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1034_PrECISE_668858.md
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# Introduction
In a research project it is common that several partners work together and
produce a lot of data related to the project. Therefore, it is important to
specify in an early stage of the project what data will be generated, how it
will be shared between the project partners and if it will be publicly
available. A data management plan (DMP) is a tool which should assist in
managing the data created during the project.
In general, the DMP of the PrECISE project will specify what data is already
available and what data will be generated, collected, and processed during the
project. It should also provide information whether and how data will be
exploited and open for public and re-use. The DMP includes information on what
standards and methodologies will be used and how the data will be handled
during and after the research project (how the data will be curated and
preserved).
The DMP is not a fixed document. It will evolve and gain more precision and
substance during the lifespan of the PrECISE project. The first version of the
DMP, including information from the first six months of the project, includes
the following:
* Methodology (Chapter 2): Data production, storage, dissemination and anaylsis
* Data Generation (Chapter 3): Data set description, research data identification
* Processing and explanation of generated data (Chapter 4): Information about the entity, which is responsible for the data, how the data is collected, an identification of the end-users of the data, and research data identification.
* Accessibility – Data sharing, archiving and preserveration (Chapter 5)
# Methodology
The DMP describes how data are managed and handled within the PrECISE project
among consortium members. In short, clinical and genetic data are produced by
UZH, whereas proteomic data are provided by ETH Zurich.
Every data point is anonymized, using a two step process: First, a pseudonym
is given by the lab information system PathPro at UZH, where only UZH has
access. Second, another independent ID is given to each data point on the
PrECISE server by AstridBio. Two PrECISE servers will be set up by AstridBio:
one for clinical data collection and storage and another one for experimental
(genomics, proteomics) data collection and storage. and the key to this second
pseudonym is only known by AstridBio. As a result, distribution of data to the
other consortium members can be regarded anonymized. Other consortium members,
other than UZH, only have access to coded (ETHZ, AstridBio) or anonymized data
(all remaining groups). The relevant contributions and data flows are
summarizes in the figure below: Data will be provided by UZH and ETHZ. Data
storage and dissemination will be done by AstridBio. The consortium members on
the right side of the figure will provide algorithm outlines, code and
analysis results as stated in the grant proposal. During the project, data and
algorithms will shared within members of the project. Upon publication,
anonymized data and algorithms/ code will be made publicly available.
In Figure 1, data production, storage, dissemination and analysis is
summarized for the PrECISE project.
Figure 1: PrECISE data production, storage, dissemination and analysis
# Data Generation
The data generation, which is illustrated in Table 1, is described below.
**_Data set reference and name:_ **
Identifier for the data set to be produced.
**_Data set description:_ **
Description of the data that will be generated or collected, its origin (in
case it is collected), nature and scale to whom it could be useful, and
whether it underpins a scientific publication. Information on the existence
(or not) of similar data and the possibilities for integration of reuse.
**_Research Data Identification_ **
The boxes (D, A, AI, U and I) symbolize a set of questions that should be
clarified for all datasets produced in this project.
**Discoverable:**
Are the data and associated software produced and/or used in the project
discoverable (and readily located), identifiable by means of a standard
identification mechanism (e.g. Digital Object Identifier).
**Accessible:**
Are the data and associated software produced and/or used in the project
accessible and in what modalities, scope, licenses (e.g. licencing framework
for research and education, embargo periods, commercial exploitation, etc.)
**Assessable and intelligible:**
Are the data and associated software produced and/or used in the project
assessable for and intelligible to third parties in contexts such as
scientific scrutiny and peer review (e.g. are the minimal datasets handled
together with scientific papers for the purpose of peer review, are data
provided in a way that judgements can be made about reliability and the
competence of those who created them)?
**Useable beyond the original purpose for which it was collected:**
Are the data and associated software produced and/or used in the project
usable by third parties even long time after the collection of the data (e.g.
is the data safely stored in certified repositories for long term preservation
and curation; is it stored together with the minimum software, metadata and
documentation to make it useful; is the data useful for the wider public needs
and usable for the likely purposes of non-specialists)?
**Interoperable to specific quality standards:**
Are the data and associated software produced and/or used in the project
interoperable allowing data exchange between researchers, institutions,
organisations, countries, etc. (e.g. adhering to standards for data
annotation, data exchange, compliant with available software applications, and
allowing re-combinations with different datasets from different origins?)
It is recommended to make an “x” to each applicable box and explain it
literally in more detail afterwards.
<table>
<tr>
<th>
**Data Nr.**
</th>
<th>
**Respon-**
**sible Bene-**
**ficiary**
</th>
<th>
**Data set reference and name**
</th>
<th>
**Data set description**
</th>
<th>
</th>
<th>
**Research data identification**
</th>
<th>
</th> </tr>
<tr>
<th>
**End user (e.g.**
**university, research**
**organization,**
**SME’s, scientific publication)**
</th>
<th>
**Existence of similar data**
**(link,**
**information)**
</th>
<th>
**Possibility for integration and**
**reuse (Y/N) + information**
</th>
<th>
**D** 1
</th>
<th>
**A** 2
</th>
<th>
**AI 3 **
</th>
<th>
**U** 3
</th>
<th>
**I** 4
</th> </tr>
<tr>
<td>
1
</td>
<td>
UZH
</td>
<td>
PPM39_r naSeq
</td>
<td>
Members of PhosphoNetPPM
project in the framework of SystemsX.ch
initiative;
members of the PrECISE project
</td>
<td>
Similar data was produced in the framework of The Cancer Genome Atlas project
(http://cancerge nome.nih.gov/ca ncersselected/pr ostatecancer)
</td>
<td>
Yes. Data can be integrated
with information on DNA and proteins from the same samples and compared to
/integrated with data from TCGA
</td>
<td>
Digital Object
Indentifiers
(DOIs) will be provided to ensure
that data and
algorithms are
discoverable.
</td>
<td>
Yes, see
Chapter
5
</td>
<td>
Data is being presently
analysed and
assembled
for
publication in a high-
impact peerreviewed journal.
</td>
<td>
Yes, see Chapter 5
</td>
<td>
Data formats adhere to
commonly used
scientific
standards (e.g.
BAM format,
VCF files etc.)
</td> </tr>
<tr>
<td>
2
</td>
<td>
UZH
</td>
<td>
PPM39_e xomeSeq
</td>
<td>
Members of PhosphoNetPPM
project in the framework of SystemsX.ch
initiative;
members of the PrECISE project
</td>
<td>
Similar data was produced in the framework of The Cancer Genome Atlas project
(http://cancerge nome.nih.gov/ca ncersselected/pr ostatecancer)
</td>
<td>
Yes. Data can be integrated with information on RNA and proteins from the same
samples and compared to
/integrated with data from TCGA
</td>
<td>
Digital Object
Indentifiers
(DOIs) will be provided to ensure
that data and
algorithms are
discoverable.
</td>
<td>
Yes, see
Chapter
5
</td>
<td>
Data is being presently
analysed and
assembled
for
publication in a high-
impact peerreviewed journal.
</td>
<td>
Yes, see Chapter 5
</td>
<td>
Data formats adhere to
commonly used
scientific
standards (e.g.
BAM format)
</td> </tr>
<tr>
<td>
3
</td>
<td>
UZH/E THZ
</td>
<td>
PPM39_s wathMS
</td>
<td>
Members of
PhosphoNetPPM
</td>
<td>
\-
</td>
<td>
Yes. Data can be integrated
</td>
<td>
Digital Object Indentifiers
</td>
<td>
Yes, see
</td>
<td>
Data is being presently
</td>
<td>
Yes, see Chapter 5
</td>
<td>
Data formats will be
</td> </tr> </table>
<table>
<tr>
<th>
**Data Nr.**
</th>
<th>
**Respon-**
**sible Bene-**
**ficiary**
</th>
<th>
**Data set reference and name**
</th>
<th>
**Data set description**
</th>
<th>
**Research data identification**
</th> </tr>
<tr>
<th>
**End user (e.g.**
**university, research**
**organization,**
**SME’s, scientific publication)**
</th>
<th>
**Existence of similar data**
**(link,**
**information)**
</th>
<th>
**Possibility for integration and**
**reuse (Y/N) + information**
</th>
<th>
**D 1 **
</th>
<th>
**A 2 **
</th>
<th>
**AI 3 **
</th>
<th>
**U 4 **
</th>
<th>
**I 5 **
</th> </tr>
<tr>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
project in the framework of SystemsX.ch
initiative;
members of the PrECISE project
</td>
<td>
</td>
<td>
with information on DNA and RNA from the same samples and compared to
/integrated
with data from
TCGA
</td>
<td>
(DOIs) will be provided to ensure
that data and
algorithms are
discoverable.
</td>
<td>
Chapter 5
</td>
<td>
analysed and
assembled
for
publication in a high-
impact peerreviewed journal.
</td>
<td>
</td>
<td>
developed as part of this study.
</td> </tr>
<tr>
<td>
4
</td>
<td>
UZH
</td>
<td>
PPM39_cl
inicopathologic
</td>
<td>
Members of PhosphoNetPPM
project in the framework of SystemsX.ch
initiative;
members of the PrECISE project
</td>
<td>
\-
</td>
<td>
Yes. Data can be integrated
with information on DNA, RNA and protein from the same samples.
</td>
<td>
Digital Object
Indentifiers
(DOIs) will be provided to ensure
that data and
algorithms are
discoverable.
</td>
<td>
Yes, see
Chapter
5
</td>
<td>
Data is being presently
analysed and
assembled
for
publication in a high-
impact peerreviewed journal.
</td>
<td>
Yes, see Chapter 5
</td>
<td>
**I** Data formats adhere to
commonly used
scientific
standards
(WHO/ISUP201 6, GCP)
</td> </tr>
<tr>
<td>
5
</td>
<td>
UZH
</td>
<td>
PPP500_ swathMS
</td>
<td>
Members of the PrECISE project
</td>
<td>
\-
</td>
<td>
Yes. Data can be integrated with clinicopathological information from the same
samples.
</td>
<td>
Digital Object
Indentifiers
(DOIs) will be provided to ensure
that data and
algorithms are
discoverable.
</td>
<td>
Yes, see
Chapter
5
</td>
<td>
Data is being presently
analysed and
assembled
for
publication in a high-
impact peerreviewed journal.
</td>
<td>
Yes, see Chapter 5
</td>
<td>
Data formats will be
developed as part of this study.
</td> </tr>
<tr>
<td>
6
</td>
<td>
UZH
</td>
<td>
PPP500_
</td>
<td>
Members of the
</td>
<td>
\-
</td>
<td>
Yes. Data can
</td>
<td>
Digital Object
</td>
<td>
Yes,
</td>
<td>
Data is being
</td>
<td>
Yes, see
</td>
<td>
Data formats
</td> </tr>
<tr>
<td>
**Data Nr.**
</td>
<td>
**Respon-**
**sible Bene-**
**ficiary**
</td>
<td>
**Data set reference and name**
</td>
<td>
**Data set description**
</td>
<td>
**Research data identification**
</td> </tr>
<tr>
<td>
**End user (e.g.**
**university, research**
**organization,**
**SME’s, scientific publication)**
</td>
<td>
**Existence of similar data**
**(link,**
**information)**
</td>
<td>
**Possibility for integration and**
**reuse (Y/N) + information**
</td>
<td>
**D 1 **
</td>
<td>
**A 2 **
</td>
<td>
**AI 3 **
</td>
<td>
**U 4 **
</td>
<td>
**I 5 **
</td> </tr>
<tr>
<td>
</td>
<td>
</td>
<td>
clinicopathologic
</td>
<td>
PrECISE project
</td>
<td>
</td>
<td>
be integrated with proteomic information from the same samples.
</td>
<td>
Indentifiers
(DOIs) will be provided to ensure
that data and
algorithms are
discoverable.
</td>
<td>
see
Chapter
5
</td>
<td>
presently
analysed and
assembled
for
publication in a high-
impact peerreviewed journal.
</td>
<td>
Chapter 5
</td>
<td>
adhere to
commonly used
scientific
standards (WHO/ISUP201
6, GCP).
</td> </tr>
<tr>
<td>
7
</td>
<td>
UZH
</td>
<td>
CRPC_he
terogeneit y_ampliS eq
</td>
<td>
Members of the PrECISE project
</td>
<td>
Similar data was produced in the framework of The Cancer Genome Atlas project
(http://cancerge nome.nih.gov/ca ncersselected/pr ostatecancer)
</td>
<td>
Yes. Data can be integrated with proteomic information from the same patients.
</td>
<td>
Digital Object
Indentifiers
(DOIs) will be provided to ensure
that data and
algorithms are
discoverable.
</td>
<td>
Yes, see
Chapter
5
</td>
<td>
Data is being presently
analysed and
assembled
for
publication in a high-
impact peerreviewed journal.
</td>
<td>
Yes, see Chapter 5
</td>
<td>
Data formats adhere to
commonly used
scientific
standards (e.g.
BAM format)
</td> </tr> </table>
Table 1: PrECISE data generation
# Chapter 4 Processing and explanation of generated data
The following sections provide some additional information to the listed data
introduced in Chapter 3. This information includes the entity, which is
responsible for the data, how the data is collected, an identification of the
end-users of the data, and research data identification.
## 4.1 PPM39_rnaSeq
This data set consists of RNA sequencing data for 39 prostate cancer patients
of the University Hospital Zurich. More specifically, for all 39 patients, the
RNA of prostate cancer tissue and the matching normal tissue was sequenced. In
addition, for 27 of the patients, a second cancer tissue sample from the same
prostate tumour was analysed the same way.
_**4.1.1 Responsible Beneficiary** _
The data was produced by UZH.
### 4.1.2 Gathering Process
RNA sequencing was performed at the Functional Genomics Center Zurich. RNAseq
libraries were generated using the TruSeq RNA stranded kit with PolyA-
enrichment (Illumina, San Diego, CA, USA). Libraries were sequenced in paired-
end mode on an Illumina HiSeq 2500 platform. After base calling, de-
multiplexing and in-silico adaptor-trimming, the data was aligned to the hg19
reference genome (UCSC version) using STAR aligner v.2.2.3 (Dobin et al.,
2013). Expression values were calculated using the FeatureCount software (Liao
et al, 2014).
### 4.1.3 End-User of the Data
Biofinformatics researchers of the PhosphonetPPM (SystemsX.ch) project and the
PrECISE project.
### 4.1.4 Research Data Identification
Within the scientific community, several initiatives have been launched to
cover the minimal standards for the annotation of a sequencing project, such
as MIGS or MISEEQE. However these standards are rather preliminary and quite
extensive.
For practical reasons, we decided to follow the example of the public data
repository Gene Expression Omnibus (GEO), which requires a minimal set of
metadata to be uploaded with every sequencing experiment that is submitted to
the archive. This set contains descriptive information and protocols for the
overall study and individual samples, and references to processed and raw data
file names. A template for this set can be found under links (see
bibliography, links - Chapter 8). The most important part of the metadata
belonging to each data file is covered by the clinical data (e.g. age, tissue
type, survival data, time to relapse, BSA levels etc.), which allows for
setting individual samples into context with each other.
A pseudonym will be provided by the lab information system PathPro of UZH
(format: GXX.XXXX_TA1, _TA2, _No, _Blood) to share data with AstridBio and
other members of the consortium. Only UZH can unblind these data. This
numbering system provides a unique indentifyer to each sample, linking all
omics, clinical and pathological data. This PathoPro identifier will be
replaced by AstridBio when setting up the data on the PrECISE server.
## 4.2 PPM39_exomeSeq
This data set consists of DNA sequencing data for 39 prostate cancer patients
of the University Hospital Zurich. More specifically, for all 39 patients, the
DNA of prostate cancer tissue, the matching normal tissue and blood was
sequenced. In addition, for 27 of the patients, a second cancer tissue sample
from the same prostate tumour was analysed the same way.
_**4.2.1 Responsible Beneficiary** _
The data was produced by UZH.
### 4.2.2 Gathering Process
DNA samples were sequenced in paired-end mode on an Illumina HiSeq 2500
platform. After base calling, de-multiplexing and in-silico adaptor-trimming
(using Trimmomatic, Bolder et al., 2014), the data was aligned to the hg19
reference genome (UCSC version) using the Bowtie2 software (Langmead et al.,
2012). Bam files containing the mapped reads were preprocessed in the
following way: Indel information was used to realign individual read using the
RealignerTargetCreator and IndelRealigner option of the Genome Analysis Tools
Kit (McKenna et al., 2010). Mate-pair information between mates was verified
and fixed using
Picard tools (http://picard.sourceforge.net) and single base were recalibrated
using GATK BaseRecalibrator. After preprocessing, variant calling was carried
out by comparing normal or tumor prostate tissue samples with matched blood
samples using the programs MuTect (Cibulskis et al., 2013 ) and ,
independently, Strelka (Saunders et al., 2012). Somatic variants that were not
detected by both programs were filtered out using CLC Genomics Workbench (CLC
Genomics Workbench 8.0.3, https://www.qiagenbioinformatics.com) as well as
those that had an entry in the dbsnp (Sherry et al., 2001) common database or
those that represented synonymous variants without predicted effects on
splicing.
### 4.2.3 End-User of the Data
Biofinformatics researchers of the PhosphonetPPM (SystemsX.ch) project and the
PrECISE project.
### 4.2.4 Research Data Identification (see also 4.1.4)
A pseudonym will be provided by the lab information system PathPro of UZH
(format: GXX.XXXX_TA1, _TA2, _No, _Blood) to share data with AstridBio and
other members of the consortium. Only UZH can unblind these data. This
numbering system provides a unique indentifyer to each sample, linking all
omics, clinical and pathological data. This PathoPro identifier will be
replaced by AstridBio when setting up the data on the PrECISE server.
## 4.3 PPM39_swathMS
This data set consists of Swath-MS proteomics data for 39 prostate cancer
patients of the University Hospital Zurich. More specifically, for all 39
patients, the protein lysates of prostate cancer tissue, and the matching
normal tissue was analysed. In addition, for 27 of the patients, a second
cancer tissue sample from the same prostate tumour was analysed the same way.
### 4.3.1 Responsible Beneficiary
The data was produced by the Institute for Molecular Systems Biology of the
ETH Zurich (ETHZ).
_**4.3.2 Gathering Process** _
Production of data has been described in detail by Tiannan et al., Nature
Medicine 2015.
### 4.3.3 End-User of the Data
Biofinformatics researchers of the PhosphonetPPM (SystemsX.ch) project and the
PrECISE project.
### 4.3.4 Research Data Identification
A pseudonym will be provided by the lab information system PathPro of UZH
(format: GXX.XXXX_TA1, _TA2, _No) to share data with AstridBio and other
members of the consortium. Only UZH can unblind these data. This numbering
system provides a unique indentifyer to each sample, linking all omics,
clinical and pathological data. This PathoPro identifier will be replaced by
AstridBio when setting up the data on the PrECISE server.
## 4.4 PPM39_clinico-pathologic
This data set consists of clinical follow-up data (age at diagnosis,
Progression-free survival time and status) and pathological data (UICC TNM
classification, Gleason grade, WHO/ISUP 2016 grade group), for 39 prostate
cancer patients of the University Hospital Zurich. More specifically, for all
39 patients, prostate cancer tissue was graded. In addition, for 27 of the
patients, a second (lower grade) cancer tissue area from the same prostate
tumour was selected and grade the same way.
_**4.4.1 Responsible Beneficiary** _
The data was produced by UZH.
### 4.4.2 Gathering Process
The 2016 WHO/ISUP classification of urological tumors was used to stage and
grade the respective lesions. Clinical follow-up data of patients of where
retrieved from the Zurich proCOC ( **Pro** state **C** ancer **O** utcomes
**C** ohort) study.
### 4.4.3 End-User of the Data
Biofinformatics researchers of the PhosphonetPPM (SystemsX.ch) project and the
PrECISE project.
### 4.4.4 Research Data Identification
A pseudonym will be provided by the lab information system PathPro of UZH
(format: GXX.XXXX) to share data with AstridBio and other members of the
consortium. Only UZH can unblind these data. This numbering system provides a
unique indentifyer to each sample, linking all omics, clinical and
pathological data. This PathoPro identifier will be replaced by AstridBio when
setting up the data on the PrECISE server.
## 4.5 PPP500_swathMS
This data set consists of Swath-MS proteomics data for 500 prostate cancer
patients of the University Hospital Zurich. More specifically, for all 500
patients, the protein lysates of prostate cancer tissue and matching normal
tissue was analysed.
### 4.5.1 Responsible Beneficiary
The data was produced by the Institute for Molecular Systems Biology of the
ETH Zurich (ETHZ).
_**4.5.2 Gathering Process** _
Production of data has been described in detail by Tiannan et al., Nature
Medicine 2015.
### 4.5.3 End-User of the Data
Biofinformatics researchers of the PhosphonetPPM (SystemsX.ch) project and the
PrECISE project
### 4.5.4 Research Data Identification
A pseudonym will be provided by the lab information system PathPro of UZH
(format: GXX.XXXX_TA1, _TA2, _No) to share data with AstridBio and other
members of the consortium. Only UZH can unblind these data. This numbering
system provides a unique indentifyer to each sample, linking all omics,
clinical and pathological data. This PathoPro identifier will be replaced by
AstridBio when setting up the data on the PrECISE server.
## 4.6 PPP500_clinico-pathologic
This data set consists of clinical follow-up data (age at diagnosis,
Progression-free survival time and status) and pathological data (UICC TNM
classification, Gleason grade, WHO/ISUP 2016 grade group), for 500 prostate
cancer patients of the University Hospital Zurich. More specifically, for all
500 patients, prostate cancer tissue was graded.
_**4.6.1 Responsible Beneficiary** _
The data was produced by UZH.
### 4.6.2 Gathering Process
The 2016 WHO/ISUP classification of urological tumors was used to stage and
grade the respective lesions. Clinical follow-up data of patients of where
retrieved from the Zurich proCOC ( **Pro** state **C** ancer **O** utcomes
**C** ohort) study.
### 4.6.3 End-User of the Data
Biofinformatics researchers of the PhosphonetPPM (SystemsX.ch) project and the
PrECISE project.
### 4.6.4 Research Data Identification
A pseudonym will be provided by the lab information system PathPro of UZH
(format: GXX.XXXX) to share data with AstridBio and other members of the
consortium. Only UZH can unblind these data. This numbering system provides a
unique indentifyer to each sample, linking all omics, clinical and
pathological data. This PathoPro identifier will be replaced by AstridBio when
setting up the data on the PrECISE server.
## 4.7 CRPC_heterogeneity_ampliSeq
This data set consists of DNA sequencing data for 10 prostate cancer patients
of the University Hospital Zurich. More specifically, for all 10 patients, the
genomic DNA of 10 different tumour areas (CRPC) and of a matching normal
tissue area will be sequenced.
_**4.7.1 Responsible Beneficiary** _
The data will be produced by UZH.
### 4.7.2 Gathering Process
DNA samples will sequenced using Amplicon sequencing on the Ion Torrent Proton
System (genes _PTEN, AR, TP53, SPOP, FOXA1_ ).
### 4.7.3 End-User of the Data
Biofinformatics researchers of the PhosphonetPPM (SystemsX.ch) project and the
PrECISE project.
### 4.7.4 Research Data Identification (see also 4.1.4)
A pseudonym will be provided by the lab information system PathPro of UZH
(format: GXX.XXXX_TA1-12, _No) to share data with AstridBio and other members
of the consortium. Only UZH can unblind these data. This numbering system
provides a unique indentifyer to each sample, linking all omics, clinical and
pathological data. This PathoPro identifier will be replaced by AstridBio when
setting up the data on the PrECISE server.
# Chapter 5 Accessibility – Data sharing, archiving and preservation
We will store clinical and experimental data on separate servers. Experimental
data will be accessible for up- and download using encrypted standard file
transfer protocols. We will separate data produced within the PrECISE project
and data uploaded from other data repositories (e.g. TCGA data). The
respective meta-data will be stored together with the clinical data. Clinical
data will be accessible via web applications using encryption. The system for
the data management will be based on the SmartBioBank software (
_http://www.smartbiobank.com_ ). This software allows for integration of
clinical and experimental/genomic data and enables data sharing and database
merging between independent research groups. The clinical data and the meta-
data should be searchable via a web-based interface.
In the beginning of the project, data will be shared exclusively between
members of the PrECISE project. As a sharing platform for small amounts of
data up to 100MB the already existing project SVN repository
_https://precise.technikon.com_ is used. This allows easy synchronization
between the partners as well as data versioning. It has to be noted that only
project partners have access to the project SVN. A data transfer agreement has
to be signed for getting access to unpublished data (see Chapter 9).
The PrECISE Executive Board will make decisions on the publication of the
data. Most journals in the biomedical field require the submission of genomic
data together with standard meta-data to public repositories. This means that
in case of publishing our findings in scientific journals, we will make the
data available to the public. In this case, the data sets will get a
persistent identifier.
External researchers will be able to apply for access to the PrECISE data
before the general release to the public. In this case an application form
needs to be filled detailing a research plan, ethical approval etc.. The
application form will be available on the PrECISE project website together
with information on approval process and data content of the PrECISE database.
The Executive board can grant access to the entire database or restrict access
to selected data sets. Non-profit organizations should cover the expenses of
the consortium related to the data migration etc. For-profit organizations
will have to pay an additional fee for data access.
The data can be used to discover new biomarkers or sets of biomarkers, which
allow for improved diagnostics of prostate cancer. In addition, different
levels of data were acquired of a complex biological systems transitioning
from one developmental stage (i.e. normal tissue) to another (i.e. tumour
tissue). Integrating these data sets may result in important discoveries that
will elucidate the dynamics of biological systems that fails to control
homeostasis resulting in disease.
No data will be deleted. Instead, preservation of raw and meta-data will be
reached by two efforts: first, setup of a data server by AstridBio; second,
publication of raw and meta-data in journals specialized in long-term
preservation of biomedical data (e.g. Nature Scientific Data using the
DataVerse platform). Through this, no costs (time/effort) to prepare the data
for sharing/preservation are involved, and data will be publicly available for
a very long time period (decades).
# Chapter 6 Summary and Conclusion
This data management plan outlines the handling of data generated within the
PrECISE project, during and after the project lifetime. As the deliverable
will be kept as a living document it will be regularly updated by the
consortium (the next time in M18 within the periodic report). The partners put
into write their plans and guarded expectations regarding valuable and
publishable data.
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Horizon 2020
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1036_5G-XHaul_671551.md
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# 1 Executive Summary
The purpose of the Data Management Plan (DMP) is to describe the main elements
of the data management policy that will be used by 5G-XHaul with regard to the
datasets generated by the project. This DMP will describe the format and the
way to store, archive and share the data created within the project. The data
may include, but not limited to, code, publications, as well as measured data,
e.g. field trials. DMP’s content will be updated from its creation (Month 6),
to the end of the project (Month 36).
The 5G-XHaul consortium will support the Open Data Initiative and will ensure
that part of the collected data will be made available to the public. Our goal
in this document is to:
1. specify the data that will be collected during the lifetime of 5G-XHaul;
2. investigate the best practices and guidelines for sharing the project outcomes, as well as facilitating open access (OA) to research data; and
3. define how the data collected in the project will be made available to third parties in contexts such as scientific scrutiny, peer review and use for research purposes.
# 2 Introduction
During the lifetime of 5G-XHaul, data of different nature will be generated
and collected. The main goal of the 5G-XHaul Data Management Plan (DMP) is to
outline the types of data foreseen for generation throughout the project, as
well as the context and procedures of this generation. It also outlines the
protocols followed to assess the generated/collected data with respect to
their sensitiveness and outlines data acquisition plan for the duration of the
project.
Following the EC template on “Guidelines on Data Management in Horizon 2020”
(
_http://ec.europa.eu/research/participants/data/ref/h2020/grants_manual/hi/oa_pilot/h2020-hi-
oadata-mgt_en.pdf_ ) , the DMP includes the following major components:
* Data set reference and name,
* Data set description,
* Standards and metadata,
* Data sharing and;
* Archiving and preservation (including storage and backup)
Following this file, the DMP clarifies that scientific generated research data
will be easily:
* Discoverable
* Accessible
* Assessable and intelligible
* Useable beyond the original purpose for which it was collected
* Interoperable to specific quality standards
The remainder of the deliverable is structured as follows. Section 3 presents
the rights to access the data generated in the project. Section 4 summarizes
the rights to access to 5G-XHaul publications. In Section 5, the data to be
gathered from the context of the project is presented. In Section 6, the data
collection processes are outlined. In Section 7, the data are handled
according to the different categories regarding confidentiality, i.e. there
are data which are confidential and need special protection, data which are
not confidential and can be shared, as well as data which depend on the
informed consent of the participant. Section 8 addressed the terms of
archiving and preservation. Finally, Section 9 summarizes the deliverable.
The 5G-XHaul consortium will attempt to maximise the visibility and
exploitation of the project results and its long-term impact, by providing as
many as possible publicly available results that can be easily accessed and
reused.
# 3 Open Access to research data
Open access (OA) to research data refers to the right to access and re-use
digital research data generated by projects. OA data repositories will be
setup by the consortium during the start of the project. However, partner
hosted repositories, as well as external repositories will be used to ensure
maximum visibility, serve as backups, and ensure availability well after the
end of the project. With respect to software modules developed during the
course of the project, whenever possible, i.e. not violating Intellectual
Property Rights (IPR), they will also be provided under open-source license to
allow for their reuse, adaptation and further enhancement to match possibly
different application contexts and serve as a baseline for future business and
research endeavours.
The project will be using the Open Access Infrastructure for Research in
Europe (OpenAIRE) ( _https://www.openaire.eu/_ ) [1][2], as well as
exploiting the expected support to be provided on research data management for
projects funded under Horizon 2020. OpenAIRE is a service that has been built
to offer exactly this functionality and may be used to reference both the
publication and the data. The lead author is responsible for getting approvals
and then sharing the data and metadata on Zenodo ( _http://zenodo.org/_ ) .
Zenodo is a repository supported by CERN and the EU OpenAire project which is
open, free, searchable and structured with flexible licensing allowing for
storing all types of data: datasets, images, presentations, publications and
software. Some of the features are:
* The repository has backup and archiving capabilities.
* The repository allows for integration github.com where the project code will be stored. GitHub provides a free and flexible tool for code developing and storage.
* Zenodo assigns all publicly available uploads a Digital Object Identifier (DOI) to make the upload easily- and uniquely-citable
EU expects funded researchers to manage and share research data in a manner
that maximizes opportunities for future research and complies with best
practice in the relevant subject domain, that is:
* The dataset has clear scope for wider research use
* The dataset is likely to have long-term value for research or other purposes
* The dataset have broad utility for reference and use by research communities
* The dataset represents a significant output of the research project
Openly accessible research data, generated during 5G-XHaul project, will be
accessed, mined, exploited, reproduced and disseminated free of charge for the
user.
# 4 Open access to scientific publications
The scientific publications will be published under the Open Access (OA)
paradigm to the largest possible extent and in accordance to the quality
standards set by the project. Since numerous publications are expected during
the course of the project, the project’s approach will be to exploit both “
**gold** “ and “ **green** ” OA practices. Publications that are expected to
be highly impactful and cited will be provided through “ **gold** ” OA to
ensure their maximum visibility. For the other publications, the “ **green** ”
OA practice will be followed, making them publicly available as soon as
possible also in their published version.
OA wil be achieved through the following steps:
1. Any paper presenting the project results will acknowledge the project: The research leading to these results has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 671551-5G-XHaul and display the EU emblem.
2. Any paper presenting the project results will be deposited by the time of publication the latest to a formal repository for scientific papers. If the organization has not a formal repository ( _https://www.openaire.eu/participate/deposit/idrepos_ ) , the paper can be uploaded in Zenodo.
3. Authors will ensure that the publisher accepts OA via self-archiving in their departments formal repository or via _http://zenodo.org/_ .
4. Authors can choose to pay “author processing charges” to ensure OA publishing, but still they have to deposit the paper in a formal repository for scientific papers (step 2).
5. Authors will ensure OA via the repository to the bibliographic metadata identifying the deposited publication. More specifically, the following will be included:
1. The terms “European Union (EU)” and “Horizon 2020”;
2. “Dynamically Reconfigurable Optical-Wireless Backhaul/Fronthaul with Cognitive Control Plane for Small Cells and Cloud-RANs-5G-XHaul”, Grant agreement number 671551;
3. Publication data, length of embargo period if applicable; and
4. A persistent identifier.
6. Each case will be examinated separately in order to decide if self-archiving of paying for OA publishing.
In all cases, publications will be treated – in terms of storage – in a
similar manner to research data, meaning that project, partner and externally
hosted repositories will be used to maximise visibility and ensure long-term
preservation of the articles. As aforementioned, research data and associated
software needed to reproduce the results presented in publications will be
also made available as much and as soon as possible.
# 5 5G-XHaul context and data description
The objectives of 5G-XHaul with respect to data gathering raise from the
_definition of the use cases_ at one of the first tasks to be carried out
within the first months of the project.Also, the usage scenarios play an
important role when thinking about data to be stored. The appropriate
identification of these datasets will be generated and collected to drive the
project’s technical work.
Preliminary examples of the datasets that will be made available in 5G-XHaul
comprise:
* real network traffic statistics and performance related information for mobile (2G/3G/4G) and fixed (xDSL) access network nodes,
* anonymous data rate requirements of mobile users derived from testbeds,
* anonymous mobility data of mobile users derived from testbeds, and
* data on channel quality, interference and error rate between different network units (RRH, BBU-pool, …).
# 6 Data collection
COSMOTE provides already three datasets listed and described below.
Telefonica will study the possibility of sharing some traffic traces from
their networks, taking into account its legal obligations for protecting the
confidentiality of its customers. The availability of the data is currently
being analysed by Telefonica legal services in order to find a common position
for all the 5G-PPP projects.
## 6.1 Dataset 1: Mobile Network Traffic Statistics (at Base Station level)
This dataset will include real network traffic statistics for a number of
3G/4G/4G+ Base Stations (BS) covering a specific (urban) geographical area,
located in Athens, Greece. More specifically, the information that will be
provided will include:
* BS configuration-related data
* Performance-related statistics for the 3G and the 4G/4G+ BSs located in the area under consideration.
### 6.1.1 Data set reference and name
##### 6.1.1.1 Base Station Configuration Data
This dataset will include the following information:
1. _BS ID_ : Starting from 1 up to the max. number of BSs that will be considered. We shall stress that this is not the real BS ID.
2. _4G/4G+ BS related configuration data_ (if the BS supports 4G): incl., the BS type (e.g. micro, macro), the number of carriers and the available bandwidth (in MHz).
3. _3G/HSPA/HSPA+ BS related configuration data_ (if the BS supports 3G), incl., the BS type (e.g. micro, macro), the number of carriers and the bandwidth per Radio Access Technology (RAT).
Additional information that could be provided is the following:
* Rough coverage per BS
* Neighboring cell relations per BS
* #inhabitants / Km 2 in the area under study
**6.1.1.2 3G/HSPA/HSPA+ Performance-related statistics (@Base Station level)**
The following performance-related data will be provided:
* _Time_ : in the form of 2015-11-25 00:00:00.0
* _Cell Name_ : The BS ID (1,2,3, …) along with the sector ID (A,B,C, …) _Speech Traffic (Erlang)_ : The total speech traffic in Erlangs.
* _Data Traffic (MB)_ : The hourly UL/DL Data traffic in MB
* _Data Rate Distribution_ : The distribution of traffic per data rate; for 8, 16, 32, 64 ,128, 256 & 384 Kbps and HSUPA data rates
* _Cell Throughput (kbps)_ : The average HSDPA throughput in Kbps
* _HSUPA Throughput (Kbps)_ : The average and max HSUPA throughput in Kbps
* _User Throughput (Kbps)_ : The average HSDPA/HSUPA throughput per User
* _Average number of Users_ : The average number of HSDPA/HSUPA Users
* _Access Failure (% of total)_ : The total access failure rate as % of total sessions (for CS and PS connections)
* _RRC Connection Setup and Access Failure rate_ : The access failure rate due to RRC failure as % of total sessions (for CS and PS connections)
* _RRC Connection Setup And Access Attempts Per Request Cause_ : The number of RRC Setup and Access requests per type; that is, for Conversational Call, International Call, Background Call, Subscirber Traffic Call, Emergency Call, Cell Resellection, Registration, High Priority Signalling, Low Priority Signalling, Call Re-establishment, SRNC Relocation.
* _DCH Rejection Rate_ : The DCH Rejection Rate (% of total sessions) for UL/DL Voice, Video and PS sessions.
* _DCH Attempts_ : The DCH Access Attempts for Voice, Video and PS sessions.
* _Drop Call Rate (% of total) for Speech Sessions per Failure Cause_ : The Drop Call Rate
(% of total sessions) for Speech Sessions per Failure Cause, i.e. due to Iu,
Radio, BTS, IuR, UE, RNC, transmission or pre-emption issues. The total number
of releases is also measured.
* _Drop Call Rate (% of total) for Video Sessions per Failure Cause_ : The Drop Call Rate (% of total sessions) for Video Sessions per Failure Cause, i.e. due to Iu, Radio, BTS, IuR, UE, RNC, transmission or pre-emption issues. The total number of releases is also measured.
* _Drop Call Rate (% of total) for PS Sessions per Failure Cause_ : The Drop Call Rate (% of total sessions) for PS Sessions per Failure Cause, i.e. due to Iu, Radio, BTS, IuR, UE, RNC or transmission issues. The total number of releases is also measured.
_Note_ : All counters are available on a per hour basis.
**6.1.1.3 4G/4G+ Performance-related statistics (@Base Station level)** The
following performance-related data will be provided:
* _Time_ : in the form of 2015-11-25 00:00:00.0
* _Cell Name_ : The BS ID (1,2,3, …) along with the sector ID (A,B,C, …)
* _Data Traffic (MB)_ : The hourly UL/DL Data traffic in MB
* _RRC Drop ratio (%)_ : The Radio Resource Control (RRC) Drop ratio
* _RRC Setup Failure Rate (%)_ : The RRC Setup Failure Rate
* _E_RAB Failure Rate (%)_ : The E-RAB Failure Rate
* _Mean Cell Throughput (Mbps)_ : The hourly average LTE UL/DL throughput in Mbps
* _Max Cell Throughput (Mbps)_ : The maximum LTE UL/DL throughput in Mbps
* _Average Number of UEs_ : The average number of mobile devices _Max number of UEs_ : The maximum number of simultaneous UE sessions.
* _Total UEs in eNB_ : The total number of UEs in the BS during the hour.
* _% Carrier Aggregation (CA) capable UES_ : The percentage of CA capable UEs over the total UEs
* _Intra-eNB HO Failure Rate (%)_ : The failure rate (% of total attempts) for Intra eNB Handovers.
* _Inter-eNB HO over X2 Failure Rate (%)_ : The failure rate (% of total attempts) for Inter eNB Handovers over the X2 interface
* _Inter-eNB HO over S1 Failure Rate (%)_ : The failure rate (% of total attempts) for Inter eNB Handovers over S1 interface
* _Number of CS Fallback (CSFB) Attempts_ : The number of CSFB attempts in idle and connected mode
* _Average PRB Usage per TTI (%)_ : The UL/DL average Physical Resource Blocks usage (i.e. PRBs Used / PRBs Available) per TTI
* _Intra-eNB Latency_ : It measures the intra-eNB latency
* Average Channel Quality Indicator (CQI): The average CQI value
* _CQI Distribution per Level (00-15) (%)_ : The CQI distribution per level for each of the 16 CQI values as percentage of total samples
* _Modulation and Code Scheme (MCS) Distribution per Order (%)_ : The % distribution of MCS usage (Low (MCS0-9), Medium (MCS 10-19), High (MCS20-28)).
* _MCS Distribution per Scheme (MCS0-MCS28) (%)_ : The MCS distribution per Scheme for each of the 29 values as percentage of total samples
* _Downlink (DL) Modulation Scheme Distribution (%)_ : The % distribution of Modulation Scheme usage in the DL (QPSK,16QAM, 64QAM)
* _Uplink (UL) Modulation Scheme Distribution (%)_ : The % distribution of Modulation Scheme usage in the UL (QPSK,16QAM, 64QAM).
* _Average Physical Uplink Control Channel (PUCCH) Signal to Interference plus Noise_ _Ratio (SINR)_ : The average PUCCH SINR
* _Average Physical Uplink Shared Channel (PUSCH) SINR_ : The average PUSCH SINR _Note_ : The above counters will be available on a per quarterly basis, apart from the average/max UEs per eNB, CQI και SINR PUCCH/PUSCH counters.
### 6.1.2 Data set description
##### 6.1.2.1 Base Station Configuration Data
This dataset will be provided in the following format:
* BS ID: A number which discriminates the BS among the others i.e., 1, 2, 3, … max.
* 4G/4G configuration data e.g., 3 sectors (A,B,C) x 20MHz o 3G/HSPA/HSPA+ configuration data, e.g.:
* 3 sectors (A,B,C) x 21Mbps
* 6 sectors (D,E,F,G,H,J) x 42Mbps
* Uplink: 5.8Mbps for all cells o Location-related data: e.g., Latitude and Longitude (37.94369217, 23.71617261)
_Note_ : All BSs’ coordinates have been dislocated from original ones; however
the relative distances have been kept, providing thus the original (displaced)
network topology.
##### 6.1.2.2 3G/HSPA/HSPA+ Performance-related statistics (@Base Station
level)
The counters described above for the 3G/HSPA/HSPA+ BSs will be given in the
following format:
##### 6.1.2.3 4G/4G+ Performance-related statistics (@Base Station level)
The counters described above for the 4G/4G+ BSs will be given in the following
format:
### 6.1.3 Standards and metadata
\-
## 6.2 Dataset 2: Fixed (Access) Network (xDSL) Traffic Statistics
The information that will be provided will be described as soon as the
consortium makes a decision on the information/dataset that will be utilized
in the context of the project.
### 6.2.1 Data set reference and name
### 6.2.2 Data set description
### 6.2.3 Standards and metadata
## 6.3 Dataset 3: Anonymous Mobility related data of mobile users
Network related data (Cell-ID, LAC/TAC, RAT, RSSI/RSRP/RSRQ/RSNR/CQI,
call/session status (idle, off-hook)) for the area under consideration (see
Dataset 1) along with user location (latitude, longitude) and time information
(timestamp) will be provided, for a limited number of users (e.g. 2-5 users).
The anonymity of the users/subscribers will be ensured by: (a) utilizing SIM
(pool) cards used by COSMOTE employees only, for network
performance/optimization purposes, that is, not belonging to real COSMOTE
subscribers and (b) utilizing part of the device IMEI (that is, last 4 digits)
for the subscriber identification (if needed).
Details about the information that will be provided will be described as soon
as the consortium makes a decision on the information/dataset that will be
utilized in the context of the project.
# 7 Data Management and sharing
The process of making the 5G-XHaul data public and publishable at the
repository will follow the procedures described in the Consortium Agreement.
## 7.1 Categories of data based on their confidentiality level
_Open/Protected/Confidential_
<table>
<tr>
<th>
Topic
</th>
<th>
Objective
</th>
<th>
Data Type
</th>
<th>
Source
</th>
<th>
Category
</th> </tr>
<tr>
<td>
Dataset 1
</td>
<td>
To be utilized for simulation purposes
</td>
<td>
Mobile network traffic statistics
@BS level
</td>
<td>
Commercial network
</td>
<td>
Confidential
</td> </tr>
<tr>
<td>
Dataset 2
</td>
<td>
To be utilized for simulation purposes
</td>
<td>
Fixed access network traffic statistics
</td>
<td>
Commercial network
</td>
<td>
Confidential
</td> </tr>
<tr>
<td>
Dataset 3
</td>
<td>
To be utilized for simulation purposes
</td>
<td>
Mobility-related statistics
</td>
<td>
Commercial network
</td>
<td>
Confidential
</td> </tr> </table>
## 7.2 Data sharing
**_COSMOTE’s Note_ ** : The data/information that will be provided by COSMOTE
is strictly confidential and is intended to be used in the context and the
timeframe of the 5G-XHaul project solely, while in case of relevant
studies/publications COSMOTE shall be informed prior to submission.
Data will be shared when the related deliverable or paper has been made
available at an Open Access (OA) repository. The normal expectation is that
data related to a publication will be openly shared. However, to allow the
exploitation of any opportunities arising from the raw data and tools, data
sharing will proceed only if all co-authors of the related publication agree.
OA to research data in 5G-XHaul will be achieved through the following steps:
1. Update of the DMP
2. Data selection
3. Data deposit into a data repository
4. License the data for reuse (Horizon 2020 recommendation is to use CC0 or CC BY)
5. Provide info on tools needed for validation: everything that could help third party in validating the data (workflow, code, etc.)
Independent of the choose, the authors will ensure that the repository:
* Gives the submitted dataset a persistent and unique identifier, to make sure that research outputs in disparate repositories can be linked back to particular researchers and grants
* Provides a landing page for each dataset, with metadata
* Helps to track if the data has been used by providing access and download statistics
* Keeps the data available in the long term, if desired
* Provides guidance on how to cite the data that has been deposited
As suggested from the European Commission, the partners will deposit at the
same time the research data needed to validate the results presented in the
deposited scientific publications. This timescale applies for data
underpinning the publication and results presented: research papers written
and published during the funding period will be made available with a subset
of the data necessary to verify the research findings. The consortium will
then make a newer, complete version of data, available within 6 months of
project completion.
Other data (not underpinning the publication) will be shared during the
project life following a granular approach to data sharing, releasing subsets
of data at distinct periods, rather than wait until the end of the project, in
order to obtain feedback from the user community and refine it as necessary.
An important aspect to take into account, is who is **allowed to access the
data** . It could happen that some of the dataset shouldn’t be publicly
accessible to everyone. In this case, a control mechanisms will be
established.
These include:
* Authentication systems that limit read access to authorized users only
* Procedures to monitor and evaluate, one to one, access requests: user must complete a request form stating the purpose for which they intend to use the data.
* Adoption of a Data Transfer Agreement that outlines conditions for access and use of the data
Each time a new dataset will be deposited, the consortiun will decide on who
is allowed to access the data. Generally speaking, anonymised and aggregate
data will be made freely available to everyone, whereas sensitive and
confidential data will only be accessed by specific authorized users.
# 8 Archiving and preservation
The Guidelines on Data Management in Horizon 2020 require defining procedures
that will be put in place for long-term preservation of the data and backup.
Datasets will be maintained for 5 years following project completion. To
ensure high-quality long-term management and maintenance of the dataset, the
consortium will implement procedures to protect information over time. These
procedures will permit a broad range of users to easily obtain, share, and
properly interpret both active and archived information, and they will ensure
that information are:
* kept up-to-date in content and format so they remain easily accessible and usable;
* protected from catastrophic events (e.g., fire and flood), user error, hardware failure, software failure or corruption security breaches, and vandalism.
Both Zenodo and OpenAIRE are purpose-built services that aim to provide
archiving and preservation of long-tail research data. In addition, the
5G-XHaul website, linking back to OpenAIRE, is expected to be available for at
least 2 years after the end of the project.
Regarding the second aspect, solutions dealing with disaster risk management
and recovery, as well as with regular backups of data and off-site storage of
backup sets, are always integrated when using the official data repositories,
i.e. Zenodo; the partners will ensure the adoptions of similar solutions when
choosing an institutional research data repository.
# 9 Conclusions
The purpose of the DMP is to support the data management life cycle for all
data that will be collected, processed or generated by the 5G-XHaul project.
The DMP is not a fixed document, but evolves during the lifespan of the
project. This document is expected to mature during the project; more
developed versions of the plan could be included as additional deliverables at
later stages. The DMP will be updated at least by the mid-term and final
review to fine-tune it to the data generated and the uses identified by the
consortium since not all data or potential uses are clear at this stage of the
project.
# 10 Bibliography
1. The OpenAIRE Project - Open Access Infrastructure for Research in Europe, _http://www.openaire.eu_
2. “OpenAire, Open access overview, What is open access?,” [Online]. Available:
_https://www.openaire.eu/open-access-overview/open-access-info/overview-of-
open-access_ .
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1039_CogNet_671625.md
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# Introduction
This deliverable includes two important topics, Data Management Plan (DMP) and
Data Protection Agencies (DPA) Notifications.
Any research project DMP should describe what data the project will generate,
whether and how it will be exploited or made accessible for verification and
re-use, and how it will be curated and preserved.
CogNet project will participate in the EC’s Pilot on Research Data, and to
this end it will deposit relevant data in a research data repository. The
benefits from data sharing will include creating a community around the
dataset, where publications allow validation of results. This will lead to
more collaboration and advanced research, citing the dataset and thus making
project results more visible with greater impact.
To maximise the extent of the data dissemination, CogNet will implement
provisions for third parties to access, mine, exploit and reproduce the data,
including the information necessary for validating project’s results.
This document establishes the plan to satisfy the EC expectations for data
management, which are the following:
* Any Horizon 2020 project is invited to submit a DMP as an early project deliverable if it is relevant to their research.
* All projects submitting a research proposal to ‘Research and Innovation Actions’ and ‘Innovation Actions’ are required to include a short outline of their general data management policy.
* All projects which are successfully funded under the Pilot on Open Research Data are expected to produce an initial DMP deliverable within the first six months of the project
* Each of the required points for the DMP should be addressed on a dataset-by-dataset basis.
* List file types, formats, and the reuse potential for other researchers.
* Where possible use existing metadata standards which will allow for potential integration with other datasets.
* Explain how your data will be shared, and the level of access to be provided (and why).
* Use a repository service to deposit your data and where possible make it and the underlying metadata accessible to third parties, free of charge.
* Arrange backup and storage procedures which are most suited to the partners and nature of your project.
* Complete more detailed DMPs at regular intervals throughout your project when changes occur or as a minimum in the run up to mid-term and final reviews.
The DMP addresses the main elements of the data management policy that will be
used by the project participants with regard to all datasets. Hence, it also
establishes some procedural mechanisms for participants with the
responsibilities of Data Controllers and Processors. For each dataset, the
following aspects will be considered:
1. _OVERVIEW OF POLICIES_ : with respect to relevant bordering international initiatives and programs.
2. _ROLES_ : identifying the different participants, the rules played and their responsibilities.
3. _DATA SET REFERENCE AND NAME_ : identifier for the data set to be produced.
4. _DATA SET DESCRIPTION_ : description of the data that will be generated or collected, its origin (in case it is collected), nature, scale, to whom it could be useful, and whether it underpins a scientific publication. Information on the existence (or not) of similar data and the possibilities for integration and reuse.
5. _STANDARDS AND METADATA_ : reference to existing suitable standards of the discipline. If these do not exist, an outline on how and what metadata will be created.
6. _DATA INFRASTRUCTURE_ : details of data production and provision assets.
7. _DATA SHARING_ : description of how data will be shared, including access procedures, embargo periods (if any), outlines of technical mechanisms for dissemination and necessary software and other tools for enabling re-use, and definition of whether access will be widely open or restricted to specific groups. Identification of the repository where data will be stored, if already existing and identified, indicating in particular the type of repository (institutional, standard repository for the discipline, etc.). In case the dataset cannot be shared, the reasons for this should be mentioned (e.g. ethical, rules of personal data, intellectual property, commercial, privacy-related, security-related).
8. _ARCHIVING AND PRESERVATION (INCLUDING STORAGE AND BACKUP)_ : description of the procedures that will be put in place for long-term preservation of the data. Indication of how long the data should be preserved, what is its approximated end volume, what the associated costs are and how these are planned to be covered.
In addition, any project that produces, collects or processes research data
should also take into account that data used to produce the research results
of the project should be:
1. _DISCOVERABLE_ : identifiable by means of a standard identification mechanism (e.g. Digital Object Identifier).
2. _ACCESSIBLE_ : the data and associated software are produced and/or used in the project accessible and in what modalities, scope, licenses (e.g. licensing framework for research and education, embargo periods, commercial exploitation, etc.).
3. _ASSESSABLE AND INTELLIGIBLE_ : covering the capacity of third parties of accessing and using data with a short learning curve.
4. _USEABLE BEYOND THE ORIGINAL PURPOSE FOR WHICH IT WAS COLLECTED_ : easing the application of the data in other domains or sectors.
5. _INTEROPERABLE TO SPECIFIC QUALITY STANDARDS_ : allowing data exchange between researchers, institutions, organisations, countries, etc. (e.g. adhering to standards for data annotation, data exchange, compliant with available software applications, and allowing re-combinations with different datasets from different origins).
The following sections of the document are:
* Section 2 - Data Management Plan: This section describes how each of the aforementioned data management aspects will be tackled in CogNet. During the different updates of this deliverable the details for each identified database will be included.
* Section 3 - Data Protection Agency Notifications: This section includes the plan to obtain the required certification and notification of the data protection agencies of the member states where research is being carried out. Notifications, when applicable, will acknowledge receipt of notification of the project in the jurisdiction of Data Protection Agencies. During the different updates of this deliverable all the notifications sent and their receipts will be included.
This deliverable is not a fixed document, and it will evolve during the
lifespan of the project to include updates on both data management related
information (e.g. details on datasets), and also updates related to the
notifications to the applicable Data Protection Agencies (including those
outside of Europe: e.g. WeFi data collected in USA). It is expected that at
least one update per year will be needed.
# Data Management Plan
In section 1 Introduction the different aspects that have to be considered
when managing each and all of the datasets have been described. The following
sub-sections present the details of each of these aspects.
## Overview of policies
Here the plan includes exploration activities to detect and apply existing
procedures from:
* Departments from the consortium that have data management guidelines.
* Groups from outside the consortium that have data management guidelines.
* Institutions/agencies that establish mandatory data protection and security policy.
* Mandatory formal standards to improve accessibility and intelligibility for third parties.
The concrete actions here comprise:
* List any relevant institution on data management, data sharing and data security.
* Clarify relevance of servers’ location with the data.
## Roles and responsibilities
### DMP coordinator
This section appoints the responsible party for data management. The DMP
coordinator, Vicomtech-IK4 (ES), as leader of the Task 1.5 Data Protection and
privacy management, is responsible for implementing the DMP, and ensuring it
is reviewed and revised.
### DMP participants
As established in the Consortium Agreement (CA) 10.7.1 each party agrees to
comply with all obligations and requirements of its corresponding national
data protection legislation and the Data Protection Directive 95/46/EEC and to
provide all legal documents and certifications strictly required for
compliance with such legislation to the Consortium designated Party for data
protection control.
While in 10.7.2 The Parties agree that any Background, Results, Confidential
Information and/or any and all data and/or information that is provided,
disclosed or otherwise made available between the Parties during the
implementation of the Action and/or for any Exploitation activities (“Shared
Information”), shall not include personal data as defined by Article 2,
Section (a) of the Data Protection Directive (95/46/EEC) (hereinafter referred
to as “Personal Data”). Accordingly each Party agrees that it will take all
necessary steps to ensure that all Personal Data is removed from the Shared
Information, made illegible, or otherwise made inaccessible (i.e. deidentify)
to the other Parties prior to providing the Shared Information to such other
Parties.
Finally, 10.7.3 notwithstanding the preceding paragraph, if Personal Data is
involved and/or needed during the implementation of the Action and/or for any
Exploitation activities, each Party who provides or otherwise make available
to any other Party Shared Information containing Personal Data (“Contributor”)
notifies this disclosure to the Consortium designated Party for data
protection control and represents that: (i) it has the authority and/or the
authorisation to disclose the Shared Information, if any, which it provides to
the Parties under this CA; (ii) where legally required and relevant, it has
obtained appropriate informed consents from all the individuals involved, or
from any other applicable institution, all in compliance with applicable
regulations; and (iii) there is no restriction in place that would prevent any
such other Party from using the Shared Information in accordance with and for
the purpose of the Agreement.
### DMP processing phases
There are four main types of research data:
* **Observational data** : captured in real time, typically cannot be reproduced exactly.
* **Experimental data** : from labs and equipment, can often be reproduced but may be expensive to do so.
* **Simulation data** : from models, can typically be reproduced if the input data is known.
* **Derived or compiled data** : after data mining or statistical analysis has been done, can be reproduced if analysis is documented.
The 5G networking data processed in CogNet centres on 3 phases:
* First, a static non-synthetic dataset ( _**observational** _ ) captured from a real infrastructure, to establish research activities with a static dataset. This dataset will enable Machine Learning algorithms simulations and training; essentially processed by WP2, WP3, WP4 and WP5.
* Second, a live synthetic ( _**simulation** _ ) / non-synthetic ( _**experimental** _ ) dataset captured in real time from a testbed or lab infrastructure to monitor it and test optimization strategies coming from the Machine Learning algorithms in a controlled infrastructure; essentially processed by WP3, WP4 and WP5.
* Third, a live non-synthetic dataset ( _**observational** _ ) captured in real time from a telco operator infrastructure to monitor it and apply optimization strategies coming from the Machine Learning algorithms. In this case, data will be the central part of the demonstration and validation in WP6.
For the first phase, CogNet has the intention of buying an anonymized
collection of information from end user devices to network infrastructure
nodes including information about network performance, application engagement
and performance. This dataset for the first phase must satisfy directives for
data protection of personal data.
It has not been declared in the Consortium Agreement (CA) who is responsible
for this first phase dataset, but all the project coordinator (TSSG) will
perform the sourcing/purchase process. The reason, CogNet is not itself a
legal entity such as a foundation or limited liability company. Thus, the
responsibility of this dataset will fall on TSSG.
Regarding the second phase, during the project some experiments using testbeds
or labs will be done and the captured datasets could be interesting to the
research community, in this case the infrastructure owner keeps the data
rights.
For the third phase, the telco operator that provides the monitored
infrastructure owns the data captured from there.
### DMP collaborative responsibilities
The responsibilities over the data depend on which EU state has jurisdiction
over the ownership and the processing of the data in the CogNet project. This
question is answered by determining who is responsible, i.e. who is
_**'controller'** _ as that term is defined in the Data Protection Directive
(95/46/EEC). This is the entity that "determines the purposes and means of the
processing of personal data".
CogNet is itself not a legal entity such as a foundation or limited liability
company. Thus, the responsibility for data protection will fall on the member
of CogNet who is the ‘ _**controller** _ ’, the entity which collects and owns
the data in question. When data is shared between parties, the recipient is a
_**'processor'** _ and may use the data only as specified by the controller.
Thus, the decisive factor is which entity collected the data. Each of these
parties is responsible for notifying its local data protection authority.
### DMP responsibilities
The following responsibilities will apply to datasets on a per-dataset basis:
* data acquisition / capture (raw) o For pre-existing datasets owned by any of the CogNet partners, the partner that owns that dataset and makes it available to the project will be the Data Controller.
* For acquisition of new dataset, we will apply what was defined in section 5 of CogNET’s Description of the Action: “ _For the purpose of the project TSSG will be the Data Controller and Robert Mullins the Point of Contact_ ”.
* For creation or capture of new datasets, the partner(s) that create or capture the dataset will be the Data Controller(s). This applies to all types of research data (observational, experimental, simulation, and derived or compiled data).
* metadata production (derived) - machine learning research participants.
* In parallel activities, each participant is responsible for their metadata and an aggregator responsible will be defined according to the volume of metadata aggregated by each individual. This means bigger volume generator establishes aggregation mechanism and format.
* In case of pipelined processing, each participant is responsible for their metadata and for aggregating it on top of the available metadata.
* data sharing – The Data Controller will determine the details of how data will be shared, including access procedures, embargo periods (if any), outlines of technical mechanisms for dissemination and necessary software and other tools for enabling re-use, and definition of whether access will be widely open or restricted to specific groups. Similarly, the Data Controller will identify the repository where data will be stored, if already existing and identified, indicating in particular the type of repository (institutional, standard repository for the discipline, etc.). Recommendations from OpenAIRE project such as depositing in Zenodo repository data and publications will be the starting point before making any decision on data sharing.
* Data archiving and preservation (including storage and backup) – depending on each dataset the data archiving and preservation procedures that will be put in place for longterm preservation of the data will be responsibility of the corresponding Data Controller. This includes the indication of how long the data should be preserved, what is its approximated end volume, what the associated costs are and how these are planned to be covered.
## Data set reference and name
As described in detail in section 2.2.3 the 5G networking data that will be
processed in CogNet pivot around 3 phases: static non-synthetic dataset
(observational), live synthetic (simulation)/non-synthetic(experimental)
dataset, and live non-synthetic dataset (observational).
Both raw captured and metadata processed information will be included in the
dataset. In each phase the dataset will be generated/bought and processed
different datasets targeting the same information but from different
infrastructures. For each dataset the corresponding Data Controller will
assure that the above information is defined:
* Contact Name - telephone and email contact details
* Date of First Version - Date the first version was completed
* Date of Last Update - Date it was last changed
## Data set description
### General description
CogNet will harvest 5G networking data with anonymized collections of
information from end user devices to network infrastructure nodes including
information about network performance, application engagement and performance.
A preliminary data category would cover:
* Device data
* Connectivity data o Primary connected network - canonical network name (/WLAN/SSID/BSSID or
/3GPP/PLMN/CellLAC/CellID or /3GPP2/SID/NID/BSID) o Specific Network
Technology – connected network interface (Wi-Fi, GPRS, EDGE, HSPA+, LTE, …)
* Usage data (total and per session) o Rx- amount of data downloaded on the network by a device (in bytes) o Tx –amount of data uploaded on the network by a device (in bytes) o avgRx - average downlink throughput observed on the network by a device (in bps) o avgTx \- average uplink throughput observed on the network by a device (in bps) o maxRx - maximum downlink throughput observed on the network by a device (in bps) o maxTx - maximum uplink throughput observed on the network by a device (in bps) o avgLatencyRx - average downlink latency observed on the network by a device (in bps) o avgLatencyTx - average uplink latency observed on the network by a device (in bps) o maxLatencyRx - maximum downlink latency observed on the network by a device
(in bps) o maxLatencyTx - maximum uplink delay latency on the network by a
device (in bps) o avgJitterRx - average downlink jitter observed on the
network by a device (in bps) o avgJitterTx - average uplink jitter observed on
the network by a device (in bps)
* KPI indicators related (local-node and global-infrastructure):
* Indicators/counters composed for too late HOs, too early HOs, HOs to a wrong cell and HOs subsequent to a connection setup (HO-related) o Number of HOs (incoming+outgoing) o Ping-pong handovers o Handover success rate (HOSR) o Bit error rate (BER) o Block error rate (BLER) o Outage
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* Average throughput o Number of calls K10 Hours and/or data communicated o Call set-up success rate (CSSR) o System/cell load information o Energy expenses o OPEX linked to energy expenses o Access delay o Call setup delay o Handover delay o Block call rate (BCR) o Drop call rate (DCR) o support for ubiquitous communication o support for emergency calls o support for lawful interception o network neutrality o QoS delivery:
* capacity
* delay
* packet loss
* guaranteed capacity
* preemption o Resource consumption
* energy
* network capacity
* compute
* storage
* caches o Resource consumption:
* signalling
* sub-optimal data paths
* management overhead (incl. monitoring)
* Contextual data (location, velocity, movement mode, etc.)
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* Session start time - Date and time of day, milliseconds resolution o Geo-location source used when session started –GPS
Where this information will be organised around:
* Geo Binned to keep a reference of the network performance with a specific area. A postprocessor will aggregate data and measures for each area cell visited by the devices
* Session records triggered by connectivity events related to:
* connectivity changes in the employed network interface
Besides, as we plan to manage data coming from virtual machines we will have
related datasets containing parameters such as:
* CPU
* RAM usage
* Disk usage
* Network usage
* User/system load
* Network traffic categories
* Incoming VNF traffic
* Outcoming VNF traffic
* VNF allocation time
* VNF instantiation time
* Number of VNF
* Types of VNF
### Format
Different features captured have standard format and units, such as the GPS
position or the speed, or _de facto_ formats such as availability,
utilization, CPU load delay or bandwidth throughput.
Moreover, the general format the probes records networking data will have an
analytical structure influenced and partially inherited from the data
collection acquired for the first phase. Hence, despite the high density of
nodes and the decentralised nature of the assets we will create a common and
homogeneous data format coming from heterogeneous infrastructures and systems.
### Accuracy and Volume
Our aim is to apply real-time policies 5G networks can be dynamically adapt to
next generation challenges. This way, live monitoring and real time decision-
making would just involve stateless data. However, the nature of the use cases
and scenarios that require Machine Learning algorithms to detect the trends
and patterns require a time window of data that spans last 24 hours in order
to include the different human behaviours during a daily life cycle.
So the different factors that come into the data volume equation are:
* the number of nodes and end devices
* the time window (historical values)
* the sampling frequency
* the number of features captured
* the metadata created for each asset
Because the input data has a short historical memory, machine learning is
required to achieve real time performance and the effect of the decisions
taken must be ready immediately (<1 second - <1 minute, depending on the SLA
and the change dimension), the dataset provided covers 24 hours.
### Origin
As described in detail in section 2.2.3 the 5G networking data that will be
processed in CogNet pivot around 3 phases: static non-synthetic dataset
(observational), live synthetic (simulation)/non-synthetic(experimental)
dataset, and live non-synthetic dataset (observational).
For the first phase, CogNet has the intention of buying an anonymized
collection of information gathered from end user devices including information
about network performance, application engagement, end user demand and usage
patterns and performance. These datasets can come from:
* telco operators partners belonging the consortium
* companies specialized in big data platform to enable network operators to optimize resources using the largest sample database of its kind
* other projects or communities that share and provide networking datasets
For the second phase, CogNet partners will collect their own datasets from
their controlled testing and experimental environments.
For the final phase, the datasets will come from telco operators that
participate in the CogNet consortium.
When dealing with new or non-anonymised data, CogNet will follow the best
practices and recommendations made by the Article 29 Data Protection Working
Party in their Opinion 05/2014 on Anonymisation Techniques 1 to
appropriately anonymise the data.
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## Standards and metadata
Network monitoring is an important function in network management because it
helps achieve three goals in network management: performance monitoring, fault
monitoring, and account monitoring.
### Standards
Concerning the standards to be considered, the list to be taken into account
for the formal dataset structure, nomenclature and taxonomy is:
* RFC 2819 2 , 4502 3 Remote Network Monitoring Management Information Base
* SMIv2 4 is the naming structure used for naming monitored objects.
* MIB-II 5 defines how the set of objects monitored can be defined. The attributes of these objects should have network monitoring values.
* NETCONF 6 is a network management protocol developed and standardized by the IETF 7 . It provides mechanisms to install, manipulate, and delete the configuration of network devices.
* YANG 8 is a data modelling language for the NETCONF network configuration protocol that was developed by the NETMOD working group in the IETF.
* IF-MAP 9 is an open standard that makes it possible for any authorized device or system to publish information to an IF-MAP server, to search that server for relevant information, and to subscribe to any updates to that information.
* SNMP/SNMPv2 10 internet standardized protocol on network management. It is used extensively for network monitoring functions such as collection errors and user statistics. SNMPv2 provides the information exchange and backbone for network monitoring.
* RMON/RMON2 11 is the standard of how to monitor internet traffic. This is a standard that is supposedly implemented by internet device vendors so that a network using RMONcompliant devices can be monitored using RMON-compliant software. RMON standard provides remote monitoring. Basically, RMON-compliant devices are created to check the status of the network.
* SMON provides network monitoring to switched networks.
* ATM-RMON is designed for ATM networks. This is a new standard based on RMON-like features.
So the data collection will be done according to standardised data
capture/recording process. The captured data will be collected employing
generic networking probes already available at the network nodes or NFV assets
and other specifically developed and deployed over the end-user devices.
### Versioning
Concerning the dataset versioning, folders structure and filenames, to help
people to find the data, the title/reference will include who created or
contributed to the data, date of creation and under what conditions it can be
accessed. Thus, each instance recording a network monitoring session would
have this naming convention:
* infrastructure identifier (telco operator or lab)
* machine learning algorithms and version applied (highlighting that the dataset is being influenced by some deployed CogNet policies)
* date and time
* _[main service(s)]_ when possible including the stimulus to the network, main service(s) processed and delivered by the network and accessed by the users
### Structure
According to the previous standards, and the structure for data aggregation
that they establish, individual metadata will be aggregated. In the case of
the metadata generation and the link to the base values, CogNet
Standardization leader T7.3 (TID) will lead and coordinate the work within the
consortium to define a format to do it.
Concerning the methodology to do it, Network Management System (NMS)
activities focuses on two alternatives:
* the management network and the production network may either be separated physically (out-band management segment) or
* the management network and the production network may share the same physical infrastructure (the VLAN segment of the network).
This aspect is still under discussion and will be decided in the WP2
Architecture deliverable (D22).
NMS should include three components:
* configuration management
* log management
* network monitoring
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This is because the decisions taken from the monitored network data effect the
network behaviour and hence, the dataset from this moment. The dataset will be
accompanied by a timed log bookmarking the decisions taken. Thus, the
documentation that will accompany the data helps secondary users to understand
and reuse it.
To control the consistency and quality of data collection, 2 consecutive
samples (24 hours each) will be taken under similar social dimension
conditions (working days, starting at the same time). Because the network
conditions can be very changeable with highly dynamic behaviour and we want to
keep these singularities no processes such as calibration, data entry
validation, peer review of data or representation will be done.
Concerning analytical and procedural information management, a preliminary
process performed by the Machine Learning ‘ _**processor’** _ will remove
repeat samples or measurements.
### Vocabulary
The variables, vocabularies, units of measurement are being defined in the WP2
activities around the definition of Use Cases and scenarios (D21). At this
moment, the parameters considered are led by typical telecom KPIs.
## Data infrastructure
For the Network monitoring ( _**'controller'** _ ), including the probes to
get networking values such as errors and user statistics, there are two
alternatives:
* the management network and the production network may either be separated physically (out-band management segment) or
* they may share the same physical infrastructure (the VLAN segment of the network).
This aspect is still under discussion and it will be decided in the WP2
Architecture deliverable (D22).
Moreover, the Network Manager (‘ _**processor’** _ ) needs infrastructure to
manage and process the data volume that comes from the Machine Learning
algorithms (e.g. performance degeneration detection or traffic
prioritization). The specific platform is still under discussion and it will
be decided in the WP2 Architecture deliverable (D22). Its implications on the
DMP will be analysed then.
## Data sharing
Here most of the aspects related to copyright and Intellectual Property Rights
(IPR) issues have been established in the 5G infrastructure Collaboration
Agreement between each beneficiary with the EC in respect of a particular 5G
Action and the Consortium Agreement.
### Ownership
The CA does not establish ownership for each data collection but how it is to
be split across partner sites in collaborative research activities.
Here, the DMP states who will own the copyright and IPR of any data that
CogNet will collect or create. Considering the dataset as the monitored values
plus the metadata and produced logs. For each dataset the ownership will be
shared:
* data captured - telco operator or lab provider not only provide infrastructure but the probes or the setup to deploy them
* metadata production - machine learning research participants o In parallel activities, each participant owns the metadata generated o In case of pipelined processing, each participant owns the metadata generated
### Licensing
Regarding the licensing for reuse or any restrictions on the reuse of third-
party data we have to keep in mind (as it is described in detail in section
2.2.3) that the 5G networking data that will be processed in CogNet pivots
around 3 phases: static non-synthetic dataset (observational), live synthetic
(simulation)/non-synthetic (experimental) dataset, and live non-synthetic
dataset (observational).For the first phase, CogNet has the intention of
buying an anonymous collection of information from end user devices to network
infrastructure nodes including information about network performance,
application engagement and performance. So this data collection cannot be
shared.
Considering permissions to reuse third-party data and any restrictions needed
on data sharing, the datasets generated in the second and the final stage the
will be in an Open Access basis (for non-commercial research/educational use
only) from the project’s end.
### Ethics
The first aspect to consider is the management of any ethical issues. This
responsibility falls on the CogNet 'controller' who have to:
* gain formal consent for data preservation, sharing and reuse.
* protect the identity of participants via anonymisation.
* assure data is stored, transferred and handled securely.
The DMP coordinator, Vicomtech-IK4 (ES) will monitor that guidelines and
recommendations collected in the DMP have been translated to project
development.
### Accessibility
The retention period of all data and information collected, stored and
processed for the dataset will be for 5-years following the end of the
project. During this period, unless otherwise decided by the consortium
members, the database functionality will remain the same as during the project
duration.
The dataset will be accessible through the CogNet portal and if no other
decision has been made with respect to the database by the consortium members,
the datasets will be attached to the CogNet wiki for open-access.
A record will track entities reusing the CogNet dataset by means of a data
sharing agreement. These third parties will not have rights for re-
distribution or of publishing the dataset partially or totally requiring the
reference to the dataset in any publication or derived product. Any potential
users that want to get access to the data will be under the DMP conditions
respecting the sensitivity of data. Here, a nondisclosure agreement would give
sufficient protection for confidential data.
Any potential user that wants the get access would be guided to:
1. submit a "request" to the dataset _**controller** _ from the CogNet consortium (TSSG). This request will contain:
* full name
* organization and department
* email address
* description of intended use
2. After reviewing the request. If the _**controller** _ approves it, the user will receive an email with a special link to verify the email address.
3. Then the user is asked to agree to and sign the following terms of access: [RESEARCHER_FULLNAME] (the "Researcher") has requested permission to use the 5G networking dataset (the "Dataset"). In exchange for such permission, Researcher hereby agrees to the following terms and conditions:
1. Researcher shall use the Database only for non-commercial research and educational purposes.
2. _**Controller** _ makes no representations or warranties regarding the Dataset, including but not limited to warranties of non-infringement or fitness for a particular purpose.
3. Researcher accepts full responsibility for his or her use of the Dataset and shall defend and indemnify _**controller** _ , including their employees, Trustees, officers and agents, against any and all claims arising from Researcher's use of the Dataset, including but not limited to Researcher's use of any copies of copyrighted dataset that he or she may create from the Dataset.
4. Researcher may provide research associates and colleagues with access to the Dataset provided that they first agree to be bound by these terms and conditions.
5. _**Controller** _ reserves the right to terminate Researcher's access to the Database at any time.
6. If Researcher is employed by a for-profit, commercial entity, Researcher's employer shall also be bound by these terms and conditions, and Researcher hereby represents that he or she is fully authorized to enter into this agreement on behalf of such employer.
7. The law of the _**Controller’s** _ country shall apply to all disputes under this agreement.
## Archiving and preservation
### Availability
The database will be accessible through the CogNet portal for five years
following the end of the project. During this period, unless otherwise decided
by the consortium members, the database functionality will remain the same as
during the project duration.
In this 5-year period and if no other decision has been made with respect to
the database by the consortium members, the datasets will be attached to the
CogNet wiki for open-access.
Once the dataset becomes public after the project end, the consortium will
publish the dataset in a chosen third-party service, ensuring that this does
not conflict with any legal jurisdiction in which data are held or the
protection of sensitive data.
### Security
Because data managed is not confidential and not related to personal data or
trade secrets and it will be anonymized the risks to data security are minimum
but still managed according to ISO 27001. The following security requirements
have been identified and are presented:
* _Access Control (Authorization)_ . CogNet dataset repository must be capable of controlling the level of access that each user has depending on their role. There must be appropriate mechanisms to define and enforce such access control (e.g. firewalls, file systems permissions, secure log-in) including physical control. _This is provided by the IT department of the dataset maintainer or by the dataset repository technology itself_ .
* _Authentication_ . It must guarantee that the system being accessed is the intended one and that the user is who claims to be. _During the project, the partners will have access using a private password. Once the datasets become public an e-mail based mechanism will grant access_ .
* _Non-Repudiation_ . To ensure the capability to prevent users from denying that data files were accessed, altered or deleted, auditing processes must be implemented. _This is provided by the IT department of the dataset maintainer or by the dataset repository technology itself_ .
* _Data Confidentiality_ . Within the scope of the project, the protection of information from unauthorized access and disclosure must be preserved by restricting per-user access and encrypting the information during transmission and also during storage. After the defined retention period expires, ensure information erasure/destruction. _This is provided by the IT department of the dataset maintainer or by the dataset repository technology itself_ .
* _Communication Security_ . Communication only flows through encrypted communication channels. _This is provided by the IT department of the dataset maintainer or by the dataset repository technology itself_ .
* _Data Integrity_ . CogNet must protect data from unauthorized, uncontrolled, or accidental alteration during storage or transmission with the use of checksum values, hash functions and digital signatures. _This is provided by the IT department of the dataset maintainer or by the dataset repository technology itself_ .
* _Availability_ . Back-up mechanisms are a desirable property, mainly to avoid Denial of Service (DoS) attacks. _This is provided by the IT department of the dataset maintainer or by the dataset repository technology itself_ .
## Discoverable
By means of the project portal the datasets can be discovered. Moreover, the
project publications and dissemination activities done in WP7 will bring
visibility of the results and datasets coming from CogNet to the academia and
research communities.
## Accessible
The datasets will be accessible through a FTP server to be downloaded
following the accessing mechanism described before.
## Assessable and intelligible
Thanks to the use of standards and a proper and documented metadata annotation
the datasets will ensure a usable dataset in a short time. The activities done
in WP7 will target this aspect.
## Useable
CogNet aims solutions for a higher and more intelligent level of automated
monitoring and management of networks and applications improve operational
efficiencies and facilitate the requirements of 5G. The same way CogNet will
employ the datasets to find underlying connections to detect trends, patterns
and symptoms towards a specific diagnostic enabling automatic decision making
to deploy preventive or corrective policies, this data that reflects human
activities could be used in other domains or sectors such as smart cities,
traffic road planning, energy production, etc.
## Interoperable
Because there is not a standard for storing this kind of information the
interoperability to allowing data exchange between researchers, institutions,
organisations, countries, etc. and the re-combination with different datasets
from different origins is very difficult. It always needs of a human
interpretation of the data structure to manually create a data map. However,
the utilization of standards for data capturing and the documented annotation
will ease the data exchange. This aspect will be reviewed in WP7 (D731, D732).
# Data Protection Agency Notifications
According to applicable data protection law it may be necessary to notify data
protection authorities in the jurisdiction where RTD activities will be
carried. At the initial stage of the CogNet project the exact requirements and
due diligence have been scoped and defined within the jurisdictions where the
research will take place.
The conclusions of the review of the applicable data protection law in the
jurisdiction where RTD activities will be carried out in CogNet are that it
will be necessary to notify the applicable Data Protection Agencies (DPA) of
Ireland, Spain, and Italy, whereas in the case of Germany and Israel we
understand that the law does not require these notifications. The notification
procedures that have been followed are described in Article 29 Working Group
document “Vadmecum on Notification Requirements” 12 .
The rest of this section presents the conclusions of the review of the
applicable data protection law in the jurisdiction where RTD activities will
be carried out in CogNet.
As stated in Section 5 of the project proposal, the notification requirements
vary from one country to another and therefore no single timeline can be
provided for completion of all notification procedures.
In addition to the initial notifications, before testing phases of the CogNet
project, the respective national DPAs of the participant countries where the
field-trials, demonstrations, and feasibility experimentations will take place
will be notified of the projects testing phases and more specifically on the
personal data of the voluntary participants that will be utilized.
## Spanish Data Protection Agency Notification: ES
This section includes the formal acknowledgement of receipt of notification of
the CogNet project in the jurisdiction of the DMP coordinator, i.e. the
document received from the Spanish Data Protection Agency on behalf of the
agency’s Director that confirms the registration of the CogNet file.
The Spanish Data Protection Agency (AEPD in the Spanish acronym) is the public
law authority overseeing compliance with the legal provisions on the
protection of personal data. In order to guarantee effective compliance with
the Organic Act on Data Protection (LOPD), which is the basis of the system to
ensure the right to protect personal data, the adequate involvement of all
agents is essential.
The AEPD is of the understanding that its functions must always be conducted
with the priority objective of guaranteeing the protection of individual
rights. Accordingly, actions are taken specifically aimed at enhancing
citizens’ capacity to effectively contribute to data protection.
Some of the outstanding activities performed by the Spanish Data Protection
Agency are the dissemination of activities and of the right to protection of
personal data, direct assistance in response to citizens’ queries, and
procedures to protect rights of individuals.
One of the most important instruments for better protecting the rights of
citizens is the registry of filing systems. The evolution of the data on the
filing systems registered at the Data Protection General Registry (RGPD) is
considered a significant reference regarding compliance with the Data
Protection Laws.
The AEPD encourages the adoption of rules that are meant to complete the legal
framework for data protection, and it also contributes to ensuring that the
right to data protection is treated correctly in the legal provisions adopted
with purposes that have no specific relation to data protection.
Many of the important topics affecting data protection are of an international
scope and certain concerns, such as security, reach well beyond national
boundaries. Therefore, this important international dimension is present in
all of the activities of the AEPD, which has been and continues to be involved
in a number of international forums.
### Agency Notification: ES
The CogNet information has being registered as a Privately Owned File (Fichero
de Titularidad Privada), by introducing the required information in the
Spanish Data Protection General Registry. As specified in Article 14 of the
Organic Act on Data Protection, the right to consult the Registry enables any
person to become acquainted of the existence of treatments of personal data,
its purpose and the identity of the data controller.
Notifications to the AEPD can be done in the following _link_ .
**Figure 1 - Notification sent to the Spanish DPA**
The information contained in the CogNet file registered in the Spanish Data
Protection Agency has also being included in Vicomtech-IK4’s Security Document
in compliance with the Organic Act on Data Protection. The information
notified to the Agency regarding personal data protection of the project is
structured as explained below.
### Private Ownership File: Detailed Information: ES
**Figure 2 - Details of the notification sent to the Spanish DPA**
## Notification Processes: IE, IT, DE, ISR
Besides the aforementioned notification to the Spanish Data Protection Agency,
the required certification and approval of the data protection agencies of the
remaining participating states (i.e. Ireland, Italy, Germany and Israel) where
research has been carried out are presented in this section.
### Notification to the Data Protection Commissioner: IE
This section includes the formal notification regarding the CogNet project to
the Irish Data Protection Authority, the office of the Data Protection
Commissioner which was established under the 1988 Data Protection Act. The
Data Protection Amendment Act, 2003, updated the legislation, implementing the
provisions of EU Directive 95/46. The Acts set out the general principle that
individuals should be in a position to control how data relating to them is
used.
Registration with the Data Protection Commissioner can be done in the
following _link_ .
The notification to the Data Protection Commissioner has not been completed at
the time of writing this first version of this deliverable. In the next update
of this document the notification receipt will be included here.
### Notification to the Garante per la protezione dei dati personali: IT
This section includes the formal notification regarding the CogNet project to
the Italian Data Protection Authority (Garante per la protezione dei dati
personali), an independent authority established to protect the fundamental
rights related to the processing of personal data, and to ensure respect for
individuals' dignity.
Notifications to the Garante per la protezione dei dati personali can be done
in the following _link_ .
The notification to the Garante per la protezione dei dati personali has not
been completed at the time of writing this first version of this deliverable.
In the next update of this document the notification receipt will be included
here.
### Notification to the Der Bundesbeauftragte für den Datenschutz und die
Informationsfreiheit: DE
The main legal source of data protection in Germany is the
(Bundesdatenschutzgesetz) (BDSG), which implements Directive 95/46/EC on data
protection. Additionally, each German state has a data protection law of its
own. There are sectoral laws, including the Telecommunication Act
(Telekommunikationsgesetz), which applies to providers of telecommunication
services. The German Act Against Unfair Competition (Gesetz gegen den
unlauteren Wettbewerb) (the “UCA”) dated 3 July 2004 and the revised German
Telecommunications Act (Telekommunikationsgesetz) (the “TA”) dated 22 June
2004 (with the TA being applicable only to telecommunications service
providers in addition to the UCA) both implemented Article 13 of the Privacy
and Electronic Communications Directive. Further, the TMA may be amended to
implement the Citizens’ Rights Directive as regards the storage of cookies.
Note that German data protection law does not apply if a controller located in
another country of the European Economic Area (EEA) collects, processes or
uses personal data within Germany.
The collection, processing and use of personal data is only admissible if
expressly permitted by the BDSG or any other legal provision, or if the data
subject has expressly consented in advance.
The Federal Republic of Germany is a federation of 16 states which are not
just provinces but states with their own original sovereign rights and
legislative responsibilities. The supreme power of the State is divided
between the federal and the state governments. The federal system of
government also affects the supervision of data protection.
In Germany there are 20 different federal and regional supervisory authorities
responsible for monitoring the implementation of data protection. Data
protection supervision in the private sector comes under the responsibility of
the states. However, there is one exception: the telecommunications and postal
services companies. Those firms are monitored by the federal government which
has assigned that task to the Federal Data Protection Commissioner (Der
Bundesbeauftragte für den Datenschutz und die Informationsfreiheit).
According to Art 4 d and 4 e of FDPA Data Controllers have to notify if they
engage in data storing and processing for the purpose of transferring data for
business reasons; all others are exempted. According to the aforementioned Act
(FDPA) all automated processing procedures have to be registered with the
competent supervisory authority. However, this rule is not applied if the
entity has appointed a data protection official (“privacy officer” under
Directive 95/46/EC).
Therefore, according to our interpretation of the German law 13 , CogNet
does not need to be registered or notified, provided a Data Protection
Official is appointed by the controller, which is the case, as Fraunhofer
Gesellschaft has been appointed CogNet Data Protection Official in Germany.
Therefore, the Data Protection Official at Fraunhofer Gesellschaft has been
duly informed by the controller and keeps the information as set out by the
law. For the CogNet project, an information note has been submitted to the
Data Protection Official at Fraunhofer Gesellschaft to comply with the
registration requirements set out in article 4e of the German Federal Data
Protection Act. As required, the Data Protection Official at Fraunhofer
Gesellschaft was informed of the elements as required under German
legislation, including the name of the controller, the name of the persons
responsible for the data processing, the persons authorised to access the data
and the purposes. Shall it be needed, a consent form will be prepared for
volunteers giving them duly information of their rights and a clear overview
of what data will be processed for what purposes during the field-trials,
demonstrations, and feasibility experimentations of the project.
### Notification to the Israeli Law Information and Technology Agency
(ILITA): ISR
EU Commission published on January 31, 2011, an announcement on the adequacy
of data protection law in Israel 14 . The Article 29 Working Party’s
favourable opinion on the level of adequacy under Israeli law 15 ,
contributed to the adoption of the decision, as well.
Article 1 of the aforementioned Commission Decision says:
1. _For the purposes of Article 25(2) of Directive 95/46/EC, the State of Israel is considered as providing an adequate level of protection for personal data transferred from the European Union in relation to automated international transfers of personal data from the European Union or, where they are not automated, they are subject to further automated processing in the State of Israel._
2. _The competent supervisory authority of the State of Israel for the application of the legal data protection standards in the State of Israel is the ‘Israeli Law, Information and Technology Authority (ILITA)’, referred to in the Annex to this Decision._
The decision set out a variety of findings that served as grounds for
declaring data protection in Israel to be in conformity with EU standards. The
Commission favourably mentions the semiconstitutional status of the right to
privacy under the Human Dignity and Liberty basic law; the similarity in
standards between the EU Data Protection Directive and Israel’s Privacy
Protection Act; the existence of data protection provisions in legislation
related to the financial, health and public sectors; the availability of
administrative and judicial remedies; and the independence of the country’s
data protection authority – the Israeli Law Information and Technology Agency
(ILITA) 16 .
Privacy is a constitutional right under Article 7 of Basic Law: Human Dignity
and Liberty. In addition, the Privacy Protection Act, 5741-1981 (“PPA”),
contains specific privacy legislation. Chapter B of the PPA deals with data
protection. The PPA entered into force in 1981. The Privacy Law requires that
the owner of a database register a database with ILITA if:
* The database contains information on more than 10,000 people;
* The database contains sensitive information on any number of people;
* The database includes information on persons, and the information was not delivered to this database by them, on their behalf or with their consent;
* The database belongs to a public body; or
* The database is used for direct mail services.
The registration system is based on registration of databases, as opposed to
data controllers. Hence, if a data controller has several databases, such as
human resources, customer data, and suppliers, it must register each database
separately. In 2014, ILITA amended the database registration procedures,
requiring the filing of a far more detailed application form than before,
specifying, amongst other things, the methods and sources of data collection
and the types of data in a database.
The term “database” refers to a collection of data processed by computer but
excludes information consisting solely of basic contact details if such
details are not in themselves likely to infringe an individual’s privacy.
The PPA restricts data transfers to third parties, including corporate
affiliates, within or outside of Israel. An additional layer of regulation
applies to international data transfers under the Privacy Protection
Regulations (Transfer of Data to Databases Outside of Israel), 2001 (the
“Transfer Regulations”). The Transfer Regulations apply to both inter- and
intra-entity transfers of personal data outside of Israel. They permit
transfers to: (i) EU Member States; (ii) other signatories of Council of
Europe Convention 108; and (iii) a country “which receives data from Member
States of the European Community, under the same terms of acceptance”.
Therefore, according to our interpretation of the Israeli PPA, at this initial
stage of the CogNet project it is not foreseen the need to obtain
authorisation from the Database Registrar.
# Conclusions
This document has provided guidelines that will deliver simple and practical
help during the implementation and validation stages of the CogNet platform,
support compliance on underlying legal framework and promote the adoption of
best data management practices.
The DMP establishes a set of guidelines meeting each of the fundamental topics
to be considered. These guidelines cover aspects such as applicable policies,
roles, standards, infrastructures, sharing strategies, data processing,
storage, retention and structure, legal compliance and compliance with market
standards and best ethical and privacy practices, identification,
accessibility, intelligibility, legitimate use for other purposes. These
guidelines will be adopted at the early stages of the project.
At the time this first version of the DMP was written, the datasets to be
utilized by the platform have not been fully defined and therefore their
description and the exact procedures to be adopted have not been discussed
extensively.
Moreover, to satisfy all the regulations, all applicable Data Protection
Agencies (DPA) of the countries of the partners belonging to the CogNet
project have been or are in the process of being notified (Ireland, Spain, and
Italy), The DPA in Germany has not been notified as, in accordance to the
Germany law, a Data Protection Official (Fraunhofer Gesellschaft) has been
appointed by the controller. With regard to Israel, no database has been
registered to the Data Protection Agency in Israel at this stage of the CogNet
project we do not foresee any dataset in Israel to have information on more
than 10,000 people, to contain sensitive information on any number of people,
to include information on persons, and the information was not delivered to
this database by them, on their behalf or with their consent, to belong to a
public body, or to be used for direct mail services.
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Horizon 2020
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1040_VirtuWind_671648.md
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# 1 ROLES AND RESPONSIBILITIES
Roles and responsibilities for maintaining and updating the Data Management
Plan (DMP) are linked to roles within VirtuWind. In case new personnel is
assigned to a relevant role, responsibilities with respect to the DMP are also
taken over. For details on the management roles and structure of VirtuWind see
Description of Action (DoA), Section 3.
The Data Management Plan is maintained by the Project Coordination Committee
(PCC). Reviews of the DMP are a regular agenda item of PCC meetings,
conference calls, and work package (WP) results will be checked with respect
to relevant information for the DMP. WP leads are responsible that results of
tasks within their work package are aligned with the definitions in the DMP.
WP leads are also responsible that the table in Section 3 of this DMP is
updated as soon as data according to the definition in Section 2 is created
within their WP (for details on the update procedure see Section 6.2). Updates
of the tables in Section 3 of the DMP are communicated from the Technical
Management Committee (TMC) to the PCC together with the minutes of the monthly
TMC calls (see also section on update procedures).
In order to ensure that this DMP is implemented and followed, reviews (by PCC
and/or TMC) of all kinds of project related documents (e.g., reports,
deliverables, publications) will include also a check for used data and the
proper documentation and use in-line with this DMP.
In case the contact person for data is leaving the project, the affiliation of
the original contact person will take over the responsibility and will assign
a new contact person.
# 2 EXPECTED DATA
VirtuWind is a three year project and will produce a number of technical
results relevant for SDN-based industrial networks. This includes data created
in lab experiments and real world tests of industrial networks (specifically
control networks of wind parks). Some data created and used in VirtuWind is
related to critical infrastructures and will not be publically accessible.
More details and reasoning will be given in the detailed description of the
specific data sets in Section 3.
Expected data will be created as:
* raw data sets of network status data from lab experiments and real world data
* data derived from raw data, including related data processing algorithms
* technical and scientific publications (processed data)
* reports and deliverables (processed data)
# 3 DATA FORMATS AND METADATA
## 3.1 Data Formats
Detailed descriptions of the expected information of each cell are given at
the end of this section.
<table>
<tr>
<th>
**Data set reference**
</th>
<th>
**Data set name**
</th>
<th>
**Data Set Description**
</th>
<th>
**Standards and metadata**
</th>
<th>
**Data sharing**
</th>
<th>
**Archiving and preservation (including storage and backup)**
</th>
<th>
**Contact Person/ source of data**
</th> </tr>
<tr>
<td>
VirtuWind01
</td>
<td>
Test Data
</td>
<td>
Test Data
</td>
<td>
NA
</td>
<td>
confidential
</td>
<td>
\-
</td>
<td>
PI, [email protected]
</td> </tr>
<tr>
<td>
Test
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td> </tr>
<tr>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td> </tr>
<tr>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td> </tr>
<tr>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td> </tr>
<tr>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td> </tr>
<tr>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td> </tr> </table>
**Table 1: Data Formats Note:**
**This table is linked to file “VirtuWind DMP.xlsx” -**
**_https://overseer1.erlm.siemens.de/repository/Document/downloadWithName/VirtuWind%20DMP.xlsx?reqCode=downloadWithName
&id=8 _ ** **_033083_ . **
**Insert text into the Excel file and update this linked table to make changes
visible!**
5
The following table gives a detailed description of the fields used in the
data formats table of Section 3.1.
<table>
<tr>
<th>
Data set reference and name
</th>
<th>
Identifier for the data set to be produced
</th> </tr>
<tr>
<td>
Data set description
</td>
<td>
Origin (in case it is collected), scale and to whom it could be useful, and
whether it underpins a scientific publication. Information on the existence
(or not) of similar data and the possibilities for integration and reuse.
</td> </tr>
<tr>
<td>
Standards and metadata
</td>
<td>
Reference to existing suitable standards of the discipline. If these do not
exist, an outline on how and what metadata will be created
</td> </tr>
<tr>
<td>
Data sharing
</td>
<td>
Gives dissemination level, where data is available (link), access procedures,
embargo periods (if any), outlines of technical mechanisms for dissemination
and necessary software and other tools for enabling re-use, and definition of
whether access will be widely open or restricted to specific groups.
Identification of the repository where data will be stored, if already
existing and identified, indicating in particular the type of repository
(institutional, standard repository for the discipline, etc.).
**In case the dataset cannot be shared, the reasons for this should be
mentioned here** (e.g. ethical, rules of personal data, intellectual property,
commercial, privacy-related, security-related).
</td> </tr>
<tr>
<td>
Archiving and preservation (including storage and backup)
</td>
<td>
In general, the procedure described in Section 5 will be applied. This cell
gives a data specific description of the procedures that will be put in place
for longterm preservation of the data (if required).
Indication of how long the data should be preserved, what is its approximated
end volume, what the associated costs are and how these are planned to be
covered (if required).
</td> </tr> </table>
## 3.2 Metadata
VirtuWind plans to create and share data in relation to project deliverables
or publications. Deliverables and publications will give all relevant
information, including the meaning of data sets, the methods of data
acquisition/processing, as well as specific methods/algorithms for usage (if
required). Thus, deliverables and publication can be considered as main piece
of metadata for all data sets created within the project.
# 4 DATA SHARING AND ACCESS
Data and metadata, as well as project related documents (release version of
document – not raw format) with dissemination level “public” will be
accessible via the project website _http://www.virtuwind.eu_ . Registration
(free of charge) is required to get access. The dissemination level is
initially proposed by the corresponding author and will be approved by PCC
(details see Section 6.2).
As far as possible depending on the publishers’ policy pre-prints of the
publications will be made available open access via the project website, as
well as OpenAIRE. In case embargo periods (e.g., given by publishers) have to
be considered, open access will be given after the embargo period expires.
VirtuWind has also a budget item allocated for open access which will be used
to provide open access for publications of high importance/relevance. The
decision making procedure for such “selected” open access publication will is
given in the VirtuWind Dissemination Plan.
Software which is required for using published data is made available via the
project website _http://www.virtuwind.eu_ according to the corresponding
license terms (e.g., open source licenses such as
EPL, …).
All modified GPL or similar copy left licensed code will be made publically
available in _www.virtuwind.eu_ at project completion.
# 5 DATA ARCHIVING AND PRESERVATION
All project related documents (raw formats), deliverables, reports,
publications, data, and other artifacts will be stored in a repository
accessible during project duration for all partners. This repository is hosted
(with backup) by the Coordinator and the link is/was distributed at the first
consortium meeting. Access to the repository is given to registered persons
from project partners only. The folder structure of the repository is managed
by the coordinator and changes of the structure need to be coordinated with
the Coordinator. Corresponding partners will keep the above mentioned
repositories operational during the project life time. After project closure,
repositories will be maintained for at least one more year. After project
closure the administrating partner can change access policies (e.g.,
restricted access / access on demand) in order to keep maintenance costs at a
minimum.
6. ANNEX:
6.1 Important Links:
Current Version of the Data management plan (pdf):
_https://overseer1.erlm.siemens.de/repository/Document/downloadWithName/Deliverable%20D1.4.docx?req_
_Code=downloadWithName &id=8033085 _
Excel file for data formats and metadata (to be updated by WP leads and TMC):
_https://overseer1.erlm.siemens.de/repository/Document/downloadWithName/VirtuWind%20DMP.xlsx?reqCo_
_de=downloadWithName &id=8033083 _
## 6.2 Update procedure for Table 1
Trigger: data (see definition in Section 2) is produced within a task
1. WP lead updates table with data format giving all required details for new data set including a proposal for the dissemination level. In case of questions, TMC/TM can assist.
2. WP lead informs TMC on update and TMC will create a new sub-version of the DMP (with updated table in Section 3).
3. TMC will inform PCC on the release of that new sub-version and PCC members can check the update on table 1 (incl. approval of dissemination level). In case PCC has objections, TM will be informed by PCC. TM then helps to clarify the open issues between PCC and WP lead.
## 6.3 Update procedure of this deliverable (except Table 1)
Updates of this deliverable (not table 1 – for updating table 1 see section
above) can be triggered by TMC and/or PCC. All releases of this deliverable
are subject to PCC approval and will follow the decision making process
described in the Consortium Agreement.
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Horizon 2020
|
1041_CHARISMA_671704.md
|
# Executive Summary
This document outlines the management guidelines and Data Management Plan for
CHARISMA, including a set of rules and guidelines for an effective execution
of the project operations in the different work packages and tasks by the
whole consortium.
The document provides an overview of the management structure and the major
management and coordination roles, focusing on the responsibilities of the
different boards and managers. It also defines the communication channels
among partners (such as email distribution lists, conference calls,
collaboration through sharing content, action points and issue tracking tool)
that will facilitate the execution of the project, as well as a set of basic
guidelines for using them.
The management guidelines provide information about general rules for planning
and reporting, defines the quality assurance roles and responsibilities,
establishes a deliverable acceptance and review process, and defines the
document management rules and tools.
This document also includes the Data Management Plan (DMP) that deals with the
project digital research data. The purpose of the DMP is to define a policy
regarding the data management life cycle for all data that will be collected,
processed and generated through the project execution. The DMP also specifies
the data that will be made available to the public and the one that will be
for internal use only.
This document is intended to complement the information already included in
the Description of Action (Annex 1 of the Grant agreement) and the Consortium
Agreement of CHARISMA.
The first part of the deliverable (Sections 1 and 2) defines the roles and
functions of the partners and the individuals involved in the management
structure providing a set of contact references that are necessary to carry
out the work effectively and deliver quality results.
The second part (Section 3) defines the project Quality Plan, which is based
on best practices implementation in a project of this nature. Quality
management of the project results is implemented as a continuous improvement
process highly related to the project agile methodologies (SCRUM), adopted to
manage the software development activities.
The third part (Section 4) is devoted to the DMP which outlines the method
used for collecting, organizing, backing up and storing the data generated in
the framework of CHARISMA.
# 1\. Management structure
With the aim of a successful execution of CHARISMA, it is key to ensure that
the project accomplishes the stated objectives, within the given deadlines and
that the results can guarantee its exploitation after the project. From a
technical point of view and from an administrative point of view it is also
very important that the timing and budget are successfully met, as stated in
the DoA.
Currently, the project consortium comprises 13 partners. An additional partner
will be incorporated to the consortium during the first months of the project
as agreed with the European Commission.
The Description of the Action (Part A) and the Consortium Agreement describe
in detail the project management goals and the project management structure,
and summarizing here their contents and obligations.
**Figure 1-1: CHARISMA Project Management Structure**
The management structure shown in Figure 1 has been set by the consortium
aiming to accomplish the following general objectives:
* Ensure that the project is conducted in accordance with EC rules;
* Reach the objectives of the project within the agreed budget and time scales;
* Coordinate the work and ensure effective communication between partners;
* Ensure the quality of the work performed as well as of the deliverables;
* Maximize the potential for exploiting results;
* Manage properly foreground and IPR matters;
* Ensure that decisions are taken on the basis of data and factual information;
* Solve any problem or conflicting situation;
* Set the quality policy, including quality objectives for the project and;
* Ensure that the appropriate infrastructure is set up to support the objectives above.
## 1.1. Governance structure
### 1.1.1. General Assembly
The General Assembly (GA) is the ultimate decision-making body of the
consortium, meaning that the GA can make decisions on contractual matters,
such as the budget, timeline, deliverables, PM shifts, adding/deleting
partners. As outlined in the Consortium Agreement, the GA consists of one
representative from each partner and is chaired by the Project Coordinator
(PC). Each Partner has nominated a representative, with budget responsibility
and able to assume the role as well as to represent contractor’s interests.
In accordance with the Consortium agreement, the GA will assume the following
responsibilities:
* Provide overall direction and policy;
* Control the satisfactory execution of the project in terms of road-map and monitor corrective actions, including deliverables review;
* Budget follow-up and transfer, including budget re-allocation and allocation of the contingency funds;
* Contractual changes including in the Consortium Agreement and selecting new contractors to enter in the partnership, termination of the contract and action against defaulting partners; Approving all reports and deliverables required in the frame of the Grant Agreement; Arbitrating on deadlock situations occurring within the WPs.
The GA shall be convened at least once a year in the framework of ordinary
meeting and at any time upon written request of the Technical Board or 1/3 of
the members of the GA in an extraordinary meeting.
Decisions will be taken by consensus whenever possible; only in case of
conflict decisions will be taken by voting. In the latter case, the quorum has
to be reached. In voting, each partner representative has one vote. Any
decision may also be taken without the need of a meeting in person if the
coordinator circulates to all members of the Consortium Body a written
document which is then agreed by the defined majority of all members of the
Consortium Body (see Section 6.2.3. of the Consortium Agreement). Such
document shall include the deadline for responses.
Any substantial changes agreed at this level would typically be reflected in
amendments to the project work plan, contractual documents or updates of the
Consortium Agreement as required.
The GA meeting will normally take place as face-to-face meeting but may also
be held by teleconference or other telecommunication means. It is required a
minimum attendance of at least two-thirds of its members (Quorum: present or
represented). The GA members are shown in the table below.
<table>
<tr>
<th>
**Participant name**
</th>
<th>
**GA Member**
</th> </tr>
<tr>
<td>
**Fundacio Privada I2CAT, Internet i Innovació Digital a Catalunya**
</td>
<td>
Eduard ESCALONA / Carles BOCK
</td> </tr>
<tr>
<td>
**Fraunhofer-Gesellschaft zur Foerderung der Angewandten Forschung E.V**
</td>
<td>
Volker JUNGNICKEL
</td> </tr>
<tr>
<td>
**National Center for Scientific Research “Demokritos”**
</td>
<td>
Tasos KOURTIS
</td> </tr>
<tr>
<td>
**APFutura Internacional Soluciones, SL**
</td>
<td>
Oriol RIBA
</td> </tr>
<tr>
<td>
**Innoroute GMBH**
</td>
<td>
Andreas FOGLAR
</td> </tr>
<tr>
<td>
**INCITES Consuting SARL**
</td>
<td>
Theodoros ROKKAS
</td> </tr>
<tr>
<td>
**JCP-Consult SAS**
</td>
<td>
Jean-Charles POINT
</td> </tr>
<tr>
<td>
**University of ESSEX**
</td>
<td>
Mike PARKER
</td> </tr>
<tr>
<td>
**Cosmote Kinites Tilepikoinonies AE**
</td>
<td>
Elina THEODOROPOULOU
</td> </tr>
<tr>
<td>
**Intracom S.A Telecom Solutions**
</td>
<td>
Spiros SPIROU
</td> </tr>
<tr>
<td>
**Telekom Slovenije DD**
</td>
<td>
Blaž PETERNEL
</td> </tr>
<tr>
<td>
**PT Inovaçao e Sitemas SA**
</td>
<td>
Victor MARQUES
</td> </tr>
<tr>
<td>
**Ethernity Networks LTD**
</td>
<td>
David LEVI
</td> </tr> </table>
Table 1-1: CHARISMA General Assembly Members
### 1.1.2. Project Coordinator
<table>
<tr>
<th>
Name
</th>
<th>
Eduard Escalona
</th> </tr>
<tr>
<td>
Address
</td>
<td>
Gran Capità 2-4 Edifici Nexus I
</td> </tr>
<tr>
<td>
Place
</td>
<td>
Barcelona
</td> </tr> </table>
The **Project Coordinator (PC)** is responsible for implementing the decisions
taken by the assembly and for tracking the overall project progress according
to the work plan. The PC provides the Commission, as the official
communication channel, with technical, managerial and financial information.
The coordinator is the authorized contact to negotiate on behalf of the
consortium meaning that the project administration will be under his
supervision, responds to important changes during the lifetime of the project
and coordinates necessary adaptation to meet the conditions of the external
environment. In this role he is assisted by the **Management Team (MT)** and
the **Technical & Innovation Manager (TIM). **
Dr. Eduard Escalona (i2CAT) is the Project Coordinator of CHARISMA. The
project coordinator chairs the general Assembly. His major tasks are:
* Consortium Agreement coordination;
* Supervision of distribution of EC’s payments to partners;
* Preparation of the support of the MT for the reports, cost statements and project documents required by the EC;
* Solution provider of all conflicts among partners;
* Organization and coordination of the internal review of EC deliverables;
* Organization of EC review meetings;
* Supervision of IPR and knowledge management;
* Representative of the consortium in dissemination events;
The PC is helped by assistants responsible for the daily administrative
supervision and financial reports and by the Technical and Innovation
Coordinator.
<table>
<tr>
<th>
</th>
<th>
Post Code
</th>
<th>
08034
</th>
<th>
</th> </tr>
<tr>
<td>
</td>
<td>
Country
</td>
<td>
España
</td>
<td>
</td> </tr>
<tr>
<td>
</td>
<td>
Telephone
</td>
<td>
+34 93 553 25 10
</td>
<td>
</td> </tr>
<tr>
<td>
</td>
<td>
Fax
</td>
<td>
+34 93 553 25 20
</td>
<td>
</td> </tr>
<tr>
<td>
</td>
<td>
E-mail
</td>
<td>
[email protected]_
</td>
<td>
</td> </tr> </table>
Table 1-2: Contact Details of the Project Coordinator
_1.1.2.1. Project management Office_
A **Project Management Office (PMO)** comprising a team of employees familiar
with administrative, legal, financial, communication and IPR issues will
support the Project Coordinator in all the above responsibilities.
The PMO gets advice from financial, legal, IPR specialists whenever required.
The PMO Team:
* Assists the Project Coordinator in the project management tasks;
* Manages the delivery and the submission of administrative and financial documents;
* Is a permanent contact point for the coordinator and all the partners regarding their participation in CHARISMA, responding to any relevant request and maintaining a high level of communication within the consortium;
* PMO is in constant communication with the Project Coordinator about the status of the project (new results, new risks, modifications, etc.). PMO is also in regular contact with the WP and Task leaders, in order to maintain a close association with the implementation team of the project.
The PMO team is in charge of the day-to-day project management providing
support to the different project bodies. In accordance with the EC
requirements, PMO are responsible for the administrative actions, including
periodic reports, Certificates on Financial Statements and communication about
submission of deliverables. Additionally, the PMO team prepares the required
logistical, legal and administrative documents and supervises the overall
running of the project.
PMO are responsible for ensuring that all of the administrative steps required
for effective progress of the project are fulfilled timely. The PMO team will
rely on the partners’ local administrative staff and is composed by i2CAT
internal staff.
<table>
<tr>
<th>
Name
</th>
<th>
Sandrine Schwartz
</th> </tr>
<tr>
<td>
Address
</td>
<td>
Gran Capità 2-4 Edifici Nexus I
</td> </tr>
<tr>
<td>
Place
</td>
<td>
Barcelona
</td> </tr>
<tr>
<td>
Post Code
</td>
<td>
08034
</td> </tr>
<tr>
<td>
Country
</td>
<td>
España
</td> </tr>
<tr>
<td>
Telephone
</td>
<td>
+34 93 553 25 10
</td> </tr>
<tr>
<td>
Fax
</td>
<td>
+34 93 553 25 20
</td> </tr>
<tr>
<td>
E-mail
</td>
<td>
[email protected]_
</td> </tr> </table>
Table 1-3: Contact Details of the PMO representative
_1.1.2.2. Technical and Innovation Management (TIM) UEssex_
The responsibility of the **Technical and Innovation Manager (TIM)** is to
maintain the technical focus of the project as a whole and to coordinate the
innovation actions. More specifically, the TIM chairs the Technical Board
(TB). Dr. Michael Parker from the University of Essex is the TIM of CHARISMA
and is responsible for:
* Coordinating the technical work;
* Monitoring the alignment of the project work with the project technical objectives;
* Coordinating the technical issues of EC reviews;
* Solving all technical conflicts among tasks;
* Coordinating the dissemination of the technical information;
* Coordinating the communication and ease the flow of information among partners;
* Coordinating all technical reports within the deadlines agreed upon with the EC;
* Supporting the Project Coordinator in supervising the progress of the project;
* Monitoring and coordinating the research and innovation actions all along the project, in cooperation with the PC and the Technical Board.
The TIM is also in regular contact with the WP and Task leaders, in order to
maintain a close association with the implementation team of the project.
With regard to innovation management, the TIM in CHARISMA will define a set of
guidelines to WPLs, to be ratified by the TB, so that the project ensures
proper sensibility to changing Cloud industry standards and technologies. This
way the TIM, in cooperation with the TB and all project partners, will be open
to detect new opportunities for CHARISMA for innovate ideas while they develop
any phase of their solution.
The TIM main innovation related tasks include:
* Monitor all the overall exploitation-related activities in the project, according to the exploitation plan defined in WP5, and put emphasis on the overall exploitation impact of the project outcomes and its branding.
* Provide support to the partners in order to define the access rights and usage of their background and project results.
* Advice the project with the right policies for protecting partners/project IPR.
* Support the project coordinator by setting up an open innovation model that, according to Innovation goals of H2020, facilitates, from the coordination point of view, the generation of Innovation all along the project and strengthen the exploitation impact of the project results.
* Provide advice about tools and mechanisms to improve the management.
<table>
<tr>
<th>
Name
</th>
<th>
Michael Parker
</th> </tr>
<tr>
<td>
Address
</td>
<td>
Wivenhoe Park
</td> </tr>
<tr>
<td>
Place
</td>
<td>
University of Essex
</td> </tr>
<tr>
<td>
Post Code
</td>
<td>
CO4 3SQ
</td> </tr>
<tr>
<td>
Country
</td>
<td>
United Kingdom
</td> </tr>
<tr>
<td>
Telephone
</td>
<td>
XXXXXXX
</td> </tr>
<tr>
<td>
Fax
</td>
<td>
XXXXXXX
</td> </tr>
<tr>
<td>
E-mail
</td>
<td>
[email protected]_
</td> </tr> </table>
Table 1-4: Contact Details of the Technical and Innovation Management
### 1.1.3. Technical Board (TB)
The **Technical Board** consists of the Coordinator and all the work package
leaders. The **TB** shall be chaired by the TIM and this board is accountable
to the GA. The TIM is responsible for calling the meeting, preparing the
agenda and writing the minutes.
The TB is overall responsible for the success and smooth running of the
technical overall management of the project, including assessment of progress
reports, maintenance of work plans, resource re-allocation (if required) and
first level conflict resolution. External factors, such unique opportunities
for dissemination or novelties should also be considered.
As mentioned above, the TB is especially involved in resolving technical
conflicts between partners. All the decisions within this group are consensus
driven. However, resolutions taken in the framework of the TB are binding and
can only be overturned by a specific alternative decision of the GA.
The TB meets regularly at least every 4 months and at any time upon request of
any member of the TB. The members’ Board are continuously in contact between
the meetings by e-mail and audio/video conferences.
The TB should aim at achieving consensus of partners on important issues.
_1.1.3.1. Work Package Leader (WPL)_
The **Work Package Leader (WPL)** is responsible for the technical management
of the work package (WP). The WPL may be supported by a number of task leaders
of the same WP that report to the WPL on a regular basis. The responsibility
of each WPL is to ensure the activities of the WP proceed according to the
project work plan. The WPL is responsible for the production of the relevant
deliverables and may delegate parts of this responsibility to other WP
participants.
The WPL reports to the PC, the TB and the TIM regarding all technical
coordination and progress reporting (e.g. periodic reports or financial
reporting). The responsibilities of a WP leader are:
* Manage and follow-up the progress and technical activity of the Work Package;
* Follow-up the timely achievement of milestone and production of deliverables; Report on the activity to the Project Management Committee;
<table>
<tr>
<th>
**Role**
</th>
<th>
**Name**
</th>
<th>
**Email**
</th>
<th>
**Affiliation**
</th> </tr>
<tr>
<td>
WP1 Leader
</td>
<td>
M.Parker
</td>
<td>
[email protected]
</td>
<td>
UESSEX
</td> </tr>
<tr>
<td>
WP2 Leader
</td>
<td>
V.Jungnickel
</td>
<td>
[email protected]
</td>
<td>
Fraunhofer
</td> </tr>
<tr>
<td>
WP3 Leader
</td>
<td>
A.Legarrea
</td>
<td>
[email protected]
</td>
<td>
i2CAT
</td> </tr>
<tr>
<td>
WP4 Leader
</td>
<td>
E.Trouva
</td>
<td>
[email protected]
</td>
<td>
National Center for
Scientific Research
“Demokritos”
</td> </tr>
<tr>
<td>
WP5 Leader
</td>
<td>
T.Rokkas
</td>
<td>
[email protected]
</td>
<td>
INCITES Consulting
</td> </tr>
<tr>
<td>
WP6 Leader
</td>
<td>
E.Escalona
</td>
<td>
[email protected]
</td>
<td>
i2CAT
</td> </tr> </table>
Table 1-5: CHARISMA Technical Board
_1.1.3.2. Task Leader (TL)_
**The Task Leader (TL)** is a partner that is appointed responsible for a
particular task. The Task Leader will report to the WPL. All TLs follow the
Document of Action and stick to specific project development plans defined by
WPLs.
### 1.1.4. External Advisory Board (EAB)
A panel of external experts will support the consortium all along the project
duration in advising on project strategy, complex technical decisions and
long-term sustainability issues. This group of expert will compose the
**External Advisory Board (EAB)** of CHARISMA project.
This Board (EAB) will serve as external adviser to the Project coordinator and
the members of the GA and TB. The EAB will meet separately and interact with
the different managing bodies being part of the management structure. In this
way, regular communication will be established between the EAB and the
different managing bodies (PC; GA and TB), in order to provide appropriate
information and assist the managing bodies with independent strategic
recommendations on the project objectives, actions and long term developments.
Resorting to such instrument provides additional quality assurance and
guidance for the project and its activities.
The communication will be done by different means: meetings, conference call,
emails, etc.
The **CHARISMA EAB** is comprised of specialists from domains relevant to
CHARISMA and will be appointed during the first year of the project.
The EAB main tasks include:
* overseeing quality of project deliverables (internal evaluation in a form of peer-reviews) when required;
* orientating the project activities towards the expected results and objectives;
* establishing the project strategy in accordance with the PC, GA and TB and adapt it during the project life, if necessary;
* providing recommendations and contribute to the implementation of the project in order to maximize the impact of the project results;
* advising and assistance on the dissemination, international discussion and promotion of project results.
The members of the EAB will represent relevant international stakeholders and
will be recognized as experts from academic and/or private sector. After a
round of consultations, the GA will select a number of distinguished
researchers and representatives of research institutions (or private sector??)
in order to carry out these responsibilities.
# 2\. Project Communication & Collaboration
## 2.1. Email Communication
The email communication reflector of the project will be kindly hosted by the
Coordinator, i2CAT who established and maintains the following email lists:
**[email protected]_ **
The purpose of this email list is to exchange information between the CHARISMA
administrative representatives and delegates. At least one administrative
participant from each partner will be included in this email list.
<table>
<tr>
<th>
**Wp1**
</th>
<th>
**[email protected]_ **
</th> </tr>
<tr>
<td>
**Wp2**
</td>
<td>
**[email protected]_ **
</td> </tr>
<tr>
<td>
**Wp3**
</td>
<td>
**[email protected]_ **
</td> </tr>
<tr>
<td>
**Wp4**
</td>
<td>
**[email protected]_ **
</td> </tr>
<tr>
<td>
**Wp5**
</td>
<td>
**[email protected]_ **
</td> </tr> </table>
The purpose of this set of emails is to exchange information between all
technical CHARISMA participants. The email address of all technical personnel
participating in the different WPs will be included in this list of 6 emails
list in accordance with the project's WPs.
All emails that will be sent to the reflector will have the following format
for their subject:
[charisma-admin] Subject
[charisma-wpx] Subject
In case a new member joins the project, an email with the specific request
should be sent to the i2CAT contacts for the project reachable at the
following email addresses:
* Eduard Escalona (i2CAT), [email protected]
* Amaia Legarrea (i2CAT), [email protected]
## 2.2. Conference-Call Collaboration
CHARISMA will utilise on a regular daily basis telephone conference calls to
evaluate the progress of the project along the planned Sprints and define
further actions.
A dedicated conference bridge (updated every 6 months) will be provided by
i2CAT. All details regarding the access to the bridge will be provided by
i2CAT technical team and will be stored in the documents repository at the
following page:
http://confluence.i2cat.net/display/CHARISMA/CHARISMA+Home
When video sharing will be required the consortium will use WebEx video
conference tool provided by Cisco and hosted by i2CAT.
Additional ad-hoc conference calls should be regulated by the following
general rules:
* The partners will schedule the date of the conference, using also appropriate support tools. (e.g., doodle).
* An invitation to the project email list with the exact time, agenda and information on how to join the conference call will be circulated, at least one day before the conference call.
* i2CAT technical team will manage the conference platform and inform the consortium how to login to that conference system.
## 2.3. Meetings
Face to face meetings will be organized periodically throughout the project
lifetime. Conference calls will also be used extensively for progress
meetings. The foreseen frequency of meetings is:
* Project Coordinator Body (PC, PMO, TIM): foreseen conference calls at least every three months and face to face meetings co-located with the General Assembly.
* General Assembly: face to face meetings at least two times every year. Conference calls to be set up when needed.
* Work packages: dedicated WP meetings will be organized by the WP leaders according to the emerging needs during the project lifetime. It is up to the WP leaders to try to arrange these meetings in co-location with General Assembly meetings.
Extraordinary meetings may be organized upon written request of:
* The TB for the GA meeting.
* Any member of the TB for a TB meeting.
Moreover, it is up to deliverable leaders to set up additional meetings or
conference calls according to the specific activities in the workplan if
required
For each meeting the following documentation will be produced and uploaded on
the respective folder in the document repository:
* Meeting minutes from which it could be easily evinced the meeting objectives, agenda, location and required participants.
* Main discussion items, decisions and actions (with owners and deadlines assigned).
* A list of participants over each day of the meeting with signatures for each day.
### 2.3.1. Hosting a face to face meeting
Throughout the project lifetime, members may be asked to host general project
meetings. Tasks involved in hosting a meeting are divided into obligatory and
recommended tasks:
Obligatory tasks:
* Provide meeting rooms with audiovisual equipment necessary for the presentations. Provide network connectivity.
General practice (recommended although not obligatory):
* Provide water, coffee breaks and lunch.
* Organize one social event (e.g., invite partners for one evening meal).
# 3\. Quality Assurance
The Project Coordinator will maintain a computer-based top-level project
management system, based on a Gantt chart work-schedule model. This will be
updated by inputs from the various managers at WP and task level. The Work
Package Leaders will be encouraged to use a formal project management system,
compatible with the one used by the Project Coordinator. Project meetings or
teleconferences will take place as required by the workplan.
## 3.1. General Rules for Planning and Reporting
The foreseen procedures and tools for the CHARISMA project are the following:
* All formal meetings (GA, TB, etc.) will be notified at least three weeks in advance. Agenda, proposed resolutions, decisions and supporting documentation will be available to all attendees at least one week before the meeting. Issuing of all documents will be via the chairman, who is responsible for compiling all submissions from partners and will be appointed at the beginning of the meeting.
* All meetings will be formally minuted and a draft version of the meeting minutes shall be sent to all the members within 10 calendar days after the completion of the meeting.
* The minutes shall be considered as accepted if, within 15 calendar days from sending, no consortium member has sent an objection in writing to the chairperson with respect to the accuracy of the draft of the minutes.
* The accepted minutes will be accessible from the documents repository:
http://confluence.i2cat.net/display/CHARISMA/Management
## 3.2. Quality Assurance Roles and Responsibilities
All documents and deliverables concerned in the Quality Assurance process will
be evaluated through a dedicated internal team, **Quality Check Team (QCT)** ,
generally composed by at least two members of the CHARISMA consortium. The
members of QCT are selected on the basis of their expertise and experience on
the subject treated in the deliverable under consideration. As general rule
the team should consist of:
* The project Technical Coordinator for the overall quality management of the deliverable, having the overall responsibility for the qualitative integrity of the document;
* The leader responsible for the work package or tasks related to the deliverable.
The review process will be iterative until final acceptance by the QCT.
This Quality Assurance procedure should be used by all partners involved in
the documents release process, more precisely:
* All partners directly involved in deliverable and prototype production.
* The QCT involved in the review of the documents produced.
## 3.3. Deliverable Acceptance Process
A clear quality process will be applied for the final acceptance of a
deliverable:
* The team responsible for the deliverable has to be in charge to verify the contents of the documents, assuring that the information contained is relevant and of importance to the research work and activity carried out in a given project task.
* The Quality Check Team will make a first internal revision of the deliverable and assess if some modifications or conflict resolution would be needed. The Quality Check Team will assure the conformity of the document with the quality criteria and also act as interface to TMB, before the final submission of the work.
The quality criteria against which every single deliverable will be evaluated
include the following:
1. **Completeness** . The deliverable must address all aspects related to the purpose and scope for which the related research activity is carried out. Each deliverable leader is responsible for the content of the document and the QCT is responsible to supervise the compliance.
2. **Depth** . Each deliverable should have a coherent depth of information with respect to the deliverable scope, purpose and type of research activity described. The lead partner has the role to verify that the contents are detailed in the correct way. The Quality Check Team should evaluate if such depth is adequate, with respect to what was stated in the Description of Action.
3. **Accuracy** . The information provided in the deliverables should be supported by real and tangible motivations. All background information used in the documents should be linked to appropriate references. Moreover, foreground information and results, should be clearly described and should be technically supported in order to avoid any misinterpretation or misunderstanding.
4. **Relevance** . Each deliverable should provide information according to the scope of the specific research work and should be focused on the key aspects of the deliverable scope.
5. **Adherence** . Each deliverable must be produced using a common project template to have a uniform appearance and structure, independently from the originals authors of the document.
The above quality criteria have to be considered by the authors when writing
and drafting the deliverables since they constitute the basic principles on
which the Quality Check Team operates for the evaluation of the work.
## 3.4. Deliverable Review Procedure
The deliverable will be distributed by the lead partner in its first full
draft version at least **two weeks before its official deadline** . The
Quality Check Team will perform the review and send their comments back to the
deliverable lead partner. Moreover, the QCT is responsible for deciding upon
any conflict in the review process.
The final rating of the deliverable draft is agreed by the Quality Check Team
and will be marked as:
* Fully accepted,
* Minor revisions required, Major revisions required, Rejected.
The above procedure is intended to be iterative. After the first round of
review from the Quality Check Team, the lead partner and the document authors
will integrate the revisions into the deliverable, addressing the comments
provided by the Quality Check Team. The final revision will be produced when
the deliverable is marked as fully accepted by the Quality Check Team. The
whole review procedure should take no more than an agreed deadline,
established before starting of the deliverable production.
In the unlikely case of a deliverable marked as rejected at the end of the
review process, the Project Coordinator will apply the needed actions to
overcome the situation.
## 3.5. Periodic Progress Reporting
In order to reflect the status and the progress of the project, a number of
management reports are produced periodically.
<table>
<tr>
<th>
**Report**
</th>
<th>
**Content**
</th>
<th>
**Responsible**
</th>
<th>
**Distribution**
</th>
<th>
**Periodicity**
</th> </tr>
<tr>
<td>
**Semester Progress Report**
</td>
<td>
Project progress and technical activities carried out by each partner.
Includes costs and efforts for each partner.
</td>
<td>
All partners
</td>
<td>
Internal/PO
</td>
<td>
Every 6 months
</td> </tr>
<tr>
<td>
**Periodic Report**
</td>
<td>
Overview of project progress in terms of achievements of each WP. Includes
costs and efforts for each partner.
</td>
<td>
All partners
</td>
<td>
EC
</td>
<td>
M18 – M30
</td> </tr>
<tr>
<td>
**Financial Statement**
</td>
<td>
Financial Statements + Audit Certificate if required
</td>
<td>
All partners
</td>
<td>
EC
</td>
<td>
M18 – M30
</td> </tr> </table>
Table 3-1: Planned CHARISMA reporting
### 3.5.1. Semester Progress Report
The Semester Progress report is made up of the following parts:
* a textual part to report on work progress by each WP towards the project objectives,
* a list of activities executed by each partner in each WP,
* effort/cost estimates and projections by each partner for each WP.
The Project Coordinator will keep the EC Project Officer informed about
changes, problems, and deviations from the work plan or the budget.
The preparation of the Semester Progress Report will be started by the Project
Management Office at the closure of the reporting period (semester) and should
be completed within the 30 days from the end of the reference period. All
partners are invited to provide timely feedback and contributions for their
parts.
### 3.5.2. Periodic Activity Report to the EC
Based on the template produced by the EC, this official activity report is
produced at M12 and at M30 by the Project Coordinator, the Technical
Coordinator and all the WP leaders based on the information contained in the
progress activity reports.
This report states explicitly the advances of the CHARISMA project against the
objectives and planned activities and should detail:
* activities carried out and obtained results,
* dissemination and exploitation actions, resource consumption.
This report will be used by the European Commission to assess the progress of
the project in the period of time that will normally coincide with technical
review meetings.
### 3.5.3. Financial Statements and Financial Summary Reports
At the end of the M12 and the M30, each partner will provide the Financial
Statements to the coordinator where all the costs referring to the period will
be declared in order to claim reimbursement to the European Commission. This
financial statement will be complemented with an audit certificate (when
required) that will ensure that the costs declared by each participant are
correct and documented.
**Special clause to the contract** : audit certificates are required when
accumulated funding surpasses 325.000 €. For example, if in Period 1, the
claimed costs for reimbursement are 100.000 €, no audit certificate is
required. If the requested funding for the project is under 325.000 €, no
audit certificate is required.
# 4\. Data Management Plan (DMP)
This section of Data Management Plan is a life document and will be subject to
change and evolve to be adapted to needs and requirements that may arise
during the project life cycle.
It is important to set up a Data Management Plan to achieve specific
dissemination objectives and ease the access of the consortium and the general
public to the data produced during the execution of CHARISMA to increase the
research impact of the project and to save time by having a common repository
to preserve the important data and maintain the integrity of the data. Making
the data available to the community of researchers interested in the area can
have a positive impact on the discovery of new applications and the general
relevance of the work. The plan for Data Management should be set up at the
beginning of the execution of the project, this way we could save time and
resources during the project lifetime. A repository facility will provide a
common reference archive where the consortium will save all the produced
documentation and instruments for the project execution, avoiding the usage of
different and replicated systems. Also, this will allow the interested parties
to consult and review these data in the future. CHARISMA consortium is devoted
to support open access to all the data produced during the project lifetime to
the greatest extent that is possible.
### 4.1.1. Data set reference and name
The infrastructure chosen to hold the documentation produced by the project
(interim reports, cost statements, working papers, and deliverables) will be
based on the Confluence solution by Atlassian:
(http://confluence.i2cat.net/display/CHARISMA/CHARISMA+Home ). The **Project
Coordinator (PC)** and the **Technical and Innovation Manager (TIM)** will be
the ultimate responsible for maintaining this platform, with coherence of
contents organization and availability to the partners. All the partners are
encouraged to contribute with documents and information that could be deemed
as useful to the project community.
### 4.1.2. Data set description
_4.1.2.1. Documents Repository_
The repository of documents will be used to share and store all documentation
relating to the execution of the project, both for official documentation (to
be sent to the European Commission) and the documentation for only internal
use: tasks and meeting reports, in-progress documentation, etc.
The choice of the document repository has been defined considering the
following general requirements:
* supports document sharing between different accounts,
* allows the definition of an organized documents structure,
* permits the versioning of the documentation, history tracking.
The following pages have been created at least the following categories of
documents:
* **Dissemination Materials** : will contain papers, articles, newsletters, brochures resulting from the project tasks, trainings information, patents, etc.
* **Documents** : will contain the repository of deliverables of the project available to all the partners.
* **Meetings** : will contain documents presented or generated during plenary meetings and conference calls minutes.
* **Templates & logos ** : in this category reference documents and guides to generate standard documents for the project have been uploaded as well as logos and visual material for media dissemination.
* **Audits** : store material exchanged among partners for audits preparation.
* **WP-x** : each WP data will have its dedicated space where the documentation specific to that WP will be accessible for all the partners including activities description, APs and others.
Note that additional categories may also be created, in case it is deemed to
be necessary. The document D5.1 includes the specifics of the confluence
document repository tool, explains how to use this repository and other
dissemination tools. It has been submitted and shared across the consortium in
M2. Further information about the Confluence tool can be found in CHARISMA
Deliverable D5.1 [1].
### 4.1.3. Document Naming Convention
_4.1.3.1. Official/Contractual Deliverables_
The official deliverable will be named using the following naming format
**CHARISMA_DX.Y_Mmm_Vx.y.ext** where:
**X:** is the WP number
**Y:** is the deliverable number
**Mmm:** is the project month in which the deliverable is finalized and sent
to the PO **x:** is the version major number **y:** is the version minor
number
**ext:** is the extension (.docx, .pdf, .ppt, .xlsx, .exe, .zip)
Note that the partner who has the responsibility for the document will have
the authority to change the version number.
_4.1.3.2. Internal/Public Documents_
The Internal documents will have the following format:
**CHARISMA _WPw_ACR_TTTd__ShortTitle_Vx.y-YYYYMMDD.ext**
Or the shorter version
**CHARISMA _WPw_ACR_TTTd__Vx.y-YYYYMMDD.ext** where:
**w:** is the WP number
**ACR:** is the partner Acronym that initiated and has the responsibility for
the document
**TTT:** is a two or three letter acronym of the following
<table>
<tr>
<th>
QRR
</th>
<th>
Quarterly Resource Report
</th> </tr>
<tr>
<td>
MAG
</td>
<td>
Meeting Agenda
</td> </tr>
<tr>
<td>
MM
</td>
<td>
Meeting Minutes
</td> </tr>
<tr>
<td>
MS
</td>
<td>
Market Studies
</td> </tr>
<tr>
<td>
SW
</td>
<td>
Software
</td> </tr> </table>
<table>
<tr>
<th>
</th>
<th>
APL
</th>
<th>
Action Points List
</th>
<th>
</th> </tr>
<tr>
<th>
TCM
</th>
<th>
Teleconference Meeting Minutes
</th> </tr>
<tr>
<th>
TP
</th>
<th>
Technical Presentation
</th> </tr>
<tr>
<th>
TPC
</th>
<th>
Technical/Research Publication (Conference)
</th> </tr>
<tr>
<th>
TPJ
</th>
<th>
Technical/Research Publication (Journal/Magazine)
</th> </tr>
<tr>
<th>
TR
</th>
<th>
Technical Report
</th> </tr> </table>
**ShortTitle:** is an optional, explanatory short title of the document **d:**
is the document number **x:** is the version major number **y:** is the
version minor number
**YYYY:** is the year
**MM:** is the month **DD:** is the day
**ext:** is the extension (.docx, .pdf, .ppt, .xlsx, .exe, .zip)
The same procedure applied to the official/contractual documents will be
applied also to the internal/public documents.
### 4.1.4. Reference documentation tools
The following tools are suggested for document processing:
* Document Processing: Microsoft Word 2010,
* Spreadsheet Processing: Microsoft Excel 2010,
* Presentations Processing: Microsoft PowerPoint 2010,
* Compression Tool: 7-Zip,
* Portable Document Format: Adobe Acrobat 8.0 or later.
In case a partner aims to use a different software tool, he/she has to assure
that the outcome is compatible with the above tools.
### 4.1.5. Code Repository
A git repository will be made available for the consortium with the purpose of
CHARISMA code storage repository. This will mean a common tool where all the
code can be appropriately versioned, committed for testing, etc. in a secured
manner. The tool it is likely to be STASH (again by Atlassian). However, this
will be decided through the project lifecycle and in consensus with all the
partners involved in code development.
### 4.1.6. Data sharing
In general, for the CHARISMA data sharing purposes, all the documentation,
reports, articles and additional data will be shared through the confluence
tool to all the CHARISMA partners. Access to this platform is secured and
granted to personal accounts, this can be requested to the project coordinator
(i2CAT) through the usual channels of communication (email, mailing lists,
etc.).
The public documentation will be made available through the public website (
_http://www.charisma5g.eu_ ) . The final version of each document in .pdf
will be uploaded to the web once they are finalised and approved by the
consortium according to the criteria previously defined in section **¡Error!
No se encuentra el origen de la referencia.** of this document.
Only publicly available information will be included in the documentation to
protect the intellectual property rights of the information shared within the
consortium. Due to the innovative nature of CHARISMA, it is expected that
partners will generate Intellectual Property that has to be protected through
patents, yet made available for other partners for their own work in the
CHARISMA project and exploited outside of the project by appropriate
licensing.
CHARISMA handling of IPR is completely in-line with Annex III of the Model
Contract and the “Guide to Intellectual Property Rules for H2020 projects”. A
summary of the way that access rights and IPR have been handled in the
project’s Consortium Agreement.
The intention of the consortium is that the research carried out as part of
CHARISMA will be eligible to be published in technical journals, conferences
and similar events.
### 4.1.7. Archiving and preservation (including storage and backup)
All the information shared through the confluence tool will be backed-up
regularly as a measure to avoid accidental or intentional data-loss in case a
disruption happens in the system hosting the data management tool. Ideally the
following the subsequent criteria will be performed:
* Make several copies of the data (e.g. original + external/local + external/remote).
* Copies should be geographically distributed (local vs. remote).
* Cloud storage is the preferred option to carry out back up of the data.
* It is recommended to execute complete backups of all the data in a monthly basis.
Data archive should be encrypted to preserve privacy of the data.
The data produced by CHARISMA will be storaged ideally uncompressed but if
there are limitations of space and if the files production starts being unable
to be handled, data will be compressed. It is recommended also to carry
periodic test in the data to try to retrieve data files and make sure those
are available.
|
https://phaidra.univie.ac.at/o:1140797
|
Horizon 2020
|
1045_ETN-FPI_676401.md
|
# Introduction
Full parallax imaging research requires databases for testing and comparison.
The research conducted in the project will encompass various capture types
(multiview, plenoptic), result images (light fields for different displays)
and compression schemes. For generating ground truth data or interactive light
field visualization the project is going to utilize datasets for real time
rendering as well.
A multiview image set is an image captured with multiple virtual or real
cameras at the same time. A plenotic image is an image captured with a
plenoptic camera. A light field image is an image captured from a scene with
the light rays described as a vector field. A practical example of this is a
light field directly describing the outgoing light rays of an autostereoscopic
3D display.
All of these images can be compressed into video streams. For multiview images
this can be a single stream or per camera streams, for plenoptic camera images
this is a single stream, for light field images, this can be a single stream
or per optical module streams.
For real time rendering of light fields it is also necessary to work with test
3D scenes imported from different 3D file formats. These are usually
containing geometry in the form of meshes or boundary representation surfaces
and materials in various formats. They can also link to various types of
texture files that describe surface properties. They can also contain scene
graph related information and animation.
For realistic environment lighting it is also necessary to use environment map
textures. With HDR imaging the changes of lighting during the day and camera
exposure settings can also be simulated in real time.
The document will cover what kinds of datasets are available for comparison
and testing. It will also introduce new datasets that will likely be created
(captured or rendered) during the project. For each dataset it will describe
data types, relevant standards and tools to interpret them. Plans for storage
and archiving procedures of the datasets will also be laid out.
The datasets will also be of use to future researchers as comparison to
existing methods for capture and compression and (rendered) image quality.
# Datasets captured or created during the project
## Captured plenoptic content
_Data set reference and name_
ETN-FPI-MIUN-PLENO
### Data set description
Plenoptic capture is currently in the planning phase. The document will be
updated once more details become available on the capture setup and the
captured data set. Contact person: Mårten Sjöström e-mail:
[email protected]
## Captured multiview content dataset from Bayer camera array
_Data set reference and name_
ETN-FPI-FHG-BAYER
### Data set description
The dataset contains video and image files captured with the following setup:
The Basler Array consists of 9 cameras in a 3x3 configuration. The cameras are
synchronized via a trigger input and have global shutter sensors. The cameras
output 1 Gigabit Ethernet data which is captured using a PC equipped with a 10
Gigabit Ethernet adapter along with a 10 Gigabit Ethernet switch.
#### (a) (b)
#### (c) (d)
_**Figure 1: Basler Camera Array. (a) Schematic drawing. The distance between
the optical axes of horizontal and vertical neighbors is 65 mm, e.g. the
inter-axial distance is 65 mm. (b) Picture of the camera array. (c) Schematic
drawing of the Basler cameras array inside the rigging box. (d) Perspective
picture of the camera array.** _
### Standards and metadata
Captured video streams are available as:
* Individual rectified video streams from 9 cameras
* Framerate: 30 FPS
* Resolution: 1920x1080 pixels
* stream of PNG images
* 8-16 bits per color channel
Captured images are available as:
* Individual rectified images from 9 cameras
* Resolution: 1920x1080 pixels
* PNG images
* 8-16 bits per color channel
Metadata:
* Camera calibration data as text file
### Data sharing
Several datasets will be captured for the project, especially during WS2.
These will be shared among project partners via a local FTP server. Generally,
download links will be available for a week and will be renewed on request.
When and how datasets will be published to the general public is to be decided
at a later date. Contact person Frederik Zilly e-mail:
[email protected]
### Archiving and preservation
Sequences will be stored on Fraunhofer IIS’s internal FTP server. Local backup
policies will be applied. All data will be mirrored.
## Captured and compressed multiview content dataset from GoPro camera array
_Data set reference and name_
ETN-FPI-FHG-GOPRO
### Data set description
The dataset contains video files captured with the following setup:
The GoPro camera array consists of 16 cameras in a 2x8 configuration. The
cameras are synchronized via special back packs and have rolling shutter
sensors. Each camera captures the videos individually on local storage (e.g.
SD-cards). The read-out is performed offline.
#### (a)
#### (b)
#### (c)
#### (d)
_**Figure 2: GoPro camera array. (a) Schematic drawing of the camera’s
centers. The horizontal translation is equidistant, i.e. the distance of two
horizontal neighbors is 60 mm in the drawing. The distance between vertical
neighbors is 70 mm. (b) Picture of the 2x8 camera array. (c) Perspective
drawing of the camera array. (d) Rear view of the cameras showing the red back
packs connected with gray cables to ensure synchronization.** _
### Standards and metadata
Captured video is available as:
* Individual rectified video streams from 16 cameras
* Framerate: 30 FPS
* Resolution: 1920x1080 pixels
* Stream of lossless PNG images converted from x264 video streams. 8 bits per color channel
Metadata:
* Camera calibration data as text file Relevant standards:
* PNG: ISO/IEC 15948:2003
* ISO/IEC 14496-10 – MPEG-4 Part 10, Advanced Video Coding
### Data sharing
Several datasets will be captured for the project especially during WS2. These
will be shared among project partners via a local FTP server. Generally
download links will be available for a week and will be renewed on request.
When and how datasets will be published to the general public is to be decided
at a later date. Contact person: Frederik Zilly e-mail:
[email protected]
### Archiving and preservation
Sequences will be stored on Fraunhofer IIS’s internal FTP server. Local backup
policies will be applied. All data are mirrored.
## Compressed multiview dataset from robot gantry
_Data set reference and name_
ETN-FPI-FHG-ROBOTGANTRY
### Data set description
The dataset contains image files captured with the following setup:
The Robot gantry is suitable to capture static scenes using a translatable
DSLR camera. The DSLR camera can be translated by 4 meters in horizontal and
50 cm in vertical direction. The images are recorded onto the camera data
storage as RAW or JPG files.
_**(a)** _
_**(b)** _
_**Figure 3: Robot gantry. (a) Picture of the full robot gantry frame. The
camera can be translated by 4 meters in horizontal and 50 cm in vertical
direction (b) Near view of a mountable DSLR camera.** _
### Standards and metadata
Captured video is available as:
* Individual rectified video streams from ~20x20 camera positions
* Framerate: N/A
* Resolution: 24 MegaPixels
* Stream of lossless PNG images.
* 8-16 bits per color channel
Metadata:
* Camera calibration data as text file Relevant standards:
* PNG: ISO/IEC 15948:2003
### Data sharing
Several datasets will be captured for the project especially during WS2. These
will be shared among project partners via a local FTP server. Generally
download links will be available for a week and will be renewed on request.
When and how datasets will be published to the general public is to be decided
at a later date. Contact person: Frederik Zilly e-mail:
[email protected]
### Archiving and preservation
Sequences will be stored on Fraunhofer IIS’s internal FTP server. Local backup
policies will be applied. All data are mirrored.
## Compressed light field dataset
### Data set reference and name
ETN-FPI-MIUN-PLENO
Data set descriptionLight field compression is currently in the planning
phase. The document will be updated if more data becomes available on light
field compression. Contact person: Mårten Sjöström e-mail:
[email protected]
## Light field datasets rendered for Holografika displays
_Data set reference and name_
ETN-FPI-HOLO
_Data set description_
The dataset contains video and image sequences rendered for one or more
Holovizio light field displays.
### Standards and metadata
Rendered video is available as
* Per optical module x264 compressed light field videos
Rendered images are available as:
* Per optical module png light field images
Metadata:
* Documentation for the display’s light field
* Holografika light field description file
* Holografika SDKs
Relevant standards:
* PNG: ISO/IEC 15948:2003
* ISO/IEC 14496-10 – MPEG-4 Part 10, Advanced Video Coding
### Data sharing
Image and video sequences can be requested from Holografika for testing and
evaluation. Inclusion in published scientific work requires the explicit
permission of Holografika. Holografika light field description files and
Holografika SDKs are available only to partners signing an NDA.
Holografika SDKs are currently only available with commercial licensing.
Contact person: Attila Barsi e-mail: [email protected]
### Archiving and preservation
Sequences are mirrored on Holografika’s internal file server. A backup server
with mirrored data is available. An offline backup of the data is available on
HDD.
# Datasets used for rendering, testing, compression and comparison
## Complex 3D scene for light field rendering
_Data set reference and name_
ETN-FPI-COMPLEX3D
### Data set description
Big Buck Bunny is a Blender community open movie project. The dataset contains
the original Blender project and various 2D, stereoscopic and multiview videos
rendered from the same dataset.
### Standards and metadata
The project files are available in Blender’s internal .blend file format.
Licensed under Create Commons 3.
Rendered video is available as
* x264 compressed 4K stereo video
* x264 compressed Full HD stereo video
* x264 compressed 4K 2D video
* x264 compressed Full HD 2D video
* yuv uncomressed 1280x768 multiview video
Rendered image files are available as
* .exr 32 bit floating point 1280x768 multiview images
* .png 1280x768 multiview images
Metadata:
* documentation for Holografika’s proprietary camera .xml format
* camera placement in Holografika’s proprietary camera .xml format
* tone mapping settings for HDR to LDR conversion
Relevant standards:
* PNG: ISO/IEC 15948:2003
* ISO/IEC 14496-10 – MPEG-4 Part 10, Advanced Video Coding
### Data sharing
2D, stereo and Multiview are available from the HTTP file servers.
* Original scene, 2D and stereo videos _http://bbb3d.renderfarming.net/explore.html_
* Scenes setup for multiview rendering, multiview video and images _http://mpegftv:[email protected]/_
### Archiving and preservation
Scenes setup for multiview rendering, multiview video and images are backed up
to a backup server and an offline backup HDD. Contact person: Attila Barsi
e-mail: [email protected]
## Simple 3D scene for light field rendering
_Data set reference and name_
ETN-FPI-SIMPLE3D
_Data set description_
This is a 3D datasets for sci-fi vehicles from the movie Star Wars. Licensed
under Create Commons 3.
_Standards and metadata_
The scene files are available in various 3D formats including MAX4, 3DS and
Lightwave .lwo formats.
### Data sharing
3D scene definition files are available at the following HTTP file server:
_http://www.scifi3d.com/list.asp?intCatID=8 &intGenreID=10 _
### Archiving and preservation
Models are mirrored on Holografika’s internal file server. A backup server
with mirrored data is available. An offline backup of the data is available on
HDD. Contact person: Attila Barsi e-mail: [email protected]
## Environment maps for 3D rendering
_Data set reference and name_
ETN-FPI-ENVMAPS
### Data set description
Environment maps for 3D scene rendering captured with ball mirrors. The
primary purpose of these maps is to enable environment lighting.
### Standards and metadata
Images are available as LDR .jpg and HDR .hdr images.
Licensing allows the free samples to be used for commercial work.
_Data sharing_ _https://www.hdri-hub.com/hdrishop/freesamples/freehdri_
### Archiving and preservation
Maps are mirrored on Holografika’s internal file server. A backup server with
mirrored data is available. An offline backup of the data is available on HDD.
Contact person: Attila Barsi e-mail: [email protected]
|
https://phaidra.univie.ac.at/o:1140797
|
Horizon 2020
|
1048_MaX_676598.md
|
# Executive Summary
The MaX Centre of Excellence aims at supporting the needs of all the
stakeholders involved in the field of materials modeling, simulation and
design by providing new instruments and services in the form of data, codes,
expertise and turnkey solutions to efficiently address the crucial challenges
of novel materials development in the exascale computing era. This document
provides a description of the strategies and solutions adopted within the MaX
CoE to establish a high level materials’ informatics framework to curate,
preserve and share all the data produced by the flagship codes. The core
technology behind this objective is the AiiDA code, a python infrastructure
designed to support different codes through a plugin interface, allow for an
automated design and implementation of complex workflows and task tracking and
able to store the full provenance of each object in a tailored database. AiiDA
parses the input and output files and runs the calculations on high
performance computing platforms, stores the data using uniform formats based
on python dictionaries and preserve the full provenance in the form of a
Directed Acyclic Graph (DAG). AiiDA also enables a social ecosystem where the
simulation workflows and results can be openly shared. Both these aspects have
been strengthened in the last version of the code. On one hand with the update
of the AiiDA plugin and workflow systems on the other, with the development of
the AiiDA REST (REpresentation Stateless Transfer) Api which also constitutes
the backbone of the materialscloud.org portal and finally with the
implementation of various exporters and converters to the most commonly used
data formats and ontologies.
# Introduction
2.1 About this document
This document is deliverable D3.4 of the MaX project, it is the updated
version of data management plan already designed and delivered in D3.1[1] at
month 6. The documents describes the types of the data produced in the
project, standards used for data and how the data is being curated, preserved
and shared. The basic types of data and the way they are organized by AiiDA
remains essentially unchanged with respect to D3.1[1] and they are hereafter
reported in a similar way. Important developments have been carried out to
ease the sharing of data and of the computational protocols used to generate
them. Such developments include a critical revision of the AiiDA plugin system
and the implementation of a REST Api compliant with the OPTIMaDe protocol.
# Description of the data
Within this project, various open-source first principle simulation codes such
as Fleur, SIESTA, Quantum ESPRESSO and YAMBO are being developed (see table
1). The materials informatics framework AiiDA has been designed to allow the
support of many different codes through a plugin interface. Plugins for the
main codes within by this Center of Excellence (CoE) are already available as
described in deliverable Table 1: The primary codes that will be used as part
of this project.
<table>
<tr>
<th>
Name
</th>
<th>
License
</th>
<th>
Main Developers
</th> </tr>
<tr>
<td>
Fleur [3, 4]
</td>
<td>
GNU-GPL
</td>
<td>
Jülich
</td> </tr>
<tr>
<td>
Quantum ESPRESSO [5, 6]
</td>
<td>
GNU-GPL
</td>
<td>
SISSA, EPFL, CIN
</td> </tr>
<tr>
<td>
SIESTA [7, 8]
</td>
<td>
SIESTA License
</td>
<td>
ICN2, BSC
</td> </tr>
<tr>
<td>
YAMBO [9, 10]
</td>
<td>
GNU-GPL
</td>
<td>
CNR, CIN
</td> </tr>
<tr>
<td>
AiiDA [11, 12]
</td>
<td>
MIT-BSD, EPFL License
</td>
<td>
EPFL, Robert Bosch
</td> </tr>
<tr>
<td>
i-PI [13, 14]
</td>
<td>
GNU-GPL/MIT-BSD
</td>
<td>
EPFL
</td> </tr> </table>
D3.2[2] of this project. A general overview of the present status of the
plugins can be found at http://www.aiida.net/plugins/ together with the
contact of the reference person for each plugin and a link to the main
repository. The page collects also numerous other plugins, for codes outside
this CoE, which are being currently developed as part of other collaborations
or by individual contributors. AiiDA promotes advanced programming models
leveraging python abstraction layers to disseminate advanced functionalities
to arbitrary quantum engines (i.e. simulation codes). It provides a model of
automatic data generation and storage, to guarantee provenance, preservation,
reproducibility and reuse. This platform is used to organize and coordinate
thousands of simulations, it allows to acquire and store a variety of
heterogeneous microscopic data from the calculations that can be subsequently
queried for the desired material properties. Details on the calculation
execution such as the parallelization scheme and execution time are also
retained to empower performance optimization. Furthermore AiiDA allows for an
automated design and implementation of complex workflows and task tracking,
based on a scripting interface for job creation and submission. The inputs,
results and computational procedures at each step of the workflows are
collected and stored in a database. All plugins, workflows and the data
produced through them are thought and designed to be openly shared as
discussed afterward.
3.1 Types of data
AiiDA provides automated solutions and various plugins for computer codes
without a need for tuning code specific parameters. It stores the
calculations, their inputs and their results (either parsed, extracted from
Extensible Markup Language (XML) outputs or from text files with the
appropriate dictionaries) in a database and its associated file repository.
This data is generated from open-source electronicstructure material
simulations codes that encompass key technologies such as allelectron,
pseudopotentials, and localised basis sets, density-functional theory,
timedependent density-functional theory, and many-body perturbation theory,
multiscale/multiphysics modelling with a focus on quantum mechanics/molecular
mechanics, solvation and electrochemistry, thermal/electrical transport, and
complex magnetic properties. The specific data to be stored in the database
within the input and output nodes of a calculation and the files to be
retrieved and stored in a local repository are determined by each code plugin.
This follows the specific characteristic of each simulations code and the
physics of the problem under study. The design of each calculation node is
documented by the plugin developers, see for example http://aiida-
core.readthedocs.io/en/stable/plugins/ quantumespresso/pw.html. AiiDA also
allows to use data from external open access databases of crystal structures
for organic and inorganic compounds such as Crystallography Open Database
(COD) [15] ,Theoretical Crystallography Open Database (TCOD) [16] and
Inorganic Crystal Structure Database (ICSD) [17] to obtain the input atomic
coordinates of crystalline materials. The platform also offers the
possibility, at the workflow level, to copy large files (for example charge
densities) to a data storage facility for later reuse and to save into the
database a symbolic link to such remote folder.
3.2 Format and scale of the data
AiiDA parses the input and output files mostly stored as text or XML and runs
the calculations/codes on high performance computing platforms. The full
provenance of each data object (inputs, outputs, calculations) is
automatically stored in database in a format that enables the simulation
results to be fully reproduced. The database has an associated repository with
text and binary (machine-independent) files. We have developed uniform formats
to define the most common raw and analyzed data irrespective of the different
plugins. These standards contain data in dictionary format, exportable for
instance to plain JavaScript Object Notation (JSON) (for example,
StructureData, ParameterData data types in AiiDA store metadata in python
dictionaries within a database). AiiDA however allows the flexibility to
define new data structures and formats which might be strongly code depended.
Currently we are using the PostgreSQL [18] open-source relational database to
store our data. Overall they presently contains around 15M+ records and over
the course of the next year it is expected to grow to 100M+ records (with 10
TB+ occupied disk space). Currently we use applications like Jmol, Visual
Molecular Dynamics (VMD), PyMOL, VESTA, XCrySDen and Blender to visualise 2D
and 3D structures, and Matplotlib, Gnuplot and Mathematica for plotting the
data.
# Data collection/generation
4.1 Methodologies for data collection/generation
Data for the project are created and collected by using the AiiDA framework
for the management of the simulations. AiiDA plugins and workflows have been
written for different simulation codes in order to support the simulation with
at least the codes used within this Center of Excellence, but also to support
other codes available in the community (See http://www.aiida.net/plugins/) .
By using AiiDA, the full provenance of all calculations is preserved from
initial inputs to final outputs, as well as all steps along the way, in the
form of a Directed Acyclic Graph (DAG). This allows any output data to be
retrospectively checked for quality if there are questions about how it was
generated. Workflows on the other hand provide a means of proactive quality
assurance whereby a series of steps is designed and implemented by a domain
expert and packaged as a workflow. A workflow can then be executed by experts
and non-experts alike, with internal checks and heuristics that attempt to
ensure the quality of data with respect to convergence and other relevant
simulation parameters. Furthermore, by having a standard way of running
particular calculations, it becomes much easier to compare and validate
results.
Raw inputs and outputs from computer simulations codes are be stored directly
so that they may be re-parsed or manually inspected if necessary. Otherwise
data are be stored as standard, code-independent objects in the AiiDA
framework (e.g. crystal structures, band structures, pseudopotentials, _k_
-point paths, etc) allowing easy querying and manipulation of results from a
variety of simulation software packages.
All naming of these input and output files are handled internally by AiiDA and
files can be retrieved for particular calculations by either issuing a query
to match specific search criteria or directly by using the Universally unique
identifier (UUID) of a known simulation.
4.2 Data quality and standards
As mentioned previously the combination of persistent provenance and workflows
is used in combination to maintain consistency and quality. Our provenance
model also acts as a form of documentation storing all the steps that lead to
any result in the database.
One aspect of the project involves the dissemination of a library of, so
called, pseudopotentials which contain information about the quantum
mechanical properties of the outermost electrons (those relevant in chemical
bonds) for the elements of the periodic table. Currently there may be many
different pseudopotentials for each element, however this makes it difficult
to compare calculations, and worse, some pseudopotentials are not accurate
enough to give reliable results for some of the calculations they are being
used in. By providing a standard set of pseudopotentials that have been
thoroughly tested we alleviate both these problems. We are also involved in
working on ontologies in collaboration with the TCOD team. During this
collaboration, we have implemented exporters for calculations managed using
AiiDA to the domain-specific ontology that is being built within the TCOD
project, so that also calculation results (such as crystal energies or atomic
forces) can be stored in a code-independent format.
# Data management, documentation and curation
5.1 Managing, storing and curating data
In AiiDA, all data (calculations, their inputs and their outputs) generated by
running high-throughput simulations on local or remote servers is naturally
stored on those computers. Moreover, relevant inputs and outputs are persisted
in the AiiDA repository, composed both of a folder-like structure and of a
database. For the latter, we use PostgreSQL, a powerful open source object-
relational database. The format for storing data (depending on the specific
type of data) is defined by the specific AiiDA data plugins, described in
detail in the code documentation. The data format of common objects (crystal
structures, band structures, force constants, etc.) is the same for all
objects of the same type, even if generated by different computer codes, to
facilitate data exchange, queries, and the bridging of different simulation
tools. Moreover, each data format is accompanied by data import and export
functions from/to standard formats (for example Crystallographic Information
File (CIF) files for crystal structures) Importer and exporter for commonly
used formats such as ASE (Atomic Simulation Environment) and PyMatgen format
have been developed. Other import/export capabilities can be transparently
added through upon necessity. Every data object is a node in the DAG where
links between nodes keeps track of the data provenance (who generated the
data, with which parameters, etc.), allowing for easy regeneration of the same
data with the same inputs. Moreover, beside common metadata (user/owner,
creation and last modification date, etc.) any further metadata can be
attached to any node of the database (data and calculations). Also, AiiDA
provides data sharing capabilities, both to share portions of the calculations
database with selected groups of users and collaborators, and to export the
data to public repositories as described in more details in D3.3[19].
Currently the full database and part of the file repository (small files) are
stored on a server at École Polytechnique Fédérale de Lausanne, Switzerland
(EPFL) while the remaining part of the file repository is stored on a Centro
Svizzero di Calcolo Scientifico - Swiss National Supercomputing Centre,
Switzerland (CSCS) server. The policy defining what a large file is depends on
the application and is defined within the AiiDA workflows used to generate the
data. On the EPFL server, there are backup scripts running every day
performing a full backup of database and an incremental backup of the file
repository. The data stored at CSCS server is also backed up daily.
5.2 Metadata standards and data documentation
There are several key pieces of simulation software that will be used for this
project as described in table 1. Typically a simulation is ran by supplying
one or more input files which along with the primary data of interest (be it a
configuration of atoms in space, the electronic structure, a material
property, etc) will typically produce auxiliary data (metadata) which can vary
greatly from software to software. To interface software with AiiDA, a plugin
is written that converts AiiDA nodes (used as input) to the actual input files
required by the code, and parses outputs allowing these to be stored in the
database in a standard way, such that it can later be queried using the AiiDA
Application Programming Interface (API). The AiiDA code itself can be
considered to be _data_ in this context. AiiDA is fully documented both in the
form of descriptions of functions that make up the API and as guides
describing steps such as the installation procedure, configuring users,
setting up codes, etc. The documentation is shipped with the code and can also
be found online at http:// aiida-core.readthedocs.org/en/latest/. To ease
the deployment procedure a dockerfile [20] for AiiDA installation with default
parameters has been realized (See
https://bitbucket.org/aiida_team/aiida_core_docker) this method will be also
extended to each code with a plugin interface with AiiDA in order to readily
install a working AiiDA and simulation code instance.
Figure 1: MaX project timeline, compared with the Swiss Marvel project.
5.3 Data preservation strategy and standards
As part of the MARVEL National Centre of Competence in Research (NCCR) project
[21], funded by the Swiss National Science Foundation (SNSF), a large storage
allocation has been purchased at CSCS which will be used to store and retain
large files until at least 2018. It is foreseen that this will be extended
until 2026 (see timeline in fig. 1). In the meantime we expect to obtain other
funding opportunities to enable further preservation of the data. This applies
also to all the data on the materialscloud.org platform that is being
developed at EPFL, where consortium members will be free to share data
publicly and privately (with selected collaborators) under the condition that
data be made publicly available within approximately a year of depositing. The
Materials Cloud platform is expected to be fully operative in April 2017.
Entire AiiDA graphs or part of them could be directly shared through the
Materials Cloud portal.
The data retained, being generated with AiiDA, will include full provenance of
all simulations carried out. Some large output files may not be preserved if
they are judged to be easy to reproduce and unlikely to be needed after the
simulation has completed. This policy is code-dependent and sensible defaults
are defined within each AiiDA plugin, but can easily be changed by the user or
group who runs the simulations and generates the data.
Together the Materials Cloud and AiiDA aim to enable researchers to trivially
adopt aspects of the Findable, Accessible, Interoperable and Re-usable (FAIR)
principles as laid out by Force11. Any calculation ran through AiiDA is
automatically interoperable (we define an open standard and provide tools to
convert data to other libraries and are adding Open Databases Integration for
Materials Design (OPTiMaDe) compatibility) and re-usable (by virtue of the
provenance tracking and workflows). The Materials Cloud on the other hand
ensures that the data is both findable and accessible. Section 7 outlines
further details.
# Data security and confidentiality
6.1 Formal information/data security standards
AiiDA adopts a distributed approach whereby an AiiDA instance (the code plus
the associated database) can be hosted on an individual’s machine, a group
server or a national or international server. Instances within a group should
be managed and secured by the group itself or an appointed administrator. We
provide a means of sharing results either with collaborators or the public at
large via the materialscloud.org website which will be running a full AiiDA
instance. In this case, EPFL and CSCS (for the AiiDA repository and the large
files, respectively) will be responsible for maintaining data security.
Data transport and access will be carried out over secure communication
channels, i.e. Secure Shell (SSH), and access to the database will be
restricted to authorised users only. The AiiDA database does not store users’
private SSH keys and therefore any possible compromise of the database does
not lead to a security breach that extends beyond the data stored in the
database itself.
6.2 Main risks to data security
Access to data and the execution of simulations is typically initiated by
opening an SSH connection. This protocol itself is considered to be highly
secure and is widely used. Also, SSH keys are used to connect rather than the
password. Moreover, these keys are not stored in the AiiDA database; instead,
AiiDA uses the keys of the Linux user under which the AiiDA daemon is running,
therefore there is no additional security risk with respect to standard SSH
connections. In any case, should a private key be obtained by an individual
other than the authorised user there are typically system logs that keep track
of all access, and the specific SSH key can be disabled to stop further
activity. In addition, AiiDA, keeps an extensive log of the activities carried
out which can be examined retrospectively in such a case.
# Data sharing and access
7.1 Suitability for sharing
The data generated in this project is highly suitable for sharing. Given that
a simulation may take many hundreds (if not thousands) of Central Processing
Unit (CPU) hours it is beneficial to the community to be able to access these
without having to recompute them. In addition to raw data there will be a
curated section of the materialscloud.org, which uses directly AiiDA as a
backend, that will contain results condensed from many simulations in a form
that gives an overview of particular property or area of interest.
AiiDA provides a social ecosystem where the simulation results, materials and
provenance data can be shared. It provides plugins to import crystal
structures from many common formats and directly from external databases such
as ICSD [17] or COD. It has also COD and TCOD exporters to export data to
these external databases. Moreover it is fully interoperable with commonly
used data formats for crystal structures such as XSF[22], ASE[23] and
Pymatgen[24].
7.2 Discovery by potential users of the research data
Data will be discoverable by the following means:
* The materialscloud.org website which will host a public facing frontend enabling access to publicly shared results.
* A private section of the website will allow dissemination with selected (authorised) collaborators. However users will be obliged to make this data publicly available approximately a year after depositing it.
* Publications will contain references to the database (including UUIDs) indicating where the data used for that study can be found.
* Publications that use results from the AiiDA repository will be encouraged to cite the paper describing the software infrastructure.
7.3 Data access and reusability
## 7.3.1 REST interface and Optimade support
A key feature to enable large-scale data access is represented by the
development of the AiiDA REST (REpresentation Stateless Transfer) Api, an
interface that assigns Universal Resource Identifiers (URIs) to the objects
stored in an AiiDA instance, thus making them obtainable via HTTP requests in
a programmatic manner. The REST Api can be coupled to
authentication/authorization modules so that the AiiDA administrator can
choose the degree of accessibility of the resources. Moreover the AiiDA
developers signed-on to provide a URI syntax compliant with the standards
defined in the OPTIMaDe ( "Open Databases Integration for Materials Design)
protocol to let the users interrogate heterogeneous databases with the same
syntax. Finally, the AiiDA REST Api can be used as an essential building block
for on-line services that expose a repository of data persisted in AiiDA. A
paramount example is materialscloud.org whose back-end largely relies on the
AiiDA REST Api.
## 7.3.2 Sharing of plugins workflows
Point A1.1 of the FAIR principles requires that “the protocol is open, free,
and universally implementable”. This is facilitated with a flexible plugins
system being developed that enables developers to extend AiiDA to support
their own codes and formats and share this functionality with the community
via the Materials Cloud. Similarly, workflows that encode scientific expertise
on how to carry out a series of steps to arrive at a result can be
disseminated via the Materials Cloud. A critically revised version of the
workflow engine and of the plugin system distribution, has been implemented in
the latest release AiiDA v0.8 and described in details in D3.3[19]. This
simplifies both the sharing and reuse of these two high level code components.
7.4 Governance of access
The ultimate decision about sharing the data will lie with the PI and the
author of the data, however in general it is expected that all data from
published work within this Center of Excellence will be made publicly
available. We are in the process of preparing a data sharing policy for the
data added to the materialscloud.org web portal, that will require users to
make their data available under a Creative Commons license after a maximum
period, which is likely to be one year.
The core of the AiiDA APIs (“aiida_core”) is released under an open-source MIT
license and is available to download for free. The full code, including the
full content of “aiida_core”, plus a set of useful additional plugins, are
distributed as the “aiida_epfl” package, available free of charge for academic
users. To download, users have to register with their academic email. The main
terms of the “aiida_epfl” license are available at
http://theossrv1.epfl.ch/aiida_download/. Finally, most of the material
simulation codes are open source. As described in the proposal, also the
remaining ones are in the process of introducing a multiple-licensing system
to comply with the open-source requirements.
# Relevant institutional, departmental or study policies on data sharing and
data security
Data will be generated by different institutions, and the data policies of the
respective institutions will apply. For data shared on materialscloud.org,
the policies of EPFL and CSCS will apply (as the data is expected to be stored
at these two institutions. In particular, EPFL provides a combined document
“Directive concerning research integrity and good scientific practice at EPFL
(LEX 3.3.2)” [25] for all data policies. The data policies from CSCS are
instead explained in the document “Data_Storage_policy_V2.pdf” [26].
|
https://phaidra.univie.ac.at/o:1140797
|
Horizon 2020
|
1049_MaX_676598.md
|
# Executive Summary
The MaX Centre of Excellence aims at supporting the needs of all the
stakeholders involved in the field of materials modeling, simulation and
design by providing new instruments and services in the form of data, codes,
expertise and turnkey solutions to efficiently address the crucial challenges
of novel materials development in the exascale computing era. This document
provides a description of the strategies and solutions adopted within the MaX
CoE to establish a high level materials’ informatics framework to curate,
preserve and share all the data produced by the flagship codes. The core
technology behind this objective is the AiiDA code, a python infrastructure
designed to support different codes through a plugin interface, allow for an
automated design and implementation of complex workflows and task tracking and
able to store the full provenance of each object in a tailored database. AiiDA
parses the input and output files and runs the calculations on high
performance computing platforms, stores the data using uniform formats based
on python dictionaries and preserve the full provenance in the form of a
Directed Acyclic Graph (DAG). AiiDA also enables a social ecosystem where the
simulation workflows and results can be openly shared.
# Introduction
2.1 About this document
This document is deliverable D3.1 of the MaX project and briefly describes the
types of data produced in the project, standards used for data and how the
data is being curated, preserved and shared. This is a living document and
will be updated continuously in the course of the project. The acronyms used
are summarised in the glossary at the end.
# Description of the data
Within this project, multiple open-source computer codes including Fleur,
SIESTA, Quantum ESPRESSO, YAMBO, etc. for electronic-structure calculations
and materials modeling are being developed (see Table 1). The materials
informatics framework AiiDA has been designed to support different codes, and
plugins are already available or are being currently developed to support all
codes of this Centre of Excellence (CoE). AiiDA promotes advanced programming
models through Python abstraction layers to disseminate advanced
functionalities to arbitrary quantum engines (i.e. simulation codes). It
provides a model of automatic data generation and storage, to guarantee
provenance, preservation, reproducibility and reuse. This platform will be
used to organise and coordinate thousands of simulations, searching for
optimal properties and performance by acquiring a variety of heterogeneous
microscopic data from the calculations. It should ideally allow for an
automated design and implementation of complex workflows and task tracking,
based on a scripting interface for job creation and submission. The results
will feed a database of structures and properties that will in turn drive
further simulations. The data thus generated will be used for instance for
data-mining and machine-learning, or to build classical neural networks to
further ramp up the time and length scales accessible to numerical modeling.
Table 1: The primary codes that will be used as part of this project.
<table>
<tr>
<th>
Name
</th>
<th>
License
</th>
<th>
Main Developers
</th> </tr>
<tr>
<td>
FLEUR [1, 2]
</td>
<td>
GNU-GPL
</td>
<td>
Jülich
</td> </tr>
<tr>
<td>
QUANTUM ESPRESSO [3, 4]
</td>
<td>
GNU-GPL
</td>
<td>
SISSA, EPFL, CIN
</td> </tr>
<tr>
<td>
SIESTA [5, 6]
</td>
<td>
SIESTA License
</td>
<td>
ICN2, BSC
</td> </tr>
<tr>
<td>
YAMBO [7, 8]
</td>
<td>
GNU-GPL
</td>
<td>
CNR, CIN
</td> </tr>
<tr>
<td>
AIIDA [9, 10]
</td>
<td>
MIT-BSD, EPFL License
</td>
<td>
EPFL, Robert Bosch
</td> </tr>
<tr>
<td>
I-PI [11, 12]
</td>
<td>
GNU-GPL/MIT-BSD
</td>
<td>
EPFL
</td> </tr> </table>
3.1 Types of data
AiiDA provides automated solutions and various plugins for computer codes
without a need for tuning code specific parameters. It stores the
calculations, their inputs and their results (either parsed, extracted from
Extensible Markup Language (XML) outputs or from text files with the
appropriate dictionaries) in a database and its associated file repository.
This data is generated from open-source electronic-structure material
simulations codes that encompass key technologies such as all-electron,
pseudopotentials, and localised basis sets, density-functional theory, time-
dependent density-functional theory, and many-body perturbation theory,
multiscale/multiphysics modelling with a focus on quantum mechanics/molecular
mechanics, solvation and electrochemistry, thermal/electrical transport, and
complex magnetic properties. The key codes are described in Table 1.
We are also using the Crystallography Open Database (COD) [13] and Theoretical
Crystallography Open Database (TCOD) [14] external open access databases of
crystal structures for organic and inorganic compounds to obtain the input
atomic coordinates of crystalline materials.
3.2 Format and scale of the data
AiiDA parses the input and output files mostly stored as text or XML and runs
the calculations/codes on high performance computing platforms. The full
provenance of each data object (inputs, outputs, calculations) is
automatically stored in database in a format that enables the simulation
results to be fully reproduced. The database has an associated repository with
text and binary (machine-independent) files. We have used a uniform format to
define the main raw and analysed data irrespective of the different plugins
(e.g. Quantum ESPRESSO). These formats contain data in dictionary format,
exportable for instance to plain JavaScript Object Notation (JSON) (for
example, StructureData, ParameterData data types in AiiDA store metadata in
python dictionaries within a database). Currently we are using the PostgreSQL
[15] open-source relational database to store our data. It contains around 1M+
records but over the course of the next year and a half it is expected to grow
to 100M+ records (with 10 TB+ occupied disk space). Currently we use
applications like Jmol, Visual Molecular Dynamics (VMD), PyMOL, VESTA,
XCrySDen and Blender to visualise 2D and 3D structures, and Matplotlib,
Gnuplot and Mathematica for plotting the data.
AiiDA provides a social ecosystem where the simulation results, materials and
provenance data and scientific workflows can be shared. It provides plugins to
import crystal structures from many common formats and directly from external
databases such as Inorganic Crystal Structure Database (ICSD) [16] or COD. It
has also COD and TCOD exporters to export data to these external databases.
This allows us to share data easily and also ensures their long-term
preservation.
# Data collection/generation
4.1 Methodologies for data collection/generation
Data for the project will be created and collected by using the AiiDA
framework for the management of the simulations. AiiDA plugins and workflows
are being written for different simulation codes in order to support the
simulation with at least the codes used within this Centre of Excellence, but
also to support other codes available in the community. By using AiiDA, the
full provenance of all calculations is preserved from initial inputs to final
outputs, as well as all steps along the way, in the form of a Directed Acyclic
Graph (DAG). This allows any output data to be retrospectively checked for
quality if there are questions about how it was generated. Workflows on the
other hand provide a means of proactive quality assurance whereby a series of
steps is designed and implemented by a domain expert and packaged as a
workflow. A workflow can then be executed by experts and non-experts alike,
with internal checks and heuristics that attempt to ensure the quality of data
with respect to convergence and other relevant simulation parameters.
Furthermore, by having a standard way of running particular calculations, it
becomes much easier to compare and validate results.
Raw inputs and outputs from computer simulations codes will be stored directly
so that they may be re-parsed or manually inspected if necessary. Otherwise
data will be stored as standard, code-independent objects in the AiiDA
framework (e.g. crystal structures, band structures, pseudopotentials, _k_
-point paths, etc) allowing easy querying and manipulation of results from a
variety of simulation software packages.
All naming of these input and output files are handled internally by AiiDA and
files can be retrieved for particular calculations by either issuing a query
to match specific search criteria or directly by using the Universally unique
identifier (UUID) of a known simulation.
4.2 Data quality and standards
As mentioned previously the combination of persistent provenance and workflows
will be used in combination to maintain consistency and quality. Our
provenance model also acts as a form of documentation storing all the steps
that lead to any result in the database.
One aspect of the project involves the dissemination of a library of, so
called, pseudopotentials which contain information about the quantum
mechanical properties of the outermost electrons (those relevant in chemical
bonds) for the elements of the periodic table. Currently there may be many
different pseudopotentials for each element, however this makes it difficult
to compare calculations, and worse, some pseudopotentials are not accurate
enough to give reliable results for some of the calculations they are being
used in. By providing a standard set of pseudopotentials that have been
thoroughly tested we alleviate both these problems. We are also involved in
working on ontologies in collaboration with the TCOD team. During this
collaboration, we have implemented exporters for calculations managed using
AiiDA to the domain-specific ontology that is being built within the TCOD
project, so that also calculation results (such as crystal energies or atomic
forces) can be stored in a code-independent format.
# Data management, documentation and curation
5.1 Managing, storing and curating data.
In AiiDA, all data (calculations, their inputs and their outputs) generated by
running highthroughput simulations on local or remote servers is naturally
stored on those computers. Moreover, relevant inputs and outputs are persisted
in the AiiDA repository, composed both of a folder-like structure and of a
database. For the latter, we use PostgreSQL, a powerful open source object-
relational database. The format for storing data (depending on the specific
type of data) is defined by the specific AiiDA data plugins, described in
detail in the code documentation. The data format of common objects (crystal
structures, band structures, force constants, etc.) is the same for all
objects of the same type, even if generated by different computer codes, to
facilitate data exchange, queries, and the bridging of different simulation
tools. Moreover, each data format is accompanied by data import and export
functions from/to standard formats (for instance Crystallographic Information
File (CIF) files for crystal structures). Further export functions can be
added transparently and will be developed during the project. Every data
object is a node in the DAG where links between nodes keeps track of the data
provenance (who generated the data, with which parameters, etc.), allowing for
easy regeneration of the same data with the same inputs. Moreover, beside
common metadata (user/owner, creation and last modification date, etc.) any
further metadata can be attached to any node of the database (data and
calculations). Also, AiiDA provides data sharing capabilities, both to share
portions of the calculations database with selected groups of users and
collaborators, and to export the data to public repositories.
Currently the full database and part of the file repository (small files) are
stored on a server at École Polytechnique Fédérale de Lausanne, Switzerland
(EPFL) while the remaining part of the file repository is stored on a Centro
Svizzero di Calcolo Scientifico Swiss National Supercomputing Centre,
Switzerland (CSCS) server. The policy defining what a large file is depends on
the application and is defined within the AiiDA workflows used to generate the
data. On the EPFL server, there are backup scripts running every day
performing a full backup of database and an incremental backup of the file
repository. The data stored at CSCS server is also backed up daily.
5.2 Metadata standards and data documentation
There are several key pieces of simulation software that will be used for this
project as described in Table 1. Typically a simulation is ran by supplying
one or more input files which along with the primary data of interest (be it a
configuration of atoms in space, the electronic structure, a material
property, etc) will typically produce auxiliary data (metadata) which can vary
greatly software to software. To interface software with AiiDA, a plugin is
written that converts AiiDA nodes (used as input) to the actual input files
required by the code, and parses outputs allowing these to be stored in the
database in a standard way, such that it can later be queried using the AiiDA
Application Programming Interface (API).
The AiiDA code itself can be considered to be _data_ in this context. AiiDA is
fully documented both in the form of descriptions of functions that make up
the API and as guides describing steps such as the installation procedure,
configuring users, setting up codes, etc. The documentation is shipped with
the code and can also be found online at http://aiida-
core.readthedocs.org/en/latest/.
5.3 Data preservation strategy and standards
As part of the MARVEL National Centre of Competence in Research (NCCR) project
[17], funded by the Swiss National Science Foundation (SNSF), a large storage
allocation is being purchased at CSCS which will be used to store and retain
large files until at least 2018. Later, data will be handled by until 2026
(see timeline in fig. 1). In the meantime we expect to obtain other funding
opportunities for further future preservation of the data. This applies also
to all the data on the materialscloud.org platform that is being developed
at EPFL, where consortium members will be free to share data publicly and
privately (with selected collaborators) under the condition that data be made
publicly available within approximately a year of depositing.
The data retained, being generated with AiiDA, will include full provenance of
all simulations carried out. Some large output files may not be preserved if
they are judged to be easy to reproduce and unlikely to be needed after the
simulation has completed. This policy is code-dependent and sensible defaults
are defined within each AiiDA plugin, but can be easily changed by the user or
group who runs the simulations and generates the data.
Figure 1: MaX project timeline, compared with the Swiss Marvel project.
# Data security and confidentiality
6.1 Formal information/data security standards
AiiDA adopts a distributed approach whereby an AiiDA instance (the code plus
the associated database) can be hosted on an individual’s machine, a group
server or a national or international server. Instances within a group should
be managed and secured by the group itself or an appointed administrator. We
provide a means of sharing results either with collaborators or the public at
large via the materialscloud.org website which will be running a full AiiDA
instance. In this case, EPFL and CSCS (for the AiiDA repository and the large
files, respectively) will be responsible for maintaining data security.
Data transport and access will be carried out over secure communication
channels, i.e. Secure Shell (SSH), and access to the database will be
restricted to authorised users only. The AiiDA database does not store users’
private SSH keys and therefore any possible compromise of the database does
not lead to a security breach that extends beyond the data stored in the
database itself.
6.2 Main risks to data security
Access to data and the execution of simulations is typically initiated by
opening an SSH connection. This protocol itself is considered to be highly
secure and is widely used. Also, SSH keys are used to connect rather than the
password. Moreover, these keys are not stored in the AiiDA database; instead,
AiiDA uses the keys of the Linux user under which the AiiDA daemon is running,
therefore there is no additional security risk with respect to standard SSH
connections. In any case, should a private key be obtained by an individual
other than the authorised user there are typically system logs that keep track
of all access, and the specific SSH key can be disabled to stop further
activity. In addition, AiiDA, keeps an extensive log of the activities carried
out which can be examined retrospectively in such a case.
# Data sharing and access
7.1 Suitability for sharing
The data generated in this project is highly suitable for sharing. Given that
a simulation may take many hundreds (if not thousands) of Central Processing
Unit (CPU) hours it is beneficial to the community to be able to access these
without having to recompute them. In addition to raw data there will be a
curated section of the materialscloud.org that will contain results
condensed from many simulations in a form that gives an overview of particular
property or area of interest.
7.2 Discovery by potential users of the research data
Data will be discoverable by the following means:
* The materialscloud.org website which will host a public facing frontend enabling access to publicly shared results.
* A private section of the website will allow dissemination with selected (authorised) collaborators. However users will be obliged to make this data publicly available approximately a year after depositing it.
* Publications will contain references to the database (including UUIDs) indicating where the data used for that study can be found.
* Publications that use results from the AiiDA repository will be encouraged to cite the paper describing the software infrastructure.
7.3 Governance of access
The ultimate decision about sharing the data will lie with the PI and the
author of the data, however in general it is expected that all data from
published work within this Centre of Excellence will be made publicly
available. We are in the process of preparing a data sharing policy for the
data added to the materialscloud.org web portal, that will require users to
make their data available under a Creative Commons license after a maximum
period, which is likely to be one year.
The core of the AiiDA APIs (“aiida_core”) is released under an open-source MIT
license and is available to download for free. The full code, including the
full content of “aiida_core”, plus a set of useful additional plugins, are
distributed as the “aiida_epfl” package, available free of charge for academic
users. To download, users have to register with their academic email. The main
terms of the “aiida_epfl” license are available at
http://theossrv1.epfl.ch/aiida_download/. Finally, most of the material
simulation codes are open source. As described in the proposal, also the
remaining ones are in the process of introducing a multiple-licensing system
to comply with the open-source requirements.
# Relevant institutional, departmental or study policies on data sharing and
data security
Data will be generated by different institutions, and the data policies of the
respective institutions will apply. For data shared on materialscloud.org,
the policies of EPFL and CSCS will apply (as the data is expected to be stored
at these two institutions. In particular, EPFL provides a combined document
“Directive concerning research integrity and good scientific practice at EPFL
(LEX 3.3.2)” [18] for all data policies. The data policies from CSCS are
instead explained in the document “Data_Storage_policy_V2.pdf” [19].
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GenTree are numbers, texts, images and sounds generated by the partners during
the project and **needed to validate** the results presented in scientific
publications and other communication documents. They include metadata, i.e.
the information describing the managed research data.
This document does not concern data preexisting the GenTree project. These
data are part of the “background” contributions supplied by the GenTree
partners and are identified in the Consortium Agreement document. This
document does not concern either data originated from third party producers.
These data are managed according to the rules defined by these third parties.
With the objective of **stimulating the use and re-use of data** and to
contribute to the development of the “Open Science” strategy of the European
Union through **data sharing** , the GenTree data policy framework provides
recommendations and rules for data management and accessibility. It is based
on existing documents published in the scientific literature, such as Michener
(2015, "Ecological data sharing” Ecologial Informatics) and the guidelines
produced by the European Commission (2016, Guidelines on Data Management in
Horizon 2020).
The **general purpose** of this document is:
1. to identify the different types of data (origin and authorship, see annex 1 for definition of terms) that will be collected during GenTree and define the different categories of users for these data during GenTree and after it is finished.
2. to provide documents and recommendations for data management and publication:
* a Data Management Plan template (Annex 2);
* recommendations for data sharing (Annex 3);
* data licenses adapted to specific Intellectual Properties (Annex 4);
c) to provide support for the production and management of discovery metadata:
- metadata standards compliant with international requirements and
constraints; - a metadata management system.
This document only focuses on data, including output data from modeling
activities. Models and modeling platforms involved in the GenTree project are
out of its scope. Intellectual property and sharing rules related to these
components are addressed in the Consortium Agreement. Plant material and DNA
are also outside the scope of this document. Their exchange and transfer will
be addressed in a separate Material Transfer Agreement document.
# 3- Data Management Plan
All GenTree research activities that collect or generate data should provide a
Data
Management Plan (DMP). Although there is no unique rule to define at which
scale the DMP should be elaborated, it must correspond to a coherent data set
relative to their management. The H2020 DMP template (Annex 2) should be
preferably used by GenTree partners. For each data set, the following
information needs to be provided: Data set reference and name, data set
description, standards and metadata used, how data will be shared, how data
will be archived and preserved (including storage and backup).
# 4- Data sharing agreement for GenTree partners
A data sharing agreement (Annex 3) has to be signed by all GenTree partners in
order to ensure the full sharing of data among partners. Data will be
immediately accessible for all partners, without any embargo. Studies based on
specific data sets must be proposed as coresearch activities to the partner(s)
owning the intellectual property rights.
# 5- Data identification/citation
GenTree data will be stored in GnpIS (
_https://urgi.versailles.inra.fr/Tools/GnpIS_ ) , a dedicated multispecies
integrative Information System which ensures confidential treatment, secured
archiving, browsing, visualizing and use and re-use of data. GnpIS is a
licensed information system that will be used as is, without further
development from its current state, by GenTree partners. GnpIS cannot be
installed at GenTree partner laboratories.
GenTree recommends that data sets should be identified by a DOI. GnpIS makes
it possible to generate DOI for data sets. Data and their DOI will remain
accessible to partners for a minimum of 10 years after they are stored in
GnpIS.
GenTree encourages the publication of data papers making the data produced by
GenTree accessible to the community of researchers or stakeholders interested
in the management and sustainable use of forest genetic resources in Europe.
GenTree encourages that publications using GenTree data clearly identify and
acknowledge such data and their Information System, either by citing the
relevant data papers or by linking to the proper DOI in the GenTree
Information System.
# 6- Metadata standards and tools for discovery
All GenTree datasets should be described by standardized metadata for
discovery purposes. GenTree metadata will be stored and managed using the same
dedicated Information System as for data, GnpIS (
_https://urgi.versailles.inra.fr/Tools/GnpIS_ ) . Metadata will follow the
ISO19115/19139 standards and will be compliant with the EU INSPIRE directive.
GenTree metadata will be fully and freely accessible.
Each Partner shall supply GnpIS, the GenTree Information System, with its
metadata as soon as data are generated. Each Partner shall also supply the
GenTree Information System with Metadata from third parties, according to the
rules defined by such third party.
# GenTree DMP template
Templates are based on H2020 and DCC templates (
_http://www.dcc.ac.uk/resources/howguides/develop-data-plan_ ) .
The purpose of the Data Management Plan (DMP) is to provide an analysis of the
main elements of the data management policy that will be used by the
applicants with regard to all the datasets that will be generated by the
project. The DMP is not a fixed document, but evolves during the lifespan of
the project. The DMP should address the points below on a dataset by dataset
basis and should reflect the current status of reflection within the
consortium about the data that will be produced.
GenTree data should be discoverable, accessible, assessable and intelligible,
useable beyond the original purpose for which they were collected and,
finally, interoperable to specific quality standards. In GenTree, there are 3
main categories of data: site description, DNA sequences and phenotypic
traits.
For each data set, the following 5 main items should be addressed and
specified:
**1- Data set reference and name** Identifier for the data set to be produced.
## **2- Data set description**
Description of the data that will be generated or collected, its origin (in
case it is collected), nature and scale and to whom it could be useful, and
whether it underpins a scientific publication. Information on the existence
(or not) of similar data and the possibilities for integration and re-use.
Questions to consider on data set description:
* What data will you create?
Guidance:
* Give a brief description of the data that will be created, noting its content and coverage
## **3- Standards and metadata**
Reference to existing suitable standards of the discipline. If these do not
exist, an outline on how and what metadata will be created.
Questions to consider on standards and metadata:
* How will you capture / create the metadata?
* Can any of this information be created automatically?
* What metadata standards will you use and why?
Guidance:
* Metadata should be created to describe the data and aid discovery. Consider how you will capture this information and where it will be recorded, e.g. in a database with links to each item, in a ‘readme’ text file, in file headers etc.
* Researchers are strongly encouraged to use community standards to describe and structure data, where these are in place. The DCC offers a catalogue of disciplinary metadata standards.
## **4- Data sharing**
Description of how data will be shared, including access procedures, embargo
periods (if any), outlines of technical mechanisms for dissemination and
necessary software and other tools for enabling re-use, and definition of
whether access will be widely open or restricted to specific groups.
Identification of the repository where data will be stored, if already
existing and identified, indicating in particular the type of repository
(institutional, standard repository for the discipline, etc.). In case the
dataset cannot be shared, the reasons for this should be mentioned (e.g.
ethical, rules of personal data, intellectual property, commercial,
privacyrelated, security-related).
Questions to consider on methods for data sharing:
* How will you make the data available to others?
* With whom will you share the data, and under what conditions?
Guidance:
* Consider where, how, and to whom the data should be made available. Will you share data via a data repository, handle data requests directly or use another mechanism?
* The methods used to share data will be dependent on a number of factors such as the type, size, complexity and sensitivity of data. Mention earlier examples to show a track record of effective data sharing. Questions to consider on restrictions for sharing:
* Are any restrictions on data sharing required? e.g. limits on who can use the data, when and for what purpose.
* What restrictions are needed and why?
* What action will you take to overcome or minimise restrictions?
Guidance:
* Outline any expected difficulties in data sharing, along with causes and possible measures to overcome these. Restrictions to data sharing may be due to participant confidentiality, consent agreements or IPR. Strategies to limit restrictions may include: anonymising or aggregating data; gaining participant consent for data sharing; gaining copyright permissions; and agreeing a limited embargo period.
Questions to consider on data repository:
* Where (i.e. in which repository) will the data be deposited?
Guidance:
* Most research funders recommend the use of established data repositories, community databases and related initiatives to aid data preservation, sharing and re-use.
* An international list of data repositories is available via Databib or Re3data. **5- Archiving and preservation (including storage and backup)**
Description of the procedures that will be put in place for long-term
preservation of the data. Indication of how long the data should be preserved,
what is its approximated end volume, what the associated costs are and how
these are planned to be covered.
Questions to consider on Preservation Plan:
* What is the long-term preservation plan for the dataset? e.g. deposit in a data repository
* Will additional resources be needed to prepare data for deposit or meet charges from data repositories?
Guidance:
* Researchers should consider how datasets that have long-term value will be preserved and curated beyond the lifetime of the grant. Also outline the plans for preparing and documenting data for sharing and archiving.
* If you do not propose to use an established repository, the data management plan should demonstrate that resources and systems will be in place to enable the data to be curated effectively beyond the lifetime of the grant.
Questions to consider on Resourcing:
* What additional resources are needed to deliver your plan?
* Is additional specialist expertise (or training for existing staff) required?
* Do you have sufficient storage and equipment or do you need to cost in more?
* Will charges be applied by data repositories?
* Have you costed in time and effort to prepare the data for sharing / preservation?
Guidance:
* Carefully consider any resources needed to deliver the plan. Where dedicated resources are needed, these should be outlined and justified. Outline any relevant technical expertise, support and training that is likely to be required and how it will be acquired. Provide details and justification for any hardware or software which will be purchased or additional storage and backup costs that may be charged by IT services.
* Funding should be included to cover any charges applied by data repositories, for example to handle data of exceptional size or complexity. Also remember to cost in time and effort to prepare data for deposit and ensure it is adequately documented to enable re-use. If you are not depositing in a data repository, ensure you have appropriate resources and systems in place to share and preserve the data.
* See UKDS guidance on costing data management.
# GenTree Data Sharing Agreement
**1\. Conditions for supplying Data and Metadata to the GenTree Information
System 1.1 Data and Metadata coming from Parties of the GenTree Project**
## a) Data and Metadata defined as Background
* Each Party shall supply the GenTree Information System with its Data and Metadata as soon as possible, if these Data and Metadata are needed for carrying out the activities of the research project, unless these Data or Metadata have been excluded or limited in the Consortium Agreement.
* The Parties will provide a citation (acknowledgement) reference for each Data set included as a Metadata into the GenTree Information System
## b) Data and Metadata (Foreground) produced by the Parties
* Each Party shall supply the GenTree Information System with its Data and Metadata as soon as they are generated if these Data and Metadata are needed for carrying out the activities of the research project.
* The Parties will provide a citation (acknowledgement) reference of each Data set provided into the GenTree Information System
### 1.2 Data and Metadata coming from third Parties
The Parties shall supply the GenTree Information System with Data and
Metadata from third parties, according to the rules defined by each such third
party.
### 2 Access policy to Data and Metadata during, outside and after the
GenTree Project
#### 2.1 Access to Metadata
Public access to the Metadata will be given once these are entered into the
GenTree Information System.
**2.2 Access to Data**
## a) Access to Data defined as Background
* Access to Data defined as Background will be given to the GenTree partners as needed for the implementation of their research activities and projects in compliance with the Consortium Agreement.
* Access to Data defined as Background will be given to the scientific community upon prior authorisation given by the Party who owns the data or rights pertaining to such data
## b) Access to Data defined as Foreground
* Access to Data defined as Foreground obtained within the GenTree Project, will be given to the GenTree partners once they are entered into the GenTree Information System, and to the public, after a maximum delay of 18 (eighteen) months after the end of GenTree.
* Parties who own the Data will endeavour to shorten this period of 18 (eighteen) months. The period of 18 (eighteen) months can be extended through a decision of the GenTree ExCom, after prior consultation of the owner the Data.
### 3 Publication/communication
#### 3.1 Metadata public release
Metadata shall be released to the Public as soon as possible. Any
publication or communication of Data and/or Metadata shall be made according
to the provisions of article 8.4 (Dissemination) of the Consortium Agreement.
#### 3.2 Referencing and acknowledgement
* Any publication of the results obtained within the GenTree project must reference the project as follows: “This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 676876”.
* Any publication of results obtained that use Data and/or Metadata shared within the GenTree Information System, must reference the Data and/or Metadata owner, the citation reference of the dataset (whenever provided in the metadata) and the project.
* Any publication of a synthesis of Data and/or Metadata shared in the GenTree information system, including for dissemination or training, must reference the Data and/or Metadata owner, the citation reference of the datasets (whenever provided in the metadata) and the project.
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and identified, indicating, in particular, the type of repository
(institutional, standard repository for the discipline, etc.). In case the
dataset cannot be shared, the reasons for this should be mentioned (e.g.
ethical, rules of personal data, intellectual property, commercial, privacy-
related, security-related).
**Archiving and preservation** (including storage and backup) - Description
of the procedures that will be put in place for the long-term preservation of
the data. An indication of how long the data should be preserved, what is its
approximated end volume, what the associated costs are and how these are
planned to be covered.
# 3 ATLAS’ Grant Agreement - Article 29 – Dissemination of results, open
access and visibility of EU funding
The Data Management Plan and Open Science Policy of project ATLAS specifically
address Article 29 of the Grant agreement. It is included here as a key
reference.
## 29.1 Obligation to disseminate results
_Unless it goes against their legitimate interests, each beneficiary must — as
soon as possible — ‘disseminate’ its results by disclosing them to the public
by appropriate means (other than those resulting from protecting or exploiting
the results), including in scientific publications (in any medium). This does
not change the obligation to protect results in Article 27, the
confidentiality obligations in Article 36, the security obligations in Article
37 or the obligations to protect personal data in Article 39, all of which
still apply._
_A beneficiary that intends to disseminate its results must give advance
notice to the other beneficiaries of — unless agreed otherwise — at least 45
days, together with sufficient information on the results it will disseminate.
Any other beneficiary may object within — unless agreed otherwise — 30 days of
receiving notification, if it can show that its legitimate interests in
relation to the results or background would be significantly harmed. In such
cases, the dissemination may not take place unless appropriate steps are taken
to safeguard these legitimate interests. If a beneficiary intends not to
protect its results, it may — under certain conditions (see Article 26.4.1) —
need to formally notify the Agency before dissemination takes place._
## 29.2 Open access to scientific publications
_Each beneficiary must ensure open access (free of charge online access for
any user) to all peerreviewed scientific publications relating to its results.
In particular, it must:_
1. _as soon as possible and at the latest on publication, deposit a machine-readable electronic copy of the published version or final peer-reviewed manuscript accepted for publication in a repository for scientific publications; Moreover, the beneficiary must aim to deposit at the same time the research data needed to validate the results presented in the deposited scientific publications._
2. _ensure open access to the deposited publication — via the repository — at the latest:_
1. _on publication, if an electronic version is available for free via the publisher, or ii. within six months of publication (twelve months for publications in the social sciences and humanities) in any other case._
3. _ensure open access — via the repository — to the bibliographic metadata that identifies the deposited publication. The bibliographic metadata must be in a standard format and must include all of the following:_
1. _the terms “European Union (EU)” and “Horizon 2020”; ii. the name of the action, acronym and grant number; iii. the publication date, and length of embargo period if applicable, and iv. a persistent identifier._ **29.3 Open access to research data**
_Regarding the digital research data generated in the action (‘data’), the
beneficiaries must:_
1. _deposit in a research data repository and take measures to make it possible for third parties to access, mine, exploit, reproduce and disseminate — free of charge — the following:_
_i. the data, including associated metadata, needed to validate the results
presented in scientific publications as soon as possible; ii. other data,
including associated metadata, as specified and within the deadlines laid down
in the 'data management plan';_
2. _provide information — via the repository — about tools and instruments at the disposal of the beneficiaries and necessary for validating the results (and — where possible — provide the tools and instruments themselves). This does not change the obligation to protect results in Article 27, the confidentiality obligations in Article 36, the security obligations in Article 37 or the obligations to protect personal data in Article 39, all of which still apply. As an exception, the beneficiaries do not have to ensure open access to specific parts of their research data if_
_the achievement of the action's main objective, as described in Annex 1,
would be jeopardised by making those specific parts of the research data
openly accessible. In this case, the data management plan must contain the
reasons for not giving access._
## 29.4 Information on EU funding — Obligation and right to use the EU emblem
_Unless the Agency requests or agrees otherwise or unless it is impossible,
any dissemination of results (in any form, including electronic) must:_
1. _display the EU emblem and_
2. _include the following text: “This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 678760”._
_When displayed together with another logo, the EU emblem must have
appropriate prominence. For the purposes of their obligations under this
Article, the beneficiaries may use the EU emblem without first obtaining
approval from the Agency. This does not, however, give them the right to
exclusive use. Moreover, they may not appropriate the EU emblem or any similar
trademark or logo, either by registration or by any other means._
## 29.5 Disclaimer excluding Agency responsibility
_**Any dissemination of results must indicate that it reflects only the
author's view and that the Agency is not responsible for any use that may be
made of the information it contains.** _
## 29.6 Consequences of non-compliance
_If a beneficiary breaches any of its obligations under this Article, the
grant may be reduced (see Article 43)._
# 4 ATLAS’ Data Management Plan (DMP)
ATLAS will generate diverse research outputs, including data, software and
scientific articles about physical oceanography, biogeochemistry, visual
surveys of biodiversity, biological rates and traits measurements, genomic
analyses, socio-economic metrics, and spatial planning. This diversity
requires an ambitious data management plan, building on existing open science
resources that are interoperable and trusted. The key to implementing ATLAS’
Data Management Plan is to appoint an information specialist (UniHB) with
experience in marine science who will act as facilitator between ATLAS
partners and six open science resources:
* ENA, the European Nucleotides Archive ( _http://www.ebi.ac.uk/ena_ )
* PANGAEA, Data Publisher for Earth and Environmental Science ( _http://www.pangaea.de_ )
* EuBI, Euro-BioImaging ( _http://www.eurobioimaging.eu/_ )
* ZENODO, EU-funded open access digital repository ( _https://zenodo.org_ )
* OpenAIRE, H2020’s research monitoring infrastructure ( _https://www.openaire.eu_ )
* EMODnet, the European Marine Observation and Data Network ( _http://www.EMODnet.eu_ )
In compliance with H2020’s Open Research Data Pilot, these resources (i.e.
ENA, PANGAEA, EuBI) ensure that _**data sets are** _ _**archived and
preserved** _ , using a _**reference and name** _ (i.e. authors, year, title,
DOI or accession number), a _**description** _ (i.e. targeted use,
geolocation, methods, link to related articles), and community _**standards
and metadata** _ (i.e. parameters semantic, formats, units, and use of
ontologies and registries). Unless authors request that data be embargoed (see
ATLAS’ Open Science Policy below), data sets archived at ENA, PANGAEA, EuBI
are available freely in open access (see Task 8.3 below). Moreover, data
sharing and reporting to the EC is maximised by disseminating metadata to
OpenAIRE and EMODnet (see Tasks 8.4 & 8.5 below). A _**list of data and
metadata expected from ATLAS’ research activities** _ is provided in Appendix
I, based on previous EU projects that conducted similar studies, i.e. HERMES,
HERMIONE and CoralFISH. The Data Management Plan of ATLAS is coordinated by
Work Package 8, and is articulated around five key objectives/tasks:
## Task 8.1 Engage ATLAS partners in H2020’s Open Research Data Pilot
This task essentially consists in effectively communicating the founding
principles and implementation steps of ATLAS’ DMP and Open Science Policy to
all partners of the project. This will be achieved by personal communication
between partners and the information specialist (UniHB) during the entire life
cycle of the project. Furthermore, ATLAS will engage in the development of
community-oriented services that help deposit, link and enrich research
outputs as part of OpenAIRE-Connect.
## Task 8.2 Assemble relevant research outputs from past EU‐funded and
nationally‐funded efforts
This task will assemble a collection of research outputs (e.g. literature,
data, maps and model outputs) available in open access from past and on‐going
research initiatives that are relevant to Atlanticecosystem‐based research.
These include projects funded under FP6 (HERMES), FP7 (HERMIONE, CoralFISH,
THOR, EURO‐BASIN, FixO3) and H2020 (AtlantOS and SponGES), and initiatives
funded in the USA (NOAA) and Canada (DFO). This task will establish a list of
data and metadata expected from ATLAS’ research activities (see Appendix I).
## Task 8.3 Safeguard and publish ATLAS’ research outputs in open access
Research articles will be published in peer-reviewed journals and other
digital material (other than data) will be deposited at ZENODO. Nucleotides
data, imaging data, and environmental data (including bathymetry, physics,
chemistry and biology) will be deposited in the selected data archives, i.e.
European Nucleotides Archive (ENA), Euro-BioImaging (EuBI) and PANGAEA,
respectively.
There are two main routes towards open access to literature and data
publications:
* **Gold open access (open access publishing)** means that data sets or articles are immediately provided in open access by the publisher. The business model of most journal publishers is shifting the payment of publication costs away from readers (subscription charges) towards the authors (Article Processing Charges, APCs). These costs can usually be borne by the university or research institute to which the researcher is affiliated, or to the funding agency supporting the research. ATLAS partners are strongly encouraged to use their institutional or ATLAS funds to publish research articles in Gold Open Access. Publishing data at ENA, EuBI and PANGAEA also follows gold open access but the cost model is different. ENA and EuBI are European Infrastructures and therefore offer their service free of charge to authors. The publication costs of data sets at PANGAEA are included in the budget of ATLAS’ UniHB partner. The information specialist (UniHB) will assist ATLAS’ partners in publishing their data in open access.
* **Green open access (self-archiving)** means that data sets or articles are archived (deposited) for free by the author in an online repository. In the case of articles submitted to a non-open access journal, a final, peer-reviewed and proofed-read version can be deposited in Green Open Access. Some journal publishers request that open access is granted only after an embargo period has elapsed. Research outputs that are published in non-open access by ATLAS partners will be deposited at ZENODO. The information specialist (UniHB) will assist ATLAS’ partners in publishing their data in open access.
## Task 8.4 Monitor and report on ATLAS research outputs using OpenAIRE
(M6‐M48)
This will be achieved by actively monitoring data and literature publications
from ATLAS partners using services from DataCite and Thompson Reuters, and by
maintaining close communication with ENA, EuBI, PANGAEA and ZENODO. The
information specialist (UniHB) will ensure that all research outputs (e.g.
literature, data, maps and model outputs) from ATLAS are cross‐linked and
monitored by OpenAIRE. The information specialist (UniHB) will also assist
ATLAS partners in engaging and using OpenAIRE-Connect. OpenAIRE will be used
to report on research outputs from the project
(https://www.openaire.eu/project/ATLAS). The following deliverables will also
provide regular updates on ATLAS research outputs:
* D8.2 18‐month progress report on ATLAS data integration in EMODnet
* D8.3 36‐month progress report on ATLAS data integration in EMODnet
* D8.4 42-month Synthesis of ATLAS research outputs available in open access
## Task 8.5 Transfer ATLAS research outputs to science and industry
stakeholders using EMODnet
ATLAS research outputs (i.e. data and linked journal publications) available
in open access will be transferred to EMODnet, using the interoperability
protocols specified by the different EMODnet thematic nodes (e.g. bathymetry,
chemistry and biology).
# 5 ATLAS’ Open Science Policy
The Open Science Policy of ATLAS provides clear rules for safeguarding,
sharing and facilitating reuse of research outputs (e.g. data, articles,
cruise reports, presentations, posters, outreach material, code, software,
tools, etc). In compliance with Article 29 of the Grant Agreement, and
following best practices described in the Data Management Plan, individuals
who are involved in ATLAS activities will:
* register at ORCID ( _http://orcid.org/_ ) and communicate their ORCID ID to the ATLAS project office ( [email protected]_ );
* ensure that Cruise Summary Reports (CSRs) (including a list of sampling activities, measured parameters and contact details of principal investigators for each parameter) are deposited in open access at ZENODO ( _https://zenodo.org/deposit/new?c=atlas_ ) no later than three months after the cruise;
* 45 days prior to submitting a manuscript for publication in a scientific journal, a draft version must be sent to the ATLAS project office (Katherine Simpson, [email protected]_ and Murray Roberts, [email protected]_ ), indicating to which journal it will be submitted. The draft can consist of the complete manuscript, or include at a minimum the title, authors and abstract. The project office will make sure that the EC is properly acknowledged in the manuscript and will send the authors a contribution number. The draft will be placed on the password protected area of the eu-atlas website and all ATLAS partners will be informed by email by the Project Office. Comments about the draft manuscript (or abstract) should be sent directly to the authors and any concerns should be raised with the project coordinator. Unless issues have been raised during a period of 30 days, it is considered that all partners have read the research output and agree with its content;
* ensure that the final version of all research outputs (e.g. data, articles, cruise reports, presentations, posters, outreach material, code, software, tools, etc) are deposited at ZENODO ( _https://zenodo.org/deposit/new?c=atlas_ ) as soon as possible and no later than three months after their production. Access to these research outputs can be restricted. Research outputs deposited in ZENODO are automatically reported to OpenAIRE and to the EC;
* ensure that their research outputs are made available preferably in gold open access (or a least green open access) as soon as possible and at the latest six months after publication; twelve months for publications in the social sciences and humanities; twenty-four months in the case of unpublished data that are part of a research thesis;
* ensure that all research outputs display the EU emblem, include the following text: “ _This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 678760 (ATLAS). This output reflects only the author’s view and the European Union cannot be held responsible for any use that may be made of the information contained therein”._
<table>
<tr>
<th>
_This project has received funding from the European Union’s Horizon 2020
research and innovation programme under grant agreement No 678760 (ATLAS).
This output reflects only the author’s view and the European Union cannot be
held responsible for any use that may be made of the information contained
therein._
</th> </tr> </table>
# 6 Appendix I – Parameters expected from research activities of ATLAS
The following table was compiled from data sets published from three previous
European projects that were similar in scope to ATLAS, i.e. HERMES, HERMIONE
and CoralFISH. We therefore expect that the parameters listed in this table
will either be measured during field and experimental campaigns organised by
or in collaboration with ATLAS partners, or re-used from already published
data sets.
<table>
<tr>
<th>
**Category of parameters**
</th>
<th>
**Parameters**
</th>
<th>
**Units**
</th> </tr>
<tr>
<td>
amount concentration of biological entity per unit mass of environmental
entity
</td>
<td>
Bacteria, abundance per unit sediment mass
</td>
<td>
10**6 #/g
</td> </tr>
<tr>
<td>
amount concentration of biological entity per unit volume of environmental
entity
</td>
<td>
Prokaryotes, abundance as single cells; Bacteria, abundance
</td>
<td>
10**9 #/cm**3
</td> </tr>
<tr>
<td>
amount concentration of chemical entity per unit volume of environmental
entity
</td>
<td>
Acid volatile sulphides; Chromium reducible sulphides; Iron ascorbic acid
extraction; Iron dithionite extraction; Ammonium; Carbon, inorganic,
dissolved; Carbon, organic, dissolved; Iron, total, dissolved; Nitrate;
Nitrite; Nitrogen, total dissolved; Oxygen; Phosphate; Silicate; Sulfite;
Sulphide; Thiosulphate; Concentration; Heptasulphide; Hexasulphide; Iron 2+;
Pentasulphide; Polythionate; Tetrasulphide; Trisulphide; Zero valent sulphur,
in pore water; Zero valent sulphur, in pore water cyanolysis; Zero valent
sulphur, in pore water ZnCl2 extraction; Sulphur, elemental; Alkalinity,
total; Chloride; Sulphate; Phospholipids; Adenylates, total
</td>
<td>
µmol/cm**3; µmol/l; µmol/ml; mmol(eq)/l; mmol/l; nmol/ml; pmol/cm**3
</td> </tr> </table>
<table>
<tr>
<th>
**Category of parameters**
</th>
<th>
**Parameters**
</th>
<th>
**Units**
</th> </tr>
<tr>
<td>
amount rate of chemical entity per unit area of environmental entity
</td>
<td>
Methane flux; Oxygen flux, sediment oxygen demand; Methane, oxidation rate,
anaerobic
</td>
<td>
mmol/m**2/day
</td> </tr>
<tr>
<td>
amount rate of chemical entity per unit volume of environmental entity
</td>
<td>
Esterase activity per sediment volume
</td>
<td>
nmol/ml/h
</td> </tr>
<tr>
<td>
mass concentration of chemical entity per unit area of
environmental entity
</td>
<td>
Carbon, organic, particulate per area, fraction; Carbon, total particulate;
Carbon, total particulate, fraction; Nitrogen, organic, particulate; Nitrogen,
organic, particulate, fraction; Carbon, organic, particulate per area;
Particle concentration, resuspendable; Marine litter, mass per area
</td>
<td>
g/m**2; kg/km**2
</td> </tr> </table>
<table>
<tr>
<th>
**Category of parameters**
</th>
<th>
**Parameters**
</th>
<th>
**Units**
</th> </tr>
<tr>
<td>
mass concentration of chemical entity per unit mass of environmental entity
</td>
<td>
Amino acid, hydrolysable per unit sediment mass; Amino acids per unit sediment
mass;
Carbohydrates, acid soluble; Carbohydrates, NaOH soluble; Carbohydrates,
total; Lipids, total; Proteins, total; Acenaphthene, fraction, per unit mass
organic carbon; Acenaphthene, per unit mass organic carbon; Acenaphthylene,
fraction, per unit mass organic carbon; Acenaphthylene, per unit mass organic
carbon; alpha-Hexachlorocyclohexane, fraction, per unit mass organic carbon;
alpha-Hexachlorocyclohexane, per unit mass organic carbon; Anthracene,
fraction, per unit mass organic carbon; Anthracene, per unit mass organic
carbon; Benz(a)anthracene, fraction, per unit mass organic carbon;
Benz(a)anthracene, per unit mass organic carbon; Benz(a)pyrene, fraction, per
unit mass organic carbon; Benz(a)pyrene, per unit mass organic carbon;
Benz(b)fluoranthene, fraction, per unit mass organic carbon;
Benz(b)fluoranthene, per unit mass organic carbon; Benz(g,h,i)perylene,
fraction, per unit mass organic carbon; Benz(g,h,i)perylene, per unit mass
organic carbon; Benz(k)fluoranthene, fraction, per unit mass organic carbon;
Benz(k)fluoranthene, per unit mass organic carbon; beta-Hexachlorocyclohexane,
fraction, per unit mass organic carbon; beta-Hexachlorocyclohexane, per unit
mass organic carbon; Chrysene, fraction, per unit mass organic carbon;
Chrysene, per unit mass organic carbon;
Dibenzo(a,h)anthracene, fraction, per unit mass organic carbon;
Dibenzo(a,h)anthracene, per unit mass organic carbon; Fluoranthene, fraction,
per unit mass organic carbon; Fluoranthene, per unit mass organic carbon;
Fluorene, fraction, per unit mass organic carbon; Fluorene, per unit mass
organic carbon; gamma-Hexachlorocyclohexane, fraction, per unit mass organic
carbon; gammaHexachlorocyclohexane, per unit mass organic carbon;
Hexachlorobenzene, fraction, per unit mass organic carbon; Hexachlorobenzene,
per unit mass organic carbon; Indeno(1,2,3-cd)pyrene, fraction, per unit mass
organic carbon; Indeno(1,2,3-cd)pyrene, per unit mass organic carbon;
Naphthalene, fraction, per unit mass organic carbon; Naphthalene, per unit
mass organic carbon; ortho,para-Dichlorodiphenyltrichloroethane, fraction, per
unit mass organic C;
</td>
<td>
µg/g; mg/kg; ng/kg
</td> </tr> </table>
<table>
<tr>
<th>
**Category of parameters**
</th>
<th>
**Parameters**
</th>
<th>
**Units**
</th> </tr>
<tr>
<td>
mass concentration of chemical entity per unit volume of environmental
entity
</td>
<td>
Chloroplastic pigment equivalents per area; Chloroplastic pigment equivalents
per volume; Chlorophyll a; Chlorophyll pigment equivalents; Phaeopigments;
Carbon, organic, total per volume; Density, sigma, in situ; Density,
sigma2000; Density, sigma-theta (0); Density, mass density; Protein, readily
soluble per sediment volume
</td>
<td>
µg/cm**3; µg/l; kg/m**3; mg/cm**3
</td> </tr>
<tr>
<td>
mass concentration of chemical entity; biological entity per unit area of
environmental entity
</td>
<td>
Bacteria, biomass as carbon; Benthos, biomass as carbon; Biomass as carbon;
Copepoda, biomass as carbon; Meiofauna, biomass as carbon; Nematoda, biomass
as carbon; Polychaeta, biomass as carbon
</td>
<td>
µg/10 cm**2; mg/m**2
</td> </tr>
<tr>
<td>
mass of biological entity
</td>
<td>
Amblyraja radiata, mass; Anarhichas denticulatus, mass; Anarhichas lupus,
mass; Brosme brosme, mass; Chimaera monstrosa, mass; Etmopterus spinax, mass;
Gadus morhua, mass; Galeus melastomus, mass; Gorgonacea, mass; Lithodes maja,
mass; Lophelia pertusa, mass;
Melanogrammus aeglefinus, mass; Molva molva, mass; Pollachius virens, mass;
Porifera, mass; Rajella fyllae, mass; Sebastes norvegicus, mass
</td>
<td>
kg
</td> </tr>
<tr>
<td>
mass rate of chemical entity per unit area of environmental entity
</td>
<td>
24-Ethylcholest-5-en-3beta-ol flux; 24-Methylcholest-5-en-3beta-ol flux;
24-Methylcholesta5,22E-dien-3beta-ol flux; all-
cis-4,7,10,13,16,19-Docosahexaenoic acid flux; all-
cis-5,8,11,14,17Icosapentaenoic acid flux; all-
cis-6,9,12,15-Octadecatetraenoic acid flux; Cholesta-5-en-3beta-ol flux;
cis-11-Hexadecenoic acid flux; cis-9-Hexadecenoic acid flux; Lipids flux;
Biogenic flux; Biopolymeric carbon flux; Calcium carbonate flux; Carbohydrate
flux; Carbon, inorganic, particulate flux per day; Carbon, organic,
particulate flux per day; Flux of total mass; Lithogenic flux per day;
Nitrogen, organic, particulate flux per day; Nitrogen, particulate flux; Opal
flux; Organic matter flux; Protein total flux; Silica, particulate flux per
day; Silicon, particulate flux; Total mass flux per day
</td>
<td>
µg/m**2/day; mg/m**2/day
</td> </tr> </table>
<table>
<tr>
<th>
**Category of parameters**
</th>
<th>
**Parameters**
</th>
<th>
**Units**
</th> </tr>
<tr>
<td>
mass rate of chemical entity per unit volume of environmental entity
</td>
<td>
Sulphate reduction rate; Methane, oxidation rate
</td>
<td>
nmol/cm**3/day
</td> </tr>
<tr>
<td>
mass rate of chemical entity; biological entity per unit volume of
environmental
entity
</td>
<td>
Bacterial biomass production of carbon
</td>
<td>
ng/l/h
</td> </tr>
<tr>
<td>
metadata
</td>
<td>
Buildup; Taxa analyzed; Replicates; Seismic line; Mesh size; Sample surface;
Time, incubation; Duration, number of days; Signal strength; Pressure, water;
Current direction; Current meter, pitch; Current meter, roll; Current meter,
tilt; Course; Direction; Duration; File size; Depth, bottom/max; Depth,
top/min; DEPTH, sediment/rock; DEPTH, water; Depth, bathymetric; Height above
sea floor/altitude; Recovery; Taxon/taxa; Position; Description; Experimental
treatment; Area/locality; Comment; Date/time end; Date/time start; Device
type; Event; File name; Gear;
Latitude 2; Longitude 2; Parameter; Reference of data; Reference/source;
Sample code/label;
Sample comment; Split; DATE/TIME; LATITUDE; LONGITUDE; ORDINAL NUMBER;
Sorting; Species; Core; File format; Log info; Number; Profile; Sample ID;
Sample, optional label/labor no; Shoot point; Site; Treatment; Type; Uniform
resource locator/link to file; Uniform resource locator/link to graphic;
Uniform resource locator/link to image; Uniform resource locator/link to
metadata file; Uniform resource locator/link to movie; Uniform resource
locator/link to raw data file; Uniform resource locator/link to sgy data file;
Uniform resource locator/link to thumbnail;
Cruise/expedition; Depth, bathymetric, maximum; Depth, bathymetric, minimum;
ELEVATION; Feature; Filter; Hooks; Identification; Legibility; Location;
Method comment; Seastate label; Section; Species code; Status
</td>
<td>
</td> </tr> </table>
<table>
<tr>
<th>
**Category of parameters**
</th>
<th>
**Parameters**
</th>
<th>
**Units**
</th> </tr>
<tr>
<td>
non-parametric quality of biological entity
</td>
<td>
Sex; Stage
</td>
<td>
</td> </tr>
<tr>
<td>
non-parametric quality of environmental entity
</td>
<td>
Coral Status; Food; Habitat; Substrate type; Morphology
</td>
<td>
</td> </tr>
<tr>
<td>
non-parametric quality of environmental entity per unit biological entity
</td>
<td>
Coral; Amblyraja radiata; Anarhichas denticulatus; Anarhichas lupus; Brosme
brosme; Chimaera monstrosa; Clupea harengus abundance as Nautical Area
Scattering Coefficient; Etmopterus spinax; Fish; Gadus morhua; Gorgonacea;
Lithodes maja; Lophelia pertusa; Melanogrammus aeglefinus; Molva molva;
Pollachius virens; Rajella fyllae; Sebastes norvegicus; Sponge
</td>
<td>
</td> </tr>
<tr>
<td>
non-parametric quality of environmental entity per unit anthropogenic entity
</td>
<td>
Marine litter, clinker; Marine litter, fabric; Marine litter, fishing net;
Marine litter, glass; Marine litter, hard plastic; Marine litter, long line;
Marine litter, metal; Marine litter, oil drum; Marine litter, soft plastic
</td>
<td>
</td> </tr>
<tr>
<td>
parametric quality of environmental entity
</td>
<td>
Food index; Porosity; Species richness; Dauwe index; Diversity; Equitability;
Shannon index of diversity
</td>
<td>
</td> </tr>
<tr>
<td>
parametric quality of environmental entity per unit biological entity
</td>
<td>
Nematoda, trophic diversity; Mesopelagic fish abundance as Nautical Area
Scattering Coefficient; Micromesistius poutassou abundance as Nautical Area
Scattering Coefficient; Plankton abundance as Nautical Area Scattering
Coefficient; Pollachius virens abundance as Nautical Area Scattering
Coefficient
</td>
<td>
</td> </tr>
<tr>
<td>
parametric quality of physical entity
</td>
<td>
Temperature, in rock/sediment; Temperature, water; Temperature, water,
potential;
Fluorescence; Turbidity; Echo backscatter; Turbidity (Formazin Turbidity
Unit); Conductivity;
Oxidation reduction (RedOx) potential; pH; Salinity; Attenuation, optical beam
transmission;
Humidity, relative; Pressure, atmospheric; Radiation, photosynthetically
active; Temperature, air;
Wind direction
</td>
<td>
degC; arbitrary units; dB; mS/cm; mV
</td> </tr> </table>
<table>
<tr>
<th>
**Category of parameters**
</th>
<th>
**Parameters**
</th>
<th>
**Units**
</th> </tr>
<tr>
<td>
ratio of biological entity per unit biological entity
</td>
<td>
Anaerobic methanotrophic archaea-1 archaea, targed with Anaerobic
methanotrophic archaea-1-
350 oligonucleotide FISH-probe; Anaerobic methanotrophic archaea-2a archaea,
targed with Anaerobic methanotrophic archaea-2a-647 oligonucleotide FISH-
probe; Anaerobic methanotrophic archaea-2a,b; Anaerobic methanotrophic
archaea-2c; Anaerobic methanotrophic archaea-3; Anaerobic methanotrophic
archaea-3 archaea, targed with Anaerobic methanotrophic archaea-3-1249
oligonucleotide FISH-probe; Archaea, Anaerobic methanotrophic archaea-1;
Archaea, GEG; Archaea, GoM Arc I; Archaea, MBG-B; Archaea, MBG-D; Archaea,
targed with
ARCH915 oligonucleotide FISH-probe; Methanomicrobiales; Methanosaeta related
Methanosarcinales; Methanosarcinales, unaffiliated; Acidobacteria; Acrobacter
spp., targed with
ARC94 oligonucleotide FISH-probe; Actinobacteria; Alphaproteobacteria;
Bacteria; Bacteria, JS1;
Bacteria, OP11; Bacteria, OP8; Bacteria, targed with EUB338(I-III)
oligonucleotide FISH-probe;
Bacteria, TG1; Bacteria, unaffiliated; Bacteria, WS2; Bacteria, WS3;
Bacteroidetes;
Betaproteobacteria; Chlorobi; Chloroflexi; Deferribacteres; Delta-
Proteobacteria;
Desulfusarcina/Desulfococcus, targed with DSS658 oligonucleotide FISH-probe;
Epsilonproteobacteria; Firmicutes; Fusobacteria; Gammaproteobacteria;
Planctomycetes;
Spirochaetes; Meiofauna; Foraminifera, benthic hyaline; Adelosina spp.;
Ammolagena clavata; Ammonia beccarii; Amphicoryna scalaris; Angulogerina
angulosa; Articulina tubulosa;
Asterigerinata mamilla; Astrononion stelligerum; Bigenerina nodosaria;
Biloculinella depressa; Biloculinella inflata; Biloculinella labiata; Bolivina
psudoplicata; Bolivina psudopunctata; Brizalina alata; Brizalina albatrossi;
Brizalina dilatata; Brizalina subaenariensis; Bulimina aculeata; Bulimina
costata; Bulimina elongata; Bulimina marginata; Bulimina striata mexicana;
Cassidulina laevigata; Cassidulina oblonga; Cassidulinoides bradyi;
Chilostomella oolina; Cibicides lobatulus; Cibicides spp.; Cibicidoides
pachyderma; Cibicidoides spp.; Cornuspira involvens; Dentalina albatrossi;
Dentalina spp.; Discammina compressa; Discanomalina semipunctata;
Discorbinella bertheloti;
Discorbis spp.; Elphidium spp.; Epistominella rugosa; Fissurina spp.;
Fursenkoina cf. acuta; Fursenkoina mexicana; Gavelinopsis praegeri; Glandulina
spp.; Globobulimina affinis; Globobulimina pseudospinescens; Globobulimina
spp.; Glomospira charoides; Guttulina cf. communis; Gyroidinoides neosoldanii;
Gyroidinoides orbicularis; Gyroidinoides umbonatus; Haynesina spp.; High
oxygen indicators; Hoeglundina elegans; Hyalinea balthica; Laevidentalina
flexuosa; Lagena spp.; Lenticulina calcar; Lenticulina cultrata; Lenticulina
orbicularis; Lenticulina
</td>
<td>
</td> </tr> </table>
<table>
<tr>
<th>
**Category of parameters**
</th>
<th>
**Parameters**
</th>
<th>
**Units**
</th> </tr>
<tr>
<td>
</td>
<td>
spp.; Low oxygen indicators; Marginulina spp.; Marginulinopsis spp.; Melonis
barleeanus; Miliolinella spp.; Miliolinella subrotunda; Neoconorbina terquemi;
Neolenticulina peregrina; Nonion asterizans; Nonion depressulum; Nonionella
turgida; Nummoloculina contraria; Oolina spp.; Paracassidulina minuta;
Patellina corrugata; Placopsilina sp.; Planorbulina mediterranensis; Planulina
ariminensis; Polymorphina spp.; Pseudoclavulina crustata; Pseudotriloculina
laevigata; Pseudotriloculina oblonga; Pullenia bulloides; Pullenia salisburyi;
Pullenia subcarinata; Pyrgo cf. murrhina; Pyrgo comata; Pyrgo elongata; Pyrgo
inornata; Pyrgo spp.; Pyrgoella sphaera; Quinqueloculina laevigata;
Quinqueloculina milletti; Quinqueloculina padana; Quinqueloculina seminula;
Quinqueloculina spp.; Quinqueloculina venusta; Quinqueloculina viennensis;
Repmanina charoides; Reussella spinulosa; Rhabdammina sp.; Robertinoides
translucens; Rosalina
bradyi; Rosalina spp.; Rotamorphina involuta; Sigmoilina costata; Sigmoilina
sigmoidea; Sigmoilinita tenuis; Sigmoilopsis schlumbergeri; Siphonaperta
aspera; Siphonina reticulata;
Siphotextularia heterostoma; Sphaeroidina bulloides; Spiroloculina sp.;
Spiroplectinella sagittula;
Textularia agglutinans; Textularia pala; Triloculina spp.; Triloculina
tricarinata; Uvigerina mediterranea; Uvigerina peregrina; Uvigerina
proboscidea; Valvulineria bradyana
</td>
<td>
</td> </tr> </table>
<table>
<tr>
<th>
**Category of parameters**
</th>
<th>
**Parameters**
</th>
<th>
**Units**
</th> </tr>
<tr>
<td>
ratio of chemical entity per unit chemical entity
</td>
<td>
Alanine; alpha-aminobutyric acid; Arginine; Asparagine; beta-Alanine; gamma-
Aminobutyric acid;
Glutamine; Glycine; Histidine; Isoleucine; Leucine; Lysine; Methionine;
Ornithine; Phenylalanine;
Serine; Threonine; Tyrosine; Valine; Nitrogen, organic, fraction; Oxygen,
saturation; Methane;
Organic matter; Ethane; n-Butane; Propane; Isobutane; Hydrate; Water content
of wet mass;
Aluminium; Calcium; Calcium carbonate; Carbon, inorganic, total; Carbon,
organic, fraction; Carbon, organic, particulate; Carbon, organic, total;
Carbon, total; Iron; Magnesium; Nitrogen, organic; Nitrogen, total; Opal,
biogenic silica; Silicon; Sulphur, total; Lithogenic material; Volcanic glass,
acidic; delta 15N; delta 15N, particulate organic nitrogen; delta 234 Uranium;
Acid volatile sulphides isotopes; Chromium reducible sulphides isotopes; delta
13C; delta 13C, adjusted/corrected; delta 13C, organic carbon; delta 13C,
particulate organic carbon; C1/C2 hydrocarbon ratio; C1/C2+ hydrocarbon ratio;
C1/C3 hydrocarbon ratio; Lead 206/Lead 207; Lead
208/Lead 206 ratio; Neodymium 143/Neodymium 144; Strontium 87/Strontium 86
ratio; epsilon-
Neodymium (0); Thorium 230/Thorium 232 ratio; Thorium 230/Uranium 234 ratio;
Uranium
234/Uranium 238 activity ratio; Uranium 238/Thorium 232 ratio;
Calcium/(Calcium+Iron) ratio; Carbon/Nitrogen ratio; Carbon/Sulphur ratio;
Nitrogen/Carbon ratio
</td>
<td>
</td> </tr> </table>
<table>
<tr>
<th>
**Category of parameters**
</th>
<th>
**Parameters**
</th>
<th>
**Units**
</th> </tr>
<tr>
<td>
unit concentration of biological entity per unit area of environmental entity
</td>
<td>
Eumeiofauna; Meiofauna, abundance; Pseudomeiofauna; Abundance per area;
Benthos, other; Copepoda indeterminata; Meiofauna, abundance of foraminifera;
Meiofauna, abundance of harpacticoida; Meiofauna, abundance of metazoa;
Cladocera; Foraminifera, benthic;
Lysianassidae; Scaphopoda; Anomonema; Chitwoodia; Comesomoides; Cricolaimus;
Cylicolaimus;
Demonema; Desmocolex; Diplolaimelloides; Echinodesmodora; Endeolophos;
Eubostrichus;
Eudiplogaster; Gairleanema; Gomphionchus; Gonionchus; Kraspedonema;
Leptonemella;
Metadasynemoides; Monoposthia; Morlaixia; Odontophoroides; Omicronema;
Parachromadorita;
Paradesmodora; Parallelocoilas; Paranticoma; Perspiria; Polysigma; Pontonema;
Prochromadora;
Psammonema; Pseudocella; Steineridora; Trichotheristus; Valvaelaimus;
Cirratulidae; Maldanidae;
Paramphinome sp.; Spionidae; Acari; Amphimonhystrella bullacauda;
Antarcticonema; Asellota;
Bathyepsilonema; Calanoidea; Cheironchus; Ciliata; Comesoma; Cyatholaimus;
Deontolaimus;
Desmodora pilosa; Desmodoroidea; Desmolorenzenia; Desmotricoma; Dorylaimopsis;
Ethmolaimidae; Goniadidae; Greeffiellinae; Greeffiellopsis; Halanonchus;
Hesionidae; Ixonema;
Karkinochromadora; Lacydoniidae; Lumbrineridae; Metacomesoma;
Metacylicolaimus;
Monhysterina; Nannolaimoides; Nephtyidae; Nicascolaimus; Notochaetosoma;
Nyctonema;
Opheliidae; Ophiuroidea; Pandolaimus; Paraethmolaimus; Paraonidae;
Parapinnanema;
Pareudesmoscolex; Phoxocephalidae; Phyllodocidae; Pilargidae; Prochaetosoma;
Pseudolella;
Quadricoma; Sabatieria bitumen; Sabatieria conicauda; Sabatieria demani;
Sabatieria lawsi; Sabatieria ornata; Sabatieria propisinna; Sabatieria
punctata; Sabatieria stekhoveni; Sabatieria vasicola; Sipuncula; Synonchium;
Tanaoidea; Tarvaia; Tetrapturus; Thoracostomopsis;
Amphinomidae; Capitellidae; Glyceridae; Terebellidae; Virus; Carrier crab;
Copepoda; Crab; Edible crab; Galeus melastomus; Gastrotricha; Kinorhyncha;
Nematoda genus sp.; Number of clones; Number of individuals; Phylotype number;
Shrimps; Small grenadier; Spinder crab; Squat lobster; Swimmer crab;
Foraminifera, benthic agglutinated; Foraminifera, benthic agglutinated
indeterminata; Foraminifera, benthic calcareous; Foraminifera, benthic
perforates indeterminata;
Foraminifera, chitineous; Insects, total counts; Ostracoda; Ameira; Ameirinae;
Ameiropsis;
Amphiascoides; Amphiascus; Archesola; Argestes; Argestidae; Atergopedia;
Bathycamptus;
Bodinia; Bradya; Bradyellopsis; Canthocamptidae; Cletodes; Cyclopoida;
Cylindronannopus;
Diarthrodella; Dizahavia; Ectinosoma; Ectinosomatidae; Enhydrosoma;
Eurycletodes; Filexilia;
Fultonia; Halectinosoma; Halophytophilus; Haloschizopera; Hastigerella;
Heterolaophonte;
</td>
<td>
#; #/10 cm**2;
#/m**2
</td> </tr> </table>
<table>
<tr>
<th>
**Category of parameters**
</th>
<th>
**Parameters**
</th>
<th>
**Units**
</th> </tr>
<tr>
<td>
</td>
<td>
Idomene; Idyanthe; Idyanthidae; Idyella; Klieosoma; Kliopsyllus; Laophonte;
Laophontodes;
Leptomesochra; Leptopsyllus; Lineosoma; Lobopleura; Malacopsyllus; Marsteinia;
Mesochra;
Mesocletodes; Metahuntemannia; Microsetella; Misophrioida; Nematovorax;
Neobradyidae;
Novocriniidae; Parameiropsis; Paramesochra; Paramesochridae;
Parapseudoleptomesochra;
Peltobradya; Perissocope; Poecilostomatoida; Pseudameira; Pseudobradya;
Pseudomesochra;
Pseudotachidiinae; Rhyncholagena; Rhynchothalestris; Robertgurneya;
Sagamiella; Sarsameira;
Scottopsyllus; Sigmatidium; Siphonostomatoida; Stenocopia; Talpina; Tegastes;
Tetragonicepsidae
; Xylora; Zosime; Dendrophyllia cornigera; Desmophylum; Lophelia; Madrepora
oculata;
Centrophorus squamosus; Helicolenus dactylopterus dactylopterus; Hexanchus
griseus; Lepidion eques; Lopheous piscatorious; Mora moro; Phycis blennoides;
Synaphobranchus kaupi;
Dendrophyllia; Madrepora; Actinonema; Adoncholaimus; Aegialoalaimidae;
Amphimonhystera;
Anticyathus; Aponema; Araeolaimus; Ascolaimus; Astomonema; Bathynox;
Benthimermithidae;
Bodonema; Bolbolaimus; Calligyrus; Catanema; Cephalanticoma; Ceramonema;
Chaetonema;
Choanolaimus; Chromadorella; Chromadoridae; Cobbia; Comesa; Comesomatidae;
Coninckia; Cyatholaimidae; Dasynemoides; Desmodorella; Desmodoridae; Didelta;
Diodontolaimus;
Diplolaimella; Diplopeltidae; Diplopeltis; Disconema; Dolicholaimus;
Doliolaimus; Draconema;
Elzalia; Enchelidiidae; Enoplinae; Enoploides; Enoplus; Epacanthion;
Euchromadora; Eurystomina;
Filoncholaimus; Gammanema; Glochinema; Gnomoxyala; Graphonema; Greeffiella;
Halichoanolaimus; Haliplectus; Halomonhystera; Hapalomus; Hopperia;
Hypodontolaimus; Intasia;
Laimella; Latronema; Leptosomatum; Linhomoeidae; Linhomoeus; Litinium;
Longicyatholaimus;
Megadesmolaimus; Mesacanthion; Mesacanthoides; Metachromadora;
Metacyatholaimus;
Metadasynemella; Metaparoncholaimus; Metasphaerolaimus; Metoncholaimus;
Micoletzkyia;
Microlaimidae; Minolaimus; Monhystera; Monhysteridae; Nannolaimus; Nemanema;
Neochromadora; Neotonchus; Noffsingeria; Odontanticoma; Odontophora;
Oncholaimellus;
Oncholaimidae; Oncholaimus; Paracomesoma; Paralongicyatholaimus;
Paramesonchium;
Paramicrolaimus; Paramonhystera; Pararaeolaimus; Parasphaerolaimus;
Parastomonema;
Paratricoma; Pareurystomina; Parodontophora; Phanoderma; Phanodermatidae;
Phanodermopsis; Pierrickia; Platycoma; Platycomopsis; Praeacanthonchus;
Promonhystera;
Prooncholaimus; Prototricoma; Pseudonchus; Pterygonema; Ptycholaimellus;
Rhynchonema;
Richtersia; Setoplectus; Setosabatieria; Siphonolaimus; Sphaerolaimidae;
Spilophorella;
</td>
<td>
</td> </tr> </table>
<table>
<tr>
<th>
**Category of parameters**
</th>
<th>
**Parameters**
</th>
<th>
**Units**
</th> </tr>
<tr>
<td>
</td>
<td>
Spinodesmoscolex; Spirobolbolaimus; Steineria; Stephanolaimus; Stylotheristus;
Symplocostoma;
Synodontium; Synonchiella; Synonchus; Thalassironus; Trefusia; Trefusialaimus;
Tripyloides;
Trissonchulus; Trochamus; Xennella; Xyala; Acantholaimus; Acanthonchus;
Aegialoalaimus;
Alaimella; Ammotheristus; Amphimonhystrella; Amphipoda; Anoplostoma; Anticoma;
Antomicron; Aplacophora; Atrochromadora; Axonolaimus; Bathyeurystomina;
Belbolla; Bivalvia;
Calanoida; Calomicrolaimus; Calyptronema; Camacolaimus; Campylaimus;
Cervonema;
Chromadora; Chromadorina; Chromadorita; Chromaspirina; Cnidaria; Crenopharynx;
Cricohalalaimus; Cumacea; Cyartonema; Dagda; Daptonema; Desmodora;
Desmolaimus;
Desmoscolecida; Desmoscolex; Dichromadora; Diplopeltoides; Diplopeltula;
Draconematidae;
Echinodermata; Eleutherolaimus; Enoplolaimus; Epsilonema; Ethmolaimus;
Eumorpholaimus;
Fenestrolaimus; Filitonchus; Gerlachius; Gnathostomulida; Halacarida;
Halalaimus; Holothuroidea;
Hydrozoa; Indeterminata; Innocuonema; Intoshia; Isopoda; Ledovitia;
Leptolaimoides;
Leptolaimus; Linhystera; Loricifera; Manganonema; Marylynnia; Metadesmolaimus;
Metepsilonema; Meyersia; Microlaimus; Molgolaimus; Monhystrella; Nauplii;
Nematoda;
Oligochaeta; Oxyonchus; Oxystomina; Paracanthonchus; Paracyatholaimoides;
Paracyatholaimus;
Paralinhomoeus; Paramesacanthion; Paramonohystera; Polychaeta; Pomponema;
Porifera;
Priapulida; Procamacolaimus; Prochromadorella; Pselionema; Pseudochromadora;
Retrotheristus;
Rhabdocoma; Rhabdodemania; Rhips; Rotifera; Sabatieria; Sipunculida;
Southerniella;
Sphaerolaimus; Spiliphera; Spirinia; Stygodesmodora; Subsphaerolaimus;
Syncarida; Synonema;
Syringolaimus; Tanaidacea; Tantulocarida; Tardigrada; Terschellingia;
Thalassoalaimus;
Thalassomonhystera; Theristus; Tricoma; Trileptium; Turbellaria; Vasostoma;
Viscosia; Wieseria;
Xyalidae; Metalinhomoeus; Adercotryma sp.; Ammodiscus sp.; Bolivina sp.;
Buliminella sp.;
Discorbinellidae indeterminata; Epistominella sp.; Fissurina sp.; Ioannella
sp.; Lagena sp.; Lagenammina sp.; Nodellum sp.; Oolina sp.; Reophax sp.;
Triloculina sp.; Gastropoda; Harpacticoida
</td>
<td>
</td> </tr>
<tr>
<td>
**Category of parameters**
</td>
<td>
**Parameters**
</td>
<td>
**Units**
</td> </tr>
<tr>
<td>
volume of biological entity
</td>
<td>
Bacteria, cell biovolume; Bacteria, heterotrophic, cell biovolume
</td>
<td>
µm**3
</td> </tr> </table>
Deliverable 10.1
|
https://phaidra.univie.ac.at/o:1140797
|
Horizon 2020
|
1052_MC-SUITE_680478.md
|
The MC-SUITE project proposes a new generation of ICT enabled process
simulation and optimization tools enhanced by physical measurements and
monitoring that can increase the competence of the European manufacturing
industry, reducing the gap between virtual modelling and real physical
processes. In order to achieve the expected results by the MC-SUITE approach,
the consortium assembled for this 36-month project consists of a unique
combination of skills and expertise including 6 SMEs, 2 universities, 2
research centres and 2 large companies. Hence, this consortium covers all the
value chain of the manufacturing products including software providers,
equipment providers, machine tool builders and end users.
The advances in the Information and Communication Technology (ICT) are
revolutionizing our everyday life. However, the manufacturing industry has not
taken complete advantage of this huge potential yet. The final aim of the MC-
SUITE project is to open the doors of the manufacturing workshops to this new
revolution. Indeed, the MC-SUITE project consortium strongly thinks that the
current ICT can have a tremendous impact in the productivity increasing the
competence and expertise of the European manufacturing companies and
especially the SMEs.
The aim of the project is to develop the MC-SUITE application based on six
different modules ready to reduce the gap between the simulated and the real
process (see Figure 2).
**1.2**
**PROJECT CONTEXT AND**
**DATA ORIGIN**
_Figure 2: MC-SUITE modules_
**_Objective 1:_ ** To develop a virtual model of the machining process.
* **MC-Virtual** obtains the final path of the tool and the quality of the real part, the cutting force
and process stability, overcoming the limits of the actual Computer Aided
Manufacturing software.
**_Objective 2:_ ** To apply multi objective optimization methods in
manufacturing processes.
* **MC-Optim** optimizes the milling process considering multiple objectives including
productivity, quality and energy consumption.
**_Objective 3:_ ** To create a complete Cyber Physical System for machine
productivity improvement.
* **MC-CyPhy** includes three different embedded systems connected to the virtual model and to
the monitoring system to increase the productivity.
**_Objective 4:_ ** To build up a monitoring system based on the cloud.
* **MC-Monitor** is a cloud-based system able to store heterogeneous data including signal coming
from internal sensors of the machine, from embedded systems and operator
authored data.
**_Objective 5:_ ** To create new services based on Big Data.
* **MC-Analytics** is a platform to treat the information of the cloud for predictive maintenance
and productivity improvement.
**_Objective 6:_ ** To combine simulated and experimental data in a complete
software suite.
* **MC-Bridge** compares the results of the virtual model with the real ones obtained from the monitoring system. This way on-line and off-line optimizations are performed both on the simulations and on the machining process.
The project will be generating two types of research data. The first type will
be large sets of numerical arrays from the machine’s sensors which will be
used for the development of the working models behind the MC-SUITE modules.
The second type will be data generated from the predictive and optimization
models in the project. Simulation and programming codes compiled in the
project will be regarded as a research data too.
One of the challenges in preserving data for long term is the choice of file
format. In the MCSuite project whenever possible data will be saved in open
formats or widely popular formats which do not depend on proprietary software.
For text files this can be .txt, for tabular data \- .csv, for image- .tif,
for audio- .mp3 and for video- .avi. Data will be generated continuously
throughout the whole duration of the project but the total amount of data is
expected to be less than 20 GB per partner.
A more detailed description of the data and the equipment which will be used
for its collection/ generation is provided in the following subsections. This
is based on the early stage plans of the project and it will be updated
together with the later versions of the Data Management Plan.
**2.1**
**M**
**ILLING**
**MACHINES**
The main milling machines that will be used in the project are the SORALUCE SV
6000, the LAGUN GVC-1000-HS and the FIDIA DL165 machining centres (see Figure
3). All machines will be equipped with a variety of sensors which will help
analysing the metal cutting processes, build process models and test the
virtual sensors developed in the MC-Suite project. Initially, investigations
will be focused on orthogonal and oblique milling of stainless steel and
aluminium.
<table>
<tr>
<th>
</th>
<th>
**2\. DATA DESCRIPTION**
</th> </tr> </table>
LAGUN GVC-1000-HS machine
SORALUCE
SV 6000 machine
FIDIA DL165
machine
_Figure 3: Machining centres used in the MC-SUITE project._
All sensor data will be presented together with some of the tool and process
characteristics. This will include cutting angles of the tool, cutting edge
radius, substrate material and tool coating (if known). Characteristics as
cutting depth, cutting speed and feed will be also added to the documentation.
**2.2**
**C**
**UTTING FORCES**
Cutting force is one of the most significant parameters in process modelling.
A range of sensors are available in the consortium. The Kistler 9255B multi-
component dynamometer plate, the Kistler 9121 3-component orthogonal
dynamometer and the Kistler rotational 9123 C dynamometer will be used as a
force measurement reference for different cutting tests (see Figure 4).
9255
B
9121
9123
C
_Figure 4: Kistler odynometers used in the project._
Dynamometers are widely used in laboratories but because of their very high
cost they are rarely applied in real production environment. A more cost
effective alternative for an indirect measurement of the forces will be
investigated in the project. It has been demonstrated that a virtual sensor
can be based on the current measured on the drive and the spindle of the
machine. Current sensors on the electric loops of the spindle and driver
motors of the SV 6000 machine have already been installed by the machine
supplier. However, external sensors might be added to verify the reliability
of the internal CNC data and to obtain higher sampling frequencies.
The cutting forces collected directly from the sensors will be saved in ASCII
files which will be structured in 4 columns. Three of the columns will be
dedicated to the force components and the fourth will be representing the
time. The processed cutting forces will be saved in separate ASCII files
together with the cutting test conditions, cutting force average and the
standard deviation of each force component.
**2.3**
**V**
**ELOCITIES AND**
**POSITIONS**
The tests will be carried on the SV 6000 SORALUCE milling machine.
Accelerometers are not integrated in the machine and will be placed on the
horizontal axes of the ram tip. The returned information is an image of the
acceleration that, once processed, provides the velocity and displacement of
the ram tip.
Encoders are electromechanical devices that generate electrical signal
proportional to the position of the measured element. They enable the
collection of accurate information in real time about the motor position and
velocity value to close the control loops.
The following encoder’s data will be used :
* Three linear encoders, Figure 5, giving information about the position following the XYZ axes of the SV milling machine.
* A rotary encoder giving information about the spindle velocity. _Figure 5: Linear encoder._
To enable a high frequency acquisition and avoid limitation from the CNC, the
encoder signals will be duplicated and processed directly in a real time
acquisition system with a frequency of up to 10 kHz. Results will be directly
exported to Matlab.
**2.4**
**M**
**ACHINE AND TOOL DYNA**
**MICS**
Frequency Response Function (FRF) of the tool tip will also be provided. FRF
is very valuable in vibration analysis to study the behaviour of the machine
and tools. The FRF will be saved in “.uff” format file.
# 2.5 VIDEO AND SOUND ACQUISITION
Video and sound of the machining processes will be recorded. Standard IP
cameras will be used to provide a general view of the machining process and
document the tests. Moreover, sound analysis can reveal information about the
occurrence of vibrations and chatter frequencies. A High Speed Filming device
FASTCAM Ultima APX-RS 250K (Figure 6) will be used to obtain videos and images
from the chip generation. Files will be saved in several formats- AVI, JPEG,
PNG (10bit) or TIFF. _Figure 6: Ultima APX-RS 250K._
**2.6**
**S**
**URFACE MEASUREM**
**E**
**NTS**
When required, surface finish quality will be measured with a non-contact
laser measurement in order to obtain information about the workpiece
roughness. Alternatively, white light interferometry (WLI) or contact probe
profilemetres can be used too. Some complimentary information will be received
from optical microscopy images.
**2.7**
**S**
**IMULATION DATA**
Many of the machining simulations in the project will be performed using the
MACHPpro simulation software. The software has a milling module which enables
prediction of process parameters such as cutting force, tool side load,
workpiece cutting force, spindle torque, spindle power and energy consumption,
bending moment, form error and chip load.
_Figure 7: MACHpro interface._
Results obtained with MACHpro will be used to provide estimation of cutting
forces and cutting process energy consumption. Outputs will be presented in
ASCII files.
# 2.8 OPTIMISATION DATA
The MC-OPTIM module will be receiving input data from a variety of simulations
performed in the MC-VIRTUAL module. Multiobjective process optimisation will
be applied to the simulation results to provide the machine operator with
optimal working conditions. Table 1 summarizes the MC-OPTIM output parameters.
_Table 1: MC-OPTIM output data details (preliminary version)._
<table>
<tr>
<th>
**Output data**
</th>
<th>
**Format**
</th> </tr>
<tr>
<td>
Spindle speed: N value for the best individual
(between a given range: Nmax-Nmin)
</td>
<td>
Boolean cast to Integer (16 bit) or double (32 bit)
</td> </tr>
<tr>
<td>
Feed Override: F for the best individual
(between a given range: Fmax-Fmin)
</td>
<td>
Integer (16 bit)
</td> </tr>
<tr>
<td>
Value for the Fitness function for the best individual
</td>
<td>
double (32 bit)
</td> </tr>
<tr>
<td>
Value for the individual objective functions that conform the Fitness function
</td>
<td>
double (32 bit)
</td> </tr>
<tr>
<td>
Pareto frontier or the output parameters for the best last 10 solutions
</td>
<td>
double (32 bit)
</td> </tr>
<tr>
<td>
Time spent to get the solution
</td>
<td>
double (32 bit)
</td> </tr>
<tr>
<td>
Data for a graphical display in 3D of the solution explored if the Fitness
function is in 2 or 3 dimensions
</td>
<td>
double (32 bit)
</td> </tr> </table>
**3.**
**DATA STORAGE AND MAI**
**NTENANCE**
**3.1**
**D**
**ATA STORAGE AND BACK**
**UPS**
At least two copies of all data generated in the project will be kept at all
times. To avoid loss of information in the event of fire or any other accident
all copies of the data will be kept at different locations. These can be the
personal computers of work colleagues based in separate offices or if
available institutional back- up servers. When this is not possible, the data
will be backed on external hard drives or DVDs which will be kept at a
different location and can be accessed by at least two different persons. USB
pen drives are not considered as a reliable backup storage media because of
their short life (1 year in average). Commercial cloud storage services may be
used but they will not be considered as a primary back up media.
Backups will be performed by each partner on the first working day of every
month or whenever new significant results are achieved. The functionality of
all data is to be checked at least once a year for any corrupted files or
format updates.
Data from sensor readings and surface measurements will be preserved in its
original raw format and shape. Any further analysis is to be performed on a
copy of the original files.
# 3.2 FILE ORGANIZATION AND NAMING
The following folder structure will be used:
## <institution>/<researcher name or initials>/<data identifier>/<date>
If necessary the _date_ folder can be broken down into to further subfolders.
When a short phrase cannot be a clear data identifier a reference number will
be used instead. However, in this case a separate _readme_ text document with
a short description of each data set and its reference number will be created
in the _researcher’s name_ folder.
The YYYY-MM-DD date convention will be used for dates in folder and file
names. This is the ISO 8601 standard and when in the beginning of the
folder/file name, items are automatically sorted chronologically. For example
the 1 st of March 2016 will be written as 2016-03-01. The following naming
system will be used for files:
## YYYY-MM-DD_project name_institution_researcher intitials_measurement
/simulation _number
For example, if there was a document containing tabular data representing
vibration measurements taken by the author of this DMP on the 1 st of March
2016, the file name of the first set for the day would be:
## 2016-03-01_UoN_NV_vibr_01.csv
**3.3**
**A**
**DDITIONAL NOTES**
For every data set a separate text document with explanatory notes will be
created and saved in the _date_ folder together with all other files. The
notes will contain full details of how and under what conditions the
information was collected or generated and explanation of the file naming
system.
In the case of experimental data the document will contain information about
the exact experimental set up, name of operators, references to specific
samples, any abbreviations or physical units used in the data set and any
other information which might be required to reproduce the results. When
relevant, notes from personal and lab logs can be added too. In the case of
data generated from simulation models the exact input parameters and
references to the simulation code will be included in the explanatory
document.
**4.**
**DATA ARCHIVING**
**AND SHAR**
**ING**
**4.1**
**D**
**ATA REPOSITORY**
All data collected, generated or processed in the project which has a
significant scientific value or is linked to a published work will be uploaded
to a public data repository. The primary repository for the MC-SUITE project
will be FigShare ( _https://figshare.com/_ ) which is a very well
established provider for scientific data and is free to use. FigShare is one
of the repositories recommended by the Nature journal and it has been chosen
as an institutional repository for a large number of highly regarded
universities and publishers. They have a very easy to navigate user interface
and unlike many of their competitors they do not impose restrictions on the
file format of the documents deposited. Maximum size of a given dataset is 20
GB and the recommended limit for a single file is 5 GB. According to the
service agreement, files on FigShare are retained for the lifetime of the
repository or at least 10 years after publishing.
MC-SUITE data will be stored under one of the Creative Commons licenses. For
figures, media, posters, papers and file sets CC-BY license will be used. CC-
BY ensures the research will be openly available but it requires that a credit
in the form of citation is given when used or referred to. In the case of
complete databases or structured datasets with highly factual data CC0 license
will be used.
CC0 similarly to CC-BY is an open license but does not legally require users
of the data to cite the source. However, the moral obligation of attribution
remains same as in the case of any research journal paper citation. In the
special case of programming or computer simulation code sharing the MIT
license will be used. MIT provides full open access to the code but also
removes any liability of the authors in the event of any legal claim or damage
caused by the use of the code.
All data with potential commercial value will be embargoed for 12 months after
the termination of the project to allow for patent filing.
MC-SUITE will not be publishing any sensitive data so there is no need of
taking any special anonymization procedures.
Data published on FigShare is automatically assigned with DataCite DOI.
Additionally, there is an option of exporting a reference file to some of the
most common reference managers like RefWorks, BibTeX, Endnote, DataCite, NLM,
DC and RefMan.
**4.2**
**D**
**ATA OWNERSHIP**
Every project partner generating, collecting or processing data in the project
will create their own FigShare account. An MC-SUITE project space which will
link the individual accounts will be created too. Publishing of relevant data
when available is the responsibility of the partner who created the data. It
is important that accounts are managed and regarded as institutional rather
than personal so that any enquiries related to the published data can be
answered even after the end of the project. This means that at least two
persons will have an access to every FigShare account at any time during and
after the project and both of them will have equal administration rights.
Partners will be reviewing their new data and publish it when significant at
least every 6 months starting at M6 of the project.
**4.3**
**M**
**ETADATA FORMAT**
Unless there is an established metadata schema for the type of data published
in the project the Dublin Core simple level schema will be used. This is a
well-established general scheme which can effectively cover a large variety of
data. The elements of the schema are as follow: title, creator (authors),
subject (e.g. Engineering), description, publisher, contributor, date, type
(e.g. sound), format, identifier (DOI), source, language, relation, coverage
(e.g. location), rights, funding. All these elements are covered on opening a
FigShare account so authors do not need to take any special actions when
publishing.
<table>
<tr>
<th>
</th>
<th>
**5\. CLOSING REMARKS**
</th> </tr> </table>
The DMP is a continuously evolving document which will be updated at the mid-
term review and the end of the project. Updates will be reflected in the Open
Research Data deliverable document due in September 2017.
All activities regarding data publishing will be coordinated and supported by
the University of Nottingham (UoN) which is also responsible to ensure that
the current DMP is followed by all partners. UoN will organise a workshop to
familiarise all interested parties with FigShare.
|
https://phaidra.univie.ac.at/o:1140797
|
Horizon 2020
|
1054_INNO-4-AGRIFOOD_681482.md
|
# Introduction
The current document constitutes the interim version of the **Data Management
Plan** (DMP) elaborated in the framework of the **INNO-4-AGRIFOOD** project,
which has received funding from the European Union’s Horizon 2020 Research and
Innovation Programme under Grant Agreement No 681482. INNO-4-AGRIFOOD is aimed
at fostering, supporting and stimulating **online collaboration for
innovation** amongst **agri-food SMEs** across Europe. To this end, the
project will enhance the service portfolio and practices of **innovation
intermediaries and SME support networks** across Europe by providing them with
a well-tailored blend of demand-driven **value propositions** , including:
* A **new generation of value added innovation support services** aimed at empowering their agri-food SME clients to capitalise on the full potential of online collaboration for innovation.
* A **suite of smart and platform-independent ICT tools** to support and optimise the delivery of the novel online collaboration for innovation support services.
* A **series of highly interactive and flexible e-training courses** that will equip them with the knowledge and skills required to successfully deliver these new services.
On top of the above mentioned, the accumulated experience and lessons learned
through INNO-4-AGRIFOOD will be translated into meaningful **guidelines** to
be diffused across Europe so as to fuel the replication of its results and
thus enable SMEs in other European sectors to tap into the promising potential
of online collaboration for innovation as well.
To this end, INNO-4-AGRIFOOD brings together and is implemented by a well-
balanced and complementary **consortium** , which is comprised of **7 partners
across 6 different European countries** , as presented in the following table.
## _Table 1: INNO-4-AGRIFOOD consortium partners_
<table>
<tr>
<th>
**Partner**
**No**
</th>
<th>
**Partner Name**
</th>
<th>
**Partner short name**
</th>
<th>
**Country**
</th> </tr>
<tr>
<td>
1
</td>
<td>
Q-PLAN INTERNATIONAL ADVISORS (Coordinator)
</td>
<td>
Q-PLAN
</td>
<td>
Greece
</td> </tr>
<tr>
<td>
2
</td>
<td>
Agenzia per la Promozione della Ricerca Europea
</td>
<td>
APRE
</td>
<td>
Italy
</td> </tr>
<tr>
<td>
3
</td>
<td>
IMP 3 rove – European Innovation Management
Academy EWIV
</td>
<td>
IMP 3 rove
</td>
<td>
Germany
</td> </tr>
<tr>
<td>
4
</td>
<td>
European Federation of Food Science and Technology
</td>
<td>
EFFoST
</td>
<td>
Netherlands
</td> </tr>
<tr>
<td>
5
</td>
<td>
BioSense Institute
</td>
<td>
BIOS
</td>
<td>
Serbia
</td> </tr>
<tr>
<td>
6
</td>
<td>
National Documentation Centre
</td>
<td>
EKT/NHRF
</td>
<td>
Greece
</td> </tr>
<tr>
<td>
7
</td>
<td>
Europa Media szolgaltato non profitkozhasznu KFT
</td>
<td>
EM
</td>
<td>
Hungary
</td> </tr> </table>
In this context, the **interim version of the DMP** presents the data
management principles set forth in the framework of INNO-4-AGRIFOOD by its
consortium partners (Chapter 2). Moreover, it builds upon the initial version
of the DMP and provides an updated list of the datasets that have been or will
be collected, processed and/or produced during the project along with an up-
to-date description for each one (Chapter 3), addressing crucial aspects
pertaining to their management and taking into account the “ _Guidelines on
Data Management_ _in Horizon 2020_ ” provided by the European Commission
(EC).
**Important remark**
The DMP is not a fixed document. To the contrary, it evolves during the
lifespan of the project. In particular, the DMP will be updated at least once
more during INNO-4-AGRIFOOD (i.e. as D7.4 at M30) as well as ad hoc when
deemed necessary in order to include new datasets, reflect changes in the
already identified datasets, changes in consortium policies and plans or other
potential external factors. Q-PLAN is responsible for the elaboration of the
DMP and will update and enrich it, when required, with the support of all
relevant members of the INNO-4-AGRIFOOD consortium.
# Data management principles
## Standards and metadata
Any open datasets produced by INNO-4-AGRIFOOD will be accompanied by data that
will facilitate their understanding and re-use by interested stakeholders.
These data may include basic details that will assist interested stakeholders
to locate the dataset, including its format and file type as well as
meaningful information about who created or contributed to the dataset, its
name and reference, date of creation and under what conditions it may be
accessed. Complementary documentation may also encompass details on the
methodology used to collect, process and/or generate the dataset, definitions
of variables, vocabularies and units of measurement as well as any assumptions
made. Finally, wherever possible consortium partners will identify and use
existing standards.
## Data sharing
The Coordinator (Q-PLAN) in collaboration with the respective Work Package
Leaders of the project, will determine how the data collected and produced in
the framework of INNO-4-AGRIFOOD will be shared. This includes the definition
of access procedures as well as potential embargo periods along with any
necessary software and/or other tools which may be required for data sharing
and re-use. In case the dataset cannot be shared, the reasons for this will be
clearly mentioned (e.g. ethical, rules of personal data, intellectual
property, commercial, privacy-related, security-related). A consent will be
requested from all external data providers in order to allow for their data to
be shared and all such data will be anonymised before sharing.
## Data archiving and preservation
The datasets of the project which will be open for sharing will be deposited
to an open data repository and will be made accessible to all interested
stakeholders, ensuring their long-term preservation and accessibility beyond
the lifetime of the project. In fact, we consider the use of _Zenodo_ as one
of the best online and open services to enable open access to INNO-4-AGRIFOOD
datasets. In this respect, the Coordinator (Q-PLAN) will be responsible for
uploading all open datasets to the repository of choice, while all partners
will be responsible for disseminating them through their professional networks
and other communication channels.
## Ethical considerations
INNO-4-AGRIFOOD entails activities which involve the collection of meaningful
data from selected individuals (e.g. the interview-based survey aimed at
revealing the needs of agri-food SMES in terms of online collaboration for
innovation support, the online survey that shed light to the training needs of
innovation intermediaries, etc.). The collection of data from participants in
such activities will be based upon a process of informed consent. Any personal
information will be handled according to the principles laid out by the
Directive 95/46/EC of the European Parliament and of the Council on the
“Protection of individuals with regard to the processing of personal data and
on the free movement of such data” (24 October 1995) and its revisions. The
participants’ right to control their personal information will be respected at
all times (including issues of confidentiality). The Coordinator (Q-PLAN) will
regulate and deal with any ethical issues that may arise during the project in
this respect, in cooperation with the Steering Committee of the project.
# Data management plan
## Overview
INNO-4-AGRIFOOD places special emphasis on the management of the valuable data
that have been or will be collected, processed and generated throughout its
activities. In this respect, the table below provides a list of the datasets
identified by INNO-4-AGRIFOOD consortium members, indicating the name of the
dataset, its linked Work Package and the respective leading consortium member
(i.e. Work Package Leader) as well as its status compared to the previous
version of the DMP.
### Table 2: List of INNO-4-AGRIFOOD datasets
<table>
<tr>
<th>
**No**
</th>
<th>
**Dataset Name**
</th>
<th>
**Linked**
**Work**
**Package**
</th>
<th>
**Work**
**Package**
**Leader**
</th>
<th>
**Status**
</th> </tr>
<tr>
<td>
1
</td>
<td>
Analysis of the agri-food value chain
</td>
<td>
WP1
</td>
<td>
BIOS
</td>
<td>
Updated
(M15)
</td> </tr>
<tr>
<td>
2
</td>
<td>
Needs of agri-food SMEs in terms of online collaboration for innovation
support
</td>
<td>
WP1
</td>
<td>
BIOS
</td>
<td>
Updated
(M15)
</td> </tr>
<tr>
<td>
3
</td>
<td>
Skills of innovation intermediaries in terms of supporting online
collaboration for innovation
</td>
<td>
WP1
</td>
<td>
BIOS
</td>
<td>
Updated
(M15)
</td> </tr>
<tr>
<td>
4
</td>
<td>
Outcomes of the INNO-4-AGRIFOOD Cocreation Workshop – E-learning
</td>
<td>
WP2
</td>
<td>
IMP 3 rove
</td>
<td>
New entry 1
(M15)
</td> </tr>
<tr>
<td>
5
</td>
<td>
Case-based training material supplemented by theoretical information on the
topic
</td>
<td>
WP2
</td>
<td>
IMP 3 rove
</td>
<td>
\-
</td> </tr>
<tr>
<td>
6
</td>
<td>
Outcomes of the INNO-4-AGRIFOOD Cocreation Workshop – Services and tools
</td>
<td>
WP3
</td>
<td>
Q-PLAN
</td>
<td>
New entry 1
(M15)
</td> </tr>
<tr>
<td>
7
</td>
<td>
Pool of agri-food SMEs
</td>
<td>
WP4
</td>
<td>
APRE
</td>
<td>
Updated
(M15)
</td> </tr>
<tr>
<td>
8
</td>
<td>
Roster of specialists database
</td>
<td>
WP4
</td>
<td>
APRE
</td>
<td>
Updated
(M15)
</td> </tr>
<tr>
<td>
9
</td>
<td>
Service testing metrics
</td>
<td>
WP4
</td>
<td>
APRE
</td>
<td>
Updated
(M15)
</td> </tr> </table>
<table>
<tr>
<th>
10
</th>
<th>
User data and learning curve of e-learning participants
</th>
<th>
WP5
</th>
<th>
EM
</th>
<th>
New entry 2
(M15)
</th> </tr>
<tr>
<td>
11
</td>
<td>
Feedback derived from e-learning participants
</td>
<td>
WP5
</td>
<td>
EM
</td>
<td>
New entry 2
(M15)
</td> </tr>
<tr>
<td>
12
</td>
<td>
Awareness creation, dissemination and stakeholder engagement
</td>
<td>
WP6
</td>
<td>
EFFoST
</td>
<td>
Updated
(M15)
</td> </tr> </table>
With the identified datasets of INNO-4-AGRIFOOD in mind, the current section
of the DMP provides meaningful information per each one, including:
* The name of the dataset.
* The type of study in the frame of which the dataset is produced.
* A concise description of the dataset.
* The methodology and tools employed for collecting/generating the data.
* The format and volume of the dataset.
* Any standards that will be used (if applicable) and/or metadata to be created.
* Potential stakeholders for whom the data may prove useful.
* Provisions regarding the confidentiality of the data.
**Important remark**
The information provided within this section reflects the current views and
plans of INNO-4-AGRIFOOD consortium partners at this stage of the project
(M15) and may be adapted and/or updated in future versions of the DMP (e.g.
through the inclusion of more elaborate descriptions of the datasets,
standards and metadata, how the datasets may be preserved, accessed and re-
used in the long-term, etc.). The template employed for collecting the
information from project partners is annexed to this document.
## Analysis of the agri-food value chain
<table>
<tr>
<th>
**Dataset name**
</th>
<th>
Analysis of the agri-food value chain.
</th> </tr>
<tr>
<td>
**Type of study**
</td>
<td>
Agri-food value chain analysis aimed at revealing the primary value chain
areas and SME actors to be targeted by the project based on both secondary and
primary research.
</td> </tr>
<tr>
<td>
**Dataset description**
</td>
<td>
Data derived from interviews with members of the Advisory and Beneficiaries
boards of INNO-4-AGRIFOOD, providing their opinions about the needs of SMEs
with respect to innovation support and the opportunities for online
collaboration for innovation.
</td> </tr>
<tr>
<td>
**Methodologies for data collection / generation**
</td>
<td>
A semi-structured questionnaire was employed in order to collect qualitative
data during the interviews.
</td> </tr>
<tr>
<td>
**Format and volume of the dataset**
</td>
<td>
The dataset is stored within a .zip file which comprises of 7 distinct
documents stored in .docx formats. The total size of the (uncompressed)
dataset is 1.12 MB.
</td> </tr>
<tr>
<td>
**Metadata and**
**standards**
</td>
<td>
Each document of the dataset is accompanied by descriptive metadata including
title, author and keywords.
</td> </tr>
<tr>
<td>
**For whom might the dataset be useful?**
</td>
<td>
The dataset provided INNO-4-AGRIFOOD consortium members with valuable
information from the perspective of agri-food stakeholders, fuelling and
complementing the agri-food value chain analysis conducted in the context of
the project.
</td> </tr>
<tr>
<td>
**Confidentiality**
</td>
<td>
The outcomes of the study that produced the dataset have been published
through the _Agri-food Value Chain Analysis Report_ , already available at
the web portal of the project. The dataset itself, used only in the context of
the project, is not intended for sharing and/or re-use, with a view to
safeguarding the privacy of interviewees. With that in mind, the dataset is
now archived at the private server of the Coordinator (Q-PLAN) and will be
preserved for at least 5 years following the end of the project, before
eventually being deleted.
</td> </tr> </table>
## Needs of agri-food SMEs in terms of online collaboration for innovation
support
<table>
<tr>
<th>
**Dataset name**
</th>
<th>
Needs of agri-food SMEs in terms of online collaboration for innovation
support.
</th> </tr>
<tr>
<td>
**Type of study**
</td>
<td>
Interview-based survey of representatives of agri-food SMEs as well as
innovation intermediaries aimed at revealing the needs, level of readiness and
profiles of agrifood SMEs in terms of online collaboration for innovation.
</td> </tr>
<tr>
<td>
**Dataset description**
</td>
<td>
The dataset contains the responses (mostly qualitative) provided by
interviewees who participated in the study, addressing different aspects of
the current situation in the EU with respect to online collaboration for
innovation amongst SMEs in the agri-food sector as well as diverse topics
relevant to collaborating for innovation by employing online means (e.g.
specific attributes of platforms and tools needed for online collaboration,
support that SMEs may seek or need in this respect, etc.).
</td> </tr>
<tr>
<td>
**Methodologies for data collection / generation**
</td>
<td>
The collection of the data was realised through a semi-structured
questionnaire administered to survey participants in the frame of interviews.
An online (web) form was employed by interviewers in order to submit a record
to the dataset.
</td> </tr>
<tr>
<td>
**Format and volume of the dataset**
</td>
<td>
The dataset is stored in spreadsheet (.xls) and .pdf formats, both of which
contain the 52 replies derived from the interview-based survey. The size of
the dataset in .xls format is 0.17MB, while in .pdf it is 0.76MB.
</td> </tr>
<tr>
<td>
**Metadata and**
**standards**
</td>
<td>
Descriptive metadata (i.e. title, author and keywords) have been created to
accompany the dataset.
</td> </tr>
<tr>
<td>
**For whom might the dataset be useful?**
</td>
<td>
The insights derived from the analysis of the data have been key in the
process of co-creating and developing the novel services and tools of
INNO-4-AGRIFOOD according to the needs of agri-food SMEs and their innovation
intermediaries.
</td> </tr>
<tr>
<td>
**Confidentiality**
</td>
<td>
The findings and conclusions of the study based on the processing and analysis
of the data within this dataset, have been openly shared through the _Agri-
food SME_ _Profiling and Needs Analysis Report_ , which is published at the
web portal of INNO4-AGRIFOOD. The raw data collected through the interview-
based survey will not be shared and/or re-used (outside the framework of the
project and/or beyond its completion) to ensure the confidentiality of the
interviewees and their responses. Hence, the dataset, currently archived at
the private server of the Coordinator (QPLAN), shall be preserved for at least
5 years following the end of the project, before eventually being deleted.
</td> </tr> </table>
## Skills of innovation intermediaries in terms of supporting online
collaboration for innovation
<table>
<tr>
<th>
**Dataset name**
</th>
<th>
Skills of innovation intermediaries in terms of supporting online
collaboration for innovation.
</th> </tr>
<tr>
<td>
**Type of study**
</td>
<td>
Online survey of staff of innovation intermediaries and SME support networks
aimed at assessing the current level of their knowledge and skills in
providing support to the online collaboration for innovation endeavours of
agri-food SMEs.
</td> </tr>
<tr>
<td>
**Dataset description**
</td>
<td>
The data collected comprise predominantly quantitative responses provided by
the participants of the online survey, including demographic information as
well as their perceived level of skills (gauged via a 5-scale Likert scale) in
different skill areas, including agri-food industry, support services,
collaboration, innovation management and soft skills.
</td> </tr>
<tr>
<td>
**Methodologies for data collection / generation**
</td>
<td>
A structured questionnaire was used in order to collect data. The
questionnaire was self-administered and survey participants were able to
access it online by following a dedicated link.
</td> </tr>
<tr>
<td>
**Format and volume of the dataset**
</td>
<td>
The dataset is stored in standard spreadsheet format (.xlsx). In total, 79
respondents from the EU filled in and successfully submitted a questionnaire.
The same number of records was collected and is now within the dataset. The
size of the dataset is 53KB.
</td> </tr>
<tr>
<td>
**Metadata and**
**standards**
</td>
<td>
The dataset is accompanied by descriptive metadata (i.e. title, author and
keywords).
</td> </tr>
<tr>
<td>
**For whom might the dataset be useful?**
</td>
<td>
The dataset has been of great use to INNO-4-AGRIFOOD consortium members,
enabling them to unearth the insight required to set the stage for the need-
driven co-creation and development of the project’s e-learning curriculum and
modules.
</td> </tr>
<tr>
<td>
**Confidentiality**
</td>
<td>
The _Skills Mapping and Training Needs Analysis Report_ , available at the
web portal of the project, provides public access to the findings of the study
in the frame of which this dataset has been produced. The records of the
database are available only to selected members of the INNO-4-AGRIFOOD project
team and are not intended for sharing and/or re-use, so as to ensure the
privacy of the study’s participants. The dataset itself is archived at the
private server of the Coordinator (Q-PLAN) and will be preserved for at least
5 years following the end of the project, before eventually being deleted.
</td> </tr> </table>
## Outcomes of the INNO-4-AGRIFOOD Co-creation Workshop – E-learning
<table>
<tr>
<th>
**Dataset name**
</th>
<th>
Outcomes of the INNO-4-AGRIFOOD Co-creation Workshop – E-learning.
</th> </tr>
<tr>
<td>
**Type of study**
</td>
<td>
The INNO-4-AGRIFOOD Co-creation Workshop which was held on the 15 th of
September 2017 at Amsterdam, the Netherlands in order to co-create, along with
stakeholders of the agri-food ecosystem, the e-learning offer of
INNO-4-AGRIFOOD.
</td> </tr>
<tr>
<td>
**Dataset description**
</td>
<td>
The dataset generated encompasses the feedback as well as the innovative
concepts and ideas provided by participants of the INNO-4-AGRIFOOD Co-creation
Workshop during the structured activities of the co-creative session dedicated
to the e-learning offer of the project. The data is mostly textual (short
sentences) and refer to (i) the appropriateness of the e-learning material
developed at the time of the workshop and (ii) supplementary ideas for
consideration in the process of developing the e-learning material of the
project.
</td> </tr>
<tr>
<td>
**Methodologies for data collection / generation**
</td>
<td>
In addition to the minutes recorded throughout the co-creation workshop, the
participants’ input from the group discussions were tabulated for each of the
draft e-learning modules (which were provided as background information) using
preprepared templates. Comments of relevant consortium members on each module
were added remotely after the event. Conclusions were then drawn on the
content and weighting of elements within each module.
</td> </tr>
<tr>
<td>
**Format and volume of the dataset**
</td>
<td>
The data collected have been integrated within the report on the _Outcomes of
the_ _INNO-4-AGRIFOOD Co-creation Workshop: Curriculum concept and key
training_ _topics_ . The report is stored in .pdf format and its size is
1.08MB.
</td> </tr>
<tr>
<td>
**Metadata and**
**standards**
</td>
<td>
The report in which the dataset has been integrated includes meaningful
information with respect to the context in which the data have been collected
as well as the methodology for collecting them. The report itself is
accompanied with basic descriptive metadata including title and type of file.
</td> </tr>
<tr>
<td>
**For whom might the dataset be useful?**
</td>
<td>
Innovation support service designers and providers as well as relevant
trainers and educators would find the dataset most useful, especially those
who operate within the agri-food sector or are interested to do so.
</td> </tr>
<tr>
<td>
**Confidentiality**
</td>
<td>
The dataset has been openly shared through the public report on the _Outcomes
of_ _the INNO-4-AGRIFOOD Co-creation Workshop: Curriculum concept and key
training_ _topics_ , which is available at the INNO-4-AGRIFOOD web portal.
</td> </tr> </table>
## Case-based training material supplemented by theoretical information on
the topic
<table>
<tr>
<th>
**Dataset name**
</th>
<th>
Case-based training material supplemented by theoretical information on the
topic.
</th> </tr>
<tr>
<td>
**Type of study**
</td>
<td>
Development of educative case studies based on the services provided in the
framework of INNO-4-AGRIFOOD blended with theoretical training building upon
existing material available to partners either from previous work or from open
sources.
</td> </tr>
<tr>
<td>
**Dataset description**
</td>
<td>
The data collected will be simple responses (plain text in English) provided
in the frame of interviews.
</td> </tr>
<tr>
<td>
**Methodologies for data collection / generation**
</td>
<td>
Data required for the development of the case studies will be collected with
the help of semi-structured questionnaires administered during interviews by
project partners. Additional data will be gathered from the existing knowledge
base of project partners (e.g. previous project documentations, previous
service provision documentations, etc.) and/or from OER repositories as well
as other third-party secondary data sources.
</td> </tr>
<tr>
<td>
**Format and volume of the dataset**
</td>
<td>
Data collected during the interviews conducted in the framework of case study
development will be stored in document format (e.g. .doc). The case studies
stemming from the interviews will be preserved in .pdf format.
</td> </tr>
<tr>
<td>
**Metadata and**
**standards**
</td>
<td>
All e-learning material developed based on the case studies will be SCORM
compliant to enable its packaging and facilitate the re-use of the learning
objects. The Articulate software, which has been used to create the e-learning
material of the project, can generate the Content Aggregation Metadata File
required.
</td> </tr>
<tr>
<td>
**For whom might the dataset be useful?**
</td>
<td>
The dataset would be quite useful for innovation intermediaries and
consultants as well as educators who would use this case-based e-learning
material in their own activities.
</td> </tr>
<tr>
<td>
**Confidentiality**
</td>
<td>
The e-learning material will be openly available to all interested
stakeholders, protected with an appropriate Creative Commons licence (to be
timely decided at a later stage of the project in consultation with the
Advisory Board of the project).
</td> </tr> </table>
## Outcomes of the INNO-4-AGRIFOOD Co-creation Workshop – Services and tools
<table>
<tr>
<th>
**Dataset name**
</th>
<th>
Outcomes of the INNO-4-AGRIFOOD Co-creation Workshop – Services and tools.
</th> </tr>
<tr>
<td>
**Type of study**
</td>
<td>
The INNO-4-AGRIFOOD Co-creation Workshop, which was held on the 15 th of
September 2017 at Amsterdam, the Netherlands with a view to co-creating
innovative ideas and designs for the innovation support services and smart
tools of the project, building upon the valuable contribution of diverse agri-
food stakeholders.
</td> </tr>
<tr>
<td>
**Dataset description**
</td>
<td>
The data include innovative concepts and ideas provided by the participants of
the workshop’s co-creative session that focused on the innovation support
services and smart tools of INNO-4-AGRIFOOD.
</td> </tr>
<tr>
<td>
**Methodologies for data collection / generation**
</td>
<td>
The data were collected during the INNO-4-AGRIFOOD Co-creation Workshop and
documented as transcript notes.
</td> </tr>
<tr>
<td>
**Format and volume of the dataset**
</td>
<td>
The data have been integrated into the report on the _Outcomes of the
INNO-4AGRIFOOD Co-creation Workshop: Innovation support_ _services and ICT
tools_ , which is stored in .pdf format. The size of the file is 5.53MB.
</td> </tr>
<tr>
<td>
**Metadata and**
**standards**
</td>
<td>
The report in which the data have been incorporated provides insights into the
objectives and methodology of the INNO-4-AGRIFOOD Co-Creation Workshop,
elaborates on the outcomes of its session on the services and tools and
translates the aforementioned outcomes into meaningful conclusions and key
potential characteristics for innovation support services and tools. Basic
descriptive metadata are provided along with the report (i.e. title and type
of file).
</td> </tr>
<tr>
<td>
**For whom might the dataset be useful?**
</td>
<td>
The dataset has contributed significantly in developing the services, smart
tools and e-learning modules of the project in line with the needs and
preferences of agrifood stakeholders in the context of INNO-4-AGRIFOOD. Beyond
the context of the project, innovation support service designers and providers
as well as ICT application developers and training providers could potentially
find the dataset and its accompanying report useful.
</td> </tr>
<tr>
<td>
**Confidentiality**
</td>
<td>
The report on the _Outcomes of the INNO-4-AGRIFOOD Co-creation Workshop:_
_Innovation support services and ICT tools_ , which includes the dataset, is
already published and available at the web portal of the project.
</td> </tr> </table>
## Pool of agri-food SMEs
<table>
<tr>
<th>
**Dataset name**
</th>
<th>
Pool of agri-food SMEs.
</th> </tr>
<tr>
<td>
**Type of study**
</td>
<td>
Deployment of INNO-4-AGRIFOOD services and tools in real-life contexts.
</td> </tr>
<tr>
<td>
**Dataset description**
</td>
<td>
The dataset consists of 2 separate lists of agri-food SMEs which may be
interested in benefiting from the innovation support services of the project
in the framework of its 3 iterative testing, validation and fine-tuning
rounds. The 1 st list includes SMEs which are either clients or among the
professional network of INNO-4-AGRIFOOD consortium partners, and to which
services may be delivered by these partners. The 2 nd list includes SMEs who
have been identified through other channels (e.g. through the
INNO-4-AGRIFOOD’s Beneficiaries and Advisory Boards, the online contact form
of the project’s web portal, etc.), and to which services may be delivered by
external innovation consultants.
</td> </tr>
<tr>
<td>
**Methodologies for data collection / generation**
</td>
<td>
In addition to the professional networks of INNO-4-AGRIFOOD (1 st list of
the dataset), several sources have been employed to identify suitable SMEs to
participate in the real-life deployment of the novel services and tools of the
project, including (among others) networks and member organisations of
INNO-4AGRIFOOD’s Advisory and Beneficiaries Boards as well as interested SMEs
which participated in the surveys launched in the context of the project or
expressed their interest through the online contact form of its web portal (2
nd list of the dataset).
</td> </tr>
<tr>
<td>
**Format and volume of the dataset**
</td>
<td>
A spreadsheet (in .xlsx format) with two separate tabs (each for one of the
two lists described above) is used to store the Pool of agri-food SMEs, which,
by the end of the project, will contain at least 125 records. In fact, the
complete dataset will contain the following data for each recorded SME: (i)
Name of the SME; (ii) Contact person; (iii) Country, (iv) City, (v) Telephone
number; and (vi) E-mail address. In the case of the 1 st list, information
about the INNO-4-AGRIFOOD consortium partner connected to a recorded SME will
also be included.
</td> </tr>
<tr>
<td>
**Metadata and**
**standards**
</td>
<td>
Descriptive and structural metadata may be created and provided at a later
stage of the project.
</td> </tr>
<tr>
<td>
**For whom might the dataset be useful?**
</td>
<td>
The dataset would be most useful for consortium partners during the real-life
deployment activities of the project.
</td> </tr>
<tr>
<td>
**Confidentiality**
</td>
<td>
The dataset will be available only to relevant INNO-4-AGRIFOOD consortium
partners and may not be disclosed or used for purposes outside the framework
of the project, unless otherwise allowed by either the project partner or the
external stakeholder that has provided the respective data.
</td> </tr> </table>
## Roster of specialists database
<table>
<tr>
<th>
**Dataset name**
</th>
<th>
Roster of specialists database.
</th> </tr>
<tr>
<td>
**Type of study**
</td>
<td>
Involvement of trained staff of innovation intermediaries and SME support
networks in the deployment of INNO-4-AGRIFOOD services and tools in real-life
contexts.
</td> </tr>
<tr>
<td>
**Dataset description**
</td>
<td>
Pool of appropriately qualified SME consultants who may participate in the
testing of project’s services and tools by providing them to agri-food SMEs.
The Roster of Specialists Database (RSD) will encompass valuable information
about the recorded consultants, such as demographics and contact details of
themselves and their affiliated organisations, data about their progress
towards completing the project’s e-learning offer and providing its services
as well as miscellaneous data that can help INNO-4-AGRIFOOD consortium members
to better match them with appropriate agri-food SMEs to service.
</td> </tr>
<tr>
<td>
**Methodologies for data collection / generation**
</td>
<td>
The RSD will be populated with consultants who will have successfully
completed the INNO-4-AGRIFOOD e-learning courses addressing the project’s
services, participated in the project’s 1 st webinar and/or have been
personally trained by a designated INNO-4-AGRIFOOD Coach. The database will be
enriched as the real-life deployment activities of INNO-4-AGRIFOOD progress
and the staff of innovation intermediaries and SME support networks gain
experience in the project’s services and e-learning modules.
</td> </tr>
<tr>
<td>
**Format and volume of the dataset**
</td>
<td>
The Roster of Specialists Database is stored in a standard spreadsheet format
and by the end of the project will comprise of at least 50 records of SME
consultants across the EU.
</td> </tr>
<tr>
<td>
**Metadata and**
**standards**
</td>
<td>
Descriptive and structural metadata may be created to accompany the dataset at
a later stage of the project.
</td> </tr>
<tr>
<td>
**For whom might the dataset be useful?**
</td>
<td>
Agri-food SMEs who would like to receive support from innovation consultants
specialised in supporting online collaboration for innovation.
</td> </tr>
<tr>
<td>
**Confidentiality**
</td>
<td>
Records in the database will remain for internal use only during the lifecycle
of the project.
</td> </tr> </table>
## Service testing metrics
<table>
<tr>
<th>
**Dataset name**
</th>
<th>
Service testing metrics.
</th> </tr>
<tr>
<td>
**Type of study**
</td>
<td>
Testing, validation and fine-tuning of the INNO-4-AGRIFOOD services and smart
tools.
</td> </tr>
<tr>
<td>
**Dataset description**
</td>
<td>
The dataset includes data collected during the iterative testing, validation
and finetuning of the INNO-4-AGRIFOOD services and smart tools, aimed at
managing ambiguity during the various iterations as well as measuring the
impact of improvements after each iteration. In particular, it contains both
qualitative and quantitative data on (i) the satisfaction of SMEs that
received INNO-4-AGRIFOOD services, (ii) the satisfaction of SMEs and
innovation consultants that have used the INNO-4-AGRIFOOD smart tools, (iii)
the impact of the INNO-4-AGRIFOOD services on the business of the SMEs that
received them, (iv) the activities performed in the framework of each
INNO-4-AGRIFOOD service provided in the context of the project, and (v)
different aspects of the services and smart tools that can be further
streamlined according to users’ needs and expectations.
</td> </tr>
<tr>
<td>
**Methodologies for data collection / generation**
</td>
<td>
In line with the _INNO-4-AGRIFOOD Metrics Model_ , this dataset will be
fuelled by the respective surveys that will run over the 3 real-life
deployment rounds of the project’s services and smart tools as well as by the
service stories that will be produced under this framework. All surveys will
employ questionnaire-based tools aiming at mining both qualitative and
quantitative data from agri-food SMEs and innovation consultants.
</td> </tr>
<tr>
<td>
**Format and volume of the dataset**
</td>
<td>
The dataset will employ a typical spreadsheet format. The volume of the
dataset’s final version will depend on the number of users that will
participate in the real-life deployment of the INNO-4-AGRIFOOD services and
smart tools.
</td> </tr>
<tr>
<td>
**Metadata and**
**standards**
</td>
<td>
Descriptive metadata will be provided (such as title, abstract, author, type
of data, data collection method and keywords).
</td> </tr>
<tr>
<td>
**For whom might the dataset be useful?**
</td>
<td>
Innovation support service designers and providers may find use in this
dataset.
</td> </tr>
<tr>
<td>
**Confidentiality**
</td>
<td>
All records will be open provided the necessary consent has been provided by
users who provided the data and any required anonymization has been performed.
</td> </tr> </table>
## User data and learning curve of e-learning participants
<table>
<tr>
<th>
**Dataset name**
</th>
<th>
User data and learning curve of e-learning participants.
</th> </tr>
<tr>
<td>
**Type of study**
</td>
<td>
Provision of e-training courses to staff of innovation intermediaries and SME
support networks.
</td> </tr>
<tr>
<td>
**Dataset description**
</td>
<td>
The dataset contains demographic data of the people who have registered to the
e-learning platform of INNO-4-AGRIFOOD and their affiliated organisations
along with data reflecting their e-learning progress.
</td> </tr>
<tr>
<td>
**Methodologies for data collection / generation**
</td>
<td>
Data are provided voluntarily by the individuals who register to the
INNO-4AGRIFOOD e-learning platform through a dedicated online form which aims
at creating the profile necessary for their registration. Moreover, the
e-learning platform automatically collects all necessary data about the online
activities of the participants who access the system via a unique username-
password combination.
</td> </tr>
<tr>
<td>
**Format and volume of the dataset**
</td>
<td>
The dataset is stored in a MySQL database and exported to standard spreadsheet
format (.csv or other). At least 150 registered participants are expected to
be recorded within the dataset by the end of the project.
</td> </tr>
<tr>
<td>
**Metadata and**
**standards**
</td>
<td>
The dataset is not intended for sharing and re-use and thus will not be
accompanied by metadata.
</td> </tr>
<tr>
<td>
**For whom might the dataset be useful?**
</td>
<td>
The dataset will be used by selected INNO-4-AGRIFOOD consortium members to
analyse the learning behaviour of the e-learning participants in the frame of
the project.
</td> </tr>
<tr>
<td>
**Confidentiality**
</td>
<td>
The data of e-learning participants will be confidential and used only in the
context of the project. With that in mind, the administrators of the
e-learning platform will have access to the data provided by e-learning
participants, apart from their password information (which will be known only
to the e-learning participants themselves). E-learning participants can
configure their profile indicating the open data they would like to share.
Still, the data of the participants’ learning curve (e.g. statistics on
accessing the e-learning, following existing material, concluding tests, etc.)
will be accessible only to the administrators of the e-learning platform as
well. Any meaningful analysis or conclusions drawn from these data will be
shared in relevant upcoming reports that will be produced by the project.
</td> </tr> </table>
## Feedback derived from e-learning participants
<table>
<tr>
<th>
**Dataset name**
</th>
<th>
Feedback derived from e-learning participants.
</th> </tr>
<tr>
<td>
**Type of study**
</td>
<td>
Testing, validation and fine-tuning of the e-learning environment.
</td> </tr>
<tr>
<td>
**Dataset description**
</td>
<td>
The dataset includes feedback on technical and content-wise aspects of the
elearning environment of INNO-4-AGRIFOOD (including the e-learning platform as
well as its constituent e-learning modules), gathered from e-learning
participants with a view to evaluating its functionalities, graphics and
content.
</td> </tr>
<tr>
<td>
**Methodologies for data collection / generation**
</td>
<td>
Data will be provided voluntarily by e-learning participants of
INNO-4-AGRIFOOD via dedicated questionnaire-based feedback forms. The
questionnaires utilised by the feedback forms employ the Likert scale (1 \-
Strongly Disagree to 5 - Strongly Agree) so that participants can quickly
provide their opinion on the functionalities and content of the different
e-learning modules as well as the platform as a whole.
</td> </tr>
<tr>
<td>
**Format and volume of the dataset**
</td>
<td>
The dataset is stored in a MySQL database and can be exported to standard
spreadsheet format (.csv or other). The volume of the final dataset will
depend on the number of people who will have provided their feedback on the
e-learning platform and/or its modules by the end of the project.
</td> </tr>
<tr>
<td>
**Metadata and**
**standards**
</td>
<td>
The dataset is not intended for sharing and re-use and thus will not be
accompanied by metadata.
</td> </tr>
<tr>
<td>
**For whom might the dataset be useful?**
</td>
<td>
The dataset will be used by selected INNO-4-AGRIFOOD consortium members to
analyse user experience on the e-learning environment and thus provide the
basis for further improvement in the future iterations in the context of the
project.
</td> </tr>
<tr>
<td>
**Confidentiality**
</td>
<td>
In order to ensure the privacy of the participants who provided their
feedback, the records of the database will remain confidential. The
administrators of the elearning platform will have access to the feedback
provided, however they will not disclose that information to third parties.
</td> </tr> </table>
## Awareness creation, dissemination and stakeholder engagement
<table>
<tr>
<th>
**Dataset name**
</th>
<th>
Awareness creation, dissemination and stakeholder engagement.
</th> </tr>
<tr>
<td>
**Type of study**
</td>
<td>
Assessment of the results and impact of the awareness creation, dissemination
and stakeholder engagement activities of the project employing an indicator-
based framework.
</td> </tr>
<tr>
<td>
**Dataset description**
</td>
<td>
Data collected during INNO-4-AGRIFOOD with a view to measuring and assessing
the performance and results of the project in terms of awareness creation,
dissemination, stakeholder engagement.
</td> </tr>
<tr>
<td>
**Methodologies for data collection / generation**
</td>
<td>
Primary data are being collected through the dissemination activity reports of
project partners regarding media products, events, external events, general
publicity, etc. Third party tools are being employed as well (e.g. Google
analytics, social media statistics, etc.).
</td> </tr>
<tr>
<td>
**Format and volume of the dataset**
</td>
<td>
The collected data are preserved in a spreadsheet format (.xlsx).
</td> </tr>
<tr>
<td>
**Metadata and**
**standards**
</td>
<td>
Descriptive metadata will be provided (such as title, type of data, data
collection method and keywords).
</td> </tr>
<tr>
<td>
**For whom might the dataset be useful?**
</td>
<td>
The dataset would be meaningful to the European Commission as well as
researchers who study relevant aspects of EU-funded projects.
</td> </tr>
<tr>
<td>
**Confidentiality**
</td>
<td>
All records will be openly shared.
</td> </tr> </table>
|
https://phaidra.univie.ac.at/o:1140797
|
Horizon 2020
|
1055_INNO-4-AGRIFOOD_681482.md
|
# Introduction
The current document constitutes the initial version of the **Data Management
Plan** (DMP) elaborated in the framework of the **INNO-4-AGRIFOOD** project,
which has received funding from the European Union’s Horizon 2020 Research and
Innovation Programme under Grant Agreement No 681482. INNO-4-AGRIFOOD is aimed
at fostering, supporting and stimulating **online collaboration for
innovation** amongst **agri-food SMEs** across Europe. To this end, the
project will enhance the service portfolio and practices of **innovation
intermediaries and SME support networks** across Europe by providing them with
a well-tailored blend of demand-driven **value propositions** , including:
* A **new generation of value added innovation support services** aimed at empowering their agri-food SME clients to capitalise on the full potential of online collaboration for innovation;
* A **suite of smart and platform-independent ICT tools** to support and optimise the delivery of the novel online collaboration for innovation support services; and
* A **series of highly interactive and flexible e-training courses** that will equip them with the knowledge and skills required to successfully deliver these new services.
On top of the above mentioned, the accumulated experience and lessons learned
through INNO-4-AGRIFOOD will be translated into meaningful **guidelines** to
be diffused across Europe so as to fuel the replication of its results and
thus enable SMEs in other European sectors to tap into the promising potential
of online collaboration for innovation as well.
INNO-4-AGRIFOOD is implemented by a well-balanced and complementary
**consortium** , which is comprised of 7 partners across 6 different European
countries, as presented in the following table.
## _Table 1: INNO-4-AGRIFOOD consortium partners_
<table>
<tr>
<th>
**Partner**
**No**
</th>
<th>
**Partner Name**
</th>
<th>
**Partner short name**
</th>
<th>
**Country**
</th> </tr>
<tr>
<td>
1
</td>
<td>
Q-PLAN International Advisors Ltd (Coordinator)
</td>
<td>
Q-PLAN
</td>
<td>
Greece
</td> </tr>
<tr>
<td>
2
</td>
<td>
Agenzia per la Promozione della Ricerca Europea
</td>
<td>
APRE
</td>
<td>
Italy
</td> </tr>
<tr>
<td>
3
</td>
<td>
IMP 3 rove – European Innovation Management
Academy EWIV
</td>
<td>
IMP 3 rove
</td>
<td>
Germany
</td> </tr>
<tr>
<td>
4
</td>
<td>
European Federation of Food Science and Technology
</td>
<td>
EFFoST
</td>
<td>
Netherlands
</td> </tr>
<tr>
<td>
5
</td>
<td>
BioSense Institute
</td>
<td>
BIOS
</td>
<td>
Serbia
</td> </tr>
<tr>
<td>
6
</td>
<td>
National Documentation Centre
</td>
<td>
EKT/NHRF
</td>
<td>
Greece
</td> </tr>
<tr>
<td>
7
</td>
<td>
Europa Media szolgaltato non profitkozhasznu KFT
</td>
<td>
EM
</td>
<td>
Hungary
</td> </tr> </table>
In this context, the **initial version of the DMP** presents the data
management principles set forth in the framework of INNO-4-AGRIFOOD by its
consortium partners. Moreover, it provides a first description of the datasets
that will be collected, processed and/or produced during the project, taking
into account the “Guidelines on Data Management in Horizon 2020” provided by
the European Commission 1 . In this respect, it addresses important relevant
points such as standards and metadata as well as outlines a provisional
approach to data sharing, archiving and preservation on a dataset by dataset
basis.
**Important remark**
The DMP is not a fixed document. To the contrary, it will evolve during the
lifespan of the project. In particular, the DMP will be updated at least twice
during INNO-4-AGRIFOOD (i.e. as D7.3 at M15 and D7.4 at M30) as well as ad hoc
when deemed necessary in order to include new datasets, reflect changes in the
already identified datasets, changes in consortium policies and plans or other
potential external factors. Q-PLAN is responsible for the elaboration of the
DMP and with the support of all Work Package Leaders will update and enrich it
when required.
# Data management principles
## Standards and metadata
All datasets produced by INNO-4-AGRIFOOD will be accompanied by data that will
facilitate their understanding and re-use by interested stakeholders. These
data may include basic details that will assist interested stakeholders to
locate the dataset, including its format and file type as well as meaningful
information about who created or contributed to the dataset, its name and
reference, date of creation and under what conditions it may be accessed.
Complementary documentation may also encompass details on the methodology used
to collect, process and/or generate the dataset, definitions of variables,
vocabularies and units of measurement as well as any assumptions made.
Finally, wherever possible consortium partners will identify and use existing
community standards.
## Data sharing
The Coordinator (Q-PLAN) in collaboration with the respective Work Package
Leaders, will determine how the data collected and produced in the framework
of INNO-4-AGRIFOOD will be shared. This includes the definition of access
procedures as well as potential embargo periods along with any necessary
software and/or other tools which may be required for data sharing and re-use.
In case the dataset cannot be shared, the reasons for this will be clearly
mentioned (e.g. ethical, rules of personal data, intellectual property,
commercial, privacy-related, security-related). A consent will be requested
from all external data providers in order to allow for their data to be shared
and all such data will be anonymised before sharing.
## Data archiving and preservation
The datasets of the project which will be open for sharing will be deposited
to an open data repository and will be made accessible to all interested
stakeholders, ensuring their long-term preservation and accessibility beyond
the lifetime of the project. At the moment, we consider the use of Zenodo 2
as one of the best online and open services to enable open access to
INNO-4-AGRIFOOD datasets but similar repositories will also be considered and
an appropriate decision will be timely made at a future update of the DMP.
Q-PLAN will be responsible for uploading all open datasets to the repository
of choice, while all partners will be responsible for disseminating them
through their professional networks and other media channels.
## Ethical considerations
INNO-4-AGRIFOOD entails activities which involve the collection of meaningful
data from selected individuals (e.g. the interview-based survey aimed at
revealing the needs of agri-food SMES in terms of online collaboration for
innovation support, the online survey that will shed light to the training
needs of innovation intermediaries in this respect, the co-creation workshop,
etc.). The collection of data from participants in these activities will be
based upon a process of informed consent. Any personal information will be
handled according to the principles laid out by the Directive 95/46/EC of the
European Parliament and of the Council on the “Protection of individuals with
regard to the processing of personal data and on the free movement of such
data” (24 October 1995) and its revisions. The participants’ right to control
their personal information will be respected at all times (including issues of
confidentiality). The Coordinator (Q-PLAN) will regulate and deal with any
ethical issues that may arise during the project in this respect, in
cooperation with the Steering
Committee of the project.
# Data management plan
In perfect alignment with the paradigm shift towards openness, INNO-4-AGRIFOOD
participates in the Horizon 2020 Open Research Data Pilot and places special
emphasis on the management of the valuable data that will be collected,
processed and generated throughout its activities. The current section
provides meaningful information in this respect per each identified dataset,
including:
* The name of the dataset.
* The type of study in the frame of which the dataset is produced.
* A concise description of the dataset.
* The methodology and tools employed for collecting/generating the data.
* The format and volume of the dataset.
* Any standards that will be used (if applicable) as well as metadata to be created.
* Potential external stakeholders for whom the data may prove useful.
* Provisions regarding the confidentiality of the data.
**Important remark**
The information provided within this section reflects the current views and
plans of INNO-4-AGRIFOOD consortium partners at this early stage of the
project (M3) and may be adapted or updated in future versions of the DMP (e.g.
through the inclusion of more elaborate descriptions of the datasets,
standards and metadata, how the datasets may be preserved, accessed and re-
used in the long-term, etc.). The template employed for collecting the
information from project partners is annexed to this document.
## Analysis of the agri-food value chain
<table>
<tr>
<th>
**Dataset name**
</th>
<th>
Analysis of the agri-food value chain (WP1)
</th> </tr>
<tr>
<td>
**Type of study**
</td>
<td>
Agri-food value chain analysis aimed at revealing the primary value chain
areas and SME actors to be targeted by the project based on both secondary and
primary research.
</td> </tr>
<tr>
<td>
**Dataset description**
</td>
<td>
Data derived from interviews with members of the Advisory and Beneficiaries
boards of INNO-4-AGRIFOOD.
</td> </tr>
<tr>
<td>
**Methodologies for data collection / generation**
</td>
<td>
A semi-structured questionnaire and a respective template will be employed in
order to collect and document data during the interviews.
</td> </tr>
<tr>
<td>
**Format and volume of the dataset**
</td>
<td>
Standard spreadsheet (.csv or other) and document (e.g. .doc) formats will be
used in order to store and preserve the dataset. The report on the agri-food
value chain analysis will accompany the dataset in pdf format.
</td> </tr>
<tr>
<td>
**Metadata and**
**standards**
</td>
<td>
Data records will be accompanied by the following metadata: a) the name of the
person that generated it, b) information about the location where it was
generated, c) reproduction rights and d) annotations made by the interviewers.
</td> </tr>
<tr>
<td>
**For whom might the dataset be useful?**
</td>
<td>
The dataset may prove meaningful for a broad array of stakeholders of the
Agrifood Ecosystem, including agri-food SMEs along with their innovation
intermediaries and providers as well as policy makers at regional, national
and EU level.
</td> </tr>
<tr>
<td>
**Confidentiality**
</td>
<td>
All records will be open, provided that the corresponding interviewees have
provided their consent in this respect within the frame of the interviews.
</td> </tr> </table>
## Needs of agri-food SMEs in terms of on-line collaboration for innovation
support
<table>
<tr>
<th>
**Dataset name**
</th>
<th>
Needs of agri-food SMEs in terms of on-line collaboration for innovation
support
(WP1)
</th> </tr>
<tr>
<td>
**Type of study**
</td>
<td>
Interview-based survey of representatives of agri-food SMEs as well as
innovation intermediaries aimed at revealing the needs, level of readiness and
profiles of agrifood SMEs in terms of online collaboration for innovation.
</td> </tr>
<tr>
<td>
**Dataset description**
</td>
<td>
The data collected will be responses (plain text in English and/or numbers)
provided by the interviewees.
</td> </tr>
<tr>
<td>
**Methodologies for data collection / generation**
</td>
<td>
The collection of the data will be realised through a semi-structured
questionnaire that will be administered to survey participants in the frame of
interviews. An online (web) form will be employed for interviewers to submit a
record to the dataset.
</td> </tr>
<tr>
<td>
**Format and volume of the dataset**
</td>
<td>
The dataset will be stored in a typical spreadsheet format (.csv or other). It
is estimated that more than 50 interviews will be conducted across Europe with
agrifood SMEs and innovation intermediaries and the same number of records
will be collected and stored in the dataset. The report on the findings of the
interviewbased survey based on the analysis of the data will complement the
dataset and will be stored in pdf format.
</td> </tr>
<tr>
<td>
**Metadata and**
**standards**
</td>
<td>
The following metadata will be created for each record: a) the name of the
person that generated it, b) information about the location where it was
generated, c) reproduction rights and d) annotations made by the interviewers.
</td> </tr>
<tr>
<td>
**For whom might the dataset be useful?**
</td>
<td>
In addition to agri-food SMEs, the dataset would also be quite useful for
innovation intermediaries and consultants that focus on agri-food SMEs,
technology and innovation providers that operate within the agri-food sector,
on-line collaboration networks and platforms providers, academics and
researchers in relevant fields (e.g. SME collaboration, open innovation, etc.)
as well as policy makers at regional, national and EU level who design and
implement innovation support policies for SMEs.
</td> </tr>
<tr>
<td>
**Confidentiality**
</td>
<td>
All records will be open. A consent will be requested from users during the
interview so that their data may be anonymised and re-used.
</td> </tr> </table>
## Skills of innovation intermediaries in terms of supporting on-line
collaboration for innovation
<table>
<tr>
<th>
**Dataset name**
</th>
<th>
Skills of innovation intermediaries in terms of supporting on-line
collaboration for innovation (WP1)
</th> </tr>
<tr>
<td>
**Type of study**
</td>
<td>
Online survey of staff of innovation intermediaries and SME support networks
aimed at assessing the current level of their knowledge and skills in
providing support to the online collaboration for innovation endeavours of
agri-food SMEs.
</td> </tr>
<tr>
<td>
**Dataset description**
</td>
<td>
The data collected will be both qualitative and quantitative responses
provided by survey participants.
</td> </tr>
<tr>
<td>
**Methodologies for data collection / generation**
</td>
<td>
A structured questionnaire will be used in order to collect data. The
questionnaire will be self-administered and survey participants will be able
to access it online by following a dedicated link.
</td> </tr>
<tr>
<td>
**Format and volume of the dataset**
</td>
<td>
The dataset will be stored in standard spreadsheet format (.csv or other). At
least 50 filled in and successfully submitted questionnaires are expected. The
same number of records will be collected and stored in the dataset. The report
presenting the analysis of the data and the identified skill gaps and training
needs of innovation intermediaries will supplement the dataset and will be
preserved in pdf format.
</td> </tr>
<tr>
<td>
**Metadata and**
**standards**
</td>
<td>
Each data record will be accompanied by metadata on a) a mandatory basis (e.g.
the country in which respondent is based; main business activities; type of
key clients) and b) on a voluntary basis (e.g. the name of the respondent, the
name of the organization the respondent works for, respondent´s email-address,
firm´s website).
</td> </tr>
<tr>
<td>
**For whom might the dataset be useful?**
</td>
<td>
Policy makers with an interest in the Agri-food Ecosystem as well as relevant
training designers and providers may also find this dataset useful.
</td> </tr>
<tr>
<td>
**Confidentiality**
</td>
<td>
Database records will be available only to selected members of the
INNO-4AGRIFOOD project team in order to safeguard the confidentiality of
participants.
</td> </tr> </table>
## Outcomes of the INNO-4-AGRIFOOD Co-creation Workshop
<table>
<tr>
<th>
**Dataset name**
</th>
<th>
Outcomes of the INNO-4-AGRIFOOD Co-creation Workshop (WP2 & WP3)
</th> </tr>
<tr>
<td>
**Type of study**
</td>
<td>
A co-creation workshop will be held at the Netherlands in order to co-create
along with project stakeholders the main value propositions of INNO-4-AGRIFOOD
(i.e. services, tools and e-learning curriculum).
</td> </tr>
<tr>
<td>
**Dataset description**
</td>
<td>
The dataset will include feedback, innovative concepts and ideas provided by
workshop participants along with their contact details.
</td> </tr>
<tr>
<td>
**Methodologies for data collection / generation**
</td>
<td>
Data will be will be collected by the dedicated minute takers of the co-
creation workshop and documented as transcript notes. The participants list
will serve as the main source of data for contact details and other relevant
background information.
</td> </tr>
<tr>
<td>
**Format and volume of the dataset**
</td>
<td>
The dataset will be preserved in simple spreadsheet or document format. The
two reports that will stem from the co-creation workshop will accompany the
dataset and will be preserved in pdf format.
</td> </tr>
<tr>
<td>
**Metadata and**
**standards**
</td>
<td>
Structural and descriptive metadata may be created.
</td> </tr>
<tr>
<td>
**For whom might the dataset be useful?**
</td>
<td>
Innovation support service designers and providers, ICT application providers
as well as relevant trainers and educators would find the dataset most useful,
especially those who operate within the agri-food sector or are interested to
do so.
</td> </tr>
<tr>
<td>
**Confidentiality**
</td>
<td>
Data will be open, provided that the person or partner who provided the
information grants permission.
</td> </tr> </table>
## Case-based training material supplemented by theoretical information on
the topic
<table>
<tr>
<th>
**Dataset name**
</th>
<th>
Case-based training material supplemented by theoretical information on the
topic
(WP2)
</th> </tr>
<tr>
<td>
**Type of study**
</td>
<td>
Development of educative case studies based on the services provided in the
framework of INNO-4-AGRIFOOD blended with theoretical training based on
existing material available to partners either from previous work or from open
sources.
</td> </tr>
<tr>
<td>
**Dataset description**
</td>
<td>
The data collected will be simple responses (plain text in English) provided
in the frame of interviews.
</td> </tr>
<tr>
<td>
**Methodologies for data collection / generation**
</td>
<td>
Data required for the development of the case studies will be collected with
the help of semi-structured questionnaires administered during the interviews
by project partners. Additional data will be gathered from the existing
knowledge base of project partners (e.g. previous project documentations,
previous service provision documentations, etc.) and/or from OER repositories
as well as other thirdparty secondary data sources.
</td> </tr>
<tr>
<td>
**Format and volume of the dataset**
</td>
<td>
Data collected during the interviews conducted in the framework of case study
development will be stored in document format (e.g. .doc). The case studies
stemming from the interviews will be preserved in pdf format.
</td> </tr>
<tr>
<td>
**Metadata and**
**standards**
</td>
<td>
All e-learning material developed based on the case studies will be SCORM
compliant to enable its packaging and facilitate the re-use of the learning
objects. The Articulate software, which will be used to create the e-learning,
can generate the Content Aggregation Metadata File required.
</td> </tr>
<tr>
<td>
**For whom might the dataset be useful?**
</td>
<td>
The dataset would be quite useful for innovation intermediaries and
consultants as well as educators who would use the e-learning material in
their own activities.
</td> </tr>
<tr>
<td>
**Confidentiality**
</td>
<td>
The e-learning material will be openly available to all interested
stakeholders, protected with an appropriate Creative Commons licence (to be
decided at a later stage of the project).
</td> </tr> </table>
## Pool of agri-food SMEs
<table>
<tr>
<th>
**Dataset name**
</th>
<th>
Pool of agri-food SMEs (WP4)
</th> </tr>
<tr>
<td>
**Type of study**
</td>
<td>
Deployment of INNO-4-AGRIFOOD services and tools in real-life contexts.
</td> </tr>
<tr>
<td>
**Dataset description**
</td>
<td>
List of agri-food SMEs which may be interested in benefiting from the online
collaboration for innovation support services of the project in the framework
of its 3 iterative testing, validation and fine-tuning rounds.
</td> </tr>
<tr>
<td>
**Methodologies for data collection / generation**
</td>
<td>
In addition to the professional networks of INNO-4-AGRIFOOD, several sources
will be employed to identify suitable SMEs to participate in the deployment of
the novel services and tools, including (among others) networks and member
organisations of the Advisory and Beneficiaries boards of the project as well
as interested SME participants in the surveys launched under WP1. Online
sources may also be utilised to complement the dataset with useful information
regarding the companies.
</td> </tr>
<tr>
<td>
**Format and volume of the dataset**
</td>
<td>
A spreadsheet (e.g. using an .xlsx format) will be created to maintain the
list of agrifood SMEs, which by the end of the project will contain at least
125 records.
</td> </tr>
<tr>
<td>
**Metadata and**
**standards**
</td>
<td>
Structural metadata may be provided.
</td> </tr>
<tr>
<td>
**For whom might the dataset be useful?**
</td>
<td>
The dataset would be most useful for consortium partners during the activities
of the project.
</td> </tr>
<tr>
<td>
**Confidentiality**
</td>
<td>
The dataset will be available only to relevant INNO-4-AGRIFOOD partners and
may not be disclosed or used for purposes outside the framework of the
project, unless otherwise allowed by either the project partner or the
external stakeholder that has provided the respective data.
</td> </tr> </table>
## Roster of specialists database
<table>
<tr>
<th>
**Dataset name**
</th>
<th>
Roster of Specialists Database (WP4)
</th> </tr>
<tr>
<td>
**Type of study**
</td>
<td>
Involvement of trained staff of innovation intermediaries and SME support
networks in the deployment of INNO-4-AGRIFOOD services and tools in real-life
contexts.
</td> </tr>
<tr>
<td>
**Dataset description**
</td>
<td>
Pool of SME consultants who have successfully completed the INNO-4-AGRIFOOD
e-training courses and may participate in the testing of services and tools of
the project by providing them to agri-food SMEs. The Roster of Specialists
Database will encompass valuable information, such as contact details and
areas of expertise of the specialists, (e.g. in terms of sector, industry,
etc.).
</td> </tr>
<tr>
<td>
**Methodologies for data collection / generation**
</td>
<td>
The Roster of Specialists Database will be enriched as the activities of WP5
progress and an increasing number of staff of innovation intermediaries and
SME support networks complete the e-learning of the project. Data on the
specialists who will participate in the e-learning as well as on the degree to
which they will have completed it will be collected/generated by the
e-learning platform.
</td> </tr>
<tr>
<td>
**Format and volume of the dataset**
</td>
<td>
The Roster of Specialists Database will be preserved in a standard spreadsheet
format and will be comprised of at least 50 SME consultants across the EU.
</td> </tr>
<tr>
<td>
**Metadata and**
**standards**
</td>
<td>
Structural metadata may be provided.
</td> </tr>
<tr>
<td>
**For whom might the dataset be useful?**
</td>
<td>
Agri-food SMEs who would, like to receive support from innovation consultants
specialised in supporting online collaboration for innovation.
</td> </tr>
<tr>
<td>
**Confidentiality**
</td>
<td>
Records in the database will be open as long as the provider of the
corresponding information has given the relevant permission.
</td> </tr> </table>
## Service testing metrics
<table>
<tr>
<th>
**Dataset name**
</th>
<th>
Service testing metrics (WP4)
</th> </tr>
<tr>
<td>
**Type of study**
</td>
<td>
Testing, validation and fine-tuning of INNO-4-AGRIFOOD services.
</td> </tr>
<tr>
<td>
**Dataset description**
</td>
<td>
Metrics collected during the iterative real-life deployment of INNO-4-AGRIFOOD
services and tools aimed at managing ambiguity during the various iterations
as well as measuring the impact of improvements after each iteration.
Indicative metrics that may be incorporated in the dedicated model of the
project and fuel the dataset include: basic metrics related to the willingness
of end-users to try the services, their level of satisfaction from them, etc.;
tailored innovative performance metrics such as number of new on-line
contacts, conversion ratio of new contacts to innovation partners, number of
innovation projects launched with partners identified through INNO-4-AGRIFOOD
services, etc.; as well as forward looking
</td> </tr>
<tr>
<td>
</td>
<td>
metrics related to the estimation of potential future growth of the developed
services.
</td> </tr>
<tr>
<td>
**Methodologies for data collection / generation**
</td>
<td>
A semi-structured questionnaire will be employed to mine both qualitative and
quantitative data from agri-food SME participants in the various deployment
rounds of the project.
</td> </tr>
<tr>
<td>
**Format and volume of the dataset**
</td>
<td>
The dataset will employ a typical spreadsheet format.
</td> </tr>
<tr>
<td>
**Metadata and**
**standards**
</td>
<td>
Structural and descriptive metadata may be provided.
</td> </tr>
<tr>
<td>
**For whom might the dataset be useful?**
</td>
<td>
Innovation support service designers and providers may find use in this
dataset.
</td> </tr>
<tr>
<td>
**Confidentiality**
</td>
<td>
All records will be open after the necessary consent has been provided by
interviewees and any required anonymization has been performed.
</td> </tr> </table>
## Personal data, profile and learning curve of e-learning participants
<table>
<tr>
<th>
**Dataset name**
</th>
<th>
Personal data, profile and learning curve of e-learning participants (WP5)
</th> </tr>
<tr>
<td>
**Type of study**
</td>
<td>
Provision of e-training courses to staff of innovation intermediaries and SME
support networks.
</td> </tr>
<tr>
<td>
**Dataset description**
</td>
<td>
Personal data of e-learning participants including statistics of accessing the
system, duration of stay and movements of the learner on the portal.
</td> </tr>
<tr>
<td>
**Methodologies for data collection / generation**
</td>
<td>
Individuals who will register to the INNO-4-AGRIFOOD e-learning will
voluntarily share the personal data and profile needed for their registration.
The e-learning platform will automatically collect all necessary data based on
the visitors accessing the system using one username-password combination.
</td> </tr>
<tr>
<td>
**Format and volume of the dataset**
</td>
<td>
Dataset will be stored in MySQL database that can be exported to standard
spreadsheet format (.csv or other). At least 150 registered learners are
expected.
</td> </tr>
<tr>
<td>
**Metadata and**
**standards**
</td>
<td>
Any potential standards to be used or metadata to be created will be
determined at a later stage of the project.
</td> </tr>
<tr>
<td>
**For whom might the dataset be useful?**
</td>
<td>
The dataset will be used to analyse the learning behaviour of the participants
in the frame of the project and thus may be useful for educators and/or
e-learning organisers targeting the same target group.
</td> </tr>
<tr>
<td>
**Confidentiality**
</td>
<td>
Personal data of training participants will be confidential, the
administrators will have access to their data, but not to password
information. Training participants can configure their profile and the open
data they want to share. Regarding the learning curve related data, the
statistics on accessing the e-learning, watching and existing material,
concluding tests, etc. will be available for the administrators of the
e-learning portal. Any analysis or conclusions drawn from these data will be
shared unanimously.
</td> </tr> </table>
## Awareness creation, dissemination and stakeholder engagement
<table>
<tr>
<th>
**Dataset name**
</th>
<th>
Awareness creation, dissemination and stakeholder engagement
</th> </tr>
<tr>
<td>
**Type of study**
</td>
<td>
Assessment of the results and impact of the awareness creation, dissemination
and stakeholder engagement activities of the project employing an indicator-
based framework.
</td> </tr>
<tr>
<td>
**Dataset description**
</td>
<td>
Metrics collected during INNO-4-AGRIFOOD with a view to measuring and
assessing the performance and results of the project in terms of awareness
creation, dissemination, stakeholder engagement.
</td> </tr>
<tr>
<td>
**Methodologies for data collection / generation**
</td>
<td>
Primary data will be collected through the activity reports of project
partners regarding media products, events, external events, general publicity,
etc. along with any supporting material (e.g. photos presentations,
participants lists, etc.). Third party tools will be used as well (e.g. Google
analytics, social media statistics, etc.).
</td> </tr>
<tr>
<td>
**Format and volume of the dataset**
</td>
<td>
All data will be collected and preserved in a document format (e.g. .doc)
along with any complementary material in their original format (e.g. JPEG,
PPT, etc.)
</td> </tr>
<tr>
<td>
**Metadata and**
**standards**
</td>
<td>
Any potential standards to be used or metadata to be created will be
determined at a later stage of the project.
</td> </tr>
<tr>
<td>
**For whom might the dataset be useful?**
</td>
<td>
The dataset would be meaningful to the EC as well as researchers who study
relevant aspects of EU-funded projects.
</td> </tr>
<tr>
<td>
**Confidentiality**
</td>
<td>
All records will be open
</td> </tr> </table>
|
https://phaidra.univie.ac.at/o:1140797
|
Horizon 2020
|
1058_NANO2ALL_685931.md
|
# INTRODUCTION
## Summary
**NANO2ALL** is a coordination and support action (CSA) aimed at establishing
a European-wide sustainable platform for mutual learning and informed dialogue
among all stakeholders (researchers including social sciences and humanities,
industry/ business, the public including Civil Society Organisations and the
media, as well as policy-makers and research funders) to improve transparency
and societal engagement in responsible nanotechnology. Engaged through
European-wide initiatives, as well as via an online platform making full use
of current communication technologies, stakeholders will exchange best
practice (e.g. from ObservatoryNano, NanoOpinion, NanoEIS and Nanodiode, among
others). They will also develop their understanding of responsible research
and innovation (RRI) and its tools (e.g. based on EthicSchool and RRI Tools).
Based on this, through national and EU level dialogue, stakeholders will
develop an action plan (MLAP) with a shared vision on existing and potential
future benefits and risks of advancing nanotechnology including ways to
support RRI along the value chain. Systemic engagement of policy-makers and
relevant communities will facilitate “buy-in” through co-development of
roadmaps to drive future Joint Stakeholder Undertakings, such as the
Nanofutures ETP. This will result in a permanent platform for future
development of trust, awareness and inclusion, providing guidance on societal
needs and concerns, and fuelling coinnovation that benefits society as a
whole.
In order to achieve the **NANO2ALL** goals, we will deliver during the
lifetime of the project activities where we will gather different types of
data. To be sure to comply with the strict requirements of the European
Commission this Data management plan (DMP) will serve as a manual to describe
the process of gathering the information, preserving and archiving it.
The main findings that will be subject to this DMP refer to:
* Task 1.1 Nano2All Multi-Stakeholder Platform, in relation to the subscriptions to project newsletters, registrations for getting enrolled in Forum discussions, emails from stakeholders about inquiries and sharing best practices in RRI
* Task 1.3. Stakeholder contact management
* Task 1.5. Engaging the Stakeholders, in relation to engagements events and other engagement activities where stakeholder data is collected
* Task 2.3 Training Needs Self-Assessment Tool, in relation to the online surveys that will be conducted to the various stakeholders
* Task 2.4 Preparing for the Dialogues, in relation to the face to face training of the stakeholders’ representatives.
* Task 3.2 Future Techno-Moral & Application Scenarios, where a Delphi survey among selected nanoexperts will be performed.
* Task 3.3. Multi-stakeholder dialogues
* Task 3.5. Monitoring and Evaluation
* Task 4.2 Expert & Stakeholders Validation Symposium
A set of planned datasets is described, including information about the
dataset itself, what it is intended to contain, and what the current plans are
to make the data available during but especially after the project. Where
appropriate, and not otherwise restricted by existing partner agreements, such
data will by default be deposited with the EU zenodo repository
(http://zenodo.org), with a general restriction that any such data should be
used for non-commercial, preferably research purposes.
The commitment to gathering and sharing data, associated with what
**NANO2ALL,** is related also with engaging relevant parties and raising
general awareness. This creates a need to consider ethical oversight, not
least because of related plans to publicise and share our results through
research publication as well as more widely targeted dissemination collateral.
As an initial outline and set of planned activities, this document provides a
summary of discussions already underway within the consortium to fully manage
project data. It will, however, be revised and updated in response to the
practical experiences of the partners and if more sensitive activities are
needed.
## DMP aim
**NANO2ALL** Data Management Plan (DMP) describes the data management life
cycle for all data sets that will be collected, processed or generated by
**NANO2ALL** project. This document is aiming at outlining the way the
consortium will collect and manage data and information from the involved
stakeholders. It contains:
* The nature of the data gathered;
* The methodology used for the collection and preservation;
* The procedure used for the analysis;
* The Ethical treatment of participant data;
* Data Open access to publications
The DMP is not a fixed document as it evolves during the lifetime of the
project. **NANO2ALL** consortium foresees to provide updates at the end of
each reporting period, inside the periodic project reports.
In the early months of the project, there are a number of specific
considerations which will be discussed in the current report, to be further
expanded in the periodic progress reports:
* How can the project handle the series of multi-stakeholder dialogues and training activities to address knowledge gaps between various types of actors and potential co-production of knowledge? This will include a consideration of the:
* different stakeholder categories that will be engaged with the project activities, and
* various activities that will involve them and generate a remarkable amount of data and knowledge.
* What data sets are currently available, and can they be shared with others?
* What data sets are going to be developed during the project lifetime?
* What form do those data sets take?
The purpose of this deliverable is to introduce some of the considerations
under discussion within the **NANO2ALL** consortium in relation to these high-
level concerns.
## Intended audience
The document is aimed at two different types of readership. First, it provides
an internal record for the project itself and all consortium members to be
able to refer to and understand what plans are in place, and what progress is
being made. Secondly, it serves as summary and stake-in-the-ground for DMP
aimed at external parties interested in the **NANO2ALL** project outcomes (the
Project Officer, researchers, and other stakeholders). Stakeholder
participants include citizens, organisations and experts already involved
Nanotechnologies, as well as those who will join specifically during the
dialogues and who, in particular, may provide input and responses as the basis
for their perspective and needs in the project.
## Document structure
The next Chapter will begin with a consideration, on the nature of the data
gathered, the methodology used for the collection and preservation, the
procedure used for the analysis, the Ethical treatment of participant data.
Finally, a brief overview of the **NANO2ALL** approach to publishing results
is introduced in the chapter Data Open access to publications.
D5.4. DATA MANAGEMEN
T PLAN
**NANO2ALL**
•
NANOTECHNOLOGY MUTUAL
LEARNING ACTION PLAN FOR TRANSPARENT AND
RESPONSIBLE UNDERSTANDING OF SCIENCE AN TECHNOLOGY
**2**
**.**
**D**
**ata set**
# DATA SET
## Description of the nature of the data
The main set of Data collected, generated and analysed in the framework of the
**NANO2ALL** project will come from Multi-Stakeholder Platform and dialogues,
and can be classified as:
1. Profile public data, including: e.g. organisational profiles and contacts, which will be provided, for public use, on the project website, the multi-stakeholder platform, the project events and the public reports.
2. Personal, survey and evaluation data, acquired by surveys, interviews and case studies developed during the project progress.
These two different types of data will each require a unique procedure for
data collection, management, preservation and sharing.
1. **NANO2ALL** project involves, by definition, engagement with those already involved with or motivated to support prosocial activities in relation to the MultiStakeholder Platform and dialogues (WP1-WP4). All such interactions will involve data, such as contact details and some description of interest and motivation. This profile public data will attempt to capture such information. With the explicit consent of stakeholders’ participants at project activities, the following contact information data will be collected:
* Name
* eMail
* Address (optional)
* Contact phone number
* Nature of interest
* Online presence (e.g., website, blog etc.)
* Any additional information (e.g., related projects, initiatives, activities, etc.)
The data will be largely free form text, and held in a project-wide data
repository. There are no specific standards relevant. The data will be stored
initially in Excel spreadsheet form, which may be converted or backed up as
CSV text.
Initially, the data will be restricted to project partners. Towards the end of
the project, those whose details are contained within the dataset will be
contacted and asked for permission to make the data available solely for non-
commercial purposes and strictly within the context of nanotechnology.
The Multi stakeholder platform (Task 1.1) has the following metadata:
1. Textual information, translated in all partner languages, about the project (objectives, impacts, expect results) and stakeholders (who they are, their benefits and advantages for being part of the project).
2. Relevant external sources (opinions, reports, initiatives and events) in a searchable format and with public access;
3. Relevant best practices, which will be available in the platform, in a searchable format, without restrictions. These practices will be result of the tasks 2.1 and 2.4, but all users are invited to share their own practices in the platform.
4. Online communication tools (social networks and discussion areas in the platform) will allow us to collect specific information such as: personal data (name, e-mail, country, etc), collected from registration forms, surveys and newsletter subscription.
5. Promotional materials, such as: flyers, newsletters, press releases and posters.
In addition, a database of relevant stakeholders contacts will be developed in
Task 1.3. This database constitutes an excel file, for private use of the
consortium and only available in Basecamp.
And in Task 1.4 a list of relevant initiatives is being produced. This list is
presented in the news and events section in the platform, in a searchable way
and public access.
During the project, the data will be curated by the Data controller (ViLabs).
With consent as indicated in the previous section, the data will be
transferred to Zenodo repository.
2. Especially, the surveys undertaken in the WPs below, will likely include personal data, or at very least will include data that may introduce a risk that data subjects could be identified, even if their survey data is anonymized:
* WP2, under task 2.3, where a short online survey across NfA contacts (NfA members cover all Nano Technology stakeholders’ categories) will be performed so as to triangulate the training needs identified in literature together with a short survey among EUSJA’s contacts to gather their training needs and training preferences, since the media’s training needs on RRI have not yet been identified by any other EU project. Based on the surveys an online tool (NANO2ALL Self-Assessment Training Tool) will be built and the work will be reported on “D2.2 : Online Training Needs Survey report” that will include questionnaires, analysis and results.
* and WP3, under task 3.2, where a Delphi survey among selected nanoexperts will be performed. Throughout the citizen and multi-stakeholder dialogues, various measures will be taken to maintain the quality of the results. The quality criteria are based on the responsive research methodology as formulated by Guba and Lincoln (1989) 1 : credibility, fairness, and satisfaction. Credibility of the results is firstly enhanced through member check. The Delphi survey, the combination of citizen and multi-stakeholder dialogues as well as the online deliberations all contribute to triangulation.
## Methodology of collection and analysis
For all the foreseen research activities the **NANO2ALL** consortium will
apply the following procedures:
* Non-disclosure and privacy protection statements complying with EU and national legislation will be signed by task leaders and WP leaders and privacy/non-disclosure statements and information on data collection and data treatment will be made available to all respondents to all research actions.
* Collected data will be: textual answers to an online survey; audio taped interviews and/or textual in depth interviews; pictures, audio and/or videotaped interviews and/or pictures,
* All collected data will be treated anonymously and numerically identified,
* Task leaders will be responsible and accountable for data storage and treatment after the end of the project. Collected raw information (texts and audio files of interviews as well as answers to questionnaires) will not be published online and/or made available to the public anyway.
* For the specific release and dissemination of success stories identified respondents will be involved in the contents design of their ‘story’ and will have to provide written and signed authorization to the release and publication of any audio/video/textual contents related to their own personal experiences.
* All original, irreplaceable electronic project data and electronic data from which individuals might be identified will be stored on Task Leaders supported media and secure server space or similar; such data will never be stored on temporary storage media nor cloud services, unless fully compliant with national and EU regulation on data protection.
* All research data will be accessible to partners of the project being in charge of the specific research action, as well as the Ethics auditors.
Especially, the surveys undertaken in WP2 and WP3 will likely include personal
data, or at very least will include data that may introduce a risk that data
subjects could be identified, even if their survey data is anonymised.
Therefore, we will introduce a threestep process to ensure that the data is
anonymised and ensure that participants retain control over the release of
research data relating to them. This will be supplemented by the projects
ethical and data protection protocol (D5.2: Data protection and ethical
protocol, delivered on M18).
First, in describing the survey purpose, **NANO2ALL** partners will populate
information in general terms on the website, within project deliverables and
when presenting the results of the case study research. Furthermore, if
information related to a particular context is so specific that the identity
of the stakeholder is obvious, **NANO2ALL** partners may choose not to release
that portion of the data, or to re-mix stakeholders’ elements in order to
disrupt the link between the stakeholder and the information.
Second, the **NANO2ALL** project will utilise a multi-dimensional consent
mechanism. Surveyed participants will be invited to consent to the survey
itself and the storage, preservation, opening and sharing of their data. Thus,
data subjects can consent to participate in the survey, and decline to consent
to providing open access to their data. This may be particularly useful to
research participants who feel they may be opening themselves up to adverse
effects if their data was made openly available. In all cases, participants
will be given access to their anonymised transcript and invited to make
corrections, amendments, additions, etc. before the survey material is
utilised in any publicly-available documents.
Third, the anonymised data will be made available after an embargo period
corresponding to one year after the close of the case study work package. This
will enable **NANO2ALL** researchers to draft and make available to
participants both project deliverables, which will be publicly available, and
journal articles or other publications, which will also be published as open
access, resulting from the case study research before they are released. This
will give survey participants an opportunity to withdraw their consent for
their survey material to be made publicly available if they have any concerns
based on what is written in the research outputs.
However, if other researchers wish to verify the material, we will construct a
managed access plan to enable such verification, with the understanding that
these other researchers will respect the wishes of the data subject. In order
to aid the preservation and re-use of the survey data, **NANO2ALL** will make
use of existing standards and infrastructure.
It will use the Zenodo repository developed by OpenAIRE/OpenAIRE plus and it
will follow their guidelines in relation to authoring appropriate and useful
metadata. The project will also use standardised format for data sharing – for
example, .txt in respect of survey transcripts originally produced in
Microsoft Word.
As such, we will avoid access barriers related to proprietary software. We
will rely on the good practice developed by the OpenAIRE team in relation to
ensuring the integrity and validity of the data we deposit.
## Internal project working environment
Basecamp.com, has been decided to be used for the internal management and
collaboration work in the project. In Basecamp, all data is encrypted via
_SSL/TLS_ when transmitted from their servers to the browser. The database
backups are also encrypted.
D5.4. DATA MANAGEMEN
T PLAN
**NANO2ALL**
•
NANOTECHNOLOGY MUTUAL
LEARNING ACTION PLAN FOR TRANSPARENT AND
RESPONSIBLE UNDERSTANDING OF SCIENCE AN TECHNOLOGY
**3**
**.**
**E**
**thical**
**T**
**reatment of**
**participant**
**data**
# ETHICAL TREATMENT OF PARTICIPANT DATA
**NANO2ALL** platform, by definition, contains personal data from registered
users and project events 2 . Moreover, WP2 and WP3 will involve the
collection of specific research data from data subjects. The purpose of this
Chapter, therefore, is to set out the basic ethical principles to be adhered
to in the project. Partners involved have many years’ experience in running
both quantitative and qualitative surveys. If and when required, they will
seek ethics approval from their respective institutions prior to any data
collection exercise.
## Ethical Requirements
A number of ethics requirements are identified and listed in _Table 1_ below.
Table 1 : Ethical requirements for **NANO2ALL**
<table>
<tr>
<th>
**# Requirement**
</th>
<th>
**Status**
</th> </tr>
<tr>
<td>
1
</td>
<td>
Copies of ethical approvals for the collection of personal data by the
competent University Data
Protection Officer / National Data Protection authority should be submitted
</td>
<td>
As and if required, approval will be sought from the relevant institution(s)
_prior_ to any data collection.
</td> </tr>
<tr>
<td>
2
</td>
<td>
The recipients of the Multi-
stakeholder platform are addressing all stakeholder groups. Because of the
open character of the platform already running, there is a risk of
stigmatization of individuals who use the services of the platform. The
project should describe a strategy to counteract the possibility of
</td>
<td>
For the operational use of the platform, data is used in compliance with the
existing Terms & Conditions between the partners. These are currently being
reviewed within the project to determine if further explicit consent may be
required, for instance, for data to be stored in a centralised location. This
will inform architectural decisions to be
</td> </tr> </table>
<table>
<tr>
<th>
</th>
<th>
stigmatization of individual recipients. In addition, a policy about the
possibility of abuse and misuse of the platform for unduly personal gain
should be in place.
</th>
<th>
reported in WP5.
</th> </tr>
<tr>
<td>
3
</td>
<td>
The platform must explicitly confirm that the existing data is publically
available
</td>
<td>
See Requirement 2, above.
In addition, an Open Data source which may be used during platform operation
will be used strictly in accordance with the terms of the provider. Such data
are typically public.
</td> </tr>
<tr>
<td>
4
</td>
<td>
Detailed information must be provided on the procedures that will be
implemented for data collection, storage, protection, retention and
destruction, and confirmation that they comply with national and EU
legislation.
</td>
<td>
In addition to the Data management plan outlined in the preceding sections,
appropriate research protocols will be developed and published D5.2: Data
protection and ethical protocol, delivered on M18.
</td> </tr>
<tr>
<td>
5
</td>
<td>
Detailed information must be provided on the informed consent procedures that
will be implemented
</td>
<td>
See requirement 4 above.
Notwithstanding research protocol details to be provided in D5.2: Data
protection and ethical protocol, delivered on M18).
</td> </tr>
<tr>
<td>
6
</td>
<td>
In case of data not publicly available, relevant authorisations must be
provided
</td>
<td>
There is currently no plan to release data which is not publically available.
As outlined under Requirement 4 and 5 above, procedures are already being set
out as guidelines for data handling.
</td> </tr>
<tr>
<td>
7
</td>
<td>
The project must clarify whether children and/or adults unable to give
informed consent will be involved
</td>
<td>
There is no plan to recruit or involve any minors or vulnerable adults.
</td> </tr>
<tr>
<td>
</td>
<td>
and, if so, justification for their participation must be provided.
</td>
<td>
</td> </tr> </table>
In the following sections, the overarching ethical and data protection
framework for the project are discussed, and will continue to be referred to
and monitored during project meetings. Any changes to the above will be
reported in the periodic progress reports and, if warranted, discussed
directly with the Project Officer.
## Reference material
The overall approach in the project, notwithstanding specific provisions under
the legislation of individual Member States, is based on the following EU
legislation:
**Directive 95/46/EC** forms the basis for legal management of personal data
and data protection in general (European Commission, 1995).
This has been extended in the **Directive 2002/58/EC** with guidance on data
protection should data be transferred via public connections (European
Commission, 2002) 3 .
In addition to these legal instruments, and leading on from Directive
95/46/EC, a working party (WP29) was set up by the European Commission to
consider and share opinion on specific issues of data protection and handling.
In the context of **NANO2ALL** , we are specifically looking at information on
anonymization at this time (WP29, 2014) 4 .
## Definitions
This section provides a few definitions as they relate specifically to the
project. This is not an exhaustive list as provided, for example, in (Article
2: "Definitions" European Commission, 1995, No L 281/38f).
<table>
<tr>
<th>
Data controller
</th>
<th>
“’controller’ shall mean the natural or legal person, public authority, agency
or any other body which alone or jointly with others determines the purposes
and means of the processing of personal data” (European Commission, 1995,
Article 2, Paragraph (d))
For **NANO2ALL** , there is discussion at the present time to establish
appropriate general guidelines for the Data controller. As an initial
framework, we are suggesting:
1. By default, SPI as project co-ordinator, will act as Data
controller;
2. For existing data, all partners will remain data controller solely in respect of the data they use for the purposes of their current activities;
</th> </tr>
<tr>
<td>
Data processor
</td>
<td>
“’processor’ shall mean a natural or legal person, public authority, agency or
any other body which processes personal data on behalf of the controller”
(European Commission, 1995, Article 2, Paragraph (e))
As above, the initial framework would correspondingly involve by default, WP
leaders (within the context of their WPs) will act as Data processors for all
data collected, generated and analysed to their perspective WP.
</td> </tr>
<tr>
<td>
Data subject
</td>
<td>
“an identified or identifiable natural person” (European Commission,
</td> </tr> </table>
1995, Article 2, Paragraph (a))
For the purposes of **NANO2ALL** , Data subjects will be all stakeholders’
participants that will be recruited to be engaged to the WP activities. They
will be presented with a separate Consent Form requesting their agreement for
the collection and specific processing of their data for the purposes of the
project surveys.
The project partners are currently reviewing any consent forms or terms and
conditions to provide a baseline for future engagement and the use of personal
data, for example, as part of dissemination materials.
D5.4. DATA MANAGEMEN
T PLAN
**NANO2ALL**
•
NANOTECHNOLOGY MUTUAL
LEARNING ACTION PLAN FOR TRANSPARENT AND
RESPONSIBLE UNDERSTANDING OF SCIENCE AN TECHNOLOGY
**4**
**.**
**D**
**ata**
**open**
**access**
**to**
**publications**
**NANO2ALL** • SOCIETAL ENGAGEMENT ON **RESPONSIBLE NANOTECHNOLOGY**
D5.1. COMMUNICATION PLAN
# DATA OPEN ACCESS TO PUBLICATIONS
Notwithstanding any provision relating to data subject rights and on the basis
of what has been outlined for specific data sets, it is by definition the
intention of the **NANO2ALL** project to share and publicise information as
widely as possible. The following briefly outlines current plans in relation
both to scientific publication as well as more general materials providing a
description of the project and reports on specific events.
## Dissemination materials
The project will produce a set of leaflets and brochures to describe the
project itself, any specific white-paper style discussion of the problems of
nanotechnology, and to highlight individual events. The specific plan for what
publications will be produced will be discussed in the annual communications
reports (D1.2-D1.4).
These are intended for publication primarily through the project website, with
additional references and links from individual partner sites as they relate
to or are relevant to the specific environment within which end-user partners
operate. Publication on the project website will also include all project
deliverables, with the exception of the two which are confidential to the
consortium, as well as informative videos providing further detailed
information about different aspects of the **NANO2ALL** activities.
## Scientific publications
Scientific publications will all be targeted at open access journals in the
first instance, unless specifically made available within a conference or
other such event. DOIs will be generated by the journal initially.
.
D5.1. COMMUNICATION
PLAN
**NANO2ALL**
•
SOCIETAL ENGAGEMENT ON
**RESPONSIBLE NANOTECHNOLOGY**
**C**
**onclusions**
**NANO2ALL** • SOCIETAL ENGAGEMENT ON **RESPONSIBLE NANOTECHNOLOGY**
D5.1. COMMUNICATION PLAN
# CONCLUSIONS
This version of the DMP as anticipated is a first version that will be updated
during the lifetime of the project and if needed more activities which are
sensitive in terms of data management plan might be added.
Although the **NANO2ALL** project has only recently begun (in October, 2015),
it is already appropriate to outline how the project will handle existing
data, from the usecase partners, along with future data. Such data will be
collected and processed in connection with the platform development (WP1) and
the Multi-stakeholder dialogues (WP3), so important to inform and support the
longer term benefits of the project.
With that in mind, in the preceding chapters, the report has introduced key
concepts and set out generalized guidelines for data management. This has to
include the identification of suitable stakeholders and interested parties,
but also take into account the value and potential sensitivity of the data
which will be generated during the project.
Such data, it is expected, will be generated as a result of operation of the
platform and the stakeholders’ engagement. But in addition, data will also be
created in terms of survey responses as part of the Dialogues preparations
planned.
In consequence, there is also a need for ethical oversight and governance, as
well as some consideration of how and when to make data and information
available. We have already outlined here the relationship between partners in
terms of data management responsibilities (Data controller vs Data processor).
We also foresee not only typical dissemination materials, such as brochures,
whitepapers, and the like, but additionally as research papers and conference
presentations, making the ethical oversight of data an important aspect of the
work being started.
This report has therefore sought to bring together all of the related strands
in a first iteration of the project data management. There are some
discussions to be held as the project progresses so that the plans outlined in
this report can be adapted as required to meet new demands as they occur, and
to exploit the experience of the close collaboration of the various partners
in the consortium.
D5.1. COMMUNICATION
PLAN
**NANO2ALL**
•
SOCIETAL ENGAGEMENT ON
**RESPONSIBLE NANOTECHNOLOGY**
|
https://phaidra.univie.ac.at/o:1140797
|
Horizon 2020
|
1059_SmartNanoTox_686098.md
|
# 1\. PROJECT DESCRIPTION
## 1.1. ABSTRACT
A definitive conclusion about the dangers associated with human or animal
exposure to a particular nanomaterial can currently be made upon complex and
costly procedures including complete NM characterisation with consequent
careful and well-controlled in vivo experiments. A significant progress in the
ability of the robust nanotoxicity prediction can be achieved using modern
approaches based on one hand on systems biology, on another hand on
statistical and other computational methods of analysis. In this project,
using a comprehensive self-consistent study, which includes invivo, in-vitro
and in-silico research, we address main respiratory toxicity pathways for
representative set of nanomaterials, identify the mechanistic key events of
the pathways, and relate them to interactions at bionano interface via careful
post-uptake nanoparticle characterisation and molecular modelling. This
approach will allow us to formulate novel set of toxicological mechanism-aware
endpoints that can be assessed in by means of economic and straightforward
tests. Using the exhaustive list of end-points and pathways for the selected
nanomaterials and exposure routs, we will enable clear discrimination between
different pathways and relate the toxicity pathway to the properties of the
material via intelligent QSARs. If successful, this approach will allow
grouping of materials based on their ability to produce the pathway-relevant
key events, identification of properties of concern for new materials, and
will help to reduce the need for blanket toxicity testing and animal testing
in the future.
## 1.2. PROJECT COORDINATION
Dr. Vladimir Lobaskin, School of Physics, University College Dublin, Belfield,
Dublin 4, Ireland. [email protected]_ Phone: +353-1-716-2432, Fax:
+353-1-283-7275.
**Data Manager:**
Philip Cotter, Information Manager, Systems Biology Ireland, Science Link
Building, University College Dublin, Belfield, Dublin 4, Ireland.
[email protected]_ Phone: +353-1-716-6310.
## 1.3. LIST OF PARTICIPATING ORGANISATIONS
1. University College Dublin / National University of Ireland, Dublin, Ireland.
2. Stockholm University, Sweden.
3. Helmholtz Zentrum München, Germany.
4. National Research Centre for the Working Environment, Denmark.
5. French National Institute for Occupational Health, France.
6. Jozef Stefan Institute, Slovenia.
7. Environmental Health Centre, Canada.
8. Imperial College London, UK.
9. University of Lorraine, France.
10. Biovia, 3DS, France.
11. Victrocell Systems, Germany
## 1.2. DEFINITIONS
_**AOP** _ – Adverse Outcome Pathway: representation of biological pathways
leading to adverse effects; _**Data Archive –** _ A device or service where
machine-readable data are acquired, managed, documented, and finally
distributed to the wider scientific community;
_**DOI** _ – Digital Object identifier – a persistent data-resource specific
identifier used to uniquely identify an entity;
_**MIE** _ – Molecular Initiating Events – the interaction if a molecule and
biomolecule/biosystem which can be observed to trigger an AOP;
_**NM, NP** _ – Nanomaterial, nanoparticle;
_**Open Data** _ – Research data made available to the wider scientific
community as evidence to support the interpretation of scientific discoveries.
Open data is described and contextualised for use in reanalysis or re-use in
further experimentation and is hosted by publicly accessible data archives;
_**Data –** _ Fact-based material generally accepted in the scientific
community as necessary to document and support research findings. Research
Data include digitally stored raw experimental data, digital laboratory
notebooks, preliminary datasets and data analysis and draft documentation such
as reports, publications and project-related communications;
_**SNT** _ / _**SmartNanoTox** _ – SmartNanoTox synonyms for the project title
‘Smart Tools for Gauging Nano Hazards’.
# 2\. DATA SUMMARY
## 2.1. PURPOSE OF THE DATA COLLECTION AND GENERATION
A heterogeneous collection of dry and wet lab experimental data has and will
be generated over the four years of the Project. These will form a basis for
mechanism-based tools for nanomaterial (NM) toxicity assessment. The project
aims at prediction of specific pulmonary adverse outcome pathways (AOPs)
caused by NM exposure with a focus on Molecular Initiating Events (MIE) and
Key Events (KE) of these AOPs. The key to predicting these events is in
understanding the interactions at bionano interface. Therefore, a significant
part of the project will be devoted to description of the pathways using
‘omics approaches and statistical models from Systems Biology and to
description of the state of NMs after contact with organisms and tissues.
## 2.2. DATA TYPES & FORMATS
The project will generate multiple data types stored in both standardised and
not yet standardised but community defined set of data formats. A description
of each data generation process within the project is tabulated in Appendix A.
Data from _in vivo_ and _in vitro_ experiments will be recorded in text and
spreadsheet forms before conversion into corresponding NANoREG templates
developed by eNanoMapper ( _http://www.enanomapper.net/_ ) . Due to the
novel nature of this project data, curation and design will be subject to
modification during the data aggregation stage.
Data from _in vivo_ and _in vitro_ NM exposure Transcriptomics and Proteomics
and lipidomics data from analysis of NP corona protein structure will consist
of raw experimental data stored in bespoke formats and manipulated by
Manufacturer developed tooling. Analysis configuration files (typically XML),
machine and laboratory generated metadata files and results, both initial
machine read files and post-processing with analytical software/protocols
(typically coma or tab delimited text formats) will also be provided for use
by project partners and potential reuse by others. Along with Histology data
and reports, these data will also be converted into eNanoMapper-NANoREG
inherited MS Excel templates for incorporation into the eNanoMapper Ontology.
Where eNanoMapper templates do not yet exist, wider community standards will
be applied. Results from Fluorescence Microspetroscopy (FMS) derived images
and their documentation, simulation data, Gene Regulatory Networks and AOP-
representing computer models, descriptors from _in silico_ computed NP
structure and descriptors of measured NP toxicity data will be stored in ISA-
TAB Nano or JSON formats with all raw experimental data and documentation
preserved in the formats produced by their analysis software.
## 2.3. IMPORTED DATA RESOURCES
For NM characterisation, bionano interactions modelling and construction of
the QSAR models, we will use NM characterisation data imported from two
previous projects: NANoREG
( _http://www.nanoreg.eu/_ ) (now succeeded by NanoReg2 (
_http://www.nanoreg2.eu/_ ) ) and NanoGenoTox (
_https://www.anses.fr/fr/node/120284_ ) . Data obtained from NANoREG and
NanoReg2 are made freely usable and shareable for non-commercial applications
vi a _CC-BY-NC-4.0_ .
Computational construction of biological pathways and Transcriptomics analysis
use entity and interaction identifiers imported from the Gene Expression
Omnibus (GEO)
( _https://www.ncbi.nlm.nih.gov/geo/_ ) and NCBI GenBank. Proteomics data
will use human and mouse protein identifiers derived from the EMBL-EBI UniProt
database ( _http://www.uniprot.org/_ ) . Molecular simulations will use
Protein structure data imported from the Protein Data Bank (
_http://www.rcsb.org/pdb/home/home.do_ ) , which is made available by _CC-
BY-4.0_ . The Chemical Information Ontology, a semantic representation of
relevant NP entities and relationships will also be imported and expanded upon
from the eNanoMapper Ontology (GNU Lesser GPL
( _http://www.gnu.org/licenses/lgpl.html_ ) .
Biological entities will use DOIs inherited from the Bioinformatics databases
used in the analysis of experimental data. Those currently in use are included
in the licence and usage policy table (Appendix B) and described in more
detail in the section “ _**DOI and Nomenclature Used** _ ”.
## 2.4. ESTIMATED DATA VOLUME
The expected size of the data varies depending on the type of experiment. The
largest volume should be reserved for Mass-Spectrometry data (~150 GB raw data
and analytical metadata) and from NP Protein corona analysis and molecular
simulation data (ca. 50-100 GB). All other combined data should not be very
space-demanding.
## 2.5. DATA USE AND RE-USE POTENTIAL
iQSAR data, combining the experimental data accumulated; ontologies and models
developed over the four years over the project may be used to:
1. provide a transparent Scientific basis for the drafting of regulations on NM use in manufacturing: the data will contribute to the development of robust systems for evaluating the health and environmental impact of engineered NMs by developing a systematic framework into which existing and emerging experimental data can be utilised to predict the interactions of NMs of industrial and economic significance with living systems.
2. reduce the financial cost of and need for animal testing currently required before a new NM may enter the market: the data and predictive tooling provided by the project may form a robust basis of a future screening strategy to predict the health impact of new NMs. Other approaches, based on learning approaches in databases, though necessary also, would less easily reduce the need for empirical testing for the broad range of entirely novel materials.
3. contribute to predictive models for designing and engineered NMs that are safe by design: by relating the physicochemical properties of NMs with their biological fate (uptake and localization) and behaviour (functional impacts and potential toxicity) we will enable the categorization of NMs by physicochemical factors that constitute a “risk” either for bioaccumulation or potentially a specific toxicity endpoint. Feeding this information back into the process development will enable manufacturers to quickly screen out particles with physicochemical properties that equate with a risk, and either develop new particles or reengineer their products to modify their properties, thereby designing out the risk factors initially, and in the longer term potentially designing the NMs to be safe.
# 3\. FAIR DATA COMPLIANCE
## 3.1. MAKING DATA FINDABLE
It is envisaged that all data collected by the consortium will be collated
together in a flexible data structure accommodating OWL Ontologies, open
source conventional MySQL RDBMS and the JSONlike MongoDB (
_https://www.mongodb.com/_ ) NoSQL database.
Data files, models and other resources used or created will be assigned a
unique and persistent identifier linked to a title, creator and creation data
attributions as well as a brief text description of content, provenance, work
package affiliation, input format and compatible formats for possible export.
Experimental data file content will be indexed and linked to their curated
analysis metadata files. Described NPs, NMs and biological entities will use
reference DOIs inherited from the external ontological and bioinformatics
databases used in the analysis of experimental results. Access, though
restricted during the development stage will be opened to the community
allowing the flow of information both into and out of the SNT database and
iQSAR system.
Though all project data will be made available on UCD School of Physics
Servers and via Zenodo, ‘omics data that has been published or is of
particular interest to the research community will be submitted to community
repositories and annotated as per the requirement of the resources to with
they are submitted, e.g. PRIDE Mass Spec data submissions require a verbose
description of Sample and Data Processing Protocols accompanying the upload of
experimental, configuration, analysis and results files. Keyword indexes built
on these submitted text descriptions are searchable from ProteomeCentral (
_http://proteomecentral.proteomexchange.org/cgi/GetDataset_ ) . Thus, placing
data in resources built for data redistribution and most commonly queried by
domain specialists.
### 3.1.1. METADATA PROVISION
Metadata will be provided to describe the data resources created.
Documentation (READMEs) will accompany any tooling or data resources created
and made available from the SNT and Zenodo file servers.
All experimental datasets added to the curated iQSAR system will be described
in terms derived from and added to the eNanoMapper Ontology. The computational
tools needed to evaluate the NM characteristics will be described according to
EMMC guidance using MODA templates.
Journal Publications and dataset submitted to community repositories will also
provide an annotated community interface for the datasets produced.
### 3.1.2. DOIs & NOMENCLATURE USED
A heterogeneous collection of data sources and types requires a large and
potentially expanding variety of Bioinformatics and Semantic tools for
information representation and disambiguation. A framework will be built
around the core data resources: QSAR ( _https://www.qsartoolbox.org/_ ) ,
NANoREG ( _http://www.nanoreg.eu/_ ) Terminology\eNanoMapper Ontology (
_http://www.enanomapper.net/_ ) , PDB (
_http://www.rcsb.org/pdb/home/home.do_ ) , UniProt Knowledgebase (
_http://www.uniprot.org/_ ) , GenBank (
_https://www.ncbi.nlm.nih.gov/genbank/_ ) and GeneOntology (
_http://www.geneontology.org/_ ) .
NP and NM identifiers are inherited from QSAR and the eNanoMapper Ontology;
information describing the composition of the Corona which develops around a
NP when NPs are exposed to living material are described using DOIs from PDB
and UniProt (Proteomics). Gene Regulatory Networks will use interaction
relationships described by GEO data using gene DOIs from GenBank
(Transcriptomics). Lipid nomenclature or DOI systems have yet to be
investigated for selection.
‘Omics interaction Network analysis will use these DOIs, which are
bidirectionally linked within each respective database. Coarse-grained network
descriptions garnered from text-data mining of peerreviewed texts will be
screened by domain experts and recorded using _recommended names_ linked to
the GenBank and UniProt Bioinformatics databases.
### 3.1.3. KEYWORD SEARCH
Development of SNT database: The database design will consider the main
challenges of data integration and interoperability in Big Data that have been
identified in the literature and, in particular, will follow the
recommendations of Kadadi, A., Agrawal, R., Nyamful, C. and Atiq, R., 2014,
October. Challenges of data integration and interoperability in big data. In
_Big Data (Big Data), 2014 IEEE_
_International Conference on_ (pp. 38-40). (
_http://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=7004486_ ) which uses
algorithmic integration of data from disparate data formats.
Using a hybrid RDBMS and NoSQL database implementation, we will attempt to
solve for the available data the challenges of i) the scope of data; ii) data
consistency; iii) optimised data querying; iv) acquire adequate information
and data processing resources and v) scalability. By having a dedicated data
manager, we also ensure that the database will be properly maintained and that
the users’ needs are taken into account when developing the various tools and
resources required. The data manager will participate in the NanoSafety
Cluster focus groups, tasked with ensuring data standardization, integration,
curation and adherence to current SOPs in the data science community.
### 3.1.4. DEVELOPMENT OF THE SMARTNANOTOX DATABASE
To provide access to project digital data resources for all partners and
facilitate the data analysis and integration the data manager will develop a
portal to the specific requirements of the project. Webcontent, various media
and more advanced project information retrieval will eventually be made public
to facilitate the re-use of the data generated in the consortium by the
scientific community. This will form the basis of the iQSARs.
To facilitate the access of all this data a bespoke user-friendly interface
will be developed. Although the final features of the portal will be
determined by the specified requirements of the consortium partners and the
access restriction imposed upon them by publication. The portal will provide
customisable access to correlation data for different NM, including
physicochemical properties, toxicology data and other meaningful data as
required.
The data will be archived on available storage resources using the
nomenclature following the recommendations of NANoREG final report to ensure a
minimal pre-requisite for system knowledge for research scientists to extract
relevant and detailed information using typical domain nomenclature.
### 3.1.5. VERSIONING
The digital capture of experimental data will typically only be produced and
processed once. Version numbers will not be required as the data resources are
not incrementally built. These resulting datasets will be assigned unique IDs
in the iQSAR system and stored using human interpretable file and folder names
on the SNT and ultimately on the Zenodo file servers.
Simulation data, such as the Gene Regulatory Network will use a Project and/or
experiment name acronym, version number and creation date data combination:
‘SNT[SYM]<EXP>[SYM]<VERSION>[SYM]<CREATION_DATE>’: _SNT_GRN-v2-02052017_ .
All data files shared and stored within the consortium will follow this naming
convention, e.g. ‘ **SmartNanoTox** _Phys-chem proposed dose range_ _upload_
**1.1 final-26072017** .xls **’.**
eNanoMapper Ontology releases - The ontology has been made available in a
public version control repository (GitHub) as an OWL language file. Any
accepted updates and additions to the source material will be managed by the
GitHub system.
### 3.1.6. METADATA STANDARDS USED OR CREATED
Work on specific metadata formats is ongoing and though not uniformly XML
based, it will compliant with DataCite’s Metadata Schema 4.0 (
_https://schema.datacite.org/_ ) . Metadata will be stored in DataCite XSD
(XML) and JSON format variants which allow conversion to ISA TAB Nano.
We will continue a discussion with NanoReg2 ( _http://www.nanoreg2.eu/_ ) IT
team on file template, implemented data formats and maximising
interoperability.
## 3.2. MAKING DATA ACCESSIBLE
### 3.2.1. DATA WHICH WILL BE MADE OPENLY AVAILABLE
Where required, restrictions will be applied to the data in accordance with
the embargo requirements of journal publication. Other results obtained using
confidential information and materials provided by Chiesi Pharmaceuticals (
_http://www.chiesigroup.com/en/_ ) , who will be supplying lung surfactant
for certain _in vitro_ experiments, will be omitted from the final data
release.
In SmartNanoTox, the following data will be made publicly available:
<table>
<tr>
<th>
**Type of activity**
</th>
<th>
**Object of study**
</th>
<th>
**Type of data produced**
</th>
<th>
**Type of data shared**
</th> </tr>
<tr>
<td>
In vivo toxicity tests in mice
</td>
<td>
IT instillation of nanomaterials
</td>
<td>
Tissue for histological evaluation and gene profiling, analysis of biological
endpoints
</td>
<td>
Toxicological endpoints
</td> </tr>
<tr>
<td>
In vivo toxicity tests in rats
</td>
<td>
Nose-only inhalation of nanomaterials
</td>
<td>
Tissue for histological evaluation and gene profiling, analysis of biological
endpoints
</td>
<td>
Toxicological endpoints
</td> </tr>
<tr>
<td>
Transcriptomics
</td>
<td>
Nanomaterials/tissues
</td>
<td>
Gene expression profile
</td>
<td>
Gene identification
</td> </tr>
<tr>
<td>
</td>
<td>
Nanomaterials
</td>
<td>
Mass-spectrometry data for the protein corona content
</td>
<td>
List of proteins in the corona
</td> </tr>
<tr>
<td>
Atomistic molecular simulation
</td>
<td>
Protein, lipid-NP interface
</td>
<td>
Energy profiles, trajectories
</td>
<td>
Potentials of mean force for
biomolecules/NM
</td> </tr>
<tr>
<td>
Coarse-grained molecular simulation
</td>
<td>
Protein, lipid-NP interface
</td>
<td>
Energy profiles
</td>
<td>
Protein descriptors, adsorption energies, protein adsorption affinity rankings
</td> </tr> </table>
### 3.2.2. MECHANISMS OF DATA ACCESSIBILITY
(Meta)Data and documentation will be made available via a shared file storage
server administered by SNT and will be mirrored on the Zenodo Open Science
file storage service ( _https://zenodo.org_ ) . Access to the SNT File
Server will initially be restricted to SNT partners. Access to Zenodo will
similarly be restricted until project completion or if requested by other
researchers beforehand.
Where data-specific online storage repositories exist, such as ProteomeXchange
( _http://www.proteomexchange.org/_ ) for Proteomics data, datasets will be
submitted to these to place data in a location where it can be best found,
accessed and reused by researchers.
We have set up the project website ‘ _http://www.smartnanotox.eu_ ’ to
publicise the activities of consortium and the associated projects, and to
provide documentation and access information to the project’s resources. A
member area within the project website has been created where experimental and
computational data may also be stored and shared between the participants and
if required, through the project website.
### 3.2.3. METHODS & SOFTWARE REQUIREMENTS
Raw experimental data may be opened in any of appropriate tool for reading or
analysing the data. For example, Mass Spectrometry data will be stored in
Thermo Xcalibur .RAW format with the accompanying mqpar.xml file which
describes the tool configuration used to run quantitative proteomics analysis
in freely available (registration required) MaxQuant (
_http://www.biochem.mpg.de/5111795/maxquant_ ) .
Generated Ontological data may be viewed on the free to use Protégé (
_http://protege.stanford.edu/_ ) .
‘Omics data interaction network analysis will be performed on Qiagen’s
Ingenuity Pathway Analysis (
_https://www.qiagenbioinformatics.com/products/ingenuity-pathway-analysis/_ )
under a pre-existing licence held by partners at UCD and on String-DB (
_https://string-db.org/_ ) (registration required) and the DAVID
Bioinformatics Resource ( _https://david.ncifcrf.gov/home.jsp_ ) (free-to-
use).
### 3.2.4. META(DATA) RESOURCES
A project management site has been created at EMDesk ( _https://emdesk.eu_ )
to assist the project members in management the project and reporting.
In its first year the consortium has already generated a considerable amount
of experimental data that has been shared, as required, between the consortium
partners. The media for these experimental data is currently stored privately
by the groups that produced them and is not yet accessible by all partners but
delivered on request via a shared cloud file sharing services.
From the second year onward, the amount of data produced within the consortium
will increase significantly and so too will the need for secure and flexible
sharing methods that can accommodate large volumes of data. To facilitate this
the SmartNanoTox file server ( _http://137.43.122.41:8080_ ) has been
established to centralise project data and make it available to all consortium
members. While access to the system requires user authentication, the data
that it will host can be shared by request or uploaded to public repositories
as data processing has been completed (unless otherwise restricted by
publication embargo). The SNT File Server infrastructure offers a resource of
approximately 36 Terabytes of shared disk space which may be accessed globally
from Linux based QTS Operating System’s desktop-emulating browser interface or
via a selection of secured and customisable file transfer protocols e.g.
WebDAV, NFS, CIFS and SSH.
The project’s data will be preserved on this system for a period of five years
under the administration of UCD’s School of Physics. However, data mirrored on
Zenodo servers should be available for a much longer, though indefinite period
(the envisioned minimum duration is 20 years). Most data will be copied to the
Zenodo file service as they are produced, and all that that is to be shared
will be transferred before the end of the project term.
## 3.3. MAKING DATA INTEROPERABLE & REUSABLE
### 3.3.1. DATA CONVERSION FOR INTEGRATION, SEARCH AND EXPORT
As the Project will build on data resources developed for the NANoREG Project
we will continue to convert data into ISA-TAB NANO file formats; using NANoREG
harmonised terminology and eNanoMapper ontology terms and relations in the
description and representation of our _in vivo_ and _in vitro_ data.
Tooling will be adopted or developed as required to transform this data into
open access formats such as XML, ISA-TAB or JSON documents allowing the import
and export of data into formats required for further processing, shared
transfer or archive.
Human-readable text documentation will also be stored in the ISO-standardised
PDF/A format to ensure long-term reusability. Content from tabular datasets
such as MS Excel spreadsheets will also be converted to RFC 4180 defined comma
delimited text files to similarly maximise their potential dispersion and
reuse. Image data will be converted and saved along with ISO-12639 Standard
TIFF formatted duplicates.
We will use a combination of semantic web technologies in conjunction with
conventional relational (MySQL) and document orientated (JSON/MongoDB)
databases to facilitate information storage, indexing and retrieval.
## 3.4. MAKING DATA REUSABLE
### 3.4.1 LICENSING & ACCESS RESTRICTIONS
There will be no restrictions imposed on the data made available by this
project though registration will be required to access the iQSAR system and
tools developed for data processing. Access to data resulting from work
involving Chiesi Pharmaceuticals is protected by a non-disclosure agreement
and will not be provided unless specifically permitted by them.
### 3.4.2 GUIDELINES FOR DATA AVAILABILITY
Data will be stored on closed file systems until fully open or partial access
is requested. Except for where temporary publication embargoes are imposed all
relevant (meta)data resources will be made accessible upon completion of
analysis, verification and integration of information into iQSAR. Mechanisms
by which data will be made accessible have been described in Section 3.2.2.
Where embargoes are imposed, citations and links to supplementary data
resources will be provided until the embargo has expired, releasing the data
to the repositories used by SNT and described in the next section.
### 3.4.3. LONG-TERM DATA AVAILABILITY
As described in section 3.2.2 and 3.2.4 Zenodo File Servers will be used to
store (meta)data and documentation relating to the projected
Where domain-specific online data repositories exist, such as GEO for high-
throughput functional Genomics data and PRIDE, as part of ProteomeXchange for
storing and redistributing Mass Spectrometry Proteomics data, etc will be used
to ensure that data is preserved beyond the lifetime of the project and will
still be freely accessible to the research community.
# 4\. RESOURCE ALLOCATION
A part-time Data Manager was appointed for one year in May 2017. They will be
responsible for implementing as close to a FAIR data framework as is possible
within the current DMP and work with each consortium partner to ensure that
the SOPs for data standardization are uniformly applied and robust enough to
handle the growing volume and spectrum of data produced.
In 2016 a QNAP TS-653A ( _https://www.qnap.com/en/product/model.php?II=213_ )
NAS was purchased to securely store and share all data produced by the project
consortium. This machine is hosted by the School of Physics, UCD and will make
data available to authorised users for at least one year beyond the timespan
allotted for the project. Approximately €3k was spent on securing the NAS and
it’s six 7.28Tb HDDs. Costs of administration will be covered for one year by
the Data Manager appointed in 2017 with subsequent devolution of tasks to
existing SNT staff at UCD.
# 5\. DATA SECURITY
## 5.1. DATA ACCESS & SHARING
With the exception of data based on work involving materials and information
provided by Chiesi pharmaceuticals there are no other data used or created by
the project partners which may be considered commercially or ethically
sensitive.
Where Institutional data sharing policies prohibit the sharing of data on
commercial ‘cloud-based’ file storage services data will be routed to the
secure shared folders of the SNT File Server.
## 5.2. DATA RECOVERY
There are currently no facilities in place for data backup, however most of
the data produced will be duplicated across collaborator infrastructure at
source. For example: all Transcriptomics, Proteomics and (the yet to be
produced) Lipidomics data will be duplicated on Systems Biology Ireland’s (
_http://www.ucd.ie/sbi/_ ) data storage servers from where it will be backed
up, weekly, to resources hosted in UCD’s Daedalus datacentre.
All data will be mirrored at no supplementary cost to the project at Zenodo
servers ( _https://zenodo.org/_ ) and opened to public access in accordance
with the guidelines for data availability. Data verification will be carried
out by generated MD5 checksums to ensure no loss in data integrity during the
transfer.
## 5.3. ETHICS COMPLIANCE
This proposal complies with ethical principles (including the highest
standards of research integrity, as set out, for instance, in the European
Code of Conduct for Research Integrity and including, in particular, avoiding
fabrication, falsification, plagiarism or other research misconduct).
# APPENDICES
## APPENDIX A – DATA GENERATION BY WORKPACKAGE
As of July 2017
<table>
<tr>
<th>
**Contact**
</th>
<th>
**WP**
</th>
<th>
**Study**
</th>
<th>
**Activity**
</th>
<th>
**Activity Description**
</th>
<th>
**Type of data for sharing**
</th>
<th>
**Shared Data Format**
</th>
<th>
**Archive**
</th>
<th>
**Vol**
</th> </tr>
<tr>
<td>
UCD
</td>
<td>
4
</td>
<td>
Proteins
</td>
<td>
Molecular simulation
</td>
<td>
Prediction of 3D structure of proteins using i-Tasser software, calculation of
molecule characteristics
</td>
<td>
coarse-grained models, sequence descriptors, structure descriptors, adsorption
energies
</td>
<td>
TDT, eNanoMapper Templates
</td>
<td>
SNT, Zenodo
</td>
<td>
10 MB
</td> </tr>
<tr>
<td>
Imperial
</td>
<td>
4
</td>
<td>
Nanomaterials
</td>
<td>
Molecular simulation
</td>
<td>
Quantum-chemical modelling of NM, calculation of material properties
</td>
<td>
NM coarse-grained presentation, NM descriptors, Hamaker constants
</td>
<td>
TDT, eNanoMapper Templates
</td>
<td>
SNT, Zenodo
</td>
<td>
10 MB
</td> </tr>
<tr>
<td>
UCD
</td>
<td>
5
</td>
<td>
AOP
</td>
<td>
Statistical Modelling
</td>
<td>
Computer simulation (CG) of the interaction of the key molecule with the NM
</td>
<td>
Programs & Computational models
</td>
<td>
Mathematica data, ISA-TAB, JSON
</td>
<td>
SNT, Zenodo
</td>
<td>
TBD
</td> </tr>
<tr>
<td>
UCD
</td>
<td>
2
</td>
<td>
Nanomaterials & Tissues
</td>
<td>
Proteomics
</td>
<td>
Omics studies for in vivo reaction to inhalation/ instillation of NMs for main
classes of NMs: gene expression, metabolomics, proteomics
</td>
<td>
MS Protein IDs
</td>
<td>
MaxQuant Data,
Tables*, eNanoMapper Templates
</td>
<td>
SNT,
PRIDE,
Zenodo
</td>
<td>
50 GB
</td> </tr>
<tr>
<td>
UCD
</td>
<td>
2
</td>
<td>
Nanomaterials
</td>
<td>
Lipidomics
</td>
<td>
Study of the content and evolution of the NM biomolecular corona and lipid
wrap/NM containing complexes from in vivo experiment
</td>
<td>
MS derived Lipid IDs
</td>
<td>
eNanoMapper Templates
</td>
<td>
SNT, Zenodo
</td>
<td>
10 GB
</td> </tr>
<tr>
<td>
SU
</td>
<td>
4
</td>
<td>
Nanomaterials
</td>
<td>
Molecular simulations
</td>
<td>
Multiscale modelling of NM corona formation and validation of the modelling
against in vivo studies
</td>
<td>
bionano interactions descriptors - adsorption free energies; binding
coordination
</td>
<td>
data sheets / Tables*
</td>
<td>
SNT, Zenodo
</td>
<td>
100MB
</td> </tr>
<tr>
<td>
HMGU
</td>
<td>
1
</td>
<td>
Lung cells
</td>
<td>
In vitro tox tests in murine cells
</td>
<td>
Cell-line exposure experiments using Vitrocell exposure systems
</td>
<td>
Cytotoxicity and biological endpoints (gene/protein-level)
</td>
<td>
data sheets / Tables*
</td>
<td>
SNT, Zenodo
</td>
<td>
</td> </tr>
<tr>
<td>
HMGU
</td>
<td>
1
</td>
<td>
Inhalation / IT instillation of
nanomaterials
</td>
<td>
In vivo tox tests in mice
</td>
<td>
Inhalation experiments: defined NMs aerosolisation, and investigation of
interactions of NM within the lung lining fluid,
alveolar epithelial cells and
macrophages
</td>
<td>
Toxicological endpoints - Tissue for histological evaluation and gene
profiling, analysis of biological endpoints
</td>
<td>
data sheets / Tables*
</td>
<td>
SNT, Zenodo
</td>
<td>
10 MB
</td> </tr>
<tr>
<td>
NRCWE
</td>
<td>
1
</td>
<td>
Nanomaterials
</td>
<td>
Surface Tension Experiments
</td>
<td>
Investigation of NM interaction with the lung alveolar fluid and lung
integrity
</td>
<td>
Surface tension profiles, dose-response curves - Effect of nanomaterials on
lung surfactant surface tension, inhibitory dose
</td>
<td>
data sheets / Tables*
</td>
<td>
SNT, Zenodo
</td>
<td>
10 MB
</td> </tr> </table>
_This project has received funding from the_
<table>
<tr>
<th>
NRCWE
</th>
<th>
3
</th>
<th>
Database for the lungspecific or
respiratory
AOP
</th>
<th>
Toxicity Pathway
Identification
</th>
<th>
Data mining in Gene
Expression (GEO) database for the known pathways for lung diseases, library of
biological processes that are perturbed by
NM uptake
</th>
<th>
Candidate Pathways
</th>
<th>
data sheets / Tables*
</th>
<th>
SNT,
GEO,
Zenodo
</th>
<th>
10 MB
</th> </tr>
<tr>
<td>
NRCWE
</td>
<td>
1&3
</td>
<td>
IT instillation of
nanomaterials
</td>
<td>
In vivo tox tests in mice
</td>
<td>
Instillation experiments:
Investigation of interactions of NM within the lung lining fluid,
alveolar epithelial cells and
macrophages
</td>
<td>
Toxicological endpoints - Tissue for histological evaluation & gene profiling,
analysis of biological endpoints
</td>
<td>
data sheets / Tables*
</td>
<td>
SNT, Zenodo
</td>
<td>
10 MB
</td> </tr>
<tr>
<td>
FIOH
</td>
<td>
3&5
</td>
<td>
Lung tissue
</td>
<td>
Histology evaluation
</td>
<td>
Tissue analysis of in vovio samples
</td>
<td>
**Verbal** evaluation, Semi-quantitative scores
</td>
<td>
TDT, Tables*, Images**, MDs
</td>
<td>
SNT, Zenodo
</td>
<td>
10 MB
</td> </tr>
<tr>
<td>
JSI
</td>
<td>
2
</td>
<td>
Labelled
Nanoparticles
</td>
<td>
Fluorimetry
microspectroscopy, FMS
</td>
<td>
NM modification for efficient tracking (fluorescent/isotopic labelling)
</td>
<td>
fluorescence spectra - procedures for labelling nanoparticles and characterize
the efficiency of labelling
</td>
<td>
spectra, synthetic pathways, data sheets & Tables*
</td>
<td>
SNT, Zenodo
</td>
<td>
50 MB
</td> </tr>
<tr>
<td>
JSI
</td>
<td>
2
</td>
<td>
Labelled
Nanoparticles, cell lines
</td>
<td>
Fluorescence microscopy, FMS, 3D STED with spectral data
</td>
<td>
NM modification for efficient tracking (fluorescent/isotopic labelling)
</td>
<td>
2D & 3D stack of 2D images (4D & 5D sets of data) - images, evaluation of
affinity constants, lifetimes of states, characteristic times of membrane
changes
</td>
<td>
Images**, text based, Video
</td>
<td>
SNT, Zenodo
</td>
<td>
20 GB
</td> </tr>
<tr>
<td>
JSI
</td>
<td>
2
</td>
<td>
Nanoparticles, Lung-lining fluid, Lipid dispersion
</td>
<td>
In vitro 'NP'-to-lipid affinity
</td>
<td>
Analysis of NM-lipid interaction for labelled NMs
</td>
<td>
autocorrelation curves analysis & stack of images - images, evaluation of
affinity constants
</td>
<td>
interaction description
</td>
<td>
SNT, Zenodo
</td>
<td>
10 MB
</td> </tr>
<tr>
<td>
UL
</td>
<td>
1&3
</td>
<td>
Lung cells
</td>
<td>
In vitro tox tests (rat & human cells)
</td>
<td>
Analysis of cell responses to
NMs
</td>
<td>
Cytotoxicity and biological endpoints (gene/protein-level), included
transcriptomics
</td>
<td>
data sheets / Tables*
</td>
<td>
SNT, Zenodo
</td>
<td>
20 GB
</td> </tr> </table>
* archived as CSV text, ** archived as TIFF
_This project has received funding from the_
## APPENDIX B – IMPORTED DATA & LICENCING
<table>
<tr>
<th>
**Resource Usage Description**
</th>
<th>
**Licence Type**
</th>
<th>
**Full description**
</th> </tr>
<tr>
<td>
eNanoMapper Templates:
NANoREG &
NanoGenotox
</td>
<td>
"The NANoREG – eNanoMapper database and NANoREG deliverables, SOPs and other
documents [hereinafter: sources] may be used by individuals, institutions,
governments, corporations or other business entities so long as the use itself
or any works derived from the use of these sources, are not intended to
generate sales or profit."
</td>
<td>
CC-BY-
NC-4.0
</td>
<td>
_http://www.rivm.nl/en/About_RIVM/International_Affairs/_
_International_Projects/Completed/NANoREG/NANoREG_specific_license_
__information_
</td> </tr>
<tr>
<td>
eNanoMapper Ontology
</td>
<td>
"The content which is created by eNanoMapper (available in the internal/
folder) is licensed under CC-BY
(https://creativecommons.org/licenses/by/3.0/). However, eNanoMapper reuses
content from external ontologies which retain their original licenses."
</td>
<td>
GNU
Lesser
GPL
</td>
<td>
_https://github.com/enanomapper_ _/ontologies/tree/master/licenses_
</td> </tr>
<tr>
<td>
GenBank
</td>
<td>
"no restrictions on the use or distribution of the GenBank data. However, some
submitters may claim patent, copyright, or other intellectual property rights
in all or a portion of the data they have submitted"
</td>
<td>
Bespoke
</td>
<td>
_http://hgdownload.soe.ucsc.edu/_ _goldenPath/hg38/bigZips/_
</td> </tr> </table>
_This project has received funding from the_
<table>
<tr>
<th>
Gene
Ontology
</th>
<th>
"You are free to copy and redistribute the material in any medium or format
and remix, transform, and build upon the material for any purpose, even
commercially"
</th>
<th>
CC-BY-4.0
</th>
<th>
_http://www.geneontology.org/page/use-and-license_
</th> </tr>
<tr>
<td>
Gene
Expression
Omnibus
</td>
<td>
"NCBI places no restrictions on the use or distribution of the GEO data.
However, some submitters may claim patent, copyright, or other intellectual
property rights in all or a portion of the data they have submitted."
</td>
<td>
Bespoke
</td>
<td>
_https://www.ncbi.nlm.nih.gov/geo/info/disclaimer.html_
</td> </tr>
<tr>
<td>
Protein Data Bank
</td>
<td>
“are free of all copyright restrictions and made fully and freely available
for both noncommercial and commercial use. Users of the data should attribute
the original authors of that structural data”
</td>
<td>
Bespoke
</td>
<td>
_https://www.rcsb.org/pdb/static.do?p=general_information/about__
_pdb/policies_references.html_
</td> </tr>
<tr>
<td>
UniProt
</td>
<td>
"free to copy, distribute, display and make commercial use of these databases
in all legislations, provided you give us credit"
</td>
<td>
CC-ND-
3.0
</td>
<td>
_http://www.uniprot.org/help/license_
</td> </tr>
<tr>
<td>
QSAR Toolbox
</td>
<td>
Free to use with accreditation
</td>
<td>
Bespoke
</td>
<td>
_http://www.oecd.org/env/ehs/risk-assessment/Eula4.pdf_
</td> </tr> </table>
_This project has received funding from the_
|
https://phaidra.univie.ac.at/o:1140797
|
Horizon 2020
|
1061_MAGNEURON_686841.md
|
**Deliverable content**
**1\. Purpose of the MAGNEURON Data Management Plan (DMP)**
MAGNEURON is a Horizon 2020 project participating in the Open Research Data
Pilot. This pilot is a part of the Open Access to Scientific Publications and
Research Data programme in H2020. The goal of the program is to foster access
to the data generated in H2020 projects.
The purpose of the DMP is to analyse the main elements of the European data
management policy that will be used by the consortium regarding all the
datasets that will be generated by the project. The DMP as a not fixed
document, has been evolving during the lifespan of the project.
This last version of the DMP includes an overview of the datasets generated by
the project, and the specific conditions that are attached to them.
The MAGNEURON project DMP primarily lists the different datasets generated by
the project and the main actions that the project implemented to manage these
datasets.
**2\. Register of the data sets generated or collected in MAGNEURON**
The register must be understood as a dynamic document, which has been updated
regularly during project lifetime.
The information listed below reflects the conception and design of the
individual work packages at the end of the project.
The data register delivers information according to Guidelines on Data
Management in Horizon 2020:
* Data set reference and name;
* Data set description;
* Standards and metadata;
* Data sharing;
* Archiving and preservation.
1. **Datasets collected within WP1**
Not relevant for this work package.
2. **Datasets collected within WP2 - Preparation of synthetic and recombinant MNPs (Phenix – Christine M.)**
<table>
<tr>
<th>
**Data set reference and name: WP2 dataset**
Person in charge: Christine Ménager (CNRS)
</th> </tr>
<tr>
<td>
**Data type**
</td>
<td>
**Data standards**
</td>
<td>
**Data generation software**
</td> </tr>
<tr>
<td>
Images (TEM pictures)
</td>
<td>
Format: TIFF
</td>
<td>
ImageJ (open source image processing program
designed for scientific multidimensional images)
</td> </tr>
<tr>
<td>
Video tapes
</td>
<td>
Format: AVI,
STK
</td>
<td>
MetaMorph Microscopy
Automation and Image
Analysis Software
</td> </tr>
<tr>
<td>
Analysis files (text, calculation) for magnetism, DLS, zeta potential,
hyperthermia, spectroscopy (UV-visible
absorption, fluorescence)
</td>
<td>
Format:
Different text and calculation formats
</td>
<td>
Different text processing and calculation software
(excel, kaleidagraph, Igor)
</td> </tr>
<tr>
<td>
A dataset containing electronic microscopy imaging of the optimized particles
and
their main characterizations (size analysis, hydrodynamic diameter, magnetism
properties) used by the different partners
of the project
</td>
<td>
Format of the data: TIFF,
JPEG and PDF
</td>
<td>
The dataset was made using imageJ, excel, word,
Malvern zetasizer software
</td> </tr> </table>
<table>
<tr>
<th>
**Estimated data size**
</th>
<th>
**Data sharing**
</th>
<th>
**Data storage and archiving**
</th> </tr>
<tr>
<td>
~500 gigabytes per year will be
generated by WP2.
</td>
<td>
The generated data have been shared only with the team of Christine Ménager
and the MAGNEURON project partners.
The MAGNEURON partners have used Dropbox for data sharing in WP2, internal
reports and EC
deliverables
</td>
<td>
The data generated in
WP2 have been stored only in the digital
format, no physical
support will be kept.
The data have been stored on the Phenix local servers. The
scientific data have
been stored during all
the project, and 5 years after the project
</td> </tr>
<tr>
<td>
25 Mb
</td>
<td>
The dataset is shared on the website zenodo: with
DOI: 10.5281/zenodo.3587537
https://zenodo.org/record/3587537#.XhL6kvxCc2w
</td>
<td>
The dataset will be stored on zenodo, and
on the PHENIX internal servers.
</td> </tr> </table>
3. **Datasets collected within WP3 - Biofunctionalization and intracellular delivery of MNPs (UOS – Jacob P.)**
<table>
<tr>
<th>
**Data set reference and name: WP3 dataset**
Person in charge: Jacob Piehler (UOS)
</th>
<th>
</th> </tr>
<tr>
<td>
**Data type**
</td>
<td>
**Data standards**
</td>
<td>
**Data generation software**
</td> </tr>
<tr>
<td>
Maps of Plasmids Used for Magnetogenetic Manipulation
</td>
<td>
Format: .cm5
</td>
<td>
Clone Manager
</td> </tr>
<tr>
<td>
Tools & Microscope-Setup for Magnetogenetic Manipulation of Cells
</td>
<td>
Format: .docx
</td>
<td>
MS-Word
</td> </tr>
<tr>
<td>
Protocol for Production of MagIcS
Nanoparticles
</td>
<td>
Format: .docx
</td>
<td>
MS-Word
</td> </tr>
<tr>
<td>
Probing Magnetic Manipulation of MagIcS Nanoparticles Inside Living Cells
</td>
<td>
Format: .docx, .AVI, .TIFF
</td>
<td>
MS-Word, Image-J,
AxioVision
</td> </tr>
<tr>
<td>
Tools for Probing Magnetogenetic
Activation of GTPases
</td>
<td>
Format: .docx, .AVI, .TIFF
</td>
<td>
MS-Word, Image-J,
AxioVision
</td> </tr>
<tr>
<td>
Script for Analysis of FRET-Ratio Images
</td>
<td>
Format: .cmv
</td>
<td>
Matlab
</td> </tr> </table>
<table>
<tr>
<th>
**Estimated data size**
</th>
<th>
**Data sharing**
</th>
<th>
**Data storage and archiving**
</th> </tr>
<tr>
<td>
~10-50 gigabytes per
year have been generated by WP3.
</td>
<td>
The generated data have been shared only with the team of Jacob
Piehler and the MAGNEURON project partners. The MAGNEURON partners have used
two software types for data sharing in WP3:
* UOS own software for sharing
the scientific data;
* Dropbox for sharing internal reports and EC deliverables.
</td>
<td>
The data generated in WP3 have been stored only in the digital format, no
physical has been kept.
The data have been stored on the UOS local servers using OMERO software. The
scientific data will be stored during all the project, and for 10 years
thereafter.
</td> </tr>
<tr>
<td>
All data
described above are about 10-50 gigabytes in sizes
</td>
<td>
These data will be published on _https://zenodo.org_
</td>
<td>
These data will be stored on _https://zenodo.org_
</td> </tr>
<tr>
<td>
including data files
</td>
<td>
raw
</td>
<td>
</td>
<td>
</td> </tr> </table>
1. **Datasets collected within WP4 - Tools for MNP manipulation in single-cell assays**
<table>
<tr>
<th>
**Data set reference and name: WP4 dataset** Person in charge: Mathieu Coppey
(Institut Curie)
</th> </tr>
<tr>
<td>
**Data type**
</td>
<td>
**Data standards**
</td>
<td>
**Data generation software**
</td> </tr>
<tr>
<td>
Maps of molecular plasmids
</td>
<td>
Format: JDK
</td>
<td>
Curie Institute application
</td> </tr>
<tr>
<td>
Document presenting the tools for magnetic manipulation
</td>
<td>
document docx
</td>
<td>
Word & PowerPoint
</td> </tr>
<tr>
<td>
Videos: raw data of manipulation of magnetic nanoparticles in single cells
</td>
<td>
Tiff videos
</td>
<td>
Recorded with
Metamorph
</td> </tr>
<tr>
<td>
Scripts for analysis
</td>
<td>
MATLAB scripts, csv file with quantification, tracks.
</td>
<td>
Coded with MATLAB
</td> </tr>
<tr>
<td>
Videos: Cellular magneticmanipulation with an endosomal
approach
</td>
<td>
AVI, TIFF,
</td>
<td>
Metamorph, ImageJ,
</td> </tr>
<tr>
<td>
Analysis: Cellular magneticmanipulation with an endosomal
approach
</td>
<td>
PowerPoint, Excel files
</td>
<td>
PowerPoint, Excel
</td> </tr> </table>
<table>
<tr>
<th>
**Estimated data size**
</th>
<th>
**Data sharing**
</th>
<th>
**Data storage and archiving**
</th> </tr>
<tr>
<td>
~1-2 terabytes per year
generated by WP4.
</td>
<td>
The generated data have been shared only with the team of Mathieu Coppey and
the MAGNEURON project partners.
The MAGNEURON partners have used two software types for data sharing in WP4:
* Xfer – Institut Curie own software – for sharing the scientific data;
* Dropbox for sharing internal reports and EC deliverables.
</td>
<td>
The data generated in WP4 have been stored only in the digital format, no
physical support have been kept.
The data have been stored on the Curie Institute local servers. The scientific
data have been stored during all the project, and the after-project scientific
data storage is to be discussed.
</td> </tr>
<tr>
<td>
Tool for magnetic
manipulation:
20kB
</td>
<td>
Open data will be published on _https://zenodo.org_
</td>
<td>
Word document
published directly on _zenodo.org_
</td> </tr>
<tr>
<td>
Magnetic manipulations in single cells:
10GB – 50GB of raw data
</td>
<td>
The dataset is shared on the website zenodo: with DOI 10.5281/zenodo.3579322
_https://zenodo.org/record/3579322#.Xigz52hKhPY_
</td>
<td>
The data will be stored directly on _zenodo.org_ with a backup on the NAS of
the team.
</td> </tr>
<tr>
<td>
Scripts for the analysis:
100Mo
</td>
<td>
The dataset is shared on the website zenodo: with DOI 10.5281/zenodo.3582962
_https://zenodo.org/record/3582962#.XigzFmhKhPY_
</td>
<td>
Scripts will be stored directly on _zenodo.org_ with a backup on the NAS of
the team.
</td> </tr>
<tr>
<td>
Endosomal approach:
80GB
</td>
<td>
Raw data will be published with description of the published experiments on
_https://zenodo.org_
</td>
<td>
The data will be stored directly on _zenodo.org_ with a backup on the NAS of
the team.
</td> </tr> </table>
1. **Datasets collected within WP5 - Biomagnetic control of stem cell differentiation (Keele – Neil T. / Michael R.)**
<table>
<tr>
<th>
**Data set reference and name: WP5 dataset** Person in charge: Alicia El Haj
(Birmingham University)
</th> </tr>
<tr>
<td>
**Data type**
</td>
<td>
**Data standards**
</td>
<td>
**Data generation software**
</td> </tr>
<tr>
<td>
Video files
</td>
<td>
Format: AVI, WMV
</td>
<td>
Microscopy Automation and
Image Analysis Software
</td> </tr>
<tr>
<td>
Still images
</td>
<td>
Format: TIF, JPEG
</td>
<td>
ImageJ (open source image processing program
designed for scientific multidimensional images)
</td> </tr>
<tr>
<td>
Graphing and statistics files
</td>
<td>
Format: various software specific
</td>
<td>
Microcal Origin, SPSS, PRISM, GraphPad, Minitab
</td> </tr>
<tr>
<td>
Analysis files
</td>
<td>
Format: MS Excel spreadsheets
</td>
<td>
MS Excel
</td> </tr>
<tr>
<td>
Data summary files
</td>
<td>
Format: ppt, docx, pdf
</td>
<td>
MS PowerPoint, MS Word,
Adobe acrobat
</td> </tr> </table>
<table>
<tr>
<th>
**Estimated data size**
</th>
<th>
**Data sharing**
</th>
<th>
**Data storage and archiving**
</th> </tr>
<tr>
<td>
~1-2 terabytes per year will be generated by WP5.
</td>
<td>
The generated data will be shared only with the team of
Alicia El Haj, and the MAGNEURON project partners.
The MAGNEURON partners will use Dropbox for data sharing in WP5.
Processed data associated with published work will be made open access via a
suitable data repository (e.g. EPrints / Keele Research repository).
</td>
<td>
The data generated in WP5 will be stored in the digital format, and in
physical copies in laboratory notebooks.
The data will be stored on the Keele University local servers and hard drives.
The scientific data will be stored during the project and for 10 years
thereafter. Place for after-project scientific data storage is to be
discussed.
</td> </tr> </table>
In more details, the following datasets have been identified in WP5
<table>
<tr>
<th>
**Data Ref**
</th>
<th>
**Name**
</th>
<th>
**Type and size**
**(Standards / metadata)**
</th>
<th>
**Sharing**
</th>
<th>
**Storage / Archiving**
</th> </tr>
<tr>
<td>
WP5.1 SH active b-catenin expt.
</td>
<td>
Active bcatenin fluorescent images
</td>
<td>
PPT, Spreadsheet (<10Mb), JPG, <1Mb per image (150+ images), Minitab file
(<1Mb per file)
</td>
<td>
Processed images and data
shared with Magneuron consortia and disseminated at conferences
</td>
<td>
Digital
(University hard
drives), Physical
(Poster)
</td> </tr>
<tr>
<td>
WP5.1 SH bcatenin ELISA expt.
</td>
<td>
B-cat ELISA
</td>
<td>
PPT, Spreadsheet
(<10Mb), Minitab file
(<1Mb per file)
</td>
<td>
Processed data shared with Magneuron consortia and disseminated at conferences
</td>
<td>
Digital
(University hard drives)
</td> </tr>
<tr>
<td>
WP5.1 SH MNP
labelling
</td>
<td>
UM206-MNP and TREKMNP
fluorescent images
</td>
<td>
PPT, TIF, <3Mb per image (100+ images)
</td>
<td>
Processed images shared with Magneuron consortia and disseminated at
conferences
</td>
<td>
Digital
(University hard drives)
</td> </tr>
<tr>
<td>
WP5.1 / WP5.2
Receptor expression (PCR)
</td>
<td>
SH and Rat receptor PCR gels and qPCR
</td>
<td>
PPT, TIF 3-13Mb per image (15+ images), spreadsheets (<10Mb
per file), Minitab files
(<2Mb per file)
</td>
<td>
Processed images shared with Magneuron consortia and disseminated at
conferences
</td>
<td>
Digital
(University hard drives),
Physical (MSc dissertation)
</td> </tr>
<tr>
<td>
WP5.1/5.2 MNP and PMA IF expt.
</td>
<td>
TH and DAT fluorescent images
</td>
<td>
PPT, Spreadsheet (<10Mb), TIF (100+ images, <25Mb per image), JPG (50+ images,
<2Mb), OIB files (20+ files, <10Mb per file), Minitab file
(<1Mb per file)
</td>
<td>
Processed images shared with Magneuron consortia and disseminated at
conferences
</td>
<td>
Digital
(University hard
drives), Physical
(Poster),
Physical (MSc dissertation)
</td> </tr>
<tr>
<td>
WP5.1/5.2 Diff.
media +/- MNP IF expt.
</td>
<td>
TH and DAT fluorescent images
</td>
<td>
PPT, JPG, (220+ images, (<2Mb per image), OIB files (20+ files, <10Mb per
file)
</td>
<td>
Processed images shared with Magneuron consortia and disseminated at
conferences
</td>
<td>
Digital
(University hard drives),
Physical (MSc dissertation)
</td> </tr> </table>
<table>
<tr>
<th>
**Data Ref**
</th>
<th>
**Name**
</th>
<th>
**Type and size**
**(Standards / metadata)**
</th>
<th>
**Sharing**
</th>
<th>
**Storage / Archiving**
</th> </tr>
<tr>
<td>
WP5.1/5.2 Diff.
media +/- MNP PCR expt.
</td>
<td>
Neuro. and Dopa. gene expression with Diff. media with/without
MNP
</td>
<td>
PPT, Spreadsheets
(<10Mb per file), Minitab files (<1Mb
per file)
</td>
<td>
Processed data shared with Magneuron consortia and disseminated at conferences
</td>
<td>
Digital
(University hard drives),
Physical (Poster and MSc
dissertation)
</td> </tr>
<tr>
<td>
WP5.2 Target receptor
homology study (Human vs Rat)
</td>
<td>
Rat Frizzled and TREK receptor homology
</td>
<td>
PPT (<10Mb)
</td>
<td>
Processed data shared with Magneuron consortia
</td>
<td>
Digital
(University hard drives)
</td> </tr>
<tr>
<td>
WP5.1 Receptor expression during differentiation
</td>
<td>
Frizzled, TREK
and BIII Tubulin fluorescent images
</td>
<td>
PPT, TIF <10Mb per image (100+ images),
JPG <5Mb per image
(50+ images)
</td>
<td>
Processed images shared with Magneuron consortia and disseminated at
conferences
</td>
<td>
Digital
(University hard drives),
Physical (MSc dissertation)
</td> </tr>
<tr>
<td>
WP5.1 Stress response gene expt.
</td>
<td>
Stress response gene expression
with TREKMNP
</td>
<td>
PPT, Spreadsheets
(<10Mb per file), Minitab files (<1Mb
per file)
</td>
<td>
Processed data shared with Magneuron consortia and disseminated at conferences
</td>
<td>
Digital
(University hard drives),
Physical (MSc dissertation)
</td> </tr>
<tr>
<td>
WP5.1 Wnt response gene expt.
</td>
<td>
Wnt associated gene expression
with UM206MNP
</td>
<td>
PPT, Spreadsheets
(<10Mb per file), Minitab files (<1Mb
per file)
</td>
<td>
Processed data shared with Magneuron consortia and disseminated at conferences
</td>
<td>
Digital
(University hard drives),
Physical (MSc dissertation)
</td> </tr>
<tr>
<td>
WP5.1 Wnt reporter expt.
</td>
<td>
SH TCF/LEF
reporter
</td>
<td>
PPT, Spreadsheets
(<10Mb per file), Minitab files (<1Mb
per file)
</td>
<td>
Processed data shared with Magneuron consortia and disseminated at conferences
</td>
<td>
Digital
(University hard
drives), Physical
(Poster)
</td> </tr>
<tr>
<td>
WP5.2 SH Neurite extension study
</td>
<td>
SH neurite extension fluorescent microscopy
</td>
<td>
PPT, TIFF, <10Mb per image (100+ images), Spreadsheet (<2Mb
per file)
</td>
<td>
Processed images shared with Magneuron consortia
</td>
<td>
Digital
(University hard drives)
</td> </tr>
<tr>
<td>
WP5.2 hDNP with MNP study
</td>
<td>
hDNP neurite extension microscopy
</td>
<td>
PPT, JPG, <2Mb per image (40+ images), Spreadsheets (<5Mb
per file), Neurite trace
file (ImageJ format,
<1Mb)
</td>
<td>
Processed images shared with Magneuron consortia
</td>
<td>
Digital
(University hard drives)
</td> </tr>
<tr>
<td>
**Data Ref**
</td>
<td>
**Name**
</td>
<td>
**Type and size**
**(Standards / metadata)**
</td>
<td>
**Sharing**
</td>
<td>
**Storage / Archiving**
</td> </tr>
<tr>
<td>
WP5.2/WP5.3 Rat
slice expt’s
</td>
<td>
Rat slice fluorescent images
</td>
<td>
PPT, TIF, <15Mb per image (300+ images)
</td>
<td>
Processed data shared with Magneuron consortia and disseminated at conferences
</td>
<td>
Digital
(University hard
drives), Physical
(Poster)
</td> </tr>
<tr>
<td>
WP5.2 Rat VM / N27 cells
</td>
<td>
Rat VM / N27 in vitro diff.
Expt’ and MNP labelling
</td>
<td>
PPT, TIFF, JPG,
<10Mb per image
(800+ images), Spreadsheet (<2Mb
per file)
</td>
<td>
Processed images shared with Magneuron consortia
</td>
<td>
Digital
(University hard drives)
</td> </tr>
<tr>
<td>
WP2/WP3 MNP and Ferritin
samples
characterisation
</td>
<td>
ACS (100F1,
Dahan MNP,
Ferritin Sample
K)
</td>
<td>
Spreadsheets (<5Mb each)
</td>
<td>
Processed data shared with Magneuron consortia
</td>
<td>
Digital
(University hard drives)
</td> </tr>
<tr>
<td>
WP5 MNP
characterisation
</td>
<td>
Micromod
250nm MNP
TEM images
</td>
<td>
PPT, TIF, <2Mb per image (100+ images)
</td>
<td>
Processed data shared with Magneuron consortia
</td>
<td>
Digital
(University hard drives)
</td> </tr> </table>
2. **Datasets collected within WP6 - Directed fibre outgrowth of neuronal cells (RUB – Rolf H.)**
<table>
<tr>
<th>
P
</th>
<th>
**Data set reference and name: WP6 dataset** erson in charge: Rolf Heumann
(Ruhr-University Bochum)
</th> </tr>
<tr>
<td>
**Data type**
</td>
<td>
**Data standards**
</td>
<td>
**Data generation software**
</td> </tr>
<tr>
<td>
Video tapes
</td>
<td>
Format: AVI, MP4
</td>
<td>
Olympus Cell Sense Dimensions Image
Analysis Software
</td> </tr>
<tr>
<td>
Still images
</td>
<td>
Format: TIF, JPEG
</td>
<td>
ImageJ (open source image processing program designed for scientific
multidimensional images) Bio-Rad Image Lab
Olympus Cell Sense Dimensions Image
Analysis Software
</td> </tr>
<tr>
<td>
Maps of molecular plasmids
</td>
<td>
Format: XDNA, DNA
</td>
<td>
Serial Cloner, SnapGene
</td> </tr>
<tr>
<td>
Graphing and statistics files
</td>
<td>
Format: XLSX, PZF, PZFX,
OPJ and further software specific formats
</td>
<td>
Microsoft Excel, Microcal Origin,
GraphPad PRISM
</td> </tr>
<tr>
<td>
Analysis files
(text, calculation)
</td>
<td>
Format: Different text and calculation formats
</td>
<td>
Different text processing and calculation software
</td> </tr> </table>
<table>
<tr>
<th>
**Estimated data size**
</th>
<th>
**Data sharing**
</th>
<th>
**Data storage and archiving**
</th> </tr>
<tr>
<td>
~1.0 terabytes per year have been
generated by WP6.
</td>
<td>
The generated data have been shared only with the team of Rolf Heumann and the
MAGNEURON project partners.
The MAGNEURON partners will use two software types for data sharing in WP6:
* FileShare (Ruhr-University Bochum
internal)
* Dropbox for sharing internal reports and EC deliverables.
</td>
<td>
The data generated in WP6 have been stored only in the digital format, no
physical support have been kept.
The data have been stored on the Ruhr-University Bochum local servers. The
scientific data have been stored during all the project, and the afterproject
scientific data storage is to be discussed.
</td> </tr>
<tr>
<td>
5.9 Mb
</td>
<td>
The dataset is shared on the website zenodo: with DOI: 10.5281/zenodo.3606263
https://zenodo.org/record/3606263#.XhyPQ
yNCdaQ
</td>
<td>
The dataset will be stored on
zenodo, and on the RUB internal servers.
</td> </tr> </table>
1. **Datasets collected within WP7 - Magnetic manipulation of cellular signalling in organotypic brain slices and in vivo models of Parkinson’s disease**
<table>
<tr>
<th>
**Data set reference and name: WP7 dataset**
Person in charge: Monte Gates (Keele)
</th> </tr>
<tr>
<td>
**Data type**
</td>
<td>
**Data standards**
</td>
<td>
**Data generation software**
</td> </tr>
<tr>
<td>
Video tapes
</td>
<td>
Format: AVI, MPEG
</td>
<td>
Microscopy Automation and Image Analysis Software
Ethovision and Tracksys processing software
</td> </tr>
<tr>
<td>
Still images
</td>
<td>
Format: TIF, JPEG,
JPEG2000
</td>
<td>
NIS elements (NIKON), and ImageJ (open source image processing) programs
designed for scientific multidimensional images
MatLab Image Processing Toolbox
</td> </tr>
<tr>
<td>
Statistics files
</td>
<td>
Format: SPSS and Prism formats (PZF)
</td>
<td>
SPSS, GraphPad Prism
</td> </tr>
<tr>
<td>
Analysis files (excel files, word docs)
</td>
<td>
Format: Different text and spreadsheet formats
</td>
<td>
Microsoft Office
</td> </tr>
<tr>
<td>
Video of slice culture protocol / embryo dissection and imaging
</td>
<td>
Combination of video (AVI) and images (JPG, TIFF)
</td>
<td>
Video and images edited in Movie Maker
</td> </tr> </table>
<table>
<tr>
<th>
**Estimated data size**
</th>
<th>
**Data sharing**
</th>
<th>
**Data storage and archiving**
</th> </tr>
<tr>
<td>
~1-2 terabytes per year will be generated by WP7.
</td>
<td>
The generated data have been shared only with the team of
Monte Gates and the MAGNEURON project partners.
The MAGNEURON partners have used Dropbox for data sharing in WP7:
</td>
<td>
The data generated in WP6 have been stored in the digital format, and in
physical copies in laboratory notebooks.
The data have been stored on the Keele University local servers and hard
drives. The scientific data have been stored during the project and will for
10 years thereafter. Place for after-project scientific data storage is to be
discussed.
</td> </tr>
<tr>
<td>
~up to 1-2 gigabytes
</td>
<td>
Video/images/description of some protocols (e.g. the tissue slicing protocol,
preparation of VM cells and imaging) will be made openly available.
</td>
<td>
Video and images will be stored on Keele University servers and the final
version will be made openly available.
</td> </tr> </table>
A video outlining some of the protocols developed in WP7 will be made openly
available. Furthermore, any accepted manuscripts arising from work in WP7 will
be made open access.
In more details, the following datasets have been identified in WP7
<table>
<tr>
<th>
**Data Ref**
</th>
<th>
**Name**
</th>
<th>
**Type and size**
**(Standards / metadata)**
</th>
<th>
**Sharing**
</th>
<th>
**Storage / Archiving**
</th> </tr>
<tr>
<td>
WP Task 7.1
Optimisation of slice culture model of
Parkinson’s disease
</td>
<td>
Optimisation of slice cutting angle
</td>
<td>
PPTX, JPG,
TIFF, ND2, <3GB
</td>
<td>
Processed summary
shared with Magneuron consortia,
disseminated at conference
</td>
<td>
Digital
(University hard
drives),
physical
(poster)
</td> </tr>
<tr>
<td>
WP Task 7.1
Optimisation of slice culture model of
Parkinson’s disease
</td>
<td>
Cell death analysis in slices
</td>
<td>
PPTX, JPG,
TIFF, ND2, XLS
Excel spreadsheets,
PZF GraphPad
Prism spreadsheet,
<15GB
</td>
<td>
Processed summary
shared with Magneuron consortia,
disseminated at conference
</td>
<td>
Digital
(University hard
drives),
physical
(poster)
</td> </tr> </table>
<table>
<tr>
<th>
**Data Ref**
</th>
<th>
**Name**
</th>
<th>
**Type and size**
**(Standards / metadata)**
</th>
<th>
**Sharing**
</th>
<th>
**Storage / Archiving**
</th> </tr>
<tr>
<td>
WP Task 7.1
Optimisation of slice culture model of
Parkinson’s disease
</td>
<td>
Retrograde labelling with FluoroGold and
FluoroRuby
</td>
<td>
PPTX, JPG, TIF,
ND2,
<36GB
</td>
<td>
Processed summary
shared with
Magneuron consortia
</td>
<td>
Digital
(University hard drives)
</td> </tr>
<tr>
<td>
WP Task 7.1
Optimisation of slice culture model of
Parkinson’s disease
</td>
<td>
Degeneration of TH in slices
</td>
<td>
PPTX, JPG, TIF,
ND2,
<10GB
</td>
<td>
Processed summary
shared with Magneuron consortia,
disseminated at conference
</td>
<td>
Digital
(University hard
drives),
physical
(poster)
</td> </tr>
<tr>
<td>
WP Task 7.1
Optimisation of slice culture model of
Parkinson’s disease
</td>
<td>
Removal of cortex to improve slice health
</td>
<td>
PPTX, JPG, TIF,
ND2, XLS Excel spreadsheets,
PZF GraphPad
Prism spreadsheet,
<3GB
</td>
<td>
Processed summary
shared with
Magneuron consortia
</td>
<td>
Digital
(University hard drives)
</td> </tr>
<tr>
<td>
WP Task 7.1
Optimisation of slice culture model of
Parkinson’s disease
</td>
<td>
Optimisation of slices derived from older
rats
</td>
<td>
PPTX, JPG, TIF,
ND2,
<15GB
</td>
<td>
Processed summary
shared with Magneuron consortia,
disseminated at conference
</td>
<td>
Digital
(University hard
drives),
physical
(poster)
</td> </tr>
<tr>
<td>
WP Task 7.1
Optimisation of slice culture model of
Parkinson’s disease
</td>
<td>
Transplantation of embryonic VM tissue
into slices
</td>
<td>
PPTX, JPG,
TIFF, ND2, XLS
Excel spreadsheets,
PZF GraphPad
Prism files,
<15GB
</td>
<td>
Processed summary
shared with Magneuron consortia,
disseminated at conference
</td>
<td>
Digital
(University hard
drives),
physical
(poster)
</td> </tr> </table>
<table>
<tr>
<th>
**Data Ref**
</th>
<th>
**Name**
</th>
<th>
**Type and size**
**(Standards / metadata)**
</th>
<th>
**Sharing**
</th>
<th>
**Storage / Archiving**
</th> </tr>
<tr>
<td>
WP Task 7.1
Optimisation of slice culture model of
Parkinson’s disease
</td>
<td>
Functional connections between
transplanted tissue and slices
</td>
<td>
PPTX, JPG, TIF,
ND2,
<20GB
</td>
<td>
Processed summary
shared with Magneuron consortia,
disseminated at conference
</td>
<td>
Digital
(University hard
drives),
physical
(poster)
</td> </tr>
<tr>
<td>
WP Task 7.2/7.3
Transplantation of
MNP-loaded dopaminergic
neurons/precursors to slice cultures
</td>
<td>
Microinjection of SH-
SY5Y cells into slices
</td>
<td>
PPTX, JPG TIF,
ND2,
<6GB
</td>
<td>
Processed summary
shared with
Magneuron consortia
</td>
<td>
Digital
(University hard drives)
</td> </tr>
<tr>
<td>
WP Task 7.2/7.3
Transplantation of
MNP-loaded dopaminergic
neurons/precursors to slice cultures
</td>
<td>
Injection of MNPs directly into slices
</td>
<td>
PPTX, JPG TIF,
ND2,
<32GB
</td>
<td>
Processed summary
shared with
Magneuron consortia
</td>
<td>
Digital
(University hard drives)
</td> </tr>
<tr>
<td>
WP Task7.2/7.3
Transplantation of
MNP-loaded dopaminergic
neurons/precursors to slice cultures
</td>
<td>
Optimisation of protocol for getting
MNPs into cell line and primary cells
</td>
<td>
PPTX, JPG TIF,
ND2,
<1GB
</td>
<td>
Processed summary
shared with
Magneuron consortia
</td>
<td>
Digital
(University hard drives)
</td> </tr>
<tr>
<td>
WP Task 7.2/7.3
Transplantation of
MNP-loaded dopaminergic
neurons/precursors to slice cultures
</td>
<td>
Injection of MNPloaded cells into slices (in
collaboration with
WP5)
</td>
<td>
PPTX, JPG TIF,
ND2, XLS Excel spreadsheets,
PZF GraphPad
Prism files,
<1GB
</td>
<td>
Processed summary
shared with
Magneuron consortia
</td>
<td>
Digital
(University hard drives)
</td> </tr>
<tr>
<td>
WP Task 7.2/7.3
Transplantation of
MNP-loaded dopaminergic
neurons/precursors to slice cultures
</td>
<td>
Establishment of E12
VM dopaminergic precursors
(neurospheres)
</td>
<td>
PPTX, JPG TIF,
ND2,
<1GB
</td>
<td>
Processed summary
shared with
Magneuron consortia
</td>
<td>
Digital
(University hard drives)
</td> </tr> </table>
<table>
<tr>
<th>
**Data Ref**
</th>
<th>
**Name**
</th>
<th>
**Type and size**
**(Standards / metadata)**
</th>
<th>
**Sharing**
</th>
<th>
**Storage / Archiving**
</th> </tr>
<tr>
<td>
WP Task 7.2/7.3
Transplantation of
MNP-loaded dopaminergic
neurons/precursors to slice cultures
</td>
<td>
Images for quantification of TH
and Tuj1 outgrowth in slices and SN/VTA
explants grafted into
slices
</td>
<td>
PPTX, JPG TIF,
ND2,
<1GB
</td>
<td>
Processed summary
shared with
Magneuron consortia
</td>
<td>
Digital
(University hard drives)
</td> </tr>
<tr>
<td>
WP Task 7.2/7.3
Transplantation of
MNP-loaded dopaminergic
neurons/precursors to slice cultures
</td>
<td>
Incubation of nanorods with cells to test compatibility
</td>
<td>
PPTX, JPG TIF,
ND2,
<1GB
</td>
<td>
Processed summary
shared with
Magneuron consortia
</td>
<td>
Digital
(University hard drives)
</td> </tr>
<tr>
<td>
WP Task 7.2/7.3
Transplantation of
MNP-loaded dopaminergic
neurons/precursors to slice cultures
</td>
<td>
Comparison of use of
E11 and E13 rat embryos to generate neurospheres
</td>
<td>
PPTX, JPG TIF,
ND2,
<1GB
</td>
<td>
Processed summary
shared with
Magneuron consortia
</td>
<td>
Digital
(University hard drives)
</td> </tr>
<tr>
<td>
WP Task 7.2/7.3
Transplantation of
MNP-loaded dopaminergic
neurons/precursors to slice cultures
</td>
<td>
Comparison of use of cell penetrating
peptide-linked MNPs and endosomal
MNPs in cells
</td>
<td>
PPTX, JPG TIF,
ND2,
<1GB
</td>
<td>
Processed summary
shared with
Magneuron consortia
</td>
<td>
Digital
(University hard drives)
</td> </tr>
<tr>
<td>
WP Task 7.2/7.3
Transplantation of
MNP-loaded dopaminergic
neurons/precursors to slice cultures
</td>
<td>
Comparison of
effects/efficacy of different
concentrations of
endosomal MNPs on
SH-SY5Y and primary cells
</td>
<td>
PPTX, JPG TIF,
ND2,
<1GB
</td>
<td>
Processed summary
shared with
Magneuron consortia
</td>
<td>
Digital
(University hard drives)
</td> </tr>
<tr>
<td>
WP Task 7.2/7.3
Transplantation of
MNP-loaded dopaminergic
neurons/precursors to slice cultures
</td>
<td>
Testing the effects of the magnet
microarray on MNPs
</td>
<td>
PPTX, JPG TIF,
ND2, AVI
<1GB
</td>
<td>
Processed summary
shared with
Magneuron consortia
</td>
<td>
Digital
(University hard drives)
</td> </tr> </table>
<table>
<tr>
<th>
**Data Ref**
</th>
<th>
**Name**
</th>
<th>
**Type and size**
**(Standards / metadata)**
</th>
<th>
**Sharing**
</th>
<th>
**Storage / Archiving**
</th> </tr>
<tr>
<td>
WP Task 7.2/7.3
Transplantation of
MNP-loaded dopaminergic
neurons/precursors to slice cultures
</td>
<td>
Use of GIRK2 and
Calbindin to differentiate between
SN and VTA dopaminergic neurons
</td>
<td>
PPTX, JPG TIF,
ND2,
<1GB
</td>
<td>
Processed summary
shared with
Magneuron consortia
</td>
<td>
Digital
(University hard drives)
</td> </tr>
<tr>
<td>
WP Task 7.2/7.3
Transplantation of
MNP-loaded dopaminergic
neurons/precursors to slice cultures
</td>
<td>
Use of E17 cortical neurons to test uptake of MNPs
</td>
<td>
PPTX, JPG TIF,
ND2,
<1GB
</td>
<td>
Processed summary
shared with
Magneuron consortia
</td>
<td>
Digital
(University hard drives)
</td> </tr>
<tr>
<td>
WP Task 7.2/7.3
Transplantation of
MNP-loaded dopaminergic
neurons/precursors to slice cultures
</td>
<td>
Assessing the effects of size and position
of magnets on MNPloaded cells
</td>
<td>
PPTX, JPG TIF,
ND2,
<2GB
</td>
<td>
Processed summary
shared with
Magneuron consortia
</td>
<td>
Digital
(University hard drives)
</td> </tr>
<tr>
<td>
WP Task 7.2/7.3
Transplantation of
MNP-loaded dopaminergic
neurons/precursors to slice cultures
</td>
<td>
Injection of neurospheres into striatum of slices
</td>
<td>
PPTX, JPG TIF,
ND2,
<1GB
</td>
<td>
Processed summary
shared with
Magneuron consortia
</td>
<td>
Digital
(University hard drives)
</td> </tr>
<tr>
<td>
WP Task 7.2/7.3
Transplantation of
MNP-loaded dopaminergic
neurons/precursors to slice cultures
</td>
<td>
tempMode
experiments using
neurospheres in
slices (in
collaboration with
WP5)
</td>
<td>
PPTX, JPG TIF,
ND2,
<20GB
</td>
<td>
Processed summary
shared with
Magneuron consortia
</td>
<td>
Digital
(University hard drives)
</td> </tr>
<tr>
<td>
WP Task 7.2/7.3
Transplantation of
MNP-loaded dopaminergic
neurons/precursors to slice cultures
</td>
<td>
Optimisation of D1 and D2 receptor antibody staining
</td>
<td>
PPTX, JPG TIF,
ND2,
<1GB
</td>
<td>
Processed summary
shared with
Magneuron consortia
</td>
<td>
Digital
(University hard drives)
</td> </tr>
<tr>
<td>
**Data Ref**
</td>
<td>
**Name**
</td>
<td>
**Type and size**
**(Standards / metadata)**
</td>
<td>
**Sharing**
</td>
<td>
**Storage / Archiving**
</td> </tr>
<tr>
<td>
WP Task 7.2/7.3
Transplantation of
MNP-loaded dopaminergic
neurons/precursors to slice cultures
</td>
<td>
Optimisation of growth of primary
cells in microfluidic devices
</td>
<td>
PPTX, JPG TIF, ND2, PZF
GraphPad Prism
files
<30GB
</td>
<td>
Processed summary
shared with
Magneuron consortia
</td>
<td>
Digital
(University hard drives)
</td> </tr>
<tr>
<td>
WP Task 7.2/7.3
Transplantation of
MNP-loaded dopaminergic
neurons/precursors to slice cultures
</td>
<td>
Improving the loading of MNPs in free-
floating primary cells
(for injection into slice)
</td>
<td>
PPTX, JPG TIF,
ND2,
<1GB
</td>
<td>
Processed summary
shared with
Magneuron consortia
</td>
<td>
Digital
(University hard drives)
</td> </tr>
<tr>
<td>
WP Task 7.2/7.3
Transplantation of
MNP-loaded dopaminergic
neurons/precursors to slice cultures
</td>
<td>
Loading of primary
VM cells into SN area of the slice
</td>
<td>
PPTX, JPG TIF,
ND2,
<1GB
</td>
<td>
Processed summary
shared with
Magneuron consortia
</td>
<td>
Digital
(University hard drives)
</td> </tr>
<tr>
<td>
WP Task 7.2/7.3
Transplantation of
MNP-loaded dopaminergic
neurons/precursors to slice cultures
</td>
<td>
spaceMode
experiments using
the magnetic device
(made by Koceila at
IC) to promote outgrowth of
transplanted MNP-
loaded primary VM
cells
</td>
<td>
PPTX, JPG TIF, ND2, PZF
GraphPad Prism
files
<20GB
</td>
<td>
Processed summary
shared with
Magneuron consortia
</td>
<td>
Digital
(University hard drives)
</td> </tr> </table>
**3\. Conclusion**
Over the last reporting period, a great effort was made to open our Data to
the general public. We decided to share them mostly through the open
repository Zenodo (https://zenodo.org/).
This repository can be linked to the European Commission Funded Research
(OpenAIRE). A DOI (Digital Object Identifier) is attributed to each dataset.
See for example _https://zenodo.org/communities/locco_ for the IC partner. We
will continue this effort over the coming months to ensure that all results
and products of the Magneuron project are being shared on an open repository.
Regarding the archiving of the numerous data that have been produced, diverse
solutions have been proposed to ensure backups and accessibility (NAS, zenodo,
dropbox, hard drives).
|
https://phaidra.univie.ac.at/o:1140797
|
Horizon 2020
|
1067_HNSciCloud_687614.md
|
Clarification in section 3 of the introduction that this first version of the
Data Management Plan includes first examples from the scientific use cases,
and that others will be added during subsequent document revisions
</td>
<td>
David Foster, Bob
Jones (CERN)
</td> </tr>
<tr>
<td>
V1.0
</td>
<td>
</td>
<td>
Final Version
</td>
<td>
Rachida Amsaghrou
(CERN)
</td> </tr> </table>
**Document Approval**
<table>
<tr>
<th>
**Issue**
</th>
<th>
**Date**
</th>
<th>
**Name**
</th> </tr>
<tr>
<td>
V0.1
</td>
<td>
04/03/2016
</td>
<td>
First draft for circulation within the collaboration
</td> </tr>
<tr>
<td>
V0.2
</td>
<td>
23/03/2016
</td>
<td>
Final draft revised by the HNSicCloud project office
</td> </tr> </table>
**Executive Summary**
This document describes the initial Data Management Plan (DMP) for the
HNSciCloud project. It addresses Project administration data collected as part
of the execution and management of the Pre-Commercial Procurement (PCP)
process within the project as well as data used as part of the scientific use
cases to be deployed on the cloud services.
The DMP for the scientific use cases is based on the Guidelines on Data
Management in the Horizon 2020 document version 2.0 of 30 October 2015\.
These guidelines state:
_“Participating projects will be required to develop a Data Management Plan
(DMP), in which they will specify what data will be open.”_
As such, this DMP focuses primarily on data sharing and re-use and in
particular on the following issues (Annex 1 of the H2020 DMP Guidelines):
* _What types of data will the project generate/collect?_
* _What standards will be used?_
* _How will this data be exploited and/or shared/made accessible for verification and re-use? If data cannot be made available, explain why._
* _How will this data be curated and preserved?_
Regular updates to this data management plan will be made according to the
following draft schedule:
<table>
<tr>
<th>
**Date**
</th>
<th>
**Milestone**
</th>
<th>
**Issue(s) to be addressed in revision**
</th> </tr>
<tr>
<td>
May 2016
</td>
<td>
Tender publication
</td>
<td>
Were all the use-cases retained?
</td> </tr>
<tr>
<td>
April 2017
</td>
<td>
Start of prototype phase
</td>
<td>
Could all the use-cases be satisfied?
</td> </tr>
<tr>
<td>
January 2018
</td>
<td>
Start of pilot phase
</td>
<td>
Could all the use-cases be deployed?
</td> </tr>
<tr>
<td>
June 2018
</td>
<td>
End of Project
</td>
<td>
Are the plans for preservation beyond the end of the project still valid?
</td> </tr> </table>
# Introduction
This Data Management Plan (DMP) addresses two distinct sets of data to be
managed by the HNSciCloud project:
* **Project administration data** : data collected as part of the execution and management of the Pre-Commercial Procurement (PCP) process within the project
* **Scientific use cases** : data managed by some of the use cases that will be supported during the pilot phase by the cloud services to be developed and procured by the PCP process.
These two sets of data are treated separately by the project and in this
document.
This is the initial version of the data management plan. It explains the
provisions for project administration data and the general approach for
scientific use cases with first examples from high energy physics use cases.
The data management plan will be reviewed and updated at major milestones in
the project, once the final set of use cases has been refined, and with the
following tentative schedule:
<table>
<tr>
<th>
**Date**
</th>
<th>
**Milestone**
</th>
<th>
**Issue(s) to be addressed in revision**
</th> </tr>
<tr>
<td>
May 2016
</td>
<td>
Tender publication
</td>
<td>
Were all the use-cases retained?
</td> </tr>
<tr>
<td>
April 2017
</td>
<td>
Start of prototype phase
</td>
<td>
Could all the use-cases be satisfied?
</td> </tr>
<tr>
<td>
January 2018
</td>
<td>
Start of pilot phase
</td>
<td>
Could all the use-cases be deployed?
</td> </tr>
<tr>
<td>
June 2018
</td>
<td>
End of Project
</td>
<td>
Are the plans for preservation beyond the end of the project still valid?
</td> </tr> </table>
**Table 1 - Tentative Data Management Plan Update Schedule**
# Project administration data
This section describes the plan for the data to be managed as part of the
execution and management of the Pre-Commercial Procurement (PCP) process
within the project.
The project office (WP1) will gather and store contact details (name, email
address, role within company, company name, and company postal address)
provided by individuals representing companies and organisations interested or
participating in the PCP process.
Such details will be used by the consortium as part of the communication plan
(WP7) and procurement process (WP2). The contact details will also be used as
the basis for statistics summarizing the level and scope of engagement in the
procurement process and will be reported (anonymously) in the mandatory
deliverable reports. Beyond the summary statistics reported in the project
deliverables, such information will be restricted to the consortium members.
The data will be maintained beyond the lifetime of the project as email lists
and entries in the supplier database of the lead procurer, which may be used
for subsequent procurement activities in the cloud services domain.
# Scientific use cases
This section describes the plan for the data to be managed as part of the
scientific use cases that will be deployed during the pilot phase of the pre-
commercial procurement process.
As a general rule, data that is created in the Cloud will be copied back to
institutional and/or discipline-specific repositories for long-term
preservation, curation and sharing. Many of these repositories (e.g. the WLCG
Tier0 and Tier1 sites) are in the process of self-certification according to
ISO 16363:
_ISO 16363:2012 defines a recommended practice for assessing the
trustworthiness of digital repositories. It is applicable to the entire range
of digital repositories. ISO 16363:2012 can be used as a basis for
certification._
As such, the procured services will not be the mechanism by which data
preservation for the use-cases presented below will be ensured. This matches
the hybrid public-commercial cloud model where the publicly operated data
centres provide data preservation facilities.
Once the PCP model has been proven to work, we may begin entrusting it with
the procurement of data preservation services, possibly initially on ISO
16363-certified sites. However, this will be outside the scope of the
HNSciCloud PCP project.
A number of the experiments that form part of the High Energy Physics (HEP)
Use Case, namely the Belle II experiment at KEK and the four main LHC
experiments at CERN (ALICE, ATLAS, CMS and LHCb) collaborate through both the
Worldwide LHC Computing Grid (WLCG) project (for data processing, distribution
and analysis) as well as DPHEP (Data Preservation of Long-term Analysis in
HEP). DPHEP maintains a portal through which information on these and other
experiments can be obtained, the status of their data preservation activities
and, in some cases, access to data released through Open Data policies. There
is significant overlap between the data preservation plans and the H2020 DMP
guidelines. For these experiments, we present the current status / plans in
the agreed DPHEP format whereas for the LHC experiments it is also presented
according to the H2020 guidelines (Annex 1). (The DPHEP format is quite
detailed but emphasizes that these “plans” are backed by “implementation” and
that there is clear evidence of data sharing and re-use. Furthermore, the data
preservation services work at a scale of 100TB – 100+PB, with an outlook to
perhaps 10EB and a duration of several decades).
This version of the Data Management Plan includes initial examples from the
scientific use cases, others will be added during subsequent document
revisions.
## H2020 Data Management Plans for the LHC Experiments
These Data Management Plans were elaborated at a Data Preservation (DPHEP)
workshop in Lisbon in February 2016 with the assistance of two of the co-
chairs of the Research Data Alliance (RDA) Interest Group on Advanced Data
Management Plans (ADMP). Representatives from the LHC experiments as well as
HNSciCloud / WLCG Tier0/1 sites contributed to the debate.
<table>
<tr>
<th>
**H2020 Annex 1 Guidelines**
</th> </tr>
<tr>
<td>
Guideline
</td>
<td>
Guidance
</td>
<td>
Statement
</td> </tr>
<tr>
<td>
Data set reference and name
</td>
<td>
_Identifier for the data set to be produced._
</td>
<td>
This Data Management Plan (DMP) refers to the data set generated by the 4 main
experiments (also know as “Collaborations”) currently taking data at CERN’s
Large Hadron Collider (LHC).
These experiments are ALICE,
ATLAS, CMS and LHCb. For the purpose of this plan, we refer to this data set
as “The LHC Data”.
In terms of Data Preservation, the software, its environment and associated
documentation must also be preserved (see below).
Further details can be found at the DPHEP portal site, with entries for each
of the above experiments:
* _http://hep-project-dphepportal.web.cern.ch/content/alice_
* _http://hep-project-dphepportal.web.cern.ch/content/atlas_
* _http://hep-project-dphepportal.web.cern.ch/content/cms_
* _http://hep-project-dphepportal.web.cern.ch/content/lhcb_
</td> </tr>
<tr>
<td>
Data set description
</td>
<td>
_Description of the data that will be generated or collected, its origin (in
case it is collected), nature and scale and to whom it could be useful,_
</td>
<td>
The 4 experiments referenced above have clear scientific goals as described in
their Technical
Proposals and via their Websites (see _https://greybook.cern.ch/greybook/_
for the official catalogue of all CERN
</td> </tr> </table>
<table>
<tr>
<th>
</th>
<th>
</th>
<th>
_and whether it underpins a scientific publication. Information on the
existence (or not) of similar data and the possibilities for integration and
reuse._
</th>
<th>
experiments that is maintained by the CERN Research Board).
Hundreds of scientific publications are produced annually.
The data is either collected by the massive detectors of the above experiments
(the raw data), is derived from it, or is the result of the simulation of
physics processes according to theoretical models and the simulated response
of the detector to these models.
Similar data – but at lower energies – have been produced by previous
experiments and comparisons of results from past, present and indeed future
experiments is routine. (See also the DPHEP portal for further information:
_http://hep-projectdphep-portal.web.cern.ch/_ )
The data behind plots in publications has been made available since many
decades via an online database: _http://hepdata.cedar.ac.uk/_ .
Re-use of the data is made by theorists, by the collaborations themselves, by
scientists in the wider context as well as for Education and Outreach.
</th> </tr>
<tr>
<td>
Standards metadata
</td>
<td>
and
</td>
<td>
_Reference to existing suitable standards of the discipline. If these do not
exist, an outline on how and what metadata will be created._
</td>
<td>
The 4 main LHC experiments work closely together through the WLCG
Collaboration on data management (and other) tools and applications. At least
a number of these have found use outside the HEP community but their initial
development has largely been driven by the scale and timeline of the above.
The ROOT framework, in particular, is used as “I/O library”
</td> </tr> </table>
<table>
<tr>
<th>
</th>
<th>
</th>
<th>
(and much more) but all LHC experiments and is a _de-facto_ standard within
HEP, also across numerous other laboratories.
The meta-data catalogues are typically experiment-specific although globally
similar. The “open data release” policies foresee the available of the
necessary metadata and other “knowledge” to make the data usable (see below).
</th> </tr>
<tr>
<td>
Data sharing
</td>
<td>
_Description of how data will be shared, including access procedures, embargo
periods (if any), outlines of technical mechanisms for dissemination and
necessary software and other tools for enabling re-use, and definition of
whether access will be widely open or restricted to specific groups.
Identification of the repository where data will be stored, if already
existing and identified, indicating in particular the type of repository
(institutional, standard repository for the discipline, etc.)._
_In case the dataset cannot be shared, the reasons for this should be
mentioned (e.g. ethical, rules of personal_
</td>
<td>
The 4 LHC experiments have policies for making data available, including
reasonable embargo periods, together with the provision of the necessary
software, documentation and other tools for re-use.
Data releases through the CERN Open
Data Portal
( _http://opendata.cern.ch/_ ) are published with accompanying software and
documentation. A dedicated education section provides access to tailored
datasets for self-
supported study or use in classrooms. All materials are shared with Open
Science licenses (e.g. CC0 or CC-BY) to enable others to build on the results
of these experiments. All materials are also assigned a persistent identifier
and come with citation recommendations.
</td> </tr> </table>
<table>
<tr>
<th>
</th>
<th>
_data, intellectual property, commercial, privacy-related, security-related)._
</th>
<th>
</th> </tr>
<tr>
<td>
Archiving and preservation (including storage and backup)
</td>
<td>
_Description of the procedures that will be put in place for longterm
preservation of the data. Indication of how long the data should be preserved,
what is its approximated end volume, what the associated costs are and how
these are planned to be covered._
</td>
<td>
The long-term preservation of LHC data is the responsibility of the Tier0 and
Tier1 sites that form part of the
WLCG Collaboration. A
Memorandum of Understanding
(MoU) outlines the responsibilities of sites that form part of this
collaboration (Tier0, Tier1s and
Tier2s).
In the case of the Tier0 and Tier1s, this includes “curation” of the data with
at least two copies of the data maintained worldwide (typically 1 copy at CERN
and at least 1 other copy distributed over the Tier1 sites for that
experiment).
The costs for data storage and “bit preservation” form part of the resource
requests that are made regularly to the funding agencies. A simply cost model
shows that the annual storage costs – even including the anticipated growth –
go down with time and remain within the funding envelope foreseen. (The
integrated costs of course rise).
Personnel from the Tier0 and Tier1 sites have followed training in ISO 16363
certification – A Standard for Trusted Digital Repositories – and self-
certification of these sites is underway.
</td> </tr>
<tr>
<td>
</td>
<td>
</td>
<td>
Any data generated on external resources, e.g. Clouds, is copied back for
long-term storage to the Tier0 or Tier1 sites. The eventual long-term storage
/ preservation of data in the Cloud would require not only that such services
are cost effective but also that they are certified according to agreed
standards, such as ISO 16363.
The data themselves should be preserved for a number of decades – at least
during the active data taking and analysis period of the LHC machine and
preferably until such a time as a future machine is operational and results
from it have been compared with those from the LHC.
The total data volume – currently of the order of 100PB – is expected to
eventually reach 5-10 EB (in circa 2035 – 2040).
Additional services are required for the long-term preservation of
documentation (digital libraries), the software to process and/or analyse the
data, as well as the environment needed to run these software packages.
Such services will be the subject of the on-going self-certification.
</td> </tr> </table>
## H2020 Data Management Plans for CTA
<table>
<tr>
<th>
**H2020 Annex 1 Guidelines**
</th> </tr>
<tr>
<td>
Guideline
</td>
<td>
Guidance
</td>
<td>
Statement
</td> </tr>
<tr>
<td>
Data set reference and name
</td>
<td>
_Identifier for the data set to be produced._
</td>
<td>
The CTA project is an initiative to build the next generation ground-based
very high energy gammaray instrument. It will serve as an open observatory to
a wide astrophysics community and will provide a deep insight into the
nonthermal high-energy universe.
</td> </tr>
<tr>
<td>
Data set description
</td>
<td>
_Description of the data that will be generated or collected, its origin (in
case it is collected), nature and scale and to whom it could be useful, and
whether it underpins a scientific publication. Information on the existence
(or not) of similar data and the possibilities for integration and reuse._
</td>
<td>
</td> </tr>
<tr>
<td>
Standards and metadata
</td>
<td>
_Reference to existing suitable standards of the discipline. If these do not
exist, an outline on how and what metadata will be created._
</td>
<td>
</td> </tr>
<tr>
<td>
Data sharing
</td>
<td>
_Description of how data will be shared, including access procedures, embargo
periods (if any),_
</td>
<td>
</td> </tr>
<tr>
<td>
</td>
<td>
_outlines of technical mechanisms for dissemination and necessary software and
other tools for enabling re-use, and definition of whether access will be
widely open or restricted to specific groups. Identification of the repository
where data will be stored, if already existing and identified, indicating in
particular the type of repository (institutional, standard repository for the
discipline, etc.)._
_In case the dataset cannot be shared, the reasons for this should be
mentioned (e.g. ethical, rules of personal data, intellectual property,
commercial, privacy-related, securityrelated)._
</td>
<td>
</td> </tr>
<tr>
<td>
Archiving and preservation (including storage and backup)
</td>
<td>
_Description of the procedures that will be put in place for long-term
preservation of the data. Indication of how long the data should be preserved,
what is its approximated end volume, what the associated costs are and how
these are planned to be covered._
</td>
<td>
</td> </tr> </table>
## Data Management Plans / Status for HEP Experiments
These plans are presented in tabular form as on the DPHEP portal site:
_http://hep-projectdphep-portal.web.cern.ch/_ . These plans will be revised
at regular intervals and the site should be consulted for the most up-to-date
information.
They were prepared as a result of a DPHEP workshop 1 held at CERN in June
2015 and form part of the DPHEP Status Report (DOI: _10.5281/zenodo.34591_ ).
The workshop aimed to:
1. Establish the motivation for long-term data preservation in HEP in terms of succinct Use
Cases o Are there a common set of Use Cases, such as those that were recently
agreed for the 4 main LHC experiments but in a more global scope?
2. Review the existing areas of "Common Projects" o Can these be extended (similarly) from their current scope - often LHC - to become more global?
3. Perform a site-experiment round-table to capture the current situation HEP-wide o These are summarized in the Status Report and in the tables shown below.
Re-use covers re-use by the Collaboration that initially acquired and analysed
the data, by theorists (e.g. to check their models), by the wider scientific
community, by the general public and for education / outreach purposes.
## Belle and Belle II
<table>
<tr>
<th>
**Preservation Aspect**
</th>
<th>
**Status (Belle II)**
</th> </tr>
<tr>
<td>
**Bit Preservation**
</td>
<td>
Preamble: The central computing system at KEK is replaced every four years.
The main user must be Belle II until the data-taking ends (in 2024).
Belle: mDST (necessary for physics analysis) is stored on the disk as well as
the tape library. The data is still frequently read by active analysis users.
All data will be preserved by migrating to the next system. We experienced
data loss in the previous data migration. Main causes of this trouble were the
short migration period, miscommunication between researchers and operators and
the lack of validation scheme after the migration. We will improve the process
of the future migration.
</td> </tr> </table>
<table>
<tr>
<th>
**Data**
</th>
<th>
Belle: raw data (1PB) and other format (incl. simulation, ~1.5PB) are stored
at the KEK central computing system. This data will be migrated to the next
system, at least (data will be preserved until 2020). However, there is no
plan thereafter, because the data will be superseded by Belle II. And a full
set of mDST was copied at PNNL in USA.
Belle II: data taking has not yet started. But raw data will be stored at KEK
and another set will be copied in some places outside Japan. Also, the
replicas of the mDST will be distributed to the world-wide collaborated
computing sites.
</th> </tr>
<tr>
<td>
**Documentation**
</td>
<td>
Belle: all documentation is stored in the local web server and INDICO system.
They are still active and accessible, but not well catalogued at all.
Belle II: Using twiki, invenio, svn and INDICO system.
</td> </tr>
<tr>
<td>
**Software**
</td>
<td>
Belle: software has been fixed since 2009 except for some patches. The
baseline of the OS is still SL5, but it was migrated to SL6. In parallel, the
Belle data I/O tool is developed and integrated in the Belle II software.
Thanks to this, the Belle data can be analysed under the Belle II software
environment. Other Belle handmade analysis tools are being integrated as well.
Software version is maintained with SVN.
Belle II: basic features witch are necessary for the coming data taking have
been implemented. But need more tuning and improvement. The software version
is maintained by SVN. SL5/6 32/64-bits, Ubuntu 14.02 LTS are supported
</td> </tr>
<tr>
<td>
**Uses Case(s)**
</td>
<td>
Continued analysis by Belle.
</td> </tr>
<tr>
<td>
**Target Community(ies)**
</td>
<td>
Belle and Belle II
</td> </tr>
<tr>
<td>
**Value**
</td>
<td>
Quantitative measures (# papers, PhDs etc) exist
Belle: During the data taking period (1999-2010), averaged number of journal
publications is ~30 papers/year and the number of PhDs is ~12/year. After the
data-taking, a moderate decreasing tendency can be seen, but the analysis is
still active.
(~20 publications/year and ~7 PhDs/year).
</td> </tr>
<tr>
<td>
**Uniqueness**
</td>
<td>
Belle: Comparing with the data from Hadron colliders, the Belle data has the
advantage of analysing the physics modes with missing energy and neutral
particles. Until the Belle II starts, these data are unique as well as _BABAR_
’s data (see _http://hepproject-dphep-portal.web.cern.ch/content/babar_ ) .
Belle II: Belle data will be superseded by 2020. After that, the data must be
unique samples.
</td> </tr>
<tr>
<td>
**Resources**
</td>
<td>
Belle / Belle II: at some stage, the Belle data must be treated as a part of
the Belle II data, and resources for the Belle data will be included in the
Belle II computing/human resources.
</td> </tr>
<tr>
<td>
**Status**
</td>
<td>
Construction of the Belle II detector/SuperKEKB accelerator as well as of the
Belle II distributed computing system.
</td> </tr>
<tr>
<td>
**Issues**
</td>
<td>
A couple of items have to be implemented in the Belle II software framework to
analyse the Belle data. Further check for the performance and reproducibility
is also necessary.
</td> </tr>
<tr>
<td>
**Outlook**
</td>
<td>
Expect to be able to analyse the Belle data within the Belle II software
framework. It provides us with less human resource to maintain the Belle
software, and longer lifetime for the Belle data analysis.
</td> </tr> </table>
## ALICE
<table>
<tr>
<th>
**Preservation Aspect**
</th>
<th>
**Status (ALICE)**
</th> </tr>
<tr>
<td>
**Bit Preservation**
</td>
<td>
On tape: data integrity check during each access request
On disk: periodically integrity checks
</td> </tr>
<tr>
<td>
**Data**
</td>
<td>
7.2 PB of raw data were acquired between 2010 and 2013 which is stored on tape
and disk in 2 replicas.
</td> </tr>
<tr>
<td>
**Documentation**
</td>
<td>
ALICE analysis train system & bookkeeping in Monalisa DB: for the last 3-4
years
Short introduction along with the analysis tools on Opendata
</td> </tr>
<tr>
<td>
**Software**
</td>
<td>
The software package “AliRoot” is published on
CVMFS
</td> </tr>
<tr>
<td>
</td>
<td>
For the Open Access the data and code packages are available on Opendata
(http://opendata.cern.ch/)
</td> </tr>
<tr>
<td>
**Uses Case(s)**
</td>
<td>
Educational purposes like the CERN Master Classes
Outreach activities
</td> </tr>
<tr>
<td>
**Target Community(ies)**
</td>
<td>
Re-use of data within the collaboration(s), sharing with the wider scientific
community, Open Access releases
</td> </tr>
<tr>
<td>
**Value**
</td>
<td>
Analysis, publications and PhDs continue to be produced
</td> </tr>
<tr>
<td>
**Uniqueness**
</td>
<td>
Unique data sets from the LHC in pp and HI
Similar data can only be collected by the other LHC experiments
</td> </tr>
<tr>
<td>
**Resources**
</td>
<td>
Since the experiment is still working, budget and FTEs are shared with the
operation of computing centre
</td> </tr>
<tr>
<td>
**Status**
</td>
<td>
First data from 2010 has been released to the public
(8 TB ≈ 10% of data)
Some analysis tools are available on Opendata for the
CERN Master class program
</td> </tr>
<tr>
<td>
**Issues**
</td>
<td>
Improve user interface
The interaction with the open-access portal is very slow due to long
communication times. E.g. the uploading of data is done by some people in the
IT department. The interaction via an automated website would be faster.
</td> </tr>
<tr>
<td>
**Outlook**
</td>
<td>
Ongoing analysis within the collaboration
Making realistic analysis available on the open-access portal
Deployment of more data
</td> </tr> </table>
## ATLAS
<table>
<tr>
<th>
**Preservation Aspect**
</th>
<th>
**Status (ATLAS)**
</th> </tr> </table>
<table>
<tr>
<th>
**Bit Preservation**
</th>
<th>
Non-Reproducible data exist in two or more geographically disparate copies
across the WLCG. The site bit preservation commitments are defined in the WLCG
Memorandum of Understanding 2 . All data to be reprocessed with most recent
software to ensure longevity.
</th> </tr>
<tr>
<td>
**Data**
</td>
<td>
Non-reproducible: RAW physics data, calibration, metadata, documentation and
transformations
(jobs).
Derived data: formats for physics analysis in collaboration, formats
distributed for education and outreach. Greatly improved by common derived
data production framework in run 2. Published results in journals and HEPDATA.
Sometimes with analysis published in Rivet and RECAST. Format lifetimes are
hard to predict, but on current experience are 5-10 years, and changes are
likely to coincide with the gaps between major running periods.
</td> </tr>
<tr>
<td>
**Documentation**
</td>
<td>
Software provenance of derived data stored in Panda database. Numerous twikis
available describing central and analysis level software. Interfaces such as
AMI and COMA contain metadata.
The publications themselves are produced via the physics result approval
procedures set out in ATLGEN-INT-2015-001 held in CDS; this sets out in detail
the expected documentation within papers and the supporting documentation
required.
</td> </tr>
<tr>
<td>
**Software**
</td>
<td>
Compiled libraries and executable of the “Athena” framework are published on
CVMFS. Software versioning is maintained on the CERN subversion server.
</td> </tr>
<tr>
<td>
**Uses Case(s)**
</td>
<td>
Main usage of data: future analysis within the collaboration
Further usage: review in collaboration and potential for outreach
</td> </tr>
<tr>
<td>
**Target Community(ies)**
</td>
<td>
Re-use of data (new analyses) within the
collaboration, open access sharing of curated data
</td> </tr>
<tr>
<td>
**Value**
</td>
<td>
Publications by the collaboration. Training of PhDs
</td> </tr>
<tr>
<td>
**Uniqueness**
</td>
<td>
Unique data sets (both pp and HI) being acquired between now and 2035\.
Similar data only acquired by other LHC experiments
</td> </tr>
<tr>
<td>
**Resources**
</td>
<td>
The active collaboration shares the operational costs with the WLCG computing
centres.
</td> </tr>
<tr>
<td>
**Status**
</td>
<td>
ATLAS replicates the non-reproducible data across the WLCG and maintains
database of software provenance to reproduce derived data. Plans to bring run
1 data to run 2 status. Master-classes exercises available on CERN Open Data
Portal, expansion considered. Some analyses published on
Rivet/RECAST.
</td> </tr>
<tr>
<td>
**Issues**
</td>
<td>
Person-power within the experiment is hard to find. Validation of future
software releases against former processing crucial. No current plans beyond
the lifetime of the experiment.
</td> </tr>
<tr>
<td>
**Outlook**
</td>
<td>
On-going development of RECAST with Rivet and collaboration with CERN IT and
the other LHC experiments via the CERN Analysis Portal as solution to problem
of analysis preservation.
</td> </tr> </table>
## CMS
<table>
<tr>
<th>
**Preservation Aspect**
</th>
<th>
Status (CMS)
</th> </tr>
<tr>
<td>
**Bit Preservation**
</td>
<td>
Follow WLCG procedures and practices
Check checksum in any file transfer
</td> </tr>
<tr>
<td>
**Data**
</td>
<td>
RAW data stored at two different T0
1. 0.35 PB 2010
2. 0.56 PB 2011
3. 2.2 PB 2012
4. 0.8 PB heavy-ion 2010-2013 Legacy reconstructed data (AOD):
* 60 TB 2010 data reprocessed in 2011 with CMSSW42 (no corresponding MC)
* 200 TB 2011 and 800 TB 2012 reprocessed in 2013 with CMSSW53 (with partial corresponding MC for 2011, and full MC for 2012)
</td> </tr> </table>
<table>
<tr>
<th>
</th>
<th>
Several reconstruction reprocessing’s
The current plan: keep a complete AOD reprocessing
(in addition to 2×RAW)
* no reconstructed collision data have yet been deleted, but deletion campaigns are planned.
* most Run 2 analyses will use miniAOD’s which are significantly smaller in size
Open data: 28 TB of 2010 collision data released in 2014, and 130 TB of 2011
collision data to be released in 2015 available in CERN Open Data Portal
(CODP)
Further public releases will follow.
</th> </tr>
<tr>
<td>
**Documentation**
</td>
<td>
Data provenance included in data files and further information collected in
CMS Data Aggregation System (DAS)
Analysis approval procedure followed in CADI
Notes and drafts stored in CDS
Presentations in Indico
User documentation in Twiki serves mainly the current operation and usage
Basic documentation and examples provided for open data users in CODP
Set of benchmark analyses reproducing published results with open data in
preparation, to be added to CODP
</td> </tr>
<tr>
<td>
**Software**
</td>
<td>
CMSSW open source and available in github and in CVFMS
Open data: VM image (CERNVM), which builds the appropriate environment from
CVFMS, available in COPD
</td> </tr>
<tr>
<td>
**Uses Case(s)**
</td>
<td>
Main usage: analysis within the collaboration Open data: education, outreach,
analysis by external users
</td> </tr>
<tr>
<td>
**Target Community(ies)**
</td>
<td>
Main target: collaboration members
Open data: easy access to old data for collaboration members and external
users
</td> </tr>
<tr>
<td>
**Value**
</td>
<td>
Data-taking and analysis is on-going, more than 400 publications by CMS
</td> </tr> </table>
<table>
<tr>
<th>
</th>
<th>
Open data: educational and scientific value, societal impact
</th>
<th>
</th> </tr>
<tr>
<th>
**Uniqueness**
</th>
<th>
Unique, only LHC can provide such data in any foreseeable time-scale
</th> </tr>
<tr>
<th>
**Resources**
</th>
<th>
Storage within the current computing resources
Open data: storage for the 2010-2011 open data provided by CERN IT, further
requests to be allocated through RRB
</th> </tr>
<tr>
<th>
**Status**
</th>
<th>
Bit preservation guaranteed in medium term within the CMS computing model and
agreements with computing tiers, but the long-term preservation beyond the
life-time of the experiment not yet addressed (storage, agreements,
responsibilities),
Open data release has resulted in
* data and software access independent from the experiment specific resources
* a timely capture of the basic documentation, which, although limited and incomplete, makes data reuse in long term possible common solutions and services.
</th> </tr>
<tr>
<th>
**Issues**
</th>
<th>
Competing with already scarce resources needed by an active experiment.
Knowledge preservation, lack of persistent information of the intermediate
analysis steps to be addressed by the CERN Analysis Preservation framework
(CAP)
* CMS has provided input for the data model and user interface design, and defining pipelines for automated ingestion from CMS services.
* The CAP use-cases are well acknowledged by CMS.
* CAP will be a valuable tool to start data preservation while the analysis is active.
Long-term reusability: freezing environment (VM) vs evolving data: both
approaches will be followed and CMS tries to address the complexity of the CMS
data format
</th> </tr>
<tr>
<th>
**Outlook**
</th>
<th>
The impact of the open data release was very positive:
• Well received by the public and the funding
</th> </tr>
<tr>
<td>
Initial Data Management Plan
</td>
<td>
**24**
</td> </tr> </table>
<table>
<tr>
<th>
</th>
<th>
agencies;
* No unexpected additional workload to the collaboration;
* The data are in use.
Excellent collaboration with CERN services developing data preservation and
open access services and with DASPOS
* Common projects are essential for long-term preservation
* Benefit from expertise in digital archiving and library services
* Fruitful discussion with other experiments. Long-term vision and planning is difficult for ongoing experiments:
* DPHEP offers a unique viewpoint. Next steps for CMS:
* Stress-test CERN Open Data Portal with the new data release
* Develop and deploy the CMS-specific interface to CERN Analysis Preservation framework.
</th> </tr> </table>
## LHCb
<table>
<tr>
<th>
**Preservation aspect**
</th>
<th>
**Status (LHCb)**
</th> </tr>
<tr>
<td>
**Bit preservation**
</td>
<td>
Data and MC samples are stored on tape and on disk. Two copies of raw data on
tape; 1 copy on tape of full reconstructed data (FULL.DST, which contains also
raw data); 4 copies of stripped data (DST) on disk for the last (N)
reprocessing. Two copies for the N-1 reprocessing. One archive replica on
tape.
</td> </tr>
<tr>
<td>
**Data**
</td>
<td>
For the long term future, LHCb plans to preserve only a legacy version of data
and MC samples. Run 1 legacy data: 1.5 PB (raw), 4 PB FULL.DST, and 1.5
stripped DST. Run 1 legacy MC: 0.8 PB DST.
Open data: LHCb plans to make 50% of analysis level data (DST) public after 5
years, 100% public 10 years after it was taken. The data will be made public
via the
Open Data portal ( _http://opendata.cern.ch/_ )
Samples for educational purposes are already public for the International
Masterclass Program and
</td> </tr> </table>
<table>
<tr>
<th>
</th>
<th>
accessible also via the Open Data portal (For Education area).
</th> </tr>
<tr>
<td>
**Documentation**
</td>
<td>
Data: dedicated webpages for data and MC samples, with details about all
processing steps.
Software: twiki pages with software tutorials, mailinglists.
Documentation to access and analyse masterclasses samples is available on LHCb
webpage and on the OpenData portal.
</td> </tr>
<tr>
<td>
**Software**
</td>
<td>
Software is organised as hierarchy of projects containing packages, each of
which contains some c++ or python code. Three projects for the framework
(Gaudi, LHCb, Phys), several “component” projects for algorithms (e.g. Lbcom,
Rec, Hlt, Analysis), one project per application containing the application
configuration (e.g. Brunel, Moore, DaVinci). Software repository: SVN.
Open access: once data will be made public, software to work with DST samples
will be released with the necessary documentation.
A virtual machine image of LHCb computing environment allows to access and
analyse the public samples available on the Open Data portal
</td> </tr>
<tr>
<td>
**Use cases**
</td>
<td>
New analysis on legacy data; analysis reproduction; outreach and education.
</td> </tr>
<tr>
<td>
**Targeted communities**
</td>
<td>
LHCb collaboration; physicists outside the collaboration; general public.
</td> </tr>
<tr>
<td>
**Value**
</td>
<td>
LHCb complementary to other LHC experiments.
</td> </tr>
<tr>
<td>
**Uniqueness**
</td>
<td>
Unique samples of pp an HI collisions collected in the forward region.
</td> </tr>
<tr>
<td>
**Resources**
</td>
<td>
Dedicated working group within LHCb computing group.
</td> </tr>
<tr>
<td>
**Status**
</td>
<td>
Legacy software and data releases defined. Development of a long-term future
validation framework ongoing. Masterclasses samples and analysis software
available via the Open Data portal. Collaboration with CERN IT and other LHC
experiments for the development of an analysis preservation framework.
</td> </tr>
<tr>
<td>
**Issues**
</td>
<td>
Main issue is manpower.
</td> </tr>
<tr>
<td>
**Outlook**
</td>
<td>
Collaboration with CERN IT and other LHC experiments on the Open Data portal
and the analysis
</td> </tr> </table>
## Summary
We have presented Data Management Plans for some of the main communities that
are candidates to use procured services through HNSciCloud. Several of these
communities have elaborated DMPs prior to the start of the project, with a
focus on data preservation, sharing, re-use and verification of their results.
Whilst these plans may be more detailed than is required by the H2020
guidelines, they nevertheless reflect the concrete work in these areas and
provide a solid basis on which data management-related work in the project can
be evaluated.
<table>
<tr>
<th>
</th>
<th>
preservation framewok.
Enrich the Open Data portal with additional
masterclasses exercise and real LHCb analysis. Exploit VM technology to
distribute LHC computing environment.
</th> </tr> </table>
|
https://phaidra.univie.ac.at/o:1140797
|
Horizon 2020
|
1069_Open4Citizens_687818.md
|
1\. Executive Summary 8
2\. Introduction 8
2.1. Structure of the document 8
2.2. Relation to other deliverables 9
3\. Data Summary 9
3.1. What is the purpose of the data collection/generation and its relation to
the objectives of the project? 9
3.1.1. Research data supports evaluation and dissemination activities 10
3.1.2. Data in the O4C platform is available for use in hackathons and reuse
beyond these events 10
3.2. What types and formats of data will the project generate/collect? 11
3.2.1. Research data 11
3.2.2. Data in the O4C platform 11
3.3. Will you re-use any existing data and how? 11
3.4. What is the origin of the data? 11
3.5. What is the expected size of the data? 12
3.6. To whom might it be useful ('data utility’) 12
4\. FAIR Data 13
4.1. Making data findable including provisions for metadata 4.1.1. Are the
data produced and/or used in the project discoverable with metadata, 14
identifiable and locatable by means of a standard identification mechanism
(e.g. persistent and unique identifiers such as Digital Object Identifiers)?
14
4.2. Making data openly accessible 14
4.2.1. Which data produced and/or used in the project will be made openly
available as the default? 14
4.2.2. How will the data be made accessible (e.g. by deposition in a
repository)? 14
4.2.3. What methods or software tools are needed to access the data? 15
4.3. Making data interoperable 15
4.3.1. Are the data produced in the project interoperable? 15
4.3.2. What data and metadata vocabularies, standards or methodologies will
you follow to make your data interoperable? 15
4.4. Increase data re-use (through clarifying licences) 15
4.4.1. How will the data be licensed to permit the widest re-use possible? 15
4.4.2. When will the data be made available for re-use? 15
4.4.3. Are the data produced and/or used in the project useable by third
parties, in particular after the end of the project? If the re-use of some
data is restricted, explain why 15
4.4.4. How long is it intended that the data remains re-usable? 16
4.4.5. Are data quality assurance processes described? 16
5\. Allocation of Resources 16
5.1. What are the costs for making data FAIR in your project? 16
5.2. How will these be covered? 16
5.3. Who will be responsible for data management in your project? 17
5.4. Are the resources for long term preservation discussed (costs and
potential value, who decides and how what data will be kept and for how long)?
17
6\. Data Security 17
6.1. What provisions are in place for data security (including data recovery
as well as secure storage and transfer of sensitive data)? 17
6.2. Is the data safely stored in certified repositories for long term
preservation and curation?18
7\. Ethical Aspects 18
7.1. Are there any ethical or legal issues that can have an impact on data
sharing? 18
7.2. Is informed consent for data sharing and long term preservation included
in questionnaires dealing with personal data? 19
8\. References 21
# 1\. Executive Summary
This midterm document – deliverable D4.6 Data Management Plan (M17) –
describes how the research data will be collected and generated throughout the
project and how it will be handled during and after the Open4Citizens project.
It describes the standards and methodologies used for data collection and
elaborates on how the data will be shared and preserved. It also covers the
data that is handled in the Open4Citizens Platform. This midterm version Data
Management Plan is written by following the Guidelines on FAIR Data Management
in Horizon 2020 v3.0, 26 July 2016 provided by the EU Commission. A third and
final version of the DMP is contractually bound to appear by M30 and will
include the final and updated plans for data management in the project.
# 2\. Introduction
This deliverable is the updated and second version of the Data Management Plan
(DMP). By answering the questions outlined in the updated version 3.0
Guidelines on Data Management in Horizon 2020 July 2016 it describes:
* the handling of research data during and after the end of the project
* what data will be collected, processed and/or generated
* which methodology and standards will be applied
* whether data will be shared/made open access and
* how data will be curated and preserved (including after the end of the project) (Guidelines on Data Management in Horizon 2020, version 3.0)
The first version of the DMP (M6) followed the ‘Guidelines on Data Management
in Horizon 2020, version 2.0, July 2015 and focused mainly on the categories
of data sets that would be generated and used during the two hackathon cycles.
This second version has a shifted focus to include the research data that is
generated throughout the project and answers to how the project will make the
research data FAIR (Findable, Accessible, Interoperable and Reusable)
(Guidelines on Data Management in Horizon 2020, version 3.0). Since the
Open4Citizens (O4C) Platform also setup to handle data the document also
covers the data that is related to the O4C Platform where applicable. A third
and final version of the DMP is contractually bound to appear by M30 and will
include the final and updated plans for data management in the project.
## 2.1. Structure of the document
The document is based on the template H2020 Programme Guidelines on FAIR Data
Management in Horizon 2020 version 3.0, 26 July 2016 and is structured after
the questions presented in the template. Section 3-6 focuses primarily on how
the research data is managed throughout the project, while section 7-9 also
includes questions that are relevant for the data in the O4C platform. Thus -
where relevant - the sections will cover both topics.
## 2.2. Relation to other deliverables
The datasets that were used in the first round of hackathons are presented in
detail in deliverable D3.2 ‘Data Mapping and Integration’ (delivered in M15).
The more specific details about the hackathons; their themes, associated
challenges, pre-hack activities and key actors are described in detail in
deliverable D3.4 ‘First Hackathon Report’ (M17) whereas the datasets that will
be identified in the upcoming second round of hackathons will be included in
D3.3 ‘Data Mapping and Integration’ (final version M26).
# 3\. Data Summary
## 3.1. What is the purpose of the data collection/generation and its
relation to the objectives of the project?
There are two main groups of data in this project:
1. the research data that is generated throughout the project and
2. the data that is uploaded to and handled in the Open4Citizens (O4C) platform.
The data that the platform will handle is in the form of datasets generated
from Open Data and authorization data for user logins.
Data collected for and generated in the O4C platform ensures that there is
sufficient and appropriate open data to support the activities related to the
project’s objectives listed in the table below. In the first year, this
especially relates to objective 2 - Exploring hackathons and objective 3 -
Overcoming citizens’ cognitive gap regarding Open Data. In the second year of
the project, the second cycle of hackathons in the five pilot locations, O4C
platform data will additionally be used and generated in the development of
OpenDataLabs (objectives 1, 3, 4, 5 and 6).
The research data generated and collected in the project during the first year
of activity relates especially to formative evaluation of objectives 2 and 3.
In the second year of the project research data collected will support both
formative evaluation related to the development of OpenDataLabs (objectives 1,
3, 4, 5 and 6) as well as summative evaluation elements, regarding the
project’s achievements over all.
<table>
<tr>
<th>
**Number**
</th>
<th>
**Objective**
</th> </tr>
<tr>
<td>
1
</td>
<td>
Creating OpenDataLabs where citizens can design new services, or improving the
existing ones, in a collaborative environment, and by using open data.
</td> </tr>
<tr>
<td>
2
</td>
<td>
Exploring hackathons as new forms of collaboration among citizens, technical
experts and public institutions that enable citizens, interest groups and
grassroots communities to understand and use the potential of open data.
</td> </tr> </table>
<table>
<tr>
<th>
3
</th>
<th>
Overcoming the cognitive gap citizens have with respect to Open Data by making
that knowledge available in form of consultants in the OpenDataLabs where
citizens will experience the practical value of Open Data in the conception,
modification, adaptation and maintenance of urban services.
</th> </tr>
<tr>
<td>
4
</td>
<td>
Combining two specific models of OpenDataLabs, specifically the solutions
development lab and the incubator models.
</td> </tr>
<tr>
<td>
5
</td>
<td>
Exploring and driving opportunities for further exploitation and
implementation of the developed and tested solutions through social network.
</td> </tr>
<tr>
<td>
6
</td>
<td>
Creating an international network of cities and organisations where the Open
Data Labs model implemented by Open4Citizens can be replicated and transferred
so generating an international movement based on network cooperation and
learning.
</td> </tr> </table>
**Table 1: Open4Citizens objectives, as stated in the project’s Description of
Action (pg. 2)**
### 3.1.1. Research data supports evaluation and dissemination activities
Research data in the Open4Citizens project is primarily generated by the
members of the five pilots to support formative and summative evaluation of
the extent to which we are achieving the project’s stated aims. Most of the
data collected for evaluation purposes is focused on the hackathon pressure-
cooker event, held over the course of a weekend in most pilots. Data
collection is carried out consistently across the pilots using standardised
data collection templates consisting of PowerPoint slides. Further data is
generated in line with the evaluation framework described in deliverable D4.1
‘Evaluation Framework’ to support reflections on individual pilots’
contributions and the overarching Open4Citizens project’s contributions to
achieving its objectives. In addition, some of this material is used for
pilot-specific and project-level dissemination activities.
### 3.1.2. Data in the O4C platform is available for use in hackathons and
reuse beyond these events
The purpose of generating and uploading datasets to the O4C Platform is to
make it possible for participants, curious citizens and other interested
stakeholders to locate and find the data that has been produced from the
activities in the emerging OpenDataLabs.
In general ‘Open Data’ means that the data is publically available and can be
used, modified, and shared freely by anyone for any purpose
(http://opendefinition.org/). These open data have a large potential to
enhance several aspects of human life, including transport, health care,
climate and even human behaviour, if they for instance are implemented in new
applications. The O4C project intends to include citizens as a driver for
innovation in the Open Data arena. However many citizens are not necessarily
able to handle Open Data, or even to imagine what to do with them. To help
closing this gap between users and data, the datasets generated and used in
the Open Data Labs is intended to inspire others to see the potential in open
data.
## 3.2. What types and formats of data will the project generate/collect?
### 3.2.1. Research data
Research data generated throughout the project includes primarily qualitative
material, as well as some quantitative information, e.g. about participants in
the hackathons and partners in the OpenDataLabs. Curated and anonymised
materials will be made publicly available. This includes the following:
* Data gathering and analysis templates (originally produced in Microsoft PowerPoint format)
* Templates and guidelines for evaluation data gathering and analysis (originally in Microsoft PowerPoint and Microsoft Word/PDF)
* Completed templates from each pilot containing evaluation material from hackathons (PowerPoint)
* Questionnaire responses (originally Microsoft Excel documents)
* Intermediate evaluation analysis outputs based on data gathered in hackathons (Excel and PDF format)
All hackathon participants sign consent forms, allowing the project to use
audio, video, visual and textual material about them. Aliases are used unless
project participants explicitly wish to be quoted or identified using their
real names.
### 3.2.2. Data in the O4C platform
The datasets that have been chosen for the hackathon cycles and uploaded to
the O4C Platform are in CSV format which allows them to be used with the
various tools on the platform.
## 3.3. Will you re-use any existing data and how?
**Research data** will consist primarily of new qualitative information
generated in the project and will therefore not re-use existing data. Some
quantitative data will also be generated as part of the evaluation data
collected, complementing qualitative data collected about users of the O4C
platform.
The data that is uploaded to the **O4C platform** will reuse existing open
data. In addition, datasets generated through O4C activities are intended to
be available for re-use through the platform.
## 3.4. What is the origin of the data?
The research data is original material and will derive from evaluation and
dissemination-related activities. The material to be shared will be produced
by the Open4Citizens consortium members. The datasets uploaded to the platform
will derive from open data repositories and the internet in general from where
the data is ‘publically available and can be used, modified, and shared freely
by anyone for any purpose’. Certain datasets may be made available by O4C
stakeholders who had not previously made this data available, i.e. generating
new open data for use in project activities and beyond.
## 3.5. What is the expected size of the data?
The total amount of research data to be made available is not yet clear. For
the first hackathon cycle, year one of the project, the total size of
evaluation and dissemination materials varies between the five pilots, from
between about 200MB to 3TB, depending on the amount of media (video and
photos) produced.
The size is not yet known for the data in the platform.
## 3.6. To whom might it be useful ('data utility’)
**Research Data**
We intend to make curated research data from the five pilot projects
available, as well as crosscutting material reflecting on the evaluation of
the project as a whole. This data will be useful for researchers,
practitioners and others wishing to duplicate or adapt the Open4Citizens model
for empowering citizens to make appropriate use of open data for improved
service delivery. The analysis material included in the research data will
highlight the strengths and challenges of the approach, allowing others to
learn from the experiences of the project. The availability of templates and
guidelines used in the project will also allow for better adoption of the
approach by others interested in making open data a common good, whether they
belong to academia, the public sector, the private sector groups or civil
society.
Materials that are specifically intended for use by ordinary citizens working
in the O4C platform or as part of an OpenDataLab will be specifically
highlighted in the platform and the labs to ensure ease of access and
understanding. These materials include the toolkits produced as part of the
project (Preliminary Hackathon Starter Kit and Citizen Data Toolkit), as well
as any additional templates and guidelines to facilitate working with and
understanding open data.
**Data in the O4C Platform**
The O4C platform is focused around helping its users gain understanding of
Open Data as well as aiding the development of new services/improve existing
services during the hackathon cycle. The data in the platform is aimed at
being used as:
* Components in digital mobile or web applications – a dynamic product to access personally meaningful or context-aware data, such as a weather or route planner app.
* Elements in concepts - i.e. mock-ups of mobile or web applications.
* Data examples for the participants to gain a greater understanding of Open Data.
* Visualisation – a statistical representation of data, such as an infographics or a narrative told as a news article (data journalism). The main objective is to communicate what is otherwise “raw numbers in a table”.
* Digital service – a product-service system with various touch points ingrained with open data. For example, a service where citizens can report faulty street objects (broken lamppost, etc.) using a smartphone application, and the government is notified about these problems and can fix them.
# 4\. FAIR Data
The O4C project is a grant recipient under Horizon 2020 and is therefore
required to deposit peerreviewed publications into an open access repository.
In addition, we wish to participate in the European Commission’s Open Research
Data (ORD) pilot to the extent that this is feasible and useful. This is in
light of the fact that the bulk of research data produced in the project is
qualitative information that cannot easily be used outside the project given
the need for contextual knowledge for its interpretation. By providing open
access to a curated selection of the research data we intend to give access to
anonymised and consolidated evaluation materials, tools and guidelines
produced as part of the project, and the underlying datasets used to address
challenges in urban services during the Open4Citizens hackathon cycle. This is
beneficial for the public, but also for researchers (www.openaire.eu, 2017a).
Providing Open Access will however not remove the author’s copyright
(www.openaire.eu, 2017a).
**Open Research Data**
It is yet to be decided by the project partners whether we will deposit in an
institutional repository of the research institutions with which we are
affiliated, a subject-based/thematic repository or if we will make use of a
centralised repository, like Zenodo that is hosted by CERN, available to all
and set up by the OpenAIRE project (www.openaire.eu, 2017b). We are likely to
use the latter option to ensure that the data is easier to find than if it is
solely deposited in an institutional repository. In addition, to increase
accessibility and findability, we are likely to make the same research data
available through the institutional repositories of the universities that are
part of the Open4Citizens consortium: Aalborg University, Politecnico di
Milano and Technische Universiteit Delft. This use also depends on the
compatibility of the institutional platforms with the type and size of data to
be uploaded. For example, the Aalborg University Research Portal, VBN (Aalborg
University Research Portal, 2017), while compatible with OpenAire (Open AIRE,
2017c) has not previously been used for the type of research material which we
are producing; video, photos and slides. Aalborg University is a signatory of
the Berlin Declaration on Open Access in the Sciences and Humanities (Berlin
Declaration, 2003), whose principles the Open4Citizens project subscribes to.
Signatories to the declaration aspire to ‘promote the Internet as a functional
instrument for a global scientific knowledge base and human reflection and to
specify measures which research policy makers, research institutions, funding
agencies, libraries, archives and museums need to consider’ (Berlin
Declaration, 2003, pg. 1). Decisions regarding the core platform to be used
and the extent to which partners’ national institutional repositories will be
used will be made in good time to make initial research data available in the
second year of the project (2017) and all relevant research data to be
available by the end of the project (project month 30, June 2018).
**Regarding Open Access Journals**
Where it is not possible to publish final peer-reviewed publications in Open
Access Journals, the project partners aim to make publications available in
institutional open access repositories such as VBN at Aalborg University.
This, however, will depend on the individual journals’ policies regarding open
access.
**Open Access through the OpenDataLabs**
An intended the lasting and living legacy of the Open4Citizens project is the
OpenDataLabs. As we are identifying the business and sustainability plans for
the OpenDataLabs, we will be prioritising openness and co-creation within the
labs as a standard approach across the five pilot locations. In the second
hackathon cycle, in 2017, this will involve identifying which guidelines and
standards for openness are applicable across all pilots and can be adopted as
standard.
**Regarding Data on the O4C platform**
The approach to data storage in the platform is also inspired by the FAIR
principles to make it easier for the participants and other interested
stakeholders to find, access and re-use the datasets and make them
interoperable with other datasets. This will be elaborated in the sections
below.
## 4.1. Making data findable including provisions for metadata 4.1.1. Are the
data produced and/or used in the project discoverable with metadata,
### identifiable and locatable by means of a standard identification
mechanism (e.g. persistent and unique identifiers such as Digital Object
Identifiers)?
For research data collected and generated in the project, a fit-for-purpose
file naming convention will be developed in accordance with best practice for
qualitative data, such as described by the UK Data Archive (2011). This will
involve identifying the most important metadata related to the various
research outputs. Key information includes content description, date of
creation, version, and pilot location.
To make the datasets in the platform easily findable searchable tags have been
added to the metadata. When uploading the data the creator of the dataset also
have the option to create new tags that corresponds to the contents of the
dataset making it easier for other users to find and reuse the data.
## 4.2. Making data openly accessible
**4.2.1. Which data produced and/or used in the project will be made openly
available as**
### the default?
Certain pilots use medical data as part of the open data in their hackathons
and OpenDataLabs. This includes the Karlstad and Barcelona pilots. The
consortium partners will be guided by the legal and ethical restrictions that
their partners adhere to, i.e. the data owners, who make relevant open data
available. Restriction regarding data availability will come into play if the
partners of the two pilots deem it necessary.
### 4.2.2. How will the data be made accessible (e.g. by deposition in a
repository)?
The research data will be made accessible through an Open AIRE compatible
repository which has yet to be decided upon.
### 4.2.3. What methods or software tools are needed to access the data?
At present Microsoft Office is used for producing research data. However, the
research data will be made available in the most appropriate open source
formats.
## 4.3. Making data interoperable
### 4.3.1. Are the data produced in the project interoperable?
During the first hackathon cycle, research data, consisting of templates, data
gathered from hackathon participants and analysis of materials has been
produced using Microsoft Office. This includes PowerPoint and Excel-formatted
files in particular. The consortium will explore the best open source software
to use instead of these formats, and will make relevant research data placed
in repositories available in these formats instead.
### 4.3.2. What data and metadata vocabularies, standards or methodologies
will you follow to make your data interoperable?
Depending on the choice of repositories we are however likely to follow the
standard of The Data Catalog Vocabulary (DCAT) (Data Catalog Vocabulary, 2014)
as it defines a standard way to publish machine-readable metadata about a
dataset where appropriate. We also intend to use common ontologies and
vocabularies for data.
## 4.4. Increase data re-use (through clarifying licences)
### 4.4.1. How will the data be licensed to permit the widest re-use
possible?
The Open4Citizens project aims to be as open as possible. We take the
guidelines developed by Open Knowledge as our starting point. Specifically, we
will explore the applicability of the Open Data
Commons Open Database License (ODbL) for data created in the project. A
project partner in Milan, OnData (http://ondata.it/), is currently developing
openness guidelines that may be applicable data used and generated in the
Open4Citizens project.
### 4.4.2. When will the data be made available for re-use?
The curated research data related to the first round of hackathons is sought
to be made available by August 2017. By the end of the project in M30 all data
that is not affected by embargo will be made available through the appropriate
repositories.
### 4.4.3. Are the data produced and/or used in the project useable by third
parties, in particular after the end of the project? If the re-use of some
data is restricted, explain why.
The curated research data made available will be available for re-use. However
it may not be feasible for researchers outside the project to interpret and
use any anonymised qualitative research data.
Raw social science research data of this nature generally requires deep
contextual knowledge to be appropriately analysed and interpreted.
The O4C Platform will form a virtual component of the five Open Data Labs. The
aim is that it will be able to exist after the O4C project has been completed.
This means that the Open Data that has been collected, generated and uploaded
to the Platform during the project lifetime will be accessible both after each
hackathon cycle and after the end of the funding period of the O4C project.
The datasets that are uploaded to the O4C platform will be shared through the
website www.opendatalab.eu where everyone will have access to them.
### 4.4.4. How long is it intended that the data remains re-usable?
We will adhere to the repository standard of the chosen repository or
repositories for the project.
### 4.4.5. Are data quality assurance processes described?
The data quality assurance processes are not described yet. These will be
developed based on experiences finding, using and creating open data sets in
the project, including the feasibility of their use in the O4C platform for
creating data-led solutions to service challenges. At the end of the first
hackathon cycle, as this document is being written, a pragmatic approach is
being taken to the quality of the open data being used in the project: at this
stage of the project quality standards are less important than making relevant
data available and using it to the extent possible, thereby learning about
what is needed in order to improve its quality. In further defining the O4C
project's data management plan with respect to research data, we will seek
inspiration in the plans of similar projects and identify good practice
guidelines for social science research data. We will use this input to ensure
that we make good quality research data available.
# 5\. Allocation of Resources
## 5.1. What are the costs for making data FAIR in your project?
Any minor costs for Open Access publishing will be covered by the overall
project dissemination budget. This will be used at the discretion of the
consortium and Aalborg University as the primary investigator. Wherever
possible, free institutional repositories will be used for data and
publications. Expected costs will be further identified in the final version
of the Data Management Plan, based on project needs to the end of the project.
## 5.2. How will these be covered?
There is no significant budget allocation in the project for making the data
FAIR. The project therefore expects to cover costs related to open access to
research data as eligible costs as part of the Horizon 2020 grant (if
compliant with the Grant Agreement conditions). For any additional costs,
sponsorship outside of the project would need to be sought, for example by
partners interested in developing OpenDataLabs or by securing additional
research funding.
## 5.3. Who will be responsible for data management in your project?
Aalborg University (AAU) and Antropologerne (ANTRO) are responsible for the
overall collection and handling of research data while Dataproces is
responsible for the data management of data in the O4C Platform. The pilot
coordinators will be responsible for the collection and handling of research
data in each pilot.
## 5.4. Are the resources for long term preservation discussed (costs and
potential value, who decides and how what data will be kept and for how long)?
This will be further developed as the business cases for the OpenDataLabs are
defined in the project. The consortium expects the main questions relating to
data preservation to be identified and answered as these business cases are
developed.
All datasets that are uploaded to the O4C Platform will be stored on a server
at Dataproces who will ensure preservation and backup throughout the project.
The aim is that the Platform will continue to be available after the O4C
project has been completed. This means that the Open Data that has been
collected, generated and uploaded to the Platform during the project life-time
will be accessible both after each hackathon cycle and after the end of the
funding period of the O4C project. The data in the O4C platform will be
available for as long as the internal server at Dataproces is up and running
and costs covered by the business case by Dataproces.
# 6\. Data Security
## 6.1. What provisions are in place for data security (including data
recovery as well as secure storage and transfer of sensitive data)?
Research data is shared between project partners and stored in collaborative
online working platforms during the project’s lifetime. These are BaseCamp
(https://3.basecamp.com), Google Drive (https://drive.google.com), and Dropbox
(https://www.dropbox.com). Some intermediate and all final versions of
evaluation data collected in the project and analysis outputs of this material
are saved in a standardised filing system with dedicated naming conventions in
the project’s BaseCamp account.
Uncurated and unanalysed material created during the project is stored locally
by the Open4Ctiizens partners according to their institutional data management
and storage guidelines. This locally stored research data includes un-
anonymised questionnaire data from hackathons, as well as consent forms signed
by hackathon and other project participants allowing for the use of personally
identifiable information about them. Consent forms will be kept beyond the end
of the Open4Citizens project. Additional research data such as personal notes,
unused photos and video clips etc. will be safely deleted and discarded after
the end of the project. This research includes all data not made publicly
available for the long term.
In the finished form of the platform the uploaded data is secure and
recoverable with daily backups, where Dataproces can go back to any file from
any day for all 365 days a year. Dataproces also fulfil all data management
comments regarding the European Personal Data Protection Act.
## 6.2. Is the data safely stored in certified repositories for long term
preservation and curation?
At the time of writing this deliverable, the Open4Citizens project partners
are identifying the most appropriate repository or repositories in which to
make research data available for the long term. An update on decisions made
will be provided in the project’s final Data Management Plan, due in M30.
# 7\. Ethical Aspects
## 7.1. Are there any ethical or legal issues that can have an impact on data
sharing?
**User-generated data**
In the second hackathon cycle it is likely that user-generated data will be
increasingly important for developing open-data driven solutions in
Open4Citizens hackathons. Examples include crowdsourced data such as that made
available in platforms like Open Street Map
(www.openstreetmap.org), public social media data, as well data scraped from
service and product review sites. The Open4Citizens consortium intends to
adopt existing standards and approaches regarding the use of this kind of
data. Our primary guide in defining an Open4Citizens guideline to using this
type of data will be the licences and guidelines available through the Open
Data Commons (https://opendatacommons.org) developed by Open Knowledge
International (https://okfn.org/) . In essence, the Open Data Commons Open
Database License (OBdL) states that the databases to which it applies allow
users to freely share, create and adapt from the database, as long as public
use is attributed, shared alike and kept open. However, as these guidelines
are voluntary, the consortium intends to engage with members of our advisory
board to determine which current approaches to openness we should adopt. We
intend, for example, to review and appropriately adopt guidelines produced by
the Open Data Institute (https://theodi.org/guides) and Open Knowledge
International, as leaders in the field, as well as any other relevant
guidelines.
We foresee that balancing participants’ rights to the intellectual property
entailed in their hackathon outputs with the desire for openness may be
challenging. We will therefore stay informed about developments in the area of
rights, ethics and openness throughout the life of the project to ensure that
we are transparently applying best practice procedures and guidelines. We
expect that debates regarding use of user-generated data in data journalism
will be one useful source of guidance.
**Data in the O4C platform**
When organising the events Open4Citizens will collect information from public
repositories, which contain Open Data. Since Open Data consist of information
databases that are public domain, the data can be freely used and
redistributed by anyone. Open4Citizens is thus not subject to any regulations
regarding proper data storage, including principles stipulated in The Data
Protection Directive 95/46/ECP and the General Data Protection Regulation (EU)
2016/679.
When providing Open Data for the hackathons and related events, Open4Citizens,
its employees, and any contributing partners of Open4Citizens or its
employees, shall not be liable for any harm arising from the use of the
collected datasets shared through the O4C Platform, including but not limited
to, how participating parties handle and develop the Open Data available on
the O4C Platform.
In regards to the Open Data from various data sources that are made available
on the O4C Platform, Open4Citizens does not guarantee that this data has been
published with the prior, necessary and informed approval that it requires.
## 7.2. Is informed consent for data sharing and long term preservation
included in questionnaires dealing with personal data?
The gathering and analysis of research data in the project is guided by
standard ethics guidelines for the social sciences (e.g. as discussed in
http://ec.europa.eu/research/participants/data/ref/h2020/other/hi/ethics-
guide-ethnoganthrop_en.pdf).
For research data collected in relation to Open4Citizens hackathons, as well
as questionnaires and other personally identifiable information generated,
informed consent is sought. All participants in hackathons are requested to
provide their consent for all data produced to be used by the project. Figure
1 represents the template consent form used for the first hackathon cycle
below. In addition all users of the platform will be asked for consent when
creating a profile in the O4C platform.
**Figure 1: Draft consent form used by all five pilots in the first hackathon
cycle of the O4C project**
|
https://phaidra.univie.ac.at/o:1140797
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Horizon 2020
|
1070_Open4Citizens_687818.md
|
# 1\. Executive Summary
This deliverable contains the first version of the Data Management Plan (M6).
It describes the nature of the data that will be collected and used in the
Open Data Lab Platform. The open data has been divided into two overarching
data categories:
1. Open Data
* **Category A** : _Open and ready to use_
* **Category B** : _Open - but not yet in a ready to use format_
2. Closed Data
* **Category C** : data that will not be used in this project
It also describes the overall architecture of the OpenDataLab Platform that
will contain the data and touch base with the current development on how the
data will be stored, made available, shared, reused and used during and after
the project. It also outlines the ethics related to working with Open Data as
well as presenting an outlook for the further work with the deliverable.
# 2\. Introduction
The purpose of this deliverable is to ‘provide an analysis of the main
elements of the data management policy that will be used in the project with
regards to all the datasets that will be generated by the project’ as stated
in the document ‘Guidelines of data Management in Horizon 2020 1 . According
to the DoA (p. 37), the aims of this deliverable (in M6) are to ‘describe the
nature of the data that will be collected and integrated and the way it will
be stored, made available and used during and after the project.’ According to
the ‘Guidelines of data Management in Horizon 2020 2 ’ p.5, the Data
Management Plan should be written from a dataset-by-dataset point of view.
However since we do not yet have the specific datasets this initial draft of
the Data Management Plan will focus on describing the overarching data
categories that will be encountered during the hackathon cycles as well as
describing the overall architecture of the OpenDataLab Platform that will
contain the data. As each pilot comes closer to defining its specific
challenges during the pre-hack phase, we will gain a clearer picture on which
datasets to collect and upload to the OpenDataLab Platform. This will
contribute to shaping the next editions of this deliverable, which are
contractually bound to appear by Month 15 and Month 30.
## 2.1. Overview on structure
This initial draft of the Data Management Plan focuses on describing the
overarching data categories that will be encountered during the hackathon
cycles followed by how each category will be managed.
Firstly the two data categories are presented in section 3.0. The subsections
describe the origin of the data as well as the possible outcome of using the
particular category (concept, mobile application, use case, etc.). Then the
structure of the Open Data Lab Platform where the data will be managed is
described in section 4.1. The subsections elaborate on access, data standards,
data sharing, archiving and preservation, reuse of the data and ethics. Lastly
the outlook for the development is presented.
# 3\. Description of the Data Categories
A large quantity of data concerning e.g. urban environments, weather, census,
etc. is being produced every day. Such data can be found, for example as
published government data sets, in private companies who have recorded the
behaviour of their customers or by scientific research, or generated by users,
who often record their activities and in some cases share such data with
friends or social networks. The term ‘Open Data’ refers to those data sets
that are made publicly available. These data have a large potential to
generate new applications and to enhance several aspects of human life,
including transport, healthcare, climate and even human behaviour. The way to
manage these data as well as realising the real potential of open data is
still to be fully discovered.
The O4C project intends to contribute to create a demand for applications
based on open data and to include citizens as a driver for innovation in the
open data arena. However many citizens are not necessarily able to handle Open
Data, or even to imagine what to do with them. To help close this gap between
users and data, a clarification of the different types of data is needed. In
general ‘Open Data’ means that the data is publically available and can be
used, modified, and shared freely by anyone for any purpose 3 . With this in
mind the following categories have been created:
3) Open Data
* Category A: _Open and ready to use_
* Category B: _Open - but not yet in a ready to use format_
4) Closed Data
* Category D: data that will not be used in this project
The categories will be described in detail in the following sections. The
colour: green, yellow and red corresponds to the level of employment of the
data.
* Green: the data is ready to use.
* Yellow: the data needs to be converted to a usable format or there are restrictions on the use
* Red: the data is not available in the O4C project
_**3.1. Open Data - Category A** _
The general topic for this category is that the data can be classified as
open. It means that it can be freely used, re-used and redistributed by anyone
- subject only, at most, to the requirement to attribute and sharealike 4 .
<table>
<tr>
<th>
**Category**
</th>
<th>
**Public Data**
</th>
<th>
**User Generated Data**
</th>
<th>
**Datasets generated by Hackathon Participants**
</th> </tr>
<tr>
<td>
Category A:
Open and ready to use
</td>
<td>
Publicly available datasets that are available in a structured format and
ready
to be used in the Platform
E.g. CSV - Comma Separated
Values
</td>
<td>
Basically any content that users of a service have created that is accessible
through a graphical user interface
Examples:
\- GPS data
</td>
<td>
Datasets that have been generated by the users for the O4C project
Merged datasets also belongs in this category
</td> </tr> </table>
**Table 1: Category A - Open and ready to use data**
## 3.1.1 Public data
The data is presented in a repository – an online platform – owned and
maintained by e.g. a company, an organization or by local, municipal or
national governments who have decided to make a selection of data available to
the public. Examples on this type of data may include data related to
demographics or urban planning, as well as to public transport or real-time
road usage. Data from this category can be located through repositories like
Open Data DK, Humanitarian Data Exchange, DR Archives, HIP.se – Health
Innovation Platform, etc. and is typically presented in a structured CSV
format. If the data has a restricted/limited use or isn’t free of charge it
belongs in Category B.
## 3.1.2 User Generated Data
When a user creates content that will be visible to the general public they
usually agree to this in the Terms of Use of the service. Examples for this
category are posts that are publically available via a graphical user
interface on e.g. Twitter. If the data has a restricted/limited use or isn’t
free of charge it belongs in Category B.
## 3.1.3 Data Generated by Hackathon Participants
This category refers to datasets that have been created during the hackathons.
The datasets can e.g. come from merging ‘Public’ or ‘User Generated Data’ into
new datasets. Since the data in this category is created from Open Data there
are no restrictions related to use, modification or sharing of the data.
## 3.1.4 Possible Outcomes and Available Datasets
It will be possible to manipulate Data from this category in the Open Data Lab
Platform. The outcome from this is mainly helping the hackathon participants
gain understanding of Open Data as well as aiding the development of new
services/improve existing ones during the hackathon cycle.
The data can be used as:
* Components in mobile or web applications
* Concepts - i.e. mockups of mobile or web application
* Data examples for the participants to gain a greater understanding of Open Data
* Some data may not be off limits because of privacy reasons, as explained in category D, but due to the fact that the owners of the datasets have not yet made them available. Thus existing Open Data can be used as components in use-cases - proof of concept - that can clear the way for opening data that is not currently available as open data.
## 3.2. Open Data - Category B
This category refers to data that needs to be converted or have certain
restrictions on its use. It means that the data is publically available but it
is not ready-to-use as the data from category A.
<table>
<tr>
<th>
**Category**
</th>
<th>
**Public Data**
</th>
<th>
**User Generated Data**
</th>
<th>
**Datasets generated by Hackathon Participants**
</th> </tr>
<tr>
<td>
Category B: Open - but not yet in an optimal format
</td>
<td>
Publically available unstructured data like:
* PDF
* Addresses
* Menus
* Library Catalogues Formats that need to be converted.
As well as CSV files with restricted downloads (e.g. number of rows/file size)
</td>
<td>
Data generated by everyday users of services and technology.
Examples:
\- Twitter
</td>
<td>
Hackathon participants creating datasets as PDF or
XLS files
or merging of datasets with restricted use
</td> </tr> </table>
**Table 2: The three sub-categories of Category B**
### 3.2.1 Public Data
Public data refers to the often _unstructured_ data that is publically
available like online menus, libraries of information, catalogues, etc. Data
from this category is typically owned by a for-profit company or an
organization (for-profit or non-profit). This category also includes data
owned by public institutions, which have not published the data in a format
that makes them immediately useable (e.g. PDF)
The unstructured data requires more preparation than structured data, since it
will have to be converted into a useable format and perhaps be extracted from
the websites by using Dataproces’ software robots.
If the use is restricted it means that there can be a limited number of
downloads. The restriction can also refer to the ownership of the data, which
means that the data is free to use but is owed by someone.
### 3.2.2 User Generated Data
User generated data is data that is quite literally generated directly by
users of everyday services and technology. Examples from this category can be
GPS data, Geo-tagged Twitter data. Also for this category the unstructured
data requires more preparation than structured data, since it will have to be
converted into a useable format and perhaps be extracted from the websites by
using Dataproces’ software robots. Restricted use can also apply which means
that there can be a limited number of downloads or a payment is required to
use the data. The restriction can also refer to the ownership of the data -
that the data is free to use but is owed by someone.
### 3.2.3 Data Generated by Hackathon Participants
This category encompasses Hackathon participants creating datasets as PDF or
XLS files or merging of the Open Data that have restricted use.
### 3.2.4 Possible Outcomes and Available Datasets
It is more time consuming to get hold of the data from Category B than
Category A. It is possible to retrieve, collect or create datasets during the
pre-hack, hack or post-hack phase but as a general rule the possible final
outcome when using data from this category is _concepts_ . If the data is
converted it “moves” to category A. Data from this category can be used as
concepts - i.e. mock-ups of mobile or web applications.
## 3.3. Closed data - Category C
Data that is not open refers to personal, medical and other sensitive data
that is not open to the public and cannot be found by the general public
online. It also encompasses data that is only meant as internal information to
public administration. Data from this category will _not_ be available in the
O4C project. The same rule applies to data that are only available after
purchase, such as business data, etc.
<table>
<tr>
<th>
**Category**
</th>
<th>
**Description**
</th> </tr>
<tr>
<td>
Category C: Closed data - data that will not be used in this project
</td>
<td>
Any data that contain personal information e.g. medical data or data that is
only meant as internal information to public administration
</td> </tr> </table>
**Table 3: Category C**
# 4\. The OpenDataLab Platform
The OpenDataLab will be the place or playground where citizens can make their
ideas more concrete. The Platform will help support this vision by leaning
against the O4C approach: Explore, Learn/Apply, Consolidate and letting the
participants build on different levels with Open Data. Starting out with
building an understanding of Open Data, then moving on to building concepts
and finally building applications and possibly a viable business (figure 1).
**Figure 1: How the Open Data Lab Platform supports the O4C approach.**
The figure (1) shows an ideal process. In real life the path will be more
intricate and winding – however, one of the goals of the platform, as well as
the O4C project, is “straightening” out the process – helping citizens getting
data and getting there!
By helping citizens concretize their ideas, concepts and applications can be
developed and where the data is not yet open a well-founded use-case can be
created and used as an argument to ask the data owner to open the data. In
order to let the participants to build with Open Data both datasets and
selected tools will be available inside the Open Data Lab Platform.
The basic function of the Platform is to allow citizens to work with Open
Data. In order to work with Open Data you must have access to datasets and you
must have access to relevant tools.
Figure 2 shows a representation of the content of the Platform. It shows the
relationship between the local hackathon teams/the hackathon participants (the
box to the right – figure 2) and the Platform (the box to the right – figure
2).
* Upload of datasets: The local hackathon teams will at all times be able to upload datasets to the platform while the hackathon participants - for now - will not have this opportunity. This division of roles is a measure to ensure relevance and quality of the uploaded datasets. It also helps ensure that there is sufficient storage space so that the platform will run smoothly.
* Access to the uploaded datasets: The Open Data Lab Platform contains five data storages - one storage for each location/country - and a selection of tools to work with Open Data. The participants at the hackathon can then access the platform, fetch datasets and use the tools to work with the Open Data. For now, each local hackathon team will only be able to access its own data storage. This is again to ensure quality, structure and stability. At a later time a feature might be added to the platform that allows mixing datasets from the different local hackathon teams.
* The platform will be linked to a developer toolbox from IBM, called Bluemix. Arrangements are being made at the time of the editing of this deliverable, to make sure that the toolbox could be linked to the platform, in order to give access to a large range of tools to analyse, search, visualise and working with data.
**Figure 2 The OpenDataLab platform structure**
## 4.1. Access to the Data inside the Open Data Lab Platform
All hackathon participants – and anyone else who accesses the Open Data Lab
Website _www.opendatalab.eu_ – can use the datasets and the tools inside.
## 4.2. Standards and metadata
Data standards for Category A (Table 1): CSV, XML, RDF, JSON, OIS, API
queries, etc.
Data standards for Category B (Table 2): formats that require transformation
to become machine readable such as PDF, JPG, TIFF, etc.
## 4.3. Data sharing
The datasets that are created during the hackathons will be shared through the
Open Data Lab website _www.opendatalab.eu_ . The essence of Open Data is
that it is open – which means that anyone can use it without asking permission
or informing the data owner about how or where it is being used and for what
purpose. The datasets that belong to Category A are therefore embraced by
unrestricted and unlimited use as well as unlimited sharing and manipulation.
The datasets from Category B might present some restrictions in terms of
usage. This has been elaborated upon in section 3.2.1.
**4.4. Archiving and preservation (including storage and backup):**
The plan is that all datasets that are uploaded to the Open Data Lab Platform
will be stored on a server at Dataproces who will ensure preservation and
backup throughout the project.
## 4.5. Tools
The tools inside the platform will allow the participant to perform different
actions, including (among others):
* visualise data,
* build understanding through data examples and use cases
* construct concepts and mobile applications
The specific data visualisations tools will be described in detail in the next
deliverable (M15). In order to build mobile applications the participants will
additionally have access to Bluemix. This toolbox includes a large number of
tools to handle, transform and analyse data, including tools to:
* transform data in HTML, PDF and Word format into Json and other appropriate formats
* create on-demand relational databases
* create apps for Web or mobile
* perform geospatial analysis
The work on the specific cases and the definition of detailed requirements
will give the project team the possibility to better define and select the
most useful tools.
## 4.6. Reuse of the data
The Open Data Lab Platform will form a virtual component of the five Open Data
Labs. The platform is also meant to be active after the O4C project has been
completed. This means that the Open Data that has been collected, generated
and uploaded to the Platform during the project lifetime will be accessible
both after each hackathon cycle and after the end of the funding period of the
O4C project. This will be elaborated in the upcoming version of the Data
Management Plan.
## 4.7. Ethics
When organising the events Open4Citizens collects information from public
repositories, which contain Open Data. Open Data consist of information
databases that are public domain, and therefore data that can be freely used
and redistributed by anyone. Open4Citizens is thus not subject to any
regulations regarding confidential or sensitive data storage, including
principles stipulated in The Data Protection Directive 95/46/ECP and the
General Data Protection Regulation (EU) 2016/679.
When providing Open Data for the events, Open4Citizens, its employees, and any
contributing partners of Open4Citizens or its employees, shall not be liable
for any harm arising from the use of the collected datasets shared through the
Open Data Lab Platform, including but not limited to, how participating
parties handle and develop the Open Data available on the Open Data Lab
Platform.
In regards to the Open data from various data sources that is made available
on the Open Data Lab Platform, Open4Citizens does not guarantee that this data
has been published with the prior, necessary and informed approval that it
requires. However the Open4Citizens team and the hackathons’ team will verify
the reliability of the source of the publication case by case.
# 5\. Conclusions and Outlook
As mentioned in the preface this first version of the Data Management Plan
presents the overarching categories and how data will enter into the Open Data
Lab Platform. The next iteration of the Data Management Plan (M15) will
provide a more detailed description of specific datasets while the final
version (M30) will present the complete and detailed description of datasets
and the final architecture of the platform. The next iteration will also
elaborate on the concrete tools that will be available in the Open Data Lab
Platform.
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# Introduction
This Data Management Plan (DMP) intends to outline the handling of data
gathered during the ARCFIRE project, from the point where it is gathered until
the archiving process at the end of the project.
1.1 Purpose
This Data Management Plan ensures that policies are in place to:
* Facilitate the generation of data and analyses of that data by ARCFIRE;
* Outline the procedures and formats for transforming raw data into processed results.
* Ensure that data required to reconstruct published results are made available online in time to facilitate the peer review process, for instance on the ARCFIRE website.
* Ensure that raw and processed data sets, together with appropriate documentation, are released in timely ways as structured archive volumes to Open Access repositories for distribution to the FIRE+ community and others beyond the duration of the ARCFIRE project.
1.2 Scope
The scope of this data management plan focuses on:
* Timely reduction of raw data into structured results, along with documentation that determines when and where the data were acquired, and for what purpose.
* Timely generation and validation of archive volumes containing standard data products and documentation.
* Timely delivery of archive volumes to open repositories for distribution to the FIRE+ community and others.
* Timely posting of new and exciting data sets and results on the Internet for public access.
* Timely announcement of the availability of results via social media
3. Responsibilities
The development, maintenance and management of the Data Management Plan in is
the responsibility of iMinds. The current responsible person is Dr. Dimitri
Staessens ([email protected]). This Data Management Plan is not
seen as a static document and will be updated during the project whenever
appropriate.
4. Change control
The validity of the Data Management Plan will be evaluated at least every six
(6) months or whenever a revision is necessitated during the project. If
changes are required, a new document will be prepared for internal use in the
consortium, indicated with a minor version: D4.1.x. These documents can be
made available upon request.
5. Relevant documents
This document is structured according to the NASA _G_ uidelines for
Development of a Project Data Management Plan (PDMP) 1 , incorporating H2020
guidelines 2 .
# Project overview
The ARCFIRE project will investigate RINA, the Recursive Internet Architecture
at scale on FIRE+ testbeds. It will use publicly available Free/Libre Open
Source Software (FLOSS) from a number of previous and currently running
projects, including
* FP7-IRATI: This EC funded project developed a GPL/LGPL licensed implementation of RINA concepts for OS/Linux, called IRATI. It is available on Github and is seen as project background for ARCFIRE. ARCFIRE will contribute to the FLOSS developments of IRATI.
* GEANT-IRINA: This EC funded project developed a FLOSS traffic generation tool for the IRATI under the Geant outward software license. ARCFIRE may use this tool. ARCFIRE will develop a much richer tool set that will be made available under a different license (GPL).
* FP7-PRISTINE: This ongoing EC funded project develops a Management System for IRATI. ARCFIRE will use and contribute further to this software.
* FWO-RINAiSense: This Flemish Government funded project is developing a smaller scale user space RINA implementation aimed at constrained resource devices. ARCFIRE may make use of publicly available software from RINAiSense.
2.1 Project objectives
ARCFIRE sets out to prove the effectiveness of the RINA architecture in
mitigating key issues observed in the past decades in the deployment of TCP/IP
as the underlying infrastructure of the global Internet. It will deploy key
experiments on testbeds in Europe (provided by FIRE+) and the U.S. (as made
available by GENI). ARCFIRE will develop the tools necessary to deploy
experiments using the RINA prototypes quickly and efficiently.
ARCFIRE will develop a framework for deploying test programs on a variety of
testbeds and gather data regarding resiliency and manageability of RINA
networks, as compared to TCP/IPbased networks.
# Testbed description
The experiments in the ARCFIRE project will run on general purpose hardware
and virtual machines running FLOSS. The four experiments will be conducted on
selected testbeds available to the project consortium. The two currently
available testbed infrastructures are provided by the Fed4FIRE project in the
EU (http://www.fed4fire.eu) and GENI (https://www.
geni.net/) in the United States.
A detailed testbed report will be delivered as part of ARCFIRE D4.2.
# ARCFIRE experimentation framework
The ARCFIRE experimentation framework has to be developed during the project
and its capabilities will impact the Data Management. We foresee that a number
of existing tools will be integrated. These tools will serve as probes and be
controlled from the ARCFIRE framework.
4.1 probes
We give a brief description of such probes below:
## 4.1.1 iperf
iPerf3 is a tool for active measurements of the maximum achievable bandwidth
on IP networks. It supports tuning of various parameters related to timing,
buffers and protocols (TCP, UDP, SCTP with IPv4 and IPv6). For each test it
reports the bandwidth, loss, and other parameters. This is a new
implementation that shares no code with the original iPerf and also is not
backwards compatible. iPerf was orginally developed by NLANR/DAST. iPerf3 is
principally developed by ESnet / Lawrence Berkeley National Laboratory. It is
released under a three-clause BSD license. It can produce output in JSON
format.
## 4.1.2 netperf
Netperf is a benchmark that can be used to measure the performance of many
different types of networking. It provides tests for both unidirectional
throughput, and end-to-end latency. The environments currently measureable by
netperf include:
* TCP and UDP via BSD Sockets for both IPv4 and IPv6
* DLPI
* Unix Domain Sockets
* SCTP for both IPv4 and IPv6
## 4.1.3 tcpdump
Tcpdump prints out a description of the contents of packets on a network
interface that match the boolean expression; the description is preceded by a
time stamp, printed, by default, as hours, minutes, seconds, and fractions of
a second since midnight. It can also be run with the -w flag, which causes it
to save the packet data to a file for later analysis, and/or with the -r flag,
which causes it to read from a saved packet file rather than to read packets
from a network interface. In all cases, only packets that match expression
will be processed by tcpdump.
The MIME type application/vnd.tcpdump.pcap has been registered with IANA for
pcap files. The filename extension .pcap appears to be the most commonly used
along with .cap and .dmp. Tcpdump itself doesn’t check the extension when
reading capture files and doesn’t add an extension when writing them (it uses
magic numbers in the file header instead). However, many operating systems and
applications will use the extension if it is present and adding one (e.g.
.pcap) is recommended.
## 4.1.4 wireshark
Wireshark is a GUI network protocol analyzer. It lets you interactively browse
packet data from a live network or from a previously saved capture file.
Wireshark’s native capture file format is pcap format, which is also the
format used by tcpdump and various other tools. Wireshark can output XML,
PostScript, csv, or plain text
## 4.1.5 RINA traffic generator (tgen)
The RINA traffic generator is a tool developed during the Geant3 IRINA
project, designed to produce CBR and Poisson-distributed traffic for the IRATI
implementation. It outputs periodic statistics in .csv format.
## 4.1.6 OML
OML is a generic software framework for measurement collection. It allows the
developer of applications to define customisable measurement points (MP)
within the application code. It consists of two main components:
OML client library: it provides an API for applications to collect
measurements that they produce. It exists for different languages including C
and Python.
• OML Server: the OML server component is responsible for collecting and
storing measurements inside a database. Currently, SQLite3 and PostgreSQL are
supported as database backends.
MPs are usually defined within the application code in the form of tupples
like:
("appname", "measurementname1:measurementtype1
measurementname2:measurementtype2") , depending on the binding used. The data
recollected in the OML server will be saved in the configured database. The
format of this data depends on the user after querying the database.
## 4.1.7 ARCFIRE tools development
ARCFIRE will develop a framework that will combine existing tools and develop
a frontend to control and deploy these tools on the FIRE testbeds quickly and
efficiently.
Scripts will be made public open source under appropriate software licenses.
We don’t plan for the tool to generate any data by itself.
4.2 Data formats
This section gives a brief summary of data formats that will be used for the
output of ARCFIRE.
4.2.1 txt
Plaintext files will be used for the metadata accompanying any ARCFIRE data
products.
## 4.2.2 pcap
Applications and libraries should use the pcap library to read savefiles,
rather than having their own code to read savefiles. If, in the future, a new
file format is supported by libpcap, applications and libraries using libpcap
to read savefiles will be able to read the new format of savefiles, but
applications and libraries using their own code to read savefiles will have to
be changed to support the new file format.
“Savefiles” read and written by libpcap and applications using libpcap start
with a per-file header. The format of the per-file header is:
* Magic number
* Major version
Minor version
* Time zone offset
* Time stamp accuracy
* Snapshot length
* Link-layer header type
The magic number is used to discern the format (byte order and timestamp).
Other important parameters are:
A 4-byte number giving the ”snapshot length” of the capture; packets longer
than the snapshot length are truncated to the snapshot length.
Following the per-file header are zero or more packets; each packet begins
with a per-packet header, which is immediately followed by the raw packet
data. The format of the per-packet header is:
* Time stamp, seconds value
* Time stamp, microseconds or nanoseconds value
* Length of captured packet data
* Un-truncated length of the packet data
All fields in the per-packet header are in the byte order of the host writing
the file. For a full description, see the manpage of pcap-savefile.
## 4.2.3 csv
RFC 4180 proposes a specification for the CSV format, and this is the
definition commonly used. However, in popular usage ”CSV” is not a single,
well-defined format. As a result, in practice the term ”CSV” might refer to
any file that
* is plain text using a character set such as ASCII, various Unicode character sets (e.g. UTF8), EBCDIC, or Shift JIS,
* consists of records (typically one record per line),
* with the records divided into fields separated by delimiters (typically a single reserved character such as comma, semicolon, or tab; sometimes the delimiter may include optional spaces),
where every record has the same sequence of fields.
Within these general constraints, many variations are in use. Therefore,
without additional information (such as whether RFC 4180 is honored), a file
claimed simply to be in ”CSV” format is not fully specified. As a result, many
applications supporting CSV files allow users to preview the first few lines
of the file and then specify the delimiter character(s), quoting rules, etc.
If a particular CSV file’s variations fall outside what a particular receiving
program supports, it is often feasible to examine and edit the file by hand
(i.e., with a text editor) or write a script or program to produce a
conforming format.
ARCFIRE .csv files will be formatted according to RFC4180.
## 4.2.4 xml
Extensible Markup Language (XML) is a markup language that defines a set of
rules for encoding documents in a format that is both human-readable and
machine-readable. It is defined by the W3C’s XML 1.0 Specification and some
associated open standards.
4.3 JSON
JSON (JavaScript Object Notation) is a lightweight data-interchange format. It
is easy for humans to read and write. It is easy for machines to parse and
generate. It is based on a subset of the JavaScript Programming Language,
Standard ECMA-262 3rd Edition - December 1999. JSON is a text format that is
completely language independent but uses conventions that are familiar to
programmers of the C-family of languages, including C, C++, C#, Java,
JavaScript, Perl, Python, and many others. These properties make JSON an ideal
data-interchange language.
4.4 Summary
A summary of the probes is given in Table 1.
Table 1: Summary of probes
<table>
<tr>
<th>
Tool
</th>
<th>
type of data gathered
</th>
<th>
raw output format
</th> </tr>
<tr>
<td>
iperf
</td>
<td>
bandwidth
</td>
<td>
txt, JSON
</td> </tr>
<tr>
<td>
netperf
</td>
<td>
bandwidth
</td>
<td>
txt
</td> </tr>
<tr>
<td>
tcpdump
</td>
<td>
raw packet data
</td>
<td>
pcap
</td> </tr>
<tr>
<td>
rina-tgen
</td>
<td>
bandwidth
</td>
<td>
txt, csv
</td> </tr> </table>
# Project data flow
ARCFIRE will generate only computer science related data and measurements, so
all processing can be done in situ after the experiment is done. ARCFIRE will
generate large quantities of network traffic, that can not be stored in its
totality and will be processed immediately after acquisition. For post-
processing, relevant subsets of that data will be stored at the facility
testbed (on the test machine or a centralised server provided by the testbed
facility until these resources need to be released. In such cases the data may
be moved to a central server at an ARCFIRE partner for further analysis. When
all analysis is done, the necessary data to reproduce the results will be
packaged and archived in a zip archive or tarball.
# Products
Products resulting from the project include raw experiment data sets, and
associated products such as statistical analysis data.
6.1 Experiment data products
The project will generate raw data such as tcpdump traces, that could reach
orders of magnitudes exceeding Terabytes of data very quickly (The iLab.t
experimentation facility provides Gigabit links). Such data will not be
archived or saved, but some traces may be filtered to illustrate key findings
in smaller data sets (not exceeding a couple of megabytes in size).
## 6.1.1 ARCFIRE data product template
Data products for ARCFIRE will be accompanied by a metadata sheet including
the information from Table 2.
# Archive location
The selected archive for ARCFIRE is the Zenodo archive. All ARCFIRE data
products will be grouped in the ARCFIRE community page 3 .
# Licensing
All open data from ARCFIRE contributed to the Zenodo repository is planned to
be released under the Creative Commons CC-BY license 4 .
Table 2: ARCFIRE data set template
<table>
<tr>
<th>
Data set reference and name
</th>
<th>
Identifier for the data set to be produced.
</th> </tr>
<tr>
<td>
Data set description
</td>
<td>
Description of the data that will be generated or collected, its origin (in
case it is collected), nature and scale and to whom it could be useful, and
whether it underpins a scientific publication. Information on the existence
(or not) of similar data and the possibilities for integration and reuse.
</td> </tr>
<tr>
<td>
Standards and metadata
</td>
<td>
Reference to existing suitab le standards of the discipline. If these do not
exist, an outline on how and what metadata will be created.
</td> </tr>
<tr>
<td>
Data sharing
</td>
<td>
Description of how data will be shared, including access procedures, embargo
periods (if any), outlines of technical mechanisms for dissemination and
necessary software and other tools for enabling reuse, and definition of
whether access will be widely open or restricted to specific groups.
Identification of the repository where data will be stored, if already
existing and identified, in dicating in particular the type of repository
(institutional, standard repository for the discipline, etc.). In case the
dataset cannot be shared, the reasons for this should be mentioned (e.g.
ethical, rules of personal data, intellectual property, commercial, privacy
related, security related).
</td> </tr>
<tr>
<td>
Archiving and preservation (including storage and backup)
</td>
<td>
Description of the procedures that will be put in place for long term
preservation of the data. Indication of how long the data should be preserved,
what is its approximated end volume, what the associated costs are and how
these are planned to be covered.
</td> </tr> </table>
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# 1\. Introduction
This document is the initial version of the Data Management Plan (DMP). The
tool DMPOnline 1 has been used for writing this document. It has been
written with the specific template created for H2020 projects. In this final
version, four plans for different datasets have been written. These can be
found in the Annex of this document following the template _Final Review DMP_
.
In STARS4ALL, the following datasets have been identified and described:
* **Photometer** . These correspond to the data generated by photometers (sensors). In STARS4ALL, a network of photometers will be deployed during the project. Citizens can expand this network in the future buying new photometers from our marketplace. These photometers will send data periodically to our system where they are stored.
* **NightKnights game** : In STARS4ALL, games with a purpose will be developed to acquire and process data from LPIs. These data will generate two datasets: i) one with the results of the tasks execution, and ii) another with the gamification process results like points, badges, etc. … The plan for the first dataset is equivalent to the plan generated for the toolbox (annex). The plan for the second dataset will be ready in the next version.
* **Cities At Night** : Cities at Night is a citizen science project that aims to create a map, similar to Google maps, of the Earth at night using night time colour photographs taken by astronauts onboard the ISS. Cities At Night is one of our initiatives and we manage the data management of the project. Two datasets are generated: i) tasks, with the definition of the task created, and ii) taskrun, with the results of the executions of these tasks
* **Community Health dataset** : STARS4ALL is a project oriented to create awareness among citizens, so for us, it is very important to measure the impact of CAs on the community. It goes without saying that this data must be open and shared with the community in order to engage newcomers and increase their participation.
## STARS4ALL - Photometers - Final review DMP
### 1\. Data summary
State the purpose of the data collection/generation
The purpose of the data collected is to monitor light pollution with the
measurements taken by our photometers (sky brightness sensors). You can find
more information here: http://tess.stars4all.eu/.
These data will be used by scientists for research purposes. It can be used to
generate prediction models on sky brightness, to measure the impact of
diferent lighting policies, etc.
Explain the relation to the objectives of the project
One of the objectives in the project is to deploy a network of photometers to
monitor light pollution.
Specify the types and formats of data generated/collected
Each photometer generates a JSON object that is sent to our system and indexed
in our database. Periodic datasets in CSV are generated and published in our
data portal and Zenodo.
The data collected is the following:
<table>
<tr>
<th>
Field ield Namea e Typeype Unitsnits
</th>
<th>
Optionalptional Descriptionescription
</th> </tr>
<tr>
<td>
seq
</td>
<td>
integer -
</td>
<td>
Sequence number. If possible use 32 bits. The sequence number will start in 1
mandatory at each device reboot.
</td> </tr>
<tr>
<td>
name
</td>
<td>
string
</td>
<td>
\-
</td>
<td>
mandatory Instrument friendly name. Should be unique as it identifies the
device
</td> </tr>
<tr>
<td>
freq
</td>
<td>
float
</td>
<td>
Hz
</td>
<td>
Raw reading as a frequency with 3 decimal digits precission (milihertz)
mandatory
NNNNN.NNN
</td> </tr>
<tr>
<td>
mag
</td>
<td>
float
</td>
<td>
</td>
<td>
Visual magnitude (formulae?) corresponding to the raw reading). Transmitted
</td> </tr>
<tr>
<td>
mag/arcsec^2 mandatory up to two decimal places NN.NN
</td> </tr>
<tr>
<td>
tamb
</td>
<td>
float
</td>
<td>
ºC madatory Ambient Temperature. Transmitted up to one decimal place
</td> </tr>
<tr>
<td>
tsky
</td>
<td>
float
</td>
<td>
Sky Temperature. Transmitted up to one decimal place az integer degrees
optional photometer optical axis Azimuth sent only on instruments with
ºC mandatory
accelerometer alt integer degrees optional photometer optical axis Altitude
(angle): sent only on instruments with accelerometer
</td> </tr>
<tr>
<td>
rev
</td>
<td>
integer - mandatory Payload data format revision number. Current
</td> </tr> </table>
Specify if existing data is being re-used (if any)
We are not aware of any reuse of this data for other purposes, for the moment.
Specify the origin of the data
Data generated in our photometer network are being collected from our network
of sensors located around the world. The upto-date map is available here:
http://dashboards.stars4all.eu/tess-chart/tess-locations.html
State the expected size of the data (if known)
A photometer network with 45 devices generates 134MB of data each month. In
the short-term, we expected to duplicate this number, reaching 200 devices in
the middle-term.
Outline the data utility: to whom will it be useful
The data generated by our photometers have a clear application in the light
pollution research field. We have data collected since 2017 in an operation
model of 24/7/365. This will provide a invaluable source of information to
understand the behaviour of the light pollution in a specific area. The
effects of light pollution in biodiversity, human health and energy wasted are
well documented in scientific literature.
#### 2.1 Making data findable, including provisions for metadata [FAIR data]
Outline the discoverability of data (metadata provision)
All data have been uploaded and indexed in our data portal
(http://data.stars4all.eu) adding the metadata defined by CKAN.
This includes:
1. Title
2. Description
3. Tags
4. License
5. Visibility
6. Source
7. Version
8. Author / Author email
9. Maintainer /email
The datasets can be found using any of the metadata defined previously from a
search field. They are also accesible in our data portal/zenodo, and re-usable
since they are published with the CC-BY 4.0 license.
The fields defined inside the datasets are described in the description field
of the dataset.
Outline the identifiability of data and refer to standard identification
mechanism. Do you make use of persistent and unique identifiers such as
Digital Object Identifiers?
Photometer datasets are uploaded to our portal and to Zenodo (in our community
https://zenodo.org/communities/stars4all ). Zenodo is used by our project to
generate DOIs and to archive (make persistent) our datasets. This also allows
indexing our data in OpenAire.
Outline naming conventions used
For naming, the following convention has been used:
Month-Year
Outline the approach towards search keyword
Our datasets are published both in our data portal and in Zenodo. A set of
tags have been defined to tag each dataset, facilitating their search. Plus,
in our data portal, based on CKAN, users can make searches based on the common
metadata defined for all datasets that has been previously described in this
section.
Outline the approach for clear versioning
Both Zenodo and our data portal have a versioning system included in the
metadata of the datasets. The version changes when a modification has been
made on a dataset. The change of the version does not imply a request of a new
DOI.
Specify standards for metadata creation (if any). If there are no standards in
your discipline describe what metadata will be created and how
There is no entry in the Metadata Standards Directory for the datasets
generated in our project.
Nevertheless, night sky brightness data is archived using the "NSBM Community
Standards for Reporting Skyglow
Observations", which was officially adopted at the 12th European Symposium for
the Protection of the Night Sky and endorsed by the International Dark Sky
Association (IDA) and by the International Astronomical Union (IAU) in Beijing
2012.
#### 2.2 Making data openly accessible [FAIR data]
Specify which data will be made openly available? If some data is kept closed
provide rationale for doing so
All data generated is made openly available.
Specify how the data will be made available
Data is available from our data portal (
http://ckan.stars4all.eu/dataset/tess-monthly-measurements ) and from our
Zenodo
community (
http://www.zenodo.org/communities/stars4al
l
)
Specify what methods or software tools are needed to access the data? Is
documentation about the software needed to access the data included? Is it
possible to include the relevant software (e.g. in open source code)?
You only need a browser to access to all data. For more documentation, access
the STARS4ALL website (http://www.stars4all.eu), Data portal section.
You can find more information here:
Data portal deliverable:
https://figshare.com/articles/D5_4_STARS4ALL_data_portal/4212555
Sensor API:
https://figshare.com/articles/D4_9_Sensor_Management_API_final_/4508690
Specify where the data and associated metadata, documentation and code are
deposited
The data are deposited in:
Data portal (including metadata) -> _http://ckan.stars4all.eu/dataset/tess-
monthly-measurements_
Zenodo Community -> _http://www.zenodo.org/communities/stars4all_
The interpretation of each measure, as well as the technical components of the
device, are open and available to general public in the following link:
_https://drive.google.com/file/d/0Bw_qv1ze9sY2Xy1BYk1Fcmx1X1k/view?usp=sharing_
Software is available on Github ( _https://github.com/STARS4ALL/photometer-
api_ ) and the API properly documented in Apiary:
_http://docs.photometer.apiary.io_
Specify how access will be provided in case there are any restrictions
You can use the contact email for data issues of our project:
[email protected]
#### 2.3 Making data interoperable [FAIR data]
Assess the interoperability of your data. Specify what data and metadata
vocabularies, standards or methodologies you will follow to facilitate
interoperability.
To facilitate interoperability, we have used the same metadata for all
datasets generated in the project. It is based in the metadata vocabulary
given by CKAN.
Data use the standard "NSBM Community Standards for Reporting Skyglow
Observations".
Specify whether you will be using standard vocabulary for all data types
present in your data set, to allow inter-disciplinary interoperability? If
not, will you provide mapping to more commonly used ontologies?
Data uses the "NSBM Community Standards for Reporting Skyglow Observations"
standard.
#### 2.4 Increase data re-use (through clarifying licenses) [FAIR data]
Specify how the data will be licenced to permit the widest reuse possible
Data is licensed with CC-BY 4.0 that allows to:
Sharehare — copy and redistribute the material in any medium or format
Adaptdapt — remix, transform, and build upon the material for any purpose,
even commercially.
Specify when the data will be made available for re-use. If applicable,
specify why and for what period a data embargo is needed
Data is available in real time through the APi and dashboards, and monthly, in
a dataset format through Zenodo and our data portal.
Specify whether the data produced and/or used in the project is useable by
third parties, in particular after the end of the project? If the re-use of
some data is restricted, explain why
Data will be used by light pollution researchers to study the impact of the
light pollution in the environment and to be used in scientific publications.
Plus, it could be used for environmental studies or commercial activities sush
as measure the quality of the sky in rural hotels environments.
There is not restriction in the use of the data. We only request to cite us
using our DOI.
Describe data quality assurance processes
The process to publish a dataset is supervised. A curator checks whether the
dataset is generated properly, before being published.
Specify the length of time for which the data will remain re-usable
Data will be continuously archived while the Zenodo platform remains open.
### 3\. Allocation of resources
Estimate the costs for making your data FAIR. Describe how you intend to cover
these costs
The cost of making our data FAIR in Zenodo is free. The cost of the data
infrastructure will be covered by the STARS4ALL foundation.
Clearly identify responsibilities for data management in your project
The data management process is managed by UPM (Universidad Politécnica de
Madrid).
Describe costs and potential value of long term preservation
We use Zenodo for long-term preservation. With these datasets, scientists will
be able to measure the evolution of the sky quality throughout the years and
to study the impact different outdoor lighting policies.
### 4\. Data security
Address data recovery as well as secure storage and transfer of sensitive data
These kind of data do not have security restrictions because they do not
provide sensitive values. Also, these data do not contain personal information
so there is not need to apply protection measurements.
Data, with the exception of datasets in Zenodo, are hosted in UPM's servers.
Only authorised personnel can access the location where these servers are
hosted and periodical backups are being done.
### 5\. Ethical aspects
To be covered in the context of the ethics review, ethics section of DoA and
ethics deliverables. Include references and related technical aspects if not
covered by the former
These datasets do not contain personal information so there is not need to
apply data protection measurements and to request permission to photometers'
owners to publish these data.
### 6\. Other
Refer to other national/funder/sectorial/departmental procedures for data
management that you are using (if any) No other issues reported.
## STARS4ALL - NightKnights - Final review DMP
### 1\. Data summary
State the purpose of the data collection/generation
Night Knights ( https://www.nightknights.eu/ ) is a Game With A Purpose
(GWAP) aimed at Data Linking; more specifically, by playing the game, players
help in classifying images taken from the International Space Station (ISS)
with respect to a given taxonomy (hence, links are created between images and
categories). In short, all actions by all players are weighted and aggregated
by the game, producing a “crowdsourced” classification of ISS pictures that
can be used for subsequent analysis of the light pollution phenomenon. Details
on the Night Knights application and its internal functioning are given in
deliverables D4.4, D4.6 and D4.10.
The data set consists of the output data of the Night Knights game. Data are
stored in a MySQL database securely accessible by means of a dedicated API, as
described in the following.
Explain the relation to the objectives of the project
One of the objectives of STARS4ALL is to implement gamification mechanisms to
increase citizen participation (O4.1 -> WP4). Another STARS4ALL objective is
to give support to LPIs (Light Pollution Initiatives) in data management (O4.4
-> WP4). For that reason, STARS4ALL has deployed a data portal for backing
them.
Specify the types and formats of data generated/collected
The format of the dataset was defined in a way to facilitate its subsequent
reuse, i.e. the game output contains all information needed for further
processing and evaluation of the data.
Data collected is divided in two categories: donetasks and results.
In the first case, the dataset contains the individual tasks executed by
users.
<table>
<tr>
<th>
Field Nameield a e
</th>
<th>
Typeype
</th>
<th>
Unitsnits Optionalptional Descriptionescription
</th> </tr>
<tr>
<td>
idUser
</td>
<td>
integer
</td>
<td>
\-
</td>
<td>
mandatory It is the identifier of the user
</td> </tr>
<tr>
<td>
taskId
</td>
<td>
integer
</td>
<td>
\-
</td>
<td>
mandatory It is the internal id of the task
</td> </tr>
<tr>
<td>
smallPhotoUrl
</td>
<td>
url
</td>
<td>
\-
</td>
<td>
mandatory It is the url of the photo used to be classified
</td> </tr>
<tr>
<td>
timestamp
</td>
<td>
datetime
</td>
<td>
\-
</td>
<td>
mandatory It represents the execution time
</td> </tr>
<tr>
<td>
choosenCategory enumerate -
</td>
<td>
It is the category choosen by the user. It can be: black, city, stars, aurora,
mandatory astronaut or none
</td> </tr>
<tr>
<td>
groundTruth boolean -
</td>
<td>
mandatory It represents if the task is a golden task
It represents if the user has been logged in the system or is an anonymous
</td> </tr>
<tr>
<td>
guest boolean -
</td>
<td>
mandatory
</td> </tr> </table>
user.
In the second case, the dataset contains the results obtained in NightKnights.
Field Nameield a e Typeype Unitsnits Optionalptional Descriptionescription
<table>
<tr>
<th>
resultId integer
</th>
<th>
\-
</th>
<th>
mandatory It is the identifier of the result
</th> </tr>
<tr>
<td>
taskId integer
</td>
<td>
\-
</td>
<td>
It is the same as the submitted task (previous dataset), so to allow for
mandatory identification.
</td> </tr>
<tr>
<td>
smallPhotoUrl url
</td>
<td>
\-
</td>
<td>
mandatory It is the url of the photo used to be classified
</td> </tr>
<tr>
<td>
class enumerate -
</td>
<td>
It is the category choosen by the user. It can be: black, city, stars, aurora,
mandatory astronaut or none
</td> </tr>
<tr>
<td>
numPlayers integer -
</td>
<td>
mandatory Number of players that were actually needed to classify the photo
The timestamp of the result (moment in which the classification was cross-
</td> </tr>
<tr>
<td>
solutionDate datetime -
</td>
<td>
mandatory
</td> </tr> </table>
validated and the photo removed from the game)
Specify if existing data is being re-used (if any)
Data generated is used in the project Cities At Night, one of the STARS4ALL
light pollution initiatives. Some examples can be found at
http://citiesatnight.org/index.php/related-projects/ .
Data is being used as a training dataset in machine learning algorithms, as
can be consulted in the following paper:
Gloria Re Calegari, Gioele Nasi and Irene Celino: "Human Computation vs.
Machine Learning: an Experimental Comparisonu an o putation vs. achine
earning: an xperi ental o parison for Image Classificatiofor I age
lassificationn", Human Computation Journal, vol. 5, issue 1, pp. 13-30, DOI:
10.15346/hc.v5i1.2, 2018. [gold open access]
Specify the origin of the data
The starting point is the NASA database, with almost half a million pictures
taken by the astronauts on the International Space Station.
These images are classified by users using the application NightKnights (
http://www.nightknights.eu ).
The dataset is generated using the NightKnights API, which is described here:
https://crowdtaskmanagement.docs.apiary.io/ Methods used:
Night Knights Game Results Collection
Night Knights Game Done Tasks Collection
State the expected size of the data (if known)
The size of the data generated depends of the number of executions and the
users who play with the game. The average per year is about 10MB, so a big
volume of data is not expected in the future.
Outline the data utility: to whom will it be useful
Data is used for:
Calculating community health indicators of the project Cities At Night. See
http://dashboards.stars4all.eu/social/
Research in the Human Computation field. Some examples are
Gloria Re Calegari, Gioele Nasi and Irene Celino: "Human Computation vs.
Machine Learning: anu an o putation vs. achine earning: an
Experimental Comparison for Image Classificationxperi ental o parison for I
age lassification", Human Computation Journal, vol. 5, issue 1, pp. 13-30,
DOI: 10.15346/hc.v5i1.2, 2018. [gold open access ]
Gloria Re Calegari and Irene Celino: "Interplay of Game Incentives, Player
Profiles and Task Difficulty inInterplay of a e Incentives, layer rofiles and
ask ifficulty in
Games with a Purposea es ith a urpose", in proceedings of the 21th
International Conference on Knowledge Engineering and
Knowledge Management - EKAW 2018, LNAI Volume 11313, pp. 306-321, DOI:
10.1007/978-3-030-03667-
6_20, 2018. [green open access (eprint) at arxiv ]
Gloria Re Calegari, Andrea Fiano and Irene Celino: "A Framework to build Games
with a Purpose for Linked ra e ork to build a es ith a urpose for inked
Data Refinementata efine ent", in proceedings of the International Semantic
Web Conference 2018, Best Paper Award Resources Track, LNCS Volume 11137, pp.
154-169, DOI: 10.1007/978-3-030-00668-6_10, 2018. [green open access (eprint)
at arxiv]
#### 2.1 Making data findable, including provisions for metadata [FAIR data]
Outline the discoverability of data (metadata provision)
As it happens with the rest of the data, all data generated in this project
have been uploaded and indexed in our data portal (http://data.stars4all.eu)
adding the metadata defined by CKAN. This includes:
1. Title
2. Description
3. Tags
4. License
5. Visibility
6. Source
7. Version
8. Author / Author email
9. Maintainer /email
The datasets of our project can be found using any of the metadata defined
previously from a search field. Also, they are accesible in our data
portal/zenodo, and re-usable since they are published with the CC-BY 4.0
license.
The fields defined inside of the datasets are described in the description
field of the dataset.
Outline the identifiability of data and refer to standard identification
mechanism. Do you make use of persistent and unique identifiers such as
Digital Object Identifiers?
NightKnights results are uploaded to our portal and Zenodo (
https://zenodo.org/communities/stars4all ). Zenodo is used to generate DOIs
and to archive (make persistent) our datasets. Also, this allows indexing our
data in OpenAire.
Outline naming conventions used
For naming donetasks datasets, the following convention has been used:
results-nightknights-year
For naming results datasets, the following convention has been used:
donetasks-nightknights-year
Outline the approach towards search keyword
Our datasets are published both in our data portal and in Zenodo. A set of
tags have been defined to tag each dataset, facilitating their search. Plus,
in our data portal, based on CKAN, users can make searches based on the common
metadata defined for all datasets that has been previously described in this
section.
Outline the approach for clear versioning
Both Zenodo and our data portal have a versioning system including in the
metadata of the datasets. The version changes when a modification has been
made on a dataset. The change of the version does not imply a request of a new
DOI.
Specify standards for metadata creation (if any). If there are no standards in
your discipline describe what metadata will be created and how
The format of the dataset was defined in a way to facilitate its subsequent
reuse, i.e. the game output contains all information needed for further
processing and evaluation of the data.
Night Knights data gives information about three main themes:
Classification results (classified photo, assigned classification, number of
players needed to come to a classification agreement)
Players’ actions (“log” of all classification actions done by anonymized game
users)
Game evaluation data (including KPIs like number of tasks
available/started/completed, number of players, total played time, throughput,
average life play, etc.)
#### 2.2 Making data openly accessible [FAIR data]
Specify which data will be made openly available? If some data is kept closed
provide rationale for doing so
All data generated have been made openly available except some personal data
such as the identifier of the user in Social Networks. That is because the
user can play in the game logging in with its twitter, facebook or google+
accounts. This personal data is closed to respect the personal data law.
Specify how the data will be made available
Data is available from our data portal. There are two different datasets: i)
one for the results , ii) one for the individual tasks. In both cases, there
are different resources (files) per year.
Specify what methods or software tools are needed to access the data? Is
documentation about the software needed to access the data included? Is it
possible to include the relevant software (e.g. in open source code)?
Only a browser is needed to access to the data. Plus, the API of the data
portal can be used. For more information, check:
https://docs.ckan.org/en/2.7/api/index.html
Specify where the data and associated metadata, documentation and code are
deposited
The data are deposited in our:
Data portal, inside of each project. ( http://data.stars4all.eu )
Zenodo Community -> http://www.zenodo.org/communities/stars4all
The source code can be found here:
https://github.com/STARS4ALL/gwap-enabler
You can find tutorials here:
https://github.com/STARS4ALL/gwap-enabler-tutorial
_https://crowdtaskmanagement.docs.apiary.io_
Specify how access will be provided in case there are any restrictions
You can use the contact email for data issues of our project:
[email protected]
#### 2.3 Making data interoperable [FAIR data]
Assess the interoperability of your data. Specify what data and metadata
vocabularies, standards or methodologies you will follow to facilitate
interoperability.
To facilitate interoperability, we have used the same metadata for all
datasets generated in the project. It is based in the metadata vocabulary
given by CKAN.
Specify whether you will be using standard vocabulary for all data types
present in your data set, to allow inter-disciplinary interoperability? If
not, will you provide mapping to more commonly used ontologies?
Vocabulary is described in the deliverables:
D4.10 Games Release (final version)
D5.7 : STARS4ALL tools for crowdsourcing activities (initial release)
D5.8 : STARS4ALL tools for crowdsourcing activities (final release)
#### 2.4 Increase data re-use (through clarifying licenses) [FAIR data]
Specify how the data will be licenced to permit the widest reuse possible
Data is licensed with CC-BY 4.0 that allows to:
Sharehare — copy and redistribute the material in any medium or format
Adaptdapt — remix, transform, and build upon the material for any purpose,
even commercially.
Specify when the data will be made available for re-use. If applicable,
specify why and for what period a data embargo is needed
There is no embargo period. Dataset are published periodically.
Specify whether the data produced and/or used in the project is useable by
third parties, in particular after the end of the project? If the re-use of
some data is restricted, explain why
These data can be used as a complement of the work done in the Cities At Night
project and the application Dark Skies ISS
( _https://crowdcrafting.org/project/darkskies/_ )
Describe data quality assurance processes
The process to publish a dataset is supervised. A curator checks whether the
dataset is generated properly, before being published.
Specify the length of time for which the data will remain re-usable
Data will be archived while the Zenodo platform remains open.
### 3\. Allocation of resources
Estimate the costs for making your data FAIR. Describe how you intend to cover
these costs
The cost of making our data FAIR in Zenodo is free. The cost of the data
infrastructure will be covered by the STARS4ALL foundation.
Clearly identify responsibilities for data management in your project
The data management process is managed by UPM (Universidad Politécnica de
Madrid).
Describe costs and potential value of long term preservation
We use Zenodo for long term preservation. With these datasets, scientists will
be able to measure the evolution of the light pollution using images taken by
astronauts of the ISS (International Space Station)
### 4\. Data security
Address data recovery as well as secure storage and transfer of sensitive data
There is only one field in the dataset that may be considered as personal
data. This field, idUser, contains the id of the user who executes the action.
Nevertheless, this field is an internal identificator (it does not contain the
user name) and the full name or another personal information cannot be traced
from this id.
Data of the data portal are hosted in UPM's servers. Only authorised personnel
can access the location where these servers are hosted and periodical backups
are being done.
### 5\. Ethical aspects
To be covered in the context of the ethics review, ethics section of DoA and
ethics deliverables. Include references and related technical aspects if not
covered by the former
These datasets do not contain personal information so there is not need to
apply data protection measurements.
The only personal information stored in our servers in the social identifier
(facebook, twitter or google+) of the users. This information is not available
in the datasets (but a user id is present in the dataset after executing an
anonymising process).
According with the Data Protection Spanish National Law, a consent is
requested to the user to store this information.
See _https://www.nightknights.eu/#/privacy_
### 6\. Other
Refer to other national/funder/sectorial/departmental procedures for data
management that you are using (if any)
No other issues reported.
## STARS4ALL - Cities At Night - Final review DMP
### 1\. Data summary
State the purpose of the data collection/generation
Cities at Night is a citizen science project that aims to create a map,
similar to Google maps, of the Earth at night using night time colour
photographs taken by astronauts onboard the ISS.
NASA has a database with almost half a million pictures taken by the
astronauts on the International Space Station.To organise all this data with
the use of computers would be infeasible since it would take extremely complex
algorithms to interpret the photographs. The human eye, however, knows
immediately if the camera was pointing at a city or simply at the stars.
For this reason we created Cities at Night, a platform with 3 apps with which
anyone can help while enjoying beautiful pictures taken from space.
Explain the relation to the objectives of the project
One of the objectives of STARS4ALL is to give support to LPIs (Light Pollution
Initiatives) in data management (O4.4). For that reason, STARS4ALL has
deployed a data portal for backing them.
Specify the types and formats of data generated/collected
The data collected is divided in two categories: task and taskrun. tasktask
defines the tasks created to be executed by users in the Dark Skies
application.
<table>
<tr>
<th>
Field Nameield a e
</th>
<th>
Typeype
</th>
<th>
Unitsnits Optionalptional Descriptionescription
</th> </tr>
<tr>
<td>
task_calibration
</td>
<td>
boolean
</td>
<td>
\-
</td>
<td>
mandatory Define if is a golden task
</td> </tr>
<tr>
<td>
task_created
</td>
<td>
datetime
</td>
<td>
\-
</td>
<td>
mandatory Define when the task was created
</td> </tr>
<tr>
<td>
task_id
</td>
<td>
integer
</td>
<td>
\-
</td>
<td>
mandatory It is the identifier of the task
</td> </tr>
<tr>
<td>
task_info
</td>
<td>
json object
</td>
<td>
\-
</td>
<td>
It is a json object with the input source (video, image, sound, etc ...) of
the
task
mandatory
</td> </tr>
<tr>
<td>
task_n_answer
</td>
<td>
integer
</td>
<td>
\-
</td>
<td>
mandatory It is the number of answers necessary to complete the task
</td> </tr>
<tr>
<td>
task__priority_0
</td>
<td>
float
</td>
<td>
\-
</td>
<td>
mandatory Priority of the task
</td> </tr>
<tr>
<td>
task_project_id
</td>
<td>
integer
</td>
<td>
\-
</td>
<td>
mandatory It is the identifier of the project associated to the task
</td> </tr>
<tr>
<td>
task_quorum
</td>
<td>
integer
</td>
<td>
\-
</td>
<td>
mandatory It is the number of different users necessary to accept the task
</td> </tr>
<tr>
<td>
task_state
</td>
<td>
enumerate -
</td>
<td>
mandatory Define the state of the task (COMPLETED)
</td> </tr>
<tr>
<td>
task_info_idiss
</td>
<td>
string -
</td>
<td>
Define the id of the task in string format. In our case, the name of the
mandatory image that we have to classify
</td> </tr>
<tr>
<td>
task_info_link
</td>
<td>
url -
</td>
<td>
mandatory Define the place where user can find the image.
</td> </tr>
<tr>
<td>
task_info_link_big
</td>
<td>
url -
</td>
<td>
mandatory Define the url of the image in high resolution
</td> </tr>
<tr>
<td>
task_info_link_small url -
</td>
<td>
mandatory Define the url of the image to be shown to the user (small
resolution)
</td> </tr> </table>
taskruntaskrun defines the executions of an specific task.
<table>
<tr>
<th>
Field Nameield a e
</th>
<th>
Typeype Unitsnits Optionalptional Descriptionescription
</th> </tr>
<tr>
<td>
task_run__calibration
</td>
<td>
boolean -
</td>
<td>
mandatory Define if the origin is a golden task
</td> </tr>
<tr>
<td>
task_run__created
</td>
<td>
datetime -
</td>
<td>
mandatory Define when the task was shown to the user to be executed.
</td> </tr>
<tr>
<td>
task_run__external_uid
</td>
<td>
string -
</td>
<td>
Define if the task was executed with an external application (using
mandatory the API)
</td> </tr>
<tr>
<td>
task_run__finish_time
</td>
<td>
datetime -
</td>
<td>
mandatory Define when the task was executed.
</td> </tr>
<tr>
<td>
task_run__id
</td>
<td>
integer -
</td>
<td>
mandatory It is the identifier of the object
</td> </tr>
<tr>
<td>
task_run__info
</td>
<td>
json object -
</td>
<td>
mandatory It is a json object with the response (result) of the user.
</td> </tr>
<tr>
<td>
task_run__project_id
</td>
<td>
integer -
</td>
<td>
mandatory It is the identifier of the project associated to the task
</td> </tr>
<tr>
<td>
task_run__task_id
</td>
<td>
integer -
</td>
<td>
mandatory It is the identifier of the task (see previous table)
</td> </tr>
<tr>
<td>
task_run__timeout
</td>
<td>
boolean -
</td>
<td>
mandatory Define if the user solved the task in the time assigned to it.
</td> </tr>
<tr>
<td>
task_run__user_id
</td>
<td>
integer -
</td>
<td>
mandatory It is the id of the user
</td> </tr>
<tr>
<td>
task_runinfo__classification enumerate -
</td>
<td>
It is the result of the classification (AURORA, CITY, BLACK,
mandatory
ISS)
</td> </tr>
<tr>
<td>
task_runinfo__cloudy enumerate -
</td>
<td>
It provides more information about the quality of ther image
mandatory shown. In our case if the level of clouds in the image
</td> </tr>
<tr>
<td>
task_runinfo__img_big url -
</td>
<td>
mandatory Image in high resolution classified
</td> </tr>
<tr>
<td>
task_runinfo__img_smal url -
</td>
<td>
mandatory Image in small resolution classified
</td> </tr> </table>
Specify if existing data is being re-used (if any)
Data generated in these crowdsourcing activities and present in this dataset
is being used in many research papers and projects such as:
EVALUATING THE ASSOCIATION BETWEEN ARTIFICIAL LIGHT-AT-NIGHT EXPOSURE AND
BREAST
AND PROSTATE CANCER RISK IN SPAIN. (
http://citiesatnight.org/index.php/portfolio-view/6799-2/ )
ARTIFICIALLY LIT SURFACE OF EARTH AT NIGHT INCREASING IN RADIANCE AND
EXTENT. (http://citiesatnight.org/index.php/portfolio-view/alan-increasing/
)
Artificial light at night and cognitive development and performance in
children, Juana Maria Delgado-Saborit,
T
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r
t
l
e
s
c
o
n
s
e
r
v
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t
i
o
n
i
n
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l
o
r
i
d
a
,
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a
n
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i
e
r
r
e
,
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,
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l
o
r
i
d
a
,
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S
A
ISGlobal (Spain) and King’s College London (UK)
EMISSI@N, Kevin G. Gaston, Environmental and Sustainability Institute of
University of Exeter, Cornwall, United
Kingdom
More information can be found in:
http://citiesatnight.org/index.php/related-projects/
http://citiesatnight.org/index.php/science-research
/
Specify the origin of the data
The starting point is the NASA database with almost half a million pictures
taken by the astronauts on the International Space Station.
These images are classified by users with the project Dark Skies (
https://crowdcrafting.org/project/darkskies/ ) that it is running in the
platform Crowdcrafting.
State the expected size of the data (if known)
According to the estimation done based on previous datasets, the size of
storing the execution of 193090 tasks takes up 625MB. These tasks comprise a
period of one and a half year.
Outline the data utility: to whom will it be useful
As it has been described in section "data re-used section", these data is
relevant for light pollution researchers because they can be used to estimate
the light pollution in cities as well as the colour of them (ambar, blue,
white, etc.).
#### 2.1 Making data findable, including provisions for metadata [FAIR data]
Outline the discoverability of data (metadata provision)
As it happens with the rest of the data, all data generated in this project
have been uploaded and indexed in our data portal (http://data.stars4all.eu)
adding the metadata defined by CKAN. This includes:
1. Title
2. Description
3. Tags
4. License
5. Visibility
6. Source
7. Version
8. Author / Author email
9. Maintainer /email
The datasets of our project can be found using any of the metadata defined
previously from a search field. Also, they are accesible in our data
portal/zenodo, and re-usable since they are published with the CC-BY 4.0
license.
The fields defined inside of the datasets are described in the description
field of the dataset.
Outline the identifiability of data and refer to standard identification
mechanism. Do you make use of persistent and unique identifiers such as
Digital Object Identifiers?
Dark Skies ISS tasks and taks runs are uploaded to our portal and Zenodo(
https://zenodo.org/communities/stars4all ). That is because, Zenodo is used
by our project to generate DOIs and to archive (make persistent) our datasets.
Also, this allows indexing our data in OpenAire.
Outline naming conventions used
For naming task datasets, the following convention has been used:
CitiesAtNight-Tasks
For naming task_run datasets, the following convention has been used:
CitiesAtNight-Tasks_run_(Year)
Outline the approach towards search keyword
Our datasets are published both in our data portal and in Zenodo. A set of
tags have been defined to tag each dataset, facilitating their search. Plus,
in our data portal, based on CKAN, users can make searches based on the common
metadata defined for all datasets that has been previously described in this
section.
Outline the approach for clear versioning
Both Zenodo and our data portal have a versioning system including in the
metadata of the datasets. The version changes when a modification has been
made on a dataset. The change of the version does not imply a request of a new
DOI.
Specify standards for metadata creation (if any). If there are no standards in
your discipline describe what metadata will be created and how
There is no entry in the Metadata Standards Directory for the datasets
generated in our project.
There is no standard used for crowdsourcing activities. Nevertheless, we use
the data model of PyBossa
( _http://docs.pybossa.com/en/latest/model.html_ ). Following the PyBossa
model, data will be stored in a database system (Postgres) as JSON objects.
Depending on their nature, projects in PyBossa belong to two different
categories: thinking or sensing. The thinking category is used in projects
where users solve problems, such as tagging images. The sensing category is
used for projects where users gather data. The generated metadata are stored
as a result of a task (taskrun). Once a quorom is reached, the value is
incorporated to the resource.
#### 2.2 Making data openly accessible [FAIR data]
Specify which data will be made openly available? If some data is kept closed
provide rationale for doing so
All data generated have been made openly available.
Specify how the data will be made available
Data is available from our data portal. There are two different datasets: i)
one for tasks , ii) one for the results. In the case of the results, there are
different resources (files) per year.
Specify what methods or software tools are needed to access the data? Is
documentation about the software needed to access the data included? Is it
possible to include the relevant software (e.g. in open source code)?
Only a browser is needed to access to the data. Plus, the API of the data
portal can be used. For more information, check:
_https://docs.ckan.org/en/2.7/api/index.html_
Specify where the data and associated metadata, documentation and code are
deposited
The data are deposited in our:
Data portal, inside of each project. ( _http://data.stars4all.eu_ )
Zenodo Community -> _http://www.zenodo.org/communities/stars4all_
Specify how access will be provided in case there are any restrictions
You can use the contact email for data issues of our project:
[email protected]
#### 2.3 Making data interoperable [FAIR data]
Assess the interoperability of your data. Specify what data and metadata
vocabularies, standards or methodologies you will follow to facilitate
interoperability.
To facilitate interoperability, we have used the same metadata for all
datasets generated in the project. It is based in the metadata vocabulary
given by CKAN.
Specify whether you will be using standard vocabulary for all data types
present in your data set, to allow inter-disciplinary interoperability? If
not, will you provide mapping to more commonly used ontologies?
There is no standard used for crowdsourcing activities. Nevertheless, we use
the data model of PyBossa ( _http://docs.pybossa.com/en/latest/model.html_ ).
#### 2.4 Increase data re-use (through clarifying licenses) [FAIR data]
Specify how the data will be licenced to permit the widest reuse possible
Data is licensed with Creative Commons License. Cities at Night by Alejandro
Sánchez de Miguel et al. Atlas of astronaut photos of Earth at night A&G
(2014) is distributed under Creative Commons Non commercial attribution –
Share 4.0
International licence. _https://doi.org/10.1093/astrogeo/atu165_ However, if
the Cities at Night data are a principal component of a science paper then co-
authorship to PI's should be offered.
Specify when the data will be made available for re-use. If applicable,
specify why and for what period a data embargo is needed
There is no embargo period. Dataset is published periodically.
Specify whether the data produced and/or used in the project is useable by
third parties, in particular after the end of the project? If the re-use of
some data is restricted, explain why
As it has been mentioned in previous sections, these data is very important
for light pollution research. Some examples can be found in:
_http://citiesatnight.org/index.php/related-projects/_
Describe data quality assurance processes
The process to publish a dataset is supervised. A curator checks whether the
dataset is generated properly, before being published.
Specify the length of time for which the data will remain re-usable
Data will be archived while Zenodo platform remains open.
### 3\. Allocation of resources
Estimate the costs for making your data FAIR. Describe how you intend to cover
these costs
The cost of making our data FAIR in Zenodo is free. The cost of the data
infrastructure will be covered by the STARS4ALL foundation.
Clearly identify responsibilities for data management in your project
The data management process is managed by UPM (Universidad Politécnica de
Madrid).
Describe costs and potential value of long term preservation
We use Zenodo for long term preservation. With these datasets, scientists will
be able to measure the evolution of the light pollution using images taken by
astronauts of the ISS (International Space Station)
### 4\. Data security
Address data recovery as well as secure storage and transfer of sensitive data
There is only one field in the dataset that may be due to security
restrictions because it may be considered as personal data. This field,
task_run_user_id, contains the id of the user who executes the action.
Nevertheless, this field is an internal identificator (not contains the name
of the user) and the full name or another personal information can not be
traced from this id.
Data of the data portal are hosted in UPM's servers. Only authorised personnel
can access the location where these servers are hosted, and periodical backups
are being done.
All the generated data in the platform Crowdcrafting by the volunteers are,
unless otherwise stated, under the _Open Data_
_Commons License_ . Check the description the project's page for further
details about which license is going to be applied .
### 5\. Ethical aspects
To be covered in the context of the ethics review, ethics section of DoA and
ethics deliverables. Include references and related technical aspects if not
covered by the former
These datasets do not contain personal information so there is not need to
apply data protection measurements.
### 6\. Other
Refer to other national/funder/sectorial/departmental procedures for data
management that you are using (if any) No other issues reported.
## STARS4ALL - Community Health - Final review DMP
### 1\. Data summary
State the purpose of the data collection/generation
In order to carry out the community health analysis in WP3, we use data from
the various LPIs to calculate a set of metrics as set out in D3.4. We are
primarily concerned with analysing the number of classifications or tasks
carried out, how these are distributed among the community, and how many
people in total are participating. Since we cover a range of project types in
the LPIs, we for now focus on a set of data that is particular to the
classification-based projects, however much of this can be easily adapted to
other types of project, too.
Explain the relation to the objectives of the project
One of the objectives on WP3 is to analyse effectivity of community awareness
activities, in our case, LPIs. Community engagement needs to be continuously
scrutinized and monitored and relevant activities and strategies need to be
adjusted accordingly.
The aim here is to set up the analysis tools that will allow us to take
informed and well-balanced decisions regarding our activities.
Specify the types and formats of data generated/collected
Contribution data are exported from individual projects, such as Dark Skies,
Globe at Night etc, and include the number of contributes over time, the
number of users who are contributing, and task metrics.
<table>
<tr>
<th>
Field ield Namea e Typeype Unitsnits Optionalptional Descriptionescription
</th> </tr>
<tr>
<td>
day datetime -
</td>
<td>
mandatory Analyzed day
</td> </tr>
<tr>
<td>
rows integer -
</td>
<td>
mandatory Number of entries processed
</td> </tr>
<tr>
<td>
contributions integer -
</td>
<td>
mandatory Number of contributions done
</td> </tr>
<tr>
<td>
users integer -
</td>
<td>
mandatory Number of users have made a contribution
</td> </tr>
<tr>
<td>
starters integer -
</td>
<td>
mandatory Number of starters
</td> </tr> </table>
Specify if existing data is being re-used (if any)
Not for the moment.
Specify the origin of the data
These data are generated by several scripts that periodically run in our
servers. The source code is here: https://github.com/STARS4ALL/STARS4ALL-
social-dashboard
State the expected size of the data (if known)
The total amount of data collected in these datasets is 8.7MB (with data
collected from two years ago). These data will depend of the amount of
contributions but we do not expect more than 10MB of data per year.
Outline the data utility: to whom will it be useful
The target of this data is to the initiative/project coordinator. With this
dataset, the coordinator will be able to measure the health of the community
behind the project.
Also, these datasets can be valuable for researchers who working with online
communities.
#### 2.1 Making data findable, including provisions for metadata [FAIR data]
Outline the discoverability of data (metadata provision)
As it happens with the rest of the data, all data generated in this project
have been uploaded and indexed in our data portal (http://data.stars4all.eu)
adding the metadata defined by CKAN. This includes:
1. Title
2. Description
3. Tags
4. License
5. Visibility
6. Source
7. Version
8. Author / Author email
9. Maintainer /email
The datasets of our project can be found using any of the metadata defined
previously from a search field. Also, they are accesible in our data
portal/zenodo, and re-usable since they are published with the CC-BY 4.0
license.
The fields defined inside of the datasets are described in the description
field of the dataset.
Outline the identifiability of data and refer to standard identification
mechanism. Do you make use of persistent and unique identifiers such as
Digital Object Identifiers?
Community health indicators are uploaded to our portal and Zenodo (
https://zenodo.org/communities/stars4all ). That is because, Zenodo is used
by our project to generate DOIs and to archive (make persistent) our datasets.
Also, this allows to index our data in OpenAire.
Outline naming conventions used
For naming, the following convention has been used: social-name_of_the_project
Outline the approach towards search keyword
Our datasets are published both in our data portal and in Zenodo. A set of
tags have been defined to tag each dataset, facilitating their search. Plus,
in our data portal, based on CKAN, users can make searches based on the common
metadata defined for all datasets that has been previously described in this
section.
Outline the approach for clear versioning
Both Zenodo and our data portal have a versioning system including in the
metadata of the datasets. The version changes when a modification has been
made on a dataset. The change of the version does not imply a request of a new
DOI.
Specify standards for metadata creation (if any). If there are no standards in
your discipline describe what metadata will be created and how
There is no entry in the Metadata Standards Directory for the datasets
generated in our project. The convention follow to describe the fields of the
dataset is decribed in D3.6 Models and methods for community health monitoring
(final release)
#### 2.2 Making data openly accessible [FAIR data]
Specify which data will be made openly available? If some data is kept closed
provide rationale for doing so
All data generated have been made openly available.
Specify how the data will be made available
Data is available from our data portal. There is one social dataset per each
project and from our Zenodo community (
_http://www.zenodo.org/communities/stars4all_ )
Specify what methods or software tools are needed to access the data? Is
documentation about the software needed to access the data included? Is it
possible to include the relevant software (e.g. in open source code)?
You only need a browser to access to the data. Plus, you can use the API of
the data portal. You can find more information here:
_https://docs.ckan.org/en/2.7/api/index.html_
Specify where the data and associated metadata, documentation and code are
deposited
The data are deposited in our:
Data portal, inside of each project. ( _http://data.stars4all.eu_ )
Zenodo Community -> _http://www.zenodo.org/communities/stars4all_
You can access to visual components here:
_http://dashboards.stars4all.eu/social/_
The source code is here: _https://github.com/STARS4ALL/STARS4ALL-social-
dashboard_
Specify how access will be provided in case there are any restrictions
You can use the contact email for data issues of our project:
[email protected]
#### 2.3 Making data interoperable [FAIR data]
Assess the interoperability of your data. Specify what data and metadata
vocabularies, standards or methodologies you will follow to facilitate
interoperability.
To facilitate interoperability, we have used the same metadata for all
datasets generated in the project. It is based in the metadata vocabulary
given by CKAN.
Standards and methodologies are described in D3.6 Models and methods for
community health monitoring.
Specify whether you will be using standard vocabulary for all data types
present in your data set, to allow inter-disciplinary interoperability? If
not, will you provide mapping to more commonly used ontologies?
Vocabulary is described in deliverable D3.6 Models and methods for community
health monitoring.
#### 2.4 Increase data re-use (through clarifying licenses) [FAIR data]
Specify how the data will be licenced to permit the widest reuse possible
Data is licensed with CC-BY 4.0 that allows to:
Sharehare — copy and redistribute the material in any medium or format
Adaptdapt — remix, transform, and build upon the material for any purpose,
even commercially.
Specify when the data will be made available for re-use. If applicable,
specify why and for what period a data embargo is needed
There is no embargo period. Dataset is published periodically.
Specify whether the data produced and/or used in the project is useable by
third parties, in particular after the end of the project? If the re-use of
some data is restricted, explain why
These data is very important for the coordinators of the LPIs (Light Pollution
Initiatives) because they can see easily the impact of their policies and the
health of their community. Also, it can be used by social researchers to
measure diferent social communities.
Describe data quality assurance processes
The process to publish a dataset is supervised. A curator checks whether the
dataset is generated properly, before being published.
Specify the length of time for which the data will remain re-usable
Data will be archived while Zenodo platform remains open.
### 3\. Allocation of resources
Estimate the costs for making your data FAIR. Describe how you intend to cover
these costs
The cost of making our data FAIR in Zenodo is free. The cost of the data
infrastructure will be covered by the STARS4ALL foundation.
Clearly identify responsibilities for data management in your project
The data management process is managed by UPM (Universidad Politécnica de
Madrid).
Describe costs and potential value of long term preservation
We use Zenodo for long term preservation. With these datasets, scientists will
be able to measure the evolution of the health on social communities.
### 4\. Data security
Address data recovery as well as secure storage and transfer of sensitive data
These kind of data do not have security restrictions because they are not
sensitive values. Also, these data do not contain personal information so
there is not need to apply protection measurements.
Data, with the exception of datasets in Zenodo, are hosted in UPM's servers.
Only authorised personnel can access the location where these servers are
hosted, and periodical backups are being done.
### 5\. Ethical aspects
To be covered in the context of the ethics review, ethics section of DoA and
ethics deliverables. Include references and related technical aspects if not
covered by the former
These datasets do not contain personal information so there is not need to
apply data protection measurements.
### 6\. Other
Refer to other national/funder/sectorial/departmental procedures for data
management that you are using (if any) No other issues reported.
|
https://phaidra.univie.ac.at/o:1140797
|
Horizon 2020
|
1076_STARS4ALL_688135.md
|
1\. Introduction 5
2\. Datasets 6
2.1. Photometers’ Network (Initial Version) 6
2.2. Photometers’ Network (Detailed Version) 7
2.3. Community Health (Initial Version) 9
2.4. Games -‐ NightKnights (Initial Version) 10
# Introduction
This document is the intermediate version of the Data Management Plan (DMP).
We have used the tool DMPOnline 1 for generating the partial DMPs for each
dataset. They have been written with the template specially created for H2020
projects. Regarding previous version of the document (D4.2), some
modifications have been done:
* In the dataset generated by the **photometer network** , we have included in each measurements the photometer's coordinates (latitude, longitude).
* We have removed the crowdsourcing dataset, integrating its data in the **community health** and **games** datasets
Also, we have added the following datasets:
* A **detailed version** of the photometers’ dataset.
* **Games dataset (initial version)** : In STARS4ALL, games with a purpose will be developed to acquire and process data from LPIs. These data will generate two datasets: i) one with the results of the tasks execution, and ii) another with the gamification process results like points, badges, etc … The plan for the first dataset is equivalent to the plan generated for the toolbox (annex). The plan for the second dataset will be ready in the next version.
* **Community Health dataset (initial version)** : STARS4ALL is a project oriented to create awareness among citizens, so for us, it is very important to measure the impact of CAs on the community. It goes without saying that this data must be open and shared with the community in order to engage newcomers and increase their participation.
# Datasets
## Photometers’ Network (Initial Version)
### Data set description
Light pollution samples will be continuously taken by the photometer network
deployed in the project.
The specification of the photometers is provided in deliverable D4.1
(https://figshare.com/s/76f01dec468f8286f781). Each photometer will send data
to a message broker, which will send it to the subscriptors. In our case, the
subscriptors will insert data in our data portal (also described in
deliverable D5.4).
Raw data will be acquired every 5 minutes. This default data acquisition rate
may be reduced to 1 minute if necessary. Raw data will be generated as a
10-row CSV in ASCII format.
This means that each photometer will generate 288K per day in the worst
scenario (1 sample / minute). Following the STARS4ALL Key Performance
Indicators, which are described in the Document of Action (Section 2.1.1.2),
at the end of the project, 250 photometers will be deployed. This amounts to a
volume of data of 72MB per day, 2.16 GB per month and 25.96GB per year. This
size has been calculated considering uncompressed data.
### Standards and metadata
Night sky brightness data will be archived using the "NSBM Community Standards
for Reporting Skyglow Observations", which was officially adopted at the 12
th European Symposium for the Protection of the Night Sky and endorsed by the
International Dark Sky Association (IDA) and by the International Astronomical
Union (IAU) in Beijing 2012 (SpS17: "Light Pollution: Protecting Astronomical
Sites and Increasing Global Awareness through Education"). More information
about the header files can be found in the following link:
_http://darksky.org/light-pollution/measuringlight-_ pollution/
The fields present in the dataset are:
* **name**
* Name of the device
* **mag**
* Magnitude measured by the photometer
* **tsky**
* Temperature of the sky measured by an infrared sensor placed in the photometer.
* **tamb**
* Ambient temperature
* **latitude** • **longitude** • **tstamp**
* Timestamp of the measurement in ISO-8601 format
### Data sharing
Users can access to the datasets in three different manners:
* From our data portal ( _http://ckan.stars4all.eu_ ). A description of this data portal can be found in deliverable D5.4.
* Using our data API (D4.9), which is used by our dashboards to visualize data.
* From our Zenodo Community 2 . Monthly datasets will be published and a Digital Object Identifier will be generated for each of them.
We are using the UPM's own servers for data storage, and in the long-term we
will hire hosting services with the funds raised by the foundation.
All data generated by this network will be open access and we do not consider
any restriction, including embargo periods.
### Archiving and preservation (including storage and backup)
As discussed in the data sharing section, data will be archived and preserved
using Zenodo. The amount and preservation of the data will depend of the
policy applied by the Zenodo consortium. According with its current policy,
there is no limitation in the public space and in the preservation time. Also,
data files are backed up nightly.
## Photometers’ Network (Detailed Version)
Scientific research data should be easily **:**
### Discoverable
Datasets with the measurements generated by photometers are accessible through
two portals:
* STARS4ALL data portal based on CKAN ( _http://ckan.stars4all.eu_ )
* STARS4ALL community in Zenodo (https://zenodo.org/communities/stars4all/)
Datasets in Zenodo have their own DOI.
**Figure 1: DOI of the dataset with April’s measurements**
### Accessible
**Are the data and associated software produced and/or used in the project
accessible and in what modalities, scope, licenses?**
Apart from using the data portals, the last 30 days of measurements from
photometers are accessible through an API (see
http://docs.photometer.apiary.io).
The associated software is available and accessible at:
* Data extraction: _https://github.com/STARS4ALL/tess-adapter_
* API: _https://github.com/STARS4ALL/photometer-api_
* CKAN image (forked from CKAN project): https://github.com/STARS4ALL/ckan
The license of the datasets and the associated software is Creative Commons
Attribution 4.0 (CC-BY4.0).
See http://opendefinition.org/licenses/cc-by/
### Assessable and intelligible
**Are the data and associated software produced and/or used in the project
assessable for and intelligible to third parties in contexts such as
scientific scrutiny and peer review?**
Each dataset is generated with a DOI and the devices, which take the
measurements, are conveniently identified (id & location). The interpretations
of each measure, as well as the technical components of the device, are open
and available to general public in the following link:
_https://figshare.com/s/66d11c0d5a1c9ac81160_
Software is available in Github (see previous point) and the API properly
documented in Apiary: http://docs.photometer.apiary.io
### Usable beyond the original purpose for which it was collected
**Are the data and associated software produced and/or used in the project
useable by third parties even long time after the collection of the data?**
Yes, this data can be used to generate future mathematical models that can
predict or allow understand, light pollution.
Many domains can benefit:
* Urban planners to measure the impact of new light sources or to analyse the urban growth.
* Biologists can study the impact of light pollution on animals or plants
* Medical doctors can use them to deep in the study of light pollution in human health.
These photometers and their measurements will provide the community an
historical dataset that shows the evolution of the light pollution. This will
be a valuable resource for scientists and public institutions.
### Interoperable to specific quality standards
**Are the data and associated software produced and/or used in the project
interoperable allowing data exchange between researchers, institutions,
organisations, countries, etc?**
Night sky brightness data will be archived using the "NSBM Community Standards
for Reporting Skyglow Observations", which was officially adopted at the 12
th European Symposium for the Protection of the Night Sky and endorsed by the
International Dark Sky Association (IDA) and by the International Astronomical
Union (IAU) in Beijing 2012\.
## Community Health (Initial Version)
### Data set description
In order to carry out the community health analysis in WP3, we use data from
the various LPIs to calculate a set of metrics as set out in D3.4. We are
primarily concerned with analysing the number of classifications or tasks
carried out, how these are distributed among the community, and how many
people in total are participating. Since we cover a range of project types in
the LPIs, we for now focus on a set of data that is particular to the
classification-based projects, however much of this can be easily adapted to
other types of project, too.
The dataset will continuously evolve throughout the project, both as the
current LPIs continue to produce data, and as new LPIs are run. This will
allow us to compare the metrics from various projects to determine what types
attract ‘healthier’ communities, while it will also allow us to manage those
communities better by being aware of their current health at a particular
moment in time.
The dataset consists of the results of a range of metrics used to measure
various aspects of community health. It is therefore derivative data from the
raw data obtained by each LPI.
We are generating four metrics per project per day.
### Standards and metadata
As data comes from numerous different projects, we do not put restrictions of
the format of the data itself, but instead require that certain attributes are
contained. Using this, we then carry out a range of calculations on the data
to make our own secondary dataset that consists of the metrics that we require
to measure community health on the dashboard discussed in D3.4.
The most critical fields required in the data provided to us by an LPI are
regarding ‘task runs’, or records of which player has completed which tasks,
as follows:
* PlayerID
* The unique identifier of the player/volunteer carrying out a specific task. This is so we can determine how many contributions each player is making, and how these are distributed among the whole community, as well as the lifetime of a player’s activity. This ID will not include reference to any kind of personal data to maintain the user’s anonymity.
* TaskID
* The unique identifier of a specific tasks, e.g. an image that is to be classified. This allows us to determine which task was completed by the player on this particular task run.
* Timestamp
* The time at which the task run was completed. This means we can calculate metrics such as the user’s lifetime (time from first task run to their last task run), and the active period of the project and each task.
### Data sharing
Data is shared to the community health analysis dashboard from each individual
LPI, as discussed above. In some cases, this is a download of the most recent
data dump available from the project (such as from the crowdcrafting platform
for Dark Skies, Lost at Night and Night Cities), or through APIs (such as for
the recently released NightKnights game).
We share the results of the community health metrics via the dashboard that
will position projects on a two-dimensional matrix, and show their path over
time to indicate their changing health. Where possible, we will release
project-specific snapshots of these metrics as datasets that can be downloaded
for additional study. Once the dashboard is stable we will aim to release a
monthly snapshot of the metrics calculated as part of this analysis.
We will ensure that datasets will not include personal data from users,
maintaining their anonymity.
### Archiving and preservation (including storage and backup)
The data for the community health analysis is currently hosted at the
University of Southampton in the UK, where it will be backed up to allow
recovery as necessary. The computed metrics data is stored in a database and
therefore it may be moved easily should we deem that it requires hosting
elsewhere during the project.
We will ensure that the data will remain available throughout the project
duration (until December 2018). We will archive the data regarding the
computed metrics for community health measures and will then be stored and
preserved by the STARS4ALL foundation servers.
Furthermore, monthly datasets will be accessible from our data portal and our
Zenodo community.
## Games -‐ NightKnights (Initial Version)
### Data set description
Night Knights ( _https://www.nightknights.eu/_ ) is a Game With A Purpose
(GWAP) aimed at Data Linking; more specifically, by playing the game, players
help in classifying images taken from the International Space Station (ISS)
with respect to a given taxonomy (hence, links are created between images and
categories). In short, all actions by all players are weighted and aggregated
by the game, producing a “crowdsourced” classification of ISS pictures that
can be used for subsequent analysis of the light pollution phenomenon. Details
on the Night Knights application and its internal functioning are given in
deliverables D4.4, D4.6 and D4.10.
The data set consists of the output data of the Night Knights game. The data
are stored in a MySQL database securely accessible by means of a dedicated
API, as described in the following.
### Standards and metadata
The format of the dataset was defined in a way to facilitate its subsequent
reuse, i.e. the game output contains all information needed for further
processing and evaluation of the data.
Night Knights data gives information about three main themes:
* Classification results (classified photo, assigned classification, number of players needed to come to a classification agreement)
* Players’ actions (“log” of all classification actions done by anonymized game users)
* Game evaluation data (including KPIs like number of tasks available/started/completed, number of players, total played time, throughput, average life play, etc.)
No personal information about game players is given, in order to respect user
privacy, including the application privacy policy available here:
_https://www.nightknights.eu/#/privacy_ .
We are also evaluating the possibility to make available the classification
results (first bullet point of the above list) in RDF format according to the
Human Computation Ontology
( _http://swa.cefriel.it/ontologies/hc#_ ), which preserves the provenance of
the collected information. In this case, each “consolidated information”
consists of an RDF triple (with subject=ISS photo, object=aggregated
classification and predicate=link) enriched with metadata about the
consolidation process.
Finally, it is worth noting that the ISS images are provided by the NASA’s
Gateway to Astronaut Photography Of Earth (courtesy of the Earth Science and
Remote Sensing Unit, NASA Johnson Space Center, _https://eol.jsc.nasa.gov/_ ),
free of any copyright restrictions. The Night Knights dataset contains links
to the original photo location.
### Data sharing
Data sharing is implemented through a secure Web API exposed on the same
server where the game is running. The full detailed documentation to access
the API is documented according to the API Blueprint format and available
online at _http://docs.crowdtaskmanagement.apiary.io_ . All API responses are
described, including their output JSON format that contains the specified
information. The actual URL to access the API is not public given, the access
restrictions explained below.
Access is regulated through token-based authentication. Token can be obtained
at the administrator authentication endpoint by providing username and
password; once obtained the token it can be used to access the API through the
Authorization header field with the ‘Bearer’ scheme. Token expires after 60
minutes; the administrator credentials can be requested by writing to the
following e-mail address: [email protected]_ . Data access is currently
restricted to project partners, also to prevent potential malicious use during
the online competition based on the use of the Night Knights game. Decision on
the actual access by third parties will be evaluated by STARS4ALL partners on
a case-by-case basis. Nevertheless, datasets with the solved tasks will be
uploaded periodically to our data portal and to Zenodo.
Currently the data is used by the University of Southampton, in order to
analyse metrics around community health, so that the engagement levels with
NightKnights may be compared with other LPIs, and allow us to notice when
intervention is required to re-engage with users. Details about the community
health dashboard are available in D3.4, and the dataset used in this is
discussed in the ‘ _Community Health data model_ ’.
The photos classified as “city”, “stars” and “black” will also be used to
improve research on light pollution, in order to extend the coverage of the
analysis of the phenomenon. The “city” photos can be used to map the light
pollution effect and “stars” and “black” photos are used for calibration in
order to measure that effect.
### Archiving and preservation (including storage and backup)
Universidad Politecnica de Madrid is responsible for backup and recovery of
the dataset. Night Knights data is stored in a MySQL database hosted in an
Aurora DB Instance (Amazon Web Services). The class of instance is
db.t2.medium (2 virtual CPUs and 4GB)
The dataset will remain available and accessible through the aforementioned
API until the actual game will be available online, which means at least until
the end of the STARS4ALL project (December 2018).
As previously commented, datasets will be preserve in our data portal and our
Zenodo community. After the end of the project, the STARS4ALL foundation will
have the responsibility of the maintenance of the data servers.
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1081_MuMMER_688147.md
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_transfers/adequacy/index_en.htm_ ) in accordance with Article 25 of the
Directive 95/46/EC and so the transfer may take place.
We will ask for ethical opinion from relevant institutional ethical
committees, e.g. the Ethical committee of social sciences of Tampere area
(Tampereen alueen ihmistieteiden eettinen toimikunta,
_http://www.uta.fi/tutkimus/etiikka/arviointitmk/kokoonpano.html_ , in Finnish
only) to ethically evaluate the planned research or relevant parts of it. VTT
is represented in the Ethical committee.
# 2.3 Storage and access provision
All data created and collected during MuMMER will be stored internally on a
private gitlab server hosted at Softbank Robotics, to which only project
partners will have access. All data in gitlab will be private by default;
where appropriately anonymized and processed data is to be made openly
available, we will make use of the University of Glasgow’s Enlighten Research
Data repository at _http://researchdata.gla.ac.uk/_ .
# 2.4 File formats
Wherever possible, we will use open and/or archival formats for all MuMMER
data. This includes TIFF for images, FLAC for audio files, MPEG-4 for video,
RTF for text, and CSV for spreadsheet data.
# 3 Description of MuMMER Datasets
## 3.1 Dataset naming Conventions
With regards to the naming convention MuMMER datasets, each name will be
created as follows:
1. Dataset number containing prefix “DS” followed by a unique identifier number, e.g. DS1
2. Partner short name who will be managing the dataset, e.g. VTT
3. Short title of the dataset summarising the data contained within the dataset, e.g. Consumer Research E.g. “DS1.VTT.Consumer Research”
## 3.2 Summary of Datasets
The following table provides the name and a short description of each dataset.
In total we envisage three high-level datasets to be collated during the
MuMMER project, encompassing a wide variety of data files.
<table>
<tr>
<th>
**Name**
</th>
<th>
**Description**
</th> </tr>
<tr>
<td>
DS1.VTT. Consumer Research
</td>
<td>
All data collected as part of the co-design activities carried out by VTT
throughout the project.
</td> </tr>
<tr>
<td>
DS2.Partner. Interaction with Robot
</td>
<td>
All data collected during interactions with the MuMMER robot, at all partner
sites.
</td> </tr>
<tr>
<td>
DS3.Partner. Software Development
</td>
<td>
All data collected for the purposes of developing and training the software
models included in the MuMMER system.
</td> </tr> </table>
In the remainder of this document, we provide an initial DMP for each of these
datasets. Note that the canonical version of the DMPs are stored in DMPonline
at _https://dmponline.dcc.ac.uk/_ ; please contact the MuMMER administrator to
be given access to the online DMPs.
# 4 MuMMER Datasets
## 4.1 Consumer Research DMP
The canonical version of this DMP is stored in DMPonline (
_https://dmponline.dcc.ac.uk/_ ). Please contact the MuMMER administrator if
you require access to the online DMP.
### 4.1.1 Data set description
WP1: Use scenarios, acceptance and success metrics will be led by VTT. The
objective of this WP is to ensure that the MuMMER robot and its implemented
and foresighted future applications will be user-driven in terms of (Human-
Robot Interaction) HRI, socially and ethically accepted, and interesting to
commercial end users. This goal will be achieved through an intensively
applied co-design approach that engages consumer users and other relevant
stakeholders in the technical development throughout the design. Consumers
will be engaged using several forms of market research including
demonstrations, workshops, discussion events, interviews and surveys. The co-
design activities will produce use scenarios to guide the development,
increase user acceptance toward robotic applications in consumer markets and
develop success metrics for human-interactive mobile robots. This DMP aims to
address the storage of MuMMER consumer research data in line with H2020
guidelines.
### Origin of data
Data collected by VTT at the following events:
* Demonstrations
* Workshops with consumers
* Discussion events
* Interviews
In addition VTT will collect data from the following:
* Surveys/questionnaires
* Consent Forms
### Nature and scale of data
* Demonstrations – photos and video recordings in Finnish. Summary of results in English to be shared with Partners
* Workshops with consumers - video recordings in local language. Summary of results in English to be shared with Partners
* Interviews - video/audio recording in Finnish. Summary of results in English to be shared with Partners
* Surveys and questionnaires – Internet questionnaire as well as paper questionnaires in word format or via digital feedback screens
* Consent Forms – paper questionnaires in RTF format
### To whom the dataset could be useful
Raw data in local language will be assessed and by VTT a summary of results
(in English) will be shared with the consortium.
Some pictures and videos may be used for dissemination purposes
### Related scientific publications
The summary data derived from these studies will be used as the basis for
scientific publications. Pictures may also be used as part of scientific
publication, where appropriate consent has been obtained.
### 4.1.2 Standards and metadata
The following metadata will be recorded regarding the consumer research data:
* Demonstrations - metadata required: demonstration host, group size, location, date and time, duration
* Workshops with consumers - metadata required: workshop host, workshop attendees, location, date and time, duration
* Interviews - metadata required: interviewer, interviewee, location, date and time, duration
* Surveys/questionnaires – metadata required: location, date and time
* Photos - saved in TIFF format with location, description and date in the title
### 4.1.3 Data sharing
Data will be shared internally with project partners using the MuMMER gitlab
server. Where appropriate, reusable data such as log files or (where
permission has been obtained) video data will be shared using the University
of Glasgow's Enlighten Research Data server.
### 4.1.4 Archiving and preservation (including storage and backup)
During the course of the MuMMER project, all data will be stored on the MuMMER
project gitlab server, which is backed up regularly. Public data will be
released through the University of Glasgow's Enlighten
Research Data server, where it will persist after the end of the project.
## 4.2 Human-Robot Interaction DMP
The canonical version of this DMP is stored in DMPonline (
_https://dmponline.dcc.ac.uk/_ ). Please contact the MuMMER administrator if
you require access to the online DMP.
### 4.2.1 Data set description
This data set will encompass all system logs, video recordings, and user
questionnaire responses arising from users interacting with the MuMMER robot,
both in lab settings and in the public deployment locations (including but not
necessarily limited to Ideapark). It will also include any after-the-fact
annotations of the video data that is gathered.
Within the project, this data will be useful for analysing the success of all
robot deployments throughout the project: this information will then be used
to inform the development and refinement of the robot scenarios, as well as to
help the developers to enhance the system for future deployments.
The data will also be used as the basis for scientific publications describing
the robot deployments.
Outside of the MuMMER project, this data could also be useful to guide the
work of other developers of similar public-space robot systems.
**4.2.2 Standards and metadata** System logs:
* Timestamped entries for every system event, internal state update, and message exchanged among the system components
* Every log includes a synchronisation signal to allow it to be matched up later with video data Videos:
* High-quality HD videos, compressed with a useful codec such as H.264
* If multiple videos are included of a session, they will be synchronised with each other using a synchronisation signal Questionnaires:
* Every questionnaire annotated to indicate the date, time, and location of the session it relates to, in order to allow questionnaire responses to be correlated with videos and/or logs Annotations:
* Any annotated data will be stored alongside the raw data.
### 4.2.3 Data sharing
Data will be shared internally with project partners using the MuMMER gitlab
server. Where appropriate, reusable data such as log files or (where
permission has been obtained) video data will be shared using the University
of Glasgow's Enlighten Research Data server.
### 4.2.4 Archiving and preservation (including storage and backup)
During the course of the MuMMER project, all data will be stored on the MuMMER
project gitlab server, which is backed up regularly. Public data will be
released through the University of Glasgow's Enlighten
Research Data server, where it will persist after the end of the project.
## 4.3 Software Development Data DMP
The canonical version of this DMP is stored in DMPonline (
_https://dmponline.dcc.ac.uk/_ ). Please contact the MuMMER administrator if
you require access to the online DMP.
### 4.3.1 Data set description
This data set will encompass all audiovisual recordings specifically designed
to help in the development of the technical components of MuMMER. Note that
this dataset is distinct from the Human-Robot Interaction dataset: while the
data in that set is related to interactions with the deployed robot system,
the data in this set is specifically gathered and designed to help in
developing and training the software components of the system.
Within the project, this data will be useful for ensuring that the components
of the robot system function appropriately in the target environment: for
example, this will include recordings made with the robot's own sensors of
users interacting in the target deployment locations within Ideapark.
The data will also be used as the basis for scientific publications describing
the technical development.
Outside of the MuMMER project, this data could also be useful to guide the
work of other developers of similar technical components, both as training and
as test data.
**4.3.2 Standards and metadata** Videos:
* High-quality HD videos, compressed with a useful codec such as H.264
* If multiple videos are included of a session, they will be synchronised with each other using a synchronisation signal
### 4.3.3 Data sharing
Data will be shared internally with project partners using the MuMMER gitlab
server. Where appropriate, reusable data such as log files or (where
permission has been obtained) video data will be shared using the University
of Glasgow's Enlighten Research Data server.
### 4.3.4 Archiving and preservation (including storage and backup)
During the course of the MuMMER project, all data will be stored on the MuMMER
project gitlab server, which is backed up regularly. Public data will be
released through the University of Glasgow's Enlighten Research Data server,
where it will persist after the end of the project.
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1085_SPARK_688417.md
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# 1\. Executive Summary
_______________________________________________________________________ 5
2. Introduction _______________________________________________________________________________ 5
1. Scope of the activities and of the deliverable _________________________________________ 5
3. the data In spark __________________________________________________________________________ 5
4. Focus on each WP ______________________________________________________________________ 10
1. WP1: CHARACTERIZATION OF USERS’ NEEDS AND EXPECTATIONS ________________ 11
2. WP2 - DEVELOPMENT OF SPARK MODULES ________________________________________ 13
3. WP3 – DEVELOPMENT AND TESTS OF SPARK PLATFORM __________________________ 15
4. WP4 – TEST AND VALIDATION IN RELEVANT ENVIRONMENT ______________________ 17
5. WP5 – VALIDATION AND DEMONSTRATIONS IN REAL OPERATIONAL
ENVIRONMENT
___________________________________________________________________________ 20
5. Archiving and Storage of the Data ______________________________________________________ 24
1. Public data __________________________________________________________________________ 24
2. Private data _________________________________________________________________________ 24
3. Format of the data to be archived and published in ZENODO. _____________________ 24
6. PROPOSED POLICY _____________________________________________________________________ 24
7. Conclusion ______________________________________________________________________________ 25
1. **EXECUTIVE SUMMARY**
The present deliverable is the result of the initial consultation among the
partners for what concerns the Data Management Plan. The activities started
soon at the beginning of the project and the issues related to storage and
sharing of the project data (both generated, processed and collected) have
been carefully analysed. The plan that will be presented in the following
gives a general overview for the project as a whole and per each WP.
The Consortium knows that as the project will evolve, also the policy to apply
for the data management will have to be updated and/or revised.
2. **INTRODUCTION**
### 2.1. SCOPE OF THE ACTIVITIES AND OF THE DELIVERABLE
This document is an initial overview on the approach the SPARK Consortium will
use for the Management of the data.
It is important to highlight here that this approach will be further detailed
and improved along the project evolution and two updates of the deliverable
are foreseen at M21 and at M36.
The deliverable falls under the headings of the WP6 and in particular within
the context of T6.1. The aim is to manage all the issues related to the
availability of the data collected and generated and processed during the
SPARK project lifecycle.
# **3\. THE DATA IN SPARK**
SPARK will deal with two main types of data:
* (Raw) Gathered Data;
* Processed/Generated Data.
Moreover, two main perspectives have been considered in order to define a plan
for the management of data. On the one hand, data can be characterized in
terms of sensitivity whenever they deal with personal data (of a person or an
external entity with respect to the consortium – e.g. a End User’s customer
participating a creative design session). On the other hand, data can be also
classified consistently with the overall purpose they play along the project
activities. These two complementary classifications are not mutually exclusive
and provide additional details to understand how to deal with the data
generated along the project. This organization allows defining a data
management policy that appropriately balances between the need of keeping
confidential what is critical for the development of the SPARK platform and
the publication of data in adequate repositories (e.g.: it supports decisions
concerning the embargo duration before making data publicly available).
The two above-mentioned additional classes can be detailed as:
* **Sensitivity** o data could be sensitive because of confidential issues that goes beyond the consortium (because of 3 rd parties rights);
* data that are sensitive because of the ethical issues; o data that are not sensitive.
* **Project strategy** o data that are relevant for the business/market exploitation of the SPARK platform;
* data that are relevant for what concerns the scientific objectives of the project. In particular:
* Analysis of the dynamics of co-creative processes of teams dealing with digital and physical prototypes;
* Development of the SAR-based responsive ICT platform, i.e. the SPARK platform;
* Study and analysis of how and to what extent the SAR technology can stimulate and enhance design creativity through a comparison against pre-defined metrics in real operational design environments.
In the following, a matrix is provided to show how the WP leader will have to
cluster the different data gathered or processed in the relevant WP, once
agreed by the consortium.
**Sensitive data**
**due to**
**confidentiality**
**issues beyond**
**the**
**consortium**
**Sensitive**
**data due**
**to ethical**
**issues**
**Non**
**-**
**sensitive**
**data**
**Recorded**
**Processed**
**Sensitivity**
**Elaboration**
### **Strategy**
Figure 1. The three dimensions to determine the management of data
In general, whatever will be the specific classification of data according to
the above presented categories, in the end they could be classified as:
* Confidential;
* Temporarily confidential (due to the expiration of the NDAs or before anonymization); - Not confidential.
A suggested policy for the dissemination plan has been initially defined in
this document for each outcome of the WP activities. This policy suggests how
the publication of the data will be managed during the project.
Other descriptors should finally have identified to characterise the data from
the technical point of view. These descriptors are:
* Format: type and corresponding extension that identify the file containing the data
* Medium of data: physical / virtual
* Projected volume: hypothesis made on the number of each data
* Data reading: the tool used to read the data in a specific format
* Metadata: information that further identifies the data and that can be used to manage (storing and searching) the data within a database.
Table 1 provides examples of the possible data readings while Table 2 provides
the structure of the metadata (Id and Format) and some application examples
related to the file used so far. Data reading and metadata should be the same
for all work packages, since they are independent from the origin of the data.
Table 1: Examples of possible data readings
<table>
<tr>
<th>
</th>
<th>
**DATA READING**
</th>
<th>
</th> </tr>
<tr>
<td>
**FORMAT**
</td>
<td>
</td>
<td>
**MOST USED TOOL(S)**
</td>
<td>
**SIZE RANGE**
</td> </tr>
<tr>
<td>
Text / .doc
</td>
<td>
</td>
<td>
MS Word, Open Office Writer
</td>
<td>
0..10 Mo
</td> </tr>
<tr>
<td>
Spreadsheet / .xls
</td>
<td>
</td>
<td>
MS Excel, Open Office Calc
</td>
<td>
0..2 Mo
</td> </tr>
<tr>
<td>
Recordings
</td>
<td>
</td>
<td>
Player: VLC
</td>
<td>
0..N Go
</td> </tr>
<tr>
<td>
Translation / .srt
</td>
<td>
</td>
<td>
Subtitles editor: notepad++, notepad
</td>
<td>
0..2 Mo
</td> </tr> </table>
Table 2: Structure of the metadata and examples
<table>
<tr>
<th>
**DATA FORMAT**
</th>
<th>
</th>
<th>
**METADATA**
</th>
<th>
</th> </tr>
<tr>
<th>
**ID**
</th>
<th>
**FORMAT**
</th>
<th>
**EXAMPLE**
</th>
<th>
**COMMENT**
</th> </tr>
<tr>
<td>
Text / .doc
&
Spreadsheet / .xls
</td>
<td>
File / Title
</td>
<td>
SPARK_[wkp]_[doc]_[version]
</td>
<td>
SPARK_WKP3_SPARKPlatform-
Architecture_v1.2
</td>
<td>
\-
</td> </tr>
<tr>
<td>
Subject
</td>
<td>
Text
</td>
<td>
Spark Platform
Architecture
</td>
<td>
\-
</td> </tr>
<tr>
<td>
Author
</td>
<td>
[company]_name_surname
</td>
<td>
VISEO_DURAND_Pierre
</td>
<td>
\-
</td> </tr>
<tr>
<td>
Responsible
</td>
<td>
[company]_name_surname
</td>
<td>
VISEO_MARTIN_Arthur
</td>
<td>
Actor in charge of the document production
</td> </tr>
<tr>
<td>
Company
</td>
<td>
Text
</td>
<td>
VISEO
</td>
<td>
\-
</td> </tr>
<tr>
<td>
Diffusion
</td>
<td>
yyyy-mm-dd
</td>
<td>
2016-06-23
</td>
<td>
Aimed date for data diffusion
</td> </tr>
<tr>
<td>
Recordings
</td>
<td>
File
</td>
<td>
SPARK_[wkp]_[doc]_[yyyymm_dd]
</td>
<td>
SPARK_WKP1_Co-
creative-design_2016-
07-15
</td>
<td>
\-
</td> </tr>
<tr>
<td>
Translation / .srt
</td>
<td>
File
</td>
<td>
SPARK_[wkp]_[doc]_[yyyymm_dd]
</td>
<td>
\-
</td>
<td>
Same as aimed record
</td> </tr> </table>
Finally, Figure 2 shows a diagram that summarises the expected outcomes
deriving from the all the task of the project. The diagram makes easier the
identification and the clustering of the type of data for each WP.
Figure 2: Summary of the outcomes for each Task (WP1-WP5).
**4\. FOCUS ON EACH WP**
This section proposes the characterisation of the data for each WP. In
particular, the following paragraphs report the part of the diagram related to
a specific WP, as a shown in Figure X and a table where the data are
classified according to the criteria described in the previous section i.e.:
* Origin of data (type specified) o Gathered data
* Processed / generated data
* Sensitivity
* Because of 3 rd parties’ rights o Because of ethical issues o Non sensitive data
* Project strategy
* Business / Commercialization o Scientifically relevant
* Suggested policy
* Description o Format o Medium of data o Projected volume o Data reading (see below) o Metadata (see below)
## 4.1. WP1: CHARACTERIZATION OF USERS’ NEEDS AND EXPECTATIONS
<table>
<tr>
<th>
Titolo
</th>
<th>
</th>
<th>
</th>
<th>
</th> </tr>
<tr>
<td>
WP1
</td>
<td>
Beginning of the project
</td>
<td>
T
1
.
1
T
1
.
2
</td>
<td>
T
1
.
3
T
1
.
4
T
1
.
5
**Deliverable**
**1**
**.**
**1**
Definition of
:
WP
1
Case studies
Metrics for creative sessions
</td>
<td>
</td> </tr> </table>
Figure 3: Expected outcomes of WP1
Table 3: Type of data and classification for WP1
<table>
<tr>
<th>
</th>
<th>
**Origin of data (Type specified)**
</th>
<th>
**Sensitivity**
</th>
<th>
**Project strategy**
</th>
<th>
**Suggested Policy**
</th>
<th>
**Description**
</th> </tr>
<tr>
<th>
**Gathered data**
</th>
<th>
**Processed / Generated data**
</th>
<th>
**Because of 3rd**
**parties rights**
</th>
<th>
**Because of**
**ethical issues**
</th>
<th>
**Non**
**sensitive data**
</th>
<th>
**Business / commercialization**
</th>
<th>
**Scientifically relevant**
</th>
<th>
**Format**
</th>
<th>
**Medium of data**
</th>
<th>
**Projected volume**
</th> </tr>
<tr>
<td>
**WP1**
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td> </tr>
<tr>
<td>
Task
1.1
</td>
<td>
</td>
<td>
Case studies
</td>
<td>
x
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
Data to be made public according to the
indication of responsible
partners
</td>
<td>
Text / .doc
</td>
<td>
Virtual
</td>
<td>
1 per case study
</td> </tr>
<tr>
<td>
Task
1.2
</td>
<td>
Existing metrics
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
x
</td>
<td>
</td>
<td>
x
</td>
<td>
To be published after the first paper
</td>
<td>
Spreadsheet
/ .xls
</td>
<td>
Virtual
</td>
<td>
1
</td> </tr>
<tr>
<td>
Task
1.2
</td>
<td>
</td>
<td>
Co-creative design metrics
</td>
<td>
</td>
<td>
</td>
<td>
x
</td>
<td>
</td>
<td>
x
</td>
<td>
To be published after the first paper
</td>
<td>
Spreadsheet
/ .xls
</td>
<td>
Virtual
</td>
<td>
1
</td> </tr> </table>
<table>
<tr>
<th>
Task
1.3
</th>
<th>
Recordings
</th>
<th>
</th>
<th>
x
</th>
<th>
x
</th>
<th>
</th>
<th>
</th>
<th>
x
</th>
<th>
Data to be made public according to the
indication of responsible
partners
</th>
<th>
Video files
</th>
<th>
Virtual
</th>
<th>
1 per case study
</th> </tr>
<tr>
<td>
Task
1.3
</td>
<td>
</td>
<td>
Transcripts/translation
</td>
<td>
x
</td>
<td>
x
</td>
<td>
</td>
<td>
</td>
<td>
x
</td>
<td>
To be published open access after
anonymization, after the
first paper
</td>
<td>
Text / .doc /
.srt
</td>
<td>
Virtual
</td>
<td>
1 per case study
</td> </tr>
<tr>
<td>
Task
1.3
</td>
<td>
</td>
<td>
Coded design protocols
</td>
<td>
x
</td>
<td>
x
</td>
<td>
</td>
<td>
</td>
<td>
x
</td>
<td>
To be published after the first paper
</td>
<td>
Text / .doc
</td>
<td>
Virtual
</td>
<td>
1
</td> </tr>
<tr>
<td>
Task
1.3
</td>
<td>
</td>
<td>
Analysis of design protocols
</td>
<td>
</td>
<td>
</td>
<td>
x
</td>
<td>
</td>
<td>
x
</td>
<td>
To be published after the first paper
</td>
<td>
Text / .doc
</td>
<td>
Virtual
</td>
<td>
1
</td> </tr>
<tr>
<td>
Task
1.4
</td>
<td>
Recordings
</td>
<td>
</td>
<td>
x
</td>
<td>
x
</td>
<td>
</td>
<td>
</td>
<td>
x
</td>
<td>
Data to be made public according to the
indication of responsible
partners
</td>
<td>
Video files
</td>
<td>
Virtual
</td>
<td>
1 per case study
</td> </tr>
<tr>
<td>
Task
1.4
</td>
<td>
</td>
<td>
Transcripts/translation
</td>
<td>
x
</td>
<td>
x
</td>
<td>
</td>
<td>
</td>
<td>
x
</td>
<td>
To be published open access after
anonymization, after the
first paper
</td>
<td>
Text / .doc /
.srt
</td>
<td>
Virtual
</td>
<td>
1 per case study
</td> </tr>
<tr>
<td>
Task
1.4
</td>
<td>
</td>
<td>
Coded design protocols
</td>
<td>
x
</td>
<td>
x
</td>
<td>
</td>
<td>
</td>
<td>
x
</td>
<td>
To be published after the first paper
</td>
<td>
Text / .doc
</td>
<td>
Virtual
</td>
<td>
1
</td> </tr>
<tr>
<td>
Task
1.4
</td>
<td>
</td>
<td>
Analysis of design protocols
</td>
<td>
</td>
<td>
</td>
<td>
x
</td>
<td>
</td>
<td>
x
</td>
<td>
To be published after the first paper
</td>
<td>
Text / .doc
</td>
<td>
Virtual
</td>
<td>
1
</td> </tr>
<tr>
<td>
Task
1.5
</td>
<td>
Interviews
</td>
<td>
</td>
<td>
x
</td>
<td>
x
</td>
<td>
</td>
<td>
</td>
<td>
x
</td>
<td>
To be published after the first paper
</td>
<td>
Text / .doc
</td>
<td>
Virtual
</td>
<td>
1 per interview
</td> </tr>
<tr>
<td>
Task
1.5
</td>
<td>
</td>
<td>
Summary of the
results
</td>
<td>
</td>
<td>
</td>
<td>
x
</td>
<td>
</td>
<td>
x
</td>
<td>
To be published after the first paper
</td>
<td>
Text / .doc
</td>
<td>
Virtual
</td>
<td>
1
</td> </tr>
<tr>
<td>
Task
1.6
</td>
<td>
</td>
<td>
Combined analysis of needs and
expectations of End
Users
</td>
<td>
</td>
<td>
</td>
<td>
x
</td>
<td>
x
</td>
<td>
x
</td>
<td>
To be published after the first paper (the
deliverable D1.2 is, however, public)
</td>
<td>
Text / .doc
</td>
<td>
Virtual
</td>
<td>
1
</td> </tr>
<tr>
<td>
Task
1.7
</td>
<td>
</td>
<td>
SPARK design specification
</td>
<td>
</td>
<td>
</td>
<td>
x
</td>
<td>
x
</td>
<td>
x
</td>
<td>
Ready for Open Access being the deliverable
D1.3 public
</td>
<td>
Text / .doc
</td>
<td>
Virtual
</td>
<td>
1
</td> </tr> </table>
## 4.2. WP2 - DEVELOPMENT OF SPARK MODULES
<table>
<tr>
<th>
Titolo
</th>
<th>
</th>
<th>
</th>
<th>
</th>
<th>
</th>
<th>
</th>
<th>
</th> </tr>
<tr>
<td>
WP2
</td>
<td>
Beginning of the project
</td>
<td>
T
2
.
1
</td>
<td>
Review of the
State of the Art
for SAR technologies
**Deliverable**
**2**
**.**
**1**
</td>
<td>
T
2
.
3
T
2
.
2
</td>
<td>
T
2
.
4
SPARK modules
developed
SAR technologies
selected
**Deliverable**
**2**
**.**
**2**
**Deliverable**
**2**
**.**
**3**
From WP
1
(
T
1
.
7
)
Metrics for the SPARK modules
</td>
<td>
T
2
.
5
SPARK modules
validated
Data about
Tracking
Interaction
Visualization
Performances
,
...
</td>
<td>
SPARK modules
T
2
.
6
Definitive set
of features
**Deliverable**
**2**
**.**
**4**
To WP
3
(
T
3
.
1
)
**(T2.2/T2.4/T2.5/T2.6)**
</td> </tr> </table>
Figure 4: Expected outcomes of WP2
Table 5: Type of data and classification for WP2
<table>
<tr>
<th>
</th>
<th>
**Origin of data (Type specified)**
</th>
<th>
**Sensitivity**
</th>
<th>
</th>
<th>
**Project strategy**
</th>
<th>
**Suggested Policy**
</th>
<th>
</th>
<th>
**Description**
</th>
<th>
</th> </tr>
<tr>
<th>
**Gathered data**
</th>
<th>
**Processed /**
**Generated data**
</th>
<th>
**Because of 3rd**
**parties rights**
</th>
<th>
**Because of**
**ethical issues**
</th>
<th>
**Non**
**sensitive data**
</th>
<th>
**Business / commercialization**
</th>
<th>
**Scientifically relevant**
</th>
<th>
**Format**
</th>
<th>
**Medium of data**
</th>
<th>
**Projected volume**
</th> </tr>
<tr>
<td>
**WP2**
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td> </tr>
<tr>
<td>
Task
2.1
</td>
<td>
Literature review
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
x
</td>
<td>
</td>
<td>
x
</td>
<td>
Ready for Open Access being the deliverable D2.1 public
</td>
<td>
Text / .doc
</td>
<td>
Virtual
</td>
<td>
1..N
</td> </tr>
<tr>
<td>
Task
2.1
</td>
<td>
Hardware
specifications
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
x
</td>
<td>
</td>
<td>
x
</td>
<td>
Ready for Open Access being the deliverable D2.1 public
</td>
<td>
Text / .doc
</td>
<td>
Virtual
</td>
<td>
1..N
</td> </tr> </table>
<table>
<tr>
<th>
Task
2.2
</th>
<th>
</th>
<th>
Modules evaluation metrics
</th>
<th>
</th>
<th>
</th>
<th>
x
</th>
<th>
</th>
<th>
x
</th>
<th>
To be published after the first paper related to the tests in T2.5
</th>
<th>
Spreadsheet
/ .xls
</th>
<th>
Virtual
</th>
<th>
1
</th> </tr>
<tr>
<td>
Task
2.3
</td>
<td>
</td>
<td>
Hardware selection
</td>
<td>
</td>
<td>
</td>
<td>
x
</td>
<td>
x
</td>
<td>
</td>
<td>
To be published at the beginning of the T5.3/5.4
</td>
<td>
Text / .doc
</td>
<td>
Virtual
</td>
<td>
1
</td> </tr>
<tr>
<td>
Task
2.4
</td>
<td>
</td>
<td>
Modules prototype description
</td>
<td>
</td>
<td>
</td>
<td>
x
</td>
<td>
x
</td>
<td>
x
</td>
<td>
To be published after the first publication or after the
exploitation strategy benefits of them, whatever comes later
</td>
<td>
Text / .doc
</td>
<td>
Virtual
</td>
<td>
1
</td> </tr>
<tr>
<td>
Task
2.5
</td>
<td>
Set of answers to
questionnaires
</td>
<td>
</td>
<td>
</td>
<td>
x
</td>
<td>
</td>
<td>
</td>
<td>
x
</td>
<td>
To be published after anonymization after the first
paper
</td>
<td>
Spreadsheet
/ .xls
</td>
<td>
Virtual
</td>
<td>
1
</td> </tr>
<tr>
<td>
Task
2.5
</td>
<td>
Modules evaluation data
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
x
</td>
<td>
</td>
<td>
x
</td>
<td>
To be published after the first paper
</td>
<td>
Spreadsheet
/ .xls
</td>
<td>
Virtual
</td>
<td>
1
</td> </tr>
<tr>
<td>
Task
2.5
</td>
<td>
</td>
<td>
Technological benchmark
</td>
<td>
</td>
<td>
</td>
<td>
x
</td>
<td>
x
</td>
<td>
x
</td>
<td>
To be published after the first publication or after the
exploitation strategy benefits of them, whatever comes later
</td>
<td>
Text / .doc
</td>
<td>
Virtual
</td>
<td>
1
</td> </tr>
<tr>
<td>
Task
2.6
</td>
<td>
</td>
<td>
SPARK platform features
</td>
<td>
</td>
<td>
</td>
<td>
x
</td>
<td>
x
</td>
<td>
</td>
<td>
To be published after the first release of the integrated
SPARK platform
</td>
<td>
Text / .doc
</td>
<td>
Virtual
</td>
<td>
1
</td> </tr> </table>
## 4.3. WP3 – DEVELOPMENT AND TESTS OF SPARK PLATFORM
Figure 5: Expected outcomes of WP3
Table 6: Type of data and classification for WP3
<table>
<tr>
<th>
</th>
<th>
**Origin of data (Type specified)**
</th>
<th>
</th>
<th>
**Sensitivity**
</th>
<th>
</th>
<th>
**Project strategy**
</th>
<th>
**Suggested Policy**
</th>
<th>
</th>
<th>
**Description**
</th>
<th>
</th> </tr>
<tr>
<th>
**Gathered data**
</th>
<th>
**Processed /**
**Generated data**
</th>
<th>
**Because of 3rd**
**parties rights**
</th>
<th>
**Because of**
**ethical issues**
</th>
<th>
**Non**
**sensitive data**
</th>
<th>
**Business / commercialization**
</th>
<th>
**Scientifically relevant**
</th>
<th>
**Format**
</th>
<th>
**Medium of data**
</th>
<th>
**Projected volume**
</th> </tr>
<tr>
<td>
**WP3**
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td> </tr>
<tr>
<td>
Task
3.1
</td>
<td>
</td>
<td>
SPARK platform architecture
</td>
<td>
</td>
<td>
</td>
<td>
x
</td>
<td>
x
</td>
<td>
</td>
<td>
To be published after the release of the
Deliverable D3.1
</td>
<td>
Text / .doc
</td>
<td>
Virtual
</td>
<td>
1
</td> </tr>
<tr>
<td>
Task
3.2
</td>
<td>
</td>
<td>
Description of SPARK platform versions
</td>
<td>
</td>
<td>
</td>
<td>
x
</td>
<td>
x
</td>
<td>
x
</td>
<td>
To be published after the end of the project
</td>
<td>
Source code
</td>
<td>
Virtual
</td>
<td>
1..N
</td> </tr>
<tr>
<td>
Task
3.3
</td>
<td>
Set of answers to
questionnaires
</td>
<td>
</td>
<td>
</td>
<td>
x
</td>
<td>
</td>
<td>
</td>
<td>
x
</td>
<td>
To be published after the first paper
</td>
<td>
Spreadsheet
/ .xls
</td>
<td>
Virtual
</td>
<td>
1
</td> </tr>
<tr>
<td>
Task
3.3
</td>
<td>
SPARK platform
evaluation data
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
x
</td>
<td>
x
</td>
<td>
x
</td>
<td>
To be published after the first paper
</td>
<td>
Text / .doc and / or
Spreadsheet
/ .xls
</td>
<td>
Virtual
</td>
<td>
1
</td> </tr> </table>
## 4.4. WP4 – TEST AND VALIDATION IN RELEVANT ENVIRONMENT
<table>
<tr>
<th>
Titolo
</th>
<th>
</th>
<th>
</th>
<th>
</th> </tr>
<tr>
<td>
WP4
</td>
<td>
T
4
.
1
T
4
.
2
</td>
<td>
Definition of
:
WP
4
Case studies
Experimental protocol
**Deliverable**
**4**
**.**
**1**
From WP
3
(
T
3
.
3
)
</td>
<td>
Observations concluded
T
4
.
3
T
4
.
4
Observations concluded
With Control Group
#
1
Control Group
#
2
\+
Test Group
Recordings
&
technical data
With ICT tools
\+
SPARK platform
Recordings of
design sessions
</td>
<td>
T
4
.
5
Data T
4
.
3
/
T
4
.
4
**Deliverable**
**4**
**.**
**2**
Analyzed
</td> </tr> </table>
Figure 6: Expected outcomes of WP4
Table 7: Type of data and classification for WP4
<table>
<tr>
<th>
</th>
<th>
**Origin of data (Type specified)**
</th>
<th>
</th>
<th>
**Sensitivity**
</th>
<th>
</th>
<th>
**Project strategy**
</th>
<th>
**Suggested Policy**
</th>
<th>
</th>
<th>
**Description**
</th>
<th>
</th> </tr>
<tr>
<th>
**Gathered data**
</th>
<th>
**Processed /**
**Generated data**
</th>
<th>
**Because of 3rd**
**parties rights**
</th>
<th>
**Because of ethical issues**
</th>
<th>
**Non**
**sensitive data**
</th>
<th>
**Business / commercialization**
</th>
<th>
**Scientificall y relevant**
</th>
<th>
**Format**
</th>
<th>
**Medium of data**
</th>
<th>
**Projected volume**
</th> </tr>
<tr>
<td>
**WP4**
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td> </tr>
<tr>
<td>
Task
4.1
</td>
<td>
</td>
<td>
Case studies
</td>
<td>
x
</td>
<td>
</td>
<td>
</td>
<td>
x
</td>
<td>
</td>
<td>
To be published after the end of the embargo
period defined in the
NDAs
</td>
<td>
Text /
.doc
</td>
<td>
Virtual
</td>
<td>
1
</td> </tr>
<tr>
<td>
Task
4.2
</td>
<td>
</td>
<td>
Testing protocol
</td>
<td>
</td>
<td>
</td>
<td>
X
</td>
<td>
</td>
<td>
x
</td>
<td>
To be published after the first paper
</td>
<td>
Text /
.doc
</td>
<td>
Virtual
</td>
<td>
1
</td> </tr> </table>
<table>
<tr>
<th>
Task
4.3
</th>
<th>
Recordings
</th>
<th>
</th>
<th>
x
</th>
<th>
x
</th>
<th>
</th>
<th>
</th>
<th>
x
</th>
<th>
Data to be made public according to the
indication of responsible
partners
</th>
<th>
Video files
</th>
<th>
Virtual
</th>
<th>
1 per case study
</th> </tr>
<tr>
<td>
Task
4.3
</td>
<td>
</td>
<td>
Transcripts
</td>
<td>
x
</td>
<td>
x
</td>
<td>
</td>
<td>
</td>
<td>
x
</td>
<td>
To be published open access after
anonymization, after the
first paper
</td>
<td>
Text / .doc / .srt
</td>
<td>
Virtual
</td>
<td>
1 per case study
</td> </tr>
<tr>
<td>
Task
4.3
</td>
<td>
</td>
<td>
Coded design protocols
</td>
<td>
x
</td>
<td>
x
</td>
<td>
</td>
<td>
</td>
<td>
x
</td>
<td>
To be published after the first paper
</td>
<td>
Text /
.doc
</td>
<td>
Virtual
</td>
<td>
1
</td> </tr>
<tr>
<td>
Task
4.3
</td>
<td>
</td>
<td>
Analysis of design protocols
</td>
<td>
</td>
<td>
</td>
<td>
x
</td>
<td>
</td>
<td>
x
</td>
<td>
To be published after the first paper
</td>
<td>
Text /
.doc
</td>
<td>
Virtual
</td>
<td>
1
</td> </tr>
<tr>
<td>
Task
4.4
</td>
<td>
Recordings
</td>
<td>
</td>
<td>
x
</td>
<td>
x
</td>
<td>
</td>
<td>
</td>
<td>
x
</td>
<td>
Data to be made public according to the
indication of responsible
partners
</td>
<td>
Video files
</td>
<td>
Virtual
</td>
<td>
1 per case study
</td> </tr>
<tr>
<td>
Task
4.4
</td>
<td>
</td>
<td>
Transcripts
</td>
<td>
x
</td>
<td>
x
</td>
<td>
</td>
<td>
</td>
<td>
x
</td>
<td>
To be published open access after
anonymization, after the
first paper
</td>
<td>
Text / .doc / .srt
</td>
<td>
Virtual
</td>
<td>
1 per case study
</td> </tr>
<tr>
<td>
Task
4.4
</td>
<td>
</td>
<td>
Coded design protocols
</td>
<td>
x
</td>
<td>
x
</td>
<td>
</td>
<td>
</td>
<td>
x
</td>
<td>
To be published open access after
anonymization, after the
first paper
</td>
<td>
Text /
.doc
</td>
<td>
Virtual
</td>
<td>
1
</td> </tr>
<tr>
<td>
Task
4.4
</td>
<td>
</td>
<td>
Analysis of design protocols
</td>
<td>
</td>
<td>
</td>
<td>
x
</td>
<td>
</td>
<td>
x
</td>
<td>
To be published after the first paper
</td>
<td>
Text /
.doc
</td>
<td>
Virtual
</td>
<td>
1
</td> </tr> </table>
<table>
<tr>
<th>
Task
4.4
</th>
<th>
</th>
<th>
SAR module performances
of the SPARK platform
</th>
<th>
</th>
<th>
</th>
<th>
x
</th>
<th>
x
</th>
<th>
x
</th>
<th>
To be published after the first publication or after
the exploitation strategy benefits of them, whatever comes later
</th>
<th>
Spreadsh eet / .xls
</th>
<th>
Virtual
</th>
<th>
1
</th> </tr>
<tr>
<td>
Task
4.5
</td>
<td>
</td>
<td>
Information
MGMT system
performances
of the SPARK platform
</td>
<td>
</td>
<td>
</td>
<td>
x
</td>
<td>
x
</td>
<td>
x
</td>
<td>
To be published after the first publication or after
the exploitation strategy benefits of them, whatever comes later
</td>
<td>
Spreadsh eet / .xls
</td>
<td>
Virtual
</td>
<td>
1
</td> </tr>
<tr>
<td>
Task
4.5
</td>
<td>
</td>
<td>
Comparison of outcomes of design
sessions run with and
without the
SPARK platform
</td>
<td>
</td>
<td>
</td>
<td>
x
</td>
<td>
x
</td>
<td>
x
</td>
<td>
To be published after the first publication or after
the exploitation strategy benefits of them, whatever comes later
</td>
<td>
Text /
.doc
</td>
<td>
Virtual
</td>
<td>
1
</td> </tr> </table>
### 4.5. WP5 – VALIDATION AND DEMONSTRATIONS IN REAL OPERATIONAL ENVIRONMENT
<table>
<tr>
<th>
</th>
<th>
**Origin of data (Type specified)**
</th>
<th>
</th>
<th>
**Sensitivity**
</th>
<th>
**Project strategy**
</th>
<th>
**Suggested Policy**
</th>
<th>
</th>
<th>
**Description**
</th>
<th>
</th> </tr>
<tr>
<th>
**Gathered data**
</th>
<th>
**Processed /**
**Generated data**
</th>
<th>
**Because of 3rd**
**parties rights**
</th>
<th>
**Because of ethical issues**
</th>
<th>
**Non**
**sensitive data**
</th>
<th>
**Business / commercialization**
</th>
<th>
**Scientifically relevant**
</th>
<th>
**Format**
</th>
<th>
**Medium of data**
</th>
<th>
**Projected volume**
</th> </tr>
<tr>
<td>
**WP5**
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td> </tr>
<tr>
<td>
Task
5.1
</td>
<td>
Recordings
</td>
<td>
</td>
<td>
x
</td>
<td>
x
</td>
<td>
</td>
<td>
</td>
<td>
x
</td>
<td>
To be published after the end of the embargo
period defined in the
NDAs
</td>
<td>
Video files
</td>
<td>
Virtual
</td>
<td>
1 per case study
</td> </tr> </table>
<table>
<tr>
<th>
Titolo
</th>
<th>
</th>
<th>
</th> </tr>
<tr>
<td>
WP5
</td>
<td>
T
5
.
1
T
5
.
2
Platform validated
With students
Platform validated
End Users
premises
**Deliverable**
**5**
**.**
**1**
**(**
**DEM**
**)**
Recordings
&
analysis of
activities
@
End Users
premises
From WP
3
(
T
3
.
3
)
</td>
<td>
@
**Deliverable**
**5**
**.**
**2**
**(**
**DEM**
**)**
Recordings
&
analysis of
design activities with students
From WP
3
(
T
3
.
3
)
</td>
<td>
T
5
.
4
</td>
<td>
SPARK Platform in design sessions with other End Users and Custo
T
5
.
3
**Deliverable**
**5**
**.**
**3**
**(**
**DEM**
**)**
**Deliverable**
**5**
**.**
**4**
Recordings
(
&
analy
**(DEM)** of Show cases
</td>
<td>
s demonstrations
rmers
Recordings
(
&
analysis
?
)
of demonstrations
Awareness on the SPARK
sis?) platform increased
</td> </tr> </table>
Figure 8: Expected outcomes of WP5
Table 9: Type of data and classification for WP5
<table>
<tr>
<th>
Task
5.1
</th>
<th>
</th>
<th>
Transcripts
</th>
<th>
x
</th>
<th>
x
</th>
<th>
</th>
<th>
</th>
<th>
x
</th>
<th>
To be published open access after
anonymization, after the
first paper
</th>
<th>
Text / .doc /
.srt
</th>
<th>
Virtual
</th>
<th>
1 per case study
</th> </tr>
<tr>
<td>
Task
5.1
</td>
<td>
</td>
<td>
Coded design protocols
</td>
<td>
x
</td>
<td>
x
</td>
<td>
</td>
<td>
</td>
<td>
x
</td>
<td>
To be published after the first paper
</td>
<td>
Text / .doc
</td>
<td>
Virtual
</td>
<td>
1
</td> </tr>
<tr>
<td>
Task
5.1
</td>
<td>
</td>
<td>
Analysis of design protocols
</td>
<td>
x
</td>
<td>
x
</td>
<td>
</td>
<td>
</td>
<td>
x
</td>
<td>
To be published after the first paper
</td>
<td>
Text / .doc
</td>
<td>
Virtual
</td>
<td>
1
</td> </tr>
<tr>
<td>
Task
5.1
</td>
<td>
</td>
<td>
SAR module performances
of the SPARK platform
</td>
<td>
</td>
<td>
</td>
<td>
X
</td>
<td>
x
</td>
<td>
x
</td>
<td>
To be published after the first publication or after
the exploitation strategy benefits of them, whatever comes later
</td>
<td>
Spreadsheet
/ .xls
</td>
<td>
Virtual
</td>
<td>
1
</td> </tr>
<tr>
<td>
Task
5.1
</td>
<td>
</td>
<td>
Information
MGMT system
performances
of the SPARK platform
</td>
<td>
</td>
<td>
</td>
<td>
X
</td>
<td>
x
</td>
<td>
x
</td>
<td>
To be published after the first publication or after
the exploitation strategy benefits of them, whatever comes later
</td>
<td>
Spreadsheet
/ .xls
</td>
<td>
Virtual
</td>
<td>
1
</td> </tr>
<tr>
<td>
Task
5.2
</td>
<td>
Recordings
</td>
<td>
</td>
<td>
</td>
<td>
x
</td>
<td>
</td>
<td>
</td>
<td>
x
</td>
<td>
To be published open access after
anonymization, after the
first paper
</td>
<td>
Video files
</td>
<td>
Virtual
</td>
<td>
1 per case study
</td> </tr>
<tr>
<td>
Task
5.2
</td>
<td>
</td>
<td>
Transcripts
</td>
<td>
x
</td>
<td>
x
</td>
<td>
</td>
<td>
</td>
<td>
x
</td>
<td>
To be published open access after
anonymization, after the
first paper
</td>
<td>
Text / .doc /
.srt
</td>
<td>
Virtual
</td>
<td>
1 per case study
</td> </tr>
<tr>
<td>
Task
5.2
</td>
<td>
</td>
<td>
Coded design protocols
</td>
<td>
x
</td>
<td>
x
</td>
<td>
</td>
<td>
</td>
<td>
x
</td>
<td>
To be published after the first paper
</td>
<td>
Text / .doc
</td>
<td>
Virtual
</td>
<td>
1
</td> </tr> </table>
<table>
<tr>
<th>
Task
5.2
</th>
<th>
</th>
<th>
Analysis of design protocols
</th>
<th>
</th>
<th>
</th>
<th>
x
</th>
<th>
</th>
<th>
x
</th>
<th>
To be published after the first paper
</th>
<th>
Text / .doc
</th>
<th>
Virtual
</th>
<th>
1
</th> </tr>
<tr>
<td>
Task
5.2
</td>
<td>
</td>
<td>
SAR module performances
of the SPARK platform
</td>
<td>
</td>
<td>
</td>
<td>
x
</td>
<td>
x
</td>
<td>
x
</td>
<td>
To be published after the first publication or after
the exploitation strategy benefits of them, whatever comes later
</td>
<td>
Spreadsheet
/ .xls
</td>
<td>
Virtual
</td>
<td>
1
</td> </tr>
<tr>
<td>
Task
5.2
</td>
<td>
</td>
<td>
Information
MGMT system
performances
of the SPARK platform
</td>
<td>
</td>
<td>
</td>
<td>
x
</td>
<td>
x
</td>
<td>
x
</td>
<td>
To be published after the first publication or after
the exploitation strategy benefits of them, whatever comes later
</td>
<td>
Spreadsheet
/ .xls
</td>
<td>
Virtual
</td>
<td>
1
</td> </tr>
<tr>
<td>
Task
5.3
</td>
<td>
Recordings
</td>
<td>
</td>
<td>
x
</td>
<td>
x
</td>
<td>
</td>
<td>
</td>
<td>
x
</td>
<td>
Data to be made public according to the
indication of responsible
partners
</td>
<td>
Video files
</td>
<td>
Virtual
</td>
<td>
1 per case study
</td> </tr>
<tr>
<td>
Task
5.3
</td>
<td>
</td>
<td>
Transcripts
</td>
<td>
x
</td>
<td>
x
</td>
<td>
</td>
<td>
</td>
<td>
x
</td>
<td>
To be published open access after
anonymization, after the first paper has been published
</td>
<td>
Text / .doc /
.srt
</td>
<td>
Virtual
</td>
<td>
1 per case study
</td> </tr>
<tr>
<td>
Task
5.3
</td>
<td>
</td>
<td>
Coded design protocols
</td>
<td>
x
</td>
<td>
x
</td>
<td>
</td>
<td>
</td>
<td>
x
</td>
<td>
To be published after the first paper
</td>
<td>
Text / .doc
</td>
<td>
Virtual
</td>
<td>
1
</td> </tr>
<tr>
<td>
Task
5.3
</td>
<td>
</td>
<td>
Analysis of design protocols
</td>
<td>
</td>
<td>
</td>
<td>
x
</td>
<td>
</td>
<td>
x
</td>
<td>
To be published after the first paper
</td>
<td>
Text / .doc
</td>
<td>
Virtual
</td>
<td>
1
</td> </tr>
<tr>
<td>
Task
5.3
</td>
<td>
</td>
<td>
SAR module performances
of the SPARK platform
</td>
<td>
</td>
<td>
</td>
<td>
x
</td>
<td>
x
</td>
<td>
x
</td>
<td>
To be published after the first publication or after
the exploitation strategy benefits of them, whatever comes later
</td>
<td>
Spreadsheet
/ .xls
</td>
<td>
Virtual
</td>
<td>
1
</td> </tr> </table>
<table>
<tr>
<th>
Task
5.3
</th>
<th>
</th>
<th>
Information
MGMT system
performances
of the SPARK platform
</th>
<th>
</th>
<th>
</th>
<th>
x
</th>
<th>
x
</th>
<th>
x
</th>
<th>
To be published after the first publication or after
the exploitation strategy benefits of them, whatever comes later
</th>
<th>
Spreadsheet
/ .xls
</th>
<th>
Virtual
</th>
<th>
1
</th> </tr>
<tr>
<td>
Task
5.4
</td>
<td>
Recordings
</td>
<td>
</td>
<td>
x
</td>
<td>
x
</td>
<td>
</td>
<td>
</td>
<td>
x
</td>
<td>
Data to be made public according to the
indication of responsible
partners
</td>
<td>
Video files
</td>
<td>
Virtual
</td>
<td>
1 per case study
</td> </tr>
<tr>
<td>
Task
5.4
</td>
<td>
</td>
<td>
Transcripts
</td>
<td>
x
</td>
<td>
x
</td>
<td>
</td>
<td>
</td>
<td>
x
</td>
<td>
To be published open access after
anonymization, after the
first paper
</td>
<td>
Text / .doc /
.srt
</td>
<td>
Virtual
</td>
<td>
1 per case study
</td> </tr>
<tr>
<td>
Task
5.4
</td>
<td>
</td>
<td>
Coded design protocols
</td>
<td>
x
</td>
<td>
x
</td>
<td>
</td>
<td>
</td>
<td>
x
</td>
<td>
To be published after the first paper
</td>
<td>
Text / .doc
</td>
<td>
Virtual
</td>
<td>
1
</td> </tr>
<tr>
<td>
Task
5.4
</td>
<td>
</td>
<td>
Analysis of design protocols
</td>
<td>
</td>
<td>
</td>
<td>
x
</td>
<td>
</td>
<td>
x
</td>
<td>
To be published after the first paper
</td>
<td>
Text / .doc
</td>
<td>
Virtual
</td>
<td>
1
</td> </tr>
<tr>
<td>
Task
5.4
</td>
<td>
</td>
<td>
SAR module performances
of the SPARK platform
</td>
<td>
</td>
<td>
</td>
<td>
x
</td>
<td>
x
</td>
<td>
x
</td>
<td>
To be published after the first publication or after
the exploitation strategy benefits of them, whatever comes later
</td>
<td>
Spreadsheet
/ .xls
</td>
<td>
Virtual
</td>
<td>
1
</td> </tr>
<tr>
<td>
Task
5.4
</td>
<td>
</td>
<td>
Information
MGMT system
performances
of the SPARK platform
</td>
<td>
</td>
<td>
</td>
<td>
x
</td>
<td>
x
</td>
<td>
x
</td>
<td>
To be published after the first publication or after
the exploitation strategy benefits of them, whatever comes later
</td>
<td>
Spreadsheet
/ .xls
</td>
<td>
Virtual
</td>
<td>
1
</td> </tr> </table>
**5\. ARCHIVING AND STORAGE OF THE DATA**
### 5.1. PUBLIC DATA
The public data will be made available through the ZENODO platform, which will
ensure the publication of such data also on OPENAIRE
(http://zenodo.org/about).
### 5.2. PRIVATE DATA
All the project gathered, generated and processed data and documents will be
archived on a web-repository managed by Viseo: CODENDI, which will ensure the
secure and safe storage of the data.
Codendi is an ALM (Application Lifecycle Management) which permits a fine
tuning of access rights.
However, video recordings of the design sessions are likely to imply very
large files (several TBs per session) that can hardly be managed via a
standard Internet connection. Therefore, all the original video recordings
will be stored in appropriate data storage devices and at least two copies of
each file will be stored in at least two different locations (at PoliMI and at
GINP) in order to reduce the risks of losing data. All the devices shall be
kept with by the involved organizations with the maximum care they dedicate to
the storage of sensible data. All private data will be stored for at least 5
years.
5.3. FORMAT OF THE DATA TO BE ARCHIVED AND PUBLISHED IN ZENODO.
The managed data will concern recordings of design sessions (with downsized
image resolution for reducing the size of the files), documents (Text files;
Spreadsheets; Presentations) as well as data acquired to assess the technical
performance of the system. All these data will be archived using standard file
format, i.e. files that the most diffused editors, readers, office suites can
handle.
**6\. PROPOSED POLICY**
All the data that are relevant and strategic from a scientific point of view
will be made available after the accomplishment of the analysis and the issue
of the first scientific publications.
For what concerns the data gathered during the testing activities, they will
be initially anonymised, so that also the ethical issues could be respected.
Data that are not confidential and that are not strategic for the business and
market will be made available at latest after 3 years from the collection or
processing.
As the project cannot be considered as a fixed element, and therefore changes
and evolutions can occur during the whole project lifecycle, the Steering
Committee can introduce modifications to the current classification as soon as
these modifications are necessary in order to avoid problems related to
confidential issues. The modifications will be registered in the minutes of
the meeting and will be integrated in the next versions of the DMP.
**7\. CONCLUSION**
This document is the first version of the Data Management Plan as it has been
conceived by the SPARK Consortium, so as to have a path that will be used for
the Management of the data that will be collected, generated and/or processed
during the project lifecycle. This deliverable is the result of the activities
of task 6.1 (WP6) that have carefully analysed all the possibilities related
to the confidentiality of the data and the importance of these data from a
strategic point of view. In addition, ethical issues related to the
sensitivity of data collected during the testing activities have been taken
into account.
Eventually, the modalities for the data archiving, storage and safeguard of
the data have been addressed.
All the data that can be made openly accessible will be uploaded by means of
Zenodo.
Finally, the approach described in this deliverable will be further detailed
and improved along the project evolution and specifically at M21 and at M36.
|
https://phaidra.univie.ac.at/o:1140797
|
Horizon 2020
|
1086_MAMI_688421.md
|
# Executive Summary
Horizon 2020 projects can participate in a limited and flexible pilot action
on open access to research data. Participating projects must develop a Data
Management Plan (DMP) specifying which data will be openly accessible. This
deliverable contains that data management plan.
As per the _Guidelines on Data Management in Horizon 2020_ (Version 2.1, 15
February 2016) 1 , this document covers:
* The handling of research data during & after the project (Sections 2 and 3)
* What data will be collected, processed or generated (see Section 4)
* What methodology & standards will be applied (Section 2)
* Whether data will be shared/made open access & how (Sections 2 and 3.3)
* How data will be curated & preserved (Section 3.4)
# Introduction
One of the primary objectives of the MAMI project is the collection and
investigation of data about middlebox manipulation of packets on the Internet.
To this end, MAMI stores data in a _Middlebox Observatory_ (or ‘Observatory’
for short). This Observatory is meant primarily for data generated in the MAMI
project itself, but may store or cache data from other measurement projects as
well.
Data being offered by the MAMI data Observatory falls into three categories.
**Data Generated by MAMI.** These are data generated by MAMI’s own probes and
experiments. For these data sets, MAMI Observatory is the authoritative
source. One example of such data is Pathspider data (see Section 4.1).
**Data Held by MAMI.** These are data that were not generated by MAMI, but
which are held within the MAMI Observatory, for example because they are
otherwise not available on demand. For these data, MAMI can also be viewed as
an authoritative source.
**Data Cached by MAMI.** These are data that are not generated by MAMI, which
may be available online, for which MAMI is not the authoritative source, and
which are held by the MAMI Observatory only for convenience.
At the time of writing, it is not at all clear that there will be any data of
the second category (held authoritatively by MAMI, but not generated by it).
We include this category here for completeness’ sake in case data of that
category should indeed exist. It also not certain that data of the third
category (data cached by MAMI) will be used in MAMI except as interesting
background data, e.g., to calibrate MAMI’s own measurements.
Since MAMI data contains IP addresses and other potentially Personally
Identifiable Information (PII), MAMI can not give out these raw data sets to
everyone (see Section 3.3). Instead, MAMI runs a query system on top of the
Observatory that will aggregate MAMI data to so called _Observations_ and
thereby only return data that is free of PII (Section 2). Researchers can
however ask for an agreement with the MAMI consortium that would allow them
access to the raw data files.
Measure
\-
ments
Obser-
vations
Analysis
Modules
Measure-
ment
Campaigns
Analysts
Jupyter
Jupyter
Jupyter
Analysis
Console
General
Public
Web Server
Figure 1: Management Architecture
# MAMI Observatory Architecture and Implementation
The management architecture is depicted in Figure 1. All access to the data
management infrastructure is mediated through a web server, nginx in this
case. The data itself is stored in two databases. One stores observations in
the format specified by MAMI project, and is used to drive the publicly-
accessible observatory website, as well as exploratory analysis. The other
database holds the raw data that from which these observations are derived.
This data might be available in different formats depending on the tool(s) and
methodologies used to collect the data (see section 4). Each data set might
have its own analysis module to derive observations. The raw measurement data
are stored in a Hadoop filesystem (HDFS) node. Observations are stored in a
MongoDB NoSQL database.
Access to these data falls into three categories:
* **Measurement Campaigns.** These may be people or machines. They are equipped with tokens that allow them to upload raw measurements to the measurement infrastructure.
* **Analysts.** These are people who write analysis modules that turn measurements and other observations into more observations for exploratory analysis of both measurements and observations.
* **General Public.** Members of the general public can access observations based on a web frontend.
The analysis modules will be written in a variety of languages, with a
preference for Python. Whenever new measurements arrive in the measurement
database, appropriate analysis modules will be triggered that transform
measurements into observations which can even trigger other analysis modules
that derive observation based on other observations.
The analysis consoles are implemented using Jupyterhub, a multi-user Jupyter
server which enables the use of Python facilities for data analysis such as
Pandas 2 .
The MAMI Observatory is managed by ZHAW; Dr. Stephan Neuhaus is the main
contact.
## Software Management
We use a software stack that consists exclusively of open-source software for
the storage and evaluation of measurements and observations. The list of this
software contains Linux (Ubuntu 14.04 LTS); 3 , HDFS; 4 , jupyterhub 5
MongoDB 6 , Python 3; 7 and nginx 8 . All of this software is available
also in previous revisions, from a variety of sources on the Internet.
The project prefers open source releases of its products. Open-source software
generated by the project, including measurement tool as described in Section 4
as well as any code developed for the Observatory itself for management and
analysis purposes, are made available and archived on GitHub, at
https://github.com/mami-project. We commit to maintaining access to the
software necessary for interacting with data in the Observatory as long as the
Observatory is in operation.
Other software that is not directly related to the operation of the
Observatory may not made generally available to the public and is stored in
the project’s github repository at gitlab. mami-project.eu, where it is backed
up daily and are transferred to off-site storage once a week.
# Data Management Plan
## Data Set Description
The measurement work in the MAMI project is largely about assessing and
classifying the types and extent of impairments to path transparency in the
Internet by middleboxes. The data generated and stored by MAMI therefore
consists of empirical observations of paths that exist in the Internet, and
the conditions that exist along with them. A typical observation will measure
whether on a certain date a certain condition was true on a certain path; for
example, “On 1 April 2016, at 12:34:45 UTC, it was possible to establish a TCP
connection from 2001:db8::dead:beef to 2001:db8:abcd::1 on port 443.”
Condition information is often derived from observation of packets and packet
headers resulting from certain active Internet measurement activity;
conditions may also contain additional data about measured parameters
associated with the condition (e.g., round-trip time as measured by packets
with given properties).
More formally, an observation consists of:
* one or more **timestamps** at which the observation is considered valid, either because it was directly measured or derived from a direct measurement taken at these times;
* a description of the **path** for which the observation is considered valid, consisting of a sequence of one or more path elements (i.e. addresses, prefixes, autonomous system numbers, or data-source-generated pseudonyms therefor);
* a description of the **condition** observed along the path, as defined by the analysis module generating the observation;
* any additional **values** associated with the condition; and
* a reference to the **source** of the observation, both the raw data (from which metadata is available) as well as the version of the analysis module that generated it.
Observations are either directly collected from vantage points around the
Internet, or they are computed from raw measurement data. Data uploaded to
MAMI comes from publicly available cloud servers as well as from Internet-
connected testbeds. The measurement run may be simple active measurements such
as pings or traceroutes, or they may be more elaborate exchanges of protocol
messages.
Some data are generated explicitly for MAMI. For example, Pathspider (see
Section 4.1) is a tool developed within the project. Pathspider does active
A/B testing of connectivity dependency and feature usability of optional
transport features (e.g., Explicit Congestion Notification (ECN), and the risk
of enabling ECN by default on the client side). In order to determine the
parameters of optional transport features, it is necessary to keep track of
certain IP and TCP header fields, something that most data sets do not do.
Pathspider will in turn be deployed on vantage points including hosts from the
Measuring Mobile Broadband Networks in Europe (MONROE) project.
MAMI data are useful to all networking researchers interested in path-related
issues. For MAMI itself, this is mainly path transparency, but it could also
be about connectivity or even about certain protocols. For example, MAMI data
might be useful to determine connectivity to and from countries whose
governments aim to control or monitor their citizens’ use of the Internet.
MAMI has not yet generated any data, but MAMI-like data have in the past been
used for scientific publications; for example, on using path transparency
observations to support protocol engineering [7], on middlebox cooperation
[9], or on the Internet-wide deployment of ECN [8].
## Standards and Metadata
MAMI data consist of observations of path conditions, and raw data from which
these observations can be derived. Raw data generated by the project or
imported to the measurement data must contain at least the following metadata,
derived from the metadata available from each data source.
* A (low-precision) timestamp at which the measurement data was created
* A (low-precision) timestamp at which the measurements were added to the Observatory
* Information about the entity (organization, individual, etc.) supplying the data
* Information about any licensing terms that may apply to the data
* Any URL for automated retrieval / re-retrieval of the data
Information about the source and target of active measurements, and timestamps
for each part of the measurement, from which path and timestamp information in
the observations are derived, are stored in the data itself.
Currently, raw data is stored as JSON [2], CSV [4], and IPFIX [1]; derived
observations are stored as JSON.
## Data Sharing
As described in Section 1, due to privacy concerns MAMI will not provide
general access to raw data sets to external users. These raw data are stored
inside MAMI with “copyright MAMI consortium, all rights reserved”. MAMI can
license these data for use by other researchers on a case-by-case basis, after
these researchers have come to an agreement with the MAMI consortium to access
the raw data and not expose any PII in any derived results.
The results from the MAMI query interface, which provides public access to the
observations stored in the MAMI database, are licensed under the Creative
Commons “Attribution 4.0 International” (CC BY 4.0) license (see
https://creativecommons.org/licenses/by/4.0/) .
The MAMI Observatory is open to all data sets where MAMI query results
involving such data sets can be shared using CC BY 4.0. However, storing MAMI-
generated data in the Observatory always has priority over third-party data,
and third-party data may be removed from the Observatory should space become
an issue. This is obviously not a problem for cached data sets, but MAMI will
even try to find a home for data sets that are not merely cached and that
would otherwise be orphaned, on a best-effort basis.
Data sets that are uploaded to MAMI, but which are later found not to be
compatible with the MAMI data-sharing license, may be removed without notice.
## Archiving and Preservation (Incl. Storage and Backup)
At the time of this first version of the DMP, the Observatory does not contain
live data. Plans for archiving are thus preliminary and fluid.
Only data that are originally generated by MAMI will be archived and curated.
At the time of writing, this includes Pathspider data and certain tracebox or
copycat data sets.
For data set storage and backup, ZHAW will back up the HDFS and MongoDB onto
external disk drives. Several drives will be used in rotation, and at least
one drive will always be stored off-site.
After the end of the project, ZHAW will prepare a final, unalterable version
of the data. These data will then be made available to researchers on request.
Support for the MAMI web site and public-facing repository query tools will be
continued as long as funds are available to sustain this long-term curation.
# Data Sources
The MAMI Observatory is managed as a single, unitary data set, containing both
observations for querying, as well as raw data for analysis and reanalysis
from which these observations are derived. In this section, we list the data
sources we presently know will provide data to the MAMI Observatory. This list
is not complete, and will expand in the future. Whenever measurement from a
new data source is added to the Observatory, it is further necessary to add
new analysis modules to generate observations from this data.
## PathSpider
PathSpider is a generalized tool for building connectivity and optional
transport feature / transport protocol A/B functionality tests. A/B testing
differentiates transient connectivity failures from connectivity failures due
to the use of a particular transport protocol or feature. It currently
supports testing of ECN connectivity and negotiation, but work is presently
underway to add support for Multipath TCP, TCP Fast Open, TCP window scale
negotiation, Stream Control Transmission Protocol (SCTP), and other protocols
and protocol features.
PathSpider functions by generating two simultaneous flows from the source to
the target, and passively observing these flows at the source to determine the
characteristics of the flow. The raw data generated by this observation
process are essentially flow data, linking characteristics of the “A” flow
(feature enabled) to those of the “B” flow (feature disabled, experimental
control). These raw flow data are analyzed by the PathSpider tool itself into
MAMI-native observation records before transmission to the Observatory.
PathSpider’s output therefore consists of { _time,path,condition,value_ }
tuples as in section 3.1:
**timestamps** Observation time of the first packet in the flow from which the
condition was derived.
**path** Path derived from the source and destination addresses of the
measurement.
**condition** Condition observed along the path; for example “ECN negotiation
successful”, “ECN negotiation causes connectivity failure”.
**values** An value associated with the condition (not yet required by present
conditions, for future use).
**source reference** Version of Pathspider used (in terms of GitHub tag or
commit hash).
Pathspider was designed for large-scale testing of millions of targets (e.g.
the Alexa top million webservers) from a set of active measurement agents;
past measurement campaigns have used DigitalOcean cloud server instances as
active measurement agents. However, for future verification of path support
for uncommon features (e.g. SCTP), it may be necessary to operate targets with
known support for these features, and passive observation of the traffic at
the targets can also be used to generate observations.
## Tracebox
Tracebox experiments attempt to contact a remote server from a vantage point
and identify the used path. Tracebox data sets are in a binary format called
warts, native to CAIDA’s Scamper tool 9 on which it is based. These data are
translated into a Tracebox-specific JSON schema for analysis at the
observatory.
Each data set contains necessary metadata about the measurement, as follows:
**version** The version of the tool used to obtain the data set. **type** The
type of data contained in the data set, e.g., tracebox. **userid** The (Unix)
user ID under which the tool ran. Often 0. **method** The method used to
gather data, e.g., ip4-tcp.
**probe** The basic probe format, e.g., ip/tcp/mss(1460)/sackp (i.e., TCP
segment, with Selective Acknowledgment and MSS of 1460 bytes).
**src** The vantage point’s source address. **dst** The experiment’s
destination address. **sport/dport** The experiment’s source and destination
TCP ports (if TCP is being used).
**result** The overall result, e.g., success
**start** The experiment’s start time, containing sec (seconds since the
Epoch), usec (microseconds) and ftime (human-readable local time, e.g.,
2016-03-23 11:57:10).
**Assorted TCP and IP options** These flags and values are often informative
only, such as tcp_seq (initial TCP sequence number), but may sometimes be
important for the protocol, such as tcp_ack.
## Copycat
Copycat is a tool for detecting differential treatment of UDP and TCP traffic
over an Internet path between two measurement agents. Copycat generates raw
IPFIX [1] flow data using the QoF [6] flow meter, which contains additional
information about TCP loss and latency. By comparing the characteristics of
UDP traffic with TCP traffic along the same path at equivalent times,
differential treatment can be detected.
Raw Copycat data contains basic QoF flows as well as TCP performance metrics;
i.e., the following IPFIX Information Elements (IEs) as well as the
appropriate reverse counterparts [5].
* octetDeltaCount
* packetDeltaCount
* protocolIdentifier
* tcpControlBits
* sourceTransportPort
* sourceIPv4Address
* ingressInterface
* destinationTransportPort
* destinationIPv4Address
* egressInterface
* sourceIPv6Address
* destinationIPv6Address
* minimumTTL
* maximumTTL
* flowEndReason
* flowId
* flowStartMilliseconds
* flowEndMilliseconds
* transportOctetDeltaCount
* transportPacketDeltaCount
* initialTCPFlags (6871 / 14)
* unionTCPFlags (6871 / 15)
* reverseFlowDeltaMilliseconds (6871 / 21)
* reverseInitialTCPFlags (6871 / 16398)
* reverseUnionTCPFlags (6871 / 16399)
* tcpSequenceCount (35566 / 1024)
* tcpRetransmitCount (35566 / 1025)
* minTcpRttMilliseconds (35566 / 1029)
* ectMarkCount (35566 / 1031)
* ceMarkCount (35566 / 1032)
* tcpSequenceLossCount (35566 / 1035)
* tcpLossEventCount (35566 / 1038)
* qofTcpCharacteristics (35566 / 1039)
* tcpRttSampleCount (35566 / 1046)
See https://github.com/britram/qof/wiki and
https://iana.org/assignments/ipfix for a full reference for relevant IE
definitions.
## Revelio
Revelio [3] is a tool for detecting IPv4 network address translation on access
networks. Revelio produces CSV-formatted data with the following fields:
**boxid** Unique identifier of the device running the Revelio client (assigned
based on MAC address).
**revelio type** The Revelio version and the platform used to deploy Revelio.
**timestamp** Time at the start of the measurement. **local IP** IP address of
the device running the Revelio client. **IGD** IP address of the WAN-facing
interface if device supports UPnP,
**STUN mapped** The public mapped address (the GRA). **trace packetSize** The
packet size of the traceroute probe.
**traceroute result** Output of traceroute to a fixed target examining the
hops within the access network
|
https://phaidra.univie.ac.at/o:1140797
|
Horizon 2020
|
1087_VICINITY_688467.md
|
# Executive Summary
_«The VICINITY project will build and demonstrate a bottom-up ecosystem of
decentralised interoperability of IoT infrastructures called virtual
neighbourhood, where users can share the access to their smart objects without
losing the control over them.»_
The present document is a deliverable “D9.4 – Data Management Plan” of the
VICINITY project (Grant Agreement No.: 688467), funded by the European
Commission’s Directorate-General for Research and Innovation (DG RTD), under
its Horizon 2020 Research and Innovation Programme (H2020). This is the third
version of the project Data Management Plan (DMP) and contains updated
information about datasets generated or collected through the project. This
includes information on whether and how it will be exploited or made
accessible for verification and re-use, and how it will be curated and
preserved.
The purpose of the Data Management Plan is to present a way forward with the
data management policy that has been used by the consortium. This is made with
regard to all the datasets
generated by the project. The datasets referred to in this document were
completed during the first stages (completed 15 th of January 2019) of the
project. The document can only reflect the intentions of the project partners
toward the final project’s datasets. This third and final version (D9.4) will
be put in effect the 31 st December 2019 and follows the H2020 guidelines on
Data Management Plans, and as stated in the Grant Agreement 688467.
Changes in this final document is caused by some changes in the organisations,
the datasets from the use cases, more legal considerations are being
presented, as well as the project have received a lot more information from
Open Call winners. As the project progressed and the partners gained insight
in results, the datasets were elaborated on. The detailed descriptions of all
the specific datasets that were collected are described and made available
under the relevant Data Management framework.
All personal data, or data directly related to the residents, was collected
after the project received a signed informed consent form from the
stakeholders participating in the pilots. The DMP will also be put in effect
after the completion of the project as it presents a structure that follows
guidelines The DMP is not a fixed document and evolved during the lifespan of
the project (Figure 1).
**Figure 1: Data Management Plan – deliverables 2016 – 2019**
Note: In order to assist the official project review process by the Commission
for the first project period (M1-M18), a preliminary version of the updated
DMP of D9.3 was delivered prior to M20 (August 2017) in order to be enable a
better assessment of the progress of the Data Management. The preliminary
version was accepted, and laid the groundwork for this third and final
version.
# Introduction
The purpose of the Data Management Plan (DMP) deliverable is to provide
relevant information concerning data collected and used by the partners of the
project VICINITY. The projects aim was to develop a solution defined as
“Interoperability as a Service”. This concept became a part of the VICINITY
Open Gateway (Figure 2), and was achieved by implementing a platform for
harvesting, converting and sharing data from IoT devices on the service layer
of the network.
**Figure 2: Domains and some of the functionalities the DMP covers**
This goal entails the need for good documentation and implementation of
descriptors, lookup-tables, privacy settings and intelligent conversion of
data formats. The strength of having a cloud-based gateway is that it should
be relatively simple to upgrade with new specifications and implement
conversion, distribution and privacy strategies. In particular, the privacy
part is considered an important aspect of the project, as VICINITY needs to
follow and adhere to strict privacy policies. It will also be necessary to
focus on possible ethical issues and access restrictions regarding personal
data so that no regulations on sensitive information are violated.
<table>
<tr>
<th>
**Purpose of access restrictions: Right of access, Art. 15:**
* Purposes of processing
* Affected categories of personal data
* Recipients or categories of recipients
* Expected storage time
* All available information on where the personal data originated
* The occurrence of automated decisions
</th> </tr> </table>
**Table 1: GDPR on right of access**
The datasets collected belong to four main domains;
1. Smart energy and buildings as demonstrated by ENERC in Portugal and tested at AAU in Denmark.
2. Mobility as demonstrated by HITS in Norway and tested at AAU.
3. Smart home as demonstrated by the Norwegian partners HITS and TINYM and tested at AAU.
4. eHealth as demonstrated by GNOMON and CERTH in Greece.
(Figure 3: Example of potential data points in use cases that generate data.).
Several standards and guidelines were taken into consideration that the
project needs to be aware within each of these fields. A number of different
vendors and disciplines are involved – and much of the information that is
available only exists in proprietary data formats. For this reason, VICINITY
will target IoT units that follow the specifications defined by oneM2M
consortium, ETSI standardization group and international groups and
committees.
The DMP has been undergone some changes in particular in regards to agreement
concerns when storing and handling data after the completion of VICINITY. This
version of the document is based on the knowledge generated through
discussions, demonstrations and preparations for deployment at pilot sites.
**Figure 3: Example of potential data points in use cases that generate
data.**
# General Principles
## 3.1. IPR management and security
As a research and innovation action, VICINITY aims at developing an open
framework and gateway – demonstrated through a subset of Value-Added Services
and the related business models. The project consortium includes partners from
private sector, public sector and end-users (Table 4). Some partners may have
Intellectual Property Rights on their technologies and data. Consequently, the
VICINITY consortium will protect that data and crosscheck with the concerned
partners before data publication.
<table>
<tr>
<th>
**Partner**
</th>
<th>
**Sector**
</th>
<th>
**Domain**
</th>
<th>
**IPR**
</th> </tr>
<tr>
<td>
ATOS Spain SA (ATOS)
</td>
<td>
Private
</td>
<td>
</td>
<td>
</td> </tr>
<tr>
<td>
Aalborg University (AAU)
</td>
<td>
Public
</td>
<td>
</td>
<td>
</td> </tr>
<tr>
<td>
Bavenir S.R.O. (BVR)
</td>
<td>
Private
</td>
<td>
</td>
<td>
</td> </tr>
<tr>
<td>
Centre for Research and Technology Hellas
</td>
<td>
Public
</td>
<td>
Health
</td>
<td>
</td> </tr>
<tr>
<td>
Climate Associates Limited (CAL)
</td>
<td>
Private
</td>
<td>
Environ mental
consulta ncy
</td>
<td>
</td> </tr>
<tr>
<td>
Enercoutim (ENERC)
</td>
<td>
Private
</td>
<td>
Energy,
Buildings
O&M
services
</td>
<td>
</td> </tr>
<tr>
<td>
Gnomon Informatics S.A. (GNOMON)
</td>
<td>
Private
</td>
<td>
</td>
<td>
</td> </tr>
<tr>
<td>
Gorenje Gospodinjski Aparati D.D. (GRN)
</td>
<td>
Private
</td>
<td>
White goods and services
</td>
<td>
</td> </tr>
<tr>
<td>
Hafenstrom AS (HITS)
</td>
<td>
Private
</td>
<td>
Mobility
</td>
<td>
</td> </tr>
<tr>
<td>
Hellenic Telecommunications Organization S.A. (OTE)
</td>
<td>
Private
</td>
<td>
</td>
<td>
</td> </tr>
<tr>
<td>
Intersoft A.S. (IS)
</td>
<td>
Private
</td>
<td>
</td>
<td>
X
</td> </tr>
<tr>
<td>
Municipality of Pilea-Hortiatis (MPH)
</td>
<td>
Public
</td>
<td>
Health
</td>
<td>
</td> </tr>
<tr>
<td>
Technical University of Kaiserslautern (UNIKL)
</td>
<td>
Public
</td>
<td>
</td>
<td>
</td> </tr>
<tr>
<td>
Tiny Mesh AS (TINYM)
</td>
<td>
Private
</td>
<td>
Building
</td>
<td>
</td> </tr>
<tr>
<td>
Universidad Politecnica de Madrid (UPM)
</td>
<td>
Public
</td>
<td>
Energy
</td>
<td>
</td> </tr> </table>
**Table 4: The VICINITY consortium includes partners from different sectors
with confidential data**
A holistic security approach has been followed, in order to protect the
pillars of information security (confidentiality, integrity, availability).
The security approach consists of a methodical assessment of security risks
followed by their impact analysis. This analysis is performed on the personal
information and data processed by the proposed system, their flows and any
risk associated to their processing.
Security measures include secure protocols (HTTPS and SSL), login procedures,
as well as protection against bots and other malicious attacks such as CAPTCHA
technologies. Moreover, the VICINITY pilot sites apply procedures related to
the data collection, their integrity and protection. The data protection and
privacy of personal information include protective measures against
infiltration as well as physical protection of core parts of the systems and
access control measures.
## 3.2. Personal Data Protection
The VICINITY architecture does not expose, use or analyse data. However, some
activities have involved human participants and utilize Value-Added Services
(VAS) that may operate on datasets. The pilots have been conducted in real
apartments and cover real use scenarios related to health monitoring, booking,
home management, governance, energy consumption and other various human
activity and behaviour analysis –related data gathering purposes. Some of the
activities that were carried out by the project gathered some basic personal
data (e.g. name, background, contact details, interest, IoT units and assigned
actions), even though the project avoided collecting such data unless data is
really necessary for the application.
Such data is protected in accordance with the EU's Data Protection Directive
95/46/EC 1 “on the protection of individuals with regard to the processing
of personal data and on the free movement of such data” and of the Council of
24 October 1995 on the protection of individuals with regard to the processing
of personal data and on the free movement of such data ( **Fehler!
Verweisquelle konnte nicht gefunden werden.** ).
Info windows containing relevant links and descriptions to GDPR Articles and
sections as well as best practice are placed throughout this deliverable.
<table>
<tr>
<th>
**General Data Protection Regulation is a common regulation for all EU/EEA
countries. GDPR introduced several changes in privacy legislations and new
privacy rules May 25, 2018. It represents a significant tightening of rules
and possibility of large fines in case of breach.**
This also includes more stringent requirements for documentation of IT systems
and security solutions (Art. 28):
• Fines up to EUR 20,000,000 or 4% of the company's turnover (Art. 79) •
Inadequate data security for the processing of personal data will have such
high financial risk that it must be reported in annual and audit reports
</th> </tr> </table>
**Table 2: General Data Protection Regulation description**
This document aims to address issues related to responsibilities as there are
considerations to be made when several partners and external stakeholders
involved. Examples of such issues are what actions that are to be undertaken
by project coordinator, responsibilities assigned to partner organisations and
the role of the ethics committee in handling data. This is even more prominent
with the number of Open Call winners that have access to the innovation
results – but which also have their own legal contacts as well as third party
suppliers. Who can ensure the data is actually gone – and who are responsible
if traces still can be found? Additionally, a project such as VICINITY also
has providers of value-adding services that are either within or outside the
ecosystem that the partners in the project have direct control over. This is
just the beginning; Who is responsible if personal data that violates the
regulations should have been stored? Or rather; what happens if the personal
data is leaked or used incorrectly?
That being said, GDPR does not represent a big issue for most the partners in
VICINITY as it only applies to data where a person can directly be identified
by the stored information. The labs are using test users, and therefore have
other concerns to made. That being said - it is still a good habit to follow
up and have proper data management strategies, regardless of what is being
stored. Thus, a reminder will be sent to the partners at the end of the
project, requesting partners and Open Call winners to remove or anonymize data
in accordance with GDPR.
WP3 and WP4 activities dealing with the implementation and deployment of core
components have been performed in Slovakia under leadership of local partners
(BVR and IS). For this reason, the solution will be reviewed for compliance
with Data Protection Act No. 122/2013 approved by National Council of the
Slovak Republic together with its amendment No. 84/2014 which already reflects
the EC directive proposal 2012/0011/COD.
WP7 and WP8 activities have been performed in Greece, Portugal and Norway
under the leadership of local partners. In the following the consortium
outlines the legislation for the countries involved in the Trial:
1. Greek Trial in Municipality of Pilea-Hortiatis, Thessaloniki, for Greece, legislation includes “Law 2472/1997 (and its amendment by Law 3471/2006) of the Hellenic Parliament”.
* Regulatory authorities and ethical committees: Hellenic Data Protection Authority http://www.dpa.gr/
2. Norwegian trials in Teaterkvarteret healthcare assisted living home in Tromsø and offices in Oslo Sciencepark, Oslo, have to comply with national legislation “Personal Data Act of 14 April No.31” 5relating to the processing of personal data.
* Each pilot demonstration has to notify regulatory body Datatilsynet pursuant to section 31 of the Personal Data Act and section 29 of the Personal Health Data Filing System Act.
3. Portuguese Trial in Martim Longo microgrid pilot site in the Algarve region, Portugal. The Portuguese renewable energy legislative base dates back to 1988, and was upgraded and reviewed multiple times since then. The most important legislative diplomas are listed; DL 189/88, DL 168/99, DL 312/2001, DL 68/2002, DL 29/2006 and DL 153/2014. The last on the list refers to also one of the most important legislative changes, being the legislative base for broad based auto-consumption, with possibility to inject excess energy in to the grid under certain conditions.
* The collection and use of personal data in Portugal are regulated by the following two laws: “Law 41/2004” (and its amendment “Law 46/2012”), and “Law 32/2008”.
Further information on how personal data collection and handling were
approached in the VICINITY project have been described in D4.3: “VICINITY
Security Services”, D5.2 “VICINITY value- added services implementation
framework”, D6.4: “VICINITY security and privacy evaluation report”, and also
addressed in the D8.2 – D8.5 that covers pilot results from smart energy,
smart buildings, smart parking and eHealth.
All personal data collection efforts of the project partners have been
established after giving subjects full details on the experiments to be
conducted, and obtaining from them a signed informed consent form (see Annex
2: VICINITY consent form template), following the respective guidelines set in
VICINITY and as described in section 3.5: Ethics and security.
<table>
<tr>
<th>
**Importance of GDPR:**
* All legal companies are given new duties
* Everyone should provide good information on how they process personal data
* Everyone should consider risk and privacy consequences
* Everyone should embed privacy in new solutions
* Many companies must establish privacy ombudsman/Privacy Advisor
* The rules also apply to businesses outside Europe
* All data processors are given new duties
* Everyone has new requirements for non-conformity management
* Everyone must be able to fulfil citizens' new rights
</th> </tr> </table>
**Table 3: Importance of GDPR as applied in VICINITY**
Beside this, certain guidelines were implemented in order to limit the risk of
data leaks;
* Keep anonymised data and personal data of respondents separate;
* Encrypt data if it is deemed necessary by the local researchers;
* Store data in at least two separate locations to avoid loss of data;
* Limit the use of USB flash drives;
* Save digital files in one of the preferred formats (see Annex 1), and
* Label files in a systematically structured way in order to ensure the
coherence of the final dataset
GDPR may be broad in its descriptions. To give some examples;
* All kinds of personal data can be stored - as long as the data cannot be combined and used to identify the person. E.g. - it's perfectly OK to save date of birth. However, if combined with an address or apartment number, potential issues may arise.
* IP address, time and login data that can be correlated with member registers used for statistical purposes represents also a risk.
* Transactions related to service or product linked to customer profiles is yet another example.
* There are other things that also come into play; whether the data sets are encrypted/hashed, how the tables are indexed, etc.
To avoid this situation, DMP is now focusing on "security by design", not
"security on demand". Recommended course of action requires a common approach
from all EU funded projects. A brief course which leads up to a written
statement indicating the partner has understood and adhere to GDPR as
presented in the course.
A more formal description of best practice principles can be found in Table 5:
Best practice for use of production data.
<table>
<tr>
<th>
**GDPR - Prominent Rights:**
* Easier Access to Own Data (Art. 43)
* Data Portability, Right to Access and Freely Transfer Data to Other Service Providers (Art. 18)
* Right to Correction (Art. 16)
•
The Right to “Be Forgotten” (Art. 17)
* The right to know if your information is subject to abuse
* Service providers; Only deal with one supervisor for the entire area (Art. 55-62)
* “Data protection by design and by default” (Art. 25)
</th> </tr> </table>
**Table 4: GDPR - Prominent Rights**
## 3.3. Participation in the Pilot on Open Research Data
VICINITY participates in the Pilot on Open Research Data 2 launched by the
European Commission along with the Horizon2020 programme. The consortium
believes firmly in the concepts of open science, and the large potential
benefits the European innovation and economy can draw from allowing reusing
data at a larger scale. Therefore, all data produced by the project may be
published with open access – though this objective will need to be balanced
with the other principles and sensitivities described below.
## 3.4. Production data
The consortium is aware that a number of privacy and data protection issues
could be raised by the activities (use case demonstration and evaluation in
WP7 and WP8) to be performed in the scope of the project. The project involves
the carrying out of data collection in all pilot applications on the virtual
neighbourhood. For this reason, human participants were involved in certain
aspects of the system development by contributing real life data. During the
development life cycle process, it was necessary to operate on datasets. None
of the datasets were based on production data, a few were generated
(synthetic), but started out from a test environment and did not contain any
personal data that will be used for future reference.
**Figure 5: The VICINITY architecture is decentralised by design**
The VICINITY architecture is decentralised by design (Figure 5). Production
data were planned being used for testing purposes, but deemed unnecessary as
relevant datasets were created for the project. Certain functionality like the
discovery function and the related search criteria, raise the need for proper
implementation of Things Ecosystem Description (TED) – which describes IoT
assets that exists in the same environment.
The public have access to the VICINITY ontology alongside the VICINITY
discovery function at the conclusion of the project. However, all data
generated through the test phase and development process will be removed.
<table>
<tr>
<th>
**BEST PRACTICE FOR PRODUCTION DATA ADOPTED BY VICINITY**
The consortium will follow what is considered best practice for handling both
copies of production data and live data.
* **Data Obfuscation and security safeguards**
Use obfuscation methods to remove/protect data or reduce the risk of personal
information being harvested on data breach, and encrypt data where
appropriate.
* **Data minimization**
Minimize the size of datasets and the number of fields used.
* **Physical/environmental protection and access control**
Restrict and secure the environment where the data is used and stored and
limit the ability to remove live data in either physical or electronic format
from the
environment. Also limit access to the data to authorized users with business
needs and who have received appropriate data protection training.
* **Retention limits and data removal**
Limit the time period for use of the data and dispose of live data at end of
use period. Destroy physical and electronic live data used for training,
testing, or research at the conclusion of the project.
* **Use Limits**
Limit through controls and education the likelihood that live data, whose
integrity is not reliable, is re-introduced into production systems or
transferred to others beyond its intended purpose.
* **Watermarking**
Include warning information on live data where possible to ensure users do not
assume it is dummy data. This applies to all pilot sites where time critical
actions have to be taken, and where forecast analysis needs to be based on
accurate data.
* **Legal Controls**
Implement Confidentiality and Non-Disclosure Agreements if applicable. This
applies to all operators responsible for living labs that address eHealth and
assisted living.
* **Responsibility for accountability, training and awareness**
Ensure that identified personnel (by role) are assigned responsibility for
compliance with any conditions of the approval for the use of live data. The
personnel responsible for the technical description of the dataset will also
serve as contact for the use of live data. This also applies to providing
safety and training sessions for all persons having access to live data. The
partners responsible for pilot sites handling real time data from living labs
will prepare information that is to be handed out to relevant stakeholders.
</th> </tr> </table>
**Table 5: Best practice for use of production data**
How these best practice principles are being implemented, are described in
more detail in section 3.5: Ethics and security and 3.9: Data sharing.
## 3.5. Ethics and security
The consortium is aware that a number of privacy and data protection issues
could be raised by the activities (use case demonstration and evaluation in
WP7 and WP8) to be performed in the scope of the project. The project involves
the carrying out of data collection in all pilot applications on the virtual
neighbourhood. For this reason, human participants became involved in certain
aspects of the project and data that was collected. This has been done in full
compliance with any European and national legislation and directives relevant
to the country where the data collections took place (INTERNATIONAL/EUROPEAN):
* The Universal Declaration of Human Rights and the Convention 108 for the Protection of Individuals with Regard to Automatic Processing of Personal Data and
* Directive 95/46/EC & Directive 2002/58/EC of the European parliament regarding issues with privacy and protection of personal data and the free movement of such data.
<table>
<tr>
<th>
**Data Protection Impact Assessment (DPIA):**
The assessment shall include at least
* a systematic description of the planned treatment activities and the purposes of the treatment, including, where relevant, the legitimate interest pursued by the treatment officer;
* an assessment of whether the treatment activities are necessary and proportionate. for the purposes,
* an assessment of the risks to the data subjects' rights and freedoms referred to in paragraph 1; and
* the planned measures to manage the risks, including guarantees, security measures and mechanisms for safeguarding personal data and demonstrating that this Regulation be respected, taking into account the rights and legitimate interests of the data subject and other persons concerned.
</th> </tr> </table>
**Table 6: Data Protection Impact Assessment (DPIA)**
In addition to this, the project further ensures that the fundamental human
rights and privacy needs of participants have been met whilst they take part
in the project. A dedicated section for providing ethical and privacy
guidelines for the execution of the Industrial Trials is presented in the
Evaluation Plans. In order to protect the privacy rights of participants, a
number of best practice principles were prepared. These include:
* No data will be collected without the explicit informed consent of the individuals under observation. This involves being open with participants about what they are involving themselves in and ensuring that they have agreed fully to the procedures/research being undertaken by giving their explicit consent.
* The owners of personal data are to be granted the right of inspection and the right to be removed from the registers.
* No data collected will be sold or used for any purposes other than the current project;
* A data minimisation policy will be adopted at all levels of the project and will be supervised by each Industrial Pilot Demonstration responsible. This will ensure that no data which is not strictly necessary to the completion of the current study will be collected;
* During the development life cycle process, it will be necessary to operate on datasets. Some of the datasets may be based on production data, while others may be generated (synthetic). These data will be removed by the end of the project.
* Any shadow (ancillary) personal data obtained during the course of the research will be immediately cancelled. However, the plan is to minimize this kind of ancillary data as much as possible. Special attention will also be paid to complying with the Council of Europe’s Recommendation R(87)15 on the processing of personal data for police purposes, Art. 2 :
_“The collection of data on individuals solely on the basis that they have a
particular racial origin, particular religious convictions, sexual behaviour
or political opinions or belong to particular movements or organisations which
are not proscribed by law should be prohibited. The collection of data
concerning these factors may only be carried out if absolutely necessary for
the purposes of a particular inquiry.”_
* Compensation – if and when provided – will correspond to a simple reimbursement for working hours lost as a result of participating in the study; special attention will be paid to avoid any form of unfair inducement;
* If employees of partner organizations, are to be recruited, specific measures will be in place in order to protect them from a breach of privacy/confidentiality and any potential discrimination; In particular their names will not be made public and their participation will not be communicated to their managers.
* Data should be pseudomised and anonymized to allow privacy to be upheld even if an attacker gains access to the system.
* Furthermore, if data has been compromised or tampering is detected, the involved parties are to be notified immediately in order to reduce risk of misuse of data gathered for research purposes.
The same concern addressed here also applies to open calls (see section 3.7:
Open Call).
These issues are be exemplified when for instance health related data is
gathered as part of service were information is exchanged within a 3rd party
ecosystem. Why are these data gathered? How are the data processed? Where are
data temporarily stored? How are aggregated data accessed? When are data
further exchanged with other actors? VICINITY allow for the interoperability
to take place, but the architecture serves as a facilitator – it cannot
determine what happens with the data afterward. Even more so if the data are
exchanged or processed within other partners ecosystems? Responsibilities
cannot be outsourced. Personal responsibilities and approach used in
protecting the privacy rights must be clarified.
<table>
<tr>
<th>
**10 National Security Agency (NSM** 3 **) Security Requirements:**
1. An established information security and certification management system in accordance with international standards, such as ISO / IEC 27001: 2017
2. Insight into the security architecture used to deliver the service.
3. Development of security in service production and at the supplier, in line with developments in technology and the threat picture over time.
4. An overview of who should have access to the company's information, where and how it should be processed and stored, and the degree of mechanisms for segregation from other customers.
5. Access management which includes encryption, activity logging, physical and logical security.
6. Security monitoring suitable for detecting incidents and actions in line with the business threat image and relevant threat actors.
7. Procedures for incident management, nonconformity and safety reporting.
8. Emergency and contingency plans to harmonize with the company's own plans.
9. That the use of subcontractors and their use of subcontractors must be approved before implementation.
10. What activities are to be performed upon termination of the contract, including the return / relocation / deletion of the company's information.
</th> </tr> </table>
**Table 7: 10 Security Requirements**
## 3.6. The VICINITY Data Management Portal
VICINITY developed a data management portal as part of the project. This
portal provides a description of the dataset along with a link to a download
section in the case the datasets have been made publicly available:
* The portal will be updated each time a new dataset has been collected and **is ready for public distribution** .
* The portal will however not contain any datasets that should **not become publicly available** .
The initial version of the portal became available during the 2nd year of the
project, in parallel to the establishment of the first versions of project
datasets that can be made publicly available. The VICINITY data management
portal will enable project partners to manage and distribute their public
datasets through a common infrastructure as described in Table 8.
<table>
<tr>
<th>
**Datasets for:**
</th>
<th>
**Datasets (continued)**
</th>
<th>
**Administrative tools**
</th> </tr>
<tr>
<td>
each IoT unit
</td>
<td>
Datasets from pilots
(see section 4.2 for examples)
</td>
<td>
List of sensor / grouping
</td> </tr>
<tr>
<td>
personal information
</td>
<td>
groups of devices
</td>
<td>
List of actions / sequences
</td> </tr>
<tr>
<td>
energy related domains
</td>
<td>
each health device
</td>
<td>
List of users
</td> </tr>
<tr>
<td>
• each interface (energy)
</td>
<td>
node/object
</td>
<td>
List of contacts
</td> </tr>
<tr>
<td>
• each measuring device (energy)
</td>
<td>
messaging
</td>
<td>
Balancing loads
</td> </tr>
<tr>
<td>
• each routing device (energy)
</td>
<td>
sequences / actions
(combination tokens / nodes)
</td>
<td>
Booking
</td> </tr>
<tr>
<td>
mobility related domains
</td>
<td>
biometric (fingerprint, retina)
</td>
<td>
Messaging
</td> </tr>
<tr>
<td>
• parking data (mobility)
</td>
<td>
camera
</td>
<td>
Criteria
</td> </tr>
<tr>
<td>
• booking (mobility)
</td>
<td>
access
</td>
<td>
Priorities
</td> </tr>
<tr>
<td>
• areas (mobility)
_(list continues in next coloumn)_
</td>
<td>
each smart home device (temperature, smoke, motion, sound)
</td>
<td>
Evaluation / feedback
</td> </tr> </table>
**Table 8: datasets stored in the VICINITY management portal**
## 3.7. Open Calls
The Open Call process of the VICINITY project involved third parties. System
integrators (Figure 6) will be one of target groups for the calls. These were
presented for opportunities to integrate IoT infrastructures based on the
VICINITY framework as well as implementation/integration of ValueAdded
Services. The calls adhered to the principles which govern Commission calls,
and were referred to in all papers. These principles all include
confidentiality: all proposals and related data, knowledge and documents are
treated in confidence.
**Figure 6: Involving 3rd parties through open calls will provide**
**VICINITY with valuable experience, and evolve interoperability**
The Project Coordinator presented a legal contract with the third parties that
are granted open calls and specified control procedures to be made compliant
with the Grant Agreement and the Consortium Agreement. This was done in order
to assure that their contributions are in line with the agreed upon work plan;
that the third party allows the Commission and the Court of Auditors to
exercise their power of control on documents and information stored on
electronic media or on the final recipient's premises.
Proposals for open calls and the deliverables that come as a result included
sections that describe how the data management principles have been
implemented. The papers followed the outlines that are presented in the legal
contract and adhere to GDPR. This also applies to sharing ideas and
intellectual properties. Furthermore, the deliverables presented how the
chosen architecture and methodologies will be handled by the stakeholders,
integrators and SME’s. That being said, very few of the Open Call winners
gathered data that would be affected by privacy concerns.
According VICINITY concept the participants can decide with whom they wish to
cooperate and to which extent. Participants are held responsible for partners
they team up with follow the same guidelines as the main project and the open
call project.
## 3.8. Standards and metadata
The data that were generated and tested through different test automation
technologies, e.g. TDL (Test description language), TTCN-3 (Test and Test
Control Notation), UTP (UML Testing Profile). The profile should mimic the
data communicated from IoT units following the oneM2M specifications. The
Systems Modeling Language 4 (SysML) is used for the collection, analysis and
processing of requirements as well as for the specification message exchanges
and overviews of architecture and behaviour specifications (Figure 7).
**Figure 7: Example of SysML model of Virtual Oslo Science City**
The project intends to share the datasets in an internally accessible
disciplinary repository using descriptive metadata as required/provided by
that repository. Additional metadata to example test datasets will be offered
within separate XML-files.
They have also been made available in XML and JSON format. Keywords will be
added as notations in SysML and modelled on the specifications defined by
oneM2M. The content will be similar to relevant data from compatible IoT
devices and network protocols. No network protocols have been defined yet, but
several have been evaluated. Files and folders will be versioned and
structured by using a name convention consisting of project name, dataset
name, date, version and ID.
## 3.9. Data sharing
The project prepared the API for internal testing through the VICINITY open
gateway.
The VICINITY open gateway is defined as Interoperability as a Service. In
other words - it is a cloudbased service that assumes the data has already
been gathered and transferred to the software running on the service layer.
These data were made available for developers and researchers in a controlled
environment, where login credentials are used to get access to the data in XML
and JSONformat (Figure 8).
**Figure 8: Data will only be provided partners with proper login
credentials**
The project focus on developing a framework that allows for a scalable and
futureproof platform upon which it can invest and develop IoT applications,
without fear of vendor lock-in or needing to commit to one connectivity
technology.
The researchers must therefore be committed to the requirements, architecture,
application programming interface (API) specifications, security solutions and
mapping to common industry protocols such as CoAP, MQTT and HTTP. Further
analysis will be performed using freely available open source software tools.
The data will also be made available as separate files.
<table>
<tr>
<th>
**Five Key Concepts (Art. 4):**
❶ **Personal Information:**
"any information about an identified or identifiable natural person (" the
data subject "); an identifiable natural person is a person who can be
directly or indirectly identified, in particular by means of an identifier,
e.g. a name, identification number, location information, an online identifier
or one or more elements specific to the physical, physiological, genetic,
psychological, economic, cultural or social identity of the said person
❷ **Processing:**
"any operation or sequence of operations done with personal data, whether
automated or not" e.g. collection, registration, organization, structuring,
storage, adaptation or modification, retrieval, consultation, use, delivery by
transmission, distribution or any other means of making available, assembling
or merging, limiting, deleting or destroying
❸ **The controller:**
"determines the purpose of the processing of personal data and the means to be
used"
</th> </tr>
<tr>
<td>
❹
</td>
<td>
**The data processor:**
"processes personal data on behalf of the controller"
</td> </tr>
<tr>
<td>
❺
</td>
<td>
**Consent:**
"any voluntary, specific, informed and unambiguous expression of the data
subject where the person in a declaration or clear affirmation gives his
consent to the processing of personal data relating to the person"
</td> </tr> </table>
**Table 9: Five Key Concepts in GDPR**
The goal is to ultimately support the Europe 2020 strategy 5 by offering the
open data portal. The Digital Agenda proposes to better exploit the potential
of Information and Communication Technologies (ICTs) in order to foster
innovation, economic growth and progress. Thus, VICINITY will support EUs
efforts in exploiting the potential offered by using ICT in areas like climate
change, managing ageing population, and intelligent transport system to
mention a few examples.
## 3.10. Archiving and preservation (including storage and backup)
As specified by the "rules of good scientific practice" the project aim to
preserve data for at least ten years. Approximated end volume of example test
dataset is currently 10 GB, but this may be subject to change as the scope of
the project may change. The VICINITY architecture itself does neither generate
nor store any information. Data in this context refers to Value-Added
Services, code and documentation produced as part of the project.
Associated costs for dataset preparation for archiving is covered by the
project itself, while long term preservation will be provided and associated
costs covered by a selected disciplinary repository. During the project data
will be stored on the VICINITY web cloud as well as being replicated to a
separate external server.
The source code and descriptions are available from the project website and
GitHub, but no actual data is stored at these locations:
* _https://vicinity2020.eu/vicinity/public-deliverables_
* _https://github.com/vicinityh2020_
# Datasets
Information that is collected or aggregated from a data source needs to adhere
to a common format. This format is used to organise the data for further
exchange, storage or manipulation. Datasets can be made up of several parts,
and in VICINITY is based on the definition found in W3 “The RDF Data Cube
Vocabulary 6 ”, section 5.1:
Some of the pilots were deployed in “living labs” with actual residents or
other human participants. Several of the activities carried out by the
project, depended on collecting some basic personal data (e.g. name,
background, contact details). VICINITY2020 avoided collecting this kind of
data. However when data was gathered the project protected the data in
accordance with “the EU's Data Protection Directive 95/46/EC 7 of the
European Parliament” and of “the Council of 24 th of October 1995 on the
protection of individuals with regard to the processing of personal data and
on the free movement of such data”. National and local legislations applicable
to the project will also be strictly applied (full list described in section
3.5: Ethics and security).
Data in a data set can be roughly described as belonging to one of the
following kinds:
* **Observations**
This is the actual data, the measured values. In a statistical table, the
observations would be the values in the table cells.
* **Organizational structure**
To locate an observation, spatial information or coordinates are necessary.
This applies also to other kind of information about origin and particular
information on the sensors that are relevant to the observations.
* **Structural metadata**
Metadata is necessary to interpret the observations. What is the unit of
measurement? Is it a normal value or a series break? Is the value measured or
estimated? These metadata are provided as attributes and can be attached to
individual observations, or to higher levels.
* **Reference metadata**
This is metadata that describes the dataset as a whole, such as categorization
of the dataset, its publisher, and a SPARQL endpoint where it can be accessed.
At the time of this deliverable was written, datasets that so far have been
provided by partners and open call winners are presented in “Table 10:
Datasets currently provided by partners” and “Table 11: Datasets currently
provided by Open Call winners”. **Fehler! Verweisquelle konnte nicht gefunden
werden.**
<table>
<tr>
<th>
**Partner**
</th>
<th>
**Sector**
</th>
<th>
**Role**
</th>
<th>
**GDPR**
</th> </tr>
<tr>
<td>
**Aalborg University (AAU)**
DS.AAU.01.GRID_Status
DS.AAU.02. Gorenje_Smart_Appliances_Sensor
DS.AAU.03. PlacePod_Parking_Sensor
DS.AAU.04. Tinymesh_Door_Sensor
</td>
<td>
Smart grid
</td>
<td>
Partner
</td>
<td>
No
</td> </tr>
<tr>
<td>
**Enercoutim (ENERC)**
DS.ENERC.01.METEO_Station
DS.ENERC.02.BUILDING_Status
DS.ENERC.03.GRID_Status
</td>
<td>
Smart energy
</td>
<td>
Partner
</td>
<td>
No
</td> </tr>
<tr>
<td>
**GNOMON Informatics SA (GNOMON)**
DS.GNOMON.01.Pressure_sensor
DS.GNOMON.02.Weight_sensor
DS.GNOMON.03.Fall_sensor
DS.GNOMON.04.Wearable_Fitness_Tracker_Sensor
DS.GNOMON.05.Beacon_Sensor
DS.GNOMON_CERTH.06.Gorenje_Smart_Appliances_Se nsor
</td>
<td>
eHealth
</td>
<td>
Partner
</td>
<td>
Yes
</td> </tr>
<tr>
<td>
**Centre for Research and Technology Hellas (CERTH)**
DS.CERTH.01.Door_Sensor
DS.CERTH.02.Motion_Sensor
DS.CERTH.03.Pressure_Mat
</td>
<td>
eHealth
</td>
<td>
Partner
</td>
<td>
Yes
</td> </tr>
<tr>
<td>
**Hafenstrom AS (HITS)**
DS.HITS.01.Parkingsensor
DS.HITS.02.SmartLight
DS.HITS.03.LaptopTeststation
DS.HITS.04.Sensio_sensors_temperature_motion_lock
DS.HITS.05.Gorenje_Smart_Appliances_Sensor
</td>
<td>
Smart parking
</td>
<td>
Partner
</td>
<td>
Yes
</td> </tr>
<tr>
<td>
**Tiny Mesh AS (TINYM)**
DS. VITIR.01.Door_Sensor
DS. VITIR.02.CO2 Sensor
</td>
<td>
Smart building
</td>
<td>
Partner
</td>
<td>
No
</td> </tr> </table>
**Table 10: Datasets currently provided by partners**
<table>
<tr>
<th>
**Open Call**
</th>
<th>
**Sector**
</th>
<th>
**Role**
</th>
<th>
**GDPR**
</th> </tr>
<tr>
<td>
**PilotThings**
DS. PilotThings.01.Building
DS. PilotThings.02.OPA_Grid
</td>
<td>
Smart building
</td>
<td>
Open
Call
</td>
<td>
Yes
</td> </tr>
<tr>
<td>
**WearHealth**
DS. WearHealth.01.SmartShirt
</td>
<td>
Smart building
</td>
<td>
Open
Call
</td>
<td>
Yes
</td> </tr>
<tr>
<td>
**SaMMY**
DS. SaMMY.Patras.Air_Temperature
DS. SaMMY.Patras.Humidity
DS. SaMMY.Patras.Water_Temperature
DS. SaMMY.Patras.Water_pH
DS. SaMMY.Patras.Water_ORP
DS. SaMMY.Patras.BerthSpace(5-15)_Occupancy
</td>
<td>
Marina
</td>
<td>
Open Call
</td>
<td>
No
</td> </tr>
<tr>
<td>
**Thinkinside Srl**
DS. ThinkInside.01.INCANT
</td>
<td>
Positioning
</td>
<td>
Open
Call
</td>
<td>
No
</td> </tr>
<tr>
<td>
**Sensinov**
DS.Sensinov.01.F2IS-VAS
</td>
<td>
Smart building
</td>
<td>
Open Call
</td>
<td>
No
</td> </tr> </table>
**Table 11: Datasets currently provided by Open Call winners**
For each dataset in VICINITY the format in “Table 12: Format of dataset
description” were specified:
<table>
<tr>
<th>
**DS. PARTICIPANTName.##.Logical_sensorname**
</th> </tr>
<tr>
<td>
**Data Identification**
</td>
<td>
</td> </tr>
<tr>
<td>
Dataset description
</td>
<td>
_Where are the sensor(s) installed? What are they monitoring/registering? What
is the dataset comprised of? Will it contain future sub-datasets?_
</td> </tr>
<tr>
<td>
Source (e.g. which device?)
</td>
<td>
_How will the dataset be collected? What kind of sensor is being used?_
</td> </tr>
<tr>
<td>
**Partners services and responsibilities**
</td> </tr>
<tr>
<td>
Partner owner of the device
</td>
<td>
_What is the name of the owner of the device?_
</td> </tr>
<tr>
<td>
Partner in charge of the data collection (if different)
</td>
<td>
_What is the name of the partner in charge of the device? Are there several
partners that are cooperating? What are their names?_
</td> </tr>
<tr>
<td>
Partner in charge of the data
analysis (if different)
</td>
<td>
_The name of the partner._
</td> </tr>
<tr>
<td>
Partner in charge of the data storage (if different)
</td>
<td>
_The name of the partner._
</td> </tr>
<tr>
<td>
WPs and tasks
</td>
<td>
_The data are going to be collected within activities of WPxx and WPxx._
</td> </tr>
<tr>
<td>
**Standards**
</td> </tr>
<tr>
<td>
Info about metadata (Production and storage dates, places) and documentation?
</td>
<td>
_What is the status with the metadata so far? Has it been defined? What is the
content of the metadata (e.g. datatypes like images portraying an action,
textual messages, sequences, timestamps etc.)_
</td> </tr>
<tr>
<td>
Standards, Format, Estimated volume of data
</td>
<td>
_Has the data format been decided on yet? What will it look like?_
</td> </tr>
<tr>
<td>
**Data exploitation and sharing**
</td> </tr>
<tr>
<td>
Data exploitation (purpose/use of the data analysis)
</td>
<td>
_Example text:_
_Production process recognition and help during the different production
phases, avoiding mistakes_
</td> </tr>
<tr>
<td>
Data access policy / Dissemination level
(Confidential, only for members of the Consortium and the
Commission Services) / Public
</td>
<td>
_Example text:_
_The full dataset will be confidential and only the members of the consortium
will have access on it. Furthermore, if the dataset or specific portions of it
(e.g. metadata, statistics, etc.) are decided to become of widely open access,
a data management portal will be created that should provide a description of
the dataset and link to a download section. Of course, these data will be
anonymized, so as not to have any potential ethical issues with their
publication and dissemination_
</td> </tr>
<tr>
<td>
Data sharing, re-use and
distribution (How?)
</td>
<td>
_Have the data sharing policies been decided yet? What requirements exist for
sharing data? How will the data be shared? Who will decide what to be shared?_
</td> </tr>
<tr>
<td>
Embargo periods (if any)
</td>
<td>
_-_
</td> </tr>
<tr>
<td>
**Archiving and preservation (including storage and backup)**
</td> </tr>
<tr>
<td>
Data storage (including backup):
where? For how long?
</td>
<td>
_Who will own the information that has been collected? How will it adhere to
partner policies? What kind of limitation is put on the archive?_
</td> </tr> </table>
**Table 12: Format of dataset description**
<table>
<tr>
<th>
**Current legal requirements as described in General Data Protection
Regulation OJ L 119, 04.05.2016:**
* Information systems and security measures must be documented (section 13 and pof. section 2-16)
* Documented internal control measures (section 14)
* Description of security objectives and strategy (section 2-3)
* Regular review of the use of IT systems, which must be documented (section 2-3)
* Establish criteria for acceptable risk (section 2-4)
* Conduct and document risk assessments (section 2-4)
* Implement and document security audits (section 2-5)
* Report to the Data Inspectorate for certain security breaches (section 2-6)
* Establish responsibility and authority structures for the use of the IT system (section 2-7)
* Configure IT systems to achieve satisfactory security (section 2-7)
* Establish access control and authorizations (section 2-8)
* Educate and train personnel in the proper use of IT systems (section 28)
* Physical measures against unauthorized access to IT systems (section 2-10)
* Document measures against authorized access to confidential
information (section 2-11)
* Encryption solutions when transferring confidential information (section 2-11)
* Ensure necessary accessibility of information (section 2-12)
* Proper backup procedures (section 2-12)
* Security measures to prevent unauthorized use and measures to detect such attempts (section 2-14)
* Measures against unauthorized changes to information (section 2- 13)
* Measures against malicious software (section 2-13)
* Proper use of cookies / cookies (Electronic Communications Act., section 2-7b)
For interactive navigation, please visit _https://gdpr-info.eu/_
_Table is based on material by Kjell Steffner, attorney-at-law, Hill & Co
Advokat _
</th> </tr> </table>
**Table 13: Current legal requirements in GDPR**
## 4.1. Description of methods for dataset description
Some example test dataset was generated by research teams from the
participants in the project as XML-files. Certain datasets were based on
semantic analysis of data from test sensors and applied to an ontology and
made available in XML and JSON format (Figure 9).
The collected dataset encompassed different methodological approaches and IoT
standards defined by the global standard initiative oneM2M. The data were
processed in different test environments like TDD (Test Driven Development),
ATDD (Acceptance Test Driven Development), PBT (Property Based Testing), BDD
(Behaviour Driven Development). The project focused on using model-based test
automation in processes with short release cycles.
**Figure 9: Datasets will be prepared and provided in XML and JSON format**
Apart from the research teams, these datasets will be useful for other
research groups, Standard Development Organisations (SDO) and technical
integrators with within the area of Internet of Things (IoT).
All datasets were shared between the participants during the lifecycle of the
project. Feedback from other participants and test implementations would
decide the dataset should be made publicly available, but there were no
requests made and only the eHealth and smart parking pilots had data that
could be of relevant. Datasets supported the framework defined by the VICINITY
ontology can be made public and presented in open access publications.
The VICINITY partners can use a variety of methods for exploitation and
dissemination of the data including:
* Using them in further research activities (outside the action)
* Developing, creating or marketing a product or process
* Creating and providing a service, or
* Using the data in standardisation activities
Restrictions:
1. All national reports (which include data and information on the relevant topic) will be available to the public through the HERON web-site or a repository or any other option that the consortium decides and after verification by the partners so as to ensure their quality and credibility.
2. After month 18 so that partners have the time to produce papers; 3) Open access to the research data itself is not applicable.
## 4.2. Datasets for smart grid from Aalborg University (AAU)
AAU will mainly deal with control design, energy management systems
implementation and Information and Communication Technology (ICT) integration
in small scale energy systems. Intensive and iterative Lab tests have been
conducted in AAU by using the hardware in the loop solution and experimental
platform to 1. restrict some features of VICINITY Gateway API to define a
stability and proper operating range for VICINITY platform; 2. ensure that
local infrastructure, to be deployed at pilot sites, operates with the
VICINITY platform as expected. The lessons learned from the Lab trial will be
forwarded to WP3 and WP4 for performance/function improvements, and to WP7 for
helping a correct deployment of VICINITY platform at the pilot sites from a
technical perspective, as well as to WP8 for helping a technical evaluation
approach design.
<table>
<tr>
<th>
**DS.AAU.01.GRID_Status**
</th> </tr>
<tr>
<td>
**Data Identification**
</td> </tr>
<tr>
<td>
Dataset description
</td>
<td>
_This dataset comprised different parameters characterising the electrical
grid from the generation to the distribution sections. The cost of the
electricity will also be considered in this dataset, so as to have full
information that enables micro-trading actions._
</td> </tr>
<tr>
<td>
Source (e.g. which device?)
</td>
<td>
_The source that feed this dataset is from the hardware in the loop simulation
and experimental platform and adapters based on Python 3._
</td> </tr>
<tr>
<td>
**Partners services and responsibilities**
</td> </tr>
<tr>
<td>
Partner owner of the device
</td>
<td>
_The devices will be the property of the test site owners, where the data
collection is going to be performed._
</td> </tr>
<tr>
<td>
Partner in charge of the data collection (if different)
</td>
<td>
_AAU_
</td> </tr>
<tr>
<td>
Partner in charge of the data
analysis (if different)
</td>
<td>
_AAU_
</td> </tr>
<tr>
<td>
Partner in charge of the data storage (if different)
</td>
<td>
_AAU_
</td> </tr>
<tr>
<td>
WPs and tasks
</td>
<td>
_The data are going to be collected within activities of WP6, WP7, and WP8._
</td> </tr>
<tr>
<td>
**Standards**
</td> </tr>
<tr>
<td>
Info about metadata (Production and storage dates, places) and documentation?
</td>
<td>
_The dataset will be accompanied with the respective documentation of its
contents. Indicative metadata may include device id, measurement date, device
owner, state of the monitored activity, etc._
</td> </tr>
<tr>
<td>
Standards, Format, Estimated volume of data
</td>
<td>
_The data are received in JSON format. Regarding the volume of data, it
depends on the motion/activity levels of the engaged devices._
</td> </tr>
<tr>
<td>
**Data exploitation and sharing**
</td> </tr>
<tr>
<td>
Data exploitation (purpose/use of the data analysis)
</td>
<td>
_Data exploitation is foreseen to be achieved through testing value-added
services, data analytics and statistical analysis._
</td> </tr>
<tr>
<td>
Data access policy / Dissemination level
(Confidential, only for members of the Consortium and the
Commission Services) / Public
</td>
<td>
_The full dataset of grid status deployed in AAU IoT-microgrid testing lab,
that is not sensitive, is accessible through a local experimental repository._
</td> </tr>
<tr>
<td>
Data sharing, re-use and
distribution (How?)
</td>
<td>
_The created dataset could be shared by using open APIs through the middleware
as well as a data management portal. Dataset from VICINITY could be used and
exploited anonymized from another European project. Dataset from sensors
deployed at seniors’ houses provide added value and be the base for other
research projects (e.g. statistical data). VICINITY could have an open portal
/ repository on its website, providing anonymized data’s information like
timestamp and description._
</td> </tr>
<tr>
<td>
Embargo periods (if any)
</td>
<td>
_None_
</td> </tr>
<tr>
<td>
**Archiving and preservation (including storage and backup)**
</td> </tr>
<tr>
<td>
Data storage (including backup):
where? For how long?
</td>
<td>
_The full dataset of grid status deployed in AAU IoT-microgrid testing lab,
that is not sensitive, is accessible through a local experimental repository.
Data exploitation is foreseen to be achieved through testing value-added
services, data analytics and statistical analysis._
</td> </tr> </table>
**Table 14: Dataset description of the AAU GRID status**
<table>
<tr>
<th>
**DS.AAU.02. Gorenje_Smart_Appliances_Sensor**
</th> </tr>
<tr>
<td>
**Data Identification**
</td> </tr>
<tr>
<td>
Dataset description
</td>
<td>
_The sensors related to Gorenje smart appliances are sensors embodied to a
Gorenje smart oven and a Gorenje smart fridge. The equipments are provided by
Gorenje partner and are deployed in AAU IoT-microgrid laboratory. The main
goal of the sensors is to automatically detect when a resident opens the
fridge or uses the oven in order to create behaviour profiles based on
relevant criteria (e.g. frequency of use, etc), produce notifications in case
of deviation from the normal standards of use and inform the call centre._
</td> </tr>
<tr>
<td>
Source (e.g. which device?)
</td>
<td>
_The data are collected by specific smart appliances (i.e. oven, fridge)
provided by the Gorenje partner and adjusted to VICINITY requirements._
</td> </tr>
<tr>
<td>
**Partners services and responsibilities**
</td> </tr>
<tr>
<td>
Partner owner of the device
</td>
<td>
_The devices will be the property of the test site owners, where the data
collection is going to be performed._
</td> </tr>
<tr>
<td>
Partner in charge of the data collection (if different)
</td>
<td>
_AAU, Gorenje_
</td> </tr>
<tr>
<td>
Partner in charge of the data
analysis (if different)
</td>
<td>
_AAU, Gorenje_
</td> </tr>
<tr>
<td>
Partner in charge of the data storage (if different)
</td>
<td>
_AAU, Gorenje_
</td> </tr>
<tr>
<td>
WPs and tasks
</td>
<td>
_The data are going to be collected within activities of WP6, WP7, and WP8._
</td> </tr>
<tr>
<td>
**Standards**
</td> </tr>
<tr>
<td>
Info about metadata (Production and storage dates, places) and documentation?
</td>
<td>
_The dataset will be accompanied with the respective documentation of its
contents. Indicative metadata may include device id, measurement date, device
owner, state of the monitored activity, etc._
</td> </tr>
<tr>
<td>
Standards, Format, Estimated volume of data
</td>
<td>
_The data are received in JSON format. Regarding the volume of data, it
depends on the motion/activity levels of the engaged devices._
</td> </tr>
<tr>
<td>
**Data exploitation and sharing**
</td> </tr>
<tr>
<td>
Data exploitation (purpose/use of the data analysis)
</td>
<td>
_Data exploitation is foreseen to be achieved through testing value-added
services, data analytics and statistical analysis._
</td> </tr>
<tr>
<td>
Data access policy / Dissemination level
(Confidential, only for members of the Consortium and the
Commission Services) / Public
</td>
<td>
_The full dataset of grid status deployed in AAU IoT-microgrid testing lab,
that is not sensitive, is accessible through a local experimental repository._
</td> </tr>
<tr>
<td>
Data sharing, re-use and
distribution (How?)
</td>
<td>
_The created dataset could be shared by using open APIs through the middleware
as well as a data management portal. Dataset from VICINITY could be used and
exploited anonymized from another European project. Dataset from sensors
deployed at seniors’ houses provide added value and be the base for other
research projects (e.g. statistical data). VICINITY could have an open
portal/repository on its website, providing anonymized data’s information like
timestamp and description._
</td> </tr>
<tr>
<td>
Embargo periods (if any)
</td>
<td>
_None_
</td> </tr>
<tr>
<td>
**Archiving and preservation (including storage and backup)**
</td> </tr>
<tr>
<td>
Data storage (including backup):
where? For how long?
</td>
<td>
_The full dataset of grid status deployed in AAU IoT-microgrid testing lab,
that is not sensitive, is accessible through a local experimental repository.
Data exploitation is foreseen to be achieved through testing value-added
services, data analytics and statistical analysis._
</td> </tr> </table>
**Table 15: Dataset description of the Gorenje smart appliances sensor in AAU
IoT-microgrid laboratory**
<table>
<tr>
<th>
**DS.AAU.03. PlacePod_Parking_Sensor**
</th> </tr>
<tr>
<td>
**Data Identification**
</td> </tr>
<tr>
<td>
Dataset description
</td>
<td>
_The PlacePod parking sensor deployed in AAU IoT-microgrid laboratory are also
installed in Tromsø pilot site. The main goal of the sensors is to
automatically detect when a car parking at the parking slot in order to create
behaviour profiles based on relevant criteria (e.g. vacant parking slot,
frequency of use, etc), produce notifications with the number of vacant
parking slots._
</td> </tr>
<tr>
<td>
Source (e.g. which device?)
</td>
<td>
_The source that feed this dataset is from the PlacePod parking sensors and an
adapter based on Python 3._
</td> </tr>
<tr>
<td>
**Partners services and responsibilities**
</td> </tr>
<tr>
<td>
Partner owner of the device
</td>
<td>
_The devices will be the property of the test site owners, where the data
collection is going to be performed._
</td> </tr>
<tr>
<td>
Partner in charge of the data collection (if different)
</td>
<td>
_AAU_
</td> </tr>
<tr>
<td>
Partner in charge of the data
analysis (if different)
</td>
<td>
_AAU_
</td> </tr>
<tr>
<td>
Partner in charge of the data storage (if different)
</td>
<td>
_AAU_
</td> </tr>
<tr>
<td>
WPs and tasks
</td>
<td>
_The data are going to be collected within activities of WP6, WP7, and WP8._
</td> </tr>
<tr>
<td>
**Standards**
</td> </tr>
<tr>
<td>
Info about metadata (Production and storage dates, places) and documentation?
</td>
<td>
_The dataset will be accompanied with the respective documentation of its
contents. Indicative metadata may include device id, measurement date, device
owner, state of the monitored activity, etc._
</td> </tr>
<tr>
<td>
Standards, Format, Estimated volume of data
</td>
<td>
_The data are received in JSON format. Regarding the volume of data, it
depends on the motion/activity levels of the engaged devices._
</td> </tr>
<tr>
<td>
**Data exploitation and sharing**
</td> </tr>
<tr>
<td>
Data exploitation (purpose/use of the data analysis)
</td>
<td>
_Data exploitation is foreseen to be achieved through testing value-added
services, data analytics and statistical analysis._
</td> </tr>
<tr>
<td>
Data access policy / Dissemination level
(Confidential, only for members of the Consortium and the
Commission Services) / Public
</td>
<td>
_The full dataset of grid status deployed in AAU IoT-microgrid testing lab,
that is not sensitive, is accessible through a local experimental repository._
</td> </tr>
<tr>
<td>
Data sharing, re-use and
distribution (How?)
</td>
<td>
_The created dataset could be shared by using open APIs through the middleware
as well as a data management portal. Dataset from VICINITY could be used and
exploited anonymized from another European project. Dataset from sensors
deployed at seniors’ houses provide added value and be the base for other
research projects (e.g. statistical data). VICINITY could have an open
portal/repository on its website, providing anonymized data’s information like
timestamp and description._
</td> </tr>
<tr>
<td>
Embargo periods (if any)
</td>
<td>
_None_
</td> </tr>
<tr>
<td>
**Archiving and preservation (including storage and backup)**
</td> </tr>
<tr>
<td>
Data storage (including backup):
where? For how long?
</td>
<td>
_The full dataset of grid status deployed in AAU IoT-microgrid testing lab,
that is not sensitive, is accessible through a local experimental repository.
Data exploitation is foreseen to be achieved through testing value-added
services, data analytics and statistical analysis._
</td> </tr> </table>
**Table 16: Dataset description of the PlacePod smart parking sensor in AAU
IoT-microgrid laboratory**
<table>
<tr>
<th>
**DS.AAU.04. Tinymesh_Door_Sensor**
</th> </tr>
<tr>
<td>
**Data Identification**
</td> </tr>
<tr>
<td>
Dataset description
</td>
<td>
_The Tinymesh door sensor deployed in AAU IoT-microgrid laboratory is provided
by Tinymesh partner. The main goal of the sensors is to automatically detect
when a door status is changed in order to create behaviour profiles based on
relevant criteria (e.g. frequency of use, etc), produce notifications with the
number of usages and cleaning requirements._
</td> </tr>
<tr>
<td>
Source (e.g. which device?)
</td>
<td>
_The data are collected by the Tinymesh door sensor provided by the Tinymesh
partner and adjusted to VICINITY requirements._
</td> </tr>
<tr>
<td>
**Partners services and responsibilities**
</td> </tr>
<tr>
<td>
Partner owner of the device
</td>
<td>
_The devices will be the property of the test site owners, where the data
collection is going to be performed._
</td> </tr>
<tr>
<td>
Partner in charge of the data collection (if different)
</td>
<td>
_AAU, Tinymesh_
</td> </tr>
<tr>
<td>
Partner in charge of the data
analysis (if different)
</td>
<td>
_AAU, Tinymesh_
</td> </tr>
<tr>
<td>
Partner in charge of the data storage (if different)
</td>
<td>
_AAU, Tinymesh_
</td> </tr>
<tr>
<td>
WPs and tasks
</td>
<td>
_The data are going to be collected within activities of WP6, WP7, and WP8._
</td> </tr>
<tr>
<td>
**Standards**
</td> </tr>
<tr>
<td>
Info about metadata (Production and storage dates, places) and documentation?
</td>
<td>
_The dataset will be accompanied with the respective documentation of its
contents. Indicative metadata may include device id, measurement date, device
owner, state of the monitored activity, etc._
</td> </tr>
<tr>
<td>
Standards, Format, Estimated volume of data
</td>
<td>
_The data are received in JSON format. Regarding the volume of data, it
depends on the motion/activity levels of the engaged devices._
</td> </tr>
<tr>
<td>
**Data exploitation and sharing**
</td> </tr>
<tr>
<td>
Data exploitation (purpose/use of the data analysis)
</td>
<td>
_Data exploitation is foreseen to be achieved through testing value-added
services, data analytics and statistical analysis._
</td> </tr>
<tr>
<td>
Data access policy / Dissemination level
(Confidential, only for members of the Consortium and the
Commission Services) / Public
</td>
<td>
_The full dataset of grid status deployed in AAU IoT-microgrid testing lab,
that is not sensitive, is accessible through a local experimental repository._
</td> </tr>
<tr>
<td>
Data sharing, re-use and
distribution (How?)
</td>
<td>
_The created dataset could be shared by using open APIs through the middleware
as well as a data management portal. Dataset from VICINITY could be used and
exploited anonymized from another European project. Dataset from sensors
deployed at seniors’ houses provide added value and be the base for other
research projects (e.g. statistical data). VICINITY could have an open portal
/ repository on its website, providing anonymized data’s information like
timestamp and description._
</td> </tr>
<tr>
<td>
Embargo periods (if any)
</td>
<td>
_None_
</td> </tr>
<tr>
<td>
**Archiving and preservation (including storage and backup)**
</td> </tr>
<tr>
<td>
Data storage (including backup):
where? For how long?
</td>
<td>
_The full dataset of grid status deployed in AAU IoT-microgrid testing lab,
that is not sensitive, is accessible through a local experimental repository.
Data exploitation is foreseen to be achieved through testing value-added
services, data analytics and statistical analysis._
</td> </tr> </table>
**Table 17: Dataset description of the Tinymesh door sensor in AAU IoT-
microgrid laboratory**
## 4.3. Datasets for smart energy from Enercoutim (ENERC)
ENERC contributed by providing the facilities and the experience in
implementing solar production integrated into municipality smart city efforts.
To this end, ENERC actively participated in the deployment, management and
evaluation of the “Smart Energy Microgrid Neighbourhood” Use Case. Its
contribution focused on the energy resource potential demand studies and
economic sustainability. ENERCS expertise allowed ICT integration with smart
city management focused on better serving its citizens.
The main aim of this project is the demonstration of a Solar Platform which
provides a set of shared infrastructures and reduces the total cost per MW as
well as improves the environmental impact compared to the stand-alone
implementation of these projects. As main responsibilities, ENERC will be in
charge of strategic technology planning and integration coordination,
designing potential models for municipal energy management, as well as
identifying the optimal ownership structure of the microgrid system with a
focus on delivering maximum social and economic benefit to the local
community.
<table>
<tr>
<th>
**DS.ENERC.01.METEO_Station**
</th> </tr>
<tr>
<td>
**Data Identification**
</td> </tr>
<tr>
<td>
Dataset description
</td>
<td>
_The weather conditions will influence the energy production, so it becomes
critical to understand the current and foreseen scenarios. It is fundamental
to constantly carry out different measures with the meteo station equipment of
the parameters that can influence both energy production and consumption over
time._
</td> </tr>
<tr>
<td>
Source (e.g. which device?)
</td>
<td>
_The sensors that feed this dataset are; temperature, humidity, wind speed and
wind direction, barometer, precipitation measurement and sun tracker._
</td> </tr>
<tr>
<td>
**Partners services and responsibilities**
</td> </tr>
<tr>
<td>
Partner owner of the device
</td>
<td>
_The devices will be the property of the test site owners, where the data
collection is going to be performed._
</td> </tr>
<tr>
<td>
Partner in charge of the data collection (if different)
</td>
<td>
_ENERC_
</td> </tr>
<tr>
<td>
Partner in charge of the data
analysis (if different)
</td>
<td>
_ENERC_
</td> </tr>
<tr>
<td>
Partner in charge of the data storage (if different)
</td>
<td>
_ENERC_
</td> </tr>
<tr>
<td>
WPs and tasks
</td>
<td>
_The data are going to be collected within activities of WP7 and WP8._
</td> </tr>
<tr>
<td>
**Standards**
</td> </tr>
<tr>
<td>
Info about metadata (Production and storage dates, places) and documentation?
</td>
<td>
_The dataset will be accompanied with the respective documentation of its
contents. Indicative metadata may include device id, measurement date, device
owner, state of the monitored activity, etc._
</td> </tr>
<tr>
<td>
Standards, Format, Estimated volume of data
</td>
<td>
_The data are received in JSON format. Regarding the volume of data, it
depends on the motion/activity levels of the engaged devices. However, it is
estimated to be 4 KB/transmission._
</td> </tr>
<tr>
<td>
**Data exploitation and sharing**
</td> </tr>
<tr>
<td>
Data exploitation (purpose/use of the data analysis)
</td>
<td>
_Due to privacy issues, the collected data are stored at a secured database
scheme at SOLAR LAB Facilities, allowing access to registered users. Data
exploitation is foreseen to be extended through envisioned value-added
services, allowing full access to specific authorised users (e.g. facility
managers), and for a broader use in an anonymised/aggregated manner for data
analytics and statistical analysis._
</td> </tr>
<tr>
<td>
Data access policy / Dissemination level
(Confidential, only for members of the Consortium and the
Commission Services) / Public
</td>
<td>
_The full dataset will be confidential and only the authorized ENERC personnel
and related end-users will have access as defined. Specific consortium members
involved in technical development and pilot deployment will further have
access under a detailed confidentiality framework._
_Furthermore, if the dataset in an anonymised/aggregated manner is decided to
become of widely open access, a data management portal will be created that
should provide a description of the dataset and link to a download section._
</td> </tr>
<tr>
<td>
Data sharing, re-use and
distribution (How?)
</td>
<td>
_The created dataset could be shared by using open APIs through the middleware
as well as a data management portal._
</td> </tr>
<tr>
<td>
Embargo periods (if any)
</td>
<td>
_None_
</td> </tr>
<tr>
<td>
**Archiving and preservation (including storage and backup)**
</td> </tr>
<tr>
<td>
Data storage (including backup):
where? For how long?
</td>
<td>
_Due to ethical and privacy issues, data will be stored in a database scheme
at the SOLAR LAB facilities, allowing only authorised access to external end-
users. A back up will be stored in an external storage device, kept by ENERC
in a secured place. Data will be kept indefinitely allowing statistical
analysis._
</td> </tr> </table>
**Table 18: Dataset description of the ENERC METEO station**
<table>
<tr>
<th>
**DS.ENERC.02.BUILDING_Status**
</th> </tr>
<tr>
<td>
**Data Identification**
</td> </tr>
<tr>
<td>
Dataset description
</td>
<td>
_The information associated to the energy consumption in buildings will allow
identifying the usage of resources for each measurement point._
</td> </tr>
<tr>
<td>
Source (e.g. which device?)
</td>
<td>
_The sensors that feed this dataset are; Cooling energy demand, heating energy
demand, hot water demand, building equipment demand_
</td> </tr>
<tr>
<td>
**Partners services and responsibilities**
</td> </tr>
<tr>
<td>
Partner owner of the device
</td>
<td>
_The devices will be the property of the test site owners, where the data
collection is going to be performed._
</td> </tr>
<tr>
<td>
Partner in charge of the data collection (if different)
</td>
<td>
_ENERC_
</td> </tr>
<tr>
<td>
Partner in charge of the data
analysis (if different)
</td>
<td>
_ENERC_
</td> </tr>
<tr>
<td>
Partner in charge of the data storage (if different)
</td>
<td>
_ENERC_
</td> </tr>
<tr>
<td>
WPs and tasks
</td>
<td>
_The data are going to be collected within activities of WP7 and WP8._
</td> </tr>
<tr>
<td>
**Standards**
</td> </tr>
<tr>
<td>
Info about metadata (Production and storage dates, places) and documentation?
</td>
<td>
_The dataset will be accompanied with the respective documentation of its
contents. Indicative metadata may include device id, measurement date, device
owner, state of the monitored activity, etc._
</td> </tr>
<tr>
<td>
Standards, Format, Estimated volume of data
</td>
<td>
_The data are received in JSON format. Regarding the volume of data, it
depends on the motion/activity levels of the engaged devices. However, it is
estimated to be 4 KB/transmission._
</td> </tr>
<tr>
<td>
**Data exploitation and sharing**
</td> </tr>
<tr>
<td>
Data exploitation (purpose/use of the data analysis)
</td>
<td>
_Due to privacy issues, the collected data are stored at a secured database
scheme at SOLAR LAB Facilities, allowing access to registered users. Data
exploitation is foreseen to be extended through envisioned value-added
services, allowing full access to specific authorised users (e.g. facility
managers), and for a broader use in an anonymised/aggregated manner for data
analytics and statistical analysis._
</td> </tr>
<tr>
<td>
Data access policy / Dissemination level
(Confidential, only for members of the Consortium and the
Commission Services) / Public
</td>
<td>
_The full dataset will be confidential and only the authorized ENERC personnel
and related end-users will have access as defined. Specific consortium members
involved in technical development and pilot deployment will further have
access under a detailed confidentiality framework._
_Furthermore, if the dataset in an anonymised/aggregated manner is decided to
become of widely open access, a data management portal will be created that
should provide a description of the dataset and link to a download section._
</td> </tr>
<tr>
<td>
Data sharing, re-use and
distribution (How?)
</td>
<td>
_The created dataset could be shared by using open APIs through the
middleware_ _as well as a data management portal._
</td> </tr>
<tr>
<td>
Embargo periods (if any)
</td>
<td>
_None._
</td> </tr>
<tr>
<td>
**Archiving and preservation (including storage and backup)**
</td> </tr>
<tr>
<td>
Data storage (including backup):
where? For how long?
</td>
<td>
_Due to ethical and privacy issues, data will be stored in a database scheme
at the SOLAR LAB facilities, allowing only authorised access to external end-
users. A back up will be stored in an external storage device, kept by ENERC
in a secured place. Data will be kept indefinitely allowing statistical
analysis._
</td> </tr> </table>
**Table 19: Dataset description of the ENERC building status**
<table>
<tr>
<th>
**DS.ENERC.03.GRID_Status**
</th> </tr>
<tr>
<td>
**Data Identification**
</td> </tr>
<tr>
<td>
Dataset description
</td>
<td>
_This dataset comprises the different parameters that characterise the
electrical grid from the generation to the distribution sections. Moreover,
the cost of the electricity will be considered in this dataset so as to have
full information that enables micro-trading actions._
</td> </tr>
<tr>
<td>
Source (e.g. which device?)
</td>
<td>
_The sensors that feed this dataset are; Electrical energy generated on-site
from RES, thermal energy generated on-site, thermal energy consumed, grid
electricity consumed, instant grid cost of energy consumed, value of energy
purchased from the grid_
</td> </tr>
<tr>
<td>
**Partners services and responsibilities**
</td> </tr>
<tr>
<td>
Partner owner of the device
</td>
<td>
_The devices will be the property of the test site owners, where the data
collection is going to be performed._
</td> </tr>
<tr>
<td>
Partner in charge of the data collection (if different)
</td>
<td>
_ENERC_
</td> </tr>
<tr>
<td>
Partner in charge of the data
analysis (if different)
</td>
<td>
_ENERC_
</td> </tr>
<tr>
<td>
Partner in charge of the data storage (if different)
</td>
<td>
_ENERC_
</td> </tr>
<tr>
<td>
WPs and tasks
</td>
<td>
_The data are going to be collected within activities of WP7 and WP8._
</td> </tr>
<tr>
<td>
**Standards**
</td> </tr>
<tr>
<td>
Info about metadata (Production and storage dates, places) and documentation?
</td>
<td>
_The dataset will be accompanied with the respective documentation of its
contents. Indicative metadata may include device id, measurement date, device
owner, state of the monitored activity, etc._
</td> </tr>
<tr>
<td>
Standards, Format, Estimated volume of data
</td>
<td>
_The data are received in JSON format. Regarding the volume of data, it
depends on the motion/activity levels of the engaged devices. However, it is
estimated to be 4 KB/transmission._
</td> </tr>
<tr>
<td>
**Data exploitation and sharing**
</td> </tr>
<tr>
<td>
Data exploitation (purpose/use of the data analysis)
</td>
<td>
_Due to privacy issues, the collected data are stored at a secured database
scheme at SOLAR LAB Facilities, allowing access to registered users. Data
exploitation is foreseen to be extended through envisioned value-added
services, allowing full access to specific authorised users (e.g. facility
managers), and for a broader use in an anonymised/aggregated manner for data
analytics and statistical analysis._
</td> </tr>
<tr>
<td>
Data access policy / Dissemination level
(Confidential, only for members of the Consortium and the
Commission Services) / Public
</td>
<td>
_The full dataset will be confidential and only the authorized ENERC personnel
and related end-users will have access as defined. Specific consortium members
involved in technical development and pilot deployment will further have
access under a detailed confidentiality framework._
_Furthermore, if the dataset in an anonymised/aggregated manner is decided to
become of widely open access, a data management portal will be created that
should provide a description of the dataset and link to a download section._
</td> </tr>
<tr>
<td>
Data sharing, re-use and
distribution (How?)
</td>
<td>
_The created dataset could be shared by using open APIs through the middleware
as well as a data management portal._
</td> </tr>
<tr>
<td>
Embargo periods (if any)
</td>
<td>
_None_
</td> </tr>
<tr>
<td>
**Archiving and preservation (including storage and backup)**
</td> </tr>
<tr>
<td>
Data storage (including backup):
where? For how long?
</td>
<td>
_Due to ethical and privacy issues, data will be stored in a database scheme
at the SOLAR LAB facilities, allowing only authorised access to external end-
users. A back up will be stored in an external storage device, kept by ENERC
in a secured place. Data will be kept indefinitely allowing statistical
analysis._
</td> </tr> </table>
**Table 20: Dataset description of the ENERC grid status**
## 4.4. Datasets for eHealth from GNOMON Informatics SA (GNOMON)
GNOMON provided its background knowledge in the specific field of assisted
living and tele care in the context of social workers. In addition, GNOMON
actively contributes in the use case pilot setup, assessment and benchmarking.
The company has developed and provided the remote care and monitoring
integrated system for people with health problems as well as of the software
applications for support and organization using information and communication
technologies of the business operation of HELP AT HOME program in the
Municipality of Pilea-Hortiatis. This infrastructure is further exploited and
extended for the scope of VICINITY project and specifically for the
realisation of the eHealth Use Case.
<table>
<tr>
<th>
**DS.GNOMON.01.Pressure_sensor**
</th> </tr>
<tr>
<td>
**Data Identification**
</td> </tr>
<tr>
<td>
Dataset description
</td>
<td>
_The sensors are in possession of patients in need of assisted living and
identified by the equivalent municipality (MPH) health care services to ensure
the validity of each case. The measurements are taken depending on the
scheduling they have agreed with their doctor. The main task of the sensor is
to monitor pressure (systolic/diastolic) and heart rate levels._
</td> </tr>
<tr>
<td>
Source (e.g. which device?)
</td>
<td>
_The dataset is collected via a combination of connected devices consisting of
a Bluetooth Blood Pressure monitor and a Connectivity Gateway based on
Raspberry pi._
</td> </tr>
<tr>
<td>
**Partners services and responsibilities**
</td> </tr>
<tr>
<td>
Partner owner of the device
</td>
<td>
_The device is property of the test site owners, where the data collection is
being performed._
</td> </tr>
<tr>
<td>
Partner in charge of the data collection (if different)
</td>
<td>
_GNOMON, CERTH, MPH_
</td> </tr>
<tr>
<td>
Partner in charge of the data
analysis (if different)
</td>
<td>
_GNOMON, CERTH, MPH_
</td> </tr>
<tr>
<td>
Partner in charge of the data storage (if different)
</td>
<td>
_GNOMON, MPH_
</td> </tr>
<tr>
<td>
WPs and tasks
</td>
<td>
_The data are being collected within activities of WP7 and WP8._
</td> </tr>
<tr>
<td>
**Standards**
</td> </tr>
<tr>
<td>
Info about metadata (Production and storage dates, places) and documentation?
</td>
<td>
_The dataset is accompanied with the respective documentation of its contents.
Indicative metadata include device id, measurement date, device owner, state
of the monitored activity, etc._
</td> </tr>
<tr>
<td>
Standards, Format, Estimated volume of data
</td>
<td>
_The data are received in XML format. In a later stage, they are converted to
JSON format and stored in a database. Regarding the volume of data, it depends
on the participation levels of the engaged patients. However, it is estimated
to be 16 KB/measurement._
</td> </tr>
<tr>
<td>
**Data exploitation and sharing**
</td> </tr>
<tr>
<td>
Data exploitation (purpose/use of the data analysis)
</td>
<td>
_Due to privacy issues, the collected data are stored at a secured database
scheme at MPH headquarters, allowing access to registered users (i.e. MPH
health care services personnel and eHealth call center). Data exploitation is
foreseen to be extended through envisioned value-added services, allowing full
access to specific authorised users (e.g. doctors), and for a broader use in
an anonymised/aggregated manner for creating behaviour profiles and clustering
patients to different medical groups._
</td> </tr>
<tr>
<td>
Data access policy / Dissemination level
(Confidential, only for members of the Consortium and the
Commission Services) / Public
</td>
<td>
_The full dataset is confidential and only the authorized MPH personnel and
related end-users have access as defined. The latter authorized groups of
users access data in a tamper-proof way with an audit mechanism triggered
simultaneously to guarantee the alignment with relevant requirements coming
from the recently introduced General Data Protection Regulation (GDPR).
Specific consortium members involved in technical development and pilot
deployment further have access under a detailed confidentiality framework._
_Furthermore, if the dataset in an anonymised/aggregated manner is decided to
become of widely open access, a data management portal will be created that
should provide a description of the dataset and link to a download section._
</td> </tr>
<tr>
<td>
Data sharing, re-use and
distribution (How?)
</td>
<td>
_The created dataset could be shared by using open APIs through the middleware
as well as a data management portal. Dataset from VICINITY could be used and
exploited anonymized from another European project. Dataset from health
devices deployed at seniors’ houses provides added value and be the base for
other research projects (e.g. statistical data). VICINITY could have an open
portal / repository on its website, providing anonymized data’s information
like timestamp and description._
</td> </tr>
<tr>
<td>
Embargo periods (if any)
</td>
<td>
_None_
</td> </tr>
<tr>
<td>
**Archiving and preservation (including storage and backup)**
</td> </tr>
<tr>
<td>
Data storage (including backup):
where? For how long?
</td>
<td>
_Due to ethical and privacy issues, data are stored in a database scheme at
the headquarters of MPH, allowing only authorised access to external end-
users. A back up is stored in an external storage device, kept by MPH in a
secured place. Data are kept indefinitely allowing statistical analysis._
</td> </tr> </table>
**Table 21: Dataset description of the GNOMON pressure sensor**
<table>
<tr>
<th>
**DS.GNOMON.02.Weight_sensor**
</th> </tr>
<tr>
<td>
**Data Identification**
</td> </tr>
<tr>
<td>
Dataset description
</td>
<td>
_The sensors are in possession of patients in need of assisted living and
identified by the equivalent municipality (MPH) health care services to ensure
the validity of each case. The measurements are scheduled to be taken
depending on the agreement between the patient and the doctor. The main task
of the sensor is to keep track of weight measurements and mass index (given
the fact that the patient provides an accurate value of his/her height)._
</td> </tr>
<tr>
<td>
Source (e.g. which device?)
</td>
<td>
_The dataset is collected via a combination of connected devices consisting of
a Bluetooth Body Composition monitor and a Connectivity Gateway based on
Raspberry pi._
</td> </tr>
<tr>
<td>
**Partners services and responsibilities**
</td> </tr>
<tr>
<td>
Partner owner of the device
</td>
<td>
_The device is a property of the test site owners, where the data collection
is being performed._
</td> </tr>
<tr>
<td>
Partner in charge of the data collection (if different)
</td>
<td>
_GNOMON, CERTH, MPH_
</td> </tr>
<tr>
<td>
Partner in charge of the data
analysis (if different)
</td>
<td>
_GNOMON, CERTH, MPH_
</td> </tr>
<tr>
<td>
Partner in charge of the data storage (if different)
</td>
<td>
_GNOMON, MPH_
</td> </tr>
<tr>
<td>
WPs and tasks
</td>
<td>
_The data are being collected within activities of WP7 and WP8._
</td> </tr>
<tr>
<td>
**Standards**
</td> </tr>
<tr>
<td>
Info about metadata (Production and storage dates, places) and documentation?
</td>
<td>
_The dataset is accompanied with the respective documentation of its contents.
Indicative metadata include device id, measurement date, device owner, state
of the monitored activity, etc._
</td> </tr>
<tr>
<td>
Standards, Format, Estimated volume of data
</td>
<td>
_The data are received in XML format. In a later stage, they are converted to
JSON format and stored in a database. Regarding the volume of data, it depends
on the participation levels of the engaged patients. However, it is estimated
to be 48 KB/measurement._
</td> </tr>
<tr>
<td>
**Data exploitation and sharing**
</td> </tr>
<tr>
<td>
Data exploitation (purpose/use of the data analysis)
</td>
<td>
_Due to privacy issues, the collected data are stored at a secured database
scheme at MPH headquarters, allowing access to registered users (i.e. MPH
health care services personnel and eHealth call centre). Data exploitation is
foreseen to be extended through envisioned value-added services, allowing full
access to specific authorised users (e.g. doctors), and for a broader use in
an anonymised/aggregated manner for creating behaviour profiles and clustering
patients to different medical groups._
</td> </tr>
<tr>
<td>
Data access policy / Dissemination level
(Confidential, only for members of the Consortium and the
Commission Services) / Public
</td>
<td>
_The full dataset is confidential and only the authorized MPH personnel and
related end-users have access as defined. The latter authorized groups of
users access data in a tamper-proof way with an audit mechanism triggered
simultaneously to guarantee the alignment with relevant requirements coming
from the recently introduced General Data Protection Regulation (GDPR).
Specific consortium members involved in technical development and pilot
deployment further have access under a detailed confidentiality framework._
_Furthermore, if the dataset in an anonymised/aggregated manner is decided to
become of widely open access, a data management portal will be created that
should provide a description of the dataset and link to a download section._
</td> </tr>
<tr>
<td>
Data sharing, re-use and
distribution (How?)
</td>
<td>
_The created dataset could be shared by using open APIs through the middleware
as well as a data management portal. Dataset from VICINITY could be used and
exploited anonymized from another European project. Dataset from health
devices deployed at seniors’ houses provides added value and be the base for
other research projects (e.g. statistical data). VICINITY could have an open
portal / repository on its website, providing anonymized data’s information
like timestamp and description._
</td> </tr>
<tr>
<td>
Embargo periods (if any)
</td>
<td>
_None_
</td> </tr>
<tr>
<td>
**Archiving and preservation (including storage and backup)**
</td> </tr>
<tr>
<td>
Data storage (including backup):
where? For how long?
</td>
<td>
_Due to ethical and privacy issues, data are stored in a database scheme at
the headquarters of MPH, allowing only authorised access to external end-
users. A back up is stored in an external storage device, kept by MPH in a
secured place. Data are kept indefinitely allowing statistical analysis._
</td> </tr> </table>
**Table 22: Dataset description of the GNOMON weight sensor**
<table>
<tr>
<th>
**DS.GNOMON.03.Fall_sensor**
</th> </tr>
<tr>
<td>
**Data Identification**
</td> </tr>
<tr>
<td>
Dataset description
</td>
<td>
_The fall sensor is a wearable sensor that is in possession of patients in
need of assisted living and identified by the equivalent municipality (MPH)
health care services to ensure the validity of each case. The main goal of the
sensor is to automatically detect when a patient falls either due to an
accident or in the case of a medical incident. The event is triggered
automatically after a fall, but a similar event is also triggered by pressing
the equivalent panic button (wearable actuator). In both cases, an automated
emergency phone call is placed to the eHealth Call Centre._
</td> </tr>
<tr>
<td>
Source (e.g. which device?)
</td>
<td>
_The dataset is collected via a combination of devices consisting of a hub
(Lifeline Vi) and a fall detector that are wirelessly connected._
</td> </tr>
<tr>
<td>
**Partners services and responsibilities**
</td> </tr>
<tr>
<td>
Partner owner of the device
</td>
<td>
_The device is the property of the test site owners, where the data collection
is being performed._
</td> </tr>
<tr>
<td>
Partner in charge of the data collection (if different)
</td>
<td>
_GNOMON, CERTH, MPH_
</td> </tr>
<tr>
<td>
Partner in charge of the data
analysis (if different)
</td>
<td>
_GNOMON, CERTH, MPH_
</td> </tr>
<tr>
<td>
Partner in charge of the data storage (if different)
</td>
<td>
_GNOMON, MPH_
</td> </tr>
<tr>
<td>
WPs and tasks
</td>
<td>
_The data are being collected within activities of WP7 and WP8._
</td> </tr>
<tr>
<td>
**Standards**
</td> </tr>
<tr>
<td>
Info about metadata (Production and storage dates, places) and documentation?
</td>
<td>
_The dataset is accompanied with the respective documentation of its contents.
Indicative metadata include device id, measurement date, device owner, state
of the monitored activity, etc._
</td> </tr>
<tr>
<td>
Standards, Format, Estimated volume of data
</td>
<td>
_An audit log containing alerts (incl. false alarms) is stored._
</td> </tr>
<tr>
<td>
**Data exploitation and sharing**
</td> </tr>
<tr>
<td>
Data exploitation (purpose/use of the data analysis)
</td>
<td>
_Due to privacy issues, the collected data are stored at a secured database
scheme at MPH headquarters, allowing access to registered users (i.e. MPH
health care services personnel and eHealth call centre). Data exploitation is
foreseen to be extended through envisioned value-added services, allowing full
access to specific authorised users (e.g. patient’s doctors), and for a
broader use in an anonymised/aggregated manner for data analytics and
statistical analysis._
</td> </tr>
<tr>
<td>
Data access policy / Dissemination level
(Confidential, only for members of the Consortium and the
Commission Services) / Public
</td>
<td>
_The full dataset is confidential and only the authorized MPH personnel and
related end-users have access as defined. The latter authorized groups of
users access data in a tamper-proof way with an audit mechanism triggered
simultaneously to guarantee the alignment with relevant requirements coming
from the recently introduced General Data Protection Regulation (GDPR).
Specific consortium members involved in technical development and pilot
deployment further have access under a detailed confidentiality framework._
_Furthermore, if the dataset in an anonymised/aggregated manner is decided to
become of widely open access, a data management portal will be created that
should provide a description of the dataset and link to a download section._
</td> </tr>
<tr>
<td>
Data sharing, re-use and
distribution (How?)
</td>
<td>
_The created dataset could be shared by using open APIs through the middleware
as well as a data management portal. Dataset from VICINITY could be used and
exploited anonymized from another European project. Dataset from fall sensors
at seniors’ houses provides added value and be the base for other research
projects (e.g. statistical data). VICINITY could have an open portal /
repository on its website, providing anonymized data’s information like
timestamp and description._
</td> </tr>
<tr>
<td>
Embargo periods (if any)
</td>
<td>
_None_
</td> </tr>
<tr>
<td>
**Archiving and preservation (including storage and backup)**
</td> </tr>
<tr>
<td>
Data storage (including backup):
where? For how long?
</td>
<td>
_Due to ethical and privacy issues, data are stored in a database scheme at
the headquarters of MPH, allowing only authorised access to external end-
users. A back up is stored in an external storage device, kept by MPH in a
secured place. Data are kept indefinitely allowing statistical analysis._
</td> </tr> </table>
**Table 23: Dataset description of the GNOMON fall sensor**
<table>
<tr>
<th>
**DS.GNOMON.04.Wearable_Fitness_Tracker_Sensor**
</th> </tr>
<tr>
<td>
**Data Identification**
</td> </tr>
<tr>
<td>
Dataset description
</td>
<td>
_The fitness sensors are sensors embodied to wearable fitness trackers such as
activity wristbands. The latter equipment is in possession of middle aged
citizens, either with a chronic health issue (e.g. obesity) or not, that are
identified by the equivalent municipality (MPH). The municipality tries to
promote fitness awareness and improve citizens’ health under the concept of a
municipal-scale competition that is based on activity related data coming from
the sensors (e.g. step counting)_
</td> </tr>
<tr>
<td>
Source (e.g. which device?)
</td>
<td>
_The data are collected by wearable fitness trackers, mainly in the form of
activity wristbands (e.g. Xiaomi MiBand)._
</td> </tr>
<tr>
<td>
**Partners services and responsibilities**
</td> </tr>
<tr>
<td>
Partner owner of the device
</td>
<td>
_The device is the property of the test subject, in this case the
participating citizen._
</td> </tr>
<tr>
<td>
Partner in charge of the data collection (if different)
</td>
<td>
_GNOMON, CERTH, MPH_
</td> </tr>
<tr>
<td>
Partner in charge of the data
analysis (if different)
</td>
<td>
_GNOMON, CERTH, MPH_
</td> </tr>
<tr>
<td>
Partner in charge of the data storage (if different)
</td>
<td>
_GNOMON, MPH_
</td> </tr>
<tr>
<td>
WPs and tasks
</td>
<td>
_The data are being collected within activities of WP7 and WP8._
</td> </tr>
<tr>
<td>
**Standards**
</td> </tr>
<tr>
<td>
Info about metadata (Production and storage dates, places) and documentation?
</td>
<td>
_The dataset is accompanied with the respective documentation of its contents.
Indicative metadata include device id, measurement date, device owner, state
of the monitored activity, etc._
</td> </tr>
<tr>
<td>
Standards, Format, Estimated volume of data
</td>
<td>
_Collection of data from wearable fitness tracker sensors is event-driven. New
data are dispatched once they are produced._
</td> </tr>
<tr>
<td>
**Data exploitation and sharing**
</td> </tr>
<tr>
<td>
Data exploitation (purpose/use of the data analysis)
</td>
<td>
_Data exploitation is foreseen to be extended through envisioned value-added
services, allowing full access to specific authorised users (e.g. doctors),
and for a broader use in an anonymised/aggregated manner for data analytics
and statistical analysis. Additionally, as one of the value-added services
introduced is related to the concept of a municipalscale competition, data
analysis also serves the needs of calculating and providing a ranking among
the competitors._
</td> </tr>
<tr>
<td>
Data access policy / Dissemination level
(Confidential, only for members of the Consortium and the
Commission Services) / Public
</td>
<td>
_The full dataset is confidential and only the authorized MPH personnel and
related end-users have access as defined. The latter authorized groups of
users access data in a tamper-proof way with an audit mechanism triggered
simultaneously to guarantee the alignment with relevant requirements coming
from the recently introduced General Data Protection Regulation (GDPR).
Specific consortium members involved in technical development and pilot
deployment further have access under a detailed confidentiality framework._
_Furthermore, if the dataset in an anonymised/aggregated manner is decided to
become of widely open access, a data management portal will be created that
should provide a description of the dataset and link to a download section._
</td> </tr>
<tr>
<td>
Data sharing, re-use and
distribution (How?)
</td>
<td>
_The created dataset could be shared by using open APIs through the middleware
as well as a data management portal. Dataset from VICINITY could be used and
exploited anonymized from another European project. Dataset from wearable
fitness trackers provide added value and be the base for other research
projects (e.g. statistical data). VICINITY could have an open portal /
repository on its website, providing anonymized data’s information like
timestamp and description._
</td> </tr>
<tr>
<td>
Embargo periods (if any)
</td>
<td>
_None_
</td> </tr>
<tr>
<td>
**Archiving and preservation (including storage and backup)**
</td> </tr>
<tr>
<td>
Data storage (including backup):
where? For how long?
</td>
<td>
_Due to ethical and privacy issues, data are stored in a database scheme at
the headquarters of MPH, allowing only authorised access to external end-
users. A back up is stored in an external storage device, kept by MPH in a
secured place. Data are kept indefinitely allowing statistical analysis._
</td> </tr> </table>
**Table 24: Dataset description of the GNOMON Wearable Fitness Tracker
Sensor**
<table>
<tr>
<th>
**DS.GNOMON.05.Beacon_Sensor**
</th> </tr>
<tr>
<td>
**Data Identification**
</td> </tr>
<tr>
<td>
Dataset description
</td>
<td>
_The beacon sensors are sensors to deployed in municipality’s sport
facilities, e.g. gym, pool, etc. and also tested at CERTH/ITI’s Smart Home.
The municipality tries to promote fitness awareness and improve citizens’
health under the concept of a municipal-scale competition that is based on
activity related data gathered by the sensors and processed accordingly (e.g.
translation of beacon signals to actual time spent in sport facilities)._
</td> </tr>
<tr>
<td>
Source (e.g. which device?)
</td>
<td>
_The data are collected by beacons deployed in municipality’s sport facilities
and at CERTH/ITI’s Smart Home._
</td> </tr>
<tr>
<td>
**Partners services and responsibilities**
</td> </tr>
<tr>
<td>
Partner owner of the device
</td>
<td>
_The device are the property of the test site owners, where the data
collection is being performed._
</td> </tr>
<tr>
<td>
Partner in charge of the data collection (if different)
</td>
<td>
_GNOMON, MPH, CERTH_
</td> </tr>
<tr>
<td>
Partner in charge of the data
analysis (if different)
</td>
<td>
_GNOMON, MPH, CERTH_
</td> </tr>
<tr>
<td>
Partner in charge of the data storage (if different)
</td>
<td>
_GNOMON, MPH, CERTH_
</td> </tr>
<tr>
<td>
WPs and tasks
</td>
<td>
_The data are being collected within activities of WP7 and WP8._
</td> </tr>
<tr>
<td>
**Standards**
</td> </tr>
<tr>
<td>
Info about metadata (Production and storage dates, places) and documentation?
</td>
<td>
_The dataset is accompanied with the respective documentation of its contents.
Indicative metadata include device id, measurement date, device owner, state
of the monitored activity, etc._
</td> </tr>
<tr>
<td>
Standards, Format, Estimated volume of data
</td>
<td>
_The collection of data from beacons is event-driven. New data are dispatched
once they are produced for example when middle-age person visits a sport
centre._
</td> </tr>
<tr>
<td>
**Data exploitation and sharing**
</td> </tr>
<tr>
<td>
Data exploitation (purpose/use of the data analysis)
</td>
<td>
_Data exploitation is foreseen to be extended through envisioned value-added
services, allowing full access to specific authorised users (e.g. doctors),
and for a broader use in an anonymised/aggregated manner for data analytics
and statistical analysis. Additionally, as one of the value-added services
introduced is related to the concept of a municipalscale competition, data
analysis also serve the needs of calculating and providing a ranking among the
competitors._
</td> </tr>
<tr>
<td>
Data access policy / Dissemination level
(Confidential, only for members of the Consortium and the
Commission Services) / Public
</td>
<td>
_The full dataset of beacons deployed in CERTH / ITI’s smart house, that is
not sensitive, are accessible through a local experimental repository._
_The full dataset of beacons deployed in houses of elderly people are
sensitive, therefore, are confidential and only the authorized MPH personnel
and related end-users have access as defined. The latter authorized groups of
users access data in a tamper-proof way with an audit mechanism triggered
simultaneously to guarantee the alignment with relevant requirements coming
from the recently introduced General Data Protection Regulation (GDPR).
Specific consortium members involved in technical development and pilot
deployment further have access under a detailed confidentiality framework._
_Furthermore, if the dataset in an anonymised/aggregated manner is decided to
become of widely open access, a data management portal will be created that
should provide a description of the dataset and link to a download section._
</td> </tr>
<tr>
<td>
Data sharing, re-use and
distribution (How?)
</td>
<td>
_The created dataset could be shared by using open APIs through the middleware
as well as a data management portal. Dataset from VICINITY could be used and
exploited anonymized from another European project. Dataset from beacons at
sport centres provide added value and be the base for other research projects
(e.g. statistical data). VICINITY could have an open portal / repository on
its website, providing anonymized data’s information like timestamp and
description._
</td> </tr>
<tr>
<td>
Embargo periods (if any)
</td>
<td>
_None_
</td> </tr>
<tr>
<td>
**Archiving and preservation (including storage and backup)**
</td> </tr>
<tr>
<td>
Data storage (including backup):
where? For how long?
</td>
<td>
_Due to ethical and privacy issues, data are stored in a database scheme at
the headquarters of MPH, allowing only authorised access to external end-
users. A back up is stored in an external storage device, kept by MPH in a
secured place. Data are kept indefinitely allowing statistical analysis._
</td> </tr> </table>
**Table 25: Dataset description of the GNOMON Beacon Sensor**
<table>
<tr>
<th>
**DS.GNOMON_CERTH.06.Gorenje_Smart_Appliances_Sensor**
</th> </tr>
<tr>
<td>
**Data Identification**
</td> </tr>
<tr>
<td>
Dataset description
</td>
<td>
_The sensors related to Gorenje smart appliances are sensors embodied to
specific house equipment such as ovens and fridges. The latter equipment are
provided by Gorenje partner and are in possession of patients in need of
assisted living and identified by the equivalent municipality (MPH) health
care services to ensure the validity of each case. Similar equipment are also
deployed in CERTH / ITI’s facilities. The main goal of the sensors is to
automatically detect when a patient opens the fridge or uses the oven in order
to create behaviour profiles based on relevant criteria (e.g. frequency of
use, etc), produce notifications in case of deviation from the normal
standards of use and inform the call centre._
</td> </tr>
<tr>
<td>
Source (e.g. which device?)
</td>
<td>
_The data are collected by specific smart appliances (i.e. oven, fridge)
provided by the Gorenje partner and adjusted to VICINITY requirements._
</td> </tr>
<tr>
<td>
**Partners services and responsibilities**
</td> </tr>
<tr>
<td>
Partner owner of the device
</td>
<td>
_The devices are the property of the test site owners, where the data
collection is being performed._
</td> </tr>
<tr>
<td>
Partner in charge of the data collection (if different)
</td>
<td>
_CERTH, GORENJE, GNOMON, MPH_
</td> </tr>
<tr>
<td>
Partner in charge of the data
analysis (if different)
</td>
<td>
_CERTH, GORENJE, GNOMON, MPH_
</td> </tr>
<tr>
<td>
Partner in charge of the data storage (if different)
</td>
<td>
_CERTH, GORENJE, GNOMON, MPH_
</td> </tr>
<tr>
<td>
WPs and tasks
</td>
<td>
_The data are being collected within activities of WP6, WP7 and WP8._
</td> </tr>
<tr>
<td>
**Standards**
</td> </tr>
<tr>
<td>
Info about metadata (Production and storage dates, places) and documentation?
</td>
<td>
_The dataset is accompanied with the respective documentation of its contents.
Indicative metadata include device id, measurement date, device owner, state
of the monitored activity, etc._
</td> </tr>
<tr>
<td>
Standards, Format, Estimated volume of data
</td>
<td>
_The collection of data from Gorenje devices is time-driven and dispatched
every 15min and it is depended on the standards that Gorenje provides._
</td> </tr>
<tr>
<td>
**Data exploitation and sharing**
</td> </tr>
<tr>
<td>
Data exploitation (purpose/use of the data analysis)
</td>
<td>
_Data exploitation is foreseen to be extended through envisioned value-added
services and for a broader use in an anonymised/aggregated manner for creating
behaviour profiles and clustering patients to different medical groups.
Significant deviation from the latter profiles is expected to produce relevant
notifications._
</td> </tr>
<tr>
<td>
Data access policy / Dissemination level
(Confidential, only for members of the Consortium and the
Commission Services) / Public
</td>
<td>
_The full dataset of Gorenje devices deployed in CERTH / ITI’s facilities,
that are not sensitive, is accessible through Gorenje Cloud in a local
experimental repository._
_The full dataset from Gorenje devices deployed in elderly’s people houses is
confidential and only the authorized MPH personnel and related end-users have
access as defined through Gorenje Cloud. The latter authorized groups of users
access data in a tamper-proof way with an audit mechanism triggered
simultaneously to guarantee the alignment with relevant requirements coming
from the recently introduced General Data Protection Regulation (GDPR).
Specific consortium members involved in technical development and pilot
deployment have access under a detailed confidentiality framework._
_Furthermore, if the dataset in an anonymised/aggregated manner is decided to
become of widely open access, a data management portal will be created that
should provide a description of the dataset and link to a download section._
</td> </tr>
<tr>
<td>
Data sharing, re-use and
distribution (How?)
</td>
<td>
_The created dataset could be shared by using open APIs through the middleware
as well as a data management portal. Dataset from VICINITY could be used and
exploited anonymized from another European project. Dataset from Gorenje
devices deployed at seniors’ houses provides added value and be the base for
other research projects (e.g. statistical data). VICINITY could have an open
portal / repository on its website, providing anonymized data’s information
like timestamp and description._
</td> </tr>
<tr>
<td>
Embargo periods (if any)
</td>
<td>
_None_
</td> </tr>
<tr>
<td>
**Archiving and preservation (including storage and backup)**
</td> </tr>
<tr>
<td>
Data storage (including backup):
where? For how long?
</td>
<td>
_Due to ethical and privacy issues, data are stored in a database scheme at
the headquarters of MPH, allowing only authorised access to external end-
users. A back up is stored in an external storage device, kept by MPH in a
secured place. Data are kept indefinitely allowing statistical analysis._
</td> </tr> </table>
**Table 26: Dataset description of the GNOMON/CERTH Gorenje Smart Appliances
Sensor**
## 4.5. Datasets for eHealth from Centre for Research and Technology Hellas
(CERTH)
CERTH / ITI contributes in the use case pilot setup for houses at Municipality
of Pilea-Hortiatis and provide its background knowledge in the field of
assisted living. It also provides its infrastructure of Smart House for cross-
domain implementation including building sensors and devices which have been
also integrated to houses at MPH.
<table>
<tr>
<th>
**DS.CERTH.01.Door_Sensor**
</th> </tr>
<tr>
<td>
**Data Identification**
</td> </tr>
<tr>
<td>
Dataset description
</td>
<td>
_Door sensors are deployed, on the one hand, in houses of patients in need of
assisted living, identified by the equivalent municipality (MPH) health care
services to ensure the validity of each case, but also in CERTH’s smart house
facilities for testing reasons. The main task of the sensor is to provide a
24/7 door status for the area of its responsibility. Data coming from this
sensor are used to create behaviour profiles based on relevant criteria and
produce notifications in case of deviation from the normal standards._
</td> </tr>
<tr>
<td>
Source (e.g. which device?)
</td>
<td>
_The dataset are collected via a combination of connected door sensors (Zwave)
and a Connectivity Gateway based on_
_Raspberry pi._
</td> </tr>
<tr>
<td>
**Partners services and responsibilities**
</td> </tr>
<tr>
<td>
Partner owner of the device
</td>
<td>
_The devices are the property of the test site owners, where the data
collection is being performed._
</td> </tr>
<tr>
<td>
Partner in charge of the data collection (if different)
</td>
<td>
_GNOMON, MPH, CERTH_
</td> </tr>
<tr>
<td>
Partner in charge of the data
analysis (if different)
</td>
<td>
_GNOMON, MPH, CERTH_
</td> </tr>
<tr>
<td>
Partner in charge of the data storage (if different)
</td>
<td>
_GNOMON, MPH, CERTH_
</td> </tr>
<tr>
<td>
WPs and tasks
</td>
<td>
_The data are being collected within activities of WP7 and WP8._
</td> </tr>
<tr>
<td>
**Standards**
</td> </tr>
<tr>
<td>
Info about metadata (Production and storage dates, places) and documentation?
</td>
<td>
_The dataset is accompanied with the respective documentation of its contents.
Indicative metadata include device id, measurement date, device owner, state
of the monitored activity, etc._
</td> </tr>
<tr>
<td>
Standards, Format, Estimated volume of data
</td>
<td>
_Τhe collection of data from door sensors is event-driven (e.g._
_through REST Services, XML format etc.)._
</td> </tr>
<tr>
<td>
**Data exploitation and sharing**
</td> </tr>
<tr>
<td>
Data exploitation (purpose/use of the data analysis)
</td>
<td>
_Data exploitation is foreseen to be extended through envisioned value-added
services and for a broader use in an anonymised/aggregated manner for creating
behaviour profiles and clustering patients to different medical groups.
Significant deviation from the latter profiles is expected to produce relevant
notifications._
</td> </tr>
<tr>
<td>
Data access policy /
Dissemination level
(Confidential, only for members of the Consortium and the
Commission Services) / Public
</td>
<td>
_The full dataset of door sensors deployed in CERTH / ITI’s smart house, that
are not sensitive, are accessible through a local experimental repository._
_The full dataset of sensors deployed in houses of elderly people are
sensitive therefore are confidential and only the authorized MPH personnel and
related end-users have access as defined. The latter authorized groups of
users access data in a tamperproof way with an audit mechanism triggered
simultaneously to guarantee the alignment with relevant requirements coming
from the recently introduced General Data Protection Regulation (GDPR).
Specific consortium members involved in technical development and pilot
deployment have access under a detailed confidentiality framework._
_Furthermore, if the dataset in an anonymised/aggregated manner is decided to
become of widely open access, a data management portal will be created that
should provide a description of the dataset and link to a download section._
</td> </tr>
<tr>
<td>
Data sharing, re-use and
distribution (How?)
</td>
<td>
_The created dataset could be shared by using open APIs through the middleware
as well as a data management portal. Dataset from VICINITY could be used and
exploited anonymized from another European project. Dataset from sensors
deployed at seniors’ houses provide added value and be the base for other
research projects (e.g. statistical data). VICINITY could have an open portal
/ repository on its website, providing anonymized data’s information like
timestamp and description._
</td> </tr>
<tr>
<td>
Embargo periods (if any)
</td>
<td>
_None_
</td> </tr>
<tr>
<td>
**Archiving and preservation (including storage and backup)**
</td> </tr>
<tr>
<td>
Data storage (including backup):
where? For how long?
</td>
<td>
_Due to ethical and privacy issues, data are stored in a database scheme at
the headquarters of MPH, allowing only authorised access to external end-
users. A back up is stored in an external storage device, kept by MPH in a
secured place. Data are kept indefinitely allowing statistical analysis._
</td> </tr> </table>
**Table 27: Dataset description of the CERTH Door Sensor**
<table>
<tr>
<th>
**DS.CERTH.02.Motion_Sensor**
</th> </tr>
<tr>
<td>
**Data Identification**
</td> </tr>
<tr>
<td>
Dataset description
</td>
<td>
_Motion sensors are deployed, on the one hand, in houses of patients in need
of assisted living, identified by the equivalent municipality (MPH) health
care services to ensure the validity of each case, but also in CERTH’s smart
house facilities for testing reasons. The main task of the sensor is to
provide the 24/7 motion levels for the area of its responsibility. Data coming
from this sensor are used to create behaviour profiles based on relevant
criteria (e.g. motions level for a specific room and time period, etc.) and
produce notifications in case of deviation from the normal standards._
</td> </tr>
<tr>
<td>
Source (e.g. which device?)
</td>
<td>
_The dataset are collected via a combination of connected motion sensors
(Zwave) and a Connectivity Gateway based on Raspberry pi._
</td> </tr>
<tr>
<td>
**Partners services and responsibilities**
</td> </tr>
<tr>
<td>
Partner owner of the device
</td>
<td>
_The devices are the property of the test site owners, where the data
collection is being performed._
</td> </tr>
<tr>
<td>
Partner in charge of the data collection (if different)
</td>
<td>
_GNOMON, MPH, CERTH_
</td> </tr>
<tr>
<td>
Partner in charge of the data
analysis (if different)
</td>
<td>
_GNOMON, MPH, CERTH_
</td> </tr>
<tr>
<td>
Partner in charge of the data storage (if different)
</td>
<td>
_GNOMON, MPH, CERTH_
</td> </tr>
<tr>
<td>
WPs and tasks
</td>
<td>
_The data are being collected within activities of WP6, WP7 and WP8._
</td> </tr>
<tr>
<td>
**Standards**
</td> </tr>
<tr>
<td>
Info about metadata (Production and storage dates, places) and documentation?
</td>
<td>
_The dataset is accompanied with the respective documentation of its contents.
Indicative metadata include device id, measurement date, device owner, state
of the monitored activity, etc._
</td> </tr>
<tr>
<td>
Standards, Format, Estimated volume of data
</td>
<td>
_The collection of data from motion sensors is event-driven (e.g._
_through REST Services, XML format etc.)._
</td> </tr>
<tr>
<td>
**Data exploitation and sharing**
</td> </tr>
<tr>
<td>
Data exploitation (purpose/use of the data analysis)
</td>
<td>
_Data exploitation is foreseen to be extended through envisioned value-added
services and for a broader use in an anonymised/aggregated manner for creating
behaviour profiles and clustering patients to different medical groups.
Significant deviation from the latter profiles is expected to produce relevant
notifications._
</td> </tr>
<tr>
<td>
Data access policy / Dissemination level
(Confidential, only for members of the Consortium and the
Commission Services) / Public
</td>
<td>
_The full dataset of door sensors deployed in CERTH / ITI’s smart house, that
is not sensitive, is accessible through a local experimental repository._
_The full dataset of sensors deployed in houses of elderly people are
sensitive therefore are confidential and only the authorized MPH personnel and
related end-users have access as defined. The latter authorized groups of
users access data in a tamperproof way with an audit mechanism triggered
simultaneously to guarantee the alignment with relevant requirements coming
from the recently introduced General Data Protection Regulation (GDPR).
Specific consortium members involved in technical development and pilot
deployment further have access under a detailed confidentiality framework._
_Furthermore, if the dataset in an anonymised/aggregated manner is decided to
become of widely open access, a data management portal will be created that
should provide a description of the dataset and link to a download section._
</td> </tr>
<tr>
<td>
Data sharing, re-use and
distribution (How?)
</td>
<td>
_The created dataset could be shared by using open APIs through the middleware
as well as a data management portal. Dataset from VICINITY could be used and
exploited anonymized from another European project. Dataset from sensors
deployed at seniors’ houses provide added value and be the base for other
research projects (e.g. statistical data). VICINITY could have an open portal
/ repository on its website, providing anonymized data’s information like
timestamp and description._
</td> </tr>
<tr>
<td>
Embargo periods (if any)
</td>
<td>
_None_
</td> </tr>
<tr>
<td>
**Archiving and preservation (including storage and backup)**
</td> </tr>
<tr>
<td>
Data storage (including backup):
where? For how long?
</td>
<td>
_Due to ethical and privacy issues, data are stored in a database scheme at
the headquarters of MPH, allowing only authorised access to external end-
users. A back up is stored in an external storage device, kept by MPH in a
secured place. Data are kept indefinitely allowing statistical analysis._
</td> </tr> </table>
**Table 28: Dataset description of the CERTH Motion Sensor**
<table>
<tr>
<th>
**DS.CERTH.03.Pressure_Mat**
</th> </tr>
<tr>
<td>
**Data Identification**
</td> </tr>
<tr>
<td>
Dataset description
</td>
<td>
_Pressure mats are deployed, on the one hand, in houses of patients in need of
assisted living, identified by the equivalent municipality (MPH) health care
services to ensure the validity of each case, but also in CERTH’s smart house
facilities for testing reasons. The main task of the device, which is deployed
on beds, is to provide the sleeping hours of the users. Data coming from this
sensor are used to create behaviour profiles based on relevant criteria and
provide notifications in case of deviation from the normal standards._
</td> </tr>
<tr>
<td>
Source (e.g. which device?)
</td>
<td>
_The dataset is collected via a sensor triggered when a person lies on the
bed, an Arduino which reads the pressure mat measurement and a Connectivity
Gateway based on Raspberry_
_Pi._
</td> </tr>
<tr>
<td>
**Partners services and responsibilities**
</td> </tr>
<tr>
<td>
Partner owner of the device
</td>
<td>
_The devices are the property of the test site owners, where the data
collection is being performed._
</td> </tr>
<tr>
<td>
Partner in charge of the data collection (if different)
</td>
<td>
_MPH, CERTH_
</td> </tr>
<tr>
<td>
Partner in charge of the data
analysis (if different)
</td>
<td>
_MPH, CERTH_
</td> </tr>
<tr>
<td>
Partner in charge of the data storage (if different)
</td>
<td>
_MPH, CERTH_
</td> </tr>
<tr>
<td>
WPs and tasks
</td>
<td>
_The data are being collected within activities of WP6, WP7 and WP8._
</td> </tr>
<tr>
<td>
**Standards**
</td> </tr>
<tr>
<td>
Info about metadata (Production and storage dates, places) and documentation?
</td>
<td>
_The dataset is accompanied with the respective documentation of its contents.
Indicative metadata include device id, measurement date, device owner, state
of the monitored activity, etc._
</td> </tr>
<tr>
<td>
Standards, Format, Estimated volume of data
</td>
<td>
_The collection of data from motion sensors is event-driven (e.g._
_through REST Services)._
</td> </tr>
<tr>
<td>
**Data exploitation and sharing**
</td> </tr>
<tr>
<td>
Data exploitation (purpose/use of the data analysis)
</td>
<td>
_Data exploitation is foreseen to be extended through envisioned value-added
services and for a broader use in an anonymised/aggregated manner for creating
behaviour profiles. Significant deviation from the latter profiles is expected
to produce relevant notifications._
</td> </tr>
<tr>
<td>
Data access policy / Dissemination level
(Confidential, only for members of the Consortium and the
Commission Services) / Public
</td>
<td>
_The full dataset of pressure mat deployed in CERTH / ITI’s smart house, that
is not sensitive, is accessible through a local experimental repository._
_The full dataset of devices deployed in houses of elderly people are
sensitive therefore are confidential and only the authorized MPH personnel and
related end-users have access as defined. The latter authorized groups of
users access data in a tamperproof way with an audit mechanism triggered
simultaneously to guarantee the alignment with relevant requirements coming
from the recently introduced General Data Protection Regulation (GDPR).
Specific consortium members involved in technical development and pilot
deployment further have access under a detailed confidentiality framework._
_Furthermore, if the dataset in an anonymised/aggregated manner is decided to
become of widely open access, a data management portal will be created that
should provide a description of the dataset and link to a download section._
</td> </tr>
<tr>
<td>
Data sharing, re-use and
distribution (How?)
</td>
<td>
_The created dataset could be shared by using open APIs through the middleware
as well as a data management portal. Dataset from VICINITY could be used and
exploited anonymized from another European project. Dataset from sensors
deployed at seniors’ houses provide added value and be the base for other
research projects (e.g. statistical data). VICINITY could have an open portal
/ repository on its website, providing anonymized data’s information like
timestamp and description._
</td> </tr>
<tr>
<td>
Embargo periods (if any)
</td>
<td>
_None_
</td> </tr>
<tr>
<td>
**Archiving and preservation (including storage and backup)**
</td> </tr>
<tr>
<td>
Data storage (including backup):
where? For how long?
</td>
<td>
_Due to ethical and privacy issues, data are stored in a database scheme at
the headquarters of MPH, allowing only authorised access to external end-
users. A back up is stored in an external storage device, kept by MPH in a
secured place. Data are kept indefinitely allowing statistical analysis._
</td> </tr> </table>
**Table 29: Dataset description of the CERTH Pressure Mat**
## 4.6. Datasets for intelligent mobility from Hafenstrom AS (HITS)
HITS provided the user requirements specifications and demonstration of
transport domain use case, while actively participating in dissemination and
exploitation activities of the project. By employing knowhow within
standardization bodies, mobility and smart city governance, HITS allowed
municipalities and smart cities to better utilize internal resources and
improve on services offered to citizens and agencies alike.
Furthermore, HITS was responsible for the Use cases “Virtual Neighbourhood of
Buildings for Assisted Living integrated in a Smart Grid Energy Ecosystem” and
“Virtual Neighbourhood of Intelligent (Transport) Parking Space”. Towards this
direction, it was the main partner to bring/arrange the required
infrastructure, in collaboration with other Consortium partners (i.e., TINYM
partner), for the use case demonstration.
<table>
<tr>
<th>
**DS.HITS.01.Parkingsensor**
</th> </tr>
<tr>
<td>
**Data Identification**
</td> </tr>
<tr>
<td>
Dataset description
</td>
<td>
_The sensors will be installed at a test site, and will register proximity of
objects of a certain size. Future subset may contain information about
temperature, humidity, noise, light and other temperature, visual and touch
related data. The sensors main task is to detect if the space is occupied.
This information will later on be integrated with identification in order to
verify that the vehicle/unit that occupies the space is licenced through
either booking or ticketing action being taken._
</td> </tr>
<tr>
<td>
Source (e.g. which device?)
</td>
<td>
_The dataset will be collected through a sensor that is mounted at the parking
site._
</td> </tr>
<tr>
<td>
**Partners services and responsibilities**
</td> </tr>
<tr>
<td>
Partner owner of the device
</td>
<td>
_The device will be the property of the test site owners, where the data
collection is going to be performed_
</td> </tr>
<tr>
<td>
Partner in charge of the data collection (if different)
</td>
<td>
_HITS_
</td> </tr>
<tr>
<td>
Partner in charge of the data
analysis (if different)
</td>
<td>
_HITS_
</td> </tr>
<tr>
<td>
Partner in charge of the data storage (if different)
</td>
<td>
_HITS_
</td> </tr>
<tr>
<td>
WPs and tasks
</td>
<td>
_The data are going to be collected within activities of WP7 and WP8._
</td> </tr>
<tr>
<td>
**Standards**
</td> </tr>
<tr>
<td>
Info about metadata (Production and storage dates, places) and documentation?
</td>
<td>
_The dataset will be accompanied with the respective documentation of its
contents. Indicative metadata include: (a) description of the experimental
setup (e.g. process system, date, etc.) and procedure which is related to the
dataset (e.g. proactive maintenance action, unplanned event, nominal
operation. etc.), (b) scenario related procedures, state of the monitored
activity and involved workers, involved system etc._
</td> </tr>
<tr>
<td>
Standards, Format, Estimated volume of data
</td>
<td>
_The data will be stored at XML format and are estimated to be 50-300 MB per
month._
</td> </tr>
<tr>
<td>
**Data exploitation and sharing**
</td> </tr>
<tr>
<td>
Data exploitation (purpose/use of the data analysis)
</td>
<td>
_Registering parking activity based upon availability, vehicle,
ownership/licence, comparing with nearby infrastructure and surrounding ITS
technology._
</td> </tr>
<tr>
<td>
Data access policy / Dissemination level
(Confidential, only for members of the Consortium and the
Commission Services) / Public
</td>
<td>
_The full dataset will be available to participants in the project. If the
dataset or specific portions of it (e.g. metadata, statistics, etc.) are
decided to become of widely open access, a data management portal will be
created that should provide a description of the dataset and link to a
download section. Of course these data will be anonymized, so as not to have
any potential ethical issues with their publication and dissemination._
</td> </tr>
<tr>
<td>
Data sharing, re-use and
distribution (How?)
</td>
<td>
_The created dataset could be shared by using open APIs through the middleware
as well as a data management portal._
</td> </tr>
<tr>
<td>
Embargo periods (if any)
</td>
<td>
_None_
</td> </tr>
<tr>
<td>
**Archiving and preservation (including storage and backup)**
</td> </tr>
<tr>
<td>
Data storage (including backup):
where? For how long?
</td>
<td>
_Data will be stored in the storage device of the developed system (computer).
A back up will be stored in an external storage device. Data will be kept
indefinitely allowing statistical analysis._
</td> </tr> </table>
**Table 30: Dataset description of the HITS parking sensor**
<table>
<tr>
<th>
**DS.HITS.02.SmartLight**
</th> </tr>
<tr>
<td>
**Data Identification**
</td> </tr>
<tr>
<td>
Dataset description
</td>
<td>
_Smart lights will be installed at the lab, and will demonstrate how light and
colours can indicate the state of access and availability. Future subset may
contain information about proximity, movement, heat sensing (infrared), sound
sensing and door contact sensors. The smart lights main task is to visually
inform about the state of the parking space. This information may later on be
integrated with indicators for occupancy, time to availability and validity._
</td> </tr>
<tr>
<td>
Source (e.g. which device?)
</td>
<td>
_The dataset will be received from a laptop in the lab._
</td> </tr>
<tr>
<td>
**Partners services and responsibilities**
</td> </tr>
<tr>
<td>
Partner owner of the device
</td>
<td>
_The device will be the property of the test site owners, where the data
collection is going to be performed_
</td> </tr>
<tr>
<td>
Partner in charge of the data collection (if different)
</td>
<td>
_HITS_
</td> </tr>
<tr>
<td>
Partner in charge of the data
analysis (if different)
</td>
<td>
_HITS_
</td> </tr>
<tr>
<td>
Partner in charge of the data storage (if different)
</td>
<td>
_HITS_
</td> </tr>
<tr>
<td>
WPs and tasks
</td>
<td>
_The data are going to be collected within activities of WP7 and WP8._
</td> </tr>
<tr>
<td>
**Standards**
</td> </tr>
<tr>
<td>
Info about metadata (Production and storage dates, places) and documentation?
</td>
<td>
_The dataset will be accompanied with the respective documentation of its
contents. Indicative metadata include: (a) description of the experimental
setup (e.g. process system, date, etc.) and procedure which is related to the
dataset (e.g. proactive maintenance action, unplanned event, nominal
operation. etc.), (b) scenario related procedures, state of the monitored
activity and involved workers, involved system etc._
</td> </tr>
<tr>
<td>
Standards, Format, Estimated volume of data
</td>
<td>
_The data will be stored at XML format and are estimated to be 50-300 MB per
month._
</td> </tr>
<tr>
<td>
**Data exploitation and sharing**
</td> </tr>
<tr>
<td>
Data exploitation (purpose/use of the data analysis)
</td>
<td>
_Registering parking activity based upon availability, vehicle,
ownership/licence, comparing with nearby infrastructure and surrounding ITS
technology._
</td> </tr>
<tr>
<td>
Data access policy / Dissemination level
(Confidential, only for members of the Consortium and the
Commission Services) / Public
</td>
<td>
_The full dataset will be available to the members of the consortium. If the
dataset or specific portions of it (e.g. metadata, statistics, etc.) are
decided to become of widely open access, a data management portal will be
created that should provide a description of the dataset and link to a
download section. Of course, these data will be anonymized, so as not to have
any potential ethical issues with their publication and dissemination._
</td> </tr>
<tr>
<td>
Data sharing, re-use and
distribution (How?)
</td>
<td>
_The created dataset could be shared by using open APIs through the middleware
as well as a data management portal._
</td> </tr>
<tr>
<td>
Embargo periods (if any)
</td>
<td>
_None_
</td> </tr>
<tr>
<td>
**Archiving and preservation (including storage and backup)**
</td> </tr>
<tr>
<td>
Data storage (including backup):
where? For how long?
</td>
<td>
_Data will be stored in the storage device of the developed system (computer).
A back up will be stored in an external storage device. Data will be kept
indefinitely allowing statistical analysis._
</td> </tr> </table>
**Table 31: Dataset description of the HITS Smart lighting**
<table>
<tr>
<th>
**DS.HITS.03.LaptopTeststation**
</th> </tr>
<tr>
<td>
**Data Identification**
</td> </tr>
<tr>
<td>
Dataset description
</td>
<td>
_The laptop test station will be installed at the workbench where the operator
normally works, and will aggregate data and process information received
wirelessly from other devices delivering data of relevance to the mobility
domain and parking in particular. Future subset may contain information about
other domains – energy, and data packages from smart home and health-devices.
The test stations main task is to process data and trigger activate and log
actions accordingly._
</td> </tr>
<tr>
<td>
Source (e.g. which device?)
</td>
<td>
_The dataset will be collected wirelessly and via USB ports._
</td> </tr>
<tr>
<td>
**Partners services and responsibilities**
</td> </tr>
<tr>
<td>
Partner owner of the device
</td>
<td>
_The device will be the property of the test site owners, where the data
collection is going to be performed_
</td> </tr>
<tr>
<td>
Partner in charge of the data collection (if different)
</td>
<td>
_HITS_
</td> </tr>
<tr>
<td>
Partner in charge of the data
analysis (if different)
</td>
<td>
_HITS_
</td> </tr>
<tr>
<td>
Partner in charge of the data storage (if different)
</td>
<td>
_HITS_
</td> </tr>
<tr>
<td>
WPs and tasks
</td>
<td>
_The data are going to be collected within activities of WP7 and WP8._
</td> </tr>
<tr>
<td>
**Standards**
</td> </tr>
<tr>
<td>
Info about metadata (Production and storage dates, places) and documentation?
</td>
<td>
_The dataset will be accompanied with the respective documentation of its
contents. Indicative metadata include: (a) description of the experimental
setup (e.g. process system, date, etc.) and procedure which is related to the
dataset (e.g. proactive maintenance action, unplanned event, nominal
operation. etc.), (b) scenario related procedures, state of the monitored
activity and involved workers, involved system etc._
</td> </tr>
<tr>
<td>
Standards, Format, Estimated volume of data
</td>
<td>
_The data will be stored at XML format and are estimated to be 50-300 MB per
month._
</td> </tr>
<tr>
<td>
**Data exploitation and sharing**
</td> </tr>
<tr>
<td>
Data exploitation (purpose/use of the data analysis)
</td>
<td>
_Registering parking activity based upon availability, vehicle,
ownership/licence, comparing with nearby infrastructure and surrounding ITS
technology._
</td> </tr>
<tr>
<td>
Data access policy / Dissemination level
(Confidential, only for members of the Consortium and the
Commission Services) / Public
</td>
<td>
_The full dataset will be available to the members of the consortium. If the
dataset or specific portions of it (e.g. metadata, statistics, etc.) are
decided to become of widely open access, a data management portal will be
created that should provide a description of the dataset and link to a
download section. Of course, these data will be anonymized, so as not to have
any potential ethical issues with their publication and dissemination._
</td> </tr>
<tr>
<td>
Data sharing, re-use and
distribution (How?)
</td>
<td>
_The created dataset could be shared by using open APIs through the middleware
as well as a data management portal._
</td> </tr>
<tr>
<td>
Embargo periods (if any)
</td>
<td>
_None_
</td> </tr>
<tr>
<td>
**Archiving and preservation (including storage and backup)**
</td> </tr>
<tr>
<td>
Data storage (including backup):
where? For how long?
</td>
<td>
_Data will be stored in the storage device of the developed system (computer).
A back up will be stored in an external storage device. Data will be kept
indefinitely allowing statistical analysis._
</td> </tr> </table>
**Table 32: Dataset description of the HITS laptop test station**
<table>
<tr>
<th>
**DS.HITS.04.Sensio_sensors_temperature_motion_lock**
</th> </tr>
<tr>
<td>
**Data Identification**
</td> </tr>
<tr>
<td>
Dataset description
</td>
<td>
_Sensors for measuring temperature, motion detection and identifying status of
door/window lock will be installed in apartments that are managed by
caretakers employed by Tromsø municipality._
_The datasets will contain general information about activities, and offer
insight that building manager, caretakers and medical staff can utilize to
offer better service and trigger messages should deviations situations occur._
</td> </tr>
<tr>
<td>
Source (e.g. which device?)
</td>
<td>
_The dataset will be received from a Sensio gateway that stores the data on an
external server, and made available to a laptop at the pilot site through an
API._
</td> </tr>
<tr>
<td>
**Partners services and responsibilities**
</td> </tr>
<tr>
<td>
Partner owner of the device
</td>
<td>
_The device will be the property of the test site owners, where the data
collection is going to be performed_
</td> </tr>
<tr>
<td>
Partner in charge of the data collection (if different)
</td>
<td>
_HITS_
</td> </tr>
<tr>
<td>
Partner in charge of the data
analysis (if different)
</td>
<td>
_HITS_
</td> </tr>
<tr>
<td>
Partner in charge of the data storage (if different)
</td>
<td>
_HITS_
</td> </tr>
<tr>
<td>
WPs and tasks
</td>
<td>
_The data are going to be collected within activities of WP7 and WP8._
</td> </tr>
<tr>
<td>
**Standards**
</td> </tr>
<tr>
<td>
Info about metadata (Production and storage dates, places) and documentation?
</td>
<td>
_The dataset will contain information on location, and be accompanied with the
respective documentation of its contents. Indicative metadata include:
scenario related procedures, state of the monitored activity and involved
workers, involved system etc._
</td> </tr>
<tr>
<td>
Standards, Format, Estimated volume of data
</td>
<td>
_The data will be stored at XML format and are estimated to be 30-50 MB per
month._
</td> </tr>
<tr>
<td>
**Data exploitation and sharing**
</td> </tr>
<tr>
<td>
Data exploitation (purpose/use of the data analysis)
</td>
<td>
_Identifying usage history used for resource planning and detecting unexpected
activities based on activity or lack of activity, as well as measured values
versus expected data._
</td> </tr>
<tr>
<td>
Data access policy / Dissemination level
(Confidential, only for members of the Consortium and the
Commission Services) / Public
</td>
<td>
_The full dataset will be available to the members of the consortium. Specific
portions will be accessible to building managers and medical staff. Parts of
the data will be anonymised, while other will available through a two-pass
data management porta. For privacy reasons, the data access will be limited,
so configuration will be made in close cooperation with the service provider._
</td> </tr>
<tr>
<td>
Data sharing, re-use and
distribution (How?)
</td>
<td>
_Due to confidentially, the created dataset will only be made accessible
through a data management portal that is open to medical staff and managers._
</td> </tr>
<tr>
<td>
Embargo periods (if any)
</td>
<td>
_None_
</td> </tr>
<tr>
<td>
**Archiving and preservation (including storage and backup)**
</td> </tr>
<tr>
<td>
Data storage (including backup):
where? For how long?
</td>
<td>
_Data will be stored in the storage device of the developed system (computer).
A back up will be stored in an external storage device. Data will be kept
indefinitely allowing statistical analysis._
</td> </tr> </table>
**Table 33: Dataset description of the Sensio sensors**
<table>
<tr>
<th>
**DS.HITS.05.Gorenje_Smart_Appliances_Sensor**
</th> </tr>
<tr>
<td>
**Data Identification**
</td> </tr>
<tr>
<td>
Dataset description
</td>
<td>
_The Gorenje smart appliances installed at the Tromsø pilot site includes a
fridge and an oven. The appliances are managed by caretakers employed by
Tromsø municipality, the tenants themselves and the building manager. The
appliances contain sensors that among other things can measure timestamps and
temperature._
_The data harvested will be used to identify usage history in order to offer
better service, identify abnormal behaviour, and otherwise generate logs that
can be used for statistical analysis._
</td> </tr>
<tr>
<td>
Source (e.g. which device?)
</td>
<td>
_The data will be collected by specific smart appliances (i.e. oven, fridge)
provided by Gorenje and adjusted to VICINITY requirements. The data will be
made available to a laptop at the pilot site through an API._
</td> </tr>
<tr>
<td>
**Partners services and responsibilities**
</td> </tr>
<tr>
<td>
Partner owner of the device
</td>
<td>
_The device will be the property of the test site owners, where the data
collection is going to be performed._
</td> </tr>
<tr>
<td>
Partner in charge of the data collection (if different)
</td>
<td>
_HITS, GORENJE_
</td> </tr>
<tr>
<td>
Partner in charge of the data
analysis (if different)
</td>
<td>
_HITS, GORENJE_
</td> </tr>
<tr>
<td>
Partner in charge of the data storage (if different)
</td>
<td>
_HITS_
</td> </tr>
<tr>
<td>
WPs and tasks
</td>
<td>
_The data are going to be collected within activities of WP6, WP7 and WP8._
</td> </tr>
<tr>
<td>
**Standards**
</td> </tr>
<tr>
<td>
Info about metadata (Production and storage dates, places) and documentation?
</td>
<td>
_The dataset will be accompanied with the respective documentation of its
contents. Indicative metadata may include device id, measurement date, device
owner, state of the monitored activity, etc._
</td> </tr>
<tr>
<td>
Standards, Format, Estimated volume of data
</td>
<td>
_A collection of data is dispatched every 15 minutes. The format is based on
standards provided by Gorenje._
</td> </tr>
<tr>
<td>
**Data exploitation and sharing**
</td> </tr>
<tr>
<td>
Data exploitation (purpose/use of the data analysis)
</td>
<td>
_Usage data to identify behaviour patterns and as mean for training disabled
users in being more self-sufficient are examples are examples of value-added
services that can be built on top of the platform. As the data pool increases,
more services are expected to be included._
</td> </tr>
<tr>
<td>
Data access policy / Dissemination level
(Confidential, only for members of the Consortium and the
Commission Services) / Public
</td>
<td>
_The full dataset of Gorenje devices deployed at the Tromsø pilot site
“Teaterkvarteret 1. Akt”, will be stored at the Gorenje Cloud in a local
experimental repository._
_The full dataset will be available to selected members of the consortium.
Specific portions will be accessible to building managers and medical staff.
Parts of the data will be anonymised, while other will available through a
two-pass data management porta. For privacy reasons, the data access will be
limited, so configuration will be made in close cooperation with the service
provider._
</td> </tr>
<tr>
<td>
Data sharing, re-use and
distribution (How?)
</td>
<td>
_Anonymised parts of the dataset will be available for training and statistic
purposes. Aggregated data that could be used to identify the user or other
privacy related information will be limited. Due to confidentially, the
created dataset will only be made accessible through a data management portal
that is open to medical staff and managers._
</td> </tr>
<tr>
<td>
Embargo periods (if any)
</td>
<td>
_None_
</td> </tr>
<tr>
<td>
**Archiving and preservation (including storage and backup)**
</td> </tr>
<tr>
<td>
Data storage (including backup):
where? For how long?
</td>
<td>
_Data will be stored in the storage device of the developed system (computer).
A back up will be stored in an external storage device. Data will be kept
indefinitely allowing statistical analysis._
</td> </tr> </table>
**Table 34: Dataset description of the Gorenje smart appliances sensor**
## 4.7. Datasets for buildings from Tiny Mesh AS (TINYM)
Tiny Mesh focus on creating new products, services and business model as part
of the Internet-ofEverything (IoE). New potential arises when IoE is used for
connecting, integrating and controlling all kinds of meters, street lights,
sensors, actuators, assets, devices, tags and other devices.
The primary role of Tiny Mesh Company was as a developer and technology
provider, with the company´s IoT solution as the main enabling technology. The
goal was to offer promising technology solutions through participation in use
cases.
TINYM contributed in the practical implementation through their work with
definitions of use case. TINYM took practical ownership of the various demo
sites through the role as of leader of WP7.
<table>
<tr>
<th>
**DS. VITIR.01.Door_Sensor**
</th> </tr>
<tr>
<td>
**Data Identification**
</td> </tr>
<tr>
<td>
Dataset description
</td>
<td>
_The sensors will be installed in the door of a room where there is a need for
monitoring usage._
_Data packet contains sensor data of movement._
</td> </tr>
<tr>
<td>
Source (e.g. which device?)
</td>
<td>
_Discrete digital input_
</td> </tr>
<tr>
<td>
**Partners services and responsibilities**
</td> </tr>
<tr>
<td>
Partner owner of the device
</td>
<td>
_The property owner Tiny-Mesh_
</td> </tr>
<tr>
<td>
Partner in charge of the data collection (if different)
</td>
<td>
_Tiny-Mesh_
</td> </tr>
<tr>
<td>
Partner in charge of the data
analysis (if different)
</td>
<td>
_Tiny-Mesh_
</td> </tr>
<tr>
<td>
Partner in charge of the data storage (if different)
</td>
<td>
_Tiny-Mesh_
</td> </tr>
<tr>
<td>
WPs and tasks
</td>
<td>
_The data are going to be collected within activities of WP7 and WP8._
</td> </tr>
<tr>
<td>
**Standards**
</td> </tr>
<tr>
<td>
Info about metadata (Production and storage dates, places) and documentation?
</td>
<td>
_Metadata about location of the sensor, network topology and network status
will be available in VITIR’s mqtt server.._
</td> </tr>
<tr>
<td>
Standards, Format, Estimated volume of data
</td>
<td>
_Data is delivered as a discrete value indicating if door has been opened or
closed, volume of data depends on the usage._
</td> </tr>
<tr>
<td>
**Data exploitation and sharing**
</td> </tr>
<tr>
<td>
Data exploitation (purpose/use of the data analysis)
</td>
<td>
_The purpose of this collection is to give input data for analysis of room
usage for analyses to the building owner and Facility manager._
</td> </tr>
<tr>
<td>
Data access policy / Dissemination level
(Confidential, only for members of the Consortium and the
Commission Services) / Public
</td>
<td>
_Data access for building manager and facility manager. Data will be
anonymized, so as not to have any potential ethical issues with their
publication and dissemination._
</td> </tr>
<tr>
<td>
Data sharing, re-use and
distribution (How?)
</td>
<td>
_Data access is confidential. Only members of the consortium, building manager
and facility manager will have access on it for privacy reasons._
</td> </tr>
<tr>
<td>
Embargo periods (if any)
</td>
<td>
_-_
</td> </tr>
<tr>
<td>
**Archiving and preservation (including storage and backup)**
</td> </tr>
<tr>
<td>
Data storage (including backup):
where? For how long?
</td>
<td>
_Unless specified otherwise by client the data will be stored in a Value-Added
Service._
</td> </tr> </table>
**Table 35: Dataset description of the VITIR Door Sensor**
<table>
<tr>
<th>
**DS. VITIR.02.CO2 Sensor**
</th> </tr>
<tr>
<td>
**Data Identification**
</td> </tr>
<tr>
<td>
Dataset description
</td>
<td>
_The sensors will be installed to measure CO2 levels. Data packet contains
sensor data of movement._
</td> </tr>
<tr>
<td>
Source (e.g. which device?)
</td>
<td>
_Data is retrieved through industry-standard meters and communicated through
Tiny-Mesh infrastructure before being made available to the consortium._
</td> </tr>
<tr>
<td>
**Partners services and responsibilities**
</td> </tr>
<tr>
<td>
Partner owner of the device
</td>
<td>
_The property owner Tiny-Mesh_
</td> </tr>
<tr>
<td>
Partner in charge of the data collection (if different)
</td>
<td>
_Tiny-Mesh_
</td> </tr>
<tr>
<td>
Partner in charge of the data
analysis (if different)
</td>
<td>
_Tiny-Mesh_
</td> </tr>
<tr>
<td>
Partner in charge of the data storage (if different)
</td>
<td>
_Tiny-Mesh_
</td> </tr>
<tr>
<td>
WPs and tasks
</td>
<td>
_The data are going to be collected within activities of WP7 and WP8._
</td> </tr>
<tr>
<td>
**Standards**
</td> </tr>
<tr>
<td>
Info about metadata (Production and storage dates, places) and documentation?
</td>
<td>
_Metadata about location of the sensor, network topology and network status
will be available in VITIR’s mqtt server._
</td> </tr>
<tr>
<td>
Standards, Format, Estimated volume of data
</td>
<td>
_Communication with meter will be on a proprietary interface according to
meter vendor. Data will be delivered as ppm_
</td> </tr>
<tr>
<td>
**Data exploitation and sharing**
</td> </tr>
<tr>
<td>
Data exploitation (purpose/use of the data analysis)
</td>
<td>
_Purpose is to give additional information about air quality inside various
office spaces to the building management._
</td> </tr>
<tr>
<td>
Data access policy / Dissemination level
(Confidential, only for members of the Consortium and the
Commission Services) / Public
</td>
<td>
_Data access is restricted to the consortium, building manager and facility
manager. Data will be anonymized, so as not to have any potential ethical
issues with their publication and dissemination._
</td> </tr>
<tr>
<td>
Data sharing, re-use and
distribution (How?)
</td>
<td>
_Data access is confidential. Only members of the consortium, building manager
and facility manager will have access on it for privacy reasons._
</td> </tr>
<tr>
<td>
Embargo periods (if any)
</td>
<td>
_-_
</td> </tr>
<tr>
<td>
**Archiving and preservation (including storage and backup)**
</td> </tr>
<tr>
<td>
Data storage (including backup):
where? For how long?
</td>
<td>
_Data will only be stored by the Value Added Service._
</td> </tr> </table>
**Table 36: Dataset description of the VITIR’s sensor for CO2.**
## 4.8. Datasets for buildings from Open Call Winner PilotThings
RTNRG – Real Time EneRGy management
Pilot Things focus is IoT Data as a service: we transform real sensors data to
human data. Our novel approach helps cities and industries to leverage IoT.
Our software Pilot Things is compliant with oneM2M worldwide standard since
the origin thanks to a collaboration with the research laboratory LAAS-CNRS.
oneM2M is based on a centralized architecture with interworking and semantic
representation capabilities. oneM2M suits very well to integrate multiple
buildings protocols. Our project fits in the smart building and energy
management use case. The main objective is to build an infrastructure for
facility management companies to help them deliver energy management services.
The extension objective is to monitor an office building energy consumption
and compare it to a grid production.
The drawing hereunder sum-up the data flow:
**Figure 10: PilotThings data flow**
Here is a dataflow from the plug to VICINITY:
**Figure 11: PilotThings global dataflow**
The oneM2M ADN client connects to oneM2M IN. Pilot Things is also a oneM2M
client. The oneM2M ADN is advised for new plugs data. It then exposes the data
to VICINITY cloud. To understand oneM2M terminology see oneM2M TS001 spec here
http://www.onem2m.org/technical/publisheddrafts/release-3
Documentation and software can be found in GitHub at:
* M2MoneM2M IPE VICINITY adapter: _https://github.com/vicinityh2020/adapter-pilot-things-one_
* This agent gets the OPA dataset and send it to VICINITY network: https://github.com/vicinityh2020/adapter-pilot-things-OPA
SPIE group ask us to connect this project to their IoT solution so called
Colligo. Colligo is based on Thingsworx platform. So we plan to develop an
interface to Thingsworxto send VICINITY and Pilot Things data. This project
will be used by SPIE operational business entities. We also introduced
VICINITY to Vinci Energie group which is a big worldwide SI. We won a smart
city challenge at vivatechlogy show. We will have more information on Vinci
objectives after the show (see _www.vivatechnology.com_ )
During the project we developed an oneM2M zwave IPE (aka driver) allowing us
to connect any commercial zwave product. The oneM2M Pilot Things VICINITY
adapter bridges Pilot Things and VN thus allowing the connection of all the
sensors we support including zwave. We now have more than 200 LoRaWan and
Sigfox sensors preintegrated in our catalog
<table>
<tr>
<th>
**DS. PilotThings.01.Building**
</th> </tr>
<tr>
<td>
**Data Identification**
</td> </tr>
<tr>
<td>
Dataset description
</td>
<td>
_This dataset includes energy consumption measurement points for buildings to
identity usage._
</td> </tr>
<tr>
<td>
Source (e.g. which device?)
</td>
<td>
_The dataset includes smartplugs energy consumption and building global energy
consumption from building management system._
</td> </tr>
<tr>
<td>
**Partners services and responsibilities**
</td> </tr>
<tr>
<td>
Partner owner of the device
</td>
<td>
_The smartplugs and the gateway are Pilot Things property._
</td> </tr>
<tr>
<td>
Partner in charge of the data collection (if different)
</td>
<td>
_Pilot Things_
</td> </tr>
<tr>
<td>
Partner in charge of the data
analysis (if different)
</td>
<td>
_Pilot Things_
</td> </tr>
<tr>
<td>
Partner in charge of the data storage (if different)
</td>
<td>
_Pilot Things_
</td> </tr>
<tr>
<td>
WPs and tasks
</td>
<td>
_The data are going to be collected during OC WP4._
</td> </tr>
<tr>
<td>
**Standards**
</td> </tr>
<tr>
<td>
Info about metadata (Production and storage dates, places) and documentation?
</td>
<td>
_The dataset will be accompanied with the respective documentation of its
contents. Indicative metadata may include device id, measurement date, device
owner, state of the monitored activity, etc._
</td> </tr>
<tr>
<td>
Standards, Format, Estimated volume of data
</td>
<td>
_The data are received using ZWave radio protocol and csv files from the
building management system._
</td> </tr>
<tr>
<td>
**Data exploitation and sharing**
</td> </tr>
<tr>
<td>
Data exploitation (purpose/use of the data analysis)
</td>
<td>
_The collected data are store in Pilot Things database and used to produce
energy consumption dashboards. The building facility manager will have access
to the dashboard and thus the data._
</td> </tr>
<tr>
<td>
Data access policy /
Dissemination level
(Confidential, only for members of the Consortium and the Commission Services)
/ Public
</td>
<td>
_The full dataset will be confidential and only the members of the consortium
will have access on it. Furthermore, if the dataset or specific portions of it
(e.g. metadata, statistics, etc.) are decided to become of widely open access,
a data management portal will be created that should provide a description of
the dataset and link to a download section. Of course, these data will be
anonymized, so as not to have any potential ethical issues with their
publication and dissemination._
</td> </tr>
<tr>
<td>
Data sharing, re-use and distribution (How?)
</td>
<td>
_The created dataset could be shared by using Pilot Things APIs as well as a
data management portal._
</td> </tr>
<tr>
<td>
Embargo periods (if any)
</td>
<td>
_-_
</td> </tr>
<tr>
<td>
**Archiving and preservation (including storage and backup)**
</td> </tr>
<tr>
<td>
Data storage (including backup):
where? For how long?
</td>
<td>
_Data will be store in Pilot Things database for 1 year period._
</td> </tr> </table>
**Table 37: Description of the PilotThings building dataset.**
<table>
<tr>
<th>
**DS. PilotThings.02.OPA_Grid**
</th> </tr>
<tr>
<td>
**Data Identification**
</td> </tr>
<tr>
<td>
Dataset description
</td>
<td>
_This dataset includes solar energy production measurement._
</td> </tr>
<tr>
<td>
Source (e.g. which device?)
</td>
<td>
_The dataset includes the net AC production per minute._
</td> </tr>
<tr>
<td>
**Partners services and responsibilities**
</td> </tr>
<tr>
<td>
Partner owner of the device
</td>
<td>
_The solar premises are LAAS/CNRS property._
</td> </tr>
<tr>
<td>
Partner in charge of the data collection (if different)
</td>
<td>
_Pilot Things gateway retrieves the data._
</td> </tr>
<tr>
<td>
Partner in charge of the data
analysis (if different)
</td>
<td>
_Pilot Things_
</td> </tr>
<tr>
<td>
Partner in charge of the data storage (if different)
</td>
<td>
_Pilot Things_
</td> </tr>
<tr>
<td>
WPs and tasks
</td>
<td>
_The data are going to be collected during OC WP4._
</td> </tr>
<tr>
<td>
**Standards**
</td> </tr>
<tr>
<td>
Info about metadata (Production and storage dates, places) and documentation?
</td>
<td>
_The dataset will be accompanied with the respective documentation of its
contents. Indicative metadata may include device id, measurement date, device
owner, state of the monitored activity, etc._
</td> </tr>
<tr>
<td>
Standards, Format, Estimated volume of data
</td>
<td>
_The data format is acsv files from the OPA._
</td> </tr>
<tr>
<td>
**Data exploitation and sharing**
</td> </tr>
<tr>
<td>
Data exploitation (purpose/use of the data analysis)
</td>
<td>
_The collected data are store in Pilot Things database and used to produce
energy consumption dashboards. The building facility manager will have access
to the dashboard and thus the data._
</td> </tr>
<tr>
<td>
Data access policy /
Dissemination level
(Confidential, only for members of the Consortium and the Commission Services)
/ Public
</td>
<td>
_The full dataset will be confidential and only the members of the consortium
will have access on it. Furthermore, if the dataset or specific portions of it
(e.g. metadata, statistics, etc.) are decided to become of widely open access,
a data management portal will be created that should provide a description of
the dataset and link to a download section. Of course, these data will be
anonymized, so as not to have any potential ethical issues with their
publication and dissemination._
</td> </tr>
<tr>
<td>
Data sharing, re-use and distribution (How?)
</td>
<td>
_The created dataset could be shared by using Pilot Things APIs as well as a
data management porta._
</td> </tr>
<tr>
<td>
Embargo periods (if any)
</td>
<td>
_-_
</td> </tr>
<tr>
<td>
**Archiving and preservation (including storage and backup)**
</td> </tr>
<tr>
<td>
Data storage (including backup):
where? For how long?
</td>
<td>
_Data will be store in Pilot Things database for 1-year period._
</td> </tr> </table>
**Table 38: Description of the PilotThings OPA Grid-dataset.**
## 4.9. Datasets for buildings from Open Call Winner WearHealth
WearHealth – Safety and Health Intelligence
**WearHealth is an intelligent decision support system that can use any 3rd
party wearables and IoT devices. 3rd party hardware is integrated into our
proprietary cognitive technologies to build an intelligent software platform
that can identify markers of workers’ occupational risks, health, workload,
and work efficiency. With these insights, businesses can detect and predict
worker safety and health risks, thus preventing accidents and managing their
workforce to improve productivity and operational efficiency.**
IoT communication infrastructures and data stream integrations are not yet
standardized in the industry and need to comply with the EU General Data
Protection Regulation. The goal of this project is to further develop our
technology and market WearHealth version 2.0 that includes a modularity
approach to connect to wearables and process the data integrated into VICINITY
following its decentralized interoperability concept.
The key functionality of the adapter is to upload semantic data from the
wearable to the Gateway. We have also implemented a Value-Added Service (VAS)
to provide real-time response to another service/adapter. The semantic data
for the service and adapter was stored in the VICINITY Cloud using the
VICINITY Neighbourhood Manager. For this, we used the VICINITY gateway API and
VICINITY agent API. The integration of wearables and our cloud services was
planned together with the VICINITY technical team and the final system was
deployed and tested in an industrial environment. The key results are: we have
built an adapter for a wearable (smart shirt) and a value-added service,
uploaded semantic data to the VICINITY cloud, improved data privacy and
security for our solution and validated the value of the integration of our
cloud service with VICINITY.
Key elements of the final WearHealth – VICINITY integration are the VICINITY
gateway API and VICINITY agent API to communicate and deliver access to the
wearables sensor semantic data to the VICINITY Neighbourhood Manager. We have
developed a custom adapter in order to access the semantic data from the
wearable via BLE and a gateway. Moreover, a value-added service (VAS) was
developed to provide real-time feedback based on the raw data from the
wearable.
The smart shirt has been successfully registered to the VICINITY Neighbourhood
Manager by integrating our gateway. The data from the smart shirt was
collected in an industrial environment. Due to the VICINITY integration, we
have validated the improvements in terms of data privacy and the value for
potential customers. In this report, we will show the final results of the
integrations and evaluations.
WearHealth improve data privacy and transparency in such form that:
* The user has full control over his own data
* Conclusions only at an aggregate level, not from each user
* All data is anonymized and stored securely
* No data shared with third parties
* No data collected without the explicit and voluntary consent of the use
* No data is used for purposes other than occupational safety and health
Based on the results of this project, WearHealth will strongly focus on the
further improving data privacy strategy and access to additional data sets to
increase the service.
<table>
<tr>
<th>
**DS. WearHealth.01.SmartShirt**
</th> </tr>
<tr>
<td>
**Data Identification**
</td> </tr>
<tr>
<td>
Dataset description
</td>
<td>
_This dataset includes a one channel ECG (Electrocardiogram) to generate heart
rate, R-R intervals, steps, activity and motion data of the users._
_This wearable uses which_
</td> </tr>
<tr>
<td>
Source (e.g. which device?)
</td>
<td>
_The main input device in the present use-case is the smart-shirt from
Ambiotex using standard BLE (Bluetooth Low Energy) transmission._
</td> </tr>
<tr>
<td>
**Partners services and responsibilities**
</td> </tr>
<tr>
<td>
Partner owner of the device
</td>
<td>
_Private owned_
</td> </tr>
<tr>
<td>
Partner in charge of the data collection (if different)
</td>
<td>
_WearHealth_
</td> </tr>
<tr>
<td>
Partner in charge of the data
analysis (if different)
</td>
<td>
_WearHealth_
</td> </tr>
<tr>
<td>
Partner in charge of the data storage (if different)
</td>
<td>
_WearHealth_
</td> </tr>
<tr>
<td>
WPs and tasks
</td>
<td>
_-_
</td> </tr>
<tr>
<td>
**Standards**
</td> </tr>
<tr>
<td>
Info about metadata (Production and storage dates, places) and documentation?
</td>
<td>
_-_
</td> </tr>
<tr>
<td>
Standards, Format, Estimated volume of data
</td>
<td>
_-_
</td> </tr>
<tr>
<td>
**Data exploitation and sharing**
</td> </tr>
<tr>
<td>
Data exploitation (purpose/use of the data analysis)
</td>
<td>
_WearHealth provides analysis on the data from the smart shirt such as heart
rate and heart rate variability to detect high workers workload to reduce
potential accidents and health related problems._
</td> </tr>
<tr>
<td>
Data access policy /
Dissemination level
(Confidential, only for members of the Consortium and the Commission Services)
/ Public
</td>
<td>
_The Worker decides who has access to the data._
</td> </tr>
<tr>
<td>
Data sharing, re-use and distribution (How?)
</td>
<td>
_The owner of the data (User or worker) will be able to share his data with
other solutions providers. The collected data during the project can be made
available in_
_anonymized form to partners for additional services. Test users (volunteers)
will sign specific documentation to allow the processing of their raw data._
</td> </tr>
<tr>
<td>
Embargo periods (if any)
</td>
<td>
_-_
</td> </tr>
<tr>
<td>
**Archiving and preservation (including storage and backup)**
</td> </tr>
<tr>
<td>
Data storage (including backup):
where? For how long?
</td>
<td>
_Data will be stored in the WearHealth cloud and compliant with the GDPR. Key
features are:_
* _Personal data is stored anonymously_
* _Encrypted data transmission and storage_
* _Data is stored in a private European data base_
* _The right to be forgotten_
* _Data is stored for the shortest time possible The following standards are employed:_
* _SHA-256_
* _SSL_
* _AES-COM at the BLE_
* _Login information are raw data are stored in physically_
* _separated servers_
* _Passwords expire every 60 days_
</td> </tr> </table>
**Table 39: Description of the WearHealth dataset.**
## 4.10. Datasets for yachting marina from Open Call Winner SaMMY
**SaMMY is an IoT cloud-based service solution for the Yachting Marina
ecosystem, which enables the Marinas to efficiently manage their resources, to
attract and efficiently service more yachts and support the local markets by
linking them with the yachting community. Its main goal is to simplify
Yachting Marinas’ operational processes, strengthening the yachters’
engagement with them as well as with the surrounding economies and also offer
easy-to-use online services to yachters and skippers.**
Taking advantage of its open architecture (FIWARE enablers and RESTful data
model interfaces) SaMMY can definitely contribute to any IoT ‘Smart City’
or/and ‘Smart Mobility’ ecosystem, enhancing the availability of Yachting-
Marina sector data resources.
VICINITY Adapter for SaMMY code repository has been uploaded in VICINITY
Github repository in URL:
_https://github.com/vicinityh2020/vicinity-adapter-sammy_
VICINITY SaMMY demo IoT dashboard for code repository has been uploaded in
VICINITY Github repository in URL: _https://github.com/vicinityh2020/vicinity-
sammy-demo_
A demo application illustrating the integration between SaMMY IoT platform and
VICINITY infrastructure is publicly available at:
https://vicinity.optionsnet.gr/demo/. The application is a web application
developed with Spring Boot, Thymeleaf and other web technologies. The values
are continuously refreshed every 10-seconds, a value that is configurable in
the application.properties files. However, user can directly request an IoT
value by pressing “Query Now” link at the desired IoT.
SaMMYacht IoT platform neither gathers nor handles any kind of personal data
as its infrastructure involves environmental and occupancy sensors. Sensors’
data are saved and kept for statistical/historical and evaluation reasons in
the cloud database of the SaMMYacht platform. On the other hand, SaMMY-
VICINITY integration does not consider these historical data, since Vicinity
adapter implemented for this integration only handles the current values of
the sensors, which in turn are ot saved. Moreover, no aggregated data
regarding the integration of the SaMMY-Vicinity are stored.
Finally, the demonstration webapp only keeps the last set of sensors’ data in
order to minimize querying the SaMMY infrastructure, which are in turn served
in the demo UI.
Concluding, due to the nature of the SaMMY IoT platform sensors and data, only
READ operations are allowed and supported, since platform does not involve any
kind of services or actuators at the IoT level. That being said, malicious
actions won‘t have any effect.
<table>
<tr>
<th>
**DS. SaMMY.Patras.Air_Temperature**
</th> </tr>
<tr>
<td>
**Data Identification**
</td> </tr>
<tr>
<td>
Dataset description
</td>
<td>
_Sensor for measuring Ait temperature, have been installed in Patras Port
Mooring for Mega-yachts._
</td> </tr>
<tr>
<td>
Source (e.g. which device?)
</td>
<td>
_The sensor that feed this dataset is Libelium Smart City Plug &Sence - Air
Temperature probe). _
</td> </tr>
<tr>
<td>
**Partners services and responsibilities**
</td> </tr>
<tr>
<td>
Partner owner of the device
</td>
<td>
_SaMMYacht (Patras Port Mooring / Patras Port Authority)._
</td> </tr>
<tr>
<td>
Partner in charge of the data collection (if different)
</td>
<td>
_SaMMYacht_
</td> </tr>
<tr>
<td>
Partner in charge of the data
analysis (if different)
</td>
<td>
_SaMMYacht_
</td> </tr>
<tr>
<td>
Partner in charge of the data storage (if different)
</td>
<td>
_SaMMYacht_
</td> </tr>
<tr>
<td>
WPs and tasks
</td>
<td>
_The data are already collected._
</td> </tr>
<tr>
<td>
**Standards**
</td> </tr>
<tr>
<td>
Info about metadata (Production and storage dates, places) and documentation?
</td>
<td>
_The dataset will contain information for Environment Temperature on Patras
Port Mega Yacht Mooring and geolocation info._
</td> </tr>
<tr>
<td>
Standards, Format, Estimated volume of data
</td>
<td>
_The data will be stored to the CLOUD and are estimated to be 5 MB per month._
</td> </tr>
<tr>
<td>
**Data exploitation and sharing**
</td> </tr>
<tr>
<td>
Data exploitation (purpose/use of the data analysis)
</td>
<td>
_Data exploitation is foreseen to be extended through envisioned value-added
services, allowing full access to specific authorised users._
</td> </tr>
<tr>
<td>
Data access policy /
Dissemination level
(Confidential, only for members of the Consortium and the Commission Services)
/ Public
</td>
<td>
_The full dataset will be available to the members of the consortium or
public._
</td> </tr>
<tr>
<td>
Data sharing, re-use and distribution (How?)
</td>
<td>
_The created dataset could be shared by using open APIs through the
middleware._
</td> </tr>
<tr>
<td>
Embargo periods (if any)
</td>
<td>
_None_
</td> </tr>
<tr>
<td>
**Archiving and preservation (including storage and backup)**
</td> </tr>
<tr>
<td>
Data storage (including backup):
where? For how long?
</td>
<td>
_Data will be stored in the storage device on the Cloud. A back up will be
stored in an external cloud storage device. Data will be kept indefinitely
allowing statistical analysis._
</td> </tr> </table>
**Table 40: Description of the Air Temperature dataset.**
<table>
<tr>
<th>
**DS. SaMMY.Patras.Humidity**
</th> </tr>
<tr>
<td>
**Data Identification**
</td> </tr>
<tr>
<td>
Dataset description
</td>
<td>
_Sensor for measuring Humidity, has been installed in Patras Port Mooring for
Mega-yachts._
</td> </tr>
<tr>
<td>
Source (e.g. which device?)
</td>
<td>
_The sensor that feed this dataset is Libelium Smart City Plug &Sence -
Humitidy probe). _
</td> </tr>
<tr>
<td>
**Partners services and responsibilities**
</td> </tr>
<tr>
<td>
Partner owner of the device
</td>
<td>
_SaMMYacht (Patras Port Mooring / Patras Port Authority)._
</td> </tr>
<tr>
<td>
Partner in charge of the data collection (if different)
</td>
<td>
_SaMMYacht_
</td> </tr>
<tr>
<td>
Partner in charge of the data
analysis (if different)
</td>
<td>
_SaMMYacht_
</td> </tr>
<tr>
<td>
Partner in charge of the data storage (if different)
</td>
<td>
_SaMMYacht_
</td> </tr>
<tr>
<td>
WPs and tasks
</td>
<td>
_The data are already collected._
</td> </tr>
<tr>
<td>
**Standards**
</td> </tr>
<tr>
<td>
Info about metadata (Production and storage dates, places) and documentation?
</td>
<td>
_The dataset will contain information for Environment Humidity on Patras Port
Mega Yacht Mooring and geo-location info._
</td> </tr>
<tr>
<td>
Standards, Format, Estimated volume of data
</td>
<td>
_The data will be stored to the CLOUD and are estimated to be 5 MB per month._
</td> </tr>
<tr>
<td>
**Data exploitation and sharing**
</td> </tr>
<tr>
<td>
Data exploitation (purpose/use of the data analysis)
</td>
<td>
_Data exploitation is foreseen to be extended through envisioned value-added
services, allowing full access to specific authorised users._
</td> </tr>
<tr>
<td>
Data access policy /
Dissemination level
(Confidential, only for members of the Consortium and the Commission Services)
/ Public
</td>
<td>
_The full dataset will be available to the members of the consortium or
public._
</td> </tr>
<tr>
<td>
Data sharing, re-use and distribution (How?)
</td>
<td>
_The created dataset could be shared by using open APIs through the
middleware._
</td> </tr>
<tr>
<td>
Embargo periods (if any)
</td>
<td>
_None_
</td> </tr>
<tr>
<td>
**Archiving and preservation (including storage and backup)**
</td> </tr>
<tr>
<td>
Data storage (including backup):
where? For how long?
</td>
<td>
_Data will be stored in the storage device on the Cloud. A back up will be
stored in an external cloud storage device. Data will be kept indefinitely
allowing statistical analysis._
</td> </tr> </table>
**Table 41: Description of the Humidity dataset.**
<table>
<tr>
<th>
**DS. SaMMY.Patras.Water_Temperature**
</th> </tr>
<tr>
<td>
**Data Identification**
</td> </tr>
<tr>
<td>
Dataset description
</td>
<td>
_Sensor for measuring Water Temperature, has been installed in Patras Port
Mooring for Mega-yachts._
</td> </tr>
<tr>
<td>
Source (e.g. which device?)
</td>
<td>
_The sensor that feed this dataset is Libelium Smart Water Plug &Sence - Water
Temperature probe). _
</td> </tr>
<tr>
<td>
**Partners services and responsibilities**
</td> </tr>
<tr>
<td>
Partner owner of the device
</td>
<td>
_SaMMYacht (Patras Port Mooring / Patras Port Authority)._
</td> </tr>
<tr>
<td>
Partner in charge of the data collection (if different)
</td>
<td>
_SaMMYacht_
</td> </tr>
<tr>
<td>
Partner in charge of the data
analysis (if different)
</td>
<td>
_SaMMYacht_
</td> </tr>
<tr>
<td>
Partner in charge of the data storage (if different)
</td>
<td>
_SaMMYacht_
</td> </tr>
<tr>
<td>
WPs and tasks
</td>
<td>
_The data are already collected._
</td> </tr>
<tr>
<td>
**Standards**
</td> </tr>
<tr>
<td>
Info about metadata (Production and storage dates, places) and documentation?
</td>
<td>
_The dataset will contain information for Water Temperature on Patras Port
Mega Yacht Mooring and geo-location info._
</td> </tr>
<tr>
<td>
Standards, Format, Estimated volume of data
</td>
<td>
_The data will be stored to the CLOUD and are estimated to be 5 MB per month._
</td> </tr>
<tr>
<td>
**Data exploitation and sharing**
</td> </tr>
<tr>
<td>
Data exploitation (purpose/use of the data analysis)
</td>
<td>
_Data exploitation is foreseen to be extended through envisioned value-added
services, allowing full access to specific authorised users._
</td> </tr>
<tr>
<td>
Data access policy /
Dissemination level
(Confidential, only for members of the Consortium and the Commission Services)
/ Public
</td>
<td>
_The full dataset will be available to the members of the consortium or
public._
</td> </tr>
<tr>
<td>
Data sharing, re-use and distribution (How?)
</td>
<td>
_The created dataset could be shared by using open APIs through the
middleware._
</td> </tr>
<tr>
<td>
Embargo periods (if any)
</td>
<td>
_None_
</td> </tr>
<tr>
<td>
**Archiving and preservation (including storage and backup)**
</td> </tr>
<tr>
<td>
Data storage (including backup):
where? For how long?
</td>
<td>
_Data will be stored in the storage device on the Cloud. A back up will be
stored in an external cloud storage device. Data will be kept indefinitely
allowing statistical analysis._
</td> </tr> </table>
**Table 42: Description of the Water Temperature dataset.**
<table>
<tr>
<th>
**DS. SaMMY.Patras.Water_pH**
</th> </tr>
<tr>
<td>
**Data Identification**
</td> </tr>
<tr>
<td>
Dataset description
</td>
<td>
_Sensor for measuring Water pH, has been installed in Patras Port Mooring for
Mega-yachts._
</td> </tr>
<tr>
<td>
Source (e.g. which device?)
</td>
<td>
_The sensor that feed this dataset is Libelium Smart Water Plug &Sence - Water
pH probe). _
</td> </tr>
<tr>
<td>
**Partners services and responsibilities**
</td> </tr>
<tr>
<td>
Partner owner of the device
</td>
<td>
_SaMMYacht (Patras Port Mooring / Patras Port Authority)._
</td> </tr>
<tr>
<td>
Partner in charge of the data collection (if different)
</td>
<td>
_SaMMYacht_
</td> </tr>
<tr>
<td>
Partner in charge of the data
analysis (if different)
</td>
<td>
_SaMMYacht_
</td> </tr>
<tr>
<td>
Partner in charge of the data storage (if different)
</td>
<td>
_SaMMYacht_
</td> </tr>
<tr>
<td>
WPs and tasks
</td>
<td>
_The data are already collected._
</td> </tr>
<tr>
<td>
**Standards**
</td> </tr>
<tr>
<td>
Info about metadata (Production and storage dates, places) and documentation?
</td>
<td>
_The dataset will contain information for Water pH on Patras Port Mega Yacht
Mooring and geo-location info._
</td> </tr>
<tr>
<td>
Standards, Format, Estimated volume of data
</td>
<td>
_The data will be stored to the CLOUD and are estimated to be 5 MB per month._
</td> </tr>
<tr>
<td>
**Data exploitation and sharing**
</td> </tr>
<tr>
<td>
Data exploitation (purpose/use of the data analysis)
</td>
<td>
_Data exploitation is foreseen to be extended through envisioned value-added
services, allowing full access to specific authorised users._
</td> </tr>
<tr>
<td>
Data access policy /
Dissemination level
(Confidential, only for members of the Consortium and the Commission Services)
/ Public
</td>
<td>
_The full dataset will be available to the members of the consortium or
public._
</td> </tr>
<tr>
<td>
Data sharing, re-use and distribution (How?)
</td>
<td>
_The created dataset could be shared by using open APIs through the
middleware._
</td> </tr>
<tr>
<td>
Embargo periods (if any)
</td>
<td>
_None_
</td> </tr>
<tr>
<td>
**Archiving and preservation (including storage and backup)**
</td> </tr>
<tr>
<td>
Data storage (including backup):
where? For how long?
</td>
<td>
_Data will be stored in the storage device on the Cloud. A back up will be
stored in an external cloud storage device. Data will be kept indefinitely
allowing statistical analysis._
</td> </tr> </table>
**Table 43: Description of the Water pH dataset.**
<table>
<tr>
<th>
**DS. SaMMY.Patras.Water_ORP**
</th> </tr>
<tr>
<td>
**Data Identification**
</td> </tr>
<tr>
<td>
Dataset description
</td>
<td>
_Sensor for measuring Water Oxidation Reduction Potential, has been installed
in Patras Port Mooring for Mega-yachts._
</td> </tr>
<tr>
<td>
Source (e.g. which device?)
</td>
<td>
_The sensor that feed this dataset is Libelium Smart Water Plug &Sence - water
ORP probe). _
</td> </tr>
<tr>
<td>
**Partners services and responsibilities**
</td> </tr>
<tr>
<td>
Partner owner of the device
</td>
<td>
_SaMMYacht (Patras Port Mooring / Patras Port Authority)._
</td> </tr>
<tr>
<td>
Partner in charge of the data collection (if different)
</td>
<td>
_SaMMYacht_
</td> </tr>
<tr>
<td>
Partner in charge of the data
analysis (if different)
</td>
<td>
_SaMMYacht_
</td> </tr>
<tr>
<td>
Partner in charge of the data storage (if different)
</td>
<td>
_SaMMYacht_
</td> </tr>
<tr>
<td>
WPs and tasks
</td>
<td>
_The data are already collected._
</td> </tr>
<tr>
<td>
**Standards**
</td> </tr>
<tr>
<td>
Info about metadata (Production and storage dates, places) and documentation?
</td>
<td>
_The dataset will contain information for Water ORP on Patras Port Mega Yacht
Mooring and geo-location info._
</td> </tr>
<tr>
<td>
Standards, Format, Estimated volume of data
</td>
<td>
_The data will be stored to the CLOUD and are estimated to be 5 MB per month._
</td> </tr>
<tr>
<td>
**Data exploitation and sharing**
</td> </tr>
<tr>
<td>
Data exploitation (purpose/use of the data analysis)
</td>
<td>
_Data exploitation is foreseen to be extended through envisioned value-added
services, allowing full access to specific authorised users._
</td> </tr>
<tr>
<td>
Data access policy /
Dissemination level
(Confidential, only for members of the Consortium and the Commission Services)
/ Public
</td>
<td>
_The full dataset will be available to the members of the consortium or
public._
</td> </tr>
<tr>
<td>
Data sharing, re-use and distribution (How?)
</td>
<td>
_The created dataset could be shared by using open APIs through the
middleware._
</td> </tr>
<tr>
<td>
Embargo periods (if any)
</td>
<td>
_None_
</td> </tr>
<tr>
<td>
**Archiving and preservation (including storage and backup)**
</td> </tr>
<tr>
<td>
Data storage (including backup):
where? For how long?
</td>
<td>
_Data will be stored in the storage device on the Cloud. A back up will be
stored in an external cloud storage device. Data will be kept indefinitely
allowing statistical analysis._
</td> </tr> </table>
**Table 44: Description of the Water ORP dataset.**
<table>
<tr>
<th>
**DS. SaMMY.Patras.BerthSpace(5-15)_Occupancy**
</th> </tr>
<tr>
<td>
**Data Identification**
</td> </tr>
<tr>
<td>
Dataset description
</td>
<td>
_Sensors for monitoring berth spaces availability at Patras Port Mooring for
Mega-yachts._
</td> </tr>
<tr>
<td>
Source (e.g. which device?)
</td>
<td>
_The sensors that feed this dataset is Libelium Smart City Plug &Sence -
UltraSound probes). _
</td> </tr>
<tr>
<td>
**Partners services and responsibilities**
</td> </tr>
<tr>
<td>
Partner owner of the device
</td>
<td>
_SaMMYacht (Patras Port Mooring / Patras Port Authority)._
</td> </tr>
<tr>
<td>
Partner in charge of the data collection (if different)
</td>
<td>
_SaMMYacht_
</td> </tr>
<tr>
<td>
Partner in charge of the data
analysis (if different)
</td>
<td>
_SaMMYacht_
</td> </tr>
<tr>
<td>
Partner in charge of the data storage (if different)
</td>
<td>
_SaMMYacht_
</td> </tr>
<tr>
<td>
WPs and tasks
</td>
<td>
_The data are already collected._
</td> </tr>
<tr>
<td>
**Standards**
</td> </tr>
<tr>
<td>
Info about metadata (Production and storage dates, places) and documentation?
</td>
<td>
_The dataset will contain information for berth space availability at Patras
Port Mega Yacht Mooring and geo-location info._
</td> </tr>
<tr>
<td>
Standards, Format, Estimated volume of data
</td>
<td>
_The data will be stored to the CLOUD and are estimated to be 5 MB per month._
</td> </tr>
<tr>
<td>
**Data exploitation and sharing**
</td> </tr>
<tr>
<td>
Data exploitation (purpose/use of the data analysis)
</td>
<td>
_Data exploitation is foreseen to be extended through envisioned value-added
services, allowing full access to specific authorised users._
</td> </tr>
<tr>
<td>
Data access policy /
Dissemination level
(Confidential, only for members of the Consortium and the Commission Services)
/ Public
</td>
<td>
_The full dataset will be available to the members of the consortium or
public._
</td> </tr>
<tr>
<td>
Data sharing, re-use and distribution (How?)
</td>
<td>
_The created dataset could be shared by using open APIs through the
middleware._
</td> </tr>
<tr>
<td>
Embargo periods (if any)
</td>
<td>
_None_
</td> </tr>
<tr>
<td>
**Archiving and preservation (including storage and backup)**
</td> </tr>
<tr>
<td>
Data storage (including backup):
where? For how long?
</td>
<td>
_Data will be stored in the storage device on the Cloud. A back up will be
stored in an external cloud storage device. Data will be kept indefinitely
allowing statistical analysis._
</td> </tr> </table>
**Table 45: Description of the Berth Spaces occupancy dataset.**
## 4.11. Datasets for INCANT from Open Call Winner Thinkinside Srl
**INCANT integrates IoT infrastructures providing indoor positioning data and
the relevant supporting services into the VICINITY ecosystem.**
Location is a first-class citizen in IoT: it can play a role in the
aggregation/processing of IoT-generated information (e.g., spatial
aggregation) or it can be the data point itself (e.g., tracking). In
particular, we focus on indoor location, where the support for location-based
services can enable and augment a rich set of application scenarios, ranging
from retail marketing, interactive spaces, to healthcare medical portable
equipment tracking, facility management and manufacturing safety of personnel,
process monitoring.
INCANT will provide a set of standard and actionable APIs, based upon the
VICINITY ones, able to overcome such fragmentation of technologies and
services in the indoor positioning area, enabling interoperability across
indoor location technology, platforms and domains with the aim of unlocking
the full potential of IoT applications. Break silos, avoid duplication of
efforts and infrastructures) will be one of the primary outcomes of the INCANT
proposal for the VICINITY ecosystem.
During the initial months of the project, we have performed the initial design
of the INCANT architecture and corresponding mapping to the existing VICINITY
infrastructure. The Real Time Locating System (RTLS) is an infrastructure used
to locate assets inside an indoor environment. It is typically based on a
network of localisation antennas connected with each other, and able to
localise a number of transmitting devices. A transmitting device can be either
a TAG or a mobile device such as, e.g., a Smartphone. The output of an RTLS
system is typically a timestamped series of location coordinates.
All adapters have been implemented and tested. The adapters allow to introduce
localisation into the IoT scenarios supported by VICINITY. The INCANT adapters
have been released as open source and can be accessed in the VICINITY GitHub
at the following link:
https://github.com/vicinityh2020/vicinity-adapter-thinkinside
The INCANT project aims at proving an indoor localisation service to the
VICINITY infrastructure and ecosystem. This will be based on the ThinkIN
platform and technology, integrated with a set of VICNITY adapters for the
collection and sharing of location data streams. This will allow to abstract
developers from the creation and management of an RTLS infrastructure. No data
will be stored in the INCANT infrastructure and no specific knowledge is
present beforehand on the Object being localised and tracked. In terms of
system architecture, the INCANT project will be running in (i) the ThinkIN
cloud infrastructure (ii) a local deployment managing the RTLS component and
the integration with VICINITY (iii) the VICINITY infrastructure. The data
exchanges between all components will occur over secure connections (https)
thus ensuring the end-to-end confidentiality and security of the data being
exchanged.
Developers or System integrators will rely on INCANT to develop specific
application scenarios. This will range from retail applications, to smart
manufacturing and smart buildings. In all cases, they will be in charge of
defining the specific policy according to which data will be managed and in
case enforce any privacy protection action that might be required to comply
with the current GDPR regulation. For the INCANT pilot and demonstrator we
have utilised a retail scenario, where we have tracked anonymously the
shopping carts and baskets moving in a retail store. Each asset is not linked
to any personal information (e.g., shopper ID or other personal identifier)
and data is utilised for store operational efficiency (e.g., detect queuing
patterns at checkouts).
<table>
<tr>
<th>
**DS. ThinkInside.01.INCANT**
</th> </tr>
<tr>
<td>
**Data Identification**
</td> </tr>
<tr>
<td>
Dataset description
</td>
<td>
_No relevant information provided._
</td> </tr>
<tr>
<td>
Source (e.g. which device?)
</td>
<td>
_No relevant information provided._
</td> </tr>
<tr>
<td>
**Partners services and responsibilities**
</td> </tr>
<tr>
<td>
Partner owner of the device
</td>
<td>
_ThinkInside_
</td> </tr>
<tr>
<td>
Partner in charge of the data collection (if different)
</td>
<td>
_ThinkInside_
</td> </tr>
<tr>
<td>
Partner in charge of the data
analysis (if different)
</td>
<td>
_ThinkInside_
</td> </tr>
<tr>
<td>
Partner in charge of the data storage (if different)
</td>
<td>
_ThinkInside_
</td> </tr>
<tr>
<td>
WPs and tasks
</td>
<td>
_None_
</td> </tr>
<tr>
<td>
**Standards**
</td> </tr>
<tr>
<td>
Info about metadata (Production and storage dates, places) and documentation?
</td>
<td>
_No relevant information provided._
</td> </tr>
<tr>
<td>
Standards, Format, Estimated volume of data
</td>
<td>
_No relevant information provided._
</td> </tr>
<tr>
<td>
**Data exploitation and sharing**
</td> </tr>
<tr>
<td>
Data exploitation (purpose/use of the data analysis)
</td>
<td>
_No relevant information provided._
</td> </tr>
<tr>
<td>
Data access policy /
Dissemination level
(Confidential, only for members of the Consortium and the Commission Services)
/ Public
</td>
<td>
_No relevant information provided._
</td> </tr>
<tr>
<td>
Data sharing, re-use and distribution (How?)
</td>
<td>
_No relevant information provided._
</td> </tr>
<tr>
<td>
Embargo periods (if any)
</td>
<td>
_No relevant information provided._
</td> </tr>
<tr>
<td>
**Archiving and preservation (including storage and backup)**
</td> </tr>
<tr>
<td>
Data storage (including backup):
where? For how long?
</td>
<td>
_No relevant information provided._
</td> </tr> </table>
**Table 46: Description of the INCANT dataset.**
## 4.12. Datasets for F2IS-VAS from Open Call Winner Sensinov
Ensuring accurate datasets through fault monitoring and isolation is crucial
for operational Internet of Things (IoT) deployments. In fact, IoT devices and
sensors can generate incorrect measurements which can be attributed to
software and hardware issues. As an example, if an IoT system is used to
perform predictive maintenance of a smart building, the collected IoT datasets
must accurately reflect the status of the monitored system. Continually
monitoring and isolating faults is an important feature in IoT.
The objective of F2I-VAS project is experimenting and validating a Value-Added
Service for fault detection in a smart building environment. F2IS-VAS relies
on IBM Watson IoT platform to capture incorrect measurements which can be
attributed to software and hardware issues.
F2I-VAS objectives are articulated around the following aspects:
* Integration of VICINITY with IBM Watson IoT platform to offer fault detection as a service providing insights to dashboard application of smart building operators. Fault detection with IBM Watson IoT is based on both machine learning of data sets as well as detection models known as SPSS (Statistical Package for the Social Sciences). We refine SPSS models for data sets pertaining to Smart Building.
* To integrate smart building infrastructure and fault detection and isolation capabilities, we need to develop adapters that translate between NGSI-LD model and the VICINITY Thing Description and VICINITY ontology to Watson IoT data model.
* To enhance the learning capabilities of our value-added service, we bring different datasets types from smart building.
Djane is available in sensinov github. Link:
_https://github.com/sensinov/djane_
The anomaly detection service is available in this link _:_
_https://github.com/mbenalaya/VCNT_F2I_VAS_
<table>
<tr>
<th>
**DS.Sensinov.01.F2IS-VAS**
</th> </tr>
<tr>
<td>
**Data Identification**
</td>
<td>
</td> </tr>
<tr>
<td>
Dataset description
</td>
<td>
The sensors are deployed on ADREAM building at LAAS-CNRS. They measure
building occupants’ comfort, energy/ water consumption, access, lighting.
Registered data types are as follows:
* Temperature
* Humidity
* Luminosity
* Energy consumption
</td> </tr>
<tr>
<td>
Source (e.g. which device?)
</td>
<td>
Smart building data is collected via connectors (software) which get data in
device-specific data format and translate it into NGSI-LD data model.
Integrated device types are:
* electricalCounter
* waterCounter
* temperature
* humidity
* luminosity
</td> </tr>
<tr>
<td>
**Partners services and responsibilities**
</td> </tr>
<tr>
<td>
Partner owner of the device
</td>
<td>
LAAS-CNRS
</td> </tr>
<tr>
<td>
Partner in charge of the data collection (if different)
</td>
<td>
Sensinov
</td> </tr>
<tr>
<td>
Partner in charge of the data analysis (if different)
</td>
<td>
Sensinov
</td> </tr>
<tr>
<td>
Partner in charge of the data storage (if different)
</td>
<td>
Sensinov
</td> </tr>
<tr>
<td>
WPs and tasks
</td>
<td>
The data are going to be collected within activities of WP2
(System implementation, testing and documentation) and WP3 (System
integration, exploitation plan and business model).
</td> </tr>
<tr>
<td>
**Standards**
</td> </tr>
<tr>
<td>
Info about metadata
(Production and storage dates, places) and documentation?
</td>
<td>
We provide metadata related to location and timestamp.
</td> </tr>
<tr>
<td>
Standards, Format, Estimated volume of data
</td>
<td>
We follow these standards to specify the data model: SAREF4BLDG and NGSI-LD.
</td> </tr>
<tr>
<td>
**Data exploitation and sharing**
</td> </tr>
<tr>
<td>
Data exploitation (purpose/use of the data analysis)
</td>
<td>
Data is used to train fault detection models and perform analysis to isolate
faulty data.
</td> </tr>
<tr>
<td>
Data access policy /
Dissemination level
(Confidential, only for members of the Consortium and the Commission Services)
/ Public
</td>
<td>
Our solution does not deal with personal data. Regulation such as free flow of
(non-personal) data will apply in the context of the digital single market. We
will comply with application regulations from this perspective.
The full dataset will be confidential and only the members of the consortium
will have access on it.
</td> </tr>
<tr>
<td>
Data sharing, re-use and distribution (How?)
</td>
<td>
The data will be processed to make learning and detect anomalies and will be
destroyed gradually.
There is not storage nor preservation of the VICINITY data after the
processing and insights generation. Vicinity Infrastructures owners have the
complete control of their data.
</td> </tr>
<tr>
<td>
Embargo periods (if any)
</td>
<td>
</td> </tr>
<tr>
<td>
**Archiving and preservation (including storage and backup)**
</td> </tr>
<tr>
<td>
Data storage (including backup):
where? For how long?
</td>
<td>
Sensinov will store data with backups.
</td> </tr> </table>
**Table 47: Description of the F2I-VAS dataset.**
## 4.13. Datasets for drEven from Open Call Winner Ubiwhere
drEVen allows any supplier of energy and owner of an OCPP-compliant Electrical
Vehicle (EV) charging station to easily become a real e-mobility Operator,
offering EV charging services with a proper transparent billing and
settlements layer. The decentralised solution uses blockchain and smart
contracts technology to process all monetary transactions and transparently
manage energy tariff and financial settlements. In a nutshell, it provides
energy suppliers with a suite of software allowing for complete management of
Electrical Vehicle stations, users’ RFID tokens, transaction monitoring and
transparent financial settlements; on the other hand, end users of the
solution (EV drivers) make use of a dedicated mobile app facilitating the
discovery of available and geo-positioned charging stations, as well as
management of all monetary transactions: topping up of different wallets,
acceptance of energy tariff and monitoring of past transactions (amount paid
to a specific provider, based on the consumed energy and provider’s specified
tariff).
Leveraging the project’s use of ontologies and abstraction layers, Ubiwhere
was able to design and implement a Vicinity adapter for this specific EV
charging station, exposing the services which effectively allow such partner
like Enercoutim to offer e-mobility services, making use of the
Vicinityenabled EV charging station. The set of services abstract the
complexities associated with the business layer (financial settlements and
billing processes), exposing also the set of Smart Contracts deployed in the
Ethereum blockchain, via common RESTful interfaces.
<table>
<tr>
<th>
**DS. Ubiwhere.##.EVCharger**
</th> </tr>
<tr>
<td>
**Data Identification**
</td>
<td>
</td> </tr>
<tr>
<td>
Dataset description
</td>
<td>
The EV charging station has been deployed at Enercoutim’s facilities,
providing information about meter readings, realtime operation status, and
information about last start and stop timestamp of an EV charging session. The
collected information references only data that has been collected during the
experiment phase, from either Ubiwhere or Enercoutim.
</td> </tr>
<tr>
<td>
Source (e.g. which device?)
</td>
<td>
The information will be relayed from the platform managing the actual charging
station which has been installed on premises. The device naturally sends this
data to a platform which, in turn, relays to our device adapter, for others
(Entities registered in Vicinity Neighbourhood) to consume, after proper
authorisation (device adapter) from Ubiwhere.
</td> </tr>
<tr>
<td>
**Partners services and responsibilities**
</td> </tr>
<tr>
<td>
Partner owner of the device
</td>
<td>
Ubiwhere
</td> </tr>
<tr>
<td>
Partner in charge of the data collection (if different)
</td>
<td>
Ubiwhere and, partially, Enercoutim (the latter during the experiment phase
only, when using the actual device)
</td> </tr>
<tr>
<td>
Partner in charge of the data analysis (if different)
</td>
<td>
Ubiwhere and Enercoutim
</td> </tr>
<tr>
<td>
Partner in charge of the data storage (if different)
</td>
<td>
Ubiwhere
</td> </tr>
<tr>
<td>
WPs and tasks
</td>
<td>
NA
</td> </tr>
<tr>
<td>
**Standards**
</td> </tr>
<tr>
<td>
Info about metadata
(Production and storage dates, places) and documentation?
</td>
<td>
Collected data during testing of this EV charging solution took place only
during the official testing period with Enercoutim, on the 08th of November.
This data has been temporarily stored in Ubiwhere’s controlled Servers (cloud-
level) for further analysis (more details afterwards) and reporting (this
deliverable only).
</td> </tr>
<tr>
<td>
Standards, Format, Estimated volume of data
</td>
<td>
Only a very small portion of data has been stored during the 08th of November,
in an SQL database. No specific data standard nor format has been followed -
the data model has been entirely designed from scratch by Ubiwhere. As for the
device adapter, the temporary collection of data followed the available
ontologies, as already documented in previous sections.
</td> </tr>
<tr>
<td>
**Data exploitation and sharing**
</td> </tr>
<tr>
<td>
Data exploitation (purpose/use of the data analysis)
</td>
<td>
The small portion of data collected during the real pilot, which has happened
at Enercoutim’s facilities, reflected the bare minimum required to assess the
viability of such a solution, by Ubiwhere alone. This data allowed us to
determine the outcome of different tests (section 4).
</td> </tr>
<tr>
<td>
Data access policy /
Dissemination level
(Confidential, only for members of the Consortium and the Commission Services)
/ Public
</td>
<td>
Access to data may be provided to the public, if required. Data which has been
deployed in the distributed ledger (Ethereum testnet), will forever be
publicly accessible.
</td> </tr>
<tr>
<td>
Data sharing, re-use and distribution (How?)
</td>
<td>
Ubiwhere sees no value in sharing such data (besides the one stored in
Ethereum), neither re-using it nor distributing.
</td> </tr>
<tr>
<td>
Embargo periods (if any)
</td>
<td>
NA
</td> </tr>
<tr>
<td>
**Archiving and preservation (including storage and backup)**
</td> </tr>
<tr>
<td>
Data storage (including backup):
where? For how long?
</td>
<td>
Collected data will be stored only until this deliverable has been officially
deemed as valid and accepted by Vicinity’s consortium. Afterwards, it shall be
archived with an encryption key held by Ubiwhere only, for 6 months,
temporarily stored in the company’s data center.
</td> </tr> </table>
**Table 48: Description of the EVcharger dataset.**
## 4.14. Datasets for MyBigFitnessTwin from Open Call Winner Nissatech
Using the VICINITY concept of interoperability as a service, this proposal has
paved the way for a new generation of fitness monitoring and analytics
services which enable improving performances of a trainee, based on the
advanced real-time analytics on the data integrated from various information
silos in a sport club. The main outcome is a novel service for a deep analysis
of the personal fitness data, obtained from fitness wearables and
smartwatches. The goal is to learn the model of the usual behaviour of a
trainee and to apply that model in the real-time for the detection of unusual
situations. The main advantage is the application of the novel algorithms for
the comparison of the fitness data in order to get a very precise modelling
and consequently very accurate anomaly detection.
We support the GDPR through the rights to be removed, the rights to have
access to your own data, and secure storage of data. All users will have to
give their consent through a contract with terms of use in order to get access
to data and use the system. Content of consent is our policy with info about
collected data and way of processing it.
The owner (the originator of the data) cannot have any control of how
aggregated data is used for statistics or tailormade offers. In order to be
either removed or have data anonymized, concrete actions need to be taken. A
request needs to be directed to a trainer who will forward request for
deletion to us, and in period of 30 days the all users data will be anonymized
or completely removed. The same way, a user needs to send a request, which
will be processed by us as soon as possible and within 30 days the latest. The
user data will be exported in CSV format.
We have implemented security protocols to avoid man-in-the-middle attacks.
These measures include encrypted data available using JWT tokens during data
transfer through a REST interface.
The data is currently physically stored on our server, but plan is that
database will be put in the cloud. There can be various Value-Added Services
prepared for collected data, and we already implemented Anomaly detection
services, as described in the report. Data are secured at rest and in transit
with encryption. We are using JWT tokens while sending data to avoid man-in-
the-middle attacks.
<table>
<tr>
<th>
**DS. Nissatech.##.smartWatch**
</th> </tr>
<tr>
<td>
**Data Identification**
</td>
<td>
</td> </tr>
<tr>
<td>
Dataset description
</td>
<td>
The sensors are wearables. Trainees in a fitness club are using them. They are
monitoring the psychophysiological
parameters of a trainee during a training (usually HIIT – High Intensive
Interval Training)
</td> </tr>
<tr>
<td>
Source (e.g. which device?)
</td>
<td>
Smartwatch Polar M600 is used. Data is collected in the real time and sent
(BTE) for further processing
Huawei smartscale and Contour plus One glucometer are used as contextualized
sensors
</td> </tr>
<tr>
<td>
**Partners services and responsibilities**
</td> </tr>
<tr>
<td>
Partner owner of the device
</td>
<td>
Nissatech
</td> </tr>
<tr>
<td>
Partner in charge of the data collection (if different)
</td>
<td>
Nissatech
</td> </tr>
<tr>
<td>
Partner in charge of the data analysis (if different)
</td>
<td>
Nissatech
</td> </tr>
<tr>
<td>
Partner in charge of the data storage (if different)
</td>
<td>
Nissatech
</td> </tr>
<tr>
<td>
WPs and tasks
</td>
<td>
The data are going to be collected within activities of WP10.
</td> </tr>
<tr>
<td>
**Standards**
</td> </tr>
<tr>
<td>
Info about metadata
(Production and storage dates, places) and documentation?
</td>
<td>
Metadata is related to the fitness context of the training, like the type of
exercises.
In addition, the data from the glucometer and smartscale are used as the
context for a fitness training.
</td> </tr>
<tr>
<td>
Standards, Format, Estimated volume of data
</td>
<td>
Data format is defined, based on Smart4Fit data format.
</td> </tr>
<tr>
<td>
**Data exploitation and sharing**
</td> </tr>
<tr>
<td>
Data exploitation (purpose/use of the data analysis)
</td>
<td>
Data analysis enables the creation of the models of the normal behavior (in a
fitness training) that will be compared with the real-time data during a
training in order to detect unusual/anomalous behavior
</td> </tr>
<tr>
<td>
Data access policy /
Dissemination level
(Confidential, only for members of the Consortium and the Commission Services)
/ Public
</td>
<td>
First point here is the authentication, by using credentials (username and
password).
Second, is that all communication is SSL-encrypted. Which means that by using
certificates and keys, trainees data is protected from outside attacks while
being sent through network
</td> </tr>
<tr>
<td>
Data sharing, re-use and distribution (How?)
</td>
<td>
There is no data sharing planned
</td> </tr>
<tr>
<td>
Embargo periods (if any)
</td>
<td>
NA
</td> </tr>
<tr>
<td>
**Archiving and preservation (including storage and backup)**
</td> </tr>
<tr>
<td>
Data storage (including backup):
where? For how long?
</td>
<td>
Nissatech
Data belongs to the partner fitness club and will be not deleted (except on
the request from a user)
</td> </tr> </table>
**Table 49: Description of the MyBigFitnessTwin dataset.**
# Conclusions
This document is the third and final version of the Data Management Plan. It
is based on knowledge harvested through describing requirements, preparing the
VICINITY architecture, and planning the pilot sites. The updated datasets have
been delivered from the participants that are responsible for the test labs
and the living labs, and describes procedures and infrastructure that have
been defined at this point in the project.
The work on semantics and privacy issues has continued. It is the process of
clarification of procedures that has led to many of the updates that are found
in this document. Certain areas still need some attention. This will in
particular matter for Open Calls, as these are still tentative and the
documents and other material is still being worked out by the VICINITY
consortium. Activities for a Data Management Portal have proceeded, and a
demonstration has been held twice that presented how the VICINITY architecture
works, how it integrates and how the concept of virtual neighbourhood
functions in practical terms. More updates are envisaged after studies of the
pilot sites proceeds, and open calls are being presented. Future versions may
have updated Consent forms as well since the upcoming GDPR may lead to changes
in how privacy and ethics issues are formulated.
Lessons learned from the work conducted in this period is there has been
introduced more IoT assets that will be integrated within the ecosystems that
will be tested. There has been a fruitful discussion between project partners,
which increases the quality of this document. Ownership of data become more
important, and will receive special attention in the next part. The Data
Management Portal is still under work, but need for each project partner to
contribute to editing / access rights will need to be managed accordingly. It
must also be noted that the partners are unable to exactly specify what kind
of datasets that will be relevant as the project proceeds. This is what they
expect to learn from the pilot sites and other tests conducted at the
workbench. It is therefore expected that the datasets may change accordingly.
The VICINITY Data Management Plan still put a strong emphasis of the
appropriate collection – and publication should the data be published – of
metadata, storing all the information necessary for the optimal use and reuse
of those datasets. This metadata will be managed by each data producer, and
will be integrated in the Data Management Portal. This is considered even more
important with the upcoming deployment of the General Data Privacy Regulations
(GDRP).
This is the final version of DMP from December 2019. It presents the final
datasets and lessons learned, alongside plans for further management of test
data and production data. The document debates information of data,
integration and reuse. The document also discuss data preservation, costs and
a detailed Management Plan for each dataset.
|
https://phaidra.univie.ac.at/o:1140797
|
Horizon 2020
|
1090_WhoLoDancE_688865.md
|
# Executive Summary
The WhoLoDancE Work Package 5 is responsible for the overall data management
infrastructure to be built and deployed by ATHENA RC with the objective to
collect, store, pre-process and manage the multimodal data acquired in the
project. Deliverable 5.1 provides technical information about the type of data
that will be produced, managed and maintained by the data management platform
and the methodologies which will be applied for the data integration and
management in order to deliver the various applications of the project.
The process of comprehending and driving conclusions on data sources related
to the project is an integral part of the WhoLoDancE data management approach
and is presented in this report. For gathering information from partners, a
special questionnaire has been designed and implemented by the project’s data
management team and has been populated by the individual data-providing
partners. More information on questionnaire structure the can be found in §3,
“Recorded dataset information”, while the results are presented in §7,
“Datasets”.
A set of dataset management practices and tools that can be used for storing,
delivering, preserving and licensing the data evaluated for the needs of the
project, complying with best practices, and generally acceptable paradigms in
the context that the project activates is then defined. The results of this
work are presented in §4, “Data Management”. Subsequently, the data model of
the project data is presented in §5, “Motion Capture Dataset ”. Finally, the
policies for ensuring data interoperability and integration across project’s
services, be it data management or end-user ones, is covered in section §6,
“Data Integration”.
# Introduction
The WhoLoDancE work package 5 is responsible for the overall data management
infrastructure to be built and deployed by ATHENA RC with the objective to
collect, store, pre-process and manage the multimodal data acquired. The data
management infrastructure will be able to deliver that data as input to
various similarity search algorithms, multimodal analysis and modeling tools
and promote data exchange between different components and modalities of
interaction to support the realization of a variety of learning scenarios. WP5
is responsible for integrating the ”groundtruth” data selected in WP2,
indexing and annotating based on the models and high-level descriptors
prepared in WP3, and creating a Learning content repository of movement data
following the requirements and theoretical guidelines defined in WP1 and
modeled in WP3.
This deliverable outlines how the data “produced” (either generated or
collected) during the WhoLoDancE project will be managed during the project
and after the project completion. In particular, it describes the practices
characterizing research data handling during and after the project, what data
will be collected, processed or generated, what methodology and standards will
be applied, whether data will be shared / made open access and how, how data
will be curated and preserved.
# Data management general approach
In the WhoLoDancE project, the produced data sets are a first class citizen
for empowering the project’s research, technological development, usage and
outreach activities. To facilitate this enhanced role of the data in the
project, the management approach of WhoLoDancE is to lay the foundations for
processes, technologies and policies related to data so that their production
and consumption via stakeholders is streamlined in a smooth and effective way.
In this direction, it is important to view data elements from various
perspectives and not as standalone artefacts but rather as integral part of
the project platform. This requires that both technological facets of data
(model, manifestation, volume, protocols, etc) and as well as policies around
them (availability, preservation, volume, etc) need to be handled when
planning their incorporation in the project platform and work plan.
The WhoLoDancE data management approach lays its foundations on the following
sources:
* The Description of Action of the Project, which describes the principles for data management as well as the baseline plan (be it explicit or implicit) for data collection and usage in the project’s lifetime.
* The availability and capacity of project partners to generate, describe and provide data, which relates to partner role, equipment, data sizing etc.
* The learning scenarios and user needs defined in D1.4, that guide the production and consumption of data in overall.
* Common practices regarding data management in the context of H2020 datasets, which relates to provenance, preservation, policies etc.
The process of comprehending and driving conclusions on those sources, is
herein called Conceptual Analysis of Datasets, and is an integral part of
WhoLoDancE data management approach and is presented in this Report.
Collecting and understanding the project’s datasets gathers information from
two sources:
* Partner statement on dataset generation: i.e. listing of datasets that will become available in the process of the project, along with several data attributes that characterize their nature, use and policies.
* Specific dataset analysis: i.e. analysis of initial datasets gathered prior to the finalization of project’s Conceptual Analysis of Datasets.
For gathering information from partners, a special questionnaire has been
designed and implemented by the project’s data management team and has been
populated by individual dataproviding partners. More information on
questionnaire structure the can be found in §3, “Recorded dataset
information”, while the results are presented in §7, “Datasets”.
The outcome of the conceptual analysis of datasets, covers a number of topics
and delivers the results presented below.
First we define the **set of dataset management practices and tools** that can
be used for storing, delivering, preserving and licensing the data evaluated
for the needs of the project, complying with best practices, and generally
acceptable paradigms in the context that the project activates. The results of
this work are presented in §4, “Data Management”.
Subsequently, the **data model** of the project motion capture dataset,
presented in §5, “Motion Capture Dataset ”, which covers:
aspects of dataset internal models (i.e. how data is structured inside
datasets) the WhoLoDancE data model which covers:
* element referencing approach (i.e. how dataset/metadata elements are cross referenced)
* descriptive metadata that allow datasets to be discovered and consumed by end users (and services)
* structural metadata that allow datasets to be handled by the system and consumed and explored by services (and users)
* semantic extensions: the approach of project for handling semantic metadata
Finally, the policies for ensuring data interoperability and integration
across project’s services, be it data management or end-user ones, is covered
in section §6, “Data Integration”.
# Recorded dataset information
To record information for the foreseen datasets to be created during the
project lifetime, a questionnaire has been created in an on-line form format
and each partner has been asked to contribute by providing information on the
datasets they foresee to generate. For each dataset, the classes of
information that have been collected are presented in the remainder of this
section.
## Dataset description
The provided description should characterise the dataset, its origin (in case
it is collected), nature and scale and to whom it could be useful, and whether
it underpins a scientific publication. Information on the existence (or not)
of similar data and the possibilities for integration and reuse. Requested
information includes:
* _**Label:** _ Please provide a (tentative) label for the dataset; _**Description:** _ Please provide a brief description of the dataset; _**Generated/Collected:** _ Method of acquisition:
* Is the dataset genuinely generated within the project, transformed or produced by aggregating content out of existing datasets / data sources?
* In the case the dataset is derived from existing datasets or imported, please indicate its origin(s). Add the dataset download URLs if available, otherwise the origin URL.
* _**Origin:** _
* In the case the dataset is derived from existing datasets or imported, please indicate its origin(s). Add the dataset download URLs if available, otherwise the origin URL.
* In the case the dataset is derived from existing datasets or imported, please indicate the licenses of the original data. Add URLs if available.
* **_Nature_ : **
* What is the nature of the dataset content? The options provided include: Optical motion capture data, audio data, biometric sensing data, 3D data, Video data, programming and code data, documentation and reporting data
* Please provide details on the nature of the data (for example "greek dance video data")
* **_Scale/Size_ : ** What is the estimated scale (size) of the dataset? (indicate size of its constituents if applicable);
* _**Potential use:** _ What is the foreseen use of the dataset by different communities for specific applications or research purposes?
* **_Scientific publications / references_ : ** Please indicate publications that are related to the dataset;
* _**Availability:** _ When will the dataset be made available to the project (indicate month/year)?
## Content and Metadata Types
* Is the dataset comprised by items of the same or different typologies? (e.g. video streams, audio streams, textual documents, tabular data etc)
* What are the data formats used? (For example, XML, CSV, FBX) Please refer to possible standards used.
* What are the metadata formats used? Please refer to possible standards used.
## Dataset use and sharing
This section includes a description of how data will be shared, including
access procedures, embargo periods (if any), outlines of technical mechanisms
for dissemination and necessary software and other tools for enabling re-use,
and definition of whether access will be widely open or restricted to specific
groups. In case the dataset cannot be shared, the reasons for this should be
mentioned (e.g. ethical, rules of personal data, intellectual property,
commercial, privacyrelated, security-related). Identification of the
repository where data will be stored, if already existing and identified,
indicating in particular the type of repository (institutional, standard
repository for the discipline, etc.). Requested information includes:
* What are the repositories the dataset or/and its metadata have been published in?
* Is there specific software required for consuming the dataset?
* Indicate the dissemination mechanisms through which the availability of the dataset will be announced (e.g. metadata catalogs).
* What is the license under which the dataset is provided (e.g. Creative Commons Attribution 4.0 ) ?
* What are the policies governing access to the dataset (e.g. the dataset is “open”, the dataset is made available to authorised users only)?
* What is the procedure for a user to consume to the dataset (e.g. standard content access API, web site download / view, behind a service API)?
* If this is a generated dataset, will there be an embargo period for providing (open) access to the dataset? If yes, how long (in months)?
* If creating or collecting data in the field how will you ensure its safe transfer into the main data management system?
## Dataset handling and synchronization
* What is the preservation strategy for the dataset?
* What is the foreseen preservation period duration?
* What are the instruments (tools) put in place to implement the preservation strategy ?
* Please provide any additional information on issues related to the handling of the dataset (synchronization, referencing, etc).
# Data Management
WhoLoDancE defines a series of practices and approaches for data storage, data
access and data preservation. These practices are briefly described in the
following sections.
## Data Storage and Repositories
WhoLoDanceE preliminary data analysis shows that there is no single data
manifestation that may cover all project’s pilot cases and data generation
needs and as such has to adopt a rather more generic approach to data and
metadata storage. As per example, datasets may contain motion capture data in
various manifestation, video or audio streams, graphs, tables etc. Furthermore
those datasets shall be described, in order to facilitate discoverability, and
interconnected in order to facilitate their consumption. As a result the data
storage elements and repositories, , presented in Figure 1 are included in the
WhoLoDancE platform. More specifically, these include:
* **Storage Layer** : A file-based object store for depositing binary data objects. The repository is implemented over a redundant store with one delayed replica and is accessible via a number of standard protocols such as FTP and HTTP, while special protocols are also available depending on the data type (e.g. streams for media objects). Items in the repository obtain URLs that can be disseminated via standard web means, yet access may be provided only with granted credentials.
* CKAN metadata repository tailormade w.r.t. configuration and plugins, to fit WhoLoDancE project data and metadata servicing needs. Offers full web UI for managing and accessing metadata and a rich set of REST web services for consuming/exploring projects datasets.
* A relational database management system (PostgreSQL) for managing dataset metadata, behind the CKAN repository and pilot-specific services.
* **An ontology management system** (Protégé) to provide management functionalities for the WhoLoDancE ontologies and access through appropriate APIs.
**Figure 1 WhoLoDancE data management infrastructure**
## Data Dissemination and Catalogues
Data dissemination has two aspects, referring either to end-users or to other
external systems / catalogues. To support both, a data catalogue based on the
CKAN 1 technology is available offering rich user interfaces to end users
for data search and discovery, as well as a REST Application Programming
Interface (API 2 ) for machine interaction.
In addition, aiming on being interoperable with external catalogues the system
is compatible to the most known and adopted interoperable standard, the Open
Archives Initiative Protocol for Metadata Harvesting (OAI-PMH 3 ).
Interoperability is the ability of two or more information systems to exchange
metadata with minimal loss of information.
The OAI-PMH is a protocol developed for harvesting metadata descriptions of
records in an archive so that services can be built using metadata from many
archives. An implementation of OAI-PMH must support representing metadata in
Dublin Core 4 schema, but may also support additional representations
depending on their nature and origin. In the current implementation, only the
default schema is supported.
Moreover, every data object residing in the WhoLoDancE platform is accessible
via a URL, which allows accessing its payload via a standard web protocol.
However, this does not preclude the involvement of an
authentication/authorization mechanism before accessing the actual dataset.
Due to the large size of project’s datasets it is required to protect
infrastructure availability via several means, one of those being limiting the
access to datasets.
## Data Preservation
In essence, every data is archived in a secure manner. This is done in two
different yet complementary ways. Every data is constantly copied in a backup
area (the periodicity of the backup procedures varies from case by case yet it
is never longer than one day). Certain storage solutions automatically store
the content in multiple copies, e.g. this is the case of the technologies
behind the file-oriented storage.
No format migration strategy or approach is in place, the data are managed
with their native format.
## Licenses
The library of motion capture data produced in the contect of WhoLoDancE is a
collection of high quality dance movement content that would be a valuable
asset for the researchers of the community. It will be decided in the
consortium what will be the approach to be taken for the access provided to
external parties to this data. A common approach would be to make the data,
available through a Creative Commons 5 license, possibly CC BY-NC-SA or CC
BY-NC-ND.
Normally the data would be made available upon request to interested parties
after registration.
# Motion Capture Dataset Modeling
Although WhoLoDancE will produce a variety of Datasets, ranging from motion
capture files to text reports, the main content outcome that will be the basis
of the project research and development work is the Dataset of motion capture
dance segments, along with the multimodal data that accompany it.
This Dataset is presented in more detail in Section 7.1 - Table 5. This
section presents the metadata modelling approach employed for it.
The Dataset is organized in the following structures:
* **Motion capture recording** : contains resources relevant to a particular motion capture segment and its relevant multimodal data, more specifically: .c3D, .fbx, .json, .txt, .mp4, .mp3
* **Collection** : a conceptual grouping of recordings, included as a metadata field in each recording, for example the Ballos Collection. A collection contains 1 to many recordings **Dance Genre** : set of collections relevant to a particular dance genre.
* **Dataset** : it contains all motion capture data captured in the motion capture sessions organized by Motek and Unige, along with the data supporting multimodal material (music, videos, etc).
The motion capture recordings have been stored in the FTP server and annotated
with metadata available through a CKAN metadata server 6 .
The metadata server offers a view to the user that emphasizes the four dance
genres. (Figure 2).
**Figure 2 The C-KAN view of the content organized by dance genre**
The first step for organizing the collected data was to create four recording
schemas corresponding to the four dance genres represented in the project
(Ballet, Contemporary, Greek Folk, Flamenco).
The recording schemas have some common fields that describe both the dances
and the files related to them (Table 1).
Table 2 presents the recording fields that are specific to Greek folk dance.
Each file (or resource) within the recording is described by a set of fields.
There is a subset that is common among the four genres (Table 3) as well as a
subset with is genre-specific and thus containing different fields for each of
the dance genres (Table 4).
The fields that are specific to each genre schema are presented in Table 4.
**Table 1 Fields that describe each recording**
<table>
<tr>
<th>
**Field name**
</th>
<th>
**Field type**
</th> </tr>
<tr>
<td>
Source
</td>
<td>
URL to the FTP server
</td> </tr>
<tr>
<td>
Author
</td>
<td>
Text
</td> </tr>
<tr>
<td>
Author email
</td>
<td>
Text
</td> </tr>
<tr>
<td>
Maintainer
</td>
<td>
Text
</td> </tr>
<tr>
<td>
Maintainer Email
</td>
<td>
Text
</td> </tr>
<tr>
<td>
Dance Genre
</td>
<td>
Text
</td> </tr>
<tr>
<td>
Description
</td>
<td>
Text
</td> </tr> </table>
**Table 2 Greek folk dance - specific recording fields**
<table>
<tr>
<th>
**Field name**
</th>
<th>
**Field type**
</th>
<th>
**Genre**
</th> </tr>
<tr>
<td>
Dance name
</td>
<td>
Text
</td>
<td>
Greek folk
</td> </tr>
<tr>
<td>
Local name
</td>
<td>
Text
</td>
<td>
Greek folk
</td> </tr>
<tr>
<td>
Region
</td>
<td>
Text
</td>
<td>
Greek folk
</td> </tr>
<tr>
<td>
Dance type
</td>
<td>
Selection from the following values: circle, face to face
</td>
<td>
Greek folk
</td> </tr>
<tr>
<td>
Dance gender
</td>
<td>
Selection from the following values: male, female, mixed
</td>
<td>
Greek folk
</td> </tr>
<tr>
<td>
Time signature
</td>
<td>
Text
</td>
<td>
Greek folk
</td> </tr> </table>
**Table 3 Common resource fields for all genres**
<table>
<tr>
<th>
**Field name**
</th>
<th>
**Field type**
</th> </tr>
<tr>
<td>
Last updated
</td>
<td>
Date
</td> </tr>
<tr>
<td>
Created
</td>
<td>
Date
</td> </tr>
<tr>
<td>
Format
</td>
<td>
Text
</td> </tr>
<tr>
<td>
License
</td>
<td>
Text
</td> </tr>
<tr>
<td>
File Type (e.g. Motion
Capture)
</td>
<td>
Text
</td> </tr>
<tr>
<td>
Dance Genre
</td>
<td>
Text
</td> </tr>
<tr>
<td>
Movement Principle
</td>
<td>
Multiple selection from the following values: Alignment and Posture, Balance,
Coordination, Directionality, Motion
Through Space, Motorics, Rhythm and Phrasing, Stillness, Symmetry, Weight
bearing vs. Gesturing
</td> </tr>
<tr>
<td>
Capture Venue
</td>
<td>
Text
</td> </tr>
<tr>
<td>
Capture day
</td>
<td>
Date
</td> </tr>
<tr>
<td>
Capture Quality
</td>
<td>
Text
</td> </tr>
<tr>
<td>
Capture Comments
</td>
<td>
Text
</td> </tr>
<tr>
<td>
Duration
</td>
<td>
Text
</td> </tr>
<tr>
<td>
Frames
</td>
<td>
Text
</td> </tr>
<tr>
<td>
Performer
</td>
<td>
Text
</td> </tr>
<tr>
<td>
Company
</td>
<td>
Text
</td> </tr>
<tr>
<td>
Dataset name
</td>
<td>
Text
</td> </tr> </table>
**Table 4 Dance-genre specific resource fields**
<table>
<tr>
<th>
**Field name**
</th>
<th>
**Field type**
</th>
<th>
**Dataset /genre**
</th> </tr>
<tr>
<td>
Type of Segment
</td>
<td>
Text
</td>
<td>
Greek folk
</td> </tr>
<tr>
<td>
Actions in Segment
</td>
<td>
Text
</td>
<td>
Greek folk
</td> </tr>
<tr>
<td>
Actions in Segment
</td>
<td>
Text
</td>
<td>
Contemporary
</td> </tr>
<tr>
<td>
Relation in space
</td>
<td>
Text
</td>
<td>
Contemporary
</td> </tr>
<tr>
<td>
Orientation
</td>
<td>
Text
</td>
<td>
Contemporary
</td> </tr>
<tr>
<td>
Body Parts Leading
</td>
<td>
Text
</td>
<td>
Contemporary
</td> </tr>
<tr>
<td>
Planes
</td>
<td>
Text
</td>
<td>
Contemporary
</td> </tr>
<tr>
<td>
Axis
</td>
<td>
Text
</td>
<td>
Contemporary
</td> </tr>
<tr>
<td>
Other Characteristics in
Motion
</td>
<td>
Text
</td>
<td>
Contemporary
</td> </tr>
<tr>
<td>
Actions in Segment
</td>
<td>
Text
</td>
<td>
Ballet
</td> </tr> </table>
**Figure 3 The metadata values for a motion capture resource (file)**
Having defined the recording and resource schemas according to the dance
types, it was decided to create a set of recordings per genre, to reflect a
categorization which is meaningful for each genre.
In total 111 recordings have been created with more than 1400 dance movement
sequences in two different formats. For each dance genre,
* Ballet: 33 recordings, according to various ballet exercises, containing 185 .fbx resources and 216 .c3d ones
* Greek folk dance: 53 recordings, to reflect different dances, containing 189 .fbx resources and 302 .c3d ones
* Contemporary dance: 13 recordings, to reflect the movement principles and improvisations, containing 727 .fbx resources and 877 .c3d ones
* Flamenco: 12 recordings, to reflect movement principles, containing 99 .fbx resources and 65 .c3d ones
More information on the resources (files) per created recording can be found
in Appendix A.
# Data Integration
Data integration will be based on a number of principles:
* Accessibility
* Discoverability
* Consumability
In line with the first principle, data access will be provided via standards’
compliant methods. For instance, where adequate formal or defacto standards
exist, those will be utilized. This may be baseline standards such as FTP,
HTTP, XML, MPEG4 etc or higher level protocols such as WebDav, OAI-ORE, etc.
In areas where no applicable standards exist, which may be the case in higher
complexity interactions, access to data will be provided by standard REST web
services.
In the direction of the second principle, data need to be described adequately
in order to support discoverability. A thorough investigation of metadata
descriptors adequate to cover the needs of the project is performed and the
results are presented in Section 5. Yet, the required supporting mechanism is
the delivery of a registry which allows data consumers to locate the dataset
in need. Discoverability will be greatly supported by standard’s compliance,
as for example OAI-PMH support will allow the diffusion of data descriptions
in other registries, promoting their recognition and reuse.
Regarding consumability, the objective is multifold: (a) completeness of data
access services and (b) facilitation of machine-to-machine exchange of data.
In this direction, full coverage of services for locating and obtaining the
dataset element in need will be provided, under the manifestation preferred
(if many) and, in particular cases the granularity of access required.
Furthermore data model will be complete and will have enough metadata, to
allow locating data via search or direct transversal of data linking and
grouping descriptors, and will utilize common, machine readable, data formats
that allow consumption of the dataset payload. Documentation will be provided
on the data model, further facilitating its utilization.
# Datasets
The datasets WhoLoDancE is called to manage belong to the following
categories:
* Motion Capture datasets, i.e., the results of the motion capture activities in the project, in .fbx, .c3d, .json and/or .csv format as well as accompanying files with music or physiometric data;
* Videos, collected to either prepare for the motion capture process or record it;
* Questionnaire and interview results
* Software, i.e. datasets resulting from the software enabling WhoLoDancE.
* Reports and deliverables
## Motion capture related datasets
**Table 5 Motion capture data dataset description**
<table>
<tr>
<th>
**Dataset name:** Motion capture data
</th> </tr>
<tr>
<td>
_**Dataset description:** _ Motion capture data produced in the motion capture
sessions organized by Motek and UniGE, processed and rigged to inverse
kinematic skeleton, along with accompanying files with relevant music and/or
physiometric data . The dataset includes motion capture data from the four
dance genres, Greek folk, contemporary, ballet and flamenco.
_**Generated/Collected:** _ Generated.
_**Origin(s):Download URL:** _ N/A
_**Origin(s):Licenses:** _ N/A
_**Nature:** _ Optical motion capture data; 3D data
_**Size/Scale:** _ 11 GB - More than 1400 dance movement sequences in two
different formats. For each dance genre,
* Ballet: 33 recordings, according to various ballet exercises, containing 185 .fbx resources and 216 .c3d ones
* Greek folk dance: 53 recordings, to reflect different dances, containing 189 .fbx resources and 302 .c3d ones
* Contemporary dance: 13 recordings, to reflect the movement principles and improvisations, containing 727 .fbx resources and 877 .c3d ones
* Flamenco: 12 recodings, to reflect movement principles, containing 99 .fbx resources and 65 .c3d ones
_**Potential use:** _ Use for the WhoLoDancE research purposes: design of
dance learning material, HLF/LLF extraction, similarity search, etc.
_**References:** _ None at the moment
</td> </tr>
<tr>
<td>
**Content and Metadata Types**
</td> </tr>
<tr>
<td>
_**Typologies:** _ Files in this dataset are of the same typology, motion
capture dance segments in two different formats
_**Data Formats: .** _ fbx,.c3d, .csv, .json motion capture formats and .mp3
and .mp4 relevant files
_**Metadata formats:** _ Available in json format through CKAN API and XML
through OAI publisher
</td> </tr>
<tr>
<td>
_**Availability date:** _ December 2016
</td> </tr>
<tr>
<td>
**Dataset Use and Sharing**
</td> </tr>
<tr>
<td>
_**Repositories:** _ Internal WhoLoDancE repository
_**Software and tools for re-use** _ : MotionBuilder, Unity, fbx viewer,
custom viewers
_**Dissemination Mechanisms** _ : Not available yet, will include the project
blending engine and learning tools for parts of the dataset.
_**License:** _ Currently internal use for the consortium research purposes.
To be decided if the data will be released under a licensing schema, possibly
Creative Commons CC BY-NC-SA or CC BY-NC-ND
_**Access policies:** _ Currently accessible only to the members of the
consortium for research purpose. According to the selected licensing schema
they could be available to external parties through a registration process
_**.** _
_**Access Procedure** _ : Web access after a registration process
_**Embargo Periods** _ : To be decided.
_**Transfer process:** _ Directly uploaded by Motek to the ftp server central
storage system
</td> </tr>
<tr>
<td>
**Dataset handling and synchronization**
_**Preservation strategy:** _ Backup at the WhoLoDancE repository and in the
interested partners’ individual storage facilities
_**Preservation period:** _ indefinite
_**Preservation implementation instruments:** _ Multiple Backups in different
locations online and offline
</td> </tr> </table>
**Table 6 Motion capture preparation videos dataset descrption**
<table>
<tr>
<th>
**Dataset name:** Motion capture preparation audio and videos
</th> </tr>
<tr>
<td>
_**Dataset description:** _ videos for preparation for motion capturing of
dance (movement principles, qualitative modules), recorded by the dance
partners prior to the motion capture sessions. For example, for the greek
dances, different videos have been recorded: full dance or parts, with and
without costumes, a) danced by a group of dancers, men and/or women, b) danced
by one or two dancers. The scope of the videos was focused on the variety of
dances and kinetic patterns and on the relation with the dance principles. In
some cases audio files with the accompanying music
</td> </tr> </table>
<table>
<tr>
<th>
has been provided
_**Generated/Collected:** _ Generated.
_**Origin(s):Download URL:** _ N/A
_**Origin(s):Licenses:** _ N/A
_**Nature:** _ Audio data, video data
_**Size/Scale:** _ Appr. 1,3 GB music audio files and 50GB video files
_**Potential use:** _ Use for the WhoLoDancE research purposes: to generate
the short list for motion capture; for the greek dances specifically, to be
used as a repository of Greek traditional dances and their kinetic patterns
for study by researchers of the field, teachers/students and use by
choreographers of any genre of dance.
_**References:** _ For Greek dances: “Improvisation in the Greek folk dances"
by Lefteris Drandakis
</th> </tr>
<tr>
<td>
**Dataset Use and Sharing**
</td> </tr>
<tr>
<td>
_**Repositories:** _ Internal WhoLoDancE repository
_**Software and tools for re-use** _ : Video and audio players
_**Dissemination Mechanisms** _ : For the project internal use only. An
exception is greek folk dances _**License:** _ For the project internal use
only.
_**Access policies:** _ For the project internal use only. For greek dances
available as supplementary material in the learning scenarios
_**Access Procedure** _ : Through the WhoLoDancE web applications
_**Embargo Periods** _ : N/A
</td> </tr>
<tr>
<td>
_**Transfer process:** _ Directly uploaded by Motek to the ftp server central
storage system
</td> </tr>
<tr>
<td>
**Dataset handling and synchronization**
_**Preservation strategy:** _ Backup at the WhoLoDancE repository and in the
interested partners’ individual storage facilities
_**Preservation period:** _ to be decided
_**Preservation implementation instruments:** _ Multiple Backups in different
locations online and offline
</td> </tr> </table>
**Table 7 Motion capture features dataset description**
<table>
<tr>
<th>
**Dataset name:** Motion capture, video and audio features
</th> </tr>
<tr>
<td>
_**Dataset description:** _ The dataset will contain features extracted from
audio, video and motion capture recordings, along with relevant motion capture
segments. Depending on the project needs, it will be around 5 to 50% of the
original data from which features will be extracted
_**Generated/Collected:** _ Generated.
_**Origin(s):Download URL:** _ N/A
_**Origin(s):Licenses:** _ N/A
_**Nature:** _ Raw and structured textual and numerical data extracted from
the computation of features from audio, video and motion capture recordings. I
will include the original video, audio and motion capture content.
_**Size/Scale:** _ 5GB
_**Potential use:** _ The dataset will be used for data analysis and machine
learning purposes. _**References:** _ None at the moment
</td> </tr>
<tr>
<td>
**Dataset Use and Sharing**
</td> </tr>
<tr>
<td>
_**Repositories:** _ Internal WhoLoDancE repository
_**Software and tools for re-use** _ : Depending on the format, Matlab or
Python with the Numpy library might be needed
_**Dissemination Mechanisms** _ : Publications on conferences and journals,
social networks and mailing lists
_**License:** _ To be discussed, possibly available under Creative commons
license
_**Access policies:** _ To be discussed
_**Access Procedure** _ : Web site download or ftp access
_**Embargo Periods** _ : The dataset will be available for the partners from
the moment of its production, they will become publicly available from the
moment of the publication of the related work in a conference / journal
_**Transfer process:** _ Secure transfer protocol, such as SFTP
</td> </tr>
<tr>
<td>
**Dataset handling and synchronization**
_**Preservation strategy:** _ Backup at the WhoLoDancE repository and in the
interested partners’ individual storage facilities
_**Preservation period:** _ indefinite
_**Preservation implementation instruments:** _ Multiple Backups in different
locations online and offline
</td> </tr> </table>
**Table 8 Motion capture videos dataset description**
<table>
<tr>
<th>
**Dataset name:** Motion capture videos
</th> </tr>
<tr>
<td>
_**Dataset description:** _ Two camera views recording all motion capture
takes
_**Generated/Collected:** _ Generated.
_**Origin(s):Download URL:** _ N/A
</td> </tr> </table>
<table>
<tr>
<th>
_**Origin(s): Licenses:** _ N/A
_**Nature:** _ Video data
_**Size/Scale:** _ 160GB
_**Potential use:** _ Use for the WhoLoDancE research purposes, for example,
semi-automatic finger tracking on mocap
_**References:** _ None at the moment
</th> </tr>
<tr>
<td>
**Dataset Use and Sharing**
</td> </tr>
<tr>
<td>
_**Repositories:** _ Internal WhoLoDancE repository
_**Software and tools for re-use** _ : Video players
_**Dissemination Mechanisms** _ : For project internal use only
_**License:** _ For project internal use only
_**Access policies:** _ For project internal use only _**Access Procedure** _
: For project internal use only
_**Embargo Periods** _ : N/A.
_**Transfer process:** _ Directly uploaded to the ftp server central storage
system
</td> </tr>
<tr>
<td>
**Dataset handling and synchronization**
_**Preservation strategy:** _ Backup at the WhoLoDancE repository and in the
interested partners’ individual storage facilities
_**Preservation period:** _ indefinite
_**Preservation implementation instruments:** _ Multiple Backups in different
locations online and
</td> </tr> </table>
offline
## Survey results datasets
<table>
<tr>
<th>
**Dataset name:** Movement principles interview and questionnaire data
</th> </tr>
<tr>
<td>
_**Dataset description:** _ The information will be collated from a number of
interviews conducted by the COVUNI team and a series of paper questionnaires
collected. All of the information gathered comes from professional
dancers/teachers/choreographers from a diverse range of disciplines.
The aim is to have an equal number of men and women.
_**Generated/Collected:** _ Generated.
_**Origin(s): Download URL:** _ N/A
_**Origin(s): Licenses:** _ N/A
_**Nature:** _ Documentation and reporting data; Questionnaire results data
_**Size/Scale:** _ 50 MB
_**Potential use:** _ For internal research purposes only
_**References:** _ None at the moment
</td> </tr>
<tr>
<td>
**Dataset Use and Sharing**
</td> </tr>
<tr>
<td>
_**Repositories:** _ Internal university private shared drive.
_**Software and tools for re-use** _ : Document viewers and sound players
_**Dissemination Mechanisms** _ : For project internal use only
</td> </tr>
<tr>
<td>
_**License:** _ For project internal use only
_**Access policies:** _ For project internal use only. Confidential and
following COVUNI ethics guidelines.
_**Access Procedure** _ : For project internal use only. Data is confidential
and only COVUNI team has permission to hear the audio recordings.
_**Embargo Periods** _ : N/A.
_**Transfer process:** _ N/A.
</td> </tr>
<tr>
<td>
**Dataset handling and synchronization**
_**Preservation strategy:** _ Backup at the WhoLoDancE repository and in the
interested partners’ individual storage facilities
_**Preservation period:** _ To be decided
_**Preservation implementation instruments:** _ N/A
</td> </tr> </table>
<table>
<tr>
<th>
**Dataset name:** Dance learning on-line survey results
</th> </tr>
<tr>
<td>
_**Dataset description:** _ The dataset includes results in spreadsheet format
by the on-line surveys conducted by ATHENA and COVUNI
_**Generated/Collected:** _ Generated.
_**Origin(s):Download URL:** _ N/A
_**Origin(s):Licenses:** _ N/A
_**Nature:** _ Questionnaire results data
_**Size/Scale:** _ 5MB
_**Potential use:** _ For internal research purposes only, to extract
conclusions on user requirements and potential learning scenarios to be
implemented within the project. _**References:** _ None at the moment
</td> </tr>
<tr>
<td>
**Dataset Use and Sharing**
</td> </tr>
<tr>
<td>
_**Repositories:** _ Internal university private shared drive.
_**Software and tools for re-use** _ : Spreadsheet viewers
_**Dissemination Mechanisms** _ : For project internal use only
_**License:** _ For project internal use only
_**Access policies:** _ For project internal use only.
_**Access Procedure** _ : For project internal use only.
_**Embargo Periods** _ : N/A.
_**Transfer process:** _ N/A.
</td> </tr>
<tr>
<td>
**Dataset handling and synchronization**
_**Preservation strategy:** _ Backup at the WhoLoDancE repository and in the
interested partners’ individual storage facilities
_**Preservation period:** _ To be decided
_**Preservation implementation instruments:** _ N/A
</td> </tr> </table>
## Software and reports
The software to be developed within WhoLoDancE will be deposited in Github and
published in repositories like Zenodo.
For the day to day management of project reports and deliverables, the project
coordinator has set up and maintains a Dropbox folder. All partners have
access to the folder and use it to exchange project reports and disseminate
the deliverables to the rest of the consortium.
# Conclusions
This deliverable contains a description of data management processes that have
been set up within the project as well as the description of the datasets
collected, processed or generated by the project and an initial plan on how
sharing, archiving and preservation of these datasets will be guaranteed.
The motion capture datasets generated within the project comprise a library of
dance movements with high quality, diverse content, valuable for a variety of
research purposes. The project consortium will use this rich data through the
data modeling and management infrastructure described in this document to
perform research in different fields, from feature extraction, conceptual
modeling and semantic analysis to advanced search algorithms and innovative
presentation approaches for the content, in the general context of a dance
learning system.
# Appendix A – Motion capture resources
## Ballet
**Table 9 Ballet motion capture .fbx and .c3d files per recording**
<table>
<tr>
<th>
**Dataset name**
</th>
<th>
**.fbx resources**
</th>
<th>
**.c3d resources**
</th>
<th>
**Total**
</th> </tr>
<tr>
<td>
Adagio
</td>
<td>
4
</td>
<td>
4
</td>
<td>
8
</td> </tr>
<tr>
<td>
Attitude
</td>
<td>
2
</td>
<td>
2
</td>
<td>
4
</td> </tr>
<tr>
<td>
Ballet Center
Variations
</td>
<td>
5
</td>
<td>
5
</td>
<td>
10
</td> </tr>
<tr>
<td>
Cou de pied
</td>
<td>
2
</td>
<td>
2
</td>
<td>
4
</td> </tr>
<tr>
<td>
Coupe
</td>
<td>
2
</td>
<td>
2
</td>
<td>
4
</td> </tr>
<tr>
<td>
Developpe
</td>
<td>
4
</td>
<td>
4
</td>
<td>
8
</td> </tr>
<tr>
<td>
Enchainement
</td>
<td>
5
</td>
<td>
5
</td>
<td>
10
</td> </tr>
<tr>
<td>
Flic Flac
</td>
<td>
2
</td>
<td>
2
</td>
<td>
4
</td> </tr>
<tr>
<td>
Fondue
</td>
<td>
6
</td>
<td>
8
</td>
<td>
14
</td> </tr>
<tr>
<td>
Fouette
</td>
<td>
1
</td>
<td>
1
</td>
<td>
2
</td> </tr>
<tr>
<td>
Frappe
</td>
<td>
8
</td>
<td>
8
</td>
<td>
16
</td> </tr>
<tr>
<td>
Grand Battement
</td>
<td>
4
</td>
<td>
6
</td>
<td>
10
</td> </tr>
<tr>
<td>
Grand Jete
</td>
<td>
2
</td>
<td>
2
</td>
<td>
4
</td> </tr> </table>
<table>
<tr>
<th>
Grand Rond de Jambe
</th>
<th>
4
</th>
<th>
4
</th>
<th>
8
</th> </tr>
<tr>
<td>
Improvisation
</td>
<td>
7
</td>
<td>
7
</td>
<td>
14
</td> </tr>
<tr>
<td>
Jump
</td>
<td>
15
</td>
<td>
15
</td>
<td>
30
</td> </tr>
<tr>
<td>
Other
</td>
<td>
3
</td>
<td>
10
</td>
<td>
13
</td> </tr>
<tr>
<td>
Pad de deux
</td>
<td>
2
</td>
<td>
3
</td>
<td>
5
</td> </tr>
<tr>
<td>
Pas de Cheval
</td>
<td>
2
</td>
<td>
2
</td>
<td>
4
</td> </tr>
<tr>
<td>
Pas Marche
</td>
<td>
6
</td>
<td>
6
</td>
<td>
12
</td> </tr>
<tr>
<td>
Passe
</td>
<td>
2
</td>
<td>
2
</td>
<td>
4
</td> </tr>
<tr>
<td>
Petit Battement
</td>
<td>
6
</td>
<td>
6
</td>
<td>
12
</td> </tr>
<tr>
<td>
Pied a la main
</td>
<td>
2
</td>
<td>
2
</td>
<td>
4
</td> </tr>
<tr>
<td>
Pirouette
</td>
<td>
18
</td>
<td>
18
</td>
<td>
36
</td> </tr>
<tr>
<td>
Plie
</td>
<td>
12
</td>
<td>
16
</td>
<td>
28
</td> </tr>
<tr>
<td>
Port de Bras
</td>
<td>
3
</td>
<td>
11
</td>
<td>
14
</td> </tr>
<tr>
<td>
Releve
</td>
<td>
6
</td>
<td>
9
</td>
<td>
15
</td> </tr>
<tr>
<td>
Rond de Jambe
</td>
<td>
12
</td>
<td>
12
</td>
<td>
24
</td> </tr>
<tr>
<td>
Rond de Jambe en
l'air
</td>
<td>
1
</td>
<td>
1
</td>
<td>
2
</td> </tr>
<tr>
<td>
Soutenu
</td>
<td>
3
</td>
<td>
3
</td>
<td>
6
</td> </tr>
<tr>
<td>
Temps Lie
</td>
<td>
2
</td>
<td>
2
</td>
<td>
4
</td> </tr>
<tr>
<td>
Tendu
</td>
<td>
15
</td>
<td>
18
</td>
<td>
33
</td> </tr>
<tr>
<td>
Tendu Jete
</td>
<td>
17
</td>
<td>
18
</td>
<td>
35
</td> </tr>
<tr>
<td>
**Total**
</td>
<td>
**185**
</td>
<td>
**216**
</td>
<td>
**401**
</td> </tr>
<tr>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td> </tr> </table>
## Contemporary
**Table 10 Contemporary dance motion capture .fbx and .c3d files per
recording**
<table>
<tr>
<th>
**Dataset name**
</th>
<th>
**.fbx resources**
</th>
<th>
**.c3d resources**
</th>
<th>
**Total**
</th> </tr>
<tr>
<td>
Alignment and
Posture
</td>
<td>
60
</td>
<td>
52
</td>
<td>
112
</td> </tr>
<tr>
<td>
Balance
</td>
<td>
73
</td>
<td>
74
</td>
<td>
147
</td> </tr>
<tr>
<td>
Coordination
</td>
<td>
62
</td>
<td>
62
</td>
<td>
124
</td> </tr>
<tr>
<td>
Directionality
</td>
<td>
237
</td>
<td>
235
</td>
<td>
472
</td> </tr>
<tr>
<td>
Emotions
</td>
<td>
0
</td>
<td>
11
</td>
<td>
11
</td> </tr>
<tr>
<td>
Motion Through
Space
</td>
<td>
32
</td>
<td>
31
</td>
<td>
63
</td> </tr>
<tr>
<td>
Motorics
</td>
<td>
76
</td>
<td>
76
</td>
<td>
152
</td> </tr>
<tr>
<td>
Multumodal_Genoa
</td>
<td>
0
</td>
<td>
110
</td>
<td>
110
</td> </tr>
<tr>
<td>
Other
</td>
<td>
9
</td>
<td>
44
</td>
<td>
53
</td> </tr>
<tr>
<td>
Rhythm and Phrasing
</td>
<td>
19
</td>
<td>
11
</td>
<td>
30
</td> </tr>
<tr>
<td>
Stillness
</td>
<td>
11
</td>
<td>
11
</td>
<td>
22
</td> </tr>
<tr>
<td>
Symmetry
</td>
<td>
78
</td>
<td>
90
</td>
<td>
168
</td> </tr>
<tr>
<td>
Weight bearing vs.
Gesturing
</td>
<td>
70
</td>
<td>
70
</td>
<td>
140
</td> </tr>
<tr>
<td>
**Total**
</td>
<td>
**727**
</td>
<td>
**877**
</td>
<td>
**1604**
</td> </tr> </table>
## Flamenco
**Table 11 Flamenco motion capture .fbx and .c3d files per recording**
<table>
<tr>
<th>
**Dataset name**
</th>
<th>
**.fbx resources**
</th>
<th>
**.c3d resources**
</th>
<th>
**Total**
</th> </tr>
<tr>
<td>
Alignment and
Posture
</td>
<td>
6
</td>
<td>
6
</td>
<td>
12
</td> </tr>
<tr>
<td>
Asymmetry
</td>
<td>
1
</td>
<td>
1
</td>
<td>
2
</td> </tr>
<tr>
<td>
Balance
</td>
<td>
2
</td>
<td>
2
</td>
<td>
4
</td> </tr>
<tr>
<td>
Coordination
</td>
<td>
3
</td>
<td>
3
</td>
<td>
6
</td> </tr>
<tr>
<td>
Directionality
</td>
<td>
3
</td>
<td>
3
</td>
<td>
6
</td> </tr>
<tr>
<td>
Flamenco combinations
</td>
<td>
20
</td>
<td>
36
</td>
<td>
56
</td> </tr>
<tr>
<td>
Motion Through
Space
</td>
<td>
3
</td>
<td>
3
</td>
<td>
6
</td> </tr>
<tr>
<td>
Motorics
</td>
<td>
2
</td>
<td>
2
</td>
<td>
4
</td> </tr>
<tr>
<td>
Rhythm and Phrasing
</td>
<td>
2
</td>
<td>
2
</td>
<td>
4
</td> </tr>
<tr>
<td>
Stillness
</td>
<td>
3
</td>
<td>
3
</td>
<td>
6
</td> </tr>
<tr>
<td>
Symmetry
</td>
<td>
3
</td>
<td>
3
</td>
<td>
6
</td> </tr>
<tr>
<td>
Weight bearing vs.
Gesturing
</td>
<td>
1
</td>
<td>
1
</td>
<td>
2
</td> </tr>
<tr>
<td>
**Total**
</td>
<td>
**49**
</td>
<td>
**65**
</td>
<td>
**114**
</td> </tr> </table>
## Greek folk dance
**Table 12 Greek folk dance motion capture .fbx and .c3d files per recording**
<table>
<tr>
<th>
**Dataset name**
</th>
<th>
**.fbx resources**
</th>
<th>
**.c3d resources**
</th>
<th>
**Total**
</th> </tr>
<tr>
<td>
Baintouska
</td>
<td>
0
</td>
<td>
5
</td>
<td>
5
</td> </tr> </table>
<table>
<tr>
<th>
Ballos
</th>
<th>
12
</th>
<th>
23
</th>
<th>
35
</th> </tr>
<tr>
<td>
Basic Steps
</td>
<td>
0
</td>
<td>
9
</td>
<td>
9
</td> </tr>
<tr>
<td>
Chaniotikos
</td>
<td>
1
</td>
<td>
6
</td>
<td>
7
</td> </tr>
<tr>
<td>
Chassapiko
</td>
<td>
2
</td>
<td>
2
</td>
<td>
4
</td> </tr>
<tr>
<td>
Enteka
</td>
<td>
4
</td>
<td>
4
</td>
<td>
8
</td> </tr>
<tr>
<td>
Forlana
</td>
<td>
1
</td>
<td>
1
</td>
<td>
2
</td> </tr>
<tr>
<td>
Gaida
</td>
<td>
3
</td>
<td>
4
</td>
<td>
7
</td> </tr>
<tr>
<td>
Ikariotiko
</td>
<td>
6
</td>
<td>
6
</td>
<td>
12
</td> </tr>
<tr>
<td>
Issos
</td>
<td>
5
</td>
<td>
5
</td>
<td>
10
</td> </tr>
<tr>
<td>
Kalamatianos
</td>
<td>
1
</td>
<td>
1
</td>
<td>
2
</td> </tr>
<tr>
<td>
Kaneloriza
</td>
<td>
4
</td>
<td>
4
</td>
<td>
8
</td> </tr>
<tr>
<td>
Karatzova
</td>
<td>
8
</td>
<td>
11
</td>
<td>
19
</td> </tr>
<tr>
<td>
Karsilamas
</td>
<td>
0
</td>
<td>
7
</td>
<td>
7
</td> </tr>
<tr>
<td>
Kastrinos
</td>
<td>
7
</td>
<td>
16
</td>
<td>
23
</td> </tr>
<tr>
<td>
Katsivelikos
</td>
<td>
6
</td>
<td>
6
</td>
<td>
12
</td> </tr>
<tr>
<td>
Kotsari
</td>
<td>
2
</td>
<td>
1
</td>
<td>
3
</td> </tr>
<tr>
<td>
Koutsos
</td>
<td>
0
</td>
<td>
9
</td>
<td>
9
</td> </tr>
<tr>
<td>
Letsina
</td>
<td>
4
</td>
<td>
4
</td>
<td>
8
</td> </tr>
<tr>
<td>
Leventikos
</td>
<td>
11
</td>
<td>
14
</td>
<td>
25
</td> </tr>
<tr>
<td>
Nisamikos
</td>
<td>
4
</td>
<td>
4
</td>
<td>
8
</td> </tr>
<tr>
<td>
Other greek dance
</td>
<td>
0
</td>
<td>
13
</td>
<td>
13
</td> </tr>
<tr>
<td>
Papadia
</td>
<td>
13
</td>
<td>
13
</td>
<td>
26
</td> </tr>
<tr>
<td>
Patima
</td>
<td>
6
</td>
<td>
6
</td>
<td>
12
</td> </tr> </table>
<table>
<tr>
<th>
Patinada
</th>
<th>
3
</th>
<th>
3
</th>
<th>
6
</th> </tr>
<tr>
<td>
Patrouninos
</td>
<td>
0
</td>
<td>
4
</td>
<td>
4
</td> </tr>
<tr>
<td>
Pentozali
</td>
<td>
2
</td>
<td>
2
</td>
<td>
4
</td> </tr>
<tr>
<td>
Pidiktos
</td>
<td>
7
</td>
<td>
7
</td>
<td>
14
</td> </tr>
<tr>
<td>
Pousnitsa
</td>
<td>
1
</td>
<td>
1
</td>
<td>
2
</td> </tr>
<tr>
<td>
Proskynitos
</td>
<td>
0
</td>
<td>
6
</td>
<td>
6
</td> </tr>
<tr>
<td>
Pyrgousikos
</td>
<td>
1
</td>
<td>
1
</td>
<td>
2
</td> </tr>
<tr>
<td>
Raiko
</td>
<td>
1
</td>
<td>
9
</td>
<td>
10
</td> </tr>
<tr>
<td>
Sera
</td>
<td>
6
</td>
<td>
10
</td>
<td>
16
</td> </tr>
<tr>
<td>
Seranitsa
</td>
<td>
4
</td>
<td>
4
</td>
<td>
8
</td> </tr>
<tr>
<td>
Sfarlys
</td>
<td>
1
</td>
<td>
1
</td>
<td>
2
</td> </tr>
<tr>
<td>
Sousta
</td>
<td>
4
</td>
<td>
4
</td>
<td>
8
</td> </tr>
<tr>
<td>
Sta Dio
</td>
<td>
1
</td>
<td>
2
</td>
<td>
3
</td> </tr>
<tr>
<td>
Sta Tria
</td>
<td>
2
</td>
<td>
3
</td>
<td>
5
</td> </tr>
<tr>
<td>
Streis
</td>
<td>
6
</td>
<td>
6
</td>
<td>
12
</td> </tr>
<tr>
<td>
Sygathistos
</td>
<td>
8
</td>
<td>
8
</td>
<td>
16
</td> </tr>
<tr>
<td>
Syrtos
</td>
<td>
1
</td>
<td>
1
</td>
<td>
2
</td> </tr>
<tr>
<td>
TikPal
</td>
<td>
3
</td>
<td>
3
</td>
<td>
6
</td> </tr>
<tr>
<td>
TikTrom
</td>
<td>
1
</td>
<td>
1
</td>
<td>
2
</td> </tr>
<tr>
<td>
Trigona
</td>
<td>
6
</td>
<td>
3
</td>
<td>
9
</td> </tr>
<tr>
<td>
Tritepati
</td>
<td>
0
</td>
<td>
4
</td>
<td>
4
</td> </tr>
<tr>
<td>
Tsamiko
</td>
<td>
13
</td>
<td>
13
</td>
<td>
26
</td> </tr>
<tr>
<td>
Vagelitsa
</td>
<td>
1
</td>
<td>
1
</td>
<td>
2
</td> </tr>
<tr>
<td>
Vlaha Naxos
</td>
<td>
3
</td>
<td>
3
</td>
<td>
6
</td> </tr>
<tr>
<td>
Zagorisio
</td>
<td>
5
</td>
<td>
14
</td>
<td>
19
</td> </tr>
<tr>
<td>
Zervodexos
</td>
<td>
0
</td>
<td>
5
</td>
<td>
5
</td> </tr>
<tr>
<td>
Zervos
</td>
<td>
2
</td>
<td>
2
</td>
<td>
4
</td> </tr>
<tr>
<td>
Zonaradikos
</td>
<td>
6
</td>
<td>
6
</td>
<td>
12
</td> </tr>
<tr>
<td>
Zorbas
</td>
<td>
1
</td>
<td>
1
</td>
<td>
2
</td> </tr>
<tr>
<td>
**Total**
</td>
<td>
**189**
</td>
<td>
**302**
</td>
<td>
**491**
</td> </tr> </table>
|
https://phaidra.univie.ac.at/o:1140797
|
Horizon 2020
|
1091_ADASANDME_688900.md
|
**Executive Summary**
This Deliverable is an update of Deliverable 10.2 ‘Data Management Plan’ and
the new additions are introduced in **Chapter 1** along with the next steps
and future updates, to be presented in the final version (M30) of this
Deliverable.
An overview of ADAS&ME overall system (discussed in D 2.1: ‘ADAS&ME
Architectural
Framework and System Specifications’) and its sub-systems is presented in
**Chapter 2** accompanied by a description of the project’s repository
(discussed in D4.1: ‘Driver/rider state reference database and integration
suite’) to highlight the data sources, profiles, distribution and storage
destinations/ access standards across the UCs and pilot sites.
**Chapter 3** provides an overview of partners responsibilities in relation to
data management and protection.
**Chapter 4** describes the data handling according to FAIR. The different
data management roles in the project are discussed in **Chapter 5** .
Data security is re-visited in this version and discussed with consideration
for GDPR compliance ( **Chapter 6** ).
This update concludes with **Chapter 7** , with the next steps to be taken and
the work to be performed and included in the final version of this
Deliverable.
The GDPR compliance questionnaire and the Data Privacy Impact Assessment
(DPIA) template can be found in **Annex 1** that will be distributed to
partners to be completed by the end of the project. The latter will be
circulated and completed two times by the end of the project. The first
assessment will be included in the final version of the Deliverable (M30) and
the final version in the final technical report.
Additional data management plan templates were prepared in this version based
on the data collected per Use Case, as presented in D7.1: ‘Evaluation
Framework’. The consolidated filled in template can be found in **Annex 2** .
1. **Introduction**
This document is an update of the version submitted in M6. The Data Management
Plans aims to define the processes of data handling during and after the end
of the project. In the first version of this Deliverable, data generated by
sensors and devices were annexed, the standards and methodologies were
presented, and the data privacy protection procedure was defined along with
guidelines on how data can be openly shared in order to comply with ORDP
requirements with regards to their storage, curation and preservation.
This update aims to address the following aspects:
* Data collection per UC pilots, as presented within D7.1 (submitted M18). Types of data along information about privacy, confidentiality, and other characteristics are presented in Table 1 (Annex 2), following the same format with the table annexed in the first version of this deliverable.
* A short description of the ADAS&ME data repository, based on D4.1, submitted in M18.
In the first version of this deliverable, Annex I, included a very detailed
and long table including all sensors and objective data collected during the
pilots in order to develop the affective state algorithms and respective DSS.
In the second version, the same table includes the data collected per UC in
each pilot site.
2. **Data architecture and storage in ADAS &Me: an overview **
The general ADAS&ME system encompasses several subsystems that are further
divided into modules, including both hardware and software components (Figure
1).
There are five different subsystems: a) the **Sensor Subsystem (SS)** , b) the
**Driver state Monitoring subsystem (DSMS)** , c) the **Environmental
Situation Awareness Subsystem (ESAS)** , d) the **ADAS &ME Core (ADAS&ME C) **
, and e) the **Vehicle Automation Subsystem (VAS)** .
Therefore, 5 different types of data are collected: _Soft sensor data, driver
monitoring data, environmental/ vehicle data, digital infrastructure data and
HMI data_ . How these data are handled in the project is presented in the
annexed tables in both this and the previous version of D10.2. Data
collection, storage and analysis is conducted always in accordance to the
project’s data privacy policy, as it has been defined within the first version
of this Deliverable and the Ethics Manual (D9.1).
Environmental Situation Awareness Subsystem
(
ESAS
)
Algorithms for Environmental Situation Determination
ADAS
&
ME Core
(
ADAS
&
ME C
)
Personalization Module
(
PM
)
Decision Support Module
(
DSM
)
HMI Controller Module
(
HMI CM
)
Vehicle Automation Subsystem
(
VAS
)
Vehicle Automation Module
(
VAM
)
Soft Sensor
data
Driver
monitoring Data
Environmental
/
Vehicle Data
Digital
Infrastructure
Data
HMI Data
Interface Module
(
IM
)
Sensor Subsystem
(
SS
)
Driver State Monitoring Subsystem
(
DSMS
)
Algorithms for Driver State Determination
p.18)
All these data will be collected and stored in the ADAS&ME repository for
sharing datasets among partners. A data repository and a suite of
optimization/machine learning algorithms was created within A4.1. The
framework, the mechanisms and the repository are described in detail in
Deliverable 4.1 – Driver/rider state reference database and integration suite.
The repository allows for sharing the sets of data collected during the pilots
with different access rights per allocated partner to ensure alignment and
confidentiality across the project. The repository is accessed through a GUI
and an API. It also offers an optimization/machine learning suit for seamless
prototyping and testing of detection algorithms as well as a RESTful interface
for the integration of customized algorithms, where partners can further use
denoising algorithms.
The repository is available (http://150.140.150.85:8000/repox/index.php) to
registered users only (Figure 2) and it is login protected with accounts
obtained by the administrator (UPatras). Registered users have access only to
their own data, which are categorized per UC.
Data are grouped under Use Cases and not necessarily per pilot site. There are
occasions where certain data are collected across UCs and pilot sites (e.g.
subjective forms and questionnaires).
GDPR compliance for all these aforementioned parts of the ADAS&ME is being
monitored and will be reported in the final version of this Deliverable.
Compliance is monitored through a dedicated questionnaire that can be found
Annex 1. Additionally, the impact of adherence to GDPR to the project will be
estimated through another questionnaire (Data Privacy Impact Assessment (DPIA;
Annex 1). This questionnaire will be distributed to ADAS&ME partners twice
until the end of the project. The first Privacy Impact Assessment (PIA) will
be reported in the final version of this deliverable in M30 and the final one
in the final technical project reporting.
**3 Partners’ Responsibilities**
In order to face data management challenges efficiently, All ADAS&ME partners
have to respect the policies set out in this DMP and datasets have to be
created, managed and stored appropriately.
The **Data controller role** within ADAS&ME Ethics will be undertaken by the
Technical Manager of the project who will directly report to the ADAS&ME
Ethics Board. The **Data controller** acts as the point of contact for Data
Protection issue and will coordinate the actions required to liaise between
different beneficiaries and their affiliates, as well as their respective Data
Protection agencies, to ensure that data collection and processing within the
scope of ADAS&ME, will be carried out according to EU and national
legislation. Regarding the ORDP, the data controller must ensure that data are
shared and easily available.
Each data producer and pilot site leader is responsible for the integrity and
compatibility of its data during the project lifetime. The data producer is
responsible for sharing its datasets through open access repositories. They
are in charge of providing the latest version of their datasets.
Regarding ethical issues, the deliverable D9.2 details all the measures that
ADAS&ME will use to comply with the H2020 Ethics requirements.
The **Data Manager** roles within ADAS&ME will directly report to the
coordination team. The **Data Managers** (CERTH and UPatras) will coordinate
the actions related to data management and in particular the compliance to
Open Research Data Pilot guideline. The data manager is responsible for
implementing the data management plan and to ensure it is reviewed and
revised.
**4 Findable, accessible, interoperable and reusable (FAIR) data**
ADAS&ME project will in principle participate in the Open Research Data Pilot
(ORDP) but data marked as “restricted” or under an “embargo” period will be
excluded. To this end, the data that will be generated during and after the
project and will be included in ORDP should be ‘FAIR’, that is **findable,
accessible, interoperable and reusable** . These requirements don’t affect
implementation choices and don’t necessarily suggest any specific technology,
standard, or implementation solution.
The FAIR principles were generated to improve the practices for data
management and datacuration, and FAIR aims to describe the principles to be
applied to a wide range of data management purposes, whether it is data
collection or data management of larger research projects regardless of
scientific disciplines.
With the endorsement of the FAIR principles by H2020 and their implementation
in the guidelines for H2020, the FAIR principles serve as a template for
lifecycle data management and ensure that the most important components for
lifecycle are covered. This is intended as an implementation of the FAIR
concept rather than a strict technical implementation of the FAIR principles.
**Making data findable, including provisions for metadata:**
* The datasets will have very rich metadata to facilitate the findability.
* All the datasets will have a Digital Object Identifiers provided by the ADAS&ME (public part of) repository or another online data storage repository (e.g., ZENODO).
* The reference used for the dataset will follow this format: ADAS&ME _WPX_AX.X_XX, including clear indication of the related WP, activity and version of the dataset. • The standards for metadata will be defined in the “Standards and metadata” section of the dataset description table (see Table 1 for the current version of the template).
**Making data openly accessible:**
* Datasets openly available will be marked as “Open” in the “Data Sharing” section of the dataset description table (see **Fel! Hittar inte referenskälla.** ).
* The repository that each dataset is stores, including Open access datasets, are mentioned in the “Archiving and Preservation” section of the dataset description table (see Table 1). Public repositories such as ZENODO will be one of the considered options.
* “Data sharing” section of the dataset description table (see Table 1) will also include information with respect to the methods or software used to access the data of each dataset.
* Data and their associated metadata will be deposed either in a public repository or in an institutional repository.
* “Data sharing” section of the dataset description table (see Table 1) will outline the rules to access the data if restrictions exist.
**Making data interoperable:**
* Metadata vocabularies, standards and methodologies will depend on the repository to be hosted (incl. public, institutional, etc.) and will be provided in the “Standards and metadata” section of the dataset description table (see Table 1).
**Increase data re-use (through clarifying licenses):**
* All the data producers will license their data to allow the widest reuse possible. More details about license types and rules will be provided in the next version “Data Sharing” section of the dataset description table (see Table 1) is the field where the data sharing policy of each dataset is defined. By default, the data will be made available for reuse. If any constrains exist, an “embargo period” or “restricted flag” will be explicitly raised in this section of Table 1\.
* The data producers will make their data available for third-parties within public repositories only for scientific publications validation purposes.
**5 Data management roles in the project**
In order to face the data management challenges efficiently, all ADAS&ME
partners have to respect the policies set out in this DMP and datasets have to
be created, managed and stored appropriately. This Chapter identifies ADAS&ME
roles related to the management of the data and their responsibilities. These
are: a) the data controller, b) the data producer and c) the data manager. The
GDPR compliance questionnaire that can be found in Annex 1 will be used in
order to define which partners will play these key roles. At this stage, the
roles have provisionally been appointed to CERTH and UPatras (as discussed in
previous chapter). However, data producers are the pilot sites per se.
The **data controller** acts as the point of contact for data protection
issues and will coordinate the actions required to liaise between different
beneficiaries and their affiliates, as well as their respective data
protection agencies, in order to ensure that data collection and processing
within the scope of ADAS&ME, will be carried out according to EU and national
legislation. The data controller must ensure that data are shared and easily
available.
Given the different pilot sites that are considered in ADAS&ME, the consortium
investigates who is the most appropriate partner to take the role of the data
controller. This role may be undertaken either by the pilot leader of each
specific pilot site or the overall data repository administrator (UPatras) or
by each data provider per se depending on the data to be collected and or
generated. The name(s) of the data controller(s) will be announced in the
final DMP version (M30). The **data producer** is any entity that produces
data within ADAS&ME scope. Each data producer is responsible for the integrity
and compatibility of its data during the project lifetime. The data producer
is responsible for sharing its anonymised datasets through open access
repositories, according to the principles and mechanisms defined in the
current document. He is in charge of providing the latest version.
Last but not least, the **data manager** (to be announced in the final version
of the DMP) will coordinate the actions related to data management, will be
responsible for the actual implementation of the DMP successive versions and
for the compliance to Open Research Data Pilot (ORDP) guidelines. As the
ADAS&ME open data will be hosted either by institutional databases or by an
open free of charge platform (e.g. Zenodo), no additional costs will be
required for hosting the data. As partners are not positive in sharing data
outside the data, due to the sensitive and innovative nature of the work
performed within the project, therefore aggregated or inferential data,
metadata and description, as well as open publications might be shared
instead. However, this decision will be made for the final version of this
document.
All research entities participating in the ADAS&ME project shall ensure that
they have entered into an appropriate data sharing agreement prior to any
personal data being shared.
**6 Data Security and GDPR**
ADAS&ME partners ensure that security mechanisms and management procedures are
in place, to: a) ensure personal (sensitive) data protection through a strict
process of data collection, anonymization, harmonization and integration and
b) guarantee data integrity and reliability, ensuring system’s high-
performance operation through the exchange of the necessary information.
The consortium research partners will fully comply at all times with all
applicable data protection legislation and regulation during this project, to
ensure the security and protection of individuals' personal information in
relation to this project. This includes compliance with the General Data
Protection Regulation (GDPR), which came into effect on 25 May 2018. The
consortium and research partners acknowledge the various new obligations and
the new rights granted to data subjects under the GDPR and are aware of the
significant fines that may be imposed should a data breach occur.
In terms of **personal data protection** , personal data will be anonymised
and strictly used for project’s purposes. Before collecting any personal data,
the Local Ethics Representative (will be responsible for informing the
involved pilot users/participants and collecting their informed consents that
will be maintained and stored based on the Grant Agreement rules and
European/local laws. No personal data will be centrally stored, without
anonymisation or pseudonymisation. No personal information will be made
available by the Local Ethics Representative to the pilot sites, i.e., ADAS&ME
partners participating in the pilots. Only one person per site (the Local
Ethics Representative) will have access to the informed consent form
containing the personal information and only that person will be aware of the
relation between the participant’s unique identifier code and their personal
identity, in order to administer the tests. In practice, the Local Ethics
Representative will collect those data required for contacting the
participants and arranging with them the sequence of the current or future
tests. The Local Ethics Representative will then issue a single Test ID
(unique identifier code) for each of them. This person (Local Ethics
Representative) will not participate in the evaluation and will not know how
each user behaved. One month before the end of the project, this reference,
i.e., the reference between the Test ID and the real-life contact details of
the participant, together with any other personal information held on the
participant will be deleted, thus safeguarding full anonymisation of the
results.
The stored data will refer to a user’s age, gender, nationality and other
information relevant to the testing objectives will be safeguarded, stored and
processed only in accordance with all applicable data protection laws and
regulations. The stored data will not contain any other identifier apart from
the Test ID. In no circumstances will a participant be asked for information
relating to their beliefs, political or sexual preferences. User-related data
will be securely and safely stored. Also, data will be scrambled where
possible and abstracted to permit its use to achieve project outcomes while
ensuring data integrity and security.
Any party which provides any data or information (the "Providing Party") to
another party (the "Receiving Party") in connection with the project will not
include any personal information relating to an identified or identifiable
natural person or data subject. To this end, the Providing Party will
anonymise or pseudonymise all data delivered to other parties to an extent
sufficient to ensure that a person without prior knowledge of the original
data and its collection cannot, from the anonymised or pseudonymised data and
any other available information, deduce the personal identity of participants.
Each party shall be solely responsible for the selection of specific database
vendors/data collectors/data providers, and for the performance (including any
breach) of its contracts between it and such database vendors/data collectors,
(to which no other project partner shall be a party, and under which no other
partner assumes any obligation or liability) and shall further warrant that it
has the authority to disclose the information, if any, which it provides to
the other parties, and that where legally required and relevant, it has
obtained appropriate informed consents from all the individuals involved.
Partners supplying special data analysis tooling, shall have the right on
written notice and without liability to terminate the license that it has
granted for such tooling to be used in connection with the project, if the
supplying partner knows or has reasonable cause to believe that the processing
of particular data through such tooling infringes the rights (including
without limitation privacy, publicity, reputation and intellectual property
rights) of any third party, including of any individual.
Each pilot site will have its own Ethics Committee and one person will be
nominated per site as responsible for following the project’s Ethics Board
recommendations and data protection.
Moreover, a Data Privacy Impact Assessment (DPIA) will be used to support this
DMP. In particular, the DPIA will assist with, and demonstrate, compliance
with the GDPR. This will be carried out within the next 6 months and as the
project evolves it will demonstrate compliance in the collection and
processing of personal information. This DPIA will ensure that collection,
handling, use and storage of personal information are reviewed to ensure
compliance with all applicable privacy legislation and regulations. The DPIA
will: • assess whether the proposed collection, handling use and storage is
legally permissible;
* assess whether the proposal is justified and proportionate;
* detail the actions necessary to ensure collection, handling, use and storage is compliant and to mitigate any risk to personal information; and
* provide a record of the decisions that are made to introduce or change the way in which personal information is collected, handled, used or stored.
An indicative template of the DPIA to be used for ADAS&ME can be found in
Annex 1.
Last but not least, whenever **authorisations for data collection** ,
processing and management have to be obtained from national bodies, those
authorisations shall be considered as documents relevant to ADAS&ME. Copies of
all relevant authorisations and approvals shall be submitted prior to
commencement of the relevant part of the research work. The cases where such
authorizations are needed are identified at the pilots by the pilot execution
teams.
**7 Conclusion and next steps**
The data collected per UC and pilot site (Table 1) and the data management
roles (Chapter 5) were defined in this version accompanied by an overview of
the ADAS&ME system and the data management repository characteristics (Chapter
2)for sharing the datasets created and stored per UC tests.
Data privacy was re-visited with consideration for different data management
roles, as required by GDPR. The overall plan related to data management to be
followed during the lifecycle of the project was further refined with
consideration for both pilot planning and respective indicators (D7.1) as well
as the ADAS&ME Ethics related policy (D10.3). A decision upon data visibility
remains to be made.
The next steps entail the following:
* Circulate the GDPR Questionnaire to further update compliance processes followed by each Pilot site and each data owner;
* Estimate DPIA twice by the end of the project (M30 an dM36, respectively);
* Finalise decisions about data openness, structure of shared data, place of sharing.
In addition, it sets the steps, roles and processes towards GDPR adoption
within the project. However, the complete adherence will be described in the
final version of this deliverable in M30.
The final version will include a description of the datasets that will be
shared outside the Consortium of the project (if any), the duration and the
sharing medium and/ technologies used. In addition, the steps taken within the
project to comply to GDPR will be recorded and reported as well as any issues,
difficulties and lessons learnt from this process, as it is new for the
project partners. The final version of the Data Management Plan will also
clearly state which datasets will be shared with the public and which parts
(or whole datasets) will not.
|
https://phaidra.univie.ac.at/o:1140797
|
Horizon 2020
|
1092_DESTINATIONS_689031.md
|
1 EXECUTIVE SUMMARY 5
2 INTRODUCTION 6
2.1 OBJECTIVES OF DESTINATIONS PROJECT 6
2.2 ROLE OF PROJECT DMP IN DESTINATIONS 6
2.3 CROSS-RELATIONS BETWEEN PROJECT DMP AND ETHICS COMPLIANCE 7
3 DATA COLLECTED AND PROCESSED IN DESTINATIONS 7
4 DMP COVERED PERIOD AND UPDATING PROCESS 8
5 DETAIL OF DATA CATEGORIES 9
6 DATA MANAGEMENT PLAN 12
6.1 WP2-WP7 12
6.2 WP9 31
6.3 WP10 35
# Executive Summary
The DESTINATIONS project embeds the process of data management and the
procedure of compliance to ethical/privacy rules set out in Ethics Compliance
Report (D1.1) into its whole work programme, for the whole research and
demonstration life cycle. The data management procedures within DESTINATIONS
project arise within the detail of the work, and not with the overall raison
d’être of the project itself, which is part of the EC Horizon 2020 programme,
Mobility for Growth sub-programme.
This document represents the first version of D1.2 Project Data Management
Plan (PDMP) related to the data collected, handled and processed by
DESTINATIONS project over the first six-monthly period (M1-M6, September 2016
– February 2017). It will be updated regularly in order to integrate the
different data typologies the project will manage period by period.
This document specifies the overall approach to data management issues adopted
by DESTINATIONS project according to the guidelines and indications defined in
the Ethics Compliance Report (D1.1) respect to PDMP plays the role of
“implementation monitoring tool”.
The Project Data Management Plan is structured as follows:
* Section 1 is an introduction of the document covering the identification of objectives for its elaboration and delivery, the role of Data Management Plan (PDMP+LDMP) into the whole DESTINATIONS project and the cross-relations between Data Management Plan (DMP) and Ethic Compliance Report (D1.1);
* Section 2 identifies the different typologies of data managed by the whole DESTINATIONS project;
* On the basis of the data typologies identified in section 2, section 3 details the specific data collected and generated by DESTINATIONS in the first six-monthly period (M1-M6, September 2016 – February 2017); Section 4 covers a two-fold role:
o Relating to demo WPs and data collected/managed at local level (project
pilot sites), section 4 represents a guided template to be filled in by pilot
sites local partners in order to generate the contributions to Local Data
Management
Plan (D1.3); o Relating to horizontal WPs and data managed/processed by expert
partners supporting sites local partners in demo WPs, section 4 provides the
description of data management procedures adopted (when applicable).
# Introduction
## Objectives of DESTINATIONS project
The DESTINATIONS project implements a set of mutually reinforcing and
integrated innovative mobility solutions in six medium small urban piloting
areas in order to demonstrate how to address the lack of a seamless mobility
offer in tourist destinations.
The overall objective of DESTINATIONS project is articulated in the following
operational goals:
* Development of a Sustainable Urban Mobility Plan (SUMP) for residents and tourists focusing on the integrated planning process that forms the basis of a successful urban mobility policy (WP2);
* Development of a Sustainable Urban Logistics Plan (SULP) targeted on freight distribution processes to be integrated into the SUMP (WP5);
* Implementation and demonstration of pilot measures to improve mobility for tourists and residents (WP3-WP7);
* Development of guidelines to sites for stakeholders engagement (WP2-WP8);
* Development of guidelines to sites for the definition of business models to sustain the site pilot measures and the future implementation of any other mobility actions/initiatives designed in SUMP (WP8):
* Development of guidelines to sites for the design, contracting and operation of ITS (WP8).
* Evaluation of results both at project level and at site level (WP9);
* Cross-fertilization of knowledge and best practice replication including cooperation with Chinese partners (WP10);
* Communication and Disseminations (WP11).
## Role of Project DMP in DESTINATIONS
The role and the positioning of Project DMP within the whole DESTINATIONS
project (in particular with Ethics Compliance Report, D1.1) is detailed in the
following:
* The Project DMP will specify the project data typologies managed in DESTINATIONS;
* Based on the identified data typologies,, the Project DMP will provide a guided template allowing project partners/site local partners (Site Managers specifically) to describe how data are collected, handled, accessed, make openly available/published (eventually). The Project DMP will be used to collect information from Site Managers on how data are collected, stored, managed and to identify the data ownership, the access rights and possible use for dissemination and exploitation;
* The Project DMP will be used by Site Managers for Local DMP included in D1.3.
## Cross-relations between Project DMP and Ethics Compliance
The Project DMP represents the “monitoring” tool (guided template to be filled
in by project partners and site local partners, in particular) to allow:
* the partners (in particular Site Managers) certify to the Project Data Manager (PDM) designated in the Ethic Compliance Report (D1.1) and the Ethic Review Board (ERB) the conformity to the requirements set in the Ethic Report itself;
* the ERB check the compliance with the requirements set out by the project Ethics Compliance Report.
# Data collected and processed in DESTINATIONS
The DESTINATIONS project covers different activities (identified in section 2)
and then it deals with an extended range of possible data to be considered.
The term “data” can mean different things and can be related to different
kind/set of information (connected to the wide range of actions taking place
during the project).
A specification of “data” in DESTINATIONS is required together with a first
comprehensive classification of the different main typologies involved.
In particular, data in DESTINATIONS can be divided between the two following
levels:
1. Data collected by the project;
2. Data processed/produced within the project.
**Data collected** by the project can be classified in the following main
categories:
* Data for SUMP-SULP elaboration (i.e. baseline, current mobility offer, needs analysis, etc.);
* Data for the design of mobility measures in demo WPs (i.e. baseline, current mobility offer, needs analysis, etc.);
* Data produced in the operation of demo mobility measures (i.e. users registration to service, validation, transactions/payment, points for green credits, etc.); Data collected to carry out the ex-ante and ex-post evaluation.
Data collected by the DESTINATIONS project are mainly related to local
activities of demo measures design, setup and implementation and then this
process deals mostly with responsibilities of Site Managers. This will be
reflected in the production of Local DMP for which each site will provide its
contribution (filling in the guided template provided by the Project DMP).
**Data processed/produced** by the project are mainly:
* SUMP/SULP;
* Outputs coming from WP8 (business principles and scenarios, ITS contracting documents, etc.), WP9 (evaluation), WP10 (transferability) and WP11 (communication).
This level of data management (process/elaboration) is related both to local
activities (SUMP/SULP) and to Horizontal WP (WP8, 9, 10, 11).
The data management process for the first group deals mostly with
responsibilities of Site Managers and will be described in the Local DMP.
Regarding the second group of data, the data management process deals mostly
with responsibilities of Horizontal WP Leaders/Task Leaders and it will be
described in the Project DMP.
# DMP covered period and updating process
The DESTINATIONS project includes a wide range of activities spanning from
users needs analysis of demo measures including SUMP/SULP (survey for data
collection, assessment of current mobility offer which could include the
management of data coming from previous survey and existing data sources,
personal interview, collection of requirements through focus groups and co-
participative events, etc.) to demo measures operation (data of users
registered to use the demo services, management of images for access control,
management of video surveillance images in urban areas, infomobility, bookings
of mobility services, payment data/validation, data on the use of services for
promotion purpose: green credits, etc.) but also from the elaboration of
SUMP/SULP to its circulation among the relevant stakeholders for its
consolidation, from data collection on baseline and ex-ante evaluation to ex-
post evaluation.
Data can be grouped in some main categories but the details varies from WP to
WP (in particular the demo ones) and from sites to sites.
Due to the duration of the project, data to be managed will also evolve during
the project lifetime.
For the abovementioned reasons, the approach used for the delivery of Project
DMP and Local DMP is to restrict each six-monthly version to the specific data
collected, handled or processed in the reference period: this will allow also
the project partners and, in particular, Site Managers, to familiarize
smoothly in touch with the issues related to data management.
This version of Project DMP covers the first six month of project activities
(M1-M6, September 2016 – February 2017). The DMP will be updated on a six-
month basis and accordingly integrated with new data collected, handled and
produced by the project.
The activities which have been taken place in this six-month period of
DESTINATIONS project are the following:
* WP2 – webinar on SUMP stakeholder and citizens engagement. Started coordination of local WP activities and provision of support to sites. Started data collection for baseline in sites;
* WP3, WP4, WP5, WP6, WP7 – Started consolidation of users needs analysis for
the design of demo services and measures
* WP8 – webinar on cross-relation within WP2, WP8 and WP9. Webinars on T8.1 to support the sites in the identification and mapping of stakeholders and on T8.3 to support the sites in the identification of key choices for the design of the “core” ITS 1 . Started identification of key principles.
* WP9 – identification of indicators categories for ex-ante/ex-post evaluation;
* WP10 – identified possible topics for cross-fertilization among partners and outside the consortium and related scheduled events;
* WP11 – fixed cooperation with SATELLITE, started developing communication and dissemination strategies and material, production of local dissemination plan for each site.
The activities more impacted by data management issues and related to this
deliverable are:
* Data collection at site level for demo WPs and WP9;
* Identification of indicators categories for ex-ante/ex-post evaluation.
# Detail of data categories
In section 4 we have identified the main activities of DESTINATIONS project
impacted by data management issues and related to this deliverable.
In the following the typologies of data produced, handled or managed by these
activities are identified 2 :
### WP2
__Task 2.2-Task 2.3 Mobility context analysis and baseline_ _ Data
collection/survey for SUMP elaboration:
* Census/demographic data;
* Economics data;
* Tourists flow;
* Accessibility in/out;
* O/D matrix;
* Traffic flow;
* Network;
* Emissions;
* Pollution;
* Questionnaires on travel behaviour, attitudes and expectations;
* On-field measuring campaign carried out during the data collection phase.
__Task 2.6 Smart metering and crowdsourcing_ _
Automatic data collection supporting SUMP development: Traffic flow;
* Passengers counting.
### WP3
__Task 3.1 Users needs analysis, requirements and design_ _
Data collection/survey for safety problem assessment at local level and design
of demo measures:
* Data about network, cycling lanes, walking path, intersections, cross points, traffic lights;
* Road safety statistics (number of incidents on the network, etc.);
* Survey on users needs and expectations;
* Reports coming from stakeholder and target users focus group;
* Statistics produced by Traffic Management System, Traffic Supervisor or similar.
### WP4
__Task 4.1 Users needs analysis, requirements and design_ _
Data collection/survey for extension/improvement of sharing services and
design of demo measures:
* Data on sharing/ridesharing service demand;
* Data on sharing/ridesharing service offer;
* Statistics produced by the platform of management of bike sharing already operated (registered users, O/D trips, etc.);
* Survey on users needs and expectations;
* Reports coming from stakeholder and target users focus group.
Data collection/survey for take up of electrical vehicles and design of demo
measures:
* Data on the demand of electrical vehicles;
* Data on the offer of electrical vehicles;
* Survey on users needs and expectations;
* Reports coming from stakeholder and target users focus group.
### WP5
__Task 5.1 Users needs analysis, requirements and design_ _ Data/collection
surveys for SULP elaboration:
* Data on shops, supply process, logistics operators, etc.;
* On-field measuring campaign carried out during the data collection phase;
* Questionnaires/survey on supply/retail process;
* Reports coming from stakeholder and target users focus group.
Data/collection surveys for demo logistics services
* Survey on users needs and expectations;
* Reports coming from stakeholder and target users focus group.
### WP6
__Task 6.1 Users needs analysis, requirements and design_ _
Data/collection for the design of demo measures for increasing awareness on
sustainable mobility:
* Data on promotional initiatives already under operation;
* Survey on users needs and expectations;
* Reports coming from stakeholder and target users focus group.
Data/collection for the design of demo measures for mobility demand
management:
* Survey on users needs and expectations;
* Reports coming from stakeholder and target users focus group.
### WP7
__Task 7.1 Users needs analysis, requirements and design_ _
Data/collection for the design of demo measures for Public Transport services:
* Data on PT service demand;
* Data on PT service offer;
* Statistics produced by the systems already operated (i.e. ticketing);
* Survey on users needs and expectations;
* Reports coming from stakeholder and target users focus group.
### WP8
N/A - The data collected in this WP in the reference period are not included
in the list of “sensible” data identified in D1.1. The data generated by this
WP over the reference period deal with the description of site background.
These data are out of the scope of the present deliverable.
### WP9
__Task 9.2 – Task 9.3 – Task 9.4 Evaluation Plan, Ex-ante/Ex-post evaluation_
_
* Baseline (BAU);
* Sustainable mobility;
* Energy consumption;
* Environmental impacts;
* Societal impacts; Health impacts.
### WP10
__Task 10.5 – International cooperation in research and innovation in China_ _
* Info about Chinese tourist flows in DESTINATIONS sites;
* Info about perspective, approach and strategies to strengthen and to make more sustainable Chinese tourist presence in DESTINATIONS sites.
### WP11
N/A – The data collected in this WP in the reference period are not included
in the list of “sensible” data identified in D1.1.
# Data management plan
## WP2-WP7
For each of the data categories identified in section 5 (sub-sections related
to WP2-WP7) Site Managers will provide the following information (to be
included in Local DMP):
<table>
<tr>
<th>
**WP2**
</th> </tr>
<tr>
<td>
**Data details**
</td> </tr>
<tr>
<td>
2.1.1
</td>
<td>
Which kind of data will be collected in your site ?
</td>
<td>
_(to be filled in by Site Managers)_
_(please indicated each data among the categories indicated for WP2 in section
5)_
</td> </tr>
<tr>
<td>
2.1.2
</td>
<td>
Please detail data typology and structure/format (if applicable)
</td>
<td>
_(to be filled in by Site Managers)_
_(please repeat for each data indicated in row ID2.1.1)_
</td> </tr>
<tr>
<td>
2.1.3
</td>
<td>
Please detail the data origin
</td>
<td>
_(to be filled in by Site Managers)_
_(please repeat for each data indicated in row ID2.1.1)_
</td> </tr>
<tr>
<td>
2.1.4
</td>
<td>
Please provide some figure allowing to estimate the data dimension
</td>
<td>
_(to be filled in by Site Managers)_
_(please repeat for each data indicated in row ID2.1.1)_
</td> </tr>
<tr>
<td>
**Data collection procedures**
</td> </tr>
<tr>
<td>
2.2.1
</td>
<td>
Please detail the procedure adopted
for data collection
</td>
<td>
_(to be filled in by Site Managers)_
_(please repeat for each data indicated in row ID2.1.1)_
</td> </tr>
<tr>
<td>
2.2.2
</td>
<td>
If a sampling process is used, please confirm that the sample is random and of
a size that can be analysed with the ability to make statistical inference for
the overall sample and for the most significant subsample breakdowns
(for reference, please see D1.1)
</td>
<td>
_(to be filled in by Site Managers)_
_(please repeat for each data indicated in row ID2.1.1)_
</td> </tr>
<tr>
<td>
2.2.3
</td>
<td>
Are data collected anonymously or not ? If not, please confirm that data are
collected in such a way preventing the tracking of personal habits or feelings
(for reference, please see
D1.1)
</td>
<td>
_(to be filled in by Site Managers)_
_(please repeat for each data indicated in row ID2.1.1)_
</td> </tr>
<tr>
<td>
**Data management and storing procedures**
</td> </tr> </table>
<table>
<tr>
<th>
**WP2**
</th> </tr>
<tr>
<td>
2.3.1
</td>
<td>
How data are stored ? Please detail where the data are stored and in which
modality/format (if
applicable)
</td>
<td>
_(to be filled in by Site Managers)_
_(please repeat for each data indicated in row ID2.1.1)_
</td> </tr>
<tr>
<td>
2.3.2
</td>
<td>
Who is the
organization responsible for data storing and management ?
</td>
<td>
_(to be filled in by Site Managers)_
_(please repeat for each data indicated in row ID2.1.1)_
</td> </tr>
<tr>
<td>
2.3.3
</td>
<td>
By whom
(organization, responsible) data are accessible ?
</td>
<td>
_(to be filled in by Site Managers)_
_(please repeat for each data indicated in row ID2.1.1)_
</td> </tr>
<tr>
<td>
2.3.4
</td>
<td>
Which international regulation will be applied for data storing and access ?
(for reference, please see D1.1)
</td>
<td>
_(to be filled in by Site Managers)_
_(please repeat for each data indicated in row ID2.1.1)_
</td> </tr>
<tr>
<td>
2.3.5
</td>
<td>
Which national regulation and applicable ‘opinion statements’ will be applied
for data storing and access ? (for reference, please see D1.1)
</td>
<td>
_(to be filled in by Site Managers)_
_(please repeat for each data indicated in row ID2.1.1)_
</td> </tr>
<tr>
<td>
**Data availability for dissemination**
</td> </tr>
<tr>
<td>
2.4.1
</td>
<td>
Are data usable for DESTINATIONS
dissemination purpose ?
Please indicate the
format
(aggregated/not aggregated)
</td>
<td>
_(to be filled in by Site Managers)_
_(please repeat for each data indicated in row ID2.1.1)_
</td> </tr>
<tr>
<td>
2.4.2
</td>
<td>
Are data planned to be published as open format ? If so, please describe the
technological solution used and he metadata format.
</td>
<td>
_(to be filled in by Site Managers)_
_(please repeat for each data indicated in row ID2.1.1)_
</td> </tr> </table>
**Table 1: Template to be filled in by Site Managers to detail WP2 data
management procedures over the reference period (included in Local DMP)**
<table>
<tr>
<th>
**WP3**
</th> </tr>
<tr>
<td>
**Data details**
</td> </tr>
<tr>
<td>
3.1.1
</td>
<td>
Which kind of data will be collected in your site ?
</td>
<td>
_(to be filled in by Site Managers)_
_(please indicated each data among the categories indicated for WP3 in section
5)_
</td> </tr>
<tr>
<td>
3.1.2
</td>
<td>
Please detail data typology and structure/format (if applicable)
</td>
<td>
_(to be filled in by Site Managers)_
_(please repeat for each data indicated in row ID3.1.1)_
</td> </tr>
<tr>
<td>
3.1.3
</td>
<td>
Please detail the data origin
</td>
<td>
_(to be filled in by Site Managers)_
_(please repeat for each data indicated in row ID3.1.1)_
</td> </tr>
<tr>
<td>
3.1.4
</td>
<td>
Please provide some figure allowing to estimate the data dimension
</td>
<td>
_(to be filled in by Site Managers)_
_(please repeat for each data indicated in row ID3.1.1)_
</td> </tr>
<tr>
<td>
**Data collection procedures**
</td> </tr>
<tr>
<td>
3.2.1
</td>
<td>
Please detail the procedure adopted
for data collection
</td>
<td>
_(to be filled in by Site Managers)_
_(please repeat for each data indicated in row ID3.1.1)_
</td> </tr>
<tr>
<td>
3.2.2
</td>
<td>
If a sampling process is used, please confirm that the sample is random and of
a size that can be analysed with the ability to make statistical inference for
the overall sample and for the most significant subsample breakdowns
(for reference, please see D1.1)
</td>
<td>
_(to be filled in by Site Managers)_
_(please repeat for each data indicated in row ID3.1.1)_
</td> </tr>
<tr>
<td>
3.2.3
</td>
<td>
Are data collected anonymously or not ? If not, please confirm that data are
collected in such a way preventing the tracking of personal habits or feelings
(for reference, please see
D1.1)
</td>
<td>
_(to be filled in by Site Managers)_
_(please repeat for each data indicated in row ID3.1.1)_
</td> </tr>
<tr>
<td>
**Data management and storing procedures**
</td> </tr> </table>
<table>
<tr>
<th>
**WP3**
</th> </tr>
<tr>
<td>
3.3.1
</td>
<td>
How data are stored ? Please detail where the data are stored and in which
modality/format (if
applicable)
</td>
<td>
_(to be filled in by Site Managers)_
_(please repeat for each data indicated in row ID3.1.1)_
</td> </tr>
<tr>
<td>
3.3.2
</td>
<td>
Who is the
organization responsible for data storing and management ?
</td>
<td>
_(to be filled in by Site Managers)_
_(please repeat for each data indicated in row ID3.1.1)_
</td> </tr>
<tr>
<td>
3.3.3
</td>
<td>
By whom
(organization, responsible) data are accessible ?
</td>
<td>
_(to be filled in by Site Managers)_
_(please repeat for each data indicated in row ID3.1.1)_
</td> </tr>
<tr>
<td>
3.3.4
</td>
<td>
Which international regulation will be applied for data storing and access ?
(for reference, please see D1.1)
</td>
<td>
_(to be filled in by Site Managers)_
_(please repeat for each data indicated in row ID3.1.1)_
</td> </tr>
<tr>
<td>
3.3.5
</td>
<td>
Which national regulation and applicable ‘opinion statements’ will be applied
for data storing and access ? (for reference, please see D1.1)
</td>
<td>
_(to be filled in by Site Managers)_
_(please repeat for each data indicated in row ID3.1.1)_
</td> </tr>
<tr>
<td>
**Data availability for dissemination**
</td> </tr>
<tr>
<td>
3.4.1
</td>
<td>
Are data usable for DESTINATIONS
dissemination purpose ?
Please indicate the
format
(aggregated/not aggregated)
</td>
<td>
_to be filled in by Site Managers)_
_(please repeat for each data indicated in row ID3.1.1)_
</td> </tr>
<tr>
<td>
3.4.2
</td>
<td>
Are data planned to be published as open format ? If so, please describe the
technological solution used and he metadata format.
</td>
<td>
_to be filled in by Site Managers)_
_(please repeat for each data indicated in row ID3.1.1)_
</td> </tr> </table>
**Table 2: Template to be filled in by Site Managers to detail WP3 data
management procedures over the reference period (included in Local DMP)**
<table>
<tr>
<th>
**WP4**
</th> </tr>
<tr>
<td>
**Data details**
</td> </tr>
<tr>
<td>
4.1.1
</td>
<td>
Which kind of data will be collected in your site ?
</td>
<td>
_(to be filled in by Site Managers)_
_(please indicated each data among the categories indicated for WP4 in section
5)_
</td> </tr>
<tr>
<td>
4.1.2
</td>
<td>
Please detail data typology and structure/format (if applicable)
</td>
<td>
_(to be filled in by Site Managers)_
_(please repeat for each data indicated in row ID4.1.1)_
</td> </tr>
<tr>
<td>
4.1.3
</td>
<td>
Please detail the data origin
</td>
<td>
_(to be filled in by Site Managers)_
_(please repeat for each data indicated in row ID4.1.1)_
</td> </tr>
<tr>
<td>
4.1.4
</td>
<td>
Please provide some figure allowing to estimate the data dimension
</td>
<td>
_(to be filled in by Site Managers)_
_(please repeat for each data indicated in row ID4.1.1)_
</td> </tr>
<tr>
<td>
**Data collection procedures**
</td> </tr>
<tr>
<td>
4.2.1
</td>
<td>
Please detail the procedure adopted
for data collection
</td>
<td>
_(to be filled in by Site Managers)_
_(please repeat for each data indicated in row ID4.1.1)_
</td> </tr>
<tr>
<td>
4.2.2
</td>
<td>
If a sampling process is used, please confirm that the sample is random and of
a size that can be analysed with the ability to make statistical inference for
the overall sample and for the most significant subsample breakdowns
(for reference, please see D1.1)
</td>
<td>
_(to be filled in by Site Managers)_
_(please repeat for each data indicated in row ID4.1.1)_
</td> </tr>
<tr>
<td>
4.2.3
</td>
<td>
Are data collected anonymously or not ? If not, please confirm that data are
collected in such a way preventing the tracking of personal habits or feelings
(for reference, please see
D1.1)
</td>
<td>
_(to be filled in by Site Managers)_
_(please repeat for each data indicated in row ID4.1.1)_
</td> </tr>
<tr>
<td>
**Data management and storing procedures**
</td> </tr> </table>
<table>
<tr>
<th>
**WP4**
</th> </tr>
<tr>
<td>
4.3.1
</td>
<td>
How data are stored ? Please detail where the data are stored and in which
modality/format (if
applicable)
</td>
<td>
_(to be filled in by Site Managers)_
_(please repeat for each data indicated in row ID4.1.1)_
</td> </tr>
<tr>
<td>
4.3.2
</td>
<td>
Who is the
organization responsible for data storing and management ?
</td>
<td>
_(to be filled in by Site Managers)_
_(please repeat for each data indicated in row ID4.1.1)_
</td> </tr>
<tr>
<td>
4.3.3
</td>
<td>
By whom
(organization, responsible) data are accessible ?
</td>
<td>
_(to be filled in by Site Managers)_
_(please repeat for each data indicated in row ID4.1.1)_
</td> </tr>
<tr>
<td>
4.3.4
</td>
<td>
Which international regulation will be applied for data storing and access ?
(for reference, please see D1.1)
</td>
<td>
_(to be filled in by Site Managers)_
_(please repeat for each data indicated in row ID4.1.1)_
</td> </tr>
<tr>
<td>
4.3.5
</td>
<td>
Which national regulation and applicable ‘opinion statements’ will be applied
for data storing and access ? (for reference, please see D1.1)
</td>
<td>
_(to be filled in by Site Managers)_
_(please repeat for each data indicated in row ID4.1.1)_
</td> </tr>
<tr>
<td>
**Data availability for dissemination**
</td> </tr>
<tr>
<td>
4.4.1
</td>
<td>
Are data usable for DESTINATIONS
dissemination purpose ?
Please indicate the
format
(aggregated/not aggregated)
</td>
<td>
_to be filled in by Site Managers)_
_(please repeat for each data indicated in row ID4.1.1)_
</td> </tr>
<tr>
<td>
4.4.2
</td>
<td>
Are data planned to be published as open format ? If so, please describe the
technological solution used and he metadata format.
</td>
<td>
_to be filled in by Site Managers)_
_(please repeat for each data indicated in row ID4.1.1)_
</td> </tr> </table>
**Table 3: Template to be filled in by Site Managers to detail WP4 data
management procedures over the reference period (included in Local DMP)**
<table>
<tr>
<th>
**WP5**
</th> </tr>
<tr>
<td>
**Data details**
</td> </tr>
<tr>
<td>
5.1.1
</td>
<td>
Which kind of data will be collected in your site ?
</td>
<td>
_(to be filled in by Site Managers)_
_(please indicated each data among the categories indicated for WP5 in section
5)_
</td> </tr>
<tr>
<td>
5.1.2
</td>
<td>
Please detail data typology and structure/format (if applicable)
</td>
<td>
_(to be filled in by Site Managers)_
_(please repeat for each data indicated in row ID5.1.1)_
</td> </tr>
<tr>
<td>
5.1.3
</td>
<td>
Please detail the data origin
</td>
<td>
_(to be filled in by Site Managers)_
_(please repeat for each data indicated in row ID5.1.1)_
</td> </tr>
<tr>
<td>
5.1.4
</td>
<td>
Please provide some figure allowing to estimate the data dimension
</td>
<td>
_(to be filled in by Site Managers)_
_(please repeat for each data indicated in row ID5.1.1)_
</td> </tr>
<tr>
<td>
**Data collection procedures**
</td> </tr>
<tr>
<td>
5.2.1
</td>
<td>
Please detail the procedure adopted
for data collection
</td>
<td>
_(to be filled in by Site Managers)_
_(please repeat for each data indicated in row ID5.1.1)_
</td> </tr>
<tr>
<td>
5.2.2
</td>
<td>
If a sampling process is used, please confirm that the sample is random and of
a size that can be analysed with the ability to make statistical inference for
the overall sample and for the most significant subsample breakdowns
(for reference, please see D1.1)
</td>
<td>
_(to be filled in by Site Managers)_
_(please repeat for each data indicated in row ID5.1.1)_
</td> </tr>
<tr>
<td>
5.2.3
</td>
<td>
Are data collected anonymously or not ? If not, please confirm that data are
collected in such a way preventing the tracking of personal habits or feelings
(for reference, please see
D1.1)
</td>
<td>
_(to be filled in by Site Managers)_
_(please repeat for each data indicated in row ID5.1.1)_
</td> </tr>
<tr>
<td>
**Data management and storing procedures**
</td> </tr> </table>
<table>
<tr>
<th>
**WP5**
</th> </tr>
<tr>
<td>
5.3.1
</td>
<td>
How data are stored ? Please detail where the data are stored and in which
modality/format (if
applicable)
</td>
<td>
_(to be filled in by Site Managers)_
_(please repeat for each data indicated in row ID5.1.1)_
</td> </tr>
<tr>
<td>
5.3.2
</td>
<td>
Who is the
organization responsible for data storing and management ?
</td>
<td>
_(to be filled in by Site Managers)_
_(please repeat for each data indicated in row ID5.1.1)_
</td> </tr>
<tr>
<td>
5.3.3
</td>
<td>
By whom
(organization, responsible) data are accessible ?
</td>
<td>
_(to be filled in by Site Managers)_
_(please repeat for each data indicated in row ID5.1.1)_
</td> </tr>
<tr>
<td>
5.3.4
</td>
<td>
Which international regulation will be applied for data storing and access ?
(for reference, please see D1.1)
</td>
<td>
_(to be filled in by Site Managers)_
_(please repeat for each data indicated in row ID5.1.1)_
</td> </tr>
<tr>
<td>
5.3.5
</td>
<td>
Which national regulation and applicable ‘opinion statements’ will be applied
for data storing and access ? (for reference, please see D1.1)
</td>
<td>
_(to be filled in by Site Managers)_
_(please repeat for each data indicated in row ID5.1.1)_
</td> </tr>
<tr>
<td>
**Data availability for dissemination**
</td> </tr>
<tr>
<td>
5.4.1
</td>
<td>
Are data usable for DESTINATIONS
dissemination purpose ?
Please indicate the
format
(aggregated/not aggregated)
</td>
<td>
_to be filled in by Site Managers)_
_(please repeat for each data indicated in row ID5.1.1)_
</td> </tr>
<tr>
<td>
5.4.2
</td>
<td>
Are data planned to be published as open format ? If so, please describe the
technological solution used and he metadata format.
</td>
<td>
_to be filled in by Site Managers)_
_(please repeat for each data indicated in row ID5.1.1)_
</td> </tr> </table>
**Table 4: Template to be filled in by Site Managers to detail WP5 data
management procedures over the reference period (included in Local DMP)**
<table>
<tr>
<th>
**WP6**
</th> </tr>
<tr>
<td>
**Data details**
</td> </tr>
<tr>
<td>
6.1.1
</td>
<td>
Which kind of data will be collected in your site ?
</td>
<td>
_(to be filled in by Site Managers)_
_(please indicated each data among the categories indicated for WP6 in section
5)_
</td> </tr>
<tr>
<td>
6.1.2
</td>
<td>
Please detail data typology and structure/format (if applicable)
</td>
<td>
_(to be filled in by Site Managers)_
_(please repeat for each data indicated in row ID6.1.1)_
</td> </tr>
<tr>
<td>
6.1.3
</td>
<td>
Please detail the data origin
</td>
<td>
_(to be filled in by Site Managers)_
_(please repeat for each data indicated in row ID6.1.1)_
</td> </tr>
<tr>
<td>
6.1.4
</td>
<td>
Please provide some figure allowing to estimate the data dimension
</td>
<td>
_(to be filled in by Site Managers)_
_(please repeat for each data indicated in row ID6.1.1)_
</td> </tr>
<tr>
<td>
**Data collection procedures**
</td> </tr>
<tr>
<td>
6.2.1
</td>
<td>
Please detail the procedure adopted
for data collection
</td>
<td>
_(to be filled in by Site Managers)_
_(please repeat for each data indicated in row ID6.1.1)_
</td> </tr>
<tr>
<td>
6.2.2
</td>
<td>
If a sampling process is used, please confirm that the sample is random and of
a size that can be analysed with the ability to make statistical inference for
the overall sample and for the most significant subsample breakdowns
(for reference, please see D1.1)
</td>
<td>
_(to be filled in by Site Managers)_
_(please repeat for each data indicated in row ID6.1.1)_
</td> </tr>
<tr>
<td>
6.2.3
</td>
<td>
Are data collected anonymously or not ? If not, please confirm that data are
collected in such a way preventing the tracking of personal habits or feelings
(for reference, please see
D1.1)
</td>
<td>
_(to be filled in by Site Managers)_
_(please repeat for each data indicated in row ID6.1.1)_
</td> </tr>
<tr>
<td>
**Data management and storing procedures**
</td> </tr> </table>
<table>
<tr>
<th>
**WP6**
</th> </tr>
<tr>
<td>
6.3.1
</td>
<td>
How data are stored ? Please detail where the data are stored and in which
modality/format (if
applicable)
</td>
<td>
_(to be filled in by Site Managers)_
_(please repeat for each data indicated in row ID6.1.1)_
</td> </tr>
<tr>
<td>
6.3.2
</td>
<td>
Who is the
organization responsible for data storing and management ?
</td>
<td>
_(to be filled in by Site Managers)_
_(please repeat for each data indicated in row ID6.1.1)_
</td> </tr>
<tr>
<td>
6.3.3
</td>
<td>
By whom
(organization, responsible) data are accessible ?
</td>
<td>
_(to be filled in by Site Managers)_
_(please repeat for each data indicated in row ID6.1.1)_
</td> </tr>
<tr>
<td>
6.3.4
</td>
<td>
Which international regulation will be applied for data storing and access ?
(for reference, please see D1.1)
</td>
<td>
_(to be filled in by Site Managers)_
_(please repeat for each data indicated in row ID6.1.1)_
</td> </tr>
<tr>
<td>
6.3.5
</td>
<td>
Which national regulation and applicable ‘opinion statements’ will be applied
for data storing and access ? (for reference, please see D1.1)
</td>
<td>
_(to be filled in by Site Managers)_
_(please repeat for each data indicated in row ID6.1.1)_
</td> </tr>
<tr>
<td>
**Data availability for dissemination**
</td> </tr>
<tr>
<td>
6.4.1
</td>
<td>
Are data usable for DESTINATIONS
dissemination purpose ?
Please indicate the
format
(aggregated/not aggregated)
</td>
<td>
_to be filled in by Site Managers)_
_(please repeat for each data indicated in row ID6.1.1)_
</td> </tr>
<tr>
<td>
6.4.2
</td>
<td>
Are data planned to be published as open format ? If so, please describe the
technological solution used and he metadata format.
</td>
<td>
_to be filled in by Site Managers)_
_(please repeat for each data indicated in row ID6.1.1)_
</td> </tr> </table>
**Table 5: Template to be filled in by Site Managers to detail WP6 data
management procedures over the reference period (included in Local DMP)**
<table>
<tr>
<th>
**WP7**
</th> </tr>
<tr>
<td>
**Data details**
</td> </tr>
<tr>
<td>
7.1.1
</td>
<td>
Which kind of data will be collected in your site ?
</td>
<td>
_(to be filled in by Site Managers)_
_(please indicated each data among the categories indicated for WP7 in section
5)_
</td> </tr>
<tr>
<td>
7.1.2
</td>
<td>
Please detail data typology and structure/format (if applicable)
</td>
<td>
_(to be filled in by Site Managers)_
_(please repeat for each data indicated in row ID7.1.1)_
</td> </tr>
<tr>
<td>
7.1.3
</td>
<td>
Please detail the data origin
</td>
<td>
_(to be filled in by Site Managers)_
_(please repeat for each data indicated in row ID7.1.1)_
</td> </tr>
<tr>
<td>
7.1.4
</td>
<td>
Please provide some figure allowing to estimate the data dimension
</td>
<td>
_(to be filled in by Site Managers)_
_(please repeat for each data indicated in row ID7.1.1)_
</td> </tr>
<tr>
<td>
**Data collection procedures**
</td> </tr>
<tr>
<td>
7.2.1
</td>
<td>
Please detail the procedure adopted
for data collection
</td>
<td>
_(to be filled in by Site Managers)_
_(please repeat for each data indicated in row ID7.1.1)_
</td> </tr>
<tr>
<td>
7.2.2
</td>
<td>
If a sampling process is used, please confirm that the sample is random and of
a size that can be analysed with the ability to make statistical inference for
the overall sample and for the most significant subsample breakdowns
(for reference, please see D1.1)
</td>
<td>
_(to be filled in by Site Managers)_
_(please repeat for each data indicated in row ID7.1.1)_
</td> </tr>
<tr>
<td>
7.2.3
</td>
<td>
Are data collected anonymously or not ? If not, please confirm that data are
collected in such a way preventing the tracking of personal habits or feelings
(for reference, please see
D1.1)
</td>
<td>
_(to be filled in by Site Managers)_
_(please repeat for each data indicated in row ID7.1.1)_
</td> </tr>
<tr>
<td>
**Data management and storing procedures**
</td> </tr> </table>
<table>
<tr>
<th>
**WP7**
</th> </tr>
<tr>
<td>
7.3.1
</td>
<td>
How data are stored ? Please detail where the data are stored and in which
modality/format (if
applicable)
</td>
<td>
_(to be filled in by Site Managers)_
_(please repeat for each data indicated in row ID7.1.1)_
</td> </tr>
<tr>
<td>
7.3.2
</td>
<td>
Who is the
organization responsible for data storing and management ?
</td>
<td>
_(to be filled in by Site Managers)_
_(please repeat for each data indicated in row ID7.1.1)_
</td> </tr>
<tr>
<td>
7.3.3
</td>
<td>
By whom
(organization, responsible) data are accessible ?
</td>
<td>
_(to be filled in by Site Managers)_
_(please repeat for each data indicated in row ID7.1.1)_
</td> </tr>
<tr>
<td>
7.3.4
</td>
<td>
Which international regulation will be applied for data storing and access ?
(for reference, please see D1.1)
</td>
<td>
_(to be filled in by Site Managers)_
_(please repeat for each data indicated in row ID7.1.1)_
</td> </tr>
<tr>
<td>
7.3.5
</td>
<td>
Which national regulation and applicable ‘opinion statements’ will be applied
for data storing and access ? (for reference, please see D1.1)
</td>
<td>
_(to be filled in by Site Managers)_
_(please repeat for each data indicated in row ID7.1.1)_
</td> </tr>
<tr>
<td>
**Data availability for dissemination**
</td> </tr>
<tr>
<td>
7.4.1
</td>
<td>
Are data usable for DESTINATIONS
dissemination purpose ?
Please indicate the
format
(aggregated/not aggregated)
</td>
<td>
_to be filled in by Site Managers)_
_(please repeat for each data indicated in row ID7.1.1)_
</td> </tr>
<tr>
<td>
7.4.2
</td>
<td>
Are data planned to be published as open format ? If so, please describe the
technological solution used and he metadata format.
</td>
<td>
_to be filled in by Site Managers)_
_(please repeat for each data indicated in row ID7.1.1)_
</td> </tr> </table>
**Table 6: Template to be filled in by Site Managers to detail WP7 data
management procedures over the reference period (included in Local DMP)**
## WP9
For each of the data categories identified in section 5 (sub-sections related
to WP9) Site Managers will provide the following information (to be included
in Local DMP):
<table>
<tr>
<th>
**WP9**
</th>
<th>
</th> </tr>
<tr>
<td>
**Data details**
</td>
<td>
</td> </tr>
<tr>
<td>
9.1.1
</td>
<td>
Which kind of data will be collected in your site ?
</td>
<td>
_(to be filled in by Site Managers)_
_(please indicated each data among the categories indicated for WP9 in section
5)_
</td> </tr>
<tr>
<td>
9.1.2
</td>
<td>
Please detail data typology and structure/format (if applicable)
</td>
<td>
_(to be filled in by Site Managers)_
_(please repeat for each data indicated in row ID9.1.1)_
</td> </tr>
<tr>
<td>
9.1.3
</td>
<td>
Please detail the data origin
</td>
<td>
_(to be filled in by Site Managers)_
_(please repeat for each data indicated in row ID9.1.1)_
</td> </tr>
<tr>
<td>
9.1.4
</td>
<td>
Please provide some figure allowing to estimate the data dimension
</td>
<td>
_(to be filled in by Site Managers)_
_(please repeat for each data indicated in row ID9.1.1)_
</td> </tr>
<tr>
<td>
**Data collection procedures**
</td>
<td>
</td> </tr>
<tr>
<td>
9.2.1
</td>
<td>
Please detail the procedure adopted
for data collection
</td>
<td>
_(to be filled in by Site Managers)_
_(please repeat for each data indicated in row ID9.1.1)_
</td> </tr>
<tr>
<td>
9.2.2
</td>
<td>
If a sampling process is used, please confirm that the sample is random and of
a size that can be analysed with the ability to make statistical inference for
the overall sample and for the most significant subsample breakdowns
(for reference, please see D1.1)
</td>
<td>
_(to be filled in by Site Managers)_
_(please repeat for each data indicated in row ID9.1.1)_
</td> </tr> </table>
<table>
<tr>
<th>
**WP9**
</th> </tr>
<tr>
<td>
9.2.3
</td>
<td>
Are data collected anonymously or not ? If not, please confirm that data are
collected in such a way preventing the tracking of personal habits or feelings
(for reference, please see
D1.1)
</td>
<td>
_(to be filled in by Site Managers)_
_(please repeat for each data indicated in row ID9.1.1)_
</td> </tr>
<tr>
<td>
**Data management and storing procedures**
</td> </tr>
<tr>
<td>
9.3.1
</td>
<td>
How data are stored ? Please detail where the data are stored and in which
modality/format (if
applicable)
</td>
<td>
_(to be filled in by Site Managers)_
_(please repeat for each data indicated in row ID9.1.1)_
</td> </tr>
<tr>
<td>
9.3.2
</td>
<td>
Who is the
organization responsible for data storing and management ?
</td>
<td>
_(to be filled in by Site Managers)_
_(please repeat for each data indicated in row ID9.1.1)_
</td> </tr>
<tr>
<td>
9.3.3
</td>
<td>
By whom
(organization, responsible) data are accessible ?
</td>
<td>
_(to be filled in by Site Managers)_
_(please repeat for each data indicated in row ID9.1.1)_
</td> </tr>
<tr>
<td>
9.3.4
</td>
<td>
Which international regulation will be applied for data storing and access ?
(for reference, please see D1.1)
</td>
<td>
_(to be filled in by Site Managers)_
_(please repeat for each data indicated in row ID9.1.1)_
</td> </tr>
<tr>
<td>
9.3.5
</td>
<td>
Which national regulation and applicable ‘opinion statements’ will be applied
for data storing and access ? (for reference, please see D1.1)
</td>
<td>
_(to be filled in by Site Managers)_
_(please repeat for each data indicated in row ID1.1)_
</td> </tr>
<tr>
<td>
**WP9**
</td> </tr>
<tr>
<td>
**Data availability for dissemination**
</td> </tr>
<tr>
<td>
9.4.1
</td>
<td>
Are data usable for DESTINATIONS
dissemination purpose ?
Please indicate the
format
(aggregated/not aggregated)
</td>
<td>
_to be filled in by Site Managers)_
_(please repeat for each data indicated in row ID9.1.1)_
</td> </tr>
<tr>
<td>
9.4.2
</td>
<td>
Are data planned to be published as open format ? If so, please describe the
technological solution used and he metadata format.
</td>
<td>
_to be filled in by Site Managers)_
_(please repeat for each data indicated in row ID9.1.1)_
</td> </tr> </table>
**Table 7: Template to be filled in by Site Managers to detail WP9 data
management procedures over the reference period (included in Local DMP)**
For each of the data categories identified in section 5 (sub-sections related
to WP9) Project Evaluation Manager (PEM) provides the following information:
<table>
<tr>
<th>
**WP9**
</th> </tr>
<tr>
<td>
**Data management and storing procedures**
</td> </tr>
<tr>
<td>
9.5.1
</td>
<td>
How data collected by sites related to exante evaluation are stored ?
</td>
<td>
Ex ante and ex post data collected by the Local Evaluation Manager (LEMs) and
Site Managers are stored in an ad hoc Excel file according to a structured
data collection template..
</td> </tr>
<tr>
<td>
9.5.2
</td>
<td>
Please detail where the data are stored and in which modality/format (if
applicable)
</td> </tr>
<tr>
<td>
9.5.3
</td>
<td>
How data will be used ?
</td>
<td>
These data will be then transposed to the Measures Evaluation Report according
to the format provided by the Satellite project. They will be used under an
aggregated format.
</td> </tr>
<tr>
<td>
9.5.4
</td>
<td>
Who is the
organization responsible for data storing and management ?
</td>
<td>
ISINNOVA
</td> </tr>
<tr>
<td>
9.5.5
</td>
<td>
By whom
(organization, responsible) data are accessible ?
</td>
<td>
Data are accessible by the ISINNOVA evaluation manager (Mr. Stefano Faberi)
and his colleagues.
</td> </tr> </table>
**Table 8: Description of WP9 data management procedures adopted by Project
Evaluation**
**Manager (PEM) over the reference period**
## WP10
<table>
<tr>
<th>
**WP10**
</th> </tr>
<tr>
<td>
**Data management and storing procedures**
</td> </tr>
<tr>
<td>
10.1.1
</td>
<td>
How data collected by sites related to exante evaluation are stored ?
</td>
<td>
Data collected from the sites are stored in an ad hoc file. These data will be
used in an aggregated format in order to better design the promotion of
DESTINATIONS sites towards possible Chinese investors and tourist stakeholder.
</td> </tr>
<tr>
<td>
10.1.2
</td>
<td>
Please detail where the data are stored and in which modality/format (if
applicable)
</td> </tr>
<tr>
<td>
10.1.3
</td>
<td>
How will be data used ?
</td> </tr>
<tr>
<td>
10.1.4
</td>
<td>
Who is the
organization responsible for data storing and management ?
</td>
<td>
GV21
</td> </tr>
<tr>
<td>
10.1.5
</td>
<td>
By whom
(organization, responsible) data are accessible ?
</td>
<td>
Data are accessible by GV21 (Mrs. Julia Perez Cerezo) and her colleagues.
</td> </tr> </table>
**Table 9: Description of WP10 data management procedures over the reference
period**
|
https://phaidra.univie.ac.at/o:1140797
|
Horizon 2020
|
1093_DESTINATIONS_689031.md
|
# Executive Summary
This document represents the first version of D1.3 Local Data Management Plan
(LDMP) relating to the data under collection/collected, handled and processed
by DESTINATIONS sites over the first six-month period (M4-M6, December 2016 –
February 2017). As the project is at the beginning of its activities (in
particular demo activities in the 6 sites have been launched in December 2016)
and this is the first edition of the document (with the Data Management
procedure to be tuned accordingly to the requirements of the Ethics Compliance
Report, Deliverable 1.1), when relevant, info are provided also to data
collection procedures/campaigns planned but still running/to be started. This
document will be updated regularly in order to integrate the different data
typologies the project will manage period by period.
This document follows the methodological approach adopted by DESTINATIONS
project and described in D1.2 (PDMP) according to the guidelines defined in
the Ethics Compliance Report (D1.1). D1.2 (PDMP) represents the framework
document of this deliverable which can be considered also an integration of
D1.2.
This deliverable is structured as follows:
* Section 2 is an introduction of the document covering the identification of objectives for its elaboration and delivery, the role of Local Data Management Plan (LDMP) into the whole DESTINATIONS project and the cross-relations with Project Data Management Plan (PDMP);
* Section 3 details the specific data collected and generated by DESTINATIONS sites in the first six-month period (M4-M6, December 2016 – February 2017). The data is presented with reference to each demo WP (WP2-WP7). For each demo WP the data is presented also per site.
# Role of Project and Local DMPs in DESTINATIONS
PDMP (D1.2) defines the overall approach assumed by the project, it identifies
the data typology involved, it describes the data collected/handled/processed
by horizontal WPs (WP8-WP11) and it sets the framework for the LDMP.
LDMP details the data collected/under collection/planned for collection by
DESTINATIONS sites over the first six-month period (M4-M6, December 2016 –
February 2017). This choice (to include also the description of data
collection just planned) has been carried out due to the initial stage of
local project activities and in particular the design of demo measures: due to
the project timing data collection is started for some measure but still
planned/on-going for others.
Data have been collected through the contribution of Site Managers (SM)
according to the template defined in PDMP (D1.2). LDMP can be considered an
integration of PDMP (D1.2) which sets the framework for approaching data
management in DESTINATIONS project.
# Local Data Management Plan
In the following sections the DESTINATIONS Local Data Management Plans are
presented. In order to improve the readability, each LDMP is presented per
(demo) WP (first level) and per site (second level).
## WP2
<table>
<tr>
<th>
**WP2 – MADEIRA**
</th>
<th>
</th> </tr>
<tr>
<td>
**Data details**
</td>
<td>
</td> </tr>
<tr>
<td>
2.1.1.1
</td>
<td>
Which kind of data have been/will be collected in your site?
</td>
<td>
* Census/demographic data (completed)
* Tourists flow (planned)
* Road network (planned)
* Passengers counting (Public transport) (planned)
* Questionnaires on travel behaviour, attitudes and expectations (planned)
</td> </tr>
<tr>
<td>
2.1.1.2
</td>
<td>
Please detail data typology and structure/format (if applicable)
</td>
<td>
Census/demographic data
* _Number of residents, age, education level_
Tourists flow
* _Number of tourists arrivals & staying by nationality _
Road network
* _Counting traffic congestions_
Passengers counting (Public transport)
* _Number of entries and exits of buses_
Questionnaires on travel behaviour, attitudes and expectations
* _Paper questionnaires_
</td> </tr>
<tr>
<td>
2.1.1.3
</td>
<td>
Please detail the data origin
</td>
<td>
Census/demographic data
* _The data is available in the website of Regional Government Statistic Department._
Tourists flow
* _The data is available in the website of Regional Government Statistic Department._
Road network
* _Visual counting or implement sensors._
Passengers counting (Public transport)
* _Sensor system on buses and Ticketing system_
Questionnaires on travel behaviour, attitudes and expectations
* _The target of questionnaires will be residents and tourists._
</td> </tr> </table>
<table>
<tr>
<th>
2.1.1.4
</th>
<th>
Please provide some figure allowing to estimate the data dimension
</th>
<th>
Questionnaires on travel behaviour, attitudes expectations
_500 questionnaires_
</th>
<th>
and
</th> </tr>
<tr>
<td>
**Data collection procedures**
</td>
<td>
</td> </tr>
<tr>
<td>
2.1.2.1
</td>
<td>
Please detail the procedure adopted
for data collection
</td>
<td>
Census/demographic data
* _Questionnaires or interviews_
Tourists flow
* Data from airport and Port of Funchal
Road network
* Data collection
Passengers counting (Public transport)
* _Data extraction from database_
Questionnaires on travel behaviour, attitudes expectations
* _Questionnaires or interviews_
</td>
<td>
and
</td> </tr>
<tr>
<td>
2.1.2.2
</td>
<td>
Is data collected anonymously or not? If not, please confirm that data is
collected in such a way preventing the tracking of personal habits or feelings
(for reference, please see
D1.1)
</td>
<td>
Questionnaires on travel behaviour, attitudes and expectations
_A sampling of the target users will be selected to be provided with the
questionnaires_
</td> </tr>
<tr>
<td>
**Data management and storing procedures**
</td> </tr>
<tr>
<td>
2.1.3.1
</td>
<td>
How data is stored? Please detail where the data is stored and in which
modality/format (if
applicable)
</td>
<td>
Census/demographic data
* _Database is stored in the office of Regional Government Statistic Department_
Tourists flow
* _Database is stored in the office of Regional Government Statistic Department_
Road network
* _The data base will be stored in CMF or SRETC office_
Passengers counting (Public transport)
* _Database in HF office_
Questionnaires on travel behaviour, attitudes and expectations
* _Questionnaires will be stored in SRETC office._
</td> </tr> </table>
<table>
<tr>
<th>
2.1.3.2
</th>
<th>
Who is the
organization responsible for data storing and management?
</th>
<th>
Census/demographic data
* _Regional Government Statistics Department_
Tourists flow
* _Regional Government Statistics Department_
Road network
* _CMF and SRETC_
Passengers counting (Public transport)
* _HF_
Questionnaires on travel behaviour, attitudes expectations
* _SRETC_
</th>
<th>
and
</th> </tr>
<tr>
<td>
2.1.3.3
</td>
<td>
Through whom
(organization, responsible) is data accessible?
</td>
<td>
Census/demographic data
* _Regional Government Statistic Department_
Tourists flow
* _Regional Government Statistic Department_
Road network
* _CMF and SRETC_
Passengers counting (Public transport)
* _HF_
Questionnaires on travel behaviour, attitudes expectations
* _SRETC_
</td>
<td>
and
</td> </tr>
<tr>
<td>
2.1.3.4
</td>
<td>
Which international regulation will be applied for data storing and access?
(for reference, please see D1.1)
</td>
<td>
_All the data is anonymous; there is no need to apply international
regulation._
</td> </tr>
<tr>
<td>
2.1.3.5
</td>
<td>
Which national regulation and applicable ‘opinion statements’ will be applied
for data storing and access? (for reference, please see D1.1)
</td>
<td>
_All the data is anonymous; there is no need to apply national regulation._
</td> </tr>
<tr>
<td>
**Data availability for dissemination**
</td> </tr>
<tr>
<td>
2.1.4.1
</td>
<td>
Is data usable for DESTINATIONS
dissemination purpose?
Please indicate the
format
(aggregated/not aggregated)
</td>
<td>
_Data can be made available by DESTINATIONS under an aggregated form_
</td> </tr>
<tr>
<td>
2.1.4.2
</td>
<td>
Is data planned to be published as open format? If so, please describe the
technological solution used and the metadata format.
</td>
<td>
_No_
</td> </tr> </table>
**Table 1: Local Data Management Plan – WP2 (MADEIRA)**
<table>
<tr>
<th>
**WP2 – RETHYMNO**
</th> </tr>
<tr>
<td>
**Data details**
</td> </tr>
<tr>
<td>
2.2.1.1
</td>
<td>
Which kind of data has been/will be collected in your site?
</td>
<td>
Data collection for SUMP elaboration:
* Census / Demographic Statistics
* Economics data
* Statistics on Tourists flow
* Statistics on accessibility incoming / outgoing;
* Data about road network;
* Statistics on traffic accidents, deaths and injuries
_The data collection described is completed_
</td> </tr>
<tr>
<td>
2.2.1.2
</td>
<td>
Please detail data typology and
structure/format (if applicable)
</td>
<td>
**Census / Demographic Statistics**
* Resident population size by sex and educational level for the municipality of Rethymno
* Resident population size by age for the Regional Unit of Rethymno
* Employment by sector annually in Rethymno
Municipality
**Economic data**
* New business openings by sector (net balance with closures)
* Secondary distribution of income account of households **Statistics on Tourists flow and distribution** ;
* Tourist arrivals & staying by nationality in municipality level
* Tourists distribution by accommodation (hotels, rented apartments, camping, other) in regional level
**Statistics on accessibility incoming / outgoing;**
* Number of ferry passengers disembarked/embarked in
regional level
* Availability of slots for incoming private boats
* Number of cruise ships visitors by months in regional
level
* Number of flight passengers in & out by days in regional level
**Data about road network**
* Car, cycling, walking network
**Statistics on traffic accidents, deaths and injuries**
* Traffic Accidents, casualties and injuries (seriously injured and slightly injured)
</td> </tr> </table>
<table>
<tr>
<th>
**WP2 – RETHYMNO**
</th> </tr>
<tr>
<td>
2.2.1.3
</td>
<td>
Please detail the data origin
</td>
<td>
Census / Demographic Statistics
* _The data is stored in a database_
Economics data
* _The data is stored in a database_
Statistics on Tourists flow and distribution
* _The data is stored in a database or in paper archive_
Statistics on accessibility incoming / outgoing
* _The data is stored in a database or in paper archive_
Data about road network
* _The data is stored in a database_
Statistics on traffic accidents, deaths and injuries
* _The data is stored in a database_
</td> </tr> </table>
<table>
<tr>
<th>
**WP2 – RETHYMNO**
</th> </tr>
<tr>
<td>
2.2.1.4
</td>
<td>
Please provide some figure allowing to estimate the data dimension
</td>
<td>
_All data is collected on an aggregated form_
**Census / Demographic Statistics**
* Resident population size by sex and educational level for the municipality of Rethymno, Resident population size by age for the Regional Unit of Rethymno,
Employment by sector annually in Rethymno
Municipality: _Data provided for 2011_
**Economic data**
* New business openings by sector (net balance with closures): _Annual data for the years 2014-2016_
* Secondary distribution of income account of households: _Annual data for 2014, income distribution regarding 5 main categories_
**Statistics on Tourists flow and distribution** ;
* Tourist arrivals & staying by nationality in municipality level: _Number of overnight stays per month provided for the years 2013-2016_
* Tourists’ distribution by accommodation in regional level: _Annual data for the years 2014- 2015 on arrivals by 2 main categories (hotels/similar establishments and camping) divided in residents and non-residents._
**Statistics on accessibility incoming / outgoing;**
* Number of ferry passengers disembarked/embarked in regional level _: Annual number of ferry passengers disembarked in each of the 2 main Cretan ports for 2014_
* Number of cruise ships visitors by months in regional level: _Annual number of cruise visitors for 2015_
* Number of flight passengers in & out by days in regional level: _Monthly data on the number of flights (arrivals/departures) and number of passengers by domestic and international flights, for the 2 main Cretan airports for 2016._
**Data about road network**
* Car, cycling, walking network: _Data regarding the total length of each type of network was gathered._
**Statistics on traffic accidents, deaths and injuries**
* Traffic Accidents, casualties and injuries (seriously injured and slightly injured): _Monthly data for the years 2015 -2016_
</td> </tr> </table>
<table>
<tr>
<th>
**WP2 – RETHYMNO**
</th> </tr>
<tr>
<td>
**Data collection procedures**
</td> </tr>
<tr>
<td>
2.2.2.1
</td>
<td>
Please detail the procedure adopted
for data collection
</td>
<td>
Census / Demographic Statistics
* _Desk research-Data extraction from database_
Economic data
* _Desk research-Data extraction from database_
Statistics on Tourists flow and distribution
* _Desk research-Data extraction from database_
Statistics on accessibility incoming / outgoing
* _Desk research-Data extraction from database_
Data about road network
* _Data collected from the Municipality’s technical department_
Statistics on traffic accidents, deaths and injuries
* _Desk research-Data extraction from database_
</td> </tr>
<tr>
<td>
2.2.2.2
</td>
<td>
If a sampling process is used, please confirm that the sample is random and of
a size that can be analysed with the ability to make statistical inference for
the overall sample and for the most significant subsample breakdowns (for
reference,
please see D1.1)
</td>
<td>
All the above data were gathered from open databases without using any
sampling process
</td> </tr>
<tr>
<td>
2.2.2.3
</td>
<td>
Is data collected anonymously or not? If not, please confirm that data is
collected in such a way preventing the tracking of personal habits or feelings
(for reference,
please see D1.1)
</td>
<td>
All above data is on an aggregated form and collected anonymously
</td> </tr> </table>
<table>
<tr>
<th>
**WP2 – RETHYMNO**
</th> </tr>
<tr>
<td>
**Data management and storing procedures**
</td> </tr>
<tr>
<td>
2.2.3.1
</td>
<td>
How is data stored? Please detail where the data is stored and in which
modality/format (if applicable)
</td>
<td>
_**Census / Demographic Statistics** _
Resident population size by sex and educational level for the municipality of
Rethymno, Resident population size by age for the Regional Unit of Rethymno,
Employment by sector annually in Rethymno Municipality:
_Database is stored in the Hellenic Statistics Authority records_
_**Economic data** _
* New business openings by sector (net balance with closures): _Database is stored in the office of Rethymno Chamber of Commerce_
* Secondary distribution of income account of households: _Database is stored in the office of the Hellenic Statistics Authority_
_**Statistics on Tourists flow and distribution** _
* Tourist arrivals & staying by nationality: _Database is stored in the office of the Association of Greek Tourism Enterprises and the Hellenic Chamber of Hotels_
* Tourists distribution by accommodation (hotels, rented apartments, camping, other) in regional level: _Database is stored in the office of the Hellenic Statistics Authority_
_**Statistics on accessibility incoming / outgoing** _
* Number of ferry passengers disembarked/embarked in regional level: _Database is stored in the office of the Port of Heraklion and Port of Chania_
* Availability of slots for incoming private boats _: Database is stored in the office of the Port of Rethymno_
* Number of cruise ships visitors by months in regional level: _Database is stored in the office of the Union of Greek Ports_
* Number of flight passengers in & out by days in regional level: _Database is stored in the office of the Civil Aviation Authority_
_**Data about road network;** _
* Car, cycling, walking network: _Database is stored in the office of the technical department of Rethymno Municipality_
_**Statistics on traffic accidents, deaths and injuries** _
* Traffic Accidents, casualties and injuries (seriously injured and slightly injured): _Database is stored in the office of the Hellenic Statistics Authority_
</td> </tr> </table>
<table>
<tr>
<th>
**WP2 – RETHYMNO**
</th> </tr>
<tr>
<td>
2.2.3.2
</td>
<td>
Who is the
organization responsible for data storing and management?
</td>
<td>
_**Census / Demographic Statistics** _
Resident population size by sex and educational level for the municipality of
Rethymno, Resident population size by age for the Regional Unit of Rethymno,
Employment by sector annually in Rethymno
Municipality: Hellenic Statistics Authority
_**Economic data** _
* New business openings by sector: _Rethymno Chamber of Commerce_
* Secondary distribution of income account of households _: Hellenic Statistics Authority_
_**Statistics on Tourists flow and distribution** _
* Tourist arrivals & staying by nationality: _Association of_
_Greek Tourism Enterprises, Hellenic Chamber of Hotels_
* Tourists distribution by accommodation: _Hellenic_
_Statistics Authority_
_**Statistics on accessibility incoming / outgoing;** _
* Number of ferry passengers disembarked/embarked _: Port of Heraklion, Port of Chania_
* Availability of slots for incoming private boats: _Port of Rethymno_
* Number of cruise ships visitors by months: _Union of Greek Ports_
* Number of flight passengers in & out by days: _Civil Aviation Authority_
_**Data about road network** _
* Car, cycling, walking network _: Rethymno Municipality_
_**Statistics on traffic accidents, deaths and injuries** _
* Traffic Accidents, casualties and injuries: _Hellenic Statistics Authority, local Police Department_
</td> </tr>
<tr>
<td>
2.2.3.3
</td>
<td>
Through whom
(organization, responsible) is data accessible?
</td>
<td>
As described in the previous section
</td> </tr>
<tr>
<td>
**WP2 – RETHYMNO**
</td> </tr>
<tr>
<td>
2.2.3.4
</td>
<td>
Which international regulation will be applied for data storing and access?
(for reference,
please see D1.1)
</td>
<td>
Regulations 2016/679; 2016/680; 2016/681 (EU)
Regulations: 2009/136, 2006/24, 2002/58, 95/46 (EC)
</td> </tr>
<tr>
<td>
2.2.3.5
</td>
<td>
Which national regulation and applicable ‘opinion statements’ will be applied
for data storing and access? (for reference,
please see D1.1)
</td>
<td>
Law 2472/1997 Protection of Individuals with regard to the
Processing of Personal Data
Law 3471/2006 Protection of personal data and privacy in the electronic
telecommunications sector and amendment of law 2472/1997
</td> </tr>
<tr>
<td>
**Data availability for dissemination**
</td> </tr>
<tr>
<td>
2.2.4.1
</td>
<td>
Is data usable for DESTINATIONS
dissemination purpose?
Please indicate the
format
(aggregated/not aggregated)
</td>
<td>
Data can be used for DESTINATIONS dissemination purpose under an aggregated
form
</td> </tr>
<tr>
<td>
2.2.4.2
</td>
<td>
Is data planned to be published as open format? If so, please describe the
technological solution used and the metadata format.
</td>
<td>
No
</td> </tr> </table>
**Table 2: Local Data Management Plan – WP2 (RETHYMNO)**
<table>
<tr>
<th>
**WP2 – LIMASSOL**
</th>
<th>
</th> </tr>
<tr>
<td>
**Data details**
</td>
<td>
</td> </tr>
<tr>
<td>
2.3.1.1
</td>
<td>
Which kind of data has been/will be collected in your site?
</td>
<td>
_**LIM2.1 Sustainable Mobility Tourist Action Plan** _
_**(SMTAP)** _
* _CO2 emissions_
* _Energy consumption_
* _Economy_
* _Questionnaires targeting the user group, for awareness level, needs and expectations_
_The data collection is on-going_
</td> </tr>
<tr>
<td>
2.3.1.2
</td>
<td>
Please detail data typology and structure/format (if applicable)
</td>
<td>
_Questionnaires targeting the user group, for awareness level, needs and
expectations_ \- _Paper questionnaires_
</td> </tr>
<tr>
<td>
2.3.1.3
</td>
<td>
Please detail the data origin
</td>
<td>
* _Public Works Department database_
* _Limassol Municipality database_
* _LTC database_
* _Questionnaires filled by tourists and local citizens_
</td> </tr>
<tr>
<td>
2.3.1.4
</td>
<td>
Please provide some figure allowing to estimate the data dimension
</td>
<td>
_30 questionnaires_
</td> </tr>
<tr>
<td>
**Data collection procedures**
</td>
<td>
</td> </tr>
<tr>
<td>
2.3.2.1
</td>
<td>
Please detail the procedure adopted
for data collection
</td>
<td>
* _CO2 emissions_
* Data extraction from database or data gathered from the field
* _Energy consumption_
* Data extraction from database or data gathered from the field
* _Economy_
* Data extraction from database and data gathered from the field
* _Questionnaires targeting the user group, for awareness level, needs and expectations_
* Data from questionnaires
</td> </tr> </table>
<table>
<tr>
<th>
**WP2 – LIMASSOL**
</th> </tr>
<tr>
<td>
2.3.2.2
</td>
<td>
If a sampling process is used, please confirm that the sample is random and of
a size that can be analysed with the ability to make statistical inference for
the overall sample and for the most significant subsample breakdowns
(for reference, please see D1.1)
</td>
<td>
* Since already existing data from current surveys will also be used, the sampling will not be random and it might be enough for statistical analysis.
* The only sampled data that will be random will be the questionnaires since this survey will involve randomly selected tourists and local citizens for questions
</td> </tr>
<tr>
<td>
2.3.2.3
</td>
<td>
Are data collected anonymously or not? If not, please confirm that data is
collected in such a way preventing the tracking of personal habits or feelings
(for reference, please see
D1.1)
</td>
<td>
The questionnaires will be anonymous
</td> </tr>
<tr>
<td>
**Data management and storing procedures**
</td> </tr>
<tr>
<td>
2.3.3.1
</td>
<td>
How is data stored? Please detail where the data is stored and in which
modality/format (if
applicable)
</td>
<td>
* All the data extracted from the mentioned databases is stored in the involved partner’s database
* The data from the questionnaires will be stored in the involved partner’s office
</td> </tr>
<tr>
<td>
2.3.3.2
</td>
<td>
Who is the
organization responsible for data storing and management?
</td>
<td>
STRATAGEM
</td> </tr>
<tr>
<td>
2.3.3.3
</td>
<td>
Through whom
(organization, responsible) is data accessible?
</td>
<td>
STRATAGEM
</td> </tr>
<tr>
<td>
**WP2 – LIMASSOL**
</td> </tr>
<tr>
<td>
2.3.3.4
</td>
<td>
Which international regulation will be applied for data storing and access?
(for reference, please see D1.1)
</td>
<td>
The Charter of Fundamental Rights of EU States
</td> </tr>
<tr>
<td>
2.3.3.5
</td>
<td>
Which national regulation and applicable ‘opinion statements’ will be applied
for data storing and access? (for reference, please see D1.1)
</td>
<td>
</td>
<td>
The Market Research Society code of conduct ISO 20252
</td> </tr>
<tr>
<td>
**Data availability for dissemination**
</td>
<td>
</td> </tr>
<tr>
<td>
2.3.4.1
</td>
<td>
Is data usable for DESTINATIONS
dissemination purpose?
Please indicate the
format
(aggregated/not aggregated)
</td>
<td>
</td>
<td>
If data is referring to personal data collected from survey/questionnaires and
from the involved partners and stakeholders, then no.
If data is referring to the final publication of the measure to be shared with
the public, then yes.
</td> </tr>
<tr>
<td>
2.3.4.2
</td>
<td>
Is data planned to be published as open format? If so, please describe the
technological solution used and the metadata format.
</td>
<td>
_No._
</td> </tr> </table>
**Table 3: Local Data Management Plan – WP2 (LIMASSOL)**
<table>
<tr>
<th>
**WP2 – ELBA**
</th>
<th>
</th> </tr>
<tr>
<td>
**Data details**
</td>
<td>
</td> </tr>
<tr>
<td>
2.4.1.1
</td>
<td>
Which kind of data have been collected in your site?
</td>
<td>
_Census/demographic data_
_Passengers, freight, vehicles flows by ferryboats_
_Car ownership rate_
_Accident rate_
_All data have been already collected_
</td> </tr>
<tr>
<td>
2.4.1.2
</td>
<td>
Please detail data typology and structure/format (if applicable)
</td>
<td>
_Census/demographic data:_
* Residents in the 8 municipalities of Elba in 2016 divided by sex
_Passengers, freight, vehicles flows by ferryboats:_
* Number of passengers from Piombino Harbour to Elba in 2016
_Car ownership rate in Elba_
* Cars owners in Elba in 2015 divided by municipality and by type of emission
_Accident rate in Elba_
* accident rate in Elba in 2015 divided by each municipality and street network
</td> </tr>
<tr>
<td>
2.4.1.3
</td>
<td>
Please detail the data origin
</td>
<td>
_Census/demographic data:_
* The data is available on the website of ISTAT
(Italian Institute of Statistics)
_Passengers, freight, vehicles flows by ferryboats:_
* The data is available on Port Authority website
_Car ownership rate in Elba, Accident rate in Elba_
* The data is available in ACI (Italian Private Car
Association) website (www.aci.it) related to 2015
</td> </tr> </table>
<table>
<tr>
<th>
**WP2 – ELBA**
</th> </tr>
<tr>
<td>
2.4.1.4
</td>
<td>
Please provide some figure allowing to estimate the data dimension
</td>
<td>
_Census/demographic data:_
Data related to one year (2016) divided by sex and municipality
_Passengers, freight, vehicles flows by ferryboats:_
Data related to one year (2016) divided by month and category
_Car ownership rate in Elba_
Data related to one year (2015) divided by municipality and type of emission
(from euro 0 to euro 6)
_Accidents rate in Elba_
Data related to one year (2015) divided by municipality and street network
</td> </tr>
<tr>
<td>
**Data collection procedures**
</td> </tr>
<tr>
<td>
2.4.2.1
</td>
<td>
Please detail the procedure adopted
for data collection
</td>
<td>
_Census/demographic data:_
* From ISTAT database (website)
_Passengers, freight, vehicles flows by ferryboats:_
* From Port Authority database (web site)
_Car ownership rate in Elba, Accident rate in Elba_
* From ACI website (www.aci.it) database (website)
</td> </tr>
<tr>
<td>
2.4.2.2
</td>
<td>
If a sampling process is used, please confirm that the sample is random and of
a size that can be analysed with the ability to make statistical inference for
the overall sample and for the most significant subsample breakdowns
(for reference, please see D1.1)
</td>
<td>
N/A
</td> </tr> </table>
<table>
<tr>
<th>
**WP2 – ELBA**
</th> </tr>
<tr>
<td>
2.4.2.3
</td>
<td>
Is data collected anonymously or not? If not, please confirm that data is
collected in such a way preventing the tracking of personal habits or feelings
(for reference, please see
D1.1)
</td>
<td>
Anonymously
</td> </tr>
<tr>
<td>
**Data management and storing procedures**
</td> </tr>
<tr>
<td>
2.4.3.1
</td>
<td>
How is data stored? Please detail where the data is stored and in which
modality/format (if
applicable)
</td>
<td>
See row 2.4.2.1
</td> </tr>
<tr>
<td>
2.4.3.2
</td>
<td>
Who is the organization responsible for data storing and management?
</td>
<td>
See row 2.4.2.1
</td> </tr>
<tr>
<td>
2.4.3.3
</td>
<td>
Through whom
(organization, responsible) is data accessible?
</td>
<td>
All data is published as open data
</td> </tr>
<tr>
<td>
2.4.3.4
</td>
<td>
Which international regulation will be applied for data storing and access?
(for reference, please see D1.1)
</td>
<td>
All the data is anonymous; there is any need to apply international regulation
</td> </tr>
<tr>
<td>
**WP2 – ELBA**
</td> </tr>
<tr>
<td>
2.4.3.5
</td>
<td>
Which national regulation and applicable ‘opinion statements’ will be applied
for data storing and access? (for reference, please see D1.1)
</td>
<td>
All the data is national regulation
</td>
<td>
anonymous; there is any need to apply
</td> </tr>
<tr>
<td>
**Data availability for dissemination**
</td>
<td>
</td> </tr>
<tr>
<td>
2.4.4.1
</td>
<td>
Are data usable for DESTINATIONS
dissemination purpose?
Please indicate the
format
(aggregated/not aggregated)
</td>
<td>
Data can be used
aggregated form
</td>
<td>
by DESTINATIONS under an
</td> </tr>
<tr>
<td>
2.4.4.2
</td>
<td>
Are data planned to be published as open format? If so, please describe the
technological solution used and the metadata format.
</td>
<td>
No
</td> </tr> </table>
**Table 4: Local Data Management Plan – WP2 (ELBA)**
<table>
<tr>
<th>
**WP2 – MALTA**
</th> </tr>
<tr>
<td>
**Data details**
</td> </tr>
<tr>
<td>
2.5.1.1
</td>
<td>
Which kind of data has been/will be collected in your site?
</td>
<td>
N/A
Awaiting data gap analysis and Strategic Objectives Framework Report to be
compiled as part of WP2.
</td> </tr> </table>
**Table 5: Local Data Management Plan – WP2 (MALTA)**
<table>
<tr>
<th>
**WP2 – LAS PALMAS**
</th>
<th>
</th>
<th>
</th> </tr>
<tr>
<td>
**Data details**
</td>
<td>
</td>
<td>
</td> </tr>
<tr>
<td>
2.6.1.1
</td>
<td>
Which kind of data has been/will be collected in your site?
</td>
<td>
</td>
<td>
Census/demographic data
Economics data
</td> </tr>
<tr>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
O/D matrix
</td> </tr>
<tr>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
Traffic flow
</td> </tr>
<tr>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
Network
</td> </tr>
<tr>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
Emissions and Pollution
</td> </tr>
<tr>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
Questionnaires on travel behaviour, attitudes and expectations
</td> </tr>
<tr>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
Tourists flow
</td> </tr>
<tr>
<td>
2.6.1.2
</td>
<td>
Please detail data typology and structure/format (if applicable)
</td>
<td>
</td>
<td>
Most of the data is collected in the SUMP drafted in 2012
Tourists flow is collected online
</td> </tr> </table>
<table>
<tr>
<th>
**WP2 – LAS PALMAS**
</th> </tr>
<tr>
<td>
2.6.1.3
</td>
<td>
origin
</td>
<td>
Please detail the data
</td>
<td>
</td>
<td>
* Census/demographic data (National, regional and
local statistical institute)
* Economic data (National, regional and local
statistical institute)
* O/D matrix
* Traffic flow (City council)
* Network (City council, Guaguas Municipales, Sagulpa)
* Questionnaires on travel behaviour, attitudes and expectations (City council, Guaguas Municipales, Sagulpa, Cinesi)
(The SUMP that has already been developed for Las Palmas de Gran Canaria in
2012 has already collected all this kind of data. However, this data needs to
be updated once the Mobility Office is implemented within CIVITAS DESTINATIONS
project)
* Emissions and Pollution (Regional network of climate station. “Red de Control y Vigilancia de la Calidad del Aire de Canarias”)
* Tourists number (Gran Canaria Tourism Board
“Patronato del Turismo de Gran Canaria” and Observatory of Tourism the City
council
“Observatorio de Turismo del Ayuntamiento de Las Palmas de Gran Canaria”)
</td> </tr>
<tr>
<td>
**Data collection pr**
</td>
<td>
**ocedure**
</td>
<td>
**s**
</td> </tr>
<tr>
<td>
2.6.2.1
</td>
<td>
Please detail the procedure adopted
for data collection
</td>
<td>
* Census/demographic data (Official statistics)
* Economic data (Official statistics)
* O/D matrix (Traffic and passenger counts, surveys)
* Traffic flow (Traffic and passenger counts)
* Network
* Emissions and Pollution (Official statistics, climate station networks)
* Questionnaires on travel behaviour, attitudes and expectations (Surveys)
* Tourists flow (Surveys, Official statistics)
</td> </tr> </table>
<table>
<tr>
<th>
**WP2 – LAS PALMAS**
</th>
<th>
</th> </tr>
<tr>
<td>
2.6.2.2
</td>
<td>
If a sampling process is used, please confirm that the sample is random and of
a size that can be analysed with the ability to make statistical inference for
the overall sample and for the most significant subsample breakdowns
(for reference, please
see D1.1)
</td>
<td>
Sampling process is random.
</td>
<td>
</td> </tr>
<tr>
<td>
2.6.2.3
</td>
<td>
Is data collected anonymously or not? If not, please confirm that data is
collected in such a way preventing the tracking of personal habits or feelings
(for reference, please see
D1.1)
</td>
<td>
Anonymously
</td>
<td>
</td> </tr>
<tr>
<td>
**Data management and storing procedures**
</td>
<td>
</td> </tr>
<tr>
<td>
2.6.3.1
</td>
<td>
How is data stored? Please detail where the data is stored and in which
modality/format (if applicable)
</td>
<td>
* Census/demographic data (Online)
* O/D matrix, Traffic flow, Network documents)
* Emissions and Pollution (Online)
* Tourists flow (Online)
</td>
<td>
(SUMP
</td> </tr> </table>
<table>
<tr>
<th>
**WP2 – LAS PALMAS**
</th>
<th>
</th> </tr>
<tr>
<td>
2.6.3.2
</td>
<td>
Who is the
organization responsible for data storing and management?
</td>
<td>
</td>
<td>
Census/demographic data (National, regional and local statistical institute)
Economic data (National, regional and local statistical institute)
</td> </tr>
<tr>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
Traffic flow (City council)
</td> </tr>
<tr>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
Network (City council, Guaguas Municipales, Sagulpa)
</td> </tr>
<tr>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
Questionnaires on travel behaviour, attitudes and expectations (City council,
Guaguas Municipales, Sagulpa, Cinesi)
</td> </tr>
<tr>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
Emissions and Pollution (Regional network of climate station. “Red de Control
y Vigilancia de la Calidad del Aire de Canarias”)
</td> </tr>
<tr>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
Tourists flow (Gran Canaria Tourism Board
“Patronato del Turismo de Gran Canaria” and Observatory of Tourism the City
council
“Observatorio de Turismo del Ayuntamiento de Las Palmas de Gran Canaria”)
</td> </tr>
<tr>
<td>
2.6.3.3
</td>
<td>
Through whom
(organization, responsible) is data accessible?
</td>
<td>
</td>
<td>
Census/demographic data (National, regional and local statistical institute)
Economics data (National, regional and local statistical institute)
</td> </tr>
<tr>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
Traffic flow (City council)
</td> </tr>
<tr>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
Network (City council, Guaguas Municipales, Sagulpa)
</td> </tr>
<tr>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
Questionnaires on travel behaviour, attitudes and expectations (City council,
Guaguas Municipales, Sagulpa, Cinesi)
</td> </tr>
<tr>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
Emissions and Pollution (Regional network of climate station. “Red de Control
y Vigilancia de la Calidad del Aire de Canarias”)
</td> </tr>
<tr>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
Tourists flow (Gran Canaria Tourism Board
“Patronato del Turismo de Gran Canaria” and Observatory of Tourism the City
council
“Observatorio de Turismo del Ayuntamiento de Las Palmas de Gran Canaria”
</td> </tr>
<tr>
<td>
**WP2 – LAS PALMAS**
</td> </tr>
<tr>
<td>
2.6.3.5
</td>
<td>
Which national regulation and applicable ‘opinion statements’ will be applied
for data storing and access? (for reference, please
see D1.1)
</td>
<td>
“Ley Orgánica 15/1999, de 13 de diciembre, de Protección de Datos de Carácter
Personal”
</td> </tr>
<tr>
<td>
**Data availability for dissemination**
</td> </tr>
<tr>
<td>
2.6.4.1
</td>
<td>
Is data usable for DESTINATIONS
dissemination purpose?
Please indicate the
format
(aggregated/not aggregated)
</td>
<td>
No
</td> </tr>
<tr>
<td>
2.6.4.2
</td>
<td>
Is data planned to be published as open format? If so, please describe the
technological solution used and the metadata format.
</td>
<td>
No
</td> </tr> </table>
**Table 6: Local Data Management Plan – WP2 (LAS PALMAS)**
## WP3
<table>
<tr>
<th>
**WP3 - MADEIRA**
</th>
<th>
</th> </tr>
<tr>
<td>
**Data details**
</td>
<td>
</td> </tr>
<tr>
<td>
3.1.1.1
</td>
<td>
Which kind of data is been/will be collected in your site?
</td>
<td>
* _Statistics on incidents on the road network (to be collected) and inside the buses_ (planned)
* _Survey to target user groups to collect needs and expectations_ (planned)
* _Data about road network_ (planned)
</td> </tr> </table>
<table>
<tr>
<th>
**WP3 - MADEIRA**
</th> </tr>
<tr>
<td>
3.1.1.2
</td>
<td>
_Please detail data typology and structure/format (if_ applicable)
</td>
<td>
_Statistics on incidents on the road network and inside the buses_
* Number of traffic accidents
* Number of accidents inside the buses
_Survey to target user group to collect needs and expectations_
* Paper questionnaires
_Data about road network_
* Counting pedestrians and identifying who is using
wheel-chair or who is mobility-impaired
* Counting traffic congestions
</td> </tr>
<tr>
<td>
3.1.1.3
</td>
<td>
Please detail the data origin
</td>
<td>
_Statistics on incidents on the road network and inside the buses_
* The data is stored in a database or in paper archive _Survey to target user group to collect needs and expectations_
* The target data of questionnaires will be schools students and professors, public transport users.
_Data about road network_
* Visual counting or implement sensors.
</td> </tr>
<tr>
<td>
3.1.1.4
</td>
<td>
Please provide some figure allowing to estimate the data dimension
</td>
<td>
_Statistics on incidents on the road network and inside the buses_
* Data of incidents on the road are not available at this moment. 30 incidents inside the bus.
_Survey to target user group to collect needs and expectations_
* 100 questionnaires
_Data about road network_
* Not available at this moment.
</td> </tr>
<tr>
<td>
**Data collection procedures**
</td> </tr>
<tr>
<td>
3.1.2.1
</td>
<td>
Please detail the procedure adopted
for data collection
</td>
<td>
_Statistics on incidents on the road network and inside the buses_
* Data extraction from database
_Survey to target user group to collect needs and expectations_
* Questionnaires or interviews
_Data about road network_
* Data collection
</td> </tr> </table>
<table>
<tr>
<th>
**WP3 - MADEIRA**
</th> </tr>
<tr>
<td>
3.1.2.2
</td>
<td>
If a sampling process is used, please confirm that the sample is random and of
a size that can be analysed with the ability to make statistical inference for
the overall sample and for the most significant subsample breakdowns
(for reference, please see D1.1)
</td>
<td>
_Survey to target user group to collect needs and expectations_
A sample of target users will be selected to be provided with the
questionnaires
</td> </tr>
<tr>
<td>
3.1.2.3
</td>
<td>
Is data collected anonymously or not? If not, please confirm that data is
collected in such a way preventing the tracking of personal habits or feelings
(for reference, please see
D1.1)
</td>
<td>
_Statistics on incidents on the road network and inside the buses_
* Anonymously
_Survey to target user group to collect needs and expectations_
* Anonymously
_Data about Road network_
* Anonymously
</td> </tr>
<tr>
<td>
**Data management and storing procedures**
</td> </tr>
<tr>
<td>
3.1.3.1
</td>
<td>
How is data stored? Please detail where the data is stored and in which
modality/format (if
applicable)
</td>
<td>
Statistics on incidents on the road network and inside the buses
* Database is stored in the office of local Police Department, and in HF office
Survey to target user group to collect needs and expectations
* Questionnaires will be stored in HF, CMF and AREAM office
Data about Road network
* The data base will be stored in CMF or AREAM
office
</td> </tr>
<tr>
<td>
3.1.3.2
</td>
<td>
Who is the
organization responsible for data storing and management?
</td>
<td>
Statistics on incidents on the road network and inside the buses
* Police Department and HF
Survey to target user group to collect needs and expectations
* HF, CMF and AREAM
Data about road network
* CMF and AREAM
</td> </tr>
<tr>
<td>
**WP3 - MADEIRA**
</td> </tr>
<tr>
<td>
3.1.3.3
</td>
<td>
Through whom
(organization, responsible) is data accessible?
</td>
<td>
Statistics on incidents on the road network and inside the buses
* Police Department and HF
Survey to target user group to collect needs and expectations
* HF, CMF and AREAM office
Data about Road network CMF and AREAM
</td> </tr>
<tr>
<td>
3.1.3.4
</td>
<td>
Which international regulation will be applied for data storing and access?
(for reference, please see D1.1)
</td>
<td>
All the data is anonymous; there is any need to apply international regulation
</td> </tr>
<tr>
<td>
3.1.3.5
</td>
<td>
Which national regulation and applicable ‘opinion statements’ will be applied
for data storing and access? (for reference, please see D1.1)
</td>
<td>
All the data is anonymous; there is any need to apply national regulation.
</td> </tr>
<tr>
<td>
**Data availability for dissemination**
</td> </tr>
<tr>
<td>
3.1.4.1
</td>
<td>
Is data usable for DESTINATIONS
dissemination purpose?
Please indicate the
format
(aggregated/not aggregated)
</td>
<td>
Data can be used by DESTINATIONS under an aggregated form
</td> </tr>
<tr>
<td>
3.1.4.2
</td>
<td>
Is data planned to be published as open format? If so, please describe the
technological solution used and the metadata format.
</td>
<td>
No
</td> </tr> </table>
**Table 7: Local Data Management Plan – WP3 (MADEIRA)**
<table>
<tr>
<th>
**WP3 – RETHYMNO**
</th> </tr>
<tr>
<td>
**Data details**
</td> </tr>
<tr>
<td>
3.2.1.1
</td>
<td>
Which kind of data has been/will be collected in your site?
</td>
<td>
_As described in WP2 – RETHYMNO:_
* Data about road network;
* Statistics on traffic accidents, deaths and injuries
_The data collection is completed_
</td> </tr> </table>
**Table 8: Local Data Management Plan – WP3 (RETHYMNO)**
<table>
<tr>
<th>
**WP3 - LIMASSOL**
</th> </tr>
<tr>
<td>
**Data details**
</td> </tr>
<tr>
<td>
3.3.1.1
</td>
<td>
Which kind of data has been/will be collected in your site?
</td>
<td>
_**LIM3.1 Increase cycling and walking in combination with special interest
tourist activities as an integrated product:** _
* CO2 emissions
* Energy consumption
* Economy
_**LIM3.2 Safe routes to school:** _ CO2 emissions
* Energy consumption
* Economy
* Questionnaires
_**LIM3.4 Attractive and accessible public places to promote intermodal
leisure trips:** _
* CO2 emissions
* Energy consumption
* Economy
_The data collection is on-going_
</td> </tr> </table>
<table>
<tr>
<th>
**WP3 - LIMASSOL**
</th> </tr>
<tr>
<td>
3.3.1.2
</td>
<td>
Please detail data typology and structure/format (if applicable)
</td>
<td>
_**3.1 Increase cycling and walking in combination with special interest
tourist activities as an integrated product:** _
* CO2 emissions
* Kg/km CO2 x total distance covered by the persons
* Energy consumption
* kWh/lt x total litres of total distance
* Economy
* Average cost of fuel x fuel savings from energy consumption
_**3.2 Safe routes to school:** _
* CO2 emissions
* Kg/km CO2 x total distance covered by the students
* Energy consumption
* kWh/lt x total litre of the total distance covered
* Economy
* Average cost of fuel x fuel savings from energy consumption
* Questionnaires
_**3.4 Attractive and accessible public places to promote intermodal leisure
trips:** _
* CO2 emissions
* Kg/km CO2 x total distance covered by the students
* Energy consumption
* kWh/lt x total litre of the total distance covered
* Economy
* Average cost of fuel x fuel savings from energy consumption
</td> </tr>
<tr>
<td>
3.3.1.3
</td>
<td>
Please detail the data origin
</td>
<td>
* Public Works Department database
* Limassol Municipality database
* LTC database
* Questionnaires filled by tourists and local citizens
</td> </tr>
<tr>
<td>
3.3.1.4
</td>
<td>
Please provide some figure allowing to estimate the data dimension
</td>
<td>
30 questionnaires
</td> </tr> </table>
<table>
<tr>
<th>
**WP3 - LIMASSOL**
</th> </tr>
<tr>
<td>
**Data collection procedures**
</td> </tr>
<tr>
<td>
3.3.2.1
</td>
<td>
Please detail the procedure adopted
for data collection
</td>
<td>
* CO2 emissions
* Data extraction from database or data gathered from the field
* Energy consumption
* Data extraction from database or data gathered from the field
* Economy
* Data extraction from database and data gathered from the field
* Questionnaires targeting the user group, for awareness level, needs and expectations
* Data from questionnaires
</td> </tr>
<tr>
<td>
3.3.2.2
</td>
<td>
If a sampling process is used, please confirm that the sample is random and of
a size that can be analysed with the ability to make statistical inference for
the overall sample and for the most significant subsample breakdowns
(for reference, please see D1.1)
</td>
<td>
* Since already existing data from current surveys will also be used, the sampling will not be random and it might be enough for statistical analysis.
* The only sampled data that will be random will be the questionnaires since this survey will involve randomly selected tourists and local citizens for questions
</td> </tr>
<tr>
<td>
3.3.2.3
</td>
<td>
Is data collected anonymously or not? If not, please confirm that data is
collected in such a way preventing the tracking of personal habits or feelings
(for reference, please see
D1.1)
</td>
<td>
The questionnaires will be anonymous
</td> </tr>
<tr>
<td>
**Data management and storing procedures**
</td> </tr>
<tr>
<td>
3.3.3.1
</td>
<td>
How is data stored? Please detail where the data is stored and in which
modality/format (if
applicable)
</td>
<td>
* All the data extracted from the mentioned databases is stored in the involved partner’s database
* The data from the questionnaires will be stored in the involved partner’s office
</td> </tr>
<tr>
<td>
**WP3 - LIMASSOL**
</td> </tr>
<tr>
<td>
3.3.3.2
</td>
<td>
Who is the
organization responsible for data storing and management?
</td>
<td>
* LTC
* STRATAGEM
* LIMA
</td> </tr>
<tr>
<td>
3.3.3.3
</td>
<td>
Through whom
(organization, responsible) is data accessible?
</td>
<td>
* LTC
* STRATAGEM
* LIMA
</td> </tr>
<tr>
<td>
3.3.3.4
</td>
<td>
Which international regulation will be applied for data storing and access?
(for reference, please see D1.1)
</td>
<td>
The Charter of Fundamental Rights of EU States
</td> </tr>
<tr>
<td>
3.3.3.5
</td>
<td>
Which national regulation and applicable ‘opinion statements’ will be applied
for data storing and access? (for reference, please see D1.1)
</td>
<td>
* The Market Research Society code of conduct
* ISO 20252
</td> </tr>
<tr>
<td>
**Data availability for dissemination**
</td> </tr>
<tr>
<td>
3.3.4.1
</td>
<td>
Is data usable for DESTINATIONS dissemination purpose?
Please indicate the
format
(aggregated/not aggregated)
</td>
<td>
The data can be used for the dissemination of the project and will be
aggregated
</td> </tr>
<tr>
<td>
3.3.4.2
</td>
<td>
Is data planned to be published as open format? If so, please describe the
technological solution used and the metadata format.
</td>
<td>
No
</td> </tr> </table>
**Table 9: Local Data Management Plan – WP3 (LIMASSOL)**
<table>
<tr>
<th>
**WP3 - ELBA**
</th>
<th>
</th> </tr>
<tr>
<td>
**Data details**
</td>
<td>
</td> </tr>
<tr>
<td>
3.4.1.1
</td>
<td>
Which kind of data has been collected in your site?
</td>
<td>
Users’ needs analysis has been carried out based on focus group with citizens
and expertise/knowledge of Municipality technicians
N/A for the scope of this document
</td> </tr> </table>
**Table 10: Local Data Management Plan – WP3 (ELBA)**
<table>
<tr>
<th>
**WP3 – MALTA**
</th> </tr>
<tr>
<td>
**Data details**
</td> </tr>
<tr>
<td>
3.5.1.1
</td>
<td>
Which kind of data has been/will be collected in your site?
</td>
<td>
No demo measures planned in WP3
</td> </tr> </table>
**Table 11: Local Data Management Plan – WP3 (MALTA)**
<table>
<tr>
<th>
**WP3 – LAS PALMAS**
</th> </tr>
<tr>
<td>
**Data details**
</td> </tr>
<tr>
<td>
3.6.1.1
</td>
<td>
Which kind of data has been/will be collected in your site?
</td>
<td>
**LPA3.1. Attractive, safe and accessible public space at major attractions**
* __Environment_ : _ CO2 emissions (not yet collected)
* __Society_ : _ Physical accessibility towards transport (under collection)
* __Transport_ : _
* Congestion levels: Average vehicle speed over total network) (not yet collected)
* Traffic levels: Average vehicles per hour by vehicle type at peak hour) (not yet collected)
* Opportunity for walking (not yet collected)
* Opportunity for cycling (not yet collected)
</td> </tr>
<tr>
<td>
3.6.1.2
</td>
<td>
Please detail data typology and structure/format (if applicable)
</td>
<td>
**LPA3.1. Attractive, safe and accessible public spaces at major attractions**
* CO2 emissions: Digital data
* Physical accessibility towards transport: Survey to target user group to collect needs and expectations. Paper questionnaires
* Congestion levels: Digital data.
* Traffic levels: Digital data
* Opportunity for walking: GIS analysis
* Opportunity for cycling: GIS analysis
</td> </tr>
<tr>
<td>
3.6.1.3
</td>
<td>
Please detail the data origin
</td>
<td>
**LPA3.1. Attractive, safe and accessible public space at major attractions**
* CO2 emissions: Data from existing pollution stations
* Physical accessibility towards transport: Paper questionnaires to tourists and public transport users
* Congestion levels: Automatic car counting
* Traffic levels – Automatic car counting
* Opportunity for walking – GIS Database
* Opportunity for cycling – GIS Database
</td> </tr>
<tr>
<td>
3.6.1.4
</td>
<td>
Please provide some figure allowing to estimate the data dimension
</td>
<td>
No data available yet Planned dimension:
* CO2 emissions: 11 stations
* Paper questionnaires to tourists and public transport users: 37
* Congestion levels: 21 measure points
* Traffic levels: 19 measure points
</td> </tr> </table>
<table>
<tr>
<th>
**WP3 – LAS PALMAS**
</th> </tr>
<tr>
<td>
**Data collection procedures**
</td> </tr>
<tr>
<td>
3.6.2.1
</td>
<td>
Please detail the procedure adopted
for data collection
</td>
<td>
* Data extraction from database or data gathering from survey to target user groups to collect needs and expectations
* Questionnaires or interviews
</td> </tr>
<tr>
<td>
3.6.2.2
</td>
<td>
If a sampling process is used, please confirm that the sample is random and of
a size that can be analysed with the ability to make statistical inference for
the overall sample and for the most significant subsample breakdowns
(for reference, please see D1.1)
</td>
<td>
For all indicators that might need data from a survey, questionnaire
A sampling of the target users have been selected to be provided with the
questionnaires
</td> </tr>
<tr>
<td>
3.6.2.3
</td>
<td>
Is data collected anonymously or not? If not, please confirm that data is
collected in such a way preventing the tracking of personal habits or feelings
(for reference, please see
D1.1)
</td>
<td>
All data is collected anonymously
</td> </tr>
<tr>
<td>
**Data management and storing procedures**
</td> </tr>
<tr>
<td>
3.6.3.1
</td>
<td>
How is data stored? Please detail where the data is stored and in which
modality/format (if
applicable)
</td>
<td>
All data will be stored in the Municipality’s databases
</td> </tr>
<tr>
<td>
3.6.3.2
</td>
<td>
Who is the organization responsible for data storing and management?
</td>
<td>
The organization responsible for data storing and management is the
Municipality of Gran Canaria Las Palmas
</td> </tr>
<tr>
<td>
**WP3 – LAS PALMAS**
</td> </tr>
<tr>
<td>
3.6.3.3
</td>
<td>
Through whom
(organization, responsible) is the data accessible?
</td>
<td>
Municipality, Public bodies (Guaguas, Sagulpa, etc.), Local Police and CINESI
</td> </tr>
<tr>
<td>
3.6.3.5
</td>
<td>
Which national regulation and applicable ‘opinion statements’ will be applied
for data storing and access? (for reference, please see D1.1)
</td>
<td>
“Ley Orgánica 15/1999, ed 13 de diciembre, de Protección de Datos de Carácter
Personal”
</td> </tr>
<tr>
<td>
**Data availability for dissemination**
</td> </tr>
<tr>
<td>
3.6.4.1
</td>
<td>
Is data usable for DESTINATIONS
dissemination purpose?
Please indicate the
format
(aggregated/not aggregated)
</td>
<td>
No
</td> </tr>
<tr>
<td>
3.6.4.2
</td>
<td>
Is data planned to be published as open format? If so, please describe the
technological solution used and the metadata format.
</td>
<td>
No
</td> </tr> </table>
**Table 12: Local Data Management Plan – WP3 (LAS PALMAS)**
## WP4
<table>
<tr>
<th>
**WP4 – MADEIRA**
</th> </tr>
<tr>
<td>
**Data details**
</td> </tr>
<tr>
<td>
4.1.1.1
</td>
<td>
Which kind of data has been/will be collected in your site?
</td>
<td>
Data on the demand of electrical vehicles (planned)
Survey of target group (planned)
</td> </tr>
<tr>
<td>
4.1.1.2
</td>
<td>
Please detail data typology and structure/format (if applicable)
</td>
<td>
Data on the demand of electrical vehicles
* Number of new electrical vehicles in the region Survey of target group
* Questionnaires to owners of new electrical vehicles
</td> </tr> </table>
<table>
<tr>
<th>
**WP4 – MADEIRA**
</th> </tr>
<tr>
<td>
**Data collection procedures**
</td> </tr>
<tr>
<td>
4.1.2.1
</td>
<td>
Please detail the procedure adopted
for data collection
</td>
<td>
Data on the demand of electrical vehicles
* Questionnaire
Survey to target group
* Questionnaires or interviews
</td> </tr>
<tr>
<td>
4.1.2.2
</td>
<td>
If a sampling process is used, please confirm that the sample is random and of
a size that can be analysed with the ability to make statistical inference for
the overall sample and for the most significant subsample breakdowns
(for reference, please see D1.1)
</td>
<td>
Survey of target group
A sampling of the target users will be selected to be provided with the
questionnaires
</td> </tr>
<tr>
<td>
4.1.2.3
</td>
<td>
Are data collected anonymously or not? If not, please confirm that data is
collected in such a way preventing the tracking of personal habits or feelings
(for reference, please see
D1.1)
</td>
<td>
Data on the demand of electrical vehicles
* Anonymously
Survey to target group
* Anonymously
</td> </tr>
<tr>
<td>
**Data management and storing procedures**
</td> </tr>
<tr>
<td>
4.1.3.1
</td>
<td>
How is data stored? Please detail where the data is stored and in which
modality/format (if
applicable)
</td>
<td>
Data on the demand of electrical vehicles
* Local vehicle sellers
Survey to target group
* The data base will be stored in AREAM office
</td> </tr>
<tr>
<td>
4.1.3.2
</td>
<td>
Who is the
organization responsible for data storing and management?
</td>
<td>
Data on the demand of electrical vehicles
* AREAM office
Survey of target group
* AREAM office
</td> </tr>
<tr>
<td>
**WP4 – MADEIRA**
</td> </tr>
<tr>
<td>
4.1.3.3
</td>
<td>
Through whom
(organization, responsible) is data accessible?
</td>
<td>
Data on the demand of electrical vehicles
* AREAM office
Survey of target group
* AREAM office
</td> </tr>
<tr>
<td>
4.1.3.4
</td>
<td>
Which international regulation will be applied for data storing and access?
(for reference, please see D1.1)
</td>
<td>
All the data is anonymous; there is any need to apply international regulation
</td> </tr>
<tr>
<td>
4.1.3.5
</td>
<td>
Which national regulation and applicable ‘opinion statements’ will be applied
for data storing and access? (for reference, please see D1.1)
</td>
<td>
All the data is
national regulation
</td>
<td>
anonymous; there is no
</td>
<td>
need to apply
</td> </tr>
<tr>
<td>
**Data availability for dissemination**
</td>
<td>
</td>
<td>
</td> </tr>
<tr>
<td>
4.1.4.1
</td>
<td>
Is data usable for DESTINATIONS
dissemination purpose?
Please indicate the
format
(aggregated/not aggregated)
</td>
<td>
Data can be used
aggregated form
</td>
<td>
by DESTINATIONS
</td>
<td>
under an
</td> </tr>
<tr>
<td>
4.1.4.2
</td>
<td>
Is data planned to be published as open format? If so, please describe the
technological solution used and the metadata format.
</td>
<td>
No
</td> </tr> </table>
**Table 13: Local Data Management Plan – WP4 (MADEIRA)**
<table>
<tr>
<th>
**WP4 - RETHYMNO**
</th> </tr>
<tr>
<td>
**Data details**
</td> </tr>
<tr>
<td>
4.2.1.1
</td>
<td>
Which kind of data have been/will be collected in your site?
</td>
<td>
Design activities of the measure are starting No data collection to be
reported
</td> </tr> </table>
**Table 14: Local Data Management Plan – WP4 (RETHYMNO)**
<table>
<tr>
<th>
**WP4 - LIMASSOL**
</th> </tr>
<tr>
<td>
**Data details**
</td> </tr>
<tr>
<td>
4.3.1.1
</td>
<td>
Which kind of data has been/will be collected in your site?
</td>
<td>
_**4.1 Electric car rentals connecting the Limassol areaairports-ports** _
* CO2 emissions
* Energy consumption
* Economy
_**4.2 Expansion of bike sharing system. Add new bikes and e-bikes for rent**
_
* CO2 emissions
* Energy consumption
* Economy
_**4.3 Promote the uptake of electric vehicles. Campaign on electro-mobility**
_
* CO2 emissions
* Energy consumption
* Economy
* Revenues
_The data collection is on-going_
</td> </tr> </table>
<table>
<tr>
<th>
**WP4 - LIMASSOL**
</th> </tr>
<tr>
<td>
4.3.1.2
</td>
<td>
Please detail data typology and structure/format (if applicable)
</td>
<td>
_**4.1 Electric car rentals connecting the Limassol areaairports-ports** _
* CO2 emissions
* Kg/km CO2 x total distance covered by the students
* Energy consumption
* kWh/lt x total litre of the total distance covered
* Economy
* Average cost of fuel x fuel savings from energy consumption
_**4.2 Expansion of bike sharing system. Add new bikes and e-bikes for rent**
_
* CO2 emissions
* Kg/km CO2 x total distance covered by the students
* Energy consumption
* kWh/lt x total litre of the total distance covered
* Economy
* Average cost of fuel x fuel savings from energy consumption
_**4.3 Promote the uptake of electric vehicles. Campaign on electro-mobility**
_
* CO2 emissions
* Kg/km CO2 x total distance covered by the students
* Energy consumption
* kWh/lt x total litre of the total distance covered
* Economy
* Average cost of fuel x fuel savings from energy consumption
* Revenues
* Total rental price x total rental days
</td> </tr>
<tr>
<td>
4.3.1.3
</td>
<td>
Please detail the data origin
</td>
<td>
* LTC
* LIMA
</td> </tr>
<tr>
<td>
**Data collection procedures**
</td> </tr> </table>
<table>
<tr>
<th>
**WP4 - LIMASSOL**
</th> </tr>
<tr>
<td>
4.3.2.1
</td>
<td>
Please detail the procedure adopted
for data collection
</td>
<td>
* CO2 emissions
* Data extraction from database or data
gathered from the field
* Energy consumption
* Data extraction from database or data
gathered from the field
* Economy
* Data extraction from database and data
gathered from the field
</td> </tr>
<tr>
<td>
4.3.2.2
</td>
<td>
If a sampling process is used, please confirm that the sample is random and of
a size that can be analysed with the ability to make statistical inference for
the overall sample and for the most significant subsample breakdowns
(for reference, please see D1.1)
</td>
<td>
Since already existing data from current surveys will also be used, the
sampling will not be random and it might be enough for statistical analysis
</td> </tr>
<tr>
<td>
4.3.2.3
</td>
<td>
Is data collected anonymously or not? If not, please confirm that data is
collected in such a way preventing the tracking of personal habits or feelings
(for reference, please see
D1.1)
</td>
<td>
The data collected will be anonymous
</td> </tr>
<tr>
<td>
**Data management and storing procedures**
</td> </tr>
<tr>
<td>
4.3.3.1
</td>
<td>
How is data stored? Please detail where the data is stored and in which
modality/format (if
applicable)
</td>
<td>
* All the data extracted from the mentioned databases is stored in the involved partner’s database
* The data from the questionnaires will be stored in the involved partner’s office
</td> </tr>
<tr>
<td>
**WP4 - LIMASSOL**
</td> </tr>
<tr>
<td>
4.3.3.2
</td>
<td>
Who is the
organization responsible for data storing and management?
</td>
<td>
LTC
</td> </tr>
<tr>
<td>
4.3.3.3
</td>
<td>
Through whom
(organization, responsible) is data accessible?
</td>
<td>
* LTC
* STRATAGEM
</td> </tr>
<tr>
<td>
4.3.3.4
</td>
<td>
Which international regulation will be applied for data storing and access?
(for reference, please see D1.1)
</td>
<td>
The Charter of Fundamental Rights of EU States
</td> </tr>
<tr>
<td>
4.3.3.5
</td>
<td>
Which national regulation and applicable ‘opinion statements’ will be applied
for data storing and access? (for reference, please see D1.1)
</td>
<td>
* The Market Research Society code of conduct and
* ISO 20252
</td> </tr>
<tr>
<td>
**Data availability for dissemination**
</td> </tr>
<tr>
<td>
4.3.4.1
</td>
<td>
Are data usable for DESTINATIONS
dissemination purpose?
Please indicate the
format
(aggregated/not aggregated)
</td>
<td>
The data will be used for the dissemination of the project and will be
aggregated
</td> </tr>
<tr>
<td>
4.3.4.2
</td>
<td>
Is data planned to be published as open format? If so, please describe the
technological solution used and the metadata format.
</td>
<td>
No
</td> </tr> </table>
**Table 15: Local Data Management Plan – WP4 (LIMASSOL)**
<table>
<tr>
<th>
**WP4 - ELBA**
</th>
<th>
</th> </tr>
<tr>
<td>
**Data details**
</td>
<td>
</td> </tr>
<tr>
<td>
4.4.1.1
</td>
<td>
Which kind of data has been collected in your site?
</td>
<td>
No data collection to be reported for the period
</td> </tr> </table>
**Table 16: Local Data Management Plan – WP4 (ELBA)**
<table>
<tr>
<th>
**WP4 - MALTA**
</th> </tr>
<tr>
<td>
**Data details**
</td> </tr>
<tr>
<td>
4.5.1.1
</td>
<td>
Which kind of data has been/will be collected in your site?
</td>
<td>
* Data on average cost of owning a car and comparable data on costs of sharing transport/using public transport (to be used in dissemination of the information campaign)
* Periodical survey data on the effectiveness of the campaign ( to be done)
* Statistics produced by the platform of management of bike and car sharing (once these start to be operated): registered users, O/D trips, etc.
_Data collection procedure is planned_
</td> </tr>
<tr>
<td>
4.5.1.2
</td>
<td>
Please detail data typology and structure/format (if applicable)
</td>
<td>
* Data on average cost of owning a car and comparable data on costs of sharing transport/using public transport (to be used in dissemination of the information campaign) – Report of findings (onetime report)
* Survey data on the effectiveness of the campaign – Bar graph format to show awareness comparison
* Statistics produced by the platform of management of bike and car sharing once these start to be operated (registered users, O/D trips, etc.) – excel format showing number of users per period
(periodic)
</td> </tr> </table>
<table>
<tr>
<th>
**WP4 - MALTA**
</th> </tr>
<tr>
<td>
4.5.1.3
</td>
<td>
Please detail the data origin
</td>
<td>
* Data on average cost of owning a car and comparable data on costs of sharing transport/using public transport (to be used in dissemination of the information campaign) – contractor to compile research and report
* Survey data on the effectiveness of the campaign – telephone surveys conducted by contractor
* Statistics produced by the platform of management of bike and car sharing once these start to be operated (registered users, O/D trips, etc.) – data provided by operators
</td> </tr>
<tr>
<td>
**Data collection procedures**
</td> </tr>
<tr>
<td>
4.5.2.1
</td>
<td>
Please detail the procedure adopted
for data collection
</td>
<td>
* Data on average cost of owning a car and comparable data on costs of sharing transport/using public transport (to be used in dissemination of the information campaign) – desk top research
* Survey data on the effectiveness of the campaign – telephone surveys done periodically (prior to launch of campaign; during the campaign; after end of campaign)
* Statistics produced by the platform of management of bike and car sharing once these start to be operated (registered users, O/D trips, etc.) – data provided by RFID cards which shows service usage
</td> </tr>
<tr>
<td>
4.5.2.2
</td>
<td>
If a sampling process is used, please confirm that the sample is random and of
a size that can be analysed with the ability to make statistical inference for
the overall sample and for the most significant subsample breakdowns
(for reference, please see D1.1)
</td>
<td>
Sampling shall only be used for the survey on Campaign Awareness. It shall be
ensured that the sample is random and of a size that can be analysed with the
ability to make statistical inference for the overall sample and for the most
significant sub-sample breakdowns
</td> </tr> </table>
<table>
<tr>
<th>
**WP4 - MALTA**
</th> </tr>
<tr>
<td>
4.5.2.3
</td>
<td>
Is data collected anonymously or not? If not, please confirm that data is
collected in such a way preventing the tracking of personal habits or feelings
(for reference, please see
D1.1)
</td>
<td>
* Data on average cost of owning a car and comparable data on costs of sharing transport/using public transport (to be used in dissemination of the information campaign) – not applicable
* Survey data on the effectiveness of the campaign – telephone surveys done periodically (prior to launch of campaign; data will be anonymous
* Statistics produced by the platform of management of bike and car sharing once these start to be operated (registered users, O/D trips, etc.) – No information regarding RFID users shall be provided, only the anonymous number of users per period
</td> </tr>
<tr>
<td>
**Data management and storing procedures**
</td> </tr>
<tr>
<td>
4.5.3.1
</td>
<td>
How is data stored? Please detail where the data is stored and in which
modality/format (if
applicable)
</td>
<td>
* Data on average cost of owning a car and comparable data on costs of sharing transport/using public transport (to be used in dissemination of the information campaign) – PDF
* Survey data on the effectiveness of the campaign – excel
* Statistics produced by the platform of management of bike and car sharing once these start to be operated (registered users, O/D trips, etc.) – excel
</td> </tr>
<tr>
<td>
4.5.3.2
</td>
<td>
Who is the
organization responsible for data storing and management?
</td>
<td>
* Data on average cost of owning a car and comparable data on costs of sharing transport/using public transport (to be used in dissemination of the information campaign) – Transport Malta
* Survey data on the effectiveness of the campaign – Transport Malta
* Statistics produced by the platform of management of bike and car sharing once these start to be operated (registered users, O/D trips, etc.) – The operators
</td> </tr>
<tr>
<td>
**WP4 - MALTA**
</td> </tr>
<tr>
<td>
4.5.3.3
</td>
<td>
Through whom
(organization, responsible) is data accessible?
</td>
<td>
* Data on average cost of owning a car and comparable data on costs of sharing transport/using public transport (to be used in dissemination of the information campaign) – Transport Malta
* Survey data on the effectiveness of the campaign – Transport Malta
* Statistics produced by the platform of management of bike and car sharing once these start to be operated (registered users, O/D trips, etc.) – The operators and Transport Malta
</td> </tr>
<tr>
<td>
4.5.3.4
</td>
<td>
Which international regulation will be applied for data storing and access?
(for reference, please see D1.1)
</td>
<td>
Directive 95/46/EC which is due to be replaced by the General Data Protection
Regulation (GDPR) EU 2016/679 which will become effective on 25 May 2018
</td> </tr>
<tr>
<td>
4.5.3.5
</td>
<td>
Which national regulation and applicable ‘opinion statements’ will be applied
for data storing and access? (for reference, please see D1.1)
</td>
<td>
Chapter 440 Data Protection Act
</td> </tr>
<tr>
<td>
**Data availability for dissemination**
</td> </tr>
<tr>
<td>
4.5.4.1
</td>
<td>
Is data usable for DESTINATIONS dissemination purpose?
Please indicate the
format
(aggregated/not aggregated)
</td>
<td>
Yes, in aggregate.
</td> </tr>
<tr>
<td>
4.5.4.2
</td>
<td>
Is data planned to be published as open format? If so, please describe the
technological solution used and the metadata format.
</td>
<td>
No
</td> </tr> </table>
**Table 17: Local Data Management Plan – WP4 (MALTA)**
<table>
<tr>
<th>
**WP4 – LAS PALMAS**
</th> </tr>
<tr>
<td>
**Data details**
</td> </tr>
<tr>
<td>
4.6.1.1
</td>
<td>
Which kind of data has been/will be collected in your site?
</td>
<td>
* Statistics on public bike systems (planned)
* Statistics on fast charging EV and e-cars (planned)
</td> </tr>
<tr>
<td>
4.6.1.2
</td>
<td>
Please detail data typology and structure/format (if applicable)
</td>
<td>
* Users by week and month
* Average users by day of the week.
* Average users by day of the week and by bike
* Average users by hour in summer and winter
* Source – Destination Matrix
* Economical saving on fuel by month
* Economical saving on mileage payment
* Number of Kwh consumed
* CO2 Tons saved
</td> </tr>
<tr>
<td>
4.6.1.3
</td>
<td>
Please detail the data origin
</td>
<td>
All the data will be stored in a database and the different statistics will be
stored in electronic format
</td> </tr>
<tr>
<td>
4.6.1.4
</td>
<td>
Please provide some figure allowing to estimate the data dimension
</td>
<td>
375 bikes, 20 e-bikes and 2 bikes for physically impaired people will be
analysed
6 fast charging EV
3 electrical cars
</td> </tr>
<tr>
<td>
**Data collection procedures**
</td> </tr>
<tr>
<td>
4.6.2.1
</td>
<td>
Please detail the procedure adopted
for data collection
</td>
<td>
Data extraction from database
Data extraction from electrical counters
</td> </tr>
<tr>
<td>
4.6.2.3
</td>
<td>
Is data collected anonymously or not? If not, please confirm that data is
collected in such a way preventing the tracking of personal habits or feelings
(for reference, please see
D1.1)
</td>
<td>
* Statistics on public bikes system
For the implementation of the public bikes system, a personal data collection
will be done in order to manage a customer database system that of course will
comply with national and international regulation regarding personal data
storing, access and management
* Statistics on fast charging EV and electrical cars.
Anonymous
</td> </tr>
<tr>
<td>
**Data management and storing procedures**
</td> </tr>
<tr>
<td>
4.6.3.1
</td>
<td>
How is data stored? Please detail where the data is stored and in which
modality/format (if
applicable)
</td>
<td>
Statistics on public bike systems
* Data is stored in NextBike server systems
Statistics on fast charging EV and electrical cars
* Data is stored in SAGULPA servers
</td> </tr>
<tr>
<td>
**WP4 – LAS PALMAS**
</td> </tr>
<tr>
<td>
4.6.3.2
</td>
<td>
Who is the
organization responsible for data storing and management?
</td>
<td>
SAGULPA
</td> </tr>
<tr>
<td>
4.6.3.3
</td>
<td>
Through whom
(organization, responsible) is the data accessible?
</td>
<td>
SAGULPA
</td> </tr>
<tr>
<td>
4.6.3.5
</td>
<td>
Which national regulation and applicable ‘opinion statements’ will be applied
for data storing and access? (for reference, please see D1.1)
</td>
<td>
“Ley Orgánica 15/1999, ed 13 de diciembre, de Protección de Datos de Carácter
Personal”
</td> </tr>
<tr>
<td>
**Data availability for dissemination**
</td> </tr>
<tr>
<td>
4.6.4.1
</td>
<td>
Is data usable for DESTINATIONS
dissemination purpose?
Please indicate the
format
(aggregated/not aggregated)
</td>
<td>
No
</td> </tr>
<tr>
<td>
4.6.4.2
</td>
<td>
Is data planned to be published as open format? If so, please describe the
technological solution used and the metadata format.
</td>
<td>
No
</td> </tr> </table>
**Table 18: Local Data Management Plan – WP4 (LAS PALMAS)**
## WP5
<table>
<tr>
<th>
**WP5 - MADEIRA**
</th>
<th>
</th> </tr>
<tr>
<td>
**Data details**
</td>
<td>
</td> </tr>
<tr>
<td>
5.1.1.1
</td>
<td>
Which kind of data has been/will be collected in your site?
</td>
<td>
* Questionnaires/survey on supply/retail process (planned)
* Data about road network (planned)
</td> </tr>
<tr>
<td>
5.1.1.2
</td>
<td>
Please detail data typology and structure/format (if applicable)
</td>
<td>
_Questionnaires/survey on supply/retail process_
* Paper questionnaires
_Data about road network_
* Counting traffic congestions
</td> </tr>
<tr>
<td>
5.1.1.3
</td>
<td>
Please detail the data origin
</td>
<td>
_Questionnaires/survey on supply/retail process_
* The target data of questionnaires will be the local commerce owners
_Data about road network_
* Visual counting or implement sensors
</td> </tr>
<tr>
<td>
5.1.1.4
</td>
<td>
Please provide some figure allowing to estimate the data dimension
</td>
<td>
_Questionnaires/survey on supply/retail process_
* 50 questionnaires
_Data about road network_
* Not available at this moment
</td> </tr>
<tr>
<td>
**Data collection procedures**
</td>
<td>
</td> </tr>
<tr>
<td>
5.1.2.1
</td>
<td>
Please detail the procedure adopted
for data collection
</td>
<td>
_Questionnaires/survey on supply/retail process_
* Questionnaires or interviews
_Data about road network_
* Data collection
</td> </tr>
<tr>
<td>
5.1.2.2
</td>
<td>
If a sampling process is used, please confirm that the sample is random and of
a size that can be analysed with the ability to make statistical inference for
the overall sample and for the most significant subsample breakdowns
(for reference, please see D1.1)
</td>
<td>
_Questionnaires/survey on supply/retail process_
A sampling of the target users will be selected to be provided with the
questionnaires.
</td> </tr> </table>
<table>
<tr>
<th>
**WP5 - MADEIRA**
</th> </tr>
<tr>
<td>
5.1.2.3
</td>
<td>
Is data collected anonymously or not? If not, please confirm that data is
collected in such a way preventing the tracking of personal habits or feelings
(for reference, please see
D1.1)
</td>
<td>
_Questionnaires/survey on supply/retail process_
* Anonymously
_Data about Road network_
* Anonymously
</td> </tr>
<tr>
<td>
**Data management and storing procedures**
</td> </tr>
<tr>
<td>
5.1.3.1
</td>
<td>
How is data stored? Please detail where the data is stored and in which
modality/format (if
applicable)
</td>
<td>
_Questionnaires/survey on supply/retail process_
* Questionnaires will be stored in CMF office
_Data about road network_
* The data base will be stored in CMF office
</td> </tr>
<tr>
<td>
5.1.3.2
</td>
<td>
Who is the
organization responsible for data storing and management?
</td>
<td>
_Questionnaires/survey on supply/retail process_
* CMF
_Data about Road network_
* CMF
</td> </tr>
<tr>
<td>
5.1.3.3
</td>
<td>
Through whom
(organization, responsible) data is accessible?
</td>
<td>
_Questionnaires/survey on supply/retail process_
* CMF
_Data about Road network_
* CMF
</td> </tr>
<tr>
<td>
5.1.3.4
</td>
<td>
Which international regulation will be applied for data storing and access?
(for reference, please see D1.1)
</td>
<td>
All the data is anonymous; there is any need to apply international regulation
</td> </tr>
<tr>
<td>
5.1.3.5
</td>
<td>
Which national regulation and applicable ‘opinion statements’ will be applied
for data storing and access? (for reference, please see D1.1)
</td>
<td>
All the data is anonymous; there is any need to apply national regulation
</td> </tr>
<tr>
<td>
**WP5 - MADEIRA**
</td>
<td>
</td> </tr>
<tr>
<td>
**Data availability for dissemination**
</td>
<td>
</td> </tr>
<tr>
<td>
5.1.4.1
</td>
<td>
Is data usable for DESTINATIONS
dissemination purpose?
Please indicate the
format
(aggregated/not aggregated)
</td>
<td>
Data can be used
aggregated form
</td>
<td>
by DESTINATIONS under an
</td> </tr>
<tr>
<td>
5.1.4.2
</td>
<td>
Is data planned to be published as open format? If so, please describe the
technological solution used and the metadata format.
</td>
<td>
No
</td>
<td>
</td> </tr> </table>
**Table 19: Local Data Management Plan – WP5 (MADEIRA)**
<table>
<tr>
<th>
**WP5 - RETHYMNO**
</th> </tr>
<tr>
<td>
**Data details**
</td> </tr>
<tr>
<td>
5.2.1.1
</td>
<td>
Which kind of data has been/will be collected in your site?
</td>
<td>
Design activities of the measure are starting No data collection to be
reported
</td> </tr> </table>
**Table 20: Local Data Management Plan – WP5 (RETHYMNO)**
<table>
<tr>
<th>
**WP5 - LIMASSOL**
</th> </tr>
<tr>
<td>
**Data details**
</td> </tr>
<tr>
<td>
5.3.1.1
</td>
<td>
Which kind of data has been/will be collected in your site?
</td>
<td>
_**5.1 Limassol city centre urban freight logistic action plan** _
* CO2 emissions
* Energy consumption
* Economy
* Questionnaires targeting the user group, for awareness level, needs and expectations
_**5.2 Promotion and creation of network for collecting of used cooking oil
(UCO)** _
* Litres of cooking oil collected from hotels and restaurants
</td> </tr> </table>
<table>
<tr>
<th>
**WP5 - LIMASSOL**
</th> </tr>
<tr>
<td>
5.3.1.2
</td>
<td>
Please detail data typology and structure/format (if applicable)
</td>
<td>
_**5.1 Limassol city centre urban freight logistic action plan** _
* CO2 emissions
* Kg/km CO2 x total distance covered by the students
* Energy consumption
* kWh/lt x total litre of the total distance covered
* Economy
* Average cost of fuel x fuel savings from energy consumption
* Questionnaires targeting the user group, for awareness level, needs and expectations
* Paper questionnaires
_**5.2 Promotion and creation of network for collecting of used cooking oil
(UCO)** _
* Litres of used cooking oil collected
_The data collection is on-going_
</td> </tr>
<tr>
<td>
5.3.1.3
</td>
<td>
Please detail the data origin
</td>
<td>
* LTC
* LIMA
* Public Works Department
* Questionnaires (to be done)
</td> </tr>
<tr>
<td>
5.3.1.4
</td>
<td>
Please provide some figure allowing to estimate the data dimension
</td>
<td>
_**5.1 Limassol city centre urban freight logistic action plan** _
* 100 shops involved
_**5.2 Promotion and creation of network for collecting of used cooking oil
(UCO)** _
* 3 hotels
* 10 restaurants
</td> </tr>
<tr>
<td>
**Data collection procedures**
</td> </tr>
<tr>
<td>
5.3.2.1
</td>
<td>
Please detail the procedure adopted
for data collection
</td>
<td>
* _CO2 emissions_
* Data extraction from database or data gathered from the field
* _Energy consumption_
* Data extraction from database or data gathered from the field
* _Economy_
* Data extraction from database and data gathered from the field
* _Questionnaires targeting the user group, for awareness level, needs and expectations_
* Data from questionnaires
</td> </tr> </table>
<table>
<tr>
<th>
**WP5 - LIMASSOL**
</th> </tr>
<tr>
<td>
5.3.2.2
</td>
<td>
If a sampling process is used, please confirm that the sample is random and of
a size that can be analysed with the ability to make statistical inference for
the overall sample and for the most significant subsample breakdowns
(for reference, please see D1.1)
</td>
<td>
* When data already exist from current surveys, they will also be used, the sampling will not be random and it might be enough for statistical analysis
* The only sampled data that will be random will be the questionnaires since this survey will involve randomly selected tourists and local citizens for questions
</td> </tr>
<tr>
<td>
5.3.2.3
</td>
<td>
Is data collected anonymously or not? If not, please confirm that data is
collected in such a way preventing the tracking of personal habits or feelings
(for reference, please see
D1.1)
</td>
<td>
The questionnaires will be anonymous
</td> </tr>
<tr>
<td>
**Data management and storing procedures**
</td> </tr> </table>
5.3.3.1 How is data stored? Please detail where the data is stored and in
which modality/format (if applicable)
* All the data extracted from the mentioned databases is stored in the relevant involved partner’s database
* The data from the questionnaires will be stored in the involved partner’s office
<table>
<tr>
<th>
5.3.3.2
</th>
<th>
Who is the organization responsible for data storing and management?
</th>
<th>
</th>
<th>
STRATAGEM
</th> </tr> </table>
5.3.3.3 Through whom STRATAGEM
(organization,
responsible) is data accessible?
<table>
<tr>
<th>
**WP5 - LIMASSOL**
</th> </tr>
<tr>
<td>
5.3.3.4
</td>
<td>
Which international regulation will be applied for data storing and access?
(for reference, please see D1.1)
</td>
<td>
The Charter of Fundamental Rights of EU States
</td> </tr>
<tr>
<td>
5.3.3.5
</td>
<td>
Which national regulation and applicable ‘opinion statements’ will be applied
for data storing and access? (for reference, please see D1.1)
</td>
<td>
* The Market Research Society code of conduct
* ISO 20252
</td> </tr>
<tr>
<td>
**Data availability for dissemination**
</td> </tr>
<tr>
<td>
5.3.4.1
</td>
<td>
Is data usable for DESTINATIONS
dissemination purpose?
Please indicate the
format
(aggregated/not aggregated)
</td>
<td>
The data will be used for the dissemination of the project and will be
aggregated
</td> </tr>
<tr>
<td>
5.3.4.2
</td>
<td>
Is data planned to be published as open format? If so, please describe the
technological solution used and the metadata format.
</td>
<td>
No
</td> </tr> </table>
**Table 21: Local Data Management Plan – WP5 (LIMASSOL)**
<table>
<tr>
<th>
**WP5 – ELBA**
</th>
<th>
</th> </tr>
<tr>
<td>
**Data details**
</td>
<td>
</td> </tr>
<tr>
<td>
5.4.1.1
</td>
<td>
Which kind of data has been collected in your site?
</td>
<td>
Surveys based on questionnaires for collecting data on goods distribution in
the island (on going)
Survey based on questionnaires targeting freight operators in the island in
order to update/integrate the available data on freight flow to the island (on
going)
</td> </tr> </table>
<table>
<tr>
<th>
**WP5 – ELBA**
</th> </tr>
<tr>
<td>
5.4.1.2
</td>
<td>
Please detail data typology and structure/format (if applicable)
</td>
<td>
_Survey of freight distribution/operators_ Paper questionnaires
</td> </tr>
<tr>
<td>
5.4.1.3
</td>
<td>
Please detail the data origin
</td>
<td>
_Survey of freight distribution/operators_
The target data of questionnaires will be the local shop owners and freight
operators
</td> </tr>
<tr>
<td>
5.4.1.4
</td>
<td>
Please provide some figure allowing to estimate the data dimension
</td>
<td>
_Survey of freight distribution/operators_
About 50 questionnaires for stores and 50 for freights operators
</td> </tr>
<tr>
<td>
**Data collection procedures**
</td> </tr>
<tr>
<td>
5.4.2.1
</td>
<td>
Please detail the procedure adopted
for data collection
</td>
<td>
Questionnaires are submitted through an interview
</td> </tr>
<tr>
<td>
5.4.2.2
</td>
<td>
If a sampling process is used, please confirm that the sample is random and of
a size that can be analysed with the ability to make statistical inference for
the overall sample and for the most significant subsample breakdowns
(for reference, please see D1.1)
</td>
<td>
A sample of the target users will be selected to be provided with the
questionnaires
</td> </tr>
<tr>
<td>
5.4.2.3
</td>
<td>
Is data collected anonymously or not? If not, please confirm that data is
collected in such a way preventing the tracking of personal habits or feelings
(for reference, please see
D1.1)
</td>
<td>
Anonymously
</td> </tr> </table>
<table>
<tr>
<th>
**WP5 – ELBA**
</th> </tr>
<tr>
<td>
**Data management and storing procedures**
</td> </tr>
<tr>
<td>
5.4.3.1
</td>
<td>
How is data stored? Please detail where the data is stored and in which
modality/format (if
applicable)
</td>
<td>
Questionnaires will be stored in Portoferraio Municipality
</td> </tr>
<tr>
<td>
5.4.3.2
</td>
<td>
Who is the
organization responsible for data storing and management?
</td>
<td>
Portoferraio Municipality
</td> </tr>
<tr>
<td>
5.4.3.3
</td>
<td>
Through whom
(organization, responsible) is data accessible?
</td>
<td>
Staff of Portoferraio and Rio Marina Municipalities working on the project
</td> </tr>
<tr>
<td>
5.4.3.4
</td>
<td>
Which international regulation will be applied for data storing and access?
(for reference, please see D1.1)
</td>
<td>
All data is anonymous; there is any need to apply international regulation
</td> </tr>
<tr>
<td>
5.4.3.5
</td>
<td>
Which national regulation and applicable ‘opinion statements’ will be applied
for data storing and access? (for reference, please see D1.1)
</td>
<td>
All Data is anonymous; there is any need to apply national regulation
</td> </tr>
<tr>
<td>
**Data availability for dissemination**
</td> </tr>
<tr>
<td>
5.4.4.1
</td>
<td>
Is data usable for DESTINATIONS
dissemination purpose?
Please indicate the
format
(aggregated/not aggregated)
</td>
<td>
Data can be used by DESTINATIONS under an aggregated form
</td> </tr>
<tr>
<td>
**WP5 – ELBA**
</td>
<td>
</td> </tr>
<tr>
<td>
5.4.4.2
</td>
<td>
Is data planned to be published as open format? If so, please describe the
technological solution used and the metadata format.
</td>
<td>
No
</td> </tr> </table>
**Table 22: Local Data Management Plan – WP5 (ELBA)**
<table>
<tr>
<th>
**WP5 - MALTA**
</th>
<th>
</th> </tr>
<tr>
<td>
**Data details**
</td>
<td>
</td> </tr>
<tr>
<td>
5.5.1.1
</td>
<td>
Which kind of data have been/will be collected in your site?
</td>
<td>
* Data on shops, supply process, logistics operators, etc.
* Survey on user needs and expectations
* Reports coming from stakeholder and target users focus group
_Data collection procedure is planned_
</td> </tr>
<tr>
<td>
5.5.1.2
</td>
<td>
Please detail data typology and structure/format (if applicable)
</td>
<td>
* Data on shops, supply process, logistics operators, etc. – _survey data collected from target group; number of registered outlets_
* Survey on user needs and expectations – _questionnaire survey_
* Reports coming from stakeholder and target users focus group – _questionnaire survey_
</td> </tr>
<tr>
<td>
5.5.1.3
</td>
<td>
Please detail the data origin
</td>
<td>
* Data on shops, supply process, logistics operators, etc. – _Valletta Local Council, Transport Malta operators’ licence database_
* Survey on user needs and expectations – _targeted_
_participants in pilot_
* Reports coming from stakeholder and target users focus group – _targeted participants in pilot_
</td> </tr> </table>
<table>
<tr>
<th>
**WP5 - MALTA**
</th> </tr>
<tr>
<td>
**Data collection procedures**
</td> </tr>
<tr>
<td>
5.5.2.1
</td>
<td>
Please detail the procedure adopted
for data collection
</td>
<td>
* Data on shops, supply process, logistics operators, etc. – desktop research
* Survey on users’ needs and expectations – questionnaire
* Reports coming from stakeholder and target users focus group – questionnaire
</td> </tr>
<tr>
<td>
5.5.2.2
</td>
<td>
If a sampling process is used, please confirm that the sample is random and of
a size that can be analysed with the ability to make statistical inference for
the overall sample and for the most significant subsample breakdowns
(for reference, please see D1.1)
</td>
<td>
No sampling will be done in this case
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td> </tr>
<tr>
<td>
5.5.2.3
</td>
<td>
Is data collected anonymously or not? If not, please confirm that data is
collected in such a way preventing the tracking of personal habits or feelings
(for reference, please see
D1.1)
</td>
<td>
Targeted participants, anonymous
</td>
<td>
once
</td>
<td>
selected,
</td>
<td>
will
</td>
<td>
not
</td>
<td>
be
</td> </tr>
<tr>
<td>
**Data management and storing procedures**
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td> </tr>
<tr>
<td>
5.5.3.1
</td>
<td>
How is data stored? Please detail where the data is stored and in which
modality/format (if
applicable)
</td>
<td>
* Data on shops, supply process, logistics operators, etc. – Official databases currently used to store respective data shall continue to be used
* Survey on users’ needs and expectations – excel
* Reports coming from stakeholder and target users focus group – PDF
</td> </tr>
<tr>
<td>
**WP5 - MALTA**
</td> </tr>
<tr>
<td>
5.5.3.2
</td>
<td>
Who is the
organization responsible for data storing and management?
</td>
<td>
* Data on shops, supply process, logistics operators, etc. – _Valletta Local Council; Transport Malta_
* Survey on users’ needs and expectations –
_Transport Malta_
* Reports coming from stakeholder and target users focus group – _Transport Malta_
</td> </tr>
<tr>
<td>
5.5.3.3
</td>
<td>
Through whom
(organization, responsible) is data accessible?
</td>
<td>
* Data on shops, supply process, logistics operators, etc. – _Valletta Local Council; Transport Malta_
* Survey on users’ needs and expectations –
_Transport Malta_
* Reports coming from stakeholder and target users focus group – _Transport Malta_
</td> </tr>
<tr>
<td>
5.5.3.4
</td>
<td>
Which international regulation will be applied for data storing and access?
(for reference, please see D1.1)
</td>
<td>
Directive 95/46/EC which is due to be replaced by the General Data Protection
Regulation (GDPR) EU 2016/679 which will become effective on 25 May 2018
</td> </tr>
<tr>
<td>
5.5.3.5
</td>
<td>
Which national regulation and applicable ‘opinion statements’ will be applied
for data storing and access? (for reference, please see D1.1)
</td>
<td>
Chapter 440 Data Protection Act
</td> </tr>
<tr>
<td>
**Data availability for dissemination**
</td> </tr>
<tr>
<td>
5.5.4.1
</td>
<td>
Is data usable for DESTINATIONS dissemination purpose?
Please indicate the
format
(aggregated/not aggregated)
</td>
<td>
Yes, non- aggregate
</td> </tr>
<tr>
<td>
5.5.4.2
</td>
<td>
Is data planned to be published as open format? If so, please describe the
technological solution used and the metadata format.
</td>
<td>
No
</td> </tr> </table>
**Table 23: Local Data Management Plan – WP5 (MALTA)**
<table>
<tr>
<th>
**WP5 – LAS PALMAS**
</th>
<th>
</th> </tr>
<tr>
<td>
**Data details**
</td>
<td>
</td> </tr>
<tr>
<td>
5.6.1.1
</td>
<td>
Which kind of data has been/will be collected in your site?
</td>
<td>
* _Number of incoming parcels on customs_ (planned)
* _Data on movements on logistics operators_
(planned)
</td> </tr>
<tr>
<td>
5.6.1.2
</td>
<td>
Please detail data typology and structure/format (if applicable)
</td>
<td>
_Info on freight distribution related to:_
* Size
* Price
* Urgent or not
* Final destination
</td> </tr>
<tr>
<td>
5.6.1.3
</td>
<td>
Please detail the data origin
</td>
<td>
Surveys on logistics operators and official data from customs
</td> </tr>
<tr>
<td>
**Data collection procedures**
</td>
<td>
</td> </tr>
<tr>
<td>
5.6.2.1
</td>
<td>
Please detail the procedure adopted
for data collection
</td>
<td>
Official data and surveys to logistic operators
</td> </tr>
<tr>
<td>
5.6.2.2
</td>
<td>
If a sampling process is used, please confirm that the sample is random and of
a size that can be analysed with the ability to make statistical inference for
the overall sample and for the most significant subsample breakdowns
(for reference, please see D1.1)
</td>
<td>
No data extraction for statistical inferences is expected at the moment
</td> </tr>
<tr>
<td>
5.6.2.3
</td>
<td>
Is data collected anonymously or not? If not, please confirm that data is
collected in such a way preventing the tracking of personal habits or feelings
(for reference, please see
D1.1)
</td>
<td>
Anonymously
</td> </tr>
<tr>
<td>
**WP5 – LAS PALMAS**
</td> </tr>
<tr>
<td>
**Data management and storing procedures**
</td> </tr>
<tr>
<td>
5.6.3.1
</td>
<td>
How is data stored? Please detail where the data is stored and in which
modality/format (if
applicable)
</td>
<td>
Electronically
</td> </tr>
<tr>
<td>
5.6.3.2
</td>
<td>
Who is the
organization responsible for data storing and management?
</td>
<td>
CTO
</td> </tr>
<tr>
<td>
5.6.3.3
</td>
<td>
Through whom
(organization, responsible) is data accessible?
</td>
<td>
CTO
</td> </tr>
<tr>
<td>
5.6.3.5
</td>
<td>
Which national regulation and applicable ‘opinion statements’ will be applied
for data storing and access? (for reference, please see D1.1)
</td>
<td>
“Ley Orgánica 15/1999, de 13 de diciembre, de Protección de Datos de Carácter
Personal”
</td> </tr>
<tr>
<td>
**Data availability for dissemination**
</td> </tr>
<tr>
<td>
5.6.4.1
</td>
<td>
Is data usable for DESTINATIONS
dissemination purpose?
Please indicate the
format
(aggregated/not aggregated)
</td>
<td>
No
</td> </tr>
<tr>
<td>
5.6.4.2
</td>
<td>
Is data planned to be published as open format? If so, please describe the
technological solution used and the metadata format.
</td>
<td>
No
</td> </tr> </table>
**Table 24: Local Data Management Plan – WP5 (LAS PALMAS)**
## WP6
<table>
<tr>
<th>
**WP6 -MADEIRA**
</th>
<th>
</th> </tr>
<tr>
<td>
**Data details**
</td>
<td>
</td> </tr>
<tr>
<td>
6.1.1.1
</td>
<td>
Which kind of data has been/will be collected in your site?
</td>
<td>
* Statistics on incidents on the road network (planned)
* Survey of target user group to collect needs and expectations (planned)
* Data about road network (planned)
</td> </tr>
<tr>
<td>
6.1.1.2
</td>
<td>
Please detail data typology and structure/format (if applicable)
</td>
<td>
_Statistics on incidents on the road network_
* Number of traffic accidents
_Survey of target user group to collect needs and expectations_
* Paper questionnaires
_Data about road network_
* Counting traffic congestions
* Average speed of public transport
</td> </tr>
<tr>
<td>
6.1.1.3
</td>
<td>
Please detail the data origin
</td>
<td>
_Statistics on incidents on the road network_
* The data is stored in a database or in paper archive _Survey of target user group to collect needs and expectations_
* Public transport users, shop owners.
_Data about road network_
* Visual counting or implement sensors. Bus exploitation (AVM) system
</td> </tr>
<tr>
<td>
6.1.1.4
</td>
<td>
Please provide some figure allowing to estimate the data dimension
</td>
<td>
_Survey of target user group to collect needs and expectations_
100 questionnaires
</td> </tr>
<tr>
<td>
**Data collection procedures**
</td>
<td>
</td> </tr>
<tr>
<td>
6.1.2.1
</td>
<td>
Please detail the procedure adopted
for data collection
</td>
<td>
_Statistics on incidents on the road network_
* Data extraction from database
_Survey of target user group to collect needs and expectations_
* Questionnaires or interviews
_Data about road network_
* Data collection
* Data extraction from database
</td> </tr> </table>
<table>
<tr>
<th>
**WP6 -MADEIRA**
</th> </tr>
<tr>
<td>
6.1.2.2
</td>
<td>
If a sampling process is used, please confirm that the sample is random and of
a size that can be analysed with the ability to make statistical inference for
the overall sample and for the most significant subsample breakdowns
(for reference, please see D1.1)
</td>
<td>
Survey of target user group to collect needs and expectations
A sampling of the target users will be selected to be provided with the
questionnaires
</td> </tr>
<tr>
<td>
6.1.2.3
</td>
<td>
Is data collected anonymously or not? If not, please confirm that data is
collected in such a way preventing the tracking of personal habits or feelings
(for reference, please see
D1.1)
</td>
<td>
_Statistics on incidents on the road network_
* Anonymously
_Survey of target user group to collect needs and expectations_
* Anonymously
_Data about Road network_
* Anonymously
</td> </tr>
<tr>
<td>
**Data management and storing procedures**
</td> </tr>
<tr>
<td>
6.1.3.1
</td>
<td>
How is data stored? Please detail where the data is stored and in which
modality/format (if applicable)
</td>
<td>
_Statistics on incidents on the road network_
* Database is stored in the office of local Police Department
_Survey of target user group to collect needs and expectations_
* Questionnaires will be stored in HF, CMF, SRETC and ARDITI office
_Data about Road network_
* The data base will be stored in CMF or HF office
</td> </tr>
<tr>
<td>
6.1.3.2
</td>
<td>
Who is the organization responsible for data storing and management?
</td>
<td>
_Statistics on incidents on the road network_
* Police Department
_Survey of target user group to collect needs and expectations_
* HF, CMF, SRETC and ARDITI
_Data about Road network_ CMF and HF
</td> </tr>
<tr>
<td>
**WP6 -MADEIRA**
</td> </tr>
<tr>
<td>
6.1.3.3
</td>
<td>
Through whom
(organization, responsible) is data accessible?
</td>
<td>
_Statistics on incidents on the road network_
* Police Department
_Survey of target user group to collect needs and expectations_
* HF, CMF, SRETC and ARDITI
_Data about Road network_ CMF and HF
</td> </tr>
<tr>
<td>
6.1.3.4
</td>
<td>
Which international regulation will be applied for data storing and access?
(for reference, please see D1.1)
</td>
<td>
All the data is anonymous; there is any need to apply international
regulation.
</td> </tr>
<tr>
<td>
6.1.3.5
</td>
<td>
Which national regulation and applicable ‘opinion statements’ will be applied
for data storing and access? (for reference, please see D1.1)
</td>
<td>
All the data is anonymous; there is any need to apply national regulation
</td> </tr>
<tr>
<td>
**Data availability for dissemination**
</td> </tr>
<tr>
<td>
6.1.4.1
</td>
<td>
Is data usable for DESTINATIONS
dissemination purpose?
Please indicate the
format
(aggregated/not aggregated)
</td>
<td>
Data can be by DESTINATIONS under an aggregated form
</td> </tr>
<tr>
<td>
6.1.4.2
</td>
<td>
Is data planned to be published as open format? If so, please describe the
technological solution used and the metadata format.
</td>
<td>
No
</td> </tr> </table>
**Table 25: Local Data Management Plan – WP6 (MADEIRA)**
<table>
<tr>
<th>
**WP6 - RETHYMNO**
</th> </tr>
<tr>
<td>
**Data details**
</td> </tr>
<tr>
<td>
6.2.1.1
</td>
<td>
Which kind of data has been/will be collected in your site?
</td>
<td>
Design activities of the measure are starting No data collection to be
reported
</td> </tr> </table>
**Table 26: Local Data Management Plan – WP6 (RETHYMNO)**
<table>
<tr>
<th>
**WP6 – LIMASSOL**
</th> </tr>
<tr>
<td>
**Data details**
</td> </tr>
<tr>
<td>
6.3.1.1
</td>
<td>
Which kind of data has been/will be collected in your site?
</td>
<td>
_**6.1 Awareness on the use of sustainable mobility modes for leisure trips**
_
* _CO2 emissions_
* _Energy consumption_
* _Economy_
* _Questionnaires targeting the user group, for awareness level, needs and expectations_
_**6.3 Bicycle challenge: competition** _
_**employees of companies** _
* _CO2 emissions_
* _Energy consumption_
* _Economy_
* _Participants’ logbook data_
_**6.4 Smart Parking Guidance System** _
* _CO2 emissions_
* _Energy consumption_
* _Economy_
_The data collection is on-going_
</td>
<td>
_**between** _
</td> </tr> </table>
<table>
<tr>
<th>
**WP6 – LIMASSOL**
</th> </tr>
<tr>
<td>
6.3.1.2
</td>
<td>
Please detail data typology and structure/format (if applicable)
</td>
<td>
_**6.1 Awareness on the use of sustainable mobility modes for leisure trips**
_
* CO2 emissions
* Kg/km CO2 x total distance covered by the students
* Energy consumption
* kWh/lt x total litre of the total distance covered
* Economy
* Average cost of fuel x fuel savings from energy consumption
* Questionnaires targeting the user group, for awareness level, needs and expectations
* Paper questionnaires
_**6.3 Bicycle challenge: competition between employees of companies** _
* CO2 emissions
* Kg/km CO2 x total distance covered by the students
* Energy consumption
* kWh/lt x total litre of the total distance covered
* Economy
* Average cost of fuel x fuel savings from energy consumption
* Participant’s logbook data
* Logbook
_**6.4 Smart Parking Guidance System** _
* CO2 emissions
* Kg/km CO2 x total distance covered by the students
* Energy consumption
* kWh/lt x total litre of the total distance covered
* Economy
* Average cost of fuel x fuel savings from energy consumption
_The data collection is on-going_
</td> </tr>
<tr>
<td>
6.3.1.3
</td>
<td>
Please detail the data origin
</td>
<td>
* LTC
* Kmeaters cycling club
* LIMA
* Public Works Department
</td> </tr> </table>
<table>
<tr>
<th>
**WP6 – LIMASSOL**
</th> </tr>
<tr>
<td>
6.3.1.4
</td>
<td>
Please provide some figure allowing to estimate the data dimension
</td>
<td>
_**6.1 Awareness on the use of sustainable mobility modes for leisure trips**
_
* _30 questionnaires_
_**6.3 Bicycle challenge: competition between employees of companies** _
* _100 employees_
_**6.4 Smart Parking Guidance System** _ \- _2000 users_
</td> </tr>
<tr>
<td>
**Data collection procedures**
</td> </tr>
<tr>
<td>
6.3.2.1
</td>
<td>
Please detail the procedure adopted
for data collection
</td>
<td>
* _CO2 emissions_
* Data extraction from database or data gathered from the field
* _Energy consumption_
* Data extraction from database or data gathered from the field
* _Economy_
* Data extraction from database and data gathered from the field
* _Questionnaires targeting the user group, for awareness level, needs and expectations_
* Data from questionnaires
</td> </tr>
<tr>
<td>
6.3.2.2
</td>
<td>
If a sampling process is used, please confirm that the sample is random and of
a size that can be analysed with the ability to make statistical inference for
the overall sample and for the most significant subsample breakdowns
(for reference, please
see D1.1)
</td>
<td>
* Since already existing data from current surveys will also be used, the sampling will not be random and it might be enough for statistical analysis
* The only sampled data that will be random will be the questionnaires since this survey will involve randomly selected tourists and local citizens for questions
</td> </tr> </table>
<table>
<tr>
<th>
**WP6 – LIMASSOL**
</th> </tr>
<tr>
<td>
6.3.2.3
</td>
<td>
Is data collected anonymously or not? If not, please confirm that data is
collected in such a way preventing the tracking of personal habits or feelings
(for reference, please see
D1.1)
</td>
<td>
The questionnaires will be anonymous apart from the bicycle challenge where
the name of the participant needs to be known as there will be a prize for the
winner. But no other personal information that it will invade the
individual’s personal life will be given
</td> </tr>
<tr>
<td>
**Data management and storing procedures**
</td> </tr>
<tr>
<td>
6.3.3.1
</td>
<td>
How is data stored? Please detail where the data is stored and in which
modality/format (if applicable)
</td>
<td>
* All the data extracted from the mentioned databases is stored in the relevant involved partner’s database
* The data from the questionnaires will be stored in the involved partner’s office
</td> </tr>
<tr>
<td>
6.3.3.2
</td>
<td>
Who is the
organization responsible for data storing and management?
</td>
<td>
* STRATAGEM
* LTC
* LIMA
</td> </tr>
<tr>
<td>
6.3.3.3
</td>
<td>
Through whom
(organization, responsible) is data accessible?
</td>
<td>
* STRATAGEM
* LTC
* LIMA
</td> </tr>
<tr>
<td>
6.3.3.4
</td>
<td>
Which international regulation will be applied for data storing and access?
(for reference, please
see D1.1)
</td>
<td>
The Charter of Fundamental Rights of EU States
</td> </tr>
<tr>
<td>
6.3.3.5
</td>
<td>
Which national regulation and applicable ‘opinion statements’ will be applied
for data storing and access? (for reference, please
see D1.1)
</td>
<td>
* The Market Research Society code of conduct
* ISO 20252
</td> </tr>
<tr>
<td>
**WP6 – LIMASSOL**
</td> </tr>
<tr>
<td>
**Data availability for dissemination**
</td> </tr>
<tr>
<td>
6.3.4.1
</td>
<td>
Is data usable for DESTINATIONS
dissemination purpose?
Please indicate the
format
(aggregated/not aggregated)
</td>
<td>
The data will be used for the dissemination of the project and will be
aggregated
</td> </tr>
<tr>
<td>
6.3.4.2
</td>
<td>
Is data planned to be published as open format? If so, please describe the
technological solution used and the metadata format.
</td>
<td>
No
</td> </tr> </table>
**Table 27: Local Data Management Plan – WP6 (LIMASSOL)**
<table>
<tr>
<th>
**WP6 – ELBA**
</th> </tr>
<tr>
<td>
**Data details**
</td> </tr>
<tr>
<td>
6.4.1.1
</td>
<td>
Which kind of data has been/will be collected in your site?
</td>
<td>
Surveys based on questionnaires on mobility services offered by the
accommodation facilities (hotels, camps ...): data about integrated packages
(hospitality and mobility) offer (to be started soon)
</td> </tr>
<tr>
<td>
6.4.1.2
</td>
<td>
Please detail data typology and structure/format (if applicable)
</td>
<td>
Paper questionnaires
</td> </tr>
<tr>
<td>
6.4.1.3
</td>
<td>
Please detail the data origin
</td>
<td>
The origin of data of questionnaires will be the owners of hotels and camps or
Bed and Breakfast or other types of accommodation facilities
</td> </tr>
<tr>
<td>
6.4.1.4
</td>
<td>
Please provide some figure allowing to estimate the data dimension
</td>
<td>
_Questionnaires:_
About 50 questionnaires according with the different type of accommodation
facilities all around the island
</td> </tr>
<tr>
<td>
**Data collection procedures**
</td> </tr>
<tr>
<td>
6.4.2.1
</td>
<td>
Please detail the procedure adopted
for data collection
</td>
<td>
_Questionnaires about integrated packages stay and mobility_
Questionnaires compiled through interviews
</td> </tr> </table>
<table>
<tr>
<th>
**WP6 – ELBA**
</th> </tr>
<tr>
<td>
6.4.2.2
</td>
<td>
If a sampling process is used, please confirm that the sample is random and of
a size that can be analysed with the ability to make statistical inference for
the overall sample and for the most significant subsample breakdowns
(for reference, please see D1.1)
</td>
<td>
A sampling of the target users will be selected to be provided with the
questionnaires
</td> </tr>
<tr>
<td>
6.4.2.3
</td>
<td>
Is data collected anonymously or not? If not, please confirm that data is
collected in such a way preventing the tracking of personal habits or feelings
(for reference, please see
D1.1)
</td>
<td>
Anonymously
</td> </tr>
<tr>
<td>
**Data management and storing procedures**
</td> </tr>
<tr>
<td>
6.4.3.1
</td>
<td>
How is data stored? Please detail where the data is stored and in which
modality/format (if applicable)
</td>
<td>
Questionnaires will be stored in Portoferraio Municipality
</td> </tr>
<tr>
<td>
6.4.3.2
</td>
<td>
Who is the organization responsible for data storing and management?
</td>
<td>
Portoferraio Municipality
</td> </tr>
<tr>
<td>
6.4.3.3
</td>
<td>
Through whom
(organization, responsible) is data accessible?
</td>
<td>
Staff of Portoferraio and Rio Marina Municipalities employed in the project
</td> </tr>
<tr>
<td>
**WP6 – ELBA**
</td> </tr>
<tr>
<td>
6.4.3.4
</td>
<td>
Which international regulation will be applied for data storing and access?
(for reference, please see D1.1)
</td>
<td>
All data is anonymous; there is any need to apply international regulation
</td> </tr>
<tr>
<td>
6.4.3.5
</td>
<td>
Which national regulation and applicable ‘opinion statements’ will be applied
for data storing and access? (for reference, please see D1.1)
</td>
<td>
All data is anonymous; there is any need to apply international regulation
</td> </tr>
<tr>
<td>
**Data availability for dissemination**
</td> </tr>
<tr>
<td>
6.4.4.1
</td>
<td>
Is data usable for DESTINATIONS
dissemination purpose?
Please indicate the
format
(aggregated/not aggregated)
</td>
<td>
Data can be used by DESTINATIONS under an aggregated form
</td> </tr>
<tr>
<td>
6.4.4.2
</td>
<td>
Is data planned to be published as open format? If so, please describe the
technological solution used and the metadata format.
</td>
<td>
No
</td> </tr> </table>
**Table 28: Local Data Management Plan – WP6 (ELBA)**
<table>
<tr>
<th>
**WP6 – MALTA**
</th> </tr>
<tr>
<td>
**Data details- MAL6.1**
</td> </tr>
<tr>
<td>
6.5.1.1
(a)
</td>
<td>
Which kind of data has been/will be collected in your site?
</td>
<td>
Data that needs to be collected includes:
1.Desk and Field research/data conducted by the appointed economic operator:
the data type can be quantitative or qualitative in nature and the tools used
may vary in line with exigencies of each respective data set
2. Field research carried out to collect data about hotel operations in the field of green mobility. These data sets will be accounted for through the hotel audit process (Findings Reports, Recommendations list and Improvement
Plans)
3. Data related to cost structures for the green mobility plan 4 .Data collection which will feed into the dissemination and awareness raising initiatives with stakeholders
Data collection procedure is planned
The above is an indicative list of the data required for Measure 6.1 and
amendments might be considered in order to implement the measure
As such the Ministry for Tourism has not compiled data but research was
compiled during the period under review i.e. M1 (Sep 16) – M6 (Feb 17) to
establish an appropriate framework for the implementation of the action:
* Research for the draft preliminary award criteria for the Green Mobility Hotel Award
* Statistical data provided by the MTA about the Chinese Market
* Technical specifications compiled to establish the model structure. This data will serve to guide the company that will be selected to carry out market testing, stakeholder consultations, launching and subsequent implementation of a pilot model structure with the hotel industry for the Green
Mobility Award and Labelling scheme
</td> </tr> </table>
<table>
<tr>
<th>
**WP6 – MALTA**
</th> </tr>
<tr>
<td>
**Data details- MAL6.1**
</td> </tr>
<tr>
<td>
6.5.1.2
(a)
</td>
<td>
Please detail data typology and structure/format (if applicable)
</td>
<td>
The data typology used for point numbers 1 to 4 (as detailed in section
6.5.1.1 (a) above) can be quantitative or qualitative in nature and the tools
used (i.e. surveys, questionnaires, interviews, statistical analysis, site
visits, comparative assessments, branding techniques) may vary in line with
the exigencies of each respective data set
Further information about the specific data typologies shall be communicated
once the Ministry for Tourism (MOT) appoints the economic operator who will be
responsible to carry out market testing, stakeholder consultations, launching
and subsequent implementation of the pilot model structure with the hotel
industry for the Green Mobility Hotel Award and labelling scheme
The data typologies used for the tasks carried out during the months under
review M1 (Sep 16) – M6 (Feb 17) included:
* Analysis of existing labelling/awards and case study analysis
* Cross tabulation and statistical analysis from market profile data
</td> </tr>
<tr>
<td>
6.5.1.3
(a)
</td>
<td>
Please detail the data origin
</td>
<td>
Further information about specific data origins used for point numbers 1 to 4
(as detailed in section 6.5.1.1 (a) above) shall be communicated once the
Ministry for Tourism appoints the economic operator who will be responsible to
carry out market testing, stakeholder consultations, launching and subsequent
implementation of the pilot model structure with the hotel industry for the
Green Mobility Hotel Award and labelling scheme.
The data origins used for the tasks carried out during the months under review
M1 (Sep 16) – M6 (Feb 17) included:
* Online research papers
* market profile survey (MTA)
* Project progression data
* Policy analysis/statistical analysis and analysis of current frameworks
* Data originating from stakeholder consultations
</td> </tr> </table>
<table>
<tr>
<th>
**WP6 – MALTA**
</th> </tr>
<tr>
<td>
**Data details- MAL6.1**
</td> </tr>
<tr>
<td>
6.5.1.4
(a)
</td>
<td>
Please provide some figure allowing to estimate the data dimension
</td>
<td>
Precise figures about the interest registered from the eligible hotels in
relation to this initiative shall be communicated once the awarded economic
operator conducts the necessary evaluation with the hotels and subsequently
after this is made available to MOT. Eligible hotels comprise those
accommodation establishments situated in the Valletta region as per the
demarcation communicated to MOT by Transport of Malta
</td> </tr>
<tr>
<td>
**Data collection procedures**
</td> </tr>
<tr>
<td>
6.5.2.1
(a)
</td>
<td>
Please detail the procedure adopted
for data collection
</td>
<td>
Further information about the procedure to be adopted for data collection
specifically in relation to points number 1 to 4 (as detailed in section 6.1.1
above) shall be communicated once the Ministry for Tourism appoints the
economic operator and once this data is made available by the awarded economic
operator to MOT
The procedures adopted for data collection for the months under review M1 (Sep
16) – M6 (Feb 17) included:
* Data extraction from online research papers
* Applicability analysis for the local tourism accommodation offering
* Data extraction from statistical tools devised by the Malta Tourism Authority
* Policy analysis/statistical analysis and analysis of current frameworks
* Analysis of data originating from stakeholder consultations
</td> </tr>
<tr>
<td>
6.5.2.2
(a)
</td>
<td>
If a sampling process is used, please confirm that the sample is random and of
a size that can be analysed with the ability to make statistical inference for
the overall sample and for the most significant subsample breakdowns
(for reference, please see D1.1)
</td>
<td>
The sample chosen for the testing phase should represent the eligible
population of hotels/accommodation establishments in terms of the following
criteria:
* number of hotel establishments by sub-region making up the Valletta region
* size of hotel establishments
* hotel categories
* years in operation and
* most recent refurbishment/extension
The approached collective accommodation establishments need to be located in
the designated area i.e. Valletta region (as per the demarcation communicated
to MOT by Transport for Malta (Site Manager)
</td> </tr> </table>
<table>
<tr>
<th>
**WP6 – MALTA**
</th> </tr>
<tr>
<td>
**Data details- MAL6.1**
</td> </tr>
<tr>
<td>
6.5.2.3
(a)
</td>
<td>
Is data collected anonymously or not? If not, please confirm that data is
collected in such a way preventing the tracking of personal habits or feelings
(for reference, please see
D1.1)
</td>
<td>
The supply side data collected is not anonymous as the awarded economic
operator has a list of eligible collective accommodation establishments which
are situated in the Valletta region and from which the sampling process should
be carried out
The findings however may be presented on a two pronged level:
\- Findings per hotel (not anonymous)
-Collective findings report (anonymous) to summarise the findings stemming from the individual hotel audits. This report will be used for information dissemination at a public level.
Since most of the data pertaining to the eligible collective accommodation
establishments will be acquired following the implementation of the hotel
audits by the awarded economic operator, the Ministry for Tourism will
undertake discussion processes with the selected economic operator to define
adequate dissemination levels. This applies mostly to the individual hotel
findings
The most appropriate modality should be discussed in order to safeguard the
confidentiality aspect when and if necessary
</td> </tr>
<tr>
<td>
**Data management and storing procedures**
</td> </tr>
<tr>
<td>
6.5.3.1
(a)
</td>
<td>
How is data stored? Please detail where the data is stored and in which
modality/format (if
applicable)
</td>
<td>
Since most of the data pertaining to the eligible collective accommodation
establishments will be acquired following the implementation of the hotel
audits by the awarded economic operator, the Ministry for Tourism will
undertake discussion processes with the awarded economic operator to define
the storage modality and to define adequate dissemination levels. This applies
mostly to the individual hotel findings
The most appropriate modality should be discussed in order to safeguard the
confidentiality aspect when and if necessary
</td> </tr>
<tr>
<td>
6.5.3.2
(a)
</td>
<td>
Who is the
organization responsible for data storing and management?
</td>
<td>
Ministry for Tourism
</td> </tr>
<tr>
<td>
**WP6 – MALTA**
</td> </tr>
<tr>
<td>
**Data details- MAL6.1**
</td> </tr>
<tr>
<td>
6.5.3.3
(a)
</td>
<td>
Through whom is
(organization,
responsible) data accessible?
</td>
<td>
Ministry for Tourism
</td> </tr>
<tr>
<td>
6.5.3.4
(a)
</td>
<td>
Which international regulation will be applied for data storing and access?
(for reference, please see D1.1)
</td>
<td>
Directive 95/46/EC which is due to be replaced by the General Data Protection
Regulation (GDPR) EU 2016/679 which will become effective on 25 May 2018
</td> </tr>
<tr>
<td>
6.5.3.5
(a)
</td>
<td>
Which national regulation and applicable ‘opinion statements’ will be applied
for data storing and access? (for reference, please see D1.1)
</td>
<td>
Data Protection Act Chapter 440
</td> </tr>
<tr>
<td>
**Data availability for dissemination**
</td> </tr>
<tr>
<td>
6.5.4.1
(a)
</td>
<td>
Is data usable for DESTINATIONS
dissemination purpose?
Please indicate the
format
(aggregated/not aggregated)
</td>
<td>
The data which can be disseminated will be qualified at a later stage in line
with 6.5.2.3 (a) and 6.5.3.1 (a)
</td> </tr>
<tr>
<td>
6.5.4.2
(a)
</td>
<td>
Is data planned to be published as open format? If so, please describe the
technological solution used and the metadata format.
</td>
<td>
No
</td> </tr> </table>
**Table 29: Local Data Management Plan – WP6 (MALTA) – MEASURE MAL 6.1**
<table>
<tr>
<th>
**WP6: MAL 6.2**
</th> </tr>
<tr>
<td>
**Data details**
</td> </tr>
<tr>
<td>
6.5.1.1
(b)
</td>
<td>
Which kind of data has been/will be collected in your site?
</td>
<td>
_Low Emission Zone:_
* Number of entries by type of vehicle
_SMS Alert_
* Number of reports
* Number of downloads
_Data collection procedure is planned_
</td> </tr>
<tr>
<td>
6.5.1.2
(b)
</td>
<td>
Please detail data typology and structure/format (if applicable)
</td>
<td>
_Low Emission Zone:_
* Number. of entries by type of vehicle: CVA database (excel)
_SMS Alert_
* Number of reports: Excel
* Number of downloads: Excel
</td> </tr>
<tr>
<td>
6.5.1.3
(b)
</td>
<td>
Please detail the data origin
</td>
<td>
_Low Emission Zone:_
* Number of entries by type of vehicle: CVA Operator
_SMS Alert_
* Number of reports: App server
* Number of downloads: App server
</td> </tr>
<tr>
<td>
**Data collection procedures**
</td> </tr>
<tr>
<td>
6.5.2.1
(b)
</td>
<td>
Please detail the procedure adopted
for data collection
</td>
<td>
_Low Emission Zone:_
* CVA Operator collects and presents data periodically to Transport for Malta as per ongoing contract
_SMS Alert_
* Number of reports: App server download
* Number of downloads: App server download
</td> </tr>
<tr>
<td>
6.5.2.2
(b)
</td>
<td>
If a sampling process is used, please confirm that the sample is random and of
a size that can be analysed with the ability to make statistical inference for
the overall sample and for the most significant subsample breakdowns
(for reference, please see D1.1)
</td>
<td>
No sampling will be adopted in this case
</td> </tr> </table>
<table>
<tr>
<th>
**WP6: MAL 6.2**
</th> </tr>
<tr>
<td>
6.5.2.3
(b)
</td>
<td>
Is data collected anonymously or not? If not, please confirm that data is
collected in such a way preventing the tracking of personal habits or feelings
(for reference, please see
D1.1)
</td>
<td>
Data made available for project use will be anonymous
</td> </tr>
<tr>
<td>
**Data management and storing procedures**
</td> </tr>
<tr>
<td>
6.5.3.1
(b)
</td>
<td>
How is data stored? Please detail where the data is stored and in which
modality/format (if
applicable)
</td>
<td>
_Low Emission Zone:_
* Number. of entries by type of vehicle: CVA Operator server
_SMS Alert_
* Number. of reports: App server
* Number of downloads: App server
</td> </tr>
<tr>
<td>
6.5.3.2
(b)
</td>
<td>
Who is the
organization responsible for data storing and management?
</td>
<td>
_Low Emission Zone:_
* Number of entries by type of vehicle: CVA Operator
_SMS Alert_
* Number of reports: Transport for Malta
* Number of downloads: Transport for Malta
</td> </tr>
<tr>
<td>
6.5.3.3
(b)
</td>
<td>
Through whom
(organization, responsible) is data accessible?
</td>
<td>
_Low Emission Zone:_
* Number of entries by type of vehicle: CVA Operator and TM _SMS Alert_
* Number of reports: Transport for Malta and
University of Malta
* Number of downloads: Transport for Malta and
University of Malta
</td> </tr>
<tr>
<td>
6.5.3.4
(b)
</td>
<td>
Which international regulation will be applied for data storing and access?
(for reference, please see D1.1)
</td>
<td>
Directive 95/46/EC which is due to be replaced by the General Data Protection
Regulation (GDPR) EU 2016/679 which will become effective on 25 May 2018
</td> </tr>
<tr>
<td>
**WP6: MAL 6.2**
</td> </tr>
<tr>
<td>
6.5.3.5
(b)
</td>
<td>
Which national regulation and applicable ‘opinion statements’ will be applied
for data storing and access? (for reference, please see D1.1)
</td>
<td>
Chapter 440 Data Protection Act
</td> </tr>
<tr>
<td>
**Data availability for dissemination**
</td> </tr>
<tr>
<td>
6.5.4.1
(b)
</td>
<td>
Is data usable for DESTINATIONS
dissemination purpose?
Please indicate the
format
(aggregated/not aggregated)
</td>
<td>
Yes in aggregate
</td> </tr>
<tr>
<td>
6.5.4.2
(b)
</td>
<td>
Is data planned to be published as open format? If so, please describe the
technological solution used and the metadata format.
</td>
<td>
No due to data protection
</td> </tr> </table>
**Table 30: Local Data Management Plan – WP6 (MALTA) – MEASURE MAL 6.2**
<table>
<tr>
<th>
**WP6: MAL 6.3**
</th> </tr>
<tr>
<td>
**Data details**
</td> </tr>
<tr>
<td>
6.5.1.1
(c)
</td>
<td>
Which kind of data has been/will be collected in your site?
</td>
<td>
* Baseline surveys to ascertain tourist travel behaviour
* Number of downloads
* Number. of users
* Mode share
_Data collection procedure is planned._
</td> </tr>
<tr>
<td>
6.5.1.2
(c)
</td>
<td>
Please detail data typology and structure/format (if applicable)
</td>
<td>
* Baseline surveys to ascertain tourist travel behaviour- survey
* Number of downloads- excel
* Number of users - excel
* Mode share - excel
</td> </tr>
<tr>
<td>
6.5.1.3
(c)
</td>
<td>
Please detail the data origin
</td>
<td>
* Baseline surveys to ascertain tourist travel behaviour – field data
* Number of downloads - App server
* Number of users - App server
* Mode share - App server
</td> </tr>
<tr>
<td>
**Data collection procedures**
</td> </tr>
<tr>
<td>
6.5.2.1
(c)
</td>
<td>
Please detail the procedure adopted
for data collection
</td>
<td>
* Baseline surveys to ascertain tourist travel behaviour – field data compiled directly with tourists
* Number of downloads - App server download
* Number of users - App server download
* Mode share - App server download
</td> </tr>
<tr>
<td>
6.5.2.2
(c)
</td>
<td>
If a sampling process is used, please confirm that the sample is random and of
a size that can be analysed with the ability to make statistical inference for
the overall sample and for the most significant subsample breakdowns
(for reference, please see D1.1)
</td>
<td>
Sample of tourists to participate in survey will be random and statistically
significant
</td> </tr> </table>
<table>
<tr>
<th>
**WP6: MAL 6.3**
</th> </tr>
<tr>
<td>
6.5.2.3
(c)
</td>
<td>
Is data collected anonymously or not? If not, please confirm that data is
collected in such a way preventing the tracking of personal habits or feelings
(for reference, please see
D1.1)
</td>
<td>
Data made available for project use will be anonymous
</td> </tr>
<tr>
<td>
**Data management and storing procedures**
</td> </tr>
<tr>
<td>
6.5.3.1
(c)
</td>
<td>
How is data stored? Please detail where the data is stored and in which
modality/format (if applicable)
</td>
<td>
* Baseline surveys to ascertain tourist travel behaviour – excel
* Number. of downloads - App server
* Number of users - App server
* Mode share - App server
</td> </tr>
<tr>
<td>
6.5.3.2
(c)
</td>
<td>
Who is the
organization responsible for data storing and management?
</td>
<td>
Transport for Malta
</td> </tr>
<tr>
<td>
6.5.3.3
(c)
</td>
<td>
Through whom
(organization, responsible) is data accessible?
</td>
<td>
Transport for Malta, University of Malta
</td> </tr>
<tr>
<td>
6.5.3.4
(c)
</td>
<td>
Which international regulation will be applied for data storing and access?
(for reference, please see D1.1)
</td>
<td>
Directive 95/46/EC which is due to be replaced by the General Data Protection
Regulation (GDPR) EU 2016/679 which will become effective on 25 May 2018
</td> </tr>
<tr>
<td>
6.5.3.5
(c)
</td>
<td>
Which national regulation and applicable ‘opinion statements’ will be applied
for data storing and access? (for reference, please see D1.1)
</td>
<td>
Chapter 440 Data Protection Act
</td> </tr>
<tr>
<td>
**WP6: MAL 6.3**
</td> </tr>
<tr>
<td>
**Data availability for dissemination**
</td> </tr>
<tr>
<td>
6.5.4.1
(c)
</td>
<td>
Is data usable for DESTINATIONS
dissemination purpose?
Please indicate the
format
(aggregated/not aggregated)
</td>
<td>
Yes in aggregate
</td> </tr>
<tr>
<td>
6.5.4.2
(c)
</td>
<td>
Is data planned to be published as open format? If so, please describe the
technological solution used and the metadata format.
</td>
<td>
No due to data protection.
</td> </tr> </table>
**Table 31: Local Data Management Plan – WP6 (MALTA) – MEASURE MAL 6.3**
<table>
<tr>
<th>
**WP6: MAL 6.4**
</th> </tr>
<tr>
<td>
**Data details**
</td> </tr>
<tr>
<td>
6.5.1.1
(d)
</td>
<td>
Which kind of data has been/will be collected in your site?
</td>
<td>
* Number of parking spaces in the city of Valletta, classified by residential and non-residential (to be collected during the operation of the measure)
* Number of daily entrants to the city (to be collected during the operation of the measure)
_Data collection procedure is planned_
</td> </tr>
<tr>
<td>
6.5.1.2
(d)
</td>
<td>
Please detail data typology and structure/format (if applicable)
</td>
<td>
* Number of parking spaces in the city of Valletta, classified by residential and non-residential – field data
* Number of daily entrants to the city – Number Plate recognition
</td> </tr>
<tr>
<td>
6.5.1.3
(d)
</td>
<td>
Please detail the data origin
</td>
<td>
* Number of parking spaces in the city of Valletta, classified by residential and non-residential – field data compiled by Local Council personnel.
* Number of daily entrants to the city – CVA Operations (data collected by ANPR Cameras)
</td> </tr>
<tr>
<td>
**Data collection procedures**
</td> </tr>
<tr>
<td>
6.5.2.1
(d)
</td>
<td>
Please detail the procedure adopted
for data collection
</td>
<td>
* Number of parking spaces in the city of Valletta, classified by residential and non-residential – on site data collection
* Number of daily entrants to the city – real time data collected by ANPR Cameras
</td> </tr> </table>
<table>
<tr>
<th>
**WP6: MAL 6.4**
</th> </tr>
<tr>
<td>
6.5.2.2
(d)
</td>
<td>
If a sampling process is used, please confirm that the sample is random and of
a size that can be analysed with the ability to make statistical inference for
the overall sample and for the most significant subsample breakdowns
(for reference, please see D1.1)
</td>
<td>
No sampling will be adopted in this case
</td> </tr>
<tr>
<td>
6.5.2.3
(d)
</td>
<td>
Is data collected anonymously or not? If not, please confirm that data is
collected in such a way preventing the tracking of personal habits or feelings
(for reference, please see
D1.1)
</td>
<td>
Data made available for project use will be anonymous
</td> </tr>
<tr>
<td>
**Data management and storing procedures**
</td> </tr>
<tr>
<td>
6.5.3.1
(d)
</td>
<td>
How is data stored? Please detail where the data is stored and in which
modality/format (if applicable)
</td>
<td>
* Number of parking spaces in the city of Valletta, classified by residential and non-residential – excel
* Number of daily entrants to the city – data will be stored as per current processes already adopted by CVA Operator
</td> </tr>
<tr>
<td>
6.5.3.2
(d)
</td>
<td>
Who is the
organization responsible for data storing and management?
</td>
<td>
* Number of parking spaces in the city of Valletta, classified by residential and non-residential – Valletta Local council
* Number of daily entrants to the city – CVA Operator
</td> </tr>
<tr>
<td>
6.5.3.3
(d)
</td>
<td>
Through whom
(organization, responsible) is data accessible?
</td>
<td>
* Number of parking spaces in the city of Valletta, classified by residential and non-residential – Valletta Local council and Transport Malta
* Number of daily entrants to the city – CVA Operator and Transport Malta as per ongoing contract
</td> </tr>
<tr>
<td>
**WP6: MAL 6.4**
</td> </tr>
<tr>
<td>
6.5.3.4
(d)
</td>
<td>
Which international regulation will be applied for data storing and access?
(for reference, please see D1.1)
</td>
<td>
Directive 95/46/EC which is due to be replaced by the General Data Protection
Regulation (GDPR) EU 2016/679 which will become effective on 25 May 2018
</td> </tr>
<tr>
<td>
6.5.3.5
(d)
</td>
<td>
Which national regulation and applicable ‘opinion statements’ will be applied
for data storing and access? (for reference, please see D1.1)
</td>
<td>
Chapter 440 Data Protection Act
</td> </tr>
<tr>
<td>
**Data availability for dissemination**
</td> </tr>
<tr>
<td>
6.5.4.1
(d)
</td>
<td>
Is data usable for DESTINATIONS
dissemination purpose?
Please indicate the
format
(aggregated/not aggregated)
</td>
<td>
Yes, in aggregate
</td> </tr>
<tr>
<td>
6.5.4.2
(d)
</td>
<td>
Is data planned to be published as open format? If so, please describe the
technological solution used and the metadata format.
</td>
<td>
No
</td> </tr> </table>
**Table 32: Local Data Management Plan – WP6 (MALTA) – MEASURE MAL 6.4**
<table>
<tr>
<th>
**WP6 - LAS PALMAS**
</th>
<th>
</th> </tr>
<tr>
<td>
**Data details**
</td>
<td>
</td> </tr>
<tr>
<td>
6.6.1.1
</td>
<td>
Which kind of data has been/will be collected in your site?
</td>
<td>
**\- LPA6.1 - Green Credits Scheme**
Statistics about urban public transport cards (ongoing)
</td> </tr>
<tr>
<td>
6.6.1.2
</td>
<td>
Please detail data typology and structure/format (if applicable)
</td>
<td>
_Statistics about urban public transport cards_
Number of monthly users of the contactless urban public transport smart card
“BonoGuagua”. The
data is stored in an electronic format
</td> </tr>
<tr>
<td>
6.6.1.3
</td>
<td>
Please detail the data origin
</td>
<td>
_Statistics about urban public transport cards._
The data is stored in a database and statistics data tables are made
depending on the needs
</td> </tr>
<tr>
<td>
6.6.1.4
</td>
<td>
Please provide some figure allowing to estimate the data dimension
</td>
<td>
_Statistics about urban public transport cards._
40 different urban public transport routes and 15 urban public transport
fares to be analysed.
</td> </tr>
<tr>
<td>
**Data collection procedures**
</td>
<td>
</td> </tr>
<tr>
<td>
6.6.2.1
</td>
<td>
Please detail the procedure adopted
for data collection
</td>
<td>
Data extraction from database
</td> </tr>
<tr>
<td>
6.6.2.2
</td>
<td>
If a sampling process is used, please confirm that the sample is random and of
a size that can be analysed with the ability to make statistical inference for
the overall sample and for the most significant subsample breakdowns
(for reference, please see D1.1)
</td>
<td>
The data collected belong to the whole database regarding urban public
transport cards.
</td> </tr>
<tr>
<td>
6.6.2.3
</td>
<td>
Is data collected anonymously or not? If not, please confirm that data is
collected in such a way preventing the tracking of personal habits or feelings
(for reference, please see
D1.1)
</td>
<td>
At first step (first six months of the project), and in order to analyse the
suitability of the chosen card to develop the Green Credits Scheme, data
collected are anonymous
However, for the implementation of the Green Credits Scheme (after business
model development), a personal data collection will be probably needed in
order to manage a customer loyalty database system that of course will comply
with national and international regulation regarding
personal data storing, access and management
</td> </tr>
<tr>
<td>
**WP6 - LAS PALMAS**
</td> </tr>
<tr>
<td>
**Data management and storing procedures**
</td> </tr>
<tr>
<td>
6.6.3.1
</td>
<td>
How is data stored? Please detail where the data is stored and in which
modality/format (if
applicable)
</td>
<td>
Data is stored in Guaguas Municipales server (Urban Public Transport Company)
</td> </tr>
<tr>
<td>
6.6.3.2
</td>
<td>
Who is the
organization responsible for data storing and management?
</td>
<td>
Guaguas Municipales (Urban Public Transport Company)
</td> </tr>
<tr>
<td>
6.6.3.3
</td>
<td>
Through whom
(organization, responsible) is data accessible?
</td>
<td>
Guaguas Municipales (Urban Public Transport Company)
</td> </tr>
<tr>
<td>
6.6.3.5
</td>
<td>
Which national regulation and applicable ‘opinion statements’ will be applied
for data storing and access? (for reference, please see D1.1)
</td>
<td>
“Ley Orgánica 15/1999, de 13 de diciembre, de Protección de Datos de Carácter
Personal”
</td> </tr>
<tr>
<td>
**Data availability for dissemination**
</td> </tr>
<tr>
<td>
6.6.4.1
</td>
<td>
Is data usable for DESTINATIONS
dissemination purpose?
Please indicate the
format
(aggregated/not aggregated)
</td>
<td>
No
</td> </tr>
<tr>
<td>
6.6.4.2
</td>
<td>
Is data planned to be published as open format? If so, please describe the
technological solution used and the metadata format.
</td>
<td>
No
</td> </tr> </table>
**Table 33: Local Data Management Plan – WP6 (LAS PALMAS)**
## WP7
<table>
<tr>
<th>
**WP7 - MADEIRA**
</th> </tr>
<tr>
<td>
**Data details**
</td> </tr>
<tr>
<td>
7.1.1.1
</td>
<td>
Which kind of data has been/will be collected in your site?
</td>
<td>
_Data on PT service demand_ (planned)
_Statistics produced by ticketing systems_ (planned)
_Survey of target user group to collect needs and expectations_ (planned)
</td> </tr>
<tr>
<td>
7.1.1.2
</td>
<td>
Please detail data typology and structure/format (if applicable)
</td>
<td>
_Data on PT service demand_
* Number of PT users
Statistics produced by ticketing systems
* Number of PT users per line and bus stop
_Survey of target user group to collect needs and expectations_
* Paper and online questionnaires
</td> </tr>
<tr>
<td>
7.1.1.3
</td>
<td>
Please detail the data origin
</td>
<td>
_Data on PT service demand_
* The data is stored in a database
Statistics produced by ticketing systems
* Number of PT users per line and bus stop
_Survey of target user group to collect needs and expectations_
* The target data of questionnaires will be public transport users _._
</td> </tr>
<tr>
<td>
7.1.1.4
</td>
<td>
Please provide some figure allowing to estimate the data dimension
</td>
<td>
_Survey of target user group to collect needs and expectations_
100 questionnaires
</td> </tr>
<tr>
<td>
**Data collection procedures**
</td> </tr>
<tr>
<td>
7.1.2.1
</td>
<td>
Please detail the procedure adopted
for data collection
</td>
<td>
_Data on PT service demand_
* Data extraction from database
Statistics produced by ticketing systems
* Data extraction from database
_Survey of target user group to collect needs and expectations_
* Questionnaires or interviews
</td> </tr> </table>
<table>
<tr>
<th>
**WP7 - MADEIRA**
</th> </tr>
<tr>
<td>
7.1.2.2
</td>
<td>
If a sampling process is used, please confirm that the sample is random and of
a size that can be analysed with the ability to make statistical inference for
the overall sample and for the most significant subsample breakdowns
(for reference, please see D1.1)
</td>
<td>
_Survey of target user group to collect needs and expectations_
A sampling of the target users will be selected to be provided with the
questionnaires
</td> </tr>
<tr>
<td>
7.1.2.3
</td>
<td>
Is data collected anonymously or not? If not, please confirm that data is
collected in such a way preventing the tracking of personal habits or feelings
(for reference, please see
D1.1)
</td>
<td>
_Data on PT service demand_
* _Anonymously_
Statistics produced by ticketing systems
* Anonymously
_Survey of target user group to collect needs and expectations_
* Anonymously
</td> </tr>
<tr>
<td>
**Data management and storing procedures**
</td> </tr>
<tr>
<td>
7.1.3.1
</td>
<td>
How is data stored? Please detail where the data is stored and in which
modality/format (if applicable)
</td>
<td>
_Data on PT service demand_
* Database is stored in Horario do Funchal (HF) office
or in a cloud service owned by HF
Statistics produced by ticketing systems
* Database is stored in HF office or in a cloud service owned by HF
_Survey of target user group to collect needs and expectations_
* Questionnaires will be stored in HF and AREAM office.
</td> </tr>
<tr>
<td>
7.1.3.2
</td>
<td>
Who is the
organization responsible for data storing and management?
</td>
<td>
_Data on PT service demand_
* Horario do Funchal (HF)
Statistics produced by ticketing systems
* HF
_Survey of target user group to collect needs and expectations_
* HF and AREAM
</td> </tr>
<tr>
<td>
**WP7 - MADEIRA**
</td> </tr>
<tr>
<td>
7.1.3.3
</td>
<td>
Through whom
(organization, responsible) is data accessible?
</td>
<td>
_Data on PT service demand_
* Horario do Funchal (HF)
Statistics produced by ticketing systems
* _HF_
_Survey of target user group to collect needs and expectations_
* _HF and AREAM_
</td> </tr>
<tr>
<td>
7.1.3.4
</td>
<td>
Which international regulation will be applied for data storing and access?
(for reference, please see D1.1)
</td>
<td>
All the data is anonymous; there is any need to apply international
regulation.
</td> </tr>
<tr>
<td>
7.1.3.5
</td>
<td>
Which national regulation and applicable ‘opinion statements’ will be applied
for data storing and access? (for reference, please see D1.1)
</td>
<td>
All the data is anonymous; there is any need to apply national regulation.
</td> </tr>
<tr>
<td>
**Data availability for dissemination**
</td> </tr>
<tr>
<td>
7.1.4.1
</td>
<td>
Is data usable for DESTINATIONS
dissemination purpose?
Please indicate the
format
(aggregated/not aggregated)
</td>
<td>
Data can be by DESTINATIONS under an aggregated form
</td> </tr>
<tr>
<td>
7.1.4.2
</td>
<td>
Is data planned to be published as open format? If so, please describe the
technological solution used and the metadata format.
</td>
<td>
No
</td> </tr> </table>
**Table 34: Local Data Management Plan – WP7 (MADEIRA)**
<table>
<tr>
<th>
**WP7 – RETHYMNO**
</th> </tr>
<tr>
<td>
**Data details**
</td> </tr>
<tr>
<td>
7.2.1.1
</td>
<td>
Which kind of data has been/will be collected in your site?
</td>
<td>
Data/collection for the design of demo measures for Public Transport services:
Data on PT service offer
_The data collection described is completed_
</td> </tr>
<tr>
<td>
7.2.1.2
</td>
<td>
Please detail data typology and structure/format (if applicable)
</td>
<td>
_Data on PT service offer:_
Number of available routes, bus lines
</td> </tr>
<tr>
<td>
7.2.1.3
</td>
<td>
Please detail the data origin
</td>
<td>
_Data on PT service offer_
Timetables from _PT operator_
</td> </tr>
<tr>
<td>
7.2.1.4
</td>
<td>
Please provide some figure allowing to estimate the data dimension
</td>
<td>
_Data on PT service offer;_
A total of 40 bus routes have been recorded
</td> </tr>
<tr>
<td>
**Data collection procedures**
</td> </tr>
<tr>
<td>
7.2.2.1
</td>
<td>
Please detail the procedure adopted
for data collection
</td>
<td>
_Data on PT service offer;_
Data on PT service offer were provided from the PT operator
</td> </tr>
<tr>
<td>
7.2.2.2
</td>
<td>
If a sampling process is used, please confirm that the sample is random and of
a size that can be analysed with the ability to make statistical inference for
the overall sample and for the most significant subsample breakdowns
(for reference, please see D1.1)
</td>
<td>
No sampling process was used
</td> </tr> </table>
<table>
<tr>
<th>
**WP7 – RETHYMNO**
</th> </tr>
<tr>
<td>
7.2.2.3
</td>
<td>
Is data collected anonymously or not? If not, please confirm that data is
collected in such a way preventing the tracking of personal habits or feelings
(for reference, please see
D1.1)
</td>
<td>
_Data on PT service offer;_
Anonymously; No personal data were collected
</td> </tr>
<tr>
<td>
**Data management and storing procedures**
</td> </tr>
<tr>
<td>
7.2.3.1
</td>
<td>
How is data stored? Please detail where the data is stored and in which
modality/format (if
applicable)
</td>
<td>
Data on PT service offer;
Database is stored in the offices of the PT operator
</td> </tr>
<tr>
<td>
7.2.3.2
</td>
<td>
Who is the
organization responsible for data storing and management?
</td>
<td>
Data on PT service offer;
PT operator
</td> </tr>
<tr>
<td>
7.2.3.3
</td>
<td>
Through whom
(organization, responsible) is data accessible?
</td>
<td>
Data on PT service offer;
PT operator
</td> </tr>
<tr>
<td>
7.2.3.4
</td>
<td>
Which international regulation will be applied for data storing and access?
(for reference, please see D1.1)
</td>
<td>
Regulations 2016/679; 2016/680; 2016/681 (EU)
Regulations: 2009/136, 2006/24, 2002/58, 95/46 (EC)
</td> </tr>
<tr>
<td>
7.2.3.5
</td>
<td>
Which national regulation and applicable ‘opinion statements’ will be applied
for data storing and access? (for reference, please see D1.1)
</td>
<td>
Law 2472/1997 Protection of Individuals with regard to the Processing of
Personal Data
Law 3471/2006 Protection of personal data and privacy in the electronic
telecommunications sector and amendment of law 2472/1997
</td> </tr>
<tr>
<td>
**WP7 – RETHYMNO**
</td> </tr>
<tr>
<td>
**Data availability for dissemination**
</td> </tr>
<tr>
<td>
7.2.4.1
</td>
<td>
Is data usable for DESTINATIONS
dissemination purpose?
Please indicate the
format
(aggregated/not aggregated)
</td>
<td>
Data can be used for DESTINATIONS dissemination purpose under an aggregated
form
</td> </tr>
<tr>
<td>
7.2.4.2
</td>
<td>
Is data planned to be published as open format? If so, please describe the
technological solution used and the metadata format.
</td>
<td>
No
</td> </tr> </table>
**Table 35: Local Data Management Plan – WP7 (RETHYMNO)**
<table>
<tr>
<th>
**WP7 - LIMASSOL**
</th> </tr>
<tr>
<td>
**Data details**
</td> </tr> </table>
<table>
<tr>
<th>
**WP7 - LIMASSOL**
</th> </tr>
<tr>
<td>
7.3.1.1
</td>
<td>
Which kind of data has been/will be collected in your site?
</td>
<td>
_**7.1 Improvement of PT routes, time tables, ticket procedure and bike
transportation on buses to make the service more attractive** _
* _CO2 emissions_
* _Energy consumption_
* _Economy_
_**7.2 Creation of an electric bus hop on hop off service in the old town** _
* _CO2 emissions_
* _Energy consumption_
* _Economy_
* _Revenues_
_**7.3 PT Traveller Information System** _
* _CO2 emissions_
* _Energy consumption_
* _Economy_
* _Revenues_
_**7.4 Mobility application and travel planner for smart phones to provide
real time information** _
* _CO2 emissions_
* _Energy consumption_
* _Economy_
_The data collection is on-going_
</td> </tr> </table>
<table>
<tr>
<th>
**WP7 - LIMASSOL**
</th> </tr>
<tr>
<td>
7.3.1.2
</td>
<td>
Please detail data
typology structure/format applicable)
</td>
<td>
and
(if
</td>
<td>
_**7.1 Improvement of PT routes, time tables, ticket procedure and bike
transportation on buses to make the service more attractive** _
_CO2 emissions_
* _Kg/km CO2 x total distance covered by the students_
_Energy consumption_
* _kWh/lt x total litre of the total distance covered_
_Economy_
* _Average cost of fuel x fuel savings from energy consumption_
_**7.2 Creation of an electric bus hop on hop off service in the old town**
CO2 emissions _
* _Kg/km CO2 x total distance covered by the students_
_Energy consumption_
* _kWh/lt x total litre of the total distance covered_
_Economy_
* _Average cost of fuel x fuel savings from energy consumption_
_Revenues_
* _Total ticket price x passengers per year_
_**7.3 PT Traveller Information System** _
_CO2 emissions_
* _Kg/km CO2 x total distance covered by the students_
_Energy consumption_
* _kWh/lt x total litre of the total distance covered_
_Economy_
* _Average cost of fuel x fuel savings from energy consumption_
_Revenues_
* _Total ticket price x passenger per year_
_**7.4 Mobility application and travel planner for smart** _
_**phones to provide real time information** _
_CO2 emissions_
* _Kg/km CO2 x total distance covered by the students_
_Energy consumption_
* _kWh/lt x total litre of the total distance covered_
_Economy_
\- _Average cost of fuel x fuel savings_
</td> </tr>
<tr>
<td>
7.3.1.3
</td>
<td>
Please detail the
data origin
</td>
<td>
* _LTC_
* _LIMA_
* _Public Works Department_
</td> </tr> </table>
<table>
<tr>
<th>
**WP7 - LIMASSOL**
</th> </tr>
<tr>
<td>
**Data collection procedures**
</td> </tr>
<tr>
<td>
7.3.2.1
</td>
<td>
Please detail the procedure adopted
for data collection
</td>
<td>
* _CO2 emissions_
* Data extraction from database or data gathered from the field
* _Energy consumption_
* Data extraction from database or data gathered from the field
* _Economy_
* Data extraction from database and data gathered from the field
</td> </tr>
<tr>
<td>
7.3.2.2
</td>
<td>
If a sampling process is used, please confirm that the sample is random and of
a size that can be analysed with the ability to make statistical inference for
the overall sample and for the most significant subsample breakdowns
(for reference, please see D1.1)
</td>
<td>
Since already existing data from current surveys will also be used, the
sampling will not be random and it might be enough for statistical analysis
</td> </tr>
<tr>
<td>
7.3.2.3
</td>
<td>
Is data collected anonymously or not? If not, please confirm that data is
collected in such a way preventing the tracking of personal habits or feelings
(for reference, please
see D1.1)
</td>
<td>
The data collected will be anonymous
</td> </tr>
<tr>
<td>
**Data management and storing procedures**
</td> </tr>
<tr>
<td>
7.3.3.1
</td>
<td>
How is data stored? Please detail where the data is stored and in which
modality/format (if applicable)
</td>
<td>
* All the data extracted from the mentioned databases is stored in the involved partner’s database
* The data from the questionnaires will be stored in the involved partner’s office
</td> </tr>
<tr>
<td>
**WP7 - LIMASSOL**
</td> </tr>
<tr>
<td>
7.3.3.2
</td>
<td>
Who is the
organization responsible for data storing and management?
</td>
<td>
LTC
</td> </tr>
<tr>
<td>
7.3.3.3
</td>
<td>
Through whom
(organization, responsible) is data accessible?
</td>
<td>
* LTC
* STRATAGEM
</td> </tr>
<tr>
<td>
7.3.3.4
</td>
<td>
Which international regulation will be applied for data storing and access?
(for reference, please see D1.1)
</td>
<td>
The Charter of Fundamental Rights of EU States
</td> </tr>
<tr>
<td>
7.3.3.5
</td>
<td>
Which national regulation and applicable ‘opinion statements’ will be applied
for data storing and access? (for reference, please see D1.1)
</td>
<td>
* The Market Research Society code of conduct
* ISO 20252
</td> </tr>
<tr>
<td>
**Data availability for dissemination**
</td> </tr>
<tr>
<td>
7.3.4.1
</td>
<td>
Is data usable for DESTINATIONS
dissemination purpose?
Please indicate the
format
(aggregated/not aggregated)
</td>
<td>
The data will be used for the dissemination of the project and will be
aggregated
</td> </tr>
<tr>
<td>
7.3.4.2
</td>
<td>
Is data planned to be published as open format? If so, please describe the
technological solution used and the metadata format.
</td>
<td>
No
</td> </tr> </table>
**Table 36: Local Data Management Plan – WP7 (LIMASSOL)**
<table>
<tr>
<th>
**WP7 - ELBA**
</th>
<th>
</th> </tr>
<tr>
<td>
**Data details**
</td>
<td>
</td> </tr>
<tr>
<td>
7.4.1.1
</td>
<td>
Which kind of data has/been will be collected in your site?
</td>
<td>
Data collection will relate to:
* Analysis of Public Transport (PT) network and offer
* Analysis of data collected during the operation by
AVM system (assessment of service performances) in order to identify any
weakness and aspects to be improved (as feedback for planning)
* Survey on satisfaction level of users on current PT offer
* Survey on users’ needs collection
Data collection for this demo WP has been planned but not operationally
launched.
</td> </tr>
<tr>
<td>
7.4.1.2
</td>
<td>
Please detail data typology and structure/format (if applicable)
</td>
<td>
* Bus lines and stops
* Coverage time
* Travelling time per line, stop by stop
* Interchange points
* Comodality (between ferry timetable and bus lines to/from Piombino and from/to Portoferraio/Rio Marina)
</td> </tr>
<tr>
<td>
7.4.1.3
</td>
<td>
Please detail the data origin
</td>
<td>
Two Public Transport Operators operating the service in Elba (CTT Nord) and
to/from Piombino, harbour to ELBA (Tiemme)
</td> </tr>
<tr>
<td>
**Data collection procedures**
</td>
<td>
</td> </tr>
<tr>
<td>
7.4.2.1
</td>
<td>
Please detail the procedure adopted
for data collection
</td>
<td>
Service contracted from CTT Nord and Tiemme
</td> </tr>
<tr>
<td>
7.4.2.2
</td>
<td>
If a sampling process is used, please confirm that the sample is random and of
a size that can be analysed with the ability to make statistical inference for
the overall sample and for the most significant subsample breakdowns
(for reference, please see D1.1)
</td>
<td>
No sampling procedure adopted
</td> </tr> </table>
<table>
<tr>
<th>
**WP7 - ELBA**
</th> </tr>
<tr>
<td>
7.4.2.3
</td>
<td>
Is data collected anonymously or not? If not, please confirm that data is
collected in such a way preventing the tracking of personal habits or feelings
(for reference, please see
D1.1)
</td>
<td>
Survey on satisfaction level of users on current PT offer
Survey on users’ needs collection
Both will be collected anonymously
</td> </tr>
<tr>
<td>
**Data management and storing procedures**
</td> </tr>
<tr>
<td>
7.4.3.1
</td>
<td>
How is data stored? Please detail where the data is stored and in which
modality/format (if applicable)
</td>
<td>
Analysis of Public Transport (PT) network and offer
* Scheduled service and timetable (planning SW, SQL database)
Analysis of data collected during the operation of service and performances
assessment
* AVM system (SQL database)
Survey
* Paper questionnaires
</td> </tr>
<tr>
<td>
7.4.3.2
</td>
<td>
Who is the
organization responsible for data storing and management?
</td>
<td>
CTT Nord and Tiemme
</td> </tr>
<tr>
<td>
7.4.3.3
</td>
<td>
Through whom
(organization, responsible) is data accessible?
</td>
<td>
CTT Nord and Tiemme, Rio Marina and Portoferraio
Municipality
</td> </tr>
<tr>
<td>
7.4.3.4
</td>
<td>
Which international regulation will be applied for data storing and access?
(for reference, please see D1.1)
</td>
<td>
All the data is anonymous; there is no need to apply international regulation
</td> </tr>
<tr>
<td>
7.4.3.5
</td>
<td>
Which national regulation and applicable ‘opinion statements’ will be applied
for data storing and access? (for reference, please see D1.1)
</td>
<td>
All the data is anonymous; there is no need to apply national regulation
</td> </tr>
<tr>
<td>
**WP7 - ELBA**
</td> </tr>
<tr>
<td>
**Data availability for dissemination**
</td> </tr>
<tr>
<td>
7.4.4.1
</td>
<td>
Is data usable for DESTINATIONS
dissemination purpose?
Please indicate the
format
(aggregated/not aggregated)
</td>
<td>
No
</td> </tr>
<tr>
<td>
7.4.4.2
</td>
<td>
Is data planned to be published as open format? If so, please describe the
technological solution used and the metadata format.
</td>
<td>
No
</td> </tr> </table>
**Table 37: Local Data Management Plan – WP7 (ELBA)**
<table>
<tr>
<th>
**WP7 - MALTA**
</th> </tr>
<tr>
<td>
**Data details**
</td> </tr>
<tr>
<td>
7.5.1.1
</td>
<td>
Which kind of data have been/will be collected in your site?
</td>
<td>
* Data on PT service demand
* Statistics produced by the systems already
operated (i.e. ticketing)
* Survey on users’ needs and expectations
_Data collection procedure is planned._
</td> </tr>
<tr>
<td>
7.5.1.2
</td>
<td>
Please detail data typology and structure/format (if applicable)
</td>
<td>
* Data on PT service demand – questionnaire with users
* Statistics produced by the systems already operated Ferry ticketing data, user data during pilot
* Survey on users’ needs and expectations – survey with users
</td> </tr>
<tr>
<td>
7.5.1.3
</td>
<td>
Please detail the data origin
</td>
<td>
* Data on PT service demand – ferry users
* Statistics produced by the systems already operated ferry operator, PT operator
* Survey on users’ needs and expectations – PT users during pilot
</td> </tr> </table>
<table>
<tr>
<th>
**WP7 - MALTA**
</th> </tr>
<tr>
<td>
**Data collection procedures**
</td> </tr>
<tr>
<td>
7.5.2.1
</td>
<td>
Please detail the procedure adopted
for data collection
</td>
<td>
* Data on PT service demand – questionnaire with users
* Statistics produced by the systems already operated Ferry ticketing data, user data during pilot
* Survey on users’ needs and expectations – survey with users
</td> </tr>
<tr>
<td>
7.5.2.2
</td>
<td>
If a sampling process is used, please confirm that the sample is random and of
a size that can be analysed with the ability to make statistical inference for
the overall sample and for the most significant subsample breakdowns
(for reference, please see D1.1)
</td>
<td>
No sampling will be adopted in this case
</td> </tr>
<tr>
<td>
7.5.2.3
</td>
<td>
Is data collected anonymously or not? If not, please confirm that data is
collected in such a way preventing the tracking of personal habits or feelings
(for reference, please see
D1.1)
</td>
<td>
Anonymously
</td> </tr>
<tr>
<td>
**Data management and storing procedures**
</td> </tr>
<tr>
<td>
7.5.3.1
</td>
<td>
How is data stored? Please detail where the data is stored and in which
modality/format (if applicable)
</td>
<td>
* Data on PT service demand – pdf
* Statistics produced by the systems already operated Ferry ticketing data, user data during pilot - excel
* Survey on users’ needs and expectations – pdf
</td> </tr>
<tr>
<td>
**WP7 - MALTA**
</td> </tr>
<tr>
<td>
7.5.3.2
</td>
<td>
Who is the
organization responsible for data storing and management?
</td>
<td>
* Data on PT service demand – Transport Malta
* Statistics produced by the systems already operated – ferry operator
* Survey on users’ needs and expectations –
Transport Malta
</td> </tr>
<tr>
<td>
7.5.3.3
</td>
<td>
Through whom
(organization, responsible) is data accessible?
</td>
<td>
* Data on PT service demand – Transport Malta
* Statistics produced by the systems already operated – ferry operator and Transport Malta as per ongoing contract
* Survey on users’ needs and expectations –
Transport Malta
</td> </tr>
<tr>
<td>
7.5.3.4
</td>
<td>
Which international regulation will be applied for data storing and access?
(for reference, please see D1.1)
</td>
<td>
Directive 95/46/EC which is due to be replaced by the General Data Protection
Regulation (GDPR) EU 2016/679 which will become effective on 25 May 2018.
</td> </tr>
<tr>
<td>
7.5.3.5
</td>
<td>
Which national regulation and applicable ‘opinion statements’ will be applied
for data storing and access? (for reference, please see D1.1)
</td>
<td>
Chapter 440 Data Protection Act
</td> </tr>
<tr>
<td>
**Data availability for dissemination**
</td> </tr>
<tr>
<td>
7.5.4.1
</td>
<td>
Is data usable for DESTINATIONS
dissemination purpose?
Please indicate the
format
(aggregated/not aggregated)
</td>
<td>
Yes, in aggregate
</td> </tr>
<tr>
<td>
7.5.4.2
</td>
<td>
Is data planned to be published as open format? If so, please describe the
technological solution used and the metadata format.
</td>
<td>
No
</td> </tr> </table>
**Table 38: Local Data Management Plan – WP7 (MALTA)**
<table>
<tr>
<th>
**WP7 – LAS PALMAS**
</th> </tr>
<tr>
<td>
**Data details**
</td> </tr>
<tr>
<td>
7.6.1.1
</td>
<td>
Which kind of data has been/will be collected in your site?
</td>
<td>
_**LPA 7.2 - Hybrid buses in the urban bus fleet** _
* _Urban Public transport buses feature (planned)_ **LPA 7.3 - Real time mobility and tourism information services**
* _Statistics about urban public transport trips at bus stops (carried out)_
</td> </tr>
<tr>
<td>
7.6.1.2
</td>
<td>
Please detail data typology and structure/format (if applicable)
</td>
<td>
_Urban Public transport buses features_
_Average age, fuel consumption, size_
_Statistics about urban public transport trips at bus stops_ _Number of
travellers at bus stops_
</td> </tr>
<tr>
<td>
7.6.1.3
</td>
<td>
Please detail the data origin
</td>
<td>
_Urban Public transport buses features_
* _The data is stored in a database and statistics data tables are made depending on the needs_
_Statistics about urban public transport trips at bus stops_
* _The data is stored in a database and statistics data tables are made depending on the needs_
</td> </tr>
<tr>
<td>
7.6.1.4
</td>
<td>
Please provide some figure allowing to estimate the data dimension
</td>
<td>
_Urban Public transport buses features_
_242 buses_
_Statistics about urban public transport trips at bus stops_ _784 bus stops,
2.8 M passenger monthly_
</td> </tr>
<tr>
<td>
**Data collection procedures**
</td> </tr>
<tr>
<td>
7.6.2.1
</td>
<td>
Please detail the procedure adopted
for data collection
</td>
<td>
_Urban Public transport buses’ features_
Data extraction from database
_Statistics about urban public transport trips at bus stops_ Data extraction
from database
</td> </tr>
<tr>
<td>
7.6.2.2
</td>
<td>
If a sampling process is used, please confirm that the sample is random and of
a size that can be analysed with the ability to make statistical inference for
the overall sample and for the most significant subsample breakdowns
(for reference, please see D1.1)
</td>
<td>
_Urban Public transport buses features_
* The data collected belong to the whole database regarding urban public transport buses
_Statistics about urban public transport trips at bus stops_
* The data collected belong to two representative months (May and October)
</td> </tr> </table>
<table>
<tr>
<th>
**WP7 – LAS PALMAS**
</th> </tr>
<tr>
<td>
7.6.2.3
</td>
<td>
Is data collected anonymously or not? If not, please confirm that data is
collected in such a way preventing the tracking of personal habits or feelings
(for reference, please see
D1.1)
</td>
<td>
_Urban Public transport buses features_
* Anonymously
_Statistics about urban public transport trips at bus stops_
* Anonymously
</td> </tr>
<tr>
<td>
**Data management and storing procedures**
</td> </tr>
<tr>
<td>
7.6.3.1
</td>
<td>
How is data stored? Please detail where the data is stored and in which
modality/format (if applicable)
</td>
<td>
_Statistics about urban public transport trips at bus stops._
_Data is stored in Guaguas Municipales server (Urban Public Transport
Company)_
</td> </tr>
<tr>
<td>
7.6.3.2
</td>
<td>
Who is the
organization responsible for data storing and management?
</td>
<td>
_Statistics about urban public transport trips at bus stops._
_Guaguas Municipales (Urban Public Transport Company)_
</td> </tr>
<tr>
<td>
7.6.3.3
</td>
<td>
Through whom
(organization, responsible) is data accessible?
</td>
<td>
_Statistics about urban public transport trips at bus stops._
_Guaguas Municipales (Urban Public Transport Company)_
</td> </tr>
<tr>
<td>
7.6.3.5
</td>
<td>
Which national regulation and applicable ‘opinion statements’ will be applied
for data storing and access? (for reference, please see D1.1)
</td>
<td>
_Statistics about urban public transport trips at bus stops._ In case a
national regulation is needed, the national regulation applicable will be “Ley
Orgánica 15/1999, de 13 de diciembre, de Protección de Datos de Carácter
Personal”
</td> </tr>
<tr>
<td>
**Data availability for dissemination**
</td> </tr>
<tr>
<td>
7.6.4.1
</td>
<td>
Is data usable for DESTINATIONS
dissemination purpose?
Please indicate the
format
(aggregated/not aggregated)
</td>
<td>
No
</td> </tr>
<tr>
<td>
**WP7 – LAS PALMAS**
</td>
<td>
</td> </tr>
<tr>
<td>
7.6.4.2
</td>
<td>
Is data planned to be published as open format? If so, please describe the
technological solution used and the metadata format.
</td>
<td>
No
</td> </tr> </table>
**Table 39: Local Data Management Plan – WP7 (LAS PALMAS)**
## WP9
<table>
<tr>
<th>
**WP9 - MADEIRA**
</th>
<th>
</th> </tr>
<tr>
<td>
**Data details**
</td>
<td>
</td> </tr>
<tr>
<td>
9.1.1.1
</td>
<td>
Which kind of data has been collected in your site?
</td>
<td>
WP9 will deal with all the data described in WP2 to WP7
</td> </tr> </table>
**Table 40: Local Data Management Plan – WP9 (MADEIRA)**
<table>
<tr>
<th>
**WP9 - RETHYMNO**
</th>
<th>
</th> </tr>
<tr>
<td>
**Data details**
</td>
<td>
</td> </tr>
<tr>
<td>
9.2.1.1
</td>
<td>
Which kind of data has been collected in your site?
</td>
<td>
_Baseline data (as described in WP2)_
* Data about road network
* Statistics on traffic accidents, deaths and injuries
</td> </tr> </table>
**Table 41: Local Data Management Plan – WP9 (RETHYMNO)**
<table>
<tr>
<th>
**WP9 - LIMASSOL**
</th>
<th>
</th> </tr>
<tr>
<td>
**Data details**
</td>
<td>
</td> </tr>
<tr>
<td>
9.3.1.1
</td>
<td>
Which kind of data has been collected in your site?
</td>
<td>
WP9 will deal with all the data described in WP2 to WP7
</td> </tr> </table>
**Table 42: Local Data Management Plan – WP9 (LIMASSOL)**
<table>
<tr>
<th>
**WP9 - ELBA**
</th>
<th>
</th> </tr>
<tr>
<td>
**Data details**
</td>
<td>
</td> </tr>
<tr>
<td>
9.4.1.1
</td>
<td>
Which kind of data has been collected in your site?
</td>
<td>
WP9 will deal with all the data described in WP2 to WP7
</td> </tr> </table>
**Table 43: Local Data Management Plan – WP9 (ELBA)**
<table>
<tr>
<th>
**WP9**
</th> </tr>
<tr>
<td>
**Data details**
</td> </tr>
<tr>
<td>
9.5.1.1
</td>
<td>
Which kind of data has been/will be collected in your site?
</td>
<td>
**MAL 2.2**
Society - Acceptance level / Awareness level
Transport - Traffic flow by vehicle (peak) -avg. vehicles per hour
**MAL4.1**
Society - Acceptance level / Awareness level
**MAL5.1**
Transport – Freight movements
Transport – Service reliability
Economy – Average Operating Costs
Energy – Vehicle Fuel Efficiency - fuel use per vkm
Environment – CO2 emission - CO2/vkm/type
Transport – Goods carried - kg
Society - Awareness level
**MAL6.1**
Society – Acceptance level / Awareness level
Society – Satisfaction
**MAL6.2**
Health – Number of polluting vehicles reported - number
Society – Acceptance level / Awareness level
**MAL6.3**
Social Interactions – No. of users
Transport – Modal split of users
**MAL6.4**
Economy – Operating
Transport – Use of space for parking
Transport – Traffic levels - vehicles/hr
Society – Satisfaction
**MAL7.1**
Society – Satisfaction
Energy – Fuel Mix
Society – Awareness level
</td> </tr> </table>
<table>
<tr>
<th>
**WP9**
</th> </tr>
<tr>
<td>
9.5.1.2
</td>
<td>
Please detail data typology and structure/format (if applicable)
</td>
<td>
**MAL 2.2**
Society - Acceptance level / Awareness level – excel table
Transport - Traffic flow by vehicle (peak) -avg. vehicles per hour – excel
table
**MAL4.1**
Society - Acceptance level / Awareness level – excel table
**MAL5.1**
Transport – Freight movements – excel table
Transport – Service reliability – excel table
Economy – Average Operating Costs – excel table
Energy – Vehicle Fuel Efficiency - fuel use per vkm – excel table
Environment – CO2 emission - CO2/vkm/type – excel table
Transport – Goods carried - kg – excel table
Society - Awareness level – excel table
**MAL6.1**
Society – Acceptance level / Awareness level – excel table
Society – Satisfaction – excel table
**MAL6.2**
Health – Number of polluting vehicles reported - number – excel table Society
– Acceptance level / Awareness level – excel table
**MAL6.3**
Social Interactions – No. of users – excel table
Transport – Modal split of users – excel table
**MAL6.4**
Economy – Operating – excel table
Transport – Use of space for parking – excel table
Transport – Traffic levels - vehicles/hr – excel table
Society – Satisfaction – excel table
**MAL7.1**
Society – Satisfaction – excel table
Energy – Fuel Mix – excel table
Society – Awareness level – excel table
</td> </tr>
<tr>
<td>
9.5.1.3
</td>
<td>
Please detail the data origin
</td>
<td>
**MAL 2.2**
Society - Acceptance level / Awareness level - survey
Transport - Traffic flow by vehicle (peak) -avg. vehicles per hour – CVA
operator
**MAL4.1**
Society - Acceptance level / Awareness level - survey
**MAL5.1**
Transport – Freight movements - survey
Transport – Service reliability - operator
Economy – Average Operating Costs - operator
Energy – Vehicle Fuel Efficiency - fuel use per vkm - operator
Environment – CO2 emission - CO2/vkm/type - operator
Transport – Goods carried – kg - operator
Society - Awareness level - survey
**MAL6.1**
Society – Acceptance level / Awareness level -survey
Society – Satisfaction - survey
**MAL6.2**
Health – Number of polluting vehicles reported – number – system server
Society – Acceptance level / Awareness level - survey
**MAL6.3**
Social Interactions – No. of users – system server
Transport – Modal split of users – survey / system server
**MAL6.4**
Economy – Operating - operator
Transport – Use of space for parking – system server
Transport – Traffic levels - vehicles/hr – CVA operator
Society – Satisfaction - survey
**MAL7.1**
Society – Satisfaction - survey
Energy – Fuel Mix - operator
Society – Awareness level - survey
</td> </tr> </table>
<table>
<tr>
<th>
**WP9**
</th>
<th>
</th> </tr>
<tr>
<td>
**Data collection procedures**
</td>
<td>
</td> </tr>
<tr>
<td>
9.5.2.1
</td>
<td>
Please detail the procedure adopted
for data collection
</td>
<td>
**MAL 2.2**
Society - Acceptance level / Awareness level - survey
Transport - Traffic flow by vehicle (peak) -avg. vehicles per hour – extracted
from CVA operator
**MAL4.1**
Society - Acceptance level / Awareness level - survey
**MAL5.1**
Transport – Freight movements - survey
Transport – Service reliability – provided by operator
Economy – Average Operating Costs – provided by operator
Energy – Vehicle Fuel Efficiency - fuel use per vkm – provided by operator
Environment – CO2 emission - CO2/vkm/type – provided by operator
Transport – Goods carried – kg – provided by operator
Society - Awareness level - survey
**MAL6.1**
Society – Acceptance level / Awareness level -survey
Society – Satisfaction - survey
**MAL6.2**
Health – Number of polluting vehicles reported – number – extracted from
system server
Society – Acceptance level / Awareness level - survey
**MAL6.3**
Social Interactions – No. of users – extracted from system server
Transport – Modal split of users – survey / system server
**MAL6.4**
Economy – Operating – provided by operator
Transport – Use of space for parking – extracted from system server
Transport – Traffic levels - vehicles/hr – extracted from CVA operator
Society – Satisfaction - survey
**MAL7.1**
Society – Satisfaction - survey
Energy – Fuel Mix – provided by operator
Society – Awareness level - survey
</td> </tr> </table>
<table>
<tr>
<th>
**WP9**
</th> </tr>
<tr>
<td>
9.5.2.2
</td>
<td>
If a sampling process is used, please confirm that the sample is random and of
a size that can be analysed with the ability to make statistical inference for
the overall sample and for the most significant subsample breakdowns
(for reference, please see D1.1)
</td>
<td>
**MAL 2.2**
Society - Acceptance level / Awareness level – survey (random)
Transport - Traffic flow by vehicle (peak) -avg. vehicles per hour – CVA
operator
**MAL4.1**
Society - Acceptance level / Awareness level – survey (random
**MAL5.1**
Transport – Freight movements – survey (random)
Transport – Service reliability - operator
Economy – Average Operating Costs - operator
Energy – Vehicle Fuel Efficiency - fuel use per vkm - operator
Environment – CO2 emission - CO2/vkm/type - operator
Transport – Goods carried – kg - operator
Society - Awareness level – survey (random)
**MAL6.1**
Society – Acceptance level / Awareness level -survey
Society – Satisfaction – survey (random)
**MAL6.2**
Health – Number of polluting vehicles reported – number – system server
Society – Acceptance level / Awareness level – survey (random)
**MAL6.3**
Social Interactions – No. of users – system server
Transport – Modal split of users – survey (random) / system server **MAL6.4**
Economy – Operating - operator
Transport – Use of space for parking – system server
Transport – Traffic levels - vehicles/hr – CVA operator
Society – Satisfaction – survey (random)
**MAL7.1**
Society – Satisfaction – survey (random)
Energy – Fuel Mix - operator
Society – Awareness level – survey (random)
</td> </tr>
<tr>
<td>
9.5.2.3
</td>
<td>
Is data collected anonymously or not? If not, please confirm that data is
collected in such a way preventing the tracking of personal habits or feelings
(for reference, please see
D1.1)
</td>
<td>
**MAL 2.2**
Society - Acceptance level / Awareness level – survey (anonymous) Transport -
Traffic flow by vehicle (peak) -avg. vehicles per hour – CVA operator
(anonymous)
**MAL4.1**
Society - Acceptance level / Awareness level – survey (anonymous) **MAL5.1**
Transport – Freight movements – survey (anonymous)
Transport – Service reliability - operator
Economy – Average Operating Costs - operator
Energy – Vehicle Fuel Efficiency - fuel use per vkm - operator
Environment – CO2 emission - CO2/vkm/type - operator
Transport – Goods carried – kg - operator
Society - Awareness level – survey (anonymous)
**MAL6.1**
Society – Acceptance level / Awareness level –survey (anonymous)
Society – Satisfaction – survey (anonymous)
**MAL6.2**
Health – Number of polluting vehicles reported – number – system server
Society – Acceptance level / Awareness level – survey (anonymous) **MAL6.3**
Social Interactions – No. of users – system server (anonymous)
Transport – Modal split of users – survey / system server (anonymous)
**MAL6.4**
Economy – Operating - operator
Transport – Use of space for parking – system server
Transport – Traffic levels - vehicles/hr – CVA operator (anonymous) Society –
Satisfaction – survey (anonymous)
**MAL7.1**
Society – Satisfaction – survey (anonymous)
Energy – Fuel Mix – operator
Society – Awareness level – survey (anonymous)
</td> </tr> </table>
<table>
<tr>
<th>
**WP9**
</th> </tr>
<tr>
<td>
**Data management and storing procedures**
</td> </tr>
<tr>
<td>
9.5.3.1
</td>
<td>
How is data stored? Please detail where the data is stored and in which
modality/format (if applicable)
</td>
<td>
**MAL 2.2**
Society - Acceptance level / Awareness level – excel table - TM
Transport - Traffic flow by vehicle (peak) -avg. vehicles per hour – excel
table - TM
**MAL4.1**
Society - Acceptance level / Awareness level – excel table - TM
**MAL5.1**
Transport – Freight movements – excel table - TM
Transport – Service reliability – excel table - TM
Economy – Average Operating Costs – excel table - TM
Energy – Vehicle Fuel Efficiency - fuel use per vkm – excel table - TM
Environment – CO2 emission - CO2/vkm/type – excel table - TM
Transport – Goods carried - kg – excel table - TM
Society - Awareness level – excel table - TM
**MAL6.1**
Society – Acceptance level / Awareness level – excel table - MOT Society –
Satisfaction – excel table - MOT
**MAL6.2**
Health – Number of polluting vehicles reported - number – excel table – TM /
UOM
Society – Acceptance level / Awareness level – excel table - TM **MAL6.3**
Social Interactions – No. of users – excel table – TM / UOM
Transport – Modal split of users – excel table – TM / UOM
**MAL6.4**
Economy – Operating – excel table – TM / VLC
Transport – Use of space for parking – excel table – TM / VLC
Transport – Traffic levels - vehicles/hr – excel table – TM / VLC
Society – Satisfaction – excel table – TM / VLC
**MAL7.1**
Society – Satisfaction – excel table TM
Energy – Fuel Mix – excel table TM
Society – Awareness level – excel table TM
</td> </tr> </table>
<table>
<tr>
<th>
**WP9**
</th> </tr>
<tr>
<td>
9.5.3.2
</td>
<td>
Who is
organization responsible for data
storing management?
</td>
<td>
the
and
</td>
<td>
**MAL 2.2**
Society - Acceptance level / Awareness level – excel table - TM
Transport - Traffic flow by vehicle (peak) -avg. vehicles per hour – excel
table - TM
**MAL4.1**
Society - Acceptance level / Awareness level – excel table - TM
**MAL5.1**
Transport – Freight movements – excel table - TM
Transport – Service reliability – excel table - TM
Economy – Average Operating Costs – excel table - TM
Energy – Vehicle Fuel Efficiency - fuel use per vkm – excel table - TM
Environment – CO2 emission - CO2/vkm/type – excel table - TM
Transport – Goods carried - kg – excel table - TM
Society - Awareness level – excel table - TM
**MAL6.1**
Society – Acceptance level / Awareness level – excel table - MOT Society –
Satisfaction – excel table - MOT
**MAL6.2**
Health – Number of polluting vehicles reported - number – excel table – TM /
UOM
Society – Acceptance level / Awareness level – excel table - TM **MAL6.3**
Social Interactions – No. of users – excel table – TM / UOM
Transport – Modal split of users – excel table – TM / UOM
**MAL6.4**
Economy – Operating – excel table – TM / VLC
Transport – Use of space for parking – excel table – TM / VLC
Transport – Traffic levels - vehicles/hr – excel table – TM / VLC
Society – Satisfaction – excel table – TM / VLC
**MAL7.1**
Society – Satisfaction – excel table TM
Energy – Fuel Mix – excel table TM
Society – Awareness level – excel table TM
</td> </tr>
<tr>
<td>
9.5.3.3
</td>
<td>
Through whom
(organization, responsible) is data accessible?
</td>
<td>
_**MAL 2.2** _
_Society - Acceptance level / Awareness level – excel table - TM_
_Transport - Traffic flow by vehicle (peak) -avg. vehicles per hour – excel
table - TM_
**MAL4.1**
Society - Acceptance level / Awareness level – excel table - TM
**MAL5.1**
Transport – Freight movements – excel table - TM
Transport – Service reliability – excel table - TM
Economy – Average Operating Costs – excel table - TM
Energy – Vehicle Fuel Efficiency - fuel use per vkm – excel table - TM
Environment – CO2 emission - CO2/vkm/type – excel table - TM
Transport – Goods carried - kg – excel table - TM
Society - Awareness level – excel table - TM
**MAL6.1**
Society – Acceptance level / Awareness level – excel table - MOT
Society – Satisfaction – excel table - MOT
**MAL6.2**
Health – Number of polluting vehicles reported - number – excel table – TM /
UOM
Society – Acceptance level / Awareness level – excel table - TM **MAL6.3**
Social Interactions – No. of users – excel table – TM / UOM
Transport – Modal split of users – excel table – TM / UOM
**MAL6.4**
Economy – Operating – excel table – TM / VLC
Transport – Use of space for parking – excel table – TM / VLC
Transport – Traffic levels - vehicles/hr – excel table – TM / VLC Society –
Satisfaction – excel table – TM / VLC
**MAL7.1**
Society – Satisfaction – excel table TM
Energy – Fuel Mix – excel table TM
Society – Awareness level – excel table TM
</td> </tr> </table>
<table>
<tr>
<th>
**WP9**
</th> </tr>
<tr>
<td>
9.5.3.4
</td>
<td>
Which international regulation will be applied for data storing and access?
(for reference, please see D1.1)
</td>
<td>
Directive 95/46/EC which is due to be replaced by the General Data Protection
Regulation (GDPR) EU 2016/679 which will become effective on 25 May 2018.
</td> </tr>
<tr>
<td>
9.5.3.5
</td>
<td>
Which national regulation and applicable ‘opinion statements’ will be applied
for data storing and access? (for reference, please see D1.1)
</td>
<td>
Chapter 440 Data Protection Act
</td> </tr>
<tr>
<td>
**WP9**
</td> </tr>
<tr>
<td>
**Data availability for dissemination**
</td> </tr>
<tr>
<td>
9.5.4.1
</td>
<td>
Is data usable for DESTINATIONS
dissemination purpose?
Please indicate the
format
(aggregated/not aggregated)
</td>
<td>
**MAL 2.2**
Society - Acceptance level / Awareness level – excel table AGGREGATED
Transport - Traffic flow by vehicle (peak) -avg. vehicles per hour – excel
table – TM AGGREGATED
**MAL4.1**
Society - Acceptance level / Awareness level – excel table – TM
AGGREGATED
**MAL5.1**
Transport – Freight movements – excel table – TM NOT AVAILABLE
Transport – Service reliability – excel table – TM NOT AVAILABLE
Economy – Average Operating Costs – excel table – TM NOT AVAILABLE
Energy – Vehicle Fuel Efficiency - fuel use per vkm – excel table – TM NOT
AVAILABLE
Environment – CO2 emission - CO2/vkm/type – excel table – TM NOT AVAILABLE
Transport – Goods carried - kg – excel table – TM NOT AVAILABLE Society -
Awareness level – excel table – TM AGGREGATED
**MAL6.1**
Society – Acceptance level / Awareness level – excel table – MOT
AGGREGATED
Society – Satisfaction – excel table – MOT AGGREGATED
**MAL6.2**
Health – Number of polluting vehicles reported - number – excel table – TM /
UOM NOT AVAILABLE
Society – Acceptance level / Awareness level – excel table – TM
AGGREGATED
**MAL6.3**
Social Interactions – No. of users – excel table – TM / UOM AGGREGATED
Transport – Modal split of users – excel table – TM / UOM
AGGREGATED
**MAL6.4**
Economy – Operating – excel table – TM / VLC NOT AVAILABLE
Transport – Use of space for parking – excel table – TM / VLC NOT AVAILABLE
Transport – Traffic levels - vehicles/hr – excel table – TM / VLC NOT
AVAILABLE
Society – Satisfaction – excel table – TM / VLC AGGREGATED **MAL7.1**
Society – Satisfaction – excel table TM AGGREGATED
Energy – Fuel Mix – excel table TM NOT AVAILABLE
Society – Awareness level – excel table TM AGGREGATED
</td> </tr>
<tr>
<td>
9.5.4.2
</td>
<td>
Is data planned to be published as open format? If so, please describe the
technological solution used and the metadata format.
</td>
<td>
No
</td> </tr> </table>
**Table 44: Local Data Management Plan – WP9 (MALTA)**
<table>
<tr>
<th>
**WP9 – LAS PALMAS**
</th>
<th>
</th> </tr>
<tr>
<td>
**Data details**
</td>
<td>
</td> </tr>
<tr>
<td>
9.6.1.1
</td>
<td>
Which kind of data has been collected in your site?
</td>
<td>
WP9 will deal with all the data described in WP2 to WP7
</td> </tr> </table>
**Table 45: Local Data Management Plan – WP9 (LAS PALMAS)**
**4 Conclusions**
D1.3 details the data typologies collected/under collection/planned for
collection for the design of demo measures. For each data typology the
procedures adopted by sites for data collection, handling and storing is
described. The description is provided per WP, per site and per data typology.
The covered period for description is M4-M6 (December 2016 – February 2017).
|
https://phaidra.univie.ac.at/o:1140797
|
Horizon 2020
|
1094_SMARTool_689068.md
|
**1\. Executive Summary**
The current deliverable is the second version of the SMARTool Data Management
Plan (DMP) and describes the procedures of data generation and collection
within the project. Specifically, the standards and the methodology that was
followed for the collection and processing are well defined. In addition, it
is described the methodology for these data to become accessible for re-use
[1]. SMARTool project includes gathering and analysis of plenty of datasets,
such as data from user requirements collection, to clinical and other patient
related data.
Specifically, datasets were collected in two ways: (i) the collected data
related to the users' personal information, demographics, clinical, imaging,
molecular, omics and other health related data anonymized on premised which
assisted in the development of the SMARTool platform, (ii) the collected data
through the application of the multivariate analysis, the data mining and the
non-imaging classification algorithm for CAD stratification, the multiscale
and multilevel site specific models for plaque progression and the noninvasive
FFR, the pharmacological therapy modulation algorithms using data mining
techniques and the virtual stent deployment approach. The second one
consistute the main output of the SMARTool platform. The second version of the
deliverable combines the feedback provided by the SMARTool partners and the
datasets they contributed to. More specifically, the deliverable was
circulated to the project partners responsible for the different tasks, who
defined the dataset descriptions and the procedure of data collection and
analysis as well as the methods and processes, which are followed to ensure
adherence with the ethics issues. In fact, most of the SMARTool datasets
include the data collection from CAD patients; the produced and processed data
were handled with caution, following all the ethical and privacy requirements
involved in such datasets.
This is the second version of the DMP of SMARTool and the datasets described
represent the reflection of the collected and processed data. These data were
enriched with more data (collected and processed) as the context of the
datasets is continuously updated during the project. However, the main
principles as well the processes of data collection, processing and
preservation were those as described in the previous version (version 1) of
the document. The deliverable is composed of different chapters and sub-
sections.
**Chapter 2** consists of
* an overview of the SMARTool project, its overall vision and objectives
* the main scope of this deliverable including the objectives of the DMP,
* the steps and actions involved in the data management cycle and,
* the background of the SMARTool DMP.
**Chapter 3** presents
* the principle of SMARTool FAIR data management
* the main categories of the SMARTool data, (iii) the data access and storage processes
* the available databases along with the level roles and permissions and,
* a detailed description of the datasets collected and processed per WP, the involved partners, the standards and metadata, the data exploitation and sharing, the archiving and preservation (including storage and backup) processes.
**Chapter 4** presents the types of data, their processes as well as the
visualization of data lineage.
**In Chapter 5,** the specific methods and tools for ensuring data security,
including the data anonymization, encryption, storage and backups are
described. The chapter also mentions the ethical considerations related to the
protection of the enrolled patients, the EU legislation and Ethics documents
and experts’ opinions and Ethical Committees, that were taken into
consideration during the lifecycle of SMARTool project.
**2\. Introduction**
A new element that has been included in the Horizon 2020 projects is the Data
Management Plan (DMP) which is related to the data generated in the framework
of the project and how this data became accessible [2]. The D4.2 – Data
Management Plan v2 is the second version of the DMP of the SMARTool project,
which has received funding by the European Union’s Horizon 2020 Programme
under Grant Agreement number: 689068. This second version of the DMP concerns
an overview of the produced datasets and the specific conditions that are
related to them.
**About the project**
Coronary artery disease (CAD) is a chronic disease with a high prevalence and
epidemic proportions in the middle aged and elderly population, accounting for
about 50% of all deaths [3], [4]. Medical therapy, lifestyle and diet
suggestions and clinical risk factors, including dyslipidemia, hypertension,
diabetes, are among the key factors in CAD patient-specific management.
Currently, an integrated site specific and patient-specific comprehensive
predictive model of plaque progression is lagging. Although several algorithms
have been developed for primary and secondary prevention of CAD, a unified
platform that incorporates all local and systemic risk factors into a
personalized clinical decision support tool for stratification, diagnosis,
prediction and treatment is not available. SMARTool is providing a Cloud
platform for enabling clinical decision support for prevention of CAD. This is
achieved through the standardization and integration of health data from
heterogeneous sources and existing patient/artery specific multiscale and
multilevel predictive models. Specifically, the SMARTool models rely on the
extension of the already available multiscale and multilevel ARTreat models
for coronary plaque assessment and progression over time. Moreover, it
utilizes non-invasive imaging by coronary computed tomography angiography
(CCTA) to provide functional site-specific assessment of arterial stenosis
based on the non-invasive Fractional Flow Reserve (SmartFFR) and provides
additional heterogeneous patient-specific models for risk stratification,
stent deployment and medical treatment prediction. More specifically, SMARTool
allows:
* **Patient-specific CAD stratification.** Specifically, the already available clinical stratification models (e.g Framingham, Genders, GES - Gene Expression Score) are implemented by patient genotyping and phenotyping (cellular/molecular markers) and examined in retrospective and prospective clinical data (EVINCI project population) towards stratifying the patients to the following categories: non-obstructive CAD, obstructive CAD and those without CAD categories.
* **Site specific plaque progression prediction.** Existing multiscale and multilevel models for plaque progression prediction (ARTreat project) are updated and refined through the incorporation of additional genotyping and phenotyping features and examined by retrospective/ prospective non-invasive CCTA imaging data plus non-imaging patient-specific information (follow-up - EVINCI population).
* **Patient-specific CAD diagnosis and CAD-related CHD treatment** Personalised and patientspecific therapeutical management (eg lifestyle changes, standard or high intensity medical therapy) are provided. Additionally, the interventional cardiologists are able to select the optimal stent type(s) and site(s) for appropriate stent deployment through the utilization of the SMARTool virtual angioplasty tool.
Additionally, the final Clinical Decision Support System (CDSS) includes a
microfluidic lab-on-chip device for blood analysis of cellular/molecular
inflammatory markers. The overall platform will be assessed in the
participating clinical sites.
_**2.1 Purpose of the SMARTool Data Management Plan** _
The DMP is a cornerstone for ensuring good data management. DMP is related to
the data management life cycle for the data, which are collected, processed
and created during SMARTool project. More specifically, the DMP concerns the
following activities:
* research data management during / after the project end
* description of the type of the collected, processed and generated data
* definition of standards that are used for data storage, safety and security
* description of the data, which are shared/enable open access
* process of data storage
* assistance in streamlining the whole research process. The DMP provides _a priori_ the required data process and facilities to store data.
The DMP: (i) includes, in combination with ethical issues, the availability of
research data, (ii) defines the measures and processes that were followed for
proper data anonymization and assurance of their privacy, and (iii) concerns
the strategy that is followed for the open data, which does not violate the
conditions of the interlinked Research and Innovation projects.
Figure 1 presents the steps and actions involved in the SMARTool data
management cycle.
_Figure 1: Overview of the SMARTool data management cycle._
As far as research data is concerned, SMARTool provides access to this data
through the CRFA platform. The imaging data is stored in the cloud-based
3DnetMedical.com DICOM compliant database from B3D’s UK based ISO-27001
accredited datacenter – providing security, redundancy, reliability and
scalability. DICOM studies can be automatically uploaded from PACS systems
through 3Dnet Gateway using 2048 bit encryption, or can be uploaded manually
by the user using SSL encryption (Figure 2).
Non-imaging clinical data is securely stored in a NoSQL MongoDB Database,
accessible only via locally installed application server. Data redundancy,
reliability and scalability is provided by the native MongoDB replica-set
feature.
MongoDB cluster members communicate (for continuous synchronization) via
encrypted TLS/SSL channels. Data is uploaded via https (secure http)
application interface and can be accessed, from within the project
infrastructure via secure RESTful API (OAuth 2.0, OpenID session token). The
OAuth2 authentication and session token issuing, renewing and revoking, is
provided by a WSO2 Identity Server deployed in the project infrastructure.
Clinical documents are acquired and stored in a Document Repository according
to IHE XDS.b (Cross-Enterprise Document Sharing) and XDS-I.b (Cross-enterprise
Document Sharing for Imaging) standards [5].
Data security and privacy for clinical documents is provided by implementing
the IHE ATNA (Audit Trail and Node Authentication) [6] and IHE XUA (Cross-
Enterprise User Assertion) profiles [7].
The purpose of the clinical data collection/generation in relation to the
objectives of the project are:
* To optimize the learning stage of SMARTool in order to extract and select discriminative markers (from any data source, ranging from history, genetics, circulating molecules and coronary anatomy by CT scan) of CAD severity and progression.
* To provide a "smart" data storage to the final CDSS, which is the main outcome of the project, adequate for: (i) the external validation of the selected panel of markers by external cohorts, and (ii) CDSS application and exploitation in clinics.
_**2.2 Background of the SMARTool Data Management Plan** _
SMARTool DMP is in accordance with the following articles of the Grant
Agreement:
* **Article 36 - Confidentiality.** During the project duration and for four years after the period set out in Article 3, the Consortium will keep confidential any data, documents or other relevant material that is defined as confidential.
* **Article 39.2 - Processing of personal data.** All personal data are processed in compliance with the applicable EU and national law on data protection. The Consortium provides adequate information and explanation to the personnel whose personal data are collected and processed and provide them with the specific privacy statement.
3. **SMARTool Data Management Plan**
_**3.1. FAIR Data** _
The SMARTool Consortium is the end-responsible for the DMP and makes the
research and clinical data **findable, accessible, interoperable and reusable
(FAIR)** , towards ensuring appropriate data management [8]. The research data
management is not an objective itself, but it is rather the way to achieve
knowledge discovery and innovation, and to accomplish data extension,
integration and reuse. More specifically, the FAIR principles will promote the
ability of machines to automatically discover and use the data, to support its
reuse by individuals (Figure 3).
To accomplish this, the data are available through well-defined APIs and the
CRFA web-based user interface. The developed software tools, which are used to
create and process the data could be made available under the open source
Apache 2.0 license, whenever possible.
To support the FAIR principles, the following practices are followed in the
SMARTool platform.
* Data discoverability and metadata provision
* Unique identifiers support persistent for a long time
* Data naming conventions
* Keywords search
* Clear versioning approach
Τhe collected, processed and generated clinical and research data are
preserved and stored in a specific format so as to ensure long-time
accessibility. To avoid file format obsolescence and avoid the risk of missing
useful information, specific actions are followed, such as the specific file
format selection with a high chance of being usable in the future.
In addition, during this deliverable the following issues are addressed:
* Specified / updated the data, which are open available.
* Specified / updated how the data and associated metadata, documentation and code are stored
* Specified / updated the access rights and restrictions
To support the data interoperability, specific actions are followed. In order
to acquire data from the legacy data sources, a specific HL7 compliant
integration layer using clinical data semantics is designed [9]. HL7
integration layer is written using HAPI (HL7 application programming
interface).
The IHE Cross-Enterprise Document Sharing (XDS) Integration Profile is adopted
to allow the registration, distribution and access across health enterprises
of patient electronic health data. In any single Hospital, the HIS (Hospital
Information System) provides the enterprise specific patient ID as well as the
historical demographic data. For Patient Medical Records the CDA Release 2 HL7
Standard (Clinical Reports) is used according to the IHE Cardiology Technical
Framework (Volume 1 (CARD TF-1): Integration Profiles [10] and Volume 2 (CARD
TF-2): Transactions [11]).
For Genomics data exchange we will adhere to HL7 IG CG_GENO, R1 Version 3
Genotype, Release1 - – January 2009, HL7 IG LOINCGENVA, R1 Version 2
Implementation Guide: Clinical Genomics; Fully LOINC-Qualified Genetic
Variation Model, Release 1. For medical images, DICOM standard is adopted. For
the refined models, standards Μarkup language (ML) format is used; ML for the
multiscale and multilevel model, Predictive Model Markup language (PMML) for
the data mining and stratification models, Portable Format for Analytics
(PFA), which is an emerging standard for statistical models [12]. All the
aforementioned standards and protocols ensure the required security in
information exchange and anonymization as well as interoperability.
To support the data re-use the following aspects were taken into
consideration:
* Specific data that are licensed to permit its possible re-use
* The period of data embargo is defined for the data which is available for re-use
* The access to the data by third parties after the end of the project is defined
* The data quality assurance processes are defined.
* The length of time for which the data is re-usable will be defined
2. _**Datasets to be gathered and processed** _
The data of SMARTool project can be categorised in the following classes:
**Cat1.** _Collected Data:_ Data that has not been subjected to quality
assurance or control
**Cat2.** _Validated Collected Data:_ Data that has been assessed in terms of
completeness, correctness, integrity, credibility
**Cat3** . _Analyzed Collected / Generated Data:_ Data which has been
validated, analysed and processed
_Table 1: Overview of data sets._
<table>
<tr>
<th>
**Dataset**
</th>
<th>
**Related WP**
</th>
<th>
**Brief description**
</th> </tr>
<tr>
<td>
Dataset 1
</td>
<td>
WP1, WP2, WP3
</td>
<td>
This dataset contains the SMARTool patients' personal information,
demographics, clinical, imaging, molecular, omics and other health related
data.(anonymized on SMARTool platform)
</td> </tr>
<tr>
<td>
Dataset 2
</td>
<td>
WP3
</td>
<td>
This dataset contains the CAD stratification, which is extracted through the
application of data mining techniques.
</td> </tr>
<tr>
<td>
Dataset 3
</td>
<td>
WP4
</td>
<td>
This dataset contains the reconstructed arteries and analysed DICOM files as
well the results of the multiscale and multilevel
</td> </tr>
<tr>
<td>
</td>
<td>
</td>
<td>
site-specific models for plaque progression prediction and the SmartFFR
results, which are used in the prognostic and diagnostic CDSS.
</td> </tr>
<tr>
<td>
Dataset 4
</td>
<td>
WP4
</td>
<td>
This dataset contains the results of the pharmacological therapy modulation
algorithms using data mining techniques. In addition, data from the virtual
stent deployment approach are also included.
</td> </tr>
<tr>
<td>
Dataset 5
</td>
<td>
WP5
</td>
<td>
This dataset contains information on users’ requirements, use cases,
questionnaires, specifications and system architecture.
</td> </tr>
<tr>
<td>
Dataset 6
</td>
<td>
WP6
</td>
<td>
The dataset contains the results of the research performed in SMARTool
project, which are communicated through public deliverables, journal and
conference presentations, as well as other dissemination channels (website,
social media, etc).
</td> </tr>
<tr>
<td>
Dataset 7
</td>
<td>
WP7
</td>
<td>
This dataset contains information related to the project management and
coordination.
</td> </tr> </table>
3. _**Data Access Procedures** _
**Public (PU).** For the data that will be available in the public, the
project web page [13] provides a description of the dataset and allows the
user to download the relevant file.
**Protected Data (PR).** Data indicated as protected may be communicated out
of the consortium, as long as the interested parties ask for access from the
SMARTool consortium, after explaining and providing evidence on how this data
will be utilised, for instance for research or commercial purposes.
**Confidential/Private (CO).** Data which are denoted as private/confidential
have been stored at a specific space, namely the databases, to which only
selected partners have access. In order for other SMARTool partners to have
access to these data, a proper written application to the partner responsible
for the data storage has been provided, accompanied with a justification of
the need for access.
**Controlled access (CA).** Raw sequencing data upon publication will be
deposited in pseudomized form in the European Genome-phenome Archive (
_https://www.ebi.ac.uk/ega/home_ ) under control of a data access committee
(DAC) representing the consortium partners.
4. **SMARTool Open Data**
The consortium of SMARTool has already or intends to make the following data
open to public. No final decision has been made yet about which of the
following will be made public.
* The collaboration with the utilization of VPH Share tools 1 , for making the models in standardized workflow, in ML format and their dissemination in the BiomedTown, in CellML repository and collaboration with the VPH Institute, where CNR is a member. The plan of the consortium was to provide in open ML format the refined site specific multiscale and multilevel model of plaque progression as well as the risk stratification algorithms
* Open access to peer reviewed scientific publications
* Statistical analysis metadata (Descriptive statistics) of the SMARTool Dataset for all the following categories (described in detail in D2.2): o Biohumoral
* Molecular
* Clinical o Omics o Lipidomics
The tests performed during the metadata analysis were:
* One-Way ANOVA test
* Krurskal Wallis test
* Tukey - Kramer Multiple Comparison Test of the ANOVA Test Output
* Tukey - Kramer Multiple Comparison Test of the Kruskal-Wallis Test Output ▪ t-Test
* Wilcoxon Test
* Fisher Exact Test
* Chi-square test
* Shapiro-Wilk Normality Test
* Imaging (CCTA DICOM files) data (retrospective/baseline collection and acquisition at follow-up)
* 3D artery reconstruction Model Examples
1. _**Legislation for Data publication** _
Regarding publication of clinical data within the European Union, under the EU
regulation (EU) No 536/2014 OF THE EUROPEAN PARLIAMENT AND OF THE COUNCIL of
16 April 2014 2 all information from clinical trials can be publicly
accessible unless its confidentiality can be justified on the basis of
protection of commercially confidential information, protection of personal
data and protection of confidential communication between EU countries and of
ensuring effective supervision of the conduct of clinical trials by EU
countries. For each EU country, national legislation, is compliant with EU
legislation (for example Greek legislation on Directive 2003/98/EC of the
European Parliament and of the Council of 17 November 2003 on the re-use of
public sector information 3 ). As far as research data is concerned,
specifically in the medicine field that often contains personal data, datasets
in their raw form cannot be made openly available as required by the Open
Research Data Pilot 4 due to conflicts with rules on protection of personal
data. The best way to fulfil the requirements of the Open Research Data Pilot
and data protection rules (GDPR5) at the same time is to anonymise personal
(research) data before making them openly available. Anonymised data are no
longer personal data, consequently data protection rules are no longer
applicable. Effective anonymisation prevents third parties from re-identifying
individuals in anonymised datasets, i.e., associating a record to a natural
person by using other sources of information. Moreover, anonymisation provides
further privacy guarantees that prevent third parties from inferring that a
person is associated with a certain property, e.g., a particular health
condition, with high probability, or even to infer the participation of a
person in a published dataset. All data on SMARTool platform are appropriately
anonymised on premise before being uploaded.
**_Tools_ **
Various tools are available for publication and sharing of anonymised open
data, such as the Openaire 5 and the Open Data Portal 6 . When possible,
data anonymisation is the best solution to avert data protection risks.
AMNESIA, a service currently being developed by OpenAIRE, will allow data
curators to anonymise their data. Amnesia is a data anonymization tool, that
allows to remove identifying information from data. Amnesia 7 not only
removes direct identifiers like names, SSNs etc but also transforms secondary
identifiers like birth date and zip code so that individuals cannot be
identified in the data. Amnesia supports _k_ anonymity and _km_ -anonymity.
2. _**Data Storage** _
During the project, the data have been collected and systematically stored in
a complex repository based on the following three components:
* Imaging data is stored in cloud-based 3DnetMedical.com DICOM compliant repository. 3DnetMedical.com conforms to the DICOM SOP’s of the Storage Service Class at Level 2 (Full). 3DnetMedical.com offers Vendor Neutral Archive (VNA) functionalities from a UK based ISO27001 accredited datacentre – providing security, redundancy, reliability and scalability through onshore outsourcing. 3DnetMedical.com follows specific privacy and security-conscious policies applicable to all of its information handling practices. The 3Dnet Dicom store is employed also for genomic data storage, following the Dicom.
* Structured clinical data (data acquired during the project for the trial population as well as data employed by the final platform) are stored in the MongoDB database;
* Clinical data acquired by means of HL7/IHE integrations with hospital sources are stored in an XDS/XDSi repository
User data (such as usernames and application privileges) are managed by a
platform's Identity Server (WSO2IS) and stored in the LDAP server embedded in
the Identity Server.
Raw sequencing data generated in WP3 will be made available upon publication
in the European Genomephenome Archive (EGA) under controlled access (DAC) to
provide long-term storage and accessibility of the data to the research
community.
**4.2.1. Databases**
_**Level roles and permissions in databases** _
To easily manage the permissions in the databases, several roles have been
defined in the following three groups of roles; Administrator, WP leaders and
Researchers, Users (Table 2, Table 3, Table 4).
_Table 2: Level roles and permissions in the 3DnetMedical.com DICOM compliant
Database._
<table>
<tr>
<th>
**Description of permissions**
</th>
<th>
**Administrator**
</th>
<th>
**WP leaders & Researchers **
</th>
<th>
**User**
</th> </tr>
<tr>
<td>
Manage user accounts
</td>
<td>
</td>
<td>
</td>
<td>
</td> </tr>
<tr>
<td>
Manage user roles and access
</td>
<td>
</td>
<td>
</td>
<td>
</td> </tr>
<tr>
<td>
Manage folders, worklists and gateways.
</td>
<td>
</td>
<td>
</td>
<td>
</td> </tr>
<tr>
<td>
Upload studies
</td>
<td>
</td>
<td>
</td>
<td>
</td> </tr>
<tr>
<td>
Delete or assign a study*
</td>
<td>
</td>
<td>
</td>
<td>
</td> </tr>
<tr>
<td>
Visualisation and manipulation of imaging data from studies in accessible
worklists and folders*
</td>
<td>
</td>
<td>
</td>
<td>
</td> </tr>
<tr>
<td>
Download data*
</td>
<td>
</td>
<td>
</td>
<td>
</td> </tr>
<tr>
<td>
Report a study*
</td>
<td>
</td>
<td>
</td>
<td>
</td> </tr>
<tr>
<td>
Access to patient information*
</td>
<td>
</td>
<td>
</td>
<td>
</td> </tr> </table>
*Depending on the user role
_Table 3: Level roles and permissions in the CRFA NoSQL MongoDB Database._
<table>
<tr>
<th>
**Description of permissions**
</th>
<th>
**Administrator**
</th>
<th>
**WP leaders & Researchers **
</th>
<th>
**User**
</th> </tr>
<tr>
<td>
Download anonymized non imaging clinical data
</td>
<td>
</td>
<td>
</td>
<td>
</td> </tr>
<tr>
<td>
Visualisation and editing of non-imaging data from studies
</td>
<td>
</td>
<td>
</td>
<td>
</td> </tr>
<tr>
<td>
Upload non imaging data for studies
</td>
<td>
</td>
<td>
</td>
<td>
</td> </tr>
<tr>
<td>
Delete and report a study*
</td>
<td>
</td>
<td>
</td>
<td>
</td> </tr>
<tr>
<td>
Access to patient information*
</td>
<td>
</td>
<td>
</td>
<td>
</td> </tr> </table>
_Table 4: Level roles and permissions in the WSO2IS embedded LDAP User
Database._
<table>
<tr>
<th>
**Description of permissions**
</th>
<th>
**Administrator**
</th>
<th>
**WP leaders & Researchers **
</th>
<th>
**User**
</th> </tr>
<tr>
<td>
Manage user accounts
</td>
<td>
</td>
<td>
</td>
<td>
</td> </tr>
<tr>
<td>
Manage user roles and access
</td>
<td>
</td>
<td>
</td>
<td>
</td> </tr>
<tr>
<td>
Manage DB, Identity Server, Integration Bus and
Application Servers
</td>
<td>
</td>
<td>
</td>
<td>
</td> </tr> </table>
_**Process** _
The 3DnetMedical.com conforms to DICOM SOP’s of the Storage Service Class at
Level 2 (Full). The DICOM Information Model is derived from the DICOM Model of
the Real World where the DICOM view of the Real-World that identifies the
relevant Real-World Objects and their relationships within the scope of the
DICOM Standard is shown. It provides a common framework to ensure consistency
between the various Information Objects defined by the DICOM Standard.
Clinical documents will be made available to the production SMARTool platform
by means of IHE XDS.b (Cross-Enterprise Document Sharing) and XDS-I.b (Cross-
enterprise Document Sharing for Imaging) integration profiles. An XDS Affinity
Domain will be defined for the platform, in order to ease clinical information
sharing with the clinical organizations that will use the platform. The
platform itself will act as a Document Consumer to query/retrieve documents
containing data used to model the patient, and as a Document Producer for
registering the documents generated by the platform.
Non imaging data will be collected directly during or after a patient's visit
via the platform's data entry application (CRFA). This data will be stored in
the SMARTool MongoDB database for processing and kept for future encounters
with the patient. In order to ease data entry activities, when non imaging
data is available in clinical documents stored in XDS.b repository the data
entry application (CRFA) will pre-fill retrievable data in the corresponding
form fields for user review. Non imaging data retention and clinical document
retention will be subject to the clinical organization policy.
**4.2.2. Datasets**
_**Origin of WP1, WP2 and WP3 datasets** _
_Table 5: Details of Dataset 1._
<table>
<tr>
<th>
**Data identification: Dataset 1**
</th> </tr>
<tr>
<td>
**Description** This dataset contains the SMARTool users' personal
information, demographics, clinical, imaging, molecular, omics and other
health related data.
More specifically, the following categories of data are also included:
* Clinical data and risk factors
* Blood tests/ Biohumoral
* Imaging - CTA scan visual/quantitative analysis: plaque composition (calcified, mixed, noncalcified) and features , nominal categories
* Circulating soluble proteins: consolidated biomarkers (hsTN, hsCRP, BNP, ALT) and inflammatory markers (IL6, IL 10, ICAM 1, VCAM, e-.selectin), values of blood concentration.
* Genetics: associated selected SNPs, selected RNA genes from bioinformatics analysis of DNA/RNA sequencing.
* Lipids: selected lipid species and concentrations in blood; names and values of plasma concentration from bioinformatics analysis.
* Circulating MN surface proteins to quantify Mon1 Mon 2 and Mon 3 subpopulations: relative and absolute concentrations in blood
</td> </tr>
<tr>
<td>
Category (Cat1, Cat2, Cat3)
</td>
<td>
Cat1, Cat2
</td> </tr>
<tr>
<td>
**Partners activities and responsibilities**
</td> </tr>
<tr>
<td>
Partner owner of the data
</td>
<td>
Clinical partners
</td> </tr>
<tr>
<td>
Partner in charge of the data analysis
</td>
<td>
CNR, LUMC, ALACRIS
</td> </tr>
<tr>
<td>
Partner in charge of the data storage
</td>
<td>
B3D, EHIT
</td> </tr>
<tr>
<td>
Related WP(s) and task(s)
</td>
<td>
WP1
Task 1.1 Clinical and imaging (CCTA) data
collection (eg Evinci baseline data)
Task 1.2 CCTA analysis under standardized criteria
WP2
Task 2.1 Biohumoral data collection and analysis at baseline and at follow-up
Task 2.2 Patient-specific phenotyping (cellular and molecular data) at
baseline and at follow-up
WP3
Task 3.1 Genomics and transcriptomics
Task 3.2 Omics data analysis
</td> </tr>
<tr>
<td>
**Standards and metadata**
</td> </tr>
<tr>
<td>
Standards, format, estimated volume of data
</td>
<td>
RAW, DICOM, XLM, JSON, BSON
Clinical data will not contain any metadata.
Volume: To be calculated at the end of patient data collection
</td> </tr>
<tr>
<td>
**Data exploitation and sharing**
</td> </tr>
<tr>
<td>
Data access policy/ Dissemination level: confidential (only for members of the
Consortium and the Commission Services) or Public
</td>
<td>
Confidential
</td> </tr>
<tr>
<td>
Data sharing, re-use, distribution, publication (How?)
</td>
<td>
Internal validation of discriminative markers by bootstrapping techniques
</td> </tr>
<tr>
<td>
Personal data protection: are they personal data?
If so, have you gained (written) consent from data subjects to collect this
information?
</td>
<td>
Personal protection according to the project ethical and legal guidelines
Written consent received.
</td> </tr>
<tr>
<td>
**Archiving and preservation (including storage and backup)**
</td> </tr>
<tr>
<td>
Data storage (including backup): where? For how long?
</td>
<td>
3DnetMedical.com DICOM compliant database
NoSQL MongoDB Database
Long term archival
</td> </tr> </table>
**Methodologies for data collection.**
For the follow-up CT acquisition, in order to achieve optimal quality for the
3D reconstruction of the desired arterial segments, the 128/256/320 MSCT
scanners are used with interval between the slices not exceeding the threshold
of 0.5 mm. For the elimination of motion or other artifacts in the acquired
images, heart rate during scan should be less than 65 beats/min and optimally
less than 60 beats/min. Nitroglycerin prior to the CTA acquisition is
administered. Also, multiple cardiac phases are captured to choose different
phases for different coronary segments, if needed. The reconstructed field of
view is reduced to maximize the number of pixels devoted to depiction of the
heart, usually a field of view of 200–250mm for coronary CTA studies of native
coronary arteries.
For the blood sampling, the responsible clinical partners collect for each
enrolled patient during venipuncture:
* N= 8/5 ml EDTA tubes (VIOLET tube)
* N=2/5ml Li-heparin tubes (GREEN tube)
* N= 2/5ml Clot activator tubes (RED tube)
During blood sampling, details are recorded such as:
* Samples collected (blood [which tubes, how many and in which order]).
* Date and time of blood sampling
* Last time of food/drink consumption and smoking. Special attention for 12h fasting and 12h refraining from smoking.
The separation of blood aliquots is described below:
_For the VIOLET tubes: EDTA N=8 tubes_
* N=6 tubes/8
Ø EDTA-Plasma is separated by centrifugation at +4°C in a refrigerated
centrifuge, 10 minutes at 1500xg. The plasma is separated from cell pellet
within 1 hour from the withdrawing, keeping the vial over this time in an ice
bath.
Samples are subdivided in aliquots about 1 ml each (18 aliquots), by using
small plastic vials (VIOLET CAPS) and keeping all the vials in an ice-bath
during the whole procedure.
* N=2 tubes/8
Ø EDTA-Whole-Blood tubes are stored at -20°C or -80°C without centrifugation
and NOT aliquoted.
_For the RED tubes: Clot Activator N=2 tubes_
* Ø Serum samples are separated by centrifugation, for 10 minutes at 1500xg. Samples are subdivided in aliquots about 1 ml each (6 aliquots), by using small plastic vials (RED CAPS) and keeping all the vials in an ice-bath during the whole procedure.
_For the GREEN tubes: Li-HEPARIN N=2 tubes_
* Ø Heparin-Plasma are separated by centrifugation, for 10 minutes at 1500xg. Samples should be subdivided in aliquots about 1 ml each (6 aliquots), by using small plastic vials (GREEN CAPS) and keeping all the vials in an ice-bath during the whole procedure.
_For the TEMPUS tubes: N=3 tubes_
* Ø TEMPUS tubes (obtained by CNR) are stored at -20°C without centrifugation and NOT aliquoted
For the blood storage, upon arrival in the lab, samples are maintained at room
temperature (18-22 °C) for 2-4 hours before transferring to freezer, in an
upright position. TEMPUS tubes can be left for up to72 hours at room
temperature prior to freezing. The TEMPUS tubes should then be frozen at -20
°C upright until freezing and storage at -20°C or -80°C (if available) until
shipping.
The blood samples are packaged appropriately with sufficient dry ice or ensure
that samples do not thaw and packaged in a manner that prevents breakage or
leakage. For mandatory safety reasons the blood samples are tested for
* HIV
* HEPATITIS B
* HEPATITIS C
_Table 6: Details of Dataset 2._
<table>
<tr>
<th>
**Data identification: Dataset 2**
</th> </tr>
<tr>
<td>
**Description:** This dataset contains the CAD stratification which is
extracted through the application of data mining techniques
</td> </tr>
<tr>
<td>
Category (Cat1, Cat2, Cat3)
</td>
<td>
Cat3
</td> </tr>
<tr>
<td>
**Partners activities and responsibilities**
</td> </tr>
<tr>
<td>
Partner owner of the data
</td>
<td>
Clinical partners
</td> </tr>
<tr>
<td>
Partner in charge of the data analysis
</td>
<td>
FORTH
</td> </tr>
<tr>
<td>
Partner in charge of the data storage
</td>
<td>
EHIT
</td> </tr>
<tr>
<td>
Related WP(s) and task(s)
</td>
<td>
WP3
Task 3.4 Multivariate analysis, data mining and non-imaging classification
algorithm for CAD stratification
</td> </tr>
<tr>
<td>
**Standards and metadata**
</td> </tr>
<tr>
<td>
Standards, format, estimated volume of data
</td>
<td>
RAW, JSON, ML,XML, PMML, PFA Volume: ~5GB
</td> </tr>
<tr>
<td>
**Data exploitation and sharing**
</td> </tr>
<tr>
<td>
Data access policy/ Dissemination level: confidential (only for members of the
Consortium and the Commission Services) or Public
</td>
<td>
Confidential
</td> </tr>
<tr>
<td>
Data sharing, re-use, distribution, publication (How?)
</td>
<td>
No data sharing, Only the data mining and nonimaging classification algorithm
will be public available in peer review journals and conferences
</td> </tr>
<tr>
<td>
Personal data protection: are they personal data?
If so, have you gained (written) consent from data subjects to collect this
information?
</td>
<td>
Personal data protection according to the project ethical and legal guidelines
</td> </tr>
<tr>
<td>
**Archiving and preservation (including storage and backup)**
</td> </tr>
<tr>
<td>
Data storage (including backup): where? For how long?
</td>
<td>
NoSQL MongoDB Database
Long term archival
</td> </tr> </table>
_**Origin of WP4 datasets** _
_Table 7: Details of Dataset 3._
<table>
<tr>
<th>
**Data identification: Dataset 3**
</th> </tr>
<tr>
<td>
**Description:** This dataset contains the results of 3D artery reconstruction
and of the multiscale and multilevel site specific models for plaque
progression and of the non-invasive SmartFFR calculation, which are all used
for the development of predictive models integrated in the prognostic and
diagnostic
CDSS
</td> </tr>
<tr>
<td>
Category (Cat1, Cat2, Cat3)
</td>
<td>
Cat3
</td> </tr>
<tr>
<td>
**Partners activities and responsibilities**
</td> </tr>
<tr>
<td>
Partner owner of the data
</td>
<td>
Clinical partners
</td> </tr>
<tr>
<td>
Partner in charge of the data analysis
</td>
<td>
FORTH
</td> </tr>
<tr>
<td>
Partner in charge of the data storage
</td>
<td>
EHIT
</td> </tr>
<tr>
<td>
Related WP(s) and task(s)
</td>
<td>
WP4
Task 4.1 Prognostic CDSS: Refinement of multiscale and multilevel site
specific models for plaque progression
Task 4.2 Diagnostic CDSS: Refinement of noninvasive FFR computation
Task 4.4 Validation of prognostic CDSS (Multiscale-multilevel site specific
plaque progression models)
Task 4.5 Validation of diagnostic CDSS (3D artery reconstruction, plaque
characterization and noninvasive FFR
</td> </tr>
<tr>
<td>
**Standards and metadata**
</td> </tr>
<tr>
<td>
Standards, format, estimated volume of data
</td>
<td>
DICOM, RAW, XLM, STL, IGES
A data volume estimation will be later provided.
</td> </tr>
<tr>
<td>
**Data exploitation and sharing**
</td> </tr>
<tr>
<td>
Data access policy / Dissemination level: confidential (only for members of
the Consortium and the Commission Services) or Public
</td>
<td>
Confidential
</td> </tr>
<tr>
<td>
Data sharing, re-use, distribution, publication (How?)
</td>
<td>
No data sharing. Only the results from the plaque progression and the non-
invasive SmartFFR will be public available in peer review journals and
conferences
</td> </tr>
<tr>
<td>
Personal data protection: are they personal data?
If so, have you gained (written) consent from data subjects to collect this
information?
</td>
<td>
Personal data protection according to the project ethical and legal guidelines
</td> </tr>
<tr>
<td>
**Archiving and preservation (including storage and backup)**
</td> </tr>
<tr>
<td>
Data storage (including backup): where? For how long?
</td>
<td>
NoSQL MongoDB Database
Long term archival
</td> </tr> </table>
_Table 8: Details of Dataset 4._
<table>
<tr>
<th>
**Data identification: Dataset 4**
</th> </tr>
<tr>
<td>
**Description:** This dataset contains the results of the pharmacological
therapy modulation algorithms (EVINCI database) using data mining techniques.
In addition, data from the virtual stent deployment approach are also
included.
</td> </tr>
<tr>
<td>
Category (Cat1, Cat2, Cat3)
</td>
<td>
Cat3
</td> </tr>
<tr>
<td>
**Partners activities and responsibilities**
</td> </tr>
<tr>
<td>
Partner owner of the data
</td>
<td>
Clinical partners
</td> </tr>
<tr>
<td>
Partner in charge of the data analysis
</td>
<td>
FINK, FORTH
</td> </tr>
<tr>
<td>
Partner in charge of the data storage
</td>
<td>
B3D, EHIT, FINK
</td> </tr>
<tr>
<td>
Related WP(s) and task(s)
</td>
<td>
WP4
Task 4.3 Treatment CDSS: Refinement of medical therapy and virtual angioplasty
decision support methods
Task 4.6 Validation of treatment CDSS
(Pharmacological therapy and virtual angioplasty tool)
</td> </tr>
<tr>
<td>
**Standards and metadata**
</td> </tr>
<tr>
<td>
Standards, format, estimated volume of data
</td>
<td>
DICOM, STL, DAT, UNV, LST, BMP, AVI
A data volume estimation will be later provided.
</td> </tr>
<tr>
<td>
**Data exploitation and sharing**
</td> </tr>
<tr>
<td>
Data access policy / Dissemination level: confidential (only for members of
the Consortium and the Commission Services) or Public
</td>
<td>
Confidential, Public only some specific results
</td> </tr>
<tr>
<td>
Data sharing, re-use, distribution, publication (How?)
</td>
<td>
Data sharing, publication in peer review journal
</td> </tr>
<tr>
<td>
Personal data protection: are they personal data?
If so, have you gained (written) consent from data subjects to collect this
information?
</td>
<td>
Personal data protection according to the project ethical and legal guidelines
</td> </tr>
<tr>
<td>
**Archiving and preservation (including storage and backup)**
</td> </tr>
<tr>
<td>
Data storage (including backup): where? For how long?
</td>
<td>
B3D, FINK
</td> </tr> </table>
_**Origin of WP5 datasets** _
_Table 9: Details of Dataset 5._
<table>
<tr>
<th>
**Data Identification: Dataset 5**
</th> </tr>
<tr>
<td>
Description: This dataset contains information related with T5.1 regarding
users’ requirements, use cases, questionnaires, functional specifications and
system architecture for the SMARTool Platform.
</td> </tr>
<tr>
<td>
Category (Cat1, Cat2, Cat3)
</td>
<td>
Cat 3
</td> </tr>
<tr>
<td>
Partners activities and responsibilities
</td> </tr>
<tr>
<td>
Partner owner of the data
</td>
<td>
Technical partners
</td> </tr>
<tr>
<td>
Partner in charge of the data analysis
</td>
<td>
B3D, FORTH, FINK, EHIT
</td> </tr>
<tr>
<td>
Partner in charge of the data storage
</td>
<td>
B3D
</td> </tr>
<tr>
<td>
Related WP(s) and task(s)
</td>
<td>
WP5
Task 5.1 User requirements, functional specifications and architecture of the
SMARTool platform
</td> </tr>
<tr>
<td>
Standards and metadata
</td> </tr>
<tr>
<td>
Standards, format, estimated volume of data
</td>
<td>
RAW, XML, UML, PDF, CSV Volume~500ΜΒ
</td> </tr>
<tr>
<td>
Data exploitation and sharing
</td> </tr>
<tr>
<td>
Data access policy / Dissemination level: confidential (only for members of
the Consortium and the Commission Services) or Public
</td>
<td>
Confidential
</td> </tr>
<tr>
<td>
Data sharing, re-use, distribution, publication (How?)
</td>
<td>
No data sharing.
</td> </tr>
<tr>
<td>
Personal data protection: are they personal data?
If so, have you gained (written) consent from data subjects to collect this
information?
</td>
<td>
This dataset does not include personal data. Questionnaires are anonymized.
</td> </tr>
<tr>
<td>
Archiving and preservation (including storage and backup)
</td> </tr>
<tr>
<td>
Data storage (including backup): where? For how long?
</td>
<td>
Data will be available through D5.1 and its annexes. As WP leader B3D will
store and backup the data.
</td> </tr> </table>
_**Origin of WP6 datasets** _
Each SMARTool partner needs to disseminate its results in accordance with
Article 29 of the Grant Agreement. The results of the research performed in
SMARTool project are communicated through public deliverables, journal and
conference presentations, as well as other dissemination channels (website,
social media, etc). On the other side, the exploitation is mainly achieved
through the main SMARTool exploitable products:
* SMARTool 3DNet™-framework: a common, centralized shareable platform that provides a better control, management and streamlining of CAD clinical workflow based on CDSS.
* SMARTool CDSS: an integrated system for supporting the clinicians in diagnosis, prognosis and treatment of CAD patients and subjects at risk.
* SMARTool Point-Of-Care-Testing (POCT), a portable device for on chip blood analysis and patient phenotyping exploitable in diagnostic CDSS.
_**Origin of WP7 datasets** _
_Table 10: Details of Dataset 7._
<table>
<tr>
<th>
**Data identification: Dataset 7.1**
</th> </tr>
<tr>
<td>
Description: This dataset contains information related to the project
management and coordination
</td> </tr>
<tr>
<td>
Category (Cat1, Cat2, Cat3)
</td>
<td>
Cat 1, Cat 2, Cat 3 (by the Commission)
</td> </tr>
<tr>
<td>
Partners activities and responsibilities
</td> </tr>
<tr>
<td>
Partner owner of the data
</td>
<td>
Joint Ownership
</td> </tr>
<tr>
<td>
Partner in charge of the data analysis
</td>
<td>
CNR
</td> </tr>
<tr>
<td>
Partner in charge of the data storage
</td>
<td>
CNR
</td> </tr>
<tr>
<td>
Related WP(s) and task(s)
</td>
<td>
WP7
T7.1 Project Management
</td> </tr>
<tr>
<td>
Standards and metadata
</td> </tr>
<tr>
<td>
Standards, format, estimated volume of data
</td>
<td>
No specific standards for this data.
Files will be in Microsoft Office in doc, .docx, .xls, .xlsx, and .pdf
formats. Volume~1GB
</td> </tr>
<tr>
<td>
Data exploitation and sharing
</td> </tr>
<tr>
<td>
Data access policy/ Dissemination level: confidential (only for members of the
Consortium and the Commission Services) or Public
</td>
<td>
Confidential: information shared only among the
Consortium and between the Consortium and the Commission
</td> </tr>
<tr>
<td>
Data sharing, re-use, distribution, publication (How?)
</td>
<td>
Effort (quantified in person months – PMs) and financial data of each partner
is collected by CNR and compiled for monitoring purposes.
Data is entered/uploaded in the EC system SyGMa in order to allow the
Commission to oversee and assess use of the resources by the consortium.
</td> </tr>
<tr>
<td>
Personal data protection: are they personal data?
If so, have you gained (written) consent from data subjects to collect this
information?
</td>
<td>
No personal data collected
</td> </tr>
<tr>
<td>
Archiving and preservation (including storage and backup)
</td> </tr>
<tr>
<td>
Data storage (including backup): where? For how long?
</td>
<td>
The data is collected for internal use in the project.
Standard daily offsite backup on CNR systems.
Length: 10 years
</td> </tr> </table>
_**4.3. Visualization of Data Lineage** _
Data lineage refers to data life cycle that included the data’s origins and
where it moves over time. It can be represented visually to discover the data
flow/movement from its source to destination via various changes and hops on
its way in the enterprise environment, how the data gets transformed along the
way, how the representation and parameters change, and how the data splits or
converges after each hop.
Data lineage answers a wide range of questions about how data is being used.
More specifically it is useful to track all calculations and transformations
actually defined.
**4.3.1. Data used in CRFA**
**_Data stored in MondoDB database_ **
**MongoDB** is a free and open-source cross-platform document-oriented
database program; it is classified as a NoSQL database program. It is used
within the project as a database engine for clinical/molecular data and data-
model storage.
MondoDB SMARTool database consists of six collections:
1. _**Studies** _ collection: contains all SMARTool patients;
2. _**Fragments** _ collection: illustrates each data showed in CRFA pages;
3. _**Hfragments** _ collection: keeps stored all historical documents of _fragments_ collection (previous versions of data entry);
4. _**Images** _ collection: contains all image references of the patients;
5. _**Institutions** _ collection: provides the name of each organization involved in the project, by distinguishing them between clinical and not clinical;
6. _**Types** _ collection: describes the structure of each page of CRFA.
Unlike SQL databases, where a table’s schema must be determined before
inserting data, MongoDB’s collections, by default, does not require its
documents to have the same schema.This implies that the document in a single
collection do not need to have the same set of fields and the data type for a
field can differ across documents within a collection. Frthermore, it is
possible to change the structure of the documents in a collection (add new
fields, remove existing fields, change the field values to a new type, update
the documents to a new structure, etc.). As a result, MongoDB provides more
flexibility in mapping of documents to an entity or an object.
Figure 4 below describe the data model of Care Report Forms (CRF) Repository.
There is a 1:N relationship between _studies/fragments_ , _studies/images,
institutions/studies,_ that is each patient case is linked to at least one
document of _fragments_ and _images,_ and more study cases can be related to a
single institution. Similarly, there is a 1:N relationship between
_fragments/types_ , i.e. there are several forms of a single type. Finally,
_hfragments_ behaviour is the same as _fragments_ collection; however, its
documents are no more linked to the correspondent study, since they refer to
historical versions.
An important difference should be clarified about data relationships: they can
be references or embedding. Reference between documents are described as
“normalized” data model. Two method for relating documents are used in
MongoDB:
* Manual references where the __id_ field of one document is saved in another document as a reference. This appears between _studies/images_ and _fragments/types_ collections.
* DBRefs are references from one document to another using the value of the first document’s __id_ field, collection name, and , optionally, its database name. This is the case of _studies/institutions_ and _studies/fragments_ collections.
Embedding allows to include related data in a single structure or document.
These schema are generally known as “denormalized” models, and take advantage
of MongoDB’s rich documents, by storing related pieces of information in the
same database record. Embedding occurs between _fragments/types,
studies/images_ .
Some examples of the data structures stored in MongoDB Database:
1. **Studies:** new study by Clinical Partner direct input
<table>
<tr>
<th>
_id
</th>
<th>
ObjectId
</th> </tr>
<tr>
<td>
_class
</td>
<td>
String
</td> </tr>
<tr>
<td>
studyCode
</td>
<td>
String
</td> </tr>
<tr>
<td>
subjectCode
</td>
<td>
String
</td> </tr>
<tr>
<td>
progressiveStudyNumber
</td>
<td>
Int32
</td> </tr>
<tr>
<td>
creationDate
</td>
<td>
Date
</td> </tr>
<tr>
<td>
Enabled
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
fragmentsDisabled
</td>
<td>
Array
</td> </tr>
<tr>
<td>
versionNumber
</td>
<td>
Int64
</td> </tr>
<tr>
<td>
createAt
</td>
<td>
Date
</td> </tr>
<tr>
<td>
lastModified
</td>
<td>
Date
</td> </tr>
<tr>
<td>
createBy
</td>
<td>
String
</td> </tr>
<tr>
<td>
lastModifiedBy
</td>
<td>
String
</td> </tr>
<tr>
<td>
Institution
</td>
<td>
Object
</td> </tr>
<tr>
<td>
fragments
</td>
<td>
Array
</td> </tr> </table>
2. **Institutions**
<table>
<tr>
<th>
**Institutions**
</th>
<th>
</th> </tr>
<tr>
<td>
_id
</td>
<td>
Strin
</td> </tr>
<tr>
<td>
Name
</td>
<td>
String
</td> </tr>
<tr>
<td>
clinical
</td>
<td>
Boolean
</td> </tr> </table>
3. **Images** : Clinical Partner direct upload
<table>
<tr>
<th>
**Images**
</th>
<th>
</th> </tr>
<tr>
<td>
_id
</td>
<td>
ObjectId
</td> </tr>
<tr>
<td>
_class
</td>
<td>
String
</td> </tr>
<tr>
<td>
fileName
</td>
<td>
String
</td> </tr>
<tr>
<td>
fileSize
</td>
<td>
Int64
</td> </tr>
<tr>
<td>
contentType
</td>
<td>
String
</td> </tr>
<tr>
<td>
sourcePath
</td>
<td>
String
</td> </tr>
<tr>
<td>
studyCode
</td>
<td>
String
</td> </tr>
<tr>
<td>
versionNumber
</td>
<td>
Int64
</td> </tr>
<tr>
<td>
createAt
</td>
<td>
Date
</td> </tr>
<tr>
<td>
lastModified
</td>
<td>
Date
</td> </tr>
<tr>
<td>
createBy
</td>
<td>
String
</td> </tr>
<tr>
<td>
lastModifiedBy
</td>
<td>
String
</td> </tr> </table>
4. **Fragments** :
1. **Exclusion Criteria** : Partner direct input
<table>
<tr>
<th>
_id
</th>
<th>
ObjectId
</th> </tr>
<tr>
<td>
_class
</td>
<td>
String
</td> </tr>
<tr>
<td>
typeCode
</td>
<td>
String
</td> </tr>
<tr>
<td>
Loked
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
Payload
</td>
<td>
Object
</td> </tr>
<tr>
<td>
Status
</td>
<td>
String
</td> </tr>
<tr>
<td>
Valid
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
Historical
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
versionNumber
</td>
<td>
Int64
</td> </tr>
<tr>
<td>
createAt
</td>
<td>
Date
</td> </tr>
<tr>
<td>
lastModified
</td>
<td>
Date
</td> </tr>
<tr>
<td>
createBy
</td>
<td>
String
</td> </tr>
<tr>
<td>
lastModifiedBy
</td>
<td>
String
</td> </tr>
<tr>
<td>
Study
</td>
<td>
Object
</td> </tr> </table>
2. **Clinical** : Clinical Partner direct input
**CTA Report CL** : Image Analysis measures by LUMC
<table>
<tr>
<th>
_id
</th>
<th>
ObjectId
</th> </tr>
<tr>
<td>
_class
</td>
<td>
String
</td> </tr>
<tr>
<td>
typeCode
</td>
<td>
String
</td> </tr>
<tr>
<td>
Payload
</td>
<td>
Object
</td> </tr>
<tr>
<td>
Status
</td>
<td>
String
</td> </tr>
<tr>
<td>
Valid
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
Historical
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
versionNumber
</td>
<td>
Int64
</td> </tr>
<tr>
<td>
createAt
</td>
<td>
Date
</td> </tr>
<tr>
<td>
lastModified
</td>
<td>
Date
</td> </tr>
<tr>
<td>
createBy
</td>
<td>
String
</td> </tr>
<tr>
<td>
lastModifiedBy
</td>
<td>
String
</td> </tr>
<tr>
<td>
study
</td>
<td>
Object
</td> </tr> </table>
3. **Lipid Profile / Biohumoral Markers / Inflammatory Markers / QCTA / Exome / RNA** : Clinical Partner direct upload
<table>
<tr>
<th>
_id
</th>
<th>
ObjectId
</th> </tr>
<tr>
<td>
_class
</td>
<td>
String
</td> </tr>
<tr>
<td>
typeCode
</td>
<td>
String
</td> </tr>
<tr>
<td>
Payload
</td>
<td>
Object
</td> </tr>
<tr>
<td>
Status
</td>
<td>
String
</td> </tr>
<tr>
<td>
Valid
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
Historical
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
versionNumber
</td>
<td>
Int64
</td> </tr>
<tr>
<td>
createAt
</td>
<td>
Date
</td> </tr>
<tr>
<td>
lastModified
</td>
<td>
Date
</td> </tr>
<tr>
<td>
createBy
</td>
<td>
String
</td> </tr>
<tr>
<td>
lastModifiedBy
</td>
<td>
String
</td> </tr>
<tr>
<td>
study
</td>
<td>
Object
</td> </tr> </table>
4. **Blood Tests** : Clinical Partner direct input
<table>
<tr>
<th>
_id
</th>
<th>
ObjectId
</th> </tr>
<tr>
<td>
_class
</td>
<td>
String
</td> </tr>
<tr>
<td>
typeCode
</td>
<td>
String
</td> </tr>
<tr>
<td>
Loked
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
Payload
</td>
<td>
Object
</td> </tr>
<tr>
<td>
Status
</td>
<td>
String
</td> </tr>
<tr>
<td>
Valid
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
Historical
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
versionNumber
</td>
<td>
Int64
</td> </tr>
<tr>
<td>
createAt
</td>
<td>
Date
</td> </tr>
<tr>
<td>
lastModified
</td>
<td>
Date
</td> </tr>
<tr>
<td>
createBy
</td>
<td>
String
</td> </tr>
<tr>
<td>
lastModifiedBy
</td>
<td>
String
</td> </tr>
<tr>
<td>
study
</td>
<td>
Object
</td> </tr> </table>
5. **Types** :
<table>
<tr>
<th>
_id
</th>
<th>
String
</th> </tr>
<tr>
<td>
_class
</td>
<td>
String
</td> </tr>
<tr>
<td>
Display
</td>
<td>
String
</td> </tr>
<tr>
<td>
shortName
</td>
<td>
String
</td> </tr>
<tr>
<td>
Order
</td>
<td>
Int32
</td> </tr>
<tr>
<td>
controllerType
</td>
<td>
String
</td> </tr>
<tr>
<td>
Definition
</td>
<td>
Object
</td> </tr>
<tr>
<td>
canReadRoles
</td>
<td>
Array
</td> </tr>
<tr>
<td>
canWriteRoles
</td>
<td>
Array
</td> </tr>
<tr>
<td>
versionNumber
</td>
<td>
Int64
</td> </tr>
<tr>
<td>
createdAt
</td>
<td>
Date
</td> </tr>
<tr>
<td>
lastModified
</td>
<td>
Date
</td> </tr>
<tr>
<td>
createBy
</td>
<td>
String
</td> </tr>
<tr>
<td>
lastModifiedBy
</td>
<td>
String
</td> </tr> </table>
**Data produced from CRFA**
_Data processing via Apache Zeppelin:_
* LUMC CTA score function definition
* Stenosis function definition
* Queries for Data Exports (.csv)
_Clinical Characteristics_
* Body Mass Index (BMI): function of height and weight
* BMI Class: function of BMI
<table>
<tr>
<th>
$%
$&
</th>
<th>
ℎ
18.50 ℎ 25.00
</th>
<th>
18.50
24.99
29.99
’ 30.00
</th> </tr> </table>
_**CTA Report CL** _
* Segment Weight Factor (seg_wf): function of System Dominance (sd)
1, ℎ
) * +0, *
, ℎ
* Stenosis Weight Factor 1 and (ste_wf_1): function of % of Lumen Diameter 1 (prox_lumen_1)
)*)1
⎧
⎪⎪1 1 2 ) 3 _1 30% | 30% 1 2 ) 3 _1 50%
⎪1.4 50% 1 2 ) 3 ) 1 70% | 70% 1 2 ) 3 ) 1 90% | 1 2 ) 3 ) 1 8 90%
1.4 _1
⎨ 1.2 1 2 ) 3 ) 1 3 1 &
⎪ 0 ℎ
⎪⎪
⎩
\- Stenosis Weight Factor 2 and (ste_wf_2): function of % of Lumen Diameter 2
(prox_lumen_2)
)*)2
⎧
⎪⎪1 1 2 ) 3 _2 30% | 30% 1 2 ) 3 _2 50%
⎪1.4 50% 1 2 ) 3 ) 2 70% | 70% 1 2 ) 3 ) 2 90% | 1 2 ) 3 ) 2 8 90%
1.4 _2
⎨ 1.2 1 2 ) 3 ) 2 3 1 &
⎪ 0 ℎ
⎪⎪
⎩
* Stenosis Weight Factor (ste_wf): function of _ste_wf_1_ and _ste_wf_2_
) * max< ) * ) 1, ) * ) 2=
* Plaque Weight Factor 1 (pla_wf_1): function of Plaque type 1 (prox_type_1)
<table>
<tr>
<th>
1.2 1 2
⎧ ) >1 1
⎪1.5
1 ) * ) 1 1.6 1 2 ) >1 1 2 | 1 2 ) >1 1 ⎨ 1.7 1 2 ) >1 1
⎪
⎩0 ℎ
\- Plaque Weight Factor 2 (pla_wf_2): function of Plaque type 2 (prox_type_2)
</th>
<th>
1
3
4
</th> </tr>
<tr>
<td>
1.2 1 2
⎧ ) >1 2
⎪1.5
1 ) * ) 2 1.6 1 2 ) >1 2 2 | 1 2 ) >1 2 ⎨ 1.7 1 2 ) >1 2
⎪
⎩0 ℎ
</td>
<td>
1
3
4
</td> </tr> </table>
* Plaque Weight Factor (ste_wf): function of _pla_wf_1_ and _pla_wf_2_
1 _ * max< 1 _ * ) 1,1 _ * ) 2=
* Segment Weight Factor (seg_wf), Stenosis Weight Factor (ste_wf), Plaque Weight Factor (pla_wf)
) @ )
* CAD Score (cad_score): function of _seg_score_
CD
@ ) @ A ) @ B
BEF
**4.3.2. Data used and produced at risk stratification algorithm**
The risk stratification algorithms uses as input all the non-imaging collected
data in the machine-learning algorithms or in the statistical analyses. The
metadata regards the classification tables of each model and the output will
be the probability of CAD depending on the outcome of the algorithm. More
specifically, the algorithm may provide the classification at each classes or
even a score, which defines one of the three classes. The data lineage is
shown in Figure 5.
**4.3.3. Data used and produced at diagnostic CDSS**
The diagnostic CDSS is based on the calculation of SmartFFR. However, the
diagnostic CDSS will also use imaging data and metrics in order to improve the
predictive value of the system. The data which are used are imaging metrics
and morphological characteristics such as the lumen area and volume, the
plaque area and volume, the volume of calcified and non-calcified plaques, the
degree of stenosis and other. These data are stored in txt files during the
run of the 3D reconstruction algorithm. The FEM solver is also provides the
simulation results as well the SmartFFR per reconstructed branch stored in
files. These data are used as input into two different approached of building
predictive models: one simplistic based on statistics and one based on machine
learning. Both approaches provide the predictive value for the diagnosis of
CAD. Figures Figure _6_ , Figure _7_ Figure _8_ present the data lineage for
the diagnostic CDSS of SMARTool.
conditions and the material properties in order to provide the pressures
required for the calculation of SmartFFR.
diagnostic CDSS of SMARTool.
**4.3.4. Data used and produced at prognostic CDSS**
The prognostic CDSS provides the prediction of site-specific plaque
progression integrating in a CDSS the plaque growth model with other SMARTool
modules. More specifically, the plaque growth model requires the reconstructed
geometries of the lumen and the arterial wall, some clinical and biohumoral
data (LDL, HDL serum concentration, monocytes count, blood pressure) and the
boundary conditions. The predictive models use additionally as input all
clinical and patients’ data and the SmartFFR. The figures Figure _9_ , Figure
_10_ , Figure _11_ present the data lineage of the prognostic CDSS.
**4.3.5. Data used and produced at treatment CDSS**
The treatment CDSS consists of the medical therapy prediction and the stent
deployment module. The medical treatment prediction combines in a machine
learning model all clinical non-imaging data including also the medical
therapy of each patient in order to provide the output of the optimal medical
therapy (Figure 12).
SMARTool.
The stent deployment module uses as input in the FEM solver the arterial
geometries of the lumen and the outer wall, the material properties of the
stent and the arterial wall and the boundary conditions. The output is the
deformed geometries in VTK format and the distribution of forces and stress in
VTK format also. Figure _13_ presents the data lineage of the stent deployment
module.
5. **Data security and ethical considerations**
_**5.1. Data security** _
The SMARTool project utilises and adopts specific methods and tools for
ensuring adequate field access and extended contact and communication between
the participants, who are sensitive in the activities that are related to
ethical concerns and research activities.
The following instructions have been used to ensure data security:
* Perform anonymization of personal data
* Encrypt data, if necessary by the local researchers
* Data storage in two separate locations for avoiding data loss
* Perform frequent backups (every 24 hours)
* Ensure the final dataset coherence by marking up files in a structured way
SMARTool databases for imaging and for non-imaging clinical data are hosted in
B3D’s secure data centre, collocated in dedicated spaces at top-tier
datacentre accredited withISO 27001 for Information Security Management, ISO
9001 for Quality Management, ISO 14001 for Environment Management. This
facility provides carrier-level support, including:
* Access control and physical security
* Environmental controls
* Power
* Network
* Fire detection and suppression
* Network protection
* Disaster Recovery
* Security Monitoring
B3D’s Information Governance has the purpose to ensure: (i) the patient
information confidentiality, and (ii) that the adherence to information
governance is built into the design of the 3DnetMedical service and derived
products provided to healthcare professionals. Information governance and
security underpins 3DnetMedical and, as an organisation, B3D strives to
achieve excellence in the services provided. B3D operates under ISO 13485:2012
(quality management system). B3D complies with ISO 62304 (medical software
development) and incorporates ISO 14971 (risk analysis) into product life
cycle. All B3D products and operations conform to industry standards including
CE Annex II of directive 93/42/EEC, DICOM, HL7 and IHE.
SMARTool non imaging clinical data and clinical documents have been encrypted
at rest by means of a transparent filesystem encryption strategy with AES-256
symmetric key cypher. Disk encryption keys have been managed in a keystore
(vaultproject.io), and encrypted themselves with a master key which was
separate from the data and the databases.
Network traffic carrying non imaging data will be encrypted via TLS/SSL
(HTTPS), with public key certificates issued and renewed by a root certificate
authority.
EHIT is an ISO 13485:2013 and ISO/IEC 27001 certified organization. This
ensures consistent information security management and product development
life cycle. Moreover, EHIT is certified for 21 IHE integration profiles.
_**5.2. Ethical issues** _
SMARTool supports the protection of the enrolled patients and make their
rights more visible by adopting the National Laws and EU Legislation and be in
compliance with the Directives as described in the following section.
**EU legislation and Ethics documents**
The Directives, Ethics Documents and experts’ opinions and Ethical Committees
are presented in the following table accompanied with a short reference note
on the general matter as well as the stated principles. All the European Union
countries of SMARTool adopted the European Directives. Additionally, the
partners of the SMARTool project follow the European Charter for researchers.
_**Table 11:** Ethical considerations in SMARTool project. _
<table>
<tr>
<th>
**Directive 1995/46/EC of the European Parliament (October 1995):**
</th> </tr>
<tr>
<td>
Protection of individuals related to personal data processing and free
movement. It concerns the protection for the individuals’ privacy and the free
movement of personal data within the European Union (EU). The Directive
defines specific criteria for the collection and utilisation of personal data.
Furthermore, the Directive stresses the obligation of each Member State to set
up an independent national authority for the monitoring of the Directive
application.
</td> </tr>
<tr>
<td>
**Charter of Fundamental Rights of the EU that became legally binding on the
EU institutions and the**
**national governments on 2009 with the entry into force of the Treaty of
Lisbon**
</td> </tr>
<tr>
<td>
The Charter includes the rights and freedoms in terms of Dignity, Freedoms,
Equality, Solidarity, Citizens' Rights, and Justice.
</td> </tr>
<tr>
<td>
**Opinion of the European Group on Ethics in Science and New Technologies No.
13 (July 1999)**
</td> </tr>
<tr>
<td>
Protection of recognisable personal health data.
</td> </tr>
<tr>
<td>
**Opinion of the European Group on Ethics in Science and New Technologies No.
26 (February**
**2012)**
</td> </tr>
<tr>
<td>
Ethics of information and communication technologies.
</td> </tr>
<tr>
<td>
**Opinion of the European Group on Ethics in Science and New Technologies No.
29 (13 October**
</td> </tr> </table>
<table>
<tr>
<th>
**2015)**
</th> </tr>
<tr>
<td>
Examination of the principal health technologies and definition of a set of
recommendations for the EU and national-level policymakers, industry and other
stakeholders, towards maximising the benefits and minimise the issues
associated with new health technologies and citizen participation in health
policy, research and practice
</td> </tr>
<tr>
<td>
**Directive 2001/20/EC of the European Parliament (April 2001)**
</td> </tr>
<tr>
<td>
Performance of clinical trials and medicine under good clinical practice.
</td> </tr>
<tr>
<td>
**Directive 2005/28/EC or Good Clinical Practice Directive of the European
Parliament and of the**
**Council(8 April 2005)**
</td> </tr>
<tr>
<td>
Presents the principles and detailed guidelines for good clinical practice
with regard to investigational medicinal products for human use, as well as
the requirements for authorisation of the manufacturing of such products.
</td> </tr>
<tr>
<td>
**Universal Declaration on the human genome and human rights adopted by UNESCO
(1997)**
</td> </tr>
<tr>
<td>
Refers to national and regional legislation on medicine, privacy and genetic
research.
</td> </tr>
<tr>
<td>
**Clinical Trials Regulation (CTR) EU No 536/2014**
</td> </tr>
<tr>
<td>
Ensures that the rules for conducting clinical trials are the same throughout
the EU. It is imperative to ensure that all Member States, in authorising and
supervising the conduct of a clinical trial, are based on identical rules.
</td> </tr>
<tr>
<td>
**Italy**
</td>
<td>
A number of ministerial decrees cover this area. A key one is Legislative
Decree no.211 of June 24, 2003 “Transposition of Directive 2001/20/EC relating
to the implementation of good clinical practice in the conduct of clinical
trials on medicinal products for clinical use”.
</td> </tr>
<tr>
<td>
Law n.675 of 31 December 1996 Tutela delle persone e di altri soggetti
rispetto al trattamento dei dati personali (published on the G.U. n.5 of8
January 1996;
</td> </tr>
<tr>
<td>
C. M. n. 6 of 2 September 2002, Attività dei comitati etici istituiti ai sensi
del decreto ministeriale 18 marzo 1998. (published on the G.U. n. 214 of 12
September 2002)
</td> </tr>
<tr>
<td>
**Finland**
</td>
<td>
Medical Research Act (L 488/1999);
</td> </tr>
<tr>
<td>
Act of the Medical Use of Human Organs and Tissues (L 101/2001),
</td> </tr>
<tr>
<td>
Act on the Status and Rights of Patients (L785/1992),
</td> </tr>
<tr>
<td>
Personal Data Act (L 523/1999);
</td> </tr>
<tr>
<td>
Good Clinical Practice (GCP) guidelines in accordance with the International
</td> </tr>
<tr>
<td>
Conference on Harmonization (www.ich.org)
</td> </tr>
<tr>
<td>
**Switzerland**
</td>
<td>
Loi fédérale sur les médicaments et les dispositifs médicaux 812.21 du 15
décembre 2000 (Chapitre 4 Section 2 Essais cliniques, Section 4 Obligation de
garder le secret et la communication de donées)
</td> </tr>
<tr>
<td>
Loi fédérale sur l’analyse génétique humaine 810.12 du 8 octobre 2004 (Section
2; Art. 5 Consentement, Art. 7 Protection des données génétiques, Art. 20.
Réutilisation du matériel biologique)
</td> </tr>
<tr>
<td>
Local: Approval by the IRB Bundesgesetz 812.21 über Arzneimittel und
Medizinprodukte (Heilmittelgesetz, HMG) as of Dezember 15, 2000
</td> </tr>
<tr>
<td>
Verordnung 812.214.2 über klinische Versuche mit Heilmitteln (VKlin) as of
October 17, 2001
</td> </tr>
<tr>
<td>
Patientinnen- und Patientengesetz 813.13 as of April 5, 2004
</td> </tr>
<tr>
<td>
Heilmittelverordnung (HMV) 812.1 as of May 21, 2008
</td> </tr>
<tr>
<td>
**UK**
</td>
<td>
Integrated Research Application Systems (IRAS)
</td> </tr>
<tr>
<td>
</td>
<td>
National Research Ethics Service (NRES)
</td> </tr>
<tr>
<td>
Department of Health’s Research Governance Framework for Health and Social
Care (2nd Edition, 2005)
</td> </tr>
<tr>
<td>
**France**
</td>
<td>
Public Health Code (articles L. 1121-1 et seq.).6
</td> </tr>
<tr>
<td>
The bioethics law of 2004 creating the French Biomedicine Agency
</td> </tr>
<tr>
<td>
The Advisory Committee on the Treatment of Research Information in the Health
</td> </tr>
<tr>
<td>
Field was created by a law n°94-548 1 July 1994
</td> </tr>
<tr>
<td>
**Germany**
</td>
<td>
Das Deutsche Referenzzentrum für Ethik in den Biowissenschaften - DRZE
</td> </tr>
<tr>
<td>
Deutscher Ethikrat
</td> </tr>
<tr>
<td>
Büro für Technikfolgen-Abschätzung beim Deutschen Bundestag - TAB
</td> </tr>
<tr>
<td>
**Spain**
</td>
<td>
Comités de Ética en Investigación Clínica, CEIC
</td> </tr>
<tr>
<td>
Royal Decree 561/1993
</td> </tr>
<tr>
<td>
Royal Decree 223/2004
</td> </tr>
<tr>
<td>
Law of Biomedical Research (2007)
</td> </tr>
<tr>
<td>
Asociación Nacional de Comités de Ética de la Investigación, ANCEI
</td> </tr>
<tr>
<td>
**Poland**
</td>
<td>
First Polish Code of Ethics (1977)
</td> </tr>
<tr>
<td>
Extraordinary National Assembly of Delegates of the Polish Society of Medicine
in Szczecin on 22 June 1984
</td> </tr>
<tr>
<td>
Code of Medical Ethics (1991)
</td> </tr>
<tr>
<td>
First Polish Research Ethics Committee (1979)
</td> </tr>
<tr>
<td>
Bioethics Committee at the Ministry of Health
</td> </tr>
<tr>
<td>
Zbiór zasad i wytycznych pt. "Dobre obyczaje w nauce"
</td> </tr>
<tr>
<td>
April 7, 2005 Order of the Minister of Health concerning the nature and extent
of inspection of clinical trials. Legislation Journal of the Republic of
Poland (2005) 69: pos. 623
</td> </tr>
<tr>
<td>
January 3, 2007 Order of the Minister of Health concerning the application
form for authorization of clinical trials, payments for authorization and
final report of the clinical trial Legislation Journal of the Republic of
Poland (2010) 222: pos. 1453
</td> </tr>
<tr>
<td>
February 11, 2011 Order of the Minister of Health concerning requirements
related with the basic documentation of clinical trials. Legislation Journal
of the Republic of Poland
(2011) 40: pos. 210
</td> </tr>
<tr>
<td>
May 12, 2012 Order of the Minister of Health concerning Good Clinical Practice
Legislation Journal of the Republic of Poland (2012) 0: pos. 489
</td> </tr>
<tr>
<td>
**Netherlands**
</td>
<td>
Personal Data Protection Act of 6 july 2000 (Wet bescherming persoonsgegevens)
</td> </tr> </table>
**6\. References**
1. “H2020 Programme Guidelines on FAIR Data Management in Horizon 2020.” .
2. “OpenAIRE | How to create a DMP Plan | Open Research Data Pilot,” _{SITE_NAME}_ . .
3. “Cardiovascular diseases statistics - Statistics Explained.” .
4. E. J. Benjamin _et al._ , “Heart Disease and Stroke Statistics—2017 Update: A Report From the American Heart Association,” _Circulation_ , p. CIR.0000000000000485, Jan. 2017.
5. “Cross-Enterprise Document Sharing - IHE Wiki.” .
6. “Audit Trail and Node Authentication - IHE Wiki.” .
7. “Cross-Enterprise User Assertion (XUA) - IHE Wiki.” .
8. M. D. Wilkinson _et al._ , “The FAIR Guiding Principles for scientific data management and stewardship,” _Sci Data_ , vol. 3, p. sdata201618, Mar. 2016.
9. “Introduction to HL7 Standards.” .
10. “IHE Volume 1 (CARD TF-1): Integration Profiles.” .
11. “IHE Volume 2 (CARD TF-2): Transactions.” .
12. “Predictive Model Markup language.” .
13. “SMARTool - Simulation Modeling of coronary ARTery disease: a tool for clinical decision support.” .
7. **Certifications**
**B3D**
**EHIT**
**END OF DOCUMENT**
8. **ANNEX**
**Data Protection**
The General Data Protection Regulation (GDPR) is the new EU’s regulation
repealing previous EU Data Protection Directive and designed to protect and
empower every subject’s data privacy located in the EU regardless of where the
processing is happening. In addition, the GDPR applies to every organisation
located in the EU that processes personal data regardless of the data
subject’s nationality and covers several activities and aspects including data
collection, processing, transfer, storage, security and the data subject
rights.
In terms of substance, the basic principles of the GDPR are similar to those
in the DPD, as are many of its definitions. Thus the GDPR requires again that
data processing adheres to the principles of lawfulness and fairness: in this
regard, the key fairness principles of the DPD are reiterated and amplified,
and include transparency; purpose limitation; data minimization; storage
limitation; accuracy and integrity 8 . Moreover, like the DPD, the GDPR sets
out further restrictions when it comes to the processing of sensitive data,
including health data10.
Data protection by design is implemented taking into account nature, scope and
context of processing, data controllers and processors, including the purposes
of processing. This includes also potential risks that can affect the rights
and freedoms of patients that are participating in the processing. All
principles of the GDPR were taken into account11.
**Key factors and definitions - Data controllers, subjects, and other actors**
Originally, it is beneficial to clarify the definitions of the various actors
involved in the processing of personal data. As per Article 4(1), the person
that the data relate to is referred to as the ‘data subject’. The protection
of data subjects against unlawful processing of their data is the main
objective of the EU data protection framework. Secondly, ‘the natural or legal
person which, alone or jointly with others, determines the purposes and means
of the processing of personal data’ is referred to as the ‘data controller’ 9
. Sometimes, however, the controller may have recourse to a third party
service provider; this entity that processes personal data on behalf of the
controller is referred to as the ‘data processor’ 10 . The controller is
able to determine the purpose himself (or jointly with others). By contrast,
the processor is subordinated to the controller and subject to his directions
concerning the processing of personal data.
**General requirements for the processing of personal data**
**Data Processing principles**
As previously noted, the basic principles of the GDPR are very similar to
those in the DPD. In this regard, in Article 5(1)(a) continues the requirement
from the DPD that data processing should adhere to the principles of:
lawfulness and fairness; however, it also adds, thirdly, the need for
‘transparency’ – a key concept that resurfaces in many subsequent more
detailed provisions of the GDPR, and places a new emphasis on the need for
data controllers and processors to demonstrate the purpose and ambit purpose
of their operations, as well as their compliance with the rules of the GDPR.
First, as to the requirement of ‘lawfulness’, this means that any data
processing must be based on a legal basis, in the shape either of the data
subject’s explicit consent, or another basis defined by law. These conditions
for lawfulness are further specified in Article 6, and – in the case of
processing of special categories of ‘sensitive’ data (where greater
restrictions apply) – Article 9 of the GDPR.
**Lawful processing basis**
As noted above, the first data processing (under Article 5(1) (a)) requires
that the processing of personal data is lawful, fair and transparent. As
regards the lawfulness aspect, the general legal bases a controller may rely
on to justify processing of personal data are further specified under Article
6 GDPR; however, as regards the processing of certain ‘special categories’ of
personal data, the controller needs to show the existence of an additional,
more restrictively drawn, processing basis under Article 9. Such special
categories (commonly referred to as ‘sensitive data’) comprise data,
“revealing racial or ethnic origin, political opinions, religious or
philosophical beliefs, or trade union membership, and the processing of
genetic data, biometric data for the purpose of uniquely identifying a natural
person, data concerning health or data concerning a natural person's sex life
or sexual orientation”.
**Data Subject Rights**
In Articles 12-22, the GDPR enumerates a number of key rights enjoyed by the
data subject in relation to processing that occurs with their personal data.
Transparency consists of various rights of the data-subject. A major aspect
relates to the data-subject's right to information and access to the
information processed about him, thereby contributing to the ‘transparency’,
which is mentioned in Article 5(a) – together with lawfulness and fairness -
as a key principle of data processing. This is reflected also in Recital 39 of
the GDPR, where it is stated: “The principle of transparency … concerns, in
particular, information to the data subjects on the identity of the controller
and the purposes of the processing and further information to ensure fair and
transparent processing in respect of the natural persons concerned and their
right to obtain confirmation and communication of personal data concerning
them which are being processed”. Here, the GDPR largely restates rights that
were already granted to data subjects under the Data Protection Directive, but
it also introduces a couple of new rights. However (as was the position under
the Directive too), the various rights are prima facie rather than absolute in
character: that is to say, they are subject to exemptions in certain
situation.
**Obligations on data controllers**
In Article 24 and following, the GDPR imposes a number of further obligations
on data controllers (and in some cases on data processors, who store and/or
process data on the controller’s behalf). The ensuing obligations include some
that ‘concretise’, or spell out in more detail how the controller should
comply with the principles of fair processing outlined under Articles 5(b)-(f)
of the GDPR. We consider these various obligations, so far as relevant to data
controllers (and processors) dealing with health data for research, below.
a. Data Protection by Design and Default
Under Article 25, the controller is required to implement data protection by
design and default, requiring a focus on the organization and technical
security of the relevant processing operations. In particular, these have as
their objective the practical implementation of the data minimisation
principle under Article 5(b). b. Data security
The processing of personal data requires security measures to prevent any
accidental disclosure or unauthorised access to the data. Data security is an
integral part of data protection that focuses on maintaining the
confidentiality, integrity and availability of information.
**Data protection in SMARTool**
Most of the SMARTool data processing in the framework of the project happened
before the Regulation's entry into force (ARTreat/EVINCI data) and that
processing was compliant with the provisions of EU law applicable at the time
(the repealed Directive 95/46/EC), as well as any relevant national data
protection laws.
The personal data of the project has been anonymised from the beginning of the
project and, therefore, the GDPR does not apply (see Recital 26 of the
Regulation).
In a broader sense, SMARTool complies with GDPR because:
* we can assume that data handled in SMARTool can be qualified as personal data and that requirements broadly pertaining to the processing of such data under the GDPR are addressed;
* Recital 33 of GDPR states that because it is not always possible to fully identify the purpose of a processing when it comes to scientific research already when the data is being collected, data subjects should be allowed to give a (broader) consent (which is our case for ARTreat and EVINCI).
Clinical partners also ensure that their process is transparent by creating
and posting a Privacy Policy that outlines:
* What data they collect
* How they store the information - How they use the information
* Whom they share the data with
* Whether they share the data with third parties
* When and how they delete the data
All these have been defined in SMARTool defining clearly the use of data,
their storing in the various databases and modules of SMARTool as well as
their use in the decision support tools. The sharing is only permitted between
the clinical and technical partners inside the consortium.
**APPENDIX**
**_Data structure_ **
Metadata refers to a set of data that described and gives information about
other data. One option to store metadata is to store that information within
the object itself. The following table illustrate the structure of a typical
document stored in each collection.
For each document, the collection and type of data entry (Clinical Partner
direct input, Clinical Partner direct upload) have been specified.
**Clinical Characteristics, “fragments” / “hfragments” structures (Clinical
Partner direct input)**
<table>
<tr>
<th>
**Clinical Characteristics**
</th>
<th>
</th> </tr>
<tr>
<td>
_id
</td>
<td>
ObjectId
</td> </tr>
<tr>
<td>
_class
</td>
<td>
String
</td> </tr>
<tr>
<td>
typeCode
</td>
<td>
String
</td> </tr>
<tr>
<td>
payload
</td>
<td>
Object
</td> </tr>
<tr>
<td>
gender
</td>
<td>
String
</td> </tr>
<tr>
<td>
yearOfBirth
</td>
<td>
String
</td> </tr>
<tr>
<td>
height
</td>
<td>
String
</td> </tr>
<tr>
<td>
weight
</td>
<td>
String
</td> </tr> </table>
<table>
<tr>
<th>
bmi
</th>
<th>
String
</th> </tr>
<tr>
<td>
bmiClass
</td>
<td>
String
</td> </tr>
<tr>
<td>
sbp
</td>
<td>
String
</td> </tr>
<tr>
<td>
dbp
</td>
<td>
String
</td> </tr>
<tr>
<td>
waist_circumference
</td>
<td>
Int32
</td> </tr>
<tr>
<td>
hip_circumference
</td>
<td>
Int32
</td> </tr>
<tr>
<td>
familyHistoryCHD
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
familiyHistoryCHDClass
</td>
<td>
String
</td> </tr>
<tr>
<td>
currentSmoking
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
currentSmokingNCigaretteDie
</td>
<td>
Int32
</td> </tr>
<tr>
<td>
pastSmoking
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
smokingInterruptedSince
</td>
<td>
Int32
</td> </tr>
<tr>
<td>
diabetesMellitus
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
metabolicSindrome
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
dyslipidemia
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
hypertension
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
obesity
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
noRiskFactors
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
alcohol
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
alcoholAmount
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
dietVegetable
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
dietVegetableAmount
</td>
<td>
Int32
</td> </tr>
<tr>
<td>
physicalActivity
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
physicalActivityAmount
</td>
<td>
Int32
</td> </tr>
<tr>
<td>
environmentHome
</td>
<td>
String
</td> </tr>
<tr>
<td>
expositionPollutants
</td>
<td>
String
</td> </tr>
<tr>
<td>
not applicable
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
AMI1_date
</td>
<td>
String
</td> </tr>
<tr>
<td>
AMI1_vessel_site
</td>
<td>
String
</td> </tr>
<tr>
<td>
AMI2_date
</td>
<td>
String
</td> </tr>
<tr>
<td>
AMI2_vessel_site
</td>
<td>
String
</td> </tr>
<tr>
<td>
CAGB1_date
</td>
<td>
String
</td> </tr>
<tr>
<td>
CABG2_date
</td>
<td>
String
</td> </tr>
<tr>
<td>
NonSTEMIUnstableAngina1
</td>
<td>
String
</td> </tr>
<tr>
<td>
NonSTEMIUnstableAngina2
</td>
<td>
string
</td> </tr>
<tr>
<td>
PCIStenting1_date
</td>
<td>
String
</td> </tr>
<tr>
<td>
PCIStenting1_site
</td>
<td>
String
</td> </tr>
<tr>
<td>
PCIStenting2_indication_for_revascularization
</td>
<td>
String
</td> </tr>
<tr>
<td>
PCIStenting2_date
</td>
<td>
String
</td> </tr>
<tr>
<td>
PCIStenting2_site
</td>
<td>
String
</td> </tr>
<tr>
<td>
PCIStenting2_indication_for_revascularization
</td>
<td>
String
</td> </tr>
<tr>
<td>
NonCardiacHosp1_date
</td>
<td>
String
</td> </tr>
<tr>
<td>
NonCardiacHosp1_spec
</td>
<td>
String
</td> </tr>
<tr>
<td>
NonCardiacHosp2_date
</td>
<td>
String
</td> </tr>
<tr>
<td>
NonCardiacHosp2_spec
</td>
<td>
String
</td> </tr>
<tr>
<td>
pastSymptoms
</td>
<td>
String
</td> </tr> </table>
<table>
<tr>
<th>
currentSymptoms
</th>
<th>
String
</th> </tr>
<tr>
<td>
oralAntidiabetics
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
insulin
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
statins
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
statinsMgDie
</td>
<td>
Int32
</td> </tr>
<tr>
<td>
statinsType
</td>
<td>
String
</td> </tr>
<tr>
<td>
aCEInhibitors
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
diuretics
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
ARB
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
bETABlockers
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
calciumAntagonists
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
aspirin
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
</td>
<td>
</td> </tr>
<tr>
<td>
novelAnticoagulants **CTA Report Core Lab**
traditionalAnticoagulants
</td>
<td>
Boolean
Boolean
</td> </tr>
<tr>
<td>
</td>
<td>
</td> </tr>
<tr>
<td>
_id nitrates
</td>
<td>
ObjectId Boolean
</td> </tr>
<tr>
<td>
</td>
<td>
</td> </tr>
<tr>
<td>
_class DAPT
</td>
<td>
Boolean String
</td> </tr>
<tr>
<td>
</td>
<td>
</td> </tr>
<tr>
<td>
typeCode otherDrugs
</td>
<td>
String Boolean
</td> </tr>
<tr>
<td>
</td>
<td>
</td> </tr>
<tr>
<td>
payload otherDrugsText
</td>
<td>
Object String
</td> </tr>
<tr>
<td>
</td>
<td>
</td> </tr>
<tr>
<td>
lumc.ctaAnalysesCompleted noTreatment
</td>
<td>
Boolean Boolean
</td> </tr>
<tr>
<td>
</td>
<td>
</td> </tr>
<tr>
<td>
lumc.imageQuality status
</td>
<td>
String String
</td> </tr>
<tr>
<td>
</td>
<td>
</td> </tr>
<tr>
<td>
ccsNotApplicable valid
</td>
<td>
Boolean
Boolean
</td> </tr>
<tr>
<td>
</td>
<td>
</td> </tr>
<tr>
<td>
TCS_HU historical
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
</td>
<td>
</td> </tr>
<tr>
<td>
TCScore versionNumber
</td>
<td>
String Int64
</td> </tr>
<tr>
<td>
</td>
<td>
</td> </tr>
<tr>
<td>
RCA_HU createAt
</td>
<td>
Date
</td> </tr>
<tr>
<td>
</td>
<td>
</td> </tr>
<tr>
<td>
RCAScore lastModified
</td>
<td>
String Date
</td> </tr>
<tr>
<td>
</td>
<td>
</td> </tr>
<tr>
<td>
LM_HU
createBy
</td>
<td>
String
</td> </tr>
<tr>
<td>
</td>
<td>
</td> </tr>
<tr>
<td>
LMScore lastModifiedBy
</td>
<td>
String String
</td> </tr>
<tr>
<td>
</td>
<td>
</td> </tr>
<tr>
<td>
LAD_HU study
</td>
<td>
Object
</td> </tr>
<tr>
<td>
</td>
<td>
</td> </tr>
<tr>
<td>
LADScore $ref
</td>
<td>
String String
</td> </tr>
<tr>
<td>
</td>
<td>
</td> </tr>
<tr>
<td>
LCX_HU $id
</td>
<td>
ObjectId
</td> </tr>
<tr>
<td>
</td>
<td>
</td> </tr>
<tr>
<td>
LCXScore
</td>
<td>
String
</td> </tr>
<tr>
<td>
nonCoronaryCardiacFindings
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
systemDominance
</td>
<td>
String
</td> </tr>
<tr>
<td>
noPlaquesInAnySegment
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
noVisibleSegmentsExist
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
noVisibleSegmentDescription
</td>
<td>
String
</td> </tr>
<tr>
<td>
plaque1
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
p1_Prox_type
</td>
<td>
String
</td> </tr>
<tr>
<td>
p1_Prox_stent
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
p1_Prox_lumen
</td>
<td>
String
</td> </tr>
<tr>
<td>
p1_Plaque_progression
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
plaque1_2
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
p1_2_Prox_type
</td>
<td>
String
</td> </tr>
<tr>
<td>
p1_2_Prox_stent
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
p1_2_Prox_lumen
</td>
<td>
String
</td> </tr>
<tr>
<td>
p1_2_Plaque_progression
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
plaque2
</td>
<td>
Boolean
</td> </tr> </table>
**CTA Report CL,**
**“fragments” /**
**“hfragments” structures (Clinical Partner direct input)**
<table>
<tr>
<th>
p2_Prox_type
</th>
<th>
String
</th> </tr>
<tr>
<td>
p2_Prox_stent
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
p2_Prox_lumen
</td>
<td>
String
</td> </tr>
<tr>
<td>
p2_Plaque_progression
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
plaque2_2
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
p2_2_Prox_type
</td>
<td>
String
</td> </tr>
<tr>
<td>
p2_2_Prox_stent
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
p2_2_Prox_lumen
</td>
<td>
String
</td> </tr>
<tr>
<td>
p2_2_Plaque_progression
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
plaque3
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
p3_Prox_type
</td>
<td>
String
</td> </tr>
<tr>
<td>
p3_Prox_stent
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
p3_Prox_lumen
</td>
<td>
String
</td> </tr>
<tr>
<td>
p3_Plaque_progression
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
plaque3_2
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
p3_2_Prox_type
</td>
<td>
String
</td> </tr>
<tr>
<td>
p3_2_Prox_stent
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
p3_2_Prox_lumen
</td>
<td>
String
</td> </tr>
<tr>
<td>
p3_2_Plaque_progression
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
plaque4
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
p4_Prox_type
</td>
<td>
String
</td> </tr>
<tr>
<td>
p4_Prox_stent
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
p4_Prox_lumen
</td>
<td>
String
</td> </tr>
<tr>
<td>
p4_Plaque_progression
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
plaque4_2
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
p4_2_Prox_type
</td>
<td>
String
</td> </tr>
<tr>
<td>
p4_2_Prox_stent
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
p4_2_Prox_lumen
</td>
<td>
String
</td> </tr>
<tr>
<td>
p4_2_Plaque_progression
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
plaque5
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
p5_Prox_type
</td>
<td>
String
</td> </tr>
<tr>
<td>
p5_Prox_stent
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
p5_Prox_lumen
</td>
<td>
String
</td> </tr>
<tr>
<td>
p5_Plaque_progression
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
plaque5_2
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
p5_2_Prox_type
</td>
<td>
String
</td> </tr>
<tr>
<td>
p5_2_Prox_stent
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
p5_2_Prox_lumen
</td>
<td>
String
</td> </tr>
<tr>
<td>
p5_2_Plaque_progression
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
plaque6
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
p6_Prox_type
</td>
<td>
String
</td> </tr>
<tr>
<td>
p6_Prox_stent
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
p6_Prox_lumen
</td>
<td>
String
</td> </tr>
<tr>
<td>
p6_Plaque_progression
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
plaque6_2
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
p6_2_Prox_type
</td>
<td>
String
</td> </tr> </table>
<table>
<tr>
<th>
p6_2_Prox_stent
</th>
<th>
Boolean
</th> </tr>
<tr>
<td>
p6_2_Prox_lumen
</td>
<td>
String
</td> </tr>
<tr>
<td>
p6_2_Plaque_progression
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
plaque7
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
p7_Prox_type
</td>
<td>
String
</td> </tr>
<tr>
<td>
p7_Prox_stent
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
p7_Prox_lumen
</td>
<td>
String
</td> </tr>
<tr>
<td>
p7_Plaque_progression
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
plaque7_2
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
p7_2_Prox_type
</td>
<td>
String
</td> </tr>
<tr>
<td>
p7_2_Prox_stent
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
p7_2_Prox_lumen
</td>
<td>
String
</td> </tr>
<tr>
<td>
p7_2_Plaque_progression
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
plaque8
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
p8_Prox_type
</td>
<td>
String
</td> </tr>
<tr>
<td>
p8_Prox_stent
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
p8_Prox_lumen
</td>
<td>
String
</td> </tr>
<tr>
<td>
p8_Plaque_progression
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
plaque8_2
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
p8_2_Prox_type
</td>
<td>
String
</td> </tr>
<tr>
<td>
p8_2_Prox_stent
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
p8_2_Prox_lumen
</td>
<td>
String
</td> </tr>
<tr>
<td>
p8_2_Plaque_progression
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
plaque9
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
p9_Prox_type
</td>
<td>
String
</td> </tr>
<tr>
<td>
p9_Prox_stent
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
p9_Prox_lumen
</td>
<td>
String
</td> </tr>
<tr>
<td>
p9_Plaque_progression
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
plaque9_2
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
p9_2_Prox_type
</td>
<td>
String
</td> </tr>
<tr>
<td>
p9_2_Prox_stent
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
p9_2_Prox_lumen
</td>
<td>
String
</td> </tr>
<tr>
<td>
p9_2_Plaque_progression
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
plaque10
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
p10_Prox_type
</td>
<td>
String
</td> </tr>
<tr>
<td>
p10_Prox_stent
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
p10_Prox_lumen
</td>
<td>
String
</td> </tr>
<tr>
<td>
p10_Plaque_progression
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
plaque10_2
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
p10_2_Prox_type
</td>
<td>
String
</td> </tr>
<tr>
<td>
p10_2_Prox_stent
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
p10_2_Prox_lumen
</td>
<td>
String
</td> </tr>
<tr>
<td>
p10_2_Plaque_progression
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
plaque11
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
p11_Prox_type
</td>
<td>
String
</td> </tr>
<tr>
<td>
p11_Prox_stent
</td>
<td>
Boolean
</td> </tr> </table>
<table>
<tr>
<th>
p11_Prox_lumen
</th>
<th>
String
</th> </tr>
<tr>
<td>
p11_Plaque_progression
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
plaque11_2
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
p11_2_Prox_type
</td>
<td>
String
</td> </tr>
<tr>
<td>
p11_2_Prox_stent
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
p11_2_Prox_lumen
</td>
<td>
String
</td> </tr>
<tr>
<td>
p11_2_Plaque_progression
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
plaque12
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
p12_Prox_type
</td>
<td>
String
</td> </tr>
<tr>
<td>
p12_Prox_stent
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
p12_Prox_lumen
</td>
<td>
String
</td> </tr>
<tr>
<td>
p12_Plaque_progression
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
plaque12_2
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
p12_2_Prox_type
</td>
<td>
String
</td> </tr>
<tr>
<td>
p12_2_Prox_stent
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
p12_2_Prox_lumen
</td>
<td>
String
</td> </tr>
<tr>
<td>
p12_2_Plaque_progression
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
plaque13
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
p13_Prox_type
</td>
<td>
String
</td> </tr>
<tr>
<td>
p13_Prox_stent
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
p13_Prox_lumen
</td>
<td>
String
</td> </tr>
<tr>
<td>
p13_Plaque_progression
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
plaque13_2
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
p13_2_Prox_type
</td>
<td>
String
</td> </tr>
<tr>
<td>
p13_2_Prox_stent
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
p13_2_Prox_lumen
</td>
<td>
String
</td> </tr>
<tr>
<td>
p13_2_Plaque_progression
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
plaque14
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
p14_Prox_type
</td>
<td>
String
</td> </tr>
<tr>
<td>
p14_Prox_stent
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
p14_Prox_lumen
</td>
<td>
String
</td> </tr>
<tr>
<td>
p14_Plaque_progression
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
plaque14_2
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
p14_2_Prox_type
</td>
<td>
String
</td> </tr>
<tr>
<td>
p14_2_Prox_stent
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
p14_2_Prox_lumen
</td>
<td>
String
</td> </tr>
<tr>
<td>
p14_2_Plaque_progression
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
plaque15
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
p15_Prox_type
</td>
<td>
String
</td> </tr>
<tr>
<td>
p15_Prox_stent
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
p15_Prox_lumen
</td>
<td>
String
</td> </tr>
<tr>
<td>
p15_Plaque_progression
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
plaque15_2
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
p15_2_Prox_type
</td>
<td>
String
</td> </tr>
<tr>
<td>
p15_2_Prox_stent
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
p15_2_Prox_lumen
</td>
<td>
String
</td> </tr> </table>
<table>
<tr>
<th>
p15_2_Plaque_progression
</th>
<th>
Boolean
</th> </tr>
<tr>
<td>
plaque16
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
p16_Prox_type
</td>
<td>
String
</td> </tr>
<tr>
<td>
p16_Prox_stent
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
p16_Prox_lumen
</td>
<td>
String
</td> </tr>
<tr>
<td>
p16_Plaque_progression
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
plaque16_2
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
p16_2_Prox_type
</td>
<td>
String
</td> </tr>
<tr>
<td>
p16_2_Prox_stent
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
p16_2_Prox_lumen
</td>
<td>
String
</td> </tr>
<tr>
<td>
p16_2_Plaque_progression
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
plaque17
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
p17_Prox_type
</td>
<td>
String
</td> </tr>
<tr>
<td>
p17_Prox_stent
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
p17_Prox_lumen
</td>
<td>
String
</td> </tr>
<tr>
<td>
p17_Plaque_progression
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
plaque17_2
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
p17_2_Prox_type
</td>
<td>
String
</td> </tr>
<tr>
<td>
p17_2_Prox_stent
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
p17_2_Prox_lumen
</td>
<td>
String
</td> </tr>
<tr>
<td>
p17_2_Plaque_progression
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
p1_seg_wf
</td>
<td>
Double
</td> </tr>
<tr>
<td>
p1_ste_wf_1
</td>
<td>
Double
</td> </tr>
<tr>
<td>
p1_ste_wf_2
</td>
<td>
Double
</td> </tr>
<tr>
<td>
p1_ste_wf
</td>
<td>
Double
</td> </tr>
<tr>
<td>
p1_pla_wf_1
</td>
<td>
Double
</td> </tr>
<tr>
<td>
p1_pla_wf_2
</td>
<td>
Double
</td> </tr>
<tr>
<td>
p1_pla_wf
</td>
<td>
Double
</td> </tr>
<tr>
<td>
p1_score
</td>
<td>
Double
</td> </tr>
<tr>
<td>
p2_seg_wf
</td>
<td>
Double
</td> </tr>
<tr>
<td>
p2_ste_wf_1
</td>
<td>
Double
</td> </tr>
<tr>
<td>
p2_ste_wf_2
</td>
<td>
Double
</td> </tr>
<tr>
<td>
p2_ste_wf
</td>
<td>
Double
</td> </tr>
<tr>
<td>
p2_pla_wf_1
</td>
<td>
Double
</td> </tr>
<tr>
<td>
p2_pla_wf_2
</td>
<td>
Double
</td> </tr>
<tr>
<td>
p2_pla_wf
</td>
<td>
Double
</td> </tr>
<tr>
<td>
p2_score
</td>
<td>
Double
</td> </tr>
<tr>
<td>
p3_seg_wf
</td>
<td>
Double
</td> </tr>
<tr>
<td>
p3_ste_wf_1
</td>
<td>
Double
</td> </tr>
<tr>
<td>
p3_ste_wf_2
</td>
<td>
Double
</td> </tr>
<tr>
<td>
p3_ste_wf
</td>
<td>
Double
</td> </tr>
<tr>
<td>
p3_pla_wf_1
</td>
<td>
Double
</td> </tr>
<tr>
<td>
p3_pla_wf_2
</td>
<td>
Double
</td> </tr>
<tr>
<td>
p3_pla_wf
</td>
<td>
Double
</td> </tr>
<tr>
<td>
p3_score
</td>
<td>
Double
</td> </tr>
<tr>
<td>
p4_seg_wf
</td>
<td>
Double
</td> </tr> </table>
<table>
<tr>
<th>
p4_ste_wf_1
</th>
<th>
Double
</th> </tr>
<tr>
<td>
p4_ste_wf_2
</td>
<td>
Double
</td> </tr>
<tr>
<td>
p4_ste_wf
</td>
<td>
Double
</td> </tr>
<tr>
<td>
p4_pla_wf_1
</td>
<td>
Double
</td> </tr>
<tr>
<td>
p4_pla_wf_2
</td>
<td>
Double
</td> </tr>
<tr>
<td>
p4_pla_wf
</td>
<td>
Double
</td> </tr>
<tr>
<td>
p4_score
</td>
<td>
Double
</td> </tr>
<tr>
<td>
p5_seg_wf
</td>
<td>
Double
</td> </tr>
<tr>
<td>
p5_ste_wf_1
</td>
<td>
Double
</td> </tr>
<tr>
<td>
p5_ste_wf_2
</td>
<td>
Double
</td> </tr>
<tr>
<td>
p5_ste_wf
</td>
<td>
Double
</td> </tr>
<tr>
<td>
p5_pla_wf_1
</td>
<td>
Double
</td> </tr>
<tr>
<td>
p5_pla_wf_2
</td>
<td>
Double
</td> </tr>
<tr>
<td>
p5_pla_wf
</td>
<td>
Double
</td> </tr>
<tr>
<td>
p5_score
</td>
<td>
Double
</td> </tr>
<tr>
<td>
p6_seg_wf
</td>
<td>
Double
</td> </tr>
<tr>
<td>
p6_ste_wf_1
</td>
<td>
Double
</td> </tr>
<tr>
<td>
p6_ste_wf_2
</td>
<td>
Double
</td> </tr>
<tr>
<td>
p6_ste_wf
</td>
<td>
Double
</td> </tr>
<tr>
<td>
p6_pla_wf_1
</td>
<td>
Double
</td> </tr>
<tr>
<td>
p6_pla_wf_2
</td>
<td>
Double
</td> </tr>
<tr>
<td>
p6_pla_wf
</td>
<td>
Double
</td> </tr>
<tr>
<td>
p6_score
</td>
<td>
Double
</td> </tr>
<tr>
<td>
p7_seg_wf
</td>
<td>
Double
</td> </tr>
<tr>
<td>
p7_ste_wf_1
</td>
<td>
Double
</td> </tr>
<tr>
<td>
p7_ste_wf_2
</td>
<td>
Double
</td> </tr>
<tr>
<td>
p7_ste_wf
</td>
<td>
Double
</td> </tr>
<tr>
<td>
p7_pla_wf_1
</td>
<td>
Double
</td> </tr>
<tr>
<td>
p7_pla_wf_2
</td>
<td>
Double
</td> </tr>
<tr>
<td>
p7_pla_wf
</td>
<td>
Double
</td> </tr>
<tr>
<td>
p7_score
</td>
<td>
Double
</td> </tr>
<tr>
<td>
p8_seg_wf
</td>
<td>
Double
</td> </tr>
<tr>
<td>
p8_ste_wf_1
</td>
<td>
Double
</td> </tr>
<tr>
<td>
p8_ste_wf_2
</td>
<td>
Double
</td> </tr>
<tr>
<td>
p8_ste_wf
</td>
<td>
Double
</td> </tr>
<tr>
<td>
p8_pla_wf_1
</td>
<td>
Double
</td> </tr>
<tr>
<td>
p8_pla_wf_2
</td>
<td>
Double
</td> </tr>
<tr>
<td>
p8_pla_wf
</td>
<td>
Double
</td> </tr>
<tr>
<td>
p8_score
</td>
<td>
Double
</td> </tr>
<tr>
<td>
p9_seg_wf
</td>
<td>
Double
</td> </tr>
<tr>
<td>
p9_ste_wf_1
</td>
<td>
Double
</td> </tr>
<tr>
<td>
p9_ste_wf_2
</td>
<td>
Double
</td> </tr>
<tr>
<td>
p9_ste_wf
</td>
<td>
Double
</td> </tr>
<tr>
<td>
p9_pla_wf_1
</td>
<td>
Double
</td> </tr>
<tr>
<td>
p9_pla_wf_2
</td>
<td>
Double
</td> </tr>
<tr>
<td>
p9_pla_wf
</td>
<td>
Double
</td> </tr> </table>
<table>
<tr>
<th>
p9_score
</th>
<th>
Double
</th> </tr>
<tr>
<td>
p10_seg_wf
</td>
<td>
Double
</td> </tr>
<tr>
<td>
p10_ste_wf_1
</td>
<td>
Double
</td> </tr>
<tr>
<td>
p10_ste_wf_2
</td>
<td>
Double
</td> </tr>
<tr>
<td>
p10_ste_wf
</td>
<td>
Double
</td> </tr>
<tr>
<td>
p10_pla_wf_1
</td>
<td>
Double
</td> </tr>
<tr>
<td>
p10_pla_wf_2
</td>
<td>
Double
</td> </tr>
<tr>
<td>
p10_pla_wf
</td>
<td>
Double
</td> </tr>
<tr>
<td>
p10_score
</td>
<td>
Double
</td> </tr>
<tr>
<td>
p11_seg_wf
</td>
<td>
Double
</td> </tr>
<tr>
<td>
p11_ste_wf_1
</td>
<td>
Double
</td> </tr>
<tr>
<td>
p11_ste_wf_2
</td>
<td>
Double
</td> </tr>
<tr>
<td>
p11_ste_wf
</td>
<td>
Double
</td> </tr>
<tr>
<td>
p11_pla_wf_1
</td>
<td>
Double
</td> </tr>
<tr>
<td>
p11_pla_wf_2
</td>
<td>
Double
</td> </tr>
<tr>
<td>
p11_pla_wf
</td>
<td>
Double
</td> </tr>
<tr>
<td>
p11_score
</td>
<td>
Double
</td> </tr>
<tr>
<td>
p12_seg_wf
</td>
<td>
Double
</td> </tr>
<tr>
<td>
p12_ste_wf_1
</td>
<td>
Double
</td> </tr>
<tr>
<td>
p12_ste_wf_2
</td>
<td>
Double
</td> </tr>
<tr>
<td>
p12_ste_wf
</td>
<td>
Double
</td> </tr>
<tr>
<td>
p12_pla_wf_1
</td>
<td>
Double
</td> </tr>
<tr>
<td>
p12_pla_wf_2
</td>
<td>
Double
</td> </tr>
<tr>
<td>
p12_pla_wf
</td>
<td>
Double
</td> </tr>
<tr>
<td>
p12_score
</td>
<td>
Double
</td> </tr>
<tr>
<td>
p13_seg_wf
</td>
<td>
Double
</td> </tr>
<tr>
<td>
p13_ste_wf_1
</td>
<td>
Double
</td> </tr>
<tr>
<td>
p13_ste_wf_2
</td>
<td>
Double
</td> </tr>
<tr>
<td>
p13_ste_wf
</td>
<td>
Double
</td> </tr>
<tr>
<td>
p13_pla_wf_1
</td>
<td>
Double
</td> </tr>
<tr>
<td>
p13_pla_wf_2
</td>
<td>
Double
</td> </tr>
<tr>
<td>
p13_pla_wf
</td>
<td>
Double
</td> </tr>
<tr>
<td>
p13_score
</td>
<td>
Double
</td> </tr>
<tr>
<td>
p14_seg_wf
</td>
<td>
Double
</td> </tr>
<tr>
<td>
p14_ste_wf_1
</td>
<td>
Double
</td> </tr>
<tr>
<td>
p14_ste_wf_2
</td>
<td>
Double
</td> </tr>
<tr>
<td>
p14_ste_wf
</td>
<td>
Double
</td> </tr>
<tr>
<td>
p14_pla_wf_1
</td>
<td>
Double
</td> </tr>
<tr>
<td>
p14_pla_wf_2
</td>
<td>
Double
</td> </tr>
<tr>
<td>
p14_pla_wf
</td>
<td>
Double
</td> </tr>
<tr>
<td>
p14_score
</td>
<td>
Double
</td> </tr>
<tr>
<td>
p15_seg_wf
</td>
<td>
Double
</td> </tr>
<tr>
<td>
p15_ste_wf_1
</td>
<td>
Double
</td> </tr>
<tr>
<td>
p15_ste_wf_2
</td>
<td>
Double
</td> </tr>
<tr>
<td>
p15_ste_wf
</td>
<td>
Double
</td> </tr>
<tr>
<td>
p15_pla_wf_1
</td>
<td>
Double
</td> </tr>
<tr>
<td>
p15_pla_wf_2
</td>
<td>
Double
</td> </tr>
<tr>
<td>
p15_pla_wf
</td>
<td>
Double
</td> </tr>
<tr>
<td>
p15_score
</td>
<td>
Double
</td> </tr>
<tr>
<td>
p16_seg_wf
</td>
<td>
Double
</td> </tr>
<tr>
<td>
p16_ste_wf_1
</td>
<td>
Double
</td> </tr>
<tr>
<td>
p16_ste_wf_2
</td>
<td>
Double
</td> </tr>
<tr>
<td>
p16_ste_wf
</td>
<td>
Double
</td> </tr>
<tr>
<td>
p16_pla_wf_1
</td>
<td>
Double
</td> </tr>
<tr>
<td>
p16_pla_wf_2
</td>
<td>
Double
</td> </tr>
<tr>
<td>
p16_pla_wf
</td>
<td>
Double
</td> </tr>
<tr>
<td>
p16_score
</td>
<td>
Double
</td> </tr>
<tr>
<td>
p17_seg_wf
</td>
<td>
Double
</td> </tr>
<tr>
<td>
p17_ste_wf_1
</td>
<td>
Double
</td> </tr>
<tr>
<td>
p17_ste_wf_2
</td>
<td>
Double
</td> </tr>
<tr>
<td>
p17_ste_wf
</td>
<td>
Double
</td> </tr>
<tr>
<td>
p17_pla_wf_1
</td>
<td>
Double
</td> </tr>
<tr>
<td>
p17_pla_wf_2
</td>
<td>
Double
</td> </tr>
<tr>
<td>
p17_pla_wf
</td>
<td>
Double
</td> </tr>
<tr>
<td>
p17_score
</td>
<td>
Double
</td> </tr>
<tr>
<td>
CAD_score
</td>
<td>
Double
</td> </tr>
<tr>
<td>
status
</td>
<td>
String
</td> </tr>
<tr>
<td>
valid
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
historical
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
versionNumber
</td>
<td>
Int64
</td> </tr>
<tr>
<td>
createAt
</td>
<td>
Date
</td> </tr>
<tr>
<td>
lastModified
</td>
<td>
Date
</td> </tr>
<tr>
<td>
createBy
</td>
<td>
String
</td> </tr>
<tr>
<td>
lastModifiedBy
</td>
<td>
String
</td> </tr>
<tr>
<td>
study
</td>
<td>
Object
</td> </tr>
<tr>
<td>
$ref
</td>
<td>
String
</td> </tr>
<tr>
<td>
$id
</td>
<td>
ObjectId
</td> </tr> </table>
**Blood Tests, “fragments” / “hfragments” structures (Clinical Partner direct
input(**
<table>
<tr>
<th>
**Blood Tests**
</th> </tr>
<tr>
<td>
_id
</td>
<td>
ObjectId
</td> </tr>
<tr>
<td>
_class
</td>
<td>
String
</td> </tr>
<tr>
<td>
typeCode
</td>
<td>
String
</td> </tr>
<tr>
<td>
loked
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
payload
</td>
<td>
Object
</td> </tr>
<tr>
<td>
FastingGlucose
</td>
<td>
Double
</td> </tr>
<tr>
<td>
FastingGlucoseMetric
</td>
<td>
String
</td> </tr>
<tr>
<td>
TotChol
</td>
<td>
Double
</td> </tr>
<tr>
<td>
TotCholMetric
</td>
<td>
String
</td> </tr>
<tr>
<td>
LDL
</td>
<td>
Double
</td> </tr>
<tr>
<td>
LDLMetric
</td>
<td>
String
</td> </tr>
<tr>
<td>
HDL
</td>
<td>
Double
</td> </tr>
<tr>
<td>
HDLMetric
</td>
<td>
String
</td> </tr>
<tr>
<td>
Triglycerides
</td>
<td>
Double
</td> </tr>
<tr>
<td>
TriglyceridesMetric
</td>
<td>
String
</td> </tr>
<tr>
<td>
UricAcidMetric
</td>
<td>
Double
</td> </tr>
<tr>
<td>
UricAcidMetricMetric
</td>
<td>
String
</td> </tr>
<tr>
<td>
Creatinine
</td>
<td>
Double
</td> </tr>
<tr>
<td>
CreatinineMetric
</td>
<td>
String
</td> </tr>
<tr>
<td>
Leukocytes
</td>
<td>
Double
</td> </tr>
<tr>
<td>
LeukocytesMetric
</td>
<td>
String
</td> </tr>
<tr>
<td>
Erythrocytes
</td>
<td>
Double
</td> </tr>
<tr>
<td>
ErythrocytesMetric
</td>
<td>
String
</td> </tr>
<tr>
<td>
Platelets
</td>
<td>
Double
</td> </tr>
<tr>
<td>
PlateletsMetric
</td>
<td>
String
</td> </tr>
<tr>
<td>
Hemoglobin
</td>
<td>
Double
</td> </tr>
<tr>
<td>
HemoglobinMetric
</td>
<td>
String
</td> </tr>
<tr>
<td>
Fibrinogen
</td>
<td>
Double
</td> </tr>
<tr>
<td>
FibrinogenMetric
</td>
<td>
String
</td> </tr>
<tr>
<td>
HTC
</td>
<td>
Double
</td> </tr>
<tr>
<td>
MCV
</td>
<td>
Double
</td> </tr>
<tr>
<td>
MCH
</td>
<td>
Double
</td> </tr>
<tr>
<td>
INR
</td>
<td>
Double
</td> </tr>
<tr>
<td>
aPTT
</td>
<td>
Double
</td> </tr>
<tr>
<td>
status
</td>
<td>
String
</td> </tr>
<tr>
<td>
valid
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
historical
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
versionNumber
</td>
<td>
Int64
</td> </tr>
<tr>
<td>
createAt
</td>
<td>
Date
</td> </tr>
<tr>
<td>
lastModified
</td>
<td>
Date
</td> </tr>
<tr>
<td>
createBy
</td>
<td>
String
</td> </tr>
<tr>
<td>
lastModifiedBy
</td>
<td>
String
</td> </tr>
<tr>
<td>
study
</td>
<td>
Object
</td> </tr>
<tr>
<td>
$ref
</td>
<td>
String
</td> </tr>
<tr>
<td>
$id
</td>
<td>
ObjectId
</td> </tr> </table>
**Inflammatory markers / Lipid profile / Biohumoral markers, “fragments” /
“hfragments” structures (Clinical Partner direct upload, data extraction)**
<table>
<tr>
<th>
**Inflammatory markers**
</th> </tr>
<tr>
<td>
_id
</td>
<td>
ObjectId
</td> </tr>
<tr>
<td>
_class
</td>
<td>
String
</td> </tr>
<tr>
<td>
typeCode
</td>
<td>
String
</td> </tr>
<tr>
<td>
Payload
</td>
<td>
Object
</td> </tr>
<tr>
<td>
Content
</td>
<td>
Array
</td> </tr>
<tr>
<td>
[0]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
param
</td>
<td>
String
</td> </tr>
<tr>
<td>
value
</td>
<td>
String
</td> </tr>
<tr>
<td>
metric
</td>
<td>
string
</td> </tr>
<tr>
<td>
[1]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
param
</td>
<td>
String
</td> </tr>
<tr>
<td>
value
</td>
<td>
String
</td> </tr>
<tr>
<td>
metric
</td>
<td>
string
</td> </tr>
<tr>
<td>
[2]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
param
</td>
<td>
String
</td> </tr>
<tr>
<td>
value
</td>
<td>
String
</td> </tr>
<tr>
<td>
metric
</td>
<td>
string
</td> </tr>
<tr>
<td>
[3]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
param
</td>
<td>
String
</td> </tr>
<tr>
<td>
value
</td>
<td>
String
</td> </tr>
<tr>
<td>
metric
</td>
<td>
string
</td> </tr>
<tr>
<td>
[4]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
param
</td>
<td>
String
</td> </tr>
<tr>
<td>
value
</td>
<td>
String
</td> </tr>
<tr>
<td>
metric
</td>
<td>
string
</td> </tr>
<tr>
<td>
[5]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
param
</td>
<td>
String
</td> </tr>
<tr>
<td>
value
</td>
<td>
String
</td> </tr>
<tr>
<td>
metric
</td>
<td>
string
</td> </tr>
<tr>
<td>
[6]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
param
</td>
<td>
String
</td> </tr>
<tr>
<td>
value
</td>
<td>
String
</td> </tr>
<tr>
<td>
metric
</td>
<td>
string
</td> </tr>
<tr>
<td>
valid
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
historical
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
versionNumber
</td>
<td>
Int64
</td> </tr>
<tr>
<td>
createAt
</td>
<td>
Date
</td> </tr>
<tr>
<td>
lastModified
</td>
<td>
Date
</td> </tr>
<tr>
<td>
createBy
</td>
<td>
String
</td> </tr>
<tr>
<td>
lastModifiedBy
</td>
<td>
String
</td> </tr>
<tr>
<td>
study
</td>
<td>
Object
</td> </tr>
<tr>
<td>
$ref
</td>
<td>
String
</td> </tr>
<tr>
<td>
$id
</td>
<td>
ObjectId
</td> </tr> </table>
<table>
<tr>
<th>
**Lipid profile**
</th>
<th>
</th> </tr>
<tr>
<td>
_id
</td>
<td>
ObjectId
</td> </tr>
<tr>
<td>
_class
</td>
<td>
String
</td> </tr>
<tr>
<td>
typeCode
</td>
<td>
String
</td> </tr>
<tr>
<td>
Payload
</td>
<td>
Object
</td> </tr>
<tr>
<td>
Content
</td>
<td>
Array
</td> </tr>
<tr>
<td>
[0]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
param
</td>
<td>
String
</td> </tr>
<tr>
<td>
value
</td>
<td>
String
</td> </tr>
<tr>
<td>
metric
</td>
<td>
string
</td> </tr>
<tr>
<td>
[1]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
param
</td>
<td>
String
</td> </tr>
<tr>
<td>
value
</td>
<td>
String
</td> </tr>
<tr>
<td>
metric
</td>
<td>
string
</td> </tr>
<tr>
<td>
[2]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
param
</td>
<td>
String
</td> </tr>
<tr>
<td>
value
</td>
<td>
String
</td> </tr>
<tr>
<td>
metric
</td>
<td>
string
</td> </tr>
<tr>
<td>
[3]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
param
</td>
<td>
String
</td> </tr>
<tr>
<td>
value
</td>
<td>
String
</td> </tr>
<tr>
<td>
metric
</td>
<td>
string
</td> </tr>
<tr>
<td>
[4]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
param
</td>
<td>
String
</td> </tr>
<tr>
<td>
value
</td>
<td>
String
</td> </tr>
<tr>
<td>
metric
</td>
<td>
string
</td> </tr>
<tr>
<td>
[5]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
param
</td>
<td>
String
</td> </tr>
<tr>
<td>
value
</td>
<td>
String
</td> </tr>
<tr>
<td>
metric
</td>
<td>
string
</td> </tr>
<tr>
<td>
[6]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
param
</td>
<td>
String
</td> </tr>
<tr>
<td>
value
</td>
<td>
String
</td> </tr>
<tr>
<td>
metric
</td>
<td>
string
</td> </tr>
<tr>
<td>
[7]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
param
</td>
<td>
String
</td> </tr> </table>
<table>
<tr>
<th>
value
</th>
<th>
String
</th> </tr>
<tr>
<td>
metric
</td>
<td>
string
</td> </tr>
<tr>
<td>
[8]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
param
</td>
<td>
String
</td> </tr>
<tr>
<td>
value
</td>
<td>
String
</td> </tr>
<tr>
<td>
metric
</td>
<td>
string
</td> </tr>
<tr>
<td>
[9]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
param
</td>
<td>
String
</td> </tr>
<tr>
<td>
value
</td>
<td>
String
</td> </tr>
<tr>
<td>
metric
</td>
<td>
string
</td> </tr>
<tr>
<td>
[10]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
param
</td>
<td>
String
</td> </tr>
<tr>
<td>
value
</td>
<td>
String
</td> </tr>
<tr>
<td>
metric
</td>
<td>
string
</td> </tr>
<tr>
<td>
[11]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
param
</td>
<td>
String
</td> </tr>
<tr>
<td>
value
</td>
<td>
String
</td> </tr>
<tr>
<td>
metric
</td>
<td>
string
</td> </tr>
<tr>
<td>
[12]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
param
</td>
<td>
String
</td> </tr>
<tr>
<td>
value
</td>
<td>
String
</td> </tr>
<tr>
<td>
metric
</td>
<td>
string
</td> </tr>
<tr>
<td>
[13]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
param
</td>
<td>
String
</td> </tr>
<tr>
<td>
value
</td>
<td>
String
</td> </tr>
<tr>
<td>
metric
</td>
<td>
string
</td> </tr>
<tr>
<td>
[14]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
param
</td>
<td>
String
</td> </tr>
<tr>
<td>
value
</td>
<td>
String
</td> </tr>
<tr>
<td>
metric
</td>
<td>
string
</td> </tr>
<tr>
<td>
[15]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
param
</td>
<td>
String
</td> </tr>
<tr>
<td>
value
</td>
<td>
String
</td> </tr>
<tr>
<td>
metric
</td>
<td>
string
</td> </tr>
<tr>
<td>
[16]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
param
</td>
<td>
String
</td> </tr>
<tr>
<td>
value
</td>
<td>
String
</td> </tr>
<tr>
<td>
metric
</td>
<td>
string
</td> </tr>
<tr>
<td>
[17]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
param
</td>
<td>
String
</td> </tr>
<tr>
<td>
value
</td>
<td>
String
</td> </tr>
<tr>
<td>
metric
</td>
<td>
string
</td> </tr>
<tr>
<td>
[18]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
param
</td>
<td>
String
</td> </tr>
<tr>
<td>
value
</td>
<td>
String
</td> </tr>
<tr>
<td>
metric
</td>
<td>
string
</td> </tr> </table>
<table>
<tr>
<th>
[19]
</th>
<th>
Object
</th> </tr>
<tr>
<td>
param
</td>
<td>
String
</td> </tr>
<tr>
<td>
value
</td>
<td>
String
</td> </tr>
<tr>
<td>
metric
</td>
<td>
string
</td> </tr>
<tr>
<td>
[20]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
param
</td>
<td>
String
</td> </tr>
<tr>
<td>
value
</td>
<td>
String
</td> </tr>
<tr>
<td>
metric
</td>
<td>
string
</td> </tr>
<tr>
<td>
[21]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
param
</td>
<td>
String
</td> </tr>
<tr>
<td>
value
</td>
<td>
String
</td> </tr>
<tr>
<td>
metric
</td>
<td>
string
</td> </tr>
<tr>
<td>
[22]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
param
</td>
<td>
String
</td> </tr>
<tr>
<td>
value
</td>
<td>
String
</td> </tr>
<tr>
<td>
metric
</td>
<td>
string
</td> </tr>
<tr>
<td>
[23]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
param
</td>
<td>
String
</td> </tr>
<tr>
<td>
value
</td>
<td>
String
</td> </tr>
<tr>
<td>
metric
</td>
<td>
string
</td> </tr>
<tr>
<td>
[24]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
param
</td>
<td>
String
</td> </tr>
<tr>
<td>
value
</td>
<td>
String
</td> </tr>
<tr>
<td>
metric
</td>
<td>
string
</td> </tr>
<tr>
<td>
[25]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
param
</td>
<td>
String
</td> </tr>
<tr>
<td>
value
</td>
<td>
String
</td> </tr>
<tr>
<td>
metric
</td>
<td>
string
</td> </tr>
<tr>
<td>
[26]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
param
</td>
<td>
String
</td> </tr>
<tr>
<td>
value
</td>
<td>
String
</td> </tr>
<tr>
<td>
metric
</td>
<td>
string
</td> </tr>
<tr>
<td>
[27]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
param
</td>
<td>
String
</td> </tr>
<tr>
<td>
value
</td>
<td>
String
</td> </tr>
<tr>
<td>
metric
</td>
<td>
string
</td> </tr>
<tr>
<td>
[28]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
param
</td>
<td>
String
</td> </tr>
<tr>
<td>
value
</td>
<td>
String
</td> </tr>
<tr>
<td>
metric
</td>
<td>
string
</td> </tr>
<tr>
<td>
[29]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
param
</td>
<td>
String
</td> </tr>
<tr>
<td>
value
</td>
<td>
String
</td> </tr>
<tr>
<td>
metric
</td>
<td>
string
</td> </tr>
<tr>
<td>
[30]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
param
</td>
<td>
String
</td> </tr> </table>
<table>
<tr>
<th>
value
</th>
<th>
String
</th> </tr>
<tr>
<td>
metric
</td>
<td>
string
</td> </tr>
<tr>
<td>
[31]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
param
</td>
<td>
String
</td> </tr>
<tr>
<td>
value
</td>
<td>
String
</td> </tr>
<tr>
<td>
metric
</td>
<td>
string
</td> </tr>
<tr>
<td>
[32]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
param
</td>
<td>
String
</td> </tr>
<tr>
<td>
value
</td>
<td>
String
</td> </tr>
<tr>
<td>
metric
</td>
<td>
string
</td> </tr>
<tr>
<td>
[33]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
param
</td>
<td>
String
</td> </tr>
<tr>
<td>
value
</td>
<td>
String
</td> </tr>
<tr>
<td>
metric
</td>
<td>
string
</td> </tr>
<tr>
<td>
[34]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
param
</td>
<td>
String
</td> </tr>
<tr>
<td>
value
</td>
<td>
String
</td> </tr>
<tr>
<td>
metric
</td>
<td>
string
</td> </tr>
<tr>
<td>
[35]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
param
</td>
<td>
String
</td> </tr>
<tr>
<td>
value
</td>
<td>
String
</td> </tr>
<tr>
<td>
metric
</td>
<td>
string
</td> </tr>
<tr>
<td>
[36]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
param
</td>
<td>
String
</td> </tr>
<tr>
<td>
value
</td>
<td>
String
</td> </tr>
<tr>
<td>
metric
</td>
<td>
string
</td> </tr>
<tr>
<td>
[37]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
param
</td>
<td>
String
</td> </tr>
<tr>
<td>
value
</td>
<td>
String
</td> </tr>
<tr>
<td>
metric
</td>
<td>
string
</td> </tr>
<tr>
<td>
[38]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
param
</td>
<td>
String
</td> </tr>
<tr>
<td>
value
</td>
<td>
String
</td> </tr>
<tr>
<td>
metric
</td>
<td>
string
</td> </tr>
<tr>
<td>
[39]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
param
</td>
<td>
String
</td> </tr>
<tr>
<td>
value
</td>
<td>
String
</td> </tr>
<tr>
<td>
metric
</td>
<td>
string
</td> </tr>
<tr>
<td>
[40]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
param
</td>
<td>
String
</td> </tr>
<tr>
<td>
value
</td>
<td>
String
</td> </tr>
<tr>
<td>
metric
</td>
<td>
string
</td> </tr>
<tr>
<td>
[41]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
param
</td>
<td>
String
</td> </tr>
<tr>
<td>
value
</td>
<td>
String
</td> </tr>
<tr>
<td>
metric
</td>
<td>
string
</td> </tr> </table>
<table>
<tr>
<th>
[42]
</th>
<th>
Object
</th> </tr>
<tr>
<td>
param
</td>
<td>
String
</td> </tr>
<tr>
<td>
value
</td>
<td>
String
</td> </tr>
<tr>
<td>
metric
</td>
<td>
string
</td> </tr>
<tr>
<td>
[43]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
param
</td>
<td>
String
</td> </tr>
<tr>
<td>
value
</td>
<td>
String
</td> </tr>
<tr>
<td>
metric
</td>
<td>
string
</td> </tr>
<tr>
<td>
[44]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
param
</td>
<td>
String
</td> </tr>
<tr>
<td>
value
</td>
<td>
String
</td> </tr>
<tr>
<td>
metric
</td>
<td>
string
</td> </tr>
<tr>
<td>
[45]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
param
</td>
<td>
String
</td> </tr>
<tr>
<td>
value
</td>
<td>
String
</td> </tr>
<tr>
<td>
metric
</td>
<td>
string
</td> </tr>
<tr>
<td>
[46]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
param
</td>
<td>
String
</td> </tr>
<tr>
<td>
value
</td>
<td>
String
</td> </tr>
<tr>
<td>
metric
</td>
<td>
string
</td> </tr>
<tr>
<td>
[47]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
param
</td>
<td>
String
</td> </tr>
<tr>
<td>
value
</td>
<td>
String
</td> </tr>
<tr>
<td>
metric
</td>
<td>
string
</td> </tr>
<tr>
<td>
[48]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
param
</td>
<td>
String
</td> </tr>
<tr>
<td>
value
</td>
<td>
String
</td> </tr>
<tr>
<td>
metric
</td>
<td>
string
</td> </tr>
<tr>
<td>
[49]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
param
</td>
<td>
String
</td> </tr>
<tr>
<td>
value
</td>
<td>
String
</td> </tr>
<tr>
<td>
metric
</td>
<td>
string
</td> </tr>
<tr>
<td>
[50]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
param
</td>
<td>
String
</td> </tr>
<tr>
<td>
value
</td>
<td>
String
</td> </tr>
<tr>
<td>
metric
</td>
<td>
string
</td> </tr>
<tr>
<td>
[51]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
param
</td>
<td>
String
</td> </tr>
<tr>
<td>
value
</td>
<td>
String
</td> </tr>
<tr>
<td>
metric
</td>
<td>
string
</td> </tr>
<tr>
<td>
[52]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
param
</td>
<td>
String
</td> </tr>
<tr>
<td>
value
</td>
<td>
String
</td> </tr>
<tr>
<td>
metric
</td>
<td>
string
</td> </tr>
<tr>
<td>
[53]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
param
</td>
<td>
String
</td> </tr> </table>
<table>
<tr>
<th>
value
</th>
<th>
String
</th> </tr>
<tr>
<td>
metric
</td>
<td>
string
</td> </tr>
<tr>
<td>
[54]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
param
</td>
<td>
String
</td> </tr>
<tr>
<td>
value
</td>
<td>
String
</td> </tr>
<tr>
<td>
metric
</td>
<td>
string
</td> </tr>
<tr>
<td>
[55]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
param
</td>
<td>
String
</td> </tr>
<tr>
<td>
value
</td>
<td>
String
</td> </tr>
<tr>
<td>
metric
</td>
<td>
string
</td> </tr>
<tr>
<td>
[56]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
param
</td>
<td>
String
</td> </tr>
<tr>
<td>
value
</td>
<td>
String
</td> </tr>
<tr>
<td>
metric
</td>
<td>
string
</td> </tr>
<tr>
<td>
[57]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
param
</td>
<td>
String
</td> </tr>
<tr>
<td>
value
</td>
<td>
String
</td> </tr>
<tr>
<td>
metric
</td>
<td>
string
</td> </tr>
<tr>
<td>
[58]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
param
</td>
<td>
String
</td> </tr>
<tr>
<td>
value
</td>
<td>
String
</td> </tr>
<tr>
<td>
metric
</td>
<td>
string
</td> </tr>
<tr>
<td>
[59]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
param
</td>
<td>
String
</td> </tr>
<tr>
<td>
value
</td>
<td>
String
</td> </tr>
<tr>
<td>
metric
</td>
<td>
string
</td> </tr>
<tr>
<td>
status
</td>
<td>
String
</td> </tr>
<tr>
<td>
valid
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
historical
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
versionNumber
</td>
<td>
Int64
</td> </tr>
<tr>
<td>
createAt
</td>
<td>
Date
</td> </tr>
<tr>
<td>
lastModified
</td>
<td>
Date
</td> </tr>
<tr>
<td>
createBy
</td>
<td>
String
</td> </tr>
<tr>
<td>
lastModifiedBy
</td>
<td>
String
</td> </tr>
<tr>
<td>
study
</td>
<td>
Object
</td> </tr>
<tr>
<td>
$ref
</td>
<td>
String
</td> </tr>
<tr>
<td>
$id
</td>
<td>
ObjectId
</td> </tr> </table>
<table>
<tr>
<th>
**Biohumoral markers**
</th> </tr>
<tr>
<td>
_id
</td>
<td>
ObjectId
</td> </tr>
<tr>
<td>
_class
</td>
<td>
String
</td> </tr>
<tr>
<td>
typeCode
</td>
<td>
String
</td> </tr>
<tr>
<td>
Payload
</td>
<td>
Object
</td> </tr>
<tr>
<td>
Content
</td>
<td>
Array
</td> </tr>
<tr>
<td>
[0]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
param
</td>
<td>
String
</td> </tr>
<tr>
<td>
value
</td>
<td>
String
</td> </tr>
<tr>
<td>
metric
</td>
<td>
string
</td> </tr>
<tr>
<td>
[1]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
param
</td>
<td>
String
</td> </tr>
<tr>
<td>
value
</td>
<td>
String
</td> </tr>
<tr>
<td>
metric
</td>
<td>
string
</td> </tr>
<tr>
<td>
[2]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
param
</td>
<td>
String
</td> </tr>
<tr>
<td>
value
</td>
<td>
String
</td> </tr>
<tr>
<td>
metric
</td>
<td>
string
</td> </tr>
<tr>
<td>
[3]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
param
</td>
<td>
String
</td> </tr>
<tr>
<td>
value
</td>
<td>
String
</td> </tr>
<tr>
<td>
metric
</td>
<td>
string
</td> </tr>
<tr>
<td>
[4]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
param
</td>
<td>
String
</td> </tr>
<tr>
<td>
value
</td>
<td>
String
</td> </tr>
<tr>
<td>
metric
</td>
<td>
string
</td> </tr>
<tr>
<td>
[5]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
param
</td>
<td>
String
</td> </tr>
<tr>
<td>
value
</td>
<td>
String
</td> </tr>
<tr>
<td>
metric
</td>
<td>
string
</td> </tr>
<tr>
<td>
[6]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
param
</td>
<td>
String
</td> </tr>
<tr>
<td>
value
</td>
<td>
String
</td> </tr>
<tr>
<td>
metric
</td>
<td>
string
</td> </tr>
<tr>
<td>
[7]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
param
</td>
<td>
String
</td> </tr>
<tr>
<td>
value
</td>
<td>
String
</td> </tr>
<tr>
<td>
metric
</td>
<td>
string
</td> </tr>
<tr>
<td>
[8]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
param
</td>
<td>
String
</td> </tr>
<tr>
<td>
value
</td>
<td>
String
</td> </tr>
<tr>
<td>
metric
</td>
<td>
string
</td> </tr>
<tr>
<td>
[9]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
param
</td>
<td>
String
</td> </tr>
<tr>
<td>
value
</td>
<td>
String
</td> </tr> </table>
<table>
<tr>
<th>
metric
</th>
<th>
string
</th> </tr>
<tr>
<td>
[10]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
param
</td>
<td>
String
</td> </tr>
<tr>
<td>
value
</td>
<td>
String
</td> </tr>
<tr>
<td>
metric
</td>
<td>
string
</td> </tr>
<tr>
<td>
[11]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
param
</td>
<td>
String
</td> </tr>
<tr>
<td>
value
</td>
<td>
String
</td> </tr>
<tr>
<td>
metric
</td>
<td>
string
</td> </tr>
<tr>
<td>
[12]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
param
</td>
<td>
String
</td> </tr>
<tr>
<td>
value
</td>
<td>
String
</td> </tr>
<tr>
<td>
metric
</td>
<td>
string
</td> </tr>
<tr>
<td>
[13]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
param
</td>
<td>
String
</td> </tr>
<tr>
<td>
value
</td>
<td>
String
</td> </tr>
<tr>
<td>
metric
</td>
<td>
string
</td> </tr>
<tr>
<td>
[14]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
param
</td>
<td>
String
</td> </tr>
<tr>
<td>
value
</td>
<td>
String
</td> </tr>
<tr>
<td>
metric
</td>
<td>
string
</td> </tr>
<tr>
<td>
[15]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
param
</td>
<td>
String
</td> </tr>
<tr>
<td>
value
</td>
<td>
String
</td> </tr>
<tr>
<td>
metric
</td>
<td>
string
</td> </tr>
<tr>
<td>
[16]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
param
</td>
<td>
String
</td> </tr>
<tr>
<td>
value
</td>
<td>
String
</td> </tr>
<tr>
<td>
metric
</td>
<td>
string
</td> </tr>
<tr>
<td>
[17]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
param
</td>
<td>
String
</td> </tr>
<tr>
<td>
value
</td>
<td>
String
</td> </tr>
<tr>
<td>
metric
</td>
<td>
string
</td> </tr>
<tr>
<td>
[18]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
param
</td>
<td>
String
</td> </tr>
<tr>
<td>
value
</td>
<td>
String
</td> </tr>
<tr>
<td>
metric
</td>
<td>
string
</td> </tr>
<tr>
<td>
[19]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
param
</td>
<td>
String
</td> </tr>
<tr>
<td>
value
</td>
<td>
String
</td> </tr>
<tr>
<td>
metric
</td>
<td>
string
</td> </tr>
<tr>
<td>
[20]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
param
</td>
<td>
String
</td> </tr>
<tr>
<td>
value
</td>
<td>
String
</td> </tr>
<tr>
<td>
metric
</td>
<td>
string
</td> </tr>
<tr>
<td>
[21]
</td>
<td>
Object
</td> </tr> </table>
<table>
<tr>
<th>
param
</th>
<th>
String
</th> </tr>
<tr>
<td>
value
</td>
<td>
String
</td> </tr>
<tr>
<td>
metric
</td>
<td>
string
</td> </tr>
<tr>
<td>
[22]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
param
</td>
<td>
String
</td> </tr>
<tr>
<td>
value
</td>
<td>
String
</td> </tr>
<tr>
<td>
metric
</td>
<td>
string
</td> </tr>
<tr>
<td>
status
</td>
<td>
String
</td> </tr>
<tr>
<td>
valid
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
historical
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
versionNumber
</td>
<td>
Int64
</td> </tr>
<tr>
<td>
createAt
</td>
<td>
Date
</td> </tr>
<tr>
<td>
lastModified
</td>
<td>
Date
</td> </tr>
<tr>
<td>
createBy
</td>
<td>
String
</td> </tr>
<tr>
<td>
lastModifiedBy
</td>
<td>
String
</td> </tr>
<tr>
<td>
study
</td>
<td>
Object
</td> </tr>
<tr>
<td>
$ref
</td>
<td>
String
</td> </tr>
<tr>
<td>
$id
</td>
<td>
ObjectId
</td> </tr> </table>
**Monocytes, “fragments” / “hfragments” structure (Clinical Partner direct
upload)**
<table>
<tr>
<th>
**Monocytes**
</th> </tr>
<tr>
<td>
_id
</td>
<td>
ObjectId
</td> </tr>
<tr>
<td>
_class
</td>
<td>
String
</td> </tr>
<tr>
<td>
typeCode
</td>
<td>
String
</td> </tr>
<tr>
<td>
payload
</td>
<td>
Object
</td> </tr>
<tr>
<td>
content
</td>
<td>
Array
</td> </tr>
<tr>
<td>
[0]
</td>
<td>
object
</td> </tr>
<tr>
<td>
row1_cell0
</td>
<td>
String
</td> </tr>
<tr>
<td>
prefix1
</td>
<td>
String
</td> </tr>
<tr>
<td>
row1_cell2
</td>
<td>
String
</td> </tr>
<tr>
<td>
row1_cell3
</td>
<td>
String
</td> </tr>
<tr>
<td>
prefix2
</td>
<td>
String
</td> </tr>
<tr>
<td>
row1_cell5
</td>
<td>
String
</td> </tr>
<tr>
<td>
row1_cell6
</td>
<td>
String
</td> </tr>
<tr>
<td>
prefix3
</td>
<td>
String
</td> </tr>
<tr>
<td>
row1_cell8
</td>
<td>
String
</td> </tr>
<tr>
<td>
row1_cell9
</td>
<td>
String
</td> </tr>
<tr>
<td>
prefix4
</td>
<td>
String
</td> </tr>
<tr>
<td>
row1_cell11
</td>
<td>
String
</td> </tr>
<tr>
<td>
row1_cell12
</td>
<td>
String
</td> </tr>
<tr>
<td>
[1]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
param
</td>
<td>
String
</td> </tr>
<tr>
<td>
metric1
</td>
<td>
String
</td> </tr>
<tr>
<td>
metric2
</td>
<td>
String
</td> </tr>
<tr>
<td>
metric3
</td>
<td>
String
</td> </tr>
<tr>
<td>
metric4
</td>
<td>
String
</td> </tr>
<tr>
<td>
metric5
</td>
<td>
String
</td> </tr>
<tr>
<td>
metric6
</td>
<td>
String
</td> </tr>
<tr>
<td>
metric7
</td>
<td>
String
</td> </tr>
<tr>
<td>
metric8
</td>
<td>
String
</td> </tr>
<tr>
<td>
metric9
</td>
<td>
String
</td> </tr>
<tr>
<td>
metric10
</td>
<td>
String
</td> </tr>
<tr>
<td>
metric11
</td>
<td>
String
</td> </tr>
<tr>
<td>
metric12
</td>
<td>
String
</td> </tr>
<tr>
<td>
[2]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
param
</td>
<td>
String
</td> </tr>
<tr>
<td>
value1
</td>
<td>
String
</td> </tr>
<tr>
<td>
value2
</td>
<td>
String
</td> </tr>
<tr>
<td>
value3
</td>
<td>
String
</td> </tr>
<tr>
<td>
value4
</td>
<td>
String
</td> </tr>
<tr>
<td>
value5
</td>
<td>
String
</td> </tr>
<tr>
<td>
value6
</td>
<td>
String
</td> </tr>
<tr>
<td>
value7
</td>
<td>
String
</td> </tr>
<tr>
<td>
value8
</td>
<td>
String
</td> </tr> </table>
<table>
<tr>
<th>
value9
</th>
<th>
String
</th> </tr>
<tr>
<td>
value10
</td>
<td>
String
</td> </tr>
<tr>
<td>
value11
</td>
<td>
String
</td> </tr>
<tr>
<td>
value12
</td>
<td>
String
</td> </tr>
<tr>
<td>
[3]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
param
</td>
<td>
String
</td> </tr>
<tr>
<td>
value1
</td>
<td>
String
</td> </tr>
<tr>
<td>
value2
</td>
<td>
String
</td> </tr>
<tr>
<td>
value3
</td>
<td>
String
</td> </tr>
<tr>
<td>
value4
</td>
<td>
String
</td> </tr>
<tr>
<td>
value5
</td>
<td>
String
</td> </tr>
<tr>
<td>
value6
</td>
<td>
String
</td> </tr>
<tr>
<td>
value7
</td>
<td>
String
</td> </tr>
<tr>
<td>
value8
</td>
<td>
String
</td> </tr>
<tr>
<td>
value9
</td>
<td>
String
</td> </tr>
<tr>
<td>
value10
</td>
<td>
String
</td> </tr>
<tr>
<td>
value11
</td>
<td>
String
</td> </tr>
<tr>
<td>
value12
</td>
<td>
String
</td> </tr>
<tr>
<td>
[4]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
param
</td>
<td>
String
</td> </tr>
<tr>
<td>
value1
</td>
<td>
String
</td> </tr>
<tr>
<td>
value2
</td>
<td>
String
</td> </tr>
<tr>
<td>
value3
</td>
<td>
String
</td> </tr>
<tr>
<td>
value4
</td>
<td>
String
</td> </tr>
<tr>
<td>
value5
</td>
<td>
String
</td> </tr>
<tr>
<td>
value6
</td>
<td>
String
</td> </tr>
<tr>
<td>
value7
</td>
<td>
String
</td> </tr>
<tr>
<td>
value8
</td>
<td>
String
</td> </tr>
<tr>
<td>
value9
</td>
<td>
String
</td> </tr>
<tr>
<td>
value10
</td>
<td>
String
</td> </tr>
<tr>
<td>
value11
</td>
<td>
String
</td> </tr>
<tr>
<td>
value12
</td>
<td>
String
</td> </tr>
<tr>
<td>
[5]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
param
</td>
<td>
String
</td> </tr>
<tr>
<td>
value1
</td>
<td>
String
</td> </tr>
<tr>
<td>
value2
</td>
<td>
String
</td> </tr>
<tr>
<td>
value3
</td>
<td>
String
</td> </tr>
<tr>
<td>
value4
</td>
<td>
String
</td> </tr>
<tr>
<td>
value5
</td>
<td>
String
</td> </tr>
<tr>
<td>
value6
</td>
<td>
String
</td> </tr>
<tr>
<td>
value7
</td>
<td>
String
</td> </tr>
<tr>
<td>
value8
</td>
<td>
String
</td> </tr>
<tr>
<td>
value9
</td>
<td>
String
</td> </tr>
<tr>
<td>
value10
</td>
<td>
String
</td> </tr>
<tr>
<td>
value11
</td>
<td>
String
</td> </tr>
<tr>
<td>
value12
</td>
<td>
String
</td> </tr> </table>
<table>
<tr>
<th>
[6]
</th>
<th>
Object
</th> </tr>
<tr>
<td>
param
</td>
<td>
String
</td> </tr>
<tr>
<td>
value1
</td>
<td>
String
</td> </tr>
<tr>
<td>
value2
</td>
<td>
String
</td> </tr>
<tr>
<td>
value3
</td>
<td>
String
</td> </tr>
<tr>
<td>
value4
</td>
<td>
String
</td> </tr>
<tr>
<td>
value5
</td>
<td>
String
</td> </tr>
<tr>
<td>
value6
</td>
<td>
String
</td> </tr>
<tr>
<td>
value7
</td>
<td>
String
</td> </tr>
<tr>
<td>
value8
</td>
<td>
String
</td> </tr>
<tr>
<td>
value9
</td>
<td>
String
</td> </tr>
<tr>
<td>
value10
</td>
<td>
String
</td> </tr>
<tr>
<td>
value11
</td>
<td>
String
</td> </tr>
<tr>
<td>
value12
</td>
<td>
String
</td> </tr>
<tr>
<td>
[7]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
Param
</td>
<td>
String
</td> </tr>
<tr>
<td>
param
</td>
<td>
String
</td> </tr>
<tr>
<td>
value1
</td>
<td>
String
</td> </tr>
<tr>
<td>
value2
</td>
<td>
String
</td> </tr>
<tr>
<td>
value3
</td>
<td>
String
</td> </tr>
<tr>
<td>
value4
</td>
<td>
String
</td> </tr>
<tr>
<td>
value5
</td>
<td>
String
</td> </tr>
<tr>
<td>
value6
</td>
<td>
String
</td> </tr>
<tr>
<td>
value7
</td>
<td>
String
</td> </tr>
<tr>
<td>
value8
</td>
<td>
String
</td> </tr>
<tr>
<td>
value9
</td>
<td>
String
</td> </tr>
<tr>
<td>
value10
</td>
<td>
String
</td> </tr>
<tr>
<td>
value11
</td>
<td>
String
</td> </tr>
<tr>
<td>
[8]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
param
</td>
<td>
String
</td> </tr>
<tr>
<td>
value1
</td>
<td>
String
</td> </tr>
<tr>
<td>
value2
</td>
<td>
String
</td> </tr>
<tr>
<td>
value3
</td>
<td>
String
</td> </tr>
<tr>
<td>
value4
</td>
<td>
String
</td> </tr>
<tr>
<td>
value5
</td>
<td>
String
</td> </tr>
<tr>
<td>
value6
</td>
<td>
String
</td> </tr>
<tr>
<td>
value7
</td>
<td>
String
</td> </tr>
<tr>
<td>
value8
</td>
<td>
String
</td> </tr>
<tr>
<td>
value9
</td>
<td>
String
</td> </tr>
<tr>
<td>
value10
</td>
<td>
String
</td> </tr>
<tr>
<td>
value11
</td>
<td>
String
</td> </tr>
<tr>
<td>
value12
</td>
<td>
String
</td> </tr>
<tr>
<td>
[9]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
param
</td>
<td>
String
</td> </tr>
<tr>
<td>
value1
</td>
<td>
String
</td> </tr>
<tr>
<td>
value2
</td>
<td>
String
</td> </tr> </table>
<table>
<tr>
<th>
value3
</th>
<th>
String
</th> </tr>
<tr>
<td>
value4
</td>
<td>
String
</td> </tr>
<tr>
<td>
value5
</td>
<td>
String
</td> </tr>
<tr>
<td>
value6
</td>
<td>
String
</td> </tr>
<tr>
<td>
value7
</td>
<td>
String
</td> </tr>
<tr>
<td>
value8
</td>
<td>
String
</td> </tr>
<tr>
<td>
value9
</td>
<td>
String
</td> </tr>
<tr>
<td>
value10
</td>
<td>
String
</td> </tr>
<tr>
<td>
value11
</td>
<td>
String
</td> </tr>
<tr>
<td>
value12
</td>
<td>
String
</td> </tr>
<tr>
<td>
[10]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
param
</td>
<td>
String
</td> </tr>
<tr>
<td>
value1
</td>
<td>
String
</td> </tr>
<tr>
<td>
value2
</td>
<td>
String
</td> </tr>
<tr>
<td>
value3
</td>
<td>
String
</td> </tr>
<tr>
<td>
value4
</td>
<td>
String
</td> </tr>
<tr>
<td>
value5
</td>
<td>
String
</td> </tr>
<tr>
<td>
value6
</td>
<td>
String
</td> </tr>
<tr>
<td>
value7
</td>
<td>
String
</td> </tr>
<tr>
<td>
value8
</td>
<td>
String
</td> </tr>
<tr>
<td>
value9
</td>
<td>
String
</td> </tr>
<tr>
<td>
value10
</td>
<td>
String
</td> </tr>
<tr>
<td>
value11
</td>
<td>
String
</td> </tr>
<tr>
<td>
value12
</td>
<td>
String
</td> </tr>
<tr>
<td>
[11]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
param
</td>
<td>
String
</td> </tr>
<tr>
<td>
value1
</td>
<td>
String
</td> </tr>
<tr>
<td>
value2
</td>
<td>
String
</td> </tr>
<tr>
<td>
value3
</td>
<td>
String
</td> </tr>
<tr>
<td>
value4
</td>
<td>
String
</td> </tr>
<tr>
<td>
value5
</td>
<td>
String
</td> </tr>
<tr>
<td>
value6
</td>
<td>
String
</td> </tr>
<tr>
<td>
value7
</td>
<td>
String
</td> </tr>
<tr>
<td>
value8
</td>
<td>
String
</td> </tr>
<tr>
<td>
value9
</td>
<td>
String
</td> </tr>
<tr>
<td>
value10
</td>
<td>
String
</td> </tr>
<tr>
<td>
value11
</td>
<td>
String
</td> </tr>
<tr>
<td>
value12
</td>
<td>
String
</td> </tr>
<tr>
<td>
[12]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
param
</td>
<td>
String
</td> </tr>
<tr>
<td>
value1
</td>
<td>
String
</td> </tr>
<tr>
<td>
value2
</td>
<td>
String
</td> </tr>
<tr>
<td>
value3
</td>
<td>
String
</td> </tr>
<tr>
<td>
value4
</td>
<td>
String
</td> </tr>
<tr>
<td>
value5
</td>
<td>
String
</td> </tr>
<tr>
<td>
value6
</td>
<td>
String
</td> </tr>
<tr>
<td>
value7
</td>
<td>
String
</td> </tr>
<tr>
<td>
value8
</td>
<td>
String
</td> </tr>
<tr>
<td>
value9
</td>
<td>
String
</td> </tr>
<tr>
<td>
value10
</td>
<td>
String
</td> </tr>
<tr>
<td>
value11
</td>
<td>
String
</td> </tr>
<tr>
<td>
value12
</td>
<td>
String
</td> </tr>
<tr>
<td>
status
</td>
<td>
String
</td> </tr>
<tr>
<td>
valid
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
historical
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
versionNumber
</td>
<td>
Int64
</td> </tr>
<tr>
<td>
createAt
</td>
<td>
Date
</td> </tr>
<tr>
<td>
lastModified
</td>
<td>
Date
</td> </tr>
<tr>
<td>
createBy
</td>
<td>
String
</td> </tr>
<tr>
<td>
lastModifiedBy
</td>
<td>
String
</td> </tr>
<tr>
<td>
study
</td>
<td>
Object
</td> </tr>
<tr>
<td>
$ref
</td>
<td>
String
</td> </tr>
<tr>
<td>
$id
</td>
<td>
ObjectId
</td> </tr> </table>
**QCTA Analysis, “fragments” / “hfragments” structures (Clinical Partner
direct upload, no data extraction)**
<table>
<tr>
<th>
**QCTA Analysis**
</th>
<th>
</th> </tr>
<tr>
<td>
_id
</td>
<td>
ObjectId
</td> </tr>
<tr>
<td>
_class
</td>
<td>
String
</td> </tr>
<tr>
<td>
typeCode
</td>
<td>
String
</td> </tr>
<tr>
<td>
payload
</td>
<td>
Object
</td> </tr>
<tr>
<td>
status
</td>
<td>
String
</td> </tr>
<tr>
<td>
valid
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
historical
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
versionNumber
</td>
<td>
Int64
</td> </tr>
<tr>
<td>
createAt
</td>
<td>
Date
</td> </tr>
<tr>
<td>
lastModified
</td>
<td>
Date
</td> </tr>
<tr>
<td>
createBy
</td>
<td>
String
</td> </tr>
<tr>
<td>
lastModifiedBy
</td>
<td>
String
</td> </tr>
<tr>
<td>
study
</td>
<td>
Object
</td> </tr>
<tr>
<td>
$ref
</td>
<td>
String
</td> </tr>
<tr>
<td>
$id
</td>
<td>
ObjectId
</td> </tr> </table>
**Exclusion criteria, “fragments” / “hfragments” structures (Clinical Partner
direct input)**
<table>
<tr>
<th>
**Exclusion Criteria**
</th> </tr>
<tr>
<td>
_id
</td>
<td>
ObjectId
</td> </tr>
<tr>
<td>
_class
</td>
<td>
String
</td> </tr>
<tr>
<td>
typeCode
</td>
<td>
String
</td> </tr>
<tr>
<td>
loked
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
payload
</td>
<td>
Object
</td> </tr>
<tr>
<td>
acuteCoronarySyndrome
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
inabilityProvideConsent
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
Pregnancy
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
lvDysfunction
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
AtrialFibrillation
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
CerebralIschemicAttack
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
ActiveCancerNeoplasticSurgery
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
Cardiomyopathy
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
CongenitalHeartDisease
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
SignificantValvularDisease
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
CoronaryRevascularization
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
CABG
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
CarotidSurgeryHistory
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
Creatinine
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
ChronicKidneyDisease
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
ActiveAutoimmuneAcuteInflammatoryDisease
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
HIV
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
HepatitisB
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
HepatitisC
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
status
</td>
<td>
String
</td> </tr>
<tr>
<td>
valid
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
historical
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
versionNumber
</td>
<td>
Int64
</td> </tr>
<tr>
<td>
createAt
</td>
<td>
Date
</td> </tr>
<tr>
<td>
lastModified
</td>
<td>
Date
</td> </tr>
<tr>
<td>
createBy
</td>
<td>
String
</td> </tr>
<tr>
<td>
lastModifiedBy
</td>
<td>
String
</td> </tr>
<tr>
<td>
study
</td>
<td>
Object
</td> </tr>
<tr>
<td>
$ref
</td>
<td>
String
</td> </tr>
<tr>
<td>
$id
</td>
<td>
ObjectId
</td> </tr> </table>
**CTA Report page, “fragments” / “hfragments” structure (Clinical Partner
direct input)**
<table>
<tr>
<th>
**CTA Report**
</th> </tr>
<tr>
<td>
_id
</td>
<td>
ObjectId
</td> </tr>
<tr>
<td>
_class
</td>
<td>
String
</td> </tr>
<tr>
<td>
typeCode
</td>
<td>
String
</td> </tr>
<tr>
<td>
payload
</td>
<td>
Object
</td> </tr>
<tr>
<td>
ccsNotApplicable
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
TCS_HU
</td>
<td>
String
</td> </tr>
<tr>
<td>
TCScore
</td>
<td>
String
</td> </tr>
<tr>
<td>
RCA_HU
</td>
<td>
String
</td> </tr>
<tr>
<td>
RCAScore
</td>
<td>
String
</td> </tr>
<tr>
<td>
LM_HU
</td>
<td>
String
</td> </tr>
<tr>
<td>
LMScore
</td>
<td>
String
</td> </tr>
<tr>
<td>
LAD_HU
</td>
<td>
String
</td> </tr>
<tr>
<td>
LADScore
</td>
<td>
String
</td> </tr>
<tr>
<td>
LCX_HU
</td>
<td>
String
</td> </tr>
<tr>
<td>
LCXScore
</td>
<td>
String
</td> </tr>
<tr>
<td>
nonCoronaryCardiacFindings
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
systemDominance
</td>
<td>
String
</td> </tr>
<tr>
<td>
noPlaquesInAnySegment
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
noVisibleSegmentsExist
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
noVisibleSegmentDescription
</td>
<td>
String
</td> </tr>
<tr>
<td>
plaque1
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
p1_Prox_type
</td>
<td>
String
</td> </tr>
<tr>
<td>
p1_Prox_stent
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
p1_Prox_lumen
</td>
<td>
String
</td> </tr>
<tr>
<td>
p1_Plaque_progression
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
plaque1_2
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
p1_2_Prox_type
</td>
<td>
String
</td> </tr> </table>
<table>
<tr>
<th>
p1_2_Prox_stent
</th>
<th>
Boolean
</th> </tr>
<tr>
<td>
p1_2_Prox_lumen
</td>
<td>
String
</td> </tr>
<tr>
<td>
p1_2_Plaque_progression
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
plaque2
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
p2_Prox_type
</td>
<td>
String
</td> </tr>
<tr>
<td>
p2_Prox_stent
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
p2_Prox_lumen
</td>
<td>
String
</td> </tr>
<tr>
<td>
p2_Plaque_progression
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
plaque2_2
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
p2_2_Prox_type
</td>
<td>
String
</td> </tr>
<tr>
<td>
p2_2_Prox_stent
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
p2_2_Prox_lumen
</td>
<td>
String
</td> </tr>
<tr>
<td>
p2_2_Plaque_progression
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
plaque3
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
p3_Prox_type
</td>
<td>
String
</td> </tr>
<tr>
<td>
p3_Prox_stent
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
p3_Prox_lumen
</td>
<td>
String
</td> </tr>
<tr>
<td>
p3_Plaque_progression
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
plaque3_2
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
p3_2_Prox_type
</td>
<td>
String
</td> </tr>
<tr>
<td>
p3_2_Prox_stent
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
p3_2_Prox_lumen
</td>
<td>
String
</td> </tr>
<tr>
<td>
p3_2_Plaque_progression
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
plaque4
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
p4_Prox_type
</td>
<td>
String
</td> </tr>
<tr>
<td>
p4_Prox_stent
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
p4_Prox_lumen
</td>
<td>
String
</td> </tr>
<tr>
<td>
p4_Plaque_progression
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
plaque4_2
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
p4_2_Prox_type
</td>
<td>
String
</td> </tr>
<tr>
<td>
p4_2_Prox_stent
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
p4_2_Prox_lumen
</td>
<td>
String
</td> </tr>
<tr>
<td>
p4_2_Plaque_progression
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
plaque5
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
p5_Prox_type
</td>
<td>
String
</td> </tr>
<tr>
<td>
p5_Prox_stent
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
p5_Prox_lumen
</td>
<td>
String
</td> </tr>
<tr>
<td>
p5_Plaque_progression
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
plaque5_2
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
p5_2_Prox_type
</td>
<td>
String
</td> </tr>
<tr>
<td>
p5_2_Prox_stent
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
p5_2_Prox_lumen
</td>
<td>
String
</td> </tr>
<tr>
<td>
p5_2_Plaque_progression
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
plaque6
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
p6_Prox_type
</td>
<td>
String
</td> </tr>
<tr>
<td>
p6_Prox_stent
</td>
<td>
Boolean
</td> </tr> </table>
<table>
<tr>
<th>
p6_Prox_lumen
</th>
<th>
String
</th> </tr>
<tr>
<td>
p6_Plaque_progression
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
plaque6_2
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
p6_2_Prox_type
</td>
<td>
String
</td> </tr>
<tr>
<td>
p6_2_Prox_stent
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
p6_2_Prox_lumen
</td>
<td>
String
</td> </tr>
<tr>
<td>
p6_2_Plaque_progression
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
plaque7
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
p7_Prox_type
</td>
<td>
String
</td> </tr>
<tr>
<td>
p7_Prox_stent
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
p7_Prox_lumen
</td>
<td>
String
</td> </tr>
<tr>
<td>
p7_Plaque_progression
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
plaque7_2
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
p7_2_Prox_type
</td>
<td>
String
</td> </tr>
<tr>
<td>
p7_2_Prox_stent
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
p7_2_Prox_lumen
</td>
<td>
String
</td> </tr>
<tr>
<td>
p7_2_Plaque_progression
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
plaque8
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
p8_Prox_type
</td>
<td>
String
</td> </tr>
<tr>
<td>
p8_Prox_stent
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
p8_Prox_lumen
</td>
<td>
String
</td> </tr>
<tr>
<td>
p8_Plaque_progression
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
plaque8_2
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
p8_2_Prox_type
</td>
<td>
String
</td> </tr>
<tr>
<td>
p8_2_Prox_stent
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
p8_2_Prox_lumen
</td>
<td>
String
</td> </tr>
<tr>
<td>
p8_2_Plaque_progression
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
plaque9
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
p9_Prox_type
</td>
<td>
String
</td> </tr>
<tr>
<td>
p9_Prox_stent
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
p9_Prox_lumen
</td>
<td>
String
</td> </tr>
<tr>
<td>
p9_Plaque_progression
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
plaque9_2
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
p9_2_Prox_type
</td>
<td>
String
</td> </tr>
<tr>
<td>
p9_2_Prox_stent
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
p9_2_Prox_lumen
</td>
<td>
String
</td> </tr>
<tr>
<td>
p9_2_Plaque_progression
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
plaque10
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
p10_Prox_type
</td>
<td>
String
</td> </tr>
<tr>
<td>
p10_Prox_stent
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
p10_Prox_lumen
</td>
<td>
String
</td> </tr>
<tr>
<td>
p10_Plaque_progression
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
plaque10_2
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
p10_2_Prox_type
</td>
<td>
String
</td> </tr>
<tr>
<td>
p10_2_Prox_stent
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
p10_2_Prox_lumen
</td>
<td>
String
</td> </tr> </table>
<table>
<tr>
<th>
p10_2_Plaque_progression
</th>
<th>
Boolean
</th> </tr>
<tr>
<td>
plaque11
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
p11_Prox_type
</td>
<td>
String
</td> </tr>
<tr>
<td>
p11_Prox_stent
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
p11_Prox_lumen
</td>
<td>
String
</td> </tr>
<tr>
<td>
p11_Plaque_progression
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
plaque11_2
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
p11_2_Prox_type
</td>
<td>
String
</td> </tr>
<tr>
<td>
p11_2_Prox_stent
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
p11_2_Prox_lumen
</td>
<td>
String
</td> </tr>
<tr>
<td>
p11_2_Plaque_progression
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
plaque12
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
p12_Prox_type
</td>
<td>
String
</td> </tr>
<tr>
<td>
p12_Prox_stent
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
p12_Prox_lumen
</td>
<td>
String
</td> </tr>
<tr>
<td>
p12_Plaque_progression
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
plaque12_2
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
p12_2_Prox_type
</td>
<td>
String
</td> </tr>
<tr>
<td>
p12_2_Prox_stent
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
p12_2_Prox_lumen
</td>
<td>
String
</td> </tr>
<tr>
<td>
p12_2_Plaque_progression
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
plaque13
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
p13_Prox_type
</td>
<td>
String
</td> </tr>
<tr>
<td>
p13_Prox_stent
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
p13_Prox_lumen
</td>
<td>
String
</td> </tr>
<tr>
<td>
p13_Plaque_progression
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
plaque13_2
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
p13_2_Prox_type
</td>
<td>
String
</td> </tr>
<tr>
<td>
p13_2_Prox_stent
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
p13_2_Prox_lumen
</td>
<td>
String
</td> </tr>
<tr>
<td>
p13_2_Plaque_progression
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
plaque14
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
p14_Prox_type
</td>
<td>
String
</td> </tr>
<tr>
<td>
p14_Prox_stent
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
p14_Prox_lumen
</td>
<td>
String
</td> </tr>
<tr>
<td>
p14_Plaque_progression
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
plaque14_2
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
p14_2_Prox_type
</td>
<td>
String
</td> </tr>
<tr>
<td>
p14_2_Prox_stent
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
p14_2_Prox_lumen
</td>
<td>
String
</td> </tr>
<tr>
<td>
p14_2_Plaque_progression
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
plaque15
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
p15_Prox_type
</td>
<td>
String
</td> </tr>
<tr>
<td>
p15_Prox_stent
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
p15_Prox_lumen
</td>
<td>
String
</td> </tr>
<tr>
<td>
p15_Plaque_progression
</td>
<td>
Boolean
</td> </tr> </table>
<table>
<tr>
<th>
plaque15_2
</th>
<th>
Boolean
</th> </tr>
<tr>
<td>
p15_2_Prox_type
</td>
<td>
String
</td> </tr>
<tr>
<td>
p15_2_Prox_stent
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
p15_2_Prox_lumen
</td>
<td>
String
</td> </tr>
<tr>
<td>
p15_2_Plaque_progression
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
plaque16
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
p16_Prox_type
</td>
<td>
String
</td> </tr>
<tr>
<td>
p16_Prox_stent
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
p16_Prox_lumen
</td>
<td>
String
</td> </tr>
<tr>
<td>
p16_Plaque_progression
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
plaque16_2
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
p16_2_Prox_type
</td>
<td>
String
</td> </tr>
<tr>
<td>
p16_2_Prox_stent
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
p16_2_Prox_lumen
</td>
<td>
String
</td> </tr>
<tr>
<td>
p16_2_Plaque_progression
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
plaque17
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
p17_Prox_type
</td>
<td>
String
</td> </tr>
<tr>
<td>
p17_Prox_stent
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
p17_Prox_lumen
</td>
<td>
String
</td> </tr>
<tr>
<td>
p17_Plaque_progression
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
plaque17_2
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
p17_2_Prox_type
</td>
<td>
String
</td> </tr>
<tr>
<td>
p17_2_Prox_stent
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
p17_2_Prox_lumen
</td>
<td>
String
</td> </tr>
<tr>
<td>
p17_2_Plaque_progression
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
status
</td>
<td>
String
</td> </tr>
<tr>
<td>
valid
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
historical
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
versionNumber
</td>
<td>
Int64
</td> </tr>
<tr>
<td>
createAt
</td>
<td>
Date
</td> </tr>
<tr>
<td>
lastModified
</td>
<td>
Date
</td> </tr>
<tr>
<td>
createBy
</td>
<td>
String
</td> </tr>
<tr>
<td>
lastModifiedBy
</td>
<td>
String
</td> </tr>
<tr>
<td>
study
</td>
<td>
Object
</td> </tr>
<tr>
<td>
$ref
</td>
<td>
String
</td> </tr>
<tr>
<td>
$id
</td>
<td>
ObjectId
</td> </tr> </table>
**CTA Acquisition page, “fragments” structure (Clinical Partner direct
input)**
<table>
<tr>
<th>
**CTA Acquisition**
</th> </tr>
<tr>
<td>
_id
</td>
<td>
ObjectId
</td> </tr>
<tr>
<td>
_class
</td>
<td>
String
</td> </tr>
<tr>
<td>
typeCode
</td>
<td>
String
</td> </tr>
<tr>
<td>
payload
</td>
<td>
Object
</td> </tr>
<tr>
<td>
ccta_date
</td>
<td>
String
</td> </tr>
<tr>
<td>
scanner_manufacturer
</td>
<td>
String
</td> </tr>
<tr>
<td>
scanner_type
</td>
<td>
String
</td> </tr>
<tr>
<td>
standardCStubeVoltage
</td>
<td>
Int32
</td> </tr>
<tr>
<td>
standardCStubeCurrent
</td>
<td>
Int32
</td> </tr>
<tr>
<td>
standardCSscanSliceThickness
</td>
<td>
Double
</td> </tr>
<tr>
<td>
standardCSscanIncrementThickness
</td>
<td>
Double
</td> </tr>
<tr>
<td>
hr_stable
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
hr_stable_value
</td>
<td>
Int32
</td> </tr>
<tr>
<td>
beta_blocking_used
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
beta_blocking_type
</td>
<td>
String
</td> </tr>
<tr>
<td>
additional_beta_blocking_used
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
additional_beta_blocking_type
</td>
<td>
String
</td> </tr>
<tr>
<td>
additional_beta_blocking_amount
</td>
<td>
String
</td> </tr>
<tr>
<td>
additional_beta_blocking_roa
</td>
<td>
String
</td> </tr>
<tr>
<td>
oral
</td>
<td>
String
</td> </tr>
<tr>
<td>
IV
</td>
<td>
String
</td> </tr>
<tr>
<td>
OtherMedicationAdministered
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
OtherMedicationAdministeredType
</td>
<td>
String
</td> </tr>
<tr>
<td>
Nitroglycerin
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
NitroglycerinAmount
</td>
<td>
String
</td> </tr>
<tr>
<td>
NitroglycerinRoa
</td>
<td>
String
</td> </tr>
<tr>
<td>
sublingualSpray
</td>
<td>
String
</td> </tr>
<tr>
<td>
tablet
</td>
<td>
String
</td> </tr>
<tr>
<td>
Native scan CTDIvol
</td>
<td>
Double
</td> </tr>
<tr>
<td>
Native sca DLP
</td>
<td>
Double
</td> </tr>
<tr>
<td>
Contrast scan CTDIvol
</td>
<td>
Double
</td> </tr>
<tr>
<td>
Contrast scan DLP
</td>
<td>
Double
</td> </tr>
<tr>
<td>
Total scan CTDIvol
</td>
<td>
Double
</td> </tr>
<tr>
<td>
Total scan DLP
</td>
<td>
Double
</td> </tr>
<tr>
<td>
Heart rate contrast scan
</td>
<td>
Int32
</td> </tr>
<tr>
<td>
Reconstruction % of R-R
</td>
<td>
Int32
</td> </tr>
<tr>
<td>
Slice Thickness
</td>
<td>
Double
</td> </tr>
<tr>
<td>
Increment thickness
</td>
<td>
Double
</td> </tr>
<tr>
<td>
kernel
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
Collimation
</td>
<td>
Int32
</td> </tr>
<tr>
<td>
Tube voltage
</td>
<td>
Int32
</td> </tr>
<tr>
<td>
Tube current
</td>
<td>
Int32
</td> </tr>
<tr>
<td>
Prospective
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
ProspectiveAmount
</td>
<td>
String
</td> </tr>
<tr>
<td>
Retrospective
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
ECGmodulation
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
ECGmodulationFrom
</td>
<td>
Int32
</td> </tr>
<tr>
<td>
ECGmodulationTo
</td>
<td>
Int32
</td> </tr>
<tr>
<td>
CCollimation
</td>
<td>
Int32
</td> </tr>
<tr>
<td>
CTube voltage
</td>
<td>
Int32
</td> </tr>
<tr>
<td>
CTube current
</td>
<td>
Int32
</td> </tr>
<tr>
<td>
CProspective
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
CProspectiveAmount
</td>
<td>
Int32
</td> </tr>
<tr>
<td>
CRetrospective
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
CECGmodulation
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
CECGmodulationFrom
</td>
<td>
Int32
</td> </tr>
<tr>
<td>
CECGmodulationTo
</td>
<td>
Int32
</td> </tr>
<tr>
<td>
Automatic Exposure Control
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
ContrastAgent
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
ContrastAgentName
</td>
<td>
String
</td> </tr>
<tr>
<td>
ContrastAgentVolume
</td>
<td>
Double
</td> </tr>
<tr>
<td>
ContrastAdministration
</td>
<td>
Double
</td> </tr>
<tr>
<td>
SalineFlush
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
SalineFlushAmount
</td>
<td>
Double
</td> </tr>
<tr>
<td>
status
</td>
<td>
String
</td> </tr>
<tr>
<td>
valid
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
historical
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
versionNumber
</td>
<td>
Int64
</td> </tr>
<tr>
<td>
createAt
</td>
<td>
Date
</td> </tr>
<tr>
<td>
lastModified
</td>
<td>
Date
</td> </tr>
<tr>
<td>
createBy
</td>
<td>
String
</td> </tr>
<tr>
<td>
lastModifiedBy
</td>
<td>
String
</td> </tr>
<tr>
<td>
study
</td>
<td>
Object
</td> </tr>
<tr>
<td>
$ref
</td>
<td>
String
</td> </tr>
<tr>
<td>
$id
</td>
<td>
ObjectId
</td> </tr> </table>
**Clinical Decision page, “fragments” structure (Clinical Partner direct
input)**
<table>
<tr>
<th>
**Clinical Decision**
</th> </tr>
<tr>
<td>
_id
</td>
<td>
ObjectId
</td> </tr>
<tr>
<td>
_class
</td>
<td>
String
</td> </tr>
<tr>
<td>
typeCode
</td>
<td>
String
</td> </tr>
<tr>
<td>
payload
</td>
<td>
Object
</td> </tr>
<tr>
<td>
plaqueProgression
</td>
<td>
String
</td> </tr>
<tr>
<td>
plaqueProgressionType
</td>
<td>
String
</td> </tr>
<tr>
<td>
nonInvasiveTesting
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
nonInvasiveTestingType
</td>
<td>
String
</td> </tr>
<tr>
<td>
angiography
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
angiographyType
</td>
<td>
String
</td> </tr>
<tr>
<td>
revascularization
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
revascularizationType
</td>
<td>
String
</td> </tr>
<tr>
<td>
oralAntidiabetics
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
oralAntidiabeticsDoseIncrease
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
Insulin
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
InsulinDoseIncrease
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
statins
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
StatinsMgDie
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
StatinsType
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
StatinsDoseIncrease
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
aCEInhibitors
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
aCEInhibitorsDoseIncrease
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
Diuretics
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
DiureticsDoseIncrease
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
ARB
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
ARBDoseIncrease
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
bETABlockers
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
bETABlockersDoseIncrease
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
calciumAntagonists
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
calciumAntagonistsDoseIncrease
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
Aspirin
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
AspirinDoseIncrease
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
novelAnticoagulants
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
novelAnticoagulantDoseIncrease
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
traditionalAnticoagulants
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
traditionalAnticoagulantDoseIncrease
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
Nitrates
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
NitratesDoseIncrease
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
DAPT
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
DAPTDoseIncrease
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
otherDrugs
</td>
<td>
</td> </tr>
<tr>
<td>
otherDrugsText
</td>
<td>
</td> </tr>
<tr>
<td>
status
</td>
<td>
String
</td> </tr>
<tr>
<td>
valid
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
historical
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
versionNumber
</td>
<td>
Int64
</td> </tr>
<tr>
<td>
createAt
</td>
<td>
Date
</td> </tr>
<tr>
<td>
lastModified
</td>
<td>
Date
</td> </tr>
<tr>
<td>
createBy
</td>
<td>
String
</td> </tr>
<tr>
<td>
lastModifiedBy
</td>
<td>
String
</td> </tr>
<tr>
<td>
study
</td>
<td>
Object
</td> </tr>
<tr>
<td>
$ref
</td>
<td>
String
</td> </tr>
<tr>
<td>
$id
</td>
<td>
ObjectId
</td> </tr> </table>
**Quality Control page, “fragments” / “hfragments” structure (Clinical Partner
direct input)**
<table>
<tr>
<th>
**Quality Control**
</th> </tr>
<tr>
<td>
_id
</td>
<td>
ObjectId
</td> </tr>
<tr>
<td>
_class
</td>
<td>
String
</td> </tr>
<tr>
<td>
typeCode
</td>
<td>
String
</td> </tr>
<tr>
<td>
payload
</td>
<td>
Object
</td> </tr>
<tr>
<td>
clinical_issue
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
clinical_notes
</td>
<td>
String
</td> </tr>
<tr>
<td>
clinical_check_completed
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
clinical_data_corrected
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
cta_report_issue
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
cta_report_notes
</td>
<td>
String
</td> </tr>
<tr>
<td>
cta_report_check_completed
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
cta_report_data_corrected
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
blood_tests_issue
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
blood_tests_notes
</td>
<td>
String
</td> </tr>
<tr>
<td>
blood_tests_check_completed
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
blood_tests_data_corrected
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
decision_making_issue
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
decision_making_notes
</td>
<td>
String
</td> </tr>
<tr>
<td>
decision_making_check_completed
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
decision_making_data_corrected
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
status
</td>
<td>
String
</td> </tr>
<tr>
<td>
valid
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
historical
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
versionNumber
</td>
<td>
Int64
</td> </tr>
<tr>
<td>
createAt
</td>
<td>
Date
</td> </tr>
<tr>
<td>
lastModified
</td>
<td>
Date
</td> </tr>
<tr>
<td>
createBy
</td>
<td>
String
</td> </tr>
<tr>
<td>
lastModifiedBy
</td>
<td>
String
</td> </tr>
<tr>
<td>
study
</td>
<td>
Object
</td> </tr>
<tr>
<td>
$ref
</td>
<td>
String
</td> </tr>
<tr>
<td>
$id
</td>
<td>
ObjectId
</td> </tr> </table>
I **nstitutions” structure**
<table>
<tr>
<th>
**Institutions**
</th>
<th>
</th> </tr>
<tr>
<td>
_id
</td>
<td>
String
</td> </tr>
<tr>
<td>
name
</td>
<td>
String
</td> </tr>
<tr>
<td>
clinical
</td>
<td>
Boolean
</td> </tr> </table>
**“Images” structure (Clinical Partner direct input)**
<table>
<tr>
<th>
**Images**
</th>
<th>
</th> </tr>
<tr>
<td>
_id
</td>
<td>
ObjectId
</td> </tr>
<tr>
<td>
_class
</td>
<td>
String
</td> </tr>
<tr>
<td>
fileName
</td>
<td>
String
</td> </tr>
<tr>
<td>
fileSize
</td>
<td>
Int64
</td> </tr>
<tr>
<td>
contentType
</td>
<td>
String
</td> </tr>
<tr>
<td>
sourcePath
</td>
<td>
String
</td> </tr>
<tr>
<td>
studyCode
</td>
<td>
String
</td> </tr>
<tr>
<td>
versionNumber
</td>
<td>
Int64
</td> </tr>
<tr>
<td>
createAt
</td>
<td>
Date
</td> </tr>
<tr>
<td>
lastModified
</td>
<td>
Date
</td> </tr>
<tr>
<td>
createBy
</td>
<td>
String
</td> </tr>
<tr>
<td>
lastModifiedBy
</td>
<td>
String
</td> </tr> </table>
**“studies” structure (Clinical Partner direct input)**
<table>
<tr>
<th>
**Studies**
</th>
<th>
</th> </tr>
<tr>
<td>
_id
</td>
<td>
ObjectId
</td> </tr>
<tr>
<td>
_class
</td>
<td>
String
</td> </tr>
<tr>
<td>
studyCode
</td>
<td>
String
</td> </tr>
<tr>
<td>
subjectCode
</td>
<td>
String
</td> </tr>
<tr>
<td>
progressiveStudyNumber
</td>
<td>
Int32
</td> </tr>
<tr>
<td>
creationDate
</td>
<td>
Date
</td> </tr>
<tr>
<td>
enabled
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
fragmentsDisabled
</td>
<td>
Array
</td> </tr>
<tr>
<td>
[0]
</td>
<td>
String
</td> </tr>
<tr>
<td>
[1]
</td>
<td>
String
</td> </tr>
<tr>
<td>
versionNumber
</td>
<td>
Int64
</td> </tr>
<tr>
<td>
createAt
</td>
<td>
Date
</td> </tr>
<tr>
<td>
lastModified
</td>
<td>
Date
</td> </tr>
<tr>
<td>
createBy
</td>
<td>
String
</td> </tr>
<tr>
<td>
lastModifiedBy
</td>
<td>
String
</td> </tr> </table>
<table>
<tr>
<th>
Institution
</th>
<th>
Object
</th> </tr>
<tr>
<td>
$ref
</td>
<td>
String
</td> </tr>
<tr>
<td>
$id
</td>
<td>
String
</td> </tr>
<tr>
<td>
fragments
</td>
<td>
Array
</td> </tr>
<tr>
<td>
[0]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
$ref
</td>
<td>
String
</td> </tr>
<tr>
<td>
$id
</td>
<td>
String
</td> </tr>
<tr>
<td>
[1]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
$ref
</td>
<td>
String
</td> </tr>
<tr>
<td>
$id
</td>
<td>
String
</td> </tr>
<tr>
<td>
[2]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
$ref
</td>
<td>
String
</td> </tr>
<tr>
<td>
$id
</td>
<td>
String
</td> </tr>
<tr>
<td>
[3]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
$ref
</td>
<td>
String
</td> </tr>
<tr>
<td>
$id
</td>
<td>
String
</td> </tr>
<tr>
<td>
[4]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
$ref
</td>
<td>
String
</td> </tr>
<tr>
<td>
$id
</td>
<td>
String
</td> </tr>
<tr>
<td>
[5]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
$ref
</td>
<td>
String
</td> </tr>
<tr>
<td>
$id
</td>
<td>
String
</td> </tr>
<tr>
<td>
[6]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
$ref
</td>
<td>
String
</td> </tr>
<tr>
<td>
$id
</td>
<td>
String
</td> </tr>
<tr>
<td>
[7]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
$ref
</td>
<td>
String
</td> </tr>
<tr>
<td>
$id
</td>
<td>
String
</td> </tr>
<tr>
<td>
[8]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
$ref
</td>
<td>
String
</td> </tr>
<tr>
<td>
$id
</td>
<td>
String
</td> </tr>
<tr>
<td>
[9]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
$ref
</td>
<td>
String
</td> </tr>
<tr>
<td>
$id
</td>
<td>
String
</td> </tr>
<tr>
<td>
[10]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
$ref
</td>
<td>
String
</td> </tr>
<tr>
<td>
$id
</td>
<td>
String
</td> </tr>
<tr>
<td>
[11]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
$ref
</td>
<td>
String
</td> </tr>
<tr>
<td>
$id
</td>
<td>
String
</td> </tr>
<tr>
<td>
[12]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
$ref
</td>
<td>
String
</td> </tr>
<tr>
<td>
$id
</td>
<td>
String
</td> </tr>
<tr>
<td>
[13]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
$ref
</td>
<td>
String
</td> </tr>
<tr>
<td>
$id
</td>
<td>
String
</td> </tr>
<tr>
<td>
[14]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
$ref
</td>
<td>
String
</td> </tr>
<tr>
<td>
$id
</td>
<td>
String
</td> </tr>
<tr>
<td>
[15]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
$ref
</td>
<td>
String
</td> </tr>
<tr>
<td>
$id
</td>
<td>
String
</td> </tr>
<tr>
<td>
[16]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
$ref
</td>
<td>
String
</td> </tr>
<tr>
<td>
$id
</td>
<td>
String
</td> </tr> </table>
**Blood Tests page, “types” structure**
<table>
<tr>
<th>
_id
</th>
<th>
String
</th> </tr>
<tr>
<td>
_class
</td>
<td>
String
</td> </tr>
<tr>
<td>
display
</td>
<td>
String
</td> </tr>
<tr>
<td>
shortName
</td>
<td>
String
</td> </tr>
<tr>
<td>
order
</td>
<td>
Int32
</td> </tr>
<tr>
<td>
controllerType
</td>
<td>
String
</td> </tr>
<tr>
<td>
definition
</td>
<td>
Object
</td> </tr>
<tr>
<td>
fields
</td>
<td>
Array
</td> </tr>
<tr>
<td>
[0]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
template
</td>
<td>
String
</td> </tr>
<tr>
<td>
[1]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
fieldGroup
</td>
<td>
Array
</td> </tr>
<tr>
<td>
[0]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
description
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
modelOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
allowInvalid
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
validators
</td>
<td>
Object
</td> </tr>
<tr>
<td>
uom2x_1
</td>
<td>
String
</td> </tr>
<tr>
<td>
[1]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
options
</td>
<td>
Array
</td> </tr>
<tr>
<td>
[0]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
name
</td>
<td>
String
</td> </tr>
<tr>
<td>
value
</td>
<td>
String
</td> </tr>
<tr>
<td>
[1]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
name
</td>
<td>
String
</td> </tr>
<tr>
<td>
value
</td>
<td>
String
</td> </tr>
<tr>
<td>
[2]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
fieldGroup
</td>
<td>
Array
</td> </tr>
<tr>
<td>
[0]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr> </table>
<table>
<tr>
<th>
type
</th>
<th>
String
</th> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
description
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
modelOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
allowInvalid
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
validators
</td>
<td>
Object
</td> </tr>
<tr>
<td>
uom2x_2
</td>
<td>
String
</td> </tr>
<tr>
<td>
[1]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
options
</td>
<td>
Array
</td> </tr>
<tr>
<td>
[0]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
name
</td>
<td>
String
</td> </tr>
<tr>
<td>
value
</td>
<td>
String
</td> </tr>
<tr>
<td>
[1]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
name
</td>
<td>
String
</td> </tr>
<tr>
<td>
value
</td>
<td>
String
</td> </tr>
<tr>
<td>
[3]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
classname
</td>
<td>
String
</td> </tr>
<tr>
<td>
fieldGroup
</td>
<td>
Array
</td> </tr>
<tr>
<td>
[0]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
description
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
modelOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
allowInvalid
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
validators
</td>
<td>
Object
</td> </tr>
<tr>
<td>
uom2x_3
</td>
<td>
String
</td> </tr>
<tr>
<td>
[1]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
options
</td>
<td>
Array
</td> </tr>
<tr>
<td>
[0]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
name
</td>
<td>
String
</td> </tr>
<tr>
<td>
value
</td>
<td>
String
</td> </tr>
<tr>
<td>
[1]
</td>
<td>
Object
</td> </tr> </table>
<table>
<tr>
<th>
name
</th>
<th>
String
</th> </tr>
<tr>
<td>
value
</td>
<td>
String
</td> </tr>
<tr>
<td>
[4]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
fieldGroup
</td>
<td>
Array
</td> </tr>
<tr>
<td>
[0]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
description
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
modelOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
allowInvalid
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
validators
</td>
<td>
Object
</td> </tr>
<tr>
<td>
uom2x_4
</td>
<td>
String
</td> </tr>
<tr>
<td>
[1]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
options
</td>
<td>
Array
</td> </tr>
<tr>
<td>
[0]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
name
</td>
<td>
String
</td> </tr>
<tr>
<td>
value
</td>
<td>
String
</td> </tr>
<tr>
<td>
[1]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
name
</td>
<td>
String
</td> </tr>
<tr>
<td>
value
</td>
<td>
String
</td> </tr>
<tr>
<td>
[5]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
fieldGroup
</td>
<td>
Array
</td> </tr>
<tr>
<td>
[0]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
description
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
modelOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
allowInvalid
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
validators
</td>
<td>
Object
</td> </tr>
<tr>
<td>
uom2x_5
</td>
<td>
String
</td> </tr>
<tr>
<td>
[1]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr> </table>
<table>
<tr>
<th>
key
</th>
<th>
String
</th> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
options
</td>
<td>
Array
</td> </tr>
<tr>
<td>
[0]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
name
</td>
<td>
String
</td> </tr>
<tr>
<td>
value
</td>
<td>
String
</td> </tr>
<tr>
<td>
[1]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
name
</td>
<td>
String
</td> </tr>
<tr>
<td>
value
</td>
<td>
String
</td> </tr>
<tr>
<td>
[6]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
fieldGroup
</td>
<td>
Array
</td> </tr>
<tr>
<td>
[0]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
description
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
modelOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
allowInvalid
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
validators
</td>
<td>
Object
</td> </tr>
<tr>
<td>
uom2x_6
</td>
<td>
String
</td> </tr>
<tr>
<td>
[1]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
options
</td>
<td>
Array
</td> </tr>
<tr>
<td>
[0]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
name
</td>
<td>
String
</td> </tr>
<tr>
<td>
value
</td>
<td>
String
</td> </tr>
<tr>
<td>
[1]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
name
</td>
<td>
String
</td> </tr>
<tr>
<td>
value
</td>
<td>
String
</td> </tr>
<tr>
<td>
[7]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
fieldGroup
</td>
<td>
Array
</td> </tr>
<tr>
<td>
[0]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr> </table>
<table>
<tr>
<th>
description
</th>
<th>
String
</th> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
modelOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
allowInvalid
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
validators
</td>
<td>
Object
</td> </tr>
<tr>
<td>
uom2x_7
</td>
<td>
String
</td> </tr>
<tr>
<td>
[1]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
options
</td>
<td>
Array
</td> </tr>
<tr>
<td>
[0]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
name
</td>
<td>
String
</td> </tr>
<tr>
<td>
value
</td>
<td>
String
</td> </tr>
<tr>
<td>
[1]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
name
</td>
<td>
String
</td> </tr>
<tr>
<td>
value
</td>
<td>
String
</td> </tr>
<tr>
<td>
[8]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
fieldGroup
</td>
<td>
Array
</td> </tr>
<tr>
<td>
[0]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
modelOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
allowInvalid
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
max
</td>
<td>
Int32
</td> </tr>
<tr>
<td>
min
</td>
<td>
Int32
</td> </tr>
<tr>
<td>
description
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
[1]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
options
</td>
<td>
Array
</td> </tr>
<tr>
<td>
[0]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
name
</td>
<td>
String
</td> </tr>
<tr>
<td>
value
</td>
<td>
String
</td> </tr>
<tr>
<td>
[1]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
name
</td>
<td>
String
</td> </tr>
<tr>
<td>
value
</td>
<td>
String
</td> </tr>
<tr>
<td>
[9]
</td>
<td>
Object
</td> </tr> </table>
<table>
<tr>
<th>
className
</th>
<th>
String
</th> </tr>
<tr>
<td>
fieldGroup
</td>
<td>
Array
</td> </tr>
<tr>
<td>
[0]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
modelOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
allowInvalid
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
max
</td>
<td>
Int32
</td> </tr>
<tr>
<td>
min
</td>
<td>
Int32
</td> </tr>
<tr>
<td>
description
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
[1]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
options
</td>
<td>
Array
</td> </tr>
<tr>
<td>
[0]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
name
</td>
<td>
String
</td> </tr>
<tr>
<td>
value
</td>
<td>
String
</td> </tr>
<tr>
<td>
[1]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
name
</td>
<td>
String
</td> </tr>
<tr>
<td>
value
</td>
<td>
String
</td> </tr>
<tr>
<td>
[10]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
fieldGroup
</td>
<td>
Array
</td> </tr>
<tr>
<td>
[0]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
modelOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
allowInvalid
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
max
</td>
<td>
Int32
</td> </tr>
<tr>
<td>
min
</td>
<td>
Int32
</td> </tr>
<tr>
<td>
description
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
[1]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr> </table>
<table>
<tr>
<th>
options
</th>
<th>
Array
</th> </tr>
<tr>
<td>
[0]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
name
</td>
<td>
String
</td> </tr>
<tr>
<td>
value
</td>
<td>
String
</td> </tr>
<tr>
<td>
[1]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
name
</td>
<td>
String
</td> </tr>
<tr>
<td>
value
</td>
<td>
String
</td> </tr>
<tr>
<td>
[11]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
fieldGroup
</td>
<td>
Array
</td> </tr>
<tr>
<td>
[0]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
description
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
modelOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
allowInvalid
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
validators
</td>
<td>
Object
</td> </tr>
<tr>
<td>
uom2x_8
</td>
<td>
String
</td> </tr>
<tr>
<td>
[1]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
options
</td>
<td>
Array
</td> </tr>
<tr>
<td>
[0]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
name
</td>
<td>
String
</td> </tr>
<tr>
<td>
value
</td>
<td>
String
</td> </tr>
<tr>
<td>
[1]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
name
</td>
<td>
String
</td> </tr>
<tr>
<td>
value
</td>
<td>
String
</td> </tr>
<tr>
<td>
[12]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
fieldGroup
</td>
<td>
Array
</td> </tr>
<tr>
<td>
[0]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
description
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
modelOptions
</td>
<td>
Object
</td> </tr> </table>
<table>
<tr>
<th>
allowInvalid
</th>
<th>
Boolean
</th> </tr>
<tr>
<td>
validators
</td>
<td>
Object
</td> </tr>
<tr>
<td>
uom2x_9
</td>
<td>
String
</td> </tr>
<tr>
<td>
[1]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
options
</td>
<td>
Array
</td> </tr>
<tr>
<td>
[0]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
name
</td>
<td>
String
</td> </tr>
<tr>
<td>
value
</td>
<td>
String
</td> </tr>
<tr>
<td>
[1]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
name
</td>
<td>
String
</td> </tr>
<tr>
<td>
value
</td>
<td>
String
</td> </tr>
<tr>
<td>
[13]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
fieldGroup
</td>
<td>
Array
</td> </tr>
<tr>
<td>
[0]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
modelOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
allowInvalid
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
max
</td>
<td>
Int32
</td> </tr>
<tr>
<td>
min
</td>
<td>
Int32
</td> </tr>
<tr>
<td>
description
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
[1]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
description
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
[2]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
description
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr> </table>
<table>
<tr>
<th>
[3]
</th>
<th>
Object
</th> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
modelOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
allowInvalid
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
max
</td>
<td>
Int32
</td> </tr>
<tr>
<td>
min
</td>
<td>
Int32
</td> </tr>
<tr>
<td>
description
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
[4]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
description
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
[4]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
options
</td>
<td>
Array
</td> </tr>
<tr>
<td>
[0]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
name
</td>
<td>
String
</td> </tr>
<tr>
<td>
value
</td>
<td>
String
</td> </tr>
<tr>
<td>
[1]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
name
</td>
<td>
String
</td> </tr>
<tr>
<td>
value
</td>
<td>
String
</td> </tr>
<tr>
<td>
canReadRoles
</td>
<td>
Array
</td> </tr>
<tr>
<td>
[0]
</td>
<td>
String
</td> </tr>
<tr>
<td>
[1]
</td>
<td>
String
</td> </tr>
<tr>
<td>
[2]
</td>
<td>
String
</td> </tr>
<tr>
<td>
[3]
</td>
<td>
String
</td> </tr>
<tr>
<td>
[4]
</td>
<td>
String
</td> </tr>
<tr>
<td>
[5]
</td>
<td>
String
</td> </tr>
<tr>
<td>
[6]
</td>
<td>
String
</td> </tr>
<tr>
<td>
[7]
</td>
<td>
String
</td> </tr>
<tr>
<td>
[8]
</td>
<td>
String
</td> </tr>
<tr>
<td>
[9]
</td>
<td>
String
</td> </tr>
<tr>
<td>
[10]
</td>
<td>
String
</td> </tr>
<tr>
<td>
canWriteRoles
</td>
<td>
Array
</td> </tr>
<tr>
<td>
[0]
</td>
<td>
String
</td> </tr>
<tr>
<td>
[1]
</td>
<td>
String
</td> </tr>
<tr>
<td>
[2]
</td>
<td>
String
</td> </tr>
<tr>
<td>
versionNumber
</td>
<td>
Int64
</td> </tr>
<tr>
<td>
createBy
</td>
<td>
String
</td> </tr>
<tr>
<td>
lastModifiedBy
</td>
<td>
String
</td> </tr> </table>
**Quality Control page, “types” structure**
<table>
<tr>
<th>
**Quality Control**
</th>
<th>
</th> </tr>
<tr>
<td>
_id
</td>
<td>
String
</td> </tr>
<tr>
<td>
_class
</td>
<td>
String
</td> </tr>
<tr>
<td>
display
</td>
<td>
String
</td> </tr>
<tr>
<td>
shortName
</td>
<td>
String
</td> </tr>
<tr>
<td>
order
</td>
<td>
Int32
</td> </tr>
<tr>
<td>
controllerType
</td>
<td>
String
</td> </tr>
<tr>
<td>
definition
</td>
<td>
Object
</td> </tr>
<tr>
<td>
fields
</td>
<td>
Array
</td> </tr>
<tr>
<td>
[0]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
template
</td>
<td>
String
</td> </tr>
<tr>
<td>
[1]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
template
</td>
<td>
String
</td> </tr>
<tr>
<td>
[2]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
template
</td>
<td>
String
</td> </tr>
<tr>
<td>
[3]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
template
</td>
<td>
String
</td> </tr>
<tr>
<td>
hideExpression
</td>
<td>
String
</td> </tr>
<tr>
<td>
[4]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
fieldGroup
</td>
<td>
Array
</td> </tr>
<tr>
<td>
[0]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
[1]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
hideExpression
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr> </table>
<table>
<tr>
<th>
templateOptions
</th>
<th>
Object
</th> </tr>
<tr>
<td>
placeholder
</td>
<td>
String
</td> </tr>
<tr>
<td>
[2]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
hideExpression
</td>
<td>
String
</td> </tr>
<tr>
<td>
modelOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
allowInvalid
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
validators
</td>
<td>
Object
</td> </tr>
<tr>
<td>
notChecked
</td>
<td>
Object
</td> </tr>
<tr>
<td>
expression
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
required
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
[3]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
hideExpression
</td>
<td>
String
</td> </tr>
<tr>
<td>
modelOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
allowInvalid
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
validators
</td>
<td>
Object
</td> </tr>
<tr>
<td>
notChecked
</td>
<td>
Object
</td> </tr>
<tr>
<td>
expression
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
required
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
[5]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
template
</td>
<td>
String
</td> </tr>
<tr>
<td>
[6]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
template
</td>
<td>
String
</td> </tr>
<tr>
<td>
hideExpression
</td>
<td>
String
</td> </tr>
<tr>
<td>
[7]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
fieldGroup
</td>
<td>
Array
</td> </tr>
<tr>
<td>
[0]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
[1]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr> </table>
<table>
<tr>
<th>
key
</th>
<th>
String
</th> </tr>
<tr>
<td>
hideExpression
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
placeholder
</td>
<td>
String
</td> </tr>
<tr>
<td>
[2]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
hideExpression
</td>
<td>
String
</td> </tr>
<tr>
<td>
modelOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
allowInvalid
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
validators
</td>
<td>
Object
</td> </tr>
<tr>
<td>
notChecked
</td>
<td>
Object
</td> </tr>
<tr>
<td>
expression
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
required
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
[3]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
hideExpression
</td>
<td>
String
</td> </tr>
<tr>
<td>
modelOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
allowInvalid
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
validators
</td>
<td>
Object
</td> </tr>
<tr>
<td>
notChecked
</td>
<td>
Object
</td> </tr>
<tr>
<td>
expression
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
required
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
[8]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
template
</td>
<td>
String
</td> </tr>
<tr>
<td>
[9]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
template
</td>
<td>
String
</td> </tr>
<tr>
<td>
hideExpression
</td>
<td>
String
</td> </tr>
<tr>
<td>
[10]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
fieldGroup
</td>
<td>
Array
</td> </tr>
<tr>
<td>
[0]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr> </table>
<table>
<tr>
<th>
label
</th>
<th>
String
</th> </tr>
<tr>
<td>
[1]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
hideExpression
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
placeholder
</td>
<td>
String
</td> </tr>
<tr>
<td>
[2]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
hideExpression
</td>
<td>
String
</td> </tr>
<tr>
<td>
modelOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
allowInvalid
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
validators
</td>
<td>
Object
</td> </tr>
<tr>
<td>
notChecked
</td>
<td>
Object
</td> </tr>
<tr>
<td>
expression
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
required
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
[3]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
hideExpression
</td>
<td>
String
</td> </tr>
<tr>
<td>
modelOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
allowInvalid
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
validators
</td>
<td>
Object
</td> </tr>
<tr>
<td>
notChecked
</td>
<td>
Object
</td> </tr>
<tr>
<td>
expression
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
required
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
[11]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
template
</td>
<td>
String
</td> </tr>
<tr>
<td>
[12]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
template
</td>
<td>
String
</td> </tr>
<tr>
<td>
hideExpression
</td>
<td>
String
</td> </tr>
<tr>
<td>
[13]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
fieldGroup
</td>
<td>
Array
</td> </tr>
<tr>
<td>
[0]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr> </table>
<table>
<tr>
<th>
key
</th>
<th>
String
</th> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
[1]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
hideExpression
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
placeholder
</td>
<td>
String
</td> </tr>
<tr>
<td>
[2]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
hideExpression
</td>
<td>
String
</td> </tr>
<tr>
<td>
modelOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
allowInvalid
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
validators
</td>
<td>
Object
</td> </tr>
<tr>
<td>
notChecked
</td>
<td>
Object
</td> </tr>
<tr>
<td>
expression
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
required
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
[3]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
hideExpression
</td>
<td>
String
</td> </tr>
<tr>
<td>
modelOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
allowInvalid
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
validators
</td>
<td>
Object
</td> </tr>
<tr>
<td>
notChecked
</td>
<td>
Object
</td> </tr>
<tr>
<td>
expression
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
required
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
canReadRoles
</td>
<td>
Array
</td> </tr>
<tr>
<td>
[0]
</td>
<td>
String
</td> </tr>
<tr>
<td>
[1]
</td>
<td>
String
</td> </tr>
<tr>
<td>
[2]
</td>
<td>
String
</td> </tr>
<tr>
<td>
[3]
</td>
<td>
String
</td> </tr>
<tr>
<td>
[4]
</td>
<td>
String
</td> </tr>
<tr>
<td>
[5]
</td>
<td>
String
</td> </tr>
<tr>
<td>
[6]
</td>
<td>
String
</td> </tr>
<tr>
<td>
[7]
</td>
<td>
String
</td> </tr> </table>
<table>
<tr>
<th>
[8]
</th>
<th>
String
</th> </tr>
<tr>
<td>
[9]
</td>
<td>
String
</td> </tr>
<tr>
<td>
canWriteRoles
</td>
<td>
Array
</td> </tr>
<tr>
<td>
[0]
</td>
<td>
String
</td> </tr>
<tr>
<td>
[1]
</td>
<td>
String
</td> </tr>
<tr>
<td>
[2]
</td>
<td>
String
</td> </tr>
<tr>
<td>
versionNumber
</td>
<td>
Int64
</td> </tr>
<tr>
<td>
lastModified
</td>
<td>
Date
</td> </tr>
<tr>
<td>
lastModifiedBy
</td>
<td>
String
</td> </tr> </table>
**RNA Sequencing page, “types” structure**
<table>
<tr>
<th>
_id
</th>
<th>
String
</th> </tr>
<tr>
<td>
_class
</td>
<td>
String
</td> </tr>
<tr>
<td>
display
</td>
<td>
String
</td> </tr>
<tr>
<td>
shortName
</td>
<td>
String
</td> </tr>
<tr>
<td>
order
</td>
<td>
Int32
</td> </tr>
<tr>
<td>
controllerType
</td>
<td>
String
</td> </tr>
<tr>
<td>
definition
</td>
<td>
Object
</td> </tr>
<tr>
<td>
canReadRoles
</td>
<td>
Array
</td> </tr>
<tr>
<td>
[0]
</td>
<td>
String
</td> </tr>
<tr>
<td>
[1]
</td>
<td>
String
</td> </tr>
<tr>
<td>
canWriteRoles
</td>
<td>
Array
</td> </tr>
<tr>
<td>
[0]
</td>
<td>
String
</td> </tr>
<tr>
<td>
versionNumber
</td>
<td>
Int64
</td> </tr>
<tr>
<td>
createdAt
</td>
<td>
Date
</td> </tr>
<tr>
<td>
lastModified
</td>
<td>
Date
</td> </tr>
<tr>
<td>
createBy
</td>
<td>
String
</td> </tr>
<tr>
<td>
lastModifiedBy
</td>
<td>
String
</td> </tr> </table>
**Exclusion Criteria page, “types” structure**
<table>
<tr>
<th>
_id
</th>
<th>
String
</th> </tr>
<tr>
<td>
_class
</td>
<td>
String
</td> </tr>
<tr>
<td>
display
</td>
<td>
String
</td> </tr>
<tr>
<td>
shortName
</td>
<td>
String
</td> </tr>
<tr>
<td>
order
</td>
<td>
Int32
</td> </tr>
<tr>
<td>
controllerType
</td>
<td>
String
</td> </tr>
<tr>
<td>
definition
</td>
<td>
Object
</td> </tr>
<tr>
<td>
Fields
</td>
<td>
Array
</td> </tr>
<tr>
<td>
[0]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
template
</td>
<td>
String
</td> </tr>
<tr>
<td>
[1]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
fieldGroup
</td>
<td>
Array
</td> </tr>
<tr>
<td>
[0]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
modelOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
allowInvalid
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
validators
</td>
<td>
Object
</td> </tr>
<tr>
<td>
notChecked
</td>
<td>
Object
</td> </tr>
<tr>
<td>
expression
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
required
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
[2]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
fieldGroup
</td>
<td>
Array
</td> </tr>
<tr>
<td>
[0]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
modelOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
allowInvalid
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
validators
</td>
<td>
Object
</td> </tr>
<tr>
<td>
notChecked
</td>
<td>
Object
</td> </tr>
<tr>
<td>
expression
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
required
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
[3]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
fieldGroup
</td>
<td>
Array
</td> </tr> </table>
<table>
<tr>
<th>
[0]
</th>
<th>
Object
</th> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
modelOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
allowInvalid
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
validators
</td>
<td>
Object
</td> </tr>
<tr>
<td>
notChecked
</td>
<td>
Object
</td> </tr>
<tr>
<td>
expression
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
description
</td>
<td>
String
</td> </tr>
<tr>
<td>
required
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
[4]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
fieldGroup
</td>
<td>
Array
</td> </tr>
<tr>
<td>
[0]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
modelOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
allowInvalid
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
validators
</td>
<td>
Object
</td> </tr>
<tr>
<td>
notChecked
</td>
<td>
Object
</td> </tr>
<tr>
<td>
expression
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
required
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
[5]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
fieldGroup
</td>
<td>
Array
</td> </tr>
<tr>
<td>
[0]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
modelOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
allowInvalid
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
validators
</td>
<td>
Object
</td> </tr>
<tr>
<td>
notChecked
</td>
<td>
Object
</td> </tr>
<tr>
<td>
expression
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
description
</td>
<td>
String
</td> </tr>
<tr>
<td>
required
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
[6]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr> </table>
<table>
<tr>
<th>
fieldGroup
</th>
<th>
Array
</th> </tr>
<tr>
<td>
[0]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
modelOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
allowInvalid
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
validators
</td>
<td>
Object
</td> </tr>
<tr>
<td>
notChecked
</td>
<td>
Object
</td> </tr>
<tr>
<td>
expression
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
required
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
[7]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
fieldGroup
</td>
<td>
Array
</td> </tr>
<tr>
<td>
[0]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
modelOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
allowInvalid
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
validators
</td>
<td>
Object
</td> </tr>
<tr>
<td>
notChecked
</td>
<td>
Object
</td> </tr>
<tr>
<td>
expression
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
required
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
[8]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
fieldGroup
</td>
<td>
Array
</td> </tr>
<tr>
<td>
[0]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
modelOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
allowInvalid
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
validators
</td>
<td>
Object
</td> </tr>
<tr>
<td>
notChecked
</td>
<td>
Object
</td> </tr>
<tr>
<td>
expression
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
required
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
[9]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
fieldGroup
</td>
<td>
Array
</td> </tr> </table>
<table>
<tr>
<th>
[0]
</th>
<th>
Object
</th> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
modelOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
allowInvalid
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
validators
</td>
<td>
Object
</td> </tr>
<tr>
<td>
notChecked
</td>
<td>
Object
</td> </tr>
<tr>
<td>
expression
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
required
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
[10]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
fieldGroup
</td>
<td>
Array
</td> </tr>
<tr>
<td>
[0]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
modelOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
allowInvalid
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
validators
</td>
<td>
Object
</td> </tr>
<tr>
<td>
notChecked
</td>
<td>
Object
</td> </tr>
<tr>
<td>
expression
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
description
</td>
<td>
String
</td> </tr>
<tr>
<td>
required
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
[11]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
fieldGroup
</td>
<td>
Array
</td> </tr>
<tr>
<td>
[0]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
modelOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
allowInvalid
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
validators
</td>
<td>
Object
</td> </tr>
<tr>
<td>
notChecked
</td>
<td>
Object
</td> </tr>
<tr>
<td>
expression
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
description
</td>
<td>
String
</td> </tr>
<tr>
<td>
required
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
[12]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr> </table>
<table>
<tr>
<th>
fieldGroup
</th>
<th>
Array
</th> </tr>
<tr>
<td>
[0]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
modelOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
allowInvalid
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
validators
</td>
<td>
Object
</td> </tr>
<tr>
<td>
notChecked
</td>
<td>
Object
</td> </tr>
<tr>
<td>
expression
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
required
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
[13]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
fieldGroup
</td>
<td>
Array
</td> </tr>
<tr>
<td>
[0]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
modelOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
allowInvalid
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
validators
</td>
<td>
Object
</td> </tr>
<tr>
<td>
notChecked
</td>
<td>
Object
</td> </tr>
<tr>
<td>
expression
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
description
</td>
<td>
String
</td> </tr>
<tr>
<td>
required
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
[14]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
fieldGroup
</td>
<td>
Array
</td> </tr>
<tr>
<td>
[0]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
modelOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
allowInvalid
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
validators
</td>
<td>
Object
</td> </tr>
<tr>
<td>
notChecked
</td>
<td>
Object
</td> </tr>
<tr>
<td>
expression
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
description
</td>
<td>
String
</td> </tr>
<tr>
<td>
required
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
[15]
</td>
<td>
Object
</td> </tr> </table>
<table>
<tr>
<th>
className
</th>
<th>
String
</th> </tr>
<tr>
<td>
fieldGroup
</td>
<td>
Array
</td> </tr>
<tr>
<td>
[0]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
modelOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
allowInvalid
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
validators
</td>
<td>
Object
</td> </tr>
<tr>
<td>
notChecked
</td>
<td>
Object
</td> </tr>
<tr>
<td>
expression
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
required
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
[16]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
fieldGroup
</td>
<td>
Array
</td> </tr>
<tr>
<td>
[0]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
modelOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
allowInvalid
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
validators
</td>
<td>
Object
</td> </tr>
<tr>
<td>
notChecked
</td>
<td>
Object
</td> </tr>
<tr>
<td>
expression
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
required
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
[17]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
fieldGroup
</td>
<td>
Array
</td> </tr>
<tr>
<td>
[0]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
modelOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
allowInvalid
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
validators
</td>
<td>
Object
</td> </tr>
<tr>
<td>
notChecked
</td>
<td>
Object
</td> </tr>
<tr>
<td>
expression
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
required
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
[18]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr> </table>
<table>
<tr>
<th>
fieldGroup
</th>
<th>
Array
</th> </tr>
<tr>
<td>
[0]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
modelOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
allowInvalid
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
validators
</td>
<td>
Object
</td> </tr>
<tr>
<td>
notChecked
</td>
<td>
Object
</td> </tr>
<tr>
<td>
expression
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
required
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
canReadRoles
</td>
<td>
Array
</td> </tr>
<tr>
<td>
[0]
</td>
<td>
String
</td> </tr>
<tr>
<td>
[1]
</td>
<td>
String
</td> </tr>
<tr>
<td>
[2]
</td>
<td>
String
</td> </tr>
<tr>
<td>
canWriteRoles
</td>
<td>
Array
</td> </tr>
<tr>
<td>
[0]
</td>
<td>
String
</td> </tr>
<tr>
<td>
[1]
</td>
<td>
String
</td> </tr>
<tr>
<td>
versionNumber
</td>
<td>
Int64
</td> </tr>
<tr>
<td>
lastModified
</td>
<td>
Date
</td> </tr>
<tr>
<td>
lastModifiedBy
</td>
<td>
String
</td> </tr> </table>
**Lipid Profile page, “types” structure**
<table>
<tr>
<th>
_id
</th>
<th>
String
</th> </tr>
<tr>
<td>
_class
</td>
<td>
String
</td> </tr>
<tr>
<td>
display
</td>
<td>
String
</td> </tr>
<tr>
<td>
shortName
</td>
<td>
String
</td> </tr>
<tr>
<td>
order
</td>
<td>
Int32
</td> </tr>
<tr>
<td>
controllerType
</td>
<td>
String
</td> </tr>
<tr>
<td>
definition
</td>
<td>
Object
</td> </tr>
<tr>
<td>
importerClass
</td>
<td>
String
</td> </tr>
<tr>
<td>
canReadRoles
</td>
<td>
Array
</td> </tr>
<tr>
<td>
[0]
</td>
<td>
String
</td> </tr>
<tr>
<td>
canWriteRoles
</td>
<td>
Array
</td> </tr>
<tr>
<td>
versionNumber
</td>
<td>
Int64
</td> </tr>
<tr>
<td>
createdAt
</td>
<td>
Date
</td> </tr>
<tr>
<td>
lastModified
</td>
<td>
Date
</td> </tr>
<tr>
<td>
createBy
</td>
<td>
String
</td> </tr>
<tr>
<td>
lastModifiedBy
</td>
<td>
String
</td> </tr> </table>
**Biohumoral markers page, “types” structure**
<table>
<tr>
<th>
_id
</th>
<th>
String
</th> </tr>
<tr>
<td>
_class
</td>
<td>
String
</td> </tr>
<tr>
<td>
display
</td>
<td>
String
</td> </tr>
<tr>
<td>
shortName
</td>
<td>
String
</td> </tr>
<tr>
<td>
order
</td>
<td>
Int32
</td> </tr>
<tr>
<td>
controllerType
</td>
<td>
String
</td> </tr>
<tr>
<td>
definition
</td>
<td>
Object
</td> </tr>
<tr>
<td>
importerClass
</td>
<td>
String
</td> </tr>
<tr>
<td>
canReadRoles
</td>
<td>
Array
</td> </tr>
<tr>
<td>
[0]
</td>
<td>
String
</td> </tr>
<tr>
<td>
[1]
</td>
<td>
String
</td> </tr>
<tr>
<td>
[2]
</td>
<td>
String
</td> </tr>
<tr>
<td>
[3]
</td>
<td>
String
</td> </tr>
<tr>
<td>
[4]
</td>
<td>
String
</td> </tr>
<tr>
<td>
canWriteRoles
</td>
<td>
Array
</td> </tr>
<tr>
<td>
[0]
</td>
<td>
String
</td> </tr>
<tr>
<td>
[1]
</td>
<td>
String
</td> </tr>
<tr>
<td>
versionNumber
</td>
<td>
Int64
</td> </tr>
<tr>
<td>
createdAt
</td>
<td>
Date
</td> </tr>
<tr>
<td>
lastModified
</td>
<td>
Date
</td> </tr>
<tr>
<td>
createBy
</td>
<td>
String
</td> </tr>
<tr>
<td>
lastModifiedBy
</td>
<td>
String
</td> </tr> </table>
**Exome Sequencing page, “types” structure**
<table>
<tr>
<th>
_id
</th>
<th>
String
</th> </tr>
<tr>
<td>
_class
</td>
<td>
String
</td> </tr>
<tr>
<td>
display
</td>
<td>
String
</td> </tr>
<tr>
<td>
shortName
</td>
<td>
String
</td> </tr>
<tr>
<td>
order
</td>
<td>
Int32
</td> </tr>
<tr>
<td>
controllerType
</td>
<td>
String
</td> </tr>
<tr>
<td>
definition
</td>
<td>
Object
</td> </tr>
<tr>
<td>
canReadRoles
</td>
<td>
Array
</td> </tr>
<tr>
<td>
[0]
</td>
<td>
String
</td> </tr>
<tr>
<td>
[1]
</td>
<td>
String
</td> </tr>
<tr>
<td>
canWriteRoles
</td>
<td>
Array
</td> </tr>
<tr>
<td>
[0]
</td>
<td>
String
</td> </tr>
<tr>
<td>
versionNumber
</td>
<td>
Int64
</td> </tr>
<tr>
<td>
createdAt
</td>
<td>
Date
</td> </tr>
<tr>
<td>
lastModified
</td>
<td>
Date
</td> </tr>
<tr>
<td>
createBy
</td>
<td>
String
</td> </tr>
<tr>
<td>
lastModifiedBy
</td>
<td>
String
</td> </tr> </table>
**Monocytes page, “types” structure**
<table>
<tr>
<th>
_id
</th>
<th>
String
</th> </tr>
<tr>
<td>
_class
</td>
<td>
String
</td> </tr>
<tr>
<td>
display
</td>
<td>
String
</td> </tr>
<tr>
<td>
shortName
</td>
<td>
String
</td> </tr>
<tr>
<td>
order
</td>
<td>
Int32
</td> </tr>
<tr>
<td>
controllerType
</td>
<td>
String
</td> </tr>
<tr>
<td>
definition
</td>
<td>
Object
</td> </tr>
<tr>
<td>
importerClass
</td>
<td>
String
</td> </tr>
<tr>
<td>
canReadRoles
</td>
<td>
Array
</td> </tr>
<tr>
<td>
[0]
</td>
<td>
String
</td> </tr>
<tr>
<td>
[1]
</td>
<td>
String
</td> </tr>
<tr>
<td>
[2]
</td>
<td>
String
</td> </tr>
<tr>
<td>
canWriteRoles
</td>
<td>
Array
</td> </tr>
<tr>
<td>
[0]
</td>
<td>
String
</td> </tr>
<tr>
<td>
[1]
</td>
<td>
String
</td> </tr>
<tr>
<td>
versionNumber
</td>
<td>
Int64
</td> </tr>
<tr>
<td>
lastModified
</td>
<td>
Date
</td> </tr>
<tr>
<td>
lastModifiedBy
</td>
<td>
String
</td> </tr> </table>
**Inflammatory markers page, “types” structure**
<table>
<tr>
<th>
_id
</th>
<th>
String
</th> </tr>
<tr>
<td>
_class
</td>
<td>
String
</td> </tr>
<tr>
<td>
display
</td>
<td>
String
</td> </tr>
<tr>
<td>
shortName
</td>
<td>
String
</td> </tr>
<tr>
<td>
order
</td>
<td>
Int32
</td> </tr>
<tr>
<td>
controllerType
</td>
<td>
String
</td> </tr>
<tr>
<td>
definition
</td>
<td>
Object
</td> </tr>
<tr>
<td>
importerClass
</td>
<td>
String
</td> </tr>
<tr>
<td>
canReadRoles
</td>
<td>
Array
</td> </tr>
<tr>
<td>
[0]
</td>
<td>
String
</td> </tr>
<tr>
<td>
[1]
</td>
<td>
String
</td> </tr>
<tr>
<td>
[2]
</td>
<td>
String
</td> </tr>
<tr>
<td>
[3]
</td>
<td>
String
</td> </tr>
<tr>
<td>
canWriteRoles
</td>
<td>
Array
</td> </tr>
<tr>
<td>
[0]
</td>
<td>
String
</td> </tr>
<tr>
<td>
[1]
</td>
<td>
String
</td> </tr>
<tr>
<td>
versionNumber
</td>
<td>
Int64
</td> </tr>
<tr>
<td>
createdAt
</td>
<td>
Date
</td> </tr>
<tr>
<td>
lastModified
</td>
<td>
Date
</td> </tr>
<tr>
<td>
createBy
</td>
<td>
String
</td> </tr>
<tr>
<td>
lastModifiedBy
</td>
<td>
String
</td> </tr> </table>
**QCTA Analysis page, “types” structure**
<table>
<tr>
<th>
_id
</th>
<th>
String
</th> </tr>
<tr>
<td>
_class
</td>
<td>
String
</td> </tr>
<tr>
<td>
display
</td>
<td>
String
</td> </tr>
<tr>
<td>
shortName
</td>
<td>
String
</td> </tr>
<tr>
<td>
order
</td>
<td>
Int32
</td> </tr>
<tr>
<td>
controllerType
</td>
<td>
String
</td> </tr>
<tr>
<td>
definition
</td>
<td>
Object
</td> </tr>
<tr>
<td>
canReadRoles
</td>
<td>
Array
</td> </tr>
<tr>
<td>
[0]
</td>
<td>
String
</td> </tr>
<tr>
<td>
[1]
</td>
<td>
String
</td> </tr>
<tr>
<td>
[2]
</td>
<td>
String
</td> </tr>
<tr>
<td>
canWriteRoles
</td>
<td>
Array
</td> </tr>
<tr>
<td>
[0]
</td>
<td>
String
</td> </tr>
<tr>
<td>
versionNumber
</td>
<td>
Int64
</td> </tr>
<tr>
<td>
createdAt
</td>
<td>
Date
</td> </tr>
<tr>
<td>
lastModified
</td>
<td>
Date
</td> </tr>
<tr>
<td>
createBy
</td>
<td>
String
</td> </tr>
<tr>
<td>
lastModifiedBy
</td>
<td>
String
</td> </tr> </table>
**Blood Sampling page, “types” structure**
<table>
<tr>
<th>
_id
</th>
<th>
String
</th> </tr>
<tr>
<td>
_class
</td>
<td>
String
</td> </tr>
<tr>
<td>
display
</td>
<td>
String
</td> </tr>
<tr>
<td>
shortName
</td>
<td>
String
</td> </tr>
<tr>
<td>
order
</td>
<td>
Int32
</td> </tr>
<tr>
<td>
controllerType
</td>
<td>
String
</td> </tr>
<tr>
<td>
definition
</td>
<td>
Object
</td> </tr>
<tr>
<td>
Fields
</td>
<td>
Array
</td> </tr>
<tr>
<td>
[0]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
template
</td>
<td>
String
</td> </tr>
<tr>
<td>
[1]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
template
</td>
<td>
String
</td> </tr>
<tr>
<td>
[2]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
fieldGroup
</td>
<td>
Array
</td> </tr>
<tr>
<td>
[0]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
template
</td>
<td>
String
</td> </tr>
<tr>
<td>
[1]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
template
</td>
<td>
String
</td> </tr>
<tr>
<td>
[2]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
template
</td>
<td>
String
</td> </tr>
<tr>
<td>
[3]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
template
</td>
<td>
String
</td> </tr>
<tr>
<td>
[3]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
fieldGroup
</td>
<td>
Array
</td> </tr>
<tr>
<td>
[0]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
template
</td>
<td>
String
</td> </tr>
<tr>
<td>
[1]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
template
</td>
<td>
String
</td> </tr>
<tr>
<td>
[2]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
template
</td>
<td>
String
</td> </tr>
<tr>
<td>
[3]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr> </table>
<table>
<tr>
<th>
type
</th>
<th>
String
</th> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
required
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
[4]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
hideExpression
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
description
</td>
<td>
String
</td> </tr>
<tr>
<td>
required
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
[4]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
fieldGroup
</td>
<td>
Array
</td> </tr>
<tr>
<td>
[0]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
template
</td>
<td>
String
</td> </tr>
<tr>
<td>
[1]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
template
</td>
<td>
String
</td> </tr>
<tr>
<td>
[2]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
template
</td>
<td>
String
</td> </tr>
<tr>
<td>
[3]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
required
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
[4]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
hideExpression
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
description
</td>
<td>
String
</td> </tr>
<tr>
<td>
required
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
[5]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
fieldGroup
</td>
<td>
Array
</td> </tr>
<tr>
<td>
[0]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr> </table>
<table>
<tr>
<th>
template
</th>
<th>
String
</th> </tr>
<tr>
<td>
[1]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
template
</td>
<td>
String
</td> </tr>
<tr>
<td>
[2]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
template
</td>
<td>
String
</td> </tr>
<tr>
<td>
[3]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
required
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
[4]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
hideExpression
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
description
</td>
<td>
String
</td> </tr>
<tr>
<td>
required
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
[6]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
fieldGroup
</td>
<td>
Array
</td> </tr>
<tr>
<td>
[0]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
template
</td>
<td>
String
</td> </tr>
<tr>
<td>
[1]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
template
</td>
<td>
String
</td> </tr>
<tr>
<td>
[2]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
template
</td>
<td>
String
</td> </tr>
<tr>
<td>
[3]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
required
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
[4]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
hideExpression
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
description
</td>
<td>
String
</td> </tr>
<tr>
<td>
required
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
[7]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
fieldGroup
</td>
<td>
Array
</td> </tr>
<tr>
<td>
[0]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
required
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
[1]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
required
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
[2]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
required
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
[8]
</td>
<td>
String
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
template
</td>
<td>
String
</td> </tr>
<tr>
<td>
canReadRoles
</td>
<td>
Array
</td> </tr>
<tr>
<td>
[0]
</td>
<td>
String
</td> </tr>
<tr>
<td>
[1]
</td>
<td>
String
</td> </tr>
<tr>
<td>
[2]
</td>
<td>
String
</td> </tr>
<tr>
<td>
canWriteRoles
</td>
<td>
Array
</td> </tr>
<tr>
<td>
[0]
</td>
<td>
String
</td> </tr>
<tr>
<td>
[1]
</td>
<td>
String
</td> </tr>
<tr>
<td>
versionNumber
</td>
<td>
Int64
</td> </tr>
<tr>
<td>
lastModified
</td>
<td>
Date
</td> </tr>
<tr>
<td>
lastModifiedBy
</td>
<td>
String
</td> </tr> </table>
**CTA Report Core Lab, “types” structure**
<table>
<tr>
<th>
**CTA Report Core Lab**
</th>
<th>
</th> </tr>
<tr>
<td>
_id
</td>
<td>
String
</td> </tr>
<tr>
<td>
_class
</td>
<td>
String
</td> </tr>
<tr>
<td>
display
</td>
<td>
String
</td> </tr>
<tr>
<td>
shortName
</td>
<td>
String
</td> </tr>
<tr>
<td>
order
</td>
<td>
Int32
</td> </tr>
<tr>
<td>
controllerType
</td>
<td>
String
</td> </tr>
<tr>
<td>
definition
</td>
<td>
Object
</td> </tr>
<tr>
<td>
Fields
</td>
<td>
Array
</td> </tr>
<tr>
<td>
[0]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
fieldGroup
</td>
<td>
Array
</td> </tr>
<tr>
<td>
[0]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
template
</td>
<td>
String
</td> </tr>
<tr>
<td>
[1]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
[2]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
options
</td>
<td>
Array
</td> </tr>
<tr>
<td>
[0]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
name
</td>
<td>
String
</td> </tr>
<tr>
<td>
value
</td>
<td>
String
</td> </tr>
<tr>
<td>
[1]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
name
</td>
<td>
String
</td> </tr>
<tr>
<td>
value
</td>
<td>
String
</td> </tr>
<tr>
<td>
[2]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
name
</td>
<td>
String
</td> </tr>
<tr>
<td>
value
</td>
<td>
String
</td> </tr>
<tr>
<td>
[3]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
name
</td>
<td>
String
</td> </tr>
<tr>
<td>
value
</td>
<td>
String
</td> </tr>
<tr>
<td>
[1]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr> </table>
<table>
<tr>
<th>
fieldGroup
</th>
<th>
Array
</th> </tr>
<tr>
<td>
[0]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
template
</td>
<td>
String
</td> </tr>
<tr>
<td>
[1]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
[2]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
hideExpression
</td>
<td>
String
</td> </tr>
<tr>
<td>
fieldGroup
</td>
<td>
Array
</td> </tr>
<tr>
<td>
[0]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
template
</td>
<td>
String
</td> </tr>
<tr>
<td>
[1]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
description
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
[2]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
depends
</td>
<td>
String
</td> </tr>
<tr>
<td>
expression
</td>
<td>
String
</td> </tr>
<tr>
<td>
[3]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
hideExpression
</td>
<td>
String
</td> </tr>
<tr>
<td>
fieldGroup
</td>
<td>
Array
</td> </tr>
<tr>
<td>
[0]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
template
</td>
<td>
String
</td> </tr>
<tr>
<td>
[1]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
description
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
[2]
</td>
<td>
Object
</td> </tr> </table>
<table>
<tr>
<th>
className
</th>
<th>
String
</th> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
depends
</td>
<td>
String
</td> </tr>
<tr>
<td>
expression
</td>
<td>
String
</td> </tr>
<tr>
<td>
[4]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
hideExpression
</td>
<td>
String
</td> </tr>
<tr>
<td>
fieldGroup
</td>
<td>
Array
</td> </tr>
<tr>
<td>
[0]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
template
</td>
<td>
String
</td> </tr>
<tr>
<td>
[1]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
description
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
[2]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
depends
</td>
<td>
String
</td> </tr>
<tr>
<td>
expression
</td>
<td>
String
</td> </tr>
<tr>
<td>
[5]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
hideExpression
</td>
<td>
String
</td> </tr>
<tr>
<td>
fieldGroup
</td>
<td>
Array
</td> </tr>
<tr>
<td>
[0]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
template
</td>
<td>
String
</td> </tr>
<tr>
<td>
[1]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
description
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
[2]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr> </table>
<table>
<tr>
<th>
depends
</th>
<th>
String
</th> </tr>
<tr>
<td>
expression
</td>
<td>
String
</td> </tr>
<tr>
<td>
[6]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
hideExpression
</td>
<td>
String
</td> </tr>
<tr>
<td>
fieldGroup
</td>
<td>
Array
</td> </tr>
<tr>
<td>
[0]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
template
</td>
<td>
String
</td> </tr>
<tr>
<td>
[1]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
description
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
[2]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
depends
</td>
<td>
String
</td> </tr>
<tr>
<td>
expression
</td>
<td>
String
</td> </tr>
<tr>
<td>
[7]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
template
</td>
<td>
String
</td> </tr>
<tr>
<td>
[8]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
template
</td>
<td>
String
</td> </tr>
<tr>
<td>
[9]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
fieldGroup
</td>
<td>
Array
</td> </tr>
<tr>
<td>
[0]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
description
</td>
<td>
String
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
[1]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
options
</td>
<td>
Array
</td> </tr> </table>
<table>
<tr>
<th>
[0]
</th>
<th>
Object
</th> </tr>
<tr>
<td>
name
</td>
<td>
String
</td> </tr>
<tr>
<td>
value
</td>
<td>
String
</td> </tr>
<tr>
<td>
[1]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
name
</td>
<td>
String
</td> </tr>
<tr>
<td>
value
</td>
<td>
String
</td> </tr>
<tr>
<td>
[2]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
name
</td>
<td>
String
</td> </tr>
<tr>
<td>
value
</td>
<td>
String
</td> </tr>
<tr>
<td>
[3]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
name
</td>
<td>
String
</td> </tr>
<tr>
<td>
value
</td>
<td>
String
</td> </tr>
<tr>
<td>
[10]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
fieldGroup
</td>
<td>
Array
</td> </tr>
<tr>
<td>
[0]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
[11]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
hideExpression
</td>
<td>
String
</td> </tr>
<tr>
<td>
fieldGroup
</td>
<td>
Array
</td> </tr>
<tr>
<td>
[0]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
[1]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
hideExpression
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
[12]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
hideExpression
</td>
<td>
String
</td> </tr>
<tr>
<td>
fieldGroup
</td>
<td>
Array
</td> </tr>
<tr>
<td>
[0]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
fieldGroup
</td>
<td>
Array
</td> </tr>
<tr>
<td>
[0]
</td>
<td>
String
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr> </table>
<table>
<tr>
<th>
id
</th>
<th>
String
</th> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
[1]
</td>
<td>
String
</td> </tr>
<tr>
<td>
hideExpression
</td>
<td>
String
</td> </tr>
<tr>
<td>
fieldGroup
</td>
<td>
Array
</td> </tr>
<tr>
<td>
[0]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
dictionary
</td>
<td>
String
</td> </tr>
<tr>
<td>
[1]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
[2]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
dictionary
</td>
<td>
String
</td> </tr>
<tr>
<td>
[3]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
[4]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
[5]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
hideExpression
</td>
<td>
String
</td> </tr>
<tr>
<td>
fieldGroup
</td>
<td>
Array
</td> </tr>
<tr>
<td>
[0]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr> </table>
<table>
<tr>
<th>
key
</th>
<th>
String
</th> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
dictionary
</td>
<td>
String
</td> </tr>
<tr>
<td>
[1]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
[2]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
dictionary
</td>
<td>
String
</td> </tr>
<tr>
<td>
[3]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
[1]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
fieldGroup
</td>
<td>
Array
</td> </tr>
<tr>
<td>
[0]
</td>
<td>
String
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
id
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
[1]
</td>
<td>
String
</td> </tr>
<tr>
<td>
hideExpression
</td>
<td>
String
</td> </tr>
<tr>
<td>
fieldGroup
</td>
<td>
Array
</td> </tr>
<tr>
<td>
[0]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
dictionary
</td>
<td>
String
</td> </tr>
<tr>
<td>
[1]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr> </table>
<table>
<tr>
<th>
key
</th>
<th>
String
</th> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
[2]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
dictionary
</td>
<td>
String
</td> </tr>
<tr>
<td>
[3]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
[4]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
[5]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
hideExpression
</td>
<td>
String
</td> </tr>
<tr>
<td>
fieldGroup
</td>
<td>
Array
</td> </tr>
<tr>
<td>
[0]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
dictionary
</td>
<td>
String
</td> </tr>
<tr>
<td>
[1]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
[2]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr> </table>
<table>
<tr>
<th>
dictionary
</th>
<th>
String
</th> </tr>
<tr>
<td>
[3]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
[2]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
fieldGroup
</td>
<td>
Array
</td> </tr>
<tr>
<td>
[0]
</td>
<td>
String
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
id
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
[1]
</td>
<td>
String
</td> </tr>
<tr>
<td>
hideExpression
</td>
<td>
String
</td> </tr>
<tr>
<td>
fieldGroup
</td>
<td>
Array
</td> </tr>
<tr>
<td>
[0]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
dictionary
</td>
<td>
String
</td> </tr>
<tr>
<td>
[1]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
[2]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
dictionary
</td>
<td>
String
</td> </tr>
<tr>
<td>
[3]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr> </table>
<table>
<tr>
<th>
[4]
</th>
<th>
Object
</th> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
[5]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
hideExpression
</td>
<td>
String
</td> </tr>
<tr>
<td>
fieldGroup
</td>
<td>
Array
</td> </tr>
<tr>
<td>
[0]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
dictionary
</td>
<td>
String
</td> </tr>
<tr>
<td>
[1]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
[2]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
dictionary
</td>
<td>
String
</td> </tr>
<tr>
<td>
[3]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
[3]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
fieldGroup
</td>
<td>
Array
</td> </tr>
<tr>
<td>
[0]
</td>
<td>
String
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
id
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr> </table>
<table>
<tr>
<th>
[1]
</th>
<th>
String
</th> </tr>
<tr>
<td>
hideExpression
</td>
<td>
String
</td> </tr>
<tr>
<td>
fieldGroup
</td>
<td>
Array
</td> </tr>
<tr>
<td>
[0]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
dictionary
</td>
<td>
String
</td> </tr>
<tr>
<td>
[1]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
[2]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
dictionary
</td>
<td>
String
</td> </tr>
<tr>
<td>
[3]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
[4]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
[5]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
hideExpression
</td>
<td>
String
</td> </tr>
<tr>
<td>
fieldGroup
</td>
<td>
Array
</td> </tr>
<tr>
<td>
[0]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
dictionary
</td>
<td>
String
</td> </tr> </table>
<table>
<tr>
<th>
[1]
</th>
<th>
Object
</th> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
[2]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
dictionary
</td>
<td>
String
</td> </tr>
<tr>
<td>
[3]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
[4]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
fieldGroup
</td>
<td>
Array
</td> </tr>
<tr>
<td>
[0]
</td>
<td>
String
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
id
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
[1]
</td>
<td>
String
</td> </tr>
<tr>
<td>
hideExpression
</td>
<td>
String
</td> </tr>
<tr>
<td>
fieldGroup
</td>
<td>
Array
</td> </tr>
<tr>
<td>
[0]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
dictionary
</td>
<td>
String
</td> </tr>
<tr>
<td>
[1]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
[2]
</td>
<td>
Object
</td> </tr> </table>
<table>
<tr>
<th>
className
</th>
<th>
String
</th> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
dictionary
</td>
<td>
String
</td> </tr>
<tr>
<td>
[3]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
[4]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
[5]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
hideExpression
</td>
<td>
String
</td> </tr>
<tr>
<td>
fieldGroup
</td>
<td>
Array
</td> </tr>
<tr>
<td>
[0]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
dictionary
</td>
<td>
String
</td> </tr>
<tr>
<td>
[1]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
[2]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
dictionary
</td>
<td>
String
</td> </tr>
<tr>
<td>
[3]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr> </table>
<table>
<tr>
<th>
templateOptions
</th>
<th>
Object
</th> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
[5]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
fieldGroup
</td>
<td>
Array
</td> </tr>
<tr>
<td>
[0]
</td>
<td>
String
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
id
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
[1]
</td>
<td>
String
</td> </tr>
<tr>
<td>
hideExpression
</td>
<td>
String
</td> </tr>
<tr>
<td>
fieldGroup
</td>
<td>
Array
</td> </tr>
<tr>
<td>
[0]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
dictionary
</td>
<td>
String
</td> </tr>
<tr>
<td>
[1]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
[2]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
dictionary
</td>
<td>
String
</td> </tr>
<tr>
<td>
[3]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
[4]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr> </table>
<table>
<tr>
<th>
label
</th>
<th>
String
</th> </tr>
<tr>
<td>
[5]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
hideExpression
</td>
<td>
String
</td> </tr>
<tr>
<td>
fieldGroup
</td>
<td>
Array
</td> </tr>
<tr>
<td>
[0]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
dictionary
</td>
<td>
String
</td> </tr>
<tr>
<td>
[1]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
[2]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
dictionary
</td>
<td>
String
</td> </tr>
<tr>
<td>
[3]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
[6]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
fieldGroup
</td>
<td>
Array
</td> </tr>
<tr>
<td>
[0]
</td>
<td>
String
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
id
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
[1]
</td>
<td>
String
</td> </tr>
<tr>
<td>
hideExpression
</td>
<td>
String
</td> </tr>
<tr>
<td>
fieldGroup
</td>
<td>
Array
</td> </tr>
<tr>
<td>
[0]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr> </table>
<table>
<tr>
<th>
key
</th>
<th>
String
</th> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
dictionary
</td>
<td>
String
</td> </tr>
<tr>
<td>
[1]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
[2]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
dictionary
</td>
<td>
String
</td> </tr>
<tr>
<td>
[3]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
[4]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
[5]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
hideExpression
</td>
<td>
String
</td> </tr>
<tr>
<td>
fieldGroup
</td>
<td>
Array
</td> </tr>
<tr>
<td>
[0]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
dictionary
</td>
<td>
String
</td> </tr>
<tr>
<td>
[1]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr> </table>
<table>
<tr>
<th>
label
</th>
<th>
String
</th> </tr>
<tr>
<td>
[2]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
dictionary
</td>
<td>
String
</td> </tr>
<tr>
<td>
[3]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
[7]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
fieldGroup
</td>
<td>
Array
</td> </tr>
<tr>
<td>
[0]
</td>
<td>
String
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
id
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
[1]
</td>
<td>
String
</td> </tr>
<tr>
<td>
hideExpression
</td>
<td>
String
</td> </tr>
<tr>
<td>
fieldGroup
</td>
<td>
Array
</td> </tr>
<tr>
<td>
[0]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
dictionary
</td>
<td>
String
</td> </tr>
<tr>
<td>
[1]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
[2]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr> </table>
<table>
<tr>
<th>
dictionary
</th>
<th>
String
</th> </tr>
<tr>
<td>
[3]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
[4]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
[5]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
hideExpression
</td>
<td>
String
</td> </tr>
<tr>
<td>
fieldGroup
</td>
<td>
Array
</td> </tr>
<tr>
<td>
[0]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
dictionary
</td>
<td>
String
</td> </tr>
<tr>
<td>
[1]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
[2]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
dictionary
</td>
<td>
String
</td> </tr>
<tr>
<td>
[3]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
[8]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
fieldGroup
</td>
<td>
Array
</td> </tr> </table>
<table>
<tr>
<th>
[0]
</th>
<th>
String
</th> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
id
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
[1]
</td>
<td>
String
</td> </tr>
<tr>
<td>
hideExpression
</td>
<td>
String
</td> </tr>
<tr>
<td>
fieldGroup
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<td>
Array
</td> </tr>
<tr>
<td>
[0]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
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String
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<tr>
<td>
dictionary
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String
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<tr>
<td>
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<td>
Object
</td> </tr>
<tr>
<td>
className
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<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
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<tr>
<td>
label
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String
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<tr>
<td>
[2]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
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<tr>
<td>
label
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String
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<tr>
<td>
dictionary
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String
</td> </tr>
<tr>
<td>
[3]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
[4]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
[5]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
hideExpression
</td>
<td>
String
</td> </tr>
<tr>
<td>
fieldGroup
</td>
<td>
Array
</td> </tr> </table>
<table>
<tr>
<th>
[0]
</th>
<th>
Object
</th> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
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String
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<tr>
<td>
dictionary
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<td>
String
</td> </tr>
<tr>
<td>
[1]
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<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
[2]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
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<tr>
<td>
dictionary
</td>
<td>
String
</td> </tr>
<tr>
<td>
[3]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
[9]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
fieldGroup
</td>
<td>
Array
</td> </tr>
<tr>
<td>
[0]
</td>
<td>
String
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
id
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
[1]
</td>
<td>
String
</td> </tr>
<tr>
<td>
hideExpression
</td>
<td>
String
</td> </tr>
<tr>
<td>
fieldGroup
</td>
<td>
Array
</td> </tr>
<tr>
<td>
[0]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
dictionary
</td>
<td>
String
</td> </tr> </table>
<table>
<tr>
<th>
[1]
</th>
<th>
Object
</th> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
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<tr>
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templateOptions
</td>
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Object
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<tr>
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label
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<tr>
<td>
[2]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
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<tr>
<td>
dictionary
</td>
<td>
String
</td> </tr>
<tr>
<td>
[3]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
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<tr>
<td>
label
</td>
<td>
String
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<tr>
<td>
[4]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
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<tr>
<td>
[5]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
hideExpression
</td>
<td>
String
</td> </tr>
<tr>
<td>
fieldGroup
</td>
<td>
Array
</td> </tr>
<tr>
<td>
[0]
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<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
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<td>
Object
</td> </tr>
<tr>
<td>
label
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<td>
String
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<tr>
<td>
dictionary
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<td>
String
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<tr>
<td>
[1]
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<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
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<tr>
<td>
templateOptions
</td>
<td>
Object
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<tr>
<td>
label
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<td>
String
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<tr>
<td>
[2]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr> </table>
<table>
<tr>
<th>
templateOptions
</th>
<th>
Object
</th> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
dictionary
</td>
<td>
String
</td> </tr>
<tr>
<td>
[3]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
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<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
[10]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
fieldGroup
</td>
<td>
Array
</td> </tr>
<tr>
<td>
[0]
</td>
<td>
String
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
id
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
[1]
</td>
<td>
String
</td> </tr>
<tr>
<td>
hideExpression
</td>
<td>
String
</td> </tr>
<tr>
<td>
fieldGroup
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<td>
Array
</td> </tr>
<tr>
<td>
[0]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
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<td>
String
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<tr>
<td>
dictionary
</td>
<td>
String
</td> </tr>
<tr>
<td>
[1]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
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<tr>
<td>
templateOptions
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<td>
Object
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<tr>
<td>
label
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String
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<tr>
<td>
[2]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
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<tr>
<td>
label
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<td>
String
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<tr>
<td>
dictionary
</td>
<td>
String
</td> </tr>
<tr>
<td>
[3]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr> </table>
<table>
<tr>
<th>
templateOptions
</th>
<th>
Object
</th> </tr>
<tr>
<td>
label
</td>
<td>
String
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<tr>
<td>
[4]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
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<td>
String
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<tr>
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templateOptions
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Object
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<tr>
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[5]
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<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
hideExpression
</td>
<td>
String
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<tr>
<td>
fieldGroup
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<td>
Array
</td> </tr>
<tr>
<td>
[0]
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<td>
Object
</td> </tr>
<tr>
<td>
className
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<td>
String
</td> </tr>
<tr>
<td>
key
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<td>
String
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<tr>
<td>
type
</td>
<td>
String
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<tr>
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templateOptions
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<td>
Object
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<tr>
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label
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String
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dictionary
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String
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<tr>
<td>
[1]
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<td>
Object
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<tr>
<td>
className
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<td>
String
</td> </tr>
<tr>
<td>
key
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String
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<tr>
<td>
type
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<td>
String
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<tr>
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templateOptions
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Object
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[2]
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<td>
Object
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<tr>
<td>
className
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<td>
String
</td> </tr>
<tr>
<td>
key
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<td>
String
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<tr>
<td>
type
</td>
<td>
String
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<tr>
<td>
templateOptions
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<td>
Object
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label
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String
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dictionary
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<td>
String
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<td>
[3]
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<td>
Object
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<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
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<tr>
<td>
type
</td>
<td>
String
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<tr>
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templateOptions
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Object
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label
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<td>
String
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<tr>
<td>
[11]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
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<td>
String
</td> </tr>
<tr>
<td>
fieldGroup
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<td>
Array
</td> </tr>
<tr>
<td>
[0]
</td>
<td>
String
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
id
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr> </table>
<table>
<tr>
<th>
templateOptions
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Object
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<tr>
<td>
label
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String
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<tr>
<td>
[1]
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<td>
String
</td> </tr>
<tr>
<td>
hideExpression
</td>
<td>
String
</td> </tr>
<tr>
<td>
fieldGroup
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<td>
Array
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<tr>
<td>
[0]
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<td>
Object
</td> </tr>
<tr>
<td>
className
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<td>
String
</td> </tr>
<tr>
<td>
key
</td>
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String
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<tr>
<td>
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</td>
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String
</td> </tr>
<tr>
<td>
templateOptions
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<td>
Object
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<tr>
<td>
label
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String
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<tr>
<td>
dictionary
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String
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<tr>
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[1]
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<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
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Object
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<tr>
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label
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String
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<tr>
<td>
[2]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
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<td>
String
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<tr>
<td>
dictionary
</td>
<td>
String
</td> </tr>
<tr>
<td>
[3]
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<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
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Object
</td> </tr>
<tr>
<td>
label
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String
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<tr>
<td>
[4]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
[5]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
hideExpression
</td>
<td>
String
</td> </tr>
<tr>
<td>
fieldGroup
</td>
<td>
Array
</td> </tr>
<tr>
<td>
[0]
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<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
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String
</td> </tr>
<tr>
<td>
type
</td>
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String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr> </table>
<table>
<tr>
<th>
label
</th>
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String
</th> </tr>
<tr>
<td>
dictionary
</td>
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String
</td> </tr>
<tr>
<td>
[1]
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Object
</td> </tr>
<tr>
<td>
className
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String
</td> </tr>
<tr>
<td>
key
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String
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<tr>
<td>
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String
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<tr>
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templateOptions
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Object
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<tr>
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label
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[2]
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Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
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String
</td> </tr>
<tr>
<td>
type
</td>
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String
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<tr>
<td>
templateOptions
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Object
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<tr>
<td>
label
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String
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<tr>
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dictionary
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String
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<tr>
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[3]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
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String
</td> </tr>
<tr>
<td>
type
</td>
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String
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<tr>
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templateOptions
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Object
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<tr>
<td>
label
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String
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<tr>
<td>
[12]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
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<td>
String
</td> </tr>
<tr>
<td>
fieldGroup
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Array
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<tr>
<td>
[0]
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String
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<tr>
<td>
className
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<td>
String
</td> </tr>
<tr>
<td>
id
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
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String
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<tr>
<td>
type
</td>
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String
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<tr>
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templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
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String
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<tr>
<td>
[1]
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String
</td> </tr>
<tr>
<td>
hideExpression
</td>
<td>
String
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<tr>
<td>
fieldGroup
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Array
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<tr>
<td>
[0]
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Object
</td> </tr>
<tr>
<td>
className
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<td>
String
</td> </tr>
<tr>
<td>
key
</td>
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String
</td> </tr>
<tr>
<td>
type
</td>
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String
</td> </tr>
<tr>
<td>
templateOptions
</td>
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Object
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<tr>
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label
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String
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<tr>
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dictionary
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String
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<tr>
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[1]
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Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
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String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr> </table>
<table>
<tr>
<th>
label
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String
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<tr>
<td>
[2]
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Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
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<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
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<tr>
<td>
dictionary
</td>
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String
</td> </tr>
<tr>
<td>
[3]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
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<tr>
<td>
label
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String
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<tr>
<td>
[4]
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<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
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<tr>
<td>
label
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String
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<tr>
<td>
[5]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
hideExpression
</td>
<td>
String
</td> </tr>
<tr>
<td>
fieldGroup
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<td>
Array
</td> </tr>
<tr>
<td>
[0]
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Object
</td> </tr>
<tr>
<td>
className
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<td>
String
</td> </tr>
<tr>
<td>
key
</td>
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String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
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<tr>
<td>
dictionary
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<td>
String
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<tr>
<td>
[1]
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<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
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<tr>
<td>
label
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<td>
String
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<tr>
<td>
[2]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
dictionary
</td>
<td>
String
</td> </tr>
<tr>
<td>
[3]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr> </table>
<table>
<tr>
<th>
key
</th>
<th>
String
</th> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
[13]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
fieldGroup
</td>
<td>
Array
</td> </tr>
<tr>
<td>
[0]
</td>
<td>
String
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
id
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
[1]
</td>
<td>
String
</td> </tr>
<tr>
<td>
hideExpression
</td>
<td>
String
</td> </tr>
<tr>
<td>
fieldGroup
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<td>
Array
</td> </tr>
<tr>
<td>
[0]
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<td>
Object
</td> </tr>
<tr>
<td>
className
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<td>
String
</td> </tr>
<tr>
<td>
key
</td>
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String
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<tr>
<td>
type
</td>
<td>
String
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<tr>
<td>
templateOptions
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<td>
Object
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<tr>
<td>
label
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String
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<tr>
<td>
dictionary
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String
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<tr>
<td>
[1]
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<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
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<tr>
<td>
label
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String
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<tr>
<td>
[2]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
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<td>
String
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<tr>
<td>
dictionary
</td>
<td>
String
</td> </tr>
<tr>
<td>
[3]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
[4]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr> </table>
<table>
<tr>
<th>
type
</th>
<th>
String
</th> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
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<tr>
<td>
label
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String
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<tr>
<td>
[5]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
hideExpression
</td>
<td>
String
</td> </tr>
<tr>
<td>
fieldGroup
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<td>
Array
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<tr>
<td>
[0]
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Object
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<tr>
<td>
className
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<td>
String
</td> </tr>
<tr>
<td>
key
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<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
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<tr>
<td>
templateOptions
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<td>
Object
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<tr>
<td>
label
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String
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<tr>
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dictionary
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String
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<tr>
<td>
[1]
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Object
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<tr>
<td>
className
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<td>
String
</td> </tr>
<tr>
<td>
key
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<td>
String
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<tr>
<td>
type
</td>
<td>
String
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<tr>
<td>
templateOptions
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Object
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label
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String
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<tr>
<td>
[2]
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Object
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<tr>
<td>
className
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<td>
String
</td> </tr>
<tr>
<td>
key
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<td>
String
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<tr>
<td>
type
</td>
<td>
String
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<tr>
<td>
templateOptions
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<td>
Object
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<tr>
<td>
label
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String
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<tr>
<td>
dictionary
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<td>
String
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<tr>
<td>
[3]
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<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
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<td>
String
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<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
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<td>
Object
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<tr>
<td>
label
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<td>
String
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<tr>
<td>
[14]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
fieldGroup
</td>
<td>
Array
</td> </tr>
<tr>
<td>
[0]
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<td>
String
</td> </tr>
<tr>
<td>
className
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<td>
String
</td> </tr>
<tr>
<td>
id
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
[1]
</td>
<td>
String
</td> </tr>
<tr>
<td>
hideExpression
</td>
<td>
String
</td> </tr>
<tr>
<td>
fieldGroup
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<td>
Array
</td> </tr> </table>
<table>
<tr>
<th>
[0]
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<th>
Object
</th> </tr>
<tr>
<td>
className
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<td>
String
</td> </tr>
<tr>
<td>
key
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<td>
String
</td> </tr>
<tr>
<td>
type
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<td>
String
</td> </tr>
<tr>
<td>
templateOptions
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<td>
Object
</td> </tr>
<tr>
<td>
label
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<td>
String
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<tr>
<td>
dictionary
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<td>
String
</td> </tr>
<tr>
<td>
[1]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
[2]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
dictionary
</td>
<td>
String
</td> </tr>
<tr>
<td>
[3]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
[4]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
[5]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
hideExpression
</td>
<td>
String
</td> </tr>
<tr>
<td>
fieldGroup
</td>
<td>
Array
</td> </tr>
<tr>
<td>
[0]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
dictionary
</td>
<td>
String
</td> </tr>
<tr>
<td>
[1]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr> </table>
<table>
<tr>
<th>
type
</th>
<th>
String
</th> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
[2]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
dictionary
</td>
<td>
String
</td> </tr>
<tr>
<td>
[3]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
[15]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
fieldGroup
</td>
<td>
Array
</td> </tr>
<tr>
<td>
[0]
</td>
<td>
String
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
id
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
[1]
</td>
<td>
String
</td> </tr>
<tr>
<td>
hideExpression
</td>
<td>
String
</td> </tr>
<tr>
<td>
fieldGroup
</td>
<td>
Array
</td> </tr>
<tr>
<td>
[0]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
dictionary
</td>
<td>
String
</td> </tr>
<tr>
<td>
[1]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
[2]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr> </table>
<table>
<tr>
<th>
templateOptions
</th>
<th>
Object
</th> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
dictionary
</td>
<td>
String
</td> </tr>
<tr>
<td>
[3]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
[4]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
[5]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
hideExpression
</td>
<td>
String
</td> </tr>
<tr>
<td>
fieldGroup
</td>
<td>
Array
</td> </tr>
<tr>
<td>
[0]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
dictionary
</td>
<td>
String
</td> </tr>
<tr>
<td>
[1]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
[2]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
dictionary
</td>
<td>
String
</td> </tr>
<tr>
<td>
[3]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
[16]
</td>
<td>
Object
</td> </tr> </table>
<table>
<tr>
<th>
className
</th>
<th>
String
</th> </tr>
<tr>
<td>
fieldGroup
</td>
<td>
Array
</td> </tr>
<tr>
<td>
[0]
</td>
<td>
String
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
id
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
[1]
</td>
<td>
String
</td> </tr>
<tr>
<td>
hideExpression
</td>
<td>
String
</td> </tr>
<tr>
<td>
fieldGroup
</td>
<td>
Array
</td> </tr>
<tr>
<td>
[0]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
dictionary
</td>
<td>
String
</td> </tr>
<tr>
<td>
[1]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
[2]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
dictionary
</td>
<td>
String
</td> </tr>
<tr>
<td>
[3]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
[4]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
[5]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr> </table>
<table>
<tr>
<th>
hideExpression
</th>
<th>
String
</th> </tr>
<tr>
<td>
fieldGroup
</td>
<td>
Array
</td> </tr>
<tr>
<td>
[0]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
dictionary
</td>
<td>
String
</td> </tr>
<tr>
<td>
[1]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
[2]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
dictionary
</td>
<td>
String
</td> </tr>
<tr>
<td>
[3]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
[13]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
fieldGroup
</td>
<td>
Array
</td> </tr>
<tr>
<td>
[0]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
fieldGroup
</td>
<td>
Array
</td> </tr>
<tr>
<td>
[0]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
template
</td>
<td>
String
</td> </tr>
<tr>
<td>
[1]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
template
</td>
<td>
String
</td> </tr>
<tr>
<td>
[2]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
template
</td>
<td>
String
</td> </tr>
<tr>
<td>
[3]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
template
</td>
<td>
String
</td> </tr> </table>
<table>
<tr>
<th>
[4]
</th>
<th>
Object
</th> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
template
</td>
<td>
String
</td> </tr>
<tr>
<td>
[5]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
template
</td>
<td>
String
</td> </tr>
<tr>
<td>
[1]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
fieldGroup
</td>
<td>
Array
</td> </tr>
<tr>
<td>
[0]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
template
</td>
<td>
String
</td> </tr>
<tr>
<td>
[1]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
depends
</td>
<td>
String
</td> </tr>
<tr>
<td>
expression
</td>
<td>
String
</td> </tr>
<tr>
<td>
[2]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
depends
</td>
<td>
String
</td> </tr>
<tr>
<td>
expression
</td>
<td>
String
</td> </tr>
<tr>
<td>
[3]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
depends
</td>
<td>
String
</td> </tr>
<tr>
<td>
expression
</td>
<td>
String
</td> </tr>
<tr>
<td>
[4]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
depends
</td>
<td>
String
</td> </tr>
<tr>
<td>
expression
</td>
<td>
String
</td> </tr>
<tr>
<td>
[5]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
depends
</td>
<td>
String
</td> </tr> </table>
<table>
<tr>
<th>
expression
</th>
<th>
String
</th> </tr>
<tr>
<td>
[6]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
depends
</td>
<td>
String
</td> </tr>
<tr>
<td>
expression
</td>
<td>
String
</td> </tr>
<tr>
<td>
[7]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
depends
</td>
<td>
String
</td> </tr>
<tr>
<td>
expression
</td>
<td>
String
</td> </tr>
<tr>
<td>
[8]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
depends
</td>
<td>
String
</td> </tr>
<tr>
<td>
expression
</td>
<td>
String
</td> </tr>
<tr>
<td>
[2]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
fieldGroup
</td>
<td>
Array
</td> </tr>
<tr>
<td>
[0]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
template
</td>
<td>
String
</td> </tr>
<tr>
<td>
[1]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
depends
</td>
<td>
String
</td> </tr>
<tr>
<td>
expression
</td>
<td>
String
</td> </tr>
<tr>
<td>
[2]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
depends
</td>
<td>
String
</td> </tr>
<tr>
<td>
expression
</td>
<td>
String
</td> </tr>
<tr>
<td>
[3]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr> </table>
<table>
<tr>
<th>
templateOptions
</th>
<th>
Object
</th> </tr>
<tr>
<td>
depends
</td>
<td>
String
</td> </tr>
<tr>
<td>
expression
</td>
<td>
String
</td> </tr>
<tr>
<td>
[4]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
depends
</td>
<td>
String
</td> </tr>
<tr>
<td>
expression
</td>
<td>
String
</td> </tr>
<tr>
<td>
[5]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
depends
</td>
<td>
String
</td> </tr>
<tr>
<td>
expression
</td>
<td>
String
</td> </tr>
<tr>
<td>
[6]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
depends
</td>
<td>
String
</td> </tr>
<tr>
<td>
expression
</td>
<td>
String
</td> </tr>
<tr>
<td>
[7]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
depends
</td>
<td>
String
</td> </tr>
<tr>
<td>
expression
</td>
<td>
String
</td> </tr>
<tr>
<td>
[8]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
depends
</td>
<td>
String
</td> </tr>
<tr>
<td>
expression
</td>
<td>
String
</td> </tr>
<tr>
<td>
[3]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
fieldGroup
</td>
<td>
Array
</td> </tr>
<tr>
<td>
[0]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
template
</td>
<td>
String
</td> </tr>
<tr>
<td>
[1]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr> </table>
<table>
<tr>
<th>
key
</th>
<th>
String
</th> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
depends
</td>
<td>
String
</td> </tr>
<tr>
<td>
expression
</td>
<td>
String
</td> </tr>
<tr>
<td>
[2]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
depends
</td>
<td>
String
</td> </tr>
<tr>
<td>
expression
</td>
<td>
String
</td> </tr>
<tr>
<td>
[3]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
depends
</td>
<td>
String
</td> </tr>
<tr>
<td>
expression
</td>
<td>
String
</td> </tr>
<tr>
<td>
[4]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
depends
</td>
<td>
String
</td> </tr>
<tr>
<td>
expression
</td>
<td>
String
</td> </tr>
<tr>
<td>
[5]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
depends
</td>
<td>
String
</td> </tr>
<tr>
<td>
expression
</td>
<td>
String
</td> </tr>
<tr>
<td>
[6]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
depends
</td>
<td>
String
</td> </tr>
<tr>
<td>
expression
</td>
<td>
String
</td> </tr>
<tr>
<td>
[7]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
depends
</td>
<td>
String
</td> </tr> </table>
<table>
<tr>
<th>
expression
</th>
<th>
String
</th> </tr>
<tr>
<td>
[8]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
depends
</td>
<td>
String
</td> </tr>
<tr>
<td>
expression
</td>
<td>
String
</td> </tr>
<tr>
<td>
[4]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
fieldGroup
</td>
<td>
Array
</td> </tr>
<tr>
<td>
[0]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
template
</td>
<td>
String
</td> </tr>
<tr>
<td>
[1]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
depends
</td>
<td>
String
</td> </tr>
<tr>
<td>
expression
</td>
<td>
String
</td> </tr>
<tr>
<td>
[2]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
depends
</td>
<td>
String
</td> </tr>
<tr>
<td>
expression
</td>
<td>
String
</td> </tr>
<tr>
<td>
[3]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
depends
</td>
<td>
String
</td> </tr>
<tr>
<td>
expression
</td>
<td>
String
</td> </tr>
<tr>
<td>
[4]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
depends
</td>
<td>
String
</td> </tr>
<tr>
<td>
expression
</td>
<td>
String
</td> </tr>
<tr>
<td>
[5]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr> </table>
<table>
<tr>
<th>
templateOptions
</th>
<th>
Object
</th> </tr>
<tr>
<td>
depends
</td>
<td>
String
</td> </tr>
<tr>
<td>
expression
</td>
<td>
String
</td> </tr>
<tr>
<td>
[6]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
depends
</td>
<td>
String
</td> </tr>
<tr>
<td>
expression
</td>
<td>
String
</td> </tr>
<tr>
<td>
[7]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
depends
</td>
<td>
String
</td> </tr>
<tr>
<td>
expression
</td>
<td>
String
</td> </tr>
<tr>
<td>
[8]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
depends
</td>
<td>
String
</td> </tr>
<tr>
<td>
expression
</td>
<td>
String
</td> </tr>
<tr>
<td>
[5]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
fieldGroup
</td>
<td>
Array
</td> </tr>
<tr>
<td>
[0]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
template
</td>
<td>
String
</td> </tr>
<tr>
<td>
[1]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
depends
</td>
<td>
String
</td> </tr>
<tr>
<td>
expression
</td>
<td>
String
</td> </tr>
<tr>
<td>
[2]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
depends
</td>
<td>
String
</td> </tr>
<tr>
<td>
expression
</td>
<td>
String
</td> </tr>
<tr>
<td>
[3]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr> </table>
<table>
<tr>
<th>
key
</th>
<th>
String
</th> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
depends
</td>
<td>
String
</td> </tr>
<tr>
<td>
expression
</td>
<td>
String
</td> </tr>
<tr>
<td>
[4]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
depends
</td>
<td>
String
</td> </tr>
<tr>
<td>
expression
</td>
<td>
String
</td> </tr>
<tr>
<td>
[5]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
depends
</td>
<td>
String
</td> </tr>
<tr>
<td>
expression
</td>
<td>
String
</td> </tr>
<tr>
<td>
[6]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
depends
</td>
<td>
String
</td> </tr>
<tr>
<td>
expression
</td>
<td>
String
</td> </tr>
<tr>
<td>
[7]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
depends
</td>
<td>
String
</td> </tr>
<tr>
<td>
expression
</td>
<td>
String
</td> </tr>
<tr>
<td>
[8]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
depends
</td>
<td>
String
</td> </tr>
<tr>
<td>
expression
</td>
<td>
String
</td> </tr>
<tr>
<td>
[6]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
fieldGroup
</td>
<td>
Array
</td> </tr>
<tr>
<td>
[0]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
template
</td>
<td>
String
</td> </tr> </table>
<table>
<tr>
<th>
[1]
</th>
<th>
Object
</th> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
depends
</td>
<td>
String
</td> </tr>
<tr>
<td>
expression
</td>
<td>
String
</td> </tr>
<tr>
<td>
[2]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
depends
</td>
<td>
String
</td> </tr>
<tr>
<td>
expression
</td>
<td>
String
</td> </tr>
<tr>
<td>
[3]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
depends
</td>
<td>
String
</td> </tr>
<tr>
<td>
expression
</td>
<td>
String
</td> </tr>
<tr>
<td>
[4]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
depends
</td>
<td>
String
</td> </tr>
<tr>
<td>
expression
</td>
<td>
String
</td> </tr>
<tr>
<td>
[5]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
depends
</td>
<td>
String
</td> </tr>
<tr>
<td>
expression
</td>
<td>
String
</td> </tr>
<tr>
<td>
[6]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
depends
</td>
<td>
String
</td> </tr>
<tr>
<td>
expression
</td>
<td>
String
</td> </tr>
<tr>
<td>
[7]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr> </table>
<table>
<tr>
<th>
templateOptions
</th>
<th>
Object
</th> </tr>
<tr>
<td>
depends
</td>
<td>
String
</td> </tr>
<tr>
<td>
expression
</td>
<td>
String
</td> </tr>
<tr>
<td>
[8]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
depends
</td>
<td>
String
</td> </tr>
<tr>
<td>
expression
</td>
<td>
String
</td> </tr>
<tr>
<td>
[7]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
fieldGroup
</td>
<td>
Array
</td> </tr>
<tr>
<td>
[0]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
template
</td>
<td>
String
</td> </tr>
<tr>
<td>
[1]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
depends
</td>
<td>
String
</td> </tr>
<tr>
<td>
expression
</td>
<td>
String
</td> </tr>
<tr>
<td>
[2]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
depends
</td>
<td>
String
</td> </tr>
<tr>
<td>
expression
</td>
<td>
String
</td> </tr>
<tr>
<td>
[3]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
depends
</td>
<td>
String
</td> </tr>
<tr>
<td>
expression
</td>
<td>
String
</td> </tr>
<tr>
<td>
[4]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
depends
</td>
<td>
String
</td> </tr>
<tr>
<td>
expression
</td>
<td>
String
</td> </tr>
<tr>
<td>
[5]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr> </table>
<table>
<tr>
<th>
key
</th>
<th>
String
</th> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
depends
</td>
<td>
String
</td> </tr>
<tr>
<td>
expression
</td>
<td>
String
</td> </tr>
<tr>
<td>
[6]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
depends
</td>
<td>
String
</td> </tr>
<tr>
<td>
expression
</td>
<td>
String
</td> </tr>
<tr>
<td>
[7]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
depends
</td>
<td>
String
</td> </tr>
<tr>
<td>
expression
</td>
<td>
String
</td> </tr>
<tr>
<td>
[8]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
depends
</td>
<td>
String
</td> </tr>
<tr>
<td>
expression
</td>
<td>
String
</td> </tr>
<tr>
<td>
[8]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
fieldGroup
</td>
<td>
Array
</td> </tr>
<tr>
<td>
[0]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
template
</td>
<td>
String
</td> </tr>
<tr>
<td>
[1]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
depends
</td>
<td>
String
</td> </tr>
<tr>
<td>
expression
</td>
<td>
String
</td> </tr>
<tr>
<td>
[2]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
depends
</td>
<td>
String
</td> </tr>
<tr>
<td>
expression
</td>
<td>
String
</td> </tr> </table>
<table>
<tr>
<th>
[3]
</th>
<th>
Object
</th> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
depends
</td>
<td>
String
</td> </tr>
<tr>
<td>
expression
</td>
<td>
String
</td> </tr>
<tr>
<td>
[4]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
depends
</td>
<td>
String
</td> </tr>
<tr>
<td>
expression
</td>
<td>
String
</td> </tr>
<tr>
<td>
[5]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
depends
</td>
<td>
String
</td> </tr>
<tr>
<td>
expression
</td>
<td>
String
</td> </tr>
<tr>
<td>
[6]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
depends
</td>
<td>
String
</td> </tr>
<tr>
<td>
expression
</td>
<td>
String
</td> </tr>
<tr>
<td>
[7]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
depends
</td>
<td>
String
</td> </tr>
<tr>
<td>
expression
</td>
<td>
String
</td> </tr>
<tr>
<td>
[8]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
depends
</td>
<td>
String
</td> </tr>
<tr>
<td>
expression
</td>
<td>
String
</td> </tr>
<tr>
<td>
[9]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
fieldGroup
</td>
<td>
Array
</td> </tr>
<tr>
<td>
[0]
</td>
<td>
Object
</td> </tr> </table>
<table>
<tr>
<th>
className
</th>
<th>
String
</th> </tr>
<tr>
<td>
template
</td>
<td>
String
</td> </tr>
<tr>
<td>
[1]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
depends
</td>
<td>
String
</td> </tr>
<tr>
<td>
expression
</td>
<td>
String
</td> </tr>
<tr>
<td>
[2]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
depends
</td>
<td>
String
</td> </tr>
<tr>
<td>
expression
</td>
<td>
String
</td> </tr>
<tr>
<td>
[3]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
depends
</td>
<td>
String
</td> </tr>
<tr>
<td>
expression
</td>
<td>
String
</td> </tr>
<tr>
<td>
[4]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
depends
</td>
<td>
String
</td> </tr>
<tr>
<td>
expression
</td>
<td>
String
</td> </tr>
<tr>
<td>
[5]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
depends
</td>
<td>
String
</td> </tr>
<tr>
<td>
expression
</td>
<td>
String
</td> </tr>
<tr>
<td>
[6]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
depends
</td>
<td>
String
</td> </tr>
<tr>
<td>
expression
</td>
<td>
String
</td> </tr>
<tr>
<td>
[7]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr> </table>
<table>
<tr>
<th>
key
</th>
<th>
String
</th> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
depends
</td>
<td>
String
</td> </tr>
<tr>
<td>
expression
</td>
<td>
String
</td> </tr>
<tr>
<td>
[8]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
depends
</td>
<td>
String
</td> </tr>
<tr>
<td>
expression
</td>
<td>
String
</td> </tr>
<tr>
<td>
[10]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
fieldGroup
</td>
<td>
Array
</td> </tr>
<tr>
<td>
[0]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
template
</td>
<td>
String
</td> </tr>
<tr>
<td>
[1]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
depends
</td>
<td>
String
</td> </tr>
<tr>
<td>
expression
</td>
<td>
String
</td> </tr>
<tr>
<td>
[2]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
depends
</td>
<td>
String
</td> </tr>
<tr>
<td>
expression
</td>
<td>
String
</td> </tr>
<tr>
<td>
[3]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
depends
</td>
<td>
String
</td> </tr>
<tr>
<td>
expression
</td>
<td>
String
</td> </tr>
<tr>
<td>
[4]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
depends
</td>
<td>
String
</td> </tr>
<tr>
<td>
expression
</td>
<td>
String
</td> </tr> </table>
<table>
<tr>
<th>
[5]
</th>
<th>
Object
</th> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
depends
</td>
<td>
String
</td> </tr>
<tr>
<td>
expression
</td>
<td>
String
</td> </tr>
<tr>
<td>
[6]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
depends
</td>
<td>
String
</td> </tr>
<tr>
<td>
expression
</td>
<td>
String
</td> </tr>
<tr>
<td>
[7]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
depends
</td>
<td>
String
</td> </tr>
<tr>
<td>
expression
</td>
<td>
String
</td> </tr>
<tr>
<td>
[8]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
depends
</td>
<td>
String
</td> </tr>
<tr>
<td>
expression
</td>
<td>
String
</td> </tr>
<tr>
<td>
[11]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
fieldGroup
</td>
<td>
Array
</td> </tr>
<tr>
<td>
[0]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
template
</td>
<td>
String
</td> </tr>
<tr>
<td>
[1]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
depends
</td>
<td>
String
</td> </tr>
<tr>
<td>
expression
</td>
<td>
String
</td> </tr>
<tr>
<td>
[2]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr> </table>
<table>
<tr>
<th>
depends
</th>
<th>
String
</th> </tr>
<tr>
<td>
expression
</td>
<td>
String
</td> </tr>
<tr>
<td>
[3]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
depends
</td>
<td>
String
</td> </tr>
<tr>
<td>
expression
</td>
<td>
String
</td> </tr>
<tr>
<td>
[4]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
depends
</td>
<td>
String
</td> </tr>
<tr>
<td>
expression
</td>
<td>
String
</td> </tr>
<tr>
<td>
[5]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
depends
</td>
<td>
String
</td> </tr>
<tr>
<td>
expression
</td>
<td>
String
</td> </tr>
<tr>
<td>
[6]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
depends
</td>
<td>
String
</td> </tr>
<tr>
<td>
expression
</td>
<td>
String
</td> </tr>
<tr>
<td>
[7]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
depends
</td>
<td>
String
</td> </tr>
<tr>
<td>
expression
</td>
<td>
String
</td> </tr>
<tr>
<td>
[8]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
depends
</td>
<td>
String
</td> </tr>
<tr>
<td>
expression
</td>
<td>
String
</td> </tr>
<tr>
<td>
[12]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr> </table>
<table>
<tr>
<th>
fieldGroup
</th>
<th>
Array
</th> </tr>
<tr>
<td>
[0]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
template
</td>
<td>
String
</td> </tr>
<tr>
<td>
[1]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
depends
</td>
<td>
String
</td> </tr>
<tr>
<td>
expression
</td>
<td>
String
</td> </tr>
<tr>
<td>
[2]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
depends
</td>
<td>
String
</td> </tr>
<tr>
<td>
expression
</td>
<td>
String
</td> </tr>
<tr>
<td>
[3]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
depends
</td>
<td>
String
</td> </tr>
<tr>
<td>
expression
</td>
<td>
String
</td> </tr>
<tr>
<td>
[4]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
depends
</td>
<td>
String
</td> </tr>
<tr>
<td>
expression
</td>
<td>
String
</td> </tr>
<tr>
<td>
[5]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
depends
</td>
<td>
String
</td> </tr>
<tr>
<td>
expression
</td>
<td>
String
</td> </tr>
<tr>
<td>
[6]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
depends
</td>
<td>
String
</td> </tr>
<tr>
<td>
expression
</td>
<td>
String
</td> </tr> </table>
<table>
<tr>
<th>
[7]
</th>
<th>
Object
</th> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
depends
</td>
<td>
String
</td> </tr>
<tr>
<td>
expression
</td>
<td>
String
</td> </tr>
<tr>
<td>
[8]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
depends
</td>
<td>
String
</td> </tr>
<tr>
<td>
expression
</td>
<td>
String
</td> </tr>
<tr>
<td>
[13]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
fieldGroup
</td>
<td>
Array
</td> </tr>
<tr>
<td>
[0]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
template
</td>
<td>
String
</td> </tr>
<tr>
<td>
[1]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
depends
</td>
<td>
String
</td> </tr>
<tr>
<td>
expression
</td>
<td>
String
</td> </tr>
<tr>
<td>
[2]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
depends
</td>
<td>
String
</td> </tr>
<tr>
<td>
expression
</td>
<td>
String
</td> </tr>
<tr>
<td>
[3]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
depends
</td>
<td>
String
</td> </tr>
<tr>
<td>
expression
</td>
<td>
String
</td> </tr>
<tr>
<td>
[4]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr> </table>
<table>
<tr>
<th>
depends
</th>
<th>
String
</th> </tr>
<tr>
<td>
expression
</td>
<td>
String
</td> </tr>
<tr>
<td>
[5]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
depends
</td>
<td>
String
</td> </tr>
<tr>
<td>
expression
</td>
<td>
String
</td> </tr>
<tr>
<td>
[6]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
depends
</td>
<td>
String
</td> </tr>
<tr>
<td>
expression
</td>
<td>
String
</td> </tr>
<tr>
<td>
[7]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
depends
</td>
<td>
String
</td> </tr>
<tr>
<td>
expression
</td>
<td>
String
</td> </tr>
<tr>
<td>
[8]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
depends
</td>
<td>
String
</td> </tr>
<tr>
<td>
expression
</td>
<td>
String
</td> </tr>
<tr>
<td>
[14]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
fieldGroup
</td>
<td>
Array
</td> </tr>
<tr>
<td>
[0]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
template
</td>
<td>
String
</td> </tr>
<tr>
<td>
[1]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
depends
</td>
<td>
String
</td> </tr>
<tr>
<td>
expression
</td>
<td>
String
</td> </tr>
<tr>
<td>
[2]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr> </table>
<table>
<tr>
<th>
type
</th>
<th>
String
</th> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
depends
</td>
<td>
String
</td> </tr>
<tr>
<td>
expression
</td>
<td>
String
</td> </tr>
<tr>
<td>
[3]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
depends
</td>
<td>
String
</td> </tr>
<tr>
<td>
expression
</td>
<td>
String
</td> </tr>
<tr>
<td>
[4]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
depends
</td>
<td>
String
</td> </tr>
<tr>
<td>
expression
</td>
<td>
String
</td> </tr>
<tr>
<td>
[5]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
depends
</td>
<td>
String
</td> </tr>
<tr>
<td>
expression
</td>
<td>
String
</td> </tr>
<tr>
<td>
[6]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
depends
</td>
<td>
String
</td> </tr>
<tr>
<td>
expression
</td>
<td>
String
</td> </tr>
<tr>
<td>
[7]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
depends
</td>
<td>
String
</td> </tr>
<tr>
<td>
expression
</td>
<td>
String
</td> </tr>
<tr>
<td>
[8]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
depends
</td>
<td>
String
</td> </tr>
<tr>
<td>
expression
</td>
<td>
String
</td> </tr> </table>
<table>
<tr>
<th>
[15]
</th>
<th>
Object
</th> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
fieldGroup
</td>
<td>
Array
</td> </tr>
<tr>
<td>
[0]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
template
</td>
<td>
String
</td> </tr>
<tr>
<td>
[1]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
depends
</td>
<td>
String
</td> </tr>
<tr>
<td>
expression
</td>
<td>
String
</td> </tr>
<tr>
<td>
[2]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
depends
</td>
<td>
String
</td> </tr>
<tr>
<td>
expression
</td>
<td>
String
</td> </tr>
<tr>
<td>
[3]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
depends
</td>
<td>
String
</td> </tr>
<tr>
<td>
expression
</td>
<td>
String
</td> </tr>
<tr>
<td>
[4]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
depends
</td>
<td>
String
</td> </tr>
<tr>
<td>
expression
</td>
<td>
String
</td> </tr>
<tr>
<td>
[5]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
depends
</td>
<td>
String
</td> </tr>
<tr>
<td>
expression
</td>
<td>
String
</td> </tr>
<tr>
<td>
[6]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr> </table>
<table>
<tr>
<th>
depends
</th>
<th>
String
</th> </tr>
<tr>
<td>
expression
</td>
<td>
String
</td> </tr>
<tr>
<td>
[7]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
depends
</td>
<td>
String
</td> </tr>
<tr>
<td>
expression
</td>
<td>
String
</td> </tr>
<tr>
<td>
[8]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
depends
</td>
<td>
String
</td> </tr>
<tr>
<td>
expression
</td>
<td>
String
</td> </tr>
<tr>
<td>
[16]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
fieldGroup
</td>
<td>
Array
</td> </tr>
<tr>
<td>
[0]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
template
</td>
<td>
String
</td> </tr>
<tr>
<td>
[1]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
depends
</td>
<td>
String
</td> </tr>
<tr>
<td>
expression
</td>
<td>
String
</td> </tr>
<tr>
<td>
[2]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
depends
</td>
<td>
String
</td> </tr>
<tr>
<td>
expression
</td>
<td>
String
</td> </tr>
<tr>
<td>
[3]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
depends
</td>
<td>
String
</td> </tr>
<tr>
<td>
expression
</td>
<td>
String
</td> </tr>
<tr>
<td>
[4]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr> </table>
<table>
<tr>
<th>
type
</th>
<th>
String
</th> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
depends
</td>
<td>
String
</td> </tr>
<tr>
<td>
expression
</td>
<td>
String
</td> </tr>
<tr>
<td>
[5]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
depends
</td>
<td>
String
</td> </tr>
<tr>
<td>
expression
</td>
<td>
String
</td> </tr>
<tr>
<td>
[6]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
depends
</td>
<td>
String
</td> </tr>
<tr>
<td>
expression
</td>
<td>
String
</td> </tr>
<tr>
<td>
[7]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
depends
</td>
<td>
String
</td> </tr>
<tr>
<td>
expression
</td>
<td>
String
</td> </tr>
<tr>
<td>
[8]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
depends
</td>
<td>
String
</td> </tr>
<tr>
<td>
expression
</td>
<td>
String
</td> </tr>
<tr>
<td>
[17]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
fieldGroup
</td>
<td>
Array
</td> </tr>
<tr>
<td>
[0]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
template
</td>
<td>
String
</td> </tr>
<tr>
<td>
[1]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
depends
</td>
<td>
String
</td> </tr>
<tr>
<td>
expression
</td>
<td>
String
</td> </tr>
<tr>
<td>
[2]
</td>
<td>
Object
</td> </tr> </table>
<table>
<tr>
<th>
className
</th>
<th>
String
</th> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
depends
</td>
<td>
String
</td> </tr>
<tr>
<td>
expression
</td>
<td>
String
</td> </tr>
<tr>
<td>
[3]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
depends
</td>
<td>
String
</td> </tr>
<tr>
<td>
expression
</td>
<td>
String
</td> </tr>
<tr>
<td>
[4]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
depends
</td>
<td>
String
</td> </tr>
<tr>
<td>
expression
</td>
<td>
String
</td> </tr>
<tr>
<td>
[5]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
depends
</td>
<td>
String
</td> </tr>
<tr>
<td>
expression
</td>
<td>
String
</td> </tr>
<tr>
<td>
[6]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
depends
</td>
<td>
String
</td> </tr>
<tr>
<td>
expression
</td>
<td>
String
</td> </tr>
<tr>
<td>
[7]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
depends
</td>
<td>
String
</td> </tr>
<tr>
<td>
expression
</td>
<td>
String
</td> </tr>
<tr>
<td>
[8]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr> </table>
<table>
<tr>
<th>
depends
</th>
<th>
String
</th> </tr>
<tr>
<td>
expression
</td>
<td>
String
</td> </tr>
<tr>
<td>
[18]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
fieldGroup
</td>
<td>
Array
</td> </tr>
<tr>
<td>
[0]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
template
</td>
<td>
String
</td> </tr>
<tr>
<td>
[1]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
depends
</td>
<td>
String
</td> </tr>
<tr>
<td>
expression
</td>
<td>
String
</td> </tr>
<tr>
<td>
canReadRoles
</td>
<td>
Array
</td> </tr>
<tr>
<td>
[0]
</td>
<td>
String
</td> </tr>
<tr>
<td>
[1]
</td>
<td>
String
</td> </tr>
<tr>
<td>
[2]
</td>
<td>
String
</td> </tr>
<tr>
<td>
[3]
</td>
<td>
String
</td> </tr>
<tr>
<td>
[4]
</td>
<td>
String
</td> </tr>
<tr>
<td>
[5]
</td>
<td>
String
</td> </tr>
<tr>
<td>
[6]
</td>
<td>
String
</td> </tr>
<tr>
<td>
[7]
</td>
<td>
String
</td> </tr>
<tr>
<td>
[8]
</td>
<td>
String
</td> </tr>
<tr>
<td>
[9]
</td>
<td>
String
</td> </tr>
<tr>
<td>
canWriteRoles
</td>
<td>
Array
</td> </tr>
<tr>
<td>
[0]
</td>
<td>
String
</td> </tr>
<tr>
<td>
[1]
</td>
<td>
String
</td> </tr>
<tr>
<td>
[2]
</td>
<td>
String
</td> </tr>
<tr>
<td>
versionNumber
</td>
<td>
Int64
</td> </tr>
<tr>
<td>
createdAt
</td>
<td>
Date
</td> </tr>
<tr>
<td>
lastModified
</td>
<td>
Date
</td> </tr>
<tr>
<td>
createBy
</td>
<td>
String
</td> </tr>
<tr>
<td>
lastModifiedBy
</td>
<td>
String
</td> </tr> </table>
**Clinical Characteristics, “types” structure**
<table>
<tr>
<th>
_id
</th>
<th>
String
</th> </tr>
<tr>
<td>
_class
</td>
<td>
String
</td> </tr>
<tr>
<td>
display
</td>
<td>
String
</td> </tr>
<tr>
<td>
shortName
</td>
<td>
String
</td> </tr>
<tr>
<td>
order
</td>
<td>
Int32
</td> </tr>
<tr>
<td>
controllerType
</td>
<td>
String
</td> </tr>
<tr>
<td>
definition
</td>
<td>
Object
</td> </tr>
<tr>
<td>
field
</td>
<td>
Array
</td> </tr>
<tr>
<td>
[0]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
template
</td>
<td>
String
</td> </tr>
<tr>
<td>
[1]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
fieldGroup
</td>
<td>
Array
</td> </tr>
<tr>
<td>
[0]
</td>
<td>
String
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
required
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
options
</td>
<td>
Array
</td> </tr>
<tr>
<td>
[0]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
name
</td>
<td>
String
</td> </tr>
<tr>
<td>
value
</td>
<td>
String
</td> </tr>
<tr>
<td>
[1]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
name
</td>
<td>
String
</td> </tr>
<tr>
<td>
value
</td>
<td>
String
</td> </tr>
<tr>
<td>
[1]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
[2]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
fieldGroup
</td>
<td>
Array
</td> </tr>
<tr>
<td>
[0]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
modelOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
allowInvalid
</td>
<td>
Boolean
</td> </tr> </table>
<table>
<tr>
<th>
templateOptions
</th>
<th>
Object
</th> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
required
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
max
</td>
<td>
Int32
</td> </tr>
<tr>
<td>
min
</td>
<td>
Int32
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
[1]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
modelOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
allowInvalid
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
required
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
max
</td>
<td>
Int32
</td> </tr>
<tr>
<td>
min
</td>
<td>
Int32
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
[2]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
modelOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
allowInvalid
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
descrption
</td>
<td>
String
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
depends
</td>
<td>
String
</td> </tr>
<tr>
<td>
expression
</td>
<td>
String
</td> </tr>
<tr>
<td>
[3]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
modelOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
allowInvalid
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
descrption
</td>
<td>
String
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
depends
</td>
<td>
String
</td> </tr>
<tr>
<td>
expression
</td>
<td>
String
</td> </tr>
<tr>
<td>
[4]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
modelOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
allowInvalid
</td>
<td>
Boolean
</td> </tr> </table>
<table>
<tr>
<th>
templateOptions
</th>
<th>
Object
</th> </tr>
<tr>
<td>
descrption
</td>
<td>
String
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
[5]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
modelOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
allowInvalid
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
descrption
</td>
<td>
String
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
[6]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
modelOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
allowInvalid
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
descrption
</td>
<td>
String
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
[7]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
modelOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
allowInvalid
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
descrption
</td>
<td>
String
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
[3]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
template
</td>
<td>
String
</td> </tr>
<tr>
<td>
[4]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
fieldGroup
</td>
<td>
Array
</td> </tr>
<tr>
<td>
[0]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr> </table>
<table>
<tr>
<th>
[1]
</th>
<th>
Object
</th> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
hideExpression
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
description
</td>
<td>
String
</td> </tr>
<tr>
<td>
options
</td>
<td>
Array
</td> </tr>
<tr>
<td>
[0]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
name
</td>
<td>
String
</td> </tr>
<tr>
<td>
value
</td>
<td>
String
</td> </tr>
<tr>
<td>
[1]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
name
</td>
<td>
String
</td> </tr>
<tr>
<td>
value
</td>
<td>
String
</td> </tr>
<tr>
<td>
[2]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
name
</td>
<td>
String
</td> </tr>
<tr>
<td>
value
</td>
<td>
String
</td> </tr>
<tr>
<td>
[5]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
fieldGroup
</td>
<td>
Array
</td> </tr>
<tr>
<td>
[0]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
[1]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
hideExpression
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
[6]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
fieldGroup
</td>
<td>
Array
</td> </tr>
<tr>
<td>
[0]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
[1]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr> </table>
<table>
<tr>
<th>
type
</th>
<th>
String
</th> </tr>
<tr>
<td>
hideExpression
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
[7]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
fieldGroup
</td>
<td>
Array
</td> </tr>
<tr>
<td>
[0]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
description
</td>
<td>
String
</td> </tr>
<tr>
<td>
required
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
[1]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
description
</td>
<td>
String
</td> </tr>
<tr>
<td>
required
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
[2]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
description
</td>
<td>
String
</td> </tr>
<tr>
<td>
required
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
[3]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
description
</td>
<td>
String
</td> </tr>
<tr>
<td>
required
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
[4]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr> </table>
<table>
<tr>
<th>
description
</th>
<th>
String
</th> </tr>
<tr>
<td>
required
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
[5]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
required
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
[8]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
template
</td>
<td>
String
</td> </tr>
<tr>
<td>
[9]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
fieldGroup
</td>
<td>
Array
</td> </tr>
<tr>
<td>
[0]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
[1]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
hideExpression
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
[10]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
fieldGroup
</td>
<td>
Array
</td> </tr>
<tr>
<td>
[0]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
[1]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
hideExpression
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr> </table>
<table>
<tr>
<th>
[11]
</th>
<th>
Object
</th> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
fieldGroup
</td>
<td>
Array
</td> </tr>
<tr>
<td>
[0]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
[1]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
hideExpression
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
[12]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
fieldGroup
</td>
<td>
Array
</td> </tr>
<tr>
<td>
[0]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
options
</td>
<td>
Array
</td> </tr>
<tr>
<td>
[0]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
name
</td>
<td>
String
</td> </tr>
<tr>
<td>
value
</td>
<td>
String
</td> </tr>
<tr>
<td>
[1]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
name
</td>
<td>
String
</td> </tr>
<tr>
<td>
value
</td>
<td>
String
</td> </tr>
<tr>
<td>
[2]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
name
</td>
<td>
String
</td> </tr>
<tr>
<td>
value
</td>
<td>
String
</td> </tr>
<tr>
<td>
[1]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
description
</td>
<td>
String
</td> </tr>
<tr>
<td>
options
</td>
<td>
Array
</td> </tr>
<tr>
<td>
[0]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
name
</td>
<td>
String
</td> </tr> </table>
<table>
<tr>
<th>
value
</th>
<th>
String
</th> </tr>
<tr>
<td>
[1]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
name
</td>
<td>
String
</td> </tr>
<tr>
<td>
value
</td>
<td>
String
</td> </tr>
<tr>
<td>
[2]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
name
</td>
<td>
String
</td> </tr>
<tr>
<td>
value
</td>
<td>
String
</td> </tr>
<tr>
<td>
[13]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
template
</td>
<td>
String
</td> </tr>
<tr>
<td>
[14]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
template
</td>
<td>
String
</td> </tr>
<tr>
<td>
[15]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
fieldGroup
</td>
<td>
Array
</td> </tr>
<tr>
<td>
[0]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
template
</td>
<td>
String
</td> </tr>
<tr>
<td>
[1]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
[16]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
hideExpression
</td>
<td>
String
</td> </tr>
<tr>
<td>
template
</td>
<td>
String
</td> </tr>
<tr>
<td>
[17]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
hideExpression
</td>
<td>
String
</td> </tr>
<tr>
<td>
fieldGroup
</td>
<td>
Array
</td> </tr>
<tr>
<td>
[0]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
description
</td>
<td>
String
</td> </tr>
<tr>
<td>
[1]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
hideExpression
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr> </table>
<table>
<tr>
<th>
label
</th>
<th>
String
</th> </tr>
<tr>
<td>
dictionary
</td>
<td>
String
</td> </tr>
<tr>
<td>
[18]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
hideExpression
</td>
<td>
String
</td> </tr>
<tr>
<td>
fieldGroup
</td>
<td>
Array
</td> </tr>
<tr>
<td>
[0]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
description
</td>
<td>
String
</td> </tr>
<tr>
<td>
[1]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
hideExpression
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
dictionary
</td>
<td>
String
</td> </tr>
<tr>
<td>
[19]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
hideExpression
</td>
<td>
String
</td> </tr>
<tr>
<td>
template
</td>
<td>
String
</td> </tr>
<tr>
<td>
[20]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
hideExpression
</td>
<td>
String
</td> </tr>
<tr>
<td>
fieldGroup
</td>
<td>
Array
</td> </tr>
<tr>
<td>
[0]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
description
</td>
<td>
String
</td> </tr>
<tr>
<td>
[21]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
hideExpression
</td>
<td>
String
</td> </tr>
<tr>
<td>
fieldGroup
</td>
<td>
Array
</td> </tr>
<tr>
<td>
[0]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr> </table>
<table>
<tr>
<th>
description
</th>
<th>
String
</th> </tr>
<tr>
<td>
[22]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
hideExpression
</td>
<td>
String
</td> </tr>
<tr>
<td>
template
</td>
<td>
String
</td> </tr>
<tr>
<td>
[23]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
hideExpression
</td>
<td>
String
</td> </tr>
<tr>
<td>
fieldGroup
</td>
<td>
Array
</td> </tr>
<tr>
<td>
[0]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
description
</td>
<td>
String
</td> </tr>
<tr>
<td>
[24]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
hideExpression
</td>
<td>
String
</td> </tr>
<tr>
<td>
fieldGroup
</td>
<td>
Array
</td> </tr>
<tr>
<td>
[0]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
description
</td>
<td>
String
</td> </tr>
<tr>
<td>
[25]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
hideExpression
</td>
<td>
String
</td> </tr>
<tr>
<td>
template
</td>
<td>
String
</td> </tr>
<tr>
<td>
[26]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
hideExpression
</td>
<td>
String
</td> </tr>
<tr>
<td>
fieldGroup
</td>
<td>
Array
</td> </tr>
<tr>
<td>
[0]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
description
</td>
<td>
String
</td> </tr>
<tr>
<td>
[1]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr> </table>
<table>
<tr>
<th>
hideExpression
</th>
<th>
String
</th> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
dictionary
</td>
<td>
String
</td> </tr>
<tr>
<td>
[2]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
hideExpression
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
[27]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
hideExpression
</td>
<td>
String
</td> </tr>
<tr>
<td>
fieldGroup
</td>
<td>
Array
</td> </tr>
<tr>
<td>
[0]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
description
</td>
<td>
String
</td> </tr>
<tr>
<td>
[1]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
hideExpression
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
dictionary
</td>
<td>
String
</td> </tr>
<tr>
<td>
[2]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
hideExpression
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
[28]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
hideExpression
</td>
<td>
String
</td> </tr>
<tr>
<td>
template
</td>
<td>
String
</td> </tr>
<tr>
<td>
[29]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
hideExpression
</td>
<td>
String
</td> </tr>
<tr>
<td>
fieldGroup
</td>
<td>
Array
</td> </tr>
<tr>
<td>
[0]
</td>
<td>
Object
</td> </tr> </table>
<table>
<tr>
<th>
className
</th>
<th>
String
</th> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
description
</td>
<td>
String
</td> </tr>
<tr>
<td>
[1]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
hideExpression
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
[30]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
hideExpression
</td>
<td>
String
</td> </tr>
<tr>
<td>
fieldGroup
</td>
<td>
Array
</td> </tr>
<tr>
<td>
[0]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
description
</td>
<td>
String
</td> </tr>
<tr>
<td>
[1]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
hideExpression
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
[31]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
template
</td>
<td>
String
</td> </tr>
<tr>
<td>
[32]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
fieldGroup
</td>
<td>
Array
</td> </tr>
<tr>
<td>
[0]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
options
</td>
<td>
Array
</td> </tr>
<tr>
<td>
[0]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
name
</td>
<td>
String
</td> </tr>
<tr>
<td>
value
</td>
<td>
String
</td> </tr> </table>
<table>
<tr>
<th>
[1]
</th>
<th>
Object
</th> </tr>
<tr>
<td>
name
</td>
<td>
String
</td> </tr>
<tr>
<td>
value
</td>
<td>
String
</td> </tr>
<tr>
<td>
[2]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
name
</td>
<td>
String
</td> </tr>
<tr>
<td>
value
</td>
<td>
String
</td> </tr>
<tr>
<td>
[3]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
name
</td>
<td>
String
</td> </tr>
<tr>
<td>
value
</td>
<td>
String
</td> </tr>
<tr>
<td>
[4]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
name
</td>
<td>
String
</td> </tr>
<tr>
<td>
value
</td>
<td>
String
</td> </tr>
<tr>
<td>
[33]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
fieldGroup
</td>
<td>
Array
</td> </tr>
<tr>
<td>
[0]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
[1]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
hideExpression
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
[34]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
template
</td>
<td>
String
</td> </tr>
<tr>
<td>
[35]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
fieldGroup
</td>
<td>
Array
</td> </tr>
<tr>
<td>
[0]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
options
</td>
<td>
Array
</td> </tr>
<tr>
<td>
[0]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
name
</td>
<td>
String
</td> </tr>
<tr>
<td>
value
</td>
<td>
String
</td> </tr>
<tr>
<td>
[1]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
name
</td>
<td>
String
</td> </tr>
<tr>
<td>
value
</td>
<td>
String
</td> </tr> </table>
<table>
<tr>
<th>
[2]
</th>
<th>
Object
</th> </tr>
<tr>
<td>
name
</td>
<td>
String
</td> </tr>
<tr>
<td>
value
</td>
<td>
String
</td> </tr>
<tr>
<td>
[3]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
name
</td>
<td>
String
</td> </tr>
<tr>
<td>
value
</td>
<td>
String
</td> </tr>
<tr>
<td>
[4]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
name
</td>
<td>
String
</td> </tr>
<tr>
<td>
value
</td>
<td>
String
</td> </tr>
<tr>
<td>
[1]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
hideExpression
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
[36]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
template
</td>
<td>
String
</td> </tr>
<tr>
<td>
[37]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
fieldGroup
</td>
<td>
Array
</td> </tr>
<tr>
<td>
[0]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
[38]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
fieldGroup
</td>
<td>
Array
</td> </tr>
<tr>
<td>
[0]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
[39]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
fieldGroup
</td>
<td>
Array
</td> </tr>
<tr>
<td>
[0]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
[1]
</td>
<td>
Object
</td> </tr> </table>
<table>
<tr>
<th>
className
</th>
<th>
String
</th> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
hideExpression
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
[2]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
hideExpression
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
[40]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
fieldGroup
</td>
<td>
Array
</td> </tr>
<tr>
<td>
[0]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
[41]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
fieldGroup
</td>
<td>
Array
</td> </tr>
<tr>
<td>
[0]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
[42]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
fieldGroup
</td>
<td>
Array
</td> </tr>
<tr>
<td>
[0]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
[43]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
fieldGroup
</td>
<td>
Array
</td> </tr>
<tr>
<td>
[0]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr> </table>
<table>
<tr>
<th>
key
</th>
<th>
String
</th> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
[44]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
fieldGroup
</td>
<td>
Array
</td> </tr>
<tr>
<td>
[0]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
[45]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
fieldGroup
</td>
<td>
Array
</td> </tr>
<tr>
<td>
[0]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
[46]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
fieldGroup
</td>
<td>
Array
</td> </tr>
<tr>
<td>
[0]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
[47]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
fieldGroup
</td>
<td>
Array
</td> </tr>
<tr>
<td>
[0]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
[48]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
fieldGroup
</td>
<td>
Array
</td> </tr>
<tr>
<td>
[0]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr> </table>
<table>
<tr>
<th>
type
</th>
<th>
String
</th> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
[49]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
fieldGroup
</td>
<td>
Array
</td> </tr>
<tr>
<td>
[0]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
[50]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
fieldGroup
</td>
<td>
Array
</td> </tr>
<tr>
<td>
[0]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
[1]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
hideExpression
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
[51]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
fieldGroup
</td>
<td>
Array
</td> </tr>
<tr>
<td>
[0]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
canReadRoles
</td>
<td>
Array
</td> </tr>
<tr>
<td>
[0]
</td>
<td>
String
</td> </tr>
<tr>
<td>
[1]
</td>
<td>
String
</td> </tr>
<tr>
<td>
[2]
</td>
<td>
String
</td> </tr>
<tr>
<td>
[3]
</td>
<td>
String
</td> </tr>
<tr>
<td>
[4]
</td>
<td>
String
</td> </tr>
<tr>
<td>
[5]
</td>
<td>
String
</td> </tr>
<tr>
<td>
[6]
</td>
<td>
String
</td> </tr>
<tr>
<td>
[7]
</td>
<td>
String
</td> </tr>
<tr>
<td>
[8]
</td>
<td>
String
</td> </tr>
<tr>
<td>
[9]
</td>
<td>
String
</td> </tr>
<tr>
<td>
[10]
</td>
<td>
String
</td> </tr>
<tr>
<td>
canWriteRoles
</td>
<td>
Array
</td> </tr>
<tr>
<td>
[0]
</td>
<td>
String
</td> </tr>
<tr>
<td>
[1]
</td>
<td>
String
</td> </tr>
<tr>
<td>
[2]
</td>
<td>
String
</td> </tr>
<tr>
<td>
versionNumber
</td>
<td>
Int64
</td> </tr>
<tr>
<td>
lastModified
</td>
<td>
Date
</td> </tr>
<tr>
<td>
lastModifiedBy
</td>
<td>
String
</td> </tr> </table>
**CTA Acquisition, “types” structure**
<table>
<tr>
<th>
_id
</th>
<th>
String
</th> </tr>
<tr>
<td>
_class
</td>
<td>
String
</td> </tr>
<tr>
<td>
display
</td>
<td>
String
</td> </tr>
<tr>
<td>
shortName
</td>
<td>
String
</td> </tr>
<tr>
<td>
order
</td>
<td>
Int32
</td> </tr>
<tr>
<td>
controllerType
</td>
<td>
String
</td> </tr>
<tr>
<td>
definition
</td>
<td>
Object
</td> </tr>
<tr>
<td>
fields
</td>
<td>
Array
</td> </tr>
<tr>
<td>
[0]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
template
</td>
<td>
String
</td> </tr>
<tr>
<td>
[1]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
fieldGroup
</td>
<td>
Array
</td> </tr>
<tr>
<td>
[0]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
datePickerPopup
</td>
<td>
String
</td> </tr>
<tr>
<td>
required
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
[1]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
required
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
[2]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
required
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
[2]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
template
</td>
<td>
String
</td> </tr>
<tr>
<td>
[3]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
fieldGroup
</td>
<td>
Array
</td> </tr>
<tr>
<td>
[0]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr> </table>
<table>
<tr>
<th>
key
</th>
<th>
String
</th> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
description
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
required
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
[1]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
description
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
required
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
[2]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
description
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
required
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
[3]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
description
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
required
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
[4]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
template
</td>
<td>
String
</td> </tr>
<tr>
<td>
[5]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
fieldGroup
</td>
<td>
Array
</td> </tr>
<tr>
<td>
[0]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr> </table>
<table>
<tr>
<th>
[1]
</th>
<th>
Object
</th> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
hideExpression
</td>
<td>
String
</td> </tr>
<tr>
<td>
[6]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
fieldGroup
</td>
<td>
Array
</td> </tr>
<tr>
<td>
[0]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
[1]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
hideExpression
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
[7]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
fieldGroup
</td>
<td>
Array
</td> </tr>
<tr>
<td>
[0]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
[1]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
hideExpression
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
[2]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
hideExpression
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr> </table>
<table>
<tr>
<th>
label
</th>
<th>
String
</th> </tr>
<tr>
<td>
[3]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
hideExpression
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
options
</td>
<td>
Array
</td> </tr>
<tr>
<td>
[0]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
name
</td>
<td>
String
</td> </tr>
<tr>
<td>
value
</td>
<td>
String
</td> </tr>
<tr>
<td>
[1]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
name
</td>
<td>
String
</td> </tr>
<tr>
<td>
value
</td>
<td>
String
</td> </tr>
<tr>
<td>
[8]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
fieldGroup
</td>
<td>
Array
</td> </tr>
<tr>
<td>
[0]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
[1]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
hideExpression
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
[9]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
fieldGroup
</td>
<td>
Array
</td> </tr>
<tr>
<td>
[0]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
[1]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
hideExpression
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr> </table>
<table>
<tr>
<th>
[3]
</th>
<th>
Object
</th> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
hideExpression
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
options
</td>
<td>
Array
</td> </tr>
<tr>
<td>
[0]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
name
</td>
<td>
String
</td> </tr>
<tr>
<td>
value
</td>
<td>
String
</td> </tr>
<tr>
<td>
[1]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
name
</td>
<td>
String
</td> </tr>
<tr>
<td>
value
</td>
<td>
String
</td> </tr>
<tr>
<td>
[10]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
template
</td>
<td>
String
</td> </tr>
<tr>
<td>
[11]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
fieldGroup
</td>
<td>
Array
</td> </tr>
<tr>
<td>
[0]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
[1]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
[2]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
[3]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr> </table>
<table>
<tr>
<th>
type
</th>
<th>
String
</th> </tr>
<tr>
<td>
[4]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
[5]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
[6]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
[7]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
[8]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
[9]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
[10]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr> </table>
<table>
<tr>
<th>
type
</th>
<th>
String
</th> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
[12]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
template
</td>
<td>
String
</td> </tr>
<tr>
<td>
[13]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
fieldGroup
</td>
<td>
Array
</td> </tr>
<tr>
<td>
[0]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
[14]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
fieldGroup
</td>
<td>
Array
</td> </tr>
<tr>
<td>
[0]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
description
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
[1]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
description
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
[15]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
fieldGroup
</td>
<td>
Array
</td> </tr>
<tr>
<td>
[0]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
[1]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr> </table>
<table>
<tr>
<th>
key
</th>
<th>
String
</th> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
hideExpression
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
[16]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
fieldGroup
</td>
<td>
Array
</td> </tr>
<tr>
<td>
[0]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
[17]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
fieldGroup
</td>
<td>
Array
</td> </tr>
<tr>
<td>
[0]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
[1]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
hideExpression
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
[2]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
hideExpression
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
[18]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
template
</td>
<td>
String
</td> </tr>
<tr>
<td>
[19]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
fieldGroup
</td>
<td>
Array
</td> </tr>
<tr>
<td>
[0]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr> </table>
<table>
<tr>
<th>
type
</th>
<th>
String
</th> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
[20]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
fieldGroup
</td>
<td>
Array
</td> </tr>
<tr>
<td>
[0]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
description
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
[1]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
description
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
[21]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
fieldGroup
</td>
<td>
Array
</td> </tr>
<tr>
<td>
[0]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
[1]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
hideExpression
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
[22]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
fieldGroup
</td>
<td>
Array
</td> </tr>
<tr>
<td>
[0]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr> </table>
<table>
<tr>
<th>
type
</th>
<th>
String
</th> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
[23]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
fieldGroup
</td>
<td>
Array
</td> </tr>
<tr>
<td>
[0]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
[1]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
hideExpression
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
[2]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
hideExpression
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
[24]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
fieldGroup
</td>
<td>
Array
</td> </tr>
<tr>
<td>
[0]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
[25]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
fieldGroup
</td>
<td>
Array
</td> </tr>
<tr>
<td>
[0]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr> </table>
<table>
<tr>
<th>
[1]
</th>
<th>
Object
</th> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
hideExpression
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
[2]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
hideExpression
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
[26]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
fieldGroup
</td>
<td>
Array
</td> </tr>
<tr>
<td>
[0]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
[27]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
fieldGroup
</td>
<td>
Array
</td> </tr>
<tr>
<td>
[0]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
[1]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
hideExpression
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
canReadRoles
</td>
<td>
Array
</td> </tr>
<tr>
<td>
[0]
</td>
<td>
String
</td> </tr>
<tr>
<td>
[1]
</td>
<td>
String
</td> </tr>
<tr>
<td>
[2]
</td>
<td>
String
</td> </tr>
<tr>
<td>
canWriteRoles
</td>
<td>
Array
</td> </tr>
<tr>
<td>
[0]
</td>
<td>
String
</td> </tr>
<tr>
<td>
[1]
</td>
<td>
String
</td> </tr>
<tr>
<td>
versionNumber
</td>
<td>
Int64
</td> </tr>
<tr>
<td>
lastModified
</td>
<td>
Date
</td> </tr>
<tr>
<td>
lastModifiedBy
</td>
<td>
String
</td> </tr> </table>
**CTA Report, “types” structure**
<table>
<tr>
<th>
_id
</th>
<th>
String
</th> </tr>
<tr>
<td>
_class
</td>
<td>
String
</td> </tr>
<tr>
<td>
display
</td>
<td>
String
</td> </tr>
<tr>
<td>
shortName
</td>
<td>
String
</td> </tr>
<tr>
<td>
order
</td>
<td>
Int32
</td> </tr>
<tr>
<td>
controllerType
</td>
<td>
String
</td> </tr>
<tr>
<td>
definition
</td>
<td>
Object
</td> </tr>
<tr>
<td>
fields
</td>
<td>
Array
</td> </tr>
<tr>
<td>
[0]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
fieldGroup
</td>
<td>
Array
</td> </tr>
<tr>
<td>
[0]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
template
</td>
<td>
String
</td> </tr> </table>
<table>
<tr>
<th>
[1]
</th>
<th>
Object
</th> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
[1]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
hideExpression
</td>
<td>
String
</td> </tr>
<tr>
<td>
fieldGroup
</td>
<td>
Array
</td> </tr>
<tr>
<td>
[0]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
template
</td>
<td>
String
</td> </tr>
<tr>
<td>
[1]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
description
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
[2]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
depends
</td>
<td>
String
</td> </tr>
<tr>
<td>
expression
</td>
<td>
String
</td> </tr>
<tr>
<td>
[2]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
hideExpression
</td>
<td>
String
</td> </tr>
<tr>
<td>
fieldGroup
</td>
<td>
Array
</td> </tr>
<tr>
<td>
[0]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
template
</td>
<td>
String
</td> </tr>
<tr>
<td>
[1]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
description
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
[2]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr> </table>
<table>
<tr>
<th>
depends
</th>
<th>
String
</th> </tr>
<tr>
<td>
expression
</td>
<td>
String
</td> </tr>
<tr>
<td>
[3]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
hideExpression
</td>
<td>
String
</td> </tr>
<tr>
<td>
fieldGroup
</td>
<td>
Array
</td> </tr>
<tr>
<td>
[0]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
template
</td>
<td>
String
</td> </tr>
<tr>
<td>
[1]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
description
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
[2]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
depends
</td>
<td>
String
</td> </tr>
<tr>
<td>
expression
</td>
<td>
String
</td> </tr>
<tr>
<td>
[4]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
hideExpression
</td>
<td>
String
</td> </tr>
<tr>
<td>
fieldGroup
</td>
<td>
Array
</td> </tr>
<tr>
<td>
[0]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
template
</td>
<td>
String
</td> </tr>
<tr>
<td>
[1]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
description
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
[2]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
depends
</td>
<td>
String
</td> </tr>
<tr>
<td>
expression
</td>
<td>
String
</td> </tr>
<tr>
<td>
[5]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr> </table>
<table>
<tr>
<th>
hideExpression
</th>
<th>
String
</th> </tr>
<tr>
<td>
fieldGroup
</td>
<td>
Array
</td> </tr>
<tr>
<td>
[0]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
template
</td>
<td>
String
</td> </tr>
<tr>
<td>
[1]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
description
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
[2]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
depends
</td>
<td>
String
</td> </tr>
<tr>
<td>
expression
</td>
<td>
String
</td> </tr>
<tr>
<td>
[6]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
template
</td>
<td>
String
</td> </tr>
<tr>
<td>
[7]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
template
</td>
<td>
String
</td> </tr>
<tr>
<td>
[8]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
fieldGroup
</td>
<td>
Array
</td> </tr>
<tr>
<td>
[0]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
description
</td>
<td>
String
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
[1]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
options
</td>
<td>
Array
</td> </tr>
<tr>
<td>
[0]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
name
</td>
<td>
String
</td> </tr>
<tr>
<td>
value
</td>
<td>
String
</td> </tr>
<tr>
<td>
[1]
</td>
<td>
Object
</td> </tr> </table>
<table>
<tr>
<th>
name
</th>
<th>
String
</th> </tr>
<tr>
<td>
value
</td>
<td>
String
</td> </tr>
<tr>
<td>
[2]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
name
</td>
<td>
String
</td> </tr>
<tr>
<td>
value
</td>
<td>
String
</td> </tr>
<tr>
<td>
[3]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
name
</td>
<td>
String
</td> </tr>
<tr>
<td>
value
</td>
<td>
String
</td> </tr>
<tr>
<td>
[9]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
fieldGroup
</td>
<td>
Array
</td> </tr>
<tr>
<td>
[0]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
[10]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
hideExpression
</td>
<td>
String
</td> </tr>
<tr>
<td>
fieldGroup
</td>
<td>
Array
</td> </tr>
<tr>
<td>
[0]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
fieldGroup
</td>
<td>
Array
</td> </tr>
<tr>
<td>
[0]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
id
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
[1]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
hideExpression
</td>
<td>
String
</td> </tr>
<tr>
<td>
fieldGroup
</td>
<td>
Array
</td> </tr>
<tr>
<td>
[0]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
dictionary
</td>
<td>
String
</td> </tr>
<tr>
<td>
[1]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr> </table>
<table>
<tr>
<th>
label
</th>
<th>
String
</th> </tr>
<tr>
<td>
[2]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
dictionary
</td>
<td>
String
</td> </tr>
<tr>
<td>
[3]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
[4]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
[5]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
hideExpression
</td>
<td>
String
</td> </tr>
<tr>
<td>
fieldGroup
</td>
<td>
Array
</td> </tr>
<tr>
<td>
[0]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
dictionary
</td>
<td>
String
</td> </tr>
<tr>
<td>
[1]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
[2]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
dictionary
</td>
<td>
String
</td> </tr>
<tr>
<td>
[3]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr> </table>
<table>
<tr>
<th>
key
</th>
<th>
String
</th> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
[1]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
fieldGroup
</td>
<td>
Array
</td> </tr>
<tr>
<td>
[0]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
id
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
[1]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
hideExpression
</td>
<td>
String
</td> </tr>
<tr>
<td>
fieldGroup
</td>
<td>
Array
</td> </tr>
<tr>
<td>
[0]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
dictionary
</td>
<td>
String
</td> </tr>
<tr>
<td>
[1]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
[2]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
dictionary
</td>
<td>
String
</td> </tr>
<tr>
<td>
[3]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
[4]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr> </table>
<table>
<tr>
<th>
type
</th>
<th>
String
</th> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
[5]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
hideExpression
</td>
<td>
String
</td> </tr>
<tr>
<td>
fieldGroup
</td>
<td>
Array
</td> </tr>
<tr>
<td>
[0]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
dictionary
</td>
<td>
String
</td> </tr>
<tr>
<td>
[1]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
[2]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
dictionary
</td>
<td>
String
</td> </tr>
<tr>
<td>
[3]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
[2]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
fieldGroup
</td>
<td>
Array
</td> </tr>
<tr>
<td>
[0]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
id
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
[1]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
hideExpression
</td>
<td>
String
</td> </tr>
<tr>
<td>
fieldGroup
</td>
<td>
Array
</td> </tr> </table>
<table>
<tr>
<th>
[0]
</th>
<th>
Object
</th> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
dictionary
</td>
<td>
String
</td> </tr>
<tr>
<td>
[1]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
[2]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
dictionary
</td>
<td>
String
</td> </tr>
<tr>
<td>
[3]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
[4]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
[5]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
hideExpression
</td>
<td>
String
</td> </tr>
<tr>
<td>
fieldGroup
</td>
<td>
Array
</td> </tr>
<tr>
<td>
[0]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
dictionary
</td>
<td>
String
</td> </tr>
<tr>
<td>
[1]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr> </table>
<table>
<tr>
<th>
type
</th>
<th>
String
</th> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
[2]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
dictionary
</td>
<td>
String
</td> </tr>
<tr>
<td>
[3]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
[3]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
fieldGroup
</td>
<td>
Array
</td> </tr>
<tr>
<td>
[0]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
id
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
[1]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
hideExpression
</td>
<td>
String
</td> </tr>
<tr>
<td>
fieldGroup
</td>
<td>
Array
</td> </tr>
<tr>
<td>
[0]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
dictionary
</td>
<td>
String
</td> </tr>
<tr>
<td>
[1]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
[2]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr> </table>
<table>
<tr>
<th>
templateOptions
</th>
<th>
Object
</th> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
dictionary
</td>
<td>
String
</td> </tr>
<tr>
<td>
[3]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
[4]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
[5]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
hideExpression
</td>
<td>
String
</td> </tr>
<tr>
<td>
fieldGroup
</td>
<td>
Array
</td> </tr>
<tr>
<td>
[0]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
dictionary
</td>
<td>
String
</td> </tr>
<tr>
<td>
[1]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
[2]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
dictionary
</td>
<td>
String
</td> </tr>
<tr>
<td>
[3]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
[4]
</td>
<td>
Object
</td> </tr> </table>
<table>
<tr>
<th>
className
</th>
<th>
String
</th> </tr>
<tr>
<td>
fieldGroup
</td>
<td>
Array
</td> </tr>
<tr>
<td>
[0]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
id
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
[1]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
hideExpression
</td>
<td>
String
</td> </tr>
<tr>
<td>
fieldGroup
</td>
<td>
Array
</td> </tr>
<tr>
<td>
[0]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
dictionary
</td>
<td>
String
</td> </tr>
<tr>
<td>
[1]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
[2]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
dictionary
</td>
<td>
String
</td> </tr>
<tr>
<td>
[3]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
[4]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
[5]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr> </table>
<table>
<tr>
<th>
hideExpression
</th>
<th>
String
</th> </tr>
<tr>
<td>
fieldGroup
</td>
<td>
Array
</td> </tr>
<tr>
<td>
[0]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
dictionary
</td>
<td>
String
</td> </tr>
<tr>
<td>
[1]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
[2]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
dictionary
</td>
<td>
String
</td> </tr>
<tr>
<td>
[3]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
[5]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
fieldGroup
</td>
<td>
Array
</td> </tr>
<tr>
<td>
[0]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
id
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
[1]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
hideExpression
</td>
<td>
String
</td> </tr>
<tr>
<td>
fieldGroup
</td>
<td>
Array
</td> </tr>
<tr>
<td>
[0]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr> </table>
<table>
<tr>
<th>
label
</th>
<th>
String
</th> </tr>
<tr>
<td>
dictionary
</td>
<td>
String
</td> </tr>
<tr>
<td>
[1]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
[2]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
dictionary
</td>
<td>
String
</td> </tr>
<tr>
<td>
[3]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
[4]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
[5]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
hideExpression
</td>
<td>
String
</td> </tr>
<tr>
<td>
fieldGroup
</td>
<td>
Array
</td> </tr>
<tr>
<td>
[0]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
dictionary
</td>
<td>
String
</td> </tr>
<tr>
<td>
[1]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
[2]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr> </table>
<table>
<tr>
<th>
key
</th>
<th>
String
</th> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
dictionary
</td>
<td>
String
</td> </tr>
<tr>
<td>
[3]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
[6]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
fieldGroup
</td>
<td>
Array
</td> </tr>
<tr>
<td>
[0]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
id
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
[1]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
hideExpression
</td>
<td>
String
</td> </tr>
<tr>
<td>
fieldGroup
</td>
<td>
Array
</td> </tr>
<tr>
<td>
[0]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
dictionary
</td>
<td>
String
</td> </tr>
<tr>
<td>
[1]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
[2]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
dictionary
</td>
<td>
String
</td> </tr>
<tr>
<td>
[3]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr> </table>
<table>
<tr>
<th>
key
</th>
<th>
String
</th> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
[4]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
[5]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
hideExpression
</td>
<td>
String
</td> </tr>
<tr>
<td>
fieldGroup
</td>
<td>
Array
</td> </tr>
<tr>
<td>
[0]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
dictionary
</td>
<td>
String
</td> </tr>
<tr>
<td>
[1]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
[2]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
dictionary
</td>
<td>
String
</td> </tr>
<tr>
<td>
[3]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
[7]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
fieldGroup
</td>
<td>
Array
</td> </tr>
<tr>
<td>
[0]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr> </table>
<table>
<tr>
<th>
id
</th>
<th>
String
</th> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
[1]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
hideExpression
</td>
<td>
String
</td> </tr>
<tr>
<td>
fieldGroup
</td>
<td>
Array
</td> </tr>
<tr>
<td>
[0]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
dictionary
</td>
<td>
String
</td> </tr>
<tr>
<td>
[1]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
[2]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
dictionary
</td>
<td>
String
</td> </tr>
<tr>
<td>
[3]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
[4]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
[5]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
hideExpression
</td>
<td>
String
</td> </tr>
<tr>
<td>
fieldGroup
</td>
<td>
Array
</td> </tr>
<tr>
<td>
[0]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr> </table>
<table>
<tr>
<th>
type
</th>
<th>
String
</th> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
dictionary
</td>
<td>
String
</td> </tr>
<tr>
<td>
[1]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
[2]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
dictionary
</td>
<td>
String
</td> </tr>
<tr>
<td>
[3]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
[8]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
fieldGroup
</td>
<td>
Array
</td> </tr>
<tr>
<td>
[0]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
id
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
[1]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
hideExpression
</td>
<td>
String
</td> </tr>
<tr>
<td>
fieldGroup
</td>
<td>
Array
</td> </tr>
<tr>
<td>
[0]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
dictionary
</td>
<td>
String
</td> </tr>
<tr>
<td>
[1]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr> </table>
<table>
<tr>
<th>
type
</th>
<th>
String
</th> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
[2]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
dictionary
</td>
<td>
String
</td> </tr>
<tr>
<td>
[3]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
[4]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
[5]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
hideExpression
</td>
<td>
String
</td> </tr>
<tr>
<td>
fieldGroup
</td>
<td>
Array
</td> </tr>
<tr>
<td>
[0]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
dictionary
</td>
<td>
String
</td> </tr>
<tr>
<td>
[1]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
[2]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
dictionary
</td>
<td>
String
</td> </tr> </table>
<table>
<tr>
<th>
[3]
</th>
<th>
Object
</th> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
[9]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
fieldGroup
</td>
<td>
Array
</td> </tr>
<tr>
<td>
[0]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
id
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
[1]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
hideExpression
</td>
<td>
String
</td> </tr>
<tr>
<td>
fieldGroup
</td>
<td>
Array
</td> </tr>
<tr>
<td>
[0]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
dictionary
</td>
<td>
String
</td> </tr>
<tr>
<td>
[1]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
[2]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
dictionary
</td>
<td>
String
</td> </tr>
<tr>
<td>
[3]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
[4]
</td>
<td>
Object
</td> </tr> </table>
<table>
<tr>
<th>
className
</th>
<th>
String
</th> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
[5]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
hideExpression
</td>
<td>
String
</td> </tr>
<tr>
<td>
fieldGroup
</td>
<td>
Array
</td> </tr>
<tr>
<td>
[0]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
dictionary
</td>
<td>
String
</td> </tr>
<tr>
<td>
[1]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
[2]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
dictionary
</td>
<td>
String
</td> </tr>
<tr>
<td>
[3]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
[10]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
fieldGroup
</td>
<td>
Array
</td> </tr>
<tr>
<td>
[0]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
id
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
[1]
</td>
<td>
Object
</td> </tr> </table>
<table>
<tr>
<th>
hideExpression
</th>
<th>
String
</th> </tr>
<tr>
<td>
fieldGroup
</td>
<td>
Array
</td> </tr>
<tr>
<td>
[0]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
dictionary
</td>
<td>
String
</td> </tr>
<tr>
<td>
[1]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
[2]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
dictionary
</td>
<td>
String
</td> </tr>
<tr>
<td>
[3]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
[4]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
[5]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
hideExpression
</td>
<td>
String
</td> </tr>
<tr>
<td>
fieldGroup
</td>
<td>
Array
</td> </tr>
<tr>
<td>
[0]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
dictionary
</td>
<td>
String
</td> </tr>
<tr>
<td>
[1]
</td>
<td>
Object
</td> </tr> </table>
<table>
<tr>
<th>
className
</th>
<th>
String
</th> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
[2]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
dictionary
</td>
<td>
String
</td> </tr>
<tr>
<td>
[3]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
[11]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
fieldGroup
</td>
<td>
Array
</td> </tr>
<tr>
<td>
[0]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
id
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
[1]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
hideExpression
</td>
<td>
String
</td> </tr>
<tr>
<td>
fieldGroup
</td>
<td>
Array
</td> </tr>
<tr>
<td>
[0]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
dictionary
</td>
<td>
String
</td> </tr>
<tr>
<td>
[1]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
[2]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr> </table>
<table>
<tr>
<th>
key
</th>
<th>
String
</th> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
dictionary
</td>
<td>
String
</td> </tr>
<tr>
<td>
[3]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
[4]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
[5]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
hideExpression
</td>
<td>
String
</td> </tr>
<tr>
<td>
fieldGroup
</td>
<td>
Array
</td> </tr>
<tr>
<td>
[0]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
dictionary
</td>
<td>
String
</td> </tr>
<tr>
<td>
[1]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
[2]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
dictionary
</td>
<td>
String
</td> </tr>
<tr>
<td>
[3]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr> </table>
<table>
<tr>
<th>
label
</th>
<th>
String
</th> </tr>
<tr>
<td>
[12]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
fieldGroup
</td>
<td>
Array
</td> </tr>
<tr>
<td>
[0]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
id
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
[1]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
hideExpression
</td>
<td>
String
</td> </tr>
<tr>
<td>
fieldGroup
</td>
<td>
Array
</td> </tr>
<tr>
<td>
[0]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
dictionary
</td>
<td>
String
</td> </tr>
<tr>
<td>
[1]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
[2]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
dictionary
</td>
<td>
String
</td> </tr>
<tr>
<td>
[3]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
[4]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr> </table>
<table>
<tr>
<th>
[5]
</th>
<th>
Object
</th> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
hideExpression
</td>
<td>
String
</td> </tr>
<tr>
<td>
fieldGroup
</td>
<td>
Array
</td> </tr>
<tr>
<td>
[0]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
dictionary
</td>
<td>
String
</td> </tr>
<tr>
<td>
[1]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
[2]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
dictionary
</td>
<td>
String
</td> </tr>
<tr>
<td>
[3]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
[13]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
fieldGroup
</td>
<td>
Array
</td> </tr>
<tr>
<td>
[0]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
id
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
[1]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
hideExpression
</td>
<td>
String
</td> </tr>
<tr>
<td>
fieldGroup
</td>
<td>
Array
</td> </tr>
<tr>
<td>
[0]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr> </table>
<table>
<tr>
<th>
type
</th>
<th>
String
</th> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
dictionary
</td>
<td>
String
</td> </tr>
<tr>
<td>
[1]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
[2]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
dictionary
</td>
<td>
String
</td> </tr>
<tr>
<td>
[3]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
[4]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
[5]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
hideExpression
</td>
<td>
String
</td> </tr>
<tr>
<td>
fieldGroup
</td>
<td>
Array
</td> </tr>
<tr>
<td>
[0]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
dictionary
</td>
<td>
String
</td> </tr>
<tr>
<td>
[1]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr> </table>
<table>
<tr>
<th>
[2]
</th>
<th>
Object
</th> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
dictionary
</td>
<td>
String
</td> </tr>
<tr>
<td>
[3]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
[14]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
fieldGroup
</td>
<td>
Array
</td> </tr>
<tr>
<td>
[0]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
id
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
[1]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
hideExpression
</td>
<td>
String
</td> </tr>
<tr>
<td>
fieldGroup
</td>
<td>
Array
</td> </tr>
<tr>
<td>
[0]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
dictionary
</td>
<td>
String
</td> </tr>
<tr>
<td>
[1]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
[2]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
dictionary
</td>
<td>
String
</td> </tr> </table>
<table>
<tr>
<th>
[3]
</th>
<th>
Object
</th> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
[4]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
[5]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
hideExpression
</td>
<td>
String
</td> </tr>
<tr>
<td>
fieldGroup
</td>
<td>
Array
</td> </tr>
<tr>
<td>
[0]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
dictionary
</td>
<td>
String
</td> </tr>
<tr>
<td>
[1]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
[2]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
dictionary
</td>
<td>
String
</td> </tr>
<tr>
<td>
[3]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
[15]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
fieldGroup
</td>
<td>
Array
</td> </tr>
<tr>
<td>
[0]
</td>
<td>
Object
</td> </tr> </table>
<table>
<tr>
<th>
className
</th>
<th>
String
</th> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
id
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
[1]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
hideExpression
</td>
<td>
String
</td> </tr>
<tr>
<td>
fieldGroup
</td>
<td>
Array
</td> </tr>
<tr>
<td>
[0]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
dictionary
</td>
<td>
String
</td> </tr>
<tr>
<td>
[1]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
[2]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
dictionary
</td>
<td>
String
</td> </tr>
<tr>
<td>
[3]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
[4]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
[5]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
hideExpression
</td>
<td>
String
</td> </tr>
<tr>
<td>
fieldGroup
</td>
<td>
Array
</td> </tr>
<tr>
<td>
[0]
</td>
<td>
Object
</td> </tr> </table>
<table>
<tr>
<th>
className
</th>
<th>
String
</th> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
dictionary
</td>
<td>
String
</td> </tr>
<tr>
<td>
[1]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
[2]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
dictionary
</td>
<td>
String
</td> </tr>
<tr>
<td>
[3]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
[16]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
fieldGroup
</td>
<td>
Array
</td> </tr>
<tr>
<td>
[0]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
id
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
[1]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
hideExpression
</td>
<td>
String
</td> </tr>
<tr>
<td>
fieldGroup
</td>
<td>
Array
</td> </tr>
<tr>
<td>
[0]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
dictionary
</td>
<td>
String
</td> </tr>
<tr>
<td>
[1]
</td>
<td>
Object
</td> </tr> </table>
<table>
<tr>
<th>
className
</th>
<th>
String
</th> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
[2]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
dictionary
</td>
<td>
String
</td> </tr>
<tr>
<td>
[3]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
[4]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
[5]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
hideExpression
</td>
<td>
String
</td> </tr>
<tr>
<td>
fieldGroup
</td>
<td>
Array
</td> </tr>
<tr>
<td>
[0]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
dictionary
</td>
<td>
String
</td> </tr>
<tr>
<td>
[1]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
[2]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
dictionary
</td>
<td>
String
</td> </tr>
<tr>
<td>
[3]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
canReadRoles
</td>
<td>
Array
</td> </tr>
<tr>
<td>
[0]
</td>
<td>
String
</td> </tr>
<tr>
<td>
[1]
</td>
<td>
String
</td> </tr>
<tr>
<td>
[2]
</td>
<td>
String
</td> </tr>
<tr>
<td>
[3]
</td>
<td>
String
</td> </tr>
<tr>
<td>
[4]
</td>
<td>
String
</td> </tr>
<tr>
<td>
[5]
</td>
<td>
String
</td> </tr>
<tr>
<td>
[6]
</td>
<td>
String
</td> </tr>
<tr>
<td>
[7]
</td>
<td>
String
</td> </tr>
<tr>
<td>
[8]
</td>
<td>
String
</td> </tr>
<tr>
<td>
canWriteRoles
</td>
<td>
Array
</td> </tr>
<tr>
<td>
[0]
</td>
<td>
String
</td> </tr>
<tr>
<td>
[1]
</td>
<td>
String
</td> </tr>
<tr>
<td>
[2]
</td>
<td>
String
</td> </tr>
<tr>
<td>
versionNumber
</td>
<td>
Int64
</td> </tr>
<tr>
<td>
createAt
</td>
<td>
Date
</td> </tr>
<tr>
<td>
lastModified
</td>
<td>
Date
</td> </tr>
<tr>
<td>
createBy
</td>
<td>
String
</td> </tr>
<tr>
<td>
lastModifiedBy
</td>
<td>
String
</td> </tr> </table>
**Lab-On-Chip page, “types” structure**
<table>
<tr>
<th>
_id
</th>
<th>
String
</th> </tr>
<tr>
<td>
_class
</td>
<td>
String
</td> </tr>
<tr>
<td>
display
</td>
<td>
String
</td> </tr>
<tr>
<td>
shortName
</td>
<td>
String
</td> </tr>
<tr>
<td>
order
</td>
<td>
Int32
</td> </tr>
<tr>
<td>
controllerType
</td>
<td>
String
</td> </tr>
<tr>
<td>
definition
</td>
<td>
Object
</td> </tr>
<tr>
<td>
fields
</td>
<td>
Array
</td> </tr>
<tr>
<td>
[0]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
template
</td>
<td>
String
</td> </tr>
<tr>
<td>
[1]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
description
</td>
<td>
String
</td> </tr>
<tr>
<td>
[2]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
description
</td>
<td>
String
</td> </tr>
<tr>
<td>
canReadRoles
</td>
<td>
Array
</td> </tr>
<tr>
<td>
[0]
</td>
<td>
String
</td> </tr>
<tr>
<td>
canWriteRoles
</td>
<td>
Array
</td> </tr>
<tr>
<td>
versionNumber
</td>
<td>
Int64
</td> </tr>
<tr>
<td>
lastModified
</td>
<td>
Date
</td> </tr>
<tr>
<td>
lastModifiedBy
</td>
<td>
String
</td> </tr> </table>
**Quality Control structure, “types” structure**
<table>
<tr>
<th>
_id
</th>
<th>
String
</th> </tr>
<tr>
<td>
_class
</td>
<td>
String
</td> </tr>
<tr>
<td>
display
</td>
<td>
String
</td> </tr>
<tr>
<td>
shortName
</td>
<td>
String
</td> </tr>
<tr>
<td>
order
</td>
<td>
Int32
</td> </tr> </table>
<table>
<tr>
<th>
controllerType
</th>
<th>
String
</th> </tr>
<tr>
<td>
definition
</td>
<td>
Object
</td> </tr>
<tr>
<td>
fields
</td>
<td>
Array
</td> </tr>
<tr>
<td>
[0]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
template
</td>
<td>
String
</td> </tr>
<tr>
<td>
[1]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
template
</td>
<td>
String
</td> </tr>
<tr>
<td>
[2]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
template
</td>
<td>
String
</td> </tr>
<tr>
<td>
[3]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
template
</td>
<td>
String
</td> </tr>
<tr>
<td>
hideExpression
</td>
<td>
String
</td> </tr>
<tr>
<td>
[4]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
fieldGroup
</td>
<td>
Array
</td> </tr>
<tr>
<td>
[0]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
[1]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
hideExpression
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
placeholder
</td>
<td>
String
</td> </tr>
<tr>
<td>
[2]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
hideExpression
</td>
<td>
String
</td> </tr>
<tr>
<td>
modelOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
allowInvalid
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
validators
</td>
<td>
Object
</td> </tr>
<tr>
<td>
notChecked
</td>
<td>
Object
</td> </tr>
<tr>
<td>
expression
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
required
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
[3]
</td>
<td>
Object
</td> </tr> </table>
<table>
<tr>
<th>
className
</th>
<th>
String
</th> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
hideExpression
</td>
<td>
String
</td> </tr>
<tr>
<td>
modelOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
allowInvalid
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
validators
</td>
<td>
Object
</td> </tr>
<tr>
<td>
notChecked
</td>
<td>
Object
</td> </tr>
<tr>
<td>
expression
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
required
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
[5]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
template
</td>
<td>
String
</td> </tr>
<tr>
<td>
[6]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
template
</td>
<td>
String
</td> </tr>
<tr>
<td>
hideExpression
</td>
<td>
String
</td> </tr>
<tr>
<td>
[7]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
fieldGroup
</td>
<td>
Array
</td> </tr>
<tr>
<td>
[0]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
[1]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
hideExpression
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
placeholder
</td>
<td>
String
</td> </tr>
<tr>
<td>
[2]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
hideExpression
</td>
<td>
String
</td> </tr>
<tr>
<td>
modelOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
allowInvalid
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
validators
</td>
<td>
Object
</td> </tr>
<tr>
<td>
notChecked
</td>
<td>
Object
</td> </tr>
<tr>
<td>
expression
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr> </table>
<table>
<tr>
<th>
label
</th>
<th>
String
</th> </tr>
<tr>
<td>
required
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
[3]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
hideExpression
</td>
<td>
String
</td> </tr>
<tr>
<td>
modelOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
allowInvalid
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
validators
</td>
<td>
Object
</td> </tr>
<tr>
<td>
notChecked
</td>
<td>
Object
</td> </tr>
<tr>
<td>
expression
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
required
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
[8]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
template
</td>
<td>
String
</td> </tr>
<tr>
<td>
[9]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
template
</td>
<td>
String
</td> </tr>
<tr>
<td>
hideExpression
</td>
<td>
String
</td> </tr>
<tr>
<td>
[10]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
fieldGroup
</td>
<td>
Array
</td> </tr>
<tr>
<td>
[0]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
[1]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
hideExpression
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
placeholder
</td>
<td>
String
</td> </tr>
<tr>
<td>
[2]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
hideExpression
</td>
<td>
String
</td> </tr>
<tr>
<td>
modelOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
allowInvalid
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
validators
</td>
<td>
Object
</td> </tr> </table>
<table>
<tr>
<th>
notChecked
</th>
<th>
Object
</th> </tr>
<tr>
<td>
expression
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
required
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
[3]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
hideExpression
</td>
<td>
String
</td> </tr>
<tr>
<td>
modelOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
allowInvalid
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
validators
</td>
<td>
Object
</td> </tr>
<tr>
<td>
notChecked
</td>
<td>
Object
</td> </tr>
<tr>
<td>
expression
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
required
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
[11]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
template
</td>
<td>
String
</td> </tr>
<tr>
<td>
[12]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
template
</td>
<td>
String
</td> </tr>
<tr>
<td>
hideExpression
</td>
<td>
String
</td> </tr>
<tr>
<td>
[13]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
fieldGroup
</td>
<td>
Array
</td> </tr>
<tr>
<td>
[0]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
[1]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
hideExpression
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
placeholder
</td>
<td>
String
</td> </tr>
<tr>
<td>
[2]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
hideExpression
</td>
<td>
String
</td> </tr> </table>
<table>
<tr>
<th>
modelOptions
</th>
<th>
Object
</th> </tr>
<tr>
<td>
allowInvalid
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
validators
</td>
<td>
Object
</td> </tr>
<tr>
<td>
notChecked
</td>
<td>
Object
</td> </tr>
<tr>
<td>
expression
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
required
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
[3]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
hideExpression
</td>
<td>
String
</td> </tr>
<tr>
<td>
modelOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
allowInvalid
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
validators
</td>
<td>
Object
</td> </tr>
<tr>
<td>
notChecked
</td>
<td>
Object
</td> </tr>
<tr>
<td>
expression
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
required
</td>
<td>
Boolean
</td> </tr>
<tr>
<td>
canReadRoles
</td>
<td>
Array
</td> </tr>
<tr>
<td>
[0]
</td>
<td>
String
</td> </tr>
<tr>
<td>
[1]
</td>
<td>
String
</td> </tr>
<tr>
<td>
[2]
</td>
<td>
String
</td> </tr>
<tr>
<td>
[3]
</td>
<td>
String
</td> </tr>
<tr>
<td>
[4]
</td>
<td>
String
</td> </tr>
<tr>
<td>
[5]
</td>
<td>
String
</td> </tr>
<tr>
<td>
[6]
</td>
<td>
String
</td> </tr>
<tr>
<td>
[7]
</td>
<td>
String
</td> </tr>
<tr>
<td>
[8]
</td>
<td>
String
</td> </tr>
<tr>
<td>
[9]
</td>
<td>
String
</td> </tr>
<tr>
<td>
[10]
</td>
<td>
String
</td> </tr>
<tr>
<td>
canWriteRoles
</td>
<td>
Array
</td> </tr>
<tr>
<td>
[0]
</td>
<td>
String
</td> </tr>
<tr>
<td>
[1]
</td>
<td>
String
</td> </tr>
<tr>
<td>
[2]
</td>
<td>
String
</td> </tr>
<tr>
<td>
versionNumber
</td>
<td>
Int64
</td> </tr>
<tr>
<td>
lastModified
</td>
<td>
Date
</td> </tr>
<tr>
<td>
lastModifiedBy
</td>
<td>
String
</td> </tr> </table>
**Clinical Decision page, “types” structure**
<table>
<tr>
<th>
**Clinical Decision**
</th> </tr>
<tr>
<td>
_id
</td>
<td>
String
</td> </tr>
<tr>
<td>
_class
</td>
<td>
String
</td> </tr>
<tr>
<td>
display
</td>
<td>
String
</td> </tr>
<tr>
<td>
shortName
</td>
<td>
String
</td> </tr>
<tr>
<td>
order
</td>
<td>
Int32
</td> </tr>
<tr>
<td>
controllerType
</td>
<td>
String
</td> </tr>
<tr>
<td>
definition
</td>
<td>
Object
</td> </tr>
<tr>
<td>
fields
</td>
<td>
Array
</td> </tr>
<tr>
<td>
[0]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
template
</td>
<td>
String
</td> </tr>
<tr>
<td>
[1]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
fieldGroup
</td>
<td>
Array
</td> </tr>
<tr>
<td>
[0]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
options
</td>
<td>
Array
</td> </tr>
<tr>
<td>
[0]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
name
</td>
<td>
String
</td> </tr>
<tr>
<td>
value
</td>
<td>
String
</td> </tr>
<tr>
<td>
[1]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
name
</td>
<td>
String
</td> </tr>
<tr>
<td>
value
</td>
<td>
String
</td> </tr>
<tr>
<td>
[2]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
name
</td>
<td>
String
</td> </tr>
<tr>
<td>
value
</td>
<td>
String
</td> </tr>
<tr>
<td>
[1]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
options
</td>
<td>
Array
</td> </tr>
<tr>
<td>
[0]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
name
</td>
<td>
String
</td> </tr>
<tr>
<td>
value
</td>
<td>
String
</td> </tr>
<tr>
<td>
[1]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
name
</td>
<td>
String
</td> </tr>
<tr>
<td>
value
</td>
<td>
String
</td> </tr> </table>
<table>
<tr>
<th>
[2]
</th>
<th>
Object
</th> </tr>
<tr>
<td>
name
</td>
<td>
String
</td> </tr>
<tr>
<td>
value
</td>
<td>
String
</td> </tr>
<tr>
<td>
[2]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
template
</td>
<td>
String
</td> </tr>
<tr>
<td>
[3]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
fieldGroup
</td>
<td>
Array
</td> </tr>
<tr>
<td>
[0]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
[1]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
hideExpression
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
options
</td>
<td>
Array
</td> </tr>
<tr>
<td>
[0]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
name
</td>
<td>
String
</td> </tr>
<tr>
<td>
value
</td>
<td>
String
</td> </tr>
<tr>
<td>
[1]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
name
</td>
<td>
String
</td> </tr>
<tr>
<td>
value
</td>
<td>
String
</td> </tr>
<tr>
<td>
[2]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
name
</td>
<td>
String
</td> </tr>
<tr>
<td>
value
</td>
<td>
String
</td> </tr>
<tr>
<td>
[3]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
name
</td>
<td>
String
</td> </tr>
<tr>
<td>
value
</td>
<td>
String
</td> </tr>
<tr>
<td>
[4]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
name
</td>
<td>
String
</td> </tr>
<tr>
<td>
value
</td>
<td>
String
</td> </tr>
<tr>
<td>
[5]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
name
</td>
<td>
String
</td> </tr>
<tr>
<td>
value
</td>
<td>
String
</td> </tr>
<tr>
<td>
[4]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
fieldGroup
</td>
<td>
Array
</td> </tr>
<tr>
<td>
[0]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr> </table>
<table>
<tr>
<th>
key
</th>
<th>
String
</th> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
[1]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
hideExpression
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
[5]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
fieldGroup
</td>
<td>
Array
</td> </tr>
<tr>
<td>
[0]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
[1]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
hideExpression
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
options
</td>
<td>
Array
</td> </tr>
<tr>
<td>
[0]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
name
</td>
<td>
String
</td> </tr>
<tr>
<td>
value
</td>
<td>
String
</td> </tr>
<tr>
<td>
[1]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
name
</td>
<td>
String
</td> </tr>
<tr>
<td>
value
</td>
<td>
String
</td> </tr>
<tr>
<td>
[6]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
template
</td>
<td>
String
</td> </tr>
<tr>
<td>
[7]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
fieldGroup
</td>
<td>
Array
</td> </tr>
<tr>
<td>
[0]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr> </table>
<table>
<tr>
<th>
[1]
</th>
<th>
Object
</th> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
hideExpression
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
[8]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
fieldGroup
</td>
<td>
Array
</td> </tr>
<tr>
<td>
[0]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
[1]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
hideExpression
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
[9]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
fieldGroup
</td>
<td>
Array
</td> </tr>
<tr>
<td>
[0]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
[1]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
hideExpression
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
[2]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
hideExpression
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr> </table>
<table>
<tr>
<th>
label
</th>
<th>
String
</th> </tr>
<tr>
<td>
[3]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
hideExpression
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
[10]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
fieldGroup
</td>
<td>
Array
</td> </tr>
<tr>
<td>
[0]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
[1]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
hideExpression
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
[11]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
fieldGroup
</td>
<td>
Array
</td> </tr>
<tr>
<td>
[0]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
[1]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
hideExpression
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
[12]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
fieldGroup
</td>
<td>
Array
</td> </tr>
<tr>
<td>
[0]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr> </table>
<table>
<tr>
<th>
type
</th>
<th>
String
</th> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
[1]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
hideExpression
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
[13]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
fieldGroup
</td>
<td>
Array
</td> </tr>
<tr>
<td>
[0]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
[1]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
hideExpression
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
[14]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
fieldGroup
</td>
<td>
Array
</td> </tr>
<tr>
<td>
[0]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
[1]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
hideExpression
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
[15]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
fieldGroup
</td>
<td>
Array
</td> </tr>
<tr>
<td>
[0]
</td>
<td>
Object
</td> </tr> </table>
<table>
<tr>
<th>
className
</th>
<th>
String
</th> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
[1]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
hideExpression
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
[16]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
fieldGroup
</td>
<td>
Array
</td> </tr>
<tr>
<td>
[0]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
[1]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
hideExpression
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
[17]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
fieldGroup
</td>
<td>
Array
</td> </tr>
<tr>
<td>
[0]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
[1]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
hideExpression
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
[18]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr> </table>
<table>
<tr>
<th>
fieldGroup
</th>
<th>
Array
</th> </tr>
<tr>
<td>
[0]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
[1]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
hideExpression
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
[19]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
fieldGroup
</td>
<td>
Array
</td> </tr>
<tr>
<td>
[0]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
[1]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
hideExpression
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
[20]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
fieldGroup
</td>
<td>
Array
</td> </tr>
<tr>
<td>
[0]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
[1]
</td>
<td>
Object
</td> </tr>
<tr>
<td>
className
</td>
<td>
String
</td> </tr>
<tr>
<td>
key
</td>
<td>
String
</td> </tr>
<tr>
<td>
type
</td>
<td>
String
</td> </tr>
<tr>
<td>
hideExpression
</td>
<td>
String
</td> </tr>
<tr>
<td>
templateOptions
</td>
<td>
Object
</td> </tr>
<tr>
<td>
label
</td>
<td>
String
</td> </tr>
<tr>
<td>
canReadRoles
</td>
<td>
Array
</td> </tr>
<tr>
<td>
[0]
</td>
<td>
String
</td> </tr>
<tr>
<td>
[1]
</td>
<td>
String
</td> </tr>
<tr>
<td>
[2]
</td>
<td>
String
</td> </tr>
<tr>
<td>
[3]
</td>
<td>
String
</td> </tr>
<tr>
<td>
[4]
</td>
<td>
String
</td> </tr>
<tr>
<td>
[5]
</td>
<td>
String
</td> </tr>
<tr>
<td>
canWriteRoles
</td>
<td>
Array
</td> </tr>
<tr>
<td>
[0]
</td>
<td>
String
</td> </tr>
<tr>
<td>
[1]
</td>
<td>
String
</td> </tr>
<tr>
<td>
[2]
</td>
<td>
String
</td> </tr>
<tr>
<td>
versionNumber
</td>
<td>
Int64
</td> </tr>
<tr>
<td>
lastModified
</td>
<td>
Date
</td> </tr>
<tr>
<td>
lastModifiedBy
</td>
<td>
String
</td> </tr> </table>
**Dummy page, “types” structure**
<table>
<tr>
<th>
_id
</th>
<th>
String
</th> </tr>
<tr>
<td>
_class
</td>
<td>
String
</td> </tr>
<tr>
<td>
display
</td>
<td>
String
</td> </tr>
<tr>
<td>
shortName
</td>
<td>
String
</td> </tr>
<tr>
<td>
order
</td>
<td>
Int32
</td> </tr>
<tr>
<td>
controllerType
</td>
<td>
String
</td> </tr>
<tr>
<td>
definition
</td>
<td>
Object
</td> </tr>
<tr>
<td>
canReadRoles
</td>
<td>
Array
</td> </tr>
<tr>
<td>
[0]
</td>
<td>
String
</td> </tr>
<tr>
<td>
canWriteRoles
</td>
<td>
Array
</td> </tr>
<tr>
<td>
[0]
</td>
<td>
String
</td> </tr>
<tr>
<td>
versionNumber
</td>
<td>
Int64
</td> </tr>
<tr>
<td>
createdAt
</td>
<td>
Date
</td> </tr>
<tr>
<td>
lastModified
</td>
<td>
Date
</td> </tr>
<tr>
<td>
createBy
</td>
<td>
String
</td> </tr>
<tr>
<td>
lastModifiedBy
</td>
<td>
String
</td> </tr> </table>
**_Data processing via Apache Zeppelin:_ **
From CRFA data has abeen produced in order to perform data processing and
analysis. This is performed through Apache Zeppelin, which is a collaborative
data analytics and visualization tool for distributes, general-purpose data
processing systems. In CRFA we use it to perform a fine-grained data export.
We also reported the LUMC CTA score function definition and stenosis function
definition, calculated by CRFA, for data export and further processing.
The following formulas refers to some data produced from CRFA, whose values
are function of some of the stored data.
**__Clinical Characteristics_ Page _ **
* Body Mass Index (BMI): function of height and weight
<table>
<tr>
<th>
• BMI Class: function of BMI
</th>
<th>
</th>
<th>
</th>
<th>
</th> </tr>
<tr>
<td>
_**_CTA Report CL Page_ ** _
</td>
<td>
$%
$&
</td>
<td>
ℎ
18.50 ℎ 25.00
</td>
<td>
18.50
24.99
29.99
’ 30.00
</td> </tr>
<tr>
<td>
• TCSore: function of TCS_HU
</td> </tr>
<tr>
<td>
H@
G H@ H@ H@
H@
</td>
<td>
1. 130 G H_I
2. 200 G H_I
3. 300 G H_I
4. G H_I
</td>
<td>
199
299
399
’ 400
</td> </tr> </table>
* RCASore: function of RCA_HU
<table>
<tr>
<th>
</th>
<th>
H@
J KH@ H@ H@
H@
</th>
<th>
1. 130 J K_I
2. 200 J K_I
3. 300 J K_I
4. J K_I
</th>
<th>
199
299
399
’ 400
</th> </tr>
<tr>
<td>
•
</td>
<td>
LMSore: function of LM_HU
</td>
<td>
</td>
<td>
</td> </tr>
<tr>
<td>
</td>
<td>
H@
L H@ H@ H@
H@
</td>
<td>
1. 130 L _I
2. 200 L _I
3. 300 L _I
4. L _I
</td>
<td>
199
299
399
’ 400
</td> </tr>
<tr>
<td>
•
</td>
<td>
LADSore: function of LAD_HU
</td>
<td>
</td>
<td>
</td> </tr>
<tr>
<td>
</td>
<td>
H@
LKMH@ H@ H@
H@
</td>
<td>
1. 130 LKM_I
2. 200 LKM_I
3. 300 LKM_I
4. LKM_I
</td>
<td>
199
299
399
’ 400
</td> </tr>
<tr>
<td>
•
</td>
<td>
LCXSore: function of LCXS_HU
</td>
<td>
</td>
<td>
</td> </tr>
<tr>
<td>
</td>
<td>
H@
L NH@ H@ H@
H@
</td>
<td>
1. 130 L N_I
2. 200 L N_I
3. 300 L N_I
4. L N_I
</td>
<td>
199
299
399
’ 400
</td> </tr> </table>
* Segment Weight Factor (seg_wf): function of System Dominance (sd)
1, ℎ
) * +0, *
, ℎ
* Stenosis Weight Factor 1 and (ste_wf_1): function of % of Lumen Diameter 1 (prox_lumen_1)
)*)1
⎧
⎪⎪1 1 2 ) 3 _1 30% | 30% 1 2 ) 3 _1 50%
⎪1.4 50% 1 2 ) 3 ) 1 70% | 70% 1 2 ) 3 ) 1 90% | 1 2 ) 3 ) 1 8 90%
1.4 _1
⎨ 1.2 1 2 ) 3 ) 1 3 1 &
⎪ 0 ℎ
⎪⎪
⎩
* Stenosis Weight Factor 2 and (ste_wf_2): function of % of Lumen Diameter 2 (prox_lumen_2)
)*)2
⎧
⎪⎪1 1 2 ) 3 _2 30% | 30% 1 2 ) 3 _2 50%
⎪1.4 50% 1 2 ) 3 ) 2 70% | 70% 1 2 ) 3 ) 2 90% | 1 2 ) 3 ) 2 8 90%
1.4 _2
⎨ 1.2 1 2 ) 3 ) 2 3 1 &
⎪ 0 ℎ
⎪⎪
⎩
* Stenosis Weight Factor (ste_wf): function of _ste_wf_1_ and _ste_wf_2_
) * max< ) * ) 1, ) * ) 2=
* Plaque Weight Factor 1 (pla_wf_1): function of Plaque type 1 (prox_type_1)
<table>
<tr>
<th>
1
</th>
<th>
)* ) 1
</th>
<th>
1.2 1 2
⎧ ) >1 1
⎪1.5
1.6 1 2 ) >1 1 2 | 1 2 ) >1 1
⎨ 1.7 1 2 ) >1 1
⎪
⎩0 ℎ
</th>
<th>
1
3
4
</th> </tr> </table>
* Plaque Weight Factor 2 (pla_wf_2): function of Plaque type 2 (prox_type_2)
⎧ 1.2 1 2) >1 2 1
⎪1.5
1 ) * ) 2 1.6 1 2 ) >1 2 2 | 1 2 ) >1 2 3
⎨ ⎪1.7 1 2 ) >1 2 4
⎩0 ℎ
* Plaque Weight Factor (ste_wf): function of _pla_wf_1_ and _pla_wf_2_
1 _ * max< 1 _ * ) 1,1 _ * ) 2=
* Segment Weight Factor (seg_wf), Stenosis Weight Factor (ste_wf), Plaque Weight Factor (pla_wf)
) @ )
* CAD Score (cad_score): function of _seg_score_
CD
@ ) @ A ) @ B
BEF
The following parameters have been produced through Zeppelin and used for data
analysis
* Degree of Max Stenosis (max_stenosis): function of % of Lumen Diameter It is the maximum % of lumen diameter among all plaques for each patient.
* Count of stents (count_stent) : function of stent.
It is the number of total stents for each patient
* Count of plaques (count_plaques): function of plaques. It is the number of total plaques for each patient.
|
https://phaidra.univie.ac.at/o:1140797
|
Horizon 2020
|
1095_SMARTool_689068.md
|
# 1\. Executive Summary
This deliverable provides the SMARTool Data Management Plan (DMP) first
version and describes how the data collected or created within the project
will be handled and which are the standards and the methodology that is
followed for the collection and processing, whether and how these data will be
accessible for re-use and whether it will be shared [1]. SMARTool project, at
its present stage, is foreseen to collect and analyse a series of datasets,
including data from user requirements to the clinical and other patient
related data. Specifically, datasets are to be collected in two ways: (i) the
data collection related to the users' personal information, demographics,
clinical, imaging, molecular, omics and other health related data, (ii) the
collected data through the application of the multivariate analysis, the data
mining and the non-imaging classification algorithm for CAD stratification,
the multiscale and multilevel site specific models for plaque progression and
the non-invasive FFR, the pharmacological therapy modulation algorithms
(EVINCI database) using data mining techniques and the virtual stent
deployment approach. The former dataset will assist in the development of the
SMARTool platform, while the latter will be the actual datasets that are
foreseen to be the main output of the SMARTool platform.
This deliverable consolidates the feedback provided by the SMARTool partners
and the datasets they contribute to.
More specifically, the deliverable was circulated to the project partners
responsible for the different tasks, who defined the dataset descriptions
according to the plans of data collection and analysis as well as the methods
and processes which are followed to ensure adherence with the ethics
requirements. Given that the majority of SMARTool datasets include the data
collection from human participants, the produced and processed data are
handled with caution, following all the ethical and privacy issues involved in
such datasets. In addition, data are not shared or made accessible, if there
is a risk of compromising the participants’ privacy.
This is the initial version of the DMP of SMARTool and the datasets described
represent the reflection of the collected and processed data till M18. These
data will be enriched with more data (collected and processed), therefore
during the evolution of the project the context of the datasets is expected to
be continuously updated. However, the main principles as well the process of
data collection, processing and preservation are expected to be those, as
described in the current document. The deliverable is composed of different
chapters and sub-sections.
**Chapter 2** presents: (i) an overview of the SMARTool project, its overall
vision and objectives, (ii) the main scope of this deliverable including the
objectives of the DMP, (iii) the steps and actions involved in the data
management cycle and, (iv) the background of the SMARTool DMP.
**Chapter 3** presents: (i) the principle of SMARTool FAIR data management,
(ii) the main categories of the project data, (iii) the data access and
storage processes, (iv) the available databases accompanied with the level
roles and permissions, (v) a detailed description of the datasets collected
and processed per WP, the involved partners, the standards and metadata, the
data exploitation and sharing, the archiving and preservation (including
storage and backup) processes.
**Chapter 4** presents the specific methods and tools for ensuring data
security, including the data anonymization, encryption, storage, backups, etc.
The chapter refers also to the ethical considerations related to the
protection of the enrolled patients, the EU legislation and Ethics documents
and experts’ opinions and Ethical Committees, that are taken into
consideration during the lifecycle of SMARTool project.
# 2\. Introduction
A new element that has been included in the Horizon 2020 projects is the DMP
which is related to the data generated in the framework of the project and how
this data will be accessible [2]. The D4.1 – Data Management Plan v1 is the
first version of the DMP of the SMARTool project, which has received funding
by the European Union’s Horizon 2020 Programme under Grant Agreement number:
689068. The DMP is not a fixed process; it is an activity that will evolve
during the lifespan of the SMARTool project. This first version of the DMP
concerns an overview of the produced datasets and the specific conditions that
are related to them.
## About the project
Coronary artery disease (CAD) is a chronic disease with a high prevalence and
epidemic proportions in the middle aged and elderly population, accounting for
about 50% of all deaths [3], [4]. Medical therapy, lifestyle and diet
suggestions and clinical risk factors, including dyslipidemia, hypertension,
diabetes, are among the key factors in CAD patient-specific management.
Currently, an integrated site specific and patient-specific comprehensive
predictive model of plaque progression is lagging. Although several algorithms
have been developed for primary and secondary prevention of CAD, a unified
platform that incorporates all local and systemic risk factors into a
personalized clinical decision support tool for stratification, diagnosis,
prediction and treatment is not available. SMARTool aims at the development of
a Cloud platform for enabling clinical decision support for prevention of CAD
and of associated major adverse cardiovascular events (MACE). This is achieved
through the standardization and integration of health data from heterogeneous
sources and existing patient/artery specific multiscale and multilevel
predictive models. Specifically, the SMARTool models rely on the extension of
the already available multiscale and multilevel ARTreat models for coronary
plaque assessment and progression over time through the utilization of non-
invasive imaging by coronary computed tomography angiography (CCTA) and are
extended with functional site-specific assessment (hemodynamically significant
plaques by noninvasive Fractional Flow Reserve (FFR) computation) and
additional heterogeneous patient-specific non-imaging data, such as patient
history, and lifestyle, exposome, biohumoral data, phenotyping and
genotyping). SMARTool allows:
* **Patient-specific CAD stratification.** Specifically, the already available clinical stratification models (e.g Framingham, Genders, GES - Gene Expression Score) are implemented by patient genotyping and phenotyping (cellular/molecular markers) and examined in retrospective and prospective clinical data (EVINCI project population) towards stratifying the patients to the following categories: nonobstructive CAD, obstructive CAD and those without CAD categories.
* **Site specific plaque progression prediction.** Existing multiscale and multilevel models for plaque progression prediction (ARTreat project) are updated and refined through the incorporation of additional genotyping and phenotyping features and examined by retrospective/ prospective noninvasive CCTA imaging data plus non-imaging patient-specific information (follow-up - EVINCI population).
* **Patient-specific CAD diagnosis and CAD-related CHD treatment** Personalised and patientspecific therapeutical management (eg lifestyle changes, standard or high intensity medical therapy) are provided. Additionally, the interventional cardiologists are able to select the optimal stent type(s) and site(s) for appropriate stent deployment through the utilization of the SMARTool virtual angioplasty tool.
Additionally, the final Clinical Decision Support System (CDSS) includes a
microfluidic lab-on-chip device for blood analysis of cellular/molecular
inflammatory markers. The overall platform will be assessed in the
participating clinical sites.
### _**2.1 Purpose of the SMARTool Data Management Plan** _
The DMP is a cornerstone for ensuring good data management. DMP is related to
the data management life cycle for the data which will be collected, processed
and created during SMARTool project. More specifically, the DMP concerns the
following activities:
* research data management during/ after the project end
* description of the type of the collected, processed and generated data
* definition of standards that are used for data storage, safety and security
* description of the data, which are shared/enable open access
* process of data storage
* assistance in streamlining the whole research process. The DMP provides _a priori_ the required data process and facilities to store data.
The DMP: (i) includes in combination with ethical issues (section 4.2) the
availability of research data, (ii) defines the measures and processes in
order for the data to be properly anonymized and ensure their privacy, and
(iii) concerns the strategy that is followed for the open data, which does not
violate the conditions of the interlinked Research and Innovation projects.
Figure 1 presents the steps and actions involved in the SMARTool data
management cycle.
_**Figure 1:** Overview of the SMARTool data management cycle. _
As far as research data is concerned, SMARTool provides access to this data
through the CRFA platform. The imaging data is stored in the cloud-based
3DnetMedical.com DICOM compliant database from B3D’s UK based ISO27001
accredited datacenter – providing security, redundancy, reliability and
scalability. DICOM studies can be automatically uploaded from PACS systems
through 3Dnet Gateway using 2048 bit encryption, or can be uploaded manually
by the user using SSL encryption (Figure 2).
Non imaging clinical data is securely stored in a NoSQL MongoDB Database,
accessible only via locally installed application server. Data redundancy,
reliability and scalability is provided by the native MongoDB replica-set
feature. MongoDB cluster members communicate (for continuous synchronization)
via encrypted TLS/SSL channels. Data is uploaded via https (secure http)
application interface and can be accessed, from within the project
infrastructure via secure RESTful API (OAuth 2.0, OpenID session token). The
OAuth2 authentication and session token issuing, renewing and revoking, is
provided by a WSO2 Identity Server deployed in the project infrastructure.
Clinical documents are acquired and stored in a Document Repository according
to IHE XDS.b (Cross-Enterprise Document Sharing) and XDS-I.b (Cross-enterprise
Document Sharing for Imaging) standards [5]. Data security and privacy for
clinical documents is provided by implementing the IHE ATNA (Audit Trail and
Node Authentication) [6] and IHE XUA (Cross-Enterprise User Assertion)
profiles [7].
_**Figure 2:** Uploading DICOM studies to 3DnetMedical.com. _
The purpose of the clinical data collection/generation in relation to the
objectives of the project are:
* To optimise the learning stage of SMARTool in order to extract and select discriminative markers (from any data source, ranging from history, genetics, circulating molecules and coronary anatomy by CT scan) of CAD severity/progression/association with MACE.
* To provide a "smart" data storage to the final CDSS which is the main outcome of the project, adequate for: (i) the external validation of the selected panel of markers by external cohorts, and (ii) CDSS application and exploitation in clinics.
### _**2.2 Background of the SMARTool Data Management Plan** _
SMARToool DMP will be in accordance with the following articles of the Grant
Agreement:
* **Article 36 - Confidentiality.** During the project duration and for four years after the period set out in Article 3, the Consortium will keep confidential any data, documents or other relevant material that is defined as confidential.
* **Article 39.2 - Processing of personal data.** All personal data are processed in compliance with the applicable EU and national law on data protection. The Consortium provides adequate information and explanation to the personnel whose personal data are collected and processed and provide them with the specific privacy statement.
# 3\. SMARTool Data Management Plan
## 3.1. FAIR Data
The SMARTool Consortium will be the end-responsible for the DMP and will make
the research and clinical data findable, accessible, interoperable and
reusable (FAIR), towards ensuring appropriate data management [8]. The
research data management is not an objective itself, but it is rather the way
to achieve knowledge discovery and innovation, and to accomplish data
extension, integration and reuse. More specifically, the FAIR principles will
promote the ability of machines to automatically discover and use the data, to
support its reuse by individuals (Figure 3).
_**Figure 3:** SMARTool FAIR data. _
To accomplish this, the data are available through well-defined APIs and the
CRFA web-based user interface. The developed within the project software
tools, which are used to create and process the data could be made available
under the open source Apache 2.0 license, whenever possible. To support the
FAIR principles, the following practices are followed in the SMARTool
platform.
* Data discoverability and metadata provision
* Unique identifiers support persistent for a long time
* Data naming conventions
* Keywords search
* Clear versioning approach
* Standards for metadata: Documentation for future metadata will be created
Τhe collected, processed and generated clinical and research data will be
preserved and stored in a specific format so as to ensure long-time
accessibility. To avoid file format obsolescence and avoid the risk of missing
useful information, specific actions are followed, such as the specific file
format selection with a high chance of being usable in the future.
In addition, it is foreseen that in the next update of this deliverable the
following issues will be addressed: Specify/update the data which will be
open available. In case of closed data a rationale will be provided
* Specify/update how the data and associated metadata, documentation and code will be stored Specify/update the access rights and restrictions
To support the data interoperability, specific actions are followed. In order
to acquire data from the legacy data sources, a specific HL7 compliant
integration layer using clinical data semantics is designed [9]. HL7
integration layer is written using HAPI (HL7 application programming
interface). The IHE Cross-Enterprise Document Sharing (XDS) Integration
Profile is adopted to allow the registration, distribution and access across
health enterprises of patient electronic health data. In any single Hospital,
the HIS (Hospital Information System) provides the enterprise specific patient
ID as well as the historical demographic data. For Patient Medical Records the
CDA Release 2 HL7 Standard (Clinical Reports) is used according to the
IHE Cardiology Technical Framework (Volume 1 (CARD TF-1): Integration Profiles
[10] and Volume 2 (CARD TF-2): Transactions [11]). For Genomics data exchange
we will adhere to HL7 IG CG_GENO, R1
Version 3 Genotype, Release1 - –January 2009, HL7 IG LOINCGENVA, R1 Version 2
Implementation
Guide: Clinical Genomics; Fully LOINC-Qualified Genetic Variation Model,
Release 1. For medical images, DICOM standard is adopted. For the refined
models, standards Μarkup language (ML) format is used; ML for the multiscale
and multilevel model, Predictive Model Markup language (PMML) for the data
mining and stratification models, Portable Format for Analytics (PFA), which
is an emerging standard for statistical models [12]. All the aforementioned
standards and protocols ensure the required security in information exchange
and anonymization as well as interoperability.
To support the data re-use the following aspects will be taken into
consideration:
* Specific data will be licensed to permit its possible re-use
* The period of data embargo will be defined for the data which is available for re-use The access to the data by third parties after the end of the project will be defined The data quality assurance processes will be defined.
* The length of time for which the data is re-usable will be defined
## 3.2. Datasets to be gathered and processed
The data of SMARTool project can be categorised in the following classes:
**Cat1.** Collected Data: Data that has not been subjected to quality
assurance or control
**Cat2** . Validated Collected Data: Data that has been assessed in terms of
completeness, correctness, integrity, credibility
**Cat3** . Analyzed Collected/ Generated Data: Data which has been validated,
analysed and processed
_**Table 1** : Overview of data sets. _
<table>
<tr>
<th>
**Dataset**
</th>
<th>
**Related WP**
</th>
<th>
**Brief description**
</th> </tr>
<tr>
<td>
Dataset 1
</td>
<td>
WP1, WP2, WP3
</td>
<td>
This dataset contains the SMARTool users' personal information, demographics,
clinical, imaging, molecular, omics and other health related data.
</td> </tr>
<tr>
<td>
Dataset 2
</td>
<td>
WP3
</td>
<td>
This dataset contains the CAD stratification which is extracted through the
application of data mining techniques
</td> </tr>
<tr>
<td>
Dataset 3
</td>
<td>
WP4
</td>
<td>
This dataset contains the results of the multiscale and multilevel site
specific models for plaque progression and the non-invasive FFR to be used in
the prognostic and diagnostic CDSS
</td> </tr>
<tr>
<td>
Dataset 4
</td>
<td>
WP4
</td>
<td>
This dataset contains the results of the pharmacological therapy modulation
algorithms (EVINCI database) using data mining techniques. In addition, data
from the virtual stent deployment approach are also included.
</td> </tr>
<tr>
<td>
Dataset 5
</td>
<td>
WP5
</td>
<td>
This dataset contains information on users’ requirements, use cases,
questionnaires, specifications and system architecture.
</td> </tr>
<tr>
<td>
Dataset 6
</td>
<td>
WP6
</td>
<td>
The dataset contains the results of the research performed in SMARTool project
which are communicated through public deliverables, journal and conference
presentations, as well as other dissemination channels (website, social media,
etc).
</td> </tr>
<tr>
<td>
Dataset 7
</td>
<td>
WP7
</td>
<td>
This dataset contains information related to the project management and
coordination.
</td> </tr> </table>
## 3.3. Data Access Procedures
**Public (PU).** For the data that will be available in the public, the
project web page [13] will provide a description of the dataset and will allow
the user to download the relevant file.
**Protected Data (PR).** Data indicated as protected may be communicated out
of the consortium, as long as the interested parties ask for access from the
SMARTool consortium, after explaining and providing evidence on how this data
will be utilised, for instance for research or commercial purposes.
**Confidential/Private (CO).** Data which are denoted as private/confidential
will be stored at a specific space, namely the databases, to which only
selected partners will have access. In order for other SMARTool partners to
have access to these data, a proper written application to the partner
responsible for the data storage will be provided, accompanied with a
justification of the need for access.
## 3.4. Data Storage
During the lifespan of the project, the data will be collected and
systematically stored in a complex repository based on the following three
components:
* Imaging data is stored in cloud-based 3DnetMedical.com DICOM compliant repository. 3DnetMedical.com conforms to the DICOM SOP’s of the Storage Service Class at Level 2 (Full). 3DnetMedical.com offers Vendor Neutral Archive (VNA) functionalities from a UK based ISO27001 accredited datacentre – providing security, redundancy, reliability and scalability through onshore outsourcing. 3DnetMedical.com follows specific privacy and security-conscious policies applicable to all of its information handling practices. The 3Dnet Dicom store is employed also for genomic data storage, following the Dicom.
* Structured clinical data (data acquired during the project for the trial population as well as data employed by the final platform) are stored in the MongoDB database;
* Clinical data acquired by means of HL7/IHE integrations with hospital sources are stored in an XDS/XDSi repository
User data (such as usernames and application privileges) will be managed by a
platform's Identity Server (WSO2IS) and stored in the LDAP server embedded in
the Identity Server.
### **3.4.1. Databases**
**3.4.1.1 Level roles and permissions in databases**
To easily manage the permissions in the databases, several roles have been
defined in the following three groups of roles; Administrator, WP leaders and
Researchers, Users (Table 2, Table 3, Table 4).
_**Table 2:** Level roles and permissions in the 3DnetMedical.com DICOM
compliant Database. _
<table>
<tr>
<th>
**Description of permissions**
</th>
<th>
**Administrator**
</th>
<th>
**WP leaders & Researchers **
</th>
<th>
**User**
</th> </tr>
<tr>
<td>
Manage user accounts
</td>
<td>
</td>
<td>
</td>
<td>
</td> </tr>
<tr>
<td>
Manage user roles and access
</td>
<td>
</td>
<td>
</td>
<td>
</td> </tr>
<tr>
<td>
Manage folders, worklists and gateways.
</td>
<td>
</td>
<td>
</td>
<td>
</td> </tr>
<tr>
<td>
Upload studies
</td>
<td>
</td>
<td>
</td>
<td>
</td> </tr>
<tr>
<td>
Delete or assign a study*
</td>
<td>
</td>
<td>
</td>
<td>
</td> </tr>
<tr>
<td>
Visualisation and manipulation of imaging data from studies in accessible
worklists and folders*
</td>
<td>
</td>
<td>
</td>
<td>
</td> </tr>
<tr>
<td>
Download data*
</td>
<td>
</td>
<td>
</td>
<td>
</td> </tr>
<tr>
<td>
Report a study*
</td>
<td>
</td>
<td>
</td>
<td>
</td> </tr>
<tr>
<td>
Access to patient information*
</td>
<td>
</td>
<td>
</td>
<td>
</td> </tr> </table>
*Depending on the user role
_**Table 3** : Level roles and permissions in the CRFA NoSQL MongoDB Database.
_
<table>
<tr>
<th>
**Description of permissions**
</th>
<th>
**Administrator**
</th>
<th>
**WP leaders & Researchers **
</th>
<th>
**User**
</th> </tr>
<tr>
<td>
Download anonymized non imaging clinical data
</td>
<td>
</td>
<td>
</td>
<td>
</td> </tr>
<tr>
<td>
Visualisation and editing of non-imaging data from studies
</td>
<td>
</td>
<td>
</td>
<td>
</td> </tr>
<tr>
<td>
Upload non imaging data for studies
</td>
<td>
</td>
<td>
</td>
<td>
</td> </tr>
<tr>
<td>
Delete and report a study*
</td>
<td>
</td>
<td>
</td>
<td>
</td> </tr>
<tr>
<td>
Access to patient information*
</td>
<td>
</td>
<td>
</td>
<td>
</td> </tr> </table>
_**Table 4:** Level roles and permissions in the WSO2IS embedded LDAP User
Database. _
<table>
<tr>
<th>
**Description of permissions**
</th>
<th>
**Administrator**
</th>
<th>
**WP leaders & Researchers **
</th>
<th>
**User**
</th> </tr>
<tr>
<td>
Manage user accounts
</td>
<td>
</td>
<td>
</td>
<td>
</td> </tr>
<tr>
<td>
Manage user roles and access
</td>
<td>
</td>
<td>
</td>
<td>
</td> </tr>
<tr>
<td>
Manage DB, Identity Server, Integration Bus and
Application Servers
</td>
<td>
</td>
<td>
</td>
<td>
</td> </tr> </table>
**3.4.1.2 Process**
The 3DnetMedical.com conforms to DICOM SOP’s of the Storage Service Class at
Level 2 (Full). The DICOM Information Model is derived from the DICOM Model of
the Real World where the DICOM view of the Real-World that identifies the
relevant Real-World Objects and their relationships within the scope of the
DICOM Standard is shown. It provides a common framework to ensure consistency
between the various Information Objects defined by the DICOM Standard.
Clinical documents will be made available to the production SMARTool platform
by means of IHE XDS.b (Cross-Enterprise Document Sharing) and XDS-I.b (Cross-
enterprise Document Sharing for Imaging) integration profiles. An XDS Affinity
Domain will be defined for the platform, in order to ease clinical information
sharing with the clinical organizations that will use the platform. The
platform itself will act as a Document Consumer to query/retrieve documents
containing data used to model the patient, and as a Document Producer for
registering the documents generated by the platform.
Non imaging data will be collected directly during or after a patient's visit
via the platform's data entry application (CRFA). This data will be stored in
the SMARTool MongoDB database for processing and kept for future encounters
with the patient. In order to ease data entry activities, when non imaging
data is available in clinical documents stored in XDS.b repository the data
entry application (CRFA) will pre-fill retrievable data in the corresponding
form fields for user review. Non imaging data retention and clinical document
retention will be subject to the clinical organization policy.
### **3.4.2. Datasets**
**3.4.2.1 Origin of WP1, WP2 and WP3 datasets**
_**Table 5:** Details of Dataset 1. _
<table>
<tr>
<th>
**Data identification: Dataset 1**
</th> </tr>
<tr>
<td>
**Description** This dataset contains the SMARTool users' personal
information, demographics, clinical, imaging, molecular, omics and other
health related data.
More specifically, the following categories of data are also included:
* Blood tests/ Biohumoral
* Imaging - CTA scan visual/quantitative analysis: plaque composition (calcified, mixed, noncalcified) and features , nominal categories
* Circulating soluble proteins: consolidated biomarkers (hsTN, hsCRP, BNP, ALT) and inflammatory markers (IL6, IL 10, ICAM 1, VCAM, e-.selectin), values of blood concentration.
* Genetics: associated selected SNPs, selected RNA genes from bioinformatics analysis of DNA/RNA sequencing.
* Lipids: selected lipid species and concentrations in blood; names and values of plasma concentration from bioinformatics analysis.
* Circulating MN surface proteins to quantify Mon1 Mon 2 and Mon 3 subpopulations: relative and absolute concentrations in blood
</td> </tr>
<tr>
<td>
Category (Cat1, Cat2, Cat3)
</td>
<td>
Cat1, Cat2
</td> </tr>
<tr>
<td>
**Partners activities and responsibilities**
</td> </tr>
<tr>
<td>
Partner owner of the data
</td>
<td>
Clinical partners
</td> </tr>
<tr>
<td>
Partner in charge of the data analysis
</td>
<td>
CNR, LUMC, ALACRIS
</td> </tr>
<tr>
<td>
Partner in charge of the data storage
</td>
<td>
B3D, EHIT
</td> </tr>
<tr>
<td>
Related WP(s) and task(s)
</td>
<td>
WP1
Task 1.1 Clinical and imaging (CCTA) data
collection (eg Evinci baseline data)
Task 1.2 CCTA analysis under standardized criteria
WP2
Task 2.1 Biohumoral data collection and analysis at baseline and at follow-up
Task 2.2 Patient-specific phenotyping (cellular and molecular data) at
baseline and at follow-up
WP3
Task 3.1 Genomics and transcriptomics
Task 3.2 Omics data analysis
</td> </tr>
<tr>
<td>
**Standards and metadata**
</td> </tr>
<tr>
<td>
Standards, format, estimated volume of data
</td>
<td>
RAW, DICOM, XLM, JSON, BSON
Clinical data will not contain any metadata.
Volume: To be calculated at the end of patient data collection
</td> </tr>
<tr>
<td>
**Data exploitation and sharing**
</td> </tr>
<tr>
<td>
Data access policy/ Dissemination level: confidential (only for members of the
Consortium and the Commission Services) or Public
</td>
<td>
Confidential
</td> </tr>
<tr>
<td>
Data sharing, re-use, distribution, publication (How?)
</td>
<td>
Internal validation of discriminative markers by bootstrapping techniques
</td> </tr>
<tr>
<td>
Personal data protection: are they personal data?
If so, have you gained (written) consent from data subjects to collect this
information?
</td>
<td>
Personal protection according to the project ethical and legal guidelines
Written consent received.
</td> </tr>
<tr>
<td>
**Archiving and preservation (including storage and backup)**
</td> </tr>
<tr>
<td>
Data storage (including backup): where? For how long?
</td>
<td>
3DnetMedical.com DICOM compliant database
NoSQL MongoDB Database
Long term archival
</td> </tr> </table>
**Methodologies for data collection.**
For the follow-up CT acquisition, in order to achieve optimal quality for the
3D reconstruction of the desired arterial segments, the 128/256/320 MSCT
scanners are used with interval between the slices not exceeding the threshold
of 0.5 mm. For the elimination of motion or other artifacts in the acquired
images, heart rate during scan should be less than 65 beats/min and optimally
less than 60 beats/min. Nitroglycerin prior to the CTA acquisition is
administered. Also, multiple cardiac phases are captured to choose different
phases for different coronary segments, if needed. The reconstructed field of
view is reduced to maximize the number of pixels devoted to depiction of the
heart, usually a field of view of 200–250mm for coronary CTA studies of native
coronary arteries.
For the blood sampling ( _samples to be sent to CNR_ ) the responsible
clinical partners collect for each enrolled patient during venipuncture:
* N= 8/5 ml EDTA tubes (VIOLET tube)
* N=2/5ml Li-heparin tubes (GREEN tube)
* N= 2/5ml Clot activator tubes (RED tube)
During blood sampling, details are recorded such as:
* Samples collected (blood [which tubes, how many and in which order]).
* Date and time of blood sampling
* Last time of food/drink consumption and smoking. Special attention for 12h fasting and 12h refraining from smoking.
The separation of blood aliquots is described below:
### For the VIOLET tubes: EDTA N=8 tubes
N=6 tubes/8
Ø EDTA-Plasma is separated by centrifugation at +4°C in a refrigerated
centrifuge, 10 minutes at 1500xg. The plasma is separated from cell pellet
within 1 hour from the withdrawing, keeping the vial over this time in an ice
bath.
Samples are subdivided in aliquots about 1 ml each (18 aliquots), by using
small plastic vials (VIOLET CAPS) and keeping all the vials in an ice-bath
during the whole procedure. N=2 tubes/8
Ø EDTA-Whole-Blood tubes are stored at -20°C or -80°C without centrifugation
and NOT aliquoted.
### For the RED tubes: Clot Activator N=2 tubes
Ø Serum samples are separated by centrifugation, for 10 minutes at 1500xg.
Samples are subdivided in aliquots about 1 ml each (6 aliquots), by using
small plastic vials (RED CAPS) and keeping all the vials in an ice-bath during
the whole procedure.
### For the GREEN tubes: Li-HEPARIN N=2 tubes
Ø Heparin-Plasma are separated by centrifugation, for 10 minutes at 1500xg.
Samples should be subdivided in aliquots about 1 ml each (6 aliquots), by
using small plastic vials (GREEN CAPS) and keeping all the vials in an ice-
bath during the whole procedure.
### For the TEMPUS tubes: N=3 tubes
* Ø TEMPUS tubes (obtained by CNR) are stored at -20°C without centrifugation and NOT aliquoted For the blood storage, upon arrival in the lab, samples are maintained at room temperature (18-22 °C) for 2-4 hours before transferring to freezer, in an upright position. TEMPUS tubes can be left for up to72 hours at room temperature prior to freezing. The TEMPUS tubes should then be frozen at -20 °C upright until freezing and storage at -20°C or -80°C (if available) until shipping.
The blood samples are packaged appropriately with sufficient dry ice or ensure
that samples do not thaw and packaged in a manner that prevents breakage or
leakage. For mandatory safety reasons the blood samples are tested for
* HIV
* HEPATITIS B
* HEPATITIS C
_**Table 6:** Details of Dataset 2. _
<table>
<tr>
<th>
**Data identification: Dataset 2**
</th> </tr>
<tr>
<td>
**Description:** This dataset contains the CAD stratification which is
extracted through the application of data mining techniques
</td> </tr>
<tr>
<td>
Category (Cat1, Cat2, Cat3)
</td>
<td>
Cat3
</td> </tr>
<tr>
<td>
**Partners activities and responsibilities**
</td> </tr>
<tr>
<td>
Partner owner of the data
</td>
<td>
Clinical partners
</td> </tr>
<tr>
<td>
Partner in charge of the data analysis
</td>
<td>
FORTH
</td> </tr>
<tr>
<td>
Partner in charge of the data storage
</td>
<td>
EHIT
</td> </tr>
<tr>
<td>
Related WP(s) and task(s)
</td>
<td>
WP3
Task 3.4 Multivariate analysis, data mining and non-imaging classification
algorithm for CAD stratification
</td> </tr>
<tr>
<td>
**Standards and metadata**
</td> </tr>
<tr>
<td>
Standards, format, estimated volume of data
</td>
<td>
RAW, JSON, ML,XML, PMML, PFA
Volume: ~5GB
</td> </tr>
<tr>
<td>
**Data exploitation and sharing**
</td> </tr>
<tr>
<td>
Data access policy/ Dissemination level: confidential (only for members of the
Consortium and the Commission Services) or Public
</td>
<td>
Confidential
</td> </tr>
<tr>
<td>
Data sharing, re-use, distribution, publication (How?)
</td>
<td>
No data sharing, Only the data mining and nonimaging classification algorithm
will be public available in peer review journals and conferences
</td> </tr>
<tr>
<td>
Personal data protection: are they personal data?
If so, have you gained (written) consent from data subjects to collect this
information?
</td>
<td>
Personal data protection according to the project ethical and legal guidelines
</td> </tr>
<tr>
<td>
**Archiving and preservation (including storage and backup)**
</td> </tr>
<tr>
<td>
Data storage (including backup): where? For how long?
</td>
<td>
NoSQL MongoDB Database
Long term archival
</td> </tr> </table>
**3.4.2.2 Origin of WP4 datasets**
_**Table 7:** Details of Dataset 3. _
<table>
<tr>
<th>
**Data identification: Dataset 3**
</th> </tr>
<tr>
<td>
**Description:** This dataset contains the results of the multiscale and
multilevel site specific models for plaque progression and the non-invasive
FFR to be used in the prognostic and diagnostic CDSS
</td> </tr>
<tr>
<td>
Category (Cat1, Cat2, Cat3)
</td>
<td>
Cat3
</td> </tr>
<tr>
<td>
**Partners activities and responsibilities**
</td> </tr>
<tr>
<td>
Partner owner of the data
</td>
<td>
Clinical partners
</td> </tr>
<tr>
<td>
Partner in charge of the data analysis
</td>
<td>
FORTH
</td> </tr>
<tr>
<td>
Partner in charge of the data storage
</td>
<td>
EHIT
</td> </tr>
<tr>
<td>
Related WP(s) and task(s)
</td>
<td>
WP4
Task 4.1 Prognostic CDSS: Refinement of multiscale and multilevel site
specific models for plaque progression
Task 4.2 Diagnostic CDSS: Refinement of noninvasive FFR computation
Task 4.4 Validation of prognostic CDSS (Multiscale-multilevel site specific
plaque progression models)
Task 4.5 Validation of diagnostic CDSS (3D artery reconstruction, plaque
characterization and noninvasive FFR
</td> </tr>
<tr>
<td>
**Standards and metadata**
</td> </tr>
<tr>
<td>
Standards, format, estimated volume of data
</td>
<td>
DICOM, RAW, XLM, STL, IGES
A data volume estimation will be later provided.
</td> </tr>
<tr>
<td>
**Data exploitation and sharing**
</td> </tr>
<tr>
<td>
Data access policy / Dissemination level: confidential (only for members of
the Consortium and the Commission Services) or Public
</td>
<td>
Confidential
</td> </tr>
<tr>
<td>
Data sharing, re-use, distribution, publication (How?)
</td>
<td>
No data sharing. Only the results from the plaque progression and the non-
invasive FFR will be public available in peer review journals and conferences
</td> </tr>
<tr>
<td>
Personal data protection: are they personal data? If so, have you gained
(written) consent from data subjects to collect this information?
</td>
<td>
Personal data protection according to the project ethical and legal guidelines
</td> </tr>
<tr>
<td>
**Archiving and preservation (including storage and backup)**
</td> </tr>
<tr>
<td>
Data storage (including backup): where? For how long?
</td>
<td>
NoSQL MongoDB Database
Long term archival
</td> </tr> </table>
_**Table 8:** Details of Dataset 4. _
<table>
<tr>
<th>
**Data identification: Dataset 4**
</th> </tr>
<tr>
<td>
**Description:** This dataset contains the results of the pharmacological
therapy modulation algorithms (EVINCI database) using data mining techniques.
In addition, data from the virtual stent deployment approach are also
included.
</td> </tr>
<tr>
<td>
Category (Cat1, Cat2, Cat3)
</td>
<td>
Cat3
</td> </tr>
<tr>
<td>
**Partners activities and responsibilities**
</td> </tr>
<tr>
<td>
Partner owner of the data
</td>
<td>
Clinical partners
</td> </tr>
<tr>
<td>
Partner in charge of the data analysis
</td>
<td>
FINK, FORTH
</td> </tr>
<tr>
<td>
Partner in charge of the data storage
</td>
<td>
B3D, EHIT, FINK
</td> </tr>
<tr>
<td>
Related WP(s) and task(s)
</td>
<td>
WP4
Task 4.3 Treatment CDSS: Refinement of medical therapy and virtual angioplasty
decision support methods
Task 4.6 Validation of treatment CDSS
(Pharmacological therapy and virtual angioplasty tool)
</td> </tr>
<tr>
<td>
**Standards and metadata**
</td> </tr>
<tr>
<td>
Standards, format, estimated volume of data
</td>
<td>
DICOM, STL, DAT, UNV, LST, BMP, AVI
A data volume estimation will be later provided.
</td> </tr>
<tr>
<td>
**Data exploitation and sharing**
</td> </tr>
<tr>
<td>
Data access policy / Dissemination level: confidential (only for members of
the Consortium and the Commission Services) or Public
</td>
<td>
Confidential, Public only some specific results
</td> </tr>
<tr>
<td>
Data sharing, re-use, distribution, publication (How?)
</td>
<td>
Data sharing, publication in peer review journal
</td> </tr>
<tr>
<td>
Personal data protection: are they personal data?
If so, have you gained (written) consent from data subjects to collect this
information?
</td>
<td>
Personal data protection according to the project ethical and legal guidelines
</td> </tr>
<tr>
<td>
**Archiving and preservation (including storage and backup)**
</td> </tr>
<tr>
<td>
Data storage (including backup): where? For how long?
</td>
<td>
B3D, FINK
</td> </tr> </table>
**3.4.2.3 Origin of WP5 datasets**
_**Table 9:** Details of Dataset 5. _
<table>
<tr>
<th>
**Data Identification: Dataset 5**
</th> </tr>
<tr>
<td>
Description: This dataset contains information related with T5.1 regarding
users’ requirements, use cases, questionnaires, functional specifications and
system architecture for the SMARTool Platform.
</td> </tr>
<tr>
<td>
Category (Cat1, Cat2, Cat3)
</td>
<td>
Cat 3
</td> </tr>
<tr>
<td>
Partners activities and responsibilities
</td> </tr>
<tr>
<td>
Partner owner of the data
</td>
<td>
Technical partners
</td> </tr>
<tr>
<td>
Partner in charge of the data analysis
</td>
<td>
B3D, FORTH, FINK, EHIT
</td> </tr>
<tr>
<td>
Partner in charge of the data storage
</td>
<td>
B3D
</td> </tr>
<tr>
<td>
Related WP(s) and task(s)
</td>
<td>
WP5
Task 5.1 User requirements, functional specifications and architecture of the
SMARTool platform
</td> </tr>
<tr>
<td>
Standards and metadata
</td> </tr>
<tr>
<td>
Standards, format, estimated volume of data
</td>
<td>
RAW, XML, UML, PDF, CSV
Volume~500ΜΒ
</td> </tr>
<tr>
<td>
Data exploitation and sharing
</td> </tr>
<tr>
<td>
Data access policy / Dissemination level: confidential (only for members of
the Consortium and the Commission Services) or Public
</td>
<td>
Confidential
</td> </tr>
<tr>
<td>
Data sharing, re-use, distribution, publication (How?)
</td>
<td>
No data sharing.
</td> </tr>
<tr>
<td>
Personal data protection: are they personal data?
If so, have you gained (written) consent from data subjects to collect this
information?
</td>
<td>
This dataset does not include personal data. Questionnaires are anonymized.
</td> </tr>
<tr>
<td>
Archiving and preservation (including storage and backup)
</td> </tr>
<tr>
<td>
Data storage (including backup): where? For how long?
</td>
<td>
Data will be available through D5.1 and its annexes. As WP leader B3D will
store and backup the data.
</td> </tr> </table>
**3.4.2.4 Origin of WP6 datasets**
Each SMARTool partner should disseminate its results in accordance with
Article 29 of the Grant Agreement. The results of the research performed in
SMARTool project are communicated through public deliverables, journal and
conference presentations, as well as other dissemination channels (website,
social media, etc). On the other side, the exploitation is mainly achieved
through the main SMARTool exploitable products:
* SMARTool 3DNet™-framework: a common, centralized shareable platform that provides a better control, management and streamlining of CAD clinical workflow based on CDSS.
* SMARTool CDSS: an integrated system for supporting the clinicians in diagnosis, prognosis and treatment of CAD patients and subjects at risk.
* SMARTool Point-Of-Care-Testing (POCT), a portable device for on chip blood analysis and patient phenotyping exploitable in diagnostic CDSS.
**3.4.2.5 Origin of WP7 datasets**
_**Table 10:** Details of Dataset 7. _
<table>
<tr>
<th>
**Data identification: Dataset 7.1**
</th> </tr>
<tr>
<td>
Description: This dataset contains information related to the project
management and coordination
</td> </tr>
<tr>
<td>
Category (Cat1, Cat2, Cat3)
</td>
<td>
Cat 1, Cat 2, Cat 3 (by the Commission)
</td> </tr>
<tr>
<td>
Partners activities and responsibilities
</td> </tr>
<tr>
<td>
Partner owner of the data
</td>
<td>
Joint Ownership
</td> </tr>
<tr>
<td>
Partner in charge of the data analysis
</td>
<td>
CNR
</td> </tr>
<tr>
<td>
Partner in charge of the data storage
</td>
<td>
CNR
</td> </tr>
<tr>
<td>
Related WP(s) and task(s)
</td>
<td>
WP7
T7.1 Project Management
</td> </tr>
<tr>
<td>
Standards and metadata
</td> </tr>
<tr>
<td>
Standards, format, estimated volume of data
</td>
<td>
No specific standards for this data.
Files will be in Microsoft Office in doc, .docx, .xls, .xlsx, and .pdf
formats. Volume~1GB
</td> </tr>
<tr>
<td>
Data exploitation and sharing
</td> </tr>
<tr>
<td>
Data access policy/ Dissemination level: confidential (only for members of the
Consortium and the
Commission Services) or Public
</td>
<td>
Confidential: information shared only among the
Consortium and between the Consortium and the Commission
</td> </tr>
<tr>
<td>
Data sharing, re-use, distribution, publication (How?)
</td>
<td>
Effort (quantified in person months – PMs) and financial data of each partner
is collected by CNR and compiled for monitoring purposes.
Data is entered/uploaded in the EC system SyGMa in order to allow the
Commission to oversee and assess use of the resources by the consortium.
</td> </tr>
<tr>
<td>
Personal data protection: are they personal data?
If so, have you gained (written) consent from data subjects to collect this
information?
</td>
<td>
No personal data collected
</td> </tr>
<tr>
<td>
Archiving and preservation (including storage and backup)
</td> </tr>
<tr>
<td>
Data storage (including backup): where? For how long?
</td>
<td>
The data is collected for internal use in the project.
Standard daily offsite backup on CNR systems. Length:
10 years
</td> </tr> </table>
# 4\. Data security and ethical considerations
## 4.1. Data security
The SMARTool project utilises and adopts specific methods and tools for
ensuring adequate field access and extended contact and communication between
the participants, who are sensitive in the activities that are related to
ethical concerns and research activities.
The following instructions will be used to ensure data security: Perform
anonymization of personal data
* Encrypt data, if necessary by the local researchers
* Data storage in two separate locations for avoiding data loss
* Perform frequent backups (every 24 hours)
* Ensure the final dataset coherence by marking up files in a structured way
SMARTool databases for imaging and for non-imaging clinical data are hosted in
B3D’s secure data centre, collocated in dedicated spaces at top-tier
datacentre accredited withISO 27001 for Information Security Management, ISO
9001 for Quality Management, ISO 14001 for Environment Management. This
facility provides carrier-level support, including:
* Access control and physical security
* Environmental controls
* Power
* Network
* Fire detection and suppression
* Network protection
* Disaster Recovery
* Security Monitoring
B3D’s Information Governance has the purpose to ensure: (i) the patient
information confidentiality, and (ii) that the adherence to information
governance is built into the design of the 3DnetMedical service and derived
products provided to healthcare professionals. Information governance and
security underpins 3DnetMedical and, as an organisation, B3D strives to
achieve excellence in the services provided.
B3D operates under ISO 13485:2012 (quality management system). B3D complies
with ISO 62304 (medical software development) and incorporates ISO 14971 (risk
analysis) into product life cycle. All B3D products and operations conform to
industry standards including CE Annex II of directive 93/42/EEC, DICOM, HL7
and IHE.
SMARTool non imaging clinical data and clinical documents will be encrypted at
rest by means of a transparent filesystem encryption strategy with AES-256
symmetric key cypher. Disk encryption keys will be managed in a keystore
(vaultproject.io), and will be encrypted themselves with a master key which
will be separate from the data and the databases.
Network traffic carrying non imaging data will be encrypted via TLS/SSL
(HTTPS), with public key certificates issued and renewed by a root certificate
authority.
EHIT is an ISO 13485:2013 and ISO/IEC 27001 certified organization. This
ensures consistent information security management and product development
life cycle. Moreover, EHIT is certified for 21 IHE integration profiles.
## 4.2. Ethical issues
SMARTool will support the protection of the enrolled patients and make their
rights more visible by adopting the National Laws and EU Legislation and be in
compliance with the Directives as described in the following section.
**EU legislation and Ethics documents**
The Directives, Ethics Documents and experts’ opinions and Ethical Committees
are presented in the following table accompanied with a short reference note
on the general matter as well as the stated principles. All the European Union
countries of SMARTool adopted the European Directives. Additionally, the
partners of the SMARTool project follow the European Charter for researchers.
_**Table 11:** Ethical considerations in SMARTool project. _
<table>
<tr>
<th>
**Directive 1995/46/EC of the European Parliament (October 1995):**
</th> </tr>
<tr>
<td>
Protection of individuals related to personal data processing and free
movement. It concerns the protection for the individuals’ privacy and the free
movement of personal data within the European Union (EU). The Directive
defines specific criteria for the collection and utilisation of personal data.
Furthermore, the Directive stresses the obligation of each Member State to set
up an independent national authority for the monitoring of the Directive
application.
</td> </tr>
<tr>
<td>
**Charter of Fundamental Rights of the EU that became legally binding on the
EU institutions and the national governments on 2009 with the entry into force
of the Treaty of Lisbon**
</td> </tr>
<tr>
<td>
The Charter includes the rights and freedoms in terms of Dignity, Freedoms,
Equality, Solidarity, Citizens' Rights, and Justice.
</td> </tr>
<tr>
<td>
**Opinion of the European Group on Ethics in Science and New Technologies No.
13 (July 1999)**
</td> </tr>
<tr>
<td>
Protection of recognisable personal health data.
</td> </tr>
<tr>
<td>
**Opinion of the European Group on Ethics in Science and New Technologies No.
26 (February 2012)**
</td> </tr>
<tr>
<td>
Ethics of information and communication technologies.
</td> </tr>
<tr>
<td>
**Opinion of the European Group on Ethics in Science and New Technologies No.
29 (13 October 2015)**
</td> </tr>
<tr>
<td>
Examination of the principal health technologies and definition of a set of
recommendations for the EU and nationallevel policymakers, industry and other
stakeholders, towards maximising the benefits and minimise the issues
associated with new health technologies and citizen participation in health
policy, research and practice
</td> </tr>
<tr>
<td>
**Directive 2001/20/EC of the European Parliament (April 2001)**
</td> </tr>
<tr>
<td>
Performance of clinical trials and medicine under good clinical practice.
</td> </tr>
<tr>
<td>
**Directive 2005/28/EC or Good Clinical Practice Directive of the European
Parliament and of the Council(8**
**April 2005)**
</td> </tr>
<tr>
<td>
Presents the principles and detailed guidelines for good clinical practice
with regard to investigational medicinal products for human use, as well as
the requirements for authorisation of the manufacturing of such products.
</td> </tr>
<tr>
<td>
**Universal Declaration on the human genome and human rights adopted by UNESCO
(1997)**
</td> </tr>
<tr>
<td>
Refers to national and regional legislation on medicine, privacy and genetic
research.
</td> </tr>
<tr>
<td>
**Clinical Trials Regulation (CTR) EU No 536/2014**
</td> </tr>
<tr>
<td>
Ensures that the rules for conducting clinical trials are the same throughout
the EU. It is imperative to ensure that all Member States, in authorising and
supervising the conduct of a clinical trial, are based on identical rules.
</td> </tr>
<tr>
<td>
**Italy**
</td>
<td>
A number of ministerial decrees cover this area. A key one is Legislative
Decree no.211 of June 24, 2003 “Transposition of Directive 2001/20/EC relating
to the implementation of good clinical practice in the conduct of clinical
trials on medicinal products for clinical use”.
</td> </tr>
<tr>
<td>
Law n.675 of 31 December 1996 Tutela delle persone e di altri soggetti
rispetto al trattamento dei dati personali (published on the G.U. n.5 of8
January 1996;
</td> </tr>
<tr>
<td>
C. M. n. 6 of 2 September 2002, Attività dei comitati etici istituiti ai sensi
del decreto ministeriale 18 marzo 1998. (published on the G.U. n. 214 of 12
September 2002)
</td> </tr>
<tr>
<td>
**Finland**
</td>
<td>
Medical Research Act (L 488/1999);
</td> </tr>
<tr>
<td>
Act of the Medical Use of Human Organs and Tissues (L 101/2001),
</td> </tr>
<tr>
<td>
Act on the Status and Rights of Patients (L785/1992),
</td> </tr>
<tr>
<td>
Personal Data Act (L 523/1999);
</td> </tr>
<tr>
<td>
Good Clinical Practice (GCP) guidelines in accordance with the International
</td> </tr>
<tr>
<td>
Conference on Harmonization (www.ich.org)
</td> </tr>
<tr>
<td>
**Switzerland**
</td>
<td>
Loi fédérale sur les médicaments et les dispositifs médicaux 812.21 du 15
décembre 2000 (Chapitre 4 Section 2 Essais cliniques, Section 4 Obligation de
garder le secret et la communication de donées)
</td> </tr>
<tr>
<td>
Loi fédérale sur l’analyse génétique humaine 810.12 du 8 octobre 2004 (Section
2; Art. 5 Consentement, Art. 7 Protection des données génétiques, Art. 20.
Réutilisation du matériel biologique)
</td> </tr>
<tr>
<td>
Local: Approval by the IRB Bundesgesetz 812.21 über Arzneimittel und
Medizinprodukte (Heilmittelgesetz, HMG) as of Dezember 15, 2000
</td> </tr>
<tr>
<td>
Verordnung 812.214.2 über klinische Versuche mit Heilmitteln (VKlin) as of
October 17, 2001
</td> </tr>
<tr>
<td>
Patientinnen- und Patientengesetz 813.13 as of April 5, 2004
</td> </tr>
<tr>
<td>
Heilmittelverordnung (HMV) 812.1 as of May 21, 2008
</td> </tr>
<tr>
<td>
**UK**
</td>
<td>
Integrated Research Application Systems (IRAS)
</td> </tr>
<tr>
<td>
National Research Ethics Service (NRES)
</td> </tr>
<tr>
<td>
Department of Health’s Research Governance Framework for Health and Social
Care (2nd
Edition, 2005)
</td> </tr>
<tr>
<td>
**France**
</td>
<td>
Public Health Code (articles L. 1121-1 et seq.).6
</td> </tr>
<tr>
<td>
The bioethics law of 2004 creating the French Biomedicine Agency
</td> </tr>
<tr>
<td>
The Advisory Committee on the Treatment of Research Information in the Health
</td> </tr>
<tr>
<td>
Field was created by a law n°94-548 1 July 1994
</td> </tr>
<tr>
<td>
**Germany**
</td>
<td>
Das Deutsche Referenzzentrum für Ethik in den Biowissenschaften - DRZE
</td> </tr>
<tr>
<td>
Deutscher Ethikrat
</td> </tr>
<tr>
<td>
Büro für Technikfolgen-Abschätzung beim Deutschen Bundestag - TAB
</td> </tr>
<tr>
<td>
**Spain**
</td>
<td>
Comités de Ética en Investigación Clínica, CEIC
</td> </tr>
<tr>
<td>
Royal Decree 561/1993
</td> </tr>
<tr>
<td>
Royal Decree 223/2004
</td> </tr>
<tr>
<td>
Law of Biomedical Research (2007)
</td> </tr>
<tr>
<td>
Asociación Nacional de Comités de Ética de la Investigación, ANCEI
</td> </tr>
<tr>
<td>
**Poland**
</td>
<td>
First Polish Code of Ethics (1977)
</td> </tr>
<tr>
<td>
Extraordinary National Assembly of Delegates of the Polish Society of Medicine
in Szczecin on 22 June 1984
</td> </tr>
<tr>
<td>
Code of Medical Ethics (1991)
</td> </tr>
<tr>
<td>
First Polish Research Ethics Committee (1979)
</td> </tr>
<tr>
<td>
Bioethics Committee at the Ministry of Health
</td> </tr>
<tr>
<td>
Zbiór zasad i wytycznych pt. "Dobre obyczaje w nauce"
</td> </tr>
<tr>
<td>
April 7, 2005 Order of the Minister of Health concerning the nature and extent
of inspection of clinical trials. Legislation Journal of the Republic of
Poland (2005) 69: pos. 623
</td> </tr>
<tr>
<td>
January 3, 2007 Order of the Minister of Health concerning the application
form for authorization of clinical trials, payments for authorization and
final report of the clinical trial Legislation Journal of the Republic of
Poland (2010) 222: pos. 1453
</td> </tr>
<tr>
<td>
February 11, 2011 Order of the Minister of Health concerning requirements
related with the basic documentation of clinical trials. Legislation Journal
of the Republic of Poland (2011) 40: pos. 210
</td> </tr>
<tr>
<td>
May 12, 2012 Order of the Minister of Health concerning Good Clinical Practice
Legislation Journal of the Republic of Poland (2012) 0: pos. 489
</td> </tr>
<tr>
<td>
**Netherlands**
</td>
<td>
Personal Data Protection Act of 6 july 2000 (Wet bescherming persoonsgegevens)
</td> </tr> </table>
|
https://phaidra.univie.ac.at/o:1140797
|
Horizon 2020
|
1096_SIM4NEXUS_689150.md
|
Executive Summary
This document presents Data Management Plan (DMP) on open access data handling
(see box 1) by SIM4NEXUS. The aim is to consider the many aspects of data
management, data and metadata generation, data preservation- maintenance- and
analysis, whilst ensuring that data is well managed at present and prepared
for preservation in the future. This Data Management Plan is compiled
according to the _Guidelines on FAIR Data Management in H2020_ 1 , and the
Guidelines to the Rules on the _Open_ _Access to Scientific Publications and
Open Data Access to Research Data in H2020_ 2 .
Thus, the sections below present the lifecycle, Box 1: Open Access
responsibilities and review processes and
<table>
<tr>
<th>
management policies of research data, produced by SIM4NEXUS. The DMP reflects
the status of discussion within the Consortium on data to be produced. It is a
_Living Document_ with iterations (M12, M30 and M48) as SIM4NEXUS evolves. An
updated version of this document will be delivered together with each
reporting period, and whenever significant changes related to DMP would occur.
This document is the update of the second release of
</th>
<th>
Open access (OA) refers to the practice of providing online access to
scientific information that is free of charge to the end-user and
reusable. 'Scientific' refers to all academic disciplines. In the context of
research &
innovation, 'scientific information' can mean:
(1) peer-reviewed scientific research articles
(published in scholarly journals) or (2) research data (data underlying
publications, curated and
raw data)
</th> </tr> </table>
the DMP. In the future, it will be delivered together with the progress
reports as it provides an overview of available research data, access and the
data management terms of use at this stage.
For SIM4NEXUS, the DMP is defined as “the development, execution and
supervision of plans, policies, programmes and practices that control,
protect, deliver and enhance the value of data and information assets”
obtained. In this regard, at the start of the project, the following processes
and procedures for data management procedures are established:
* Data governance, such as standards management and guidelines
* Data architecture, analysis, and design including data modelling
* Data maintenance, administration, and data mapping across building blocks and solution modules
* Data security management including data access, archiving, privacy, and security
* Data quality management including query management, data integrity, data quality, and quality assurance
* Reference and master data management including data integration, external data transfer, master data management, reference data
* Document, record, and content management
* Metadata management, i.e., metadata definition, discovery, publishing, metrics, and standardization.
Readers are the project members and research institutions using the data
collected and produced during the project period.
Changes with respect to the DoA
No changes with respect to the DoA
Dissemination and uptake
The deliverable is publicly available, based on the participation of SIM4NEXUS
to the Pilot on Open Research Data in Horizon 2020 3 . Special attention
will be paid to how personal data will be properly catered together with other
important data and/or scientific information.
Short Summary of results (<250 words)
As SIM4NEXUS participates in the Pilot on Open Research Data in Horizon 2020,
it is required to submit in the first 6 months of the project the Data
Management Plan. This document aims to improve and maximise access and re-use
of research data generated by project actions. Participating in the Open
Research Data Pilot does not necessarily mean opening up all research data. In
a sense, the document determines and explains which of the research data
generated and/or collected will be made open. Several iterations of this
document will be released as project evolves.
Evidence of accomplishment
The deliverable itself can act as the evidence of accomplishment. Also,
communication (Teleconferences, emails) between EURECAT, EPSILON and the
project Coordinator (WUR) can be revealed as evidence.
1. Introduction
1.1 Scope
This document describes the SIM4NEXUS Data Management Plan (DMP, see box) that
corresponds to Deliverable D4.2 of the SIM4NEXUS Technical Annex. The DMP:
* Provides a description of how the research data collected, processed, and generated will be handled during and after the SIM4NEXUS.
* Describes which standards and methodology for data collection and generation will be followed, how data will be shared and be curated and preserved.
The document follows the template provided by the European Commission on DMP
4 . The DMP is prerequisite for SIM4NEXUS as it participates in the Open
Research Data Pilot 5 , thus first version was delivered at an early stage
of the project (Month 6).
An updated version of this document has been, and will be provided together
with the first two progress reports (M12, M30) and whenever significant
Box 2: Data Management Plan
A Data Management Plan (DMP) is a _key element_ of good data management; it
describes the data management life cycle for the data to be collected,
processed, and generated by a Horizon 2020 project.
As part of making research data findable, accessible, interoperable, and re-
usable (FAIR), a DMP should include information on: (i) the
handling of research data during and after the end of the project, (ii) what
data will be
collected, processed, and generated, (iii) which methodology and standards
will be applied, (iv) whether data will be shared/made open access, and (v)
how data will be curated and preserved (including after the end of the
project). A DMP is required for all projects
participating in the extended ORD pilot unless they opt out of the ORD pilot;
however,
projects that opt are encouraged to submit a DMP on voluntary basis.
changes occur. At month 48 the final version will be provided as Appendix to
deliverable D4.7 Data Management Report.
1.2 Structure of the document
DMP deliverable is organized as follows:
* Section 1 is the introductory chapter, which provides the scope of the deliverable
* Section 2 presents the key questions that DMP addresses as tailored for SIM4NEXUS
* Section 3 contains information on digital data sets generated or collected in SIM4NEXUS for each Work Package. It will be updated per reporting period until the end of the project
* Section 4 contains a data summary for all Work Package data sets. It will be updated per reporting period until the end of the project
* Section 5 contains information of FAIR data for SIM4NEXUS and will be updated as the project evolves
* Sections 6 address issues related to data security & ethical aspects
* Section 7 answers FAIR data key questions related to all datasets produced or gathered in.
2. Background on SIM4NEXUS DMP
1. General
<table>
<tr>
<th>
Box 3: Research Data
</th> </tr> </table>
<table>
<tr>
<th>
data that derives new scientific findings is stable. This does not imply, that
data currently being unused is useless, as it can be of high value in future.
Prerequisite for meaningful use, re-use or recombination of data is to be well
documented according to accepted and trusted standards. These standards form a
key pillar of science because they enable the recognition of suitable data. To
ensure this, agreements on standards, quality level and sharing practices are
to be discussed. Strategies should be fixed to preserve and store data over a
</th>
<th>
Research data Refers to information, facts or numbers, collected to be
examined and
considered as a basis for reasoning, discussion,
or calculation. In a research context, examples of data include statistics,
results of experiments, measurements, observations resulting from
fieldwork, survey results, interview recordings
and images. The focus is on research data that is available in digital form.
Users can normally access, mine, exploit, reproduce and
disseminate openly accessible research data free of charge (Figure 1: Open
Access and
dissemination of scientific data (EC, 2017)).
</th> </tr> </table>
It is a well-known phenomenon that data is increasing exponentially, while the
use and re-use of defined period to ensure its availability and reusability
after the end of SIM4NEXUS. Based on the EU guidelines and because SIM4NEXUS
utilizes data for various pilots, the H2020 Programme requires for such
projects to participate in the Open Research Data Pilot 6 (ORD Pilot).
Figure 1: Open Access and dissemination of scientific data (EC, 2017)
2. Why is a Data Management Plan needed?
SIM4NEXUS participates in the Open Access and the Open Research Data Pilot of
the European Research Council (ERC). The DMP specifies the implementation of
the pilot for: data generated and collected, standards in use, workflow to
make data accessible for use, reuse and verification by the community, and
definition of a strategy of curation and preservation of the data. Therefore,
we refer to the SIM4NEXUS Grant Agreement (GA), Article 29.3 on “Open Access
to research data”:
_'Regarding the digital research data generated in the action (‘data’), the
beneficiaries must: (a) deposit in a research data repository and take
measures to make it possible for third parties to access, mine, exploit,
reproduce, and disseminate - free of charge for any user - the following: (i)
the data, including associated metadata, needed to validate the results
presented in scientific publications as soon as possible; (ii) other data,
including associated metadata, as specified and within the deadlines laid down
in the data management plan;_
_(b) provide information - via the repository - about tools and instruments at
the disposal of the beneficiaries and necessary for validating the results
(and - where possible – provide the tools and instruments themselves)._
The data management policy described in this document reflects the current
state of consortium agreements 7 on data management. Collecting data sets on
environmental systems at scale can be an expensive and time-consuming process.
By making a large part of them available, this project will make a long-
lasting and significant contribution to the research and industrial
communities.
All datasets will be released in open formats (e.g., JSON, XML) respecting
available standards, with proper documentation supporting their use by other
researches. Since we will engage end-users in the development of the SIM4NEXUS
platform, it is imperative to carefully address data management issues
connected to the use of external data; whist we will also deal with highly
sensitive data, which may be confidential. In the development of the SIM4NEXUS
platform we will explicitly deal with security issues from the technical
perspective and in the DMP, delivered in WP4 with security and privacy issues
arranged from the management perspective.
The following categories of data are considered within the project:
* personal status (age, gender, etc.),
* socio-economic (city of residence, social status, marital status, and income category), • social network, and
* domain related.
Data will be stored in a project database developed by EPSILON and managed by
the project coordinator. All data and especially personal data will be
securely stored in a project server. The final server specifications and the
system architecture may change during the execution of the project. However, a
first definition made by EURECAT and DHI (Serious Game needs) can be checked
in “D4.3 Game land systems requirements”. EPSILON is the responsible partner
to provide the cloud server, which will be scalable when required in terms of
processing power and storage space. SIM4NEXUS will use cloud server solution
for maximum performance (e.g. quick access for the user to the content, quick
processing and communication with SIM4NEXUS services/modules).
Project participants will have secured web access to the previously anonymized
data, which will have been automatically checked for consistency, homogeneity
and completeness, whist manual audits will be performed as per the standard
operation procedures previously defined.
After project completion, and in case of no objection by project partners and
user, anonymization is preserved (i.e., a user cannot be identified from their
data) and data may be published in an Open Data portal (e.g., _http://open-
data.europa.eu_ ) for future research always consistent with exploitation and
Intellectual Property Rights (IPR) requirements.
3. Who is responsible for the implementation of the
DMP?
The responsible partner is EPSILON, co-Leader in WP4, led by EURECAT, though
all WP Leaders and coLeaders shall be involved in the compliance of the DMP.
The partners agreed to deliver datasets and metadata produced or collected in
SIM4NEXUS according to the signed GA (Article 29.3) which is in line with
rules described in the DMP. The Project and the Scientific Officer are also
central players in the implementation of the DMP and will track the compliance
of the rules agreed.
4. What kind of data will be affected by DMP?
The main purpose of a DMP is to describe Research Data with the metadata
attached to make them discoverable, accessible, assessable, usable beyond the
original purpose and exchangeable between researchers.
Thus, SIM4NEXUS focuses more on the production process and tools than on
production of research or observation data and so the amount of Research Data
which SIM4NEXUS intend to produce is limited, at least at this stage of the
project.
As already presented the Open Research Data Pilot applies to two types of
data:
1 “the data, including associated metadata, needed to validate the results
presented in scientific publications as soon as possible.”
1\. “other data, including associated metadata, as specified and within the
deadlines laid down in the data management plan–that is, per the individual
judgement by each project.”
According to the “Guidelines on Data Management in Horizon 2020” (2015) the
DMP describes the handling of _numerical datasets_ processed or collected
during SIM4NEXUS lifetime.
_“Research data refers to information, in particular facts or numbers,
collected to be examined and considered and as a basis for reasoning,
discussion, or calculation. In a research context, examples of data include
statistics, results of experiments, measurements, observations resulting from
fieldwork, survey results, interview recordings and images. The focus is on
research data that is available in digital form.”_
The DMP includes clear descriptions and rationale for the access regimes that
are foreseen for collected data sets. Therefore, the DMP leaves explicitly
open the handling, use and curation of products like tools, software and
written documents. Thus, we restrict the focus of our DMP to numerical data
products like produced model data or observation data.
3 Digital data sets generated or collected in SIM4NEXUS
The intention of the DMP is to present data management plans of the Work
Packages. The information listed below reflects the conception and design of
the individual Work Packages at the beginning of the project, updated
accordingly with current status. As the operational phase of the project
started in June 2016, the data collection and generation is at a very first
state and is rather restricted to the ongoing fast track for the Sardinia
area. The objective of the fast track aims to:
1. Identify difficulties related to datasets collection from the different stakeholders and projects to run models and have a comprehensive view of all nexus components in the area
2. Identify the obstacle and find solution to harmonize data at level of scale and spatial distribution
Thus, the data register will deliver information according to Annex 1 of the
H2020 guidelines (2015):
1. Data set reference and name: Identifier for the data set to be produced.
2. Data set description: Descriptions of data that will be generated or collected, its origin (in case it is collected), nature and scale and to whom it could be useful, and whether it underpins a scientific publication. Information on the existence (or not) of similar data and the possibilities for integration and reuse.
3. Standards and metadata: Reference to existing suitable standards, in case they do not exist, an outline on how and what metadata will be created.
4. Data sharing: Description of how data will be shared, including access procedures, embargo periods (if any), outlines of technical mechanisms for dissemination and necessary software and other tools for enabling re-use, and definition of whether access will be widely open or restricted to specific groups. Identification of the repository where data will be stored, if already existing and identified, indicating the type of repository (institutional, standard repository for the discipline, etc.). In case the dataset cannot be shared, the reasons for this should be mentioned (e.g. ethical, rules of personal data, intellectual property, commercial, privacy-related, security-related).
5. Archiving and preservation (including storage and backup): Description of the procedures that will be put in place for long-term preservation of the data. Indication of how long the data should be preserved, what is its approximated end volume, what the associated costs are and how these are planned to be covered.
FAIR data of the ORD Pilot, stands as an acronym for Findable, Accessible,
Interoperable, and Reused data. The following table presents the template to
be used to report datasets related to each WP. Table 1. WPXX datasets
<table>
<tr>
<th>
WPXX – Dataset YY
</th>
<th>
Description
</th> </tr>
<tr>
<td>
Data set reference and name
</td>
<td>
</td> </tr>
<tr>
<td>
Data set description
</td>
<td>
</td> </tr>
<tr>
<td>
Standards and metadata
</td>
<td>
</td> </tr>
<tr>
<td>
Data sharing
</td>
<td>
</td> </tr>
<tr>
<td>
Archiving and Preservation
(including storage and back-up)
</td>
<td>
</td> </tr>
<tr>
<td>
Reported by
</td>
<td>
(WP leader) / (WP co-leader)
</td> </tr> </table>
EPSILON is responsible to communicate with the WP Leaders and co-Leaders and
collect the required information; assigned people for reporting and updating
the above template are shown in Table 2.
Assign persons.
Table 2. Assign persons
<table>
<tr>
<th>
WP
</th>
<th>
Assigned person
</th> </tr>
<tr>
<td>
WP1
</td>
<td>
Chrysi Laspidou (WP leader) / Mark Howells (WP co-leader)
</td> </tr>
<tr>
<td>
WP2
</td>
<td>
Maria Witmer (WP leader) / Janez Sušnik (WP co-leader)
</td> </tr>
<tr>
<td>
WP3
</td>
<td>
Dragan Savic (WP leader) / Maria Blanco (WP co-leader)
</td> </tr>
<tr>
<td>
WP4
</td>
<td>
Xavier Domingo (WP leader) / Marc Bonazountas (WP co-leader)
</td> </tr>
<tr>
<td>
WP5
</td>
<td>
Floor Brouwer (WP leader) / Maïté Fournier (WP co-leader)
</td> </tr>
<tr>
<td>
WP6
</td>
<td>
Alexandre Bredimas (WP leader) / Chengzi Chew (WP co-leader)
</td> </tr>
<tr>
<td>
WP7
</td>
<td>
Guido Schmidt (WP leader) / Frank Wechsung (WP co-leader)
</td> </tr>
<tr>
<td>
WP8
</td>
<td>
George Beers (WP leader)
</td> </tr> </table>
The time plan (Figure 2: DMP Time plan) for the next 6 months asks to consider
input from both the Sardinia pilot case and from each WP. As soon as
simulations are completed more input will become available in terms of data
and data ontologies that will be stored in the database and semantic
repository. Additionally, bilateral teleconferences will take place between
EPSILON and WP Leaders are regularly discussing in available and created data
and information and terms of use (i.e. whether data will be publicly available
or not).
3.1 Data set reference and name
Data set reference and naming will be implemented to employ a standard
identification mechanism for each data set according the metadata standard
implemented. Zenodo 8 (a popular repository for research data, will be
extensively exploited throughout the project) assigns all publicly available
uploads a Digital Object Identifier (DOI) to make the upload easily and
uniquely citeable. Zenodo supports harvesting of all content via the OAI-PMH
protocol 9 .
3.2 Data set description
Data sets related to the project’s DMP: (i) data sets referred to project
publications (deliverables and papers) (ii) curated and/or raw data collected
produced during the project.
SIM4NEXUS data sets collection and production is mainly linked to WP3 applying
the thematic models (i.e. E3ME-FTT, MAGNET, CAPRI, IMAGE-GLOBIO, OSeMOSYS,
SWIM MAgPIE) selected for the project within the individual Case Studies (as
specified in the Task 3.3 of WP3) to realise a partial simulation of the Nexus
components under different scenarios, feeding into the development of the
Serious Game. Based on the testing, the partners will then collect and
organize the data into a semantic database that houses the complexity science
tools (WP1). It will also review and select the most suitable integration
methodologies for the Case Studies and the Serious Game (for WP4). Thereby,
integrated complexity science models will be developed for all the Case
Studies. These complexity science models will then be used to run many
scenarios.
Thus, SIM4NEXUS produces raw data with some parts summarized in deliverables
and scientific publications. This raw data, underpinning the published work,
constitute the main research data sets that will be made publicly available if
the WP leaders give their permission. In cases where release of complete raw
data sets is impossible due to, for example, privacy or personal data concerns
(such as packet traces involving networking usage of trial participants), we
will strive to find _data sanitation_ and _anonymization_ approaches that
enable publishing as large parts of the data as possible. Any scripts used for
post-processing the raw data will also be shared.
_Data layer_
The data layer is responsible for storing the SIM4NEXUS data according to
different strategies:
* As files: For example, raster data can be stored in raster files formats such as JPEG, TIFF or GeoTiff. Vector data can be stored in various formats as ESRI Shapefile, MapInfo, DXF, etc.
* Within a database: This is managed by a (spatial) database management system such as Oracle Spatial, ESRI ARCSDE or Postgres/PostGIS. Spatial Databases are used for higher data volumes or when data must be accessed and updated by many users, when security policy is required, when complex spatial and non-spatial operation must be performed on the database and when the data must be integrated with non-spatial data.
3.3 Standards and Metadata
As mentioned data will be shared in relation to (i) publications (deliverables
and papers) and (ii) curated and/or raw data. For the data linked to
scientific publication, the publication will serve as the main piece of
metadata documentation for the shared data. When this is not seen as being
adequate for the comprehension of the raw data, a report will be shared along
with the data explaining their meaning and methods of acquisition. However,
for both data categories the metadata standard structure of Zenodo repository.
_For Public Availability of Data,_ data will be shared when the related
deliverable or paper or data set has been made available at an open access
(OA) repository from the responsible partner/owner of the data. It is expected
that data related to a publication will be openly shared. However, to allow
the exploitation of any opportunities arising from the raw data and tools,
data sharing will proceed only if all co-authors of the related publication
agree. The Lead author is responsible for getting approvals and then sharing
the data and metadata on Zenodo. The Lead Author will also create an entry on
OpenAIRE 10 to link the publication to the data. OpenAIRE is a service built
to offer this functionality and may be used to reference both the publication
and the data. A link to the OpenAIRE entry will then be submitted to the
SIM4NEXUS Website Administrator (FT) by the Lead Author.
In view of the precautions for protection of personal data, it is explicitly
confirmed that the data collected will be publicly available, after care is
taken with regards to rules of confidentiality, anonymity, and protection.
Anonymized final data sets will be open access and procedures are set as to
how data will be preserved and archived in the repository. We are aware of
post-publication risks to local researchers and end-users in our research
sites and will mitigate all reasonable risk before publication according to
the ethical and IPR requirements set.
However, “Opting Out” remains a choice for data owners, as it is possible that
even though comprehensive measures are taken to ensure the safety of
participants, researchers and their environment, it is only after a SIM4NEXUS
report or peer reviewed article is published and generation of date sets is
realized, that the question of open access arises. Open access does not entail
an absolute obligation to publish all data, and it is up to researchers and
associated organization to decide whether data is suitable and ethical to be
published or not.
3.4 Archiving and Preservation
To ensure archiving and preservation of long-tail research data during the
project, a repository with a web catalogue service will be built and
maintained after the project competition. The Web Catalogue Services provides
the system with a smarter interface to the SIM4NEXUS repository (geo-
database). There are many technologies that can be exploited and adopted to
perform this function.
3.4.1 Web catalogue service description
The CSW interface is based on the OGC™ Catalog Services Specification Version
2.0.2. The interaction between a client and a server is accomplished using a
standard request-response model of the HTTP protocol. That is, a client sends
a request to the server using HTTP, and expects to receive a response to the
request or an exception message.
Repository service access is based upon the HTTP protocol with client and
server requests and responses using XML or JSON. Client applications can use
this interface for executing service repository queries and receiving service
repository metadata results. Basically, the essential purpose of a Catalog
Service is to enable a user to locate, access, and make use of resources in an
open, distributed system by providing facilities for retrieving, storing, and
managing many kinds of resource descriptions.
The metadata repository managed by the catalog can store a multitude of
resource descriptions that conform to any standard Internet media type, as:
* XML schemas
* Audio annotations
* Specification documents
* Style sheets for generating detailed topographic maps.
Furthermore, arbitrary relationships among catalog items can be expressed by
creating links between any two resource descriptions. For example, a service
offer may be associated with descriptions of the data sets that can be
acquired using the service. A catalogue can function as a stand-alone service
or it can interact with other affiliated catalogues within a federation that
spans multiple administrative domains. The federation then effectively
enlarges the total search space within which resource descriptions may be
discovered.
When a catalog is linked to a peer catalog, it makes the resource descriptions
managed by the peer implicitly available to its own clients. Each catalog
client connects to a single catalog service as its main point of contact with
the federation. This is the agent node; the propagation of request messages to
neighboring nodes is invisible to the client. It is not necessary to know
where the metadata repositories are located or how they are accessed.
The CSW catalogue profile is intended to provide a flexible, general-purpose
catalogue service that can be adapted to meet the needs of diverse communities
of practice within the geospatial domain. In the SIM4NEXUS framework we try to
respect the following rules:
1. Communicate information adopting standard protocols (e.g. XML/JSON, OGC standards, etc.).
2. Try to adopt a solution that allows for the maximum interoperability among actors who will process the data stored.
3. State of the art technologies that will be used in the context of the Web Catalogue Service include:
* pyCSW ( _http://pycsw.org/_ )
* GeoNetwork ( _http://geonetwork-opensource.org/_ )
* Micka ( _http://micka.bnhelp.cz/_ )
* CKAN ( _https://ckan.org/_ )
pyCSW
The pyCSW is OGC compliant python implementation that permits clients to make
searches on metadata. The CSW interface is based upon the OGC™ Catalog
Services Specification Version 2.0.2. The interaction between a client and a
server is accomplished using a standard request-response model of the HTTP
protocol.
GeoNetwork
GeoNetwork open source is a standard based and decentralized spatial
information management system, designed to enable access to geo-referenced
databases and cartographic products from a variety of data providers through
descriptive metadata, enhancing the spatial information exchange and sharing
between organizations and their audience, using the capacities and the power
of the Internet. The system provides a broad community of users with easy and
timely access to available spatial data and thematic maps from
multidisciplinary sources, that may in the end support informed decision
making. The main goal of the software is to increase collaboration within and
between organizations for reducing duplication and enhancing information
consistency and quality and to improve the accessibility of a wide variety of
geographic information along with the associated information, organized and
documented in a standard and consistent way. Main Features:
* Instant search on local and distributed geospatial catalogues
* Uploading and downloading of data, documents, PDF’s, and any other content
* An interactive Web map viewer that combines Web Map Services from distributed servers around the world
* Online map layout generation and export in PDF format
* Online editing of metadata with a powerful template system
* Scheduled harvesting and synchronization of metadata between distributed catalogues
* Groups and user’s management
* Fine grained access control
From technical point of view, GeoNetwork has been developed following the
principles of a Free and Open Source Software (FOSS) and is based on
International and Open Standards for services and protocols, like the ISO-
TC211 and the Open Geospatial Consortium (OGC™) specifications. The
architecture is largely compatible with the OGC™ Portal Reference
Architecture, i.e. the OGC™ guide for implementing standardized geospatial
portals. Indeed, the structure relies on the same three main modules
identified by the OGC Portal Reference Architecture that are focused on
spatial data, metadata, and interactive map visualization. The system:
* Is fully compliant with the OGC™ specifications for querying and retrieving information from Web Catalog (CS-W)
* It supports the most common standards to specifically describe geographic data (ISO19139 and FGDC) and the international standard for general documents (Dublin Core)
* It uses standards (OGS WMS) also for visualizing maps through the Internet
A common use case is the harvesting of geospatial data in a shared
environment. In fact, within the geographic information environment, the
increased collaboration between data providers and their efforts to reduce
duplication have stimulated the development of tools and systems to
significantly improve the information sharing and guarantee an easier and
quicker access of data from a variety of sources without undermining the
ownership of the information.
The harvesting functionality in GeoNetwork is a mechanism of data collection
in perfect accordance with both rights to data access and data ownership
protection. Through the harvesting functionality it is possible to collect
public information from the different GeoNetwork nodes installed around the
world and to copy and store periodically this information locally. In this
way, a user from a single-entry point can get information also from
distributed catalogues.
MICKA
MICKA is a meta-information catalogue that fully complies with the ISO 19115
standard and is fully compliant with the INSPIRE principles. It can be
integrated with map applications. It is multilingual. The web catalogue
service uses OGC specifications.
MICKA is a complex system for metadata management used for building Spatial
Data Infrastructure (SDI) and geo-portal solutions. It contains tools for
editing and management of metadata for spatial information, web services and
other sources (documents, web sites, etc.). It includes online metadata search
engine, portrayal of spatial information and download of spatial data to local
computer.
MICKA is compatible with obligatory standards for European SDI building
(INSPIRE). Therefore, it is ready to be connected with other nodes of prepared
networked metadata catalogues (its compatibility with pilot European geo-
portal is continuously being tested). Functions include:
* Spatial data metadata (ISO 19115)
* Spatial services metadata (ISO 19119)
* Dublin Core metadata (ISO 15836)
* Feature catalogue support (ISO 19110)
* OGC CSW 2.0.2 support (catalogue service)
* User defined metadata profiles
* INSPIRE metadata profile
* Web interface for metadata editing
Multilingual (both user interface and metadata records). Currently 16
languages are supported. It is possible to dynamically extend the system for
other languages. Context help is multilingual and import from the following
metadata formats is supported:
* ESRI ArcCatalog
* ISO 19139
* OGC services (WMS, WFS, WCS, CSW)
* Feature catalogue XML
* Export – ISO 19139, GeoRSS • Support of thesauri and gazetteers.
* Display of changes with GeoRSS
Template base interface with possibilities to change according to user
requirements is available with possibility in deep cooperation with any map
clients for display of on-line map services.
MICKA stores metadata in a relational database and it is edited by dynamically
generated forms. Therefore, it is possible to amend other standards or
profiles. It is possible to switch between profiles while editing. Individual
profiles can be distributed into sections. With the help of control elements,
it is possible to duplicate individual items, select from code lists or
connects to supporting applications. Checking of mandatory items is enabled
while editing.
The MICKA integrated application is divided into 3 independent components:
* Metadata creation
* Metadata importing
* Metadata Management
CKAN
CKAN is a powerful data management system that makes data accessible – by
providing tools to streamline publishing, sharing, finding, and using data. It
is aimed at data publishers (national and regional governments, companies, and
organizations) wanting to make their data open and available CKAN implements
several of the administrative services that are described in the data
management plan and provides both an attractive end-user client as well as a
Web Service API.
CKAN is built with Python on the backend and JavaScript on the frontend, and
uses The Pylons web framework and SQLAlchemy as its ORM. Its database engine
is PostgreSQL and its search is powered by SOLR. It has a modular architecture
that allows extensions to be developed to provide additional features such as
harvesting or data upload.
CKAN uses its internal model to store metadata about the different records,
and presents it on a web interface that allows users to browse and search this
metadata. It also offers a powerful API that allows third-party applications
and services to be built
3.4.2 Zenodo Repository
After the project completion of the project, the final dataset will be
transferred to the Zenodo repository which ensures sustainable archiving of
the final research data sets and publications produced.
Zenodo is built and developed by researchers, in the context of The OpenAIRE
project, that in the vanguard of the open access and open data movements in
Europe, commissioned by the EC to support their nascent Open Data policy by
providing a catch-all repository for EC funded research. One of its mayor
advantages is it works closely with GitHub, enabling users to make the work
they share on GitHub 11 citable by archiving one of your GitHub repositories
and assigning a DOI with the data archiving tool Zenodo.
4 Data Summary – Specifics
This section must be understood as a living section that will be further
updated in future iteration as the project evolves, and more input in terms of
data and date reporting comes from the SIM4NEXUS partners. The purpose of this
section is to provide an executive summary of the different SIM4NEXUS data
addressing the following issues:
Provide a summary addressing the following issues:
* State the purpose of the data collection/generation
* Explain the relation to the objectives of the project
* Specify the types and formats of data generated/collected
* Specify if existing data is being re-used (if any)
* Specify the origin of the data
* State the expected size of the data (if known)
* Outline the data utility: to whom will it be useful
* Defines how this data is going to be accessible both for internal or public use
4.1 System Dynamics models data sets
EPSILON has implemented the SIM4NEXUS repository (currently using Dropbox
infrastructure) for the efficient management of the SIM4NEXUS datasets. The
service supports cloud storage, file sharing and synchronization and client
software. The file organization structure adopted in the context of the
project, supports the easy identification and revisions of the datasets
provided by each partner. For each of the SIM4NEXUS cases, a specific folder
has been created in the file hosting service. The folder contains the
following sub-folders: 01-ModelData, 02-ArbitraryData, 03-ThematicData,
04-ClimateData. The 1 st folder provides the outputs from the selected
models applied in each case study. The file type of the outputs is either
Microsoft Excel Open XML Spreadsheet (.XLSX File Extension) or Commas
Separated Values files (.CSV File Extension). The sub-folder 02-ArbitraryData
contains information about the relevant case Study. These include the
conceptual model of the case, the concept harmonization process etc. The 3 rd
sub-folder contains the thematic datasets of each case study along with its
metadata. Finally, the sub-folder 04-ClimateData contains the climate datasets
such as precipitation, relative humidity, long-wave downward solar radiation
at the ground, long-wave downward solar radiation at the ground, daily maximum
air temperature, daily minimum air temperature, and wind speed at 10m height.
The datasets in this folder are of a generic file type with .DAT file
extension. Each dataset of this type may contain data in binary or text
format. A standardised name has been assigned to each dataset in the following
format:
_Country code_Earth System Model_Simulation Method_Period_Time frequency.dat_
In this way, all the necessary elements of each dataset such as the way that
each dataset has been produced (i.e. model and simulation methods), the
addressed area, and the duration and time frequency, are provided. Each
subfolder in the file hosting service contains files with descriptive
information about the available datasets.
At the moment, none of this datasets are intended for public availability, as
they are only useful in the context of building System Dynamics Models, and
specific for the different scenarios of the case studies (only Sardinia so
far).
It has been decided internally that only people involved on each case study
will have access to the specific case study folder. This is a default initial
status, which can be revoked if the Case Study leader considers it is
convenient. The access is by default read-only, in the same way, if there is a
need for edition access, this can be requested to EPSILON by the Case Study
leader. As this datasets are only useful for the specific case studies’ System
Dynamic Models generation, it has been decided to not make them available
publicly. At this stage of the project, this decision only affects to
Sardinia’s Case Study.
However, some baselines for some case studies are being developed and may be
of interest for the scientific community. In that case, SIM4NEXUS project will
make these baselines available under Open Access.
4.2 Semantic repository
A Semantic Repository is being developed to store information related to the
concepts, properties and restrictions from the Nexus procedures; to improve
the data integration of diverse sources and, finally, to give a better
analytical power. This repository, which is currently focused in provide
support to the information flow between the System Dynamics Models and the
Serious Game User interface, will also allow for knowledge storage related to
the Nexus, policies, etc., coming from WP1 and WP2.
A triple-store is being used to be the base of the repository and an ontology
to semantically represent the stored data. The ontology is still under
development and represents the SIM4NEXUS knowledge and scope. Currently, the
top concept is the ‘Session’, which represent a game or session in the Serious
Game. Linked to the ‘Session’ there are a ‘User’, representing the player, and
a ‘StudyCase’ which, in turn, it is related with a specific ‘Model’, a SDM. In
order to represent the Nexus state through the game, the ‘Session’ has a list
of ‘StateEvolution’, which represent the way from one state (‘State’) to the
new state (‘State’), applying certain polices (‘Policy’). The ‘State’ is
defined by ‘NexusComponent’ (for instance ‘Climate and environment’ or
‘Water’) and these components have a specifics parameters (‘Parameter’).
The ontology is defined using the Web Ontology Language (OWL), a Semantic Web
language designed to represent rich and complex knowledge about things, and
relations between things. Some existing ontologies, related to the Nexus, have
been analysed to be involved in the SIM4NEXUS context:
* WatERP ontology, which reflects the water manager’s expertise to manage water supply and demand. The novelty of WatERP ontology lies on including man interactions with the natural paths as a mechanism to understand how affect into the water resources management with the objective to match supply with demand, these interactions could range from infrastructures to management decisions.
* WEFNexus ontology, which concern Water, Energy and Food derived by the European Directives: Article 2 of EU Directive 98/83/EC that defines the water intended for human consumption; Article 2 of EU Directive 2003/30/EC that defines bio-fuels; Article 2 of EU Regulation 178/2002/EC that defines food.
At the moment, the ontology is not available publicly, as it is in an initial
state. As soon as it gets a stable version, will be made available broadly.
4.3 3D map and terrain data in serious game
The 3D map available in current Serious Game user interface prototype is
rendered from 2 sources – a height map and a texture overlay:
* The height map data comes from NASA Shuttle Radar Topographic Mission (SRTM) v4.1 and is distributed freely by USGS. The SRTM data is available with a 90m resolution. Not all data from
SRTM is being used in the serious game, only data related to the geographical
regions of the 12 case studies are used. This data is downloaded from the USGS
web and then stored as mesh elements within the serious game client.
* The texture overlay is from Google Earth, it is downloaded from Google and then stored within the serious game client.
Consequently, we are not generating this data, but reusing existing publicly
available datasets.
4.4 Other considerations
As stated in D.9.1, SIM4NEXUS project, foresee to provide both short and long-
term benefits for the involved decision-makers and their associated networks.
Given the above, « _the only ethics issues involved in the SIM4NEXUS study
concern general ethical issues of informed consent, anonymity and
confidentiality associated with the voluntary involvement of human
participants in the European Union_ ».
SIM4NEXUS will not involve types of data related to sensitive topics (well
described in D.9.1), which might generate uncomfortable situations such as
psychological stress, any kind of anxiety or humiliation, deception, or any
potential increased danger to participants, or gathering of personal data from
participants. Thus, SIM4NEXUS, has defined approaches for the following
issues:
1. Collection and processing of personal data (described in D.9.1)
2. General ethics commitments (described in D.9.1)
3. Storing and sharing information (described in D.9.1)
4. Accessing and using of information (described in D.9.1)
5. Protection of Information (described in D.9.1)
In addition, deliverable D9.2 provides templates of the informed consent forms
and information sheets. This information is confidential, and only for the
consortium members (including the Commission Services). Also, SIM4NEXUS
consortium, agreed on signing an ethics agreement based on the European Code
of Conduct for Research Integrity, published by the European Science
Foundation
(http://www.esf.org/fileadmin/Public_documents/Publications/Code_Conduct_ResearchIntegrity.pdf)
and the ethical principles for conducting community-based participatory
research, as defined by the National Co-ordinating Centre for Public
Engagement of Durham University, UK (www.publicengagement.ac.uk). This will
ensure the fair and equal power relationships between researchers and
participants.
Last but not least, Deliverable 9.3 stipulates a keen awareness of most
ethical issues, as they were presented to the Social Sciences Ethics Committee
(SEC). « _The SEC is convinced that fair and respectful treatment in terms of
inconvenience, consent and privacy is assured_ ».
5 FAIR Data – Specifics
This section must be understood as a living section that will be further
updated in future iteration as the project evolves, and more input in terms of
data and data reporting comes from the SIM4NEXUS partners.
Intellectual property rights (IPR) management in SIM4NEXUS project, is a
substantial part of its data management plan. Usually data content and their
system are treated as one parameter, but when the matter comes to IPR a
distinction between the databases and data content is of utmost importance. It
is imperative for other users to know how they can reuse both the data
collected, assembled, or generated and the databases where these are included.
The Open Data Commons group (http://opendatacommons.org) developed the
following tools to govern the use of data sets. The three ODC licenses are:
* Public Domain Dedication and License (PDDL): This makes the use of the database and its content free to the public domain.
* Attribution License (ODC-By): Users can make use of the database and its content in new and different ways, but they need to provide an attribution to the source of the data and/or the database.
* Open Database License (ODC-ODbL): ODbL stipulates that any use of the database must provide attribution, and any new outcomes must use the same terms of licensing (also an unrestricted version of the new product must always be accessible).
In addition, it is acceptable to articulate for SIM4NEXUS project a set of
“community norms” that can be used complementary to the use of formal
licenses.
At this version of the D4.2 this section is briefly answered in the section 9
Questions & Answers on FAIR data, and thus is structured here to give the
outline of future DMP iterations.
5.1 Making data findable, including provisions for metadata
This section will be updated on next iterations to provide detailed
information on how data will be made discoverable, and more specifically:
* Discoverability of data (metadata provision)
* Identifiability of data and refer to standard identification mechanism
* Use of persistent and unique identifiers such as Digital Object Identifiers
* Naming and conventions used
* Approach towards search keyword
* Approach for clear versioning
* Specify standards for metadata creation
* Type of metadata created and how
As detailed in section 4, all data, information, and knowledge considered
relevant for the scientific community will be made accessible under Open
Access. At the moment, all datasets, and other means of information storage
are in draft status, and this information has not been defined for each
dataset. When a dataset is set to be accessible publicly, this information
will be fulfilled and the DMP updated accordingly.
5.2 Making data openly accessible
This section will be updated on next iterations to provide detailed
information on how data will be made accessible, assessable and intelligible
more specifically:
* Specifics on which data will be made openly available • Which data is kept closed and provide the rationale?
* How the data will be made available
* What methods and software tools are used to access the data
* Documentation of software needed to access the data included
* Inclusion of relevant software
* Data and associated metadata, documentation and code deposit • Provision of access provided in case of restrictions
As detailed in section 4, and 5.1, all data, information, and knowledge
considered relevant for the scientific community will be made accessible under
Open Access. At the moment, all datasets, and other means of information
storage are in draft status, and this information has not been defined for
each dataset. When a dataset is set to be accessible publicly, this
information will be fulfilled and the DMP updated accordingly.
5.3 Data interoperability
This section will be updated on next iterations to provide detailed
information on how data will be made interoperable to specific quality
standards and more in detail:
* Assess the interoperability of project data
* Specifics on data/metadata vocabularies, standards, methodologies followed
* Use of standard vocabulary for all data types present to allow inter-disciplinary interoperability
* Provision of mapping to more commonly used ontologies
To assure data interoperability, SIM4NEXUS project will follow state of the
art ontologies and standards. The two main elements which will store and make
available information and services in SIM4NEXUS publicly are the Knowledge
Elicitation Engine, and the Semantic Repository. The Knowledge Elicitation
Engine is being implemented under Service Oriented Architecture principles,
following the OGC standards and services defined for information publication,
discovery, exchange, etc. (WPS, WFS, GML…). Please refer to deliverable D4.3,
section 3.2 for more information. In regards to the Semantic Repository,
please refer to section 4.2 in this document for more information.
5.4 Increased data re-use
This section will be updated on next iterations to provide detailed
information on how data will be made usable beyond the original purpose for
which it was collected, and more in detail:
* Data licensing to permit the widest reuse possible
* Data availability for re-use
* Why and for what period a data embargo is induced
* Data useable by third parties after the end of the project
* Restriction of re-use of some data
* Data quality assurance processes
* Length of time for which the data will remain re-usable
As detailed in section 4, 5.1, and 5.2, all data, information, and knowledge
considered relevant for the scientific community will be made accessible under
Open Access. At the moment, all datasets, and other means of information
storage are in draft status, and this information has not been defined for
each dataset. When a dataset is set to be accessible publicly, this
information will be fulfilled and the DMP updated accordingly.
6 Ethical & Security aspects
6.1 General
Within the SIM4NEXUS study only general ethical issues are concerned such as
informed consent, anonymity and confidentiality associated with the voluntary
involvement of human participants in the European Union. Types of such data
collected in SIM4NEXUS are various user interviews, opinions and reviews
associated with project's components. Non-exhaustive list is as follows:
* Stored involvement of Serious Game users to gain insight into the decisions and behaviors of the players and to allow further analysis
* The visualization and interaction tool - to collect information from users so that the Knowledge Elicitation Engine (KEE) can learn from user decisions
* A series of interviews with key stakeholders and decision makers – in particular those which might be affected most by a Nexus-compliant implementation of policies, or which behavioral change is central to the achievement of a resource efficient Europe
* Planned contacts with representative persons of the targeted users. Interviews should be carried out by phone or face-to-face when convenient. Interviews should help define the expected functionalities/ services to be offered, test the price that could be acceptable and identify distribution channels to access these clients
* The end-users, potential developers, and partners, etc. will be provided the opportunity to test and review the latest products and services
* Methodology and procedures for sensitive data processing and storing will be specified as a part of the ethics Deliverable 9.1. It is important to emphasize that special efforts will be devoted to anonymize information and securing accessibility. Mechanisms to delete personal data will be provided in an easy and usable manner
6.2 Intellectual Property Rights (IPR)
Intellectual Property Rights (IPR) will receive special attention from the
beginning. All rules regarding management of knowledge and IPR will be
governed by the Consortium Agreement (CA). SIM4NEXUS was based on DESCA
(Consortium Agreement Model) H2020 model for the Consortium Agreement (CA).
SIM4NEXUS will not act in contradiction with the rules laid down in Annex II
of the Grant Agreement. The CA will address background and foreground
knowledge, ownership, protected third party components of the products, and
protection, use and dissemination of results and access rights.
The following principles will be applied:
* Confidentiality: During the project duration and beyond (Section 10 of the GA – nondisclosure of the information for a period of 4 years after the end of the project), the contractors shall treat any information, which is designated as property by the disclosing contractors, as confidential. They also shall impose the same obligations to their employees and suppliers.
* Pre-existing know how: Each Contractor is and remains the sole owner of its IPR over its preexisting know-how. The Contractors will identify and list the pre-existing know-how over which they may grant access rights for the project. The Contractors agree that the access rights to the pre-existing know-how needed for carrying out their own work under the project shall be granted on a royalty-free basis.
* Ownership and protection of knowledge: The ownership of the knowledge developed within the project will be governed by an open source license.
* Open data: Data and results obtained during the project that are based on open public sector data will be made available free of charge.
7 Questions & Answers on FAIR data
Furthermore, the goal of this document is to clarify a series of questions
related to all datasets produced or gathered in SIM4NEXUS project. In this
conclusive section, we describe how the DMP answers to these questions. In the
following table, we report the characteristics of the dataset together with
the questions to which the DMP should answer.
Table 3. SIM4NEXUS data characteristic description.
<table>
<tr>
<th>
Data
Characteristic
</th>
<th>
Question SIM4NEXUS Answer
</th> </tr>
<tr>
<td>
Discoverable
</td>
<td>
Are data and associated software produced and/or used in the SIM4NEXUS
discoverable (and readily located), identifiable by means of a standard
identification mechanism (e.g.
Digital Object Identifier)?
</td>
<td>
Data produced within the project can be discoverable in SIM4NEXUS Database and
will be uniquely identified most probably by RESTful (Representational State
Transfer) service pointing to Database resource.
Third party datasets will be referenced and identified with data name and
version, thus discoverable in their original data repositories.
Existing software that is used should be background information of a given
partner and as such could be documented but not discoverable.
This may also apply to modifications made as part of the SIM4NEXUS case.
</td> </tr>
<tr>
<td>
Accessible
</td>
<td>
Are data and associated software produced and/or used in SIM4NEXUS accessible
and in what modalities, scope, licenses (e.g. licensing framework for research
and education, embargo periods, commercial exploitation, etc.)?
</td>
<td>
Intermediate data (i.e. non-final data produced during the processing chain
elaboration) will be stored in the database but only Consortium Members will
access to them.
Final products data instead will be stored in the Database and freely
accessible also by externals through a dedicated web-service obtaining data
from the database.
</td> </tr>
<tr>
<td>
Assessable and intelligible
</td>
<td>
Are data and associated software produced and/or used in the project
accessible for and intelligible to third parties in contexts such as
scientific scrutiny and peer review (e.g. are the minimal datasets handled
together with scientific papers for peer review, is data provided in a way
that judgments can be made about their reliability and the competence of those
who created them)?
</td>
<td>
Final products data will be freely accessible for and intelligible to third
parties.
</td> </tr>
<tr>
<td>
Data
Characteristic
</td>
<td>
Question
</td>
<td>
SIM4NEXUS Answer
</td> </tr>
<tr>
<td>
Useable
beyond the
original
purpose for which it was collected
</td>
<td>
Are data and associated software produced and/or used in SIM4NEXUS project
useable by third parties even long time after the collection of the data (e.g.
is the data safely stored in certified repositories for long term preservation
and curation; is it stored together with the minimum software, metadata, and
documentation to make it useful; is the data useful for the wider public needs
and usable for the likely purposes of nonspecialists)?
</td>
<td>
Final products data will be useable by third parties even long time after
their production. Thanks to infrastructure (i.e. IaaS –
Infrastructure as a Service that will be applied to SIM4NEXUS) behind the
database, data retention will have no physical limit about size and the time
of validity (just economic limits related to maintenance). The historical data
will be accessible through same interface and methods of the most recent ones
also thanks to dedicated storage methodologies.
</td> </tr>
<tr>
<td>
Interoperable
to specific
quality standards
</td>
<td>
Are data and associated software produced and/or used in the project
interoperable allowing data exchange between researchers, institutions,
organizations, countries, etc. (e.g. adhering to standards for data
annotation, data exchange, compliant with available software applications, and
allowing re-combinations with different datasets from different origins)?
</td>
<td>
The web-service (providing data) will be based on well-known protocols (i.e.
Data Access Protocol - DAP 2.0, RESTful/HTTP), so all data can be accessed in
a standardized way, through a compliant HTTP / DAP client. Moreover, data will
be stored, when possible, with recognized state of the art standards and
protocols, thus assuring interoperability and maximizing exploitation of
results.
</td> </tr> </table>
References
* Article 43.2 of Regulation (EU) No 1290/2013 of the European Parliament and of the Council, of 11 December 2013, laying down the rules for participation and dissemination in "Horizon 2020 - the Framework Programme for Research and Innovation (2014-2020)" and repealing Regulation (EC) No 1906/2006.
* Guidelines on Data Management in Horizon 2020, _http://ec.europa.eu/research/participants/data/ref/h2020/grants_manual/hi/oa_pilot/h2020hi-oa-data-mgt_en.pdf_
* Open Access to Scientific Publications and Research Data in Horizon 2020 Guidelines, _https://ec.europa.eu/research/participants/data/ref/h2020/grants_manual/hi/oa_pilot/h2020 -hi-oa-pilot-guide_en.pdf_
* Open Research Data Pilot – ORD pilot: _https://www.openaire.eu/opendatapilot_ • SIM4NEXUS Grant agreement & SIM4NEXUS consortium agreement
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Horizon 2020
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1097_DECISIVE_689229.md
|
**Executive summary**
</th> </tr> </table>
As stated in the proposal, the WP 3.3 takes part in the “Pilot on Open
Research Data 1 ” as suggested in the H2020 guidelines. It aims at making
final results and when possible, intermediate results of this WP (mainly in
the form of spatial datasets) available for free to access, reuse, repurpose
and redistribute.
This deliverable aims at defining the specific data management plan (DMP) for
the WP 3.3 which is a key element to comply with the Open Research Data
requirement. This document follows the guidelines on the “FAIR Data Management
in Horizon 2020” 2 . It describes the data that will be produced by the
WP3.3, the file formats and the metadata standard used and the sharing and the
archiving strategy.
The DMP for the overall project has been described in D8.4 and the DMP
described here is specific to the WP 3.3.
This document is the first version of the DMP. It will be updated over the
course of the project to add details on the data that will be published or if
there are some changes in the consortium policies or consortium composition.
<table>
<tr>
<th>
**2.**
</th>
<th>
**ADMIN DETAILS**
</th> </tr> </table>
**Project Name:** DMP for the work package 3.3 of the DECISIVE Project, funded
by Horizon 2020 ("spatial approach for designing the decentralized urban
biowaste valorization network") DMP title
**Grant Title:** 689229
**Principal Investigator / Researcher:** Irstea
**Description:** The growing attractiveness of cities leads to increasing
population, thus rising energetic and food demands in urban areas. This makes
urban waste management increasingly challenging, both in terms of logistics
and environmental or health impacts. To decrease the cities’ environmental
impacts and to contribute to a better resilience of urban areas towards energy
or food supply crisis, waste management systems have to be improved to
increase recycling of resources and local valorization. In this context, The
DECISIVE project proposes to change the present urban metabolism for organic
matter (foods, plants, etc.), energy and biowaste to a more circular economy
and to assess the impacts of these changes on the whole waste management
cycle. Thus, the challenge will be to shift from an urban “grey box”, implying
mainly goods importation and extra-urban waste management, to a cooperative
organization of intra- and peri-urban networks enabling circular local and
decentralized valorization of biowaste, through energy and bio products
production. Such a new waste management paradigm is expected to increase the
sustainability of urban development by: 1-promoting citizens awareness about
waste costs and values; 2-promoting renewable energy production and use in the
city; 3-developing an industrial ecology approach that can promote the
integration between urban and peri-urban areas, by providing valuable
agronomic by-products for urban agriculture development and so improving the
balance of organic products and waste in the city; 4-developing new business
opportunities and jobs. In order to achieve these objectives, the project
DECISIVE will develop and demonstrate eco-innovative solutions, addressed to
waste operators and public services.
The work package 3.3 of the project, handled by Irstea, is focused on _a
spatial approach for designing the decentralized urban biowaste valorization
network_ . It also takes part in the “ _Pilot on Open Research Data_ ” as
suggested in the H2020 guidelines. Therefore final results and when possible,
intermediate results of the 3.3 work package (mainly in the form of spatial
datasets) will be free to access, reuse, repurpose and redistribute.
**Funder:** European Commission (Horizon 2020)
<table>
<tr>
<th>
**3.**
</th>
<th>
**DATA SUMMARY**
</th> </tr> </table>
The DECISIVE project proposes to change the present urban metabolism for
organic matter to a more circular economy (food waste, partly in connection
with further digestible biowastes to heat and power from biogas and further
material products, resulting from SSF processes) and to assess the impacts of
these changes on the whole waste management cycle. One objective of the
project, addressed by the work package 3.3, is to **design the decentralized
urban biowaste valorization network** based on **a spatial analysis** .
In a first step, **data will be gathered** about:
* Locations and if possible characteristics of the sources of organic matter
* Locations and characteristics of the potential output for the products coming out of the process (energy, digestate, by-products)
* Location of rural, peri-urban and urban farms in the target areas
* Location of the current biowaste treatment systems (incineration, landfilling, biological, etc.)
Information will be either collected from existing database (OpenStreetMap,
some official database like BD Topo or SIREN in France, etc.) or created based
on surveys.
In a second step, those data, completed with other information (AD 3 or SSF
4 characteristics, rules and legislation, social and economic factors,
infrastructures), will be incorporated in a **multi-criteria analysis tool**
to optimized the location of a network of AD and SSF sites. The spatial data
are saved in **Shapefile** format, an open and interoperable format recognized
as the de-facto standard for all GIS vector data or saved in **GeoTIFF** for
raster data. The final sizes of the different datasets remain unknown at this
stage of the project but they should be small enough to remain easily
manageable.
The project data may be useful from an operational and methodological point of
view. In the cities used as case studies, the results may be already exploited
as decision tools by local authorities to design new biowaste networks, but
those data may also serve as guidelines for the replication of the method in
other contexts.
<table>
<tr>
<th>
**4.**
</th>
<th>
**FAIR DATA**
</th> </tr> </table>
**4.1. Making data findable, including provisions for metadata:**
All the data published in the framework of the work package 3.3 of the
DECISIVE project and included in the “ _Pilot on Open Research Data_ ”
procedure will comply with the **INSPIRE Directive** 5 .
Therefore, all data will be provided with a **standard XML metadata** . The
metadata will be published on metadata catalog like the _European Union Open
Data Portal_ to insure their good discoverability.
The metadata will contain in particular:
* Clear information about the content of the data (title, abstract, etc.) to insure a good understanding of its content
* A set of keywords including keywords from officials thesaurus (GEMET 6 , etc.) and tailor made keywords to optimize the searching process ▪ Date of creation and revision, contacts of creators
The metadata will be created manually for each dataset with GIS software
(ESRI-ArcGIS or QSphere: a plugin for QGIS Software). In the case of
versioning, the version of the data will be clearly expressed in the file name
as defined by the file naming schema of the Decisive Project (Deliverable 8.4,
ex: _DECISIVE-WP3.3- title-partner_name-Vxx_ , where **Vxx** indicates the
version number: V01, V02, etc.) but also in the title and the abstract of the
metadata.
All dataset will have a **unique resource identifier** (DOI) provided by the
repository _Zenodo_ 7 .
# 4.2. Making data openly accessible
The final results of the work package 3.3 will be openly accessible in the
selected repository (Zenodo). The intermediate data will also be published in
the same repository, except if:
* The owners of the data sources or raw data request to keep it close
* The data contain some personal information
Note: The data processing will heavily modify the raw data. Therefore, the
final results will show only anonymized information.
Method for data sharing and data repository:
The data and the metadata will be available through Zenodo (www.zenodo.org), a
generalpurpose open access repository created by OpenAIRE. All data will be
published in open and standard formats to allow a good interoperability.
# 4.3. Making data interoperable
All data and metadata will be published based on open and standard file
format:
* GIS vector data are saved in Shapefile format. It is recognized as the standard format for spatial vector data and it is based on open specifications for data operability among most of the GIS software.
* GIS raster data are saved in GeoTIFF format.
* Metadata files are saved in XML
The data vocabulary used in attribute tables will be specific to the theme of
the project (waste management, energy, network, etc.). Therefore, a definition
of the technical names or concepts will be included in the metadata or in a
side document (PDF).
Moreover, to comply with the INSPIRE Directive; some of the keywords used in
the metadata will come from official thesaurus like GEMET.
# 4.4. Increase data re-use (through clarifying licenses)
## \- Timeframe for data sharing
Data will be shared as soon as the _Communication and Dissemination_ and the
_Work package 3_ leaders have given their agreement. The data will be
maintained during the project timeframe and they will remain available after
the project on the repository (Zenodo).
## \- Expected reuse
The project data may be useful from an operational and methodological point of
view. In the case studies areas, the results may be already exploited as
decision tools by local authorities to design new biowastes networks. But the
data may also serve as guidelines for the replication of the method in other
contexts.
## \- Ownership and Licensing
The data will be owned by Irstea and will be published under _Creative Commons
license_ 8 with at least an attribution (CC **BY** ) or attribution and non-
commercial (CC **BY-NC** ). However, the final selection of the license is
still under discussion and may be different according to the dataset. In the
case of information based on third-party data with a copyleft license, the
license will be adapted in consequences.
<table>
<tr>
<th>
**5.**
</th>
<th>
**ALLOCATION OF RESOURCES**
</th> </tr> </table>
## \- Responsible for data management
The data management of the WP3.3 is handled by the GIS service of Irstea. This
service is responsible for:
* the data capture or gathering (if others partners are involved)
* the quality control
* the metadata creation or edition
* the storage and backup
* the publication in the selected repository
## \- Resourcing
To comply with the FAIR requirement 9 , the Decisive project will have to
cover the cost of data management, storage backup and publication:
<table>
<tr>
<th>
**Resources**
</th>
<th>
**Description**
</th>
<th>
**Cost**
</th> </tr>
<tr>
<td>
Human resources
</td>
<td>
_Format the data, create the metadata, publication in repository, etc._
</td>
<td>
20 man-day (estimation)
</td> </tr>
<tr>
<td>
Storage and backup
</td>
<td>
_Running cost of Irstea IT services_
</td>
<td>
Not assessed yet
</td> </tr>
<tr>
<td>
Repository
</td>
<td>
_The repository Zenodo is free of charge_
</td>
<td>
Free of charge
</td> </tr> </table>
After the end of project, the data will remain available on the repository
(Zenodo) without added cost as the services is free of charges.
<table>
<tr>
<th>
**6.**
</th>
<th>
**DATA SECURITY**
</th> </tr> </table>
\- Data security
No sensitive data will be published.
## \- Storage and backup
Data and the metadata will be stored in the external repository _Zenodo_ which
will insure their long term preservation. Moreover, the data will be stored
additionally in an own Irstea platform with a secure NetApp® data storage
system. The data will be daily saved and replicated to an external data center
that guarantees the preservation of long-term data as described in D8.4.
<table>
<tr>
<th>
**7.**
</th>
<th>
**SUMMARY OF DMP TECHNICAL CHOICES**
</th> </tr>
<tr>
<td>
**Topics**
</td>
<td>
**Description**
</td> </tr>
<tr>
<td>
_GIS data_
</td>
<td>
Shapefile format for vector data GeoTIFF format for raster data
</td> </tr>
<tr>
<td>
_Metadata_
</td>
<td>
XML format
Compliant with INSPIRE Directive
</td> </tr>
<tr>
<td>
_Repository_
</td>
<td>
Zenodo (www.zenodo.org)
</td> </tr>
<tr>
<td>
_Additional backup_
</td>
<td>
Irstea platform
</td> </tr> </table>
<table>
<tr>
<th>
**8.**
</th>
<th>
**ETHICAL ASPECTS**
</th> </tr> </table>
Until now, no ethical or legal issues were identified.
* No sensitive or personal data will be published.
* If sensitive or personal information is used, from a survey for example, a _Consent Form_ will be required as defined by the Deliverable 8.4. Moreover, the spatial analysis will make the data anonymous or impossible to link to any raw data.
<table>
<tr>
<th>
**9.**
</th>
<th>
**OTHER**
</th> </tr> </table>
Data will be compliant with the INSPIRE Directive 10 .
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Horizon 2020
|
1099_Quaco_689359.md
|
# Executive summary
_The QUACO Data Management Plan (DMP) shows the tools and life cycle of data
generated and used during and after the project. All data generated in this
project (both on PCP and on technical development) will be centrally stored at
CERN who will act as central repository for archiving also after the end of
the project. The DMP also outline the accessibility of the data, which is in
line with the project’s Open Access policy._
# INTRODUCTION
The main deliverable of QUACO PCP is a potential component of the HL-LHC
project. For this reason, the HL-LHC Data management Plan concepts are the
basis for this document.
Data Management is a process that provides an efficient way of sharing
knowledge, information and thinking among the project's participants and
stakeholders.
In this document, we will define:
* The type of Data that will be generated and managed;
* The life cycle of the data and in particular the data control process; The tools used to manage the data.
The process of communication and dissemination of the data it is part of the
Deliverable 8.1.
# DATA HANDLED BY THE PROJECT
## OVERVIEW
The QUACO project will produce two different type of documents and data. From
one side the information exchange on the PCP process, from the other the
information related to the design and fabrication of the MQYY first of a kind
magnet.
The type of documents and data and the way they will be treated will be very
different.
## DATA LINKED TO THE PCP PROCESS
QUACO is a collaborative acquisition process. There will be documents and data
related to the exchange of information with/among QUACO partners and the
interaction with: Partner labs interested in the use of the PCP instrument
in the future;
* Industrial suppliers;
* Public;
* Stakeholders.
There will be also data created by the analysis of the interaction of these
four groups with QUACO.
## DATA LINKED TO THE PRODUCTION OF THE FIRST OF A KIND MAGNET
The QUACO technical specification [1] gives a detailed list of technical and
managerial documents and data that will be produced during the three phases.
Among them:
* Technical Documents such as the Conceptual Design Report of the magnet;
* Managerial Documents such as the Development and Manufacturing plan;
* Acquisition Documents such as the technical specifications for the tendering of tooling and components;
* 2D and 3D models such as the As-built 2D and 3D CAD manufacturing drawings;
* Data such as the control parameters during the winding process or the dimensional checks.
* Contract Follow up Documents such as minutes and visit reports.
There will be also data created by the internal exchange among QUACO partners
on the progress done by the suppliers.
# LIFE CYCLE AND DATA CONTROL PROCESS
## OVERVIEW
The PCP Project has a Data Management Plan because needs that:
* Data required for the project is identified, traced and stored;
* Documents are approved for adequacy prior to issue;
* Documents are reviewed and updated as necessary;
* Changes to Data and Documents are identified;
* Relevant versions of applicable documents are available at points of use;
* Documents remain legible and readily identifiable;
* Documents of external origin are identified and their distribution controlled.
To manage and control a document we shall establish several sub processes:
1. **Identification** : what kind of document shall be managed.
2. **Labeling** : how the document shall be named.
3. **Lifecycle** : how shall be ensured the adequacy of the document before distribution.
4. **Availability** : how shall be ensured that the document arrives to the right person and can access it as long as required.
5. **Traceablity** : how it is record the changes and location of the document.
Except for the labelling process, the same sub processes are applicable for
the data that will be handled by the project.
## IDENTIFICATION OF DATA MANAGED
The QUACO project shall manage and control all documents required to guaranty
the full life cycle of the Project.
Points 2.2 and 2.3 list the type of data and documents identified to follow
the PCP process and the production of the first of a kind MQYY.
Among those documents we can distinguish two classes:
* Baseline documents: Documents that will have to be stored after the end of QUACO.
* Non-baseline documents: documents that are required for the well functioning of the project but that which storage will not be considered critical after Phase 3.
The management and control sub processes of these two types of documents will
be handled differently.
The HL-LHC Configuration, Quality and Resource Officer ensures the training of
the different QUACO partners [2] on the identification of the data to be
managed.
## LABELING
Baseline Documents shall follow the HL-LHC Quality plan naming convention [3]
and shall be labelled with an EDMS number and with a document name. Non-
Baseline Documents do not need to have a document name.
## LIFECYCLE
The lifecycle of a document includes the publishing (proofreading, peer
review, authorization, printing), the versioning and the workflow involved on
this two processes.
Concerning peer reviewing as a general rule:
* Baseline documents shall be peer reviewed (verification process) by a group of people knowledgeable on the subject and those interfacing with the system/process described in the document. As default the peer review is done by the QUACO PMT or one of its members.
* Non Baseline documents peer review process is generally managed by the author.
In particular for the Tender Documents (Baseline Documents)
* The JTEC reviews the Prior Information Notice, the draft Invitation to Tender, the draft Subcontracts, as prepared by the Lead Procurer in accordance with the laws applicable to it and the Specific PCP Requirements and submit to the STC all above documents for approval
Concerning Authorization the process is adapted to the type of document. As a
general rule:
* For Baseline documents, the STC gives the Final approval of technical and contractual specifications for the PCP tender, Approval of tender selection and Management of knowledge (IPR), dissemination & exploitation documents. The Project Coordinator always approve all Baseline Documents.
* For Non Baseline documents the process depends mainly on the type of document, but are mainly approved by the WP Leader.
Every time there is a change in the lifecycle of a document a new version of
the document shall be created. Changes are traced by the revision index. The
revision index will increase by 0.1 for minor changes. In case of major
changes will be the first digit that will be moved to the next integer.
## AVAILABILITY
Table 1 and Table 2 give the general guidelines for the visibility and storage
of documents in the project. The process of communication and dissemination of
the data it is part of the Deliverable 8.1.
_Table 1: Visibility guidelines_
<table>
<tr>
<th>
**Document class**
</th>
<th>
**Visibility**
</th> </tr>
<tr>
<td>
Baseline documents
</td>
<td>
Financial, resource oriented and with sensitive information
</td>
<td>
QUACO Partners and EU Stakeholders
</td> </tr>
<tr>
<td>
Commercial
</td>
<td>
QUACO Partners, EU Stakeholders and
Partner labs interested in the use of the PCP instrument in the future
</td> </tr>
<tr>
<td>
Technical
</td>
<td>
QUACO Partners, EU Stakeholders and
Partner labs
In some cases Industrial partners*
</td> </tr>
<tr>
<td>
Non Baseline documents
</td>
<td>
Technical documents
</td>
<td>
QUACO Partners, EU Stakeholders and
Partner labs
In some cases Industrial partners*
</td> </tr>
<tr>
<td>
Scientific publications
</td>
<td>
Worldwide
</td> </tr>
<tr>
<td>
Outreach
</td>
<td>
Worldwide
</td> </tr> </table>
(*) Follows IP rules described in the Tendering Documentation and on the Grant
Agreement
_Table 2: Storage time and format requirements_
<table>
<tr>
<th>
**Document class**
</th>
<th>
**Storage time**
</th>
<th>
**Format**
</th> </tr>
<tr>
<td>
Baseline documents
</td>
<td>
Forever
</td>
<td>
Native format and at least a long term readable format
</td> </tr>
<tr>
<td>
Non Baseline documents
</td>
<td>
Limited time
</td>
<td>
Native format or long term readable format
</td> </tr> </table>
## TRACEABILITY
Traceability includes the record of the lifecycle of the document and the
metadata that describes the document. A document is fully trace if we can
retrieve:
* Label including version number,
* Properties (Author, creation date, title, description)
* Life cycle information,
* Storage location,
* List of actions and comments with their author linked to changes in the life cycle.
Baseline documents shall be fully traced. For Non Baseline documents it is not
required a complete traceability of the actions and comments with their author
linked to changes in the life cycle.
# TOOLS
CERN has two documentation management systems EDMS and CDS. EDMS is the tool
used for the control of engineering documents and presentations. CDS is the
tool used for the control of scientific documents, meetings documentation and
graphic records.
To ensure the long term storage of Baseline documents they shall be stored in
EDMS. Non Baseline documents can be stored in another documentation management
system that can ensure the correct level of approval, availability and
traceability.
_Table 3: Recommended tools_
<table>
<tr>
<th>
**Document class**
</th>
<th>
**Tool**
</th> </tr>
<tr>
<td>
Baseline documents
</td>
<td>
EDMS
</td> </tr>
<tr>
<td>
Non Baseline documents
</td>
<td>
Meetings: Indico, EDMS, SharePoint
Technical: EDMS (requiring approval process), SharePoint
Scientific: CDS
Commercial: CFU, EDMS
Outreach: WWW
</td> </tr>
<tr>
<td>
Data
</td>
<td>
Technical: MTF
Non-Technical: SharePoint
Outreach: Twitter, LinkedIn
</td> </tr> </table>
# LINKS TO THE TOOLS
CDS: https://cds.cern.ch/
EDMS: https://edms.cern.ch/project/CERN-0000154893
Indico: https://indico.cern.ch/category/7138/
LinkedIn: _https://www.linkedin.com/in/quaco/en_
MTF: https://edms5.cern.ch/asbuilt/plsql/mtf_eqp_sel.adv_list_top
Twitter: https://twitter.com/HL_LHC_QUACO
SharePoint: https://espace.cern.ch/project-HL-LHC-Technical-
coordination/QUACO/_layouts/15/start.aspx#/ WWW: https://quaco.web.cern.ch/
# TEMPLATES
The HL-LHC Quality support Unit maintains a series of templates that are
accessible in the EDMS [4].
# CONCLUSIONS
The QUACO Project has identified the different type of documents and data that
have to be managed to ensure its full life cycle. The different sub process
such as labelling, publishing or traceability have been analyzed and adapted
to the project. Finally different tools have been identified and deployed to
ensure the sub processes.
|
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Horizon 2020
|
1101_STARR_689947.md
|
# Deliverable Description
This document is a deliverable of the STARR project, which is funded by the
European Union’s Horizon 2020 Program under Grant Agreement #689947. It is a
follow-up to Deliverables 1.6 and 1.7, which described what data the project
would generate, how they would be produced and analyzed. The following
deliverable also aims to detail how the data related to the STARR project will
be disseminated and afterwards shared and preserved.
# 1\. Scope of the document
The STARR project Data Management Plan (DMP) primarily lists the different
datasets were produced by the project, the main exploitation perspectives for
each of those datasets, and the major management principles the project were
implemented to handle those datasets.
The purpose of the DMP is to provide an analysis of the main elements of the
data management policy that were used by the consortium with regard to all the
datasets that were generated by the project.
As mentioned in the previous versions of this deliverable, the data management
plan covers the whole data life cycle.
_Figure 1: Steps in the data life cycle. Source: From University of Virginia
Library, Research Data Services_
# 3\. Responsability for the data
<table>
<tr>
<th>
**Person in charge of the data management during the oroject**
</th>
<th>
Margarita Anastassova (personal and non-personal data)
[email protected]_
Franziska Boehm (specific focus on EU regulations about personal data
management) [email protected]_
</th> </tr>
<tr>
<td>
**Partners’ reponsability**
</td>
<td>
Every partner is responsible for the data they are collecting. This is valid
for both personal and non-personal data. The data has been collected,
combined, stored and transmitted according to the relevant national, European
and institutional regulations. As far as the management of personal data is
concerned, the partners were supported by FIZ Karlsruhe, providing guidance on
personal data management in an EU perspective.
</td> </tr>
<tr>
<td>
**Data management policy**
</td>
<td>
All personal data collection in STARR was done within the remit of formal
ethics clearances obtained at our testing sites and granted by the relevant
university and/or local health officials. Thus, any patient-related data, such
as data from pre-exiting health record data and behavioural data depicted, for
example, by keyboard presses, video, audio, motion tracking falls under the
ethics clearance. Some nonpatient data, such as requirements from stakeholders
in WP3, in the form of anonymous questionnaire responses & focus group
opinions, can be gathered more informally.
The legal basis for the personal data processing is the participant’s consent,
obtained in accordance with the rules to which the collecting partner is
subject.
The most relevant standards regarding data handling, in this experimental
context with patients, concern the area of ethics, data protection and
privacy. They are listed below:
\- Directive 95/46/EC: Protection of individuals with regard to the processing
of personal data and on the free movement of such data.
</td> </tr> </table>
<table>
<tr>
<th>
</th>
<th>
-Directive 99/5/EC: Radio equipment and telecommunications terminal equipment and the mutual recognition of their conformity.
* Directive 98/44/EC: Legal protection of biotechnological inventions.
* Art. 7, 8 Charter of Fundamental Rights
* Art. 8 European Convention on Human Rights
* Case Law by the European Court of Human Rights
* Documents of the Aricle 20 Working Party
* Ilves report on E-health
* GDPR regulation (2016/679).
* Draft data protection regulation.
All of our patient data is reported as anonymized group summaries in the
project deliverables and in peer reviewed publications. Anonymous individual
quotes describing requirements are also be used in these reports. During
system evaluation, user performance data (e.g. task execution time and number
of errors) is transient and context sensitive (to the particular task, sensors
and user). There is no public value in the data and hence no foreseen need for
public access beyond that of the therapist. Nonetheless we have no objection
in principle to releasing this data in the spirit of Open Access if requested.
</th> </tr>
<tr>
<td>
**Ownership and access to data**
</td>
<td>
A consortium agreement was negotiated and signed by all the parties in order
to inter alia specify the terms and conditions pertaining to ownership, access
rights, exploitation of background and results and dissemination of results,
in compliance with the grant agreement and Regulation n°1290/2013 of December
11th, 2013. The consortium agreement was based on the DESCA Horizon 2020 Model
Consortium Agreement with the necessary adaptations considering the specific
context and the parties involved in the project. Its basic principles are as
follows:
\- The parties exhaustively identified the background intellectual property
they brought to the project, and assessed its availability for access rights
as regards to potential third parties’ rights over such background;
</td> </tr> </table>
<table>
<tr>
<th>
</th>
<th>
* Ownership of results including joint results generated by two or more parties goes to the party(ies) having generated such results;
* The owning parties take all appropriate measures for the protection of the results capable of commercial or industrial exploitation, notably through intellectual property rights when relevant;
* The parties use their best efforts to exploit and disseminate the results, either directly or indirectly, for instance by out-licensing said results;
* Each party gives access rights (through licenses) to their background and results to the other parties for the implementation of the project and/or for the exploitation of those other parties’ own results (under fair and reasonable conditions).
Knowledge management follows the strategy presented in the Figure below. The
IPR activities are organized according to the different project phases.
In principle, Foreground is managed according to the provisions of the
European Commission, and the access to the foreground created throughout the
project lifetime is specified by the Consortium Agreement. As a general rule,
the foreground is considered as a property of the Contractor generating it,
and in this sense the originator is entitled to use and to license such right
without any financial compensation to or the consent of the other
Contributors. In case of licensing to third parties, the Contributors shall be
informed in advance and appropriate financial compensation shall be given to
them. Starting from these
</th> </tr> </table>
<table>
<tr>
<th>
</th>
<th>
basic rules, other particular situations have been treated in WP9
(Dissemination & Exploitation):
* If the features of a joint invention, design or work are such that it is not possible to separate them, the Contributors could agree that they may jointly apply to obtain and/or maintain the relevant rights and shall strive to set up a joint exploitation agreements in order to do so;
* An originator of the foreground could decide not to seek protection of its Foreground. In this case, another contractor interested in such protection might apply for it, advising the other Contractors. In case several Contractors are interested, an agreement is necessary between them.
Concerning access rights, each Contractor shall take appropriate measures to
ensure that it can grant Access Rights and fulfill the obligations under the
EU Contract. The Contractors agree that Access Rights are granted on a
nonexclusive basis, and that, if not otherwise provided in the Consortium
Agreement or granted by the owner of the Foreground or Background, the Access
Rights does not include the right to grant sub-licenses. The Consortium
agreement has one section or one appendix to define which access rights to the
background may be granted. Access rights to foreground and background needed
for the execution of the project are be granted on a royalty-free basis to the
project partners.
Publication and dissemination of foreground are granted with the approval of
the Consortium, making sure that the period of secrecy needed for IP
protection is respected. Contractors have to inform the Consortium and the
Commission of its intention to publish on its foreground. Adequate references
to the Contract with the EC shall be given in the publication. Publication can
be impeded if another contractor can show that the secrecy of the foreground
is not guaranteed.
</th> </tr>
<tr>
<td>
**Publication of research data**
</td>
<td>
Since in STARR certain research activities rely on the processing of personal
data of stroke survivors, the privacy and the protection of their personal
data should be ensured. However, this does not exclude the publication of
aggregated, properly anonymized data, e.g. in statistical form. In this way
both the privacy of the stroke survivors are respected and scientific results
can be disseminated
</td> </tr> </table>
# 4\. Data set description
The project partners have identified the dataset that was produced during
different phases of the project. The list is provided below, while the nature
and details for each dataset are given in the subsequent sections.
<table>
<tr>
<th>
**Data set name**
</th>
<th>
**Personal identifiable data**
</th> </tr>
<tr>
<td>
**Type of data**
</td>
<td>
Name, gender, age, marital status, deprivation index, hand dominance,
educational level, type of job, hobbies, socio-familiar support
</td> </tr>
<tr>
<td>
**Format**
</td>
<td>
Documents (paper and digital such as Word documents or Excel sheets),
pictures, audio or video recordings, inputs to the STARR application
</td> </tr>
<tr>
<td>
**Source**
</td>
<td>
This data comes from:
* Questionnaires (paper originals, responses captured in an Excel spreadsheet), analysis (e.g. charts) within the spreadsheet
* Interviews transcribed to Word documents
(anonymized), analyzed data from the interviews
* Focus groups: discussions transcribed to Word documents (anonymized), synthesis of themes captured in Word documents
* Consent forms: signed paper documents
* Inputs to the application
* Video or audio recordings of user testing and studies
* Pictures from user testing and studies
</td> </tr>
<tr>
<td>
**Reuse and sharing**
</td>
<td>
Only relevant anonymized and aggregated data are transmitted and reused by the
partners not collecting the data.
</td> </tr>
<tr>
<td>
**Diffusion principles**
</td>
<td>
Within the consortium only respecting the limitations mentioned above.
</td> </tr>
<tr>
<td>
**Scientific publications**
</td>
<td>
No personal identifiable data was used in scientific publications.
</td> </tr>
<tr>
<td>
**Relevant documentation**
</td>
<td>
Personal identifiable data was not used in any documentation.
</td> </tr>
<tr>
<td>
**Archiving and preservation**
**(including storage and backup)**
</td>
<td>
The data is stored by the partner collecting it (on their own computers and/or
institutional servers).
</td> </tr> </table>
<table>
<tr>
<th>
**Data set name**
</th>
<th>
**Sensitive data (except Health data)**
</th> </tr>
<tr>
<td>
**Type of data**
</td>
<td>
Ethnic group, sex life or orientation, political opinions, religious beliefs
or other beliefs of a similar nature
</td> </tr>
<tr>
<td>
**Format**
</td>
<td>
Documents (paper and digital such as Word documents or Excel sheets), audio
recordings or videos, inputs to the STARR application
</td> </tr>
<tr>
<td>
**Source**
</td>
<td>
This data comes from:
* Questionnaires (paper originals, responses captured in an Excel spreadsheet), analysis (e.g. charts) within the spreadsheet
* Interviews transcribed to Word documents
(anonymized), analyzed data from the interviews
* Focus groups: discussions transcribed to Word documents (anonymized), synthesis of themes captured in Word documents
* Inputs to the application
* Audios or videos recordings of user testing and studies
</td> </tr>
<tr>
<td>
**Reuse and sharing**
</td>
<td>
Only anonymized and aggregated relevant data are transmitted and reused by the
partners not collecting the data.
</td> </tr>
<tr>
<td>
**Diffusion principles**
</td>
<td>
Within the consortium only respecting the limitations mentioned above.
</td> </tr>
<tr>
<td>
**Scientific publications**
</td>
<td>
No sensitive data was used in scientific publications.
</td> </tr>
<tr>
<td>
**Relevant documentation**
</td>
<td>
Sensitive data was not included in any documentation.
</td> </tr>
<tr>
<td>
**Archiving and preservation**
**(including storage and backup)**
</td>
<td>
The data is stored by the partner collecting it (on their own computers and/or
institutional servers).
</td> </tr> </table>
Concerning sensitive data, it also includes health data. However, we chose to
separate it and create a specific category, as this is the major type of data
that was collected and analyzed during the project. It is further detailed in
the table below.
<table>
<tr>
<th>
**Data set name**
</th>
<th>
**Health data (physical + mental)**
</th> </tr>
<tr>
<td>
**Type of data**
</td>
<td>
Medical history, blood pressure, lipidic profile, glycaemia, heart rate, diet,
toxic habits, disabilities, weight, depression, stress, pain, motor function,
physical activity, canes and wheel chair use, gait analysis, skeleton
movements in the game, emotions, behavior,
</td> </tr>
<tr>
<td>
</td>
<td>
adherence to treatment, information needed and obtained from the psychological
model analysis
</td> </tr>
<tr>
<td>
**Format**
</td>
<td>
Documents (paper and digital such as Word documents or Excel sheets), videos,
inputs to the STARR
application, visual sensing (Kinect or equivalent)
</td> </tr>
<tr>
<td>
**Source**
</td>
<td>
This data comes from:
* Questionnaires (paper originals, responses captured in an Excel spreadsheet), analysis (e.g. charts) within the spreadsheet
* Interviews / Focus groups transcribed to Word documents (anonymized), analyzed data from the interviews
* Inputs to the application via questions or sensing
* Video or audio recordings of user testing and studies
* Movement recordings by the games and training applications
* Movement recordings by the wearables
</td> </tr>
<tr>
<td>
**Reuse and sharing**
</td>
<td>
Only relevant anonymized and aggregated data are transmitted and reused by the
partners not collecting the data.
</td> </tr>
<tr>
<td>
**Diffusion principles**
</td>
<td>
Within the consortium only respecting the limitations mentioned above.
</td> </tr>
<tr>
<td>
**Scientific publications**
</td>
<td>
Health data was used in scientific publications in the form of anonymized,
statistical data or anonymized, aggregated patient description.
</td> </tr>
<tr>
<td>
**Relevant documentation**
</td>
<td>
Health data was only included in health-related files in OSA and HOP (the
medical partners).
</td> </tr>
<tr>
<td>
**Archiving and preservation**
**(including storage and backup)**
</td>
<td>
The data is stored by the partner collecting it (on their own computers and/or
institutional servers).
</td> </tr> </table>
<table>
<tr>
<th>
**Data set name**
</th>
<th>
**Website traffic**
</th> </tr>
<tr>
<td>
**Type of data**
</td>
<td>
Email addresses, Google Analytics data, HTTP cookies
</td> </tr>
<tr>
<td>
**Format**
</td>
<td>
CSV files, HTTP cookies
</td> </tr>
<tr>
<td>
**Source**
</td>
<td>
This data comes from user activity on the My Stroke Guide website.
</td> </tr>
<tr>
<td>
**Reuse and sharing**
</td>
<td>
My Stroke Guide website collects email addresses, which are only used by the
Stroke Association, with consent of the users and are not shared with any
third parties.
My Stroke Association collects Google Analytics data from the website, which
is used anonymously to assess site use and is not shared with any third
parties.
</td> </tr>
<tr>
<td>
**Diffusion principles**
</td>
<td>
This data is not diffused.
</td> </tr>
<tr>
<td>
**Scientific publications**
</td>
<td>
This data is not included in any scientific publications.
</td> </tr>
<tr>
<td>
**Archiving and preservation**
**(including storage and backup)**
</td>
<td>
The data is stored by the My Stroke Association (on their own computers and/or
institutional servers).
</td> </tr> </table>
# 5\. Data volume
The volume of data collected by the STARR platform is limited to the time
during which the pilot was run by each patient, as well as to the total number
of participating patients and the quantity of information that was be sent
from the client agents to the platform. The total amount of information
gathered from telemonitoring without including the smart-space was under
100Gbytes (N = 20, effective time pilot = 2 months, daily system usage = 12
hours, sensors data rate = 1Kbytes/s, which estimate a total of 52Gbytes).
This information is duly protected as described in section 7.
# 7\. Data security
The STARR project uses methods that emphasize good field access and extended
contact and trust building with participants. Due to the sensitive nature of
some of the topics that were discussed in interviews and focus groups, data
security is of vital importance. The following guidelines have been followed
throughout the project in order to ensure the security of the data:
* Keep anonymized data and personal data of respondents separate;
* Encrypt data if it is deemed necessary by the local researchers;
* Store data in at least two separate locations to avoid loss of data;
* Do not store personal data on USB drives;
* Save digital files in one of the preferred formats (see table above), and
* Label files in a systematically structured way in order to ensure the coherence of the final dataset;
* Whenever possible and according to the ICT policies of each organization, on all devices (desktops, laptops, external hard disks, USB drives, etc.) that are for archiving, storing and data transferring is encrypted.
Security of long-term preservation:
Long-term preservation of the data will be carried out by three partners:
* CEA – preservation of the data for two years after the end of the project, for publishing purposes;
* Hopale and Osakidetza – the data on the health record is to be stored at OSA for 30 years which is a requirement under Spanish law. In HOP, this data will be stored for 20 years, as required by French law.
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Horizon 2020
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1104_SecureCloud_690111.md
|
Executive Summary i
1 Building a DMP in the Context Of H2020 2
1.1 Purpose of the SecureCloud Data Management Plan (DMP) 2
1.2 Background Of The SecureCloud DMP 2
2 SecureCloud Data Context 3
2.1 The Smart Grid Use Case 3
2.1.1 Existing Standards 4
2.1.2 Data Model 5
2.1.3 Protocols 5
2.2 Smart Grids in SecureCloud 7
2.2.1 COPEL 7
2.2.2 Israel Electric Corporation IEC 8
3 Data Description 9
3.1 COPEL 9
3.1.1 Data Set Description 9
3.2 Israel Electric Corporation IEC 10
3.2.1 Data Set Description 10
Appendices 13
A Zenodo Repository 14
Bibliography 15
# Building a DMP in the Context Of H2020
## Purpose of the SecureCloud Data Management Plan (DMP)
SecureCloud is a Horizon 2020 project participating in the Open Research Data
Pilot. This pilot is part of the Open Access to Scientific Publications and
Research Data programme in H2020 [1]. The goal of the program is to foster
access to data generated in H2020 projects. Open Access refers to a practice
of giving online access to all scholarly disciplines information that is free
of charge to the end-user. In this way data becomes re-usable, and the benefit
of public investment in the research will be improved. The EC provided a
document with guidelines for projects participants in the pilot. The
guidelines address aspects like research data quality, sharing and security.
According to the guidelines, projects participating will need to develop a
DMP. The DMP describes the types of data that will be generated or gathered
during the project, the standards that will be used, the ways how the data
will be exploited and shared for verification or reuse, and how the data will
be preserved. This document has been produced following these guidelines and
aims to provide a consolidated plan for SecureCloud partners in the data
management plan policy that the project will follow. The document is the first
version of the DMP, delivered in M6 of the project.
## Background Of The SecureCloud DMP
The SecureCloud DMP is written in reference to the Article 29.3 in the Model
Grant Agreement called Open access to research data (research data
management). Project participants must deposit their data in a research data
repository and take measures to make the data available to third parties. The
third parties should be able to access, mine, exploit, reproduce and
disseminate the data. This should also help to validate the results presented
in scientific publications. In addition Article 29.3 suggests that
participants will have to provide information, via the repository, about tools
and instruments needed for the validation of project outcomes. The DMP will be
important for tracking all data produced during the SecureCloud project.
Article 29 [2] states that project beneficiaries do not have to ensure access
to parts of research data if such access would be lead to a risk for the
projects goals. In such cases, the DMP must contain the reasons for not
providing access.
# SecureCloud Data Context
## The Smart Grid Use Case
Traditionally, the term ”grid” is used for an electricity system that may
support all or some of the following four operations: electricity generation,
electricity transmission, electricity distribution, and electricity control.
the SmartGrid uses two-way flows of electricity and information to create an
automated and distributed advanced energy delivery network.
The SmartGrid can be regarded as an electric system that uses information,
two-way, cyber-secure communication technologies, and computational
intelligence in an integrated fashion across electricity generation,
transmission, substations, distribution and consumption to achieve a system
that is clean, safe, secure, reliable, resilient, efficient, and sustainable.
This description covers the entire spectrum of the energy system from the
generation to the end points of consumption of the electricity. The ultimate
SmartGrid is a vision. It is a loose integration of complementary components,
subsystems, functions, and services under the pervasive control of highly
intelligent management-and-control systems.
However smart technologies improve the observability and/or the
controllability of the power system. Thereby Smart Grid technologies help to
convert the power grid from a static infrastructure to be operated as
designed, to a flexible, living infrastructure operated proactively. The Smart
Grid is integrating the electrical and information technologies in between any
point of generation and any point of consumption. Examples:
* Smart metering could significantly improve knowledge of what is happening in the distribution grid, which nowadays is operated rather blindly.
* The controllability of the distribution grid is improved by load control and automated distribution switches.
* Common to most of the Smart Grid technologies is an increased use of communication and IT technologies, including an increased interaction and integration of formerly separated systems.
European Technology Platform Smart Grid defines smart grid as follows: A
SmartGrid is an electricity network that can intelligently integrate the
actions of all users connected to it generators, consumers and those that do
both in order to efficiently deliver sustainable, economic and secure
electricity supplies. A SmartGrid employs innovative products and services
together with intelligent monitoring, control, communication, and self-healing
technologies to:
* better facilitate the connection and operation of generators of all sizes and technologies;
* allow consumers to play a part in optimizing the operation of the system;
* provide consumers with greater information and choice of supply;
* significantly reduce the environmental impact of the whole electricity supply system;
* deliver enhanced levels of reliability and security of supply.
Smart Grid deployment must include not only technology, market and commercial
considerations, environmental impact, regulatory framework, standardization
usage, ICT (Information and Communication Technology) and migration strategy
but also societal requirements and governmental edicts.
### Existing Standards
The IEC 62357 Reference Architecture [3] addresses the communication
requirements of the application in the power utility domain. Its scope is the
convergence of data models, services and protocols for efficient and future-
proof system integration for all applications. This framework comprises
communication standards including semantic data models, services and protocols
for the abovementioned intersystem and subsystem.
ABNT NBR 14522
The Brazilian Standard is ABNT NBR 14522, Data Exchange for electricity
metering systems. It defines the standard for the exchange of information in
the electricity metering system in order to achieve compatibility between
systems and electricity metering equipment from different sources.
This pattern consists of the following items:
* conventional communication reader-meter,
* directional communication reader-meter,
* synchronous remote communication,
* user exits,
* communication-computer reader,
* public format,
* expanded public format,
* FK7 format,
* operational program format.
Load format parameters:
* 1/2 magnetic tape format,
* display codes,
* standardized readings.
### Data Model
The Power supply companies today face the urgent task of optimizing their core
processes. This is the only way that they can survive in this competitive
environment. The vital step here is to combine the large number of autonomous
IT systems into a homogeneous IT landscape. However, conventional network
control systems can only be integrated with considerable effort because they
do not use uniform data standards. Network control systems with a standardized
data format for source data based on the standardized Common Information Model
(CIM), in accordance with IEC 61970, offer the best basis for IT integration.
The CIM defines a common language and data modeling with the objective of
simplifying the exchange of information between the participating systems and
applications via direct interfaces. The CIM was adopted by IEC TC 57 and fast-
tracked for international standardization. The standardized CIM data model
offers a very large number of advantages for power suppliers and
manufacturers:
* Simple data exchange for companies that are near each other.
* Standardized CIM data remains stable, and data model expansions are simple to implement. It results to be simpler, faster and less risky upgrading the energy management systems.
* The CIM application program interface creates an open application interface. The aim is to use this to interconnect the application packages of all kinds of different suppliers using Plug and Play to create an EMS.
The CIM forms the basis for the definition of important standard interfaces to
other IT systems. The working group in IEC TC 57 plays a leading role in the
further development and international standardization of IEC 61970 and the
CIM. Working group WG14 (IEC 61968 Standards) in the TC 57 is responsible for
standardization of interfaces between systems, especially for the power
distribution area. Standardization in the outstation area is defined in IEC
61850. With the extension of document IEC 61850 for communication to the
control centre, there are overlaps in the object model between IEC 61970 and
IEC 61850. The CIM data model describes the electrical network, the connected
electrical components, the additional elements and the data needed for network
operation as well as the relations between these elements. The Unified
Modeling Language (UML), a standardized, objectoriented method that is
supported by various software tools, is used as the descriptive language. CIM
is used primarily to define a common language for exchanging information via
direct interfaces or an integration bus and for accessing data from various
sources. The CIM model is subdivided into packages such as basic elements,
topology, generation, load model, measurement values and protection. The sole
purpose of these packages is to make the model more transparent. Relations
between classes may extend beyond the boundaries of packages.
The ABNT NBR 14522 data definition can be found in deliverable 5.1.
### Protocols
Communication technology has continued to develop rapidly over the past few
years and the TCP/IP protocol has also become the established network protocol
standard in the power supply sector. The modern communication standards as
part of the IEC 62357 reference architecture (e.g. IEC 61850) are based on
TCP/IP and provide full technological benefits for the user. The protocol used
by Copel is defined in ABNT NBR 14522\.
IEC 61850 Communication networks and systems in substations
Since being published in 2004, the IEC 61850 communication standard has gained
more and more relevance in the field of substation automation. It provides an
effective response to the needs of the open, deregulated energy market, which
requires both reliable networks and extremely flexible technology flexible
enough to adapt to the substation challenges of the next twenty years. IEC
61850 has not only taken over the drive of the communication technology of the
office networking sector, but it has also adopted the best possible protocols
and configurations for high functionality and reliable data transmission.
Industrial Ethernet, which has been hardened for substation purposes and
provides a speed of 100 Mbit/s, offers enough bandwidth to ensure reliable
information exchange between IEDs (Intelligent Electronic Devices), as well as
reliable communication from an IED to a substation controller. The definition
of an effective process bus offers a standardized way to digitally connect
conventional as well as intelligent CTs and VTs to relays. More than just a
protocol, IEC 61850 also provides benefits in the areas of engineering and
maintenance, especially with respect to combining devices from different
vendors.
Telecontrol IEC 60870-5
IEC 60870-5 provides a communication profile for sending basic telecontrol
messages between two systems, which uses permanent directly connected data
circuits between the systems. The IEC Technical Committee 57 (Working Group
03) have developed a protocol standard for Telecontrol, Teleprotection, and
associated telecommunications for electric power systems. The result of this
work is IEC 60870-5, Telecontrol equipment and systems. Five documents specify
the base IEC 60870-5:
* IEC 60870-5-1, Transmission frame formats
* IEC 60870-5-2, Link transmission procedures
* IEC 60870-5-3, General structure of application data
* IEC 60870-5-4, Definition and coding of application information elements
* IEC 60870-5-5, Basic application functions
IEC TC 57 has also generated companion standards:
* IEC 60870-5-101, Transmission Protocols, companion standard for basic telecontrol tasks
* IEC 60870-5-102, Companion standard for the transmission of integrated totals in electric power systems (this standard is not widely used)
* IEC 60870-5-103, Transmission protocols, Companion standard for the informative interface of protection equipment
* IEC 60870-5-104, Transmission Protocols, Network access for IEC 60870-5-101 using standard transport profiles
## Smart Grids in SecureCloud
The SecureCloud project will consider use cases in the area of smart grids.
Smart grid applications offer the opportunities to consider many of the
requirements that a sensitive big data applications may have when executing in
the cloud. First, smart grid applications consider a growing volume of data.
Smart meters and sensors for monitoring distribution and transmission grids
are being deployed and are capable of continuously collecting and transmitting
data. Adequate use of this data enables energy distributors not only to
optimise their infrastructure, but also to reduce the environmental impact of
supplying power to a given load or region. Second, these promising data
analysis opportunities require having access to detailed information about
energy consumption.
In the first use case we consider, smart meters collect detailed power
consumption data from a residential or industrial consumer. Collecting data at
granularities of minutes, or even seconds, enables for sophisticated
applications that prevent power theft, detect power quality issues, in order
to calculate and prevent penalties for fault duration, among other
applications for triggering adaptation actions that increase efficiency or
robustness of the power grid. Currently, these applications are deployed on
dedicated servers maintained by utilities and system integrators. Therefore,
they cannot be exploited for all customers because this would require a large
data storage and processing infrastructure. Using cloud computing can help to
provide such an infrastructure. Nevertheless, once this data is under control
of an energy provider, an adversary who compromises this providers
infrastructure, e.g., a malicious employee or an oppressive government, could
gain access to them. The data therefore needs to be processed in a secure
fashion and never be readable in a non-encrypted form outside the secure
container.
### COPEL
Copel _Companhia Paranaense de Energia_ , the largest company of the State of
Parana, was founded on´
October 26, 1954 with ownership control held by the State of Parana. The
Company went public in April´
1994 (BM&FBovespa) and, in 1997, it was the first company of the Brazilian
electricity sector to be listed at the New York Stock Exchange. As from June
2002, the brand is also present at the European Economic Community, having
been listed at Latibex the Latin American arm of the Madrid Stock Exchange. As
of May 7, 2008, Copels shares were ranked at Level 1 of So Paulo Stock
Exchange (BM&FBovespa) Corporate Governance.
The Company directly serves 4,391,313 consuming units, across 395 cities and
1,113 locations
(districts, villages and settlements), located in the State of Parana. This
network consists of 3.5 million´ homes, 89 thousand plants, 373 thousand
commercial establishments and 369 thousand rural properties.
The staff is composed of 8,653 employees.
Copels structure comprises the operation of:
* An own generating complex composed of 20 power plants (18 hydroelectric plants, 1 thermal plant and 1 wind plant), whose installed capacity totals 4,754 MW;
* The transmission system totaling 2,302 km of lines and 33 substations (all of them automated);
* The distribution system, which consists of 192,508 km of lines and network of up to 230 kV enough to spin four times round the Earth through the Equator line and 362 substations (100% automated);
* The optical telecommunication system Paranas Infoway), which has 9,793 km of OPGW cables´ installed between the main ring and urban radials (self-sustained cables), totaling 18,212 km and reached 41,153 clients distributed reaching 399 cities of the State of Parana and 3 cities of the State´ of Santa Catarina.
COPEL as a large utility will contribute to the requirements collection from
an end-users point-of-view and will provide access to real-world Smart Meter
measurements.
The data provided by Copel are:
* Historic Data of consumers: Ref. 3.1.1 Smart meter data covers consumption data (i.e. energy usage as well as historical consumption), production data.
### Israel Electric Corporation IEC
Israel Electric Corporation is the main supplier of electrical power in
Israel. IEC builds, maintains and operates power generation stations, sub-
stations, as well as the transmission and distribution networks.
The company is the sole integrated electric utility in the State of Israel and
generates, transmits and distributes substantially all the electricity used in
the State of Israel. The State of Israel owns approximately 99.85% of the
Company.
Since its establishment and up to today, IEC builds infrastructure, generates,
transmits, and supplies electricity to 2.6 million customers. The Companys
main activities take place within the State of Israel. It generates,
transmits, distributes, and supplies most of the electricity used in the
Israeli economy according to licenses granted by virtue of the Electricity
Sector Law, 1996. In addition, the Company acts as administrator of the
countrys electricity system.
The data provided by Israel Electric Corporation IEC concern 3.2.1:
* Distribution Management System
* Transmission System
* Smart Home
# Data Description
## COPEL
Copel collects data from Group A (high voltage customers) for at least 13
months.
The data is useful to application developers. The data analysis or the
application can feed a research paper.
### Data Set Description
Data set reference and name
Consumers Group A: Historic Data.
Standards and metadata
1. Data Capture Methods:
* Data is exported by an Oracle Database;
* There is no standard or metodology associated with the kind of data that Copel exports;
* Copel can name folders and files according to the customers’ number or the meters; Additionally, all data are obtained based in ABNT NBR 14522 standard and all captured data have an associated time stamp. The data definition can be found in deliverable 5.1.
* The dataset will be exported only once.
2. Metadata:
* Copel are not able to define/describe metadata issues. Maybe some examples can help to better understand this need.
Data sharing
Data sharing criteria were not defined yet. It will depend on the anonymizing
process in order to not expose customers personal information.
* Data consists in historic values of voltage, current, consumption, power usage, power factor and geographic location. The exporting format is CSV and XLSX. Copel expects no more than 100 GB of data;
* Data must be anonimized before sharing.
COPEL will adopt the Zenodo repository (appendix A) to make the data available
to the research community.
Archiving and preservation (including storage and backup) Copel recommends to
fit with the application requirements.
## Israel Electric Corporation IEC
### Data Set Description
The data will be generated by 3 simulators in the IEC lab:
* Energy generation simulator (EGS)
* Energy transmission and distribution simulator (ETDS)
* Smart homes simulator (SHS)
The simulated data contains the momentary state of each sub-system during the
simulator execution and simulated IED’s (Intelligent Electrical Devices that
are usually installed un the grid) data. The method for data capture is: when
a value is changed (updated), it is written to the data base. Note: The user
has to know that he will use mostly simulated data that is generated by
simulated processes and not data that is generated by real field equipment.
Standards and metadata
The data of the above mentioned simulators is stored in an SQL server by SCADA
system according to SCADA standards.
Then procedure that runs automatically every hour (or other time slot that
will be decided), extracts the data to text files.
The folders in the repository are in hierarchic structure:
1. IEC
a) EGS
a) CONFIGURATION-ID
a) MODEL-DESCRIPTION OR DATE
2. ETDS
3. SHS
Note that CONFIGURATION-ID is updated due to change of configuration either in
the subsystem itself or change of configuration in a subsystem which has
relationships with it.
The data of the different files in the IEC dataset will be synchronized by the
date of generation which appears in the file name.
Some of the metadata could be created manually and some not. For example the
model-description file is a free text file describing the model and the data.
The metadata (that describes the data items), which are the columns names will
be generated automatically.
Data sharing
IEC has started to store data in a repository for some of the simulators.
The repository will be available on a server in the IEC lab network. This
server can be accessed also from the internet by IEC provided access rights to
the lab. The files could be copied and processed by each partner in the
project.
There will not be any restrictions on using the data.
IEC will publish the dataset in a disciplinary repository. Analysis will be
performed using freely available Open Source Software tools. IEC will adopt
the Zenodo repository (appendix A) to make the data available to the research
community.
Archiving and preservation (including storage and backup)
The data of the simulators is stored in SQL data base. It will be extracted
from the data base to text files in CSV format.
Currently our estimation of the volume of the data is 10 MB/day. This amount
could increase as some of the simulators are in development process and will
produce more data.
IEC preserves data for a log-term period of at least 7 years. Approximated end
volume of dataset is 100 GB. Associated cost in preparing the dataset to be
ready for archiving will be covered by project itself.
Test dataset IEC will be generated in a test laboratory by IEC team. Dataset
could be useful to other research groups working on similar research questions
in the area of energy generation and distribution. IEC plans to make our
dataset publicly accessible to the all project needs.
Example of Data Set Description
<table>
<tr>
<th>
Electricity Chain Systems
</th>
<th>
Data set and reference name
</th>
<th>
Data set description
</th>
<th>
Standards and Metadata
</th>
<th>
Data Sharing
</th>
<th>
Archiving and preservation
</th> </tr>
<tr>
<td>
Distribution Management System (DMS) operational mode normal and under attack
</td>
<td>
Modbus/TCP normal
</td>
<td>
network behavior of normal DMS operation
</td>
<td>
Standard protocol TCP/IP
</td>
<td>
pcap file available on file server
</td>
<td>
Files saved on external storage for a year
</td> </tr>
<tr>
<td>
Modbus/TCP attack
</td>
<td>
network behavior of
DMS under attack
</td>
<td>
Standard protocol TCP/IP
</td>
<td>
pcap file available on file server
</td>
<td>
Files saved on external storage for a year
</td> </tr>
<tr>
<td>
Attacker action
</td>
<td>
Standard protocol TCP/IP
Specific attacker activity
</td>
<td>
Standard protocol TCP/IP
Specific attacker activity
</td>
<td>
pcap file available on file server
</td>
<td>
Files saved on external storage for a year
</td> </tr>
<tr>
<td>
Transmission System
</td>
<td>
IEC-60870-5-104 IEC-61850-9 normal
</td>
<td>
traffic between national
communication center and substation in normal operation
</td>
<td>
IEC-60870-5-104
IEC-61850-9
</td>
<td>
pcap file available on file server
</td>
<td>
Files saved on external storage for a year
</td> </tr>
<tr>
<td>
IEC-60870-5-104 IEC-61850-9 attack
</td>
<td>
traffic between national
communication center and substation under attack
</td>
<td>
IEC-60870-5-104
IEC-61850-9
</td>
<td>
pcap file available on file server
</td>
<td>
Files saved one xternal storage for a year
</td> </tr>
<tr>
<td>
Attacker action
</td>
<td>
network attacker activity
</td>
<td>
network specific
attacker activity
</td>
<td>
pcap file available on file server
</td>
<td>
Files saved on external storage for a year
</td> </tr>
<tr>
<td>
Smart Home
</td>
<td>
Zigbee normal
</td>
<td>
common set to actuator
and sensors normal
</td>
<td>
Zigbee and Modbus/TCP
</td>
<td>
pcap file available on file server
</td>
<td>
Files saved on external storage for a year
</td> </tr>
<tr>
<td>
Zigbee attack
</td>
<td>
common set to actuator
and sensors normal
</td>
<td>
Zigbee and Modbus/TCP
</td>
<td>
pcap file available on file server
</td>
<td>
Files saved on external storage for a year
</td> </tr>
<tr>
<td>
Attacker action
</td>
<td>
network attacker activity
</td>
<td>
Specific attacker activity
</td>
<td>
pcap file available on file server
</td>
<td>
Files saved on external storage for a year
</td> </tr> </table>
Table 3.1: Example of Data Set Description
# Appendices
# A Zenodo Repository
The SecureCloud project has chosen Zenodo repository for data sharing. Zenodo
builds and operates a simple and innovative service that enables researchers,
scientists, EU projects and institutions to share, preserve and showcase
multidisciplinary research results (data and publications) that are not part
of the existing institutional or subject-based repositories of the research
communities. Zenodo enables researchers, scientists, EU projects and
institutions to:
* easily share the long tail of small research results in a wide variety of formats including text, spreadsheets,, audio, video, and images across all fields of science.
* display their research results and receive credit by making the research results citable and integrating them into existing reporting lines to funding agencies like the European Commission.
* easily access and reuse shared research results.
Deliverables:
* An open digital repository for everyone and everything not served by a dedicated service; the so called long tail of research results.
* Integration with OpenAIRE infrastructure and assured inclusion in OpenAIRE corpus.
* Easy upload and semi-automatic metadata completion by communication with existing online services such as DropBox for upload, Mendeley/ORCID/CrossRef/OpenAIRE for upload and pre-filling metadata.
* Easy access to research results via an innovative viewing option, open APIs, integration with existing online services, and the preservation of community independent data formats.
* A safe and trusted service by combining community based curation with short- and long-term archival and digital preservation strategies in accordance with best practices.
* Persistent identifiers, Digital Object Identifiers (DOIs), for sharing research results.
* Service hosting according to industry best practices in CERNs professional data centres.
* An easy way to link research results with other results and products, funding sources, institutions, and licenses.
# Bibliography
1. Guidelines on Data Management in Horizon 2020 Version 2.1, 15 February 2016, http://ec.europa.eu/research/participants/data/ref/h2020/grants_ manual/hi/oa_pilot/h2020-hi-oa-data-mgt_en.pdf
2. Guidelines on Open Access to Scientific Publications and Research Data in Horizon 2020 Version
2.1, 15 February 2016, http://ec.europa.eu/research/participants/data/ref/
h2020/grants_manual/hi/oa_pilot/h2020-hi-oa-pilot-guide_en.pdf
3. IEC TR 62357-1:2012 Power systems management and associated information exchange - Part 1:
Reference architecture, https://webstore.iec.ch/publication/6918
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**Abbreviations**
CA: Consortium Agreement
GA: Grant Agreement
DoA: Description of Action (Annex I of the Grant Agreement)
GAs: General Assembly
MB: Management Board
PAB: Project Advisory Board
WP: Workpackage
QM: Quality Management
CS: Case Study
EC: European Commission
PO: Project Officer
PR: Project Review
DM: Deliverable Manager
DDP: Deliverable Development Plan RP: Reporting Period
iv Month Year
# 1\. DATA SUMMARY
DAFNE advocates an integrated water resources management approach, which
addresses the water-energy-food (WEF) nexus explicitly and from a novel
perspective. The project’s core is the development of a decision-analytic
framework (DAF) that will enable the extensive, quantitative analysis of the
anticipated effects of alternative planning options on the broad range of
heterogeneous and often competing interests in transboundary river basins of
fast developing countries in Africa.
Data in DAFNE will be:
* Collected from multiple sources (e.g. ground stations, remote sensing) to characterize the hydrologic regime, engineering developments, social and economic systems, terrestrial and aquatic ecosystems. A baseline scenario will be defined considering the present situation of the system;
* Generated from models, to build future scenarios and assess effects of changes in the system.
Existing data, available in public repositories or from institutional partners
will be re-used whenever possible, while localized field surveys will be
carried out to inspect local scale issues or to compensate for eventual data
lack.
# 2\. FAIR DATA
DAFNE will adopt three different tools for data management:
* A project **intranet,** composed by shared directories, managed by ETHZ (ETH Zurich) and built using both institutional storage facilities (ETH Zurich _polybox_ cloud storage) - to store collected data - and external services ( _Dropbox Business Solution_ ) - for filing generated data in a password-protected way and with access restricted to project partners;
* The **DAFNE Geoportal** , publishing all relevant project data, both public and with restricted access, targeted to be used by project stakeholders during the project. The Geoportal will be powered by a webGIS platform and equipped with customizable user and roles permission tools, providing a subset of common metadata for each dataset, and data viewing and download functionalities. DAFNE Geoportal will be online and working during the lifetime of the project, while the Summer School and the MOOC course will provide the knowledge to ensure its maintenance also after the project completion;
* **Zenodo** 1 repository, to publish and maintain final project outcomes, deliverables and scientific publications, with all the data produced or needed for verification. Project information and related publications and data uploaded to Zenodo will automatically become visible on the OpenAIRE portal. 2
As for H2020 guidelines on Data Management, this document describes the data
management life cycle in DAFNE and will be updated over the course of the
project whenever significant changes related to its contents will occur.
## 2.1 MAKING DATA FINDABLE, INCLUDING PROVISIONS FOR METADATA
DAFNE will adopt the Zenodo repository to publish project outcomes, including
datasets. Using this repository, all the public data of the project will be
provided with a Digital Object Identifier (DOI )and a common dataset of
metadata (based on _Dublin Core_ 3 ).
Keywords will be sourced by the standard dictionaries, like the USGS water
dictionary 4 .
Versions of each dataset will report main and minor changes with dot notation.
Main version change will occur after significant changes in the data (e.g.
change in the data structure, massive correction or update, changes in the
procedure for data collection or generation, …), while minor version changes
will occur after data updates or limited correction. Any changes will also be
mentioned in the description metadata field.
Naming convention is reported, whenever applicable, in the attached data
catalogue (Annex 1 5 )
## 2.2 MAKING DATA OPENLY ACCESSIBLE
Data generated in DAFNE, if relevant for project deliverables or scientific
publications, will be published in the Zenodo repository, together with
associated metadata.
Data collected will be published on the same repository, unless limitations
specified in the attached data catalogue (Annex 1).
Data with restricted access will be published in the DAFNE Geoportal, which
will be made accessible through user identification to all project partner and
stakeholders.
Data will be stored using standard formats specified in Annex 1 for each
dataset and any open source software and tool developed within DAFNE needed to
access data will be published on Zenodo or on other public software code
repositories, like Github 6 .
## 2.3 MAKING DATA INTEROPERABLE
DAFNE strives to integrate data and information from different disciplines and
domains: in order to provide a common understanding on data within the project
itself, the use of the _Dublin Core Metadata Element set_ vocabulary will be
adopted.
Whenever possible and useful, more discipline-specific metadata will be also
adopted, as those defined by OGC 7 for geospatial data.
**2.4 INCREASE DATA RE-USE (THROUGH CLARIFYING LICENCES)** Generated data will
be licenced with CC-BY-SA licence 8 9 .
Collected data will be generally subject to the same licence, published on
Zenodo repository to be accessible to third parties without any time
restriction, unless with the limitations reported in Annex 1\.
Quality assurance process will be performed through the following steps:
* Collected data
1. Storage of raw datasets, without any further processing, in a dedicated folder in the intranet;
2. Data check and editing for assuring positional, attribute and temporal quality, completeness and consistency, under the responsibility of the project partner listed as reference for each dataset in Annex 1;
3. Compilation of metadata (both generic and domain specific where applicable) reporting a brief summary with the editing done;
4. Storage of the final version of the datasets in a dedicated folder in the intranet;
5. Uploading of the datasets on DAFNE Geoportal, if relevant for sharing with project stakeholders, and/or on Zenodo repository, if compliant with limitation detailed in Annex 1 and if relevant for maintaining also after the project lifetime.
* Generated data
1. Compiling metadata (both generic and domain specific where applicable) reporting a brief summary specifying the generation process (lineage);
2. Storing the final version of the generated datasets in a dedicated folder in the intranet;
3. Uploading of the datasets on DAFNE Geoportal, if relevant for sharing with project stakeholders, and/or on Zenodo repository, if relevant for maintaining also after the project lifetime.
# 3\. ALLOCATION OF RESOURCES
The data catalogue reported in Annex 1 identifies, for each dataset, the
responsible project partner. Costs are included in the tasks related to data
collection and generation and cannot be listed separately.
The DAFNE Geoportal will be hosted on the Politecnico di Milano (POLIMI)
servers using internal resources.
Costs of the project intranet on _polybox_ are covered by internal ETHZ
resources. As already mentioned in Section 2, _Dropbox Business_ accounts will
also be activated in order to have more space where to store and share raw
datasets and simulation outputs. Costs related to these accounts will be
changing according to the numbers of accounts and disk space needed for each
of them and they will be covered by project budget.
# 4\. DATA SECURITY
The tools mentioned in Section 2 of the present document are hosted partially
on external services (e.g. Zenodo) and partially on ETHZ and POLIMI servers
protected by firewall and institutional security policies.
More precisely:
* The **intranet** is relying both on ETHZ storage facilities and on external storage services; accessibility is anyway reserved only to registered users, via _HTTP Secure protocol_ (https), for both upload and download functionalities;
* **DAFNE Geoportal** will be hosted at POLIMI **:** data upload will be performed from registered users through _Secure Shell Protocol_ (SSH), while download functionalities will be available for registered project partners and stakeholders, unless if not differently specified in Annex 1;
* **Zenodo** repository is hosted at CERN and it is subject to its rules for data security as reported at _https://zenodo.org/policies_
All datasets maintained on the ETH Zurich _polybox_ Intranet and DAFNE
Geoportal will be periodically subject to incremental backup in order to avoid
data loss.
**5\. ETHICAL ASPECTS**
Ethical aspects related to data management have been already addressed in the
Deliverable D8.2.
# 6\. OTHER ISSUES
The Zambezi Watercourse Commission (ZAMCOM) has implemented ZAMWIS 9 , a
system to support Riparian States with an efficient and timely means of
sharing data and information on water resources in the Zambezi River basin.
ZAMCOM is involved in DAFNE as a key stakeholder for the Zambezi: data
collected by ZAMWIS will be used as primary source of data for DAFNE, while
data produced in the DAFNE project will be made available for integration into
ZAMWIS.
9 Zambezi Water Resources Information System (http://zamwis.wris.info/)
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1 EXECUTIVE SUMMARY 7
2 INTRODUCTION 8
3 PRINCIPLES 8
3.1 PARTICIPATION IN THE PILOT ON OPEN RESEARCH DATA 8
3.2 THE I-PROGNOSIS DATA MANAGEMENT PORTAL 9
3.3 COMMON PROCEDURES 11
3.3.1 Data Collection 11
3.3.2 Data Access Procedures 12
4 I-PROGNOSIS DATASETS 12
4.1 DATASETS NAMING 12
4.2 SUMMARY OF THE I-PROGNOSIS DATASETS 13
4.3 DATASETS BREAKDOWN 15
4.3.1 Personal & Clinical Data 15
4.3.2 Sensed and Captured GData/SData 18
4.3.3 Intervention Data 46
4.3.4 Requirements Data 63
APPENDIX I – DATASET DESCRIPTION TEMPLATE 70
# LIST OF MAIN ABBREVIATIONS
API Application Programming Interface
BDI Beck Depression Inventory
CSV Comma Separated Values
DMP Data Management Plan
DoA Description of the Action
EC European Commission
EDF European Data Format
GData Generic Data
HDF5 Hierarchical Data Format 5
HRA Health Research Authority
JSON JavaScript Object Notation
MoCA Montreal Cognitive Assessment
NMSQuest Parkinson’s Disease Non Motor Symptoms Questionnaire
PDSS Parkinson’s Disease Sleep Scale
PGS Personalised Game Suite
RBD-SQ REM Sleep Behaviour Questionnaire
SData Specific Data
UPDRS Unified Parkinson Disease Rating Scale
XML Extensible Markup Language
# EXECUTIVE SUMMARY
This deliverable is the initial version of the Data Management Plan (DMP) of
the iPROGNOSIS project, in accordance to the regulations of the Pilot action
on Open Access to Research Data of the Horizon 2020 programme (H2020). It
contains provisional information about the data that will be produced and
collected within the project, whether and how it will be made accessible for
re-use and further exploitation, and how it will be curated and preserved.
Based on the guidelines on Data Management in Horizon 2020 1 , a dataset
description template was initially drafted to provide the main pillar for the
dataset descriptions (see Appendix I). All relevant datasets were recognized
and a detailed list was produced, based on the datasets that have been
described within the DoA to be produced within the life span of the project.
The present deliverable consolidates all the partners’ feedback and provision
for the datasets they contribute to.
The project at its present stage is foreseen to develop a series of datasets,
related to issues ranging from user requirements to the intervention and
sensor captured data stemming from patients with PD and also healthy
participants. Specifically, datasets are planned to be collected in two ways:
the development data collection and the data that will be collected during the
deployment of i-PROGNOSIS system. The former dataset will help the development
and improvement of the algorithms and systems of the i-PROGNOSIS, while the
latter will constitute the actual datasets that are foreseen to be the main
input of the i-PROGNOSIS system.
Given that the majority of the i-PROGNOSIS datasets involve data collection
from human participants, the respective data produced either raw or processed,
should be carefully handled, under thorough consideration of ethical and
privacy issues involved in such datasets. For all the identified i-PROGNOSIS
datasets, specific parts that can be made publicly available have been
identified in the current first version of the project’s DMP. The public
datasets of the i-PROGNOSIS project will become available through a common
repository that will be formulated on the basis of the iPROGNOSIS “data
management portal” based on the CKAN open source platform 2 for open
datasets or similar system.
This is an initial version of the Data Management Plan of the i-PROGNOSIS
system. Having said that, the datasets described at this stage, represent an
early reflection on the data that we foresee to be collected. During the
evolution of the project, we expect that there will some changes either to the
content of the datasets or the information classification. However, main
principles - as described within this deliverable - is expected to remain
intact until the end of the project, thus forming the main strategic axes of
the overall Data Management Plan.
The overall DMP will be delivered at the end of the i-PROGNOSIS project (M48)
within the deliverable “D5.7 Report on open research data management”.
# INTRODUCTION
During the lifetime of i-PROGNOSIS, data of different nature will be generated
and collected. These data are user-related and thus require a clear plan on
how they are to be managed, i.e., stored, accessed, protected against
unauthorized or improper use, etc. Thus, the main goals of i-PROGNOSIS Data
Management Plan (DMP) are:
1. Outline of the types of data foreseen for generation at this stage of the project, including the context and procedures of this generation, as well as the degree of privacy and confidentiality of the data.
2. Outline of the protocols that will be followed to assess the generated/collected data with respect to their sensitiveness.
3. Outline of the data acquisition plan for the duration of the project.
4. Outline of the measures that are foreseen for the adequate management of the data from the ethical and security points of view.
The remainder of the deliverable is structured as follows. In Section 3, the
common data collection procedures are outlined. A short description about
specifications of the data management portal is provided, in addition to
different types of classified information. Finally, Section 4 elaborates on
each dataset, based on the provided template of Appendix I.
# PRINCIPLES
## PARTICIPATION IN THE PILOT ON OPEN RESEARCH DATA
The Data Management Plan will guide all activities regarding the
anonymisation, exchange and release of data gathered during the project, as
required for participating in the Open Research Data Pilot of the H2020
framework. Datasets that are to be produced within the i-PROGNOSIS project
span from users' demographic and clinical data to sensor data, intervention
data and outcome measures of several interventions. These data will allow the
research community to benchmark algorithms with respect to several aspects of
Parkinson's disease (PD) detection and treatment, providing a common basis for
augmented policy decision making. Since iPROGNOSIS data collection phases
involve human participants, data collected will contain sensitive, personal
information, and, as a result, the focus is also placed on possible ethical
issues and access restrictions regarding personal data, so that no regulations
on sensitive information are violated (see also D1.2 "Ethics and safety
manual").
In this scope, this version of the deliverable describes in detail the
datasets that are to be collected in each data collection phase, by each
different technical or medical partner. Individual data collection mechanisms
are treated as individual cases and therefore individual data management plans
are foreseen, including the following information - as described in the
available data management plan template and guidelines of the European
Commission (EC) 1 : i) dataset reference and name, ii) dataset description,
iii) standards and metadata, iv) data sharing, v) archiving and preservation
(including storage and backup).
As part of the Data Management Plan, the i-PROGNOSIS consortium aims at
developing a central database station, where data and evaluation outcomes of
all relevant data collection phases will be deposited. Furthermore, a central
Data Management Portal will be developed, in order to serve for the main
access point to the publicly available datasets. The i-PROGNOSIS Data
Management Portal will be based on open-source data portal platforms, such as
CKAN and/or Zenodo 3 , allowing for each partner to publish its datasets to
the research community.
## THE i-PROGNOSIS DATA MANAGEMENT PORTAL
The i-PROGNOSIS consortium will explore the possibility to develop its data
management portal based on the popular open source software CKAN 2 , and it
will be accessible through a portal (endpoint) at the following address:
"ckan.i-prognosis.eu"
CKAN may offer a better control over the stored data as they can be installed
and run completely under the control of the research institution.
CKAN is a powerful data management system that makes data accessible – by
providing tools to streamline publishing, sharing, finding and using data.
CKAN is aimed at data publishers (national and regional governments, companies
and organisations) wanting to make their data open and available. The software
supports strong integration with third-party content management systems
(CMSs), such as Drupal and WordPress.
The portal’s features include among others:
* Complete catalogue system with easy to use web interface and a powerful
API.
* Integration with the profile database system.
* Data visualisation and analytics.
* Fine access controls.
* Store the raw data and metadata.
* Search by keyword or filter by tags. See dataset information at a glance.
* Rich application programming interface (API), and over 60 extensions including link checking, comments, and analytics.
* Records datasets’ change logs and versioning information.
* Allows deep customization of its features.
* Supports unrestricted (non standards-compliant) metadata.
In addition, i-PROGNOSIS will investigate the possibility to publish a subset
of the data within the Zenodo repository service. Zenodo builds and operates a
simple and innovative service that enables researchers, scientists, EU
projects and institutions to share, preserve and showcase multidisciplinary
research results (data and publications) that are not part of the existing
institutional or subject-based repositories of the research communities.
Zenodo enables researchers, scientists, EU projects and institutions to:
* Easily share the long tail of small research results in a wide variety of formats including text, spreadsheets, audio, video, and images across all fields of science.
* Display their research results and receive credit by making the research results citable and integrating them into existing reporting lines to funding agencies like the EU.
* Easily access and reuse shared research results.
Some of the features provided by the Zenodo service are:
* An open digital repository for everyone and everything not served by a dedicated service; the so-called “long tail” of research results.
* A modern look and feel in line with current trends in state-of-the-art online services.
* Integration with OpenAIRE infrastructure and assured inclusion in OpenAIRE corpus.
* Easy upload and semi-automatic metadata completion by communication with existing online services such as DropBox for upload,
Mendeley/ORCID/CrossRef/OpenAIRE for upload and pre-filling metadata.
* Easy access to research results via an innovative viewing option, open APIs, integration with existing online services, and the preservation of community independent data formats.
* A safe and trusted service by combining community-based curation with short- and long-term archival and digital preservation strategies in accordance with best practices.
* Persistent identifiers, Digital Object Identifiers (DOIs), for sharing research results.
* Service hosting according to industry best practices in CERN’s professional data centres.
* An easy way to link research results with other results and products, funding sources, institutions, and licenses.
* Supports Dublin Core, MARC and MARCXML for metadata exporting.
* Complies with OAI-PMH for data dissemination.
## COMMON PROCEDURES
### Data Collection
3.3.1.1 Development Data Collection
The newly introduced development data acquisition period is the first and the
earliest collection period and it is highly correlated with software
development and implementation. The existence of non-artificial data is
mandatory for the effective development of software-related, and not limited,
to minor tremor or dysphonia detection. Moreover, the need for data is further
augmented due to the fact that only limited publicly available corpora exist,
and the existing ones incorporate different data capturing methods than
i-PROGNOSIS, constituting them not suitable for our needs.
Furthermore, it is crucial for software implementation to capture and have
access to raw data (e.g., unprocessed speech signals). The above is one of the
main differences with the rest of the data collection periods, where sensitive
data will be manipulated with proper methods.
_Goals of the development data collection period:_
* Aid the implementation of the processing algorithms (machine learning approaches require data during their training phase).
* Evaluate the effectiveness of various data descriptors. The latter is essential in order to successfully analyse, understand and translate the raw signals to meaningful information, as well as, to initially train the machine learning algorithms.
Development datasets will only be publicly available in the form of processed
data. Raw signal data will remain confidential and will be available within
the consortium, only after the data collectors provide their approval to share
the data.
3.3.1.2 Data to be collected during the deployment of i-PROGNOSIS
The goal of the data collection at this stage of the project is to build the
predictive algorithm for PD detection and evaluate it against the medical
golden standard. In addition, the efficacy of the interventions regimen will
be measured by medical partners, in terms of the specified medical evaluation
protocol (see D2.2 "Data collection and medical evaluation protocol").
Data collected during the deployment of i-PROGNOSIS will be fully anonymised
and will be offered to third parties in a processed format. Opening data
during this stage of the project in a raw format will not be an option, due to
privacy issues. Clinical data will be used to label the processed data, thus
forming the required “semantic ground truth” for other researchers to apply
their own machine learning techniques and statistical analyses and to compare
their findings.
### Data Access Procedures
3.3.2.1 Public
For the sections of the dataset that will be made publicly available, a
respective Web page will be created on the data management portal that will
provide a description of the dataset. External researchers will have
unrestricted access, following the steps as indicated by the data management
portal of the i-PROGNOSIS project. In addition, any third party stakeholder
will be explicitly informed about the publication that need to be cited if
part of the dataset has been used for publications. All public datasets should
be strictly anonymised.
3.3.2.2 Protected
Data denoted as protected can be shared out of the consortium, as long as
interested parties _a priori_ request access from the consortium, explaining
how these datasets will be used, e.g., research or commercial purposes.
Appropriate forms will be created and be available through the data management
portal, from where interested parties can use them and request for explicit
access to certain datasets. Upon the approval by the i-PROGNOSIS partners,
interested parties will be provided with credentials, in order to download the
requested datasets.
3.3.2.3 Confidential/Private
The private part of the datasets will be stored at a specific and designated
private space of the partner responsible for the dataset, namely the data
collector, on dedicated hard disk drives, to which only members of the data
collector, whose work directly relates to these data will have access. In
order for other i-PROGNOSIS partners to obtain access to these data, each
partner must provide a proper written request to the data collector, including
a justification over the need of access to the particular data. Once deemed
necessary, the data collector will provide the respective data to the partner.
# i-PROGNOSIS DATASETS
## DATASETS NAMING
Concerning the convention followed for naming the project datasets, it should
be noted that the name of each dataset comprises:
1. A prefix " **DS** " indicating a dataset.
2. Its unique identification number depending on the dataset category (see next Section), e.g., " **DS1** " for datasets belonging to the category of personal and clinical data, " **DS2** " for sensed and captured GData or SData, " **DS3** " for interventions data and “ **DS4** ” for requirements data.
3. Since sensed and captured GData or SData or interventions data contain different types of data, such as physiological recordings, audio, and visual features, further distinction takes place as follows: " **DS2.1** " for physiological features, " **DS2.2** " for visual features, " **DS3.1** " for serious games’ metrics, etc.
4. A short name indicative of its content and purpose.
For example, body and gesture-related features collected during the
interventions data collection phase form a dataset named:
<table>
<tr>
<th>
_Prefix_
</th>
<th>
_Category identification number_
</th>
<th>
_Type of data discriminator_
</th>
<th>
_Indicative Name_
</th> </tr>
<tr>
<td>
" **DS** "+
</td>
<td>
" **3.** "+
</td>
<td>
" **1-** "+
</td>
<td>
" **BodyAndGesture** "+
</td> </tr> </table>
i.e., " **DS3.1-BodyAndGesture** "
## SUMMARY OF THE I-PROGNOSIS DATASETS
The i-PROGNOSIS datasets are divided in the following categories:
1. Personal and clinical data.
2. Sensed and captured GData or SData.
3. Interventions data.
4. Requirements data.
Personal and clinical data will accompany and annotate Sensed and captured
GData or SData and Interventions data, as essential metadata, thus allowing
for the semantic annotation of the collected datasets.
**TABLE 1** presents cumulatively all datasets that are initially planned to
be collected within the i-PROGNOSIS project with respect to each
aforementioned category.
**TABLE 1** Indicative Datasets to be collected within the i-PROGNOSIS project
<table>
<tr>
<th>
**Dataset**
**#**
</th>
<th>
**Dataset Name**
</th>
<th>
**Description**
</th> </tr>
<tr>
<td>
</td>
<td>
**PERSONAL & CLINICAL DATA **
</td> </tr>
<tr>
<td>
1.1
</td>
<td>
**DS1.1-**
**ElectronicHealthRecordData**
</td>
<td>
It contains information related to medical evaluation protocols, such as
health screening results and interventions’ outcome measures.
</td> </tr>
<tr>
<td>
</td>
<td>
**GDATA / SDATA**
</td> </tr>
<tr>
<td>
2.1
</td>
<td>
**DS2.1-VoiceQualityAnalysis**
</td>
<td>
It contains voice data and vocal features captured using the smartphone
microphone.
</td> </tr>
<tr>
<td>
2.2
</td>
<td>
**DS2.2-PhotosFacialAnalysis**
</td>
<td>
It contains facial features extracted from photos taken with the mobile phone
camera (selfies for masked face etc.).
</td> </tr>
<tr>
<td>
2.3
</td>
<td>
**DS2.3-ActivityAnalysis**
</td>
<td>
It contains activity-related (steps, heart rate, skin temperature) features
derived from the smartwatch.
</td> </tr>
<tr>
<td>
2.4
</td>
<td>
**DS2.4-PhysioSignalAnalysis**
</td>
<td>
It contains features derived from physiological data acquisition such as
electrocardiography derived from the
</td> </tr> </table>
<table>
<tr>
<th>
</th>
<th>
</th>
<th>
Smart TV remote.
</th> </tr>
<tr>
<td>
2.5
</td>
<td>
**DS2.5-TypingPatternAnalysis**
</td>
<td>
It contains keystroke dynamics-related features collected during typing on a
virtual keyboard of a touch screenenabled smartphone.
</td> </tr>
<tr>
<td>
2.6
</td>
<td>
**DS2.6-**
**ExploratoryWalkabilityAnalysis**
</td>
<td>
It contains de-personalised location information and derived features from
GPS, Wi-Fi, Mobile Network and IMU originating from the users’ smartphone and
smartwatch.
</td> </tr>
<tr>
<td>
2.7
</td>
<td>
**DS2.7-TextSentimentAnalysis**
</td>
<td>
It contains SMS text and/or tweets collected from the users’ smartphone along
with associated sentiment classification
</td> </tr>
<tr>
<td>
2.8
</td>
<td>
**DS2.8-FoodIntakeAnalysis**
</td>
<td>
The dataset is composed of objective quantification of meal mechanics as well
as derivative information about the user’s eating behavioural elements (meal
total intake and duration, number of bites, eating rate and eating rate
changes across the meal).
</td> </tr>
<tr>
<td>
2.9
</td>
<td>
**DS2.9-BowelSoundsAnalysis**
</td>
<td>
It contains pre-processed bowel sound signals captured from the smart belt.
</td> </tr>
<tr>
<td>
2.10
</td>
<td>
**DS.10-TremorAnalysis**
</td>
<td>
It contains IMU based (accelerometer, gyroscope and magnetometer) derived
features, originating from the user’s smartphone and smartwatch.
</td> </tr>
<tr>
<td>
**INTERVENTIONS**
</td> </tr>
<tr>
<td>
3.1
</td>
<td>
**DS3.1-SeriousGamesMetrics**
</td>
<td>
In-game metrics and performance such as scores, achievements, difficulty
level, etc. of all the gaming sessions (exergames, dietary, handwriting,
emotion and voice games).
</td> </tr>
<tr>
<td>
3.2
</td>
<td>
**DS3.2-BodyGestureAnalysis**
</td>
<td>
The dataset consists of postures and gestures tracking experiments, as well as
balance and gait features, especially during the Exergames where Kinect will
be the main sensor.
</td> </tr>
<tr>
<td>
3.3
</td>
<td>
**DS3.3-SleepStageAnalysis**
</td>
<td>
It comprises sleep stage data based on accelerometer, heart rate, and skin
temperature measurements of users, captured by the smartwatch during their
sleep (and provided they are wearing the smartwatch on their wrist).
</td> </tr>
<tr>
<td>
3.4
</td>
<td>
**DS3.4-GaitAnalysis**
</td>
<td>
It contains accelerometer, gyroscope and pedometer data captured from the
smart watch/band with respect to rhythm guidance in terms of sound cues
</td> </tr>
<tr>
<td>
3.5
</td>
<td>
**DS3.5-VoiceEnhancementAnalysis**
</td>
<td>
It contains voice data captured using the smartphone microphone.
</td> </tr>
<tr>
<td>
**REQUIREMENTS**
</td> </tr>
<tr>
<td>
4.1
</td>
<td>
**DS4.1-FocusGroupsDataset**
</td>
<td>
It contains data gathered within focus groups during the requirements
elicitation phase.
</td> </tr>
<tr>
<td>
4.2
</td>
<td>
**DS4.2-WebSurveyDataset**
</td>
<td>
It contains the questions and the qualitative answers to the questions of the
i-PROGNOSIS Web survey, conducted within the context of the identification of
user requirements and system specifications, from ~2000 anonymous survey
participants.
</td> </tr> </table>
## DATASETS BREAKDOWN
In the following, datasets are described based on the template of Appendix I.
### Personal & Clinical Data
<table>
<tr>
<th>
**DATA SET REFERENCE**
**NAME**
</th>
<th>
**DS1.1-ElectronicHealthRecordData**
</th> </tr>
<tr>
<td>
**DATA SET DESCRIPTION**
</td> </tr>
<tr>
<td>
**Generic description**
</td> </tr>
<tr>
<td>
This dataset will contain the i-PROGNOSIS users' personal information,
demographics and health related data (assessment batteries’ scores,
comorbidities, medication).
This dataset will facilitate subsequent analysis with respect to the wide
GData and SData collection, as well as to clinical assessment tests towards
research on the stealth assessment and screening of early signs of PD. In
addition, results of the medical evaluation as represented within these
datasets, will allow to evaluate the effectiveness of the user participation
in the interventions (i.e., their involvement in the Serious Games).
</td> </tr>
<tr>
<td>
**Origin of data**
</td> </tr>
<tr>
<td>
This dataset a) will be collected by i-PROGNOSIS App and b) will be entered
into the i-PROGNOSIS system by the clinical sites. The data collected through
the mobile App will be taken during the first use of the mobile phone app; the
user will be asked to provide short personal information, demographics and
health related data by multiple choice questions. During the SData collection
and intervention phases, the relevant clinical data for each user will be
entered into the system from the recruiting clinical site (AUTH, KCL or TUD).
In case of participants who will follow all the projects’
</td> </tr> </table>
<table>
<tr>
<th>
phases, multiple instances of follow up assessment tests’ scores will be
available.
</th> </tr>
<tr>
<td>
**Nature and scale of data**
</td> </tr>
<tr>
<td>
The dataset will be collected during all phases of the project: the
GData/SData collection and the intervention phases. The questions delivered
through the iPROGNOSIS App will be designed in a multiple choice format, so
that the obtained information will be in a numeric format. Health related data
at this stage will include: 1. Diagnosis of Parkinson`s disease: yes / no, 2.
Physical handicap: yes / no, 3. Family history of Parkinson`s disease: yes /
no.
The process of clinical assessment will take place in three countries (UK,
Germany, and Greece), where health-centric partners are based, in order to
facilitate the medical monitoring of the users and the subsequent clinical
validation of PD risk. The clinical data will include all the relevant medical
information collected by the recruiting clinical centres. Some of the clinical
examinations include among others:
Unified Parkinson Disease Rating Scale (UPDRS), Parkinson’s Disease Non Motor
Symptoms Questionnaire (NMSQuest), Montreal Cognitive Assessment (MoCA),
Parkinson’s Disease Sleep Scale (PDSS), REM Sleep Behaviour Questionnaire
(RBDSQ), Beck Depression Inventory (BDI), Parkinson Fatigue Scale, Senior
fitness test and BERG balance scale.
During the initial pilot phase more than 5000 older adults (above 55 years of
age) is expected to participate, whereas some 80 and 60 will take part in the
SData phase and the intervention phases.
The data will be stored as records of a profile database, part of the
i-PROGNOSIS repository.
</td> </tr>
<tr>
<td>
**To whom the dataset could be useful**
</td> </tr>
<tr>
<td>
The dataset will be used to classify and rate the obtained sensed and captured
further GData and SData, intervention data and requirements data in respect to
age, gender and health state dependent values.
In addition, clinical data will be used for ground truth purposes, to train
the machine learning algorithms responsible for the second stage early PD
detection.
Also, part of this dataset will be each intervention user’s baseline and
follow up data, for the evaluation of their health state progress through the
use of the serious games Personalised Game Suite.
</td> </tr>
<tr>
<td>
**Related scientific publication(s)**
</td> </tr>
<tr>
<td>
This kind of dataset is collected in every type of clinical trial in the field
of PD or otherwise. Example:
Martinez-Martin et al. EuroInf: A Multicenter Comparative Observational Study
of Apomorphine and Levodopa Infusion in Parkinson's Disease. Mov Disord. 2015
Apr;30(4):510-6.
</td> </tr>
<tr>
<td>
**Indicative existing similar data sets** (including possibilities for
integration and reuse)
</td> </tr>
<tr>
<td>
Not applicable within the application context of the novel technologies used
in iPROGNOSIS.
</td> </tr>
<tr>
<td>
**STANDARDS AND METADATA**
</td> </tr> </table>
<table>
<tr>
<th>
Clinical data will not contain any metadata, instead the name of the
examination and the measurement score will be provided. There would be a
possibility to support electronic health data exchange, via partial
implementation of the CEN/ISO 13606 standard as declared the base standard of
the European Interoperability Framework for Health.
</th> </tr>
<tr>
<td>
**DATA SHARING**
</td> </tr>
<tr>
<td>
**Access type**
</td> </tr>
<tr>
<td>
Personal identification as stated in the consent forms will be kept separate
from any research and health-related data, which will be pseudonymised (only
initials and date of birth will be kept). Furthermore no personal data that
could identify an individual person e.g., name, date of birth is obtained. The
data for personal identification of the users will be kept locally at each
recruitment clinical centre, outside this dataset. This dataset will only
include personal and clinical data relevant to the i-PROGNOSIS protocols. Due
to ethical reasons, only averaged group data could become publicly available,
while individual data will be private to serve the i-PROGNOSIS R&D objectives.
</td> </tr>
<tr>
<td>
**Access Procedures**
</td> </tr>
<tr>
<td>
For the parts of the dataset that will be made publicly available, a
respective Web page will be created on the data management portal that will
provide a description of the dataset and links to a download section. The
private part of this dataset will be stored at a specifically designated
private space of clinical partners, in dedicated hard disk drives, on which
only members of the clinical research teams will have access.
</td> </tr>
<tr>
<td>
**Embargo periods** (if any)
</td> </tr>
<tr>
<td>
The applicable datasets will be publicly available 2 years after the end of
the project to allow the consortium prepare and submit the scientific
publications.
</td> </tr>
<tr>
<td>
**Technical mechanisms for dissemination**
</td> </tr>
<tr>
<td>
For the public part of the dataset, a link will be provided from the Data
management portal. The link will be provided in all relevant i-PROGNOSIS
publications. A technical publication describing the dataset and acquisition
procedure will be published.
</td> </tr>
<tr>
<td>
**Necessary S/W and other tools for enabling re-use**
</td> </tr>
<tr>
<td>
The dataset will be in a numeric manner and therefore designed to allow easy
reuse with commonly available tools.
</td> </tr>
<tr>
<td>
**Repository where data will be stored**
</td> </tr>
<tr>
<td>
The public part of this dataset will be accommodated at the data management
portal of the project Website, hosted by Microsoft Azure-based i-PROGNOSIS
Cloud infrastructure.
</td> </tr>
<tr>
<td>
**ARCHIVING AND PRESERVATION** (including storage and backup)
</td> </tr>
<tr>
<td>
**Data preservation period**
</td> </tr>
<tr>
<td>
The dataset will be preserved as long as there are regular downloads. After
that it would be made accessible by request and preserved by AUTH at least
until the end of
</td> </tr>
<tr>
<td>
the project. Locally, the personal and clinical data will be preserved based
on the national and departmental practises about scientific data handling.
</td> </tr>
<tr>
<td>
**Approximated end volume of data**
</td> </tr>
<tr>
<td>
Some MBs.
</td> </tr>
<tr>
<td>
**Indicative associated costs for data archiving and preservation**
</td> </tr>
<tr>
<td>
There are no costs associated with data preservation in institutional servers.
However, data publicly archived and preserved in the Microsoft Azure-based
i-PROGNOSIS Cloud infrastructure, will cost approximately 20 Euros (€) per
month for one year (at least) after the end of the project.
</td> </tr>
<tr>
<td>
**Indicative plan for covering the above costs**
</td> </tr>
<tr>
<td>
The costs associated with the data archiving and preservation will be covered
by the i-PROGNOSIS project budget.
</td> </tr>
<tr>
<td>
**PARTNERS ACTIVITIES AND RESPONSIBILITIES**
</td> </tr>
<tr>
<td>
**Partner Owner / Data**
**Collector**
</td>
<td>
**TUD, KCL, AUTH, MICROSOFT**
</td> </tr>
<tr>
<td>
**Partner in charge of the data analysis**
</td>
<td>
**Both clinical and technical partners**
</td> </tr>
<tr>
<td>
**Partner in charge of the data storage**
</td>
<td>
**TUD, KCL, AUTH**
</td> </tr>
<tr>
<td>
**WPs and Tasks**
</td> </tr>
<tr>
<td>
The data are going to be collected within activities of WP4, WP6 and WP7, to
mainly serve the research efforts of T4.1 - T4.5, T6.1 - T6.5, T7.1, T7.3 and
T7.4.
</td> </tr> </table>
### Sensed and Captured GData/SData
<table>
<tr>
<th>
**DATA SET REFERENCE NAME**
</th>
<th>
**DS2.1-VoiceQualityAnalysis**
</th> </tr>
<tr>
<td>
**DATA SET DESCRIPTION**
</td> </tr>
<tr>
<td>
**Generic description**
</td> </tr>
<tr>
<td>
Speech dataset for the detection and identification of voice-related PD
symptoms for early PD detection. The database will contain speech data
collected from phone calls recorded from the i-PROGNOSIS smartphone dialler
application. The data set includes extracted voice features and several
annotations about the speech data and the underlying speakers.
</td> </tr>
<tr>
<td>
**Origin of data**
</td> </tr>
<tr>
<td>
The dataset will be collected by recording conversations using the i-PROGNOSIS
smartphone dialler application during GData and SData collection phase.
</td> </tr>
<tr>
<td>
**Nature and scale of data**
</td> </tr>
<tr>
<td>
The goal of this dataset is to enable the detection and identification of
voice-related PD symptoms for early PD detection. The dataset will contain the
raw speech signals recorded by the i-PROGNOSIS smartphone application during
the GData and SData
</td> </tr> </table>
<table>
<tr>
<th>
collection phase as well as extracted features. Annotations including date and
time of the recordings along with speaker information (e.g., ID, gender, PD
scale) will provide the ground truth and will facilitate the subsequent
analysis of the speech recordings. The dataset will contain raw speech data
(e.g. 44.1 kHz sampling rate, wav unencoded) and text files for the
annotations.
_Data Format:_ Encoded (mp3, aac) or unencoded (wav, raw) audio waveforms; XML
or plain text files for annotations/metadata; HDF5 for feature files.
The dataset will be on the order of 0.5 MB to 5 MB per minute of speech
depending on the encoding and sampling rate.
</th> </tr>
<tr>
<td>
**To whom the dataset could be useful**
</td> </tr>
<tr>
<td>
The dataset will be used within the project for the development and evaluation
of automatic signal processing and pattern recognition algorithms for the
extraction of discriminative features for the detection and identification of
voice-related PD symptoms for the early detection of PD. This data will be
used in WP3 and WP6, supporting the early PD symptoms detection and for the
overall assessment of the iPROGNOSIS system. This dataset may also be useful
in the future for other researchers who want to explore voice-related PD
symptoms.
</td> </tr>
<tr>
<td>
**Related scientific publication(s)**
</td> </tr>
<tr>
<td>
This database will build the foundation of our research and development of
algorithms towards the identification and detection of voice-related PD
symptoms for early PD detection. We plan to propose our findings on ICASSP,
Interspeech or other voice and biomedical-related conferences.
</td> </tr>
<tr>
<td>
**Indicative existing similar data sets** (including possibilities for
integration and reuse)
</td> </tr>
<tr>
<td>
To the best of our knowledge no long-term speech database covering voice
recordings of healthy and PD subjects is (publicly) available.
</td> </tr>
<tr>
<td>
**STANDARDS AND METADATA**
</td> </tr>
<tr>
<td>
The dataset will be accompanied with detailed documentation of its contents.
Indicative metadata include: (a) description of the experimental setup and
procedure that led to the generation of the dataset and (b) documentation of
the variables recorded in the dataset.
</td> </tr>
<tr>
<td>
**DATA SHARING**
</td> </tr>
<tr>
<td>
**Access type**
</td> </tr>
<tr>
<td>
Due to ethical reasons, only the raw data, captured by normal healthy control
subjects, (during the development data collection phase) as well as a subset
of the extracted features of the collected datasets could become publicly
available, while the rest of them will be private to serve the i-PROGNOSIS R&D
objectives.
The inclusion of a (normal healthy control) subject’s data in the public part
of this dataset will be done on the basis of appropriate informed consent to
data publication.
</td> </tr>
<tr>
<td>
**Access Procedures**
</td> </tr>
<tr>
<td>
For the parts of the dataset that will be made publicly available, a
respective web
</td> </tr> </table>
<table>
<tr>
<th>
page will be created on the data management portal that will provide a
description of the dataset and links to a download section. The private part
of this dataset will be stored at a specifically designated private space of
FRAUNHOFER, in dedicated hard disk drives, on which only members of the
FRAUNHOFER research team will have access.
</th> </tr>
<tr>
<td>
**Embargo periods** (if any)
</td> </tr>
<tr>
<td>
The applicable datasets will be publicly available two years after the end of
the project to allow the consortium prepare and submit the scientific
publications.
</td> </tr>
<tr>
<td>
**Technical mechanisms for dissemination**
</td> </tr>
<tr>
<td>
For the public part of the dataset, a link will be provided from the Data
management portal. The link will be provided in all relevant i-PROGNOSIS
publications. A technical publication describing the dataset and acquisition
procedure will be published.
</td> </tr>
<tr>
<td>
**Necessary S/W and other tools for enabling re-use**
</td> </tr>
<tr>
<td>
The dataset will be designed to allow easy reuse and access with commonly
available tools (e.g. Matlab, Python, VLC, GVIM) and software libraries (e.g.
Tensorflow, FFMPEG, HDF5 C++ API, C++ STL), because the data will be stored
primarily in common file formats and generic data containers.
</td> </tr>
<tr>
<td>
**Repository where data will be stored**
</td> </tr>
<tr>
<td>
The public data will be hosted on the central database (Microsoft Azure-based
iPROGNOSIS Cloud infrastructure) that will serve the needs of the Data
Management Portal of the i-PROGNOSIS project.
</td> </tr>
<tr>
<td>
**ARCHIVING AND PRESERVATION** (including storage and backup)
</td> </tr>
<tr>
<td>
**Data preservation period**
</td> </tr>
<tr>
<td>
The public part of the dataset will be preserved as long as there are regular
downloads. After that it would be made accessible by request and preserved by
AUTH at least until the end of the project. The private part of the dataset
will be preserved by FRAUNHOFER at least until the end of the project.
</td> </tr>
<tr>
<td>
**Approximated end volume of data**
</td> </tr>
<tr>
<td>
The dataset is expected to consume up to 100 GB depending on the encoding and
the length and quantity of the speech signals. (E.g. single channel audio
waveform 16 bit, 44.1 kHz is of size 5.3 MB/min, single channel mp3 192 Kbps
is of size 1.44 MB/min)
</td> </tr>
<tr>
<td>
**Indicative associated costs for data archiving and preservation**
</td> </tr>
<tr>
<td>
There are no costs associated with data preservation in institutional servers.
However, data publicly archived and preserved in the Microsoft Azure-based
i-PROGNOSIS Cloud infrastructure, will cost approximately 20 Euros (€) per
month for one year (at least) after the end of the project..
</td> </tr>
<tr>
<td>
**Indicative plan for covering the above costs**
</td> </tr>
<tr>
<td>
The costs associated with the data archiving and preservation will be covered
by the i-PROGNOSIS project budget.
</td> </tr>
<tr>
<td>
**PARTNERS ACTIVITIES AND RESPONSIBILITIES**
</td> </tr>
<tr>
<td>
**Partner Owner / Data Collector**
</td>
<td>
**TUD, KI, FRAUNHOFER**
</td> </tr>
<tr>
<td>
**Partner in charge of the data analysis**
</td>
<td>
**FRAUNHOFER**
</td> </tr>
<tr>
<td>
**Partner in charge of the data storage**
</td>
<td>
**FRAUNHOFER**
</td> </tr>
<tr>
<td>
**WPs and Tasks**
</td> </tr>
<tr>
<td>
The data is going to be collected within activities of WP3 and WP6, to mainly
serve the research efforts of T3.3, T6.1, T6.2 and T6.3.
</td> </tr> </table>
<table>
<tr>
<th>
**DATA SET REFERENCE NAME**
</th>
<th>
**DS2.2-FacialAnalysis**
</th> </tr>
<tr>
<td>
**DATA SET DESCRIPTION**
</td> </tr>
<tr>
<td>
**Generic description**
</td> </tr>
<tr>
<td>
Facial image dataset for the detection and identification of masked face
symptoms for early PD detection. The database will contain selfie images
collected from the frontal camera of the smartphone of the user. The data set
will include annotations about a) the bounding box of the recognized face of
the user b) self-assessment of the emotional state of the user.
</td> </tr>
<tr>
<td>
**Origin of data**
</td> </tr>
<tr>
<td>
The dataset will be collected from the user smartphone by collecting the
(selfie) images obtained during GData and SData collection phases and
detecting/recognizing the face of the user.
</td> </tr>
<tr>
<td>
**Nature and scale of data**
</td> </tr>
<tr>
<td>
The goal of this dataset is to enable the detection and identification of
masked face symptoms for early PD detection. The dataset will contain the
images (selfie) images obtained during GData and SData collection phases.
Annotations including date and time of the recordings along with bounding box
of the identified user face in the selfie will provide the ground truth and
will facilitate the subsequent face expression analysis task. The dataset will
contain JPEG selfie images and XML (or text) files for the annotations.
Data Format: JPEG files for selfie images from the frontal camera of the
smartphone, XML or plain text files for annotations/metadata.
</td> </tr>
<tr>
<td>
**To whom the dataset could be useful**
</td> </tr>
<tr>
<td>
The dataset will be used within the project for the development and evaluation
of automatic facial expression recognition algorithms which will then be used
to detect masked face symptoms for the early detection of PD. This data will
be used in WP3 and WP6, supporting the early PD symptoms detection and for the
overall assessment of the i-PROGNOSIS system. This dataset may also be useful
in the future for other researchers who want to explore masked face PD
symptoms.
</td> </tr>
<tr>
<td>
**Related scientific publication(s)**
</td> </tr> </table>
<table>
<tr>
<th>
This database will build the foundation of our research and development of
algorithms towards the masked face PD symptoms for early PD detection. We plan
to propose our findings on computer vision/image processing- and biomedical-
related conferences and journals (e.g., ICIP conference).
</th> </tr>
<tr>
<td>
**Indicative existing similar data sets** (including possibilities for
integration and reuse)
</td> </tr>
<tr>
<td>
To the best of our knowledge no image database covering face expressions of
healthy and PD subjects is (publicly) available.
</td> </tr>
<tr>
<td>
**STANDARDS AND METADATA**
</td> </tr>
<tr>
<td>
The dataset will be accompanied with detailed documentation of its contents.
Indicative metadata include: a) description of the procedures that led to the
generation of the dataset and b) documentation of the contents of the dataset.
</td> </tr>
<tr>
<td>
**DATA SHARING**
</td> </tr>
<tr>
<td>
**Access type**
</td> </tr>
<tr>
<td>
Due to ethical reasons, only the data captured by a subset of the patients,
during the GData and SData collection phases, as well as normal healthy
control subjects could become publicly available, while the rest of them will
be private to serve the iPROGNOSIS R&D objectives.
The inclusion of a (normal healthy control) subject’s data in the public part
of this dataset will be done on the basis of appropriate informed consent to
data publication.
</td> </tr>
<tr>
<td>
**Access Procedures**
</td> </tr>
<tr>
<td>
For the portions of the dataset that will be made publicly available, a
respective web page will be created on the data management portal that will
provide a description of the dataset and links to a download section. The
private part of this dataset will be stored at a specifically designated
private space of CERTH, in dedicated hard disk drives, on which only members
of the CERTH research team will have access.
</td> </tr>
<tr>
<td>
**Embargo periods** (if any)
</td> </tr>
<tr>
<td>
The applicable datasets will be publicly available two years after the end of
the project to allow the consortium prepare and submit the scientific
publications.
</td> </tr>
<tr>
<td>
**Technical mechanisms for dissemination**
</td> </tr>
<tr>
<td>
For the public part of the dataset, a link will be provided from the Data
management portal. The link will be provided in all relevant i-PROGNOSIS
publications. A technical publication describing the dataset and its
annotations will be produced.
</td> </tr>
<tr>
<td>
**Necessary S/W and other tools for enabling re-use**
</td> </tr>
<tr>
<td>
The dataset will be designed to allow easy reuse and access with commonly
available tools and software libraries.
</td> </tr>
<tr>
<td>
**Repository where data will be stored**
</td> </tr>
<tr>
<td>
The public part of this dataset will be accommodated at the data management
portal of the project Website, hosted by Microsoft Azure-based i-PROGNOSIS
Cloud
</td> </tr>
<tr>
<td>
infrastructure.
</td> </tr>
<tr>
<td>
**ARCHIVING AND PRESERVATION** (including storage and backup)
</td> </tr>
<tr>
<td>
**Data preservation period**
</td> </tr>
<tr>
<td>
The public part of the dataset will be preserved on the Data Management Portal
of the i-PROGNOSIS project at least until the end of the project. The private
part of the dataset will be preserved by CERTH at least until the end of the
project.
</td> </tr>
<tr>
<td>
**Approximated end volume of data**
</td> </tr>
<tr>
<td>
Each selfie image in JPEG format has size 1-2 MBs, so a dataset of e.g., 1000
images is expected to consume up to 2 GB.
</td> </tr>
<tr>
<td>
**Indicative associated costs for data archiving and preservation**
</td> </tr>
<tr>
<td>
There are no costs associated with data preservation in institutional servers.
However, data publicly archived and preserved in the Microsoft Azure-based
i-PROGNOSIS Cloud infrastructure, will cost approximately 20 Euros (€) per
month for one year (at least) after the end of the project.
</td> </tr>
<tr>
<td>
**Indicative plan for covering the above costs**
</td> </tr>
<tr>
<td>
The costs associated with the data archiving and preservation will be covered
by the i-PROGNOSIS project budget.
</td> </tr>
<tr>
<td>
**PARTNERS ACTIVITIES AND RESPONSIBILITIES**
</td> </tr>
<tr>
<td>
**Partner Owner / Data Collector**
</td>
<td>
**CERTH**
</td> </tr>
<tr>
<td>
**Partner in charge of the data analysis**
</td>
<td>
**CERTH**
</td> </tr>
<tr>
<td>
**Partner in charge of the data storage**
</td>
<td>
**CERTH, AUTH**
</td> </tr>
<tr>
<td>
**WPs and Tasks**
</td> </tr>
<tr>
<td>
The data is going to be collected within activities of WP3 and WP6, to mainly
serve the research efforts of T3.4, T6.1 and T6.2.
</td> </tr> </table>
<table>
<tr>
<th>
**DATA SET REFERENCE NAME**
</th>
<th>
**DS2.3-ActivityAnalysis**
</th> </tr>
<tr>
<td>
**DATA SET DESCRIPTION**
</td> </tr>
<tr>
<td>
**Generic description**
</td> </tr>
<tr>
<td>
This dataset will include accelerometer, gyroscope, pedometer, estimated
calories, distance travelled, motion type (walking, jogging or running), heart
rate, skin temperature as well as altimeter and barometer when available to
identify steps climbing. The origin of the data will be from a smart
watch/band regarding activity analysis.
</td> </tr>
<tr>
<td>
**Origin of data**
</td> </tr>
<tr>
<td>
The dataset will be captured by the accelerometer, gyroscope, pedometer heart
rate and skin temperature sensors of the smart watch that will be worn by the
participants
</td> </tr> </table>
<table>
<tr>
<th>
during all the i-PROGNOSIS data collection phases.
</th> </tr>
<tr>
<td>
**Nature and scale of data**
</td> </tr>
<tr>
<td>
The first part of the dataset is going to be collected during the development
data collection phase where the subjects will follow specific activity
scenarios. The second part of the dataset will be collected mainly during the
SData and Interventions phases, where smart watches/bands will be provided to
80 and 60 participants, respectively. Moreover, the GData phase will
contribute to the dataset, however, the contribution is expected to be limited
since the smart watch/band is optional at GData. At these phases, i.e., GData,
SData and Interventions, the subjects will not perform specific activity
tasks, instead, the data will be captured throughout their daily routine
activities.
The dataset will include: i) the measures (double precision) of the
acceleration force in g units (9,81 m/s 2 ) that is applied to the smart
watch/band on all three physical axes (x, y, and z), ii) the measures (double
precision) of the smart watch/band’s rotation in rad/s around each of the
three physical axes, and iii) the absolute number of steps taken, the steps
ascended and descended (based on the smart watch’s altimeter), the number of
kilocalories (kcals) burned, the distance travelled (in cm) along with the
speed (cm/s), the pace (ms/m) and the motion type (walking, jogging, running,
etc.), the heart rate (beats/min) and the skin temperature.
The minimum sampling frequency for each sensor will be based on the outcome of
the analysis of the development data (where the sampling frequency will be the
highest possible).
Data format: TXT or CSV file.
The dataset will be in the order of ~5 MB per participant per hour of usage.
Usually the smart watch/band owners use it for at least 12 hours per day.
</td> </tr>
<tr>
<td>
**To whom the dataset could be useful**
</td> </tr>
<tr>
<td>
The collected data will be used for the development and evaluation of the
activity analysis of the i-PROGNOSIS project. The dataset could be useful in
analysing the activity levels and patterns over the course of the day.
Moreover, it may be possible to identify symptoms and signs related to
Parkinson’s Disease, such as tremor.
</td> </tr>
<tr>
<td>
**Related scientific publication(s)**
</td> </tr>
<tr>
<td>
The dataset will accompany the research results in the field of activity
analysis through a smart watch/band of people with PD. At least one
publication is intended to be made in the IEEE Biomedical Engineering journal
(or similar).
The following publication will be taken into account:
Sharma, Vinod, et al. "SPARK: personalized Parkinson disease interventions
through synergy between a smartphone and a smartwatch." International
Conference of Design, User Experience, and Usability. Springer International
Publishing, 2014.
</td> </tr>
<tr>
<td>
**Indicative existing similar data sets** (including possibilities for
integration and reuse)
</td> </tr>
<tr>
<td>
To the best of our knowledge, there is no available smart watch/band-based
activity dataset for PD-related. Related datasets include data captured from
smartwatches (some of them along with datasets originating from smartphones).
For example:
</td> </tr> </table>
<table>
<tr>
<th>
_Crowdsignals.io_ ( _http://crowdsignals.io_ ) . Crowdsignals creates the
largest set of rich, longitudinal mobile and sensor data recorded from
smartphones and smartwatches available to the community.
_Dbworld_ ( _http://permalink.gmane.org/gmane.comp.db.dbworld/54394_ ) :
Dbworld dataset contains free dataset for downloads originating from
smartphone+smartwatch mobile, sensor, and human activity. It is part of the
CrowdSignals.io data collection campaign.
_Heterogeneity Activity Recognition Dataset_
( _https://archive.ics.uci.edu/ml/datasets/Heterogeneity+Activity+Recognition_
) . The Heterogeneity Human Activity Recognition (HHAR) dataset from
smartphones and smartwatches is a dataset devised to benchmark human activity
recognition algorithms (classification, automatic data segmentation, sensor
fusion, feature extraction, etc.) in real-world contexts; specifically, the
dataset is gathered with a variety of different device models and use-
scenarios, in order to reflect sensing heterogeneities to be expected in real
deployments.
</th> </tr>
<tr>
<td>
**STANDARDS AND METADATA**
</td> </tr>
<tr>
<td>
The dataset will be accompanied with detailed documentation of its contents.
Indicative metadata include: a) description of the experimental setup and
procedure that led to the generation of the dataset, b) health status of the
subject, and c) documentation of the variables recorded in the dataset.
</td> </tr>
<tr>
<td>
**DATA SHARING**
</td> </tr>
<tr>
<td>
**Access type**
</td> </tr>
<tr>
<td>
Due to ethical issues, only part of the dataset will be publicly available.
More specifically, the data that correspond to a subset of the PD patients, as
well as the healthy control subjects, captured at the development phase of the
i-PROGNOSIS project will become publicly available. The rest of the data will
be private to serve the i-PROGNOSIS R&D objectives. The inclusion of a
subject’s data in the public part of this dataset will be done on the basis of
appropriate informed consent to data publication.
</td> </tr>
<tr>
<td>
**Access Procedures**
</td> </tr>
<tr>
<td>
The access procedures for the publicly available sub-dataset and the private
subdataset that will serve the project’s R&D objectives are described in
Sections 3.3.2.1 and Section 3.3.2.3 respectively.
</td> </tr>
<tr>
<td>
**Embargo periods** (if any)
</td> </tr>
<tr>
<td>
The applicable datasets will be publicly available 2 years after the end of
the project to allow the consortium to prepare and submit the scientific
publications.
</td> </tr>
<tr>
<td>
**Technical mechanisms for dissemination**
</td> </tr>
<tr>
<td>
For the public part of the dataset, a respective link will be provided through
the Data management portal. The link will be provided in all relevant
i-PROGNOSIS publications. A technical publication describing the dataset and
acquisition procedure will be published.
</td> </tr>
<tr>
<td>
**Necessary S/W and other tools for enabling re-use**
</td> </tr>
<tr>
<td>
The dataset will be designed to allow easy reuse with commonly available tools
and software libraries, such as Excel, Matlab, .NET etc.
</td> </tr>
<tr>
<td>
**Repository where data will be stored**
</td> </tr>
<tr>
<td>
The dataset will be accommodated at the data management portal of the project
Website, hosted by Microsoft Azure-based i-PROGNOSIS Cloud infrastructure.
</td> </tr>
<tr>
<td>
**ARCHIVING AND PRESERVATION** (including storage and backup)
</td> </tr>
<tr>
<td>
**Data preservation period**
</td> </tr>
<tr>
<td>
The public part of this dataset will be preserved online for as long as there
are regular downloads. After that it would be made accessible by request. The
private part of the dataset will be preserved by AUTH at least until the end
of the project.
</td> </tr>
<tr>
<td>
**Approximated end volume of data**
</td> </tr>
<tr>
<td>
The dataset is expected to be several Gigabytes, provided that each
participant is expected to produce at least 20MB per day and the duration is
at least 22 months (SData and Interventions phase).
</td> </tr>
<tr>
<td>
**Indicative associated costs for data archiving and preservation**
</td> </tr>
<tr>
<td>
There are no costs associated with data preservation in institutional servers.
However, data publicly archived and preserved in the Microsoft Azure-based
i-PROGNOSIS Cloud infrastructure, will cost approximately 20 Euros (€) per
month for one year (at least) after the end of the project.
</td> </tr>
<tr>
<td>
**Indicative plan for covering the above costs**
</td> </tr>
<tr>
<td>
The costs associated with the data archiving and preservation will be covered
by the i-PROGNOSIS project budget.
</td> </tr>
<tr>
<td>
**PARTNERS ACTIVITIES AND RESPONSIBILITIES**
</td> </tr>
<tr>
<td>
**Partner Owner / Data Collector**
</td>
<td>
**AUTH, MICROSOFT**
</td> </tr>
<tr>
<td>
**Partner in charge of the data analysis**
</td>
<td>
**AUTH**
</td> </tr>
<tr>
<td>
**Partner in charge of the data storage**
</td>
<td>
**AUTH, MICROSOFT**
</td> </tr>
<tr>
<td>
**WPs and Tasks**
</td> </tr>
<tr>
<td>
The data are going to be collected within the activities of WP3, WP4 and WP6,
to mainly serve the research efforts of T3.2, T4.3, T6.1, T6.2, T6.3 and T6.4.
</td> </tr> </table>
<table>
<tr>
<th>
**DATA SET REFERENCE NAME**
</th>
<th>
**DS2.4-PhysioSignalAnalysis**
</th> </tr>
<tr>
<td>
**DATA SET DESCRIPTION**
</td> </tr>
<tr>
<td>
**Generic description**
</td> </tr>
<tr>
<td>
This dataset will contain features derived from physiological data acquisition
(e.g. electrocardiography), as well as raw data. Whenever suitable,
annotations will be associated with the data to provide ground truth and
facilitate the subsequent analysis of pre-recorded data. Annotations may be
generated by the users in real
</td> </tr> </table>
<table>
<tr>
<th>
time and recorded simultaneously with the biosignal data (e.g., by means of a
manual trigger) or produced by a human expert upon revision of pre-recorded
data.
</th> </tr>
<tr>
<td>
**Origin of data**
</td> </tr>
<tr>
<td>
The dataset will be collected using the i-PROGNOSIS physiological data
acquisition devices, such as the Smart TV remote and/or the Microsoft Band.
</td> </tr>
<tr>
<td>
**Nature and scale of data**
</td> </tr>
<tr>
<td>
The datasets will be collected in guided and controlled testing scenarios,
producing SData, and include data acquired from interaction between the elder
and everyday living sensorial artefacts. The goal is to identify changes in
the cardiac and motion (i.e. tremor) patterns, which may relate to changes in
health status, behaviours or other aspects relevant to PD.
The dataset will contain raw data, as well as temporal, spectral and non-
linear features extracted from the raw time series. Examples of such features
are the Very Low Frequency (VLF), Low Frequency (LF), High Frequency (HF) and
related spectral indicators associated with the Heart Rate Variability (HRV),
instant heart rate, interbeat intervals, heart rate histogram, amongst others.
_Data Format:_ Comma-Separated Values (CSV) or Hierarchical Data Format 5
(HDF5)
The dataset is expected to include individualized records per user per device
usage session, possibly segmented in files of duration compatible with easily
manageable post-processing (e.g. 1 hour segments).
</td> </tr>
<tr>
<td>
**To whom the dataset could be useful**
</td> </tr>
<tr>
<td>
The collected data will be used within the project for the development and
evaluation of data mining and signal processing algorithms that incorporate
cardiac and motion data in the identification of early indicators of PD.
Physiological data can also be useful to follow-up the interventions, and also
to assess the progress and status of the disease (if possible). This data will
primarily feed WP4 and WP7, supporting the design of the interventions and to
the overall assessment of the i-PROGNOSIS system. Together with the
annotations, it may also be useful in the future for other researchers and
practitioners working not only in PD but in other medical specialties as well.
</td> </tr>
<tr>
<td>
**Related scientific publication(s)**
</td> </tr>
<tr>
<td>
The dataset will contribute to extend our research results in the field of
physiological data sensing to the specific case of people with PD. We envision
part of the dataset to include data collected from healthy controls. Recent
publications from the project team concerning multimodal physiological
datasets include:
. P. da Silva, C. Carreiras, A. ouren o, A. Fred, R. C. das Neves, and R.
Ferreira, “Offthe-person electrocardiography: Performance assessment and
clinical correlation,”
Health and Technology, vol. 4, no. 4, pp. 309–318, 2015.
_http://link.springer.com/article/10.1007/s12553-015-0098-y_
. P. da Silva, A. ouren o, A. Fred, N. Raposo, and M. A. de Sousa, “Check Your
Biosignals Here: A new dataset for off-the-person ECG biometrics,” Computer
Methods and Programs in Biomedicine, vol. 113, no. 2, pp. 503–514, 2014.
_http://www.sciencedirect.com/science/article/pii/S0169260713003891_
</td> </tr> </table>
<table>
<tr>
<th>
. Gamboa, . P. da Silva, and A. Fred, “ iMotion: a new re- search resource for
the study of behavior, cognition, and emotion,” Multimedia Tools and
Applications, vol. 73, no. 1, pp. 345–375, 2014
_http://link.springer.com/article/10.1007%2Fs11042-013-1602-x_
</th> </tr>
<tr>
<td>
**Indicative existing similar data sets** (including possibilities for
integration and reuse)
</td> </tr>
<tr>
<td>
To the best of our knowledge there are no datasets available (at least
publicly) that simultaneously target the specific case of PD and contain the
physiological data sources envisioned by the project.
</td> </tr>
<tr>
<td>
**STANDARDS AND METADATA**
</td> </tr>
<tr>
<td>
The dataset will be accompanied with detailed documentation of its contents.
Indicative metadata include: a) description of the experimental setup and
procedure that led to the generation of the dataset, b) type of activity
performed c) documentation of the variables recorded in the dataset, d) manual
annotations provided by the subjects, and e) manual annotations provided by
human experts.
</td> </tr>
<tr>
<td>
**DATA SHARING**
</td> </tr>
<tr>
<td>
**Access type**
</td> </tr>
<tr>
<td>
Due to ethical reasons, only the raw data, captured by normal healthy control
subjects, (during the development data collection phase) as well as a subset
of the extracted features of the collected datasets could become publicly
available, while the rest of them will be private to serve the i-PROGNOSIS R&D
objectives. The inclusion of a (normal healthy control) subject’s data in the
public part of this dataset will be done on the basis of appropriate informed
consent to data publication.
</td> </tr>
<tr>
<td>
**Access Procedures**
</td> </tr>
<tr>
<td>
The recorded data will be primarily stored as CSV or HDF5 files containing the
raw data streams and the features derived from the data. The files will hosted
on the private i-PROGNOSIS central database that will serve the needs of the
Data Management Portal of the project, or in a suitable analogous digital
space, with protected access reserved only to the relevant members of the
i-PROGNOSIS project team or people for which the consortium partners decide
that access to the data is of relevant interest to the execution of the
project. Only anonymised data will be provided, unless otherwise deemed
necessary for the adequate pursuit of the project goals.
</td> </tr>
<tr>
<td>
**Embargo periods** (if any)
</td> </tr>
<tr>
<td>
The applicable datasets will be available two years after the end of the
project to allow the consortium the preparation and submission of scientific
publications.
</td> </tr>
<tr>
<td>
**Technical mechanisms for dissemination**
</td> </tr>
<tr>
<td>
A technical publication describing the dataset and acquisition procedure will
be published is expected. Dissemination will be mostly performed through
postprocessed data and result analysis by means of relevant i-PROGNOSIS
publications.
</td> </tr>
<tr>
<td>
**Necessary S/W and other tools for enabling re-use**
</td> </tr>
<tr>
<td>
The dataset will be designed to allow easy reuse and access with commonly
available
</td> </tr>
<tr>
<td>
tools and software libraries.
</td> </tr>
<tr>
<td>
**Repository where data will be stored**
</td> </tr>
<tr>
<td>
The data will be hosted on the central database (Microsoft Azure-based
iPROGNOSIS Cloud infrastructure) that will serve the needs of the Data
Management Portal of the i-PROGNOSIS project.
</td> </tr>
<tr>
<td>
**ARCHIVING AND PRESERVATION** (including storage and backup)
</td> </tr>
<tr>
<td>
**Data preservation period**
</td> </tr>
<tr>
<td>
The private part of the dataset will be preserved on the Data Management
Portal of the i-PROGNOSIS project at least until the end of the project.
</td> </tr>
<tr>
<td>
**Approximated end volume of data**
</td> </tr>
<tr>
<td>
The dataset is expected to be several hundreds of MB, considering that the
devices can produce up to 5KB of data per second.
</td> </tr>
<tr>
<td>
**Indicative associated costs for data archiving and preservation**
</td> </tr>
<tr>
<td>
There are no costs associated with data preservation in institutional servers.
However, data publicly archived and preserved in the Microsoft Azure-based
i-PROGNOSIS Cloud infrastructure, will cost approximately 20 Euros (€) per
month for one year (at least) after the end of the project.
</td> </tr>
<tr>
<td>
**Indicative plan for covering the above costs**
</td> </tr>
<tr>
<td>
The costs associated with the data archiving and preservation will be covered
by the i-PROGNOSIS project budget.
</td> </tr>
<tr>
<td>
**PARTNERS ACTIVITIES AND RESPONSIBILITIES**
</td> </tr>
<tr>
<td>
**Partner Owner / Data Collector**
</td>
<td>
**AUTH, PLUX, MICROSOFT**
</td> </tr>
<tr>
<td>
**Partner in charge of the data analysis**
</td>
<td>
**AUTH, PLUX**
</td> </tr>
<tr>
<td>
**Partner in charge of the data storage**
</td>
<td>
**AUTH, PLUX, MICROSOFT**
</td> </tr>
<tr>
<td>
**WPs and Tasks**
</td> </tr>
<tr>
<td>
The data are going to be collected within activities of WP4, WP6 and WP7, to
mainly serve the research efforts of T4.1, T4.5, T6.1, T6.4, T7.3 and T7.4.
</td> </tr> </table>
<table>
<tr>
<th>
**DATA SET REFERENCE NAME**
</th>
<th>
**DS2.5-TypingPatternAnalysis**
</th> </tr>
<tr>
<td>
**DATA SET DESCRIPTION**
</td> </tr>
<tr>
<td>
**Generic description**
</td> </tr>
<tr>
<td>
This dataset will include typing patterns in the form of keystroke dynamics-
related features, extracted during the users' typing on a virtual keyboard (of
a touch screenenabled smartphone) so as to be used as indicators towards the
formulation of early PD detections tests. Features will be accompanied by
metadata in order to facilitate the analysis within the i-PROGNOSIS project,
as well as, by other researchers outside of the project.
</td> </tr> </table>
<table>
<tr>
<th>
**Origin of data**
</th> </tr>
<tr>
<td>
The dataset will be collected as part of the development of the i-PROGNOSIS
first stage of early PD detection. It will comprise a development sub-dataset
and corresponding metadata collected by a small number of users, as well as, a
deployment sub-dataset and corresponding metadata collected by a larger number
of users (in the range of hundreds). Both data collection procedures will be
accompanied by ethics approval.
</td> </tr>
<tr>
<td>
**Nature and scale of data**
</td> </tr>
<tr>
<td>
The dataset will be collected during the interaction of users with the virtual
keyboard of a touch screen-enabled smartphone, i.e., when they will be typing
on the keyboard. The development sub-dataset will be collected when
development users' will be typing specific text excerpts in a controlled
environment using specific smartphones. Indicative features that will comprise
the dataset are the hold time (the time a key is pressed), the press latency
(the time between pressing a key and fully releasing it), flight time (the
time in-between releasing a key and pressing the next key) and release latency
(the time between releasing a key and pressing the next key), all measured in
milliseconds (ms). The deployment sub-dataset will comprise the same features
as the development sub-dataset, but it will be collected through the
iPROGNOSIS PD detection application [via the **typing pattern service** (see
Section 6 of D2.1)] that will be released to the public for download. As a
result, it is expected that the dataset will be formulated through a
significantly larger number of users' typing on their smartphone keyboard. In
the case of the deployment dataset, keystroke dynamics will be collected from
each typing session, i.e., starting when the keyboard becomes active and
finishing when the keyboard is suppressed. In the case of the deployment
dataset, _keys pressed will not be recorded_ in order to comply with personal
data privacy and protection regulations.
The dataset is expect to be composed of 40 records for the development
subdataset, while the deployment sub-dataset scale cannot be defined as it
depends on the number of users that will download the i-PROGNOSIS PD detection
application, the number of those that will allow for the respective service to
collect typing pattern data and their frequency of actions that require typing
(e.g., composing SMSs/Tweet or e-mails).
_Data Format:_ For each text excerpt typed (development sub-dataset) a CSV
file will be generated, including the values of features and metadata. For
each typing session (deployment sub-dataset) a structured XML file will be
generated, including values of features and metadata.
</td> </tr>
<tr>
<td>
**To whom the dataset could be useful**
</td> </tr>
<tr>
<td>
The development sub-dataset will be used to initialise the i-PROGNOSIS machine
learning algorithms as far as the typing pattern inference is concerned, that
will be further trained by the deployment sub-dataset.
The deployment sub-dataset will be used for training the i-PROGNOSIS machine
learning algorithms in order for the i-PROGNOSIS investigators to examine
whether it is useful to employ this type of information towards the
realisation of early PD detection tests based on the users' interaction with
their everyday digital devices,
</td> </tr> </table>
<table>
<tr>
<th>
such as the smartphone.
Moreover, the dataset could be useful for biomedical researchers that are
interested in studying the relationship between the interfacing (requiring
motor skills) of people with digital devices and psychomotor impairments, such
as PD. The later constitutes a relatively new research subject. Adequate use
of these data presupposes at least basic background regarding PD
symptomatology, as well as, basic knowledge in data analysis methodology and
experience in the use of statistical software packages.
</th> </tr>
<tr>
<td>
**Related scientific publication(s)**
</td> </tr>
<tr>
<td>
The development sub-dataset will accompany the research results regarding the
feasibility of early PD detection tests based on users' interaction with
everyday digital devices. Research results are planned to be published
initially in the indicative journals and/or conferences provided below:
* IEEE Transactions on Biomedical Engineering (Journal)
* Annual International Conference of the IEEE Engineering in Medicine and Biology Society (Conference)
* Human Computer Interaction International (Conference)
The respective research field is relatively new. A recent publication has been
made targeting the psychomotor impairment detection based on users' natural
typing (see Giancardo et al. (2015) at _Indicative existing similar data sets_
)
</td> </tr>
<tr>
<td>
**Indicative existing similar data sets** (including possibilities for
integration and reuse)
</td> </tr>
<tr>
<td>
A similar dataset has been made available by Giancardo et al. (2015), linked
to the publication:
Giancardo, ., Sánchez-Ferro, A., Butterworth, I., Mendoza, C. S., & Hooker, J.
M. (2015). Psychomotor impairment detection via finger interactions with a
computer keyboard during natural typing. Scientific reports, 5, article no:
9678, doi:10.1038/srep09678
The dataset includes keystroke dynamics and it is part of the supplementary
information accompanying the publication and can be found through:
_http://www.nature.com/articleassets/npg/srep/2015/150409/srep09678/extref/srep09678-s1.pdf_
i-PROGNOSIS investigators plan to use this dataset and the respective
experiment protocol in order to configure the framework for the collection and
analysis of the development sub-dataset.
</td> </tr>
<tr>
<td>
**STANDARDS AND METADATA**
</td> </tr>
<tr>
<td>
The European Data Format+ (EDF+) (
_http://www.edfplus.info/specs/edfplus.html_ ) will be exploited for the
production of the metadata files.
The development sub-dataset will be accompanied by a detailed description of
its contents. Indicative metadata include: a) description of the experiment
set-up and the procedure that led to the generation of the dataset, b)
demographics, anthropometrics and basic health-record data of the experiment
participants, c) description of the keystroke dynamics-related features, and
d) annotation of features
</td> </tr> </table>
<table>
<tr>
<th>
based on (b).
The deployment sub-dataset will be annotated based on the following indicative
metadata: a) basic health record data of the users and b)
smartphone/smartwatch device model.
</th> </tr>
<tr>
<td>
**DATA SHARING**
</td> </tr>
<tr>
<td>
**Access type**
</td> </tr>
<tr>
<td>
The development sub-dataset will be publicly available (see Section 3.3.2.1),
accompanied by the respective ethics approval. Due to ethical compliance, the
deployment sub-dataset will be confidential and only i-PROGNOSIS partners will
be able to access it after following the appropriate procedure (see Section
3.3.2.3)
</td> </tr>
<tr>
<td>
**Access Procedures**
</td> </tr>
<tr>
<td>
The deployment sub-dataset that will be confidential will be handled according
the framework reported in Section 3.3.2.3. The development sub-dataset that
will be publicly available will be open for third-party stakeholders to
download based on the procedure described in Section 3.3.2.1.
</td> </tr>
<tr>
<td>
**Embargo periods** (if any)
</td> </tr>
<tr>
<td>
The development sub-dataset is planned to be publicly available after month 24
of the project to allow i-PROGNOSIS investigators to prepare and submit the
respective scientific publications, but allow also for third-party researchers
to conduct further analysis and comment on the i-PROGNOSIS results before the
end of the project.
</td> </tr>
<tr>
<td>
**Technical mechanisms for dissemination**
</td> </tr>
<tr>
<td>
The development sub-dataset will be available through the i-PROGNOSIS data
management portal (see Section 3.2). Links redirecting to the portal and the
available dataset will be provided through a dedicated page of the i-PROGNOSIS
project website ( _www.i-prognosis.eu_ ) .
A technical description providing information on the experiment protocol
through which the development sub-dataset was captured will accompany the
development sub-dataset.
</td> </tr>
<tr>
<td>
**Necessary S/W and other tools for enabling re-use**
</td> </tr>
<tr>
<td>
As the publicly available compressed dataset (development sub-dataset) will
comprise standard CSV files, no specialised software or tools will be required
for the dataset to be parsed and reused, other than a (de-)compression
software and a basic text editor (minimum requirement to access the content of
the CSV file).
</td> </tr>
<tr>
<td>
**Repository where data will be stored**
</td> </tr>
<tr>
<td>
The development sub-dataset will be stored in AUTH secure servers, as well as
in the Microsoft Azure-based i-PROGNOSIS Cloud infrastructure (data centres
are located in Ireland) where the data management portal will be installed.
The deployment subdatasets will be also stored in the i-PROGNOSIS Cloud
infrastructure, but access will be restricted (Section 3.3.2.3).
</td> </tr>
<tr>
<td>
**ARCHIVING AND PRESERVATION** (including storage and backup)
</td> </tr>
<tr>
<td>
**Data preservation period**
</td> </tr>
<tr>
<td>
The public part of this dataset will be preserved online for as long as there
are regular downloads and at least one year after the end of the project.
After that it would be made accessible by direct request to the data
collector. The confidential part of the dataset will be preserved by the
owners at least one year after the end of the project.
</td> </tr>
<tr>
<td>
**Approximated end volume of data**
</td> </tr>
<tr>
<td>
The development sub-dataset is expected to be approximately 4 MB (including
metadata), based on the number of participants (40) and the approximate size
of features recorded during a 10 min typing session (~ 70 KB).
The deployment dataset is expected to be in the range of hundreds of Gigabytes
(GB), based on the expected number of the i-PROGNOSIS App users (in the range
of thousands), an average of 10 typing activities per day for at least six
months, an average typing activity duration of 5 min, and a 7 KB size of
typing pattern features per one minute of typing.
</td> </tr>
<tr>
<td>
**Indicative associated costs for data archiving and preservation**
</td> </tr>
<tr>
<td>
The development sub-dataset will be stored on AUTH servers. There are no costs
associated with the latter means of archiving and preservation. The
development and deployment sub-datasets will be also archived and preserved in
the Microsoft Azurebased i-PROGNOSIS Cloud infrastructure. The indicative cost
of the latter means of archiving and preservation is 20 Euros (€) per month
for one year (at least) after the end of the project.
</td> </tr>
<tr>
<td>
**Indicative plan for covering the above costs**
</td> </tr>
<tr>
<td>
The costs associated with the data archiving and preservation will be covered
by the i-PROGNOSIS project budget.
</td> </tr>
<tr>
<td>
**PARTNERS ACTIVITIES AND RESPONSIBILITIES**
</td> </tr>
<tr>
<td>
**Partner Owner / Data Collector**
</td>
<td>
**AUTH, MICROSOFT**
</td> </tr>
<tr>
<td>
**Partner in charge of the data analysis**
</td>
<td>
**AUTH**
</td> </tr>
<tr>
<td>
**Partner in charge of the data storage**
</td>
<td>
**AUTH, MICROSOFT**
</td> </tr>
<tr>
<td>
**WPs and Tasks**
</td> </tr>
<tr>
<td>
The development sub-dataset will be collected within WP3 and WP6, to mainly
serve the research efforts of T3.2, T3.7, T6.2 and T6.3.
</td> </tr> </table>
<table>
<tr>
<th>
**DATA SET**
**REFERENCE NAME**
</th>
<th>
**DS2.6-ExploratoryWalkabilityAnalysis**
</th> </tr>
<tr>
<td>
**DATA SET DESCRIPTI**
</td>
<td>
**ON**
</td> </tr>
<tr>
<td>
**Generic description**
</td>
<td>
</td> </tr> </table>
<table>
<tr>
<th>
Dataset for building an anonymous and depersonalised user sociability profile,
based on information (derived features) originating by location
sensors/modules (e.g. Wi-Fi, GPS).
</th> </tr>
<tr>
<td>
**Origin of data**
</td> </tr>
<tr>
<td>
The derived information will originate from data by the smartphone and
smartwatch embedded location and IMU (GPS, GSM, Wi-Fi, accelerometer,
gyroscope, and magnetometer) sensors and modules during the GData and SData
collection periods.
</td> </tr>
<tr>
<td>
**Nature and scale of data**
</td> </tr>
<tr>
<td>
The collected data will be captured while the user carries her/his smartphone
or wears her/his smartwatch while commuting. More specifically, the dataset
will contain derived de-personalised features based on the fusion of multiple
sensors and modules such as: Wi-Fi, GSM, GPS and IMU from the user’s
smartphone and GPS and IMU from the user’s smartwatch device. Since
anonymisation is of outmost importance when dealing with location information,
all derived features will be free of any absolute world coordinates or any way
to derive them by processing.
_Data format:_ JSON/XML for data representation as well as for any relevant
annotations.
It is expected that the dataset, will be composed of an equal number of PD
patients and healthy subjects; however, the number of subjects is heavily
related with the overall user participation.
The data volume per subject is anticipated to be in the order of ~10-15 MB per
subject.
</td> </tr>
<tr>
<td>
**To whom the dataset could be useful**
</td> </tr>
<tr>
<td>
The collected data will be used for the development and evaluation of the
location and the physical activity service (i.e. background services, see D2.1
for additional information) as well as the module responsible for building the
user’s behavioural profile.
Furthermore, the dataset will be of particular interest to the researchers and
health professionals that are willing to explore the underlying information
contained in location data regarding the sociability of potential PD patients.
</td> </tr>
<tr>
<td>
**Related scientific publication(s)**
</td> </tr>
<tr>
<td>
A paper describing the dataset is expected to be published. Furthermore, a
paper describing the approach of de-personalising location information and
fusing different location data sources is also target for publication. More
information on the latter can be found in D8.3 (Dissemination plan; “A paper
presenting an approach for generating user profiles based on de-personalized
location information”).
</td> </tr>
<tr>
<td>
**Indicative existing similar data sets** (including possibilities for
integration and reuse)
</td> </tr>
<tr>
<td>
To this day, publicly available location based PD datasets are non-existent.
</td> </tr>
<tr>
<td>
**STANDARDS AND METADATA**
</td> </tr>
<tr>
<td>
The dataset will be accompanied by a complete documentation containing:
</td> </tr> </table>
<table>
<tr>
<th>
1. A description of the overall procedure regarding the collection of the data.
2. A brief definition of how the derived location-based features were generated.
3. A brief definition of the de-personalisation processes.
4. An annotation file, indicating if the subject (indicated solely by a subject ID) is a diagnosed PD patient or a healthy individual. If deemed informative or necessary, additional non-identifiable information may be included in this section.
</th> </tr>
<tr>
<td>
**DATA SHARING**
</td> </tr>
<tr>
<td>
**Access type**
</td> </tr>
<tr>
<td>
Since all identifiable information, like correlation to absolute world
coordinates, will be stripped clean off of the dataset, access type can be
classified as publicly available (i.e. can be publicly shared).
</td> </tr>
<tr>
<td>
**Access Procedures**
</td> </tr>
<tr>
<td>
A respective webpage will be created on the data management portal that will
include a brief description of the dataset as well as the publication(s) that
need to be cited in case any part of the dataset is used.
</td> </tr>
<tr>
<td>
**Embargo periods**
</td> </tr>
<tr>
<td>
The applicable dataset will be publicly available two years after the end of
the project to allow the consortium prepare and submit the scientific
publications.
</td> </tr>
<tr>
<td>
**Technical mechanisms for dissemination**
</td> </tr>
<tr>
<td>
The data management link leading to the dataset will be included in all
relevant publications. A complete technical publication describing in-depth
the dataset as well as the data acquisition procedures will be published.
</td> </tr>
<tr>
<td>
**Necessary S/W and other tools for enabling re-use**
</td> </tr>
<tr>
<td>
A typical JSON/XML (e.g. libxerces, libJSON, JSON-java) parsing library is the
only requirement for accessing the content of the dataset. Such libraries are
typical in most (if not all) programming languages.
</td> </tr>
<tr>
<td>
**Repository where data will be stored**
</td> </tr>
<tr>
<td>
The entirety of the dataset will be accommodated at the data management portal
of the project’s website, hosted by Microsoft Azure-based i-PROGNOSIS Cloud
infrastructure.
</td> </tr>
<tr>
<td>
**ARCHIVING AND PRESERVATION** (including storage and backup)
</td> </tr>
<tr>
<td>
**Data preservation period**
</td> </tr>
<tr>
<td>
The dataset will be preserved online for as long as there are regular
downloads. After that it would be made accessible by request.
</td> </tr>
<tr>
<td>
**Approximated end volume of data**
</td> </tr>
<tr>
<td>
Depending on the magnitude of user participation, the end volume of data is
expected to be in the range of ~15-25 GB.
</td> </tr>
<tr>
<td>
**Indicative associated costs for data archiving and preservation**
</td> </tr>
<tr>
<td>
There are no costs associated with data preservation in institutional servers.
However, data publicly archived and preserved in the Microsoft Azure-based
i-PROGNOSIS Cloud infrastructure, will cost approximately 20 Euros (€) per
month for one year (at least) after the end of the project.
</td> </tr>
<tr>
<td>
**Indicative plan for covering the above costs**
</td> </tr>
<tr>
<td>
The costs associated with the data archiving and preservation will be covered
by the i-PROGNOSIS project budget.
</td> </tr>
<tr>
<td>
**PARTNERS ACTIVITIES AND RESPONSIBILITIES**
</td> </tr>
<tr>
<td>
**Partner Owner / Data Collector**
</td>
<td>
**AUTH**
</td> </tr>
<tr>
<td>
**Partner in charge of the data analysis**
</td>
<td>
**AUTH**
</td> </tr>
<tr>
<td>
**Partner in charge of the data storage**
</td>
<td>
**AUTH**
</td> </tr>
<tr>
<td>
**WPs and Tasks**
</td> </tr>
<tr>
<td>
The entire “ExploratoryWalkabilityAnalysis” dataset will be collected during
WP3, to mainly serve the research efforts of T3.2, T3.7 and T4.4.
</td> </tr> </table>
<table>
<tr>
<th>
**DATA SET REFERENCE**
**NAME**
</th>
<th>
**DS2.7-TextSentimentAnalysis**
</th> </tr>
<tr>
<td>
**DATA SET DESCRIPTION**
</td> </tr>
<tr>
<td>
**Generic description**
</td> </tr>
<tr>
<td>
SMS/Tweet(Twitter message) datasets in different languages will be collected
from the users’ smartphones. These messages will be manually annotated by a
value indicating the sentiment classification (at minimum three classes will
be used: positive, negative, neutral or n/a).
</td> </tr>
<tr>
<td>
**Origin of data**
</td> </tr>
<tr>
<td>
The dataset will be collected from i-Prognosis users’ smartphones SMS or
Twitter messages (User consent will be provided).
</td> </tr>
<tr>
<td>
**Nature and scale of data**
</td> </tr>
<tr>
<td>
The goal of this dataset is to enable the detection of depression symptoms for
early PD detection.
Data Format: plain text files of SMS/Tweets with annotations/metadata.
</td> </tr>
<tr>
<td>
**To whom the dataset could be useful**
</td> </tr>
<tr>
<td>
The dataset will be used within the project for the development and evaluation
of depression recognition algorithms as a symptom for the early detection of
PD. This data will be used in WP3 and WP6, supporting the early PD symptoms
detection and for the overall assessment of the i-PROGNOSIS system. This
dataset may also be useful in the future for other researchers who want to
explore depression symptoms from short messages.
</td> </tr> </table>
<table>
<tr>
<th>
**Related scientific publication(s)**
</th> </tr>
<tr>
<td>
This database will build the foundation of our research and development of
algorithms towards detecting depression symptoms from short messages for early
PD detection. We plan to publish our findings on natural language processing-
related conferences and journals.
</td> </tr>
<tr>
<td>
**Indicative existing similar data sets** (including possibilities for
integration and reuse)
</td> </tr>
<tr>
<td>
Many manually annotated databases are already available for SMS/twitter
sentiment analysis. To name a few:
* Stanford Twitter Corpus: _http://help.sentiment140.com/for-students_
* HCR and OMD datasets: _https://bitbucket.org/speriosu/updown_
* Sentiment Strength Corpora: _http://sentistrength.wlv.ac.uk/_
* Sanders: _http://www.sananalytics.com/lab/twitter-sentiment/_
* SemEval: _http://www.cs.york.ac.uk/semeval-2013/task2/_
</td> </tr>
<tr>
<td>
**STANDARDS AND METADATA**
</td> </tr>
<tr>
<td>
SMS/tweet messages are ASCII text with a length of 160/140 characters
respectively. The available metadata will be the available sentiment
labels(classes).
</td> </tr>
<tr>
<td>
**DATA SHARING**
</td> </tr>
<tr>
<td>
**Access type**
</td> </tr>
<tr>
<td>
Due to ethical reasons, only the SMS/Tweets collected by a subset of the
patients during the initial phases by normal healthy control subjects could
become publicly available, while the rest of them will be private to serve the
i-PROGNOSIS R&D objectives.
</td> </tr>
<tr>
<td>
**Access Procedures**
</td> </tr>
<tr>
<td>
For the portions of the dataset that will be made publicly available, a
respective web page will be created on the data management portal that will
provide a description of the dataset and links to a download section. The
private part of this dataset will be stored at a specifically designated
private space of CERTH, in dedicated hard disk drives, on which only members
of the CERTH research team will have access.
</td> </tr>
<tr>
<td>
**Embargo periods** (if any)
</td> </tr>
<tr>
<td>
The applicable datasets will be publicly available two years after the end of
the project to allow the consortium prepare and submit the scientific
publications.
</td> </tr>
<tr>
<td>
**Technical mechanisms for dissemination**
</td> </tr>
<tr>
<td>
For the public part of the dataset, a link will be provided from the Data
management portal. The link will be provided in all relevant i-PROGNOSIS
publications. A technical publication describing the dataset and its
annotations will be produced.
</td> </tr>
<tr>
<td>
**Necessary S/W and other tools for enabling re-use**
</td> </tr>
<tr>
<td>
No specialised software or tools will be required for the dataset to be parsed
and reused, other than a basic text editor.
</td> </tr>
<tr>
<td>
**Repository where data will be stored**
</td> </tr>
<tr>
<td>
The public data will be hosted on the central database that will serve the
needs of the Data Management Portal of the i-PROGNOSIS project, hosted by
Microsoft Azurebased i-PROGNOSIS Cloud infrastructure.
</td> </tr>
<tr>
<td>
**ARCHIVING AND PRESERVATION** (including storage and backup)
</td> </tr>
<tr>
<td>
**Data preservation period**
</td> </tr>
<tr>
<td>
The public part of the dataset will be preserved on the Data Management Portal
of the i-PROGNOSIS project at least until the end of the project. The private
part of the dataset will be preserved by CERTH at least until the end of the
project.
</td> </tr>
<tr>
<td>
**Approximated end volume of data**
</td> </tr>
<tr>
<td>
Each text/SMS message is less than 500 bytes (assuming utf-8 encoding for
tweets), so a corpus of 10000 SMS/Tweets is expected to consume up to 5 MB
(per language).
</td> </tr>
<tr>
<td>
**Indicative associated costs for data archiving and preservation**
</td> </tr>
<tr>
<td>
The private part of the dataset will be stored on a dedicated hard drive, with
very small costs for its preservation. Data publicly archived and preserved in
the Microsoft ,Azure-based, i-PROGNOSIS Cloud infrastructure, will cost
approximately 20 Euros (€) per month for one year (at least) after the end of
the project.
</td> </tr>
<tr>
<td>
**Indicative plan for covering the above costs**
</td> </tr>
<tr>
<td>
The costs associated with the data archiving and preservation will be covered
by the i-PROGNOSIS project budget.
</td> </tr>
<tr>
<td>
**PARTNERS ACTIVITIES AND RESPONSIBILITIES**
</td> </tr>
<tr>
<td>
**Partner Owner / Data Collector**
</td>
<td>
**CERTH**
</td> </tr>
<tr>
<td>
**Partner in charge of the data analysis**
</td>
<td>
**CERTH**
</td> </tr>
<tr>
<td>
**Partner in charge of the data storage**
</td>
<td>
**CERTH, AUTH**
</td> </tr>
<tr>
<td>
**WPs and Tasks**
</td> </tr>
<tr>
<td>
The data is going to be collected within activities of WP3 and WP6, to mainly
serve the research efforts of T3.4, T6.1 and T6.2.
</td> </tr> </table>
<table>
<tr>
<th>
**DATA SET REFERENCE NAME**
</th>
<th>
**DS2.8-FoodIntakeAnalysis**
</th> </tr>
<tr>
<td>
**DATA SET DESCRIPTION**
</td> </tr>
<tr>
<td>
**Generic description**
</td> </tr>
<tr>
<td>
Dataset for the objective quantification of meal mechanics, based on the real-
time recording of the cumulative intake curve during a meal, extrapolated by
the weightloss recording from a personal weighting scale placed under the
user’s plate. This dataset will also include derivative information about the
specific values of the user’s eating behavioural elements, e.g.,: meal total
intake and duration, number of bites,
</td> </tr> </table>
<table>
<tr>
<th>
eating rate and eating rate changes across the meal.
</th> </tr>
<tr>
<td>
**Origin of data**
</td> </tr>
<tr>
<td>
The dataset will be produced by use of Mandometer electronic plate scales
during the SData collection phase. It is possible that the dataset will be
complemented by data collected during the intervention phase if/when the
Mandometer is used.
</td> </tr>
<tr>
<td>
**Nature and scale of data**
</td> </tr>
<tr>
<td>
It is expected that the datasets will be collected mainly during the SData
phase, and potentially during the intervention phase. The users will be asked
to use the Mandometers during their main (breakfast, lunch, dinner) meals
across a pre-set number of days (e.g., 5 times/week). The Mandometers will be
supposed to be used throughout the duration of a main meal.
The dataset will contain the raw weight-loss data series generated by the
removal of food from a plate during the meal, the corrected cumulative intake
curve of the user for each meal, the extracted meal behavioural indicators
(e.g., the meal total intake and duration, the recorded number of bites, the
total eating rate and the modelled eating rate changes across the meal).
Potentially, the dataset will also include averaged values per user across
multiple recorded meals. Additionally, the dataset will contain around-the-
meal self-rated subjective information concerning the meal, e.g., perceived
fullness before/after, food taste evaluation etc.
_Data Format:_ XML files for all the described measures.
The dataset will be on the order of 0.5 MB per recorded meal. The size will be
significantly higher if the food pictures are also included in the dataset.
</td> </tr>
<tr>
<td>
**To whom the dataset could be useful**
</td> </tr>
<tr>
<td>
The dataset will facilitate evaluation of the progress of each individual and
their comparison with healthy, age-matched control populations. This
comparison can be valuable for health professionals who care for patients with
Parkinson patients, the patients themselves and researchers who want to
explore the underlying shifts that happen in eating behaviour due to
Parkinson’s disease.
</td> </tr>
<tr>
<td>
**Related scientific publication(s)**
</td> </tr>
<tr>
<td>
The dataset will complement our research and clinical results in the field of
eating behaviour quantification. A subset of the investigated population,
participating in the SData phase, without having diagnosed Parkinson disease
will be the reference control population. Corresponding publications
describing and validating different components of the described methodology
are:
Ioakimidis I, Modjtaba Z, Eriksson-Marklund L, Bergh C, Grigoriadis A, &
Södersten P. (2011). “Description of Chewing and Food Intake over the Course
of a Meal.” Physiology & Behavior,104 (5,: 761–769.
Papapanagiotou V, Diou C, Langlet B, Ioakimidis I, & Delopoulos A. (2015). “A
parametric Probabilistic Context-Free Grammar for food intake analysis based
on continuous meal weight measurements.” Conf Proc IEEE Eng Med Biol
Soc.,78537856.
</td> </tr>
<tr>
<td>
**Indicative existing similar data sets** (including possibilities for
integration and reuse)
</td> </tr> </table>
<table>
<tr>
<th>
While there are many age stratified datasets about qualitative measures
pertaining to the nutritional profile of national or local populations, to the
best of our knowledge there exists no comparable dataset for Parkinson
patients or aged-matched control group.
Available national (Sweden) dataset example:
Riksmaten 2010 ( _http://www.livsmedelsverket.se/matvanor-halsa--
miljo/kostrad-ochmatvanor/matvanor---undersokningar/riksmaten-2010-11---
vuxna/_ ) : In this dataset, a representative sample of 1800 individuals
between 18–80 years, living in Sweden, were invited to participate in the
survey. The data collection took place between May 2010 and July 2011\.
</th> </tr>
<tr>
<td>
**STANDARDS AND METADATA**
</td> </tr>
<tr>
<td>
The dataset will be accompanied with detailed documentation of its contents.
Indicative metadata include: a) description of the experimental setup and
procedure that led to the generation of the dataset, b) type of consumed food
(probably identified through mobile-recorded pictures), c) documentation of
the variables recorded in the dataset, and d) the relative “positioning” of an
individual in the corresponding population distribution.
</td> </tr>
<tr>
<td>
**DATA SHARING**
</td> </tr>
<tr>
<td>
**Access type**
</td> </tr>
<tr>
<td>
Due to ethical reasons, only the data captured by a subset of the patients and
normal healthy control subjects, could become publicly available, while the
rest of them will be private to serve the i-PROGNOSIS R&D objectives.
The inclusion of a (normal healthy control) subject’s data in the public part
of this dataset will be done on the basis of appropriate informed consent to
data publication. It will be investigated further whether the complementary
food pictures can also become publicly available.
</td> </tr>
<tr>
<td>
**Access Procedures**
</td> </tr>
<tr>
<td>
For the portions of the dataset that will be made publicly available, a
respective web page will be created on the data management portal that will
provide a description of the dataset. The private part of this dataset will be
stored at a specifically designated private space of KI, in encrypted hard
disk drives, on which only members of the KI research team, whose work
directly relates to these data will have access. For further i-PROGNOSIS
partners to obtain access to these data, they should provide a proper request
to the KI primarily responsible, including a justification over the need to
have access to these data. Once deemed necessary, KI will provide the
respective data portions to the partner.
</td> </tr>
<tr>
<td>
**Embargo periods** (if any)
</td> </tr>
<tr>
<td>
The applicable datasets will be publicly available 2 years after the end of
the project to allow the consortium prepare and submit the scientific
publications.
</td> </tr>
<tr>
<td>
**Technical mechanisms for dissemination**
</td> </tr>
<tr>
<td>
For the public part of the dataset, a link to this will be provided from the
Data management portal. The link will be provided in all relevant i-PROGNOSIS
</td> </tr>
<tr>
<td>
publications. A technical publication describing the dataset and acquisition
procedure will be published.
</td> </tr>
<tr>
<td>
**Necessary S/W and other tools for enabling re-use**
</td> </tr>
<tr>
<td>
A typical XML parsing library is the only requirement for accessing the
content of the dataset. Such libraries are typical in most (if not all)
programming languages.
</td> </tr>
<tr>
<td>
**Repository where data will be stored**
</td> </tr>
<tr>
<td>
The public part of this dataset will be accommodated at the data management
portal of the project website, hosted within Microsoft Azure-based i-PROGNOSIS
Cloud infrastructure.
</td> </tr>
<tr>
<td>
**ARCHIVING AND PRESERVATION** (including storage and backup)
</td> </tr>
<tr>
<td>
**Data preservation period**
</td> </tr>
<tr>
<td>
The public part of this dataset will be preserved online for as long as there
are regular downloads. After that it would be made accessible by request. The
private part of the dataset will be preserved by KI at least until 10 years
after the publication of the scientific results.
</td> </tr>
<tr>
<td>
**Approximated end volume of data**
</td> </tr>
<tr>
<td>
The dataset will be on the order of 0.5 MB per recorded meal. The size will be
significantly higher if the food pictures are also included in the dataset. A
first estimation of the dataset, including food pictures would be in the range
from ~2-7 GB.
</td> </tr>
<tr>
<td>
**Indicative associated costs for data archiving and preservation**
</td> </tr>
<tr>
<td>
Probably parts from three hard disk drives in the KI lab (primary, backup 1
and backup 2) will be allocated for the dataset. There are no costs associated
with its preservation. Data publicly archived and preserved in the Microsoft
Azure-based iPROGNOSIS Cloud infrastructure, will cost approximately 20 Euros
(€) per month for one year (at least) after the end of the project.
</td> </tr>
<tr>
<td>
**Indicative plan for covering the above costs**
</td> </tr>
<tr>
<td>
The costs associated with the data archiving and preservation will be covered
by the i-PROGNOSIS project budget.
</td> </tr>
<tr>
<td>
**PARTNERS ACTIVITIES AND RESPONSIBILITIES**
</td> </tr>
<tr>
<td>
**Partner Owner / Data Collector**
</td>
<td>
**KI**
</td> </tr>
<tr>
<td>
**Partner in charge of the data analysis**
</td>
<td>
**KI**
</td> </tr>
<tr>
<td>
**Partner in charge of the data storage**
</td>
<td>
**KI**
</td> </tr>
<tr>
<td>
**WPs and Tasks**
</td> </tr>
<tr>
<td>
The data are going to be collected within activities of WP3, WP4 and WP6, to
mainly serve the research efforts of T3.2, T3.7, T4.1, T4.5, T6.1 and T6.4.
</td> </tr> </table>
<table>
<tr>
<th>
**DATA SET REFERENCE NAME**
</th>
<th>
**DS2.9-BowelSoundsAnalysis**
</th> </tr> </table>
<table>
<tr>
<th>
**DATA SET DESCRIPTION**
</th> </tr>
<tr>
<td>
**Generic description**
</td> </tr>
<tr>
<td>
This dataset will include bowel sound data captured from a smart belt towards
bowel immobility related to increased constipation detection, since
constipation is one of the earliest non-motor symptoms in PD patients.
</td> </tr>
<tr>
<td>
**Origin of data**
</td> </tr>
<tr>
<td>
The dataset will be captured by a custom-made smart belt that will carry
distributed microphones covering the abdomen area and will be worn either
directly on the skin or above clothes.
</td> </tr>
<tr>
<td>
**Nature and scale of data**
</td> </tr>
<tr>
<td>
The first part of the dataset is expected to be collected during the
development data collection phase where the subjects (healthy control and PD
patients) will wear the smart belt for at least 1 hour in laboratory
environment. The second part of the dataset will be collected mainly during
the SData and Interventions phases, where smart belts will be given to 80
(potential PD patients and healthy controls) and 60 (identified PD patients)
subjects, respectively. The recording will take place at each subject’s
indoor/outdoor environment. Since the bowel sounds, according to the
literature, exist in the range of 100 – 1000/1500 Hz, the raw signal will be
filtered (high-pass filtering at 80 Hz in order to eliminate the influence of
cardiac and pulmonary sounds).
Data format: TXT or CSV file
Considering three recording channels, an average usage of 4 hours per day and
3 kHz sampling frequency, the dataset is estimated to be on the order of ~500
MB per subject per day.
</td> </tr>
<tr>
<td>
**To whom the dataset could be useful**
</td> </tr>
<tr>
<td>
The collected data will be used for the development and evaluation of
constipation detection algorithms (through sound-based intestinal motility
assessment). Moreover, the dataset may be useful to identify other
gastrointestinal disorders, such as obstructions, ascites, infections and
trauma. The fact that there are not any available datasets of this kind, makes
this dataset valuable to the research community, especially to the
gastroenterologists.
</td> </tr>
<tr>
<td>
**Related scientific publication(s)**
</td> </tr>
<tr>
<td>
The dataset will accompany the research results in the field of bowel sounds
analysis for constipation detection of people with PD. One publication is
intended to be made in the World Journal of Gastroenterology (or similar).
</td> </tr>
<tr>
<td>
**Indicative existing similar data sets** (including possibilities for
integration and reuse)
</td> </tr>
<tr>
<td>
To the best of our knowledge, there is no available bowel sounds dataset. To
this end, the dataset that will be collected during i-PROGNOSIS project is
expected to have major impact.
</td> </tr>
<tr>
<td>
**STANDARDS AND METADATA**
</td> </tr>
<tr>
<td>
The dataset will be accompanied with detailed documentation of its contents.
</td> </tr> </table>
<table>
<tr>
<th>
Indicative metadata include: a) description of the experimental setup and
procedure that led to the generation of the dataset, b) health status of the
subject, and c) documentation of the variables recorded in the dataset.
</th> </tr>
<tr>
<td>
**DATA SHARING**
</td> </tr>
<tr>
<td>
**Access type**
</td> </tr>
<tr>
<td>
Due to ethical issues, only part of the dataset will be publicly available.
More specifically, the data that correspond to a subset of the PD patients as
well as the healthy control subjects, captured at the initial phase of the
i-PROGNOSIS project will become publicly available. The rest of the data will
be private to serve the iPROGNOSIS R&D objectives. The inclusion of a
subject’s data in the public part of this dataset will be done on the basis of
appropriate informed consent to data publication.
</td> </tr>
<tr>
<td>
**Access Procedures**
</td> </tr>
<tr>
<td>
The access procedures for the publicly available sub-dataset and the private
subdataset that will serve the project’s R&D objectives are described in
Sections 3.3.2.1 and Section 3.3.2.3 respectively.
</td> </tr>
<tr>
<td>
**Embargo periods** (if any)
</td> </tr>
<tr>
<td>
The applicable datasets will be publicly available 2 years after the end of
the project to allow the consortium prepare and submit the scientific
publications.
</td> </tr>
<tr>
<td>
**Technical mechanisms for dissemination**
</td> </tr>
<tr>
<td>
For the public part of the dataset, a link to this will be provided from the
Data management portal. The link will be provided in all relevant i-PROGNOSIS
publications. A technical publication describing the dataset and acquisition
procedure will be published.
</td> </tr>
<tr>
<td>
**Necessary S/W and other tools for enabling re-use**
</td> </tr>
<tr>
<td>
The dataset will be designed to allow easy reuse with commonly available tools
and software libraries, such as Excel, Matlab, .NET etc.
</td> </tr>
<tr>
<td>
**Repository where data will be stored**
</td> </tr>
<tr>
<td>
The public part of this dataset will be accommodated at the data management
portal of the project website, hosted by Microsoft Azure-based i-PROGNOSIS
Cloud infrastructure.
</td> </tr>
<tr>
<td>
**ARCHIVING AND PRESERVATION** (including storage and backup)
</td> </tr>
<tr>
<td>
**Data preservation period**
</td> </tr>
<tr>
<td>
The public part of this dataset will be preserved online for as long as there
are regular downloads. After that it would be made accessible by request. The
private part of the dataset will be preserved by AUTH at least until the end
of the project.
</td> </tr>
<tr>
<td>
**Approximated end volume of data**
</td> </tr>
<tr>
<td>
The dataset is expected to be several Terabytes, provided that each
participant is expected to produce at least 500MB per day and the duration is
at least 22 months
</td> </tr>
<tr>
<td>
(SData and Interventions phase).
</td> </tr>
<tr>
<td>
**Indicative associated costs for data archiving and preservation**
</td> </tr>
<tr>
<td>
Probably four dedicated hard disk drives will be allocated for the private
dataset. There are no costs associated with its preservation. Data publicly
archived and preserved in the Microsoft Azure-based i-PROGNOSIS Cloud
infrastructure, will cost approximately 20 Euros (€) per month for one year
(at least) after the end of the project.
</td> </tr>
<tr>
<td>
**Indicative plan for covering the above costs**
</td> </tr>
<tr>
<td>
The costs associated with the data archiving and preservation will be covered
by the i-PROGNOSIS project budget.
</td> </tr>
<tr>
<td>
**PARTNERS ACTIVITIES AND RESPONSIBILITIES**
</td> </tr>
<tr>
<td>
**Partner Owner / Data Collector**
</td>
<td>
**PLUX, AUTH**
</td> </tr>
<tr>
<td>
**Partner in charge of the data analysis**
</td>
<td>
**PLUX, AUTH**
</td> </tr>
<tr>
<td>
**Partner in charge of the data storage**
</td>
<td>
**PLUX, AUTH**
</td> </tr>
<tr>
<td>
**WPs and Tasks**
</td> </tr>
<tr>
<td>
The data are going to be collected within the activities of WP3 and WP6, to
mainly serve the research efforts of T3.6, T6.1, T6.3 and T6.4.
</td> </tr> </table>
<table>
<tr>
<th>
**DATA SET REFERENCE NAME**
</th>
<th>
**DS2.10-TremorAnalysis**
</th> </tr>
<tr>
<td>
**DATA SET DESCRIPTION**
</td> </tr>
<tr>
<td>
**Generic description**
</td> </tr>
<tr>
<td>
Dataset for detecting the presence of PD minor tremor in the upper limbs. The
dataset will include derived features from 3-axis acceleration, orientation as
well as magnetic data streams.
</td> </tr>
<tr>
<td>
**Origin of data**
</td> </tr>
<tr>
<td>
The data will originate from the smartphone and smartwatch embedded IMU
(gyroscope, accelerometer and magnetometer) sensors during the GData and SData
collection periods.
</td> </tr>
<tr>
<td>
**Nature and scale of data**
</td> </tr>
<tr>
<td>
The collected data will be captured during the user’s interaction with the
smartphone/smartwatch devices. More specifically, smartphone generated data
will be collected while the user performs certain actions while holding the
device (e.g., during a voice call). On the other hand, data originating from
the smartwatch device will be captured while the user performs everyday
activities while wearing the smartwatch.
_Data format:_ JSON/XML for data representation as well as for any relevant
annotations.
</td> </tr> </table>
<table>
<tr>
<th>
It is expected that the dataset, will be composed of an equal number of PD
patients and healthy subjects; however, the number of subjects is heavily
related with the overall user participation.
The data volume per subject is anticipated to be in the order of ~5-10 MB per
subject.
</th> </tr>
<tr>
<td>
**To whom the dataset could be useful**
</td> </tr>
<tr>
<td>
The collected data will be used for the development and evaluation of the
minor tremor detection module (i.e. handling service, see D2.1 for more
information) of the i-PROGNOSIS smartphone detection application.
Furthermore, the dataset will be of particular interest to the researchers and
health professionals that are willing to explore the underlying information
contained in IMU data regarding PD tremor.
</td> </tr>
<tr>
<td>
**Related scientific publication(s)**
</td> </tr>
<tr>
<td>
A paper describing the dataset is expected to be published. Furthermore, a
paper describing the approach for detecting PD minor tremor is also target for
publication. More information on the latter can be found in D8.3
(Dissemination plan; “A paper presenting a method for detecting the presence
of minor-tremor, based on IMU data collected by the smartphone/smartwatch”).
</td> </tr>
<tr>
<td>
**Indicative existing similar data sets** (including possibilities for
integration and reuse)
</td> </tr>
<tr>
<td>
To this day, publicly available IMU based PD tremor datasets are non-existent.
</td> </tr>
<tr>
<td>
**STANDARDS AND METADATA**
</td> </tr>
<tr>
<td>
The dataset will be accompanied by a complete documentation containing: a) a
description of the overall procedure regarding the collection of the data, b)
a brief definition of how the derived IMU-based features were generated and c)
an annotation file, indicating if the subject (indicated solely by a subject
ID) is a diagnosed PD patient or a healthy individual. If deemed informative
or necessary, additional non-identifiable information may be included in this
section.
</td> </tr>
<tr>
<td>
**DATA SHARING**
</td> </tr>
<tr>
<td>
**Access type**
</td> </tr>
<tr>
<td>
Since all identifiable information will be stripped clean off of the dataset,
access type can be classified as publicly available (i.e. can be publicly
shared).
</td> </tr>
<tr>
<td>
**Access Procedures**
</td> </tr>
<tr>
<td>
A respective webpage will be created on the data management portal that will
include a brief description of the dataset as well as the publication(s) that
need to be cited in case any part of the dataset is used.
</td> </tr>
<tr>
<td>
**Embargo periods**
</td> </tr>
<tr>
<td>
The applicable dataset will be publicly available two years after the end of
the project to allow the consortium prepare and submit the scientific
publications.
</td> </tr>
<tr>
<td>
**Technical mechanisms for dissemination**
</td> </tr>
<tr>
<td>
The data management portal will provide a link leading to the dataset, which
will be
</td> </tr>
<tr>
<td>
included in all relevant publications. A complete technical publication
describing indepth the dataset as well as the data acquisition procedures will
be published.
</td> </tr>
<tr>
<td>
**Necessary S/W and other tools for enabling re-use**
</td> </tr>
<tr>
<td>
A typical JSON/XML (e.g., libxerces, libJSON, JSON-java) parsing library is
the only requirement for accessing the content of the dataset. Such libraries
are typical in most (if not all) programming languages.
</td> </tr>
<tr>
<td>
**Repository where data will be stored**
</td> </tr>
<tr>
<td>
The entirety of the dataset will be accommodated at the data management portal
of the project’s website, hosted by Microsoft Azure-based i-PROGNOSIS Cloud
infrastructure..
</td> </tr>
<tr>
<td>
**ARCHIVING AND PRESERVATION** (including storage and backup)
</td> </tr>
<tr>
<td>
**Data preservation period**
</td> </tr>
<tr>
<td>
The dataset will be preserved online for as long as there are regular
downloads. After that it would be made accessible by request.
</td> </tr>
<tr>
<td>
**Approximated end volume of data**
</td> </tr>
<tr>
<td>
Depending on the magnitude of user participation, the end volume of data is
expected to be in the range of ~15-25 GB.
</td> </tr>
<tr>
<td>
**Indicative associated costs for data archiving and preservation**
</td> </tr>
<tr>
<td>
Data archived and preserved in the Microsoft Azure-based i-PROGNOSIS Cloud
infrastructure, will cost approximately 20 Euros (€) per month for one year
(at least) after the end of the project.
</td> </tr>
<tr>
<td>
**Indicative plan for covering the above costs**
</td> </tr>
<tr>
<td>
The costs associated with the data archiving and preservation will be covered
by the i-PROGNOSIS project budget.
</td> </tr>
<tr>
<td>
**PARTNERS ACTIVITIES AND RESPONSIBILITIES**
</td> </tr>
<tr>
<td>
**Partner Owner / Data Collector**
</td>
<td>
**AUTH**
</td> </tr>
<tr>
<td>
**Partner in charge of the data analysis**
</td>
<td>
**AUTH**
</td> </tr>
<tr>
<td>
**Partner in charge of the data storage**
</td>
<td>
**AUTH**
</td> </tr>
<tr>
<td>
**WPs and Tasks**
</td> </tr>
<tr>
<td>
The entire dataset will be collected during WP3, to mainly serve the research
efforts of T3.2, T3.7 and T4.4.
</td> </tr> </table>
### Intervention Data
<table>
<tr>
<th>
**DATA SET REFERENCE NAME**
</th>
<th>
**DS3.1-SeriousGames**
</th> </tr>
<tr>
<td>
**DATA SET DESCRIPTION**
</td> </tr>
<tr>
<td>
**Generic description**
</td> </tr>
<tr>
<td>
This dataset will include in-game metrics and games’ performance of all the
sessions
</td> </tr> </table>
<table>
<tr>
<th>
in a semantically annotated way (by adopting proper ontologies for each
specific domain) so as to facilitate subsequently analysis with respect to the
clinical assessment tests towards research on stealth assessment and screening
of PD early signs and disease progress through the suite (linear correlation
of in-game metrics with clinical assessment tests).
</th> </tr>
<tr>
<td>
**Origin of data**
</td> </tr>
<tr>
<td>
The dataset will be collected using the i-PROGNOSIS Personalised Game Suite
(PGS) which will accommodate different types of serious gaming interventions.
</td> </tr>
<tr>
<td>
**Nature and scale of data**
</td> </tr>
<tr>
<td>
The datasets will be collected during the serious gaming interventions of
iPROGNOSIS. The serious games will ask the user to go under specific exercises
or gaming tests while mechanisms in the background will track and collect the
user’s performance. In the case of Exergames, the user will be asked to
perform specific exercises which will be captured by the Kinect sensor.
The dataset will contain metrics like reaction time, player’s path / optimum
path, goal time, movement range, balance, min and max angles of movements,
wrong choices and much more in-game metrics that will arise during the design
requirements of the serious games.
_Data Format:_ RDF triples or JSON
The dataset is expected to be composed of 180 records per serious gaming
session.
</td> </tr>
<tr>
<td>
**To whom the dataset could be useful**
</td> </tr>
<tr>
<td>
The collected data will be used for the development and evaluation of the
Personalized Game Suite in terms of usability, acceptance, effectiveness and
user’s assessment. The different parts of the (semantically annotated where
applicable) dataset could be useful in the benchmarking of a series of serious
games, focusing either on the effectiveness axis as the primary role of the
interventions, as well as in detecting and assessing, if possible, the
disease’s progress and status. The latter will feed the T4.5 which is intended
to dynamically recommend game adaptations for personalised and optimised use
as well as keeping the users in the “flow zone” which represents the feeling
of being complete and energized focus in an activity with a high level of
enjoyment and fulfilment towards increased adherence. Finally, this dataset
will be part of the overall evaluation of the i-PROGNOSIS contributing to the
validation of the pilot applications and interventions.
</td> </tr>
<tr>
<td>
**Related scientific publication(s)**
</td> </tr>
<tr>
<td>
The dataset will accompany our research results in the field of human activity
monitoring of people with Parkinson’s. A subset of similar dataset, including
recordings from elderly people without Parkinson’s going through Exergames,
will be used as initial input to this dataset. Corresponding publications are:
Bamparopoulos, G., Konstantinidis, E., Bratsas, C., & Bamidis, P. D. (2016).
Towards exergaming commons: composing the exergame ontology for publishing
open game data. Journal of Biomedical Semantics, 7(1), Article nr 4.
http://doi.org/10.1186/s13326-016-0046-4
Konstantinidis, E., Bamparopoulos, G., & Bamidis, P. (2016). Moving Real
Exergaming
</td> </tr> </table>
<table>
<tr>
<th>
Engines on the Web: The webFitForAll case study in an active and healthy
ageing living lab environment. IEEE Journal of Biomedical and Health
Informatics, 1–1.
http://doi.org/10.1109/JBHI.2016.2559787
</th> </tr>
<tr>
<td>
**Indicative existing similar data sets** (including possibilities for
integration and reuse)
</td> </tr>
<tr>
<td>
It should be noted that there are no serious games datasets available online.
To the best of our knowledge, the only open dataset regarding serious games,
and more specifically Exergames performed by elderly people, is the one that
AUTH has published a couple of years before described in:
Bamparopoulos, G., Konstantinidis, E., Bratsas, C., & Bamidis, P. D. (2016).
Towards exergaming commons: composing the exergame ontology for publishing
open game data. Journal of Biomedical Semantics, 7(1), Article nr 4.
http://doi.org/10.1186/s13326-016-0046-4
</td> </tr>
<tr>
<td>
**STANDARDS AND METADATA**
</td> </tr>
<tr>
<td>
The dataset will be accompanied with detailed documentation of its contents.
Indicative metadata include: a) description of the experimental setup and
procedure that led to the generation of the dataset, b) type of exercise or
game, c) documentation of the variables recorded in the dataset, and d)
semantic annotation based on existing ontologies.
</td> </tr>
<tr>
<td>
**DATA SHARING**
</td> </tr>
<tr>
<td>
**Access type**
</td> </tr>
<tr>
<td>
Due to ethical reasons, only the data captured by a subset of the patients
during the initial phases by normal healthy control subjects could become
publicly available, while the rest of them will be private to serve the
i-PROGNOSIS R&D objectives.
The inclusion of a (normal healthy control) subject’s data in the public part
of this dataset will be done on the basis of appropriate informed consent to
data publication.
</td> </tr>
<tr>
<td>
**Access Procedures**
</td> </tr>
<tr>
<td>
An ontology that describes Exergames using the Web Ontology Language (OWL) is
available at _http://purl.org/net/exergame/ns#_ . The acquired game results
will be automatically converted to RDF triples and published on the web as
open data, accessible through a SPARQL Endpoint. The data will be accessible
from a SPARQL endpoint that is available at _http://www.fitforall.gr/sparql_
, where queries can be made using the GET or POST method. In order to
facilitate access, links to a download section where the datasets will be
downloaded as JSON files will be provided if required. The private part of
this dataset will be stored at a specifically designated private space of
AUTH, in dedicated hard disk drives, on which only members of the AUTH
research team whose work directly relates to these data will have access. For
further i-PROGNOSIS partners to obtain access to these data, they should
provide a proper request to the AUTH primarily responsible, including a
justification over the need to have access to these data. Once deemed
necessary, AUTH will provide the respective data portions to the partner.
Another option is to provide a public link to download the dataset through the
iPROGNOSIS data management portal.
</td> </tr> </table>
<table>
<tr>
<th>
**Embargo periods** (if any)
</th> </tr>
<tr>
<td>
The applicable datasets will be publicly available two years after the end of
the project to allow the consortium prepare and submit the scientific
publications.
</td> </tr>
<tr>
<td>
**Technical mechanisms for dissemination**
</td> </tr>
<tr>
<td>
For the public part of the dataset, a link to this, as well as to the SPARQL
endpoint _http://www.fitforall.gr/sparql_ , will be provided from the Data
management portal. The link and the SPARQL endpoint will be provided in all
relevant i-PROGNOSIS publications. A technical publication describing the
dataset and acquisition procedure will be published.
</td> </tr>
<tr>
<td>
**Necessary S/W and other tools for enabling re-use**
</td> </tr>
<tr>
<td>
The dataset will be designed to allow easy reuse with commonly available tools
and software libraries.
</td> </tr>
<tr>
<td>
**Repository where data will be stored**
</td> </tr>
<tr>
<td>
The public part of this dataset will be accommodated at AUTH servers,
accessible by the SPARQL endpoint _http://www.fitforall.gr/sparql_ . In
addition, a subset of these data will be hosted to the central database that
will serve the needs of the Data Management Portal of the i-PROGNOSIS project.
</td> </tr>
<tr>
<td>
**ARCHIVING AND PRESERVATION** (including storage and backup)
</td> </tr>
<tr>
<td>
**Data preservation period**
</td> </tr>
<tr>
<td>
The public part of this dataset will be preserved online for as long as there
are regular downloads. After that it would be made accessible by request. The
private part of the dataset will be preserved by AUTH at least until the end
of the project.
</td> </tr>
<tr>
<td>
**Approximated end volume of data**
</td> </tr>
<tr>
<td>
The dataset is expected to be several hundreds of MB, provided that each
session is expected to produce a volume of ~80 KB of data.
</td> </tr>
<tr>
<td>
**Indicative associated costs for data archiving and preservation**
</td> </tr>
<tr>
<td>
The dataset will be stored in the serious gaming server hosted by AUTH. There
are no costs associated with its preservation. In the case that Microsoft
Azure will accommodate the serious gaming server, there will be an additional
cost of approximately 20 Euros (€) per month for one year
</td> </tr>
<tr>
<td>
**Indicative plan for covering the above costs**
</td> </tr>
<tr>
<td>
The costs associated with the data archiving and preservation will be covered
by the i-PROGNOSIS project budget.
</td> </tr>
<tr>
<td>
**PARTNERS ACTIVITIES AND RESPONSIBILITIES**
</td> </tr>
<tr>
<td>
**Partner Owner / Data Collector**
</td>
<td>
**AUTH**
</td> </tr>
<tr>
<td>
**Partner in charge of the data analysis**
</td>
<td>
**AUTH**
</td> </tr>
<tr>
<td>
**Partner in charge of the data storage**
</td>
<td>
**AUTH**
</td> </tr>
<tr>
<td>
**WPs and Tasks**
</td> </tr> </table>
The data are going to be collected within activities of WP4, WP6 and WP7, to
mainly serve the research efforts of T4.1, T4.5, T6.1, T6.4, T7.3 and T7.4.
<table>
<tr>
<th>
**DATA SET REFERENCE NAME**
</th>
<th>
**DS3.2-BodyAndGesture**
</th> </tr>
<tr>
<td>
**DATA SET DESCRIPTION**
</td> </tr>
<tr>
<td>
**Generic description**
</td> </tr>
<tr>
<td>
Dataset for human identification, postures and gestures tracking experiments,
especially during the Exergames where Kinect will be the main sensor. The
dataset is also planned to include balance and gait features.
</td> </tr>
<tr>
<td>
**Origin of data**
</td> </tr>
<tr>
<td>
The dataset will be collected using a Kinect2 sensor during the exergaming
intervention.
</td> </tr>
<tr>
<td>
**Nature and scale of data**
</td> </tr>
<tr>
<td>
It is expected that the datasets will be collected mainly during the
exergaming interventions. The Exergames will ask the user to go under specific
exercises which will be captured by Kinect. The approximate duration of each
session is expected to be close to 45min.
The dataset will contain the user’s silhouette as this is provided by the
Kinect SKD (Skeleton with bones and joints). It will be investigated further
whether the RGB and depth image will be collected.
_Data Format:_ PNG/JPG for images (both RGB and depth), JSON for the
coordinates of the joints and bones, XML or TXT for annotations.
The dataset will be on the order of ~2-5 GB per recording hour.
</td> </tr>
<tr>
<td>
**To whom the dataset could be useful**
</td> </tr>
<tr>
<td>
The collected data will be used for the development and evaluation of the
human activity monitoring and the Exergames intervention of the i-PROGNOSIS
project. The different parts of the dataset could be useful in the
benchmarking of a series of human tracking methods, focusing either on human
identification, on posture and gesture analysis and tracking as well as in
detecting, if possible, symptoms and signs of those appear at people with
Parkinson’s.
</td> </tr>
<tr>
<td>
**Related scientific publication(s)**
</td> </tr>
<tr>
<td>
The dataset will accompany our research results in the field of human activity
monitoring of people with Parkinson’s. A subset of similar dataset, including
recordings from elderly people without Parkinson’s going through Exergames,
will be used as initial input to this dataset. Corresponding publications are:
Konstantinidis, E. I., Antoniou, P. E., Bamparopoulos, G., & Bamidis, P. D.
(2014). A lightweight framework for transparent cross platform communication
of controller data in ambient assisted living environments. Information
Sciences, 300, 124–139. http://doi.org/10.1016/j.ins.2014.10.070
Konstantinidis, E. I., Billis, A. S., Bratsas, C., & Bamidis, P. D. (2016).
Active and Healthy
</td> </tr> </table>
<table>
<tr>
<th>
Ageing Big Dataset streaming on demand. In Proceedings of the 18th
International Conference on Human-Computer Interaction. Toronto, Canada.
</th> </tr>
<tr>
<td>
**Indicative existing similar data sets** (including possibilities for
integration and reuse)
</td> </tr>
<tr>
<td>
It should be noted that although several RGB-D datasets stemming from Kinect
sensor dealing with human activity analysis are publicly available (see
datasets below), to the best of our knowledge, there is no available any
Parkinson monitoring dataset yet.
_G3D_ ( _http://dipersec.king.ac.uk/G3D/G3D.html_ ) : G3D dataset contains a
range of gaming actions captured with Microsoft Kinect. The Kinect enabled us
to record synchronised video, depth and skeleton data. The dataset contains 10
subjects performing 20 gaming actions: _punch right, punch left, kick right,
kick left, defend, golf swing, tennis swing forehand, tennis swing backhand,
tennis serve, throw bowling ball, aim and fire gun, walk, run, jump, climb,
crouch, steer a car, wave, flap and clap_ .
_MSRC-12 Kinect gesture dataset_ (
_http://research.microsoft.com/enus/um/cambridge/projects/msrc12/_ ) : The
Microsoft Research Cambridge-12 Kinect gesture data set consists of sequences
of human movements, represented as bodypart locations, and the associated
gesture to be recognized by the system.
_RGB-D Person Re-identification Dataset_ ( _http://old.iit.it/en/datasets-
andcode/datasets/rgbdid.html_ ) : A new dataset for person re-identification
using depth information. The main motivation is that the standard techniques
(such as _SDALF_ ) fail when the individuals change their clothing,
therefore they cannot be used for longterm video surveillance. Depth
information is the solution to deal with this problem because it stays
constant for a longer period of time.
_DGait Database_ ( _http://www.cvc.uab.es/DGaitDB/Summary.html_ ) : DGait is
a new gait database acquired with a depth camera. This database contains
videos from 53 subjects walking in different directions.
</td> </tr>
<tr>
<td>
**STANDARDS AND METADATA**
</td> </tr>
<tr>
<td>
The dataset will be accompanied with detailed documentation of its contents.
Indicative metadata include: a) description of the experimental setup and
procedure that led to the generation of the dataset, b) type of exercise in
case the dataset produced during exergames, c) documentation of the variables
recorded in the dataset, and d) annotated posture, action and activity.
</td> </tr>
<tr>
<td>
**DATA SHARING**
</td> </tr>
<tr>
<td>
**Access type**
</td> </tr>
<tr>
<td>
Due to ethical reasons, only the data captured by a subset of the patients
during the initial phases by normal healthy control subjects could become
publicly available, while the rest of them will be private to serve the
i-PROGNOSIS R&D objectives.
The inclusion of a (normal healthy control) subject’s data in the public part
of this dataset will be done on the basis of appropriate informed consent to
data publication. It will be investigated further whether the silhouette
(coordinates of joints and bones) subset of the dataset could be publicly
available.
</td> </tr>
<tr>
<td>
**Access Procedures**
</td> </tr> </table>
<table>
<tr>
<th>
For the portions of the dataset that will be made publicly available, a
respective web page will be created on the data management portal (the use of
the CAC-playback manager 4 will be assessed) that will provide a description
of the dataset, links to a download section and a playback possibility in case
the playback manager approach is followed. The private part of this dataset
will be stored at a specifically designated private space of AUTH, in
dedicated hard disk drives, on which only members of the AUTH and CERTH
research team whose work directly relates to these data will have access. For
further i-PROGNOSIS partners to obtain access to these data, they should
provide a proper request to the AUTH/CERTH primarily responsible, including a
justification over the need to have access to these data. Once deemed
necessary, AUTH/CERTH will provide the respective data portions to the
partner.
Cac-playback manager: Konstantinidis, E. I., Billis, A. S., Bratsas, C., &
Bamidis, P. D. (2016). Active and Healthy Ageing Big Dataset streaming on
demand. In
Proceedings of the 18th International Conference on Human-Computer
Interaction. Toronto, Canada.
</th> </tr>
<tr>
<td>
**Embargo periods** (if any)
</td> </tr>
<tr>
<td>
The applicable datasets will be publicly available 2 years after the end of
the project to allow the consortium prepare and submit the scientific
publications.
</td> </tr>
<tr>
<td>
**Technical mechanisms for dissemination**
</td> </tr>
<tr>
<td>
For the public part of the dataset, a link to this will be provided from the
Data management portal. The link will be provided in all relevant i-PROGNOSIS
publications. A technical publication describing the dataset and acquisition
procedure will be published.
</td> </tr>
<tr>
<td>
**Necessary S/W and other tools for enabling re-use**
</td> </tr>
<tr>
<td>
The dataset will be designed to allow easy reuse with commonly available tools
and software libraries. In case of supporting the online playback of the
datasets, libraries for a variety of programming languages will be released
(e.g. _http://www.cacframework.com/_ )
</td> </tr>
<tr>
<td>
**Repository where data will be stored**
</td> </tr>
<tr>
<td>
The public part of this dataset will be accommodated at the data management
portal of the project website.
</td> </tr>
<tr>
<td>
**ARCHIVING AND PRESERVATION** (including storage and backup)
</td> </tr>
<tr>
<td>
**Data preservation period**
</td> </tr>
<tr>
<td>
The public part of this dataset will be preserved online for as long as there
are regular downloads. After that it would be made accessible by request. The
private part of the dataset will be preserved by AUTH at least until the end
of the project.
</td> </tr>
<tr>
<td>
**Approximated end volume of data**
</td> </tr>
<tr>
<td>
The dataset is expected to be several gigabytes, provided that each recording
hour is expected to be ~2-5 GB.
</td> </tr>
<tr>
<td>
**Indicative associated costs for data archiving and preservation**
</td> </tr>
<tr>
<td>
Probably two dedicated hard disk drives will be allocated for the dataset; one
for the public part and one for the private. In this case the costs associated
with its preservation will be according to the hardware cost (hard drives) .
Azure will be also considered as candidate data archiving means although the
volume of the data (1-2 TB) would make this choice very expensive.
</td> </tr>
<tr>
<td>
**Indicative plan for covering the above costs**
</td> </tr>
<tr>
<td>
Small one-time costs covered by i-PROGNOSIS. In case of Azure, the cost will
be higher.
</td> </tr>
<tr>
<td>
**PARTNERS ACTIVITIES AND RESPONSIBILITIES**
</td> </tr>
<tr>
<td>
**Partner Owner / Data Collector**
</td>
<td>
**AUTH, CERTH**
</td> </tr>
<tr>
<td>
**Partner in charge of the data analysis**
</td>
<td>
**AUTH, CERTH**
</td> </tr>
<tr>
<td>
**Partner in charge of the data storage**
</td>
<td>
**AUTH**
</td> </tr>
<tr>
<td>
**WPs and Tasks**
</td> </tr>
<tr>
<td>
The data are going to be collected within activities of WP3, WP4 and WP6, to
mainly serve the research efforts of T3.2, T3.7, T4.1, T4.5, T6.1 and T6.4.
</td> </tr> </table>
<table>
<tr>
<th>
**DATA SET REFERENCE NAME**
</th>
<th>
**DS3.3-SleepStageAnalysis**
</th> </tr>
<tr>
<td>
**DATA SET DESCRIPTION**
</td> </tr>
<tr>
<td>
**Generic description**
</td> </tr>
<tr>
<td>
This dataset will include physiological (heart rate and skin temperature) data
and IMU (accelerometer and gyroscope) data, captured by the smartwatch
(Microsoft Band 2) collected during the users' sleep, to be i) used for the
development of or ii) collected during the targeted nocturnal intervention
(TNI), i.e., an i-PROGNOSIS intervention that will use pacifying sounds in
order to reinstate a satisfactory sleep stage when a sleep disturbance episode
is detected. Features will be accompanied by metadata in order to facilitate
the analysis within the i-PROGNOSIS project, as well as, by other researchers
outside of the project.
</td> </tr>
<tr>
<td>
**Origin of data**
</td> </tr>
<tr>
<td>
The dataset will be collected as part of the development of the i-PROGNOSIS
supportive interventions and the respective data collection phase. It will
comprise a development sub-dataset and corresponding metadata, collected by
experiment participants during a short period of time (~2 days), as well as, a
deployment subdataset and corresponding metadata collected by users during a
significantly longer period (in the range of months) during the interventions
data collection period. Both data collection procedures will be accompanied by
ethics approval.
</td> </tr>
<tr>
<td>
**Nature and scale of data**
</td> </tr>
<tr>
<td>
The data collection leading to the formulation of the dataset will take place
during the users' sleep. The development sub-dataset will be collected based
on an
</td> </tr> </table>
<table>
<tr>
<th>
experiment protocol, i.e., the participants (PD patients) will be asked to
wear the smartwatch during their sleep for ~2 consecutive nights and
afterwards report on their sleep quality. Indicative features that will
comprise the dataset are the heart rate (beats per minute - 1 Hz sampling
frequency), the skin temperature (degrees Celsius - 1 Hz sampling frequency),
accelerometer data (X, Y, and Z acceleration in g units or meter per second
squared, 62 Hz sampling frequency) and gyroscope data (X, Y, Z angular
velocity in degrees per second, 62 Hz sampling frequency) as captured and
outputted by the Microsoft Band 2. The deployment sub-dataset will comprise
the same features as the development sub-dataset, but it will be collected
through the iPROGNOSIS PD interventions application [via the **targeted
nocturnal intervention (TNI) service** (see Section 6 of D2.1)] that will be
provided to a subset of interventions users to use (~10 PD patients out of 60
interventions users) based on a pre-interventions medical evaluation.
The dataset is expected to be composed of 60 (30 participants × 2 nights)
records for the development sub-dataset, while the deployment sub-dataset will
comprise approximately 1800 (~6 months interventions period × ~30 nights × ~10
interventions users) records.
_Data Format:_ For each sleep session (development sub-dataset) four CSV
files, one per feature (heart rate, skin temperature, accelerometer and
gyroscope data), will be generated, including the values of features and
timestamps, as well as a CSV or EDF+ file including metadata. For each sleep
session (deployment sub-dataset) a structured XML file will be generated,
including all the aforementioned features and metadata.
</th> </tr>
<tr>
<td>
**To whom the dataset could be useful**
</td> </tr>
<tr>
<td>
The development sub-dataset will be used to develop a sleep stage/disturbances
recognition model focusing on sleep disturbances that PD patients experience.
The latter model will be used as part of the targeted nocturnal intervention
(TNI) and the triggering of pacifying sounds will occur based on the model
outputs.
The deployment sub-dataset will be used for validation and fine-tuning of the
model produced during development, as well as for evaluating the effectiveness
of the TNI in real life scenarios.
Moreover, the dataset could be useful for biomedical researchers that are
interested in studying the inference of sleep disturbances, experienced by PD
patients, based on data captured by commercially available wearable sensors.
Adequate use of these data presupposes at least basic background regarding PD
symptomatology, as well as, basic knowledge in data analysis methodology and
experience in the use of statistical software packages.
</td> </tr>
<tr>
<td>
**Related scientific publication(s)**
</td> </tr>
<tr>
<td>
The development sub-dataset will accompany the research results regarding the
feasibility of a sleep stage/disturbances recognition model based on data
captured by commercially available wearable sensors, such as smartwatches.
Research results are planned to be published initially in the indicative
journals and/or conferences provided below:
* IEEE Transactions on Biomedical Engineering (Journal)
* Elsevier International Journal of Medical Informatics (Journal)
</td> </tr> </table>
<table>
<tr>
<th>
\- Annual International Conference of the IEEE Engineering in Medicine and
Biology
Society (Conference)
</th> </tr>
<tr>
<td>
**Indicative existing similar data sets** (including possibilities for
integration and reuse)
</td> </tr>
<tr>
<td>
To the researchers' knowledge there are no similar datasets publicly available
at the moment. However, it is expected that such dataset will be available in
the near future through the Michael J. Foundation for Parkinson's Research and
Intel collaborative "Fox Insight" clinical study (
_https://foxinsight.michaeljfox.org/_ ) that started in 2015 and aims at
collecting big data arising from users' (healthy and PD patients) interaction
with smartphones and smartwatches. The latter interaction will also lead to
the generation of smartwatch data during the users' sleep.
i-PROGNOSIS investigators will proceed to all the necessary actions to access
the latter dataset (when available) and exploit it for the development of the
sleep stage/disturbance model of the TNI in conjunction with the development
subdataset.
</td> </tr>
<tr>
<td>
**STANDARDS AND METADATA**
</td> </tr>
<tr>
<td>
The European Data Format+ (EDF+) (
_http://www.edfplus.info/specs/edfplus.html_ ) will be exploited for the
production of the metadata files.
The development sub-dataset will be accompanied by a detailed description of
its contents. Indicative metadata include: a) description of the experiment
set-up and the procedure that led to the generation of the dataset, b)
anthropometrics and basic health-record data of the experiment participants,
c) description of the sleep-stagerelated features, d) annotation of features
based on (b), and e) ground truth based on self-reported sleep quality by
participants and sleep stage evaluation as provided by the Microsoft Health
mobile application.
The deployment sub-dataset will be annotated based on the following indicative
metadata: a) basic health record data and anthropometrics of the interventions
users, b) timestamps and duration of sleep disturbances detected, c)
timestamps of starting/stopping the playback of pacifying sounds, and d)
statistics on the usage of the TNI.
</td> </tr>
<tr>
<td>
**DATA SHARING**
</td> </tr>
<tr>
<td>
**Access type**
</td> </tr>
<tr>
<td>
The development sub-dataset will be protected (see Section 3.3.2.2),
accompanied by the respective ethics approval. Due to ethical compliance, the
deployment subdataset will be confidential and only i-PROGNOSIS partners will
be able to access it after following the appropriate procedure (see Section
3.3.2.3)
</td> </tr>
<tr>
<td>
**Access Procedures**
</td> </tr>
<tr>
<td>
The deployment sub-dataset that will be confidential will be handled according
the framework reported in Section 3.3.2.3. The development sub-dataset that
will be protected will be available for third-party stakeholders to download
based on the procedure described in Section 3.3.2.1.
</td> </tr>
<tr>
<td>
**Embargo periods** (if any)
</td> </tr>
<tr>
<td>
The development sub-dataset is planned to be available as protected after
month 32
</td> </tr> </table>
<table>
<tr>
<th>
of the project to allow i-PROGNOSIS investigators to prepare and submit the
respective scientific publications, but enable also third-party researchers to
conduct further analysis and comment on the i-PROGNOSIS results before the end
of the project.
</th> </tr>
<tr>
<td>
**Technical mechanisms for dissemination**
</td> </tr>
<tr>
<td>
The development sub-dataset will be available through either the CKAN-based
iPROGNOSIS data management portal (see Section 3.2). Links redirecting to the
portal and the available dataset will be provided through a dedicated page of
the iPROGNOSIS project website ( _www.i-prognosis.eu_ ) .
A technical description providing information on the experiment protocol
through which the development sub-dataset was captured will accompany the
development sub-dataset.
</td> </tr>
<tr>
<td>
**Necessary S/W and other tools for enabling re-use**
</td> </tr>
<tr>
<td>
As the publicly available compressed dataset (development sub-dataset) will
comprise standard CSV files, no specialised software or tools will be required
for the dataset to be parsed and reused, other than a (de-)compression
software and a basic text editor (minimum requirement to access the content of
the CSV file).
</td> </tr>
<tr>
<td>
**Repository where data will be stored**
</td> </tr>
<tr>
<td>
The development sub-dataset will be stored in AUTH secure servers, as well as
in the Microsoft Azure-based i-PROGNOSIS Cloud infrastructure (European data
centres are located in Ireland) where the CKAN-based data management portal
will be installed. The deployment sub-datasets will be also stored in the
i-PROGNOSIS Cloud infrastructure, but access will be restricted (Section
3.3.2.3).
</td> </tr>
<tr>
<td>
**ARCHIVING AND PRESERVATION** (including storage and backup)
</td> </tr>
<tr>
<td>
**Data preservation period**
</td> </tr>
<tr>
<td>
The public part of this dataset will be preserved online for as long as there
are regular downloads and at least one year after the end of the project.
After that it would be made accessible by direct request to the data
collector. The confidential part of the dataset will be preserved by the
owners at least for one year after the end of the project.
</td> </tr>
<tr>
<td>
**Approximated end volume of data**
</td> </tr>
<tr>
<td>
The development sub-dataset is expected to be approximately 600 MB (including
metadata), based on the number of experiment participants (30) and the
approximate size of features recorded during an average 7-hour sleep (~10 MB),
for 2 nights.
The deployment dataset is expected to be in the range of tens of gigabytes
(GB), based on the expected number of the i-PROGNOSIS interventions users that
will test the TNI (~10 users), an average 7-hour sleep per night for ~6 months
(~30 days per month), and an average size of features of ~10 MB per 7-hour
sleep.
</td> </tr>
<tr>
<td>
**Indicative associated costs for data archiving and preservation**
</td> </tr>
<tr>
<td>
The development sub-dataset will be stored on AUTH servers. There are no costs
associated with the latter means of archiving and preservation. The
development and
</td> </tr>
<tr>
<td>
deployment sub-datasets will be also archived and preserved in the Microsoft
Azurebased i-PROGNOSIS Cloud infrastructure. The indicative cost of the latter
means of archiving and preservation is 20 Euros (€) per month for one year (at
least) after the end of the project.
</td> </tr>
<tr>
<td>
**Indicative plan for covering the above costs**
</td> </tr>
<tr>
<td>
The costs associated with the data archiving and preservation will be covered
by the i-PROGNOSIS project budget.
</td> </tr>
<tr>
<td>
**PARTNERS ACTIVITIES AND RESPONSIBILITIES**
</td> </tr>
<tr>
<td>
**Partner Owner / Data Collector**
</td>
<td>
**AUTH, MICROSOFT**
</td> </tr>
<tr>
<td>
**Partner in charge of the data analysis**
</td>
<td>
**AUTH**
</td> </tr>
<tr>
<td>
**Partner in charge of the data storage**
</td>
<td>
**AUTH, MICROSOFT**
</td> </tr>
<tr>
<td>
**WPs and Tasks**
</td> </tr>
<tr>
<td>
The development sub-dataset will be collected within WP4 and WP6, to mainly
serve the research efforts of T4.2 and T6.4.
</td> </tr> </table>
<table>
<tr>
<th>
**DATA SET REFERENCE NAME**
</th>
<th>
**DS3.4-GaitAnalysis**
</th> </tr>
<tr>
<td>
**DATA SET DESCRIPTION**
</td> </tr>
<tr>
<td>
**Generic description**
</td> </tr>
<tr>
<td>
This dataset will include accelerometer, gyroscope and pedometer data captured
from a smart watch/band regarding gait rhythm identification and analysis so
as to provide personalised cueing and guidance in case of gait freezing
episodes.
</td> </tr>
<tr>
<td>
**Origin of data**
</td> </tr>
<tr>
<td>
The dataset will be captured by the accelerometer, gyroscope and pedometer
sensors of the smart watch/band that will be worn by the participants mainly
during the GData, SData and Interventions phases.
</td> </tr>
<tr>
<td>
**Nature and scale of data**
</td> </tr>
<tr>
<td>
The first part of the dataset is going to be collected during the development
data collection phase where the subjects will follow specific walking
scenarios. The second part of the dataset will be collected mainly during the
SData and Interventions phases, where smart watches/bands will be provided to
80 and 10 participants, respectively. Moreover, the GData phase will
contribute to the dataset; however, the contribution is expected to be limited
since the smart watch/band is optional at GData. At these phases, i.e., GData,
SData and Interventions, the subjects will not perform specific walking tasks,
instead, the data will be captured throughout their daily routine activities.
The dataset will include: i) the measures (double precision) of the
acceleration force in g units (9,81 m/s 2 ) that is applied to the smart
watch/band on all three physical axes (x, y, and z), ii) the measures (double
precision) of the smart watch/band’s
</td> </tr> </table>
<table>
<tr>
<th>
rotation in rad/s around each of the three physical axes, and iii) the
absolute number of steps taken. Since the pedometer sensor provides the total
number of steps the wearer has taken since the last factory-reset of the smart
watch/band, the absolute number of steps, that will be contained in the
dataset, will be determined by taking the difference between the sensors
returned values. The above data will be annotated with the health status of
the subject, i.e., healthy control or Parkinson’s disease patient.
The minimum sampling frequency for accelerometer and gyroscope data is 8Hz.
However, this value is too high for pedometer data. Consequently, pedometer
data will be recorded every 15 seconds.
Data format: TXT or CSV file.
The dataset will be on the order of ~1.3 MB per participant per hour of usage
and usually the smart watch/band owners use it for at least 12 hours per day.
</th> </tr>
<tr>
<td>
**To whom the dataset could be useful**
</td> </tr>
<tr>
<td>
The collected data will be used for the development and evaluation of the
personalised gait rhythmic guidance intervention of the i-PROGNOSIS project.
The dataset could be useful in analysing the gait patterns, speed,
periodicity, complexity and habits as well as in detecting sudden freezing
episodes and falls. Moreover, it may be possible to identify symptoms and
signs of those appear at people with Parkinson’s Disease, such as tremor.
</td> </tr>
<tr>
<td>
**Related scientific publication(s)**
</td> </tr>
<tr>
<td>
The dataset will accompany the research results in the field of rhythm gait
analysis and freezing episodes’ identification of people with PD. At least one
publication is intended to be made in the IEEE Biomedical Engineering journal
(or similar).
</td> </tr>
<tr>
<td>
**Indicative existing similar data sets** (including possibilities for
integration and reuse)
</td> </tr>
<tr>
<td>
To the best of our knowledge, there is no available smart watch/band-based
accelerometer/gyroscope and pedometer dataset for PD-related gait freezing
detection. Related datasets include data captured from standalone inertia
sensors attached to specific parts of the body, and/or accelerometer/gyroscope
of a smartphone. For example:
_Daphnet_ ( _https://archive.ics.uci.edu/ml/datasets/Daphnet+Freezing+of+Gait_
) : Daphnet dataset contains the annotated readings of 3 acceleration sensors
at the hip, thigh and ankle of 10 Parkinson's disease patients that experience
freezing of gait during walking tasks. Users performed three kinds of tasks:
straight line walking, walking with numerous turns, and finally a more
realistic activity of daily living (ADL) task, where users went into different
rooms while fetching coffee, opening doors, etc.
_OU-ISIR_ ( _http://www.am.sanken.osaka-u.ac.jp/BiometricDB/InertialGait.html_
) : OU-ISIR database contains accelerometer and gyroscope data captured from:
i) 3 inertia sensors located around user’s waist and ii) a smartphone which
includes only a triaxial accelerometer located at the back waist of the users.
There were three walking scenarios: flat level, slope up and slope down.
_ZJU-GaitAcc_ ( _http://www.cs.zju.edu.cn/~gpan/database/gaitacc.html_ ) :
The ZJUGaitAcc dataset contains the gait acceleration series of 175 subjects,
who were
</td> </tr> </table>
<table>
<tr>
<th>
equipped with 5 Wii remotes, acting as the inertia sensors, fastened at 5 body
locations: the left upper arm, the right wrist, the right side of the pelvis,
the left thigh, and the right ankle.
_Gait Dataset_ ( _http://www.cs.mcgill.ca/~jfrank8/data/gait-dataset.html_ )
: This dataset was collected at McGill University using the HumanSense open-
source Android data collection platform. It contains the raw sensor data
collected from a mobile phone in the pocket of 20 individuals, performing two
separate 15 minute walks.
</th> </tr>
<tr>
<td>
**STANDARDS AND METADATA**
</td> </tr>
<tr>
<td>
The dataset will be accompanied with detailed documentation of its contents.
Indicative metadata include: a) description of the experimental setup and
procedure that led to the generation of the dataset, b) health status of the
subject, and c) documentation of the variables recorded in the dataset.
</td> </tr>
<tr>
<td>
**DATA SHARING**
</td> </tr>
<tr>
<td>
**Access type**
</td> </tr>
<tr>
<td>
Due to ethical issues, only part of the dataset will be publicly available.
More specifically, the data that correspond to a subset of the Parkinson’s
Disease patients as well as the healthy control subjects, captured at the
initial phase of the iPROGNOSIS project will become publicly available. The
rest of the data will be private to serve the i-PROGNOSIS R&D objectives. The
inclusion of a subject’s data in the public part of this dataset will be done
on the basis of appropriate informed consent to data publication.
</td> </tr>
<tr>
<td>
**Access Procedures**
</td> </tr>
<tr>
<td>
See Section 3.3.2.1 and Section 3.3.2.3.
</td> </tr>
<tr>
<td>
**Embargo periods** (if any)
</td> </tr>
<tr>
<td>
The applicable datasets will be publicly available 2 years after the end of
the project to allow the consortium prepare and submit the scientific
publications.
</td> </tr>
<tr>
<td>
**Technical mechanisms for dissemination**
</td> </tr>
<tr>
<td>
For the public part of the dataset, a link to this will be provided from the
Data management portal. The link will be provided in all relevant i-PROGNOSIS
publications. A technical publication describing the dataset and acquisition
procedure will be published.
</td> </tr>
<tr>
<td>
**Necessary S/W and other tools for enabling re-use**
</td> </tr>
<tr>
<td>
The dataset will be designed to allow easy reuse with commonly available tools
and software libraries, such as Excel, Matlab, .NET etc.
</td> </tr>
<tr>
<td>
**Repository where data will be stored**
</td> </tr>
<tr>
<td>
The public part of this dataset will be accommodated at the data management
portal of the project website, hosted by Microsoft Azure-based i-PROGNOSIS
Cloud infrastructure.
</td> </tr>
<tr>
<td>
**ARCHIVING AND PRESERVATION** (including storage and backup)
</td> </tr>
<tr>
<td>
**Data preservation period**
</td> </tr>
<tr>
<td>
The public part of this dataset will be preserved online for as long as there
are regular downloads. After that it would be made accessible by request. The
private part of the dataset will be preserved by AUTH at least until the end
of the project.
</td> </tr>
<tr>
<td>
**Approximated end volume of data**
</td> </tr>
<tr>
<td>
The dataset is expected to be several gigabytes, provided that each
participant is expected to produce at least 6 MB per day and the duration is
at least 22 months (SData and Interventions phase).
</td> </tr>
<tr>
<td>
**Indicative associated costs for data archiving and preservation**
</td> </tr>
<tr>
<td>
Probably two dedicated hard disk drives will be allocated for the dataset; one
for the public part and one for the private. There are no costs associated
with its preservation. Azure will be also considered as candidate data
archiving means.
</td> </tr>
<tr>
<td>
**Indicative plan for covering the above costs**
</td> </tr>
<tr>
<td>
The costs associated with the data archiving and preservation will be covered
by the i-PROGNOSIS project budget.
</td> </tr>
<tr>
<td>
**PARTNERS ACTIVITIES AND RESPONSIBILITIES**
</td> </tr>
<tr>
<td>
**Partner Owner / Data Collector**
</td>
<td>
**AUTH, MICROSOFT**
</td> </tr>
<tr>
<td>
**Partner in charge of the data analysis**
</td>
<td>
**AUTH**
</td> </tr>
<tr>
<td>
**Partner in charge of the data storage**
</td>
<td>
**AUTH, MICROSOFT**
</td> </tr>
<tr>
<td>
**WPs and Tasks**
</td> </tr>
<tr>
<td>
The data are going to be collected within the activities of WP3, WP4 and WP6,
to mainly serve the research efforts of T3.2, T4.3, T6.1, T6.2, T6.3 and T6.4.
</td> </tr> </table>
<table>
<tr>
<th>
**DATA SET REFERENCE**
**NAME**
</th>
<th>
**DS3.5-VoiceEnhancementAnalysis**
</th> </tr>
<tr>
<td>
**DATA SET DESCRIPTION**
</td> </tr>
<tr>
<td>
**Generic description**
</td> </tr>
<tr>
<td>
Speech database for the design and implementation of the voice enhancement
algorithms within the assistive interventions software. The database will
contain speech data collected from phone calls recorded from the i-PROGNOSIS
smartphone dialler application. The data set includes several annotations
about the speech data and the underlying speakers.
</td> </tr>
<tr>
<td>
**Origin of data**
</td> </tr>
<tr>
<td>
The dataset will be collected by recording conversations using the i-PROGNOSIS
smartphone dialler application during SData and Intervention Data collection
phase.
</td> </tr>
<tr>
<td>
**Nature and scale of data**
</td> </tr>
<tr>
<td>
The goal of this dataset is to enable the development and implementation of
voice enhancement algorithms for speech from PD patients. The dataset will
contain the raw speech signals recorded by the i-PROGNOSIS smartphone dialler
application
</td> </tr> </table>
<table>
<tr>
<th>
during the SData and Intervention data collection phase. Annotations including
date and time of the recordings along with speaker information (e.g. ID,
gender, PD scale) will provide the necessary information and will facilitate
the development and evaluation of the speech enhancement algorithms. The
dataset will contain raw speech data (e.g. 44.1 kHz sampling rate, wav
unencoded) and text files for the annotations.
_Data Format:_ Encoded (mp3, aac) or unencoded (wav, raw) audio waveforms; XML
or plain text files for annotations/metadata.
The dataset will be on the order of 0.5 MB to 5 MB per minute of speech
depending on the encoding and sampling rate.
</th> </tr>
<tr>
<td>
**To whom the dataset could be useful**
</td> </tr>
<tr>
<td>
The dataset will be used within the project for the development and evaluation
of automatic signal processing speech enhancement algorithm for speech from PD
patients. This data will be used in WP4 and WP6, supporting the assistive
intervention software and for the overall assessment of the i-PROGNOSIS
system. This dataset may also be useful in the future for other researchers
who want to explore PD patients’ speech and develop speech enhancement
algorithms.
</td> </tr>
<tr>
<td>
**Related scientific publication(s)**
</td> </tr>
<tr>
<td>
This database will build the foundation of our research and development of
algorithms for the automatic enhancement of the speech from PD patients within
the assistive intervention software. We plan to propose our findings on
ICASSP, Interspeech or other voice and biomedical-related conferences.
</td> </tr>
<tr>
<td>
**Indicative existing similar data sets** (including possibilities for
integration and reuse)
</td> </tr>
<tr>
<td>
To the best of our knowledge no speech database for the development and
evaluation of speech enhancement algorithms for the speech of PD patients is
available.
</td> </tr>
<tr>
<td>
**STANDARDS AND METADATA**
</td> </tr>
<tr>
<td>
The dataset will be accompanied with detailed documentation of its contents.
Indicative metadata include: (a) description of the experimental setup and
procedure that led to the generation of the dataset and (b) documentation of
the variables recorded in the dataset.
</td> </tr>
<tr>
<td>
**DATA SHARING**
</td> </tr>
<tr>
<td>
**Access type**
</td> </tr>
<tr>
<td>
Due to ethical reasons, only the data captured by a subset of the patients
during the initial phases by normal healthy control subjects could become
publicly available, while the rest of them will be private to serve the
i-PROGNOSIS R&D objectives.
The inclusion of a (normal healthy control) subject’s data in the public part
of this dataset will be done on the basis of appropriate informed consent to
data publication.
</td> </tr>
<tr>
<td>
**Access Procedures**
</td> </tr>
<tr>
<td>
For the portions of the dataset that will be made publicly available, a
respective web
</td> </tr> </table>
<table>
<tr>
<th>
page will be created on the data management portal that will provide a
description of the dataset, links to a download section and a playback
possibility in case the playback manager approach is followed. The private
part of this dataset will be stored at a specifically designated private space
of FRAUNHOFER, in dedicated hard disk drives, on which only members of the
FRAUNHOFER research team will have access.
</th> </tr>
<tr>
<td>
**Embargo periods** (if any)
</td> </tr>
<tr>
<td>
The applicable datasets will be publicly available two years after the end of
the project to allow the consortium prepare and submit the scientific
publications.
</td> </tr>
<tr>
<td>
**Technical mechanisms for dissemination**
</td> </tr>
<tr>
<td>
For the public part of the dataset, a link will be provided from the Data
management portal. The link will be provided in all relevant i-PROGNOSIS
publications. A technical publication describing the dataset and acquisition
procedure will be published.
</td> </tr>
<tr>
<td>
**Necessary S/W and other tools for enabling re-use**
</td> </tr>
<tr>
<td>
The dataset will be designed to allow easy reuse and access with commonly
available tools (e.g. Matlab, Python, VLC, GVIM) and software libraries (e.g.
Tensorflow, FFMPEG, HDF5 C++ API, C++ STL), because the data will be stored
primarily in common file formats and generic data containers.
</td> </tr>
<tr>
<td>
**Repository where data will be stored**
</td> </tr>
<tr>
<td>
The public data will be hosted on the central database that will serve the
needs of the Data Management Portal of the i-PROGNOSIS project.
</td> </tr>
<tr>
<td>
**ARCHIVING AND PRESERVATION** (including storage and backup)
</td> </tr>
<tr>
<td>
**Data preservation period**
</td> </tr>
<tr>
<td>
The public part of the dataset will be preserved on the Data Management Portal
of the i-PROGNOSIS project. The private part of the dataset will be preserved
by FRAUNHOFER at least until the end of the project.
</td> </tr>
<tr>
<td>
**Approximated end volume of data**
</td> </tr>
<tr>
<td>
The dataset should be expected to consume up to 10 GB depending on the
encoding and the length and quantity of the speech signals. (E.g. single
channel audio waveform 16 bit, 44.1 kHz is of size 5.3 MB/min, single channel
mp3 192 Kbps is of size 1.44 MB/min)
</td> </tr>
<tr>
<td>
**Indicative associated costs for data archiving and preservation**
</td> </tr>
<tr>
<td>
The public data will be archived and preserved in the Microsoft Azure-based
iPROGNOSIS Cloud infrastructure, will cost approximately 20 Euros (€) per
month for one year (at least) after the end of the project. The private
dataset will be stored on a dedicated hard drive.
</td> </tr>
<tr>
<td>
**Indicative plan for covering the above costs**
</td> </tr>
<tr>
<td>
The costs associated with the data archiving and preservation will be covered
by the i-PROGNOSIS project budget.
</td> </tr>
<tr>
<td>
**PARTNERS ACTIVITIES AND RESPONSIBILITIES**
</td> </tr>
<tr>
<td>
**Partner Owner / Data Collector**
</td>
<td>
**TUD, KI**
</td> </tr>
<tr>
<td>
**Partner in charge of the data analysis**
</td>
<td>
**FRAUNHOFER**
</td> </tr>
<tr>
<td>
**Partner in charge of the data storage**
</td>
<td>
**FRAUNHOFER**
</td> </tr>
<tr>
<td>
**WPs and Tasks**
</td> </tr>
<tr>
<td>
The data is going to be collected within activities of WP4 and WP6, to mainly
serve the research efforts of T4.3, T6.3 and T6.4.
</td> </tr> </table>
### Requirements Data
<table>
<tr>
<th>
**DATA SET REFERENCE**
**NAME**
</th>
<th>
**DS4.1-FocusGroupsDataset**
</th> </tr>
<tr>
<td>
**DATA SET DESCRIPTION**
</td> </tr>
<tr>
<td>
**Generic description**
</td> </tr>
<tr>
<td>
This dataset aims at providing a non-exhaustive list of end-user requirements,
as these have been elicited through a series of focus groups and personal
interviews with experts (both patient groups and clinicians) in PD.
</td> </tr>
<tr>
<td>
**Origin of data**
</td> </tr>
<tr>
<td>
KCL and relevant partners gathered pertinent data and established focus groups
data set, so as to then design a final paper-based user requirement
questionnaire that was used as an acquisition means in four focus groups. This
dataset contains information from all stakeholders (developers and clinicians)
involved in the development of the iPROGNOSIS system and its potential users
(healthcare professionals, and patients). The main components of the focus
groups questionnaire included: a) demographics, e.g. gender, age, health
problem related to PD, b) technology adoption, c) iPROGNOSIS App design-
oriented questions, d) i-PROGNOSIS App delivery through existing smart phones,
e) usability aspects of the specialized gadgets, such as smartwatch (Microsoft
Band was provided as an example), smart belt, Mandometer and a ECG-enabled TV
remote control, f) general questions about the PGS interventions and finally,
g) some specialized questions about specific interventions, such as the sleep
and gait interventions. For a more detailed description on the questions and
the results produced from the analyses, one is referred to D2.1 _\- First
version of user requirements analysis_ .
</td> </tr>
<tr>
<td>
**Nature and scale of data**
</td> </tr>
<tr>
<td>
The focus groups were conducted among health professionals such as nurses,
therapists, clinicians and researchers as well as patients and carers. The
subjects of the focus groups were about the design of the i-PROGNOSIS project
and expectations of it, the design of the application and special gadgets that
will be used in i-PROGNOSIS (smart belt, Mandometer, TV smart remote control,
etc.). Finally, the questions focus on the i-PROGNOSIS interventions. The
participants were carefully recruited for the face-to-face focus groups. The
dataset contains the output of the systematic analysis
</td> </tr> </table>
<table>
<tr>
<th>
Data format: The dataset will be made available as a single Microsoft Excel
file (.xlsx) by merging the CSV files of all languages. All questions and
answers will be translated in English.
</th> </tr>
<tr>
<td>
**To whom the dataset could be useful**
</td> </tr>
<tr>
<td>
The dataset and the respective outcomes and findings will be used to shape the
user requirements and the technical specifications of the i-PROGNOSIS system.
In addition, this kind of datasets could be exploited by researchers that
investigate to build new technologies of patients with PD relating to self-
care, as well as, developers, system architects and user experts that are
interested to develop a similar to i-PROGNOSIS ICT-based system for health
management.
</td> </tr>
<tr>
<td>
**Related scientific publication(s)**
</td> </tr>
<tr>
<td>
The dataset will accompany the research results regarding the prospective
users' attitude towards ICT-based self-care solutions and the i-RPOGNOSIS
system against PD. Research results and findings is planned to be published
initially in one of the indicative conferences provided below:
* International Conference on e-Health (Conference)
* Human Computer Interaction International (Conference)
</td> </tr>
<tr>
<td>
**Indicative existing similar data sets** (including possibilities for
integration and reuse)
</td> </tr>
<tr>
<td>
There are no similar datasets with open access. Results of relevant end-user
focus groups target to participatory design of symptomatic domains to be
measured as well as interventions (through serious games) design., i.e.,
Serrano, J. A., Larsen, F., Isaacs, T., Matthews, H., Duffen, J., Riggare, S.,
... & Graessner, H. (2015). Participatory design in parkinson's research with
focus on the symptomatic domains to be measured. Journal of Parkinson's
disease, 5(1), 187196.
McNaney, R., Balaam, M., Holden, A., Schofield, G., Jackson, D., Webster, M.,
... & Olivier, P. (2015, April). Designing for and with People with
Parkinson's: A Focus on Exergaming. In Proceedings of the 33rd annual ACM
conference on Human Factors in Computing Systems (pp. 501-510). ACM.
</td> </tr>
<tr>
<td>
**STANDARDS AND METADATA**
</td> </tr>
<tr>
<td>
For this dataset, the metadata correspond to the participant profile as well
as the settings of the focus group, and mainly include: a) occupational and
expertise of the participants (nurse, therapist, clinician, researcher,
patient, carers), b) the country where the focus group conducted, c) number of
the participants and d) health status relating to PD.
</td> </tr>
<tr>
<td>
**DATA SHARING**
</td> </tr>
<tr>
<td>
**Access type**
</td> </tr>
<tr>
<td>
Personal identification as stated in the consent forms will be kept separate
from any research and health-related data, which will be pseudonymised (only
initials and date of birth will be kept). All paper based data will be stored
in an access restricted, locked building with access for the research team.
Electronic data will be saved on a
</td> </tr> </table>
<table>
<tr>
<th>
secured NHS server and CKAN-based i-PROGNOSIS data management portal.
</th> </tr>
<tr>
<td>
**Access Procedures**
</td> </tr>
<tr>
<td>
Personal and clinical data will be accessible by technical, medical and
scientific personnel being involved in the i-PROGNOSIS project and not by any
third-party users. When applicable, the publicly available dataset will be
open for third-party stakeholders to download based on the procedure described
in Section 3.3.2.1.
</td> </tr>
<tr>
<td>
**Embargo periods** (if any)
</td> </tr>
<tr>
<td>
The dataset is planned to be publicly available two (2) years after the end of
the project to allow i-PROGNOSIS investigators to prepare and submit the
respective scientific publications regarding the final version of user
requirements.
</td> </tr>
<tr>
<td>
**Technical mechanisms for dissemination**
</td> </tr>
<tr>
<td>
The dataset will be available either through the CKAN-based i-PROGNOSIS data
management portal (see Section 3.2) or other out of the project open access
repository. Links redirecting to the portal and the available dataset will be
provided through a dedicated page of the i-PROGNOSIS project website (
_www.i-prognosis.eu_ ) .
</td> </tr>
<tr>
<td>
**Necessary S/W and other tools for enabling re-use**
</td> </tr>
<tr>
<td>
The dataset will be kept in a numeric manner and therefore designed to allow
easy reuse with commonly available tools.
</td> </tr>
<tr>
<td>
**Repository where data will be stored**
</td> </tr>
<tr>
<td>
The data will be hosted to the central database that will serve the needs of
the Data Management Portal of the i-PROGNOSIS project and part of them in a
secured NHS server.
</td> </tr>
<tr>
<td>
**ARCHIVING AND PRESERVATION** (including storage and backup)
</td> </tr>
<tr>
<td>
**Data preservation period**
</td> </tr>
<tr>
<td>
The dataset will be preserved as long as there are regular downloads. After
that it would be made accessible by request and preserved by AUTH at least
until the end of the project. Data set will be stored as per HRA regulations
and requirements.
</td> </tr>
<tr>
<td>
**Approximated end volume of data**
</td> </tr>
<tr>
<td>
The approximate size of the dataset is a couple of tens of MB.
</td> </tr>
<tr>
<td>
**Indicative associated costs for data archiving and preservation**
</td> </tr>
<tr>
<td>
The downloaded CSV files and the cumulative Microsoft Excel file will be
stored in an AUTH server, both not imposing any additional costs. The
Microsoft Excel file that will be made publicly available will be also
archived and preserved in the Microsoft Azure-based i-PROGNOSIS Cloud
infrastructure. The indicative cost of the latter means of archiving and
preservation is 20 Euros (€) per month for one year (at least) after the end
of the project.
</td> </tr>
<tr>
<td>
**Indicative plan for covering the above costs**
</td> </tr>
<tr>
<td>
The costs associated with the data archiving and preservation will be covered
by the i-PROGNOSIS project budget.
</td> </tr>
<tr>
<td>
**PARTNERS ACTIVITIES AND RESPONSIBILITIES**
</td> </tr>
<tr>
<td>
**Partner Owner / Data Collector**
</td>
<td>
**TUD, KCL, AUTH**
</td> </tr>
<tr>
<td>
**Partner in charge of the data analysis**
</td>
<td>
**All**
</td> </tr>
<tr>
<td>
**Partner in charge of the data storage**
</td>
<td>
**AUTH**
</td> </tr>
<tr>
<td>
**WPs and Tasks**
</td> </tr>
<tr>
<td>
The data are going to be collected within the activities of WP2, to mainly
serve the research efforts of T2.1 and T2.4.
</td> </tr> </table>
<table>
<tr>
<th>
**DATA SET REFERENCE NAME**
</th>
<th>
**DS4.2-WebSurveyDataset**
</th> </tr>
<tr>
<td>
**DATA SET DESCRIPTION**
</td> </tr>
<tr>
<td>
**Generic description**
</td> </tr>
<tr>
<td>
The dataset contains the questions and the qualitative answers to the
questions of the i-PROGNOSIS Web-based questionnaire (Web survey), conducted
within the context of the identification of user requirements and system
specifications, from ~2000 anonymous survey participants.
</td> </tr>
<tr>
<td>
**Origin of data**
</td> </tr>
<tr>
<td>
The dataset is populated by the answers of participants to the questions of
the iPROGNOSIS Web survey. The Web survey consists of three sections
corresponding to three groups of survey participants, i.e., healthy adults, PD
patients, and experts in PD (including physicians and carers). Each
participant answers the questions of her/his corresponding section based on
the group s/he belongs to. Each section is further divided in three parts,
i.e. demographics, PD detection and interventions. The section corresponding
to the group of healthy adults does not include the interventions part. The
participant is redirected to the corresponding section based on a question
allowing her/him to denote the group s/he belongs to. If no option is
selected, the participant is redirected to the survey section corresponding to
healthy adults. There are six versions of the Web survey corresponding to six
languages, i.e., English, Greek, French, German, Portuguese and Swedish.
</td> </tr>
<tr>
<td>
**Nature and scale of data**
</td> </tr>
<tr>
<td>
The Web survey is conducted electronically and it is available online. The
questionnaires were designed using the Google Forms online software. The
answers to the questions are qualitative and are stored in Google Sheets files
(one per language) that are updated with new entries (an entry corresponds to
a participant in the survey) automatically. The current dataset consists of
six Google Sheets files with a total of ~2000 entries, but it is updated
frequently as the Web survey is planned to be online until the updated user
requirements will be produced (month 28 of the project). The dataset (Google
Sheets files) can be downloaded manually as CSV files.
_Data format:_ The dataset will be made available as a single Microsoft Excel
file (.xlsx) by merging the CSV files of all languages. All questions and
answers will be translated
</td> </tr> </table>
<table>
<tr>
<th>
in English.
</th> </tr>
<tr>
<td>
**To whom the dataset could be useful**
</td> </tr>
<tr>
<td>
The dataset and the respective results will be used to shape the identified
user requirements and the technical specifications of the i-PROGNOSIS system.
The dataset could be of interest to researchers that investigate the
relationship of similar groups of users with new technology relating to self-
care, as well as, developers, system architects and user experts that are
interested to develop a similar to i-PROGNOSIS ICT-based system for health
management.
Adequate use of these data presupposes at least basic knowledge in data
analysis methodology and experience in the use of statistical software
packages.
</td> </tr>
<tr>
<td>
**Related scientific publication(s)**
</td> </tr>
<tr>
<td>
The dataset will accompany the research results regarding the prospective
users' attitude towards ICT-based self-care solutions and the i-RPOGNOSIS
system against PD. Research results is planned to be published initially in
one of the indicative journals and/or conferences provided below:
* Elsevier International Journal of Medical Informatics (Journal)
* IOS Journal of Parkinson's Disease (Journal)
* International Conference on e-Health (Conference)
* Human Computer Interaction International (Conference)
</td> </tr>
<tr>
<td>
**Indicative existing similar data sets** (including possibilities for
integration and reuse)
</td> </tr>
<tr>
<td>
There are no similar datasets with open access. Results of similar surveys are
available through publications and may be used for shaping the i-PROGNOSIS
user requirements, i.e.,
Zhao, Y., Heida, T., van Wegen, E. E., Bloem, B. R., & van Wezel, R. J.
(2015). E-health support in people with Parkinson’s disease with smart
glasses: a survey of user requirements and expectations in the Netherlands.
Journal of Parkinson's disease, 5(2), 369-378.
</td> </tr>
<tr>
<td>
**STANDARDS AND METADATA**
</td> </tr>
<tr>
<td>
For this dataset, the metadata correspond to the participant-provided
information via the first part of each web survey section, i.e., the
demographics part, and mainly include: a) the age of the participant, b) the
country of residence, c) their occupational category, d) health status
relating to PD, e) expertise (in case of health care professionals), and (f)
familiarity with new technology and new means of communication. Thus, no
standard is followed.
</td> </tr>
<tr>
<td>
**DATA SHARING**
</td> </tr>
<tr>
<td>
**Access type**
</td> </tr>
<tr>
<td>
The dataset will be publicly available (see Section 3.3.2.1).
</td> </tr>
<tr>
<td>
**Access Procedures**
</td> </tr>
<tr>
<td>
The publicly available dataset will be open for third-party stakeholders to
download based on the procedure described in Section 3.3.2.1.
</td> </tr> </table>
<table>
<tr>
<th>
**Embargo periods** (if any)
</th> </tr>
<tr>
<td>
The dataset is planned to be publicly available after month 28 of the project
to allow i-PROGNOSIS investigators to prepare and submit the respective
scientific publications regarding the final version of user requirements.
</td> </tr>
<tr>
<td>
**Technical mechanisms for dissemination**
</td> </tr>
<tr>
<td>
The dataset will be available through either the CKAN-based i-PROGNOSIS data
management portal (see Section 3.2). Links redirecting to the portal and the
available dataset will be provided through a dedicated page of the i-PROGNOSIS
project website ( _www.i-prognosis.eu_ ) .
A technical description providing information on how the web-survey was
structured and conducted will accompany the dataset.
</td> </tr>
<tr>
<td>
**Necessary S/W and other tools for enabling re-use**
</td> </tr>
<tr>
<td>
As the compressed dataset will consist of a Microsoft Excel file (.xlsx), only
a (de)compression software and the Microsoft Excel software (or other
compatible with this type of file software) will be required for data parsing
and re-use.
</td> </tr>
<tr>
<td>
**Repository where data will be stored**
</td> </tr>
<tr>
<td>
The dataset will be stored in AUTH secure servers, as well as in the Microsoft
Azurebased i-PROGNOSIS Cloud infrastructure (data centres are located in
Ireland) where the CKAN-based data management portal will be installed.
External repositories will also be considered as alternatives, to increase
visibility and dissemination efforts.
</td> </tr>
<tr>
<td>
**ARCHIVING AND PRESERVATION** (including storage and backup)
</td> </tr>
<tr>
<td>
**Data preservation period**
</td> </tr>
<tr>
<td>
The dataset will be preserved online for as long as there are regular
downloads and at least for one year after the end of the project. After that
it would be made accessible by request.
</td> </tr>
<tr>
<td>
**Approximated end volume of data**
</td> </tr>
<tr>
<td>
The approximate size of the dataset is ~ 3 MB based on the size of each entry
(~ 1.2 KB) and the expected final number of entries (participants in the
survey) (~ 2500 to 3000 participants - entries).
</td> </tr>
<tr>
<td>
**Indicative associated costs for data archiving and preservation**
</td> </tr>
<tr>
<td>
The original Google Sheets files comprising the dataset are stored in the
Google drive account of the i-PROGNOSIS project that is free of charge. The
downloaded CSV files and the cumulative Microsoft Excel file are stored in an
AUTH server, both not imposing any additional costs. The Microsoft Excel file
that will be made publicly available will be also archived and preserved in
the Microsoft Azure-based iPROGNOSIS Cloud infrastructure. The indicative cost
of the latter means of archiving and preservation is 20 Euros (€) per month
for one year (at least) after the end of the project.
</td> </tr>
<tr>
<td>
**Indicative plan for covering the above costs**
</td> </tr>
<tr>
<td>
The costs associated with the data archiving and preservation will be covered
by the
</td> </tr>
<tr>
<td>
i-PROGNOSIS project budget.
</td> </tr>
<tr>
<td>
**PARTNERS ACTIVITIES AND RESPONSIBILITIES**
</td> </tr>
<tr>
<td>
**Partner Owner / Data Collector**
</td>
<td>
**AUTH**
</td> </tr>
<tr>
<td>
**Partner in charge of the data analysis**
</td>
<td>
**AUTH**
</td> </tr>
<tr>
<td>
**Partner in charge of the data storage**
</td>
<td>
**AUTH**
</td> </tr>
<tr>
<td>
**WPs and Tasks**
</td> </tr>
<tr>
<td>
The data are going to be collected within the activities of WP2, to mainly
serve the research efforts of T2.1 and T2.4.
</td> </tr> </table>
# APPENDIX I – DATASET DESCRIPTION TEMPLATE
The table template used to describe each dataset along with clarifications on
each field:
<table>
<tr>
<th>
**DATA SET REFERENCE NAME**
</th>
<th>
**DS#.#- <DatasetName> **
</th> </tr>
<tr>
<td>
**DATA SET DESCRIPTION**
</td> </tr>
<tr>
<td>
**Generic description**
</td> </tr>
<tr>
<td>
_ <Provide a summary of data to be collected> _
</td> </tr>
<tr>
<td>
**Origin of data**
</td> </tr>
<tr>
<td>
_ <How will the dataset be produced, e.g. mobile phone app logs or Microsoft
Band and a sampling rate of xx Hz etc.> _
</td> </tr>
<tr>
<td>
**Nature and scale of data**
</td> </tr>
<tr>
<td>
_ <Description of file format, e.g. TXT files, estimated size, number of
participants> _
</td> </tr>
<tr>
<td>
**To whom the dataset could be useful**
</td> </tr>
<tr>
<td>
_ <What research purposes will the dataset facilitate, e.g. The dataset will
be valuable for benchmarking algorithms for activity analysis etc.> _
</td> </tr>
<tr>
<td>
**Related scientific publication(s)**
</td> </tr>
<tr>
<td>
_ <Either existing ones in case we get open data from external to the project
repositories, or future publications that we intend to make, e.g. description
of indicative research area like neuroscience or name a few scientific
conferences or journals that will be targeted> _
</td> </tr>
<tr>
<td>
**Indicative existing similar data sets** (including possibilities for
integration and reuse)
</td> </tr>
<tr>
<td>
_ <List other already existing datasets or repositories> _
</td> </tr>
<tr>
<td>
**STANDARDS AND METADATA**
</td> </tr>
<tr>
<td>
_ <Reference to existing suitable standards of the discipline. Format, e.g.
EDF format for biosignals. _Metadata_ should contain information such as: (a)
description of the experimental setup and procedure that led to the generation
of the dataset, (b) documentation of the variables recorded in the dataset and
(c) annotated experiment state of the monitored person per time interval. If
these do not exist, provide an outline on how and what metadata will be
created.> _
</td> </tr>
<tr>
<td>
**DATA SHARING**
</td> </tr>
<tr>
<td>
**Access type**
</td> </tr>
<tr>
<td>
_ <E.g., open - can be publicly shared, protected - can be shared but the
participants have to provide their consent, or confidential - cannot be shared
outside the project> _
</td> </tr>
<tr>
<td>
**Access Procedures**
</td> </tr>
<tr>
<td>
_ <Explain how will you handle private (if any) and publicly available
datasets, e.g., depending on the access type account for each dataset a
relevant access procedure should be defined. Access procedure should contain
information about the developed _
</td> </tr>
<tr>
<td>
_area within the portal that will let third-party users download files > _
</td> </tr>
<tr>
<td>
**Embargo periods** (if any)
</td> </tr>
<tr>
<td>
_ <When will the data be published and access provided> _
</td> </tr>
<tr>
<td>
**Technical mechanisms for dissemination**
</td> </tr>
<tr>
<td>
_ <Accompany datasets with a technical description of the dataset and the way
data were captured> _
</td> </tr>
<tr>
<td>
**Necessary S/W and other tools for enabling re-use**
</td> </tr>
<tr>
<td>
_ <Code/libraries/open source software to read and process data so as to allow
for reproducible research> _
</td> </tr>
<tr>
<td>
**Repository where data will be stored**
</td> </tr>
<tr>
<td>
_ <i-PROGNOSIS portal dedicated download section, institutional portals or
standard repository for the discipline> _
</td> </tr>
<tr>
<td>
**ARCHIVING AND PRESERVATION** (including storage and backup)
</td> </tr>
<tr>
<td>
**Data preservation period**
</td> </tr>
<tr>
<td>
_ <For how long should the data be preserved? We should take into account any
national regulations as well> _
</td> </tr>
<tr>
<td>
**Approximated end volume of data**
</td> </tr>
<tr>
<td>
_ <E.g., ### GB raw signal> _
</td> </tr>
<tr>
<td>
**Indicative associated costs for data archiving and preservation**
</td> </tr>
<tr>
<td>
_ <Hard disk drives, servers> _
</td> </tr>
<tr>
<td>
**Indicative plan for covering the above costs**
</td> </tr>
<tr>
<td>
_ <Within project, whole consortium, local partner, centrally> _
</td> </tr>
<tr>
<td>
**PARTNERS ACTIVITIES AND RESPONSIBILITIES**
</td> </tr>
<tr>
<td>
**Partner Owner / Data Collector**
</td>
<td>
_ <Partner Acronym> _
</td> </tr>
<tr>
<td>
**Partner in charge of the data analysis**
</td>
<td>
_ <Partner Acronym> _
</td> </tr>
<tr>
<td>
**Partner in charge of the data storage**
</td>
<td>
_ <Partner Acronym> _
</td> </tr>
<tr>
<td>
**WPs and Tasks**
</td> </tr>
<tr>
<td>
_ <During which stage of the project will data be captured?> _
</td> </tr> </table>
|
https://phaidra.univie.ac.at/o:1140797
|
Horizon 2020
|
1107_AEOLIX_690797.md
|
**Executive Summary**
According to the "Guidelines on Data Management in Horizon 2020" 1 , the
Data Management Plan has the aim to produce data so that researchers may
benefit by their use directly, and / or to apply their methods based on data
generated by Research in Horizon 2020. The AEOLIX Data Management Plan governs
all data generated and collected during the project, the standards that will
be used, how the research data will be preserved and what parts of the
datasets will be shared for verification or reuse. The documentation of this
plan is part of work package 4 (WP4) Collaborative data exchange certification
framework.
The data relevant in the AEOLIX project can be roughly divided into three
categories:
1. Data retrieved from project participants, LL actors and other stakeholders by non-technical means, e.g. by participation in surveys, through interviews etc.
2. Data used in the technical implementation of the AEOLIX platform, e.g. position data of vehicles or cargo, scheduling data (ETA), custom or billing information.
3. Project internal administration data such as meeting agendas and minutes, cost statement, internal reports.
The category 1 data will be mainly used in the WP 2 (Lessons learned,
strategic needs and requirements) which is focused on experiences and future
needs of potential AEOLIX users and WP6 (AEOLIX evaluation framework) which
concentrates on the results achieved in the LL operations.
Category 2 data is used in the application of the AEOLIX platform at the
different living labs and is of relevance to WP3 (AEOLIX IT Ecosystem) dealing
with the development of the AEOLIX system and WP5 (Verification with Living
Labs) which aims at the implementation of AEOLIX in real-word situations, i.e.
the Living Labs (LL).. The handling of category 2 data is considered highly
complex and is can only be partly described in the present document. The
details will be presented in the following deliverables of WP4:
* D4.1: Best practices for AEOLIX collaborative data sharing
* D4.2: AEOLIX high level architecture guideline for collaborative data exchange
* D4.3: Guidelines for an AEOLIX framework for legal, trusted, interoperable and high quality data exchange
Category 3 data is AEOLIX consortium internal and may have different levels of
confidentiality. The agenda of AEOLIX general assemblies may for example be
available to the public whereas cost statements of individual partners will
not be disclosed outside the consortium.
The format of the plan follows the Horizon 2020 template.
* To comply with the Horizon 2020 Open Data Access Guidelines
* To embed the AEOLIX project in the EU policy on data management which is increasingly geared towards providing open access to data that is gathered with funds from the EU.
* To ensure that all possible data will be accessible to other researchers, helping to streamline the research process from start to finish.
**1\. Introduction**
##### 1.1 Purpose of Document
The purpose of the present document is the provision of a Data Management Plan
(DMP). The AEOLIX Data Management Plan (DMP) is defined within the context of
the Grant Agreement. The full text of this Deliverable is addressing the “Open
Access to Research Data” Objective. The document describes the level of
confidentiality of different data sets, defines data governance and privacy
principles, and the strategy towards contributing to the Open Data Research
Pilot. It has been prepared by taking into account the template of the
“Guidelines on Data Management in Horizon 2020”. In addition, to that the
project has dedicated a specific Work Package called ‘’Data governance’’ with
the aim to address all issues related with data within the project. The
intention of the AEOLIX project is to publish no confidential results under
Open Access, regarding all scientific publications produced along the project
lifecycle. Although it is preferable to publish in online publications free of
charge, when it is not possible it will be up to the partner who will cover
the associated costs. The AEOLIX Open Access strategy relates to the EU “Open”
paradigm for publishing project results, which foresees two possible ways of
accessing the published results: Gold Open Access, which grants immediate
access through a publisher, and Green Open Access. The consortium will decide
which data will be part of an open data scheme to become available for the
Open Access Data initiative. Various mechanisms to protect identities and
sensitive information will be enforced as a part of the data management
actions. The DMP is Deliverable D4.6 due on month 6 of the project. After that
due date the DMP will remain a living document throughout the project's
lifetime. The present first version may evolve during the project according to
the progress of project activities.
### 1.2 Procedures to update the Data Management Plan
The strategy for updating the present document has not been fully developed at
this early stage of the AEOLIX project. However, certain factors have been
identified that may trigger the publication of new versions of the DMP, such
as
* Change of the scope of a Work Package (WP);
* Addition/Deletion of deliverables from WPs;
* Input from Living Labs (LL) operation;
* Inputs from work on deliverables D4.1, D4.2 and/or D4.3.
##### 1.3 Intended audience
This deliverable is aimed to the AEOLIX Consortium as a reference document
with the objective of covering all aspects of data management for all WPs.
However, it may be of special importance to the LL within the project
consortium as they may need to exchange data with the partners active in the
development of the AEOLIX architecture in WP3. This is a public document which
can be downloaded by external stakeholders which might be interested in
cooperate with AEOLIX consortium.
**2\. Literature Review of Data Management**
### 2.1 Definition
The Data Management Plan will describe the data management life cycle for the
data to be collected, processed and/or generated by the AEOLIX project, during
the project lifetime itself, and once it is completed. This will help to
better manage the data, meet funder requirements, and help others use these
data when they are shared, ensuring that these data are well managed in the
present, and prepared for preservation in the future.
### 2.2 Personal Data
Personal Data refers to any information relating to an identified or
identifiable natural person, meaning by identifiable person the one who can be
identified, directly or indirectly, in particular by reference to an
identification number or to one or more factors specific to his physical,
physiological, mental, economic, cultural or social identity. Data is
considered personal when someone is able to connect information to a specific
person, even when the person or entity that is holding the personal data
cannot make the connection directly (e.g. name, address, e-mail), but has or
may have access to information allowing such identification (e.g. through
telephone numbers, credit card numbers, license plate numbers, etc.).
#### 2.2.1 Personal Data Protection
The fundamental right to the protection of personal data is explicitly
recognised in Article 8 of the Charter of Fundamental Rights of the European
Union, and in Article16 of the Treaty on the functioning of the European
Union, according to which _everybody has the right to the protection of
personal data concerning them_ . Such data must be processed fairly for
specified purposes and on the basis of the consent of the person concerned or
some other legitimate basis laid down by law.
#### 2.2.2 Personal Data Processing and movement
The European Parliament and Council Directive 95/46/EC _on the protection of
individuals with regard to the processing of personal data and on the free
movement of such data_ establishes on its Article 6 that Personal Data must be
processed fairly and lawfully, and collected for specified, explicit and
legitimate purposes and not further processed in a way incompatible with those
purposes. These data must be also adequate, relevant and not excessive in
relation to the purposes for which they are collected and/or further
processed, and also accurate (and, where necessary, kept up to date) keeping
them in a form which permits identification of data subjects for no longer
than is necessary for the purposes for which the data were collected or for
which they are further processed.
The Article 7 continues to determine the rules for processing data, allowing
this only if the data subject has unambiguously given his consent, meaning by
this (as per Article 2) “ _any freely given specific and informed indication
of his wishes by which the data subject signifies his agreement to personal
data relating to him being processed”._ Another circumstance required for
processing personal data is that it is necessary:
* for the performance of a contract to which the data subject is party or in order to take steps at the request of the data subject prior to entering into a contract;
* for the compliance with a legal obligation to which the controller is subject;
* in order to protect the vital interests of the data subject;
* for the performance of a task carried out in the public interest or in the exercise of official authority vested in the controller or in a third party to whom the data are disclosed;
* for the purposes of the legitimate interests pursued by the controller or by the third party or parties to whom the data are disclosed, except where such interests are overridden by the interests for fundamental rights and freedoms of the data subject which require protection.
#### 2.2.3 Information given to the data subject
According to the Article 10 of the Directive, the subject must be informed by
the controller of the data or his representative about from whom data relating
to him are collected, specifically about:
* the identity of the controller and of his representative, if any;
* the purposes of the processing for which the data are intended;
* any further information such as the recipients or categories of recipients of the data, whether replies to the questions are obligatory or voluntary, as well as the possible consequences of failure to reply, the existence of the right of access to and the right to rectify the data concerning him in so far as such further information is necessary, having regard to the specific circumstances in which the data are collected, to guarantee fair processing in respect of the data subject.
Article 11 establishes that the same information will be given to the data
subject where the data have not been obtained from him, at the time of
undertaking the recording of personal data or if a disclosure to a third party
is envisaged , no later than the time when the data are first disclosed,
except for the cases where, in particular for processing for statistical
purposes or for the purposes of historical or scientific research, the
provision of such information proves impossible or would involve a
disproportionate effort or if recording or disclosure is expressly laid down
by law.
## 3\. AEOLIX data
##### 3.1 Data summary
The data relevant in the AEOLIX project can be roughly divided into three
categories:
1. Data retrieved from project participants, LL actors and other stakeholders by non-technical means, e.g. by participation in surveys, through interviews etc.
2. Data used in the technical implementation of the AEOLIX platform (supply chain data coming from the living labs, other open data- e.g. statistics, GPS, metadata from standards or information exchanges)
3. Project internal administration data such as meeting agendas and minutes, cost statement, internal reports.
The category 1 data will be mainly used in the WP 2 (Lessons learned,
strategic needs and requirements) and WP6 (AEOLIX evaluation framework). WP2
is focused on experiences and future needs of potential AEOLIX users and will
gather its data by a set of measures:
1. Web based survey to establish the state-of-the-art and the problem awareness (lessons learned);
2. Analysis of information obtained in other WPs;
3. Individual interviews based on a defined questionnaire;
4. Delphi analysis in physical meetings.
Delphi is used in complex situations and its aim is to reach the correct
response through consensus. Whereas in the original technique question rounds
are administered in writing, the WP2 of the AEOLIX projects has focused on
physical meetings where the selected experts have been invited to express
their opinions about the proposed issues, answering several questions and
deciding together a consensus about those.
WP6 concentrates on the results achieved in the LL operations. As the
methodology on how to obtain the results is part of the work of WP6 (Cost-
Benefit Analysis evaluation tool, Multi-Criteria Analysis and Analytic
Hierarchy Process), the exact nature of the resulting data cannot be described
at the time of the writing of the present data management plan.
Category 2 data is used in the application of the AEOLIX platform at the
different living labs and is of relevance to WP3 and WP5. This data will carry
a big variety of information (supply chain data, geographic positions,
schedules, legal data, price information, etc.) of different levels of
confidentiality. Furthermore, the data will be present in many different
formats that will be processed by the AEOLIX connectivity engine. The concept
of the AEOLIX platform does not imply the long term storage of the data under
processing; however, short term storage of real-time data may not be
completely avoidable.
However, the handling of this data is highly complex and is not completely in
the scope of the present document. However, some considerations are made in
clause 3.2 of DMP. Further details will be presented in the following
deliverables of WP4:
1. D4.1: Best practices for AEOLIX collaborative data sharing
2. D4.2: AEOLIX high level architecture guideline for collaborative data exchange
3. D4.3: Guidelines for an AEOLIX framework for legal, trusted, interoperable and high quality data exchange
Category 3 data is AEOLIX consortium internal and may have different levels of
confidentiality. The agenda of AEOLIX general assemblies may for example be
available to the public whereas cost statements of individual partners will
not be disclosed outside the consortium.
##### 3.2 Accessibility of data
The raw category 1 data (e.g. results from the web survey, qualitative KPIs
from LL evaluations) will be kept in restricted storage areas as they are
considered private and/or confidential.
However, the aggregated results derived from the different data gathering
methods will be made available through publicly available AEOLIX project
deliverables, mainly of WP2 and WP6. They will be available through the AEOLIX
website
Category 2 data is mostly owned data that the data owners do only want to
disclose to partners with which they have a business relation. The nature of
the data exchanges between AEOLIX partners,
e.g. in the context of a LL, may be manifold and aspects such as
confidentiality or data security are usually covered by mutual agreements
between the partners that exchange and process data.
Consequently, category 2 data is not publicly available to any party outside
of the AEOLIX consortium and even within the consortium strict rules for data
access have to be established (see D4.3). This is of special importance as
data from many partners will run through the AEOLIX platform, where it may be
manipulated (e.g. data conversion) and exchanged between different data
handling/displaying entities. The concept of the AEOLIX platform does not
imply the long term storage of the data under processing; however, short term
storage of real-time data may not be completely avoidable.
During the operation of the AEOLIX project, partners of the consortium will
have access to the web based project management tool project place. This tool
allows the exchange and sharing of any kind of information. Within the AEOLIX
context, it will be mainly used to exchange and archive category 3 data.
##### 3.3 Interoperability of data
Data interoperability only applies to category 2 data and is at the heart of
the AEOLIX project. The whole AEOLIX concept aims at achieving data
interoperability between all players in the technical area of logistics. The
AEOLIX Platform represents a critical way forward of supply chain
interoperability through decentralized information sharing. AEOLIX is
established via cloud services where data, application, on-premises and cloud-
based processes and services from multiple actors can be connected - enhancing
collaboration and interoperability, potentially across the entire freight
transportation system. Data governance in AEOLIX will ensure the proper Data
Management of important and sensitive data including information for
customers, product designs, to be appropriately managed, anonymized, encrypted
and sanitized, managing risks which should arise upon their access by third
parties. The details of achieving data interoperability will be presented in
the deliverables of WP3.
##### 3.4 Reusability of data
The concepts of the reuse of data apply to category 2 data and will be
investigated throughout the operation of the LLs in WP5 (AEOLIX Verification
with Living Labs). Within WP8 (Collaborative business models and AEOLIX
exploitation) the principles for the exploitation of the AEOLIX platform will
be developed. This will include also aspects of data reusability. The
following aspects will be covered by WP8
* How the data will be licenced to permit the widest reuse possible
* How the data produced and/or used in the project is useable by third parties, in particular
after the end of the project
* How long the data will remain re-usable
##### 3.5 Allocation of resources to Data Management
The AEOLIX project has a whole WP dedicated to data management issues. WP4
(Collaborative data exchange certification framework) is considered
complementary to the technical approach for a collaborative architecture as
set up in WP3 (AEOLIX IT Ecosystem) and aims to establish a sustained flow of
information across modes, entities and countries enabled by interoperable
systems and standards, allowing the re-use of data and trustworthiness of the
data content. AEOLIX has assigned the WP leader PTV to provide a mechanism for
controlling changes related to the data, data processes, and the data
architecture of AEOLIX and act as data protection officer. WP4 addresses the
structural problems and barriers of data exchange and sharing and develop
solutions to overcome. The AEOLIX governance model is to address the different
levels of data exchange starting reaching from strategic over tactical to
operational issues. Key stakeholders in industry and administrations are
involved.
More specifically WP4 aims to:
* Identify and assess key AEOLIX practices for collaborative data sharing (liaise with LL)
* Define an architecture for cross entity data exchange and sharing
* Develop a framework for trusted, interoperable and high quality data exchange including legal aspect
* Develop a certification framework to certify company, who want to join the AEOLIX platform
In total a resource of 112 man months has been allocated to WP4. However, this
also includes the development of certification schemes and the organisation
and execution of two TESTFEST events.
##### 3.6 Data security
The AEOLIX consortium will use the web based projectplace to tool to store and
exchange category 3 data within the partnership. Access to projectplace is not
public and the ERTICO project management team will assign and manage the
access rights of the project partners to the tool. AEOLIX stakeholders should
stay in control on who has access to their data. Privacy and data security are
very important strategic requirements.
The Conceptual Security and Privacy Taxonomy will be applied. It contains four
main Big Data security & privacy principles:
* Data confidentiality topic: safeguarding the confidentiality of personal data.
* Data provenance topic: safeguarding the integrity and validation of personal data.
* System health topic: safeguarding the availability personal data.
* Public policy, social, and cross-organizational topics: safeguarding the specific Big Data and privacy & data protection requirements.
Project participants are already covered by a Consortium Agreement which
encompasses NonDisclosure clauses as well as their Contract with the EC which
itself includes clauses on the treatment of data. However, NDAs are
implemented to cover future users, as well as through the implementation of
security measures in their processing systems aimed at a level of protection,
according also to the principle of proportionality, which should be at least
the same as that provided to their own confidential information.
##### 4\. Data confidentiality
The data to be considered as confidential as not so much related to personal
data or information as these are protected under basic data protection
legislation, as analysed in the previous section. Here it is particularly
technical, commercial information that are placed within the scope of
protection. This information may have intellectual property rights protection
over it, but it may well be the case that it is unprotected raw data, that
however still possesses business value for the disclosing party. The preferred
means through which to protect confidential information exchanged during
execution of the AEOLIX project are (NDAs) and these are included in the
present Consortium Agreement which has been signed by all partners. However,
after the end of the research project, when results will be rolled-out to
organizations which are not partners in AEOLIX and therefore not covered by
the present Consortium Agreement, it is essential that comprehensive NDAs are
signed prior to any disclosure of information. Such agreements should be
drafted by the Project Coordinator and entered whenever deemed important
before confidential information exchanges.
Regarding confidentiality measures of the different AEOLIX categories:
1. Data retrieved from project participants, LL actors and other stakeholders by non-technical
means, e.g. by participation in surveys, through interviews etc.
Participants in interviews or other activities must be asked to sign a
relevant form, in order for their consent to be demonstrable in writing. The
form must be composed in accordance with legal requirements, i.e. among others
to describe how the information will be used, how the person concerned may
review or amend them. Such data are responses to interviews, or views from
DELPHI studies. More details can be found in Data per WP.
2. Data used in the technical implementation of the AEOLIX platform
These supply chain data coming from the living labs are often commercial data
(shipper’s data like invoices, inventory data, GPS position data, delivery
information, data coming from services of the Living lab). This information
constitutes valuable commercial business information and if unauthorized
access was granted to them could cause serious damage to AEOLIX partners.
Therefore, as a general principle: This data will not be shared outside the
consortium and access within the consortium will be strictly limited to only
those parties agreed by the data owner. Other types of data are coming from
the evaluation and impact assessment evaluation results. Data in this category
require the agreement of all commercial parties related, to eliminate
sensitive information. More details can be found in Data per WP.
3. Project internal administration data such as meeting agendas and minutes, cost statement, internal reports.
These administrative data and project minutes are Confidential, only for
members of the Consortium (including the Commission services). More details
can be found in Data per WP.
## 5\. Detailed Data Management Aspects per WP
The data that is collected or generated in AEOLIX, contributes to different
purposes depending of the objective of the WP they relate to. The following
sub clauses provide an overview of the deliverables per WP giving also the
origin of the data in tables.
The tables will deliver the following information:
* **Data set reference and name:** Pointer to the deliverable containing the data
* **Data set description:** Descriptions of the data that will be generated or collected
* **Data collection procedures:** Description how the data was collected or produced
* **Data privacy** : Description how data is kept confidential, if applicable
* **Data sharing/ confidentiality** : Description of how data will be shared, e.g. publicly available or consortium internal only
### 5.1 Data in WP1
Table 1 – Data in WP1
<table>
<tr>
<th>
**WP1**
</th>
<th>
**Project Management**
</th> </tr>
<tr>
<td>
**Data set reference and name**
</td>
<td>
Quality and Risk Management Plan
</td> </tr>
<tr>
<td>
**Data set description**
</td>
<td>
The AEOLIX Quality Plan will define all quality processes and procedures to be
applied throughout the project, including documents and Deliverable
Management.
</td> </tr>
<tr>
<td>
**Data collection procedures**
</td>
<td>
Both Quality and Risk Management Plan will be developed under PRINCE2
Principles which are: Business justification, Roles and responsibilities,
Learn from experience, Manage by exceptions, Manage by stages, Tailor to suite
the environment, Focus on product.
</td> </tr>
<tr>
<td>
**Data privacy**
</td>
<td>
Not applicable
</td> </tr>
<tr>
<td>
**Data sharing/confidentiality**
</td>
<td>
Confidential, only for members of the Consortium (including the Commission
services)
</td> </tr>
<tr>
<td>
</td>
<td>
</td> </tr>
<tr>
<td>
**Data set reference and name**
</td>
<td>
Coordination Plan for a harmonised coordination of the LLs
</td> </tr>
<tr>
<td>
**Data set description**
</td>
<td>
Each Living Lab develops a plan, agreed with the EC, with the aim to
demonstrate progress in the AEOLIX LLs
</td> </tr>
<tr>
<td>
**Data collection procedures**
</td>
<td>
Each LL will include a Gantt Chart (What the various activities are, When each
activity begins and ends, How long each activity is scheduled to last, Where
activities overlap with other activities, and by how much, The start and end
date of the whole project).
</td> </tr>
<tr>
<td>
**Data privacy**
</td>
<td>
Not applicable
</td> </tr>
<tr>
<td>
**Data sharing/confidentiality**
</td>
<td>
Confidential, only for members of the Consortium (including the Commission
services)
</td> </tr>
<tr>
<td>
</td>
<td>
</td> </tr>
<tr>
<td>
**Data set reference and name**
</td>
<td>
Yearly Management and Progress reports.
</td> </tr>
<tr>
<td>
**Data set description**
</td>
<td>
The AEOLIX Yearly Management and Progress reports will inform the PO and
reviewers of the progress made by the Consortium.
</td> </tr>
<tr>
<td>
**Data collection procedures**
</td>
<td>
Produced by the AEOLIX management team
</td> </tr>
<tr>
<td>
**Data privacy**
</td>
<td>
Not applicable
</td> </tr>
<tr>
<td>
**Data sharing/confidentiality**
</td>
<td>
Confidential, only for members of the Consortium (including the Commission
services)
</td> </tr>
<tr>
<td>
</td>
<td>
</td> </tr>
<tr>
<td>
**Data set reference and name**
</td>
<td>
Final report.
</td> </tr>
<tr>
<td>
**Data set description**
</td>
<td>
The AEOLIX Final Report will summarise the work performed by the Consortium
members and the interaction with any external stakeholders
</td> </tr>
<tr>
<td>
**Data collection procedures**
</td>
<td>
Produced by the AEOLIX management team
</td> </tr>
<tr>
<td>
**Data privacy**
</td>
<td>
Not applicable
</td> </tr>
<tr>
<td>
**Data sharing/confidentiality**
</td>
<td>
Public
</td> </tr> </table>
### 5.2 Data in WP2
Table 2 – Data in WP2
<table>
<tr>
<th>
**WP2**
</th>
<th>
**Lessons learned, strategic needs and requirements**
</th> </tr>
<tr>
<td>
**Data set reference and name**
</td>
<td>
Lessons learned
</td> </tr>
<tr>
<td>
**Data set description**
</td>
<td>
The result is a deliverable giving a clear vision of the technical and non-
technical elements that led previous solutions in the logistics field to
either success or to failure.
</td> </tr>
<tr>
<td>
**Data collection procedures**
</td>
<td>
Analysis of the existing approaches, screening of projects, commercial
solutions and roadmaps within the domain of AEOLIX.
</td> </tr>
<tr>
<td>
**Data collection method**
</td>
<td>
Web-based survey among stakeholders (from Europe and beyond) to identify
additional current and future needs and gaps in current provisions.
</td> </tr>
<tr>
<td>
**Data privacy**
</td>
<td>
The individual survey answers will be treated with confidentiality.
</td> </tr>
<tr>
<td>
**Data Sharing/confidentiality**
</td>
<td>
This data will be publicly available as AEOLIX deliverable D2.1
</td> </tr>
<tr>
<td>
</td>
<td>
</td> </tr>
<tr>
<td>
**Data set reference and name**
</td>
<td>
New and revised general needs and requirements
</td> </tr>
<tr>
<td>
**Data set description**
</td>
<td>
The requirements are divided into two groups:
1. Functional requirements that define specific behaviour or functions with a focus on the utility
2. Non-functional requirements (that define the quality and general behaviour of the actors involved in the system operations
</td> </tr>
<tr>
<td>
**Data collection procedures**
</td>
<td>
The data is coming from the living labs in the project and much of the data
will be collected via deliverables D5.2 Connectivity Gap Analysis and D5.3
Logistics Business Needs and Data Identification Report.
</td> </tr>
<tr>
<td>
**Data privacy**
</td>
<td>
Not applicable
</td> </tr>
<tr>
<td>
**Data Sharing/confidentiality**
</td>
<td>
This data will be publicly available as AEOLIX deliverable D2.2
</td> </tr>
<tr>
<td>
</td>
<td>
</td> </tr>
<tr>
<td>
**Data set reference and name**
</td>
<td>
AEOLIX global needs and requirements
</td> </tr>
<tr>
<td>
**Data set description**
</td>
<td>
Information to be collected:
1. Trends in business needs as to demand and supply in logistics services
2. New types of processes envisaged and new kinds of ICT and information services needed
3. Trends in ICT technologies (including Secure, resilient and trusted communications and information storage and processing) 4. Trends in ITS technologies especially those focusing on the driver, the vehicles and the cargo.
5 Current and future expected legislative and standards requirements
</td> </tr>
<tr>
<td>
**Data collection procedures**
</td>
<td>
Questionnaire as basis for individual interviews followed by a two round
Delphi analysis in physical meetings.
</td> </tr>
<tr>
<td>
**Data privacy**
</td>
<td>
The individual interview answers and the discussions during the Delphi
analysis will not be disclosed.
</td> </tr>
<tr>
<td>
**Data Sharing/confidentiality**
</td>
<td>
This data will be publicly available as AEOLIX deliverable D2.3
</td> </tr>
<tr>
<td>
</td>
<td>
</td> </tr>
<tr>
<td>
**Data set reference and name**
</td>
<td>
AEOLIX reference book
</td> </tr>
<tr>
<td>
**Data set description**
</td>
<td>
Continuous upgraded information on AEOLIX requirements
</td> </tr>
<tr>
<td>
**Data collection procedures**
</td>
<td>
Establishment of a task force consisting of representatives from logistics
actors, software developers, authorities and academia from within the
consortium, that performs a continuous monitoring of the evolutions of the
demand and supply in the logistics market, of the ICT domain, of the
information sharing tools etc.
</td> </tr>
<tr>
<td>
**Data privacy**
</td>
<td>
Not applicable
</td> </tr>
<tr>
<td>
**Data Sharing/confidentiality**
</td>
<td>
The AEOLIX Reference Book will be continually communicated via a dedicated
space on the AEOLIX website and via direct meetings with representatives of
other WPs.
</td> </tr>
<tr>
<td>
</td>
<td>
</td> </tr>
<tr>
<td>
**Data set reference and name**
</td>
<td>
KPIs and evaluation criteria
</td> </tr>
<tr>
<td>
**Data set description**
</td>
<td>
Indicators of local and global levels, e.g.
Network efficiency, Environmental impact, Economical sustainability, Traffic
network management, Driver specific metrics, Goods specific metrics
</td> </tr>
<tr>
<td>
**Data collection procedures**
</td>
<td>
Evaluation criteria and performance targets will be set in collaboration with
the LL leaders and will take into account use cases, LL setup, historical data
and projection and models of the expected impact of each of the applications
at each site.
</td> </tr>
<tr>
<td>
**Data privacy**
</td>
<td>
Not applicable
</td> </tr>
<tr>
<td>
**Data Sharing/confidentiality**
</td>
<td>
This data will be publicly available as AEOLIX deliverable D2.5 and will serve
as input to WP6.1 Evaluation framework WP 6.2 AEOLIX Living Labs business
impacts, WP6.4 Business Paradigm Shift Analysis and WP 7.4 Business Model
Scenarios.
</td> </tr> </table>
### 5.3 Data in WP3
Table 3 – Data in WP3
<table>
<tr>
<th>
**WP3**
</th>
<th>
**AEOLIX IT Ecosystem**
</th> </tr>
<tr>
<td>
**Data set reference and name**
</td>
<td>
AEOLIX Ecosystem technical architecture specification
</td> </tr>
<tr>
<td>
**Data set description**
</td>
<td>
Definition of the AEOLIX IT architecture that enables the real-time data-
sharing platform, supporting the connectivity network and interoperability of
the different systems, applications and objects of the ecosystems through
AEOLIX IT platform services
</td> </tr>
<tr>
<td>
**Data collection procedures**
</td>
<td>
_Definition of the AEOLIX Reference Architecture_ : Description of the
different ecosystem services and elements that will comprise the AEOLIX Cloud
Ecosystem, detailing the connectivity engine communication protocols that will
be used to interact among them, the collaborative ecosystem builder module and
detailing the services to support the functionalities.
</td> </tr> </table>
<table>
<tr>
<th>
</th>
<th>
_Definition of Platform services_ : Exchange of specific information among the
systems of the ecosystem and the security in those processes
_Definition of additional AEOLIX Platforms services_ : Management of the
ecosystem resources (reporting, statistical information, user administration,
support tickets, ecosystem status, admin newsletter, etc.)
</th> </tr>
<tr>
<td>
**Data privacy**
</td>
<td>
Not applicable
</td> </tr>
<tr>
<td>
**Data sharing/confidentiality**
</td>
<td>
This data is publicly available as AEOLIX deliverable D3.1
</td> </tr>
<tr>
<td>
</td> </tr>
<tr>
<td>
**Data set reference and name**
</td>
<td>
Connectivity engine reference implementation
</td> </tr>
<tr>
<td>
**Data set description**
</td>
<td>
Specification and provision of the Connectivity Infrastructure Services of the
AEOLIX Ecosystem based on development of data pipelines and cloud messaging
technology
</td> </tr>
<tr>
<td>
**Data collection procedures**
</td>
<td>
Not applicable
</td> </tr>
<tr>
<td>
**Data privacy**
</td>
<td>
The software source code will be non-public. IPR details are for further
study.
</td> </tr>
<tr>
<td>
**Data sharing/confidentiality**
</td>
<td>
The textual description is publicly available as AEOLIX deliverable D3.2
</td> </tr>
<tr>
<td>
</td> </tr>
<tr>
<td>
**Data set reference and name**
</td>
<td>
AEOLIX Intelligent Dashboard
</td> </tr>
<tr>
<td>
**Data set description**
</td>
<td>
Definition and development of the AEOLIX dashboard as the component that
enables logistic stakeholders to create and configure the collaborative
environments composed by those stakeholders that are part of the logistic
network required for the transport of goods, and aiming to share information
between them.
AEOLIX provides an open AEOLIX API to provide access to main
AEOLIX IT Connectivity Services and a Software Development Toolkit (SDK) to
enable developers to join the ecosystem or creating the opportunity to develop
new pan-EU logistics applications based on the platform capabilities and
AEOLIX toolkit capabilities.
</td> </tr>
<tr>
<td>
**Data collection procedures**
</td>
<td>
Not applicable
</td> </tr>
<tr>
<td>
**Data privacy**
</td>
<td>
The software source code will be non-public. IPR details are for further
study.
</td> </tr>
<tr>
<td>
**Data sharing/confidentiality**
</td>
<td>
The textual description is publicly available as AEOLIX deliverable D3.3
</td> </tr>
<tr>
<td>
</td> </tr>
<tr>
<td>
**Data set reference and name**
</td>
<td>
Services and interoperability adapters
</td> </tr>
<tr>
<td>
**Data set description**
</td>
<td>
Provision of the technical specification of the AEOLIX Data interoperability
services and security recommendations for a secure development of AEOLIX
ecosystem and services.
</td> </tr>
<tr>
<td>
**Data collection procedures**
</td>
<td>
Control methods proposal, for the main security risks in the architecture.
Identification of the most suitable security solutions.
</td> </tr>
<tr>
<td>
**Data privacy**
</td>
<td>
Logistics stakeholders use different standards and formats:
* EDIFACT
* ODETTE
* GS1 Standards
* Common Framework
</td> </tr>
<tr>
<td>
**Data sharing/confidentiality**
</td>
<td>
This data is publicly available as AEOLIX deliverable D3.4
</td> </tr>
<tr>
<td>
</td> </tr>
<tr>
<td>
**Data set reference and name**
</td>
<td>
AEOLIX API and SDK
</td> </tr>
<tr>
<td>
**Data set description**
</td>
<td>
Provision of the procedure and necessary tools to allow developers, public and
private services to publish services and apps in the central functionalities
provided by AEOLIX for seamless integration into dashboards and intelligent
applications.
Development and implementation of standard interface mapping procedures.
</td> </tr>
<tr>
<td>
**Data collection procedures**
</td>
<td>
Not applicable
</td> </tr>
<tr>
<td>
**Data privacy**
</td>
<td>
The software source code will be non-public. IPR details are for further
study.
</td> </tr>
<tr>
<td>
**Data sharing/confidentiality**
</td>
<td>
The textual description is publicly available as AEOLIX deliverable D3.5
</td> </tr>
<tr>
<td>
</td>
<td>
</td> </tr>
<tr>
<td>
**Data set reference and name**
</td>
<td>
AEOLIX Toolkit
</td> </tr>
<tr>
<td>
**Data set description**
</td>
<td>
The toolkit provides a set of functional tools and component that can manage
the multitude of logistics information flows that will be accessible through
AEOLIX.
</td> </tr>
<tr>
<td>
**Data collection procedures**
</td>
<td>
Not applicable
</td> </tr>
<tr>
<td>
**Data privacy**
</td>
<td>
The software source code will be non-public. IPR details are for further
study.
</td> </tr>
<tr>
<td>
**Data sharing/confidentiality**
</td>
<td>
The textual description is publicly available as AEOLIX deliverable D3.6
</td> </tr> </table>
### 5.4 Data in WP4
Table 4 – Data in WP4
<table>
<tr>
<th>
**WP4**
</th>
<th>
**Collaborative data exchange certification framework**
</th> </tr>
<tr>
<td>
**Data set reference and name**
</td>
<td>
Best practices for AEOLIX collaborative data sharing
</td> </tr>
<tr>
<td>
**Data set description**
</td>
<td>
AEOLIX Architecture for Collaborative Data Exchange; Complete overview of the
high level governance architecture related to the integration, securing,
management of data flows and organisation within AEOLIX.
</td> </tr>
<tr>
<td>
**Data collection procedures**
</td>
<td>
Practices from different use cases, industry collaborations or PPP are the
basis for this task. Topics and approaches will be defined from the LL and
WP3.
</td> </tr>
<tr>
<td>
**Data privacy**
</td>
<td>
Not applicable
</td> </tr>
<tr>
<td>
**Data Sharing/confidentiality**
</td>
<td>
This data will be publicly available as AEOLIX deliverable D4.1
</td> </tr>
<tr>
<td>
</td>
<td>
</td> </tr>
<tr>
<td>
**Data set reference and name**
</td>
<td>
AEOLIX high level architecture guideline for collaborative data exchange
</td> </tr>
<tr>
<td>
**Data set description**
</td>
<td>
Definition of generic data exchange and sharing architecture for AEOLIX
describing the processes related to the integration, securing, management and
measurement of data flows and organisation.
</td> </tr>
<tr>
<td>
**Data collection procedures**
</td>
<td>
Based on the AEOLIX LL requirements, taking into account other research
initiatives (e.g. NEXTRUST).
</td> </tr>
<tr>
<td>
**Data privacy**
</td>
<td>
Not applicable
</td> </tr>
<tr>
<td>
**Data Sharing/confidentiality**
</td>
<td>
This data will be publicly available as AEOLIX deliverable D4.2
</td> </tr>
<tr>
<td>
</td>
<td>
</td> </tr>
<tr>
<td>
**Data set reference and name**
</td>
<td>
Guidelines for an AEOLIX framework for legal, trusted, interoperable and high
quality data exchange
</td> </tr>
<tr>
<td>
**Data set description**
</td>
<td>
Guidelines for legal and organisational framework:
1. Information ownership, data access levels and scope, data liability as treated within the EU member states.
2. Data re-use and sharing
3. Data quality and accuracy
4. Data interoperability
</td> </tr>
<tr>
<td>
**Data collection procedures**
</td>
<td>
Extension of work from WP4.2
</td> </tr>
<tr>
<td>
**Data privacy**
</td>
<td>
Not applicable
</td> </tr>
<tr>
<td>
**Data Sharing**
</td>
<td>
This data will be publicly available as AEOLIX deliverable D4.3, a high level
description of the data management within AEOLIX will be publicly available in
deliverable D4.6.
</td> </tr>
<tr>
<td>
**Archiving and preservation**
**(including storage and backup)**
</td>
<td>
</td> </tr>
<tr>
<td>
</td>
<td>
</td> </tr>
<tr>
<td>
**Data set reference and name**
</td>
<td>
Specification for certification framework for the AEOLIX platform
</td> </tr>
<tr>
<td>
**Data set description**
</td>
<td>
Framework for certifying companies, who want to join the AEOLIX platform.
Objectives:
1. Evaluation of compliancy of devices and services with the data governance model
2. Specification of the AEOLIX certification framework to check compliancy with the data governance model
</td> </tr>
<tr>
<td>
**Data collection procedures**
</td>
<td>
The data from the guidelines in D4.2 and D4.3 will be used as input to develop
the certification framework. Feedback will be included by performing a test
certification at the TESTFESTs.
</td> </tr>
<tr>
<td>
**Data privacy**
</td>
<td>
Not applicable
</td> </tr>
<tr>
<td>
**Data Sharing/confidentiality**
</td>
<td>
This data will be publicly available as AEOLIX deliverable D4.5, the results
from the TESTFEST events will be public in deliverable D4.5
</td> </tr> </table>
### 5.5 Data in WP5
Table 5 – Data in WP5
<table>
<tr>
<th>
**WP5**
</th>
<th>
**Verification with Living Labs**
</th> </tr>
<tr>
<td>
**Data set reference and name**
</td>
<td>
Connectivity gap analysis
</td> </tr>
<tr>
<td>
**Data set description**
</td>
<td>
Definition of the data flow and gaps as experienced by the logistics partners
and official agencies which collaborate to improve supply chain performance
across specified corridors
</td> </tr>
<tr>
<td>
**Data collection procedures**
</td>
<td>
Data Issue Analysis for each LL, describing the specific data gaps that
currently hamper effective operations.
</td> </tr>
<tr>
<td>
**Data privacy**
</td>
<td>
Not applicable
</td> </tr>
<tr>
<td>
**Data sharing**
</td>
<td>
This data is publicly available as AEOLIX deliverable D5.2
</td> </tr>
<tr>
<td>
</td>
<td>
</td> </tr>
<tr>
<td>
**Data set reference and name**
</td>
<td>
Logistics Business Needs and Data Identification
</td> </tr>
<tr>
<td>
**Data set description**
</td>
<td>
Analysis of the business needs data needed and the sources of such data and
the format available and needed in each LL.
</td> </tr>
<tr>
<td>
**Data collection procedures**
</td>
<td>
Identification of the specific sources of data needed and the
</td> </tr>
<tr>
<td>
</td>
<td>
format in which it exists and it is provided.
Definition of procedures and mechanisms required for authorization of data
access and import, and the level of data analysis and manipulation needed by
users within the Intelligent Dashboard, and which data will be supplied by or
sent to partners’ existing in-house systems, and what Toolkit solutions may be
deployed.
</td> </tr>
<tr>
<td>
**Data privacy**
</td>
<td>
Not applicable
</td> </tr>
<tr>
<td>
**Data sharing/confidentiality**
</td>
<td>
This data is publicly available as AEOLIX deliverable D5.3
</td> </tr> </table>
###### 5.6 Data in WP6
Table 6 – Data in WP6
<table>
<tr>
<th>
**WP6**
</th>
<th>
**Collaborative data exchange certification framework**
</th> </tr>
<tr>
<td>
**Data set reference and name**
</td>
<td>
AEOLIX evaluation framework
</td> </tr>
<tr>
<td>
**Data set description**
</td>
<td>
Framework for assessing the impacts of the AEOLIX project:
1. Identification of the operational benefits
2. Mechanism depicting the market penetration
</td> </tr>
<tr>
<td>
**Data collection procedures**
</td>
<td>
Cost-Benefit Analysis (CBA) evaluation tool, Multi-Criteria Analysis (MCA) and
Analytic Hierarchy Process (AHP). KPIs defined on based of those tools
complemented by the KPIs identified in WP2.5.
</td> </tr>
<tr>
<td>
**Data privacy**
</td>
<td>
Not applicable
</td> </tr>
<tr>
<td>
**Data Sharing/confidentiality**
</td>
<td>
This data will be publicly available as AEOLIX deliverable D6.1
</td> </tr>
<tr>
<td>
</td>
<td>
</td> </tr>
<tr>
<td>
**Data set reference and name**
</td>
<td>
AEOLIX impacts
</td> </tr>
<tr>
<td>
**Data set description**
</td>
<td>
AEOLIX Living Labs impact assessment, will present the results living lab
evaluation including socio-economic impacts of the AEOLIX project on society
and the environment. Continuous presentation of quick win impacts as
identified from the initial implementation of the LLs.
</td> </tr>
<tr>
<td>
**Data collection procedures**
</td>
<td>
As described in AEOLIX evaluation framework
</td> </tr>
<tr>
<td>
**Data privacy**
</td>
<td>
Not applicable
</td> </tr>
<tr>
<td>
**Data Sharing/confidentiality**
</td>
<td>
This data will be publicly available as AEOLIX deliverables D6.2, D6.3 and
D6.4. D6.3 AEOLIX quick win impacts will be updated quarter-yearly as of
project month 15.
</td> </tr>
<tr>
<td>
</td>
<td>
</td> </tr>
<tr>
<td>
**Data set reference and name**
</td>
<td>
Business Paradigm Shift analysis
</td> </tr>
<tr>
<td>
**Data set description**
</td>
<td>
Business Paradigm Shift analysis describing the modification of the business
models required by the implementation of the AEOLIX ecosystem and the new
collaboration schemes among the stakeholders.
</td> </tr>
<tr>
<td>
**Data collection procedures**
</td>
<td>
Based on the impacts of the Living Labs in comparison to a benchmark. Paradigm
shift analysis by a dynamic monitoring of the performance and the impacts of
the pilots.
</td> </tr>
<tr>
<td>
**Data privacy**
</td>
<td>
Not applicable
</td> </tr>
<tr>
<td>
**Data Sharing/confidentiality**
</td>
<td>
This data will be publicly available as AEOLIX deliverable D6.5
</td> </tr>
<tr>
<td>
**Archiving and preservation**
**(including storage and backup)**
</td>
<td>
</td> </tr> </table>
###### 5.7 Data in WP7
WP7 (Dissemination, Communication and Market Outreach) aims to maximise market
take-up of the AEOLIX project objectives, concepts and outputs as realised in
the AEOLIX Living labs, to all relevant logistic stakeholders and public
authorities both during and after the life of the AEOLIX project by raising
awareness about the AEOLIX project, its activities and its achievements. This
WP does not deal with data in itself. However, it may be necessary to define
the status of certain documents, e.g. slide sets of speakers at a conference.
The details of the related proceedings will be defined within WP7.
###### 5.8 Data in WP8
WP8 (Collaborative business models and AEOLIX exploitation) addresses all
deployment/business aspects that are essential for ensuring sustainable after-
project life of the AEOLIX platform, piloted services and established Living
Labs. This WP does not deal with data in itself. However, certain data used in
the development of e.g. deliverable D8.6 (Cost benefit Analysis) may need
consideration in regards to data privacy. These data are related to costs and
benefits of the different systems. These data will need to be confidential
internal to the consortium for analysis but can be disseminated externally as
a overall evaluation of the project. The details of the related proceedings
will be defined within WP8.
**6\. Ethics**
The AEOLIX partners are to comply with the ethical principles as set out in
Article 34 of the Grant Agreement, which states that they must ensure that the
activities under the action have an exclusive focus on civil applications, and
must be carried out in compliance with:
ethical principles (including the highest standards of research integrity —
as set out, for instance, in the European Code of Conduct for Research
Integrity and including, in particular, avoiding fabrication, falsification,
plagiarism or other research misconduct) and applicable international, EU
and national law.
Partners will deploy all the efforts needed to ensure a high level of
transparency in data treatment and compliance with national and European
legislations. The ethics procedures in the involved countries will be analysed
and, if needed, partners will collect the necessary permissions in their
entity / country. The involved AEOLIX parties that will lead the design and
implementation of the overall system architecture and data feeding components,
has notified the National Authority for Protection of Personal Data (Garante
per la protezione dei dati personali, no: 2014-061000196946) in order to be
qualified, as required by the national law (Legislative Decree no. 196 of 30th
June 2003), to manage data about position and tracking of persons and objects
for research activities.
Deliverables D9.1 PODP – Requirement 1, describe the AEOLIX procedures for
research actions involving data collection. Each beneficiary must submit to
the coordinator in good time notifications for activities raising ethical
issues (e.g. personal data collection) and before the beginning of an activity
raising an ethical issue (e.g. prior to any personal data being collected),
the coordinator must submit to the Agency copy of:
* The Ethics Committee opinion required under national law and
* Any notification or authorisation for activities raising ethical issues required under national law.
This act should have had received favourable opinions from the relevant Ethics
Committee and, if applicable, the regulatory approvals of the competent
national or local authorities in the countries where the research is going to
be carried out.
**7\. Confidentiality**
All AEOLIX partners must keep any data, documents or other material
confidential during the implementation for the project and for four years
after the project period set out in Article 3 (36 months); as per Article 36
of the Grant Agreement (GA). Further detail on confidentiality can be found in
Article 36 of the GA. This confidential information will only be used to
implement the Agreement; consequently the partners may disclose confidential
information to their personnel or third parties involved in the action only if
they: need to know to implement the Agreement, and; are bound by an
obligation of confidentiality.
Nevertheless, the confidentiality obligations no longer apply if:
* the disclosing party agrees to release the other party;
* the information was already known by the recipient or is given to him without obligation of confidentiality by a third party that was not bound by any obligation of confidentiality;
* the recipient proves that the information was developed without the use of confidential information;
* the information becomes generally and publicly available, without breaching any confidentiality obligation, or
* the disclosure of the information is required by EU or national law.
|
https://phaidra.univie.ac.at/o:1140797
|
Horizon 2020
|
1109_REFLEX_691685.md
|
**ABBREVIATIONS**
<table>
<tr>
<th>
DMP
</th>
<th>
Data management plan
</th> </tr>
<tr>
<td>
DSM
</td>
<td>
Demand side management
</td> </tr>
<tr>
<td>
DWH
</td>
<td>
Data warehouse
</td> </tr>
<tr>
<td>
EIM
</td>
<td>
Exploitation and Innovation Manager
</td> </tr>
<tr>
<td>
EMS
</td>
<td>
Energy models system
</td> </tr>
<tr>
<td>
LCA
</td>
<td>
Life Cycle Assessment
</td> </tr>
<tr>
<td>
RES
</td>
<td>
Renewable Energy Sources
</td> </tr> </table>
**SCOPE OF THE DOCUMENT**
This document provides the draft version of the Data Management Plan (DMP) for
the REFLEX project according to the Open Research Data Pilot (ORD pilot) under
Horizon 2020. The purpose of the DMP is to support the data management life
cycle of all data that will be collected, processed or generated by the
project.
The document structure and contents are based on the Guidelines on FAIR Data
Management in Horizon 2020 (Version 3.0, 26 July 2016) and on the Guidelines
on Open Access to Scientific Publications and Research Data in Horizon 2020
(Version 2.1, 15 February 2016). The document was generated by using the
Digital Curation Centre's DMP online tool. The following sections outline the
types of collected and generated data, how these data will be exploited and
made accessible for verification and re-use and how data will be curated and
preserved upon closure of the project. Changes are reserved – the DMP will be
continuously updated during the project.
**ADMIN DETAILS**
**Project Name:** REFLEX (Horizon 2020 DMP)
**Grant Agreement No.:** 691685
**Principal Investigator / Researcher:** Prof. Dominik Möst, TU Dresden
**Project Description:**
The future energy system is challenged by the intermittent nature of
renewables and requires therefore several flexibility options. Still, the
interaction between different options, the optimal portfolio and the impact on
environment and society are unknown. It is thus the core objective of REFLEX
to analyse and evaluate the development towards a low-carbon energy system
with focus on flexibility options in the EU to support the implementation of
the SET-Plan. The analysis are based on a modelling environment that considers
the full extent to which current and future energy technologies and policies
interfere and how they affect the environment and society while considering
technological learning of low-carbon and flexibility technologies.
For this purpose, REFLEX brings together the comprehensive expertise and
competences of known European experts from six different countries. Each
partner focusses on one of the research fields techno-economic learning,
fundamental energy system modelling or environmental and social life cycle
assessment. To link and apply these three research fields in a compatible way,
an innovative and comprehensive energy models system (EMS) is developed, which
couples the models and tools from all REFLEX partners. It is based on a common
database and scenario framework. The results from the EMS will help to
understand the complex links, interactions and interdependencies between
different actors, available technologies and impact of the different
interventions on all levels from the individual to the whole energy system. In
this way, the knowledge base for decision-making concerning feasibility,
effectiveness, costs and impacts of different policy measures will be
strengthened, which will assist policy makers and support the implementation
of the SETPlan. Stakeholders will be actively involved during the entire
project from definition of scenarios to dissemination and exploitation of
results via workshops, publications and a project website.
**Nature:** Research and innovation actions based on energy systems modelling
**Research Questions:**
1. How do current and future energy technologies and policies interfere?
2. What will be an optimal combination of flexibility options to cope with the future flexibility needs?
3. How do these technologies and policy measures affect the environment, economy and society?
**Purpose:**
Support for the implementation of the SET-Plan:
* Analysing the impacts of technological development and innovation on the energy system and its dynamics
* Comparative assessment of the impacts and the sustainability performance of all relevant energy technologies
* Assessing the related impacts on the environment, society and economy - Analysing of technology policy measures in the framework of the SET-Plan
* Understanding the complex links/interactions/interdependencies between the different actors, the available technologies and the impact of the different interventions on all levels from the individual to the whole energy system
* Providing model based decision support tools for the different actors in the energy system in order to facilitate handling the complex system
**Funder:** European Commission (Horizon 2020)
**1 DATA SUMMARY**
The core objective of the REFLEX project is to analyse and evaluate the
development towards a low-carbon energy system in the EU up to the year 2050.
The focus is laying on flexibility options to support a better system
integration of Renewable Energy Sources (RES). The analysis and the assessment
of REFLEX are based on a modelling environment that considers the full extent
to which current and future energy technologies and policies interfere and how
they affect the environment and society.
### 1.1 PURPOSE OF DATA COLLECTION AND GENERATION
The purpose for data collection and their preparation within REFLEX is to
provide the needed input data for the applied mathematical energy system
models. The model pool of the REFLEX partners contains bottom-up simulation
tools and fundamental system optimisation models on national and therewith
also on European level as well as approaches for Life Cycle Assessment (LCA).
Typically, one model cannot cover all aspects of an energy system or the
implications of specific policies. Each of these different models focuses on a
specific sector or aspect (heat, electricity, mobility, environmental/ social
impacts etc.) of the energy system.
For analysing and answering the given research questions (see Admin Details
above), the different models and approaches will be coupled to a so called
integrated Energy Model System (EMS). The analysis by applying the EMS allows
to perform an in-depth and at the same time holistic assessment of the system
transformation and shall contribute to the scientific underpinning of the SET-
Plan. The final result data of the EMS helps to understand and investigate the
complex links, interactions and interdependencies between the different actors
and technologies within the energy system as well as their impact on society
and environment.
The results of a model based analysis depend not only on the chosen
methodology, but also on the quality of the data used. For a consistent
analysis within the EMS in REFLEX, a common database with harmonised datasets
is needed. It will be implemented in a form of a Data Warehouse 1 (DWH). The
DWH of the REFLEX project will contain four groups of data (see Table 1 for an
overview), which will be explained in more detail in the following sections.
**Table 1: Groups of data within the DWH of the REFLEX project**
<table>
<tr>
<th>
**Data group**
</th>
<th>
**Description/Contains**
</th> </tr>
<tr>
<td>
**Existing model input data**
</td>
<td>
* Data collected, generated or purchased from commercial providers by a project partner before the start of the REFLEX project
* Input data collected, generated or purchased from commercial providers by a project partner in the context of projects on behalf of other clients run in parallel to the REFLEX project
</td> </tr>
<tr>
<td>
**Collected and generated new model input data**
</td>
<td>
* Data collected from public available sources or purchased from commercial providers by a project partner in the context of REFLEX
* Data collected through surveys conducted by a project partner in the context of REFLEX
* Data generated based on existing, new collected or new purchased data by a project partner in the context of REFLEX
</td> </tr>
<tr>
<td>
**Generated intermediate model output data**
</td>
<td>
\- Intermediate results of the model applications for data exchange between
the models or for further assessments within the project; due to this the 2
nd and the 3 rd data group could overlap
</td> </tr>
<tr>
<td>
**Generated final result data of the EMS**
</td>
<td>
\- Final results of REFLEX generated by model applications, e.g. CO2-emission,
energy demand, technology impact evaluation etc.
</td> </tr>
<tr>
<td>
</td>
<td>
</td> </tr> </table>
### 1.2 EXISTING MODEL INPUT DATA
Energy system models need many different input data for modelling the real
world. Each of the applied models in REFLEX has already been used as stand-
alone applications. Therefore each model has its own database with already
existing data. By this input data are rather model specific and an un-
conditional application over several models is limited. They originate from
own previous work and own assumptions of the project partners as well as from
literature and have been developed over many years. Some of this existing data
will be re-used in the REFLEX project, if they are up to date or if no better
data are available.
Not all of the existing data are relevant for the DWH. Only data that
* are needed for more than one model within the EMS and therefore have to be harmonized and/or
* are needed to validate the results presented in scientific publications (so called
"underlying data") will be included in the DWH.
The harmonization of input data is necessary to ensure a consistent analysis
within the EMS. For the same information the same dataset (values) have to be
used in all models. The consortium decides which of the existing datasets will
be used. They will be included in the DWH and will be provided to all models
before initializing the EMS run. Table 2 gives an overview of the existing and
re-used input datasets/parameters.
# Table 2: Existing essential (re-used) datasets in REFLEX
<table>
<tr>
<th>
Sub- Period
Spatial
Dataset (category) categories (from Source
Reference (Quantity) until)
</th>
<th>
Values per scenario
(Quantity)
</th> </tr>
<tr>
<td>
Buildings average life time of heating systems 1
</td>
<td>
0
</td>
<td>
2015
2050
</td>
<td>
EU28
</td>
<td>
EU regulation, other projects, own assumptions
</td>
<td>
</td> </tr>
<tr>
<td>
Buildings compliance rate index 1
</td>
<td>
0
</td>
<td>
2015
2050
</td>
<td>
EU28
</td>
<td>
EU regulation, other projects, own assumptions
</td>
<td>
</td> </tr>
<tr>
<td>
Buildings energy efficiency increase due to mandatory commissioning 1
</td>
<td>
0
</td>
<td>
2015
2050
</td>
<td>
EU28
</td>
<td>
EU regulation, other projects, own assumptions
</td>
<td>
</td> </tr>
<tr>
<td>
Buildings energy efficiency investement cost (with national financial
incentives) 1
</td>
<td>
0
</td>
<td>
2015
2050
</td>
<td>
EU28
</td>
<td>
EU regulation, other projects, own assumptions
</td>
<td>
</td> </tr>
<tr>
<td>
Buildings minimum energetic standards 1
</td>
<td>
0
</td>
<td>
2015
2050
</td>
<td>
EU28
</td>
<td>
EU regulation, other projects, own assumptions
</td>
<td>
</td> </tr>
<tr>
<td>
Buildings thermal renovation 1
</td>
<td>
0
</td>
<td>
2015
2050
</td>
<td>
EU28
</td>
<td>
EU regulation, other projects, own assumptions
</td>
<td>
</td> </tr>
<tr>
<td>
Power plants availability
</td>
<td>
6
</td>
<td>
2010
2050
</td>
<td>
EU28
</td>
<td>
DIW 2013
</td>
<td>
</td> </tr>
<tr>
<td>
Power plants efficiency
</td>
<td>
11
</td>
<td>
2010
2050
</td>
<td>
EU28
</td>
<td>
DIW 2013
</td>
<td>
</td> </tr>
<tr>
<td>
Power plants emisson factor
</td>
<td>
3
</td>
<td>
2010
2050
</td>
<td>
EU28
</td>
<td>
UBA 2014
</td>
<td>
</td> </tr>
<tr>
<td>
Power plants interest rate
</td>
<td>
0
</td>
<td>
2010
2050
</td>
<td>
EU28
</td>
<td>
IEA et al. 2010
</td>
<td>
</td> </tr>
<tr>
<td>
Power lifetime of investment
</td>
<td>
6
</td>
<td>
2010
2050
</td>
<td>
EU28
</td>
<td>
IEA et al. 2010
</td>
<td>
</td> </tr>
<tr>
<td>
Power plants load change cost (depreciation)
</td>
<td>
0
</td>
<td>
2010
2050
</td>
<td>
EU28
</td>
<td>
DIW 2013, Traber & Kemfert 2011, own assumptions
</td>
<td>
</td> </tr>
<tr>
<td>
Power plants load change cost (fuel factor)
</td>
<td>
0
</td>
<td>
2010
2050
</td>
<td>
EU28
</td>
<td>
DIW 2013, Traber & Kemfert 2011
</td>
<td>
</td> </tr>
<tr>
<td>
Power plants operations management cost fixed
</td>
<td>
18
</td>
<td>
2010
2050
</td>
<td>
EU28
</td>
<td>
DIW 2013, VGB PowerTech 2011a
</td>
<td>
</td> </tr>
<tr>
<td>
Power plants operations management cost variable
</td>
<td>
11
</td>
<td>
2010
2050
</td>
<td>
EU28
</td>
<td>
DIW 2013, Traber & Kemfert 2011
</td>
<td>
</td> </tr>
<tr>
<td>
Power plants specific investment 2
</td>
<td>
11
</td>
<td>
2010
2050
</td>
<td>
EU28
</td>
<td>
DIW 2013
</td>
<td>
</td> </tr>
<tr>
<td>
Power plants start-up cost (depreciation)
</td>
<td>
18
</td>
<td>
2010
2050
</td>
<td>
EU28
</td>
<td>
Traber & Kemfert 2011
</td>
<td>
</td> </tr>
<tr>
<td>
Power plants start-up cost (fuel factor)
</td>
<td>
18
</td>
<td>
2010
2050
</td>
<td>
EU28
</td>
<td>
Traber & Kemfert 2011
</td>
<td>
</td> </tr>
<tr>
<td>
Tertiary & residential sector tax energy carrier 1
</td>
<td>
3
</td>
<td>
2015
2050
</td>
<td>
EU28
</td>
<td>
EU regulation, other projects, own assumptions
</td>
<td>
</td> </tr>
<tr>
<td>
Vehicles CO 2 standard 1
</td>
<td>
2
</td>
<td>
2015
2050
</td>
<td>
EU28
</td>
<td>
EU regulation, own assumptions
</td>
<td>
</td> </tr>
<tr>
<td>
Vehicles fuel consumption factors
</td>
<td>
4
</td>
<td>
2015
2050
</td>
<td>
EU28
</td>
<td>
GHG-TransPoRD project, ASSIST project
</td>
<td>
</td> </tr>
<tr>
<td>
1 2
Scenario-dependent Only for technologies for which no experience curve data
are available or will be generated
</td>
<td>
</td> </tr> </table>
### 1.3 COLLECTED AND GENERATED NEW MODEL INPUT DATA
Some of the needed input data for the models will be updated or newly defined
according to the research questions and the focus of the analysis within the
REFLEX project. Therefor publicly and commercially available data will be
used. Unavailable data will be generated by empirical surveys and/or
appropriate assumptions. These data will be included in the DWH and in that
way provided as harmonized datasets for all models within the EMS. This group
includes data for:
* Scenario framework
* Demand side management Experience curves
#### 1.3.1 DATA FOR SCENARIO FRAMEWORK
These data describe the overall framework for the model-based analysis and
include the main macro-economic and societal drivers as well as techno-
economic parameters and regulations/conditions of the political environment.
Therefor the defined scenario storylines for REFLEX will be translated into
quantitative model input parameters until the year 2050, which is the defined
horizon for the analysis. They may be defined on a European level, or be
further distinguished on a national, sectoral or technological level. The
macro-economic trends and the societal drivers are likely to be based upon
official projections provided by the European Commission (e.g. the upcoming
Reference Scenario). All political assumptions will be elaborated considering
current and past policy implementations and will be discussed with the
European Commission and stakeholders. Table 3 gives an overview of the data
for the scenario framework.
# Table 3: Data for scenario framework
<table>
<tr>
<th>
Dataset (category)
</th>
<th>
Subcategories
(Quantity)
</th>
<th>
Period (from until)
</th>
<th>
Spatial Reference
</th>
<th>
Source
</th>
<th>
Values per scenario
(Quantity)
</th> </tr>
<tr>
<td>
GDP-Gross domestic product
</td>
<td>
0
</td>
<td>
2000 2050
</td>
<td>
NUTS 0 (EU28+NO+CH)
</td>
<td>
Capros et al. 2016 (Assumptions for NO+CH based on other Horizon 2020
projects)
</td>
<td>
330
</td> </tr>
<tr>
<td>
POP-population
</td>
<td>
0
</td>
<td>
2000 2050
</td>
<td>
NUTS 0 (EU28+NO+CH)
</td>
<td>
Capros et al. 2016(Assumptions for NO+CH based on other Horizon 2020 projects)
</td>
<td>
330
</td> </tr>
<tr>
<td>
Price electricity (initial average cost of gross electricity generation)
</td>
<td>
0
</td>
<td>
2000 2050
</td>
<td>
NUTS 0 (EU28+NO+CH)
</td>
<td>
(Assumptions for NO+CH based on other
Horizon 2020 projects)
</td>
<td>
330
</td> </tr>
<tr>
<td>
Price fossil energy carrier
</td>
<td>
3
</td>
<td>
2015
2050
</td>
<td>
EU28
</td>
<td>
Capros et al. 2016
</td>
<td>
24
</td> </tr>
<tr>
<td>
ETS-CO 2 -Price 1
</td>
<td>
0
</td>
<td>
2015
2050
</td>
<td>
EU28
</td>
<td>
Capros et al. 2016, own assumptions
</td>
<td>
8
</td> </tr>
<tr>
<td>
NON-ETS-CO 2 -Price 1
</td>
<td>
0
</td>
<td>
2015
2050
</td>
<td>
EU28
</td>
<td>
own assumptions
</td>
<td>
8
</td> </tr>
<tr>
<td>
Vehicles CO 2 standard 1
</td>
<td>
2
</td>
<td>
2015
2050
</td>
<td>
EU28
</td>
<td>
Capros et al. 2016
</td>
<td>
16
</td> </tr>
<tr>
<td>
1
Scenario-dependent
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td> </tr> </table>
#### 1.3.2 DATA FOR DEMAND SIDE MANAGEMENT
Relevant data for investigating system flexibility by Demand Side Management
(DSM) are rarely available from public and commercial sources. In particular
the available database for the tertiary sector with regard to DSM is
incomplete. Therefore, an empirical survey on DSM in the tertiary sector will
be conducted with the aim to improve the model input data and to fill data
gaps. Based on the analysis of the collected specific empirical data, existing
datasets will be extended as well as new datasets generated. The design of the
survey will be established by the REFLEX partners and it will be conducted for
10 European countries by an international market research institute. The DSM
data for further countries will be deduced from the survey results. Relevant
model input parameters for modelling DSM options which should be deduced from
the empirically ascertained data are given in Table 4\.
# Table 4: Parameters for modelling DSM options
<table>
<tr>
<th>
Dataset (category)
</th>
<th>
Subcategories
(Quantity)
</th>
<th>
Period (from until)
</th>
<th>
Spatial Reference
</th>
<th>
Source
</th>
<th>
Values per scenario
(Quantity)
</th> </tr>
<tr>
<td>
DSM potential (share of flexible load per energy usage process)
</td>
<td>
50
</td>
<td>
2015
2050
</td>
<td>
NUTS 0
(EU28+NO
+CH)
</td>
<td>
empirical survey, public and commercial sources
</td>
<td>
28000
</td> </tr>
<tr>
<td>
DSM cost (activation cost per energy usage process)
</td>
<td>
50
</td>
<td>
2015
2050
</td>
<td>
NUTS 0
(EU28+NO
+CH)
</td>
<td>
empirical survey, public and commercial sources
</td>
<td>
28000
</td> </tr>
<tr>
<td>
DSM time of interfere (maximum load reduction time)
</td>
<td>
50
</td>
<td>
2015
2050
</td>
<td>
NUTS 0
(EU28+NO
+CH)
</td>
<td>
empirical survey, public and commercial sources
</td>
<td>
28000
</td> </tr>
<tr>
<td>
DSM number of interventions (frequency of DSM measures)
</td>
<td>
50
</td>
<td>
2015
2050
</td>
<td>
NUTS 0
(EU28+NO
+CH)
</td>
<td>
empirical survey, public and commercial sources
</td>
<td>
28000
</td> </tr>
<tr>
<td>
DSM shifting time (allowed points of time or time periods/frames for DSM)
</td>
<td>
50
</td>
<td>
2015
2050
</td>
<td>
NUTS 0
(EU28+NO
+CH)
</td>
<td>
empirical survey, public and commercial sources
</td>
<td>
28000
</td> </tr>
<tr>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td> </tr> </table>
#### 1.3.3 DATA FOR EXPERIENCE CURVES
To enable endogenous modelling of technological developments and due to this
to production cost reductions, experience curves for the most relevant
technologies for each sector will be developed and implemented in the sectoral
models. Special attention will be given to determination of uncertainty ranges
of progress ratios (i.e. the slopes of the experience curves), as these can
have a major impact on modelling results, especially for long-term modelling
until 2050. In addition, especially for technologies that depend strongly on
either the available geographical potential (e. g. wind onshore, offshore,
biomass) or on raw material prices, decomposition of the experience curve
using a multi-level experience will be performed. This allows determination of
the most important factors behind cost development, such as variations in
steel or oil prices, as well as scale effects. The needed empirical data for
defining the experience will be surveyed by means of interviewing experts,
specific survey methods and by analysing detailed statistics (e. g.
construction, production and consumer price indices as well as installed
capacities and cost developments in the electricity, heat and mobility
sector). Table 5 gives an overview of the technologies for which
## experience curves will be developed.
**Table 5: Defined technologies per sector for which experience curves will be
developed**
<table>
<tr>
<th>
Category
</th>
<th>
Technology
</th> </tr>
<tr>
<td>
Electricity Generation
</td>
<td>
CCS (Membrane, Oxyfuel, Pre, Post)
</td>
<td>
</td> </tr>
<tr>
<td>
Electricity Generation
</td>
<td>
CCGT (Gas)
</td>
<td>
</td> </tr>
<tr>
<td>
Electricity Generation
</td>
<td>
EFCC (Gas)
</td>
<td>
</td> </tr>
<tr>
<td>
Electricity Generation
</td>
<td>
Biomass: digestion
</td>
<td>
</td> </tr>
<tr>
<td>
Electricity Generation
</td>
<td>
Biomass: gassification
</td>
<td>
</td> </tr>
<tr>
<td>
Electricity Generation
</td>
<td>
Biomass: combustion
</td>
<td>
</td> </tr>
<tr>
<td>
Electricity Generation
</td>
<td>
Concentrated Solar Power (CSP)
</td>
<td>
</td> </tr>
<tr>
<td>
Electricity Generation
</td>
<td>
Geothermal: (dry, flash, binary)
</td>
<td>
</td> </tr>
<tr>
<td>
Electricity Generation
</td>
<td>
Photovoltaics: modules (mono/poly, CdTe)
</td>
<td>
</td> </tr>
<tr>
<td>
Electricity Generation
</td>
<td>
Photovoltaics: system level aspects
</td>
<td>
</td> </tr>
<tr>
<td>
Electricity Generation
</td>
<td>
Photovoltaics: CdTe
</td>
<td>
</td> </tr>
<tr>
<td>
Electricity Generation
</td>
<td>
Wind: onshore
</td>
<td>
</td> </tr>
<tr>
<td>
Electricity Generation
</td>
<td>
Wind: offshore
</td>
<td>
</td> </tr>
<tr>
<td>
Electricity Generation
</td>
<td>
CHP
</td>
<td>
</td> </tr>
<tr>
<td>
Electricity Generation
</td>
<td>
micro-CHP
</td>
<td>
</td> </tr> </table>
**Table 5 (continuation)**
<table>
<tr>
<th>
Category
</th>
<th>
Technology
</th> </tr>
<tr>
<td>
Electricity Generation
</td>
<td>
Pulverized Coal-fired
</td> </tr>
<tr>
<td>
Electricity Generation
</td>
<td>
(P)FBC (Coal)
</td> </tr>
<tr>
<td>
Electricity Generation
</td>
<td>
IGCC (Coal)
</td> </tr>
<tr>
<td>
Electricity Generation
</td>
<td>
Gas Turbine (inlc. Mini- and Micro-Gas-Turbine)
</td> </tr>
<tr>
<td>
Electricity Generation
</td>
<td>
Nuclear (generation 2, 3 and 4)
</td> </tr>
<tr>
<td>
Electricity Generation
</td>
<td>
Steam turbine (coal/gas)
</td> </tr>
<tr>
<td>
Electricity Generation
</td>
<td>
Photovoltaics: CIGS
</td> </tr>
<tr>
<td>
Electricity Storage
</td>
<td>
Battery: Lithium-(Ion, Polymer, Air)
</td> </tr>
<tr>
<td>
Electricity Storage
</td>
<td>
Battery: Redox-Flow
</td> </tr>
<tr>
<td>
Electricity Storage
</td>
<td>
CAES
</td> </tr>
<tr>
<td>
Electricity Storage
</td>
<td>
Flywheel
</td> </tr>
<tr>
<td>
Electricity Storage
</td>
<td>
Pumped Storage Plants
</td> </tr>
<tr>
<td>
Electricity Storage
</td>
<td>
Battery: Molten Salt
</td> </tr>
<tr>
<td>
Electricity Storage
</td>
<td>
Battery: NiMH
</td> </tr>
<tr>
<td>
Electricity Storage
</td>
<td>
Battery: NiCd
</td> </tr>
<tr>
<td>
Heating/cooling
</td>
<td>
Electric boiler (P2H)
</td> </tr>
<tr>
<td>
Heating/cooling
</td>
<td>
Heat Pump (air/air)
</td> </tr>
<tr>
<td>
Heating/cooling
</td>
<td>
Heat Pump (air/water/ground)
</td> </tr>
<tr>
<td>
Heating/cooling
</td>
<td>
Solar Thermal (process heat or large scale)
</td> </tr>
<tr>
<td>
Heating/cooling
</td>
<td>
Air conditioning
</td> </tr>
<tr>
<td>
Heating/cooling
</td>
<td>
Thermal Energy Storage: (sensible, underground, Phase Change, TCS, Ice)
</td> </tr>
<tr>
<td>
Heating/cooling
</td>
<td>
Night-Storage Heaters
</td> </tr>
<tr>
<td>
Heating/cooling
</td>
<td>
Electric boilers for district heating
</td> </tr>
<tr>
<td>
Heating/cooling
</td>
<td>
Gas- or oil boiler
</td> </tr>
<tr>
<td>
Heating/cooling
</td>
<td>
Fridges, freezers: cooling
</td> </tr>
<tr>
<td>
Industry
</td>
<td>
Air separation: membrane
</td> </tr>
<tr>
<td>
Industry
</td>
<td>
Air separation: conventional
</td> </tr>
<tr>
<td>
Industry
</td>
<td>
Aluminium electrolysis
</td> </tr>
<tr>
<td>
Industry
</td>
<td>
Chemical & mechanical pulp production
</td> </tr>
<tr>
<td>
Industry
</td>
<td>
Electric arc and induction furnaces: copper, cink, etc.
</td> </tr>
<tr>
<td>
Industry
</td>
<td>
Electrolysis (wet chemical): copper, cink, etc.
</td> </tr>
<tr>
<td>
Industry
</td>
<td>
Electrolysis, chemical industry
</td> </tr>
<tr>
<td>
Industry
</td>
<td>
ODC, chlorine electrolysis
</td> </tr>
<tr>
<td>
Industry
</td>
<td>
Fischer-Tropsch-synthesis
</td> </tr>
<tr>
<td>
Industry
</td>
<td>
Industrial CCS
</td> </tr>
<tr>
<td>
Industry
</td>
<td>
Industrial heat
</td> </tr>
<tr>
<td>
Industry
</td>
<td>
Cement and raw mill: cement production
</td> </tr>
<tr>
<td>
Industry
</td>
<td>
Electric booster: container glas production
</td> </tr>
<tr>
<td>
Industry
</td>
<td>
HIsarna process: steel production
</td> </tr>
<tr>
<td>
Industry
</td>
<td>
ULCOWIN process: steel production
</td> </tr>
<tr>
<td>
Mobility
</td>
<td>
Battery electric vehicles
</td> </tr>
<tr>
<td>
Mobility
</td>
<td>
Fuel Cell vehicles
</td> </tr>
<tr>
<td>
Mobility
</td>
<td>
Plug-in hybrid electric car
</td> </tr>
<tr>
<td>
Mobility
</td>
<td>
Flexifuel car
</td> </tr>
<tr>
<td>
Mobility
</td>
<td>
Overhead wiring trucks
</td> </tr>
<tr>
<td>
Mobility
</td>
<td>
Biofuels (jet, vehicle, marine)
</td> </tr>
<tr>
<td>
Mobility
</td>
<td>
CNG vehicles
</td> </tr>
<tr>
<td>
Mobility
</td>
<td>
LPG car
</td> </tr>
<tr>
<td>
Mobility
</td>
<td>
Diesel vehicles
</td> </tr>
<tr>
<td>
Mobility
</td>
<td>
Gasoline car
</td> </tr>
<tr>
<td>
Power to X
</td>
<td>
Power to Hydrogen
</td> </tr>
<tr>
<td>
Power to X
</td>
<td>
**Power to Methane**
</td> </tr>
<tr>
<td>
Power to X
</td>
<td>
Power to Methanol
</td> </tr>
<tr>
<td>
Power to X
</td>
<td>
**Hydrogen synthesis, ammonia**
</td> </tr>
<tr>
<td>
Electricity end-use
</td>
<td>
Lighting: LED
</td> </tr>
<tr>
<td>
Electricity end-use
</td>
<td>
**Elevators/escalators**
</td> </tr>
<tr>
<td>
Electricity end-use
</td>
<td>
Data centers
</td> </tr>
<tr>
<td>
Electricity end-use
</td>
<td>
**Building Energy Management/Automation systems**
</td> </tr>
<tr>
<td>
Electricity end-use
</td>
<td>
Ventilation
</td> </tr>
<tr>
<td>
Electricity end-use
</td>
<td>
**Dishwashers, dryers, washing machines**
</td> </tr>
<tr>
<td>
Other
</td>
<td>
Power Grids (HVDC point-to-point, also meshed systems)
</td> </tr>
<tr>
<td>
</td>
<td>
</td> </tr> </table>
In order to estimate the potential of alternative fuel technologies, both
regarding Europe (e. g. MID in Germany or UKTS in UK) and regarding Asia and
North America, mobility patterns and related market potentials will be derived
from available mobility surveys. The analysis will be focused on major common
driving patterns. The reason for the analysis of main global passenger car
markets is to identify the global market penetration of electric vehicles,
which will influence the demand for Li-ion batteries substantially in order to
assess the future prices of electric vehicle batteries and fuel cells based on
the learning curve theory. In this sense, the global automotive market
(especially including Northern America and Asia) will be taken into account
for investigating the uptake of alternative car technology in Europe.
Emission degression data will also be considered in REFLEX. However, the
content, nature and scope of these data are still under discussion and will be
determined during the project.
### 1.4 GENERATED INTERMEDIATE MODEL OUTPUT DATA
By coupling the different approaches of the RFELEX partners, the systems
boundaries of each stand-alone model will be partly disbanded and most
exogenous parameters of each model will become endogenous variables of the
EMS. This will be done by using the relevant output data of one model as input
data of another model.
To achieve a stable final state of the EMS within each REFLEX scenario
storyline, several iterations with all models are performed. The generated
results during these iterations are needed for the data exchange between the
different models. Therefore these data will be included in the DWH and will be
provided to each model by using a data interface. Table 6 shows the relevant
datasets for the data exchange within the EMS.
# Table 6: Datasets as intermediate results for data exchange between models
within the EMS
<table>
<tr>
<th>
Dataset (category)
</th>
<th>
Subcategories
(Quantity)
</th>
<th>
Period (from until)
</th>
<th>
Spatial Reference
</th>
<th>
Source
</th>
<th>
Values per scenario/iteration (Quantity)
</th> </tr>
<tr>
<td>
Price electricity
(hourly, wholesale and retail prices incl. taxes)
</td>
<td>
0
</td>
<td>
2015
2050
</td>
<td>
NUTS 0 (EU28+NO+CH)
</td>
<td>
Applied energy system models
</td>
<td>
12,264,000
</td> </tr>
<tr>
<td>
Demand electricity (hourly)
</td>
<td>
50
</td>
<td>
2015
2050
</td>
<td>
NUTS 0 (EU28+NO+CH)
</td>
<td>
Applied energy system models
</td>
<td>
12,264,000 - 245,280,000
</td> </tr>
<tr>
<td>
Demand electricity for mobility (yearly)
</td>
<td>
7
</td>
<td>
2015
2050
</td>
<td>
NUTS 0 (EU28+NO+CH)
</td>
<td>
Applied energy system models
</td>
<td>
1,050
</td> </tr>
<tr>
<td>
Demand district heating (yearly)
</td>
<td>
3
</td>
<td>
2015
2050
</td>
<td>
NUTS 0 (EU28+NO+CH)
</td>
<td>
Applied energy system models
</td>
<td>
450
</td> </tr>
<tr>
<td>
Power plants installed capacity and operating (yearly and hourly)
</td>
<td>
22
</td>
<td>
2015
2050
</td>
<td>
NUTS 0 (EU28+NO+CH)
</td>
<td>
Applied energy system models
</td>
<td>
5,280 - 67,200
</td> </tr>
<tr>
<td>
Power plants emissions (yearly)
</td>
<td>
132
</td>
<td>
2015
2050
</td>
<td>
NUTS 0 (EU28+NO+CH)
</td>
<td>
Applied energy system models
</td>
<td>
15,840
</td> </tr>
<tr>
<td>
Power plants demand energy (yearly)
</td>
<td>
22
</td>
<td>
2015
2050
</td>
<td>
NUTS 0 (EU28+NO+CH)
</td>
<td>
Applied energy system models
</td>
<td>
2,640
</td> </tr>
<tr>
<td>
Mobility demand energy (yearly)
</td>
<td>
56
</td>
<td>
2015
2050
</td>
<td>
NUTS 0 (EU28+NO+CH)
</td>
<td>
Applied energy system models
</td>
<td>
6,720
</td> </tr>
<tr>
<td>
Mobility emissions (yearly)
</td>
<td>
56
</td>
<td>
2015
2050
</td>
<td>
NUTS 0 (EU28+NO+CH)
</td>
<td>
Applied energy system models
</td>
<td>
6,720
</td> </tr>
<tr>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td> </tr> </table>
### 1.5 GENERATED FINAL RESULT DATA OF THE EMS
After achieving a stable state based on several iterations with all models
within the EMS within each REFLEX scenario storyline, the result data of the
different models will be collected and combined within the DWH to the final
result data of the EMS. These data will be analysed to derive the key findings
and are the basis for answering the research questions of the REFLEX project
(see ADMIN DETAILS). Table 7 gives an overview on the major result data of the
EMS.
# Table 7: Major result data of the EMS
<table>
<tr>
<th>
Dataset (category)
</th>
<th>
Subcategories
(Quantity)
</th>
<th>
Period (from until)
</th>
<th>
Spatial Reference
</th>
<th>
Source
</th>
<th>
Values per scenario
(Quantity)
</th> </tr>
<tr>
<td>
Price electricity average yearly
(wholesale and retail prices incl. taxes)
</td>
<td>
0
</td>
<td>
2015
2050
</td>
<td>
NUTS 0 (EU28+NO+CH)
</td>
<td>
Applied energy system models
</td>
<td>
120-240
</td> </tr>
<tr>
<td>
Demand electricity (yearly)
</td>
<td>
50
</td>
<td>
2015
2050
</td>
<td>
NUTS 0 (EU28+NO+CH)
</td>
<td>
Applied energy system models
</td>
<td>
6000
</td> </tr>
<tr>
<td>
Demand district heating (yearly)
</td>
<td>
3
</td>
<td>
2015
2050
</td>
<td>
NUTS 0 (EU28+NO+CH)
</td>
<td>
Applied energy system models
</td>
<td>
450
</td> </tr>
<tr>
<td>
Power plants installed capacity (yearly)
</td>
<td>
22
</td>
<td>
2015
2050
</td>
<td>
NUTS 0 (EU28+NO+CH)
</td>
<td>
Applied energy system models
</td>
<td>
2640
</td> </tr>
<tr>
<td>
Power plants operation (yearly)
</td>
<td>
22
</td>
<td>
2015
2050
</td>
<td>
NUTS 0 (EU28+NO+CH)
</td>
<td>
Applied energy system models
</td>
<td>
2640
</td> </tr>
<tr>
<td>
Power plants emissions (yearly)
</td>
<td>
132
</td>
<td>
2015
2050
</td>
<td>
NUTS 0 (EU28+NO+CH)
</td>
<td>
Applied energy system models
</td>
<td>
15,840
</td> </tr>
<tr>
<td>
Net transfer capacities between countries installed (yearly)
</td>
<td>
143
</td>
<td>
2015
2050
</td>
<td>
NUTS 0 (EU28+NO+CH)
</td>
<td>
Applied energy system models
</td>
<td>
572
</td> </tr>
<tr>
<td>
Net transfer capacities between countries operation (yearly)
</td>
<td>
143
</td>
<td>
2015
2050
</td>
<td>
NUTS 0 (EU28+NO+CH)
</td>
<td>
Applied energy system models
</td>
<td>
572
</td> </tr>
<tr>
<td>
Mobility demand energy (yearly)
</td>
<td>
56
</td>
<td>
2015
2050
</td>
<td>
NUTS 0 (EU28+NO+CH)
</td>
<td>
Applied energy system models
</td>
<td>
6,720
</td> </tr>
<tr>
<td>
Mobility emissions (yearly)
</td>
<td>
56
</td>
<td>
2015
2050
</td>
<td>
NUTS 0 (EU28+NO+CH)
</td>
<td>
Applied energy system models
</td>
<td>
6,720
</td> </tr>
<tr>
<td>
Life cycle environmental and resource impacts
</td>
<td>
19
</td>
<td>
2015
2050
</td>
<td>
NUTS 0 (EU28+NO+CH)
</td>
<td>
Applied energy system models
</td>
<td>
2280
</td> </tr>
<tr>
<td>
Life cycle human health (damage / toxicity)
</td>
<td>
2
</td>
<td>
2015
2050
</td>
<td>
NUTS 0 (EU28+NO+CH)
</td>
<td>
Applied energy system models
</td>
<td>
240
</td> </tr>
<tr>
<td>
Life cycle societal impacts (risk level)
</td>
<td>
5
</td>
<td>
2015
2050
</td>
<td>
NUTS 0 (EU28+NO+CH)
</td>
<td>
Applied energy system models
</td>
<td>
600
</td> </tr>
<tr>
<td>
Costs external
</td>
<td>
1
</td>
<td>
2015
2050
</td>
<td>
NUTS 0 (EU28+NO+CH)
</td>
<td>
Applied energy system models
</td>
<td>
120
</td> </tr>
<tr>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td> </tr> </table>
### 1.6 DATA UTILITY
REFLEX collects and generates a certain amount of research data. On one hand,
these data are necessary to meet the objectives of the project. On the other
hand most of the collected and generated data will be useful for further
research and even for the energy industry.
#### 1.6.1 DATA FOR SCENARIO FRAMEWORK
The collected and prepared data for the scenario framework are tailored to the
aim and scope as well as the specific research questions of the REFLEX project
and the applied models to answering them. They are thus primarily useful as
underlying data to ensure the transparency of the generated results and the
comparability of the project outcomes to other studies with similar analysing
scope. However, the data will be used for updating the existing databases of
the different models and will be used for further model based research of the
project partners outside of REFLEX.
#### 1.6.2 DATA FOR DEMAND SIDE MANAGEMENT
The empirical survey on DSM aims to provide the needed model input data for
the REFLEX project. With the collected specific empirical data the database
for investigating system flexibility by DSM will be improved in general,
because relevant data – especially for the tertiary sector – are rarely
available from public and commercial sources. Thus, existing datasets will be
extended as well as new datasets generated. Furthermore, the survey data will
allow the identification of promising energy applications and DSM potentials
in the selected sector in different European countries. These data have a high
potential for re-use after the end of the REFLEX project within further
research projects. They are useful for other researchers and for stakeholders
from industry and policy making.
#### 1.6.3 DATA FOR EXPERIENCE CURVES
Endogenizing technological learning through experience curves allows for an
enhanced assessment of the evaluation of impacts from policy measures or
alternative incentive schemes on realizable future cost reduction. In
addition, in view of current rapid and necessary changes in energy systems
(driven partially by policies and partially by markets) and the ensuing need
for flexibility, the endogenous modelling of the cost development of existing
and new energy-related technologies in bottom-up models will become even more
important.
However, the data and experience curves required to do so are not readily
available. A comprehensive review of many energy supply (and some energy
demand) technologies have been published by Junginger et al. (2010), but these
require updating. Since then, recent studies have been published for some
individual technologies (e. g. Bolinger and Wiser 2012; Candelise et al. 2013
or Chen et al. 2012). However, an up-to-date overview is not available.
Especially with regard to technologies needed for increasing the flexibility
in energy systems (such as storage technologies or DSM-devices) little or no
experience curves have been published. Thus, to advance the energy models
included in REFLEX beyond the state-of-the-art by implementing these
experience curves, data collection will be required to devise or update
experience curves for existing technologies and to estimate experience curves
for new technologies.
Furthermore, it will require smart and innovative incorporation and
interlinkage of these experience curves in various sectoral energy models to
comprehensively assess the effects of technological learning and the demand
for increased flexibility in energy systems. The outcome – a state-of-the-art
and up-to-date overview of experience curves and underlying database – could
benefit other energy models outside the project, i.e. developed in the EU as
well as worldwide) to meet the challenges of modelling our changing energy
systems for the coming decades.
#### 1.6.4 GENERATED INTERMEDIATE MODEL OUTPUT DATA
These data are only intermediate results of the EMS and will transfer between
the models during the iteration process of the EMS. A relevant benefit of
these data for further applications outside the framework of REFLEX is not
expected.
#### 1.6.5 GENERATED FINAL RESULT DATA OF THE EMS
The overall objective of REFLEX is to support the SET-Plan by strengthening
the knowledge base for transition paths towards a low carbon energy system
based on a cross-sectoral analysis for the entire energy system of the
European Union. Due to the complexity of this system, it is obvious that the
implementation of the SET-Plan requires in-depth knowledge on the
interrelationship between the different energy sectors (electricity, heat and
mobility), energy technologies but also on the interdependencies between
energy and non-energy industries, environment (beyond greenhouse gas
emissions) and society.
The result data of the EMS within REFLEX helps to understand and investigate
the complex links, interactions and interdependencies between the different
actors and technologies within the energy system as well as their impact on
society and environment. Based on the EMS result data, recommendations for
effective strategies for a transition of the European energy system to a low-
carbon system will be derived. Policy makers at EU level as well as at
regional level can use these findings when developing policy measures.
Furthermore, the data of the REFLEX project can be used as a reference or
starting point for further research work on the future design of the energy
system of the European Union.
### 1.7 DATA PROTECTION AND EXPLOITATION STRATEGY
In order to ensure efficient implementation of dissemination and exploitation
activities amongst the participants, a Consortium Agreement (CA) was signed by
all partners. The CA is among other things dealing with the exact details on
the participants’ background data, the rights to, the protection of and the
exploitation of data/results generated solely and/or jointly during the
project. Moreover the CA sets up specific rules on how to deal with
dissemination activities and to ensure open access to all peer-reviewed
scientific publications. The model used is the DESCA template, version 1.0
(www.desca-2020.eu).
The following basic rules apply:
* All participants define their individual background data required for their successful participation in the project (own model input data and commercial model input data purchased before the start of the project). The rights to this background remain with the respective owner but royalty-free access to other participants is granted if not restricted by third parties and if it is required to enable other participants to carry out their research and development activities in the context of the project.
* During the project: Background data that is acquired by individual partners during the project, e. g. in the context of projects on behalf of other clients run in parallel, will be treated as pre-existing background data.
* The property rights to data collected and data/results generated during the project belong to those involved in its collection and generation. When more than one consortium member is involved in the creation of results it will be jointly owned by the respective consortium member.
Dissemination and exploitation of data and results will be executed in
accordance with EU laws and with respect to specific laws in the participating
countries. Before any dissemination activity will take place, respective legal
aspects will be examined and clarified. This is particularly the case for data
from the DSM-survey and data purchased from commercial providers. The
possibility for protection of generated results within the project
(consortium) will be also examined before publication. All participants have
departments specifically devoted to managing intellectual property. These
departments will manage the relevant protection processes.
Within REFLEX the dissemination and exploitation of data will be coordinated
by the Exploitation and Innovation Manager (EIM) regarding knowledge
management and innovation activities. The EIM is responsible for:
* maintaining a registry of background data,
* maintaining a registry of data gathered and generated in the work packages during the project,
* assessing the opportunities for exploitation, for example by following political events in the energy sector or searches of other scientific databases for similar developments and
* proposing specific exploitation measures, e. g. policy briefs and events.
In REFLEX periodic analysis of transfer opportunities to adjust the
exploitation strategies takes place. All consortium partners contribute to the
exploitation plan of the project throughout its life span. Thereby, the EIM is
in close contact and regularly informed about the exploitation plans of the
partners to use synergies and to ensure the best and suitable use and
exploitation of results. Furthermore, the EIM will regularly advise the
consortium and individual partners about possible strategies.
The exploitation strategy is outlined below:
* First it will be decided whether to disseminate a dataset and in what way.
* Participants inform the EIM and other consortium members if they wish to publish or disseminate any datasets, whether in a direct way or indirectly.
* Before any dissemination activity may take place the participants must examine the possibility of protecting generated results.
* Upon (affirmative) dissemination decision the dataset will be made available (regarding different dissemination types see section 2.2)
**2 FAIR DATA**
All collected and generated data will be implemented in the DWH in a
standardized way. The DWH includes several databases which are depending on
the structure and contents of the different datasets. For managing the
databases the database-management-tool “Mesap” will be used, which provides
also several data preparing and identifier mapping functions. Mesap is
developed and commercially provided by the Seven2one GmbH.
A selection of existing data as well as data collected and generated during
the project will be made available to interested research groups and
interested parties from policy and industry. The following section outlines
how the data will be exploited and made accessible for verification and re-use
and how data will be curated and preserved upon closure of the project.
### 2.1 MAKING DATA FINDABLE
For making data findable, a data catalogue will be prepared, which will be
implemented in the REFLEX project website ( _www.reflex-project.eu_ ). The
catalogue gives an overview of all provided datasets and affords the metadata
of them. The scope and design of the metadata will be oriented on the metadata
structure of the “Open Power System Data” platform ( _www.data.open-power-
system-data.org/_ ) see Table 8.
# Table 8: Scope and contents of the metadata for a provided dataset
<table>
<tr>
<th>
**Category**
</th>
<th>
**Content**
</th> </tr>
<tr>
<td>
**Name**
</td>
<td>
Name of dataset - a concise one (short but informative)
</td> </tr>
<tr>
<td>
**ID**
</td>
<td>
Dataset identifier
</td> </tr>
<tr>
<td>
**Description/Notes**
</td>
<td>
Short description of scope/contents of the dataset. Also specific remarks (e.
g. restrictions, data gaps etc.)
</td> </tr>
<tr>
<td>
**Keywords**
</td>
<td>
List of used keywords for the dataset
</td> </tr>
<tr>
<td>
**Version**
</td>
<td>
Dataset version given as number and/or date. Also the information if it is the
latest available version
</td> </tr>
<tr>
<td>
**Last changes**
</td>
<td>
Short description of changes to previous dataset version
</td> </tr>
<tr>
<td>
**Timescale**
</td>
<td>
If the dataset is a time series:
* Values for which years e. g.: - 2010-2050 (yearly)
* 2010-2030 (5-year-steps)
* 2010, 2012, 2017, 2022
* Values structure in the course of year if applicable e. g.: - seasonal
* hourly
* quarter-hourly
* Structure of type days if used
</td> </tr>
<tr>
<td>
**Spatial reference**
</td>
<td>
Spacial reference of values with scope and level of
differentiation/aggregation e. g.: - EU 28 (NUTS 0)
\- EU 28 + X (NUTS 3) - Poland, Germany,…
</td> </tr>
<tr>
<td>
**Sectoral reference**
</td>
<td>
Sectoral reference of values, if applicable e. g.: - Households, Industy,
Traffic…
* Road Traffic, Rail Traffic, Aviation...
* C24_C25 , D, E (Codes from Eurostat)
</td> </tr>
<tr>
<td>
**Sources**
</td>
<td>
Used sources to prepare/provide the dataset, if possible with links to the
primary data/original input data
</td> </tr>
<tr>
<td>
**Attribution**
</td>
<td>
Recommended text for attribution
</td> </tr>
<tr>
<td>
**Contact**
</td>
<td>
Contact information for questions/remarks
</td> </tr>
<tr>
<td>
**Access**
</td>
<td>
Terms of data access/usage (licence, free of charge, XX € protective charge
etc.)
</td> </tr>
<tr>
<td>
**Field documentation**
</td>
<td>
List of used fields within the dataset with following subcategories:
* field name: e. g. capacity_installed
* type (format): e. g. number (float)
* unit: e. g. MW
* description: e. g. installed electrical capacity at the end of year
</td> </tr>
<tr>
<td>
</td>
<td>
</td> </tr> </table>
Each dataset can be unambiguously identified via the combination of dataset
name and the version label. Both will be included in the unique dataset ID.
With regard to the publication and transparency of results of the project work
(e. g. in journals), the preparation of tailored dataset packages for the
publications is considered. A package contains together with a short content
description of the package a compilation of:
* the metadata of the published results
* the metadata of the relevant datasets required to verify the results, as long as these can be made available.
It will be discussed within the REFLEX consortium, if for these dataset
packages digital object identifiers (DOI) will be used, which could be easily
included in publications as a reference.
### 2.2 MAKING DATA OPENLY ACCESSIBLE
Within REFLEX three different possibilities for data dissemination will be
considered, as described in Table 9.
Both, the collected input data as well as generated data, will be made
available, mostly as open access according to the guidelines of the EU. The
specification of data which will be made openly available is still under
discussion. It will be decided within the project consortium case by case and
after that updated in this section of the DMP.
# Table 9: Possibilities of data dissemination considered in REFLEX
<table>
<tr>
<th>
**Dissemination**
</th>
<th>
**Description**
</th> </tr>
<tr>
<td>
**Open Access Publication**
</td>
<td>
Owners will be granting royalty-free access of a meaningful selection of
generated results to other participants and to the public, possibly restricted
by appropriate embargo periods and/or respecting restrictions from editors of
scientific journals and organizers of conferences.
</td> </tr>
<tr>
<td>
**Commercial Exploitation**
</td>
<td>
Data suitable for commercial exploitation (e. g. for a commercial re-use by
consulting companies) will be managed by the project partner ESA 2 which was
funded after completion of the EU funded innovation project ESA2 explicitly
with the purpose to exploit research results (including research data) related
to (coupled) energy systems modelling.
</td> </tr>
<tr>
<td>
**Indirect**
</td>
<td>
Parts of the generated data will be disseminated only indirectly as part of
intermediate or final results of models and/or as qualitative outcome based on
post-analysis of results.
</td> </tr>
<tr>
<td>
</td>
<td>
</td> </tr> </table>
The access to all provided data will be offered via the project website (
_www.reflexproject.eu/_ ). The planned formats are:
* csv
* xlsx
* sql
In the case of an _Open Access Publication_ , the dataset can be easily
downloaded. The download links for different formats are given within the
metadata in the category “Access”.
In case of _Commercial Exploitation_ of a dataset, a registration procedure
for all those interested in such datasets will be implemented. This includes
the opportunity, to differ the conditions for access depending on type of the
inquirer or planned re-usage (e. g. dataset is free of charge for public
scientific institutions for scientific work, but with a charge in case of
commercial re-use by a company). After registration of the request of a
dataset, a timelimited download link will be provided via e-mail to the
registered contact together with the terms of usage and as the case may be
with the invoice. The requisition will be implemented in the metadata, which
are available free of charge in any case, in the category “Access”.
### 2.3 MAKING DATA INTEROPERABLE
To increase the interoperability of provided data, commonly used vocabularies
for the metadata contents as well as for the identifiers and the contents of
the identifiers within the datasets will be applied. These include
standardized name conventions and codes used in official statistics (e. g. for
countries, regions etc.). Furthermore, specific energy system topics related
name conventions will be orientated on the “Open Power System Data” platform (
_www.data.open-power-system-data.org/_ ). An additional mapping procedure or
the provision of mapping tools for data users is not envisaged.
### 2.4 INCREASE DATA RE-USE
The datasets will be made available to third parties as soon as they are
generated, prepared and reviewed for publication/commercial exploitation and
when the conditions of dissemination are decided and possible protections of
dataset are clarified within the consortium. However, additional restrictions
by setting appropriate embargo periods and/or respecting restrictions from
editors of scientific journals and organizers of conferences are also
possible. A generally valid statement regarding the embargo periods is not
possible at the moment. It can differ from case to case.
The use of Creative-Commons-Licences (CC) for provided data will be discussed
within the consortium. Table 10 gives an overview of conceivable licences for
the different types of dissemination. Which licence is used will be decided
dataset by dataset and after that updated in this section of the DMP.
# Table 10: Conceivable licences for the different types of dissemination
<table>
<tr>
<th>
**Dissemination**
</th>
<th>
**Data group Licences**
</th> </tr>
<tr>
<td>
**Open Access Publication**
</td>
<td>
Existing model input data
</td>
<td>
</td> </tr>
<tr>
<td>
Collected and generated new model input data
</td> </tr>
<tr>
<td>
Generated final result data of the EMS
</td>
<td>
</td> </tr>
<tr>
<td>
**Commercial Exploitation**
</td>
<td>
Existing model input data
</td>
<td>
</td> </tr>
<tr>
<td>
Collected and generated new model input data
</td> </tr>
<tr>
<td>
Generated final result data of the EMS
</td>
<td>
</td> </tr>
<tr>
<td>
**Indirect**
</td>
<td>
</td>
<td>
</td> </tr>
<tr>
<td>
</td>
<td>
</td>
<td>
</td> </tr> </table>
Regarding the data quality assurance the processes given in Table 11 are
implemented in the project work.
# Table 11: Processes of data quality assurance
<table>
<tr>
<th>
**Data group**
</th>
<th>
**Processes**
</th> </tr>
<tr>
<td>
**Existing model input data**
</td>
<td>
* Harmonization of model input data to ensure a consistent analysis within the EMS and regarding the defined scenario storylines. For the same information have to be used the same dataset (values) in all models. (Consortium decision)
* Harmonized data will be provided to all models before initializing the EMS run
</td> </tr>
<tr>
<td>
**Collected and generated new model input data**
</td>
<td>
* Minimum two internal reviews of the generated new model input data
* Additionally Peer-Reviews in case of publication in Journals
* Harmonization of model input data to ensure a consistent analysis within the EMS and regarding the defined scenario storylines. For the same information have to be used the same dataset (values) in all models. (Consortium decision)
* Harmonized data will be provided to all models before initializing the EMS run
</td> </tr>
<tr>
<td>
**Generated intermediate model output data**
</td>
<td>
\- Check of plausibility of intermediate output data of a model during EMS
runs by the responsible modeller before implementation of the data in the DHW
for data transfer to another model
</td> </tr>
<tr>
<td>
**Generated final result data of the EMS**
</td>
<td>
* Minimum two internal reviews of the generated final result data
* Additionally Peer-Reviews in case of publication in Journals
</td> </tr>
<tr>
<td>
</td>
<td>
</td> </tr> </table>
The consortium will continue to provide the data via the REFLEX project
website for a limited period of time after the end of the REFLEX project. The
project website will be maintained during this period. After this period, an
appropriate reference/link to the final data repository will be integrated in
the REFLEX project website, which is no longer being maintained after that.
The final repository for these data has not been chosen yet. The choice of
repository will depend on:
* location of repository
* research domain
* costs
* open access options
* prospect of long-term preservation.
The following approaches for long term data provision (or a combination of
them) are conceivable:
1. The data remain in the DWH of the project partner ESA². The data provision is transferred to the website of the ESA² Company ( _www.esa2.eu_ ).
2. Another repository could be ZENODO _https://zenodo.org/_ . This is online, free of charge storage created through the European Commission’s OpenAIREplus project and is hosted at CERN, Switzerland. It encourages open access deposition of any data format, but also allows deposits of content under restricted or embargoed access. Contents deposited under restricted access are protected against unauthorized access at all levels. Access to metadata and data files is provided over standard protocols such as HTTP and OAI-PMH. Data files are kept in multiple replicas in a distributed file system, which is backed up to tape every night. Data files are replicated in the online system of ZENODO.
Data files have versions attached to them, whilst records are not versioned.
Derivatives of data files are generated, but the original content is never
modified. Records can be retracted from public view; however, the data files
and records are preserved. The uploaded data is archived as a Submission
Information Package in ZENODO. Files stored in ZENODO will have MD5 checksum
of the file content, and it will be checked against their checksum to assure
that a file content remains correct. Items in the ZENODO will be retained for
the lifetime of the repository which is also the lifetime of the host
laboratory CERN which currently has an experimental programme defined for the
next 20 years. Each dataset can be referenced at least by a unique persistent
identifier (DOI), in addition to other forms of identifications provided by
ZENODO.
3. A third option is provided by the Technische Universität Dresden, which is currently setting up an institutional, inter-disciplinary repository with long-term archive in the project OpARA. It will provide open access long-term storage of data, including metadata and will go into production in 2017.
Other institutional and thematic repositories will be considered and evaluated
in the next months. The procedure will be discussed and decided at the end of
the project lifespan. In any case the data will be available for third parties
after the end of the project. The length of time for which the data will
remain re-usable is not restricted.
3. **ALLOCATION OF RESOURCES**
The EIM is responsible for data management within the REFLEX project (see
section 1.7). The estimated costs for making REFLEX data FAIR are 50.000 Euro.
The costs include:
* the clarification of data protection and licences,
* the final preparation of data by each project partner for publishing (without effort/costs for data collection/purchasing/generation etc.),
* the processes for assurance of data quality,
* the development and implementation of the data catalogue in the project website,
* the implementation of the registration procedure for access to commercially exploited datasets,
* the data hosting and backup for security,
* the data updating and maintenance of the data and of the data provision
during the project lifespan. These costs are covered by the project funds,
mainly by the budgeted personnel costs.
The costs for long term preservation after the end of the project are
difficult to estimate at the moment. They depend mainly on the level of
convenience of data providing (with/without comprehensive search routines
and/or additional consulting) but also on the scope and size of the collected
and generated datasets. The permanent costs of preserving datasets on the
ZENODO repository will be free of charge as long as the single dataset storage
is no greater than the maximum 2GB of data. The permanent costs of preserving
datasets on the OpARA repository are planned to be free of charge for TUD
members. But the final decision on costs has not been taken.
The costs for long term preservation shall be covered by the collected charges
from the commercial exploitation of datasets during the project lifespan and
after.
4. **DATA SECURITY**
Most of the data handled in the REFLEX project are not sensitive regarding the
laws governing data protection and data security. An exception represents the
data from the DSM-survey. A provision/publication of these data is only
possible in an anonymous form.
The DWH as well as the data provision via websites will be implemented on
servers with regular backup and data recovery procedures.
5. **ETHICAL ASPECTS**
The data collection, data storage, data usage, data generation and data
dissemination in this project do not affect to ethical issues.
6. **OTHER**
No other national/funder/sectorial/departmental procedures for data management
will be used.
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# Executive Abstract
The goal of the Data Management Plan (DMP) is to detail the data to be used
and generated by the project. This is a precise requirement since ESPRESSO is
a Pilot on H2020 Open Research Data initiative. Therefore, the ESPRESSO
project will make publicly available the data generated and that will include
data regarding the community of stakeholders, market analysis, best practice
assessment and other activities to be carried on during the CSA. In addition,
corresponding metadata generated within the project will be made available
through open data portals (including the European Open Data Portal). This will
ensure the widest public accessibility as well as long-term preservation.
Indeed, ESPRESSO will ensure that all the data generated is properly collected
accessed, curated, preserved, and eventually made public after any possible
data-ownership issue has been cleared.
For this reason, a specific Data Management Plan is planned. This actual
document is the last of the reports that are due on M06, M14, and M24.
# Regulation (EU) 2016/679 of the European Parliament and of the Council of
27 April 2016
The ESPRESSO project has worked in accordance with the Regulation (EU)
2016/679 of the European Parliament and of the Council of 27 April 2016, whose
main aims are to:
* lay down rules relating to the protection of natural persons with regard to the processing of personal data and rules relating to the free movement of personal data,
* protect fundamental rights and freedoms of natural persons and in particular their right to the protection of personal data,
* neither restrict nor prohibit the free movement of personal data within the Union, for the management of the data collected.
In particular, the project team has focused on the principles of:
* “lawfulness, fairness and transparency”, thus processing data lawfully, fairly and in a transparent manner in relation to the data subject;
* “purpose limitation”, thus ensuring that the data has been collected for specified, explicit and legitimate purposes and not further processed in a manner that is incompatible with those purposes;
* “data minimization”, thus ensuring that the collected data has been treated in an adequate and relevant manner and limited to what is necessary in relation to the purposes for which they are processed;
* “storage limitation”, thus keeping them in a form which permits identification of data subjects for no longer than is necessary for the purposes for which the personal data are processed; the only possibility for which the personal data may be stored for longer periods is only for statistical purposes in accordance with Article 89(1) in order to safeguard the rights and freedoms of the data subject;
* “integrity and confidentiality”, thus processing data in a manner that ensures appropriate security of the personal data, including protection against unauthorised or unlawful processing and against accidental loss, destruction or damage, using appropriate technical or organisational measures.
# Data managed through the ESPRESSO website
This section describes the data that are being managed directly by the
ESPRESSO project. The section is subdivided into different groups to cover all
the different types of information collected.
## Personal Data/registry of SmaCStak participants
The ESPRESSO system collects and stores information about the personal data of
the SmaCStak participants that give their consent to record their personal
data in the project ESPRESSO database. This is done through a form available
from the website at the following address:
_https://espressoprojekt.typeform.com/to/fo1DK0_ and that collects the
following information:
* Name
* Surname
* Email address
* Company name
* Company website
* Country
* City
* Nature of the company. Be it:
* Public o Private o Industry o Research Institution o University o NGO o SDO o NSB o ESO
* City/Municipality
* Regional Planning Association o Other
**Figure 1. SmaCStak registration page. Available _here_ . **
Below, screenshots of the questions for being registered as SmaCStak
participant are presented.
Those registering to the SmaCStak by filling in the form above will have their
personal data stored herewith: _https://www.typeform.com_ . To this
database, only Mr. Mario Conci of Trentino Innovation and Mr. Jan-Philipp
Exner of the University of Kaiserslautern have access and can extract regular
copies locally stored for analysis. Names and email addresses are also stored
on Mailchimp.org, used for sending the project ESPRESSO newsletter.
## Personal Data/registry of participants to the ESPRESSO Atlas of smart
cities and standards
The ESPRESSO system also collects and stores information about the personal
data of the participants to the ESPRESSO Atlas of smart cities and standards
that give their consent to record their personal data in the project ESPRESSO
database. The Atlas is accessible through the website: _http://www.espresso-
project.eu/_ .
The platform is composed by a multitier architecture, which the presentation
layer, the business logic layer, the service layer, and the data access layer.
The **Presentation Layer** represents the web application through which the
content is available to the end users. It is developed in AngularJS, an open
source framework with Model-View-ViewModel (MVVM) architecture. The UI
component is developed using Angular Material with responsive patterns, in
order to extend the compatibility and ensure the same user experience through
different devices, included the mobile devices. The GIS features and 3D
visualization are developed over the framework NASA Web World Wind, an open
source virtual globe with advanced features for managing and viewing
geometries and display of map layers.
The **Business Logic Layer** is developed in NodeJs, and handles the
manipulation of the Smart Cities data and the related ancillary information
(i.e. geographic information, documents, attributes, sections, etc.). At this
level the permissions and the filters are managed, ensuring data integrity and
proper handling of sensitive and confidential data. Regarding the data
visibility, it handles different types of users with different permissions
associated in order to limit both the visibility of some information and the
operations that can be executed (Note that the data source of the users has
not be defined yet).
The **Service Layer** deals with data exchange with other layers through a
RESTful architecture. In addition, it provides public APIs for a limited data
exposure and for the data insert/update in order to make possible integration
with third-party software. This also ensures a greater facility for importing
data and therefore a benefit for the data population in order to enrich the
platform
Finally, **the Data Access Layer** provides access to the database, for which
it was used PostgresSQL with PostGIS, a relational database with an extension
to provide support for the geo-data.
The Smart Cities data is organized over a dynamic template.
Each Smart City contains a set of basic attributes (i.e. identifier,
coordinates, name, website, etc.) and a set of categories. Then, each category
can contain a set of subcategories or a set of attributes. The template can be
represented as follow:
• Smart City o Basic attributes
* …
* Basic attributes o Category A o …
* Category Z o Sub-Category A o … o Sub-Category Z o Attribute A o …
* Attribute Z
The architecture allows to customize the smart city template by inserting and
editing of categories and attributes. It means that new categories and new
attributes can be created and added (as well as they can be removed) to
existing smart cities without changing the database structure.
Service Layer
Business Logic Layer
Presentation Layer
Data Access Layer
**Figure 2. Platform diagram.**
## Personal Data taken within the Conceptual Standards Framework survey
The ESPRESSO system also collected personal data within the survey launched
for identifying the open standards currently being used and most beneficial,
and what gaps and weaknesses there are that could be addressed by the ESPRESSO
project.
The short survey was mainly addressed to the members of the SmaCStak
community. The closing date for taking part was Tuesday 28th June 2016\.
The personal/demographic data collected included:
* name and surname
* type of institution/organisation
* geographical scope of the institution/organisation
* role/position
* department/unit
* location of the institution/organisation
As a disclaimer, at the beginning of the survey it was included the following
privacy statement:
_"All responses recorded, including any personal information you provide, will
be kept strictly confidential. Your input will only be used in combination
with the responses of others participating in the survey. Our research
examines the opinions of groups of respondents. Your individual responses will
not be shown to anyone outside the study team"._
As stated in the above mentioned statement, any personal data has been
included in any ESPRESSO project deliverables. Furthermore, all personal data
collected within this survey will be deleted once the ESPRESSO project comes
to an end.
## Personal Data taken within the Smart City Standartization survey
The ESPRESSO system also collected personal data within the survey launched
for defining the ESPRESSO’s project CASSIOPEiA (Definition of a ConceptuAl
StandardS InterOPErability frAmework) and to define testbeds.
The link to access the survey is the following one:
_https://sabina24.typeform.com/to/JDjCyI_ .
The ESPRESSO project is conducting a detailed requirements-engineering
campaign in order to establish the baseline for interoperability between the
various Smart City sectors. Indeed, a first step towards the definition of a
Smart City interoperability scope is the identification and selection of Use
Cases of relevance, to be used further real-life test-beds within ESPRESSO.
Those filling in the survey had and will have (since the survey is still
opened) a part in this effort, by sharing needs and requirements for
standardization in Smart City solutions, projects, initiatives, or even by
providing the Consortium with a “best practice” case.
The survey collects the following personal information:
* Name
* Company name
* E-mail address (optional)
Below, screenshots of the questions are presented.
**9\. Conclusion**
Understanding the needs of the stakeholders – as primary input into the
various activities of the project - has been achieved using surveys, polls,
events and webinars.
ESPRESSO will ensure that all the data generated is properly collected
accessed, curated, preserved, and eventually made public after any possible
data-ownership issue has been cleared.
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1114_SEARMET_692299.md
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10\. Embryo flushing
The data and resulting scienfic findings would also be beneficial to animal
breeders who are interested in aceiving greater efficiency in production of
cattle of specific types or sexes.
Researchers could also use the data generated from this research regarding
bovines to link to other animal models in efforts to better understand
reproduction, in particular reproductive problems.
# 2.1 Making data findable, including provisions for metadata [FAIR data]
Outline the discoverability of data (metadata provision)
The metadata is stored in Dublin Core format. All the data is captured through
measurements and sampling by the researchers of EMU. Some of the data can be
created automatically, but mainly all the data is produced bt the researchers
doing experiments and collecting data from those experiments. For the metadata
standarsd we are using appropritate sample size and correct sampling
methology.
**Outline the identifiability of data and refer to standard identification
mechanism. Do you make use of persistent and unique identifiers such as
Digital Object Identifiers?**
EMU DSpace mints (produce for the first time) DOIs via DataCite for deposited
research outputs.
**Outline naming conventions used**
All files will be named uniformly when storing them for public use, based upon
the following criteria:
No special characters such as "/ \ : * ? " < > [ ] & $ will be used in names.
Underscores (_) will be used to separate terms not spaces
Names will be 30 charcters or less in length
Names can specify the year and month of creation (YYYY-MM) at the end of the
name
Names will be descriptive of what information they contain - so that they are
understandable to someone who is unfamilar with the research.
Some information could be described in metadata including the following:
-Project or experiment name or acronym
-Researcher name/initials
-Date or date range of experiment
-Type of data
-Conditions
-Version number of file
-Three-letter file extension for application-specific files
**Outline the approach towards search keyword**
Library will provide basic discovery metadata online (title, author, subjects,
keywords, department, etc.).
**Outline the approach for clear versioning**
Versions of files will only be stored and made available when relevant
Whenever possible, obsolete versions will be discarded or deleted (whilst
retaining the original 'raw' copy)
Files with multiple versions will include the letter V and the number of the
version before the date - for example V1 V2 V3, etc.
If appropriate, version control software e.g. Subversion, TortoiseSVN will be
used
**Specify standards for metadata creation (if any). If there are no standards
in your discipline describe what metadata will be created and how**
Dublin Core
# 2.2 Making data openly accessible [FAIR data]
**Specify which data will be made openly available? If some data is kept
closed provide rationale for doing so**
All metadata will made openly available via EMU Dspace. Any data that will be
kept closed will be done so either for IRP reasons or to protect
confidentiality issues.
**Specify how the data will be made available** All the data will be available
via DSpace.
**Specify what methods or software tools are needed to access the data? Is
documentation about the software needed to access the data included? Is it
possible to include the relevant software (e.g. in open source code)?**
Metadata will be available in conventional formats such as pdf files,
individual level data would be accessible using R software.
**Specify where the data and associated metadata, documentation and code are
deposited**
The data and associated metadata, documentation and code are deposited in
Estonian University of Life Sciences Library digital repository EMU DSpace.
**Specify how access will be provided in case there are any restrictions**
Data is freely accessible. Restricted data is accessible via password
protection.
# 2.3 Making data interoperable [FAIR data]
**Assess the interoperability of your data. Specify what data and metadata
vocabularies, standards or methodologies you will follow to facilitate
interoperability.**
The metadata will be created by experiments and different measurements. Some
of the information about bovines can be created automatically.
The metadata is storaged at first in the University of Life Sciences personal
server spaces and after data publication in DSpace.
Whenever possible interoperatable files, formats will be used such as ,CSV.
**Specify whether you will be using standard vocabulary for all data types
present in your data set, to allow inter-disciplinary interoperability? If
not, will you provide mapping to more commonly used ontologies?**
Whereever possible standard vocabulary will be used for data sets. No mapping
to more commonly used ontologies will be offered.
**2.4 Increase data re-use (through clarifying licenses) [FAIR data] Specify
how the data will be licenced to permit the widest reuse possible** Unless
protected via IRP, no licenses will be needed to use data.
**Specify when the data will be made available for re-use. If applicable,
specify why and for what period a data embargo is needed**
The data will be made available after articles have been published in peer-
reviewed journals.
As the time to publish or seek patents may vary greatly, no precise time
period can be stated.
**Specify whether the data produced and/or used in the project is useable by
third parties, in particular after the end of the project? If the re-use of
some data is restricted, explain why**
Restrictions to data sharing may be due to participant confidentiality,
consent agreements or IPR. Restrictions are required until the data is
published. After that all the data can be used by third parties. Restrictions
are needed in order to be published in peer-reviewed journals. Limited embrago
period until data is published.
**Describe data quality assurance processes**
The consistency and quality of data collection will be controlled and
documented with repeated samples or measurements, standardised data capture,
data entry validation, peer-review of data or representation with controlled
vocabularies.
**Specify the length of time for which the data will remain re-usable**
In perpetuity
# Allocation of resources
**Estimate the costs for making your data FAIR. Describe how you intend to
cover these costs**
Free of charge.
**Clearly identify responsibilities for data management in your project**
Data Management is handled by researchers during collection and by staff of
the EMU
Library during curation and preservation
**Describe costs and potential value of long term preservation** Its
librarians voluntary job.
# Data security
**Address data recovery as well as secure storage and transfer of sensitive
data**
EMU DSpace is maintained and operated by Estonian University of Life Sciences
Library. All data files are stored in Estonian University of Life Sciences
storage service in two independent copies. Preservation and back-ups of the
data are ensured by the EMU DSpace preservation policies: daily backups,
security updates, tightly controlled administrative access. Sensitive data is
available only for SEARMET group members via login.
# Ethical aspects
**To be covered in the context of the ethics review, ethics section of DoA and
ethics deliverables. Include references and related technical aspects if not
covered by the former**
No research involving human participants is done. All the researchers have
given consent for data preservation and sharing. For animal experiments there
are licenses obtained from the local ethics committee in accordance with the
EU guidelines. These licences are preserved in EMU DSpace.
Sensitive data will be securely stored at EMU DSpace.
# Other
**Refer to other national/funder/sectorial/departmental procedures for data
management that you are using (if any)**
N/A
SEARMET Deliverable #2.3
<table>
<tr>
<th>
</th>
<th>
**H2020-TWINN-2015** **Grant Agreement no**
This project is funded under the **EUROPEAN COMMISSION** in the
Framework Programme for Research and Innovation (2014-2020).
</th> </tr>
<tr>
<td>
Call:
</td>
<td>
Work programme **H2020** under **“Spreading Excellence and Widening
Participation”, call: H2020-TWINN-2015: Twinning** (Coordination and Support
Action).
</td> </tr>
<tr>
<td>
Project full title:
</td>
<td>
Scientific Excellence in Animal Reproductive Medicine and Embryo Technology
</td> </tr>
<tr>
<td>
Project acronym:
</td>
<td>
SEARMET
</td> </tr>
<tr>
<td>
Work Package (WP):
</td>
<td>
2
</td> </tr>
<tr>
<td>
Deliverable (D):
</td>
<td>
2.3
</td> </tr>
<tr>
<td>
Due date of deliverable:
</td>
<td>
20
</td>
<td>
</td> </tr>
<tr>
<td>
Author(s):
</td>
<td>
Ülle Jaakma
</td> </tr>
<tr>
<td>
Contributor(s):
</td>
<td>
</td> </tr>
<tr>
<td>
Start date of project:
</td>
<td>
1 January 2016
</td>
<td>
Duration: 36 Months
</td> </tr> </table>
<table>
<tr>
<th>
**Dissemination Level**
</th>
<th>
</th> </tr>
<tr>
<td>
**PU**
</td>
<td>
Public
</td>
<td>
_PU_
</td> </tr>
<tr>
<td>
**CO**
</td>
<td>
Confidential, only for members of the consortium (including the Agency
Services)
</td>
<td>
</td> </tr> </table>
Version Page
|
https://phaidra.univie.ac.at/o:1140797
|
Horizon 2020
|
1118_SONNETS_692868.md
|
**Executive Summary**
The SONNETS project participates in the Pilot on Open Research Data launched
by the European Commission along with the Horizon 2020 programme. The main
purpose of a Data Management Plan (DMP) is to describe Research Data with the
metadata attached to make them discoverable, accessible, assessable, usable
beyond the original purpose and exchangeable between researchers.
SONNETS general strategy for data management follows the Guidelines on Data
Management in Horizon 2020 and will specify the types of data to be generated
and collected along with their respective identification and classification
methods, the standards and metadata to be used, the corresponding data
exploitation and availability perspectives as well as the data sharing and
preservation schemes to be used. The DMP will be implemented through the Task
1.3 lead by ATOS.
The SONNETS project aims at accelerating the transformation of the public
sector through the identification, analysis and take-up of emerging
technologies that hold the potential to transform and infuse real value to
public services. In order to achieve this goal, the SONNETS consortium will
carry desk-based research, workshops and interviews, make use of data
discovery tools, construct a wide network of collaborators, engage a group of
renowned experts and develop a knowledge repository, which will produce a huge
amount of data. Hence the importance for SONNETS project to define a data
management policy.
SONNETS involves primary data collection: (1) semi-structured interviews; (2)
focus group discussions and stakeholder workshops; (3) Communication via
SONNETS group. These sources will be the bases for the creation of seven
SONNETS datasets:
* Dataset 1: Contact Users’ Database
* Dataset 2: Stakeholders’ Database
* Dataset 3 Stakeholders’ Interviews
* Dataset 4 Stakeholders’ Surveys
* Dataset 5 Societal and Public Sector Needs Dataset 6 Emerging Technologies Dataset 7 SONNETS roadmap and briefs.
These data sets will be handled as editable file (.doc) or Data will be shared
in relation to publications (deliverables and papers).
SONNETS project intends to make its research data available for open access
and use by other researchers and members of the public. In furtherance of this
goal, SONNETS will deposit its research data into a digital repository and
make the data available for access and reuse under a Creative Commons
Attribution License.
Finally, the SONNETS data will be backed up regularly and stored on the ATOS
Institutions server. Furthermore, for potential sensitivities (e.g. personal
data of interviewees) of the data being collected, the project has established
a system for protecting data while it is being processed.
1. **Introduction**
**1.1 Purpose and scope**
This document describes the Data Management Plan (DMP) with respect to the
Horizon 2020 – EURO-6-2015 CSA Project SOcietal Needs aNalysis and Emerging
Technologies in the public Sector (Grant Agreement No. 692868).
The mission of the SONNETS project, as described in its proposal and
subsequent grant agreement, calls on providing an ever-evolving methodological
framework for public sector organizations and related stakeholders to
accelerate the transformation of the public sector through the identification,
analysis and takeup of emerging technologies that hold the potential to
transform and infuse real value to public services.
In order to tackle the above-mentioned goal, the SONNETS consortium will carry
desk-based research, workshops and interviews, make use of data discovery
tools, construct a wide network of collaborators, engage a group of renowned
experts and develop a knowledge repository (the SONNETS LinkedIn group,
https://www.linkedin.com/groups/8520112). These activities will produce a huge
amount of data, hence the importance for SONNETS project to define a data
management policy.
The purpose of the DMP is to provide an analysis of the main elements of the
data management policy that will be used by the consortium with regard to all
the datasets that will be generated by the project.
**1.2 Structure**
This document provides a description of the Data Management Plan (DMP) within
the SONNETS project through the following sections:
* _Section 2_ defines the data management strategy to be followed during the SONNETS project according to the Guidelines on Data Management in Horizon 2020 published by the EC services.
* _Section 3_ details the information to be included in the SONNETS data tables, listing the different datasets that will be produced by the project, the main exploitation perspectives for each of those datasets, and the major management principles the project will implement to handle those datasets
* _Section 4_ presents the main conclusions and finally the _References section_ provides information about the documentation used to produce this deliverable.
2. **Data Management strategy**
The general strategy for data management, according to the Guidelines on Data
Management in Horizon 2020 1 , will be based on the identification and
classification of data generated and collected, standards and metadata to be
used, exploitation and availability of data as well as how the data will be
shared and archiving preservation of the information.
The SONNETS DMP will cover all the data life cycle. Hence, the task _T1.3 Data
Management_ in WP1 will be devoted to formulate and continuously evolve the
SONNETS research data management plan in accordance with the H2020 guidelines
regarding Open Research Data. In Task 1.3, the metadata, procedures and file
formats for note-taking, recording, transcribing, storing visual data from
participatory techniques, and anonymising semi-structured interview and focus
group discussion data will be developed and agreed. ATOS will be the leader
for this task, though all partners are involved in the compliance of the DMP.
1. **Types of data to be generated/collected**
Data to be generated/extracted/collected will be obtained with the
collaboration of researchers, the external experts (both the Experts Group and
Experts Advisory Group) and other collaborators.
1. **Data sources**
SONNETS involves **primary data collection** : 1) semi-structured interviews;
2) focus group discussions and stakeholder workshops; 3) Communication via
SONNETS group
1. _Semi-structured interviews with individuals_ : The team anticipates undertaking 20-25 semi-structured interviews in WP2 and WP3. Data will be collected and stored using digital audio recording (e.g. MP3) where interviewees permit. In case they do not, interviews will be undertaken in pairs to enable detailed note-taking. Interview notes will be typed up according to agreed formats and standards. During data analysis, the data will be accessible only by certified members of the project team. The research project will remove any direct identifiers in the data before sending it to a digital repository.
2. _Focus group discussions and stakeholder workshops_ : Focus groups and workshops conducted within WP2 and WP3 will involve two researchers. Whether recorded or not, the event will be transcribed or documented using agreed formats and standards for handling the issue of multiple voices, interruptions, labelling of participatory and visual activities, and so on.
All transcripts will be in Microsoft Word. All the researchers will be
reasonably fluent in both English and the main language in which interviews
and focus groups will be conducted, so that transcriptions will be translated
into English.
3. _Communication via SONNETS collaboration tool_ : The consortium has created a dedicated Web site and a SONNETS LinkedIn Group. On one hand, the project site has been established using a content management system (Drupal 7) so that data users can participate in adding site content over time in the Blogs section ( _http://www.sonnets-project.eu/blog_ ) , making the site self-sustaining. For preservation, we will supply periodic copies of the data to ATOS own digital data repository. That repository will be the ultimate home for the data. On the other hand, the consortium agrees to use LinkedIn to manage and distribute the communication data of the SONNETS network. In addition to the research community, we expect these data will be used by practitioners and policymakers.
2. **Type of data**
The data to be collected can be:
* **No personal data** : this information is not affected by Data Protection legislation
* **Personal data** : data which relate to an individual who can be identified 2 o (a) from those data, or
o (b) from those data and other information which is in the possession of, or
is likely to come into the possession of, the data controller, and includes
any expression of opinion about the individual and any indication of the
intentions of the data controller or any other person in respect of the
individual.
In that case, SONNETS follows the European directives:
* Data Protection Directive 3
* Directive on privacy and electronic communications. 4
The activities focussed on the ethical and privacy issues and compliance to
legislation in SONNETS are carried out in WP6 and are applied in accordance to
what have been defined in D6.2 PODP- Requirement No. 2.
2. **Standards and metadata to be used**
Data will be shared in relation to publications (deliverables and papers). As
such, the publication will serve as the main piece of metadata for the shared
data. Hence, formats to be used mainly include .doc, .pdf and .xls files,
which substantially reduce the amount of metadata. Other standards do not
apply to this project.
3. **Exploitation, availability of data and re-use**
SONNETS project intends to make its research data available for open access
and use by other researchers and members of the public. In furtherance of this
goal, SONNETS will deposit its research data into a digital repository (Zenodo
5 or similar) and make the data available for access and reuse under a
Creative Commons Attribution License 5 .
For sensitive/restricted data, if any, access restrictions will be enforced
(e.g. by requiring specific credentials) or at least limited in the detail
available, e.g. by granting an open access exclusively through aggregation,
while providing the specific data to authorized users only.
Additionally, the Grant Agreement and the SONNETS Consortium Agreement are to
be referred to for further details on the ownership, management of
intellectual property and access.
4. **Archiving and preservation**
Our data will need to be backed up regularly; because of likely problems with
viruses and hardware in developing countries, so that up-to-date versions are
stored on the ATOS Institutions server. Qualitative data will be backed up and
secured by the coordinator on a regular basis and metadata will include clear
labelling of versions and dates.
There are some potential sensitivities (e.g. personal data of interviewees) of
the data being collected, so the project has established a system for
protecting data (see deliverable D6.2 POPD - Requirement No. 2 for further
details) while it is being processed, including use of passwords and safe
back-up hardware.
3. **SONNETS Datasets**
As mentioned in the previous chapter, the DMP should address the points below
on a dataset by dataset basis and should reflect the current status of
reflection within the consortium about the data that will be produced.
* **Dataset reference and name:** Identifier for the data set to be produced.
* **Dataset description** : Description of the data that will be generated or collected, its origin (in case it is collected), nature and scale and to whom it could be useful, and whether it underpins a scientific publication. Information on the existence (or not) of similar data and the possibilities for integration and reuse.
* **Standards and metadata** : it includes reference to existing suitable standards of the discipline. If these do not exist, an outline on how and what metadata will be created.
* **Data sharing** : A detailed description of how data will be shared, including access procedures, embargo periods (if any), outlines of technical mechanisms for dissemination and necessary software and other tools for enabling re-use, and definition of whether access will be widely open or restricted to specific groups. Identification of the repository where data will be stored, if already existing and identified, indicating in particular the type of repository (institutional, standard repository for the discipline, etc.).
In case the dataset cannot be shared, the reasons for this should be mentioned
(e.g. ethical, rules of personal data, intellectual property, commercial,
privacy-related, security-related).
* **Archiving and preservation** : Description of the procedures that will be put in place for long-term preservation of the data. Indication of how long the data should be preserved, what is its approximated end volume, what the associated costs are and how these are planned to be covered.
Within SONNETS, the consortium plans to create seven separate datasets. The
datasets will have the structure in accordance with the guide of Horizon 2020
for the Data Management Plan.
1. **DATASET 1: Contact users’ database**
<table>
<tr>
<th>
**1**
</th>
<th>
**Dataset reference and name**
</th> </tr>
<tr>
<th>
DATASET 1: Contact users’ database
</th> </tr>
<tr>
<td>
**2**
</td>
<td>
**Dataset description**
</td> </tr>
<tr>
<td>
The data collected are implemented in Excel and refer to the following
sections and purposes:
* Contact users’ register for newsletter subscriptions, it contains name and e-mail (both mandatory). This dataset is automatically generated when visitors sign up to the newsletter form available on the project website. The register will be used in order to send issues of the project newsletters.
* Contact user’s personal details with regard to messages sent to the website through the Contact form. It includes name, e-mail, message (all mandatory) and (possibly) phone. The contact details will be used to address the enquiry/request and to send
</td> </tr>
<tr>
<td>
</td>
<td>
information in the scope of the SONNETS project.
</td> </tr>
<tr>
<td>
**3**
</td>
<td>
**Standards and metadata**
</td> </tr>
<tr>
<td>
This dataset can be imported from, and exported to a CSV, TXT or Excel file.
</td> </tr>
<tr>
<td>
**4**
</td>
<td>
**Data sharing**
</td> </tr>
<tr>
<td>
The mailing list will be used for disseminating the project newsletter to a
targeted audience. An analysis of newsletter subscribers may be performed in
order to assess and improve the overall visibility of the project.
As it implies personal data, the access to the dataset is restricted to
SONNETS consortium.
</td> </tr>
<tr>
<td>
**5**
</td>
<td>
**Archiving and preservation (including storage and backup)**
</td> </tr>
<tr>
<td>
The dataset will be preserved in Atos’ servers.
</td> </tr> </table>
# Table 2: DATASET 1: Contact users’ database
**3.2 DATASET 2: Stakeholders’ database**
<table>
<tr>
<th>
**1**
</th>
<th>
**Dataset reference and name**
</th> </tr>
<tr>
<th>
DATASET 2: Stakeholders’ database
</th> </tr>
<tr>
<td>
**2**
</td>
<td>
**Dataset description**
</td> </tr>
<tr>
<td>
This dataset is related to the stakeholders that will build the SONNETS
network and contains the name, surname, job function, domain field/expertise,
e-mail contact and location as well as project interests or benefits, what
they can contribute, and future actions & communication.
* Contact users’ register for Experts Group.
* Contact users’ register for Expert Advisory Board
This dataset is used to extend and disseminate the information about the
project as well as to get feedback from these collaborators.
</td> </tr>
<tr>
<td>
**3**
</td>
<td>
**Standards and metadata**
</td> </tr>
<tr>
<td>
This dataset can be imported from, and exported to a CSV, TXT or Excel file
</td> </tr>
<tr>
<td>
**4**
</td>
<td>
**Data sharing**
</td> </tr>
<tr>
<td>
For these sensitive/restricted data, access restrictions will be enforced
(e.g. by requiring specific credentials) or at least limited in the detail
available, e.g. by granting an open access exclusively through aggregation,
while providing the specific data to authorized users only.
</td> </tr>
<tr>
<td>
**5**
</td>
<td>
**Archiving and preservation (including storage and backup)**
</td> </tr>
<tr>
<td>
The dataset will be preserved in Atos’ servers.
</td> </tr> </table>
# Table 3: DATASET 2: Stakeholders’ database
**3.3 DATASET 3: Stakeholders’ interviews**
<table>
<tr>
<th>
**1**
</th>
<th>
**Dataset reference and name**
</th> </tr>
<tr>
<th>
DATASET 3: Stakeholders’ interviews
</th> </tr>
<tr>
<td>
**2**
</td>
<td>
**Dataset description**
</td> </tr>
<tr>
<td>
This dataset will contain the answers of people who will participate in the
SONNETS individual interviews, workshops and focus groups carried within WP2
and WP3 activities.
</td> </tr>
<tr>
<td>
**3**
</td>
<td>
**Standards and metadata**
</td> </tr>
<tr>
<td>
Data will be collected and stored using digital audio recording (e.g. MP3)
where interviewees permit. In any case, the data from these sources will be
always held in transcript form in accessible .doc file format (Word).
</td> </tr>
<tr>
<td>
**4**
</td>
<td>
**Data sharing**
</td> </tr>
<tr>
<td>
This dataset will be used to produce analytical reports on the most important
societal and public sector needs as well to identify the most appropriate
technologies to tackle these tasks. Results will be mainly shared through the
WP2, WP3 and WP4 deliverables.
</td> </tr>
<tr>
<td>
**5**
</td>
<td>
**Archiving and preservation (including storage and backup)**
</td> </tr>
<tr>
<td>
The dataset will be preserved in Atos’ servers.
</td> </tr> </table>
# Table 4: DATASET 3 Stakeholders’ interviews
**3.4 DATASET 4: Stakeholders’ questionnaires**
<table>
<tr>
<th>
**1**
</th>
<th>
**Dataset reference and name**
</th> </tr>
<tr>
<th>
DATASET 4: Stakeholders’ questionnaires
</th> </tr>
<tr>
<td>
**2**
</td>
<td>
**Data set description**
</td> </tr>
<tr>
<td>
This dataset will contain the answers of people who will participate in the
SONNETS questionnaires carried within WP2 and WP3 activities. The surveys will
be built using Limesurvey or similar and will be hosted at the project website
( _www.sonnets-project.eu_ ) .
</td> </tr>
<tr>
<td>
**3**
</td>
<td>
**Standards and metadata**
</td> </tr>
<tr>
<td>
This dataset can be imported from, and exported to a CSV, TXT or Excel file.
</td> </tr>
<tr>
<td>
**4**
</td>
<td>
**Data sharing**
</td> </tr>
<tr>
<td>
This dataset will be used to produce analytical reports on the most important
societal and public sector needs as well to identify the most appropriate
technologies to tackle these tasks. Results will be mainly shared through the
WP2, WP3 and WP4 deliverables.
</td> </tr>
<tr>
<td>
**5**
</td>
<td>
**Archiving and preservation (including storage and backup)**
</td> </tr>
<tr>
<td>
The dataset will be preserved in the internal project repository (Owncloud)
</td> </tr> </table>
# Table 5: DATASET 4 Stakeholders’ surveys
**3.5 DATASET 5: List of societal and public sector needs**
<table>
<tr>
<th>
**1**
</th>
<th>
**Dataset reference and name**
</th> </tr>
<tr>
<th>
DATASET 5: List of societal and public sector needs
</th> </tr>
<tr>
<td>
**2**
</td>
<td>
**Dataset description**
</td> </tr>
<tr>
<td>
Dataset for analysis of societal and public sector needs is the basis of the
WP2. Data will be collected from stakeholders. Data collection techniques used
will be mainly stakeholder interviews, focus groups and data collection sheets
filled by stakeholders. Literature data will also be used. The dataset will be
useful for all users of the project outcomes. Metadata will be comprised of
contextual information about the data in a text based document.
</td> </tr>
<tr>
<td>
**3**
</td>
<td>
**Standards and metadata**
</td> </tr>
<tr>
<td>
This dataset is a combination of Excel/WORD/PDF documents.
</td> </tr>
<tr>
<td>
**4**
</td>
<td>
**Data sharing**
</td> </tr>
<tr>
<td>
This dataset will be mainly shared through the WP2 and WP4 deliverables, which
are public.
</td> </tr>
<tr>
<td>
**5**
</td>
<td>
**Archiving and preservation (including storage and backup)**
</td> </tr>
<tr>
<td>
The dataset will be preserved in project internal repository (Owncloud), in
the project website ( _http://www.sonnets-project.eu/downloads_ ) and in a
digital repository (Zenodo or similar).
</td> </tr> </table>
# Table 6: DATASET 5 societal and public sector needs
**3.6 DATASET 6: List of emerging technologies**
<table>
<tr>
<th>
**1**
</th>
<th>
**Dataset reference and name**
</th> </tr>
<tr>
<th>
DATASET 6: List of emerging technologies
</th> </tr>
<tr>
<td>
**2**
</td>
<td>
**Dataset description**
</td> </tr>
<tr>
<td>
Dataset for analysis of emerging technologies is the basis of the WP3. Data
will be collected from stakeholders. Data collection techniques used will be
mainly stakeholder interviews, focus groups and data collection sheets filled
by stakeholders. Literature data will also be used. The dataset will be useful
for all users of the project outcomes. Metadata will be comprised of
contextual information about the data in a text based document.
</td> </tr>
<tr>
<td>
**3**
</td>
<td>
**Standards and metadata**
</td> </tr>
<tr>
<td>
This dataset is a combination of Excel/WORD/PDF documents.
</td> </tr>
<tr>
<td>
**4**
</td>
<td>
**Data sharing**
</td> </tr>
<tr>
<td>
This dataset will be mainly shared through the WP3 and WP4 deliverables, which
are public.
</td> </tr>
<tr>
<td>
**5**
</td>
<td>
**Archiving and preservation (including storage and backup)**
</td> </tr>
<tr>
<td>
The dataset will be preserved in project internal repository (Owncloud), in
the project website ( _http://www.sonnets-project.eu/downloads_ ) and in
</td> </tr>
<tr>
<td>
</td>
<td>
a digital repository (Zenodo or similar).
</td> </tr> </table>
# Table 7: DATASET 6 Emerging technologies
**3.7 DATASET 7: SONNETS roadmap and briefs**
<table>
<tr>
<th>
**1**
</th>
<th>
**Dataset reference and name**
</th> </tr>
<tr>
<th>
DATASET 7: SONNETs roadmap and briefs
</th> </tr>
<tr>
<td>
**2**
</td>
<td>
**Dataset description**
</td> </tr>
<tr>
<td>
This dataset will be developed on the analysis of the identified technologies
regarding technology readiness level, relevant international research
programmes and important actors and on the identification on research needs.
The dataset will provide a set of recommendations (namely, the SONNETS briefs)
for policy makers, researchers and representatives of the public sector.
Metadata will be comprised of contextual information about the data in a text
based document.
</td> </tr>
<tr>
<td>
**3**
</td>
<td>
**Standards and metadata**
</td> </tr>
<tr>
<td>
This dataset is a combination of /WORD/PDF documents.
</td> </tr>
<tr>
<td>
**4**
</td>
<td>
**Data sharing**
</td> </tr>
<tr>
<td>
This dataset will be mainly shared through the WP3 and WP4 deliverables, which
are public.
</td> </tr>
<tr>
<td>
**5**
</td>
<td>
**Archiving and preservation (including storage and backup)**
</td> </tr>
<tr>
<td>
The dataset will be preserved in project internal repository (Owncloud), in
the project website ( _http://www.sonnets-project.eu/downloads_ ) and in a
digital repository (Zenodo or similar).
</td> </tr> </table>
# Table 8: DATASET 8 SONNETS roadmap and briefs
**4 Conclusions**
This Data Management Plan provides an overview of the data that SONNETS
project will produce together with related challenges and constraints that
need to be taken into consideration.
The analysis contained in this report allows anticipating the procedures and
infrastructures to be implemented by SONNETS project to efficiently manage the
data it will produce. Nearly all project partners will be owners or/and
producers of data.
The SONNETS Research Data Management Plan will put a strong emphasis on the
appropriate collection – and publication should the data be published – of
metadata, storing all the information necessary for the optimal use and reuse
of those datasets.
Specific attention will be given to ensuring that the data made do not break
either partner IPR rules or regulations and good practices related to personal
data protection. For this latter point, systematic anonymization of personal
data will be made.
**5 References**
1. Guidelines on Data Management in Horizon 2020, V2.0, 30 October 2015, _http://ec.europa.eu/research/participants/data/ref/h2020/grants_manual/hi/_ _oa_pilot/h2020-hi-oa-data-mgt_en.pdf_ .
2. Guidelines on Open Access to Scientific Publication and Research Data in
Horizon 2020, Version 2.0, 30, October 2015,
_http://ec.europa.eu/research/participants/data/ref/h2020/grants_manual/hi/_
_oa_pilot/h2020-hi-oa-pilot-guide_en.pdf_ .
3. SONNETS Grant Agreement No. 692868 (Grant Agreement-692868SONNETS.pdf)
4. SONNETS consortium agreement (SONNETS_CA_v2.0_signed.pdf).
5. Key definitions of the Data Protection Act, ico (Information Commissioner’s Office), _https://ico.org.uk_ .
6. _http://eur-lex.europa.eu/legal-content/en/ALL/?uri=CELEX:31995L0046_
7. _http://eurlex.europa.eu/LexUriServ/LexUriServ.do?uri=OJ:L:2002:201:0037:0047:en:P_ _DF_ .
8. _http://zenodo.org_
9. _https://creativecommons.org/licenses_ /
|
https://phaidra.univie.ac.at/o:1140797
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Horizon 2020
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1120_ARCHES_693229.md
|
1 Introduction 11 2 Data summary 12
2.1 Purpose 12
2.2 Types and formats 12
2.3 Origin 13
2.4 Re‐use 14
2.5 Expected size 15
2.6 Utility 16
3. FAIR data 18
1. Making data findable, including provisions for metadata 18
2. Making data openly accessible 18
3. Making data interoperable 19 3.4 Increase data re‐use 20 4 Allocation of resources 21
5. Data security 22
6. Ethical aspects 23
1. Principles of participation 24
2. Principles of consent 24
3. Principles of security 24
4. Principles of privacy 25
5. Deliverables 25
7. Conclusions 26
References 27
Annex A Ethics requirements 29
A.1 Information sheets 29
1. Information sheet for people with learning disabilities 29
2. Information sheet for people with sensory disabilities 32
A.2 Informed consents 34
1. Informed consent for people with learning disabilities 34
2. Informed consent for people with sensory disabilities 36
Figure 1: EDM class hierarchy [18]. 20
Figure 2: ARCHES Information Letter for people with learning disabilities –
general description. 29 Figure 3: ARCHES Information Letter for people with
learning disabilities – the research process (1/2). 30
Figure 4: ARCHES Information Letter for people with learning disabilities –
the research process (2/2). 31
<table>
<tr>
<th>
Figure 5: ARCHES Information Sheet for people with sensory disabilities (1/2).
</th>
<th>
32
</th> </tr>
<tr>
<td>
Figure 6: ARCHES Information Sheet for people with sensory disabilities (2/2).
</td>
<td>
33
</td> </tr>
<tr>
<td>
Figure 7: ARCHES Consent Form for people with learning disabilities (1/2).
</td>
<td>
34
</td> </tr>
<tr>
<td>
Figure 8: ARCHES Consent Form for people with learning disabilities (2/2).
</td>
<td>
35
</td> </tr>
<tr>
<td>
Figure 9: ARCHES Consent Form for people with sensory disabilities.
</td>
<td>
36
</td> </tr> </table>
# Abbreviations
**API** : Application Programming Interface
**ARCHES** : Accessible Resources for Cultural Heritage EcoSystems
**BERA** : British Educational Research Association
**BSL** : British Sign Language
**CA:** Consortium Agreement
**CH** : Cultural Heritage
**CIDOC** : International Committee of
Documentation
**CMS** : Central Management Server
**Coprix** : (partner short name) Coprix Media
**CRM** : Conceptual Reference Model
**DMP** : Data Management Plan
**DoA:** Description of Action
**EC** : European Commission
**EDM** : European Data Model
**EEAB** : External Expert Advisory Board
**FAIR** : Findable, Accessible, Interoperable and Reusable
**FLG** : (partner short name) Fundación Lázaro Galdiano
**GA:** Grant Agreement
**H2020** : Horizon 2020
**ICOM** : International Council of Museums
**IPR:** Intellectual Property Rights
**KHM** : (partner short name) Kunst‐historisches Museum Wien
**LIDO** : Lightweight Information Describing Objects
**MBBAA** : (partner short name) Centro Regional de Bellas Artes de Oviedo
**MN** : (partner short name) Moritz Neumüller –
ArteConTacto
**ORDP** : Open Research Data Pilot
**ORO** : Open Research Online
**OU** : (partner short name) The Open University
**SignTime** : (partner short name) Sign Time GmbH
**Tree** : (partner short name) Treelogic Telemática y
Lógica Racional para la Empresa Europea S.L.
**Thyssen** : (partner short name) FundaciónColección Thyssen‐Bornemisza
**UBAH** : (partner short name) University of Bath
**VCS** : Version Control System
**VRVis** : (partner short name) VRVis Zentrum für Virtual Reality und
visualisierung forschungs ‐ GmbH
**V &A ** : (partner short name) Victoria & Albert Museum
**WC** : (partner short name) The Wallace Collection
**WP** : Work Package
# Introduction
ARCHES [2] (“Accessible Resources for Cultural Heritage Ecosystems”) is a
Horizon 2020 (H2020) project which aims to generate more inclusive
environments at museums and cultural heritage (CH) sites, so that people with
differences and difficulties associated with perception, memory, cognition and
communication can easily access and understand art. To this end, ARCHES brings
together three key aspects: a participative research methodology with the
active involvement of the previously cited target audiences organised in the
so called exploration groups; the re‐use of available digital resources
provided by the partners and external sources; and the development of
innovative technologies that will be implemented and fine‐tuned by the
tech‐partners with the feedback from the exploration groups. As a consequence,
the generation, collection, re‐use and preservation of data is crucial for the
smooth running of ARCHES and needed the collaboration of all the partners
within the consortium: University of Bath (UBAH), The Open University (OU),
Sign Time GmbH (SIGN), Neumüller Moritz – ArteConTacto (MN), Centro Regional
de Bellas Artes de Asturias – Fine Arts Museum of Asturias (MBBAA), Coprix
Media
(Coprix), VRVis Zentrum für Virtual Reality and Visualization Forschungs GmbH
(VRVis), KHMMuseumsverband – Kunsthistorisches Museum Wien (KHM), The Wallace
Collection (WC), Fundación Colección Thyssen Bornemisza – Thyssen Museum
(Thyssen), Fundación Lázaro Galdiano – Lázaro Galdiano Museum (FLG) and
Victoria & Albert Museum (V&A).
This document, the revised version of the ARCHES Data Management Plan,
describes the research data generated and collected during the project, as
well as the data (i.e. the partner museums digital assets) that has been used
throughout the project. The strategy to make data FAIR (findable, accessible,
interoperable and re‐usable) is commented in the following sections.
# Data summary
## Purpose
The purpose of the data collection/generation is the support of the ARCHES
technical developments and complementary activities to validate the proposed
solutions in real environments and organise demonstration activities open to
everyone according to the objectives and guidelines defined in the document
Description of Action (DoA). In particular, data collection and generation are
crucial to achieve the following specific objectives:
* To develop and evaluate strategies which enable an exploration of the value, form and function of mainstream technologies by and for people with differences and difficulties associated with perception, memory, cognition and communication.
* To develop and evaluate the use of mainstream technologies to enable the inclusion of people with such disabilities as museums visitors and consumers of art.
* To identify sources – Internet, internal archives, libraries, etc. – that can provide digital cultural resources and take advantage of their possibilities to integrate this content into innovative tools, applications and functionalities.
* To validate the technological outcomes in operational environments based on a participatory research methodology consisting of three pilot exercises in museums.
* To promote the tools and applications developed in ARCHES by means of on‐site demonstration activities all around Europe.
Likewise, data collection/generation is in close connection with the tasks
scheduled in the work plan, especially in the context of the technical work
packages – i.e., WP3 “Development of an accessible software platform”, WP4
“Development of applications for handheld devices” and WP5 “On‐site
multisensory activities” – paving the way for the system validation and pilot
exercises in WP6. Therefore, the DMP will be a living document where new
datasets may be incorporated at any time all along the lifespan of the project
following the principles of FAIR data.
## Types and formats
ARCHES will handle different types and formats of data according to the
multiple research areas and technical developments. In principle, these are
grouped into four categories:
* **Multimedia research data** : A combination of sound, images, video and text components to represent the works of art in different ways and support the integration of digital surrogates into the web and mobile applications as well as the generation of complementary resources and tools. Photographs, 3D models, sketches, interactive guides, avatars and serious games are also considered within this category.
* **Metadata** : Data associated to the multimedia content used to better describe and understand each of the works of art. This includes information about the author, style, period and other details regarding the history and characteristics of the paintings, sculptures and other CH assets. Subtitles are other form of metadata that will be generated together with the videos to become accessible to people with hearing difficulties.
* **User data** : Based on the findings in WP3 and WP4 no user data is collected in the platform or apps. This also better complies with GDPR and ensures the privacy of the users. The access preferences of the users are just saved on the users device for usability purposes and are not sent to any server.
* **Exploration data** : Data generated for/by the exploration groups regarding their involvement in the project, from the recruitment phase in the UK, Spain and Austria to the interaction with the research teams to provide feedback on the methodology, activities and technologies. Therefore, information sheets, consent forms and multiple materials to assess the outcomes and allow expressing opinions and feelings – e.g. surveys, questionnaires, interviews, etc. – were generated, stored and preserved under this category.
The above information is summarised in Table 1 with the most common formats
employed by the partners when managing these datasets. We will decide the ones
that fit best, depending on the input sources and the applications. This will
also be aligned with the definition of system requirements and architecture in
the deliverables D3.1 and D4.1.
**Table 1: Types and formats of data generated/collected.**
<table>
<tr>
<th>
</th>
<th>
**Type of data**
</th>
<th>
**Description**
</th>
<th>
**Formats**
</th> </tr>
<tr>
<td>
**1**
</td>
<td>
**Multimedia research data**
</td>
<td>
Image
</td>
<td>
JPG, PNG, TIF, GIF, BMP
</td> </tr>
<tr>
<td>
High‐resolution photograph
</td>
<td>
TIF, PNG, RAW
</td> </tr>
<tr>
<td>
3D model (where applicable)
</td>
<td>
PLY, DXF,
COLLADA, STL,
OBJ, PDF, U3D, FBX
</td> </tr>
<tr>
<td>
Audio and video
</td>
<td>
MP3, MP4, MOV,
AVI, WMV, MPG, MPEG, 3GP
</td> </tr>
<tr>
<td>
**2**
</td>
<td>
**Metadata**
</td>
<td>
Subtitles, captions
</td>
<td>
SRT, VTT, QT
</td> </tr>
<tr>
<td>
Metadata
</td>
<td>
JSON, plain text
</td> </tr>
<tr>
<td>
**3**
</td>
<td>
**User data**
</td>
<td>
None
</td>
<td>
</td> </tr>
<tr>
<td>
**4**
</td>
<td>
**Exploration data**
</td>
<td>
Information sheet
</td>
<td>
DOC, DOCX, PDF
</td> </tr>
<tr>
<td>
Informed consent
</td>
<td>
DOC, DOCX, PDF
</td> </tr>
<tr>
<td>
Feedback
</td>
<td>
DOC, DOCX, XLS,
XLSX, PDF, JPG,
PNG, MP4, 3GP
</td> </tr> </table>
## Origin
We identify four different origins for the data collected/generated in ARCHES:
* **Consortium partners** : Information and digital CH assets mainly gathered by the six participating museums, i.e., MBBAA, KHM, WC, Thyssen, FLG and V&A, so as to present them to the exploration groups in Spain, Austria and the UK. This data will be obtained from their digital archives, guides, library, etc. and exploited by means of state of the art technologies – e.g. augmented reality, avatars, relief printers and models, context‐sensitive tactile audio guides and advanced image processing techniques.
ARCHES acquired new image (photographs, models, drawings) and video datasets
(e.g. when enough documentation was not available or the development of
accessible resources required additional multimedia content) that could be
used in the project. In these cases, image and video datasets were produced at
the participating museums using the most common file formats. Text files
relating to information about museum objects and images were generated in
formats such as DOC, PDF or TXT, although it may have been processed and
converted to other schemes.
* **Exploration groups** : ARCHES created new data based on the regular activities of the exploration groups prepared for them at the six ARCHES museums. It informed how the platforms, apps and activities were designed and developed with the purpose of improving their functionalities and accessibility. Qualitative and quantitative data resulting from diverse methods – e.g. interviews and discussions with participants, along with direct observation – was collected in audio, video and written formats taking the differences and difficulties of the individuals into account as done with the consent forms.
* **External sources** : Repositories of CH assets of several institutions – such as Europeana [3], DBpedia [4] and the Rijksmuseum [5] – that publicly and freely release digital content on the Internet, useful to test the functionalities of the new tools and complement the online experience. Images, videos, audio and metadata were collected through the corresponding Application Programming Interfaces (API). The list of CH institutions continuously grew in parallel with the organisation of the sessions where the exploration groups evaluate the types of contents to be included in the platform and applications according to their needs and expectations. In the final platform data collected and combined by the Museums themselves was used in order to ensure completeness and quality.
## Re‐use
As stated in the DoA and described in the previous sections, a significant
part of the research data consisted of existing data coming from museums, CH
sites and other related sources to be used for the development of technology
such as apps, websites and sensory activities (e.g. 3D reliefs). This is
clearly aligned with the expected impacts listed in the work programme under
the topic Reflective‐6‐2015 [6], where promoting “the use of digital cultural
heritage allowing its reinterpretation towards the development of a new shared
culture in Europe” and exploiting “the rich and diverse European digital
cultural heritage in a sustainable way” are two of the cornerstones.
On the one hand, ARCHES re‐used data already stored on the Central Management
Server (CMS) of each of the partner museums. For example, in case of the WC,
this is MuseumPlus and eMuseumPlus. Therefore, there was no problem in
providing specific digital resources to the technological developers once
these are selected by the exploration groups with the support of OU, UBAH as
well as the educators and experts working in the museums. This enabled the
generation of tactile images among others. In addition, some images and
complementary information was directly retrieved from the public websites, in
particular when referring to masterpieces:
* MBBAA: _http://www.museobbaa.com/en/collection/permanent‐collection/_
* KHM: _https://www.google.com/culturalinstitute/beta/partner/kunsthistorisches‐museumvienna‐museum‐of‐fine‐arts?hl=en_
* WC: _http://wallacelive.wallacecollection.org/eMuseumPlus_
* Thyssen: _https://www.museothyssen.org/coleccion_
* FLG: _http://www.flg.es/museo/la‐coleccion/bases‐de‐datos_
* V&A: _https://www.vam.ac.uk/collections_
On the other hand, different options were considered to collect and re‐use
already‐existing digital CH resources, making use of the APIs released by the
multiple institutions as pointed out in the previous section. These APIs
provide direct access to a great deal of artworks in several formats, from
images to 3D models together with videos, text and audio tracks. Europeana [3]
– MBBAA, KHM, Thyssen and FLG are Europeana contributors – the Rijksmuseum [5]
and the Finnish National Gallery [7] were explored during the initial phase.
Furthermore portals like Google Arts and Culture [8] – part of the KHM,
Thyssen, FLG and V&A collections can be found here – WikiArt (The Visual Art
Encyclopaedia) [9] or DBpedia [4] also make digitization of paintings, 3D
objects and associated metadata available to the general public.
The consortium paid special attention to Intellectual Property Rights (IPRs)
regarding data re‐use from external and internal sources. We respected the
terms and conditions described on the corresponding websites and/or the
copyright licenses under Creative Commons [10]. Similarly, neither the WC nor
the V&A will allow their digital assets to be re‐used for commercial purposes
and, specifically, the WC will not allow any re‐use of the assets by third
parties for any reason without permission. The other participating museums in
Spain and Austria allowed the sharing of the artworks that were selected by
the exploration groups.
After the takeover of the app and platform development by SIGN from TREE, the
data for the apps and platform was collected by use of a management backend,
developed as part of WP3 and stored on the server of SIGN.
## Expected size
Multiple factors were taken into account to estimate the size of the data.
Based on the types identified in section 2.2, we expect multimedia research
data to represent the highest percentage of storage needs. Format, resolution,
size, length and other variables were analysed throughout the duration of
ARCHES to ensure that the hardware resources are enough to store and preserve
data, especially when the platform and apps become public and new institutions
are sought to share their contents.
Some examples of data provided by the participating museums can be found in
Table 2. Although metadata did not require much space, images (especially
those employed to design 3D reliefs) are in high resolution for accurate
results, ranging between 5 and 100 MB each. Likewise, data related to the
generation of tactile reliefs can vary from many MB to several GB. In
addition, the consortium was analysing the reproduction of 3D models with
different materials, colours and dimensions taking advantage of existing or
new scans. Depending on the method, the size was several GB and up to TB.
**Table 2: Examples of data provided by the museums.**
<table>
<tr>
<th>
</th>
<th>
**Painting**
</th>
<th>
**3D object**
</th>
<th>
**Fabric**
</th> </tr>
<tr>
<td>
**Title**
</td>
<td>
Madame de Pompadour
</td>
<td>
Celadon dish
</td>
<td>
Strawberry thief
</td> </tr>
<tr>
<td>
**Photo**
</td>
<td>
</td>
<td>
</td>
<td>
</td> </tr>
<tr>
<td>
**Place of origin**
</td>
<td>
Paris (France)
</td>
<td>
Zheijiang (China)
</td>
<td>
London (UK)
</td> </tr>
<tr>
<td>
**Date**
</td>
<td>
1758
</td>
<td>
14 th century
</td>
<td>
1883
</td> </tr>
<tr>
<td>
**Artist/maker**
</td>
<td>
François Boucher
</td>
<td>
Unknown
</td>
<td>
William Morris / Morris & Co.
</td> </tr>
<tr>
<td>
**Materials/techniques**
</td>
<td>
Oil on canvas
</td>
<td>
Stoneware, glazed
</td>
<td>
Indigo‐discharged and block‐printed cotton
</td> </tr>
<tr>
<td>
**Credit Line**
</td>
<td>
Bequeathed by John Jones
</td>
<td>
Bequeathed by Mr Arthur Hurst
</td>
<td>
Given by Morris & Co.
</td> </tr>
<tr>
<td>
**Museum No.**
</td>
<td>
487‐1882
</td>
<td>
C.1‐1940
</td>
<td>
T.586‐1919
</td> </tr>
<tr>
<td>
**Gallery location**
</td>
<td>
Europe 1600‐1815 (Room 3)
</td>
<td>
Ceramics galleries (Room 145)
</td>
<td>
British galleries (Room 125)
</td> </tr>
<tr>
<td>
**Description**
</td>
<td>
Madame de Pompadour was the official mistress of King Louis XV. She
was also an influential
patron of the arts and a leader of taste. S
devoted supporter of
the Sèvres porcelain factory and keen
collector of Japanese
lacquer, she furnished
her residences with fine
furniture and porcelain.
[…]
</td>
<td>
Green‐glazed stonewares from
Zhejiang were the most common type of
Chinese ceramics exported to the Middle
East before 1400. This dish was thrown and carved before being given a thick
green
'celadon' glaze, which has pooled in the
incised decoration and carved fluting. […]
</td>
<td>
Morris was inspired to draw this design after finding thrushes
stealing fruit in his garden. This
complicated and
colourful pattern is
printed by the indigo
discharge method and took a long time to
produce. Consequently, it was expensive to buy.
[…]
</td> </tr>
<tr>
<td>
**Other**
</td>
<td>
</td>
<td>
Audio description available:
_http://bit.ly/2nRAfmM_
</td>
<td>
</td> </tr> </table>
Because exploration data did mainly consist of written documents as well as
photographs and videos usually taken with a smartphone, they only account for
several GB.
## Utility
Collected/generated data will be useful for different target audiences:
* **People with differences and difficulties associated with perception, memory, cognition and communication** : Co‐creation is a crucial concept in ARCHES where end users are continuously involved in the value chain from the very beginning. Multimedia research data and multisensory activities allowed them to explore new forms of interaction with CH resources through the Internet and/or at the museum. Metadata was a good support to multimedia content so as to make the available information accessible to all. Besides, in addressing the combination of state of the art technologies and digital CH assets, the exploitation and re‐use of data built bridges not only between cultures but also between the heritage communication and a historically silenced audience, traditionally sidelined by mainstream cultural and intellectual activity. Papers and handouts/prints were also used, in order to make the workshops tactile and interactive.
* **Researchers** : Social researchers took advantage of the documents to obtain feedback from the participants as well as information sheets and consent forms for people with learning difficulties and sensory impairments. It facilitated communication and paved the way to replicate the approach and methodology in other contexts, especially in the field of education. From a technological perspective, high‐resolution photographs, 3D scans and other datasets were of interest to design new functionalities and applications in the field of CH.
* **Museums** : The multimedia research data, exploration data and multisensory activities allowed museums to define more inclusive strategies in the future building on top of the ARCHES results. Moreover, the generated material may be employed for other purposes, such as marketing and preservation, attracting new audiences to the facilities. Likewise, the generated digital content may be integrated in their own websites and other networks to boost dissemination and the reuse of these surrogates.
* **General public** : Although specifically designed for the aforementioned target groups, access to multimedia data through the online platform and smartphone applications are granted to all end users on a free basis. In addition, the outputs will be available at the participating museums at least during the open days to be organised at the end of the project.
# FAIR data
This section deals with four key aspects within the DMP, i.e., findable,
accessible, interoperable and reusable data.
## Making data findable, including provisions for metadata
The collection and generation of consistent and accurate metadata
significantly contribute to the improvement of data search and the
identification of digital resources. In this particular context where CH
assets are the main concern, metadata facilitates the finding of similar
content across different online collections and repositories, such as the
external sources cited in the previous sections. In order to ensure that
interoperability among different approaches and structures, the adoption of a
common standard was intended. Therefore, the consortium analysed the schemas
used by the participating museums as well as the most relevant actors in the
CH field.
The implementation of a metadata scheme was based on the requirements of the
participating museums and the research groups in order to ensure high
usability and full accessibility.
Likewise, the consortium defined a unique identifier for each of the digital
resources to facilitate the handling of videos, images, etc. among
professionals. The methodology enabled the storage and preservation of, for
example, multiple photographs of the same artwork keeping their connection for
further re‐use, studies and cross search.
For data that requires versioning, the consortium took advantage of Version
Control Systems (VCS). For example, the source code was maintained in GIT
repositories. Furthermore, the documents were also versioned in repositories
during design and authoring.
## Making data openly accessible
The ARCHES project proposes a general strategy to make data openly accessible.
Through the platform, apps and multisensory activities the multimedia content
is openly available.
Regarding the exploration data, when it does not rise ethical and privacy
issues, the consortium benefits from the available platform Open Research
Online (ORO) [11], the OU’s repository of research publications and other
research outputs. ORO is an open access resource that can be searched and
browsed freely by the general public.
Open access to the content of the repositories is subjected to IPRs. As a
consequence, each partner carefully assessed on a case‐by‐case basis which
results – and to what extent – were made public. In particular, museum data
was retained by the project partners in accordance to the Grant Agreement (GA)
and Consortium Agreement (CA). For example, WC digital assets, such as
high‐resolution images, cannot be made openly available to third parties
without permission. On the contrary, other WC digital assets, such as
low‐resolution images and video content is already publicly available online
on different websites [12], [13] and can be used by third parties for
non‐commercial purposes. Similarly, V&A digital assets are openly available to
third parties for non‐commercial purposes. The other participating museums
decided on the strategy ‐ with respect to making data openly accessible ‐ once
the digital CH assets to be included in the solutions were selected in the
exploration sessions.
Technology developers evaluated the outputs and intermediate results that
became public since most developments contain traces of copyrighted material
(e.g. use of high‐resolution photographs) and, thus, developers do not have
the full copyright on the derivative outputs.
In the context of communication and dissemination actions, different
activities were planned to achieve the maximum impact from the outset by
taking advantage of the generated data. These are described in deliverable
D7.2 “Communication plan, activities and publications – 1 st version” and
include a dedicated website [2] – where reports about research and
technological results that are part of the public deliverables are available –
social networks, mass media, networking in different events, etc. Moreover,
the consortium encourages publication in open access journals and the adoption
of green or gold models for open access. Again, ORO was used to this end as
well as the website.
## Making data interoperable
The use of the most common and standardised formats for each of the data types
(see Table 1) is the first step to make data interoperable with other systems
and for professionals in other disciplines. Free online converters can be
found browsing the web to easily obtain a compatible file.
Regarding the vocabularies and ontologies, ARCHES proceeded as outlined in
section 3.1, i.e. analysing the schemas and methodologies used by the
participating museums and other partners with the purpose of developing a
joint strategy to ensure interoperability among their systems. The following
paragraphs describe some popular schemas and how they are connected to provide
data openly.
The Conceptual Reference Model (CRM) [14] developed by the International
Committee of Documentation (CIDOC) of the International Council of Museums
(ICOM) is a well‐known and extensively used semantic model based on earlier
standards. CIDOC‐CRM establishes relationships among implicit and explicit
concepts for CH documentation to transform isolated and inhomogeneous metadata
into a valuable and coherent global resource. CRMdig [15] has extended
CIDOC‐CRM in the framework of the 3D‐Coform project [16].
The Lightweight Information Describing Objects (LIDO) is an XML harvesting
scheme intended to act as a gateway and provider of museum object metadata to
online databases and repositories. Therefore, it does not replace CIDOC‐CRM
but builds on top of this and other data schemas. The strength of LIDO lies in
“its ability to support the full range of descriptive information about museum
objects” [17].
The Europeana Data Model (EDM) used by the Europeana network [3] aims to
guarantee the preservation of the original data from the diverse metadata
schemas, while accommodating the rich variety of community standards for
museums, archives and digital libraries. In particular, LIDO is one of the
standard intermediary schemas that can be mapped to EDM in a straightforward
manner. The EDM class hierarchy is presented in Figure 1.
**Figure 1: EDM class hierarchy [18].**
## Increase data re‐use
ARCHES is committed to the exploitation and sharing of the outcomes generated
(knowledge, technologies, data, etc.) in the framework of the activities
defined in the work plan. Free applications, public deliverables, open
publications and reports or access to research data are indicative of this
commitment. However, all the aspects related to IPR were thoroughly assessed
for each result and addressed according to the clauses of both the GA and the
CA and considering the particular interests and policies of each of the
partners. Data re‐use is not an exception.
The platform and smartphone applications can be used as they are after the end
of the project. Updates and support will depend on the decision of the museums
to invest in further content. The technical development, if viable, will
follow the plan developed in WP7.
Because multimedia research data and metadata are mostly related to the
digital CH assets owned by the six museums, the re‐use by third parties will
be subjected to their particular business strategies. Data will be licensed
following the same principles each museum apply to its digital collection
available on the Internet through its website, Google Art, Europeana and other
means all along the execution and after the completion of ARCHES. Outputs
derived from the use of digital CH assets will also be released/protected
accordingly in agreement with the technical developers (if any) involved. To
this end, access to datasets in our repositories may be conditioned to the
registration of the researchers and entities willing to take advantage of this
content. In order to obtain the necessary rights, they will be asked to get in
contect. If approved by the consortium, access will be granted.
Some designs – such as the relief printer – and tools will not be released
since the owner will apply for a patent. Making the data public and allowing
its re‐use would be a violation that would invalidate the process. Therefore,
an embargo period is foreseen in this particular situation.
The consortium jointly worked towards the achievement of the maximum quality
of the results that were tested by the exploration groups in the museums.
# Allocation of resources
The estimated costs for making data FAIR were already considered when drafting
the budget for the project before being submitted to the European Commission.
The implementation of dedicated spaces in data repositories was deemed to be
an important part of the approach as well as the access to the collected and
generated data. Consequently, the consortium does not need to make any
distinction or to consider additional resources to fulfil the expectations in
this regard.
Depending on the repository, there will be different people responsible for
the storage, access and curation of data:
* The contributions of ARCHES to the ORO repository of the OU are managed by the Research Manager Prof Jonathan Rix (OU).
* The multimedia/metadata repository for the interaction with the online platform and applications for handheld devices are managed by Christoph Aschberger (SIGN).
* Intermediate and final data to generate 3D reliefs, 3D printer design and tactile audio guide are managed by Mr Andreas Reichinger (VRVis).
# Data security
All data was kept in compliance with the “Data protection act” (1998) [19],
the “Freedom of information act” (2000) [20] and the “General Data Protection
Regulation” (GDPR) 2018 [24]. Research notes and visual records along with
interview material and transcripts will be kept in secure conditions. ARCHES
is registered with the Faculty of Data Protection Officer at OU. Therefore,
any personal information will be kept on an OU secure server.
* OU aims to keep collected datasets resulting from the participation of the exploration groups in the pilot exercises and validation separated from personal identity information. Any key linking codes to identity information such as names, addressed and telephone numbers were kept secure and separate from the dataset accessible only to the investigators. This, however, was not always acceptable to the exploration groups and the methods they chose to develop. Consequently, partners were flexible in balancing their needs for privacy with the needs for representation. This is a key aspect of the research approach being adopted. The methodology was submitted to the Open University Data Protection Officer and was recorded on 23 rd December 2016\.
* SIGN dealt with different types of data. Regarding the infrastructure for multimedia research data and metadata storage, backups will be made on a daily basis following the company’s policy. Access to these backups will were restricted to the persons working in the project. The data was stored inside the EU on servers in Frankfurt, Germany and backups in Vienna, Austria.
* Although most data generated for the multisensory activities is intended to be used only by the developers, VRVis has a reserved space on a secure data server in its facility, which only members of VRVis can access. An easily browse‐able folder hierarchy is used: Each museum and each object has its own main directory. There, VRVis collects all input data and data generated during the project. VRVis is not recording or storing any personalized data.
# Ethical aspects
The research carried out in ARCHES adheres to the “Ethical guidelines for
educational research” [21] edited by the British Educational Research
Association (BERA) and the “GDPR” (2018) [24]. It also followed the OU policy
documents “Ethical principles for research involving human participants” and
“The code of practice for research and those conducting research”. The OU
insisted that all explorations groups operate under these guidelines as well
as any local or national policies which are relevant. In fact, the research
protocol for the ARCHES project already received a favourable opinion by the
Open University Human Research Ethics Committee.
Given the possible vulnerable nature of the participants in the exploration
groups, the consortium recognised the need to be sensitive to several key
aspects when referring to data management and how participants get involved in
the pilots and validation (e.g. information sheets and information consents),
collaborate (e.g. access to data and audio visual content generation) and
provide feedback (e.g. surveys and questionnaires):
* The need to be constantly alert to the potential breaches of confidentiality between exploration group members.
* Consent as an ongoing, unfolding process, particularly in relation to people with learning difficulties. This is particularly relevant given that the project is endeavouring to give these participants a research voice.
* The need to balance our desire to gather data. Our presence as observers to practices rooted in everyday relationships elsewhere means we will engage in discussion with relevant management or services at the earliest opportunity if we have evidence for practices, which cause us concern about individual well‐being.
* Issues with privacy in relation to subsequent use of data beyond the initial confines of the exploration groups. We regard images and other audiovisual footage as the property of the individual. If subsequently we used any material, we needed to seek further specific permission.
WP8 “Ethics requirements” focused on the activities tackling ethics in
relation to the exploration groups. The university research teams (OU and
UBAH) have considerable personal expertise within the field of ethics, but
this was reinforced by the presence of an independent, international ethical
specialist on the External Expert Advisory Board (EEAB). The work package was
executed throughout the duration of the project to ensure the informed,
consensual and secure involvement of people with intellectual and sensory
impairments. In this context, the key objectives were:
* Develop a range of mechanisms to inform participants about the project and to assure we are working with their consent throughout.
* Maintain ethical standards in a manner and form appropriate to that laid out by national and institutional documents and committees.
* Operate with key participatory principles, agreed with the exploration groups that underpin research practices and relationships throughout the project.
* Ensure all participants understand the importance of rigorously applying these ethical principles.
In order to achieve these objectives, ARCHES was organised and managed in line
with principles of participation, consent, security and privacy. The main
aspects connected with the DMP are outlined in the following subsections (and
the full version can be found in the DoA).
## Principles of participation
* ARCHES is enabling the research voice of the members of the exploration groups.
* ARCHES must ensure the members of the exploration groups are active, recognised and willing participants.
* Individual members of the exploration groups will join the project on a volunteer basis and will be able to leave whenever they so wish.
* Data collection methods will also be developed in collaboration with the exploration group so they can best identify, capture and record their experiences and views.
* Diverse forms of communication must be used to engage with authentic user perspectives and the diverse forms of evidence that this produces must be valued and treated as significant markers of certainty.
* Reports will be provided twice a year to the EEAB on ethical issues, in relation to principles of participation, consent, security and privacy.
## Principles of consent
* Consent and assent is an ongoing, unfolding process, to which the research teams need to be alert at all times. It will be demonstrated by engagement as well as through verbal or signed agreement.
* Consent will be made via the communication medium in which the person is most adept (verbal and/or augmented communication), and recorded with the person’s initials (or alternative if necessary), witnessed by an advocate.
* The prospective members of the exploration groups need to meet the researchers before the project begins. Informed consent will be sought following this meeting. Agreement to participate will be viewed as provisional consent.
* Consent is provisional upon the research being conducted within the outlined framework, continuing to develop within participant expectations, and there being no adverse change in the person’s ability to give consent.
* Participants will be encouraged to share information with people they trust, who in turn will be encouraged to ask questions.
* Consent and assent materials must be accessible to people with the range of sensory and intellectual impairments, to ensure all participants are consensually involved in the project.
* Information will be given verbally, supported by sign/symbols/illustrations and repeated on more than one occasion.
* Supporters and other professionals involved must give their informed consent to participate using the agreement form, which will be completed prior to any data collection taking place.
## Principles of security
* There is a need to be constantly alert to the potential for breaches of confidentiality and trust between exploration group members, impacting upon personal and collective well‐being.
* Written records or audio recordings of consent will be maintained and updated as appropriate when new members join or if additional consents are sought.
* The UK researchers will hold appropriate Disclosure and Barring certificates.
## Principles of privacy
* Research notes and visual records along with interview material and transcripts will be kept in secure conditions.
* The project will be registered with the Open University Faculty Data Protection Officer.
* Images and other audio visual footage are property of the individual. Each individual will be informed in person of the possible use of photography and other data collection methods as part of ARCHES research sessions. If subsequently we wish to use any material, we will need to seek further specific permission.
The aspects described in section 5 will also be taken into account in this
field.
## Deliverables
* Full ethical clearance for the collection of personal data from the appropriate committee at the participating universities was acquired prior to the end of the third month of the project, involving oversight of all consent materials. This was reflected in deliverable D8.2 “POPD – Requirement No. 3”.
* A range of accessible consent material in English (see Annex A) was developed by the end of the second month of the project in deliverable D8.3 “POPD – H – Requirement No. 4”. Versions in Spanish and German will be available two months prior to the start of the research groups in Spain and Austria.
* All participants were provided with an accessible letter (see Annex A) and access to an online video – one version for British Sign Language (BSL) speakers [22] and other version for the general public – explaining the research process and offering and opt‐out option [23].
* Detailed information on the informed consent procedures implemented were provided in deliverable D8.1 “H – Requirement No. 2”.
# Conclusions
In this deliverable the strategy to deal with data collection, generation,
management and exploitation has been described.
The section dealing with the data summary presented the purpose, types and
formats, re‐use, origin, expected size and utility of the data collected and
generated in ARCHES. Different categories were defined based on the diverse
characteristics and needs of the project, involving the consortium partners as
well as external sources.
The approach to make data findable, accessible, interoperable and re‐usable
was described in the corresponding section, where the use of a common data
scheme to facilitate searches among different collections and the
implementation of a platform to favour interoperability among different
systems were commented.
No extra resources were allocated for the actions described in the DMP since
they fall within the common activities of the partners in charge of data
management. Similarly, the security measures adopted for the ARCHES project
were aligned with their own policies in this field – in particular, OU has
submitted its plan to The Open University Data Protection Officer. However,
this does not prevent from considering complementary measures if sensitive
data is handled.
Ethical aspects were monitored in the framework of WP8 “Ethics requirements”.
The information sheets and informed consents provided in Annex A were
translated into Spanish and German before the exploration groups in these
countries are built and following the same principles detailed in section 6
and applied for the first exploration group in the UK. The proposed protocol
has already received the confirmation from The Open University Ethics
Committee.
|
https://phaidra.univie.ac.at/o:1140797
|
Horizon 2020
|
1121_ARCHES_693229.md
|
# Introduction
ARCHES [2] (“Accessible Resources for Cultural Heritage Ecosystems”) is a
Horizon 2020 (H2020) project which aims to generate more inclusive
environments at museums and cultural heritage (CH) sites, so that people with
differences and difficulties associated with perception, memory, cognition and
communication can easily access and understand art. To this end, ARCHES brings
together three key aspects: a participative research methodology with the
active involvement of the previously cited target audiences organised in the
so called exploration groups; the re-use of available digital resources
provided by the partners and external sources; and the development of
innovative technologies that will be implemented and fine-tuned by the
technology partners with the feedback from the exploration groups. As a
consequence, the generation, collection, re-use and preservation of data is
deemed to be crucial for the smooth running of ARCHES and will need the
collaboration of all the partners within the consortium: Treelogic (Tree),
University of Bath (UBAH), The Open University (OU), Sign Time GmbH
(SignTime), Neumüller Moritz – ArteConTacto (MN), Centro Regional de Bellas
Artes de Asturias – Fine Arts Museum of Asturias (MBBAA), Coprix Media
(Coprix), VRVIs Zentrum für Virtual Reality and Visualization Forschungs
(VRVis), KHM-Museumsverband –
Kunsthistorisches Museum Wien (KHM), The Wallace Collection (WC), Fundación
Colección Thyssen Bornemisza – Thyssen Museum (Thyssen), Fundación Lázaro
Galdiano – Lázaro Galdiano Museum (FLG) and Victoria & Albert Museum (V&A).
This document, the first version of the ARCHES Data Management Plan, describes
the research data that will be generated and collected during the project, as
well as the data (i.e. the partner museums digital assets) that will be used
throughout the project. The strategy to make data FAIR (findable, accessible,
interoperable and re-usable) is commented in the following sections.
Nevertheless it is worth noting that updates and more details will be added as
ARCHES progresses so as to provide the guidelines and strategies to follow
beyond the completion for exploiting data collected and generated within
ARCHES. A final version will be released in month 36 of the project.
# Data summary
## Purpose
The purpose of the data collection/generation is the support of the ARCHES
technical developments and complementary activities to validate the proposed
solutions in real environments and organise demonstration activities open to
everyone according to the objectives and guidelines defined in the document
Description of Action (DoA). In particular, data collection and generation are
crucial to achieve the following specific objectives:
* To develop and evaluate strategies which enable an exploration of the value, form and function of mainstream technologies by and for people with differences and difficulties associated with perception, memory, cognition and communication.
* To develop and evaluate the use of mainstream technologies to enable the inclusion of people with such disabilities as museums visitors and consumers of art.
* To identify sources – Internet, internal archives, libraries, etc. – that can provide digital cultural resources and take advantage of their possibilities to integrate this content into innovative tools, applications and functionalities.
* To validate the technological outcomes in operational environments based on a participatory research methodology consisting of three pilot exercises in museums.
* To promote the tools and applications developed in ARCHES by means of on-site demonstration activities all around Europe.
Likewise, data collection/generation is in close connection with the tasks
scheduled in the work plan, especially in the context of the technical work
packages – i.e., WP3 “Development of an accessible software platform”, WP4
“Development of applications for handheld devices” and WP5 “On-site
multisensory activities” – paving the way for the system validation and pilot
exercises in WP6. Therefore, the DMP will be a living document where new
datasets may be incorporated at any time all along the lifespan of the project
following the principles of FAIR data.
## Types and formats
ARCHES will handle different types and formats of data according to the
multiple research areas and technical developments. In principle, these are
grouped into four categories:
* **Multimedia research data** : A combination of sound, images, video and text components to represent the works of art in different ways and support the integration of digital surrogates into the web and mobile applications as well as the generation of complementary resources and tools. Photographs, 3D models, sketches, interactive guides, avatars and serious games are also considered within this category.
* **Metadata** : Data associated to the multimedia content used to better describe and understand each of the works of art. This includes information about the author, style, period and other details regarding the history and characteristics of the paintings, sculptures and other CH assets. Subtitles are other form of metadata that will be generated together with the videos to become accessible to people with hearing difficulties.
* **User data** : Credentials to access the platform and applications to interact with the community as well as user preferences to personalise how the content is presented. These preferences will not be based
on the identification of impairments but on the selection of the desired
functionalities from a wide range of options, avoiding the management of
potential sensitive information.
* **Exploration data** : Data generated for/by the exploration groups regarding their involvement in the project, from the recruitment phase in the UK, Spain and Austria to the interaction with the research teams to provide feedback on the methodology, activities and technologies. Therefore, information sheets, consent forms and multiple materials to assess the outcomes and allow expressing opinions and feelings – e.g. surveys, questionnaires, interviews, etc. – will be generated, stored and preserved under this category.
The above information is summarised in Table 1 with the most common formats
employed by the partners when managing these datasets. We will decide the ones
that best fit the depending on the input sources and the applications. This
will also be aligned with the definition of system requirements and
architecture in the deliverables D3.1 and D4.1.
**Table 1: Types and formats of data generated/collected.**
<table>
<tr>
<th>
</th>
<th>
**Type of data**
</th>
<th>
**Description**
</th>
<th>
**Formats**
</th> </tr>
<tr>
<td>
**1**
</td>
<td>
**Multimedia research data**
</td>
<td>
Image
</td>
<td>
JPG, PNG, TIF, GIF, BMP
</td> </tr>
<tr>
<td>
High-resolution photograph
</td>
<td>
TIF, PNG, RAW
</td> </tr>
<tr>
<td>
3D model
</td>
<td>
PLY, DXF,
COLLADA, STL,
OBJ, PDF, U3D, FBX
</td> </tr>
<tr>
<td>
Audio and video
</td>
<td>
MP3, MP4, MOV,
AVI, WMV, MPG, MPEG, 3GP
</td> </tr>
<tr>
<td>
**2**
</td>
<td>
**Metadata**
</td>
<td>
Subtitles, captions
</td>
<td>
SRT, VTT, QT
</td> </tr>
<tr>
<td>
Metadata
</td>
<td>
JSON, plain text
</td> </tr>
<tr>
<td>
**3**
</td>
<td>
**User data**
</td>
<td>
Credentials
</td>
<td>
TBD
</td> </tr>
<tr>
<td>
Preferences
</td>
<td>
TBD
</td> </tr>
<tr>
<td>
**4**
</td>
<td>
**Exploration data**
</td>
<td>
Information sheet
</td>
<td>
DOC, DOCX, PDF
</td> </tr>
<tr>
<td>
Informed consent
</td>
<td>
DOC, DOCX, PDF
</td> </tr>
<tr>
<td>
Feedback
</td>
<td>
DOC, DOCX, XLS,
XLSX, PDF, JPG,
PNG, MP4, 3GP
</td> </tr> </table>
## Origin
We identify four different origins for the data collected/generated in ARCHES:
* **Consortium partners** : Information and digital CH assets mainly gathered by the six participating museums, i.e., MBBAA, KHM, WC, Thyssen, FLG and V&A, so as to present them to the exploration groups in Spain, Austria and the UK. This data will be obtained from their digital archives, guides, library, etc. and exploited by means of state of the art technologies – e.g. augmented reality, avatars, relief printers and models, context-sensitive tactile audio guides and advanced image processing techniques.
ARCHES may also acquire new image (photographs, models, drawings) and video
datasets (e.g. when enough documentation is not available or the development
of accessible resources require additional multimedia content) that can be
used in the project. In these cases, image and video datasets will be produced
at the participating museums using the most common file formats. Text files
relating to information about museum objects and images will be generated in
formats such as DOC, PDF or TXT, although it may be processed and converted to
other schemes.
* **Exploration groups** : ARCHES will create new data based on the weekly activity of the exploration groups involved in the activities prepared for them at the six ARCHES museums. It will inform how the platforms, apps and activities are designed and developed with the purpose of improving their functionalities and accessibility. Qualitative and quantitative data resulting from diverse methods – e.g. interviews and discussions with participants, along with direct observation – will be collected in audio, video and written formats taking the differences and difficulties of the individuals into account as done with the consent forms.
* **Users** : In order to register and log in to the platform, they will provide a name and valid email address. The system will store the options they select for the customised presentation and visualisation to enable the corresponding functionalities.
* **External sources** : Repositories of CH assets of several institutions – such as Europeana [3], DBpedia [4] and the Rijksmuseum [5] – that publicly and freely release digital content on the Internet, useful to test the functionalities of the new tools and complement the online experience. Images, videos, audio and metadata will be collected through the corresponding Application Programming Interfaces (API). The list of CH institutions will continuously grow in parallel with the organisation of the sessions where the exploration groups evaluate the types of contents to be included in the platform and applications according to their needs and expectations.
## Re-use
As stated in the DoA and described in the previous sections, a significant
part of the research data will consist of existing data coming from museums,
CH sites and other related sources to be used for the development of
technology such as apps, websites and sensory activities (e.g. 3D reliefs).
This is clearly aligned with the expected impacts listed in the work programme
under the topic Reflective-6-2015 [6], where promoting “the use of digital
cultural heritage allowing its reinterpretation towards the development of a
new shared culture in Europe” and exploiting “the rich and diverse European
digital cultural heritage in a sustainable way” are two of the cornerstones.
On the one hand, ARCHES will re-use data already stored on the Central
Management Server (CMS) of each of the partner museums. For example, in case
of the WC, this is MuseumPlus and eMuseumPlus. Therefore, there will be no
problem in providing specific digital resources to the technological
developers once these are selected by the exploration groups with the support
of OU, UBAH as well as the educators and experts working in the museums. This
will enable the generation of tactile images among others. In addition, some
images and complementary information may be directly retrieved from the public
websites, in particular when referring to masterpieces:
* MBBAA: _http://www.museobbaa.com/en/collection/permanent-collection/_
* KHM: _https://www.google.com/culturalinstitute/beta/partner/kunsthistorisches-museum-viennamuseum-of-fine-arts?hl=en_
* WC: _http://wallacelive.wallacecollection.org/eMuseumPlus_
* Thyssen: _https://www.google.com/culturalinstitute/beta/u/0/partner/museo-thyssen-bornemisza_
_?hl=en_
* FLG: _http://www.flg.es/museo/la-coleccion/bases-de-datos_
* V&A: _https://www.vam.ac.uk/collections_
On the other hand, different options will be considered to collect and re-use
already-existing digital CH resources, making use of the APIs released by the
multiple institutions as pointed out in the previous section. These APIs
provide direct access to a great deal of artworks in several formats, from
images to 3D models together with videos, text and audio tracks. Europeana [3]
– MBBAA, KHM, Thyssen and FLG are Europeana contributors – the Rijksmuseum [5]
and the Finnish National Gallery [7] will be explored during the initial
phase. Furthermore portals like Google Arts and Culture [8] – part of the KHM,
Thyssen, FLG and V&A collections can be found here – WikiArt (The Visual Art
Encyclopaedia) [9] or DBpedia [4] also make digitisations of paintings, 3D
objects and associated metadata available to the general public.
The consortium will pay special attention to Intellectual Property Rights
(IPRs) regarding data re-use from external and internal sources. We will
respect the terms and conditions described on the corresponding websites
and/or the copyright licenses under Creative Commons [10]. Similarly, neither
the WC nor the V&A will allow their digital assets to be re-used for
commercial purposes and, specifically, the WC will not allow any re-use of the
assets by third parties for any reason without permission. The other
participating museums in Spain and Austria will take a decision on how the
digital content is shared once the artworks are selected by the exploration
groups.
## Expected size
Multiple factors should be taken into account to estimate the size of the
data. Based on the types identified in section 2.2, we expect multimedia
research data to represent the highest percentage of storage needs. Format,
resolution, size, length and other variables will be analysed throughout the
duration of ARCHES to ensure that the hardware resources are enough to store
and preserve data, especially when the platform and apps become public and new
institutions are sought to share their contents.
Some examples of data provided by the participating museums can be found in
Table 2. Although metadata will not require much space, images (especially
those employed to design 3D reliefs) have to be in high resolution for
accurate results, ranging between 5 and 100 MB each. Likewise, data related to
the generation of tactile reliefs can vary from many MB to several GB. In
addition, the consortium is currently analysing the reproduction of 3D models
with different materials, colours and dimensions taking advantage of existing
or new scans. Depending on the method, the size could be several GB and up to
TB.
**Table 2: Examples of data provided by the museums.**
<table>
<tr>
<th>
</th>
<th>
**Painting**
</th>
<th>
**3D object**
</th>
<th>
**Fabric**
</th> </tr>
<tr>
<td>
**Title**
</td>
<td>
Madame de Pompadour
</td>
<td>
Celadon dish
</td>
<td>
Strawberry thief
</td> </tr>
<tr>
<td>
**Photo**
</td>
<td>
</td>
<td>
</td>
<td>
</td> </tr>
<tr>
<td>
**Place of origin**
</td>
<td>
Paris (France)
</td>
<td>
Zheijiang (China)
</td>
<td>
London (UK)
</td> </tr>
<tr>
<td>
**Date**
</td>
<td>
1758
</td>
<td>
14 th century
</td>
<td>
1883
</td> </tr>
<tr>
<td>
**Artist/maker**
</td>
<td>
François Boucher
</td>
<td>
Unknown
</td>
<td>
William Morris / Morris & Co.
</td> </tr>
<tr>
<td>
</td>
<td>
**Painting**
</td>
<td>
**3D object**
</td>
<td>
**Fabric**
</td> </tr>
<tr>
<td>
**Materials/techniques**
</td>
<td>
Oil on canvas
</td>
<td>
Stoneware, glazed
</td>
<td>
Indigo-discharged and block-printed cotton
</td> </tr>
<tr>
<td>
**Credit Line**
</td>
<td>
Bequeathed by John Jones
</td>
<td>
Bequeathed by Mr Arthur Hurst
</td>
<td>
Given by Morris & Co.
</td> </tr>
<tr>
<td>
**Museum No.**
</td>
<td>
487-1882
</td>
<td>
C.1-1940
</td>
<td>
T.586-1919
</td> </tr>
<tr>
<td>
**Gallery location**
</td>
<td>
Europe 1600-1815 (Room 3)
</td>
<td>
Ceramics galleries (Room 145)
</td>
<td>
British galleries (Room 125)
</td> </tr>
<tr>
<td>
**Description**
</td>
<td>
Madame de Pompadour was the official mistress of King Louis XV. She
was also an influential
patron of the arts and a leader of taste. S
devoted supporter of
the Sèvres porcelain factory and keen
collector of Japanese
lacquer, she furnished
her residences with fine
furniture and porcelain. […]
</td>
<td>
Green-glazed stonewares from
Zhejiang were the most common type of
Chinese ceramics exported to the Middle
East before 1400. This dish was thrown and carved before being given a thick
green
'celadon' glaze, which has pooled in the
incised decoration and carved fluting. […]
</td>
<td>
Morris was inspired to draw this design after
finding thrushes stealing
fruit in his garden. This complicated and
colourful pattern is
printed by the indigo
discharge method and took a long time to
produce. Consequently,
it was expensive to buy. […]
</td> </tr>
<tr>
<td>
**Other**
</td>
<td>
</td>
<td>
Audio description available:
_http://bit.ly/2nRAfmM_
</td>
<td>
</td> </tr> </table>
User data should not be a problem in terms of size, since it will be
restricted to contact details, username and password as well as the
configuration options.
Because exploration data will mainly consist of written documents as well as
photographs and videos usually taken with a smartphone, they will all account
for several GB.
## Utility
Collected/generated data will be useful for different target audiences:
* **People with differences and difficulties associated with perception, memory, cognition and communication** : Co-creation is a crucial concept in ARCHES where end users are continuously involved in the value chain from the very beginning. Multimedia research data will allow them to explore new forms of interaction with CH resources through the Internet or at the museum. Metadata will be a good support to multimedia content so as to make the available information accessible to all. Besides, in addressing the combination of state of the art technologies and digital CH assets, the exploitation and re-use of data will build bridges not only between cultures but also between the heritage communication and a historically silenced audience, traditionally sidelined by mainstream cultural and intellectual activity.
* **Researchers** : Social researchers may take advantage of the documents to obtain feedback from the participants as well as information sheets and consent forms for people with learning difficulties and sensory impairments. It will facilitate the communication and pave the way to replicate the approach and methodology in other contexts, especially in the field of education. From a technological perspective, high-resolution photographs, 3D scans and other datasets may be of interest to design new functionalities and applications in the field of CH.
* **Museums** : Both multimedia research data and exploration data will allow museums to define more inclusive strategies in the future building on top of the ARCHES results. Moreover, the generated material may be employed for other purposes, such as marketing and preservation, attracting new audiences to the facilities. Likewise, the generated digital content may be integrated in their own websites and other networks to boost dissemination and the re-use of these surrogates.
* **General public** : Although specifically designed for the aforementioned target groups, access to multimedia data through the online platform and smartphone applications will be granted to all on a free basis. In addition, the outputs will be available at the participating museums at least during the open days to be organised at the end of the project.
# FAIR data
This section deals with four key aspects within the DMP, i.e., findable,
accessible, interoperable and re-usable data.
## Making data findable, including provisions for metadata
The collection and generation of consistent and accurate metadata
significantly contribute to the improvement of data search and the
identification of digital resources. In this particular context where CH
assets are the main concern, metadata will also facilitate the finding of
similar content across different online collections and repositories, such as
the external sources cited in the previous sections. In order to ensure that
interoperability among different approaches and structures, the adoption of a
common standard is recommended. Therefore, the consortium will analyse the
schemas used by the participating museums as well as the most relevant actors
in the CH field.
The implementation of a metadata scheme will be exploited for the search
functionality in the online platform and applications for handheld devices to
allow the user to find specific CH assets by date, data, author, title,
origin, type of artwork, etc. Keyword-based search will also be developed to
detect specific words and terms free-text fields, such as description, history
and others.
Likewise, the consortium will define a unique identifier for each of the
digital resources to facilitate the handling of videos, images, etc. among
professionals. The methodology will enable the storage and preservation of,
for example, multiple photographs of the same artwork keeping their connection
for further re-use, studies and cross search.
For data that requires versioning, the consortium will take advantage of
Version Control Systems (VCS). For example, source code will be maintained in
GIT and SVN repositories; executable object code can be versioned on
MyGet.org. Furthermore, the documents can also be versioned in repositories
during design and authoring.
## Making data openly accessible
The ARCHES project proposes a general strategy in which different alternatives
will be developed to make data openly accessible. Aside from the envisioned
platform, apps and multisensory activities where multimedia content will be
available for specific actions, different repositories will contain the four
different types of data generated/collected by the partners.
When referring to the final version of multimedia research data and associated
metadata, a dedicated repository managed by the coordinator (Tree) will be
used to deposit this information. This has already been considered in the
system architecture as pointed out in the deliverable D3.1 “Report on system
architecture definition”. A REST-API will facilitate the sharing of the stored
digital resources in an easy way by means of an HTTP browser that will call
API methods.
User data will not be accessible for people outside the consortium for privacy
and security reasons. Even within the consortium, only the person in charge of
the technical development of the platform at Tree will be allowed to handle
this data. Passwords will be hashed.
Regarding the exploration data, when it does not rise ethical and privacy
issues, the consortium will benefit from the available platform Open Research
Online (ORO) [11], the OU’s repository of research publications and other
research outputs. ORO is an open access resource that can be searched and
browsed freely by the general public.
Open access to the content of the repositories will be subjected to IPRs. As a
consequence, each partner will carefully assess on a case-by-case basis which
results – and to what extent – can be made public. In particular, museum data
may be retained by the project partners in accordance to the Grant Agreement
(GA) and Consortium Agreement (CA). For example, WC digital assets, such as
high-resolution images, cannot be made openly available to third parties
without permission. On the contrary, other WC digital assets, such as
lowresolution images and video content is already (and will be) publically
available online on different websites [12], [13] and can be used by third
parties for non-commercial purposes. Similarly, V&A digital assets are openly
available to third parties for non-commercial purposes. The other
participating museums will decide on the strategy to adopt with respect to
making data openly accessible once the digital CH assets to be included in the
solution are selected in the exploration sessions planned for the end of 2017
and 2018.
Technology developers will also evaluate the outputs and intermediate results
that can become public since most developments may contain traces of
copyrighted material (e.g. use of high-resolution photographs) and, thus,
developers might not have the full copyright on the derivative outputs.
In the context of communication and dissemination actions, different
activities are planned to achieve the maximum impact from the outset by taking
advantage of the generated data. These are described in deliverable D7.2
“Communication plan, activities and publications – 1 st version” and include
a dedicated website [2] – where reports about research and technological
results that are part of the public deliverables will be available – social
networks, mass media, networking in different events, etc. Moreover, the
consortium will encourage publication in open access journals and the adoption
of green or gold models for open access. Again, ORO may be used to this end as
well as the website.
## Making data interoperable
The use of the most common and standardised formats for each of the data types
(see Table 1) is the first step to make data interoperable with other systems
and for professionals in other disciplines. Free online converters can be
found browsing the web to easily obtain a compatible file.
Regarding the vocabularies and ontologies, ARCHES will proceed as outlined in
section 3.1, i.e. analysing the schemas and methodologies used by the
participating museums and other partners with the purpose of developing a
joint strategy to ensure interoperability among their systems. The following
paragraphs describe some popular schemas and how they are connected to provide
data to the Europeana network [3] (the other way around is possible too). This
is an initial suggestion that should be studied in depth by the consortium.
The Conceptual Reference Model (CRM) [14] developed by the International
Committee of Documentation (CIDOC) of the International Council of Museums
(ICOM) is a well-known and extensively used semantic model based on earlier
standards. CIDOC-CRM establishes relationships among implicit and explicit
concepts for CH documentation to transform isolated and inhomogeneous metadata
into a valuable and coherent global resource. CRMdig [15] has extended CIDOC-
CRM in the framework of the 3D-Coform project [16].
The Lightweight Information Describing Objects (LIDO) is an XML harvesting
scheme intended to act as a gateway and provider of museum object metadata to
online databases and repositories. Therefore, it does not replace CIDOC-CRM
but builds on top of this and other data schemas. The strength of LIDO lies in
“its ability to support the full range of descriptive information about museum
objects” [17].
The Europeana Data Model (EDM) used by the Europeana network [3] aims to
guarantee the preservation of the original data from the diverse metadata
schemas, while accommodating the rich variety of community standards for
museums, archives and digital libraries. In particular, LIDO is one of the
standard intermediary schemas that can be mapped to EDM in a straightforward
manner. The EDM class hierarchy is presented in Figure 1.
**Figure 1: EDM class hierarchy [18].**
## Increase data re-use
ARCHES is committed to the exploitation and sharing of the outcomes generated
(knowledge, technologies, data, etc.) in the framework of the activities
defined in the work plan. Free applications, public deliverables, open
publications and reports or access to research data are indicative of this
commitment. However, all the aspects related to IPR should be thoroughly
assessed for each result and addressed according to the clauses of both the GA
and the CA and considering the particular interests and policies of each of
the partners. Data re-use is not an exception.
The platform and smartphone applications will be kept updated and running for
a period of at least two years after the completion of the project, allowing
the consortium to improve the functionalities, add new content and initiate
the commercial exploitation in accordance with the business plan developed in
WP7. During this period, the access to the repositories will be supported as
usual, so that third parties will be able to re-use data following the
conditions defined and agreed by the partners.
Because multimedia research data and metadata are mostly related to the
digital CH assets owned by the six museums, the re-use by third parties will
be subjected to their particular business strategies. Data will be licensed
following the same principles each museum apply to its digital collection
available on the Internet through its website, Google Art, Europeana and other
means all along the execution and after the completion of ARCHES. Outputs
derived from the use of digital CH assets will also be released/protected
accordingly in agreement with the technical developers (if any) involved. To
this end, access to datasets in our repositories may be conditioned to the
registration of the researchers and entities willing to take advantage of this
content. In order to obtain the necessary credentials, they will be asked to
provide a valid e-mail and additional contact details as well as some relevant
information concerning the exploitation actions to be carried out. If approved
by the consortium, a username and password (or alternatively an API key) will
be generated.
Some designs – such as the relief printer – and tools will not be released
since the owner will apply for a patent. Making the data public and allowing
its re-use would be a violation that would invalidate the process. Therefore,
an embargo period is foreseen in this particular situation.
The consortium will jointly work towards the achievement of the maximum
quality of the results that will be tested by the exploration groups in the
museums.
# Allocation of resources
The estimated costs for making data FAIR were already considered when drafting
the budget for the project before being submitted to the European Commission.
The implementation of dedicated spaces in data repositories was deemed to be
an important part of the approach as well as the access to the collected and
generated data. Consequently, the consortium does not need to make any
distinction or to consider additional resources to fulfil the expectations in
this regard.
Depending on the repository, there will be different people responsible for
the storage, access and curation of data:
* The contributions of ARCHES to the ORO repository of the OU will be managed by the Research Manager Prof Jonathan Rix (OU).
* The multimedia/metadata repository for the interaction with the online platform and applications for handheld devices will be managed by the Technical Manager Ms Ana Belén Rodríguez (Tree).
* Intermediate and final data to generate 3D reliefs, 3D printer design and tactile audio guide will be managed by Mr Andreas Reichinger (VRVis).
Costs associated to the long term preservation of data generated in the
project will be assumed by the aforementioned partners as part of their daily
operating costs.
# Data security
All data will be kept in compliance with the “Data protection act” (1998) [19]
and the “Freedom of information act” (2000) [20]. Research notes and visual
records along with interview material and transcripts will be kept in secure
conditions. ARCHES is registered with the Faculty of Data Protection Officer
at OU. Therefore, any personal information will be kept on an OU secure
server.
* OU will aim to keep collected datasets resulting from the participation of the exploration groups in the pilot exercises and validation separated from personal identity information. Any key linking codes to identity information such as names, addressed and telephone numbers would then be kept secure and separate from the dataset accessible only to the investigators. This, however, may not be acceptable to the exploration groups and the methods they choose to develop. Consequently, partners will be flexible in balancing their needs for privacy with the needs for representation. This is a key aspect of the research approach being adopted. The methodology was submitted to the Open University Data Protection Officer and was recorded on 23 rd December 2016.
* Tree will deal with different types of data. In particular, passwords to log in to the user’s profile will be hashed so that nobody (except for the user) can see this information. Regarding the infrastructure for multimedia research data and metadata storage, backups will be made on a daily basis following the company’s policy. Access to these backups will be restricted to the persons working in the project.
* Although most data generated for the multisensory activities will be intended to be used only by the developers, VRVis has a reserved space on a secure data server in its facility, which only members of VRVis can access. An easily browse-able folder hierarchy is used: Each museum and each object has its own main directory. There, VRVis collects all input data and data generated during the project.
# Ethical aspects
The research carried out in ARCHES adheres to the “Ethical guidelines for
educational research” [21] edited by the British Educational Research
Association (BERA) and the “Data protection act” (1998) [19]. It will also
follow the OU policy documents “Ethical principles for research involving
human participants” and “The code of practice for research and those
conducting research”. The OU will insist that all explorations groups operate
under these guidelines as well as any local or national policies which are
relevant. In fact, the research protocol for the ARCHES project already
received a favourable opinion by the Open University Human Research Ethics
Committee.
Given the possible vulnerable nature of the participants in the exploration
groups, the consortium recognises the need to be sensitive to several key
aspects when referring to data management and how participants get involved in
the pilots and validation (e.g. information sheets and information consents),
collaborate (e.g.
access to data and audio visual content generation) and provide feedback (e.g.
surveys and questionnaires):
* The need to be constantly alert to the potential breaches of confidentiality between exploration group members.
* Consent as an ongoing, unfolding process, particularly in relation to people with learning difficulties. This is particularly relevant given that the project is endeavouring to give these participants a research voice.
* The need to balance our desire to gather data. Our presence as observers to practices rooted in everyday relationships elsewhere means we will engage in discussion with relevant management or services at the earliest opportunity if we evidence practices which cause us concern about individual well-being.
* Issues with privacy in relation to subsequent use of data beyond the initial confines of the exploration groups. We regard images and other audio visual footage to be the property of the individual. If subsequently we wish to use any material, we will need to seek further specific permission.
WP8 “Ethics requirements” focuses on the activities tackling ethics in
relation to the exploration groups. The university research teams (OU and
UBAH) have considerable personal expertise within the field of ethics, but
this will be reinforced by the presence of an independent, international
ethical specialist on the External Expert Advisory Board (EEAB). The work
package will be executed throughout the duration of the project to ensure the
informed, consensual and secure involvement of people with intellectual and
sensory impairments. In this context, the key objectives will be to:
* Develop a range of mechanisms to inform participants about the project and to assure we are working with their consent throughout.
* Maintain ethical standards in a manner and form appropriate to that laid out by national and institutional documents and committees.
* Operate with key participatory principles, agreed with the exploration groups that will underpin research practices and relationships throughout the project.
* Ensure all participants understand the importance of rigorously applying these ethical principles.
In order to achieve these objectives, ARCHES will be organised and managed in
line with principles of participation, consent, security and privacy. The main
aspects connected with the DMP are outlined in the following subsections (and
the full version can be found in the DoA).
## Principles of participation
* ARCHES is enabling the research voice of the members of the exploration groups.
* ARCHES must ensure the members of the exploration groups are active, recognised and willing participants.
* Individual members of the exploration groups will join the project on a volunteer basis and will be able to leave whenever they so wish.
* Data collection methods will also be developed in collaboration with the exploration group so they can best identify, capture and record their experiences and views.
* Diverse forms of communication must be used to engage with authentic user perspectives and the diverse forms of evidence that this produces must be valued and treated as significant markers of certainty.
* Reports will be provided twice a year to the EEAB on ethical issues, in relation to principles of participation, consent, security and privacy.
## Principles of consent
* Consent and assent is an ongoing, unfolding process, to which the research teams need to be alert at all times. It will be demonstrated by engagement as well as through verbal or signed agreement.
* Consent will be made via the communication medium in which the person is most adept (verbal and/or augmented communication), and recorded with the person’s initials (or alternative if necessary), witnessed by an advocate.
* The prospective members of the exploration groups need to meet the researchers before the project begins. Informed consent will be sought following this meeting. Agreement to participate will be viewed as provisional consent.
* Consent is provisional upon the research being conducted within the outlined framework, continuing to develop within participant expectations, and there being no adverse change in the person’s ability to give consent.
* Participants will be encouraged to share information with people they trust, who in turn will be encouraged to ask questions.
* Consent and assent materials must be accessible to people with the range of sensory and intellectual impairments, to ensure all participants are consensually involved in the project.
* Information will be given verbally, supported by sign/symbols/illustrations and repeated on more than one occasion.
* Supporters and other professionals involved must give their informed consent to participate using the agreement form, which will be completed prior to any data collection taking place.
## Principles of security
* There is a need to be constantly alert to the potential for breaches of confidentiality and trust between exploration group members, impacting upon personal and collective well-being.
* Written records or audio recordings of consent will be maintained and updated as appropriate when new members join or if additional consents are sought.
* The UK researchers will hold appropriate Disclosure and Barring certificates.
## Principles of privacy
* Research notes and visual records along with interview material and transcripts will be kept in secure conditions.
* The project will be registered with the Open University Faculty Data Protection Officer.
* Images and other audio visual footage are property of the individual. Each individual will be informed in person of the possible use of photography and other data collection methods as part of ARCHES research sessions. If subsequently we wish to use any material, we will need to seek further specific permission.
The aspects described in section 5 will also be taken into account in this
field.
## Deliverables
* Full ethical clearance for the collection of personal data from the appropriate committee at the participating universities was acquired prior to the end of the third month of the project, involving oversight of all consent materials. This was reflected in deliverable D8.2 “POPD – Requirement No. 3”.
* A range of accessible consent material in English (see Annex A) was developed by the end of the second month of the project in deliverable D8.3 “POPD – H – Requirement No. 4”. Versions in Spanish and German will be available two months prior to the start of the research groups in Spain and Austria.
* All participants are being provided with an accessible letter (see Annex A) and access to an online video – one version for British Sign Language (BSL) speakers [22] and other version for the general public – explaining the research process and offering and opt-out option [23].
* Detailed information on the informed consent procedures implemented were provided in deliverable D8.1 “H – Requirement No. 2”.
# Conclusions
In this deliverable the initial strategy to deal with data collection,
generation, management and exploitation has been described. More information
will continuously be included by the consortium partners until the final
version is released in September 2019. This will allow us to improve and fine
tune the preliminary actions (including the identification of datasets) as
well as implement new measures if necessary.
The section dealing with the data summary presented the purpose, types and
formats, re-use, origin, expected size and utility of the data collected and
generated in ARCHES. Different categories were defined based on the diverse
characteristics and needs of the project, involving the consortium partners as
well as external sources.
The approach to make data findable, accessible, interoperable and re-usable
was drafted in the corresponding section, where the use of a common data
scheme to facilitate searches among different collections and the
implementation of APIs to favour interoperability among different systems were
commented. In addition, it was clearly stated that the re-use of existing
digital CH assets fell within the objectives of the project.
No extra resources will be allocated for the actions described in the DMP
since they fall within the common activities of the partners in charge of data
management. Similarly, the security measures adopted for the ARCHES project
will be aligned with their own policies in this field – in particular, OU has
submitted its plan to The Open University Data Protection Officer. However,
this does not prevent from considering complementary measures if sensitive
data is handled.
Ethical aspects will be monitored in the framework of WP8 “Ethics
requirements”. The information sheets and informed consents provided in Annex
A will be translated into Spanish and German before the exploration groups in
these countries are built and following the same principles detailed in
section 6 and applied for the first exploration group in the UK. The proposed
protocol has already received the confirmation from The Open University Ethics
Committee.
|
https://phaidra.univie.ac.at/o:1140797
|
Horizon 2020
|
1123_WeGovNow_693514.md
|
1 Introduction 5
2 Data summary 5
2.1 Administrative information 5
2.2 Purpose of data collation 5
2.3 Type and format of data collated 6
2.4 Re-use of existing data 7
2.5 Expected size of data 7
2.6 Expected data utility 7
3 Preserving access to quantitative WeGovNow platform evaluation data 8
3.1 Making quantitative WeGovNow platform evaluation data findable 8
3.2 Making quantitative WeGovNow platform evaluation data openly accessible 9
3.3 Making quantitative WeGovNow platform data interoperable 9
3.4 Increase re-use of quantitative WeGovNow platform evaluation data 10
4 Allocation of resources 10
5 Data security 10
6 Ethical aspects 11
7 Preserving access to publications 12
# Introduction
According to available guidance, H2020 projects are to provide a first version
of the Data Management Plan (DMP) within the first six months of the project
1 . The initial DMP should be updated during the project lifetime. The
present document represents a final update of the initial version of the DMP.
The DMP presented throughout this document starts with a summary of the data
collated in the framework of the WeGovNow pilot evaluation (Chapter 2). This
is followed by a description of how access quantitative data collated for the
purposes of formative pilot platform evaluation will be preserved (Chapter 3).
Next, the resources allocated to this are assessed (Chapter 4) and measures
for ensuring data security are described (Chapter 5). This is followed by a
description how ethical aspects and data privacy according to GDPR has been
addressed (Chapter 6). Finally, it is described how access to publications is
preserved (Chapter 7).
# Data summary
## Administrative information
Grant Agreement No.: 693514
Acronym: WeGovNow
DPM version: 2.3
Planned update: n. a.
DMP responsible: Lutz Kubitschke
## Purpose of data collation
The WeGovNow project is to develop a new type of online platform for citizen
engagement, thereby integrating existing software applications and newly
developed ones in accordance with a multi-staged development approach (Figure
1).
_Figure 1 – Multi-staged development approach of the WeGovNow online platform_
Phase I Phase II Phase III Phase IV
**Basic Iterative prototype Platform piloting**
**Platform architecture**
### component refinement & under day to day development integration extension
conditions
The WeGovNow platform will has been piloted in three municipalities from
project month 25 onwards. During the pilot evaluation phase data have been
gathered in relation to different evaluation objectives, namely whether
1. the WeGovNow pilot system works as anticipated (viability perspective)
2. the WeGovNow pilot system is worth being maintained after the ending of the pilot duration (sustainability perspective)
Outcomes have been feed into the development of guidance on the further
mainstreaming of the WeGovNow pilot platform after the ending of the pilot
project duration, including recommendations on further exploitation of the
WeGovNow pilot platform as a major project output.
## Type and format of data collated
Different types of evaluation data have been gathered throughout the project’s
pilot phase, including quantitative data:
* _Quantitative evaluation data generated by means of automated monitoring of_ _platform usage:_
The WeGovNow online platform developed includes different functional software
components which enable the pilot users to post and manipulate different types
of content. This is reflected by the case-based data set including the
following variables:
* User account created
* User account automatically validated o User account manual validation requested o User account manually validated o Date of birth of registered user account holder o Sex of registered user account holder
* No. of original contributions posted through the WeGovNow First Life platform component
* No. of comments posted through the WeGovNow First Life platform component
* No. of original contributions posted through the WeGovNow Community
Maps component o No. of original contributions updated through the WeGovNow
Community
Maps component o No. of comments posted through the WeGovNow Community Maps
component
* No. of objects created through the WeGovNow First Life component o No. of objects updated through the WeGovNow First Life component o No. of objects deleted through the WeGovNow First Life component o No. of votes casted d through the WeGovNow LiquidFeedback component
These data have been processed in a common spread sheet processing programme
format (Microsoft Excel).
* _Qualitative evaluation data gathered by means of different data gathering methods:_ Semi-structured interviews were conducted with a number of selected stakeholders at the pilot sites for the purposes of the viability assessment, focussing on identifying potential impacts of the pilot service on the various stakeholder groups involved. Outcomes were documented in terms of a common reporting template. Further to this qualitative evaluation data has been gathered by means focus group sessions. In thematic regard, these events focused on gathering the stakeholders’ perceptions of the utility, usability and reliability of the pilot services, including the technical infrastructure through which the pilot services are delivered at each of the pilot sites. Also, perceived impacts have been addressed. Outcomes were again documented in terms of a common reporting template. All reporting templates were processed as a text files with help of common word processing software (MS Word).
## Re-use of existing data
The WeGovNow project specifically has developed and piloted a new type of
civic engagement platform integrating various participation functions. There
was no existing data available that could be utilised for the purpose of pilot
platform evaluation in the framework of WeGovNow.
## Expected size of data
The following data volume was generated:
* _Quantitative data gathered_ :
A case-based variable set was generated for 11.833 instances overall.
* _Quantitative data gathered:_
Overall, 18 aggregated reporting templates have been processed.
## Expected data utility
Data gathered by means of the project’s multi-method evaluation approach
specifically relate to the utilisation of the newly developed WeGovNow pilot
platform in three participating pilot municipalities. These data are of
utility primarily for formative evaluation purposes to support the further
mainstreaming of the pilot platform, the latter representing a key output of
the project. The pilot platform may however undergo further optimisation prior
to envisaged mainstreaming after the ending of the pilot project. Due to the
formative nature of the evaluation design adopted for the purpose of the
WeGovNow project, the long term value of the evaluation data to be generated
can be assessed as very low. Type and nature of the evaluation data gathered
do not offer opportunities for subsequent research, e.g. in terms of secondary
analyses or replication of results. 2 However, the quantitative data set
that has become available from automated usage monitoring during the overall
project’s pilot phase may potentially be utilised by others for verifying any
published results referring to these data, albeit with a rather low
probability given the strong RTD-focus of the current project.
# Preserving access to quantitative WeGovNow platform evaluation data
## Making quantitative WeGovNow platform evaluation data findable
Beyond the project duration, it is planned to make an anonymous case-level
data set from the automated pilot platform usage monitoring available through
the GESIS Datorium repository. The data set consists of 11.833 instances and
11 coded variables. To this end, the data set is intended to be made available
after the formal closing of the project.
The user ID which might in conjunction with registration data stored in the
pilot platform, at least theoretically, enable the identification of an
individual who’s platform usage activities have been monitored is not included
in the quantitative data set that has been generated for formative evaluation
purposes. Also, no person related information stemming from the required
registration of the pilot users to the pilot platform has been derived from
the platform for evaluation purposes. Hence, no personal data or any other
information that would theoretically allow a third party to infer a pilot
user’s identify has been included in the data set generated for formative
evaluation purposes. This ensures anonymity of the data set to be publicly
preserved.
Findability will be supported by relying on the metadata framework applied by
the data sharing repository to be utilized (GESIS Datorium) 3 . In
particular, the following metadata will be provided:
1. Title
2. Principle investigator & Institution
3. Publisher
4. Publication Year
5. Availability
6. Subject Area
7. Topic Classification
In contrast to “replicablility” in this sense, we understand “reproducibility”
as making available data sets to others for verifying published results. C.f.
e.g. Peng, R. (2009): Reproducible research and Biostatistics. In:
Biostatistics, 10 (3): 405-408.
3 This meta data framework is compatible with the codebook standard of the
Data Documentation Initiative (DDI 2). c.f. Wolfgang Zenk-Möltgen and Monika
Linne (2014): Metadatenschema zu datorium - Data
Sharing Repositorium. GESIS-Technical Reports 2014/103
8. Abstract
9. Geographical Area
10. Data collection Mode
11. Survey Period
12. Rights
13. Notes
14. File description
15. Research Data Type
16. Language
17. Number of Variables
18. Number of Units
19. Unit Type
20. Software
These metadata will be fed by means of a persistent digital identifier (DOI)
into the international community of DataCite and thus become findable at an
international level. 3
## Making quantitative WeGovNow platform evaluation data openly accessible
As mentioned above, an anonymous case-level data set derived by means of
automated pilot platform usage monitoring will be uploaded to the GESIS
Datorium data sharing repository. An account has already been registered.
The data set will be made available in terms of an OpenDocument Spreadsheet
(.ods). In conjunction with the metadata to be provided the data will be
immediately usable with help of commonly available spreadsheet software. No
particular documentation of access software will thus be required.
Access to this data is planned to be provided to everybody. Setting up a Data
Access Comitee, e.g. for concluding particular data access agreements, is
considered not necessary.
## Making quantitative WeGovNow platform data interoperable
As discussed earlier the type and nature of the data set generated for the
purposes of for formative evaluation of the WeGovNow pilot platform is not
expected to be of high longterm value (c.f. 2.6). When it comes to potentially
re-combinations with different datasets from different origins in particular,
no value is seen at all due to the formative evaluation approach pursued.
As discussed earlier as well, the quantitative data set that has become
available from automated usage monitoring might be utilised by others for
verifying any published results referring to these data, albeit with a rather
low probability given the strong RTD-focus of the current project. This will
be supported by a documentation of the all variables and numeric codes
included in the data set that will be provided on the data sharing repository
(GESIS Datorium). Where possible available standard codes have been followed,
for example in the case of identification of date of birth data were coded
according to ISO 8601\.
## Increase re-use of quantitative WeGovNow platform evaluation data
The data set includes automatically derived monitoring data on pilot platform
utilization. Prior to further processing, the data derived from the pilot
platform underwent quality assurance procedure in relation to data integrity
in terms of a plausibility assessment.
The data set will be made publicly available on GESIS Datorium following the
closing of the WeGovNow project. No embargo period is foreseen.
No particular restrictions on the re-use of the data are planned to be
imposed. The appropriateness of different licensing agreements under the
Creative Commons licensing framework is currently explored.
The data set will remain re-usable until the repository withdraws or goes out
of business.
# Allocation of resources
Costs for data preparation and documentation are covered by the project
budget. The costs for data preparation to be FAIR cannot be exactly specified
at the current stage. However, expenses for data set preparation, data
management and additional documentation concerning those data to be made
openly accessible are estimated to not exceed 0.5 person months. No additional
expenses are expected to accrue for purchasing supportive tools, e.g. for
working with DDI, and repository charges for data submission.
The lead partner (empirica) of the project’s evaluation work package (WP4)
takes responsibility for data management. Lutz Kubitschke and Sonja Müller are
responsible for data storage, archiving and publication.
# Data security
During the project, all evaluation data has been stored at the server of the
lead organization (empirica) responsible for the evaluation work package
(WP4), with daily backup at an institutional off-site server. The team member
responsible for storage is supported by empirica’s IT team. Back up are
checked manually at two weeks intervals. No additional costs are accruing for
storage and back-up.
When it comes to quantitative monitoring data, sensitive data (the user
account ID recorded by the pilot platform) has been separated to create an
anonymised data set. Beyond the user ID , no data item processed and stored is
assessed as sensitive.
When it comes to qualitative evaluation data generated for evaluation
purposes, this data is planned to be stored locally at empirica’s servers for
10 years, whereby no costs will be associated with local storage. As described
above, qualitative raw data will not be made available to external parties
preventing data privacy threats. Only to project members will be granted
access on request, with clearance of a non-disclosure agreement.
# Ethical aspects
Informed consent for the sharing of anonymised evaluation data and long term
preservation was included during data collation. Sensitive data was separated
and kept secure.
When it comes to the processing of personal data in the framework of the local
validation trials which have been implemented in the three WeGovNow pilot
municipalities GDPR compliance was ensured. During the overall project’s pilot
phase, the WeGovNow platform has been operated by three municipalities under
day-to-day, namely in Turin, in the London Borough of Southward and in San
Doná di Piave. With help of the platform, each pilot municipality offered a
publicly available pilot service to its citizens until the end of the formal
project duration. In technological respect, the individual platform components
(software applications) were hosted by different consortium members according
to a Software-as-aService (SaaS) deployment model. Pilot users entered data
into the pilot platform through a common web interface. User data were shared
across the platform components, either directly between individual components
or by means of a common logger data base.
From a legal point of view, two different perspectives deserved attention in
framework of the WeGovNow pilots when it comes to GDPR requirements, namely an
external one and an internal one:
* External: One the one hand, a legal relationship was established between the pilot service provider (pilot municipality) and the pilot service user (citizen).
* Internal: On the other hand, a legal relationship was established between the pilot service provider (pilot municipality) and those parties processing data on its behalf in accordance with a SaaS deployment model (technology partners hosting one or more platform component remotely).
As far as the processing of personal data was concerned, the GDPR creates
obligations for the WeGovNow municipalities offering the pilot service (“data
controllers”) to their citizens (“data subjects”) with help of WeGovNow
component providers (“data processors”). These obligations were mat in various
ways:
* Terms of use statement (ToU) complying with GDPR requirements was developed by each pilot site and made available through the local pilot platform instances to the pilot users. Consent was requested prior to user registration.
* A data privacy statement was developed by each pilot site and made available through the local platform instances to the pilot users. Consent was requested prior to user registration. In this context, user were informed about which types of personally identifiable information was collected about them across the WeGovNow platform components, how the data is used and how users can control the information that is gathered. Current data protection legislation as well as the new GDPR put an obligation on data controllers to ensure data subjects can rectify remove or block incorrect data about themselves. Users were also informed about their various rights in relation to data protection as stipulated by GDPR.
* To be able to respond to user request in relation to these rights, it was identified in advance what personal data were held within the individual WeGovNow platform components, where it comes from, who it is shared it with, how its processing can be restricted and how it can be erased. Also, a process was identified din advance how the consortium would react if a pilot user asked to have his/her personal data deleted, for example. In such a case the pilot municipality receiving such a request from one of its citizens was able to rely upon a commonly agreed procedure for informing the partners concerned, monitor how the user claim is met and provide informed feedback on this matter to the pilot user.
* The GDPR makes privacy by design an explicit legal requirement under the term ‘data protection by design and by default’. As a general rule, personal data was processed within and across the WeGovNow platform software components only for those purposes intended to be achieved by the component.
When it comes to the legal relationship between the pilot municipalities and
the technical partners hosting one or more software components remotely, GDPR
differentiates between the “data controller” (the pilot municipalities) and
the “data processor” (the technical partners). Processing of personal data on
behalf of a data controller requires an assignment in writing between both
parties according to GDPR. Therefore a data processing agreement was concluded
bilaterally between each of the three pilot municipalities and each WeGovNow
component provider prior to the starting of the local pilots. Overall, 12 data
processing agreements were hence concluded.
# Preserving access to publications
Project partners have relied on research results from WeGovNow for authoring
scientific papers for journals and book chapters, as well as presenting
conference papers in relevant disciplines. To preserve their accessibility
these have been made available on different repositories as follows:
* The Zenodo open access repository
* The open access repository of the University of Heidelberg
* The open access repository of the University of Turin
* The Liquid Democracy Journal (permanently archived at German National Library)
* The ACM digital Library
* IEEE Xplore Digital Library
* International Conference on Cartography and GIS (ICC&GIS)
**Title**
**)**
**Year**
**(**
**Authors**
**Journal/book/conferen**
**ce**
**Type**
**Link to**
**publication**
<table>
<tr>
<th>
**Title (Year)**
</th>
<th>
**Authors**
</th>
<th>
**Journal/book/conferen ce**
</th>
<th>
**Type**
</th>
<th>
**Link to publication**
</th> </tr>
<tr>
<td>
A Fair Distance Function (2017)
</td>
<td>
Behrens, J. and B. Swierczek
</td>
<td>
The Liquid Democracy Journal on electronic participation, collective
moderation, and voting systems
</td>
<td>
Other
</td>
<td>
_http://www.liq_ _uid-democracyjournal.org/issu_ _e/5/_
</td> </tr>
<tr>
<td>
LiquidFeeback’s Issue Limiter (2017)
</td>
<td>
Behrens, J., Nitsche, A. and B. Swierczek
</td>
<td>
The Liquid Democracy Journal on electronic participation, collective
moderation, and voting systems
</td>
<td>
Other
</td>
<td>
_http://www.liq_ _uid-democracyjournal.org/issu_ _e/5/_
</td> </tr>
<tr>
<td>
Unified User
Management with
LiquidFeedback (2018)
</td>
<td>
Behrens, J. and B. Swierczek
</td>
<td>
The Liquid Democracy Journal on electronic participation, collective
moderation, and voting systems
</td>
<td>
Other
</td>
<td>
_http://www.liq_ _uid-democracyjournal.org/issu_ _e/6/_
</td> </tr>
<tr>
<td>
Data Quality Concept for eGovernment Web-Map
Based Services (2018)
</td>
<td>
Noskov A., Zipf A. and A.
Rousell
</td>
<td>
Proceedings 7th
International
Conference on
Cartography and GIS
</td>
<td>
Conference proceedings
</td>
<td>
_https://iccgis20_ _18.cartographygis.com/7ICCGI_ _S_Proceedings/_
_7_ICCGIS_2018_
_%20(34).pdf_
</td> </tr>
<tr>
<td>
Computer Vision
Approaches for Big Geo-
Spatial Data: Quality
Assessment of Raster Tiled
Web Maps for Smart City
Solutions (2018)
</td>
<td>
Noskov A.
</td>
<td>
Proceedings 7th
International
Conference on
Cartography and GIS
</td>
<td>
Conference proceedings
</td>
<td>
_https://www.ge_ _og.uniheidelberg.de/_ _md/chemgeo/g_ _eog/gis/noskov_
_2018rastertiles_
_qualityinitial.pd_
_f_
</td> </tr>
<tr>
<td>
Open Source Tools for
Coastal Dynamics
Monitoring (2018)
</td>
<td>
Noskov A.
</td>
<td>
Proc. SPIE 10773, Sixth
International
Conference on Remote
Sensing and
Geoinformation of the
Environment
</td>
<td>
Conference proceedings
</td>
<td>
_https://www.ge_ _og.uniheidelberg.de/_ _md/chemgeo/g_ _eog/gis/noskov_
_2018osrccoastd_
_yn.pdf_
</td> </tr>
<tr>
<td>
Backend and Frontend Strategies for Deployment of WebGIS Services (2018)
</td>
<td>
Noskov A. and A. Zipf
</td>
<td>
Proc. SPIE 10773, Sixth
International
Conference on Remote
Sensing and
Geoinformation of the
Environment
</td>
<td>
Conference proceedings
</td>
<td>
_https://www.ge_ _og.uniheidelberg.de/_ _md/chemgeo/g_ _eog/gis/noskov_
_2018fbswebgis._
_pdf_
</td> </tr>
<tr>
<td>
Smart City WebGIS
Applications: Proof of
Work Concept for Highlevel Quality-of-Service
Assurance (2018)
</td>
<td>
Noskov, A.
</td>
<td>
ISPRS Ann.
Photogramm. Remote
Sens. Spatial Inf. Sci.,
IV-4/W7
</td>
<td>
Conference proceedings
</td>
<td>
_https://www.ge_ _og.uniheidelberg.de/_ _md/chemgeo/g_ _eog/gis/noskov_
_zipf2018pow.pd_
</td> </tr> </table>
<table>
<tr>
<th>
**Title (Year)**
</th>
<th>
**Authors**
</th>
<th>
**Journal/book/conferen ce**
</th>
<th>
**Type**
</th>
<th>
**Link to publication**
</th> </tr>
<tr>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
_f_
</td> </tr>
<tr>
<td>
Definition of Contour Lines
Interpolation Optimal
Methodas for EGovernment Solutions
(2018)
</td>
<td>
Noskov, A. and A. Zipf
</td>
<td>
ISPRS Ann.
Photogramm. Remote
Sens. Spatial Inf. Sci.,
IV-4/W8
</td>
<td>
Conference proceedings
</td>
<td>
_https://www.ge_ _og.uniheidelberg.de/_ _md/chemgeo/g_ _eog/gis/noskov_
_zipf2018interp._ _pdf_
</td> </tr>
<tr>
<td>
Modelling and Assessing Spatial Big Data: Use Cases of the OpenStreetMap Full-
History Dump (2019)
</td>
<td>
Noskov, A. et al.
</td>
<td>
Spatial Planning in the Big Data Revolution
</td>
<td>
Book chapter
</td>
<td>
_http://ar.nkov.com/i/Nosk_ _ovGrinbergerPa_ _papesiosRousell_
_TroiloZipf2019L_ _owLevelFHD.pd_
_f_
</td> </tr>
<tr>
<td>
Open-Data Driven
Embeddable Quality
Management Services for
Map-Based Web
Applications (2019)
</td>
<td>
Noskov, A. and A. Zipf
</td>
<td>
Big Earth Data
</td>
<td>
Journal Article
</td>
<td>
_https://www.ge_ _og.uniheidelberg.de/_ _md/chemgeo/g_ _eog/gis/noskov_
_zipf2019embqu_
_ality.pdf_
</td> </tr>
<tr>
<td>
From E-Government to We-Government: an analysis towards participatory public
services in the context of the H2020 WeGovNow
(2018)
</td>
<td>
Tsampoulati
dis, I.,
Kompatsiaris
, I. and N.
Komninos
</td>
<td>
Information Society and Smart Cities Conference University of Cambridge,
United
Kingdom
</td>
<td>
Conference proceedings
</td>
<td>
_https://zenodo._ _org/record/257_ _8929#.XJoSoKAi_
_FLw_
</td> </tr>
<tr>
<td>
La Pubblica
Amministrazione responsabile : un caso di digital welfare (2018)
</td>
<td>
Visentin, M.
and G. Antonini
</td>
<td>
Rivista Italiana di Public Management
</td>
<td>
Journal Article
</td>
<td>
_https://zenodo._ _org/record/257_ _9141#.XJoSXaAi_
_FLx_
</td> </tr>
<tr>
<td>
First Life, the
Neighborhood Social
Network: a Collaborative
Environment for Citizens
(2016)
</td>
<td>
Antonini, A. et al.
</td>
<td>
Proceedings of the 19th ACM Conference on
Computer Supported
Cooperative Work and
Social Computing
Companion
</td>
<td>
Conference publication
</td>
<td>
_https://iris.unit_
_o.it/handle/231_ _8/1646139#.XH_ _QHFaAiFhE_
</td> </tr>
<tr>
<td>
WeGovNow: a map based platform to engage the local civic society (2018)
</td>
<td>
Boella, G. et al.
</td>
<td>
WWW '18 Companion
Proceedings of the The Web Conference 2018
</td>
<td>
Conference publication
</td>
<td>
_https://dl.acm._ _org/citation.cfm_ _?id=3191560_
</td> </tr>
<tr>
<td>
WeGovNow: an integrated platform for social engagement in shaping future
cities (2018)
</td>
<td>
Boella, G. et al.
</td>
<td>
4th Italian Conference on ICT for Smart Cities And Communities 2018
</td>
<td>
Conference publication
</td>
<td>
_https://iris.unit_
_o.it/handle/231_ _8/1693782#.XH_
_P_aqAiFhE_
</td> </tr>
<tr>
<td>
Back to public: Rethinking
</td>
<td>
Lupi, L. et al.
</td>
<td>
Proceedings of the
</td>
<td>
Conference
</td>
<td>
_https://iris.unit_
_o.it/handle/231_
</td> </tr>
<tr>
<td>
**Title (Year)**
</td>
<td>
**Authors**
</td>
<td>
**Journal/book/conferen ce**
</td>
<td>
**Type**
</td>
<td>
**Link to publication**
</td> </tr>
<tr>
<td>
the public dimension of institutional and private initiatives on an urban data
platform (2016)
</td>
<td>
</td>
<td>
2016 IEEE International
Smart Cities
Conference (ISC2)
</td>
<td>
publication
</td>
<td>
_8/1646137#.XH_
_QHiKAiFhE_
</td> </tr>
<tr>
<td>
MiraMap: A We-
Government Tool for
Smart Peripheries in Smart
Cities (2016)
</td>
<td>
De Filippi, F. at al.
</td>
<td>
IEEE Access Journal
</td>
<td>
Journal Article
</td>
<td>
_https://ieeexplo_ _re.ieee.org/doc_ _ument/7444140_
</td> </tr> </table>
|
https://phaidra.univie.ac.at/o:1140797
|
Horizon 2020
|
1125_I-Media-Cities_693559.md
|
# Executive Summary
This Data Management Plan (DMP) describes all elements of the data management
life cycle of I-MediaCities. It presents the type of data will be collected,
processed and generated by the project and how the IMedia-Cities Consortium
plans to manage these datasets in the respect of the FAIR-principles of data
management, as needed for a European project participating in Horizon 2020.
It provides information on the type of data the project will collect, process
and generate, the partner responsible for the DMP during the course of the
project, how this data will be handled during and after the project lifetime
and what methods and standards will be applied to the data.
The DMP also includes all rules and provisions on data preservation, security
and ethics for I-Media-Cities. These rules and provisions were already listed
and in article 6 on IPR Management of D1.1 and D9.1 on the ethics of the
project. This deliverable will detail the specific data protection and ethical
rules that apply to the different datasets collected, analyzed and generated
within the project.
Finally, a Data Register listing all details for all the open datasets,
planned for in I-Media-Cities, is provided.
# 1\. Introduction
This Data Management Plan (DMP) provides all the information needed to answer
any question that might arise concerning the data collected, processed and
generated within the project. There is a multitude of diverse research and
user-generated data planned for, which makes such a concrete Data Management
Plan and its updated versions (at M16, M24 and M36) an absolute necessity.
This document lists all the datasets that are going to be produced within the
project, and provides an analysis of all the main elements of the data
management policy the project will implement to handle those datasets.
I-Media-Cities is part of the OPEN Research Data Pilot, a flexible pilot
running under Horizon 2020, whose goal it is to improve visibility, reach more
people and have a greater impact – for science, society and your own career.
Consequently, the DMP provides information of how the research data of the
project follows and adheres to the FAIR-principles of data management. It is
also important to note that a key component of the project is that all
datasets generated within the project will be made open, as long as there are
not limitations due to copyright provisions or privacy laws.
The DMP also develops rules and general principles designed in D 1.1 (Quality
Assurance, Risk Management and IPR) D.9.1 (POPD – Requirement N° 4).
This Data Management Plan provides information on the following elements of
the Data Management lifecycle:
* Type and volume of Data collected, processed and generated within the project
* The ways the project data complies with the FAIR-principles of data management
* Rules of preservation and archiving
* Ethical rules of data management
* Data Quality Assurance
* Resourcing of data management
* Data security provisions
# 2\. Data of I-Media-Cities
When speaking of the data used, collected, processed and generated within
I-Media-Cities, a distinction has to be made between the data and metadata
existing before the project ( _Background_ ) and the data and metadata created
within the project ( _Foreground),_ as defined in D 1.1.
I-Media-Cities will use, collect, process and generate both types of data and
metadata, but the IPRrelated solutions for these two types differ greatly:
1. _The Background of the project_
The Background is any information, in hard copy or in electronic form,
including, without limitation, documents, drawings, models, designs, data
memoranda, tapes, records, and databases developed before or independent of
performance under the project that is necessary for the performance of Project
Work and exploitation of its results.
All rights and obligations connected to the background of the project are
listed in the Consortium Agreement and in D1.1, article 6.3. 1 The
Coordinator, in collaboration with all partners, will ensure that the project
complies with these rights and obligations.
Concretely, the Consortium Agreement provides all the details on what
constitutes Background for every partner and, to summarize, partners will
retain ownership of the Background they contributed to the project.
2. _The Foreground of the project_
The Foreground means the results, including information, materials and
knowledge, generated in a given project, whether or not they can be protected.
It includes Intellectual property rights, similar forms of protection and
unprotected know-how. Thus, foreground includes the tangible and intangible
results of the project. Results generated outside the project scope do not
constitute the Foreground.
Ownership and IPR of metadata, data and technological solutions created within
the project are all dealt with in D1.1 2 and the Consortium Agreement 3 ,
and in general the following rules apply:
* Metadata produced within the project: to be made freely available, but the IP remains with the institution(s) who produced it.
* User-generated content and metadata: IP will be regulated when users register to any I-MediaCities service or platform.
* Technological solutions created within the project: IP and ownership remain with the partner(s) who developed it. If there are several partners who developed the technological solution, a shared ownership is granted.
# 3\. Data Register
Since I-Media-Cities generates a lot of datasets in order to achieve the
project’s goals, they are classified and detailed in a Data Register, to
enable their proper and full management. The Data register lists all datasets
of a project and includes their volume (for some estimations), methodology,
standards and handling of all data during and after the end of the project.
This Data Register has to be understood as a living document, which will be
updated regularly during the project lifetime in combination with the updates
of the Data Management Plan (see chapter 8). This article lists and details
the possible values of the different datasets and to provide for some context
to go along with the Data register.
Below, a preliminary list of all generated datasets of version 1 of the Data
Register, completed with their names and the Work Package they are attached
to.
_Data generated within all Work Packages_
Deliverables that are reports and documents
Every Work Package has a number of deliverables attached to it, reports that
are under responsibility of specific partners. Some of these deliverables are
reports or documents that need to be considered research data and are vital to
understanding the research project as a whole. To this effect, the project
will provide information on all documents that are relevant to the research
project of I-Media-Cities in the Data register. All these documents will be
kept at the same place and the details of their data management can be found
in the data register (Annex I).
## _Datasets generated within WP3_
* Performance data of website
* List of email subscribers for I-Media-Cities Newsletter
## _Datasets generated within WP5_
* Account details for end-users
* User-generated metadata
* User-generated data
* Software
## _Datasets generated within WP6_
* I-Media-Cities Data model
* Additional Metadata created for the I-Media-Cities Metadata Repository Software
## _Datasets generated within WP7_
* Metadata generated through the use of automatic analysis tools
* Software
## _Datasets generated within WP8_
Software
_See Annex I for the I-Media-Cities Data Register, version 1._
## 3.1. Data Register Component 1: Data Summary
To keep a structured overview, the first component of every data set
description in the Data Register is a Data Summary. It delivers the following
information in accordance with the guidelines on fair data management in
Horizon 2020 projects 4 :
* Data set reference and name: Identifier for the data set.
* Data set purpose: Description of the data that will be collected and generated and its purpose for the project and its objectives.
* Standards and metadata: Specify the types and formats of data generated/collected.
* Data Origin: Specify the origin of the data and whether or not existing data is being re-used.
* Data size: States the expected size of the data (when known). The expected total size of every dataset in the data Register will be added as soon as the Coordinator and the WP leaders have a better understanding of the specifics of this dataset. As soon as the WP Leaders can make an educated estimation on the expected dataset size, this information will be added as ‘expected total size’ to the dataset in the Data Register. This will be done during one of the planned updates of the Data Management Plan, following the estimation (M16, M24, M36).
Since the project applies an agile approach to platform design and
development, storage is foreseen to be expandable to be able to store any
unforeseen quantity of data the project produces.
* Data utility: Specify to whom the data will be useful.
## 3.2. Data Register component 2: FAIR data-principles
In accordance with the requirements for European H2020-projects, I-Media-
Cities will make sure the research generated within the project adheres to the
FAIR-principles, which means that there are clear rules on their management
that will improve their findability, accessibility, interoperability and
reusability.
This Article will go into more detail for each of the different components
that help define the FAIR-principles of I-Media-Cities. It provides further
information on all the principles’ components and deals with the questions
that need answering in order to comply to the FAIR DATA Guidelines. 5
### 3.2.1. Making Data Findable
To make the collected and generated data of I-Media-Cities findable, every
dataset is characterized by specifications on the ways they are made findable
through metadata, versioning and other identification mechanisms.
Next to these identification methods, every dataset also provides the
information on the main digital location, used for their storage. To help make
the open data of I-Media-Cities discoverable, the project website includes a
central catalogue of all open datasets, which includes rules and regulations
for accessing these datasets.
In general, the following rules and provisions apply for the different
datasets:
#### a. _Reports and other documents_
* In accordance with the mandatory properties of the DataCite metadata scheme 6 , which is a part of the DDI-standard for research metadata (version 3) 7 , all reports and documents collected or generated in I-Media-Cities has, at a minimum, the following metadata attached to it: Persistent and unique identifier (file name), Title, Keywords/tags, Author/Creator, Subject, Time of Publication, Publisher (Always I-MediaCities Consortium), Version, File size.
* All metadata are added to the properties of the document itself. For deliverables, next to the metadata being added to the properties, some of the metadata will also be published on the document control and title pages.
* The file name is the unique identifier I-Media-Cities uses for reports and other documents. It is constructed by the following standard identification mechanisms:
_For deliverables_ :
Start with IMC (Project code for I-Media-Cities), followed by the number of
the deliverable (can be found in list of deliverables in the Grant Agreement),
followed by the title of the deliverable or annex to the deliverable and the
version of the document. All these elements are connected by dashes.
Example: IMC-D7.1-Report on state of the art for moving image analysis-v.
1.3-final
_For documents that are not deliverables_ :
Start with IMC, followed by the type of the document (e.g. questionnaire,
minutes …) and the title and the version of the document. All these elements
are connected by dashes.
Example: IMC-Minutes-Kick-off meeting Brussels May 23-24-version 1.2-final
Clear Versioning is provided for all documents. All deliverables will have a
version number on the title page and all other documents will have a version
number in the file name. The last version of a document will always carry the
label ‘final’ at the end of the file name, after the version number of the
document.
The final versions of public documents and deliverables will be added to the
project web site _www.imediacities.eu_ and on the CORDIS portal 8 . It is
worth to note that information/documents/deliverables for the internal use
within the consortium and for the monitoring procedures of the EU will not be
made publicly available but will be available on the protected project Work
Cloud, B2DROP 9 , where the project partners have unlimited access to all
versions of all documents and reports.
#### b. _Films, Photographs and text-files (contents) created within the
project_
User-generated contents, which means any film, photograph and document,
created by partners, researchers and the general public, using the I-Media-
City services, and then saved in the I-Media-Cities repository. The user-
generated content does not belong to the Background of the project.
All rights and obligations concerning user-generated contents are listed and
detailed in the IPR-provisions connected to every single I-Media-City service
and must be approved by users, before they are able to save their data in the
I-Media-City repository.
Every Film, Photograph and text-file created within I-media-Cities has a title
and a source (metadata elements) attached to them, when they are entered into
the I-Media-Cities repository. Once entered into the repository, they receive
a unique numerical identifier, dedicated to the I-media-Cities repository.
c. _Metadata collected, processed or created within the project_
* The following types of metadata are collected and in part generated within I-Media-
Cities:
* Technical Metadata: these contain information on the technical aspects of the films, photographs and documents, such as size, format, color …
* Descriptive Metadata: these contain information on the subject of the films, photographs and documents, such as title, director/author, summary, keywords …
* Administrative Metadata: these contain information on the rights status, creation, preservation and quality control of the films, photographs and documents, such as source/publisher, rights holder.
* All metadata connected to the films, photographs and text-files (Background or usergenerated) of I-Media-Cities, which is created within the project, are attached to a linked and open metadata model. Since a specific metadata model and repository is created for I-Media-Cities, the structure and standards used for this model are detailed and described in D6.1 _Content metadata subsets selection and analysis of metadata schemas and vocabularies_ (M 10).
* All metadata connected to the films, photographs and documents (background or usergenerated) of I-Media-Cities, can derive from four sources:
* Previously existing metadata in the databases of the Project partners of I-MediaCities (this metadata is considered part of the Background, since it is collected not created)
* Automatic A/V Analysis Tools o Metadata generated by the software
* Users (both project partners and external users)
Rules of ownership and IPR management of all metadata collected, processed and
generated within I-Media-Cities, are subject to the regulations and provisions
detailed in D1.1 Quality Control, Risk Management and IPR, already published
on the project website 10 .
The approach I-Media-Cities will follow to maintain the distinction between
metadata that is considered background (already present in the partners’
databases) and the metadata generated within and for the purpose of the
project, is based upon the definition of the generating source of the
metadata.
##### d. Software and its intellectual property rights (Patents, copyrights
and Trademarks)
* All software developed in I-Media-Cities are governed by the rights and obligations attributed to the results and the software of the project, as listed and detailed in D1.1 11 and the Consortium Agreement 12 .
* The rights owners of any software developed in I-Media-Cities, is clearly indicated in the copyright information of the software.
#### e. _Personal data_
* Personal data means any information relating to an identified or identifiable natural person.
* An email address will function as the unique identifier for personal data.
* Users can be asked to register an account on I-Media-Cities in order to be able to submit data into the project. When creating their account, users will also have to approve the Terms of Use, the Privacy Policy and the Disclaimer attached to this type of account.
* All users can always have access to, edit or even cancel their own account and personal data, by logging in via their email address and their personal password.
* All rules and regulations connected to personal data, included informed consent for data sharing and long term preservation, are listed and detailed in deliverable 9.1 13 on ethics and in the IPR section of deliverable 1.1 14 .
### 3.2.2. Making Data Openly Accessible
Openness of data and free access is one of the key concepts of the project. As
much as possible, and respecting all Intellectual Property involved, but also
taking into considerations the principles of the Public Sector Directive
2003/98/EC, all data will be made openly and freely accessible. All data
concerned are: content, metadata (including user-generated), and research
produced within the project.
To that avail, all publications, metadata and other content that are produced
as a direct result of the project will be made openly available, which is
known as a “gold” model. The project will also encourage researchers using its
resources, to make their works (or a significant section of it) available via
and within the e-environment and eco-system of I-Media-Cities. In other words,
research that is produced by using and accessing the content made available
via I-Media-Cities, will be made available via the project e-environments.
For every dataset, the following specifications on their accessibility is
detailed in the Data Register:
a. _Is the dataset open available or not_ _._
Open data is defined as follows:
_**Open data is data that can be freely used, re-used and redistributed by
anyone - subject only, at most, to the requirement to attribute and share-
alike.** _ 15
As a general rule, the project will openly make available the contents and
metadata produced within the project through a “gold” model. This is due to
the fact that the consortium strongly wants to encourage other stakeholders in
using the material generated by the project, enhancing the replicability of
the proposed approach and wider fruition of the technology.
Some datasets generated in I-Media-Cities will not be openly accessible,
depending on the copyright protection rules and ethics rules attached to the
generated datasets. For background, the copyright status of the content and
corresponding metadata, is identified by and under the responsibility of the
partners who contribute these datasets to the project, and listed by CRB,
which is responsible for the Data Management Plan. For the foreground, The
Management Committee will be responsible for planning and identifying the
copyright rules pertaining to every dataset belonging to the foreground, and
CRB is responsible for listing the copyright protection pertaining to every
dataset in the Data Register. The accessibility status is listed in the Data
Register and will always be made clear to users before they can start working
on and with datasets.
b. _How will the dataset be made available?_
An open data license will be applied to all datasets that are openly
available. This license will list and determine all intellectual property
rights that exist on the datasets, and is conformant with the principles set
forth in the Open Definition above. If user-generated data or content need to
be anonymized in order to be able to make it openly available, I-MediaCities
will do so.
Open data needs to be technically open as well as legally open. To this end,
I-Media-Cities will make sure that all open data is available in bulk in a
machine-readable format or through an API and for a reasonable price. The
project will also verify which part of the dataset is available for download.
The Data Register will list with every dataset, which project partner is
responsible for making it technically open.
Data will be priced at no more than a reasonable cost for reproduction or even
as a free download from the Internet. The open data license will mention the
cost of the dataset or will provide instructions on how and where information
on the cost for downloading the dataset can be found.
The project website will have clear instructions on how to access the
different open datasets and will try to avoid a Data Request Methodology as
much as possible. If such a methodology is needed, the project website will
also list the full details instructions for this DRM.
#### c. _Methods and software necessary to access the dataset_
The methods used to share data are dependent on a number of factors, such as
the type, size, complexity and sensitivity of the dataset.
For easy data availability, the project will use one of the following methods
of online publication to share data:
* Files will be provided for download via the project website
* Via ftp servers
* As an API
* Linked metadata structure
* Via 3 rd party websites
If the project uses specific software to share the data, the partners will
make sure to primarily use open source software. If dedicated software, which
is not open source, is necessary to access the data, the software will have
clear indicators on its copyright situation, rules of use and will provide a
manual for users.
#### d. _The location of the deposited datasets_
All open datasets will be available through the project website. The user-
generated metadata and content will be deposited in the dedicated I-Media-
Cities repositories, which will be developed as a part of the project. The
project website will provide information on how to access these datasets and
the used methodology.
The repositories will be hosted at CINECA partner location, in Italy. CINECA
will provide access, during the project lifetime, for users to storage
resources in the amount needed by all I-Media-Cities services. After the
project lifetime, this storage will be subject to contractual agreements made
between CINECA and the Managing organization, in line with the developed
Business Plan. Consequently I-Media-Cities users will be allowed to access the
iRODS data repository. Actually two iRODS server instances are installed and
running on CINECA PICO HPC cluster 16 , intended to enable new "BigData"
classes of applications, related to the management and processing of large
quantities of data, coming both from simulations and experiments. The storage
area is composed by high throughput disks for a total amount of about 4 PB,
connected with a large capacity tape library for a total actual amount of 12
PByte (expandable to 16 PByte). Also Virtual Machines have been deployed and
equipped with an iRODS server development version in order to perform advanced
tests on the infrastructure before going on production.
#### e. _Are there any restrictions on the datasets_
If any restrictions apply to a generated dataset, for whatever reason (e.g.
ethical, rules of personal data, intellectual property, commercial, privacy-
related, security-related), the Data Register indicates this restriction and
provides information on the accessibility of the dataset and the request
methodology users must follow to gain access. This information is also
available through the project website.
### 3.2.3. Making Data Interoperable
Metadata interoperability is fundamental for the project. Interoperability
denotes the ability of diverse systems and organizations to work together
(inter-operate). In this case, it is the ability to interoperate - or intermix
- different datasets. 17 In order to make the data and metadata of IMedia-
Cities interoperable with other datasets, the following specifications are
added and detailed by the Coordinator to every dataset in the Data Register:
* The open data license applied to the open datasets will allow for the intermixing of the dataset with other open datasets, even if these are external to I-Media-Cities.
* A clear indication on which (meta)data standard was used for the dataset. The project will make largely use of open standards in the case of A/V content, the main metadata standard that is relevant is the one developed following a standardization mandate from the European Commission to the European Committee of Standardization (CEN). CEN produced two European Standards (EN) to facilitate the interoperability of film databases:
* EN 15744:2009 “Film Identification – Minimum metadata set for cinematographic works”;
* EN 15907:2010 “Film Identification – Enhancing interoperability of metadata – Element sets and structures”.
These standards constitute the base for the activities of the project.
* In order to open the structured information gathered in the project, Open Linked Data will be adopted so that it can be interlinked and become more useful through semantic queries. This type of data builds upon standard Web technologies, such as HTTP, RDF and URI’s.
* Specifically for the identification of the A/V contents, the project will foster the adoption of Persistent Identifiers in the project and among the different cultural holding institutions. Among the main mature standards the DOI 18 and the Handles 19 are effective means of identifying an entity over the internet.
* Specifically for the identification of names, the project will foster the adoption of the main mature standards such as ORCID 20 that provide a persistent digital identifier that distinguishes researchers or ISNI 21 .
* Since I-Media-Cities will create its own metadata ontologies and vocabularies, details on how they are linked to commonly used metadata standards will be provided on the project website. I-Media-Cities strives to use a linked open data model for its database.
### 3.2.4. Increase Data Reuse
In order to increase the re-use of the open datasets of I-Media-Cities, the
project partners will use the dissemination and communication channels to
spread the word on the availability of the open datasets:
* I-Media-Cities will promote their available open datasets through the project website and the other available dissemination tools, described in deliverable 3.3, the dissemination and communication plan of I-Media-Cities 22 .
* I-Media-Cities will make every effort to communicate the availability of its open datasets to channels that directly provide information to one or more of the 6 target groups and related projects, as described in D3.3 23 .
In order to encourage re-use, the special section on the project website that
will deal with all the open datasets, includes the following information:
* Catalogue with all open datasets of the project.
* All information on the IPR rules and the open data licenses used for the different datasets.
As a general rule, the project will apply the licenses that permit the widest
re-use possible. All information on the methodology and location needed to
access the open datasets.
* A clear indication on how long the datasets will remain re-usable.
* A clear contact point for any questions related to the access and re-use of the project datasets.
* If there are any expected difficulties or restrictions in data sharing and re-use, the open data information page will provide this information, along with causes and possible measures to overcome these. Possible strategies to limit restrictions may include: anonymizing or aggregating data; gaining participant consent for data sharing; gaining copyright permissions; and agreeing a limited embargo period.
A key component of the project is the openness of the solution developed.
Although some components might involve some IP and require protection, the
project is conceived in order to provide a solution that is open and easy to
adopt by as many institutions as possible, in as many fields as possible.
Concretely, I-Media-Cities will actively seek to involve other research
centers and collection-holding institutions to use the tools it will produce,
already during the project lifetime and after. The system will be designed to
enable more archives, cities and institutions to join the project. Equally
encouraged by the partnership is the fact that the concept and the underlying
technological solutions are employed by others, on completely different
subject matters.
In order to enhance the chance that the open datasets will be re-used, they
will also be published on 3 rd party data providing websites, such as GitHub
24 . Also thanks to the open link to Europeana via the EFG content
aggregator, content and data will also be – as much as possible and compatible
with the Europeana metadata policies – made available via that channel. This
link will be drafted considering the evolution of Europeana portal and its
relation with aggregators as EFG, which are now under reflection following
Council conclusions 25 .
Lastly, a key objective of the project is the ability to process queries
across language barriers. This will be made possible by defining and
implementing all necessary tools, which will be defined in D6.1, such as
ontologies, thesauri and authority files. This part is considered as
strategic, since I-Media-Cities will function as a pilot and a blueprint to
solve this standing roadblock to effective sharing of collections and metadata
across European heritage and research institutions. Therefore, it is a
strategic objective of the project that this part of the development and the
activities is completely open in its concept, results, methods, technical
solutions and results.
## 3.3. Data Register Component 3: Data Preservation and Archiving
The long-term preservation plan for every I-Media-Cities dataset will also
require the following information to be added to every dataset of the Data
Register:
* The retention period of every dataset in the dedicated (Meta)data repositories. This period will depend on the type of data and will always be in compliance with the rules set out in D9.1 on ethics for I-Media-Cities and D1.1 on the IPR management plan for the project, but will also be influenced by the results reached in D3.1 Platform Sustainability report with business model scenarios.
* The central preservation location of every dataset will be defined. This will almost always be one of the two repositories foreseen in the project system.
* Clear indications about what will happen with the dataset after the planned retention period of the dataset is finished.
# 4\. Data Quality Assurance
I-Media Cities will apply a Data Quality Assurance methodology to avoid data
contamination, incorrect, inaccurate or not recorded data and to generally
prevent errors entering a dataset. The Data Quality Plan will apply quality
assurance rules to the data before, during and after collection.
## 4.1 Data Quality Assurance before Collection
To avoid errors, I-media-Cities will strictly define and enforce the format
and metadata standards used for the different datasets. The technical
solutions will be designed in such a way that automatically enforces the use
of the defined standards and formats.
_See D6.1 for the I-Media-Cities metadata standards definition._
## 4.2 Data Quality Assurance during Collection
To ensure user-generated data quality, a specific methodology will be applied
to eliminate mistakes during data entry and evaluation. This methodology was
defined in D4.1 on Research Activities and Platform Content Enrichment.
By designing the data and metadata repositories in a specific way, before
starting to use them, the project will also enhance the data quality during
the collection process. In order to reach this goal, the following rules will
be applied during the design phase:
* The use of consistent terminology
* Data is atomized
* Guide data entry through field value definition
_See D6.1 on the Metadata Model and D6.2 on the Metadata repository_
## 4.3 Data Quality Assurance after Collection
Statistical data could be generally provided by CINECA exploiting statistical
tools as for example Elastic Search or Kibana, first producing logs containing
information about database content, subsequently parsing log files in order to
produce statistical visual representation of such information.
Other specific quality assurance mechanisms, such as checksums, are not
foreseen within the project at this point in the project, as they would not
only require implementations at technical partner CINECA, but would demand a
technological readiness level of the metadata providing partners (FHI
partners), which surpasses both the technological capabilities of several of
these partners as well as the available resources of all partners for this
section of the project. The Consortium is certain, however, that visualizing
statistical analysis of logged information, can provide a solution which is
sufficient for quality assurance after storage.
# 5\. Resourcing
Since the design and use of the meta(data) repositories, the preparation of
the project data and the application of the FAIR-principles to the datasets
were already foreseen during the composition of the project proposal, no extra
resources should be needed to fulfill these requirements.
The relevant project partners will make sure that no extra resources are
needed to ensure preparation and preservation of the datasets during the
lifetime of the project. If there are any costs or charges attached to the
preservation of the datasets after the lifetime of the project, these will be
planned for and added to the business model scenarios that will be developed
through the living lab methodology.
The following project partners are responsible for the data management during
the lifetime of the project:
* All FHI partners are responsible for managing the correctness and quality of the data before uploading it into the project. This includes the rights status and copyright restrictions that might apply to the content. It is the responsibility of the content provider to provide the correct information to the project database and the WP Leaders, through the Coordinator or one of the WP leaders. Data provided by an FHI partner, which was already present in their local database and ingested into the project as background, cannot be edited by another partner or user. Only the partner that has contributed the data can edit it.
* WP Leader DIF is responsible for managing the standardization of all datasets coming from the content providing partners to the project. This means managing the preparation before upload into the project repository in accordance with the chosen standards. Specific preparation guidelines and manuals have been created for any data contributing party and have been added to D4.2.
* WP Leader CINECA is responsible for managing the preservation and security of the data once it has been stored in the I-Media-Cities repositories.
* WP Leader CRB is responsible for managing the data register and making the datasets compliant with its FAIR-principles.
If any unforeseen or unplanned resources are needed to complete and deliver
any part of the project plan, the problem resolution mechanism, defined in the
Risk Management Plan 26 , will be started. This means, that the relevant WP
Leader will inform the Project Coordinator of the problem, and will analyze
and try to manage the problem inside the related project team. In the case the
insufficiency of resources can’t be resolved or entails risks for the whole
project, its solution is referred to the Project Coordinator and Management
Committee. The Project Coordinator will discuss the problem at the first
Management Committee following their discovery and will devise a solution
proposal that must be approved by the Management Committee.
# 6\. Data Security
All the data is stored on secured servers at CINECA. In order to ensure the
continuity or the uninterrupted provisioning of the IIA service (the research
service at CINECA), CINECA exploits Preventive, Detective and Corrective
measures in order to reduce or eliminate various threats/incidents that may
occur. The control measures are applied at different levels of CINECA’s
infrastructure, main control measures adopted include: Uninterruptible power
supply (UPS), Server Room conditioning and cooling and different types of
connectivity, including firewall and VPN services.
All the equipment is in redundant configuration.
HA clustering performs hardware/software faults detecting, and immediately
restarts the application on another system node, making the fault transparent
vs system users.
As EUDAT 27 partner, CINECA will support access to the CINECA data
repository, also providing the web portal with authentication mechanisms
implemented by the EUDAT consortium. Technologies developed within EUDAT will
be exploited and coupled with novel web tools in order to expose interfaces to
allow users browsing and visualizing files from the CINECA repository.
Proper APIs will be included in the IMC web portal interface to activate data
access using, for example, EUDAT federated identity management services (i.e.
B2ACCESS) or invoking iRODS procedures in order to directly connect to the
CINECA iRODS server instance.
Data access and authentication within I-Media-Cities infrastructure will be
implemented as a novel Account Service, managing system accounts through the
I-Media-Cities web portal interface and mapping I-Media-Cities users to iRODS
users so as to maintain the correspondence between accounts on the system in
case they pertain to the same physical person.
In EUDAT, access policies for data deposited directly in the Common Data
Infrastructure (CDI) through user-facing services (for instance B2SHARE) are
determined by the depositor. While the EUDAT CDI encourages open access,
depositors can currently choose to keep data private. Consequently access
policies for I-Media-Cities data stored in the CDI by a partner community will
be determined by CINECA data managers as part of the community and
subsequently respected across all copies maintained in the CDI.
Moreover the EUDAT2020 project in the future will develop finer-grained
authorisation for data sharing, enabling "group" or "approved reader"
authorisation modes. Within the CDI framework, authorisation decisions (the
granting of access rights) will remain with the relevant data owners.
The EUDAT approach in terms of Access policies could obviously impact on the
I-Media-Cities Security Service architecture and development, even if no
specific high-level requirements has been already identified in order to
determine how access should be granted to a specific resources within the
IMedia-Cities infrastructure.
To make sure no data will be lost, a data recovery plan generally provides
basic replication mechanisms conceived in order to preserve data from failures
or in case of system takeovers. This kind of approach includes file system
backup as well as database support for replication. Upon such kind of basic
and very general mechanisms, currently supported from the most part of HPC
centres, CINECA offer includes more sophisticated high level mechanisms for
data preservation and metadata management. CINECA-”Backup Servers” is an
important control measure adopted in order to maintain a backup of the data
contained at application and database servers as well as file system servers
for both production and staging environment. The Advanced Data Backup
mechanism enables recovery of the pertinent service from incident/disasters
that cause a complete loss of the data.
CINECA also supports high-level data and metadata management tools relying
upon services developed within the EUDAT consortium and offers high level
support for storage and data preservation. To this extent individual services
in the EUDAT CDI provide shorter or longer periods of data preservation
depending on Service Level Agreements (SLA). The B2DROP service (which could
be easily integrated within the IMC infrastructure) is aimed at short-term
sharing of active research data, B2SHARE is aimed at the medium-term storage
and sharing of near-publication and published data and B2SAFE is aimed at
longer-term preservation and backup of community data repositories.
Backup of data in the EUDAT CDI is performed at two levels: multiple replicas
of data can be stored at different sites (also on different geographical
locations) through the B2SAFE service, governed by user-defined policies; data
may additionally be backed up at an individual site, depending on the nature
of the storage systems in use at that site.
I-Media-Cities will apply extra security measures when dealing with the
transfer of sensitive data. If such data needs to be transferred, the project
will use a File Transfer protocol combined with an SSL Certificate to add an
extra layer of security to the transfer of files. Encrypting the data this way
is one of the safest ways to make sure that sensitive data is protected during
transfer. In fact, CINECA provides file transfer mechanism based on GridFTP
which supports x509 proxy certificates as well as SSL encryption. GridFTP is a
very effective tool for data transfer: it enhances the standard ftp service
making it more reliable and faster. The transfer performance with GridFTP may
be easily raised of 45 times the ones provided by scp-like tools. GridFTP was
developed by the Globus alliance as part of an open-source toolkit for data
management. It is a client-server application: on CINECA HPC platforms a
server is available.
# 7\. Ethical Aspects of Data Management Plan
All ethics and legal issues connected to data sharing in I-Media-Cities are
covered in D9.1 POD Requirement N° 4 28 and the IPR-section of D1.1 29 .
In this Article, we provide a summary of all rules, regulations and provisions
planned for to deal with any ethical challenge that might arise with data
sharing in I-Media-Cities.
* When dealing with long term preservation or sharing of personal data of any kind, the project will ask the user for informed consent. This informed consent will be achieved through requesting the user to approve the Terms of Use, the Privacy Policy and the Disclaimer, in order to be able to register his account and submit any kind of personal data into the platform or any of its web services. These documents will be added to every web service of I-media-Cities and might differ from each other to fit the specific technical abilities of a specific web service. The Consortium will make sure that all web services will have a clearly marked and dedicated area where users can, at all times during their visit, find and read the terms and policies that apply.
Templates for the Terms of Use, the Disclaimer and the Privacy Policy of
I-Media-Cities have been added as annexes to this document.
_See Annex II for the Terms of Use template_
_See Annex III for the Privacy Policy template_
_See Annex IV for the Disclaimer template_
* The project will make sure that all legislative standards and acts of ethics are followed when dealing with collecting, processing and sharing data in the project.
* The Terms of Use and the Privacy Policy, attached to any part of the project where data is collected from users, will inform those users on how their data will be used, who it will be shared with and the details of its storage.
* If data needs to be anonymized before it can be made open, the project will do so.
# 8\. Updates of the Data Management Plan
The Data Management Plan as well as the Data Register will be updated yearly
to make sure that all information included in the plan and the Register are
still valid and that specific information previously unavailable is added.
This update will happen at least once a year at M16 (after first project
review), M24 and M36 and an overview listing of all updates to the Data
Management Plan (including Data register updates) will be added as an Annex to
the updated versions of this deliverable (with references to the chapters).
The updated version of this document will be available via the results section
of the project website, where it will replace the previous version of this
deliverable.
It is the responsibility of the Coordinator to manage the updates of the Data
Management Plan and the Data Register.
# 9\. Conclusion
This Data Management Plan provides an overview of all aspects of collecting,
processing and generating data within I-Media-Cities, as well as all the
related challenges and constraints that need to be taken into consideration.
I-Media-Cities has constructed a Data Register that lists all datasets
collected, processed and/or generated within the project and details all
relevant data management information for each of them. Every dataset has a
summary that indicates, among other things, the unique identifiers of the
dataset. Since I-Media-Cities is part of the H2020 project and of its Open
Research Data Pilot, the Data Register will include the information of how
every dataset complies to the FAIR-principles of data management, as well the
details on its storage and preservation.
The last three sections of this DMP provides information on the ways data
quality is assured throughout and after the project lifetime, the security
provisions that protect the datasets, the ethical rules that apply and the way
the resources are planned for all parts of data management throughout the
project.
A first version of the Data register with all datasets foreseen in I-Media-
Cities, containing the detailed data management information for each dataset,
has been created and added as an annex to this document. Since the Data
Register is a living document, it will be updated throughout the project
lifetime, to include previously unforeseen datasets.
Templates for the Terms of Use, Privacy Policy and I-Media-Cities in order to
collect the informed consent of users.
An update planning for this deliverable is also provided and details when
updates are scheduled, who is responsible for them and where updated versions
or update overview information can be found.
|
https://phaidra.univie.ac.at/o:1140797
|
Horizon 2020
|
1129_ENLIVEN_693989.md
|
# 3 Introduction
## 3.1 Project Summary
ENLIVEN (‘Encouraging Lifelong Learning for an Inclusive and Vibrant Europe’ –
H2020 Young Society 2015) is a research project supported by the European
Commission’s Horizon 2020 research framework (Project No. 693989). The project
responds to Call YOUNG-3-2015 (‘Lifelong learning for young adults: better
policies for growth and inclusion in Europe’). Its duration is 36 months and
it commenced on 1st October 2016.
ENLIVEN’s overarching objective is to provide an innovative model and
mechanism to support policy debate, policy formation and policy evaluation in
lifelong learning, focussing on the needs of today’s young adults, and
integrating theoretical and empirical perspectives from social and computer
sciences. It will generate an evidence-based analysis of where, when and why
policies have been effective, and develop a computer-based intelligent system
to improve policy-making. The project draws on two research fields – social
science and computer science – and combines expertise from both. Partner
research institutions are located in nine countries (Australia, Austria,
Belgium, Bulgaria, Estonia, Italy, Slovakia, Spain, and United Kingdom); some
research tasks will also be undertaken under a specific arrangement with
researchers at a university in Denmark.
**Objectives**
The specific objectives of ENLIVEN are to:
* Map and critically assess key elements of programmes implemented at EU, national and regional levels to support access to and participation in adult learning among excluded population groups and those at risk of social exclusion, assess how these have addressed disadvantage, inequality, and social exclusion, and helped overcome barriers to participation, and in what ways participation in education and training benefits the social and economic inclusion of population groups suffering from exclusion and cumulative disadvantage.
* Assess the impact of “system characteristics” (of initial and adult education, the labour market, the economy, and social protection) on aggregate participation rates (overall, and in various segments of adult education markets), and on the distribution of participation (with special reference to disadvantaged young adults and using gender-sensitive approaches).
* Assess the role of lifelong learning in developing a productive, efficient and competitive economy through investigating what learning potential and innovation ability exists within workplaces, what organisational models favour innovation and innovative training, and how effective learning actions are.
* Identify and map the nature and availability of data about adult and lifelong learning, and integrate these with new research findings from across the ENLIVEN project and, using data mining, establish a knowledge base for the development of an Intelligent Decision Support System to support policy making;
* Design and implement an Intelligent Decision-making Support System (IDSS), and test how this could adapt to new knowledge and learn from restoring and updating users’ experience interactively.
## 3.2 Types of Data
The main types of data the ENLIVEN project will be handling are as follows:
* _Existing national and international datasets_ . These are both quantitative (e.g. LFS, AES, PIAAC, ESS) and qualitative (e.g. reports from Eurydice, Cedefop). All these data are either anonymised (in the case of the quantitative data), or in the public domain. 1 None contain private or personal details regarding identifiable individuals. We will ensure we utilise existing datasets wherever possible, to ensure best practice in the project.
* Existing collections of aggregated data (e.g. Eurostat’s dissemination database, UNESCO’s database on education).
* Existing accessible quantitative scientific-use microdata sets e.g. LFS, AES, CVTS obtained from Eurostat
* Other quantitative scientific-use micro datasets not obtained from Eurostat (e.g.
PIAAC data obtained from the OECD) o Reports from (mainly) European agencies,
containing aggregated statistical data and various forms of qualitative data
e.g. Eurydice, Cedefop
* _Fieldwork data generated by ENLIVEN researchers_ . This will come from interviews with policy actors, programme managers, and young adults in educational, community and workplace situations. Fieldwork research will be conducted in a sample of EU member states selected to represent a diversity of socio-economic characteristics and institutional environments (AT, BE, BG, DK, EE, ES, IT, SK, UK), and in Australia.
* Qualitative Interviews with participants in educational programmes and with participants in organisational case studies (managers; workers in their first 10 years of employment) and their analysis
* Data collection within the participatory observation/action research phase of the case studies (in WP5) and their analysis.
* Expert Interviews; Interviews with representatives of educational providers/policy makers in the field of adult learning (WPs 1, 2, and 3) and with business interest organisations and trade unions/social movement organisations (WP7) and their analysis
## 3.3 Organisation of the ENLIVEN project
The ENLIVEN project is organised in eleven work packages (WPs). Nine of these
involve research of various kinds; one is concerned with dissemination of the
research and engagement with policy and practitioner communities; one is
concerned with project management and the integration of the various elements
of the research. The research WPs can be considered in three clusters: each
involves distinct types of empirical research. The broad relationship between
these WPs is shown in Fig. 1.1.
WP8
**Knowledge**
**Discovery for Evidence**
**-**
**based Policy**
**-**
**making**
WP9
**Development of**
**IDSS for Evidence**
**-**
**based Policy**
**-**
**making**
**WP4 Determinants of**
**Participation in Adult**
**Learning: A Macro**
**-**
**systemic**
**Approach**
WP5
**Organisational Structure of**
**Early Careers: HRM & Innovation **
WP6
**Quality of Work & Young **
**adults motivation & well **
**-**
**being**
WP7
**Understanding Role of**
**Young Workers’ Activism**
WP10
**Engagement, Dissemination & Impact **
WP11
**Project Management & Integration **
WP1
**Mapping EU & National **
**Policies & Programmes **
WP2
**Constraints on & **
**Facilitators of Participation**
WP3
**Role of EU governance in**
**Adult Learning Policy**
**Figure 1.1**
**ENLIVEN:**
**:**
**Organisational Structure**
# 4 Data Management
## 4.1 Principles
Data Management Plans (DMPs) are a key element of good data management. The
DMP for Enliven has been written with reference to the Guidelines on Data
Management in Horizon 2020 which specifically ask for provisions to be made
'FAIR', that is findable, accessible, interoperable and reusable. ENLIVEN will
strictly adhere to the ‘Data Protection Directive’ (Directive 95/46/EC) 16 on
the protection of individuals with regard to the processing of personal data
and on the free movement of such data, the Charter of Fundamental Rights of
The European Union and the European Charter for Researchers, including the
Code of Conduct for the Recruitment of Researchers. The European Commission
has now issued a new directive on the Protection of Personal Data. The
Regulation entered into force on 24 May 2016, and it will apply from 25 May
2018. The ENLIVEN researchers will comply with its requirements.
The DMP covers:
* What data the project will produce
* How the data will be used, managed and stored
* How the data will be made accessible for future research
Additional information on data preservation and data sharing protocols are
covered in full in our _Deliverable 11.2 Ethical Procedures (Part A)_ .
The Project Team will seek guidance from the University of Nottingham’s
relevant Professional Services where any uncertainties occur, including
Libraries, Research and Learning Resources (LRLR) and Information Services
(IS).
## 4.2 Organisation
The Coordinator, Professor John Holford, and the ENLIVEN Management Board,
supported by the Project Co-ordination Team at the University of Nottingham (a
Senior Research Fellow, Sharon Clancy, and a Project Administrator, Ruth
Elmer), are responsible for ensuring that data are handled by all Consortium
Members according to the DMP.
The DMP will be updated as required through the life of the project.
Responsibility for this lies with the Project Co-ordinator, supported at
Nottingham by the Project Administrator and the Senior Research Fellow.
DMP updating will take place whenever significant changes arise. These might
include new data emerging, changes in consortium policies, changes in
consortium composition and external factors (e.g. new consortium members
joining or old members leaving). In any case, the DMP will be reviewed on a
six monthly basis at every second Management Board meeting. It will be a
standing agenda item for the meetings, to allow for discussion and updating on
an ongoing basis.
## 4.3 Types of Data
Beyond research literature and reports published by various authorities and
institutions, the types of data which ENLIVEN will manage include:
* Aggregated secondary statistical data (tables) obtained from national statistical providers, Eurostat or other European or transnational data providers (CEDEFOP; Eurofound; OECD, UNESCO)
* Secondary data in the form of anonymised scientific use micro data sets, obtained mainly from Eurostat, and partly from other European or transnational organisations (e.g. ESS data from the ESS ERIC; the EJS data from CEDEFOP 2 , PIAAC data from the OECD;) 3 . and
* primary data (gathered by project researchers), including data based on qualitative non- or semi-standardised interviews, small-scale written surveys and data gathered via participator observation during the field work (action research phase) in WP 5.
### 4.3.a Aggregated secondary statistical data
Aggregated statistical data will be obtained from the various data
repositories (e.g. Eurostat dissemination data base); data will be arranged,
analysed, stored and reported in an appropriate form.
**4.3.b Secondary data in form of anonymised micro data sets:**
Micro data sets will be obtained according to the applicable procedures. Rules
set by the data providers (in particular, by Eurostat) for data storage, use,
dissemination and the publication of results based on the analysis will be
strictly followed.
#### 4.3.c Primary data collection
Within the ENLIVEN project, primary data collection will predominantly involve
the collection of qualitative data. Interviews will take place with the
following categories of people (the categories are not mutually exclusive):
policy makers; representatives of business interest organisations;
representatives and members of trade unions and social movement organisations;
participants in educational programmes; participants in organisational case
studies (managers, line managers; HRD/HRM specialists; legal employee
representatives; employees in their first 10 years of occupational careers).
Particular emphasis will be given to interviews with employees in their early
careers, as 3-5 young adults working for the organisations will be interviewed
twice, with the goal of learning about their previous learning biographies and
their current perception of learning opportunities available in the workplace.
Records will take the form of interview recordings and transcripts.
Participatory observation and action research case studies are undertaken as
part of WPs 5-7. WP 5 has an action research phase, in which small projects
(learning projects, workshops, training) will be arranged with the members of
the case study organisations. These will be tailored to the particular
conditions of the organisation concerned, will run for up to 6 months and
allow for on-site participatory observations for at least 4 working days.
Records will be taken in the form of research notes made by the researchers
involved in the field phase.
## 4.4 Data Use and Protection
In order to adhere to the principles of the Data Protection Directive and
national legislation, and to the project’s own ethical principles, the ENLIVEN
consortium will apply the standards and procedures set out below. These are
designed to minimise the risk of misuse of data by third parties, and to
safeguard anonymity of research participants (in respect both of secondary use
of existing datasets and data collected by the ENLIVEN project).
**4.4.a Secondary analysis of EUROSTAT and related scientific-use micro data
files** International quantitative micro datasets used (e.g. LFS, AES, PIAAC,
ESS) are anonymised. None contain private or personal details regarding
identifiable individuals. In the case of data made available by the European
Statistical System, Eurostat imposes a strict framework to avoid misuse of
(already anonymised) datasets. The key emphasis is on avoiding leakage of data
to third parties who may break the anonymization by merging datasets with
other datasets (for example, register data from the Social Security system).
In WPs 2-4, mainly 4 scientific-use micro datasets provided by Eurostat will
be used (LFS, AES, CVTS). When preparing the scientific-use files, Eurostat
minimises the risk of disclosure of research participants’ identities by
removing information and by merging information into broader categories. It
also requires research organisations applying to use these micro data to
comply with detailed standards, which is, in this instance, the University of
Leuven. Data will only be shared with ENLIVEN partners who were listed in the
University of Leuven’s data request. All partners will fully comply with the
requirements 5 set for the use of Eurostat’s micro data files, in particular
with regard to safe storage of micro data and safeguarding of respondents’
identities in outputs based on the micro data used.
With regard to safe storage of micro data, ENLIVEN partners will:
* Ensure that they identify a senior researcher who will be responsible for storing the medium containing the confidential data in his/her office and will only allow access to them to the authorised researchers identified above.
* Ensure that research premises where the micro data are used and stored are secure.
* Ensure that only authorised researchers who have signed a declaration of confidentiality have access to the micro data and that data are not copied in any way
* Store Eurostat Data-CD-ROMs and any information provided by Eurostat for data decryption in separate locked cabinets, making it unlikely that any third party can get both sets of information required for using the data sets.
* Take precautions to prevent any copies of the micro data being made, and to prevent the data being transmitted via the internet or within an organisation’s network (e.g. by using a standalone computer connected neither with the internet nor with the organisation’s local network). No printer will be attached to computers used for micro-data analysis.
* Document all use of the microdata set (who has used the dataset, on which dates, and for what purposes).
* Share data only as required amongst the members of the national research team undertaking a specific piece of research; that team will be responsible for keeping the data safe and for removing identifying information. Any such data will be shared only on this anonymised basis with the wider (international) ENLIVEN project research team.
* Ensure that all data, and other confidential items, are kept securely (i.e. accessible only to relevant members of the team), and that when such data and items are shared between partners, this is done in a secure way.
* Retain all data, and all other confidential items, on a secure password-protected site at the University of Nottingham (the “R: Drive”). Only members of the team will have access to this file store; it is managed by the Project Administrator (Ruth Elmer).
* Destroy datasets (and any output based on the datasets) in accordance with contractual requirements (e.g. for safeguarding non-disclosure of individuals, households, enterprises).
### 4.4.b Interview Transcription and Data Protection
The procedures applied for the processing of qualitative data in line with the
requirements of data protection and ethical principles have been developed in
detail in deliverable 11.2 and are summarised in Tables at 4.1 and 4.2 in the
annex. They include:
* Safeguarding anonymity of research participants;
* Safeguarding confidential information;
* Restricting recording of sensitive information to the minimum required for answering the research questions and excluding any information from recordings which is either not relevant or potentially harmful;
* Ensuring fully informed consent from research participants.
Due to the difficulty in guaranteeing that recorded interviews will not
contain personally identifiable data, such recordings will be subject to the
Data Protection Act (or equivalent legislation in other countries). To ensure
the security of the recording and its content, a research-institute-owned
encrypted digital recorder or personally owned devices complying with the
University of Nottingham’s policy and procedures relating to the use of mobile
devices and remote working as set out in the Information Security Policy 6
will be used.
Transcription will be carried out where possible within the premises of the
research partners. In case of the use of external specialised service
providers, the following rules will be applied. Recordings will be securely
transferred to transcribers, for instance by secure upload to an approved
transcriber’s website. Encryption keys will be sent to the recipient by some
other means, e.g. telephone, email, and will not be sent with the encrypted
recording. Similar care will be taken with security of transfers of completed
transcriptions from transcribers and to the safe return and/or secure deletion
of recordings. Transcription agencies used will have a track record of
academic transcription work and will be expected to sign undertakings
confirming that the information is kept strictly confidential.
Informed consent forms will be distributed to all participants. They will be
collected and stored in a locked facility, separate from the research
material. Interviews will be analysed only in line with predefined research
questions, as stated in the proposal and agreed on with the research
participants in the informed consent process. The use of data for
transcription purposes and the access to anonymised data sets for future
projects will also be outlined in the informed consent forms.
Confidential or sensitive data will be kept in our password-protected data
confidential ENLIVEN filestore, on the Research drive (“R: Drive”) of the
University of Nottingham’s computer system. Only members of the team have
access to this filestore. The University of Nottingham's Research Filestore is
centrally managed and provided by University of Nottingham Information
Services, and is subject to IS Business Continuity Planning, IS Information
Security Policy and IS Change and Release standards. The service includes
failover support across two separate data centres. Full backups to tape are
performed once a week, while incremental backups are performed nightly. Tapes
are retained for 112 days, after which they are re-used or securely disposed
of. Access to the service requires authorised University of Nottingham
credentials, and is restricted by the institutional firewall.
Data storage will comply with the UK Data Protection Act, equivalent national
legislation, and applicable University of Nottingham and partner institutional
policies. The Nottingham policy requires that data be retained intact for a
period of at least seven years from the date of any publication which is based
upon them. Data will be stored in their original form – i.e. tapes/discs etc.
will not be deleted and reused, but kept securely.
## 4.5 Documentation
### 4.5.a Documentation Reports
Publications (e.g. books, articles in peer reviewed and professional journals,
and presentations at scientific conferences), along with engagement with
policy-makers and other public communities play a vital role in raising
awareness about ENLIVEN results. To this end:
* All publications will be made available by open access.
* The project’s web site, maintained by the University of Edinburgh, will have hierarchical levels for public engagement, interested stakeholders and detailed scientific information to increase and further participation and collaboration.
* Unless required otherwise by confidentiality undertakings given to research participants, data sets (anonymised as indicated below) used for analysis will be deposited on institutional data repositories and/or through the UK Data Archive at the University of Essex and/or through national data archives. (The Co-ordinator will approach the UK Data Archive with a view to making all datasets generated by the project available through that source at the end of the project.)
### 4.5.b Project Management Documents
**All internal board, committee, work package and meeting/workshop reports
will not be included in the ENLIVEN Dropbox folder, but will be stored on the
University of Nottingham’s system.** Confidential items will be maintained in
a password-protected confidential ENLIVEN filestore on the University of
Nottingham’s computer system. This will be accessible to members of all
project teams, but will otherwise be confidential until and unless it is
agreed that they should be made public.
**On the basis of this management documentation, annual reports on the
project’s activities and progress will be prepared (using the quarterly
Coordinators Reports used for Management Board Meetings). These reports will
be approved by the Management Board. One report will be prepared which will be
for public use (e.g. on website and for Advisory Board). However, if there is
any sensitive or IPR-restricted information there will be a ‘private’ version
too that this can be included in.**
#### 4.5.c Website
The web site is hierarchical in design with initial entry levels designed to
promote the subject and the project in general, and H2020, in order to
stimulate public engagement and to engage with interested stakeholders. In
line with our data utility objectives, intermediate public levels will also be
designed to support school children and their curricular activities. Higher
open access research levels will detail scientific and technical progress as
well as comprehensive information on dissemination activities.
In addition to providing links to each partner’s respective normal research
web pages, the web site will include various resources such as on-line
publications, benchmark problems, discussion groups and links to other related
sites.
## 4.6 Hardware and Software
### 4.6.a Intelligent Decision Support System
University of Nottingham resources and equipment (a desktop and a laptop)
supported by funding from the ENLIVEN project will be utilised for the IDSS
component of the project. IBM SPSS modeller software will be used. The
University of Nottingham possesses a group licence for this software.
Additional software usage will be covered by open source licences.
### 4.6.b Data Backup and Recovery
The University of Nottingham's Research Filestore (“R: Drive”) is centrally
managed and provided by
University of Nottingham Information Services (IS), and is subject to IS
Business Continuity Planning, IS Information Security Policy and IS Change and
Release standards. The service includes failover support across two separate
data centres. Full backups to tape are performed once a week, while
incremental backups are performed nightly. Tapes are retained for 112 days,
after which they are reused or securely disposed of. Access to the service
requires authorised University of Nottingham credentials, and is restricted by
the institutional firewall.
## 4.7 Intellectual Property and Ownership
### 4.7.a Intellectual Property
The ENLIVEN partners will comply with intellectual property rights regulations
and provisions in accordance with Horizon 2020 principles:
* All data generated will be centrally stored on the Co-ordinator’s central computer system (University of Nottingham), using the University’s standard archiving data control procedures and routinely (daily) backed up onto secure areas on a central server, in line with the above statement on data backup and recovery.
* The Project Administrator (Ruth Elmer) will hold management responsibility for these curation activities.
* All Consortium members are responsible for complying with the intellectual property regulations and provisions.
* At the earliest opportunity and appropriate times, data will be made publicly available by presentation at internal and external conferences and by publication in peer-reviewed journals. This is outlined in the ENLIVEN Dissemination plan.
* All data presented or published will be anonymised. Immediately after publication access to original anonymised data sets will be freely available on appropriate request, adhering to relevant regulatory requirements and ethical use of data approval. A condition of such access will be subsequent acknowledgment by those applying.
### 4.7.b Joint ownership
Joint ownership is governed by Grant Agreement Article 26.2 with the following
additions:
_Unless otherwise agreed: - Each of the joint owners shall be entitled to use
their jointly owned Results for non-commercial research activities on a
royalty-free basis, and without requiring the prior consent of the other joint
owner(s), and each of the joint owners shall be entitled to otherwise Exploit
the jointly owned Results and to grant non-exclusive licenses to third parties
(without any right to sub-license), if the other joint owners are given: (a)
At least 45 calendar days advance notice; and (b) Fair and Reasonable
compensation_
## 4.8 Open Access
Data with an acknowledged long-term value will be preserved and remain
accessible and useable for future research. Data which supports and validates
published research will also be preserved and, as far as possible, openly
accessible to other interested researchers. All output, including academic
publications, will be made available by ‘Open Access’ so that every paper will
be available to readers without additional cost. Where Green route publishing
is not possible, authors will be provided with funding to publish through the
Gold route.
All data presented or published will be in an anonymised form. Immediately
post-publication access to original anonymised data sets will be made freely
available on appropriate request, wherever possible through institutional data
repositories. Release of such datasets will be in accordance with relevant
regulatory requirements, especially on the ethical use of data. A condition of
access and reuse of the data will be subsequent acknowledgment of ENLIVEN as a
source.
## 4.9 Quality Assurance
ENLIVEN has an annual Quality Assurance System reporting plan which details
the project deliverables each partner is responsible for delivering, and the
peer reviewers responsible for checking them. The document also details the
timescales for each deliverable, including the formal date for submission and
internal timelines. The latter include dates for first drafts to be sent to
the Co-ordinator by the Responsible Partner, the deadline for the Nottingham
team to distribute to all Partners, the deadline for feedback from all
Partners and the date for feedback from Peer Reviewers. Certain protocols and
procedures are also outlined: e.g., when documents are sent to all Partners,
this is for _comment only_ , mainly in respect of accuracy in relation to
statements about partners’ countries or specific research input. Feedback must
be given in the body of an email ( _not_ via word ‘tracking’ on the document
itself as this creates a very high volume of work). Partners have three
working days to respond with any comment; otherwise, correctness is assumed.
Finally, the _peer reviewer offers detailed review/amendment_ regarding
overall coherence and quality (with reference to the Quality Criteria
document) - and comments and changes can be made in word tracking.
# 5 Work-Package-specific Data Issues
This section sets requirements for data management related to specific work-
package tasks.
## 5.1 WP1: Mapping European and national policies and programmes, and their
contribution to economic and social inclusion
This WP maps and investigates, at European, national, and subnational levels,
**policies and funding schemes** to tackle disadvantage, inequality and social
exclusion. It includes a **comparative study** of policies and programmes in
selected European countries and Australia, and analyses the role system
characteristics (policy regimes) play in participation and inequalities. The
main methods used are critical discourse analysis of **policy documents and
funding schemes** . Country-based analysis of selected education programmes
involving **in-depth case studies** (based in part on interviews) will collect
data about individuals’ employment (e.g., ex-ante and ex-post, working
conditions), empowerment (e.g., self-perceptions of personal and collective
agency) and active citizenship (e.g., political participation, participation
in community life). Learners will be selected for interview according to
programme, funding received, orientation, and provider (public/private).
## 5.2 WP2: Constraints and facilitators of access and participation
This WP **analyses** national institutional architecture and qualifications
frameworks **using policy documents and key informant interviews.** Within
each participating country, we will produce **a map of the institutional
architecture** with a view to assessing its flexibility and inclusivity, based
on policy documents and reviews, including Eurydice reports. All partners will
analyse aggregated administrative and survey data, national and local policy
documents, and qualifications frameworks, to illuminate institutional
architecture in their jurisdiction. Up to ten **key informant interviews**
will be conducted in each jurisdiction **with policy-makers, service providers
and service users** to provide **commentary** on the extent to which official
accounts are consistent with the practical experience of service users and
those involved in delivery of services. **Interviews with** **young adults**
(some of whom will be potentially vulnerable adults) will explore barriers to
participation among various groups.
## 5.3 WP3: The role of European governance in adult education & learning
policy
This WP **maps the main actors** contributing to adult education policy
developments in Europe. It identifies key policy actors at global, regional,
national and sub-national levels via a thorough **web search.** It improves
understanding of the coordinating function of European governance in adult
education by identification and examination of governance mechanisms
coordinated by or under the supervision of EU shared institutions, how they
work, and how they have developed. It also looks at evidence of these
governance mechanisms, and related EU policies, influencing public and
regulatory agencies intervention in adult education markets at national and
sub-national levels. It also **analyses the development of taxonomies and
indicators in European adult education** and their **use of crossnational
survey data (e.g. PIAAC)** to inform policy. Finally, it examines how
taxonomies and indicators have developed in European adult education, and
interrogates how PIAAC connects with governance mechanisms under the
supervision of EU shared institutions.
## 5.4 WP4: Improving our understanding of the effect of system
characteristics by building stronger data and adding a longitudinal, regional
& sectoral focus
This WP constructs a **pseudo-panel data set on lifelong learning
participation** , starting from **the time series of the Labour Force Survey
(LFS),** in order to investigate how lifelong learning participation and
system characteristics have developed over time. LFS also permits breaking-up
of the sample by NUTSregion (Nomenclature of Territorial Units for Statistics)
and by employment sector. In order to improve develop **richer contextual
information,** the LFS-based pseudo-panel data set will be enriched with
**more detailed information from other datasets** (e.g., AES and PIAAC). To
facilitate longitudinal, regional and sectoral analysis, indicator time series
will be constructed to be linked to the panel data set. New indicators will be
developed for characteristics that have not yet been fully covered, such as
indicators of the demand for general, sector-specific and job-specific skills.
However, micro data sets will not be merged in a way to avoid any risk of
potentially compromising the anonymity of any individual units represented.
## 5.5 WPs5–7: Studying the role of workplace learning and patterns of work
organisations for early career structuration; qualitative interviews on
learning biographies
WP 5 and 6 utilise **in-depth organisational case studies** . 16 in-depth
organisational case studies will be undertaken, drawing on 64 interviews with
managers, line managers, HRD experts and legal representatives of employees,
and 64 in-depth interviews with adults in their first 10 years of their
occupational career will be conducted. Each organisational case study will
include two interviews with each of four managers, HRM/HRD professionals and
line managers and at least one interview with an employee interest
representative. During an **action research phase** small ‘learning projects’
will be arranged with the members of the case study organisation, tailored to
the particular organisation, and will run for up to 6 months. In WP6, some
potentially **sensitive data** will be collected (for example, **personal
reports on observed conflicts in the workplace);** the young adults (aged
18-35) will be invited to share important aspects of their learning
biographies, focusing on job-related non-formal and informal learning in the
workplace.
In WP7, beyond a broad review of the relevant literature, **Case studies**
will be implemented in three countries (ES, AT, SK) on three initiatives
advocating better employment conditions during early career stages, including
young employees among their activists. Case studies will be based on two
expert interviews (one with an official speaker for the initiatives, one with
a representative of a corresponding employer interest organisation) and one
interview with a young activist. Where feasible and not running against the
outlined ethical principles, the activist interviewed will be employed by the
organisations studied in WP5 and 6. For the interviews with the young
activists, particular safeguards will be taken to shield them from any
potential harm resulting from their participation in the research. Further
detailed data management in these workpackages is provided in Table at 4.3
below.
## 5.6 WP8: Knowledge discovery on evidence-based policy making in
participating countries; & WP9: Establishment of Intelligent Decision Support
System for evidence-based policy making
This work package involves knowledge discovery based on data available from
international organisations (e.g., European Commission, OECD, UNESCO) and
other ENLIVEN research findings to establish a **knowledge based methodology**
(Case Based Reasoning: CBR) designed to facilitate analytical insights,
informed governing of ‘policy problems’, and modelling of policy making on
ALE/LLL programmes in the EU. A knowledge based system requires knowledge
acquisition, which will begin with an in-depth analysis, working with other
WPs, of **existing data** (e.g. ESS, LFS/SILC, Eurydice, Cedefop and Eurostat)
and case studies. Based on the analysis, data mining techniques will identify
case representation; investigations will then be conducted based on case
representation and training in the case base to identify similarity measure
models. These different stages are conducted iteratively, until the
performance of the CBR system provides satisfactory evaluation metrics
(accuracy and user feedback, etc.). Establishing an IDSS is an iterative
process. Findings from WPs1-7 will contribute to **building common models** of
policy making, reflected in the IDSS procedure.
## 5.7 WP10-11: Dissemination and Project Management & Integration
A **data repository** , comprising anonymised data gathered during the
project, and main findings, will be maintained for further use. This will
include transcripts of interviews as well as data collected for case studies.
On publication of a related output, or on conclusion of the project, these
data will be transferred from the University of Nottingham’s Research Drive to
the University’s Research Data Management Repository (
_https://rdmc.nottingham.ac.uk/_ ) and/or to the UK Data Archive (
_http://www.data-archive.ac.uk/home_ ) .
Where feasible, partners will also deposit their data in institutional or
national data repositories. This will be done in strict accordance with
European and individual country Data Protection legislation, and in the light
of the standards and guidance developed by the Consortium of European Social
Science Data Archives (CESSDA : _https://cessda.net/_ ) . This will include
ensuring that permission has been given by individuals and organisations that
contribute data in any form not already public. All stored data will be
thoroughly anonymised (except in the case of expert interviewees who
explicitly decide not to be anonymous).
The data will be citable and will be given a Digital Object Identifier code
(DOI) within the data repository which will be guaranteed for 7 years, in line
with Table 2, below.
# 6 Appendices
## 6.1 Table 1: Key elements of the framework to ensure anonymization within
the ENLIVEN research process (for storage/use within the project): (to be
refined within the research project)
<table>
<tr>
<th>
**Level**
</th>
<th>
**Item**
</th>
<th>
**Description**
</th>
<th>
**When**
</th> </tr>
<tr>
<td>
1
</td>
<td>
Removing names/using replacements
</td>
<td>
Family name (removed); forename (replaced by a name typical for the gender,
generation and the community and **not** the name of another person in the
sample surveyed); replacing the name of the employing organisation; replacing
the name of the
geographic entity (town, etc.)
</td>
<td>
Immediately, when transcriptions are made.
</td> </tr>
<tr>
<td>
2
</td>
<td>
Removing
indirect identifier
</td>
<td>
Transforming information on educational attainment, age, migrant background of
a particular kind, household characteristics into categories (e.g.
25 to a 25-29 age brackets).
</td>
<td>
When including transcripts in the R: Drive.
</td> </tr>
<tr>
<td>
3
</td>
<td>
Removing information allowing an indirect identification
</td>
<td>
Removing information which allows the
identification of a person by her/his particularities, habits etc. for an
observer familiar with the particular context.
</td>
<td>
When choosing
information for publication (biographical vignettes, quotations); before
preparing interview transcripts for archiving (if any).
</td> </tr>
<tr>
<td>
4
</td>
<td>
Alienation and strategic replacement of important information
</td>
<td>
Replacing important (“telling”) details which potentially include a risk of
de-anonymization by related details capable of communicating comparable
information.
</td>
<td>
When presenting information in biographical vignettes.
</td> </tr> </table>
## 6.2 Table 2: Processing of data in the qualitative research implemented by
the ENLIVEN project
**Expert Interviews; Interviews with Qualitative Interviews with participants
in Data collection within the**
**representatives of business interest organisations educational programmes &
with participant in participatory observation/action and trade unions/social
movement organisations organisational case studies _research phase of the case
studies_ **
<table>
<tr>
<th>
**Informed consent**
</th>
<th>
Forms on informed consent will be collected and stored on the R: Drive
separate from other research material.
</th>
<th>
Forms on informed consent will be collected and stored on the R: Drive
separate from other research material.
</th>
<th>
Informed consent forms for participants in the action research phase will be
will be collected and stored on the R: Drive separate from other research
material
</th> </tr>
<tr>
<td>
**Collection**
</td>
<td>
Face-to-face interviews or phone interviews; onsite interviews in surveyed
organisations
</td>
<td>
Face-to-Face interviews (all); on-site case study enterprises (managers, line-
managers); neutral environment at choice of the interviewee (all others)
</td>
<td>
Participatory
observation/participation in the
action research module
</td> </tr>
<tr>
<td>
**Recording**
</td>
<td>
Personal identifiers (name, age, function, related information): paper and
pencil; experts are free to decide whether their names/the names of their
organisations should be reported or should anonymised; main interview: digital
voice recording (a numeric code links the voice recording to the personal
identifier)
</td>
<td>
Personal identifiers (name, age, function, related information): paper and
pencil; main interview: digital voice recording (a numeric code links the
voice recording to the personal identifier)
</td>
<td>
Research Diary for on-site participant observation – paper and pencil; all
personal identifiers will immediately be replaced by codes representing
particular research participants (or omitted, where not relevant for the
research activity)
</td> </tr> </table>
**Organisation** When participants choose anonymity or are **and storage**
regarded as vulnerable: personal identifiers (single paper copy); information
will be stored in a locked compartment. Information on personal identifies
will be collected and stored electronically only in an anonymised version,
attributing pseudonyms, using broader categories (e.g. age); two digital
copies of the main interview will be stored in the R: drive separate from
other research material; copies will be stored in a locked compartment
separate from the papers with the personal identifiers; interviews will be
transcribed (verbatim transcription); passages where personal
When participants choose anonymity or are Notes must be stored in a locked
regarded as vulnerable: personal identifiers (single room and a locked
compartment
paper copy); information must be stored in a locked compartment. Information
on personal identifiers will be collected and stored electronically only in an
anonymised version, attributing pseudonyms and using broader categories (e.g.
age); two digital copies of the main interview will be stored in the R: drive
separate from other research material; copies will be stored in a locked
compartment, separate from the papers with personal identifiers; interviews
will be transcribed (verbatim); passages where personal identifiers of the
interviewee or related
<table>
<tr>
<th>
</th>
<th>
identifiers of the interviewee or related persons (e.g. name of a co-worker)
are revealed will not be transcribed, but will be immediately replaced in a
way safeguarding anonymity [e.g. “the interviewee”; “an interviewee’s co-
worker]; individual transcripts will be further anonymised by the responsible
researcher prior to compiling the transcripts within the software package
used. Transcripts will be held only within the originating partner research
organisation and the University of Nottingham R: Drive.
</th>
<th>
persons (e.g. name of a co-worker) are revealed will not be transcribed, but
will be immediately replaced in a way safeguarding anonymity [e.g. “the
interviewee”; “an interviewee’s co-worker]; individual transcripts will be
further anonymised by the responsible researcher prior to compiling the
interview transcripts within the software package used; only anonymised
interviews will be saved in the R Drive. Transcripts will be held only within
the originating partner research organisation and the University of Nottingham
R: Drive.
</th>
<th>
</th> </tr>
<tr>
<td>
**Adaption and alteration**
</td>
<td>
When participants choose anonymity or are regarded as vulnerable: personal
identifiers will be anonymised; verbatim transcripts will be anonymised in a
two-step procedure (immediate replacement of identifiers during transcription;
further removal of any information helping to identify the interviewee prior
to compilation of interviews; a summary of each interview will provided
[10,000 signs] and translated into English; for the summary, any further
information which risks disclosure of respondent identity will
be removed/replaced by more general information; at this stage, any sensitive
information (e.g. on health condition, on sexual orientation etc.) not
required for the research process will also either be removed or replaced by a
more neutral statement (e.g. a description of a concrete case of misconduct
will be replaced by a statement that a case of misconduct has been reported).
</td>
<td>
Personal identifiers will be anonymised; verbatim transcripts will be
anonymised in a two-step procedure (immediate replacement of identifiers
during transcription; further removal of any information helping to identify
the interviewee prior to the compilation of interviews; a summary of each
interview will provided [10,000 signs] and translated into English; for the
summary, any further information which risks disclosure of the respondent’s
identity will be removed/replaced by more general information; at this stage,
any sensitive information (e.g. on health condition, on sexual orientation
etc.) not required for the research process will also either be removed or
replaced by a more neutral statement (e.g. a description of a concrete case of
misconduct will be replaced by a statement that a case of misconduct has been
reported).
</td>
<td>
Researchers will produce a detailed summary of their notes as an electronic
document for each day of observation, but removing all personal identifiers;
potentially harmful information/sensitive information not relevant for the
project must not be included in the summaries
</td> </tr> </table>
**Retrieval and** Interview transcripts (with alterations required for
Interview transcripts (with alterations required for (Anonymised) summaries of
on-site **consultation** anonymisation) will be stored [in original
anonymisation) will be stored [in original language] visits/participatory
observation will
language] within the software used for qualitative within the software used
for qualitative text analysis, be shared within the local research
text analysis, separately in each partner’s separately in each partner’s
organisation (and saved team organisation (and saved on the University of on
the University of Nottingham R: Drive);
<table>
<tr>
<th>
</th>
<th>
Nottingham R: Drive); otherwise, within the partnership, only translated
interview summaries will be exchanged and compiled in one file (this will be
the base for cross-country comparative analysis).
</th>
<th>
otherwise, within the partnership, only translated interview summaries will be
exchanged and compiled in one file (this will be the base for crosscountry
comparative analysis).
</th>
<th>
</th> </tr>
<tr>
<td>
**Use**
</td>
<td>
Interviews will be analysed only in line with predefined research questions,
as stated in the proposal and agreed with the research participants in the
informed consent process.
</td>
<td>
Interviews will be analysed only in line with predefined research questions,
as stated in the proposal and agreed on with the research participants in the
informed consent process.
</td>
<td>
Interviews will be analysed only in line with predefined research questions,
as stated in the proposal and agreed on with the research participants in the
informed consent process.
</td> </tr>
<tr>
<td>
**Dissemination**
</td>
<td>
Original language interview transcripts will not be disseminated but will
remain with the team of the research organisation responsible for the
research. Fully anonymised transcripts will be saved on the University of
Nottingham R: Drive. They will also be made available along with summaries (in
English, and from which all information potentially disclosing the
participants’ identities is removed) at the end of the project on a suitable
data archive.
</td>
<td>
Original language interview transcripts will not be disseminated, but will
remain with the team of the research organisation responsible for the
research. Fully anonymised transcripts will be saved on the University of
Nottingham R: Drive. They will also be made available, along with summaries
(in English, and from which all information potentially disclosing the
participants’ identities is removed) at the end of the project on a suitable
data archive.
</td>
<td>
Research notes will not be disseminated, but will remain with the team of the
research organisation responsible.
</td> </tr>
<tr>
<td>
**Alignment and**
**Combination**
</td>
<td>
Expert interviews will be analysed in combination, however, no further risks
to confidentiality of information are expected
</td>
<td>
Interview transcripts of respondents from each organisation will be analysed
in combination; special attention will be paid to the risk of disclosure of
respondents’ identities when bringing the information from the various
interviews and information from the participatory observations together;
information which carries the risk of disclosure will be omitted
</td>
<td>
Summaries of research notes will be combined for each organisation studied and
merged with the transcripts of the interviews; special attention will be paid
to the risk of disclosing respondents’ identities when material is brought
together; information which carries
the risk of disclosure will be omitted
</td> </tr> </table>
**Deletion/** Data will be retained intact for a period of at least
**Destruction** seven years from the date of any publication which is based
upon them. Data will be stored in their original form – i.e. tapes/discs etc.
will not be deleted and reused, but kept securely. Except
Data will be retained intact for a period of at least Personal field notes
(paper and seven years from the date of any publication which pencil) will be
destroyed at the end is based upon them. Data will be stored in their of the
project (on acceptance of the original form – i.e. tapes/discs etc. will not
be final report).
deleted and reused, but kept securely. Except where
where individually agreed by the research individually agreed by the research
participant, all participant, all records which could lead to records which
could lead to identification of identification of individual persons will be
individual persons will be destroyed at the end of destroyed at the end of the
project (on acceptance the project (on acceptance of the final report by the
of the final report by the Commission). Commission).
23
|
https://phaidra.univie.ac.at/o:1140797
|
Horizon 2020
|
1130_E2District_696009.md
|
EXECUTIVE SUMMARY
The European Commission (EC) is enabling access to, and reuse of, research
data generated by Horizon 2020 projects through the Open Research Data Pilot
(ORD Pilot) as part of its ambition to make research data and publications
openly available with a view to accelerating scientific progress, aiding the
validation of project outcomes, and making scientific research more
transparent in general.
As the E2District project is participating in the ORD Pilot, project partners
are required to deliver a Data Management Plan (DMP). The DMP is not a fixed
document and will, therefore, evolve throughout the E2District project
according to the progress of project activities. Subsequently, a mid-term DMP
will be required and a final DMP when the project reaches completion.
Therefore, this deliverable provides the first version of the E2District Data
Management Plan for the datasets captured or processed inside the project,
according to the guidelines published by the EC. The purpose of the DMP is to
identify how data collected or generated by the E2District project will be
organised, stored and shared and to specify what type of data will be made
publicly available by the project (open access) and how. Suitable public
repositories to store the data will also be identified and, where necessary,
tools that help others to use the data will be provided.
This report has been prepared by taking into consideration the template of the
“Guidelines on Data Management in Horizon 2020”, 1 guidance via DMP Online
2 and OpenAIRE/EUDAT webinar and presentations 2 .
Data Management Plan (DMP)
## 1.1 AIMS AND OBJECTIVES OF THE DATA MANAGEMENT PLAN
The purpose of this deliverable is to describe the data management life cycle
for all datasets that are being collected, processed and generated by the
E2District research project and the specific conditions that are attached to
them. The report outlines, as far as it is possible to do so at this stage,
how research data will be handled during and after the project, what precise
data will be collected, processed or generated, what methodology and standards
will be applied, whether data will be shared/made open access and how it will
be shared, and how the data will be curated & preserved.
## 1.2 INTENDED AUDIENCE
The E2District consortium partners are the primary audience for the DMP. The
report aims to establish clear practices in relation to data management
between the consortium’s five partner organisations.
The second audience for this report comprises the E2District project
dissemination target groups as identified by the E2District Dissemination and
Communication Plan (D6.1). This group includes DHC managers, DHC operators,
subscribers, end-users, DHC owners, local authorities, designers, technical
providers, investors and the community of researchers involved in related
projects/initiatives.
As a participant of the Horizon 2020 Open Data pilot, the project is committed
to Open Access Publishing and is prioritising publication venues and promoting
Open Access to its publications where possible. Where feasible, the project
will openly make available through open access repositories, baseline data
from the demo sites, statistics and measurements from experiments, business
models and key stakeholder surveys and questionnaires. In conjunction with an
espousal of FAIR DATA practice, the DMP’s establishment of consistent data
practices will increase the efficiency of data handling throughout the
lifespan of the project. Thus, the data will reach more people, have a greater
impact, avoid duplication of efforts and preserve data for future researchers.
## 1.3 UPDATING THE DATA MANAGEMENT PLAN
This is the initial DMP which will be updated throughout the project cycle
whenever significant changes arise in the project such as (i) new datasets,
(ii) changes in consortium policies and/or (iii) external factors.
A mid-term DMP will be released in M18 which will address a number of
questions suggested in the Horizon 2020 guidelines 3 (EC DG R&I, 2015):
1. Discoverable
Are the data and associated software produced and/or used in the project
discoverable (and readily located), identifiable by means of a standard
identification mechanism (e.g. Digital Object Identifier)?
2. Accessible
1
Are the data and associated software produced and/or used in the project
accessible and in what modalities, scope, licenses (e.g. licensing framework
for research and education, embargo periods, commercial exploitation, etc.)?
3. Assessable and Intelligible:
Are the data and associated software produced and/or used in the project
assessable for and intelligible to third parties in contexts such as
scientific scrutiny and peer review (e.g. are the minimal datasets handled
together with scientific papers for the purpose of peer review, are data
provided in a way that judgments can be made about their reliability and the
competence of those who created them?).
4. Usable beyond the original purpose for which it was collected
Are the data and associated software produced and/or used in the project
useable by third parties even long time after the collection of the data (e.g.
is the data safely stored in certified repositories for long term preservation
and curation; is it stored together with the minimum software, metadata and
documentation to make it useful; is the data useful for the wider public needs
and usable for the likely purposes of non-specialists)?
5. Interoperable to specific quality standards:
Are the data and associated software produced and/or used in the project
interoperable allowing data exchange between researchers, institutions,
organisations, countries, etc. (e.g. adhering to standards for data
annotation, data exchange, compliant with available software applications, and
allowing recombinations with different datasets from different origins)?
The mid-term DMP will be followed by a final report at the end of the project.
## 1.4 PURPOSE OF DATA COLLECTION/GENERATION & RELATION TO PROJECT OBJECTIVES
The main objective of the E2District project is to develop, deploy, validate,
and demonstrate a novel cloud enabled District Management and Decision Support
framework for DHC systems, which will deliver compound energy cost savings of
30%. A diverse range of data will be collected and generated by the E2District
project for the purpose of achieving all of the project’s objectives.
All of E2District’s work packages except WP1 and WP7 are dependent, to varying
degrees, on data collection and generation:
* WP1 will specify, gather and document requirements and use-cases for the E2District framework that capture realistic expectations and required features from the stakeholder point of view. These requirements will be used in WP2, WP3, WP4 and WP6 to develop the individual E2District technologies, platform architecture and business models. There is no dataset currently defined for WP1 as any related data will be defined and collated in WP2, WP3, WP4 and WP6.
* WP2 will address SMART Objective 1 4 by developing and validating a District Simulation Platform that consists of physical and numerical simulation models of production and demand assets, which will be used as an Asset Portfolio Decision Support tool. WP2 will require the collection and generation of a variety of data including semantic information files that will be used to ensure interoperability between simulation platforms (District Simulation Platform, Supervisory Controllers and Production Scheduling Optimiser), District Simulation Platform parametric study results and District Information Files for demonstration sites comprising data relating to building geometry, thermal properties, HVAC systems, energy sources, heating/cooling schedules, lightings, district heating production, storage, occupancy profiles and weather data. Data relating to the Veolia demonstration sites including any DHN historical monitoring data, any data concerning the DHN design and equipment, any DHN operation rules and practices and any data generated through the tools developed in the project (eg. optimisation tools) will not be available for use in publications.
* WP3 will focus on the development and validation of all the key control, optimisation, diagnostics and prosumer engagement algorithms and modules for reducing the energy consumption of a DHC system based on the historical data and real measurements, real prices and flexible assets, and for influencing the demand to be more efficient at the user level. Hence, it directly addresses SMART Objectives 2 & 3\. As such, WP3 requires the collection and generation of a variety of data including DSP data, BMS data, weather data, matlab data and behavioural model calibration survey data. As with data collected and generated in relation to Veolia demonstration sites for WP2, all data collected and generated for WP3 relating to Veolia sites will not be used in publications.
* WP4 will address SMART Objective 4 5 by developing and validating a scalable District Operation System that will integrate all key control, optimisation, diagnostics and prosumer engagement modules into a cloud-enabled DHC management platform. A variety of sensor data relating to the CIT testbed site will be collected and generated during the execution of this work package.
* WP5 will focus on the integration and deployment of the developed district simulation platform and operation system (and respective modules) to the E2District demonstration site. It validates and analyses the energy savings achieved from the demonstration, and the experience and lessons learned to WP6 to develop business models and replication studies. Thus, it addresses the deployment requirements of SMART Objectives 2 6 , 3 7 and 4\. Fulfilment of WP5 requires the collection and generation of a variety of data including baseline data, KPI’s, electricity consumption data, gas consumption data, heat data, building/areas set point data, monitoring data.
* WP6 will, first, address SMART Objective 5 8 by developing new business models and services for the operators, designers and integrators of DHC systems and will provide studies and guidelines for the replication of the E2District technology. Secondly, it will develop dissemination, exploitation and awareness-raising, based on a global dissemination approach (dissemination targets, channels and instruments), to openly discuss, validate, and disseminate the results to the wider stakeholder and scientific community. WP6 requires the collection and generation of a variety of data including DHN historical monitoring data, data concerning the DHN design and equipment and data generated through the tools developed in the project (eg. economic evaluation tools). Data collected and generated in WP6 that is relative to Veolia sites will not be used in publications.
## 1.5 DATASET DESCRIPTION
The following datasets have been identified by the E2District project
partners. This list may be adapted in future versions of the DMP as the
project develops.
<table>
<tr>
<th>
#
</th>
<th>
Dataset (DS) name
</th>
<th>
Responsible Partner
</th>
<th>
Related
WP
</th> </tr>
<tr>
<td>
1
</td>
<td>
VeoliaSites_Data
</td>
<td>
VERI
</td>
<td>
WP2-
T2.3/WP3-
T3.2/WP3T3.5
</td> </tr>
<tr>
<td>
2
</td>
<td>
VeoliaSites-BusinessModels_Data
</td>
<td>
VERI
</td>
<td>
WP6-T6.3
</td> </tr>
<tr>
<td>
3
</td>
<td>
Data Templates for Simulation Interoperability
</td>
<td>
CSTB
</td>
<td>
WP2-T2.1
</td> </tr>
<tr>
<td>
4
</td>
<td>
District Simulation Platform Parametric Study Results
</td>
<td>
CSTB
</td>
<td>
WP2-T2.2 & WP3
</td> </tr>
<tr>
<td>
5
</td>
<td>
District Information Files for Demonstration Sites
</td>
<td>
CSTB & ACC
</td>
<td>
WP2-T2.3
</td> </tr>
<tr>
<td>
6
</td>
<td>
Behavioural Model Calibration Survey Data
</td>
<td>
CIT
</td>
<td>
WP3-T3.4
</td> </tr>
<tr>
<td>
7
</td>
<td>
Supervisory Control and Production
Scheduling Optimisation Simulation-based Evaluation Data
</td>
<td>
UTRC
</td>
<td>
WP3-T3.5
</td> </tr>
<tr>
<td>
8
</td>
<td>
Simulation Data and Use-cases of the Existing District System (baseline
simulation)
</td>
<td>
UTRC
</td>
<td>
WP3-T3.5
</td> </tr>
<tr>
<td>
9
</td>
<td>
CIT Sensor Data
</td>
<td>
CIT
</td>
<td>
WP4
</td> </tr>
<tr>
<td>
10
</td>
<td>
Acciona Baseline and Performance Evaluation
</td>
<td>
ACC
</td>
<td>
WP5
</td> </tr> </table>
## 1.6 ETHICS
The E2District partners will comply with the ethical principles as set out in
Article 34 of the Grant Agreement, which asserts that all project activities
must be carried out in compliance with:
1. Ethical principles (including the highest standards of research integrity - as set out, for instance, in the European Code of Conduct for Research Integrity 9 \- and including, in particular, avoiding fabrication, falsification, plagiarism or other research misconduct)
2. Applicable international, EU and national law.
# 2 DATA SHARING
## 2.1 BACKGROUND DATA
With regard to Background data, the E2District Consortium Agreement states as
follows:
‘According to the Grant Agreement (Article 24) Background is defined as “data,
knowhow or information (…) that is needed to implement the action or exploit
the results”. Because of this need, Access Rights have to be granted in
principle, but parties must identify and agree amongst them on the Background
for the project.
#### 2.1.1 PARTY 1: (CSTB)
As to CSTB, it is agreed between the parties that, to the best of their
knowledge, the following background is hereby identified and agreed upon for
the Project. Specific limitations and/or conditions, shall be as mentioned
hereunder:
<table>
<tr>
<th>
Describe Background
</th>
<th>
Specific limitations and/or conditions for implementation (Article 25.2 Grant
Agreement)
</th>
<th>
Specific limitations and/or conditions for exploitation (Article
25.3 Grant Agreement)
</th> </tr>
<tr>
<td>
District Simulation Platform (DIMOSIM, v2.03)
This platform includes several modules:
* A graphical district editor (to create or edit district configuration files)
* A citygml import function for importing existing data on districts
* A simulation kernel
* Import and export functions from and to excel (load profiles, results …)
* A global performance analysis
(energy, environment and costs)
* A classification module for comparing and classifying different energy concepts and parameters on an existing or new district
* A data exchange module for
coupling any district controller or optimiser
Functions of DIMOSIM, v2.03:
* Import of all building parameters and generation of building models:
* Building parameters: floor, window and wall area, orientation, etc.
* Building system parameters:
consumer, prosumer, local storage or production, etc.
Import of all district parameters and generation of district model:
* Hydronic network parameters: connections and distances
* Electric network parameters:
</td>
<td>
CSTB grants a free license of use to all
E2DISTRICT partners
for the use of a partial or full compiled version
(executable) of the DIMOSIM simulation platform, as well as an access right to
the user guide.
</td>
<td>
</td> </tr>
<tr>
<td>
</td> </tr>
<tr>
<td>
connections and distances
Import of Energy HUB parameters and generation of HUB model: ▪ Central
thermal and electrical production (configuration and sizing)
▪ Central thermal and electrical storage (configuration and sizing)
Generation of building electrical load profiles (therefore, a tool available
at CSTB, based on statistical data, is used):
▪ Each building is divided in sublevels (e.g. apartments), for which a load
profile is generated ▪ From the sublevels, a general load profile for each
building is generated from the sum of all sublevels. These load profiles are
then connected to the electrical grid model.
Sizing of the thermal system and network
* Based on the nominal heat demand of each building, the tool sizes all network connections automatically, based on expert rules, using a database of district heating pipes and insulation
* The Energy HUB is also sized automatically based on the heat load of the heating district network.
Sizing of the electric grid
▪ All grid connections are sized automatically, based on expert rules
</td>
<td>
</td>
<td>
</td> </tr> </table>
#### 2.1.2 PARTY 2: (ACCIONA)
As to **ACCIONA** , it is agreed between the parties that, to the best of
their knowledge, the following Background is hereby identified and agreed upon
for the Project. Specific limitations and/or conditions shall be as mentioned
hereunder:
<table>
<tr>
<th>
Describe Background
</th>
<th>
Specific limitations and/or condition for
implementation (Article 25.2 Grant Agreement)
</th>
<th>
Specific limitations and/or conditions for exploitation
(Article 25.3 Grant
Agreement)
</th> </tr> </table>
<table>
<tr>
<th>
Background that is covered under specific research agreements and
confidentiality agreements and therefore subject to third party rights.
</th>
<th>
Right for using the
Background within the
Project
</th>
<th>
This Background shall not be used until an exploitation agreement is signed,
which will reflect the conditions on which royalties are provided
</th> </tr>
<tr>
<td>
All knowledge, technical information and experience related to other
construction and research project in which ACCIONA is or has been involved
</td>
<td>
Right for using the Background within the Project
</td>
<td>
This Background shall not be used until an exploitation agreement is signed,
which will reflect the conditions on which royalties are provided
</td> </tr>
<tr>
<td>
Know-how and experience
on energy efficient buildings design and passive strategies integration.
</td>
<td>
Right for using the Background within the Project
</td>
<td>
This Background shall not be used until an exploitation agreement is signed,
which will reflect the conditions on which royalties are provided
</td> </tr>
<tr>
<td>
Knowledge about implementation of
Renewable Energy systems in buildings, integration of systems to generate
electricity and new energy distribution systems by electrical and thermal
micro-grids
</td>
<td>
Right for using the Background within the Project
</td>
<td>
This Background shall not be used until an exploitation agreement is signed,
which will reflect the conditions on which royalties are provided
</td> </tr>
<tr>
<td>
Background in patents and current applications
</td>
<td>
Right for using the Background within the Project
</td>
<td>
This Background shall not be used until an exploitation agreement is signed,
which will reflect the conditions on which royalties are provided
</td> </tr>
<tr>
<td>
Know-how relating to production (renewable), demand, control and storage
systems at building level
</td>
<td>
Right for using the Background within the Project
</td>
<td>
This Background shall not be used until an exploitation agreement is signed,
which will reflect the conditions on which royalties are provided
</td> </tr>
<tr>
<td>
Background of software tools developed for the energy management of
buildings
</td>
<td>
Right for using the Background within the Project
</td>
<td>
This Background shall not be used until an exploitation agreement is signed,
which will reflect the conditions on which royalties are provided
</td> </tr>
<tr>
<td>
Know-how on façade structures design backed up by previous real projects and
those co-financed by EC where Acciona take active part or coordinate, on
composite and aluminum façade multifunctional systems and including also
knowledge in connexion between different parts.
</td>
<td>
Right for using the Background within the Project
</td>
<td>
This Background shall not be used until an exploitation agreement is signed,
which will reflect the conditions on which royalties are provided
</td> </tr> </table>
#### 2.1.3 PARTY 3 (VEOLIA)
As to VEOLIA, it is agreed between the parties that, to the best of their
knowledge, the following Background is hereby identified and agreed upon for
the Project. Specific limitations and/or conditions shall be as mentioned
hereunder:
<table>
<tr>
<th>
Describe Background
</th>
<th>
Specific limitations and/or conditions for implementation
(Article 25.2 Grant
Agreement)
</th>
<th>
Specific limitations and/or conditions for exploitation
(Article 25.3 Grant
Agreement)
</th> </tr>
<tr>
<td>
Technical characteristics of VEOLIA sites and its components/assets
</td>
<td>
Restricted to project implementation needs and to
its duration
</td>
<td>
Not usable for exploitation
</td> </tr>
<tr>
<td>
VEOLIA heating and cooling sites working/historical data
</td>
<td>
Restricted to project implementation needs and to
its duration
</td>
<td>
Not usable for exploitation
</td> </tr>
<tr>
<td>
VEOLIA heating and cooling sites operational KPIs and their calculation
methods
</td>
<td>
Restricted to project implementation needs and duration
</td>
<td>
Not usable for exploitation
</td> </tr>
<tr>
<td>
VEOLIA models corresponding to components of heating and cooling networks
</td>
<td>
Restricted to project implementation needs and duration
</td>
<td>
Not usable for exploitation
</td> </tr>
<tr>
<td>
Veolia current and classical business models general characteristics and
principles
</td>
<td>
Restricted to project implementation needs and duration
</td>
<td>
Not usable for exploitation
</td> </tr> </table>
#### 2.1.4 PARTY 4 (CIT)
As to CIT, it is agreed between the parties that, to the best of their
knowledge, the following Background is hereby identified and agreed upon for
the Project. Specific limitations and/or conditions shall be as mentioned
hereunder:
<table>
<tr>
<th>
Describe Background
</th>
<th>
Specific limitations and/or conditions for implementation (Article 25.2 Grant
Agreement)
</th>
<th>
Specific limitations and/or conditions for exploitation
(Article 25.3 Grant
Agreement)
</th> </tr>
<tr>
<td>
NICORE: Application
</td>
<td>
Restricted to project
</td>
<td>
Not usable for exploitation.
</td> </tr>
<tr>
<td>
Enablement Platform based
</td>
<td>
implementation needs and to
</td>
<td>
</td> </tr>
<tr>
<td>
on Generic Event Driven
</td>
<td>
its duration.
</td>
<td>
</td> </tr>
<tr>
<td>
System Architecture combined with dynamic
</td>
<td>
Excluded: NICORE: all
</td>
<td>
</td> </tr>
<tr>
<td>
Service composition and invocation principles.
</td>
<td>
elements of source code.
</td>
<td>
</td> </tr> </table>
## 2.2 OPEN ACCESS
According to article 29.2 of the Grant Agreement: 10
E2District, as a Horizon 2020 beneficiary, must ensure open access (free of
charge online access for any user) to all peer-reviewed scientific
publications relating to its results.
In particular, the E2District project must:
* As soon as possible, and at the latest on publication, deposit a machinereadable electronic copy of the published version or final peer-reviewed manuscript accepted for publication in a repository for scientific publications; moreover, the research data needed to validate the results presented in the deposited scientific publications must be deposited at the same time.
* Ensure open access to the deposited publication - via the repository - at the latest: on publication, if an electronic version is available for free via the publisher, or within six months of publication (twelve months for publications in the social sciences and humanities) in any other case.
* Ensure open access - via the repository - to the bibliographic metadata that identify the deposited publication.
As outlined in article 29.2 of the Grant Agreement, all E2District
bibliographic metadata will be in a standard format and will include all of
the following:
* The terms “European Union (EU)” and “Horizon 2020”;
* The name of the action, acronym and grant number;
* The publication date, and length of embargo period if applicable, and A persistent identifier.
In accordance with the above guidelines, E2District, as a participant of the
Horizon 2020 Open Data pilot, is committed to Open Access Publishing and is
prioritising publication venues and promoting Open Access to its publications
where possible. Where feasible, the project will openly make available through
open access repositories, baseline data from the demo sites, statistics and
measurements from experiments, business models and key stakeholder surveys and
questionnaires.
## 2.3 OPEN DATA
The Data Management Plan establishes the approach of the project in relation
to open research data as much as it is possible to currently define and
further detail will be provided in the mid-term and final plans.
As stated in article 2.2.2 of the Grant Agreement:
E2District is voluntarily participating in the Horizon 2020 Open Data pilot
11 . Therefore, the project will openly make available, baseline data from
demo sites, statistics and measurements from experiments, business models and
key stakeholder surveys and questionnaires, except when the release of
datasets collected from the project is considered to:
* Impact results that are expected to be commercially or industrially exploited
* Be incompatible with the need for confidentiality in connection with security issues.
* Be incompatible with existing rules on the protection of personal data. Would jeopardise the achievement of the main aim of the action.
* Be incompatible with existing rules on the protection of personal data.
* Would jeopardise the achievement of the main aim of the action.
* Create other legitimate reason to not take part in the Pilot.
In conjunction with an espousal of FAIR DATA practice 12 , this Data
Management Plan’s establishment of consistent data practices will increase the
efficiency of data handling throughout the lifespan of the project and ensure
that the data will reach more people, have a greater impact, avoid duplication
of efforts and be preserved for future researchers.
## 2.4 ACCESSIBILITY
Specifically, the data generated and collected by E2District will be
accessible to the consortium through the CIT NICORE platform, using the
available APIs and webservices. In addition, the data will be available
through the consortium’s SVN repository. The consortium will also follow the
dissemination validation process (see D6.1
Dissemination and Communication Plan, Section 6.3) to validate and approve for
the public dissemination of the data. The data will then be shared publicly on
the E2District website.
# 3 DATA MANAGEMENT PLAN
## 3.1 DATASET 1: (VEOLIA) VEOLIASITES_DATA
<table>
<tr>
<th>
</th>
<th>
**VEOLIA**
</th> </tr>
<tr>
<td>
**Work Package/Task**
**Nos. re: dataset**
**Data Manager**
</td>
<td>
WP2-T2.3 (validation of the model) / WP3-T3.2 / WP3-T3.5
VERI
</td> </tr>
<tr>
<td>
**Dataset reference and name**
</td>
<td>
VeoliaSites_Data
</td> </tr>
<tr>
<td>
**Data set description**
</td>
<td>
Any DHN historical monitoring data, any data concerning the DHN design and
equipment, any DHN operation rules and practices, any data generated through
the tools developed in the project (eg. optimisation tools) relative to the
VEOLIA sites. This data will not be used in publications.
</td> </tr>
<tr>
<td>
**Availability**
</td>
<td>
Project partners needing an access to these data during the project
</td> </tr>
<tr>
<td>
**E2District Project Metadata**
</td>
<td>
</td> </tr>
<tr>
<td>
**Metadata specific to dataset**
</td>
<td>
Historical DHN sites and DHN sites design data located on VEOLIA IT platform;
Generated data located on VEOLIA IT platform
</td> </tr>
<tr>
<td>
**Standards**
</td>
<td>
Not defined
</td> </tr>
<tr>
<td>
**Data Sharing**
</td>
<td>
Data will be accessible to project partners needing an access to these data
during the project through VERI IT platform
</td> </tr>
<tr>
<td>
**Archiving and**
**Preservation (including storage and back-up)**
</td>
<td>
Not communicated
</td> </tr> </table>
## 3.2 2: (VEOLIA) VEOLIASITES-BUSINESSMODELS_DATA
<table>
<tr>
<th>
**Description**
</th> </tr>
<tr>
<td>
</td> </tr>
<tr>
<td>
**Work Package/Task**
**Nos. re: dataset**
**Data Manager**
</td>
<td>
WP6-T6.3
VERI
</td> </tr>
<tr>
<td>
**Dataset reference and name**
</td>
<td>
VeoliaSites-BusinessModels_Data
</td> </tr>
<tr>
<td>
**Data set description**
</td>
<td>
Any DHN historical monitoring data, any data concerning the DHN design and
equipment, any data generated through the tools developed in the project (eg.
economic evaluation tools) relative to the VEOLIA sites. This data will not be
used in publications.
</td> </tr>
<tr>
<td>
**Availability**
</td>
<td>
Project partners needing an access to these data during the project
</td> </tr>
<tr>
<td>
**E2District Project Metadata**
</td>
<td>
</td> </tr>
<tr>
<td>
**Metadata specific to dataset**
</td>
<td>
DHN sites design data located on VEOLIA IT platform, generated data located on
VEOLIA IT platform
</td> </tr>
<tr>
<td>
**Standards**
</td>
<td>
Not defined
</td> </tr>
<tr>
<td>
**Data Sharing**
</td>
<td>
Data will be accessible to project partners needing an access to these data
during the project through VERI IT platform
</td> </tr>
<tr>
<td>
**Archiving and**
**Preservation (including storage and back-up)**
</td>
<td>
Not communicated
</td> </tr> </table>
## 3.3 3: (CSTB) DATA TEMPLATES FOR SIMULATION INTEROPERABILITY
<table>
<tr>
<th>
</th>
<th>
**Description**
</th> </tr>
<tr>
<td>
**Work Package/Task**
**Nos. re: dataset**
**Data Manager**
</td>
<td>
WP 2 - Task 2.1
Data Manager: Vincent Partenay (CSTB)
</td> </tr>
<tr>
<td>
**Dataset reference and name**
</td>
<td>
Data Templates for simulation interoperability
</td> </tr>
<tr>
<td>
**Data set description**
</td>
<td>
Templates of semantic information files that will be used to ensure
interoperability between simulation platforms (District Simulation Platform,
Supervisory Controllers and Production Scheduling Optimizer)
Two types:
1. District Information File, which is based on a semantic tree structure allowing to gather only district topology, building and system properties to allow energy simulation
2. Co-simulation Data File, which is also based on a semantic tree structure but only to exchange data dynamically between coupled platforms of the project.
</td> </tr>
<tr>
<td>
**Availability**
</td>
<td>
Consortium
</td> </tr>
<tr>
<td>
**E2District Project Metadata**
</td>
<td>
European Union; H2020; Energy Efficiency Optimised District Heating and
Cooling’; E2District; GA696009
</td> </tr>
<tr>
<td>
**Metadata specific to dataset**
</td>
<td>
Data template / interoperability / co-simulation
</td> </tr>
<tr>
<td>
**Standards**
</td>
<td>
For District Information File: based on citygml international standard
(http://www.citygml.org/)
For Co-simulation Data File: XML schema
</td> </tr>
<tr>
<td>
**Data Sharing**
</td>
<td>
Through Nicore
Even potentially accessible to all the partners, these data mainly concern the
groups involved in running the simulation platforms
</td> </tr>
<tr>
<td>
**Archiving and**
**Preservation (including storage and back-up)**
</td>
<td>
According to Nicore architecture
Preservation period: inherently subject to time constraints of the simulation
platform operation
</td> </tr> </table>
## 3.4 4: (CSTB) DISTRICT SIMULATION PLATFORM PARAMETRIC STUDY RESULTS
<table>
<tr>
<th>
</th>
<th>
**Description**
</th> </tr>
<tr>
<td>
**Work Package/Task**
**Nos. re: dataset**
**Data Manager**
</td>
<td>
WP 2 - Task 2.2 (also relevant to WP3)
Data Manager: Vincent Partenay (CSTB)
</td> </tr>
<tr>
<td>
**Dataset reference and name**
</td>
<td>
District Simulation Platform Parametric Study Results
</td> </tr>
<tr>
<td>
**Data set description**
</td>
<td>
In WP2, task 2.2, several elementary physical models are being developed and
integrated into the District Simulation Platform. Once this modelling task
done, a global parametric study for different system configurations and
various climate zones shall be carried out. The data set in this case are the
results of this parametric study as load time series (hourly, on an annual
basis) and final integrated values relative to KPI’s calculation defined in
WP1.1
</td> </tr>
<tr>
<td>
**Availability**
</td>
<td>
Consortium and Open Access
</td> </tr>
<tr>
<td>
**E2District Project Metadata**
</td>
<td>
European Union; H2020; Energy Efficiency Optimised District Heating and
Cooling’; E2District; GA696009
</td> </tr>
<tr>
<td>
**Metadata specific to dataset**
</td>
<td>
The district information file (using template elaborated in WP2.1) for each
simulated configuration
</td> </tr>
<tr>
<td>
**Standards**
</td>
<td>
XML or JSON
</td> </tr>
<tr>
<td>
**Data Sharing**
</td>
<td>
Through Nicore
</td> </tr>
<tr>
<td>
**Archiving and**
**Preservation (including storage and back-up)**
</td>
<td>
To be stored at least along the project life
</td> </tr> </table>
## 3.5 5: (CSTB) DISTRICT INFORMATION FILES FOR DEMONSTRATION SITES
<table>
<tr>
<th>
</th>
<th>
**Description**
</th> </tr>
<tr>
<td>
**Work Package/Task**
**Nos. re: dataset**
**Data Manager**
</td>
<td>
WP - Tasks 2.3
Data Manager: ACCIONA + CSTB
</td> </tr>
<tr>
<td>
**Dataset reference and name**
</td>
<td>
District Information Files for Demonstration Sites
</td> </tr>
<tr>
<td>
**Data set description**
</td>
<td>
For each demonstration site, a District Information File will be filled
accordingly from their specific properties for simulation objectives. These
data will be inherited from intrinsic data that will be at disposal, but also
from monitoring systems (for calibration)
</td> </tr>
<tr>
<td>
**Availability**
</td>
<td>
Consortium; Open Access only for CIT test site data
</td> </tr>
<tr>
<td>
**E2District Project Metadata**
</td>
<td>
European Union; H2020; Energy Efficiency Optimised District Heating and
Cooling’; E2District; GA696009
</td> </tr>
<tr>
<td>
**Metadata specific to dataset**
</td>
<td>
Building geometry, thermal properties, HVAC systems, energy sources,
heating/cooling schedules, lightings, district heating production, storage,
occupancy profiles, weather data
</td> </tr>
<tr>
<td>
**Standards**
</td>
<td>
Based on citygml international standard (http://www.citygml.org/) and ADE
Energy
</td> </tr>
<tr>
<td>
**Data Sharing**
</td>
<td>
Through Nicore
</td> </tr>
<tr>
<td>
**Archiving and**
**Preservation (including storage and back-up)**
</td>
<td>
To be stored as long as controlling systems (deployed on demonstration sites
requiring these calibrated district information models) will be operating
</td> </tr> </table>
## 3.6 6: (CIT) BEHAVIOURAL MODEL CALIBRATION SURVEY DATA
<table>
<tr>
<th>
</th>
<th>
**Description**
</th> </tr>
<tr>
<td>
**Work Package/Task**
**Nos. re: dataset**
**Data Manager**
</td>
<td>
WP 3/T 3.4
Julia Blanke
</td> </tr>
<tr>
<td>
**Dataset reference and name**
</td>
<td>
Behavioural Model Calibration Survey Data
</td> </tr>
<tr>
<td>
**Data set description**
</td>
<td>
This dataset contains the survey data collected on the CIT campus for the
purpose of calibrating the behavioural model.
</td> </tr>
<tr>
<td>
**Availability**
</td>
<td>
Private
</td> </tr>
<tr>
<td>
**E2District Project Metadata**
</td>
<td>
European Union; H2020; Energy Efficiency Optimised District Heating and
Cooling’; E2District; GA696009
</td> </tr>
<tr>
<td>
**Metadata specific to dataset**
</td>
<td>
Behavioural survey data
</td> </tr>
<tr>
<td>
**Standards**
</td>
<td>
</td> </tr>
<tr>
<td>
**Data Sharing**
</td>
<td>
Access to this data will be restricted - to be used for model calibration only
- in order to protect the personal data of the survey participants.
</td> </tr>
<tr>
<td>
**Archiving and**
**Preservation (including storage and back-up)**
</td>
<td>
The data will be stored in Excel files on CIT servers.
</td> </tr> </table>
## 3.7 7: (CIT) CIT SENSOR DATA
<table>
<tr>
<th>
</th>
<th>
**Description**
</th> </tr>
<tr>
<td>
**Work Package/Task**
**Nos. re: dataset**
**Data Manager**
</td>
<td>
WP4
Christian Beder
</td> </tr>
<tr>
<td>
**Dataset reference and name**
</td>
<td>
CIT Sensor Data
</td> </tr>
<tr>
<td>
**Data set description**
</td>
<td>
This dataset contains all the sensor data collected on CIT campus during the
E2D project. Data is collected from the main campus BMS, the Nimbus BMS, as
well as through LoRa based sensors distributed across the campus. Each data
point comprises of a timestamp, the sub-system the data was collected from,
the name of the particular data point, as well as its value. The semantics of
the data stream is such, that each data point remains valid until a new data
point is indicating a change in value.
</td> </tr>
<tr>
<td>
**Availability**
</td>
<td>
Consortium; Open Access for subsets
</td> </tr>
<tr>
<td>
**E2District Project Metadata**
</td>
<td>
European Union; H2020; Energy Efficiency Optimised District Heating and
Cooling’; E2District; GA696009
</td> </tr>
<tr>
<td>
**Metadata specific to dataset**
</td>
<td>
BMS data, sensor data
</td> </tr>
<tr>
<td>
**Standards**
</td>
<td>
MongDB database, key-value pairs
</td> </tr>
<tr>
<td>
**Data Sharing**
</td>
<td>
Data will be accessible to the E2D consortium through the NiCore platform.
This platform provides access through a variety of services, including SOAP
WebServices, subscription to live data via AMQP, or by directly accessing the
MongoDB database through its API.
</td> </tr>
<tr>
<td>
**Archiving and**
**Preservation (including storage and back-up)**
</td>
<td>
The data is stored on a MongoDB database on a server at CIT
</td> </tr> </table>
## 3.8 DATASET 8: (UTRC) SIMULATION DATA AND USE-CASES OF THE EXISTING CIT
DISTRICT SYSTEM (BASELINE SIMULATION)
<table>
<tr>
<th>
</th>
<th>
**Description**
</th> </tr>
<tr>
<td>
**Work Package/Task**
**Nos. re: dataset**
**Data Manager**
</td>
<td>
WP3/T3.5
Kostas Kouramas
</td> </tr>
<tr>
<td>
**Dataset reference and name**
</td>
<td>
Simulation data and use-cases of the existing CIT district system (baseline
simulation)
</td> </tr>
<tr>
<td>
**Data set description**
</td>
<td>
A record of heating use-cases for the CIT demo-site and data generated from
running simulations on the DSP platform for these use-cases.
</td> </tr>
<tr>
<td>
**Availability**
</td>
<td>
Consortium and Open Access
</td> </tr>
<tr>
<td>
**E2District Project Metadata**
</td>
<td>
European Union; H2020; Energy Efficiency Optimised District Heating and
Cooling’; E2District; GA696009
</td> </tr>
<tr>
<td>
**Metadata specific to dataset**
</td>
<td>
DSP data; BMS data; Weather data
</td> </tr>
<tr>
<td>
**Standards**
</td>
<td>
Matlab mat files; CSV files; Excel Sheets; NICORE data-base (?)
</td> </tr>
<tr>
<td>
**Data Sharing**
</td>
<td>
The data will be accessible to the consortium through the CIT NICORE platform,
using the available APIs and web-services. In addition, the data will be
available through the consortium SVN repository. The consortium will also
follow the dissemination validation process (see D6.1 Dissemination and
Communication Plan, Section 6.3) to validate and approve for the public
dissemination of the data. The data will then be shared publicly on the
E2District web-site.
</td> </tr>
<tr>
<td>
**Archiving and**
**Preservation (including storage and back-up)**
</td>
<td>
The data will be store in the NICORE platform data-base, E2District SVN
repository and the project web-site for the duration of the project.
</td> </tr> </table>
## 3.9 DATASET 9: (UTRC) SUPERVISORY CONTROL AND PRODUCTION SCHEDULING
OPTIMISATION SIMULATION-BASED EVALUATION DATA
<table>
<tr>
<th>
</th>
<th>
**Description**
</th> </tr>
<tr>
<td>
**Work Package/Task**
**Nos. re: dataset**
**Data Manager**
</td>
<td>
WP3/T3.5
Kostas Kouramas
</td> </tr>
<tr>
<td>
**Dataset reference and name**
</td>
<td>
Supervisory control and Production Scheduling Optimisation simulation-based
evaluation data
</td> </tr>
<tr>
<td>
**Data set description**
</td>
<td>
Comparison data of the Energy consumption cost, heating generation and demand
(kW), comfort (deg. C) from the simulationbased analysis and comparison with
baseline of the control and optimisation algorithms, for a number of use-case
scenarios that will be determined as part of the work in T3.5.
</td> </tr>
<tr>
<td>
**Availability**
</td>
<td>
Consortium
</td> </tr>
<tr>
<td>
**E2District Project Metadata**
</td>
<td>
European Union; H2020; Energy Efficiency Optimised District Heating and
Cooling’; E2District; GA696009
</td> </tr>
<tr>
<td>
**Metadata specific to dataset**
</td>
<td>
DSP data; Matlab data; BMS data; Weather data
</td> </tr>
<tr>
<td>
**Standards**
</td>
<td>
Matlab mat files; CSV files; Excel Sheets; NICORE data-base
</td> </tr>
<tr>
<td>
**Data Sharing**
</td>
<td>
The data will be accessible to the consortium through the CIT NICORE platform,
using the available APIs and web-services. In addition, the data will be
available through the consortium SVN repository.
</td> </tr>
<tr>
<td>
**Archiving and**
**Preservation (including storage and back-up)**
</td>
<td>
The data will be available for the duration of the project through the above
data-sharing means.
</td> </tr> </table>
## 3.10 DATASET 10: (ACC) ACCIONA BASELINE AND PERFORMANCE EVALUATION
<table>
<tr>
<th>
</th>
<th>
**Description**
</th> </tr>
<tr>
<td>
**Work Package/Task**
**Nos. re: dataset**
**Data Manager**
</td>
<td>
WP5
Jose C. Esteban
</td> </tr>
<tr>
<td>
**Dataset reference and name**
</td>
<td>
Acciona Baseline and Performance Evaluation.
</td> </tr>
<tr>
<td>
**Data set description**
</td>
<td>
The dataset is the collection of calculated items related to the description
of the behaviour in the baseline situation and after the implementation of the
E2District measures into the Cork demonstration; they are based on cross-
compared data extracted from the dataset of BMS and sensor of the CIT (WP4
collection).
The standards of the IPMVP (International Performance Measurement and
Verification Protocol) of the EVO (Efficiency Validation Organization) are
used to follow the improvements.
</td> </tr>
<tr>
<td>
**Availability**
</td>
<td>
Consortium
</td> </tr>
<tr>
<td>
**E2District Project Metadata**
</td>
<td>
European Union; H2020; Energy Efficiency Optimised District Heating and
Cooling’; E2District; GA696009
</td> </tr>
<tr>
<td>
**Metadata specific to dataset**
</td>
<td>
Baseline, KPI; Electricity consumption; Gas consumption; Heat; Building/Areas
Set Point Data; Monitoring data.
</td> </tr>
<tr>
<td>
**Standards**
</td>
<td>
IPVMP.
BacNet for BMS integration
ModBus in some meter readings
Analysis of metering data to be stored in .csv, .xls or .xlsx format
</td> </tr>
<tr>
<td>
**Data Sharing**
</td>
<td>
Data are available for the E2District consortium, through the data sharing
platform, Tortoise SVN Platform.
</td> </tr>
<tr>
<td>
**Archiving and**
**Preservation (including storage and back-up)**
</td>
<td>
Daily export of measurement, metering and set point data, to configure a
monthly summary file.
Update and storage of the baseline and performance verification minimum every
three months, and upload to the SVN platform.
</td> </tr> </table>
# 4 CONCLUSION
This document has established the E2District project’s approach to data
management for the datasets captured or processed inside the project,
according to the guidelines published by the EC. The plan has identified how
data collected or generated by the E2District project will be organised,
stored and shared and has specified what type of data will be made publicly
available by the project (open access) in so far as it is possible to do so at
this stage of the project. Suitable public repositories to store the data have
also been identified.
This Data Management Plan is not a fixed document and will, therefore, evolve
throughout the E2District project according to the progress of project
activities.
|
https://phaidra.univie.ac.at/o:1140797
|
Horizon 2020
|
1131_DOMINO_696074.md
|
# 1 Introduction
DOMINO is an EU-funded research project that intends to prompt energy
reductions in households. It uses a smart plug technology to raise awareness
for energy consumption and to change the participants’ behaviour towards
energy saving. The project has chosen a playful approach in form of an energy
saving game called “DOMINO Challenge” which is played in teams of five
households each. The challenge takes place in Brussels, Berlin, and Naples and
will involve around 4000 households. Including preparation and evaluation the
project extends over a period of almost three years (March 2016 till February
2019 approximately). The project results will allow making assumptions on the
overall potential of smart plugs for reducing energy consumption in households
and will enable related initiatives to benefit from the DOMINO experience.
This Data Management Plan describes the fundamentals of the data management
concerning the DOMINO Challenge as executed by Plugwise and its DOMINO
Challenge partners. The plan consists of various generally applicable Plugwise
privacy and security policies and refers also to the specific DOMINO Challenge
policies. Within the DOMINO project, Plugwise has been assigned to develop and
deliver:
* Project database
* DOMINO website
* DOMINO App
* Smart plugs
* Testing of the equipment and software
* Logistic activities
For a better understanding of the course of the DOMINO Challenge, the course
of the action can be found below:
* Potential participants from Brussels, Berlin and Naples learn about the challenge and become interested in participating.
* They sign up via the DOMINO website individually. They receive six smart plugs and a “gateway” by postal mail from Plugwise. (The gateway is the Plugwise router called “Stretch”, that allows for communication with the plugs (ZigBee protocol) and the app via the existing home router by WiFi.)
* They download the DOMINO app.
* They install the plugs in their home by plugging them into a power outlet and then plugging in an electric appliance, e.g. a refrigerator or a washing machine.
* They monitor their appliances’ electricity consumption for one month via the app.
* In month two and three, they receive energy saving recommendations via the app for their choice of appliances. They can compare their consumption data to anonymised data of other users via the website. They can comment on how useful the recommendations are.
* If they have not assembled a complete team (of 5 players) at the beginning, they can find another player during their 3-month-cycle and hand over the plugs to this person.
* The process starts over until a team of 5 people has played.
* Once all team members have played (anticipated date September 2018), the Usage Data will be evaluated and the winners will be determined.
* The last person of a team sends the plugs back to the consortium partners with help of postage labels which will be available on the DOMINO website.
In the data management plan, it is differentiated between Personal Data and
Usage Data. Usage Data is collected through the stretch and stored in one
cloud (called “Plugwise cloud”). Personal Data is collected through the
website and through the app and is stored in a different cloud called “DOMINO
cloud”.
Personal data collected through the website include:
* First Name, Name
* Physical address incl. street number, street name city, zip code and country (used for shipment of smart plug equipment)
* Email address
Address data is collected to ship the hardware to the players of the game. The
email address is used to send them information on their energy savings and
important procedural steps in the DOMINO Challenge.
Additional Personal Data collected through the app, include:
* Age (recommended but optional)
* Gender (recommended but optional)
* Password
* Choice of language
* Name and type of device connected for all the plugs except the joker plug (recommended but optional)
* Number of people in the household of the player (recommended but optional)
* Electricity price (recommended but optional)
* Electricity consumption from the past year (recommended but optional)
* Information on appliances: i.e. energy efficiency class, year of construction and consumption as indicated by the manufacturer (recommended but optional)
* Absence periods during the DOMINO challenge - retrospectively (recommended but optional)
This type of data is collected for research purposes that are non-commercial
and serve the goal of increasing the general knowledge on electricity
consumption in European households which – ultimately – can inform activities
on reducing electricity consumption.
Usage data include:
* current and historical consumption on o Monthly basis o Weekly basis o Daily basis o Hourly basis
* settings and scheduling data
This type of data is collected both for research purposes that are non-
commercial and for the participants so that they get well-structured insights
into their own consumption patterns.
# 2 Plugwise general data and privacy policies
## 2.1 Plugwise gateway and applications
The DOMINO Challenge is based on two different cloud environments in which
data is collected and stored.
For the purpose of the Challenge a specific and separate DOMINO cloud
environment has been developed which allows the participants to register (via
the specially developed DOMINO registration website). Before the registration
can be completed, the participant will have to fill in the Consent Form (see
chapter 6) after which certain Personal Data will be stored in the DOMINO
cloud for further use during the Challenge, e.g. for determining the winners
of the challenge.
The Personal Data will not be available outside this specific DOMINO cloud.
Plugwise will not have general access to any Personal Data from users of
Plugwise gateway- and application based systems.
* The application is available via the relevant app stores and is downloadable without registration.
* During the registration process via the website the user has the possibility to make available to Plugwise his Usage Data by explicitly declaring his permission by ticking a dedicated box.
* Each Plugwise gateway has a unique serial number (ID) which, upon activation through the application, will be connected to the Plugwise cloud based database.
* The Plugwise gateway, called the Stretch, will make a secure internet connection (HTTPS) to the Plugwise database(s) by means of a unique password each time the participant/user uses the gateway and application.
## 2.2 Usage Data
At installation, the user will make a secure connection between the
application and the Plugwise gateway only when physically within his existing
WLAN network at home. The Plugwise gateway then provides the application
automatically with a secure ID and Access Key which allows communication
between the application and the Plugwise gateway and Plugwise plugs.
After installation of the Plugwise plugs and initiating the system, the user
can access his Plugwise system from outside the house/WLAN network.
Plugwise will, through the Plugwise Cloud, receive and have access to the
Usage Data from the devices/appliances that are connected through the plugs to
the Plugwise system.
This concerns data only regarding the connected devices themselves, i.e.
* the current and historical consumption
* and settings and scheduling data. (This refers to the moments in time in which plugs are switched off/on and the schedule (as set in the app) is activated.)
The historical consumption is available to the corresponding player during the
3 months of his cycle. It includes consumption data in Watts per hour on
* Monthly basis
* Weekly basis
* Daily basis
* Hourly basis
This Usage Data is stored in the Plugwise database in the Plugwise Cloud,
based on the Plugwise gateway ID only and will always be anonymous and
therefore cannot be traced back to natural persons and/or address details.
Through signing the consent form of the DOMINO challenge, the DOMINO player
explicitly gives consent to link his Personal Data to the collected Usage
Data, for specific situations, i.e. if he/his team wins the challenge or if
the Smart Plug equipment has not been passed on to the next player.
Plugwise guarantees that any and all Usage Data that is stored in its
database(s) (third party database included) will not in any way and/or for
whatever reason be made available to third parties, for example for commercial
reasons.
After handing over the Plugwise plugs to the next team member, the Plugwise
system will register the new participant (via the Log-in on the app) and the
new team member will only be able to see the historical Usage Data from the
date of his first log-in.
In other words, Usage Data collected earlier (from earlier participants in the
same team) will not be available for the next participants.
## 2.3 Purpose of collecting Data
### 2.3.1 System control
Plugwise collects Usage Data to allow players to gain insights into their
appliances’ electricity consumption and control it, if they want to reduce it
by switching the connected appliances and/or lighting on or off. Insights into
consumption and control of devices is also possible from outside the WLAN
network. In this case, data will be transferred via the internet to the
Plugwise application of a smartphone or tablet.
Access to the plugs via the DOMINO cloud, is only possible once the
participant has made an initial connection to the Plugwise gateway (Stretch)
and when he has received the secure ID and Access Key during that process on
his phone.
It is not possible to use the Cloud based controls (for example switching
appliances on or off) without this initial local communication.
### 2.3.2 Analysis of Data
The DOMINO consortium expects to generate data on energy consumption of
participants related to different appliances (Usage data) and other optional
anonymised data on:
* household size,
* region,
* age,
* gender,
* last year’s electricity consumption,
* electricity price,
* connected appliances: energy consumption as indicated by the manufacturer, energy efficiency class, year of construction
and in how far recommendations on saving energy have an effect on behaviour.
For this reason feedback clicks for energy saving recommendations will be
collected.
In the app the participants are asked to indicate long absence periods (i.e.
holidays) retrospectively so that they can be taken into account during data
analysis, as well. The data mentioned above is collected for research purposes
that are non-commercial and serve the goal of increasing the knowledge on
electricity consumption in different types which can – ultimately – form the
basis for activities on reducing electricity consumption. Anonymised data will
be analysed by the responsible consortium partners (adelphi, IBGE and ANEA).
Plugwise will make requested anonymised data available to the aforementioned
partners for analysis and assure that it will be accessible in readable format
(excel, open officeI), within maximum 2 weeks after the request and without
violation of anonymity of the data.
Metadata (e.g. IP address, location of app user etc.) is not collected with
the app. The DOMINO website collects data on the number of users that visited
the site from the various target countries. Those are stored for 7 days and
then removed (standard procedure).
### 2.3.3 Sharing of Personal Data within the DOMINO Challenge teams
In view of the key element of the DOMINO Challenge, the creation of teams to
compete for a prize (for example, highest total electricity savings within a
geographical area), will be supported by the regional DOMINO partners (ANEA,
adelphi, and IBGE) in case a complete team cannot be formed by participants
themselves. All team member households/participants have be informed for this
case that their names will be made known to other team members. This is also
covered in the consent form.
### 2.3.4 Public availability of Usage Data
The DOMINO Challenge administrator Kees Schouten will keep the Usage Data
collected throughout the challenge. Anonymised data resulting from the
subsequent analysis will be made publicly available through an open access
area on the DOMINO website.
### 2.3.5 DOMINO Administrator
The DOMINO challenge administrator will set up an administrator dashboard and
as such will be able to create an account for the regional DOMINO partners.
Regional partners cannot get access to the database of Plugwise so they cannot
see actual Usage Data. The regional partners can only see if a specific
participant/user does indeed use the Plugwise plugs. At all times it is
guaranteed through measures and procedures described earlier (see sections 2.1
and 2.2) that the Usage Data cannot in any way be connected to Personal Data.
2.4 **Plugwise System and Usage Data Security and Retention period** The
Plugwise ZigBee network, gateway and plugs are highly reliable and secure, due
to the use of a 128.bit AES encryption. The Usage Data will be stored for an
indefinite period in the Plugwise cloud based database, provided by Web
Services Hosting Companies Amazon.com and will be hosted in Ireland.
Plugwise guarantees that the data storage (also through their contracted third
parties) will be according to the latest and industry accepted Security
Standards, in accordance with national legislation and in full harmony with
the new General Data Protection Regulation (that succeeds the EU Data
Protection Directive (95/46/EC)).
## 2.5 Deletion of Usage data
Due to the fact the Usage Data is fully anonymous, Plugwise can in no
practical way delete specific participant/user related Usage Data from its
databases, since the database does only register gateway ID related data, and
does not know what Usage Data relates to which participant/user. Within the
Plugwise organisation the Data Management is allocated to the Data protection
employee and falls under the responsibility of the Plugwise Chief Information
Officer (CIO), Theo Vroege. Reference is made to the attachment AWS, outlining
the Security measures of Amazon.com, the main hosting services partner of
Plugwise (cf. Chapter 5).
## 2.6 Logistics
The DOMINO website will generate a list of participants for the first cycle of
DOMINO, including their Personal Data (Name, Address, Postal code & Country),
which will be transferred by Plugwise to an Excel list for logistic/shipping
purposes. Plugwise will hand over this list to its (out-sourced) logistics
centre. The logistic centre will ensure that a DOMINO package will be sent to
the listed addresses.
Plugwise will separately (before packaging and shipping) scan all unique
Stretch IDs allocated to the DOMINO Challenge, to ensure that, upon first
installation, the Plugwise back-end will recognize the Stretch ID as part of
the DOMINO Challenge.
The individual shipping address will in no way be connected to the unique
Stretch ID. The ID list will not be known by third parties nor communicated
outside of Plugwise.
Before the actual start of the Challenge the first participants will receive
the DOMINO set per FedEx postal services. The administration and handling over
of the sets to FedEx will be handled by the independent logistic handling
company on mentioned out-sourced basis. They will use the formats as described
in Chapter 8.
# 3 DOMINO Registration and Consent procedures
Potential participants of the DOMINO Challenge will be approached via the
DOMINO website, social media and offline channels, such as newspaper articles,
project presentation during relevant events or flyers. The target group
consists of men and women, who are approximately 28-49 years old, tech savvy
and own a smartphone. Via the consent form they confirm that they have
informed other members of their household that they take part in the DOMINO
project and assure to have obtained consent from the other members as well.
The idea is to target people who are living in the areas of Berlin (Germany),
Brussels (Belgium) and Naples (Italy). Communication channels will be chosen
to particularly fit this group.
The DOMINO web portal is the unique entry point into the challenge. All
relevant information for potential participation in the DOMINO challenge,
including this data management plan, will be available on the website.
Potential participants who want to sign up through the website are asked to
read the digital informed consent form (cf. Chapter 6 of this report) and
confirm that they agree with the information provided. If they have questions
on the consent form or on the data policy, they can submit them through an
online form available on the website before participating in the DOMINO
challenge. A response will be given to them via email from the data protection
officer of the project team within 24 hours on weekdays. Possible risks and
discomforts of participants during the research project, e.g. people need to
invest some time and effort for setting up the plugs and passing them on to
the next person, or participants not saving any energy at all, if they do not
follow energy saving tips, are laid out in the consent form and generally they
are assumed to remain at a tolerable level.
The form will be available in German, French, Dutch and Italian so that
participants in the three target regions will be able to fully understand it.
All participants in the DOMINO Challenge are specifically and explicitly
informed about the way that their Personal and Usage Data will be collected
and administered. On the DOMINO website potential participants will be able to
formally agree to the registration and administration of the personal
information including:
* First Name, Last Name
* Physical address incl. city, zip Code and country
* Email address
* Choice of language
The data above needs to be collected for sending the plugs to participants,
for contacting them in case they receive a prize or have not passed the plugs
on.
_Figure1_ : Required information for registration
The agreement will be gathered via so called opt-in option (“I agree to the
terms and conditions” button). Via the URL www.dominoenergy.eu potential
participants will get to the registration page.
The terms and conditions are available for reading in a separate page of the
registration process. In case a potential participant does not tick the
acceptance button, he/she cannot be registered and cannot participate in the
challenge.
After successful registration the first cycle participants will receive a
challenge package and will be able to download the DOMINO application from the
relevant stores (Android and iOS).
To activate the app the participant will have to log in by using the correct
user name, i.e. the email address (as defined by themselves on the DOMINO
website).
The DOMINO Challenge will also use the Personal Data to communicate to the
participants in the following manner:
* Send them a prize if they win the challenge.
* Send them a reminder if they have not passed on the plugs to the next person in their team.
* Send them automated information on the savings after month two and three of their cycle.
* Send them the invitation to a voluntary project evaluation sheet to a random selection of participants from each country.
* Send them an encouraging email to stick to the energy saving habits they have learned during the project.
The notification of the winners by email (9 teams including overall 45
households) will be done by the project partners For this they will evaluate
the anonymised user data of the participants of the three regions and indicate
the team IDs with the best performances to the data manager at Plugwise. For
this purpose only, he will pair the user data with the personal data, at the
end of the challenge or in the case of Italy at the end of each cycle and
communicate the email addresses to the project team who will then inform the
winners.
## 3.1 Deletion of Personal Data
All personal data of all the participants will be deleted within the 90 days
following the nomination of the winners. Postal addresses will be deleted 90
days after the equipment has been sent out.
# 4 Ethics Issues
## 4.1 Recruitment
Recruitment of participants of the DOMINO Challenge will be done via websites,
in newsletters, on social media channels, as well as presenting the challenge
in personal interactions in dedicated workshops, meetings or events.
No specific part of the population in the DOMINO assigned geographical target
areas are excluded from the challenge, taking into consideration the
specifically defined DOMINO target group characteristics. Participants are
accepted on a first-come-first-serve basis if they fulfil the minimum
requirements for participating in the challenge as described above.
Children under the age of 18 years are not allowed to sign up for the DOMINO
Challenge. If they are part of a household that participates in the challenge
the person/parent in charge of the child (legal guardian) has to declare his
or her consent regarding the participation of the child to the person who
signed up for the challenge. Children over 14 additionally have to be informed
about the character of the DOMINO challenge and declare their consent to the
person who signed up for the challenge.
Participation in the DOMINO Challenge is voluntary and can be ended by the
participants at any time without consequences.
## 4.2 Sensitive Personal Data
At no time during the DOMINO Challenge data will be requested or processed in
relation to:
* Health
* Sexual lifestyle
* Ethnicity
* Political opinion
* Religious or philosophical conviction
## 4.3 Incidental findings policy
The consortium expects to generate data on consumption of participants by
household size, region, and gender and in how far recommendations on saving
energy have an effect on behaviour. Other anticipated findings are data on
electricity consumption of diverse household appliances.
Researcher’s duty of care towards their research group suggests that the
researcher should disclose unexpected findings that are beneficial to the
participants or that could prevent damages to them. This guiding principle is
mostly known and applied in the context of medical or psychological studies.
Since the DOMINO Challenge is not a medical study, we do not expect any
incidental findings that could harm health or well-being of participants.
### 4.3.1 General policy
Therefore, if the project team comes across general and “benign” incidental
findings in the course of this research project, the assumed policy it not to
disclose them to the participants nor to other designated parties. Possible
risks and discomforts of participants during the research project are laid out
in the consent form and generally they are assumed to remain at a tolerable
level.
### 4.3.2 Exception to the general policy
However, in order to assume a reasonable standard of care that should be
present in all research (also non-medical), there is one exception to the
general policy: In the unlikely event, that findings are made which indicate
_fundamentally harmful/negative economic or_ _social consequences_ for the
participant, the consortium will inform the participant of these consequences,
This approach is intended to protect the participant, as well as the
researcher.
The procedure to deal with fundamentally harmful findings is laid out below.
1. The consortium leader adelphi will set up a phone call with all project partners within three weeks to discuss the recommendations to be made to this particular participant to avert future damaging consequences.
2. The consortium will get a second expert opinion from the external advisor on ethics issues, Axel Carlberg (http://www.upspring.se/contact.html) within four weeks. Axel Carlberg has extensive experience in ethics analysis in divers EU projects and has declared his availability for the DOMINO project, if necessary.
3. The consortium will inform the EC about the findings and get feedback on the suggested recommendation after step one and two have been executed.
4. If the strategy is agreed on, the regional partners will inform the respective participant about the findings and recommendation in their national languages. The first contact on that issue will be made by email to set up a date when to talk on the phone. If the participant so wishes, one or more follow-up calls can be arranged after a few weeks. The goal of the calls with the participants is to enable him to deal with the consequences in an (for him) appropriate and satisfactory way.
5. Budget for the extra work will be shared by the partners equally. In case a high number of participants need extra care over a longer period of time, some budget from the dissemination activities might be reallocated to this task (in agreement with the EC).
The general outcomes of the project will, as outlined in the project proposal,
be published on the project website and is thus accessible for all
participants. This is to say that they are transparently informed of research
results that the consortium set out to acquire. (Participants might not always
keep all the details on purposes of the DOMINO Challenge readily available in
their head.)
5 **Attachment Security Benefits Amazon Web Services**
# _Security Benefits of Amazon Web Services (AWS)_
Cloud security at AWS is the highest priority. As an AWS customer, Plugwise
benefits from a data centre and network architecture built to meet the
requirements of the most securitysensitive organisations.
We refer to: _https://aws.amazon.com/privacy/_
An advantage of the AWS cloud is that it allows its customers to scale and
innovate, while maintaining a secure environment.
## 5.1 Designed for Security
The AWS Cloud infrastructure is operated in AWS data centres and is designed
to satisfy the requirements of AWS’ most security-sensitive customers. The AWS
infrastructure has been designed to provide high availability, while putting
strong safeguards in place for customer privacy. All data is stored in highly
secure AWS data centres. Network firewalls built into Amazon VPC, and web
application firewall capabilities in AWS WAF allows creation of private
networks, and control access to the instances and applications.
_Figure 3_ : Global Infrastructure of AWS
## 5.2 Highly automated
AWS proposes build security tools, which are tailored for a unique
environment, size, and global requirements. Building security tools from the
ground up allows AWS to automate many of the routine tasks security experts
normally spend time on. This means AWS security experts can spend more time
Amazon Web Services focusing on measures to increase the security of the AWS
Cloud environment.
AWS customers also automate security engineering and operations functions
using a comprehensive set of APIs and tools. Identity management, network
security and data protection, and monitoring capabilities can be fully
automated and delivered using popular software development methods. Customers
take an automated approach to responding to security issues. When automating
and using the AWS services, rather than having people monitor the security
position and reacting to an event, the system can monitor, review, and
initiate a response.
## 5.3 Highly available
AWS builds its data centres in multiple geographic Regions. Within the
Regions, multiple Availability Zones exist to provide resiliency. AWS designs
data centres with excess bandwidth, so that if a major disruption occurs there
is sufficient capacity to load-balance traffic and route it to the remaining
sites, minimizing the impact on our customers. Customers also leverage this
Multi-Region, Multi-AZ strategy to build highly resilient applications at a
disruptively low cost, to easily replicate and back up data, and to deploy
global security controls consistently across their business.
## 5.4 Highly accredited
AWS environments are continuously audited, with certifications from
accreditation bodies across the globe. This means that segments of your
compliance have already been completed.
To help Plugwise meet specific government, industry, and company security
standards and regulations, AWS provides certification reports that describe
how the AWS Cloud infrastructure meets the requirements of an extensive list
of global security standards.
Customers of AWS inherit many controls operated by AWS into their own
compliance and certification programs, and run security assurance efforts in
addition to actually maintaining the controls themselves.
6 **Attachment Consent Form**
## Brief introduction of the project
DOMINO is an EU-funded research project that intends to prompt energy
reductions in households. It uses a smart plug technology, where plugs measure
electricity consumption of different appliances, to raise awareness for energy
consumption and to change the participants’ behaviour towards energy saving.
The project has chosen a playful approach in form of an energy saving game
called “DOMINO Challenge” which is played in teams of five households each.
The challenge takes place in Brussels, Berlin, and Naples and will involve
around 4000 households. The project results will allow making assumptions on
the overall potential of smart plugs for reducing energy consumption in
households and will enable related initiatives to benefit from the DOMINO
experience. Including preparation and evaluation the project extends over a
period of almost three years (March 2016 till end of February 2019 .
## Incidental findings
If during the course of the project, the project team comes about incidental
findings that would have potential or real fundamentally harmful and negative
economic or social consequences for a participant, the project team will
disclose these findings to the participant. Benign incidental findings will
not be disclosed to participants or third parties.
## Point of contact, if questions on this form arise
Different organisations are responsible for the DOMINO project in the three
regions. For any further questions please contact adelphi in Berlin (Lena
Domröse; [email protected]), Agenzia Napoletana Energia e Ambiente in Naples
(Michele Macaluso; [email protected]), or Brussels Environment in Brussels
(Xavier Van Roy; [email protected]). For data protection queries,
please contact Plugwise in the Netherlands (Theo Vroege; [email protected]
).
_Terms and conditions to which I agree by ticking the box_ 1 _:_
* My participation in the DOMINO Challenge requires the provision of Personal Data. I agree to provide personal information, such as:
* First Name, Name
* Physical address, i.e. street name, street number, city, zip code and country
(used for shipment of smart plug equipment)
* Email address
* If I am the first player of my team, I agree that my name and address data will be transferred by the smart plug producer Plugwise to its (out-sourced) logistics centre and used by FEDEX for shipment.
* I understand that my postal address will be deleted 90 days after the equipment has been sent out.
* The DOMINO app will request and collect personal data on my:
* Age
* Gender
* Password
* Choice of language
* Number of people in the household
* Electricity price
* Electricity consumption from the past year
* Type/name of appliances connected to the plugs except for the joker plug
* Appliances; i.e. information on energy efficiency class, year of construction and consumption as indicated by the manufacturer
* Absence periods during the DOMINO challenge (retrospectively)
* I understand that my personal data will not be given or sold to any other third party for commercial or other reasons.
* I understand that the information requested by the app will help the research team in their analysis and it is not mandatory to provide but recommended.
* I understand that the processing of my personal data relies on my consent, which is necessary to participate to the contest, and is required till the end of the contest to have the chance to win a prize.
* I understand that my name can be seen by other members of my team.
* I understand that participation in the DOMINO challenge is voluntary and can be ended at any time without affecting the lawfulness of processing based on my consent before its withdrawal.
* I agree that my personal data (except for the postal address) will be safely stored until the end of the DOMINO project, i.e. until the end of February 2019. Then it will be deleted.
1. agree that my personal data will be processed by Plugwise and stored in a cloud from Amazon.com, in Ireland.
* I understand that I have the right to request access to my _personal_ data, the right to rectification, erasure, or restriction of processing or to object to processing of personal data.
* I agree that the research team collects and analyses the following _usage_ data:
o the current and historical consumption of my appliances on
Monthly basis
Weekly basis
Daily basis
Hourly basis o scheduling data, meaning data on the schedule chosen by myself
to switch appliances on and off
* The collected _usage_ data will be stored anonymously and separately from the personal data in a secure data base of Plugwise and I understand that this data cannot be deleted from the data base after the project is over because it has been anonymised. It will therefore be stored there for an indefinite period.
* If I click on feedback buttons in the tips and alerts section of the app, I agree that the number of my clicks will be collected. I understand that the collection is necessary so that it can be evaluated which team has most clicks because this is one way to win the DOMINO challenge.
* I agree that my anonymised consumption data will be compared to anonymised data of other users and that general outcomes of the project will be published on the project website and will thus be accessible for all participants and the public.
* I agree that my Usage data will be available outside my local WLAN network when I use the app outside my house and could be accessed by third parties via computer espionage.
* During the DOMINO challenge, I agree to receive certain messages and notifications via the DOMINO app, for example energy saving tips, reminders to set goals and information on the end of my cycle.
* During my 3-month long cycle of DOMINO, I agree to receive monthly emails with information on my monetary and energy savings.
* After my cycle is over, I agree to receive an email asking for evaluating the challenge encouraging me to stick to the energy saving habits I may have adopted.
* When I have allowed push notifications from the app, I understand that the app will be running permanently on my phone.
1. have the right to lodge a complaint with my national Data Protection Authority, if I have doubts concerning data protection measures.
* I have informed members of my household that my household is part of a research project of the European Commission that deals with energy efficiency of household appliances and collects usage data on appliances that are connected to the smart plugs.
* I assure that other household members have expressed their consent to being part of the DOMINO Challenge. Violation of this clause can lead to my exclusion from the DOMINO challenge.
* I understand that the DOMINO Challenge only works if the plugs are passed on from one household to the next and I agree to pass the plugs on to another participating household in due time after my usage cycle has ended. If I am the last player, I will send the plugs back to the responsible project partner in my region within 14 days using the postage label that will be provided on the website: www.dominoenergy.eu.
* If there is reason to believe that I did not pass on the plugs to another household, I agree to receive a notification email from the project team to remind me of that duty.
* I understand that when the smart plugs are used by the next player, statistical consumption data of my appliances, or data on the type of appliances connected, will not be available anymore.
* In case that my team belongs to the winners, I agree that Usage Data will be connected to my Personal Data (Email-address) in order to inform me about the award and to send me the prize.
* I understand that there are possible risks and discomforts, as mentioned above (or: in the data management plan), that might occur during the research project. The risks are: I might not save any energy at all. (For example because I do not (want to) follow the recommendations that are sent to me via the app.
* I understand that I will have to invest some time and effort for setting up the plugs and passing them on to the next person. (It is assumed that setting the system up will take between 10 minutes and 1 hour depending on the technical expertise of people.)
* I refrain from connecting medical equipment or erotic toys to any of the plugs and from naming them correspondingly because obtaining data of this sort of appliances is not desired during the project.
* In the unlikely event, that incidental findings are made which indicate real or potential fundamentally harmful/negative economic or social consequences for me, I allow the project team to contact me.
I understand that the project team and their companies are not liable for any
damage or harm that might occur, unless it is a consequence of intentional or
grossly negligent behaviour.
7 **Attachment Logistics**
### _Fedex Excel file definition_
<table>
<tr>
<th>
Excel column
</th>
<th>
Description
</th>
<th>
Trans Field #
</th> </tr>
<tr>
<td>
A (*)
</td>
<td>
Unique Identification Number of the line
</td>
<td>
1
</td> </tr>
<tr>
<td>
B
</td>
<td>
Recipient Company name
</td>
<td>
11
</td> </tr>
<tr>
<td>
C
</td>
<td>
Recipient Contact Name
</td>
<td>
12
</td> </tr>
<tr>
<td>
D
</td>
<td>
Address line 1
</td>
<td>
13
</td> </tr>
<tr>
<td>
E
</td>
<td>
Address line 2
</td>
<td>
14
</td> </tr>
<tr>
<td>
F
</td>
<td>
Recipient City
</td>
<td>
15
</td> </tr>
<tr>
<td>
G (*)
</td>
<td>
Recipient State code
</td>
<td>
16
</td> </tr>
<tr>
<td>
H (*)
</td>
<td>
Recipient Country code or Country name
</td>
<td>
50
</td> </tr>
<tr>
<td>
I (*)
</td>
<td>
Recipient Postal code
</td>
<td>
17
</td> </tr>
<tr>
<td>
J (*)
</td>
<td>
Recipient Phone number
</td>
<td>
18
</td> </tr>
<tr>
<td>
K (*)
</td>
<td>
Recipient E-mail
</td>
<td>
1202
</td> </tr>
<tr>
<td>
L
</td>
<td>
Recipient VAT or Tax number
</td>
<td>
118
</td> </tr>
<tr>
<td>
M (*)
</td>
<td>
Shipment Reference
</td>
<td>
25
</td> </tr>
<tr>
<td>
N
</td>
<td>
Line numbering (drag down till last line)
</td>
<td>
38
</td> </tr> </table>
#### Fedex Excel Template
<table>
<tr>
<th>
1
</th>
<th>
11
</th>
<th>
12
</th>
<th>
13
</th>
<th>
14
</th>
<th>
15
</th>
<th>
16
</th>
<th>
50
</th>
<th>
17
</th>
<th>
18
</th>
<th>
1202
</th>
<th>
118
</th>
<th>
25
</th> </tr>
<tr>
<td>
Bus. Code
</td>
<td>
Company Name
</td>
<td>
Contact Name
</td>
<td>
Address Line 1
</td>
<td>
Address Line2
</td>
<td>
City
</td>
<td>
State Code
</td>
<td>
Country Code
</td>
<td>
Zip
</td>
<td>
Phone
</td>
<td>
E-mail
</td>
<td>
VAT_Tax Nr.
</td>
<td>
Reference
</td> </tr>
<tr>
<td>
Manda tory Unique ID
</td>
<td>
Mandatory if no Contact
</td>
<td>
Mandatory if no Company
</td>
<td>
Mandatory
</td>
<td>
</td>
<td>
Manda tory
</td>
<td>
Mandatory
</td>
<td>
Mandatory
</td>
<td>
Manda tory
</td>
<td>
Mandatory
</td>
<td>
</td>
<td>
</td>
<td>
</td> </tr>
<tr>
<td>
US-CA
</td> </tr>
<tr>
<td>
Max.
Length
</td>
<td>
Max. Length
</td>
<td>
Max. Length
</td>
<td>
Max.
Length
</td>
<td>
Max. Length
</td>
<td>
Max.
Length
</td>
<td>
2 Letter ID
</td>
<td>
2 Ltr ID
</td>
<td>
Max.
Length
</td>
<td>
Numeric field
</td>
<td>
Max
</td>
<td>
Max
</td>
<td>
Max.
</td> </tr>
<tr>
<td>
20
</td>
<td>
35
</td>
<td>
35
</td>
<td>
35
</td>
<td>
35
</td>
<td>
35
</td>
<td>
10
</td>
<td>
Max. 15
</td>
<td>
60
</td>
<td>
18
</td>
<td>
35
</td> </tr> </table>
|
https://phaidra.univie.ac.at/o:1140797
|
Horizon 2020
|
1134_RESCCUE_700174.md
|
# Introduction
This document is developed as part of RESCCUE (RESilience to cope with Climate
Change in Urban arEas - a multisectorial approach focusing on water) project,
which has received funding from the European Union’s Horizon 2020 Research and
Innovation program, under the Grant Agreement number 700174.
This document is an updated version of the Data Management Plan (DMP)
presented in M6 (D8.2) and corresponds to Deliverable 8.5 of Work Package 8
(WP8) – Project Management. WP8 ensures an optimal coordination and management
of RESCCUE, guaranteeing the effective implementation of the project
activities.
This document is to be used by all partners to efficiently handle data, and
make sure that the several obligations that RESCCUE has in terms of data are
properly fulfilled by all partners at all time. The Data Management Plan (DMP)
also ensures an effective implementation of the _Open Research Data Pilot_
initiative of the European Commission and addresses the points set in the DMP
template of the EC Guidelines on FAIR Data Management in HORIZON 2020 (EC,
2016b). Nevertheless, this has to be balanced with the protection of
scientific information, commercialisation and Intellectual Property Rights
(IPR).
The DMP establishes the ~~data management~~ life cycle for the data to be
collected, processed, and/or generated which is to be followed by all partners
in the consortium. Moreover, it details what kind of data will be reused and
produced, which of the data generated by RESCCUE project will be open and how
these data will be exploited and made accessible (for verification and/or
reuse) and, on the contrary, which of the data will be preserved, considering
also that this project deals with some cases of Critical Infrastructures (CI)
and that Directive 2008/114/EC on CI must be respected (EC, 2008).
# Objectives and methodology
The goal of a DMP is to consider the many aspects of data management, metadata
generation, data preservation and analysis, which ensures that data are well-
managed in the present, and prepared for preservation in the future.
On the other hand, it is of key importance to make sure that the research data
is findable, accessible, interoperable and re-usable (FAIR).As stated in the
Guidelines on FAIR Data Management in Horizon 2020 (EC, 2016b), a DMP must
include information on:
* the handling of research data during and after the end of the project
* what data will be collected, processed and/or generated
* which methodology and standards will be applied
* whether data will be shared/made open access and
* how data will be curated and preserved (including after the end of the project) Consequently, these several items are included in this Deliverable 8.5, describing the data management life cycle for the data to be collected, processed and generated by the RESCCUE Project.
The methodology to produce this DMP, is based on the Guidelines on FAIR Data
Management in Horizon 2020 (EC, 2016b) the Digital Curation Centre (DCC)
online tool called DMP Online ( _https://dmponline.dcc.ac.uk_ ) and the
Guidelines on Open Access to Scientific Publications and Research Data in
Horizon 2020 (EC, 2016a).
The document has been divided into the following sections:
3. Research data management and sharing
4. Metadata and FAIR data
5. Protection of critical infrastructures and sensitive information
6. Ethics and Legal Compliance
7. Responsibilities and Resources
8. References
# Research data management and sharing
## Data classification and management
RESCCUE project deals with the resilience of cities, specifically in the case
studies of Barcelona, Bristol and Lisbon, in terms of urban services response
in critical situations derived from climate change. The assessment of the
response and interdependencies of the urban services (water services,
transport, telecommunication, energy supply or solid waste collection) both
for current and future scenarios is therefore the basis of the research. The
expected results involve, among others, the hazard, vulnerability and risk
assessment of the urban services operation, including the identification of
critical infrastructures.
In order to achieve all the project objectives, data is being complied,
methodologies are developed, models are built and finally datasets are
generated.
### 3.1.1 Output data
Table 1 presents the datasets that will be generated by RESCCUE. For each
dataset, the following characteristics are described: brief description of the
result; WP where these data are generated; producer/owner of the results; date
(project’s month) in which these data are expected; type and data format;
estimated size of data; naming conventions to be used and expected end users.
**Table 1 – Summary of the data generated in RESCCUE**
<table>
<tr>
<th>
**Description of result**
</th>
<th>
**Associated WP**
</th>
<th>
**Result owner(s)**
</th>
<th>
**Delivery date**
</th>
<th>
**Type and data format**
</th>
<th>
**Estimated data size**
</th>
<th>
**Naming conventions**
</th>
<th>
**End-users**
</th> </tr>
<tr>
<td>
Climate downscaled projections, decadal and seasonal simulations
</td>
<td>
1
</td>
<td>
FIC
</td>
<td>
M18
</td>
<td>
Ascii files with climate data per variable, model, scenario, etc.
</td>
<td>
-Climate
timescale: 500,000 files
(30 Gb)
-Decadal timescale:42,000 files (16 Gb)
-Seasonal timescale:500 files (80 Mb)
</td>
<td>
-For zip file:
Variable_Model_Scenario_City
.zip
-For plain text file:
Variable_Model_Scenario_Stat ionId.txt
</td>
<td>
Model owners or other climate researchers that
will use this information as inputs for their research
</td> </tr>
<tr>
<td>
Extreme climate scenarios
</td>
<td>
1
</td>
<td>
FIC,
Aquatec
</td>
<td>
M24
</td>
<td>
KML polygons with climate data per variable, horizon and scenario.
</td>
<td>
300 files (4Mb)
</td>
<td>
-For climate/decadal simulations:
CITY_variable_threshold_Retu rnPeriod_TimePeriod_quantile
.kml
-For seasonal forecast:
CITY_variable_threshold_scen ario_criterion_seasonal.kml
</td>
<td>
Model owners or other climate researchers that
will use this information as inputs for their research
</td> </tr>
<tr>
<td>
Drought and water quality
analysis
</td>
<td>
2
</td>
<td>
Cetaqua
</td>
<td>
M36
</td>
<td>
Excel sheets presenting the water contributions at each reservoir for the
future scenarios
Excel sheets presenting the evolution of quality problems in the Llobregat's
river at the DWTP of Sant Joan Despí for the future
scenarios
</td>
<td>
2 Excel files 20 Mb (x2)
</td>
<td>
Contributons_Ter_Llobregat_r eservoirs.xlsx
Water_Quality_Llobregat_SJD. xlsx
</td>
<td>
Water companies and the Catalan Water Agency
</td> </tr>
<tr>
<td>
Urban drainage simulations in Barcelona
</td>
<td>
2
</td>
<td>
Aquatec, BCASA
</td>
<td>
M36
</td>
<td>
Shape file presenting hydraulic behaviour of sewer system for the several
scenarios (5
different return periods for the current (1) and future (4) scenarios 🡪 TOTAL:
25 simulations)
Shape files presenting the pedestrian hazard maps for the several scenarios (5
different
</td>
<td>
75 shape files of data (of
250 Mb each)
TOTAL: 18.75 Gb
</td>
<td>
Name_of_city_Hazard_target_
Return_period_Scenario_Time _period
</td>
<td>
Other researchers, all the stakeholders that might
have flooded assets and
the general population of
the city
</td> </tr> </table>
<table>
<tr>
<th>
</th>
<th>
</th>
<th>
</th>
<th>
</th>
<th>
return periods for the current (1) and future
(4) scenarios 🡪 TOTAL: 25 simulations)
Shape files presenting the vehicular hazard maps for the several scenarios (5
different
return periods for the current (1) and future
(4) scenarios 🡪 TOTAL: 25 simulations)
</th>
<th>
</th>
<th>
</th>
<th>
</th> </tr>
<tr>
<td>
Assessment of marine model impacts
</td>
<td>
2
</td>
<td>
Aquatec
</td>
<td>
M36
</td>
<td>
MOHID files and Ascci data (Time series simulations of E.Coli concentration in
the
Barcelona bathing water) for current (1) and future scenarios (2)
</td>
<td>
Ascii files of concentration distribution every hour for
10 years of continuous simulations (7GB). A total
of 30 years simulations
between baseline and future scenarios (TOTAL=21GB).
</td>
<td>
Name_of_city_Scenario_Time
_period
</td>
<td>
Waste water operators, public administrations and general population
</td> </tr>
<tr>
<td>
Assessment of bursting pipes impacts in Barcelona
</td>
<td>
2
</td>
<td>
Aquatec, AB
</td>
<td>
M36
</td>
<td>
Shape files presenting the hazard maps for several scenarios
</td>
<td>
2 shape files of data (of
250 Mb each) 🡪 TOTAL: 500 Mb
</td>
<td>
Name_of_city_Scenario_Time
_period
</td>
<td>
Water companies and other stakeholders that might have flooded assets
</td> </tr>
<tr>
<td>
Simulations of the
electric model in Barcelona
</td>
<td>
2
</td>
<td>
IREC, Endesa
</td>
<td>
M36
</td>
<td>
Maps Images and data in table format presenting the impacts
</td>
<td>
4 cases with 3 PR for 3 scenarios (36 Figures)
~150Mb
</td>
<td>
Simulation_City_Sector_Scena rio_PR_case
</td>
<td>
All the stakeholders that have critical infrastructures depending on the
electric network
</td> </tr>
<tr>
<td>
Simulation of hazards on
the traffic model
</td>
<td>
2
</td>
<td>
Barcelona CC
</td>
<td>
M36
</td>
<td>
Maps and ascii information presenting the impacts
</td>
<td>
25 shape files of data (of 250 Mb each) 🡪 TOTAL:
6.25 Gb
</td>
<td>
Name_of_city_Hazard_target_
Return_period_Scenario_Time _period
</td>
<td>
Local police and other public administrations
</td> </tr>
<tr>
<td>
Urban drainage simulations in Lisbon
</td>
<td>
2
</td>
<td>
Hidra and CML
</td>
<td>
M36
</td>
<td>
1\. Lisbon citywide drainage system
(1D GIS based model)
Image files presenting sewer capacity for 4 return periods of current
situation (results
</td>
<td>
1\. 4 png files (1 Mb each)
→ TOTAL: 4 Mb
</td>
<td>
NameOfTheCity_Model_Scena rio_ReturnPeriod
</td>
<td>
Other researchers, all the stakeholders that might
have flooded assets and
the general population of
the city
</td> </tr> </table>
<table>
<tr>
<th>
</th>
<th>
</th>
<th>
</th>
<th>
</th>
<th>
taken from Lisbon Drainage Master Plan
2016-2030)
2. Alcântara drainage system (1D SWMM Model)
Image files presenting main hydraulic variables (flow capacity, flow rate and
velocity) for 5 return periods of current
situation and 3 return periods for 2 future
scenarios (most severe and most probable)
3. Lisbon downtown catchments J and L (1D/2D Combined Model
SWMM+BASEMENT)
Raster files presenting water depths for 5 return periods of current situation
and and
3 return periods for 2 future scenarios (most severe and most probable)
</th>
<th>
2. 3x11 png files (2 Mb each)
→ TOTAL: 66 Mb
3. 11 Raster files (1.5 Mb each)
→ TOTAL: 16.5 Mb
</th>
<th>
</th>
<th>
</th> </tr>
<tr>
<td>
Simulations of the energy distribution model in Lisbon
</td>
<td>
2
</td>
<td>
EDP
</td>
<td>
M36
</td>
<td>
DXF files presenting the simulation of the impact in electrical infrastructure
for Lisbon using the information of urban drainage models. 6 Scenarios: i)
Lisbon
Low/Medium/High Voltage Grid at normal configuration (2 simulations); ii)
Lisbon municipality citywide drainage (2
simulations); iii) Lisbon municipality estuary water (1 simulation); iv)
Lisbon downtown
catchments J an L (7 simulations); iv-1) 1 primary substation out of service
(4
simulations); iv-2) 9 secondary substations
out of service (14 simulations)
</td>
<td>
30 files of data. Total: 6
Mb
</td>
<td>
City, Low, Medium, High
Voltage Grid,
Urban Drainage model approaches and electrical infrastructure results
Contingency Plan for different scenarios for simulations of iv), iv-1) and
iv-2)
</td>
<td>
All the stakeholders that have critical infrastructures depending on the
electric network
</td> </tr>
<tr>
<td>
Urban drainage simulations in Bristol
</td>
<td>
2
</td>
<td>
BCC
</td>
<td>
M36
</td>
<td>
Shape files presenting the depth, extent, hazard and velocity maps for the
several
scenarios (5 different return periods (RP) per catchment (7) – TOTAL: 35
simulations).
</td>
<td>
8Mb per catchment per
RP. 7 catchments = 56Mb per RP. TOTAL: 5 RP x 56 x 3 = 0.85Gb.
</td>
<td>
Catchment name_Mreturn period
</td>
<td>
Other researchers, all the stakeholders that might
have flooded assets and
the general population of
the city
</td> </tr> </table>
<table>
<tr>
<th>
Tidal and
Fluvial
Flooding simulations in Bristol - Central Area
Flood Risk
Assessment
(CAFRA)
</th>
<th>
2
</th>
<th>
BCC
</th>
<th>
M36
</th>
<th>
Shape files presenting the depth, extent, hazard, maps for the several
scenarios (32 RP (mixed combination events), 4 epochs, 3 emissions scenarios,
with existing defences.
TOTAL – 24 simulations (however equivalency runs exist, for instance current
Flood Zone 2 is equivalent to future Flood Zone 3 so some represent 2
scenarios))
</th>
<th>
Varying sizes of ASCII grid and DAT file format
400Mb per run x 24 =
9.6Gb
</th>
<th>
CAFRA_Versions_RP_Fluvialele ment_Tidalelement_EpochEm
missions_DepthMaximumoutp
uts_Gridsize
</th>
<th>
Other researchers, all the stakeholders that might
have flooded assets and
the general population of
the city
</th> </tr>
<tr>
<td>
Tidal and
Fluvial
Flooding simulations in Bristol -
Avonmouth
Strategic
Flood Risk
Assessment
</td>
<td>
2
</td>
<td>
BCC
</td>
<td>
M36
</td>
<td>
Shape files presenting the hazard maps for the several scenarios (5 RP per
epoch, 3 epochs plus various wave and surge
components and different failure scenarios.
Current defended/undefended/blockage.
2073 defended/undefended/breach. 2110 defended/undefended/breach. TOTAL – 118
simulations
</td>
<td>
Varying sizes of ASCII and dat files TOTAL = 60Gb
</td>
<td>
AVM_gridsize_RPtidal_RPfluvi al_epoch_withdefences_block
age_breach_modeldesignruns
</td>
<td>
Other researchers, all the stakeholders that might
have flooded assets and
the general population of
the city
</td> </tr>
<tr>
<td>
Integrated flooding – traffic simulations in Bristol
</td>
<td>
2
</td>
<td>
Uni Exeter
</td>
<td>
M36
</td>
<td>
Shape files presenting the impacts for all the simulated events. XML files
outputted via
micro-simulation traffic
CSVs for graphical analysis
</td>
<td>
Approximately 2-5GB per flood event simulation
</td>
<td>
NameOfCity_EventType_Retur nPeriod_AdditionalInfo.FileTyp
e
</td>
<td>
Local police and other public administrations
</td> </tr>
<tr>
<td>
Integrated flood and waste sectorial model
</td>
<td>
2
</td>
<td>
Cetaqua
</td>
<td>
M26
</td>
<td>
Shapefiles with the location of potentially unstable containers in Barcelona
for the
different scenarios
</td>
<td>
1.5 GB
</td>
<td>
For the actual event (to validate the model):
Containers_0_31_07_2011;
Containers_50_31_07_2011;
Containers_100_31_07_2011 For the design storms: Containers_0_T2;
Containers_0_T5;
Containers_0_T10
Containers_50_T2;
Containers_50_T5;
Containers_50_T10
</td>
<td>
Model owners or other researchers that will use
this information as inputs
for their research. Also the
Barcelona City Council will use this model to prevent containers’
instabilities
</td> </tr> </table>
<table>
<tr>
<th>
</th>
<th>
</th>
<th>
</th>
<th>
</th>
<th>
</th>
<th>
</th>
<th>
Containers_100_T2;
Containers_100_T5;
Containers_100_T10
</th>
<th>
</th> </tr>
<tr>
<td>
Flood impact assessment in the energy sector
</td>
<td>
3
</td>
<td>
IREC
</td>
<td>
M36
</td>
<td>
Maps Images and data in table format presenting the impacts
</td>
<td>
5 PR for 3 scenarios (15
Figures + optimization table) ~200Mb
</td>
<td>
Hazards_City_Sector_Scenario _PR_case
</td>
<td>
All the stakeholders that have critical infrastructures depending on the
electric network
</td> </tr>
<tr>
<td>
Flood direct damage assessments
</td>
<td>
3
</td>
<td>
Uni Exeter,
Cetaqua,
Aquatec
</td>
<td>
M36
</td>
<td>
1.Depth damage curves and shape files with the impacts for all the scenarios.
KML/KMZ files for use in Google Earth
2.CSVs for graphical analysis
</td>
<td>
1.Approximately 2-5GB per flood event simulation
2.Excel File xls 5 Mb
(curves) shape files 100 Mb (damage maps)
</td>
<td>
NameOfCity_EventType_Retur nPeriod_AdditionalInfo.FileTyp e
T1_damages.shp
T5_damages.shp
T10_damages.shp
T100_damages.shp
T500_damage.shp
</td>
<td>
Other researchers, public administrations and insurance companies
</td> </tr>
<tr>
<td>
Flood indirect damage assessments
</td>
<td>
3
</td>
<td>
Cetaqua
</td>
<td>
M36
</td>
<td>
Ascii files presenting the impacts for all the simulated events
</td>
<td>
Ascii files 10 Mb (no definitive)
</td>
<td>
Flood_indirect_damage (no definitive)
</td>
<td>
Other researchers, public administrations and insurance companies
</td> </tr>
<tr>
<td>
Assessment of transport
indirect damages
</td>
<td>
3
</td>
<td>
Cetaqua, Uni Exeter
</td>
<td>
36
</td>
<td>
1.Shape files presenting the impacts for all the simulated events. XML files
outputted
via micro-simulation traffic
2.CSVs for graphical analysis
</td>
<td>
1.Approximately 2-5GB per flood event simulation
2.Ascii files 10 Mb
(no definitive)
</td>
<td>
NameOfCity_SubArea_EventTy pe_ReturnPeriod_
AdditionalInfo.fileType
Transport_indirect_damage (no definitive)
</td>
<td>
Other researchers, public administrations and insurance companies
</td> </tr>
<tr>
<td>
Adaptation measures and strategies database
</td>
<td>
5
</td>
<td>
Cetaqua
</td>
<td>
18
</td>
<td>
Database containing all the strategies compiled
</td>
<td>
Not required (in cloud)
</td>
<td>
https://resccue.herokuapp.co
m
</td>
<td>
Other researchers, service operators and public administrations
</td> </tr> </table>
As it is later explained in section 4, the data generated in RESCCUE project
(i.e. datasets summarized in Table 1) will be made publically available and
discoverable, by publishing the metadata in the Inspire portal and uploading
the datasets in Zenodo. More details related to that can be found in that
section.
### 3.1.2 Input data
In addition to the information presented in Table 1, it is also important to
show the information that the RESCCUE project reuses to generate its outputs.
In Table 2 there is a brief overview of the data needed (reused) in each WP
and the source(s) to obtain them.
The data necessary to develop RESCCUE project is being collected by the
responsible and contributors involved in each task. In general, though,
primary data is being collected at the case studies (Barcelona, Lisbon and
Bristol) by the case-study responsibles (Aquatec, LNEC and University of
Exeter, respectively).
Regarding the data used or reused from other sources, some of this information
is private (it belongs to some of the project stakeholders) or has been
purchased to use it on the project (as some of the climate information).
Therefore, the RESCCUE partners are not allowed to share it, but only to use
it to generate the outputs.
**Table 2 – Summary of input data used in the RESCCUE project classified per
Work Package**
<table>
<tr>
<th>
**WORK PACKAGE**
</th>
<th>
**INPUTS**
</th> </tr>
<tr>
<th>
**Type of data**
</th>
<th>
**Source(s)**
</th> </tr>
<tr>
<td>
**WP1-Climate Change Scenarios**
</td>
<td>
Climatic data/models
</td>
<td>
-Public data from: PCMDI
GHCN-daily
ISH/ISD
-AEMet
-IPMA
-Met Office
</td> </tr>
<tr>
<td>
Future climate scenarios
</td>
<td>
Public data from IPCC
</td> </tr>
<tr>
<td>
**WP2-Hazard Assessment for Urban Services Operation**
</td>
<td>
Field data, sensor data and physical data from the several sectorial models
and studies
implemented in the 3 research sites
</td>
<td>
-Public data
-Know-how and networks data from Barcelona CC (BCASA), CML, Bristol CC, Endesa, EDP and Wessex Water
</td> </tr>
<tr>
<td>
**WP3-Vulnerability & Risk **
**Assessment for Urban Services Operation**
</td>
<td>
Data quantifying the impacts of identified hazards in urban areas
</td>
<td>
-Public data on impacts for the three research sites
-Know-how (such as damage curves or other methodologies) from UNEXE, Aquatec, CETaqua and LNEC
</td> </tr>
<tr>
<td>
**WP4-Integration in a software tool**
</td>
<td>
Information of the location of infrastructures and services from the three
research sites,
as well as their interdependencies, redundancies and other key parameters
</td>
<td>
-City councils
-Urban services providers such as water utilities, electricity
providers, waste management services, etc.
</td> </tr>
<tr>
<td>
**WP5-Resilience and adaptation strategies ready for market uptake**
</td>
<td>
Resilience strategies
</td>
<td>
-Public information coming from previous research projects (such as CORFU, PREPARED, RESIN,
BRIGAID – see Aneex 2 of
D5.1)
-Expert knowledge from RESCCUE partners to complete the methodology proposed
</td> </tr>
<tr>
<td>
**WP6-Validation Platform & First Applications **
</td>
<td>
Resilience studies undertaken by third parties
</td>
<td>
-Reports from C40, 100RC,
UN-Habitat and others
</td> </tr>
<tr>
<td>
**WP7-Dissemination & Exploitation **
</td>
<td>
Information of key stakeholders and personal data
</td>
<td>
-Information of the key stakeholders that have been identified for the project -Personal data of the attendees of RESCCUE workshops and other public events, complying with the
GDPR regulations
</td> </tr>
<tr>
<td>
**WP8-Project Management**
</td>
<td>
Personal data
</td>
<td>
-Personal data of the attendees of RESCCUE PCM meetings, complying with the
GDPR regulations
</td> </tr> </table>
### 3.1.3 Data quality
All data generated and collected in RESCCUE undergo a quality check in order
to analyse its individual plausibility and consistency. Collection and
generation of climate and service related data follow established standards
such as INSPIRE implementing rules, metadata and data specifications; OpenMI;
WaterML; GML and OGC geospatial data services.
When needed, additional measures are taken in order to ensure the quality of
data. As an example, in “D1.1 - Data collection and quality control report.
Summary of studies on climate variables at the research cities”, a thorough
analysis of all the data compiled was undertaken, ensuring consistency,
removing outliers and homogenizing the information.
Open standardised and interchangeable formats are used whenever possible and
adequate to ensure the long-term usability of data. Proprietary software-
specific data formats are avoided, with exception to those widely spread and
openly documented or eventually related to software platforms used by the
project teams.
## Data sharing
### 3.2.1 Open access to peer-reviewed scientific publications
RESCCUE research partners publish scientific publications including project
results in Open Access. Open access can be defined as the practice of
providing on-line access to scientific information that is free of charge to
the end-user To meet this requirement, beneficiaries ensure that these
publications can be read online, downloaded and printed (free of charge,
online access to any user).
The links to abstracts of research articles published in scientific journals
are also available in the project website (www.resccue.eu).
The open access to publications procedure comprises 3 steps:
1. Selecting the open access route (green or gold open access)
2. Providing open access to publications
3. Depositing the data in repositories (online archive) in order to allow for replicability of the results
Thus, all scientific publications generated by the RESCCUE project will be
made available both online through open access in peer reviewed scientific
journals and at RESCCUE web page.
### 3.2.2 Open access to research data
RESCCUE is part of the EC’s initiative Open Research Data Pilot. Within the
framework of Horizon 2020, the Open Research Data Pilot aims to improve and
maximise the access to and re-use of research data generated by projects. The
Open Research Data Pilot applies to two types of data:
1. Data, including metadata, needed to validate the results presented in scientific publications (published in scholarly journals);
2. Other data (e.g. curated data not directly attributable to a publication or raw data), including associated metadata.
The research data that will be produced, as it could be seen in Table 1, will
be of interest to other researchers, public administrations, service operators
and other stakeholders as well as the general population. In order to allow
for replicability of research results, the information generated (final
results as specified in Table 1) will be made available so other can reproduce
the methodologies used in RESCCUE.
Accordingly, the data generated in the project will be available in a research
data repository so that it will be possible to access, mine, exploit,
reproduce and disseminate it, free of charge for any user. Possible
repositories to include these data are: Registry of Research Data Repositories
(www.re3data.org) or Zenodo (zenodo.org). After an analysis of both,
**Zenodo** has been selected as the data repository to be used in RESCCUE.
According to the Exploitation Plan (Deliverable 7.4) the exploitation of the
results generated from RESCCUE project must be ensured up to four years after
the project end. Therefore, both the Zenodo repository and the RESCCUE website
(including the deliverables and scientific publications) will be completely
operative, at least, until May 2024. Additionally, all the Gold Open Access
scientific publications will be available for an unlimited period after the
end of the project.
# Metadata and FAIR data
The Guidelines on FAIR Data Management in Horizon 2020 (EC, 2016b), clearly
state that making research data findable, accessible, interoperable and re-
usable (FAIR), is on the main roles of the DMP.
## Making data findable, including provisions for metadata
In order to make data findable, the main tool is to assure that data used and
produced in the project can be discoverable with **metadata** . Since there
are several ISO metadata standards produced by ISO committees including ISO
19115 (Geographic information — Metadata) and ISO 19119 (Geographic
information — Services), the RESCCUE consortium will take advantage of the
schemas already defined to define its metadata. Therefore, common criteria
will be followed for all the RESCUE generated data while following the
requirements of one of these standards.
In addition, the European INSPIRE Directive (2007/2/EC) aims to create a
European Union (EU) spatial data infrastructure. This Directive requests that
Member States shall ensure that metadata are created for the spatial data
sets, and that those metadata are kept up to date. In order to do so, INSPIRE
created an online portal called “INSPIRE GeoPortal” (
_http://inspiregeoportal.ec.europa.eu/_ ) that can be used to store and
search for metadata. This portal will be used in RESCCUE in order ensure that
the project data will be findable.
Given that data will be linked to each of the research sites, Barcelona,
Bristol and Lisboa, to a certain service and timeframe, naming conventions are
established in order to clearly identify the dataset by its name. All these
can be seen in Table 1, together with information corresponding to the several
datasets generated in the project.
Taking advantage of those naming conventions, a set of keywords will be
defined for each dataset, in order to ease the search of the metadata. These
keywords will be defined in accordance with the terminology that is defined in
the glossary of the RESCCUE project.
All these metadata will be stored in the “INSPIRE GeoPortal” making sure that
all the RESCCUE partners follow the same criteria. In addition, the Keyword
“RESCCUE” will always be included in order to easily track the project
results. An internal guide on how to generate the metadata of the RESCCUE
results will be prepared and circulated to all the RESCCUE partners.
## Making data openly accessible
The A from FAIR stands for accessible, which is precisely the main goal of the
Open Research data pilot that was presented in section 3.2.2. As stated there,
the research data generated by the RESCCUE Project will be shared in the
Zenodo repository.
This is of special relevance for the data used to publish results, in order to
ensure replicability of research results. Only on the cases in which the key
stakeholders do not give permission to disclose the results (when the
vulnerabilities of the networks that they manage are being presented), the
data will not be updated to Zenodo. On the rest of the cases, the RESCCUE
results will be found there.
## Making data interoperable
Making data interoperable, means that data exchange and re-use between
researchers, institutions, organisations, countries, etc., should be
available. The main goal of all this is to facilitate the re-combination of
the data produced with different datasets from other origins. In order to do
this, the use of standard formats and of available (open) software
applications is promoted.
In RESCCUE, the main pathway to make data interoperable is to include the
metadata in the INSPIRE GeoPortal as presented before, as well as upload the
datasets to the Zenodo repository, so other researchers are able to use this
information with different software applications (no matter if they are open
or not).
Finally, a terminology glossary has been prepared, using the most common
ontologies available from each of the fields that RESCCUE is dealing with. It
can be found in D5.1, including all the definitions that are of interest for
the project and thus, the results obtained will be easily understood and
therefore re-used by others.
## Increase data re-use
As explained before, data will be licensed to permit the widest re-use
possible, when no limitations are identified by the key stakeholders.
All data generated and collected in RESCCUE (see Table 1) undergoes a quality
check in order to analyse its individual plausibility and consistency, making
sure that others can directly use it to do their assessments and validate the
research done by the RESCCUE team.
As in some cases similar results will be generated for different case studies,
data harmonisation will also be of critical importance both for increasing
data re-use in general, but also to ease the comparison of RESCCUE results in
the three research sites.
In this sense, an Exploitation Plan (Deliverable 7.4) has been developed to
ensure the use (and re-use) of project results (and data). In this
deliverable, it is ensured that the results of RESCCUE project will be
exploited, at least, during four years after the end of the project. More
details about some of the RESCCUE detailed business plans can be also seen in
Deliverable 7.3).
# Data security
## Data confidentiality
Due to the topics addressed in RESCCUE Project, some of the results and
datasets generated could be potentially used for unfair purposes, that is, to
cause a complete collapse of a city through the failure of its main urban
services. Accordingly, apart from detailing what key data the project will
reuse and generate, how it will be exploited or made accessible (as explained
in “D7.7 Dissemination and Exploitation Plan”), in the context of RESCCUE
project it is very important to determine which of this data needs to be
protected and how it will be done.
As stated in the guidance document “Guidelines for the classification of
research results” (European Commission, 2015), the results of a project must
be classified if their unauthorized disclosure could adversely impact the
interests of the EU or of one (or more) of its Member States. E.g. some of the
information produced by a project could potentially be used to plan terrorist
attacks or avoid detection of criminal activities. However, while RESCCUE
deals with critical infrastructures and utilities research (e.g. buildings and
urban areas; energy, water, transport and communications networks; supply
chains; financial infrastructures, etc.), the results on criticalities or
vulnerabilities will not reach a level of detail that would imply a risk.
Therefore, there is no need in protecting the project results with the
category of EU CONFIDENTIAL. Nevertheless, in order to protect the results
concerning critical infrastructures at the case study areas, a couple of
measures have been taken.
The first measure is already tackled in the Consortium Agreement, where all
the project partners have agreed not to use confidential information otherwise
than for the purpose for which it was disclosed, that is the development of
the tasks of RESCCUE project.
The second measure is to establish the dissemination level of several
deliverables as **confidential** . The list of confidential deliverables are
listed below:
* D2.2: Multi-hazards assessment related to water cycle extreme events for current scenario
* D2.3: Multi-hazards assessment related to water cycle extreme events for future scenarios
* D3.4: Impact assessments of multiple hazards in case study areas
* D4.1: Report from HAZUR® implementation in each city
* D4.2: City RESILIENCE Assessment software (HAZUR® Assessment)
* D4.3: City RESILIENCE Management software (HAZUR® Manager)
* D5.3: Functional design of a resilience assessment operational module
Nevertheless, in order to not limit the exploitation of the project results, a
public version of deliverables 2.2, 2.3, 3.4 and 4.1 will be developed and
thus available at the RESCCUE web page ( _www.resccue.eu_ ) . These public
versions will be, respectively, 2.4, 2.5, 3.5 and 4.4, being a compacted
version of the original deliverable avoiding the information or results
affecting critical infrastructures.
## Protection of sensitive information
As presented earlier, some of the assessments being done in the RESCCUE
project are dealing with Critical Infrastructures (CI). The Directive
2008/114/EC (EC, 2008), defines “critical infrastructure as an asset, system
or part thereof located in Member States which is essential for the
maintenance of vital societal functions, health, safety, security, economic or
social well-being of people, and the disruption or destruction of which would
have a significant impact in a Member State as a result of the failure to
maintain those functions”.
It must be noticed thought that RESCCUE project deals with a scope at a city
level and therefore, there is not a lot of details of the specific critical
infrastructures and other sensitive information used. Consequently, although
information about CI is used, no sensitive data regarding these
infrastructures is disclosed and therefore, there would be no security
measures to be taken in this sense (unless, as mentioned earlier, the key
stakeholders specifically request that).
In Spain, the CNPIC (Centro Nacional para la Protección de Infraestructuras
Críticas) is the Centre that manages CI, which depends from the Ministry of
Interior. Although, as explained before, they still haven’t identified all the
CI from all the fields, they are using this principle in order to identify
them: “the infrastructures that have been considered as critical for now, are
the ones that might have impacts to the whole city (e.g. Barcelona).
Therefore, localized impacts, even being severe, they are not considered
critical as of now”.
Finally, since the Hazur tool will be compiling information from several
networks, containing several CI, general recommendations are proposed for the
use of the Hazur tool and the presentation of final results:
* Represent CI by its "zone of influence" rather than a singular point to not reveal its location
* Completely anonymize the data of CI so that there is no spatial attributes stored within HAZUR
* In order to establish interdependencies, use generalized nomenclature e.g. "Exchange box #12", so the infrastructures affected by this CI know that it is an exchange box but do not know anything else.
## Systems security
Sensitive or confidential information that will not be made publicly
available, will be stored in at least two systems: Hazur and Basecamp.
Consequently, the security characteristics of both systems was assessed to
make sure that the minimum standards were reached. Hazur is hosted in
classical servers in a OVH data center with maximum physical security (servers
can only be physically accessed by authorized employees, access restricted by
security badge control system, video surveillance and security personnel, 24/7
on-site, rooms fitted with smoke detection systems and technicians on site
24/7) and high availability infrastructure (Systematic double power supply and
generators with an initial autonomy of 48 hrs.)
The servers have the following characteristics:
<table>
<tr>
<th>
**Virtualization**
</th>
<th>
</th>
<th>
</th>
<th>
64-bit OpenVZ
</th> </tr>
<tr>
<td>
**SLA**
</td>
<td>
</td>
<td>
</td>
<td>
99.98%, reboot in 10 mins in the event of hardware failure
</td> </tr>
<tr>
<td>
**Scalability**
</td>
<td>
</td>
<td>
</td>
<td>
Upgrade whenever you want from our control panel. No need to transfer our data
nor to reinstall our VPS.
</td> </tr>
<tr>
<td>
**Anti-DDoS**
</td>
<td>
</td>
<td>
</td>
<td>
Included
</td> </tr>
<tr>
<td>
**IP**
</td>
<td>
</td>
<td>
</td>
<td>
1 IPv4 and 1 IPv6 included (all ports open)
</td> </tr>
<tr>
<td>
**Management**
</td>
<td>
</td>
<td>
</td>
<td>
Web Control Panel, RESTful API, KVM, root access
</td> </tr> </table>
**Reboot and reinstallation** Unlimited, at any time via the Control Panel
**Monitoring** Detailed monitoring and key performance indicators
**Backup** Once a week
OptiCits is considering different options to upgrade the software hosting in
order to increase both the software performance and the data access security.
Regarding Basecamp, they guarantee the security and confidentiality of the
information stored there, by using encrypted protocols via HTTPS. Whenever
data is in transit, everything is encrypted, and sent using HTTPS. Any files
uploaded are stored and are encrypted at rest, and backups of data are
encrypted using GPG.
Additionally, all data is written to multiple disks instantly, backed up
daily, and stored in multiple locations. Uploaded files are stored on servers
that use modern techniques to remove bottlenecks and points of failure.
The servers operate at full redundancy. The systems are engineered to stay up
even if multiple servers fail. Their state-of-the-art servers are protected by
biometric locks and round-theclock interior and exterior surveillance
monitoring. Only authorized personnel have access to the data center. 24/7/365
onsite staff provides additional protection against unauthorized entry and
security breaches.
Their software infrastructure is updated regularly with the latest security
patches. Their products run on a dedicated network which is locked down with
firewalls and carefully monitored. While perfect security is a moving target,
they work with security researchers to keep up with the state-of-the-art in
web security.
# Ethics and Legal Compliance
Ethics is taken into account in the way data is stored, regarding who can
see/use it and how long it is kept.
The consent for data preservation and sharing obtained from data producers, or
data owners, are strictly fulfilled according to applicable license rules. The
identity of external participants is secured through anonymization, when and
if applicable. The terms of use, curation and sharing of all datasets made
available in the scope of RESCCUE, by data producers and data owners, are
established in formal consent agreements.
The formal consent agreements state who will own the copyright on the data to
be collected or created, along with the license(s) for its use and reuse. When
applicable, permissions to reuse third-party data and any restrictions needed
on data sharing will also be referred in the associated metadata.
However, ethics should not only be taken into account in the way data is
stored and shared, but also regarding many other issues as all consortium
members are subject to the EU Directive on Data Protection and its national
transpositions. Therefore, there are several aspects to be considered, such as
details on what type of personal data is being collected; details on data
transference to non-EU countries; or examples of the Information Sheets and
Consent forms to be used.
According to the recent General Data Protection Regulation (GDPR) (EU)
2016/679, entering into application on the 25 th of May 2018, there is one
set of data protection rules for all companies operating in the EU, wherever
they are based. Specifically, the regulation contains provisions and
requirements pertaining to the processing of personally identifiable
information of individuals (formally called data subjects in the GDPR) inside
the European Union, and applies to all enterprises, regardless of location,
that are doing business with the European Economic Area. Business processes
that handle personal data must be built with data protection by design and by
default, meaning that personal data must be stored using pseudonymisation or
full anonymisation, and use the highest-possible privacy settings by default,
so that the data is not available publicly without explicit consent, and
cannot be used to identify a subject without additional information stored
separately. No personal data may be processed unless it is done under a lawful
basis specified by the regulation, or if the data controller or processor has
received explicit, opt-in consent from the data subject.
Taking into consideration all these requirements, several measures have been
adopted in the framework of RESCCUE project regarding personal data
protection. The project manager (Aquatec) is the only one authorized for
collecting and processing personal data. Due to this role, Aquatec must
clearly disclose any data collection, declare the lawful basis and purpose for
data processing, how long data is being retained, and if it is being shared
with any thirdparties or outside of the EU.
To accomplish this requirements, Aquatec has prepared a consent form to be
disclosed each time personal data is collected, for example, at the
registration form of periodic project meetings or other project’s events
involving people from RESCCUE consortium and external ones. This consent form
informs that:
* Aquatec is the responsible for collecting and using personal data of project’s partners and other attendees to RESCCUE events
* Personal data include: name, ID number, contact numbers, mailing, email addresses, photographic or video images
* Personal data will be disclosed only to the European Commission and only when necessary (i.e. justification of partners’ contributions, travel expenditures, etc.)
* Aquatec will keep data securely according to the GDPR
* Aquatec will not disclose “sensitive personal data” as defined in the GDPR ( _i.e._ data concerning health such as alimentary intolerances) or address, telephone or email details without your explicit consent unless the disclosure is strictly necessary to protect your vital interests
* Aquatec will record personal data during a period of 4 years after the project end after that period, personal data will be destroyed.
A model of the consent form to be used before collecting this data is included
in the Annex of this deliverable and will be applicable from the 25 th of
May 2018 at each event or procedure collecting such personal data.
# Responsibilities and Resources
Aquatec (as Project Coordinator) as well as the leaders of WP1 to WP6 (where
most of the project data is produced) are responsible to ensure that it is
duly reviewed and timely revised. However, it is important to highlight that
all beneficiaries must implement the DMP.
The data management activities within each WP should be assured by the person
responsible for data management at WP level.
RESCCUE has 3 research sites that will be studied across several project
activities and WPs. The coordination of data management is to be done by the
WP leader and the people responsible for each research site: Cetaqua, FIC,
Opticits, UNEXE, LNEC and Aquatec.
However, as presented in Table 1, the owners of each dataset are clearly
identified and they are precisely the ones responsible for the data management
activities. So after generating each dataset, the partner that owns it should
be in charge of uploading the information in a repository and making the
dataset findable by generating the corresponding metadata.
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https://phaidra.univie.ac.at/o:1140797
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Horizon 2020
|
1135_I-REACT_700256.md
|
1 INTRODUCTION
# 1.1 PURPOSE OF THE DOCUMENT
This document describes how we defined the Data Management Plan (DMP) of
I-REACT and contains its second version. The first version of the DMP plan has
been delivered by the end of the first six months of the project and this new
version is a refinement of the former one. The DMP covers the data management
life cycle of the collected, processed and generated data during the project.
Specifically, the DMP is used to define the guidelines for data management in
the project in order to ensure a high level of data quality, security, and
accessibility.
According to the "Guidelines on FAIR Data Management in Horizon 2020" [RDOI],
"the DMP is intended to be a living document in which information can be made
available on a finer level of granularity through updates as the
implementation of the project progresses and when significant changes occur.".
This deliverable contains the second version of the DMP. The first version of
the DMP plan has been delivered by the end of the first six months of the
project. The DMP will be updated, providing finer details, during the periodic
evaluation/assessment of the project (i.e., at the midterm review and at the
final review).
We prepared the Data Management Plan by following the template provided in the
"Guidelines on FAIR Data Management in Horizon 2020" [RDOI] document.
Specifically, we generated the DMP of I-REACT by using the DMP online [RD02]
tool, which is compatible and compliant with the requirements set out in Annex
1 of the "Guidelines on FAIR Data Management in Horizon 2020" [RDOI]. DMP
online supports the generation of the DMP and allows exporting it in different
electronic formats (e.g., pdf file, doc file).
The DMP of I-REACT follows the FAIR principles, i.e., public research data are
made Findable, Accessible, Interoperable and Re-usable.
# 1.2 STRUCTURE OF THE DOCUMENT
The document is organized as it follows:
* Chapter 1 is this introduction and description of the document itself;
* Chapter 2 contains the current version of the Data Management Plan of I-REACT. Specifically, the content of Chapter 2 corresponds to the export of the Data Management Plan (DMP) that we created by means of the DMP online [RD02] tool; Chapter 3 draws conclusions.
### 1\. DATA SUMMARY
The I-REACT project aims at supporting emergency activities related to hazards
(e.g., floods and fires) by integrating data from several external data
sources. Specifically, the I-REACT project aims at (i) supporting emergency
activities related to hazards (e.g., floods and fires) and (ii) generating
predictive and forecast models. To achieve the main goals of the project,
several heterogeneous data must be properly collected, transformed and
combined in order to tackle the problem from different perspectives.
Specifically, publicly available open data and external data sources will be
integrated with the content generated by the users of the I-REACT system
(e.g., the user generated reports). The integration of several data sources
will allow generating models based on complementary information that can be
used to support the different activities related to hazards and emergency
events in the I-REACT project. Both first responders and citizens will use and
exploit, at different granularities, the data collected, transformed, and
generated by I-REACT.
The collected data are mainly georeferenced data related to hazards and
emergency events (floods and fires). Based on the analysis of the available
data sources performed so far, the currently imported data, and the use cases
of the project, the following types of external data have been currently
identified as potentially useful for the I-REACT project:
* Weather forecast data
* Flood maps
* Copernicus land monitoring service data
* European flood awareness system data
* European forest fire information system data
* Disaster event historical data
* Global administrative area data
* Statistical data
* Social media data (e.g., tweets)
As reported in the above list, several external data sources will be re-used
by I-REACT, each one providing a different facet of the hazard/emergency event
we are facing. Both open and freely available data and non-public data will be
used. Open and freely available data will be preferred.
In the I-REACT project, standard data formats (e.g., GeoJSON and Shapefiles),
and metadata (e.g., INSPIRE complaint metadata) will be used in order to
improve findability, accessibility, interoperability and re-usability of the
data.
The I-REACT project will also generate data, based on the data (e.g., reports)
generated by the users of I-REACT, for instance by means of mobile
applications, and the transformation and analysis of the collected data.
The main types of data generated by I-REACT are the followings:
* User generated reports
* Risk maps and weather forecast maps Flood and fire nowcasts and forecasts
* Climate change maps
* UAV imagery
Also, the data generated internally by I-REACT will be represented by means of
standard data formats (e.g., GeoJson) and will be enriched with standard
metadata.
Table 1 reports the data imported so far in the backend of I-REACT, with the
expected size (per file) and the partner that is responsible for importing the
data in the backend.
<table>
<tr>
<th>
RESPONSIBLE
</th>
<th>
DESCRIPTION
</th>
<th>
FORMAT
</th>
<th>
SIZE
</th> </tr>
<tr>
<td>
GeoVille
</td>
<td>
Copernicus EMS Delineation Maps (crisis information, administrative
boundaries, area of interest, general information, hydrography, land cover,
physiography, points of interest, populated places, settlements,
transportation, utilities)
</td>
<td>
Shapefile
</td>
<td>
Depending on zoom level
</td> </tr>
<tr>
<td>
EOXPLORE
</td>
<td>
Flood Forecast Period 1 — 5 year return period and
Flood Forecast Period 2 — 20 year return period
</td>
<td>
GeoJSON
</td>
<td>
MB
</td> </tr>
<tr>
<td>
EOXPLORE
</td>
<td>
Fire Hotspots Comune and Points
</td>
<td>
GeoJSON
</td>
<td>
< 5 MB
</td> </tr>
<tr>
<td>
EOXPLORE
</td>
<td>
Fire Weather Index
</td>
<td>
GeoJSON
</td>
<td>
< 5MB
</td> </tr>
<tr>
<td>
CSI
</td>
<td>
ARPA Hydrogeological Alert Bulletin
</td>
<td>
GeoJSON
</td>
<td>
< IMB
</td> </tr>
<tr>
<td>
CSI
</td>
<td>
FMI Weather alert (Wind, Snow-ice, Thunderstorms, Low temperature, Forest-
fire, Rain)
</td>
<td>
GeoJSON
</td>
<td>
N
3.5 MB
</td> </tr>
<tr>
<td>
TU Vienna
</td>
<td>
Sentinel-I Flood Delineation Mapping
</td>
<td>
GeoTlFF
</td>
<td>
Sentinel-I: rv 50 MB
Envi ASAR: N IOMB
</td> </tr>
<tr>
<td>
FMI
</td>
<td>
Probability of gust limit 1, 2
</td>
<td>
GeoJSON
</td>
<td>
rv 2.5MB (per lead time)
</td> </tr>
<tr>
<td>
FMI
</td>
<td>
Probability of wind limit 1, 2
</td>
<td>
GeoJSON
</td>
<td>
N2.5MB
</td> </tr>
<tr>
<td>
FMI
</td>
<td>
Probability of temperature limit 1, 2,
</td>
<td>
GeoJSON
</td>
<td>
N2.5MB
</td> </tr>
<tr>
<td>
FMI
</td>
<td>
Probability of precipitation limit 1, 2, 3,
</td>
<td>
GeoJSON
</td>
<td>
N2.5MB
</td> </tr>
<tr>
<td>
FMI
</td>
<td>
Temperature
</td>
<td>
GeoJSON
</td>
<td>
N2.5MB
</td> </tr>
<tr>
<td>
FMI
</td>
<td>
Wind
</td>
<td>
GeoJSON
</td>
<td>
N2.5MB
</td> </tr>
<tr>
<td>
FMI
</td>
<td>
Precipitation 3h and 24h
</td>
<td>
GeoJSON
</td>
<td>
N2.5MB
</td> </tr>
<tr>
<td>
FMI
</td>
<td>
Pressure
</td>
<td>
GeoJSON
</td>
<td>
N2.5MB
</td> </tr>
<tr>
<td>
FMI
</td>
<td>
Ensemble forecasts
</td>
<td>
NetCDF
</td>
<td>
N500MB
</td> </tr>
<tr>
<td>
FMI
</td>
<td>
25th, 50th, and 75th percentile of daily max. temperature forecast
distribution
</td>
<td>
NetCDF
</td>
<td>
N13MB
</td> </tr> </table>
Table 1 — Imported data
The size of the data collected and generated depends on the considered data
sources. The size of the datasets and files associated with the several data
sources exploited by the project varies from some MBs to tens of GBs.
The collected and generated data will be useful for several end users and
stakeholders. Specifically, the users of the I-REACT system (both citizens and
first responders) will benefit from the data generated by the system.
Moreover, third parties could also be interested in the data collected and
generated by I-REACT, also for supporting decisions not directly related to
the management of emergency events.
### 2\. FAIR DATA
#### 2.1 MAKING DATA FINDABLE, INCLUDING PROVISIONS FOR METADATA [FAIR DATA]
We will share the public data related to the I-REACT publications in the
Zenodo repository ( _https://www.zenodo.org/_ ). Zenodo provides a set of
basic functionalities that allows publishing data and searching them by means
of keywords. Moreover, Zenodo automatically assigns a DOI to each new uploaded
dataset and allows specifying metadata, which can be profitably exploited to
find the shared datasets.
The uploaded files will be identifiable and versioned by using a name
convention consisting of project name, dataset name, version, and date.
For the data for which it is appropriate, the standard INSPIRE
(http://inspiregeoportal.ec.europa.eu/) metadata will be used to enrich the
shared data.
#### 2.2 MAKING DATA INTEROPERABLE [FAIR DATA]
The sharing of the data will follow the ORD pilot principle "as open as
possible, as closed as necessary". Specifically, when it is possible (i.e.,
when the data can be released as open without violating any copyright), the
research data needed to validate the results presented in the published
scientific papers will be made available. To support the sustainability of the
project, according to the defined business plan, the data that are fundamental
for the sustainability of the I-REACT project and are not used in the
published scientific papers will not be disclosed.
As already introduced before, the public datasets, and the related metadata,
will be made available through the Zenodo repository. Standard data formats
and metadata will be used to improve accessibility to the data by means of
standard freely available tools.
We will use standard data formats (e.g., GeoJSON and Shapefiles) for
representing the data used by I-REACT and standard metadata (INSPIRE complaint
metadata) to improve interoperability and re-usability of the data. This
solution improves the interoperability of the modules of I-REACT and the
interoperability with respect to external users interested in using the data
of I-REACT.
#### 2.3 INCREASE DATA RE-USE (THROUGH CLARIFYING LICENSES) [FAIR DATA]
The data collected from external sources, or generated transforming external
data, will use the same license of the original data sources. For the subset
of internally generated data that will be open, not based on external data
sources, an open license will be used.
The public research data associated with the published research papers will be
made available as soon as the accepted papers will be published. The research
data associated with the published research papers will be made available as
soon as the accepted papers will be published. An embargo period will be
applied only if the policy of the related scientific publication enforces it.
The public research data published on Zenodo will be available for third
parties also after the end of project and will be freely available as long as
the Zenodo service will be available.
### 3\. ALLOCATION OF RESOURCES
The public data will the published in the free Zenodo repository. Hence, there
are no specific costs related to the storage of the public data.
Regarding the subset of non-publicly disclosed data exploited by the I-REACT
project, the costs of the management of the data, and the related privacy
issues, are already part of the costs related to the cloud architecture that
will be used to implement the I-REACT system.
Regarding the long-term preservation of the data, we plan to cover the costs
for the management of the non-public data by the revenue associated with the
exploitation of the data itself and the related services.
### 4\. DATA SECURITY
Security and privacy are two important issues managed by the project.
Specifically, Task "T2.4: Security & Privacy by design" addresses the security
and privacy issues by adopting a security and privacy by design methodology. A
detailed description of the selected methodology is reported in the
deliverable "D2.4: Report on Privacy and Security". The solution described in
D2.4 will be further specialized during the project based on the new privacy
and security issues that will emerge during the project. Any further changes
to the privacy and security solution that will arise during the project will
be included in the following versions of the DMP.
Regarding the secure storage of the data, the project will use an architecture
based on cloud services to store the data. The used services provide the
functionalities needed to address secure storage and data security. Regarding
sensible data, a secure and privacy by design methodology will be used to
avoid the disclosure of sensible and personal data.
### 5\. ETHICAL ASPECTS
According to the content of the ethics review and ethics section of DOA, the
designed solution will imply the collection and the processing of personal
data once operational. The management of such data will be regulated by a
clear term of service, which must be read and accepted by users, and which
will follow the EU data protection regulation. Note that data regarding
financial details, sexual lifestyles, ethnicity, political opinion, religious
or philosophical conviction will not be included in the Information
Architecture. Different features of the system will rely on locationbased
technologies to determine the position of people and "objects" (such as
infrastructures, resources, vehicles, etc.), which is crucial for the
implementation of the I-REACT products.
Regarding the localization of people, two different categories will be
distinguished:
First responders — who are the emergency operators and/or volunteers. Since
they are professional users, they must accept the geolocation of their devices
during the working time through a formal agreement with their employee. Their
positioning is needed to send in-field reports and it is also aimed at
monitoring their safety during the emergency response phase. Citizens — who
must read and accept specific term of service in order to submit geolocalized
reports through the I-REACT mobile application. Optionally, they can allow the
I-REACT system to locate their position, which will be shared only with
subscribed authorities in case of emergency in order to effectively perform
search and rescue operations. With respect to the data retrieved from Social
Networks, the regulation included in their terms of service will be applied.
All personal data will be solely used for the project purpose, and they will
be only accessible by the data owner. The geolocated reports will include only
the user category and an ID that is not linkable with the user personal data.
Personal data will not be subject of any exploitation and will not be
distributed to any third party. Such data will be collected, stored, and
processed following Privacy-by-design approaches in order to guarantee
confidentiality, and anonymity. Thus, the data that will be publicly made
available will be anonymized. I-REACT does not foresee the management of
health data.
### 6\. OTHER
Regarding the privacy issues, the data will be managed in compliance with the
European regulation, as already described in Sections 4 and 5 of this Data
Management Plan.
# 3 CONCLUSIONS
This deliverable describes how the data management plan of the I-REACT project
has been generated and contains its second version. A first version was
released at the end of the first size months of the project and this updated
version was released in November 2017. The DMP of lREACT will be periodically
updated, providing finer details, in time with the following periodic
evaluation/assessment of the project (i.e., at the final review).
END OF THE DOCUMENT
|
https://phaidra.univie.ac.at/o:1140797
|
Horizon 2020
|
1139_COMPASS_710543.md
|
**1\. Introduction**
This Data Management Plans (DMP) describes the data management life cycle for
the data to be collected, processed and/or generated by COMPASS (710543). As
part of making research data findable, accessible, interoperable and re-usable
(FAIR), this DMP includes information on a) the handling of research data
during and after the end of the project, b) what data will be collected,
processed and/or generated, c) which methodology and standards will be
applied, d) whether data will be shared/made open access and e) how data will
be curated and preserved (including after the end of the project). This DMP is
prepared according to the specifications for participating in the EU Pilot on
Open Research Data (ODR).
### 1.1. Background Information on COMPASS (710543) Project
The concept of “Responsible Research and Innovation” has been around for
almost a decade. So far, it has been the subject of discussion among policy-
makers and researchers; without including the key actors of Responsible
Research and Innovation: businesses.
The aim of the COMPASS (710543) project is to make Responsible Research and
Innovation accessible, comprehensible and feasible for Small and Medium-Sized
Enterprises. The COMPASS (710543) project will set the stage on which SMEs
will be able to co-create their own visions of Responsible Research and
Innovation – for their particular sector and for their particular business
context. Physical and virtual interaction will take place in “Responsible
Innovation Labs” for SMEs in the areas of nanotechnology, healthcare and ICT.
The tools and services designed in these labs will be tailored to the needs of
innovative SMEs. SMEs will get the chance to test the tools in their daily
operations. They will get access to all services on the “Responsible
Innovation COMPASS”, a custom-made web platform.
### 1.2. Information on Data
The project objectives require collection and processing of information which
is not available from other sources. As the concept of “Responsible Research
and Innovation” is still fairly novel and not much data is available on it,
the project will need to generate its own data sets. It will rely on industry
experts, businesses and civil society actors to provide first-hand information
about their experiences, views and opinions.
The project will build up the following data sets: 1) Stakeholder & multiplier
database; 2) Website; 3) Interviews; 4) Responsible Innovation Labs; and 5)
Pilot Testing.
The data sets will be appropriately handled by the consortium in accordance
with confidentiality obligations (article 36) and principles of processing
data (article 39.2) outlined in the Grant Agreement.
This project has received funding from the European Union's Horizon 2020
research and innovation programme under grant agreement No 710543
### 1.3. Timetable for Updates
**2\. Stakeholder & Multiplier Database 2.1. Data Set Description **
The COMPASS (710543) project will set up a stakeholder & multiplier database
to compile contact information of actors that might be interested in taking an
active part in the project (e.g. as participants in the Responsible Innovation
Labs) or receiving and disseminating project output (e.g. innovation
networks).
The database is being compiled and administered by consortium partner FBLC and
owned by all COMPASS (710543) partners.
The compilation of the COMPASS (710543) stakeholder database will be the
central point of reference for all stakeholder interaction during the running
time of the project. These data stored in the COMPASS (710543) stakeholder
database will be used for the following tasks:
* Persona analysis (i.e. analysis of influence / position in an organisation, analysis of relevance / type of organisation) in order to determine an efficient communication strategy with the platform’s stakeholders; stakeholder contacts will be stored in a WP leader (FLBC) access database.
* Monitoring participation of the stakeholders in the online and on-site interactions in order to provide analyses of stakeholder participation according to (a) type of organisation, (b) country, (c) gender balance and (d) areas of expertise. The results of these analyses will be used for guiding further stakeholder recruitment efforts in order to achieve a balanced representation of different stakeholder groups and to ensure gender balance.
* Collecting input from the stakeholders (feedback on the Responsible Innovation COMPASS and roadmaps and online knowledge repository prepared by the consortium and new ideas through “idea sourcing”) as important source of the Responsible Innovation COMPASS content and information provision functionality.
### 2.2. Standards and Metadata
Data is documented in form of entries (rows) and different fields, including
type of entity, country, sector, basic information and contact details into an
MS Office Excel File. Each entry includes metadata in form of a) information
about who (i.e. which of the consortium partners or advisory board members)
has provided the contact and will need to establish first contact between the
project and the respective stakeholder/multiplier and b) which of the project
tasks/activities might be of interest for the respective
stakeholder/multiplier.
No particular metadata standards are being applied.
### 2.3. Ethical and Legal Issues
Data contained in the COMPASS (710543) stakeholder & multiplier database will
not be publicly available, but solely serve the purpose of involving
potentially interested stakeholders and multipliers in the different project
activities.
The stakeholder data base does not include any ethically questionable
material.
Personal data (i.e. name, organisation, position, email, telephone number)
which are not publicly accessible, but provided by individuals during the
course of the project, will be treated confidentially by FBLC.
Any data contained in the COMPASS (710543) stakeholder & multiplier database
will be anonymised for further use (i.e. academic or other publications).
COMPASS (710543) parties will make sure that any data that is used outside of
the scope of the COMPASS (710543) project will be unlinked (anonymised) from
any personal data information.
### 2.4. Access and Sharing
Data storage in the scope of the COMPASS (710543) project will be secured so
as for the data not to become accessible to unwanted third parties and to be
protected against disaster and risk.
Data contained in the COMPASS (710543) stakeholder & multiplier database will
not be publicly available; i.e. these personal data will not be shared with
third parties nor attributed to commercial use.
Sharing of personal data among COMPASS (710543) consortium partners will take
place only if consented by the stakeholder/multiplier in question, and shall
be organised through the server-based and password-protected “WU Owncloud
system” (https://owncloud.wu.ac.at/).
### 2.5. Re-Use and Distribution
Information contained in the database that has been obtained from publicly
available sources in the Internet (such as name of the entity, country, or
URL) might be re-used for education or further research purposes in the
future.
Any contact information is not expected to be re-used for education, research
or non-profit purposes. Data contained in this dataset will not be distributed
to third parties (see section 2.4).
### 2.6. Archiving and Preservation (including Storage and Backup)
**2.6.1. Spaces for Data Storage and Respective Data Security Measures** The
stakeholder & multiplier database will be stored on state-of-the-art secured
laptops, protected by most recent and regularly updated anti-virus software.
The FBLC team with access to the stakeholder database will adhere to a level
of confidentiality (as outlined in the COMPASS (710543) Grant Agreement
article 39.2). Furthermore, the consortium partners will comply with WU
institutional data protection policy (“WU Information Security Policy” ; “The
Organization of Information Security at WU”; “WU Directive on Confidentiality
Classification”; “WU Directive on Data Erasure and Disposal” ) which outlines
measures guaranteeing “protection from loss and damage to information”
(availability), “protection from unauthorised access and disclosure of
information (confidentiality)”, “protection from unintended and manipulative
modification of information (integrity)”, and “protection from loss of
nonrepudiation or comprehensibility of information flows”.
#### Duration of data storage and access
Data stored in the COMPASS (710543) stakeholder database will be securely
stored within FBLC secure servers for the total duration of the COMPASS
(710543) project and for 6 months after the completion of the project.
Security measures as outlined above will be kept active as long as the
respective data is in use or until terminated.
#### Procedures for data destruction/deletion
Data will be destroyed according to the WU Directive on Data Erasure and
Disposal in order to guarantee proper off- and online data protection. COMPASS
(710543) consortium parties will apply appropriate tools and procedures for
data deletion in order to guarantee irreversibility.
**3\. Website Subscriptions Data Set 3.1. Data Set description**
The COMPASS (710543) consortium maintains a website for the purpose of making
RRI more accessible to SMEs. To complement the stakeholder & multiplier
database, the website offers a sign-up function: Website users are able to
sign up to the website to receive news about the project. To subscribe, it is
only necessary to enter a name and email address as well as agree to the
website Terms and Conditions (see section 3.3). The website subscriptions data
set thus compiles contact information of additional actors that might be
interested in taking an active part in the project (e.g. as participants in
the Responsible Innovation Labs) or receiving and disseminating project output
(e.g. innovation networks). The website subscriptions data set will be
administered by WU.
The website is created using WordPress software. There are three software
plugins (plugins for WordPress) COMPASS (710543) uses to collect website users
data as an email subscription data:
1. Email Subscribers – a plug developed for WordPress sites that allows to create forms where users can enter their name and email address to subscribe to receive news from the respective website. COMPASS (710543) uses a double opt-in system, where the user receives an email to confirm his/her subscription to the COMPASS (710543) website news.
2. Maintenance Mode – a plug developed for WordPress sites that displays a “Coming soon” webpage and allows interested users to leave their email address to be informed of when the site is launched. COMPASS (710543) uses a single opt-in system, where the user enters their email address only and this email address is entered into the database of interested users without them having to reconfirm his/her subscription to the COMPASS (710543) website news.
3. Newsletter - Newsletter is a newsletter system plug-in for WordPress sites: it is used for list building, creating, sending and tracking emails. The plug in also collects data on whether the users have opened the email sent. It allows to create forms where users can enter their name and email address to subscribe to receive news from the respective website. COMPASS (710543) uses a single opt-in system, where the user enters their email address only and this email address is entered into the database of interested users without them having to reconfirm his/her subscription to the COMPASS (710543) website news.
The email addresses and names collected through these plugins are stored on
COMPASS (710543) website server and are visible online to COMPASS (710543)
consortium partners. The data can only be exported from the site by COMPASS
(710543) consortium partners when they log-in to the COMPASS website
confirming their identity.
**3.2. Standards and Metadata**
No particular metadata standards are being applied.
### 3.3. Ethical and Legal Issues
It is clearly stated on the website sign-up page ( _http://innovation-
COMPASS.eu/sign-up/_ ) that by signing up users agree to accept COMPASS
(710543) Terms and Conditions ( _http://innovation-COMPASS.eu/terms-and-
conditions/_ ).
By subscribing to the COMPASS (710543) website, users authorise COMPASS
(710543) to process personal data for, and only for, the following purposes:
* To send users information on COMPASS (710543) activities and project updates, both as general newsletters and specific communications from the COMPASS (710543) project
* To send users information about the opportunity to participate in COMPASS (710543) activities, either in face-to-face format (workshops, seminars, etc.) or online, via the internet (contributions to the contents of the COMPASS (710543) website, participation in surveys, etc.).
Users are informed that they may at any time exercise their rights to
unsubscribe. Each communication they receive will detail free means whereby
they can request not to receive further communications from COMPASS (710543).
When users are asked to supply personal details to complete a form, they will
be informed of the recipient of the data and the purpose for which it is being
collected; the identity and address of the body responsible for the file (file
manager). Users at all times may exercise their rights of access,
rectification, cancellation and opposition to the treatment of their data. The
personal data collected will only be used and/or transferred for the purpose
specified, and always with the consent of the user.
### 3.4. Access and Sharing
Data storage in the scope of the COMPASS (710543) project will be secured so
as for the data not to become accessible to unwanted third parties and to be
protected against disaster and risk.
Data contained in the website subscriptions data set will not be publicly
available; i.e. these personal data will not be shared with third parties nor
attributed to commercial use.
Sharing of personal data among COMPASS (710543) consortium partners will take
place only if consented by the stakeholder/multiplier in question, and shall
be organised through the server-based and password-protected “WU Owncloud
system” (https://owncloud.wu.ac.at/).
### 3.5. Re-Use and Distribution
As this dataset contains contact information only, we do not expect any re-use
for education, research or non-profit purposes. Data contained in this dataset
will not be distributed to third parties (see section 3.4).
### 3.6. Archiving and Preservation (including Storage and Backup)
#### Procedures for the storage of personal data
Data storage in the scope of the COMPASS (710543) project will be secured so
as for the data not to become accessible to unwanted third parties and to be
protected against disaster and risk.
#### Duration of data storage and access
Data stored in the COMPASS (710543) project website will be stored for the
total duration of the COMPASS (710543) project and for 6 months after the
completion of the project.
Safe procedures outlined during the project’s running time will be kept active
as long as the respective data is in use or until terminated. **Procedures for
data destruction/deletion**
Data will be destroyed according to the WU Directive on Data Erasure and
Disposal in order to guarantee proper off- and online data protection. COMPASS
(710543) consortium parties will apply appropriate tools and procedures for
data deletion in order to guarantee irreversibility.
#### Technical safety measures for personal data
As regards any personal data compiled in COMPASS (710543) project website, all
data will be subject to strict safety procedures as well as technical
measures. At WU data will be stored on WU institutionalised SAN system. Safety
measures include physical protection of server data centres, SAN system
protection includes Data Protection Manager and TSM as well as physical
protection.
**4.Interviews Data Set 4.1. Data Set description**
In order to collect first-hand insights on the potential of RRI in two of the
three the project’s key innovation fields, three consortium partners (WU, DMU
and UCLan CY) will undertake 30 fact-finding interviews with key industries
representatives across Europe. Interviews will cover critical responsibility
issues as well as success factors and barriers for adoption of RRI in
industry.
These interviews will include, besides expert knowledge on responsible
innovation policy issues, the collection of personal data (i.e. name,
organisation, position, type of organisation, country, email).
Interviews will be conducted based on a semi-structured interview guideline
and, subject to interviewees’ permission, recorded using digital audio
recording (e.g. MP3). Interviews will be transcribed verbatim. Consortium
partner UCLan CY will analyse the collected information and compile it in a
synthesis report (D1.2 Synthesis report “Success factors and barriers for
mainstreaming RRI in SMEs”).
### 4.2. Standards and Metadata
Metadata will include:
* About the interviewee:
* Affiliation to key innovation field (i.e. healthcare or nanotechnology)
* About the interviewer: o Name
* Affiliation (i.e. consortium partner institution)
* About the interview: o Date
* Length
* Form of conduct (i.e. face-to-face, telephone, skype or other) No particular metadata standards are being applied.
### 4.3. Ethical and Legal Issues
Potential participants will be identified by all consortium partners.
Interviews will be conducted face-to-face, via skype or telephone by
experienced interviewers from UCLan CY, WU and DMU. All potential interview
partners will receive detailed information about their involvement in the
project. Participants will be asked to demonstrate their understanding of
their involvement by consenting explicitly to their participation in the data
collection activity. Each interview partner will receive an information sheet
form clearly stating the following:
* Details of who will be conducting the study;
* Details about who is sponsoring the study and what the terms of the sponsorship are (i.e. who will 'own' the data and how the data will be used): In this case, reference will be made to the EU Horizon 2020 funding;
* Details about the nature, purpose and duration of the study;
* What kinds of procedures will be used and what the participant will be asked to do;
* Details about any hazards, inconveniences and risks associated with the study;
* What benefits are attached to the study;
* What procedures will be employed to maintain confidentiality and anonymity (e.g. removing personal details from data/reports, keeping data in locked files);
* What will happen to the data (how it will be used, how it will be stored, in what form it will be disseminated and if it is likely to be used for further analysis);
* How to withdraw from the study;
* Details about who to contact if questions or problems arise.
Participants will also be provided with clear opportunities to provide
feedback regarding their participation. The procedure regarding informed
consent will be carried out prior to each interview. Participants will confirm
having received:
* Information on the purpose of the study;
* The opportunity to ask any questions related to this study, and received satisfactory answers to questions and additional details;
* Information that participation is voluntary;
* Information, that words and phrases from the interview may be included in the final report and publications to come from this research but any quotations will be kept anonymous;
* Information on withdrawal;
* Information, that the interview will be recorded using audio recording equipment.
The interviews do not include any highly sensitive personal data. Any data
related to personal data collected by the COMPASS (710543) project will be
anonymised for further use. COMPASS (710543) parties will make sure that any
data that is used outside of the scope of the COMPASS (710543) project will be
unlinked (anonymised) from any personal data information.
### 4.4. Access and Sharing
Analysis of the collected information will be compiled in a synthesis report
(D1.2 Synthesis report “Success factors and barriers for mainstreaming RRI in
SMEs”). The report will be openly accessible and available as free download on
the project website.
#### Procedures for the circulation of personal data
Sharing of personal data (i.e. name, organisation, position, type of
organisation, country, email address) among COMPASS (710543) consortium
partners will take place only if necessary to achieve project output and if
consented by the stakeholder/multiplier in question.
#### Confidentiality and safety procedures of personal data handling
All consortium partners will store collected data in state-of-the-art secured
computers, protected by most recent and regularly updated anti-virus software.
Data will only be accessible to the COMPASS (710543) consortium who will
adhere to a level of confidentiality (as outlined in the COMPASS (710543)
grant agreement article 39.2). Furthermore, the consortium partners will
comply with WU institutional data protection policy (“WU Information Security
Policy”; “The Organization of Information Security at WU”; “WU Directive on
Confidentiality Classification”; “WU Directive on Data Erasure and Disposal” )
which outlines measures guaranteeing “protection from loss and damage to
information” (availability), “protection from unauthorised access and
disclosure of information (confidentiality)”, “protection from unintended and
manipulative modification of information (integrity)”, and “protection from
loss of non-repudiation or comprehensibility of information flows”.
### 4.5. Re-Use and Distribution
The synthesis report (D1.2 Synthesis report “Success factors and barriers for
mainstreaming RRI in SMEs”) due in M14 of the project will be openly
accessible and available as free download on the project website at least
until the end of the project duration. Insight from the task will feed
directly into the preparation of the Responsible Innovation Labs (WP2) and
will thus become immediately available to up to 75 SMEs and SME support
organisations.
## 4.6. Archiving and preservation (including storage and backup)
#### Spaces for data storage and respective data security measures
Consortium partners WU, DMU and UCLan CY will store interview data in state-
of-the-art secured computers on secured server-based storage drives, protected
by most recent and regularly updated anti-virus software. Sharing of personal
data among COMPASS (710543) consortium partners will be organised through the
server-based and protected WU Owncloud system (i.e. IT Clowd system). Sharing
of (anonymised) data with third parties for transcription purposes will also
be organised through WU Owncloud system. Third parties will store data in
state-of-the-art secured computers on secured server-based storage drives and
agree to adhere to a level of confidentiality (as outlined in the COMPASS
(710543) grant agreement article 39.
#### Duration of data storage and access
Data will be stored for the total duration of the COMPASS (710543) project and
for 6 months after the completion of the project unless explicit consent is
sought from participants for portions of it to be kept longer or included in
dissemination/deliverable activities. Safe procedures outlined during the
project’s running time will be kept active as long as the respective data is
in use or until terminated. A data subject will have the right to have his or
her personal data erased and no longer processed where the personal data are
no longer necessary in relation to the purposes for which they are collected
or otherwise processed (“Right to be Forgotten”).
#### Procedures for data destruction/deletion
Data will be destroyed according to the WU Directive on Data Erasure and
Disposal in order to guarantee proper off- and online data protection. COMPASS
(710543) consortium parties will apply appropriate tools and procedures for
data deletion in order to guarantee irreversibility.
#### Technical safety measures for personal data
As regards any personal data compiled in personal interviews all data will be
subject to strict safety procedures as well as technical measures. All
collected data will be stored on secure servers or, for physical data such as
signed informed consent sheets, in a physically secure location such as a
locked cupboard/drawer within one of the partner institutions until
destruction. At WU data will be stored on WU institutionalised SAN system.
Safety measures include physical protection of server data centres, SAN system
protection includes Data Protection Manager and TSM as well as physical
protection. The primary university server location and storage system of UCLan
Cyprus is in the UCLan UK campus and supported by a dedicated WAN link to the
UCLan Cyprus campus, for which there exists a backup line supported by CYNET
(Cyprus Research and Academic Network). The storage system is supported by a
SAN (Storage Area Network) for which all necessary physical security and
cybersecurity measures are taken to ensure data protection. Thus, the users
are encouraged to use the network drives, which are provided by this SAN
system.
# 5\. Responsible Innovation Labs Data Set
## 5.1. Data Set description
The Responsible Innovation Labs will combine virtual and in situ interaction
between stakeholders from industry, policy-making and civil society
organisations. Any personal data collected via labs will not be publicly
available (i.e. these personal data will not be shared with third parties and
are not attribute to any commercial use).
The operation of stakeholder interaction in the form of Responsible Innovation
Labs will include, besides expert knowledge on responsible innovation policy
issues, the collection of personal data (i.e. name, organisation, position,
type of organisation, country, email).
Prior to the Responsible Innovation Labs participants will receive an email
including information (e.g. that the labs will be recorded) and a link to
attend the virtual meeting. The virtual meetings will be held via GoToMeeting.
By clicking on the link participants agree to the recording of the meeting.
## 5.2. Standards and Metadata
Metadata will include affiliation to key innovation field (i.e. healthcare or
nanotechnology) and region, company size, type of company, participants’
position in the company and contact between company and the project
consortium.
Metadata will also include date, length and form of conduct (i.e. face-to-
face, webinar or other) of the Responsible Innovation Lab.
Data will be documented in form of entries (rows) into an MS Office Excel or
Word file and in form of audio files.
No particular metadata standards will be applied.
## 5.3. Ethical and Legal Issues
The Responsible Innovation Labs do not contain any ethically questionable
material.
Any data related to personal data collected by the COMPASS (710543) project
will be anonymised for further use. COMPASS (710543) consortium partners will
make sure that any data that is used outside of the scope of the COMPASS
(710543) project will be unlinked (anonymised) from any personal data
information.
Potential participants will be selected by the institutions conducting the
labs, namely DMU, FBLC and SDS. All participants will receive detailed
information about their involvement in the project. Participants will be asked
to demonstrate their understanding of their involvement by consenting
explicitly to their participation in any data collection activity.
## 5.4. Access and Sharing
Analysis of the collected information will be compiled in a methods report
(D2.1 Responsible Innovation Lab Methods Report) and 3 roadmaps (D2.2
Responsible Innovation Lab Report & Roadmap 1, D2.3 Responsible Innovation Lab
Report & Roadmap 2, D2.4 Responsible
Innovation Lab Report & Roadmap 3). Insights will further feed into the Co-
Creation Method Kit (D2.5 Responsible Innovation Co- Creation Method Kit) and
a comparative assessment report (D2.6 Comparative assessment report). These
reports will be openly accessible and available as free downloads on the
project website.
#### Procedures for the circulation of personal data
Sharing of personal data (i.e. name, organisation, position, type of
organisation, country, email address) among COMPASS (710543) consortium
partners will take place only if necessary to achieve project output and if
consented by the stakeholder in question.
#### Confidentiality and safety procedures of personal data handling
As regards the Responsible Innovation Labs all consortium partners will store
collected data in state-of-the-art secured computers, protected by most recent
and regularly updated antivirus software. Data will only be accessible to the
COMPASS (710543) consortium partners who will adhere to a level of
confidentiality (as outlined in the COMPASS (710543) grant agreement article
39.2). Furthermore, the consortium partners will comply with WU institutional
data protection policy (“WU Information Security Policy” ; “The Organization
of Information Security at WU”; “WU Directive on Confidentiality
Classification”; “WU Directive on Data Erasure and Disposal” ) which outlines
measures guaranteeing “protection from loss and damage to information”
(availability), “protection from unauthorised access and disclosure of
information (confidentiality)”, “protection from unintended and manipulative
modification of information (integrity)”, and “protection from loss of non-
repudiation or comprehensibility of information flows”.
## 5.5. Re-Use and Distribution
The reports (D2.1 Responsible Innovation Lab Methods Report, D2.2 Responsible
Innovation Lab Report & Roadmap 1, D2.3 Responsible Innovation Lab Report &
Roadmap 2, D2.4 Responsible Innovation Lab Report & Roadmap 3, D2.5
Responsible Innovation Co- Creation Method Kit, D2.6 Comparative assessment
report) will be openly accessible and available as free download on the
project website at least until the end of the project duration. Insight from
the task will feed directly into the preparation of the Responsible Innovation
Compass (WP3) and will thus become publicly available.
## 5.6. Archiving and preservation (including storage and backup)
#### Spaces for data storage and respective data security measures
The DMU, FBLC and SDS teams will store data in state of the art secured
laptops on secured server-based storage drives, protected by most recent and
regularly updated anti-virus software.
Sharing of personal data among COMPASS (710543) consortium partners will be
organised through the server-based and protected WU Owncloud system (i.e. IT
Clowd system).
#### Duration of data storage and access
Data will be stored securely for the total duration of the COMPASS (710543)
project and for 6 months after the completion of the project unless explicit
consent is sought from participants for portions of it to be kept longer or
included in dissemination/deliverable activities.
#### Procedures for data destruction/deletion
Data will be destroyed according to the WU Directive on Data Erasure and
Disposal in order to guarantee proper off- and online data protection. COMPASS
(710543) consortium parties will apply appropriate tools and procedures for
data deletion in order to guarantee irreversibility.
#### Technical safety measures for personal data
As regards any personal data compiled in Responsible Innovation Labs all data
will be subject to strict safety procedures as well as technical measures. All
collected data will be stored on a secure DMU, FBLC or SDS server or, for
physical data, in a physically secure location such as a locked
cupboard/drawer within one of the partner institutions until destruction. Data
will be made available to COMPASS consortium within the secure WU OwnCloud
storage system.
# 6.Pilot-Testing Data Set
## 6.1. Data Set description
Pilot testing & demonstration is dedicated to testing the Responsible
Innovation Compass prototype modules developed in WP2 & WP3 in cooperation
with potential users to ensure their usability and market readiness. The pilot
testing phase includes demonstration activities, showcasing and gathering
feedback on three particular features of the Responsible Innovation Compass:
the co-creation method kit, the Responsible Innovation Self-Check tool and the
Responsible Innovation Roadmaps.
The “test groups” will be selected from (a) participants of the Responsible
Innovation Labs (WP2); (b) networks of organisations in the Multiplier
Programme and; (c) the networks of the consortium partners.
Any personal data collected will not be publicly available (i.e. these
personal data will not be shared with third parties and are not attribute to
any commercial use). The operation of stakeholder interaction will include the
collection of personal data (i.e. name, organisation, position, type of
organisation, country, email).
## 6.2. Standards and Metadata
Metadata will include affiliation to key innovation field (i.e. healthcare or
nanotechnology) and region, company size, type of company, participants’
position in the company and contact between company and the project
consortium.
Data will be documented in form of entries (rows) into an MS Office Excel File
or Word File and in form of audio files.
No particular metadata standards will be applied.
## 6.3. Ethical and Legal Issues
The pilot testing & demonstration work package will not contain any ethically
questionable material.
Any personal data collected will not be publicly available (i.e. these
personal data will not be shared with third parties and are not attribute to
any commercial use).
## 6.4. Access and Sharing
Output of the pilot testing phase will continuously feed back into WP3 for
modifications and fine-tuning in order to optimise the finalized Responsible
Innovation Compass. The work package will feed recommendations for revision
into WP2 (method kit) and WP3 (self-check tool).
Analysis of the collected information will be compiled in a strategy (D4.1
Piloting and demonstration strategy) and reviews and recommendations (D4.2
Review and recommendations for revision of the Responsible Innovation Co-
Creation method Kit, D4.3 Review and recommendations for revision of the
Responsible Innovation Self-Check, D4.4 User feedback and implementation
report on Responsible Innovation Roadmaps).
These reports will be openly accessible and available as free downloads on the
project website.
#### Procedures for the circulation of personal data
Sharing of personal data (i.e. name, organisation, position, type of
organisation, country, email address) among COMPASS (710543) consortium
partners will take place only if necessary to achieve project output and if
consented by the stakeholder in question.
#### Confidentiality and safety procedures of personal data handling
As regards the pilot testing phase all consortium partners will store
collected data in state-ofthe-art secured computers, protected by most recent
and regularly updated anti-virus software. Data will only be accessible to the
COMPASS (710543 consortium partners who will adhere to a level of
confidentiality (as outlined in the COMPASS (710543) grant agreement article
39.2). Furthermore, the consortium partners will comply with WU institutional
data protection policy (“WU Information Security Policy” ; “The Organization
of Information Security at WU”; “WU Directive on Confidentiality
Classification”; “WU Directive on Data Erasure and Disposal” ) which outlines
measures guaranteeing “protection from loss and damage to information”
(availability), “protection from unauthorised access and disclosure of
information (confidentiality)”, “protection from unintended and manipulative
modification of information (integrity)”, and “protection from loss of non-
repudiation or comprehensibility of information flows”.
## 6.5. Re-Use and Distribution
The reviews and reports (D4.1 Piloting and demonstration strategy, D4.2 Review
and recommendations for revision of the Responsible Innovation Co-Creation
method Kit, D4.3 Review and recommendations for revision of the Responsible
Innovation Self-Check, D4.4 User feedback and implementation report on
Responsible Innovation Roadmaps) will be openly accessible and available as
free download on the project website at least until the end of the project
duration. Insight from the task will feed directly into the revision of the
Responsible Innovation Compass (WP3) and the Method Kit (WP2), and will thus
become publicly available.
Potential use of the Responsible Innovation Compass for profit purposes after
COMPASS (710543) project has ended will be detailed in D5.8 Business and
exploitation plan.
## 6.6. Archiving and preservation (including storage and backup)
#### Spaces for data storage and respective data security measures
The COMPASS (710543) consortium partners will store data in state of the art
secured laptops on secured server-based storage drives, protected by most
recent and regularly updated antivirus software.
Sharing of personal data among COMPASS (710543) consortium partners will be
organised through the server-based and protected WU Owncloud system (i.e. IT
Clowd system).
#### Duration of data storage and access
Data will be stored securely for the total duration of the COMPASS (710543)
project and for 6 months after the completion of the project unless explicit
consent is sought from participants for portions of it to be kept longer or
included in dissemination/deliverable activities.
#### Procedures for data destruction/deletion
Data will be destroyed according to the WU Directive on Data Erasure and
Disposal in order to guarantee proper off- and online data protection. COMPASS
(710543) consortium parties will apply appropriate tools and procedures for
data deletion in order to guarantee irreversibility.
#### Technical safety measures for personal data
As regards any personal data compiled in the pilot testing phase all data will
be subject to strict safety procedures as well as technical measures. All
collected data will be stored in the EBN cloud storage system, which is
available to EBN staff only (the EBN cloud is secure and password protected),
or on a secure DMU, FBLC or SDS server or, for physical data such as signed
informed consent sheets, in a physically secure location such as a locked
cupboard/drawer within one of the partner institutions until destruction. Data
will be made available to COMPASS consortium within the secure WU OwnCloud
storage system.
|
https://phaidra.univie.ac.at/o:1140797
|
Horizon 2020
|
1140_UMI-Sci-Ed_710583.md
|
introduction and actual use of state of the art technologies (Ubiquitous
Learning, Mobile Learning, Internet of Things). European countries need as
potential employees youngsters who are able to think creatively, apply new
knowledge in an effective way, become continuously competitive in a highly
demanding working environment through constant self-monitoring and thus be
able to stand up for all the challenges that work based learning brings. The
ability to switch efficiently between different disciplines such as STEM
disciplines depends on processing effectively the educational material based
on clearly defined outcomes, expanding a broad repertoire of ICT
communication, problem-solving and decision-making skills, and using the
collective knowledge represented in networks, based on working environments.
_The orientation of UMI-Sci–Ed is entrepreneurial and multidisciplinary in an
effort to raise young boys’ and girls’ motivation in science education and to
increase their prospects in choosing a career in pervasive, mobile computing
and IoT (UMI)._
Under this scope, the project should deliver meta-level solutions to link
school, community and third-level initiatives together and should foster a
model that looks to strongly broaden impacts from the current cohort (e.g.
students competing in STEM/UMI competitions) to the entire ‘education’
population. The most effective way to do this will be to build communities of
practice (CoPs) within active clusters where a body of knowledge already
exists. The project should also provide tools, particularly technology, to
support this; however, the creation of this toolset should empower the
stakeholder group and this should be considered carefully. New technology is
often rejected, so approaches where participants can help co-create the
toolkit should be prioritised. In this context adopting a ‘making’ culture may
prove highly beneficial and a productive way of building a tangible bridge
between theory and action; it could foster pilots that enable exploration and
validation of technical, pedagogical and community-based innovation
approaches.
In order to achieve the abovementioned, this project investigates important
parameters on the introduction of UMI technologies in science education
supported by the implementation of a CoPs format. By carefully exploiting
state of the art technologies in order to design educational tools and
activities, the project aims to offer novel educational services by
implementing innovative pedagogies so as to enhance students’ and teachers’
creativity, socialisation and scientific citizenship. We aim to develop an
integrated yet open fully integrated training environment for 14-16 years old
students based on a selection of methodological processes and UMI
applications. The training environment will consist of an open repository of
educational material, educational means, training activities, a platform to
support CoPs through socialization, delivery specific of educational material,
entrepreneurship training, showcases, self-evaluation, mentoring, and
conceptualisation of content and information management. This project’s core
objectives are the following:
**O1 Novel educational services:** To develop a training mechanism, as a
methodology, for young students, containing guidelines for UMI learning under
the CoPs format, roles and structures.
**O2 Career consultancy services:** To foster innovation and support promising
scientific careers. The career consultancy will be carried out by conducting a
series of piloting UMI-SciEd activities and scenarios using CoPs and UMI. It
includes linking the market needs to the project stakeholders through the
platform, formation and management of CoPs using social computing tools and
adaptation of specific, specially selected technological tools used for
establishing CoPs.
**O3 Supporting software tools:** To design and implement an integrated
learning environment which shall actually support all stakeholders to form
CoPs as a facilitating mechanism for UMI learning in the science education.
**O4 Supporting hardware tools:** To integrate, package and delivery a
necessary hardware kit to support training and to develop the accompanied
programming environment allowing the interaction with the hardware kit and
supporting young students to realize their ideas from the beginning of their
training.
**O5 Dissemination of the project ideas and results:** To disseminate the use
of UMI technologies in real educational settings and promote their added
pedagogical value to youngsters (male and female) in science education mostly
but alternatively also in emerging disciplines of STEM (Technology,
Engineering, Mathematics) and to convey project scientific achievements and
RTD results both internally among project partners and externally to European
and International research communities, potential users and
industrial/commercial organizations.
**Rationale and changes introduced in DMP revision**
The first objective of initial DMP was to define and classify the most
important datasets that will be produced and exchanged throughout the lifetime
of the project among project partners and participants. As such, four datasets
have been defined, i.e., _Educational Scenarios Derivatives_ , _Research data_
, _Educational material_ and _Platform & market analysis data _ . A
description of these datasets has been given to the best possible extent.
However, as noted in section 1.3 of DMP v1.0, with only a preliminary design
and development of the UMI-Sci-Ed project platform (which consists the major
means of project data collection, generation and exchange) in hand, several
issues regarding the collection, organisation and presentation of data could
not be accurately predicted and major decisions had to be made for several
aspects of data management. On the other hand, while the datasets were
correctly defined, the definition and development of educational scenarios and
the evaluation framework that will drive the educational activities and the
main educational research directions, respectively, were not yet mature enough
in order to fully specify the data types and the metadata that are important
for the project and the research activities involved. Last, but not least, the
initial DMP development followed the Horizon 2020 directions available at the
beginning of the project and was based on DMP online platform 2 provided by
Data Curation Centre (DCC). In the meantime, Horizon 2020 released a new
template 3 as well as new guidelines on Open Access 4 ]. Taking into
account the abovementioned issues, together with several decisions that were
made by the consortium in the meantime, a major revision of the initial DMP
was necessary. Overall, the DMP has been revised in the following major
aspects:
* The initial datasets have been enriched with two more datasets. In specific: _DS5 Project working data_ is introduced in order to keep track and of internal working documents, that were decided to be circumvented through the _Filedepot_ feature of the project platform; _DS6 - Other data_ is introduced in order to include all other data that will be exchanged through the project platform that cannot be classified in one of the rest datasets.
* Each of the datasets, either new or existent in the DMP v1.0 has been described in accordance with the project platform content types. Data curation and preservation methods have been fully clarified and Zenodo has been decided to be used as a repository of research data.
* A set of metadata has been defined for each of the most important platform content types based on the LRMI/DCMI specification following the Schema.org metadata structure.
* The DMP has been rewritten following the new template provided by Horizon 2020.
**DMP as a living document: Next steps**
The revised DMP described in this deliverable can be considered as a
consistent and mature plan for data management. However, as clearly described
above, the vast majority of data is going to be collected, generated and
exchanged via the project platform. The project platform on the other hand is
going to be enriched, change and expand, according to the needs and results of
research activities that will lead to accomplishment of the project
objectives. Therefore, the DMP is going to evolve in conjunction with the
project platform and the project, as a whole, evolution. In this context,
while the major decisions regarding the DMP have been already made, several
issues will need reconsideration, revision and improvement. The next important
step is the conclusion and evaluation of the local pilots phase (T6.2). This
will provide a compact base of data as well as significant experience of the
detailed utilisation of the platform that will drive the next steps of the
platform implementation and the research methods to be subsequently followed.
As such, it is expected that several decisions and specifications included in
this document regarding the data and metadata management will be revised
accordingly.
1. **Data Summary**
1. **Introduction**
UMI-Sci-Ed is going to generate, use, circulate and disseminate a big amount
of diverse data. These include data that will support the educational and
training activities, the artefacts and derivatives of the piloting and
implementation phases, data that will drive the research process that will
take place in the context of the project, etc. These data will be both
qualitative and quantitative and may either be automatically or manually
generated. The origin will differ substantially, e.g., data will be generated
by the participants to the educational activities (e.g., students, tutors,
researchers, project members or professionals), researchers that will process
and analyse the activities and data created in the context of the educational
scenarios implementation, sensors or other artefacts producing data in the
context of the pilot and implementation phases as well as the UMI-Sci-Ed
platform itself. Furthermore, it is clear that the generated data will follow
different formats and standards due their diverse nature and post-processing
requirements. Almost all types of data will be managed through the UMI-Sci-Ed
platform 5 that has been created in order to support the activities that
will be realized in the context of the project.
Aiming to provide easy access and processing capabilities, the consortium
revised the original dataset categories with respect to the data that will be
generated throughout the project from four (4) to six (6). In addition to the
datasets described in the DMP v1.0, two more datasets have been introduced,
namely, the _Project working data_ and the _Other data_ . The main idea behind
this categorisation is the provision of a simple structure while keeping the
major relevant data collections compact, in terms of the origin of creation,
and the postprocessing that will be applied to the data collections. In
specific, the six datasets are:
* **_DS1: Educational scenarios derivatives._ ** This is a family of datasets that will contain all the raw or processed data that will be generated during the execution of the pilots as learning artefacts, mainly by the students and the tutors. Each educational scenario will eventually have its own dataset.
* _**DS2: Research data.** _ This dataset will include all the quantitative and qualitative data (pre- and post-processed questionnaires, reflections, evaluations, etc.) that will support educational research.
* _**DS3: Educational material.** _ This dataset will include all the educational material that will be developed by the partners, tutors, professionals, etc. that will participate in the preparation of the educational scenarios and the pilots.
* _**DS4: Platform and market analysis data.** _ This dataset will include information about the usage of the platform that will be developed in the context of the project in the form of log files and evaluation forms as well as overall evaluation data that will be used for market analysis and exploitation.
* _**DS5: Project working data.** _ This dataset includes working documents in the context of the project, such as presentations, draft deliverable documents, deliverable review documents, etc.
* _**DS6: Other data.** _ This dataset includes other types of information that will be gathered in the UMI-Sci-Ed platform throughout the project that cannot be categorized in one of the above datasets.
The UMI-Sci-Ed platform that hosts the above mentioned data has been organised
in specific content types to support the activities of the project, as
described in D4.2. These include _UMI Scenario_ , _UMI Project_ , _Group
Article_ , _Repository Entry_ 6 , _Blog_ , _Survey_ , _Wiki_ , _Forum Topic_
, _Poll_ , and _Event_ . In addition, a _Filedepot_ is included in the
platform that supports information exchange among project partners. In the
following section, each of six datasets is described separately, in relation
with the above-mentioned content types, following the guidelines of the H2020
template for DMP [2].
2. **Datasets description**
**1.2.1 DS1: Educational scenarios derivatives**
This is a family of datasets containing the raw or assembled and processed
intermediate or final data that will be created by the participants as
learning artefacts during the pilots that will take place within WP6 (Tasks
6.2 and 6.3). This kind of data will be mainly generated and collected in the
context of UMI projects ( _UMI Project_ content type) that will implement the
predefined educational scenarios (UMI scenarios), that is, the basis of the
above mentioned activities. However, relevant data can be uploaded in the
_Blog_ , _Forum Topic_ , _Survey_ , etc. content types that are related to UMI
projects.
The data of this dataset will be generated manually by students and tutors or
automatically by UDOO devices themselves running pieces of codes created by
the participants. UMI-SciEd partners and researchers may also create data,
e.g., during demonstration or dissemination activities.
The dataset may comprise software code (e.g. programs for managing
experiments, programs for building hardware), data produced by running
experiments or building solutions, student reports, etc., following several
diverse formats, e.g., text, images, audio, video, scripts, numerical
(spreadsheet) and binary sensor data, etc. Open and/or wellestablished format
standards will be considered and used as first options to guarantee easy
exchange and processing using non-proprietary software. Customised format
standards might also be used if necessary for the execution of the UMI
projects; in such case, documentation and/or open source software tools to
access the data will be released too. Available standards describing raw data
from sensors such as Sensor Model Language (SML) and Sensor Observation
Service (SOS) will be examined for adoption in the project. The data size
cannot be predicted yet since it depends on the level of participation of
students and their tutors. However, there is no restriction posed by technical
or administrative issues.
Data of this dataset may be categorized as raw data and processed data. Raw
data represent all the artefacts of the students’ activities that take place
with the use of UDOO devices. Processed data represent all material
(documents, statistics, reports, etc.) that will be generated by the students
after processing the raw data in cases that such processing is required in the
UMI scenarios and UMI projects.
The main purpose of these datasets is to support the activities of the
students and tutors during the realisation of UMI projects and to allow groups
and CoPs interaction when working on the same UMI project. In addition,
specific samples of these data may be used to support research reasoning and
outcomes or proof of concept and applicability of the UMISci-Ed platform and
hardware devices. Therefore, they are closely related with O1, O3 and O4
objectives and they will be used as input to the assessment of the training
methodology, the UMI-Sci-Ed platform, and the UDOO hardware kit, respectively,
that are under development in the context of the project.
**1.2.2 DS2: Research data**
This is a data set containing the research data that will be collected during
the UMI projects realisation (implementation of the UMI scenarios) in the
classroom (T6.2 and T6.3), the CoPs study and evaluation phases (T2.3 and
T2.4) as well as overall evaluation phase (T7.2 and T7.3). The data will
mainly be collected through the _Survey_ or _Poll_ content types of the
platform and may be exchanged (in aggregated and anonymised format) through
the _Group Article_ , _Repository Entry_ content types and the _Filedepot_ .
_UMI Scenario_ contents can be used in research activities as well.
Each pilot study will produce a number of different files including both
qualitative and quantitative data. Indicative information to be collected is
as follows:
* Pilot/workshop information (goals, hardware kits, programming language, group formation, success criteria, Gantt charts etc.)
* Project partners responsible for the pilot/workshop, demographic information of participants (age, gender, etc.), tutors/mentors information.
* Video/audio recordings, log interactions and observations.
* Student assessments.
* Tutors' reflections.
* Corporate sector feedback.
* Interviews with the participants (students, tutors, parents, etc.).
* Different questionnaires addressed to participants (students, tutors, researchers) for collecting qualitative and quantitative data before, during and after the project activities performed in the context of the pilots/workshops.
The above mentioned generated data will mainly be used in the context of
research activities of UMI-Sci-Ed, including assessment of novel educational
services, evaluation of software and hardware tools, as well as dissemination
of the project ideas and results, as described in O1, O3, O4, and O5
objectives of the project, respectively. Therefore, they will support and
accompany publications (which by default are included in this dataset as
well). In this context, these data will be useful for researchers within the
framework of UMI-Sci-Ed, as well as external researchers that are active in
similar or complementary research areas. Existing data by previous research
efforts (out of the context of the project) may be used and analysed in order
to provide comparative results.
Several data formats will be necessary to support the research and evaluation
process including, but not restricted to, text, spreadsheets and audio/video.
Open and/or wellestablished format standards will be considered and used as
first options to guarantee easy exchange of the information. Estimation of the
generated data volume cannot be made since it depends on the activities of
project partners. However, there is no restriction posed by technical or
administrative issues.
**1.2.3 DS3: Educational material**
This dataset contains the educational material and resources that will be
developed in the context of tasks T2.2, T5.2 and T6.1 in order to support the
pilots. The dataset will include two general types of educational material,
i.e., material to be used by the tutors and material to be used by the
students. The material will either be developed by the consortium partners
participating in educational activities, external stakeholders and
professionals as well as the tutors themselves, or it is already available (by
organisations outside the framework of UMI-Sci-Ed) and will be redistributed,
mainly in the form of URLs. The educational material will mainly be available
through the _UMI Scenario_ , _UMI Project_ , _Repository Entry_ , _UMI Forum
Topic_ , and _Wiki_ content types of the UMI-Sci-Ed platform. While the
educational material is mainly intended to be used by the target groups of the
project, i.e., tutors and students in the context of UMI-Sci-Ed as well as the
general public, some material will support research efforts and publications
as well. Indicative information to be generated and distributed includes:
* Educational (UMI) Scenarios' detailed descriptions including activity plans, session/lesson plans hardware/software to be used, etc.
* Supporting documentation and presentations for UDOO devices.
* Guidelines, templates, handouts, instructive videos, presentations and documentation for the scientific topics under consideration in the UMI Scenarios and UMI Projects.
From the above description, it is evident that this dataset is mainly related
to O1 and O2 UMI-Sci-Ed objectives that deal with novel educational services
development and career consultancy services support. The data generated and
distributed can be of audio, video, document, presentation and other types.
Common and well-established formats will be used to guarantee easy exchange
and processing. The expected size of data cannot be estimated since it depends
on the productivity of the project participants and partners. However, there
is no restriction posed by technical or administrative issues.
**1.2.4 DS4: Platform and market analysis data**
The purpose of this dataset is to provide information about the usage of the
UMI-Sci-Ed platform as well as general evaluation of the project and the
offered services. Data to be collected can be categorized in two major parts,
that is, log files and evaluation data. Both will include information
regarding
* user info (e.g., ID or name for registered users, submitted info by unregistered users)
* user category (according to the actors that will be defined in the specifications of the platform)
* date/time
* visiting duration
* visited pages/tools
* platform and overall services evaluation data (through evaluation forms that will be defined in the specifications of the platform) as well as questionnaires and interviews for the services offered in general by the project.
Platform usage log data will be collected automatically by the platform via
proper software modules as text files (.csv or .xml) while evaluation data
will be provided by the interaction of the users with the platform through
proper evaluation forms and questionnaires that will be processed to provide
spreadsheets or numerical tables. Furthermore, interviews (as audio or video
files) may be required to collect qualitative data. These data will be
properly aggregated in order (a) to get feedback for the quality of services
that the platform and the project (as a whole) offers to the users, (b) to
measure the visibility of the project, and (c) to be combined with other data
in order to draw conclusions in educational research, drive market analysis
and subsequent exploitation plans in the context of Task 8.3. Therefore, this
dataset’s data will be useful for the consortium in the context of the O3 and
O5 UMI-Sci-Ed objectives that are related to the supporting software tools
assessment and the dissemination of the project ideas and results,
respectively.
The data size cannot be predicted since it has to do with the visibility and
utilisation of the UMI-Sci-Ed platform throughout and after the project.
However, there is no restriction posed by technical or administrative issues.
**1.2.5 DS5: Project working data**
This dataset includes working documents in the context of the project, such as
draft deliverable documents, deliverable evaluation forms, deliverable
revisions, progress reports, presentations, meeting minutes, etc., as well as
other auxiliary material, that are created and exchanged through the UMI-Sci-
Ed platform _Filedepot_ by the project partners in order to fulfil the typical
project requirements. Thus, this dataset is related with all the objectives of
the project and is used for monitoring the activities and progress of the
project as a whole. It mainly consists of text files or presentations;
however, other data types may be occasionally used, in well-established
formats that have been agreed among the partners.
**1.2.6 DS6: Other data**
This dataset includes other types of information that will be gathered in the
UMI-Sci-Ed platform by any registered user throughout the project and cannot
be categorised in one of the _DS1_ \- _DS5_ datasets while still relevant and
useful for the project, e.g., forum and blog discussions, announcements, etc.
This dataset includes, in general, auxiliary data that are exchanged among the
platform users in the context of educational scenarios/projects design and
implementation and can be of any type. Their size depends on the utilisation
of the platform throughout the project. They are not directly connected with
the objectives of the project, however post-processing of them may be useful
in qualitative analysis that could be used in research efforts of the project
partners.
**1.3 Datasets and UMI-Sci-Ed platform relations**
The plurality and diversity of data that will be generated, gathered and
exchanged through the UMI-Sci-Ed platform, as described in the previous
section, make their classification and organisation a rather involved task.
Furthermore, the platform, tools and methods are expected to be updated,
expanded or shrunk in several aspects in order to support effectively the
CoPs’ and educational activities throughout the duration of the project.
Therefore, while the described datasets seem to cover the data that will be
gathered in the framework of the project, the described in sections 1.1 and
1.2 relations between the platform content types and datasets, i.e., in which
content types the _DS1_ \- _DS6_ data are expected to be uploaded, may change.
In order to ease the presentation and keep track of the changes that may be
necessary in the future, Table 1 provides the platform content types that will
be used for collecting or exchanging data of the defined datasets.
<table>
<tr>
<th>
**Content Type**
</th>
<th>
**DS1: Educational scenarios derivatives**
</th>
<th>
**DS2: Research data**
</th>
<th>
**DS3: Educational material**
</th>
<th>
**DS4: Platform and market analysis data**
</th>
<th>
**DS5:**
**Project working data**
</th>
<th>
**DS6:**
**Other Data**
</th> </tr>
<tr>
<td>
**UMI Scenario**
</td>
<td>
</td>
<td>
×
</td>
<td>
×
</td>
<td>
×
</td>
<td>
</td>
<td>
</td> </tr>
<tr>
<td>
**UMI Project**
</td>
<td>
×
</td>
<td>
</td>
<td>
×
</td>
<td>
×
</td>
<td>
</td>
<td>
</td> </tr>
<tr>
<td>
**Group Article**
</td>
<td>
</td>
<td>
×
</td>
<td>
</td>
<td>
×
</td>
<td>
</td>
<td>
×
</td> </tr>
<tr>
<td>
**Repository Entry**
</td>
<td>
</td>
<td>
×
</td>
<td>
×
</td>
<td>
×
</td>
<td>
</td>
<td>
</td> </tr>
<tr>
<td>
**Forum Topic**
</td>
<td>
×
</td>
<td>
</td>
<td>
×
</td>
<td>
×
</td>
<td>
</td>
<td>
×
</td> </tr>
<tr>
<td>
**Blog**
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
×
</td>
<td>
</td>
<td>
×
</td> </tr>
<tr>
<td>
**Survey**
</td>
<td>
×
</td>
<td>
×
</td>
<td>
</td>
<td>
×
</td>
<td>
</td>
<td>
</td> </tr>
<tr>
<td>
**Poll**
</td>
<td>
×
</td>
<td>
×
</td>
<td>
</td>
<td>
×
</td>
<td>
</td>
<td>
</td> </tr>
<tr>
<td>
**Wiki**
</td>
<td>
</td>
<td>
</td>
<td>
×
</td>
<td>
×
</td>
<td>
</td>
<td>
</td> </tr>
<tr>
<td>
**Filedepot**
</td>
<td>
</td>
<td>
×
</td>
<td>
</td>
<td>
×
</td>
<td>
×
</td>
<td>
</td> </tr> </table>
**Table 1 Datasets and UMI-Sci-Ed platform content types relation**
2. **Fair Data**
**2.1 Making data findable, including provisions for metadata**
The UMI-Sci-Ed platform is the major place of data collection and presentation
either raw or processed data of the project. Additionally, publications and
relative research data will be uploaded to Zenodo ( _https://zenodo.org/_ ) in
order to expand visibility. All data of the content types described in Chapter
1 that are uploaded in the project platform, including attachments, have a
unique and persistent identifier of URI type that is automatically generated
by the platform. Research data uploaded to Zenodo will also have a unique DOI
provided by Zenodo platform.
A first set of metadata has been declared for each of the content types of the
platform focusing in educational research while general metadata are also
going to be generated. The specific set for each content type has been decided
as a compromise between a full descriptive set of metadata for educational
research and low requirements of user input, so that to avoid end user
dissatisfaction that will prevent users from using the platform. Therefore,
_UMI Scenario_ and _UMI Project_ content types that are basically developed by
project partners require a significant set of metadata (provided by user
input) during the setup of a scenario or a project. In all other cases user
input related to metadata has been minimised.
Following the same principle, attachments in the platform are classified in
two major categories: attachments including educational material that supports
educational scenarios and activities under the _UMI Scenario_ content type and
attachments for all other content types. In the former case, educational
material is classified in 5 categories, that is, Source Code, URL, Digital
Document, Media Object and Rich Text, each one accompanied by a detailed set
of metadata that will be generated either by user input or automatically by
the platform. In the latter case, attachments are categorized as file
attachments, image/photos and YouTube videos and are accompanied by a minimum
set of metadata that will be generated by user input in order to ease file and
media exchange.
The metadata schema for educational research in the framework of the project
will follow the Learning Resource Metadata Initiative (LRMI) version 1.1 of
Dublin Core Metadata Initiative (DCMI) 7 , properly adapted and expanded to
fulfil the needs of the project. Table 2 provides the properties of
_Schema.org/CreativeWork_ that were adopted from LRMI specification, new
properties introduced, the metadata collection method in the platform and some
clarifying comments. Table 3 summarizes the use of _AlignmentObject_ type and
its properties by providing specific examples and value sets that are going to
be used as a basis for detailed metadata in the context of educational
research. A new set of values has been defined in addition to the recommended
by LRMI 1.1 values for the _alignmentType_ property. These are going to be
used primarily with _UMI Scenario_ and secondarily with _UMI Project_ content
types and are expected to enhance discoverability and reusability of UMI-Sci-
Ed data.
The preliminary set of metadata that has been specified for the most important
content types of the project platform is presented in detail in Appendix A.
All metadata are saved in the platform following Schema.org 8 structure.
Metadata and their value sets are going to be finalised after the local
pilots’ period (T6.2) based on the evaluation of the data that will be
gathered.
<table>
<tr>
<th>
**Property**
</th>
<th>
**LRMI**
</th>
<th>
**New**
</th>
<th>
**Generation method**
</th>
<th>
**Comments**
</th> </tr>
<tr>
<td>
**educationalAlignment**
</td>
<td>
×
</td>
<td>
</td>
<td>
User input
</td>
<td>
This property is of _schema.org/AlignmentObject_ type which is further
described in Table 3.
</td> </tr>
<tr>
<td>
**educationalUse**
</td>
<td>
×
</td>
<td>
</td>
<td>
Automatically by the platform
</td>
<td>
“Educational scenario plan” and
“Educational scenario implementation” are the values used for _UMI Scenario_
and _UMI Project_ content types in the context of UMI-Sci-Ed. The value set
may be revised according to UMI-Sci-Ed future needs.
</td> </tr>
<tr>
<td>
**timeRequired**
</td>
<td>
×
</td>
<td>
</td>
<td>
User input
</td>
<td>
</td> </tr>
<tr>
<td>
**typicalAgeRange**
</td>
<td>
×
</td>
<td>
</td>
<td>
Automatically by the platform
</td>
<td>
Value depends on the _educationalRole_ value and may be “14-16” for “learner”
or “22-” for other values.
</td> </tr>
<tr>
<td>
**interactivityType**
</td>
<td>
×
</td>
<td>
</td>
<td>
User input or automatically by the platform
</td>
<td>
Generation method depends on the platform content type.
</td> </tr>
<tr>
<td>
**learningResourceType**
</td>
<td>
×
</td>
<td>
</td>
<td>
User input or automatically by the platform
</td>
<td>
Specific content types have predefined values.
</td> </tr>
<tr>
<td>
**artefactType**
</td>
<td>
</td>
<td>
×
</td>
<td>
User input
</td>
<td>
A new property that provides means for characterization of learning artefacts.
</td> </tr>
<tr>
<td>
**licence**
</td>
<td>
×
</td>
<td>
</td>
<td>
User input
</td>
<td>
This corresponds to the LRMI property useRightsUrl as defined in Schema.org
</td> </tr>
<tr>
<td>
**educationalRole**
</td>
<td>
×
</td>
<td>
</td>
<td>
User input
</td>
<td>
This is a property of
_schema.org/EducationalAudience_ type and includes the values “learner”,
“teacher”, “author”, “manager”, as specified in IEEE LOM. It is mainly used
for _UMI Scenario_ and _UMI Project_ content types.
</td> </tr> </table>
# Table 2 LRMI properties adopted in UMI-Sci-Ed
_UMI Scenario_ , _UMI Project_ , _Group Article_ , _Repository Entry_ , _Blog_
, _Wiki_ and _Forum Topic_ content types, include a mandatory field for Key
Terms or Tags. All of them will be mapped to
_schema.org/CreativeWork:keywords_ property for metadata generation.
Therefore, all datasets (see Table 1) will be accompanied by keywords.
Keywords are available as a list (autocomplete feature) while typing new key
terms or tags in the abovementioned content types in order to enhance
reusability. Keywords are searchable through the main search function of the
platform.
Versioning of _UMI Scenario_ , _UMI Project_ and _Wiki_ content types is
preserved automatically by the platform. Additionally, versioning is available
for _UMI Scenario_ , _UMI Project_ and _Wiki_ content types by user input. For
file attachments, users are suggested not to delete (replace) older versions
of the same file in order to keep tracking of the file changes. Decimal
numbering with two numbers preceded by “ _v_ ” and separated by “ _._ ” will
be followed, indicating major or minor changes. For file attachments, version
may be put in the end of file name followed, optionally, by language
indication, e.g., “ _UMI-SCI-Ed CTI Scenario My Social Panel v3.1 en.docx_ ”.
<table>
<tr>
<th>
**alignmentType value**
</th>
<th>
**LRMI**
</th>
<th>
**New**
</th>
<th>
**educationalFramework value or example**
</th>
<th>
**targetName value set or example**
</th> </tr>
<tr>
<td>
**“learningOutcomes”**
</td>
<td>
</td>
<td>
×
</td>
<td>
Ex: “Revised Bloom Taxonomy”
</td>
<td>
Ex: “Describe a simple local area computer network”
</td> </tr>
<tr>
<td>
**“educationalLevel”**
</td>
<td>
×
</td>
<td>
</td>
<td>
Ex: “Greek upper secondary school”
</td>
<td>
Ex: “year 10 (on K-12 scale)”
</td> </tr>
<tr>
<td>
**“educationalSubject”**
</td>
<td>
×
</td>
<td>
</td>
<td>
Ex: “Greek upper secondary school”
</td>
<td>
Ex: “Informatics’ Applications
(Curriculum 932/14-4-2014/τΒ)”
</td> </tr>
<tr>
<td>
**“umiDomain”**
</td>
<td>
</td>
<td>
×
</td>
<td>
“Horizon 2020 UMI-Sci-Ed”
</td>
<td>
“Ubiquitous”, “Mobile”, “IoT”
</td> </tr>
<tr>
<td>
**“educationalScenarioOri enation”**
</td>
<td>
</td>
<td>
×
</td>
<td>
“Horizon 2020 UMI-Sci-Ed”
</td>
<td>
“Acquire new Knowledge”,
“Develop new Skills”, “Attain
Attitudes”
</td> </tr>
<tr>
<td>
**“pedagogicalTheory”**
</td>
<td>
</td>
<td>
×
</td>
<td>
“Horizon 2020 UMI-Sci-Ed”
</td>
<td>
Ex: “Active Learning”
</td> </tr>
<tr>
<td>
**“requires”**
</td>
<td>
×
</td>
<td>
</td>
<td>
“hardware resources”,
“software resources”,
“other resources”
</td>
<td>
Ex: “Arduino IDE”
</td> </tr>
<tr>
<td>
**activityType**
</td>
<td>
</td>
<td>
×
</td>
<td>
“Horizon 2020 UMI-Sci-Ed”
</td>
<td>
“Introductory”, “Core”
“Concluding” or “Auxiliary”
</td> </tr>
<tr>
<td>
**“learningObjectives”**
</td>
<td>
</td>
<td>
×
</td>
<td>
Ex: “Revised Bloom Taxonomy”
</td>
<td>
Ex: “List UMI applications”
</td> </tr>
<tr>
<td>
**“difficulty”**
</td>
<td>
</td>
<td>
×
</td>
<td>
“IEEE LOM 5.8”
</td>
<td>
“Easy”, “Intermediate” or
“Advanced”
</td> </tr> </table>
# Table 3 Properties of Schema.org/Intangible/AlignmentObject used in UMI-
Sci-Ed
**2.2 Making data openly accessible**
All data will be generated or collected via the UMI-Sci-Ed platform and will
be made openly available to registered users with the following exceptions due
to legal and contractual reasons:
1. Raw educational research data (part of _DS1_ dataset), i.e., survey and poll answers, interviews, observation notes, etc., that include or may collectively reveal the identity of student participants. This kind of data will be properly anonymised before use allowing information on demographic characteristics (e.g., age, gender) of students only (see Chapter 5).
2. Raw platform use log data ( _DS4_ dataset) or platform survey and evaluation data that include sensitive personal information of users. These data will be accessible only to
the platform administrator and available only to officially authorised
persons. They may be released to the consortium partners for processing and
evaluation after anonymisation or exclusive permission of the steering
committee upon a fully justified request. These data are not going to be
shared outside the project consortium in any case (see also Chapter 5).
3. Files uploaded to _Filedepot_ which are by default created and exchanged between the project partners for internal use only.
4. Any data that are related to business activities and goals of corporate consortium partners.
Furthermore, registered users that participate in a Group (supporting CoPs’
activities) have the capability to restrict specific types of the platform
content to Group visibility only, as described in deliverable “D4.2 –
Intermediate version of the system”.
All data will be available through UMI-Sci-Ed platform after registration. In
addition, research or platform data ( _DS2_ and _DS4_ datasets) supporting
publications (as well as the publications themselves) will be openly available
in Zenodo, after proper anonymisation, as described in (a) and (b) above. No
special methods or tools are necessary to access the data with the exception
of UDOO sensor data that will be gathered when implementing educational
scenarios. The necessary software will be available in open-source code and
will be accessible with a special link via the UMI-Sci-Ed platform.
All data will be deposited in CTI servers dedicated to the project and
accessed through the UMI-Sci-Ed platform. In addition, publications and
accompanying data will be deposited to Zenodo and other institutional/national
databases. Access to UMI-Sci-Ed platform is provided according to deliverable
“D4.1 – System Requirements and Architectural Design”. The platform
administrator can additionally provide massive access to data (either via the
platform or directly to CTI servers) to consortium members, the project
officer and the reviewers, after registration to the platform and proper
consent of the project coordinator. All registered users are uniquely
identifiable by the platform.
3. **Making data interoperable**
The data that will be produced during the project and will be publicly made
available will follow well-established and massively used format standards,
open as much as possible, with preference to .csv, .pdf, .xml, .png, .jpeg
formats. This will allow reusability between researchers, institutions and
organisations not participating in the project consortium.
Data and metadata vocabularies, as described in section 2.1 especially for
educational research purposes, as well as with commonly used metadata for the
platform content, as described in Schema.org, will offer enhanced
interoperability and inter-disciplinary use. A preliminary set of the planned
metadata and vocabularies for the content types of the platform are provided
in detail in Appendix A.
4. **Increase data re-use (through clarifying licences)**
Data produced by the project will be available through the platform or
published on Zenodo platform under a Creative Common (CC) 9 proper license.
In this context, _UMI Scenario_ , _UMI Project_ and _Repository Entry_ content
types, include a license field to be filled by the user (as a list of
available CC licensing types) that applies to all the content included in them
as well. Research data and publications will follow the same procedure in
Zenodo.
All data that are viewable by registered users of the platform or uploaded in
Zenodo or are accessible via the project web site 10 are considered to be
available for re-use by third parties by the moment they are made available in
public. No embargo period will be applied. Re-use of data is restricted only
when they are related to business activities and goals of corporate consortium
partners. Data will be and remain re-usable for a minimum time of two (2)
years after the end of the project. The project partners will decide which
data will be preserved for a longer period by the end of the project.
Raw data processing, aggregation and presentation for research activities will
take place following standardised methodologies and tools as described in
detail in deliverable “D7.1 – Evaluation concept-framework and piloting” in
order to assure processed data quality. Data related to deliverables and
project reporting follow the predefined by the project document control
special procedure that includes: (a) Use of predefined and agreed templates,
(b) Review and revision process and (c) Approval from the steering committee.
Raw and processed data will be kept in the platform and archived to CTI
servers following standardised procedures that guarantee data integrity. A
short description of the infrastructure, software and procedures used can be
found in Chapter 4.
3. **Allocation of resources**
The costs for making the data FAIR have been pre-allocated as part of Horizon
2020 grant in the context of Work Package 1 (Project management). No extra
costs will be necessary for platform data curation and preservation during the
project and for a period of two (2) years after the end of the project.
Research data uploaded to Zenodo will not require extra costs. The project
partners (through the Steering Committee) will decide what platform data will
require longer term preservation. These data may be uploaded to Zenodo as well
(depending on data volume) at no cost. Otherwise extra costs will burden CTI
which is responsible for the maintenance of the platform and the project
servers.
CTI is responsible for data management. The platform administrator and the
project coordinator (both in CTI) are the key persons for data management
during the project. Furthermore, the coordinator of each partner is
responsible for any data (intended to be scant or none) that are locally kept
by the project partners outside the project platform.
4. **Data security**
All data will be stored and preserved in CTI project servers. UMI-Sci-Ed
platform is running on a server with Ubuntu 16.04 LTS operating system
installed. LTS stands for “Long Term Support” and it means that Canonical (the
company behind Ubuntu) provides support (security updates etc) for at least 5
years. As a web server, we have installed the latest version of Apache web
server with PHP 7.0 and as database server UMI-Sci-Ed platform stores the data
to the latest version of MySQL Server Community Edition.
For better resources management and allocation, we use virtualization
technologies in our servers. Regarding the project, UMI-Sci-Ed server is a
virtual machine that runs on an ESXi infrastructure. The bare metal ESXi
server runs on a Dell R430 machine with redundant power supply and redundant
hard disk drives (SAS HDD – RAID1).
The physical location of the server is CTI’s computer room. Our computer room
is equipped with UPS systems, systems that maintain the temperature and the
humidity levels inside the computer room and special fire suspension systems
(Waterless Fire Suppressant). Finally, only authorized personnel can access
the computer room by using personal ID cards. Also, any entrance to the
computer room is logged and the entrance is video recorded 24/7.
For the backup of UMI-Sci-Ed server, we use one extra, independent server
(backup server). Our backup plan is executed daily at 2:00 and we keep 60
restore points. This means that we can restore any instance of UMI-Sci-ed
server 60 from the past 60 days. All backups are created and transferred to
the backup server via a secure connection.
UMI-Sci-Ed development team checks in a regular basis servers’ system logs and
ensures that all the necessary system updates are installed in our servers.
Furthermore, CTI’s cyber security department monitors daily the internet
traffic from/to CTI’s network to prevent any malicious actions, attempts for
unauthorized access etc.
Finally, as noted in Chapter 3, for longer than 2 years after the end of the
project preservation, properly selected sets of data (that include research
data) will be deposited to Zenodo.
5. **Ethical aspects**
The project meets national legal and ethical requirements of the partners’
countries. The project will not study vulnerable populations. No sensitive
personal data will be recorded. As we are interested in UMI/STEM use and
career potential of both genders, gender of participating students and tutors
will be probably recorded, but in anonymous questionnaires, with general
descriptors like country, school name, geographic region, as well as opinion
about project actions, etc. In spite of being sensitive, all collected data
will be treated with care and respect, and only the results of their
processing will be published. However, whenever personal data collection is
required, this will be made with the explicit written consent of the
individuals involved. Especially for children (which are unable to give
informed consent), the consortium has already provided compliance with the
ethical standards and guidelines of Horizon 2020 (Annex 1, DoA, part B,
section 5.1, p. 76). The description and the justification of the methodology
adopted and the composition of the sample are provided in Chapter 2 (sections
2.1 and 2.2) of deliverable “D9.2 – H - Requirement No. 1”. Furthermore,
detailed information on the informed consent procedures that will be
implemented regarding the participation of humans, as well as clarifications
on how assent will be ensured for children is provided in section 2.3 of D9.2.
More specifically, among the datasets under consideration in Chapter 1, _DS3_
and _DS5_ , educational material and project working data, do not, by
definition, include personal student data. Other auxiliary data falling in
_DS6_ , on the other hand, are unlikely to carry personal student data. Major
importance for ethical aspects is data collected as part of _DS2_ and _DS4_
datasets, i.e., research and platform data, respectively. For _DS2_ , all data
will be stored without any reference to the student interacting with the
platform and will be password protected. In the case of the tutors collecting
or generating information (interviews, assessments, reflections, etc.), the
information will be directly stored to the platform via proper forms with no
reference to student personal data. In all the abovementioned cases, only
demographic student data will be stored (e.g., gender, age) and will be
password protected. Following the same strategy, _DS4_ data including platform
log files will be encrypted, statistically processed and aggregated to ensure
anonymity. Aggregated and anonymised data may be shared within the consortium
and may be made publicly available when they support research publications via
the CTI platform and Zenodo. Finally, for _DS1_ data that include educational
derivatives, the original intention of the project was to anonymise all
learning artefacts created by students with reference only to groups and not
the individuals. In this context, artefacts that inevitably contain personal
student data (i.e., not fully anonymised) will be marked as sensitive and will
be password protected and/or encrypted. However, this last case will be
further examined by the consortium in cooperation with local tutors in the
context of providing recognition (visible for the members of the Cops) to
active students. Further information on ethical aspects and relative
procedures can be found in deliverables “D9.1 – POPD - Requirement No. 2” and
“ D7.1 – Evaluation concept-framework and piloting”.
Finally, the educational material will include a section about plagiarism,
code of ethics, and plain advice on how could students properly avoid it by
properly referencing their sources.
6. **Other**
No other national/funder/sectorial/departmental procedure for data management
will be used.
**Appendix A: Metadata tables for UMI-Sci-Ed platform content types**
This section provides the preliminary set of metadata that have been specified
for the _UMI Scenario_ , _UMI Project_ , _Group Article_ , _Wiki_ , _Blog_ ,
_Forum Topic_ and _Repository Entry_ content types of the platform.
Furthermore, the set of metadata that are kept in case of _Files and Media_
attachments to the above content types (excluding UMI Scenario that includes a
special set of metadata for attachments) is also given. It has to be noted
that the tables given below may undergo changes after the local small scale
pilots, in order to better support the objectives of the project and the
usability of the project platform.
Table 4 shows the metadata that have been specified for the main parameters of
_UMI Scenario_ content type together with the mandatory attached file. The
table includes the metadata property and the type of metadata according to
Schema.org, the value space, the platform field that the platform user
utilizes for input and some comments (when necessary). An empty platform field
indicates that metadata are created automatically without any user input. Each
educational scenario (that corresponds to _UMI Scenario_ content type)
includes a set of activities that are supported by their own set of metadata.
The set of metadata defined for the activity are presented in Table 5. Each
activity can further include several types of educational material, i.e.,
pieces of source code, URLs, digital documents (text, presentation or
spreadsheet), media objects, or rich text parts. The corresponding set of
metadata is not included here for brevity.
Tables 6, 7, 8, 9, 10 and 11 present the metadata that have been specified for
_UMI Project_ , _Group Article_ , _Wiki_ , _Blog_ (including both the blog and
a blog comment), _Forum Topic_ (including both the forum topic and a Forum
Post) and _Repository Entry_ content types.
_Files and Media_ attachments may be files, YouTube videos (via URL) and
images or photos. Table 12 provides the metadata that have been for file
attachments.
<table>
<tr>
<th>
**Metadata property**
</th>
<th>
**Metadata typeof**
</th>
<th>
**Value space**
</th>
<th>
**Platform field**
</th>
<th>
**Comments**
</th> </tr>
<tr>
<td>
**name**
</td>
<td>
CreativeWork
</td>
<td>
Text
</td>
<td>
Title
</td>
<td>
</td> </tr>
<tr>
<td>
**alternateName**
</td>
<td>
CreativeWork
</td>
<td>
Text
</td>
<td>
Abbreviation
</td>
<td>
</td> </tr>
<tr>
<td>
**version**
</td>
<td>
CreativeWork
</td>
<td>
Text
</td>
<td>
Version
</td>
<td>
</td> </tr>
<tr>
<td>
**inLanguage**
</td>
<td>
CreativeWork
</td>
<td>
Text, as defined in IETF BCP47
</td>
<td>
Language
</td>
<td>
</td> </tr>
<tr>
<td>
**description**
</td>
<td>
CreativeWork
</td>
<td>
Text
</td>
<td>
Synopsis
</td>
<td>
</td> </tr>
<tr>
<td>
**keywords**
</td>
<td>
CreativeWork
</td>
<td>
Text
</td>
<td>
Key Terms
</td>
<td>
</td> </tr>
<tr>
<td>
**educationalRole**
</td>
<td>
Ιntangible > Audience > EducationalAudience
</td>
<td>
"teacher", "learner",
"author", "manager"
</td>
<td>
Audience
</td>
<td>
</td> </tr>
<tr>
<td>
**audienceType**
</td>
<td>
Intangible > Audience
</td>
<td>
"public" or UMI Group
</td>
<td>
Group content
visibility, Groups audience
</td>
<td>
</td> </tr>
<tr>
<td>
**targetName, alignmentType = "learningOutcomes"**
</td>
<td>
Ιntangible >
AlignmentObject
</td>
<td>
Text
</td>
<td>
Expected Learning Outcomes
</td>
<td>
alignmentFramework = "Revised Bloom Taxonomy"
</td> </tr>
<tr>
<td>
**timeRequired**
</td>
<td>
CreativeWork
</td>
<td>
ISO 8601
</td>
<td>
Expected duration
</td>
<td>
Months, days, hours, minutes
</td> </tr>
<tr>
<td>
**alignmentFramework**
</td>
<td>
Ιntangible >
AlignmentObject
</td>
<td>
Text
</td>
<td>
Framework
</td>
<td>
</td> </tr>
<tr>
<td>
**targetName, alignmentType = "educationalLevel"**
</td>
<td>
Ιntangible >
AlignmentObject
</td>
<td>
Text
</td>
<td>
Level
</td>
<td>
alignmentFramework is defined by the field "Framework"
</td> </tr>
<tr>
<td>
**targetName, alignmentType = "educationalSubject"**
</td>
<td>
Ιntangible >
AlignmentObject
</td>
<td>
Text
</td>
<td>
Subject
</td>
<td>
alignmentFramework is defined by the field "Framework"
</td> </tr>
<tr>
<td>
**targetName, alignmentType = "umiDomain"**
</td>
<td>
Ιntangible >
AlignmentObject
</td>
<td>
"Ubiquitous", "Mobile",
"IoT"
</td>
<td>
UMI Domain
</td>
<td>
alignmentFramework = "Horizon2020 UMI-Sci-Ed"
</td> </tr>
<tr>
<td>
**targetName, alignmentType =**
**"educationalScenarioOrien ation"**
</td>
<td>
Ιntangible >
AlignmentObject
</td>
<td>
"Acquire new Knowledge",
"Develop new Skills", "Attain
Attitudes"
</td>
<td>
UMI-Sci-Ed Orientation/Focus
</td>
<td>
alignmentFramework = "Horizon2020 UMI-Sci-Ed"
</td> </tr>
<tr>
<td>
**targetName, alignmentType = "pedagogicalTheory"**
</td>
<td>
Ιntangible >
AlignmentObject
</td>
<td>
Text
</td>
<td>
Pedagogical Theory
</td>
<td>
alignmentFramework = "Horizon2020 UMI-Sci-Ed"
</td> </tr>
<tr>
<td>
**interactivityType = "mixed"**
</td>
<td>
CreativeWork
</td>
<td>
"active", "expositive" or
"mixed"
</td>
<td>
Interactivity Type
</td>
<td>
Only one out of the three values
</td> </tr>
<tr>
<td>
**targetName,**
**alignmentType = "requires"**
</td>
<td>
Ιntangible >
AlignmentObject
</td>
<td>
Text
</td>
<td>
Resources - Hardware
</td>
<td>
alignmentFramework = "UMI-Sci-Ed hardware resources"
</td> </tr>
<tr>
<td>
**targetName,**
**alignmentType = "requires"**
</td>
<td>
Ιntangible >
AlignmentObject
</td>
<td>
Text
</td>
<td>
Resources - Software
</td>
<td>
alignmentFramework = "UMI-Sci-Ed software resources"
</td> </tr>
<tr>
<td>
**targetName,**
**alignmentType = "requires"**
</td>
<td>
Ιntangible >
AlignmentObject
</td>
<td>
Text
</td>
<td>
Resources - Software
</td>
<td>
alignmentFramework = "UMI-Sci-Ed other resources"
</td> </tr>
<tr>
<td>
**identifier**
</td>
<td>
CreativeWork
</td>
<td>
URI
</td>
<td>
</td>
<td>
</td> </tr>
<tr>
<td>
**author**
</td>
<td>
Person
</td>
<td>
Registered User
</td>
<td>
</td>
<td>
</td> </tr>
<tr>
<td>
**dateCreated**
</td>
<td>
CreativeWork
</td>
<td>
Date
</td>
<td>
</td>
<td>
</td> </tr>
<tr>
<td>
**dateModified**
</td>
<td>
CreativeWork
</td>
<td>
Date
</td>
<td>
</td>
<td>
</td> </tr>
<tr>
<td>
**genre**
</td>
<td>
CreativeWork
</td>
<td>
"UMI Scenario"
</td>
<td>
</td>
<td>
Fixed value
</td> </tr>
<tr>
<td>
**disambiguatingDescription**
</td>
<td>
</td>
<td>
"UMI-Sci-Ed Educational
Scenario"
</td>
<td>
</td>
<td>
Fixed value
</td> </tr>
<tr>
<td>
**provider:name**
</td>
<td>
Organization:Text
</td>
<td>
"Horizon2020 UMI-Sci-Ed"
</td>
<td>
</td>
<td>
Fixed value
</td> </tr>
<tr>
<td>
**educationalUse**
</td>
<td>
CreativeWork
</td>
<td>
"Educational Scenario plan"
</td>
<td>
</td>
<td>
Fixed value
</td> </tr>
<tr>
<td>
**typicalAgeRange**
</td>
<td>
CreativeWork
</td>
<td>
"14-16"
</td>
<td>
</td>
<td>
Fixed value
</td> </tr>
<tr>
<td>
**accessibilityAPI**
</td>
<td>
CreativeWork
</td>
<td>
Text (as defined in https://www.w3.org/wiki/W ebSchemas/Accessibility)
</td>
<td>
</td>
<td>
TBD
</td> </tr>
<tr>
<td>
**accessibilityControl**
</td>
<td>
CreativeWork
</td>
<td>
</td>
<td>
TBD
</td> </tr>
<tr>
<td>
**accessibilityFeature**
</td>
<td>
CreativeWork
</td>
<td>
</td>
<td>
TBD
</td> </tr>
<tr>
<td>
**accessibilityHazard**
</td>
<td>
CreativeWork
</td>
<td>
</td>
<td>
TBD
</td> </tr>
<tr>
<td>
**hasPart**
</td>
<td>
CreativeWork
</td>
<td>
_UMIScenarioFile_
</td>
<td>
</td>
<td>
For the UMI Scenario File.
</td> </tr>
<tr>
<td>
**hasPart**
</td>
<td>
CreativeWork
</td>
<td>
_EducationalActivity_
</td>
<td>
</td>
<td>
For the UMI Activities, there can be more than one Activities.
</td> </tr>
<tr>
<td>
**UMI Scenario File**
</td> </tr>
<tr>
<td>
**name**
</td>
<td>
CreativeWork > DigitalDocument
</td>
<td>
Text
</td>
<td>
</td>
<td>
The name of the file
</td> </tr>
<tr>
<td>
**Metadata property**
</td>
<td>
**Metadata typeof**
</td>
<td>
**Value space**
</td>
<td>
**Platform field**
</td>
<td>
**Comments**
</td> </tr>
<tr>
<td>
**fileformat**
</td>
<td>
CreativeWork > DigitalDocument
</td>
<td>
Text (MIME format - see IANA site)
</td>
<td>
</td>
<td>
The type of the file
</td> </tr>
<tr>
<td>
**identifier**
</td>
<td>
CreativeWork > DigitalDocument
</td>
<td>
URI
</td>
<td>
</td>
<td>
</td> </tr>
<tr>
<td>
**author**
</td>
<td>
Person
</td>
<td>
Registered User
</td>
<td>
</td>
<td>
Inherited from UMIScenario
</td> </tr>
<tr>
<td>
**license**
</td>
<td>
CreativeWork
</td>
<td>
URL
</td>
<td>
</td>
<td>
Inherited from UMIScenario
</td> </tr>
<tr>
<td>
**datePublished**
</td>
<td>
CreativeWork > DigitalDocument
</td>
<td>
Date
</td>
<td>
</td>
<td>
</td> </tr>
<tr>
<td>
**isPartOf**
</td>
<td>
CreativeWork > DigitalDocument
</td>
<td>
UMIScenario
</td>
<td>
</td>
<td>
</td> </tr> </table>
# Table 4 Metadata defined for the _UMI Scenario_ content type.
<table>
<tr>
<th>
**Metadata property**
</th>
<th>
**Metadata typeof**
</th>
<th>
**Value space**
</th>
<th>
**Platform field**
</th>
<th>
**Comments**
</th> </tr>
<tr>
<td>
**name**
</td>
<td>
CreativeWork
</td>
<td>
Text
</td>
<td>
Title
</td>
<td>
</td> </tr>
<tr>
<td>
**description**
</td>
<td>
CreativeWork
</td>
<td>
Text
</td>
<td>
Description and Steps
</td>
<td>
</td> </tr>
<tr>
<td>
**interactivityType**
</td>
<td>
CreativeWork
</td>
<td>
"active", "expositive" or
"mixed"
</td>
<td>
Interactivity Type
</td>
<td>
</td> </tr>
<tr>
<td>
**targetName, alignmentType = "activityType"**
</td>
<td>
Ιntangible >
AlignmentObject
</td>
<td>
"Introductory", "Core"
"Concluding" or "Auxiliary"
</td>
<td>
Type of Activity
</td>
<td>
alignmentFramework = "Horizon2020 UMI-Sci-Ed"
</td> </tr>
<tr>
<td>
**targetName, alignmentType = "learningObjectives"**
</td>
<td>
AlignmentObject
</td>
<td>
Text
</td>
<td>
Learning Objectives
</td>
<td>
alignmentFramework = "Revised Bloom Taxonomy"
</td> </tr>
<tr>
<td>
**timeRequired**
</td>
<td>
CreativeWork
</td>
<td>
ISO 8601
</td>
<td>
Expected duration
</td>
<td>
</td> </tr>
<tr>
<td>
**educationalRole**
</td>
<td>
Ιntangible > Audience > EducationalAudience
</td>
<td>
"teacher", "learner",
"author", "manager"
</td>
<td>
Audience
</td>
<td>
</td> </tr>
<tr>
<td>
**dateCreated**
</td>
<td>
CreativeWork
</td>
<td>
Date
</td>
<td>
</td>
<td>
</td> </tr>
<tr>
<td>
**dateModified**
</td>
<td>
CreativeWork
</td>
<td>
Date
</td>
<td>
</td>
<td>
</td> </tr>
<tr>
<td>
**author**
</td>
<td>
Person
</td>
<td>
Registered User
</td>
<td>
</td>
<td>
Inherited from _UMIScenario_
</td> </tr>
<tr>
<td>
**version**
</td>
<td>
CreativeWork
</td>
<td>
Text
</td>
<td>
</td>
<td>
Inherited from _UMIScenario_
</td> </tr>
<tr>
<td>
**inLanguage**
</td>
<td>
CreativeWork
</td>
<td>
Text, as defined in IETF BCP47
</td>
<td>
</td>
<td>
Inherited from _UMIScenario_
</td> </tr>
<tr>
<td>
**provider:name**
</td>
<td>
Organization:Text
</td>
<td>
"Horizon2020 UMI-Sci-Ed"
</td>
<td>
</td>
<td>
Inherited from _UMIScenario_
</td> </tr>
<tr>
<td>
**license**
</td>
<td>
CreativeWork
</td>
<td>
URL
</td>
<td>
</td>
<td>
Inherited from _UMIScenario_
</td> </tr>
<tr>
<td>
**accessibilityAPI**
</td>
<td>
CreativeWork
</td>
<td>
Text (as defined in https://www.w3.org/wiki/W ebSchemas/Accessibility)
</td>
<td>
</td>
<td>
Inherited from _UMIScenario_
</td> </tr>
<tr>
<td>
**accessibilityControl**
</td>
<td>
CreativeWork
</td>
<td>
</td>
<td>
Inherited from _UMIScenario_
</td> </tr>
<tr>
<td>
**accessibilityFeature**
</td>
<td>
CreativeWork
</td>
<td>
</td>
<td>
Inherited from _UMIScenario_
</td> </tr>
<tr>
<td>
**accessibilityHazard**
</td>
<td>
CreativeWork
</td>
<td>
</td>
<td>
Inherited from _UMIScenario_
</td> </tr>
<tr>
<td>
**identifier**
</td>
<td>
CreativeWork
</td>
<td>
URI
</td>
<td>
</td>
<td>
</td> </tr>
<tr>
<td>
**genre**
</td>
<td>
CreativeWork
</td>
<td>
"Educational Activity"
</td>
<td>
</td>
<td>
Fixed value
</td> </tr>
<tr>
<td>
**disambiguatingDescription**
</td>
<td>
CreativeWork
</td>
<td>
"UMI-Sci-Ed Educational
Activity"
</td>
<td>
</td>
<td>
Fixed value
</td> </tr>
<tr>
<td>
**educationalUse**
</td>
<td>
CreativeWork
</td>
<td>
"Educational Scenario implementation"
</td>
<td>
</td>
<td>
Fixed value
</td> </tr>
<tr>
<td>
**typicalAgeRange**
</td>
<td>
CreativeWork
</td>
<td>
"14-16"
</td>
<td>
</td>
<td>
Fixed value
</td> </tr>
<tr>
<td>
**isPartOf**
</td>
<td>
CreativeWork
</td>
<td>
UMIScenario
</td>
<td>
</td>
<td>
</td> </tr>
<tr>
<td>
**learningResourceType**
</td>
<td>
CreativeWork
</td>
<td>
"learning activity"
</td>
<td>
</td>
<td>
Fixed value
</td> </tr>
<tr>
<td>
**hasPart**
</td>
<td>
CreativeWork
</td>
<td>
_UMIActivityFile_
</td>
<td>
</td>
<td>
For the UMI Activity File
</td> </tr>
<tr>
<td>
**hasPart**
</td>
<td>
CreativeWork
</td>
<td>
_UMIEducationalMaterial_
</td>
<td>
</td>
<td>
For the Educational Material
</td> </tr>
<tr>
<td>
**UMI Activity File**
</td> </tr>
<tr>
<td>
**dateCreated**
</td>
<td>
CreativeWork
</td>
<td>
Date
</td>
<td>
</td>
<td>
</td> </tr>
<tr>
<td>
**dateModified**
</td>
<td>
CreativeWork
</td>
<td>
Date
</td>
<td>
</td>
<td>
</td> </tr>
<tr>
<td>
**name**
</td>
<td>
CreativeWork > DigitalDocument
</td>
<td>
Text
</td>
<td>
</td>
<td>
Filename
</td> </tr>
<tr>
<td>
**fileformat**
</td>
<td>
CreativeWork > DigitalDocument
</td>
<td>
Text (MIME format - see IANA site)
</td>
<td>
</td>
<td>
</td> </tr>
<tr>
<td>
**identifier**
</td>
<td>
CreativeWork > DigitalDocument
</td>
<td>
URI
</td>
<td>
</td>
<td>
</td> </tr>
<tr>
<td>
**license**
</td>
<td>
CreativeWork
</td>
<td>
URL
</td>
<td>
</td>
<td>
Inherited from _UMIScenario_
</td> </tr>
<tr>
<td>
**isPartOf**
</td>
<td>
CreativeWork > DigitalDocument
</td>
<td>
UMIScenario
</td>
<td>
</td>
<td>
</td> </tr> </table>
# Table 5 Metadata defined for an activity that is part of a _UMI Scenario_
content type.
<table>
<tr>
<th>
**Metadata property**
</th>
<th>
**Metadata typeof**
</th>
<th>
**Value space**
</th>
<th>
**Platform field**
</th>
<th>
**Comments**
</th> </tr>
<tr>
<td>
**name**
</td>
<td>
CreativeWork
</td>
<td>
Text
</td>
<td>
Title
</td>
<td>
</td> </tr>
<tr>
<td>
**version**
</td>
<td>
CreativeWork
</td>
<td>
Text
</td>
<td>
Version
</td>
<td>
</td> </tr>
<tr>
<td>
**isBasedOn**
</td>
<td>
CreativeWork
</td>
<td>
_UMIScenario_
</td>
<td>
UMI Scenario
</td>
<td>
</td> </tr>
<tr>
<td>
**inLanguage**
</td>
<td>
CreativeWork
</td>
<td>
Text, as defined in IETF BCP47
</td>
<td>
Language
</td>
<td>
</td> </tr>
<tr>
<td>
**description**
</td>
<td>
CreativeWork
</td>
<td>
Text
</td>
<td>
Description
</td>
<td>
</td> </tr>
<tr>
<td>
**keywords**
</td>
<td>
CreativeWork
</td>
<td>
Text
</td>
<td>
Key Terms
</td>
<td>
</td> </tr>
<tr>
<td>
**timeRequired**
</td>
<td>
CreativeWork
</td>
<td>
ISO 8601
</td>
<td>
Duration
</td>
<td>
Months, days, hours, minutes
</td> </tr>
<tr>
<td>
**interactivityType = "mixed"**
</td>
<td>
CreativeWork
</td>
<td>
"active", "expositive" or
"mixed"
</td>
<td>
Interactivity Type
</td>
<td>
</td> </tr>
<tr>
<td>
**targetName,**
**alignmentType =**
**"difficulty"**
</td>
<td>
AlignmentObject
</td>
<td>
"Easy", "Intermediate" or
"Advanced"
</td>
<td>
Difficulty
</td>
<td>
alignmentFramework = "IEEE LOM
5.8"
</td> </tr>
<tr>
<td>
**targetName,**
**alignmentType = "requires"**
</td>
<td>
Ιntangible >
AlignmentObject
</td>
<td>
Text
</td>
<td>
Resources - Hardware
</td>
<td>
alignmentFramework = "UMI-Sci-Ed hardware resources"
</td> </tr>
<tr>
<td>
**targetName,**
**alignmentType = "requires"**
</td>
<td>
Ιntangible >
AlignmentObject
</td>
<td>
Text
</td>
<td>
Resources - Software
</td>
<td>
alignmentFramework = "UMI-Sci-Ed software resources"
</td> </tr>
<tr>
<td>
**targetName,**
**alignmentType = "requires"**
</td>
<td>
Ιntangible >
AlignmentObject
</td>
<td>
Text
</td>
<td>
Resources - Software
</td>
<td>
alignmentFramework = "UMI-Sci-Ed other resources"
</td> </tr>
<tr>
<td>
**targetName, alignmentType = "learningOutcomes"**
</td>
<td>
Ιntangible >
AlignmentObject
</td>
<td>
Text
</td>
<td>
Expected Learning Outcomes
</td>
<td>
alignmentFramework = "Revised Bloom Taxonomy"
</td> </tr>
<tr>
<td>
**license**
</td>
<td>
CreativeWork
</td>
<td>
URL
</td>
<td>
License
</td>
<td>
Creative Commons URL
</td> </tr>
<tr>
<td>
**educationalRole**
</td>
<td>
Ιntangible > Audience > EducationalAudience
</td>
<td>
"learner"
</td>
<td>
</td>
<td>
Fixed value
</td> </tr>
<tr>
<td>
**identifier**
</td>
<td>
CreativeWork
</td>
<td>
URI
</td>
<td>
</td>
<td>
</td> </tr>
<tr>
<td>
**author**
</td>
<td>
Person
</td>
<td>
Registered User
</td>
<td>
</td>
<td>
</td> </tr>
<tr>
<td>
**dateCreated**
</td>
<td>
CreativeWork
</td>
<td>
Date
</td>
<td>
</td>
<td>
</td> </tr>
<tr>
<td>
**dateModified**
</td>
<td>
CreativeWork
</td>
<td>
Date
</td>
<td>
</td>
<td>
</td> </tr>
<tr>
<td>
**genre**
</td>
<td>
CreativeWork
</td>
<td>
"UMI Project"
</td>
<td>
</td>
<td>
Fixed value
</td> </tr>
<tr>
<td>
**disambiguatingDescription**
</td>
<td>
CreativeWork
</td>
<td>
"UMI-Sci-Ed Project"
</td>
<td>
</td>
<td>
Fixed value
</td> </tr>
<tr>
<td>
**provider:name**
</td>
<td>
Organization:Text
</td>
<td>
"Horizon2020 UMI-Sci-Ed"
</td>
<td>
</td>
<td>
Fixed value
</td> </tr>
<tr>
<td>
**educationalUse**
</td>
<td>
CreativeWork
</td>
<td>
"Educational Scenario implementation"
</td>
<td>
</td>
<td>
Fixed value
</td> </tr>
<tr>
<td>
**typicalAgeRange**
</td>
<td>
CreativeWork
</td>
<td>
"14-16"
</td>
<td>
</td>
<td>
Fixed value
</td> </tr>
<tr>
<td>
**accessibilityAPI**
</td>
<td>
CreativeWork
</td>
<td>
Text (as defined in https://www.w3.org/wiki/W ebSchemas/Accessibility)
</td>
<td>
</td>
<td>
TBD
</td> </tr>
<tr>
<td>
**accessibilityControl**
</td>
<td>
CreativeWork
</td>
<td>
</td>
<td>
TBD
</td> </tr>
<tr>
<td>
**accessibilityFeature**
</td>
<td>
CreativeWork
</td>
<td>
</td>
<td>
TBD
</td> </tr>
<tr>
<td>
**accessibilityHazard**
</td>
<td>
CreativeWork
</td>
<td>
</td>
<td>
TBD
</td> </tr>
<tr>
<td>
**hasPart**
</td>
<td>
CreativeWork
</td>
<td>
_UMIProjectFile_
</td>
<td>
</td>
<td>
For the UMI Project File.
</td> </tr>
<tr>
<td>
**hasPart**
</td>
<td>
CreativeWork
</td>
<td>
_FileAttachment_ ,
_YouTubeVideo_ , or
_Image/Photo_
</td>
<td>
</td>
<td>
Files and Media
</td> </tr>
<tr>
<td>
**UMI Project file**
</td> </tr>
<tr>
<td>
**name**
</td>
<td>
CreativeWork > DigitalDocument
</td>
<td>
Text
</td>
<td>
</td>
<td>
The name of the file
</td> </tr>
<tr>
<td>
**fileformat**
</td>
<td>
CreativeWork > DigitalDocument
</td>
<td>
Text (MIME format - see IANA site)
</td>
<td>
</td>
<td>
The type of the file
</td> </tr>
<tr>
<td>
**identifier**
</td>
<td>
CreativeWork > DigitalDocument
</td>
<td>
URI
</td>
<td>
</td>
<td>
</td> </tr>
<tr>
<td>
**author**
</td>
<td>
Person
</td>
<td>
Registered User
</td>
<td>
</td>
<td>
</td> </tr>
<tr>
<td>
**license**
</td>
<td>
CreativeWork
</td>
<td>
URL
</td>
<td>
</td>
<td>
Inherited from _UMIProject_
</td> </tr>
<tr>
<td>
**datePublished**
</td>
<td>
CreativeWork > DigitalDocument
</td>
<td>
Date
</td>
<td>
</td>
<td>
</td> </tr>
<tr>
<td>
**isPartOf**
</td>
<td>
CreativeWork > DigitalDocument
</td>
<td>
_UMIProject_
</td>
<td>
</td>
<td>
</td> </tr> </table>
# Table 6 Metadata defined for the _UMI Project_ content type.
<table>
<tr>
<th>
**Metadata property**
</th>
<th>
**Metadata typeof**
</th>
<th>
**Value space**
</th>
<th>
**Platform field**
</th>
<th>
**Comments**
</th> </tr>
<tr>
<td>
**name**
</td>
<td>
CreativeWork
</td>
<td>
Text
</td>
<td>
Title
</td>
<td>
</td> </tr>
<tr>
<td>
**keywords**
</td>
<td>
CreativeWork
</td>
<td>
Text
</td>
<td>
Tags
</td>
<td>
</td> </tr>
<tr>
<td>
**description**
</td>
<td>
CreativeWork
</td>
<td>
Text
</td>
<td>
Summary
</td>
<td>
</td> </tr>
<tr>
<td>
**articleBody**
</td>
<td>
CreativeWork > Article
>
</td>
<td>
Text
</td>
<td>
Body
</td>
<td>
</td> </tr>
<tr>
<td>
**audienceType**
</td>
<td>
Intangible > Audience
</td>
<td>
"public" or _UMIGroup_
</td>
<td>
Group content visibility, Groups audience
</td>
<td>
</td> </tr>
<tr>
<td>
**identifier**
</td>
<td>
CreativeWork
</td>
<td>
URI
</td>
<td>
</td>
<td>
</td> </tr>
<tr>
<td>
**author**
</td>
<td>
Person
</td>
<td>
Registered User
</td>
<td>
</td>
<td>
</td> </tr>
<tr>
<td>
**dateCreated**
</td>
<td>
CreativeWork
</td>
<td>
Date
</td>
<td>
</td>
<td>
</td> </tr>
<tr>
<td>
**dateModified**
</td>
<td>
CreativeWork
</td>
<td>
Date
</td>
<td>
</td>
<td>
</td> </tr>
<tr>
<td>
**genre**
</td>
<td>
CreativeWork
</td>
<td>
"UMI Group Article"
</td>
<td>
</td>
<td>
Fixed value
</td> </tr>
<tr>
<td>
**disambiguatingDescription**
</td>
<td>
CreativeWork
</td>
<td>
"UMI-Sci-Ed Group Article"
</td>
<td>
</td>
<td>
Fixed value
</td> </tr>
<tr>
<td>
**provider:name**
</td>
<td>
Organization:Text
</td>
<td>
"Horizon2020 UMI-Sci-Ed"
</td>
<td>
</td>
<td>
Fixed value
</td> </tr>
<tr>
<td>
**interactivityType**
</td>
<td>
CreativeWork
</td>
<td>
"expositive"
</td>
<td>
</td>
<td>
Fixed value
</td> </tr>
<tr>
<td>
**ResourceType**
</td>
<td>
</td>
<td>
"Article"
</td>
<td>
</td>
<td>
Fixed value
</td> </tr>
<tr>
<td>
**license**
</td>
<td>
CreativeWork
</td>
<td>
URL
</td>
<td>
</td>
<td>
TBD
</td> </tr>
<tr>
<td>
**accessibilityAPI**
</td>
<td>
CreativeWork
</td>
<td>
Text
</td>
<td>
</td>
<td>
TBD
</td> </tr>
<tr>
<td>
**accessibilityControl**
</td>
<td>
CreativeWork
</td>
<td>
Text
</td>
<td>
</td>
<td>
TBD
</td> </tr>
<tr>
<td>
**accessibilityFeature**
</td>
<td>
CreativeWork
</td>
<td>
Text
</td>
<td>
</td>
<td>
TBD
</td> </tr>
<tr>
<td>
**accessibilityHazard**
</td>
<td>
CreativeWork
</td>
<td>
Text
</td>
<td>
</td>
<td>
TBD
</td> </tr>
<tr>
<td>
**hasPart**
</td>
<td>
CreativeWork
</td>
<td>
_FileAttachment_ ,
_YouTubeVideo_ , or
_Image/Photo_
</td>
<td>
</td>
<td>
Files and Media
</td> </tr> </table>
# Table 7 Metadata defined for the _Group Article_ content type.
<table>
<tr>
<th>
**Metadata property**
</th>
<th>
**Metadata typeof**
</th>
<th>
**Value space**
</th>
<th>
**Platform field**
</th>
<th>
**Comments**
</th> </tr>
<tr>
<td>
**name**
</td>
<td>
CreativeWork
</td>
<td>
Text
</td>
<td>
Title
</td>
<td>
</td> </tr>
<tr>
<td>
**description**
</td>
<td>
CreativeWork
</td>
<td>
Text
</td>
<td>
Subject
</td>
<td>
</td> </tr>
<tr>
<td>
**articleBody**
</td>
<td>
CreativeWork > Article
</td>
<td>
Text
</td>
<td>
Body
</td>
<td>
</td> </tr>
<tr>
<td>
**keywords**
</td>
<td>
CreativeWork
</td>
<td>
Text
</td>
<td>
Tags of Wiki
</td>
<td>
</td> </tr>
<tr>
<td>
**version**
</td>
<td>
CreativeWork
</td>
<td>
Text
</td>
<td>
Version
</td>
<td>
</td> </tr>
<tr>
<td>
**text**
</td>
<td>
CreativeWork > Comment
</td>
<td>
</td>
<td>
Revision log message
</td>
<td>
</td> </tr>
<tr>
<td>
**audienceType**
</td>
<td>
Intangible > Audience
</td>
<td>
"public" or _UMIGroup_
</td>
<td>
Group content
visibility, Groups audience
</td>
<td>
</td> </tr>
<tr>
<td>
**license**
</td>
<td>
CreativeWork
</td>
<td>
https://creativecommons.or g/licenses/by/4.0/
</td>
<td>
</td>
<td>
Fixed value
</td> </tr>
<tr>
<td>
**identifier**
</td>
<td>
</td>
<td>
URI
</td>
<td>
</td>
<td>
</td> </tr>
<tr>
<td>
**author**
</td>
<td>
Person
</td>
<td>
Registered User
</td>
<td>
</td>
<td>
</td> </tr>
<tr>
<td>
**contributor**
</td>
<td>
Person
</td>
<td>
Registered User
</td>
<td>
</td>
<td>
</td> </tr>
<tr>
<td>
**dateCreated**
</td>
<td>
CreativeWork
</td>
<td>
Date
</td>
<td>
</td>
<td>
</td> </tr>
<tr>
<td>
**dateModified**
</td>
<td>
CreativeWork
</td>
<td>
Date
</td>
<td>
</td>
<td>
</td> </tr>
<tr>
<td>
**genre**
</td>
<td>
CreativeWork
</td>
<td>
"UMI Wiki"
</td>
<td>
</td>
<td>
Fixed value
</td> </tr>
<tr>
<td>
**disambiguatingDescription**
</td>
<td>
CreativeWork
</td>
<td>
"UMI-Sci-Ed Wiki"
</td>
<td>
</td>
<td>
Fixed value
</td> </tr>
<tr>
<td>
**provider:name**
</td>
<td>
Organization:Text
</td>
<td>
"Horizon2020 UMI-Sci-Ed"
</td>
<td>
</td>
<td>
Fixed value
</td> </tr>
<tr>
<td>
**interactivityType**
</td>
<td>
CreativeWork
</td>
<td>
"expositive"
</td>
<td>
</td>
<td>
Fixed value
</td> </tr>
<tr>
<td>
**learningResourceType**
</td>
<td>
CreativeWork
</td>
<td>
"wiki page"
</td>
<td>
</td>
<td>
Fixed value
</td> </tr>
<tr>
<td>
**typicalAgeRange**
</td>
<td>
CreativeWork
</td>
<td>
"14-"
</td>
<td>
</td>
<td>
Fixed value.
</td> </tr>
<tr>
<td>
**accessibilityAPI**
</td>
<td>
CreativeWork
</td>
<td>
Text
</td>
<td>
</td>
<td>
TBD
</td> </tr>
<tr>
<td>
**accessibilityControl**
</td>
<td>
CreativeWork
</td>
<td>
Text
</td>
<td>
</td>
<td>
TBD
</td> </tr>
<tr>
<td>
**accessibilityFeature**
</td>
<td>
CreativeWork
</td>
<td>
Text
</td>
<td>
</td>
<td>
TBD
</td> </tr>
<tr>
<td>
**accessibilityHazard**
</td>
<td>
CreativeWork
</td>
<td>
Text
</td>
<td>
</td>
<td>
TBD
</td> </tr> </table>
# Table 8 Metadata defined for the _Wiki_ content type.
<table>
<tr>
<th>
**Metadata property**
</th>
<th>
**Metadata typeof**
</th>
<th>
**Value space**
</th>
<th>
**Platform field**
</th>
<th>
**Comments**
</th> </tr>
<tr>
<td>
**name**
</td>
<td>
CreativeWork
</td>
<td>
Text
</td>
<td>
Title
</td>
<td>
</td> </tr>
<tr>
<td>
**keywords**
</td>
<td>
CreativeWork
</td>
<td>
Text
</td>
<td>
Tags
</td>
<td>
</td> </tr>
<tr>
<td>
**description**
</td>
<td>
CreativeWork
</td>
<td>
Text
</td>
<td>
Summary
</td>
<td>
</td> </tr>
<tr>
<td>
**sharedContent**
</td>
<td>
CreativeWork > Article
> SocialMediaPosting >
BlogPosting - typeOf
CreativeWork >
Article: articleBody
</td>
<td>
Text
</td>
<td>
Body
</td>
<td>
</td> </tr>
<tr>
<td>
**audienceType**
</td>
<td>
Intangible > Audience
</td>
<td>
"public" or _UMIGroup_
</td>
<td>
Group content visibility, Groups audience
</td>
<td>
</td> </tr>
<tr>
<td>
**identifier**
</td>
<td>
CreativeWork
</td>
<td>
URI
</td>
<td>
</td>
<td>
</td> </tr>
<tr>
<td>
**author**
</td>
<td>
Person
</td>
<td>
Registered User
</td>
<td>
</td>
<td>
</td> </tr>
<tr>
<td>
**dateCreated**
</td>
<td>
CreativeWork
</td>
<td>
Date
</td>
<td>
</td>
<td>
</td> </tr>
<tr>
<td>
**dateModified**
</td>
<td>
CreativeWork
</td>
<td>
Date
</td>
<td>
</td>
<td>
</td> </tr>
<tr>
<td>
**sharedContent**
</td>
<td>
CreativeWork
</td>
<td>
_FileAttachment_ ,
_YouTubeVideo_ , or
_Image/Photo_
</td>
<td>
</td>
<td>
Files and Media
</td> </tr>
<tr>
<td>
**license**
</td>
<td>
CreativeWork
</td>
<td>
URL
</td>
<td>
</td>
<td>
TBD
</td> </tr>
<tr>
<td>
**accessibilityAPI**
</td>
<td>
CreativeWork
</td>
<td>
Text
</td>
<td>
</td>
<td>
TBD
</td> </tr>
<tr>
<td>
**accessibilityControl**
</td>
<td>
CreativeWork
</td>
<td>
Text
</td>
<td>
</td>
<td>
TBD
</td> </tr>
<tr>
<td>
**accessibilityFeature**
</td>
<td>
CreativeWork
</td>
<td>
Text
</td>
<td>
</td>
<td>
TBD
</td> </tr>
<tr>
<td>
**accessibilityHazard**
</td>
<td>
CreativeWork
</td>
<td>
Text
</td>
<td>
</td>
<td>
TBD
</td> </tr>
<tr>
<td>
**genre**
</td>
<td>
CreativeWork
</td>
<td>
"UMI Blog"
</td>
<td>
</td>
<td>
Fixed value
</td> </tr>
<tr>
<td>
**disambiguatingDescription**
</td>
<td>
CreativeWork
</td>
<td>
"UMI-Sci-Ed Blog"
</td>
<td>
</td>
<td>
Fixed value
</td> </tr>
<tr>
<td>
**provider:name**
</td>
<td>
Organization:Text
</td>
<td>
"Horizon2020 UMI-Sci-Ed"
</td>
<td>
</td>
<td>
Fixed value
</td> </tr>
<tr>
<td>
**Blog post (Comment)**
</td> </tr>
<tr>
<td>
**about**
</td>
<td>
CreativeWork
</td>
<td>
Text
</td>
<td>
Subject
</td>
<td>
</td> </tr>
<tr>
<td>
**text**
</td>
<td>
CreativeWork > Comment
</td>
<td>
Text
</td>
<td>
Comment
</td>
<td>
</td> </tr>
<tr>
<td>
**author**
</td>
<td>
Person
</td>
<td>
Registered User
</td>
<td>
</td>
<td>
</td> </tr>
<tr>
<td>
**dateCreated**
</td>
<td>
CreativeWork
</td>
<td>
Date
</td>
<td>
</td>
<td>
</td> </tr>
<tr>
<td>
**dateModified**
</td>
<td>
CreativeWork
</td>
<td>
any date
</td>
<td>
</td>
<td>
</td> </tr>
<tr>
<td>
**parentItem**
</td>
<td>
CreativeWork > Comment
</td>
<td>
_UMIBlog_
</td>
<td>
</td>
<td>
</td> </tr>
<tr>
<td>
**identifier**
</td>
<td>
CreativeWork
</td>
<td>
URI
</td>
<td>
</td>
<td>
</td> </tr>
<tr>
<td>
**sharedContent**
</td>
<td>
CreativeWork
</td>
<td>
_FileAttachment_ ,
_YouTubeVideo_ , or
_Image/Photo_
</td>
<td>
</td>
<td>
</td> </tr>
<tr>
<td>
**author**
</td>
<td>
Person
(RegisteredUser)
</td>
<td>
Registered User
</td>
<td>
</td>
<td>
</td> </tr>
<tr>
<td>
**dateCreated**
</td>
<td>
CreativeWork
</td>
<td>
Date
</td>
<td>
</td>
<td>
</td> </tr>
<tr>
<td>
**license**
</td>
<td>
CreativeWork
</td>
<td>
URL
</td>
<td>
</td>
<td>
TBD
</td> </tr>
<tr>
<td>
**accessibilityAPI**
</td>
<td>
CreativeWork
</td>
<td>
Text
</td>
<td>
</td>
<td>
TBD
</td> </tr>
<tr>
<td>
**accessibilityControl**
</td>
<td>
CreativeWork
</td>
<td>
Text
</td>
<td>
</td>
<td>
TBD
</td> </tr>
<tr>
<td>
**accessibilityFeature**
</td>
<td>
CreativeWork
</td>
<td>
Text
</td>
<td>
</td>
<td>
TBD
</td> </tr>
<tr>
<td>
**accessibilityHazard**
</td>
<td>
CreativeWork
</td>
<td>
Text
</td>
<td>
</td>
<td>
TBD
</td> </tr>
<tr>
<td>
**genre**
</td>
<td>
CreativeWork
</td>
<td>
"UMI Blog Comment"
</td>
<td>
</td>
<td>
Fixed value
</td> </tr>
<tr>
<td>
**disambiguatingDescription**
</td>
<td>
CreativeWork
</td>
<td>
"UMI-Sci-Ed Blog Comment"
</td>
<td>
</td>
<td>
Fixed value
</td> </tr>
<tr>
<td>
**provider:name**
</td>
<td>
Organization:Text
</td>
<td>
"Horizon2020 UMI-Sci-Ed"
</td>
<td>
</td>
<td>
Fixed value
</td> </tr> </table>
# Table 9 Metadata defined for the _Blog_ content type.
<table>
<tr>
<th>
**Metadata property**
</th>
<th>
**Metadata typeof**
</th>
<th>
**Value space**
</th>
<th>
**Platform field**
</th>
<th>
**Comments**
</th> </tr>
<tr>
<td>
**name**
</td>
<td>
CreativeWork
</td>
<td>
Text
</td>
<td>
Title
</td>
<td>
</td> </tr>
<tr>
<td>
**description**
</td>
<td>
CreativeWork
</td>
<td>
Text
</td>
<td>
Subject
</td>
<td>
</td> </tr>
<tr>
<td>
**genre**
</td>
<td>
CreativeWork
</td>
<td>
"UMI General Discussion
Forum" or "UMI Platform
Support Forum"
</td>
<td>
Forums
</td>
<td>
Depending on user selection
</td> </tr>
<tr>
<td>
**sharedContent**
</td>
<td>
CreativeWork > Article
> SocialMediaPosting > DiscussionForumPosti ng - typeof
CreativeWork >
Article: articleBody
</td>
<td>
Text
</td>
<td>
Body
</td>
<td>
</td> </tr>
<tr>
<td>
**identifier**
</td>
<td>
CreativeWork
</td>
<td>
URI
</td>
<td>
</td>
<td>
</td> </tr>
<tr>
<td>
**author**
</td>
<td>
Person
</td>
<td>
Registered User
</td>
<td>
</td>
<td>
</td> </tr>
<tr>
<td>
**dateCreated**
</td>
<td>
CreativeWork
</td>
<td>
Date
</td>
<td>
</td>
<td>
</td> </tr>
<tr>
<td>
**dateModified**
</td>
<td>
CreativeWork
</td>
<td>
Date
</td>
<td>
</td>
<td>
</td> </tr>
<tr>
<td>
**sharedContent**
</td>
<td>
CreativeWork
</td>
<td>
_FileAttachment_ ,
_YouTubeVideo_ , or
_Image/Photo_
</td>
<td>
</td>
<td>
Files and Media
</td> </tr>
<tr>
<td>
**license**
</td>
<td>
CreativeWork
</td>
<td>
URL
</td>
<td>
</td>
<td>
TBD
</td> </tr>
<tr>
<td>
**accessibilityAPI**
</td>
<td>
CreativeWork
</td>
<td>
Text
</td>
<td>
</td>
<td>
TBD
</td> </tr>
<tr>
<td>
**accessibilityControl**
</td>
<td>
CreativeWork
</td>
<td>
Text
</td>
<td>
</td>
<td>
TBD
</td> </tr>
<tr>
<td>
**accessibilityFeature**
</td>
<td>
CreativeWork
</td>
<td>
Text
</td>
<td>
</td>
<td>
TBD
</td> </tr>
<tr>
<td>
**accessibilityHazard**
</td>
<td>
CreativeWork
</td>
<td>
Text
</td>
<td>
</td>
<td>
TBD
</td> </tr>
<tr>
<td>
**disambiguatingDescription**
</td>
<td>
CreativeWork
</td>
<td>
"UMI-Sci-Ed Forum"
</td>
<td>
</td>
<td>
Fixed value
</td> </tr>
<tr>
<td>
**provider:name**
</td>
<td>
Organization:Text
</td>
<td>
"Horizon2020 UMI-Sci-Ed"
</td>
<td>
</td>
<td>
Fixed value
</td> </tr>
<tr>
<td>
**Forum Post (Comment)**
</td> </tr>
<tr>
<td>
**about**
</td>
<td>
CreativeWork
</td>
<td>
Text
</td>
<td>
Subject
</td>
<td>
</td> </tr>
<tr>
<td>
**text**
</td>
<td>
CreativeWork > Comment
</td>
<td>
Text
</td>
<td>
Comment
</td>
<td>
</td> </tr>
<tr>
<td>
**author**
</td>
<td>
Person
</td>
<td>
Registered User
</td>
<td>
</td>
<td>
</td> </tr>
<tr>
<td>
**dateCreated**
</td>
<td>
CreativeWork
</td>
<td>
Date
</td>
<td>
</td>
<td>
</td> </tr>
<tr>
<td>
**dateModified**
</td>
<td>
CreativeWork
</td>
<td>
any date
</td>
<td>
</td>
<td>
To be shown in view mode
</td> </tr>
<tr>
<td>
**parentItem**
</td>
<td>
CreativeWork > Comment
</td>
<td>
_UMIForumTopic_
</td>
<td>
</td>
<td>
</td> </tr>
<tr>
<td>
**sharedContent**
</td>
<td>
CreativeWork
</td>
<td>
_FileAttachment_ ,
_YouTubeVideo_ , or
_Image/Photo_
</td>
<td>
</td>
<td>
Files and Media
</td> </tr>
<tr>
<td>
**identifier**
</td>
<td>
CreativeWork
</td>
<td>
URI
</td>
<td>
</td>
<td>
To be shown in view mode
</td> </tr>
<tr>
<td>
**license**
</td>
<td>
CreativeWork
</td>
<td>
URL
</td>
<td>
</td>
<td>
TBD
</td> </tr>
<tr>
<td>
**accessibilityAPI**
</td>
<td>
CreativeWork
</td>
<td>
Text
</td>
<td>
</td>
<td>
TBD
</td> </tr>
<tr>
<td>
**accessibilityControl**
</td>
<td>
CreativeWork
</td>
<td>
Text
</td>
<td>
</td>
<td>
TBD
</td> </tr>
<tr>
<td>
**accessibilityFeature**
</td>
<td>
CreativeWork
</td>
<td>
Text
</td>
<td>
</td>
<td>
TBD
</td> </tr>
<tr>
<td>
**accessibilityHazard**
</td>
<td>
CreativeWork
</td>
<td>
Text
</td>
<td>
</td>
<td>
TBD
</td> </tr>
<tr>
<td>
**genre**
</td>
<td>
CreativeWork
</td>
<td>
"UMI Forum Comment"
</td>
<td>
</td>
<td>
Fixed value
</td> </tr>
<tr>
<td>
**disambiguatingDescription**
</td>
<td>
CreativeWork
</td>
<td>
"UMI-Sci-Ed Forum
Comment"
</td>
<td>
</td>
<td>
Fixed value
</td> </tr>
<tr>
<td>
**provider:name**
</td>
<td>
Organization:Text
</td>
<td>
"Horizon2020 UMI-Sci-Ed"
</td>
<td>
</td>
<td>
Fixed value
</td> </tr> </table>
# Table 10 Metadata defined for the _Forum Topic_ content type.
<table>
<tr>
<th>
**Metadata property**
</th>
<th>
**Metadata typeof**
</th>
<th>
**Value space**
</th>
<th>
**Platform field**
</th>
<th>
**Comments**
</th> </tr>
<tr>
<td>
**name**
</td>
<td>
CreativeWork
</td>
<td>
Text
</td>
<td>
Title
</td>
<td>
</td> </tr>
<tr>
<td>
**keywords**
</td>
<td>
CreativeWork
</td>
<td>
Text
</td>
<td>
Key terms
</td>
<td>
</td> </tr>
<tr>
<td>
**author**
</td>
<td>
Person
</td>
<td>
Registered User
</td>
<td>
Authors
</td>
<td>
Multiple inputs are allowed
</td> </tr>
<tr>
<td>
**genre**
</td>
<td>
CreativeWork
</td>
<td>
"Educational Material",
“Learning Artefact”,
"Research Material",
"Other"
</td>
<td>
Type
</td>
<td>
Fixed value
</td> </tr>
<tr>
<td>
**description**
</td>
<td>
CreativeWork
</td>
<td>
Text
</td>
<td>
Subject
</td>
<td>
</td> </tr>
<tr>
<td>
**license**
</td>
<td>
CreativeWork
</td>
<td>
URL
</td>
<td>
License
</td>
<td>
Creative Commons URL
</td> </tr>
<tr>
<td>
**identifier**
</td>
<td>
</td>
<td>
URI
</td>
<td>
</td>
<td>
</td> </tr>
<tr>
<td>
**dateCreated**
</td>
<td>
CreativeWork
</td>
<td>
Date
</td>
<td>
</td>
<td>
</td> </tr>
<tr>
<td>
**dateModified**
</td>
<td>
CreativeWork
</td>
<td>
Date
</td>
<td>
</td>
<td>
</td> </tr>
<tr>
<td>
**disambiguatingDescription**
</td>
<td>
CreativeWork
</td>
<td>
"UMI-Sci-Ed Repository
Entry"
</td>
<td>
</td>
<td>
Fixed value
</td> </tr>
<tr>
<td>
**provider:name**
</td>
<td>
Organization:Text
</td>
<td>
"Horizon2020 UMI-Sci-Ed"
</td>
<td>
</td>
<td>
Fixed value
</td> </tr>
<tr>
<td>
**interactivityType**
</td>
<td>
CreativeWork
</td>
<td>
"expositive"
</td>
<td>
</td>
<td>
Fixed value
</td> </tr>
<tr>
<td>
**learningResourceType**
</td>
<td>
CreativeWork
</td>
<td>
"wiki page"
</td>
<td>
</td>
<td>
Fixed value
</td> </tr>
<tr>
<td>
**typicalAgeRange**
</td>
<td>
CreativeWork
</td>
<td>
"14-"
</td>
<td>
</td>
<td>
Fixed value.
</td> </tr>
<tr>
<td>
**accessibilityAPI**
</td>
<td>
CreativeWork
</td>
<td>
Text
</td>
<td>
TBD
</td>
<td>
</td> </tr>
<tr>
<td>
**accessibilityControl**
</td>
<td>
CreativeWork
</td>
<td>
Text
</td>
<td>
TBD
</td>
<td>
</td> </tr>
<tr>
<td>
**accessibilityFeature**
</td>
<td>
CreativeWork
</td>
<td>
Text
</td>
<td>
TBD
</td>
<td>
</td> </tr>
<tr>
<td>
**accessibilityHazard**
</td>
<td>
CreativeWork
</td>
<td>
Text
</td>
<td>
TBD
</td>
<td>
</td> </tr>
<tr>
<td>
**hasPart**
</td>
<td>
CreativeWork
</td>
<td>
_FileAttachment_ ,
_YouTubeVideo_ , or
_Image/Photo_
</td>
<td>
</td>
<td>
Files and Media
</td> </tr>
<tr>
<td>
**learningResourceType**
</td>
<td>
CreativeWork
</td>
<td>
Text
</td>
<td>
</td>
<td>
Inherited from Files and Media
</td> </tr>
<tr>
<td>
**artefactType**
</td>
<td>
CreativeWork
</td>
<td>
Text
</td>
<td>
</td>
<td>
Inherited from Files and Media
</td> </tr>
<tr>
<td>
**resourceType**
</td>
<td>
</td>
<td>
Text
</td>
<td>
</td>
<td>
Inherited from Files and Media
</td> </tr> </table>
# Table 11 Metadata defined for the _Repository Entry_ content type.
<table>
<tr>
<th>
**Metadata property**
</th>
<th>
**Metadata typeof**
</th>
<th>
**Value space**
</th>
<th>
**Platform field**
</th>
<th>
**Comments**
</th> </tr>
<tr>
<td>
**description**
</td>
<td>
CreativeWork
</td>
<td>
Text
</td>
<td>
Description
</td>
<td>
</td> </tr>
<tr>
<td>
**genre**
</td>
<td>
CreativeWork
</td>
<td>
"Educational Material",
"Artefact", "Research
Material", "Other"
</td>
<td>
Type of Attachment
</td>
<td>
</td> </tr>
<tr>
<td>
**learningResourceType**
</td>
<td>
CreativeWork
</td>
<td>
Text
</td>
<td>
File content characterization
</td>
<td>
Takes value if genre = "Educational Material"
</td> </tr>
<tr>
<td>
**artefactType**
</td>
<td>
CreativeWork
</td>
<td>
Text
</td>
<td>
Takes value if genre = "Artefact"
</td> </tr>
<tr>
<td>
**resourceType**
</td>
<td>
</td>
<td>
Text
</td>
<td>
Takes value if genre = "Research Material" or "Other"
</td> </tr>
<tr>
<td>
**dateCreated**
</td>
<td>
CreativeWork
</td>
<td>
Date
</td>
<td>
</td>
<td>
</td> </tr>
<tr>
<td>
**dateModified**
</td>
<td>
CreativeWork
</td>
<td>
Date
</td>
<td>
</td>
<td>
</td> </tr>
<tr>
<td>
**provider:name**
</td>
<td>
Organization:Text
</td>
<td>
"Horizon2020 UMI-Sci-Ed"
</td>
<td>
</td>
<td>
Fixed Value
</td> </tr>
<tr>
<td>
**license**
</td>
<td>
CreativeWork
</td>
<td>
URL
</td>
<td>
</td>
<td>
TBD
</td> </tr>
<tr>
<td>
**accessibilityAPI**
</td>
<td>
CreativeWork
</td>
<td>
Text
</td>
<td>
</td>
<td>
TBD
</td> </tr>
<tr>
<td>
**accessibilityControl**
</td>
<td>
CreativeWork
</td>
<td>
Text
</td>
<td>
</td>
<td>
TBD
</td> </tr>
<tr>
<td>
**accessibilityFeature**
</td>
<td>
CreativeWork
</td>
<td>
Text
</td>
<td>
</td>
<td>
TBD
</td> </tr>
<tr>
<td>
**accessibilityHazard**
</td>
<td>
CreativeWork
</td>
<td>
Text
</td>
<td>
</td>
<td>
TBD
</td> </tr>
<tr>
<td>
**identifier**
</td>
<td>
CreativeWork
</td>
<td>
URI
</td>
<td>
</td>
<td>
</td> </tr>
<tr>
<td>
**typicalAgeRange**
</td>
<td>
CreativeWork
</td>
<td>
"14-"
</td>
<td>
</td>
<td>
Fixed value
</td> </tr>
<tr>
<td>
**isPartOf**
</td>
<td>
CreativeWork
</td>
<td>
_UMIProject_ , _UMIBlog_ ,
_UMIBlogPost_ ,
_UMIForumTopic_ ,
_UMIGroupArticle_ ,
_UMIForumPost_ , _UMIWiki_ ,
_UMIRepositoryEntry_
</td>
<td>
</td>
<td>
</td> </tr>
<tr>
<td>
**hasPart**
</td>
<td>
CreativeWork
</td>
<td>
_AttachmentFile_
</td>
<td>
</td>
<td>
For the Attached File
</td> </tr>
<tr>
<td>
**Attachment File**
</td> </tr>
<tr>
<td>
**name**
</td>
<td>
CreativeWork > DigitalDocument
</td>
<td>
Text
</td>
<td>
</td>
<td>
</td> </tr>
<tr>
<td>
**fileformat**
</td>
<td>
CreativeWork
</td>
<td>
Text (MIME format - see IANA site)
</td>
<td>
</td>
<td>
</td> </tr>
<tr>
<td>
**identifier**
</td>
<td>
CreativeWork
</td>
<td>
URI
</td>
<td>
</td>
<td>
</td> </tr>
<tr>
<td>
**isPartOf**
</td>
<td>
CreativeWork > DigitalDocument
</td>
<td>
_UMIProject_ , _UMIBlog_ ,
_UMIBlogPost_ ,
_UMIForumTopic_ ,
_UMIGroupArticle_ ,
_UMIForumPost_ , _UMIWiki_ ,
_UMIRepositoryEntry_
</td>
<td>
</td>
<td>
</td> </tr> </table>
**Table 12 Metadata defined for _Files and Media_ supporting several content
types. NOTE: These metadata are for a file attachment. YouTube videos do not
have an Attachment file and include a “contentUrl” property instead. Image or
photo attachments on the other hand include a “caption” property in place of
the “description” property. **
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Horizon 2020
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1141_OpenUP_710722.md
|
# Summary
This document provides the description of what data OpenUP project will
generate, how it will be stored and managed and how it will be preserved after
the end of the project. PPMI prepared this document as the main partner
leading task 1.4 with inputs from other consortium partners and with a review
from UoA. This Data Management Plan complies with H2020 requirements 1 and
is based on the DMP template 2 provided by the e-Infrastructures Austria
project. The plan will be updated whenever changes to the project are made due
to inclusion of new data sets, changes in consortium policies or other
external factors.
# Explanatory Remarks
For creating a common ground of understanding, in the current deliverable we
use the following definition of dataset:
A dataset is any set of data (no matter how many files it materialises) that
is worth to be considered as a unit for data management activities 3
Any of the following can be considered as a possible dataset in the context of
a project:
* Any dataset produced by aggregating data from data providers for analysing it;
* Any dataset produced by aggregating data from data providers for building an integrated dataset out of the aggregated data (e.g. this is the case of Knowledge Bases);
* The material of a training course;
* A dataset documenting and providing evidence for either a report or a publication produced in the context of project activities.
In the case of OpenUP we have identified three main categories of datasets:
Core Datasets, i.e., datasets related to the main project activities (review-
assess-disseminate) and worth to be used by the project. These datasets pre-
exist OpenUP and are publicly available;
Produced Datasets, datasets resulting from the operation and evaluation of
OpenUP’s use cases and pilot applications. These may include but are not
limited to data collected through questionnaires, workshops, interviews, and
desktop research.
Project Related Data, i.e., datasets resulting from the operation of the
OpenUP project and produced by the OpenUP consortium. These datasets are
collections of standard material produced by a research project, e.g.
deliverables, dissemination material, training material, scientific
publications.
# Data Collection
<table>
<tr>
<th>
</th>
<th>
</th> </tr>
<tr>
<td>
Data Collection
</td>
<td>
</td>
<td>
Data collection activities will be carried out in WP3, WP4, WP5, WP6 and WP7.
Main data collection methods include survey, interviews, workshops, focus
groups, crowdsourcing, and web mining. Main data that the consortium will
generate include opinions, attitudes and practices of researchers regarding
open peer review, alternative dissemination and altmetrics.
</td> </tr>
<tr>
<td>
</td>
<td>
</td>
<td>
The raw data collected from surveys will be stored in a suitable format (e.g.
Excel files, ODS or CSV files).
</td> </tr>
<tr>
<td>
</td>
<td>
</td>
<td>
The recorded data from focus groups, workshops and interviews will be stored
as recordings in a suitable format (MP3, OGG or WMA format) on the internal
servers of the task leading organisations. If focus groups, workshops and
interviews are not recorded, the written summaries will be saved in a suitable
format (Word documents, text files). The data from crowd-sourcing will be
stored in Google Spreadsheets that anyone with the link can access.
</td> </tr>
<tr>
<td>
</td>
<td>
</td>
<td>
Files will be converted to open file formats where possible for long term
storage and in these cases the researchers will anonymise the data.
</td> </tr> </table>
3 Renear, A.H., Sacchi, S. and Wickett, K.M. (2010) Definitions of dataset in
the scientific and technical literature.
Proceedings of the American Society for Information Science and Technology,
47(1): 1–4. DOI:
_10.1002/meet.14504701240_
# Data storage, selection and preservation and data sharing
<table>
<tr>
<th>
</th>
<th>
</th> </tr>
<tr>
<td>
Data Storage
</td>
<td>
</td>
<td>
The raw data collected from the survey will be stored in a suitable format
(e.g. Excel files, ODS or CSV files). The documents will be saved on the
internal institutional server of the task leader (PPMI). The server is
protected by passwords known only to the researchers working on OpenUP
project. Unauthorised users will not be able to access the data. The documents
available on the server are backed up regularly. Once processed and
anonymised, the data will be published on an online repository (eg. Zenodo)
and will be available for the use by third parties.
</td> </tr>
<tr>
<td>
</td>
<td>
</td>
<td>
The recorded data from focus groups, workshops and interviews will be stored
as recordings in a suitable format (MP3, OGG or WMA format) on the internal
servers of the task leading organisations. If focus groups, workshops and
interviews are not recorded, the written summaries will be saved in a suitable
format (Word documents, text files) and will be also stored on the internal
servers of the responsible institution. The data will be backed up regularly.
The servers are password protected and only researchers working on the project
will be able to access the files. The written summaries will be anonymised and
will also be made available on the online repository for third parties’ use.
</td> </tr>
<tr>
<td>
</td>
<td>
</td>
<td>
The data from crowd-sourcing will be stored in Google Spreadsheets that anyone
with the link can access. Participants can enter data in the spreadsheet
either anonymously or by identifying themselves.
</td> </tr>
<tr>
<td>
</td>
<td>
</td>
<td>
All the collected data, apart from the crowd-sourced data, will be anonymised.
The information provided will be analysed and presented in project reports
grouped together and will not be used individually.
</td> </tr>
<tr>
<td>
Data storage
</td>
<td>
</td>
<td>
The Raw data will be stored on the institutional servers of the organisations
that have generated them. The data will be stored for two years after the end
of the project. After that, the data will be destroyed.
</td> </tr>
<tr>
<td>
</td>
<td>
</td>
<td>
The anonymised data will be stored in an online repository (e.g. Zenodo).
</td> </tr>
<tr>
<td>
Data Sharing
</td>
<td>
</td>
<td>
The raw data will only be accessed by researchers that collected the data. The
anonymised summaries of interviews and workshops or survey results will be
made accessible to other partners as well as third parties.
</td> </tr> </table>
# Documentation and Metadata
The following table outlines an example structure of the metadata that project
partners will include to describe their data.
<table>
<tr>
<th>
</th>
<th>
</th> </tr>
<tr>
<td>
Project and GA number
</td>
<td>
OpenUP 710722
</td> </tr>
<tr>
<td>
Data type
</td>
<td>
Text document/ numerical data/ survey data
</td> </tr>
<tr>
<td>
Description
</td>
<td>
Description of the variables and description of the data included
</td> </tr>
<tr>
<td>
Data state
</td>
<td>
Processed
</td> </tr>
<tr>
<td>
Data source
</td>
<td>
Interview/ workshop/ focus group/ data mining/ survey / crowdsourced data
</td> </tr>
<tr>
<td>
Media type
</td>
<td>
The format the data is stored in
</td> </tr>
<tr>
<td>
Licence or use
constraints
</td>
<td>
Licence (if any)
</td> </tr>
<tr>
<td>
Size
</td>
<td>
Size of the file
</td> </tr> </table>
# Ethics and Legal Compliance
The OpenUP project will mostly collect data on opinions, attitudes and
practices related to open peer review, innovative dissemination and novel
impact assessments methods used by the researchers. The consortium will also
collect some personal data (such as country, gender, career stage or a
researcher). Contact emails and names of researchers will be used when
inviting them to participate in the survey, interviews or workshops. Once the
data is collected, the researchers will anonymise it and present it in
aggregated manner. The procedures for data handling of OpenUP are described in
D8.1 data handling procedures document available _here_ . Also, participants
of interviews, workshops and survey will be informed about the OpenUP project
and the research activities through informed consent procedures that are
described in _D8.2._
# Responsibilities and Resources
PPMI is the lead for developing DMP and implementing it together with
participation of UoA. PPMI is also responsible to ensure that the plan is
reviewed and revised during the project duration. The DMP will be updated
whenever important changes to the project occur due to inclusion of new
datasets, changes in consortium policies or external factors. All involved
partners are responsible for the compliance with the DMP and the procedures
for data collection, handling and preservation.
The contact person for communication:
Viltė Banelytė
[email protected]_
+37062336168
No additional resources than those already outlined in the budget of OpenUP
will be needed for data management and archiving.
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Horizon 2020
|
1142_LiRichFCC_711792.md
|
simulation parameters, synthesis protocols etc.), and it will have moderate
size on the order of typically few tens of GB.
Depending on the Work Package involved in data generation, data may not only
be useful for members inside the consortium but also for other academic
institutions or for industry that might want to do benchmarking of new models,
protocols or materials in comparison with existing battery technology.
**3.2 How will data be managed internally?**
All LiRichFCC partners provide appropriate storage facilities for research
data and provide controlled accesses as well as appropriate infrastructure.
They also support free access to research data considering ethical, legal,
economic, and contractual framework conditions.
**3.3 What data can be made public?**
For those data that can be made public, it needs to be ensured that it is
findable, accessible, interoperable, and reusable (FAIR). To this end,
proprietary formats (see 3.1) will be converted into international standard
formats such as ASCII and stored as text files. That way, scientists and
development engineers from all over the world which are researching on the
field of Liion batteries or the synthesis and electrochemistry of new Li-rich
cathode materials for Li-ion batteries will benefit from the LiRichFCC
program.
The consortium is currently preparing the following data sets to be made
publicly available on the project website as well as selected repositories:
<table>
<tr>
<th>
**Partner**
</th>
<th>
**Record Name**
</th>
<th>
**ID**
</th>
<th>
**Data Type**
</th>
<th>
**Short description**
</th>
<th>
**Type of data**
**within record**
</th> </tr>
<tr>
<td>
**CEA**
</td>
<td>
AlF3 coatings
</td>
<td>
CEA01
</td>
<td>
exp
</td>
<td>
XRD of AlF3 coated materials and XRD
acquisition protocol
</td>
<td>
XRD, protocols
</td> </tr>
<tr>
<td>
**CEA**
</td>
<td>
AlF3-coated Li2VO2F
</td>
<td>
CEA02
</td>
<td>
exp
</td>
<td>
Electrochemical results of galvanostatic cycling of 1wt%, 2wt%,
3wt% AlF3 coated
Li2VO2F
</td>
<td>
charge/discharge, protocols
</td> </tr>
<tr>
<td>
**CEA**
</td>
<td>
Separator study
</td>
<td>
CEA03
</td>
<td>
exp
</td>
<td>
Electrochemical results of the study of the effect of the separator nature
(Celgard alone,
Whatman+Celgard))
</td>
<td>
charge/discharge, protocols
</td> </tr>
<tr>
<td>
**CEA**
</td>
<td>
Electrolyte additives
</td>
<td>
CEA04
</td>
<td>
exp
</td>
<td>
Electrochemical results of galvanostatic cycling with the different additives
(LiBoB, LiODFB, FEC
and glycolide)
</td>
<td>
charge/discharge, protocols
</td> </tr>
<tr>
<td>
**CEA**
</td>
<td>
Voltage window
</td>
<td>
CEA05
</td>
<td>
exp
</td>
<td>
Electrochemical results of the study on the cutoff effect (1V4.1v, 1.3V-4.1V,
1.5V4.1V, 1.5V-4.3V)
</td>
<td>
charge/discharge, protocols
</td> </tr>
<tr>
<td>
**CEA**
</td>
<td>
Protocolls - CEA
</td>
<td>
CEA06
</td>
<td>
exp
</td>
<td>
Corresponding
protocols for
</td>
<td>
supplement to CEA01 - CEA05
</td> </tr>
<tr>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
electrochemical testing as well as electrode making and coin cell mounting
</td>
<td>
</td> </tr>
<tr>
<td>
**DTU**
</td>
<td>
XRD
characterization
</td>
<td>
DTU01
</td>
<td>
exp
</td>
<td>
Collection of XRD data from LiRichFCC
materials
</td>
<td>
XRD, operando
XRD, protocols
</td> </tr>
<tr>
<td>
**DTU**
</td>
<td>
Atomic
Simulation
Environment
</td>
<td>
DTU02
</td>
<td>
code
</td>
<td>
Tools and Python modules for setting up, manipulating, running, visualizing
and analyzing atomistic
simulations
</td>
<td>
code
</td> </tr>
<tr>
<td>
**DTU**
</td>
<td>
Simulated structural data
</td>
<td>
DTU03
</td>
<td>
theory
</td>
<td>
Structural data of LiRichFCC materials obtained with DFT and cluster expansion
methods
</td>
<td>
structural data
</td> </tr>
<tr>
<td>
**KI**
</td>
<td>
Ceramic synthesis
</td>
<td>
KI01
</td>
<td>
exp
</td>
<td>
Ceramic Synthesis, characterization and electrochemical performance
</td>
<td>
XRD, EIS,
protocols
</td> </tr>
<tr>
<td>
**KI**
</td>
<td>
Li2MnO2F
</td>
<td>
KI02
</td>
<td>
exp
</td>
<td>
Li2MnO2F synthesis by ball milling. XRD, ICP-MS and TEM characterization.
Galvanostatic
charge/discharge cycles measured at
C/10 (22,4 mAg-1)
</td>
<td>
XRD, ICP-MS,
TEM,
charge/discharge, protocols
</td> </tr>
<tr>
<td>
**KIT**
</td>
<td>
Na-rich FCC
</td>
<td>
KIT01
</td>
<td>
exp
</td>
<td>
Attempt to synthesize sodium-rich disordered rock-salt oxyfluoride material,
to be used as a cathode in Na-ion batteries.
</td>
<td>
XRD, SEM,
charge/discharge, protocols
</td> </tr>
<tr>
<td>
**UU**
</td>
<td>
Interface data
</td>
<td>
UU01
</td>
<td>
exp
</td>
<td>
Interfacial
characterization data
</td>
<td>
XPS
</td> </tr> </table>
The “Atomic Simulation Environment” developed by DTU is already available as
open source freeware at _https://wiki.fysik.dtu.dk/ase/_ . All structural
data obtained via simulation have been uploaded on NOMAD. In addition, the
consortium has already published and plans to publish key raw data together
with scientific publications.
**3.4 What processes have been implemented?**
The partners of the LiRichFCC consortium combine over a century of experience
in research data handling, and have developed efficient ways to archive and
share data. Nonetheless, research has become increasingly more
interdisciplinary, and amounts of data generated are on the rise. Therefore,
especially for collaborative work within individual work packages, the
partners follow internal codes and standards for making data findable.
**Parameter sets, methods and protocols** will be stored in text documents
that follow standardized naming conventions jointly defined by the LiRichFCC
partners to ensure maximum findability, accessibility and interoperability.
**Aggregated data** in the form of presentations, reports (deliverables),
publications, or patents follow standardized naming conventions. For example,
presentations and reports include the name of the project, the corresponding
work package, and the date. Deliverables can be identified by their
deliverable number, publications have unique DOIs, and patents are numbered
per international standards. Public aggregated data will by default be made
available on the project webpage ( _www.lirichfcc.eu_ ) as well as in a yet-
to-be-determined professional repository.
Aggregated data to be shared will always be in a format that can be used and
understood by a computer. They will typically be stored in PDF formats that
are either standardised or otherwise publicly known so that anyone can develop
new tools for working with the documents.
**Raw experimental or theoretical data** that have been identified as non-
restricted will be converted into a standard, non-proprietary format (ASCII
text file) and combined with necessary meta data in the form of a text
document and PDF file. Such data will be available on the project website as
well as from a professional repository.
General consideration regarding publication, depositing or patenting of
research data are summarized by the Figure below that has been reproduced from
the H2020 Program Guidelines to the Rules on Open Access to Scientific
Publications and Open Access to Research Data in Horizon 2020:
# OUTLOOK
The project has ended on Sept. 30, 2019. The consortium plans a series of
scientific publications based on project results and prepares selected data
sets for publication (see 3.3).
|
https://phaidra.univie.ac.at/o:1140797
|
Horizon 2020
|
1144_EVERLASTING_713771.md
|
**PROJECT OBJECTIVES**
</th> </tr> </table>
EVERLASTING is focussing on model based battery management systems (BMS) for
Li-ion batteries. New or improved BMS features will be developed by performing
intensive research activities in the field of physical testing, simulations,
modelling and validation on battery cell and pack level to improve the
reliability, lifetime, performance and safety of Li-ion batteries when being
used in electric vehicles.
Today, batteries are not yet the ideal energy container they were promised to
be. They are expensive, fragile and potentially dangerous. Moreover the
current electric vehicle cannot compete yet with traditional vehicles when it
comes to driving range and flexibility. EVERLASTING intends to bring Li-ion
batteries closer to this ideal by focusing on the following technology areas:
* Predicting the behaviour of battery systems in all circumstances and over their full lifetime. This enables accurate dimensioning and choice of the correct battery type, leading to lower cost. It also facilitates the development of a powerful battery management system during all stages of its evolution from idea to fully tested product.
* Sensing signals beyond the standard parameters of current, voltage and temperature. This multi-sensing approach, over early, mid and late stage time domains, provides more varied and in-depth data on the status of the battery facilitating a pro-active and effective management of the batteries, preventing issues rather than mitigating them.
* Monitoring the status of the battery by interpreting the rich sensor data. By intelligently combining this information with road, vehicle and driver data we intend to offer accurate higher-level driver feedback. This induces a bigger trust and hence a lower range anxiety.
* Managing the battery in a proactive way, based on a correct assessment of its status. Efficient thermal management and load management results in increased reliability and safety and leads to lower overall cost through an increased lifetime.
* Defining a standard BMS architecture and interfaces and gathering the necessary support in the market. This allows an industry of standard BMS components to flourish which will result in lower cost.
<table>
<tr>
<th>
**2 DATA MANAGEMENT P**
</th>
<th>
**LAN**
</th> </tr>
<tr>
<td>
</td>
<td>
</td> </tr>
<tr>
<td>
**2.1 INTRODUCTION**
</td>
<td>
</td> </tr> </table>
A new element in Horizon 2020 is the use of Data Management Plans. A **Data
Management Plan** (DMP) describes the data management life cycle for all
datasets to be collected, processed or generated by a research project. The
purpose of the Data Management Plan (DMP) is to provide an analysis of the
main elements of the data management policy that will be used by the
applicants with regard to all the datasets that will be generated by the
project.
Special attention will be given to “ **Open Access** ” to scientific
information and “ **Open Research Data** ”. Open access (OA) refers to the
practice of providing online access to scientific information that is free of
charge to the end-user and reusable without restrictions. In the context of
research and innovation, 'scientific information' can mean: peer-reviewed
scientific research articles (published in scholarly journals) or research
data (underlying data from publications, curated data and/or raw data).
Why give open access to publications and data in Horizon 2020?
Firstly, for the benefits of society in general. Modern research builds on
extensive scientific dialogue and advances by improving earlier work. The
Europe 2020 strategy for a smart, sustainable and inclusive economy underlines
the central role of knowledge and innovation in generating growth. Broader
access to scientific publications and data therefore helps to:
* build on previous research results (improved quality of results)
* encourage collaboration and avoid duplication of effort (greater efficiency)
* speed up innovation (faster progress to market means faster growth)
* involve citizens and society (improved transparency of the scientific process).
Secondly, also the EVERLASTING partners benefit from giving open access to
publications and research data by increasing the visibility of the project
results, leading to more citations for the research partners and increased
collaboration potential for setting up new projects or for the quicker
adoption and valorisation of the built up knowledge.
**Deliverable D8.2 “Data Management Plan”** describes the handling of research
data that will be collected, processed or generated by the EVERLASTING project
according to the guidelines for data management in the H2020 Online Manual.
This deliverable will evolve during the lifetime of the project in order to
describe the status of the project's reflections on data management.
EVERLASTING Deliverable D8.2 “Data Management Plan” must be considered as a “
**living document** ” and will be updated when important updates are
available: new datasets, updates on existing datasets, changes in consortium
policies (e.g. on exploitation of results and patenting) or other external
reasons (e.g. changes in consortium members and suggestions from advisory
board).
The DMP will be updated as a minimum in time with the periodic
evaluation/assessment of the project. This update will be in parallel with the
update of **D8.1 “Dissemination and exploitation plan”** . Obviously, there is
a close link to D8.1 “Dissemination and exploitation plan” since the generated
data within the EVERLASTING project is input for future exploitation and
dissemination activities. Some of the generated datasets will be used as
“underlying data” for the dissemination of EVERLASTING project results and the
Data Management Plan will describe how these datasets will be shared as “Open
Data”. By sharing data, we further improve the quality level and impact of the
dissemination activities within EVERLASTING.
## 2.2 SCOPE OF DATA MANAGEMENT PLAN VERSION 2.0
The DMP is not a fixed document, but evolves during the lifespan of the
project.
The scope of this **second version of the EVERLASTING DMP** contains the
updated status of reflection within the consortium about the overall types of
data that have been or will be collected or produced during the project and on
how and what data will be made open available to external stakeholders such as
research institutes and companies. In this version of the EVERLASTING DMP,
more detailed datasets and their specific handling will be described. The
first open datasets have been uploaded in the **4TU.Centre for Research Data**
**repository** (see 2.5.3 Data Inventory).
Existing guidance material will be used to structure this work such as the use
of **DMPOnline**
( _https://dmponline.dcc.ac.uk_ ) , which has been developed by the Digital
Curation Centre to help writing data management plans. **OpenAIRE** also
provides a range of resources, FAQs, webinars and support pages. OpenAIRE can
also be contacted via the local representatives in all EU countries: the
**National Open Access Desks** or NOADs. More information can be found on
_www.openaire.eu_ .
The **Horizon 2020 FAIR DMP template,** which is available on DMPOnline, has
been designed to be applicable to any Horizon 2020 project that produces,
collects or processes research data. A single DMP should be developed to cover
its overall approach. However, where there are specific issues for individual
datasets (e.g. regarding openness), these should be described in the specific
dataset. In general terms, research data should be 'FAIR' i.e. findable,
accessible, interoperable and re-usable.
## 2.3 OPEN ACCESS SCIENTIFIC PUBLICATIONS
In this section, the intended scientific dissemination will be described
briefly since **“underlying data”** of scientific publications forms the core
part of the “open data research pilot”.
The scientific dissemination will be implemented through:
* The publishing of the generated results in open access peer-reviewed scientific journals
* The presentation of project results in scientific conferences and events
Partners will publish scientific articles with Green or Gold-standard open
access to share results generated from the project. TU/e will ensure that
rules in the Consortium Agreement are respected concerning scientific
publications before their submission to journals. Prior to any disclosure
(conference, publications, defence of PhD theses or Masters) the protection of
the project progress must be secured. The project will generate research data
in a wide range of levels of detail from simulation and lab results to
demonstrator validation. Most data will be associated with results that may
have a potential for commercial or industrial protection and therefore cannot
be made accessible for verification and reuse in general due to intellectual
property protection measures. However, relevant data necessary for the
verification of results published in scientific journals can be made
accessible on a case by case basis. The decision concerning the publication of
data will be made by the Management Board, as the decision-making body of the
consortium. Research data of public interest such as those underlying
scientific publications will be made available via open access data
repositories while hyperlinks to these datasets will be placed on the
EVERLASTING website.
Below you can find the list of planned scientific publications, as mentioned
in the project proposal, which will generate “underlying data”. Besides these
publications some deliverables will also generate “open data” like D2.3
“Report containing aging test profiles and test results” (Type: ORDP & Due
Date: M42). For the most recent details on our dissemination activities, we
refer to the **M18 update of D8.1 “Dissemination and exploitation plan”** .
<table>
<tr>
<th>
Topic
</th>
<th>
Dissemination method
</th>
<th>
Partners
</th> </tr>
<tr>
<td>
WP1: Improved simulation and modelling tools
</td>
<td>
</td> </tr>
<tr>
<td>
Dissemination of research results with respect to tools and methods achieving
a relevant prediction of pack behaviour, with coupled controls, including
validation (Siemens)
</td>
<td>
Conference presentation
</td>
<td>
Siemens PLM
</td> </tr>
<tr>
<td>
Presentation of (pre-)industrialized new models, tools and features allowing
the modelling and prediction of battery pack behaviour and BMS coupling
</td>
<td>
Siemens user conference (including major carmakers, suppliers, OEMs,
battery suppliers…)
</td>
<td>
Siemens PLM
</td> </tr>
<tr>
<td>
Demonstrating multi-level battery modelling, simulation, reduction and control
coupling applied to automotive transportation
</td>
<td>
Simulation@Siemens (Siemens divisions)
</td>
<td>
Siemens PLM
</td> </tr>
<tr>
<td>
Modelling order reduction techniques
</td>
<td>
Conference
presentation,
Journal paper (Open Access)
</td>
<td>
TUM, TU/e
</td> </tr>
<tr>
<td>
WP2: Increased reliability
</td>
<td>
</td> </tr>
<tr>
<td>
The use of self-learning algorithms for estimation of SoH
</td>
<td>
Conference
presentation, Journal paper (Open Access)
</td>
<td>
VITO,
ALGOLiON
</td> </tr>
<tr>
<td>
Test procedures for BMS testing
</td>
<td>
Conference presentation, Standard proposal
</td>
<td>
TÜV SÜD, VITO
</td> </tr>
<tr>
<td>
WP3: Extended vehicle range
</td>
<td>
</td> </tr>
<tr>
<td>
Challenges of parametrization of physico-chemical battery model and comparison
to standard electro chemical battery models
</td>
<td>
Conference presentation
</td>
<td>
RWTH
</td> </tr>
<tr>
<td>
Deduction of minimum parameter set for physico-chemical model and adaption of
model for application on embedded hardware
</td>
<td>
Journal paper (Open Access)
</td>
<td>
RWTH
</td> </tr>
<tr>
<td>
Maximizing operation range of Li-Ion batteries to maximize energy output
without losing safety
</td>
<td>
Conference paper, Journal Paper (Open
Access)
</td>
<td>
RWTH
</td> </tr>
<tr>
<td>
Embedded BMS hardware requirements towards execution of extended battery
models
</td>
<td>
Conference paper
</td>
<td>
RWTH
</td> </tr>
<tr>
<td>
Costs for more complex BMS hardware, enabling the extraction of more energy
vs. higher capacity of battery cells
</td>
<td>
Conference paper
</td>
<td>
RWTH
</td> </tr>
<tr>
<td>
Development of common BMS software towards integration of more complex battery
models
</td>
<td>
Conference paper
</td>
<td>
RWTH
</td> </tr>
<tr>
<td>
Advanced BMS Models and Model Adaptation
</td>
<td>
University courses and seminars
</td>
<td>
RWTH
</td> </tr>
<tr>
<td>
Full Physico-Chemical Parameterization of the Battery Cell
</td>
<td>
Journal paper (Open Access)
</td>
<td>
RWTH
</td> </tr>
<tr>
<td>
Aging behaviour of the Battery Cell
</td>
<td>
Journal paper (Open Access)
</td>
<td>
RWTH
</td> </tr>
<tr>
<td>
Methodology for onBoard estimation of lithium loss and anode and cathode
degradation
</td>
<td>
Journal paper (Open Access)
</td>
<td>
RWTH,
ALGOLiON
</td> </tr>
<tr>
<td>
Drive power prediction of electric busses
</td>
<td>
Conference
presentation and journal paper (Open Access)
</td>
<td>
TU/e
</td> </tr>
<tr>
<td>
Online real-time vehicle parameter estimation
</td>
<td>
Conference
presentation and journal paper (Open Access)
</td>
<td>
TU/e
</td> </tr>
<tr>
<td>
Energy Management of Electrified Auxiliaries of electric busses
</td>
<td>
Conference
presentation and journal paper (Open Access)
</td>
<td>
TU/e
</td> </tr>
<tr>
<td>
Range extension and optimization: tradeoff between travel-time and energy
consumption
</td>
<td>
Conference
presentation and Journal paper (Open Access)
</td>
<td>
TU/e
</td> </tr> </table>
WP4: Safer batteries
Battery and cell safety and failure modes, early degradation Conference CEA
mechanisms of materials leading to cell thermal runaway presentation,
Journal paper (Open
Access)
Safety monitoring of Li-ion batteries: signal processing for battery
Conference CEA,
safety applications, multi-sensing safety algorithms presentation, ALGOLiON
journal paper (Open Access),
demonstration at trade exhibitions, trade
journal article
Multi-scale, multi-physics modelling of safety hazards in Li-ion Conference
CEA
batteries presentation,
Journal paper (Open Access)
WP5: Longer battery life
Active and passive thermal management of Li-ion batteries Conference CEA, VITO
presentation,
Journal paper (Open
Access)
Battery reconfiguration Conference VITO
presentation,
Journal paper (Open
Access)
Influence of cell-to-cell parameter variations on cell balancing Conference
TUM
presentation,
Journal paper (Open
Access)
Optimal utilization of dissipative cell balancing Conference TUM
presentation,
Journal paper (Open
Access)
Optimal utilization of non-dissipative cell balancing Conference TUM
presentation,
Journal paper (Open Access)
WP6: Standardized architecture
BMS architecture Standard proposal LION
Smart
Requirements and architecture concept of a highly modular Journal paper (Open
TUM
prototyping hardware platform. Access)
Implementation of a selection of the developed technologies in WP2- Journal
paper (Open LION
W5 according to their readiness levels on standardized BMS Access) Smart WP7:
Demonstrator
Battery pack demonstrator, integrated in electric vehicle Presentation to
VOLTIA
scientific community and general public
## 2.4 DATA REPOSITORIES
The EVERLASTING project will collect or generate a lot of different types of
datasets. Some of these datasets will be only for internal use between the
EVERLASTING consortium partners and will be stored in EVERLASTING
repositories, while other datasets will be made publicly accessible in open
access repositories.
**2.4.1 DATA REPOSITORIES - DATA OWNER**
The main data type collected in the EVERLASTING project is **BMS related
measurement/testing/simulation data** of which one of the partners is the
owner. If the data is considered to be privacy related, it is the
responsibility of the data owner to inform the impacted person(s) and provide
the necessary terms and agreements.
The data owner will store its own data on **data owner servers** following
internal data management procedures during the research project. Most partners
have a dedicated Data Protection Officer (DPO) and some partners have a
Research Data Management team (RDM).
### 2.4.2 DATA REPOSITORIES - EVERLASTING
To facilitate the exchange of information and data between the EVERLASTING
partners, the project coordinator has set-up 2 dedicated sites:
**EVERLASTING SharePoint site** to exchange all general information and
documents like MB and GA meeting minutes, WP related documents, such as the
Grant Agreement and the Consortium Agreement, deliverables and publications
(for more information see D9.1 “Project collaborative platform”).
**EVERLASTING Secure FTP site** to facilitate the exchange of research data
between the EVERLASTING partners.
**2.4.3 DATA REPOSITORIES - OPEN ACCESS**
A data repository is a digital archive collecting and displaying datasets and
their metadata. A lot of data repositories also accept publications, and allow
linking between publications and their underlying data. During the first 6
months, the EVERLASTING partners have been studying the different options
where to store the data that can be made openly accessible. An overview of
repositories can be found at **Re3data** ( _www.re3data.org_ ) .
The EVERLASTING partners prefer to use an open access data repository which is
co-developed by one of the own partners and the choice was made to use the “
**4TU.Centre for Research Data** ” **repository** as **main data repository
for EVERLASTING “Open Data”** .
The 4 Technical Universities in the Netherlands have created this research
data repository ( _http://data.4tu.nl_ ) and are further developing this.
TU/e has an own RDM group and can help the EVERLASTING partners in using this
repository. It is possible to deposit EVERLASTING data as open data or deposit
it with embargo when it contains confidential data. The **TU/e RDM team** can
help in offering guidelines for sustainable data formats and metadata
standards, as well as support for dealing with sensitive data and licensing.
As complementary solution for storing open access publications and open
research data, the EVERLASTING partners can use the “Zenodo” repository (
_www.zenodo.org)_ . Zenodo repository is provided by OpenAIRE and hosted by
CERN. Zenodo is a catch-all repository that enables researchers, scientists,
EU projects and institutions to:
* Share research results in a wide variety of formats including text, spreadsheets, audio, video, and images across all fields of science.
* Display their research results and get credited by making the research results citable and integrating them into existing reporting lines to funding agencies like the European Commission.
* Easily access and reuse shared research results.
* Integrate their research outputs with the OpenAIRE portal.
## 2.5 DATA SUMMARY
**2.5.1 INTRODUCTION**
EVERLASTING is focussing on model based battery management systems (BMS) for
Li-ion batteries.
New or improved BMS features will be developed by performing intensive
research activities in the field of physical testing, simulations, modelling
and validation on battery cell and pack level to improve the reliability,
lifetime, performance and safety of Li-ion batteries when being used in
electric vehicles. The first 3 years of the EVERLASTING project, the research
activities will be mainly performed in the different labs of the research
partners. In a later phase, also real-life validation activities will be
performed on the electric van from VOLTIA and the electric bus from VDL ETS.
The main data type collected in the EVERLASTING project is **BMS related
measurement/testing/simulation data** . Some of the research data (such as
lifetime and safety test data) obtained in the project will be publicly shared
via the Open Research Data Pilot. In this chapter, the reader can find the
first available open datasets and can also get an idea of the different
datasets to be expected the remaining period from the EVERLASTING project.
Before elaborating on the datasets in the data inventory we first describe the
FAIR Data principles.
### 2.5.2 FAIR DATA
The datasets will be made available following the **FAIR Data principles**
which means findable, accessible, interoperable and re-usable.
#### Making data findable, including provisions for metadata [fair data]
_Outline the discoverability of data (metadata provision)_
All data underlying journal articles will be archived and made available via
the data repository of **4TU.Centre for Research Data** ( _http://data.4tu.nl_
) . It is a data repository for technical and scientific research data,
mainly from The Netherlands. It offers data archiving services also to
researchers at non-Dutch institutions and to H2020 projects such as the
EVERLASTING project.
4TU.Centre for Research Data makes data openly available with a user license
which is comparable to a CC BY-NC license.
**Discoverability** of the data in 4TU.Centre for Research Data is achieved
by:
* Adding bibliographic (descriptive) metadata to each dataset (according to the Datacite metadata standard);
* Allowing the metadata to be harvested by Google and portals such as Narcis
( _http://www.narcis.nl_ ) ;
* Presenting metadata in the open linked data RDF format;
* Providing a DOI to each dataset;
* Linking the data set to the accompanying publication.
Recognisability or visibility of the data as originating from the EVERLASTING
project will be enhanced by creating a special collection of these data in
4TU.Centre for Research Data.
_Outline the identifiability of data and refer to standard identification
mechanism. Do you make use of persistent and unique identifiers such as
Digital Object Identifiers?_
Each data set deposited in 4TU.Centre for Research Data is assigned a **DOI**
so that it is uniquely identifiable and can be cited in scholarly articles.
#### Making data openly accessible [fair data]
_Specify which data will be made openly available? If some data is kept closed
provide rationale for doing so._
Partners will publish scientific articles with Green or Gold-standard open
access to share results generated from the project. TU/e will ensure that
rules in the Consortium Agreement are respected concerning scientific
publications before their submission to journals. Prior to any disclosure
(conference, publications, defence of PhD theses or Masters) the protection of
the project progress must be secured. The project will generate research data
in a wide range of levels of detail from simulation and lab results to
demonstrator validation. Most data will be associated with results that may
have a potential for commercial or industrial protection and therefore cannot
be made accessible for verification and reuse in general due to intellectual
property protection measures. However, relevant data necessary for the
verification of results published in scientific journals can be made
accessible on a case by case basis. The decision concerning the publication of
data will be made by the Management Board, as the decision-making body of the
consortium.
_Specify how the data will be made available_
The data will be made openly available via the data archive of 4TU.Centre for
Research Data ( _http://data.4tu.nl_ ) .
_Specify where the data and associated metadata, documentation and code are
deposited_
As part of the dataset, a data guide will be deposited with data specific
information (parameters and/or variables used, column headings, codes/symbols
used, etc.) and with information on the provenance of the data. When software
is needed to reuse the data, the code will also be deposited together with the
data itself.
#### Making data interoperable [fair data]
_Assess the interoperability of your data. Specify what data and metadata
vocabularies, standards or methodologies you will follow to facilitate
interoperability._
_Specify whether you will be using standard vocabulary for all data types
present in your data set, to allow inter-disciplinary interoperability? If
not, will you provide mapping to more commonly used ontologies?_
These questions do not yet apply to the project. To make the data
interchangeable between the project partners and future users of the data, we
are developing a metadata scheme.
Related to standard vocabulary for data types, we can refer to our white
papers (see D8.1 for more details) in which we will explain the definitions of
some data types such as SOC and SOH.
#### Increase data re-use (through clarifying licenses) [fair data]
_Specify how the data will be licenced to permit the widest reuse possible_
Data sets deposited in 4TU.Centre for Research Data will have a user license
comparable to a CC BY-NC license. 4TU.Centre for Research Data is in the
process of offering data depositors a choice of different Creative Commons
user licenses. When this is realized a CC BY license will be selected.
_Specify when the data will be made available for re-use. If applicable,
specify why and for what period a data embargo is needed_
Data will be made available for re-use immediately upon publication of the
accompanying article or after a certain embargo period. This is on a case-by-
case basis.
_Specify the length of time for which the data will remain re-usable_
4TU.Centre for Research Data archives data for a minimum of 15 years. Data
from the EVERLASTING project are provided in a format that is recommended by
4TU.Centre for Research Data for long term preservation and re-use. 4TU.Centre
for Research Data has received a Data Seal of Approval which guarantees that
the research data deposited here will continue to be able to be found and
shared in the future.
_Describe costs and potential value of long term preservation_
The costs of archiving data in 4TU.Centre for Research Data depends on the
size of the data. If the data is deposited on behalf of one of the partners of
4TU.Centre for Research Data (the 4 special Dutch universities, including
Eindhoven University of Technology) and doesn’t exceed 100 Gb per year, then
4TU.Centre for Research Data can absorb these costs. Above 100 Gb per year,
the costs will be € 3,60 per Gb per 15 years. Archiving data from non-partners
is free till 10 Gb per year. Above 10 Gb, the costs are € 4,50 per Gb per 15
year.
**2.5.3 DATA INVENTORY**
The data inventory is structured alphabetically per partner. This version of
the DMP plan will elaborate on all expected datasets more in detail on topics
such as description, formats, metadata and whether the data will be
shared/made open access or not. In this section, based on the current
insights, the reader can get an view on the datasets to be expected from the
EVERLASTING project.
The first open datasets, from TU/e and TUM, are available in the 4TU.Centre
for Research Data repository.
#### ALGOLiON
Raw Data: cell and small pack measurements for current, voltage, temperature
and single frequency impedance data.
Raw Data Format: CSV time series.
Derived Parameters: electrochemical based parameters to diagnose the state of
safety and predict the development of safety hazards.
Derived Parameters Format: time domain normalized values.
Analysis of Derived Parameters: statistical analysis of hazard parameters. The
goal is to obtain data that will be helpful in identifying how the signature
of a healthy battery differs from the signature of a damaged battery. The work
will collect sufficient data to ascertain that the signatures are consistent
across cells, various conditions of cells and charge-discharge cycles. A large
statistical base will be used to quantify the extent to which signatures are
repeatable across these platforms.
Modelling: trace the development of internal short circuits to determine
empirical time constant for changes in cell resistance and state of safety;
projected time to threshold for thermal runaway.
#### CEA
<table>
<tr>
<th>
</th>
<th>
**Data Description**
</th>
<th>
Access
</th> </tr>
<tr>
<td>
WP4-T4.2: Multi-sensing strategy
</td> </tr>
<tr>
<td>
Meta Data
</td>
<td>
* Type of sensors and specifications (Temperature, constraint gauge, IR, voltage, current).
* Sensor implementation (Number of sensors, location…)
* Cell ID
* Further experiment information if needed
</td>
<td>
EVERLASTING CONSORTIUM
</td> </tr>
<tr>
<td>
Data
</td>
<td>
• Raw data coming from the multi-sensing experiments Format : CSV
</td>
<td>
EVERLASTING CONSORTIUM
</td> </tr>
<tr>
<td>
Data
</td>
<td>
• Post treated data coming from the multi-sensing experiments Format : CSV
</td>
<td>
CEA
</td> </tr>
<tr>
<td>
WP4-T4.3: Safety Testing Campaign
</td> </tr>
<tr>
<td>
Meta Data
</td>
<td>
• Conditions of tests, Cell ID…
</td>
<td>
OPEN ACCESS
</td> </tr>
<tr>
<td>
Data
</td>
<td>
• Raw data coming from the Safety Testing Campaign Format : CSV
</td>
<td>
OPEN ACCESS
</td> </tr>
<tr>
<td>
Data
</td>
<td>
• Post treated data coming from the Safety Testing Campaign Format : CSV
</td>
<td>
EVERLASTING CONSORTIUM
</td> </tr>
<tr>
<td>
WP4-T4.4: Post Mortem Analysis
</td> </tr>
<tr>
<td>
Meta Data
</td>
<td>
* Protocols
* Pictures
* Cell Size, weight…
</td>
<td>
OPEN ACCESS
</td> </tr>
<tr>
<td>
Data
</td>
<td>
• Characterization Raw data Format : tbd
</td>
<td>
OPEN ACCESS
</td> </tr>
<tr>
<td>
Data
</td>
<td>
• Post treated data Format : tbd
</td>
<td>
EVERLASTING CONSORTIUM
</td> </tr>
<tr>
<td>
WP4-T4.5: Thermal and electrical modelling
</td> </tr>
<tr>
<td>
Meta Data
</td>
<td>
* Physical models (set of equations chosen to describe the model)
* Software version
</td>
<td>
OPEN ACCESS
</td> </tr>
<tr>
<td>
Data
</td>
<td>
• Model implementation (full access software) Format : COMSOL software
</td>
<td>
CEA
</td> </tr>
<tr>
<td>
Data
DEMONSTRAT
OR
SIMPLIFIED
CASE
</td>
<td>
* Data Input:
* Simple Geometry
* Mesh (Finite element)
* State of the art material physical specifications and models Format : COMSOL Application
* Data Output:
* Simulation Results Format: CSV
</td>
<td>
OPEN ACCESS
</td> </tr>
<tr>
<td>
Data & Meta
Data
DEMONSTRAT
OR
EVERLASTING
</td>
<td>
• Data Output :
\- Simulation results Format : CSV
</td>
<td>
OPEN ACCESS
</td> </tr>
<tr>
<td>
CASE
</td>
<td>
* Data and Meta Data input generated by EVERLASTING partners:
* Material specifications and models deduced from experimental characterization
* Experimental safety tests measurements (voltage and temperature time records) Format: CSV
* COMSOL Application
</td>
<td>
EVERLASTING CONSORTIUM
</td> </tr>
<tr>
<td>
WP4-T4.6: Safety warning and prediction algorithms
</td>
<td>
</td> </tr>
<tr>
<td>
Input Data
</td>
<td>
• Database from multi-sensing measurements: exploitation Format: CSV
</td>
<td>
EVERLASTING
CONSORTIUM
(Common data with ALGOLiON)
</td> </tr>
<tr>
<td>
Output Meta
Data
</td>
<td>
• Detection & Learning algorithms Format: Matlab Code
</td>
<td>
EVERLASTING CONSORTIUM:
(Share knowledge with ALGOLiON)
</td> </tr>
<tr>
<td>
Output Data
</td>
<td>
• Results: statistics, figures, precision and robustness of the algorithms
Format : tbd
</td>
<td>
OPEN ACCESS
</td> </tr>
<tr>
<td>
WP5: Longer battery life
</td>
<td>
</td> </tr>
<tr>
<td>
Data
</td>
<td>
* Thermal and hydraulic simulations results.
Detailed comparison with experimental performances Format : tbd
* Experimental tests :
Detailed tests results in terms of thermal performance Format : tbd
</td>
<td>
EVERLASTING CONSORTIUM
</td> </tr>
<tr>
<td>
Data
</td>
<td>
• Experimental tests:
General evaluation of 2 cooling solutions
</td>
<td>
OPEN ACCESS
</td> </tr> </table>
#### LION Smart
<table>
<tr>
<th>
**WP**
</th>
<th>
**Data**
</th>
<th>
**Format**
</th>
<th>
**Metadata**
</th>
<th>
</th>
<th>
</th> </tr>
<tr>
<td>
WP6
</td>
<td>
Reduced order electrochemical cells simulation models
</td>
<td>
Matlab/Simulink
</td>
<td>
Documentation simulation models
</td>
<td>
of
</td>
<td>
the
</td> </tr>
<tr>
<td>
WP6
</td>
<td>
Cells internal states and parameter estimation procedures and models
</td>
<td>
Matlab/Simulink
</td>
<td>
Documentation simulation models
</td>
<td>
of
</td>
<td>
the
</td> </tr>
<tr>
<td>
WP6
</td>
<td>
BMS simulation models
</td>
<td>
Matlab/AmeSIM
</td>
<td>
Documentation simulation models
</td>
<td>
of
</td>
<td>
the
</td> </tr> </table>
#### RWTH Aachen
<table>
<tr>
<th>
</th>
<th>
**Electrical cell**
**measurements**
</th>
<th>
**Simulations with physicochemical model**
</th>
<th>
**Post mortem laboratory data**
</th> </tr>
<tr>
<td>
Data
</td>
<td>
* Voltage
* Current
* Temperature
* (Complex impedance data)
</td>
<td>
* Internal Cell Potentials
* Currents
* Temperatures
</td>
<td>
* Sizes
* Weights
* Microscopic Pictures
* ICP Data
</td> </tr>
<tr>
<td>
Format
</td>
<td>
CSV time series
</td>
<td>
Binary file with Matlab parsing script
</td>
<td>
Text Files, jpeg, Excel
</td> </tr>
<tr>
<td>
Metadata
</td>
<td>
* Measurement Program
* Cell ID
* Time
* Further measurement conditions (e.g. ageing test the
cell is part of)
</td>
<td>
* Simulation revision
* Parameter set (Stripped XML
File)
</td>
<td>
* Protocol of cell opening
* Cell ID
</td> </tr> </table>
#### Siemens
Some of the simulation results in WP1 (data produced by the simulators, not
the models and simulator themselves) may be considered as open: if the data
used for parameterizing the models is public or if the partners give their
approval for making simulation results public.
_**TU/e** _
#### _Current data set_
<table>
<tr>
<th>
**Journal Publications**
</th>
<th>
</th>
<th>
</th>
<th>
</th>
<th>
</th>
<th>
</th>
<th>
</th> </tr>
<tr>
<td>
Lead partner
</td>
<td>
W
P
</td>
<td>
Status
</td>
<td>
Open access
</td>
<td>
RDM
</td>
<td>
Year
</td>
<td>
Authors
</td>
<td>
Title
</td>
<td>
Journal
</td> </tr>
<tr>
<td>
TUE
</td>
<td>
1
</td>
<td>
Accepted
</td>
<td>
Green
</td>
<td>
Data DOI: _https://doi.org/10.41_
_21/uuid:0d19258e8fe2-44b7-b390cfc205669528_
</td>
<td>
2018
</td>
<td>
N. Jin, D. Danilov, P.M.J. Van den Hof, M.C.F.
Donkers
</td>
<td>
Parameter Estimation of an
Electrochemistry-based
Lithium-ion battery model using a Two-Step Procedure and
Sensitivity Analysis
</td>
<td>
Int J Energy Research
</td> </tr> </table>
The data set contains the Matlab simulation files needed to reproduce the
results of the paper. The data set is open accessible and available in
4TU.Centre for Research Data repository under following link:
_https://data.4tu.nl/repository/uuid:0d19258e-8fe2-44b7-b390-cfc205669528_
#### _Expected data sets_
Following data sets are expected in the next period. All data sets will be
open, unless confidential information was used to generate them.
<table>
<tr>
<th>
**WP**
</th>
<th>
**Data**
</th>
<th>
**Format**
</th>
<th>
**Metadata**
</th> </tr>
<tr>
<td>
WP1
</td>
<td>
Implemented battery simulation models Implementation of parameter estimation
procedures
</td>
<td>
Matlab
</td>
<td>
Documentation of the simulation models
</td> </tr>
<tr>
<td>
WP3
</td>
<td>
Simulation models implementing range estimation techniques
</td>
<td>
Matlab and AmeSIM
</td>
<td>
Documentation of the simulation models
</td> </tr> </table>
##### TUM
<table>
<tr>
<th>
**Journal Publications**
</th>
<th>
</th>
<th>
</th>
<th>
</th>
<th>
</th>
<th>
</th>
<th>
</th> </tr>
<tr>
<td>
Lead partner
</td>
<td>
W
P
</td>
<td>
Status
</td>
<td>
Open access
</td>
<td>
RDM
</td>
<td>
Year
</td>
<td>
Authors
</td>
<td>
Title
</td>
<td>
Journal
</td> </tr>
<tr>
<td>
TUM
</td>
<td>
1
</td>
<td>
Published
</td>
<td>
Gold
</td>
<td>
Data DOI: _https://doi.org/10.41_
_21/uuid:c10a6b3fefe9-41ce-99f64093df68c653_
</td>
<td>
2017
</td>
<td>
J. Sturm, F.B. Spingler, B. Rieger, A. Rheinfeld, A. Jossen
</td>
<td>
Non-Destructive Detection of
Local Aging in Lithium-Ion
Pouch Cells by Multi-Directional
Laser Scanning
</td>
<td>
Journal of the
Electrochemica l Society
</td> </tr> </table>
<table>
<tr>
<th>
**Journal Publications**
</th>
<th>
</th>
<th>
</th>
<th>
</th>
<th>
</th>
<th>
</th> </tr>
<tr>
<td>
Lead partner
</td>
<td>
WP
</td>
<td>
Status
</td>
<td>
Open access
</td>
<td>
RDM
</td>
<td>
Year
</td>
<td>
Authors
</td>
<td>
Title
</td>
<td>
Journal
</td> </tr>
<tr>
<td>
TUM
</td>
<td>
5
</td>
<td>
review
</td>
<td>
Yes
</td>
<td>
Reserved DOI:
_https://doi.org/10.4_
_121/uuid:65ed2b759cb7-469d-b55af4599e5b2126_
</td>
<td>
</td>
<td>
Ilya Zilberman,
Alexander Rheinfeld,
Andreas Jossen
</td>
<td>
Uncertainties in Entropy due to Temperature Path Dependent Voltage
Hysteresis in Li-Ion Cells
</td>
<td>
Journal of
Power
Sources
</td> </tr> </table>
<table>
<tr>
<th>
**Task**
</th>
<th>
**Author**
</th>
<th>
**Publication**
</th>
<th>
**Existing dataset used**
</th>
<th>
**Dataset generated**
</th> </tr>
<tr>
<td>
T5.3
</td>
<td>
Ilya
Zilberman
</td>
<td>
Statistical analysis of self-discharge in lithium-ion cells
</td>
<td>
</td>
<td>
• 1.1 Check Up and EIS Measurement of 48 LG MJ1 cells: Distribution of the
capacity and of the impedance
</td> </tr>
<tr>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
• 1.2 OCV measurement of 48 LG MJ1 cells
</td> </tr>
<tr>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
• 1.3 Self-discharge measurement of 24 LG MJ1 cells (SOC, T)
</td> </tr>
<tr>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
• 1.4 Correlation analysis between single cell parameters
</td> </tr>
<tr>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
• 1.5 Simulation of the influence of different self-discharge rates on the
cell voltage within the battery pack
</td> </tr>
<tr>
<td>
T5.3
</td>
<td>
Ilya
Zilberman
</td>
<td>
Influence of cell-to-
cell variations, thermal conditions and balancing on the scalability of large
lithium-ion battery packs
</td>
<td>
1.1, 1.2, 1.3,
1.5
</td>
<td>
Thermal characterization
* 2.1 Heat capacity measurement of the LG MJ1 cell
* 2.2 Heat generation measurement over the whole SOC range for 0.2C, 0.5C and 1C charge and discharge
Electrical characterization
</td> </tr>
<tr>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
• 2.3 EIS and pulse measurements (SOC, T)
</td> </tr>
<tr>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
• 2.4 Simulation of electrical long-term and short-term behaviour of a battery
pack with different balancing circuits
</td> </tr>
<tr>
<td>
T5.3
</td>
<td>
Ilya
Zilberman
</td>
<td>
Analysis of balancing effort during the ageing of the battery module
</td>
<td>
1.5, 2.4 •
•
</td>
<td>
3.1 Module aging data with controlled temperature gradient
3.2 Balancing effort during the aging of the module
</td> </tr>
<tr>
<td>
T5.4
</td>
<td>
Sebastian Ludwig
</td>
<td>
Multi-objective nondissipative balancing
</td>
<td>
2.4 •
</td>
<td>
4.1 Temperature distribution of a battery module during a driving profile with
applied multi-objective non-dissipative balancing
</td> </tr>
<tr>
<td>
T1.1
</td>
<td>
Johannes Sturm
</td>
<td>
Multi-dimensional
simulation of internal short
circuits
</td>
<td>
ECM parameters •
(RWTH)
•
</td>
<td>
4.2 Progression of the temperature within the cell during an internal short
circuit
4.3 Progression of the voltage during an internal short circuit
</td> </tr>
<tr>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
•
</td>
<td>
4.4 Influence of different cooling conditions for a state of the battery for
an internal short circuit
</td> </tr>
<tr>
<td>
T1.2
</td>
<td>
Johannes Sturm
</td>
<td>
Internal cell state estimation via electro chemical
based EKF
</td>
<td>
ECM parameters •
(RWTH)
•
</td>
<td>
5.1 Different approximation methods for solid state diffusion
5.2 Estimation of the anode potential with Extended Kalman Filter (EKF) during
CCCV charge
</td> </tr> </table>
##### TUV SUD
TUV SUD performs electrical cell measurements and cycling tests (WP2) and
abuse tests (WP4). These tests are recorded in great detail (15 Gb from 5 Cell
tests) because it might be useful also for other partners at a further stage
in the project.
<table>
<tr>
<th>
</th>
<th>
**Electrical cell measurements and cycling (WP2)**
</th>
<th>
**Abuse Testing on Cells and Packs (WP4)**
</th> </tr>
<tr>
<td>
Data
</td>
<td>
* Voltage
* Current
* Temperature
* Impedance data
</td>
<td>
* Voltage
* Current
* Temperature
* Videos
* Photos
</td> </tr>
<tr>
<td>
Format
</td>
<td>
* CSV
* Open source video and picture codecs
</td>
<td>
* CSV
* Open source video and picture codecs
</td> </tr>
<tr>
<td>
Metadata
</td>
<td>
Test report containing:
* Measurement Program
* Cell ID
* Time
* Further experiment information if needed
</td>
<td>
Test report containing:
* Measurement Program
* Test Set-up
* Cell/Pack ID
* Time
* Further experiment information if needed
</td> </tr> </table>
##### VITO
<table>
<tr>
<th>
1\. Data Summary
</th> </tr>
<tr>
<td>
What is the purpose of the data collection/generation and its relation to the
objectives of the project?
</td>
<td>
The tests consist of ageing commercial Li-ion cells within several stress
conditions (temperature, current, …). The results will allow to better
understand how these cells age when exposed to these conditions which can be
similar to the conditions that the cells will undergo in an electric vehicle.
Additionally these tests will be used to build an ageing model that can
estimate the state of health of a battery and also predict its lifetime.
</td> </tr>
<tr>
<td>
What types and formats of data will the project generate/collect?
</td>
<td>
The ageing tests will generate measurements collected at cell level. The data
will contain time, current, voltage, impedance and temperature values.
</td> </tr>
<tr>
<td>
Will you re-use any existing data and how?
</td>
<td>
We might use some data collected from previous EU projects.
</td> </tr>
<tr>
<td>
What is the origin of the data?
</td>
<td>
The data will be generated from experimental measurements.
</td> </tr>
<tr>
<td>
What is the expected size of the data?
</td>
<td>
Some gigabits
</td> </tr>
<tr>
<td>
To whom might it be useful
('data utility')?
</td>
<td>
Researchers working on Li-ion battery ageing; Li-ion battery user in
automotive applications
</td> </tr>
<tr>
<td>
2\. FAIR data
</td> </tr>
<tr>
<td>
Are the data produced and/or used in the project discoverable with metadata,
identifiable and locatable by means of a standard identification mechanism
(e.g. persistent and unique identifiers such as Digital Object
Identifiers)?
</td>
<td>
We can use metadata according to standards.
</td> </tr>
<tr>
<td>
What naming conventions do you follow?
</td>
<td>
A naming agreed within the consortium.
</td> </tr>
<tr>
<td>
Will search keywords be provided that optimize possibilities for reuse?
</td>
<td>
yes
</td> </tr>
<tr>
<td>
Do you provide clear version numbers?
</td>
<td>
yes
</td> </tr>
<tr>
<td>
What metadata will be created? In case metadata standards do not exist in your
discipline, please outline what type of metadata will be created and how.
</td>
<td>
An excel file with an overview of all the tests is usually created which makes
easier to follow up the tests and to identify the test conditions and results.
</td> </tr> </table>
##### VOLTIA & VDL ETS
As demonstrator partners, VOLTIA and VDL ETS, can support the other
participants by supplying real vehicle data of their existing e-fleet to help
verifying and optimising algorithms and models. In the final year, VOLTIA and
VDL ETS also play a role in the real life validation of the EVERLASTING BMS
features in their respective electric vehicles.
<table>
<tr>
<th>
**Task**
</th>
<th>
**Author**
</th>
<th>
**Existing dataset used**
</th>
<th>
**Dataset generated**
</th> </tr>
<tr>
<td>
T3.1 T3.3
</td>
<td>
VOLTIA
</td>
<td>
Driving data from operation of electric utility vans. The data consists of:
GPS coordinates, driving range, battery SOC, maximal, current and average
speed, voltage, current, energy flow, temperature on cell and pack level.
Monitored vehicles have been driven more than 1 million km cumulatively.
</td>
<td>
Driving data from operation of electric utility vans.
Measurement of data during specially designed driving profiles: voltage,
current and SOC will be measured with high data sampling rate (5Hz).
</td> </tr>
<tr>
<td>
T5.1
</td>
<td>
VOLTIA
</td>
<td>
\-
</td>
<td>
Experimental validation of prototype of passive cooling system of battery pack
</td> </tr>
<tr>
<td>
T7.5
</td>
<td>
VOLTIA, TÜV SÜD
</td>
<td>
\-
</td>
<td>
Measurement of electrical, thermal and functional parameters of demonstrator
battery pack.
</td> </tr> </table>
<table>
<tr>
<th>
**3**
</th>
<th>
**CONCLUSIONS**
</th> </tr> </table>
EVERLASTING Deliverable D8.2 “Data Management Plan” must be considered as a “
**living document** ” and will be updated when important updates are
available: new datasets, updates on existing datasets, changes in consortium
policies (e.g. on exploitation of results, patenting, …) or other external
reasons (e.g. changes in consortium members, suggestions from advisory board,
…).
#### The “Data Management Plan” will be updated as a minimum in time with the
periodic evaluation/assessment of the project and in parallel with the update
of D8.1
**“Dissemination and exploitation plan”** .
The scope of this **second version of the EVERLASTING DMP** contains the
updated status of reflection within the consortium about the overall types of
data that have been or will be collected or produced during the project and on
how and what data will be made open available to external stakeholders such as
research institutes and companies. The first open datasets have been uploaded
in the **4TU.Centre for Research Data** **repository** (see 2.5.3 Data
Inventory).
|
https://phaidra.univie.ac.at/o:1140797
|
Horizon 2020
|
1154_Rafts4Biotech_720776.md
|
# 1 INTRODUCTION
## 1.1 PURPOSE OF THE DOCUMENT
The goal of this document is to provide a technical description and the
guidelines of Raft4Biotech data management plan version 1. The Deliverable
outlines how the research data collected or generated will be handled during
and after the Raft4Biotech action, describes which standards and methodology
for data collection and generation will be followed, and whether and how data
will be shared. This document follows the template provided by the European
Commission in the
Participant Portal 1 .
Although a DMP is not required for this call Biotec-03-2016 because this
particular call is not adhered to the Open Research Data Pilot, we
nevertheless considered data sharing as an important component of our
dissemination strategy and also an important component of our Responsible
Research and Innovation (RRI) actions. We therefore endeavour to delineate the
main actions to generate a DMP and to identify specific datasets that will be
disseminated using free access/use and also the numbers of channels chosen for
their dissemination. The _Rafts4Biotech_ project DMP will be written in
reference to the Art 29.3 in the Annotated Model Grant Agreement 2 called
“Open Access to Research Data” (research data management). Open access refers
to a practice of giving online access to all scholarly disciplines information
that is free of charge to the end-user. In this way data becomes re-usable,
and the benefit of public investment in the research will be improved. Project
participants must deposit their data in a research data repository and take
measures to make the data available to third parties. The third party should
be able to access, mine, exploit, reproduce and disseminate the data. This
should also help to validate the results presented in scientific publications.
In addition Article 29.3 suggests that participants will have to provide
information, via the repository, about tools and instruments needed for the
validation of project outcomes.
The Data Management plan is a document that is submitted to the EU as project
**Deliverable 8.1.** in June 2017, at early stage of the project. It is
therefore important to note that the document will evolve and further develop
during the project’s life cycle.
# 3 RAFT4BIOTECH DATA MANAGEMENT PLAN
## 2.1 OBJECTIVE OF THE RAFT4BIOTECH PROJECT
The objective of the _Rafts4Biotech_ project is to develop a new and versatile
generation of microbial chassis to produce industrially relevant reactions
confined in subcellular membrane compartments, allowing their fine regulation
to achieve optimal performance and isolation from cellular metabolism to
prevent toxic cross-reactivity. The _Rafts4Biotech_ project will produce a new
generation of reliable and robust synthetic microbial chassis platforms
(SMCPs) in which industrial production processes are confined in bacterial
lipid rafts, thus SMCPs will be released from their classical off-genome
limitations and optimized for the industrial production of numerous
biochemical processes. The _Raft4Biotech_ project will use two of the most
biotechnologically relevant microbial systems, the gram-positive bacterium
_Bacillus subtilis_ and the gram-negative bacterium _Escherichia coli_ , to
engineer synthetic bacterial lipid rafts to optimize the performance of three
challenging biochemical processes of growing biotechnological interest,
including 1) the production of a new class of lipophilic antibiotics to fight
resistant infections. The industrial production of these antimicrobials cannot
be achieved in conventional cell factories because of their inherent toxicity
to bacterial cells and the natural capacity of these lipophilic molecules to
inhibit essential activity in bacterial membranes. 2) the implementation of
complex biochemical pathways of commercial value spatially confined in
synthetic bacterial lipid rafts. Subcellular confinement of these processes in
specific highly lipophilic areas of the bacterial membrane will increase the
efficiency of the targeted enzymatic processes that can be applied to
industrial manufacturing. The general aim of the _Rafts4Biotech_ project is
therefore to develop an innovative technology to optimize multistep enzymatic
processes using a new generation of SMCPs.
dissemination stands, different dissemination channels will be considered
depending on whether the dataset comes in form of a scientific publication or
in contrast, is a large dataset, likely produced by the number of -omics
research disciplines of _Rafts4Biotech_ project.
In the flow-chart below, we delineated the main actions to identify the
specific datasets that will be disseminated.
**Figure 1 Flowchart of the Data Management Plan designed for _Rafts4Biotech_
. **
# 3 DATA SET DESCRIPTION
The data for the _Rafts4Biotech_ project will be produced by various
researchers from the participating laboratories using general guidelines and
procedures to standardize the operative procedure of data and help to decide
which data is dedicated to dissemination and which data requires IPR
protection.
## 3.1 TRANSCRIPTOMIC DATA
Transcriptomic data will be generated via RNA-Seq using high-throughput
sequencing platforms. The resulting raw data (fastq files) and the alignment
information (BAM files) will be stored in the European Nucleotide Archive 4
(ENA) which accepts sequence reads and associated analyses. Data submitted to
ENA is automatically exchanged between International Nucleotide Sequence
Database Collaboration 5 (INSDC) partners: NCBI and DDBJ. These data bases
make data publically available to the community allowing for new discoveries
by comparing these raw data sets with others. Secondly, analyzed data files
will be deposited in the EBI Array Express Archive of Functional Genomics Data
6 . This ensures that transcriptome experiments are available for reuse and
combined analyses. Both, the EBI and the NCBI curates data that is deposit to
their archives, which ensure uniformity of the transcriptome data and its
cognate experiments.
## 3.2 PROTEOMIC DATA
Proteomic data will be generated by LC-MS/MS and SRMlabel-free. Upon
publication data will be freely made available through public databases and
supplementary material. Processed and raw data will be stored either in
ProteomeXchange international public database 7 or PRIDE European repository
7 .
## 3.3 METABOLOMIC DATA
The metabolomic data will be made available freely in the standard formats
defined by the Metabolomics Standards Initiative 8 to enable free and open
sharing of metabolomics data. The specific format will depend on whether
targeted or untargeted methods are being used. Besides the specific data
format the reported metadata are possibly even more crucial and are routinely
reported in our internal storage system. Within the project we will use the
OpenBis platform to store, annotate, and exchange metabolomics data 9 . Upon
publication data will be freely made available through public databases,
supplementary material and public accessible ETH storage for larger sets,
thereby ensuring preservation and re-use.
## 3.4 GENETIC TOOLS AND CONSTRUCTS
Regarding genetic tools and constructs that become available during the
completion of _Rafts4Biotech_ , plasmids and useful genetic constructs that
are released from IPR protection will be deposited at the Addgene non-profit
public repository 10 . Genetic sequences will be available to any requesting
laboratory. Addgene develops a strong interaction between participating
laboratories to implement data curation and standardization of the samples
that are stored in their DNA bank.
# 4 DATA SET STORAGE
During the life cycle of the project data will be stored and systematically
organised in a repository called ‘Data4Raft‘. A website will be generated not
only to be the showcase of all online communication material and also all the
dissemination material (papers, conference abstracts, conference talk videos,
posters), but also (via internal access-restricted intranet to the Data4Raft
repository) to share, preserve, cite, explore and analyse research data. It
will facilitate to make data available to others at the end of the project,
and allows to replicate others work.
# 5 DATA DISSEMINATION
Following the recommendation of the Exploitation Committee of _Rafts4Biotech_
to the Steering Committee, dataset will be disseminated by appropriate
channels, mostly depending on whether the dataset comes in form of a
scientific publication or in contrast, is a large omics dataset.
## 5.1 PUBLICATIONS
Open access will be mandatory for research publications. Open access
publishing (gold open access) will be the preferred option when is available.
However, high-impact journals such as _Science_ and _Nature_ do not provide
this option. Even in the cases of the high-impact journals that offer the gold
open-access option, the publication fees are excessive in such this precludes
many laboratories to publish in gold open access. As we have a clear intention
to cause the highest impact as possible, publication in high-impact journals
will be pursued during the course of this project. In case that the journal
does not have the gold open access option or the publication fees for open
access cannot be assumed by the reporting laboratory, we will choose self-
archiving publishing (green open access) as an option to publish in open
access. This option is free and suitable for such already accepted manuscripts
that become public after a defined embargo period of 2-6 months.
As reference, we will use Europe PubMed Central (Europe PMC) repository 11
This repository is the recommended repository for Green Open Access
publication in other European Funding actions such as ERC grants (European
Research Council). EuropePMC provides access to life sciences articles, books,
patents and clinical studies as well as links to online databases such as
Uniprot, European Nucleotide Archive (ENA), Protein Data Bank Europe (PDBE)
and BioStudies.
Europe PMC meets the agreed definition of a repository as determined by Jisc
and endorsed by RLUK, SCONUL, ARMA and UKCoRR.
## 5.2 IPR AND PUBLIC AVAILABILITY OF DATA
While the data produced and collected in the framework of the _Rafts4Biotech_
project will be publicly available, precautious will be taken regarding
intellectual property rights (IPR). To protect IPR, the ECR will be free to
choose any available form of protection (Patent, Trademark, Industrial design,
Copyright, Trade-secret, Confidentiality). The choice of the most suitable
form should be made on the basis of the specificities of the action and the
type of result. The ECR will develop the IPR section that has been previously
defined in the Consortium Agreement (CA). Rules of confidentiality established
in the CA will define access rights, license agreements and timing for public
availability of the data sets.
## 5.3 PRESERVATION OF THE FINAL DATA SET
Although the WEB-based ‘Data4Raft’ is a useful tool to store the data during
the life cycle of the project, it is not geared towards the long-term
preservation of the data. Therefore, at the completion of the project, the
final dataset will be transferred to a sustainable repository, which ensures
sustainable archiving of the final research data.
Several such repository services promoting sustained access to digital
research data are currently offered and the ECR will search and attempt to
identify a repository promoting sustained access to digital research data.
**END OF THE DOCUMENT**
Project: Rafts 4 Biotech D8.1 Presentation of the Data Management Deliverable
ID: D.8.1 Plan (DMP)
Grant Agreement: 720776 Call ID: H2020-NMBP-BIO-2016 Page: 10 of 12
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Horizon 2020
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1155_ZAero_721362.md
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<table>
<tr>
<th>
**3.**
</th>
<th>
**Data Formats**
</th> </tr> </table>
Initially, data formats used for data representation within the ZAero project
(or at individual project partners) may be proprietary and/or of complex
structure. However, when preparing data for open access, the goal is to use
standard data formats that are well known and easy to handle. Any data that is
present in a highly proprietary complicated format must be converted to easily
readable data formats before publication. For example, HDF5, XML, or standard
image formats (.png, .jpg) are favoured over complex proprietary binary
formats. Every data set that is published will be accompanied by a simple
“readme.txt” text file that provides general information about the data as
well as a detailed description of the data format.
<table>
<tr>
<th>
**4.**
</th>
<th>
**Software and Interoperability**
</th> </tr> </table>
In some cases it makes sense to provide data with software that is able to
load the data and/or process the data in order to make it easier to work with
it. From the current status within the project it is not yet completely clear,
how and if such software can be provided with the data. For each data set
there will be a decision if and how dedicated software can be provided
together with the data set. Preferably, we promote Python as programming
language to work with scientific open access data from the ZAero project.
Since Python has become very popular as the language of choice in research,
this would make it easy for a large number of researchers to get access to the
data and use it.
<table>
<tr>
<th>
**5.**
</th>
<th>
**Licences for Data**
</th> </tr> </table>
For open access publication of pure data, the Creative Commons Attribution 4.0
International Public License (IPL) [5] will be considered in the first place.
This license essentially provides the right to freely copy and redistribute
the material in any medium or format. Re-mixing, transforming, and building
upon the material are explicitly permitted. This is granted for any purpose.
The licensor cannot revoke these freedoms as long as the license terms are
followed.
The use of the IPL is the preferable choice for data publication in ZAero. In
specific cases a different license might be considered.
<table>
<tr>
<th>
**6.**
</th>
<th>
**ZAero Data Sets**
</th> </tr> </table>
The following list includes the data sets which are expected to be acquired
and/or used within the ZAero project. Each data set is characterized by:
* Code: a code consisting of “ZAero-“ and a 3-letter code (e.g. “ZAero-DTD”). - Data set full name: a full name for the data set.
* Responsible beneficiary: the main responsible project partner.
* Description: a general description of the data set.
* Data Format: the expected format of the data.
* Data set size: the expected size of a single data set instance.
* Data sharing: information about whether the data set is confidential (not published) or published under open access criteria.
The subsequent list of data sets defines mainly the type of data. During the
run of the project it can be expected that multiple instances for each data
set will be acquired. For example, dry material triangulation data might be
generated at a first trial and later repeated with modified parameters. In
this case, the specific instances of data sets should additionally be tagged
by
* a date when the data set was acquired and
* a textual description of the conditions/environment under which the data was captured or initially generated.
Multiple instances of a single type of data set will be tagged with a running
number. For example, data sets for code “ZAero-FCD” (see below) will be
referred to as ZAero-FCD-1, ZAero-FCD-2, etc. Additionally, data sets that are
uploaded to the Zenodo portal, will receive a unique DOI.
For the data sets listed below the entry “Data sharing” describes whether data
sharing is considered. “Publication” means that the plan is to publish the
data set (or at least specific instances). “Confidential” means that the
respective data set is not considered for publication.
<table>
<tr>
<th>
**Code**
</th>
<th>
**ZAero-FCD**
</th> </tr>
<tr>
<td>
**Data set full name**
</td>
<td>
Fibre orientation sensor calibration data
</td> </tr>
<tr>
<td>
**Responsible beneficiary**
</td>
<td>
Ideko
</td> </tr>
<tr>
<td>
**Description**
</td>
<td>
Data used to evaluate calibration accuracy of
Profactor’s fibre orientation sensor: calibration images, position information
etc.
</td> </tr>
<tr>
<td>
**Data format**
</td>
<td>
HDF5 file
</td> </tr>
<tr>
<td>
**Data set size**
</td>
<td>
~40 MB
</td> </tr>
<tr>
<td>
**Data sharing**
</td>
<td>
Publication
</td> </tr> </table>
<table>
<tr>
<th>
**Code**
</th>
<th>
**ZAero-LCD**
</th> </tr>
<tr>
<td>
**Data set full name**
</td>
<td>
Laser triangulation sensor calibration data
</td> </tr>
<tr>
<td>
**Responsible beneficiary**
</td>
<td>
Ideko
</td> </tr>
<tr>
<td>
**Description**
</td>
<td>
Data used to evaluate calibration accuracy of Profactor’s laser triangulation
sensor: calibration images, position information, etc.
</td> </tr>
<tr>
<td>
**Data format**
</td>
<td>
HDF5 file
</td> </tr>
<tr>
<td>
**Data set size**
</td>
<td>
~40 MB
</td> </tr>
<tr>
<td>
**Data sharing**
</td>
<td>
Publication
</td> </tr> </table>
<table>
<tr>
<th>
**Code**
</th>
<th>
**ZAero-DTD**
</th> </tr>
<tr>
<td>
**Data set full name**
</td>
<td>
Automated dry material placement triangulation data
</td> </tr>
<tr>
<td>
**Responsible beneficiary**
</td>
<td>
PROFACTOR
</td> </tr>
<tr>
<td>
**Description**
</td>
<td>
Profile depth data that is generated by the in-line depth sensor from
Profactor in the dry material placement shop floor at Danobat.
</td> </tr>
<tr>
<td>
**Data format**
</td>
<td>
16-bit PNG uncompressed image files in combination with simple text-file
providing meta-information about the raw depth data (e.g. calibration
information, position information, etc.)
</td> </tr>
<tr>
<td>
**Data set size**
</td>
<td>
~2 GB
</td> </tr>
<tr>
<td>
**Data sharing**
</td>
<td>
Publication
</td> </tr> </table>
<table>
<tr>
<th>
**Code**
</th>
<th>
**ZAero-FTD**
</th> </tr>
<tr>
<td>
**Data set full name**
</td>
<td>
Automated fibre placement triangulation data
</td> </tr>
<tr>
<td>
**Responsible beneficiary**
</td>
<td>
PROFACTOR
</td> </tr>
<tr>
<td>
**Description**
</td>
<td>
Profile depth data that is generated by the in-line depth
sensor from Profactor in the AFP test environment at MTorres and FIDAMC
</td> </tr>
<tr>
<td>
**Data format**
</td>
<td>
16-bit PNG uncompressed image files in combination with simple text-file
providing meta-information about the raw depth data (e.g. calibration
information, position information, etc.)
</td> </tr>
<tr>
<td>
**Data set size**
</td>
<td>
~1 GB
</td> </tr>
<tr>
<td>
**Data sharing**
</td>
<td>
Publication
</td> </tr> </table>
<table>
<tr>
<th>
**Data set short name**
</th>
<th>
**ZAero-FOS**
</th> </tr>
<tr>
<td>
**Data set full name**
</td>
<td>
Dry material fibre orientation sensor data
</td> </tr>
<tr>
<td>
**Responsible beneficiary**
</td>
<td>
PROFACTOR
</td> </tr>
<tr>
<td>
**Description**
</td>
<td>
Dense fibre orientation data. For a dense grid of points over the surface of a
scanned ADMP part fibre orientations are determined. Each measurement position
is stored as a tuple of 6 floating point values: x, y, z (position as 3D
point), vx, vy, vz (fibre orientation as 3D-vector).
</td> </tr>
<tr>
<td>
**Data format**
</td>
<td>
HDF5 file containing a single Nx6 matrix of floating point values.
</td> </tr>
<tr>
<td>
**Data set size**
</td>
<td>
~10 GB (Probably, data sets will be down-sampled or only parts of it will be
made publicly available to make data easily usable)
</td> </tr>
<tr>
<td>
**Data sharing**
</td>
<td>
Publication
</td> </tr> </table>
<table>
<tr>
<th>
**Data set short name**
</th>
<th>
**ZAero-CTD**
</th> </tr>
<tr>
<td>
**Data set full name**
</td>
<td>
Curing monitoring: temperature data
</td> </tr>
<tr>
<td>
**Responsible beneficiary**
</td>
<td>
AGI
</td> </tr>
<tr>
<td>
**Description**
</td>
<td>
Temperature profile (temperature over time at different locations) during CFRP
curing
</td> </tr>
<tr>
<td>
**Data format**
</td>
<td>
Xyz coordinate for each location, temperature value in °C, time stamp
</td> </tr>
<tr>
<td>
**Data set size**
</td>
<td>
< 1 MB
</td> </tr>
<tr>
<td>
**Data sharing**
</td>
<td>
Confidential
</td> </tr> </table>
<table>
<tr>
<th>
**Data set short name**
</th>
<th>
**ZAero-CFD**
</th> </tr>
<tr>
<td>
**Data set full name**
</td>
<td>
Curing monitoring: flow front data
</td> </tr>
<tr>
<td>
**Responsible beneficiary**
</td>
<td>
AGI
</td> </tr>
<tr>
<td>
**Description**
</td>
<td>
Flow front propagation at different locations during CFRP resin injection
</td> </tr>
<tr>
<td>
**Data format**
</td>
<td>
Xyz coordinate for data points, yes/no value on resin arrival, time stamp
</td> </tr>
<tr>
<td>
**Data set size**
</td>
<td>
< 1 MB
</td> </tr>
<tr>
<td>
**Data sharing**
</td>
<td>
Confidential
</td> </tr> </table>
<table>
<tr>
<th>
**Data set short name**
</th>
<th>
**ZAero-CDD**
</th> </tr>
<tr>
<td>
**Data set full name**
</td>
<td>
Curing monitoring: degree of curing data
</td> </tr>
<tr>
<td>
**Responsible beneficiary**
</td>
<td>
AGI
</td> </tr>
<tr>
<td>
**Description**
</td>
<td>
Information on the degree of curing during resin curing in a CFRP fabrication
process
</td> </tr>
<tr>
<td>
**Data format**
</td>
<td>
Xyz coordinate for each location, value for degree of curing, time stamp
</td> </tr>
<tr>
<td>
**Data set size**
</td>
<td>
< 1 MB
</td> </tr>
<tr>
<td>
**Data sharing**
</td>
<td>
Confidential
</td> </tr> </table>
<table>
<tr>
<th>
**Data set short name**
</th>
<th>
**ZAero-SPE**
</th> </tr>
<tr>
<td>
**Data set full name**
</td>
<td>
Schema Specification
</td> </tr>
<tr>
<td>
**Responsible beneficiary**
</td>
<td>
DS
</td> </tr>
<tr>
<td>
**Description**
</td>
<td>
Schema used in defining the content of the manufacturing database.
(Also used for Effects of Defects Database)
</td> </tr>
<tr>
<td>
**Data format**
</td>
<td>
MSWord document
</td> </tr>
<tr>
<td>
**Data set size**
</td>
<td>
~70 KB
</td> </tr>
<tr>
<td>
**Data sharing**
</td>
<td>
Publication
</td> </tr> </table>
<table>
<tr>
<th>
**Data set short name**
</th>
<th>
**ZAero-MDB**
</th> </tr>
<tr>
<td>
**Data set full name**
</td>
<td>
Manufacturing Data Base
</td> </tr>
<tr>
<td>
**Responsible beneficiary**
</td>
<td>
DAS
</td> </tr>
<tr>
<td>
**Description**
</td>
<td>
Manufacturing database for test model; Will contain simulated and/or measured
data. Whole model data to show the full sweep of zero defect simulation and
decision methods. One version of this data set is expected for each
demonstration within the project.
</td> </tr>
<tr>
<td>
**Data format**
</td>
<td>
HDF5 file
</td> </tr>
<tr>
<td>
**Data set size**
</td>
<td>
~5 GB
</td> </tr>
<tr>
<td>
**Data sharing**
</td>
<td>
A few representative versions of data sets are planned to be published.
Probably, these data sets will be reduced before publication to meet
confidentialy concerns.
</td> </tr> </table>
<table>
<tr>
<th>
**Data set short name**
</th>
<th>
**ZAero-EDB**
</th> </tr>
<tr>
<td>
**Data set full name**
</td>
<td>
Effects of Defects Database (EDB)
</td> </tr>
<tr>
<td>
**Responsible beneficiary**
</td>
<td>
DS
</td> </tr>
<tr>
<td>
**Description**
</td>
<td>
Database which tabulates the results of multiple FE analysis for use as a
lookup during manufacture to determine the knock-down factors to apply for
measured defects.
</td> </tr>
<tr>
<td>
**Data format**
</td>
<td>
HDF5 file
</td> </tr>
<tr>
<td>
**Data set size**
</td>
<td>
~20 MB
</td> </tr>
<tr>
<td>
**Data sharing**
</td>
<td>
Publication
</td> </tr> </table>
<table>
<tr>
<th>
**Data set short name**
</th>
<th>
**ZAero-FEA**
</th> </tr>
<tr>
<td>
**Data set full name**
</td>
<td>
Effects of Defects FE analysis results
</td> </tr>
<tr>
<td>
**Responsible beneficiary**
</td>
<td>
DS
</td> </tr>
<tr>
<td>
**Description**
</td>
<td>
Datasets for multiple FE analysis runs used to determine the effect of
different defects and defect locations on the properties of laminates. Use to
create the lookup EDB for use during manufacture to determine the knock-down
factors to apply for measured defects. Many (100 - 1000s) instances are
expected.
</td> </tr>
<tr>
<td>
**Data format**
</td>
<td>
Proprietary file format
</td> </tr>
<tr>
<td>
**Data set size**
</td>
<td>
~10 GB
</td> </tr>
<tr>
<td>
**Data management**
</td>
<td>
Confidential
</td> </tr> </table>
<table>
<tr>
<th>
**Data set short name**
</th>
<th>
**ZAero-ALU**
</th> </tr>
<tr>
<td>
**Data set full name**
</td>
<td>
Automated fiber placement lay-up definition
</td> </tr>
<tr>
<td>
**Responsible beneficiary**
</td>
<td>
MTorres
</td> </tr>
<tr>
<td>
**Description**
</td>
<td>
Definition of paths for lay-up on AFP machines.
</td> </tr>
<tr>
<td>
**Data format**
</td>
<td>
CATIA file
</td> </tr>
<tr>
<td>
**Data set size**
</td>
<td>
~5 MB
</td> </tr>
<tr>
<td>
**Data sharing**
</td>
<td>
Confidential
</td> </tr> </table>
<table>
<tr>
<th>
**Data set short name**
</th>
<th>
**ZAero-DLU**
</th> </tr>
<tr>
<td>
**Data set full name**
</td>
<td>
Automated dry material placement lay-up definition
</td> </tr>
<tr>
<td>
**Responsible beneficiary**
</td>
<td>
Danobat
</td> </tr>
<tr>
<td>
**Description**
</td>
<td>
Definition of paths for lay-up on ADMP machines.
</td> </tr>
<tr>
<td>
**Data format**
</td>
<td>
JT format (could be provided in any other CAD data format)
</td> </tr>
<tr>
<td>
**Data set size**
</td>
<td>
~5 MB
</td> </tr>
<tr>
<td>
**Data management**
</td>
<td>
Confidential
</td> </tr> </table>
<table>
<tr>
<th>
**7.**
</th>
<th>
**Allocation of Resources**
</th> </tr> </table>
Costs for making data FAIR in the ZAero project will not be extensive. Due to
the use of the Zenodo repository, no additional costs for infrastructure
concerning storage and online availability of the data will occur. Concerning
efforts related to preparation of the data for easy re-use, all partners need
to work together to define clear interfaces within the project anyway. If the
definition of internal interfaces is done in a good way, this will ease
preparation of data for publication. We therefore expect that all efforts will
be covered by the activities within the individual technical work packages
and/or the dissemination work package.
As a coordinator, Profactor will be responsible for keeping an overview and
guiding data management across the whole project. However, individual partners
are associated to data sets and declared “responsible beneficiary” as
specified in the tables above. As such, partners are expected to supervise
data acquisition, storage, and documentation for “their” data sets. In case
specific versions of data sets that allow interesting (scientific) insights
are identified, these will be presented and discussed in the regular telephone
conferences or at general meetings. Activities for publication of data sets
(preparation of scientific publications, upload to online repository, etc.)
will be driven by the respective responsible beneficiaries.
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https://phaidra.univie.ac.at/o:1140797
|
Horizon 2020
|
1157_DYNACOMP_722096.md
|
# DATA SUMMARY
The purpose of the data will be to create a database of material properties
regarding the dynamic behavior of the following **three composite materials:**
* **HexPly® M91** (UD prepreg system, area weight of 268 gsm, cpt of 0.25 mm) with PES and Polymide particles), that shows evidence of displaying a good dynamic behaviour.
* **RTM250** (RTM resin with core shell particles), using a **Hexcel® 46290 WB1010 (5-harness)** (area weight of 280 gsm, cpt of 0.3 mm) fabric, that shows evidence of displaying a good dynamic behaviour.
* **HexPly® 8552** (UD prepreg system with PES), which is not is not suitable for high rate impact behavior.
The following properties will be collected as a function of loading rate:
* Longitudinal tension (LT)
* Transversal tension (TT)
* Longitudinal Compression (LC)
* Transversal Compression (TC)
* In-Plane Sehar (IPS)
* Interlamintar Toughness Mode I (GIC) and Mode II (GIIC)
* Intralaminar toughness (G1+)
* Impact (IMP)
The following micromechanical properties will be collected as a function of
loading rate:
* Hardness
* Indentation elastic modulus
The following micromechanical properties will be collected as a function of
impact energy and indentation strain rate:
* Dynamic hardness
* Coefficient of restitution
The data generated will be numerical, including the load-displacement curves,
as well as the mechanical properties derived from such curves.
The origin of the data will be mechanical tests carried out at the
participating institutions. The data will be used internally and will serve as
a database against which to compare the results of the simulations that will
be produced in the framework of the project.
_Project nº 722096. H2020-MSCA-ITN 2016 European Industrial Doctorate_
______________________________________________________________________________
# FAIR DATA
## Making data findable, including provisions for metatata
A large number of tests will be produced. Each test will generally produce a
CSV file, with the data acquired from the mechanical testing machine, that is
typically load, displacement and time. The Core Scientific Metadata Model
(CSMD) will be considered as a reference.
The names of the files must identify the test unambiguously. **The following
criteria is proposed:**
XXX_YY_ZZ_NN
where:
XXX: refers to material type: M91, RTM250 or 8552.
YY: refers to type of test: LT, TT, LC, TC, IPS, GIC, GIIC, G1+,IMP
ZZ: cross head speed
NN: number of sample
A header will be added with the information about the material, the stacking
of the composite laminate, the type of test and the dimensions of the
specimen.
**For micromechanical test results:**
Impact indentation:
AAAA_BBB_CC_D
where:
AAAA: refers to material type: M91, RTM250 or 8552.
BBB: refers to impulse load
CC: refers to impulse distance
D: refers to number of test: 1,2,...o each type.
Strain rate-constant quasistatic indentation:
AAAA_BBB_CC_D
where:
DYNACOMP _Data management plan_
______________________________________________________________________________
AAAA: refers to material type: M91, RTM250 or 8552.
BBB: refers to maximum load
CC: refers to strain rate
D: refers to number of test: 1,2,...o each type.
## Making data openly accessible
Initially, and due to the commercial value of the data, the **data generated
will be confidential and only opened to the members of the consortium** (see
ownership of results in the Consortium Agreement, CA). **When results of the
research are made available** , for instance through a research publication,
**and clearance is received** from the industrial partners (HEXCEL and MM),
**efforts will be made to make the accompanying data publicly available
through an open repository** . In such case, due to the simplicity of the data
(CSV file), no special software is needed to access the data. The _Zenodo_
repository will be considered in those cases.
## Making data interoperable
The data is easily interoperable as the mechanical tests provide standard
signals such as load, displacement and time.
## Increase data re-use
In those case where the data is publicly available (for instance linked to a
publication, see section 6.2), it is expected that the data will be licensed
under Public Domain.
# ALLOCATION OF RESOURCES
The costs are minimal as open and free tools are considered for making data
publicly availability. The project management office (PMO) at IMDEA is
responsible for data management, under the supervision of the project
coordinator (D. Jon Molina), which is covered by funding received by funding
from the REA.
# DATA SECURITY
Zenodo open repository is considered safe for long-term preservation.
_Project nº 722096. H2020-MSCA-ITN 2016 European Industrial Doctorate_
______________________________________________________________________________
# ETHICAL ASPECTS
Whenever data is considered publicly available, written consent will be seek
from the participating partners.
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Horizon 2020
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1158_enCOMPASS_723059.md
|
Executive Summary 5
1 DATA SUMMARY 6
2 FAIR DATA 8
2.1 Making data findable, including provisions for metadata 8
2.2 Making data openly accessible 8
2.3 Making data interoperable 8
2.4 Increase data re-use (through clarifying licenses) 8
3 ALLOCATION OF RESOURCES 9
4 DATA SECURITY 10
5 ETHICAL ASPECTS 11
6 OTHER 13
# **EXECUTIVE SUMMARY**
The Commission is running a flexible pilot under Horizon 2020 called the Open
Research Data Pilot (ORD pilot).
Projects participating in the pilot must submit a first version of the DMP (as
a deliverable) within the first 6 months of the project. The DMP needs to be
updated over the course of the project whenever significant changes arise.
The enCOMPASS project is taking part in this pilot and this is the initial
version of the Data Management Plan set up according to the ORD pilot
specifications.
Therefore, this document describes the data, how it will be created, how it
will be stored and backed-up, who owns it and who is responsible for the
different data.
# DATA SUMMARY
The enCOMPASS project will collect data related to energy consumption in
households, in schools and in public buildings in pilots located in Germany,
in the city of Haßfurt, in Greece, in Athens and Thessaloniki, and in
Switzerland, in the municipality of Gambarogno.
_Purpose of data collection_
The purpose of the data collection is to implement and validate an integrated
socio-technical approach to behavioural change for energy saving, by
developing innovative user-friendly digital tools for making energy data
consumption available and understandable for the different users and
stakeholders (residents, employees, pupils, building managers, utilities, ICT
providers) empowering them to collaborate to achieve energy savings and manage
their energy needs in energy efficient, cost-effective and comfort-preserving
ways.
_Relation to the objectives of the project_
In order to provide information to energy users about their consumption levels
and their potential for improvement it is necessary to collect, process, and
analyse a variety of data sources, from smart meter reading, providing
detailed energy consumption, to building characteristics, to user
psychographic data. _Types and format of data_
There are 3 major types of data:
* **Feature data** : these data describe the structural properties of a building (e.g. the surface area, the type of insulation, the lighting installation, etc.) or of an individual (age, education, environmental attitude, comfort preferences, etc.). These data are both quantitative and qualitative.
* **Sensor data** : these data are data streams regarding observed measurements including energy consumption, temperature, humidity, luminance, presence.
* **Interaction data** : these data represent the interaction of the users with the enCOMPASS platform such as logs of accesses, actions performed and so on.
_Re-use of existing data_
The data used in the enCOMPASS project is being collected and generated by the
project itself.
_Origin of data_
* Psychographic data of residential users will be provided by the users themselves through voluntary answering to data collection questionnaires in an anonymous way.
* Electrical and thermal energy consumption will be provided by smart meter readings, where available. Power consumption of individual appliances will be provided by smart plugs, where available.
* Local comfort measurements in households and buildings, such as temperature, humidity, luminance, will be collected by in-situ sensors, installed by the project.
* Presence of humans in monitored buildings and rooms will be also be monitored by in-situ sensors installed by the project.
* Interaction data is generated by the use of the enCOMPASS platform by the users. The data can refer to interaction with web and mobile apps.
_Users of the data_
The collected data will be mainly used to build profile of users in order to
provide meaningful and effective suggestions for energy efficiency. The data
is also collected to experimentally verify the impact of the energy saving
suggestions. Data will be therefore used by the researchers of the enCOMPASS
project. Other participants of the enCOMPASS project will also have access to
the data (e.g. building managers, home owners, utility operators, etc.) but
specific interfaces will be provided to them to access the data and therefore
they are not expected to directly access the datasets.
# FAIR DATA
## MAKING DATA FINDABLE, INCLUDING PROVISIONS FOR METADATA
The data generated by the enCOMPASS project will be annotated using public
metadata standards where appropriate. In particular, the DDI (Data
Documentation Initiative _http://www.ddialliance.org/_ ) will be used for
socio-economic data where possible, and OGC's Observation and Measurements
( _http://www.opengeospatial.org/standards/om_ ) will be used for sensor
generated data.
Provided the data is not covered by non-disclosure agreements and provided
that it does not violate ethics, it will be published using the Zenodo
platform ( _https://zenodo.org_ ), which enables the association of a DOI to
the generated data sets.
Data sets will be catalogued by a structured approach to a naming convention
defined as follows:
**enCOMPASS_TYP_AAA_CC_PP_BTY_BLDID_vXX_(short_title)**
* T: Type of data, one of FET (feature), SEN (sensor), INT (interaction)
* AAA: Author organisation (use the acronyms of the contract)
* CC_PP: Country and Place of origin, one of: DE_HS for Haßfurt, GR_AT for Athens, GR_TH for Thessaloniki, CH_GM for Gambarogno.
* BTY: building type, one of: RES (residential), SCH (school), PUB (public)
* BLDID: building id, a unique id associated to each building in the project vXX: progressive version number
This convention might be revised according to the specific needs arising
during the project.
## MAKING DATA OPENLY ACCESSIBLE
All data will be made available after anonymisation where required and
appropriate. Before publishing data sets, even after anonymisation, clearance
must be issued by the data owners (typically the utilities and the
municipalities)
Data will be published using an open platform such as Zenodo, aiming for
compliancy with the OpenAIRE initiative.
Restricted data will not be published online and will not be made available to
the public.
## MAKING DATA INTEROPERABLE
The interoperability of the collected data will be ensued by the compliance to
standard ontologies as much as possible, in particular, as stated in Section
2.1, to the DDI (Data Documentation Initiative _http://www.ddialliance.org/_ )
for socio-economic data where possible, and OGC's Observation and Measurements
( _http://www.opengeospatial.org/standards/om_ ) for sensor generated data.
## INCREASE DATA RE-USE (THROUGH CLARIFYING LICENSES)
Access to data will be Open Access as it is published on Zenodo. Data will be
available to the public as long as the Zenodo repository will be available.
Copies of the data will be retained by the partners for a maximum of three
years after the project’s end.
# ALLOCATION OF RESOURCES
The cost for making enCOMPASS data fair can be ascribed to Task 8.4 "Open data
and standards", where a total of 7 months has been allocated. Part of these
resources will be thus devoted to the preparation and organisation of data for
making it FAIR compliant.
The enCOMPASS project has designed Stelios Krinidis of CERTH as Data Manager
and each site has an individual data manager, which reports to the project
Data Manager. These persons are:
* Greek pilot: Konstantinos Arvanitis (WVT)
* German pilot: Felix Zösch (SHF)
* Swiss pilot: Marco Bertocchi (SES)
The cost for long term preservation are dramatically reduced thanks to the
availability of the Zenodo platform.
# DATA SECURITY
Sensitive data is transmitted over the Internet only by secure channels such
as _ssh_ and _sftp_ . In general the enCOMPASS project does not deal with data
that can be directly traced to a single individual (anonymization). There will
be certain type of data which the enCOMPASS users will generate in conjunction
with the use of the platform (login ids, emails, passwords) that will be
transmitted only over https secured channels.
The enCOMPASS servers where data are temporarily stored for data processing
and elaboration are protected by state-of-the-art techniques and they are
constantly monitored and regularly updated to limit the risk of intrusion.
# ETHICAL ASPECTS
Although enCOMPASS does not introduce any critical ethical issues or problems,
several considerations typical to ICT and IoT applications and on-site trials
shall be taken into account. The consortium is fully aware of these and has
the necessary experience to address them seamlessly.
enCOMPASS proposed solutions does not expose, use or analyse personal
sensitive data for any purpose. In this respect, no ethical issues related to
personal sensitive data are raised by the technologies to be employed in Pilot
sites foreseen in Germany, Greece and Switzerland. However, enCOMPASS
consortium is fully aware of the privacy-related implications of the proposed
solutions and respects the ethical rules and standards of H2020, and those
reflected in the Charter of Fundamental Rights of the European Union.
Generally speaking, ethical, social and data protection considerations are
crucial and will be given all due attention. enCOMPASS will address any
ethical and other privacy issues in **WP3** , as well as allocated specific
**Task 3.2** to review the deployed solutions for privacy and security. Thus
enCOMPASS will assure the investigation, management and monitoring of ethical
and privacy issues that could be relevant to its envisaged technological
solution and will establish a close-cooperation with the Ethics Helpdesk of
the European Commission.
The consortium is aware that a number of privacy and data protection issues
could be raised by the activities (in WP3-WP5) to be performed within the
scope of the project. The project involves the carrying out of data collection
in all pilots in order to assess the effectiveness of the proposed solution.
For this reason, human participants will be involved in certain aspects of the
project and data will be collected. This will be done in full compliance with
any European and national legislation and directives relevant to the country
where the data collections are taking place (INTERNATIONAL/EUROPEAN):
* The Universal Declaration of Human Rights and the Convention 108 for the Protection of Individuals with Regard to Automatic Processing of Personal Data.
* Directive 95/46/EC & Directive 2002/58/EC of the European parliament regarding issues with privacy and protection of personal data and the free movement of such data.
* Core ethical issues and with the European Charter of Fundamental Human Rights and as well as with any relevant EU standard in the fields of privacy and data protection.
In addition to this, to further ensure that the fundamental human rights and
privacy needs of participants are met, the privacy-preserving data collection
activities within encompass project will further comprise of the writing of
detailed ethical guidelines for the project (deliverable D10.1). In order to
protect the privacy rights of participants (e.g. public building employees,
students, visitors, etc.), a number of best practice principles will be
followed. These include:
* No data will be collected without the explicit informed consent of the individuals under observation. This involves being open with participants about what they are involving themselves in and ensuring that they have agreed fully to the procedures/research being undertaken by giving their explicit consent.
* No data collected will be sold or used for any purposes other than the current project.
* A data minimisation policy will be adopted at all levels of the project and will be supervised by each Industrial Pilot Demonstration responsible. This will ensure that no data, which is not strictly necessary to the completion of the current study will be collected.
* Any shadow (ancillary) personal data obtained during the course of the research will be immediately cancelled. However, the plan is to minimize this kind of ancillary data as much as possible. Special attention will also be paid to complying with the Council of Europe’s Recommendation R(87)15 on the processing of personal data for police purposes, Art.2:
“The collection of data on individuals solely on the basis that they have a
particular racial origin, particular religious convictions, sexual behaviour
or political opinions or belong to particular movements or organisations which
are not proscribed by law should be prohibited. The collection of data
concerning these factors may only be carried out if absolutely necessary for
the purposes of a particular inquiry.”
* Compensation, if and when provided, will correspond to a simple reimbursement for hours lost as a result of participating in the study; special attention will be paid to avoid any form of unfair inducement.
* If employees of partner organizations, are to be recruited, specific measures will be in place in order to protect them from a breach of privacy/confidentiality and any potential discrimination; in particular, their names will not be made public and their participation will not be communicated to their managers.
More information related to the ethical aspects related to the data collected
and utilized within encompass project will be documented at deliverable D10.1.
# OTHER
The implementation and deployment of core components will be performed in
Germany, Greece and Switzerland, under the leadership of each Pilot Site
responsible (SHF, WVT and SES respectively). In the following, the consortium
outlines the legislation for the countries involved in the pilots:
1. German Pilot Trials in schools, residential and public buildings in Haßfurt, Germany have to comply with German legislation “German Federal Data Protection Act (BDSG)” and the responsible state data protection acts, relating to the collection, processing and use of personal data: _http://www.gesetze-iminternet.de/bdsg_1990/index.html_
* Federal regulatory authorities and ethical committees: German Federal Data Protection Authority (BfDI) _http://www.bfdi.bund.de/_
* State regulatory authorities: Bavarian Data Protection Authority _https://www.datenschutzbayern.de/_
2. Greek Pilot in National Documentation Centre in Athens and Residential & Offices buildings of WATT+VOLT customer portfolio in Greece, have to comply with Greek legislation “Law 2472/1997 (and its amendment by Law 3471/2006) of the Hellenic Parliament”.
* Regulatory authorities and ethical committees: Hellenic Data Protection Authority _http://www.dpa.gr/_
3. Swiss Pilot Trials in Locarno have to comply with Swiss federal legislation “235.1 Legge federale sulla protezione dei dati (LPD) / Federal Act on Data Protection (FADP)” relating to the processing of personal data: _https://www.admin.ch/opc/en/classified-compilation/19920153/index.html_ and _https://www.admin.ch/opc/it/classified-compilation/19920153/201401010000/235.1.pdf_
* Regulatory authorities and ethical committees: Federal Council _https://www.admin.ch/gov/en/start.html_
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Horizon 2020
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1159_CPaaS.io_723076.md
|
# Introduction
As described with the H2020 guidelines, Research funding organisations, as
well as organisations undertaking publicly funded research, have an obligation
to optimize the use the funds they have been granted. Part of this
optimization is that data sets resulting from public funded research must be
made available to other researchers to either verify the original results
(which is integral part of proper scientific approach), or to build upon them.
In order to achieve this high-level objective, a data management policy has to
be implemented and thoroughly followed by the CPaaS.io consortium as a whole,
even if –per se- not all CPaaS.io partners will be involved in all aspects of
those policies/principles.
The Data Management Plan (DMP) is a living document (with two formal versions
of the same deliverable released in M6 and M30 respectively) that describes
the data management policy (i.e. the management principles) and collected and
generated data sets. It covers all aspects introduced in the “Guidelines on
Data Management in Horizon 2020”, which are:
1. Precise description of the collected and generated data (nature of data, related domain ontologies, standards and data formats used,…)
2. Detail about various aspects of the data management (how it is stored, by whom, under which responsibility, how it is secured, how it is sustained and backed up)
3. Sharing principles (licensing, access methods,...)
4. Detail about how the privacy is maintained
In this second iteration of the Data Management Plan we provide the current
and final status on data generation and management and some final statements.
## Delta with D7.2
## This section highlight updates brought to version 2 of the Data Management
Plan: • Enhanced user Experience: Section2.1 has been extended in order to
include the Sapporo Snow
## Festival event;
## • 3 applications from Japan in Section 2;
## • Final statements in Section 3 for EU partners and Data Management Plans
for Japanese partners.
# CPaaS.io Research Data
This section introduces the different EU-side use-cases as described in the
CPaaS.io Description of Work document and the applications built upon them. It
also describes the collected data (meaning the semantically annotated raw-data
with no extra added value) and the generated data (meaning the semantic value-
added information built from the annotated raw-data using various technics
like analytics or reasoning). Part of the information described in this
section can be found in a more complete form in CPaaS.io deliverable D2.1 [1].
The 3 use-cases considered in CPaaS.io are:
* Managing Fun and Sport events, which derives into 3 different applications at three different locations (and therefore based on 3 distinct data sets in Amsterdam, Sapporo and Tokyo)
* Waterproof Amsterdam
* Yokosuka Emergency Medical
The 5 derived applications are:
* Enhanced User Experience
* Sapporo Visitor Experience
* Tokyo Public Transportation (case 1 and case 2)
* Waterproof Amsterdam
* Yokosuka Emergency Medical Care
## Data from Enhanced User Experience application
### Short description
The core idea of this application is to use IoT sensors and analytics to
enhance people’s experience while visiting or participating at a fun or sports
event. Wearables and mobile phones are used as sensors in order to learn about
the activities of event participants. Event participants may include members
of the audience, but also performing artists or athletes. For instance AGT has
previously equipped referees and cheerleaders in basketball matches with
wearable sensors and created content based on the analysed data for
consumption on site and for distribution via TV broadcasting, social media and
other digital distribution channels 1 . Furthermore the application uses
sensor deployed at the venue to measure and analyse fan behaviour and
engagement.
**Data collected for the Enhanced User Experience application (Color Run and
Sappor Snow Festival)** Table 1 summarizes the data from the Enhanced User
Experience application covering both events, the Color Run and the Sapporo
Snow Festival.
**Table 1: Data collected for Managing Fun and Sport events scenario**
<table>
<tr>
<th>
**Biometric data**
</th>
<th>
</th> </tr>
<tr>
<td>
**Detailed Description**
</td>
<td>
This data set includes a range of measurements from wearables such as
wristbands, chest straps and smart sportswear that provides biometric
measurements including heart rate, breathing rate and galvanic skin response,
burned calories measurements and skin temperature.
</td> </tr>
<tr>
<td>
**OGD or private data**
</td>
<td>
Private
</td> </tr>
<tr>
<td>
**Personal Data**
</td>
<td>
Yes
</td> </tr>
<tr>
<td>
**Hosting**
</td>
<td>
External, CPaaS.io
</td> </tr>
<tr>
<td>
**Data Provider**
</td>
<td>
AGT, YRP
</td> </tr>
<tr>
<td>
**Format**
</td>
<td>
JSON, RDF
</td> </tr>
<tr>
<td>
**Update Frequency**
</td>
<td>
up to every 200ms
</td> </tr>
<tr>
<td>
**Update Size**
</td>
<td>
~1 KB
</td> </tr>
<tr>
<td>
**Data Source**
</td>
<td>
Sensor
</td> </tr>
<tr>
<td>
**Sensor**
</td>
<td>
Wristband, chest strap, smart shirts
</td> </tr>
<tr>
<td>
**Number of Sensors per person**
</td>
<td>
~6
</td> </tr> </table>
<table>
<tr>
<th>
**GPS Traces**
</th>
<th>
</th> </tr>
<tr>
<td>
**Detailed Description**
</td>
<td>
GPS traces include positional data including altitude information as delivered
by GPS devices.
</td> </tr>
<tr>
<td>
**OGD or private data**
</td>
<td>
Private
</td> </tr>
<tr>
<td>
**Personal Data**
</td>
<td>
Yes
</td> </tr>
<tr>
<td>
**Hosting**
</td>
<td>
External, CPaaS.io
</td> </tr>
<tr>
<td>
**Data Provider**
</td>
<td>
AGT, YRP
</td> </tr>
<tr>
<td>
**Format**
</td>
<td>
common GPS formats (GPX, KML, CSV, NMEA)
</td> </tr>
<tr>
<td>
**Update Frequency**
</td>
<td>
Up to 1s
</td> </tr>
<tr>
<td>
**Update Size**
</td>
<td>
< 1KB
</td> </tr>
<tr>
<td>
**Data Source**
</td>
<td>
Sensor
</td> </tr>
<tr>
<td>
**Sensor**
</td>
<td>
GPS sensor in wristbands and mobile phones
</td> </tr>
<tr>
<td>
**Number of Sensors per person**
</td>
<td>
1-2
</td> </tr> </table>
<table>
<tr>
<th>
**Motion Data**
</th>
<th>
</th> </tr>
<tr>
<td>
**Detailed Description**
</td>
<td>
Motion data that measures hand and body movements based on accelerometer and
gyroscope sensors
</td> </tr>
<tr>
<td>
**OGD or private data**
</td>
<td>
Private
</td> </tr>
<tr>
<td>
**Personal Data**
</td>
<td>
Yes
</td> </tr>
<tr>
<td>
**Hosting**
</td>
<td>
External, CPaaS.io
</td> </tr>
<tr>
<td>
**Data Provider**
</td>
<td>
AGT, YRP
</td> </tr>
<tr>
<td>
**Format**
</td>
<td>
JSON
</td> </tr>
<tr>
<td>
**Update Frequency**
</td>
<td>
Up to every 16 ms
</td> </tr>
<tr>
<td>
**Update Size**
</td>
<td>
~ 200 byte per sensor reading
</td> </tr>
<tr>
<td>
**Data Source**
</td>
<td>
Sensors
</td> </tr>
<tr>
<td>
**Sensor**
</td>
<td>
Accelerometer and gyroscope sensors of mobile phones, wristband and other
wearables
</td> </tr>
<tr>
<td>
**Number of Sensors per person**
</td>
<td>
2-3
</td> </tr> </table>
<table>
<tr>
<th>
**Step Counts**
</th>
<th>
</th> </tr>
<tr>
<td>
**Detailed Description**
</td>
<td>
This data set contains step counts.
</td> </tr>
<tr>
<td>
**OGD or private data**
</td>
<td>
Private
</td> </tr>
<tr>
<td>
**Personal Data**
</td>
<td>
Yes
</td> </tr>
<tr>
<td>
**Hosting**
</td>
<td>
External, CPaaS.io
</td> </tr>
<tr>
<td>
**Data Provider**
</td>
<td>
AGT, YRP
</td> </tr>
<tr>
<td>
**Format**
</td>
<td>
JSON
</td> </tr>
<tr>
<td>
**Update Frequency**
</td>
<td>
Up to 1Hz
</td> </tr>
<tr>
<td>
**Update Size**
</td>
<td>
~ 200 byte per sensor reading
</td> </tr>
<tr>
<td>
**Data Source**
</td>
<td>
Sensors
</td> </tr>
<tr>
<td>
**Sensor**
</td>
<td>
Step count measurement of wristband
</td> </tr>
<tr>
<td>
**Number of Sensors per**
</td>
<td>
1-2
</td> </tr>
<tr>
<td>
**person**
</td>
<td>
</td> </tr> </table>
<table>
<tr>
<th>
**Environmental Data**
</th>
<th>
</th> </tr>
<tr>
<td>
**Detailed Description**
</td>
<td>
This data set environmental data such light intensity and barometric pressure.
The data is primarily collected from wearable sensors.
</td> </tr>
<tr>
<td>
**OGD or private data**
</td>
<td>
Private
</td> </tr>
<tr>
<td>
**Personal Data**
</td>
<td>
Yes
</td> </tr>
<tr>
<td>
**Hosting**
</td>
<td>
External, CPaaS.io
</td> </tr>
<tr>
<td>
**Data Provider**
</td>
<td>
AGT, YRP
</td> </tr>
<tr>
<td>
**Format**
</td>
<td>
JSON
</td> </tr>
<tr>
<td>
**Update Frequency**
</td>
<td>
Up to 1Hz
</td> </tr>
<tr>
<td>
**Update Size**
</td>
<td>
~ 200 byte per sensor reading
</td> </tr>
<tr>
<td>
**Data Source**
</td>
<td>
Sensors
</td> </tr>
<tr>
<td>
**Sensor**
</td>
<td>
Sensors in wristband
</td> </tr>
<tr>
<td>
**Number of Sensors per person**
</td>
<td>
1-2
</td> </tr> </table>
<table>
<tr>
<th>
**Mobile Camera videos**
</th>
<th>
</th> </tr>
<tr>
<td>
**Detailed Description**
</td>
<td>
This data set contains videos recorded by mobile cameras worn by Color Run
participants.
</td> </tr>
<tr>
<td>
**OGD or private data**
</td>
<td>
Private
</td> </tr>
<tr>
<td>
**Personal Data**
</td>
<td>
Yes
</td> </tr>
<tr>
<td>
**Hosting**
</td>
<td>
External, CPaaS.io
</td> </tr>
<tr>
<td>
**Data Provider**
</td>
<td>
AGT, YRP
</td> </tr>
<tr>
<td>
**Format**
</td>
<td>
MP4
</td> </tr>
<tr>
<td>
**Update Frequency**
</td>
<td>
30fps
</td> </tr>
<tr>
<td>
**Update Size**
</td>
<td>
(~45kbps)
</td> </tr>
<tr>
<td>
**Data Source**
</td>
<td>
Mobile Camera
</td> </tr>
<tr>
<td>
**Sensor**
</td>
<td>
GoPro Hero4 Camera
</td> </tr>
<tr>
<td>
**Number of Sensors per person**
</td>
<td>
1
</td> </tr> </table>
### Data generated by the Enhanced User Experience application (Color Run and
Sapporo Snow Festival)
The Enhanced User Experience application generates or uses the following types
of data
1. **User Activity** . Mainly on motion data and therefore private information. A user activity is always linked to a user and therefore personal information. The re-use of the data is possible within the boundaries defined in the consent forms used to collect the data.
2. **Dominant Colors** . Provides information about the prevailing colour in a video feed and is used for detecting colour stations in the Color Run. The output is a colour value, duration and location. The generated can be provided in anonymised form, but requires further examination to what degree it can be opened.
3. **Clothing Analysis** . Clothing Analysis uses deep learning techniques to determine metrics based on clothing styles derived from images. By nature this metrics are linked to user and therefore reflect private data that can only be reused in the boundaries of the consent forms used to collect the data.
4. **Running Type.** This classifies whether the participant ran more like a fun or an ambitious runner during an event. This is personal data.
5. **Dance Energy.** Provides a measure of the accumulated energy overtime while dancing per user. This is personal data.
6. **Emotions.** Refers to people’s emotion during the event. This is personal data, but also aggregated data is used.
7. **Tube Rider Classification.** Classifies the tube ride based on how intensely the tube rotated. This is personal data.
8. **Event Sentiment.** This is an aggregated measure of sentiments per event derived from Tweets.
9. **Ride Bumpiness.** Provides a metric for the bumpiness of a ride. This is personal dat.
10. **Throw Intensity.** This provides a measure for the intensity of throwing a ball such as a snow ball. This is personal dat.
**Table 2: Data generated for the Enhanced User Experience application**
<table>
<tr>
<th>
**Types of generated data**
</th>
<th>
**Based on…**
</th>
<th>
**Anonymised**
**Y/N**
</th>
<th>
**Open**
**Y/N**
</th> </tr>
<tr>
<td>
User Activity
</td>
<td>
Motion Data
</td>
<td>
N
</td>
<td>
reusable, but not open
</td> </tr>
<tr>
<td>
Dominant Colour
</td>
<td>
Mobile Camera Videos
</td>
<td>
Y
</td>
<td>
Reusable, but not fully open
</td> </tr>
<tr>
<td>
Clothing Analysis
</td>
<td>
Mobile Camera Videos, Public Images
</td>
<td>
N
</td>
<td>
Reusable, but not open
</td> </tr>
<tr>
<td>
Running Type
</td>
<td>
Motion Data
</td>
<td>
N
</td>
<td>
N
</td> </tr>
<tr>
<td>
Dance Energy
</td>
<td>
Motion Data
</td>
<td>
N
</td>
<td>
N
</td> </tr>
<tr>
<td>
Emotions
</td>
<td>
Video Data
</td>
<td>
N
</td>
<td>
N
</td> </tr>
<tr>
<td>
Tube Rider Classification
</td>
<td>
Motion Data
</td>
<td>
N
</td>
<td>
N
</td> </tr>
<tr>
<td>
Event Sentiment
</td>
<td>
Tweets
</td>
<td>
Y
</td>
<td>
N
</td> </tr>
<tr>
<td>
Ride Bumpiness
</td>
<td>
Motion Data
</td>
<td>
N
</td>
<td>
N
</td> </tr>
<tr>
<td>
Throw Intensity
</td>
<td>
Motion Data
</td>
<td>
N
</td>
<td>
N
</td> </tr> </table>
## Data from Waterproof Amsterdam
### Short description
Extreme rainfall and periods of continued drought are occurring more and more
often in urban areas. Because of the rainfall, peak pressure on a
municipality’s sewerage infrastructure needs to be load balanced to prevent
flooding of streets and basements. With drought, smart water management is
required to allow for optimal availability of water, both underground as well
as above ground.
The Things Network develops the Amsterdam Waterproof application, which is a
software tool creating a network of smart, connected rain buffers, be it rain
barrels, retention rooftops or buffer otherwise, that can be both monitored
and controlled centrally by the water management authority. Third party
hardware providers will connect their buffers to this tool for uplink and
downlink data transmission. External data such as weather data and sewerage
capacity are added, in order to calculate the optimal filling degree of each
buffer and so operate a pump or valve in the device. Waternet is the local
water management company who will be the main user of the application.
### Data collected for the Waterproof Amsterdam application
In the section below are the data sets used for the Waternet application. It
consists of device data (rain buffer information), public weather data and
government data about physical infrastructure. Device data will be stored in
the application and could be stored in CPaaS, especially as it contains
private data like name and address of device owner. As this stage however we
cannot determine whether this privacy data will be shared by the vendors of
the devices, who are also the ones maintaining them. They are the only actor
who has direct contact with the end user and/or owner of the device.
(Historical) weather data is publicly available on the web, so there is no
need to store this data. It will be provided by a subscription data feed from
the web. The third data set is already owned and stored by Waternet, so there
is also no need for storage capabilities.
**Table 3: Data collected for the Waterproof Amsterdam scenario**
<table>
<tr>
<th>
**Weather data**
</th>
<th>
</th> </tr>
<tr>
<td>
**Detailed Description**
</td>
<td>
Upcoming weather displaying periods of heavy rain or drought
</td> </tr>
<tr>
<td>
**OGD or private data**
</td>
<td>
OGD
</td> </tr>
<tr>
<td>
**Personal Data**
</td>
<td>
No
</td> </tr>
<tr>
<td>
**Hosting**
</td>
<td>
Platform
</td> </tr>
<tr>
<td>
**Data Provider**
</td>
<td>
KNMI – Dutch weather forecast agency
</td> </tr>
<tr>
<td>
**Format**
</td>
<td>
HDF5/JSON
</td> </tr>
<tr>
<td>
**Update Frequency**
</td>
<td>
Hourly
</td> </tr>
<tr>
<td>
**Update Size**
</td>
<td>
20kb
</td> </tr>
<tr>
<td>
**Data Source**
</td>
<td>
Sensors
</td> </tr>
<tr>
<td>
**Sensor**
</td>
<td>
Water sensor
</td> </tr>
<tr>
<td>
**Number of Sensors**
</td>
<td>
unknown
</td> </tr> </table>
<table>
<tr>
<th>
**Rain buffer information**
</th>
<th>
</th> </tr>
<tr>
<td>
**Detailed Description**
</td>
<td>
Specific information about each rainbuffer (rooftop, barrel, underground
storage)
* Buffer size and type
* Filling degree
* Temperature
* Location
* Battery status
* Pump/valve capacity
* Active pump/valve hours
* Owner name, address, contact information
</td> </tr>
<tr>
<td>
**OGD or private data**
</td>
<td>
Private
</td> </tr>
<tr>
<td>
**Personal Data**
</td>
<td>
Yes – anonymised and not open
</td> </tr>
<tr>
<td>
**Hosting**
</td>
<td>
Platform
</td> </tr>
<tr>
<td>
**Data Provider**
</td>
<td>
Rain buffer hardware provider
</td> </tr>
<tr>
<td>
**Format**
</td>
<td>
JSON
</td> </tr>
<tr>
<td>
**Update Frequency**
</td>
<td>
Hourly
</td> </tr>
<tr>
<td>
**Update Size**
</td>
<td>
10b
</td> </tr>
<tr>
<td>
**Data Source**
</td>
<td>
Sensors
</td> </tr>
<tr>
<td>
**Sensor**
</td>
<td>
Water sensor or infrared sensor
</td> </tr>
<tr>
<td>
**Number of Sensors**
</td>
<td>
1 per buffer
</td> </tr> </table>
<table>
<tr>
<th>
**Sewerage processing capacity**
</th> </tr>
<tr>
<td>
**Detailed Description**
</td>
<td>
Geographical data on water infrastructure depicting remaining capacity of
sewerage
</td> </tr>
<tr>
<td>
**OGD or private data**
</td>
<td>
Private
</td> </tr>
<tr>
<td>
**Personal Data**
</td>
<td>
No
</td> </tr>
<tr>
<td>
**Hosting**
</td>
<td>
External
</td> </tr>
<tr>
<td>
**Data Provider**
</td>
<td>
Waternet
</td> </tr>
<tr>
<td>
**Format**
</td>
<td>
XML
</td> </tr>
<tr>
<td>
**Update Frequency**
</td>
<td>
Hourly
</td> </tr>
<tr>
<td>
**Update Size**
</td>
<td>
1kb
</td> </tr>
<tr>
<td>
**Data Source**
</td>
<td>
Sensors, maps
</td> </tr>
<tr>
<td>
**Sensor**
</td>
<td>
Water sensor
</td> </tr>
<tr>
<td>
**Number of Sensors**
</td>
<td>
unknown
</td> </tr> </table>
**Data generated by the Waterproof Amsterdam application** The Waterproof
Amsterdam generates different types of data.
1. Open/close command per buffer. This is the most important data generated, as it determines when an actuator inside a buffer should be operated (valve open or pump on). Based on all data sources available, an algorithm will determine which conditions are required to perform a certain command. The commands can be open and close, or a value in between as different water discharge mechanisms have different capacities (i.e. a percentage of full capacity)
2. Aggregated remaining buffer capacity per area. Waternet as the primary user of the application needs to monitor the total remaining capacity to buffer rain water, to understand whether there will be plenty capacity to catch up rain water in moments of heavy rainfall.
3. Aggregated litres of rain water processed per area. This is a metric to be used to show the impact the micro buffer network has generated over time. These insights may be used for PR and marketing purposes to stimulate individuals and companies to also buy and install such rain buffers.
The open data in the table below can be reused to perform analytics on
historical data, and could be open data through a public (graphical or
application) interface for third parties to interact with.
**Table 4: Data generated for the Waterproof Amsterdam application**
<table>
<tr>
<th>
**Types of generated data**
</th>
<th>
**Based on…**
</th>
<th>
**Anonymised**
**Y/N**
</th>
<th>
**Open**
**Y/N**
</th> </tr>
<tr>
<td>
Open/close command per buffer
</td>
<td>
All data sets
</td>
<td>
Y
</td>
<td>
N
</td> </tr>
<tr>
<td>
Aggregated remaining buffer capacity
(street, area, city level)
</td>
<td>
Individual rain buffers filling degree and location, map
</td>
<td>
Y
</td>
<td>
Y
</td> </tr>
<tr>
<td>
Aggregated litres processed by the buffers
</td>
<td>
Individual rain buffer pump hours run and pump capacity, map
</td>
<td>
Y
</td>
<td>
Y
</td> </tr> </table>
## Data from Sapporo Visitor Experience application
### Short description
The Sapporo _Visitor Experience application_ is part of a set of applications
that will use the CPaaS.io platform to manage fun and sports event. This
application focuses on tourist information services including event
information and public transportation information.
### Data collected for the Sapporo Visitor Experience application
We established Sapporo Open Data Association whose members are data providers
such as Sapporo City, companies and organizations in tourism, sports and
transportations. This association collects data from those members, transforms
them to Open Data and publishes them on the Sapporo Open Data
Portal.
<table>
<tr>
<th>
**Sightseeing Spot**
</th>
<th>
</th> </tr>
<tr>
<td>
**Detailed Description**
</td>
<td>
Comments, pictures and locations of popular sightseeing spots
</td> </tr>
<tr>
<td>
**OGD or private data**
</td>
<td>
OGD
</td> </tr>
<tr>
<td>
**Personal Data**
</td>
<td>
No
</td> </tr>
<tr>
<td>
**Hosting**
</td>
<td>
Sapporo Open Data Association
</td> </tr>
<tr>
<td>
**Data Provider**
</td>
<td>
Sapporo City
</td> </tr>
<tr>
<td>
**Format**
</td>
<td>
PDF, HTML, CSV, JPEG
</td> </tr>
<tr>
<td>
**Update Frequency**
</td>
<td>
Yearly
</td> </tr>
<tr>
<td>
**Update Size**
</td>
<td>
N/A
</td> </tr>
<tr>
<td>
**Data Source**
</td>
<td>
Web
</td> </tr>
<tr>
<td>
**Sensor**
</td>
<td>
Ucode marker
</td> </tr>
<tr>
<td>
**Number of Sensors**
</td>
<td>
11
</td> </tr> </table>
<table>
<tr>
<th>
**Meal**
</th>
<th>
</th> </tr>
<tr>
<td>
**Detailed Description**
</td>
<td>
Restaurant name, location, opening hours, menu, service
</td> </tr>
<tr>
<td>
**OGD or private data**
</td>
<td>
Private
</td> </tr>
<tr>
<td>
**Personal Data**
</td>
<td>
No
</td> </tr>
<tr>
<td>
**Hosting**
</td>
<td>
Sapporo Open Data Association
</td> </tr>
<tr>
<td>
**Data Provider**
</td>
<td>
Hotel, Sapporo Station Shopping Mall
</td> </tr>
<tr>
<td>
**Format**
</td>
<td>
PDF, HTML, CSV, JPEG
</td> </tr>
<tr>
<td>
**Update Frequency**
</td>
<td>
Monthly
</td> </tr>
<tr>
<td>
**Update Size**
</td>
<td>
N/A
</td> </tr>
<tr>
<td>
**Data Source**
</td>
<td>
Web
</td> </tr>
<tr>
<td>
**Sensor**
</td>
<td>
Ucode marker
</td> </tr>
<tr>
<td>
**Number of Sensors**
</td>
<td>
11
</td> </tr> </table>
<table>
<tr>
<th>
**Hotel**
</th>
<th>
</th> </tr>
<tr>
<td>
**Detailed Description**
</td>
<td>
Hotel name, location, number of rooms, access
</td> </tr>
<tr>
<td>
**OGD or private data**
</td>
<td>
Private
</td> </tr>
<tr>
<td>
**Personal Data**
</td>
<td>
No
</td> </tr>
<tr>
<td>
**Hosting**
</td>
<td>
Sapporo Open Data Association
</td> </tr>
<tr>
<td>
**Data Provider**
</td>
<td>
Hotels
</td> </tr>
<tr>
<td>
**Format**
</td>
<td>
PDF, HTML, CSV, JPEG
</td> </tr>
<tr>
<td>
**Update Frequency**
</td>
<td>
Monthly
</td> </tr>
<tr>
<td>
**Update Size**
</td>
<td>
N/A
</td> </tr>
<tr>
<td>
**Data Source**
</td>
<td>
Paper
</td> </tr>
<tr>
<td>
**Sensor**
</td>
<td>
N/A
</td> </tr>
<tr>
<td>
**Number of Sensors**
</td>
<td>
N/A
</td> </tr> </table>
<table>
<tr>
<th>
**Other Facilities**
</th>
<th>
</th> </tr>
<tr>
<td>
**Detailed Description**
</td>
<td>
Location of ATM, Toilet, Rental Locker, etc.
</td> </tr>
<tr>
<td>
**OGD or private data**
</td>
<td>
Private
</td> </tr>
<tr>
<td>
**Personal Data**
</td>
<td>
No
</td> </tr>
<tr>
<td>
**Hosting**
</td>
<td>
Sapporo Open Data Association
</td> </tr>
<tr>
<td>
**Data Provider**
</td>
<td>
Sapporo Station Shopping Mall
</td> </tr>
<tr>
<td>
**Format**
</td>
<td>
PDF, HTML, CSV, JPEG
</td> </tr>
<tr>
<td>
**Update Frequency**
</td>
<td>
Monthly
</td> </tr>
<tr>
<td>
**Update Size**
</td>
<td>
N/A
</td> </tr>
<tr>
<td>
**Data Source**
</td>
<td>
Web
</td> </tr>
<tr>
<td>
**Sensor**
</td>
<td>
Ucode marker
</td> </tr>
<tr>
<td>
**Number of Sensors**
</td>
<td>
11
</td> </tr> </table>
<table>
<tr>
<th>
**Snow Festival**
</th>
<th>
</th> </tr>
<tr>
<td>
**Detailed Description**
</td>
<td>
Event information in Sapporo Snow Festival
</td> </tr>
<tr>
<td>
**OGD or private data**
</td>
<td>
Private
</td> </tr>
<tr>
<td>
**Personal Data**
</td>
<td>
No
</td> </tr>
<tr>
<td>
**Hosting**
</td>
<td>
Sapporo Open Data Association
</td> </tr>
<tr>
<td>
**Data Provider**
</td>
<td>
Operating Committee of Sapporo Snow
Festival
</td> </tr>
<tr>
<td>
**Format**
</td>
<td>
PDF, HTML, CSV, JPEG
</td> </tr>
<tr>
<td>
**Update Frequency**
</td>
<td>
Yearly
</td> </tr>
<tr>
<td>
**Update Size**
</td>
<td>
</td> </tr>
<tr>
<td>
**Data Source**
</td>
<td>
Web
</td> </tr>
<tr>
<td>
**Sensor**
</td>
<td>
Ucode marker
</td> </tr>
<tr>
<td>
**Number of Sensors**
</td>
<td>
11
</td> </tr> </table>
<table>
<tr>
<th>
**Stadium Data**
</th>
<th>
</th> </tr>
<tr>
<td>
**Detailed Description**
</td>
<td>
Location, facilities and drawing of stadiums
</td> </tr>
<tr>
<td>
**OGD or private data**
</td>
<td>
OGD
</td> </tr>
<tr>
<td>
**Personal Data**
</td>
<td>
No
</td> </tr>
<tr>
<td>
**Hosting**
</td>
<td>
Sapporo Open Data Association
</td> </tr>
<tr>
<td>
**Data Provider**
</td>
<td>
Sapporo City
</td> </tr>
<tr>
<td>
**Format**
</td>
<td>
PDF, HTML, CSV, JPEG
</td> </tr>
<tr>
<td>
**Update Frequency**
</td>
<td>
Yearly
</td> </tr>
<tr>
<td>
**Update Size**
</td>
<td>
N/A
</td> </tr>
<tr>
<td>
**Data Source**
</td>
<td>
Web, Paper
</td> </tr>
<tr>
<td>
**Sensor**
</td>
<td>
N/A
</td> </tr>
<tr>
<td>
**Number of Sensors**
</td>
<td>
N/A
</td> </tr> </table>
<table>
<tr>
<th>
**Train & Bus Data **
</th>
<th>
</th> </tr>
<tr>
<td>
**Detailed Description**
</td>
<td>
Time table, Operating status, Line, Real time location, bus stop, etc.
</td> </tr>
<tr>
<td>
**OGD or private data**
</td>
<td>
OGD & private
</td> </tr>
<tr>
<td>
**Personal Data**
</td>
<td>
No
</td> </tr>
<tr>
<td>
**Hosting**
</td>
<td>
Sapporo Open Data Association
</td> </tr>
<tr>
<td>
**Data Provider**
</td>
<td>
Sapporo City, Hokkaido Chuo Bus
</td> </tr>
<tr>
<td>
**Format**
</td>
<td>
PDF, HTML, CSV, JPEG
</td> </tr>
<tr>
<td>
**Update Frequency**
</td>
<td>
Monthly
</td> </tr>
<tr>
<td>
**Update Size**
</td>
<td>
N/A
</td> </tr>
<tr>
<td>
**Data Source**
</td>
<td>
File
</td> </tr>
<tr>
<td>
**Sensor**
</td>
<td>
N/A
</td> </tr>
<tr>
<td>
**Number of Sensors**
</td>
<td>
N/A
</td> </tr> </table>
<table>
<tr>
<th>
**Express Way Data**
</th>
<th>
</th> </tr>
<tr>
<td>
**Detailed Description**
</td>
<td>
Information of service area
</td> </tr>
<tr>
<td>
**OGD or private data**
</td>
<td>
Private
</td> </tr>
<tr>
<td>
**Personal Data**
</td>
<td>
No
</td> </tr>
<tr>
<td>
**Hosting**
</td>
<td>
Sapporo Open Data Association
</td> </tr>
<tr>
<td>
**Data Provider**
</td>
<td>
NEXCO East Japan
</td> </tr>
<tr>
<td>
**Format**
</td>
<td>
PDF, HTML, CSV, JPEG
</td> </tr>
<tr>
<td>
**Update Frequency**
</td>
<td>
Yearly
</td> </tr>
<tr>
<td>
**Update Size**
</td>
<td>
N/A
</td> </tr>
<tr>
<td>
**Data Source**
</td>
<td>
Web
</td> </tr>
<tr>
<td>
**Sensor**
</td>
<td>
N/A
</td> </tr>
<tr>
<td>
**Number of Sensors**
</td>
<td>
N/A
</td> </tr> </table>
<table>
<tr>
<th>
**Underground Map Data**
</th>
<th>
</th> </tr>
<tr>
<td>
**Detailed Description**
</td>
<td>
Drawing of underground map
</td> </tr>
<tr>
<td>
**OGD or private data**
</td>
<td>
Private
</td> </tr>
<tr>
<td>
**Personal Data**
</td>
<td>
No
</td> </tr>
<tr>
<td>
**Hosting**
</td>
<td>
Sapporo Open Data Association
</td> </tr>
<tr>
<td>
**Data Provider**
</td>
<td>
Sapporo Station Shopping Mall
</td> </tr>
<tr>
<td>
**Format**
</td>
<td>
PDF, HTML, CSV, JPEG
</td> </tr>
<tr>
<td>
**Update Frequency**
</td>
<td>
Yearly
</td> </tr>
<tr>
<td>
**Update Size**
</td>
<td>
N/A
</td> </tr>
<tr>
<td>
**Data Source**
</td>
<td>
File
</td> </tr>
<tr>
<td>
**Sensor**
</td>
<td>
Ucode marker
</td> </tr>
<tr>
<td>
**Number of Sensors**
</td>
<td>
11
</td> </tr> </table>
**Note:** Japanese applications including Sapporo visitor experience are based
on u2 architecture, ucode architecture Version 2. Data collected from hardware
device is only ucode, a non-semantic 128-bit id umber. Only after a process
called ucode resolution process is performed by an application, a meaningful
data is provided to the application in a context-sensitive manner. Ucode
itself has no notion of open vs proprietary / anonymous vs public.
**Table 5: Data collected for the Sapporo Visitor Experience application**
<table>
<tr>
<th>
**Devices**
</th>
<th>
**Types of data**
</th>
<th>
**Anonymised**
**Y/N**
</th>
<th>
**Open**
**Y/N**
</th> </tr>
<tr>
<td>
ucode marker
</td>
<td>
128 bit unique identifier, ucode.
</td>
<td>
N
</td>
<td>
n/a
</td> </tr> </table>
### Data generated by the Sapporo Visitor Experience application
**Note:** The data generated such as guidance uses open data map. However,
particular guidance based on the user’s current location is a private data and
should be anonymised.
**Table 6: Data generated for the Sapporo Visitor Experience application**
<table>
<tr>
<th>
**Types of generated data**
</th>
<th>
**Based on…**
</th>
<th>
**Anonymised**
**Y/N**
</th>
<th>
**Open**
**Y/N**
</th> </tr>
<tr>
<td>
Sightseeing spot guidance
</td>
<td>
Location information (from ucode marker and GPS, ditto in rows below) and
Sightseeing Spot data.
</td>
<td>
Y
</td>
<td>
N
</td> </tr>
<tr>
<td>
Meal Guidance
</td>
<td>
Location information and Meal
(restaurant, etc.) data.
</td>
<td>
Y
</td>
<td>
N
</td> </tr>
<tr>
<td>
Hotel Guidance
</td>
<td>
Location information and hotel data.
</td>
<td>
Y
</td>
<td>
N
</td> </tr>
<tr>
<td>
Facility Guidance (ATM, toilet, etc.)
</td>
<td>
Location information and facility data
</td>
<td>
Y
</td>
<td>
N
</td> </tr>
<tr>
<td>
Snow Festival event guidance
</td>
<td>
Location information and Snow Festival data
</td>
<td>
Y
</td>
<td>
N
</td> </tr>
<tr>
<td>
Stadium Guidance
</td>
<td>
Location information and Stadium data
</td>
<td>
Y
</td>
<td>
N
</td> </tr>
<tr>
<td>
Train and Bus Guidance
</td>
<td>
Location information and Train and Bus data.
</td>
<td>
Y
</td>
<td>
N
</td> </tr>
<tr>
<td>
Expressway Guidance
</td>
<td>
Location information and Expressway guidance
</td>
<td>
Y
</td>
<td>
N
</td> </tr>
<tr>
<td>
Underground Route Guidance
</td>
<td>
Location information from ucode marker (GPS is not available!) and Underground
Map data.
</td>
<td>
Y
</td>
<td>
N
</td> </tr> </table>
## Data from Tokyo Public Transportation: (a) Open Data Challenge contest
### Short description
One prominent use case of Tokyo Public Transportation open data is the open
data contest that uses data from public transportation operators in Tokyo
area, i.e., Open Data Challenge for Public Transportation in Tokyo.
One contest has already been performed and the second one is under way: From
July 17, 2018 to March
31, 2019.
URL: _https://tokyochallenge.odpt.org/en/index.html_
Third one is already announced to start on January 16, 2019.
The contest is held by the Association for Open Data of Public Transportation
(ODPT). See URL: _http://www.odpt.org/_ I t is chaired by Dr. Ken Sakamura,
the director of YRP UNL, the Japanese coordinator of CPaaS.io. YRP and MSJ,
both CPaaS.io partners, are members of ODPT.
The explanation of the open data provision framework within the context of
Open Data Challenge for Public Transportation in Tokyo is in order.
The contest makes available many types of data from many raw data sources from
public transportation operators in Tokyo area. However, they are all in
proprietary formats and CPaaS.io can't mention them in a public document. They
are covered by NDA.
From these diverse data sources, the contest developer site uses program
modules that understand these formats and produces unique data format (JSON-
LD) for all types of data so that ALL the information is provided in this
single format.
So, the participants in the contest only need to know the API to access this
JSON-LD data and the semantics of JSON-LD data. They do not have to deal with
the idiosyncrasies of various data formats and network protocols used by the
public transportation operators in the greater Tokyo metropolitan area.
There are many types of data available in the contest. There are dynamic data
available, but the details change from time to time and is hard to keep track.
So, the author refers the reader to the contest developer site. URL:
https://ckan-tokyochallenge.odpt.org/dataset
As of now (December 28, 2018), there are 152 data sets made available from the
contest site. (https://ckan-tokyochallenge.odpt.org/dataset )
The JSON-LD data set has the semantics which is explained by a document
available at the developer site of the contest. (It is readable only after one
registers itself at the website. See the contest site for details.)
The following URL is the generic API and semantics initially used some ago for
the contest framework (in Japanese). URL: https://docs.odpt.org/
However, the developer site has English translation of the more focused and
updated document and so the interested reader is encouraged to register in the
contest (there is NO OBLIGATION to submit the final contest work).
### Data collected for the Open Data Challenge Contest
Due to the reasons mentioned above, we cannot really mention how and by means
of which device the data is collected.
So instead, below, the generic description of data made available is listed
(quoted from the URL of the contest site.)
**Table 7: Data Collected for the two Tokyo Public Transportation
applications**
<table>
<tr>
<th>
**Devices**
</th>
<th>
**Types of data**
</th>
<th>
**Anonymised**
**Y/N**
</th>
<th>
**Open**
**Y/N**
</th> </tr>
<tr>
<td>
n/a
</td>
<td>
n/a
</td>
<td>
n/a
</td>
<td>
Not really in the original format.
</td> </tr> </table>
* List: Published Data types.
➢ Railway
⟡ Tokyo Metro
* Static data, including train/station timetable, and dynamic data, such as train location information and status information
⟡ Bureau of Transportation, Tokyo Metropolitan Government
* Static data, including train/station timetable of Toei Transportation, Tokyo Sakura Tram (Toden Arakawa Line), and Nippori-Toneri Liner, and dynamic data, such as status information, and train location information of Tokyo Sakura Tram (Toden Arakawa Line)
* JR East
* Static data, including train/station timetable, and dynamic data, such as train location information and status information, of multiple railway lines in Greater Tokyo
* Odakyu
* Static data and dynamic data, such as status information
* Keio
* Static data, including train/station timetable, and dynamic data, such as status information ⟡ Keisei
* Static data, including station timetable, and dynamic data, such as status information ⟡ Keikyu
* Static data, including station timetable, and dynamic data, such as status information ⟡ Seibu
* Static data, including station timetable, and dynamic data, such as status information
* Tokyu
* Static data and dynamic data, such as status information
* TWR Rinkai Line
* Static data, including train/station timetable, and dynamic data, such as status information
* Tobu
* Static data, including station timetable
* Yurikamome
* Static data, including station timetable
* Bus
* Bureau of Transportation, Tokyo Metropolitan Government
* Static data, including timetable of Toei Bus, and bus location information as dynamic data
* Odakyu Bus
* data, including timetable ⟡ Kanto Bus
* data, including timetable
* Keio Bus
* Static data, including timetable
* Kokusai Kogyo
* Static data, including timetable ⟡ JR Bus Kanto
* Static data, including timetable ⟡ Seibu Bus
* Static data, including timetable ⟡ Tokyu Bus
* Static data, including timetable ⟡ Tobu Bus
* Static data, including timetable ⟡ Nishi Tokyo Bus
* Static data, including timetable
* Airline
* All Nippon Airways
* Static data, including flight timetable, and dynamic data, such as real-time arrival/departure information
* Tokyo International Air Terminal
* Static data, including flight timetable, and dynamic data, such as real-time arrival/departure information
* Narita International Airport
* data, such as real-time arrival/departure information
* Japan Airport Terminal
* Flight timetable, and dynamic data, such as real-time arrival/departure information
* Japan Airlines
* Static data, including flight timetable, and dynamic data, such as real-time arrival/departure information
* -station map and facility information of train stations
● The intra-station map and the facility information of the train stations
around Shinjuku station and Tokyo station are being prepared by Ministry of
Land Infrastructure, Transportation and Tourism anticipating the Tokyo Olympic
and Paralympic Games in 2020. The data are made available in this challenge.
### Data generated by Open Data Challenge Contest
As mentioned above, from the diverse data sources of many transportation
operators, the contest developer site uses program modules that understand
these formats and produces unique data format (JSON-LD) for all types of data
so that ALL the information is provided in this single format. The generated
data is stored in a ckan website.
From the data in the ckan website, many apps create their own data (guidance,
visualization, etc.). But it is beyond the scope of this document to
characterise the existing contest apps and their generated data. **Table 8:
Data generated for the**
<table>
<tr>
<th>
**Types of generated data**
</th>
<th>
</th>
<th>
**Based on…**
</th>
<th>
**Anonymised**
**Y/N**
</th>
<th>
**Open**
**Y/N**
</th> </tr>
<tr>
<td>
Many in JSON-LD format
</td>
<td>
Many
</td>
<td>
</td>
<td>
Varies
</td>
<td>
Yes, in
JSON-LD format to contestants.
</td> </tr> </table>
## Data from Tokyo Public Transportation (b): Tokyo Management of Service
Vehicles
### Short description
Tokyo Management of Service Vehicles provides a platform mobility tracking
infrastructure to enable management of service vehicles using monitoring,
planning, coordination, analysing of things with mobility, such as service
vehicles.
### Data collected for the Tokyo Management of Service Vehicles application
1. Time tables and real-time traffic information of public transportation such as railways, buses, and airplanes. Data are owned by transportation operators. For public transportation, it is public. For public service-related transportation, it depends on services.
2. Geographical data for routes, locations of railway stations, bus stops, airports, etc. Data are owned by transportation operators.
3. Data of origination and destination, such as maps, facility lists. Data are owned by transportation operators. Some are public, some (such as origination places) are companyconfidential.
4. Driver information. It is owned by transportation companies. It needs privacy protection.
**Table 9: Data Collected for the Tokyo Management of Service Vehicles (* not
yet defined)**
<table>
<tr>
<th>
**Devices**
</th>
<th>
**Types of data**
</th>
<th>
**Anonymised**
**Y/N**
</th>
<th>
**Open**
**Y/N**
</th> </tr>
<tr>
<td>
Smartphone
</td>
<td>
Location, time
</td>
<td>
N
</td>
<td>
Y
</td> </tr>
<tr>
<td>
Smartphone
</td>
<td>
Device-id
</td>
<td>
Y
</td>
<td>
N
</td> </tr>
<tr>
<td>
Smartphone
</td>
<td>
Image
</td>
<td>
N
</td>
<td>
N
</td> </tr>
<tr>
<td>
*
</td>
<td>
Route
</td>
<td>
N
</td>
<td>
N
</td> </tr>
<tr>
<td>
*
</td>
<td>
Weather
</td>
<td>
N
</td>
<td>
Y
</td> </tr>
<tr>
<td>
*
</td>
<td>
Road information
</td>
<td>
N
</td>
<td>
Y
</td> </tr> </table>
### Data generated by the Tokyo Management of Service Vehicles application
Route logs: generated from mobility tracking data. The data is owned by
transportation providers. Stored inside transportation providers services on
top of CPaaS.io
**Table 10: Data generated for the two Tokyo Management of Service Vehicles
application (* Not yet defined)**
<table>
<tr>
<th>
**Types of generated data**
</th>
<th>
**Based on…**
</th>
<th>
**Anonymised**
**Y/N**
</th>
<th>
**Open**
**Y/N**
</th> </tr>
<tr>
<td>
Route logs
</td>
<td>
Time-series-mobility data
</td>
<td>
N
</td>
<td>
*
</td> </tr> </table>
## Data from Yokosuka Emergency Medical Care application
### Short description
Yokosuka Emergency Medical Care Application provides a platform to improve
quality and efficiency of medical information sharing situation of sick person
and to reduce time to start initial treatment after ambulance arrival.
### Data collected for the Yokosuka Emergency Medical Care application
Yokosuka Emergency Medical Care application collects IDs and location from
smartphone in ambulance. This application also collects the video image in
ambulances from camera(s) in the ambulances but not stored.
Summarised in the table below:
**Table 11: Data collected for the Yokosuka Emergency Medical Care
application**
<table>
<tr>
<th>
**Devices**
</th>
<th>
**Types of data**
</th>
<th>
**Anonymised**
**Y/N**
</th>
<th>
**Open**
**Y/N**
</th> </tr>
<tr>
<td>
Smartphone
</td>
<td>
Location, time
</td>
<td>
N
</td>
<td>
Y
</td> </tr>
<tr>
<td>
Smartphone
</td>
<td>
Device-id
</td>
<td>
N
</td>
<td>
N
</td> </tr>
<tr>
<td>
camera
</td>
<td>
video image in ambulance (not stored)
</td>
<td>
N
</td>
<td>
N
</td> </tr> </table>
### Data generated by the Yokosuka Emergency Medical Care application
Yokosuka Emergency Medical Care application generated a location map of
ambulances.
Summarised in the table below:
**Table 12: Data generated for the Yokosuka Emergency Medical Care
application**
<table>
<tr>
<th>
**Types of generated data**
</th>
<th>
**Based on…**
</th>
<th>
**Anonymised**
**Y/N**
</th>
<th>
**Open**
**Y/N**
</th> </tr>
<tr>
<td>
Location map of ambulances
</td>
<td>
location and time from smartphone in ambulances
</td>
<td>
N
</td>
<td>
N
</td> </tr> </table>
# CPaaS.io Research Data management plan
CPaaS.io project follows the principle that research data will be handled and
managed by those organisations/institutions that either collects or generates
the research data. The CPaaS.io project comprise a number of partners that are
involved directly in either:
* Producing the actual data during the trials, or
* Developing tools and enablers (e.g. analytics, reasoners, etc.) that are needed as core elements in the CPaaS.io system architecture, or
* Elaborating upon the produced data (using the aforementioned enablers) in order to produce new value-added knowledge.
The individual roles and duties of such partners and the research data
management plans that are in place in the organisations taking part in
CPaaS.io are described in the following sub-sections.
## AGT International (AGT)
### Data collection (from sensors)
The data collected by AGT has been described in Section 2.1 and is used for
generated the data as described in Table 2 and for developing the Enhanced
User Experience application. As described in D2.2 the collected data is
enriched with additional metadata.
### Data generation
The data generated by AGT has been described in Table 2 and is used in the
Enhanced User Experience application.
### Data Management
We have implemented appropriate technical and organizational measures to
ensure generated data is protected from unauthorized or unlawful processing,
accidental loss, destruction or damage. We review our information collection,
storage and processing practices regularly, including physical security
measures, to guard against unauthorized access to our systems. We restrict
access to generated data to only those employees, contractors and agents who
strictly need access to this information, and who are subject to strict
contractual confidentiality obligations.
_**Update at the end of the Project:** _
In cases where consent forms and legislation allow, the data will be
maintained beyond the project mainly for presenting the results of the
project.
## University of Surrey (UoS)
ICS at the University of Surrey is not involved neither in the production of
raw data nor in the exploitation or generation of higher-level information out
of it. However, UoS is focussing on architecture work where particular
attention is paid to ensuring that 1/ all privacy-related requirements are
thoroughly taken into account 2/ important part of the data is publicly
available following the project Open Data policy.
To this respect UoS is aiming at providing a bridge between CPaaS.io and
another FIRE project called FIESTA-IoT, two projects where UoS is actively
involved. UoS will in particular aim at involving CPaaS.io in either the 2 nd
Open Call of FIESTA-IoT or as a fellow contributor to that project via a
cooperation agreement to be discussed between the two projects after both POs
have been consulted on that matter. In both cases, CPaaS.io could play two
non-exclusive distinct roles:
* Data-provider: playing this role the CPaaS.io project would inject its data or part of its data (either raw data or inferred data) to the FIESTA-IoT so that so-called experimenters can make use of it using the FIESTA-IoT enablers; or
* Experimenter: playing this role, CPaaS.io could reuse additional data sets produced by the FIESTAIoT collaborators for testing our new own algorithms (e.g. Analytics) and techniques.
**Data collection (from sensors)**
UoS does not participate in any data collection
**Data generation**
UoS does not generate any new data from the project data sets
### Data Management
UoS does not manage any gathered or generated data
_**Update at the end of the Project:** _
The foreseen collaboration between CPaaS.io and FIESTA-IoT could not take
place due to technical issues. It will be discussed –early 2019- whether the
data collected internally as part of our smartBuilding test-bed can be
ingested to the CPaaS.io platform. That data would be then open to students
and used for the sake of machine learning experiments.
## Bern University of Applied Sciences (BFH)
The BFH is not directly involved in the implementation of the envisaged use
cases. Its main research focus is in the data management concepts – in
particular the usage of Linked Data and Open Government Data as well as data
quality annotations, the application of MyData approaches, and in the
validation of the use cases. Hence it is not collecting, generating or storing
any data.
However, as part of its exploitation, validation and knowledge transfer
activities, BFH is planning to connect some sensors via the LoRa testbed
network that another institute (Institute for Energy and Mobility Research in
Biel) is currently setting up. What data will be collected and for what
purposes exactly will be defined at a later stage; a related data management
plan will be drawn up before any data collection starts.
### Data collection
BFH is not collecting any data for the main use cases of CPaaS.io. It may
collect and make available some sensor data through the LoRa network at BFH
for testing and validation purposes; details will be determined at a later
stage.
### Data generation
BFH is not generating any data for the main use cases of CPaaS.io. It may link
public data sources (e.g., from the Swiss Open Government Data portal at
_www.opendata.swiss_ ) with the sensor data collected through the LoRa
network at BFH for testing and validation purposes; potential use cases will
be determined at a later stage.
### Data Management
BFH is not managing any data for the main use cases of CPaaS.io. Data
collected and generated for testing and validation purposes through the LoRa
network at BFH will likely be made available publicly, in the spirit of open
data research, unless the data could allow to infer any information about
individuals.
Details are to be determined at a later stage.
_**Update at the end of the Project:** _
BFH used a single TTN sensor to generate some test data. This data however is
not being persisted beyond the end of the project.
## OdinS
OdinS as a partner involved on the security and privacy aspects, will check
and support the project to check that data access and sharing activities will
be implemented in compliance with the privacy and data collection rules and
regulations, as they are applied nationally and in the EU, as well as with the
H2020 rules. Concerning the results of the project, these will become publicly
available based on the IPRs as described in the Consortium Agreement.
Due to the nature of the data involved, some of the results that will be
generated by each project phase will be restricted to authorized users, while
other results will be publicly available. Data access and sharing activities
will be rigorously implemented in compliance with the privacy and data
collection rules and regulations, as they are applied nationally and in the
EU, as well as with the H2020 rules.
### Data collection (from sensors)
OdinS will not be involved in the data generation of data from sensors,
working exclusively in the architecture aspects of the data collections and
its consequence over the security and privacy components.
### Data generation
OdinS is not involved in the production of raw data, but as part of the Task
4.1 User Empowerment Component Definition and the definition of access control
policies and use consent solution, OdinS will generate information associated
to data for controlling access and sharing data between entities and
components that will use the platform.
### Data Management
As the raw data included in the data sources, will be gathered from sensor
nodes and information management systems, those could be seen as highly
sensitive. Therefore, access to raw data can only take place between the
specific end users based on the policies associated and the partners involved
in the analysis of the data. For the models to function correctly, the data
will have to be included into the CPaaS.io repository. The results of the data
analytics are set to be anonymised and made available to the subsequent layers
of the framework, which will then allow the possibility for external industry
stakeholders to use the results of the project for their own purposes.
## NEC
NEC is not directly involved in the production or raw data. NEC’s focuses are
in the architecture (system integration including transferability and semantic
interoperability) area and cloud-edge processing of the data. FIWARE’s
resources such as the Generic Enablers and NEC’s IoT Platform can support
storage and exploitation of data from use cases for generating higher-level
analytical results. NEC pays particular attention to privacy related
requirements as well as the Open Data policy of CPaaS.io.
**Data collection**
NEC is not planning to collect any raw data for the use cases of CPaaS.io.
### Data generation
NEC is not generating data for the main use cases, NEC may exploit shared data
from use cases and generate higher level data as a result. Potential use cases
will be determined at a later stage.
### Data management
While NEC is not directly involved with the use cases, it will take part in
data transferability and management via the provided IoT Platform. NEC has
implemented necessary organizational and technical measures for the usage of
the data and its protection from unauthorized persons.
## The Things Network
#### Data collection (from sensors)
The data collected by The Things Network has been described in Section 2.2 and
is used for generated the data as described in table 2 and for developing the
Waterproof Amsterdam application. As described in D2.4 the collected data is
enriched with additional metadata.
#### Data generation
The data generated by The Things Network has been described in and is used in
the Waterproof Amsterdam application. Private data from owners of a rain
buffer is anonymised. Based on an algorithm, data from various sources is
processed by the application to determine the optimal filling degree for each
individual rain buffer. The results may be used for automated control of
buffers, or push notifications to trigger manual control.
**Data Management** Open data such as weather data will be streamed into the
application and not stored locally.
Private data from external sources such as device location will be stored in
the application and only released in an anonymised and aggregated manner.
Personal details about a device, such as name, address and contact details
will also be stored in the application in a secure account server. These data
may be transferred to CPaaS.io at some time, easing security and privacy
demands on the application end and transferring those to CPaaS.io
Parts of the personal data, such as buffer location, size and processed
litres, will be released in an aggregated, anonymised manner (e.g. on a heat
map) per area of a city or the city as a whole.
Readily available data from Waternet about sewerage capacity will abide by the
policies of Waternet. These policies are not yet clear at the moment.
We restrict access to generated data to only those employees, contractors and
agents who strictly need access to this information, and who are subject to
strict contractual confidentiality obligations.
## YRP
### Data collection (from sensors)
The data collected by YRP has been described in Section 2.4 and is used for
generated the data as described in table 6 for developing the Tokyo Public
Transportation application. It also has been described in Section 2.6 and is
used generated the data as described in table 12 for developing the Yokosuka
Emergency Medical Care application. As described in D2.4 the collected data is
enriched with additional metadata.
### Data generation
The data generated by YRP has been described in Section 2.4 and is used in the
Tokyo Public Transportation application. It also has been described in 2.6 and
is used in Yokosuka Emergency Medical Care application. Neither Tokyo Public
Transportation nor Yokosuka Emergency Medical Care application generates
private data.
### Data Management
Tokyo Public Transportation application coverts data such as timetables,
location of trains and provides converted data. This application does not
manage private data.
Yokosuka Emergency Medical Care application manages the location of ambulances
and provides a map of the location. This application streams private data,
such as video image in ambulance and does not store locally.
Explicitly private data that was gathered during Sapporo Snow Festival in
February 2018 was collected with explicit user agreement. (The English
translation of the agreement is part of D2.12 Final Ethics Report.). That data
was exchanged with AGT under a mutually signed data exchange agreement.
## Microsoft Japan
**Data collection (from sensors)**
MSJ is not directly involved in the production or raw data.
**Data generation**
MSJ is not generating data for the main use cases.
### Data Management
While MSJ is not directly involved with the data management of each CPaaS.io
use cases, MSJ has implemented necessary organizational and technical measures
for the usage of private data and its protection from unauthorized persons in
its cloud service.
In the case of collecting data from visitors to Sapporo Snow Festival in
February 2018, MSJ personnel acted as an agent of YRP which was responsible
for the gathering and storage of personal data in Japan.
## ACCESS
### Data collection (from sensors)
ACC collects data from smartphones. The collected data are location, time,
device ID of the smartphone and the images taken by the camera of the smart
phone.
### Data generation
ACC generates route logs: generated from mobility tracking data in Tokyo
Management of Service Vehicles application. The data is owned by
transportation providers. This data is stored inside transportation provider’s
services on top of CPaaS.io.
### Data Management
ACC manages the data with the usual access control system so that they are not
leaked to non-owners. Also, the identity of smartphones are crucial for the
application in so far as the route log is generated, and are not anomyzed
within the application domain.
## Ubiquitous Computing Technology
### Data collection (from sensors)
Ubiquitous Computing Technology (UCT) is not planning to collect any raw data
for the use cases of CPaaS.io.
**Data generation**
UCT does not generate any new data from the project data sets
### Data Management
UCT does not manage any gathered or generated data
## University of Tokyo
### Data collection (from sensors)
University of Tokyo (UoT) was not planning to collect any raw data for the use
cases of CPaaS.io in 2016. However, as of 2018, it has now facilities for
monitoring indoor temperature, lighting, human movement, humidity and other
data from environmental sensors.
**Data generation**
UoT does not generate any new data from the project data sets.
### Data Management
UoT does not manage any gathered or generated data other than the pure sensor
data (non-private data).
# Conclusions
In this second version of the Data Management Plan deliverable we presented
the CPaaS.io approach towards data management as handled by the EU CPaaS.io
consortium and final statements made at the end of the project as far as
management of data is concerned.
|
https://phaidra.univie.ac.at/o:1140797
|
Horizon 2020
|
1160_5GPagoda_723172.md
|
1\. Introduction 5
2\. Data Types 8
2.1. Data Types of the Project 8
2.2. Levels of Confidentiality and Flow of Data 9
2.3. Data Formats 10
2.4. Expected Data of the Project 11
3\. Collection, Storage and Use of Research Data and Analysed Research Data
13
4\. Storage and Use of Project Data 14
4.1. Storage of Data 14
4.2. Documenting of Data 15
5\. Data Sharing 17
5.1. Depositing Research Data 17
5.2. Open Access Publishing 17
5.3. Other Dissemination 18
6\. Opting Out Research Data 19
7\. Summary and Perspectives 20
# Introduction
This document outlines the principles and processes for data collection,
annotation, analysis and distribution as well as storage and security of data
within the EU/JP 5G!Pagoda project. The procedures will be adopted by all
project partners throughout the project in order to ensure that all project
related data are well-managed according to contractual obligations as well as
applicable legislation both during and after the project.
5G!Pagoda is committed to openness with respect to research data and results,
as manifested by its participation in the Pilot on Open Research Data in
Horizon 2020. Therefore, as a further dissemination channel, 5G!Pagoda will
share datasets on which its results are derived as explained in this Open Data
Management Plan. Public research repositories are used to maximize the
visibility and impact of the research carried out by partners by making such
data available for reuse by 3rd parties and reproducibility of the project’s
results.
The Grant Agreement of the 5G!Pagoda project (as an Open Data Pilot
participant) obligates the project to deposit [digital research data generated
in the project] in a research data repository and take measures to make it
possible for third parties to access, mine, exploit, reproduce and disseminate
— free of charge for any user — the following:
1. the data, including associated metadata, needed to validate the results presented in scientific publications as soon as possible;
2. other data, including associated metadata, as specified and within the deadlines laid down in the 'data management plan', i.e. this document.
The Grant Agreement contains an option to discard the obligation to deposit a
part of research data in the case where the achievement of the action's main
objective, described in Annex 1 of the Grant Agreement, would be jeopardised.
In such case, the Open Data Management Plan must contain the reasons for not
giving access.
As the obligation to deposit research data in a databank does not change the
obligation to protect results, take care of confidentiality and security
obligations, or the obligations to protect personal data, the Open Data
Management Plan addresses these topics. This document details, how the
seemingly contradicting commitments to share and protect are implemented
within the project.
The Open Data Management Plan has, on the other hand, also served the purpose
of acting as a tool to agree on the data processing of the 5G!Pagoda project
consortium. The production of the Open Data Management Plan has helped the
consortium to identify situations, where the practices were thought to be
agreed upon and where a common understanding on practices was thought to have
been achieved, but where such in fact did not exist.
Documents related to the Open Data Management Plan are the 5G!Pagoda project
Grant Agreement, the Consortium Agreement, the Coordination Agreement, and the
Project Management, Quality and IPR Guide. Some of the deliverables also
contain information which link to the Open Data Management Plan. The
relationships are described below in Table 1.
**Table 1 Documents related to the Open Data Management Plan.**
<table>
<tr>
<th>
Related document
</th>
<th>
Relationship to the Open Data Management Plan
</th> </tr>
<tr>
<td>
The Grant Agreement
</td>
<td>
* Article 27 details the obligation to protect results
* Article 29 details dissemination of results including open access to publications and research data
* Article 36 details confidentiality obligations
</td> </tr>
<tr>
<td>
</td>
<td>
* Article 39 details obligations to protect personal data, if applicable
* Annex 1, Part B, Chapter 2.4 and Chapter 2.5 detail open access publication and research data management principles.
</td> </tr>
<tr>
<td>
Consortium Agreement
</td>
<td>
Chapter 4.1 on the General principles: “ _Each Party undertakes to notify
promptly, in accordance with the governance structure agreed in the
Coordination Agreement and directly to the Coordinator, any significant
information, fact, problem or delay likely to affect the Project, submission
of the deliverables or reports in accordance with the Grant Agreement and_
_shall promptly provide all information reasonably required by the Coordinator
to carry out its tasks._
_Each Party shall take reasonable measures to ensure the accuracy of any
information or materials it supplies to the Coordinator or the Parties.”_ This
is a general declaration of the partners to abide by the rights and
obligations set out in the Grant Agreement.
</td> </tr>
<tr>
<td>
Coordination Agreement
</td>
<td>
* Chapter 4.1 on the General principles: _“Each Party undertakes to take part in the efficient implementation of the Coordinated Project, and to cooperate, perform and fulfil, promptly and on time, all of its obligations in the said project and under this Coordination Agreement as may be reasonably required from it and in a manner of good faith._
_Each Party undertakes to notify promptly, in accordance with the governance
structure of the Coordinated Project, and as required for the coordination
under this Coordination Agreement, any significant information, fact, problem
or delay likely to affect the project(s) or the coordination.”_ This is a
general declaration of the partners to abide by the rights and obligations set
out in the Coordinated Project, Project Plan, part of the Grant Agreement.
* Chapter 8.4 on Publication defines publication principles.
</td> </tr>
<tr>
<td>
Project Management, Quality and IPR Guide
</td>
<td>
The Project Management, Quality and IPR Guide defines the quality criteria for
all work conducted in the 5G!Pagoda project.
</td> </tr>
<tr>
<td>
Yearly report on standardization, dissemination and exploitation achievements
</td>
<td>
Contributions to standardization will enable 5G!Pagoda to achieve broader
recognition of its results by a wide industry community, stimulate higher
levels of interoperability and contribute to establishing economies of scale
for 5G!Pagoda applications. Moreover, close coordination between research
projects and standardization organizations, particularly through running and
validated testbeds, is an important mechanism for exploitation of results and
for inspiring and initiating innovation.
</td> </tr>
<tr>
<td>
Report on innovation achieved and forthcoming industrial exploitation
</td>
<td>
This public report summarises the innovations achieved in the project and the
activities and plans to exploit these innovations and other project results.
</td> </tr> </table>
5G!Pagoda does not intend collecting any data that may expose private
information (i.e., user contextrelated data). If during the course of the
project, this becomes a necessity, the 5G!Pagoda consortium is committed to
respect privacy and comply with the EU respective directives and national law:
any data deemed private will not be made publicly available, and if it has to
be made public, it will be appropriately anonymized. The project may generate
quantitative models of various aspects of the 5G!Pagoda system (e.g.,
performance of specific network function, performance of a service over a
specific network slice, network or application level traces, dependence of
end-user experience on specific system and environment parameters, etc.),
which will be made available via open-access publications, technical reports,
and the project’s public deliverables. The partners will evaluate the
applicability of standards with respect to various aspects of data
publication, indexing, storage, preservation, privacy, etc., and will report
on them in this Open Data Management Plan (i.e. deliverable D1.2).
Where applicable, each contributor to a publication as an outcome of 5G!Pagoda
will acquire a persistent identifier from the ORCID registry and each
published data set or other research data object will be assigned a persistent
identifier from DataCite, which builds on OIDs. The consortium will strive to
publish data on repositories indexed by the Registry of Research Data
Repositories, which provides an XML schema and an API for querying research
data. The selected repositories should support the Open Archives Initiative
Protocol for Metadata Harvesting (OAI-PMH) standard for repository
interoperability and cross-archive search. The consortium will ensure that
appropriate metadata are provided for the published data, including the
acknowledgement to the funding of EU, H2020, and the Japanese Ministry of
Internal Affairs and Communications, the name of the project, the grant number
and the name of the action.
The ODMP may be re-evaluated during the project. On each updated version, the
necessary corrective actions will be included, to ensure that research data
remain discoverable, accessible, assessable and intelligible, interoperable
and (re)usable beyond their original purpose. The European Commission as well
as the Japanese Ministry of Internal Affairs and Communications will be
accordingly notified.
# Data Types
## Data Types of the Project
In the 5g!Pagoda project there are four basic types of data (Figure 1):
research data, analysed research data, project data and reports and
communication data.
**Research data** covers the data collected on the project subject matter and
it refers to information, in particular facts or numbers, collected to be
examined and considered as a basis for reasoning, discussion, or calculation.
In a research context, examples of data include statistics, results of
experiments, measurements, observations resulting from fieldwork, survey
results, interview recordings and images. The focus is on research data that
is available in digital form.
Users can normally access, mine, exploit, reproduce and disseminate openly
accessible research data free of charge. /H2020 Online Manual/
Based on discussion with the consortium members, it has become apparent that
it is difficult at this stage to identify the kind of data to be collected on
the project. However, once such data become identified, this Open Data
Management will be accordingly updated.
**Analysed research data** means the reports composed of the research data.
Analysed data also refers to qualitative and quantitative data analyses
conducted on the data. Reviews of earlier published data and records will be
utilised to some degree. This data will be considered as analysed research
data for the purposes of this document. Project related workshops and
stakeholder engagement events are public events and the workshop notes of
project partners will be treated in the same way as analysed research data
(i.e. the notes will be shared within the consortium).
**Figure 1 Data types.**
**Project data** includes administrative and financial project data, including
contracts, partner information and periodic reports, as well as accumulated
data on project meetings, teleconferences and other internal materials. This
data is confidential to the project consortium and to the European Commission.
Project data includes mainly MS Office documents, in English, which ensures
ease of access and efficiency for project management and reporting. Most of
the project data is stored in the password protected OwnCloud repository,
administrated by Aalto University.
**Reports and other communication data** includes deliverables, presentations
and for example articles. This data type also refers to the contents of the
5G!Pagoda project website.
Each data type is treated differently with regard to the level of
confidentiality (see Chapter 2.2). Some of the data falls under the EU, Japan,
and national laws on data protection and for this reason the project is
obliged to seek necessary authorisations and to fulfil notification
requirements.
The project will assume the principle of using commonly used data formats for
the sake of compatibility, efficiency and access. The preferred means of data
types is MS Office compatible formats, where applicable.
## Levels of Confidentiality and Flow of Data
Overall, there are three basic levels of confidentiality, namely Public,
Confidential to consortium (including Commission Services), and Confidential
to the Partner.
**Figure 2 Data types displayed in three levels of confidentiality.**
Figure 2 displays how the previously mentioned data types are positioned in
the level of confidentiality context. Figure 3 displays the data in more
granularity.
**Figure 3 Data distributed into the three levels of confidentiality in more
detail.**
## Data Formats
Recommended file formats are presented in the following table. See best
practices for file formats from _Stanford University Library_ .
**Table 2 Recommended file formats.**
<table>
<tr>
<th>
**Text format**
</th>
<th>
**File extension**
</th> </tr>
<tr>
<td>
Acrobat PDF/A
</td>
<td>
.pdf
</td> </tr>
<tr>
<td>
Comma-Separated Values
</td>
<td>
.csv
</td> </tr>
<tr>
<td>
Open Office Formats
</td>
<td>
.odt, .ods, .odp
</td> </tr>
<tr>
<td>
Plain Text (US-ASCII, UTF-8)
</td>
<td>
.txt
</td> </tr>
<tr>
<td>
XML
</td>
<td>
.xml
</td> </tr>
<tr>
<td>
**Image / Graphic formats**
</td>
<td>
**File extension**
</td> </tr>
<tr>
<td>
JPEG
</td>
<td>
.jpg
</td> </tr>
<tr>
<td>
JPEG2000
</td>
<td>
.jp2
</td> </tr>
<tr>
<td>
PNG
</td>
<td>
.png
</td> </tr>
<tr>
<td>
SVG 1.1 (no java binding)
</td>
<td>
.svg
</td> </tr>
<tr>
<td>
TIFF
</td>
<td>
.tif, .tiff
</td> </tr>
<tr>
<td>
**Audio formats**
</td>
<td>
**File extension**
</td> </tr>
<tr>
<td>
AIFF
</td>
<td>
.aif, .aiff
</td> </tr>
<tr>
<td>
WAVE
</td>
<td>
.wav
</td> </tr>
<tr>
<td>
**Motion formats**
</td>
<td>
**File extension**
</td> </tr>
<tr>
<td>
AVI (uncompressed)
</td>
<td>
.avi
</td> </tr>
<tr>
<td>
Motion JPEG2000
</td>
<td>
.mj2, .mjp2
</td> </tr> </table>
## Expected Data of the Project
Table 3 presents the data expected to be created during the project.
D1.2 – Open Data Management Plan
<table>
<tr>
<th>
**5G!Pagoda**
</th>
<th>
**Version 1.0**
</th>
<th>
</th>
<th>
</th>
<th>
</th>
<th>
</th>
<th>
</th>
<th>
**Page 12 of 20**
</th> </tr> </table>
**Table 3 Expected data of the project.**
<table>
<tr>
<th>
</th>
<th>
**Publications**
</th>
<th>
**Reports & Deliverables **
</th>
<th>
**Software**
</th>
<th>
**Open Research Data**
</th>
<th>
**Opted Out Research Data**
</th>
<th>
**Contribution to Open Sources**
</th>
<th>
**Contribution to Standars**
</th> </tr>
<tr>
<td>
**Partner**
</td>
<td>
Gold
</td>
<td>
Green
</td>
<td>
Consortium
</td>
<td>
Public
</td>
<td>
Consortium
</td>
<td>
Public
</td>
<td>
Yes 1)
</td>
<td>
No
</td>
<td>
May be
</td>
<td>
Yes 2)
</td>
<td>
No
</td>
<td>
May be
</td>
<td>
Yes 3)
</td>
<td>
No
</td>
<td>
May be
</td>
<td>
Yes 4)
</td>
<td>
No
</td>
<td>
May be
</td> </tr>
<tr>
<td>
AALTO
</td>
<td>
X
</td>
<td>
X
</td>
<td>
X
</td>
<td>
X
</td>
<td>
X
</td>
<td>
</td>
<td>
</td>
<td>
X
</td>
<td>
</td>
<td>
</td>
<td>
X
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
X
</td>
<td>
</td>
<td>
</td>
<td>
X
</td> </tr>
<tr>
<td>
UT
</td>
<td>
Δ
</td>
<td>
X
</td>
<td>
X
</td>
<td>
X
</td>
<td>
X
</td>
<td>
</td>
<td>
</td>
<td>
X
</td>
<td>
</td>
<td>
</td>
<td>
X
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
X
</td>
<td>
FGIMT2020,5GMF
</td>
<td>
</td>
<td>
</td> </tr>
<tr>
<td>
Ericsson
</td>
<td>
X
</td>
<td>
X
</td>
<td>
X
</td>
<td>
X
</td>
<td>
X
</td>
<td>
</td>
<td>
</td>
<td>
X
</td>
<td>
</td>
<td>
</td>
<td>
X
</td>
<td>
</td>
<td>
</td>
<td>
X
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
X
</td> </tr>
<tr>
<td>
Orange
</td>
<td>
X
</td>
<td>
X
</td>
<td>
X
</td>
<td>
X
</td>
<td>
</td>
<td>
X
</td>
<td>
</td>
<td>
X
</td>
<td>
</td>
<td>
</td>
<td>
X
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
X
</td>
<td>
3GPP, ETSI NFV, ITU-T
</td>
<td>
</td>
<td>
</td> </tr>
<tr>
<td>
FOKUS
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td> </tr>
<tr>
<td>
EI
</td>
<td>
X
</td>
<td>
X
</td>
<td>
X
</td>
<td>
X
</td>
<td>
X
</td>
<td>
X
</td>
<td>
</td>
<td>
X
</td>
<td>
</td>
<td>
</td>
<td>
X
</td>
<td>
</td>
<td>
X
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
X
</td> </tr>
<tr>
<td>
MI
</td>
<td>
X
</td>
<td>
X
</td>
<td>
X
</td>
<td>
X
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
ITU-T
</td>
<td>
</td>
<td>
</td> </tr>
<tr>
<td>
DG
</td>
<td>
X
</td>
<td>
X
</td>
<td>
X
</td>
<td>
X
</td>
<td>
X
</td>
<td>
</td>
<td>
</td>
<td>
X
</td>
<td>
</td>
<td>
</td>
<td>
X
</td>
<td>
</td>
<td>
</td>
<td>
X
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td> </tr>
<tr>
<td>
KDDI
</td>
<td>
</td>
<td>
</td>
<td>
X
</td>
<td>
X
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
X
</td>
<td>
</td>
<td>
</td>
<td>
X
</td>
<td>
</td>
<td>
</td>
<td>
X
</td>
<td>
</td>
<td>
3GPP, 5GMF
</td>
<td>
</td>
<td>
</td> </tr>
<tr>
<td>
HITACHI
</td>
<td>
</td>
<td>
</td>
<td>
X
</td>
<td>
X
</td>
<td>
X
</td>
<td>
</td>
<td>
</td>
<td>
X
</td>
<td>
</td>
<td>
</td>
<td>
X
</td>
<td>
</td>
<td>
</td>
<td>
X
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
X
</td> </tr>
<tr>
<td>
NESIC
</td>
<td>
</td>
<td>
</td>
<td>
X
</td>
<td>
X
</td>
<td>
X
</td>
<td>
</td>
<td>
</td>
<td>
X
</td>
<td>
</td>
<td>
</td>
<td>
X
</td>
<td>
</td>
<td>
</td>
<td>
X
</td>
<td>
</td>
<td>
</td>
<td>
X
</td>
<td>
</td> </tr>
<tr>
<td>
WU
</td>
<td>
X
</td>
<td>
X
</td>
<td>
X
</td>
<td>
X
</td>
<td>
X
</td>
<td>
</td>
<td>
</td>
<td>
X
</td>
<td>
</td>
<td>
</td>
<td>
X
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
X
</td>
<td>
ITU-TFG IMT2020, SG13
</td>
<td>
</td>
<td>
</td> </tr>
<tr>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td> </tr>
<tr>
<td>
If you answered yes to the following items, please give detailed
comments/explanations below!
</td>
<td>
</td>
<td>
</td> </tr>
<tr>
<td>
Yes 1)
</td>
<td>
Describe the data, give also format and expected amount (MB, GB)
</td>
<td>
</td>
<td>
</td> </tr>
<tr>
<td>
Yes 2)
</td>
<td>
If you are opting out data (keep it confidential), describe the data and give
reasons for opting out
</td>
<td>
</td>
<td>
</td> </tr>
<tr>
<td>
Yes 3)
</td>
<td>
If you are contributing to open sources, describe the contributing data and
the open source instance
</td>
<td>
</td>
<td>
</td> </tr>
<tr>
<td>
Yes 4)
</td>
<td>
If you are contributing to standards preparation, describe the instance to
contribute to
</td>
<td>
</td>
<td>
</td> </tr>
<tr>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td> </tr>
<tr>
<td>
**Detailed comments**
</td>
<td>
</td>
<td>
</td> </tr>
<tr>
<td>
**Partner**
</td>
<td>
**Comment**
</td>
<td>
</td>
<td>
</td> </tr>
<tr>
<td>
AALTO
</td>
<td>
4) Aalto may contribute to ITU-T IMT 2020 Network Softwarization Working Group
along with The University of Tokyo
</td>
<td>
</td>
<td>
</td> </tr>
<tr>
<td>
UT
</td>
<td>
4) UT will contribute to ITU-T IMT-2020 and 5GMF Network Architecture WG
</td>
<td>
</td>
<td>
</td> </tr>
<tr>
<td>
Ericsson
</td>
<td>
</td>
<td>
</td>
<td>
</td> </tr>
<tr>
<td>
Orange
</td>
<td>
</td>
<td>
</td>
<td>
</td> </tr>
<tr>
<td>
FOKUS
</td>
<td>
</td>
<td>
</td>
<td>
</td> </tr>
<tr>
<td>
EI
</td>
<td>
3) EURECOM is interested to contribute the code around Open Air Interface
(OAI) to the OAI Software Alliance
</td>
<td>
</td>
<td>
</td> </tr>
<tr>
<td>
MI
</td>
<td>
4) MI will contribute to ITU-T SG20
</td>
<td>
</td>
<td>
</td> </tr>
<tr>
<td>
DG
</td>
<td>
</td>
<td>
</td>
<td>
</td> </tr>
<tr>
<td>
KDDI
</td>
<td>
4) KDDI will contribute to 3GPP SA2 WG and 5GMF Technology Promotion Group
</td>
<td>
</td>
<td>
</td> </tr>
<tr>
<td>
HITACHI
</td>
<td>
</td>
<td>
</td>
<td>
</td> </tr>
<tr>
<td>
NESIC
</td>
<td>
</td>
<td>
</td>
<td>
</td> </tr>
<tr>
<td>
WU
</td>
<td>
4) Waseda will contribute to ITU-T FG IMT-2020 and may be to SG13
</td>
<td>
</td>
<td>
</td> </tr> </table>
# Collection, Storage and Use of Research Data and Analysed Research Data
The research data will be collected following jointly agreed guidelines and
principles in order to guarantee the achievement of sound research data and to
make the reusability of the research data possible. Possible data flows are
presented in Figure 4.
**Figure 4 Open access to scientific publication and research data in the
wider context of dissemination**
**and exploitation (Source: H2020 Programme, Guidelines on Open Access to
Scientific Publications and Research Data in Horizon 2020, Version 3.1, 25
August 2016).**
# Storage and Use of Project Data
## Storage of Data
The data accumulated in the 5G!Pagoda project will be analysed and stored
according to the principles detailed in this plan. Overall the detailed
research data will be stored by the organisations which has collected it, and
the project data will be stored by Aalto University. Both the central
repository and the databases of the individual organisations will be secured
using the latest security protocols, and access to data will be granted only
for persons nominated by the project partners.
All project administrative data will be stored at a dedicated database for the
5G!Pagoda project. The project uses the OwnCloud repository (https://
_https://5g-pagoda.aalto.fi/owncloud/_ ) , which is a secure, password
protected document repository and archive system. Access to the database is
managed by the coordinator and provided for project consortium and other
parties as deemed necessary by the project team. The project data is stored on
Aalto University servers, not in the cloud, for added security. The project
data management structure and categories are as follows.
Workpackages
WP1
WP2
WP3
WP4
WP5
WP6
Templates
Standards-Activities
Project Meetings
Physical Meetings
ConfCalls
Market Analysis
Deliverables
WP1
WP2
WP3
WP4
WP5
WP6
Admin Relevant
Original DoA
Grant Agreement
Consortium Agreement
Coordination Agreement
The 5G!Pagoda Project Management, Quality and IPR Guide details the project
internal management structure and processes, as well as quality and reporting
practices. Related to project data management, best practices for data
generation and sharing have been applied. This includes set rules for version
control, whereby the partners are encouraged to use unified methods for naming
the documents by the Task or Deliverable name, with a corresponding version
number. The documents are stored in the database and preferably shared within
the consortium via a link to the database rather than e-mail attachments. All
deliverables have a unified look and feel with a unified template which helps
the reviewers in their project evaluation.
The project coordinator assumes the responsibility for timely documentation
and sharing of project management related documents and materials. Each Work
Package (WP) leader monitors the timely documentation of WP related
requirements within the consortium. Each task leader ensures the timely
production of the deliverable for which he/she is responsible. With the
strongly inter-related and intertwined Tasks in the different Work Packages,
the same previously described principles regarding e.g. confidentiality levels
and data types will be applied.
## Documenting of Data
The stored data needs to be provided with metadata that complies with an
international metadata standard. To facilitate this, the following questions
should be answered already during the research work:
* Who are the creators and what are their affiliations
* Where the data is located and is there a persistent identifier
* What is the license chosen to allow reuse
* How, when and by whom the data has been collected/ created
* How the data has been prepared for analysis
* What kind of data manipulations have taken place
* How and what methods have been used to analyse the data
* What instruments and devices have been used
* Which scientific publications are based on this data
* What is the software used to process and analyse the data
Available metadata standards are listed e.g. in these sites:
* _General Metadata standards_ listed by the Digital Curation Center (DCC) UK
* _Discipline-specific metadata standards_ listed by the Digital Curation Center (DCC) UK
* _Metadata standards by topic_ listed by the Research Data Alliance (RDA)
It is imperative to get a persistent identifier for data so that it is
findable and citable. The most common identifier is Digital Object Identifier
(DOI). Appropriate data repositories provide persistent identifiers for data
sets.
The 5G!Pagoda project intends to use _DataSite_ digital identifier for
research data. For more details about DOI, the interested reader may refer to
th e _International DOI Foundation (IDF_ ) .
To license a dataset requires either 1) that all creators agree to release the
data they have created using the same license; or 2) the ownership of datasets
is transferred to one legal entity. One straightforward and effective way of
doing this is to attach _Creative Commons_ _Licences_ ( _CC BY_ o r _CC0_ )
to the data deposited. Th e _EUDAT B2SHARE_ t ool includes a built-in
license wizard that facilitates the selection of an adequate license for
research data.
Table 4 presents the planned Open Access Data of the project. For repositories
see Chapter 5.
**Table 4 Curation of stored open data.**
<table>
<tr>
<th>
**Partner**
</th>
<th>
**Data type**
</th>
<th>
**Repository**
</th>
<th>
**Metadata standard**
</th>
<th>
**License standard**
</th> </tr>
<tr>
<td>
All partners
</td>
<td>
PU deliverables
</td>
<td>
Participant Portal
</td>
<td>
</td>
<td>
</td> </tr>
<tr>
<td>
AALTO
</td>
<td>
Publications
</td>
<td>
TBD
</td>
<td>
TBD
</td>
<td>
TBD
</td> </tr>
<tr>
<td>
UT
</td>
<td>
Publications
</td>
<td>
TBD
</td>
<td>
TBD
</td>
<td>
TBD
</td> </tr>
<tr>
<td>
Ericsson
</td>
<td>
Publications
</td>
<td>
TBD
</td>
<td>
TBD
</td>
<td>
TBD
</td> </tr>
<tr>
<td>
Orange
</td>
<td>
Publications
</td>
<td>
TBD
</td>
<td>
TBD
</td>
<td>
TBD
</td> </tr>
<tr>
<td>
Orange
</td>
<td>
Software
</td>
<td>
TBD
</td>
<td>
TBD
</td>
<td>
TBD
</td> </tr>
<tr>
<td>
EI
</td>
<td>
Publications
</td>
<td>
TBD
</td>
<td>
TBD
</td>
<td>
TBD
</td> </tr>
<tr>
<td>
EI
</td>
<td>
Software
</td>
<td>
TBD
</td>
<td>
TBD
</td>
<td>
TBD
</td> </tr>
<tr>
<td>
MI
</td>
<td>
Publications
</td>
<td>
TBD
</td>
<td>
TBD
</td>
<td>
TBD
</td> </tr>
<tr>
<td>
DG
</td>
<td>
Publications
</td>
<td>
TBD
</td>
<td>
TBD
</td>
<td>
TBD
</td> </tr>
<tr>
<td>
WU
</td>
<td>
Publications
</td>
<td>
TBD
</td>
<td>
TBD
</td>
<td>
TBD
</td> </tr> </table>
# Data Sharing
All parties have signed the Coordination Agreement and the European Partners
have signed/accessed to the project Grant Agreement and Consortium Agreement,
which all together detail the parties’ rights and obligations, including – but
not limited to – obligations regarding data security and the protection of
privacy. These obligations and the underlying legislation will guide all of
the data sharing actions of the project consortium.
The 5G!Pagoda project has committed to participate in the Pilot on Open
Research Data in Horizon 2020, which is an expression of the larger Open
Access initiative of the European Commission 1 . Participation in the pilot
is manifested on two levels: a) depositing research data in an open access
research database or repository and b) choosing to provide open access to
scientific publications which are derived from the project research. At the
same time, the consortium is dedicated to protect the privacy of the
informants and companies.
## Depositing Research Data
Following the principles of the European Commission Open Data pilot, the
applicable research data gathered in the project will be made available to
other researchers through open access database or repositories, in order to
increase the potential exploitation of the project work.
To make research data openly accessible, the following matters shall be
considered:
* Specify which data will be made openly available? If some data is kept closed, provide rationale for doing so
* Specify how the data will be made available
* Specify what methods or software tools are needed to access the data? Is documentation about the software needed to access the data included? Is it possible to include the relevant software (e.g. in open source code)?
* Specify where the data and associated metadata, documentation and code are deposited
* Specify how access will be provided in case there are any restrictions
* Assess the interoperability of your data. Specify what data and metadata vocabularies, standards or methodologies you will follow to facilitate interoperability.
There are online research data archives, which may be subject-based/thematic,
institutional or centralised. Useful listings of repositories include the
Registry of _Research Data Repositories_ a nd _Databib_ . The Open Access
Infrastructure for Research in Europe (OpenAIRE) provides additional
information and support on linking publications to underlying research data.
Some repositories like _Zenodo_ (an OpenAIRE and CERN collaboration), allows
researchers to deposit both publications and data, while providing tools to
link them. Zenodo and some other repositories as well as many academic
publishers also facilitate linking publications and underlying data through
persistent identifiers and data citations.
## Open Access Publishing
All peer-reviewed scientific publications relating to results are published so
that open access (free of charge, online access for any user) is ensured.
Publications will either immediately be made accessible online by the
publisher (Gold Open Access), or publications are available through an open
access repository after an embargo period, which according to the Grant
Agreement can be six months at maximum (Green Open Access). Possible Gold Open
Access journals include IEEE Access Magazine. For all other articles, the
researchers aim at publishing them in a Green Open Access repository. The
coordinator, Aalto University, has a Green Open Access repository the
5G!Pagoda consortium can use, at _https://aaltodoc.aalto.fi/?locale-
attribute=en_ .
A repository for scientific publications is an online archive. Institutional,
subject-based and centralised repositories are all acceptable choices.
The _Open Access Infrastructure for Research in Europe_ _(OpenAIRE)_ i s the
recommended entry point for researchers to determine what repository to
choose. It also offers support services for researchers, such as the National
Open Access Desks. Other useful listings of repositories are:
* _Registry_ _of Open Access Repositories (ROAR)_
* _Directory_ _of Open Access Repositories_ _(OpenDOAR)_
A machine-readable electronic copy of the published version or final peer-
reviewed manuscript accepted for publication will be available in a repository
for scientific publications. Electronic copies of publications will have
bibliographic metadata in a standard format and will include "European Union
(EU)" and "Horizon 2020", the name of the action, acronym and grant number;
publication date, and length of embargo period if applicable, and identifier.
_ORCID_ identifier is used to identify the authors of publications in the
5G!Pagoda project.
## Other Dissemination
In addition to the above, the project will release yearly a public report on
standardization, dissemination and exploitation achievements (deliverables
D6.2, D6.3, and D6.4). At the end of the project, a report on innovation
achieved and forthcoming industrial exploitation will be also released (D6.5).
# Opting Out Research Data
The 5G!Pagoda project can opt out at any stage and so free itself
retroactively from the obligations associated with the conditions – if:
* participation is incompatible with the obligation to protect results that can reasonably be expected to be commercially or industrially exploited
* participation is incompatible with the need for confidentiality in connection with security issues
* participation is incompatible with rules on protecting personal data
* participation would mean that the project's main aim might not be achieved
* the project will not generate / collect any research data or
* there are other legitimate reasons
Alternatively, the 5G!Pagoda project can also choose to keep selected datasets
or even all data closed for any of the reasons above, via this Data Management
Plan.
At this phase of the project no research data is identified and thus opting
out is not relevant.
# Summary and Perspectives
The Open data management plan will be updated during the project lifetime if
new practices for data management are introduced.
|
https://phaidra.univie.ac.at/o:1140797
|
Horizon 2020
|
1161_SAFE STRIP_723211.md
|
**Executive Summary**
In Horizon 2020 a limited pilot action on open access to research data has
been implemented. Participating projects have been required to develop a Data
Management Plan (DMP).
This deliverable provides the first version of the DMP elaborated by the SAFE
STRIP project. The purpose of this document is to provide an overview of the
main elements of the data management policy. It outlines how research data
will be handled during the project and describes what data will be collected,
processed or generated and following what methodology and standards, whether
and how this data will be shared and/or made open, and how it will be curated
and preserved. Besides, an initial list of data types, metadata and global
data collection processes are also provided in this document.
The SAFE STRIP Data Management Plan refers to the latest EC DMP guidelines.
This version has explicit recommendations for full life cycle management
through the implementation of the FAIR principles, which state that the data
produced shall be Findable, Accessible, Interoperable and Reusable (FAIR).
Since the data management plan is expected to evolve during the project while
taking into account the progress of the work, especially regarding sensors
specifications, use cases, system architecture, pilot preparation and data
modelling and fusion activities, an updated version will be produced as
foreseen (deliverable D2.3) in M29.
Deliverable D2.2 SAFE STRIP Initial Data Management Plan Version 1.0Date
08/11/2017
6
1. **Introduction**
**1.1 SAFE STRIP main concept and objectives**
**SAFE STRIP** aims to introduce a **disruptive technology** that will achieve
**to embed C-ITS applications in existing road infrastructure** , including
novel I2V and V2I, as well as VMS/VSL functions **into low-cost, integrated
strips markers on the road** . The SAFE STRIP vision is to make roads self-
explanatory (with personalised in-vehicle messages) and forgiving (due to
advanced cooperative functions) for all road users (trucks, cars and
vulnerable road users, such as PTWs riders) and all vehicle generations (non-
equipped, C-ITS equipped, autonomous), with reduced maintenance cost, full
recyclability and added value services, as well as supporting real-time
predictive road maintenance functions.
A brief overview of the specific project objectives is given below:
1. To develop a novel micro/nano sensorial system integrated in road pavement tapes/ markers; that will provide advanced safety functions to all road users at a fraction of the cost of current I2V/V2I nodes and roadside equipment.
2. To support predictive infrastructure maintenance, through dynamic road embedded sensors input.
3. To make road infrastructure (mainly for highways and interurban roads but also for city rings and selected rural roads) self-explanatory (through personalised info in own language and preferred format provided by the system to each driver/rider) and forgiving (through key I2V/V2I info provided to the cooperative system of the vehicle; such as dynamic speed limit and friction coefficient factor); for all vehicle types.
4. To extend this notion to parking depots, key intermodal nodes, such as railway crossings, harbour loading/uploading areas and logistic depots and work zone areas.
5. To reduce the infrastructure operational (including VMS/VSL info and toll collection functions), installation and maintenance costs.
6. To provide key info to C-ITS equipped and autonomous vehicles about road, weather and traffic conditions ahead, to support dynamic trajectory estimation and optimisation.
7. To support a wide range of added value services.
8. To evaluate the system in a controlled environment.
In order to realise its vision, SAFE STRIP will implement two complementary as
well as alternative solutions, one to address equipped vehicles (namely,
intelligent vehicles with on board sensors and C-ITS or automation
applications) and one to address nonequipped vehicles (the great majority of
current vehicle fleets, including also vehicles that are very difficult to
equip with rich on-board sensorial platforms, like PTW’s).
**1.2 Purpose of the Document**
The purpose of this deliverable ( _D2.2-Data Management Plan_ ) is to provide
an analysis of the main elements of the data management policy that will be
used by the consortium with regard to all the datasets that will be generated
and/or collected by the project consortium.
As SAFE STRIP is a H2020 funded project the Data Management Plan (DMP) must at
least cover the specific aspects about project’s datasets. Particularly, in
accordance to the EC DMP guidelines and template1, describes the data
management life cycle for all datasets to be collected, processed or generated
by a research project. It must cover:
* the handling of research data during & after the project
* what data will be collected, processed or generated
* what methodology & standards will be applied whether data will be shared /made open access & how how data will be curated & preserved.
The DMP plays a crucial role in project’s success for two main reasons. On the
one hand, it ensures the availability and the quality of the datasets, which
will be used/produced in the technical Work Packages of the project (WP2-WP6),
serving the achievement of project’s objectives. On the other hand, it
provides a thorough elaboration of the state of all datasets and corresponding
software, proving that they are assessable and useable by third parties.
**1.3 Intended audience**
The SAFE STRIP project addresses highly innovative concepts. As such, foreseen
intended audience of the project is the scientific community in the areas of
intelligent transport systems, with emphasis on wireless communications, road
safety, and automotive engineering. In addition, due to the strong expected
impact of the project on their respective domains, the other expected audience
consists in automotive industrial communities, telecom operator and
standardization organizations.
**1.4 Interrelations**
This deliverable is directly related to project activity _A2.4-Data Modelling
and Fusion_ , which defines the data models and protocols for all data to be
exchanged among system modules, during the project lifecycle. Furthermore,
project activities _A1.3-Use cases and application scenarios_ and
_A2.2-Sensors specifications_ , provide the necessary input for the
specification of the various data types and models that are concerned in the
project. In particular, from the use cases and applications defined in A1.3,
we derive the types of data associated to each application, whereas A2.2
provides insight about the data to be collected and processes by sensors which
are installed in various locations of the system architectures.
2. **Data Summary**
**2.1 Data Collection and Sharing Process**
In line with SAFE STRIP objectives, data collection in SAFE STRIP concerns the
two key following data clusters:
1. Data that will be generated and exchanged at different communication nodes of the overall system architecture, according to the foreseen use cases.
2. Additional data that need to be collected, **on top of the above** , during **technical validation activities and Pilots with users in a real-life operational environment** during the project that will allow the meaningful evaluation of the system performance and acceptance as well as the impact assessment to be held (in the context of WP6). Those data can be raw data (the ones of cluster 1 above plus some additional data that need to be collected for evaluation purposes regarding the driving performance upon the warning/notifications provided by the system and, in addition, subjective/qualitative data that will be collected in the context of interviews and focus groups).
Both clusters of data will be exhaustively specified in the following version
of the DMP (for M29) upon the outcomes of _D1.2: “SAFE STRIP Use Cases and
application scenarios”_ , _D2.1:_ _“System architecture, sensor
specifications, fabrication, maintenance, installation and security
requirements and Risk Assessment”_ and will be pragmatically verified – before
being reported – after the three first evaluation iterations that will have
been held until M29.
Apart from the above, a set of data has been collected already in the context
of A1.1 surveys and A1.3 workshop – anonymised – that have been used for
recognizing stakeholders’ needs and views on the system use cases and
implementation approach.
In the remaining section we present the various data categories of the
aforementioned data clusters. Where applicable, we provide the full details
that describe the data model for each category, i.e. data structure and data
type of each element. It is expected that the full details for all SAFE STRIP
data categories will be defined in the context of the activities _A2.2-Sensors
specifications_ and _A2.4-Data Modelling and Fusion_ and will be included in
the next updated version of this deliverable ( _D2.3SAFE STRIP Final Data
Management Plan_ ) on M29.
2. **V2I & V2V Exchanged Messages **
The types of the exchanged I2V and V2V messages can be classified into:
* **Decentralized Environmental Notification Messages** 2
(DENMs – ETSI EN 302 637-3)
* **Cooperative Awareness Messages** 3
(CAMs - ETSI EN 302 637-2)
DENMs contain information related to an event that has potential impact on
road safety and are triggered by an event occurrence (event-triggered
messages). On the other hand, CAMs inform road users and roadside
infrastructure about each other’s position, dynamics and attributes. This is
achieved by periodically exchange of information among vehicles (V2V) and
between vehicles and road side infrastructure (V2I). The data associated with
the aforementioned types is presented in the remaining sections.
**2.2.1 Decentralized Environmental Notification Messages**
In our application the DENMs, which are exchanged between Road-Side-Unit (RSU)
and Vehicles, are providing information related to road wear and road
conditions based on a number of sensors data. DENMs, which are event-based
messages, are giving specific information about the event such as **Event
Type** , **Event Location** , **Transmission Area** , **Event Duration** and
are constructed by the Data Elements DE (contain one single data) & Data
Frames DF (contain more than one data element in a predefined order) described
in the clause below. The general structure of a DENM is depicted at the
following figure.
Each container is composed of a sequence of data elements (DE) and/or data
frames (DF). A DE and a DF is either optional or mandatory.
* ITS PDU Header
* Management Container o _actionID_ o _detectionTime_ o _referenceTime_ o _termination_ o _eventPosition_ o _relevanceDistance_ o _relevanceTrafficDirection_ o _transmissionInterval_ o _station type_
* Situation Container o _informationQuality_ o _eventType_
* _causeCode_
* _subCauseCode_ o _linkedCause_ o _eventHistory_
* Location Container o _EventSpeed_ o _EventPositionHeading_ o _Traces_ o _roadType_
* A la carte Container o _LanePosition_
* _ImpactReduction_ o _ExternalTemperature_
* _RoadWorks_
* _PositioningSolution_ o _stationaryVehicle_
Each of these DE &DF belongs to one of the following categories4:
* **Vehicle information:** the DE or DF describes one or a set of in vehicle data.
* **GeoReference information:** the DE or DF provides geographical description of the data.
* **Road topology information:** the DE or DF describes one or a set of road topology information.
* **Traffic information:** the DE or DF describes one or a set of road traffic information.
* **Infrastructure information:** the DE or DF describes one or a set of ITS infrastructure information.
* **Personal information:** the DE or DF describes one or a set of ITS personal information.
* **Communication information:** the DE or DF describes one or a set of data that are relevant to the ITS application layer or ITS facilities layer communication protocol.
* **Other information:** the DE or DF that does not belong to any of the above categories.
The Data Elements DE & Data Frames DF, which are described below, are giving
information related to **ITS PDU Header** 4 **(** Figure 1 **)**
* **DF_ItsPduHeader (DataType_ 114) – Communication Information**
**Category** o Common message header for application and facilities layer
messages. It is included at the beginning of an ITS message as the message
header. The DF shall include the following information:
* **protocolVersion:** version of the ITS message and/or communication protocol
* **messageID:** Type of the ITS message:
* denm(1): Decentralized Environmental Notification
Message (DENM) as specified in ETSI EN 302 637-3
[i.3]
* cam(2): Cooperative Awareness Message (CAM) as specified in ETSI EN 302 637-2 [i.2]
* poi(3): Point of Interest message as specified in ETSI TS 101 556-1 [i.11]
* spat(4): Signal Phase And Timing (SPAT) message as specified in SAE J2735 [i.12]
* map(5): MAP message as specified in SAE J2735 [i.12]
* ivi(6): In Vehicle Information (IVI) message as defined in ISO TS 19321 [i.13]
* ev-rsr(7): Electric vehicle recharging spot reservation message, as defined in ETSI TS 101 556-3 [i.14]
▪ **stationID:** the identifier of the ITS-S that generates the ITS message in
question. It shall be represented as specified hereafter (DataType 77).
* **DE_StationID (DataType_ 77) - Communication Information Category** o Identifier for an ITS-S (Intelligent Transport Systems Station). The ITS-S ID may be a pseudonym. It may change over space and/or over time.
o Integer value which ranges from 0 to 4294967295
The Data Elements DE & Data Frames DF, which are described below, are giving
information related to **Management Container (** Figure 1 **)** .
* **DF_ActionID (DataType_ 102) - Communication Information Category** o Identifier used to describe a protocol action taken by an ITS-S. For example, it describes an action taken by an ITS-S to trigger a new DENM as defined in ETSI EN 302 637-3 [i.3] after detecting an event.
The DF shall include a sequence of the following data:
* **originatingStationID:** ID of the ITS-S that takes the action. It shall be presented as defined in _**DE_StationID** _
* **sequenceNumber:** a sequence number. It shall be presented as defined in _**DE_SequenceNumber** . _
* **DE_SequenceNumber (DataType_ 68) - Other Information Category** o Sequence number o Integer value which ranges from 0 to 65535.
* **DE_Timestamplts (DataType_ 82) - Other Information Category** o Number of milliseconds since 2004-01-01T00:00:00.000Z, as specified in ISO 8601 [i.10].
EXAMPLE: The value for TimestampIts for 2007-01-01T00:00:00.000Z is 94 694 401
000 milliseconds, which includes one leap second insertion since
2004-01-01T00:00:00.000Z. It should range from 0 to 4398046511103\.
* (0) if utcStartOf2004
* (1 )oneMillisecAfterUTCStartOf2004
* **DF_ReferencePosition (DataType_ 124) – GeoReference Information**
**Category** o The geographical position of a position or of an ITS-S. It
represents a geographical point position. The DF shall include a sequence of
the following information:
* **latitude** : latitude of the geographical point
* **longitude** : longitude of the geographical point
* **positionConfidenceEllipse** : accuracy of the geographical position
* **altitude** : altitude and altitude accuracy of the geographical point
* **DF_Latitude (DataType_ 41) – GeoReference Information Category**
* Absolute geographical latitude in a WGS84 coordinate system, providing a range of 90 degrees in north or in south hemisphere. Positive values are used for latitude in north of the Equator, negative values are used for latitude in south of the Equator. When the information is unavailable, the value shall be set to 900 000 001\.
* Unit: 0.01 microdegree
* **DF_Longitude (DataType_ 44) - GeoReference Information Category** o Absolute geographical longitude in a WGS84 co-ordinate system, providing a range of 180 degrees to the east or to the west of the prime meridian. Negative values are used for longitudes to the west; positive values are used for longitudes to the east. When the information is unavailable, the value shall be set to 1 800 000 001\.
o Unit: 0.01 microdegree
* **DF_PosConfidenceEllipse (DataType_ 119) - GeoReference Information**
**Category** o DF that provides the horizontal position accuracy in a shape of
ellipse with a predefined confidence level (e.g. 95 %). The centre of the
ellipse shape corresponds to the reference position point for which the
position accuracy is evaluated. The DF shall include a sequence of the
following information:
* **semiMajorConfidence** : half of length of the major axis, i.e. distance between the centre point and major axis point of the position accuracy ellipse ( _**DE_SemiAxisLength** _ ).
* **semiMinorConfidence** : half of length of the minor axis, i.e. distance between the centre point and minor axis point of the position accuracy ellipse ( _**DE_SemiAxisLength** _ ).
* **semiMajorOrientation** : orientation direction of the ellipse major axis of the position accuracy ellipse with regards to the WGS84 north ( _**DE_HeadingValue** _ ).
* **DE_SemiAxisLength (DataType_67) - GeoReference Information**
**Category** o Absolute position accuracy in one of the axis direction as
defined in a shape of ellipse with a predefined confidence level (e.g. 95 %).
The required confidence level is defined by the corresponding standards
applying the DE. The value shall be set to:
* (1) if the accuracy is equal to or less than 1 cm
* (n > 1 and n < 4 093) if the accuracy is equal to or less than n cm
* (4 093) if the accuracy is equal to or less than 4 093 cm
* (4 094) if the accuracy is out of range, i.e. greater than 4 093 cm
* (4 095) if the accuracy information is unavailable o Unit: 1 cm.
**NOTE** : The fact that a position coordinate value is received with
confidence set to 'unavailable(4095)' can be caused by several reasons, such
as:
* the sensor cannot deliver the accuracy at the defined confidence level because it is a low-end sensor,
* the sensor cannot calculate the accuracy due to lack of variables, or o there has been a vehicle bus (e.g. CAN bus) error.
In all 3 cases above, the reported position coordinate value may be valid and
used by the application.
If a position coordinate value is received and its confidence is set to
'outOfRange(4094)', it means that the reported position coordinate value is
not valid and therefore cannot be trusted. Such value is not useful for the
application.
* **DE_HeadingValue (DataType_35) – GeoReference, Vehicle & Road **
**Topology Information Category** o Orientation of a heading with regards to
the WGS84 north.
* wgs84North(0)
* wgs84East(900)
* wgs84South(1800)
* wgs84West(2700)
* unavailable(3601) o Unit: 0.01 degree.
* **DF_Altitude (DataType_ 103) - GeoReference Information Category** o Altitude and accuracy of an altitude in a WGS84 co-ordinate system.
The DF shall include a sequence of the following information:
* **altitudeValue** : altitude of a geographical point.
* **altitudeConfidence** : accuracy of the reported altitudeValue within a specific confidence level.
* **DF_AltitudeValue (DataType_ 9) - GeoReference Information Category** o Altitude in a WGS84 co-ordinate system. When the information is not available, the DE shall be set to 800 001. For altitude equal or greater than 8 000 m, the DE shall be set to 800 000. For altitude equal or less than -1 000 m, the DE shall be set to -100 000\.
o Unit: 0.01 metre
* **DE_AltitudeConfidence (DataType_8) - GeoReference Information**
**Category** o Absolute accuracy of a reported altitude value of a
geographical point for a predefined confidence level (e.g. 95 %). The required
confidence level is defined by the corresponding standards applying the usage
of this DE. The value shall be set to:
* 0 if the altitude accuracy =< 0.01 metre
* 1 if the altitude accuracy =< 0.02 metre
* 2 if the altitude accuracy =< 0.05 metre
* 3 if the altitude accuracy =< 0.1 metre
* 4 if the altitude accuracy =< 0.2 metre
* 5 if the altitude accuracy =< 0.5 metre
* 6 if the altitude accuracy =< 1 metre
* 7 if the altitude accuracy =< 2 metres
* 8 if the altitude accuracy =< 5 metres
* 9 if the altitude accuracy =< 10 metres
* 10 if the altitude accuracy =< 20 metres
* 11 if the altitude accuracy =< 50 metres
* 12 if the altitude accuracy =< 100 metres
* 13 if the altitude accuracy =< 200 metres
* 14 if the altitude accuracy is out of range, i.e. greater than 200 metres
* 15 if the altitude accuracy information is unavailable
**NOTE** : The fact that an altitude value is received with confidence set to
'unavailable(15)' can be caused by several reasons, such as:
* the sensor cannot deliver the accuracy at the defined confidence level because it is a low-end sensor,
* the sensor cannot calculate the accuracy due to lack of variables, or o there has been a vehicle bus (e.g. CAN bus) error.
In all 3 cases above, the reported altitude value may be valid and used by the
application.
If an altitude value is received and its confidence is set to
'outOfRange(14)', it means that the reported altitude value is not valid and
therefore cannot be trusted. Such value is not useful for the application.
* **DE_RelevanceDistance (DataType_ 61) - GeoReference Information**
**Category** o DE describing a distance of relevance for information indicated
in a message, for example, it may be used to describe the distance of
relevance of an event indicated in a DENM as defined in ETSI EN 302 637-3
[i.3]. The value shall be set to:
* 0 if relevance distance < 50 m
* 1 if relevance distance < 100 m
* 2 if relevance distance < 200 m
* 3 if relevance distance < 500 m
* 4 if relevance distance < 1000 m
* 5 if relevance distance < 5 km
* 6 if relevance distance < 10 km
* 7 if relevance distance > 10 km
* **DE_RelevanceTrafficDirection (DataType_ 62) - GeoReference**
**Information Category** o DE describing a traffic direction that is relevant
to information indicated in a message. For example, it may be used to describe
traffic direction which is relevant to an event indicated by a DENM as defined
in ETSI EN 302 637-3 [i.3], The terms "upstream",
"downstream" and "oppositeTraffic" are relative to the event position.
The value shall be set to:
* 0 if allTrafficDirections
* 1 if upstreamTraffic
* 2 if downstreamTraffic
* 3 if oppositeTraffic(3)
o The terms "upstream", "downstream" and "oppositeTraffic" are relative to the
event position
* **DE_ValidityDuration (DataType_ 88) - Traffic Information Category** o At the end of this validity duration, the event is regarded as terminated.
All information related to it may be deleted.
o Unit: Second (It could range from 0 to 86400)
* **DE_TransmissionInterval (DataType_ 86) - Communication Information**
**Category** o Time interval between two consecutive message transmissions.
o Unit: Millisecond
* **DE_StationType (DataType_78) - Other Information Category** o The type of an ITS-S. The station type depends on the integration environment of ITS-S into vehicle, mobile devices or at infrastructure.
(0 … 255)
* unknown(0)
* pedestrian(1)
* cyclist(2)
* moped(3)
* motorcycle(4)
* passengerCar(5)
* bus(6)
* lightTruck(7)
* heavyTruck(8)
* trailer(9)
* specialVehicles(10)
* tram(11)
* roadSideUnit(15)
The Data Elements DE & Data Frames DF, which are described below, are giving
information related to **Situation Container (** Figure 1 **)** .
* **DE_InformationQuality (DataType_39) - Other Information Category** Quality level of provided information (0 … 7) o Unavailable (0) o Lowest (1)
o Highest (7)
* **DF_CauseCode (DataType_104) - Traffic Information Category** o Encoded value of a traffic event type. The DF shall include the following information:
* causeCode: the type of a direct cause of a detected event. It shall be presented as defined in _**CauseCodeType** _
* subCauseCode: sub type of the direct cause. It shall be presented as defined in _**SubCauseCodeType.** _
The values of causeCodeType and subCauseCode are defined in clause 7.1.4 of
ETSI EN 302 637-3 [i.3].
* **DE_CauseCodeType (DataType_ 10) - Traffic Information Category** o Value of the direct cause code of a detected event. The Cause Codes are described as following:
* adverseWeatherCondition-Adhesion ( **Direct Cause Code** 🡪
6): the type of event is low adhesion
* adverseWeatherCondition-ExtremeWeatherCondition ( **Direct Cause Code** 🡪 17): the type of event is extreme weather condition
* adverseWeatherCondition-Precipitation ( **Direct Cause Code** 🡪 19): the type of event is precipitation
* adverseWeatherCondition-Visibility ( **Direct Cause Code** 🡪
18): the type of event is low visibility
* hazardousLocation-SurfaceCondition ( **Direct Cause Code** 🡪 9): the type of event is abnormal road surface condition
* roadworks ( **Direct Cause Code** 🡪 3): the type of the event is roadwork
* **DE_SubCauseCodeType (DataType_ 81) - Traffic Information Category** o Type of **Sub Cause** of a detected event as defined in ETSI EN 302
637-3 [i.3]” **.**
* **DE_AdverseWeatherCondition – AdhensionSubCauseCode (DataType_ 4) - Traffic Information Category** Low Road Adhension is due to:
* unavailable (Sub Causes Code 🡪 0) o heavy frost on the road (Sub Causes Code 🡪 1) o fuel on the road (Sub Causes Code 🡪 2) o mud on the road (Sub Causes Code 🡪 3) o snow on the road (Sub Causes Code 🡪 4) o ice on the road (Sub Causes Code 🡪 5) o black ice on the road (Sub Causes Code 🡪 6) o oil on the road (Sub Causes Code 🡪 7) o loose gravel or stone fragments detached from a road surface or from a hazard (Sub Causes Code 🡪 8) o instant black ice on the road surface (Sub Causes Code 🡪 9)
* salted road (Sub Causes Code 🡪 10)
* **DE_AdverseWeatherCondition –**
**ExtremeWeatherConditionSubCauseCode (DataType_ 5) - Traffic Information
Category**
Extreme Weather Condition is:
o unavailable (Sub Causes Code 🡪 0) o strong wind (Sub Causes Code 🡪 1) o
damaging hail (Sub Causes Code 🡪 2) o hurricane (Sub Causes Code 🡪 3) o
thunderstorm (Sub Causes Code 🡪 4) o tornado (Sub Causes Code 🡪 5) o blizzard
(Sub Causes Code 🡪 6) **DE_AdverseWeatherCondition –
PrecipitationSubCauseCode**
**(DataType_ 6) - Traffic Information Category** Precipitation is:
<table>
<tr>
<th>
o unavailable
</th>
<th>
(Sub Causes Code 🡪 0)
</th> </tr>
<tr>
<td>
o heavy rain
</td>
<td>
(Sub Causes Code 🡪 1)
</td> </tr>
<tr>
<td>
o heavy snow fall
</td>
<td>
(Sub Causes Code 🡪 2)
</td> </tr>
<tr>
<td>
o soft hail
</td>
<td>
(Sub Causes Code 🡪 3)
</td> </tr> </table>
* **DE_AdverseWeatherCondition – VisibilitySubCauseCode (DataType_ 7) - Traffic Information Category** Low visibility due to:
o unavailable (Sub Causes Code 🡪 0) o fog (Sub Causes Code 🡪 1) o smoke (Sub
Causes Code 🡪 2) o heavy snow fall (Sub Causes Code 🡪 3) o heavy rain (Sub
Causes Code 🡪 4) o heavy hail (Sub Causes Code 🡪 5) o sun glare (Sub Causes
Code 🡪 6) o sand storm (Sub Causes Code 🡪 7)
* **DE_HazardousLocation – SurfaceConditionSubCauseCode (DataType_ 33) - Traffic Information Category** Hazardous Location in case:
o unavailable further detailed information of the road surface condition
(Sub Causes Code 🡪 0) o the road surface is damaged by earthquake (Sub Causes
Code 🡪 2) o road surface is damaged by subsidence (Sub Causes Code 🡪 4) o road
surface is damaged due to snow drift (Sub Causes Code 🡪 5) o road surface is
damaged by strong storm (Sub Causes Code 🡪 6) o road surface is damaged due to
pipe burst (Sub Causes Code 🡪 7) o road surface damage is due to falling ice
(Sub Causes Code 🡪 9)
The sensors can be connected to On-Road-Unit (ORU) and/or Road-Side-Unit (RSU)
and can provide information about the Temperature, Humidity, Barometric Air
Pressure, Wind Direction & Speed, Precipitation Quantity, Solar Radiation,
Chemicals Detection (e.g. oil) on Road and Road Wear (string gauges)5 , 6\.
The Data Elements DE & Data Frames DF, which are described below, are giving
information related to **A la Carte Container (** Figure 1 **)** .
* **DE_Temperature (DataType_ 83) - Other Information Category** o Temperature. For temperature equal to or less than -60 °C, the value shall be set to -60. For temperature equal to or greater than 67 °C, the value shall be set to 67.
* equalOrSmallerThanMinus60Deg (-60)
* oneDegreeCelsius(1)
* equalOrGreaterThan67Deg (67) o Unit: Degree Celsius (It could range from -60 to 67) **DE_RoadworksSubCauseCode (DataType_ 66) ) - Traffic Information Category**
RoadWork Type:
o unavailable further detailed information on roadworks
(Sub Causes Code 🡪 0) o a major roadworks is ongoing (Sub Causes Code 🡪 1) o a
road marking work is ongoing (Sub Causes Code 🡪 2) o slow moving road
maintenance work is ongoing (Sub Causes Code 🡪
3) o a short term stationary roadwork is ongoing (Sub Causes Code 🡪 4) o a
vehicle street cleaning work is ongoing (Sub Causes Code 🡪 5) o winter service
work is ongoing (Sub Causes Code 🡪 6)
Table 1 shows an example of DENM message with RoadWorks cause-code selected.
Table 1: Example of DENM generated by a roadside unit.
<table>
<tr>
<th>
ETSI ITS (DENM)
DENM
header
protocolVersion: currentVersion (1) messageID: denm (1)
stationID: 2 denm management actionID originatingStationID: 148
sequenceNumber: 33 detectionTime: 2158628750 (2158628.750 s) referenceTime:
2158628750 (2158628.750 s) eventPosition latitude: (45.8510750 deg) longitude:
(11.0006270 deg) positionConfidenceEllipse semiMajorConfidence: unavailable
(4095) semiMinorConfidence: unavailable (4095) semiMajorOrientation:
unavailable (3601) altitude
altitudeValue: (800.00 m)
altitudeConfidence: unavailable (15) relevanceDistance: lessThan500m (3)
relevanceTrafficDirection: allTrafficDirections (0) validityDuration: Unknown
(60 sec) stationType: roadSideUnit (15) situation informationQuality: lowest
(1) eventType
causeCode: roadworks (3) subCauseCode: 0
</th> </tr> </table>
**2.2.2 Cooperative Awareness Messages**
Cooperative awareness within road users means that vehicles and infrastructure
become aware of each other’s presence thanks to periodic messages containing
each other’s position, dynamics and attributes. The information to be
exchanged is packed up in the periodically transmitted Cooperative Awareness
Message (CAM) whose construction, management and processing is part of the ITS
Communication architecture ETSI EN 302 6657\. The requirements on the
performance of the CAM and the quality of its data elements are derived from
the Basic Set of Applications (BSA) defined in ETSI TR 102 6388. Figure 2
shows the general structure of a CAM message.
Each container consists of a sequence of DE and DF similarly to DENM. Here is
a description of typical containers:
* ITS PDU Header
* Basic Container o stationType o referencePosition
* latitude
* longitude
* positionConfidenceEllipse
* altitude
* High Frequency Container o Basic Vehicle Container HF
* heading
* speed
* driveDirection
* vehicleLength
* vehicleWidth
* longitudinalAcceleration
* curvature
* yawRate OR
o Basic RSU Container HF o Protected Communication Zone
* protectedZoneType
* protecetedZoneLatitude
* protectedZoneLongitude
* protectedZoneRadius
* Low Frequency Container o vehicleRole o exteriorLights o pathHistory
* ItemX: deltaLatitude
* ItemX: deltaLongitude
* ItemX: deltaAltitude
The content of CAM varies from the type of ITS-S station. In SAFE STRIP two
types of stations will be used to identify the presence of equipped vehicles.
The CAM messages will be used to notify the vehicle that it is approaching a
location with equipped infrastructure and vice versa the vehicles will send a
notification to the infrastructure about their presence. Table 2 shows the
implementation of two CAM messages from a vehicle and a roadside unit.
Table 2: Example of CAM generated by: (a) a vehicle, (b) a road-side unit.
<table>
<tr>
<th>
ETSI ITS (CAM)
CAM
header
protocolVersion: currentVersion (1) messageID: cam (2) stationID: 200 cam
generationDeltaTime: 30204 camParameters basicContainer
stationType: passengerCar (5) referencePosition
latitude: (45.8501000 deg)
longitude: Unknown (11.0012658 deg) positionConfidenceEllipse
semiMajorConfidence: (12.00 m) semiMinorConfidence: (11.50 m)
semiMajorOrientation: (36.01 deg) altitude
altitudeValue: (173.60 m)
altitudeConfidence: unavailable (15)
highFrequencyContainer: basicVehicleContainerHighFrequency (0)
basicVehicleContainerHighFrequency heading headingValue: (129.7 deg)
headingConfidence: (1) speed
speedValue: (20.50 m/s, 73.80 km/h)
</th> </tr> </table>
<table>
<tr>
<th>
speedConfidence: (0.16 m/s) driveDirection: unavailable (2) vehicleLength
vehicleLengthValue: (3.8 m)
vehicleLengthConfidenceIndication: noTrailerPresent (0) vehicleWidth: (2.0 m)
longitudinalAcceleration
longitudinalAccelerationValue: (1.61) longitudinalAccelerationConfidence:
(1.02) curvature
curvatureValue: straight (0)
curvatureConfidence: unavailable (7)
curvatureCalculationMode: yawRateUsed (0) yawRate yawRateValue: straight (0.00
deg/sec) yawRateConfidence: unavailable (8) lowFrequencyContainer:
basicVehicleContainerLowFrequency vehicleRole: specialTransport (2)
exteriorLights: 12 [0001 0010]
pathHistory: 1 item
Item 0
PathPoint pathPosition deltaLatitude: (0.0000003 deg) deltaLongitude:
(0.0000002 deg) deltaAltitude: (0.01 m)
</th> </tr>
<tr>
<td>
(a)
</td> </tr>
<tr>
<td>
ETSI ITS (CAM)
CAM
header
protocolVersion: currentVersion (1) messageID: cam (2)
stationID: 2 cam
generationDeltaTime: (35748) camParameters basicContainer
stationType: roadSideUnit (15) referencePosition latitude: (45.8510750 deg)
longitude: (11.0006270 deg) positionConfidenceEllipse
</td> </tr>
<tr>
<td>
semiMajorConfidence: unavailable (4095) semiMinorConfidence: unavailable
(4095) semiMajorOrientation: unavailable (3601) altitude
altitudeValue: unavailable (800001) altitudeConfidence: unavailable (15)
highFrequencyContainer: rsuContainerHighFrequency (1)
rsuContainerHighFrequency
protectedCommunicationZonesRSU: 1 item
Item 0
ProtectedCommunicationZone protectedZoneType: cenDsrcTolling (0)
protectedZoneLatitude: (45.8510750 deg) protectedZoneLongitude: (11.0006270
deg) protectedZoneRadius: (300)
</td> </tr>
<tr>
<td>
(b)
</td> </tr> </table>
The data content transmitted from the two stations is different: the vehicles
are moving objects, therefore it is necessary to spread information about
vehicle dynamics. On the contrary, RSUs are characterized by static
coordinates and protected areas. For the same reason vehicles send CAMs and
DENMs at higher rates as opposed to roadside units: typically the vehicles
transmit 10[mex/s] while roadside units sends out CAM messages at 1[Hz]. In
ETSI EN 302 637-23 the mandatory fields are defined, such as the input source
of the message and the proper operation of the Cooperative Awareness service.
Here the most relevant containers from SAFE STRIP perspective are reported.
The Data Elements DE & Data Frames DF, which are described below, provide
information related to **ITS PDU Header**
* **DF_ItsPduHeader (DataType_ 114) – Communication Information**
**Category** o ITS PDU header of the CAM, it’s composed by three containers:
* **protocolVersion** version of the ITS message and/or communication protocol;
* **messageID** Type of the ITS message. Following message type values are assigned in the present document: denm(1): Decentralized Environmental Notification Message, cam(2): Cooperative Awareness Message, poi(3): Point of Interest message, spat(4): Signal Phase And Timing (SPAT) message, map(5): MAP message, ivi(6): In Vehicle Information (IVI) message and ev-rsr(7): Electric vehicle recharging spot reservation message; **stationID:** the identifier of the ITS-S that generates the ITS message in question. It shall be represented as specified hereafter (DataType 77).
Generation Delta Time is a container described in ETSI EN 302 637-2, it is not
included into ITS-PDU Header, nor into High Frequency Container, but it is
mandatory to evaluate the time validity.
* **generationDeltaTime** this is the reference time of the CAM, considered as time of the CAM generation. It shall be used as time basis for application with predefined time validity.
The following DF and DE comprise information of the Basic container, which
provides basic information of the originating ITS-station.
* **DE_StationType (DataType_78) - Other Information Category** o The type of an ITS-S: unknown, pedestrian, cyclist, moped, motorcycle, passengerCar, bus, lightTruck, heavyTruck, trailer, specialVehicles, tram, roadSideUnit.
* **DF_referencePosition (DataType_124) - GeoReference Information**
**Category** o Position at the reference point of the originating ITS-S. The
measurement time shall correspond to generationDeltaTime. This parameter shall
be used to locate the vehicle and roadside units into absolute coordinate
system. The DF shall include a sequence of the following information:
* latitude: latitude of the geographical point,
* longitude: longitude of the geographical point,
* positionConfidenceEllipse accuracy of the geographical position,
* altitude: altitude and altitude accuracy of the geographical point.
* **DF_PosConfidenceEllipse (DataType_119) - GeoReference Information**
**Category** o positionConfidenceEllipse provides the accuracy of the measured
position with the 95 % confidence level. The DF shall include a sequence of
the following information:
* semiMajorConfidence: half of length of the major axis, i.e. distance between the centre point and major axis point of the position accuracy ellipse,
* semiMinorConfidence: half of length of the minor axis, i.e. distance between the centre point and minor axis point of the position accuracy ellipse,
* semiMajorOrientation: orientation direction of the ellipse major axis of the position accuracy ellipse with regards to the WGS84 north.
The following DF and DE comprise information of the high Frequency Container,
which contains the high frequency signals of the vehicle or roadside units. An
example of DF and DE includes car acceleration, speed and yaw rate. Thanks to
this information the following scenario could be realised: the infrastructure
and a vehicle can determine if a user is properly approaching an intersection,
or any other obstacle. On the contrary, the roadside units could disseminate
as high frequency container the position of a CEN DSRC Tolling Station.
* **DF_Heading (DataType_112) – GeoReference, Vehicle & Road Topology **
**Information Category** o Heading in a WGS84 co-ordinates system. The DF
shall include a sequence of the following information:
* headingValue: a heading value,
* headingConfidence: the accuracy of the reported heading value with a predefined confidence level.
* **DF_Speed (DataType_126) - Vehicle Information Category** o It describes the speed and corresponding accuracy of the speed information for a moving object (e.g. vehicle). The DF shall include the a sequence of the following information:
* speedValue,
* speedConfidence: accuracy of the reported speed value.
* **DE_DriveDirection (DataType_22) - Vehicle Information Category** o It denotes whether a vehicle is driving forward or backward.
* **DF_vehicleLength (DataType_131) - Vehicle Information Category** o Length of vehicle and accuracy indication information. The DF shall include a sequence of the following information:
* vehicleLengthValue: length of vehicle,
* vehicleLengthConfidenceIndication: indication of reported length value confidence
* **DE_VehicleWidth (DataType_95) - Vehicle Information Categor** o Width of a vehicle, including side mirrors.
* **DF_LongitudinalAcceleration (DataType_129) - GeoReference**
**Information Category** o It indicates the vehicle acceleration at
longitudinal direction and the accuracy of the longitudinal acceleration.
The DF shall include a sequence of the following information:
* longitudinalAccelerationValue: longitudinal acceleration value at a point in time,
* longitudinalAccelerationConfidence: accuracy of the reported longitudinal acceleration value with a predefined confidence level.
* **DF_Curvature (DataType_107) - Vehicle Information Category** o It describes the curvature of the vehicle trajectory and the accuracy. The curvature detected by a vehicle represents the curvature of actual vehicle trajectory. The DF shall include a sequence of the following information:
* curvatureValue: detected curvature of the vehicle trajectory,
* curvatureConfidence: accuracy of the reported curvature value with a predefined confidence level.
* **DE_CurvatureCalculationMode (DataType_13) - Vehicle Information**
**Category** o It describes whether the yaw rate is used to calculate the
curvature for a reported curvature value.
* **DF_YawRate (DataType_132) - Vehicle Information Category** o Yaw rate of vehicle at a point in time. The DF shall include a sequence of the following information:
* yawRateValue: yaw rate value at a point in time,
* vehicleLengthConfidenceIndication: accuracy of reported yaw rate value.
The following DF and DE comprise information of the low Frequency Container,
which contains information changing with more relaxed dynamics, such as the
exterior-lights, or the vehicle role. This information allows the user to
react to different type of operations: public transport, roadwork vehicles
etc.
* **DE_VehicleRole (DataType_94) - Vehicle Information Category** o Role played by a vehicle at a point in time: default, publicTransport, specialTransport, dangerousGoods, roadWork, rescue, emergency, safetyCar, agriculture, commercial, military, roadOperator, taxi.
* **DE_ExteriorLights (DataType_28) - Vehicle Information Category** o This DE describes the status of the exterior light switches of a vehicle.
* **DF_ProtectedCommunicationZonesRSU (DataType_122) –**
**Infrastructure & Communication Information Category ** o DF that describes a
list of protected communication zones by a road side ITS-S (Road Side Unit
RSU). It may provide up to 16 protected communication zones information. Each
protected communication zone shall be presented as
DF_ProtectedCommunicationZone.
* **DF_ProtectedCommunicationZone (DataType_121) – Infrastructure & **
**Communication Information Category** o DF that describes a zone of
protection inside which the ITSG5 communication should be restricted. It shall
include a sequence of the following information :
* protectedZoneType: type of the protected zone,
* expiryTime: (otional) time at which the validity of the protected communication zone will expire,
* protectedZoneLatitude: latitude of the centre point of the protected communication zone,
* protectedZoneLongitude: longitude of the centre point of the protected communication zone,
* protectedZoneRadius: (optional) radius of the protected communication zone in metres,
* protectedZoneID: (optional) the ID of the protected communication zone. o A protected communication zone may be defined around a CEN DSRC road side equipment
**2.3 TMC Messages**
Standard RDS-TMC user messages provide the following five basic items of
explicit, broadcast information9:
* **Event description** , giving details of:
o road event situation o general traffic problems o weather situations
In some cases more information about the severity of the event is given.
**Location** (road segment or point location of event occurrence)
* **Direction and Extent** , identifying the adjacent segments or specific point locations also affected by the incident, and where appropriate the direction of traffic affected.
* **Duration** (indication of expected problem duration)
* **Diversion advice** (showing whether or not end-users are recommended to find and follow an alternative route)
Optional information can be added to any message using one or more additional
RDS data groups. This optional addition can give greater detail or can deal
with unusual situations. Any number of additional fields can in principle be
added to each basic message, subject only to a maximum message length of five
RDS data groups4.
4. **User model**
In the context of SAFE STRIP, any user is an entity that has its own data
model. The elements of the User model are shown below.
Table 3: User model in SAFE STRIP
<table>
<tr>
<th>
**Field Name**
</th>
<th>
**Type**
</th>
<th>
**Required**
</th>
<th>
**Details**
</th> </tr>
<tr>
<td>
**_id**
</td>
<td>
objectid
</td>
<td>
True
</td>
<td>
Unique id
</td> </tr>
<tr>
<td>
**email**
</td>
<td>
string
</td>
<td>
True
</td>
<td>
Email
</td> </tr>
<tr>
<td>
**password**
</td>
<td>
string
</td>
<td>
True
</td>
<td>
Password
</td> </tr>
<tr>
<td>
**name**
</td>
<td>
string
</td>
<td>
True
</td>
<td>
First/ last name
</td> </tr>
<tr>
<td>
**phone**
</td>
<td>
string
</td>
<td>
True
</td>
<td>
Phone number
</td> </tr>
<tr>
<td>
**fcm_token**
</td>
<td>
string
</td>
<td>
True
</td>
<td>
FireCloud Message token
</td> </tr> </table>
All data related to user personal information will not be open by any means,
but it will be stored according to the SAFE STRIP data privacy policy (Section
6.3).
5. **Data Collection and Management in Pilots**
Data management in Pilots will follow the principles below as a minimum, at
each pilot site level. This list will be updated in the next DMP version
(deliverable D2.3):
* Each Pilot site must ensure the data collection according to the evaluation requirement and with the right accuracy.
* Each pilot site must provide, in a timely fashion, the requested data to perform test analysis. Data must be provided with its metadata or a complete description. To enable an accurate usage, data must be generated in compliance with evaluation requirements (formats, measurements, etc.) that will be defined in the technical validation and evaluation plans of A5.6 and A6.1 respectively.
* Each pilot site must provide an easy access to collected data and a secure environment for data storage.
* Each pilot site needs to have all the necessary tools to check data quality and to read raw data. Automated scripts will be provided to process large dataset in order to ease and enable post-processing of aggregated data, whereas the logging of the raw data will be pre-defined in a homogeneous way for all types of them.
The data that will be collected during technical validation and user trials
activities are as follows:
* Subjective anonymised data from users (drivers) and other stakeholders (i.e. TMC operators) – collected at **pilot site level** related to system evaluation and acceptance. Indicative list of evaluation indicators to be collected is as follows (list is not exhaustive – will be finalized in technical and pilot evaluation plans): usage, user satisfaction, user compliance (following the system’s advice), user acceptance (perceived/rated by users), usability ratings (perceived by users), system efficiency rates, willingness to have and use the system.
* Driving performance data from drivers/riders that will be logged during trials in the logging mechanisms installed in the vehicles and the mobile devices **– collected at pilot site level, but in an homogeneous way and upon common protocols,** related with speed, lateral deviation, safety headway, acceleration/deceleration, lane merging and overtaking, etc.
* **Data from sensors** . This includes the data that will be collected by the various sensors, e.g. road sensors for _humidity_ , _temperature_ , _oil_ _detection_ , _road wear_ , as well as tyre sensors for _friction_ , RFID sensors for _parking slots_ , _car position_ , _speed limit_ , etc. This data will be collected by the hardware sensors and will be stored in the cloud via the SAFE STRIP communication modules (i.e., through LTE), according to the data model defined in Section 2.2. In particular, for parking slots and relevant information, the SAFE STRIP data model will be compatible with the ETSI TR 102 638 standard8. For any data element that no reference to the existing standards occurs (e.g. _vehicle_ _lane position_ for autonomous vehicles) _,_ the project will propose a new data model to be potentially included in the existing standards.
* **TMC messages** , as the ones shown in section 2.3. Storage of those messages will be done centrally, by the means of the OMNIA cloud repository (provided by SWARCO). Hence, the corresponding data model will adhere to the one supported by the OMNIA Web API. Only data that do not confront any IPR related issues will be available as open data. A final decision about this will be made in the context of activity A2.4 and be included in the next version of the DMP. Access to all data of the OMNIA repository will be done in an authenticated manner.
* **Vehicle position** received from the GPS device **.** This can be stored in the cloud in an anonymised format **,** according to the data model defined in Section
2.2 (“event position”).
The final definition of the data, the way and place they will be logged will
be defined in _D5.4-Test sites set-up and experimental technical validation
plan_ , _D5.5-Updated experimental technical validation plan and results –
final report_ , D6.1- _Initial report on Pilot framework and plans_ and
_D6.2-Final report on Pilot framework and plans_ , and will be also presented
in the next version of DMP (deliverable D2.3). All test sites participating in
either the technical and/or user-based evaluation of the system are obliged to
follow the common plans, where the experiments will be defined in detail. In
specific, the validation plans, starting from Key Performance Indicators that
will be set, will define the number of scenarios and test cases, the duration
of tests and test runs, the number of situations per specific event, the
number of test vehicles, the variation in users, the variation in situations
(weather, traffic, traffic context, etc.) together with the validation
approaches and data collection tools (questionnaires, user surveys protocols,
logging mechanisms on different ends – e.g., the vehicle, mobile phone, the
cloud, or other) that will be used to store all types of data in need in an
appropriate and consistent manner and format (in order to allow aggregated and
crosssite post processing).
Additional requirements related to ORDP are defined in section 4 to guarantee
that the collected data will be provided in compliance to European Commission
Guidelines1 on Data Management in Horizon 2020.
**2.6 Data Flow in SAFE STRIP Test Environment**
The test data management process carried out by activity A3.4 will define the
architecture that enables the implementation of the data collection processes
in all SAFE STRIP modules. Data generated during SAFE STRIP system operation
will be stored in the long term in a cloud repository, as data storage at the
local infrastructure will be volatile due to the limited capabilities of those
devices and their low energy requirements. However, interconnection is
foreseen to the OMNIA platform provided by SWARCO, which is also built upon a
cloud architecture. OMNIA can be seen as a gateway to TMC data, hence access
to this data will be performed in an authenticated manner via the OMNIA Rest
web API. It is not yet clear of the OMNAI platform will provide all required
resources for hosting the SAFE STRIP cloud or only part of it, as at the time
of this writing the system architecture (activity A2.1) is not yet finished.
The test data management process will focus on services that will be deployed
on the Pilot Site Test Servers and that will comprise the central SAFE STRIP
cloud. Special care will be given so that for each specific pilot site test a
dedicated VM will collect and store all the data created by the pilot site.
Figure 3 shows the data flow in the SAFE STRIP test environment. Finally, the
aggregated and validated data will be fed into the SAFE Decision Support for
posting the warning message to the drivers.
**Sensor data**
**(**
**Road**
**,**
**Parking slots**
**,**
**etc**
**.)**
**Pilot site VM**
**OMNIA cloud**
**(**
**TMC data**
**)**
**Decision Support**
**System**
**Pilot site specific data sources**
**(**
**mobile phone**
**,**
**static data**
**,**
**user**
**questionnaires**
**)**
**Data Aggregation**
**Data import**
**/**
**export**
**SAFE STRIP Cloud**
**Data enrichment**
**User feedback**
**Data Fusion**
3. **FAIR Data Principles**
**3.1 Making data findable, including provisions for metadata**
According to the principle of making data discoverable, all data to be made
openly available in SAFE STRIP will have rich metadata. The foreseen data
quality assurance process of the project will take special care in order to
prevent storage of data for open access, unless adequate metadata will be
provided.
All data in SAFE STRIP will be encoded in adherence to a set of standards
(e.g. ETSI TS 102 894-2, etc.) that are mentioned in Section 3.3. Adherence to
those standards will ensure that the appropriate metadata will be provided,
making data visible in a searchable context based on the semantics of data.
Also the use of persistent and unique in the form of Digital Object
Identifiers will be supported in order to facilitate data search and
discovery.
The naming conventions used for SAFE STRIP data will be as follows. The DE
introduced in the previous section, and the connection among them (see e.g.
Figures 1-4), where the **Cause Code** (TPEGS-TEC (CEN ISO/TS 21219-15))
corresponds to the **Direct Cause Code of** **DE_CauseCodeType** **(DataType_
10)** and the **Sub Cause Code** (TPEGS-TEC (CEN
ISO/TS 21219-15)) corresponds to the **Sub Causes Code** of the following DE:
* **DE_AdverseWeatherCondition – AdhensionSubCauseCode (DataType_ 4)**
* **DE_AdverseWeatherCondition – ExtremeWeatherConditionSubCauseCode (DataType_ 5)**
* **DE_AdverseWeatherCondition – PrecipitationSubCauseCode (DataType_ 6)**
* **DE_AdverseWeatherCondition – VisibilitySubCauseCode (DataType_ 7)**
* **DE_HazardousLocation – SurfaceConditionSubCauseCode (DataType_ 33)** **DE_RoadworksSubCauseCode (DataType_ 66)**
The aforementioned standards (DATEX II, TPEG) and the metadata they provide
will be applied in order to make data searchable. For enabling keyword search-
based data discovery off-the-self solutions will be deployed for allowing ease
and fast search (see also section 3.2).
slippery road” 10
weather conditions” 4
visibility” 11
works” 4
**3.1.1 Data documentation**
In addition to metadata inherited by the aforementioned standards, further
documentation and metadata will be provided where applicable. The initial
documentation details that will be included for each service are shown in the
template shown below.
Table 4: Data documentation template
<table>
<tr>
<th>
**Basic**
</th>
<th>
</th>
<th>
**Service 1**
</th>
<th>
**Service 2**
</th>
<th>
**Service n**
</th>
<th>
**Advanced**
</th> </tr>
<tr>
<td>
Data name
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
Definition of variables
</td> </tr>
<tr>
<td>
Who
created/contributed data
</td>
<td>
to
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
Vocabularies
</td> </tr>
<tr>
<td>
Date created
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
Units of Measurement
</td> </tr>
<tr>
<td>
Data modified
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
File format and type
</td> </tr>
<tr>
<td>
Conditions
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
Methods
</td> </tr>
<tr>
<td>
**Basic**
</td>
<td>
**Service 1**
</td>
<td>
**Service 2**
</td>
<td>
**Service n**
</td>
<td>
**Advanced**
</td> </tr>
<tr>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
used/assumptions made/analytical procedural info; description
</td> </tr> </table>
The role of this template will be to accompany any data to be accessed and
used by other than the individuals/organizations who collected the data. The
details that will define and create the specific dataset profile can be:
_Basic:_ details defining the origin, the type, the creation dates and the way
in which this data was created.
_Advanced_ : include definitions of variables, the methods used and applied in
order to gather and capture data based on common standards (incl. any
procedural steps taken). A detailed and technical specification of data might
be included like the variables that define the specific dataset, the units of
measurements and the assumptions applied for the data collection (e.g. zero
might mean no activity recorded, therefore the patient is not active). The
format and file types used for collecting and storing the data (and size) can
be of help if external users show interest in re-using a dataset (i.e.
checking compatibility with s/w they use in order to aggregate or analyse data
utilise import/export functionalities).
**3.2 Making data openly accessible**
All data described in Section 1 will be made available with open access with
the exception of those datasets that will be assessed by the project
consortium as being subject to IPR violation, e.g. proprietary data owned by
consortium beneficiaries or third legal parties (e.g. suppliers), in
accordance with the policies outlined in DoASection 5, the Ethics requirement
deliverables of the project (D9.1, D9.2), as well as the project CA especially
concerning data that will be generated or made available as background assets
by one or more beneficiaries. In case restrictions such as the aforementioned
ones will occur, access will be granted only to authorised users.
All datasets will be available for sharing and re-use via the
management/exploitation portals of the project. These portals will be the
common means for exchanging data either among the partners of the project or
between the consortium and third parties. The specifications of how and which
of the portals will be used in the context of data sharing and reuse will be
defined in the next months (up to M9) by the consortium and will be included
in the final version of the DMP. These specifications have to be referred to
any new features that have to be added to the current portal infrastructure
for dealing with the different data manipulation needs.
All datasets will be stored in a private cloud-based repository, e.g. ZENODO
( _https://zenodo.org/_ ) a free service developed by CERN under the EU FP7
project OpenAIREplus (grant agreement no.283595). The repository shall also
include information regarding the software, tools and instruments that were
used by the dataset creator(s) so that secondary data users can access and
then validate the results. The SAFE STRIP data collection can be accessed in
ZENODO repository in a similar address as the following link:
_https://zenodo.org/collection/ <<safestrip _ > >. The datasets in the cloud
repository will be linked to the management/exploitation portals of the
project, as well, and they will be assigned to DOIs in order for 3 rd
parties to have access to them. Through the use of the above repository (or
similar, e.g. ownCloud), we will ensure that the most up to date security
features will be applied out of the box, e.g., firewall, password protection,
encryption, etc. If any IPR issue exist on sharing, they will be handed
accordingly. Moreover, all necessary material that supplements each dataset
(software for parsing the datasets, standards documents, etc.) will be
provided by the consortium via the management/exploitation portals.
**3.3 Making data interoperable**
Data interoperability is foreseen in the project through conformance to
standards Specifically, SAFE STRIP data will confront to the following
standards:
* ETSI TS 102 894-24
* ETSI EN 302 637-2 12
* ETSI EN 302 637-32
* DATEX II, TPEG2-TEC- and TMCEvents for safety messages 13
**3.4 Increase data re-use (through clarifying licenses)**
SAFE STRIP participates in the Pilot on Open Research Data launched by the
European Commission along with the Horizon2020 Programme. As such, all data
produced by the project are to be published with open access. Particularly,
access will be given through Creative Commons CC0 license for all datasets at
this project stage, unless it is defined otherwise.
All datasets will be maintained for the entire duration of the project as well
as for 2 additional years after its conclusion. After the project ends, all
datasets will be stored in a centralized facility in order to minimize
maintenance costs. Datasets with acknowledged long-term value may be kept for
longer period of time. The long-term value of a dataset will be decided
according to exploitation plan as well as its relation to a scientific
publication.
For the time period in which the data will be available for open access, no
restrictions will be imposed on their access. In case IPR protected or
proprietary data will be generated or made available as background assets in
the context of the project by one or more beneficiaries, access to this data
will be treated in accordance to the rules and regulations foreseen in the
project Consortium Agreement. For all this data, access will be granted only
to authorised users.
All data to be available as open data will undergo a quality assurance
process.
At a first level, each partner in charge follows specific procedures to assure
the quality of the data and conformance to standards as referred to each
dataset’s description in Section 2. Those procedures regard calibration of the
measurements, as well as, internal post-measurement review. In a higher level,
quality will be also monitored periodically on the different versions of the
dataset as conservation experts of the consortium will review each update.
Finally, additional peer-reviews will take place in case of publication in
Journals.
**4 SAFE STRIP Open Strategy & Participation in the Open Research Data Pilot
**
The SAFE STRIP project has agreed to participate in the Pilot on Open Research
Data in Horizon 2020 and uses the specific Horizon 2020 guidelines associated
with ‘open’ access to ensure that the results of the project results provide
the greatest impact possible. SAFE STRIP will ensure the open access 14 to
all peer-reviewed scientific publications relating to its results and will
provide access to the research data needed to validate the results presented
in deposited scientific publications.
The following lists the minimum fields of metadata that should come with a
SAFE STRIP project-generated scientific publication in the Library section of
the project web site ( _http://safestrip.eu/publications-index-p/_ ) , where
a Publication Index has been anticipated:
* The terms: “European Union (EU)”, “Horizon 2020”
* Name of the action (Research and Innovation Action)
* Acronym and grant number (SAFE STRIP, 723211)
* Publication date
* Publication type
* Publication place
* Publication name
* Publication authors
* Length of embargo period if applicable
* Persistent identifier
Apart from the scientific publications index, SAFE STRIP will publish all its
public Deliverables and dissemination material in its web site (under
“Library”). When referencing Open access data, SAFE STRIP will include at a
minimum the following statement demonstrating EU support (with relevant in-
formation included into the repository metadata):
\- “This project has received funding from the European Union’s Horizon 2020
research and innovation program under grant agreement No 723211”.
Finally, SAFE STRIP will target **Open Access journals** , when applicable,
whereas it will also target Gold OA publications and Green OA, wherever Gold
OA is not possible. The target is to maximize the impact on scientific
excellence through result publication in open access yet highly appreciated
journals, without releasing any confidential information that could
potentially violate the security nature of the project.
The SAFE STRIP consortium will strive to make many of the collected datasets
open access. When this is not the case, the data sharing section for that
particular dataset will describe why access has been restricted (see also
section 3.2).
In regards to the specific repositories available to the SAFE STRIP
consortium, numerous project partners maintain institutional repositories that
will be listed in the next DMP version (Deliverable D2.3), where project
scientific publications and in some instances, research data will be
deposited. The use of a specific repository will depend primarily on the
primary creator of the publication and on the data in question.
Some other project partners will not operate publically accessible
institutional repositories. When depositing scientific publications they shall
use either a domain specific repository or use the EU recommended service
OpenAIRE (http://www.openaire.eu) as an initial step to finding resources to
determine relevant repositories.
Project research data shall be deposited on an online data repository (see
also Section 3.2) about ZENODO. In summary, as a baseline SAFE STRIP partners
shall deposit:
* _Scientific publications_ – on their respective institute repositories in addition
(when relevant) to the SAFE STRIP online data repository
* _Research data_ – to the SAFE STRIP online data collection (when possible)
* _Other project output files_ – to the SAFE STRIP online data collection (when relevant)
This version of the DMP does not include the actual metadata about the
Research Data being produced in SAFE STRIP project. Details about technical
means and services for building repositories and accessing to this metadata
will be provided in the next version of the DMP (deliverable D3.2). A template
document is defined in section 3.1.1 and will be used by project partners to
provide all requested information.
5. **Allocation of Resources**
In order to face the data management challenges efficiently, all SAFE STRIP
partners have to respect the policies set out in this DMP and datasets have to
be created, managed and stored appropriately. The Data controller role within
SAFE STRIP will be undertaken by Dionysis Kehagias (CERTH/ITI) who will
directly report to the SAFE STRIP Ethics Board and the Project Management Team
(PMT). The Data controller acts as the point of contact for Data Protection
issue and will coordinate the actions required to liaise between different
beneficiaries and their affiliates, as well as their respective Data
Protection agencies, in order to ensure that data collection and processing
within the scope of SAFE STRIP, will be carried out according to EU and
national legislation. Regarding the ORDP, the data controller must ensure that
data are shared and easily available. Each data producer and WP leader is
responsible for the integrity and compatibility of its data during the project
lifetime. The data producer is responsible for sharing its datasets through
open access repositories. He is in charge of providing the latest version. In
summary, the Data Manager will coordinate the actions related to data
management, will be responsible for the actual implementation of the DMP
successive versions and for the compliance to Open Research Data Pilot
guidelines.
As the SAFE STRIP open data will be hosted by an open free of charge platform
(e.g. Zenodo), no additional costs will be required for hosting the data.
6. **Ethical and Security Aspects**
1. **Ethical issues**
Regarding ethical issues, the deliverables D9.1 and D9.2 detail all the
principles, processes and measures that SAFE STRIP will use to comply with the
H2020 Ethics requirements. In addition, security and privacy requirements will
be addressed in _D2.1: “System architecture, sensor specifications,
fabrication, maintenance, installation and security requirements and Risk
Assessment”_ for M18.
2. **Data Protection and Security**
To avoid any data losses and ensure fast and reliable access to the data
common storage facility to be deployed in SAFE STRIP, appropriate redundancy
mechanisms will be deployed (e.g. RAID-1, RAID-5 etc.) for the online data
repository. In addition to online data, an appropriate number of off-line
copies will be maintained in hard disks or network-attached storage (NAS)
owned by every partner in charge of their data. For the time of this writing,
the total volume of the generated data has not yet been estimated accurately.
A separate checklist has been prepared and should be used by all data
providers not only during evaluations but also technical testing and
validation. Any data that will be stored as a result of regular checks and
tests performed by the service administrators wish to perform regular checks
and tests and want to be able to create a database:
<table>
<tr>
<th>
**Checklist** :
* How will the raw data be stored and backed up during the research?
* How will the processed data be stored and backed up during the research?
* Which storage medium will you use for your storage and backup strategy? Network storage; personal storage media (e.g. external hard drives); cloud storage
* Are backups made with sufficient frequency so that you can restore in the event of data loss?
</th> </tr> </table>
Each site and service responsible should ensure that the research data is
regularly backed-up and stored in secure and safe location. There is a common
“rule-of-thumb” to only store data that you actually need in three different
copies. It is advised that copies can be stored in both local and remote
storage units/locations.
**6.3 SAFE STRIP data privacy policy**
SAFE STRIP system aims to be a cost-efficient safety and comfort provision
cooperative system. It performs so with due respect, as all required
participants’ physical, vehicle, driving performance and subjective data (e.g.
driving background data) will be stored separately from identification
information and be securely protected by relevant mechanisms.
Personal data on subjects will be used in strictly confidential terms and will
be published only as integrated statistics (anonymously) and under no
circumstances as individual data set. All test data will be anonymized. Only
one person per site (relevant Pilot responsible) will have access to the
relation between test participants’ code and identity, in order to administer
the tests. People who will analyse data will receive anonymised and coded
information. One month after the tests end, this reference will be deleted,
thus safeguarding full anonymisation. The stored data will only refer to
users’ age, gender, nationality and driving profile. No other identifier will
be kept. Nevertheless, **stored data will by no means relate to a person’s
health, religious, political and sexual beliefs and preferences.** In
addition, SAFE STRIP pilot participants **will not receive any medication,
related to project research.**
As such, anonymised data will be publicly available for other researchers;
still with no possibility for identification of participants. In addition,
participants will be recorded only if they provide consent.
To avoid risks related to the processing of personal data such as identity
theft, discriminatory profiling or continuous surveillance, the principle of
proportionality has to be respected. Data can be used only for the initial
purpose for which they were collected. Anonymisation or pseudonymisation is a
way to prevent violations of privacy and data protection rules. Processing has
to be limited to what is truly necessary and less intrusive means for
realising the same end have to be considered.
All this information falls under the European legislation for the lawful
processing of personal data:
* Regulation (EU) 2016/679 of the European Parliament and of the Council of 27 April 2016 on the protection of natural persons with regard to the processing of personal data and on the free movement of such data, and repealing Directive 95/46/EC (General Data Protection Regulation).
* Directive 95/46/EC of the European parliament and the Council (1995) on the protection of individuals with regard to the processing of personal data and on the free movement of such data.
* Directive 2006/24/EC of the European Parliament and of the Council of 15 March 2006 on the retention of data generated or processed in connection with the provision of publicly available electronic communications services or of public communications networks and amending Directive 2002/58/EC.
* Directive 2002/58/EC of the European Parliament and of the Council, concerning the processing of personal data and the protection of privacy in the electronic communications sector.
* Reform of the legislative framework for personal data protection (In January 2012, the European Commission proposed a reform of the Directive 95/46/CE, which constituted until now the basic instrument for personal data protection, in the form of a global Regulation on data protection 2012/001 (COD), supplemented by Directive 2012/0010 (COD) concerning the processing of personal in the area of police and judicial cooperation in criminal matters).
* Art.29 Data Protection Working party: Working Document on Privacy on the Internet.
Still, apart from the above, attention will be paid to national legislation,
that has to be respected even if the users have given their consent for the
processing of their personal data.
As such, during the SAFE STRIP Pilot tests:
1. Drivers/riders (but also other stakeholders) participating in the trials will give their names, address, and contact phone, together with age, gender, nationality and, if any, functional problem type (not medical term of impairment), to a single person in each pilot site, to be stored in a protected local database (to contact them and arrange for the tests). The contact person will issue a single Participant ID for each of them. This person will not participate in the evaluation and will not know how each user behaved.
2. The names, address and contact phone will be kept in the database only for the duration of each trial (short term trials-up to 1 week, long term trials- up to 1 month). Such data will not be communicated to any other partner or even person in each pilot site. Once the test ends, they will be deleted.
3. Each month the anonymised data will be re-sorted randomly, to mix participants order.
4. Since personal data will be deleted, no follow-up studies with the same people will be feasible.
5. Personal data will be used in strictly confidential terms and will be published as statistics (anonymously).
6. In pilots, all necessary safety precautions will be taken (i.e. use of professional drivers or drivers with valid driving license under normal traffic conditions, and , in case of need, using a test car with double pedals and a driving instructor as codriver).
**7 Conclusions – Next Steps**
The first data management analysis contained in this report allows
anticipating the procedures and infrastructures to be implemented in SAFE
STRIP to efficiently manage the generated and/or collected data.
This document points to present the general principles of DMP and to summarize
the current state of each and every identified dataset, containing information
regarding brief descriptions, standards and metadata specifications,
activities and responsibilities of each partner, access, data sharing and
reuse policies and archiving/preservation strategies over the respective
datasets from each and every related partner.
Finally, open issues have been recognized, i.e., the role and the
functionality of the management/exploitation portals as a common means for
data sharing have to be specified, and the metadata specifications which
cannot be explicitly defined yet. These open issues are taken into sincere
consideration and they are to be addressed in the next period.
The next version of the data management plan (D2.3), which is scheduled for
M29, will include more detailed dataset descriptions according to the process
defined in this version. Until M29, WP1 will have been completed, but also WP2
and WP3 will be approaching their ending (both end at M33), therefore the main
input regarding the data types from sensors and the end user applications and
services will have been comprehensively defined in detail. Hence, an enhanced
and possibly revised version of the SAFE STRIP data models that have been
introduced in this initial version of the DMP will be provided.
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1162_LessThanWagonLoad_723274.md
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# Executive summary
The aim of this document is to provide the procedure to be adopted by the
project partners to collect and utilize the data from the customers involved
in the implementation of the business models under WP08 and research packages
WP1-7, 9-
11\.
The adopted procedure will follow the guidelines developed by the European
Commission in the document “Guidelines on Data Management in Horizon 2020” 1
. This Data Management Plan (DMP) is also in line with the methodology of the
DMP online tool developed by the Digital Curation Centre 2 .
It has to be highlighted that a DMP is mandatory for the projects
participating in the Open Research Data Pilot and for the “research and
innovation actions” or “innovation actions”. The project is a coordination and
support action participating in the Open Research Data Pilot.
Besides this it has to be noted that this DMP is intended to be a “living
document in which information will be made available on a finer level of
granularity through updates as the implementation of the project progresses”
3
This Draft of the Data Management Plan, submitted at M6 (31st October 2016),
describes a plan for data collection and utilization while the Data Management
Plan will be updated when the data is collected and the plan will state how it
should be utilized.
The adopted procedure includes the following steps: 1) Data set description 2)
Standard and metadata 3) Data sharing 4) Archiving and preservation (including
storage and backup) and 5) Ethical aspects.
The project partners agree on the procedure described in this document and on
providing further information on the data to be collected and utilized in the
deliverable “Data Management Plan”.
# Introduction
The main objective of the project is to develop a smart specialized logistics
cluster for the chemical industry in the Port of Antwerp in order to shift
transport volumes from road to rail freight.
The developed solutions and concepts will be reviewed by the Advisory Board as
well as the General Assembly and potential customers.
This work will be performed within WP10 and WP12 and in specific within “Task
10.2”, “Task 10.3” and “Task 12.1”.
This document first describes the data set that will be collected, then
describes any applicable standards and metadata, and finally deals with data
sharing, archiving and preservation and ethical aspects.
<table>
<tr>
<th>
**1.**
</th>
<th>
**Data Set Description**
</th> </tr> </table>
Definition of Data Set Description (according to the H2020 guidelines):
_“Description of the data that will be generated or collected, its origin (in
case it is collected), nature and scale and to whom it could be useful, and
whether it underpins a scientific publication. Information on the existence
(or not) of similar data and the possibilities for integration and reuse”._
Data that will be generated or collected in the frame of practically every WP:
from market information to product specifications.
The first three WPs are pure R&D packages. Within these packages the
consortium wants to develop a working LessThanWagonLoad concept.
First a viable LWL concept is created preferably together with a potential
customer. Simultaneously, the consortium is going to develop a Automatic Wagon
Loading System (AWLS), this has to enable the developed LWL concept and make
it profitable. The data generated during these packages are commercially
sensitive so will be handled as confidential during the 3 years of the project
and 4 years after the end of the project. Within WP 9 a validation of this
system will be researched at the Italian terminal operator, ISC. The data will
be handled with the same care.
For WP4, 5 and 6 some market research is necessary to really pinpoint the
issues in the chemical industry and translate these problems or necessities
into working solutions. The market research for the two WPs will executed
together and coordinated by Essenscia, the Belgian federation of chemical
companies. Essenscia acts as neutral partner and will make the data anonymous
if the responses are commercially sensitive. The market research will be
handled as confidential. Nevertheless the preferences and requirements which
will be discovered during the research will be used to create public reports
but with anonymous and aggregated data. The dissemination of the market
research and the different solutions will be the topics of a concluding
conference organized for interested parties within the sector.
Within WP7 the main goal is to find possible destinations for viable mixed
train products. The process to select these destinations is twofold. First of
all a top-down approach is used to map the potential. In order to construct
this analysis, Eurostat data is used and drilled down to NUTS3 level. The
outcome of this exercise will be reviewed by Lineas’ market experts. The top-
down approach will be used as a long/short list from where the high potential
destinations will be selected. Together with the Business Development
department the outcome will be translated into concrete business cases with
potential roll-out. Due to the commercial sensitivity of the data, these
regions will be handled confidential.
Work package 8 tries to summarize everything which was researched before this
package and has the main goal to create a coherent business case for every
element. These business cases will consist of costs and potential benefits
which are only for internal use.
WP 11 calculates the environmental benefit of all preceding WPs and solutions
based on potential modal shift because of the enhanced product offering. These
reports will be made public to promote rail freight transport in Europe.
**At this stage of the project it is still not totally clear if there could be
any additional type of data involved or collected.** This will become clear
only once the project partners start working on the specific WP. In main order
this will be qualitative data, but for example in WP07 this will also include
some quantitative data.
Besides this here are some additional considerations that will be taken into
account:
* The data will originate from the customers and will be collected by partners of the consortium
* This data will be directly used by and for analysis
* The data will underpin scientific publications published by the partners. However this will be done only on the overall aggregated level and not individual customer level.
The data and thereby the outcome of these work packages can be useful for
every intermodal operator, terminal operator or any other players with the
supply chain. Those companies are competitors of Lineas and Lineas Intermodal.
For this reason the deliverables as well as the data will be confidential
during the project and four years after the end date of the project.
Based on the best of our knowledge, similar data has not been generated before
for research purposes in this specific setting. Data will be collected by
specific research participants. The participants will be recruited by the
consortium, using approaches that fit best with their customers and in ways
that maximize engagement. One of the methods that could be used is described
below:
The Consortium will send letters and survey questionnaires to target customers
to generate and gauge interest in their participation in market research or
the new developed business model. Based on the survey feedback, the Consortium
will identify the customers for interaction. The Consortium will invite the
identified customers to a short presentation and workshop to explain the
project background, the underlying economic assumptions and the potential
benefits. So the questionnaire is used as a starting point for further
interaction.
Those who wish to participate in the project will give permission for their
data to be used, specifying what data they are happy to share and how it can
be used. Finally it’s important to clarify that all data will be anonymised or
aggregated with other data in order to make it impossible to trace it back to
the client.
<table>
<tr>
<th>
**2.**
</th>
<th>
**Standards and metadata**
</th> </tr> </table>
Definition of Standards and metadata (according to the H2020 guidelines):
_“Reference to existing suitable standards of the discipline. If these do not
exist, an outline on how and what metadata will be created”._
The project focuses on the development a smart specialized logistics cluster
for the chemical industry in the Port of Antwerp in order to shift transport
volumes from road to rail freight. For this purpose, market information is
needed from relevant players inside the current chemical cluster. Accordingly
the data will be described and categorized with appropriate metadata.
Metadata is _“structured information that describes, explains, locates, or
otherwise makes it easier to retrieve, use, or manage an information resource.
Metadata is often called data about data or information about information”_ 4
_._
This means that during the project, the collected data will also be assigned
appropriate metadata, which will make it easier to manage the data itself.
However at this stage of the project we cannot define what metadata will be
collected. Because for example concerning WP2 and WP3, the Automated Wagon
Loading System does not exist -to the best of our knowledge. Also the metadata
might differ across the countries due to different data collection
regulations.
Nevertheless the data created or translated by the consortium will be FAIR at
all time. (Findable, Accessible, Interoperable a Re-usable). Although, it
should be mentioned that the project data and deliverables should be
considered as ‘photos’: The photo of today could be fairly different from the
photo of within a year or so.
<table>
<tr>
<th>
**3.**
</th>
<th>
**Data sharing**
</th> </tr> </table>
Definition of Data sharing (according to the H2020 guidelines):
_“Description of how data will be shared, including access procedures, embargo
periods (if any), outlines of technical mechanisms for dissemination and
necessary software and other tools for enabling re-use, and definition of
whether access will be widely open or restricted to specific groups.
Identification of the repository where data will be stored, if already
existing and identified, indicating in particular the type of repository
(institutional, standard repository for the discipline, etc.). In case the
dataset cannot be shared, the reasons for this should be mentioned (e.g.
Ethical, rules of personal data, intellectual property, commercial, privacy-
related, security- related)”._
The collected data will be a mix of confidential and public information. I t s
usage will be subject to customers and consortium’s partners consent. This
will be asked with a survey questionnaire that will be sent out to customer’s
i.e potential research participants. The survey questionnaires will include an
Informed Consent Form (ICF) which needs to be signed by the research
participants potentially interested in using the data.
The signature of the ICF will be a pre-requisite for the development of the
customized implementation plan. The ICF will include a short description of
the project mentioning that data collected from the customers will be used in
the framework of the project. It will be discussed with the client what data
and in what form can be shared with the project consortium.
The consortium consists of partners that operate in countries in which data
and privacy protection all have adopted the European legislation:
_“Client data is always treated confidentiality and all data management will
in particular be in line with privacy regulation of the European Union. It
will nevertheless be possible to use the data within the project.”_
The format of the agreements between the customers and project partners will
be defined during the activities, when they occur. However, the data that will
be shared for the purpose of implementing the work packages will be subject to
individual and commercial negotiations with the customers.
In general data privacy has not been identified as a major barrier within the
consortium. In specific the Consortium partners were asked to assess how data
and privacy protection are defined in their countries. Also they were asked if
the approach to data management is clearly regulated and if this presents a
barrier for the projects activities.
<table>
<tr>
<th>
**4.**
</th>
<th>
**Archiving and preservation**
</th> </tr> </table>
Definition of archiving and preservation (according to the H2020 guidelines):
_“Description of the procedures that will be put in place for long-term
preservation of the data. Indication of how long the data should be preserved,
what is its approximated end volume, what the associated costs are and how
these are planned to be covered”._
The customer data is deleted after the duration of the project. As all
customers are directly associated with one consortium partners with which a
bilateral contract for data handling is signed additionally to the Informed
Consent Form (ICF), this bilateral agreement might allow the data storing and
use beyond the project duration.
The data created by the consortium partners (not customer bound) in order to
develop the AWLS; analyze the environmental impact, etc. will be saved at
least 4Y after the end date of the project. Afterwards an evaluation takes
place to see if further archiving is necessary.
If needed, Non-Disclosure Agreements (NDAs) will be signed with the parties
that use the data for further work in the project (for instance by the
research partners).
To safely store the information in a manner that it is still accessible for
the consortium partners, a dedicated portal is created via the Targetprocess
tool. Every partner has been given access to this portal and is responsible
for his or her own password.
The table below provides a summary of the measures for data storing.
_Deliverable: D12.5 – Data management plan_
Table 3: Measures for data storing
<table>
<tr>
<th>
</th>
<th>
**Storage of data – description of method**
</th>
<th>
**Duration of storage**
</th>
<th>
**Comments**
</th> </tr>
<tr>
<td>
**Customer data**
</td>
<td>
All clients’ data should be kept confidential and anonymized **-** Paper
records such as questionnaires are stored in a locked filing cabinet **-**
Digital data is stored in a specific folder with restricted access. **-**
These set of data can only be consulted by members who need this information.
</td>
<td>
Project duration
</td>
<td>
The data can be shared within the consortium, however it must be kept
confidential by the partners
The whole data set will not be publically available and any publication
dissemination will obscure the client’s individual data.
</td> </tr>
<tr>
<td>
**AWLS – WP 2,3, 11**
</td>
<td>
The data related to the project is going to be stored on our servers and
Targetprocess for potential future patent.
</td>
<td>
Project duration and longer if necessary
</td>
<td>
Maybe needed for potential patent
</td> </tr>
<tr>
<td>
**Market data – WP1, WP4-7, 9- 10**
</td>
<td>
The plan is to store data in xls, csv- formats or in a data base.
</td>
<td>
Project duration and longer if necessary
</td>
<td>
No further comment
</td> </tr>
<tr>
<td>
**Business cases – WP8**
</td>
<td>
The data related to the project is going to be stored on our servers and
Targetprocess.
</td>
<td>
Project duration + 3Y
</td>
<td>
No further comment
</td> </tr> </table>
13
_Data management plan_
<table>
<tr>
<th>
**5.**
</th>
<th>
**Ethical aspects**
</th> </tr> </table>
Are there any ethical or legal issues that can have an impact on data sharing?
These can also be discussed in the context of the ethics review. If relevant,
include references to ethics deliverables and ethics chapter in the
Description of the Action (DoA).
Is informed consent for data sharing and long term preservation included in
questionnaires dealing with personal data?
The consortium shall carry out the action in compliance with:
(a) Ethical principles (including the highest standards of research integrity)
and (b) Applicable international, EU and national law.
The consortium shall respect the highest standards of research integrity — as
set out, for
instance, in the European Code of Conduct for Research Integrity This implies
notably compliance with the following essential principles:
* honesty;
* reliability;
* objectivity;
* impartiality;
* open communication;
* duty of care;
* fairness and
* responsibility for future science generations
This means that consortium shall ensure that persons carrying out research
tasks:
* present their research goals and intentions in an honest and transparent manner;
* design their research carefully and conduct it in a reliable fashion, taking its impact on society into account;
* use techniques and methodologies (including for data collection and management) that are appropriate for the field(s) concerned;
* exercise due care for the subjects of research — be they human beings, animals, the
* environment or cultural objects;
* ensure objectivity , accuracy and impartiality when disseminating the results;
* allow — in addition to the open access obligations under Article 29.3 of the Grant Agreement as much as possible and taking into account the legitimate interest of the beneficiaries — access to research data, in order to enable research to be reproduced;
* make the necessary references to their work and that of other researchers;
* refrain from practicing any form of plagiarism, data falsification or fabrication;
* avoid double funding, conflicts of interest and misrepresentation of credentials or other research misconduct.
_Data management plan_ Activities raising ethical issues must comply with the
‘ethics requirements’ set out as deliverables in Annex 1 of the Grant
Agreement.
The documents must be kept on file and be submitted upon request by the
coordinator to the Agency (see Article 52 of the Grant Agreement). If they are
not in English, they must be submitted together with an English summary, which
shows that the action tasks in question are covered and includes the
conclusions of the committee or authority concerned (if available)
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Executive Summary 5
Introduction 6
Legal framework 7
Grant agreement and Consortium agreement 7
Personal data protection in MUV: EU regulations 7
Open Access in H2020 8
MUV’s Data management plan (DMP) 8
Data summary 8
Data localization and metadata 12
Making data openly accessible 14
Making data interoperable 17
Responsibilities for data management 17
Data re-use 19
Data security 20
Conclusions 20
Annex I data sources 21
### Abbreviations
<table>
<tr>
<th>
CA
</th>
<th>
Consortium Agreement
</th>
<th>
EURAXES
</th>
<th>
European Charter for
Researchers and Code
Conduct
</th> </tr>
<tr>
<td>
CSV
</td>
<td>
Comma-separated Values
</td>
<td>
ECGA
</td>
<td>
European Comission Grant Agreement
</td> </tr>
<tr>
<td>
DFS
</td>
<td>
Distributed File System
</td>
<td>
GDPR
</td>
<td>
Eurpean Genera Data Protection Regulation
</td> </tr>
<tr>
<td>
DMP
</td>
<td>
Data Management Plan
</td>
<td>
MUV
</td>
<td>
Mobility Urban Values
</td> </tr>
<tr>
<td>
IPR
</td>
<td>
Intellectual Property Rights
</td>
<td>
SQL
</td>
<td>
Structured Query Language
</td> </tr>
<tr>
<td>
EB
</td>
<td>
Ethics Board
</td>
<td>
NO-SQL
</td>
<td>
Not Only Structured Query Language
</td> </tr>
<tr>
<td>
EC
</td>
<td>
European Comission
</td>
<td>
WP
</td>
<td>
Work Package
</td> </tr> </table>
# Executive Summary
This deliverable contains the first version of the Data Management Plan (M6).
It describes the nature of the data that will be collected and used during the
MUV project. The data has been divided into four overarching data categories:
* **Participants’ data** . MUV is a research project that will involve real users during some of its research activities. Our research involves the collection of personal data, first, for requirement elicitation, second, for valuation purposes and, third, to improve the quality of experience within a real-time feedback loop.
* **Data for expert systems** . As stated in D6.1, MUV envisions to use several distinct data sources to feed the recommendation engine, one of the central components of the MUV platform. This data will be used by the application to take decisions in order to offer and promote more sustainable mobility decisions to the users.
* **Data for evaluation** . Also identified in D6.1, different data sources have been selected to measure the impact of the MUV project. These datasets are either from the data generated by the application, from the data collected for the expert systems or from external data sources (like national statistics institutes). These datasets will be the base for publication of research papers.
* **Code** . Files containing the code of the platform is considered also as output data of the project. These datasets are designed for compilation/interpretation by a compiler/interpreter of the specific language. This category also comprehends the configuration files and scripts needed to run any needed tool.
It also describes the governance model of this data which is based on several
aspects: i) the current legislation, ii) the ethics related to working with
data, iii) the technical aspects regarding data storage and processing and iv)
the responsible parties for collecting and accessing the data.
# Introduction
This deliverable reports the management procedures that will be followed when
dealing with the data of MUV, including generated datasets, gathered datasets,
research data and code. It has been designed to be compliant with the
Guidelines on FAIR Data Management in Hoirizon 2020 1 and reviewed with the
Checklist for a Data Management Plan v4.0 2 . Due to the evolving nature of
MUV and its methodology, based on co-creation design and AGILE development,
the datasets of MUV will be collected (or even created) during and after the
project lifetime. Moreover, MUV is a project with many different datasets.
Hence, MUV has envisioned several deliverables to report different aspects of
data. Specifically:
**D6.1 Data Preparation (M6):** This deliverable has been already submitted
and presents in detail the different datasets that will be processed and
valorised in MUV and the techniques that will be used to prepare them for
further analysis.
**D1.4 Data Management Plan (M7):** This deliverable, which deals with the
management procedures of all the data generated or used in MUV, including the
code and the research data (publications).
**D6.2 Detailed data management and privacy plan (M10):** This deliverable
will specify the procedures for the privacy and security of the data collected
and generated in MUV (including anonymization).
**D1.4 Data Management Plan - Update (M19, M31):** Updates of the DMP to adapt
to the evolving nature of MUV.
**D10.1 H - Requirement No. 1 (M10):** Details on the procedures and criteria
that will be used to identify / recruit research participants, including how
to ethically manage this data. **D10.2 POPD - Requirement No. 2 (M10):**
Confirmation by all the competent Data Protection Officers from the National
Data Protection Offices, including the files declared with personal data and
how they will be managed according to the legislation and with ethical
considerations.
**D10.3 GEN - Requirements No. 3 (M4):** The Ethics report including how to
ethically treat the data generated in MUV.
In order to avoid information repetition, but at the same time produce a self-
contained deliverable, this D1.4 will summarize the identified categories of
datasets envisioned in MUV and will include all the tables describing these
datasets in the Annex. More information regarding specific procedures on how
to prepare the data for data quality and for data re-use will be found in
D6.1. Information about the procedures to secure the data and assure its
privacy will be found in D6.2. Any ethical consideration will be found in the
deliverables of WP10, specifically D10.1, D10.2 and D10.3.
# Legal framework
The objective of this section is to describe the legal framework of the
project linked with the data policy. In this sense this section reviews the
IPR framework established by the ECGA, the Consortium Agreement (CA) and the
overarching European legislation regarding data protection.
## Grant agreement and Consortium agreement
MUV has not participated in the Research Data Pilot (article 29.3 of the ECGA)
hence this legal framework on data management derives from the articles of the
ECGA and the CA. Of them, main points to take into consideration are the
following:
· The ECGA defines in article 26 the ownership of the project results. These
results are any tangible or intangible output of the actions including data
and information and are owned by the institution that generates them. In case
the results are generated by two or more institution rules defined in article
26.2 of the ECGA and article 8.2 of MUV’s CA must be applied.
· Unless it goes against the legitimate interest of the beneficiaries the
results must be disseminated by disclosing them. This means that the
beneficiaries have the right to protect the results in case the institution
plans to protect or exploit the results.
· As defined in the article 8.4 of the CA 30 calendar days prior to any
publication notice must be given to the other parties. Objections must be
raised in writing within 15 calendar days after the receipt of the notice. The
publication will be permitted if no objection is made within this time limit.
## Personal data protection in MUV: EU regulations
The project is committed to observe the EU Data Protection Directive 94/46/EC
when dealing with data and the forthcoming European General Data Protection
Regulation.
MUV has a whole Work Package dedicated to Ethical Issues (WP10). This WP will
deal and manage all ethical issues that arise during the project lifetime,
including those related to data gathering, publications and datasets. MUV also
leverages on an Ethics Board (EB), which is an external group of experts with
special competences in Ethics, protection of personal data and security. Upon
request of the EC the consortium established an EB, as they could provide
valuable knowledge, advice and guidance on the work planned in the project and
the data related to it.
IPR’s and exploitation rights (including data) are extensively covered in D1.3
“Plan for managing Knowledge and Intellectual Property” (M7).
# Open Access in H2020
The workflow related to publications is clearly stated in the CA (article 8),
and further evolved in D1.3. The same instructions are provided in MUV’s
Project Management Handbook.
MUV will attempt to open access to datasets with multiple purposes. Most of
these datasets can be reused, but the terms of use will be properly specified.
It is expected that during the project certain contents can be generated and
they can be of interest for researchers. With this purpose, the project will
identify which datasets can be made public, and which only re-used by project
members. This will be treated on a case-by-case basis. Data research will be
published following the same approach as for publications. This means that
data will be accessible within the 6 months after its publication.
# MUV’s Data management plan (DMP)
## Data summary
**Purpose of the data collection**
MUV project envisions four main categories of data to be collected during the
project lifetime.
* **Participants’ data** . MUV is a research project that will involve real users during some of its research activities. Our research involves the collection of personal data, first, for requirement elicitation, second, for valuation purposes and, third, to improve the quality of experience within a real-time feedback loop.
* **Data for expert systems** . As stated in D6.1, MUV envisions to use several distinct data sources to feed the recommendation engine, one of the central components of the MUV platform. This data will be used by the application to take decisions in order to offer and promote more sustainable mobility decisions to the users.
* **Data for evaluation** . Also identified in D6.1, different data sources have been selected to measure the impact of the MUV project. These datasets are either from the data generated by the application, from the data collected for the expert systems or from external data sources (like national statistics institutes). These datasets will be the base for publication of research papers.
* **Code** . Files containing the code of the platform is considered also as output data of the project. These datasets are designed for compilation/interpretation by a compiler/interpreter of the specific language. This category also comprehends the configuration files and scripts needed to run any needed tool.
**Relation to the objectives of the project**
MUV general objective is to “ _lever behavior change in local communities in
an entirely novel approach to reducing urban traffic. Rather than focus on
infrastructure, it raises citizen awareness on the quality of the urban
environment to promote a shift towards more sustainable and healthy mobility
choices. MUV solutions will be open, co-created with a strong learning
community of users and stakeholders, and piloted in a set of diverse urban
neighborhoods spread across Europe: Amsterdam (NL), Barcelona (ES), Fundao
(PT), Ghent (BE), Helsinki (FI), Palermo (IT). In order to ensure the
effectiveness of the mobility solutions and really match the communities and
stakeholders’ needs, all the project’s main activities (co-creation sessions,
software development and impacts’ analysis) will iterate three times during
the piloting phase._
_Real impact is measured with an evidence-based approach to maximize economic
viability and Social Return on Investment (SROI) and drive replicability and
the scaling up and out of MUV solutions._ ”
From the objective definition, it raises naturally the need for the three
envisioned categories of data. Firstly, **participants’ data** to be able to
create the communities that will allow MUV solutions to be “ _open, co-created
with a strong learning community of users and stakeholders, and piloted in a
set of diverse urban neighborhoods spread across Europe”_ and to elicit the
requirements of MUV “ _to ensure the effectiveness of the mobility solutions
and really match the communities and stakeholders’ needs”_ .
Secondly, **data for expert systems** to “ _raise citizen awareness on the
quality of the urban environment to promote a shift towards more sustainable
and healthy mobility choices”_ . Finally, **data for evaluation** to measure
real impact “ _with an evidence-based approach to maximize economic viability
and Social Return on Investment (SROI) and drive replicability and the scaling
up and out of MUV solutions._ ”
Relation of data with specific objectives is shown in the Table 1\.
<table>
<tr>
<th>
understanding the neighborhoods’ peculiarities and emerging values to **define
an effective behavior change strategy**
</th>
<th>
A recommendation engine is one of the central components of the MUV platform.
It will be fed with several data sources regarding user profile, environmental
context and public transport system and will produce and present specific
smart and sustainable choices to the user
</th> </tr>
<tr>
<td>
**co-designing site-specific solutions** for better and more liveable urban
environments
</td>
<td>
Co-creation and co-design is the methodology of the project. Hence, data will
be collected to be able to run these activities (participants’ personal data).
The results of these activities will also be collected (requirements,
validation).
</td> </tr>
<tr>
<td>
**developing scalable digital solutions and technologies** to improve globally
the experience of urban mobility
</td>
<td>
Heterogeneous datasets will be collected from all the different pilot areas.
These datasets will be obtained through city's open data portals, through the
MUV application and through the monitoring stations of MUV. In conclusion, MUV
data model will be defined to be able to escalate to any new region or area.
</td> </tr>
<tr>
<td>
**raising awareness among citizens** **on the importance of sustainable and
healthy mobility choices** , reducing private vehicular traffic and its
negative externalities (pollution, noise, deterioration of urban
infrastructures, time wasted in traffic jams, cost …) and encouraging local
consumption
</td>
<td>
Several datasets regarding environmental measurements will be collected,
either from the cities’ open portals or from the monitoring stations installed
in MUV. A visualization of these datasets will be offered to the user to raise
awareness on the topic of sustainable mobility.
</td> </tr>
<tr>
<td>
**analyzing, visualizing and sharing mobility and environmental data** to
build an effective decision support system for multiple stakeholders
</td>
<td>
As mentioned in the same objective, these
datasets will be collected to fed the recommendation engine and build a
complete decision system.
</td> </tr>
<tr>
<td>
**integrating new co-created mobility solutions into urban policy-making** and
planning processes at neighborhood level
</td>
<td>
Outputs of the co-creation activities will be collected, with special focus to
requirement elicitation and validation of the solution.
</td> </tr>
<tr>
<td>
**bringing the whole experiment to the market through an innovative business
model** in order to improve urban transportation in crowded neighborhoods and
cities all over the world
</td>
<td>
MUV data model will be open and scalable to allow the experiment to be moved
to new regions and cities.
</td> </tr> </table>
_Table 1: Relation of gathered data with the objectives of MUV_
**Type and format of the data**
MUV will collect several datasets and the formats of such datasets vary
depending on their sources. Specifically, we can identify 7 types of datasets
inside the three categories described.
* Participants’ data.
1. Personal details of the co-creation participants, like name, age, contact details, etc. The format of this dataset will be a file declared in each national data protection office.
○ Outputs of the activities, including requirement elicitation and validation
feedback. Format will be unstructured text in tables.
* Data for expert systems.
1. Data from MUV application like users’ profile data or users’ behaviour. Format of these datasets will be SQL tables.
○ Social media data, extracted from public social network interaction. Format
of these datasets will be non-structured text stored in No-SQL databases.
○ Data from open data portals, including environmental and public
transportation data. Format of this data may vary depending on the specific
portal but it is usually in CSV, XML or JSON format.
○ Monitoring stations data, from the monitoring stations created in MUV.
Format of these datasets will be JSON stored in NO-SQL databases. ● Data for
evaluation
○ User engagement evaluation data, composed by the interaction of the users
with the platform. Will be stored in structured files.
○ Research publications done during the life of MUV. The format of these
publications will be either LATEX 3 or MS-WORD / Open Office.
* Code
1. Code sentences will be stored in language specific files.
**Origin of the data**
There are six possible origins of the data:
* Open data portals from the municipalities, connected through an API.
* MUV application, including the interaction of the user with the application.
* Social media data from social media networks, specially twitter.
* MUV monitoring stations data, from the stations created and deployed within the project.
* Data from the participants in the co-creation activities.
* Research publications and code created by the MUV researchers / developers.
**Size of the data**
We do not envision very large datasets in MUV but the exact size is difficult
to predict. Datasets from open data portals are in the scale of some megabytes
and data collected by the MUV application can be stored in traditional SQL
tables with one entry per user. In addition, data from the participants is
expected to be of some kilobytes.
Social media data and MUV monitoring stations data is the data that can
represent a higher challenge in terms of size. However, we cannot predict such
size at this moment, since we need to define the frequency and the scope of
this collection through the co-creation activities. Finally, research papers
and code files are usually not exceeding few megabytes.
## Data localization and metadata.
Data will be stored in a cloud defined in WP4. The cloud infrastructure will
contain a specific module for data storage that will be designed and
partitioned according to the needs of the different datasets regarding
security, format and processing capabilities. Details of this architecture
will be presented in D4.1.
Storing formats for each one of the datasets have been designed and defined in
D6.1 and depend on the needs of each dataset. We will use five types of data
storage technologies
* Plain files are the oldest and simplest way of storing a data. Data is stored in a file with a specific format. A very known example of this type of storage is the Comma Separated Value (CSV) file, where commas are used to separate different values and end of lines indicate new rows. Files are used because they are easy to transfer and because they can be ingested easily in other storage or processing technologies.
* Relational Databases are the most known and used storage technology. Relational databases are based on formal tables which contain strongly-typed variables (types of variables like integer, float, string etc. are predefined). For example, a client of a company has a name (string), an age (int), etc. Each new client is a new instance of the table with the same fields but different values. To uniquely identify each instance of the table, a value that never repeats must be selected, this value is called the Primary Key of the table. Moreover, different tables need a connection, for example each client may have ordered several different commands, so there should be a new table which represents each command with its own primary key.
Again each command can be composed by several products, which should be
represented in a new table. The link between different tables is done using a
foreign key (e.g. the id of the client is stored in the command table). The
main advantages of relational databases are their strong schema, which assures
availability of the data, and the specific language designed to operate over
them (called SQL), which facilitates operations and the possibility to apply
any preparation technique directly. The drawbacks are the lack of scalability
(the relations between tables introduce checks and delays) and their poor
flexibility (predefined types and mandatory fields) which do not deal well
with low quality and high volume data.
* Non-relational databases are a new type of databases which appeared few years ago to deal with the flexibility problems of relational databases. In non-relational databases, usually known as Not Only SQL (NO-SQL), information is stored in documents which contain few mandatory fields and are not strongly-typed. It is on the designer hands to define indices that permit the location of specific documents in a collection of documents. NO-SQL databases are fast, can deal with high volume of data and can store not cleaned data, while at the same time can locate easily the datasets (if the indices are well designed). However, NO-SQL datasets do not allow data processing and connectors with specific external technologies (like Elastic search, R, etc.) are needed.
* Distributed file systems (DFS) are designed for data processing. In DFS data is partitioned into blocks and distributed among different nodes and partitions. Using this approach, data processing techniques can run in parallel, especially with the use of the MapReduce paradigm 4 . DFS are very convenient for data preparation techniques, especially for high-volume, high-velocity and high-heterogeneity. Moreover, availability is assured through replication. However, they do not contain relations between different datasets and are not designed to easily locate data.
* Versioning systems are softwares that act as a folder that is uploaded to a server to avoid any possible loss, but that track not only the files included in the folder, but also the changes from one version to another. Versioning systems are used for code development and for paper production.
Naming convention in MUV will follow the tables of identified datasets
explained in D6.1 and also in the Annex I of this document. Moreover, metadata
will be added to open data in order to classify and facilitate location of the
data. This metadata will be in the form of tags regarding the nature of the
data i.e. transport, environmental, etc. For code and research papers, well-
known standards and best practices will be used.
MUV identifies the following stakeholders of the data:
* Users of the application, that will visualize processed data to help them take more sustainable and healthier decisions. Their interests are on data directly involved with their interaction with the application, e.g. recommendations, pollution map, transit map, etc.
* MUV developers, that will use MUV data as a base for further developments. Their interests are on data that measures the activity of the users, e.g. profiling of the users and their level of activity in the platform, impact of their activity, etc. they are also interested in the code of the project.
* MUV evaluators / researchers, that will use MUV data to evaluate or measure the impact of the MUV platform. Their interests are on impact of MUV in the cities, e.g. traffic, pollution, noise, etc. They are also interested in the research publications of the project.
## Making data openly accessible.
As explained in D1.3, the MUV consortium will attempt to maximise the
visibility and exploitation of the project and its long-term impact, by
providing as many as possible publicly available results that can be easily
discovered and reused. Specifically, appropriately identified datasets
(anonymized end-user data whenever possible according to the “Article 29 Data
Protection Working Party” 5 , as well as, wherever possible, software
releases) will be generated and collected to drive the project’s technical
work. With this purpose, the project will identify which datasets can be made
public, and which only reused by project members. This will be treated on a
case-by-case basis.
Open data
**Open data** will be stored and made available through Symphony ( _Symphony_
) . Moreover, all datasets regarding municipalities will be also made
accessible through the municipalities’ open data portals.
**Research data** will be made accessible through Zenodo ( _Zenodo_ )
**Code** will be made accessible through github ( _Github_ )
All these datasets will be included in a table detailing the following fields:
* Type of dataset
* Format
* File type
* Owner
* Open data set (Y/N)
* Licensing
* Comments
When publishing the databases, any needed software tools or tool needed to
access to the data will be included in the description of the data-base.
Table 2 summarizes the level of access to each dataset
<table>
<tr>
<th>
**Category**
</th>
<th>
**Subcategory**
</th>
<th>
**Openness**
</th>
<th>
**Comments**
</th> </tr>
<tr>
<td>
Participant’s data
</td>
<td>
Personal details
</td>
<td>
Private
</td>
<td>
Personal details of the participants will be private
</td> </tr>
<tr>
<td>
Outputs of the activities
</td>
<td>
Confidential / public
</td>
<td>
outputs of the activities will drive to the requirements and evaluation.
Depending on the nature of the outputs will be kept confidential (within the
consortium) or open
</td> </tr>
<tr>
<td>
Data for expert systems
</td>
<td>
Users’ profile data
</td>
<td>
Private / confidential
</td>
<td>
Personal data will be kept private. Other types of data (profiling) will be
confidential within the consortium for evaluation and improvement needs.
</td> </tr>
<tr>
<td>
Users’ behaviour data
</td>
<td>
Private/confi dential
</td>
<td>
Same as previous
</td> </tr>
<tr>
<td>
social media data
</td>
<td>
Public
</td>
<td>
Only public social media data will be used.
</td> </tr>
<tr>
<td>
Environmen
tal data
</td>
<td>
Public
</td>
<td>
Environmental data is gathered from the open data portal
</td> </tr>
<tr>
<td>
MUV
monitoring stations data
</td>
<td>
Public
</td>
<td>
Monitored data will be made public
</td> </tr>
<tr>
<td>
Public transport data
</td>
<td>
Public
</td>
<td>
Transport data will be made public
</td> </tr>
<tr>
<td>
Traffic data
</td>
<td>
Public/confid ential
</td>
<td>
Publicly available data will be open. We envision the possibility to gather
private traffic data (mainly from google maps) which will be confidential for
use only inside the consortium
</td> </tr>
<tr>
<td>
Data for evaluation
</td>
<td>
Recommen
dation system input data
</td>
<td>
N/A
</td>
<td>
This is a set of parameters to be used as input to the recommendation system.
These are parameters from other already identified datasets and their
confidentiality level will be maintained
</td> </tr>
<tr>
<td>
User
engagemen
t evaluation data
</td>
<td>
N/A
</td>
<td>
Same as before
</td> </tr>
<tr>
<td>
KPI evaluation data
</td>
<td>
Public
</td>
<td>
KPIs data will be made public for evaluation and maximization of impact.
</td> </tr>
<tr>
<td>
Research publications data
</td>
<td>
Public
</td>
<td>
Research publications will be under open or green access.
</td> </tr>
<tr>
<td>
Code
</td>
<td>
Code files
</td>
<td>
According to CA
</td>
<td>
Taking into account firstly the IPR and the Consortium Agreement, code will be
made open when possible.
</td> </tr>
<tr>
<td>
</td>
<td>
Scripts and configuratio n files
</td>
<td>
According to CA
</td>
<td>
Same as before
</td> </tr> </table>
_Table 2: Levels of access for the MUV datasets_
We foresee MUV evaluation data to be used actively and extensively for
research purposes, including the publication of papers to conferences and
journals. Eventually, data for expert systems can also be used for research,
especially in more technical publications regarding data science and
artificial intelligence. We don’t foresee research use for participant’s data,
and if this situation arises, only aggregated results (anonymized) will be
allowed to be published (according to Article 6 of the GDPR 6 ). Data
published in open data portals (and identified in D6.3) will be kept at least
2 years beyond the life of the project. Other data sources will be tied to the
life of the website of MUV (1 year after the end of the project). Any kind of
research data will be held in persistent research repositories. No associated
costs are envisioned for the data repositories. Either research data
repositories and open data repositories are free of charge and maintained by
non-profit organizations or administrations.
Several activities are envisioned in the scope of T6.3 - Open data
valorisation in order to preserve the datasets and to make them persistent and
easy to locate and use. Aspects like open formats, use of standards and linked
data will be taken into account.
If an entity or particular from outside the consortium asks for access to
personal or closed data, the General Assembly will decide either to give
access or not. The proprietary of the data set can exercise his veto as
specified in the CA.
In case there is a request to access private content by a third party/external
entity, the question will be evaluated by the project coordinator and those
partners involved. In case the request is internal, the part requesting access
will contact the project coordinator, who will take it to the General Assembly
(GA) and the GA will evaluate, jointly with the hosting partner, whether it is
possible or not. All decisions will follow the _European Charter for_
_Researchers and Code of Conduct_ (EURAXESS), the Consortium Agreement and the
Grant Agreement.
## Making data interoperable.
For research data collected and generated in the project, a fit-for-purpose
file naming convention will be developed in accordance with best practice for
qualitative data, such as described by the UK Data Archive (2011). This will
involve identifying the most important metadata related to the various
research outputs. Key information includes content description, date of
creation, version, and location.
To make the datasets in the platform easily findable searchable tags will be
added to the metadata. When uploading the data the creator of the dataset also
have the option to create new tags that corresponds to the contents of the
dataset making it easier for other users to find and re-use the data.
MUV is strongly committed to produce any kind of data in open formats. For
files, Comma Separated Values (CSV) 7 files will be preferred, when possible
including links to other files and hence, promoting linked data 8 .
Depending on the choice of repositories we are however likely to follow the
standard of The Data Catalog Vocabulary (DCAT) (Data Catalog Vocabulary, 2014)
as it defines a standard way to publish machine-readable metadata about a
dataset where appropriate.
We also intend to use common ontologies and vocabularies for data.
## Responsibilities for data management.
Each partner will be solely responsible of the quality and accuracy of the
data provided and of making data accessible, table summarizes partners per
datasets. The PC (PUSH) will provide support and advice to the consortium.
Table 3 summarizes these responsibilities for each one of the MUV datasets.
<table>
<tr>
<th>
**Category**
</th>
<th>
**Subcategory**
</th>
<th>
**Openness**
</th>
<th>
**Responsible partner**
</th>
<th>
**Partners with limited accessing rights**
</th> </tr>
<tr>
<td>
Participa nt’s data
</td>
<td>
Personal details
</td>
<td>
Private
</td>
<td>
Amsterdam pilot ->AAU
Barcelona pilot -> I2CAT
Fundao pilot -> BAG
Ghent pilot -> LUCA
Helsinki pilot -> FVH
Palermo pilot -> PUSH
</td>
<td>
AMST
IMI BCN
FUNDAO
GENT
MUNI PAL
</td> </tr>
<tr>
<td>
Outputs of the activities
</td>
<td>
Confidenti al / public
</td>
<td>
Amsterdam pilot ->AAU
Barcelona pilot -> I2CAT
Fundao pilot -> BAG
Ghent pilot -> LUCA
Helsinki pilot -> FVH
Palermo pilot -> PUSH
</td>
<td>
All
</td> </tr>
<tr>
<td>
Data for expert systems
</td>
<td>
Users’ profile data
</td>
<td>
Private / confidenti
al
</td>
<td>
PUSH
</td>
<td>
I2CAT, ISMB, WAAG, LUCA
</td> </tr>
<tr>
<td>
Users’ behaviour data
</td>
<td>
Private/co nfidential
</td>
<td>
PUSH
</td>
<td>
I2CAT, ISMB, WAAG, LUCA
</td> </tr>
<tr>
<td>
social media data
</td>
<td>
Public
</td>
<td>
ISMB
</td>
<td>
PUSH, I2CAT, WAAG, LUCA
</td> </tr>
<tr>
<td>
Environment al data
</td>
<td>
Public
</td>
<td>
I2CAT
</td>
<td>
All
</td> </tr>
<tr>
<td>
MUV
monitoring stations data
</td>
<td>
Public
</td>
<td>
WAAG
</td>
<td>
I2CAT, ISMB, PUSH, LUCA
</td> </tr>
<tr>
<td>
Public transport data
</td>
<td>
Public
</td>
<td>
I2CAT
</td>
<td>
All
</td> </tr>
<tr>
<td>
Traffic data
</td>
<td>
Public/con fidential
</td>
<td>
I2CAT
</td>
<td>
PUSH, ISMB, WAAG, LUCA
</td> </tr>
<tr>
<td>
Data for evaluatio n
</td>
<td>
Recommenda
tion system input data
</td>
<td>
N/A
</td>
<td>
ISMB
</td>
<td>
I2CAT, WAAG, PUSH
</td> </tr>
<tr>
<td>
User engagement evaluation
</td>
<td>
N/A
</td>
<td>
ISMB
</td>
<td>
I2CAT, WAAG, PUSH
</td> </tr>
<tr>
<td>
</td>
<td>
data
</td>
<td>
</td>
<td>
</td>
<td>
</td> </tr>
<tr>
<td>
KPI evaluation data
</td>
<td>
Public
</td>
<td>
ISMB
</td>
<td>
ALL
</td> </tr>
<tr>
<td>
Research publications data
</td>
<td>
Public
</td>
<td>
LUCA
</td>
<td>
All
</td> </tr>
<tr>
<td>
Code
</td>
<td>
Code files
</td>
<td>
According to CA
</td>
<td>
PUSH
</td>
<td>
N/A
</td> </tr>
<tr>
<td>
</td>
<td>
Scripts and
configuration files
</td>
<td>
According to CA
</td>
<td>
PUSH
</td>
<td>
N/A
</td> </tr> </table>
_Table 3: Summary of the responsible partners and partners with accessing
rights to the MUV datasets_
Regarding research data and research publications, prior to uploading any
database to zenodo, the responsible has to inform the GA 30 days in advance.
Every six months the PC will request the consortium for pre-printed
manuscripts (accepted), slides and posters, and non-protected raw data
supporting papers and deliverables for updating the project public repository
in zenodo.
## Data re-use.
MUV data will be licensed from the creation of the dataset to ensure that it
is used according to the defined terms. Any re-used dataset by MUV will
respect the previous license of the dataset.
**Research Publications** : Each publication will be licensed on a case-by-
case basis, taking into account embargo periods if applicable. Questionnaires
are licensed under Creative
Commons Attribution-Non Commercial 4.0. Video Footage is licensed under
Creative Commons Attribution-Non Commercial 4.0 (strictly forbidden to use it,
totally or partially, for commercial purposes).
**Private data:** Private data will be licensed by the owner (partner) of the
dataset. **Confidential data:** Confidential data will be licensed by the
owner (partner) of the dataset, which has to include the rights to allow the
partners with accessing rights to access to such information.
**Public data:** Public data will be licensed under the Open Data Commons Open
Database
License (ODbL) 9 which is an open license for data (and databases) which
requires attribution (owner of the data must be kept when reusing it) and
share-alike (the same license must be applied when re-using data).
Regarding data quality, D6.1 specifically describes the processes that will be
used to ensure data quality, including cleaning and enrichment (to remove or
correct erroneous samples) and curating to ensure perdurability and easy
classification of the datasets. For publications and research data, all
content will be peer-reviewed.
## Data security
Data security in MUV will be treated in a specific deliverable, D6.2 Detailed
Data Management and Privacy Plan which is a confidential deliverable that
assures that any security procedure is not disclosed publicly.
# Conclusions
MUV considers four types of data: Participant’s data, Data for expert systems,
Data for evaluation and Code. In this deliverable we analyze these four types
of data from several aspects. Firstly, its relation with the objectives of the
project. Secondly, the procedures and techniques that will be used to store
them and assure its accessibility. Finally, their nature
(open/private/confidential), their license and their owners.
It is important to note that, except for research data and code, which have
very standardized procedures to define these aspects, all the other datasets
are very heterogeneous (from a wide range of different sources) and with
different privacy, preparation and ethics requirements. Hence, three specific
deliverables must be considered together with the Data Management Plan
presented here, to obtain all the picture of the data activities of MUV.
Firstly, D6.1 for data preparation techniques, including cleaning and
curation. Secondly, D6.2 for data privacy and security, including
anonymization. Finally, D10.1, D10.2 and D10.3 for ethics and legal
requirements regarding data.
|
https://phaidra.univie.ac.at/o:1140797
|
Horizon 2020
|
1164_RE4_723583.md
|
# 1\. EXECUTIVE SUMMARY
According to the Guidelines on Open access to Scientific Publications and
Research Data for projects funded or co-funded under Horizon 2020, Europe 2020
strategy underlines the central role of knowledge and innovation in growth
generation. For these reasons the European Union strives to improve access to
scientific information and to boost the benefits of public investment in the
research funded under the EU Framework Programme Horizon 2020.
The present document constitutes the **second issue** of the Deliverable D8.4
Data Management Plan in the framework of the RE 4 project, dedicated to Task
T8.5 under the work package WP8. The Data Management Plan (DMP) identifies the
results that should be subject of RE 4 dissemination and exploitation and
analyses the main data uses, users and explore the restrictions related to IPR
according with the Consortium Agreement, defining the data assurance processes
that are to be applied during and after the completion of the project. This
document is prepared in compliance with the template provided by the
Commission in the Annex 1 of the “Guidelines on Data Management in Horizon
2020“.
Main updates at month 18:
\- Dataset description, data sharing, storage, preservation and
responsibilities (Table 4) - Datasets shared publically at month 18 (Table 5)
# 2\. INTRODUCTION
This document constitutes the **second issue** of Data Management Plan (DMP)
in the EU framework of the RE 4 project under Grant Agreement No 723583. The
objective of the DMP is to establish the measures for promoting the findings
during the project’s life and detail what data the Project will generate,
whether and how it will be exploited or made accessible for verification and
re-use, and how it will be curated and preserved. The DMP enhances and ensures
relevant project´s information transferability and takes into account the
restrictions established by the Consortium Agreement. In this framework, the
DMP sets the basis for both Dissemination Plan and Exploitation Plan. The
first version of the DMP was delivered at month 6, at month 18 the DMP is
updated in parallel with the different versions of Dissemination and
Exploitation Plans. It is acknowledged that not all data types will be
available at the start of the project, thus whenever important, if any changes
occur to the RE 4 project due to inclusion of new data sets, changes in
consortium policies or external factors, the DMP will be updated in order to
reflect actual data generated and the user requirements as identified by the
RE 4 consortium participants.
The overall goal of the RE 4 project is to promote new technological
solutions for the design and development of structural and non-structural pre-
fabricated elements with high degree of recycled materials and reused
structures from partial or total demolition of buildings. The developed
technology will aim at energy efficient new construction and refurbishment,
thus minimizing environmental impacts. The RE 4 -Project targets the
demonstration of suitable design concepts and building elements produced from
CDW in an industrial environment, considering perspective issues for the
market uptake of the developed solutions. The technical activities will be
supported by LCA and LCC analyses, certification and standardization
procedures, demonstration activities, professional training, dissemination,
commercialisation and exploitation strategy definition, business modelling and
business plans.
The overarching purpose is to develop a RE 4 prefabricated energy-efficient
building concept that can be easily assembled and disassembled for future
reuse, containing up to 65% in weight of recycled materials from CDW (ranging
from 50% for the medium replacement of the mineral fraction, up to 65% for
insulating panels and concrete products with medium mineral replacement
coupled with the geopolymer binder). The reusable structures will range from
15-20% for existing buildings to 80-90% for the RE 4 prefabricated building
concept.
RE 4 Project comprises seven technical work packages (WPs) as follows:
* WP1 - Mapping and analysis of CDW reuse and recycling in prefabricated elements
* WP2 - Strategies for innovative sorting of CDW and reuse of structures from dismantled
* WP3 - Innovative concept for modular/easy installation and disassembly of eco-friendly prefabricated elements
* WP4 - Technical characterization of CDW-derived materials for the production of building
* WP5 - Development of precast components and elements from CDW
* WP6 - Pilot level demonstration of CDW based prefabricated elements
* WP7 - Life-cycle and HSE analysis and certification/standardization strategy definition
To facilitate the technical work there are three transversal work packages to
coordinate all the work packages, disseminate and communications project
results and to ensure compliance with the ethics requirements.
* WP8 - Training, dissemination and exploitation
* WP9 - Project Management
* WP10 - Ethics requirements
This document has been prepared to describe the data management life cycle for
all data sets that will be collected, processed or generated by RE 4
Project. It is a document outlining how research data will be handled during
the project, and after the project is completed. It describes what data will
be collected, processed or generated and what methodologies and standards are
to be applied. It also defines if and how this data will be shared and/or made
open, and how it will be curated and preserved.
# 3\. OPEN ACCESS
Open access can be defined as the practice of providing on-line access to
scientific information that is free of charge to the reader and that is
reusable. In the context of R&D, open access typically focuses on access to
“scientific information”, which refers to two main categories:
* Peer-reviewed scientific research articles (published in academic journals).
* Scientific research data (data underlying publications and/or raw data).
It is important to note that:
* Open access publications go through the same peer review process as non-open access publications.
* As an open access requirement comes after a decision to publish, it is not an obligation to publish: it is up to researchers whether they want to publish some results or not.
* As the decision on whether to commercially exploit results (e.g. through patents or otherwise) is made before the decision to publish (open access or not), open access does not interfere with the commercial exploitation of research results. 1
Benefits of open access:
* Unprecedented possibilities for the dissemination and exchange of information due to the advent of the internet and electronic publishing.
* Wider access to scientific publications and data can help to accelerate innovation, foster collaboration and avoid duplication of effort, build on previous research results, involve citizens and society.
Figure 1. Open Access benefits
The EC capitalizes on open access and open science as it lowers barriers to
accessing publicly-funded research. This increases research impact, the free-
flow of ideas and facilitates a knowledge-driven society at the same time
underpinning the EU Digital Agenda (OpenAIRE Guide for Research Administrators
- EC funded projects). Open access policy of European Commission is not a goal
in itself, but an element in promotion of affordable and easy accessible
scientific information for the scientific community itself, but also for
innovative small businesses.
## 3.1 Open Access to peer-reviewed scientific publications
Open access to scientific peer-reviewed publications has been anchored as an
underlying principle in the Horizon 2020 Regulation and the Rules of
Participation and is consequently implemented through the relevant provisions
in the Grant Agreement.
More specifically, Article 29: “Dissemination of results, Open Access,
Visibility of EU Funding” of RE 4 Grant Agreement establishes the obligation
to ensure open access to all peer-reviewed articles produced by RE 4 .
## _Article 29.2 Open access to scientific publications in RE_ 4 _GA_
Each beneficiary must ensure open access (free of charge online access for any
user) to all peer reviewed scientific publications relating to its results.
In particular, it must:
1. as soon as possible and at the latest on publication, deposit a machine-readable electronic copy of the published version or final peer-reviewed manuscript accepted for publication in a repository for scientific publications;
Moreover, the beneficiary must aim to deposit at the same time the research
data needed to validate the results presented in the deposited scientific
publications.
2. ensure open access to the deposited publication — via the repository — at the latest:
1. on publication, if an electronic version is available for free via the publisher, or
2. within six months of publication (twelve months for publications in the social sciences and humanities) in any other case.
3. ensure open access — via the repository — to the bibliographic metadata that identify the deposited publication.
The bibliographic metadata must be in a standard format and must include all
of the following:
* the terms “European Union (EU)” and “Horizon 2020”;
* the name of the action, acronym and grant number;
* the publication date, and length of embargo period if applicable; \- a persistent identifier.
3.1.1 Green open access
The green open access is also called self-archiving and means that the
published article or the final peer-reviewed manuscript is archived by the
researcher in an online repository before, after or alongside its publication.
Access to this article is often delayed (embargo period). Publishers recoup
their investment by selling subscriptions and charging pay-per-download/view
fees during this period during an exclusivity period. This model is promoted
alongside the “Gold” route by the open access community of researchers and
librarians, and is often preferred.
3.1.2 Gold open access
This type of open access is sometimes called open access publishing, or author
pays publishing and means that a publication is immediately provided in open
access mode by the scientific publisher. Associate costs are shifted from
readers to the university or research institute to which the researcher is
affiliated, or to the funding agency supporting the research. This model is
usually the one promoted by the community of well-established scientific
publishers in the business.
### 3.2 Open Access to research data
“Research data” refers to information, in particular facts or numbers,
collected to be examined and considered and as a basis for reasoning,
discussion, or calculation. In a research context, examples of data include
statistics, results of experiments, measurements, observations resulting from
fieldwork, survey results, interview recordings and images. The focus is on
research data that is available in digital form.
## _Article 29.3 Open access to research data in RE_ 4 _GA_
Regarding the digital research data generated in the action (‘data’), the
beneficiaries must:
1. deposit in a research data repository and take measures to make it possible for third parties to access, mine, exploit, reproduce and disseminate — free of charge for any user — the following:
1. the data, including associated metadata, needed to validate the results presented in scientific publications as soon as possible;
2. other data, including associated metadata, as specified and within the deadlines laid down in the 'data management plan' (see Annex 1 of RE 4 GA);
2. provide information — via the repository — about tools and instruments at the disposal of the beneficiaries and necessary for validating the results (and — where possible — provide the tools and instruments themselves).
This does not change the obligation to protect results in Article 27, the
confidentiality obligations in Article 36, the security obligations in Article
37 or the obligations to protect personal data in Article 39, all of which
still apply.
The beneficiaries do not have to ensure open access to specific parts of their
research data if the achievement of the action's main objective, as described
in Annex 1, would be jeopardized by making those specific parts of the
research data openly accessible. In this case, the data management plan must
contain the reasons for not giving access to third parties.
### 3.3 Dissemination & Communication and Open Access
For the implementation of RE 4 Project, there is a complete dissemination
and communication set of activities scheduled, with the objectives of raising
awareness in the research community, industry and wide public (e-newsletters,
e-brochures, poster or events, are foreseen for the dissemination of RE 4 to
key groups potentially related to the project results’ exploitation).
Likewise, RE 4 website, webinars, press releases or videos, for instance,
will be developed for a communication to a wider audience. Details about all
those dissemination and communication elements are provided in the deliverable
D8.2 Communication and Dissemination Plan. The Data Management Plan and the
actions derived are part of the overall RE 4 dissemination and communication
strategy, which is included in the above mentioned D8.2.
# 4\. OBJECTIVES OF DATA MANAGEMNET PLAN
The purpose of RE 4 Data Management Plan (DMP) is to provide a management
assurance framework and processes that fulfil the data management policy that
will be used by the RE 4 project partners with regard to all the dataset
types that will be generated by the RE 4 project. The aim of the DMP is to
control and ensure quality of project activities, and to
effectively/efficiently manage the material/data generated within the RE 4
project. It also describes how data will be collected, processed, stored and
managed holistically from the perspective of external accessibility and long
term archiving.
The content of the DMP is complementary to other official documents that
define obligations under the Grant Agreement (GA) and associated annexes, and
shall be considered a living document and as such will be the subject of
periodic updating as necessary throughout the lifespan of the project.
Figure 2. RE 4 Data Management Plan overview
# 5\. RE 4 PROJECT WEBSITE, STORAGE AND ACCESS
RE 4 project website is used for storing both public and private documents
related to project and dissemination, and is meant to be live for the whole
project duration and minimum 2 years after the project end. Public section of
the website contains mainly public deliverables, brochure, (roll up) poster,
presentations, scientific papers, newsletters, magazine article, videos,
photos, etc. Private section of the project website includes confidential
deliverables, work packages related documentation, and is used as the main
exchange of information among the Project partners.
The website www.re4.eu was launched on 1st December 2016, its design is done
by dissemination leader FENIX that is also in charge of website maintenance
and regular update. It is dynamic and interactive tool in order to ensure a
clear communication and wide dissemination of project news, activities and
results. The website is of primary importance due to the expected impact on
the target audiences. It was designed to give quick, simple and neat
information. The website is regularly updated with news and events related to
RE 4 Project, press releases, magazine articles, scientific papers, etc. The
website is available in English, but translation to partners´ languages is
considered as well in order to break the language barrier and enable wide and
effective communication of project results at national level.
To ensure the safety of the data, the partners will use their available local
file servers to periodically create backups of the relevant materials. The RE
4 Project website itself already has its own backup procedures.
In addition to the RE 4 Project website, the Project Coordinator established
a temporary ftp access for the first period of the Project to all project
partners. Dropbox was created for the RE 4 Project to manage living
documents (e.g. contact list).
The Project Coordinator (CETMA) of the RE 4 along with the Dissemination and
Exploitation Manager (FENIX) will be in charge for data management and all the
relevant issues.
Figure 3. RE 4 Project website
# 6\. DATA MANAGEMENT PLAN IMPLEMENTATION
Partners of the RE 4 Project demonstrate relevant management capabilities
necessary to support and provide major contribution to all the activities
envisaged in the Project work. The general and technical Project management is
handled by the Coordinator of the Project, CETMA. The main roles and
instruments comprising the Project management structure include:
* _Project Management Committee_ (PMC): representatives of each partner, the highest decision board and its main task is the Project governance, the overall responsibility of all technical, financial, legal, administrative, ethical, and dissemination issues of the Project. It encompasses the following main roles:
* Project Coordinator (PC): PMC chairman, responsible for the overall management, communication, and coordination of the entire Project (supervision and approval of reports and technical deliverables, first liaison and communication with the EU Institutions, monitoring of the progress of the Project according to the work-plan, ensuring that the technical objectives of the Project as a whole are met, budget controlling, reporting of the major changes from the agreed work-plan to the PMC).
* Dissemination and Exploitation Manager (DEM): responsible for dissemination and communication (website, press releases, newsletters, etc.), for exploitation planning (support and liaising to companies, SMEs and industrials), continuous assessment of the market potential of the developed know-how in the Project.
* Risk Manager and Quality (RQM): assessment, and – along with the support of the PC – the management of administrative and technical risks and the development of the Quality Plan.
* _Scientific and Technical Committee_ (STC): under the control of and in compliance with the decision of PMC responsible for the planning, execution and controlling of the Project, as regards issues of both scientific and technical nature. From a technical point of view, the Project is broken down into a number of work packages, each of them addressing a specific area of work. The STC encompasses the following roles:
* Scientific and Technical Manager (STM): ensuring that the S&T objectives of the Project are met with quality and time. STM is expected to lead the S&T activities undertaken within the Project and is responsible for resolving any issues of S&T nature that might occur.
* Work Packages Leaders (WPL): responsible for managing their work package as a selfcontained entity. Their tasks include among others coordinating, monitoring, and assessing the progress of the WP to ensure that output performance, costs, and timelines are met.
* Each WP is further subdivided into its large components tasks, which are allocated a Task Leader responsible for coordination.
The Project management also encompasses an experienced Financial Responsible
(FR) who is in charge for the financial and administrative Project management
and supervision. Finally, an _End_ _User and Interest Group_ (EIG) has been
already named to provide inputs for products requirements and to evaluate the
Project results and achievements. It includes external experts already
identified and is chaired by Prof Hebel from Swiss Federal Institute of
Technology Zurich, that is currently holding also the position of Assistant
Professor of Architecture and Construction at the Future Cities Laboratory in
Singapore. The main role of the EIG is to observe the work tackled in RE 4
and envisage possible inconsistencies between the market expectations and the
technical work to assure a high level of innovation and to find a suitable
balance between the waste managers, architects and endusers requirements and
the developed technical solutions.
Figure 4. Management structure on RE 4 Project
Table 1: RE 4 partners and their role in the project
<table>
<tr>
<th>
**#**
</th>
<th>
**Partner short name**
</th>
<th>
**Partner legal name**
</th>
<th>
**Partner role in RE 4 project **
</th> </tr>
<tr>
<td>
**1.**
</td>
<td>
**CETMA**
</td>
<td>
CENTRO DI RICERCHE
EUROPEO DI TECNOLOGIE
DESIGN E MATERIALI
</td>
<td>
Project coordinator, mapping the current best practices related to reuse and
recycling of CDW in prefabricated elements, diagnosis of CDW management in the
EU, current status on policy measures and regulatory frameworks,
</td> </tr> </table>
<table>
<tr>
<th>
</th>
<th>
</th>
<th>
</th>
<th>
development of materials incorporating CDW, Portland cement and alkali
activated binders.
</th> </tr>
<tr>
<td>
**2.**
</td>
<td>
**ACCIONA**
</td>
<td>
ACCIONA
INFRAESTRUCTURAS S.A.
</td>
<td>
Leader of the demonstration activities, in charge of assembling and testing
some of the final components in real scale pilot buildings.
</td> </tr>
<tr>
<td>
**3.**
</td>
<td>
**CBI**
</td>
<td>
CBI Betonginstitutet AB
</td>
<td>
Scientific leader, development of suitable concrete formulations and concrete
component development in particular façade applications, performance and
durability testing of the prefabricated elements and of larger precast and
timber elements, requirements concerning the quality and properties of
materials for concrete and other cement based product, LCA and LCC.
</td> </tr>
<tr>
<td>
**4.**
</td>
<td>
**CDE**
</td>
<td>
CDE GLOBAL LIMITED
</td>
<td>
Developing an innovative separating system for CDW, based on weight criteria,
and of providing recycled materials for R&D and demo activities, innovative
strategies and processes for separating CDW based on weight criteria,
collection of representative samples of CDW sorted material.
</td> </tr>
<tr>
<td>
**5.**
</td>
<td>
**CREAGH**
</td>
<td>
CREAGH CONCRETE PRODUCTS LIMITED
</td>
<td>
Production of RE4 prefabricated component and their assembly into demo
building, manufacturing and testing of the prefabricated elements prototypes,
quality control and characterization, HSE analysis.
</td> </tr>
<tr>
<td>
**6.**
</td>
<td>
**FENIX**
</td>
<td>
FENIX TNT SRO
</td>
<td>
Dissemination and exploitation leader, development of business modelling and
business plans, IPR management, market assessment, data management.
</td> </tr>
<tr>
<td>
**7.**
</td>
<td>
**QUB**
</td>
<td>
THE QUEEN'S UNIVERSITY OF BELFAST
</td>
<td>
Technical characterization of recycled material for structural and non-
structural elements, characterisation of mineral aggregates, assessment of
variability effects and investigation on the alkali activation potential of
ceramic waste, development of prefabricated components, refinement and
production of pre-fab test elements, certification strategies, technical
documentation and standardization.
</td> </tr>
<tr>
<td>
**8.**
</td>
<td>
**ROS**
</td>
<td>
ROSWAG ARCHITEKTEN
GESELLSCHAFT VON
ARCHITEKTEN MBH
</td>
<td>
Design of innovative concept for modular/easy installation and disassembly of
eco-friendly prefabricated elements, current status of construction of
prefabricated elements with reused/recycled material, definition of
sustainable strategies for the disassembly and reuse of structures and
components from dismantled buildings.
</td> </tr>
<tr>
<td>
**9.**
</td>
<td>
</td>
<td>
</td>
<td>
</td> </tr>
<tr>
<td>
**10.**
</td>
<td>
**STRESS**
</td>
<td>
SVILUPPO TECNOLOGIE E
RICERCA PER L'EDILIZIA
SISMICAMENTE SICURA
ED ECOSOSTENIBILE
SCARL
</td>
<td>
Life-cycle and HSE analysis and certification /standardization strategy
definition, scaled-up processes, inputs related to S-LCA, support for the
definition of data to be collected, development of the BIM-compatible DSS and
platform for CDW estimation and management, refurbishment of residential
and/or commercial building: installation of the panels/blocks on an existing
façade.
</td> </tr>
<tr>
<td>
**11.**
</td>
<td>
**NTUST**
</td>
<td>
National Taiwan
University of Science and Technology
</td>
<td>
Demonstration of RE 4 technologies outside EU.
</td> </tr>
<tr>
<td>
**12.**
</td>
<td>
**VORTEX**
</td>
<td>
VORTEX HYDRA S.R.L.
</td>
<td>
Extruded products (roof tiles, floor tiles and façade products) obtained using
CDW materials, to supply a demo line to the consortium capable to produce
extruded products using the CDW material, to assist the testing phase of the
obtained products following the Standards and to use its experience in this
field to achieve the final target, design and adapt the prefabricated elements
production line.
</td> </tr>
<tr>
<td>
**13.**
</td>
<td>
**ACR+**
</td>
<td>
ASSOCIATION DES CITES
ET DES REGIONS POUR LE
RECYCLAGE ET LA
GESTION DURABLE DES
RESSOURCES
</td>
<td>
Assessment of economic instruments of CDW management for European
representative countries, dissemination and communication activities.
</td> </tr>
<tr>
<td>
**14.**
</td>
<td>
**STAM**
</td>
<td>
STAM Srl
</td>
<td>
Study of innovative sorting solutions for the recycling of CDW and strategies
for the reuse of structures from dismantled buildings, main features of
materials resulting from total or partial demolition of buildings, innovative
strategies and processes for sorting CDW based on advanced robotic system.
</td> </tr> </table>
# 7\. RESEARCH DATA
“Research data” refers to information, in particular facts or numbers,
collected to be examined and considered as a basis for reasoning, discussion,
or calculation. In a research context, examples of data include statistics,
results of experiments, measurements, observations resulting from fieldwork,
survey results, interview recordings and images. The focus is on research data
that is available in digital form.
As indicated in the Guidelines on Data Management in Horizon 2020 (European
Commission, Research & Innovation, October 2015), scientific research data
should be easily:
1. DISCOVERABLE
The data and associated software produced and/or used in the project should be
discoverable (and readily located), identifiable by means of a standard
identification mechanism (e.g. Digital Object Identifier).
2. ACCESSIBLE
Information about the modalities, scope, licenses (e.g. licencing framework
for research and education, embargo periods, commercial exploitation, etc.) in
which the data and associated software produced and/or used in the project is
accessible should be provided.
3. ASSESSABLE and INTELLIGIBLE
The data and associated software produced and/or used in the project should be
easily assessable for and intelligible to third parties in contexts such as
scientific scrutiny and peer review (e.g. the minimal datasets are handled
together with scientific papers for the purpose of peer review, data is
provided in a way that judgments can be made about their reliability and the
competence of those who created them).
4. USEABLE beyond the original purpose for which it was collected The data and associated software produced and/or used in the project should be useable by third parties even long time after the collection of the data (e.g. the data is safely stored in certified repositories for long term preservation and curation; it is stored together with the minimum software, metadata and documentation to make it useful; the data is useful for the wider public needs and usable for the likely purposes of non-specialists).
5. INTEROPERABLE to specific quality standards
The data and associated software(s) produced and/or used in the Project should
be interoperable allowing data exchange between researchers, institutions,
organisations, countries, etc.
Some examples of research data include:
* Documents (text, Word), spreadsheets
* Questionnaires, transcripts, codebooks
* Laboratory notebooks, field notebooks, diaries
* Audiotapes, videotapes
* Photographs, films
* Test responses, slides, artifacts, specimens, samples
* Collection of digital objects acquired and generated during the process of research
* Database contents (video, audio, text, images)
* Models, algorithms, scripts
* Contents of an application (input, output, logfiles for analysis software, simulation software, schemas)
* Methodologies and workflows
* Standard operating procedures and protocols.
In addition to the other records to manage, some kinds of data may not be
sharable due to the nature of the records themselves, or to ethical and
privacy concerns (e.g. preliminary analyses, drafts of scientific papers,
plans for future research, peer reviews, communication with partners, etc.).
Research data also do not include trade secrets, commercial information,
materials necessary to be held confidential by researcher until they are
published, or information that could invades personal privacy. Research
records that may also be important to manage during and beyond the project
are: correspondence, project files, technical reports, research reports, etc.
# 8\. RE 4 DATA SETS
Projects are required to deposit the research data - the data, including
associated metadata, needed to validate the results presented in scientific
publications as soon as possible; and other data, including associated
metadata, as specified and within the deadlines laid down in a data management
plan (DMP).
At the same time, projects should provide information (via the chosen
repository) about tools and instruments at the disposal of the beneficiaries
and necessary for validating the results, for instance specialised software(s)
or software code(s), algorithms, analysis protocols, etc. Where possible, they
should provide the tools and instruments themselves.
The types of data to be included within the scope of the RE 4 Data
Management Plan shall as a minimum cover the types of data that is considered
complementary to material already contained within declared project
deliverables. In order to collect the information generated during the
project, the template for data collection will be circulated periodically
every 6 months. The scope of this template is to detail the research results
that will be developed during the RE 4 Project detailing the kind of results
and how it will be managed. The responsibility to define and describe all
nongeneric data sets specific to an individual work package is with the WP
leader.
## _Data set reference and name_
Identifier for the data set to be produced. All data sets within this DMP have
been given a unique field identifier and are listed in the table 4 (Collection
of project results and sharing strategy).
## _Data Set Description_
A data set is defined as a structured collection of data in a declared format.
Most commonly a data set corresponds to the contents of a single database
table, or a single statistical data matrix, where every column of the table
represents a particular variable, and each row corresponds to a given member
of the data set in question. The data set may comprise data for one or more
fields. For the purposes of this DMP data sets have been defined by generic
data types that are considered applicable to the RE 4 project. For each data
set, the characteristics of the data set have been captured in a tabular
format as enclosed in table 4 (Collection of project results and sharing
strategy).
## _Standards & Metadata _
Metadata is defined as “data about data”. It is “structured information that
describes, explains, locates, and facilitates the means to make it easier to
retrieve, use or manage an information resource”.
Metadata can be categorised in three types:
* Descriptive metadata describes an information resource for identification and retrieval through elements such as title, author, and abstract.
* Structural metadata documents relationships within and among objects through elements such as links to other components (e.g., how pages are put together to form chapters).
* Administrative metadata manages information resources through elements such as version number, archiving date, and other technical information for the purposes of file management, rights management and preservation.
There are a large number of metadata standards which address the needs of
particular user communities.
## _Data Sharing_
During the period, when the Project is live, the sharing of data shall be
defined by the configuration rules defined in the access profiles for the
project participants. Each individual project data set item shall be allocated
a 3 character “dissemination classification” for the purposes of defining the
data sharing restrictions. The classification shall be an expansion of the
system of confidentiality applied to deliverables reports provided under the
RE 4 Grant Agreement.
PU: Public
RE: restricted to a group specified by the consortium
CO: Confidential, only for members of the consortium; Commission services
always included.
The three above levels are linked to the “Dissemination Level” specified for
all RE 4 deliverables.
All material designated with a PU dissemination level is deemed uncontrolled.
In case the dataset cannot be shared, the reasons for this should be mentioned
(e.g. ethical, rules of personal data, intellectual property, commercial,
privacy-related, or security-related).
Data will be shared when the related deliverable or paper has been made
available at an open access repository. The expectation is that data related
to a publication will be openly shared. However, to allow the exploitation of
any opportunities arising from the raw data and tools, data sharing will
proceed only if all co-authors of the related publication agree. The Lead
author is responsible for getting approvals and then sharing the data and
metadata on Zenodo (www.zenodo.org), a popular repository for research data.
The Lead Author will also create an entry on OpenAIRE (www.openaire.eu) in
order to link the publication to the data.
OpenAIRE is a service that implements the Horizon 2020 Open Access mandate for
publications and its Open Research Data Pilot and may be used to reference
both the publication and the data. A link to the OpenAIRE entry will then be
submitted to the RE 4 Website Administrator (FENIX) by the Lead Author.
## _Data archiving and preservation_
Both Zenodo and OpenAIRE are purpose-built services that aim to provide
archiving and preservation of long-tail research data. In addition, the RE 4
website, linking back to OpenAIRE, is expected to be available for at least 2
years after the end of the Project. At the formal project closure all the data
material that has been collated or generated within the Project and classified
for archiving shall be copied and transferred to a digital archive
(coordinator responsibility).
The document structure and type definition will be preserved as defined in the
document breakdown structure and work package groupings specified. At the time
of document creation the document will be designated as a candidate data item
for future archiving. This process is performed by the use of codification
within the file naming convention (see Section 10). The process of archiving
will be based on a data extract performed within 12 weeks of the formal
closure of the RE 4 Project.
The archiving process shall create unique file identifiers by the
concatenation of “metadata” parameters for each data type. The metadata index
structure shall be formatted in the metadata order. This index file shall be
used as an inventory record of the extracted files, and shall be validated by
the associated WP leader.
Figure 5. OpenAIRE website
Figure 6. ZENODO repository
# 9\. DATA SETS TECHNICAL REQUIREMENTS
The applicable data sets are restricted to the following data types for the
purposes of archiving. The technical characteristics of each data set are
described in the following sections. The copy rights with respect to all data
types shall be subject to IPR clauses in the GA, but shall be considered to be
royalty free. The use of file compression utilities, such as “WinZip” is
prohibited. No data files shall be encrypted.
## 9.1 Engineering CAD drawings
The .dwg file format is one of the most commonly used design data formats,
found in nearly every design environment. It signifies compatibility with
AutoCAD technology. Autodesk created .dwg in 1982 with the launch of its first
version of AutoCAD software. It contains all the pieces of information a user
enters, such as: Designs, Geometric data, Maps, Photos.
## 9.2 Static graphical images
Graphical images shall be defined as any digital image irrespective of the
capture source or subject matter. Images should be composed such to contain
only objects that are directly related to RE 4 activity and do not breach
IPR of any third parties.
Image files are composed of digital data and can be of two primary formats of
“raster” or “vector”. It is necessary to represent data in the rastered state
for use on a computer displays or for printing. Once rasterized, an image
becomes a grid of pixels, each of which has a number of bits to designate its
colour equal to the colour depth of the device displaying it. The RE 4
project shall only use raster based image files. The allowable static image
file formats are JPEG and PNG.
There is normally a direct positive correlation between image file size and
the number of pixels in an image, the colour depth, or bits per pixel used in
the image. Compression algorithms can create an approximate representation of
the original image in a smaller number of bytes that can be expanded back to
its uncompressed form with a corresponding decompression algorithm. The use of
compression tools shall not be used unless absolutely necessary.
## 9.3 Animated graphical images
Graphic animation is a variation of stop motion and possibly more conceptually
associated with traditional flat cell animation and paper drawing animation,
but still technically qualifying as stop motion consisting of the animation of
photographs (in whole or in parts) and other non-drawn flat visual graphic
material. The allowable animated graphical image file formats are AVI, MPEG,
MP4, and MOV. The WP leader shall determine the most suitable choice of format
based on equipment availability and any other factors. This is mainly valid
for the RE 4 project promo video, which is expected to contain animated
graphical images, infographics and on site interviews.
Table 2: Video formats
<table>
<tr>
<th>
**Format**
</th>
<th>
**File**
</th>
<th>
**Description**
</th> </tr>
<tr>
<td>
MPEG
</td>
<td>
.mpg
.mpeg
</td>
<td>
MPEG. Developed by the Moving Pictures Expert Group. The first popular video
format on the web. Used to be supported by all browsers, but it is not
supported in HTML5 (See MP4).
</td> </tr>
<tr>
<td>
AVI
</td>
<td>
.avi
</td>
<td>
AVI (Audio Video Interleave). Developed by Microsoft. Commonly used in video
cameras and TV hardware. Plays well on Windows computers, but not in web
browsers.
</td> </tr>
<tr>
<td>
WMV
</td>
<td>
.wmv
</td>
<td>
WMV (Windows Media Video). Developed by Microsoft. Commonly used in video
cameras and TV hardware. Plays well on Windows computers, but not in web
browsers.
</td> </tr>
<tr>
<td>
QuickTime
</td>
<td>
.mov
</td>
<td>
QuickTime. Developed by Apple. Commonly used in video cameras and TV hardware.
Plays well on Apple computers, but not in web browsers. (See MP4)
</td> </tr>
<tr>
<td>
RealVideo
</td>
<td>
.rm
.ram
</td>
<td>
RealVideo. Developed by Real Media to allow video streaming with low
bandwidths. It is still used for online video and Internet TV, but does not
play in web browsers.
</td> </tr>
<tr>
<td>
Flash
</td>
<td>
.swf
.flv
</td>
<td>
Flash. Developed by Macromedia. Often requires an extra component (plug-in) to
play in web browsers.
</td> </tr>
<tr>
<td>
Ogg
</td>
<td>
.ogg
</td>
<td>
Theora Ogg. Developed by the Xiph.Org Foundation. Supported by HTML5.
</td> </tr>
<tr>
<td>
WebM
</td>
<td>
.webm
</td>
<td>
WebM. Developed by the web giants, Mozilla, Opera, Adobe, and Google.
Supported by HTML5.
</td> </tr> </table>
<table>
<tr>
<th>
</th>
<th>
</th>
<th>
</th>
<th>
This project has received funding from the European
Union’s Horizon 2020 research and innovation programme under grant agreement
No 723583
</th>
<th>
</th> </tr>
<tr>
<td>
</td>
<td>
</td>
<td>
</td> </tr>
<tr>
<td>
MPEG-4 or MP4
</td>
<td>
.mp4
</td>
<td>
MP4. Developed by the Moving Pictures Expert Group. Based on QuickTime.
Commonly used in newer video cameras and TV hardware. Supported by all HTML5
browsers. Recommended by YouTube.
</td> </tr> </table>
## 9.4 Audio data
An audio file format is a file format for storing digital audio data on a
computer system. The bit layout of the audio data (excluding metadata) is
called the audio coding format and can be uncompressed, or compressed to
reduce the file size, often using lossy compression. The data can be a raw
bitstream in an audio coding format, but it is usually embedded in a container
format or an audio data format with defined storage layer. The allowable
animated audio file formats is MP3 or MP4. This is mainly valid for the RE 4
Project promo video, which is expected to contain interviews with key
partners, voice over and music.
Table 3: Audio formats
<table>
<tr>
<th>
**Format**
</th>
<th>
**File**
</th>
<th>
**Description**
</th> </tr>
<tr>
<td>
MIDI
</td>
<td>
.midi
.mid
</td>
<td>
MIDI (Musical Instrument Digital Interface). Main format for all electronic
music devices like synthesizers and PC sound cards. MIDI files do not contain
sound, but digital notes that can be played by electronics. Plays well on all
computers and music hardware, but not in web browsers.
</td> </tr>
<tr>
<td>
RealAudio
</td>
<td>
.rm
.ram
</td>
<td>
RealAudio. Developed by Real Media to allow streaming of audio with low
bandwidths. Does not play in web browsers.
</td> </tr>
<tr>
<td>
WMA
</td>
<td>
.wma
</td>
<td>
WMA (Windows Media Audio). Developed by Microsoft. Commonly used in music
players. Plays well on Windows computers, but not in web browsers.
</td> </tr>
<tr>
<td>
AAC
</td>
<td>
.aac
</td>
<td>
AAC (Advanced Audio Coding). Developed by Apple as the default format for
iTunes. Plays well on Apple computers, but not in web browsers.
</td> </tr>
<tr>
<td>
WAV
</td>
<td>
.wav
</td>
<td>
WAV. Developed by IBM and Microsoft. Plays well on Windows, Macintosh, and
Linux operating systems. Supported by HTML5.
</td> </tr>
<tr>
<td>
Ogg
</td>
<td>
.ogg
</td>
<td>
Theora Ogg. Developed by the Xiph.Org Foundation. Supported by HTML5.
</td> </tr>
<tr>
<td>
MP3
</td>
<td>
.mp3
</td>
<td>
MP3 files are actually the sound part of MPEG files. MP3 is the most popular
format for music players. Combines good compression (small files) with high
quality. Supported by all browsers.
</td> </tr>
<tr>
<td>
MPEG-4 or MP4
</td>
<td>
.mp4
</td>
<td>
MP4. Developed by the Moving Pictures Expert Group. Based on QuickTime.
Commonly used in newer video cameras and TV hardware. Supported by all HTML5
browsers. Recommended by YouTube.
</td> </tr> </table>
## 9.5 Textual data
A text file is structured as a sequence of lines of electronic text. These
text files shall not contain any control characters including end-of-file
marker. In principle the least complicated form of textual file format shall
be used as the first choice.
On Microsoft Windows operating systems, a file is regarded as a text file if
the suffix of the name of the file is "txt". However, many other suffixes are
used for text files with specific purposes. For example, source code for
computer programs is usually kept in text files that have file name suffixes
indicating the programming language in which the source is written. Most
Windows text files use "ANSI", "OEM", "Unicode" or "UTF-8" encoding.
Prior to the advent of Mac OS X, the classic Mac OS system regarded the
content of a file to be a text file when its resource fork indicated that the
type of the file was "TEXT". Lines of Macintosh text files are terminated with
CR characters.
Being certified Unix, macOS uses POSIX format for text files. Uniform Type
Identifier (UTI) used for text files in macOS is "public.plain-text".
## 9.6 Numeric data
Numerical Data is information that often represents a measured physical
parameter. It shall always be captured in number form. Other types of data can
appear to be in number form i.e. telephone number, however this should not be
confused with true numerical data that can be processed using mathematical
operators.
## 9.7 Process and test data
Standard Test Data Format (STDF) is a proprietary file format originating
within the semiconductor industry for test information, but it is now a
Standard widely used throughout many industries. It is a commonly used format
produced for/by automatic test equipment (ATE). STDF is a binary format, but
can be converted either to an ASCII format known as ATDF or to a tab delimited
text file. Software tools exist for processing STDF generated files and
performing statistical analysis on a population of tested devices. RE 4
innovation development shall make use of this file type for system testing.
## 9.8 Microsoft Office Application Suite
RE 4 Project partners shall use the currently MS supported operating system
and convert from any previous obsolete releases.
The types of specific applications available within the current Microsoft
Windows operating system shall be used to support all project activities in
preference to any other software solutions. The data file types associated
with these applications shall be saved in the default format and be in
accordance with the file naming convention as specified in Section 10.
At the Microsoft Office Application level the “file properties” shall be
configured using the
“document properties” feature. This is accessed via “Info” dropdown within the
“File” menu. The “properties” and “advanced properties” present a data entry
box under the “Summary” as shown in the figure below.
Figure 7. Data Entry Box – Summary
Title: Duplication of the name used for the data file name
Subject: Identifier for RE 4 work package discrimination and shall be of the
following format RE4_WPxx.
Author: Name of the person creating the document and be formatted to have the
surname stated first as follows: surname_firstname_secondname
Manager: Name of the author’s immediate line manager and be formatted to have
the surname stated first as follows: surname_firstname_secondname
Company: Company name of the author to be stated as follows: companyname_RE4
participant number
Keywords: Free format text and should contain key words that would be relevant
and useful to future data searches. The keywords should all be in lower case
and separated with commas Comments: Description of file contents in free
format text
Hyperlink base: Blank
The tickbox indicating “Save Thumbnails for All Word Documents” shall be
untagged.
## 9.9 Adobe Systems
Portable Document Format (PDF) is a file format developed by Adobe Systems for
representing documents in a manner that is independent of the original
application software, hardware, and operating system used to create those
documents. A PDF file can describe documents containing any combination of
text, graphics, and images in a device independent and resolution independent
format. These documents can be one page or thousands of pages, very simple or
extremely complex with a rich use of fonts, graphics, colour, and images. PDF
is an open standard, and anyone may write applications that can read or write
PDFs royalty-free. PDF files are especially useful for documents such as
magazine articles, product brochures, or flyers in which you want to preserve
the original graphic appearance online.
# 10\. NAMING CONVENTION
All files irrespective of the data type are named in accordance with the
following document file naming convention:
**[PROJECT]** _[WORKPACKAGE]_[TASK]_ **[TITLE]** _ **[VERSION]_[**
DISSEMINATIONCLASS]_[ARCHIVE] Where:
* [PROJECT] is RE 4 for all document types (mandatory)
* [WORKPACKAGE] is the RE 4 project work package number, with WP as a prefix
* [TASK] is the RE 4 project task number, with T as a prefix
* [TITLE] represents the description of the data item contents excluding capitalisation and punctuation characters (mandatory)
* [VERSION] is the version number consisting of integer numbers only without leading zeros, prefixed with V (mandatory)
* [DISSEMINATIONCLASS] is the dissemination classification allocated to a document type that define the data access post archiving, consists of the characters CO and a suffix of a single number in the range 1 to 3
* [ARCHIVE] this is a single character defining the allocation of the data item for future archiving and is represented by a Y or N.
# 11\. EXPECTED PROJECT RESULTS AND RESEARCH DATA
Expected RE 4 Project results described by tasks are listed in the table
below. The table template is circulated periodically (at month 18, month 36)
in order to monitor the results and set the strategy for their sharing.
Table 4: Collection of project results and sharing strategy
<table>
<tr>
<th>
**WP number and name**
</th>
<th>
**WP**
**lead**
</th>
<th>
**Task number and name**
</th>
<th>
**Duration**
</th>
<th>
**Task lead**
</th>
<th>
**Dataset name**
</th>
<th>
**Dataset description**
</th>
<th>
**Format**
</th>
<th>
**Level**
</th> </tr>
<tr>
<td>
WP1 Mapping
and analysis of CDW reuse and recycling in prefabricated elements
</td>
<td>
CETMA
</td>
<td>
Task 1.1 Diagnosis of CDW management in the EU
</td>
<td>
M1-M6
</td>
<td>
CETMA
</td>
<td>
Data collection on CDW
</td>
<td>
Report outlining the current CDW management situation, not only in
the participating countries but in all
European countries, against the background of national waste
management plans and prevention programmes.
</td>
<td>
pdf
</td>
<td>
public
</td> </tr>
<tr>
<td>
Statistics assessment
</td>
<td>
Report assessing the information and data collected into the
deliverable D1.1 (Data collection on CDW), in order to compare
them with data coming from other studies in progress at the time of the
Project proposal writing.
</td>
<td>
pdf
</td>
<td>
public
</td> </tr>
<tr>
<td>
Task 1.2 Current status of construction of prefabricated elements with
reused/recycled material
</td>
<td>
M1-M6
</td>
<td>
ROS
</td>
<td>
Data collection on prefab construction with and without CDW
</td>
<td>
Report providing an overview of the state of the art regarding
construction of prefabricated
elements with and without CDW
and recycling technologies and plants of the different CDW
</td>
<td>
pdf
</td>
<td>
public
</td> </tr>
<tr>
<td>
Task 1.3 Current status on policy measures and regulatory frameworks
</td>
<td>
M1-M9
</td>
<td>
CETMA
</td>
<td>
Current status on policy measures and regulatory framework
</td>
<td>
Report outlining EU policies and regulations about prefabricated elements
integrating recycled materials from CDW.
</td>
<td>
pdf
</td>
<td>
public
</td> </tr>
<tr>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
Certification framework
</td>
<td>
Report providing a template including all the possible
certification issues related to RE 4 final products.
</td>
<td>
xls
</td>
<td>
public
</td> </tr>
<tr>
<td>
**Data Shari**
</td>
<td>
**ng**
</td>
<td>
Public reports will be shared on RE 4 project website public section,
eventually on ZENODO.
</td>
<td>
**Data Archi preserv**
</td>
<td>
**ving and**
**ation**
</td>
<td>
Regular backup of data on server, managed by IT departments.
Data will be stored on the RE 4 project website, which already has its
backup procedures.
</td>
<td>
**Data management Responsibilities**
</td>
<td>
Sonia Saracino
</td> </tr> </table>
<table>
<tr>
<th>
**WP number and name**
</th>
<th>
**WP**
**lead**
</th>
<th>
**Task number and name**
</th>
<th>
**Duration**
</th>
<th>
**Task lead**
</th>
<th>
**Dataset name**
</th>
<th>
**Dataset description**
</th>
<th>
**Format**
</th>
<th>
**Level**
</th> </tr>
<tr>
<td>
WP2
Strategies for innovative
sorting of
CDW and
reuse of structures from dismantled buildings
</td>
<td>
STAM
</td>
<td>
Task 2.1 CDW material specifications
</td>
<td>
M1-M4
</td>
<td>
STAM
</td>
<td>
CDW average composition among EU
</td>
<td>
Qualitative and quantitative literature
analysis on composition of
CDW in different European areas and in different
building typologies, with main reference to the
material classes in which the CDW can be divided.
</td>
<td>
pdf
</td>
<td>
public
</td> </tr>
<tr>
<td>
CDE's separating system performance requirements
</td>
<td>
Definition of how the weight-based separation system has to work and
</td>
<td>
pdf
</td>
<td>
public
</td> </tr> </table>
<table>
<tr>
<th>
</th>
<th>
</th>
<th>
</th>
<th>
</th>
<th>
</th>
<th>
</th>
<th>
which macro-classes of
materials will have to be separated in Task 2.3.
</th>
<th>
</th>
<th>
</th> </tr>
<tr>
<th>
STAM's sorting system input material definition
</th>
<th>
Definition of the status of separated CDW as
required for the proper
working of robot-based
sorting system in Task 2.4
(material classes, sizes, shapes, etc).
</th>
<th>
pdf
</th>
<th>
public
</th> </tr>
<tr>
<th>
</th>
<th>
</th>
<th>
</th>
<th>
</th>
<th>
STAM's sorting system performance requirements
</th>
<th>
Definition of the required productivity, classes of
materials to be sorted
from CDW and output size of fragments for the
robot-based sorting
system to be developed in Task 2.4.
</th>
<th>
pdf
</th>
<th>
public
</th> </tr>
<tr>
<th>
</th>
<th>
Task 2.2 Definition of sustainable strategies for the disassembly and reuse of
structures and components from dismantled buildings
</th>
<th>
M4-M15
</th>
<th>
ROS
</th>
<th>
Building Typology Identification
</th>
<th>
Literature review of existing building stock in 5
European countries (Italy, Spain, UK, Sweden and
Germany) with regards to numbers of buildings, construction typology and
</th>
<th>
pdf
</th>
<th>
confide
ntial*
</th> </tr> </table>
<table>
<tr>
<th>
</th>
<th>
</th>
<th>
</th>
<th>
</th>
<th>
</th>
<th>
</th>
<th>
energy efficiency standards.
</th>
<th>
</th>
<th>
</th> </tr>
<tr>
<th>
Summary of requirements and standards with regards to building demolition in
Italy, Spain, UK, Sweden and Germany
</th>
<th>
Summary of standards and national codes in relation to the demolition of
buildings for five European countries (Italy, Spain, UK, Sweden and Germany).
</th>
<th>
pdf
</th>
<th>
confide
ntial*
</th> </tr>
<tr>
<th>
Sustainable Dismantling Strategy with focus on high reusability
</th>
<th>
Literature review of state of the art demolition processes and
development of innovative strategy based on experts knowledge.
</th>
<th>
pdf
</th>
<th>
confide
ntial*
</th> </tr>
<tr>
<th>
</th>
<th>
Task 2.3 Innovative strategies and processes for separating CDW based on
weight criteria
</th>
<th>
M4-M15
</th>
<th>
CDE
</th>
<th>
Process flow diagram
</th>
<th>
To be completed
</th>
<th>
pdf
</th>
<th>
confide
ntial*
</th> </tr>
<tr>
<th>
Component size and selection
</th>
<th>
To be completed
</th>
<th>
pdf
</th>
<th>
confide
ntial*
</th> </tr>
<tr>
<th>
Throughput
</th>
<th>
To be completed
</th>
<th>
pdf
</th>
<th>
confide
ntial*
</th> </tr>
<tr>
<th>
Power usage
</th>
<th>
To be completed
</th>
<th>
pdf
</th>
<th>
confide
ntial*
</th> </tr>
<tr>
<th>
</th>
<th>
output material definition
</th>
<th>
To be completed
</th>
<th>
pdf
</th>
<th>
confide
ntial*
</th> </tr>
<tr>
<th>
Task 2.4 Innovative strategies and processes for sorting
</th>
<th>
M4-M20
</th>
<th>
STAM
</th>
<th>
Technical requirements for STAM's sorting system components
</th>
<th>
According to the functional specifications for sorting system outlined
</th>
<th>
pdf
</th>
<th>
confide
ntial*
</th> </tr> </table>
<table>
<tr>
<th>
</th>
<th>
</th>
<th>
CDW based on advanced robotic system
</th>
<th>
</th>
<th>
</th>
<th>
</th>
<th>
in Task 2.1, definition of
the detailed specification
of each single component to be integrated.
</th>
<th>
</th>
<th>
</th> </tr>
<tr>
<th>
Sorting system design
</th>
<th>
Detailed 3D design of the sorting system, including commercial and
customized parts to be assembled.
</th>
<th>
stp/dwg
</th>
<th>
confide
ntial*
</th> </tr>
<tr>
<th>
</th>
<th>
</th>
<th>
</th>
<th>
CDW classification algorithm compiled file
</th>
<th>
Software algorithm to real-time extract material
information from the data coming from sensors.
</th>
<th>
TBD (dll or other)
</th>
<th>
confide
ntial*
</th> </tr>
<tr>
<th>
CDW NIR data
</th>
<th>
Raw and processed data converted from the
physical signals detected from sensors about
hyperspectral response of different CDW fragments.
</th>
<th>
TBD (csv, xls, etc.)
</th>
<th>
confide
ntial*
</th> </tr>
<tr>
<th>
Performance calculation and results
</th>
<th>
Test campaign on the integrated sorting system in both laboratory and
industrial environment,
with relevant calculation of performance indexes
(productivity, error rate, etc.).
</th>
<th>
xls,
MATLAB
</th>
<th>
confide
ntial*
</th> </tr>
<tr>
<th>
</th>
<th>
</th>
<th>
</th>
<th>
Evaluation and final considerations
</th>
<th>
Collection and evaluation of results from the test campaign.
</th>
<th>
pdf
</th>
<th>
confide
ntial*
</th> </tr>
<tr>
<td>
</td>
<td>
</td>
<td>
Task 2.5 BIM-compatible DSS and platform for CDW estimation and management
</td>
<td>
M4-M24
</td>
<td>
STRESS
</td>
<td>
Platform as a support for Collection plant managers, recyclers and
construction/ demolition companies.
</td>
<td>
Estimation of the types and quantities of CDW that will be generated during
construction/
demolition, with possible utilization options and related logistic references.
</td>
<td>
ICT tool; pdf for
the tool guideline
</td>
<td>
confide
ntial*
</td> </tr>
<tr>
<td>
**Data Sharing**
</td>
<td>
</td>
<td>
All public documents in pdf will be shared on RE 4 project website public
section and ZENODO, confidential reports on RE 4 project website private
section.
</td>
<td>
**Data Archiving and preservation**
</td>
<td>
Regular backup of data on server, managed by IT departments.
Data will be stored on the RE4 project website which already has its backup
procedures.
</td>
<td>
**Data management**
**Responsibilities**
</td>
<td>
Tomasso Zerbi
</td> </tr> </table>
<table>
<tr>
<th>
**WP number and name**
</th>
<th>
**WP**
**lead**
</th>
<th>
**Task number and name**
</th>
<th>
**Duration**
</th>
<th>
**Task lead**
</th>
<th>
**Dataset name**
</th>
<th>
**Dataset description**
</th>
<th>
**Format**
</th>
<th>
**Level**
</th> </tr>
<tr>
<td>
WP3
Innovative concept for modular/easy installation and disassembly of eco-
friendly prefabricated elements
</td>
<td>
ROS
</td>
<td>
Task 3.1 Definition of indicators for easy installation, disassembly,
recycling and reuse for newly developed prefabricated elements
</td>
<td>
M1-M12
</td>
<td>
ROS
</td>
<td>
European design scenario - loads and material properties
</td>
<td>
Report on housing stock, legal requirements and
construction options
Structural Calculations
</td>
<td>
pdf, xls
</td>
<td>
public
</td> </tr>
<tr>
<td>
List and rating of indicators for easy installation
</td>
<td>
Catalogue of building components with specific
data regarding design and
dimensions
</td>
<td>
pdf
</td>
<td>
public
</td> </tr> </table>
<table>
<tr>
<th>
</th>
<th>
</th>
<th>
</th>
<th>
</th>
<th>
</th>
<th>
</th>
<th>
</th>
<th>
</th>
<th>
</th> </tr>
<tr>
<th>
Concept design of dismountable building system
</th>
<th>
Set of Drawings M1:100 Structural Calculations
</th>
<th>
dwg, pdf,
xls
</th>
<th>
public
</th> </tr>
<tr>
<th>
Concept design of dismountable connections
</th>
<th>
Set of Drawings M1:20 Details 1:5
</th>
<th>
dwg, pdf
</th>
<th>
public
</th> </tr>
<tr>
<th>
Task 3.2 Design concept for prefabricated elements for the refurbishment of
residential or commercial build
</th>
<th>
M3-M30
</th>
<th>
ROS
</th>
<th>
Strategy for the thermal optimisation of existing façade elements
</th>
<th>
Report on housing stock, legal requirements and
construction options
</th>
<th>
pdf
</th>
<th>
public
</th> </tr>
<tr>
<th>
Design of façade/roof element for extensions
</th>
<th>
Set of Drawings M1:50 Details 1:10
</th>
<th>
dwg, pdf
</th>
<th>
public
</th> </tr>
<tr>
<th>
Task 3.3 Design concept for the development of components for the new
construction of residential or commercial buildings
</th>
<th>
M3-M30
</th>
<th>
ROS
</th>
<th>
Design of structural system
</th>
<th>
Set of Drawings M1:50 Details 1:10
</th>
<th>
dwg, pdf
</th>
<th>
public
</th> </tr>
<tr>
<th>
</th>
<th>
Concept design of foundations
</th>
<th>
Set of Drawings M1:50 Details 1:10
</th>
<th>
dwg, pdf
</th>
<th>
public
</th> </tr>
<tr>
<th>
Concept design of slab elements
</th>
<th>
Set of Drawings M1:50 Details 1:10
</th>
<th>
dwg, pdf
</th>
<th>
public
</th> </tr>
<tr>
<th>
</th>
<th>
Concept design of bearing / non-bearing walls
</th>
<th>
Set of Drawings M1:50 Details 1:10
</th>
<th>
dwg, pdf
</th>
<th>
public
</th> </tr>
<tr>
<th>
Concept design of facade elements
</th>
<th>
Set of Drawings M1:50 Details 1:10
</th>
<th>
dwg, pdf
</th>
<th>
public
</th> </tr>
<tr>
<td>
</td>
<td>
</td>
<td>
Task 3.4: Numerical modelling to support the prototypes design and to predict
the prototypes performance
</td>
<td>
M7-M33
</td>
<td>
STRESS
</td>
<td>
FE models and results of numerical simulations (structural, thermal and fire
resistance data)
</td>
<td>
FE analysis results, including performances
(thermal, structural and fire resistance) of the
developed prefabricated
elements
</td>
<td>
pdf
</td>
<td>
public
</td> </tr>
<tr>
<td>
**Data Sharing**
</td>
<td>
</td>
<td>
All public documents in will be shared on RE 4 project website public
section and ZENODO, confidential reports on RE 4 project website private
section.
</td>
<td>
**Data Archiving and preservation**
</td>
<td>
Regular backup of data on server, managed by IT departments.
Data will be stored on the RE 4 project website which already has its backup
procedures.
</td>
<td>
**Data management**
**Responsibilities**
</td>
<td>
Andrea Klinge
</td> </tr> </table>
<table>
<tr>
<th>
**WP number and name**
</th>
<th>
**WP**
**lead**
</th>
<th>
**Task number and name**
</th>
<th>
**Duration**
</th>
<th>
**Task lead**
</th>
<th>
**Dataset name**
</th>
<th>
**Dataset description**
</th>
<th>
**Format**
</th>
<th>
**Level**
</th> </tr>
<tr>
<td>
WP4 Technical characterisation of CDW-derived materials for the production of
building elements
</td>
<td>
QUB
</td>
<td>
Task 4.1 Collection of representative samples of CDW sorted material
</td>
<td>
M3-M6
</td>
<td>
CDE
</td>
<td>
Collection of North & South samples - mixed source
</td>
<td>
Unsorted CDW samples collected from two different recycling plants. The first
recycling plant located in UK (N-EU), the
second one located in Southern France
(S-EU)
</td>
<td>
N/A
</td>
<td>
N/A
</td> </tr>
<tr>
<td>
Delivery of samples to partners n/a
</td>
<td>
Unsorted CDW samples from the above two recycling plants delivered to CDE and
QUB.
</td>
<td>
N/A
</td>
<td>
N/A
</td> </tr>
<tr>
<td>
Physical assessment of CDW samples
</td>
<td>
Manual sorting and sieving of unsorted
CDW
</td>
<td>
MS word
</td>
<td>
confide
ntial*
</td> </tr>
<tr>
<td>
Assessment methods
</td>
<td>
Manual sorting and sieving of unsorted CDW in order to determine its precise
composition in terms of the following: ▪ Silt/clay (< 0.075 mm)
* Fine sand (0.075-0.6 mm)
* Medium/coarse sand (0.6-4 mm)
* Mixed concrete/mineral aggregate (4-8 mm)
* Mixed concrete/mineral aggregate (8-16 mm)
* Mixed concrete/mineral aggregate (16-
20 mm)
* Mixed concrete/mineral aggregate (> 20 mm)
* Mixed mortar/plaster (> 1.7 mm)
* Ceramics (bricks & tiles > 1.7 mm)
* Glass (> 1.7 mm)
* Steel (nails, re-bars, hooks, tags etc. > 1.7 mm)
Lightweight (mixed plastics/wood > 1.7 mm)
</td>
<td>
MS word
</td>
<td>
confide
ntial*
</td> </tr> </table>
<table>
<tr>
<th>
</th>
<th>
</th>
<th>
</th>
<th>
</th>
<th>
</th>
<th>
Results and discussion
</th>
<th>
Deliverable 4.1
</th>
<th>
MS word
</th>
<th>
confide
ntial*
</th> </tr>
<tr>
<th>
Task 4.2 Characterisation
of CDW-derived materials
</th>
<th>
M5-M11
</th>
<th>
QUB
</th>
<th>
Chemical and durability characterisation of mineral fraction
</th>
<th>
The following types of tests were performed:
* Petrographic description
* Constituent classification of coarse aggregate
* Water-soluble chloride salt content
* Water-soluble sulfate content
* Carbonate content of fine aggregates
* Organic matter (humus & fulvo acid content)
* Water soluble components from recycled aggregate
* Total/bulk chemistry of the material
* Resistance to freezing & thawing
* Resistance to weathering
* Volume stability-drying shrinkage Alkali-silica reactivity
</th>
<th>
pdf
</th>
<th>
public
</th> </tr>
<tr>
<th>
</th>
<th>
</th>
<th>
Geometrical and physical characterisation of mineral fraction
</th>
<th>
The following types of tests were performed:
* Grading
* Flakiness index
* Flow coefficient of fine aggregates
* Resistance to fragmentation
* Resistance to wear
Particle density and water absorption
</th>
<th>
pdf/xls
</th>
<th>
public
</th> </tr>
<tr>
<th>
</th>
<th>
</th>
<th>
Lightweight fraction characterisation
</th>
<th>
The following types of tests were performed on Wood&Plastic and Rigid Plastic
fractions:
* Grain size
* Density
* Water absorption
The following types of tests were performed on Wood Flakes fraction: ▪ Grain
size
</th>
<th>
pdf
</th>
<th>
public
</th> </tr> </table>
<table>
<tr>
<th>
</th>
<th>
</th>
<th>
</th>
<th>
</th>
<th>
</th>
<th>
</th>
<th>
▪ Density
Moisture content
</th>
<th>
</th>
<th>
</th> </tr>
<tr>
<th>
Fine fraction characterisation
</th>
<th>
The following types of tests were performed:
* Liquid limit
* Plastic limit
* Plasticity
* XRF analysis
* XRD analysis
* FTIR Spectroscopy
* DTA-TGA
* Soluble components (humus, fulvo acid, sulfates & chlorides
* Soluble components (alkalis)
* Activity index
Reactivity/isothermal calorimetry
</th>
<th>
pdf
</th>
<th>
public
</th> </tr>
<tr>
<th>
</th>
<th>
</th>
<th>
</th>
<th>
</th>
<th>
Physical assessment of timber from CDW
</th>
<th>
The following types of tests were described in detail:
* In situ strength assessment
* Visual on-site inspection
* Assessment on chemical contamination
* On-site separation
In addition, a reprocessing exercise was performed
</th>
<th>
pdf
</th>
<th>
public
</th> </tr>
<tr>
<td>
</td>
<td>
</td>
<td>
Task 4.3 Variability of the chemical-physical features of CDW-derived
materials and effect on technological properties of developed products
</td>
<td>
M9-M18
</td>
<td>
QUB
</td>
<td>
Variability of the chemicalphysical features of CDW aggregate and effect on
compressive strength development assessment
</td>
<td>
The following types of tests were performed:
* Grading of fine and coarse aggregate
* Constituent classification of coarse aggregate
* Water absorption & particle density
* Water-soluble chloride content
* Water-soluble sulfate content
* Slump of fresh OPC concrete
* Fresh density of OPC concrete
* Hardened density of OPC concrete
</td>
<td>
pdf
</td>
<td>
public
</td> </tr> </table>
<table>
<tr>
<th>
</th>
<th>
</th>
<th>
</th>
<th>
</th>
<th>
</th>
<th>
</th>
<th>
▪ Compressive strength of OPC concrete Tensile strength of OPC concrete
</th>
<th>
</th>
<th>
</th> </tr>
<tr>
<th>
Variability of the chemicalphysical features and effect on the insulation
assessment
</th>
<th>
The following types of tests were performed on Wood&Plastic and Rigid Plastic
fractions used for insulating mortars:
* Grading
* Water absorption & particle density
* Consistency of insulating mortars
* Fresh density of insulating mortars
* Hardened density of insulating mortars
* Flexural strength of insulating mortars
* Compressive strength of insulating mortars
* Thermal conductivity of insulating mortars
* Specific heat capacity of insulating mortars
* Water vapour resistance factor of insulating mortars
The following types of tests were performed on Wood flakes fraction used for
insulating wood-based panels:
* Grading
* Water absorption & particle density
* Density of wood-based insulating panels
* Thermal conductivity of wood-based insulating panels
* Specific heat capacity of wood-based insulating panels
Water vapour resistance factor of woodbased insulating panels
</th>
<th>
pdf
</th>
<th>
public
</th> </tr>
<tr>
<td>
</td>
<td>
</td>
<td>
Task 4.4 Definition of quality classes for utilisation in different
applications
</td>
<td>
M16-M20
</td>
<td>
RISE (CBI)
</td>
<td>
Quality classes
</td>
<td>
Definition of quality classes for each
CDW sorted fraction (mineral aggregate, lightweight aggregate & timber)
</td>
<td>
pdf
</td>
<td>
public
</td> </tr>
<tr>
<td>
Potential applications for recovered CDW-derived materials
</td>
<td>
Classification of each CDW sorted fraction (mineral aggregate, lightweight
aggregate & timber) according to the potential for structural and
nonstructural applications
</td>
<td>
pdf
</td>
<td>
public
</td> </tr>
<tr>
<td>
Task 4.5 Development of alkali activated binders from sorted brick and tiles
waste
</td>
<td>
M11-M20
</td>
<td>
QUB
</td>
<td>
Investigate the potential for alkali activation of ceramic (bricks and tiles)
fraction
</td>
<td>
Development of alkali activated binder from ceramic CDW
</td>
<td>
pdf
</td>
<td>
confide
ntial*
</td> </tr>
<tr>
<td>
**Data Sharing**
</td>
<td>
</td>
<td>
All public documents in will be shared on RE 4 project website public
section and ZENODO, confidential reports on RE 4 project website private
section.
</td>
<td>
**Data Archiving and preservation**
</td>
<td>
Regular backup of data on server, managed by IT departments.
Data will be stored on the RE 4 project website which already has its backup
procedures.
</td>
<td>
**Data management Responsibilities**
</td>
<td>
Konstantinos Grigoriadis
</td> </tr> </table>
<table>
<tr>
<th>
**WP number and name**
</th>
<th>
**WP**
**lead**
</th>
<th>
**Task number and name**
</th>
<th>
**Duration**
</th>
<th>
**Task lead**
</th>
<th>
**Dataset name**
</th>
<th>
**Dataset description**
</th>
<th>
**Format**
</th>
<th>
**Level**
</th> </tr>
<tr>
<td>
WP5
Development of precast components and elements from CDW
</td>
<td>
RISE (
CBI
)
</td>
<td>
Task 5.1: Development of materials incorporating CDW, Portland cement and
alkali activated binders
</td>
<td>
M2-M18
</td>
<td>
CETMA
</td>
<td>
Formulations of concrete with CDW+OPC
</td>
<td>
Mix designs of ordinary concrete mix and self-compacting concrete mixes, based
on 70-100%
replacement of virgin aggregates with CDW aggregates, and using
OPC as binder. Fresh and hardened concrete properties for both mix designs,
including durability performance.
</td>
<td>
xls
</td>
<td>
confide
ntial*
</td> </tr>
<tr>
<td>
Formulations of concrete with CDW+AAB
</td>
<td>
Mix designs of ordinary concrete mix, based on up to 100%
replacement of virgin aggregates with CDW aggregates, and using
alternative material as binders (Fly ash and slag). Fresh and hardened
concrete properties for both mix designs, including durability performance.
</td>
<td>
xls
</td>
<td>
confide
ntial*
</td> </tr>
<tr>
<td>
Formulations of lightweight concrete with CDW+OPC and CDW+AAB
</td>
<td>
Formulations of lightweight concrete with CDW+OPC and CDW+AAB
</td>
<td>
xls
</td>
<td>
confide
ntial*
</td> </tr>
<tr>
<td>
Formulations of earth
plaster and adhesive from CDW
</td>
<td>
Formulations of earth plaster and adhesive from CDW
</td>
<td>
xls
</td>
<td>
confide
ntial*
</td> </tr>
<tr>
<td>
Task 5.2: Development of prefabricated components
</td>
<td>
M6-M24
</td>
<td>
QUB
</td>
<td>
Development of building blocks
</td>
<td>
Blocks prepared with 100 % recycle aggregate varying the cement
content and bulk density of the block to achieve a minimum
</td>
<td>
pdf
</td>
<td>
confide
ntial*
</td> </tr> </table>
<table>
<tr>
<th>
</th>
<th>
</th>
<th>
</th>
<th>
</th>
<th>
</th>
<th>
</th>
<th>
strength of 7.3 MPa. Data is compared to control blocks whenever appropriate.
</th>
<th>
</th>
<th>
</th> </tr>
<tr>
<th>
Development of reconstituted tiles
</th>
<th>
Reconstituted tiles will be developed using unsorted CDW
material. A proper binder will be tested in order to optimise the
process. Moulding and extrusion processes will be used for the above purpose.
</th>
<th>
pdf
</th>
<th>
confide
ntial*
</th> </tr>
<tr>
<th>
</th>
<th>
</th>
<th>
</th>
<th>
</th>
<th>
Development of timber beams and columns (structural support system) and
weatherboarding
</th>
<th>
The use of recycled laminated timber, laminated veneer lumber,
plywood box and nail plated timber beams in new structural timber elements and
systems will be investigated.
</th>
<th>
pdf
</th>
<th>
confide
ntial*
</th> </tr>
<tr>
<th>
</th>
<th>
</th>
<th>
</th>
<th>
</th>
<th>
Development of insulation panels
</th>
<th>
Two different types of insulating panels will be investigated: wood fibre
panels and composite plastic panels.
</th>
<th>
pdf
</th>
<th>
confide
ntial*
</th> </tr>
<tr>
<th>
Task 5.3: Development of prefabricated elements
</th>
<th>
M6-M31
</th>
<th>
RISE (CBI)
</th>
<th>
Cladding panel for ventilated façade applications
</th>
<th>
Design, dimensions and type of material to use. Production of labscale
specimens.
</th>
<th>
Blueprint/ Prototype
</th>
<th>
confide
ntial*
</th> </tr>
<tr>
<th>
</th>
<th>
Sandwich element with integrated insulation
</th>
<th>
Design, dimensions and type of material to use. Production of labscale
specimens.
</th>
<th>
Blueprint/ Prototype
</th>
<th>
confide
ntial*
</th> </tr>
<tr>
<th>
</th>
<th>
Load bearing concrete element
</th>
<th>
Design, dimensions and type of material to use. Production of labscale
specimens.
</th>
<th>
Blueprint/ Prototype
</th>
<th>
confide
ntial*
</th> </tr>
<tr>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
Non-load bearing internal partition wall
</td>
<td>
Design, dimensions and type of material to use. Production of labscale
specimens.
</td>
<td>
Blueprint/ Prototype
</td>
<td>
confide
ntial*
</td> </tr>
<tr>
<td>
Please fill in data description
</td>
<td>
Refinement of mix designs for the production of up-scaled structural elements.
</td>
<td>
Blueprint/ Prototype
</td>
<td>
confide
ntial*
</td> </tr>
<tr>
<td>
Task 5.4: Refinement and production of pre-fab test elements
</td>
<td>
M18-M31
</td>
<td>
QUB
</td>
<td>
Development of mix designs suitable for a range of applications
</td>
<td>
Refinement of mix designs for the production of up-scaled nonstructural
elements such as building blocks.
</td>
<td>
pdf
</td>
<td>
confide ntial
</td> </tr>
<tr>
<td>
</td>
<td>
</td>
<td>
Development of formulations for building blocks with a reduced carbon
footprint
</td>
<td>
Test results on factory-scale prototypes
</td>
<td>
pdf
</td>
<td>
confide
ntial*
</td> </tr>
<tr>
<td>
Task 5.5: Performance and durability testing of larger precast and timber
elements
</td>
<td>
M20-M31
</td>
<td>
RISE (CBI)
</td>
<td>
Test results on sandwich elements
</td>
<td>
Test results on factory-scale prototypes
</td>
<td>
xls
</td>
<td>
confide
ntial*
</td> </tr>
<tr>
<td>
Test results on load bearing elements
</td>
<td>
Test results on factory-scale prototypes
</td>
<td>
xls
</td>
<td>
confide
ntial*
</td> </tr>
<tr>
<td>
</td>
<td>
</td>
<td>
Test results on cladding panels
</td>
<td>
Design, dimensions and type of material to use. Production of labscale
specimens.
</td>
<td>
xls
</td>
<td>
confide
ntial*
</td> </tr>
<tr>
<td>
**Data Sharing**
</td>
<td>
</td>
<td>
All data produced within WP5 are confidential and will be shared with other
project partners on RE 4 project website private section.
</td>
<td>
**Data Archiving and preservation**
</td>
<td>
Regular backup of data on server, managed by IT departments.
Data will be stored on the RE 4 project website which already has its backup
procedures.
</td>
<td>
**Data management**
**Responsibilities**
</td>
<td>
Linus Brander
</td> </tr> </table>
<table>
<tr>
<th>
**WP number and name**
</th>
<th>
**WP**
**lead**
</th>
<th>
**Task number and name**
</th>
<th>
**Duration**
</th>
<th>
**Task lead**
</th>
<th>
**Dataset name**
</th>
<th>
**Dataset description**
</th>
<th>
**Format**
</th>
<th>
**Level**
</th> </tr>
<tr>
<td>
WP6 Pilot level demonstration of CDW based prefabricated elements
</td>
<td>
ACCI ONA
</td>
<td>
Task 6.1 Design and adapt the prefabricated elements production line
</td>
<td>
M23-M28
</td>
<td>
VORTE
X
</td>
<td>
Technical and technological information and data related to the line
adjustment for the extrusion of concrete with CDW inside for roof tiles/floor
tiles/facade
</td>
<td>
Description of applied modification of the
production line for the usage of the CDW
materials. Picture/videos
of the production process.
Technical reports.
</td>
<td>
pdf, dwg, jpeg
</td>
<td>
confide ntial*
</td> </tr>
<tr>
<td>
Task 6.2 Manufacture and testing of the prefabricated elements prototypes,
quality control and characterization
</td>
<td>
M26-M32
</td>
<td>
CREAG
H
</td>
<td>
Production of the prefabricated elements to build the RE 4 demonstrator
</td>
<td>
Industrial scale production of selected elements developed in WP5. Namely
concrete precast structural and nonstructural façade panels and support
structures, as well as timber frame structural and nonstructural elements,
extruded roof and floor tiles and façade elements. Furthermore, insulating
panels with plastic a plastic and wood are to be manufactured.
</td>
<td>
samples;
pdf; jpg
</td>
<td>
confide
ntial*
</td> </tr>
<tr>
<td>
Tests results on the prefabricated elements
</td>
<td>
Standardized tests will be carried out by RE4
partners to control the quality of the produced
</td>
<td>
xls
</td>
<td>
confide
ntial*
</td> </tr> </table>
<table>
<tr>
<th>
</th>
<th>
</th>
<th>
</th>
<th>
</th>
<th>
</th>
<th>
</th>
<th>
elements. All the results
obtained from these tests will be collected and will feed task 7.5.
</th>
<th>
</th>
<th>
</th> </tr>
<tr>
<th>
Task 6.3 New residential and/or commercial buildings made of a high ratio of
waste: design and construction of the demonstrator
</th>
<th>
M18-M36
</th>
<th>
ACCIO NA
</th>
<th>
Design of the RE 4 demonstrator
</th>
<th>
Detailed design of the RE 4 demonstrators using the
RE4 elements developed in WP5.
</th>
<th>
pdf
</th>
<th>
confide
ntial*
</th> </tr>
<tr>
<th>
Construction of the RE 4 demonstrator
</th>
<th>
Construction of the RE 4 demonstrators using the selected RE 4 elements
</th>
<th>
pf, jpg, avi
</th>
<th>
public
</th> </tr>
<tr>
<td>
</td>
<td>
</td>
<td>
Task 6.4 Refurbishment of residential and/or commercial building: installation
of the panels/blocks on an existing façade
</td>
<td>
M31-M36
</td>
<td>
STRESS
</td>
<td>
Results of tests. Testing a and validation of the RE4 elements for
refurbishment at STREES facility.
</td>
<td>
Description of the set-up of the installation of the façade panels.
</td>
<td>
pdf
</td>
<td>
public
</td> </tr>
<tr>
<td>
Task 6.5 Disassembly demonstration of conventional
building vs. 6.3 and 6.4 demonstrators
</td>
<td>
M18-M40
</td>
<td>
ACCIO NA
</td>
<td>
Disassembly demonstration of RE4 demonstrator VS conventional building
</td>
<td>
Demonstration and monitoring of the easy
disassembly of the RE4 demonstrator an
comparison with the demolition of
conventional building
</td>
<td>
pf, jpg, avi
</td>
<td>
public
</td> </tr>
<tr>
<td>
</td>
<td>
</td>
<td>
Disassembly demonstration of 6,3 demonstrator
</td>
<td>
Demonstration and monitoring of the easy
disassembly of the Task
6.3 demonstrator (the one refurbished)
</td>
<td>
pf, jpg
</td>
<td>
public
</td> </tr> </table>
<table>
<tr>
<th>
</th>
<th>
</th>
<th>
</th>
<th>
</th>
<th>
</th>
<th>
Evaluation and quantification of the disassembly demonstration
</th>
<th>
Evaluation and quantification of the
disassembly efficiency
using the indicators and the deconstruction
strategy developed in WP3
and WP2 respectively in order to be able to
compare the process
properly in a standardized way.
</th>
<th>
pdf
</th>
<th>
public
</th> </tr>
<tr>
<th>
Task 6.6 Monitoring and techno-economic analysis of the performance.
Validation
</th>
<th>
M36-M40
</th>
<th>
ACCIO NA
</th>
<th>
Monitoring strategy
</th>
<th>
Definition of the monitoring plan (type and number of sensors,
acquisition system,
monitoring period, etc.) to monitor properly the
energy efficiency of the RE 4 demonstrators.
</th>
<th>
pdf
</th>
<th>
confide
ntial*
</th> </tr>
<tr>
<th>
Results of the monitoring. Validation performance.
</th>
<th>
Data analysis of all the measurements taken in order to validate the
performance of the RE 4 demonstrator.
</th>
<th>
Pdf, xls
</th>
<th>
confide
ntial*
</th> </tr>
<tr>
<th>
Techno-economic analysis
</th>
<th>
Validation of the RE 4 building from a techno-
economic point of view, comparing the results obtained in the monitoring
</th>
<th>
Pdf, xls
</th>
<th>
confide
ntial*
</th> </tr>
<tr>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
with the model carried out in WP3.
</td>
<td>
</td>
<td>
</td> </tr>
<tr>
<td>
**Data Sharing**
</td>
<td>
</td>
<td>
Demonstration activities will be shared on RE 4 project website, social
network profiles. Public reports will be shared on RE 4 project website
public section, confidential reports and data on private section.
</td>
<td>
**Data Archi preservati**
</td>
<td>
**ving and on**
</td>
<td>
Regular backup of data on server, managed by IT department.
Data will be stored on the RE 4 project website which already has its backup
procedures.
</td>
<td>
**Data management**
**Responsibilities**
</td>
<td>
María Casado
</td> </tr> </table>
<table>
<tr>
<th>
**WP number and name**
</th>
<th>
**WP**
**lead**
</th>
<th>
**Task number and name**
</th>
<th>
**Duration**
</th>
<th>
**Task lead**
</th>
<th>
**Dataset name**
</th>
<th>
**Dataset description**
</th>
<th>
**Format**
</th>
<th>
**Level**
</th> </tr>
<tr>
<td>
WP7 Life-cycle and HSE analysis and certification/s tandardization strategy
definition
</td>
<td>
STRESS
</td>
<td>
Task7.1 Inputs related to scaled-up processes
</td>
<td>
M28-M30
</td>
<td>
STRESS
</td>
<td>
Scaled-up processes
</td>
<td>
Industrial data collection on state of art competing products (to be utilized
as a benchmark). Conceptual design of the RE4 processes scaled-up at
industrial scale (materials and energy balances, equipment list and layout,
process flow diagrams…)
</td>
<td>
pdf
</td>
<td>
public
</td> </tr>
<tr>
<td>
Task7.2 Goal & Scope definition
</td>
<td>
M13-M24
</td>
<td>
STRESS
</td>
<td>
Goal and Scope Report
</td>
<td>
Development of common assessment framework for LCA, LCCA an S-LCA, in order to
compare, in an integrated and consistent way, environment, economic and social
impacts of the RE4 technologies/products versus standard solutions.
</td>
<td>
pdf (LCA,
LCCA and
S-LCA); jpeg
(graphic represen tation)
</td>
<td>
public
</td> </tr> </table>
<table>
<tr>
<th>
</th>
<th>
</th>
<th>
Task7.3 Inventory
</th>
<th>
M18-M32
</th>
<th>
RISE (CBI)
</th>
<th>
Meta data for all data: reference, year of inventory, data valid to year,
regional validity, technical specification, modules included.
</th>
<th>
Definition and collection of all data for the LCA/LCC/SLCA analysis
</th>
<th>
xlsx
</th>
<th>
Genera l life
cycle data are
public.
We
don't
know any
confide ntial data yet.
</th> </tr>
<tr>
<th>
</th>
<th>
</th>
<th>
If good quality on data, material impact data for modules A1-A5, B1-B6, C1-C4
according EN15804.
</th>
<th>
xlsx
</th>
<th>
Genera l life
cycle data are
public.
We
don't
know any
confide ntial data yet.
</th> </tr>
<tr>
<th>
Building element data: type, weight, dimensions, service life, including
materials, etc.
</th>
<th>
xlsx
</th> </tr>
<tr>
<th>
Processes environmental data: for Waste management, transport, construction,
deconstruction, upgrading material etc
</th>
<th>
xlsx
</th> </tr>
<tr>
<th>
</th>
<th>
</th>
<th>
Task7.4 Assessment and interpretation
</th>
<th>
M21-M40
</th>
<th>
RISE (CBI)
</th>
<th>
LCA Result in tables and figures Impact categories and module according EN 15
804
</th>
<th>
Definition and interpretation of the LCA,LCC and SLCA results.
</th>
<th>
xlsx
</th> </tr> </table>
<table>
<tr>
<th>
</th>
<th>
</th>
<th>
</th>
<th>
</th>
<th>
</th>
<th>
for all scenarios and sensitivity analysis
</th>
<th>
</th>
<th>
</th>
<th>
</th> </tr>
<tr>
<td>
</td>
<td>
</td>
<td>
Task7.5 HSE issues analysis
</td>
<td>
M21-M42
</td>
<td>
CREAG
H
</td>
<td>
Products HSE analysis report
</td>
<td>
Assessing potential risks for the environment and for the
workers’ health, due to the
investigated processes and
related to possible presence or release of dangerous substances
</td>
<td>
pdf
</td>
<td>
confide
ntial*
</td> </tr>
<tr>
<td>
</td>
<td>
</td>
<td>
Processes HSE analysis report
</td>
<td>
pdf
</td>
<td>
confide
ntial*
</td> </tr>
<tr>
<td>
</td>
<td>
</td>
<td>
Task7.6 Certification strategies, technical documentation and contribution to
standardization
</td>
<td>
M31-M42
</td>
<td>
QUB
</td>
<td>
Development of technical documentation in the form of
technical data sheets
TDS/DoP
</td>
<td>
Summarising the performance and other technical
characteristics of the product,
machine, component, material, a subsystem or software in
sufficient detail to be used by an engineer.
</td>
<td>
pdf
</td>
<td>
public
</td> </tr>
<tr>
<td>
</td>
<td>
</td>
<td>
Drafting of preliminary EPD documentation based on the existing product
categories for the EPD (EN 15804) and complying with the ISO
14025-Type III
</td>
<td>
Communicating transparent and comparable information about the life-cycle
environmental impact of the products, collected and
quantified according to the
criteria defined by international standards.
</td>
<td>
pdf
</td>
<td>
public
</td> </tr>
<tr>
<td>
Analysis of the most convenient certification strategy for each expected
RE4 project
</td>
<td>
Considering the four steps in the process for certification:
Application (including testing);
Evaluation (qualification criteria); Decision (second
</td>
<td>
pdf
</td>
<td>
public
</td> </tr>
<tr>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
review); Surveillance (the product in the marketplace).
</td>
<td>
</td>
<td>
</td> </tr>
<tr>
<td>
**Data Sharing**
</td>
<td>
</td>
<td>
All public documents in pdf, xls and jpg will be shared on RE 4 project
website public section and ZENODO, confidential reports and data on RE 4
project website private section.
</td>
<td>
**Data Archi preservati**
</td>
<td>
**ving and on**
</td>
<td>
Regular backup of data on server, managed by IT departments.
Data will be stored on the RE 4 project website which already has its backup
procedures.
</td>
<td>
**Data management**
**Responsibilities**
</td>
<td>
Loredana Napolano
</td> </tr> </table>
<table>
<tr>
<th>
**WP number and name**
</th>
<th>
**WP**
**lead**
</th>
<th>
**Task number and name**
</th>
<th>
**Duration**
</th>
<th>
**Task lead**
</th>
<th>
**Dataset name**
</th>
<th>
**Dataset description**
</th>
<th>
**Format**
</th>
<th>
**Level**
</th> </tr>
<tr>
<td>
WP8 Training, dissemination and exploitation
</td>
<td>
FENIX
</td>
<td>
Task 8.1 Use of economic instruments and waste management performances
</td>
<td>
M1-M9
</td>
<td>
ACR+
</td>
<td>
Use of economic instruments
</td>
<td>
Assessment of economic instruments of CDW
management for European representative countries.
</td>
<td>
pdf
</td>
<td>
public
</td> </tr>
<tr>
<td>
Task 8.2 Business modelling and business plans
</td>
<td>
M18-M42
</td>
<td>
FENIX
</td>
<td>
Business Models and Business Plans for RE 4
</td>
<td>
Business models based on the new value chain, business plans focused on the
sustainability strategy and replicability.
</td>
<td>
pdf
</td>
<td>
Confid ential
</td> </tr>
<tr>
<td>
Task 8.3 Market assessment,
Exploitation and IPR management
</td>
<td>
M1-M42
</td>
<td>
FENIX
</td>
<td>
Market Assessment
</td>
<td>
Preliminary market analysis of the European market of CDW recycling and reuse
</td>
<td>
pdf
</td>
<td>
public
</td> </tr>
<tr>
<td>
Exploitation Plan and IPR strategy
</td>
<td>
Identification of the key exploitable results, exploitation forms,
</td>
<td>
pdf
</td>
<td>
Confid ential
</td> </tr> </table>
<table>
<tr>
<th>
</th>
<th>
</th>
<th>
</th>
<th>
</th>
<th>
</th>
<th>
</th>
<th>
competition, risk analysis, potential obstacles
</th>
<th>
</th>
<th>
</th> </tr>
<tr>
<th>
IPR manual
</th>
<th>
Background knowledge and existing patents mapping,
potentially overlapping IPR, optimal IPR protection
</th>
<th>
pdf
</th>
<th>
Confid ential
</th> </tr>
<tr>
<th>
</th>
<th>
Task 8.4 Dissemination
</th>
<th>
M1-M42
</th>
<th>
FENIX
</th>
<th>
Images: Images and logos from project partners, photos/videos from
dissemination events, project promo videos consisting of animated graphical
images, filming, voice over and music. Promo materials shared online.
_The owner gives permission to FENIX to use images for dissemination purposes
of RE 4 _
</th>
<th>
.eps,.jpe g,.png, mpeg,
.avi,
.mp4, pdf
</th>
<th>
public
</th> </tr>
<tr>
<th>
Dissemination and Communication Plan
</th>
<th>
Report identifying target audiences, key messages, communication channels,
roles and timelines
</th>
<th>
pdf
</th>
<th>
public
</th> </tr>
<tr>
<th>
Task 8.5 Data Management
</th>
<th>
M1-M6
</th>
<th>
FENIX
</th>
<th>
Data Management Plan
</th>
<th>
Report analysing the main data uses and restrictions
related to IPR according to the Consortium Agreement
</th>
<th>
pdf
</th>
<th>
public
</th> </tr>
<tr>
<th>
Task 8.6 Training
</th>
<th>
M24-M42
</th>
<th>
FENIX
</th>
<th>
Data sheets, videos for training purposes (webinars, workshops, social
profiles)
</th>
<th>
pdf
</th>
<th>
public
</th> </tr>
<tr>
<td>
**Data Sharing**
</td>
<td>
All dissemination and promo material will be shared on RE 4 project website,
social network profiles, videos on YouTube, thematic portals.
Public reports will be shared on RE 4 project website public section,
confidential reports on private section.
</td>
<td>
**Data Archiving and preservation**
</td>
<td>
Regular backup of data on server, managed by IT departments.
Data will be stored on the RE 4 project website which already has its backup
procedures.
</td>
<td>
**Data management**
**Responsibilities**
</td>
<td>
Petra Colantonio
</td> </tr> </table>
<table>
<tr>
<th>
**WP number and name**
</th>
<th>
**WP**
**lead**
</th>
<th>
**Task number and name**
</th>
<th>
**Duration**
</th>
<th>
**Task lead**
</th>
<th>
**Dataset name**
</th>
<th>
**Dataset description**
</th>
<th>
**Form at**
</th>
<th>
**Level**
</th> </tr>
<tr>
<td>
WP9 Project Management
</td>
<td>
CETMA
</td>
<td>
Task 9.1 Project organization and planning
</td>
<td>
M1-M42
</td>
<td>
CETMA
</td>
<td>
Strategic Action Plan
</td>
<td>
Report defining the general approach to quality assurance and the procedures
to be
followed for the production of outcomes such as deliverables or reports. Risks
management plan is included.
</td>
<td>
pdf
</td>
<td>
Confid ential
</td> </tr>
<tr>
<td>
Task 9.2 Risk
management
</td>
<td>
M1-M42
</td>
<td>
CETMA
</td>
<td>
Risks management plan
</td>
<td>
N/A: it is included in the Strategic Action
Plan
</td>
<td>
N/A
</td>
<td>
N/A
</td> </tr>
<tr>
<td>
Task 9.3 Monitoring and evaluation
</td>
<td>
M1-M42
</td>
<td>
CETMA
</td>
<td>
Monitoring and evaluation Plan
</td>
<td>
Report defining strategies, methods and tools to manage the Project and track
its
progress with respect to the Strategic Action Plan (SAP)
</td>
<td>
pdf
</td>
<td>
Confid ential
</td> </tr>
<tr>
<td>
Task 9.4 Reporting to the EC
</td>
<td>
M1-M42
</td>
<td>
CETMA
</td>
<td>
First short interim management report
</td>
<td>
Report providing the status of activities performed in RE 4 Project from M1
to M6
</td>
<td>
pdf
</td>
<td>
Confid ential
</td> </tr>
<tr>
<td>
Second short interim management report
</td>
<td>
Report providing the status of activities performed in RE 4 Project from M7
to M12
</td>
<td>
pdf
</td>
<td>
Confid ential
</td> </tr>
<tr>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
Third short interim management report
</td>
<td>
Report providing the status of activities performed in RE 4 Project from M19
to M24
</td>
<td>
pdf
</td>
<td>
Confid ential
</td> </tr>
<tr>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
Final short interim management Report
</td>
<td>
Report providing the status of activities performed in RE 4 Project from M30
to M36
</td>
<td>
pdf
</td>
<td>
Confid ential
</td> </tr>
<tr>
<td>
**Data Shari**
</td>
<td>
**ng**
</td>
<td>
Reports will be shared on RE 4 project website private section.
</td>
<td>
**Data Arch preser**
</td>
<td>
**iving and vation**
</td>
<td>
Regular backup of data on server, managed by IT departments.
Data will be stored on the RE 4 project website which already has its backup
procedures.
</td>
<td>
**Data management Responsibilities**
</td>
<td>
Sonia Saracino
</td> </tr> </table>
<table>
<tr>
<th>
**WP number and name**
</th>
<th>
**WP**
**lead**
</th>
<th>
**Task number and name**
</th>
<th>
**Duration**
</th>
<th>
**Task lead**
</th>
<th>
**Dataset name**
</th>
<th>
**Dataset description**
</th>
<th>
**Format**
</th>
<th>
**Level**
</th> </tr>
<tr>
<td>
WP10 Ethics requirements
</td>
<td>
CETMA
</td>
<td>
N/A
</td>
<td>
M1-M42
</td>
<td>
CETMA
</td>
<td>
EPQ - Requirements
</td>
<td>
Report providing further information about the possible harm to the
environment caused by the
research and state the measures that will be taken to mitigate the risks and
ensuring that appropriate health and safety procedures conforming to
relevant local/national guidelines/legislation are followed for the staff
involved in this Project.
</td>
<td>
pdf
</td>
<td>
Confid ential
</td> </tr>
<tr>
<td>
NEC - Requirements
</td>
<td>
Report examining ethical issues involved in the RE 4
Project, especially those related to the participation of a non-EU country in
the research activities.
</td>
<td>
pdf
</td>
<td>
Confid ential
</td> </tr>
<tr>
<td>
**Data Sharing**
</td>
<td>
Reports will be shared on RE 4 project website private section.
</td>
<td>
**Data Archiving and preservation**
</td>
<td>
Regular backup of data on server, managed by IT departments.
Data will be stored on the RE 4 project website which already has its backup
procedures.
</td>
<td>
**Data management Responsibilities**
</td>
<td>
Sonia Saracino
</td> </tr> </table>
*To be kept confidential at least till the end of the RE 4 project.
<table>
<tr>
<th>
</th>
<th>
This project has received funding from the European
Union’s Horizon 2020 research and innovation programme under grant agreement
No 723583
</th>
<th>
</th> </tr> </table>
# 12\. PUBLICATIONS
The RE 4 Consortium is willing to submit papers for scientific/industrial
publication during the course of the RE 4 Project. In the framework of the
Dissemination and Communication Plan agreed by the GA, R&D partners are
responsible for the preparation of the scientific publications, while the
Scientific and Technical Committee (Scientific and Technical Manager and WP
leaders) is responsible for review and final approval.
As a general approach, the R&D partners are responsible for the scientific
publications as well as for the selection of the publisher considered as more
relevant for the subject of matter. Each publisher has its own policies on
self-archiving:
* **Green open access** : researchers can deposit the final version of their published article (peerreviewed manuscript) into a subject-based repository or an institutional repository before, after or alongside its publication. Access to this article is often delayed (embargo period). Publishers recoup their investment by selling subscriptions and charging pay-perdownload/view fees.
* **Gold open access:** author pays publishing, a publication is immediately provided in open access mode by the scientific publisher. Associate costs are shifted from readers to the university or research institute to which the researcher is affiliated, or to the funding agency supporting the research. (e.g. _http://www.springer.com/gp/_ , _https://www.elsevier.com/_ , _https://www.oasis-open.org/_ , _http://www.sherpa.ac.uk/romeo/index.php_ )
After the paper is published and license for open access in obtained, R&D
partner will contact Dissemination and Exploitation Manager (FENIX), who is
responsible for RE 4 data management, and he will upload the publication
into RE 4 project website and deposit the publication in the OpenAIRE or
Zenodo repository indicating the project it belongs to in the metadata.
For adequate identification of accessible data, all the following metadata
information will be included:
* Information about the grant number, name and acronym of the action: **European Union**
**(UE), Horizon 2020 (H2020), Research Innovation Action (RIA), RE 4 acronym,
GA N° 723583 **
* Information about the publication date and embargo period if applicable: **Publication date, Length of embargo period**
* Information about the persistent identifier (for example a **Digital Object Identifier** , DOI), if any, provided by the publisher (for example an **ISSN number** )
For more detailed rules and processes about OpenAIRE, Zenodo, it is possible
to find within FAQ on the link https://www.openaire.eu/support/faq.
_RE4_Deliverable D8.4_Data Management Plan update (M18)_FINAL_V3.0_
_© RE 4 Consortium - This document and the information contained are RE 4
consortium property and shall not be copied or disclosed to any third party
without RE 4 consortium prior written authorisation **54** _
<table>
<tr>
<th>
</th>
<th>
This project has received funding from the European Union’s Horizon 2020
research and innovation programme under grant agreement No 723583
</th>
<th>
</th> </tr> </table>
# 13\. RE 4 DATA MANAGEMENT PLAN PROGRESS
The Table 5 lists datasets shared publically at month 18. The other public
deliverables already available at M18 (D1.2, D1.3, D1.4, D1.5, D4.3 and the
updated version of D8.4) will be shared publically after the final approval of
the European Commission.
Table 5: Datasets shared publically at month 18
<table>
<tr>
<th>
**WP**
</th>
<th>
**WP**
**lead**
</th>
<th>
**Dataset name**
</th>
<th>
**Form at**
</th>
<th>
**Type**
</th>
<th>
**Open access**
</th>
<th>
**Links**
</th> </tr>
<tr>
<td>
WP1
</td>
<td>
CETMA
</td>
<td>
RE4_D1.1_Data collection on CDW_Final_V1.0
</td>
<td>
pdf
</td>
<td>
Deliverable
</td>
<td>
RE 4 website public
</td>
<td>
_http://www.re4.eu/documents/deliverables_
</td> </tr>
<tr>
<td>
RE4_Valorization of construction and
demolition wastes: RE4 building solutions_
AMAM2017_CETMA
</td>
<td>
pdf
</td>
<td>
Paper
</td>
<td>
RE 4 website public,
ProScience proceedings,
ZENODO
</td>
<td>
_https://www.scientevents.com/proscience/download/val_
_orization-of-industrial-wastes-sus-con-building-solutions/_
(DOI:10.14644/amamicam.2017.001) _https://doi.org/10.5281/zenodo.1185180_
</td> </tr>
<tr>
<td>
WP2
</td>
<td>
STAM
</td>
<td>
RE4_D2.1_CDW specifications and material requirements for prefabricated
structures_Final_V2.0
</td>
<td>
pdf
</td>
<td>
Deliverable
</td>
<td>
RE 4 website public, ZENODO
</td>
<td>
_http://www.re4.eu/documents/deliverables_
_https://doi.org/10.5281/zenodo.1175607_
</td> </tr>
<tr>
<td>
RE4_Indexing and sorting robot based on hyperspectral and reflectance
information for CDW recycling_HISER conference_STAM
</td>
<td>
pdf
</td>
<td>
Paper
</td>
<td>
RE 4 website public, ZENODO
</td>
<td>
_http://www.re4.eu/documents/publications/scientificpublications_
_https://doi.org/10.5281/zenodo.1175617_
</td> </tr>
<tr>
<td>
WP4
</td>
<td>
QUB
</td>
<td>
RE4_D4.1_Composition of materials from demolition and available volumes of
sorted fractions_Final_V2.0
</td>
<td>
Pdf
</td>
<td>
Deliverable
</td>
<td>
RE 4 website public, ZENODO
</td>
<td>
_http://www.re4.eu/documents/deliverables_
_https://doi.org/10.5281/zenodo.1185211_
</td> </tr> </table>
_RE4_Deliverable D8.4_Data Management Plan update (M18)_FINAL_V3.0_
_© RE 4 Consortium - This document and the information contained are RE 4
consortium property and shall not be copied or disclosed to any third party
without RE 4 consortium prior written authorisation **55** _
<table>
<tr>
<th>
</th>
<th>
This project has received funding from the European Union’s Horizon 2020
research and innovation programme under grant agreement No 723583
</th>
<th>
</th> </tr> </table>
<table>
<tr>
<th>
</th>
<th>
</th>
<th>
RE4_D4.2_Geometrical, physical and chemical characterisation of CDW-derived
materials_Final_V2.0
</th>
<th>
pdf
</th>
<th>
Deliverable
</th>
<th>
RE 4 website public, ZENODO
</th>
<th>
_http://www.re4.eu/documents/deliverables_
_https://doi.org/10.5281/zenodo.1185221_
</th> </tr>
<tr>
<td>
WP8
</td>
<td>
FENIX
</td>
<td>
RE4_D8.1_Website creation_FENIX_Final_V0.3
</td>
<td>
pdf
</td>
<td>
Deliverable
</td>
<td>
RE 4 website public
</td>
<td>
_http://www.re4.eu/documents/deliverables_
</td> </tr>
<tr>
<td>
RE4_D8.2 Communication and Dissemination Plan_Final_V2.0
</td>
<td>
pdf
</td>
<td>
Deliverable
</td>
<td>
RE 4 website public
</td>
<td>
_http://www.re4.eu/documents/deliverables_
</td> </tr>
<tr>
<td>
RE4_D8.3_Promo Material Design_Final_V2.0
</td>
<td>
pdf
</td>
<td>
Deliverable
</td>
<td>
RE 4 website public
</td>
<td>
_http://www.re4.eu/documents/deliverables_
</td> </tr>
<tr>
<td>
RE4_D8.4_Data Management Plan_Final_V2.0
</td>
<td>
pdf
</td>
<td>
Deliverable
</td>
<td>
RE 4 website public
</td>
<td>
_http://www.re4.eu/documents/deliverables_
</td> </tr>
<tr>
<td>
RE4_D8_5_UseOfEconomicInstruments_Final _V4.0
</td>
<td>
pdf
</td>
<td>
Deliverable
</td>
<td>
RE 4 website public
</td>
<td>
_http://www.re4.eu/documents/deliverables_
</td> </tr>
<tr>
<td>
RE4_D8_6_MarketAssessment_Final_V2.0
</td>
<td>
pdf
</td>
<td>
Deliverable
</td>
<td>
RE 4 website public
</td>
<td>
_http://www.re4.eu/documents/deliverables_
</td> </tr>
<tr>
<td>
RE4-Folder-A4-EN-WEB (2)
RE4-Infographic
RE4_KoM_POSTER
RE4_roll up poster
</td>
<td>
Png
Pdf
Pdf
Pdf
</td>
<td>
Promo
materials
</td>
<td>
RE 4 website public
</td>
<td>
_http://www.re4.eu/documents/promo-material_
</td> </tr>
<tr>
<td>
Re4_first newsletter_Sep 2017
</td>
<td>
pdf
</td>
<td>
e-Newsletter
</td>
<td>
RE 4 website public
</td>
<td>
_http://www.re4.eu/documents/promomaterial/newsletters_
</td> </tr>
<tr>
<td>
La Gazzetta Del Mezzogiorno_Kick-off meeting
RE4_14.09.16
RE4_CBI-nytt 2 2016
RE4_KoM_QuotidianoDiPuglia_14112016
RE4_LaGazzetta_KoM_09092016
RE4_LaRepubblica_Nov2016
</td>
<td>
Pdf
Pdf
Jpg
Jpg jpg
</td>
<td>
Publication
</td>
<td>
RE 4 website public
</td>
<td>
_http://www.re4.eu/documents/publications/popularized_ _-publications_
</td> </tr> </table>
_RE4_Deliverable D8.4_Data Management Plan update (M18)_FINAL_V3.0_
_© RE 4 Consortium - This document and the information contained are RE 4
consortium property and shall not be copied or disclosed to any third party
without RE 4 consortium prior written authorisation **56** _
<table>
<tr>
<th>
</th>
<th>
This project has received funding from the European
Union’s Horizon 2020 research and innovation programme under grant agreement
No 723583
</th>
<th>
</th> </tr> </table>
# 14\. CONCLUSION
This report contains the second release of the Data Management Plan and
represents the status of the mandatory quality requirements at the month 18.
This report should be read in association with all the referenced documents,
appendices and including the EC Grant and Consortium Agreement, annexes and
guidelines. The report will be subject to revisions as required to meet the
needs of the RE 4 project and will be formally reviewed at month 36 to
ensure ongoing fitness for purpose.
At month 18 more detailed information about the dataset description, sharing,
archiving, preservation and responsibilities was updated by each WP leader and
outcomes can be seen in the table 4 (Collection of project results and sharing
strategy). Data which were already shared publically for RE 4 project with
open access are listed in the table 5 (Datasets shared publically at month 18)
with links where they can be accessed and downloaded. The other public
deliverables already submitted (D1.2, D1.3, D1.4, D1.5, D4.3 and the updated
version of D8.4) will be shared publically after the final approval of the
European Commission.
|
https://phaidra.univie.ac.at/o:1140797
|
Horizon 2020
|
1166_Z-Fact0r_723906.md
|
# 2 Introduction
The amount of data generated is continuously increasing while use and re-use
of data to derive new scientific findings is relatively stable. This
information would be useful in the future if the data is well documented
according to accepted and trusted standards which enable the recognition of
suitable data by negotiated agreements on standards, quality level and sharing
practices. For this purpose, DMP defines strategies to preserve and store data
over the defined period of time in order to ensure their availability and re-
usability after the end of Z-Fact0r project.
According to the Guidelines of ORD Pilot in H2020, research data refers to
information, in particular facts or numbers, collected to be examined and
considered and as well as basis for reasoning, discussion, or calculation. The
overall objective of Z-Fact0r project is to develop zerodefect manufacturing
strategies for on-line production. Z-Fact0r aims to contribute to the
eradication of defects in manufacturing, providing better quality of products,
increasing flexibility, and reducing production costs. Thus, research
activities are more focused on the production process and tools than on
production of research or observation of data, so the amount of research data
which will be produced within the project is limited, at least at this stage
of the project.
## 2.1.1 Participation in the pilot on open research data
The EC is running a flexible pilot under H2020 called the ORD pilot. The ORD
pilot aims to improve and maximize access and re-use of research data
generated by H2020 projects and takes into account the need to balance
openness and protection of scientific information, commercialization and IPR,
privacy concerns, security as well as data management and preservation issues.
The 2017 work programme of ORD pilot has been extended to cover all the
thematic areas of Horizon 2020.
Following the recommendation of the EC, Z-Fact0r project is participating in
the ORD Pilot and DMP is D9.4 deliverable (D.9.4) due M6 of the project. The
DMP of Z-Fact0r project has been prepared by taking into account the document
template of the “Guidelines on DMP in H2020”.
This document will be updated and augmented with new datasets and results,
according to the progress of the activities of the Z-Fact0r project. Also, the
DMP will be updated to include possible changes in the consortium composition
and policies over the course of the project.
The procedures that will be implemented for data collection, storage, access,
sharing policies, protection, retention and destruction will be according to
the requirements of the national legislation of each partner and in line with
the EU standards.
## 2.1.2 Building a DMP in the context of H2020
The EC provided a document with guidelines for project participating in the
pilot. The guidelines address aspects like research data quality, sharing and
security. Following these guidelines, DMP will be developed with aim to
provide a consolidated plan for Z-Fact0r partners in the data management plan
policy that the project will follow.
The consortium will comply with the requirements of Directive 95/46/EC of the
European Parliament and of the Council of 24 October 1995 on the protection of
individuals with regard to the processing of personal data and on the free
movement of such data. The consortium will preserve the right to privacy and
confidentiality of data of the survey participants, by providing them two
documents: The Participant Information Sheet and the Consent Form. These
documents will be sent electronically and will provide information about how
the answers will be used and what the purpose of the survey is.
The participants will be assured that their answers will be used only for the
purposes of the specific survey. The voluntary character of participation will
be stated explicitly in the Consent Form. Before conducting the survey, the
consortium will examine and follow the requirements of the national
legislation in line with the EU standards, whether the proposed data
collection requires special local/national ethical/legal permission.
An ethical approach will be adopted and maintained throughout the fieldwork
process. The responsible partners will assure that the EU standards regarding
ethics and Data Management are fulfilled. Each partner will proceed with the
survey according to the provisions of the national legislation that are
adjusted in line with the respective EU Directives for Data Management and
ethics.
The consortium will follow a transparent recruitment process for the
engagement of stakeholders and inclusion/exclusion criteria for all the
surveys will be explained in the Participant Information Sheet.
Each partner will send an invitation (by mail) to participants/third parties
that have neither the role in Z-Fact0r project nor professional relationship
with the consortium to participate in the survey. The consortium will also
examine whether personal data will be collected and how to secure the
confidentiality in such a case.
The Steering Committee of the project will also ensure that EU standards are
followed. The issue of informed consent for all survey procedures, all
participants will be provided with a Participant Information Sheet and Consent
Form to provide informed consent. The default position for all data relating
to residents and staff will be anonymous.
## 2.2 Z-Fact0r Data Management Plan (DMP)
### 2.2.1 General description
This document outlines the first version of the project’s DMP. The DMP is
presented as D9.4 public deliverable (Month 6) of WP9, DCE.
The main purpose of DMP is to provide an analysis of the main elements of data
management policy that will be used by the consortium with regard to all the
datasets that will be generated by the project (e.g. numerical, images, etc.).
This document describes the Research Data with the metadata attached, and
presents an overview of datasets to be produced by the project, their
characteristics and the management processes to make them discoverable,
accessible, assessable, usable beyond the original purpose, and disseminated
between researchers. It also introduces the specifications of the dedicated
Data Management Portal developed by the project in the context of the ORD
Pilot, allowing the efficient management of the project’s datasets and
providing proper OA on them for further analysis and reuse. In addition, the
DMP of Z-Fact0r project reflects the current status of discussion within the
consortium about the data that will be produced.
### 2.2.2 Activities of Data Management Plan
The DMP is a dynamic document, updated throughout the whole project lifecycle.
The final version of this report will be delivered by the end of the project,
reflecting on lessons learnt and describing the plans implemented for
sustainable storage and accessibility of the data, even beyond the project’s
lifetime.
A Knowledge Management system will be developed, which incorporates in a
structured way, the technical and business knowledge created during the
project. The activities of the Z-Fact0r concerning the data management are
planned as follows:
* Knowledge management – to be led by the DEM, in which the DMP will be delivered.
* A knowledge management document will be created, based on DMP, describing how the acquired data and knowledge will be shared and/or made open, and how it will be maintained and preserved. The identifiable project data will be provided in a manner to define the relevant knowledge, increase partners’ awareness, validate the result, and timeframe of actions.
* Technology watch - All partners will be responsible for periodically updating the knowledge management system with outcomes of research work conducted by other groups and any new patents/patent applications, i.e. to ensure that ongoing relevant technological developments and innovations are identified, analysed, and hopefully built upon during the course of the project.
### 2.2.3 Register on numerical datasets generated or collected in Z-Fact0r
The goal of the DMP is to describe numerical model or observation datasets
collected or created by Z-Fact0r during the runtime of the project. The
register on numerical datasets has to be understood as a living document,
which will be updated regularly during the project´s lifetime.
The operational phase of the project started in October 2016, so there is no
dataset generated or collected until delivery date of this DMP (M6). However,
this is not a fixed document so it will be updated and augmented with new
datasets and results during the duration of Z-Fact0r project.
The information listed below reflects the conception and design of the
individual partners in the different work packages at the beginning of the
project. The data register will deliver information according to the
information detailed in _Annex 1 of the GA document_ :
* Dataset reference and name: identifier for the dataset to be produced.
* Dataset description: descriptions of the data that will be generated or collected, its origin or source (in case it is collected), nature and scale and to whom it could be useful, and whether it underpins a scientific publication. Information on the existence (or not) of similar data and the possibilities for integration and reuse.
* Partners activities and responsibilities: partner owner of the device, in charge of the data collection, data analysis and/or data storage, and WPs and tasks it is involved.
* Standards and metadata: reference to existing suitable standards of the discipline. If these do not exist, an outline on how and what metadata will be created. Format and estimated volume of data.
* Data exploitation and sharing: description of how data will be shared, including access procedures and policy, embargo periods (if any), outlines of technical mechanisms for dissemination and necessary software and other tools for enabling re-use, and definition of whether access will be widely open or restricted to specific groups. Identification of the repository where data will be stored, if already existing and identified, indicating in particular the type of repository (institutional, standard repository for the discipline, etc.) and if this information will be confidential (only for members of the CCS) or public. In case a dataset cannot be shared, the reasons for this should be mentioned (e.g. ethics, rules of personal data, intellectual property, commercial, privacy-related, security-related).
* Archiving and preservation (including storage and backup): description of the procedures that will be put in place for long-term preservation of the data. Indication of how long the data should be preserved, what is its approximated end volume, what the associated costs are and how these are planned to be covered.
### 2.2.4 Metadata for Data Management
An initial plan of research data has been explored in _Annex 1 of the_ GA. The
dataset list is provided in the table below, while the nature and details of
each dataset are presented in the next section.
_Table 1. Research data that will be collected and generated during Z-Fact0r._
<table>
<tr>
<th>
**Research Data**
</th>
<th>
**Partners**
</th> </tr>
<tr>
<td>
Data structures with production machine signatures (healthy and deteriorated
conditions)
</td>
<td>
ATLANTIS
</td> </tr>
<tr>
<td>
Machine Deterioration thresholds for predicting production of defected
products
</td>
<td>
ATLANTIS
</td> </tr>
<tr>
<td>
RCA data structures for identifying the root cause of a defect in upstream
stages
</td>
<td>
CERTH/ITI
</td> </tr>
<tr>
<td>
Data from the comparative assessment (i.e. with and without Z-Fact0r) in the 3
use cases: difference in production cost/waste/scrap, in detection efficiency,
in singlestage production defect rate, in average multistage production defect
rate, in production output quality (qualified output / total output produced),
in defect propagation to downstream stages
</td>
<td>
MICROSEMI,
INTERSEALS,
DURIT
</td> </tr>
<tr>
<td>
Defect detection efficiency data: false alarm rate, precision, recall,
F-Measure
</td>
<td>
ALL PARTNERS
</td> </tr>
<tr>
<td>
Defect prediction efficiency data: positive prediction rate
</td>
<td>
ALL PARTNERS
</td> </tr>
<tr>
<td>
Discrete Event Modelling – cost function generation to optimize production
with green scheduling
</td>
<td>
BRUNEL
</td> </tr>
<tr>
<td>
Validation and verification of KPIs to assess the direct impact of system
level to the final cost
</td>
<td>
BRUNEL, EPFL
</td> </tr>
<tr>
<td>
Context aware models and associated algorithms
</td>
<td>
EPFL
</td> </tr>
<tr>
<td>
Additive manufacturing methodologies for rework and repair
</td>
<td>
CETRI
</td> </tr>
<tr>
<td>
Improved functionalities of i-LiKe knowledge management and DSS suite
</td>
<td>
HOLONIX
</td> </tr> </table>
Partners will characterize their research data and associated software and/or
used in the project whether these are discoverable, accessible, assessable and
intelligible, useable beyond the project’s life and interoperable. In
specific, research data can be discovered by means of an identification
mechanism such as Digital Object Identifier and accessible by defining
modalities, the scope of the action, establish the licenses and define the
IPR.
Otherwise research data will be assessable and intelligible allowing third
parties to make assessments. Also, the dataset will be useable beyond the
original purpose for which it was collected or usable to third parties after
the collection of the data for long periods (repositories, preservation and
curation). Finally, research data will offer interoperability to specific
quality standards and allow data exchange between researchers, institutions,
organizations, countries, re-combinations with different datasets, data
exchange, compliant with available software applications.
### 2.2.5 Data description
In order to collect the information about the research data that will be
generated in different activities of the Z- Fact0r project, we have elaborated
a template to be completed by the consortium partners.
This template includes the following information items:
* Dataset reference and name: name, homepage, publisher, maintainer
* Dataset description: description, provenance, usefulness, similar data, re-use and integration
* Standards and metadata: metadata description, vocabularies and ontologies
* Data sharing: license, URL dataset description, openness, software necessary, repository
* Archiving and preservation: preservation, growth, archive, size
_2.2.5.1 Dataset per partner_
All partners have identified the data that will be produced in the different
project activities;
<table>
<tr>
<th>
**DS.CETRI.Z-Repair_AM_processing**
</th> </tr>
<tr>
<td>
**Data Identification**
</td> </tr>
<tr>
<td>
Dataset description
</td>
<td>
Formulation of the inks or paste for additive manufacturing repairing
processes & printing/deposition protocols.
</td> </tr>
<tr>
<td>
Source
</td>
<td>
Various characterization techniques, e.g. microscopy, viscometer, printing
station.
</td> </tr>
<tr>
<td>
**Partners activities and responsibilities**
</td> </tr>
<tr>
<td>
Partner owner device
</td>
<td>
CETRI
</td> </tr>
<tr>
<td>
Partner in charge of data collection
</td>
<td>
CETRI
</td> </tr>
<tr>
<td>
Partner in charge of
data analysis
</td>
<td>
CETRI
</td> </tr>
<tr>
<td>
Partner in charge of data storage
</td>
<td>
CETRI
</td> </tr>
<tr>
<td>
WPs and tasks
</td>
<td>
T2.4 in WP2
</td> </tr>
<tr>
<td>
**Standards**
</td> </tr>
<tr>
<td>
Info about metadata (Production and storage dates, places, and documentation)
</td>
<td>
The metadata include:
1. The characteristics of the materials to be deposited.
2. The user requirements as obtained by the end users.
</td> </tr>
<tr>
<td>
Standards, Format, Estimated volume of data.
</td>
<td>
No standards apply. The format will be in the form of spreadsheets and images
(TIFF of JPG). Estimated volume is < 10 MB.
</td> </tr>
<tr>
<td>
**Data exploitation and sharing**
</td> </tr>
<tr>
<td>
Data exploitation (purpose/use of the
data analysis)
</td>
<td>
The results of the study have the potential to be exploited by CETRI along
with the Z-Fact0r end users MICROSEMI, DURIT and INTERSEALS, as well as by
SIR, towards the implementation of integrating new processes in their
production lines.
</td> </tr>
<tr>
<td>
Data access policy /
Dissemination level
</td>
<td>
In general, the data will be confidential with the exception of possible
future publications in the case the consortium permits such activities.
</td> </tr>
<tr>
<td>
Data sharing, re-use and distribution
</td>
<td>
CETRI will generate 3 sets of data for the 3 Z-Fact0r end-users (MICROSEMI,
DURIT, INTERSEALS). Each set will be shared with the individual partners in
the form of raw data and complete reports in order to receive feedback during
the project implementation.
</td> </tr>
<tr>
<td>
Embargo periods
</td>
<td>
No
</td> </tr>
<tr>
<td>
**Archiving and preservation (including storage and backup)**
</td> </tr>
<tr>
<td>
Data storage (including backup)
</td>
<td>
Data will be stored into a computer and an external hard disc and will be send
frequently to the individual end-users. The data will be stored permanently in
a computer in CETRI facilities.
</td> </tr> </table>
<table>
<tr>
<th>
**DS.CETRI.Z-Repair_laser_processing**
</th> </tr>
<tr>
<td>
**Data Identification**
</td> </tr>
<tr>
<td>
Dataset description
</td>
<td>
Measurements of the laser source couples to measurements of the processed
surface.
</td> </tr>
<tr>
<td>
Source
</td>
<td>
Laser source. Laser Power Meter Microscopy.
</td> </tr>
<tr>
<td>
**Partners activities and responsibilities**
</td> </tr>
<tr>
<td>
Partner owner device
</td>
<td>
CETRI
</td> </tr>
<tr>
<td>
Partner in charge of data collection
</td>
<td>
CETRI
</td> </tr>
<tr>
<td>
Partner in charge of
data analysis
</td>
<td>
CETRI
</td> </tr>
<tr>
<td>
Partner in charge of data storage
</td>
<td>
CETRI
</td> </tr>
<tr>
<td>
WPs and tasks
</td>
<td>
T2.4 in WP2
</td> </tr>
<tr>
<td>
**Standards**
</td> </tr>
<tr>
<td>
Info about metadata
(Production and
</td>
<td>
The metadata include:
1. The type/origin of the processed material.
2. The conditions of the experiments.
</td> </tr>
<tr>
<td>
storage dates, places, and documentation)
</td>
<td>
</td> </tr>
<tr>
<td>
Standards, Format, Estimated volume of data.
</td>
<td>
No standards apply. The format will be in the form of spreadsheets and images
(TIFF of JPG). Estimated volume is < 10 MB.
</td> </tr>
<tr>
<td>
**Data exploitation and sharing**
</td> </tr>
<tr>
<td>
Data exploitation (purpose/use of the
data analysis)
</td>
<td>
The results of the study have the potential to be exploited by
CETRI along with the Z-Fact0r end users MICROSEMI, DURIT and INTERSEALS, as
well as by SIR, towards the implementation of integrating new processes in
their production lines.
</td> </tr>
<tr>
<td>
Data access policy /
Dissemination level
</td>
<td>
In general, the data will be confidential with the exception of possible
future publications in the case the consortium permits such activities.
</td> </tr>
<tr>
<td>
Data sharing, re-use and distribution
</td>
<td>
CETRI will generate 3 sets of data for the 3 Z-Fact0r end-users (MICROSEMI,
DURIT and INTERSEALS). Each set will be shared with the individual partners in
the form of raw data and complete reports in order to receive feedback during
the project implementation.
</td> </tr>
<tr>
<td>
Embargo periods
</td>
<td>
No
</td> </tr>
<tr>
<td>
**Archiving and preservation (including storage and backup)**
</td> </tr>
<tr>
<td>
Data storage (including backup)
</td>
<td>
Data will be stored into a computer and an external hard disc and will be send
frequently to the individual end-users.
The data will be stored permanently in a computer in CETRI facilities.
</td> </tr> </table>
<table>
<tr>
<th>
**DS.DURIT. Production line. Demo 3.**
</th> </tr>
<tr>
<td>
**Data Identification**
</td> </tr>
<tr>
<td>
Dataset description
</td>
<td>
Data collected:
* Dimensions/shapes/surface and 3D details
* Superficial defects like cracks
</td> </tr> </table>
<table>
<tr>
<th>
Source
</th>
<th>
The data will be collected by different sensors and imaging devices such as
cameras. Installed in the production line after green machining and after
finishing operations. Ideally installed in machine for real time inspection
although very difficult to implement at the current time.
</th> </tr>
<tr>
<td>
**Partners activities and responsibilities**
</td> </tr>
<tr>
<td>
Partner owner device
</td>
<td>
DURIT
</td> </tr>
<tr>
<td>
Partner in charge of data collection
</td>
<td>
DURIT
</td> </tr>
<tr>
<td>
Partner in charge of
data analysis
</td>
<td>
DURIT
</td> </tr>
<tr>
<td>
Partner in charge of data storage
</td>
<td>
DURIT
</td> </tr>
<tr>
<td>
WPs and tasks
</td>
<td>
The data are going to be collected in WP5 and WP6.
</td> </tr>
<tr>
<td>
**Standards**
</td> </tr>
<tr>
<td>
Info about metadata (Production and storage dates, places, and documentation)
</td>
<td>
The dataset will be accompanied by information regarding:
* Drawings and sequence of operations.
* Batch of material used.
* Operators involved.
* Date, time.
* Temperature and relative humidity in the metallurgy section.
</td> </tr>
<tr>
<td>
Standards, Format, Estimated volume of data.
</td>
<td>
Our tests will be in a specific type of pieces. The volume of data depends of
the quantity of order.
</td> </tr>
<tr>
<td>
**Data exploitation and sharing**
</td> </tr>
<tr>
<td>
Data exploitation (purpose/use of the
data analysis)
</td>
<td>
Production process recognition and help during the different production
phases, avoiding mistakes. Support of quality checks and production batches
recalls
</td> </tr>
<tr>
<td>
Data access policy /
Dissemination level
</td>
<td>
The full dataset will be confidential and only the members of the consortium
will have access on it. Furthermore, if the dataset or specific portions of it
(e.g. metadata, statistics, etc.) are to become of widely OA, a data
management portal will be created that should provide a description of the
dataset and link to a download section. Of course, these data will be
</td> </tr>
<tr>
<td>
</td>
<td>
anonymized, so as not to have any potential ethical issues with their
publication and dissemination.
</td> </tr>
<tr>
<td>
Data sharing, re-use and distribution
</td>
<td>
Data sharing is dependent of DURIT, DURIT´s customers and partner
requirements.
</td> </tr>
<tr>
<td>
Embargo periods
</td>
<td>
None
</td> </tr>
<tr>
<td>
**Archiving and preservation (including storage and backup)**
</td> </tr>
<tr>
<td>
Data storage (including backup)
</td>
<td>
All information belongs to the industrial partner that owns the shop floor.
All data will respect the partner policies. All data has to be stored till the
end of life/warranty of the produced component. Probably also stored at DURIT
servers at the cloud.
</td> </tr> </table>
<table>
<tr>
<th>
**DS.EPFL.01_KMDSS**
</th> </tr>
<tr>
<td>
**Data Identification**
</td> </tr>
<tr>
<td>
Dataset description
</td>
<td>
Z-Fact0r Knowledge Management and Decision Support System Dataset.
</td> </tr>
<tr>
<td>
Source
</td>
<td>
Device Manager, Event Manager, Semantic Context Manager, ZFact0r Repository.
</td> </tr>
<tr>
<td>
**Partners activities and responsibilities**
</td> </tr>
<tr>
<td>
Partner owner device
</td>
<td>
The device will be owned by Z-Fact0r End-users (MICROSEMI, INTERSEALS, DURIT),
where the data collection will be performed.
</td> </tr>
<tr>
<td>
Partner in charge of data collection
</td>
<td>
Various partners related to the specific event and/or operation.
</td> </tr>
<tr>
<td>
Partner in charge of
data analysis
</td>
<td>
Various partners related to the specific event and/or operation.
</td> </tr>
<tr>
<td>
Partner in charge of data storage
</td>
<td>
EPFL will store data related to KMDSS (various partners can handle the rest of
the data).
</td> </tr>
<tr>
<td>
WPs and tasks
</td>
<td>
The data will be collected within the activities of WP2, WP3 and WP4.
</td> </tr>
<tr>
<td>
**Standards**
</td> </tr>
<tr>
<td>
Info about metadata (Production and storage dates, places, and documentation)
</td>
<td>
Indicative metadata include: Input from the Sensor Network (through the Device
Manager), the overall model of Production activities (through Z-Fact0r
Repository), shop-floor events data from the Event Manager, and context –
aware knowledge stemming from the Semantic Context Manager (Ontology).
</td> </tr>
<tr>
<td>
Standards, Format, Estimated volume of data.
</td>
<td>
Data can be available in XML or JSON format. Estimation of the volume of data
cannot be predicted in advance of a real use of the technology at the shop
floor level.
</td> </tr>
<tr>
<td>
**Data exploitation and sharing**
</td> </tr>
<tr>
<td>
Data exploitation (purpose/use of the
data analysis)
</td>
<td>
The collected data will be used for better understanding of the processes and
activities evolving in the shop-floor which will provide actionable knowledge
in the form of a set of recommendations to (i) supervise and provide feedback
for all the processes executed in the production line, (ii) evaluate
performance parameters and responding to defects, keeping historical data,
(iii) send efficiently alarms to initiate actions, filter out false alarms,
increase confidence levels (through previously acquired knowledge) of early
defect detection and prediction, etc.
</td> </tr>
<tr>
<td>
Data access policy /
Dissemination level
</td>
<td>
Accessible to Z-Fact0r consortium members including the commission services as
defined in the Z-Fact0r GA.
</td> </tr>
<tr>
<td>
Data sharing, re-use and distribution
</td>
<td>
The sharing of this data is yet to be decided together with the industrial
partners.
</td> </tr>
<tr>
<td>
Embargo periods
</td>
<td>
None
</td> </tr>
<tr>
<td>
**Archiving and preservation (including storage and backup)**
</td> </tr>
<tr>
<td>
Data storage (including backup)
</td>
<td>
Data will be stored in a dedicated repository.
</td> </tr> </table>
<table>
<tr>
<th>
**DS.EPFL.02.SemanticContextManager**
</th> </tr>
<tr>
<td>
**Data Identification**
</td> </tr> </table>
<table>
<tr>
<th>
Dataset description
</th>
<th>
Context-aware shop-floor analysis and semantic model for the annotation and
description of the knowledge to represent manufacturing system performance.
</th> </tr>
<tr>
<td>
Source
</td>
<td>
Z-Fact0r repository.
</td> </tr>
<tr>
<td>
**Partners activities and responsibilities**
</td> </tr>
<tr>
<td>
Partner owner device
</td>
<td>
Z-Fact0r End-users (MICROSEMI, INTERSEALS, DURIT)
</td> </tr>
<tr>
<td>
Partner in charge of data collection
</td>
<td>
EPFL
</td> </tr>
<tr>
<td>
Partner in charge of
data analysis
</td>
<td>
EPFL
</td> </tr>
<tr>
<td>
Partner in charge of data storage
</td>
<td>
EPFL
</td> </tr>
<tr>
<td>
WPs and tasks
</td>
<td>
The data will be collected within the activities of WP3 and in particular
T3.5.
</td> </tr>
<tr>
<td>
**Standards**
</td> </tr>
<tr>
<td>
Info about metadata (Production and storage dates, places, and documentation)
</td>
<td>
Data from Z-Fact0r repository (data concerning machines, workers, actors,
activities and processes, production data logs, etc.).
</td> </tr>
<tr>
<td>
Standards, Format, Estimated volume of data.
</td>
<td>
Generated output will be the semantic enrichment of shop-floor data for
representation of processes, actors, alarms, actions, work-pieces/products,
etc., e.g. as RDF Triplets. Standards:
W3C-OWL, RDF. Less than 2GB.
</td> </tr>
<tr>
<td>
**Data exploitation and sharing**
</td> </tr>
<tr>
<td>
Data exploitation (purpose/use of the
data analysis)
</td>
<td>
Data is required for the Z-Fact0r ontology development. Ontology describes
semantic models. The ontology will be used in order to drive the semantic
framework. Furthermore, it will be used for data integration, visualization,
inferencing /reasoning. The ontology will describe the basic entities of the
project and model relevant structures of multi-stage manufacturing processes.
</td> </tr>
<tr>
<td>
Data access policy /
Dissemination level
</td>
<td>
Accessible to Z-Fact0r consortium members including the commission services as
defined in the Z-Fact0r GA.
</td> </tr>
<tr>
<td>
Data sharing, re-use and distribution
</td>
<td>
The Ontology will be uploaded in a server where it will be accessible to
Z-Fact0r consortium members including the commission services.
</td> </tr>
<tr>
<td>
Embargo periods
</td>
<td>
None
</td> </tr>
<tr>
<td>
**Archiving and preservation (including storage and backup)**
</td> </tr>
<tr>
<td>
Data storage (including backup)
</td>
<td>
Data will be stored in a dedicated repository. No expiry date – revisions will
be kept.
</td> </tr> </table>
<table>
<tr>
<th>
**DS.HOLONIX.ProductionManagement**
</th> </tr>
<tr>
<td>
**Data Identification**
</td> </tr>
<tr>
<td>
Dataset description
</td>
<td>
Collections of data from industrial partner’s production plant, operators, and
elaborated data from other Z-Modules. These collections of data contain
information about machine conditions, plant conditions, process KPIs of an
Industrial production plant.
</td> </tr>
<tr>
<td>
Source
</td>
<td>
Industrial partners’ production plant with its operators and other Z-Modules.
</td> </tr>
<tr>
<td>
**Partners activities and responsibilities**
</td> </tr>
<tr>
<td>
Partner owner device
</td>
<td>
Industrial partners
</td> </tr>
<tr>
<td>
Partner in charge of data collection
</td>
<td>
Industrial partner with support of HOLONIX presumably
</td> </tr>
<tr>
<td>
Partner in charge of
data analysis
</td>
<td>
HOLONIX and Z-Modules
</td> </tr>
<tr>
<td>
Partner in charge of data storage
</td>
<td>
HOLONIX
</td> </tr>
<tr>
<td>
WPs and tasks
</td>
<td>
T3.2 in WP3
</td> </tr>
<tr>
<td>
**Standards**
</td> </tr>
<tr>
<td>
Info about metadata (Production and storage dates, places, and documentation)
</td>
<td>
A set of RESTful APIs will be released with documentation of how to require
data from datasets.
</td> </tr>
<tr>
<td>
Standards, Format, Estimated volume of data.
</td>
<td>
No estimation has been done so far.
</td> </tr>
<tr>
<td>
**Data exploitation and sharing**
</td> </tr>
<tr>
<td>
Data exploitation (purpose/use of the
data analysis)
</td>
<td>
Support on the monitoring of production machine, production performance and
process both for operators and other monitoring modules.
</td> </tr>
<tr>
<td>
Data access policy /
Dissemination level
</td>
<td>
Collections of data of Production management module should be accessible only
for Z-Fact0r consortium partners only.
</td> </tr>
<tr>
<td>
Data sharing, re-use and distribution
</td>
<td>
Data sharing should not be possible with users outside of the project. A set
of RESTFul APIs will be implemented to share data between partners.
</td> </tr>
<tr>
<td>
Embargo periods
</td>
<td>
None
</td> </tr>
<tr>
<td>
**Archiving and preservation (including storage and backup)**
</td> </tr>
<tr>
<td>
Data storage (including backup)
</td>
<td>
Physical place to store production data has still to be decided, data will be
stored at least for all the duration of the project.
</td> </tr> </table>
<table>
<tr>
<th>
**DS.HOLONIX.Repository**
</th> </tr>
<tr>
<td>
**Data Identification**
</td> </tr>
<tr>
<td>
Dataset description
</td>
<td>
The repository is a collection of dataset coming from various sources
including sensors, operator notes, production line installed at industrial
partners of the project as well as data incoming from Z-modules as results of
their calculation.
</td> </tr>
<tr>
<td>
Source
</td>
<td>
Z-Fact0r industrial partner’s production plant and Z-modules.
</td> </tr>
<tr>
<td>
**Partners activities and responsibilities**
</td> </tr>
<tr>
<td>
Partner owner device
</td>
<td>
Z-Modules’ responsible partner and industrial partners.
</td> </tr>
<tr>
<td>
Partner in charge of data collection
</td>
<td>
Z-Modules and industrial partners.
</td> </tr>
<tr>
<td>
Partner in charge of
data analysis
</td>
<td>
Various Z-modules
</td> </tr>
<tr>
<td>
Partner in charge of data storage
</td>
<td>
HOLONIX
</td> </tr>
<tr>
<td>
WPs and tasks
</td>
<td>
T3.2
</td> </tr>
<tr>
<td>
**Standards**
</td> </tr>
<tr>
<td>
Info about metadata (Production and storage dates, places, and documentation)
</td>
<td>
A set of RESTful APIs will be released with documentation of how to require
data from datasets.
</td> </tr>
<tr>
<td>
Standards, Format, Estimated volume of data.
</td>
<td>
JSON will be data exchange format between Repository and ZModules. No
estimation of data volume has been done so far.
</td> </tr>
<tr>
<td>
**Data exploitation and sharing**
</td> </tr>
<tr>
<td>
Data exploitation (purpose/use of the
data analysis)
</td>
<td>
The datasets collected should be used by various modules of the project for
the pursue of Zero defects production objective.
</td> </tr>
<tr>
<td>
Data access policy /
Dissemination level
</td>
<td>
Still to be clarified, for the nature of the dataset collected, only the
members of consortium should have rights to access to the datasets with
appropriate authorization/authentication policy.
</td> </tr>
<tr>
<td>
Data sharing, re-use and distribution
</td>
<td>
No discussion about this matter has been done so far, and should not be shared
with entities outside of Z-Fact0r consortium.
</td> </tr>
<tr>
<td>
Embargo periods
</td>
<td>
None
</td> </tr>
<tr>
<td>
**Archiving and preservation (including storage and backup)**
</td> </tr>
<tr>
<td>
Data storage (including backup)
</td>
<td>
Still to be decided where the collected datasets will be stored definitely.
</td> </tr> </table>
<table>
<tr>
<th>
**DS.CONFINDUSTRIA.Events &Roadmapping **
</th> </tr>
<tr>
<td>
**Data Identification**
</td> </tr>
<tr>
<td>
Dataset description
</td>
<td>
Info regarding the demand for Zero Defect production and a global matching
coming from a Desk Research based on technology brokerage system available at
CONFINDUSTRIA.
List of potential tradefairs and events.
</td> </tr> </table>
<table>
<tr>
<th>
</th>
<th>
List of potential customer/visitors of workshop and potential companies
interested in Z-Fact0r technology.
</th> </tr>
<tr>
<td>
Source
</td>
<td>
Internet, specific DBs (we have not selected anyone).
</td> </tr>
<tr>
<td>
**Partners activities and responsibilities**
</td> </tr>
<tr>
<td>
Partner owner device
</td>
<td>
CONFINDUSTRIA
</td> </tr>
<tr>
<td>
Partner in charge of data collection
</td>
<td>
CONFINDUSTRIA
</td> </tr>
<tr>
<td>
Partner in charge of
data analysis
</td>
<td>
CONFINDUSTRIA
</td> </tr>
<tr>
<td>
Partner in charge of data storage
</td>
<td>
CONFINDUSTRIA
</td> </tr>
<tr>
<td>
WPs and tasks
</td>
<td>
WP 7: **T7.3** Roadmap for wider adoption and take-up
WP 8: **T8.2** Adoption Plan for increasing Awareness
WP 9: **T9.2** To identify the relevant conference or event
</td> </tr>
<tr>
<td>
**Standards**
</td> </tr>
<tr>
<td>
Info about metadata (Production and storage dates, places, and documentation)
</td>
<td>
It will be used the only available and not confidential (public) data and
documentation coming from Z-Fact0r results in order to define our strategy and
desk research
</td> </tr>
<tr>
<td>
Standards, Format, Estimated volume of data.
</td>
<td>
\--
</td> </tr>
<tr>
<td>
**Data exploitation and sharing**
</td> </tr>
<tr>
<td>
Data exploitation (purpose/use of the
data analysis)
</td>
<td>
It will be used the only available and not confidential (public) data and
documentation coming from Z-Fact0r results in order to define our strategy and
desk research
</td> </tr>
<tr>
<td>
Data access policy /
Dissemination level
</td>
<td>
* It will be respected the project rules about confidentiality, by using and disseminate the only public data
* Our results will be public
</td> </tr>
<tr>
<td>
Data sharing, re-use and distribution
</td>
<td>
* Our results will be public, they could be shared and re-used as a model:
* Research method structure
* Roadmap structure
* Business Network created
</td> </tr>
<tr>
<td>
Embargo periods
</td>
<td>
None
</td> </tr>
<tr>
<td>
</td>
<td>
</td> </tr>
<tr>
<td>
**Archiving and preservation (including storage and backup)**
</td> </tr>
<tr>
<td>
Data storage (including backup)
</td>
<td>
Original data and results will be kept in our company server at least until
the all project duration and audit period. Results will be also shared with
partners and kept in the project repository.
</td> </tr> </table>
<table>
<tr>
<th>
**DS.ATLANTIS.ES-DSS**
</th> </tr>
<tr>
<td>
**Data Identification**
</td> </tr>
<tr>
<td>
Dataset description
</td>
<td>
Dataset for insufficient glue detection obtained by cameras and lasers at the
glue implementation machine.
The camera images will be processed and not saved anywhere, while the metadata
of insufficient glue placement will be used for analysis and detection.
Data will be used for early detection of failures.
The metadata will be able to send notifications and alarms to the responsible
control operators and glue workers.
</td> </tr>
<tr>
<td>
Source
</td>
<td>
The dataset will be collected by using cameras and lasers at the glue machine
</td> </tr>
<tr>
<td>
**Partners activities and responsibilities**
</td> </tr>
<tr>
<td>
Partner owner device
</td>
<td>
The device will be owned to the industry (MICROSEMI), where the data
collection is going to be performed.
</td> </tr>
<tr>
<td>
Partner in charge of data collection
</td>
<td>
Various partners related to the specific incident and/or operation.
</td> </tr>
<tr>
<td>
Partner in charge of
data analysis
</td>
<td>
Various partners related to the specific incident and/or operation.
</td> </tr>
<tr>
<td>
Partner in charge of data storage
</td>
<td>
ATLANTIS will store data related to ES-DSS (various partners can handle the
rest of the data).
</td> </tr>
<tr>
<td>
WPs and tasks
</td>
<td>
The data are going to be collected within activities of WP3 and more
specifically within activities of T3.1, T3.2, T3.3 and T3.4.
</td> </tr>
<tr>
<td>
**Standards**
</td> </tr>
<tr>
<td>
Info about metadata
(Production and
</td>
<td>
The dataset will be accompanied with a detailed documentation of its contents.
Indicative metadata include:
</td> </tr>
<tr>
<td>
storage dates, places, and documentation)
</td>
<td>
1. description of the experimental setup (e.g. location, date, etc.) and procedure that led to the generation of the dataset,
2. annotated detection of insufficient glue, activity, business process, state of the monitored activity.
</td> </tr>
<tr>
<td>
Standards, Format, Estimated volume of data.
</td>
<td>
The data will be stored at XML format and are estimated to be 1GB per day.
</td> </tr>
<tr>
<td>
**Data exploitation and sharing**
</td> </tr>
<tr>
<td>
Data exploitation (purpose/use of the
data analysis)
</td>
<td>
The collected data will be used for the development of the activities analysis
and incident detection methods of the Z – Fact0r project and all the tasks,
activities and methods that are related to it.
</td> </tr>
<tr>
<td>
Data access policy /
Dissemination level
</td>
<td>
The full dataset will be confidential and only the members of the consortium
will have access on it.
</td> </tr>
<tr>
<td>
Data sharing, re-use and distribution
</td>
<td>
The sharing of this data is yet to be decided along with the industrial
partners.
</td> </tr>
<tr>
<td>
Embargo periods
</td>
<td>
None
</td> </tr>
<tr>
<td>
**Archiving and preservation (including storage and backup)**
</td> </tr>
<tr>
<td>
Data storage (including backup)
</td>
<td>
Data will be stored in a DB. RAID and other common backup mechanism will be
utilized to ensure data reliability and performance improvement and to avoid
data losses.
</td> </tr> </table>
<table>
<tr>
<th>
**DS.ATLANTIS.Evaluation**
</th> </tr>
<tr>
<td>
**Data Identification**
</td> </tr>
<tr>
<td>
Dataset description
</td>
<td>
Values of the KPIs for:
1. Technical indicators.
2. User/Stakeholders acceptance.
3. Indicators for accessing the impact of the project on the factories.
</td> </tr>
<tr>
<td>
Source
</td>
<td>
The dataset will be collected from Z-Fact0r industrial partners, technology
providing partners and User/Stakeholders - the tool/solution beneficiaries.
</td> </tr> </table>
<table>
<tr>
<th>
**Partners activities and responsibilities**
</th> </tr>
<tr>
<td>
Partner owner device
</td>
<td>
The device will be owned by the consortium.
</td> </tr>
<tr>
<td>
Partner in charge of data collection
</td>
<td>
ATLANTIS with the respective and responsible partners per toolkit/task/plant.
</td> </tr>
<tr>
<td>
Partner in charge of
data analysis
</td>
<td>
ATLANTIS.
</td> </tr>
<tr>
<td>
Partner in charge of data storage
</td>
<td>
ATLANTIS will store analysed data related to Solution Evaluation.
</td> </tr>
<tr>
<td>
WPs and tasks
</td>
<td>
The data are going to be collected through demonstrations in relevant
environment, specifically within T5.3 activity in collaboration with WP6.
</td> </tr>
<tr>
<td>
**Standards**
</td> </tr>
<tr>
<td>
Info about metadata (Production and storage dates, places, and documentation)
</td>
<td>
Collected data from the execution of the demonstrations at the operational
environment of the pilot sites (WP6) as well as the users’ acceptance and
overall impact will be analysed and documented - Report on Solution
Validation.
</td> </tr>
<tr>
<td>
Standards, Format, Estimated volume of data.
</td>
<td>
Alphanumeric
</td> </tr>
<tr>
<td>
**Data exploitation and sharing**
</td> </tr>
<tr>
<td>
Data exploitation (purpose/use of the
data analysis)
</td>
<td>
Solution validation will be synthesised and documented in the form of report -
deliverable.
</td> </tr>
<tr>
<td>
Data access policy /
Dissemination level
</td>
<td>
The full dataset will be confidential, the reports will be public.
</td> </tr>
<tr>
<td>
Data sharing, re-use and distribution
</td>
<td>
Data will be shared among involved partners.
</td> </tr>
<tr>
<td>
Embargo periods
</td>
<td>
None
</td> </tr>
<tr>
<td>
**Archiving and preservation (including storage and backup)**
</td> </tr>
<tr>
<td>
Data storage (including backup)
</td>
<td>
To avoid data losses during the project and to ensure data reliability
analysed data will be stored for up to two years after the project life by
ATLANTIS.
</td> </tr> </table>
<table>
<tr>
<th>
**DS.ATLANTIS.ReverseSupplyChain**
</th> </tr>
<tr>
<td>
**Data Identification**
</td> </tr>
<tr>
<td>
Dataset description
</td>
<td>
Dataset for gathered data during the manufacturing process, obtained by
cameras, lasers and other measurement sensors. The camera images will be
processed and not saved anywhere while the metadata for all the other sensors
will be used for analysis in order to activate the Reverse – flow process in
the Reverse Supply Chain.
Data will be used for defect detection in the reverse supply chain. The
metadata will be able to send notifications and alarms to the responsible
machine operators for removal the defected parts, special inspection, return
to previous internal tier (upstream stage) or external tier (other production
line or external supplier). Standards and prototypes shall be included in the
data for comparison with the defected parts and setting acceptance levels.
</td> </tr>
<tr>
<td>
Source
</td>
<td>
Cameras, lasers and measurement instruments at different points of the
production lines
</td> </tr>
<tr>
<td>
**Partners activities and responsibilities**
</td> </tr>
<tr>
<td>
Partner owner device
</td>
<td>
The device will be owned to the industry, where the data collection is going
to be performed.
</td> </tr>
<tr>
<td>
Partner in charge of data collection
</td>
<td>
Various partners related to the specific incident and/or operation.
</td> </tr>
<tr>
<td>
Partner in charge of
data analysis
</td>
<td>
ATLANTIS will analyse the data in order to provide answers and reliable use of
the Reverse Supply Chain.
</td> </tr>
<tr>
<td>
Partner in charge of data storage
</td>
<td>
ATLANTIS will store data related to Reverse Supply Chain (various partners can
handle the rest of the data).
</td> </tr>
<tr>
<td>
WPs and tasks
</td>
<td>
The data are going to be collected within activities of WP2 and more
specifically within activities of T2.5.
</td> </tr>
<tr>
<td>
**Standards**
</td> </tr>
<tr>
<td>
Info about metadata (Production and storage dates, places, and documentation)
</td>
<td>
The dataset will be accompanied with a detailed documentation of its contents.
Indicative metadata include:
(a) description of the experimental setup (e.g. location, date, etc.) and
procedure that led to the generation of the dataset,
</td> </tr>
<tr>
<td>
</td>
<td>
(b) annotated detection of a defective part in the production line, the cause
of the defect, the acceptable standards and limits of the part, as well as
return point in the production process.
</td> </tr>
<tr>
<td>
Standards, Format, Estimated volume of data.
</td>
<td>
The data will be stored at XML format and are estimated to be 100ΜΒ per day.
</td> </tr>
<tr>
<td>
**Data exploitation and sharing**
</td> </tr>
<tr>
<td>
Data exploitation (purpose/use of the
data analysis)
</td>
<td>
The collected data will be used for the development of the activities analysis
and defect detection methods in the production lines of the Z – Fact0r project
plants and all the tasks, activities and methods that are related to it. The
Reverse Supply Chain shall be able to use the data in order to decide whether
or not a defective part should return to a previous tier.
</td> </tr>
<tr>
<td>
Data access policy /
Dissemination level
</td>
<td>
The full dataset will be confidential and only the members of the consortium
will have access on it.
</td> </tr>
<tr>
<td>
Data sharing, re-use and distribution
</td>
<td>
The sharing of this data is yet to be decided along with the industrial
partners
</td> </tr>
<tr>
<td>
Embargo periods
</td>
<td>
None
</td> </tr>
<tr>
<td>
**Archiving and preservation (including storage and backup)**
</td> </tr>
<tr>
<td>
Data storage (including backup)
</td>
<td>
Data will be stored in a DB. RAID and other common backup mechanism will be
utilized to ensure data reliability and performance improvement and to avoid
data losses.
</td> </tr> </table>
<table>
<tr>
<th>
**DS.DATAPIXEL.3DPointcloud**
</th> </tr>
<tr>
<td>
**Data Identification**
</td> </tr>
<tr>
<td>
Dataset description
</td>
<td>
High accuracy and high resolution 3D Pointclouds of scanned parts. The
Pointcloud is a list of 3D points, and can be structured and unstructured
</td> </tr>
<tr>
<td>
Source
</td>
<td>
DATAPIXEL 3D Scanner
</td> </tr>
<tr>
<td>
**Partners activities and responsibilities**
</td> </tr>
<tr>
<td>
Partner owner device
</td>
<td>
DATAPIXEL
</td> </tr>
<tr>
<td>
Partner in charge of data collection
</td>
<td>
DATAPIXEL
</td> </tr>
<tr>
<td>
Partner in charge of
data analysis
</td>
<td>
DATAPIXEL
</td> </tr>
<tr>
<td>
Partner in charge of data storage
</td>
<td>
DATAPIXEL and Z-Fact0r repository
</td> </tr>
<tr>
<td>
WPs and tasks
</td>
<td>
WP2 and WP3
</td> </tr>
<tr>
<td>
**Standards**
</td> </tr>
<tr>
<td>
Info about metadata (Production and storage dates, places, and documentation)
</td>
<td>
Metadata includes part identification, date and time of data collection,
equipment. Pointcloud is part of the information associated with the
manufactured parts.
</td> </tr>
<tr>
<td>
Standards, Format, Estimated volume of data.
</td>
<td>
ASCII list of X Y Z is the most common format. Typically, Pointclouds have a
size between 100 K to 10M points, or 3Mbytes to 300 Mbytes.
</td> </tr>
<tr>
<td>
**Data exploitation and sharing**
</td> </tr>
<tr>
<td>
Data exploitation (purpose/use of the
data analysis)
</td>
<td>
Main use will be the automatic detection of defects by two methods: CAD based
inspection and GD&T analysis.
</td> </tr>
<tr>
<td>
Data access policy /
Dissemination level
</td>
<td>
Confidential, except parts authorized by the industrial partners.
</td> </tr>
<tr>
<td>
Data sharing, re-use and distribution
</td>
<td>
The data will be shared using the Sensor network manager, and stored in the
repository for further future analysis.
</td> </tr>
<tr>
<td>
Embargo periods
</td>
<td>
None
</td> </tr>
<tr>
<td>
**Archiving and preservation (including storage and backup)**
</td> </tr>
<tr>
<td>
Data storage (including backup)
</td>
<td>
Data will be stored in the Z-Fact0r repository and by the 3D Pointcloud
analysis software.
</td> </tr> </table>
<table>
<tr>
<th>
**DS.DATAPIXEL.CADModel**
</th> </tr>
<tr>
<td>
**Data Identification**
</td> </tr> </table>
<table>
<tr>
<th>
Dataset description
</th>
<th>
The CADModel is the description of the surfaces and geometries of the designed
part. It is representing the 3D information of the manufactured part model,
and will be utilized for CAD based inspection.
</th> </tr>
<tr>
<td>
Source (e.g. which
device?)
</td>
<td>
Industrial partner’s CAD modelling software and DATAPIXEL 3D Pointcloud
Analysis software.
</td> </tr>
<tr>
<td>
**Partners activities and responsibilities**
</td> </tr>
<tr>
<td>
Partner owner of the device
</td>
<td>
Industrial partners (MICROSEMI, INTERSEALS, DURIT) and DATAPIXEL.
</td> </tr>
<tr>
<td>
Partner in charge of the data collection (if different)
</td>
<td>
Same
</td> </tr>
<tr>
<td>
Partner in charge of the data analysis (if different)
</td>
<td>
DATAPIXEL
</td> </tr>
<tr>
<td>
Partner in charge of the data storage (if different)
</td>
<td>
DATAPIXEL and Z-Fact0r repository
</td> </tr>
<tr>
<td>
WPs and tasks
</td>
<td>
WP2 and WP3
</td> </tr>
<tr>
<td>
**Standards**
</td> </tr>
<tr>
<td>
Info about metadata (Production and storage dates, places) and documentation?
</td>
<td>
Metadata includes part identification, date and time of data generation. CAD
Model is part of the information associated with the manufactured parts.
</td> </tr>
<tr>
<td>
Standards, Format, Estimated volume of data.
</td>
<td>
STEP format
</td> </tr>
<tr>
<td>
**Data exploitation and sharing**
</td> </tr>
<tr>
<td>
Data exploitation
(purpose/use of the
data analysis)
</td>
<td>
Main use will be the automatic detection of deviations based in local regions
and the extraction of nominal values for GD&T analysis.
</td> </tr>
<tr>
<td>
Data access policy /
Dissemination level
</td>
<td>
Confidential, except parts authorized by the industrial partners.
</td> </tr>
<tr>
<td>
(Confidential, only for members of the _CCS_ ) /
Public
</td>
<td>
</td> </tr>
<tr>
<td>
Data sharing, re-use and distribution
(How?)
</td>
<td>
The data will be shared by the 3D Pointcloud Analysis module, and stored in
the repository for further future analysis.
</td> </tr>
<tr>
<td>
Embargo periods
(if any)
</td>
<td>
None
</td> </tr>
<tr>
<td>
**Archiving and preservation (including storage and backup)**
</td> </tr>
<tr>
<td>
Data storage (including backup): where? For how long?
</td>
<td>
Data will be stored in the Z-Fact0r repository and by the 3D Pointcloud
analysis software.
</td> </tr> </table>
<table>
<tr>
<th>
**DS.DATAPIXEL.DeviationMaps**
</th> </tr>
<tr>
<td>
**Data Identification**
</td> </tr>
<tr>
<td>
Dataset description
</td>
<td>
The deviation map is a 3D representation of surface deviations calculated
between a captured Pointcloud and the reference CAD model. The deviation map
is represented as a list of regions with their corresponding deviation.
Typically, the regions are polygonal regions with their associated deviation.
</td> </tr>
<tr>
<td>
Source
</td>
<td>
DATAPIXEL 3D Pointcloud Analysis software.
</td> </tr>
<tr>
<td>
**Partners activities and responsibilities**
</td> </tr>
<tr>
<td>
Partner owner device
</td>
<td>
DATAPIXEL
</td> </tr>
<tr>
<td>
Partner in charge of data collection
</td>
<td>
DATAPIXEL
</td> </tr>
<tr>
<td>
Partner in charge of
data analysis
</td>
<td>
DATAPIXEL
</td> </tr>
<tr>
<td>
Partner in charge of data storage
</td>
<td>
DATAPIXEL and Z-Fact0r repository.
</td> </tr>
<tr>
<td>
WPs and tasks
</td>
<td>
WP2 and WP3
</td> </tr>
<tr>
<td>
**Standards**
</td> </tr>
<tr>
<td>
Info about metadata (Production and storage dates, places, and documentation)
</td>
<td>
Metadata includes part identification, date and time of data generation.
Deviation Map is part of the information associated with the manufactured
parts.
</td> </tr>
<tr>
<td>
Standards, Format, Estimated volume of data.
</td>
<td>
A polygonal mesh with an associated deviation number in mm. Most common
formats are STL with annotated deviations and PLY. Typically, deviation maps
have a size between 100 K to 1M polygons, or 10Mbytes to 100 Mbytes.
</td> </tr>
<tr>
<td>
**Data exploitation and sharing**
</td> </tr>
<tr>
<td>
Data exploitation (purpose/use of the
data analysis)
</td>
<td>
Main use will be the automatic detection of defects based in local deviations.
A deviation threshold can be defined to identify defects.
</td> </tr>
<tr>
<td>
Data access policy /
Dissemination level
</td>
<td>
Confidential, except parts authorized by the industrial partners.
</td> </tr>
<tr>
<td>
Data sharing, re-use and distribution
</td>
<td>
The data will be shared by the 3D Pointcloud Analysis module, and stored in
the repository for further future analysis.
</td> </tr>
<tr>
<td>
Embargo periods
</td>
<td>
None
</td> </tr>
<tr>
<td>
**Archiving and preservation (including storage and backup)**
</td> </tr>
<tr>
<td>
Data storage (including backup)
</td>
<td>
Data will be stored in the Z-Fact0r repository and by the 3D Pointcloud
analysis software.
</td> </tr> </table>
<table>
<tr>
<th>
**DS.DATAPIXEL.MeasurementPlan**
</th> </tr>
<tr>
<td>
**Data Identification**
</td> </tr>
<tr>
<td>
Dataset description
</td>
<td>
The MP is a definition of the GD& to be measured in the Pointcloud. The MP
contains a detailed definition of geometrical elements and the tolerances
associated to them. This information is the input to the Geometrical Feature
Extraction module of the
3D Pointcloud Analysis software
</td> </tr>
<tr>
<td>
Source
</td>
<td>
DATAPIXEL 3D Pointcloud Analysis software. Normally the MP is extracted from
the geometrical information contained in the CAD model
</td> </tr>
<tr>
<td>
**Partners activities and responsibilities**
</td> </tr>
<tr>
<td>
Partner owner device
</td>
<td>
DATAPIXEL
</td> </tr>
<tr>
<td>
Partner in charge of data collection
</td>
<td>
DATAPIXEL
</td> </tr>
<tr>
<td>
Partner in charge of
data analysis
</td>
<td>
DATAPIXEL
</td> </tr>
<tr>
<td>
Partner in charge of data storage
</td>
<td>
DATAPIXEL and Z-Fact0r repository
</td> </tr>
<tr>
<td>
WPs and tasks
</td>
<td>
WP2 and WP3
</td> </tr>
<tr>
<td>
**Standards**
</td> </tr>
<tr>
<td>
Info about metadata (Production and storage dates, places, and documentation)
</td>
<td>
Metadata includes project identification, date and time of data generation. MP
is part of the project information.
</td> </tr>
<tr>
<td>
Standards, Format, Estimated volume of data.
</td>
<td>
The standard format for MP can be QIF or DMO.
</td> </tr>
<tr>
<td>
**Data exploitation and sharing**
</td> </tr>
<tr>
<td>
Data exploitation (purpose/use of the
data analysis)
</td>
<td>
Main use will be automatic measurement of dimensions and geometries based in
nominal values and tolerances. This information will be used for defect
detection and process analysis.
</td> </tr>
<tr>
<td>
Data access policy /
Dissemination level
</td>
<td>
Confidential, except parts authorized by the industrial partners.
</td> </tr>
<tr>
<td>
Data sharing, re-use and distribution
</td>
<td>
The data will be shared by the 3D Pointcloud Analysis module, and stored in
the repository for further future analysis.
</td> </tr>
<tr>
<td>
Embargo periods
</td>
<td>
None
</td> </tr>
<tr>
<td>
**Archiving and preservation (including storage and backup)**
</td> </tr>
<tr>
<td>
Data storage (including backup)
</td>
<td>
Data will be stored in the Z-Fact0r repository and by the 3D Pointcloud
analysis software.
</td> </tr> </table>
<table>
<tr>
<th>
**DS.DATAPIXEL.MeasurementResults**
</th> </tr>
<tr>
<td>
**Data Identification**
</td> </tr> </table>
<table>
<tr>
<th>
Dataset description
</th>
<th>
The Measurement Results are the set of measurement values extracted from the
Pointcloud based in the MP.
</th> </tr>
<tr>
<td>
Source
</td>
<td>
DATAPIXEL 3D Pointcloud Analysis software.
</td> </tr>
<tr>
<td>
**Partners activities and responsibilities**
</td> </tr>
<tr>
<td>
Partner owner device
</td>
<td>
DATAPIXEL
</td> </tr>
<tr>
<td>
Partner in charge of data collection
</td>
<td>
DATAPIXEL
</td> </tr>
<tr>
<td>
Partner in charge of
data analysis
</td>
<td>
DATAPIXEL
</td> </tr>
<tr>
<td>
Partner in charge of data storage
</td>
<td>
DATAPIXEL and Z-Fact0r repository.
</td> </tr>
<tr>
<td>
WPs and tasks
</td>
<td>
WP2 and WP3
</td> </tr>
<tr>
<td>
**Standards**
</td> </tr>
<tr>
<td>
Info about metadata (Production and storage dates, places, and documentation)
</td>
<td>
Metadata includes part identification, date and time of data generation.
Measurement Results is part of the information associated with the
manufactured parts.
</td> </tr>
<tr>
<td>
Standards, Format, Estimated volume of data.
</td>
<td>
The standard format for MP can be QIF or DMO.
</td> </tr>
<tr>
<td>
**Data exploitation and sharing**
</td> </tr>
<tr>
<td>
Data exploitation (purpose/use of the
data analysis)
</td>
<td>
Main use will be the automatic detection of based in geometrical deviations.
</td> </tr>
<tr>
<td>
Data access policy /
Dissemination level
</td>
<td>
Confidential, except parts authorized by the industrial partners.
</td> </tr>
<tr>
<td>
Data sharing, re-use and distribution
</td>
<td>
The data will be shared by the 3D Pointcloud Analysis module, and stored in
the repository for further future analysis.
</td> </tr>
<tr>
<td>
Embargo periods
</td>
<td>
None
</td> </tr>
<tr>
<td>
**Archiving and preservation (including storage and backup)**
</td> </tr>
<tr>
<td>
Data storage (including backup)
</td>
<td>
Data will be stored in the Z-Fact0r repository and by the 3D Pointcloud
analysis software.
</td> </tr> </table>
<table>
<tr>
<th>
**DS.CERTH/IRETETH.DataConditioning**
</th> </tr>
<tr>
<td>
**Data Identification**
</td> </tr>
<tr>
<td>
Dataset description
</td>
<td>
Data collected by DATAPIXEL’s laser system or other complementary data sources
for defect detection.
</td> </tr>
<tr>
<td>
Source
</td>
<td>
Should be defined by DATAPIXEL.
</td> </tr>
<tr>
<td>
**Partners activities and responsibilities**
</td> </tr>
<tr>
<td>
Partner owner device
</td>
<td>
DATAPIXEL
</td> </tr>
<tr>
<td>
Partner in charge of data collection
</td>
<td>
DATAPIXEL + the relevant end user (manufacturer) depending on the use case.
</td> </tr>
<tr>
<td>
Partner in charge of
data analysis
</td>
<td>
IRETETH/CERTH
</td> </tr>
<tr>
<td>
Partner in charge of data storage
</td>
<td>
TO BE DEFINED
</td> </tr>
<tr>
<td>
WPs and tasks
</td>
<td>
WP2 / T2.1 - T2.2
</td> </tr>
<tr>
<td>
**Standards**
</td> </tr>
<tr>
<td>
Info about metadata (Production and storage dates, places, and documentation)
</td>
<td>
Should be discussed between DATAPIXEL and IRETETH/CERTH.
</td> </tr>
<tr>
<td>
Standards, Format, Estimated volume of data.
</td>
<td>
Should be determined by DATAPIXEL. In IRETETH/CERTH we are open to use
different data formats with a preference in raw data formats. As far as the
volume, the more the better. Ideally, we would like to have hundreds of
measurements per product (e.g. 500 per case including defected and non-
defected).
</td> </tr>
<tr>
<td>
**Data exploitation and sharing**
</td> </tr>
<tr>
<td>
Data exploitation (purpose/use of the
data analysis)
</td>
<td>
This should be discussed within the relevant partners.
</td> </tr>
<tr>
<td>
Data access policy /
Dissemination level
</td>
<td>
This should be discussed within the consortium and approved by the DEM.
</td> </tr>
<tr>
<td>
</td>
<td>
</td> </tr>
<tr>
<td>
Data sharing, re-use and distribution
</td>
<td>
This should be discussed within the relevant partners.
</td> </tr>
<tr>
<td>
Embargo periods
</td>
<td>
None
</td> </tr>
<tr>
<td>
**Archiving and preservation (including storage and backup)**
</td> </tr>
<tr>
<td>
Data storage (including backup)
</td>
<td>
We need to see who is responsible for the data storage task.
</td> </tr> </table>
<table>
<tr>
<th>
**DS.SIR.RoboticCellData**
</th> </tr>
<tr>
<td>
**Data Identification**
</td> </tr>
<tr>
<td>
Dataset description
</td>
<td>
Collections of data from SIR robotic deburring cell. These collections of data
contain information about machine conditions, algorithms, machine data, plant
conditions, process KPIs.
</td> </tr>
<tr>
<td>
Source
</td>
<td>
SIR robotic deburring cell.
</td> </tr>
<tr>
<td>
**Partners activities and responsibilities**
</td> </tr>
<tr>
<td>
Partner owner device
</td>
<td>
SIR
</td> </tr>
<tr>
<td>
Partner in charge of data collection
</td>
<td>
SIR with support of technological partners involved in the task.
</td> </tr>
<tr>
<td>
Partner in charge of
data analysis
</td>
<td>
SIR with support of technological partners involved in the task.
</td> </tr>
<tr>
<td>
Partner in charge of data storage
</td>
<td>
SIR
</td> </tr>
<tr>
<td>
WPs and tasks
</td>
<td>
T2.3 in WP2
</td> </tr>
<tr>
<td>
**Standards**
</td> </tr>
<tr>
<td>
Info about metadata (Production and storage dates, places, and documentation)
</td>
<td>
\--
</td> </tr>
<tr>
<td>
Standards, Format, Estimated volume of data.
</td>
<td>
Mainly consisting in MS documents released using the following formats (.doc,
.pptx and .xls files, images for visualizing and conceptualizing the use cases
will be released as PDF files), UML
</td> </tr>
<tr>
<td>
</td>
<td>
documents, machine algorithms (various type of informatics languages C#,
RAPID, etc).
</td> </tr>
<tr>
<td>
**Data exploitation and sharing**
</td> </tr>
<tr>
<td>
Data exploitation (purpose/use of the
data analysis)
</td>
<td>
The datasets collected should be used by SIR to achieve the objectives of
T2.3.
</td> </tr>
<tr>
<td>
Data access policy /
Dissemination level
</td>
<td>
Confidential
</td> </tr>
<tr>
<td>
Data sharing, re-use and distribution
</td>
<td>
No discussion about this matter has been done so far, and should not be shared
with entities outside of Z-Fact0r consortium.
</td> </tr>
<tr>
<td>
Embargo periods
</td>
<td>
None
</td> </tr>
<tr>
<td>
**Archiving and preservation (including storage and backup)**
</td> </tr>
<tr>
<td>
Data storage (including backup)
</td>
<td>
Documents will be store in FREEDCAMP document management system. Machine data,
algorithms and machines backups will be store in the SIR internal repository.
</td> </tr> </table>
<table>
<tr>
<th>
**DS.SIR.IndustrialPartnersData**
</th> </tr>
<tr>
<td>
**Data Identification**
</td> </tr>
<tr>
<td>
Dataset description
</td>
<td>
Collections of data from industrial partners. These collections of data
contain information about machine conditions, plant conditions, process KPIs
of an Industrial production plant.
</td> </tr>
<tr>
<td>
Source
</td>
<td>
Industrial partners’ production plant, internal reports, operators.
</td> </tr>
<tr>
<td>
**Partners activities and responsibilities**
</td> </tr>
<tr>
<td>
Partner owner device
</td>
<td>
Industrial partners.
</td> </tr>
<tr>
<td>
Partner in charge of data collection
</td>
<td>
Industrial partner with support of SIR.
</td> </tr>
<tr>
<td>
Partner in charge of
data analysis
</td>
<td>
Task leader
</td> </tr>
<tr>
<td>
Partner in charge of data storage
</td>
<td>
Task leader and SIR
</td> </tr>
<tr>
<td>
WPs and tasks
</td>
<td>
WP1
</td> </tr>
<tr>
<td>
**Standards**
</td> </tr>
<tr>
<td>
Info about metadata (Production and storage dates, places, and documentation)
</td>
<td>
\--
</td> </tr>
<tr>
<td>
Standards, Format, Estimated volume of data.
</td>
<td>
Mainly consisting in MS documents released using the following formats (.doc,
.pptx and .xls files, images for visualizing and conceptualizing the use cases
will be released as PDF files) and
UML documents. The metadata standard proposed is the CERIF.
</td> </tr>
<tr>
<td>
**Data exploitation and sharing**
</td> </tr>
<tr>
<td>
Data exploitation (purpose/use of the
data analysis)
</td>
<td>
Only for members of the CCS.
</td> </tr>
<tr>
<td>
Data access policy /
Dissemination level
</td>
<td>
The information leading to the preparation of the following deliverable might
be confidential as the following deliverables are marked as confidential:
* D1.1 Z-Fact0r User requirements DURIT M3
* D.1.3 Z-Fact0r system architecture EPFL M5
* D1.5 Report on Z-Fact0r strategy implementation and risk analysis EPFL M18
</td> </tr>
<tr>
<td>
Data sharing, re-use and distribution
</td>
<td>
For the time being data are expected to be used internally as input by the
other WPs. However, D1.2 Report on the analysis of SoA, existing and past
projects initiatives due by CERTH at M2 and D1.4 Z-Fact0r Use Cases due by
INTERSEALS at M6 are expected to be released publicly.
</td> </tr>
<tr>
<td>
Embargo periods
</td>
<td>
None
</td> </tr>
<tr>
<td>
**Archiving and preservation (including storage and backup)**
</td> </tr>
<tr>
<td>
Data storage (including backup)
</td>
<td>
Documents are stored in FREEDCAMP document management system. Data and
documents will be up to five years after the project completion. Revisions
will be stored in the FREEDCAMP document management system.
</td> </tr> </table>
_2.2.5.2 Dataset per task_
In addition, data information that will be generated in the different tasks,
has been identified by the different partners;
<table>
<tr>
<th>
Task: **T1.1- T1.5, T2.1-T2.5, T6.1-T6.3**
</th> </tr>
<tr>
<td>
WP: WP1 + WP2 + WP6
</td> </tr>
<tr>
<td>
WP Leader: SIR
</td> </tr>
<tr>
<td>
Author: G. Tinker (MICROSEMI)
</td> </tr> </table>
#### 1) Scope
* State the purpose of the data generation/ collection
Main aims for MICROSEMI are for the improvement of the dispense process and
its analysis. Other opportunities for the system and the data might be
learning how much glue might be needed for a new size die (prediction) and
checking LCP panels for surface defects prior to dispensing.
* Explain the relation to the objectives of the project/WP/Task
The data being collected will enable the KPIs to be monitored and to generate
history for prediction and correction of the process.
#### 2) Types
* Are the data digital/hard copies or both? Digital
* What types of data will the WP generate/collect? Specify the types and formats of data
generated/collected (for example .xls files, .ppt files, emails, .doc files)
MICROSEMI preference for data would be:
* xls for sensor history data
* xls for a volumetric measurement of the glue dispensed o jpeg images of the surface
* Is the data generated or collected from other sources under certain terms and conditions? TBC – not believed to be a requirement at this stage
* How is generated/collected? Specify the origin of the data and instruments/tools that will be used.
TBC – Possibilities of either Pointcloud clouds from DATAPIXEL or micro –
profilometry data generated by CERTH or both may need to be utilised
* State the expected size of the data (if known)
Currently unknown but Good IT infrastructure at MICROSEMI means Data size
should not be a constraint
* Standards None
#### 3) Ownership
* Is another organization contributing to the data development?
TBC – If the answer does end up being yes, it will be a member of the Z-Fact0r
project **4) Reuse of existing data**
* Specify if existing data is being re-used (if any)
No Data is currently being collected other than the process improvement
project that has already been completed. **5) Data use**
* How will this data be exploited and/or shared/made accessible for verification and re-use? Outline the data utility: to whom will it be useful Much depends on:
Who wants access?
How often they want access?
How big the files are?
MICROSEMI does have an FTP site – terms of access to this will have to be
agreed by MICROSEMI and the members of the Z-Fact0r project. **6)
Dissemination Level of Data**
* Confidentiality/ Sensitive data. If data cannot be made available, explain why. Who will have access?
TBC – this depends a little on the data being collected and if it is deemed
sensitive.
#### 7) Storage and disposal
* How will this data be stored?
Probably on a local PC with the option to back up data (depending on size) to
the MICROSEMI servers at Caldicot.
* How long is it required to keep the data? Expire date. Will revisions be kept?
Duration of the project, and potentially five years after the completion of
the project.
<table>
<tr>
<th>
</th>
<th>
Task: **T1.1 -T1.5**
</th>
<th>
</th> </tr>
<tr>
<td>
WP: 1 USER REQUIREMENTS – SPECIFICATIONS – USE CASE ANALYSIS
</td> </tr>
<tr>
<td>
WP Leader: SIR
</td> </tr>
<tr>
<td>
Author: Marcello Pellicciari (SIR)
</td> </tr> </table>
#### 1) Scope
* State the purpose of the data generation/ collection
Qualitative and quantitative data will be produced:
1. WP1 data generated and collected are aimed at defining both the user and system requirements and use cases (T.1.1 and T.1.4)
2. Bibliographic and data-based information (e.g. Cordis) for T.1.2 State of the art to analyse new, live and past projects, initiatives in the field.
3. Workflow and UML diagrams, blue prints will be generated to design the ZFact0r architecture (T.1.3)
4. Report on Z-Fact0r strategy and risk analysis (T.1.5) to monitor the status of the manufacturing process in real time.
* Explain the relation to the objectives of the project/WP/Task Data are related to all tasks WP1. (See above)
#### 2) Types
* Are the data digital/hard copies or both?
Digital data and documents will be produced.
* What types of data will the WP generate/collect? Specify the types and formats of data
generated/collected (for example .xls files, .ppt files, emails, .doc files)
Data are preserved in their incoming format, Files generated and used will be
mainly consisting in MS documents released using the following formats (.doc,
.pptx and .xls files, images for visualizing and conceptualizing the use cases
will be released as PDF files).
* Is the data generated or collected from other sources under certain terms and conditions?
No
* How is generated/collected? Specify the origin of the data and instruments/tools that will be used.
Unified Modelling Language will be used
* State the expected size of the data (if known) Not yet a clear idea.
* Standards Not at the moment.
#### 3) Ownership
Is another organization contributing to the data development?
To date no other external organization is contributing to the data development
activities of WP1.
#### 4) Reuse of existing data
Specify if existing data is being re-used (if any)
For the time being data are expected to be used internally as input by the
other WPs. However, D1.2 Report on the analysis of SoA, existing and past
projects initiatives due by CERTH at M2 and D1.4 Z-Fact0r Use Cases due by
INTERSEALS at M6 are expected to be released publicly.
#### 5) Data use
How will this data be exploited and/or shared/made accessible for
verification and re-use? Outline the data utility: to whom will it be useful
D1.2 Report on the analysis of SoA, existing and past projects initiatives due
by CERTH at M2 and D1.4 Z-Fact0r Use Cases due by INTERSEALS at M6 are
expected to be released publicly.
#### 6) Dissemination Level of Data
Confidentiality/ Sensitive data. If data cannot be made available, explain
why. Who will have access?
The information leading to the preparation of the following deliverable might
be confidential as the following deliverables are marked as confidential:
* D1.1 Z-Fact0r User requirements DURIT M3 o D.1.3 Z-Fact0r system architecture EPFL M5
* D1.5 Report on Z-Fact0r strategy implementation and risk analysis EPFL M18
#### 7) Storage and disposal
* How will this data be stored?
Documents are stored in FREEDCAMP document management system. Content creator
upload the relevant file.
* How long is it required to keep the data? Expire date. Will revisions be kept?
Data and documents will be up to five years after the project completion.
Revisions will be stored in the FREEDCAMP document management system.
<table>
<tr>
<th>
Task: T.1-USER REQUIREMENTS
</th> </tr>
<tr>
<td>
WP: WP1 + WP6
</td> </tr>
<tr>
<td>
WP Leader: SIR
</td> </tr>
<tr>
<td>
Author: E. Soares (DURIT)
</td> </tr> </table>
#### 1) Scope
* State the purpose of the data generation/ collection
Automated quality control, with high accuracy level and predictive system for
defect generation based on online continuous monitoring.
* Explain the relation to the objectives of the project/WP/Task
The data collected will enable to detect probability or trends that lead to
defects that normally result in scrapping the parts.
#### 2) Types
* Are the data digital/hard copies or both?
Both
* What types of data will the WP generate/collect? Specify the types and formats of data generated/collected (for example .xls files, .ppt files, emails, .doc files) o xls for sensor history data o jpeg images of the defects
* Is the data generated or collected from other sources under certain terms and conditions? Possibly collected by sensors at an bench top apparatus.
* How is generated/collected? Specify the origin of the data and instruments/tools that will be used.
Optical and physical sensors to be studied.
* State the expected size of the data (if known) A few MB per type of part. Perhaps 1 GB per day.
* Standards
#### 3) Ownership
* Is another organization contributing to the data development? Only partners from Z-Fact0r **4) Reuse of existing data**
* Specify if existing data is being re-used (if any) No Data is currently being collected
#### 5) Data use
* How will this data be exploited and/or shared/made accessible for verification and re-use? Outline the data utility: to whom will it be useful
System software, cloud where DURIT servers are stored and some local pc. data
will be used mainly by Quality Department **6) Dissemination Level of Data**
* Confidentiality/ Sensitive data. If data cannot be made available, explain why. Who will have access?
Partners from Z-Fact0r can have access during the project. In our premises
access is limited to quality operators.
#### 7) Storage and disposal
* How will this data be stored?
Probably on a local PC + DURIT servers at the cloud.
* How long is it required to keep the data? Expire date. Will revisions be kept?
five years minimum
<table>
<tr>
<th>
Task: **T1.4, T6.2**
</th> </tr>
<tr>
<td>
WP: WP1 (User Requirements), WP6 (Demonstration activities)
</td> </tr>
<tr>
<td>
WP Leader: SIR, INTERSEALS
</td> </tr>
<tr>
<td>
Author: Pierino Izzo (INTERSEALS)
</td> </tr> </table>
#### 1) Scope
* State the purpose of the data generation/ collection
* Explain the relation to the objectives of the project/WP/Task
The INTERSEALS actual data are generated and exploited to plan and manage the
Customer orders, to planning the production, getting feedback by the
production phase, managing of the maintenance.
The Z-Fact0r will useful for all the objectives of the software itself:
Z-DETECT, ZPREDICT, Z-PREVENT, (Z-REPAIR), Z-MANAGER.
This data could then be connected to the INTERSEALS ERP and Quality Data
Management.
#### 2) Types
* Are the data digital/hard copies or both? The data are digital.
* What types of data will the WP generate/collect? Specify the types and formats of data generated/collected (for example .xls files, .ppt files, emails, .doc files). They could be of two kinds: .xls, SQL formats, emails.
* Is the data generated or collected from other sources under certain terms and conditions? For Z-Fact0r, there isn’t this possibility.
* How is generated/collected? Specify the origin of the data and instruments/tools that will be used.
The data for Z-Fact0r will be generate by:
* FT-IR (Infrared spectroscopy): for material checking.
* System control (CoMo by Kistler) that concentrate the data from the sensor cavities pressure.
* Injection moulding machine parameters: these data can be achieved by the connection to the PLC of the injection Machine (the PLC can be Siemens, Omron, Moog).
* Data from visual and dimensional checking machine (DATAPIXEL will be involved in this)
* Data from the worker that work beside the production cell and will communicate with the software using Augmented Reality.
* State the expected size of the data (if known) At the moment, our servers are of about 150Gbyte.
* Standards
SQL server
#### 3) Ownership
* Is another organization contributing to the data development?
No, we have all the competence to generate and manage the data. **4) Reuse of
existing data**
* Specify if existing data is being re-used (if any) The existing data are re-used as really useful:
* for making quotations
* for process study
* quality control
* traceability
* claim answer
#### 5) Data use
How will this data be exploited and/or shared/made accessible for
verification and re-use?
Outline the data utility: to whom will it be useful See point 4
#### 6) Dissemination Level of Data
Confidentiality/ Sensitive data. If data cannot be made available, explain
why. Who will have access?
The data could be accessible after signing the INTERSEALS NDA.
#### 7) Storage and disposal
* How will this data be stored?
Workstation server, SQL server
* How long is it required to keep the data? Expire date. Will revisions be kept?
At least for 6 months for the dynamic production data and five years for the
static data.
<table>
<tr>
<th>
Task: **Task 1.3**
</th> </tr>
<tr>
<td>
WP: WP 1
</td> </tr>
<tr>
<td>
WP Leader: SIR
</td> </tr>
<tr>
<td>
Author: EPFL
</td> </tr> </table>
#### 1) Scope
* State the purpose of the data generation/collection
Development of the architecture of the Z-Fact0r system (i.e. functional view,
information view, deployment view, etc.) & the definition and description of
the main components
* Explain the relation to the objectives of the project/WP/Task
A complete description of the modules included in the detailed view is
provided in order to point out the responsibilities of each module and their
interactions with the global System Architecture
#### 2) Types
* Are the data digital/hard copies or both? Digital
* What types of data will the WP generate/collect? Specify the types and formats of data generated/collected (for example .xls files, .ppt files, emails, .doc files) Emails, doc files, .vpp files etc.
* Is the data generated or collected from other sources under certain terms and conditions? No
* How is generated/collected? Specify the origin of the data and instruments/tools that will be used.
Visual Paradigm V14.0 for component diagrams
* State the expected size of the data (if known)
Not known
* Standards
UML for component diagrams
**3) Ownership**
Is another organization contributing to the data development?
#### ALL Z-Fact0r partners 4) Reuse of existing data
Specify if existing data is being re-used (if any)
No
#### 5) Data use
* How will this data be exploited and/or shared/made accessible for verification and re-use? Outline the data utility: to whom will it be useful
As described in GA document. **6) Dissemination Level of Data**
* Confidentiality/ Sensitive data. If data cannot be made available, explain why. Who will have access?
As described in GA document. Accessible to Z-Fact0r consortium members
including the commission services. Based on further discussions and agreement
between partners, part of data (e.g. overall approach and architecture etc.)
could be published in the form of an article or conference proceedings for
dissemination purposes.
#### 7) Storage and disposal
* How will this data be stored?
All the collected info/data will be delivered in the deliverable D1.3
* How long is it required to keep the data? Expire date. Will revisions be kept?
Duration of the project, and potentially five years after the completion of
the project for further research; but it will be with consent of the
consortium members in case the data is to be accessed and used for the purpose
of academic exercise (e.g. teaching and publications)
<table>
<tr>
<th>
Task: **Task 1.5**
</th> </tr>
<tr>
<td>
WP: WP 1
</td> </tr>
<tr>
<td>
WP Leader: SIR
</td> </tr>
<tr>
<td>
Author: EPFL
</td> </tr> </table>
#### 1) Scope
* State the purpose of the data generation/ collection
Monitoring the application of the various Z-Fact0r strategies and risk
analysis will determine how well & successful the implementation of the
strategies will be in the use cases, aligned with the project objectives.
* Explain the relation to the objectives of the project/WP/Task
Data collected and generated will support part, machine and process level
itself continuous monitoring in real time. Actions of correctness will be
suggested in case of error occurrence. Re-evaluations of the deployed
strategies will be conducted. Also, manufacturing equipment, part and process
status measurement analysis will be adapted to provide the means for process
validation. Z-Fact0r strategies developed according to objectives.
#### 2) Types
* Are the data digital/hard copies or both? Digital
* What types of data will the WP generate/collect? Specify the types and formats of data
generated/collected (for example .xls files, .ppt files, emails, .doc files)
Data types could be: .doc files, emails, SQL DB programs, .XML files etc.
* Is the data generated or collected from other sources under certain terms and conditions?
* How is generated/collected? Specify the origin of the data and instruments/tools that will be used.
Machine sensors, network infrastructure /middleware (device manager)/ shop-
floor (Z-
Fact0r repository for machine processes, part condition and worker’s actions)
* State the expected size of the data (if known)
Not known
* Standards
#### 3) Ownership
Is another organization contributing to the data development?
ALL Z-Fact0r partners
#### 4) Reuse of existing data
Specify if existing data is being re-used (if any)
Data will be reused for corrective actions on the deployed strategies and
actions will be suggested based on correlations by the automatic decision
support mechanism.
#### 5) Data use
* How will this data be exploited and/or shared/made accessible for verification and re-use?
Outline the data utility: to whom will it be useful As described in GA
document. **6) Dissemination Level of Data**
* Confidentiality/ Sensitive data. If data cannot be made available, explain why. Who will have access?
Confidential, only for members of the consortium, Commission Services
#### 7) Storage and disposal
* How will this data be stored?
* How long is it required to keep the data? Expire date. Will revisions be kept?
Duration of the project, and potentially five years after the completion of
the project.
<table>
<tr>
<th>
Task: **Task 2.2**
</th> </tr>
<tr>
<td>
WP: 2
</td> </tr>
<tr>
<td>
WP Leader: EPFL
</td> </tr>
<tr>
<td>
Author: IRETETH/CERTH
</td> </tr> </table>
#### 1) Scope
* State the purpose of the data generation/ collection Data needed for formulation of data driven model.
* Explain the relation to the objectives of the project/WP/Task
Defect prediction from process inputs, correlated to Z-DETECT Module. **2)
Types**
* Are the data digital/hard copies or both? Digital
* What types of data will the WP generate/collect? Specify the types and formats of data
generated/collected (for example .xls files, .ppt files, emails, .doc files)
Can accept data in any format (*.csv, *.xls, etc). Data output will be in the
form of matlab files, (*.mat, *.m, etc).
* Is the data generated or collected from other sources under certain terms and conditions?
Data are collected from the manufacturing processes (end users’ collection
systems)
* How is generated/collected? Specify the origin of the data and instruments/tools that will be used.
State the expected size of the data (if known)
Standards
#### 3) Ownership
Is another organization contributing to the data development? No
#### 4) Reuse of existing data
Specify if existing data is being re-used (if any)
No
#### 5) Data use
* How will this data be exploited and/or shared/made accessible for verification and re-use?
Outline the data utility: to whom will it be useful As described on GA
document. **6) Dissemination Level of Data**
* Confidentiality/ Sensitive data. If data cannot be made available, explain why. Who will have access?
Confidential, only for members of the consortium, CCS.
#### 7) Storage and disposal
How will this data be stored?
How long is it required to keep the data? Expire date. Will revisions be kept?
Duration of the project, and potentially five years after the completion of
the project.
<table>
<tr>
<th>
Task: **Task 2.5**
</th> </tr>
<tr>
<td>
WP: WP 2
</td> </tr>
<tr>
<td>
WP Leader: EPFL
</td> </tr>
<tr>
<td>
Author: EPFL
</td> </tr> </table>
#### 1) Scope
* State the purpose of the data generation/ collection
Supervise and provide feedback for all the processes executed in the
production line, evaluating performance parameters and responding to defects,
keeping historical data. Send efficiently alarms to initiate actions, filter
out false alarms, increase confidence levels (through previously acquired
knowledge) of early defect detection and prediction.
* Explain the relation to the objectives of the project/WP/Task
KMS refers to an information and communication technology system for managing
knowledge in organizations for supporting creation, capture, storage and
dissemination of information. Facilitate the adoption of risk-based thinking
(in line with ISO 9001:2015) at enterprise level by supporting faster and
better decision making at shop-floor. Link the 5 intertwined zero-defect
strategies (i.e. Z-PREDICT, Z-PREVENT, Z-DETECT, ZREPAIR and Z-MANAGE).
Implement the designed Z-MANAGE strategy and interface with MES and/or other
high level manufacturing systems in-place.
* Provide the inference engine a second layer of autonomous decision support in relation to the 5 Z-Fact0r strategies.
* Update the monitoring and inspection conditions and constraints of the ES-DSS.
* Define rights for data sharing and exchanging internally with various enterprise systems and decision making units, as well as externally with customers and suppliers
#### 2) Types
* Are the data digital/hard copies or both? Digital
* What types of data will the WP generate/collect? Specify the types and formats of data
generated/collected (for example .xls files, .ppt files, emails, .doc files)
Data should be available at XML or JSON format.
* Is the data generated or collected from other sources under certain terms and conditions?
* How is generated/collected? Specify the origin of the data and instruments/tools that will be used.
It needs inputs from the Sensor Network (through the Device Manager), the
overall model of Production activities (through Z-Fact0r Repository) and
context – aware knowledge stemming from the Semantic Context Manager
(Ontology)
* State the expected size of the data (if known)
Estimation of the volume of data can be done only by the source.
* Standards
#### 3) Ownership
Is another organization contributing to the data development? Yes
#### 4) Reuse of existing data
Specify if existing data is being re-used (if any)
No
#### 5) Data use
How will this data be exploited and/or shared/made accessible for
verification and re-use? Outline the data utility: to whom will it be useful
Reaction to Incident detection, re-adaptation of the production processes and
notifying components of Z-Fact0r which has subscribed for these events.
#### 6) Dissemination Level of Data
Confidentiality/ Sensitive data. If data cannot be made available, explain
why. Who will have access? As described on GA document.
#### 7) Storage and disposal
* How will this data be stored?
In a main KM server and also into local terminals when appropriate indications
have been disseminated.
* How long is it required to keep the data? Expire date. Will revisions be kept?
Duration of the project, and potentially five years after the completion of
the project.
<table>
<tr>
<th>
Task: **T2.5 / T3.4**
</th> </tr>
<tr>
<td>
WP: WP2 / WP3
</td> </tr>
<tr>
<td>
WP Leader: CERTH / EPFL
</td> </tr>
<tr>
<td>
Author: Ziazios Konstantinos (ATLANTIS)
</td> </tr> </table>
#### 1) Scope
* State the purpose of the data generation/ collection o Early stage decision support system o Reverse supply chain system
* Explain the relation to the objectives of the project/WP/Task o Data will be used to model the early stage DSS.
o Models for the supply chain
#### 2) Types
* Are the data digital/hard copies or both? Digital.
* What types of data will the WP generate/collect? Specify the types and formats of data generated/collected (for example .xls files, .ppt files, emails, .doc files) o Images o CSV o JSON o Binary
* Is the data generated or collected from other sources under certain terms and conditions? Collected from sensors.
* How is generated/collected? Specify the origin of the data and instruments/tools that will be used.
o From laser scanning o From user input o Batch files
* State the expected size of the data (if known) Several GBs per day.
* Standards
Not known at this stage.
#### 3) Ownership
* Is another organization contributing to the data development? Only partners of the consortium **4) Reuse of existing data**
* Specify if existing data is being re-used (if any)
No
#### 5) Data use
How will this data be exploited and/or shared/made accessible for
verification and re-use?
Outline the data utility: to whom will it be useful o Used for modelling o To
visualise processes o Create visual KPI’s
#### 6) Dissemination Level of Data
Confidentiality/ Sensitive data. If data cannot be made available, explain
why. Who will have access?
* Avoiding storage of sensitive data. Stored encrypted always.
* Limited access to confidentiality data.
#### 7) Storage and disposal
How will this data be stored?
* On cloud only for the training / modelling period.
* No production data will store outside the shop floor.
How long is it required to keep the data? Expire date. Will revisions be kept?
Duration of the project, and potentially five years after the completion of
the project.
<table>
<tr>
<th>
Task: **T2.1, T2.2, T3.1**
</th> </tr>
<tr>
<td>
WP: WP2 / WP3
</td> </tr>
<tr>
<td>
WP Leader: CERTH / EPFL
</td> </tr>
<tr>
<td>
Author: Toni Ventura (DATAPIXEL)
</td> </tr> </table>
#### 1) Scope
* State the purpose of the data generation/ collection o High accuracy and high resolution 3D Pointcloud of scanned parts. o CAD Model by description of the surfaces and geometries of the designed part.
* Deviation map of a 3D representation of surface deviations calculated between a captured Pointcloud and the reference CAD model.
* MP, definition of the GD&T to be measured in the Pointcloud.
* Explain the relation to the objectives of the project/WP/Task
Data generation of WP2 and WP3 will be connected with Z-DETECT activities, in
particular with T2.1, T2.2 and T3.1.
#### 2) Types
* Are the data digital/hard copies or both? Digital
* What types of data will the WP generate/collect? Specify the types and formats of data
generated/collected (for example .xls files, .ppt files, emails, .doc files)
ASCII list of X Y Z, STEP format, STL with annotated deviations and PLY and
QIF or
DMO.
* Is the data generated or collected from other sources under certain terms and conditions?
Data will be stored in the Z-Fact0r repository and by the 3D Pointcloud
analysis software.
* How is generated/collected? Specify the origin of the data and instruments/tools that will be used.
DATAPIXEL 3D Scanner, 3D Pointcloud Analysis software and Industrial partner’s
CAD modelling software.
* State the expected size of the data (if known)
Typically, Pointcloud have a size between 100 K to 10M points, or 3Mbytes to
300 Mbytes and deviation maps have a size between 100 K to 1M polygons, or
10Mbytes to 100 MBytes.
* Standards
Metadata includes part identification, date and time of data generation,
collection, equipment. Pointcloud, CAD model, deviation map and measurement
results are part of the information associated with the manufactured parts.
Also, MP is part of the project information.
#### 3) Ownership
Is another organization contributing to the data development? Only partners
of the consortium
#### 4) Reuse of existing data
Specify if existing data is being re-used (if any)
No
#### 5) Data use
How will this data be exploited and/or shared/made accessible for verification
and re-use? Outline the data utility: to whom will it be useful
Main use will be the automatic detection of defects by two methods: CAD based
inspection and G&T analysis. Also, for automatic measurement of dimensions and
geometries based in nominal values and tolerances and local deviations. This
information will be used for defect detection and process analysis. The data
will be shared using the Sensor network manager,
3D Pointcloud Analysis module and stored in the repository for further future
analysis **6) Dissemination Level of Data**
Confidentiality/ Sensitive data. If data cannot be made available, explain
why. Who will have access?
Confidential, except parts authorized by the industrial partners.
#### 7) Storage and disposal
* How will this data be stored?
Data will be stored in the Z-Fact0r repository and by the 3D Pointcloud
analysis software.
* How long is it required to keep the data? Expire date. Will revisions be kept?
Duration of the project, and potentially five years after the completion of
the project.
<table>
<tr>
<th>
Task: **T3.2**
</th> </tr>
<tr>
<td>
WP: WP3
</td> </tr>
<tr>
<td>
WP Leader: CERTH
</td> </tr>
<tr>
<td>
Author: Simone Parrotta (HOLONIX)
</td> </tr> </table>
#### 1) Scope
* State the purpose of the data generation/ collection
Within this task data from sensors will be integrated and stored in Z-Fact0r.
Retrieved data will came from sensors and systems from industrial partners.
* Explain the relation to the objectives of the project/WP/Task
The task will define and develop the middleware and related tools for the
Z-Fact0r sensor data integrations.
#### 2) Types
* Are the data digital/hard copies or both?
Digital data will be stored in Cloud Based DB.
* What types of data will the WP generate/collect? Specify the types and formats of data generated/collected (for example .xls files, .ppt files, emails, .doc files)
XML, JSON, CSV
* Is the data generated or collected from other sources under certain terms and conditions?
Proper term and conditions will be defined later during the project
* How is generated/collected? Specify the origin of the data and instruments/tools that will be used.
Data will be collected from: new sensors placed in the shop floor to support
the processes monitoring; PLC; legacy systems.
* State the expected size of the data (if known)
* Standards
XML, JSON, CSV
#### 3) Ownership
Is another organization contributing to the data development?
Z-Fact0r industrial partners will provide confidential data regarding their
processes. **4) Reuse of existing data**
Specify if existing data is being re-used (if any)
#### 5) Data use
* How will this data be exploited and/or shared/made accessible for verification and re-use? They will be used internally for system testing and validation.
* Outline the data utility: to whom will it be useful **6) Dissemination Level of Data**
* Confidentiality/ Sensitive data. If data cannot be made available, explain why. Who will have access?
Data confidentiality/ sensitive have been already mentioned in the GA.
Processes data from industrial consortium partners should be kept confidential
based on internal company policies.
#### 7) Storage and disposal
* How will this data be stored?
Data will be stored within Z-Fact0r repository.
* How long is it required to keep the data? Expire date. Will revisions be kept? At least five years after the project ends.
<table>
<tr>
<th>
Task: **Task 3.5**
</th> </tr>
<tr>
<td>
WP: WP 3
</td> </tr>
<tr>
<td>
WP Leader: CERTH
</td> </tr>
<tr>
<td>
Author: EPFL
</td> </tr> </table>
#### 1) Scope
* State the purpose of the data generation/collection
Data is required for the Z-Fact0r ontology development. Ontology describes
semantic models. The ontology will be used in order to drive the semantic
framework. Furthermore, it will be used for data integration, visualization,
inferencing/reasoning.
* Explain the relation to the objectives of the project/WP/Task
Context-aware shop-floor analysis and semantic model for the annotation and
description of the knowledge to represent manufacturing system performance.
The ontology will describe the basic entities of the project and model
relevant structures of multi-stage manufacturing processes.
#### 2) Types
* Are the data digital/hard copies or both? Digital
* What types of data will the WP generate/collect? Specify the types and formats of data
generated/collected (for example .xls files, .ppt files, emails, .doc files)
Required input will be data from Z-Fact0r repository (data concerning
machines, workers, actors, activities and processes, production data logs,
etc.), e.g. in XML, CSV, etc.
Generated output will be the semantic enrichment of shop-floor data for
representation of processes, actors, alarms, actions, work-pieces/products,
etc., e.g. as RDF Triplets
* Is the data generated or collected from other sources under certain terms and conditions? Data from Z-Fact0r repository (data concerning machines, workers, actors, activities and processes, production data logs, etc.)
* How is generated/collected? Specify the origin of the data and instruments/tools that will be used.
Data will be stored in a dedicated repository
* State the expected size of the data (if known)
Less than 1GB
* Standards
W3C-OWL, RDF
#### 3) Ownership
* Is another organization contributing to the data development? Z-Fact0r End-users (MICROSEMI, INTERSEALS, DURIT) **4) Reuse of existing data**
* Specify if existing data is being re-used (if any)
No
#### 5) Data use
* How will this data be exploited and/or shared/made accessible for verification and re-use? Outline the data utility: to whom will it be useful
The ontology will be used in order to drive the semantic framework.
Furthermore, it will be used for data integration, visualization,
inferencing/reasoning. **6) Dissemination Level of Data**
* Confidentiality/ Sensitive data. If data cannot be made available, explain why. Who will have access?
Accessible to Z-Fact0r consortium members including the commission services
#### 7) Storage and disposal
* How will this data be stored?
The Ontology will be uploaded in a server where it will be accessible to
Z-Fact0r consortium members including the commission services
* How long is it required to keep the data? Expire date. Will revisions be kept?
Duration of the project, and potentially five years after the completion of
the project.
<table>
<tr>
<th>
Task: **T4.1 - T4.3**
</th> </tr>
<tr>
<td>
WP: 4
</td> </tr>
<tr>
<td>
WP Leader: Brunel University London
</td> </tr>
<tr>
<td>
Author: Brunel
</td> </tr> </table>
#### 1) Scope
* State the purpose of the data generation/ collection
The purpose of the data collection and generation is to facilitate the
building of the eventbased model, green scheduler using the KPIs,
implementation of the scheduler and extracting cost functions. The raw data
will be collected from plants and the output will provide the metrics for
process and control optimisation to minimise defect.
* Explain the relation to the objectives of the project/WP/Task
The fulfilment of the tasks will lead to achieving: An event-base modelling
platform and green scheduler to identify the key parameters that influence and
have the largest effect of creation of defects in the productions process as
well as energy consumption and carbon emissions. They will assist in
customisation of the measurement of the KPIs for each industrial partner in
the consortium, and build the framework for implementing control and
optimisation solutions to minimise defect.
#### 2) Types
* Are the data digital/hard copies or both?
Mainly digital
* What types of data will the WP generate/collect? Specify the types and formats of data
generated/collected (for example .xls files, .ppt files, emails, .doc files)
Mainly DB driven files that can be converted to CSV, JSON, TXT, HTML, and SML
* Is the data generated or collected from other sources under certain terms and conditions? The agreed T&C of the consortium
* How is generated/collected? Specify the origin of the data and instruments/tools that will be used.
PLC, SCADA, Production Management Systems, Internet, and project Intranet.
* State the expected size of the data (if known) Large but not known at this stage.
* Standards
Control Area Network, TCP/IP.
#### 3) Ownership
* Is another organization contributing to the data development? Members of the consortium. **4) Reuse of existing data**
* Specify if existing data is being re-used (if any)
N/A
#### 5) Data use
How will this data be exploited and/or shared/made accessible for
verification and re-use? Outline the data utility: to whom will it be useful
Members of the consortium, in addition the results of the R&D project will be
disseminated according to the consortium agreement in the form of conference,
journal, specialist magazine/website outlets.
#### 6) Dissemination Level of Data
Confidentiality/ Sensitive data. If data cannot be made available, explain
why. Who will have access?
N/A within the sensitive and project oriented data will remain within the
boundaries of the consortium
#### 7) Storage and disposal
* How will this data be stored?
In local data storages defined and design specifically for the project. Brunel
University SERG Laboratories will have a dedicated storage and computing
facility for the project. The data will then be stored and utilised in
accordance with the T&C of the consortium agreement.
* How long is it required to keep the data? Expire date. Will revisions be kept?
Duration of the project, and potentially five years after the completion of
the project for further research; but it will be with consent of the
consortium members in case the data is to be accessed and used for the purpose
of academic exercise (e.g. teaching and publications)
<table>
<tr>
<th>
Task: **Task 4.2**
</th> </tr>
<tr>
<td>
WP: WP 4
</td> </tr>
<tr>
<td>
WP Leader: BRUNEL
</td> </tr>
<tr>
<td>
Author: EPFL
</td> </tr> </table>
#### 1) Scope
* State the purpose of the data generation/ collection
For the validation and verification of the KPI models, i.e. Productivity,
Efficiency, Quality (Customer Satisfaction), Environmental Impact, and
Inventory levels.
* Explain the relation to the objectives of the project/WP/Task
#### 2) Types
* Are the data digital/hard copies or both? Digital
* What types of data will the WP generate/collect? Specify the types and formats of data
generated/collected (for example .xls files, .ppt files, emails, .doc files)
.docs & formats for discrete event simulation (descriptive) models using off-
the-shelf simulation packages
* Is the data generated or collected from other sources under certain terms and conditions?
* How is generated/collected? Specify the origin of the data and instruments/tools that will be used.
Already installed actuators and sensors will be used for monitoring and
evaluating the KPIs.
* State the expected size of the data (if known)
N/A
* Standards
#### 3) Ownership
* Is another organization contributing to the data development? **4) Reuse of existing data**
* Specify if existing data is being re-used (if any)
#### 5) Data use
* How will this data be exploited and/or shared/made accessible for verification and re-use?
Outline the data utility: to whom will it be useful
Based on the prediction of the expected results and depending on the
measurements received for the evaluation of the KPIs through the use-cases
utilization they will be finetuned in order for afterwards on-line & real-time
application of them. Corrective actions will be considered for the production
line based on the results received. **6) Dissemination Level of Data**
* Confidentiality/ Sensitive data. If data cannot be made available, explain why. Who will have access?
The final deliverable report associated with this task will be public.
All the other data will be Accessible to Z-Fact0r consortium members.
#### 7) Storage and disposal
* How will this data be stored?
* How long is it required to keep the data? Expire date. Will revisions be kept?
Duration of the project, and potentially five years after the completion of
the project
<table>
<tr>
<th>
Task: **Task 4.4**
</th> </tr>
<tr>
<td>
WP: WP 4
</td> </tr>
<tr>
<td>
WP Leader: BRUNEL
</td> </tr>
<tr>
<td>
Author: EPFL
</td> </tr> </table>
#### 1) Scope
* State the purpose of the data generation/ collection
Design and development of the cost functions for each of the KPIs.
* Explain the relation to the objectives of the project/WP/Task
Models will be industry specific and will be defined as monetary loss
functions due to loss of Productivity (OEE, OLE, Resource Utilisation),
Efficiency (energy consumption per produced unit), Quality (process and
product quality loss models), Environmental Loss (emissions of pollutants per
produced unit), and Inventory (storage, and work-in-process).
#### 2) Types
* Are the data digital/hard copies or both? Digital
* What types of data will the WP generate/collect? Specify the types and formats of data generated/collected (for example .xls files, .ppt files, emails, .doc files) .xls , .doc files
* Is the data generated or collected from other sources under certain terms and conditions?
* How is generated/collected? Specify the origin of the data and instruments/tools that will be used.
Financial data from end-users.
* State the expected size of the data (if known)
* Standards
#### 3) Ownership
Is another organization contributing to the data development? Z-Fact0r end-
users.
#### 4) Reuse of existing data
Specify if existing data is being re-used (if any)
No
#### 5) Data use
* How will this data be exploited and/or shared/made accessible for verification and re-use?
Outline the data utility: to whom will it be useful
At a second stage, a validation and verification process of the direct
observations and experiments on the Shop-floor and direct measurement of costs
against system state and contrastively with the simulated ones. **6)
Dissemination Level of Data**
* Confidentiality/ Sensitive data. If data cannot be made available, explain why. Who will have access?
#### 7) Storage and disposal
* How will this data be stored?
* How long is it required to keep the data? Expire date. Will revisions be kept?
Duration of the project, and potentially five years after the completion of
the project.
<table>
<tr>
<th>
Task: **T7.3, T8.2, T9.2**
</th> </tr>
<tr>
<td>
WP: 7-8-9
</td> </tr>
<tr>
<td>
WP Leader: INOVA+, CETRI
</td> </tr>
<tr>
<td>
Author: CONFINDUSTRIA
</td> </tr> </table>
#### 1) Scope
* State the purpose of the data generation/ collection
Regularly, Data generation and Collection is an important requirement aimed to
develop activities/tasks, to allow Analysis, to measure performances and to
measure the achievement of own objectives.
* Explain the relation to the objectives of the project/WP/Task
Data generation, and, in particular, data collection will be useful in order
to develop market analysis and Customer Adoption Plan. We also recognize the
importance of these options, generate and collecting data, by thinking to the
planned workshops, in which results achieved will be shared (in respect to the
privacy needed).
#### 2) Types
* Are the data digital/hard copies or both? Mainly digital.
* What types of data will the WP generate/collect? Specify the types and formats of data generated/collected
.xls files, .ppt files, emails, .doc files
* Is the data generated or collected from other sources under certain terms and conditions? Since we will have to use available info and results from other WP we will respect the required IP protection, confidentiality of data.
* How is generated/collected? Specify the origin of the data and instruments/tools that will be used.
Data will be collected from website, available DB and we will use the only not
confidential results/information that we could share in order to develop our
tasks (Roadmapping, Customer Adoption plan and DCE).
* State the expected size of the data (if known)
Unknown
* Standards
#### 3) Ownership
* Is another organization contributing to the data development? **4) Reuse of existing data**
* Specify if existing data is being re-used (if any)
We will use existing data coming from web, available surveys or other WPs.
#### 5) Data use
How will this data be exploited and/or shared/made accessible for
verification and re-use?
Outline the data utility: to whom will it be useful
For their definitions and objective, our tasks and results will be useful for
the all project partners and some of them for some potential customers, since
our tasks imply Partners and customers sharing.
#### 6) Dissemination Level of Data
Confidentiality/ Sensitive data. If data cannot be made available, explain
why. Who will have access?
No confidentiality.
#### 7) Storage and disposal
* How will this data be stored?
Z fact0r Shared digital folders.
* How long is it required to keep the data? Expire date. Will revisions be kept?
Duration of the project, and potentially five years after the completion of
the project.
<table>
<tr>
<th>
Task: **8.1-8.5 & 9.1-9.6 **
</th> </tr>
<tr>
<td>
WP:8,9
</td> </tr>
<tr>
<td>
WP Leader: CETRI
</td> </tr>
<tr>
<td>
Author: Dr Souzanna Sofou (CETRI)
</td> </tr> </table>
#### 1) Scope
* State the purpose of the data generation/ collection
1. Website: project dissemination and product innovation delivery.
2. Innovation Management Strategy: Form the IM strategy for the ultimate use and dissemination of project Results.
3. Innovation Management Roadmap: Design and implement WPs 8 and 9\.
4. DMP Questionnaire: Manage Research Data, MetaData, before and after project duration.
5. Deliverables: [D.8.1-D.8.5], [D9.1-D9.5]: as explained in the vi) W. vii) Publications: dissemination and communication activities. viii) Z-Fact0r leaflet: communication activity for project wider acceptance.
ix) Z-Fact0r poster: communication activity for project wider acceptance.
* Explain the relation to the objectives of the project/WP/Task As explained above
#### 2) Types
* Are the data digital/hard copies or both?
_Digital:_
i) Website ii) Innovation Management Strategy iii) Innovation Management
Roadmap iv) DMP Questionnaire
5. Deliverables: [D.8.1-D.8.5], [D9.1-D9.5]
6. Publications (hard copies may also be sent for journal & conference publications) _Hard Copies:_
7. Z-Fact0r leaflet viii) Z-Fact0r poster
* What types of data will the WP generate/collect? Specify the types and formats of data
generated/collected (for example .xls files, .ppt files, emails, .doc files)
<table>
<tr>
<th>
_i)_ Website
</th>
<th>
developed by wordpress
</th> </tr>
<tr>
<td>
_ii)_ Innovation Management Strategy
</td>
<td>
.ppt and .pdf file
</td> </tr>
<tr>
<td>
_iii)_ Innovation Management Roadmap:
</td>
<td>
.xls file
</td> </tr>
<tr>
<td>
_iv)_ DMP Questionnaire:
</td>
<td>
.doc file
</td> </tr>
<tr>
<td>
_v)_ Deliverables: [D.8.1-D.8.5], [D9.2-D9.5]
</td>
<td>
doc files, .pdf files website
</td> </tr>
<tr>
<td>
_vi)_ Publications
</td>
<td>
.doc files, .pdf files
</td> </tr>
<tr>
<td>
_vii)_ Z-Fact0r leaflet
</td>
<td>
.cdr file, .ppt file, .pdf file
</td> </tr>
<tr>
<td>
_viii)_ Z-Fact0r poster
</td>
<td>
.cdr file, .ppt file, .pdf file
</td> </tr> </table>
* Is the data generated or collected from other sources under certain terms and conditions? i), iii), Data taken also from the GA. iv) Data collected from participants.
5. Data generated during project duration, data from other deliverables will be used.
6. Research Data generated.
7. Data taken also from the GA.
* How is generated/collected? Specify the origin of the data and instruments/tools that will be used.
Not applicable for WP8 and WP9.
* State the expected size of the data (if known) For digital files: less than 100Mb.
For Hard Copies:
Z-Fact0r leaflet: print on both sides A4 size,
Z-Fact0r poster: print on one side, according to conference restrictions.
* Standards
#### 3) Ownership
According to the ownership model.
* Is another organization contributing to the data development?
According to the ownership model. **4) Reuse of existing data**
* Specify if existing data is being re-used (if any)
Data from other WP´s might be used in dissemination and communication files.
#### 5) Data use
How will this data be exploited and/or shared/made accessible for
verification and re-use? Outline the data utility: to whom will it be useful
All WP8 and 9 data will be useful to the consortium for the ultimate use and
dissemination of project results.
#### 6) Dissemination Level of Data
Confidentiality/ Sensitive data. If data cannot be made available, explain
why. Who will have access?
<table>
<tr>
<th>
Website
</th>
<th>
Public
</th> </tr>
<tr>
<td>
Innovation Management
Strategy
</td>
<td>
Private, the DEM has created the file, only the project partners have access
</td> </tr>
<tr>
<td>
Innovation Management Roadmap:
</td>
<td>
Private, the DEM has created the file, only the project partners have access
</td> </tr>
<tr>
<td>
DMP Questionnaire:
</td>
<td>
Private, Only the project partners have access
</td> </tr>
<tr>
<td>
Deliverables: [D.8.1D.8.5], [D9.1-D9.5]:
</td>
<td>
D8.1, D8.2, D8.3, D8.5, D9.2, D9.3, D9.5: Confidential D8.4, D9.1, D9.4:
Public
</td> </tr>
<tr>
<td>
Publications
</td>
<td>
Public, dissemination rules apply
</td> </tr>
<tr>
<td>
Z-Fact0r leaflet
</td>
<td>
Public, dissemination rules apply
</td> </tr>
<tr>
<td>
Z-Fact0r poster
</td>
<td>
Public, dissemination rules apply
</td> </tr> </table>
#### 7) Storage and disposal
* How will this data be stored?
All digital files will be stored in FREEDCAMP All hard copy files will be
stored by the DEM as well as all consortium parties.
* How long is it required to keep the data? Expire date. Will revisions be kept?
Duration of the project, and potentially five years after the completion of
the project.
_2.2.5.3 Dataset per WP_
Data information of the partners has been used to define the complete RDI that
will be generated in each WP;
<table>
<tr>
<th>
**WP1**
</th>
<th>
**User requirements, specifications, use case analysis**
</th>
<th>
WP leader: **SIR**
</th> </tr> </table>
**Objective** : Development of the architecture of the Z-Fact0r system and the
definition and description of the main components. A complete description of
the modules included in the detailed view in order to point out the
responsibilities of each module and their interactions with the global System
Architecture. Qualitative and quantitative data generated and collected are
aimed at defining both the user and system requirements and use, prepare the
bibliographic and databased information, design the workflow and UML diagrams
and report on Z-Fact0r strategy and risk analysis to monitor the status of the
manufacturing process in real time.
**Data description:** The data being collected will enable the KPI’s to be
monitored and to generate history for prediction and correction of the
process. Digital data and documents will be preserved in their incoming
format, files generated and used will be mainly consisting in MS documents
released using the following formats (.doc, .pptx, .vpp and .xls files,
emails, SQL DB programs, images for visualizing and conceptualizing the use
cases will be released as PDF files).
**Instrument and tools:** Unified Modelling Language, TBC for either
Pointclouds or micro– profilometry data. Machine sensors, network
infrastructure /middleware (device manager)/ shopfloor (Z-Fact0r repository
for machine processes, part condition and worker’s actions).
<table>
<tr>
<th>
**WP2**
</th>
<th>
**Production-process monitoring, detect life-cycle management and
remanufacturing**
</th>
<th>
WP
**EPFL**
</th>
<th>
leader:
</th> </tr> </table>
**Objective** : Data generation will be needed for formulation of data driven
model for the defect prediction from process inputs, correlated to Z-DETECT
Module. Supervise and provide feedback for all the processes executed in the
production line, evaluating performance parameters and responding to defects,
keeping historical data. Send efficiently alarms to initiate actions, filter
out false alarms, increase confidence levels (through previously acquired
knowledge) of early defect detection and prediction.
**Data description:** Data generated will be digital and any format data can
be accepted (.CSV, .XLS, etc). However, data output will be in the form of
matlab files, (.XLM, .JSON, .MAT, .M, etc).
**Instrument and tools:** Inputs from the Sensor Network (through the Device
Manager), the overall model of Production activities (through Z-Fact0r
Repository) and context – aware knowledge stemming from the Semantic Context
Manager (Ontology) will be necessary.
**Terms & Conditions of data generated: ** Data are collected from the
manufacturing processes (end users’ collection systems).
<table>
<tr>
<th>
**WP3**
</th>
<th>
**Data management and early stage DSS for inspection and control**
</th>
<th>
WP leader: **CERTH**
</th> </tr> </table>
**Objective** : Data is required for the Z-Fact0r ontology development.
Ontology describes semantic models for the annotation and description of the
knowledge to represent manufacturing system performance. The ontology will be
used in order to drive the semantic framework. Furthermore, it will be used
for data integration, visualization, inferencing/reasoning. Data from sensors
will be integrated and stored in Z-Fact0r. Retrieved data will came from
sensors and systems from industrial partners.
**Data description:** Digital data will be stored in Cloud Based DB and
required input will be data from Z-Fact0r repository (data concerning
machines, workers, actors, activities and processes, production data logs,
etc.). Generated output will be the semantic enrichment of shop-floor data for
representation of processes, actors, alarms, actions, work-pieces/products,
etc., e.g. RDF Triplets, .CSV, .XML, .JSON.
**Instrument and tools:** Data will be collected from new sensors placed in
the shop floor to support the processes monitoring; PLC; legacy systems.
**Data re-use:** The ontology will be re-used in order to drive the semantic
framework. Furthermore, it will be used for data integration, visualization,
inferencing/reasoning.
<table>
<tr>
<th>
**WP4**
</th>
<th>
**System modelling for fast forward cost functions**
</th>
<th>
WP leader: **BRUNEL**
</th> </tr> </table>
**Objective** : The purpose of the data collection and generation is to
facilitate the building, validation and verification of the KPI models,
implementation of the scheduler and extracting cost functions.
The raw data will be collected from plants and the output will provide the
metrics for process and control optimisation to minimise defect.
**Data description:** Data generate will be mainly digital and DB driven files
can be converted to CSV, XLS, TXT, HTML and XML.
**Instrument and tools:** Data and instruments that will be used are PLC,
SCADA, Production Management Systems, Internet, and project Intranet. Already
installed actuators, sensors and financial data from end-users will be used
for monitoring and evaluating the KPIs.
**Data re-use:** Data reutilization will be fine-tuned in order for afterwards
on-line & real-time application of them and corrective actions will be
considered for the production line based on the results received.
<table>
<tr>
<th>
**WP5**
</th>
<th>
**Integration & Testing Validation **
</th>
<th>
WP leader: **ATLANTIS**
</th> </tr> </table>
**Objective** : Diverse set of technologies will be developed and all s/w and
h/w components and platforms will be integrated throughout a predefined
integration methodology. The technology validation plan is to be defined and
executed while applying corrective design and reimplementation on all detected
errors. Furthermore, the methodology validation throughout the demonstration
in relevant environments will be used as well as the evaluation data and
feedback that are going to be collected, analyzed and documented.
**Data description:** Data generated will be made available from the Z-Fact0r
components that will be integrated into the complete system. Multiple formats
will be available, however, all compatible to commonly agreed standards, most
probably Business to Manufacturing Markup Language (B2MML) in XML form.
Moreover, from the evaluation part of the WP data will come out in XLS format.
**Instrument and tools:** Data will be provided by the Z-Fact0r components
that form the 5 strategies. For this WP, primary, non-analysed data are not
considered, rather than the results of their analysis by the Z-Fact0r tools
and components. Data related to end user evaluation will be most probably
collected using online forms and questionnaires, that will allow transfer into
XLS files.
**Data re-use:** The collected data will be evaluated and considered in order
to reach a better understanding of the processes and activities evolved in the
shop-floors. Combined with the input from the technical validation plan this
will lead to fine-tuning of the Z-Fact0r components and the lessons learned
could be transformed into actionable knowledge.
<table>
<tr>
<th>
**WP6**
</th>
<th>
**Demonstration activities**
</th>
<th>
WP leader: **INTERSEALS**
</th> </tr> </table>
**Objective** : Automated quality control, with high accuracy level and
predictive system for defect generation based on online continuous monitoring.
The data collected will enable to detect probability or trends that lead to
defects that normally result in scrapping the parts.
**Data description:** Data generate will be digital and hard copies and data
format will be .xls for sensor history data and volumetric measurement and
.jpeg images of the defects, and also SQL formats, emails, etc.
**Instrument and tools:** Possibly collected by sensors at a bench top
apparatus and optical and physical sensors to be studied. FT-IR (Infrared
spectroscopy): for material checking. System control (CoMo by Kistler) that
concentrate the data from the sensor cavities pressure. Injection moulding
machine parameters: achieved by the connection to the PLC of the injection
Machine (the PLC can be Siemens, Omron, Moog). Data from visual and
dimensional checking machine. Data from the worker that work beside the
production cell and will communicate with the software using Augmented
Reality.
**Data re-use:** The existing data are re-used as really useful for making
quotations, for process study, quality control, traceability and claim answer.
<table>
<tr>
<th>
**WP 7,**
**8 & 9 **
</th>
<th>
**Valorization, market replication,**
**dissemination/ communication/exploitation**
</th>
<th>
WP leader **: INNOVA, CETRI**
</th> </tr> </table>
**Objective** Data generation and collection is an important requirement to
develop activities/tasks, allow analysis, measure performances and measure the
achievement of WP objectives as: Website (dissemination and innovation
delivery), DMP Questionnaire (Manage Research Data, MetaData, before, during
and after project duration), Innovation Management Strategy (for the ultimate
use and dissemination of results) and Roadmap (planning WP7, design and
implement WPs 8 and 9), Publications (dissemination and communication
activities), Z-Fact0r leaflet and poster (communication activity wider
acceptance) and Market analysis and Customer Adoption Plan.
**Data description:** Data generated will be digital for Website, Innovation
Management Strategy, Innovation Management Roadmap, DMP Questionnaire,
Deliverables and Publications (hard copies may also be sent for journal &
conference publications), and hard copies in the case of ZFact0r leaflet and
poster. Format of data generated will be .xls, .ppt, .pdf, .doc and .cdr files
and emails.
**Instrument and tools:** Data will be collected from website and available
DB, with only nonconfidential results/information that could be shared for the
development of WP activities (Roadmapping, Customer Adoption plan and DCE).
In conclusion, data and data management-related challenges under Z-Fact0r are
identified and addressed mainly within WP1 (T1.1, T1.4), WP2 and WP3. As
described in the proposal, data to be used in the project will include: on-
line (nearly) real-time and historical data related to (i) the product
(desired specifications; quality inspection results, etc.); (ii) the
production equipment and environment (e.g. temperature, pressure, vibrations,
etc.); (iii) manufacturing/ production and maintenance (e.g. capacity,
planning, etc.). Sources of these data will be: existing sensors and actuators
(such as sensors embedded in production machinery, quality inspection
equipment, etc.), as well as new novel sensors and actuators (such as laser
scanning, visual and/or IR cameras, noncontact profilometers, etc.), and
enterprise systems. The type of sensors/ actuators and data to be used will be
defined and finalized per Z-Fact0r use case on the basis of the required
metrics at product and workstation level at single manufacturing stage, and
also at multiple stage. Additionally, non-research data collected related to
Innovation Management like the IPR registry that includes the IP strategy per
Result are confidential and are only stored in the FREEDCAMP repository and
the website private area, with no access rights for members outside the
consortium.
### 2.2.6 Policies for access, sharing and re-use
Data generated during Z-Fact0r project will be confidential. Ownership and
management of intellectual property and access will be limited to the project
consortium partners. For this purpose, policies for access, sharing, and re-
use have been established:
_2.2.6.1 Partners Background_
Partners have identified their background for the action (data, know-how or
information generated before they acceded to the Agreement), which will be
accessible to each other partners to implement their own tasks (under to legal
restrictions or limits previously defined in the CA). The partners should be
able to access, mine, exploit, reproduce and disseminate the data. This should
also help to validate the results presented in scientific publications. The
partner´s background, acquired prior to the starting date of the project, will
remain the sole property of the originating partner, provided that it was
presented in the CA.
_2.2.6.2 Data Ownership and Access_
The full dataset will be confidential and only the members of the consortium
will have access on it.
Special consideration will be taken for the project dissemination dataset
(e.g. leaflet, brochures, posters, etc.) that will be considered as public
information. As described in GA, data generated are expected to be used
internally as input by the other WPs. All the partners will have free-access
to the results generated during the project, the information needed for
implementing their own tasks under the action and for exploiting their own
results. Also, this information will be available to EU institutions, bodies,
offices or agencies, for developing, implementing or monitoring EU policies,
however such access rights are limited to non-commercial and non-competitive
use.
Regarding ORD Pilot, data that will be generated in the OA will be decided
during the course of the project and can include; final peer-reviewed
scientific research articles that will be published in the online repository
after the publication, research data including data underlying publications,
curated data and/or raw data and public deliverables of the project (described
in GA). If any document or dataset are decided to become of OA, a special
section into the data management portal (FREEDCAMP) will be created that
should provide a description of the item and link to a download section. Of
course, these data will be anonymized, so as not to have any potential ethical
issues with their publication and dissemination.
_2.2.6.3 Naming rules_
All data files will be saved using a standardized, consistent file naming
protocol agreed by the project partners, which will include relevant metadata
to ensure their accessibility. The metadata standard proposed is the CERIF.
_2.2.6.4 Storage Information_
Documents of the dataset will be stored at the data management portal (
**FREEDCAMP** ) created and maintained by CERTH/ITI, while links to the portal
will exist at the Z-Fact0r website. The Data Management Portal developed by
the project ( **FREEDCAMP** ) in the context of the ORD Pilot, allows the
efficient management of the project’s datasets and provides the proper OA of
them for further analysis and reuse.
The dataset will remain at the data management portal for the whole project
duration, as well as for at least 2 years after the end of the project.
Finally, after the end of the project, the portal is going to be accommodated
with other portals at the same server, so as to minimize the needed costs for
its maintenance.
_2.2.6.5 Data sharing and dissemination_
Data will be reused for corrective actions on the deployed strategies and
actions will be suggested based on correlations by the automatic decision
support mechanism. Research data results will be disseminated according to the
CA in the form of conference, articles in a journal, specialist
magazine/website outlets or conference proceedings for dissemination purposes.
All patent applications and all other publications will require prior
agreement in respect to content and the publication media. To this end, each
partner should notify the consortium members about the content and material
they wish to publish/disseminate and a 21 days’ evaluation period will be
provided as stated in the CA.
_2.2.6.6 IPR management and security_
As an innovation action close to the market, Z-Fact0r project covers high-TRL
technologies and aims at developing marketable solutions. The project
consortium includes nine industrial partners from the private sector, in
particular, CETRI, ATLANTIS (Technical Management), HOLONIX, DATAPIXEL, SIR,
INOVA, MICROSEMI (demonstrator/end user), INTERSEALS
(demonstrator), and DURIT (demonstrator). Those partners obviously have
Intellectual Property Rights on their technologies and data, on which their
economic sustainability is at stake. Consequently, the Z-Fact0r consortium
will protect that data and get approval of concerned partners before every
data publication.
The data management portal will be equipped with authentication mechanisms, so
as to handle the identification of the persons/organizations that download
them, as well as the purpose and the use of the downloaded dataset.
_2.2.6.7 Data expire date_
Copyright statements of the Z-Fact0r project will protect any written material
produced during its lifetime. As described in GA, the information and data
supplied by all project partners and documents produced during the project
will be protected for a period of five years after the project completion
unless there are agreements between the partners.
After the end of the project, the partners should keep for five years the
original documents, digital and digitalized documents, records and other
supporting documentation in order to prove the proper implementation of the
action and the costs they declare as eligible.
## 2.3 Data currently being produced in Z-Fact0r
This version of the DMP does not include the actual metadata about the data
being produced in ZFact0r because there is no dataset generated or collected
until delivery date of this deliverable (M6).
Further details will be provided in the next updated version.
# 3 Data Management related to Zero-defects Manufacturing
The quality and performance data of the Manufacturing enterprises will be
considered private and will only be available after granting permission. On
the other hand, the research data about modelling procedures, KPI validation,
event modelling, inspection and real-time quality control, as well as the
system optimization, which will be collected/generated during Z-Fact0r will be
distributed freely.
# 4 Data Management Portal (FREEDCAMP)
The Data Management Portal, a web based portal name as “FREEDCAMP”, is being
used within the Z-Fact0r project for the purposes of the management of the
various datasets that will be produced by the project, as well as, for
supporting the exploitation perspectives for each of those datasets. FREEDCAMP
Portal will need to be flexible in terms of the parts of datasets that are
made publicly available. Special attention is going to be given on ensuring
that the data made publicly available violates neither IPR issues related to
the project partners, nor the regulations and good practices around personal
data protection.
## 4.1 FREEDCAMP portal functionalities
The FREEDCAMP Portal is accessed through a web based platform which enables
its users to easily access and effectively manage the various datasets created
throughout the development of the project.
Regarding the user authentication, as well as the respective permissions and
access rights, the following three user categories are foreseen:
* **Admin;** the Admin has access to all of the datasets and the functionalities offered by the DMP and is able to determine and adjust the editing/access rights of the registered members and users (OA area). Finally, the Admin is able to access and extract the analytics, concerning the visitors of the portal.
* **Member;** when someone successfully registers to the portal and is given access permission by the Admin, she/he is then considered as a “registered Member”. All the registered members will have access to and be able to manage most of the collected datasets.
Knowledge sharing and public documents, apart from the admin and the
registered members, as **OA** area will be available for users who will not
need to register and they will have access to some specific datasets, as well
as to project outcomes.
Figure 1 shows the Login page of the FREEDCAMP portal.
Figure
1\.
Login Page of the
FREEDCAMP
Portal
FREEDCAMP portal will be easily and effectively managed by the members. A
variety of graphs, pie charts etc. is going to be employed for helping members
to easily understand and elaborate the data. In particular, the architecture
of the portal presents special interfaces organized to comply the information.
All tasks and datasets available in the DMP will be accompanied by a short
description of the item (Figure 2 and 3).
Figure 2. Data access page of the FREEDCAMP portal.
Figure
3\.
F
ile access of the
FREEDCAMP
P
ortal.
Dataset will be structured in three different folders into FREEDCAMP portal;
Tasks, Discussion, Files. Draft documents and deliverables, and other data
will be uploaded on specific tasks folders, and final version documents will
be uploaded into the file section of appropriate folder.
In addition, technical and progress meetings will be scheduled in the
FREEDCAMP portal calendar (Figure 4).
Figure 4. Calendar access of the FREEDCAMP Portal.
## 4.2 Data Backup: Private Area of the Project Website
As described in Deliverable 9.1, the website private area can be used by all
the partners:
i) for storage of files and confidential deliverables ii) for providing
feedback on work in progress iii) for exchanging information about upcoming
events, conferences, etc.
The website Private Area is only accessible by the consortium partners using a
username and password, and will be used as a backup repository to store data
that are either confidential or data that will be made public after a release
date that has been identified by the data owner.
## 4.3 Open Access Section
A special free access section into the data management portal (FREEDCAMP) will
be created to upload the documents, data and datasets and other information
that are decided to become of OA. The description of the item and the link to
a download section will be available into this section. Of course, these data
will be anonymized, so as not to have any potential ethical issues with their
publication and dissemination.
# 5 Future Work
A spreadsheet has been created that will be used throughout the project for
the continuous logging of data and datasets, as well as the related
information that has been previously presented. Figure 5 shows the spreadsheet
of Z-Fact0r data and datasets.
Figure 5. Spreadsheet of Z-Fact0r data and datasets.
Additionally, the release date of the datasets that will be available in the
open research data pilot will be defined in this xls.
More specifically, after the IP protection route has been defined for each
“result” in the IPR registry currently being developed, dissemination actions
will take place for some of them (for example for those for which a patent
application has been submitted). As soon as a dissemination action is
complete, data can be uploaded in the open research data pilot.
## 5.1 Roadmap of actions to update the DMP
This deliverable is a dynamic document and will be updated and augmented
throughout the whole project lifecycle with new datasets and results according
to the progress of the activities of the ZFact0r project. Also, the DMP will
be updated to include possible changes in the consortium composition and
policies over the course of the project.
For that purpose, the final version of this report will be delivered 6 month
before the end of the project (M36), reflecting on lessons learnt and
describing the plans implemented for sustainable storage and accessibility of
the data, even beyond the project’s duration.
# 6 Conclusions
This report includes the DMP and describes the RDI that will be generated
during Z- Factor project and the challenges and constraints that need to be
taken into account for managing it. In addition, it describes the updated
procedures and the infrastructure used in the project to efficiently manage
the produced data, named as FREEDCAMP Portal. The DMP is identified as
starting point for the discussion with the community about the Z-Fact0r data
management strategy and reflects the procedures planned by the work packages
at the beginning of the project.
An elaborated questionnaire has been distributed between the consortium
partners, asking them what kind of data they were expecting to produce and
collect during the project. From this information, it has become clear that
currently only work packages 1, 2 and 3, are planning to generate or collect
data that can be classified as relevant information according to the
definition of the European Commission. Nonetheless, DMP is not a fixed
document and it can be the case that this situation evolves during the
lifespan of the project. Thus, the DMP will be updated and augmented with new
datasets and results twice during project lifetime with the Project Periodic
Reports.
Regarding storage information, documents generated during the project will be
stored in FREEDCAMP Portal which is the document management system of the
project. This information, data and documents produced during the project will
be protected for a period of two years after the project completion, as it is
described in GA.
# 7 Glossary
## Participant Information Sheet
The information sheet is an important part of recruiting research
participants. It ensures that the potential participants have sufficient
information to make an informed decision about whether to take part in your
research or not
(
_http://www.kcl.ac.uk/innovation/research/support/ethics/training/infosheet.aspx_
).
## Consent Form
A form signed by a participant to confirm that he or she agrees to participate
in the research and is aware of any risks that might be involved.
## Metadata
Metadata is data that describes other data. Meta is a prefix that in most
information technology usages means "an underlying definition or description."
Metadata summarizes basic information about data, which can make finding and
working with particular instances of data easier.
( _http://whatis.techtarget.com/definition/metadata_ ) or _http://www.data-_
_archive.ac.uk/media/54776/ukda062-dps-preservationpolicy.pdf_
## Repository
A digital repository is a mechanism for managing and storing digital content.
Repositories can be subject or institutional in their focus. (
_http://www.rsp.ac.uk/start/before-youstart/_ _what-is-arepository/)_
# 8 Bibliography
* Guidelines on Data Management in Horizon 2020, Version 2.0, 30 October 2015:
_http://ec.europa.eu/research/participants/data/ref/h2020/grants_manual/hi/oa_pilot/h2020-hi-
oapilotguide_en.pdf_
* Guidelines on OA to Scientific Publications and Research Data in Horizon 2020, Version
2.0, 30 October 2015:
_http://ec.europa.eu/research/participants/data/ref/h2020/grants_manual/hi/oa_pilot/h2020-hi-
oapilotguide_en.pdf_
* Webpage of European Commission regarding OA:
_http://ec.europa.eu/research/science-society/open_access_
|
https://phaidra.univie.ac.at/o:1140797
|
Horizon 2020
|
1167_SUREAL-23_724136.md
|
**Introduction**
The focus of SUREAL-23 is on the particulate emissions from contemporary light
duty direct injection (DI) internal combustion (IC) engines (Diesel and
gasoline) that will be addressed by homologation standards beyond Euro-6,
especially for nanoparticles smaller than the current regulation cut-off limit
of 23 nm with the threshold of at least 10 nm.
Within this context, the objectives of SUREAL-23 are:
* to complement existing standard instrumentation by introducing extensive size and composition characterization of exhaust particles especially for sizes below 23 nm,
* to support future emissions compliance through technical development in real driving emissions measurement,
* to fully characterize the nature of the particulate emissions which potentially evade current emission control technology and regulations,
* to contribute to future definitions of particulate emissions limits of the “Super Low Emission Vehicles”.
The aim of WP7 is to establish effective project management bodies and
decision-making procedures, monitor the progress of the project activities
versus important objectives & project milestones and to apply relevant
corrective actions if necessary. Moreover, it includes the dissemination of
the project's results and maximize its impact to the most wide and relevant
audience, as well as the establishment of links with relevant projects in EU,
USA and Japan. Finally, the creation of an exploitation plan defining the
technologies which can contribute to current regulatory measurements and can
be taken up into new products, has to be performed.
This deliverable, _D7.7 “Data management plan (Final Version)”,_ aims to
update the 2 nd version of the DMP (D7.6 “ _Data management plan (2 nd
Version) _ ”) about the management and accessibility of project’s data and
findings with emphasis in those that are accessible via the H2020 Open
Research Data Project (OpenAIRE).
The updated DMP includes descriptions about a) the data / reports generated
and provided openly to third parties b) the data sharing (taking into
considerations any legitimate reasons for not sharing data) and d) data
archiving and preservation; for the second half of the project (1839M).
# Abbreviations list
<table>
<tr>
<th>
APTL
</th>
<th>
Aerosol and Particle Technology Laboratory
</th> </tr>
<tr>
<td>
EC
</td>
<td>
European Commission
</td> </tr>
<tr>
<td>
CA
</td>
<td>
Consortium Agreement
</td> </tr>
<tr>
<td>
CERTH
</td>
<td>
Centre for Research and Technology – Hellas
</td> </tr>
<tr>
<td>
CPERI
</td>
<td>
Chemical Process and Energy Resources Institute
</td> </tr>
<tr>
<td>
DG-GRO
</td>
<td>
Directorate-General "Growth"
</td> </tr>
<tr>
<td>
DMP
</td>
<td>
Data Management Plan
</td> </tr>
<tr>
<td>
DOI
</td>
<td>
Digital Object Identifier
</td> </tr>
<tr>
<td>
DoW
</td>
<td>
Description of Work
</td> </tr>
<tr>
<td>
INEA
</td>
<td>
Innovation Networks Executive Age
</td> </tr>
<tr>
<td>
NEDC
</td>
<td>
New European Driving Cycle
</td> </tr>
<tr>
<td>
PMP
</td>
<td>
Particle Measurement Program
</td> </tr>
<tr>
<td>
UNECE
</td>
<td>
United Nations Economic Commission for Europe
</td> </tr>
<tr>
<td>
WLTP
</td>
<td>
Worldwide harmonized Light vehicles Test Procedures
</td> </tr> </table>
# Short Project Overview
A large proportion of the total number of particles emitted from direct
injection engines are below 23 nm and the EU aims to regulate those emissions
and impose limits for new light-duty vehicles. Before SUREAL-23, accounting
for sub-23 nm particles was not a straightforward choice due to the absence of
accurate quantification methods, especially under real driving conditions. The
project aimed to increase the knowledge regarding the nature of sub-23 nm
particles from different engine/fuel combinations under different operating
conditions. It also aimed to overcome the relevant barriers by introducing
novel measurement technology for concentration, size and composition
measurements. In parallel, state of the art aerosol measurement techniques
were advanced for better compatibility with sub-23 nm exhaust particles as
well as onboard use. The developed instrumentation was used to assess sub-23
nm particle emissions from both Diesel and GDI vehicles accounting for effects
of the fuel, lubricants, after-treatment and driving conditions for existing
and near-future vehicle configurations. The most suitable concepts for on-
board use have been evaluated accordingly. The project provided measurement
technologies that will complement and extend established particle measurement
protocols, sustaining the extensive investments that have already been made by
industry and regulation authorities. It delivered a systematic
characterisation of sub23 nm particles to facilitate future particle emission
regulations as well as to assess any potential trade-off between advances in
ICE technology towards increased efficiency and emissions. The consortium
consisted of European and US organizations, which are leaders in the field of
aerosol and particle technology.
# OBJECTIVES
The deliverable _D7.7. “Data management plan (Final Version)”_ objectives are
to describe: a) the data and reports generated and provided openly to third
parties b) the data sharing and c) the data archiving and preservation during
the second half of the project (18-39M) including provisions for the data
preservation beyond the project.
SUREAL-23 is registered in the OpenAIRE Project - in a _volunteer basis_ \- in
order to widely share its findings (Figure 1). For this purpose specific
information/outcomes was deposited in ZENODO ( _https://zenodo.org/_ ) ; the
European repository for the EU funded research.
It is mentioned that the creation and update of the current DMP was
facilitated by the OpenAIRE proposed web tool _DMPonline_ (
_https://dmponline.dcc.ac.uk_ ) .
_Figure_
_1_
_. SUREAL_
_-_
_23_
_registration in OpenAIRE_
_| Explore_
# DATA SUMMARY
SUREAL-23 project generates:
1. experimental results and findings related to the assessment of the developed sub-23nm particle sampling and measurement techniques (6 in total) and their demonstration, as described and discussed in reports delivered by M39;
2. experimental data and scientific findings based on a test matrix that combines sub-23 nm particle emissions measurements emitted by Gasoline Direct Injection (GDI), Diesel and CNG engines / vehicles with standard and under development measurement and sampling techniques.
The up to date mature outcomes of the project were presented in relevant
international conferences and published in scientific journals.
Additionally, selected scientific reports and publications are available (in
*.pdf formats) in the _open access_ European repository ZENODO, thus in
OpenAIRE. Project’s public deliverables are also available in ZENODO /
OpenAIRE and project’s website as well.
Final data and reports are stored in Coordinator's (APTL/CPERI/CERTH) data
server. Completed deliverables and reports are also available in the common
_private_ "members area" accessed through project's website (
_http://sureal-23.cperi.certh.gr_ ) (Figure 2).
_Figure 2. Common, private "members area" accessed through project's website._
Project’s scientific outcomes are expected to be useful to the scientific and
academic society related to engine development, aerosol science, emission
control and particle measurements. Moreover, the automotive industry and the
instrumentation developers could be also interested. Last but not least
vehicle particulate emissions regulators is expected to pay special attention
on the sub-23 nm emission measurements, as already expressed during the
Clustering meetings of the GV-02-2016 research projects organized by INEA and
the two public events organized by the project i.e the Joint Workshop (10-11
October, 2018, Thessaloniki) and the Final Workshop (10 December, 2019, Lyon)
(see Deliverables _D7.2 “First Dissemination Report”_ and _D7.3 “Final
Dissemination Report”_ ).
# FAIR DATA
## Making data findable
Identifiability is obtained for each report / deliverable by filling a
standard introductory table that includes information such as author's and co-
author's names and affiliation, associated work-package and technical activity
(task), date (due and delivered), responsible partner and (if any) involved
partners.
During document's preparation a versioning strategy is followed in order to
facilitate the document's exchange among partners.
All relevant scientific publications have a Digital Object Identifier (DOI)
issued by the Journal itself, while the project’s reports / deliverables
selected to be deposited at ZENODO repository, receive a DOI as well.
Additionally, each deposition at ZENODO is linked with related keywords that
at least include the “sub-23 nm particle emissions” term. Item’s versioning is
also sustained by ZENODO (all versions are cited by using the same DOI).
## Making data openly accessible
As already mentioned, selected Open Access project's data / reports,
deliverables, research papers and public presentations are deposited in the
European repository for EC funded research ZENODO. Consequently, items are
accessible via OpenAIRE.
During the whole duration of the project (1-39M) the below selected items were
deposited at ZENODO with author’s permission (Figure 3a & b), including
project’s public deliverables with technical / scientific content. The rest of
project’s public deliverables relevant to management such as dissemination
reports, DMP and exploitation plan are open accessed via project’s website, in
the “NEWS” section ( _http://sureal-23.cperi.certh.gr/public-deliverables/_ )
.
_Figure 3a. List of deposited SUREAL-23 items at ZENODO repository (1/2)_
_Figure 4b. List of deposited SUREAL-23 items at ZENODO repository (2/2)_
As clearly stated at the Consortium Agreement (CA), pre-existing data is of
each partner's intellectual property. Such data is subject to each project
partner's policy governing intellectual property.
In case of "close" and confidential data / reports / outcomes produced in the
frame of the project, they will be available only after an invention
disclosure or provisional patent is filed. This mainly concerns either the
data of the novel measurement instrumentation or the emission data of engines
or vehicles that automotive industry provided under specific terms of use.
Assessed research results were shared within the scientific community via
presentations at conferences, workshops and other dissemination events (see
Deliverable _D7.2_ “ _First Dissemination Report_ ” and _D7.3 “Final
Dissemination Report”_ ).
Whenever available, the DOI of each published item is given at the project’s
website, section “NEWS” (e.g. Figure 5).
_Figure 5. Example of web announcement of project’s publication, including
information about its DOI._
## Making data interoperable
Interoperability of produced reports and outcomes is obtained by following
standard measurement protocols such as the Particle Measurement Program (PMP)
protocol, standard driving cycles for vehicle homologation (e.g. New European
Driving Cycle (NEDC), Worldwide harmonized Light vehicles Test Procedure
(WLTP)), as well as European and/or international certified measurement
methods.
Additionally, a standard technical and scientific vocabulary is used in order
to document all research outputs.
## Increase data re-use
The open data / reports are available for re-use through the practices already
described at paragraph 3.2. by the time that each report is completed,
delivered and approved by EC.
Stakeholders from research, industry and regulatory authorities - that already
mentioned in chapter 2 - is expected to be interested in the data/reports re-
use. It is also mentioned that EC and especially Directorate-General "Growth"
(DG-GRO), as well as the United Nations Economic Commission for Europe
(UNECE), Particle Measurement Programme have already express their interest of
re-using SUREAL-23 open reports for regulatory purposes.
Re-use of the most remarkable findings of the project will be also ensured via
a _joint_ _publication_ of the three GV-2-2016 “sub-23nm” clustering projects
PEMS4NANO (G.A.724145), DownToTen (G.A. 724085 ) and SUREAL-23 that is
currently under preparation.
The above provisions for data accessibility and re-use do not apply for the
results and produced knowledge that considered confidential or IP protected.
In such cases - that mainly referred to parts or concepts of the developed
measurement techniques - the involved partners are investigating manners of IP
protection (see D7.4 “ _Exploitation Plan_ ”).
# ALLOCATION OF RESOURCES
Experimental data and related reports are produced, prepared and assessed in
the frame of the technical and dissemination work-packages of SUREAL-23
project which covers the required costs of man-effort and any other required
goods or services.
Mrs. Eleni Papaioannou coordinates the project, as well as the Data Management
activities, while Mr. Apostolos Tsakis is responsible for the data storage and
security and Mrs. Penelope Baltzopoulou for the OpenAIRE and Zenodo
repositories.
# DATA SECURITY
With regards to data security and storage, final experimental data and
completed scientific and technical reports are stored on Coordinator's Data
Server which is mirrored, routinely backed up and daily versioned.
Data security is ensured by implementation of local (intra-laboratory) and
central (CERTH's information department) firewalls.
# ETHICAL ASPECTS
No ethical issues are expected as already assessed according to EU rules and
regulations and described at the SUREAL-23 "Description of Work" (DoW)
document.
|
https://phaidra.univie.ac.at/o:1140797
|
Horizon 2020
|
1174_ULISENS_726499.md
|
# DATA SET REFERENCE, NAME AND DESCRIPTION
This section shows a description of the information to be gathered, the nature
and the scale of the data generated or collected during the project. These
data are listed below:
ULISENS Project Parameters and Data are divided into Confidential and Non
Confidential Information:
<table>
<tr>
<th>
**Confidential Information**
</th>
<th>
**Non Confidential Information:**
</th> </tr>
<tr>
<td>
* Names of the providers of prototypes and their industrial details: Company names and addresses.
* Technical files including all compulsory documents for demonstrators, with standards and applicable directives
* Prototype testing results
* Declaration of Conformity and Notified body testing results
* Pilot plant design, parameters and critical data for the upscaling of the production.
* Jobs and quality procedures.
* Details of potential customers and interests related to the
commercialization plan
</td>
<td>
* Names of stakeholders in which the demonstrators are installed including details as names, addresses and other.
* Results of the demonstration activities carried out
* Contents of the training actions.
</td> </tr> </table>
# STANDARDS AND METADATA
The main objectives of the ULISENS project are not scientific publications.
However, _Open Access (OA) will be implemented in peer-review publications
(scientific research articles published in academic journals), conference
proceedings and workshop presentations carried out during and after the end of
the project. In addition, non-confidential PhD or Master Thesis and
presentations will be disseminated in OA._
The publications issued during the project will include the Grant Number,
acronym and a reference to the H2020 Programme funding, including the
following sentence:
“Project ULISENS has received funding from the European Union´s Horizon 2020
research and innovation programme under grant agreement No 726499”. In
addition, all the documents generated during the project will indicate in the
Metadata the reference of the project: ULISENS H2020 726499.
Each paper will include the terms Horizon 2020, European Union (EU), the name
of the action, acronym and the grant number, the publication date, the
duration of embargo period (if applicable) and a persistent identifier (e.g.
DOI).
The purpose of the requirement on metadata is to maximise the discoverability
of publications and to ensure the acknowledgment of EU funding. Bibliographic
data mining is more efficient than mining of full text versions. The inclusion
of information relating to EU funding as part of the bibliographic metadata is
necessary for adequate monitoring, production of statistics, and assessment of
the impact of Horizon 2020 [2].
# DATA SHARING
All the scientific publications of the Horizon 2020 project will be
automatically aggregated to the OpenAIRE portal (provided they reside in a
compliant repository). Each project has its own page on OpenAIRE ( _Figure 1_
) featuring project information, related project publications and datasets and
a statistics section. BIOTICA will ensure that if there were scientific papers
derived from ULISENS project, they will be available as soon as possible in
OpenAIRE, taking into account embargo period (in case they exist).
**_FIGURE 1_ : ULISENS INFORMATION IN OPENAIRE WEB ( _WWW.OPENAIRE.EU_ ) **
BIOTICA will check periodically if the list of publications is completed. In
case there are articles not listed it is necessary to notify to the portal.
The steps to follow to publish an article and the subsequent OA process are:
The final peer-reviewed manuscript is added to an OA repository.
The reference and the link to the publication should be included in the
publication list of the progress Report.
When the publication is ready, the author has to send it to the coordinator,
who will report to the EC through the publication list included in the
progress reports. Once the EC has been notified by the coordinator about the
new publication, the EC will automatically aggregate it at the OpenAIRE
portal.
# ARCHIVING AND PRESERVATION
In order to achieve an efficient access to research data and publications in
ULISENS project, Open Access (OA) model will be applied. Open access can be
defined as the practice of providing on-line access to scientific information
that is free of charge to the end-user. Open Access will be implemented in
peerreview publications (scientific research articles published in academic
journals), conference proceedings and workshop presentations carried out
during and after the end of the project. In addition, nonconfidential PhD or
Master Thesis and presentations will be disseminated in OA.
Open access is not a requirement to publish, as researchers will be free to
publish their results or not. This model will not interfere with the decision
to exploit research results commercially e.g. through patenting [3].
The publications made during ULISENS project will be deposited in an open
access repository (including the ones that are not intended to be published in
a peer-review scientific journal). The repositories used by project partners
will be:
ZENODO will be used by ULISENS
As stated in the Grant Agreement (Article 29.3): _“As an exception, the
beneficiaries do not have to ensure open access to specific parts of their
research data if the achievement of the action´s main objective, as described
in Annex I, would be jeopardized by making those specific parts of the
research data openly accessible. In this case, the data management plan must
contain the reasons for not giving access”._
This rule will be followed only in some specific cases, in those that will be
necessary to preserve the main objective of the project.
Dissemination Plan
Research Results
Data Management Plan
Research
Decision to
disseminate /
share
Decision to
exploit/
protect
Publications
Depositing
research data
Gold OA
Green OA
Restricted access
and/or use
Access and use
free of charge
Patenting (or
other form of
protection)
And/or
According to the “Guidelines on Open Access to Scientific Publications and
Research Data in Horizon 2020” [2], there are two main routes of open access
to publications:
**Self-archiving (also referred to as “green open access”):** in this type of
publication, the published article or the final peer-reviewed manuscript is
archived (deposited) by the author \- or a representative - in an online
repository before, alongside or after its publication. Some publishers request
that open access be granted only after an embargo period has elapsed.
**Open access publishing (also referred to as “gold open access”):** in this
case, the article is immediately provided in open access mode as published. In
this model, the payment of publication costs is shifted away from readers
paying via subscriptions. The business model most often encountered is based
on one-off payments by authors. These costs (often referred to as Article
Processing Charges, APCs) can usually be borne by the university or research
institute to which the researcher is affiliated, or to the funding agency
supporting the research.
As a conclusion, the process involves two steps, firstly BIOTICA will deposit
the publications in the repositories and then they will provide open access to
them. Depending on the open access route selected self-archiving (Green OA) or
open access publishing (Gold OA), these two steps will take place at the same
time or not. In case of self-archiving model, embargo period will have to be
taken into account (if any).
## GREEN OPEN ACCESS (SELF-ARCHIVING)
This model implies that researchers deposit the peer-reviewed manuscript in a
repository of their choice (e.g. ZENODO). Depending on the journal selected,
the publisher may require and embargo period between 6 and 12 months. The
process to follow for ULISENS project is:
1. BIOTICA prepares a publication for a peer-review journal.
2. After the publication has been accepted for publishing, the partner will send the publication to the project coordinator.
3. BIOTICA will notify the publication details to the EC, through the publication list of the progress report. Then, the publication details will be updated in OpenAIRE.
4. The publication may be stored in a repository (with restricted access) for a period of between 6 and 12 months (embargo period) as a requirement of the publisher.
5. Once the embargo period has expired, the journal gives Open Access to the publication and the partner can give Open Access in the repository.
Partner prepares the
publication
Partner notifies to
project coordinator
Partner stores the publication in a
repository with restricted access
Publication in
OpenAIRE
Coordinator
notifies EC
Partner gives Open
Access to the
publication
Embargo period
**_FIGURE 3_ : STEPS TO FOLLOW IN GREEN OPEN ACCESS PUBLISHING WITHIN ULISENS
PROJECT **
## GOLD OPEN ACCESS (OPEN ACCESS PUBLISHING)
When using this model, the costs of publishing are not assumed by readers and
are paid by the authors, this means that these costs will be borne by the
university or research institute to which the researcher is affiliated, or to
the funding agency supporting the research. These costs can be considered
eligible during the execution of the project.
The process foreseen in ULISENS project is:
1. The partner prepares a publication for a peer-reviewed journal.
2. When the publication has been accepted for publishing, the partner sends the publication to the project coordinator.
3. The coordinator will notify the publication details to the EC, through the publication list of the progress report. Then, the publication details will be updated in OpenAIRE.
4. The partner pays the correspondent fee to the journal and gives Open Access to the publication. This publication will be stored in an Open Access repository.
Partner prepares the
publication
Partner notifies to
project coordinator
Partner pays the fees and gives
Open Access to the publication
Publication in OpenAIRE
Coordinator
notifies EC
_**FIGURE 4: STEPS TO FOLLOW IN GOLD OPEN ACCESS PUBLISHING WITHIN ULISENS
PROJECT** _
# BIBLIOGRAPHY
1. E. Commission, “Guidelines on Data Management in Horizon 2020. Version 2.1,” 15 February 2016.
2. E. Commission, “Guidelines on Open Access to Scientific Publications and Research Data in Horizon 2020. Version 2.0,” 30 October 2015\.
3. E. Commission, Fact sheet: Open Access in Horizon 2020, 9 December 2013.
|
https://phaidra.univie.ac.at/o:1140797
|
Horizon 2020
|
1175_CITADEL_726755.md
|
# Executive Summary
This deliverable aims to present a plan for the data management, collection,
generation, storage and preservation related to CITADEL activities. In this
action, we envision five different types of data: data related to the use
cases, data related to the meta-analysis to be done in the social-sciences
tasks, data coming from publications, public deliverables and open source
software.
The document presents, following the EC template [1], how these different
types of data will be collected, who the main beneficiaries are, and how
CITADEL will store them, manage them, and make them accessible, findable and
re-usable. The text continues with the foreseen resources needed for the
openness and data to finalize with security and ethical aspects that will be
taken into consideration in the context of CITADEL.
This plan is the first version of the data management plan, which will be
updated in subsequent versions (M12, M24 and M36) as part of the Technical
Reports, having as input the work carried out in the use cases (WP5), the
social and technical work packages (WP2 – WP4) and the dissemination
activities (WP6).
# Introduction
## About this deliverable
This deliverable focuses on the management of the data in CITADEL. In CITADEL
there will be two different data, the first strand related to the publications
generated as part of the research activities, and the second strand related to
the data collected from citizens, users and non-users of digital public
services, as well as from civil servants, that will be used as part of the
implementation of the different key results established in the project.
## Document structure
The document follows the established H2020 template for a Data Management Plan
(DMP) [1]. Section 2 presents a summary of what the purpose of the data
collection and generation is in the case of CITADEL. Section 3 explains how
the data and metadata will be made fair, and thus accessible, findable and
reusable. Section 4 briefly explains how the financial resources for this
openness are envisioned at this stage to be allocated. Section 5 and 6 focus
on the security and ethical aspects respectively. Section 7 presents the
conclusions and future work.
# Data Summary
The purpose of the data to be collected in CITADEL is twofold. On one hand, to
understand why citizens are not currently using a certain digital public
service, even if they do have the skills for it. On the other hand to
understand what the opinion is of citizens that are using a digital public
service and how this digital service can be improved or created from scratch
with the participation of the citizens.
To realize this, CITADEL will develop the following Key Results (KR) [2]:
* **KR1: CITADEL Recommendations and guidelines to transform PA** . A set of guidelines derived from the use cases and surveys will focus on the re-definition of the public policies, processes and services to be adapted to citizens´ and organizations’ (users and non-users) expectations. The use of these CITADEL recommendations will allow PAs save significant money in the re-design of their processes.
* **KR2: CITADEL Information monitoring service:** This service monitors and analyses available citizens´ (user and non-users) data (e.g. feedback, open data, demographic statistics, preferences and so on). The objective is to extract, using big data, semantics and privacy preserving technologies, relevant information required for the formulation of the recommendations (KR1). This service is composed of two important assets:
* Monitoring services asset: Based on semantics, this asset will select the relevant information in each moment and in each situation. o Analysis services asset: Based on the relevant information provided by the monitoring asset, this asset will provide KPI (Key performance Indicators) or if possible recommendations.
* **KR3: CITADEL tool supported methodology for services co-creating.** This methodology will guide and support PAs in the co-creation process and will be customized for taking into account the characteristics of each PA. The adaptation could be done based on different aspects like, legal systems, scope and audience or the target group.
* **KR4: CITADEL Co-creation collaborative tool** that allows the PA, the Private Sector and citizens to co-create new public services at a conceptual level.
* **KR5: CITADEL Discovery service,** that allows discovering digital public services based on the citizen’s profile and the result of semantic analysis of the data such as preferences, utilization, opinions and so on.
* **KR6: CITADEL Assessment services** that are responsible for allowing those citizens that use digital public services to evaluate them providing useful information for improving them.
* **KR7: CITADEL Security toolkit:** A set of assets integrated in CITADEL:
* Dedicated asset for the integration of privacy regulations
* Implementation of cloud- and device-based personal data privacy features o Privacy-by-Default features
* **KR8: CITADEL Ecosystem** is the main result of the project and aggregates both the social and technical aspects. CITADEL Ecosystem will include not only the services provided by the other objectives of CITADEL (KR2: Information monitoring service; KR3: Co-creation collaborative tool; KR4: Discovery service; CITADEL DISCOVERY SERVICE (KR2): Assessment services and KR6: Security toolkit) but also those outcomes focused on the transformation of the CITADEL Recommendations for the re-definition of the public processes (KR1: Recommendations to transform PA).
All the CITADEL services to enable both the pillars of ‘Understand to
transform’ and ‘Co-create to transform’ will be integrated as part of the
CITADEL ICT Enablers. The high level architecture of the different components
and their link to the CITADEL KR’s are shown in the next figure.
**Figure 1.** CITADEL High level architecture and the link to CITADEL’s
envisioned Key Results
Out of all the Key Results envisioned for CITADEL, the ones that are more
data-oriented, either by using, generating or analyzing data are KR2 - CITADEL
Information monitoring service, CITADEL DISCOVERY SERVICE (KR2) - CITADEL
Discovery service, KR6 - CITADEL Assessment services and KR7- Security
toolkit, which aims to apply privacy by design and security by design
principles as well as anonymization and pseudo-anonymization techniques.
In CITADEL three distinct environments are envisioned, namely, a development
environment, an integration environment and dedicated environments for each of
the use cases.. The development environment will be deployed in the
corresponding sites of the technological partners (imec, FINCONS and
TECNALIA). The integration environment will be deployed at TECNALIA’s DevOps
infrastructure and will include the different components developed by the
technological partners. For testing purposes, these partners will use
synthetic, fake, data or ‘persona’ data, but never real data, anonymized or
not, coming from the use cases. The third environment is the customized
environment for the four distinct CITADEL use cases. The CITADEL platform and
the already existing IT systems of the four different use cases will be
integrated by means of REST APIs, and will be deployed on the PAs own sites.
The data that will be collected from these will follow the principles
underlined along this deliverable, fulfilling always the GDPR requirements.
**Figure 2.** CITADEL Envisioned environments
Different types of data will be collected and generated in the context of the
project. These can be summarized as follows:
1. Data related to the execution of the use cases, testing KR1 –KR8.
2. Data related to the results of the analysis and metadata analysis of the social-related work packages (WP2 and WP3), which will be embedded (partially or wholly) in KR1 – KR8.
3. Data related to scientific publications.
4. CITADEL public deliverables.
5. CITADEL Open Source Software
## Data related to the use cases
The aim of this section is to establish what the purpose is of the collection
and generation of data within CITADEL, which the sources of this data are and
how this is important to realize the Key Results of CITADEL. Furthermore, we
also determine the baseline for the formats for the data both collected and
generated and the main beneficent of the collection and generation of such
data.
Use cases are a key pillar of CITADEL as they will validate the work (in WP5)
to be performed in the social and technological work packages (WP2-WP4).
In principle, for the real execution of the use cases, CITADEL trials will
involve real end users (i.e. data subjects) using a test bed designed to
emulate operational services they are using or intend to use. Wherever
possible, use cases will use synthetic (i.e. fictional but representative)
data, avoiding the use of personal data from operational systems at the PAs.
However, this does not mean that no personal data will be collected from
participating citizens. In such cases, anonymized data from users of digital
public services will be collected. These data can originate either from the
databases already in production at the Public Administrations where citizens’
data are stored, which in the event they are not anonymized, they will be
anonymized or de-anonymized through a dedicated anonymization component of
CITADEL, as part of the module ‘Security Management’ of Figure 1. In the event
surveys are to be launched, as for instance in the case of the VARAM’s use
case, in order to capture and understand why digitally skilled citizens are
not using the offered online governmental services, or to understand the civil
servants’ willingness to change, the users will be required to provide some
personal data (such as age group, education level), but this will be stored in
a confidential manner. Data will be anonymised once data collection has been
completed. This is especially relevant for CITADEL Information monitoring
service (KR2) and CITADEL Evaluation Service (KR6).
For the CITADEL Discovery service (KR2), in which citizens are recommended
digital public services, based on their profile, this will be pseudo-
anonymized, taking into consideration generic characteristics such as services
already being used by that specific citizen as well as age, for instance in
the cases of ICTU. This data will be used following the European GDPR
legislation for personal data.
The generic sources of data for CITADEL Information monitoring service (KR2),
CITADEL Discovery Service (KR5) and CITADEL Evaluation Service (KR6) are
envisioned as follows:
* **Open Data** coming in a first phase, from EU-wide open data portals such as, the
European Data portal 1 , and the EU open data portal 2 . In a second
stage, it might be
needed to consider other open data portals at EU, national, regional or local
level. This will be updated in subsequent technical reports due M12, M24 and
M36. The format of such data is preferably JSON, CSV, TSV (Tab separated
values), or when relevant, SDMX for statistical data. The exact data sets to
be used are part of the study to be performed in D2.1.
* **Synthetic data** , generated for testing purposes. This data will be preferably in JSON.
* **Citizens’ data** stored in the governments participating in CITADEL, namely, Antwerp, Regione Puglia, the Netherlands and Latvia. As stated before, not be shared with the technological partners. Furthermore, for the CITADEL Information monitoring service (KR2) and CITADEL Evaluation Service (KR6) data will be (pseudo-)anonymized. The preferred formats are JSON or CSV.
The collected data will be curated and processed as part of CITADEL
Information monitoring service (KR2), CITADEL Discovery Service (KR5) and
CITADEL Evaluation Service (KR6), with the main aim to provide recommendations
(KR1) to the Public Administrations, which will be shown, mostly, in the form
of KPI reports. The format for the generated metadata will be preferably JSON,
RDF and XML. The size of the expected datasets is unknown at this stage of the
project.
Furthermore, each use case will need certain specific data for the execution
of their own characteristics. This data will be useful only for each of the PA
that owns / collect the data. Next, we proceed to describe, by use case, the
data available at this stage of the project.
### Stad Antwerpen
For Stad Antwerp, this is the current data available:
* **Back-end data of** the Antwerp citizen platform (antwerpen.be):
* These data are not anonymized, neither is Stad Antwerpen authorized to use the data for other purposes than the stated objective (i.e. delivery of the requested service). This is part of the citizen platform’s general conditions and is confirmed by legal partner, Time.Lex.
* In January 2017, a censored back-end data sample has been delivered to Tecnalia to give insight in the data structure. Conducting trials on the data thus is possible. Hence, back-end data will always be stored on local servers and managed by Digipolis (the externalised IT-department from Stad Antwerpen). This policy applies to the use case within CITADEL as well.
* **Front-end data** Google analytics data of the platform o These data are anonymised and aggregated and can be used within the CITADEL project.
* **Marketing research data,** such as: Antwerp Monitor, Panel surveys, and Surveys that are conducted in the local city desk, which are anonymised and aggregated and can be used within the CITADEL project. A specific survey to measure usability within the use case can be conducted on Stad Antwerpen’s own panel
### VARAM
Current available data in the use case of VARAM include:
1. Latvia's e-index results raw data;
2. National Citizens' portal www.latvija.lv, Google Analytics Data;
3. Service catalogue and service descriptions of more than 2000 public services;
4. Public opinion evaluation on public sector services;
5. Regional development data.
6. HotJar (Analytics and Feedback application) installation in portal
All existing data are anonymous. There might be personalised data when CITADEL
co-creation tools are used. However, as expressed before, the intention is to
use fictional but representative data in order to evaluate the adequateness of
CITADEL ICT Enablers for cocreation.
### ICTU
ICTU does not collect any data such as names or addresses but the current
system does collect IP addresses or other information by means of ‘cookies’.
ICTU, however, will generate data during the co-creation activities. These
data will be gathered will through focus group of youngsters turning 18 or
parents/caretakers for, with a need analyses; prototyping testing, etc., users
for prototyping, customer satisfaction surveys and qualitative research
(interviews, workshops) on target audience and needs analysis (and
validation). For such data, inform of consents will be collected and signed.
### Reglione Puglia
In the case of Regione Puglia, existing data includes tourist flow, prices and
services, personal data of business players, promotion data (photo,
description, and activities), special offers, leisure activities and events,
pathways, attractors, social data, google analytics. These data are anonymized
or (pseudo-) anonymized. In the case this is not feasible, making participant
subjects easily recognisable, fictional data will be sought to be used
instead.
## Data related to the analysis and metadata analysis of socialrelated work
packages
In the context of the activities related to ‘Understand to transform’,
vignette studies will be carried out to understand the willingness of civil
servants to changes. Vignette studies are mostly used for qualitative research
studies and they can be defined as “short stories about hypothetical
characters in specified circumstances, to whose situation the interviewee is
invited to respond” [3]. In the context of CITADEL, these studies will be used
as a complementary technique to other data collection methods. Before the
vignette studies are carried out, civil servants participating in them will be
required to sign informed consent forms. Their answers will be used, processed
and stored in an anonymously manner.
Moreover, in those activities for Understanding the citizens, surveys and
questionnaires will be launched to understand why citizens are or are not
using digital public services. This is especially relevant in the case of
VARAM. The data provided by the participants to these questionnaires will be
treated in an anonymous way. Data allowing to identify participants will be
deleted prior to data analysis.
In the context of the activities related to ‘Co-create to transform’, a
metadata analysis of existing co-creation, co-production and co-design
approaches will be performed. The origin of the data is theoretical and
empirical studies, papers and projects’ deliverables (e.g. from FP7 or H2020),
from which the CITADEL partners own the rights, either because they are the
authors, for the access fees paid to editorial companies, they are published
following an open access policy or are publicly available. For this metadata
analysis, no standard format will be used, but rather an ad-hoc one, created
for the purpose of CITADEL needs. The results will be exposed mainly in the
form of a deliverable, a study, which will be published on the CITADEL website
and as part of the rules that will be used to customize the co-creation
methodology.
The data created as part of the previously explained (meta-)data analysis in
the context of both ‘Understand’ and ‘Co-create’ will serve as input data for
the implementation of the ICT enablers to be developed in the project. The
exact format is at this stage still to be decided, but it will be customized
for the needs of the CITADEL ICT Enablers. The transmission of this data will
be done programmatically.
## Data related to scientific publications
As part of the dissemination activities, CITADEL will publish scientific
publications in conferences and journals. Following the EC Mandate on Open
Access [4], CITADEL adheres to the Open Access policy choosing the most
appropriate route for each case. CITADEL favours whenever possible, the
‘green’ open access, in which the published article or the final peerreviewed
manuscript will be deposited in an online repository, before, at the same time
as, or after publication, ensuring that the embargo period requested by
certain publishers has elapsed.
The format in which the data related to the scientific publications will be
accessible pdf files. The metadata to be used will be compliant with that of
the repository where the paper is to be deposited and will be compliant with
the format requested by Open Aire, as to ease the index.
The data related to the scientific publications will be relevant to scientists
in the field of innovation in the public sector as well as developers and
technical-oriented communities.
## CITADEL public deliverables
All information and material related to the public such as public
deliverables, brochures, posters and so, on will be freely available on the
project website in the form of accessible pdf files. When IPR of foreground
knowledge needs to be protected, the corresponding disclosures will be
published.
All deliverables include a set of keywords and a brief description that are
aimed to facilitate the indexing and search of the deliverables in search
engines. The keywords in each deliverable aim to stress the main topics
addressed in the document, be it a report or a software – related document.
The audience of the public deliverables of CITADEL range from general
audiences, interested in the activities performed in the project, to more
specialized audiences, interested in social sciences, innovation in the public
sector, ICT enablers for the Public Sector, or experiences gathered through
the pilots.
## CITADEL Open Source Software
CITADEL will develop ICT Enablers as means to help Public Administrations to
aid in their transformation. The source code will be released, whenever IPR of
the partners is not breached, under a friendly open source licensing schema.
CITADEL ICT Enablers will be developed in a variety of programming languages
but deployed using a container-based approach following a micro-services [5]
architecture. The size of the source code, the readme files, the user manual
and technical specifications as well as the docker scripts cannot be known at
the moment.
The open source software is aimed at developers of ICT providers of the PAs
and PA providers’ IT departments.
# Fair Data
This section focuses on the following aspects, namely, on the feasibility and
appropriateness of making data findable, openly accessible, interoperable and
reusable in the context of CITADEL.
## Data related to the use cases
At this stage, for all use cases, both the collected and generated data,
anonymized or fictional, are not envisioned to be made openly accessible. In
principle, all data collected, stored and processed will be treated as
strictly confidential, and kept for a specific period of time as stated on the
consent form. This time period shall be no longer than necessary to achieve
the aims of the scenario and to validate the project objectives, and after
this point, the data will be destroyed as required.
The openness of the fictional data for testing purposes is, however, an option
that will be evaluated as part of the exploitation and sustainability strategy
and the final decision will be provided in subsequent updates of the Data
Management Plan. In the event they are finally released, they will follow
machine – readable formats such as CSV, RDF/XML or RDF/JSON, and when
appropriate, the DCAP-AP format [7]. Following that structure, it will
immediately make the use cases’ data immediately identifiable, openly
accessible, semantically interoperable and re-usable. Each metadata set will
be accompanied by its licensing schema.
### Stad Antwerpen
As stated in Section 2.1.1, the back-end data from this scenario will always
be stored on local servers and managed by the externalized IT-department from
Stad Antwerpen, Digipolis. The other data, anonymized or pseudo-anonymized, as
well as the synthetic data that will be used when needed, will also be stored
in the instance of CITADEL that will be created for the Stad Antwerp scenario
(see Figure 2. CITADEL Envisioned environments). Access to the data will be
restricted to citizens under their pseudonyms as well as authorized members of
the CITADEL implementation team who might require access to the data. VARAM
In the case of VARAM, and as represented in Figure 2. CITADEL Envisioned
environments, the data related to this use case will also be stored in VARAM’s
premises, following the Latvian legislation procedures on data protection. The
data gathered through the surveys as well as the citizens’ data will be
anonymized or when not possible, it will be pseudo-anonymized.
For the activities in which citizens’ participation will be required in a
proactive way, such as for the co-creation phase, the approach to be followed
will be under a pseudo-anonym procedure. That is, real data of users will not
be used or disclosed, but rather fictional although representative data will
be favored.
Access to the data will be restricted to citizens under their pseudonyms as
well as authorized members of the CITADEL implementation team who require
access to the data.
### ICTU
As in the previous cases, in ICTU the same approach for the usage of the data
of the citizens participating in the co-creation activities will be sought,
that is, carry out these activities under a pseudo – anonym. Access to the
data will be restricted to citizens under their pseudonyms as well as
authorized members of the CITADEL implementation team who require access to
the data.
ICTU will also store its own data in its own CITADEL customized environment.
### Regione Puglia
Regione Puglia will also host a customized CITADEL environment in its own
premises, following the Italian legislation on data protection. As in the
previous use cases, access to the data will be restricted to citizens under
their pseudonyms as well as authorized members of the CITADEL implementation
team who require access to the data.
## Data related to the analysis and metadata analysis of social – related
workpackages
Data related to the metadata analysis to be performed in the social sciences
work packages will be released mainly as statistical data, following when
feasible, standardized formats (e.g. SPSS).
In the context of the work to be done in the strand of ‘Understand to
transform’, the software AutoMap 3 is envisioned to be used to analyse the
willingness of civil servants to change processes, policies or services,
either in conjunction with citizens (co-design, co-create) or individually.
AutoMap delivers as output a specific format DyNetML as well as standard
formats such as CSV or XML. The personal data of the civil servants
participating in the questionnaires and vignette studies will not be
disclosed, being therefore, anonymized.
The licensing schema for these data is currently under evaluation but the
project will favour its openness as much as possible. However, depending on
the licensing schema selected, the reusability may be limited.
The collected and generated data will be stored as part of the results of
CITADEL on the project’s website and it will be available 3 years beyond the
time frame of the project. In addition, where relevant for secondary research,
data will be deposited in open access social data archives such as DANS or
Gesis.
## Data related to scientific publications
The project will favour, whenever possible, the ‘green’ open access, in which
the published article or the final peer-reviewed manuscript will be deposited
in an online repository, before, at the same time as, or after publication,
ensuring that the embargo period requested by certain publishers has elapsed.
The Consortium will ensure open access to the publication within a maximum of
six months. CITADEL partners have the liberty to choose the repository where
they will deposit their publications, although Open Aire – compliant and Open
Aire - indexed repositories such as Zenodo [5] will be favoured. The partner
TECNALIA will use its own repository, already indexed by Open Aire.
For the case of the scientific publications, a persistent identification
number will be provided when uploading the publications to the selected
repository / repositories.
## Data related to deliverables
For the project’s publications on the website, the naming convention to be
used will be <<Dx.y Deliverable name _ date in which the deliverable was
submitted.pdf>>. All deliverables include a set of keywords and a brief
description that are aimed to facilitate the indexing and search of the
deliverables in search engines.
The deliverables will be stored at TECNALIA’s hosting provider, and for three
years beyond the duration time frame of the project
## Open Source Software
CITADEL has envisioned following a freemium business model for the ICT
enablers and the CITADEL ecosystem which implies a free version of the
software as well as a premium one. The free versions of the open source
components of the CITADEL ICT Enablers will be released as open source in a
source code repository, namely GitHub, which will be stored at TECNALIA’s
premises.
The free versions of these components will be accessible, findable and
reusable by any developer that is interested in the CITADEL ICT Enablers.
Furthermore, CITADEL will explore the possibility of developers outside of the
consortium to experience with such components, which will ensure the uptake
and sustainability of the software results of the project.
Alongside every software component, a readme file as well as technical
specifications document will be released and made available, and in addition
the docker script so as to be able to deploy the container in any desired
infrastructure.
# Allocation of resources
CITADEL does not envision an additional need for beyond the duration of the
project to handle data or making the data fair. As expressed before, open
access repositories will be favoured.
In the case of open source software, the partner TECNALIA will ensure that the
github repository is available after the project duration, either by keeping
it in its own premises or by transferring it to existing open source projects
and communities, such as JoinUp [7].
# Data Security
CITADEL will develop the ICT enablers adhering to the security – by – design
and privacy – by design principles that allow the security and audit standards
to remain consistent across multiple environments.
Furthermore, as part of the CITADEL Security toolkit (KR7) and the overall
CITADEL ecosystem (KR8), the following components are at this stage foreseen
in the architecture (see next figure):
* 1) Access Management, which grant authorized users the right to use a service, while preventing access to non-authorized users of CITADEL;
* 2) Credentials Management to manage credential information such as user names and passwords, and
* 3) Anonymization, which is responsible for removing personally identifiable information from user data or pseudonymizing the personally identifiable information.
**Figure 3.** Security components envisioned in the CITADEL ecosystem as part
of the ICT Enablers
Moreover, CITADEL will ensure that the General Data Protection Regulation
(GDPR), which will enter into force in May 2018, is ensured, especially in
regards to protection of private data. The security components shown above
will be implemented with that principle in mind.
# Ethical Aspects
The basis of ethical research is the principle of informed consent. All
participants in CITADEL use cases will be informed of all aspects of the
research that might reasonably be expected to influence willingness to
participate. Moreover, project researchers will discuss before and after each
practical exercise (e.g. interview, co-creation session, etc.) to maintain on-
going consent. Participants will be recruited by each organization leading the
use cases (VARAM, ICTU, Stad Antwerp, and Regione Puglia) and other supporting
organizations (e.g. InnovaPuglia, FINCONS) and will cover more than one type
of citizens. If participants wish to withdraw from the participation in the
use cases at any time, they will be able to do it, and their data, even the
pseudo-anonymized data, will be destroyed.
A specific task has been included in CITADEL to ensure that ethical principles
are used throughout the use cases, which are clustered together for management
purposes in the work package related to the use cases (WP5). This task
involves the performance of a privacy impact assessment per use case.
CITADEL also has a task dedicated to analyse technical and operational issues
related to privacy, and identify suitable solutions to be included in the
CITADEL framework. Furthermore, CITADEL has as one of the key results to be
delivered a Security Toolkit, which looks for the application of security-by-
design and privacy-by-design principles
# Conclusions
This deliverable has presented the plan for the management of data in the
CITADEL project. In this action, several types of data will be collected and
generated. Specifically, we envision that there will be five different types
of data, namely data coming from the use cases, from the metadata analysis to
be performed in the social sciences work packages, from publications,
deliverables and open source software. This data will be anonymized or pseudo-
anonymized using, when relevant, an anonymization engine as part of the ICT
enablers to be developed. In the event this engine cannot be used, such as in
surveys to civil servants or directly to the citizens, manual anonymization
techniques will be used. A combination of real data coming from the different
Public Administrations with fictional but relevant data will be sought. GDPR
compliance will be ensured.
All data and metadata generated will follow machine readable formats, such as
CSV, RDF or JSON.
Data from the use cases will be stored in the use cases’ premises fulfilling
the relevant legislations. Data from publications will be stored in Open Aire
indexed repositories favouring the green model whenever possible. Other
publications such as deliverables will be stored at TECNALIA’s hosting
services.
This deliverable will be updated in subsequent releases, namely in M12, M24
and M36 as part of the technical reports. It is envisioned that in those
versions the aspects that at this stage are not fully clear (e.g. naming
conventions, versioning, dataset size) will be clarified as work progresses in
all the work packages.
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https://phaidra.univie.ac.at/o:1140797
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Horizon 2020
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1177_FRAME_727073.md
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# 1 Introduction
The main contribution of D7.4 is to introduce the changes undergone in Work
Package 5 which now relies on various data sources to achieve the new
objectives described in the Grant amendment: on the one hand, providing new
Total Productivity Estimates for European countries, and on the other hand,
determining the respective roles of drivers of unemployment in explaining why
some European countries recovered faster than others after the Great
Recession. To do so, Work Package 5 now involves different empirical
estimations. Consequently, two Work Packages are currently using data. The
report is organized in two main sections related to Work Packages 5 and 6
respectively. The conclusion summarizes the key implications regarding the
replicability of the project findings.
# 2 Work Package 6: estimations of the R&D and Technologies adoption
parameters
The first set of estimations relates to R&D parameters. P1-P3 are estimated
based on patent data and OECD data related to Research and Development (R&D)
expenditures. P4 and P5 are based on previous work developed by Comin and
Mestieri (2014), known as the CHAT dataset. Finally, P6 is estimated by
aggregating and merging two sources of data: micro-data coming from ZEW and
the confidential dataset summarizing all agreements between firms and the
Fraunhofer institutes.
## 2.1 Data description: micro datasets
### 2.1.1 Mannheim Innovation Panel (MIP)
The MIP represents an annual survey conducted by ZEW on the behalf of the
German Ministry of Research and Education. The MIP provides information about
the introduction of new products, services and processes, the expenditures for
innovations and how the economic success achieved with new products, new
services and improved processes. In addition, the survey gives information
about the factors which promote and also hinder innovation activities of
enterprises. The innovation survey from ZEW lays an important basis for
evaluating Germany’s technological performance. It is also the basis for the
German Community Innovation Survey (CIS), which constitutes the EU science and
technology statistics surveys carried out each two years by EU member states
and number of ESS member countries.
### 2.1.2 PATSTAT
PATSTAT contains bibliographical and legal status patent data from leading
industrialised and developing countries. This is extracted from the EPO’s
databases and is provided as raw data or online. The PATSTAT product line
forms a unique basis for conducting sophisticated analyses of bibliographic
and legal status data. It has become a standard in the field of patent
intelligence and statistics. The PATSTAT product line consists of three
individual databases. They are available in raw data format or via PATSTAT
Online, a web-based interface to the databases. For the conditions related to
different access modalities, see https://www.epo.org/searchingfor-
patents/business/patstat.html#tab-2.
## 2.2 Data description: macro datasets
### 2.2.1 GERD dataset - OECD
The OECD data is available online and covers a wide range of OECD and non-OECD
countries. Data is available from 1981 onwards. RD data reported in this
dataset have been collected according to 2002 guidelines of the Frascati
Manual, which have been now superseded by the Manual’s 2015 edition. Revised
definitions are not expected to revise significantly the major indicators.
Data are provided in million national currency (for the euro zone, pre-EMU
euro or EUR), million current PPP USD and million constant USD (2010 prices
and PPPs). This table contains research and development (R&D) expenditure
statistics. Data includes gross domestic R&D expenditures by sector of
performance (business enterprise, government, higher education, private non-
profit, and total intramural) and by source of funds (business enterprise,
government - including public general university funds -, higher education,
private non-profit and funds from abroad - including funds from enterprises
and other funds from abroad).
### 2.2.2 CHAT dataset
The Cross-country Historical Adoption of Technology (CHAT) dataset is an
unbalanced panel with information on the adoption of over 100 technologies in
more than 150 countries since 1800.It contains information on the diffusion of
about 104 technologies in 161 countries during the last 200 years. It extends
the data used in Comin and Hobijn (2004) and Comin, Hobijn, and Rovito (2006).
Almost all of our source data is only available at an annual frequency. For
some of the older technologies, like steamships, data go back until the early
Nineteenth Century. The last year in the sample is 2003. Some data, especially
in the earlier part of the sample, is not available at an annual frequency.
The technology measures in CHAT capture a similar intuition. They are either:
(i) the number of capital goods specifically related to accomplishing
particular tasks, (ii) the amounts of particular tasks that have been
accomplished, (iii) the number of users of a particular manner to accomplish a
task. For more details about CHAT, see http://www.nber.org/papers/w15319.pdf.
## 2.3 Access to the datasets
### 2.3.1 Micro-data: replication of results vs confidentiality
The MIP data is composed of micro-data (German firms) and their use for
scientific purposes is free of charge. The ZEW places value on the fact that
the whole scientific community benefits from the MIP. The data is placed at
the disposal of external users in an anonymous form (Scientific Use File /
Education Use File) for scientific, non-commercial purposes. At the moment
more than 100 scientists use the Scientific Use files, and over a dozen of
researchers visit ZEW’s Research Data Centre every year. For more information
about the Data Centre, please see https://kooperationen.zew.de/en/zew-
fdz/home.html.
PatStat is widely used in the academic community to extract patent data. Even
if the latter is private, most of universities with faculties in Economics and
Economics of Innovation acquires licenses for its use. The scripts linked to
the patent extraction can be found on the GitHub depository in order to
replicate the results for each sector and country over time. The only
exception to an opened access to the data concerns the micro-data about the
confidential agreements between the Fraunhofer institutes and German firms to
license, or to perform R&D. The FRAME project could benefit from a specific
allowance to use the data within the FRAME project only (see Appendix A for
more details).
### 2.3.2 Aggregated data: replication and public sources
Besides the limitations previously mentioned, the other data sources are
already publicly available: the GERD dataset is available online 1 , CHAT
dataset 2 . To ease the replication of the research results across Work
Packages, the detailled calibrated estimates will be summarized on the project
website. Moreover, the scripts used in extracting patent data are already
available on the GitHub depository 3 . Doing so allows people with licences
to PatStat to replicate the queries and estimations of P1-P3.
# 3 Work Package 5
The reference method developed by Basu, Fernald, and Kimball (2006) cannot be
easily applied in the European context. The lack of available quaterly data
does not allow a direct application of their methodology to the European case.
However, the EU-KLEMS database provides an alternative by providing series of
annual TFP measures. The main drawback is linked to the assumption behind the
estimation: constant returns to scale and do not adjust for changes in factor
utilization. Work Package 5 proposes to extend these estimations by relying
this assumption and uses EU-KLEMS as a starting point.
## 3.1 TFP estimation: EU countries vs the USA
### 3.1.1 Main datasets: description and access
Work Package 5 uses outputs and inputs measures at the industry level (for
further details, see O’Mahony and Timmer (2009) and Jäger (2017)). Two main
sources of data are used to evaluate the added-value of the developed
methodology. First, the EU KLEMS is used for EU countries to provide annual
industry-level growth accounting data. Second, the World KLEMS data has been
used to assess the relevancy of the methodology to estimate the TFP for the
USA. EU-KLEMS is maintained and updated on the official website of the
initiative: http: //www.euklems.net/. Therefore, data is freely available
and recently updated. The EU KLEMS updates in the new ISIC Rev. 4 industry
classification are provided on a country by country basis. Similarly, the use
of capacity utilization is also available on the European Commission website
and freely available https://ec.europa.eu/info/business-economy-
euro/indicatorsstatistics/economic-databases/business-and-consumer-
surveys/download-business-a nd consumer-survey-data_en. Regarding the USA,
the industry measures used to estimate the TFP are coming from another
publicly dataset, available online http://www.worldklems.net/ data.htm. The
US capacity utilization data comes from the Federal Reserve Board’s monthly
reports on Industrial Production and Capacity Utilization (G.17) 4 The data
is constructed by the Federal Reserve on the basis of an underlying Census
Bureau survey of manufacturing firms, the Census Bureau’s Quarterly Survey of
Plant Capacity (QSPC).
In order to tackle endogeneity, Work Package 5 relies on different variables
to instrument the relationship between the capacity utilization and TFP.
Therefore, secondary datasets are also involved.
### 3.1.2 Secondary datasets: description and access
Work Package 5 relies on different sources of shocks to tackle endogeneity:
shock in oil prices, monetary policy shocks, and fiscal policy shocks. Oil
prices are computed by deflating the Brent Europe price of oil with each
country’s GDP deflator for European country. The latter relies on two public
available data sources: the Brent Europe price (COILBRENTEU) from the
World Bank 5 and the OECD GDP data see https://data.oecd.org/gdp/gross-
domesticproduct-gdp.htm. For the USA, oil prices are retrieved from FRED
(Federal Reserve Bank of St. Louis) available online at
https://fred.stlouisfed.org/series/WPU114112153.
The variable linked to uncertainty has been computed based on the number of
journal articles per year and country, and known as the Economic Policy
Uncertainty (EPU). This data source is already widely used in the economic and
finances academic communities 6 . The EPU database is maintained and
available by various US universities, its use is also free of charge and
available online: www.policyuncertainty.com.
The variable based on monetary shocks comes from the ECB policy announcements
which are used in Jarocinski and Karadi (2018) but is not yet publicly
available. For the moment, the study is under review and the summary of the
monetary shocks has been directly communicated to the Work Package 5 team by
Jarocinski and Karadi. When the study will be published, it will be possible
to replicate the estimations with the specified monetary shocks. For the
United States, the WP5 team uses the series of narratively identified monetary
policy shocks from the seminal work of Romer and Romer (2004), as updated in
Wieland and Yang (2016) and provided at an annual frequency in the latter
paper 7 .
For fiscal shocks, WP5 mainly relies on a database on fiscal consolidation
shocks compiled by Alesina et al.(2015), which identifies changes in taxes and
government spending motivated by debt and deficit reduction concerns, and
therefore arguably unrelated to productivity shocks. Their database, which
builds on earlier efforts by Pescatori et al. (2011), is available at the
annual level for all countries in our sample between 1978 and 2014 8 . For
the United States, a measure of exogeneous tax changes developed by Romer and
Romer (2010) 9 is used and available at the quarterly level for the period
1945-2007.
## 3.2 Drivers of unemployment
The aim is to evaluate how much discount factors can explain of the actual
dynamics of unemployment by comparing the unemployment rate predicted by the
model to the actual data for each of the four countries we consider. To
evaluate the extent to which variation in discounts can explain unemployment
variability across EU countries, WP5 relies on realized country specific
returns on stock market data. Data is provided by WRDS.The estimations are
also compared with the Leading Economic Indicators from OECD, as these
measures have been shown to predict stock maret returns (see Zhu and Zhu, 2014
on Financial Research Letters) 10 . Before doing that, WP5 explores the
qualitative predictions of the model by discussing the Impulse-Response
Functions (IRFs) which involves different datasets due to the calibration
exercises.
### 3.2.1 Data description
SDF are estimated with data collected onstock market returns as a measure of
risky return in each country. WRDS offers measures of national stock market
returns and are computed (by WRDS) as weighted averages of price variations of
each stock in each stock exchange. The discussion of the IRF involves
different data sources to calibrate the model. First, to benchmark the model,
the project team of WP5 tries to mimic the results of Hall (2017) by using US
data. The latter is about the stock market prices and dividends. The data has
been collected by Professor Robert Shiller. Secondly, the authors discuss the
results in the light of previous contributions for EU countries, namely Elsby
et al.(2013). The WP5 project team does not follow the same methodology as
Elsby et al. (2013) but computes the productivity shocks by dividing the GDP
of various countries by the total number of workers in these countries. D5.1
and D5.2 use the calibration existing in the literature but is rather US-
biased. In order to provide relevant estimates for the EU countries, the
authors have calibrated the model with OECD data to characterize the labour
market dynamics with the: NRR (net replacement rates, for the new
calibration), DUR_D (unemployed persons by duration, to calibrate job-finding
and separation rates) and MEI (for quarterly measures of unemployment).
### 3.2.2 Access to the datasets
The market returns are estimated based on the computation of a private
database provider, Wharton Research Data Services (WRDS). WRDS offers a
dataset called “World Indices by WRDS”. WRDS computes of stock market returns
at the country level, so what WRDS does her is to aggregate the stock market
returns of listed companies from the security issue level for a given country.
The access to the data requires a license and can be accessed online:
https:// wrds-www.wharton.upenn.edu/pages/support/manuals-and-overviews/wrds-
world-index/. The Euro OverNight Index Average (EONIA) as a measure of the
net risk-free return. The data is provided by the Statistical Data Warehouse
of the European Central Bank. All series are expressed in percent per annum
and available at monthly frequency. The data collected by Robert
Shiller is available publicly at
http://www.econ.yale.edu/shiller/data/ie_data.xls. Such dataset has monthly
observations about prices Pt and dividends dt, which have been provided by
Standard and Poor’s. Finally, the productivity shocks are also based on
another public source of data, see
http://appsso.eurostat.ec.europa.eu/nui/show.do?dataset=nama_10_ gdp&lang=en
and http://appsso.eurostat.ec.europa.eu/nui/show.do?dataset=namq_10_
pe&lang=en. Finally, the calibration of the labour market parameters is also
by definition available for replication and for different countries to fit the
researchers’ needs for replicability purposes as well. For more information
about the data, please see https://stats.oecd.org/
Index.aspx?DataSetCode=NRR.
# 4 Conclusion
Broadly speaking, the FRAME project will ease the access to the data and
scripts for replication within the scientific community. Most of the sources
of data is publicly available, either by being in opened access already, or by
belonging to national statistical offices. Consequently, the use, archiving
and, preservation of these datasets follow the rules of the respective
institutions. However, due to confidential agreement and copyrights, two micro
sources of information will not be available (i.e. PatStat and Fraunhofer
Institutes licensing agreements). The confidentiality linked to the data does
not impact the replication of the macro-findings across Work Packages because
the aggregated estimations to define the calibration of the model will be
provided. Only the micro-estimations from Work Package 6 will not be possible
to replicate.
The archiving policy of the data and scripts involved in the FRAME project
will follow the ZEW Data Protection Policy. Consequently, the latter will be
available for replication and scientific purposes for the next 10 years at the
ZEW data center 11 . The upload of the different scripts linked to each type
of modelling will be achieved after receiving the acceptance of scientific
journals of the related studies. Doing so will ensure the publication of the
FRAME results and their replication. The access to the different datasets
involved within the project is centralized on the website 12 to cover the
largest spectrum of users. The project website links to the official sources
of the data but the datasets are not associated to official PID. It is
therefore likely that the url used to cross-link the datasources might be
affected.
The Data Management Plan has been designed to be the most user friendly as
possible by providing access to most of the data involved in the modelling
approaches but also in calibrating the models. The depository will be updated
over the submission process of the different deliverables. The deliverables
will be available on the project website after receiving the acceptance of the
deliverables by the European Commission. The scripts linked to the different
models will be made available for download when the project members will
receive the acceptance of their related articles into scientific journals. The
scripts linked to the modelling approaches are based on the Dynare software,
which is available for free.
# A Agreement for the exploitation of the Fraunhofer dataset
FinalDataManagementPlan
FinalDataManagementPlan
FinalDataManagementPlan
FinalDataManagementPlan
FinalDataManagementPlan
FinalDataManagementPlan
FinalDataManagementPlan
FinalDataManagementPlan
FinalDataManagementPlan
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Horizon 2020
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1180_SHiELD_727301.md
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# Executive Summary
The objective of this deliverable is to present a data management plan for the
SHIELD project. This document covers a wide set of activities such as data
collection, generation, storage and preservation. In this action, we envision
five different types of data: data related to the use cases, data coming from
publications, public deliverables and open source software.
The document presents, following the EC template [1], how these different
types of data will be collected, who the main beneficiaries are, and how
SHIELD will store them, manage them, and make them accessible, findable and
re-usable. The text continues with the foreseen resources needed for the
openness and data to finalize with security and ethical aspects that will be
taken into consideration in the context of SHIELD.
This plan is the first version of the data management plan, which will be
updated in subsequent versions (M18 and M36) as part of the Technical Reports,
having as input the work carried out in the use cases (WP6), the social and
technical work packages (WP2 – WP5) and the dissemination activities (WP7).
# Introduction
## About this deliverable
This deliverable focuses on the management of the data in SHIELD. In this
context there are two different types of data: those related to the
publications generated as part of research activities, and those related to
the data collected from citizens, users and non-users of digital public
services, as well as from civil servants, that will be used as part of the
implementation of the different key results established in the project.
## Document structure
The document follows the established H2020 template for a Data Management Plan
(DMP) [1]. Section 2 presents the data summary of what the purpose of the data
collection and generation is. Section 3 explains how the data will be made
fair, and thus findable, accessible, interoperable and reusable. Section 4
briefly explains how the financial resources for this openness are envisioned
at this stage to be allocated. Section 5 and 6 outline security and ethical
aspects respectively. And finally Section 7 presents the conclusions and
future work.
# Data Summary
## Purpose of the data collection/generation and its relation to the
objectives of the project
The following list of SHIELD‘s project objectives and related key results (KR)
provides a description for each KR specifying the purpose of the data
collection/generation (if any): • **(O1) Systematic protection of health data
against threats and cyber-attacks** .
* **KR01: Knowledge base of generic security issues that may affect a system** . The purpose is to create a knowledge base which captures threats that should be managed by the architecture and regulatory data protection requirements (supporting objective O4). This knowledge base does not capture nor user's health data nor users, and it only manages threats and compliance issues in specific end-to-end applications. For the SHIELD use cases we will use fake data just to prove the benefits of the results. o **KR02: Tool that provides an automated analysis of data structures in order to identify sensitive elements that may be vulnerable to specific threats.** Data structure used to have flaws and weaknesses during the storage or exchange of data. The purpose is to analyse/collect the schema of these structures. SHiELD pilots will be used to identify sensitive data, and it will be traced during the pilots to ensure its privacy aspects and that access rights requirements are kept.
* **KR03: Security requirements identification tool** : this tool will allow models of end-to-end applications to be created, and security threats and compliance issues affecting that application to be automatically identified. We will just list security threats and compliance issues according to ‘security by design’ principles.
* **(O2) Definition of a common architecture for secure exchange of health data across European borders.**
* **KR04: SHiELD open architecture and open secure interoperability API:** the purpose is to create a SHiELD architecture which is composed by the results of epSOS project but also with tools brought by SHIELD partners such as the anonymisation mechanisms. Furthermore the health data interchanged is fake, and we do not use real users data. SHIELD pilots will invent users for each scenario. Basically the approach is to allow citizens and healthcare providers the possibility for accessing their health data from other countries
* **KR05: SHiELD (Sec)DevOps tool:** the purpose is twofold. During development time, a set of architectural patterns (mainly in Java) are stored in order to check data protection security mechanisms. During run time a set of tools provide monitoring facilities alerting the operator of the system that a threat is likely to occur.
* **(O3) Assurance of the protection and privacy of the health data exchange.** This objective is addressed mostly in WP5, led by IBM based on their expertise in novel data security mechanisms for securing the exchanged data among the different Member States. This data is protected before, during and after it is exchanged.
* **KR06: Data protection mechanisms** : the purpose is to collect a suite of security mechanisms to address data protection threats and regulatory compliance issues in end-to-end heterogeneous systems. This includes (but not limited to) tamper detection for mobile devices, data protection mechanisms, and consent-based access control mechanisms. o **KR07: Privacy protection mechanisms:** these privacy mechanisms address different aspects of privacy protection and regulation of data. These include methods for sensitive information identification. The purpose is to use and develop methods to mask private sensitive information dynamically on the fly as well as methods able to anonymize data while enabling analysis on the data.
* **(O4) To understand the legal/regulatory requirements in each member state, which are only partly aligned by previous EU directives and regulations and provide recommendations to regulators for the development of new/improved regulations** . o **KR08: Legal recommendations report.** For this KY we are not going to use private data. The purpose is to create a common regulatory framework where the legal requirements regarding security among the state members are aligned.
* **(O5) Validation of SHiELD in different pilots across three Member States** o **KR09: Pilots:** the purpose is to test implementations which are deployed in three Member States, supporting validation scenarios defined. The collected data will be used to prove that scenarios are working.
* **KR10: Best practices:** the purpose of the data used is to describe lessons learned and best practices for protecting health data
* **(O6) Dissemination of SHiELD results** o **KR11: Publications:** the purpose is to collect the scientific papers, white papers, popular press articles, media and social media we are producing **.** o **KR12: Take up opportunities: its purpose is to identify the main** users, standards bodies and regulators.
## Types and formats
* During these first 6 months we are just considering the format suggested in [2] we are considering a Patient Summary as an identifiable “dataset of essential and understandable health information” that is made available “at the point of care to deliver safe patient care during unscheduled care [and planned care] with its maximal impact in unscheduled care”; it can also be defined at a high level as: “the minimum set of information needed to assure healthcare coordination and the continuity of care” [2]. From a technical point of view, we will use as readable formats such as CSV, XML or JSON. Examples of the XML format are described in [3] which is the official Metada Registry. SHIELD project manages structured and unstructured data collected during current and past patient hospitalizations. For example: **Structured data** refers to kinds of data with a high level of organization, such as information in a relational database. For example:
* SDO (discharge form) that contains 5 .txt files where each field is separated by
“;”
* ED (Emergency Department) dataset. o Medical images o Treatment forms.
* Constant collection forms.
* **Unstructured data** refers to information that either does not have a pre-defined _data_ _model_ or is not organized in a pre-defined manner. Unstructured information is typically _text_ \- heavy, but may contain data such as dates, numbers, and facts as well. Examples of are:
* Reports of complementary tests (radiology, pathological anatomy, endoscopy, etc.)
* Discharge letter. o Monitoring of evolutions in external consultations.
Both documents are typically uploaded in PDF format.
## Re-use of existing data
We will reuse the existing and available data provided in epSOS
(http://www.epsos.eu/home/epsos-results-outlook.html) just to check the
feasibility of the solutions provided in SHIELD.
## Origin of the data
The data is based on the scenarios provided in SHIELD, and more precisely on
the three member states requirements (UK, Italy, Spain (Basque country)). The
data used in the scenarios are not real, and do not belong nor describe any
individual. Additionally we follow the principle of informed consent (see
section 6 “Ethical aspects”). The use of these data can help us to demonstrate
the technology developed.
## Expected size of the data
At this stage of the project it still hard to define precisely the data size
and ingestion rate. However, it can be useful to go into details regarding the
dimension of the most important data involved in the use cases:
* **Medical images** : include all the bio-images such as ultrasound scan, MRI (magnetic resonance imaging) or CT (computer tomography) scan. Considering that the **Computerized Tomography** uses 3D x-rays to make detailed pictures of structures inside of the body, it takes **pictures in slices** , like a loaf of bread. This means that each slice it’s a picture, the number of pictures can be from 30 for simple examinations to 1000+ for sophisticated examinations. This scan can be repeated several times (2-6) to reduce noise and to ensure high quality of the exam. In conclusion we will have from 30 to 1000 images each one with the size of 5 MB times 2-6 series; we can say that a single CT examination for a patient will have the size of 300 MB – 30 GB depending on the kind of investigation;
* **SDO and ED dataset** : is around 1 Kb per patient since any images are included and since the information is codified (.txt format).
## To whom might it be useful ('data utility')?
The results of SHIELD might be useful for healthcare providers, governments,
and patients.
# FAIR DATA
This data management plan follows the FAIR (Findable Accessible Interoperable
Reusable) principles.
## Data findable
There are different types of data:
* Data related to the use cases
* Data coming from publications
* Data coming from public deliverables
* Open source software
### Data related to the use cases
During the lifetime of the project and especially during the trials execution,
SHiELD partners expect several types of data to be generated, mainly health
data, location data, personal data (“fake” names, addresses, contact
information, IP addresses, etc.), pseudonymised data (user names, device
identifiers, etc.), traffic data, as well as others.
The first step in development of the use case studies will be to produce a
high level outline of the scenario to be used in the project. Starting from
epSOS data exchange gateway, a set up for subsequent validation experiments
will be deployed. Since these experiments will involve some novel security
mechanisms whose value is not yet proven, current patient data will not be
used directly in the use cases. Instead, an equivalent test system will be
implemented by using synthetic patient data to verify that security is
effective without compromising the data exchange interoperability requirements
and that SHiELD solutions are compliant to European General Data Protection
Regulation 679/2016.
The second step of the project will see the creation of synthetic data sets
which may be sampled or combined randomly and associated with fictitious
Patients.
This synthetic set of medical information will include the minimum patient
summary dataset for electronic exchange developed in the epSOS project [4]
defined according to the clinical point of view keeping in mind the medical
perspective of the final users (medical doctors and patients).
SHIELD WP6 deliverable 6.1 describes a set of scenarios, and all digitalized
data included in Electronic Health Records (EHR) includes as example:
* Patient’s personal data
* Medical histories & Progress notes
* Diagnoses
* Acute and chronic medications
* Allergies
* Vacinations
* Radiology images
* Lab and test results
* Clinical parameters (blood pressure, heart rate, capillary glucose,…)
For each scenario it is going to be necessary to stablish which are going to
be the minimum clinical data in order to try to manage the patient in the most
efficient way. On the one hand, it will be necessary to establish the
sensitivity and security of the data, but on the other hand it is essential to
provide the health professionals with the minimum imprescriptible data in
order to perform an efficient management and also provide security in the
management of the patient. One of the scopes of SHiELD, is to stablish the
minimum data necessary for each scenario just to improve the clinical
management of foreign patients while traveling along Europe.
In this way we need to:
* Denominate the fields to include, its format and range of values that can adopt.
* The classification of the field as part of the minimum set or if its inclusion is recommended, corresponding to each Health Service the final decision to include it or not.
* Inclusion of the field and its value as part of the attributes of the document as a "tag" to identify the essential elements of its content without having to open (decrypt) the document.
To codify different fields of the minimum dataset that will be exchange we
have:
* **SNOMED CT** or **SNOMED Clinical Terms:** is a systematically organized computer processable collection of medical terms providing codes, terms, synonyms and definitions used in clinical documentation and reporting. SNOMED CT is considered to be the most comprehensive, multilingual clinical healthcare terminology in the world. The primary purpose of SNOMED CT is to encode the meanings that are used in health information and to support the effective clinical recording of data with the aim of improving patient care. SNOMED CT provides the core general terminology for electronic health records. SNOMED CT comprehensive coverage includes: clinical findings, symptoms, diagnoses, procedures, body structures, organisms and other etiologies, substances, pharmaceuticals, devices and specimens. between different Health Systems we have:
* **ICD-10** is the 10th revision of the International Statistical Classification of Diseases and Related Health Problems, a medical classification list by the World Health Organization. It contains codes for diseases, signs and symptoms, abnormal findings, complaints, social circumstances, and external causes of injury or diseases. The code set allows more than 14,400 different codes and permits the tracking of many new diagnoses. The codes can be expanded to over 16,000 codes by using optional subclassifications.
This is just a brief list of medical data; indeed, it represents only a subset
of the whole set of medical information that could be involved in SHiELD
project. For documents including sensitive information we will remove and hide
all data. For example, Figure 1 represents a dismissal letter in which
sensitive information can be found. It can be seen that in the discharge
letter the sensitive information are **name and surname** of the patient and a
**clinical record number** that is bound to the patient. All these data will
be removed.
Figure 1: Document containing clinical record number and name.
Regarding medical images, Figure 2represents a slice of a simulated patient.
Within this figure some sensitive information are circled in blue:
* **FANTOCCIO** is the space dedicated to the patient name and surname;
* **PID** is the internal patient ID, it means that the code identifies the patient within hospital internal system;
* **Acc.num** is a progressive number in the hospital internal system;
Figure 2: Slice of a simulated patient
Additional to synthetic data regarding to patient past hospitalization, SHiELD
project can include mobile data that can be useful for diagnostic purposes.
Data could come from both mobile and wearable devices; some examples of
datasets are provided:
* GPS tracks ( _e.g._ localization); - Posts ( _e.g._ social registrations); - Last known activities:
* SMS sent at time XX.XX;
* Weather data;
* Activity tracker - Chronic patient monitoring;
* Drug therapy.
This data coming from wearable devices are not directly health related but
allow for health related conclusions after processing.
### Metadata
All publications will be indexed by using Digital Object Identifiers or
similar mechanisms to be discovered and identified. All papers in journals and
magazines will use this identifier.
Concerning the naming convention, we will use the following: <<Dx.y
Deliverable name _ date in which the deliverable was submitted.pdf>>.
Each paper or deliverable contains a keywords section that can be used to
optimize possibilities for re-use.
Each deliverable is tagged with a clear version number as indicated on Figure
1, Figure 2 and
Figure 3. This is part of the metadata that each deliverable contains.
Additionally
* Editor(s): who is/are the main leaders of this document
* Responsible Partner: who is/are the main responsible partner of this document
* Status-Version: draft, released, final
* Date: submission date
* Distribution level (CO, PU): confidential or public access according to SHIELD proposal
* Project Number: SHIELD project number
* Project Title: SHIELD title
* Title of Deliverable
* Due Date of Delivery to the EC: date to be sent to the European Commission (EC)
* Workpackage responsible for the Deliverable
* Editor(s):Who edit this deliverable
* Contributor(s): who have contributed
* Reviewer(s): reviewers
* Approved by: people who internally approved it to be submitted to EC
* Recommended/mandatory readers
* Abstract: it summarises this document
* Keyword List: a set of words which can provide an overview of the topic of this deliverable
* Disclaimer: copyrights if any
Each document registers its revision history: version number, data, reason for
the modification, and by whom it is modified.
Figure 3: Deliverable front page where version is shown
Figure 4: Document description contains version number
Figure 5: Page headers contains version number
## Data openly accessible
Data related to the use cases are going to be accessible through the SHIELD
deliverables which will be published on the website ( _http://www.project-
shield.eu/_ ) . All deliverables include a set of keywords and a brief
description that are aimed to facilitate the indexing and search of the
deliverables in search engines. Scientific publications are going to be
published as Open Data, we will use Open Aire [3] – compliant repositories.
For example TECNALIA use its own repository, already indexed by Open Aire.
There are other repositories such as Zenodo [4] that can be used. The
deliverables will be stored at AIME’s hosting provider, and for three years
beyond the duration time frame of the project
All data produced will be made available through the use of deliverables,
papers in journals/magazines/conferences, or repositories. The data used for
proving functionalities are not real, and they are going to be distributed
using open source repositories, which will be easily accessible by using a
browser.
According to the SHIELD Grant Agreement (GA) page 15 “The SHiELD DevOps and
solution will be as open source as possible (taking into account exploitation
plans and the IPR issues that might arise from the usage of proprietary
background)”. But basically all tools are following a freemium licensing
schema, where there is a public version that can be released as open source
and a commercial edition. All these software will be released at the end of
the project, because they are going to be mature enough.
At this moment, there is no specific arrangements, restrictions of use (apart
from GA), there is no data access committee, and licenses depend on each tool
used in SHIELD.
## Data interoperable
Basically SHIELD project will produce a platform based on OpenNCP [6] which is
interoperable with other software. The structures used for data exchange
follow the eHealth DSI Interoperability Specifications [7]. Most of the
vocabularies used follow the traditional software engineering artefacts
descriptions, and for the eHealth domain we are using the HL7 [8] which
specifications do not have a cost.
## Increase data re-use (through clarifying licences)
Data stemming from the use cases will be delivered through the appropriate
deliverables. Our approach is to extend a branch of the OpenNCP, and to add
SHIELD functionalities. Once we have finalised the project we integrate these
functionalities to the OpenNCP community, and this community will maintain
this platform. At the time of writing, we do not envision any embargo on data.
# Allocation of resources
SHIELD does not envision additional resources for handling data management.
SHIELD will use open access repositories as much as possible for the following
data:
* data related to the use cases
* data related to the meta-analysis
* data coming from publications
* data coming from public deliverables
* open source software
Obviously there is an indirect cost for making data FAIR in our project. But
we consider as part of the activities of the SHIELD project. All partners in
the SHIELD project are responsible for data management.
# Data security
SHIELD will ensure that the General Data Protection Regulation (GDPR), which
will enter into force in May 2018, is ensured, especially in regards to
protection of private data. In addition SHIELD project provides the following
key results dealing with data security:
* [KR03] Security requirements identification tool
* KR04] SHiELD open architecture and open secure interoperability API
* [KR06] Data protection mechanisms: a suite of security mechanisms that address data protection threats and regulatory compliance issues in end-to-end heterogeneous systems
* [KR07] Privacy tool: it monitors the data access attempts to ensure that only valid requests are accepted and only the data that is really needed is provided
# Ethical aspects
The basis of ethical research is the principle of informed consent as stated
in our proposal. All participants in SHIELD use cases will be informed of all
aspects of the research that might reasonably be expected to influence
willingness to participate. Project researchers will clarify questions and
obtain permission from participants before and after each practical exercise
(e.g. interview, co-creation session, etc.) to maintain on-going consent.
Participants will be recruited by each organization leading the use cases
(Osakidetza, FCSR, Lanc) and other supporting organizations (e.g. Ibermatica,
Aimes) and will cover more than one type of citizens. If participants wish to
withdraw from the participation in the use cases at any time, they will be
able to do it, and their data, even the pseudo-anonymized data, will be
destroyed.
In WP1 there is a task entitled as “Task 1.3 Ethical trials management” where
we ensure that ethical principles are used throughout the use cases, which are
clustered together for management purposes in the work package related to the
use cases. Further explanations on ethical matters will be gathered in
deliverable D1.6 Ethical protocols and approvals.
# Conclusions
The document describes our SHIELD data management plan according to the
established H2020 template for a Data Management Plan (DMP) [1]. This document
is alive during the whole project, and it will be updated on a regular basis.
Data Summary section indicates the purpose of the data collection and
generation. This is a complex task, because there are several data which can
be managed. Each data will be made FAIR (findable, accessible, interoperable
and reusable). SHIELD project’s key results are briefly explains how the
financial resources for this openness are envisioned at this stage to be
allocated. Section 5 and 6 outline security and ethical aspects respectively,
and finally Section 7 presents the conclusions and future work
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https://phaidra.univie.ac.at/o:1140797
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Horizon 2020
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1183_ECHOES_727470.md
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# INTRODUCTION AND OVERVIEW
## General overview of data collection activities in ECHOES
ECHOES is a mixed methods project where the key methodological strength comes
from combining qualitative and quantitative techniques with an
interdisciplinary approach from the perspectives of Social Sciences and
Humanities, including sociology, psychology, political science, and economics.
This allows us to study individual and collective energy choices and energy-
related behaviour across the EU 28 plus Norway and Turkey to identify and
close knowledge gaps on factors that are drivers or barriers for choices
enabling sustainable energy transitions. In turn, this will allow us to
formulate innovative policy advice with a toolbox for how to further the
European Energy Union, providing an insight of the interrelationships between
the identified factors and their interrelations with technological,
regulatory, and investment-related aspects to implement the SET-plan and meet
the challenges specified in the SET-plan integrated roadmap. The work in the
project is delivered in different work packages, which have their own
methodological approaches.
## Purpose and scope of this document
This data management plan (DMP) first gives a comprehensive overview of all
data types collected in ECHOES, including a detailed mapping of data
collection activities to work packages and ECHOES partners (see Section 2).
Afterwards, each partner’s responsibilities with respect to data collections,
data handling and analysis, as well as data storage are identified (Section
3). In the final section, the document defines ECHOES standards for all data
collections (including research ethical standards), data storage and handling,
data documentation, data anonymization, access to data for exploitation and
future use, and data deletion. It needs to be noted, that a data management
plan is understood to be a dynamic document that will be adjusted and adapted
during the course of the project. It will be integrated into the ECHOES
project handbook (deliverable 1.2) which will be implemented in ECHOES as a
WIKI-based knowledge / procedures database constantly updated. In that
respect, the DMP describes the ECHOES data management at the point in time it
was delivered to the European Commission. The
document was written with reference to the Guidelines to FAIR data management
in Horizon 2020
(
_http://ec.europa.eu/research/participants/data/ref/h2020/grants_manual/hi/oa_pilot/h2020-hi-
oa-datamgt_en.pdf)_ _._
## Future revisions of this document
A data management plan is a dynamic document, which will be constantly
updated. Since the DMP will become part of the dynamic project handbook
(deliverable D1.2), it will be integrated in its revision cycles. However, a
formal revision of the DMP will be provided on an annual basis, which means in
January 2018 and January 2019.
# DATA COLLECTION
ECHOES is a mixed-methods project and its methodological strength comes from
combining qualitative and quantitative techniques with an interdisciplinary
approach. This allows us to study individual and collective energy choices and
energy-related behaviour across the EU 28 plus Norway and Turkey to identify
and close knowledge gaps on factors that are drivers or barriers for choices
enabling transitions to sustainable energy. ECHOES includes data collection
and handling activities in all WPs (excluding WP1) which strongly depend on
each other.This complexity demands a strict coordination between the different
tasks and WPs as they depend on each other and input from preceding tasks is
not only required within the same WP but also in other WPs.
Table 1 presents an overview of the various data collections that are part of
ECHOES (see the first column) and indicates which WP(s) participate in each
data collection or data handling. For example, the first data collection is
“Literature search” and WPs 2-6 contribute to this data collection.
#### _Table 1:_ _Data collection methods used in different WPs_
<table>
<tr>
<th>
**Method/WP**
</th>
<th>
**WP1**
</th>
<th>
**WP2**
</th>
<th>
**WP3**
</th>
<th>
**WP4**
</th>
<th>
**WP5**
</th>
<th>
**WP6**
</th>
<th>
**WP7**
</th>
<th>
**WP8**
</th> </tr>
<tr>
<td>
Literature search
</td>
<td>
</td>
<td>
✓
</td>
<td>
✓
</td>
<td>
✓
</td>
<td>
✓
</td>
<td>
✓
</td>
<td>
Synthesizes data
</td>
<td>
</td> </tr>
<tr>
<td>
Document study
</td>
<td>
</td>
<td>
✓
</td>
<td>
✓
</td>
<td>
</td>
<td>
✓
</td>
<td>
✓
</td>
<td>
</td> </tr>
<tr>
<td>
International survey
</td>
<td>
</td>
<td>
✓
</td>
<td>
Coordinates
</td>
<td>
✓
</td>
<td>
✓
</td>
<td>
✓
</td>
<td>
</td> </tr>
<tr>
<td>
Local surveys
</td>
<td>
</td>
<td>
✓
</td>
<td>
✓
</td>
<td>
</td>
<td>
✓
</td>
<td>
</td> </tr>
<tr>
<td>
Quantitative experiments
</td>
<td>
</td>
<td>
</td>
<td>
✓
</td>
<td>
</td>
<td>
</td>
<td>
</td> </tr>
<tr>
<td>
Interviews
</td>
<td>
</td>
<td>
✓
</td>
<td>
</td>
<td>
✓
</td>
<td>
✓
</td>
<td>
</td> </tr>
<tr>
<td>
Case studies/site visits
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
✓
</td>
<td>
✓
</td>
<td>
</td> </tr>
<tr>
<td>
Netnography
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
✓
</td>
<td>
</td> </tr>
<tr>
<td>
Workshop
</td>
<td>
</td>
<td>
✓
</td>
<td>
</td>
<td>
✓
</td>
<td>
✓
</td>
<td>
✓
</td> </tr>
<tr>
<td>
Focus Groups
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
✓
</td>
<td>
✓
</td>
<td>
</td> </tr>
<tr>
<td>
Discussion events
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
✓
</td>
<td>
</td>
<td>
</td> </tr> </table>
## Data types
Table 2 presents key characteristics of each data collection in ECHOES. The
first column indicates the type of data collection, the second column
indicates where the data for each data collection type come from, the third
column indicates how the data are collected, the fourth column indicates
whether data from a given data collection type will be published in an open
access mode at the end of the project, the next column lists the tasks and/or
WPs that contribute to a given data collection type, and finally the last
column names all partners involved in a given data collection. Note that the
same data collection type may be used in several independent data collections
in different WPs.
##### _Table 2:_ _Details of data collection_
<table>
<tr>
<th>
**Type of data collection**
</th>
<th>
**Source of data**
</th>
<th>
**How are data collected**
</th>
<th>
**Open access**
</th>
<th>
**WP/task**
</th>
<th>
**Partners (lead partners underlin ed)**
</th> </tr>
<tr>
<td>
Literature review data
</td>
<td>
Published papers and reports
(qualitative data and quantitative input to
meta-analysis in
WP4)
</td>
<td>
Papers are located through databases such as Web of Science and Scopus using
appropriate search words (e.g., “electric mobility”, “energy conservation”).
Relevant information, such as what factors influence the purchase of electric
vehicles, is retrieved from the identified papers.
</td>
<td>
Y
</td>
<td>
WP2, 3.1,
4.1, 5.1,
6.1, 7.1 1
</td>
<td>
_VTT,_
_NTNU,_
_ROMA3,_
_JR, IUE,_
_EI_ , ULEI,
UACEG,
TECN
</td> </tr>
<tr>
<td>
Document study data
</td>
<td>
Documents published by relevant stakeholders, such as policy-makers
and regulators,
NGOs and
professional organizations (qualitative data, quantitative data for
WP2 database)
</td>
<td>
Documents published by relevant stakeholders are located in the
European Commission’s document database, and through search engines such as
Google and Google Scholar using appropriate search words (e.g., “electric
mobility”, “Energy Union”).
In addition, relevant policy papers are located through databases such as Web
of Science and Scopus. These policy papers can indicate additional potentially
useful sources of information that will be subsequently explored as well.
Relevant information, such as what policy measures were successful in
increasing the uptake of electric vehicles, is retrieved from the identified
documents.
</td>
<td>
Y
</td>
<td>
2.1, 2.2,
3.2, WP5,
WP6, 7.1
</td>
<td>
_VTT,_
_NTNU,_
_JR, IUE,_
_EI_ ,
TECN, EUAS,
ENEL,
ENEF,
ROMA3
</td> </tr>
<tr>
<td>
International survey data
</td>
<td>
Responses of
participants in the international survey (quantitative data)
</td>
<td>
WP3 will coordinate the efforts related to the multi-national survey.
Empirical WPs 4-6 will provide questionnaire items to be used in the survey.
The survey will be subsequently implemented in EU-28 plus Norway and Turkey by
a professional company that specializes in conducting surveys. The survey will
cover at least 9000 households.
</td>
<td>
Y
</td>
<td>
WP2, 3.3, 4.3, 5.1,
5.2, WP6,
7.1
</td>
<td>
_VTT,_
_NTNU,_
_EI, JR,_
_IUE_ ,
ROMA3,
TECN
</td> </tr> </table>
<table>
<tr>
<th>
Local survey data
</th>
<th>
Responses of
participants in the local survey (quantitative data)
</th>
<th>
WP3 will coordinate the efforts related to the national surveys. The national
surveys will be implemented either as separate small-scale surveys, or as add-
ons to the general survey with questions relevant only for the local analyses.
Empirical WP 6 will provide questionnaire items to be used in the surveys.
</th>
<th>
Y
</th>
<th>
WP2,
WP3,
WP4, 6.1,
6.2, 7.1
</th>
<th>
_VTT,_
_NTNU,_
_ROMA3,_
_IUE, EI_ ,
UACEG,
ULEI,
TECN
</th> </tr>
<tr>
<td>
Quantitative experiments
</td>
<td>
Responses of experimental participants (quantitative data)
</td>
<td>
WP4 develops the design of psychological experiments, which are subsequently
carried out in six countries (Bulgaria, Finland, Germany, Italy, Norway,
Spain, and Turkey). The experiments will partly be conducted online as local
additions to the international survey, partly offline in the laboratories of
the participating partners.
</td>
<td>
Y
</td>
<td>
3.3, 4.2,
7.1
</td>
<td>
_NTNU,_
_ULEI, EI_ ,
ROMA3,
VTT, IUE,
JR
</td> </tr>
<tr>
<td>
Interview data
</td>
<td>
Responses of interviewees
(qualitative data)
</td>
<td>
Interviews with relevant stakeholders will be conducted as part of WPs 2 and
6. Interviewees will be selected according to availability in Turkey, Norway,
Italy, Finland, Germany, Austria, Bulgaria and/or Spain.
</td>
<td>
Y
</td>
<td>
2.1, 2.2,
WP3,
WP5, 6.2,
7.1
</td>
<td>
_VTT,_
_NTNU,_
_JR, IUE,_
_EI_ , ULEI,
UACEG TECN,
ROMA3
</td> </tr>
<tr>
<td>
Case study
data / Site visit data
</td>
<td>
Observational data from site visits, available structural framework data
(qualitative and quantitative data)
</td>
<td>
Case studies will be conducted as part of WPs 5 and 6. Cases will be analysed
based on their structural framework and available data about their success
criteria and site visits. Cases will be selected according to availability in
Turkey, Norway, Italy, Finland, Germany, Austria, Bulgaria and/or Spain.
</td>
<td>
Y
</td>
<td>
WP3, 5.5,
5.6, 6.2,
6.4 , 7.1
</td>
<td>
_NTNU,_
_ROMA3,_
_TECN,_
_IUE, EI_ ,
JR, ULEI,
UACEG,
VTT,
STMK
</td> </tr>
<tr>
<td>
Netnography
</td>
<td>
Publicly accessible information on people’s behaviour on the internet
(qualitative and quantitative data)
</td>
<td>
Ethnographic research of people’s publicly accessible behaviour on the
internet will be performed as part of WP6.
</td>
<td>
Y
</td>
<td>
WP3,
WP6, 7.1
</td>
<td>
_NTNU,_
_IUE, EI_ ,
VTT, JR,
ROMA3
</td> </tr>
<tr>
<td>
Workshop
data
</td>
<td>
Responses of workshop
participants (qualitative data)
</td>
<td>
Data on views of relevant experts and stakeholders are collected during an
expert workshop, during two policy-makers workshops and during a workshop for
EU member states organized by TECN.
</td>
<td>
Y
</td>
<td>
2.4, WP3, WP5, 6.4,
7.1, 7.3,
7.4, 8.3
</td>
<td>
_VTT,_
_NTNU,_
_JR, IUE,_
_EI,_
_TECN_ ,
UACEG,
ROMA3,
ULEI,
EUAS,
ENEF,
</td> </tr>
<tr>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
GAP,
STMK,
ENEL
</td> </tr>
<tr>
<td>
Focus group data
</td>
<td>
Responses of participants in focus group discussions (qualitative data)
</td>
<td>
WPs 5 and 6 prepare the protocol for focus group discussions, which are
subsequently carried out in six countries (Austria Bulgaria, Finland, Italy,
Norway, Spain, and Turkey).
</td>
<td>
Y
</td>
<td>
3.3, 5.2,
6.1, 6.2,
7.1
</td>
<td>
_NTNU,_
_JR, IUE,_
_EI_ ,
UACEG,
ULEI,
VTT,
TECN,
ROMA3,
EUAS,
GAP,
STMK
</td> </tr>
<tr>
<td>
Discussion event data
</td>
<td>
Responses of national stakeholders
(qualitative data)
</td>
<td>
JR organizes a discussion event with national stakeholders where their views
and opinions will be discussed and recorded.
</td>
<td>
Y
</td>
<td>
5.4, 7.1
</td>
<td>
_JR, EI_ ,
NTNU,
TECN,
UACEG,
IUE,
ROMA3,
GAP,
VTT
</td> </tr> </table>
# PARTNER RESPONSIBILITIES
This section describes the data collections and data processing each WP is
responsible for. The international survey as the largest data collection in
ECHOES with the highest need for coordination is described in an extra chapter
3.8. For a complete overview of data collection responsibilities see
_**Appendix I** _ (constantly updated during the project).
## WP2 (VTT)
VTT (WP2 lead beneficiary) is responsible for the formulation of SSH database
and SSH indicators. The SSH data will be collected on the national, regional
and the EU level. The formulation of the database will start from analysing
the existing national and EU level databases to evaluate the existing gaps in
the quantitative data and EU policies, regulations and directives. VTT will
first gather existing information on both quantitative and qualitative SSH
data, relevant databases, missing data, ideas and suggestions on important
literature via a web-based survey addressed to all consortium partners (task
2.1 and 2.2). VTT will also arrange an expert workshop, planned in July 2017
2 , to get feedback and ideas from policy-makers and other stakeholders of
the future data requirements (task 2.4).
VTT directly supports the formulation of which variables need to be measured
and the collection of relevant new data, both quantitative and qualitative,
based on the results from WP3-WP7. Those results will come from the
international survey (inputs from WP2 itself, WP3, WP4, WP5 and WP6), local
surveys (inputs from WP2 itself, WP3, WP4 and WP6), quantitative experiments
(inputs from WP3 and WP4), interviews (inputs from WP2 itself, WP3, WP5, WP6
and WP7), case studies (inputs from WP3, WP5 and WP6) and focus groups (inputs
from WP3, WP5 and WP6).
The empirical work of WP4, WP5 and WP6 will be coordinated by WP3 and then
transferred into the database created by WP2. VTT will then formulate
indicators that support modelling, analysing and comparison of transition to
sustainable energy systems (task 2.3). The final outcome will be a
comprehensive database that is a combination of SSH relevant quantitative and
qualitative data from existing databases and newly collected quantitative and
qualitative data. The database will be open source and offered under the
regulations of the pilot on open research data in H2020. This will therefore
ensure high usefulness.
## WP3 (NTNU)
NTNU (WP3 lead beneficiary) is responsible for preparing the conceptual
ground, defining, shaping and harmonizing the empirical work of WP4, WP5, WP6
and coordinating the data collection across WP4, WP5, WP6 in order to transfer
the data into the database created in WP2. WP3 will closely coordinate with
WP7 to ensure a seamless transition between the pre-empirical, empirical and
post-empirical stages covered in WP7.
NTNU will start by synthesizing the state-of-art from the three ECHOES
perspectives (micro, meso and macro levels) on energy choices and energy
transitions through the identification of existing knowledge and knowledge
gaps to be closed to foster the Energy Union (task 3.1). This prepares and
supports the literature review in WP4, WP5 and WP6 and structures the final
synthesis conducted in WP7. WP3 will then develop a strategy document to
foster the implementation of existing knowledge and to allow targeted work on
filling identified knowledge gaps in WP3, WP4, WP5, WP6, WP7 (task 3.2).
On the empirical side, WP3 ensures that all research questions are addressed,
makes sure that existing knowledge is used, and together with WP4, WP5, WP6
determines which questions are most relevant for the data collection. WP3 will
coordinate ECHOES data collection and produce a complete documentation of data
collection activities (task 3.3). The data collection includes survey-based
experiments, psychological experiments, focus groups among households and
among social units and experts, case studies and netnography, and secondary
analysis of official documents, reports and research literature. WP3 is also
responsible for coordinating the conduction of the multinational survey (EU 28
+ Norway and Turkey), accommodating all items/questions that are best dealt
with in the form of questionnaires (for details see subsection 3.8).
## WP4 (ROMA TRE)
ROMA3 (WP4 lead beneficiary) is responsible for gathering knowledge and data
on individual energy choices in a group context. WP4 builds on WP3 (literature
study, identification of research gaps and theoretical innovation) and will
complement results from quantitative and qualitative case studies. Firstly, a
meta-analysis will be conducted with the aim of shedding light on identity
processes and energy related behaviours. The meta-analysis will provide
conceptual and statistical guidelines for how to design subsequent surveys and
experiments across the ECHOES consortium (task 4.1). Empirically, survey and
experimental quantitative methods will be combined, and the obtained data will
be subjected to statistical analysis (task 4.2). WP4 also contributes a set of
items to the largescale multinational survey (task 4.3). All data will be
transferred to WP3.
## WP5 (JR)
JR (WP5 leader beneficiary) is responsible for gathering knowledge and data on
collective behaviour driven by “energy cultures”. WP5 will first develop a
research typology on energy lifestyles, integrating the literature study
carried out in WP3 (task 5.1). Quantitative and qualitative assessments of
European lifestyles will be applied. The multinational survey will provide
extensive quantitative information and will give an assessment of typical
collective energy lifestyles. Qualitative research will be carried out through
focus groups that will allow deep insight in the self-definition and key
dimensions of selected energy lifestyle types (task 5.2).
WP5 will secondly focus on the development of an “energy memory” research
guide with the literature inputs from WP3 (task 5.3). Multinational studies
such as discussion events and national reports on “energy memory” will be
produced, complemented by the outputs of discussion events to be organized
with national stakeholders (task 5.4). WP5 will then closely cooperate with
WP6 in assessing the factors enabling collective energy consumer action
through multinational studies and small national assessment (task 5.5). A
qualitative assessment via case studies in selected partner countries will
identify collective energy practices (task 5.6). All the data gathered trough
quantitative and qualitative assessments will then be transferred to WP3.
## WP6 (IUE)
IUE (WP6 leader beneficiary) is responsible for gathering knowledge and data
on formal social units as drivers behind collective decision-making. WP6 will
first identify the variables and dimensions that affect decision-making
processes for target stakeholders through a literature review based on
previous research. Based on the knowledge gathered trough the literature
review, a design of the empirical quantitative and qualitative research will
be developed (task 6.1).
Quantitative techniques will be implemented to quantify attitudes, opinions,
and other selected variable relevant to energy choices and energy related
behaviour within formal social units. Following that, a combination of several
qualitative different formats such as focus groups, in-depth interviews,
netnography, surveys (including the multinational survey) and case studies
will be implemented in selected countries (see table 2). These methods will be
applied to three types of social units: formal social units, collective
decision-making units and individual consumers engaging in joint contracts
(task 6.2).
The evaluation of the results obtained from the empirical assessment and
statistical analysis will then reveal the best practices and successful
implementation and will expose the lower level dynamics affecting energy
choices. A dissemination workshop will be organized among the EU member states
and associate countries in order to spread the evidence from good practices
and successful implementations (tasks 6.3 and 6.4). The final result is a
synthesis study that delivers suggestions and recommendations derived from the
analysis, and it will be used as a basis for the development of a decision
tree algorithm as an instrument to establish integrated governance and energy
conservation (task 6.5). All data will be transferred to WP3.
## WP7 (EI)
In the ECHOES project, EI (WP7 leader beneficiary) is responsible for
providing a knowledge synthesis for the Energy Union and by doing that
involved on the analysis/synthesis of all data produced in ECHOES. WP7 brings
together the knowledge gathered in WP3 with the knowledge produced in WP4, WP5
and WP6 with the aim of identifying the driving factors of energy related
choices and behaviour that have importance and practical relevance (task 7.1).
The identification is followed by the ranking of the driving factors according
to their potential impact on energy related choices and behaviours and
according to their potential for being implemented as part of energy policy.
Through an in-depth analysis of all relevant policy documents gathered in
previous WPs, WP7 will gain a holistic consolidated knowledge (task 7.2).
WP7 will then consult stakeholders from all relevant areas (market, politics,
and environmental agencies) to scrutinize the scientific results obtained in
ECHOES. For this purpose, two policy-maker workshops are foreseen (task 7.3).
The creation of policy-ready recommendations represents the final outcome of
WP7 and it consists of suggesting how best to exploit the new knowledge in
light of policy-making. This is the result of a continuous interaction with
all stakeholder groups, including policy makers (task 7.4).
## WP8 (TECN)
In the ECHOES project, TECN (WP8 leader beneficiary) is responsible for the
communication, dissemination and impact of the obtained project’s results. WP8
has developed the Data Management Plan for ECHOES – this document. All
information about the data collection standards, data coding, referencing and
processing and about the exploitation of the data during the project and
beyond is included in the Data Management Plan.
As its next task, WP8 will bundle the data packages for the dissemination and
exploitation. This will be done in close cooperation with the respective WP
leaders and the result will be the basis for the project final exploitation
strategy and corresponding business plan (task 8.2). WP8 will design a
strategy for the communication and dissemination of results, taking into
consideration different targets, dissemination channels and activities and
means of verification, in close cooperation with WP2. A joint workshop will be
organized by TECN during the SET Plan Conference/EERA Joint Programmed in
order to communicate and disseminate the results (task 8.3). The strategy for
the exploitation of the results represents the main outcome of WP8 and will
support the consortium’s joint efforts to maximize the project’s impact and
also the business plan for individual partners (task 8.4).
## The international survey
The largest collection of primary data will come from a multinational survey
that will collect views, opinions and answers in simulated choice experiment
in the EU-28 plus Norway and Turkey. The survey will cover at least 9000
households that span 30 countries and 27 languages. WP3 will accommodate all
items/questions that are best dealt with in the form of questionnaires. WP3
then coordinates the conduction of the survey, regularly controls the progress
of the data collection, and finally distributes the relevant data to the
respective researchers/WPs.
WP4 will contribute with a question block to the survey with the objective of
extending, validating and generalizing the findings on the factors driving
energy related decision-making at the individual level. WP5 will, through the
multinational survey, collect data that will enable to identify the most
important energy lifestyles and to see if there are comparable patterns of
energy lifestyles across Europe. WP6 will use the multinational survey to
quantify attitudes, opinions and other selected variables relevant to energy
choices and energy related behaviour within formal social units.
The results of the ECHOES multinational survey on factors driving individual
and collective energy choices will be coordinated by WP3 and then transferred
to WP7. WP7 will exploit the data from the multinational survey for deriving
policy-ready recommendations. The data gathered from the ECHOES multinational
effort will also be available for future use as an open source in the SSH
database created in WP2.
The ECHOES multinational survey data collection will be carried out by an
experienced data collection company that will have the responsibility for
recruiting participants, translating the instructions/survey questions,
hosting the online survey on secure servers, and for collecting and cleaning
the data.
# DATA MANAGEMENT
This chapter describes the procedures applied in ECHOES for the different
steps of data collection, management, storage, and publication in detail.
## Formal ethics approval
The EU commission Ethics summary report (Ref. Ares (2016)2334779 - 19/05/2016)
identifies three ethics issues to be managed in ECHOES: 1) Involvement of
human participants, 2) data collection and processing and 3) involvement of
non-EU countries.
### Involvement of human participants
There are three sub-points related to the involvement of human participants to
be managed:
1. Details on the procedures and criteria that will be used to identify/recruit research participants must be provided.
2. Detailed information must be provided on the informed consent procedures that will be implemented for the participation of humans.
3. Templates of the informed consent forms and information sheet must be submitted on request.
###### Details on the procedures and criteria that will be used to
identify/recruit research participants
Participants to the 30 country quantitative survey will be recruited from
already registered members of a web-panel that the data collection institution
operates. The participants will be sampled to be representative for each
country. Participants are 18 years or older and must be able to give informed
consent. They will be informed about the aim of the study, the collected data,
data handling, storage and anonymization procedures as well as publication of
the anonymized data and its inclusion in the open data pilot. By following the
link to participate they explicitly give their consent to participate
(templates for the consent/information sheet see point 3). Participants will
earn points in the point system of their panel operator as reward for their
participation.
Participants of the other empirical work (experiments, focus groups) will be
recruited locally from the general population 18 years and older or the
population of experts/stakeholders relevant for the topic of the data
collection. Only participants will be recruited that are able to give informed
consent and information sheets and consent forms according to the templates
under point 3 will be used. Participants will be recruited through mailing
lists, newspaper advertisement, snowball systems, posters, or the like. To
increase motivation participants will participate in a lottery of a smaller
gift card (e.g. 50 Euro) or receive other small incentives like for example
chocolate bars. Expenses that they have for participating will be reimbursed.
In the netnography, only publically available internet sources will be used.
Wherever individuals are identifiable in the raw data, their consent to use
the data for the analysis will be collected before the data is used. Usage of
internet data will be conducted after clearance by the relevant data
protection authorities. For observational data, the same standards will be
applied.
###### Informed consent procedures
Before participation in the online survey panel members will be invited to
participate in the survey by e-mail. In the e-mail the information from the
informed consent form is presented and a link to the survey is included. The
participants are instructed that by clicking the link they consent to
participate in the study as described in the information included in the mail.
For the other empirical studies, information is presented in written form when
participants are recruited. It will be repeated immediately before the data
collections are started and the consent form is signed by the participant
before they enter the experiments or focus group. Participants are also
informed that they can retreat their consent until the data is anonymized
without any disadvantages and without having to give a reason.
###### Informed consent forms and information sheet
The information sheets and consent forms will be based on the standard form
provided by the Norwegian Centre for Research Data (NSD) and will be in line
with national regulations. Future updates of the data management plan will
include a documentation of all consent forms/information sheets used in ECHOES
(see _**Appendix II** _ )
### Data collection and Processing
There are three sub-points related to Data collection and Processing
1. Copies of opinion or confirmation by the competent Institutional Data Protection Officer and/or authorization or notification by the National Data Protection Authority must be submitted (which ever applies according to the Data Protection Directive (EC Directive 95/46, currently under revision, and the national law)).
2. If the position of a Data Protection Officer is established, their opinion/confirmation that all data collection and processing will be carried according to EU and national legislation, should be submitted.
3. Detailed information must be provided on the procedures that will be implemented for data collection, storage, protection, retention and destruction and confirmation that they comply with national and EU legislation.
###### Copies of opinion (sub-points 1 and 2)
All partners that collect and process data have confirmed they will do so
according to Data Protection Directive 95/46 and the national law. All
confirmations by the institutional or national data protection officers
regarding the conduction of data collections in ECHOES in accordance with EU
and national legislation will be collected in _**Appendix III** _ of the data
management plan as soon as they are received. They will be provided at the
earliest possible time point prior to starting the data collections.
**Information on the procedures for data collection, storage, protection,
retention and destruction.**
This document (D8.1), The project Plan (D1.1) as well as the Project Handbook
(D1.2) provides this information. All procedures are in accordance the
principles of the “Guidelines on Data Management in Horizon 2020”.
###### Further data collection and procession standards
Procedures in this section, on ECHOES data collection, storage, protection,
retention and destruction, are based on the “Guidelines on Data Management in
Horizon 2020”.
The data collection, storage, protection, retention and destruction will be
conducted according to National and EU legislation, as all partners dealing
with data have declared. ECHOES includes different forms of data collection (a
large quantitative survey based on a panel of a professional survey provider,
local surveys, psychological experiments, workshops, netnography, and
interviews), and the main principles for all types of data collections is to
anonymize the datasets at the earliest possible point in the process by
separating the identifying information from the rest of the data, to store all
raw data on encrypted and password secured servers cleared for storage of
medical research data and to delete directly or indirectly identifying
personal data as soon as the purpose of collecting and quality controlling the
data is served. The conducting party will ensure that data collection and
anonymization will comply to EU legislation and ECHOES project handbook. NTNU
has the responsibility to curate all raw-data.
In more detail, the main quantitative survey data will already be anonymized
when it is received from the survey company by ECHOES. ECHOES will only
collaborate with survey companies which can document the clearance by all
national and EU legislative bodies relevant for the study. The contract with
the survey company will specify the consent procedures (only panel members who
have given their consent will be recruited, it will be explicitly informed
that clicking the link to the online survey equals consent to participate,
this consent can be retracted at any point until the data is anonymized). The
data set will be anonymized by the survey company removing all directly or
indirectly identifying information before sending it to the ECHOES research
team. After a quality control by ECHOES the survey company will immediately
delete the coding tables coupling identifying information to the survey data.
All informants and subjects in interviews and the experiments will give a
written informed consent to their participation, either by receiving and
signing a _consent and information_ letter or email, ensuring they understand
what the consent is concerning and what the consequences of participation will
be. Identifying information will be stored separated from the study data.
After quality control of the data, the identifying information will be
deleted. Video and audio material from interview sessions will be deleted
after transcription and quality control.
All raw data will be stored and protected on ECHOES encrypted servers for
secure data storage that meets the Act relating to Personal Data Filing
Systems (Personregisterloven). Personal data directly linking person and data
will always be kept separate from the datasets. Identifying personal data will
be retained for a maximum of 1 year after collection completion to allow for
thorough quality control. All such data will thus be deleted by march 2018 the
latest.
<table>
<tr>
<th>
**Procedures for data protection**
</th> </tr>
<tr>
<td>
* Data will be anonymized at the earliest possible point in time
* Non-anonymised data will be stored under strictest precautions and under no circumstances made public
* Data produced in one WP will always be anonymised before it is shared with the other WPs
* Consent is sought before storing collected material in electronic form
* The personal data collected through interviews generate personal data, this data will be anonymized during transcription
* Any text produced based on research results will respect this principle of privacy and anonymity
* All secondary data sources shall contain already anonymized datasets, which will not allow identifying individuals directly or indirectly
* Activities carried out outside the EU will be executed in compliance with the legal obligations in the country where they are carried out
* The activities must also be allowed in at least one EU Member State
* All data transferred between project partners (within or outside the EU) will be restricted to anonymized data and transfer will only be made in encrypted form via secured channels
</td> </tr> </table>
### Involvement of non-EU countries
ECHOES non-EU partners (partners NTNU, IUE and EUAS) have confirmed that the
ethical standards and guidelines of Horizon2020 will be rigorously applied,
regardless of the country in which the research is carried out.
### Local data collections
As shown in Table 2, ECHOS will produce 10 categories of data in addition to
the international survey (described separately in Section 4.1.5), involving
all scientific partners and WPS 2-8. The involved WPs are responsible for raw
data production and monitoring, and data preparation (including translation to
English and transcription if necessary). Each partner producing raw-data will
upload the raw-data (including audio/video files of interviews or focus
groups) to the protected servers at the _Services for sensitive data_ (TSD),
which are NTNUs solution for sensitive data including medical information (see
also Section 4.3.1). Each partner is responsible for the anonymization of the
data collected. WP3 is responsible for the overall curation of the collected
raw-data and the distribution of the anonymized data to the ECHOES partners.
WP2 is responsible for transfer and long-term storage of anonymized, while all
relevant WPs are responsible to utilize relevant data. See the constantly
updated **_Appendix I_ ** for details.
All informants and subjects in interviews and the experiments will give a
written informed consent to their participation, either by receive and sign a
_consent and information_ letter or email, ensuring they understand what the
consent is concerning and what the consequences of participation will be.
Identifying information will be stored separated from the study data. After
quality control of the data the identifying information will be deleted. Video
and audio material from interview sessions will be deleted after transcription
and quality control.
### International survey
The ECHOES survey effort is coordinated and administrated in WP 3, while
scientific design of the questionnaires and the ancillary choice experiments
are defined in WPs 4-6. A professional service company is needed to a)
translate the questionnaire into all 26 official languages (EU + Norway and
Turkey), b) to program the questionnaire in an adequate online-survey tool, c)
recruit survey participants (according the selection criteria defined by the
consortium) and d) to send out the links to this questionnaire to
participants. Emphasis is put on hiring a company that specialize in the
above-mentioned tasks and that has a proven record of accomplishment of multi-
national efforts to ensure the highest quality data material. We will collect
several quotes for the ECHOES survey service, and choose the one offering the
best value for money ratio.
Companies in question must document their routines and procedures for data
management, sensitive information and personal data – and thus prove that all
data collection services and processing will be carried out according to EU
and national legislation. The chosen company will transfer anonymized data to
the ECHOES project server through a secure file transferring system.
The conduct of the ECHOES survey follows a stepwise process:
<table>
<tr>
<th>
**Procedure for the international survey (all conducted by ECHOES partners)**
</th> </tr>
<tr>
<td>
1. Collection of (preliminary) results of the psychological laboratory experiments and the focus group discussions, each of which is implemented in 6 countries
2. Derivation of key research questions that a) could not be conclusively answered by the experiments or group discussions, or b) for which high heterogeneity between the countries was identified requiring the collection of additional data from all 30 countries.
3. Identified questions are then implemented in the ECHOES questionnaire, a) as questions being equal for all respondents, or b) as design variables, i.e. ancillary information that varies by the respondents to identify the impact of these variables on her/his answers in the following questions or choice experiments. An example of such a design variable is the (varying!) description of a certain energy policy instrument, along with the question whether the respondent would vote in favour of or against that policy in an opinion poll.
4. Econometric/statistical analyses of the data are then carried out to identify the effectiveness of the design variables (e.g. policy variables) on behaviour.
</td> </tr> </table>
## Data collection procedures
In order to achieve quality assurance, quality control and consistency
throughout the project; specific data collection procedures will be added to
the DMP as they are developed by the involved partners ahead of the different
data collections. All procedures will be developed to meet general scientific
quality criteria for data collections 3 as indicated in the following table:
<table>
<tr>
<th>
**Quality standards**
</th> </tr>
<tr>
<td>
**Accuracy**
Is the data collected correct and complete? Are the data entry procedures
reliable?
**Efficiency**
Are the resources used to collect data the most economical available to
achieve those objectives?
**Effectiveness**
Have the objectives been achieved?
Have the specific results planned been achieved?
**Feasibility and timeliness**
Can data be collected and analysed cost effectively? Can it provide current
information in a timely manner?
**Relevance**
What is the relevance of the data/information/evidence to primary
stakeholders?
Is data collection compatible with other efforts? Does it complement,
duplicate or compete?
**Security**
Is the confidentiality ensured?
**Utility**
Does data provide the right information to answer the questions posed?
</td> </tr> </table>
### Literature review
The ECHOES literature review is the first data collection to be conducted. The
literature review includes all three perspectives and technology foci of
ECHOES, WPs 3, 4, 5, 6 and 7 are involved, starting at different times and
with adjoining scopes. To ensure quality, consistency and to avoid unnecessary
overlap between partners and WPSs, WP3 has a specific focus on coordination.
All partners will use similar templates (see _**Appendix V** _ ), a local
literature review database on the ECHOES SharePoint will store all references
reviewed, and finally the following procedure shall be followed:
<table>
<tr>
<th>
**Procedure for the literature review**
</th> </tr>
<tr>
<td>
1. Keep a log that tracks all steps taken while performing the literature review
2. Choose a topic, define relevant research questions.
3. Define the scope of the review
4. Select the databases to be used for searches
5. Conduct searches; keep track of all search words and combination of search words
6. Review the literature and fill out the relevant templates according to your findings
7. Register the references reviewed on the literature review database
8. Store the data in their respective folders on SharePoint
</td> </tr> </table>
### Document study
The document study procedure will be developed ahead of the data collection
and documented in an updated DMP.
### Local survey(s)
The local survey procedures will be developed ahead of the data collection and
documented in an updated DMP.
### Quantitative experiments
Procedures for the quantitative experiments will be developed ahead of the
data collection and documented in an updated DMP.
### Interview data
Interview data procedure will be developed ahead of the data collection and
documented in an updated DMP.
### Case study/site visits
Case study procedure will be developed ahead of the data collection and
documented in an updated DMP.
### Netnography
Netnography procedure will be developed ahead of the data collection and
documented in an updated DMP.
### Workshop data
Workshop data collection procedure will be developed ahead of the data
collection and documented in an updated DMP.
### Focus group data
Focus group data collection procedure will be developed ahead of the data
collection and documented in an updated DMP.
### Discussion event data
Discussion event data collection procedure will be developed ahead of the data
collection and documented in an updated DMP.
## Data documentation
All collected data shall include a metafile when stored on the ECHOES secure
storage solution and/or the ECHOES SharePoint server. The file will later be
made available for external users of the data. This metafile shall explain the
kind of data included, involved personnel, date and duration of the data
collection, variable names/labels, recruiting procedures, response rates,
whether or not it is anonymized, related WPs and tasks, and finally a summary.
_**Appendix IV** _ provides two templates for qualitative and quantitative
data sets that will be adapted during the course of ECHOES.
## Data storage and curation
All non-anonymized data will be stored and protected on ECHOES encrypted
server space for secure data storage, described in 4.4.1. Anonymized data will
be stored in the ECHOES SharePoint solution described in 4.4.2 in encrypted
form (see 4.4.3). WP3 (NTNU) is responsible for the curation of all data
collected in ECHOES and its save storage. The storage solutions for non-
anonymized raw-data and anonymized data include daily backup routines to
prevent data loss. All data files will be assigned a persistent and unique
Digital Object Identifier (DOI) by the end of the project through services
such as Figshare, Zenodo or Dryad.
### Non-anonymized raw-data
The non-anonymized raw-data will be stored on a secure server, hosted and
operated by the University of Oslo (UiO) and their _Services for sensitive
data_ (TSD) (
_https://www.uio.no/english/services/it/research/storage/sensitivedata/_ )
that meets the Act relating to Personal Data Filing Systems
(Personregisterloven). This secure storage solution is NTNUs standard for
sensitive data. ECHOES will establish a server space as soon as first data is
produced. The server used will either be a windows 2012 server or the Linux
RedHat 6.0. Backup is performed by the nominal UiO backup system, using an
encryption key that only exists on that terminal-server and in two safes in
two separate locations.
Data import/export is provided by a "file-sluice" that carries data between a
storage area inside of the solution to/from outside of the solution. All users
can import data to the server, and work with the data on the server using
tools such as R, PSPP, SPSS, STATA, MATLAB, SAS etc. Data can only be exported
by the coordinator and administrators.
Access to the server is provided first through an encrypted SSH tunnel to one
of the login machines. Then either RPD (windows) or Spice (Linux) to access
the ECHOES project. TSD use a two-factor login, users will be given a one-time
password by smartphone/Yubikey. When establishing a user account in the
system, the users have to be identity checked, for example by logging inn
through the Norwegian Bank ID or MinID system (for the administering users
located in Norway) or similar systems in other countries. User guides are
available at the TSD homepage 4 .
The folder structure on the server will be based on the ECHOES WP structure,
and separate folders for each kind of data for all relevant WPs. Datasets that
belong to multiple WPs will be stored in sub-folders of WP3.
### Anonymized data
All data collection and processing that will be done during ECHOES will be
carried out according to national legislation and the EU Directive 95/46/EC /
General Data Protection Regulation (Regulation (EU) 2016/679). The consortium
and the partners are responsible for following the ethical procedures in their
respective countries (see Section 4.1).
Anonymized data will be stored at the ECHOES SharePoint solution in encrypted
and password-protected form (see Section 4.4.3). ECHOES partners have access
to this solution through personal logins provided by NTNU. The overall folder
structure is based on the ECHOES WP structure; each WP folder includes a data
sub-folder and these will include folders for the specific kinds of data
produced.
### Encryption standards and procedures
All data files will be transferred via secure connections and in state-of-the-
art encrypted and password-protected form (for example with the open source
7-zip tool providing full AES-256 encryption: _http://www.7-zip.org/_ ) .
Passwords will not be exchanged via e-mail but in personal communication
between the partners. The encryption solutions will be chosen in accordance
with the ECHOES partners’ IT supports.
### File name standards
To ensure that data files as well as any other file in ECHOES has a clear name
identifying the content, the following file name standards are used. All
documents shall be numbered by their type of document, and the assigned
subsequent numbering within each WP (first deliverable of WP1: **D1.1,** first
deliverable of WP 2: **D2.1** )
**XXX** : Identifies which main category the document belongs to. In order to
always easily identify the files, the project name **ECHOES-** shall be
included as a prefix to all document categories.
**YYY** : Will always be a number assigned subsequently for each new doc in
the XXX category and WP. **ZZZ** : Issue number
<table>
<tr>
<th>
**XXX**
</th>
<th>
**XXX explanation**
</th>
<th>
**YYY**
</th>
<th>
**ZZZ**
</th> </tr>
<tr>
<td>
D
</td>
<td>
Deliverable
</td>
<td>
1.1, 1.2, 2.1, 2.2 etc.
</td>
<td>
1,2,3,etc.
</td> </tr>
<tr>
<td>
MAN
</td>
<td>
Management
</td>
<td>
1.1, 1.2, 2.1, 2.2 etc.
</td>
<td>
1,2,3,etc
</td> </tr>
<tr>
<td>
DAT
</td>
<td>
Data files
</td>
<td>
1.1, 1.2, 2.1, 2.2 etc.
</td>
<td>
1,2,3,etc
</td> </tr>
<tr>
<td>
DOC
</td>
<td>
Data documentation file
</td>
<td>
1.1, 1.2, 2.1, 2.2 etc.
</td>
<td>
1,2,3,etc
</td> </tr>
<tr>
<td>
NOT
</td>
<td>
Notes
</td>
<td>
1.1, 1.2, 2.1, 2.2 etc.
</td>
<td>
1,2,3,etc
</td> </tr>
<tr>
<td>
MOM
</td>
<td>
Minutes of meeting
</td>
<td>
1.1, 1.2, 2.1, 2.2 etc.
</td>
<td>
1,2,3,etc
</td> </tr>
<tr>
<td>
PRE
</td>
<td>
Presentations
</td>
<td>
1.1, 1.2, 2.1, 2.2 etc.
</td>
<td>
1,2,3,etc
</td> </tr>
<tr>
<td>
PAP
</td>
<td>
Journal paper manuscript
</td>
<td>
1.1, 1.2, 2.1, 2.2 etc.
</td>
<td>
1,2,3,etc
</td> </tr> </table>
The file-name shall always consist of: document number, document title and
issue (in this order). Underscore shall be used between document number, issue
number and document title. There shall be no open spaces in the document
title. Logical short versions of words can be used in the document title part
of the filename in order to shorten the filename. If the document is a draft
version, this is indicated by “DR” after Issue# and underscore.
Example (this document, first issue): **ECHOES-D8.1_DMP_1**
## ECHOES Data base
An open access database will be created to gather both quantitative and
qualitative SSH data. The data will be compiled from public and non-commercial
databases. In addition, database will compile and store data based on the
results from WP3-WP7 of this project and WP2’s own data and information
collection via a web-based survey(s), workshops, and interviews. The
researchers have a duty of transparency to fully inform how the data will be
used and to what purpose the data is for. Thus, the ethically compliant data
collection will be guided by proportionality (proportional of WP2 research
aims) and follow the legal safeguards to minimize any risks related to
unauthorized release of personal and private information. The empirical work
of WP4, WP5 and WP6 will be coordinated by WP3 and then transferred into the
database created by WP2. The data handling procedures defined in this document
apply to the operation of the ECHOES database.
## Deletion of data
Identifying personal data will be retained for a maximum of 1 year after
collection completion to allow for thorough quality control. All such data
will thus be deleted by march 2018 the latest. Anonymized data will not be
deleted but stored and made available for future use through the open data
pilot.
## Dissemination and exploitation of the data
Although the dissemination and exploitation strategy of the project is yet
under development and will be published through deliverables D8.2 and D8.3,
different type of data or data packages are likely to be used for
dissemination and exploitation purposes:
* _Database (see Section 4.5)_ : the data base is expected to be one of the main exploitable results of the ECHOES project. The database will be open source and offered under the regulations of the pilot on open research data in H2020. This open access character’s compatibility with ECHOES’ exploitation strategy will be analysed during the project (WP8) and restrictions to open access use will be kept at a minimum and only implemented if strong exploitation benefits stand against. In any case, the procedures for data management set in this document, particularly regarding WP2, will be followed and respected.
* _International survey_ : in addition to the use of data from the international survey (WP3) for deriving policy recommendations in WP7, data on user preferences and energy use strategies might be of interest for utilities and other commercial agents in the energy fields. In this regard, the exploitation pathways will be analysed throughout the project, especially though the bundling of anonymized data packages. The procedures for data management set in this document, particularly regarding WP3, will be followed and respected.
* _ECHOES contact database_ : For project dissemination purposes an ECHOES contact database is being created in WP8. All the partners contribute to this database by providing information of persons and organisations to be informed or contacted during the project. Once collected, this data will be stored on a secure server, hosted and operated by the University of Oslo (UiO) and their Services for sensitive data (TSD) ( https://www.uio.no/english/services/it/research/storage/sensitive-data/) that meets the Act relating to Personal Data Filing Systems (Personregisterloven).
## Open data pilot
ECHOES provides access to all primary data collected and secondary data
aggregations where publication of the data does not collide with copyrights of
the initial data providers. This is in line with the H2020 open data pilot
openaire (see _https://www.openaire.eu/opendatapilot_ ) . Data will be made
available as soon as ECHOES primary research and publication interests are
fulfilled. No embargo period is implemented once the ECHOES publications are
finished and no restrictions are foreseen to be put on the re-use of the data
at this point. WP2 with the ECHOES database is responsible for providing open
access to the data.
### General principles
All data in ECHOES shall be open access if no other important principles stand
against it. In this respect, the Consortium Agreement and the Grant Agreement
are binding, especially Section 9 (“Access rights”) and Section 10 (“Non
disclosure of information”) of the Consortium Agreement are relevant for
determining potential need for access restriction to ECHOES data.
### Size of the data
The size of the data files is not determined at this point of the ECHOES
project yet. The international survey is expected to have at least 9000
participants, the focus group interviews will typically have between 6 and 12
participants. This section will be updated as soon as more information is
available.
### Target group for the data use
The data provided in ECHOES will be of interest for policy makers, businesses
in the energy sector, stakeholder groups and other researchers. They will be
documented and presented in a way that makes them accessible for non-
scientists.
### Access procedures
The data made available through the open data pilot will be fully accessible
without any restrictions (if not exploitation benefits require an embargo
period, see Section 4.7). To keep track of the use of the data and its
outreach, ECHOES will implement a registration procedure in the WP2 database
(see Section 4.5) where an interested external user has to register in the
system with name, affiliation and the reason for the wish to access the data.
Access will be granted for all interested users, but with a registration, it
will be possible to roughly track what the data is used for. The validity of
the provided e-mail addresses is checked by sending a confirmation link before
opening the database.
### Documentation procedures
All data files provided by ECHOES include a documentation of the content of
the data file and the context the data was collected in (see Section 4.2.2).
This is important to ensure usefulness of the data for researchers and
analysist not included in the data collection. The documentation procedures
will be constantly updated during the ECHOES project.
### Securing interoperability
For social science data it is essential to document the use and source of
theoretical concepts leading to data collections to ensure interoperability
across different user groups. ECHOES will create a glossary defining key terms
and concepts used in the project. This glossary will be part of the data
documentation. Furthermore, sources for theoretical concepts and variables
measures will be documented to ensure comparability with previous and future
use. For quantitative data, the psychometric performance of the variables will
be documented. The use of theoretical concepts will be standardized within
ECHOES and with previous use of the variables and concepts wherever possible
(there will be a number of newly developed concepts in ECHOES, which cannot be
aligned with previous work).
### Search keywords and data identification
Each data set will be assigned a unique and persistent Digital Object
Identifier (DOI) to make it identifiable when stored in the ECHOES database or
a data repository. Each fill will be tagged with keywords for search purposes:
ECHOES is always a keyword, furthermore, keywords describing the type of the
data (e.g., “interview”, “survey”), the participants (“representative sample
EU”), the type of topics included (e.g., “energy culture”, “identity”).
### File types
Each data file in ECHOES will be made available with an accompanying
documentation of its content (see Section 4.2.2). Qualitative data such as
interview transcripts will be made available in form of text documents (in
PDF, TXT, RTF and DOC format) in their original language and translation to
English. Quantitative data will be made available in standard data formats for
popular statistical program packages to make reuse as easy as possible (ASCII,
R, STATA, SPSS).
### Curation of the data after the end of ECHOES
All data in ECHOES will be made available through the ECHOES database for at
least 5 years after the end of ECHOES, which means until the End of October
2024. During this period, NTNU is responsible for curating the data and for
hosting costs. After that time, the data files will be transferred into a data
repository that is the state of the art at that time.
# APPENDIX I: DETAILED DATA COLLECTION RESPONSIBILITIES
The following table presents a complete summary of all data collections in
ECHOES with the responsibilities indicated. The table will be constantly
updated during the project as soon as data collections are started.
# APPENDIX II: CONSENT FORMS
This appendix collects all consent forms and information sheets used in
ECHOES. The first included document is the template for ECHOES information
sheets based on the general template provided by the Norwegian Centre for
Research Data (NSD).
(1) Consent form template from NSD.
#### Request for participation in research project
_**[This template serves as an example of an information letter.** _
_**Clear text and insert your own.** _
_**NB - all information should be concise and easy to understand]** _
##### "[Insert _project title_ ]"
**Background and Purpose**
[ **Describe** the purpose of the project and briefly sketch the main research
topics. Indicate whether the project is a master’s or Ph.D. project at the
institution, whether it is implemented as commissioned research, in
cooperation with other institutions, etc.]
<table>
<tr>
<th>
[ **Describe** how the sample has been selected and/or why the person has been
requested to
</th> </tr>
<tr>
<td>
participate.]
</td>
<td>
</td> </tr> </table>
**What does participation in the project imply?**
[ **Describe** the main features of the project: data collection that requires
active participation (surveys, interviews, observation, tests, etc.,
preferably describing approximate duration), and any collection of data about
the participant from other sources (registers, records, student files, other
informants, etc.). **Describe** the types of data to be collected (e.g.
"questions will concern..."), and the manner(s) in which data will be
collected (notes, audio/video recordings, etc.).
If parents are to give consent on behalf of their children, inform that they
can request to see the questionnaire/interview guide etc. If multiple sample
groups are to be included in the project, it must be explicitly indicated what
participation entails for each group, alternatively, a separate information
letter must be made for each group.]
**What will happen to the information about you?**
All personal data will be treated confidentially. [ **Describe** who will have
access to personal data (e.g. only the project group, student and supervisor,
data processor, etc.), and how personal data/recordings will be stored to
ensure confidentiality (e.g. if a list of names is stored separately from
other data ).]
[ **Describe** whether participants will be recognizable in the publication or
not.]
The project is scheduled for completion by [ **insert** date]. [ **Describe**
what will happen to personal data and any recordings at that point. If the
data will not be made anonymous by project completion: state the purpose of
further storage/use, where data will be stored, who
<table>
<tr>
<th>
will have access, as well as the final date for anonymization (or information
about personal
</th> </tr>
<tr>
<td>
data being stored indefinitely).]
</td>
<td>
</td> </tr> </table>
**Voluntary participation**
It is voluntary to participate in the project, and you can at any time choose
to withdraw your consent without stating any reason. If you decide to
withdraw, all your personal data will be made anonymous. [For patients and
others in dependent relationships, it must be stated that it will not affect
their relationships with clinicians or others, if they do not want to
participate in the project, or if they at a later point decide to withdraw.]
If you would like to participate or if you have any questions concerning the
project, please contact [ **Insert** name and telephone number of the project
leader. In student projects contact information of the supervisor should also
be inserted].
The study has been notified to the Data Protection Official for Research, NSD
- Norwegian Centre for Research Data.
#### Consent for participation in the study
<table>
<tr>
<th>
**[Consent may be attained in writing or verbally. If consent is obtained in
writing from**
</th> </tr>
<tr>
<td>
**the participant, you can use the formulation below. If parents/guardians are
to give**
</td>
<td>
</td> </tr>
<tr>
<td>
**consent on behalf of their children or others with reduced capacity to give
consent, the**
</td> </tr>
<tr>
<td>
**consent form must be adapted, and the participant’s name should be
stated.]**
</td>
<td>
</td> </tr> </table>
I have received information about the project and am willing to participate
\-------------------------------------------------------------------------------------------------------------
(Signed by participant, date)
**[Checkboxes can be used (in addition to signature) if the project is
designed in such a way that the participant can choose to give consent to some
parts of the project without participating in all parts (e.g. questionnaire,
but not interview), or if information is to be obtained from other sources,
especially when the duty of confidentiality must be set aside in order for the
information about the participant to be disclosed._Examples: - I agree to
participate in the interview / - I agree that information about me may be
obtained from teacher/doctor/register - I agree that my personal information
may be published/saved after project completion]_ **
# APPENDIX III: CONFIRMATIONS BY DATA PROTECTION OFFICERS
All confirmations regarding the conduction of data collection in accordance
with national and international law, especially EC Directive 95/46 and the
General Data Protection Regulation (Regulation (EU) 2016/679) will be
collected in this appendix as soon as they are available.
# APPENDIX IV: DATA DOCUMENTATION TEMPLATES
The following two templates shall be used to document the necessary background
of the data files for internal and external use in ECHOES.
1. Data documentation template for qualitative data in ECHOES
2. Data documentation template for quantitative data in ECHOES
_Data documentation template for qualitative data in ECHOES_
Name of the data set: ___________ Date the data set was finalized:
_________________
Date/time period the data was collected: ____________ to ____________ .
Responsible partner for the collection of the data:
_________________________________ (name)
____________________________(institution)
Data produced in WP: ____________ Task: ____________
Data anonymized on (date): _______________ by __________________________
_Information about the participants:_
Number: __________ Age: _________________ Sex: ___________________
Participants’ background: ____________________________________________________
Recruitment procedure: _____________________________________________________
Original language of the material:
_____________________________________________
Data collected by (interviewer):
_______________________________________________
Transcribed by: ___________________________________________________________
Transcription rules: ________________________________________________________
Translated to English by: ___________________________________________________
Ethically cleared by: _____________________________________ on (date):
_______________
Interview guidelines (or the like):
_______________________________________________________
Size of the data (e.g. number of words):
_________________________________________________
Short summary: ______________________________________________ _Data
documentation template for quantitative data in ECHOES_
Name of the data set: ___________ Date the data set was finalized:
_________________
Date/time period the data was collected: ____________ to ____________ .
Responsible partner for the collection of the data:
_________________________________ (name)
____________________________(institution)
Data produced in WP: ____________ Task: ____________
Data anonymized on (date): _______________ by __________________________
_Information about the participants:_
Number: __________ Age: _________________ Sex: ___________________
Participants’ representative for which population:
__________________________________________
Recruitment procedure: _____________________________________________________
Response rate: ___________________________________________________________
Original language of the material:
_____________________________________________
Translated to English by: ___________________________________________________
Ethically cleared by: _____________________________________ on (date):
_______________
Variables in the dataset:
<table>
<tr>
<th>
**Variable name**
</th>
<th>
**Variable type**
</th>
<th>
**Variable label**
</th>
<th>
**Answering format / value labels**
</th>
<th>
**Comments**
</th> </tr>
<tr>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td> </tr>
<tr>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
<td>
</td> </tr> </table>
_Variable types:_
* T = text
* D = date / time
* B = binary / dichotomous
* C = categorical
* O = ordered categorical / ordinal
* I = interval / ratio / Likert scales with 5 or more categories
Short summary: ______________________________________________
# APPENDIX V: LITERATURE REVIEW TEMPLATE
The following template is used in the ECHOES literature review.
Paper #
Title
Details (Authors,
Journal etc.)
Keywords
Main
Technology Foci
and sub
\-
foci
The Type of
Assestment
(
regional,
national etc.)
Type of Formal Social Unit
formal social units,
(
collective decision
\-
maki
ng
bodies etc.)
Definitions
)
Terms etc.
(
Objective(s)
Methodology
Main Indicators,
Dimensions or
Variables used
or identified
Results
1
2
…
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https://phaidra.univie.ac.at/o:1140797
|
Horizon 2020
|
1184_KONFIDO_727528.md
|
# Executive Summary
The document provides an initial data management plan concerning the data
processed, generated and preserved during and after KONFIDO Action, as well as
relative concerns generated from their usage. The deliverable aims to define a
framework outlining KONFIDO’s policy for data management. In particular, this
deliverable covers topics like information about the data, metadata content
and format, policies for access, sharing and re-use and longterm storage and
data management. During the KONFIDO Action, health data will be processed and
the processing of this personal data is submitted to strict data protection
legal rules. In addition, the data management plan will be affected by future
results of the work performed in WP2-WP7. Therefore, the initial framework
presented in this deliverable will further evolve during the Action.
# Introduction
The document provides an initial data management plan concerning the data
processed, generated and preserved during and after KONFIDO Action, as well as
relative concerns generated from their usage. The deliverable aims to define a
framework outlining KONFIDO’s policy for data management. In particular, this
deliverable covers topics like information about the data, metadata content
and format, policies for access, sharing and re-use and longterm storage and
data management. This deliverable is “living” and strongly linked with the
work taking place in the Action’s Work Packages. We note here that throughout
the duration of the Action, KONFIDO consortium will closely follow the
activities in the eHealth Network (eHN), the Joint Action to Support the
eHealth Network (JAseHN) and the eHealth Digital Service Infrastructure
(eHDSI).
## Action goal
KONFIDO Action will advance the state of the art of eHealth technology with
respect to security aspects by providing a holistic approach – i.e. targeting
all architectural layers of an IT infrastructure, namely: _storage_ ,
_dissemination_ , _processing_ , and _presentation_ – to address challenges of
secure storage and exchange of eHealth data, protection and control over
personal data, and security of health related data gathered by mobile devices.
KONFIDO will build on and extend the results of a best of breed selection of
successful projects (namely: epSOS, STORK, DECIPHER, EXPAND and ANTILOPE).
The KONFIDO approach will be implemented in a technological framework that
will rely on six technology pillars, namely:
i. The new security extensions provided by some of the main CPU vendors; ii.
Physical Unclonable Function (PUF)-based security solutions based on photonic
technologies; iii. Homomorphic encryption mechanisms; iv. Customised
extensions of selected Security Information and Event Management (SIEM)
solutions;
5. A set of disruptive logging and auditing mechanisms developed in other technology sectors - such as blockchain - and transferred to the healthcare domain and
6. A customised STORK-compliant eID implementation. Given the recent advances in the field, KONFIDO will consider a customized eIDAS-compliant cross-border eID implementation.
The usability of the proposed solutions will be tested in a realistic setup,
deployed on top of a federated cloudbased infrastructure where data will be
exchanged and services interoperate cross-border. Building on results that are
already widely accepted and relying on a handful of complementary technologies
(some of which are already at a high level of maturity), KONFIDO has a
dramatic potential in terms of cross-sector transfer of innovation in the
field of coordinated care towards improved acceptance of healthcare solutions.
## Work Package goal
The deliverable is written in the context of WP1 – Project Management. The
main goal of this WP is to guarantee the successful realisation and conclusion
of the Action including the project administration and control, risk
management, problem handling and quality assurance on management levels. This
WP makes sure, that the Action runs to budget, is on time and the expected
results are achieved. This goal will be reached by ensuring the correct and
efficient collaboration between partners.
## Deliverable goal
The deliverable aims to define a framework outlining KONFIDO’s policy for data
management. The deliverable is based on the guidelines provided in “
_Guidelines on Data Management in Horizon 2020_ ” published by the European
Commission and aims to answer the following issues:
* What types of data will the Action generate/collect?
* What standards will be used?
* How will this data be exploited and/or shared/made accessible for verification and re-use?
* How will this data be curated and preserved?
As mentioned above, the document is dynamic and will be periodically updated
in parallel with the development work taking place in other WPs. Overall, a
common policy will be defined indicating the procedures for the data
management during and after the Action, as well as at internal and external
level.
## Deliverable structure
The deliverable is divided into following sections:
* Section 2 describes the Action’s approach regarding open access to scientific publications;
* Section 3 refers to the Action’s approach regarding open access to research data;
* Section 4 describes the datasets that will be used in the Action, provided by SUNDHED and PAUSIL;
* Section 5 details any standards and metadata that will be utilised in the framework of KONFIDO;
* Section 6 includes a description of the data sharing policies regarding access, sharing, re-use and distribution;
* Section 7 refers to archiving and presentation in the context of KONFIDO and - Section 8 concludes the document.
# Open access to scientific publications
Open access to scientific publications refers to free of charge online access
for users. Open access will be achieved through the following steps:
1. Any paper presenting the Action results will acknowledge the Action: “ _The research leading to these results has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 727528-KONFIDO_ ” and display the EU emblem.
2. Any paper presenting the Action results will be deposited at least by the time of publication to a formal repository for scientific papers. If the organisation does not support a formal repository ( _https://www.openaire.eu/participate/deposit/idrepos_ ) , the paper can be uploaded in the European sponsored repository for scientific papers: _http://zenodo.org/_ .
3. Authors can choose to pay “author processing charges” to ensure open access publishing, but still they have to deposit the paper in a formal repository for scientific papers (step 2).
4. Authors will ensure open access via the repository to the bibliographic metadata identifying the deposited publication. More specifically, the following will be included:
* The terms “ _European Union (EU)_ ” and “ _Horizon 2020_ ”;
* “ _KONFIDO - Secure and Trusted Paradigm for Interoperable eHealth Services_ ”, Grant agreement number 727528;
* Publication data, length of embargo period if applicable; and
* A persistent identifier.
5. Each case will be examined separately in order to decide on self-archiving or paying for open access publishing.
# Open access to research data
Open access to research data refers to the right to access and re-use digital
research data generated by Actions. EU expects funded researchers to manage
and share research data in a manner that maximises opportunities for future
research and complies with best practices in the relevant subject domain. That
is:
* The dataset has clear scope for wider research use;
* The dataset is likely to have long-term value for research or other purposes; The dataset has broad utility for reference and use by research communities;
* The dataset represents a significant output of the research project.
Openly accessible research data generated during KONFIDO Action will be
accessed, mined, exploited, reproduced and disseminated free of charge for the
user. Specifically, the " _Guidelines on Data Management in Horizon 2020_ "
clarifies that the beneficiaries must:
_“Deposit in a research data repository and take measures to make it possible
for third parties to access, mine, exploit, reproduce and disseminate — free
of charge for any user — the following:_
_i. The data, including associated metadata, needed to validate the results
presented in scientific publications as soon as possible; ii. Other data,
including associated metadata.”_
The following section describes some sample datasets that we are planning to
use in the framework of KONFIDO. The provided datasets are, at this early
stage of the Action, possible examples which are probably subject to change
with the evolution of the Action.
For each dataset that we are going to share in the project lifetime, policies
for access and sharing as well as policies for re-use and distribution will be
defined and applied. A generic guideline is provided in Section 6 " _Data
sharing_ " and Section 7 " _Archiving and preservation_ ".
# Datasets description
KONFIDO pilots involve the cross-border exchange of patient data. The end-user
partners of the consortium have access to such types of data; however, it is
not clear yet if the Action will require the use of real patient data for the
pilots. In case this is needed, KONFIDO partners will follow best practices
during pilot definition, including data anonymisation or pseudoanonymisation,
ethical approvals for working with these data, informed consents of patients
etc. All patient data will be handled according to national law and
guidelines. This deliverable is a “live” document; therefore, it will be
updated as the progress progresses and all relevant issues will be addressed.
Below, we provide a brief overview of the datasets that can be provided to the
KONFIDO pilots by SUNDHED and PAUSIL partners, if needed.
## SUNDHED dataset
The 1 st KONFIDO end-user workshop gave valuable input in regards to the
potential dataset. The typical scenario will involve a consultation or
treatment situation where the general practitioner or hospital doctor will
have a limited time to get an overview of the patient. Therefore, the dataset
needs to be short and to the point.
The actual nature of the required data will vary a lot from patient to
patient, so it will be very hard to pinpoint a one-size-fits-all. For example,
a patient with a broken leg will primarily need x-rays, while a COPD patient
might need data on current medication and lung function. In all the
situations, the doctors at the end-user workshop agreed on Electronic Health
Record (EHR) notes not being important for the vast majority of the patients.
A minimum dataset that would cover most of the basic needs would include:
* Diagnose data based on ICPC-2 and/or ICD-10 codes;
* Access to laboratory test results based on IUPAC codes;
* Current medication based on ATC codes;
* Open prescriptions for the patient – preferred useable to local pharmacies;
* X-rays pictures or descriptions if the bandwidth is limited or the pictures are very large or scans;
* EHR notes should be made available but, in a situation with limited time on hand, some kind of filter would need to be used to help the doctor get an overview. Often the notes are generated by dictation and data are not structured;
* Specific data for chronic patients like INR values, lung function, HbA1c or similar. It is very important that all members of the health data ecosystem agree on the measurement values and standards, so that there is no doubt on how a doctor will interpret the numbers.
## PAUSIL dataset
The PAUSIL Information System (IS) can be decomposed into three main areas:
_Management and Accounting IS_ , _Clinical IS_ and _Communication and web
services_ . For the purpose of KONFIDO Action, the Clinical IS is described in
the Figure 1. The Clinical IS (CIS) is a computer-based infrastructure
designed to collect, store and manipulate data related to the healthcare
delivery process. CIS data consists of structured/unstructured text
represented in a proprietary format. CIS can be, in turn, divided into
Diagnostic IS and Emergency Department IS (EDIS). The Diagnostic IS includes
the Laboratory IS (Lab IS) and the Radiology Information Service (RIS). The
Lab IS integrates and manages the results of patients’ tests with the
Electronic Health Record (EHR) and Transfusion Medicine. Data managed by RIS
system are digital radiological images (DICOM format) that represent large
volumes of data. The picture archiving and communications system (PACS) is
designed for paper and film archiving. It provides archiving, viewing, and
distribution of medical images to radiologists, physicians of other
specialties, and doctors in other hospitals and it is integrated with EHRs
too. All the system integrations are realised using the standard HL7 that
refers to a set of international standards for transfer of clinical and
administrative data between software applications used by various healthcare
providers.
**Figure 1. PAUSIL Information System**
EDIS is based on a dedicated application that collects patient information
from the triage to the discharge or admission, following the work flow
described in Figure 2. Each dataset comes in specific formats, as specified in
Table 2.
**Figure 2. ED workflow**
<table>
<tr>
<th>
**Data**
</th>
<th>
**Description**
</th>
<th>
**Size**
</th>
<th>
**Type**
</th>
<th>
**Structure**
</th> </tr>
<tr>
<td>
CIS
</td>
<td>
Clinical information system
</td>
<td>
~15 GB/500 GB
</td>
<td>
Text
</td>
<td>
Structured/unstructured
</td> </tr>
<tr>
<td>
EDIS
</td>
<td>
Emergency department information system
</td>
<td>
~5 GB
</td>
<td>
Text
</td>
<td>
Structured/unstructured
</td> </tr>
<tr>
<td>
RIS
</td>
<td>
Radiology information system
</td>
<td>
~6.5 TB/130 GB
</td>
<td>
Text
</td>
<td>
Structured/unstructured
</td> </tr>
<tr>
<td>
PACS
</td>
<td>
Picture archiving and communications system
</td>
<td>
~19.5 TB/8 TB
</td>
<td>
Image
</td>
<td>
Unstructured
</td> </tr>
<tr>
<td>
Lab IS
</td>
<td>
Laboratory information system
</td>
<td>
~2 GB
</td>
<td>
Text
</td>
<td>
Structured/unstructured
</td> </tr>
<tr>
<td>
EHR
</td>
<td>
Electronic health record
</td>
<td>
~9 TB
</td>
<td>
Text
</td>
<td>
Structured/unstructured
</td> </tr> </table>
**Table 1. Emergency Department Dataset**
# Standards and metadata
Metadata is needed on the datasets. This will provide transparency and
traceability to make security and audit possible. The law and regulations
demand a large degree of control when handling this type of data. Also, unique
identifies need to be in place for patients and doctors to make sure that data
is going to the right people at the right time.
Most of the data will come from the local system with lots of metadata and
this will greatly vary from country to country, hospital to hospital and
system to system. Some of the base data needed will include:
* Time stamp for when the data was generated;
* Time stamp for exchange of data;
* Health professional to contact regarding the data, organisation, email and phone number;
* Log with access and actions regarding the dataset;
* Unique identifiers on the doctors who access the data;
* Unique identifiers for the patient;
* Country code on data and access log;
* Data on who added or edited the data;
* Data on possible shielding of certain types of data.
Standards can be selected to help the data exchange, but the variation in the
employed systems across Europe is very large and there will be hundreds of
standards and even big variations within the same standards and/or versions of
these. Datasets should be as standardized, if possible, but the exchange of
data will be very hard by using defined standards.
To use standards, you do not only need to invent new or develop current
standards, but implement them into the actual systems which is a very
difficult task. The reason for this is that you do not only need the
technological support, but you also really need to make it happen. It is a
rare thing that an organisation can adapt to a new standard with an existing
system since that system will likely be integrated with other systems. Also,
the systems/organisations asking others to adapt to a standard will not have
the budget or the governance in the other organisation. Experience shows that
you need to be able to handle a multitude of standards, if you want to
integrate a large number of systems and KONFIDO is aiming to potentially
integrate a very large number of systems.
A standard for integrating to KONFIDO should be defined, but it is a must that
it can co-exist with current standards and interfaces in the system. Even on a
European level it is hard to imagine all countries moving at the same pace.
# Data sharing
The data sharing policy concerning KONFIDO Action is still under definition.
Most of the existing data are privately held by KONFIDO data owners. For the
time being, the consortium has not decided how the data/results generated
within the Action will be treated. This will be an on-going process and is
expected to be finalised later in the course of the Action as it closely
related to the definition and the requirements of the KONFIDO use cases. The
issues identified so far concern:
* The definition of data owner(s);
* The definition of incentives concerning the data providers;
* The identification of user groups and the access policies concerning the data;
* The definition of access procedure and embargo periods;
* The compliance with corresponding legal and ethical issues.
Open access to research data will be achieved in KONFIDO through the following
steps:
1. Prepare the " _Data Management Plan_ " (current document) and update it as needed;
2. Select what data we will need to retain to support validation of the Action finding (the datasets described in Section 4);
3. Deposit the research data into an online research data repository. In deciding where to store Action data, the following choice will be performed, in order of priority:
* An institutional research data repository, if available;
* An external data archive or repository already established in the KONFIDO research domain (to preserve the data according to recognised standards);
* The European sponsored repository: _http://zenodo.org/_ ;
* Other data repositories (searchable here: _http://www.re3data.org_ ) , if the aforementioned ones are ineligible.
4. License the data for re-use (Horizon 2020 recommendation is to use CC0 or CC BY);
5. Provide information on the tools needed for validation, i.e. everything that could help a third party in validating the data (workflow, code, etc.). Independent of the selected repository, the authors will ensure that the repository:
* Gives the submitted dataset a persistent and unique identifier to make sure that research outputs in disparate repositories can be linked back to particular researchers and grants;
* Provides a landing page for each dataset, with metadata;
* Helps track if the data has been used by providing access and download statistics;
* Keeps the data available in the long term, if desired;
* Provides guidance on how to cite the data that has been deposited.
Even following the previously described steps, each case will be examined
separately in order to select the most suitable online repository.
## Policies for access and sharing
As suggested by the European Commission, the partners will deposit at the same
time the research data needed to validate the results presented in the
deposited scientific publications. This timescale applies for data
underpinning the publication and results presented _._ Research papers written
and published during the funding period will be made available with a subset
of the data necessary to verify the research findings. The consortium will
then make a newer, complete version of data, available within 6 months of
Action completion. This embargo period is requested to allow time for
additional analysis and further publication of research findings to be
performed.
Other data (not underpinning the publication) will be shared during the Action
life following a granular approach to data sharing and releasing subsets of
data at distinct periods rather than waiting until the end of the Action, in
order to obtain feedback from the user community and refine it as necessary.
An important aspect to take into account is who is allowed to access the data.
It could be the case that part of a dataset should not be publicly accessible
to everyone. In this case, control mechanisms will be established, including:
* Authentication systems that limit read access to authorised users only;
* Procedures to monitor and evaluate access requests one by one. A user must complete a request form stating the purpose for which they intend to use the data;
* Adoption of a Data Transfer Agreement that outlines conditions for access and use of the data.
Each time a new dataset will be deposited, the consortium will decide who is
allowed to access the data. Generally speaking, anonymised and aggregate data
will be made freely available to everyone, whereas sensitive and confidential
data will only be accessed by specific authorised users.
## Policies for re-use and distribution
A key aspect of data management is to define policies in order for users to
learn the existence of data and the content it contains. People will not be
interested in a set of unlabeled files published on a website. To attract
interest, partners will describe accurately the content of published dataset
and, each time a new dataset is deposited, disseminate the information using
the appropriate means (i.e., mailing list, press release, Facebook, website
news) based on the type of data and the interested target audience.
Research data will be made available in a way that can be shared and easily
re-used by others. That means:
1. Sharing data using open file format (whenever possible), so that they can be implemented by both proprietary and open source software;
2. Using format based on an underlying open standard;
3. Using format which is interoperable among diverse internal and external platforms and applications;
4. Using format which does not contain proprietary extensions (whenever possible).
Documenting datasets, data sources and the methodology used for acquiring the
data establishes the basis for the interpretation and appropriate usage of the
data. Each generated/collected and deposited dataset will include
documentation to help users to re-use it.
As recommended, the license that will be applied to the data is CC0 or CC BY.
If limitations exist for the generated data, these restrictions will be
clearly described and justified.
Potential issues that could affect how data can be shared and used may include
the need to protect participant confidentiality, comply with informed consent
agreement, protect Intellectual Property Rights, submit patent applications
and protect commercial confidentiality. Possible measures that may be applied
to address these issues include encryption of data during storage and
transfer, anonymisation of personal information, development of Data Transfer
Agreements that specify how data may be used by an end user, specification of
embargo periods, and development of procedures and systems to limit access to
authorised users only.
# Archiving and presentation
Datasets will be maintained for 5 years following Action completion. To ensure
high-quality long-term management and maintenance of the dataset, the
consortium will implement procedures to protect information over time. These
procedures will permit a broad range of users to easily obtain, share and
properly interpret both active and archived information and they will ensure
that information is:
* Kept up-to-date in content and format so that they remain easily accessible and usable;
* Protected from catastrophic events (e.g., fire and flood), user error, hardware failure, software failure or corruption, security breaches, and vandalism.
Regarding the second aspect, solutions dealing with disaster risk management
and recovery, as well as with regular backups of data and off-site storage of
backup sets, are always integrated when using the official data repositories
(i.e., _http://zenodo.org/_ ) ; the partners will ensure the adoption of
similar solutions when choosing an institutional research data repository.
Partners are encouraged to claim costs for resources necessary to manage and
share data; these will be clearly described and justified. Arrangements for
post-action data management and sharing must be made during the life of the
Action. Costs associated with long-term curation and preservation, such as
POSF (Pay Once, Store Forever) storage, will be purchased before the Action
ends.
# Conclusions
The purpose of the Data Management Plan is to support the data management life
cycle for all data that will be collected, processed or generated by KONFIDO
Action. The Data Management Plan is not a fixed document, but evolves during
the lifespan of the Action. This document is expected to mature during the
Action; more developed versions of the plan could be included as additional
deliverables at later stages. The Data Management Plan will be updated at
least by the mid-term and final review to fine-tune it to the data generated
and the uses identified by the consortium since not all data or potential uses
are clear at this stage of the Action.
|
https://phaidra.univie.ac.at/o:1140797
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Horizon 2020
|
1185_CrowdHEALTH_727560.md
|
# 1\. Executive Summary
This deliverable is the first version of the Data Management Plan (DMP) of the
CrowdHEALTH project and it compiles a first description of the datasets
according to the FAIR template. The overall purpose of this document is to
support the data management life cycle for all data that will be collected,
processed or generated by the project.
CrowdHEALTH participates in the Open Research Data Pilot of the H2020
Programme [1], and a DMP is required for all the projects that are
participating in this programme. The DMP is usually intended to be submitted
by month 6 of the project, and the European Commission recommends using a
template that follows the FAIR Data Management Plan Model [2] in order to make
the research data **F** indable, **A** ccessible, **I** nteroperable and **R**
e-usable. However, due to CrowdHEALTH complexity in what regards data, which
comes from the fact that the project deals with heterogeneous sets of data,
from different sources that are initially not interoperable, and taking into
account the high level of privacy and confidentiality of most datasets,
together with the large amount of datasets identified, the FAIR Model was not
appropriate to gather relevant information from the pilots (Use Cases) as a
starting point. A more practical kind of information about the datasets was
needed by the technicians to understand what type of data will be made
available and how it will be used by the pilots. Consequently, some extra time
was needed to create appropriate questionnaires and collect relevant
information.
The whole procedure to gather information about partners’ datasets, and in
particular those provided by the Use Cases who have access to them, has been
described in the introduction of this deliverable and forms for the
compilation of information can be found in annexes. The reason for sharing
information about these steps and the procedure that we have designed previous
to the elaboration of the first DMP is that we proposed what can be seen as a
good practice, which could be appreciated and replicated, and probably
improved for its implementation in other projects of similar complexity
regarding available data and datasets to be researched and used by the
developments.
The project has identified a list of 13 datasets and a FAIR questionnaire has
been completed for each one.
The DMP is a living document that will be updated for the mid-term and the
final review of the project.
# 2\. Introduction
## 2\. 1 Objective
CrowdHEALTH DMP has been conceived to support the data management life cycle
for all data that will be collected, processed or generated by the project.
Moreover, the CrowdHEALTH DMP aims to identify best practices for gathering
information about the variety of data to be used in the project that could
optimise the development, specific standards for the generated data and assess
their suitability for sharing and reuse in accordance with official
guidelines.
## 2\. 2 Document Structure
Section 2 - this introduction - explains what a DMP contains, the datasets
identified during the project, how the CrowdHEALTH consortium proceeded to
gather the information, and why we started to complete simpler templates and
finally completed the FAIR Data Management Plan Template. Section 3 contains
inputs to the FAIR Model following the template for all project Datasets.
Annexes contain the templates of the questionnaires for the Use Cases partners
that were used previously to complete the FAIR Data Management Plan Template
and some additional information.
## 2\. 3 What the Data Management Plan should contain as described in the
Grant Agreement
From the Grant Agreement p. 193: “ _CrowdHEALTH Data Management Plan will
comply with the EC Data Management Plan template_ [2] _and will specify how
the generated data will be easily discovered and accessed, ensuring open
access by adopting the adequate licensing scheme (e.g. Creative Commons
License)._
_The plan will also generate wider interest towards improvements achieved by
the project in order to facilitate and potentiate exploitation opportunities.
CrowdHEALTH capitalizes on the development of a well-defined Data Management
Plan (DMP), including: (i) Data Types, Formats, Standards and Capture Methods;
(ii) Ethics and Intellectual Property, (iii) Access, Data Sharing and Reuse;
(iv) Resourcing; (v) Deposit and Long-Term Preservation, and (vi) Short-Term
Storage and Data Management._
_Moreover, the plan will describe quality evaluating tools/procedures, which
will prove the data intelligibility, and will define the type of accompanying
information in the form of metadata or short description to allow potential
users to gain awareness on the data concepts and evaluate their suitability
for future use._
## 2\. 4 Key Performance Indicators of the Data Management Plan
As stated in the Description of Action (DoA) [3], these will be the means to
measure and Key Performance Indicators (KPIs) of the first evaluation of DMP:
* _At least 5 open data management packages uploaded_
* _At least 5 repositories, where open data management packages are uploaded_
In case of not providing access to data, the DMP must contain the reasons for
not giving access. It is too early to address the KPIs at this early stage of
the project development. This will be addressed in further versions of the DMP
and towards the end of the project.
## 2\. 5 Data classification as identified during the Kick-off meeting
CrowdHEALTH project is complex in what regards data, for the diversity, the
privacy and confidentiality of collected data, and the different sources from
where data come from, and as such operates on the basis of very different and
heterogeneous datasets.
The first questions to the Use Case partners related to the types of datasets
to be considered, their characteristics and descriptions. Answering those
questions, and providing details regarding the available and potentially
available data to be shared within the context of the project, was essential
to start defining the high level architecture of the system.
During the kick-off meeting, the consortium identified two basic stages with
respect to the availability of datasets:
1. Existing datasets _provided by the use cases_ or open datasets that we may use for the project purposes
2. Datasets of aggregated data _generated (i.e. collected) during the project pilots_
Subsequently, an initial and tentative classification guided the consortium
towards the understanding of what the project will face in terms of data.
<table>
<tr>
<th>
1) _**EHR Data** (Stockholm University Hospital and Hospital La Fe) _
</th> </tr>
<tr>
<td>
Disease-specific (e.g. obesity, diabetes, etc.) in standardised formats (e.g.
HL7, FHIR, etc)
</td> </tr>
<tr>
<td>
2) _**Living Lab and Chronic Disease Management Data** (DFKI, BioAssist) _
</td> </tr>
<tr>
<td>
a) Nutrition data, lifestyle, activities, biosignals
</td> </tr>
<tr>
<td>
b) Standardisation (ADLs, IADLs, Open mHealth, FHIR, PHRs)
</td> </tr>
<tr>
<td>
3) _**Public Health Data** (Ministry of Education, Science and Sport,
University of Ljubljana ) _
</td> </tr>
<tr>
<td>
1. Somatic and motor development data sets from SLOfit system (anthropometric measurements of children 1 );
2. In a second step, nutrition, physical activity, sedentariness, sleep, resting heart-rate, socioeconomic status and parental physical activity.
</td> </tr>
<tr>
<td>
4) _**Social Health Data** (Care Across, BioAssist) _
</td> </tr>
<tr>
<td>
a) Information shared through online platforms with different stakeholders
(e.g. physicians, patients, etc) regarding diagnosis, treatment, co-
morbidities
</td> </tr>
<tr>
<td>
b) Other sources with information about health behaviours related to
interactions between patients and patients-care givers
</td> </tr>
<tr>
<td>
5) _**Non-Medical Data** (ALL) _
</td> </tr>
<tr>
<td>
a) Open and linked data, e.g. World bank or WHO Statistics, Socio-Economic and
Financial Data, etc.
</td> </tr> </table>
## 2\. 6 Pilot on Open Research Data in Horizon 2020
CrowdHEALTH participates in the Pilot on Open Research Data in Horizon 2020
[1] and will offer open access to scientific results reported in publications,
to relevant scientific data and to data generated throughout the project
lifetime in its numerous demonstrators, provided that they are anonymized and
fully respect national and EU privacy regulations. It will aim to improve and
maximise access to and re-use of scientific data generated by the project
i.e., system performance data, user validations, etc.
For those projects that are participating in the Pilot on Open Research Data
in Horizon 2020, the European Commission suggests a template for the DMP
called FAIR Data Management Plan Template. However, this template has been
designed to include information progressively, and after the kick-off meeting
of the project, it was clear that gathering some initial information about the
datasets not included in the FAIR template was needed, not only for the
purposes of preparing the DMP but to provide some information to the project
developers in order to avoid any delay in the definition of the project
architecture and the work package technical development.
## 2\. 7 Original questions asked by the technical WPs
After the kick-off meeting, the leaders of the CrowdHEALTH technical WPs
needed detailed information about the datasets to start their work. Their
questions were gathered into a list that can be found in Annex 1\.
## 2\. 8 Questions to Use Cases partners (UC)
In reality, we have used the pretext of the DMP deliverable – in which we need
to determine what the project intends to do with the data produced within the
project for each dataset– to concretise and better understand the CrowdHEALTH
use cases starting points in terms of available data.
The original questions asked by the technical WPs were merged and reordered
with some basic details that were also needed for this first version of the
DMP. The questionnaire has been distributed amongst the Use Cases partners,
and replies allowed technical WP staff understanding how each Use Case operate
in terms of policy making, what are their prioritised policies, how they
evaluate them and what is expected as project outputs for the creation and
evaluation of policies.
We understand now that not all inputs gathered at that initial stage are
relevant and need to be included in the final version of the DMP. However, we
considered that it would be easier for Use Cases to provide all initial
information related to data and how data is being used in their context into
one single document, which could then serve as a basis to be consulted by the
technical partners.
This questionnaire can be found in Annex 2. The replies to this questionnaire
were compiled in a document restricted to the project participants - D2.1a,
Questions to Use Cases for the DMP. This document served as an input to WP5
and WP6 deliverables.
## 2\. 9 The First Project Dataset Questionnaire form
Annex 3 shows an initial _dataset form_ oriented to the DMP preparation. This
form allowed the consortium to identify all the datasets and their owners and
the most important characteristics of each dataset. At the moment, the owners
of the datasets are the 6 Use Cases. The replies not relevant for the DMP have
been transferred to WP5 and WP6 teams.
## 2\. 10 The initial list of datasets
Thanks to the compiled forms, we identified the project’s first list of
datasets (some of which will be combined to create the envisioned holistic
health records – this is the case of HULAFE, DFKI, CRA):
* DATASET 1: HULAFE
* DATASET 2: Stockholm County Council/ Karolinska University Hospital/SwedeHeart
* DATASET 3: Bio Data (DFKI)
* DATASET 4: Nutritional Data (DFKI)
* DATASET 5: Activity Data (DFKI)
* DATASET 6: Biosignals and Activity Data (BIO)
* DATASET 7: BIO-PHRs (Personal Health Records) (BIO)
* DATASET 8: Medication (BIO)
* DATASET 9: Allergies Dataset (BIO)
* DATASET 10: Social Data dataset (BIO)
* DATASET 11: CancerPatientData Dataset (CRA)
* DATASET 12: SLOfit Dataset (ULJ)
* DATASET 13: Lifestyle Dataset (ULJ)
## 2\. 11 The FAIR Data Management Template Questionnaire
The initial forms and steps were required to understand the nature of the
datasets. It allowed to design the project architecture and to start the
development with less risks of a delay. But in order to share the information
later with other researchers, the public, etc., and to start the preparation
of the DMP draft, the consortium needed to fill in a more complete form: the
FAIR Data Management Template.
As described in the Guidelines of FAIR Data Management in Horizon 2020 [2],
this template is a set of questions that the partners should answer with a
level of detail appropriate to the project. For this first version it is not
required to provide detailed answers to all the questions.
The DMP is intended to be a living document in which information can be made
available gradually through successive updates as the implementation of the
project. New versions will be produced for the periodic reviews.
Section 3 shows the replies to the FAIR questionnaire of our use cases, what
is the core of this deliverable and will be updated in future versions of this
plan.
# 3\. CrowdHEALTH FAIR Data Management Plan
This section gathers all FAIR forms completed with information from the
project datasets.
## 3\. 1 DATASET 1: HULAFE
<table>
<tr>
<th>
**DATASET 01**
</th>
<th>
**NAME: Hospital La Fe Dataset (HULAFE)**
</th> </tr>
<tr>
<td>
**1\. Data Summary**
</td> </tr>
<tr>
<td>
1.1 Purpose
</td> </tr>
<tr>
<td>
_The purpose of the data is mainly to:_
* _Allow the data-driven modelling partners to develop risk stratification models for the risk of obesity, the chances of improving the condition, and the identification of population with obesity that have not been diagnosed yet –the under-diagnosis of obesity and overweight is a common and worldwide problem-._
* _Allow the forecasting modelling partners and causal modelling partners to develop forecasting and causal models for aiding in the improvement of public health policies for overweight and obesity, regarding the goal of promoting the systematic detection of obesity and overweight citizens._
* _To help policy makers in evaluating their actions by monitoring population trends in obesity on a clinical and regional level by using key performance indicators and the developed data-driven models._
* _To aid policy makers in creating and evaluating policies relating to adult obesity and overweight by performing big data analysis on collected data. HHRs will provide an opportunity for performing data analysis and building health risk predictive models._
* _To support the endocrinology service with information related to patients that at medium/high risk of becoming obese to carry out preventive actions, as well as giving feedback related to the already diagnosed obese patients._
* _To improve the healthcare management, clinical and public health dashboards introducing Key Performance Indicators to improve the treatment and follow-up of patients with obesity and overweight._
</td> </tr>
<tr>
<td>
1.2 Types and formats of data
</td> </tr>
<tr>
<td>
_The data are structured. There are numeric and categorical variables. The
data are originally stored in SQL databases, and they are gathered through SQL
queries and exported in Excel, CSV or any other text formats for research and
data-analysis purposes._
</td> </tr>
<tr>
<td>
1.3 Re-use of existing data
</td> </tr>
<tr>
<td>
_Existing data could NEVER be used for other research projects._
</td> </tr>
<tr>
<td>
1.4 Origin
</td> </tr>
<tr>
<td>
_The data comes from Business Intelligence datamarts that at the same time
comes from some of the integrated databases of the Hospital La Fe Information
Systems IRIS and ORION._
_Additionally, data from the Primary Health Care System will be available and
integrated. This data comes from the databases of the Primary Care Information
Systems ABUCASIS._
</td> </tr> </table>
<table>
<tr>
<th>
1.5 Expected size
</th> </tr>
<tr>
<td>
_N > 10.000 individuals, D > 85 variables. _
</td> </tr>
<tr>
<td>
1.6 Data utility
</td> </tr>
<tr>
<td>
_Endocrinologists, the endocrinology service is the main supporter of this
action as they need the information feedback to improve their health care
attention on obese citizens and patients; Data analysts and researchers, might
benefit from the data as they will provide answers on different research
questions regarding the problem of overweight and obesity; Public Health
Institutions, data might provide evidence regarding public health policy
making on the promotion of physical activity, nutrition habits and systematic
detection of obesity and overweight; Primary care professionals, who are the
first health care professionals to diagnose and treat obesity; and hospital
managers, who require information and indicators to take evidence-based
decisions._
</td> </tr>
<tr>
<td>
**2 FAIR Data**
</td> </tr>
<tr>
<td>
**2.1 Making data findable, including provisions for metadata**
</td> </tr>
<tr>
<td>
2.1.1 Are the data produced and/or used in the project discoverable with
metadata, identifiable and locatable by means of a standard identification
mechanism?
</td> </tr>
<tr>
<td>
_The data uses a Unique Random Identifier, they are anonymized and de-
identified. We use generalization and suppression mechanisms to guarantee de-
identification._
</td> </tr>
<tr>
<td>
2.1.2 What naming conventions do you follow?
</td> </tr>
<tr>
<td>
_None._
</td> </tr>
<tr>
<td>
2.1.3 Will search keywords be provided that optimize possibilities for re-use?
</td> </tr>
<tr>
<td>
_No._
</td> </tr>
<tr>
<td>
2.1.4 Do you provide clear version numbers?
</td> </tr>
<tr>
<td>
_We use the date of extraction._
</td> </tr>
<tr>
<td>
2.1.5 What metadata will be created?
</td> </tr>
<tr>
<td>
_None._
</td> </tr>
<tr>
<td>
**2.2 Making data openly accessible**
</td> </tr>
<tr>
<td>
2.2.1 Which data produced and/or used in the project will be made openly
available as the default?
</td> </tr>
<tr>
<td>
_The dataset will only be available for the data analyst partners during the
CrowdHEALTH project upon the signature of a Non-Disclosure Agreement._
</td> </tr>
<tr>
<td>
2.2.2 How will the data be made accessible?
</td> </tr>
<tr>
<td>
_The data will be made accessible using a Hospital La Fe cloud repository._
</td> </tr> </table>
<table>
<tr>
<th>
2.2.3 What methods or software tools are needed to access the data?
</th> </tr>
<tr>
<td>
_A web browser._
</td> </tr>
<tr>
<td>
2.2.4 Is documentation about the software needed to access the data included?
</td> </tr>
<tr>
<td>
_No._
</td> </tr>
<tr>
<td>
2.2.5 Is it possible to include the relevant software?
</td> </tr>
<tr>
<td>
_N/A._
</td> </tr>
<tr>
<td>
2.2.6 Where will the data and associated metadata, documentation and code be
deposited?
</td> </tr>
<tr>
<td>
_Hospital La Fe Cloud._
</td> </tr>
<tr>
<td>
2.2.7 Have you explored appropriate arrangements with the identified
repository?
</td> </tr>
<tr>
<td>
_No._
</td> </tr>
<tr>
<td>
2.2.8 If there are restrictions on use, how will access be provided?
</td> </tr>
<tr>
<td>
_Encrypted and compressed files will be available to those partners who signed
a Confidential NonDisclosure Agreement. After the signature a login and a
password will be provided to these partners to get access to those files._
</td> </tr>
<tr>
<td>
2.2.9 Is there a need for a data access committee?
</td> </tr>
<tr>
<td>
_No._
</td> </tr>
<tr>
<td>
2.2.10 Are there well described conditions for access?
</td> </tr>
<tr>
<td>
_To be described by the end of November._
</td> </tr>
<tr>
<td>
2.2.11 How will the identity of the person accessing the data be ascertained?
</td> </tr>
<tr>
<td>
_User login and password._
</td> </tr>
<tr>
<td>
**2.3 Making data interoperable**
</td> </tr>
<tr>
<td>
2.3.1 Are the data produced in the project interoperable?
</td> </tr>
<tr>
<td>
_No._
</td> </tr>
<tr>
<td>
2.3.2 What data and metadata vocabularies, standards or methodologies will you
follow to make your data interoperable?
</td> </tr>
<tr>
<td>
_Only the information about diagnosis are described using ICD-9-CM codes._
</td> </tr>
<tr>
<td>
2.3.3 Will you be using standard vocabularies for all data types present in
your data set, to allow interdisciplinary interoperability?
</td> </tr>
<tr>
<td>
_No._
</td> </tr> </table>
<table>
<tr>
<th>
2.3.4 In case it is unavoidable that you use uncommon or generate project
specific ontologies or vocabularies, will you provide mappings to more
commonly used ontologies?
</th> </tr>
<tr>
<td>
_No._
</td> </tr>
<tr>
<td>
**2.4 Increase data re-use (through clarifying licenses)**
</td> </tr>
<tr>
<td>
2.4.1 How will the data be licensed to permit the widest re-use possible?
</td> </tr>
<tr>
<td>
_The data will not be licensed for any re-use._
</td> </tr>
<tr>
<td>
2.4.2 When will the data be made available for re-use?
</td> </tr>
<tr>
<td>
_Data will never be available for re-use._
</td> </tr>
<tr>
<td>
2.4.3 Are the data produced and/or used in the project useable by third
parties, in particular after the end of the project?
</td> </tr>
<tr>
<td>
_The use of the data is limited to the framework of the project. Any re-use of
the data after the project is forbidden by the Legal & Ethical Committee of
Hospital La Fe and the Spanish law for data protection (Ley Orgánica 15/1999,
de 13 de diciembre, de Protección de Datos de Carácter Personal). Once the
project is finished any data should be destroyed by any partner doing research
with them. _
</td> </tr>
<tr>
<td>
2.4.4 How long is it intended that the data remains re-usable?
</td> </tr>
<tr>
<td>
_N/A_
</td> </tr>
<tr>
<td>
2.4.5 Are data quality assurance processes described?
</td> </tr>
<tr>
<td>
_No._
</td> </tr>
<tr>
<td>
**3 Allocation of resources**
</td> </tr>
<tr>
<td>
3.1 What are the costs for making data FAIR in your project?
</td> </tr>
<tr>
<td>
_Unknown._
</td> </tr>
<tr>
<td>
3.2 How will these be covered?
</td> </tr>
<tr>
<td>
_Note that costs related to open access to research data are eligible as part
of the Horizon 2020 grant (if compliant with the Grant Agreement conditions)._
</td> </tr>
<tr>
<td>
3.3 Who will be responsible for data management in your project?
</td> </tr>
<tr>
<td>
_María Luisa Correcher Palau (head of the Business Intelligence Technologies
of Hospital La Fe)_
</td> </tr>
<tr>
<td>
3.4 Are the resources for long term preservation discussed?
</td> </tr>
<tr>
<td>
_No._
</td> </tr>
<tr>
<td>
**4 Data security**
</td> </tr>
<tr>
<td>
4.1 What provisions are in place for data security?
</td> </tr>
<tr>
<td>
_Hospital La Fe has a Data Processing Center with both server and storage
infrastructure racks and communications infrastructure with separated
electrical panel area, with fire detection and protection systems, UPS and air
conditioning units for the area's own air conditioning and with RF-120
fireresistant walls. The access to the data is allowed only to authorized
staff with an active directory with user logins and encrypted passwords. The
Data Processing Center has also disk to disk and tape backup systems._
</td> </tr>
<tr>
<td>
4.2 Is the data safely stored in certified repositories for long term
preservation and curation?
</td> </tr>
<tr>
<td>
_Yes._
</td> </tr>
<tr>
<td>
**5 Ethical aspects**
</td> </tr>
<tr>
<td>
5.1 Are there any ethical or legal issues that can have an impact on data
sharing?
</td> </tr>
<tr>
<td>
_Data from Electronic Health Records (EHRs) must be anonymized and de-
identified to protect patients’ privacy and obey the data protection
legislations specially when the data is to be used for research purposes
within any research project._
_Sensitive private information such as the name, surname, date of birth,
address, zip codes, and identity numbers must be suppressed. A random unique
identifier is needed to link the information of patients without any relation
to them in a way such that backtracking the patient is completely impossible._
</td> </tr>
<tr>
<td>
5.2 Is informed consent for data sharing and long term preservation included
in questionnaires dealing with personal data?
</td> </tr>
<tr>
<td>
_No questionnaires are going to be used in this Use Case._
</td> </tr>
<tr>
<td>
**6 Other issues**
</td> </tr>
<tr>
<td>
6.1 Do you make use of other national/funder/sectorial/departmental procedures
for data management? If yes, which ones?
</td> </tr>
<tr>
<td>
_N/A_
</td> </tr> </table>
## 3\. 2 DATASET 2: Stockholm County Council/ Karolinska University
Hospital/SwedeHeart (KI)
<table>
<tr>
<th>
**DATASET 02**
</th>
<th>
**NAME: Stockholm County Council/ Karolinska University Hospital/SwedeHeart**
</th> </tr>
<tr>
<td>
**1\. Data Summary**
</td> </tr>
<tr>
<td>
1.1 Purpose
</td> </tr>
<tr>
<td>
_A patient diagnosed with one of listed ICD10 Codes for cardiovascular
diseases in Stockholm will be registered in two different databases. The first
one is the Stockholm County Database (VAL) which is responsible for collecting
information from all the hospitals in Stockholm County. The second one is the
SwedeHeart quality registry which is collecting information only for patients
with cardiovascular diseases nationally. More specifically, Swedheart is a
procedural and surgery-related registry, which gathers information, based on
the disease, patient’s risk profile, medical treatment processes and
laboratory results among others. The two databases are independent which means
that different information for the same patient is registered._
_We expect that the extraction of information from the databases _for the same
period_ (2006-2012) will give us the possibility to track the same patients.
Our assumption is strong and it is based on other research projects, which
have actualized similar procedures (SCREAM project). _
_Aims:_
* _Provide the relevant data to the health analytics partners in order to develop models that can be used from the policy makers in order to identify causality relationships between events, forecast the development of cardiovascular diseases on a population based sample, explore clinical pathways and produce risk stratification outcomes._
* _Create a new policy making data driven process factoring in information from sources that are currently functioning independently._
* _Support the cardiovascular and policy making research community in Sweden._
</td> </tr>
<tr>
<td>
1.2 Types and formats of data
</td> </tr>
<tr>
<td>
_Both databases use the same format: .csv_
_The data will be static, meaning that we are going to get two extracts, one
from the VAL database and one from the SwedeHeart Quality registry._
</td> </tr>
<tr>
<td>
1.3 Re-use of existing data
</td> </tr>
<tr>
<td>
_As included in the databases described above_
</td> </tr>
<tr>
<td>
1.4 Origin
</td> </tr>
<tr>
<td>
_Already mentioned._
</td> </tr> </table>
<table>
<tr>
<th>
1.5 Expected size
</th> </tr>
<tr>
<td>
_80.000 unique patients._
* _Phase 1 : 5.000 (December 2017)_
* _Phase 2: 25.000 (September 2018)_
* _Phase 3: 80.000 (March 2019)_
</td> </tr>
<tr>
<td>
1.6 Data utility
</td> </tr>
<tr>
<td>
_KI’s use case scenario stakeholders._
</td> </tr>
<tr>
<td>
**2 FAIR Data**
</td> </tr>
<tr>
<td>
**2.1 Making data findable, including provisions for metadata**
</td> </tr>
<tr>
<td>
2.1.1 Are the data produced and/or used in the project discoverable with
metadata, identifiable and locatable by means of a standard identification
mechanism?
</td> </tr>
<tr>
<td>
_N/A_
</td> </tr>
<tr>
<td>
2.1.2 What naming conventions do you follow?
</td> </tr>
<tr>
<td>
None
</td> </tr>
<tr>
<td>
2.1.3 Will search keywords be provided that optimize possibilities for re-use?
</td> </tr>
<tr>
<td>
_No_
</td> </tr>
<tr>
<td>
2.1.4 Do you provide clear version numbers?
</td> </tr>
<tr>
<td>
_Yes. Extraction date_
</td> </tr>
<tr>
<td>
2.1.5 What metadata will be created?
</td> </tr>
<tr>
<td>
_None_
</td> </tr>
<tr>
<td>
**2.2 Making data openly accessible**
</td> </tr>
<tr>
<td>
2.2.1 Which data produced and/or used in the project will be made openly
available as the default?
</td> </tr>
<tr>
<td>
_Samples of the dataset will be shared to the CrowdHEALTH partners responsible
for the data analysis during the project upon the signature of a Non-
Disclosure Agreement. The data should be used for training scripts, systems or
algorithms. The partners should have already implemented the GDPR regulations
in their organizations._
_It must be stated that, it is important to pseudonymise/code, and sometimes
double-code, the data and ensure that it is shared in a secure manner. If the
data is to be sent outside KI, it must be encrypted, or external partners may
be given access to a secure server at KI._
</td> </tr>
<tr>
<td>
2.2.2 How will the data be made accessible?
</td> </tr>
<tr>
<td>
_By deposition in a repository_
</td> </tr> </table>
<table>
<tr>
<th>
2.2.3 What methods or software tools are needed to access the data?
</th> </tr>
<tr>
<td>
_Pull/Push methods_
</td> </tr>
<tr>
<td>
2.2.4 Is documentation about the software needed to access the data included?
</td> </tr>
<tr>
<td>
_No_
</td> </tr>
<tr>
<td>
2.2.5 Is it possible to include the relevant software?
</td> </tr>
<tr>
<td>
_No_
</td> </tr>
<tr>
<td>
2.2.6 Where will the data and associated metadata, documentation and code be
deposited?
</td> </tr>
<tr>
<td>
_N/A_
</td> </tr>
<tr>
<td>
2.2.7 Have you explored appropriate arrangements with the identified
repository?
</td> </tr>
<tr>
<td>
_No_
</td> </tr>
<tr>
<td>
2.2.8 If there are restrictions on use, how will access be provided?
</td> </tr>
<tr>
<td>
_N/A_
</td> </tr>
<tr>
<td>
2.2.9 Is there a need for a data access committee?
</td> </tr>
<tr>
<td>
_No_
</td> </tr>
<tr>
<td>
2.2.10 Are there well described conditions for access?
</td> </tr>
<tr>
<td>
_No_
</td> </tr>
<tr>
<td>
2.2.11 How will the identity of the person accessing the data be ascertained?
</td> </tr>
<tr>
<td>
_N/A_
</td> </tr>
<tr>
<td>
**2.3 Making data interoperable**
</td> </tr>
<tr>
<td>
2.3.1 Are the data produced in the project interoperable?
</td> </tr>
<tr>
<td>
_-_
</td> </tr>
<tr>
<td>
2.3.2 What data and metadata vocabularies, standards or methodologies will you
follow to make your data interoperable?
</td> </tr>
<tr>
<td>
_ICD CODE 10_
</td> </tr>
<tr>
<td>
2.3.3 Will you be using standard vocabularies for all data types present in
your data set, to allow interdisciplinary interoperability?
</td> </tr>
<tr>
<td>
_Yes_
</td> </tr> </table>
<table>
<tr>
<th>
2.3.4 In case it is unavoidable that you use uncommon or generate project
specific ontologies or vocabularies, will you provide mappings to more
commonly used ontologies?
</th> </tr>
<tr>
<td>
_Yes_
</td> </tr>
<tr>
<td>
**2.4 Increase data re-use (through clarifying licenses)**
</td> </tr>
<tr>
<td>
2.4.1 How will the data be licensed to permit the widest re-use possible?
</td> </tr>
<tr>
<td>
_The data will not be licensed for any re-use._
</td> </tr>
<tr>
<td>
2.4.2 When will the data be made available for re-use?
</td> </tr>
<tr>
<td>
_Data will never be available for re-use_
</td> </tr>
<tr>
<td>
2.4.3 Are the data produced and/or used in the project useable by third
parties, in particular after the end of the project?
</td> </tr>
<tr>
<td>
_No_
</td> </tr>
<tr>
<td>
2.4.4 How long is it intended that the data remains re-usable?
</td> </tr>
<tr>
<td>
_-_
</td> </tr>
<tr>
<td>
2.4.5 Are data quality assurance processes described?
</td> </tr>
<tr>
<td>
_No_
</td> </tr>
<tr>
<td>
**3 Allocation of resources**
</td> </tr>
<tr>
<td>
3.1 What are the costs for making data FAIR in your project?
</td> </tr>
<tr>
<td>
_No costs have been calculated for this purpose at the moment_
</td> </tr>
<tr>
<td>
3.2 How will these be covered?
</td> </tr>
<tr>
<td>
_If any costs are incurred, this will be covered from CrowdHEALTH_
</td> </tr>
<tr>
<td>
3.3 Who will be responsible for data management in your project?
</td> </tr>
<tr>
<td>
_LIME, Karolinska Institutet IT department. The IT department will assign
three data security managers:_
* _Controller: the person who decides on the purpose of the personal data processing, irrespective of whether anyone else carries out the actual processing._
* _Processor: the person who carries out the personal data processing on behalf of the controller. In GDPR the processor has a direct liability and direct responsibilities. This means, for example, that agreement relations should be updated and that assistants must themselves observe a large proportion of the requirements in GDPR._
* _The Data Protection Officer (DPO) is a physical person who represents the liable instance in matters which relate to GDPR. Requirements governing the appointment of representatives for authorities._
</td> </tr>
<tr>
<td>
3.4 Are the resources for long term preservation discussed?
</td> </tr>
<tr>
<td>
_N/A_
</td> </tr>
<tr>
<td>
**4 Data security**
</td> </tr>
<tr>
<td>
4.1 What provisions are in place for data security?
</td> </tr>
<tr>
<td>
_Karolinska Institutet is following the General Data Protection Regulation
(GDPR)_
_Data that will be used for the CrowdHEALTH project may only be gathered for
specific, expressly stated and justified purposes and may not be subsequently
processed in a manner which is incompatible with these purposes._
</td> </tr>
<tr>
<td>
4.2 Is the data safely stored in certified repositories for long term
preservation and curation?
</td> </tr>
<tr>
<td>
_Yes_
</td> </tr>
<tr>
<td>
**5 Ethical aspects**
</td> </tr>
<tr>
<td>
5.1 Are there any ethical or legal issues that can have an impact on data
sharing?
</td> </tr>
<tr>
<td>
_After receiving ethical approval to handle the data, it is important that all
personal data is stored and worked on safely, and it needs to be protected
against unauthorized access. Personal data may only be stored in systems and
solutions that are approved for personal data at KI._
</td> </tr>
<tr>
<td>
5.2 Is informed consent for data sharing and long term preservation included
in questionnaires dealing with personal data?
</td> </tr>
<tr>
<td>
_We are not intending to use any questionnaires_
</td> </tr>
<tr>
<td>
**6 Other issues**
</td> </tr>
<tr>
<td>
6.1 Do you make use of other national/funder/sectorial/departmental procedures
for data management? If yes, which ones?
</td> </tr>
<tr>
<td>
_N/A_
</td> </tr> </table>
## 3\. 3 DATASET 3: Bio Data (DFKI)
<table>
<tr>
<th>
**DATASET 3**
</th>
<th>
**NAME: Bio Data (DFKI)**
</th> </tr>
<tr>
<td>
**1\. Data Summary**
</td> </tr>
<tr>
<td>
1.1 Purpose
</td> </tr>
<tr>
<td>
_This data is collected to get an insight in the physical stress a user had
throughout the day._
</td> </tr>
<tr>
<td>
1.2 Types and formats of data
</td> </tr>
<tr>
<td>
_The collected data will be BioSnapshots (Heartrate, step counter - once per
minute) and sleep data (total sleep duration, list of individual sleep
intervals (REM, deep sleep, light sleep, awake…) with their respective
durations._
_The data will be stored in an SQL database and made available to the
CrowdHEALTH platform through the HHRs communicated through the CrowdHEALTH
platform._
</td> </tr>
<tr>
<td>
1.3 Re-use of existing data
</td> </tr>
<tr>
<td>
_No_
</td> </tr>
<tr>
<td>
1.4 Origin
</td> </tr>
<tr>
<td>
_The data will be aggregated by fitness trackers, i.e. fitbit devices._
</td> </tr>
<tr>
<td>
1.5 Expected size
</td> </tr>
<tr>
<td>
We expect to have data from up to 100 participants, entering per day about 1-2
sleep entries and up to 1440 BioSnapshot entries for at least a year. This
amounts to roughly 1.8 GB of data.
</td> </tr>
<tr>
<td>
1.6 Data utility
</td> </tr>
<tr>
<td>
_The data might be useful to detect relations between food intake, physical
activity, overall mood and sleep patterns, which are all three collected from
the same individuals over the same periods of time._
</td> </tr>
<tr>
<td>
**2 FAIR Data**
</td> </tr>
<tr>
<td>
**2.1 Making data findable, including provisions for metadata**
</td> </tr>
<tr>
<td>
2.1.1 Are the data produced and/or used in the project discoverable with
metadata, identifiable and locatable by means of a standard identification
mechanism?
</td> </tr>
<tr>
<td>
_Yes_
</td> </tr>
<tr>
<td>
2.1.2 What naming conventions do you follow?
</td> </tr>
<tr>
<td>
_Generally the java naming conventions are followed._
</td> </tr>
<tr>
<td>
2.1.3 Will search keywords be provided that optimize possibilities for re-use?
</td> </tr>
<tr>
<td>
_No_
</td> </tr> </table>
<table>
<tr>
<th>
2.1.4 Do you provide clear version numbers?
</th> </tr>
<tr>
<td>
_No because the data will be gathered over time no versioning is possible._
</td> </tr>
<tr>
<td>
2.1.5 What metadata will be created?
</td> </tr>
<tr>
<td>
_The whole metadata and interoperability layer will be added via HRR and the
FHIR extensions._
</td> </tr>
<tr>
<td>
**2.2 Making data openly accessible**
</td> </tr>
<tr>
<td>
2.2.1 Which data produced and/or used in the project will be made openly
available as the default?
</td> </tr>
<tr>
<td>
_Information gathered in the DFKI use case will be anonymized and made
available to CrowdHEALTH partners._
</td> </tr>
<tr>
<td>
2.2.2 How will the data be made accessible?
</td> </tr>
<tr>
<td>
_For our raw Data there will be pull interfaces and regular dumps. It will be
made available to the partners in deployed CrowdHEALTH platform instances by
being integrated into the HHRs of patients selected by the use case partners
HULAFE and CRA._
</td> </tr>
<tr>
<td>
2.2.3 What methods or software tools are needed to access the data?
</td> </tr>
<tr>
<td>
_The raw data will be provided as JSON and SQL Dumps, the SQL Dumps are
created from a mariadb 10 server._
</td> </tr>
<tr>
<td>
2.2.4 Is documentation about the software needed to access the data included?
</td> </tr>
<tr>
<td>
_No software will be needed for the raw data, there most likely will be a form
of data documentation._
</td> </tr>
<tr>
<td>
2.2.5 Is it possible to include the relevant software?
</td> </tr>
<tr>
<td>
_-_
</td> </tr>
<tr>
<td>
2.2.6 Where will the data and associated metadata, documentation and code be
deposited?
</td> </tr>
<tr>
<td>
_Data can be accessed directly with an API key associated to every involved
partner, but the best way to access it is via HHR._
</td> </tr>
<tr>
<td>
2.2.7 Have you explored appropriate arrangements with the identified
repository?
</td> </tr>
<tr>
<td>
_-_
</td> </tr>
<tr>
<td>
2.2.8 If there are restrictions on use, how will access be provided?
</td> </tr>
<tr>
<td>
_RESTful or RPC pull interface on the DFKI aggregation platform._
</td> </tr>
<tr>
<td>
2.2.9 Is there a need for a data access committee?
</td> </tr>
<tr>
<td>
_No_
</td> </tr> </table>
<table>
<tr>
<th>
2.2.10 Are there well described conditions for access?
</th> </tr>
<tr>
<td>
_No_
</td> </tr>
<tr>
<td>
2.2.11 How will the identity of the person accessing the data be ascertained?
</td> </tr>
<tr>
<td>
_The access will be controlled by API keys_
</td> </tr>
<tr>
<td>
**2.3 Making data interoperable**
</td> </tr>
<tr>
<td>
2.3.1 Are the data produced in the project interoperable?
</td> </tr>
<tr>
<td>
_Yes, but only in the HHR/FHIR representation._
</td> </tr>
<tr>
<td>
2.3.2 What data and metadata vocabularies, standards or methodologies will you
follow to make your data interoperable?
</td> </tr>
<tr>
<td>
_FHIR and the proposed FHIR extensions_
</td> </tr>
<tr>
<td>
2.3.3 Will you be using standard vocabularies for all data types present in
your data set, to allow inter-disciplinary interoperability?
</td> </tr>
<tr>
<td>
_yes_
</td> </tr>
<tr>
<td>
2.3.4 In case it is unavoidable that you use uncommon or generate project
specific ontologies or vocabularies, will you provide mappings to more
commonly used ontologies?
</td> </tr>
<tr>
<td>
_Yes, mappings to used source data sources will be made available_
</td> </tr>
<tr>
<td>
**2.4 Increase data re-use (through clarifying licenses)**
</td> </tr>
<tr>
<td>
2.4.1 How will the data be licensed to permit the widest re-use possible?
</td> </tr>
<tr>
<td>
_The raw data will be only available for all partners in CrowdHEALTH, the data
integrated from it into the HHR/FHIR formatted data will be available under
same restrictions as the HULAFE and CRA data._
</td> </tr>
<tr>
<td>
2.4.2 When will the data be made available for re-use?
</td> </tr>
<tr>
<td>
_As soon as it is aggregated it will be available._
</td> </tr>
<tr>
<td>
2.4.3 Are the data produced and/or used in the project useable by third
parties, in particular after the end of the project?
</td> </tr>
<tr>
<td>
_The data will be reusable by third parties._
</td> </tr>
<tr>
<td>
2.4.4 How long is it intended that the data remains re-usable?
</td> </tr>
<tr>
<td>
_There will be no data maintenance from DFKI, the data is provided as-is._
</td> </tr>
<tr>
<td>
2.4.5 Are data quality assurance processes described?
</td> </tr>
<tr>
<td>
_No_
</td> </tr>
<tr>
<td>
**3 Allocation of resources**
</td> </tr>
<tr>
<td>
3.1 What are the costs for making data FAIR in your project?
</td> </tr>
<tr>
<td>
_The HHR/FHIR formatted data is already defined FAIR._
</td> </tr>
<tr>
<td>
3.2 How will these be covered?
</td> </tr>
<tr>
<td>
_-_
</td> </tr>
<tr>
<td>
3.3 Who will be responsible for data management in your project?
</td> </tr>
<tr>
<td>
_Jan Janssen (DFKI)_
</td> </tr>
<tr>
<td>
3.4 Are the resources for long term preservation discussed?
</td> </tr>
<tr>
<td>
_The raw data will only be preserved for internal access by DFKI, the data
integrated from it into the HHR/FHIR formatted data will be under the same
long-term preservation policies as any other data from HULAFE and CRA._
</td> </tr>
<tr>
<td>
**4 Data security**
</td> </tr>
<tr>
<td>
4.1 What provisions are in place for data security?
</td> </tr>
<tr>
<td>
_Raw data will be stored encrypted in a server room within the DFKI premises
with appropriate access restrictions._
</td> </tr>
<tr>
<td>
4.2 Is the data safely stored in certified repositories for long term
preservation and curation?
</td> </tr>
<tr>
<td>
_No, the raw data will not be safely stored for long term preservation and
curation, at least not for public access._
</td> </tr>
<tr>
<td>
**5 Ethical aspects**
</td> </tr>
<tr>
<td>
5.1 Are there any ethical or legal issues that can have an impact on data
sharing?
</td> </tr>
<tr>
<td>
_The collected raw data cannot be shared without any ethical or legal issues._
</td> </tr>
<tr>
<td>
5.2 Is informed consent for data sharing and long term preservation included
in questionnaires dealing with personal data?
</td> </tr>
<tr>
<td>
_Yes_
</td> </tr>
<tr>
<td>
**6 Other issues**
</td> </tr>
<tr>
<td>
6.1 Do you make use of other national/funder/sectorial/departmental procedures
for data management? If yes, which ones?
</td> </tr>
<tr>
<td>
_No_
</td> </tr> </table>
## 3\. 4 DATASET 4: Nutritional Data (DFKI)
<table>
<tr>
<th>
**DATASET 4**
</th>
<th>
**NAME: Nutritional Data (DFKI)**
</th> </tr>
<tr>
<td>
**1\. Data Summary**
</td> </tr>
<tr>
<td>
1.1 Purpose
</td> </tr>
<tr>
<td>
_This data is collected to get insight into the nutritional behaviour of
individuals, that is food composition, food types, quantity and nutritional
values._
</td> </tr>
<tr>
<td>
1.2 Types and formats of data
</td> </tr>
<tr>
<td>
_General nutritional information about ingredients, recipes, allergens,
allergies and diets._ _Manually logged nutritional habits of the patients._
</td> </tr>
<tr>
<td>
1.3 Re-use of existing data
</td> </tr>
<tr>
<td>
_Partially._
_Some of the Ingredient and Recipe Data is collected from third party
services, and some of those data will not be publicly available, but in the
scope of the project all the data will be accessible._
</td> </tr>
<tr>
<td>
1.4 Origin
</td> </tr>
<tr>
<td>
* _The nutritional information comes from public domain sources like the USDA Food Nutrition database._
* _Recipes are gathered from several public domain sources, and some proprietary sources._
* _LanguaL information is gathered from LanguaL._
* _Translations are made with the help of DeepL._
* _Lucene and Stanford NLP are used for matching purposes._
* _The source is always marked and a cleanup based on the source is always possible._
* _Only public domain data can be shared externally, in the scope of the project every consortium member gets access to all of the data._
</td> </tr>
<tr>
<td>
1.5 Expected size
</td> </tr>
<tr>
<td>
_We expect to have data from up to 60 participants, entering about 2-10 dishes
per day for a target duration of 1.5 years. The data will stored at DFKI, but
only aggregated nutritional information will be pushed into the CrowdHEALTH
platform. The collected data with all details will amount to 68 GB, from which
7 GB of aggregated nutritional information will be pushed into the CrowdHEALTH
platform by using optimal datatypes._
</td> </tr>
<tr>
<td>
1.6 Data utility
</td> </tr>
<tr>
<td>
_The data might be useful to detect relations between food intake, physical
activity, overall mood and sleep patterns, which are all three collected from
the same individuals over the same periods of time (please refer to
information about the different datasets of DFKI regarding the data collected
and correlated)._
</td> </tr> </table>
<table>
<tr>
<th>
**2 FAIR Data**
</th> </tr>
<tr>
<td>
**2.1 Making data findable, including provisions for metadata**
</td> </tr>
<tr>
<td>
2.1.1 Are the data produced and/or used in the project discoverable with
metadata, identifiable and locatable by means of a standard identification
mechanism?
</td> </tr>
<tr>
<td>
_Yes_
</td> </tr>
<tr>
<td>
2.1.2 What naming conventions do you follow?
</td> </tr>
<tr>
<td>
_Generally the java naming conventions are followed._
</td> </tr>
<tr>
<td>
2.1.3 Will search keywords be provided that optimize possibilities for re-use?
</td> </tr>
<tr>
<td>
_No_
</td> </tr>
<tr>
<td>
2.1.4 Do you provide clear version numbers?
</td> </tr>
<tr>
<td>
_No because the data will be gathered over time no versioning is possible._
</td> </tr>
<tr>
<td>
2.1.5 What metadata will be created?
</td> </tr>
<tr>
<td>
_The whole metadata and interoperability layer will be added via HRR and the
FHIR extensions._
</td> </tr>
<tr>
<td>
**2.2 Making data openly accessible**
</td> </tr>
<tr>
<td>
2.2.1 Which data produced and/or used in the project will be made openly
available as the default?
</td> </tr>
<tr>
<td>
_Every piece of information gathered in the DFKI use case will be available to
all CrowdHEALTH partners._
</td> </tr>
<tr>
<td>
2.2.2 How will the data be made accessible?
</td> </tr>
<tr>
<td>
_For the aggregated data there will be pull interfaces and regular dumps. It
will be made available to the partners in deployed CrowdHEALTH platform
instances by being integrated into the HHRs of patients selected by the use
case partners HULAFE._
</td> </tr>
<tr>
<td>
2.2.3 What methods or software tools are needed to access the data?
</td> </tr>
<tr>
<td>
_The raw data will be provided as JSON and SQL Dumps, the SQL Dumps are
created from a mariadb_
_10 server_
</td> </tr>
<tr>
<td>
2.2.4 Is documentation about the software needed to access the data included?
</td> </tr>
<tr>
<td>
_No software will be needed for the raw data, there most likely will be a form
of data documentation._
</td> </tr>
<tr>
<td>
2.2.5 Is it possible to include the relevant software?
</td> </tr>
<tr>
<td>
_-_
</td> </tr> </table>
<table>
<tr>
<th>
2.2.6 Where will the data and associated metadata, documentation and code be
deposited?
</th> </tr>
<tr>
<td>
_Data can be accessed directly with an API key associated to every involved
partner, but the best way to access it is via HHR._
</td> </tr>
<tr>
<td>
2.2.7 Have you explored appropriate arrangements with the identified
repository?
</td> </tr>
<tr>
<td>
_-_
</td> </tr>
<tr>
<td>
2.2.8 If there are restrictions on use, how will access be provided?
</td> </tr>
<tr>
<td>
_RESTful or RPC pull interface on the DFKI aggregation platform._
</td> </tr>
<tr>
<td>
2.2.9 Is there a need for a data access committee?
</td> </tr>
<tr>
<td>
_No_
</td> </tr>
<tr>
<td>
2.2.10 Are there well described conditions for access?
</td> </tr>
<tr>
<td>
_No_
</td> </tr>
<tr>
<td>
2.2.11 How will the identity of the person accessing the data be ascertained?
</td> </tr>
<tr>
<td>
_The access will be controlled by API keys_
</td> </tr>
<tr>
<td>
**2.3 Making data interoperable**
</td> </tr>
<tr>
<td>
2.3.1 Are the data produced in the project interoperable?
</td> </tr>
<tr>
<td>
_Yes, but only in the HHR/FHIR representation._
</td> </tr>
<tr>
<td>
2.3.2 What data and metadata vocabularies, standards or methodologies will you
follow to make your data interoperable?
</td> </tr>
<tr>
<td>
_FHIR and the proposed FHIR extensions._
</td> </tr>
<tr>
<td>
2.3.3 Will you be using standard vocabularies for all data types present in
your data set, to allow interdisciplinary interoperability?
</td> </tr>
<tr>
<td>
_Yes_
</td> </tr>
<tr>
<td>
2.3.4 In case it is unavoidable that you use uncommon or generate project
specific ontologies or vocabularies, will you provide mappings to more
commonly used ontologies?
</td> </tr>
<tr>
<td>
_Yes, mappings to used source data sources will be made available_
</td> </tr>
<tr>
<td>
**2.4 Increase data re-use (through clarifying licenses)**
</td> </tr>
<tr>
<td>
2.4.1 How will the data be licensed to permit the widest re-use possible?
</td> </tr>
<tr>
<td>
_The aggregated data will be only available for all partners in CrowdHEALTH,
the data integrated from it into the HHR/FHIR formatted data will be available
under same restrictions as the HULAFE data._
</td> </tr> </table>
<table>
<tr>
<th>
2.4.2 When will the data be made available for re-use?
</th> </tr>
<tr>
<td>
_As soon as it is aggregated it will be available._
</td> </tr>
<tr>
<td>
2.4.3 Are the data produced and/or used in the project useable by third
parties, in particular after the end of the project?
</td> </tr>
<tr>
<td>
_The data will be reusable by third parties._
</td> </tr>
<tr>
<td>
2.4.4 How long is it intended that the data remains re-usable?
</td> </tr>
<tr>
<td>
_There will be no data maintenance from DFKI, the data is provided as-is._
</td> </tr>
<tr>
<td>
2.4.5 Are data quality assurance processes described?
</td> </tr>
<tr>
<td>
_No_
</td> </tr>
<tr>
<td>
**3 Allocation of resources**
</td> </tr>
<tr>
<td>
3.1 What are the costs for making data FAIR in your project?
</td> </tr>
<tr>
<td>
_The HHR/FHIR formatted data is already defined FAIR._
</td> </tr>
<tr>
<td>
3.2 How will these be covered?
</td> </tr>
<tr>
<td>
_-_
</td> </tr>
<tr>
<td>
3.3 Who will be responsible for data management in your project?
</td> </tr>
<tr>
<td>
_Jan Janssen (DFKI)_
</td> </tr>
<tr>
<td>
3.4 Are the resources for long term preservation discussed?
</td> </tr>
<tr>
<td>
_The aggregated data will only be preserved for internal access by DFKI, the
data integrated from it into the HHR/FHIR formatted data will be under the
same long-term preservation policies as any other data from HULAFE._
</td> </tr>
<tr>
<td>
**4 Data security**
</td> </tr>
<tr>
<td>
4.1 What provisions are in place for data security?
</td> </tr>
<tr>
<td>
_Raw data will be stored encrypted in a server room within the DFKI premises
with appropriate access restrictions._
</td> </tr>
<tr>
<td>
4.2 Is the data safely stored in certified repositories for long term
preservation and curation?
</td> </tr>
<tr>
<td>
_No, the raw data will not be safely stored for long term preservation and
curation, at least not for public access._
</td> </tr>
<tr>
<td>
**5 Ethical aspects**
</td> </tr>
<tr>
<td>
5.1 Are there any ethical or legal issues that can have an impact on data
sharing?
</td> </tr>
<tr>
<td>
_The collected raw data cannot be shared without any ethical or legal issues._
</td> </tr>
<tr>
<td>
5.2 Is informed consent for data sharing and long term preservation included
in questionnaires dealing with personal data?
</td> </tr>
<tr>
<td>
_Yes_
</td> </tr>
<tr>
<td>
**6 Other issues**
</td> </tr>
<tr>
<td>
6.1 Do you make use of other national/funder/sectorial/departmental procedures
for data management? If yes, which ones?
</td> </tr>
<tr>
<td>
_No._
</td> </tr> </table>
## 3\. 5 DATASET 5: Activity Data (DFKI)
<table>
<tr>
<th>
**DATASET 5**
</th>
<th>
**NAME: Activity Data (DFKI)**
</th> </tr>
<tr>
<td>
**1\. Data Summary**
</td> </tr>
<tr>
<td>
1.1 Purpose
</td> </tr>
<tr>
<td>
_This data is collected to get in amount, duration, intensity and kind of
physical activities of individuals._
</td> </tr>
<tr>
<td>
1.2 Types and formats of data
</td> </tr>
<tr>
<td>
_Data of physical activities as detected from fitness trackers extended by
qualitative information provided by the participants about what activities
(social or physical) they performed._
_Automatically and manually logged (or manually enhanced) information about
the activities carried out._
</td> </tr>
<tr>
<td>
1.3 Re-use of existing data
</td> </tr>
<tr>
<td>
_Partially._
_Some of the Activity Types are gathered from public domain sources_
</td> </tr>
<tr>
<td>
1.4 Origin
</td> </tr>
<tr>
<td>
_Several public domain terminologies, DFKI added and user added data._
_The source is always marked and a cleanup based on the source is always
possible._
_Only public domain data can be shared externally, in the scope of the project
every consortium member gets access to all of the data._
</td> </tr>
<tr>
<td>
1.5 Expected size
</td> </tr>
<tr>
<td>
_We expect to have data from up to 100 participants, entering about 2-10
entries per day for a period of 1,5 years. This will amount then to approx. 1
GB of data using optimal datatypes._
</td> </tr>
<tr>
<td>
1.6 Data utility
</td> </tr>
<tr>
<td>
_The data might be useful to detect relations between food intake, physical
activity, overall mood and sleep patterns._
</td> </tr> </table>
<table>
<tr>
<th>
**2 FAIR Data**
</th> </tr>
<tr>
<td>
**2.1 Making data findable, including provisions for metadata**
</td> </tr>
<tr>
<td>
2.1.1 Are the data produced and/or used in the project discoverable with
metadata, identifiable and locatable by means of a standard identification
mechanism?
</td> </tr>
<tr>
<td>
_Yes_
</td> </tr>
<tr>
<td>
2.1.2 What naming conventions do you follow?
</td> </tr>
<tr>
<td>
_Generally the java naming conventions are followed._
</td> </tr>
<tr>
<td>
2.1.3 Will search keywords be provided that optimize possibilities for re-use?
</td> </tr>
<tr>
<td>
_No_
</td> </tr>
<tr>
<td>
2.1.4 Do you provide clear version numbers?
</td> </tr>
<tr>
<td>
_No because the data will be gathered over time no versioning is possible._
</td> </tr>
<tr>
<td>
2.1.5 What metadata will be created?
</td> </tr>
<tr>
<td>
_The whole metadata and interoperability layer will be added via HRR and the
FHIR extensions._
</td> </tr>
<tr>
<td>
**2.2 Making data openly accessible**
</td> </tr>
<tr>
<td>
2.2.1 Which data produced and/or used in the project will be made openly
available as the default?
</td> </tr>
<tr>
<td>
_Every piece of information gathered in the DFKI use case will be available to
all CrowdHEALTH partners._
</td> </tr>
<tr>
<td>
2.2.2 How will the data be made accessible?
</td> </tr>
<tr>
<td>
_For our raw Data there will be pull interfaces and regular dumps. It will be
made available to the partners in deployed CrowdHEALTH platform instances by
being integrated into the HHRs of patients selected by the use case partners
HULAFE and CRA._
</td> </tr>
<tr>
<td>
2.2.3 What methods or software tools are needed to access the data?
</td> </tr>
<tr>
<td>
_The raw data will be provided as JSON and SQL Dumps, the SQL Dumps are
created from a mariadb_
_10 server_
</td> </tr>
<tr>
<td>
2.2.4 Is documentation about the software needed to access the data included?
</td> </tr>
<tr>
<td>
_No software will be needed for the raw data, there most likely will be a form
of data documentation._
</td> </tr>
<tr>
<td>
2.2.5 Is it possible to include the relevant software?
</td> </tr>
<tr>
<td>
_-_
</td> </tr> </table>
<table>
<tr>
<th>
2.2.6 Where will the data and associated metadata, documentation and code be
deposited?
</th> </tr>
<tr>
<td>
_Data can be accessed directly with an API key associated to every involved
partner, but the best way to access it is via HHR._
</td> </tr>
<tr>
<td>
2.2.7 Have you explored appropriate arrangements with the identified
repository?
</td> </tr>
<tr>
<td>
_-_
</td> </tr>
<tr>
<td>
2.2.8 If there are restrictions on use, how will access be provided?
</td> </tr>
<tr>
<td>
_RESTful or RPC pull interface on the DFKI aggregation platform._
</td> </tr>
<tr>
<td>
2.2.9 Is there a need for a data access committee?
</td> </tr>
<tr>
<td>
_No._
</td> </tr>
<tr>
<td>
2.2.10 Are there well described conditions for access?
</td> </tr>
<tr>
<td>
_No._
</td> </tr>
<tr>
<td>
2.2.11 How will the identity of the person accessing the data be ascertained?
</td> </tr>
<tr>
<td>
_The access will be controlled by API keys_
</td> </tr>
<tr>
<td>
**2.3 Making data interoperable**
</td> </tr>
<tr>
<td>
2.3.1 Are the data produced in the project interoperable?
</td> </tr>
<tr>
<td>
_Yes, but only in the HHR/FHIR representation._
</td> </tr>
<tr>
<td>
2.3.2 What data and metadata vocabularies, standards or methodologies will you
follow to make your data interoperable?
</td> </tr>
<tr>
<td>
_FHIR and the proposed FHIR extensions_
</td> </tr>
<tr>
<td>
2.3.3 Will you be using standard vocabularies for all data types present in
your data set, to allow interdisciplinary interoperability?
</td> </tr>
<tr>
<td>
_Yes._
</td> </tr>
<tr>
<td>
2.3.4 In case it is unavoidable that you use uncommon or generate project
specific ontologies or vocabularies, will you provide mappings to more
commonly used ontologies?
</td> </tr>
<tr>
<td>
_Yes, mappings to used source data sources will be made available_
</td> </tr>
<tr>
<td>
**2.4 Increase data re-use (through clarifying licenses)**
</td> </tr>
<tr>
<td>
2.4.1 How will the data be licensed to permit the widest re-use possible?
</td> </tr>
<tr>
<td>
_The raw data will be only available for all partners in CrowdHEALTH, the data
integrated from it into the HHR/FHIR formatted data will be available under
same restrictions as the HULAFE and CRA data._
</td> </tr> </table>
<table>
<tr>
<th>
2.4.2 When will the data be made available for re-use?
</th> </tr>
<tr>
<td>
_As soon as it is aggregated it will be available._
</td> </tr>
<tr>
<td>
2.4.3 Are the data produced and/or used in the project useable by third
parties, in particular after the end of the project?
</td> </tr>
<tr>
<td>
_The data will be reusable by third parties._
</td> </tr>
<tr>
<td>
2.4.4 How long is it intended that the data remains re-usable?
</td> </tr>
<tr>
<td>
_There will be no data maintenance from DFKI, the data is provided as-is._
</td> </tr>
<tr>
<td>
2.4.5 Are data quality assurance processes described?
</td> </tr>
<tr>
<td>
_No._
</td> </tr>
<tr>
<td>
**3 Allocation of resources**
</td> </tr>
<tr>
<td>
3.1 What are the costs for making data FAIR in your project?
</td> </tr>
<tr>
<td>
_The HHR/FHIR formatted data is already defined FAIR._
</td> </tr>
<tr>
<td>
3.2 How will these be covered?
</td> </tr>
<tr>
<td>
_-_
</td> </tr>
<tr>
<td>
3.3 Who will be responsible for data management in your project?
</td> </tr>
<tr>
<td>
_Jan Janssen (DFKI)_
</td> </tr>
<tr>
<td>
3.4 Are the resources for long term preservation discussed?
</td> </tr>
<tr>
<td>
_The raw data will only be preserved for internal access by DFKI, the data
integrated from it into the HHR/FHIR formatted data will be under the same
long-term preservation policies as any other data from HULAFE and CRA._
</td> </tr>
<tr>
<td>
**4 Data security**
</td> </tr>
<tr>
<td>
4.1 What provisions are in place for data security?
</td> </tr>
<tr>
<td>
_Raw data will be stored encrypted in a server room within the DFKI premises
with appropriate access restrictions._
</td> </tr>
<tr>
<td>
4.2 Is the data safely stored in certified repositories for long term
preservation and curation?
</td> </tr>
<tr>
<td>
_No, the raw data will not be safely stored for long term preservation and
curation, at least not for public access._
</td> </tr>
<tr>
<td>
**5 Ethical aspects**
</td> </tr>
<tr>
<td>
5.1 Are there any ethical or legal issues that can have an impact on data
sharing?
</td> </tr>
<tr>
<td>
_The collected raw data cannot be shared without any ethical or legal issues._
</td> </tr>
<tr>
<td>
5.2 Is informed consent for data sharing and long term preservation included
in questionnaires dealing with personal data?
</td> </tr>
<tr>
<td>
_Yes._
</td> </tr>
<tr>
<td>
**6 Other issues**
</td> </tr>
<tr>
<td>
6.1 Do you make use of other national/funder/sectorial/departmental procedures
for data management? If yes, which ones?
</td> </tr>
<tr>
<td>
_No._
</td> </tr> </table>
## 3\. 6 DATASET 6: Biosignals and Activity Data (BIO)
<table>
<tr>
<th>
**DATASET 6**
</th>
<th>
**NAME: Biosignals and Activity Data**
</th> </tr>
<tr>
<td>
**1\. Data Summary**
</td> </tr>
<tr>
<td>
1.1 Purpose
</td> </tr>
<tr>
<td>
_Chronic disease management calls for constant monitoring of specific vital
signs, while physical activity levels are an important parameter for assessing
a patient’s lifestyle and wellness. Proper analysis of these data can assist
clinicians in effective decision-making and can provide policy makers the
means to measure the impact of relevant policies with respect to a
population’s health, changes in patient behaviour and quality of life._
</td> </tr>
<tr>
<td>
1.2 Types and formats of data
</td> </tr>
<tr>
<td>
_The dataset includes historical data. More specifically, the types of
measurements included are:_
* _Oxygen saturation_
* _Blood pressure_
* _Blood glucose_
* _Spirometry_
* _Weight_
* _Step count_ _Mood_
_Each measurement is accompanied by a timestamp. The data are currently
structured in a custom format for internal use in the BioAssist platform and
can be extracted in JSON or CSV format. However, they will be shared with
CrowdHEALTH in a FHIR format_ 2 _as Observations._
</td> </tr>
<tr>
<td>
1.3 Re-use of existing data
</td> </tr>
<tr>
<td>
_The dataset comprises data collected within the frame of BioAssist’s piloting
activities throughout the past year and is continuously growing. All data that
are already available will be used and new data will be continuously fed to
the CrowdHEALTH platform._
</td> </tr>
<tr>
<td>
1.4 Origin
</td> </tr>
<tr>
<td>
_The data are collected from patients participating in BioAssist’s piloting
operations. Biosignal measurements are collected from a variety of sensors
(pulse oximeter, blood pressure meter, glucometer, weighing scale) and
activity data are collected from wearable activity trackers. This dataset also
includes patients’ daily mood, which is self-reported via BioAssist’s app._
</td> </tr>
<tr>
<td>
1.5 Expected size
</td> </tr>
<tr>
<td>
_Approximately 100 patients are enrolled in BioAssist’s pilots. Each patient
performs daily measurements of at least two types of biosignals and mood self-
report, and uses the platform’s communication features twice a week on
average. Within the next couple of months, these patients will also be
provided with activity trackers, which will allow them to record their daily
step count. This amounts to a dataset of more than 100,000 observations, which
is continuously growing._
</td> </tr> </table>
<table>
<tr>
<th>
1.6 Data utility
</th> </tr>
<tr>
<td>
_To the patients’ attending doctors, as well as potentially interested policy
makers._
</td> </tr>
<tr>
<td>
**2 FAIR Data**
</td> </tr>
<tr>
<td>
**2.1 Making data findable, including provisions for metadata**
</td> </tr>
<tr>
<td>
2.1.1 Are the data produced and/or used in the project discoverable with
metadata, identifiable and locatable by means of a standard identification
mechanism?
</td> </tr>
<tr>
<td>
_Yes, the data include identifiers (patient name, observation ID), but after
anonymisation they will not be identifiable._
</td> </tr>
<tr>
<td>
2.1.2 What naming conventions do you follow?
</td> </tr>
<tr>
<td>
_At the moment, the dataset utilises the SNOMED terminology, but any other
standard can be applied if required (e.g. LOINC)._
</td> </tr>
<tr>
<td>
2.1.3 Will search keywords be provided that optimize possibilities for re-use?
</td> </tr>
<tr>
<td>
_Yes._
</td> </tr>
<tr>
<td>
2.1.4 Do you provide clear version numbers?
</td> </tr>
<tr>
<td>
_No._
</td> </tr>
<tr>
<td>
2.1.5 What metadata will be created?
</td> </tr>
<tr>
<td>
_Biosignal measurements are accompanied by the sensor model and in some cases
with metadata created by the sensor related to the quality of the
measurement._
</td> </tr>
<tr>
<td>
**2.2 Making data openly accessible**
</td> </tr>
<tr>
<td>
2.2.1 Which data produced and/or used in the project will be made openly
available as the default?
</td> </tr>
<tr>
<td>
_The dataset shall become available to the consortium after anonymization,
when permission by the Greek Data Protection Authority is obtained. Legal
restrictions do not permit open access to the data._
</td> </tr>
<tr>
<td>
2.2.2 How will the data be made accessible?
</td> </tr>
<tr>
<td>
_The data will be pushed to the CrowdHEALTH big data platform and underlying
store._
</td> </tr>
<tr>
<td>
2.2.3 What methods or software tools are needed to access the data?
</td> </tr>
<tr>
<td>
_The platform provides an API for obtaining the data. This API can be used to
access them._
</td> </tr>
<tr>
<td>
2.2.4 Is documentation about the software needed to access the data included?
</td> </tr>
<tr>
<td>
_Not Required._
</td> </tr> </table>
<table>
<tr>
<th>
2.2.5 Is it possible to include the relevant software?
</th> </tr>
<tr>
<td>
_N/A_
</td> </tr>
<tr>
<td>
2.2.6 Where will the data and associated metadata, documentation and code be
deposited?
</td> </tr>
<tr>
<td>
_Legal constraints do not permit us to provide open access to these data. The
data will be deposited on a local deployment of the CrowdHEALTH platform and
will be accessible only within the project._
</td> </tr>
<tr>
<td>
2.2.7 Have you explored appropriate arrangements with the identified
repository?
</td> </tr>
<tr>
<td>
_N/A_
</td> </tr>
<tr>
<td>
2.2.8 If there are restrictions on use, how will access be provided?
</td> </tr>
<tr>
<td>
_The data will only be accessible for use to authorised users of the
platform._
</td> </tr>
<tr>
<td>
2.2.9 Is there a need for a data access committee?
</td> </tr>
<tr>
<td>
_We do not foresee the need for a data access committee at the moment. This
will be verified when the process for obtaining permission by the Greek Data
Protection Authority is completed._
</td> </tr>
<tr>
<td>
2.2.10 Are there well described conditions for access?
</td> </tr>
<tr>
<td>
_No._
</td> </tr>
<tr>
<td>
2.2.11 How will the identity of the person accessing the data be ascertained?
</td> </tr>
<tr>
<td>
_This will be handled by the CrowdHEALTH user authentication and access
control components._
</td> </tr>
<tr>
<td>
**2.3 Making data interoperable**
</td> </tr>
<tr>
<td>
2.3.1 Are the data produced in the project interoperable?
</td> </tr>
<tr>
<td>
_The data are currently in custom format, but will be shared within the
project in FHIR format._
</td> </tr>
<tr>
<td>
2.3.2 What data and metadata vocabularies, standards or methodologies will you
follow to make your data interoperable?
</td> </tr>
<tr>
<td>
_At the moment, the dataset utilises the SNOMED terminology, but any other
standard can be applied if required (e.g. LOINC)._
</td> </tr>
<tr>
<td>
2.3.3 Will you be using standard vocabularies for all data types present in
your data set, to allow interdisciplinary interoperability?
</td> </tr>
<tr>
<td>
_Yes._
</td> </tr>
<tr>
<td>
2.3.4 In case it is unavoidable that you use uncommon or generate project
specific ontologies or vocabularies, will you provide mappings to more
commonly used ontologies?
</td> </tr>
<tr>
<td>
_There is no need to provide mappings._
</td> </tr> </table>
<table>
<tr>
<th>
**2.4 Increase data re-use (through clarifying licenses)**
</th> </tr>
<tr>
<td>
2.4.1 How will the data be licensed to permit the widest re-use possible?
</td> </tr>
<tr>
<td>
_The data will not be available for re-use._
</td> </tr>
<tr>
<td>
2.4.2 When will the data be made available for re-use?
</td> </tr>
<tr>
<td>
_N/A_
</td> </tr>
<tr>
<td>
2.4.3 Are the data produced and/or used in the project useable by third
parties, in particular after the end of the project?
</td> </tr>
<tr>
<td>
_N/A_
</td> </tr>
<tr>
<td>
2.4.4 How long is it intended that the data remains re-usable?
</td> </tr>
<tr>
<td>
_N/A_
</td> </tr>
<tr>
<td>
2.4.5 Are data quality assurance processes described?
</td> </tr>
<tr>
<td>
_N/A_
</td> </tr>
<tr>
<td>
**3 Allocation of resources**
</td> </tr>
<tr>
<td>
3.1 What are the costs for making data FAIR in your project?
</td> </tr>
<tr>
<td>
_There may be costs related to e.g. legal support, but they cannot be
estimated at the moment. All other known costs are already covered by the
project._
</td> </tr>
<tr>
<td>
3.2 How will these be covered?
</td> </tr>
<tr>
<td>
_This will depend on the nature of the costs that will arise._
</td> </tr>
<tr>
<td>
3.3 Who will be responsible for data management in your project?
</td> </tr>
<tr>
<td>
_BioAssist’s CTO._
</td> </tr>
<tr>
<td>
3.4 Are the resources for long term preservation discussed?
</td> </tr>
<tr>
<td>
_This has not been discussed yet._
</td> </tr>
<tr>
<td>
**4 Data security**
</td> </tr>
<tr>
<td>
4.1 What provisions are in place for data security?
</td> </tr>
<tr>
<td>
_BioAssist has developed state-of-the-art security, business continuity and
data retention plans which are currently in place._
</td> </tr>
<tr>
<td>
4.2 Is the data safely stored in certified repositories for long term
preservation and curation?
</td> </tr>
<tr>
<td>
_At the moment, all data are stored only on BioAssist’s database._
</td> </tr>
<tr>
<td>
**5 Ethical aspects**
</td> </tr>
<tr>
<td>
5.1 Are there any ethical or legal issues that can have an impact on data
sharing?
</td> </tr>
<tr>
<td>
_Ethical issues have already been resolved. Permission from the Greek Data
Protection Authority is required before the (anonymised) dataset can be shared
with the partners. Legal restrictions do not permit open access to this
dataset._
</td> </tr>
<tr>
<td>
5.2 Is informed consent for data sharing and long term preservation included
in questionnaires dealing with personal data?
</td> </tr>
<tr>
<td>
_Yes, all patients that are supplying data to this dataset have signed
informed consent._
</td> </tr>
<tr>
<td>
**6 Other issues**
</td> </tr>
<tr>
<td>
6.1 Do you make use of other national/funder/sectorial/departmental procedures
for data management? If yes, which ones?
</td> </tr>
<tr>
<td>
_Not at the moment._
</td> </tr> </table>
## 3\. 7 DATASET 7: BIO-PHRs (Personal Health Records) (BIO)
<table>
<tr>
<th>
**DATASET 7**
</th>
<th>
**NAME: BIO-PHRs (Personal Health Records)**
</th> </tr>
<tr>
<td>
**1\. Data Summary**
</td> </tr>
<tr>
<td>
1.1 Purpose
</td> </tr>
<tr>
<td>
_Medical test results of patients enrolled in BioAssist’s pilots are collected
from primary healthcare providers, in order to provide their doctors with
access to this information and support them in monitoring their patients’
health status. Analysis of such data can be useful in tasks such as assessment
of clinical pathways._
</td> </tr>
<tr>
<td>
1.2 Types and formats of data
</td> </tr>
<tr>
<td>
_The dataset contains historical data. Each entry includes a timestamp,
category, description, comments, result type (numeric/description), result
value and unit, as well as a reference range. Currently the data are
structured in a custom format for internal use in the BioAssist platform and
can be extracted in JSON or CSV format. However, they will be shared with
CrowdHEALTH in a FHIR format as Observations._
</td> </tr>
<tr>
<td>
1.3 Re-use of existing data
</td> </tr>
<tr>
<td>
_The dataset comprises PHR data collected within the frame of BioAssist’s
piloting activities throughout the past year and is continuously growing. All
data that are already available will be used and new data will be continuously
fed to the CrowdHEALTH platform automatically whenever new results are
generated._
</td> </tr>
<tr>
<td>
1.4 Origin
</td> </tr>
<tr>
<td>
_The data are collected from primary healthcare providers via appropriate web
services and stored on the BioAssist platform. Some entries may be manual
inputs from the platform’s users._
</td> </tr>
<tr>
<td>
1.5 Expected size
</td> </tr>
<tr>
<td>
_Approximately 100 patients are enrolled in BioAssist’s pilots. The average
number of entries in a patient’s PHR is more than 200, although some patients’
PHRs may contain no entries yet._
</td> </tr>
<tr>
<td>
1.6 Data utility
</td> </tr>
<tr>
<td>
_To the patients’ attending doctors, as well as potentially interested policy
makers._
</td> </tr>
<tr>
<td>
**2 FAIR Data**
</td> </tr>
<tr>
<td>
**2.1 Making data findable, including provisions for metadata**
</td> </tr>
<tr>
<td>
2.1.1 Are the data produced and/or used in the project discoverable with
metadata, identifiable and locatable by means of a standard identification
mechanism?
</td> </tr>
<tr>
<td>
_No._
</td> </tr> </table>
<table>
<tr>
<th>
2.1.2 What naming conventions do you follow?
</th> </tr>
<tr>
<td>
_At the moment, the dataset utilises the SNOMED terminology, but any other
standard can be applied if required (e.g. LOINC)._
</td> </tr>
<tr>
<td>
2.1.3 Will search keywords be provided that optimize possibilities for re-use?
</td> </tr>
<tr>
<td>
_Yes_
</td> </tr>
<tr>
<td>
2.1.4 Do you provide clear version numbers?
</td> </tr>
<tr>
<td>
_No_
</td> </tr>
<tr>
<td>
2.1.5 What metadata will be created?
</td> </tr>
<tr>
<td>
_No metadata is created._
</td> </tr>
<tr>
<td>
**2.2 Making data openly accessible**
</td> </tr>
<tr>
<td>
2.2.1 Which data produced and/or used in the project will be made openly
available as the default?
</td> </tr>
<tr>
<td>
_The dataset shall become available to all partners in the CrowdHEALTH project
after anonymisation, when permission by the Greek Data Protection Authority is
obtained. Legal restrictions do not permit open access to the data._
</td> </tr>
<tr>
<td>
2.2.2 How will the data be made accessible?
</td> </tr>
<tr>
<td>
_The data will be pushed to the CrowdHEALTH GW._
</td> </tr>
<tr>
<td>
2.2.3 What methods or software tools are needed to access the data?
</td> </tr>
<tr>
<td>
_All software tools required to access the data will be developed as
components of the CrowdHEALTH platform._
</td> </tr>
<tr>
<td>
2.2.4 Is documentation about the software needed to access the data included?
</td> </tr>
<tr>
<td>
_Not Required._
</td> </tr>
<tr>
<td>
2.2.5 Is it possible to include the relevant software?
</td> </tr>
<tr>
<td>
_N/A_
</td> </tr>
<tr>
<td>
2.2.6 Where will the data and associated metadata, documentation and code be
deposited?
</td> </tr>
<tr>
<td>
_Legal constraints do not permit us to provide open access to these data. The
data will be deposited on a local deployment of the CrowdHEALTH platform and
will be accessible only within the project._
</td> </tr>
<tr>
<td>
2.2.7 Have you explored appropriate arrangements with the identified
repository?
</td> </tr>
<tr>
<td>
_N/A_
</td> </tr> </table>
<table>
<tr>
<th>
2.2.8 If there are restrictions on use, how will access be provided?
</th> </tr>
<tr>
<td>
_The data will only be accessible for use to authorised users of the
platform._
</td> </tr>
<tr>
<td>
2.2.9 Is there a need for a data access committee?
</td> </tr>
<tr>
<td>
_We do not foresee the need for a data access committee at the moment. This
will be verified when the process for obtaining permission by the Greek Data
Protection Authority is completed._
</td> </tr>
<tr>
<td>
2.2.10 Are there well described conditions for access?
</td> </tr>
<tr>
<td>
_No._
</td> </tr>
<tr>
<td>
2.2.11 How will the identity of the person accessing the data be ascertained?
</td> </tr>
<tr>
<td>
_This will be handled by the CrowdHEALTH user authentication and access
control components._
</td> </tr>
<tr>
<td>
**2.3 Making data interoperable**
</td> </tr>
<tr>
<td>
2.3.1 Are the data produced in the project interoperable?
</td> </tr>
<tr>
<td>
_The data are currently in a custom format, but will be shared within the
project in FHIR format._
</td> </tr>
<tr>
<td>
2.3.2 What data and metadata vocabularies, standards or methodologies will you
follow to make your data interoperable?
</td> </tr>
<tr>
<td>
_At the moment, the dataset does not utilize any standard. A standard will be
applied depending on the standard that will eventually be used for dataset 6._
</td> </tr>
<tr>
<td>
2.3.3 Will you be using standard vocabularies for all data types present in
your data set, to allow interdisciplinary interoperability?
</td> </tr>
<tr>
<td>
_It is not certain at the moment._
</td> </tr>
<tr>
<td>
2.3.4 In case it is unavoidable that you use uncommon or generate project
specific ontologies or vocabularies, will you provide mappings to more
commonly used ontologies?
</td> </tr>
<tr>
<td>
_There is no need to provide mappings._
</td> </tr>
<tr>
<td>
**2.4 Increase data re-use (through clarifying licenses)**
</td> </tr>
<tr>
<td>
2.4.1 How will the data be licensed to permit the widest re-use possible?
</td> </tr>
<tr>
<td>
_The data will not be available for re-use._
</td> </tr>
<tr>
<td>
2.4.2 When will the data be made available for re-use?
</td> </tr>
<tr>
<td>
_N/A_
</td> </tr>
<tr>
<td>
2.4.3 Are the data produced and/or used in the project useable by third
parties, in particular after the end of the project?
</td> </tr>
<tr>
<td>
_N/A_
</td> </tr> </table>
<table>
<tr>
<th>
2.4.4 How long is it intended that the data remains re-usable?
</th> </tr>
<tr>
<td>
_N/A_
</td> </tr>
<tr>
<td>
2.4.5 Are data quality assurance processes described?
</td> </tr>
<tr>
<td>
_N/A_
</td> </tr>
<tr>
<td>
**3 Allocation of resources**
</td> </tr>
<tr>
<td>
3.1 What are the costs for making data FAIR in your project?
</td> </tr>
<tr>
<td>
_There may be costs related to e.g. legal support, but they cannot be
estimated at the moment. All other known costs are already covered by the
project._
</td> </tr>
<tr>
<td>
3.2 How will these be covered?
</td> </tr>
<tr>
<td>
_This depends on the nature of the costs that will arise._
</td> </tr>
<tr>
<td>
3.3 Who will be responsible for data management in your project?
</td> </tr>
<tr>
<td>
_BioAssist’s CTO._
</td> </tr>
<tr>
<td>
3.4 Are the resources for long term preservation discussed?
</td> </tr>
<tr>
<td>
_This has not been discussed yet._
</td> </tr>
<tr>
<td>
**4 Data security**
</td> </tr>
<tr>
<td>
4.1 What provisions are in place for data security?
</td> </tr>
<tr>
<td>
_BioAssist has developed state-of-the-art security, business continuity and
data retention plans which are currently in place._
</td> </tr>
<tr>
<td>
4.2 Is the data safely stored in certified repositories for long term
preservation and curation?
</td> </tr>
<tr>
<td>
_At the moment, all data are stored only on BioAssist’s database._
</td> </tr>
<tr>
<td>
**5 Ethical aspects**
</td> </tr>
<tr>
<td>
5.1 Are there any ethical or legal issues that can have an impact on data
sharing?
</td> </tr>
<tr>
<td>
_Ethical issues have already been resolved. Permission from the Greek Data
Protection Authority is required before the (anonymised) dataset can be shared
with the partners. Legal restrictions do not permit open access to this
dataset._
</td> </tr>
<tr>
<td>
5.2 Is informed consent for data sharing and long term preservation included
in questionnaires dealing with personal data?
</td> </tr>
<tr>
<td>
_Yes, all patients that are supplying data to this dataset have signed
informed consent._
</td> </tr>
<tr>
<td>
**6 Other issues**
</td> </tr>
<tr>
<td>
6.1 Do you make use of other national/funder/sectorial/departmental procedures
for data management? If yes, which ones?
</td> </tr>
<tr>
<td>
_Not at the moment._
</td> </tr> </table>
## 3\. 8 DATASET 8: Medication (BIO)
<table>
<tr>
<th>
**DATASET 8**
</th>
<th>
**NAME: Medication**
</th> </tr>
<tr>
<td>
**1\. Data Summary**
</td> </tr>
<tr>
<td>
1.1 Purpose
</td> </tr>
<tr>
<td>
_Patients’ prescribed medications are recorded by their attending doctors on
the BioAssist platform, in order to maintain a comprehensive profile for each
patient and encourage medication adherence with reminders. This data can be
used for patient profiling and assessment of clinical pathways._
</td> </tr>
<tr>
<td>
1.2 Types and formats of data
</td> </tr>
<tr>
<td>
_This dataset includes prescribed medications of patients enrolled in
BioAssist’s pilots. Each entry includes active ingredient, brand name, drug
form (e.g. tab, capsule, etc.), strength and dose time. It is currently
structured in a custom format, but will be shared within the CrowdHEALTH in a
FHIR format._
</td> </tr>
<tr>
<td>
1.3 Re-use of existing data
</td> </tr>
<tr>
<td>
_Medication data are constantly updated. All modifications to the medication
list are logged (with timestamps). The dataset includes data collected within
the past year and new data are added continuously._
</td> </tr>
<tr>
<td>
1.4 Origin
</td> </tr>
<tr>
<td>
_Medication data is mainly collected from manual inputs from doctors._
</td> </tr>
<tr>
<td>
1.5 Expected size
</td> </tr>
<tr>
<td>
_Prescribed medications are also recorded for all patients (approximately 100
people) enrolled in BioAssist’s pilots and this data can be linked to the
other datasets. The average number of medication doses for each patient is
more than 2 per day. BioAssist will also pursue additions to the dataset with
data from Greek national authorities following the respective approvals._
</td> </tr>
<tr>
<td>
1.6 Data utility
</td> </tr>
<tr>
<td>
_To the patients’ attending doctors, as well as potentially interested policy
makers._
</td> </tr>
<tr>
<td>
**2 FAIR Data**
</td> </tr>
<tr>
<td>
**2.1 Making data findable, including provisions for metadata**
</td> </tr>
<tr>
<td>
2.1.1 Are the data produced and/or used in the project discoverable with
metadata, identifiable and locatable by means of a standard identification
mechanism?
</td> </tr>
<tr>
<td>
_No._
</td> </tr>
<tr>
<td>
2.1.2 What naming conventions do you follow?
</td> </tr>
<tr>
<td>
_N/A_
</td> </tr> </table>
<table>
<tr>
<th>
2.1.3 Will search keywords be provided that optimize possibilities for re-use?
</th> </tr>
<tr>
<td>
_N/A_
</td> </tr>
<tr>
<td>
2.1.4 Do you provide clear version numbers?
</td> </tr>
<tr>
<td>
_No_
</td> </tr>
<tr>
<td>
2.1.5 What metadata will be created?
</td> </tr>
<tr>
<td>
_No metadata are created_
</td> </tr>
<tr>
<td>
**2.2 Making data openly accessible**
</td> </tr>
<tr>
<td>
2.2.1 Which data produced and/or used in the project will be made openly
available as the default?
</td> </tr>
<tr>
<td>
_The dataset shall become available to the consortium after anonymization,
when permission by the Greek Data Protection Authority is obtained. Legal
restrictions do not permit open access to the data._
</td> </tr>
<tr>
<td>
2.2.2 How will the data be made accessible?
</td> </tr>
<tr>
<td>
_The data will be pushed to the CrowdHEALTH GW._
</td> </tr>
<tr>
<td>
2.2.3 What methods or software tools are needed to access the data?
</td> </tr>
<tr>
<td>
_All software tools required to access the data will be developed as
components of the CrowdHEALTH platform._
</td> </tr>
<tr>
<td>
2.2.4 Is documentation about the software needed to access the data included?
</td> </tr>
<tr>
<td>
_Not Required._
</td> </tr>
<tr>
<td>
2.2.5 Is it possible to include the relevant software?
</td> </tr>
<tr>
<td>
_N/A_
</td> </tr>
<tr>
<td>
2.2.6 Where will the data and associated metadata, documentation and code be
deposited?
</td> </tr>
<tr>
<td>
_Legal constraints do not permit us to provide open access to these data. The
data will be deposited on a local deployment of the CrowdHEALTH platform and
will only be accessible within the project._
</td> </tr>
<tr>
<td>
2.2.7 Have you explored appropriate arrangements with the identified
repository?
</td> </tr>
<tr>
<td>
_N/A_
</td> </tr>
<tr>
<td>
2.2.8 If there are restrictions on use, how will access be provided?
</td> </tr>
<tr>
<td>
_The data will only be accessible for use by authorised users of the
platform._
</td> </tr>
<tr>
<td>
2.2.9 Is there a need for a data access committee?
</td> </tr>
<tr>
<td>
_We do not foresee the need for a data access committee at the moment. This
will be verified when the process for obtaining permission by the Greek Data
Protection Authority is completed._
</td> </tr> </table>
<table>
<tr>
<th>
2.2.10 Are there well described conditions for access?
</th> </tr>
<tr>
<td>
_No_
</td> </tr>
<tr>
<td>
2.2.11 How will the identity of the person accessing the data be ascertained?
</td> </tr>
<tr>
<td>
_This will be handled by the CrowdHEALTH user authentication and access
control components._
</td> </tr>
<tr>
<td>
**2.3 Making data interoperable**
</td> </tr>
<tr>
<td>
2.3.1 Are the data produced in the project interoperable?
</td> </tr>
<tr>
<td>
_The data are currently in custom format, but will be shared within the
project in FHIR format._
</td> </tr>
<tr>
<td>
2.3.2 What data and metadata vocabularies, standards or methodologies will you
follow to make your data interoperable?
</td> </tr>
<tr>
<td>
_At the moment, the dataset does not utilize any standard. A standard may be
applied depending on the standard that will eventually be used for dataset 6._
</td> </tr>
<tr>
<td>
2.3.3 Will you be using standard vocabularies for all data types present in
your data set, to allow interdisciplinary interoperability?
</td> </tr>
<tr>
<td>
_It is not certain at the moment._
</td> </tr>
<tr>
<td>
2.3.4 In case it is unavoidable that you use uncommon or generate project
specific ontologies or vocabularies, will you provide mappings to more
commonly used ontologies?
</td> </tr>
<tr>
<td>
_There is no need to provide mappings._
</td> </tr>
<tr>
<td>
**2.4 Increase data re-use (through clarifying licenses)**
</td> </tr>
<tr>
<td>
2.4.1 How will the data be licensed to permit the widest re-use possible?
</td> </tr>
<tr>
<td>
_The data will not be available for re-use._
</td> </tr>
<tr>
<td>
2.4.2 When will the data be made available for re-use?
</td> </tr>
<tr>
<td>
_N/A_
</td> </tr>
<tr>
<td>
2.4.3 Are the data produced and/or used in the project useable by third
parties, in particular after the end of the project?
</td> </tr>
<tr>
<td>
_N/A_
</td> </tr>
<tr>
<td>
2.4.4 How long is it intended that the data remains re-usable?
</td> </tr>
<tr>
<td>
_N/A_
</td> </tr>
<tr>
<td>
2.4.5 Are data quality assurance processes described?
</td> </tr>
<tr>
<td>
_N/A_
</td> </tr>
<tr>
<td>
**3 Allocation of resources**
</td> </tr>
<tr>
<td>
3.1 What are the costs for making data FAIR in your project?
</td> </tr>
<tr>
<td>
_There may be costs related to e.g. legal support, but they cannot be
estimated at the moment. All other known costs are already covered by the
project._
</td> </tr>
<tr>
<td>
3.2 How will these be covered?
</td> </tr>
<tr>
<td>
_This will depend on the nature of the costs that will arise._
</td> </tr>
<tr>
<td>
3.3 Who will be responsible for data management in your project?
</td> </tr>
<tr>
<td>
_BioAssist’s CTO._
</td> </tr>
<tr>
<td>
3.4 Are the resources for long term preservation discussed?
</td> </tr>
<tr>
<td>
_This has not been discussed yet._
</td> </tr>
<tr>
<td>
**4 Data security**
</td> </tr>
<tr>
<td>
4.1 What provisions are in place for data security?
</td> </tr>
<tr>
<td>
_BioAssist has developed state-of-the-art security, business continuity and
data retention plans which are currently in place._
</td> </tr>
<tr>
<td>
4.2 Is the data safely stored in certified repositories for long term
preservation and curation?
</td> </tr>
<tr>
<td>
_At the moment, all data are stored only on BioAssist’s database._
</td> </tr>
<tr>
<td>
**5 Ethical aspects**
</td> </tr>
<tr>
<td>
5.1 Are there any ethical or legal issues that can have an impact on data
sharing?
</td> </tr>
<tr>
<td>
_Ethical issues have already been resolved. Permission from the Greek Data
Protection Authority is required before the (anonymised) dataset can be shared
with the partners. Legal restrictions do not permit open access to this
dataset._
</td> </tr>
<tr>
<td>
5.2 Is informed consent for data sharing and long term preservation included
in questionnaires dealing with personal data?
</td> </tr>
<tr>
<td>
_Yes, all patients that are supplying data to this dataset have signed
informed consent._
</td> </tr>
<tr>
<td>
**6 Other issues**
</td> </tr>
<tr>
<td>
6.1 Do you make use of other national/funder/sectorial/departmental procedures
for data management? If yes, which ones?
</td> </tr>
<tr>
<td>
_Not at the moment._
</td> </tr> </table>
## 3\. 9 DATASET 9: Allergies Dataset (BIO)
<table>
<tr>
<th>
**DATASET 9**
</th>
<th>
**NAME: Allergies Dataset**
</th> </tr>
<tr>
<td>
**1\. Data Summary**
</td> </tr>
<tr>
<td>
1.1 Purpose
</td> </tr>
<tr>
<td>
_Users of the BioAssist platform can manually input their allergies, adding
useful information for attending doctors. These data can be utilized for
patient profiling._
</td> </tr>
<tr>
<td>
1.2 Types and formats of data
</td> </tr>
<tr>
<td>
_This dataset includes recorded allergies of patients enrolled in BioAssist’s
pilots. Allergy data can be considered static. Entries are allergens in text
format._
</td> </tr>
<tr>
<td>
1.3 Re-use of existing data
</td> </tr>
<tr>
<td>
_This is a static dataset. Only existing data will be used._
</td> </tr>
<tr>
<td>
1.4 Origin
</td> </tr>
<tr>
<td>
_Allergy data is mainly collected from manual inputs from doctors._
</td> </tr>
<tr>
<td>
1.5 Expected size
</td> </tr>
<tr>
<td>
_This dataset does not contain a large number of records, as very few patients
enrolled in BioAssist’s pilots have reported allergies. However, this data
will be useful when linked to the other datasets._
</td> </tr>
<tr>
<td>
1.6 Data utility
</td> </tr>
<tr>
<td>
_Patients’ attending doctors._
</td> </tr>
<tr>
<td>
**2 FAIR Data**
</td> </tr>
<tr>
<td>
**2.1 Making data findable, including provisions for metadata**
</td> </tr>
<tr>
<td>
2.1.1 Are the data produced and/or used in the project discoverable with
metadata, identifiable and locatable by means of a standard identification
mechanism?
</td> </tr>
<tr>
<td>
_No._
</td> </tr>
<tr>
<td>
2.1.2 What naming conventions do you follow?
</td> </tr>
<tr>
<td>
_No naming conventions are followed at the moment._
</td> </tr>
<tr>
<td>
2.1.3 Will search keywords be provided that optimize possibilities for re-use?
</td> </tr>
<tr>
<td>
_-_
</td> </tr> </table>
<table>
<tr>
<th>
2.1.4 Do you provide clear version numbers?
</th> </tr>
<tr>
<td>
_No_
</td> </tr>
<tr>
<td>
2.1.5 What metadata will be created?
</td> </tr>
<tr>
<td>
_No metadata are created._
</td> </tr>
<tr>
<td>
**2.2 Making data openly accessible**
</td> </tr>
<tr>
<td>
2.2.1 Which data produced and/or used in the project will be made openly
available as the default?
</td> </tr>
<tr>
<td>
_The dataset shall become available to all partners after anonymization, when
permission by the Greek Data Protection Authority is obtained._
</td> </tr>
<tr>
<td>
2.2.2 How will the data be made accessible?
</td> </tr>
<tr>
<td>
_The data will be pushed to the CrowdHEALTH GW._
</td> </tr>
<tr>
<td>
2.2.3 What methods or software tools are needed to access the data?
</td> </tr>
<tr>
<td>
_All software tools required to access the data will be developed as
components of the CrowdHEALTH platform._
</td> </tr>
<tr>
<td>
2.2.4 Is documentation about the software needed to access the data included?
</td> </tr>
<tr>
<td>
_Not Required._
</td> </tr>
<tr>
<td>
2.2.5 Is it possible to include the relevant software?
</td> </tr>
<tr>
<td>
_N/A_
</td> </tr>
<tr>
<td>
2.2.6 Where will the data and associated metadata, documentation and code be
deposited?
</td> </tr>
<tr>
<td>
_Legal constraints do not permit us to provide open access to these data. The
data will be deposited on a local deployment of the CrowdHEALTH platform and
will be accessible only within the project._
</td> </tr>
<tr>
<td>
2.2.7 Have you explored appropriate arrangements with the identified
repository?
</td> </tr>
<tr>
<td>
_N/A_
</td> </tr>
<tr>
<td>
2.2.8 If there are restrictions on use, how will access be provided?
</td> </tr>
<tr>
<td>
_The data will only be accessible for use to authorised users of the
platform._
</td> </tr>
<tr>
<td>
2.2.9 Is there a need for a data access committee?
</td> </tr>
<tr>
<td>
_We do not foresee the need for a data access committee at the moment. This
will be verified when the process for obtaining permission by the Greek Data
Protection Authority is completed._
</td> </tr>
<tr>
<td>
2.2.10 Are there well described conditions for access?
</td> </tr>
<tr>
<td>
_No._
</td> </tr> </table>
<table>
<tr>
<th>
2.2.11 How will the identity of the person accessing the data be ascertained?
</th> </tr>
<tr>
<td>
_This will be handled by the CrowdHEALTH user authentication and access
control components._
</td> </tr>
<tr>
<td>
**2.3 Making data interoperable**
</td> </tr>
<tr>
<td>
2.3.1 Are the data produced in the project interoperable?
</td> </tr>
<tr>
<td>
_The data are currently in custom format, but will be shared within the
project in FHIR format._
</td> </tr>
<tr>
<td>
2.3.2 What data and metadata vocabularies, standards or methodologies will you
follow to make your data interoperable?
</td> </tr>
<tr>
<td>
_At the moment, the dataset does not utilize any standard. A standard may be
applied depending on the standard that will eventually be used for dataset 6._
</td> </tr>
<tr>
<td>
2.3.3 Will you be using standard vocabularies for all data types present in
your data set, to allow interdisciplinary interoperability?
</td> </tr>
<tr>
<td>
_Yes._
</td> </tr>
<tr>
<td>
2.3.4 In case it is unavoidable that you use uncommon or generate project
specific ontologies or vocabularies, will you provide mappings to more
commonly used ontologies?
</td> </tr>
<tr>
<td>
There is no need to provide mappings.
</td> </tr>
<tr>
<td>
**2.4 Increase data re-use (through clarifying licenses)**
</td> </tr>
<tr>
<td>
2.4.1 How will the data be licensed to permit the widest re-use possible?
</td> </tr>
<tr>
<td>
_The data will not be available for re-use._
</td> </tr>
<tr>
<td>
2.4.2 When will the data be made available for re-use?
</td> </tr>
<tr>
<td>
_N/A_
</td> </tr>
<tr>
<td>
2.4.3 Are the data produced and/or used in the project useable by third
parties, in particular after the end of the project?
</td> </tr>
<tr>
<td>
_N/A_
</td> </tr>
<tr>
<td>
2.4.4 How long is it intended that the data remains re-usable?
</td> </tr>
<tr>
<td>
_N/A_
</td> </tr>
<tr>
<td>
2.4.5 Are data quality assurance processes described?
</td> </tr>
<tr>
<td>
_N/A_
</td> </tr>
<tr>
<td>
**3 Allocation of resources**
</td> </tr>
<tr>
<td>
3.1 What are the costs for making data FAIR in your project?
</td> </tr>
<tr>
<td>
_There may be costs related to e.g. legal support, but they cannot be
estimated at the moment. All other known costs are already covered by the
project._
</td> </tr>
<tr>
<td>
3.2 How will these be covered?
</td> </tr>
<tr>
<td>
_This will depend on the nature of the costs that will arise._
</td> </tr>
<tr>
<td>
3.3 Who will be responsible for data management in your project?
</td> </tr>
<tr>
<td>
_BioAssist’s CTO._
</td> </tr>
<tr>
<td>
3.4 Are the resources for long term preservation discussed?
</td> </tr>
<tr>
<td>
_This has not been discussed yet._
</td> </tr>
<tr>
<td>
**4 Data security**
</td> </tr>
<tr>
<td>
4.1 What provisions are in place for data security?
</td> </tr>
<tr>
<td>
_BioAssist has developed state-of-the-art security, business continuity and
data retention plans which are currently in place._
</td> </tr>
<tr>
<td>
4.2 Is the data safely stored in certified repositories for long term
preservation and curation?
</td> </tr>
<tr>
<td>
_At the moment, all data are stored only on BioAssist’s database._
</td> </tr>
<tr>
<td>
**5 Ethical aspects**
</td> </tr>
<tr>
<td>
5.1 Are there any ethical or legal issues that can have an impact on data
sharing?
</td> </tr>
<tr>
<td>
_Ethical issues have already been resolved. Permission from the Greek Data
Protection Authority is required before the (anonymised) dataset can be shared
with the partners. Legal restrictions do not permit open access to this
dataset._
</td> </tr>
<tr>
<td>
5.2 Is informed consent for data sharing and long term preservation included
in questionnaires dealing with personal data?
</td> </tr>
<tr>
<td>
_Yes, all patients that are supplying data to this dataset have signed
informed consent._
</td> </tr>
<tr>
<td>
**6 Other issues**
</td> </tr>
<tr>
<td>
6.1 Do you make use of other national/funder/sectorial/departmental procedures
for data management? If yes, which ones?
</td> </tr>
<tr>
<td>
_Not at the moment._
</td> </tr> </table>
## 3\. 10 DATASET 10: Social Data dataset (BIO)
<table>
<tr>
<th>
**DATASET 10**
</th>
<th>
**NAME: Social Data dataset**
</th> </tr>
<tr>
<td>
**1\. Data Summary**
</td> </tr>
<tr>
<td>
1.1 Purpose
</td> </tr>
<tr>
<td>
_Social data collected from the BioAssist platform provide insights into the
patients’ behaviour, lifestyle and wellbeing. They can be utilised for patient
profiling, as well as assessment of policies targeting patient behaviour._
</td> </tr>
<tr>
<td>
1.2 Types and formats of data
</td> </tr>
<tr>
<td>
_This dataset includes logs of the interaction of BIO’s pilot users with the
platform’s social features. These logs provide information on contact
management (e.g. number of people in contact list, number of invitations
sent), videocalls (i.e. time, duration, frequency), events (e.g.
notifications) and multimedia content shared (e.g. number of files uploaded by
the patient, number of files uploaded by the patient’s contacts)._
</td> </tr>
<tr>
<td>
1.3 Re-use of existing data
</td> </tr>
<tr>
<td>
_This dataset includes historical data which are continuously updated. All
currently available data will be use and new data will be periodically
imported to the CrowdHEALTH platform throughout the course of the project._
</td> </tr>
<tr>
<td>
1.4 Origin
</td> </tr>
<tr>
<td>
_Social data are collected automatically by the BioAssist platform._
</td> </tr>
<tr>
<td>
1.5 Expected size
</td> </tr>
<tr>
<td>
_The dataset contains hundreds of thousands of records._
</td> </tr>
<tr>
<td>
1.6 Data utility
</td> </tr>
<tr>
<td>
_Policy makers and caregivers._
</td> </tr>
<tr>
<td>
**2 FAIR Data**
</td> </tr>
<tr>
<td>
**2.1 Making data findable, including provisions for metadata**
</td> </tr>
<tr>
<td>
2.1.1 Are the data produced and/or used in the project discoverable with
metadata, identifiable and locatable by means of a standard identification
mechanism?
</td> </tr>
<tr>
<td>
_No._
</td> </tr>
<tr>
<td>
2.1.2 What naming conventions do you follow?
</td> </tr>
<tr>
<td>
_No naming conventions are followed at the moment._
</td> </tr> </table>
<table>
<tr>
<th>
2.1.3 Will search keywords be provided that optimize possibilities for re-use?
</th> </tr>
<tr>
<td>
_-_
</td> </tr>
<tr>
<td>
2.1.4 Do you provide clear version numbers?
</td> </tr>
<tr>
<td>
_No_
</td> </tr>
<tr>
<td>
2.1.5 What metadata will be created?
</td> </tr>
<tr>
<td>
_-_
</td> </tr>
<tr>
<td>
**2.2 Making data openly accessible**
</td> </tr>
<tr>
<td>
2.2.1 Which data produced and/or used in the project will be made openly
available as the default?
</td> </tr>
<tr>
<td>
_The dataset shall become available to the consortium after anonymization,
when permission by the Greek Data Protection Authority is obtained. Legal
restrictions do not permit open access to the data._
</td> </tr>
<tr>
<td>
2.2.2 How will the data be made accessible?
</td> </tr>
<tr>
<td>
_The data will be pushed to the CrowdHEALTH GW._
</td> </tr>
<tr>
<td>
2.2.3 What methods or software tools are needed to access the data?
</td> </tr>
<tr>
<td>
_All software tools required to access the data will be developed as
components of the CrowdHEALTH platform._
</td> </tr>
<tr>
<td>
2.2.4 Is documentation about the software needed to access the data included?
</td> </tr>
<tr>
<td>
_Not Required._
</td> </tr>
<tr>
<td>
2.2.5 Is it possible to include the relevant software?
</td> </tr>
<tr>
<td>
_N/A_
</td> </tr>
<tr>
<td>
2.2.6 Where will the data and associated metadata, documentation and code be
deposited?
</td> </tr>
<tr>
<td>
_Legal constraints do not permit us to provide open access to these data. The
data will be deposited on a local deployment of the CrowdHEALTH platform and
will be accessible only within the project._
</td> </tr>
<tr>
<td>
2.2.7 Have you explored appropriate arrangements with the identified
repository?
</td> </tr>
<tr>
<td>
_N/A_
</td> </tr>
<tr>
<td>
2.2.8 If there are restrictions on use, how will access be provided?
</td> </tr>
<tr>
<td>
_The data will only be accessible for use to authorised users of the
platform._
</td> </tr>
<tr>
<td>
2.2.9 Is there a need for a data access committee?
</td> </tr>
<tr>
<td>
_We do not foresee the need for a data access committee at the moment. This
will be verified when the process for obtaining permission by the Greek Data
Protection Authority is completed._
</td> </tr> </table>
<table>
<tr>
<th>
2.2.10 Are there well described conditions for access?
</th> </tr>
<tr>
<td>
_No._
</td> </tr>
<tr>
<td>
2.2.11 How will the identity of the person accessing the data be ascertained?
</td> </tr>
<tr>
<td>
_This will be handled by the CrowdHEALTH user authentication and access
control components._
</td> </tr>
<tr>
<td>
**2.3 Making data interoperable**
</td> </tr>
<tr>
<td>
2.3.1 Are the data produced in the project interoperable?
</td> </tr>
<tr>
<td>
_Currently the data are structured in a custom format for internal use in our
systems. A new data model for sharing this data with CrowdHEALTH is being
developed._
</td> </tr>
<tr>
<td>
2.3.2 What data and metadata vocabularies, standards or methodologies will you
follow to make your data interoperable?
</td> </tr>
<tr>
<td>
_At the moment, the dataset does not utilize any standard._
</td> </tr>
<tr>
<td>
2.3.3 Will you be using standard vocabularies for all data types present in
your data set, to allow interdisciplinary interoperability?
</td> </tr>
<tr>
<td>
_This will be determined at a later stage._
</td> </tr>
<tr>
<td>
2.3.4 In case it is unavoidable that you use uncommon or generate project
specific ontologies or vocabularies, will you provide mappings to more
commonly used ontologies?
</td> </tr>
<tr>
<td>
_This will be determined at a later stage._
</td> </tr>
<tr>
<td>
**2.4 Increase data re-use (through clarifying licenses)**
</td> </tr>
<tr>
<td>
2.4.1 How will the data be licensed to permit the widest re-use possible?
</td> </tr>
<tr>
<td>
_The data will not be available for re-use._
</td> </tr>
<tr>
<td>
2.4.2 When will the data be made available for re-use?
</td> </tr>
<tr>
<td>
_N/A_
</td> </tr>
<tr>
<td>
2.4.3 Are the data produced and/or used in the project useable by third
parties, in particular after the end of the project?
</td> </tr>
<tr>
<td>
_N/A_
</td> </tr>
<tr>
<td>
2.4.4 How long is it intended that the data remains re-usable?
</td> </tr>
<tr>
<td>
_N/A_
</td> </tr>
<tr>
<td>
2.4.5 Are data quality assurance processes described?
</td> </tr>
<tr>
<td>
_N/A_
</td> </tr>
<tr>
<td>
**3 Allocation of resources**
</td> </tr>
<tr>
<td>
3.1 What are the costs for making data FAIR in your project?
</td> </tr>
<tr>
<td>
_There may be costs related to e.g. legal support, but they cannot be
estimated at the moment. All other known costs are already covered by the
project._
</td> </tr>
<tr>
<td>
3.2 How will these be covered?
</td> </tr>
<tr>
<td>
_This will depend on the nature of the costs that will arise._
</td> </tr>
<tr>
<td>
3.3 Who will be responsible for data management in your project?
</td> </tr>
<tr>
<td>
_The company’s CTO._
</td> </tr>
<tr>
<td>
3.4 Are the resources for long term preservation discussed?
</td> </tr>
<tr>
<td>
_This has not been discussed yet._
</td> </tr>
<tr>
<td>
**4 Data security**
</td> </tr>
<tr>
<td>
4.1 What provisions are in place for data security?
</td> </tr>
<tr>
<td>
_BioAssist has developed state-of-the-art security, business continuity and
data retention plans which are currently in place._
</td> </tr>
<tr>
<td>
4.2 Is the data safely stored in certified repositories for long term
preservation and curation?
</td> </tr>
<tr>
<td>
_At the moment, all data are stored only on BioAssist’s database._
</td> </tr>
<tr>
<td>
**5 Ethical aspects**
</td> </tr>
<tr>
<td>
5.1 Are there any ethical or legal issues that can have an impact on data
sharing?
</td> </tr>
<tr>
<td>
_Ethical issues have already been resolved. Permission from the Greek Data
Protection Authority is required before the (anonymised) dataset can be shared
with the partners. Legal restrictions do not permit open access to this
dataset._
</td> </tr>
<tr>
<td>
5.2 Is informed consent for data sharing and long term preservation included
in questionnaires dealing with personal data?
</td> </tr>
<tr>
<td>
_Yes, all patients that are supplying data to this dataset have signed
informed consent._
</td> </tr>
<tr>
<td>
**6 Other issues**
</td> </tr>
<tr>
<td>
6.1 Do you make use of other national/funder/sectorial/departmental procedures
for data management? If yes, which ones?
</td> </tr>
<tr>
<td>
_Not at the moment._
</td> </tr> </table>
## 3\. 11 DATASET 11: CancerPatientData Dataset (CRA)
<table>
<tr>
<th>
**DATASET 11**
</th>
<th>
**CancerPatientData Dataset (CareAcross)**
</th> </tr>
<tr>
<td>
**1\. Data Summary**
</td> </tr>
<tr>
<td>
1.1 Purpose
</td> </tr>
<tr>
<td>
_The data is collected from patients in order to receive personalised coaching
and medical information. These are central themes for our use case, in terms
of coaching adherence & information impact. _
</td> </tr>
<tr>
<td>
1.2 Types and formats of data
</td> </tr>
<tr>
<td>
_They are structured data about patient’s daily life and information received.
Mostly consisting of numbers of food portions or Boolean flags about specific
diagnoses and treatments._
</td> </tr>
<tr>
<td>
1.3 Re-use of existing data
</td> </tr>
<tr>
<td>
_No_
</td> </tr>
<tr>
<td>
1.4 Origin
</td> </tr>
<tr>
<td>
_Patients_
</td> </tr>
<tr>
<td>
1.5 Expected size
</td> </tr>
<tr>
<td>
_For each patient, we expect to collect about 30 data points. It is expected
that 1,000 patients will have entered data throughout this project._
</td> </tr>
<tr>
<td>
1.6 Data utility
</td> </tr>
<tr>
<td>
_On an individual level, to the patient. On a research level, to health care
professionals and policy makers._
</td> </tr>
<tr>
<td>
**2 FAIR Data**
</td> </tr>
<tr>
<td>
**2.1 Making data findable, including provisions for metadata**
</td> </tr>
<tr>
<td>
2.1.1 Are the data produced and/or used in the project discoverable with
metadata, identifiable and locatable by means of a standard identification
mechanism?
</td> </tr>
<tr>
<td>
_Yes: input is defined as a Question and the possible answers are put down as
Answers._
</td> </tr>
<tr>
<td>
2.1.2 What naming conventions do you follow?
</td> </tr>
<tr>
<td>
_All Questions and Answers have “value”s._
</td> </tr>
<tr>
<td>
2.1.3 Will search keywords be provided that optimize possibilities for re-use?
</td> </tr>
<tr>
<td>
_No_
</td> </tr> </table>
<table>
<tr>
<th>
2.1.4 Do you provide clear version numbers?
</th> </tr>
<tr>
<td>
_No_
</td> </tr>
<tr>
<td>
2.1.5 What metadata will be created?
</td> </tr>
<tr>
<td>
_N/A_
</td> </tr>
<tr>
<td>
**2.2 Making data openly accessible**
</td> </tr>
<tr>
<td>
2.2.1 Which data produced and/or used in the project will be made openly
available as the default?
</td> </tr>
<tr>
<td>
_The data subjects are patients themselves who have agreed to provide data in
a way that their individual records do not get shared beyond the purposes of
this service, and not beyond the confines of the company._
_Aggregated data will be made available to the CrowdHEALTH platform, but not
open to the public or otherwise beyond the scope of this project._
</td> </tr>
<tr>
<td>
2.2.2 How will the data be made accessible?
</td> </tr>
<tr>
<td>
_N/A_
</td> </tr>
<tr>
<td>
2.2.3 What methods or software tools are needed to access the data?
</td> </tr>
<tr>
<td>
_N/A_
</td> </tr>
<tr>
<td>
2.2.4 Is documentation about the software needed to access the data included?
</td> </tr>
<tr>
<td>
_N/A_
</td> </tr>
<tr>
<td>
2.2.5 Is it possible to include the relevant software?
</td> </tr>
<tr>
<td>
_N/A_
</td> </tr>
<tr>
<td>
2.2.6 Where will the data and associated metadata, documentation and code be
deposited?
</td> </tr>
<tr>
<td>
_N/A_
</td> </tr>
<tr>
<td>
2.2.7 Have you explored appropriate arrangements with the identified
repository?
</td> </tr>
<tr>
<td>
_N/A_
</td> </tr>
<tr>
<td>
2.2.8 If there are restrictions on use, how will access be provided?
</td> </tr>
<tr>
<td>
_N/A_
</td> </tr>
<tr>
<td>
2.2.9 Is there a need for a data access committee?
</td> </tr>
<tr>
<td>
_N/A_
</td> </tr> </table>
<table>
<tr>
<th>
2.2.10 Are there well described conditions for access?
</th> </tr>
<tr>
<td>
_N/A_
</td> </tr>
<tr>
<td>
2.2.11 How will the identity of the person accessing the data be ascertained?
</td> </tr>
<tr>
<td>
_N/A_
</td> </tr>
<tr>
<td>
**2.3 Making data interoperable**
</td> </tr>
<tr>
<td>
2.3.1 Are the data produced in the project interoperable?
</td> </tr>
<tr>
<td>
_No specific standard exists for the variety and breadth of data we are
collecting. However, the structure is such that the data can be
interoperable._
_On the other hand, as discussed, the individual data will be contained within
the Use Case and only aggregated data will be shared with the central
CrowdHEALTH platform._
_The aggregated data will be amenable to further analysis by the CrowdHEALTH
platform. This data will be exported in a structure which will contain field-
value pairs; the exact structure of that export cannot be determined in
advance, because the level of detail and aggregation depends on the findings
of the analysis performed within the Use Case._
</td> </tr>
<tr>
<td>
2.3.2 What data and metadata vocabularies, standards or methodologies will you
follow to make your data interoperable?
</td> </tr>
<tr>
<td>
_The extent of such standards followed for the aggregated data structures will
depend on the findings. We expect these to evolve as the data gets richer._
</td> </tr>
<tr>
<td>
2.3.3 Will you be using standard vocabularies for all data types present in
your data set, to allow interdisciplinary interoperability?
</td> </tr>
<tr>
<td>
_N/A_
</td> </tr>
<tr>
<td>
2.3.4 In case it is unavoidable that you use uncommon or generate project
specific ontologies or vocabularies, will you provide mappings to more
commonly used ontologies?
</td> </tr>
<tr>
<td>
_The data entered is structured, but it is patient-oriented; therefore,
mappings can be provided where necessary. However, such formal ontologies are
usually created in a system-orientation. For example, “dry mouth” and “mouth
sores” mean completely different things to the patient, but may actually be
reflected onto one single element (as opposed to two elements) within an
ontology._
</td> </tr>
<tr>
<td>
**2.4 Increase data re-use (through clarifying licenses)**
</td> </tr>
<tr>
<td>
2.4.1 How will the data be licensed to permit the widest re-use possible?
</td> </tr>
<tr>
<td>
_Data will not be licensed but will remain property of CareAcross._
</td> </tr>
<tr>
<td>
2.4.2 When will the data be made available for re-use?
</td> </tr>
<tr>
<td>
_N/A_
</td> </tr> </table>
<table>
<tr>
<th>
2.4.3 Are the data produced and/or used in the project useable by third
parties, in particular after the end of the project?
</th> </tr>
<tr>
<td>
_No_
</td> </tr>
<tr>
<td>
2.4.4 How long is it intended that the data remains re-usable?
</td> </tr>
<tr>
<td>
_N/A_
</td> </tr>
<tr>
<td>
2.4.5 Are data quality assurance processes described?
</td> </tr>
<tr>
<td>
_N/A_
</td> </tr>
<tr>
<td>
**3 Allocation of resources**
</td> </tr>
<tr>
<td>
3.1 What are the costs for making data FAIR in your project?
</td> </tr>
<tr>
<td>
_We can approximate that the costs to construct such datasets will be
moderate, if their provision is as frequent as already discussed with partners
managing the centralised repository (no more frequent than every 6 months)._
</td> </tr>
<tr>
<td>
3.2 How will these be covered?
</td> </tr>
<tr>
<td>
_The efforts for aggregated data that will be included in an open access
scheme, will be covered as part of the corresponding grant portion. The rest
will be covered by the company._
</td> </tr>
<tr>
<td>
3.3 Who will be responsible for data management in your project?
</td> </tr>
<tr>
<td>
_The company CEO (Thanos Kosmidis)_
</td> </tr>
<tr>
<td>
3.4 Are the resources for long term preservation discussed?
</td> </tr>
<tr>
<td>
_Data is preserved indefinitely._
</td> </tr>
<tr>
<td>
**4 Data security**
</td> </tr>
<tr>
<td>
4.1 What provisions are in place for data security?
</td> </tr>
<tr>
<td>
_Best practices are being used for data security, using state-of-the-art
commercial cloud providers. Frequent backups are being scheduled. The current
commercial cloud provider is Heroku (powered by Salesforce)._
</td> </tr>
<tr>
<td>
4.2 Is the data safely stored in certified repositories for long term
preservation and curation?
</td> </tr>
<tr>
<td>
_Data is safely stored in state of the art cloud-based repositories._
</td> </tr>
<tr>
<td>
**5 Ethical aspects**
</td> </tr>
<tr>
<td>
5.1 Are there any ethical or legal issues that can have an impact on data
sharing?
</td> </tr>
<tr>
<td>
_These data sets consist of sensitive patient data, contributed by the
patients themselves. The Terms of our Service and our Privacy Policy restrict
the level of sharing, only to an aggregate level (as discussed above and
beyond)._
</td> </tr>
<tr>
<td>
5.2 Is informed consent for data sharing and long term preservation included
in questionnaires dealing with personal data?
</td> </tr>
<tr>
<td>
_Patients agree with the aforementioned Terms of Service and Privacy Policy as
part of the registration process._
</td> </tr>
<tr>
<td>
**6 Other issues**
</td> </tr>
<tr>
<td>
6.1 Do you make use of other national/funder/sectorial/departmental procedures
for data management? If yes, which ones?
</td> </tr>
<tr>
<td>
_N/A_
</td> </tr> </table>
## 3\. 12 DATASET 12: SLOfit Dataset - (ULJ)
<table>
<tr>
<th>
**DATASET 12**
</th>
<th>
**NAME: SLOfit (ULJ)**
</th> </tr>
<tr>
<td>
**1\. Data Summary**
</td> </tr>
<tr>
<td>
1.1 Purpose
</td> </tr>
<tr>
<td>
_Purpose of the SLOfit data collection is:_
* _to enable children and parents to compare their somatic and physical fitness development with the development of their peers and identify the needs for improvement_
* _to permit teachers to use the analysed data to identify children with special developmental needs, to follow the development of every individual child and adjust the teaching process to the needs and capabilities of children_
* _to equip school physicians and pediatricians with holistic information from multiple sources in order to enable tailoring of custom-made interventions as well as monitoring and evaluating the effects of small scale interventions;_
* _to help policy makers in evaluating their actions by monitoring population trends in physical fitness and obesity on a national level (the SLOfit data serves as scientific backbone for most of the policies, related to improvement of physical activity of children and youth and the policies, related to school physical education)_
* _creation of a collection of data on lifestyle and social economic environment beside already collected records from standard physical fitness testing. Linking the upgraded SLOfit database with the e-health system (eHR) within the CrowdHEALTH (CH) platform will provide an integrated view of the patient. This will allow more accurate forecasting of predictive health risk of children and youth and performing causal analysis and consequently the shaping of an efficient public health policy framework and the provision of integrated healthcare services within educational and health-care system._
* _to aid policy makers in creating and evaluating policies relating to childhood obesity and physical activity by performing big data analysis on collected data. HHRs will provide an opportunity for performing causal analysis and building health risk predictive models. Any intervention such as increasing physical activity, reducing obesity or general morbidity on the level of class, school, municipality, and region or on the national level could be evaluated. Moreover, It will constitute a useful tool to directly evaluate the trends among groups and an optimal means to promptly highlight the impact of the various policies._
* _to provide illustrative visualizations of the collected data on the individual, school, regional and national level. Via the CH platform, every child, his/her parents, school physician and policy makers will be see the status and development of child’s physical fitness, physical activity and linked health risks on individual and aggregated level. The integration of predictive models into this platform would also provide policy makers with a useful tool to forecast trends, simulate effects of intervention and measuring the impact of interventions and relevant policies in the context of physical fitness, physical activity and obesity._
</td> </tr> </table>
<table>
<tr>
<th>
1.2 Types and formats of data
</th> </tr>
<tr>
<td>
_Data are stored in MS SQL database, but will be exported to Excel._
</td> </tr>
<tr>
<td>
1.3 Re-use of existing data
</td> </tr>
<tr>
<td>
_Yes, the SLOfit data already exist but data from every year of measurement
are not linked. Therefore, for the purpose of CrowdHEALTH the data will be
linked into cohort data and used in visualizations._
</td> </tr>
<tr>
<td>
1.4 Origin
</td> </tr>
<tr>
<td>
_SLOfit is a national surveillance system for somatic and physical fitness
development of children and youth in Slovenia, which was formerly known as
Sports Educational Chart. The system was implemented in 1982 on a sample of
Slovenian schools and after 5 years of testing, it was introduced to all
Slovenian primary and secondary schools. Therefore, SLOfit enables annual
monitoring of somatic and physical fitness status of children in all Slovenian
schools from 1987 onwards. Every April, almost the entire Slovenian
population, aged 6 to 19 (220,000 students) is measured by 8 fitness tests and
3 anthropometric measurements (see _www.slofit.org_ ) . _
</td> </tr>
<tr>
<td>
1.5 Expected size
</td> </tr>
<tr>
<td>
_About 200,000 students with record for every year of measurement (between 1
and 14). Every record will have about 20 values (data columns)._
</td> </tr>
<tr>
<td>
1.6 Data utility
</td> </tr>
<tr>
<td>
* _For decision makers, the SLOfit data might enable scientific backbone for most of the policies, related to improvement of physical activity of children and youth as well as policies for conquer child obesity. It might also provide proper data for health risk predictive models, based on physical fitness data, and simulation of physical activity interventions on physical fitness and prevalance of obesity._
* _For researchers, the SLOfit data might provide answers on the patterns of physical and motor development and their interrelatedness, and the relation between physical activity, sedentariness, Socio-Economic Status (SES), parental physical activity and physical fitness. The data analysis might provide answers on how much physical activity is necessary for retaining adequate level of physical fitness, and what ratios of physical activity and sedentariness still provide sufficient physical fitness. It might also provide proper data for designing individual somatic and motor development forecast._
* _For physicians, the SLOfit data on the individual level might provide holistic view on health of individual schoolchild and effects of interventions on physical fitness as important indicator of child’s health._
* _For parents, the SLOfit data on the individual level might provide alerts if the SLOfit data analyzed through the CrowdHEALTH platform suggests that recommendations for physical fitness or activity are not met that currently do not exist._
</td> </tr> </table>
<table>
<tr>
<th>
**2 FAIR Data**
</th> </tr>
<tr>
<td>
**2.1 Making data findable, including provisions for metadata**
</td> </tr>
<tr>
<td>
2.1.1 Are the data produced and/or used in the project discoverable with
metadata, identifiable and locatable by means of a standard identification
mechanism?
</td> </tr>
<tr>
<td>
_No._
</td> </tr>
<tr>
<td>
2.1.2 What naming conventions do you follow?
</td> </tr>
<tr>
<td>
_None._
</td> </tr>
<tr>
<td>
2.1.3 Will search keywords be provided that optimize possibilities for re-use?
</td> </tr>
<tr>
<td>
_No._
</td> </tr>
<tr>
<td>
2.1.4 Do you provide clear version numbers?
</td> </tr>
<tr>
<td>
_No._
</td> </tr>
<tr>
<td>
2.1.5 What metadata will be created?
</td> </tr>
<tr>
<td>
_None._
</td> </tr>
<tr>
<td>
**2.2 Making data openly accessible**
</td> </tr>
<tr>
<td>
2.2.1 Which data produced and/or used in the project will be made openly
available as the default?
</td> </tr>
<tr>
<td>
_The SLOfit dataset will not be openly available due to voluntary
restrictions. Over 30 years, the SLOfit team has invested a lot of resources
into collection of data and dataset management, so open access to data is not
fair way to exploit possibilities of dataset for the SLOfit team. Members of
the SLOfit team want to have an overview of the SLOfit dataset usage and
collaboration in possible analysis._
</td> </tr>
<tr>
<td>
2.2.2 How will the data be made accessible?
</td> </tr>
<tr>
<td>
_N/A_
</td> </tr> </table>
<table>
<tr>
<th>
2.2.3 What methods or software tools are needed to access the data?
</th> </tr>
<tr>
<td>
_N/A_
</td> </tr>
<tr>
<td>
2.2.4 Is documentation about the software needed to access the data included?
</td> </tr>
<tr>
<td>
_N/A_
</td> </tr>
<tr>
<td>
2.2.5 Is it possible to include the relevant software?
</td> </tr>
<tr>
<td>
_N/A_
</td> </tr>
<tr>
<td>
2.2.6 Where will the data and associated metadata, documentation and code be
deposited?
</td> </tr>
<tr>
<td>
_N/A_
</td> </tr>
<tr>
<td>
2.2.7 Have you explored appropriate arrangements with the identified
repository?
</td> </tr>
<tr>
<td>
_N/A_
</td> </tr>
<tr>
<td>
2.2.8 If there are restrictions on use, how will access be provided?
</td> </tr>
<tr>
<td>
_N/A_
</td> </tr>
<tr>
<td>
2.2.9 Is there a need for a data access committee?
</td> </tr>
<tr>
<td>
_N/A_
</td> </tr>
<tr>
<td>
2.2.10 Are there well described conditions for access?
</td> </tr>
<tr>
<td>
_N/A_
</td> </tr>
<tr>
<td>
2.2.11 How will the identity of the person accessing the data be ascertained?
</td> </tr>
<tr>
<td>
_N/A_
</td> </tr> </table>
<table>
<tr>
<th>
**2.3 Making data interoperable**
</th> </tr>
<tr>
<td>
2.3.1 Are the data produced in the project interoperable?
</td> </tr>
<tr>
<td>
_Yes, at least within the consortium_
</td> </tr>
<tr>
<td>
2.3.2 What data and metadata vocabularies, standards or methodologies will you
follow to make your data interoperable?
</td> </tr>
<tr>
<td>
_No standard data vocabularies will be used_
</td> </tr>
<tr>
<td>
2.3.3 Will you be using standard vocabularies for all data types present in
your data set, to allow interdisciplinary interoperability?
</td> </tr>
<tr>
<td>
_No, we will create custom codes for our data and share it with the partners
in the consortium._
</td> </tr>
<tr>
<td>
2.3.4 In case it is unavoidable that you use uncommon or generate project
specific ontologies or vocabularies, will you provide mappings to more
commonly used ontologies?
</td> </tr>
<tr>
<td>
_Yes._
</td> </tr>
<tr>
<td>
**2.4 Increase data re-use (through clarifying licenses)**
</td> </tr>
<tr>
<td>
2.4.1 How will the data be licensed to permit the widest re-use possible?
</td> </tr>
<tr>
<td>
_Copyright will be retained by the University of Ljubljana. No re-use will be
publicly available._
</td> </tr>
<tr>
<td>
2.4.2 When will the data be made available for re-use?
</td> </tr>
<tr>
<td>
_N/A_
</td> </tr>
<tr>
<td>
2.4.3 Are the data produced and/or used in the project useable by third
parties, in particular after the end of the project?
</td> </tr>
<tr>
<td>
_No._
</td> </tr>
<tr>
<td>
2.4.4 How long is it intended that the data remains re-usable?
</td> </tr>
<tr>
<td>
_N/A_
</td> </tr> </table>
<table>
<tr>
<th>
2.4.5 Are data quality assurance processes described?
</th> </tr>
<tr>
<td>
_No._
</td> </tr>
<tr>
<td>
**3 Allocation of resources**
</td> </tr>
<tr>
<td>
3.1 What are the costs for making data FAIR in your project?
</td> </tr>
<tr>
<td>
_N/A_
</td> </tr>
<tr>
<td>
3.2 How will these be covered?
</td> </tr>
<tr>
<td>
_N/A_
</td> </tr>
<tr>
<td>
3.3 Who will be responsible for data management in your project?
</td> </tr>
<tr>
<td>
_Gregor Starc, Faculty of Sport, University of Ljubljana._
</td> </tr>
<tr>
<td>
3.4 Are the resources for long term preservation discussed?
</td> </tr>
<tr>
<td>
_Cost of long term preservation of the SLOfit dataset is approximately EUR
3,000 per year. Decision is upon Faculty of Sport and Ministry of Education.
Currently, the dataset has no time limit in preservation._
</td> </tr>
<tr>
<td>
**4 Data security**
</td> </tr>
<tr>
<td>
4.1 What provisions are in place for data security?
</td> </tr>
<tr>
<td>
_Faculty of Sport, University of Ljubljana has a Data Center with both server
and storage infrastructure, which are accessible only to authorized personnel,
have fire detection system and video surveillance system, and air cooling. The
access to the data is allowed only to authorized staff with an active
directory with user logins and encrypted passwords. The Data Center has also
disk to disk backup systems._
</td> </tr>
<tr>
<td>
4.2 Is the data safely stored in certified repositories for long term
preservation and curation?
</td> </tr>
<tr>
<td>
_Yes_
</td> </tr>
<tr>
<td>
**5 Ethical aspects**
</td> </tr>
<tr>
<td>
5.1 Are there any ethical or legal issues that can have an impact on data
sharing?
</td> </tr>
<tr>
<td>
_Data are protected by the Personal data protection legislation. However,
individual consents have been acquired that allow for sharing anonymized
data._
</td> </tr>
<tr>
<td>
5.2 Is informed consent for data sharing and long term preservation included
in questionnaires dealing with personal data?
</td> </tr>
<tr>
<td>
_Yes._
</td> </tr>
<tr>
<td>
**6 Other issues**
</td> </tr>
<tr>
<td>
6.1 Do you make use of other national/funder/sectorial/departmental procedures
for data management? If yes, which ones?
</td> </tr>
<tr>
<td>
_None._
</td> </tr> </table>
### 3.13 DATASET 13: Lifestyle Dataset (ULJ)
<table>
<tr>
<th>
**DATASET 13**
</th>
<th>
**NAME: Lifestyle Dataset (ULJ)**
</th> </tr>
<tr>
<td>
**1\. Data Summary**
</td> </tr>
<tr>
<td>
1.1 Purpose
</td> </tr>
<tr>
<td>
_Purpose of the Lifestyle data collection is:_
* _to enable children and parents to compare their lifestyle habits (i.e. physical activity, sedentariness, sleep) with the recommendations and identify the needs for improvement;_
* _to equip school physicians and pediatricians with holistic information from multiple sources in order to enable tailoring of custom-made interventions as well as monitoring and evaluating the effects of small scale interventions;_
* _creation of a collection of data on lifestyle and social economic environment beside already collected records from standard physical fitness testing. Linking the upgraded SLOfit database (the SLOfit + Lifestyle database) with the e-health system (eHR) within the CrowdHEALTH (CH) platform will provide an integrated view of the patient. This will allow more accurate forecasting of predictive health risk of children and youth and performing causal analysis and consequently the shaping of an efficient public health policy framework and the provision of integrated healthcare services within educational and health-care system._
* _to aid policy makers in creating and evaluating policies relating to childhood obesity and physical activity by performing big data analysis on collected data. HHRs will provide an opportunity for performing causal analysis and building health risk predictive models. Any intervention such as increasing physical activity, reducing obesity or general morbidity on the level of class, school, municipality, and region or on the national level could be evaluated._
* _to provide illustrative visualizations of the collected data on the individual, school, regional and national level. Via the CH platform, every child, his/her parents, school physician and policy makers will be see the status and development of child’s physical fitness, physical activity and linked health risks on individual and aggregated level. The integration of predictive models into this platform would also provide policy makers with a useful tool to forecast trends, simulate effects of intervention and measuring the impact of interventions and relevant policies in the context of physical fitness, physical activity and obesity._
</td> </tr>
<tr>
<td>
1.2 Types and formats of data
</td> </tr>
<tr>
<td>
_Data will be stored in MS SQL database, but will be exported to Excel._
</td> </tr>
<tr>
<td>
1.3 Re-use of existing data
</td> </tr>
<tr>
<td>
_No, the Lifestyle data will be collected within CrowdHEALTH pilot
environment, except of physical fitness data which will be a subset of the
SLOfit database._
</td> </tr> </table>
<table>
<tr>
<th>
1.4 Origin
</th> </tr>
<tr>
<td>
_The Lifestyle dataset will be implemented on a sample of 2,000 students
within CrowdHEALTH pilot environment in Slovenia (Škofja Loka municipality).
It will include data about physical activity, sedentariness, SES, sleep,
resting heart-rate, peers, academic achievement, height and body mass of
parents, and parental physical activity. The data will be collected and
entered by students and their parents. Beside these data the Lifestyle dataset
will include also physical fitness data which will be extracted from the
SLOfit dataset._
</td> </tr>
<tr>
<td>
1.5 Expected size
</td> </tr>
<tr>
<td>
_About 2000 students with record for 2 years of measurement in the course of
the CrowdHEALTH project (2018, 2019), Every record will have about 20 values
(data columns)._
</td> </tr>
<tr>
<td>
1.6 Data utility
</td> </tr>
<tr>
<td>
_For decision makers, the Lifestyle data might enable scientific backbone for
most of the policies, related to improvement of physical activity of children
and youth as well as policies for conquer child obesity. It might also provide
proper data for health risk predictive models, based on physical fitness data,
and simulation of physical activity interventions on physical fitness and
prevalence of obesity._
_For researchers, the Lifestyle data might provide answers on the patterns of
physical and motor development and their interrelatedness, and the relation
between physical activity, sedentariness, SES, parental physical activity and
physical fitness. The data analysis might provide answers on how much physical
activity is necessary for retaining adequate level of physical fitness, and
what ratios of physical activity and sedentariness still provide sufficient
physical fitness. It might also provide proper data for designing individual
somatic and motor development forecast._
_For physicians, the Lifestyle data combined with the SLOfit data on the
individual level might provide holistic view on health of individual
schoolchild and effects of interventions on physical fitness as important
indicator of child’s health._
_For parents, the Lifestyle data on the individual level might provide alerts
if the Lifestyle data analyzed through the CrowdHEALTH platform suggests that
recommendations for physical activity, sleep or sedentary are not met that
currently do not exist._
</td> </tr>
<tr>
<td>
**2 FAIR Data**
</td> </tr>
<tr>
<td>
**2.1 Making data findable, including provisions for metadata**
</td> </tr>
<tr>
<td>
2.1.1 Are the data produced and/or used in the project discoverable with
metadata, identifiable and locatable by means of a standard identification
mechanism?
</td> </tr>
<tr>
<td>
_No._
</td> </tr> </table>
<table>
<tr>
<th>
2.1.2 What naming conventions do you follow?
</th> </tr>
<tr>
<td>
_None._
</td> </tr>
<tr>
<td>
2.1.3 Will search keywords be provided that optimize possibilities for re-use?
</td> </tr>
<tr>
<td>
_No._
</td> </tr>
<tr>
<td>
2.1.4 Do you provide clear version numbers?
</td> </tr>
<tr>
<td>
_No._
</td> </tr>
<tr>
<td>
2.1.5 What metadata will be created?
</td> </tr>
<tr>
<td>
_None._
</td> </tr>
<tr>
<td>
**2.2 Making data openly accessible**
</td> </tr>
<tr>
<td>
2.2.1 Which data produced and/or used in the project will be made openly
available as the default?
</td> </tr>
<tr>
<td>
_None._
_The Lifestyle dataset will not be openly available due to voluntary
restrictions. The dataset will present an upgrade of the SLOfit database. Over
30 years, the SLOfit team has invested a lot of resources into collection of
data and dataset management, so open access to data is not fair way to exploit
possibilities of dataset for the SLOfit team. Members of the SLOfit team want
to have an overview of the dataset usage and collaboration in possible
analysis._
</td> </tr>
<tr>
<td>
2.2.2 How will the data be made accessible?
</td> </tr>
<tr>
<td>
_N/A_
</td> </tr>
<tr>
<td>
2.2.3 What methods or software tools are needed to access the data?
</td> </tr>
<tr>
<td>
_N/A_
</td> </tr>
<tr>
<td>
2.2.4 Is documentation about the software needed to access the data included?
</td> </tr>
<tr>
<td>
_N/A_
</td> </tr> </table>
<table>
<tr>
<th>
2.2.5 Is it possible to include the relevant software?
</th> </tr>
<tr>
<td>
_N/A_
</td> </tr>
<tr>
<td>
2.2.6 Where will the data and associated metadata, documentation and code be
deposited?
</td> </tr>
<tr>
<td>
_N/A_
</td> </tr>
<tr>
<td>
2.2.7 Have you explored appropriate arrangements with the identified
repository?
</td> </tr>
<tr>
<td>
_N/A_
</td> </tr>
<tr>
<td>
2.2.8 If there are restrictions on use, how will access be provided?
</td> </tr>
<tr>
<td>
_Users will be able to access their own data via password. Aggregated data
will be publicly accessible._
</td> </tr>
<tr>
<td>
2.2.9 Is there a need for a data access committee?
</td> </tr>
<tr>
<td>
_N/A_
</td> </tr>
<tr>
<td>
2.2.10 Are there well described conditions for access?
</td> </tr>
<tr>
<td>
_N/A_
</td> </tr>
<tr>
<td>
2.2.11 How will the identity of the person accessing the data be ascertained?
</td> </tr>
<tr>
<td>
_n/a_
</td> </tr>
<tr>
<td>
**2.3 Making data interoperable**
</td> </tr>
<tr>
<td>
2.3.1 Are the data produced in the project interoperable?
</td> </tr>
<tr>
<td>
_Yes, at least within the consortium._
</td> </tr>
<tr>
<td>
2.3.2 What data and metadata vocabularies, standards or methodologies will you
follow to make your data interoperable?
</td> </tr>
<tr>
<td>
_No standard data vocabularies will be used._
</td> </tr> </table>
<table>
<tr>
<th>
2.3.3 Will you be using standard vocabularies for all data types present in
your data set, to allow interdisciplinary interoperability?
</th> </tr>
<tr>
<td>
_No, we will create custom codes for our data and share it with the partners
in the consortium._
</td> </tr>
<tr>
<td>
2.3.4 In case it is unavoidable that you use uncommon or generate project
specific ontologies or vocabularies, will you provide mappings to more
commonly used ontologies?
</td> </tr>
<tr>
<td>
_Yes._
</td> </tr>
<tr>
<td>
**2.4 Increase data re-use (through clarifying licenses)**
</td> </tr>
<tr>
<td>
2.4.1 How will the data be licensed to permit the widest re-use possible?
</td> </tr>
<tr>
<td>
_Copyright will be retained by the University of Ljubljana. No re-use will be
publicly available._
</td> </tr>
<tr>
<td>
2.4.2 When will the data be made available for re-use?
</td> </tr>
<tr>
<td>
_N/A_
</td> </tr>
<tr>
<td>
2.4.3 Are the data produced and/or used in the project useable by third
parties, in particular after the end of the project?
</td> </tr>
<tr>
<td>
_No_
</td> </tr>
<tr>
<td>
2.4.4 How long is it intended that the data remains re-usable?
</td> </tr>
<tr>
<td>
_N/A_
</td> </tr>
<tr>
<td>
2.4.5 Are data quality assurance processes described?
</td> </tr>
<tr>
<td>
_No._
</td> </tr>
<tr>
<td>
**3 Allocation of resources**
</td> </tr>
<tr>
<td>
3.1 What are the costs for making data FAIR in your project?
</td> </tr>
<tr>
<td>
_None_
</td> </tr> </table>
<table>
<tr>
<th>
3.2 How will these be covered?
</th> </tr>
<tr>
<td>
_N/A_
</td> </tr>
<tr>
<td>
_3.3 Who will be responsible for data management in your project?_
</td> </tr>
<tr>
<td>
_Gregor Starc, Faculty of Sport, University of Ljubljana._
</td> </tr>
<tr>
<td>
3.4 Are the resources for long term preservation discussed?
</td> </tr>
<tr>
<td>
_There will be no additional costs for long term preservation if the Lifestyle
data will be integrated into the SLOfit database. Decision on data keeping is
upon Faculty of Sport and Ministry of Education. Currently, the dataset has no
time limit in preservation._
</td> </tr>
<tr>
<td>
**4 Data security**
</td> </tr>
<tr>
<td>
4.1 What provisions are in place for data security?
</td> </tr>
<tr>
<td>
_Faculty of Sport, University of Ljubljana has a Data Center with both server
and storage infrastructure, which are accessible only to authorized personnel,
have fire detection system and video surveillance system, and air cooling. The
access to the data is allowed only to authorized staff with an active
directory with user logins and encrypted passwords. The Data Center has also
disk to disk backup systems._
</td> </tr>
<tr>
<td>
4.2 Is the data safely stored in certified repositories for long term
preservation and curation?
</td> </tr>
<tr>
<td>
_Yes._
</td> </tr>
<tr>
<td>
**5 Ethical aspects**
</td> </tr>
<tr>
<td>
5.1 Are there any ethical or legal issues that can have an impact on data
sharing?
</td> </tr>
<tr>
<td>
_Data are protected by the Personal data protection legislation. However,
individual consents have been acquired that allow for sharing anonymised
data._
</td> </tr>
<tr>
<td>
5.2 Is informed consent for data sharing and long term preservation included
in questionnaires dealing with personal data?
</td> </tr>
<tr>
<td>
_Yes._
</td> </tr>
<tr>
<td>
**6 Other issues**
</td> </tr>
<tr>
<td>
6.1 Do you make use of other national/funder/sectorial/departmental procedures
for data management? If yes, which ones?
</td> </tr>
<tr>
<td>
_No_
</td> </tr> </table>
# 4\. Conclusion remarks
It is important to highlight that using easier questionnaires previous to the
FAIR Data Management Plan template in projects with complex and non-homogenous
datasets could be a best practice; it helps to prevent delays in the starting
of technical work into the project and at the same time helps to the User
Cases groups to reflect about some basic questions about their datasets for
the Open Data Pilot in H2020 previously to complete the DMP template.
This gradual effort took more of time than foreseen in the first months of the
project but it is proving to be mutually beneficial for the work progress of
both the technical and the use cases groups.
In November 2017 already initial datasets (HULAFE and SLOfit) have been made
available within the consortium for research and analysis. Additional datasets
are expected to be available by the end of 2017, while a complete plan has
been developed in order to make all datasets available within 2018.
|
https://phaidra.univie.ac.at/o:1140797
|
Horizon 2020
|
1186_IASIS_727658.md
|
# 1 Introduction
## 1.1 Purpose and scope of the document
Heterogeneous data sources are integrated into the iASiS knowledge graph in
order to offer an integrated view of data presented in different formats, e.g.
clinical notes, images, publications, web data. Diverse techniques are
implemented for transforming these heterogeneous data into a unified schema,
and curate and integrate this data. Furthermore, techniques for traversing and
exploring the iASiS knowledge graph are demanded in order to exploit the
benefits of the knowledge graph. This document describes the data management
techniques implemented in iASiS to transform data in diverse formats into the
unified schema of the knowledge graph; these techniques are the result of task
T5.4 of WP5. Moreover, this document presents the query processing techniques
implemented by Ontario, which is a federated SPARQL query engine able to
explore the iASiS knowledge graph and their knowledge graphs. This document is
structured as follows: Section 2 presents RML, a mapping language for
transforming raw data into RDF triples, as well as the SPARQL query language;
main characteristics of existing federated query approaches are also
described. Section 3 presents the semantic enrichment process implemented in
iASiS, as well as the main characteristics of the current version of the iASiS
knowledge graph. Section 4 outlines the main features of Ontario and
illustrates how it is integrated into the iASiS framework. Finally, the
conclusions and outcomes reached during the execution of tasks T5.4 and T5.5
are outlined, as well as the next steps to be followed in WP5.
## 1.2 Relationship with other documents
This document is related to the following deliverables of WP5: i) D5.1 where
the iASiS data sources are defined; ii) D5.5 where the privacy-aware
techniques are reported presented; iii) D5.6 where the final version of the
privacy aware strategies will be described; and iv) D5.7, D5.8, and D5.9 where
the iASiS data management and analytics services will be reported.
# 2 Background
## 2.1 RML, a Mapping Language for RDF
Big Data is usually presented in different formats, e.g. images, unstructured
text, or tabular, being required the definition of mapping rules in order to
transform data in these diverse formats into a unified schema. RML 1 is one
of the existing mapping languages; it expresses mappings to transform sources
represented in tabular format, e.g. CSV or relational, into RDF. Each mapping
rule in RML is represented as a **Triple Map** which consists of the following
parts; Figure 1 presents an overview of the main concepts of the RML mapping
language.
* A **Logical Source** (rr:logicalSource) that refers to a data source from where data is collected; it is composed of the following components:
1. **Source** (rml:source) - is an input source, can be JSON, XML, or CSV;
○ **Iterator** (rml:iterator) - is not required when it comes to tabular input
sources, like relational databases. It is needed in case of hierarchical or
structured data sources. The iterator (rml:iterator) determines the iteration
pattern by the input source and specifies the extraction of the data displayed
during each iteration;
○ **Reference Formulation** (rr:referenceFormulation) - as RML deals with
different data serializations with various ways to refer to their elements,
RML defines the reference. Such reference is specified based on the source of
the input data file, e.g. in case of a JSON file, a Reference Formulation
would be "JSONPath", in case of an XML file, a Reference Formulation would be
"XPath".
* A **Subject Map** (rr:subjectMap) - defines the subject of the generated RDF triples.
* Several **Predicate-Object Maps** (rr:predicateObjectMap), combining:
1. **Predicate** Maps (rr:predicate) expressing the predicate of the RDF triple;
○ **Object** Maps (rr:objectMap) expressing the object of the RDF triple; ○ A
**Referencing Object Map** , that indicates the reference to another **Triples
Maps** (rr:parentTriplesMap).
**Figure 1: An Overview of the RML mapping schema. Based on Dimou et al** 2
**.**
## 2.2 RDF and SPARQL Query Language
Resource Description Framework (RDF) – is a model developed by the W3C
consortium for describing the metadata of resources. The main goal is to
present statements in the form, that is equally well perceived by both, the
person and the machine. RDF provides interoperability between applications
that exchange computer-understood information via the Web. It also pays
attention to the means that automatically process Web resources. RDF
distinguishes three different type of values:
* **IRI** (Internationalized Resource Identifier) – denotes entities, act as an identifier, representing the same resource;
* **Literals** – denote entities;
* **Blank nodes** – represent a resource, but without a specific identifier. Finally, an RDF triple is composed and relates three elements:
* **Subject** \- a described resource, represented by a URI reference or a blank node; ● **Predicate** \- the property of a resource, represented by a URI reference;
* **Object** \- property value, represented by a URI reference, blank node or a literal.
Thus, the subject can be represented by an IRI or a blank node, the object can
be an IRI, a blank node or a literal. Links between nodes are always specific,
that is why a predicate can be only represented by an IRI. The basic building
block in RDF is represented by a triplet "Subject-Predicate-Object".
SPARQL 3 is a W3C standard query language used to define and manipulate data
in the RDF format. SPARQL queries comprise triple patterns including
conjunction and disjunction operations. The main query clauses supported by
SPARQL are SELECT, CONSTRUCT, ASK, and DESCRIBE. An evaluation of a SPARQL
query **Q** over an RDF graph **G** , corresponds to the set instantiations of
the variables in the SELECT clause of **Q** against RDF triples in **G.** A
SPARQL query can include different operators, e.g. JOIN, UNION, and OPTIONAL.
Moreover, FILTER can be used in order to filter from the output the
instantiations of the variables of the SELECT clause of **Q** that meet a
certain condition. The basic building block in the WHERE clause of a SPARQL
query is the triple pattern, or a triple with variables. A Basic Graph pattern
is the conjunction of several triple patterns, where a conjunction corresponds
to the JOIN operator. Finally, Basic Graph patterns can be connected with the
JOIN, UNION, or OPTIONAL operators. The following SPARQL query expresses the “
_Mutations of the type Confirmed somatic variant located in transcripts which
are translated as proteins that interact with the drug Docetaxel_ “.
PREFIX rdf: < http://www.w3.org/1999/02/22-rdf-syntax-ns#>
PREFIX iasis: <http://project-iasis.eu/vocab/>
SELECT DISTINCT ?mutation
WHERE {
?mutation **rdf:type** **iasis:Mutation** .
?mutation **iasis:mutation_chromosome** ?chromosome .
?mutation **iasis:mutation_start** ?start.
?mutation **iasis:mutation_NucleotideSeq** ?nucleotideSeq.
?mutation **iasis:mutation_isClassifiedAs_mutationType** ?type.
?mutation **iasis:mutation_somatic_status 'Confirmed somatic variant'** .
?mutation **iasis:mutation_cds** ?cds.
?mutation **iasis:mutation_aa** ?aa.
?mutation **iasis:mutation_isLocatedIn_transcript** ?transcript .
?transcript **iasis:translates_as** ?protein .
?drug **iasis:drug_interactsWith_protein** ?protein .
?drug **iasis:label** **'docetaxel'** }
This query is composed of 12 triple patterns; each triple, e.g. “?mutation
**rdf:type** **iasis:Mutation** ” corresponds to a triple pattern, where
“?mutation” corresponds to a variable, “ **rdf:type** ” to a predicate, and “
**iasis:Mutation** ” a mutation. Triple patterns are connected using the “.”
operator which corresponds to a JOIN. The 12 triple patterns in the WHERE
clause of the query comprise a Basic Graph Pattern.
### 2.3 Federated Query Engines and Existing Approaches
A federation of SPARQL endpoints is a set of RDF datasets that can be accessed
via SPARQL endpoints. A SPARQL endpoint is a Web service that provides a Web
interface to query RDF data following the SPARQL protocol 4 . RDF datasets
comprise sets of RDF triples; predicates of these triples can be from more
than one Linked Open Vocabulary 5 , e.g. FOAF 6 or DBpedia ontology.
Additionally, proprietary vocabularies can be used to describe the RDF
resources of these triples, and controlled vocabularies like VoID 7 , can be
used to describe the properties of the RDF data accessible through a given
SPARQL endpoint. Queries against federations of SPARQL endpoints are posed
through federated SPARQL query engines. A generic architecture of a federated
SPARQL query engine is based on the mediator and wrapper architecture 8 .
Wrappers translate SPARQL subqueries into calls to the SPARQL endpoints as
well as convert endpoint answers into the query engine internal structures.
The mediator rewrites original queries into subqueries that can be executed by
the data sources of the federation. Moreover, the mediator collects the
answers of evaluating the subqueries over the selected queries, merges the
results and produces the answer of a federated query; mainly, it is composed
of three components:
* **Source Selection and Query Decomposition** \- decomposes queries into subqueries, and selects the endpoints that are capable of executing each subquery. Simple subqueries comprise a list of triple patterns that can be evaluated against at least one endpoint.
* **Query Optimizer** \- identifies execution plans that combine subqueries and physical operators implemented by the query engine. Statistics about the distribution of values may allow for the identification of combination of subqueries and plans that reduce execution time.
* **Query Engine** \- implements different physical operators to combine tuples from different endpoints. Physical operators implement logical SPARQL like JOIN, UNION, or OPTIONAL.
FedX 9 , ANAPSID 10 , and MULDER 11 are exemplars federated SPARQL query
engines able to execute queries over a federation of SPARQL endpoints. FedX
implements source selection techniques able to contact the SPARQL endpoints on
the fly to decide the subqueries of the original query that can be executed
over the endpoints of the federation. Thus, FedX relies on zero knowledge
about the content of the SPARQL endpoints to perform the tasks of Source
Selection and Decomposition. ANAPSID exploits information about the predicates
of the RDF datasets accessible via the SPARQL endpoints of the federation to
select relevant sources, decompose the original queries, and find efficient
execution plans. Moreover, ANAPSID implements physical operators able to
adjust schedulers of query executions to the current conditions of SPARQL
endpoints, i.e. if one of the SPARQL endpoint is delayed or blocked, ANAPSID
is able to adjust the query plans in order to keep producing results in an
incremental fashion. Finally, MULDER is a federation SPARQL engine that relies
on the description of the properties and links of the classes in the RDF
graphs accessible from SPARQL endpoints, to decompose the original queries
into the minimal number of subqueries required to evaluate the original query
into the relevant SPARQL endpoints. MULDER utilises the RDF Molecule Templates
(RDF-MTs) to describe the classes in an RDF graph and its links; it also
exploits the physical operators implemented in ANAPSID to provide efficient
query executions of SPARQL queries, i.e. MULDER provides Source Selection and
Query Decomposition and Query Optimizer components which effectively exploit
the ANAPSID query engine.
Ontario is the query engine implemented in the iASiS framework for accessing
data from the iASiS knowledge graph. Similar to MULDER, Ontario relies on RDF
Molecule Templates (RDF-MTs) for describing the RDF classes included in the
iASiS knowledge graph. Additionally, Ontario maintains in the RDF-MTs metadata
describing the data privacy and access control regulations imposed by the
provider of the data used to populate the RDF classes of the knowledge graph.
Moreover, Ontario relies on adaptive physical operators to be able to adjust
query execution plans to the condition of the SPARQL endpoints that make
accessible the iASiS knowledge graph. More importantly, contrary to existing
federated SPARQL query engines, Ontario is able to execute SPARQL queries over
data sources that are not integrated in the knowledge graph and are stored in
raw formats, e.g. CSV or JSON. This feature of Ontario allows for executing
queries over both RDF graphs and against data collections that are not
physically integrated into the knowledge graph, providing thus a virtual
integration of data sources.
# 3 The Semantic Enrichment for Creating the iASiS Knowledge Graph
In this section, the semantic enrichment component is described; it is able to
transform and integrate heterogeneous data sources into the iASiS knowledge
graph. First, the iASiS data-driven pipeline is explained. Then, the pipeline
implemented by the semantic enrichment component is defined, as well as RML
mappings utilised to transform raw data into RDF. Finally, the main
characteristics of the version
1.0 of the iASiS knowledge graph are reported.
## 3.1 The iASiS data-driven pipeline
The iASiS data-driven pipeline transforms Big clinical and biomedical data
into actionable insights for the support of precision medicine and decision
making; the main steps of the pipeline are presented in Figure 2\. The
pipeline receives Big data sets as input, e.g. electronic clinical nodes,
genomic data, medical images, and open data, and produces as output the iASiS
knowledge graph. Moreover, tasks of knowledge discovery are performed in order
to detect potential novel patterns and associations between entities in the
iASiS knowledge graph, e.g. signatures of long survival patients of lung
cancer.
First, the aim of the **knowledge extraction methods** is to create ontology
annotated data sets from data represented in different formats, e.g. clinical
notes, medical images, hospital-derived genomic data; biomedical ontologies
are used for annotation. Clinical data is collected from the clinical partners
and anonymisation techniques are performed in order to remove confidential
attributes, e.g. national identifier, name, address, and profession. **EHR
text analysis** and **Image analysis** components are executed in order to
extract relevant information from the clinical notes and medical images, e.g.
treatments, diagnostics, and results of clinical tests. Biomedical
vocabularies, e.g. UMLS or SNOMED are used to represent the knowledge
extracted from the clinical notes. Additional knowledge extraction tasks, i.e.
genomic and open data analysis are performed over open data collections; these
techniques also annotate data using terms from Biomedical ontologies, e.g.
UMLS and the Human Phenotype Ontology (HPO).
**Figure 2: The iASiS Pipeline for Knowledge Management and Discovery**
Once the annotated datasets are produced, tasks of knowledge graph creation
are performed in order to semantically describe and integrate annotated data
into the **iASiS** **knowledge graph** ; the iASiS unified schema is utilised
for describing the data. Annotations for biomedical ontologies, e.g. UMLS,
SNOMED, and HPO, are exploited to curate and integrate the entities in the
annotated datasets; also, **entity linking techniques** are used to connect
these entities to equivalent entities in other knowledge graphs, e.g. DBpedia
and Bio2RDF; DBpedia Spotlight 11 solves the tasks of entity extraction,
disambiguation, and linking. Moreover, RML mapping rules are used by the
Semantic Enrichment component in order to create the RDF triples that populate
the iASiS knowledge graph.
Once the iASiS knowledge graph is created, it can be explored and consulted by
using Ontario; moreover, Ontario allows for performing federated queries
between the iASiS knowledge graph, as well as the knowledge graphs linked to
it, e.g. DBpedia and Bio2RDF. Results of executing a federated query can be
used as input of the tasks of Data Analytics or Knowledge Discovery, enabling
thus the discovery of patterns among entities in the iASiS knowledge graph.
Data privacy and access control policies are verified and enforced by all the
components in the pipeline.
## 3.2 The Knowledge Graph Creation Process
Creating a knowledge graph from heterogeneous data sources requires the
description of the entities in the data sources using RDF vocabularies, as
well as the performance of curation and integration tasks in order to reduce
data quality issues, e.g. missing values or duplicates. Figure 3 depicts the
pipeline followed during knowledge graph creation; it is composed of three
main components:
* **Semantic Enrichment** \- transforms annotated data into RDF; it relies on rules in a mapping language, e.g. RML, to generate the RDF triples that correspond to the semantic description of the input data. The iASiS unified schema and properties from existing RDF vocabularies like RDFS and OWL, are utilised as predicates and classes. Annotations in the input data are also represented as RDF triples. The RDF representation of these annotations are linked to the corresponding entities in the knowledge graph, e.g. the resource of the UMLS annotation C00031149 is associated with the resource of the PubMed publication 28381756. Moreover, equivalences and semantic relations between annotations are represented in the knowledge graph. These relationships allow for detecting entities annotated with equivalent annotations and that may correspond to the same real-world entities, i.e. they are duplicates; thus, equivalent annotations represent the input to the tasks of knowledge integration.
* **Knowledge Creation & Integration ** \- receives an initial version of the iASiS knowledge graph that may include duplicates and it outputs a new version of the knowledge graph from where duplicates are removed. In order to detect if two entities correspond to the same real-world entity, i.e. they are duplicates, similarity measures are utilised, e.g. GADES 12 or Jaccard 13 ; all the entities in an RDF class of the knowledge graph are compared pairwise. Then, a 1-1 perfect weighted matching algorithm is performed in order to identify duplicates in the class; thus, if two entities are matched, they are considered as equivalent entities and merged in the knowledge graph. Fusion policies 14 are followed to decide how equivalent entities are merged in a knowledge graph; the fusion policies include:
○ **Union** \- creates a new entity with the union of the properties of the
matched entities.
○ **Semantics based Union** \- creates a new entity with the union of the
properties of the matched entities. Only _most general properties_ are kept in
case of properties related by the _subproperty_ relationship; furthermore, if
two properties are equivalent, only one of them is kept in the resulting
entity.
○ **Authoritative Merge** \- creates a new entity with the properties of the
entity with the data provided from an authoritative source.
* **Interlinking** \- receives the iASiS knowledge graph and a list of existing knowledge graphs, e.g. DBpedia or Bio2RDF, and outputs a new version of the iASiS knowledge graph where entities are linked to equivalent entities in the input knowledge graphs. Entity Linking tools like DBpedia Spotlight are used for linking; additionally, link traversal techniques are performed to further identify links with other knowledge graphs, e.g. Bio2RDF or DrugBank.
## 3.3 RML Mapping Rules to Create the iASiS Knowledge Graph
In order to transform data from heterogeneous data sources into the version
1.0 of the iASiS knowledge graph, 66 RML mapping rules have been defined.
These mapping rules are defined based on the classes of the iASiS unified
schema available as an instance of the ontology development tool VoCol 15 ;
the mappings are available in the GitHub repository 16 of the LUH team. The
RML mappings are executed by the Semantic Enrichment component defined above.
Given the number of rules and the size of the data sources, optimisation
techniques have been implemented with the aim of scaling up. Empirically,
scalability has been evaluated, and the Semantic Enrichment component is able
to generate knowledge graphs in the order of Terabytes. Furthermore, the
Semantic Enrichment component is able to detect data quality issues in the
input data collections; it has been empowered with data curation capabilities
that
allow
for
detecting
missing
values,
and
malformed
names
and
identifiers.
**Figure 3: The Pipeline for Knowledge Graph Creation**
The following is one exemplar RML mapping rule for defining the properties of
a Drug. This rule is used to transform data in CSV format into RDF data. This
RML mapping rule describes the data source from which the data is extracted
and the format of this data, i.e. CSV. Additionally, it specifies the URL of
the class subject; finally, two properties of the class Drug are defined. The
Semantic Enrichment component receives the mapping and iterates over each row
of the file to transform the values of the columns of the raw file according
to the mappings in the rules annotated with
Class Subject and Class Properties labels. Once the end of file is reached, an
RDF file is produced as output; this RDF file corresponds to the input of the
knowledge integration component.
### 3.4 Example of Knowledge Graph Creation
In this section, the pipeline for creating the iASiS knowledge graph is
illustrated with an example. Suppose input data describing a drug, is received
in a tabular format, e.g. as a CSV file, then, an RDF graph describing the
drugs in the file is created. These RDF graphs are called **simple RDF
molecules** or group of RDF triples that share the same subject. RML mapping
rules are defined and executed to transform raw data into the RDF triples that
comprise the resulting RDF molecules. Furthermore, these mapping rules
indicate the format of the URIs of the resources that appear as subjects or
objects of the RDF molecules created during their execution. In this case,
three URIs are created, i.e. for the drug, publication, and variation.
The same process is
repeated for all the RML
mappings that define the
RDF classes in the iASiS
knowledge graph in terms
of
the
available
data
sources. In case several
simple RDF molecules are
defined for the same real-
world entity, e.g. the drug
Docetaxel, the process of
_knowledge integration_
is
executed.
This
process
determines RDF molecules that represent equivalent entities of a class,
according to the available fusion policies. These simple RDF molecules are
then merged or integrated into a complex RDF molecule that represent all the
properties of the real-world entity, that are represented in the different
simple RDF molecules.
Finally, entity linking techniques allow for discovering links between
entities in the iASiS knowledge
graph
and
equivalent
entities
in
existing
knowledge
graphs,
e.g.
DBpedia. In this case, the
resource representing the
drug Docetaxel in the
iASiS knowledge graph is
linked to the resource
that represents the same
drug
in
DBpedia;
the
owl:sameAs property
is
utilised to represent these
type of links. Linking the
iASiS
knowledge
graph
with other knowledge graphs not only allows for exploring properties that are
not represented in the original knowledge graph (e.g. dbo:atcPrefix), but also
enables the identification of data quality issues like missing values or
duplicates.
# 4 Ontario: A Federated Query Engine
In this section, the characteristics of Ontario are presented. It is a
federated SPARQL query engine being able to evaluate SPARQL queries over the
iASiS knowledge graph and the knowledge graphs that are linked to it, e.g.
DBpedia and Bio2RDF. Further, examples of federated queries are utilised to
illustrate the main features implemented in Ontario in order to scale up to
federation of large knowledge graphs. Finally, the RDF Molecule Templates
(RDF-MTs), i.e. the source description formalism of Ontario, is utilised as
the basis to provide an analysis of the main properties of the iASiS knowledge
graph.
## 4.1 SPARQL Query Processing in Ontario
Ontario is a federated query engine able to execute queries against a
federation of data sources accessible via a Web Access Interface, e.g. SPARQL
endpoints; Figure 4 depicts the main components of the Ontario architecture.
First, Ontario maintains the description of the data sources in the federation
in terms of RDF molecule templates (RDF-MTs). Thus, a data source is described
in terms of the RDF classes of the data in the data sources, and the
properties of these classes; additionally, data privacy and access policies
imposed by data providers are represented in RDF-MTs. An RDF Molecule Template
(RDF-MT) 17 is characterised by the following properties:
* **rdfMT:hasClass** represents a class **C** of the entities described by the RDF-MT, e.g. iasis:Patient.
* **rdfMT:hasProperty** represents the properties of the class of the entities described by the RDFMT, e.g. **iasis:hasBiopsy** and **iasis:hasTumorStage** .
* **rdfMT:hasPolicy** allows for representing the operations that can be performed over a property whose data is provided by a given data source. The object of the rdfMT:policies is related to an entity which is an instance of the class **rdfMT:AuthorizationEntity;** which relates a property with access rights authorized by a data provider **s** to an access consumer **c** during a given time period.
* **rdfMT:hasCardinality** represents the cardinality of the entities in the class **C** .
* **rdfMT:endpoint** corresponds to the Web access interface to access entities of the class **C** .
* **rdfMT:linkedTo** represents the links between the class **C** and other classes in the knowledge graph or in other knowledge graphs.
**Figure 4: The Ontario Architecture**
Given a SPARQL query Q, the **Source Selection and Query Decomposer**
identifies a **query decomposition** of Q; it is composed of subqueries (SQi)
of Q, and the data sources where these subqueries can be executed, e.g. in
Figure 4 subquery SQ2 is executed over @KG1, @KG2, and @KG3. Metadata
represented in the RDF-MTs are exploited during source selection and query
decomposition, in order to identify the minimal number of data sources that
have to be contacted to produce a complete answer of the query, i.e. a
solution to the problem of finding the relevant sources for a federated query.
Next, the **Query Optimizer** receives the query decomposition and finds an
**efficient query plan** , i.e. a plan of Q over the relevant data sources
that produces all the answers but in the minimal time. Different **physical
operators** 18 are implemented by the Query Engine to collect the answers of
the subqueries against the relevant data sources, e.g. SMJOIN or gJOIN, as
well as to produce the query answer. In Figure 4, a multiway-join operator
connects the subqueries over @KG1, @KG2, and @KG3. This operator is able to
adjust query execution schedulers to the conditions of the sources, e.g. data
transfer delays and unexpected source workload; moreover, it allows for
producing query answers incrementally, thus, users do not have to wait until
all the subqueries are completely executed to receive the first answer and can
collect answer continuously. Techniques implemented in Ontario have been
empirically evaluated and compared with state-of-the-art approaches. Observed
experimental results suggest that Ontario is not only able to scale up to
large data sources, but also is outperforming the state of the art in queries
over a large number of RDF data sources.
The main features of Ontario are illustrated in Section 4.2 with an example.
Furthermore, Section 4.3 presents an analysis of the characteristics of the
iASiS knowledge graph based on the metadata encoded in the RDF-MTs and links.
The results suggest that the current version of the iASiS knowledge graph
represents a relatively good number of connections between biomedical
concepts, e.g. drugs, publications, variants, tumors, and annotations.
Nevertheless, these connections need to be extended, being part of future work
of WP5, through the analysis and discovery of new links between the concepts
represented in the iASiS knowledge graph.
### 4.2 Example of Federated Query Processing
Consider the following SPARQL query **Q** that produces “ _Mutations of the
type Confirmed somatic variant located in transcripts which are translated as
proteins that are transporters of the drug Docetaxel_ “.
**Q:**
PREFIX rdf: < http://www.w3.org/1999/02/22-rdf-syntax-ns#>
PREFIX iasis: <http://project-iasis.eu/vocab/>
PREFIX drugbank: <http://bio2rdf.org/drugbank_vocabulary:>
PREFIX owl: <http://www.w3.org/2002/07/owl#>
SELECT DISTINCT ?mutation
WHERE {
?mutation **rdf:type** **iasis:Mutation** .
?mutation **iasis:mutation_somatic_status 'Confirmed somatic variant'** .
?mutation **iasis:mutation_isLocatedIn_transcript** ?transcript .
?transcript **iasis:translates_as** ?protein .
?drug **iasis:drug_interactsWith_protein** ?protein .
?protein **iasis:label** ?proteinName .
?drug **iasis:label** **'docetaxel'** .
?drug **owl:sameAs** ?drug1 .
?drug1 **drugbank:transporter** ?transporter .
?transporter **drugbank:gene-name** ?proteinName . }
To execute this query, both the iASiS and the Bio2RDF knowledge graphs need to
be consulted. Ontario maintains metadata about these two knowledge graphs,
represented in RDF molecule templates, and it is able to select them as
relevant sources for the query. Then, once the RDF molecule templates are
selected, Ontario decomposes query **Q** into subqueries **SQ1** and **SQ2,**
and executes them over the iASiS and Bio2RDF knowledge graphs, respectively.
**SQ1:**
PREFIX rdf: < http://www.w3.org/1999/02/22-rdf-syntax-ns#>
PREFIX iasis: < _http://project-iasis.eu/vocab/_ >
PREFIX owl: <http://www.w3.org/2002/07/owl#>
SELECT DISTINCT ?mutation ?proteinName ?drug1
WHERE {
?mutation **rdf:type** **iasis:Mutation** .
?mutation **iasis:mutation_somatic_status 'Confirmed somatic variant'** .
?mutation **iasis:mutation_isLocatedIn_transcript** ?transcript .
?transcript **iasis:translates_as** ?protein .
?drug **iasis:drug_interactsWith_protein** ?protein .
?protein **iasis:label** ?proteinName .
?drug **iasis:label** **'docetaxel'** .
?drug **owl:sameAs** ?drug1 .
}
**SQ2:**
PREFIX drugbank: <http://bio2rdf.org/drugbank_vocabulary:>
SELECT DISTINCT ?proteinName ?drug1
WHERE {
?drug1 **drugbank:transporter** ?transporter .
?transporter **drugbank:gene-name** ?proteinName .
}
As a result, **24 mutations** of the protein **ABCB1** and **11 mutations** of
the protein **ABCG2** are identified. It is important to highlight that
without the integration of COSMIC data into the iASiS knowledge graph and the
linking of the corresponding entities with Bio2RDF, this federated query could
not be executed. Thus, these results illustrate not only the features of
Ontario as a federated query engine, but also the benefits of semantically
describing and integrating heterogeneous data sources into a knowledge graph.
### 4.3 Main Characteristics of the iASiS Knowledge Graph
The current version of the iASiS knowledge graph has 236,512,819 RDF triples,
26 RDF classes, and in average, 6.98 properties per entity. Figure 5 shows the
distribution of number of entities per RDF-MT in the iASIS knowledge graph and
in the RDF-MTs from DBpedia (with prefix _dbo:_ ) and Bio2RDF (with prefix
_bio2rdf:_ ) that are connected to its RDF-MTs. As observed, some RDF-MTs are
populated with a relatively large number of entities, e.g. the annotations of
the publications, and the mutations; in average there are 86,934.00 entities
per class.
Figure 5: Number of Entities per RDF-MT in the iASiS Knowledge Graph, and
related RDF-MTs in DBpedia and Bio2RDF
RDF-MTs of the iASiS knowledge graph and connected knowledge graphs are used
to describe the main characteristics of the integrated data and their
connections. To conduct this analysis, an undirected graph with the RDF-MTs of
the iASiS, DBpedia, and Bio2RDF knowledge graphs was built. Figure 6 shows
this graph; RDF-MTs correspond to 35 nodes in the graph, while 58 edges
represent links among RDF-MT. It can be observed that all the RDF-MTs are
connected to at least one RDF-MT, i.e. there are no isolated classes.
**Figure 6: RDF-MTs in the iASiS, DBPedia, and Bio2RDF**
**Knowledge Graphs**
Using network analysis, several graph measures were computed; Table 1 reports
on the results of these measures. Clustering coefficient measures the tendency
of nodes who share the same connections in a graph to become connected. If the
neighbourhood is fully connected, the clustering coefficient is 1.0 while a
value close to 0.0 means that there is no connection in the neighbourhood.
Transitivity measures if RDF-MTs are transitively connected; values close to
1.0 indicate that almost all the RDF-MTs are related, while low values
indicate that many RDF-MTs are not related. Clustering coefficient and
transitivity are both relatively low, i.e. 0.224 and 0.23, respectively. This
suggests that many more connections between RDF classes can be included in
future versions of the iASiS knowledge graph.
**Table 1: Graph Analysis of the RDF-MTs of the iASiS Knowledge Graph**
<table>
<tr>
<th>
RDF-MT graph property
</th>
<th>
Value
</th> </tr>
<tr>
<td>
Number of RDF-MTs (nodes)
</td>
<td>
35
</td> </tr>
<tr>
<td>
Number of connections (edges)
</td>
<td>
58
</td> </tr>
<tr>
<td>
Clustering coefficient
</td>
<td>
0.224
</td> </tr>
<tr>
<td>
Transitivity
</td>
<td>
0.23
</td> </tr>
<tr>
<td>
Avg. number of neighbours
</td>
<td>
2.629
</td> </tr> </table>
# 5 Conclusions and Future Work
The outcomes of performing the tasks T5.4 and T5.5 of WP5 have been presented
in this document. The reported results have allowed for the understanding of
the main data management techniques implemented in the iASiS framework. These
techniques enable the transformation and curation of heterogeneous data
sources into RDF, as well as their integration into the iASiS knowledge graph.
Moreover, the Ontario federated query engine is described in terms of its
components. The depicted examples illustrate the functionalities of the
developed techniques, and the reported statistics of the iASiS knowledge graph
facilitate the understanding of the amount of knowledge represented in the
version 1.0 of the iASiS knowledge graph, as well as the opportunities that it
offers for knowledge exploration and discovery. As the number of sources to be
integrated in the iASiS knowledge graph will increase, techniques for
physically distributing the iASiS knowledge graph will be demanded.
Furthermore, caching techniques for keeping the results of the most frequent
federated queries will facilitate the execution of queries at large scale. All
these tasks are part of future work in WP5.
|
https://phaidra.univie.ac.at/o:1140797
|
Horizon 2020
|
1187_DELTA-FLU_727922.md
|
D4.1 Data Management Plan
# DATA SUMMARY
DELTA-FLU participates by default in the H2020 Open access to research data
pilot, which aims to improve and maximize access to and re-use of research
data generated by H2020 funded projects. This initial Data Management Plan
(DMP) will detail the management of the research data generated and collected
during the project, in compliance with article 29.3 of the H2020 Grant
Agreement. The DMP will follow the principles of FAIR data management as set
out in the Guidelines on FAIR data management in H2020. This DMP will include
information on the handling of research data during and after the project,
what data will be collected, processed and/or generated, which methodology and
standards will be applied, whether data will be shared/made open access, and
how data will be curated and preserved. The initial DMP will be updated over
the course of DELTA-FLU, whenever significant changes arise, such as the
generation of new data, or changes in consortium composition. DELTA-FLU will
be advised by the data access committees available at partner institutes
and/or the ethical body overseeing consent-giving procedures, regarding the
appropriate management and use of the research data.
## PURPOSE OF DATA COLLECTION/GENERATION AND RELATION TO THE OBJECTIVES OF
THE PROJECT
The primary aim of data collection and generation within DELTA-FLU is to
advance knowledge on the key viral, host-related, and environmental factors
that determine the dynamics of avian influenza (AI) in poultry and other host
species, with the ultimate goal to improve prevention and control strategies
against this disease. By establishing interdisciplinary research focusing on
key questions of AI, DELTA-FLU will collect and generate data to determine 1)
potential for some highly pathogenic avian influenza viruses (HPAIV, e.g. H5N8
clade 2.3.4.4) to be maintained in wild bird populations and spread over long-
distances, 2) key viral, host, and environmental factors for incursion of
HPAIV from wild birds into poultry holdings, 3) roles of viral, host, and
environmental factors in the transition of low pathogenic avian influenza
virus to HPAIV in poultry, 4) effect of flock immunity against AI on early
detection and viral genetic drift, and 5) viral genetic factors that allow
reassortants of avian and mammalian influenza viruses to transmit efficiently
among pigs. Data will be used to inform about experimental design, analytic
tools and methods, as well as modelling and simulation tools, in order to
achieve these primary aims.
## TYPES AND FORMATS OF DATA TO BE GENERATED/ COLLECTED
DELTA-FLU will collect and generate a range of data based on which targeted
prevention and control strategies can be designed, as summarized in the table
below. These data will largely originate from public databases, databases
available from partner’s own projects and research activities in work packages
1 – 3. Standards for collecting, curating and preserving the data are
integrated in the research activities of DELTA-FLU to ensure data is kept
securely and protected from inappropriate use or disclosure. Where
appropriate, DELTA-FLU will leverage existing standards, and continue to
develop existing ones for data representation, for example for sequence-based
experiments. The research data generated by DELTA-FLU will be curated in such
a way that the formats will be compatible with publicly available data
repositories.
<table>
<tr>
<th>
Type of data
</th>
<th>
Origin of data
</th>
<th>
Format
</th>
<th>
Expected size of data
</th> </tr>
<tr>
<td>
Virus sequences
</td>
<td>
Public databases (Genbank: https://www.ncbi.nlm.nih.gov/genbank/ or GISAID:
http://platform.gisaid.org), work packages 1 to 3)
</td>
<td>
FASTA
</td>
<td>
Depends on amount of metadata (order of MB)
</td> </tr>
<tr>
<td>
Wild bird tracking data
</td>
<td>
Public database
(https://www.movebank.org/), work package 1
</td>
<td>
.csv,
Movebank format
</td>
<td>
Depends on number of birds and duration of tracking (order or GB)
</td> </tr>
<tr>
<td>
Animal trial data (tissue tropism of viruses, excretion dynamics of viruses,
virulence of viruses in infected birds)
</td>
<td>
Public databases (PubMed: https://www.ncbi.nlm.nih.gov/pubmed or Web of
Science: http://apps.webofknowledge.com/), work packages 1 to 3
</td>
<td>
Word, Excel
</td>
<td>
Order of MB
</td> </tr> </table>
## RE-USE OF EXISTING DATA
In DELTA-FLU, partners will make use of specific existing datasets collected
from public databases, databases available from partner’s own projects and
research activities, or datasets made available by one or more partners to the
consortium. The re-use of existing data is necessary to value and integrate
obtained results into the current state of knowledge. These include the
following data, sources, and reuse:
<table>
<tr>
<th>
Data/ source
</th>
<th>
Re-use
</th>
<th>
Expected size of data
</th> </tr>
<tr>
<td>
Virus sequences / Genbank
</td>
<td>
Creation of phylogenetic trees
</td>
<td>
Order of MB
</td> </tr>
<tr>
<td>
Virus sequences / GISAID
</td>
<td>
Creation of phylogenetic trees
</td>
<td>
Order of MB
</td> </tr>
<tr>
<td>
Animal trial data / Pubmed, Web of Science, unpublished data of partners
</td>
<td>
Evolution of genetic and phenotypic characteristics of viruses in different
avian hosts
</td>
<td>
Order of MB
</td> </tr> </table>
Before making datasets available amongst partners or via open access data
repositories, DELTA-FLU will review any agreement(s) with third parties to
evaluate any restrictive use of the data by the consortium partners, and
evaluate whether or not ethical and other restrictions on further use apply.
In addition, whether permissions for use of the data are needed from third
parties will be evaluated.
## DATA UTILITY: TO WHOM WILL IT BE USEFUL
DELTA-FLU will collect and generate a range of data, as described above, based
on which targeted prevention and control strategies can be designed, including
policy strategies and risk assessment. These data might be useful to a wide
range of actors involved in prevention and control of avian Influenza virus
outbreaks. These actors include, but are not limited to:
* Research institutes and their individual scientists (such as in the areas of virology, epidemiology, molecular biology, pathology, ecology, ornithology, risk assessment and modelling, and animal and human health)
* International public and animal health and food safety authorities (e.g. EU, FAO, EFSA, WHO, ECDC, OIE, OFFLU)
* International nature conservation organizations (e.g. Wetlands International, Wetlands and Wildfowl Trust)
* National governments and international policy-making authorities (e.g. EU)
* Poultry production industry, including poultry production companies, farmers, and international associations representing the industry (e.g. European Live Poultry and Hatching Egg Association, Association of Poultry Processors and Poultry Trade in the EU).
# FAIR DATA
## MAKING DATA FINDABLE, INCLUDING PROVISIONS FOR METADATA
DELTA-FLU will ensure that all data stored provide sufficient information
about each data set. Typically, this will include information about
experimental protocols, samples, virus strains, reagents, experimental
conditions, and data analysis models and approaches. DELTA-FLU will use
internationally accepted naming conventions and indicators, such as the
taxonomic international accepted names for pathogens, the common pathogen
titration methodologies expressing doses such as TCDI50, PFU, etc., developed
and/or optimized specific detection and analyzing methods (e.g. WHO or OIE
standards), as well as modelling/simulation exercises. Scenario-based
mathematical models will be described in a publication submitted to an
appropriate (open access) peer-reviewed journal for publication, which will be
finable by its unique Digital Object Identifier. Sequence data will be
uploaded with additional information/metadata. Search keywords will be
provided e.g. for data in publications as well as for data in public
databases. In addition, version numbers will be provided to enable a clear
traceability of currentness of data and of documents
There is a broad range of metadata which will be created especially in
connection to sequencing data (location, origin of samples, species sampled
etc.) but also to data collected from satellite tracking (time, location,
species, other tracking data). The metadata will be collected following the
schemes provided within the working packages and also by related projects
(e.g. sequence metadata collection scheme of COMPARE).
## MAKING DATA OPENLY ACCESSIBLE
Before making datasets available amongst partners or via open access data
repositories, DELTA-FLU will review any agreement(s) with third parties to
evaluate any restrictive use of the data by the consortium partners, and
evaluate whether or not ethical and other restrictions on further use apply.
In addition, whether permissions for use of the data are needed from third
parties will be evaluated. In cases where transferred data generates new data,
e.g. upon data analysis by one or more partners, the DELTA-FLU Consortium
Agreement will address Intellectual Property issues that may arise from
ownership of the processed data and use of the tools on these data. Prior to
sharing of data, all researchers in DELTA-FLU will review their data obtained
in the project for exploitation potential and adequate knowledge protection
where appropriate, and communicate without delay any decision to protect data
generated in DELTA-FLU to the Executive Board of DELTA-FLU. Adequate
protection of knowledge shall take into account the need for proper
dissemination of results and openly sharing of data without any unjustified
delay. DELTA-FLU will actively seek to publish the data in peer-reviewed
scientific journals, and will strive to publish in open access journals (“Gold
model”). In case publication in open access journals is not feasible, the
publications will be made available via the DELTA-FLU website, and the
repositories of the participating universities and institutes, respecting any
embargo periods of the journal (“Green model”).
The availability of the data generated in DELTA-FLU has three main access
levels. The first level is public data, in which data will be made permanently
and freely available through for example open access publication and data
storage in publicly accessible databases. The second level is managed access
data, in which data will be stored under strict security provisions and access
to these data will be provided to ( _bona fide_ ) researchers or actors able
to demonstrate that they meet the requirements for privacy protection and
security as assessed by the relevant data access committees (typically drawn
from the ethical body overseeing consent-giving procedures, or data access
committees available at partner institutes). The third level of access is
private data, in which data will be made available only to defined closed
consortia that will retain all access management functions. We anticipate that
private data will comprise only a minor portion of all DELTA-FLU data sets.
Typically, this concerns data for which potential dual use or biosecurity
issues have been identified in the ethical assessment.
Data sharing with third parties will be subject to a data-sharing agreement
established by the DELTA-FLU Steering Committee. The agreement will specify
the conditions of data use, criteria for access, and acknowledgements.
Consortium partners who wish to withhold patentable or proprietary data can do
so, and advice on this point will be given by the Steering Committee. Data
sharing and data access and related issues will in principle be discussed and
decided upon in the Steering Committee, duly advised by the consortium
partners’ data management officers, and the project’s Scientific Advisory
Board and Ethical Committee. If deemed appropriate and necessary, a data
access committee will be established during the course of the project.
In making data available, DELTA-FLU discriminates raw and final research data.
Raw research data that will not be considered for a patent application by
consortium partners, but has been used to generate scientific papers, will be
made publicly available two years after the project end at the latest, and
stored in a repository of the partner’s choice. Final research data will be
made publicly available only after i) relevant scientific publications based
on the data have been accepted, or ii) a patent application has been
published, or iii) two years after the project end at the latest.
The public data will be available following the access conditions of the used
databases (e.g. EPiFLU or Genbank for sequence data or Movebank for animal
movement data). Restricted data will be stored locally and access will be
granted by the responsible partners upon request, including information about
necessary software tools.
Movebank is a free online infrastructure created to help researchers manage,
share, analyze and archive animal movement data. The Movebank project is
hosted by the Max Planck Institute for Ornithology in coordination with the
North Carolina Museum of Natural Sciences, the Ohio State University and the
University of Konstanz. Movebank collaborates with numerous governmental
agencies, universities and conservation organizations, and has received
funding from the National Science Foundation, the German Aerospace Center, the
German Science Foundation and NASA. Movebank has long-term (>20 years) funding
through the Max Planck Society and the University of Konstanz and is intended
to serve as a global data archive for animal movement data. Movebank is a
resource open to all researchers and organizations regardless of species,
study area or source of funding. Movebank users retain ownership of their data
and can choose whether or not to make their data available to the public. The
database is designed for datasets that include multiple locations of
individual animals, commonly referred to as tracking data. It also supports
data collected by other bio-logging sensors attached to animals as well as
information about animals, tags and deployments. During data collection, data
on movement is continuously added to the database; this data can be made
visible to the public directly, via a map tool on the Movebank homepage. After
completion of specific project, data can be curated and metadata included and
published in the Movebank Data Repository with specific DOI and under Creative
Commons license, enabling the data to be used by other researchers.
GenBank is the NIH genetic sequence database, an annotated collection of all
publicly available DNA sequences. GenBank is part of the International
Nucleotide Sequence Database Collaboration, which comprises the DNA DataBank
of Japan (DDBJ), the European Nucleotide Archive (ENA), and GenBank at NCBI.
These three organizations exchange data on a daily basis. There are several
ways to search and retrieve data from GenBank. The GenBank database is
designed to provide and encourage access within the scientific community to
the most up-to-date and comprehensive DNA sequence information. Therefore,
NCBI places no restrictions on the use or distribution of the GenBank data.
However, some submitters may claim patent, copyright, or other intellectual
property rights in all or a portion of the data they have submitted. NCBI is
not in a position to assess the validity of such claims, and therefore cannot
provide comment or unrestricted permission concerning the use, copying, or
distribution of the information contained in GenBank. Some authors are
concerned that the appearance of their data in GenBank prior to publication
will compromise their work. GenBank will, upon request, withhold release of
new submissions for a specified period of time.
GISAID EpiFlu database is a non-profit organization that provides public
access to the most complete collection of genetic sequence data of influenza
viruses and related clinical and epidemiological data. Furthermore, the
database provides rapid sharing of influenza virus data. “The Initiative
involves public– private partnerships between the non-profit organization
_Freunde von GISAID e.V._ [4] and governments of the Federal Republic of
Germany, the official host of the GISAID EpiFlu™ database” “The database
access agreement of GISAID ensures that contributors of virus sequence data do
not forfeit intellectual property rights to the data”.
PubMed comprises over 28 million citations for biomedical literature from
MEDLINE, life science journals, and online books. PubMed citations and
abstracts include the fields of biomedicine and health, covering portions of
the life sciences, behavioral sciences, chemical sciences, and bioengineering.
PubMed also provides access to additional relevant web sites and links to the
other NCBI molecular biology resources. PubMed is a free resource that is
developed and maintained by the National Center for Biotechnology Information
(NCBI), at the U.S. National Library of Medicine (NLM), located at the
National Institutes of Health (NIH).
Web of Science connects publications and researchers through citations and
controlled indexing in curated databases spanning every discipline. It allows
cited reference searches to track prior research and monitor current
developments in over 100 years’ worth of content that is fully indexed,
including 59 million records and backfiles dating back to 1898. It is
maintained by Clarivate Analytics, that provides a comprehensive citation
search. It gives access to multiple databases that reference cross-
disciplinary research, which allows for in-depth exploration of specialized
sub-fields within an academic or scientific discipline.
## MAKING DATA INTEROPERABLE
To ensure the interoperability of data, where appropriate, DELTA-FLU will
leverage existing standards, and continue to develop existing ones for data
representation. DELTA-FLU will make the generated and collected research data
available via appropriate, dedicated, and, where possible, open access
archives and database repositories. In addition, DELTA-FLU will upload basic
datasets in standardized forms in a primary database as required by the
journal in which the consortium partners publish their results.
As the project activities and the consortium partners span multiple scientific
disciplines, partners will agree on standard vocabularies for all data types
present in DELTA-FLU generated and/or collected data sets, as well as on
common terminology, key words, units and formats to be applied to the data and
other documents.
## INCREASE DATA RE-USE (THROUGH CLARIFYING LICENCES)
In DELTA-FLU, partners will make use of specific datasets collected from
public databases, data-bases available from partner’s own projects and
research activities, or datasets made available by one or more partners to the
consortium. Therefore, the majority of the data will be stored by the partner
who is performing the experiments or using the tools. In those cases, the data
remain under the control of the partner who delivered the data, and management
and ethical procedures will follow those of the original project under which
the data were made available.
Before making datasets available amongst partners, DELTA-FLU will review any
agreement(s) with third parties to evaluate any restrictive use of the data by
the consortium partners, and evaluate whether or not ethical and other
restrictions on further use apply. In addition, whether permissions for use of
the data are needed from third parties will be evaluated. In cases where
transferred data generates new data, e.g. upon data analysis by one or more
partners, the Consortium Agreement addresses IP issues that may arise from
ownership of the processed data and use of the tools on these data. Detailed
arrangements on the data use and related knowledge management issues have been
agreed upon in the Consortium Agreement.
The data generated and collected in the project will be made openly available
to third parties after the end of the project. Possible restrictions could be
put in place due to the need to finalize pa-tenting processes or publish
scientific works based on the data.
Final research data stored locally will remain re-usable until the end of the
project and for an additional 10-years term if adequate financial sponsors
will be found (if necessary). Raw and final re-search data stored in other
open repositories will remain re-usable for a minimum of 10 years.
All partners of DELTA-FLU have compromised about harmonizing methods and
protocols to assure quality (milestones and deliverables). The majority of the
applied diagnostic methods are standardized. In sum, only those methods will
be used which have already been validated and accredited in partners lab to
ensure highest quality standards. This ensures high quality data, which can be
compared between different partners and other laboratories.
# ALLOCATION OF RESOURCES
Each partner in DELTA-FLU is responsible for data stored locally and for
complying with their own standard operating procedures (SOPs), and any DELTA-
FLU specific SOP that may be drawn up if deemed necessary, as well as for
complying to the relevant legal and ethical requirements. Data sharing, data
access and related issues will in principle be discussed and decided upon in
the Steering Committee.
Final research data stored locally will remain re-usable until the end of the
project and for an additional 10-years term if adequate financial sponsors
will be found (if necessary). Raw and final research data stored in open
repositories, such as EMBL-EBI ENA/GenBank and Movebank, will remain re-usable
for a minimum of 10 years.
Consortium partners strive to publish research data as supporting materials
together with their publications as much as possible, to facilitate future re-
use of data by other researchers. Cost for open access publications are
foreseen in the DELTA-FLU budget (around 2000 - 8000 euros per partner).
Currently, we do not have a cost estimate for the staff time necessary to make
data FAIR in this project.
# DATA SECURITY
As mentioned above, each partner in DELTA-FLU is responsible for data stored
locally and for complying with their own standard operating procedures (SOPs),
and any DELTA-FLU specific SOP that may be drawn up if deemed necessary, as
well as for complying to the relevant legal and ethical requirements. The
following general terms regarding data protection will be followed by the
partners in DELTA-FLU with regard to raw and final research data which is not
stored in open repositories:
* The amount of data collected is relevant and not excessive;
* Data are stored securely;
* Data are fairly and lawfully processed;
* Data accuracy is ensured (i.e. all reasonable efforts to ensure data accuracy are undertaken);
* Data are used only in ways that are compatible with the original consent or agreement;
* Relevant national and international regulations regarding data protection will be applied.
Data storage facilities will be maintained in accordance with manufacturers’
guidelines. Data will be backed up at regular intervals, and stored safely and
securely, in accordance with the consortium partners’ organizational policy.
# ETHICAL ASPECTS
Ethical issues that may have an impact on data sharing will be assessed during
the project as part of work package 4, task 4.3 (Management of the research
data generated and collected during the project). Animal trial data collection
follows all national and EU laws.
DELTA-FLU will not generate, collect or deal with personal data.
|
https://phaidra.univie.ac.at/o:1140797
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Horizon 2020
|
1188_E2mC_730082.md
|
# INTRODUCTION
The Data Management Plan (DMP) is a deliverable required for all projects
participating in the ORD Pilot (Open Research Data Pilot). In the previous
work programmes, the ORD pilot included only some areas of Horizon 2020, now,
under the revised version of the 2017 work programme, the Open Research Data
pilot has been extended to cover all the thematic areas of Horizon 2020. It
aim to encourage the data management following the principle "as open as
possible, as closed as necessary".
“ _The ORD pilot aims to improve and maximize access to and re-use of research
data generated by Horizon 2020 projects and takes into account the need to
balance openness and protection of scientific information, commercialization
and Intellectual Property Rights (IPR), privacy concerns, security as well as
data management and preservation questions_ ” (from “Guidelines on FAIR Data
Management in Horizon 2020”).
The structure of the document follows the “Guidelines on FAIR Data Management
in
Horizon 2020”, provided by the Directorate-General for Research & Innovation
of the European Commission
(
_http://ec.europa.eu/research/participants/data/ref/h2020/grants_manual/hi/oa_pilot/h20_
_20-hi-oa-data-mgt_en.pdf_ )
## Scope of the document
The final goal of this document is to show how the research data of the
project are **findable, accessible, interoperable and reusable** (FAIR).
This deliverable describes the data management life cycle for the data to be
collected, processed and/or generated by the project. It includes information
on:
* the handling of research data during and after the end of the project
* what type of data will be collected, processed and/or generated
* which methodology and standards will be applied
* whether data will be shared/made open access and
* how data will be curated and preserved (including after the end of the project)
It reports also some considerations on the ethical aspects and data security
and also an assessment on costs related to data management.
The DMP is intended to be a living document and it will be updated over the
course of the project whenever significant changes arise. In any case, it will
be updated as a minimum in time with the periodic reviews of the project.
# OVERVIEW ON PROJECT DATA
## Data types, collection and generation
<table>
<tr>
<th>
**Data types, collection and generation**
</th>
<th>
</th> </tr>
<tr>
<td>
▪ What types and formats of data will the project generate/collect?
</td>
<td>
The E2mC will use data from crowdsourcing and social media (e.g. images,
videos, text messages). The project will generate, from that data, mainly maps
in support to the Emergency responders, which include a layer relevant to the
social elaborated information.
</td> </tr>
<tr>
<td>
▪ Purpose of the data collection/generation and its relation to the objectives
of the project
</td>
<td>
The main goal of the project is to produce better, more qualitative and more
timely maps using information either to be collected from social media or
solicited from the crowd information, enriching the already available
information from the satellite sources.
</td> </tr>
<tr>
<td>
▪ Expected size of the data
</td>
<td>
The size of the data will be in the range of few Terabytes
</td> </tr>
<tr>
<td>
▪ Origin of the data
</td>
<td>
Crowdsourcing and social media
</td> </tr> </table>
## Existing data
<table>
<tr>
<th>
**Existing data**
</th>
<th>
</th> </tr>
<tr>
<td>
▪ Re-use of existing data
</td>
<td>
Existing datasets from social media and crowdsourcing sources related to past
disaster events will be re-used as well as products delivered by the
Copernicus Emergency management Service. This reuse will enable the testing of
the system on a set of “cold cases”, giving hints and suggestions on the set-
up of the procedures and their reliavility.
</td> </tr>
<tr>
<td>
▪ Way to re-use, to what end
</td>
<td>
Data will be re-used through reprocessing of social media and crowdsourced
data in combination with satellite images and visual interpretation in order
to demonstrate the feasibility of
</td> </tr> </table>
<table>
<tr>
<th>
**Existing data**
</th>
<th>
</th> </tr>
<tr>
<td>
</td>
<td>
deriving useful and geolocated information on the disaster location and extent
form these unconventional sources.
</td> </tr> </table>
## Data usability
<table>
<tr>
<th>
**Data usability**
</th>
<th>
</th> </tr>
<tr>
<td>
▪ To whom might it be useful?
</td>
<td>
These data might be useful to the Service Provider of the Copernicus Emergency
Management Service in order to provide an enhanced service to the European
Commission and, ultimately, to the Civil Protection of the European Member
States and to the Humanitarian Aid actors.
In fact, the outcomes of the project and the released data and maps, can
suggest also other applications of the delivered system, by selecting other
keywords and enabling the location and the mapping of other events of
interest, also exploiting the multilingual capability of the system.
</td> </tr> </table>
# “FAIR” DATA APPROACH
## Data “Findability”
<table>
<tr>
<th>
**Data “Findability”**
</th>
<th>
</th> </tr>
<tr>
<td>
▪ Are the data produced and/or used in the project discoverable with metadata,
identifiable and locatable by means of a standard identification mechanism
(e.g. persistent and unique identifiers such as Digital Object Identifiers)?
</td>
<td>
E2mC deployed a master PostgresSQL/PostGIS database where to store the data
produced by the project (indexes over social media data streams, data
contributed form crowdsourcing, satellite images …). All the data are saved to
this database through a REST interface called
DataAPI. The API is developed by using the Swagger/Open API standard and the
documentation is availabl e _here_ .
</td> </tr>
<tr>
<td>
▪ What naming conventions do you follow?
</td>
<td>
The only naming convention adopted is the one related to the creation of each
specific event workspace: dYYYY-MM-DD_NameOfTheEvent
The values coming from the social networks do not follow any naming
convention, nevertheless the data findability for each kind of resource is
guaranteed through the usage of well defined tags.
</td> </tr>
<tr>
<td>
▪ Will search keywords be provided that optimize possibilities for re-use?
</td>
<td>
Yes, through search interfaces using local cache/db and existing service API
available by social
</td> </tr>
<tr>
<td>
▪ Do you provide clear version numbers?
</td>
<td>
Yes
</td> </tr>
<tr>
<td>
▪ What metadata will be created? In case metadata standards do not exist in
your discipline, please outline what type of metadata will be created and how
</td>
<td>
We will use INSPIRE compliant metadata for the generated geospatial datasets
and services
</td> </tr> </table>
## Data “Accessibility”
<table>
<tr>
<th>
**Data “Accessibility”**
</th> </tr>
<tr>
<td>
▪ Which data produced and/or used in the project will be made openly available
as the default? If certain datasets cannot be shared (or need to be shared
under restrictions), explain why, clearly separating legal and contractual
reasons from voluntary restrictions (Note that in multi-
beneficiary projects it is also possible for specific beneficiaries to keep
their data closed if relevant provisions are made in the consortium agreement
and are in line with the reasons for opting out)
</td>
<td>
Maps and derived information will be made openly available.
As for data from social media and crowdsourcing, it has to be decided which
data can be shared, indeed there could be ethical and legal issues related to
this data that need to be further discussed with the ethical expert.
</td> </tr>
<tr>
<td>
▪ How will the data be made accessible (e.g. by deposition in a repository)?
</td>
<td>
A web-based access will be allowed for working on the project master database
and the project Social&Crowd Platform.
</td> </tr>
<tr>
<td>
▪ What methods or software tools are needed to access the data?
</td>
<td>
A web browser to access the project
Social&Crowd Platform and different FOSS SW package to access the data
generated (images, PDF documents, database)
</td> </tr>
<tr>
<td>
▪ Is documentation about the software needed to access the data included?
</td>
<td>
Yes, it will be part of the metadata
</td> </tr>
<tr>
<td>
▪ Is it possible to include the relevant software (e.g. in open source code)?
</td>
<td>
Yes, the SW of the Social&Crowd Platform will be released as an open source
software for Commission use only
</td> </tr>
<tr>
<td>
▪ Where will the data and associated metadata, documentation and code be
deposited? Preference should be given to certified repositories which support
open access where possible.
</td>
<td>
Yes, are available through the project Social&Crowd Platform _here_ with the
following usr/pwd: demouser/demouser
The project code is stored on the e-GEOS Gitlab repository _here_ . The
credentials to access must be requested at the following email address:
[email protected]_
</td> </tr>
<tr>
<td>
▪ Have you explored appropriate
</td>
<td>
It is not necessary to take any particular
</td> </tr>
<tr>
<td>
**Data “Accessibility”**
</td>
<td>
</td> </tr>
<tr>
<td>
arrangements with the identified repository?
</td>
<td>
arrangement with Gitlab.
</td> </tr>
<tr>
<td>
▪ If there are restrictions on use, how will access be provided?
</td>
<td>
The administrator access to Gitlab will be grated to Commission and Code
maintainers only
</td> </tr>
<tr>
<td>
▪ Is there a need for a data access committee?
</td>
<td>
No
</td> </tr>
<tr>
<td>
▪ Are there well described conditions for access (i.e. a machine readable
license)?
</td>
<td>
No
</td> </tr>
<tr>
<td>
▪ How will the identity of the person accessing the data be ascertained?
</td>
<td>
The access to the master database and to the Social&Crowd platform will be
strictly controlled and credential will be generated/preconfigured through a
service admin facility.
Nonetheless we have no means to ensure that the provided credentials are not
used by a different person
</td> </tr> </table>
## Data “Interoperability”
<table>
<tr>
<th>
**Data “Interoperability”**
</th>
<th>
</th> </tr>
<tr>
<td>
▪ Are the data produced and/or used in the project discoverable with metadata,
identifiable and locatable by means of a standard identification mechanism
(e.g. persistent and unique identifiers such as Digital Object Identifiers)?
</td>
<td>
Yes, intermediate and final data/products will be stored in the project
storage with unique resources identifiers. In some cases (e.g. Social Data),
there will be the need to respect the Terms & Conditions of the specific
Social Network used.
</td> </tr>
<tr>
<td>
▪ What data and metadata vocabularies, standards or methodologies will you
follow to make your data interoperable?
</td>
<td>
The project will use INSPIRE compliant metadata associated, where possible, to
vocabularies such as GEMET
</td> </tr>
<tr>
<td>
▪ Will you be using standard vocabularies for all data types present in your
data set, to allow inter-
</td>
<td>
As far as possible standard vocabularies will be used, exploiting also
progress done by other FP7/H2020 projects in the field of
</td> </tr>
<tr>
<td>
**Data “Interoperability”**
</td>
<td>
</td> </tr>
<tr>
<td>
disciplinary interoperability?
</td>
<td>
standardization and interoperability
</td> </tr>
<tr>
<td>
▪ In case it is unavoidable that you use uncommon or generate project specific
ontologies or vocabularies, will you provide mappings to more commonly used
ontologies?
</td>
<td>
Yes
</td> </tr> </table>
## Data “Re-usability”
<table>
<tr>
<th>
**Data “Re-usability”**
</th>
<th>
</th> </tr>
<tr>
<td>
▪ How will the data be licensed to permit the widest re-use possible?
</td>
<td>
Data and products generated within the Copernicus frame (CEMS) will be
publicly available according to the Copernicus terms and conditions
https://www.copernicus.eu/en/how/howaccess-data ….
Data and products generated within exploitation frame other than Copernicus,
will be available under the license
Creative Commons Attribution +
ShareAlike (CC BY-SA)
https://en.wikipedia.org/wiki/Creative_Co mmons_license
Social data are accessible under the terms and conditions of services
(Twitter,
Youtube and Flickr)
</td> </tr>
<tr>
<td>
▪ When will the data be made available for re-use? If an embargo is sought to
give time to publish or seek patents, specify why and how long this will
apply, bearing in mind that research data should be made available as soon as
possible.
</td>
<td>
Data will be made available for re-use at the end of the project. During the
course of the project, some datasets may be released after validation. There
could be some limitations due to big amount of data.
</td> </tr>
<tr>
<td>
▪ Are the data produced and/or used in the project useable by third parties,
in particular after the end of the project?
If the re-use of some data is
restricted, explain why
</td>
<td>
Yes
</td> </tr>
<tr>
<td>
**Data “Re-usability”**
</td>
<td>
</td> </tr>
<tr>
<td>
▪ How long is it intended that the data remains re-usable?
</td>
<td>
The data will remain re-usable at least for one year after project completion
</td> </tr>
<tr>
<td>
▪ Are data quality assurance processes described?
</td>
<td>
Data Quality assessment will be part of final maps preparation during
demonstration phases. The quality assessment of outputs is ultimately a
project goal.
</td> </tr> </table>
# ETHICAL ASPECTS AND DATA SECURITY
## Ethical aspects
<table>
<tr>
<th>
**Ethical aspects**
</th>
<th>
</th> </tr>
<tr>
<td>
▪ Are there any ethical or legal issues that can have an impact on data
sharing? These can also be discussed in the context of the ethics review. If
relevant, include references to ethics deliverables and ethics chapter in the
Description of the Action (DoA).
</td>
<td>
As stated in the DoA-part B section 5.1.1
_“The E2mC Project involves humans, but only for registering information
relevant to an emergency crisis,_not requiring sensitive_ _information about
identity, health, etc_ ., and as volunteer contributor to a crowdsourcing
platform.” _
So, the ethical/legal issues in place are:
1. Management of personal data (i.e. registering information)
2. Information related to the images and videos collected (via crowdsourcing application) during a disaster as persons or vehicles can potentially be detected
Detailed information on privacy/confidentiality and the procedures that will
be implemented for data collection, storage, access, sharing policies,
protection, retention and destruction along with the confirmation that they
comply with national and EU legislation are provided in D7.1 and D7.2.
</td> </tr>
<tr>
<td>
▪ Is informed consent for data sharing and long term preservation included in
questionnaires dealing with personal data?
</td>
<td>
The Digital Informed Consent form is filled when registering and creating an
account on the platform, to agree to the Terms and Conditions and to the
Privacy Policy. Details are provided in D7.1. No other use of personal data is
performed by the platform service.
</td> </tr> </table>
## Data security
<table>
<tr>
<th>
**Data security**
</th>
<th>
</th> </tr>
<tr>
<td>
▪ What provisions are in place for data security (including data recovery as
well as secure storage and transfer of sensitive data)?
</td>
<td>
The master database will be stored in a controlled cloud environment to ensure
high data security and reliability
</td> </tr>
<tr>
<td>
▪ Is the data safely stored in certified repositories for long term
preservation and curation?
</td>
<td>
No.
See also 3.4 Data “Re-usability”
</td> </tr> </table>
# ALLOCATION OF RESOURCES
## Costs related to data management
<table>
<tr>
<th>
**Costs related to data management**
</th>
<th>
</th> </tr>
<tr>
<td>
▪ What are the costs for making data FAIR in your project?
</td>
<td>
Most of the FAIR Data Principles are already taken into account. Further
evaluation will be made on the basis of the final data ensemble (code
repository, demonstration phases) in terms of code, output data and source
data.
</td> </tr>
<tr>
<td>
▪ How will these be covered? Note that costs related to open access to
research data are eligible as part of the Horizon 2020 grant (if compliant
with the Grant Agreement conditions)
</td>
<td>
Data will be stored in a commercial cloud facility. Cost for storage and
management are included in the project budget to cover the period of usage
within the project lifecycle.
</td> </tr>
<tr>
<td>
▪ Are the resources for long term preservation discussed (costs and potential
value, who decides and how what data will be kept and for how long)?
</td>
<td>
The amount of data stored for each event is in the order of tens of MB. Given
that, no rolling archive policies have been put in place. Nevertheless, the
system database has been designed in a way that each activation is not related
to another one. This separation allows the administrator to backup and put
offline old data in any moment, if needed.
</td> </tr> </table>
## Data management responsible
The nominated data management responsible is: Mariano Alfonso Biscardi
(Software Developer and Architect at e-Geos).
# CONCLUSIONS
This DMP issue contains all information available at this point in time,
further information will be included in the next issues of the document.
The E2mC project rely on the collection and use of a variety of crowdsourced
data relevant to an emergency crisis. Such data (e.g. images, video, text
messages etc. not requiring sensitive information about identity, health,
etc.), will be collected through social media, crowdsourcing means and through
volunteer contributors. They will be further analysed, processed (e.g.
geolocated) and arranged for exploitation in the project master database and
in the Social&Crowd Platform developed by the project consortium.
Further to this, any ethical and legal issues related to the usage and sharing
of the collected data shall be carefully discussed with the ethical expert as
to put in place proper informed consent forms, disclaimer, protection and
security measures etc. in line with personal data protection policies and
applicable IPR, if any.
Operators of the service (e.g., Project partners, commission personnel,
Copernicus Emergency services providers…) will access the project master
database and the Social&Crowd Platform on a controlled basis (username/pwd
mechanism).
All the data will be stored in an external Cloud facility for the duration of
the project.
At the finalisation of the Service Design phase of the project, details on the
identification of the repositories or archive as well as data management
responsibilities and roles will be discussed and allocated to partners.
|
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Horizon 2020
|
1192_SCREEN_730313.md
|
# 1\. Data Summary
What is the purpose of the data collection/generation and its relation to the
objectives of the project?
_The regions participating to SCREEN sent data on their smart specialization
strategies with specific focus on the areas and sectors to be prioritized for
the diffusion of circular economy focus sectors, R &D capabilities, education
capabilities, and emerging ideas. _
What types and formats of data will the project generate/collect?
_Excel files only, like the one shown in the following figure._
Will you re-use any existing data and how?
_Due to the scope of the Action, that is a Coordination Action, only existing
data provided by the participating regions will be collected and analysed. The
main scope is to collect data in a way that can be used for the project
purposes, basically matchmaking between different regional initiatives and
possible synergies._
What is the origin of the data?
_Regional data base of other regional sources, only from the participating
regions_
What is the expected size of the data?
## Extremely variable, depending on their availability in each region;
however, for the project purposes, they are contained in small excel files
like the following one
To whom might it be useful ('data utility')?
_To other regions willing to use the tools developed by the project for the
analysis of potential cross-regional value chains. Also to relevant public and
private stakeholders interested to be part of a circular economy value chain._
# 2\. FAIR data
## **2.1 Making data findable, including provisions for metadata**
Are the data produced and/or used in the project discoverable with metadata,
identifiable and locatable by means of a standard identification mechanism
(e.g. persistent and unique identifiers such as Digital Object Identifiers)?
## Not applicable: only Excel files
What naming conventions do you follow?
_Name of the deliverable, according to the list in the Grant Agreement, with
extension xls or xlsx._
Will search keywords be provided that optimize possibilities for re-use?
_No_
Do you provide clear version numbers?
## Yes, complying with the version of the deliverable or internal report
What metadata will be created? In case metadata standards do not exist in your
discipline, please outline what type of metadata will be created and how.
## No metadata will be created
_**2.2 Making data openly accessible** _
Which data produced and/or used in the project will be made openly available
as the default?
## Data on the smart specialization of the participating regions, related to
the circular economy. Data on the mapping made by each region about potential
circular economy value chains
How will the data be made accessible (e.g. by deposition in a repository)?
_All the technical reports are public and available on the project website
_http://www.screenlab.eu/Deliverables.html_ . These reports also contain the
above mentioned excel files. _
What methods or software tools are needed to access the data?
_Any tool able to read Excel files_
Is documentation about the software needed to access the data included?
_No need of any documentation_
Is it possible to include the relevant software (e.g. in open source code)?
## Free software available on the Internet (such as Open Office and similar)
Where will the data and associated metadata, documentation and code be
deposited? _Not applicable_
Have you explored appropriate arrangements with the identified repository? Not
applicable
If there are restrictions on use, how will access be provided?
_No restrictions_
Is there a need for a data access committee?
## No
Are there well described conditions for access (i.e. a machine readable
license)?
How will the identity of the person accessing the data be ascertained? _Not
applicable_
### 2.3. Making data interoperable
Are the data produced in the project interoperable, that is allowing data
exchange and re-use between researchers, institutions, organisations,
countries, etc. (i.e. adhering to standards for formats, as much as possible
compliant with available (open) software applications, and in particular
facilitating re-combinations with different datasets from different origins)?
## Yes, Excel file format (xls od xlsx) compliant with open software
applications
What data and metadata vocabularies, standards or methodologies will you
follow to make your data interoperable?
## No metadata, the standard adopted is the excel file one
Will you be using standard vocabularies for all data types present in your
data set, to allow inter-disciplinary interoperability? In case it is
unavoidable that you use uncommon or generate project specific ontologies or
vocabularies, will you provide mappings to more commonly used ontologies?
## Not applicable
_**2.4. Increase data re-use (through clarifying licences)** _
How will the data be licensed to permit the widest re-use possible?
_Not applicable_
When will the data be made available for re-use?
## Immediately after the publication of the related SCREEN deliverable
Are the data produced and/or used in the project useable by third parties, in
particular after the end of the project? If the re-use of some data is
restricted, explain why. _Yes, without any restriction_
How long is it intended that the data remains re-usable?
_3 years or more_
Are data quality assurance processes described?
_No_
# 3\. Allocation of resources
What are the costs for making data FAIR in your project?
## No specific cost, being public deliverable containing excel files
How will these be covered? Note that costs related to open access to research
data are eligible as part of the Horizon 2020 grant (if compliant with the
Grant Agreement conditions). _Not applicable, covered by the project_
Who will be responsible for data management in your project?
## Project Coordinator
Are the resources for long term preservation discussed (costs and potential
value, who decides and how what data will be kept and for how long)?
_No_
# 4\. Data security
What provisions are in place for data security (including data recovery as
well as secure storage and transfer of sensitive data)?
## Back up assured by the service provider hosting the SCREEN web site, plus
the deliverable repository managed by the European Commission
Is the data safely stored in certified repositories for long term preservation
and curation?
_Not applicable, see the previous answer._
# 5\. Ethical aspects
Are there any ethical or legal issues that can have an impact on data sharing?
These can also be discussed in the context of the ethics review. If relevant,
include references to ethics deliverables and ethics chapter in the
Description of the Action (DoA).
## No
Is informed consent for data sharing and long term preservation included in
questionnaires dealing with personal data?
## Yes
**6\. Other issues**
Do you make use of other national/funder/sectorial/departmental procedures for
data management? If yes, which ones?
## No
|
https://phaidra.univie.ac.at/o:1140797
|
Horizon 2020
|
1195_R2PI_730378.md
|
# EXECUTIVE SUMMARY
The Data Management Plan (DMP) is a deliverable of the R2PI project, which is
funded by Horizon 2020 Programme with Grant Agreement number 730378\.
The Transition from Linear 2 Circular: Policy and Innovation (R2PI) project is
participating in the Horizon 2020 pilot action on Open Access to Research
Data. Following the Guidelines on FAIR Data Management in Horizon 2020 1 ,
this Data Management Plan (DMP) describes the data management life cycle of
the R2PI project.
Creating
data
Processing
data
Analyzing
data
Presenting
data
Giving
access
to
data
Reusing
data
Figure 1 [Data Management Lifecycle]
The DMP includes an overview of the datasets to be generated by R2PI and
specific conditions attached to them. Specifically, the DMP outlines the
handling of research data during and after the the project, including
* types of data to be collected and/or generated and processed
* methodology and standards to be applied
* if data will be shared/made openly accessible and
* how data will be curated and preserved (including after the end of the project)
In combination with Deliverables 10.1 & 10.2 Ethical Requirements, the DMP
specifies measures to ensure data are properly shared while ensuring the
privacy of informants and respondents. This DMP first describes the R2PI
project and overall principle of data collection and access. It then
introduces the R2PI dataset overview. The DMP closes with the introduction of
specific datasets to be collected, following the template in the the
Guidelines on FAIR Data Management in Horizon 2020.
This is the 1st version of the DMP. The DMP is a living document, which will
be elaborated or updated along with the project progress or when significant
changes occur.
1
# INTRODUCTION OF R2PI AND ITS RESEARCH METHODOLOGY
R2PI is a EU-funded project under the Horizon 2020 programme, which examines
the shift from the broad concept of a Circular Economy (CE) to one of a
Circular Economy Business Models (CEBM), by tackling both market failure
(business, consumers) and policy failure (conflicts, assumptions, unintended
consequence). The goal of the R2PI project is to develop sustainable business
models that would facilitate the circular economy and to propose policy
packages and business guidelines that will support implementation of these
business models.
To achieve the concept’s ambitions, the research design employs mixed-methods,
with a strong emphasis on case studies but also including desktop research,
feasibility assessments (including surveys where applicable), policy
formulation & stakeholder involvement. The ultimate goal of the project is to
see the widespread implementation of the CE based on successful Business
Models to ensure sustained economic development, to minimize environmental
impact and to maximize social welfare.
# GENERAL DATA MANAGEMENT PRINCIPLES
The DMP of R2PI will follow the principle "as open as possible, as closed as
necessary", as suggested in EC Open Research Data (ORD) pilot. Meanwhile,
considering the ethical requirements, the open data plan should not represent
risks for compromising the privacy of informants participating in the
different interviews, surveys or case studies. The DMP will therefore assess
when, what and how data can be shared within a sound research ethical
framework.
The figure below illustrates the policy to ensure the R2PI project has open
access to research data and publications.
Figure 2 [R2PI research data and publication]
# R2PI DATASET SUMMARY
## Overview
R2PI seeks to combine the formal analysis of business models for circularity
with on-going commercial practice. Specifically, it draws on case-studies of
real enterprises in a breadth of sectors to illustrate, and in some cases
refute or correct, those elements considered to be important for successful
Circular Economy business models. To achieve the concept’s ambitions, the
project will focus on case-studies as the core of its work. Particularly, R2PI
will generate data designed to study the success factors of circular business
model transition through 15 in-depth case studies and policy analysis.
R2PI hybrid methodology framework is built upon a combination of
1. **scientific methods and tools** , based on a broad understanding of systems innovation approaches, including technological and non-technological innovation, such as business models’ evaluation for circularity, behavioural economics and consumer behaviour analysis, rigorous environmental measurements and ex-post policy and regulation analysis and policy formation (policy packages methodology), and
2. **on-going “on the ground”** work through interactive case studies, to investigate and foster circular business model good practices. Research data in R2PI
**Research data** refers to information, in particular facts or numbers,
collected to be examined and considered as a basis for reasoning, discussion,
or calculation. In a research context, examples of data include statistics,
results of experiments, measurements, observations resulting from fieldwork,
survey results, interview recordings and images. The focus is on research data
that is available in digital form (European Commission 2017).
R2PI has 3 main stages to collect research data:
### (1) General conceptualization and scoping
To achieve the project ambitions and objectives, R2PI will focus on case-
studies as the core of its work. A preliminary phase (Work Package 2 – WP2)
will set the scene it terms of a conceptual framework and the policy context,
through desktop research methodology, intended to ground further research in
time-space context. While defining the elements of Business Models for
Circular Economy and thereby selection criteria for the case-studies, it will
determine the boundaries of the scope. This conceptual and contextual
framework will therefore further support the consortium in identifying and
selecting relevant case-studies (WP3).
### (2) Case study selection and analysis
R2PI will select 15 case studies (WP3) and conduct in-depth analysis,
including on-going (from WP4) and piloted (from WP5) circular business models.
The individual analysis will take into account the value-chain of each
specific case: suppliers, customers, stakeholders, regulators and where the
initiative lay (e.g. entrepreneurial, corporate, municipal, etc.), intended
and unintended consequences of the wider policy package, as well as any nudges
that may have been in place – whether by design or by default. The project
shall inventory the case-studies and their results, extracting commonalities,
differences and key factors of success (WP6).
### (3) Policy packages and business guidelines development
The ex-post policy analysis is conducted in Task 2.4 to evaluate the extent to
which policy measures to promote circular economy both induce economic,
environmental and social impacts, and facilitate the uptake and functioning of
these business models. This task promotes two of the project's objectives.
First, the evaluation of individual policy instruments is part of building
systematic understanding of the role of policy, the way it may incentivize
and/or hinder the road to success. Second, it is a necessary first step in
building policy packages to promote circular economy business model (CEBM).
This evaluation will be carried out in the context of WP2 using qualitative
methods.
Further, WP7 will develop policy packages for enabling policymakers and
business guidelines for enabling businesses to implement effective measures to
facilitate the shift to circular economy and benefit from its advantages. The
policy packages methodology is based on a mix of tools complementing one
another, including policy instruments’ relation matrix, causal mapping
technique, actors’ relation analysis and more.
The data management overview, including data collection and deposition
summaries, is summarized as below.
Figure 3 [R2PI research data management method]
Based on the 3 stages, the key R2PI datasets to be collected and processed are
listed in the table below. The descriptions of each dataset, following the
FAIR principle, are provided in the following sections.
This list is indicative and may be adapted in the next versions of the DMP,
taking into consideration project developments.
<table>
<tr>
<th>
No.
</th>
<th>
Dataset name
</th>
<th>
Brief introduction
</th>
<th>
Responsible partners
</th>
<th>
Related work package(s)
</th> </tr>
<tr>
<td>
1
</td>
<td>
Economic
Actor Survey
</td>
<td>
Investigating the drivers, success factors and impacts of CEBM
</td>
<td>
* University of Malta
* Sapir Academic College
</td>
<td>
WP4
</td> </tr>
<tr>
<td>
2
</td>
<td>
CEBM Case
Study
</td>
<td>
Investigating the drivers, success factors and impacts of
CEBM
</td>
<td>
* University of Malta
* University of Santiago de Compostela
* Carbon Trust
* Business Models Inc.
* CSCP
</td>
<td>
WP4
</td> </tr> </table>
# SPECIFIC R2PI DATASETS DESCRIPTION
The DMP template suggested in the Guidelines on FAIR Data Management in
Horizon 2020 is used to describe the key datasets and specific conditions
attached, according to the current plans for gathering and analysis of data as
well as the methods and processes foreseen to be applied to ensure compliance
with ethics requirements. Economic Actor Survey
<table>
<tr>
<th>
**DMP component**
</th>
<th>
**Issues to be addressed**
</th> </tr>
<tr>
<td>
1\. Data
summary
</td>
<td>
* **Purpose and relation to the project objective** : The Economic Actor Survey will seek to gauge the extent to which firms engage in circular economy practices and measure their degree of circularity, as well as to understand the key motivators, barriers and policies that influence firms’ level of circularity. The ultimate aim is to assist businesses in their route to circularity and the adoption of new circular economy business models, and inform policies both at the national and EU-level.
* **Data formats:**
* Data will be collected via an online survey, and will consist of three key sections, namely questions related to business’s circular economy activities and/or policies (e.g. waste minimisation initiatives, environmental auditing, etc.), the motives, barriers and policies that influence the uptake of circular activities, and basic business characteristics (e.g. firm size, industry). No personal information regarding company or respondent name, address or contact details will be collected;
* Where possible, we will use online and/or electronic archives. This will involve extracting and processing quantitative and qualitative data, including participants, objectives and outcomes;
* Data will be input and stored in a spreadsheet format (e.g. Excel), to ensure accessibility to partners and researchers;
* No personal information will be stored in the project database, and any final reports or publications based on this survey will present information only in terms of averages or ranges.
* **Origin of the data and if existing datasets is being reused:** The University of Malta and Sapir Academic College with collaboration from the partners will form the survey questions. The project partners will conduct the survey with the economic actors of circular economy. The targets of the survey are businesses operating in Europe. The businesses already form part of existing business directories and databases which are held by the project consortium partners.
* WP4 will include the identification and re-use of existing databases for addressing all the relevant issues and stakeholders. The survey will not however build on existing databases.
* **Data utility:** The data might be useful for the research community (academics) on investigating circular economy, especially on business models and their market uptake. The data will also be useful for policymakers in order to analyse different barriers and policies that influence circularity.
</td> </tr>
<tr>
<td>
2\. FAIR Data
</td>
<td>
R2PI will develop metadata that is compliant with the Data Documentation
Initiative (DDI), the most relevant international standard for describing the
data produced by
</td> </tr>
<tr>
<td>
2.1. Making data findable, including provisions for metadata
</td>
<td>
surveys and other observational methods in the social, behavioural, economic,
and health sciences.
The Guidelines on FAIR Data Management in Horizon 2020 has listed suggestions
on further support for developing the DMP. Following the listed support, the
Consortium has chosen ZENODO 2 as the scientific publication and data
repository for the project outcomes. All metadata will be stored on ZENODO in
JSON-format according to a defined JSON schema. Metadata can be exported in
several standard formats such as MARCXML, Dublin Core, and DataCite Metadata
Schema (according to the OpenAIRE Guidelines).
These types of metadata will be produced and archived through ZENODO:
* Data Citation with Digital Object Identifier (DOI)
* Keywords to facilitate search and optimize possibilities for re-use
</td> </tr>
<tr>
<td>
2.2 Making data openly
accessible
</td>
<td>
* R2PI will make selected data available on the project website.
* The R2PI survey data and metadata will be deposited in ZENODO, providing open access to data files and metadata over standard protocols such as HTTP and OAIPMH.
</td> </tr>
<tr>
<td>
2.3. Making
data interoperable
</td>
<td>
• The data will be made available on ZENODO under a CC-BY licence. A 24-month
embargo period following the completion of the project will be applied to
allow research findings to be written up.
</td> </tr>
<tr>
<td>
2.4. Increase data re-use (through clarifying licences)
</td>
<td>
* Data objects will be deposited in ZENODO under open access to data files and metadata, permitting its use and reuse, as well as protecting privacy of its users.
* The aggregated dataset will be disseminated as soon as possible through the project website and other R2PI dissemination activities (WP9). In the case of the underlying data of a publication, this might imply an embargo period for green open access publications.
</td> </tr>
<tr>
<td>
3\. Allocation of resources
</td>
<td>
* Overall, the project coordinator (CSCP) will direct the data management process, with the Executive Management Board of the project responsible for ensuring that metadata production, cross-checks, back-up and other quality control activities are maintained. The lead researchers of the respective tasks will be responsible for routine supervision of the dataset development.
* In principle, all partners are responsible for survey data generation, metadata production and data quality, coordinated by the task leaders (UoM and Sapir).
* Dataset storage, backup, archiving and sharing will be in the majority of cases the responsibility of the partners who own the data and/or the servers in which they will be stored.
</td> </tr>
<tr>
<td>
4\. Data
security
</td>
<td>
The data will be backed up regularly, including
* sharing data through ZENODO, where data files and metadata are backed up nightly and replicated into multiple copies in the online system;
* uploading research data to Freedcamp 3 , the project management system used
</td> </tr> </table>
<table>
<tr>
<th>
</th>
<th>
by R2PI consortium;
* regular email sharing with the partners to ensure up-to-date versions are stored on partners’ server;
* requesting the task leaders to back up qualitative data on a regular basis and include clear labelling of versions and dates in metadata
</th> </tr>
<tr>
<td>
5\. Ethical
aspects
</td>
<td>
* **Informed consent** : Prior to the survey, informants will be given enough information about their involvement and what it means for them to be able to make an informed decision as to whether they wish to participate. The principle of informed consent will also apply to the storage of their data and the use of their data in any analyses or reports/publications.
* **Confidentiality** : throughout all stages of the survey process (including all stages of data sharing, analysis, report-writing etc.) the identity of participants will be concealed.
* All research and all data storage will be closely monitored for compliance with the appropriate ethical guidelines. Each researcher will be responsible for reviewing the ethical concerns that could be raised in relation to each and every element of their research. In doing so they will have regard to the ethical guidelines of their professional organisation, their institution, and pan-European guidelines. In all cases, where there are a number of different guidelines, all research will conform to the more stringent of the available criteria.
* As it is impossible to predict with 100% certainty the ethical issues that will arise, the Executive Board will encourage debate within and between work packages on ethical standards in research and what they require in this project.
</td> </tr>
<tr>
<td>
6\. Other
</td>
<td>
N/A
</td> </tr> </table>
## Circular Economy Business Model Case Studies
<table>
<tr>
<th>
**DMP component**
</th>
<th>
**Issues to be addressed**
</th> </tr>
<tr>
<td>
1\. Data summary
</td>
<td>
• **Purpose and relation to the project objective** : R2PI combines the formal
analysis of business models for circularity with on-going commercial practice.
15 casestudies of circular economy business models form the core of the
project work. Specifically, the case studies will contribute to the following
objectives:
* To collect empirical data to understand the factors driving (hindering) the success of Circular Economy Business Models (CEBM);
* To collect empirical data that would support and contribute to the definition and categorisation of CEBM;
* To analyse success in terms of environmental assessment, social and economic parameters;
* To understand the interests and roles played by different actors along the value chain;
* To identify the role of innovation and the knowledge infrastructure in the implementation of CEBM;
* To understand the role of Information and Communication Technology (ICT) in supporting the development of CEBM;
</td> </tr> </table>
<table>
<tr>
<th>
</th>
<th>
* To quantify the economic, social and environmental effects of specific CEBM and to estimate its expanding impact;
* To identify potential policies to support the transition to a Circular Economy
* **Data formats:** The case studies will generate various datasets from diverse research methods combined, such as desk research, interviews, surveys, and interactive workshops, etc. Where possible, we will use online and/or electronic archives. Data will be recorded in various format, such as
* Microsoft Word 2007 for text based documents (e.g. transcript of
interviews);
* MP3 or WAV for audio files;
* Quicktime or Windows Media Video for video files (e.g. video of workshops and interviews);
* SAV files for storing quantitative data analysis;
These file formats have been chosen because they are accepted formats in
widespread use. Files will be converted to open file formats where possible
for ensuring long term storage.
* **Origin of the data and if existing datasets are being reused:** 15 cases will be selected based on defined criteria, which should be sufficient to allow making generic recommendations in terms of policy making and business models transferability at the level of the EU. After defining the list of cases, desk research, in-depth interviews, dynamic group discussions and surveys, etc. will be conducted. Data will also be collected by the core team of R2PI through on-site visits and in-depth stakeholder interviews for those cases demonstrating particular innovation or circularity. After the data collection, a case study inventory database will be developed, incorporating the case studies analysed. The categorical attributes of each case study will be documented in this inventory to enhance the transparency of the findings and strengthen the repeatability and upscale-ability of the research and best practices identified.
* **Data utility** : the data might be useful for researchers on investigating circular economy, especially on business model drivers, success factors and impacts.
</th> </tr>
<tr>
<td>
2. FAIR Data
2.1. Making data findable, including provisions for metadata
</td>
<td>
R2PI will develop metadata that is compliant with the Data Documentation
Initiative (DDI), which is the most relevant international standard for
describing the data produced by surveys and other observational methods in the
social, behavioural, economic, and health sciences.
The Guidelines on FAIR Data Management in Horizon 2020 has listed suggestions
on further support for developing DMP. Following the listed support, the
Consortium has chosen ZENODO as the scientific publication and data repository
for the project outcomes. All metadata is stored on ZENODO in JSON-format
according to a defined JSON schema. Metadata can be exported in several
standard formats such as MARCXML, Dublin Core, and DataCite Metadata Schema
(according to the OpenAIRE Guidelines).
These types of metadata will be produced and archived through ZENODO:
* Data Citation with Digital Object Identifier (DOI)
* Keywords to facilitate search and optimize possibilities for re-use
</td> </tr>
<tr>
<td>
2.2 Making data openly
accessible
</td>
<td>
* An inventory of cases will be developed. This inventory will consist of a tabular collection of the main attributes/context of each case study. It will synthesize the characteristics of the CEBM selection, with respect to features including size, impact level, composition, industrial sector, impact on environment, society and the economy and upscale-ability.
* Full data access policy will be restricted to WP4 participants, in order to protect the sensitive information of the companies. Access to the audio and video recordings of interviews will only be provided to bona fide researchers under a data sharing agreement.
* Besides, the R2PI survey data and metadata will be deposited in ZENODO, providing open access to data files and metadata over standard protocols such as HTTP and OAI-PMH.
</td> </tr>
<tr>
<td>
2.3. Making
data interoperable
</td>
<td>
* The data will be stored in widely applied formats to allow long-term and wide use.
* The data will be made available on ZENODO under a CC-BY licence. A 24-month embargo period will be applied to allow research findings to be written up.
</td> </tr>
<tr>
<td>
2.4. Increase data re-use (through clarifying licences)
</td>
<td>
* Data objects will be deposited in ZENODO under open access to data files and metadata, permitting its use and reuse, as well as protecting privacy of its users.
* The aggregated dataset will be disseminated as soon as possible through the project website and other R2PI dissemination activities (WP9). In the case of the underlying data of a publication, this might imply an embargo period for green open access publications.
</td> </tr>
<tr>
<td>
3\. Allocation of resources
</td>
<td>
* Overall, the project coordinator (CSCP) will direct the data management process, with the Executive Management Board of the project responsible for ensuring that metadata production, cross-checks, back-up and other quality control activities are maintained. The lead researchers of the respective tasks will be responsible for routine supervision of the dataset development.
* In principle, all partners are responsible for research data generation, metadata production and data quality, coordinated by the task leader (Carbon Trust).
* Dataset storage, backup, archiving and sharing will be in the majority of cases the responsibility of the partners who own the data and/or the servers in which they will be stored.
</td> </tr>
<tr>
<td>
4\. Data
security
</td>
<td>
The data will be backed up regularly, including
* sharing data through ZENODO, where data files and metadata are backed up nightly and replicated into multiple copies in the online system;
* uploading research data to Freedcamp 4 , the project management system used by R2PI consortium;
* regular email sharing with the partners to ensure up-to-date versions are stored on partners’ server;
* requesting the task leaders to back up qualitative data on a regular basis and include clear labelling of versions and dates in metadata
Extra resources, such as physical storage media and cloud, are needed to
</td> </tr> </table>
<table>
<tr>
<th>
</th>
<th>
accomplish the storage and maintenance activities described above.
</th> </tr>
<tr>
<td>
5\. Ethical
aspects
</td>
<td>
* **Informed consent** : Prior to the interviews, survey and workshops, informants will be given enough information about their involvement and what it means for them to be able to make an informed decision as to whether they wish to participate. The principle of informed consent will also apply to the storage of their data and the use of their data in any analyses or reports/publications.
* **Confidentiality** : throughout all stages of the research process (including all stages of data sharing, analysis, report-writing etc.) the identity of participants will be concealed, unless prior consent to reveal these names is granted beforehand by participating firms.
* All research and all data storage will be closely monitored for compliance with the appropriate ethical guidelines. Each researcher will be responsible for reviewing the ethical concerns that could be raised in relation to each and every element of their research. In doing so they will have regard to the ethical guidelines of their professional organisation, their institution, and pan-European guidelines. In all cases, where there a number of different guidelines, all research will conform to the more stringent of the available criteria.
* As it is impossible to predict with 100% certainty the ethical issues that will arise, the Executive Board will encourage debate within and between work packages on ethical standards in research and what they require in this project.
</td> </tr>
<tr>
<td>
6\. Other
</td>
<td>
• N/A
</td> </tr> </table>
|
https://phaidra.univie.ac.at/o:1140797
|
Horizon 2020
|
1196_EUCalc_730459.md
|
# Executive Summary
This deliverable outlines the first version of the EU calculator Data
Management Plan. The DMP as outlines only deals with process data (not
personal data gather during the EU calculator workshops). Accordingly, this
document provides:
6. the characterization of the main types of data expected to be collected and produced during the time frame of the project,
7. the list of licences detailing data re-use policies and intellectual property rights to be adopted,
8. the list of repositories and strategies to make the data accessible after the termination of the project,
9. the first version of the metadata standards to be adopted by the EU calculator both for collected and produced data.
10. The first outline of model documentation guidelines.
# Introduction
## Importance of the plan and objectives
The appropriate management of data is an essential, often forgotten,
undertaking of responsible research. At the start of a new research project,
it is important to lay down the basic principles relating to data and data
management. The creation of a shared Data Management Plan (DMP) at the
beginning of the project meets the requirement of informing the involved
partners on important procedures and processes regarding data collection,
processing, storing and distribution. Furthermore, the DMP is pivotal in
guaranteeing that the data collected and processed can be easily and properly
shared within the consortium and distributed beyond the project lifetime. A
data management plan helps achieve optimal handling, organizing, documenting
and enhancing of research data. It is particularly important for facilitating
data sharing, ensuring the sustainability and accessibility of data in the
long-term, and allowing data to be reused for future research.
For maximum effectiveness, the DMP must start when research is being designed
and needs to consider both how data and information will be managed during the
research and how they will be shared afterwards. This involves thinking
critically of how research data can be shared, what might limit or prohibit
data sharing, and whether any steps can be taken to remove such limitations.
In the context of the European Calculator project, plans regarding the usage
of data and model documentation started during its preparation phase. The time
and thoughts devoted to the data management issues were preliminary and hence
it is now time to undertake a more concerted effort in elaborating the MP for
the European Calculator project. The DMP for a research project in the H2020
program should elaborate on the following aspects (without any particular
order of importance).
* Handling of research data during and after the end of the project
* What data will be collected, processed and/ or generated
* Which methodology and standards will be applied
* Whether data will be shared/made open access and how data will be curated and preserved (including after the end of the project)
These aspects are a mandatory requirement in the Programme Guidelines on FAIR
Data Management in Horizon 2020 1 . The guidelines from the European
Commission on the requirements for the DMP focus on the issues of data
documentation, standards applied and data re-use.
During the lifetime of the European Calculator project the DMP will be updated
in D11.7 (Month 35). The DMP described in the sections below is to be taken as
a first iteration document to synchronize the knowledge of partners on their
datarelated responsibilities and to outline the major guiding principles of
the EU calculator project in regard to its data policy. I addition, future
iterations will also explore potential synergies of the EU calculator DMP with
those being developed by INNOPATHS and REINVENT.
## FAIR data principles
The open data policy of the European Union favours the implementation of the
G8 Open Data Charter 2 and the FAIR Guiding Principles 3 for scientific
data. The latter highlights core principles as enumerated below:
**To be Findable:**
* Data are assigned a globally unique and persistent identifier
* Data are described with rich metadata
* Metadata clearly and explicitly include the identifier of the data it describes Data are registered or indexed in a searchable resource
**To be Accessible:**
* Data are retrievable by their identifier using a standardized communications protocol
* The protocol is open, free, and universally implementable
* The protocol allows for an authentication and authorization procedure, where necessary
* Metadata are accessible, even when the data are no longer available
**To be Interoperable:**
* Data use a formal, accessible, shared, and broadly applicable language for knowledge representation.
* Data use vocabularies that follow FAIR principles
* Data include qualified references to other data
**To be Reusable:**
* Data are richly described with a plurality of accurate and relevant attributes
* Data are released with a clear and accessible data usage license
* Data are associated with detailed provenance
* Data meet domain-relevant community standards
The EU calculator project will adhere to these principles during the
elaboration and fulfilment of its DMP.
# Data origin and type
The European calculator project will make use of a number of heterogeneous
data sources that can be broadly classified as external and internal to the
project. By external it is meant that datasets are acquired from institutions
external to the European calculator consortium. By internal it is meant that
the data is owned by the partners or generated within the European Calculator
activities. As a rule, external open-datasets that are regularly updated will
be favoured although one cannot discard the possibility of the project
requiring particular external datasets subject to licensing.
## External data
### Open sources
The European calculator project will connect to open data sources that are
regularly maintained and updated from European and Global institutions. For
example, it is foreseen to substantially rely on Eurostat and OECD data, both
as model baseline and as input for statistical relations to quantify some of
the model dynamics. External data repositories such the Database on Shared
Socioeconomic Pathways hosted at IIASA 4 and climate data from initiatives
like CORDEX 5 and the ODYSEE database 6 are also foreseen to be accessed
and data retrieved during the running time of the project. Databases from
existing and closed EU-funded projects such as Heat Road Maps 7 project will
also be scanned for their potential in supplying the European calculator
project with data. For example datasets from the TRACCS 8 project or the EU
Buildings Database 9 are also expected to be used during the project.
### Paying sources
At the time of writing there is not yet a clear indication that datasets
falling into this category are required. This section will be updated in the
next iteration the ECDMP.
## Internal data
In regard to internal datasets, these will refer mostly to the data generated
during the lifetime of the project and are foreseen to comprise mostly model
outputs and expert opinions gathered during the sectoral expert-consultation
workshops. Regarding the latter, there is in place a common procedure to
guarantee confidentiality of personal data (see Del. 12.1). The expert
consultation workshops will supply the team of the European Calculator with
opinions on the modelling approaches undertaken and evidence base support on
the choice of particular ambition levers.
To a lesser extent, datasets and model processes owned by the institutions
comprising the European Calculator project will also be used. For example,
published datasets like the BPIE Building observatory database or dietary
patterns and decarbonisation pathways will be used to inform on the
development of the model.
## Data types
The following section describes the current data types forecast to be
generated during the project. Note that this is a preliminary judgement based
on the author’s understanding of the EU Calculator model. This section will be
updated periodically as part of the ECDMP life cycle.
<table>
<tr>
<th>
**Identifier**
</th>
<th>
**Label**
</th>
<th>
**Description**
</th>
<th>
**Type**
</th>
<th>
**Main responsible**
</th>
<th>
**Access**
</th> </tr>
<tr>
<td>
**OTS**
</td>
<td>
Observed time
series (historical data)
</td>
<td>
Observations of climate, socio-
economic variables and emissions.
</td>
<td>
Numeric
</td>
<td>
WP leaders of the respective modules.
</td>
<td>
Public
</td> </tr>
<tr>
<td>
**FTS**
</td>
<td>
Future time series
(generated data)
</td>
<td>
Projections of climate, socio-
economic variables and emissions.
</td>
<td>
Numeric
</td>
<td>
WP leaders of the respective modules.
</td>
<td>
Public
</td> </tr>
<tr>
<td>
**LL**
</td>
<td>
Levels of levers
</td>
<td>
Levels of technology or lifestyle ambition.
</td>
<td>
Numeric
</td>
<td>
WP leaders of the respective modules.
</td>
<td>
Public
</td> </tr>
<tr>
<td>
**CP**
</td>
<td>
Constant parameters
</td>
<td>
Time-invariable parameters required for the model.
</td>
<td>
Numeric
</td>
<td>
WP leaders of the respective modules.
</td>
<td>
Public
</td> </tr>
<tr>
<td>
**MC**
</td>
<td>
Model/module code
</td>
<td>
Source code of modules and model.
</td>
<td>
Code
</td>
<td>
WP leaders of the respective modules & CLIMACT.
</td>
<td>
Public
</td> </tr>
<tr>
<td>
**MD**
</td>
<td>
Model/module documentation
</td>
<td>
Documentation of model/module code
</td>
<td>
Text
</td>
<td>
WP leaders of the respective modules & CLIMACT.
</td>
<td>
Public
</td> </tr> </table>
_Table 1 – Types of data considered in the European Calculator project._
# Metadata standards and data formats
All the data used within the project will be available using non-proprietary
formats and documented accordingly via the use of extensive metadata
descriptions and EU-calculator naming conventions. The metadata descriptions
will contain the required elements to guarantee that data are easily
discovered. Table 2 enumerates and describes the foreseen metadata elements to
be used when documenting Observed Time Series (OTS), Future Time Series (FTS),
Level of Levers (LL) and Constant Parameter (CP) data in the EU calculator
project.
<table>
<tr>
<th>
**Attribute name**
</th>
<th>
**Description**
</th> </tr>
<tr>
<td>
_**ID** _
</td>
<td>
Unique identifier of the dataset.
</td> </tr>
<tr>
<td>
_**Title** _
</td>
<td>
Dataset title.
</td> </tr>
<tr>
<td>
_**Summary** _
</td>
<td>
Abstract related to the title attribute.
</td> </tr>
<tr>
<td>
_**Variable** _
</td>
<td>
Short variable name.
</td> </tr>
<tr>
<td>
_**Unit** _
</td>
<td>
Unit of the variable.
</td> </tr>
<tr>
<td>
_**Activity** _
</td>
<td>
Name of the project.
</td> </tr>
<tr>
<td>
_**Tags** _
</td>
<td>
List of keywords commonly used to describe the subject.
</td> </tr>
<tr>
<td>
_**Frequency** _
</td>
<td>
Time frequency of the variable.
</td> </tr>
<tr>
<td>
_**Period and reference** _
</td>
<td>
Time period for which the variable was calculated and respective reference
year.
</td> </tr>
<tr>
<td>
_**Institution** _
</td>
<td>
URL of the home page of the institution compiling or producing the data.
</td> </tr>
<tr>
<td>
_**Contact** _
</td>
<td>
Email contact of the main responsible for the data as compiled or produced for
the EU calculator project.
</td> </tr>
<tr>
<td>
_**Contributors and role** _
</td>
<td>
Any name of person contributing for the data compiled or produced for the EU
calculator project, as well as the respective role.
</td> </tr>
<tr>
<td>
_**Methods summary** _
</td>
<td>
Brief description of the methodology used to compile/calculate the data.
</td> </tr>
<tr>
<td>
_**Data filling** _
</td>
<td>
Description of the approach to fill in missing country data.
</td> </tr>
<tr>
<td>
_**Source data** _
</td>
<td>
Any relevant sources used to compile or produce the data.
</td> </tr>
<tr>
<td>
_**Quality control** _
</td>
<td>
Description of the quality control process before data publication.
</td> </tr>
<tr>
<td>
_**Comment** _
</td>
<td>
Miscellaneous information about the data/methods used to derive the dataset.
</td> </tr>
<tr>
<td>
_**References** _
</td>
<td>
Any additional references.
</td> </tr>
<tr>
<td>
_**Date created** _
</td>
<td>
Date of data creation (YYYY-MM-DD).
</td> </tr>
<tr>
<td>
_**Data type** _
</td>
<td>
Type of data
</td> </tr>
<tr>
<td>
_**Workpackage and task** _
</td>
<td>
Project WP and task from which the data originates.
</td> </tr>
<tr>
<td>
_**Version status** _
</td>
<td>
Version of the data and its status for usage.
</td> </tr> </table>
_Table 2 – Metadata elements for OTS, PTS, LL and CP data._
Datasets on the Levels of Levers (LL), Observed/Future Time Series (OTS/FTS)
and Constant parameters (CP) will be made publically available following the
CSV or XLS tabular data standard. OTS refers to historical data collected from
sources. The difficulty is to find data for every country and to fill the
gaps. This data is used as input of for the EU calculator model. In this
respect it should be noticed that 1) only credible sources could be used that
have primary/secondary access to the data, i.e. owning/overseeing the system
producing/recording the data or legally obliged to collect data (statistical
offices) and, 2) if possible, use the data from EU level/international bodies
that gather the data from member state level organizations by law, such as
Eurostat, the IEA or the World Bank.
Data coded as FTS are generated by the model. This is the "matrix of
possibilities" that will be used by the Web application; hence, this data is
an output of the model and INPUT of the Web application. LL data are created
by each WP in cooperation with the stakeholders. The LL data refers to
scenarios built for each lever and are therefore inputs of the model. Finally,
CP data refers to constants (physical, geographic, etc...; for example country
entity, mass to kcal conversion) that are required and input for the model.
The documentation of this data obeys to a template whose first version is
described in section 8.
Module Documentation (MD) data will be made available in PDF format. As for
the Model Code (MC) this will be made available in the KNIME and Python
formats.
* KNIME is an open source data analytics platform
( _https://www.knime.com/_ ) . It integrates various components for machine
learning and data mining and possess a graphical user interface to allow
assembly of nodes for data preprocessing (e.g., extraction, transformation,
etc), for modeling and data analysis and visualization.
* Python is a high-level programming language for general-purpose programming. It is widely used in the scientific community given its scalability and philosophy emphasizing code readability.
# Policy of data re-use
## Open source licence
The data produced in the European Calculator project will be stored as a
comprehensive database and therefore illegible for intellectual property
rights (IPR) and subjected to licencing. When it comes to intellectual
property rights and licences for data, the central notion is the database as
“a collection of independent works, data or other materials arranged in a
systematic or methodological way and individually accessible by electronic or
other means” 10 . The database notion is not restricted to a data collection
stored in traditional database management systems, it relates also to data
stored in a file and organized in a well-structured manner.
The data resulting from the European Calculator will be published under the
Creative Commons Attribution International License **CC-BY-4.0** (
_https://creativecommons.org/licenses/by/4.0/_ ) , and the Open Data Commons
Attribution Licence **ODC v1.0** ( _https://opendatacommons.org/licenses/by/_
) . These licences are permissive and do not include a copyleft clause 11 .
They allow sharing (copy, redistribute and use the data) as long as the user
entity gives appropriate credit.
# Data storage and preservation
## Model data storage
Data used in the development of the EU calculator model will be stored online
using the Amazon S3 ( _https://aws.amazon.com/en/s3/_ ) solution. Inputs for
the model covering the data described in section Data origin and type3 and
documented according the standards described in section 4 will be stored in an
EU calculator dependency. The details on how to upload both the data and
metadata will be made available in the next iteration of the DMP.
## Long-term storage plan
The data of the European Calculator project will be stored in a research data
repository like PANGAEA ( _www.pangaea.de_ ) . PANGAEA is a free publishing
repository for environmental data (although some small fees might apply in
case of data from big projects) with a Digital Object Identifier (DOI)
service. From this generic storage place for data, the EU calculator team will
reach out to other data repositories in order to increase the visibility and
secure long-term preservation of our outputs by exploring the following
possibilities:
* Linking the data sets produced in the European calculator to OpenEI.org ( _http://en.openei.org/wiki/Main_Page_ ) . The Open Energy Information (OpenEI.org) initiative is a free, open source knowledge-sharing platform created to facilitate access to data, models, tools, and information that accelerate the transition to clean energy systems through informed decisions. OpenEI strives to make energy-related data and information searchable, accessible, useful to both people and machines
* Linking the data sets produced in the European calculator to European’s Union Open Data Portal ( _https://data.europa.eu/euodp/en/data/_ ) . The European Union Open Data Portal is the single point of access to a growing range of data from the institutions and other bodies of the European Union. The portal aims to promote their innovative use and unleash their economic potential. It also aims to help foster the transparency and the accountability of the institutions and other bodies of the European Union. The Open Data Portal is managed by the Publications Office of the European Union.
* Upload our datasets to the Open Energy Modelling Initiative ( _www.openmod-initiative.org/_ ) . The Open Energy Modelling Initiative is more in line with the thematic focus of the European Calculator and hence
the project outputs could gain more visibility for the community if announced
through this channel.
## Model documentation
One of the most important criteria of the EU Calculator is the transparency of
the model. This can only be achieved by allowing anyone to understand the
model and to find the source of every data that we are using. The data we are
collecting are evolving quickly. It is important to keep track of the version
of every data we are using. Finally, every assumption/hypothesis that we are
taking are influencing the final result of the calculator, we need to track
them and to explain them with details.
* For Observed Time Series data that we are collecting as input of the model, we need to document the place where we found them, the date, the owner, as well as the method we are using to fill the missing data.
* For Future Time Series built regarding expert opinion and stakeholder consultations, we need to document the critical hypothesis that we took to build our matrices of scenarios;
* For Levels of Levers data, it is crucial to track the assumption we made to define the lifestyle ambition or the levels of technology.
* For Model Code, everyone should be able to understand the code (Knime or Python) without having to know how to code. This is only possible by documenting the code with a deep Model Documentation (MD) inside the code itself.
### Model versioning
The EU Calculator is a large project with several complicated
modules/components where multiple developers are working together at the same
time. It is simply not feasible to keep track of every modification and to be
able to go back to an older version (in needed) without having something
called version control.
Multiple Version Control Systems (VCS) are on the market. We propose to use
the popular and open source GIT system in the EU Calculator project and to
host our code using the Bitbucket solution ( _https://bitbucket.org/product_
) .
Git is a free and open source distributed version control system designed to
handle everything from small to very large projects with speed and efficiency.
It outclasses SCM tools like Subversion, CVS, Perforce, and ClearCase with
features like cheap local branching, convenient staging areas, and multiple
workflows. ( _https://git-scm.com/_ ) .
Git will allow us to keep track of the changes in the code but it is the task
of every developer to document his code and the version he is pushing to the
server. The code of the European Calculator can be found at the following
address (you need to be registered to access it):
_https://bitbucket.org/eucalcmodel/_
# Responsibilities and duties
Each work-package leader institution is responsible for supervising the
creation, compilation and adequate documentation of data during the life time
of the project. This includes making the data available for the rest of the
team, as well as guaranteeing that the data compiled or generated adheres to
the metadata and format standards described in the ECDMP. PIK will support the
partners with doubts regarding the metadata standards. It also falls within
each work package the responsibility to keep the module documentation for the
EU calculator model up to gate and compliant with the template provided.
CLIMACT is responsible for supervising the model documentation, curating the
code and making it available in the predefined format.
|
https://phaidra.univie.ac.at/o:1140797
|
Horizon 2020
|
1197_EU-MACS_730500.md
|
# 1\. INTRODUCTION
## The study
To support further product development and effective widespread uptake of
climate services, as a means to boost mitigation of and adaptation to climate
change as well as capabilities to cope with climate variability, the European
Commission has included several actions in its current research programme
Horizon 2020 (H2020). Essentially these actions follow from the logic to
implement the European Research and Innovation Roadmap for Climate Services
(cf. European Commission, 2015)
EU-MACS and its twin project MARCO deal with analysis of the climate services
market. In addition demonstration calls were launched on the added value of
climate services for supposedly high value added sectors with hitherto little
uptake of climate services (SC5-01-2016-2017), while other actions focus more
on networking activities interlinking to better connect relevant players, such
as the Coordination and Support Action (SC5-05b-2015) called Climateurope. In
addition the ERANET for climate services (ERA4CS) is a programme that contains
both testing of particular types of climate services in selected sectors and
exploration of suitable climate service types for selected sectors.
An extremely important sub-programme in H2020 is the COPERNICUS Climate Change
Service (C3S) programme, which aims to generate a very comprehensive coherent
and quality assured climate data set meant to support mitigation and
adaptation planning, implementation and monitoring. In due course, also coping
capabilities of (current) climate variability are addressed.
In this framing, EU-MACS – European Market for Climate Services – will analyse
market structures and drivers, obstacles and opportunities from scientific,
technical, legal, ethical, governance and socioeconomic vantage points. The
analysis is grounded in economics and social sciences, embedding innovation
theories on how service markets with public and private features can develop,
and how innovations may succeed.
## The scope and remit of this report
The project EU-MACS does not produce new climate services itself, neither is
it engaged in the processing of climate data or impact data as such. Instead,
EU-MACS collects different types of (meta)information regarding the ‘field of
climate services’ and the ‘climate services market’ (the market being a subset
of the field). Apart from literature study, the typical ways of collecting
information are surveys, interviews, workshops and internet based interaction
formats. In addition, the project assembles stakeholder lists based on
snowballing among the consortium partners and some key contacts, contacts
during outreach activities, and stakeholder feedback via the website and
twitter. Furthermore, purpose designed interaction formats, such as Living Lab
embedded workshops and web based explorations on climate service design
preferences, are used.
Given the nature of these data not only maximization of access to research
data is an issue, but also data protection considerations. As regards data
protection, two types of potential limitations on openness play a role, being:
(1) personal contact information, and (2) attribution of characteristics or
opinions to participating organisations or their representatives. The
implication is that for a part of the collected information only meta-
information can be provided freely, whereas the actual research files (e.g.
interviews) can only be made available under stringent conditions (such as
‘view only’) and only for the academic purpose of peer review and duplication.
This report is meant to give the consortium partners guidance in how to
collect, store, share and describe data with the aim to promote the objective
of open access to research data, under the conditions of the Grant Agreement
and the Consortium Agreement, while abiding to the EU regulation on data
protection, (EU) 2016/679 1 ). In addition to providing guidelines, the
project EU-MACS pursues also an open data pilot in order to tangibly promote
the access to research data via EUDAT 2 .
This Deliverable is a living document, which may be revised during the
project. The first version will be formally submitted as Deliverable to the EC
Project Portal.
## Structure of the report
Chapter 2 briefly reviews the principal guidelines regarding open data access
and data protection for this project. Chapter 3 gives a detailed account of
the collected and processed information. Chapter 4 presents the guidelines for
properly assembling and storing the project’s datasets. Chapter 5 describes
the implementation steps planned in order to effectively support open data
access by means of EUDAT, while honouring applicable data protection
principles. Annex 1 – provides an insight in the description of the metadata.
# 2\. PRINCIPAL GUIDELINES
## Open data
* Information generated by the project EU-MACS, either as intermediate or as final product, should as much as possible be made available for reuse by third parties;
* To make availability meaningful for third parties the information should be:
* Easy to find and identify o Described in accompanying meta-information in order to adequately facilitate the judgement of a potential user regarding the usefulness of the considered data; this should include information on the origin, observation technology, and post-processing of the information
* Clearly organised in datasets with adequate use of object/variable labels and definitions o Submitted to a commonly agreed quality control prior to definitive submission o Accurate (at the time of its collection)
* Information planned to be made accessible for third parties should be reviewed with respect to data protection regulation _prior to its submission_ to facilities meant for retrieval by third parties and should be sufficiently anonymised or conversely sections of the considered datasets should not be submitted to such facilities; in the latter case meta-information disclosing that such information exists and is in possession of one or more consortium partners can be submitted to such facilities;
* All consortium parties should inform the consortium coordinator about the data collected and processed in the EU-MACS project by using a preformatted table provided by the coordinator; this meta-information table will be made available to all consortium partners; the coordinator should see to it that the meta-information table is regularly updated during the project;
* If there are no objections from the point of view of the Grant Agreement and of the Consortium
Agreement, and if the considered data are not subject to sharing limitations
due to Regulation EU 2016/679, collected and processed data are supposed to be
made available for third parties by means of the Open Data Pilot (see below).
An exception can be made for those data of which a majority of the consortium
is of the opinion that there is no value added for third parties or if the
manager of the data repository service is not interested in the submission;
* For data that cannot be shared, meta-information will be made available for third parties, as much as possible;
* After submission of data for the Open Data Pilot, and after quality control has shown that all conditions are met, consortium parties are not obliged to provide further support free of charge with respect to possible maintenance of the submitted data. An exception to this principle applies when preventable inadequacies are detected after submission and quality control.
* Files to be made accessible for third parties will be declared under Creative Commons Licensing system; the recommended options are 3 :
* Attribution-ShareAlike 4.0 International (CC BY-SA 4.0) o Attribution 4.0 International (CC BY 4.0)
## Open data pilot
* In the Open Data Pilot data generated in the EU-MACS project will be uploaded to the EUDAT facility ( _https://www.eudat.eu/_ ) and/or to the Finnish Social Science Data Archive (FSD; _http://www.fsd.uta.fi/en/data/index.html_ ) depending on the nature of the files, while an effort will be made to advertise the availability of these data to third parties;
* It is the ambition of the Open Data Pilot to upload all eligible data generated in the EU-MACS project, where eligibility refers to limitations following from the Grant Agreement, the Consortium Agreement, or the Regulation EU 2016/679;
* In order to be able to upload eligible data in a systemized way a set of preparatory actions should be carried out related to selection, quality assurance, data protection review, metainformation, consent from third parties, and formatting of datasets;
* In order to formulate guidelines for selection, quality assurance, data protection review and metainformation the Executive Board will nominate an Open Data Pilot (ODP) committee; the ODP committee will oversee the adequate implementation of the Open Data Pilot, with support from the project’s information integrity officer;
* The ODP committee will manage a table of potentially eligible data in the common workspace of the EU-MACS project, and ensure it is regularly updated regarding the declaration of eligible datasets;
* The ODP committee can delegate the actual technical preparations and uploading of datasets to the designated partner(s);
# 3\. TYPES OF DATASETS IN EU-MACS
## Survey data
Up to now, one survey has been held by means of an online questionnaire. The
questionnaire counts 42 questions. Some questions are multistage and others
allow for more than one entry. The questionnaire is addressing both climate
service providers and users, for whom partly common and partly distinct
questions are presented. Apart from respondent characterization all questions
have to do with awareness of, experiences with, and pre-conditions for climate
services and their development. The online questionnaire starts with an
explanation of the purpose of the questionnaire and the characteristics of the
EU-MACS project.
The response types include numerical information, predetermined choice
options, and text.
The current number of valid questionnaires is about 150, but we aim to raise
this to 200 at least.
The questionnaire results have been collected in an EXCEL file. The
respondents are anonymous, and only needed to indicate type of organisation
and country from which they operate.
In case of future surveys with identifiable respondents, the publishable file
contains a respondent sequence number, while the actual respondents’
identities are stored separately.
More information on the survey and its results can be found in Deliverable
1.1, and to some extent in Deliverable 1.2. ( _http://eu-macs.eu/outputs/#_
>> Reports)
## Interviews
Various consortium partners have conducted interviews, mostly by phone or
video connection, sometimes face-to-face. Most interviews are of a semi-
structured nature. Interviewees are informed in advance (by email) about the
purpose and context of the interview, and are requested to sign and return an
informed consent form (scanned pdf). Interview summaries are sent to the
interviewees for comments and approval. Interviews have also been recorded
(voice).
Neither the recordings nor the individual interview summaries are public
research material, but may be made accessible for academic peer review
purposes. Syntheses of clusters of interviews can be considered for
publication. Also the basic sets of questions for the semi-structured
interviews can be published.’ Synthesizing interpretations of the interviews
are presented in various WP1 deliverables.
## Workshops
The Work Packages 2, 3, and 4 (hereafter WP2, WP3, WP4) organize workshops
with representatives from the focus sectors, finance (WP2), tourism (WP3) and
urban planning (WP4). The programme and activity descriptions (assignments) of
these workshops are available as internal documents. The same applies to the
internal detailed reports of the workshops. Summaries (impressions) of the
workshops are published in the project’s communication channels, such as the
Newsletter. The programme, activity descriptions (assignments) and the
published summaries are eligible for publication and submission of copies to
third parties. As with the interviews the detailed internal reports are only
available for academic peer review purposes. Some highlights of the results
and their interpretation will be presented in WP2, WP3 and WP4 deliverables.
## Web based interactive explorations (bidding games)
For WP2 and WP3 internet based serial questionnaires are used to elicit
information on inclinations and tendencies of different types of users with
respect to climate service product features and provision modes. The
anonymized results will be made available for 3 rd party use, provided the
respondent numbers are large enough. Summaries of the results and their
interpretation will be made presented in WP2 and WP3 deliverables.
## Stakeholder registry
The stakeholder registry combines the collected contact information from
interviews, workshops, encounters in events, and other contacts, with the
exception of the internet based survey responders. The stakeholder registry is
only for internal project purposes and is not supposed to be accessible for
people outside the EU-MACS consortium. A protected version of the stakeholder
registry is available in the protected common work space of the project.
The stakeholder registry will be evicted after the conclusion of the project.
# 4\. PRACTICAL GUIDELINES FOR ASSEMBLY AND STORAGE OF DATASETS
## Survey data
#### Data format
Even though original source data need to be kept, in case errors are found
later, the target dataset discussed here is a database format which enables
easy use, both as such (e.g. as EXCEL ® or ACCESS ®) and as source file for
another data handling programme (SAS®, MATLAB®, etc.), and enables also
metadata specification.
**Meta data can in principle contain the following variables:**
* Creation date / period
* Creator (name; email)
* Owner(s) (organisation(s) + prime contact)
* Original purpose; project
* Level of openness; Allowed types of re-use (incl. license version of Creative Commons 4.0)
* Number of variables
* Variable list (incl. applied units, resolution, observation method, post-processing steps)
* Observation or measurement period
* Number of observations
* Number of respondents (if not same as number of observations)
* Anonymization method
* Kind of quality check (none; visual; simple checks on allowable ranges and plausible scores; …)
* Handling of missing data (none; …)
#### Storage
* During the project: in the common workspace
* In the Finnish Social Science Data Archive (FSD) and linked from the EUDAT service at the latest starting at the end of the project
## Interviews
#### Data format
* Audio and audiovisual files of recorded interviews
* Transcripts of interviews (text file)
#### Meta data
Interview datasets are in principle not public, but their archiving should
nevertheless allow for tractability in case of later needs for peer review.
For each audio- and audiovisual file a separate small (text) document has to
be made, containing the metadata of the file of interest. In additional a
summary meta-data overview for all (audio)visual file and transcripts belong
to the same interview collection can be made.
* Creation date / period
* Creator (name; email)
* Owner(s) (organisation(s) + prime contact)
* Purpose; project
* Level of openness; Allowed types of re-use (see Creative Commons 4.0) – if any
* Interviewee(s) – if allowed to disclose
* Length of audio-recording in minutes
* Length of transcript (number of words)
* Used questionnaire (if any)
* Informed consent form
#### Storage
* During the project: transcript text files in the (protected) common workspace; (audio)visual files offline
* Files should be kept available for at least 5 years after the end of the project in case of peer review requests
## Workshops
#### Data format
* Audio and audiovisual files of recorded interviews
* Transcripts of interviews (text file)
#### Meta data
Workshop datasets are in principle not public, but their archiving should
nevertheless allow for tractability in case of later needs for peer review.
See interviews for suggested meta data contents. For workshop files the
precise relevant contents is case specific.
#### Storage
* During the project: in the (protected) common workspace;
* Files should be kept available for at least 5 years after the end of the project in case of peer review requests
## Web based interactive explorations (bidding games)
**Data format**
Easy accessible and usable format, i.e. EXCEL®, ACCESS®, or comparable
**Meta data**
Same guidelines as for surveys.
**Storage**
Same guidelines as for surveys.
## Stakeholder registry
**Data format**
Same guidelines as for surveys.
**Meta data**
None, except indication on openness, i.e. only for project internal use.
#### Storage
* During the project: in the protected common workspace;
* After the project: dataset should be discarded
# 5\. IMPLEMENTATION STEPS
## Organising data sets and their storage during the project
* WP leaders in cooperation with the coordinator and the information integrity officer will review that the relevant datasets are properly uploaded to the common workspace. Where necessary protected subsections and/or keyword protection by file will be applied.
* The established situation will be reviewed by the information integrity officer
* First, the situation for already existing files will be reviewed in January and February 2018. Subsequently the same procedure will applied to newly created files.
* The evolution in the stored datasets will be monitored by the information integrity officer.
## Archiving datasets after the project has ended
* Designated eligible files will be transferred to the Finnish Social Science Data Archive (FSD) and announced to EUDAT by the coordinator.
* Designation of eligible files will be prepared by the Open Data Pilot (ODP) committee and approved by the General Assembly of the project. These decisions should be made before the termination of the project (31.12.2018).
* The agreements between FMI, FSD, and EUDAT will be reviewed by the information integrity officer.
# 6\. SUMMARY OF DATA GENERATED DURING THE PROJECT
## Brief description of generated data by type of information
In the various EU-MACS work packages, information was collected and evaluated
on the basis of a web survey, interviews and workshops.
#### Web-Surveys
The web survey aimed to collect information from providers and users of
climate services on barriers related to the development and uptake / use of
climate services in political, economic, social, technological, ethical and
legal / regulatory respects. It also addressed topics such as resourcing and
quality issues.
The survey was organised in two branches; one provider-specific and one user-
specific branch. Each branch itself was organised in three loops. The survey
consists of 121 questions in total including open and different forms of
closed questions (e.g. multiple choice, rating on 5-Point Likert Scales etc).
Participants were routed through the survey based on their responses. None of
the participants had to go through all questions.
169 participants took part in the survey, of which 109 considered themselves
as a provider (which also includes intermediary providers or so-called
purveyors) and 60 as a user. None of the participants had to provide any kind
of personalized data. The results of survey are available as an excel file.
In WP2 and WP5 web surveys failed to attract notable numbers of respondents.
The modest material from these surveys has been used to provide further
support to certain statements and insights In D2.1 and D5.2, but there is no
point in uploading these files to a repository.
#### Interviews
Interviews were mainly conducted in the three case study sectors finance,
tourism and urban planning with both, providers and users of climate services.
Additional interviews were conducted in WP1 related to quality assurance with
climate services providers only and in WP5 to receive additional views on the
conclusions drawn.
In many cases the interviews provided a forum to discuss also confidential
details, only to be shared in a safe environment and with the knowledge that
the shared information will only be used in an aggregated manner. Furthermore,
the informed consent form for the interviewees promises confidentiality of the
interview. Nevertheless, all interviews have been either recorded (with
approved transcripts created afterwards) or trans-scripted.
The information collected during interviews are stored as recorded files and /
or (approved) trans-scripted text documents. In only very few cases the
documentation only contains of some notes taken during the interviews.
#### Workshops
Workshops were conducted in two out of three case study sectors, i.e. tourism
and urban planning. Participants represented mostly users of climate services
but also few climate services providers. Main parts of the workshops were
dedicated to Constructive Technology Assessment (CTA). The CTA part of the
workshop offered a set of specific viewpoints to consider scenarios of using
climate services, while at the same time giving ample space for discussion of
aspects stakeholders find important. Another element of the workshops was a
discussion on typical business cases for climate services use in the two
sectors.
Even though, proper documentation of the workshops has been done, workshops
did not generate material which merits to make generally available that goes
beyond the processed products and syntheses as can be found in the respective
deliverables of WP3 and WP4.
## Proposition for data to submitted to open data repository
Not all information collected in EU-MACS through a variety of interaction
formats can be made accessible for the reasons given above. The original files
are stored for five years by the organisations that collected the information.
Access can be granted upon request to the owner of the information under
conditions in accordance with the Consortium Agreement. However, the original
files will under no circumstances be made available for commercial purposes.
While the survey does not contain any personalized or confidential
information, the interview records and transcripts do. The same applies to a
proper workshop documentation. Therefore, the following information on the
various interactions and information will be made available:
* The survey will be made available both in terms of a file providing some meta-information as well as the excel file providing all responses from all 169 participants.
* For interviews and workshops, only meta-information will be provided.
The meta-information follow the guidelines and principles presented in chapter
4 and are made available as pdf files (see also annex 2), which will be
uploaded to the Finnish Social Science Data Archive and announced to EUDAT by
the coordinator.
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https://phaidra.univie.ac.at/o:1140797
|
Horizon 2020
|
1198_OPEUS_730827.md
|
# Background
This is the deliverable report D9.3 “Data Management Plan” for OPEUS project’s
Work Package 9, as required by the project’s Grant Agreement number 730827. It
defines the data management processes that will be followed throughout the
project’s lifetime. Some of these processes may change or more data types may
be collected, which will then be documented in an update of this data
management plan as the project evolves. As such, this deliverable is a living
document that will be updated and will officially be released to the public as
D9.4 “Data Management Implementation” at M30 (i.e. April 2019).
# Objective/Aim
This deliverable aims to:
* demonstrate compliance with all applicable legislation in relation to the data being collected and used
* report on the data to be collected for use in the delivery of the OPEUS project
* present consortium decisions with respect to making the data FAIR and the respective mechanisms to support these decisions
* discuss the security processes to be applied, including data recovery as well as secure storage and transfer of sensitive data
* summarise any related ethical aspects e.g. with respect to (sensitive) personal data, informed consent, etc.
## Intended audience
This Data Management Plan (DMP) is for all the researchers within OPEUS and it
is considered to be a living document. Being a public document, this
Deliverable may also serve as a guide for other researchers working on similar
initiatives beyond the specific project.
# Data Summary
The purpose of the data collection/generation and the relationship of the data
to the objectives of the OPEUS project:
The aim of OPEUS is to develop a simulation methodology and accompanying
modelling tool to evaluate, improve and optimise the energy consumption of
rail systems, with a particular focus on in-vehicle innovation.
The main objectives of OPEUS comprise:
* the definition a simplified but universal energy requirements outlook for European urban rail systems
* the development a comprehensive rail energy usage simulation methodology
* the development of an energy consumption simulation modelling tool for assessment purposes and applicable to urban, regional, high speed and freight duty cycles
* the further assessment of the role and optimisation potential of driver advisory systems (DAS) in relation to control strategies for different representative duty cycles and traction types
* the further assessment of the potential for energy usage optimisation of novel technologies (e.g. next generation ESSs) and strategies (e.g. engine power-off at low loads) ▪ the provision of a critique of the energy consumption outlook for railway systems.
To do this, three components can be defined within the OPEUS approach:
* the energy simulation model
* the energy use requirements
* the energy usage outlook and optimisation strategies recommendation.
To fulfil its objectives and achieve its aim, OPEUS needs to access, collect,
create, process and manage various data types. As the project develops, it may
be necessary to involve additional data; this will be included in the next
version of this deliverable, D9.4 “Data Management Implementation”, due for
completion at M30 (the final month of OPEUS project delivery).
Types of data collected and/or generated:
* _Data from previous EU-funded collaborative projects:_
The OPEUS concept builds upon and extensive range of knowledge and outcomes
generated by a number of key EU-funded, collaborative projects including,
MERLIN, OSIRIS, CleanER-D, RailEnergy and ModUrbanRoll. The details of their
anticpated key inputs to be used are included to the DoA PartB (page 9). OPEUS
consortium does not control datasets from these previous projects, but it will
exploit them as maybe appropriate to augment the delivery of OPEUS’s project
activities.
* _Data provided by / generated by OPEUS consortium partners:_
In delivering an improved simulation tool and testing it within a relevant
environment, data from consortium partners will be used e.g. operational data
from STAV.
Dataset description:
The following template, below at _Table 1: Dataset Description Template_ ,
will be used to collate information about, and for describing, the datasets
that are used and/or produced by the OPEUS project. The compiled descriptions
of these datasets will be provided in D9.4 “Data Management Implementation” at
M30 (i.e. April 2019):
_Table 1: Dataset Description Template_
<table>
<tr>
<th>
**Dataset Reference**
</th>
<th>
**OPEUS_WPX_TX.X_vXX:**
Each dataset will have a reference that will be generated by the combination
of the name of the project, the Work Package and Task in which it is generated
and its version (for example:
OPEUS_WP3_T3.2_v01)
</th> </tr>
<tr>
<td>
**Dataset Name**
</td>
<td>
Name of the dataset
</td> </tr>
<tr>
<td>
**Dataset Description**
</td>
<td>
Each dataset will have a full data description explaining the data provenance,
origin and usefulness. Reference may be made to existing data that could be
reused.
</td> </tr>
<tr>
<td>
**Standards and metadata**
</td>
<td>
* The metadata attributes list
* The used methodologies
</td> </tr>
<tr>
<td>
**File format**
</td>
<td>
All the format that defines data
</td> </tr>
<tr>
<td>
**Data Origin**
</td>
<td>
Specify the origin of the data (including whether created or collected)
</td> </tr>
<tr>
<td>
**Data Size**
</td>
<td>
State the expected size of the data
</td> </tr>
<tr>
<td>
**Data Sharing**
</td>
<td>
Explanation of the sharing policies related to the dataset between the next
options:
* **Open** : Open for public disposal
* **Embargo** : It will become public when the embargo period applied by the publisher is over. In case it is categorized as embargo the end date of the embargo period must be written in DD/MM/YYYY format.
* **Restricted** : Only for project internal use.
</td> </tr>
<tr>
<td>
**Data Utility**
</td>
<td>
Outline to whom the dataset could be useful – potential secondary users
</td> </tr>
<tr>
<td>
**Archiving and Preservation**
</td>
<td>
The preservation guarantee and the data storage during and after the project
(for example: databases,
institutional repositories, public repositories, etc.)
</td> </tr>
<tr>
<td>
**Re-used existing data**
</td>
<td>
Yes / No. If “Yes”, state the re-used data and how/from where they were
retrieved
</td> </tr> </table>
Open Access approach:
The OPEUS consortium has agreed to follow an “Open Access” approach (as much
as possible depending on the specific data type) following the respective
Horizon 2020 guidelines 1 to ensure that the results of the project results
provide the greatest impact possible. OPEUS will ensure the open access 3 to
all peer-reviewed scientific publications relating to its results and will
provide access to the research data needed to validate the results presented
in deposited scientific publications. Publications and research data made
available to third parties will not contain any personal information.
The following lists the minimum fields of metadata that should come with an
OPEUS projectgenerated scientific publication in a repository:
* The terms: “European Union (EU)”, “Horizon 2020”
* Name of the action (Research and Innovation Action)
* Acronym and grant number (OPEUS, 730827)
* Publication date
* Length of embargo period, if applicable
* Persistent identifier
When referencing open access data, OPEUS will include at a minimum the
following statement demonstrating EU support (with relevant information being
included into the repository metadata):
“The OPEUS project has received funding from the European Union’s Horizon2020
research and innovation programme under grant agreement No 730827”.
The OPEUS consortium will strive to make its datasets open access. When this
is not the case, the “Data Sharing” element for that particular dataset will
describe why access has been restricted.
Regarding specific repositories where OPEUS datasets will be held during and
after the project, they will be noted in the “Archiving and Preservation”
field of Table 1 (above). In cases where the project partners maintain
institutional repositories, these will be listed in OPEUS deliverable D9.4
“Data Management Implementation” at M30 (i.e. April 2019). The project
scientific publications and, in some instances, research data will be
deposited on the institutional repository depending primarily on the primary
creator of the publication and on the data in question. In cases where the
project partners do not operate publicly accessible institutional
repositories, they will use either a domain specific repository or the EU
recommended service OpenAIRE ( _http://www.openaire.eu_ ) as an initial step
to finding resources to determine relevant repositories for depositing the
scientific publications and the respective data.
The repository will also include information regarding the software, tools and
instruments that were used by the dataset creator(s) so that secondary data
users can access and then validate the results.
In summary, as a baseline OPEUS partners will deposit:
* Scientific publications – on their respective institute repositories in addition (when relevant) to a public OPEUS repository (such as ZENODO)
* Research data – to the public OPEUS repository collection (when possible)
* Other project output files – to the OPEUS public repository collection (as relevant).
This DMP does not include the actual metadata about the Research Data being
produced in OPEUS project. Details about building a repository and accessing
this metadata will be provided in deliverable D9.4 “Data Management
Implementation” at M30.
# FAIR Data
OPEUS will in principle participate in the Open Research Data Pilot (ORDP) but
data marked as “Restricted” or under an “Embargo” period will be excluded. To
this end, the data that will be generated during and after the project and
will be included in ORDP should be ‘FAIR’, that is Findable, Accessible,
Interoperable and Reusable. These requirements do not affect implementation
choices and do not necessarily suggest any specific technology, standard, or
implementation solution.
The FAIR principles were generated to improve the practices for data
management and datacuration, aiming to describe the principles to be applied
to a wide range of data management purposes, whether it is data collection or
data management of larger research projects regardless of scientific
disciplines.
With the endorsement of the FAIR principles by H2020 and their implementation
in the guidelines for H2020, they serve as a template for lifecycle data
management and ensure that the most important components for lifecycle are
covered. This is intended as an implementation of the FAIR concept rather than
a strict technical implementation of the FAIR principles:
* Making data _Findable_ , including provisions for metadata:
▫ The datasets will have very rich metadata to facilitate the findability
▫ All the datasets will have a Digital Object Identifier provided by an OPEUS
public repository (e.g., ZENODO)
▫ The reference used for the dataset will follow the format:
OPEUS_WPX_TX.X_vXX, including clear indication of the related WP, activity and
version of the dataset
▫ The standards for metadata will be defined in the “Standards and metadata”
section of the dataset description table (see Table 1, above).
* Making data openly _Accessible_ :
<table>
<tr>
<th>
▫
</th>
<th>
Datasets openly available are marked as “Open” in the “Data Sharing” section
of the dataset description table (see Table 1).
</th> </tr>
<tr>
<td>
▫
</td>
<td>
The repository in which each dataset is stored, including open access
datasets, is mentioned in the “Archiving and Preservation” section of the
dataset description table (see Table 1)
</td> </tr>
<tr>
<td>
▫
</td>
<td>
The “Data Sharing” section of the dataset description table (Table 1) will
also include information with respect to the methods or software used to
access the data of each dataset
</td> </tr>
<tr>
<td>
▫
</td>
<td>
The data and their associated metadata will be deposed either in a public
repository or in an institutional repository
</td> </tr>
<tr>
<td>
▫
</td>
<td>
The “Data Sharing” section of the dataset description table (see Table 1) will
outline the rules to access the data, if restrictions exist.
</td> </tr> </table>
* Making data _Interoperable_ :
▫ Metadata vocabularies, standards and methodologies will depend on the
repository to be hosted (incl. public, institutional, etc.) and will be
provided in the “Standards and metadata” section of the dataset description
table (Table 1).
* Making data _Reusable_ :
<table>
<tr>
<th>
▫
</th>
<th>
All the data producers will license their data if applicable to allow the
widest reuse possible. More details about license types and rules will be
provided in OPEUS deliverable D9.4 “Data Management Implementation” at M30.
</th> </tr>
<tr>
<td>
▫
</td>
<td>
The “Data Sharing” section of the dataset description table (see Table 1) is
the field where the data sharing policy of each dataset is defined. By
default, the data will be made available for reuse. If any constrains exist,
an “Embargo” period or “Restricted” flag will be explicitly raised in this
section of Table 1.
</td> </tr>
<tr>
<td>
▫
</td>
<td>
The data producers will make their data available for third-parties within
public repositories only for scientific publications validation purposes.
</td> </tr> </table>
# Allocation of Resources
In order to face the data management challenges efficiently, all OPEUS
partners have to respect the policies set out in this DMP and datasets have to
be identified, created, managed and stored appropriately. The OPEUS roles
related to the management of the data are:
* The _data controller_ **,** who acts as the point of contact for data protection issues and will coordinate the actions required to liaise between different beneficiaries and their affiliates, as well as their respective data protection agencies, in order to ensure that data collection and processing within the scope of OPEUS, will be carried out according to EU and national legislation. The data controller must ensure that data are shared and easily available
* The _data producer_ **,** which is any entity that produces data within OPEUS’s scope. Each data producer is responsible for the integrity and compatibility of its data during the project lifetime. The data producer is responsible for sharing its anonymised datasets through open access repositories, according to the principles and mechanisms defined in the current document. They are in charge of providing the latest version
* The _data manager_ **,** who will coordinate the actions related to data management, will be responsible for the actual implementation of the DMP successive versions and for the compliance to Open Research Data Pilot (ORDP) guidelines. As the OPEUS open data will be hosted either by institutional databases or by an open free of charge platform (e.g.
ZENODO), no additional costs will be required for hosting the data.
Responsibilities:
UNEW, as project coordinator, is responsible for implementing the DMP.
In principle, all partners are responsible for data generation, metadata
production and data quality. Specific responsibilities are to be assigned
depending on the data and the internal organisation in the WPs and Tasks where
data is created.
Dataset storage and backup, data set archiving and sharing will be, in the
majority of cases, the responsibility of the partner(s) who owns the data
and/or the servers in which they will be stored.
# Data Security
Data sharing, storage, backup/recovery and long term data preservation
A “repository” is a mechanism used by a project consortium to make its project
results (i.e., publications and scientific data) publicly available and free
of charge for any user.
Several options are considered/suggested by the EC in the frame of the Horizon
2020 programme to this aim:
* For depositing scientific publications:
▫ Institutional repository of the research institutions
▫ Subject-based/thematic repository
▫ Centralised repository
* For depositing generated research data:
▫ A research data repository which allows third parties to access, mine,
exploit,
▫ Reproduce and disseminate free of charge
▫ Centralised repository
The Consortium is aware of the mandate for open access of publications in the
H2020 projects and the participation of the project in the ORDP. The academic
institutions participating in OPEUS have available their own repositories
which in fact are linked to OpenAIRE. These institutional repositories will be
used to deposit the publications generated by them.
A scientific publication and data repository, such as ZENODO (the repository
set up by the EC’s OpenAIRE initiative in order to unite all the research
results arising from EC funded projects), will be used for the sharing and
preservation of project outcomes. The Consortium will ensure that scientific
results that will not be protected and can be useful for the research
community will be duly and timely deposited in the chosen scientific results
repository, free of charge to any user.
Considering ZENODO as the repository, the short- and long-term storage of the
research data will be secured since they are stored safely in same cloud
infrastructure as research data from CERN's Large Hadron Collider. It uses
digital preservation strategies to storage multiple online replicas and to
back up the files (data files and metadata are backed up on a nightly basis).
Therefore, this repository fulfils the main requirements imposed by the EC for
data sharing, archiving and preservation of the data generated in OPEUS.
# Ethical Aspects
There are no ethical issues related to data management in OPEUS, as the
project does not handle personal (sensitive) data.
# Conclusions
This deliverable provides an overview of the data that OPEUS project will
produce together with related data processes and requirements that need to be
taken into consideration.
The document gives an overview of the anticipated dataset types, defines a set
of attributes to be used for describing each dataset, and presents the open
access aspects to be followed by the consortium. These include a description,
standards, methodologies, sharing and storage methods.
With respect to making the data FAIR and the respective mechanisms to support
these decisions are described, with the allocation of resources, data security
and ethical aspects are presented.
This deliverable is a living document which will be updated during the project
lifetime as needed, including more detailed information regarding the
collected / generated data. The next official updated version will be released
as deliverable D9.4 “Data Management Implementation”, at M30 (i.e., April
2019), providing more information on such as:
* The descriptions of the different datasets, including their reference, file format, standards, methodologies and metadata and repository to be used
* Institutional repositories in cases where the project partners maintain such a repository
* The use of an appropriate Open Access repository to enable the sharing and reuse of data from the project
* How the data is being curated and will be preserved.
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https://phaidra.univie.ac.at/o:1140797
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Horizon 2020
|
1199_RINGO_730944.md
|
# 1\. DATA SUMMARY
_State the purpose of the data collection/generation_
ICOS collects observational data on greenhouse gas concentrations and fluxes
in atmosphere, ecosystem, and marine environments. This data is targeted to
scientific use to increase our knowledge of the greenhouse gas cycles and the
budget of Europe and surrounding regions.
_Explain the relation to the objectives of the project_
The RINGO project aims at improving the methods and data used and generated by
ICOS. The improved methods will increase the quality, amount, and FAIRness of
ICOS data.
_Specify the types and formats of data generated/collected_
The RINGO project collects several different types of data:
* CO2 ambient mole fraction and ecosystem flux data from the pre-ICOS period that will be reprocessed into higher quality data, very similar to the current ICOS Level 2 data and INGOS datasets for CH4 and N2O. This data is stored in the WMO and Fluxnet community defined Comma and Tab separated data format as clear ASCII text.
* Observation data of vertical profiles of greenhouse gas mole fractions from observations using air cores is stored into a community defined ACSII format, the development, definition, and documentation of this processing is part of RINGO.
* Additional raw data is usually also some comma or tab separated ASCII file.
_Specify if existing data is being re-used (if any)_
Raw instrument pre-ICOS data is used to generate the higher quality Level 2
data as described in the previous section.
_Specify the origin of the data_
All raw data is generated by approved pre-ICOS or candidate ICOS
instrumentation.
_State the expected size of the data (if known)_
Raw data volume per site is about 40 MB (ecosystem, 20 sites), 20 MB
(atmosphere, 10 sites) or several Kb (marine, five sites) per day. The Level 2
data products are about 1-20 MB per year.
_Outline the data utility: to whom will it be useful_ :
* Scientists in many fields from climatology, biogeochemistry, biology, agriculture, forestry etc. - General public.
* Scholars, students.
* Policy makers.
## 2.1 MAKING DATA FINDABLE, INCLUDING PROVISIONS FOR METADATA [FAIR DATA]
_Outline the discoverability of data (metadata provision)_
All raw data and Level 2 data products from RINGO will be published together
with the relevant metadata through the ICOS Carbon Portal and follow its FAIR
principles and mechanisms. The metadata is exposed through the CP search app,
the B2FIND service, through the Datacite through DOIs of the collections of
the datasets and in the future other portals of portals (e.g. EOSC; GEOSS).
_Outline the identifiability of data and refer to standard identification
mechanism. Do you make use of persistent and unique identifiers such as
Digital Object Identifiers?_
At ingestion, all data objects get assigned a Persistent Identifier (PID)
based on the handle system. This PID contains the sha-256 checksum of the data
object and resolves into a landing page that contains the relevant metadata
and a link to the data object. Collections of data, per station and per
release of one or more years, will be generated and minted a Datacite DOI and
the metadata of these collections, including attribution information, will be
stored at Datacite.
_Outline naming conventions used_
Each data object has an object specification that links to the metadata that
describes the data format and describes the data content ontology (data
columns, variables, units). File names are preserved but are considered
redundant for machine to machine interpretation and not interpreted. Each
community follows its own conventions for file naming to support internal
processing.
_Outline the approach towards search keyword_
Search keywords will be linked to the data object specification. All metadata
elements can be searched for as keyword using the open SPARQL query, and the
Carbon Portal search app.
_Outline the approach for clear versioning_
Part of the metadata for each data object is the data version. Each data
object of higher version links to its previous (and eventual next) version.
Specify standards for metadata creation (if any). If there are no standards in
your discipline describe what metadata will be created and how
All metadata is open linked data based on RDF, accessible through an open
SPARQL endpoint (W3C standards). Naming convention of the metadata entries
follows ISO19115 where possible. Collections and their metadata will also be
findable from the Datacite search engine.
## 2.2 MAKING DATA OPENLY ACCESSIBLE [FAIR DATA]
_Specify which data will be made openly available? If some data is kept closed
provide rationale for doing so_
All raw and higher level data products originating from the RINGO project are
provided following the ICOS data licence which is CC4BY. Raw data (Level 0)
objects are not directly downloadable through the web pages, there we ask to
contact the thematic center first in order to provide the user with the best
service to interpret the data and to inform us of the actual data
requirements, but access is allowed and will be given without further
conditions.
Experimental data from the RINGO project is in principle, kept closed for
users from outside the relevant RINGO task until the end of the project, but
will be provided with open access according to the ICOS Data Policy after the
project end. Descriptive metadata will always be openly available for all
data.
_Specify how the data will be made available_
All data is made available through the ICOS Carbon Portal through standard
https transfer. The PIDs of the data can be found through the interactive
search interface at Carbon Portal or by queries through the open SPARQL
endpoint or through other portals (of portals) like the B2FIND or GEOPortal.
All PIDs resolve through the Handle (or DOI) system into a landing page that
contains all relevant metadata and a link to the actual data object for direct
download.
_Specify what methods or software tools are needed to access the data? Is
documentation about the software needed to access the data included? Is it
possible to include the relevant software (e.g. in open source code)?_
All metadata and data can be accessed through restful APIs with standard web
browsers and internet tools such as wget or curl and javascript or python
code. All ICOS Carbon Portal code is open and licensed under GPL v3 through
Github.
_Specify where the data and associated metadata, documentation and code are
deposited_
* Metadata: At Carbon Portal server _https://meta.icos-cp.eu_ as RDF and B2FIND (CKAN, OAI-PMH)
* Datacite for collections of data
* Data: At Thematic Centers, Carbon Portal data service _https://data.icos-cp.eu_ and B2SAFE at CSC (Finland) and KFz Jülich (Germany)
### \- Code: https://github.com/ICOS-Carbon-Portal
_Specify how access will be provided in case there are any restrictions_
While access to RINGO raw data (Level 0) objects is open, and not subject to
any restrictions or conditions, these data are not directly downloadable via
the corresponding landing pages. Instead, interested users are requested to
contact the relevant ICOS thematic center, which will provide the data as well
as assistance on how to best use and interpret it.
## 2.3 MAKING DATA INTEROPERABLE [FAIR DATA]
_Assess the interoperability of your data. Specify what data and metadata
vocabularies, standards or methodologies you will follow to facilitate
interoperability._
RINGO and ICOS data and metadata follow standards that are well-anchored in
the Earth Science community, including relevant ISO specifications and the
INSPIRE directive. This ensures smooth uptake and usage of the data products
by all designated scientific user communities, including support for automated
harvesting of data and metadata by machine-driven processing e.g. in the
cloud.
The metadata associated with all data objects stored in the ICOS Carbon Portal
is stored in a RDF database, based on an open ontology based on OWL, which is
part of the RDF database. Read-only access to the metadata repository is given
through an open SPARQL endpoint. All metadata is also exported to the B2FIND
repository where it is also linked with the PIDs of the data objects in B2SAFE
through CKAN. The B2FIND repository is again linked to the GEOPortal for
global access to the metadata form other portals and portals of portals.
The landing pages of the data object will allow for content negotiation to
deliver the metadata in the format and vocabulary of the respective community
standards. This translation using equivalences will be dynamic and online,
will be anchored in the ontology and thus open and easy to maintain and
update.
_Specify whether you will be using standard vocabulary for all data types
present in your data set, to allow interdisciplinary interoperability? If not,
will you provide mapping to more commonly used ontologies?_
As described in the previous item, we plan to support all relevant standard
vocabularies by mapping the ontologies to the ICOS standard dynamically.
## 2.4 INCREASE DATA RE-USE (THROUGH CLARIFYING LICENSES) [FAIR DATA]
_Specify how the data will be licenced to permit the widest reuse possible_
Data will be provided in general according to the ICOS data Policy and using
the Creative Commons Attribution 4.0 International (CC4BY) licence.
_Specify when the data will be made available for re-use. If applicable,
specify why and for what period a data embargo is needed_
In general, the data is available directly after ingestion/generation. Some
data, e.g. related to development work being performed as part of the RINGO
DoA, will be made available after the end of the project, see section 2.2
above.
_Specify whether the data produced and/or used in the project is useable by
third parties, in particular after the end of the project? If the re-use of
some data is restricted, explain why_
All data that falls under the CC4BY license is available to all third parties.
RINGO experimental data results are restricted to the consortium until the end
of the project. They will become available under CC4BY within 2 years after
the end of the project.
_Describe data quality assurance processes_
Quality assurance is at the heart of ICOS and the reason of the existence of
the research infrastructure. The quality assurance procedures are described in
the relevant papers and reports that are published by ICOS, the Thematic
Centers and their contributors:
### \- ETC Guidelines and instructions: http://www.icos-
etc.eu/icos/documents/instructions \- ATC ICOS station specifications:
https://icos-atc.lsce.ipsl.fr/node/99/27248
\- ATC Data processing: Hazan, L., Tarniewicz, J., Ramonet, M., Laurent, O.,
and Abbaris, A.: Automatic processing of atmospheric CO2 and CH4 mole
fractions at the ICOS Atmosphere Thematic Centre, Atmos. Meas. Tech., 9,
4719-4736, _https://doi.org/10.5194/amt-9-4719-2016_ , 2016 - OTC:
_https://otc.icos-cp.eu/data-levels-quality-access_
_Specify the length of time for which the data will remain re-usable_
ICOS is a long term infrastructure that is foreseen to exist for at least
20-25 years. This would guarantee operation and data availability until 2040.
Following OAIS recommendations, periodic consultations with the designated
scientific end-user communities are planned to ensure that the data remain
fully FAIR throughout this time.
# 3\. ALLOCATION OF RESOURCES
_Estimate the costs for making your data FAIR. Describe how you intend to
cover these costs_
Most of the work to make ICOS and RINGO data FAIR is performed at the Carbon
Portal and the ICOS Thematic Centers and concerns at least 50% of the cost of
these. Total cost is thus roughly 3 M€ per year.
Hardware costs are about 10 k€ per year. EUDAT/CDI services like B2FIND and
B2SAFE are about 50 k€ per year.
_Clearly identify responsibilities for data management in your project_
The data management responsibilities are clearly described in the ICOS Data
Policy document and the ICOS Data Lifecycle document. The latter is under
continuous development. In general Carbon Portal is responsible for the data
identification and minting of PIDs and DOIs, publishing of data and metadata
and archiving to the trusted repository. The Thematic Centers of ICOS are
responsible for the data curation and provenance. The project participants and
task leaders are responsible for the provenance of the raw data and in some
cases of curation of the experimental results.
_Describe costs and potential value of long-term preservation_
The trusted repositories at B2SAFE from EUDAT at CSC and KFA Jülich will
preserve all ICOS data objects for the foreseeable future. B2SAFE will be part
of the EOSC service portfolio. Cost is covered from the ICOS budget that is
secured for the long term (>20 years).
# 4\. DATA SECURITY
_Address data recovery as well as secure storage and transfer of sensitive
data_
Transfer of sensitive data is not applicable. Data is backupped at all
individual instances of ICOS, these locations are the stations, experiments,
thematic centers, and Carbon Portal. All raw data and higher-level data is
streamed at ingestion to a trusted repository from EUDAT CDI (B2SAFE) that
replicates the data over two centers in Europe (in Finland and Germany), that
each also provide a full backup. All data objects are identified with a
persistent identifier minted by ICOS CP that contains the AES256 checksum of
the data for unique identification and consistency check. NOTE: the (primary)
repository in this context is the Carbon Portal (which operates the “ICOS
repository”), not the EUDAT data center!
# 5\. ETHICAL ASPECTS
_To be covered in the context of the ethics review, ethics section of DoA and
ethics deliverables. Include references and related technical aspects if not
covered by the former_
None
# 6\. OTHER
_Refer to other national/funder/sectorial/departmental procedures for data
management that you are using (if any)_ Documentation for Atmosphere, Ocean
and Ecosystem community, see section 2.4. All other information is at
_https://github.com/ICOS-Carbon-Portal/meta_
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1200_VetBioNet_731014.md
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## 1\. Data Summary
### 1.1 Purpose of data collection/generation and its relation to the
objectives of the project
The H2020 topic under which VetBioNet has been funded (INFRAIA-01-2016-2017)
participates per default in the open access to research data pilot which aims
to improve and maximize access to and re-use of research data generated by
H2020 projects. This data management plan is part of this pilot and describes
the data VetBioNet will generate, whether and how they will be exploited or
made accessible for verification and re-use, and how they will be curated and
preserved.
VetBioNet will collect and generate data to advance research on epizootic and
zoonotic diseases, with the final objective of strengthening the present
European capacity and competence to meet the challenges of (re)emerging animal
infectious diseases. VetBioNet will collect data generated from its Joint
Research Activities (JRA), which are designed to improve the scientific and
technological standards of the integrated services provided by the network
infrastructures. For this, the _FAIR principles_ (Findability, Accessibility,
Interoperability, and Reusability) and _ARRIVE guidelines_ for scientific
reporting will be adopted. Moreover, VetBioNet will also collect data from
Transnational Access (TNA) research projects on a voluntary basis, namely when
the owners of the data at stake agree to make them available and with a
possible delay of 2 years after the end of their TNA projects.
### 1.2 Types and formats of data generated/collected
VetBioNet JRA will generate data of several types, including phenotypic,
genotypic and sequencing data from pathogens and hosts. JRA and Networking
Activities (NA) will also deliver novel guidance documents, training tools,
harmonized protocols and other scientific information of interest for the
research community. The final research data produced by JRA will be curated in
a harmonized way to fit the purpose and stored in publicly available standard
data repositories (e.g. Gene Expression Omnibus, Sequence Read Archive, etc.)
or in the dedicated VetBioNet database, facilitating sharing of information
among project partners and finally the wider community of scientists and end-
users through specifically designed interfaces.
Research data (phenotypic, genotypic, sequencing and other kinds of data)
generated by researchers outside the VetBioNet consortium through TNA
Activities will be covered by data management on a case-by-case basis, as
these data may be required to be protected by confidentiality terms specific
to each researcher selected for conducting a TNA project within VetBioNet. To
stimulate the public sharing of these data, specific agreements will be sought
with researchers conducting TNA projects - allowing them to openly share only
a part of their research data and with a possible delay of 2 years after the
end of their TNA projects. Further details on the types and formats of data to
be generated or collected by VetBioNet are included in the table here below:
<table>
<tr>
<th>
**Type**
</th>
<th>
**Source**
</th>
<th>
**Volume**
</th>
<th>
**Format**
</th> </tr>
<tr>
<td>
Spreadsheet
</td>
<td>
JRA and TNA Activities
(Laboratory experiments / simulation / observation / compilation)
</td>
<td>
Small files, up to few megabytes per experiment
</td>
<td>
XLSX
CSV
</td> </tr>
<tr>
<td>
Image
</td>
<td>
JRA Activities
(Laboratory experiments)
</td>
<td>
Few terabytes per experiment
</td>
<td>
RAW
TIFF
</td> </tr>
<tr>
<td>
Sequencing data
</td>
<td>
JRA Activities
(Laboratory experiments)
</td>
<td>
200GB per sequence
</td>
<td>
FASTQ
FASTA
SAM/BAM
GFF/GTF
BED
VCF
</td> </tr> </table>
### 1.3 Existing data re-used, origin of data, expected size and who will
find data useful
VetBioNet will use existing genomic data and related publications from NADIR
(FP7 project, GA n. 228394). Particularly, data generated by NADIR experiments
on fishes, chickens and sheep could be re-used. These data will not be
available for use outside the VetBioNet network unless the owners give
permission.
Overall, the data collected, generated and made openly available by the
project will originate from VetBioNet JRA and NA, and possibly from VetBioNet
TNA Activities and NADIR final results. It is expected that VetBioNet will not
publicly share some experimental data to be obtained through WP7 (
_Experimental models for animal infectious diseases_ ) and WP8
( _Development of novel analytical tools and reagents to help interrogate the
host pathogen interaction_ ) activities, because these data could be included
at a later stage in scientific publications or used for pursuing patents.
It is expected that, due to the nature of the studies, mainly small size data
sets will be collected, at the exception of imaging files, sequencing data and
data from instrumented behavioral and physiological monitoring. The largest
amount of raw data is expected to come from imaging and NGS (next generation
sequencing) genomic data.
The data that VetBioNet will make openly available will be useful for the
veterinary field, particularly for the scientific community interested in
animal infectious diseases, characterizing models and livestock production.
The data will also be useful to policy makers, funders and industry who have
an interest in biosecurity for animal diseases and zoonoses through
sustainable and safe production of livestock species for the public good.
## 2\. FAIR Data
### 2.1. Making foreground data findable, including provisions for metadata
VetBioNet final research data will be stored in publicly available standard
data repositories or in the project dedicated database, which will be
accessible through VetBioNet website ( _http://www.vetbionet.eu/_ ) . The
data stored in VetBioNet database will be searchable with metadata and a
standard identification mechanism will be used to describe the data.
The naming convention followed will be:
[work package].[task].[(JRA/TNA id].[text description].[version].[format]
Search keywords will be provided to optimize possibilities for re-use of data
and raw data will be curated accordingly. The keywords to be chosen will
relate to animal species and pathogens analyzed by the project and followed
methodologies.
Clear version numbers will be provided.
Metadata will be produced to describe how the data were analyzed and summarize
important portions of data.
A simple form will be designed by WP7, and will be filled by partners involved
in the development of experimental models for animal infectious diseases.
Researchers will use the taxonomic international accepted names for pathogens,
the common pathogen titration methodologies expressing doses such as TCDI50,
pfu, etc. The form will include the following fields: Animal species,
Pathogen, Doses, Route of inoculation, Biosecurity requirements (following OIE
classification), Mortality percentage, Clinical signs, Pathogenesis, Immunity
and Reference to published data.
This form could show in summary the main project achievements, and could
facilitate the search for information about animal models related to a
particular pathogen, animal species, etc.
### 2.2. Making foreground data openly accessible
It is the goal of VetBioNet to share final research data (i.e., factual data
on which summary statistics are based). Data sharing with third parties will
be subject to a data-sharing agreement established by the IPUDC (Intellectual
Property Use and Dissemination Committee). The agreement will indicate the
conditions of use, criteria for access, and acknowledgements. Project
participants who wish to withhold patentable or proprietary data can do so,
and advice on this point will be given by the IPUDC.
Final research data will be made openly available only after one of the 3
following criteria are met:
* relevant scientific publications based on the data at stake have been accepted;
* a patent application has been published; 2 years after the project end.
Biosecurity reasons will be considered before making data openly available, as
well as possible IP protection measures that will allow further exploitation
of produced data. As explained in previous paragraphs, VetBioNet will also
collect and publicly share data from TNA research projects on a voluntary
basis, with the consent of the owners of the data.
Finally, VetBioNet partners are committed to give public access to the raw
data that will not be subject to a patent application, at the latest two years
after the end of the project. VetBioNet partners will autonomously store the
raw data used to generate scientific papers in a repository of their choice.
A part of the data made openly available by VetBioNet will be accessible by
means of MS Office applications. To access the statistical data publicly
shared by VetBioNet, Graphpad Prism or similar software will be needed.
Documentation about the software needed to access the data will be provided
upon request.
All freeware programs needed to access the project open data will be provided
in the data repositories used by VetBioNet.
Where possible, open data and associated metadata, documentation and codes
will be deposited in certified repositories which support open access.
Access to open data stored in the VetBioNet database and other (e.g.
institutional) repositories will be provided to registered users (name,
affiliation and email will be requested) who have accepted the terms of use.
Data that could raise societal concerns (such as biosecurity risks) will not
be shared in open access mode.
The VetBioNet Executive Committee (i.e. the decision-implementing body of the
project) will act as data access committee, and will be supported by two
experts in social sciences and ethical evaluation.
### 2.3. Making data interoperable
Formats of data will be decided by the leaders of each Joint Research Activity
Leader following, when possible, the guidelines already developed in the NADIR
project and in accordance with the work of Networking Activity 3 (Best
practices for biosafety, biosecurity and quality management in farmed animal
high containment facilities). To ensure interoperability of data across the
project and where applicable (e.g., data on gene sequences), project
participants will upload basic datasets in standardized forms in a primary
database as required by the journal in which they publish their results. Where
adequate, data generated in VetBioNet will be defined according to the Animal
Trait Ontology for Livestock (ATOL:
_http://www.atolontology.com/index.php/en/_ ) . ATOL is an on-going
initiative and project participants will contribute to the development of the
ontology when relevant. Most partners in the project are ISO 9001 certified.
Partners will agree on common terminology, key words, units and formats and
apply those to the data and other documents to be incorporated in the
consortium’s database. Standard vocabularies for all data types present in
VetBioNet data sets will be employed, to allow interdisciplinary
interoperability.
### 2.4. Increase data re-use (through clarifying licences)
FAIR principles will be applied, also for optimal formatting of data.
Final research data will be made openly available through publicly available
standard data repositories or the dedicated VetBioNet database only after one
of the 3 following criteria are met:
* relevant scientific publications based on the data at stake have been accepted;
* a patent application has been published; 2 years after the project end.
The data produced and/or used in the project will be made openly available to
third parties after the end of the project. Possible restrictions could be put
in place due to the need to finalize patenting processes or publish scientific
works based on project data.
Final research data stored in the project dedicated database will remain re-
usable until the end of the project and for an additional 10-years term if
adequate financial sponsors will be found. Raw and final research data stored
in other open repositories will remain re-usable for a minimum of 10 years.
Each animal experiment conducted by VetBioNet will require approval from an
ethical committee. In order to ensure data quality, guidelines will be
produced by INRA and shared among all project participants to establish common
procedures for acquiring, storing and amending data. Finally, _ARRIVE
guidelines_ will be adopted for ensuring metadata quality.
## 3\. Allocation of resources
All data produced will initially be recorded in numbered workbooks, signed and
dated and retained at each respective site, or stored on network attached file
systems that will be regularly archived and automatically backed up. Data will
be checked for quality and accuracy and all protocols recorded and adapted to
clear guidelines. Where necessary, data will be archived in each institution’s
archive system. Data will be made fully available to the consortium in a
timely manner after passing each partner’s quality control criteria and after
Intellectual Property considerations have been taken into account. To enable
long-term accessibility and validation, data will be stored in formats that
are open, non-proprietary, and in common use by the research community.
Each partner of VetBioNet will be responsible for uploading its own final
research data on publicly available standard data repositories or the
dedicated VetBioNet database. Management of final research data uploaded on
the VetBioNet database is the responsibility of EAAP. Once final research data
are collected and stored in the VetBioNet database (which will have a maximum
capacity of 5 terabytes), there will be a monthly preservation cost of about
750 Euro.
Therefore, final research data will be preserved in one of the following
databases: 1. publicly available standard data repositories (e.g. Gene
Expression Omnibus, Sequence Read Archive, etc.); 2. VetBioNet database until
the end of the project and for an additional 10-years term if adequate
financial sponsors will be found.
Open raw data will be stored and preserved by the project partners who
generated/collected them. Raw data will be retained and remain accessible for
at least 10 years after completion of the project. Data storage facilities
will be maintained in accordance with the manufacturer’s warranty and
guidelines and data backed up at regular intervals and stored safely and
securely.
Moreover, project participants are encouraged to publish research data as
supporting materials together with their publications, to facilitate
preservation of data for future re-use by other projects or research
initiatives.
## 4\. Data security
If not stored in publicly available standard data repositories, VetBioNet
final research data will be stored in the project dedicated database (primary
site) and in a secondary site, which will be geographically distant from the
primary one. Data backups will be then run from the secondary site, without
any impact on the primary project database.
## 5\. Ethical aspects
Ethical issues that can have an impact on data sharing will be analyzed in WP4
( _Ethical aspects, 3Rs and social impact_ ).
The project will not launch questionnaires dealing with personal data.
## 6\. Other issues
Currently, VetBioNet partners do not make use of other
national/funder/sectorial/departmental procedures for data management.
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1201_EUROVOLC_731070.md
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metadata formats are _SEED_ and _QuakeML_ ). In other fields, like _GNSS_ the
data format ( _RINEX – Receiver Independent Exchange Format_ ) has been
established, but the metadata structure is being created within _EPOS_ . In
fields where standards do not exist (f. ex. in geochemistry and for some
hazard products), they are defined within the relevant harmonization group of
_EPOS_ .
_EUROVOLC_ will adopt the existing data and metadata standards implemented in
_EPOS_ and the _EUROVOLC_ Data Management Plan assumes that wherever possible,
the implementation of data access will utilize the _EPOS VO-TCS_ , which also
forms one of the Virtual Access Activities of _EUROVOLC_ (WP20). These data,
which follow the data and metadata standards defined in _EPOS_ and for which
services already have been created, will be Findable, Accessible and Re-
usable, and through further development of the _EPOS_ _Integrated Core
Services_ ( _ICS_ ) the data are expected to become interoperable during the
lifetime of _EUROVOLC_ . _EUROVOLC_ thus adheres to the _FAIR_ data
principles. For data and products where standards do not exist, _EUROVOLC_
will define and implement standards, emphasizing their full implementation
within the _EPOS VO-TCS_ . For some data however, it is to be expected that
either data or metadata, or even both will need to be managed by the suppliers
themselves.
Many of the _EUROVOLC_ partners are active partners in the _EPOS VO-TCS_ where
they contribute either as (i) _Service Providers_ responsible for aggregating,
collecting and ensuring access to the _DDSS_ for the _EPOS ICS_ and the _EPOS
VO-TCS_ , or as (ii) _Data Suppliers_ providing the _DDSS_ to the Service
Providers and grant the rights of redistribution through _EPOS_ by signing a
supplier letter. In _EUROVOLC_ these roles are expected to be maintained to
ensure the continued provision of access beyond the duration of _EUROVOLC_ .
In general, the management of data networked in _EUROVOLC_ falls into three
main categories 1. Data and metadata made accessible through the _EPOS-VO-TCS_
.
2. Metadata made accessible through the _EPOS VO-TCS_ , but access to the data themselves managed by the Data Supplier.
3. Access to data and metadata managed by the Data Supplier.
EUROVOLC will emphasize data management under category 1, but some DDSS will
still fall under category 2 and 3.
The EUROVOLC project has three types of activities: i) _Networking_ , ii)
_Joint Research_ , and iii) _Trans-national_ and _Virtual Access_ . Although
there may be some restricted access, the focuses of many of the Networking
activities in _EUROVOLC_ (WP4, 5 and 6) are to open access to existing
volcanological data from the _Volcano Observatories_ ( _VO_ ) and
_Volcanological_ _Research Institutions_ ( _VRI_ ), constructing new databases
and/or applying the metadata structures defined in _EPOS_ to make the data
findable and accessible. The _Networking_ activities will also harmonize and
define best practices for data collection and the curation of datasets to
serve as testbeds for testing and validation of new models developed in future
research, as well as in _EUROVOLCs_ _Joint Research_ activities. These include
for example models for volcanic plumeatmosphere interaction, volcanic ash
transport and dispersion, and new eruption early warning processes (WP8 and
WP9).
The _Trans-national Access_ ( _TA_ ) activities (WP13-WP19) will open access
to the observational infrastructures of the _VOs_ and _VRIs_ to enable
collection of new data and modeling results. The data collected within the
_TA_ activities will be made accessible, and whenever possible, through the
_EPOS_ services. These data may include time-limited embargoes while the
researchers are processing the results for publications. The data policy
statement for the TA projects is published in the 1 st TA call for
applications on the EUROVOLC website ( _www.eurovolc.eu_ ) .
The _Virtual Access_ activities (WP18 and WP20-25) will develop and provide
access services (some in realtime) to data, data products software and
modeling tools at the providers websites, which will be prominently linked to
the _EUROVOLC_ web site ( _www.eurovolc.eu_ ) . At month 6, the data are not
foreseen to be otherwise distributed through _EUROVOLC_ .
**_Specific data and products to be networked in _EUROVOLC_ : _ **
# Atmospheric gas and aerosol observations
Access will be opened to existing _VO_ data on eruptions and volcano-
atmosphere interaction, mainly from Italian and Icelandic volcanic areas and
also from French overseas volcanic areas (WP4.1). These data products include:
* ( **a** ) Construction of a database of Icelandic tephra layers and its implementation within the _Catalogue of Icelandic Volcanoes_ ( _CIV_ ) ( _www.icelandicvolcanoes.is_ ) , an open-access web resource maintained by IMO for long-term access for the Icelandic Volcano Observatory, stakeholders and general users. The tephra database will be accessible to registered users through the _CIV_ . This work includes the definition of data and metadata standards and will consider experience from the _DynVolc_ data base ( _http://wwwobs.univ-bpclermont.fr/SO/televolc/dynvolc/index.php_ ) , which contains a multi-disciplinary dataset for explosive eruptive events in French territories (including magmatic textures, porosity, crystal size distribution). Expected data volume is <100 Gb.
( **b** ) Access to selected data from other existing tephra datasets from
Deception Island, Eyjafjallajökull, Sakurajima, Campi Flegrei and others,
definition of standards and construction of a database. Details to be defined
during the project and provided in a later update to the DMP. Expected volume
of the data is unknown at month 6\.
* Access to a remote-sensing database for real-time monitoring of volcanic activity, early warning and determination of volcanic plume parameters. The database includes quantitative parameters (plume top height and velocity, grain-size distribution, Mass Eruption Rate and fine ash concentration in the plume) retrieved from processed satellite data for several volcanoes (Icelandic, Italian, French) and following Earth-Observation standards for data and metadata. Also access to eruption data from high-speed, visible and thermal infrared cameras, Lidar system (UVVIS), FTIR, video surveillance system, L-Band Doppler radars, ASHER and PLUDIX instruments, Optical-Particle- and Chemical-Particle Counters as well as ash and gas data from Unmanned Aerial Vehicles (UAV). Access and data policy will follow those of _EPOS_ . More specific definitions of these datasets, their standards and their volume will be defined in later updates to the DMP.
* Access to ash/tephra datasets (plume height, initial velocity, mass flux rates, etc.) obtained from meteo/volcano observations of recent eruptions of Icelandic and Italian volcanoes, made available through the _European Catalogue of Volcanoes_ ( _ECV_ ). The _ECV_ is an open-access web resource to be constructed in WP11 of _EUROVOLC_ . The tephra dataset will be a complete, official and multidisciplinary database and test bed that can be used for benchmarking of all current models, from 1D-colum models, such as f. ex. _PPM_ , to _VATD_ numerical forecast models, such as _NAME_ . Data and metadata standards have not been specified. Expected volume of the data is <100 Gb.
# Geochemical gas monitoring across VOs
Standards and protocols will be defined for volcanic gas observations and
access opened to new and existing gas observations (WP5). These include:
* Datasets collected in field surveys carried out in _EUROVOLC_ to determine best practice standards.
* Atmospheric gas data from volcanic areas across Europe, which will be made available through the _EPOS VO-TCS_ . The use cases for integrating the geochemical data in _EPOS_ will be summarized at the end of the project.
Details of the data and metadata standards and implementation of services will
be provided in a later update to the DMP. Expected volume of the data is <100
Gb.
# Volcano geophysical and geochemical observations of sub-surface processes
Access will be made to volcanic data not yet implemented in _EPOS-IP_ and the
data will be standardized according to _EPOS_ standards (WP6.1).
* The networked data include: seismic and infrasound array parameters, processed continuous gravity, borehole strainmeter data, geodetic campaign surveys, collection of rocks, volcanological parameters (e.g. lava flow thickness, area, volume, effusion rate), chemical and isotope analyses of volcanic glass and minerals, analysis of pyroclastite (e.g. grain size distribution, component abundance analysis, tephra isopach maps). Which datasets will be selected for networking will be determined by month 18 and the expected volume of the data will be evaluated at that time.
* Services will be implemented to provide access to case studies from recent eruptions in Italy and Iceland. The datasets will contain multidisciplinary data from specific eruptions of Mt. Etna, as well as the Grímsvötn 2011 and Eyjafjallajökull 2010 eruptions. The volume will be defined later in the project and provided in an update to the DMP.
Access will be Initiated to multidisciplinary observations from the Krafla
Volcano Laboratory in the Krafla caldera (WP6.2), where magma has been
penetrated in boreholes at shallow level and several wells have detected a
superheated environment below 1800-2000 m depth. These data, which are largely
owned by the Landsvirkjun power company include:
* (i) borehole data (loggings and geothermal fluids) in Landsvirkjun’s database, (ii) seismic data, in a _SeisComp3_ data base, from permanent surface and borehole stations, and seismic data from field experiments, (iii) geodetic data, (iv) surface measurement data including resistivity (TEM and MT) gravity and magnetic and (v) surface manifestation monitoring data. Wherever possible, access to the data will be through the _EPOS VO-TCS_ . Which data and the volume will be determined by month 18, when an update to the DMP will be made.
* A platform to access data not accessible through the _EPOS VO-TCS_ will be implemented at Landsvirkjun’s web site and will be managed by them. The access to the database will be open to the project members by conditions which fulfill the company’s security requirements.
# **Data and products compiled or generated in Joint Research Activities**
__European Catalogue of Volcanoes and Volcanic Areas (ECV) and related
volcanic hazards – Version 1.0_ _ Existing databases of European _VOs_ on
active volcanoes and volcanic areas will be unified for the purpose of
creating the first coherent pan-European volcano catalogue (WP11). The _ECV_
will contain detailed information, graphs, maps and references on the
characteristics and hazards of selected volcanoes. It will be an open-access
web resource for scientists at _VOs_ and _VRIs_ , stakeholders and the public.
Access to the integrated data will be through the web interface of the
catalogue and will follow the same construction as the Icelandic catalogue,
_CIV_ ( _www.icelandicvolcanoes.is_ ) . Initially the _ECV_ will be
implemented and maintained at the _IMO_ . Long-term hosting and management of
the _EVC_ will be decided during EUROVOLC; possibly at the _EPOS VO-TCS_ .
Data volume will be estimated in the next update of the DMP.
## Assimilation of geophysical data to initialize Volcanic Ash Transport and
Dispersal Models
Ash and volcanic plume data (grain-size parameters, mass eruption rate, et c.)
will be compiled to test algorithms developed in _EUROVOLC_ (WP8). These
include data from ground-based observations like weather radars, thermal and
visible imaging cameras, ASHERs, ash samples from field surveys, infrasound,
as well as satellite data. The datasets may somewhat overlap with data to be
networked in WP4. Future updates of the DMP will specify which data from WP8
will be prepared for open access.
## Tools and techniques for rapid characterization of volcanic plumes
Plume observations and derived products from the mobile _Icelandic Atmospheric
Observatory_ ( _IAO_ ) will be made available in near-real time to researchers
and operational practitioners on the _IAO_ website, which will be constructed
(WP22). The data set includes observations with Lidar, ceilometer, weather
station, visual cameras and wind measurements. Descriptions of access and
estimated volume of the data will be provided in the next update of the DMP.
## Database construction for testing and quality control of Eruption Early
Warning algorithms
To test algorithms for Eruption Early Warning developed in _EUROVOLC_ (WP9.7),
multiparametric data (including seismic, infrasound, GNSS, tilt and InSAR)
preceding the eruptions of Eyjafjallajökull 2010, Grímsvötn 2011 and
Bárdarbunga 2014 will compiled, as well as already available data from over 25
eruptions of Etna and Piton de la Fournaise. These datasets may somewhat
overlap with those planned for networking in WP6.1. Future updates of the DMP
will specify which data from WP9 will be prepared for open access.
## Integration of petrological and real-time monitoring data
Cutting-edge petrological studies will be combined with real-time monitoring
data (deformation, gas chemistry and other geophysical phenomena) from recent
eruptions (WP10.2). The monitoring data may be part of the datasets of WP6.1.
Future updates of the DMP will specify which data from WP10 will be prepared
for open access.
**_Standards defined in EUROVOLC_ **
EUROVOLC will produce summaries of best practices and standards, as well as
summaries of lessons learned and information on tools and guidelines for
managing volcanic hazards. All these will be submitted as different
deliverable reports (in pdf format). The reports will be made accessible as a
type of _EUROVOLC_ standards product through the _EPOS VO-TCS_ in a service
similar to the existing _EPOS WP11-DDSS-031_ service for reports on volcanic
activity (see Appendix I). The service will be built during _EUROVOLC_ and
will include the following reports on: a) Standards
EUROVOLC standard of best practices (D2.3)
Best practice for direct sampling of fumarolic gases (D5.1)
Best practice for ground-based, remote-sensing measurements of volcanic plumes
(D5.2) Best practice in petrological monitoring of eruptions (D10.4)
Guidelines to assess the monitoring level of European volcanoes (D11.3)
2. Information and lessons learned for response to and management of volcanic hazard
Forensic examination of multidisciplinary data from past volcanic crisis
events (D2.2)
EUROVOLC outreach box (D3.5)
Inception of a Scientific Advisory Group (D7.2)
On-line searchable catalog of pre-existing volcanic hazard assessment tools
(D12.1)
3. Guidelines on Volcano community – stakeholder interaction
Outcome of the VAAC workshop (D4.4)
Consultation document on European Civil Protection needs and inventory of
hazard communication styles (D7.1)
During the first year of EUROVOLC, surveys are being carried out in the
Networking work packages (WP4 and WP6) to catalogue the available and suitable
data to be networked. The results of these efforts will be available by month
12, at which time an update of the DMP will be made defining the specific data
sets and products to be networked in EUROVOLC, how they will be managed
(category 1, 2 or 3) and estimates of their volume.
1. **MAKING DATA FINDABLE** _(dataset description: metadata, persistent and unique identifiers e.g.,_
_DOI)_
<table>
<tr>
<th>
_EUROVOLC_ will, wherever possible, adopt the existing metadata
structures/standards defined in _EPOS_ ( _https://www.epos-ip.org/_ ) . This
entails mapping metadata for _EUROVOLC_ data and products to the existing
_EPOS_ standards and making the metadata accessible to the _EPOS Integrated
Core Service_ ( _ICS_ ) and the _EPOS Volcanological Thematic Core service_ (
_VO-TCS_ ). By doing so, the data will be _findable_ through the _EPOS_
services.
− The corresponding data themselves may also be made _accessible_ through
existing _EPOS services_ (category 1), in which case they will either be
assigned _Digital Object Identifiers_ ( _DOI_ ) by the Data Suppliers
themselves, or through _EPOS_ .
− For data which are findable through _EPOS_ services, but only accessible on
the Data Suppliers websites (category 2), links will be provided on the _EPOS
VO-TCS_ site. These data may or may not be assigned _DOIs_ by the Data
Suppliers.
− Data and metadata, which will only be accessible on the Data Suppliers sites
(category 3), may or may not adhere to _EPOS_ data and metadata standards and
may or may not be assigned _DOI_ s.
Domain names and key words have been defined for the main thematic fields
within _EPOS_ . The domain name for volcanological data is __Volcano
Observations_ _ . To facilitate searches and discovery of volcanological data,
the following key words have already been defined for the main DDSS:
Volcanic activity Faults Satellite
Volcanic plume Soil fluxes Radar
Volcanic tremor Thermal anomaly SAR
Vent opening Chemical analysis Aviation Colour Code
Lava flow Isotope BET
Tephra fallout Thin sections HASSET magmatic rocks Infrared QVAST
As more types of data and products are networked through _EPOS_ in the future,
additions to these key words will be implemented in later, updated versions of
the _EPOS_ _Volcano Observations_ standards. First versions of standards
definitions specifically made within the _Volcano Observations_ domain are
maintained on gitlab, where updated versions are also expected to be
maintained. The final metadata format required by _EPOS ICS_ is _EPOS DCAT-AP_
format. All metadata from the _Volcano Observations_ domain is mapped into
this format to make the data searchable and discoverable within _EPOS_ .
Standards, for data and their corresponding metadata which fall outside the
domain of _Volcano Observations_ _(f. ex. geodetic, seismic, satellite)_ , are
maintained by their respective domains in _EPOS_ . _EUROVOLC_ data falling
under these other domains will, wherever possible, be mapped to the
corresponding community standards to make them findable through the _EPOS_
services and accessible through the avenues decided by the Data Suppliers.
Additional metadata standards are expected to be required for some of the data
networked in _EUROVOLC_ , f. ex. the tephra databases in WP4. These metadata
structures will be defined as required during the project and specified in
future updates of the _EUROVOLC_ DMP.
</th> </tr> </table>
2. **MAKING DATA OPENLY ACCESSIBLE** _(which data will be made openly available and if some datasets remain closed, the reasons for not giving access; where the data and associated metadata, documentation and code are deposited (repository?); how the data can be accessed (are relevant software tools/methods provided?)_
<table>
<tr>
<th>
In general, the data access policy of _EUROVOLC_ will follow that of _EPOS_
making the data access as open as possible, as closed as necessary. _EPOS’s_
classification of user access is:
1. Anonymous access;
2. Registered (identified) access;
3. Authorized access (Identified and authenticated, requiring specific permissions).
EPOS’s classification of access rights is:
4. Open access (freely accessible for download or use);
5. Restricted access (available under restrictions set by the Service Providers and Data Suppliers);
6. Embargoed access (available after a predefined, limited time not exceeding 3 years). Access to metadata for all three data-access classes is always freely open.
As detailed in section 1 of this DMP, efforts will be made to make the
metadata for the networked _EUROVOLC_ data and products (from Work Packages 4,
5 and 6) according to the _EPOS_ standards and therefore findable through the
_EPOS_ services. In addition, access to large amounts of data and information
will also be generated through new or existing web services, like the
Catalogue of Icelandic Volcanoes ( _www.icelandicvolcanoes.is_ ) and the
European Catalogue of Volcanoes (website to be established).
Access to the data and products gathered or generated within _EUROVOLC_ will,
in general be open, but may have initial embargoes to protect publication of
research findings and writing of student theses. Where embargoes apply, this
status and the time frame will be clearly indicated on the _EPOS VO-TCS_
service. Wherever possible, the access to data will be provided through the
_EPOS VO-TCS_ , where the access will be according to definitions of a – f
above, as stipulated by each Data Supplier. In cases where only the metadata
are findable on the _EPOS VO-TCS_ , the data themselves will need to be
accessed at the web sites of the Data Suppliers or by special arrangements
with them. Rules of access to these data (anonymous, registered, or
authorized) will be determined by the Data Suppliers. Limitations and/or
restrictions to data access, as specified by the _EUROVOLC_ partners, are
detailed in the _EUROVOLC_ Consortium Agreement, (Attachment 1: Background
included, p. 41-51) accessible at:
_https://public.3.basecamp.com/p/mmLkXiB2CXvG1tYCcid4AQNJ_
Regarding access to data and products from the Krafla caldera, owned by
Landsvirkjun power company (WP6.2), these will only be accessible to
registered users during the project at the web site of the Data Supplier
Landsvirkjun. Specifics of the access to the data selected to be networked in
_EUROVOLC_ will be developed during the project.
Data generated and used within the TA activities, will be accessible at the
end of the TA projects or after a reasonable embargo period, and definitions
of their data and metadata standards are a required part of the final report
from the TA users. Wherever possible, these standards will adhere to _EPOS_
standards.
Access to software developed within _EUROVOLC_ will be defined in later stages
of _EUROVOLC_ and the details of the access provided in an update to the DMP.
</th> </tr> </table>
1. **MAKING DATA INTEROPERABLE** _(which standard or field-specific data and metadata vocabularies and methods will be used)_
_EUROVOLC_ aims to apply the data formats and metadata standards specified in
the thematic domains of _EPOS_ and make the metadata for all networked data
accessible on the _EPOS VO-TCS_ , thus making the data findable through the
_EPOS ICS_ and _VO-TCS_ services. The vocabularies used in the metadata
descriptions will be those defined by the different thematic domains of _EPOS_
, which will also make the _EUROVOLC_ data interoperable with other _EPOS_
datasets.
For data where metadata standards do not exist in _EPOS_ , _EUROVOLC_ will
define the required standards and harmonize them with other metadata standards
in the _Volcano Observations_ domain and other relevant domains which use the
same data.
Later DMP updates will specify which datasets will require standards to be
defined and implemented.
2. **INCREASE DATA RE-USE** _(what data will remain re-usable and for how long, is embargo foreseen; how the data is licensed; data quality assurance procedures)_
The data distributed by the _EPOS_ infrastructure will be owned by the
original Data Suppliers, who will sign a Memorandum of Understanding (MoU)
with Service Providers, regarding their rights to redistribute the data and
service the data access to _EPOS ICS and EPOS VO-TCS_ . The Service Providers
will sign a contract with the _EPOS-ERIC_ ( _EPOS European Research
Infrastructure Consortium_ ) legal entity, specifying their responsibilities
regarding the long-term maintenance of the data access services. Several of
the partners in _EUROVOLC_ are already committed to become Service Providers
to the _EPOS-ERIC_ and, where needed, are expected to take over this
responsibility for the data and products generated and networked by _EUROVOLC_
.
Statements describing the level of quality checks and procedures for all the
metadata, data and products networked in _EUROVOLC_ will be expected from all
the Data Suppliers. The dissemination of these information will be decided
during the project. _EPOS_ assumes, the responsibility of quality control
rests with the Data Supplier.
3. **ALLOCATION OF RESOURCES and DATA SECURITY** _(estimated costs for making the project data open access and potential value of long-term data preservation; procedures for data backup and recovery; transfer of sensitive data and secure storage in repositories for long term preservation and curation)_
The costs of making both pre-existing and new data openly accessible within
_EUROVOLC_ will be covered by the project. This includes, mapping the metadata
and data into the required _EPOS_ standards, or when standards do not exist,
defining and implementing them.
Long-term data preservation is the responsibility of the Data Suppliers. Long-
term maintenance of the services provided through the Data Suppliers own web
sites will also be the responsibility of the Suppliers and at their own costs.
Long-term maintenance of data services accessible through the _EPOS_
infrastructure, will follow the _EPOS_ convention, that is, the Data Suppliers
will sign an MoU with the Service Providers, who will then sign a contract
with _EPOS-ERIC_ to guarantee long-term maintenance of the data service. The
costs of maintaining the services will be covered by the Service Providers,
_EPOS-ERIC_ or a combination of the two. The specifics will be determined in
the final year of EPOS, 2019, and will be provided in a later update of the
DMP.
The descriptions of data preservation and archiving will also be provided in a
later updates of the DPM.
# **Appendix I DDSS elements to be produced within EPOS-IP project
(2015-2019)**
List of Data, Data products, Software and Services (DDSS) to be delivered in
the EPOS VO-TCS (WP11 of EPOSIP project) during the Implementation phase of
EPOS. Standards for data formats and structures for metadata are defined, and
interoperable services made to make the data findable and accessible. The EPOS
ID of the elements are shown in the first column, where the WP11 refers to the
_EPOS-IP_ work package hosting the VOTCS. The category, or scientific field of
the element is shown in the second columns and the name of the product in the
third column.
The high priority elements will be implemented before the end of 2018, while
the medium priority ones are planned for implementation during 2019-2020.
<table>
<tr>
<th>
**DDSS - ID**
</th>
<th>
**Category**
</th>
<th>
**DDSS Name**
</th> </tr>
<tr>
<td>
</td>
<td>
**High priority (end of 2018)**
</td> </tr>
<tr>
<td>
WP11-DDSS-001
</td>
<td>
Seismological data
</td>
<td>
Velocity seismic waveforms
</td> </tr>
<tr>
<td>
WP11-DDSS-002
</td>
<td>
Seismological data
</td>
<td>
Acceleration /Accelerometer waveforms
</td> </tr>
<tr>
<td>
WP11-DDSS-003
</td>
<td>
Geodetic data
</td>
<td>
GNSS raw data (Rinex Data)
</td> </tr>
<tr>
<td>
WP11-DDSS-004
</td>
<td>
Geodetic data
</td>
<td>
Borehole strainmeter and pressure data colocated with strain meter
</td> </tr>
<tr>
<td>
WP11-DDSS-005
</td>
<td>
Geodetic data
</td>
<td>
Tiltmeter
</td> </tr>
<tr>
<td>
WP11-DDSS-006
</td>
<td>
Geodetic data
</td>
<td>
Tide gauge
</td> </tr>
<tr>
<td>
WP11-DDSS-007
</td>
<td>
Geodetic data
</td>
<td>
Continuous gravity
</td> </tr>
<tr>
<td>
WP11-DDSS-018
</td>
<td>
Satellite data
</td>
<td>
RAW SAR data and SAR SLC
</td> </tr>
<tr>
<td>
WP11-DDSS-019
</td>
<td>
Satellite data
</td>
<td>
VIS/IR Sensors onboard polar orbiting satellites (AVHRR, MODIS)
</td> </tr>
<tr>
<td>
WP11-DDSS-023
</td>
<td>
Ground-based remote sensing data&products
</td>
<td>
Ground-based visible and thermal / IR camera
</td> </tr>
<tr>
<td>
WP11-DDSS-024
</td>
<td>
Ground-based remote sensing data&products
</td>
<td>
Ground-based doppler radar near-source eruptive parameters
</td> </tr>
<tr>
<td>
WP11-DDSS-031
</td>
<td>
Volcanological /petrological
</td>
<td>
Reports on volcanic activity
</td> </tr>
<tr>
<td>
WP11-DDSS-032
</td>
<td>
Volcanological /petrological
</td>
<td>
Aviation colour codes for volcanoes
</td> </tr>
<tr>
<td>
WP11-DDSS-036
</td>
<td>
Geochemical /petrological
</td>
<td>
Chemical analysis and physical properties of gas, water and rocks
</td> </tr>
<tr>
<td>
WP11-DDSS-047
</td>
<td>
Satellite data
</td>
<td>
Volcanic Plume (Ash + SO2)
</td> </tr>
<tr>
<td>
WP11-DDSS-049
</td>
<td>
Satellite data
</td>
<td>
Thermal anomaly (lava flow)
</td> </tr>
<tr>
<td>
WP11-DDSS-050
</td>
<td>
Satellite data
</td>
<td>
Wrapped Differential Interferograms (Phase and Amplitude)
</td> </tr>
<tr>
<td>
WP11-DDSS-056
</td>
<td>
Geohazards
</td>
<td>
Spatial probability analysis/maps
</td> </tr>
<tr>
<td>
WP11-DDSS-057
</td>
<td>
Geohazards
</td>
<td>
Lava flow invasion hazard maps
</td> </tr>
<tr>
<td>
WP11-DDSS-058
</td>
<td>
Geohazards
</td>
<td>
Tephra fallout hazard maps for explosive volcanoes
</td> </tr>
<tr>
<td>
WP11-DDSS-059
</td>
<td>
Geohazards
</td>
<td>
PDCs hazard maps (EH;GVF; Deception)
</td> </tr>
<tr>
<td>
WP11-DDSS-060
</td>
<td>
Geohazards
</td>
<td>
Probabilistic volcanic hazard assessment (maps)
</td> </tr>
<tr>
<td>
WP11-DDSS-064
</td>
<td>
Geohazards
</td>
<td>
Effects on health and recommendations for response to SO2 from volcanic
eruptions
</td> </tr>
<tr>
<td>
WP11-DDSS-065
</td>
<td>
Geohazards
</td>
<td>
Daily ash/gas forecasting maps
</td> </tr>
<tr>
<td>
WP11-DDSS-070
</td>
<td>
Data services
</td>
<td>
Software catalogue for petrological to geophysical modelling
</td> </tr>
<tr>
<td>
WP11-DDSS-072
</td>
<td>
Satellite data
</td>
<td>
Mean LOS velocity
</td> </tr> </table>
<table>
<tr>
<th>
**Medium priority (2019- 2020)**
</th> </tr>
<tr>
<td>
WP11-DDSS-008
</td>
<td>
Physico-chemical parameters in water (river water or groundwater)
</td>
<td>
River stage data
</td> </tr>
<tr>
<td>
WP11-DDSS-009
</td>
<td>
Physico-chemical parameters in water (river water or groundwater)
</td>
<td>
Electrical conductivity in river
</td> </tr>
<tr>
<td>
WP11-DDSS-010
</td>
<td>
Physico-chemical parameters in water (river water or groundwater)
</td>
<td>
River temperature
</td> </tr>
<tr>
<td>
WP11-DDSS-011
</td>
<td>
Physico-chemical parameters in water (river water or groundwater)
</td>
<td>
Piezometer data
</td> </tr>
<tr>
<td>
WP11-DDSS-012
</td>
<td>
Physico-chemical parameters in water (river water or groundwater)
</td>
<td>
Groundwater electrical conductivity
</td> </tr>
<tr>
<td>
WP11-DDSS-013
</td>
<td>
Physico-chemical parameters in water (river water or groundwater)
</td>
<td>
Groundwater temperature
</td> </tr>
<tr>
<td>
WP11-DDSS-014
</td>
<td>
Physico-chemical parameters in water (river water or groundwater)
</td>
<td>
Atmospheric temperature
</td> </tr>
<tr>
<td>
WP11-DDSS-015
</td>
<td>
Physico-chemical parameters in water (river water or groundwater)
</td>
<td>
Atmospheric pressure
</td> </tr>
<tr>
<td>
WP11-DDSS-016
</td>
<td>
Geochemical data
</td>
<td>
Fumarole temperature
</td> </tr>
<tr>
<td>
WP11-DDSS-017
</td>
<td>
Geochemical data
</td>
<td>
CO2 concentration in groundwater
</td> </tr>
<tr>
<td>
WP11-DDSS-022
</td>
<td>
Ground-based remote sensing data&products
</td>
<td>
Ground-based radar data
</td> </tr>
<tr>
<td>
WP11-DDSS-025
</td>
<td>
Ground-based remote sensing data&products
</td>
<td>
Ground-based UV scanner spectra
</td> </tr>
<tr>
<td>
WP11-DDSS-026
</td>
<td>
Rock sample properties
</td>
<td>
Collections of magmatic rocks
</td> </tr>
<tr>
<td>
WP11-DDSS-027
</td>
<td>
Seismological data
</td>
<td>
Earthquake parameters (hypocentral or magnitude)
</td> </tr>
<tr>
<td>
WP11-DDSS-028
</td>
<td>
Seismological data
</td>
<td>
Tremor parameters (amplitude information)
</td> </tr>
<tr>
<td>
WP11-DDSS-029
</td>
<td>
Seismological data
</td>
<td>
ShakeMaps
</td> </tr>
<tr>
<td>
WP11-DDSS-030
</td>
<td>
Geodetic data
</td>
<td>
GNSS time series
</td> </tr>
<tr>
<td>
WP11-DDSS-033
</td>
<td>
Volcanological /petrological
</td>
<td>
Catalogue of eruptions
</td> </tr>
<tr>
<td>
WP11-DDSS-034
</td>
<td>
Volcanological /petrological
</td>
<td>
Maps of recent and past lava flows
</td> </tr>
<tr>
<td>
WP11-DDSS-035
</td>
<td>
Volcanological /petrological
</td>
<td>
Maps of faults
</td> </tr>
<tr>
<td>
WP11-DDSS-041
</td>
<td>
Geochemical data
</td>
<td>
Soil CO2 fluxes
</td> </tr>
<tr>
<td>
WP11-DDSS-043
</td>
<td>
Satellite data
</td>
<td>
Brightness temperature and/or surface temperature by Optical Satellite
(Visible, IR)
</td> </tr>
<tr>
<td>
WP11-DDSS-045
</td>
<td>
Ground-based remote sensing data&products
</td>
<td>
Processed atmospheric Lidar Data for mapping airborne ash
</td> </tr>
<tr>
<td>
WP11-DDSS-046
</td>
<td>
Ground-based remote sensing data&products
</td>
<td>
SO2 flux
</td> </tr>
<tr>
<td>
WP11-DDSS-051
</td>
<td>
Satellite data
</td>
<td>
InSAR lava flow maps
</td> </tr>
<tr>
<td>
WP11-DDSS-052
</td>
<td>
Satellite data
</td>
<td>
Eruptive physical parameters (e.g. MER *, altitude)
</td> </tr>
<tr>
<td>
WP11-DDSS-053
</td>
<td>
Ground-based remote sensing data&products
</td>
<td>
Ground-based Doppler radar spectra
</td> </tr>
<tr>
<td>
WP11-DDSS-054
</td>
<td>
Geohazards
</td>
<td>
SO2 concentration probabilistic hazard maps
</td> </tr>
<tr>
<td>
WP11-DDSS-055
</td>
<td>
Geohazards
</td>
<td>
Volcanic hazard Event Tree
</td> </tr>
<tr>
<td>
WP11-DDSS-061
</td>
<td>
Software
</td>
<td>
Spatial probability Tool (QVAST)
</td> </tr>
<tr>
<td>
WP11-DDSS-062
</td>
<td>
Software
</td>
<td>
Event Tree HASSET tool
</td> </tr>
<tr>
<td>
WP11-DDSS-063
</td>
<td>
Software
</td>
<td>
Bayesian Event Tree (BET) tools: computation and visualization of
probabilistic long- and short-term volcanic hazard
</td> </tr>
<tr>
<td>
WP11-DDSS-067
</td>
<td>
Data services
</td>
<td>
Station information
</td> </tr>
<tr>
<td>
WP11-DDSS-073
</td>
<td>
Seismological data
</td>
<td>
Infrasound waveforms
</td> </tr> </table>
WP11-DDSS-1, -3, -5, -6, -7, 18, -23, -31, -36, -57, -58, -60, initially made
available by some partners in 2018, will be made available by additional
partners in 2019-2020.
|
https://phaidra.univie.ac.at/o:1140797
|
Horizon 2020
|
1202_OpenRiskNet_731075.md
|
# SUMMARY
This report describes the first updated version of the data management plan
(DMP) for the OpenRiskNet e-infrastructure projects. The current DMP covers
the general aspects of the OpenRiskNet data management based on the FAIR
(findable, accessible, interoperable and reusable) guidelines, ethics
considerations for re-sharing of public datasets and the first examples of
shared data sources including diXa, BridgeDb, WikiPathways, AOP-Wiki and
ToxCast/Tox21. More specific data source and clearly-defined measures will be
added in parallel to their integration into the infrastructure, which will
follow the time plan enforced by the case study requirements on data
availability.
# INTRODUCTION
The European Commission is running a flexible pilot under Horizon 2020 called
the Open Research Data Pilot (ORD Pilot). The ORD pilot aims to improve and
maximise access to and re-use of research data generated by Horizon 2020
projects and takes into account the need to balance openness and protection of
scientific information, commercialisation and Intellectual Property Rights
(IPR), privacy concerns, security as well as data management and preservation
questions [1 ] . Open data is data that is free to access, re-use,
repurpose, and redistribute. The Open Research Data Pilot aims to make the
research data generated by selected Horizon 2020 projects accessible with as
few restrictions as possible, while at the same time protecting sensitive data
from inappropriate access [2 ] . Projects starting from January 2017
are by default part of the Open Data Pilot, including the Research
infrastructures (including e-Infrastructures) are required to participate in
the ORD Pilot. Since one of the main aims of OpenRiskNet is to allow for a
simpler, more harmonised access to public, open data sources and workflows and
enrich these with semantic annotation to improve their interoperability
between each other and with predictive toxicology and risk assessment
software, OpenRiskNet is fully supporting the ORD Pilot, is developing best-
practice approach and trying to act as a role model for data management and
sharing,
To help optimising the potential for future sharing and re-use of data, the
OpenRiskNet Data Management Plan (DMP) helps the partners to consider any
problems or challenges that may be encountered and helps them to identify ways
to overcome these. This DMP is a “living” document developed using the online
tool described above and given as a snapshot of the current status (November
2018) in this document, It outlines how the research data collected or
generated, including redistribution of existing data sources as well as
results from the i n silico investigations performed as part of the case
studies, are handled during and after a research project. It follows the
Guidelines on FAIR Data Management in Horizon 2020 [1 ] and is based
around the resources available to the project partners in a realistic way
taking the current knowledge into account. The ongoing activities to keep the
DMP up to date follow an online, distributed approach as outlined in the
Guidelines for creating an online DMP (see Figure 1) [3 ] . Here, we
summarise the concepts for the description of data sets as well as data
sharing and archiving approaches adopted in the DMP first followed by the DMP
in relevant version of the time of this writing. Since the OpenRiskNet case
studies are under ongoing development, which define the integrated data
sources, the current DMP covers the general aspects of the
OpenRiskNet data management but also specific and clearly-defined measures for
the first data sources integrated. Additional sources will be added in
parallel to the data integration.
**Figure 1** . DMP tool interface used to create the OpenRiskNet plan [4
]
## DATA SET DESCRIPTION
This section give the general concepts of what is considered data in
OpenRiskNet and a listing of what kind of datal the project collects, makes
available for redistribution and sharing oor generates as part of the case
studies (i n silico only), and to whom might they be useful later. More
information and specific details are given in the DMP below.
Data refers to:
* Data generated in i n vivo, i n vitro, i n chemico and i n silico experiments broadly related to toxicology and risk assessment in the form of raw, processed and summary data as well as metadata to describe the type of data and how it was produced (protocols and method descriptions for the experimental, processing and analysis procedures);
* More specifically, data and metadata needed to execute the case studies and validate results in scientific publications, and
* Other curated and/or raw data and metadata that may be required for validation purposes or with reuse value.
The metadata provided with the datasets allow to answer questions to enable
data to be found and understood, ideally according to the particular standards
applied. Such questions include but are not limited to:
* What is the data about?
* Who created it and why?
* In what forms it is available?
* Standards applied.
Finally the **metadata** , **documentation** and **standards** will
help in making the data FAIR (Findable, Accessible, Interoperable and Re-
usable) by not only provide the technical requirements like global, persistent
identifier and clear access protocols (data application programming interfaces
(APIs)) and licenses but by harmonizing and improving the scientific
interoperability of the data by semantic annotations and allowing combination
and enrichment of data sets using linked-data approaches (Combined OpenAPI and
JSON-LD description of data APIs).
Data integrated, for which the integration is in progress or produced can be
grouped into the following areas:
* Existing toxicology, chemical properties and bioassay databases for redistribution
* Existing omics databases for redistribution
* Existing knowledge bases for redistribution and information extracted by data mining
* Intermediate or final results of i n silico studies performed as part of the case studies
## DATA SHARING
According to the ORD Pilot programme, by default as much of the resulting data
as possible should be archived as Open Access. Most data handled in
OpenRiskNet is is provided by international publicly funded projects, not-for-
profit consortia or governmental and regulatory agencies. This data sources
are already available under open-data licenses and will be redistributed by
OpenRiskNet in a restructured and enriched form under the same license. Newly
generated data are results from the improved processing, analysis and
modelling workflows developed as examples in the case studies and OpenRiskNet
is fully committed to make these publicly available either as part of the
publicly shared workflows or, if they have value outside the case studies, as
a separate information and knowledge source. Working together with associated
partners especially from the commercial sector (service providers and end
users from SMEs and larger industry) might put some restriction on the sharing
of data generated in these collaborations. Such legitimate reasons for not
sharing resulting data will be explained in the DMP in the rare cases they
have to be applied. Additionally, OpenRiskNet is committed to protect personal
data and IPR agreements and to responsible data sharing and is taking all
steps reasonably necessary to ensure that data is treated securely and in
accordance with the OpenRiskNet privacy policy (see section below on the
Privacy Policy). No personal data will be transferred to an organization or a
country unless there are adequate controls in place including the security of
data and personal information. Complementing these general data sharing
policies, the DMP describes any ethical or legal issues that can have an
impact on data sharing. Since in many cases, the data production is not under
control of the OpenRiskNet partners and is only redistributed by them, the
obligation to guarantee that the data is generated from high quality, ethical
research and can be shared under an open license is in the hand of the
original data provider or the primary data distributor. This includes the
obligation to operate in conformity with the requirements of their own
institution, and fulfil all necessary national and international regulatory
and ethical requirements. OpenRiskNet is working together with the original
data providers as well as ethical experts on producing workflows and checklist
(see attachments) for the ethics evaluation and on documenting the measures
adopted during the data generation process (ethical approval of the i n vivo
and i n vitro experiments by the relevant authorities) as well as for
protection personal data e.g. by anonymization of data before sharing.
Whenever agreed on by the data provider and technical feasible, this data is
made available to the OpenRiskNet user as part of the data service
description.
## ARCHIVING AND PRESERVATION
To ensure that publicly funded research outputs can have a positive impact on
future research, for policy development, and for societal change, it is also
important to assure the availability of data for a long period beyond the
lifetime of a project. This does not refer only to storage in a research data
repository, but also to consider the usability of the data. One of the main
goals of the infrastructure created by the OpenRiskNet project is to harmonise
data, make it interoperable and sustainable and in some cases even enable data
sharing or replace existing data sharing solutions. Therefore, the project has
a special obligation for preserving data not only produced in the project but
also from other projects redistributed by OpenRiskNet and software or any code
produced to perform specific analyses or to render the data as well as being
clear about any proprietary or open source tools that will be needed to
validate and use the preserved data. OpenRiskNet is build upon software
engineering and infrastructure components developed, supported and adopted by
a large community guaranteeing, on one hand, some stability and sustainability
of the data sharing, accessing and processing solutions provided even in the
relatively quickly changing field of microservice architectures and
deployments. On the other hand, the containerization approach adopted by
OpenRiskNet allows for the storage of the data and software in the version
used during the execution of the analysing and modelling workflows allowing
for complete and exact repeatability using the same code and improved
reproducibility due to better documentation.
It has to be noted here that many of the data sources are only redistributed
by OpenRiskNet. The primary data provider for e.g. diXa and ToxCast are big
European infrastructures or US agencies, ELIXIR and US EPA, respectively. For
these, data archiving and preservation have to be guaranteed by these
institutions. However, OpenRiskNet and more specifically the OpenRiskNet
partner responsible for the integration into the OpenRiskNet infrastructure is
in charge of maintaining and updating the alternative method to access the
data (OpenRiskNet-compliant data API), guaranteeing that the data available
within OpenRiskNet is on the same technical and curation level and at the same
version as in the primary source, and sustaining the solution beyond the
OpenRiskNet project. The same is true for data sources, where OpenRiskNet also
takes the responsibility of hosting the data and thus, becomes the primary
data source. In the later case, archiving and preservation of the data source
containers is of uttermost importance since otherwise there is the danger that
the data is lost completely. Negotiations with the Birmingham Environment for
Academic Research (BEAR) are underway to provide archiving and preservation
facilities for containerised data and software services for at least the next
5 years, for which this cannot be guaranteed by the service provider.
# DATA MANAGEMENT PLAN (DMP)
This data management plan will address all data-related problems or challenges
that may be encountered by partners during the execution of the project. It
consists of general guidelines and project-internal rules and regulations
dealing with the type of data collected, data sharing following the FAIR
principles, hard- and software resources as well as with data security,
privacy and ethics. Additionally, more details for specific data sources on
all these aspects will be provided whenever necessary.
## 1\. DATA SUMMARY
<table>
<tr>
<th>
**Summary of the data addressing the following issues:**
* State the purpose of the data collection/generation
* Explain the relation to the objectives of the project
* Specify the types and formats of data generated/collected
* Specify if existing data is being re-used (if any)
* Specify the origin of the data
* State the expected size of the data (if known)
* Outline the data utility: to whom will it be useful
</th> </tr> </table>
### 1.1 Purpose of the data collection
The main purpose in collecting and use of data and metadata in the OpenRiskNet
project is to fulfill its main objectives in providing and improving solutions
on data availability to the risk assessment scientific community, data
quality, interoperability, standardization and sustainability and overcome
some of the data-related issues, e.g.:
* Fragmentation of data across different databases;
* Low quality due to insufficient data curation;
* Poor explanation and insufficient details on experimental design and protocols applied;
* Data available in different formats and with different annotations.
Another goal is to generate guidelines and templates for data exchange,
harmonise the use of ontologies as well as develop criteria and solutions for
controlling the quality of a dataset or i n silico tool, for quantifying the
uncertainty of predictive models and for improving the repeatability and
reproducibility of processing, analysis and modelling workflows.
### 1.2 Relation to the objectives of the project
The OpenRiskNet project aims to establish the infrastructure and services
functions providing a centralised and standardised set of data and computing
resources, accompanied by standardised operating procedures and guidance:
* Provision of quality sources of data to facilitate more accurate evaluation of toxicity;
* Data infrastructure will offer a centralised repository for data created during other research programs, including the import of relevant research i n vitro, i n vivo and human data from other sources.
* Well-designed data import facilities to support ongoing data collection according to quality guidance.
* Use and further development of data annotation and exchange standards for describing toxicity data based on application programming interfaces in order to reduce errors and enable data integration from different laboratories, including data sources outside the program
* Integrate regulatory reporting requirements with respect to metadata and documentation details and completeness as well as to export options into file formats like ISA, ToxML and OECD harmonised templates.
The OpenRiskNet project aims also to develop and optimise computational models
and automated and reliable analysis workflows in order to increase the
mechanistic understanding of toxicity:
* The models will permit identification of mechanistic links between omics data at different levels of functional organisation;
* The models will help to advance the understanding of the relationship between toxicity, architecture, function and risk;
* Computational sensitivity analyses components will aid in identifying most sensitive parameters relevant to toxicity and guide further data acquisition and experiments towards increased chemical safety.
The data sources integrated during the project are highly relevant to the
predictive toxicology and risk assessment community and therefore, are used to
showcase and evaluate the concepts and solutions provided by OpenRiskNet and
how these are addressing the aims just mentioned. Additionally, they are used
in the case studies to provide the example workflows on how to apply and
combine the different tools for effective problem solving for the different
aspects of risk assessment.
### 1.3 Types and formats of data
OpenRiskNet is structured around the concept of semantic-annotated application
programming interfaces, which will also be used to search and access data from
OpenRiskNet-compliant data sources. As serialised exchange format, JSON or the
semantically annotated form JSON-LD is recommended and enforced whenever
possible. These formats will cover mainly the metadata associated to the data
and in the case of small numbers of readouts (experimental toxicology
endpoints) per sample also the data. Especially for omics data or imaging
techniques, these files will be accompanied by data in standard file formats
to keep the compatibility and interoperability with tools developed in these
areas like gene- and pathway-enrichment approaches or image recognition
software, respectively. Additionally, to be able to integrate legacy data and
provide the final results in the format required for e.g. regulatory
reporting, OpenRiskNet is supporting import and export of standard file
formats like ISA (-tab or -json), ToxML and OECD harmonised templates.
However, such technical file format conversion solutions require a scientific
harmonization of the metadata completeness and data description levels, which
are better defined for summary data used for regulatory purpose than for raw
data, where the reporting style is very dependent on the individual data
provider and application area. Therefore, we are working together with other
big projects (EU-ToxRisk and NanoCommons) to define the amount and content of
metadata, which has to be provided for each experimental assay or
computational investigation, and data formats if no standards exist so far as
well as providing the means for future additions and adaptations based on
flexible data schema specifications. However, only the strict usage of
ontologies in this data and metadata descriptions can guarantee that the
information is easily understood by the user or automatically transferred
between services.
### 1.4 Reuse of data
For data and computational models, we use, as much as possible, existing data,
software tools, open and readily available to all partners. We will aim at re-
usable and extensible tools. OpenRiskNet is not producing any new experimental
data but is reusing data from publicly available data sources or if absolutely
needed for interaction with an associated partner or a case study, data from
other projects not yet under an open license. It is the clear goal of the
project, to provide all input data independent of the original source as well
as the results from the processing, analysis and modelling workflows under an
open-data license and provide it in a easy way for reuse by others. On one
hand, making sharing, accessing and reusing of data easier is the main goal of
the data solutions provided by OpenRiskNet and the integration of the
reference data sources. On the other hand, results from the i n silico
investigations are considered as equally valuable for sharing and reuse
especially with the goal to improve the evaluability, repeatability and
reproducibility of these computational studies. Full documentation of the
workflows including intermediate results and permanent storage of the final
outcomes highly annotated by metadata describing the procedures is, therefore,
another central goal of the OpenRiskNet infrastructure.
### 1.5 Origin of the data
As mentioned before at many places, the main data integrated, used and
provided for easy reuse by OpenRiskNet are coming from other publicly funded
research and infrastructure projects or institutions and are already in the
public domain or will be made public available soon. However, users might also
want to access commercial data services provided by associated partners or and
use their in-house data as part of the infrastructure and partly share them
with a selected users under a specific license. These considerations lead to
three different classes grouping the origins of the data:
* Data and models owned and provided by OpenRiskNet Partners and Associated Partners as part of the project work under an open-data license;
* Open Source data and models provided under the license mentioned by the owners;
* Data from third parties including associated partners and commercial services of OpenRiskNet partner, and not yet available in existing open databases used provided under the conditions specified by the data owner and included in a formal agreement.
For all these data sources, the original license of data usage as to be
considered and also applied (in the original or more restricted form) to the
version integrated in OpenRiskNet environments. To prevent unauthorised data
access even in virtual environments shared by multiple users like the
reference environment, an authentication and authorisation service is
integrated in OpenRiskNet infrastructure, which also handles the license
management. In the same way, commercial software or free software requiring a
registration is handled.
### 1.6 Expected size of the data
As described above the idea of the OpenRiskNet infrastructure is not to
combine data from different sources into one data warehouse but to access the
data from its original source and use the interoperability layer added to the
data services to harmonise them. In this way, no additional capacity for
storage of the original data is needed. However, two aims of the OpenRiskNet
project might lead to additional requirements on data storage.
1. Some of the data sources considered for integration are not yet available in open-accessible databases, these cannot be accessed via application programming interfaces or don’t comply with the FAIR principles. In such cases, OpenRiskNet will negotiate with the data owners if the data should be either transferred to standard data repositories or if the existing solution should be improved within the framework of the associated partner programme.
2. Most if not all of the data sources should also be provided in a form suitable for in-house deployment. Even if the user system administrator setting up the in-house virtual research environment (VRE) is responsible for providing the required resources for such deployments, the data sources have to be containerised and provided to the users for download via the OpenRiskNet service catalogue. To assess the needed storage space for the containers and to give the users guidance on the needed computational resources for the VRE, expected sizes for all data services are given in section 1.8 below.
### 1.7 Utility of data and models
OpenRiskNet solutions will work towards making data available to its main
stakeholders, researchers, risk assessors and regulators, in an easy
accessible, standardised and harmonised way in order to be able to base
conclusions and recommendations about the safeness of a chemical, drug,
cosmetic ingredient and nanomaterial on all available evidence. The same
principles are applied also to data processing, analysis and modelling tools
involved in risk assessment.
The access to the data infrastructure part of OpenRiskNet by industry has the
merit of providing a wide spectrum of data, with which industry could perform
parts of research and development activities and to lower the barriers to real
innovation resulting in new products, processes and services. Close
cooperation with the regulatory agencies is also key to push the regulatory
acceptance of the integrated tools and workflows.
Possible beneficiaries of the data, computational models and e-infrastructure:
* Industry represented by chemicals, pharma, food, cosmetics or other consumer products companies which are required use all available information and to address the ‘3Rs’ principles and report on alternative methods used (including i n silico);
* Regulatory agencies (e.g. ECHA, EMA, EFSA);
* SMEs as they frequently do not have in-house tools and knowledge resources for the regulatory risk assessment requirements;
* R&D community: the translation of these methods to industrial and regulatory science will result in a deeper understanding of biological response to perturbations supporting e.g. better designed and safer drugs and clinical practice;
* Consumers: OpenRiskNet aims to support integration of apps that can be used by consumers on their mobile phones supporting everyday activities, such as obtaining knowledge on ingredients in products they are purchasing or using.
### 1.8 Specific information on individual shared data sources
In this section, we will address specific information and requirements of
individual data sources provided by OpenRiskNet with respect to their purpose,
origin, relationship to the project, data type and format, size and potential
users. Section 2.5 and 5.3 below are fulfilling the same purpose for issues on
FAIR data sharing and ethics. These are meant as additions or clarifications
to the general descriptions relevant only for the specific data set /
database. Points completely covered by the general remarks will not be
repeated here again and thus some of the subsections above will not appear in
the database descriptions
#### 1.8.1 diXa
Like OpenRiskNet, diXa was an e-infrastructure project for collecting data
from different research projects and make them publicly available via a common
interface. Thus, its goals and objectives fit those of the OpenRiskNet
project. As part of the integration of diXa Data Warehouse into the
OpenRiskNet infrastructure, the data access will be semantically annotated and
further harmonised to other OpenRiskNet services.
1.8.1.1 Additional details to 1.1 Purpose of the data collection and 1.2
Relation to the objectives of the project
The Data Infrastructure for Chemical Safety (diXa) project (
_http://www.dixa-fp7.eu_ _/_ ) __ was funded by EU FP7 to provide a
single resource for the capture of toxicogenomics data produced by past,
present and future EU research projects, and to ensure sustainability of such
a resource for use by the wider research community. Therefore, the diXa Data
Warehouse was established ( _http://wwwdev.ebi.ac.uk/fg/dixa/index.htm_
_l_ ).
Data from the diXa Data Warehouse as well as other sources (i.e. NCBI GEO and
EBI ArrayExpress) is currently being used in a meta-analysis for genotoxicity
prediction using data from multiple i n vitro cell models as part of the TGX
case study. The results using only the human data have been presented as
poster at EUROTOX 2018
( _https://doi.org/10.1016/j.toxlet.2018.06.60_ _8_ ) __ .
1.8.1.2 Additional details to 1.3 Types and formats of data, 1.4 Reuse of data
and 1.5 Origin of the data
The diXa Data Warehouse comprises of 95 studies, 29609 samples and 469
compounds (including solvents). I n vitro human and rodent data and i n vivo
rodent data were collected from the EU FP6 projects carcinoGENOMICS, PredTox,
NewGeneris, Predictomics, the EU FP7 projects ESNATS, Predict-iv, the Dutch
project of the Netherlands Toxicogenomics Centre, the Japanese project Open
TG-GATEs and the US project DrugMatrix. Most studies consist of
transcriptomics data, whereas a few also contain metabolomics and/or
proteomics data. It should be noted that the 2 i n vivo human studies of the
EU FP7 Envirogenomarkers project do not contain data as these have been
retracted based on objections from the Swedish biobank with regard to personal
data protection.
In addition, 188 human disease transcriptomics data sets have been added to
the data warehouse.
Metadata for all studies and disease data sets are captured in the ISA-tab
format.
In addition to this ‘omics data collection links to other globally-available
chemical/toxicological databases were provided.
The diXa Data Warehouse has been further used in the EU FP7 project HeCaToS
coordinated by UM. In this project the data warehouse has become part of EBI’s
BioStudies ( _https://www.ebi.ac.uk/biostudies_ _/_ ) __ and the data
generated in HeCaToS were directly uploaded to BioStudies. Upon public release
of these data they can also be used within OpenRiskNet.
1.8.1.3 Additional details to 1.6 Expected size of the data
The raw data of the ~30,000 samples are at least 400 GB in size.
#### 1.8.2 BridgeDb
The BridgeDb project was set up to provide both identifier mapping data and a
general framework that provides an API to access identifier mapping data [5
] . BridgeDb is used in smaller and larger projects, the latter including
WikiPathways, Cytoscape and Open PHACTS [6 ] . It is available in
various forms, including an Open API web service, Java library,
Docker image, and BioConductor package. The platform supports two kinds of
identifiers.
The first are simple identifier-data source combinations. The second is
Internationalised Resource Identifiers, for use in semantic web technologies.
1.8.2.1 Additional details to 1.1 Purpose of the data collection and 1.2
Relation to the objectives of the project
Data interoperability requires identifier mappings. The mapping data is
collected by the BridgeDb project and reshared in OpenRiskNet (possible
because of the open licenses).
Availability of identifier mappings allows simplifications of workflows. Data
is part of the BridgeDb Docker services, and either preloaded (as in the
current OpenRiskNet services) or loaded when the service is fired up (this
approach is currently not actively used in OpenRiskNet).
1.8.2.2 Additional details to 1.3 Types and formats of data, 1.4 Reuse of data
and 1.5 Origin of the data
BridgeDb identifier mapping databases are commonly available in two formats:
Derby data files and as link sets. Both formats have been developed for
different use cases. BridgeDb identifier mapping databases are available under
open licenses or CC-Zero.
Identifier mapping is essential to data set interoperability. Existing
identifier mappings databases suffice for the current needs, but mapping
databases are expected to be needed for other entities, like nanomaterials and
AOP entities (e.g. stressors, key events, outcomes).
1.8.2.3 Additional details to 1.6 Expected size of the data
Identifier mappings databases are released under the data management plan of
the
BridgeDb project. Data is shared in different ways depending on the type of
entity. Metabolics identifier mappings databases are released on Figshare, and
gene-variant databases are planned to be released on Figshare or Zenodo. The
gene/protein and interaction mapping databases are currently still released
using a custom approach, using a download server and not actively archived
yet. The sizes of these databases vary, but typically are in the order of
500MB to 1GB in size. Exception are the gene-variant databases which are much
larger. All sizes are still well within the scope of what archival websites
allow.
1.8.2.4 Additional details to 1.7 Utility of data and models
Identifier mapping is essential to data set interoperability since there are
multiple competing identifier systems available for labeling e.g. chemical
compounds, genes and pathways. Existing identifier mappings databases suffice
for the current needs, but mapping databases are expected to be needed for
other entities, like nanomaterials and AOP entities (e.g. key events,
outcomes). Additionally, access to these tools from other services for e.g.
cross-database searches and data curation and enrichment will be facilitated
by the OpenRiskNet integration.
#### 1.8.3 WikiPathways
WikiPathways is a molecular pathway database, established by the WikiPathways
team, a collaboration between the Department of Bioinformatics of Maastricht
University and the Gladstone Institute, San Francisco. Its purpose is to
facilitate the contribution and maintenance of pathway information by the
biology community by utilizing the open, collaborative platform of
WikiPathways.
1.8.3.1 Additional details to 1.1 Purpose of the data collection and 1.2
Relation to the objectives of the project
The contents of WikiPathways comprise of molecular pathways, consisting of
nodes that are annotated for genes, proteins, and metabolites, which can be
utilised for omics data analysis through pathway analysis in PathVisio. The
WikiPathways database captures the biological knowledge in biological pathway
diagrams, supported by scientific literature. Because molecular pathways can
describe processes in any field of biology, it is relevant for toxicological
risk assessment workflows. Pathways describe the connections between
biological entities and show how a disturbance by a chemical or nanomaterial
could cause downstream effects.
1.8.3.2 Additional details to 1.3 Types and formats of data, 1.4 Reuse of data
and 1.5 Origin of the data
The molecular pathways in WikiPathways are developed and curated by
researchers, and are based on scientific literature. Pathways are available in
multiple formats, including but not limited to the original Graphical Pathway
Markup Language (GPML), Resource Description Framework (RDF), gene lists (GMT
format), and nanopublications. The CC-Zero license puts no restrictions on
reuse.
1.8.3.3 Additional details to 1.6 Expected size of the data
The complete collection of GPML files is less than 100MB.
1.8.3.4 Additional details to 1.7 Utility of data and models
Biological pathways are used for data analysis, biological interpretation of
omics data, and data integration.
#### 1.8.4 AOP-Wiki
The AOP-Wiki is the primary repository of qualitative, mechanistic Adverse
Outcome Pathway (AOP) knowledge. It was developed by the Organisation for
Economic and Co-operation and Development (OECD), representing a collaboration
between the
European Commission DG Joint Research Centre and US Environmental Protection
Agency. The AOP-Wiki is part of the AOP-Knowledge Base, which was launched by
the OECD to allow everyone to build AOPs.
1.8.4.1 Additional details to 1.1 Purpose of the data collection and 1.2
Relation to the objectives of the project
The AOP-Wiki data comprises of mechanistic toxicological knowledge relevant
for risk assessment. While most of the knowledge is present as free-text,
literature-supported descriptions, essential aspects, such as biological
processes, objects, cell types, and stressor chemicals that cause a
disturbance, among other things are annotated with ontologies and chemical
identifiers. Therefore, the AOP-Wiki serves as a knowledge base for
toxicological effects related to a variety of chemicals, which summarises
relevant literature.
1.8.4.2 Additional details to 1.3 Types and formats of data, 1.4 Reuse of data
and
1.5 Origin of the data
Knowledge in the AOP-Wiki data is stored partly as free text and partly as
ontology annotations and chemical identifiers. The data originates from the
AOP-Wiki database, and is supported by scientific literature that is gathered
and written by researchers. The contents of the AOP-Wiki are reviewed by the
OECD Extended Advisory Group on Molecular Screening and Toxicogenomics
(EAGMST). Nightly exports of the AOP-Wiki contents are available, but only
quarterly downloads are stored and maintained permanently on the Wiki which
allows citation when the information is reused.
1.8.4.3 Additional details to 1.6 Expected size of the data
While the contents of the AOP-Wiki are increasing rapidly on a daily basis,
the latest permanent download of the data (October 2018) does not exceed 12Mb.
1.8.4.4 Additional details to 1.7 Utility of data and models
In order to perform risk assessment, one has to gather all relevant knowledge
about the mechanistic effects of a compound that requires assessment. The AOP-
Wiki allows for reusing mechanistic knowledge of toxicological events upon
disturbance by a stressor, often a chemical. As the AOPs are developed in a
way that knowledge is separated in biological events (called Key Events) and
are chemical-agnostic, their major purpose is the re-usability of
toxicological knowledge. Therefore, the contents of the AOP-Wiki can be
relevant for each risk assessment workflow, providing mechanistic information
about biological processes and linking these together.
#### 1.8.5 ToxCast
The United States Environmental Protection Agency Toxicity forecaster
(ToxCast) 1 has generated toxicity screening data on thousands of chemicals
in commerce and of interest to the agency and general public. The project also
uses computational approaches to prioritise and rank chemicals for risk
assessments and regulatory decision making. The data is publicly available,
widely distributed and can be annotated to fit into the OpenRiskNet data
harmonisation and integration framework.
1.8.5.1 Additional details to 1.1 Purpose of the data collection and 1.2
Relation to the objectives of the project
The ToxCast research project data is generated on high-throughput in vitro
toxicity screens for a variety of chemicals and biological targets. One of the
goals of the project is to prioritise and evaluate the potential human health
risk of chemicals in a cost efficient way. The data generated also includes
computational and predictive models to predict toxicity potential of the
chemicals in humans. The results of these analysis are being used actively to
inform the context of decision making such as endocrine disruptor screening.
The data can be integrated with other data services of the OpenRiskNet
infrastructure such as ontology mapping, pathway identification and mapping,
and AOP development tools that. Users of the OpenRiskNet service will be able
to take advantage of the information gaps filled through the integration of
these datasets and models to develop predictive toxicology and risk assessment
models e.g. read-across models. Examples of such uses are created in the case
studies collecting evidence from all available data sources to create profiles
of specific compounds, complementing omics data in bioinformatics workflows,
data-driven developing and validating AOP, and model building based on
chemical and biological data. The US EPA is also making the data publicly
available and accessible through various means. As the project progresses, and
more chemicals are screened, the agency makes periodic updates to the public
release as more data is generated.
1.8.5.2 Additional details to 1.3 Types and formats of data, 1.4 Reuse of data
and 1.5 Origin of the data
The data currently available on the ToxCast dashboard includes over 9076
chemicals tested in 1192 assays (as at November 26, 2018) that map to hundreds
of genes in both humans and rats. The chemicals screened span various uses
including industrial, individual, food additive and potentially safer
alternatives to already existing older chemicals. The assays tested are
usually of two types: i.) cell-based assays which measure changes in cellular
response to the test substances; and ii) Biochemical assays which measure the
activity of a biological macromolecule. The cell typically used may be human
or rat primary cells and cell lines. To inform chemical safety decisions, the
computational toxicology research group at the US EPA makes both the archived
and current versions of the data available to the public through 1.) a
database called invitroDB which is a MySQL download of all the data, 2.)
summary data in flat-file format (e.g. comma-separated value files and tab
separated files), 3.) concentration response plots in pdf format, and 4.) a
ToxCast dashboard web application which serves as a portal for users to search
and query the data. No personal data is collected during the data generation
process as all data in this set is in vitro and commercial cell lines are used
for the testing.
1.8.5.3 Additional details to 1.6 Expected size of the data
The current version of the invitroDB database download is less than 8GB. The
accompanying summary files and analysis pipeline and concentration response
plots have a cumulative size of less than 30GB.
1.8.5.4 Additional details to 1.7 Utility of data and models
The high-throughput screening toxicity data and models available in ToxCast
cover a wide chemical and biological space useful for risk assessment and as
such of great value to the OpenRiskNet stakeholders. Integrating this dataset
to the OpenRiskNet infrastructure will allow for easier access to the data by
users who may not have background or expertise to setup and run the local
databases and modelling pipelines. In addition being able to access this data
from the OpenRiskNet service will also create greater utility for the data as
it can be directly cross-referenced and used for modelling or analysis with
other data in the service.
## 2\. FAIR DATA
### 2.1 Making data findable, including provisions for metadata
<table>
<tr>
<th>
_●_
</th>
<th>
Outline the discoverability of data (metadata provision)
</th> </tr>
<tr>
<td>
_●_
</td>
<td>
Outline the identifiability of data and refer to standard identification
mechanism. Do you make use of persistent and unique identifiers such as
Digital Object Identifiers?
</td> </tr>
<tr>
<td>
_●_
</td>
<td>
Outline naming conventions used
</td> </tr>
<tr>
<td>
_●_
</td>
<td>
Outline the approach towards search keyword
</td> </tr>
<tr>
<td>
_●_
</td>
<td>
Outline the approach for clear versioning
</td> </tr>
<tr>
<td>
_●_
</td>
<td>
Specify standards for metadata creation (if any). If there are no standards in
your discipline describe what metadata will be created and how
</td> </tr> </table>
OpenRiskNet is integrating existing data sources and make them easier
findable, accessible and interoperable. This is based, on one hand, on the
metadata provided by the data sources and, on the other hand, on the
interoperability layer, which harmonises these metadata into service
descriptions and data schemata, which can be queried through the OpenRiskNet
discovery service.
The description of the capabilities of a database and the data schema allows
for:
* Accessing specific search functionality, and
* Identify the data fields to be searched (e.g. where information on the biological assays are stored);
* Finding the best format for data exchange;
* Understanding all the data and tools, with transparent access to metadata describing the experimental setup or computational approaches.
In the case that the original data sources don’t provide all features required
by the FAIR principles as e.g. unique identifiers, the interoperability layer
added to the service in the context of OpenRiskNet can add these features and
in this way improve the quality of the data source.
### 2.2 Making data accessible
<table>
<tr>
<th>
_●_
</th>
<th>
Specify which data will be made openly available? If some data is kept closed
provide rationale for doing so
</th> </tr>
<tr>
<td>
_●_
</td>
<td>
Specify how the data will be made available
</td> </tr>
<tr>
<td>
_●_
</td>
<td>
Specify what methods or software tools are needed to access the data? Is
documentation about the software needed to access the data included? Is it
possible to include the relevant software (e.g. in open source code)?
</td> </tr>
<tr>
<td>
_●_
</td>
<td>
Specify where the data and associated metadata, documentation and code are
deposited
</td> </tr>
<tr>
<td>
_●_
</td>
<td>
Specify how access will be provided in case there are any restrictions
</td> </tr> </table>
The OpenRiskNet approach will enable the early and transparent sharing and
analysis of data between organisations involved in many sectors and programs.
OpenRiskNet APIs and the used transfer formats were openly released
immediately after their definition had reached a first stable form and updates
will be made available throughout the project. In the prioritization of
services to be integrated, open source tools are favoured for the use in the
case studies and the reference workflows targeting specific question but
commercial services will be equally important to sustain the infrastructure in
the long run. However, also these commercial services are required to openly
share their API definitions and data formats to allow for integration and
combination with other tools.
Open Standards applied:
* Data and models are stored and served using well-developed and widely applied standards and technologies that promote data reuse and integration, such as JSON-LD, RDF and related semantic web technologies;
* OpenRiskNet resources are aligned with activities of toxicology communities like OpenTox in developing open standards for predictive toxicology resources;
* Tools to access study data and metadata description in standards file formats such as ISA, already in use in a number of omics, toxicogenomic and nanosafety resources (e.g. ToxBank, diXa, eNanoMapper), further simplify the integration;
* Model descriptions are provided encoded guided by suitable open standards (e.g. QMRF, BEL, SBML) and annotated advancing appropriate minimal information standards (MIRIAM) for dissemination through appropriate repositories (e.g. BioModels) to cover the extended requirements of the semantic interoperability layer of OpenRiskNet.
OpenRiskNet does not propose to create new file standards rather to employ the
existing approaches as to define a core set of information, on which the
scientific community agrees that they are important to document, but which can
also be modified and extended if necessary for a specific application. For
defining this core set, regulatory files formats like **OECD** **harmonised
templates (OECD HT)** [7 ] and **Standard** **for Exchange of
Nonclinical Data (SEND)** [8 ] are considered. Even if these file formats
are too limited and do not have the flexibility to be used outside regulatory
purposes and especially for early stage research and method development, the
OpenRiskNet partners developing the guidelines are including as much
information needed for these reports as possible in the data transfer
templates.
### 2.3 Making data interoperable
<table>
<tr>
<th>
_●_
</th>
<th>
Assess the interoperability of your data. Specify what data and metadata
vocabularies, standards or methodologies you will follow to facilitate
interoperability
</th> </tr>
<tr>
<td>
_●_
</td>
<td>
Specify whether you will be using standard vocabulary for all data types
present in your data set, to allow inter-disciplinary interoperability? If
not, will you provide mapping to more commonly used ontologies?
</td> </tr> </table>
OpenRiskNet interoperability layer opens possibilities to provide data
schemata, which describe the format of the data using a controlled vocabulary:
* Metadata standards and data documentation approaches consider the existing standards that can be consolidated and the equivalent data that can be retrieved independent of the file format;
* Developments towards the integration of ontologies under a single framework are ongoing together with partner projects mainly from the EU NanoSafety Cluster, which will contribute to the goal of automatic harmonization and annotation of datasets. The goal is not to develop new ontologies. Instead, already existing ontologies (e.g. OBI, ChEBI, PATO, UO, NCIT, EFO, OAE, eTOX, eNanoMapper, MPATH, etc.) are consolidated and integrated into applications ontologies for the toxicology community and specifically for the requirements of OpenRiskNet service annotation.
* Another requirements to establish the comprehensive use of ontologies and in this way foster the interoperability not only of the major data sources but also user-provided data are user-friendly capturing frameworks supporting the selection of ontology terms during data curation and an ontology mapping service resolving issues of using synonyms from different ontologies (e.g. CAS numbers can be annotated using the National Cancer Institute Thesaurus, the EDAM ontology or even the Chemical Information Ontology reused in the eNanoMapper ontology, where it is available under the term “CAS registry number”). OpenRiskNet is working with experts in the field to integrate such tools in the infrastructure.
* Allowing mapping between related items in different database (e.g. different gene-identifiers, linking genes to proteins or RNA identifiers, or mapping between equivalent chemical structures in different databases. BridgeDb, which can perform such mappings and is already part of the OpenRiskNet Services, is thus a core interoperability service.
Additionally, we provide guidelines and training on the usage of standard data
transfer/sharing formats and ontologies in the context of OpenRiskNet:
* Best-practice examples like diXa and ToxBank are used to create templates for data storage and sharing;
* Data schemata for different endpoints as already available in file formats like ISA and ToxML will be transformed into more flexible data transfer approach able to accompany modifications needed due to changed and enhanced experimental and computational protocols.
An important part of these procedure is searching and accessing data from
different sources supported by the semantic annotation of the data sources
based e.g on the
Bioschemas and BioAssays ontology:
* The databases will be accessible by the OpenRiskNet APIs (similar like the computational tools) including the interoperability layer;
* Searches throughout multiple databases will be possible, removing the need to search in everyone independently
* The interoperability layer can be used to inspect the data schema and find out if the needed information is available from the databank and if it can be provided in a form for further analysis.
OpenRiskNet provides to its potential data managers or developers complete
control over the provided data and associated functionalities but requires
from them to describe the interfaces and transfer formats in a generally
understandable OpenRiskNet-compliant way through the interoperability layer.
This work is based on and extends:
* OpenTox APIs, which were designed to cover the field of QSAR-based predictive toxicology with dataset generation, model building, prediction and validation;
* Open PHACTS APIs, which handle knowledge collection and sharing;
* Various other APIs for accessing databases like BioStudies, EGA, ToxBank, and PubChem.
### 2.4 Increase data re-use (through clarifying licenses)
<table>
<tr>
<th>
_●_
</th>
<th>
Specify how the data will be licenced to permit the widest reuse possible
</th> </tr>
<tr>
<td>
_●_
</td>
<td>
Specify when the data will be made available for re-use. If applicable,
specify why and for what period a data embargo is needed
</td> </tr>
<tr>
<td>
_●_
</td>
<td>
Specify whether the data produced and/or used in the project is useable by
third parties, in particular after the end of the project? If the re-use of
some data is restricted, explain why
</td> </tr>
<tr>
<td>
_●_
</td>
<td>
Describe data quality assurance processes
</td> </tr>
<tr>
<td>
_●_
</td>
<td>
Specify the length of time for which the data will remain re-usable
</td> </tr> </table>
Most of the data sources are already available in the public domain.
OpenRiskNet will redistribute the data using the same license as the original
data provider or, if this is demanded by the data provider, in a more
restricted form. New data will be made available through the OpenRiskNet
access methods as soon as it is released by the original data provider, i.e.
no additional embargo period will be enforced by the OpenRiskNet project.
Thus, users can access the same data with respect to the number of datasets
and version of each dataset either from the original service provider, via
e.g. a web interface specifically design for the data warehouse, or through
the OpenRiskNet mechanism, where the latter has the advantage of easy
integration into workflows and interoperability with other data sources and
software tools. Besides this simpler access, OpenRiskNet aims to improve the
quality of the data with the following measures:
Data quality assurance processes:
* Tools for performing automatic validation, analysis and (pre)processing are developed to find inconsistencies in the data and databases and, in this way, improve the quality of the source and are made available in OpenRiskNet, e.g. _http://arrayanalysis.org/_ and _ https://github.com/BiGCAT-U M _ . Additionally, efforts to establish a general, cross-database data curation framework, in which users can flag possible errors in the data and semantic annotation, is supported.
* Some partners (e.g. UM) developed their own pipelines for quality control and analysis of sequencing data (RNA-seq and MeDIP-seq).
* We also integrate tools for manual curation of datasets. The modified dataset are stored (similar to the pre-reasoned datasets) in the original databases as a new version of the dataset or in other OpenRiskNet-compliant databases with a link to the original source.
Quality assurance in the processing, analysis and modelling tools:
* Protocolling of the performed calculations increasing the repeatability and reproducibility of the studies, is supported by the automatic logging and auditing functionalities of modern microservices frameworks as well as the integrated workflow management systems.
* Validation of the services are enforced by the consortium and appropriate measures of uncertainty are requested for all models.
### 2.5 Specific information on individual shared data sources
#### 2.5.1 diXa
The diXa Data Warehouse is open access. Data retrieval as well as upload takes
place via the diXa Data Warehouse webpage
_http://wwwdev.ebi.ac.uk/fg/dixa/index.htm_ _l_ using the ISA-tab format
for the metadata. There is no API available for diXa.
Since the diXa Data Warehouse has become part of BioStudies diXa’s data can
also be accessed here. Data uploaded via BioStudies using the PageTab format
(Page layout Tabulation format) will be part of BioStudies and therefore, will
not be visible via the diXa Data Warehouse. The same is true the other way
around.
BioStudies also provides other formats, such as JSON and XML. Furthermore,
BioStudies is part of the ELIXIR infrastructure and an Rest API 2 is
available.
#### 2.5.2 BridgeDb
The BridgeDb software is available under the OSI-approved Apache License 2.0.
Identifier mappings files are available under open licenses too, following the
open licenses of the upstream resources (Ensembl, Rhea) or CCZero in case of
the metabolite mapping database. The BridgeDb web service and data for
identifier mappings is made available on the OpenRiskNet cloud using an
OpenAPI specification wrapped around a REST services.
#### 2.5.3 WikiPathways
All contents of WikiPathways are licenced with the Creative Commons CC0
waiver, which states that all contents of the database are free to share and
adapt. WikiPathways adopts a customised quality assurance protocol to curate
the database, which is done on a weekly basis.
#### 2.5.4 AOP-Wiki
The AOP-Wiki provides quarterly downloads for the complete database, which are
permanently maintained by the OECD. The AOP-Wiki does not provide licence
information, but states that the data can be reused. All AOPs undergo review
by EAGMST to ensure the quality of the contents of the AOP-Wiki.
<table>
<tr>
<th>
**FAIR Principles**
</th>
<th>
**WikiPathways**
</th>
<th>
**AOP-Wik i**
</th> </tr>
<tr>
<td>
F1. (Meta)data are assigned and globally unique and persistent identifiers
</td>
<td>
2
</td>
<td>
1
</td> </tr>
<tr>
<td>
F2. Data are described with rich metadata
</td>
<td>
1
</td>
<td>
1
</td> </tr>
<tr>
<td>
F3. Metadata clearly and explicitly include the identifier of the data they
describe
</td>
<td>
2
</td>
<td>
2
</td> </tr>
<tr>
<td>
F4. (Meta)data are registered or indexed in a searchable resource
</td>
<td>
2
</td>
<td>
2
</td> </tr>
<tr>
<td>
A1.1. The protocol is open, free and universally implementable
</td>
<td>
2
</td>
<td>
2
</td> </tr>
<tr>
<td>
A1.2. The protocol allows for an authentication and authorization where
necessary
</td>
<td>
2
</td>
<td>
2
</td> </tr>
<tr>
<td>
A2. Metadata are accessible, even when the data are no longer available
</td>
<td>
2
</td>
<td>
2
</td> </tr>
<tr>
<td>
I1. (Meta)data use a formal, accessible, shared, and broadly applicable
language for knowledge representation
</td>
<td>
2
</td>
<td>
2
</td> </tr>
<tr>
<td>
I2. (Meta)data use vocabularies that follow FAIR principles
</td>
<td>
1
</td>
<td>
1
</td> </tr>
<tr>
<td>
I3. (Meta)data include qualified references to other (meta)data
</td>
<td>
2
</td>
<td>
1
</td> </tr>
<tr>
<td>
R1.1 (Meta)data are released with a clear and accessible data usage license
</td>
<td>
2
</td>
<td>
1
</td> </tr>
<tr>
<td>
R1.2. (Meta)data are associated with detailed provenance
</td>
<td>
1
</td>
<td>
1
</td> </tr>
<tr>
<td>
R1.3. (Meta)data meet domain-relevant community standards
</td>
<td>
1
</td>
<td>
2
</td> </tr> </table>
**Table 1.** Compliance to FAIR principles [9 ] by AOP-Wiki and
WikiPathways. Score meanings:
1 = partial compliance, 2 = compliance
#### 2.5.5 ToxCast
All data produced by the U.S EPA including ToxCast is by default in the public
domain (U.S. Public Domain license) and is not subject to domestic copyright
protection under 17 U.S.C. § 105. This allows to reproduce the work in print
or digital form, create derivative works, perform the work publicly, display
the work and distribute copies or digitally transfer the work to the public by
sale or other transfer of ownership, or by rental, lease, or lending. The
release currently used in OpenRiskNet is a REST API developed by Douglas
Connect of the official summary data release made available as downloadable
CSV files. These were restructured to fit the OpenRiskNet concept and to make
them fit for semantic annotation.
## 3\. ALLOCATION OF RESOURCES
<table>
<tr>
<th>
**Explain the allocation of resources, addressing the following issues** :
* Estimate the costs for making your data FAIR. Describe how you intend to cover these costs
* Clearly identify responsibilities for data management in your project
* Describe costs and potential value of long term preservation
</th> </tr> </table>
Making data FAIR is a central task of the integration of data source in the
OpenRiskNet infrastructure. Many of the sources considered for integration
already follow the FAIR principles and no additional cost are foreseen other
than the effort needed to make the sources OpenRiskNet-compliant. For data
sources not at a sufficient high level, the OpenRiskNet partners owning the
data or responsible for its integrating covers the costs of the integration
from their allocated budget. In the case of third-party data sources, the
integration will be performed in collaboration of the associated partners
partly financially supported through the Implementation Challenge and an
OpenRiskNet partner designated as main contact point of the associated
partner.
## 4\. DATA SECURITY
<table>
<tr>
<th>
_●_
</th>
<th>
Address data recovery as well as secure storage and transfer of sensitive data
</th> </tr> </table>
The OpenRiskNet approach on data recovery, secure storage and transfer of
sensitive data includes:
* Responsible and secure management processes for personal data including anonymisation, encryption, logging of data usage as well as data deletion after usage are implemented;
* To ensure that all ethical guidelines are followed by all OpenRiskNet Partners and Associated Partners and implemented in every step of the infrastructure, a p rivacy by design approach is followed in the project, documented in the OpenRiskNet
privacy policy (see below) and controlled by an independent Data Protection
Officer;
* The most sensible way to protect sensitive data offered by the OpenRiskNet infrastructure is to bring the virtual environment and all data sources behind a company’s firewall by in-house deployment.
## 5\. ETHICAL ASPECTS
<table>
<tr>
<th>
_●_
</th>
<th>
To be covered in the context of the ethics review, ethics section of DoA and
ethics deliverables. Include references and related technical aspects if not
covered by the former
</th> </tr> </table>
The ethical aspects are covered by the Protection Of Personal Data (POPD)
requirements and submitted deliverables:
* D6.1 - NEC - Requirement No. 3: Statements regarding the full compliance of third country participants to the H2020 rules
* D6.2 - POPD - Requirement No. 4: Copies of the previous ethical approvals of data to be collected and used in the project, approvals which must also allow the possible secondary use of data
* D6.3 - POPD - Requirement No. 5: Consent forms and information sheets before interviews and surveys and any other data and personal data collection activity in the project
* D6.4 - POPD - Requirement No. 6: A statement by third party testers that they will comply with the applicable EU law and H2020 rules
* D6.5 - POPD - Requirement No. 7: Data Protection Officer Report 1
And the two future deliverables:
* D6.6 - POPD - Requirement No. 8: Data Protection Officer Report 2 \- D6.7 - POPD - Requirement No. 9: Data Protection Officer Report 3
Based on the ethics report received as part of the evaluation of the proposal
and the first report of the Data Protection Officer summarizing the relevant
regulations and legislation for the different data types and test models,
OpenRiskNet has worked together with an external expert to develop elements on
an ethics review framework and ethics requirements checklist, which have to be
considered in the project and the infrastructure implementation before
integrating a data source.
One important component for working on the project case studies and the future
usage of the e-infrastructure is access to existing data provided by other
European projects in this field or from international consortia. The
OpenRiskNet platform is based on a data management system that will make
existing data sources mainly from i n vitro human and animal and i n vivo
animal experiments available to all stakeholders in a harmonised way. In this
context, one important additional task of the DMP, becoming even more relevant
with the new EU General Data Protection Regulation (GDPR) in effect since May
2018, is to support and give recommendations in achieving the highest impact
without jeopardising the ethics integrity of the OpenRiskNet infrastructure.
To fulfil the requirements from the ethics review, a step-by-step decision
process was developed addressing how important legacy data sources need to be
handled by the project. On top of the workflows provided in the first review
of the Data Protection Officer, a hierarchical data source analysis and
evaluation of the ethical implications for OpenRiskNet was performed.
Different categories of data sources have been analysed, including references
to the legislation in place and the conditions for primary, secondary and
tertiary data collection and use. Also special measures that need to be
considered for some specific cases are included.
Data sources considered:
I. Human Biomaterial Use, Collection or Storage (Donors) II. Primary Results
Data processing (Clinics)
3. a) Compound Storage/Processing (Commercial cell lines data providers), and b) Secondary Results Processing (Experimentalists)
4. Tertiary Use - Storage/Provider - No Processing (Database)
Based on the assessment of each of these categories, a checklist is proposed
for each data source to be included and/or used in the OpenRiskNet platform (
|
https://phaidra.univie.ac.at/o:1140797
|
Horizon 2020
|
1204_OpenRiskNet_731075.md
|
# SUMMARY
This report is based on the initial data management plan (DMP) (Deliverable
3.1) [1 ] and includes the final DMP for the OpenRiskNet
e-infrastructure project. The current document covers the aspects of the
OpenRiskNet data management based on the FAIR (findable, accessible,
interoperable and reusable) guidelines, ethics considerations for re-sharing
of public datasets, and details on the data sources made available and will be
sustained after the project as OpenRiskNet data sources. These are alternative
access to US EPA ToxCast/Tox21 data, intensities and fold changes obtained by
processing the TG-GATEs and DrugMatrix transcriptomics data, BridgeDb,
semantic annotated versions of WikiPathways, AOP-Wiki and AOP-DB in RDF
format, ToxicoDB including the dataset for the TGX case study, the nano
Daphnia dataset, ToxPlanet, SCAIview and as a specific feature of OpenRiskNet
also the library of risk assessment workflows in form of Jupyter notebooks.
Sustainability of OpenRiskNet developments and achievements other than data
will be covered in the separate sustainability plan being part of the second
Periodic Report.
# INTRODUCTION
The European Commission is enforcing the openness of data produced in all
projects under Horizon 2020 based on the Open Research Data Pilot (ORD Pilot).
The ORD pilot aims to improve and maximise access to and re-use of research
data and takes into account the need to balance openness and protection of
scientific information, commercialisation and Intellectual Property Rights
(IPR), privacy concerns, security as well as data management and preservation
questions [2 ] . Open data is data that is free to access, re-use,
repurpose, and redistribute. The Open Research Data Pilot aims to make the
research data generated by selected Horizon 2020 projects accessible with as
few restrictions as possible, while at the same time protecting sensitive data
from inappropriate access. Projects starting from January 2017 are by default
part of the Open Data Pilot, including the Research infrastructures (including
e-Infrastructures) are required to participate in the ORD Pilot. Since one of
the main aims of OpenRiskNet is to allow for a simpler, more harmonised access
to public, open data sources and workflows and enrich these with semantic
annotation to improve their interoperability between each other and with
predictive toxicology and risk assessment software, OpenRiskNet is fully
supporting the ORD Pilot, is developing best-practice approach and trying to
act as a role model for data management and sharing [2 ] .
To help optimising the potential for future sharing and re-use of data, the
OpenRiskNet Data Management Plan (DMP) helped the partners to consider any
problems or challenges that may be encountered and helped them to identify
ways to overcome these. Throughout the project, the DMP was handled as a
“living” document and is available here in its final version as to November
2019. It outlines how the research data collected or generated, including
redistribution of existing data sources as well as results from the in
silico investigations performed as part of the case studies, were handled by
the
OpenRiskNet consortium and associated partners. It follows the Guidelines on
FAIR Data Management in Horizon 2020 [2 ] and OpenAIRE guidelines [3
] , and is based around the resources available to the project partners in a
realistic way taking the current knowledge into account. The activities of all
OpenRiskNet partners to keep the DMP up to date followed an online,
distributed approach as outlined in the above mentioned guidelines for
creating an online DMP (see Figure 1).
In this report the general concepts for the description of datasets, as well
as data sharing and archiving approaches adopted in the DMP are described
first. This is then followed by the actual DMP in its final version as by the
time of this writing. It covers the general aspects of the OpenRiskNet data
management but also specific and clearly-defined measures for the data sources
integrated by the project into the OpenRiskNet infrastructure specifically
also including the library of toxicology and risk assessment workflows.
**Figure 1** . DMPonline 1 tool used to guide the creation of the
OpenRiskNet plan
( _https://dmponline.dcc.ac.uk/plans/1626_ _1_ ) __
## DATA SET DESCRIPTION
This section give the general concepts of what is considered data in
OpenRiskNet and a listing of what kind of data and information the project
collected, made available for redistribution and sharing or generated as part
of the case studies (in silico only), and to whom they might be useful later.
More information and specific details are given in the DMP below.
Data refers to:
* Data generated in in vivo, in vitro, in chemico and in silico experiments, the first three coming from third-parties including other projects and associated partners, broadly related to toxicology and risk assessment in the form of raw, processed and summary data as well as metadata to describe the type of data and how it was produced (protocols and method descriptions for the experimental, processing and analysis procedures including the computational workflows);
* More specifically, data and metadata, which form part of an OpenRiskNet service or were needed to execute the case studies and validate results in scientific publications, and
* Other curated and/or raw data and metadata that may be required for validation purposes or with reuse value.
The metadata provided with the datasets allows to answer questions to enable
data to be found and understood, according to the particular standards
applied. Such questions include but are not limited to:
* What is the data about?
* Who created it and why?
* In what forms is it available?
* Which standards were applied.
Finally, the **metadata** , **documentation** and **standards** were
selected and are now provided to make the data FAIR (Findable, Accessible,
Interoperable and Re-usable) as well as to guide future project. They are not
only satisfying the technical requirements like global, persistent identifier,
clear access protocols (data application programming interfaces (APIs)) and
licenses but are an attempt for harmonizing and improving the scientific
interoperability of the data by semantic annotations and allowing combination
and enrichment of datasets using linked-data approaches (Combined OpenAPI and
JSON-LD description of data APIs).
Data integrated can be grouped into the following areas:
* Existing toxicology, chemical properties and bioassay databases for redistribution;
* Existing omics databases for redistribution;
* Existing knowledge bases for redistribution and information extracted by data mining;
* Document libraries;
* Computational workflows for redistribution and adaptation to specific scientific questions; and
* Intermediate or final results of in silico studies performed as part of the case studies as part of the Jupyter notebooks.
## DATA SHARING
According to the ORD Pilot programme, the resulting data should be archived as
Open Access by default as much as possible. Most data handled in OpenRiskNet
is provided by international publicly funded projects, not-for-profit
consortia or governmental and regulatory agencies. This data sources are
already available under open-data licenses and will be redistributed by
OpenRiskNet in a restructured and enriched form under the same license. Newly
generated data are results from the improved processing, analysis and
modelling workflows developed as examples in the case studies, including the
workflows themselves, and OpenRiskNet makes these publicly available either as
part of the publicly shared workflows or, if they have value outside the case
studies, as a separate information and knowledge source. Working together with
associated partners especially from the commercial sector (service providers
and end users from SMEs and larger industry) has put some restrictions on the
sharing of data generated in these collaborations. The legitimate reasons for
not sharing resulting data is explained in the DMP in the one case they had to
be applied (ToxPlanet chemical hazard and toxicology literature repository).
OpenRiskNet was fully committed to protect personal data and IPR agreements
and to responsible data sharing and has taken all steps reasonably necessary
to ensure that data is treated securely and in accordance with the OpenRiskNet
privacy policy (see section below on the Privacy Policy). No personal data was
transferred to an organization or a country unless there were adequate
controls in place including the security of data and personal information.
Complementing these general data sharing policies, the DMP below describes any
ethical or legal issue that has an impact on data sharing of specific data
sources. Since in many cases, the data production was not under the control of
the OpenRiskNet partners and the data was only redistributed by them, the
obligation to guarantee that the data is generated from high quality, ethical
research and can be shared under an open license is in the hand of the
original data provider or the primary data distributor. This includes the
obligation to operate in conformity with the requirements of their own
institution, and fulfil all necessary national and international regulatory
and ethical requirements. OpenRiskNet concortium members were working together
and will continue to do so with the original data providers as well as ethical
experts on producing workflows and a checklist (see attachments for a draft
version) for the ethics evaluation and on documenting the measures adopted
during the data generation process (ethical approval of the in vivo and in
vitro experiments by the relevant authorities) as well as for protection
personal data e.g. by anonymization of data before sharing. Whenever provided
by the data provider and when technical feasible, this licensing, legal and
ethics information was made available to the OpenRiskNet user as part of the
data service description.
## ARCHIVING AND PRESERVATION
To ensure that publicly funded research outputs can have a positive impact on
future research, for policy development, and for societal change, it is also
important to assure the availability of data for a long period beyond the
lifetime of a project. This does not refer only to storage in a research data
repository, but also to consider the usability of the data. One of the main
goals of the infrastructure created by the OpenRiskNet project was to
harmonise data, make it interoperable and sustainable and in some cases even
enable data sharing or replace existing data sharing solutions. Therefore, the
project had a special obligation for preserving data not only produced in the
project but also from other projects redistributed by OpenRiskNet and software
or any code produced to perform specific analyses or to render the data as
well as being clear about any proprietary or open source tools that will be
needed to validate and use the preserved data. OpenRiskNet was building on
software engineering and infrastructure components developed, supported and
adopted by a large community guaranteeing, on the one hand,stability and
sustainability of the data sharing, accessing and processing solutions
provided even in the relatively quickly changing field of microservice
architectures and deployments. On the other hand, the containerization
approach adopted by OpenRiskNet allows for the storage of the data and
software in the version used during the execution of the analysing and
modelling workflows allowing for complete and exact repeatability using the
same code and improved reproducibility due to better documentation.
It has to be noted here that many of the data sources are only redistributed
by OpenRiskNet. The primary data provider for e.g. ToxicoDB, ToxCast/Tox21 and
TG-GATEs are international research groups or agencies. Raw data archiving and
preservation have to be guaranteed by these institutions. However, OpenRiskNet
and more specifically the OpenRiskNet partner responsible for the integration
into the OpenRiskNet infrastructure are in charge of maintaining and updating
the alternative method to access the data (OpenRiskNet-compliant data API),
guaranteeing that the data available within OpenRiskNet is on the same
technical and curation level and at the same version as in the primary source,
and sustaining the solution beyond the OpenRiskNet project. The same is true
for data sources, where OpenRiskNet also takes the responsibility of hosting
the data and thus, becomes the primary data source. In the later case,
archiving and preservation of the data source containers is of uttermost
importance since otherwise there is the danger that the data is lost
completely. Short-term sustainability (2 years) is secured by the reference
infrastructure running at Johannes-Gutenberg Universität Mainz. Negotiations
with the Birmingham Environment for Academic Research (BEAR) are underway to
provide mid- to long-term archiving and preservation facilities for
containerised data and software services for at least the next 5 years.
# DATA MANAGEMENT PLAN (DMP)
This data management plan addresses all data-related measures adopted by the
OpenRiskNet project including problems or challenges that were encountered by
partners during the execution of the project. It consists of general
guidelines and project-internal rules and regulations dealing with the type of
data collected, data sharing following the FAIR principles, hard- and software
resources as well as with data security, privacy and ethics. Additionally,
more details for specific data sources on all these aspects are provided
whenever necessary.
## 1\. DATA SUMMARY
<table>
<tr>
<th>
**Summary of the data addressing the following issues:**
* State the purpose of the data collection/generation
* Explain the relation to the objectives of the project
* Specify the types and formats of data generated/collected
* Specify if existing data is being re-used (if any)
* Specify the origin of the data
* State the expected size of the data (if known)
* Outline the data utility: to whom will it be useful
</th> </tr> </table>
### 1.1 Purpose of the data collection
The main purpose in collecting and use of data and metadata in the OpenRiskNet
project was to fulfill its main objectives in providing and improving
solutions on data availability to the toxicology and risk assessment
scientific community, data quality, interoperability, standardization and
sustainability and overcome some of the data-related issues, e.g.:
* Fragmentation of data across different databases without common ways to query the data;
* Low quality, interpretability and reusability due to insufficient data curation;
* Poor explanation and insufficient details on experimental design and protocols applied;
* Data available in different formats and with different annotations.
Another goal was to generate guidelines and templates for data exchange,
semantic annotations and harmonised use of ontologies as well as develop
criteria and solutions for controlling the quality of a dataset or in silico
tool for quantifying the uncertainty of predictive models and for improving
the repeatability and reproducibility of processing, analysis and modelling
workflows.
### 1.2 Relation to the objectives of the project
The OpenRiskNet project aimed to establish an e-infrastructure and services
functions providing a centralised and standardised set of data and computing
resources, accompanied by standardised operating procedures and guidance:
* Provision of quality sources of data to facilitate a more accurate evaluation of toxicity;
* Data infrastructure offering a centralised repository for data created during other research programs, including the import of relevant research in vitro, in vivo and human data from other sources.
* Well-designed data import facilities to support ongoing data collection according to quality guidance.
* Use and further development of data annotation and exchange standards for describing toxicity data based on application programming interfaces in order to reduce errors and enable data integration from different laboratories, including data sources outside the program
* Integrate regulatory reporting requirements with respect to metadata and documentation details and completeness.
The OpenRiskNet project aims also to develop and optimise computational models
and automated and reliable analysis workflows in order to increase the
mechanistic understanding of toxicity:
* Models permitting identification of mechanistic links between omics data at different levels of functional organisation;
* Models helping to advance the understanding of the relationship between toxicity, architecture, function and risk;
* Computational sensitivity analyses components aiding in identifying most sensitive parameters relevant to toxicity and guide further data acquisition and experiments towards increased chemical safety.
The data sources integrated during the project are highly relevant to the
predictive toxicology and risk assessment community and therefore, are used to
showcase and evaluate the concepts and solutions provided by OpenRiskNet and
how these are addressing the aims just mentioned. Additionally, they were used
in the case studies to provide the example workflows on how to apply and
combine the different tools for effective problem solving for the different
aspects of risk assessment.
### 1.3 Types and formats of data
OpenRiskNet was structured around the concept of semantic-annotated
application programming interfaces, which can be used to search and access
data from OpenRiskNet-compliant data sources. As serialised exchange format,
JSON or the semantically annotated form JSON-LD is recommended and enforced
whenever possible. These formats cover mainly the metadata associated to the
data and in the case of small numbers of readouts (experimental toxicology
endpoints) per sample also the data. Especially for omics data or imaging
techniques, these files will be accompanied by data in standard file formats
to keep the compatibility and interoperability with tools developed in these
areas like gene- and pathway-enrichment approaches or image recognition
software, respectively. Working together with other big projects (EU-ToxRisk
and NanoCommons), the amount and content of metadata were defined, which has
to be provided for each experimental assay or computational investigation, and
data and protocol/test method description formats have been created in the
cases standards didn’t exist so far providing the means for future additions
and adaptations based on flexible data schema specifications to cover
scientific advances. However, only the strict usage of ontologies in this data
and metadata descriptions can guarantee that the information is easily
understood by the user or automatically transferred between services.
### 1.4 Reuse of data
Since OpenRiskNet aimed at improving the reusability of data and software
tools, it was the clear major goal of the project, to provide all input data
independent of the original source as well as the results from the processing,
analysis and modelling workflows under an open-data license and offer it in an
easy way for reuse by others. On one hand, making sharing, accessing and
reusing of data easier was achieved by the data solutions provided by
OpenRiskNet and the integration of the reference data sources. On the other
hand, results from the in silico investigations are considered as equally
valuable for sharing and reuse especially with the goal to improve the
evaluability, repeatability and reproducibility of these computational
studies. Full documentation of the workflows including intermediate results
and permanent storage of the final outcomes highly annotated by metadata
describing the procedures was, therefore, organized and promoted for adoption
by other projects using the capacities of workflow management tools like
Jupyter storing not only the computational workflows but also the produced
results.
### 1.5 Origin of the data
As mentioned before, the main data integrated, used and provided for easy
reuse by OpenRiskNet are coming from other publicly funded research and
infrastructure projects or institutions and are already in the public domain
or will be made public available soon. However, users might also want to
access commercial data services provided by associated partners or use their
in-house data as part of the infrastructure and partly share them with a
selected users under a specific license. These considerations lead to three
different classes grouping the origins of the data:
* Data and models owned and provided by OpenRiskNet consortium members and associated Partners as part of the project work under an open-data license;
* Open Source data and models provided under the license mentioned by the owners;
* Data from third parties including associated partners and commercial services of OpenRiskNet partner, and not yet available in existing open databases provided under the conditions specified by the data owner and included in a formal agreement.
For all these data sources, the original license of data usage has to be
considered and applied (in the original or more restricted form) to the
version integrated in OpenRiskNet environments. To prevent unauthorised data
access even in virtual environments shared by multiple users like the
reference environment, an authentication and authorisation service was
integrated in OpenRiskNet infrastructure, which also handles the license
management. In the same way, commercial software or free software requiring a
registration is handled. In cases, where even more protection was needed, the
data services continued to be operated by the data provider and only
restricted but harmonized access using the OpenRiskNet authentication and
authorisation service was integrated into the infrastructure. In this way, the
data provider keeps complete control over the data and can shield it against
attacks to obtain unauthorised access, which would be easier possible if the
containerised data is deployed into virtual environments on local machines.
### 1.6 Expected size of the data
The idea of the OpenRiskNet infrastructure was not to combine data from
different sources into one data warehouse but to access the data from its
original source and use the interoperability layer added to the data services
to harmonise them. In this way, no additional capacity for storage of the
original, mainly raw data was needed. However, two aims of the OpenRiskNet
project led to additional requirements on data storage.
1. Some of the data sources considered for integration are not yet available in open-accessible databases, cannot be accessed via application programming interfaces from these original sources or don’t comply with the FAIR principles.
2. Data sources were made available in a form suitable for in-house deployment. Even if the user system administrator setting up the in-house virtual environment (VE) is responsible for providing the required resources for such deployments, the data sources have to be containerised and provided to the users for download via the OpenRiskNet service catalogue.
Sizes for all data services are given in section 1.8 below together with other
more specific details. This information can be used by OpenRiskNet users to
assess the needed storage space for the containers and to give guidance on the
needed computational resources for the VE.
### 1.7 Utility of data and models
OpenRiskNet solutions make data available to its main stakeholders
(researchers, risk assessors and regulators) in an easy accessible,
standardised and harmonised way in order to be able to base conclusions and
recommendations about the safety of a chemical, drug, cosmetic ingredient and
nanomaterial on all the available evidence. The same principles are applied
also to data processing, analysis and modelling tools involved in risk
assessment.
The access to the data infrastructure part of OpenRiskNet by academia,
industry, risk assessors and regulators has the merit of providing a wide
spectrum of data, with which users can perform parts of research and
development activities and to lower the barriers to real innovation resulting
in new products, processes and services. Close cooperation with the regulatory
agencies is key to push the regulatory acceptance of the integrated tools and
workflows.
Possible beneficiaries of the data, computational models and e-infrastructure:
* Software developers in academia and industry developing advanced risk assessment approaches based on the data and provide these to risk assessment experts;
* Industry represented by chemicals, pharma, food, cosmetics or other consumer products companies which are required to use all available information and to address the ‘3Rs’ principles and report on alternative methods used (including in silico);
* Regulatory agencies (e.g. ECHA, EMA, EFSA);
* SMEs as they frequently do not have in-house tools and knowledge resources for the regulatory risk assessment requirements;
* R&D community as the translation of these methods to industrial and regulatory science will result in a deeper understanding of biological response to perturbations supporting e.g. better designed and safer drugs and clinical practice;
* Consumers as OpenRiskNet infrastructure support the integration and development of apps that can be used by consumers on their mobile phones supporting everyday activities, such as obtaining knowledge on ingredients in the products they are purchasing or using.
### 1.8 Specific information on individual shared data sources
In this section, specific information and requirements of individual data
sources provided and sustained by OpenRiskNet with respect to their purpose,
origin, relationship to the project, data type and format, size and potential
users are summarized. Section 2.5 below is fulfilling the same purpose for
issues on FAIR data sharing. These are meant as additions or clarifications to
the general descriptions relevant only for the specific dataset/database.
Points completely covered by the general remarks are not repeated here and
thus some of the subsections above will not appear in the following
descriptions. OpenRiskNet sustained data sources are:
* Library of OpenRiskNet computational workflows
* BridgeDb
* WikiPathways
* AOP-Wiki
* AOP-DB
* ToxCast/Tox21
* TG-GATEs
* DrugMatrix
* Nano Daphnia dataset
* ToxicoDB
* ToxPlanet
* SCAIView annotated document corpora
#### **1.8.1 Repository of OpenRiskNet computational workflows**
OpenRiskNet has created a large number of computational workflows in relation
to the work performed in the case studies and to demonstrate the functionality
of single OpenRiskNet tools and how they can be combined to address complex
risk assessment tasks profiting from the improved harmonization and
interoperability. These build an important part of the achievements of
OpenRiskNet and need to be sustained as an information and training resource.
1.8.1.1 Additional details to 1.1 Purpose of the data collection and 1.2
Relation to the objectives of the project
As just described, the workflows build a central piece of the OpenRiskNet
activities and achievements. They not only document the performed work but are
also a way to offer the workflows composed of the computational procedure, the
intermediate data and results for reuse by others. These users of the resource
can rerun the workflow to show repeatability and more importantly modify the
workflows to specifically address their risk assessment questions.
1.8.1.2 Additional details to 1.3 Types and formats of data, 1.4 Reuse of data
and 1.5 Origin of the data
The workflows are in the specific format used to exchange Jupyter, Squonk and
nextflow workflows. Since they are highly related to the service development
and integration, they are stored in a way in which they can be linked to other
development resources and disseminated to computational toxicologies and tool
developments. GitHub was created exactly for this purpose. An OpenRiskNet
organisation has been set up in GitHub covering multiple repositories
designated for the development, support and dissemination activities of the
project. Besides source code control, issue tracking and developer
documentation, one of the repositories is dedicated for sharing and
disseminating workflows mainly in the form of Jupyter or Squonk notebooks and
can be accessed at https://github.com/OpenRiskNet/notebooks. All OpenRiskNet
workflows have been uploaded there and are publically available under open-
source licences to allow reuse, modification and any publication of derived
work. Sustainability of the content is guaranteed by the GitHub environment.
1.8.1.3 Additional details to 1.6 Expected size of the data
The overall size of the workflows is well below 1GB and is covered by the
standard offering of GitHub.
1.8.1.4 Additional details to 1.7 Utility of data and models
Computational workflows offer the optimal way to present functionality of
OpenRiskNet services and the e-infrastructure in total. They help users get
easily started by allowing to rerun the predefined workflows (since all needed
data and service access routes are defined) and adapt them to their questions
at hand. Examples for specific risk assessment task are already available and
the coverage of the full risk assessment framework will be continuously
improved by additional workflows provided by the OpenRiskNet consortium
members also after the end of the project and by opening up the workflows
repository (read and write access) to all users to provide and share their
workflows and even let others comment on and improve them.
#### **1.8.2 BridgeDb**
The BridgeDb project was set up to provide both identifier mapping data and a
general framework that provides an API to access identifier mapping data [4
] . BridgeDb is used in smaller and larger projects, the latter including
WikiPathways, Cytoscape and Open
PHACTS [5 ] . It is available in various forms, including an Open API
web service, Java library,
Docker image, and BioConductor package. The platform supports two kinds of
identifiers.
The first are simple identifier-data source combinations. The second is
Internationalised Resource Identifiers, for use in semantic web technologies.
1.8.2.1 Additional details to 1.1 Purpose of the data collection and 1.2
Relation to the objectives of the project
Data interoperability requires identifier mappings. The mapping data is
collected by the BridgeDb project and reshared in OpenRiskNet (possible
because of the open licenses).
Availability of identifier mappings allows simplifications of workflows. Data
is part of the BridgeDb Docker services, and either preloaded (as in the
current OpenRiskNet services) or loaded when the service is fired up (this
approach is currently not actively used in OpenRiskNet).
1.8.2.2 Additional details to 1.3 Types and formats of data, 1.4 Reuse of data
and
1.5 Origin of the data
BridgeDb identifier mapping databases are commonly available in two formats:
Derby data files and as link sets. Both formats have been developed for
different use cases. BridgeDb identifier mapping databases are available under
open licenses or CC-Zero.
Identifier mapping is essential to data set interoperability. Existing
identifier mappings databases suffice for the current needs, but mapping
databases are expected to be needed for other entities, like nanomaterials and
AOP entities (e.g. stressors, Key Events, outcomes).
1.8.2.3 Additional details to 1.6 Expected size of the data
Identifier mappings databases are released under the data management plan of
the
BridgeDb project. Data is shared in different ways depending on the type of
entity. Metabolics identifier mappings databases are released on Figshare, and
gene-variant databases are planned to be released on Figshare or Zenodo. The
gene/protein and interaction mapping databases are currently still released
using a custom approach, using a download server and not actively archived
yet. The sizes of these databases vary, but typically are in the order of
500MB to 1GB in size. Exception are the gene-variant databases which are much
larger. All sizes are still well within the scope of what archival websites
allow.
1.8.2.4 Additional details to 1.7 Utility of data and models
Identifier mapping is essential to data set interoperability since there are
multiple competing identifier systems available for labeling e.g. chemical
compounds, genes and pathways. Existing identifier mappings databases suffice
for the current needs, but mapping databases are expected to be needed for
other entities, like nanomaterials and AOP entities (e.g. Key Events,
outcomes). Additionally, access to these tools from other services for e.g.
cross-database searches and data curation and enrichment will be facilitated
by the OpenRiskNet integration.
#### **1.8.3 WikiPathways**
WikiPathways is a molecular pathway database, established by the WikiPathways
team, a collaboration between the Department of Bioinformatics of Maastricht
University and the Gladstone Institute, San Francisco. Its purpose is to
facilitate the contribution and maintenance of pathway information by the
biology community by utilizing the open, collaborative platform of
WikiPathways.
1.8.3.1 Additional details to 1.1 Purpose of the data collection and 1.2
Relation to the objectives of the project
The contents of WikiPathways comprise of molecular pathways, consisting of
nodes that are annotated for genes, proteins, and metabolites, which can be
utilised for omics data analysis through pathway analysis in PathVisio. The
WikiPathways database captures the biological knowledge in biological pathway
diagrams, supported by scientific literature. Because molecular pathways can
describe processes in any field of biology, it is relevant for toxicological
risk assessment workflows. Pathways describe the connections between
biological entities and show how a disturbance by a chemical or nanomaterial
could cause downstream effects.
1.8.3.2 Additional details to 1.3 Types and formats of data, 1.4 Reuse of data
and 1.5 Origin of the data
The molecular pathways in WikiPathways are developed and curated by
researchers, and are based on scientific literature. Pathways are available in
multiple formats, including but not limited to the original Graphical Pathway
Markup Language (GPML), Resource Description Framework (RDF), gene lists (GMT
format), and nanopublications. The CC-Zero license puts no restrictions on
reuse.
1.8.3.3 Additional details to 1.6 Expected size of the data
The complete collection of GPML files is less than 100MB.
1.8.3.4 Additional details to 1.7 Utility of data and models
Biological pathways are used for data analysis, biological interpretation of
omics data, and data integration.
#### **1.8.4 AOP-Wiki**
The AOP-Wiki is the primary repository of qualitative, mechanistic Adverse
Outcome Pathway (AOP) knowledge. It was developed by the Organisation for
Economic and Co-operation and Development (OECD), representing a collaboration
between the
European Commission DG Joint Research Centre and US Environmental Protection
Agency. The AOP-Wiki is part of the AOP-Knowledge Base, which was launched by
the OECD to allow everyone to build AOPs.
1.8.4.1 Additional details to 1.1 Purpose of the data collection and 1.2
Relation to the objectives of the project
The AOP-Wiki data comprises of mechanistic toxicological knowledge relevant
for risk assessment. While most of the knowledge is present as free-text,
literature-supported descriptions, essential aspects, such as biological
processes, objects, cell types, and stressor chemicals that cause a
disturbance, among other things are annotated with ontologies and chemical
identifiers. Therefore, the AOP-Wiki serves as a knowledge base for
toxicological effects related to a variety of chemicals, which summarises
relevant literature.
1.8.4.2 Additional details to 1.3 Types and formats of data, 1.4 Reuse of data
and
1.5 Origin of the data
Knowledge in the AOP-Wiki data is stored partly as free text and partly as
ontology annotations and chemical identifiers. The data originates from the
AOP-Wiki database, and is supported by scientific literature that is gathered
and written by researchers. The contents of the AOP-Wiki are reviewed by the
OECD Extended Advisory Group on Molecular Screening and Toxicogenomics
(EAGMST). Nightly exports of the AOP-Wiki contents are available, but only
quarterly downloads are stored and maintained permanently on the Wiki which
allows citation when the information is reused. For the OpenRiskNet service,
the AOP-Wiki has been transformed into the Turtle-syntax, which described the
data in RDF, providing semantic annotations of the database and its contents,
and includes persistent identifiers to improve interoperability with external
tools and resources.
1.8.4.3 Additional details to 1.6 Expected size of the data
While the contents of the AOP-Wiki are increasing rapidly on a daily basis,
the latest permanent download of the data (October 2019) does not exceed 15MB.
The RDF that is exposed in the Virtuoso SPARQL endpoint on the OpenRiskNet
e-Infrastructure has a size of approximately 6MB.
1.8.4.4 Additional details to 1.7 Utility of data and models
In order to perform risk assessment, one has to gather all relevant knowledge
about the mechanistic effects of a compound that requires assessment. The AOP-
Wiki allows for reusing mechanistic knowledge of toxicological events upon
disturbance by a stressor, often a chemical. As the AOPs are developed in a
way that knowledge is separated in biological events (called Key Events) and
are chemical-agnostic, their major purpose is the re-usability of
toxicological knowledge. Therefore, the contents of the AOP-Wiki can be
relevant for each risk assessment workflow, providing mechanistic information
about biological processes and linking these together.
#### **1.8.5 AOP-DB**
The AOP-DB (The Adverse Outcome Pathway Database) serves to link molecular
targets identified as molecular initiating events (MIEs) and key events (KEs)
in the AOP-Wiki (https://aopwiki.org) to publically available data (e.g. gene-
protein, pathway, species orthology, chemical, disease), in addition to
ToxCast assay information.
1.8.5.1 Additional details to 1.1 Purpose of the data collection and 1.2
Relation to the objectives of the project
The AOP-DB service provides information related to AOPs that extend the
existing AOP-Wiki service. Currently, the AOP-DB SPARQL endpoint contains a
variety of resource types, all of which are linked to genes that are present
in the AOP-Wiki. It has the links of those genes with diseases, ToxCast
assays, and protein-protein interactions. By the integration of these
different types of information from several databases allows for convenient
extensions of knowledge captured in AOPs.
1.8.5.2 Additional details to 1.3 Types and formats of data, 1.4 Reuse of data
and 1.5 Origin of the data
The AOP-DB is a SQL database that integrates many types of data of several
databases and repositories. The AOP-DB RDF that is developed in the
OpenRiskNet project contains only a small section of the complete AOP-DB. The
data that is captured in the AOP-DB RDF originates from a variety of public
data sources, including NCBI genes, DisGeNet,
Comparative Toxicogenomics Database (CTD), and EPA ToxCast Data available
through the EPA Chemistry Dashboard ( _https://comptox.epa.gov/dashboar_
_d_ ) __ .
1.8.5.3 Additional details to 1.6 Expected size of the data
At the time of November 2019, the data that is loaded into the Virtuoso SPARQL
endpoint on the OpenRiskNet e-Infrastructure has a size of approximately
320MB.
1.8.5.4 Additional details to 1.7 Utility of data and models
Similarly to the AOP-Wiki, the AOP-DB provides knowledge related to AOPs,
which can be used to inform risk assessments. However, it can also generate
hypotheses by creating links between chemical interactions and adverse effects
by integrating the various resources that it captures. Therefore, the AOP-DB
has multiple purposes, for several types of user communities.
#### **1.8.6 ToxCast/Tox21**
The United States Environmental Protection Agency (US EPA) Toxicity forecaster
(ToxCast) 2 has generated toxicity screening data on thousands of chemicals
in commerce and of interest to the agency and the general public. The project
also uses computational approaches to prioritise and rank chemicals for risk
assessments and regulatory decision making. Toxicology in the 21st Century
(Tox21) was created to continue supporting regulations by developing better
toxicity assessment methods and publicly sharing of the generated data and is
a federal collaboration among US EPA, NIH, including the National Center for
Advancing Translational Sciences and the National Toxicology Program at the
National Institute of Environmental Health Sciences, and the Food and Drug
Administration (FDA). The number of tested environmental chemicals was
increased to 10,000 (called the Tox21 10K library) while the number of
endpoints was reduced to 200 endpoints coming from about 70 quantitative high-
throughput screening assays.
Both data sources are publicly available from the EPAas a MySQL database dump
with additional supporting CSV files with information on the assays and
chemicals used, are widely distributed and applied in risk assessment and can
be annotated to fit into the OpenRiskNet data harmonisation and integration
framework.
1.8.6.1 Additional details to 1.1 Purpose of the data collection and 1.2
Relation to the objectives of the project
The ToxCast research project data was generated on high-throughput in vitro
toxicity screens for a variety of chemicals and biological targets. One of the
goals of the project was to prioritise and evaluate the potential human health
risk of chemicals in a cost efficient way. Processing and analysis workflows
as well as computational and predictive models are also provided to estimate
the toxicity potential of the chemicals in humans. The results of these
analyses are being used actively to inform the context of decision making such
as endocrine disruptor screening. To even expand these screening activities,
the US EPA, National Toxicology Program (NTP) headquartered at the National
Institute of Environmental Health Sciences, National Center for Advancing
Translational Sciences (NCATS), and the Food and Drug Administration (FDA)
formed the Tox21 Consortium. Tox21 is a US federal research collaboration
focused on driving the evolution of Toxicology in the 21st Century by
developing methods to rapidly and efficiently evaluate the safety of
commercial chemicals, pesticides, food additives/ contaminants, and medical
products. 3 To date, the Tox21 Consortium has been successful generating
data on pharmaceuticals and thousands of data poor chemicals, developing a
better understanding of the limits and applications of in vitro methods, and
enabling the new data generated to be incorporated into regulatory decisions.
Tox21 data is publicly available through the National Library of Medicine’s
PubChem 4 , the
EPA’s CompTox Dashboard 5 also providing the ToxCast data, and NTP’s
Chemical Effects in Biological Systems 6 . Even if the first can be accessed
via APIs, PubChem doesn’t offer the rich metadata and mechanistic annotation
available from US EPA. The other two sources
2 _ https://www.epa.gov/chemical-research/toxicity-forecastin g _
3456 _ https://comptox.epa.gov/dashboar https://tox21.gov/wp-
content/uploads/2019/02/Tox21_FactSheet_Oct2018.pd
https://pubchem.ncbi.nlm.nih.gov https://tripod.nih.gov/tox2 1 _ _/_
_d_ _f_
are currently only accessible via web frontends and no APIs are available at
the moment even if the US EPA is planning to release an API to their CompTox
database in the future. To allow for the data to be integrated with other data
services of the OpenRiskNet infrastructure such as ontology mapping, pathway
identification and mapping, and AOP development tools that already now,
OpenRiskNet transferred the MySQL database provided by the US EPA as an
alternative access point for computational toxicologists into a data
management solution easy to access by less experienced users and automated
workflows. Users of the OpenRiskNet service are able to take advantage of the
information gaps filled through the integration of these datasets and models
to develop predictive toxicology and risk assessment models e.g. read-across
models. Examples of such uses are created in the case studies collecting
evidence from all available data sources to create profiles of specific
compounds, complementing omics data in bioinformatics workflows, data-driven
developing and validating AOP, and model building based on chemical and
biological data.
1.8.6.2 Additional details to 1.3 Types and formats of data, 1.4 Reuse of data
and
1.5 Origin of the data
The ToxCast and Tox21 data as provided by the US EPA includes over 9076
chemicals tested for 1473 toxicity endpoints (as at November, 2019) that map
to hundreds of genes, biological pathways and cellular mechanisms in both
humans and rats. The chemicals screened span various uses including
industrial, individual, food additive and potentially safer alternatives to
already existing older chemicals. The assays tested are usually of two types:
i.) cell-based assays which measure changes in cellular response to the test
substances; and ii) biochemical assays which measure the activity of a
biological macromolecule. The cell typically used may be human or rat primary
cells and cell lines. To inform chemical safety decisions, the computational
toxicology research group at the US EPA makes both archived and current
versions of the data available to the public through 1.) a database called
invitroDB which is a MySQL download of all the data, 2.) summary data in flat-
file format (e.g. comma-separated value files and tab separated files), 3.)
concentration response plots in pdf format, and 4.) a CompTox dashboard
(replacing the ToxCast dashboard) which serves as a portal for users to search
and query the data. As the Tox21 project progresses and more chemicals are
screened in more assays, the US EPA make periodic updates to the public
release. At the time of this writing (November 2019), the invitroDB MySQL
database is available in the version 3.2 (released 5 August 2019).
The data is released under the US government public domain license 1.0 and is
not subject to domestic copyright protection under 17 U.S.C. § 105. It states
that unless the work falls under an exception, anyone may, without restriction
under U.S. copyright laws, reproduce the work in print or digital form, create
derivative works, perform the work publicly, display the work and distribute
copies or digitally transfer the work to the public by sale or other transfer
of ownership, or by rental, lease, or lending. Therefore, the redistribution
of the data by OpenRiskNet under the same conditions is completely covered by
the license. With respect to ethics aspects, no personal data is collected
during the data generation process as all data is from in vitro experiments
and commercial cell lines are used for the testing.
OpenRiskNet has transferred the data into the OpenRiskNet-compliant
EdelweissData management solution, with the advantage to provide the data via
APIs in a semantically annotated form. For this, data was extracted from the
MySQL dump of the invitroDB in version 3.1 and 3.2. Additional information on
the assays and compounds were extracted from the provided CSV files and
enriched by additional chemical identifiers taken from the PubChem service. It
was then transferred into table format for the data and metadata in JSON
format and the data schema was semantically annotated. These steps prepared
the data for upload to the EdelweissData system fully integrated into the
OpenRiskNet infrastructure, from which data and metadata can be accessed via
the APIs in JSON format for re-use in OpenRiskNet services and workflows.
1.8.6.3 Additional details to 1.6 Expected size of the data
The current version of the invitroDB database available from EdelweissData has
a total size of approximately 50 GB of data and metadata.
1.8.6.4 Additional details to 1.7 Utility of data and models
The high-throughput screening toxicity data and models available in ToxCast
cover a wide chemical and biological space useful for risk assessment and as
such of great value to the OpenRiskNet stakeholders. Integrating this dataset
to the OpenRiskNet infrastructure will allow for easier access to the data by
users who may not have background or expertise to setup and run the local
databases and modelling pipelines. In addition being able to access this data
from the OpenRiskNet service will also create greater utility for the data as
it can be directly cross-referenced and used for modelling or analysis with
other data in the service.
#### **1.8.7 TG-GATEs**
Open TG-GATEs [6 ] is a Japanese public toxicogenomic database resulting
from two joint government-private sector projects [7 ] no date); [8
] , no date) organized by the National Institute of Biomedical
Innovation, National Institute of Health Sciences and multiple pharmaceutical
companies. The Japanese Toxicogenomics Project generated gene expression and
toxicity data in rats and the primary cultured hepatocytes of rats and humans
following exposure to 170 compounds (mainly pharmaceutical products). The
follow-up Toxicogenomics Informatics Project discovered over 30 different
safety biomarkers using the data and generated additional data for verifying
the biomarkers and analyzing their mechanisms.
1.8.7.1 Additional details to 1.1 Purpose of the data collection and 1.2
Relation to the objectives of the project
Open TG-GATEs is the public toxicogenomics database developed so that a wider
community of researchers can utilize the fruits of TGP and TGP2 research. This
database provides public access to data on 170 of the compounds catalogued in
TG-GATEs. Data searching can be refined using either the name of a compound or
the pathological findings by organ as the starting point. Gene expression data
linked to phenotype data in pathology findings is available for download as a
CEL file. Such data has to run through different processing steps before it
can be used in risk assessment e.g. to run biological pathway enrichment
analysis to identify areas of concern and get mechanistic insight. Even if
these are standard procedures performed by bioinformaticians and are used to
optimize the information available in the data, risk assessors first want to
have a quick look at the data to understand the cellular mechanisms caused by
a chemical. Therefore, OpenRiskNet provides processed data (intensities and
fold changes) generated using a standardized approach, which can directly be
used for gene, pathway and mechanism analysis and linked to AOPs using tools
like AOP-Wiki and AOP-DB described above.
1.8.7.2 Additional details to 1.3 Types and formats of data, 1.4 Reuse of data
and
1.5 Origin of the data
Open TG-GATEs datasets are based on the Affymetrix platforms Rat230-2 (for a
rat) and HG-U133_Plus_2 (for a human) using in vivo and in vitro samples
exposed to the chemicals in different concentrations and different timepoints.
More information on the exposure scenario and the number of compounds are
given in the table below.
<table>
<tr>
<th>
**Organ or cell type**
</th>
<th>
**Organism**
</th>
<th>
**Study type**
</th>
<th>
**Dose type**
</th>
<th>
**Dose level**
</th>
<th>
**No. of compounds tested**
</th> </tr>
<tr>
<td>
Liver
</td>
<td>
Rat
</td>
<td>
In vivo
</td>
<td>
Repeat dose
</td>
<td>
Control, low, middle and high
</td>
<td>
143
</td> </tr>
<tr>
<td>
Liver
</td>
<td>
Rat
</td>
<td>
In vivo
</td>
<td>
Single dose
</td>
<td>
Control, low, middle and high
</td>
<td>
158
</td> </tr>
<tr>
<td>
Kidney
</td>
<td>
Rat
</td>
<td>
In vivo
</td>
<td>
Repeat dose
</td>
<td>
Control, low, middle and high
</td>
<td>
41
</td> </tr>
<tr>
<td>
Kidney
</td>
<td>
Rat
</td>
<td>
In vivo
</td>
<td>
Single dose
</td>
<td>
Control, low, middle and high
</td>
<td>
41
</td> </tr>
<tr>
<td>
Liver
</td>
<td>
Rat
</td>
<td>
In vitro
</td>
<td>
Single dose
</td>
<td>
Control, low, middle and high
</td>
<td>
145
</td> </tr>
<tr>
<td>
Liver
</td>
<td>
Human
</td>
<td>
In vitro
</td>
<td>
Single dose
</td>
<td>
Control, low, middle and high
</td>
<td>
158
</td> </tr> </table>
For the generation of the processed data to be provided by OpenRiskNet, the
raw data were downloaded from ftp.biosciencedbc.jp/archive/open-
tggates/LATEST. Data was stored and processed locally using the R scripting
language, which contains well maintained and documented libraries for gene
expression analysis. Generally the workflow consisted of two independent parts
- extraction of intensity readouts and calculation of fold changes.
The steps for intensity data file generations include (see also Figure 2 for a
schematic representation):
1. For every CEL file, **the** **intensity readouts and probe IDs were extracted** and stored as a new dataset in the form of a CSV file. The CSV file contains only two columns - probe ID and intensity.
2. For every CEL file, the relevant **metadata** **associated with the particular assay was extracted** (such as organism, organ, study type, compound, dose level, dose type, route of exposure, duration, vehicle) and stored in the form of a JSON file. This is crucial information for easy and efficient searching/filtering in any subsequent data analysis.
3. Both databases provide only compound names as chemical identifiers. In order to support easier searching/filtering through compounds, we **extracted** **additional chemical identifiers from the PubChem and CACTUS** (NIH/NCI) services, such as CAS number, SMILES string, InChI, InChI key, IUPAC name, PubChem ID. This information also became part of the metadata file.
4. For every CEL file, the **intensity** **data and metadata files were combined and uploaded to the EdelweissData** as a separate dataset. Along with that, a short description in a human-readable format was generated from the metadata and uploaded along with the dataset to EdelweissData
**Figure 2.** Workflow to generate intensity readouts for DrugMatrix and Open
TG-GATEs data and upload to EdelweissData. Critical steps in the workflow are
numbered according to the description in the main text.
Fold changes were produced using the following workflow (see also Figure 3 for
a schematic representation):
1. Firstly, **every** **CEL file was normalized** using the single-channel array normalization function of the SCAN.UPC library available through Bioconductor [9 ] [ 10]. The latter has been shown to have the same or better performance as the other competing methods (such as RMA), while providing a crucial advantage due to a one-at-a-time normalization of CEL files (hence there is no need to reprocess all the affymetrix datasets, when an existing database is updated) [9 ]
2. For every unique set of conditions (compound, dose, organ or cell type, study type, vehicle, route, duration) the corresponding **treatment** **and control CEL files were identified** .
3. Normalized data of treatment and control CEL files were used as an input for the **differential expression analysis of microarray data** using the very well known limma library available through Bioconductor [11 ] . The empirical Bayes statistics for differential expression has been used to calculate the t- and p-values for every probe ID. Note that for the in vivo studies we used the usual t-test/ANOVA as it considers two independent groups of samples and fits the linear model to the expression data of each gene, while for the in vitro studies we used the paired t-test statistics, which consider dependent groups of samples.
4. Additionally, to aid the further analysis of fold changes, we have converted the probe ID column to the corresponding **gene** **identifiers** (gene symbol, Entrez ID, Ensembl ID) using the AnnotationDbi library and rat2302.db or hgu133plus2.db array annotation data available through Bioconductor. Processed data has been stored in the form of CSV files with multiple columns (probe ID, gene symbol, Ensembl ID, Entrez ID, logarithm of fold change, average expression, t, p-value, adjusted p-value, B).
5. For every processed file (i.e. for every set of conditions) the **relevant metadata associated with the particular assay was extracted** (such as organism, organ, study type, compound, dose level, dosing type, route of exposure, vehicle, duration) and stored in the form of a JSON file. As for the intensities, this is crucial for easy and efficient searching/filtering in any subsequent data analysis.
6. The **additional** **chemical identifiers** generated in step 3 of the previous workflow were again used as part of the metadata file.
7. For every set of conditions the **processed** **data and metadata files were combined and uploaded to the EdelweissData** as a separate dataset.
**Figure 3.** Workflow for the calculation of fold-changes of DrugMatrix and
Open TG-GATEs datasets. Critical steps in the workflow are enumerated and
harmonized with the description in the main text.
1.8.7.3 Additional details to 1.6 Expected size of the data
The overall size of the TG-GATEs processed data is approximately 160GB. The
much larger raw data in CEL format is not managed by OpenRiskNet but can be
accessed at the original source.
1.8.7.4 Additional details to 1.7 Utility of data and models
Processed data created using standardized normalization and processing
procedures offer the advantage that they can be directly combined with similar
data from other sources. At the moment, this can be done for TG-GATEs and
DrugMatrix but will be extended to other sources in the future. The
information can then be combined and re-used in OpenRiskNet services for gene
and pathway enrichment analysis using tools like WikiPathways as well as
linked to AOPs based on the knowledge covered e.g. in AOP-Wiki and AOP-DB.
#### **1.8.8 DrugMatrix**
DrugMatrix is one of the world’s largest toxicogenomic reference resources
provided by the National Toxicology Program of the US Department of Health and
Human Services. It provides access to the toxicogenomic profiles of over 600
different compounds generated with Affymetrix and Codelink microarrays. While
both types of microarray cover liver, kidney, thigh muscles, heart and
cultured hepatocytes, the Codelink microarrays additionally cover bone marrow,
spleen, intestine and brain.
1.8.8.1 Additional details to 1.1 Purpose of the data collection and 1.2
Relation to the objectives of the project
As for TG-GATEs, OpenRiskNet provides processed data (intensities and fold
changes) generated using standardized approach for normalization and
processing starting from the DrugMatrix transcriptomics data, which can
directly be used for gene, pathway and mechanism analysis and linked to AOPs
using tools like AOP-Wiki and AOP-DB described above via simple to use APIs.
1.8.8.2 Additional details to 1.3 Types and formats of data, 1.4 Reuse of data
and
1.5 Origin of the data
Only data from the Affymetrix microarrays is available as OpenRiskNet service
up to now.
All datasets are based on the Affymetrix microarray platform RG230-2.0 and
male Sprague Dawley rats.
<table>
<tr>
<th>
**Organ or cell type**
</th>
<th>
**Study type**
</th>
<th>
**Dose type**
</th>
<th>
**Dose level**
</th>
<th>
**No. of compounds tested**
</th> </tr>
<tr>
<td>
Heart
</td>
<td>
In vivo
</td>
<td>
Repeat dose
</td>
<td>
Control, low and high
</td>
<td>
88
</td> </tr>
<tr>
<td>
Kidney
</td>
<td>
In vivo
</td>
<td>
Repeat dose
</td>
<td>
Control, low and high
</td>
<td>
139
</td> </tr>
<tr>
<td>
Liver
</td>
<td>
In vivo
</td>
<td>
Repeat dose
</td>
<td>
Control, low and high
</td>
<td>
200
</td> </tr>
<tr>
<td>
Thigh Muscle
</td>
<td>
In vivo
</td>
<td>
Repeat dose
</td>
<td>
Control, low and high
</td>
<td>
21
</td> </tr>
<tr>
<td>
Cultured Hepatocytes
</td>
<td>
In vitro
</td>
<td>
Single dose
</td>
<td>
Control and high
</td>
<td>
125
</td> </tr> </table>
1.8.8.3 Additional details to 1.6 Expected size of the data
For the generation of the processed data to be provided by OpenRiskNet, the
raw data were downloaded from
_https://ntp.niehs.nih.gov/results/drugmatrix/index.htm_ _l_ , __ then
processed using the workflows described for TG-GATEs above and uploaded to the
EdelweissData system for easy access in OpenRiskNet workflows. The overall
size of DrugMatrix datasets is 40 GB.
1.8.8.4 Additional details to 1.7 Utility of data and models
See section 1.8.7.4 for a general description of the utility of
transcriptomics processed
data.
#### **1.8.9 KIT Daphnia data on nanoparticles**
"KIT Daphnia data on nanoparticles" dataset is based on "Meta-analysis of
Daphnia magna nanotoxicity experiments in accordance with test guidelines"
study [12 ] and contains the raw “original_daphnia” data file and its
eight derived processed files.
1.8.9.1 Additional details to 1.1 Purpose of the data collection and 1.2
Relation to the objectives of the project
The "original_daphnia" data is compiled from research articles in which
nanotoxicity toward Daphnia Magna was measured according to the test
guidelines from OECD and US EPA. Toxic response caused by nanomaterials have
been assayed; however, the assay outcomes were varied between research
articles. Therefore, this data set was compiled for meta-analysis to figure
out what may be the cause of data heterogeneity between diverse assay
outcomes.
This data contains physicochemical properties of nanomaterials, experimental
conditions for assays and toxic response measurement. Most of physicochemical
properties of nanomaterials were taken from the research articles without
considering measurement details for them since measurement details were often
absent in the articles. Therefore, further details could only be found in
reference research article. Since nanomaterials were easily aggregated in
media, dispersion methods were applied to make nanomaterials being nano-sized
particle. Centrifuge, stir, sonication, and filter were four dispersion
methods used among research articles.
The eight processed files are prepared by the authors and contain numeric
values obtained from quantitative values of the raw "original_daphnia" data.
They are “carbon_pec50”, containing the carbon-based nanomaterials with EC50
values,
“coated_m_pec50” and “coated_m_class”, containing the coated metal
nanoparticle data,
“fullerene_class”, containing the fullerene nanomaterials data, “metal_pec50”
and “metal_class”, containing the metal nanoparticle data, “meox_pec50” and
“meox_class”, containing the metal oxide nanoparticle data. The eight
processed files are used to build models, the ones with the label “pec50” are
used for regression models and the ones with label “class” are used for
classification models.
This nano datasets are very important for standardization and validation of
test methods for regulatory usage and identifying reasons for discrepancies in
results, even if test guidelines exist, and are a good example of a relatively
small data source profiting highly from data management solutions integrated
in the infrastructure and the harmonization and interoperability effort of
OpenRiskNet. Integration into OpenRiskNet made it publicly available following
the FAIR principles.
1.8.9.2 Additional details to 1.3 Types and formats of data, 1.4 Reuse of data
and
1.5 Origin of the data
The Daphnia data as used in the meta-analysis (Shin, Hyun Kil & Seo, Myungwon
& Shin, Seong & Kim, Kwang-Yon & Park, June-Woo & No, Kyoung Tai. (2018)) was
available only from the author composed out of the raw “original_daphnia” data
file and its eight derived processed files, all being in tabular CSV (comma
separated values) format. By providing it on the EdelweissData system, the
data and associated metadata is now available through the APIs in JSOn format
and can be reused in an interoperable way in combination with other data
sources. Even if the data sources provided by the OpenRiskNet consortium cover
nanomaterials only to a very small extent, the uptake of OpenRiskNet solutions
e.g. by the NanoCommons infrastructure will result in new interoperable
resources in the near future. Models for nanomaterials using the data are
already part of the infrastructure.
1.8.9.3 Additional details to 1.6 Expected size of the data
The total size of the data and metadata of this small data source are below 1
MB.
1.8.9.4 Additional details to 1.7 Utility of data and models
Even if this is a very small data source, it provides important information on
the reproducibility of nanomaterial hazard data generated following a
validated OECD guideline. It can now guide researchers with the task to
perform equivalent experiments on new nanomaterials, and encourage them to
provide the results in the same level of detail and preferable also to deposit
the data on an OpenRisk data solution allowing combined analysis and
monitoring progress with respect to reproducibility.
#### **1.8.10 ToxicoDB**
ToxicoDB is a web application to mine large and small scale toxicogenomics
datasets. To better understand the molecular mechanisms underlying compound
toxicity, great efforts have been made in screening of drugs/chemicals by
various groups to generate datasets such as Open-TG GATEs and DrugMatrix.
1.8.10.1 Additional details to 1.1 Purpose of the data collection and 1.2
Relation to the objectives of the project
The inspiration for this application comes from the need for a common ground
for analyzing toxicogenomics datasets with maximum overlap and consistency.
The ToxicoDB will provide an intuitive interface for all users (including
users that are not computational-savvy) to mine the complex toxicogenomic
data. This is in line with OpenRiskNet’s objectives.
1.8.10.2 Additional details to 1.3 Types and formats of data, 1.4 Reuse of
data and
1.5 Origin of the data
ToxicoBD will provide curated toxicogenomics datasets, which includes gene
expression data and toxicological information on drugs and chemicals used to
generate these gene expression data. These datasets are and will be obtained
from publicly available resources, such as the diXa data warehouse, NCBI GEO,
EBI’s ArrayExpress. All datasets are and will be preprocessed and checked for
quality.
1.8.10.3 Additional details to 1.6 Expected size of the data
As a resource provided by an associated partner of OpenRiskNet, the data
management and sustainability is in the hand of this institution. As work on
ToxicoDB is still ongoing it is unclear what the size of the data will be.
Currently, only data from TG-GATEs and DrugMatrix are available. However, by
integrating the service into the OpenRiskNet infrastructure, OpenRiskNet
guarantees that the metadata will be publicly available.
1.8.10.4 Additional details to 1.7 Utility of data and models
Users of the database can find drug and gene annotations, visualize gene
expression data for datasets as well drugs of interest and will be able to
download these data.
#### **1.8.11 ToxPlanet**
ToxPlanet aggregates and curates toxicology and chemical hazard information
from over 500 sources. A web-based GUI as well as access via APIs to the
commercial service are possible.
1.8.11.1 Additional details to 1.1 Purpose of the data collection and 1.2
Relation to the objectives of the project
ToxPlanet gives access to regulatory reports and other structured and
unstructured information sources. In this way, it is a comprehensive and well
organized source for legacy data and documents the current state of regulation
for a large number of compounds. This information and can be used in all
application areas of OpenRiskNet partly automatically extracted by the text-
mining workflows developed in OpenRiskNet.
1.8.11.2 Additional details to 1.3 Types and formats of data, 1.4 Reuse of
data and
1.5 Origin of the data
The data is mainly in the form of reports available in pdf format and
originate from all major regulatory agencies and literature sources. Most of
the documents are in the public domain while the search and browsing features
of ToxPlanet are proprietary and their use needs a commercial license.
1.8.11.3 Additional details to 1.6 Expected size of the data
Due to the commerciality of the service, the data is managed by the provider.
However, by integrating the service into the OpenRiskNet infrastructure,
OpenRiskNet guarantees that the metadata will be publicly available.
1.8.11.4 Additional details to 1.7 Utility of data and models
OpenRiskNet provides text mining workflows to extract data from the ToxPlanet
repository under the restriction that the user needs to acquire a license
first.
#### **1.8.12 SCAIView**
The information retrieval system SCAIView allows for semantic searches in
large text collections by combining free text searches with the ontological
representations of entities derived by JProMiner. SCAIView gives answers to
questions such as “Which genes / proteins are related to a certain disease,
pathway or epigenetics?”. SCAIView´s key features are:
* A user-friendly search environment with a query builder supporting semantic queries with biomedical entities
* Fast and accurate search and retrievals, based on the newest technologies of
semantic search engines
* Visualization and ranking of the most relevant entities and documents
* Exportation of the search results in various file formats
Documents are retrieved by precisely formulated questions using ontological
representations of biomedical entities. The entities are embedded in
searchable hierarchies and span from genes, proteins, accompanied single-
nucleotide polymorphisms to chemical compounds and medical terminologies.
SCAIView supports the selection of the suitable entities by an autocompletion
functionality and a knowledge base for each entity. This includes a
description of the entity, structural information, pathways and links to
relevant biomedical databases like _EntrezGen_ _e_ , __ _ dbSN P _
, _ KEG G _ , __ _ G O _ and _DrugBank_ . __ SCAIView represents
the search results using a color-coded highlighting of the different entity-
classes, statistical search results and various ranking functions. The
selected biomedical entities are found by an approximate search algorithm
implemented in the Fraunhofer-Gesellschaft information extraction tool
JProMiner® which additionally disambiguates synonyms of entities to unique
identifiers in public available entity databases. The SCAIView data collection
comprises three different public document collections and an index of
extracted biomedical entities from these documents.
1.8.12.1 Additional details to 1.1 Purpose of the data collection and 1.2
Relation to the objectives of the project
The main purpose of the data collection is to enable a semantic search
functionality for researchers for public available full text collections. The
documents are retrieved via free text queries in combination with semantic or
ontological search of biomedical entities of interest. The biomedical entities
are embedded in searchable hierarchies and span from genes, proteins,
accompanied SNPs to chemical compounds and medical terminology. With
Ontological Filtering, it is possible to restrict the result to a subset e.g.
genes on a
KEGG pathway or in a Cytoband region.
Advanced retrieval technology allows answering complex queries such as:
* Which genes/proteins are related to a certain context (e.g. disease/pathway/epigenetics)?
* Give me an overview of relevant biomedical concepts in my subcorpus
* Which drugs are relevant for this context?
* To which diseases is my gene associated?
* Which chromosomes show linkage to the disease?
* Which variations are mentioned in the context of the disease and could they be found in dbSNP?
* What other diseases are possibly co-occurring with my relevant disease?
The collection has been used in the Data Cure case study to find relevant
information for chemical compounds and their cancer hazard to humans.
1.8.12.2 Additional details to 1.3 Types and formats of data, 1.4 Reuse of
data and
1.5 Origin of the data
The data collection has been derived from publicly available xml collections
from the _U.S_ _._ _National_ _Library_ _of_ _Medicine_ (NLM: _PubMe_
_d_ , _ PM C _ under _Term_ _s_ _ & _ _Conditions_ _of_ _NLM_ ) __
and the _ Unite d _ _States_ _and_ _Trademark_ _Office_ (USPTO: _PatF_
_T_ ). The annotated files are converted to JSON format and can be downloaded
via the SCAIView API.
1.8.12.3 Additional details to 1.6 Expected size of the data
The size of the raw xml data is: PubMed 40G, PMC 61G, Patents 79G. The
processed data comprises: PubMed 664G, PMC 431G, Patents 179G. The publication
numbers increase constantly every year in the _NL_ _M_ and the _ USPTO
_ . The processed data even grows at a larger rate since more terminologies
and ontologies are indexed each year.
The processed data is loaded into a _ sol r _ _enterprise_ _search_
_engine_ and is hosted and regularly updated by _ Fraunhofer SCA I _ __
under the following _Terms & Condition _ _s_ .
## 2\. FAIR DATA
### 2.1 Making data findable, including provisions for metadata
<table>
<tr>
<th>
_●_
</th>
<th>
Outline the discoverability of data (metadata provision)
</th> </tr>
<tr>
<td>
_●_
</td>
<td>
Outline the identifiability of data and refer to standard identification
mechanism. Do you make use of persistent and unique identifiers such as
Digital Object Identifiers?
</td> </tr>
<tr>
<td>
_●_
</td>
<td>
Outline naming conventions used
</td> </tr>
<tr>
<td>
_●_
</td>
<td>
Outline the approach towards search keyword
</td> </tr>
<tr>
<td>
_●_
</td>
<td>
Outline the approach for clear versioning
</td> </tr>
<tr>
<td>
_●_
</td>
<td>
Specify standards for metadata creation (if any). If there are no standards in
your discipline describe what metadata will be created and how
</td> </tr> </table>
OpenRiskNet was integrating existing data sources and made them more easily
findable, accessible and interoperable. This was based, on the one hand, on
the metadata provided by the data sources and, on the other hand, on the
interoperability layer and exploitation of semantic web standards (Resource
Description Framework, RDF) , which harmonises these metadata leading to data
service descriptions and data schemata, which can be queried through the
OpenRiskNet discovery service.
The description of the capabilities of a database and the data schema allow
for:
* Accessing specific search functionality, and
* Identify the data fields to be searched (e.g. where information on the biological assays are stored);
* Finding the best format for data exchange;
* Understanding all the data and tools, with transparent access to metadata describing the experimental setup or computational approaches.
In the case that the original data sources don’t provide all the features
required by the FAIR principles as e.g. unique persistent identifiers or clear
access protocols, OpenRiskNet worked together with the data providers to
either integrate this into the original service, transfer the data to more
advanced data management solutions provided by OpenRiskNet or provide missing
features as part of the interoperability layer added to the service in the
context of OpenRiskNet all leading to improved quality of the data source.
### 2.2 Making data accessible
<table>
<tr>
<th>
_●_
</th>
<th>
Specify which data will be made openly available? If some data is kept closed
provide rationale for doing so
</th> </tr>
<tr>
<td>
_●_
</td>
<td>
Specify how the data will be made available
</td> </tr>
<tr>
<td>
_●_
</td>
<td>
Specify what methods or software tools are needed to access the data? Is
documentation about the software needed to access the data included? Is it
possible to include the relevant software (e.g. in open source code)?
</td> </tr>
<tr>
<td>
_●_
</td>
<td>
Specify where the data and associated metadata, documentation and code are
deposited
</td> </tr>
<tr>
<td>
_●_
</td>
<td>
Specify how access will be provided in case there are any restrictions
</td> </tr> </table>
The OpenRiskNet approach enables the easy and transparent sharing and analysis
of data between organisations involved in many sectors and programs.
OpenRiskNet APIs and the used transfer formats were openly released
immediately after their definition had reached a stable form at the end of the
project. However, updates might be necessary to follow scientific and
technical advances. In the prioritization of services to be integrated, open
source tools were favoured for the use in the case studies and the reference
workflows targeting specific question but commercial services are equally
important to build in the specific requirements of restricted access rights
and to help sustain the infrastructure in the long run. However, also these
commercial services had to openly share their API definitions and data formats
as well as provide features to make medatadata even of such restricted data
findable to allow for integration and combination with other tools and comply
with the FAIR principles.
Open Standards applied:
* Data and models are stored and served using well-developed and widely applied standards and technologies that promote data reuse and integration, such as JSON-LD, RDF and related semantic web technologies;
* OpenRiskNet resources are aligned with activities of toxicology communities like OpenTox, NanoCommons and EU-ToxRisk in developing open standards for predictive toxicology resources;
* Tools to access study data and metadata description in standards file formats already in use in a number of omics, toxicogenomic and nanosafety resources (e.g. ToxBank, diXa, eNanoMapper), further simplify the integration;
* Model descriptions are provided encoded guided by suitable open standards (e.g. QMRF, BEL, SBML) and annotated advancing appropriate minimal information standards (MIRIAM) for dissemination through appropriate repositories (e.g. BioModels) to cover the extended requirements of the semantic interoperability layer of OpenRiskNet.
OpenRiskNet did not create new file standards but rather employed existing
approaches as to define a core set of information, on which the scientific
community agrees that they are important to document, but which can also be
modified and extended if necessary for a specific application. For defining
this core set, regulatory files formats like **OECD** **harmonised templates
(OECD HT)** [13 ] and **Standard** **for Exchange of Nonclinical Data
(SEND)** [14 ] were included when collection the requirements for file
transfer. Even if these file formats are too limited and do not have the
flexibility to be used outside regulatory purposes and especially for early
stage research and method development, the guidelines for data and metadata
management proposed by OpenRiskNet and is continued to be developed in
NanoCommons and other projects ensures that all relevant metadata and
information needed for regulatory reporting is included in the data transfer
templates.
### 2.3 Making data interoperable
<table>
<tr>
<th>
_●_
</th>
<th>
Assess the interoperability of your data. Specify what data and metadata
vocabularies, standards or methodologies you will follow to facilitate
interoperability
</th> </tr>
<tr>
<td>
_●_
</td>
<td>
Specify whether you will be using standard vocabulary for all data types
present in your data set, to allow inter-disciplinary interoperability? If
not, will you provide mapping to
</td> </tr> </table>
more commonly used ontologies?
OpenRiskNet interoperability layer opens possibilities to provide data
schemata, which describe the format of the data using a controlled vocabulary:
* Metadata standards and data documentation approaches consider the existing standards that can be consolidated and the equivalent data that can be retrieved independent of the file format;
* Developments towards the integration of ontologies under a single framework have been started and are ongoing together with partner projects mainly from the EU NanoSafety Cluster, which will contribute to the goal of automatic harmonization and annotation of datasets. The goal is not to develop new ontologies. Instead, already existing ontologies (e.g. OBI, ChEBI, PATO, UO, NCIT, EFO, OAE, eTOX, eNanoMapper, MPATH, etc.) are consolidated and integrated into applications ontologies for the toxicology community and specifically for the requirements of OpenRiskNet service annotation.
* Another requirements to establish the comprehensive use of ontologies and in this way foster the interoperability not only of the major data sources but also user-provided data are user-friendly capturing frameworks supporting the selection of ontology terms during data curation and an ontology mapping service resolving issues of using synonyms from different ontologies (e.g. CAS numbers can be annotated using the National Cancer Institute Thesaurus, the EDAM ontology or even the Chemical Information Ontology reused in the eNanoMapper ontology, where it is available under the term “CAS registry number”). OpenRiskNet was working with experts in the field to integrate such tools in the infrastructure and has partly integrated them into data management services provided by OpenRiskNet partners. However, since OpenRiskNet was not a major primary data provider, it is even more important that these tools are now made available and easily accessible to the community for integration in new and existing data sources of EU funded research projects.
* Allowing mapping between related items in different databases (e.g. different gene-identifiers, linking genes to proteins or RNA identifiers, or mapping between equivalent chemical structures in different databases. BridgeDb, which can perform such mappings and is part of the OpenRiskNet Services, is thus a core interoperability service.
Additionally, we provide guidelines and training on the usage of standard data
transfer/sharing formats and ontologies in the context of OpenRiskNet. Best
practice examples like ToxCast, AOP-Wiki and AOP-DB show how semantic
annotation can be applied either directly by providing the data as RDF or via
semantic annotated OpenAPI definitions or both and used to make the data and
information understandable and, in this way, easier integrable and re-usable.
An important and heavily used part of improved data management throughout the
OpenRiskNet project is searching and accessing data from different sources
supported by the semantic annotation of the data sources based e.g on the
Bioschemas and BioAssays ontology:
* The databases are accessible by the OpenRiskNet APIs (similar to the computational tools) including the interoperability layer;
* Searches throughout multiple databases are possible, removing the need to search
in everyone independently
* The interoperability layer can be used to inspect the data schema and find out if the needed information is available from the databank and if it can be provided in a form for further analysis.
All this work was based on and extended:
* OpenTox APIs, which were designed to cover the field of QSAR-based predictive toxicology with dataset generation, model building, prediction and validation;
* Open PHACTS APIs, which handle knowledge collection and sharing;
* Various other APIs for accessing databases like BioStudies, EGA, ToxBank, and
PubChem; which led to a fast uptake by the community due to existing
familiarity with the underlying concepts.
### 2.4 Increase data re-use (through clarifying licenses)
<table>
<tr>
<th>
_●_
</th>
<th>
Specify how the data will be licenced to permit the widest reuse possible
</th> </tr>
<tr>
<td>
_●_
</td>
<td>
Specify when the data will be made available for reuse. If applicable, specify
why and for what period a data embargo is needed
</td> </tr>
<tr>
<td>
_●_
</td>
<td>
Specify whether the data produced and/or used in the project is usable by
third parties, in particular after the end of the project? If the re-use of
some data is restricted, explain why
</td> </tr>
<tr>
<td>
_●_
</td>
<td>
Describe data quality assurance processes
</td> </tr>
<tr>
<td>
_●_
</td>
<td>
Specify the length of time for which the data will remain re-usable
</td> </tr> </table>
Most of the data sources are already available in the public domain.
OpenRiskNet redistributes the data using the same license as the original data
provider or, if this is demanded by the data provider, in a more restricted
form. New data is made available through the OpenRiskNet access methods as
soon as it is released by the original data provider, i.e. no additional
embargo period is/was enforced by the OpenRiskNet project. Thus, users can
access the same data with respect to the number of datasets and version of
each dataset either from the original service provider, via e.g. a web
interface specifically designed for the data warehouse, or through the
OpenRiskNet mechanism, where the latter has the advantage of easy integration
into workflows and interoperability with other data sources and software
tools. Besides this simpler access, OpenRiskNet improved the quality of the
data with the following measures:
Data quality assurance processes:
* Tools for performing automatic validation, analysis and (pre)processing are developed to find inconsistencies in the data and databases and, in this way, improve the quality of the source and are made available in OpenRiskNet, e.g. _http://arrayanalysis.org/_ and _ https://github.com/BiGCAT-U M _ . Additionally, efforts to establish a general, cross-database data curation framework, in which users can flag possible errors in the data and semantic annotation, is supported.
* Some partners (e.g. UM) developed their own pipelines for quality control and analysis of sequencing data (RNA-seq and MeDIP-seq).
* We also integrate tools for automatic or manual curation of datasets as well as deriving processed data. The modified dataset are stored (similar to the pre-reasoned datasets) in OpenRiskNet-compliant databases with a link to the original source. Discussions were started, are underway and will continue with the original data providers to transfer the curated datasets back into the original database so that users preferring to use the data from the primary source are also profiting from the curation effort.
Quality assurance in the processing, analysis and modelling tools:
* Protocolling of the performed calculations increasing the repeatability and reproducibility of the studies, is supported by the automatic logging and auditing functionalities of modern microservices frameworks as well as the integrated workflow management systems.
* Validation of the services were enforced by the consortium and appropriate measures of uncertainty were requested for all models.
### 2.5 Specific information on individual shared data sources
#### 2.5.1 Workflows
All OpenRiskNet workflows created as part of the case studies or demonstrating
the functionalities of individual services or combinations of services are
available under open licenses from the Github repository. These can be
downloaded, re-executed to demonstrate repeatability and modified to answer
specific scientific questions of the user.
#### 2.5.2 BridgeDb
The BridgeDb software is available under the OSI-approved Apache License 2.0.
Identifier mappings files are available under open licenses too, following the
open licenses of the upstream resources (Ensembl, Rhea) or CCZero in case of
the metabolite mapping database. The BridgeDb web service and data for
identifier mappings is made available on the OpenRiskNet cloud using an
OpenAPI specification wrapped around a REST services.
#### 2.5.3 WikiPathways
All contents of WikiPathways are licenced with the Creative Commons CC0
waiver, which states that all contents of the database are free to share and
adapt. WikiPathways adopts a customised quality assurance protocol to curate
the database, which is done on a weekly basis.
#### 2.5.4 AOP-Wiki
The AOP-Wiki provides quarterly downloads for the complete database, which are
permanently maintained by the OECD. The AOP-Wiki does not provide licence
information, but states that the data can be reused. All AOPs undergo review
by EAGMST to ensure the quality of the contents of the AOP-Wiki.
<table>
<tr>
<th>
**FAIR Principles**
</th>
<th>
**WikiPathways**
</th>
<th>
**AOPWiki**
</th> </tr>
<tr>
<td>
F1. (Meta)data are assigned and globally unique and persistent identifiers
</td>
<td>
2
</td>
<td>
1
</td> </tr>
<tr>
<td>
F2. Data are described with rich metadata
</td>
<td>
1
</td>
<td>
1
</td> </tr>
<tr>
<td>
F3. Metadata clearly and explicitly include the identifier of the data they
describe
</td>
<td>
2
</td>
<td>
2
</td> </tr>
<tr>
<td>
F4. (Meta)data are registered or indexed in a searchable resource
</td>
<td>
2
</td>
<td>
2
</td> </tr>
<tr>
<td>
A1.1. The protocol is open, free and universally implementable
</td>
<td>
2
</td>
<td>
2
</td> </tr>
<tr>
<td>
A1.2. The protocol allows for an authentication and authorization where
necessary
</td>
<td>
2
</td>
<td>
2
</td> </tr>
<tr>
<td>
A2. Metadata are accessible, even when the data are no longer available
</td>
<td>
2
</td>
<td>
2
</td> </tr>
<tr>
<td>
I1. (Meta)data use a formal, accessible, shared, and broadly applicable
language for knowledge representation
</td>
<td>
2
</td>
<td>
2
</td> </tr>
<tr>
<td>
I2. (Meta)data use vocabularies that follow FAIR principles
</td>
<td>
1
</td>
<td>
1
</td> </tr>
<tr>
<td>
I3. (Meta)data include qualified references to other (meta)data
</td>
<td>
2
</td>
<td>
1
</td> </tr>
<tr>
<td>
R1.1 (Meta)data are released with a clear and accessible data usage license
</td>
<td>
2
</td>
<td>
1
</td> </tr>
<tr>
<td>
R1.2. (Meta)data are associated with detailed provenance
</td>
<td>
1
</td>
<td>
1
</td> </tr>
<tr>
<td>
R1.3. (Meta)data meet domain-relevant community standards
</td>
<td>
1
</td>
<td>
2
</td> </tr> </table>
**Table 1.** Compliance to FAIR principles [15 ] by AOP-Wiki and
WikiPathways. Score meanings: 1 = partial compliance, 2 = compliance
#### 2.5.5 AOP-DB
The AOP-DB provides data that originates from a variety of data sources. The
OpenRiskNet services that exposes the AOP-DB RDF contains data from NCBI,
ToxCast, CTD and DisGeNET. NCBI and ToxCast are produced by the U.S.
Government and the information is by default in the public domain. The
DisGeNET database is made available under the Attribution-NonCommercial-
ShareAlike 4.0 International (CC BY-NC-SA 4.0) license. CTD is subject to the
terms of reuse by the MDI Biological Laboratory and NC State University (
_http://ctdbase.org/about/legal.js_ _p_ ) __ .
#### 2.5.6 ToxCast
All data produced by the U.S EPA including ToxCast/Tox21 is by default in the
public domain (U.S. Public Domain license) and is not subject to domestic
copyright protection under 17 U.S.C. § 105. This allows to reproduce the work
in print or digital form, create derivative works, perform the work publicly,
display the work and distribute copies or digitally transfer the work to the
public by sale or other transfer of ownership, or by rental, lease, or
lending. The release currently provided as part of OpenRiskNet is using the
EdelweissData system including a REST API developed by Edelweiss Connect and
is based on the MySQL database dump and additional downloadable CSV files. The
data was extracted and restructured to fit the OpenRiskNet concept and
semantic annotated.
#### 2.5.7 TG-GATEs
Open TG-GATEs is provided as a public source by the Japanease National
Institutes of Biomedical Innovation, Health and Nutrition. OpenRiskNet used
standard procedures to normalize and processed and now provides the data under
the Attribution CC BY 4.0 license based on the EdelweissData platform, which
is designed to comply to the FAIR principles.
#### 2.5.8 DrugMatrix
DrugMatrix is provided as a public source by the National Toxicology Program
of the US Department of Health and Human Services. As for TG-GATEs,
OpenRiskNet used standard procedures to normalize and processed and now
provides the data under the Attribution CC BY 4.0 license based on the
EdelweissData platform, which is designed to comply to the FAIR principles.
#### 2.5.9 KIT Daphnia data on nano particles
KIT Daphnia data on nano particles dataset is provided by the Department of
predictive toxicology, Korea Institute of Toxicology. OpenRiskNet provides the
data under the Attribution CC BY 4.0 license based on the EdelweissData
platform, which is designed to comply to the FAIR principles.
#### 2.5.10 ToxicoDB
ToxicoDB is provided by the University Health Network, Toronto, Canada and is
available under GNU Lesser General Public License 3 (LGPLv3.0). The database
provides toxicological data and toxicogenomics datasets (incl. TG-GATEs and
DrugMatrix) from publicly available sources and thereby complying to the FAIR
principles.
#### 2.5.11 ToxPlanet
ToxPlanet is available under a commercial license. However, metadata is
publicly available to comply with the FAIR principles.
#### 2.5.12 SCAIview
The main resources on which SCAIView builds (namely PubMed and US Patents) are
very metadata rich and SCAIView makes this metadata searchable and accessible
via API. SCAIView harmonizes the metadata of the different sources into a
common JSON schema and therefore makes the data more interoperable and
reusable. SCAIView adds additional metadata from text mining by adding
semantic annotations. These annotations are derived from publically available
ontologies (eg. OBO foundry). Each annotation contains a referable identifier
(URI), a source, a preferred label and workflow provenance on how this
annotation has been produced. I.e. the generated data is by definition FAIR.
## 3\. ALLOCATION OF RESOURCES
<table>
<tr>
<th>
**Explain the allocation of resources, addressing the following issues** :
* Estimate the costs for making your data FAIR. Describe how you intend to cover these costs
* Clearly identify responsibilities for data management in your project
* Describe costs and potential value of long term preservation
</th> </tr> </table>
Making data FAIR was a central task of the integration of data source in the
OpenRiskNet infrastructure. Many of the sources integrated already follow the
FAIR principles at least to some extent and limited additional budget was
needed other than for the effort needed to make the sources OpenRiskNet-
compliant. For data sources not at a sufficient high level, the OpenRiskNet
partners owning the data or responsible for its integrating covered the costs
of the integration from their allocated budget. In the case of third-party
data sources, the integration was performed in collaboration with the
associated partners partly financially supported through the Implementation
Challenge with an OpenRiskNet partner designated as main contact point of the
associated partner.
## 4\. DATA SECURITY
<table>
<tr>
<th>
_●_
</th>
<th>
Address data recovery as well as secure storage and transfer of sensitive data
</th> </tr> </table>
The OpenRiskNet approach on data recovery, secure storage and transfer of
sensitive data included:
* Responsible and secure management processes for personal data including anonymisation, encryption, logging of data usage as well as data deletion after usage are implemented;
* To ensure that all ethical guidelines are followed by all OpenRiskNet Partners and Associated Partners and implemented in every step of the infrastructure, a p rivacy by design approach was followed in the project, documented in the OpenRiskNet privacy policy (see below) and controlled by an independent Data Protection
Officer;
* The most sensible way to protect sensitive data offered by the OpenRiskNet infrastructure was to bring the virtual environment and all data sources behind a company’s firewall by in-house deployment;
* All these data protection measured were documented and distributed as part of the terms of usage to guide existing and future data providers and research projects in need for secure and ethical data management.
## 5\. ETHICAL ASPECTS
<table>
<tr>
<th>
_●_
</th>
<th>
To be covered in the context of the ethics review, ethics section of DoA and
ethics deliverables. Include references and related technical aspects if not
covered by the former
</th> </tr> </table>
The ethical aspects are covered by the Protection Of Personal Data (POPD)
requirements and deliverables:
* D6.1 - NEC - Requirement No. 3: Statements regarding the full compliance of third country participants to the H2020 rules
* D6.2 - POPD - Requirement No. 4: Copies of the previous ethical approvals of data to be collected and used in the project, approvals which must also allow the possible secondary use of data
* D6.3 - POPD - Requirement No. 5: Consent forms and information sheets before interviews and surveys and any other data and personal data collection activity in the project
* D6.4 - POPD - Requirement No. 6: A statement by third party testers that they will comply with the applicable EU law and H2020 rules
* D6.5 - POPD - Requirement No. 7: Data Protection Officer Report 1
* D6.6 - POPD - Requirement No. 8: Data Protection Officer Report 2 ● D6.7 - POPD - Requirement No. 9: Data Protection Officer Report 3
Based on the ethics report received as part of the evaluation of the proposal
and the three reports of the **Data** **Protection Officer (DPO)** (D6.5,
D6.6 and D6.7) summarising the relevant regulations and legislation for the
different data types and test models, OpenRiskNet has worked also together
with an external expert to develop elements on an ethics review framework and
a document in the form of a “Proposal for ethics requirement for data
providers to European e-infrastructures like OpenRiskNet” was generated
(version 3 in **Annex** **1** ). This document was further reviewed and
improved based on the recommendation received from the DPO.
The OpenRiskNet platform is based on a federated data management system that
makes existing data sources provided by other European projects in this field
or from international consortia mainly from in vitro human and animal and in
vivo animal experiments available to all stakeholders in a harmonised way.
Such a federated systems imposes highest demands on data management and
sharing with respect to data security, data integrity and ethics becoming even
more relevant with the new EU General Data Protection Regulation (GDPR) in
effect since May 2018\. Thus, not only OpenRiskNet but the complete scientific
community is in demand of support and recommendations to achieve the highest
impact without jeopardising the ethics integrity of the e-infrastructure.
As a starting point and to fulfil the requirements from the ethics review, a
step-by-step decision process was first designed addressing how important
legacy data sources need to be handled by the project. On top of the workflows
provided in the reviews of the Data Protection Officer, a hierarchical data
source analysis and evaluation of the ethical implications for OpenRiskNet was
performed. Different categories of data sources have been analysed, including
references to the legislation in place and the conditions for primary,
secondary and tertiary data collection and use. Also special measures that
need to be considered for some specific cases are included.
Data type and additional aspects considered:
* General requirements
* Additional Requirements for Human Data
○ Basic requirements for ALL types of human data
○ Further requirements according to type of biomaterial being provided
■ Commercial Cell Lines
■ Research Using, Producing or Collecting Human Cells and Tissues
(EXCLUDING Embryonic Stem Cells (hESC) and Human-Induced Pluripotent Stem Cell
(hIPSC)
■ Research using Human Embryonic Stem Cells hESCs and
Human-induced Pluripotent Stem Cell (hiPSC) Lines
■ Research using Clinical Trial Data
* Additional Requirements for Animal Data
**Reasoning**
The European Commission is enforcing strong data management guidelines
following the FAIR principles (Findable, Accessible, Interoperable and Re-
usable) on all funded projects and the open, public sharing of all data
generated in these projects. However, this sharing of research data needs to
be following all ethics requirements and highest standards of privacy
protection. To support the definition of such standards, OpenRiskNet has
developed this checklist to help data providers understand the requirements
for privacy-protecting data sharing and to guide them in fulfilling the
existing guidelines and the ethics requirements for the specific data
supplied. The checklist has general requirements that apply to all data types,
and additional requirements specifically for data used in the toxicology and
risk assessment fields. The final goal is to allow for an evaluation following
the relevant criteria and to generate a statement confirming that the
applicable requirements are fulfilled before a dataset can be used.
Since data management and sharing is becoming a more centralised, European or
even global effort spanning different infrastructures and disciplines,
recommendations and guidelines adopted by OpenRiskNet cannot be developed
autonomously and have to be aligned and harmonised with the ongoing
discussions on the EU level and with changing regulations. Therefore, this
document needs to be considered only as an initial attempt to cover issues in
the specific scientific area and it will be provided to working groups
established within the governance of the European Open Science cloud (EOSC) on
FAIR data requirements. Until additional feedback is collected and other
players are consulted (i.e. EOSC) the information included in Annex 1 will not
be implemented yet as an online tool linked to the OpenRiskNet infrastructure,
but kept as an input document to the e-infrastructure community.
The current draft document of the checklist proposes specific ethics
evaluation steps for each data source to be included and/or used in the
OpenRiskNet platform based on the assessment to one of the data categories
listed above (animal in vitro and in vivo, human
in vivo,...) aligned with specific regulatory and ethical requirements. Even
if the obligation to fulfil all necessary national and international
regulatory and ethical requirements including obtaining legal and ethics
clearance of all experiments and to operate in conformity with the
institutional regulations is ultimately in the hands of the original data
producer, OpenRiskNet is committed to continue providing the framework for the
ethics evaluation described above to all providers of data services
(OpenRiskNet internal, associated partners and other third-parties) and
supported the execution and documentation of the data source evaluation.
However, as already mentioned, all scientific disciplines are now facing the
same challenge how to document ethical and privacy-protecting generation,
management and sharing of research data within the open research data and open
science goals of the European Union. Therefore, we did not enforce the
adoption of the checklist as a requirement for data services on OpenRiskNet
since this would be very specific to this e-infrastructure while the services
are also of interest in a more general setting like e.g. the European Open
Science Cloud. This makes it clear that discussions on ethics standard have to
pushed to a higher level and harmonised across all relevant European
infrastructures or even better globally to avoid that data providers have to
deal with multiple parly contradicting, incomplete or outdated requirements,
guidelines and checklists depending on the setting their services are used in.
To funnel our experience, know-how and results of the ethics reviews into
these discussions, we opened the checklist now available in a draft version
for comments, raised the importance of clear licenses, privacy-protection and
ethics guidelines at different project but even more important ELIXIR and EOSC
meetings (including the Building EOSC through the Horizon 2020 projects
current status and future directions” workshop, 9 – 10 Sep 2019 in Brussels),
and engaged with interested parties and the EOSC-Secretariat to improve and
extend the OpenRiskNet guidelines and checklist to make them fit for multiple
disciplines and infrastructures and to foster the uptake of by the scientific
community.
### 5.1 Privacy Policy
The privacy policy 2 is implemented (version 3 from 20 November 2019) that
discloses the ways OpenRiskNet website manages the content, the personal data
or analytics on website usage. Specifically, the disclaimer implemented refers
to the following aspects related to the Personal Data Protection and Privacy
Policy:
* Website content disclaimer
* External links disclaimer
* Copyright and acknowledgement of sources
* Data Protection and Privacy Policy
○ Responsibility for the processing of Personal Data
○ Categories of Personal Data processed by OpenRiskNet
○ Storage of Personal Data
○ Sharing of Personal Data
○ Data Protection Rights under the General Data Protection Regulation (GDPR)
■ Service Providers
■ Analytics
○ Security and Integrity
○ Cookies
○ SSL or TLS encryption
○ Email communication
○ Changes of the Data Protection and Privacy Policy
* Legal effect of disclaimer
* Contact details
The latest version of the Privacy Policy is included in **Annex 2** .
### 5.2 Terms of use
The terms of use 3 (version 3 from 20 November 2019) regulate the use of the
OpenRiskNet infrastructure and is structured as follows:
* Definition of terms used
* About the OpenRiskNet e-infrastructure
* Data providers and data users of OpenRiskNet e-infrastructure
* Use of OpenRiskNet e-infrastructure
* Confirmation of Acceptance of the terms of use
The latest version of the OpenRiskNet e-infrastructure terms of use is
included in **Annex**
**3** .
# GLOSSARY
The list of terms or abbreviations with the definitions, used in the context
of OpenRiskNet project and the e-infrastructure development is available at:
_https://github.com/OpenRiskNet/home/wiki/Glossary_
|
https://phaidra.univie.ac.at/o:1140797
|
Horizon 2020
|
1205_DESIR_731081.md
|
# Executive Summary
This document is the first version of the Data Management Plan (DMP) for data
collected and created by DESIR. It describes the datasets generated during the
course of the project, how the data will be produced and analysed. It details
also how the data generated will be shared, disseminated and preserved.
<table>
<tr>
<th>
</th>
<th>
</th>
<th>
Nature of the deliverable
</th> </tr>
<tr>
<td>
</td>
<td>
R
</td>
<td>
Document, report
</td> </tr>
<tr>
<td>
</td>
<td>
DEM
</td>
<td>
Demonstrator, pilot, prototype
</td> </tr>
<tr>
<td>
</td>
<td>
DEC OTHER
</td>
<td>
Websites, patent fillings, videos, etc.
</td> </tr>
<tr>
<td>
✓
</td>
<td>
ORDP
</td>
<td>
Open Research Data Pilot
</td> </tr>
<tr>
<td>
</td>
<td>
</td>
<td>
</td> </tr>
<tr>
<td>
</td>
<td>
</td>
<td>
Dissemination level
</td> </tr>
<tr>
<td>
✓
</td>
<td>
P
</td>
<td>
Public
</td> </tr>
<tr>
<td>
</td>
<td>
CO
</td>
<td>
Confidential only for members of the consortium (including the Commission
Services)
</td> </tr>
<tr>
<td>
</td>
<td>
EU-RES
</td>
<td>
Classified Information: RESTREINT UE (Commission Decision 2005/444/EC)
</td> </tr>
<tr>
<td>
</td>
<td>
EU-CON
</td>
<td>
Classified Information: CONFIDENTIEL UE (Commission Decision 2005/444/EC)
</td> </tr>
<tr>
<td>
</td>
<td>
EU-SEC
</td>
<td>
Classified Information: SECRET UE (Commission Decision 2005/444/EC)
</td> </tr> </table>
Disclaimer
DESIR has received funding from the European Union’s Horizon 2020 research and
innovation programme under grant agreement No 731081. This publication
reflects the views only of the author, and the Commission cannot be held
responsible for any use which may be made of the information contained
therein.
# Introduction
The DESIR project sets out to strengthen the sustainability of DARIAH and
firmly establish it as a long-term leader and partner within arts and
humanities communities. By DESIR’s definition, sustainability is an evolving
6-dimensional process, divided into the following challenges:
1. Dissemination: DESIR will organise a series dissemination events, including workshops in the US and Australia, to promote DARIAH tools and services and initiative collaborations.
2. Growth: DESIR sets out to prepare the ground for establishing DARIAH membership in six new countries: the UK, Finland, Spain, Switzerland, Czech Republic and Israel.
3. Technology: DESIR will widen the DARIAH research infrastructure in three areas, vital for DARIAH’s long-term sustainability: entity-based search, scholarly content management, visualization and text analytic services.
4. Robustness: DESIR will make DARIAH’s organizational structure and governance fit for the future and develop a detailed business plan and marketing strategy.
5. Trust: DESIR will measure the acceptance of DARIAH, especially in new communities, and define mechanisms to support trust and confidence in DARIAH.
6. Education: Through training and teaching DESIR will promote the use of DARIAH tools and services.
Funded under the Work Programme part European Research infrastructures
(including eInfrastructures), DESIR participates to the Open Research Data
Pilot (ORDP). As such, this document introduces the first version of the
project Data Management Plan (DMP). The DMP describes the data management life
cycle for the data to be collected, processed and/or generated by the DESIR
Consortium. It includes information on how the research data will be handled
during and after the end of the project, what data will be collected or
generated, which methodology and standards will be applied, how the data will
be disseminated and shared, how the data will be curated and shared during and
after the end of the project.
The DMP is to be considered as a living document: the information contained
therein will be updated following the implementation of the project and when
significant changes occur. Moreover, in a second phase, the Consortium intends
to deliver a version which will contain recommendations to the community and
specifically tailored to Digital Humanities. Research data generated and
processed in DESIR will be FAIR (findable, accessible, interoperable and
reusable).
# What kind of data is considered in the DMP
According to the _Guidelines to the Rules on Open Access to Scientific
Publications and Open Access to Research Data in Horizon 2020_ (Version 3.0,
26 July 2016), research data “ _refers to information, in particular facts or
numbers, collected to be examined and considered as a basis for reasoning,
discussion, or calculation._ _In a research context, examples of data include
statistics, results of experiments, measurements, observations resulting from
fieldwork, survey results, interview recordings and images. The focus is on
research data that is available in digital form._ ”
DESIR focuses on the sustainability of the DARIAH ERIC and will develop
Actions consisting primarily of accompanying measures such as standardisation,
dissemination, awarenessraising and communication, networking, coordination,
support services, policy dialogues and mutual learning exercises. As such, we
expect that limited research data will be generated during the course of the
project.
# Structure of the template
Each dataset will cover issues identified in the template below. The data will
follow best practises defined in the _Guidelines on FAIR Data Management in
Horizon 2020_ to make the DESIR research data findable, accessible,
interoperable and re-usable 1 .
<table>
<tr>
<th>
Data Summary
</th>
<th>
•
</th>
<th>
State the purpose of the data collection/generation
</th> </tr>
<tr>
<td>
</td>
<td>
•
</td>
<td>
Explain the relation to the objectives of the project
</td> </tr>
<tr>
<td>
</td>
<td>
•
</td>
<td>
Specify the types and formats of data generated/collected
</td> </tr>
<tr>
<td>
</td>
<td>
•
</td>
<td>
Specify if existing data is being re-used (if any)
</td> </tr>
<tr>
<td>
</td>
<td>
•
</td>
<td>
Specify the origin of the data
</td> </tr>
<tr>
<td>
</td>
<td>
•
</td>
<td>
State the expected size of the data (if known)
</td> </tr>
<tr>
<td>
</td>
<td>
•
</td>
<td>
Outline the data utility: to whom will it be useful
</td> </tr>
<tr>
<td>
2. FAIR Data
2.1. Making data findable, including
provisions for
metadata
</td>
<td>
• •
•
</td>
<td>
Outline the discoverability of data (metadata provision)
Outline the identifiability of data and refer to standard identification
mechanism.
Do you make use of persistent and unique identifiers such as Digital Object
Identifiers?
</td> </tr>
<tr>
<td>
</td>
<td>
•
</td>
<td>
Outline naming conventions used
</td> </tr>
<tr>
<td>
</td>
<td>
•
</td>
<td>
Outline the approach towards search keyword
</td> </tr>
<tr>
<td>
</td>
<td>
•
</td>
<td>
Outline the approach for clear versioning
</td> </tr>
<tr>
<td>
</td>
<td>
•
</td>
<td>
Specify standards for metadata creation (if any). If there are no standards in
your discipline describe what type of metadata will be created and how
</td> </tr>
<tr>
<td>
2.2 Making data
openly accessible
</td>
<td>
•
</td>
<td>
Specify which data will be made openly available? If some data is kept closed
provide rationale for doing so
</td> </tr>
<tr>
<td>
</td>
<td>
•
</td>
<td>
Specify how the data will be made available
</td> </tr>
<tr>
<td>
</td>
<td>
•
</td>
<td>
Specify what methods or software tools are needed to access the data? Is
documentation about the software needed to access the data included? Is it
possible to include the relevant software (e.g. in open source code)?
</td> </tr>
<tr>
<td>
</td>
<td>
•
</td>
<td>
Specify where the data and associated metadata, documentation and code are
deposited
</td> </tr>
<tr>
<td>
</td>
<td>
•
</td>
<td>
Specify how access will be provided in case there are any restrictions
</td> </tr>
<tr>
<td>
2.3. Making data interoperable
</td>
<td>
* Assess the interoperability of your data. Specify what data and metadata vocabularies, standards or methodologies you will follow to facilitate interoperability.
* Specify whether you will be using standard vocabulary for all data types present in your dataset, to allow inter-disciplinary interoperability? If not, will you provide mapping to more commonly used ontologies?
</td> </tr>
<tr>
<td>
2.4. Increase data re-use (through
clarifying licences)
</td>
<td>
* Specify how the data will be licenced to permit the widest reuse possible
* Specify when the data will be made available for re-use. If applicable, specify why and for what period a data embargo is needed
* Specify whether the data produced and/or used in the project is useable by third parties, in particular after the end of the project? If the re-use of some data is restricted, explain why
* Describe data quality assurance processes
* Specify the length of time for which the data will remain reusable
</td> </tr>
<tr>
<td>
3\. Allocation of resources
</td>
<td>
* Estimate the costs for making your data FAIR. Describe how you intend to cover these costs
* Clearly identify responsibilities for data management in your project
* Describe costs and potential value of long term preservation
</td> </tr>
<tr>
<td>
4\. Data security
</td>
<td>
• Address data recovery as well as secure storage and transfer of sensitive
data
</td> </tr>
<tr>
<td>
5\. Ethical aspects
</td>
<td>
• To be covered in the context of the ethics review, ethics section of DoA and
ethics deliverables. Include references and related technical aspects if not
covered by the former
</td> </tr>
<tr>
<td>
6\. Other
</td>
<td>
• Refer to other national/funder/sectorial/departmental procedures for data
management that you are using (if any)
</td> </tr> </table>
# Datasets description
The DESIR Consortium identified the datasets that will be produced during the
different phases of the project. The list is, however, to be considered
indicative and datasets may be adapted, added or removed following the
evolution of the project.
## Datasets
1. Source code of software developed in WP4
2. Survey conducted in WP6
<table>
<tr>
<th>
Dataset 1: Source code of software developed in WP4
</th> </tr>
<tr>
<td>
Data Summary
</td>
<td>
Computer source code generated as part of the programming activities in WP4,
through development activities by the project partners contributing to WP4 and
through the Code Sprint planned for Summer 2018\.
The efforts will follow the recommendations to encourage best practices in
research software: _https://softdev4research.github.io/recommendations/_
</td> </tr>
<tr>
<td>
2\. FAIR Data 2.1. Making data findable, including provisions for metadata
</td>
<td>
The code will be licenced under OSI approved licenses. When no other choice is
preferred (e.g. when contributing to an existing ecosystem), the default
license will be Apache-2.0 or EUPL-1.2 (In each case the decision for or
against Copyleft and other implications can be readdressed.).
The source code will be designed, formatted and commented according to the
established standards and practices of the corresponding programming
languages. Additional documentation will be provided in text-based form along
with the code.
The identifiers, versions, licenses and contributors will be collected
according to existing open source standards. The code repositories will be
registered via DataCite or Zenodo.
</td> </tr>
<tr>
<td>
2.2 Making data openly accessible
</td>
<td>
The source code will be made publicly available.
The code will be licenced under OSI approved licenses. When no other choice is
preferred (e.g. when contributing to an existing ecosystem), the default
license will be Apache-2.0 or EUPL-1.2 (In each case the decision for or
against Copyleft and other implications can be readdressed.).
</td> </tr>
<tr>
<td>
2.3. Making data interoperable
</td>
<td>
By using the established Github platform and adhering to each programming
language’s individual software design standard, interoperability with third-
party development will be ensured.
</td> </tr>
<tr>
<td>
2.4. Increase data re-use (through clarifying licences)
</td>
<td>
Code specifically developed within DESIR will be published on GitHub using the
responsible partner's or the DARIAH ERIC's organisational account, using
appropriate OSI approved licenses, see 2.2. Where existing solutions are
extended, applicable platforms will be chosen on a per-case basis, and efforts
will be undertaken to merge
</td> </tr>
<tr>
<td>
</td>
<td>
improvements back upstream. This will allow re-use and discoverability and
constitutes the publication.
</td> </tr>
<tr>
<td>
3\. Allocation of resources
</td>
<td>
Publication of source code on GitHub is part of the development process and
free of charge. Archiving of code repositories will be done by consortium
members.
</td> </tr>
<tr>
<td>
4\. Data security
</td>
<td>
Source code will be archived in institutional repositories at the end of the
project, on Zenodo and/or the DARIAH repository.
</td> </tr>
<tr>
<td>
5\. Ethical aspects
</td>
<td>
n/a
</td> </tr>
<tr>
<td>
6\. Other
</td>
<td>
n/a
</td> </tr> </table>
<table>
<tr>
<th>
Dataset 2: Survey conducted in WP6
</th> </tr>
<tr>
<td>
Data Summary
</td>
<td>
This survey aims to identify cross-disciplinary DARIAH communities and new
core groups, considering gender and diversity as main variables. It also aims
to explore to what extent is DARIAH is reaching such research communities in
terms of use and access. It is expected to analyse to what extent these
communities perceive DARIAH as a reliable, trustworthy and sustained
infrastructure.
Generated data will be the source to the empirical study of DARIAH’s usage in
new communities, and define new strategies and the data will be exported in
xsl format.
Original data will be collected from researchers defined within target groups
(three cross-disciplinary communities and core groups: i) early career
researchers, including MA and PhD students; ii) academics without permanent
institutional affiliations (no reliable access to RIs); iii) academics with
cross-disciplinary backgrounds and research interests (not clearly associated
merely with one academic discipline).
Data size cannot be specified yet and no specific data re-use is expected
within this data collection.
The collected data will be mostly useful for WP6 study. The analysed data will
be, nonetheless, useful for future comparative studies.
</td> </tr>
<tr>
<td>
2\. FAIR Data 2.1. Making data findable, including provisions for metadata
</td>
<td>
Files will have common identifiers (respondents’ identification number and
country codes) and identification numbers will be consistent across all data
files.
Users will be given access to contextual, multilevel and thematic data.
</td> </tr>
<tr>
<td>
2.2 Making data openly accessible
</td>
<td>
In accordance with data protection, only anonymous data will be available to
users.
No specific methods or software tools are needed to access the data to be made
available.
</td> </tr>
<tr>
<td>
2.3. Making data interoperable
</td>
<td>
n/a
</td> </tr>
<tr>
<td>
2.4. Increase data re-use (through clarifying licences)
</td>
<td>
Analysed data and final report will be published under Creative Commons
Attribution 4.0 License, allowing data re-use by third parties after the end
of the project
</td> </tr>
<tr>
<td>
</td>
<td>
Data quality will be assured through quality assessment, including the quality
and comparability of measurement instruments, the assessment of target-groups
sample composition and the output quality of the survey.
</td> </tr>
<tr>
<td>
3\. Allocation of resources
</td>
<td>
The data collection and analysis is part of WP6 tasks within the project and
no additional costs are expected.
</td> </tr>
<tr>
<td>
4\. Data security
</td>
<td>
Data will be archived in institutional repositories at the end of the project
and/or the DARIAH repository.
</td> </tr>
<tr>
<td>
5\. Ethical aspects
</td>
<td>
Research data will be generated from the participation of humans through a
survey. Respondent, which will be anonymous and will participate on a
volunteer basis, will be fully aware of the nature and purpose of the
research, what their role in it will be, and how the data they provide will be
subsequently used. The template of the survey will has been included in D6.1
and will be provided on request.
</td> </tr>
<tr>
<td>
6\. Other
</td>
<td>
n/a
</td> </tr> </table>
|
https://phaidra.univie.ac.at/o:1140797
|
Horizon 2020
|
1207_VESSEDIA_731453.md
|
# Chapter 1 Introduction
The H2020 programme has implemented a pilot action on open access to research
data. VESSEDIA, as a participating project to this pilot action, is required
to develop a **Data Management Plan (DMP)** . This DMP has been identified in
the Description of Action (DoA) as VESSEDIA deliverable D6.2. This document is
drafted according to the **“Horizon 2020 FAIR**
**Data Management Plan template”** . The major aim of the DMP is to ensure
that our research data is FAIR – **F** indable, **A** ccessible, **I**
nteroperable, and **R** e-usable.
Thus, by the DMP we aim to address data set references and names, data set
descriptions, standards and metadata, data sharing and archiving and
preservation (including storage and backup) on a dataset by dataset basis.
The DMP is intended to be a **living document** . It will be periodically
revised to reflect changes in the data that may be made available by the
project, and to provide additional information on the datasets as this
information is developed during the specifications of the experimental phases.
All partners have contributed to the document, particularly through the use of
a project wide questionnaire.
Since each partner will generate and use data, the document is organized with
one section per partner. Each section is structured following the **6-points
structure** described thereafter:
1. **Data summary** gives a description of the data.
2. **FAIR data** ensures that data are **F** indable, **A** ccessible, **I** nteroperable, and **R** e-usable.
1. **Making data findable, including provisions for metadata** gives a description of the data.
2. **Making data openly accessible** explains how the data are made openly accessible.
3. **Making data interoperable** assesses the interoperability of the data.
4. **Increase data re-use (through clarifying licences)** discusses how the reusability of the data is ensured.
3. **Allocation of resources** refers to costs and other resources for making the data FAIR.
4. **Data security** addresses data recovery as well as secure storage and transfer of sensitive data.
5. **Ethical aspects** clarifies if there any ethical or legal issues that can have an impact on data sharing.
In the following, we will address the methodology that was used to set up the
deliverable. Furthermore, we will provide a Data summary, followed by a
summary of the use of FAIR data in the VESSEDIA project. Finally, allocation
of resources, management of data security, as well as ethical aspects will be
elaborated. .
# Chapter 2 Methodology
In order to compile the data management plan, a questionnaire was first
elaborated, covering the main questions that need to be answered in the
template provided by the European Commission.
In a second phase, each project partner responded to the questionnaire,
filling it with as much detail as possible at this stage of the project.
Completed questionnaires were stored for analysis and traceability in the
project’s SVN repository.
In a third phase, the Data Management Plan was created as a synthesis of the
questionnaire results, attempting to take advantage of commonalities between
responses, in order to provide a simple view of data management procedures
within the consortium.
Further revisions of the document will be based on updates to partner
questionnaires. Therefore, the DMP will be updated at least by the mid-term
and final review to be able to finetune it to the data generated and the uses
identified by the consortium.
In addition, a confidential index of datasets will be created and maintained
in the project when the datasets are created. The DMP itself is a confidential
document. Therefore the information about the datasets will only be provided
to the EU and the reviewers.
The VESSEDIA project will consider open licenses and open availability for the
datasets. The reasons for not offering open access will be documented in the
partner questionnaires and in the appendix describing the datasets.
# Chapter 3 Data Summary
## 3.1 Generalities
VESSEDIA is a research project that produces mainly data through **Research
and software Developments (R &D) ** .
The partners will use background R&D data, such as original research articles
and other projects’ reports and tools, to do research, and produce new data of
various kinds, including reports and articles, source and binary code (mainly
for x86_64 platforms running Linux, MS Windows and MacOS X, as well as IoT
target platforms), archives (containing various kinds of files), log files
(containing execution traces and results, proof traces, intermediate results,
etc.), text files (containing documentation, traces, etc.), script files (e.g.
with compilation, installation and execution procedures), etc.
**Various tools will be used and developed** by the project:
* Software development tools (commonly called CASE tools),
* Software V&V tools (for the testing and analysis of code and models),
* Modelling tools (for the development of software models),
* Documentation writing tools (such as text editors),
* Configuration management tools (mainly GIT, SVN and Zenodo), - Workflow management tools (often internal to partner organisations), - etc.
The second category of tools is the main purpose of the project on which the
partners will focus.
**The data will be formatted** as much as possible using standard format types
(and respective naming conventions):
* Reports are mostly in HTML, Markdown, Office Open XML, OpenDocument, and LaTeX format, and therefore files have the corresponding extensions (.docx, .pptx, .xlsx, tex, .html, .md, .pdf, etc.).
* Source files of tools are plain text files using standard naming convention corresponding to the programming languages and computing platforms used: .c, .cpp, .ml, .java, bash, sh, perl, .py, etc. Some text documentation (e.g. README) and script files (e.g.
Makefile) are often distributed with these source files (they have no specific
extension).
These lists are not exhaustive.
**Documentation** is produced alongside with research activities, either when
new results are found or when planned deliverables need to be produced.
Metadata, as stated above, are automatically associated to the tools that
serve to prepare the documentation (text editors, MS Office, LibreOffice,
LaTeX, etc.), or by the **software development tools** (gcc, MS Visual C++,
Ocaml, etc.). The **metadata types** for documentation have been chosen by the
project among the most widespread standards. The metadata types for software
files are imposed by the development tools used on Linux, MS Windows and MacOS
computing platforms.
The size of these kinds of data is not known nor bounded.
The data will be **useful to several parties** :
* The Computer Science community, especially to those interested in Formal Methods and Testing, Embedded systems, and IoT. This includes all possible kinds of organisations, ranging from academia to SME and industries, mainly those producing safety and security critical software.
* The project members as well as other similar research projects members (either national or European) for cross-fertilisation purposes.
**Their purpose** is manifold and includes:
* Research
* Training: users, students, etc.
* Consultancy
* Applications developments: tools will be used on various kinds of use-cases to demonstrate their effectiveness and usage on real cases
* Tools developments: new and improved tools
* Certification: data is useful for CC evaluation and certification purposes.
## 3.2 Partners specificities
Below are the specificities of the project partners in terms of data types,
collection and generation. Accessibility of data will further be dealt with in
section 4.2.
**TEC:** As the project Coordinator, Technikon will generate data for the
Quality Management of the project. This data will contain text, tables,
graphs, electronic measurements (e.g. SRAM start-up behaviour) and maybe also
some code. Data formats are chosen accordingly, to make the data shareable and
accessible in the long term. Data will be reused from scientific publications,
conclusions from conference papers, technical data sheets from manufacturers
and from other research projects, such as UNIQUE, HECTOR, certMILS, etc.
**CEA:** The Frama-C V&V platform, the Papyrus Eclipse project, the Diversity
tool and the 6LowPan use-case are part of the background material.
**DA:** DA is building its use-cases and possibly complementary analysis
tools. DA will use an internal development methodology for this purpose. Its
use-case will be partly available publicly, possibly on the project’s website.
Confidential parts will not be available.
**SLAB:** SLAB data consists of reports and evaluations and will be publicly
accessible.
**FOKUS:** FOKUS data consists mainly of reports, specifications, some source
code, and scientific articles that are produced by original research
activities of the project.
**INRIA:** The Contiki source code is part background material it is available
publicly on Github.
**TUAS:** The Economic rationale, as a tool for supporting decision making,
can be used for a wide range of products/systems' owners/designers/builders,
not only applicable to products/systems close in nature to the Vessedia pool
of use-cases. So the Economic rationale has a lot of potential for being
integrated and re-used.
**KU Leuven:** The VeriFast V&V tool is part of the background material.
**FD:** FD will be using the benchmark SPEC for testing efficiency of our
tools.
**AMO:** AMO will use an internal development methodology.
# Chapter 4 FAIR Data
In this chapter we describe what is done to make the data FAIR ( **F**
indable, **A** ccessible, **I** nteroperable, and **R** e-usable).
## 4.1 Making data findable, including provisions for metadata
### 4.1.1 Generalities
The project’s data is discoverable. As described above, the standards chosen
for identifying data correspond to naming conventions (file extensions) at the
data files level. Such files are generally compact, easy to manage and can be
managed by standard applications, as follows:
* HTML files (suffixes .html, .htm) can be opened by any web browser
* Markdown files (suffix .md) can be handled by any text editor (e.g. emacs)
* Microsoft Office documents (suffixes .docx, .xlsx, .pptx, etc) must be handled by MS Office
* Microsoft Project documents (suffix .mpp) can be edited using MS Project or any compatible tool on Linux
* OpenOffice documents (suffixes .odt, .xml, .rtf, etc.) can be handled by OpenOffice and LibreOffice
* LaTeX format (suffix .tex) must be handled by LaTex
* Acrobat PDF format (suffix .pdf) must be opened by Adobe Acrobat.
* Source files of tools (suffixes .h, .c, .cpp, .ml, .mli, bash, sh, .bat, .perl, .py, etc.) must be handled by the development tools (e.g. MS Visual C++, gnu tools, etc.) or by any text editor (e.g. emacs)
* Assembly files (suffixes .s, .asm) for Intel x86 and ARM architectures mainly that will be handled by assemblers or assembly code analysers.
* Proof script files must be handled by proof tools (such as Qed, Why3, PVS, AltErgo, Z3 and Coq)
* Binary files are mostly the result of the development tool chains (suffixes .exe, .com, .o, .so, .a, .dll, .lib, etc.) and can be handled using some debugger (e.g. gdb) or profiling tools or binary code analysers.
These files naming conventions have the benefit of allowing Operating Systems
to identify them easily as well as associating some tool(s) for opening them
automatically. This association can be changed easily in each OS.
### 4.1.2 Partners specificities
Below are the specificities for each project partner: **TEC:** None.
**CEA:** Versions of Frama-C will be named by an element of the periodic table
of chemical elements and a version number.
Papyrus follows the Eclipse release train and has a persistent identifier
according to the release. Plugins for Papyrus, developed within VESSEDIA, may
have their own persistent identifier but it is loosely correlated to the
Papyrus identifier.
Diversity’s components will be made available at different moments of the
projects according to their maturity. They will have an incremental persistent
identifier.
**DA:** Data will be available at the delivery date in the public deliverable
reports. No specific identifiers are used.
**SLAB:** None.
**FOKUS:** The report on formal methods will have a version number that also
refers to the version number of Frama-C.
**INRIA:** None.
**TUAS:** Online E- training materials will also be generated by TUAS. Notice
that the volume of data of TUAS is hard to assess at this point. For example,
the ISO standard documents are large documents (approximately 80 pages).
**KU Leuven:** Platform/software versions information are used too.
**FD:** None.
**AMO:** None.
## 4.2 Making data openly accessible
### 4.2.1 Generalities
The major part of the project data is accessible. The project produces
whenever possible:
1. Software components with an open-source licence (e.g. LGPL, Berkeley, GPL and MIT), and
2. Public documents (such as deliverables or white papers or thesis reports).
When such components are deemed to be of sufficiently high quality, open
source software components can be downloaded on a public web site, on the
project web site or on Gitlab or through some Linux packages manager (such as
Synaptic on Ubuntu).
Other software components can be made available to partners in prototype
state, depending on the author’s decision. They may then be made available on
the internal and/or external project web site or may be transmitted directly
between partners.
Finally, when data is classified as confidential, it will not be available
outside of the project.
Data embedded in some public document is public data and becomes visible.
In order to access public data, web browsers are the most suitable. Users may
also use, when necessary, some FTP, GIT or SVN client software (e.g.
filezilla, rapidsvn, etc.) to transfer the data to a local computer.
In terms of locations, potential users can find the project’s public data on
the following places:
* Documents, dissemination of reports, open source prototype tools, test cases, white papers and demonstration videos can be found on the project’s web site: _https://vessedia.eu_ . Additionally, a contact page of the project website will allow interested persons to contact the project coordination team through some contact form
* In the case of conference papers, they can be obtained as part of conference proceedings, if their organisers decide to let the material be publicly available.
Otherwise, they can be obtained within the paper versions of these
proceedings.
### 4.2.2 Partners’ specificities
Here are some specificities for each project partner:
**TEC:** It is possible that some public data will also become available
through a thesis developed within the scope of the VESSEDIA project. The
thesis may be closed for a few years after its finalization. Any relevant
information (research data like SRAM readouts or statistical evaluations) will
be shared with the partners internally via the project subversion repository
( _https://vessedia.technikon.com_ ) . Other information, which is also
relevant for the global publis will be shared via the project website, e.g.
the “results section:” https://vessedia.eu/results.
**CEA:** Frama-C can be found on the public web site _http://www.frama-c.com_
, in the repositories of the major Linux distributions such as Ubuntu, as
well as in the repository of the OCaml package manager (
_https://opam.ocaml.org_ ) .
Papyrus is and Eclipse project and it is available at
_http://www.eclipse.org/papyrus_ . The source code of Papyrus is available in
an Eclipse repository, available on the Papyrus website.
Diversity is distributed open source as an Eclipse project and can be found at
the public website of EFM
_https://projects.eclipse.org/projects/modeling.efm_ and associated Eclipse
repositories. Sharing other components that are not part of the distribution
will be done directly by CEA according to some agreements.
The 6LowPan use-case provided by CEA is confidential and its source code will
not be made public. However, it can be transferred to project partners after a
NDA has been signed with CEA.
Some further data may come from PhD students, master degree or postdoc theses.
**DA:** None.
**SLAB:** None.
**FOKUS:** Data will be available and useful for the wider public but is not
usable for nonspecialists.
**INRIA:** The verified Contiki OS source code will be provided on a
dedicated, publicly hosted repository.
**TUAS:** Data is discoverable and accessible to a certain degree. TUAS will
share data with project partners and also with students of their university.
**KU Leuven:** Versions of VeriFast, together with its documentation will be
public and available on the author’s server at
_https://people.cs.kuleuven.be/~bart.jacobs/verifast/_
**FD:** Data produced by FD will be available to people inside Program
Analysis field, and more generally to Computer Science Scientists.
**AMO:** Most AMO data is public but some reports are confidential and will
thus not be available outside of the project.
**All:** To guarantee open access to scientific publications and research
data, several repositories were selected (see above). They are convenient to
access and also easy to use. Those repositories allow to easily share the long
tail of small research results in a wide variety of formats including text,
spreadsheets, audio, video, and images across all fields of science.
Further, each uploaded publication and dataset receives a persistent
identifier (DOI), which ensures the long term preservation.
## 4.3 Making data interoperable
The project’s reports are trivially interoperable as they can be edited and
reused with any compatible editor (see above).
In terms of software produced by the project, these are not easily
interoperable with other software tools as they generally require the same
tool equipment. Software source code can be reused by installing the same
development tools as for their production, to read, edit and recompile them.
Verification data (predicates, lemma, axioms, proofs, proof scripts, etc.) is
generally added to the source code and can only be processed by the same
specialised tools as the project (Frama-C, Verifast, Flinder, Why3, VeriFast,
etc.) and thus is not interoperable with other tools.
* Frama-C state and script files can only be read by Frama-C.
* Software models are stored in XMI (XML Metadata Interchange) format are also specialised data that can be exchanged with other tools accepting XML. XMI data is compliant to EMF-UML2 1 that is am implementation of UML2 by EMF.
* Diversity has a proper language and the UML format can be used to exchange data.
Other data in text format such as log or trace files can be edited and reused.
## 4.4 Increase data re-use (through clarifying licences)
Data reuse is an issue specific to each partner who produces data. Below is
described how each partner plans to manage data reuse within the VESSEDIA
project:
**TEC:** Management and project quality data do not need any licence and there
is no restriction on the reuse of third-party data, nor any restrictions to
share it.
Quality of data produced is ensured by internal checks such as peer reviews.
**CEA:**
* **Licencing issues**
* In terms of licensing, an open source (LGPL 2.1) version of Frama-C has been released since the beginning of the VESSEDIA project, and new versions will be published during the course of the project, which will incorporate extensions made within the project. The latest release, version 15 - Phosphorus, has been published on May 31 th , 2017. New components that will be developed for FramaC platform and that will be distributed get an open-source licence whenever possible.
* Papyrus and all of its plugins are open source and distributed under the Eclipse Public License 1.0 (EPL 1.0). Papyrus implements open-source standards provided by the Object Management Group (OMG). Within VESSEDIA, new plugins developed for Papyrus will continue to be open-source and licensed under EPL 1.0.
* Diversity is under an open source process as an Eclipse project “Eclipse Formal
Modelling Project” (EFM for short), licensed under Eclipse Public License 1.0
(EPL 1.0): A first open source version of Diversity will be realised at the
beginning of the VESSEDIA project. New versions with upgrades will be released
during the project in the same context. New formal analysis modules and
gateways of Diversity that will be developed will be licenced under EPL and
distributed through the EFM project whenever possible.
* The CEA 6LowPan application is confidential and will only be shared within the project between partners that signed a NDA with CEA.
* **Sharing**
* Frama-C will be shared only when CEA estimates that it is mature enough and by means of new public versions (as for version 15 - Phosphorus). Some new components may be restricted when they are not part of the Frama-C distribution or when they are subject to some agreement with a third party.
* New plugins developed for Papyrus, within VESSEDIA, will be shared only when CEA estimates that they are mature enough. Some new components may be restricted if they are subject to some agreement with a third party.
* Diversity will be shared only when CEA estimates that it is mature enough and by means of new public versions (under EFM project, EPL licence). Some new components may be restricted if they are not part of the Diversity distribution or if they are subject to some agreement with a third party.
* When data is part of some agreement with another company, access will be defined by both parties specifically.
* Otherwise and if data is not part of the Frama-C distribution, it can be accessed through particular agreements with CEA.
* In the case of Papyrus, if the data is not part of the Papyrus distribution it can be accessed through particular agreements with CEA.
* For Diversity: when data is part of some agreement with another company, access will be defined by both parties specifically. Otherwise, if data is not part of the Diversity distribution it can be accessed through particular agreements with CEA.
* **Data quality** is ensured at CEA in several ways: articles and deliverables are project internally and peer reviewed in order to ensure a high quality standard; software components are intensively tested and peer reviewed within the development team (who are assumed to understand the internals of the tools as well as mastering the programming languages used). Furthermore, bug tracking systems and discussion lists ensure that users can report bugs, discuss issues and contact the development team to solve problems. See https://frama-c.com/support.html for instance.
**DA:** Parts of the use case shared on the public project site, and public
reports (project deliverables), unless duly mentioned, have no particular
license. Then, there is no restriction on the reuse and sharing of these
unlicensed data.
**SLAB:** Same as for DA.
**FOKUS:** FOKUS will continue updating a report on formal methods that is
licenced under
_https://creativecommons.org/licenses/by-nc-sa/3.0_ . There are a few
restrictions, see _https://creativecommons.org/licenses/by-nc-sa/3.0/_
Potential users can find reports and data on the VESSEDIA website and on the
website of Fraunhofer FOKUS _https://www.fokus.fraunhofer.de_ . The manual
“ACSL by Example” is accessible at
_https://gitlab.fokus.fraunhofer.de/verification/open-acslbyexample.git_
**INRIA:** Concerning Contiki OK, the new source code will be part of Contiki
and will adopt the same BSD license. There is no restriction on the reuse of
third-party data, nor any restrictions to share it. New versions are available
on the _http://www.contiki-os.com_ main site.
The quality of Contiki is ensured by peer review and checks. New distributions
of the OS are intensively tested before release. Bugs are centralized through
the Contiki support (at _http://www.contiki-os.org/community.html_ ) in
order to be processed.
**TUAS:** ISO/IEC will licence only the final version of the standard
document. All the draft versions of ISO/IEC standards will stay available for
any expert willing to give comments through a national body or a liaison
organisation.
**KU Leuven:** KUL is using permissive open source license (MIT)/permissive
creative commons license.
**FD:** FD data will be open-source. The precise license has still to be
determined.
**AMO:** AMO reports are public but some reports are confidential and will
thus not be available outside of the project. There are no restrictions for
the reuse, sharing or access of data.
# Chapter 5 Allocation of resources
## 5.1 Generalities
The costs for making the data FAIR are different for each partner and will be
discussed below.
In terms of **data management** , each partner is responsible for handling its
internal data:
* The management of data shared in the project, such as on the project’s SVN, is done by TEC and the partners. The administrator is TEC, who established rules for partners to use this facility to store, share, modify and query data. Data volumes are not limited so far and will be reasonable.
* Data which is made public on the project’s web site has a shared responsibility even though the management of this server is done by TEC.
* Other published data, such as articles, white papers and tools, engage the responsibility of authoring partners as well as the entire project, who approves such data before publication.
## 5.2 Partners specificities
In terms of **long term preservation** , each partner has a different policy.
**TEC:** Costs incur for server provision and maintenance (expected costs of
about 2000€). TEC has foreseen to retain the project’s data for 3 years after
project. The expected costs are those that arise through server provision and
maintenance.
**CEA:** The Frama-C website is hosted on a rented server. Costs for renting
the server are part of the lab’s operating costs, and are shared among all
projects and industrial contracts that are using Frama-C. The same applies to
Papyrus. There are no costs for Diversity.
Components of Frama-C and Papyrus that are not released will be kept until
mature enough, unless a cooperation agreement is signed with another
organisation that makes use of it. Frama-C as well as public plug-ins are
archived on the public web site _http://www.frama-c.com._ Papyrus and its
plugins are available at _http://www.eclipse.org/papyrus_ and the source code
repository is available on the website.
For Diversity, data that is not released will be kept until mature enough,
unless a cooperation agreement is signed with another organisation that makes
use of it. Diversity source code is available at the following public
repositories _http://git.eclipse.org/c/efm/org.eclipse.efmmodeling.git_ (for
the GUI and modelling part) and
_http://git.eclipse.org/c/efm/org.eclipse.efmsymbex.git_ (for the formal
analysis modules).
In terms of data preservation, tools will be kept for several years (including
released versions and tool prototypes). Released tools survive years and
prototypes decay unless researchers take up in the scope of a new project.
This is the case for Frama-C, Papyrus and Diversity.
**DA:** There are no extra costs as data is stored on the project’s sites.
**SLAB:** There are no extra costs as data is stored on the project’s sites.
**FOKUS:** No cost as it is hosted by Fraunhofer FOKUS. Maintenance of server
is guaranteed by basic funding of Fraunhofer FOKUS. There are no plans to
limit the availability.
**INRIA:** No costs are incurred to make data FAIR.
The data will be publicly hosted on Github on a free account and retained as
long as Github provides free public repositories.
Applications specific data as well as non open-source modules will be
retained.
**TUAS:** No costs are incurred to make data FAIR.
TUAS data will be discoverable and accessible to a certain degree. When this
is the case, it will be usable beyond the original purpose, e.g. the project’s
ISO standard will enable building new ISO standards, new tools, etc.
**KU Leuven:** KUL data will be kept indefinitely. KUL will use Zenodo for
storage. We already prepare the data for review by reviewers of the scientific
publications. Extra effort for preservation is expected to be minimal.
**FD:** There are no extra costs as data is stored on the project’s sites.
**AMO:** There are no extra costs as data is stored on the project’s sites.
# Chapter 6 Data Security
Data security is concerned by the secure handling, storage, loss prevention,
recovery and transfer of project data, especially for sensitive data. Data
security is managed differently by each partner, as follows:
**TEC:** Data is secured by saving it regularly on external server (daily),
which also avoids losses. Data can be recovered by seeking for the latest copy
on external server (max. 24 hours are lost). Sensitive data is handled inside
the SVN repositories.
**CEA:** Frama-C related data is stored on a server which runs a regularly
updated Linux distribution, and only accessible through ssh by a few
administrators.
Data on the server is part of a git repository that is mirrored in several
places, physically distinct from the main server.
Data recovery can be done by simply cloning from one of the mirror
repositories.
The 6LowPan application is sensitive data and is stored by CEA internal
servers and its security is compliant with the CEA security policies.
**DA & AMO: ** The security of DA publicly available data relies on the
project’s server managed by TEC. Data is duplicated into DA internal
repository, including official deliverables (following ISO 9001 quality
management systems - requirements).
Sensitive data will be handled by any means in accordance to the project
public web site management.
**SLAB:** The security of data of SLAB data is built into the project’s server
managed by TEC. Data is backed up into an SLAB internal repository.
Sensitive emails are encrypted by PGP. Secure data can be stored in an offline
network.
**FOKUS:** Security will be maintained by Fraunhofer’s IT infrastructure.
Backups are maintained. GIT- repositories can simply be fully-cloned, making
recovery from a clone very easy.
**INRIA:** The data will be as secure as Github allows. Loss of data is
prevented by Github by offering cutting-edge redundancy services. All
contributors have copies of the repository on their work station. Data
recovery can be done by simply cloning from one of the mirror repositories.
There is no sensitive data.
**TUAS:** Internal TUAS security measures enable data to be stored securely
and avoid any loss of data. Sensitive data will be handled according to
effective national and EU-wide privacy regulations.
**KU Leuven:** Security and recovery procedures are dealt with by Zenodo.
There is no sensitive data.
**FD:** The security of data of FD data is built into the project’s server
managed by TEC as well as in the Git servers.
# Chapter 7 Ethical Aspects
To the best of our knowledge, neither the project nor its later use, nor other
planned exploitation of the results have an impact on ethics, but complies
with the general social rules of protecting the citizens’ right on its own
data and purpose of communications. The partners will conform to their current
national legislation and regulations.
# Chapter 8 Summary and Conclusion
The Data Management Plan of VESSEDIA describes the activities of the partners
related to datasets and is a key element of good data management. In the above
sections we described the methods used for these activities and provided an
extensive definition of the data and their formats. We described how data is
made FAIR in the VESSEDIA project as well as the choices made by the partners
for the allocation of resources and data security. We have structured all
these items into Generalities and Partners Specificities. This document
contains a summary of all the information available as of 9 th June, 2017\.
All partners intend to create data and make it available within the
consortium.
The DMP needs to be updated in the course of the project whenever significant
changes arise, such as new data, changes in consortium policies (e.g. new
innovation potential, decision to file for a patent), changes in consortium
composition and external factors (e.g. new consortium members joining or old
members leaving). This will be done within the periodic reports in M18 and
M36.
The partners’ questionnaires have helped to understand how each partner uses
and manages data. Through the questionnaire, we have also been able to
establish that a set of common practices and tools already exist to manage
data, allowing date to be shared and reused easily.
|
https://phaidra.univie.ac.at/o:1140797
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Horizon 2020
|
1208_DECIDE_731533.md
|
# Executive Summary
This deliverable aims to present a plan for the data management, collection,
generation, storage and preservation related to DECIDE activities. In this
action, we envision five different types of data: data related to the use
cases, data related to the meta-analysis to be done in the social-sciences
tasks, data coming from publications, public deliverables and open source
software.
The document presents, following the EC template [1], how these different
types of data will be collected, who the main beneficiaries are, and how
DECIDE will store them, manage them, and if the project will make them
accessible, findable and re-usable. The text continues with the foreseen
resources needed for the openness and data to finalize with security and
ethical aspects that will be taken into consideration in the context of
DECIDE.
This plan is the first version of the data management plan, which will be
updated in subsequent versions (M18 and M36) as part of the Technical Reports,
having as input the work carried out in the use cases (WP6), the technical
work packages (WP2 – WP5) and the dissemination activities (WP8).
# Introduction
## About this deliverable
This deliverable focuses on the management of the data in DECIDE. In DECIDE
there will be two different data strands. The first strand relates to the
publications generated as part of the research activities, and the second
strand relates to the data collected from the use cases, that will be used as
part of the implementation of the different key results established in the
project.
## Document structure
The document follows the established H2020 template for a Data Management Plan
(DMP) [1]. Section 2 presents a summary of what the purpose of the data
collection and generation is in the case of DECIDE. Section 3 explains how the
data and metadata will be made fair, and thus accessible, findable and
reusable. Section 4 briefly explains how the financial resources for this
openness are envisioned at this stage to be allocated. Section 5 and 6 focus
on the security and ethical aspects respectively. Section 7 presents the
conclusions and future work.
# Data Summary
DECIDE aims to create a set of tools to design, deploy and operate multi-cloud
aware applications in an ecosystem of reliable, interoperable and legally
compliant services. DECIDE will not generate any data beyond those of the
public deliverables and open access publications. DECIDE will use data in the
use cases. AIMES’ use case with its clinical trials application may seem
especially relevant but it should be noted that all data used shall be
fictional.
To realize the main goal of the project, DECIDE will develop the following Key
Results (KR):
* **KR1: Multi-cloud native applications DevOps framework:** This Key Result integrates KR2KR5, explained next, in addition to the integration and extension of existing tools in the Open Source (OS) communities covering development, continuous integration (CI), continuous quality (CQ) and continuous delivery (CD).
* **KR2: DECIDE ARCHITECT:** ARCHITECT will provide architectural patterns and modelling practices for implementation, optimization and deployment of multi-cloud native applications. Apart from the theoretical description of the patterns and explanations of how they can be implemented, DECIDE ARCHITECT will provide a supporting tool, with suggestions on which pattern is to be applied and in which order.
* **KR3: DECIDE OPTIMUS:** DECIDE will address Multi-cloud deployment simulations as well. DECIDE OPTIMUS aims to simulate the behaviour, in stressful conditions, of the profiled and classified components of a multi-cloud native application deployed on multiple CSPs and using multiple cloud services, so as to provide the most adequate candidate deployment topologies for that application, based on a predefined prioritized set of requirements (e.g. for example, legal compliance, performance and cost, vs. cost, security, legal awareness) defined by the developer. The candidate topologies will be calculated by making use of big data optimization algorithms such as Dandelion codes, genetic algorithms, or Harmony search.
* **KR4: Advanced Cloud Service (meta-) intermediator (ACSmI): ACSmI** will provide means to assess continuously, through real time verification, the fulfillment by the cloud services of their non-functional properties fulfilment and legal compliance. ACSmI will also provide a cloud services store where companies and developers across Europe can easily access centrally negotiated deals of compliant and accredited cloud services for applications developed by the software sector.
* **KR5: DECIDE ADAPT:** ADAPT is DECIDE’s self-adaptation tool for the multi-cloud native applications. It is a Software tool to deploy, monitor and (semi-) automatically self-adapt multi-cloud native applications.
Out of all the Key Results envisioned for DECIDE, the one that is most data-
related is the ACSmI (KR4). In the timeframe of the project, the ACSmI will
use fictional user data to contract the services from the CSPs and it will
provide encryption techniques for login-related information. The ACSmI will be
the DECIDE tool involved in the contracting of the CSP services, but the data
relevant for the execution of the use cases will not be stored in the ACSmI
but rather in the services offered by the selected CSPs and therefore,
external to DECIDE’s tools. Following the vision and goal of DECIDE of
contracting only legally compliant services, the ACSmI will only contract only
CSP services from CSPs that state that they comply with the laws and
regulations relevant for the domain of DECIDE’s use cases. This is especially
relevant for the case of the AIMES study.
In DECIDE two distinct environments are envisioned, namely, an integration
environment, and a production environment. The integration environment will be
deployed at Innovati’s DevOps infrastructure and will include the different
components developed by the technological partners. For testing purposes,
these partners will use synthetic, fictional, data or ‘persona’ data, but
never real data, anonymized or not, coming from the use cases. The production
environment’s final location is at this stage not yet decided.
Different types of data will be collected and generated in the context of the
project. These can be summarized as follows:
1. Data related to the execution of the use cases, testing KR1 –KR5.
2. Data related to the ACSmI
3. Data related to scientific publications.
4. DECIDE public deliverables.
5. DECIDE Open Source Software
## Data related to the use cases
The aim of this section is to establish what the purpose is of the collection
and generation of data within DECIDE, which the sources of this data are, if
relevant, and how this is important to realize the Key Results of DECIDE.
Use cases are a key pillar of DECIDE as they will validate the work (in WP6)
to be performed in the social and technological work packages (WP2-WP5).
Next we proceed to describe the data available at this stage of the project.
### AIMES
With respect to the AIMES’ use case, the following considerations with respect
to data need to be stated:
* No personal or identifiable data will be used as part of the AIMES use case for the DECIDE project
* The streamline use case and other eHealth services will not use, host or process patient identifiable data as part of DECIDE
* Any eHealth related services which need to be accessed as part of demonstrating the viability and usability of tools will require the creation of a separate instance which is entirely separate to any service which hosts sensitive/personal data. In the event data is required to test the DECIDE tools, this data will be ‘fake’ and randomly generated.
While the AIMES use case does not involve the processing of personal data as a
constitutive part, it can never be completely excluded that some accidental
processing of personal data might take place, especially in relation to the
use case partner’s own employees. Any such processing shall however be minor
and will take place in accordance with applicable law.
### ARSYS
Data used and stored in the ARSYS use case are typically not personal, but
rather non-personal data concerning the business continuity of ARSYS.
While the ARSYS use case does not involve the processing of personal data as a
constitutive part, it can never be completely excluded that some accidental
processing of personal data might take place, especially in relation to the
use case partner’s own employees. Any such processing shall however be minor
and will take place in accordance with applicable law.
### INNOVATI
INNOVATI’s data needed for the execution of the use case is fictional, used
only for testing purposes, aiming to simulate the behaviour of the system in
production. INNOVATI stores only fictional data for the use case.
While the INNOVATI use case does not involve the processing of personal data
as a constitutive part, it can never be completely excluded that some
accidental processing of personal data might take place, especially in
relation to the use case partner’s own employees. Any such processing shall
however be minor and will take place in accordance with applicable law.
## Data related to scientific publications
DECIDE will publish scientific publications in conferences and journals as
part of the planned dissemination activities. Following the EC Mandate on Open
Access [2], DECIDE adheres to the Open Access policy, choosing the most
appropriate route for each case. Whenever possible, DECIDE favours the ‘green’
open access route, in which the published article or the final peer-reviewed
manuscript will be deposited in an online repository, before, at the same time
as, or after publication, ensuring that the embargo period requested by
certain publishers has elapsed.
Scientific publications data are often made available using accessible pdf
files. The metadata to be used will be compliant with the format requested by
OpenAire as well as the one requested by the repository where the papers are
to be deposited.
The format in which the data related to the scientific publications will be
accessible pdf files. The metadata to be used will be compliant with that of
the repository where the paper is to be deposited and will be compliant with
the format requested by Open Aire, as to ease the index.
DECIDE’s partners will use Zenodo [3] for their joint publications. Some
institutions, like TECNALIA, have developed their own Open Aire compliant
repositories, where TECNALIA researchers have to upload their contributions
to. When TECNALIA’s researchers do so, these publications are automatically
indexed in OpenAire.
## DECIDE public deliverables
All information and material related to the public such as public
deliverables, brochures, posters etc., will be freely available on the project
website in the form of accessible pdf files. When IPR of foreground knowledge
needs to be protected, the corresponding disclosures will be published.
All deliverables include a set of keywords and a brief description, which are
meant to facilitate the indexing and search of the deliverables in search
engines. The keywords in each deliverable aim to stress the main topics
addressed in the document, be it a report or a software- related document.
The audience of the public deliverables of DECIDE range from general
audiences, interested in the activities performed in the project, to more
specialized audiences such as developers and operators of multi-cloud
application or those who wish to learn about the benefits of DECIDE through
the experiences gathered through the pilots.
## DECIDE Open Source Software
DECIDE will develop the ICT tools mentioned above. The source code will be
released, whenever the IPR of the partners is not breached, under a friendly
open source licensing schema still to be decided as part the of the
exploitation activities.
DECIDE tools will be developed in a variety of programming languages but
deployed using a container-based approach following a micro-services [4]
architecture. The size of the source code, the readme files, the user manual
and technical specifications as well as the docker scripts cannot be known at
the moment.
The open source software is aimed at developers and operators of multi-cloud
native applications.
# Fair Data
This section focuses on the following aspects, namely, on the feasibility and
appropriateness of making data findable, openly accessible, interoperable and
reusable in the context of DECIDE.
## Data related to the use cases
At this stage, for all use cases, both the collected and generated data,
anonymized or fictional, are not envisioned to be made openly accessible. In
principle, if needed, all data collected, stored and processed will be
fictional and treated as strictly confidential, and kept for a specific period
of time as stated on the consent form. This time period shall be no longer
than necessary to achieve the aims of the scenario and to validate the project
objectives, and after this point, the data will be destroyed as required.
### AIMES
AIMES’ use case data will not be made accessible, and thus not ‘FAIR’ 1 .
### ARSYS
ARSYS’ use case data will not be made accessible, and thus not ‘FAIR’.
### INNOVATI
INNOVATI’s use case data will not be made accessible, and thus not ‘FAIR’.
## Data related to scientific publications
The project will favour, whenever possible, the ‘green’ open access, in which
the published article or the final peer-reviewed manuscript will be deposited
in an online repository, before, at the same time as, or after publication,
ensuring that the embargo period requested by certain publishers has elapsed.
The Consortium will ensure open access to the publication within a maximum of
six months. DECIDE partners have the liberty to choose the repository where
they will deposit their publications, although Open Aire – compliant and Open
Aire - indexed repositories such as Zenodo [3] will be favoured. The partner
TECNALIA will use its own repository, already indexed by Open Aire.
For the case of the scientific publications, a persistent identification
number will be provided when uploading the publications to the selected
repository / repositories.
## Data related to deliverables
For the project’s publications on the website, the naming convention to be
used will be << _Dx.y Deliverable name _ date in which the deliverable was
submitted.pdf_ >>. All deliverables include a set of keywords and a brief
description that are aimed to facilitate the indexing and search of the
deliverables in search engines, in accordance to the defined template.
The deliverables will be stored at TECNALIA’s hosting provider, and for three
years beyond the duration time frame of the project.
## Open Source Software
DECIDE has envisioned a freemium business model for the project’s Key Results
which implies a free version of the software as well as a premium one. In
addition to offering a free version of the software, these free versions of
the open source components of the DECIDE KRs will be released as open source
in a source code repository, namely GitHub, which will be stored at Innovati’s
premises.
The free versions of these components will be findable and reusable by any
developer that is interested in the DECIDE Key Results, in agreement with the
open source licenses of the components. Furthermore, DECIDE will explore the
possibility of developers outside of the consortium to experience with such
components, which will ensure the uptake and sustainability of the software
results of the project.
For every software component, a readme file as well as the technical
specification document will be released. Moreover, a docker script will also
be released with the aim of facilitating the deployment of the DECIDE
containerized components in any desired infrastructure.
# Allocation of resources
DECIDE does not foresee additional needs for resources beyond the duration of
the action to handle data or making the data fair. As expressed before, open
access repositories will be favoured.
In the case of open source software, the partner TECNALIA will ensure that the
github repository is available after the project duration, either by keeping
it in its own premises or by transferring it to existing open source projects
and communities.
# Data Security
Out of the Key Results envisioned in DECIDE, the one that is most affected by
security requirements is the ACSmI. To this respect, a Security management
component has been included. The aim of this component is to be in charge of
designing and developing the means to guarantee the secure operation of the
ACSmI, including: Identity propagation and federated authentication and
authorization. The sub-modules included in this component are:
* The _User management_ is responsible for gathering information to create, delete and modify users.
* The _Policy Manager,_ is responsible for creating, deleting and modifying the policies of the ACSmI and when a new user is created, is responsible for assigning the policies that apply to this new user and to properly update the user registry.
* The _Role Manager_ , is responsible for creating, deleting and modifying the roles of the ACSmI. When a new user is created it is also responsible for assigning the roles to this user. This component will also properly update the user registry.
* The _Authentication Manager,_ is responsible for the authentication of the users of the ACSmI (This activity will be carried out at DECIDE framework level, if the ACSmI is integrated). If the credentials are valid, the authentication manager will provide to the console the token of the identification for propagating the identity to the rest of the module when appropriate.
* The _User Registry_ , is a database where all the information related to the users is stored.
* The _Data Encryption_ , is responsible for encrypting the data within the ACSmI in order to keep these data secure in case of cyber-attacks.
* The _Back-up_ is responsible for carrying out incremental back-ups in order to allow the recovery of data of the ACSmI should this be necessary.
* The _Communication security_ is responsible for providing secure communication using SSL transport layer encryption both between client and platform and between platform and cloud infrastructures.
Moreover, DECIDE will ensure that the General Data Protection Regulation
(GDPR), which will enter into force in May 2018, is complied with. The
security components shown above will be implemented with that goal in mind.
# Ethical Aspects
The basis of ethical research is the principle of informed consent. All
participants in DECIDE use cases evaluation will be informed of all aspects of
the research that might reasonably be expected to influence their willingness
to participate. Moreover, project researchers will discuss before and after
each practical exercise (e.g. interview, co-creation session, etc.) to
maintain on-going consent. Participants will be recruited by each organization
leading the use cases (AIMES, ARSYS and INNOVATI) to perform the planned
qualitative assessment of the DECIDE Key Results. For instance, in the case of
the evaluation of the usability of the tools this will be performed through
questionnaires (see D6.1). This data will be anonymized and reported as
aggregated data (when relevant) in the documents related to the evaluation of
DECIDE outcomes. If participants wish to withdraw from the participation in
the use cases at any time, they will be able to do it, and their data, even
anonymized data, will be destroyed.
# Conclusions
This deliverable has presented the plan for the management of data in the
DECIDE project. In this action, mostly data related to scientific publications
will be generated. Data coming from fictional users logging into the ACSmI
will be encrypted and they will not be made available compliant with the FAIR
principles. Furthermore, data coming from the use cases will also be
fictional, especially relevant in the case of Innovati and AIMES, and in
principle also not disclosed as FAIR.
Data coming from publications will be stored in Open Aire indexed repositories
favouring the green model whenever possible. Other publications such as
deliverables will be stored at TECNALIA’s hosting services.
This deliverable will be updated in subsequent releases, namely in M18 and M36
as part of the technical reports. It is envisioned that in those versions the
aspects that at this stage are not fully clear will be clarified as work
progresses in all the work packages.
References
1. European Commission;, "Data Management," July 2016. [Online]. Available: http://ec.europa.eu/research/participants/docs/h2020-funding-guide/cross-cutting-issues/openaccess-data-management/data-management_en.htm#A1-template. [Accessed 9 January 2017].
2. European Commission;, "Guidelines on Open Access to Scientific Publications and Research Data,"
July 2016. [Online]. Available:
http://ec.europa.eu/research/participants/data/ref/h2020/grants_manual/hi/oa_pilot/h2020-hioa-
pilot-guide_en.pdf. [Accessed December 2016].
3. Zendo, “Zenodo,” [Online]. Available: www.zenodo.org. [Accessed 2016].
4. C. Richardson, “Microservice architecture patterns and best practices,” 2017. [Online]. Available: https://microservices.io/. [Accessed March 2017].
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Horizon 2020
|
1209_REASSURE_731591.md
|
# Introduction
## REASSURE objectives
Implementing cryptography on embedded devices is an ongoing challenge: every
year new implementation flaws are discovered and new attack paths are being
used by real life adversaries. Whilst cryptography can guarantee many security
properties, it crucially depends on the ability to keep the used keys secret
even in face of determined adversaries.
Over the last two decades a new type of adversary has emerged, able to obtain,
from the cryptographic implementation, side channel leakage such as recording
of response times, power or EM signals, etc. To account for such adversaries,
sophisticated security certification and evaluation methods (Common Criteria,
EMVCo, FIPS...) have been established to give users assurance that security
claims have withstood independent evaluation and testing. Recently the
reliability of these evaluations has come into the spotlight: the Taiwanese
citizen card proved to be insecure, and Snowden’s revelations about NSA’s
tampering with FIPS standards eroded public confidence. REASSURE will:
1. improve the efficiency and quality of all aspects of certification using a novel, structured detectmapexploit approach that will also improve the comparability of independently conducted evaluations,
2. cater for emerging areas such as the IoT by automating leakage assessment practices in order to allow resistance assessment without immediate access to a testing lab,
3. deliver tools to stakeholders, such as reference data sets and an open-source leakage simulator based on instruction-level profiles for a processor relevant for the IoT,
4. improve existing standards by actively pushing the novel results to standardization bodies.
REASSURE’s consortium is ideal to tackle such ambitious tasks. It features two
major circuits manufacturers (NXP, MORPHO), a highly respected side channel
testing lab (Riscure), an engaged governmental representative (ANSSI), and two
of the most prominent research institutions in this field (UCL, University of
Bristol).
## Data sharing policy
Very early in the construction of the REASSURE project, it was decided that
not all leakage traces would be made available publicly (the consortium
explicitly opted out of the open access pilot plan). Indeed, parts of the
power traces manipulated by industrial partners correspond to evaluations of
their own, or their customer’s, product, and these data sets are critical from
a security point of view, and also from a customer’s confidence point of view.
Similarly, some elements, such as the exact identification of the product
being evaluated, might not always be fully described in order not to expose
company-specific products.
Yet, as discussed in Sec.2.2, it is the conviction of the consortium that
largely sharing experimental data is paramount to improve the comparability
and quality of evaluations. Consequently, whilst REASSURE will not commit to
sharing all experimental data, we will in practice provide as many practically
useful data sets as possible.
# Data summary
## Purpose of data collection/generation
Data generated in the framework of REASSURE mainly consists in data sets
related to side-channel analysis, i.e. leakage traces (e.g. power or
electromagnetic traces) acquired or simulated when performing side-channel
analysis of a given device. This data will then be processed in order to try
and expose the cryptographic keys manipulated by the device or, on the
contrary, to assess the efficiency of side-channel countermeasures by showing
that these keys cannot be recovered.
## Purpose of data sharing
The efficiency of a side-channel attack depends on a large number of factors,
including the quality of the physical data (which in turn depends on the
measuring equipment, amount of noise, skills and knowledge of the person
performing the measurement), the knowledge of the implementation and device’s
behaviour, the quality of the exploitation strategy...As a consequence,
comparing different attack techniques is very difficult: when a paper
describing a new attack technique appears in the literature, it is not always
easy to decide whether it yields better results because of its intrinsic
quality, or due to more favorable initial data. For the same reason, comparing
the efficiency different countermeasures is quite hard to achieve.
By sharing leakage traces at various stages of their processing, REASSURE aims
to:
* Allow reproducing experiments, either to validate claims or as a tool facilitating learning.
* Allow a fair comparison of specific substeps of attacks, by making it possible to compare the efficiency of different methods when exploiting _the same data_ .
* Provide a common reference basis: in the future, it is our hope to see new results documented by applying them on some openly-accessible reference leakage traces.
* Remove the burden of having to actually run the acquiring of physical leakage traces. This should prove useful for researchers working on side channel evaluation who have a mathematical/algorithmical background (with strong expertise in signal processing, information extraction or statistical evaluation), but little to no knowledge on physical measurements or access to laboratories allowing them to practically generate and collect traces.
## Target audience
REASSURE-related data are expected to be of use for:
* academic researchers;
* device manufacturers;
* implementers of embedded cryptographic algorithms;
* evaluators assessing the side-channel resistance bodies;
* working groups and standardization bodies, such as the JHAS, ANSSI or BSI, to help them decide on the relevance of newly published attacks.
## Types and formats of data
Data include:
1. Raw data acquired when performing physical measurements of a given chip using specific laboratory equipment.
2. Simulated data, generated by running a simulator emulating a chip’s behaviour based on a more or less sophisticated model.
3. Post-processed data, generated based on raw or simulated data by applying some post-treatment (e.g. point-of-interest identification, template modeling...). Post-treatment is an iterative process, so various levels of post-processed data may exist.
Raw and simulated data typically consist in a set of one-dimensional (e.g.
power consumption) or multidimensional (e.g. electromagnetic emanations)
information collected per unity of time. Post-processed data may take a
similar form, or be the output of a statistical treatment – and, more
generally, of any mathematical function – applied to the initial data. Typical
examples are reduced data sets after points-of-interest identification or
dimensionality reduction, or a probability density function when a template
attack is performed.
## Expected size of data
Depending on the sampling frequencies and measurement precision, the size of
leakage data can greatly vary, from a couple of megabytes to several
gigabytes.
# Data sharing principles
Data generated by REASSURE must be shared according to the FAIR principle,
meaning that it should be findable, accessible, interoperable and re-usable.
We describe below the general principles that will be followed to enforce
this. These principles will be further refined as the project evolves.
## Sharing policy
As our objective is to allow as widespread use of the data as possible, open
licensing schemes similar to the Creative Commons, are being considered.
Consortium members, especially academic partners, consider aligning themselves
with the University of Bristol “Open Access Software License”, which seems
well in line with REASSURE’s intents. The compliance of this with partner’s
institutional regulations is being investigated.
## Data localization
The exact storage process to be used by REASSURE is still under evaluation.
Most likely will it be a mixture of partner’s institutional storage facilities
and, especially for very large data pieces, mass storage solutions, e.g.
cloud-based. Data replication will be considered to ensure both easy access
and disaster recovery.
## Data referencing
Data will be made findable mostly through REASSURE’s dissemination activities.
Links towards data repositories will be provided on the project’s website, as
well as in all related scientific publication, position papers and
deliverables. In particular, scientific publications will clearly identify the
data used to obtain results and, whenever not prohibited by confidentiality
reasons, will provide direct access to these data.
## (Meta-)Data format
Each data set will be accompanied by a clear description of
* The device under evaluation.
* The equipment (hardware and/or software) used for data acquisition or generation.
* The treatment applied to the data.
* The identity of the involved team(s).
* The format of the data file.
* Any additional information deemed relevant.
Considering the large number of possible configurations and variability of
potentially relevant parameters in the measurement setup, no standard naming
convention or metadata format will be used. Instead, the configuration will
consist in a text describing the device, equipment and measurement setup in a
clear and unambiguous way.
As described in the introduction, some elements, such as the exact
identification of the product being evaluated, might not always be fully
described in order not expose company-specific products. More generic
information, such as the bus size and operating frequency, will then be
provided.
## Data lifetime
Nothing in the nature of the data – such as privacy-preserving reasons – makes
it necessary to destroy them after a given period of time, hence no action
will be taken to delete them nor limit their replication. The consortium’s
goal is to maintain data availability for at least three years after project
end.
Appendices
# A University of Bristol – Open Access Software Licence
Copyright (c) 2016, The University of Bristol, a chartered corporation having
Royal Charter number RC000648 and a charity (number X1121) and its place of
administration being at Senate House, Tyndall Avenue, Bristol, BS8 1TH, United
Kingdom.
All rights reserved
Redistribution and use in source and binary forms, with or without
modification, are permitted provided that the following conditions are met:
1. Redistributions of source code must retain the above copyright notice, this list of conditions and thefollowing disclaimer.
2. Redistributions in binary form must reproduce the above copyright notice, this list of conditions andthe following disclaimer in the documentation and/or other materials provided with the distribution. THIS
SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY
EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DIS-
CLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR
ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
(INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON
ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
(INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
Any use of the software for scientific publications or commercial purposes
should be reported to the University of Bristol (OSI-
[email protected] and quote reference REASSURE, H2020 project
731591). This is for impact and usage monitoring purposes only.
Enquiries about further applications and development opportunities are
welcome. Please contact elisabeth.
[email protected]
[end of document]
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Horizon 2020
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1210_SPECIAL_731601.md
|
**Introduction**
Processes related to quality, risk and data management within SPECIAL are
summarised as follows in the Description of Work:
##
____________________________________________________________________________
**T7.3 Quality, risk and data management** (Lead: ERCIM; Participants: WU;
Duration: M1-M36) Quality Assurance and risk management is intended to ensure
the production of concrete and high-quality results in line with the project
work plan. To achieve this goal, a Quality Assurance Team is appointed to:
* Define and widely distribute the Quality Plan, to be a reference for all project participants; Encourage and verify that standards, procedures and metrics are defined, applied and evaluated;
* Adopt a procedure for identifying, estimating, treating and monitoring risks;
* Perform monthly Quality and Risk Reviews communicated to the General Assembly for appropriate action;
* Define a statement on the promotion of gender equality within SPECIAL practices and procedures; Produce a Data Management Plan (DMP) in accordance to [DM_H2020], as described in Section 3.2.5 below.
**____________________________________________________________________________**
This deliverable reports on the processes put in place by the consortium to
ensure that all contractual reports are delivered to the expected level of
quality and timeliness.
It also re-visits and updates the risk table and mitigation measures proposed
in the original Description of Work at proposal time, taking into account the
experience gained during the first six months of operation.
Finally, the Data Management Plan (DMP), contractually due at M6, makes the
third and final chapter of deliverable D7.3.
<table>
<tr>
<th>
**2**
</th>
<th>
**Quality**
</th> </tr> </table>
# 2.1 Overview
This chapter details the internal Quality Assurance processes put in place at
the start of the project to ensure the production of quality deliverables
throughout the lifetime of project SPECIAL.
In the Description of Work, the quality plan is one of the output of Task 7.3
‘Quality, Risk and Data Management’. T7.3 is under the responsibility of the
project coordinator (ERCIM), with the active participation of the scientific
coordinator (WU).
Task T7.3 is active for the full duration of the project. Quality processes
described here will apply to all SPECIAL deliverables listed in annex I of
this document, as well as to possible additions to this list that may result
from annual project reviews.
# 2.2 Quality Assurance process
## 2.2.1 Definition
As per the above definition of Task 7.3 ‘Quality, risk and data management’,
Quality Assurance is intended to guarantee the production of concrete and
high-quality results in line with the project work plan. To achieve this goal,
a Quality Assurance team is appointed to define and widely distribute the
Quality Plan, to be a reference for all project participants, to encourage and
verify that standards, procedures and metrics are defined, applied and
evaluated.
With this in mind, the consortium has defined a Quality Assurance process
based on a time line and a set of actions to be repeated for each project
deliverable. In graphical terms, this is the sequence of events that will
ensure proper internal review of the SPECIAL deliverables:
**Figure 2.1 - The SPECIAL Quality Assurance process**
Two internal reviewers for each project deliverable have been appointed by the
Project Steering Committee (PSC) in the early stages of the project. Figure 3
below shows the detailed list of project deliverables together with their
assigned internal reviewers. In a nutshell, the review process described in
Figure 1 requires that:
* The author of the deliverable supplies a table of content for review at least four weeks before submission, and a first draft of the deliverable at the latest two weeks before submission;
* Internal reviewers write their review report using the internal review checklist described in annex 2 and send it to the author within a week;
* The author implements the changes and sends the final version back to the reviewers, to the WP leader and to the scientific coordinator, no later than two days before the deadline;
* Once last comments are resolved among all players and taken on board by the author, the deliverable is submitted to the EC by the project coordinator via the continuous reporting tool on the participant portal.
The end result of the implementation of this process is expected to be a set
of quality deliverables delivered on time.
## 2.2.2 Implementation
_2.2.2.1 Internal reviewers_
At the kick-off meeting of the project, which took place at the European
Commission in Luxembourg on 16-17 January 2017, the consortium discussed in
further details and confirmed the Quality Assurance procedures described in
the Grant Agreement.
As a concrete result, a session at the kick-off meeting was dedicated to
reviewing each single deliverable of Year 1, to reach common understanding of
what exactly had to be delivered, and to identify the more suitable partners
to review Year 1 deliverables.
This exercise yielded the following table of internal reviewers for Year 1
deliverables, as shown in this slide extracted from the WP7 presentation at
the kick-off meeting:
**Figure 2.2 - Initial nomination of internal reviewers at the kick-off
meeting**
All SPECIAL internal reviewers were made aware of this internal review process
and agreed to comply with the timeline described in 2.2.1 above.
This initial list of internal reviewers was quickly extended after the kick-
off meeting to all deliverables over the full duration of the project. The
final list of deliverables and associated internal reviewers is captured in
the table below (sorted by delivery date then by WP), which is available on
the project repository to all project participants, ensuring the clear and
transparent implementation of our Quality Assurance process:
**Table 2.1 - List of Internal Reviewers for SPECIAL Deliverables**
_2.2.2.2 Continuous monitoring_
The SPECIAL consortium planned to hold a minimum of one plenary Telco or one
quarterly face-toface meeting every month of the project. Each one of these
physical or virtual meetings will be the occasion to review upcoming
deliverables and to ensure at management level that progress is on schedule
and that potential issues are under control.
Monitoring and control actions from the coordination team have been further
described in earlier deliverables D7.1 ‘Technical/Scientific coordination
plan’ and D7.2 ‘Administrative management and support plan’.
Should any unplanned event arise that negatively impacts the submission
deadline, the coordinator will contact the project officer at once to justify
the delay and to request in writing an approval to postpone the deliverable by
a reasonable amount of time.
# 2.3 Supporting Tools
With a consortium of nine beneficiaries, the tools required to manage the
Quality Assurance process for deliverables do not require a high level of
sophistication. Simple yet efficient methods will ensure the consistent
quality of SPECIAL deliverables.
## Deliverable templates
With the first deliverables due at M2, WP7 produced an initial deliverable
template in the first month of the project. This MS Word template includes the
logo of the project, which was produced during the kick-off meeting at M1.
This template is available to partners on the BSCW project repository, and it
is the one used here for this deliverable.
Approaching the more technical deliverables of M5 and M6, the scientific
partners opted for collaborative writing based on the Latex document
preparation system 1 . A Latex template is now available to partners who
need or prefer the superior functionality offered by Latex for collaborative
editing.
The outcome of the collaborative editing, whether done in MS Word or in Latex,
is a SPECIAL deliverable in PDF format that looks the same regardless of the
software that produced it.
## E-Mail
In line with the internal review process described in 2.2.1, partners
circulate via e-mail the successive versions of the draft and final
deliverables.
## BSCW
The final version of each deliverable is available to the consortium after
submission to the EC, via a shared folder on the project repository (BSCW).
**Figure 2.3 - SPECIAL deliverables on the project repository**
Shortly before each project review, a special BSCW folder will be created for
the reviewers and populated with the submitted versions of the deliverables
for the period. Reviewers will be granted secure access to and invited to
download from this location.
## Web site
Once approved by the project officer at the project review, public
deliverables for the reviewed period will be made available on the project web
site 2 .
# 2.4 Current State
At M6 of SPECIAL, the relevant procedures are well in place to ensure that the
project will deliver quality deliverables, on time and in a controlled
fashion. For the full duration of SPECIAL, each project deliverable has been
assigned two internal reviewers, and all project participants know what their
role is in supporting the successful implementation of the deliverable Q&A
processes. Should there be any doubt during the course of the project, all
necessary documentation is available on the project repository.
The consortium is committed to follow this procedure and to submit all SPECIAL
deliverables on time and to the best possible level of quality.
<table>
<tr>
<th>
**3**
</th>
<th>
**Risks**
</th> </tr> </table>
# 3.1 Overview
This chapter details the internal framework for risk management put in place
at the start of the project to ensure the smooth running of the SPECIAL
project.
In the Description of Work, the risk management plan is one of the outputs of
Task 7.3 ‘Quality, Risk and Data Management’. T7.3 is under the responsibility
of the project coordinator (ERCIM), with the active participation of the
scientific coordinator (WU).
In this chapter we: (i) outline the sound and practically applicable risk
management methodology we will employ throughout the project; (ii) define
procedures and role assignments to tackle risk identification, evaluation,
monitoring and mitigation; and (iii) provide and overview of the risk register
which will be used to support SPECIAL’s workforce and governance structures to
contribute to and follow the entire process.
The framework for Risk Management described here will apply to all risks
identified to date, as well as to possible additions to this list that may be
added as the project progresses.
# 3.2 Risk Management Methodology
The PRINCE2 risk management procedure 3 provides guidelines to project
managers with respect to the identification and evaluation of potential risks
and their ongoing monitoring. Although in the DOW we focused on negative
risks, it is worth noting that in PRINCE2 risks can have either a negative and
a positive impact on success of the project.
The risk management methodology described herein and depicted in _Figure 3.1_
is closely aligned with PRINCE2’s five steps to risk management:
1. Risk identification involves the identification of the source, casuse and effect of the risk should it materialise. Potential threats are recorded in a risk register (i.e. a list potential risks, including proposed mitigation measures).
2. Risk assessment involves estimating both the impact and the probability of the risk materialising (probability is ranked as low, medium or high).
3. Risk planning involves proposing risk mitigation measures and assessing said mitigation strategies for seconary risks.
4. Implementation is concerned with monitoring the risks, taking action if needs be and monitoring the effectiveness of any actions taken.
5. Communication is a continuous process that aims to ensure all stakeholders are kept up to date with respect to potential risks and risk management activities
**Figure 3.1 - The PRINCE2 RISK management Procedure**
**(Source http://prince2.wiki/File:Slide44.PNG)**
# 3.3 Roles and Responsibilities
## Task leader responsibilities
* Communicate potential risks to the work package leader
* Assist the work package leader with risk management activities, namely identification, assessment, planning and implementation
* Communicate risk status updates to the work package leader
## Work package leader responsibilities
* Communicate potential risks to the project co-ordinators
* Perform risk management activities (i.e. identification, assessment, planning and implementation) for work package specific risks
* Assist the the project co-ordinators with risk management activities (i.e. identification, assessment, planning and implementation) across the various work packages • Communicate risk status updates to the project co-ordinators
## Project Co-ordinators (Scientific/Technical and Administrative)
* Provide feedback to the work package leaders on risk identification, assessment, planning and implementation
* Perform risk management activities (i.e. identification, assessment, planning and implementation) for risks that span multiple work package
* Communicate risk status updates to the General Assembly
# 3.4 Current State
At a minimum the risk register should include the following information:
* Unique risk number for each risk
* Author
* Date registered
* Risk Category (e.g. managerial, Implementation, impact)
* Detailed description of the risk
* Impact and probability
* Relevant work package(s)
* Proposed risk-mitigation measures
* Risk owner (e.g. task leader, work package leader, technical/scientific co-ordinator, administrative co-ordinator)
* Status
_Table 3.1_ provides a list of the initially identified risks from the DOW,
along with the measures foreseen to mitigate those risks, that will be
migrated into the risk register and expanded to include additional risks
identified during the 1 st six months of the project.
**Table 3.1 - Critical Risks for Implementation**
<table>
<tr>
<th>
**Risk num**
</th>
<th>
**Author**
</th>
<th>
**Date Registered**
</th>
<th>
**Category**
</th>
<th>
**Description of risk**
</th>
<th>
**Impact and probability**
</th>
<th>
**WPs**
**involved**
</th>
<th>
**Proposed risk-mitigation measures**
</th>
<th>
**Owner**
</th>
<th>
**Status**
</th> </tr>
<tr>
<td>
1
</td>
<td>
All
</td>
<td>
Jan 2017
</td>
<td>
_Managerial_
</td>
<td>
IPR, licensing or other legal / ethics related issues arise among partners.
</td>
<td>
__Internal_ _
__Risk; Low_ _
__Probability_ _
</td>
<td>
WP7
</td>
<td>
The consortium foresees a number of measures to proactively face such issues,
including a Consortium Agreement listing background/foreground. To name some
measures: alternative licensing of specific S/W elements is allowed to
withdraw related barriers, a clear IPR to inventor(s) policy is adopted,
bodies that can handle stages of conflict before they escalate to a problem,
and lastly, most partners have been cooperating in various contexts and
already have
well-established relationships.
</td>
<td>
ERCIM
</td>
<td>
Open
</td> </tr>
<tr>
<td>
2
</td>
<td>
All
</td>
<td>
Jan 2017
</td>
<td>
_Managerial_
</td>
<td>
A partner / body underperforms, defaults or faces other severe operation
issues.
</td>
<td>
__Internal_ _
__Risk; Low_ _
__Probability_ _
</td>
<td>
WP1, WP2, WP3, WP4,
WP5, WP6, WP7, WP8
</td>
<td>
Governance structure and procedures (meetings,
teleconference, collaboration tools, etc.) allow close monitoring of partner
activities that allow any turbulence to be spotted
</td>
<td>
ERCIM
</td>
<td>
Open
</td> </tr> </table>
<table>
<tr>
<th>
</th>
<th>
</th>
<th>
</th>
<th>
</th>
<th>
</th>
<th>
</th>
<th>
</th>
<th>
promptly. Moreover, measures are foreseen under the Grant and Consortium
Agreement terms for the handling of any defaulting or underperforming partner.
</th>
<th>
</th>
<th>
</th> </tr>
<tr>
<td>
3
</td>
<td>
All
</td>
<td>
Jan 2017
</td>
<td>
_Managerial_
</td>
<td>
Lack of required competences for the completion of project’s tasks.
</td>
<td>
__Internal_ _
__Risk;_ _
__Medium_ _
__Probability_ _
</td>
<td>
WP1, WP2,
WP3, WP4,
WP5, WP6, WP7, WP8
</td>
<td>
The consortium members are selected specifically to fill in the pieces of the
expertise required in the project. Any underperforming partner in technical or
other activities related to lack of expertise will be identified early in the
project and immediate rectification measures will be taken.
</td>
<td>
ERCIM
</td>
<td>
Open
</td> </tr>
<tr>
<td>
4
</td>
<td>
All
</td>
<td>
Jan 2017
</td>
<td>
_Implement ation_
</td>
<td>
Policy framework and engine do not scale to the
demanded volume and
velocity
</td>
<td>
__Internal_ _
__Risk; Low_ _
__Probability_ _
</td>
<td>
WP1, WP2, WP3, WP4
</td>
<td>
The technology partners, led by the architects of the Big Data Europe project
(TF) will from the start of the project onwards focus on solutions that scale.
Scalability demands and requirements will be oriented by the use case partners
and developed in an agile manner with the combined expertise of TUB,
WU and CeRICT on
</td>
<td>
WU
</td>
<td>
Open
</td> </tr> </table>
<table>
<tr>
<th>
</th>
<th>
</th>
<th>
</th>
<th>
</th>
<th>
</th>
<th>
</th>
<th>
</th>
<th>
distributed systems, scalable Linked Data query answering and reasoning, and
reasoning about policies.
</th>
<th>
</th>
<th>
</th> </tr>
<tr>
<td>
5
</td>
<td>
All
</td>
<td>
Jan 2017
</td>
<td>
_Implement ation_
</td>
<td>
Dependency chains between related tasks
</td>
<td>
__Internal_ _
__Risk; Low_ _
__Probability_ _
</td>
<td>
WP1, WP2, WP3
</td>
<td>
WP2’s tasks related to the development of a policy language depend on the
requirements elicited from the use cases. In turn, WP 3 depends partly on
WP2’s inputs. So any delay or variation in the definition of the use cases may
have a domino effect on some of the core technical work packages. If
necessary, WP2 may start with a generic approach, by assessing the scalability
of the main policy constructs introduced in the literature, studying scalable
implementations whenever possible. The agile development process adopted by
SPECIAL will help to compensate any delayed specific input coming from the use
cases, allowing WP2 and WP3 to start their work in advance, and integrate use-
case specific
</td>
<td>
WU
</td>
<td>
Open
</td> </tr> </table>
<table>
<tr>
<th>
</th>
<th>
</th>
<th>
</th>
<th>
</th>
<th>
</th>
<th>
</th>
<th>
</th>
<th>
requirements when they become available.
</th>
<th>
</th>
<th>
</th> </tr>
<tr>
<td>
6
</td>
<td>
All
</td>
<td>
Jan 2017
</td>
<td>
_Implement ation_
</td>
<td>
Introduction of new standards, law and certifications affecting the success of
the project activities
</td>
<td>
__External_ _
__Risk; Low_ _
__Probability_ _
</td>
<td>
WP1, WP2,
WP3, WP4,
WP5
</td>
<td>
As with technological changes, the consortium will monitor pertinent standards
and the responsible partners will provide recommendations on how to address
these in the project in case changes arise. The fact that there are two rounds
of design and development in the project also allows for appropriate
adjustments.
</td>
<td>
WU
</td>
<td>
Open
</td> </tr>
<tr>
<td>
7
</td>
<td>
All
</td>
<td>
Jan 2017
</td>
<td>
_Implement ation_
</td>
<td>
Issues obtaining quality simulated data
</td>
<td>
__Internal_ _
__Risk; Low_ _
__Probability_ _
</td>
<td>
WP3, WP5
</td>
<td>
The use case partners will take all necessary actions to ensure that the
simulated data is both realistic and representative of the real data used in
the proposed usecase scenarios.
</td>
<td>
WU
</td>
<td>
Open
</td> </tr>
<tr>
<td>
8
</td>
<td>
All
</td>
<td>
Jan 2017
</td>
<td>
Impact
</td>
<td>
Applicability of project results
</td>
<td>
__Internal_ _
__Risk; Low_ _
__Probability_ _
</td>
<td>
WP5
</td>
<td>
We have chosen use cases from diverse domains (telecom and financial sectors)
that expose complementary critical aspects of the challenges we want to solve
with respect to privacy-aware solutions that scale to Big Data. Moreover,
</td>
<td>
ERCIM
</td>
<td>
Open
</td> </tr> </table>
<table>
<tr>
<th>
</th>
<th>
</th>
<th>
</th>
<th>
</th>
<th>
</th>
<th>
</th>
<th>
</th>
<th>
we will actively (by having allocated resources in our work plan) liaise with
other ICT-14 projects and the CSA in the ICT-18 call to guarantee
applicability of results and impact.
</th>
<th>
</th>
<th>
</th> </tr>
<tr>
<td>
9
</td>
<td>
All
</td>
<td>
Jan 2017
</td>
<td>
_Impact_
</td>
<td>
Insufficient end-user engagement in the pilot hacking challenges.
</td>
<td>
__Internal_ _
__Risk; Low_ _
__Probability_ _
</td>
<td>
WP5
</td>
<td>
The project team will use all available dissemination channels to promote
awareness, e.g. through standardisation activities, existing networks, social
media outlets. Rewarding services could be included to increase the level of
user engagement.
</td>
<td>
ERCIM
</td>
<td>
Open
</td> </tr> </table>
<table>
<tr>
<th>
**4**
</th>
<th>
**Data Management Plan**
</th> </tr> </table>
The data management plan described here-in relates to synthesised data ONLY.
PROX is the only partner in the project that will collect data via user
studies. This data will NOT be made available (even in an anonymized format).
Specific guidance with respect to the assessment of ethical issues concerning
the collection, processing and disclosure of personal data are described in
deliverable D.8.1 SPECIAL Ethics Guidelines.
# 4.1 Data Summary
_What is the purpose of the data collection/generation and its relation to the
objectives of the project?_
* All three industry partners will provide their expertise and domain knowledge in order to enable the generation of simulated data, which we will use in our public challenges.
* Additionally, synthesised data (e.g. datasets, queries and policies) will be generated and used for benchmarking and stress testing purposes throughout the projects.
_What types and formats of data will the project generate/collect?_
The following types of data will be generated:
* Synthesised ledger entries containing processing and sharing events – who processed/shared, what data, for what purpose, with whom, under what usage conditions.
* Synthesised usage policies – stipulating what data can be used for what purpose by whom and under what usage conditions.
* Synthesised domain specific data (e.g. telecoms and financial data) – encrypted simulated telecoms and financial data and associated metadata.
* Ontologies used to describe ledge entries, usage policies, domain specific data and metadata (e.g. temporal, provenance, permissions, obligations).
* Certain aspects of the General Data Protection Regulation (GDPR) that are necessary for compliance checking within the remit of SPECIAL will be made available in a machine readable format.
All public data will be published according to the 5-star deployment scheme
for Open Data 4 4 :
Tim Berners-Lee, the inventor of the Web and initiator of the Linked Data
project, suggested a 5star deployment scheme for Linked Data. The 5 Star
Linked Data system is cumulative. Each additional star presumes the data meets
the criteria of the previous step(s).
☆ Data is available on the Web, in whatever format.
☆☆ Available as machine-readable structured data, (i.e., not a scanned image).
☆☆☆ Available in a non-proprietary format, (i.e. CSV, not Microsoft Excel).
☆☆☆☆ Published using open standards from the W3C (RDF and SPARQL).
☆☆☆☆☆ All of the above and links to other Linked Open Data.
**Figure 4.1 - 5 Star Linked Data**
_Will you re-use any existing data and how?_
* Where possible existing ontologies will be used used to describe both data and metadata (e.g. temporal, provenance, permissions, obligations). PROV 5 and OWL-Time 6 ontologies can be used to represent provenance and temporal information respectively. Additionally there are a number of general event vocabularies such as the Event 7 ontology and the LODE 8 ontology that could potentially be adapted/extended in order to model our data processing events. Likewise, vocabularies for expressing policies such as the Open Digital Rights Language 9 which the W3C’s Permissions and Obligations working group hopes to standardise in the near future, could be adapted/extended.
_What is the origin of the data?_
* Synthesised ledger entries, usage policies, domain specific data will be generated by the SPECIAL team.
* PROV 5 , OWL-Time 6 , Events 7 , LODE 8 and ODRL 9 are ontologies that are openly available for reuse. Any adaptations/extensions developed by SPECIAL will likewise be given back to the community.
What is the expected size of the data?
At this stage of the project it is difficult to quantity. Size can have many
meanings:
* number of data sets
* number of data fields
* size of data fields (an image is usually bigger that a binary field
_To whom might it be useful ('data utility')?_
* Computer Science, Semantic Web and Privacy researchers can make use of some/all of the data for reesearch and benchmarking purposes.
* Companies and researchers may leverage and extend the ontologies developed by SPECIAL.
* Legislators, companies, and researchers may benefit from the subset of the GDPR that will be made availble in a machine readable format.
# 4.2 Findable, accessible, interoperable and reusable (FAIR) data
This section is based on the guidelines for effective data management in in
the course of a Horizon 2020 project, provided by the European Commission 10
. The primary objective is to ensure that research data is findable,
accessible, interoperable and re-usable (FAIR).
## 4.2.1 Making data findable, including provisions for metadata
Are the data produced and/or used in the project discoverable with metadata,
identifiable and locatable by means of a standard identification mechanism
(e.g. persistent and unique identifiers such as Digital Object Identifiers)?
All synthesised data will be made available as Linked Data. Underpinning the
Linked Data Web is a set of best practices for publishing and interlinking
structured data, know as the Principles of Linked Data. These principles are
defined by Tim Berners-Lee 11 as follows:
” 1. Use URIs as names of things.
2. Use HTTP URIs so that people can look up those names.
3. When someone looks up a URI, provide useful information, using the standards (RDF*, SPARQL).
4. Include links to other URIs. so that they can discover more things. ”
The term thing is used to refer to both real world entities and abstract
concepts, commonly referred to as resources. The LDW builds on the existing
web infrastructure, by using HyperText Transfer Protocol (HTTP) URIs to
identify things, as well as documents. However, URIs only support a subset of
the American Standard Code for Information Interchange (ASCII) character set.
Later the W3C introduced Internationalised Resource Identifiers (IRIs), that
provides support for the richer Unicode character set. Although the principles
defined by Berners-Lee refer to URIs, as there is a mapping from IRIs to URIs,
it is also possible to use IRIs.
However, it is not enough to simply use URIs to refer to things. According to
the Linked Data principles, it should be feasible to use the URI to return a
description of the resource (commonly referred to as dereferencing). As URIs
often represent real world entities, it is common practice to use different
URIs to represent the resource and the document that describes it. Two
different strategies, that can be used to dereference URIs exist, namely 303
redirects and Hash URI’s. In the case of 303 redirects, when a client attempts
to dereference a resource, the server responds with a 303 See Other, and a URI
for the document that describes this resource. The client subsequently uses
this new URI to retrieve the description of the resource. Whereas, in the case
of hash URIs a # separator is used to append an identifier, which identifies
the resource, to the end of the URI. Prior to attempting to dereference the
resource, the client strips off the # and the identifier, making it possible
to distinguish between the physical resource and the document that describes
it. The final principle refers to the linking of URI’s. Just like the web of
documents uses reference links to enable humans and machines to navigate web
pages, the web of data is constructed in a similar fashion. By using RDF to
describe resources, it is possible not only to link structured data, but also
to describe complex relations between resources in a machine readable format.
What naming conventions do you follow?
All class, properties and instances will be provided with a unique IRI, that
will be accessible via the SPECIAL web server. The prefix used for all public
data items will be https://data.specialprivacy.eu/.
As the data management plan is a living document more specific naming
conventions will be worked out at a later stage if needs be.
Will search keywords be provided that optimize possibilities for re-use?
SPECIAL will adopt the Comprehensive Knowledge Archive Network (CKAN) 11
web-based open source management system, which is developed by the Open
Knowledge Foundation, for the storage and distribution of open data. CKAN is
already the tool of choice for many national and local governments, research
institutions, and other organisations which collect a lot of data. CKAN
provides powerful search and faceting, browsing over distributed data sources.
Do you provide clear version numbers?
The CKAN ckanext-datasetversions 12 extension provides support for different
versions of a dataset.
What metadata will be created? In case metadata standards do not exist in your
discipline, please outline what type of metadata will be created and how.
CKAN provides a rich set of metadata for each dataset. The following
information can be found on the CKAN website 13 : “
* _Title – allows intuitive labelling of the dataset for search, sharing and linking._
* _Unique identifier – dataset has a unique URL which is customizable by the publisher._
* _Groups – display of which groups the dataset belongs to if applicable. Groups (such as science data) allow easier data linking, finding and sharing amongst interested publishers and users._
* _Description – additional information describing or analysing the data. This can either be static or an editable wiki which anyone can contribute to instantly or via admin moderation._
* _Data preview – preview .csv data quickly and easily in browser to see if this is the dataset you want._
* _Revision history – CKAN allows you to display a revision history for datasets which are freely editable by users (as is thedatahub.org)_
* _Extra fields – these hold any additional information, such as location data (see geospatial feature) or types relevant to the publisher or dataset. How and where extra fields display is customizable._
* _Licence – instant view of whether the data is available under an open licence or not. This makes it clear to users whether they have the rights to use, change and re-distribute the data._
* _Tags – see what labels the dataset in question belongs to. Tags also allow for browsing between similarly tagged datasets in addition to enabling better discoverability through tag search and faceting by tags._
* _Multiple formats (if provided) – see the different formats the data has been made available in quickly in a table, with any further information relating to specific files provided inline._
* _API key – allows access every metadata field of the dataset and ability to change the data if you have the relevant permissions via API. “_
If needs be, CKAN allows for the specification of additional metadata items in
the form of name value pairs.
## 4.2.2 Making data openly accessible
Which data produced and/or used in the project will be made openly available
as the default? If certain datasets cannot be shared (or need to be shared
under restrictions), explain why, clearly separating legal and contractual
reasons from voluntary restrictions.
* All three industry partners will provide their expertise and domain knowledge in order to enable the generation of simulated data, which we will use in our public challenges. PROX is the only partner in the project that will collect data via user studies. This data will NOT be made available (even in an anonymized format).
* Additionally, synthesised data (e.g. datasets, queries and policies) will be generated and used for benchmarking and stress testing purposes throughout the projects.
Note that in multi-beneficiary projects it is also possible for specific
beneficiaries to keep their data closed if relevant provisions are made in the
consortium agreement and are in line with the reasons for opting out.
By default, commercially sensitive data belonging to SPECIAL usecase partners
Prox, TLabs and TR will be closed
How will the data be made accessible (e.g. by deposition in a repository)?
As stated in section 4.2.1, synthesised data is will be made available as
Linked Data.
What methods or software tools are needed to access the data?
Data will be accessible via HTTP and queryable using RDF (Resource Description
Framework) query languages such as SPARQL
Is documentation about the software needed to access the data included?
Resource Description Framework (RDF) is a standard model for data interchange
on the Web. Relevant documentation is provided by the W3C.
Is it possible to include the relevant software (e.g. in open source code)?
A pointer to documentation on the relevant standards can be include in open
source code.
Where will the data and associated metadata, documentation and code be
deposited? Preference should be given to certified repositories which support
open access where possible.
As stated in section 4.2.1, synthesised data is will be accessible via the
SPECIAL web server. The prefix used for all public data items will be
https://data.specialprivacy.eu/.
Have you explored appropriate arrangements with the identified repository?
Not applicable.
If there are restrictions on use, how will access be provided?
All synthesised data will be publically available
Is there a need for a data access committee?
Not at the moment.
Are there well described conditions for access (i.e. a machine readable
license)?
The default license for SPECIAL public data will be CC-BY.
How will the identity of the person accessing the data be ascertained?
Not applicable.
## 4.2.3 Making data interoperable
Are the data produced in the project interoperable, that is allowing data
exchange and re-use between researchers, institutions, organisations,
countries, etc. (i.e. adhering to standards for formats, as much as possible
compliant with available (open) software applications, and in particular
facilitating re-combinations with different datasets from different origins)?
Where possible data will be published as Resource Description Framework (RDF).
RDF is a standard model for data interchange on the Web. Relevant
documentation is provided by the W3C.
What data and metadata vocabularies, standards or methodologies will you
follow to make your data interoperable?
By default, SPECIAL will adopt the RDF data model, and meta language such as
RDFS and OWL. Like RDF both RDFS and OWL are W3C specifications
Additionally we will reuse existing RDF ontologies such as PROV 5 , OWL-Time
6 , Events 7 , LODE 8 and ODRL 9 .
Will you be using standard vocabularies for all data types present in your
data set, to allow inter-disciplinary interoperability?
Yes, where possible we will reuse standard vocabularies.
In case it is unavoidable that you use uncommon or generate project specific
ontologies or vocabularies, will you provide mappings to more commonly used
ontologies?
Should existing existing ontologies and vocabularies not meet our needs we
will be sure to publish our extensions or new ontologies so that others can
reuse.
## 4.2.4 Increase data re-use (through clarifying licences)
How will the data be licensed to permit the widest re-use possible?
The default license for SPECIAL public data will be CC-BY.
When will the data be made available for re-use? If an embargo is sought to
give time to publish or seek patents, specify why and how long this will
apply, bearing in mind that research data should be made available as soon as
possible.
Generally speaking, data will be made available when papers are published or
releases of the SPECIAL software are deployed.
Are the data produced and/or used in the project useable by third parties, in
particular after the end of the project? If the re-use of some data is
restricted, explain why.
All synthesised data will be publically available.
How long is it intended that the data remains re-usable?
The specialprivacy.eu domain name has been reserved by ERCIM/W3C – the project
coordinator – for 5 years. It has been registered on 17/01/2017 and will
expire on 17/01/2022, over-running the project life time by two years. A
sustainability plan will be put in place during year 3 of the project.
Are data quality assurance processes described?
Data quality assurance processes will be described before the 1 st public
release.
# 4.3 Allocation of resources
What are the costs for making data FAIR in your project?
As we aim to use the existing web infrastructure at the current point in time
no additional costs are foreseen.
How will these be covered? Note that costs related to open access to research
data are eligible as part of the Horizon 2020 grant (if compliant with the
Grant Agreement conditions).
Not applicable.
Who will be responsible for data management in your project?
The technical/scientific co-ordinator.
Are the resources for long term preservation discussed (costs and potential
value, who decides and how what data will be kept and for how long)?
A sustainability plan will be put in place during year 3 of the project.
# 4.4 Data security
What provisions are in place for data security (including data recovery as
well as secure storage and transfer of sensitive data)?
For security reasons, six months after the end of the project and with the
approval of the project coordinator, ERCIM will copy the whole website to a
“static” version which will replace the online dynamic version. Content will
still be available online but will stop being editable.
The servers use RAID hardware and redundant power-supplies to ensure high
efficiency and availability of our services. Those machines are hosted in a
secured machine room in Sophia Antipolis, France. It is a limited-access
facility, with air conditioning and uninterrupted power supplies. The systems
are backed-up daily and are up and running 24/7.
Is the data safely stored in certified repositories for long term preservation
and curation?
A sustainability plan will be put in place during year 3 of the project.
# 4.5 Ethical aspects
Are there any ethical or legal issues that can have an impact on data sharing?
These can also be discussed in the context of the ethics review. If relevant,
include references to ethics deliverables and ethics chapter in the
Description of the Action (DoA).
Given that we will only share synthesised data there are no foreseen ethical
or legal implication.
Is informed consent for data sharing and long term preservation included in
questionnaires dealing with personal data?
Given that we will only share synthesised data, informed consent is not a
consideration.
# 4.6 Other issues
Do you make use of other national/funder/sectorial/departmental procedures for
data management? If yes, which ones?
No
<table>
<tr>
<th>
**5**
</th>
<th>
**Conclusion**
</th> </tr> </table>
As early as Project Month 1, the SPECIAL consortium has put procedures in
place to ensure that the project will deliver quality deliverables, on time
and in a controlled fashion. At Project Month 6, those procedures are
completely documented, well publicised within the consortium, and adhered to
by all project participants.
The good team work on each deliverable between its owners, its participants,
its internal reviewers and the coordination team reinforces the good spirit
within the consortium, keeps everyone aware of issues and progress, and
ensures that all partners remain committed to the global success of the
project.
For a project and a consortium the size of SPECIAL, we feel that the processes
and tools put in place to limit risk and ensure quality are adequate and will
prove sufficient and successful over the project life time.
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https://phaidra.univie.ac.at/o:1140797
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Horizon 2020
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1211_GOAL_731656.md
|
1. Introduction to the background of the research;
2. Objectives of the evaluation study;
3. Detailed description of the different phases of the study, and the participant’s expected actions and contributions;
4. Statement on the eligibility criteria of the study;
5. Clear description of possible risks in participation;
6. Description of the benefits (for the participant or researcher) of participating in the study;
7. Confidentiality clause (i.e. who has access to data);
8. Statement of voluntary participation (i.e. participants are always able to drop out of the study without any argumentation given);
9. Possible compensation of travel costs or other (financial) compensation for participating in the study;
10. Signature page.
## 5.1 Informed Consent for Final Demonstration
Below in Figure 1 and Figure 2, the final informed consent forms used for the
GOAL final demonstration are presented in English and Dutch respectively.
_Figure 1: Informed Consent form used in the GOAL Final Demonstration
(English)._
_Figure 2: Informed Consent form used in the GOAL Final Demonstration
(Dutch)._
These informed consent forms were provided to all users in the four phases of
the final GOAL demonstration. For phase 1 and 2 an English information leaflet
was provided additionally (see Figure 3).
_Figure 3: Information about the GOAL project in Phase 1 of the final
demonstration._
The information leaflet on the previous page for Phase 1 and 2 was updated and
translated to Dutch for the Dutch phase 3 participants (see Figure 4 below).
_Figure 4: Information leaflet used in Phase 3 of the GOAL final demonstration
(Dutch)._
Finally, the leaflet was updated once again for the fourth and final phase of
the evaluation (see Figure 5 below).
_Figure 5: Information leaflet for the fourth and final phase of the GOAL
final demonstration (Dutch)._
Additionally, in phase four, users were provided an information “cheatsheet”
to help them navigate through the different application, and specifically for
working with the Fitbit activity trackers:
_Figure 6: Fitbit and GOAL Application "Cheat Sheet" Page #1/2._
_Figure 7: Fitbit and GOAL Application "Cheat Sheet" Page #2/2._
# 6 Other issues
N/A
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https://phaidra.univie.ac.at/o:1140797
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Horizon 2020
|
1212_WaterSpy_731778.md
|
# Introduction
This document deals with the research data produced, collected and preserved
during the project. This data can either be made publicly available or not
according to the Grant Agreement and to the need of the partners to preserve
the intellectual property rights and related benefits derived from project
results and activities.
Essentially, the present document will answer to the following main questions:
* What types of data will the project generate/collect?
* What data is to be shared for the benefit of the scientific community?
* What data cannot be made available? Why?
* What format will the shared data have?
* How will this data be exploited and/or shared/made accessible for verification and re-use?
* How will this data be curated and preserved?
The data that can be shared will be made available as Open access research
data; this refers to the right to access and re-use digital research data
under the terms and conditions set out in the Grant Agreement. Openly
accessible research data can typically be accessed, mined, exploited,
reproduced and disseminated free of charge for the user.
The WaterSpy project abides to the European Commission's vision that
information already paid for by the public purse should not be paid for again
each time it is accessed or used, and that it should benefit European
companies and citizens to the full. This means making publicly-funded
scientific information available online, at no extra cost, to European
researchers, innovative industries and citizens, while ensuring long-term
preservation.
The Data Management Plan (DMP) is not a fixed document, but evolves during the
lifespan of the project. The following are basic issues that will be dealt
with for the data that can be shared:
* **Data set reference and name**
The identifier for the datasets to be produced will have the following format:
WaterSpy_[taskx.y]_[descriptive name]_[progressive version number]_[date of
production of the data]
* **Data set description**
Description of the data that will be generated or collected will include:
* Its origin, nature, scale and to whom it could be useful, and whether it underpins a scientific publication. Information on the existence (or not) of similar data and the possibilities for integration and reuse.
* Its format
* Tools needed to use the data (for example specialised software)
* Accessory information such as possible video registration of the experiment or other.
* **Standards and metadata**
Reference to existing suitable standards of the discipline. If these do not
exist, an outline on how and what metadata will be created, if necessary.
* **Data sharing**
Description of how data will be shared, including access procedures, embargo
periods (if any), outlines of technical mechanisms for dissemination and
necessary software and other tools for enabling re-use, and definition of
whether access will be widely open or restricted to specific groups.
Identification of the repository where data will be stored, if already
existing and identified, indicating in particular the type of repository
(institutional, standard repository for the discipline, etc.). In case the
dataset cannot be shared, the reasons for this should be mentioned (e.g.
ethical, rules of personal data, intellectual property, commercial, privacy-
related, security-related).
* **Archiving and preservation (including storage and backup) and access modality**
Description of the procedures that will be put in place for long-term
preservation of the data. Indication of how long the data should be preserved,
what is its approximated end volume, what the associated costs are and how
these are planned to be covered.
# Types of data generated within the project
The project is expected to generate the following types of data:
1. Data from the preliminary experiments
1. regarding the identification of the IR bands of the targeted bacteria (WP2-T2.2)
2. required to improve the S/N ratio of an optimized QCL-ATR-detector configuration (as need identified in WP5)
2. Data regarding the Quantum Cascade Lasers (QCL) testing and characterization (WP3)
3. Data regarding the photodetector testing and characterization (WP4)
4. Data from the testing and characterization of the molecular recognition elements (MREs) used for the smart surface (WP5-T5.4). This also includes work on possible mobile smart surfaces.
5. Simulation and testing data collected during the development of the ultrasound (US) pre-concentration module (WP5-T5.2)
6. Simulation data collected during the optimization of the fluid flow in the microfluidic cell (WP5-T5.3)
7. Design of the main processing unit and firmware (WP5-T5.6-T5.7)
8. Modulation scheme approach and signal conditioning concept data (WP5-T5.5)
9. Mid-project testing results (WP6-T6.3)
10. Lab validation results of the WaterSpy device (WP6-T6.4)
11. Device performance assessment data after the field tests (WP7-T7.3)
12. Impact assessment data and system usability analysis (WP7-T7.4)
## Data to be shared and not
Some of the above data will be shared with the scientific community, following
the principles of open data sharing. In particular:
* Data from category **(a)** might be shared with the scientific community, following research publications by CNR, TUW and FAU. In case of accepted publications, selected datasets will be shared with the community for verification purposes.
* Data from categories **(b)** and **(c)** will not be shared under any circumstances, since those refer to prototypes and possibly commercial products of ALPES and VIGO.
* Data from category **(d)** might be shared with the scientific community, following a research publication by CNR and TUW. In case of accepted publications, selected datasets will be shared with the community for verification purposes.
* Data from category **(e)** might be shared with the scientific community, following a research publication by TUW. In case of accepted publications, selected datasets will be shared with the community for verification purposes.
* Data from category **(f)** are not expected to be publishable. Nevertheless, if a related publication is accepted, selected datasets will be shared with the community for verification purposes.
* Data from category **(g)** will not be shared under any circumstances, since those refer to prototypes and possibly commercial products of CyRIC and NTUA.
* Data from category **(h)** might be shared with the scientific community, following research publications by FAU. In case of accepted publications, selected datasets will be shared with the community for verification purposes.
* Data from category **(i)** will not be shared, since those will be intermediate data, used to improve the system.
* Data from categories **(j)** , **(k)** and **(l)** might be shared with the scientific community, following research publications by all partners. In case of accepted publications, selected datasets will be shared with the community for verification purposes. Particular emphasis will be paid to IPR protection, since possibly sensitive data might be available particularly in the results of those tests.
# Management plan for the different sharable categories of data
In the sections that follow, details on the data to be shared with the
community are presented. Focus is put on: a) Dataset description
b) Standards and metadata, where applicable
## Data from the preliminary experiments, regarding fingerprint regions of
bacteria
The data related to the preliminary experiments for the fingerprint regions of
bacteria identification are mainly the output of WP2, in particular they are
generated as an output of task T2.2 and are mostly experimental measurements.
The following types of data will be produced:
1. Protocol and results
2. Experimental data and figures, used also in the deliverables (D2.2 and D2.3)
_Information about tools and instruments:_
Documentation related documents will be available either in the Microsoft Word
format or as a PDF file. Measured data will be available in the form of Bruker
OPUS software, an Excel table or Origin files derivate from the Origin
software.
_Standards and metadata:_
To guarantee a high academic standard, results will be documented considering
the typical nomenclature of the particular field of research.
Measured data are either self-explanatory or will be prepared or described by
an additional document. Metadata will be made available for each document or
data set.
_Accessibility:_
Once a dataset is made available on the project repository, it will freely be
available to the community, in the way described in the appropriate section
below. No embargo period is foreseen.
## Data from testing and characterization of the MREs
Data from the testing and characterization of the molecular recognition
elements (MREs) used for the smart surface will be mainly generated as an
output of WP5, in particular as an output of task T5.4. Such data will include
the description of the experimental procedure, as well as measurements needed
for the characterization of the MREs selected, such as ELISA, surface plasmon
resonance (SPR) and fluorescence steady state measurements.
The following types of data will be produced:
1. Protocol and results from the experiments and characterization
2. Scientific publications related to the _smart surface_ development and preparation using the selected MREs
_Information about tools and instruments:_
Documentation related documents will be available either in the Microsoft Word
format or as PDF file. Measured data will be available as Excel table or
Origin files derivate from Origin software.
_Standards and metadata:_
To guarantee a high academic standard, results will be documented considering
the typical nomenclature of the particular field of research.
Measured data are either self-explanatory or will be prepared or described by
an additional document. Metadata will be made available for each document or
data set.
_Accessibility:_
Once a dataset is made available on the project repository, it will freely be
available to the community, in the way described in the appropriate section
below. No embargo period is foreseen.
## Data related to the development of the US pre-concentration module
Data from the testing and characterization of the ultrasound (US) pre-
concentration module used for sample preconcentration will be mainly generated
as an output of WP5, in particular as an output of task T5.2 and T5.3. Such
data will include the description of the experimental set-up and the
experimental procedure. In addition, operational parameters of the ultrasound
device as well as IR data (being FTIR spectra or QCL readings) will be
recorded.
The following types of data will be produced:
* Scientific publications
* Documentation of experimental set-ups
* Documentation of experimental results
* Documentation of hardware related topics
* Measured data
* Source code
_Information about tools and instruments:_
Publications and documentation related documents will be available either in
the Microsoft Word format or as PDF file. For reading original FTIR data, the
Bruker OPUS software is required. Selected measured data will be available as
MATLAB data file, as an Excel table or depending on the size of the dataset
also as plain text files. Large files may be compressed to .zip files.
The source code that may be made available can be read by any text editor but
specialized software (mainly MATLAB) will be needed to execute the programs.
_Standards and metadata:_
To guarantee a high academic standard, results will be documented considering
the typical nomenclature of the particular field of research.
Measured data are either self-explanatory or will be prepared or described by
an additional document.
Source code will be commented and, if useful, explanatory examples will be
made. Metadata will be made available for each document or data set.
_Accessibility:_
Once a dataset is made available on the project repository, it will freely be
available to the community, in the way described in the appropriate section
below. No embargo period is foreseen.
## Data related to the laser modulation scheme and signal conditioning
_Data set description:_
The data in this category will be mainly the output of WP5 T5.5 and be mostly
the result of theoretical investigations. Additionally, measurements that
prove the developed concept will be part of the generated data. The following
types of data will be produced:
* Scientific publications
* Documentation of theoretical investigations
* Documentation of simulation results
* Documentation of hardware related topics
* Measured data
* Source code
_Information about tools and instruments:_
For reading the data, no special tools are necessary. Publications and
documentation related documents will be available either in the Microsoft Word
format or as PDF file. Measured data will be available as Excel table or
depending on the size of the dataset as plain text files. Large files may be
compressed to .zip files.
The source code that may be made available can be read by any text editor but
specialized software (mainly MATLAB) will be needed to execute the programs.
_Standards and metadata:_
To guarantee a high academic standard, results will be documented considering
the typical nomenclature of the particular field of research.
Measured data are either self-explanatory or will be prepared or described by
an additional document.
Source code will be commented and, if useful, explanatory examples will be
made. Metadata will be made available for each document or data set.
_Accessibility:_
Once a dataset is made available on the project repository, it will freely be
available to the community, in the way described in the appropriate section
below. No embargo period is foreseen.
## Data related to the lab validation of the WaterSpy device
The data in this category is collected during lab tests of the WaterSpy
device. Data collected is related to the developments of T6.4. The task is
planned to run from M20 to M30. Data that may be suitable for publicly sharing
could be related to the overall device testing and its outputs, as well as the
possible testing of individual components performance. The data will be
available after ensuring the protection of possible IP issues and following
the acceptance of related scientific or technical publications.
Data will be shared in the form of .zip packets and may include: spreadsheets
with experimental results, photos from the setup, documents describing the
experiment and the sample (standards) used.
At the moment, it is not possible to estimate the file size, but it is not
expected to be higher than 100MB per experiment, mostly due to the possible
presence of images.
The possible use or re-use by the scientific community is provided by adhering
to terms and conditions set out in the Grant Agreement.
_Information about tools and instruments_ : It is not foreseen at the moment
the need to use specialised tools for using the research data, apart from
common spreadsheet and text file editors. If any specialised instrument is
required for a particular dataset, then information about this tool will be
published along with the dataset. If the tool is freely available, a link for
downloading it will also be provided.
Any scientific papers to be published that will make use of the datasets, will
also be associated to the datasets and be accessible through the same page.
_Standards and metadata_ : We will also create metadata for easier data
discovery, including through search engines. During system validation,
reference samples of know concentration of the target analytes will be used
(standards). Information about these standards, including bacteria
concentration, ionic strength, salt content, pH etc. will also be published
along with the dataset.
_Accessibility_ : Once a dataset is made available on the project repository,
it will freely be available to the community, in the way described in the
appropriate section below. No embargo period is foreseen.
## Device performance assessment data
Data from this category might be shared with the scientific community,
following research publications by all partners. In case of accepted
publications, selected datasets will be shared with the community for
verification purposes. Particular emphasis will be paid to IPR protection,
since possibly sensitive data might be available particularly in the results
of those tests.
The data in this category is generated and collected during and after the
field validation of the WaterSpy device. Data collected is related to the
developments of T7.3. The task is planned to run from M30 to M36. Data that
may be suitable for publicly sharing could be related to the overall device
testing and its outputs, as well as the possible testing of individual
components performance. The data will be available after ensuring the
protection of possible IP issues and following the acceptance of related
scientific or technical publications.
Data will be shared in the form of .zip packets and may include: spreadsheets
with experimental results, photos from the setup, documents describing the
validations, information about the test site and the comparison measurements
with golden standard procedures.
At the moment, it is not possible to estimate the file size, but it is not
expected to be higher than 100MB per experiment, mostly due to the possible
presence of images.
The possible use or re-use by the scientific community is provided by adhering
to terms and conditions set out in the Grant Agreement.
_Information about tools and instruments_ : It is not foreseen at the moment
the need to use specialised tools for using the research data, apart from
common spreadsheet and text file editors. If any specialised instrument is
required for a particular dataset, then information about this tool will be
published along with the dataset. If the tool is freely available, a link for
downloading it will also be provided.
Any scientific papers to be published that will make use of the datasets, will
also be associated to the datasets and be accessible through the same page.
_Standards and metadata_ : We will also create metadata for easier data
discovery, including through search engines. During system field testing,
comparison with golden standard methodologies is foreseen, in order to
generate ground truth data. Information about the golden standard methodology
used for comparison will also be published.
_Accessibility_ : Once a dataset is made available on the project repository,
it will freely be available to the community, in the way described in the
appropriate section below. No embargo period is foreseen.
## Data related to impact assessment and system usability analysis
The impact of the WaterSpy pilot campaigns in both locations will be evaluated
after the demos. Initial situation information will first be collected, in
order to be able to evaluate the effect of the device use.
Assessment will be carried out based on the KPIs mentioned in the DoA. Section
1.1 of this document. Impact assessment takes into consideration the RoI for
the end-users.
System usability will also be evaluated with the help of the end-users.
The data in this category is generated and collected during and after the
field validation of the WaterSpy device. Data collected is related to the
developments of T7.4. The task is planned to run from M31 to M36. Data that
may be suitable for publicly sharing could be related to the impact assessment
of the device use or to the usability analysis. The data will be available
after ensuring the protection of possible IP issues and following the
acceptance of related scientific or technical publications.
Data will be shared in the form of .zip packets and may include: photos from
the demos, documents describing the initial situation, possible financial
considerations, information about the test site, system usability
questionnaires analysis.
At the moment, it is not possible to estimate the file size, but it is not
expected to be higher than 100MB per experiment, mostly due to the possible
presence of images.
The possible use or re-use by the scientific community is provided by adhering
to terms and conditions set out in the Grant Agreement.
_Information about tools and instruments_ : It is not foreseen the need to use
specialised tools for using the research data, apart from common spreadsheet
and text file editors.
Any scientific papers to be published that will make use of the datasets, will
also be associated to the datasets and be accessible through the same page.
_Standards and metadata_ : We will also create metadata for easier data
discovery, including through search engines. If data related to financial
projections will be published, information about the methods used for the
projections will also be published.
_Accessibility_ : Once a dataset is made available on the project repository,
it will freely be available to the community, in the way described in the
appropriate section below. No embargo period is foreseen.
# Data sharing
All sharable data will be published and hosted as per individual availability
on the project’s public website i.e. _www.waterspy.eu_ . Partners generating
the data are also encouraged to publish the sharable data on other online
repositories (for example, Zenodo.org).
The WaterSpy website has a friendly and easy to use navigation. It will be
modified in due time to accommodate additional sections (pages) where the
publishable data will be stored. The consortium will make sure that available
data will be easily recoverable by any interested party.
The data will be made available on the website through adaptive webpages. The
pages will cover the topics and project descriptive information to an
appropriate level for each set of information or dataset.
The data will be formatted as per the description of each section, provided
previously in this document, and will be presented for access along with the
necessary links to download the appropriate software tools, if necessary.
The pages will be available to the public domain, enriched with the necessary
metadata and will be open to web crawlers for search engine listing, so they
will be available to the public through standard web searches.
Despite the publicly available pages, the downloadable data will be presented
along with possible restrictions provided in each previously described
section. This means that the following will apply on the website in order to
gain access to the information:
1. Terms and Conditions will apply and will have to be accepted prior to any download
2. Registration will be compulsory (free of charge) to maintain a data access record
3. For certain and limited number of datasets, a form will be available to request access to the data, but this will be subject to approval from the consortium
Downloadable formats will be:
1. PNG, BMP and JPEG file formats
2. ZIP and other public domain compressed archives
3. PDF formatted documents
4. WMV, MP4 or AVI formats for possible videos
All available datasets will be downloadable in their entirety.
# Archiving, preservation and access modality
The data in all the various formats will be stored in standard storage. It has
been mentioned in the previous section that no specialised storage or access
is necessary, as all datasets will be downloadable in their entirety.
All the information, including the descriptive metadata, when available, will
be available throughout the lifetime of the website, which is expected to be
in the public domain for a period of at least five (5) years after the
completion of the project. Due to the possibly large size of individual
downloadable files, the storage used will be based on a Cloud based services
and it is estimated (based on current prices) to be according to the table
below:
_Geographically Monthly Price Estimated Estimated Price per Estimated Price
over 5_
_Redundant Storage for per GB Storage month years_
_Disaster/Recovery requirements_
_purposes_
<table>
<tr>
<th>
_First 1 TB/Per month_
_Next 49 TB/Per month_
_(1-50TB)_
</th>
<th>
€0.0358 per GB
</th>
<th>
1 TB (1024 GB)
</th>
<th>
€36.66 per month
</th>
<th>
€2,199.60
</th> </tr>
<tr>
<th>
€0.0352 per GB
</th>
<th>
1 TB (1024 GB)
</th>
<th>
€36.05 per month
</th>
<th>
€2,162.69
</th> </tr> </table>
_€4,362.29_
Please note that we estimate a maximum of no more than 2 TB of data to be made
publicly available. The cost of storage will be covered by the consortium
members.
Please note that the prices are based on the Microsoft Azure current pricing (
_http://azure.microsoft.com/enus/pricing/details/storage/_ ) .
Please note that some data may be stored under the facilities of the
consortium members that own them, but will still be referenced via the
website. Data may also be shared with the community through public
repositories.
# Conclusions
The procedures described in this document have been designed to guarantee
smooth project execution. They describe the way that deliverables submission
will be handled, the way that project communications will take place and also
the project dissemination and meetings’ organisation procedures. They will be
used by all partners for the entire project duration.
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https://phaidra.univie.ac.at/o:1140797
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Horizon 2020
|
1213_UNICORN_731846.md
|
**1 Executive Summary**
The results of the UNICORN project will be openly published to communicate and
spread the knowledge to all interested communities and stakeholders. Published
results generate wider interest towards the improvements achieved by the
project in order to facilitate and potentiate exploitation opportunities. The
goal of this deliverable is listing publishable results and research data and
investigating the appropriate methodologies and open repositories for data
management and dissemination. The UNICORN's partners aim to offer as much
information as possible generated by the project through open access. Such
information includes scientific publications issued by the UNICORN consortium,
white papers published, open source code generated, anonymous interview
results, or mock-up datasets used for gathering customer feedback.
In general, there are two types of project results which differ in the way
they are published; namely publications and other research data. Our
publication strategy follows the ideas of open access and open research data.
Figure 1 depicts how research activities lead to different results and how
these results are disseminated and exploited. Scientific publications and
related research data is as far as possible published openly. On the other
hand, not all data collected or generated can be published openly, as it may
contain private information or interfere with legal aspects. This kind of data
must be identified and protected accordingly. One of the targets of the
project is to deliver a specification/standard which, however, will not be
publicly available.
2. **Introduction**
1. **Purpose of The Document**
Each project in the EC's Horizon 2020 program has to define what kind of
results are generated or collected during the project's runtime and when and
how it is published openly. This document describes which results will be
published in UNICORN within the whole project duration. For all results
generated or collected during UNICORN a description is provided including the
purpose of the document, the standards and metadata used for storage, the
facility used for sharing openly, based on the EC template recommended
(European Commission, 2013). This document is updated on a regular basis.
However, it does not describe how the results are exploited, which is part of
D5.1, D5.4 and D5.5.
2. **Terminology**
**Open Access** : Open access means unrestricted access to research results.
Often the term open access is used for naming free online access to peer-
reviewed publications. Open access is expected to enable others to a) build on
top of existing research results,
2. avoid redundancy,
3. participate in open innovation, and
4. read about the results of a project or inform citizens.
All major publishers in computer science - like ACM, IEEE, Elsevier, or
Springer - participate in the idea of open access. Both green, or gold open
access levels are promoted. Green open access means that authors eventually
are going to publish their accepted, peer-reviewed articles themselves, e.g.
by deposing it to their own institutional repositories. Gold open access means
that a publisher is paid (e.g. by the authors) to provide immediate access on
the publishers website and without charging any further fees to the readers.
**Open Research Data** : Open research data is related to the long-term
deposit of underlying or linked research data needed to validate the results
presented in publications. Following the idea of open access, all open
research data needs to be openly available, usually meaning online
availability. In addition, standardized data formats and metadata has to be
used to store and structure the data. Open research data is expected to enable
others to:
1. understand and reconstruct scientific conclusions, and
2. to build on top of existing research data.
**Metadata** : Metadata defines information about the features of other data.
Usually metadata is used to structure larger sets of data in a descriptive
way. Typical metadata are names, locations, dates, storage data type, and
relations to other data sets. Metadata is very important when it comes to
index and search larger data sets for a specific kind of information.
Sometimes metadata can be retrieved automatically from a dataset, but often it
needs some manual classification also. The well-known tags in MP3-recordings
are a good example why metadata is necessary to find a specific kind of genre
or composer in a larger number of songs.
3. **Structure of The Document**
The rest of the document is structured into two sections. Section 2 defines
the process that will be applied to all results collected or generated during
UNICORN. The process defines if a result has to be published or not. In
addition, we provide a summary of all publishing platforms to be used by the
UNICORN consortium. Section 3 lists publications and other public related data
that is already or may be generated or collected during UNICORN. For each
result, a short description, the chosen way of open access, and a long-term
storage solution are specified according to the EC's data management
guidelines (European Commission, 2013).
3. **Publishing Infrastructure for Open Access**
The UNICORN publication infrastructure consists of a process and several web-
based publication platforms that together provide long-term open access to all
publishable, generated or collected results of the project. The implementation
of the project fully complies with law in national and EU level and especially
with the **Directive 95/46** related to the Protection of personal data. More
specifically, there are not cases where personal data information or sensitive
information of internet users is collected (IP addresses, email addresses or
other personal information) or processed. For the whole duration of the
project, from the beginning to its end, the Data Protection Officer (DPO – Ms.
Meltini Christodoulaki (FORTH)) will carefully examine the legality of the
activities and the tools (including platforms) that will be produced for not
violating the personal data of internet users. In the potential future case
where the UNICORN consortium will collect, record, store or process any
personal information, it will be ensured that this will be done on a basis of
respecting citizens’ rights, preventing their identification and keeping their
anonymization. Both the process and the used web-based platforms are described
in the following subsections. The section ends with some research items and
data that they project partners foresee that they will be reflected on
respective publication actions. The research data will be the main subject of
analysis with respect to data management in the next section.
1. **Publishing Process**
UNICORN partners defined a simple, deterministic process that decides if a
result in UNICORN must be published or not. The term result is used for all
kind of artefacts generated during UNICORN like white papers, scientific
publications, and anonymous usage data. By following this process, each result
is either classified public or nonpublic. Public means that the result must be
published under the open access policy. Non-public means that it must not be
published.
For each result generated or collected during UNICORN runtime, the following
questions must be answered to classify it:
1. _Does a result provide significant value to others or is it necessary to understand a scientific conclusion?_
If this question is answered with yes, then the result is classified as
public. If this question is answered with no, the result is classified as non-
public. Such a result could be code that is very specific to UNICORN platform
(e.g., a database initialization) which is usually of no scientific interest
to anyone, nor does it add any significant contribution.
2. _Does a result include personal information that is not the author's name?_
If this question is answered with yes, the result is classified as non-public.
Personal information beyond the name must be removed if it should be
published. This also bares witness on the repetitive nature of the publishing
process, where results which are deemed in the beginning as non-publishable
can become publishable once privacy-related information is removed from them.
3. _Does a result allow the identification of individuals even without the name?_
If this question is answered with yes, the result is classified as non-public.
Sometimes data inference can be used to superimpose different user data and
reveal indirectly a single user's identity. As such, in order to make a result
publishable, the included information must be reduced to a level where single
individuals cannot be identified. This can be performed by using established
anonymisation techniques to conceal a single user's identity, e.g.,
abstraction, dummy users, or non-intersecting features.
4. _Does a result include business or trade secrets of one or more partners of UNICORN?_
If this question is answered with yes, the result is classified as non-public,
except if the opposite is explicitly stated by the involved partners. Business
or trade secrets need to be removed in accordance to all partners'
requirements before it can be published.
5. _Does a result name technologies that are part of an ongoing, project-related patent application?_
If this question is answered with yes, then the result is classified as non-
public. Of course, results can be published after patent has been filed.
6. _Can a result be abused for a purpose that is undesired by society in general or contradict with societal norms and UNICORN’s ethics?_
If this question is answered with yes, the result is classified as non-public.
_7\. Does a result break national security interests for any project partner?_
If this question is answered with yes, the result is classified as non-public.
**3.2 Publishing Platforms**
In UNICORN, we use several platforms to publish our results openly. The
following list presents the platforms used during the project and describes
their concepts for publishing, storage, and backup.
1. **The project Website**
The partners in the project consortium decided early to setup a project-
related website. This website describes the mission and the general approach
of UNICORN and its development status. A blog informs about news on a regular
basis. Later in the project the developed UNICORN platform will be announced.
A dedicated area for downloads is used to publish reports and white papers as
well as scientific publications (in pre-camera ready form, or through links to
the publisher’s websites in case these are not open access). All documents are
published using the portable document format (PDF). All downloads are enriched
by using simple metadata information, such as the title and the type of the
document. The website is hosted by partner FORTH. All webpage-related data is
backed on a regular basis. All information on the project website can be
accessed without creating an account. The webpage is backed up once per month.
Web-Link: _http://www.UNICORN-project.eu/_
2. **Zenodo**
Zenodo is a research data archive / online repository which helps researchers
to share research results in a wide variety of formats for all fields of
science. It was created through EC's OpenAIRE+ project and is now hosted at
CERN using one of Europe's most reliably hardware infrastructures. Data is
backed nightly and replicated to different locations. Zenodo not only supports
the publication of scientific papers or white papers, but also the publication
of any structured research data (e.g., using XML). Zenodo provides a connector
to GitHub that supports open collaboration for source code and versioning for
all kinds of data. All uploaded results are structured by using metadata, like
for example the contributors' names, keywords, date, location, kind of
document, license, and others. Considering the language of textual metadata
items, English is preferred. All metadata is licensed under CC0 license
(Creative Commons 'No Rights Reserved'). The property rights or ownership of a
result does not change by uploading it to Zenodo.
All public results generated or collected during the project lifetime will be
uploaded to Zenodo for long-term storage and open access.
Web-Link: _http://zenodo.org_
3. **GitHub**
GitHub is a well-established online repository which supports distributed
source code development, management, and revision control. It is primarily
used for source code data. It enables world-wide collaboration between
developers and provides also some facilities to work on documentation and to
track issues. GitHub provides paid and free service plans. Free service plans
can have any number of public, open-access repositories with unlimited
collaborators. Private, non-public repositories require a paid service plan.
Many open-source projects use GitHub to share their results for free. The
platform uses metadata like contributors' nicknames, keywords, time, and data
file types to structure the projects and their results. The terms of service
state that no intellectual property rights are claimed by the GitHub Inc. over
provided material. For textual metadata items, English is preferred. The
service is hosted by GitHub Inc. in the United States. GitHub uses a rented
Rackspace hardware infrastructure where data is backed up continuously to
different locations.
All source-code components that are implemented during this project and
decided to be public will be uploaded to an open access GitHub repository.
Web-Link: _https://github.com/_
**3.3 Project Artefacts**
This section just attempts to enumerate specific datasets, software and
research items (from which publications can be produced) and whether all these
artefacts can be publishable or not according to which means. More detailed
analysis spanning the different categories of artefacts is provided in the
next chapter.
# Table 1: Project Artefacts
<table>
<tr>
<th>
**Artefact Type**
</th>
<th>
**Artefact**
</th>
<th>
**Publication Means**
</th> </tr>
<tr>
<td>
**Research Item**
</td>
<td>
Cloud Governance Mechanisms
</td>
<td>
Zenodo, open-access journals, project web site, research-oriented social media
</td> </tr>
<tr>
<td>
**Research Item**
</td>
<td>
Continuous Security and Data Privacy Enforcement Mechanisms
</td>
<td>
Zenodo, open-access journals, project web site, research-oriented social media
</td> </tr>
<tr>
<td>
**Research Item**
</td>
<td>
Risk, Cost and Vulnerability Assessment
</td>
<td>
Zenodo, open-access journals, project web site, research-oriented social media
</td> </tr>
<tr>
<td>
**Research Item**
</td>
<td>
Policy Validation & Identity Signing
</td>
<td>
</td>
<td>
Zenodo, open-access journals, project web site, research-oriented social media
</td> </tr>
<tr>
<td>
**Software**
</td>
<td>
Privacy by Design Libraries
</td>
<td>
</td>
<td>
Github, Zenodo
</td> </tr>
<tr>
<td>
**Software**
</td>
<td>
Orchestrator for Microservices
</td>
<td>
</td>
<td>
Github, Zenodo
</td> </tr>
<tr>
<td>
**Software**
</td>
<td>
Cloud Monitoring specialized
Microservices
</td>
<td>
for
</td>
<td>
Github, Zenodo
</td> </tr>
<tr>
<td>
**Software**
</td>
<td>
Elasticity Control specialized Microservices
</td>
<td>
for
</td>
<td>
Github, Zenodo
</td> </tr>
<tr>
<td>
**Software**
</td>
<td>
Eclipse Che Plugin
</td>
<td>
</td>
<td>
Github, Zenodo
</td> </tr>
<tr>
<td>
**Dataset**
</td>
<td>
Anonynous usage statistics
</td>
<td>
</td>
<td>
Zenodo
</td> </tr>
<tr>
<td>
**Dataset**
</td>
<td>
UseCase data
</td>
<td>
</td>
<td>
Non-publishable
</td> </tr>
<tr>
<td>
**Dataset**
</td>
<td>
...
</td>
<td>
</td>
<td>
Zenodo
</td> </tr> </table>
**4 Project Results / Data Sets**
In this section, we first introduce the template that has been proposed by the
EC in order to form the description of the data that will be produced in the
context of UNICORN. Every use case will fill in such a template and
subsequently all the templates will be collected with the beginning of WP6,
the demonstration applications work package of the project.
Then, two sub-sections will shortly focus on two types of specific artefacts
produced by UNICORN, i.e., publications and software and finally the third one
will be dedicated to the analysis of all data produced in UNICORN according to
the previously presented template.
# Table 2: Initial Dataset Template
<table>
<tr>
<th>
**Initial Dataset Template**
</th> </tr>
<tr>
<td>
**Dataset reference name**
</td>
<td>
Identifier for the data set to be produced.
</td> </tr>
<tr>
<td>
**Dataset description**
</td>
<td>
Description of the data that will be generated or collected, its origin (in
case it is collected), nature and scale and to whom it could be useful, and
whether it underpins a
</td> </tr>
<tr>
<td>
</td>
<td>
scientific publication. Information on the existences (or not) of similar data
and the possibilities for integration and reuse.
</td> </tr>
<tr>
<td>
**Standards and metadata**
</td>
<td>
Reference to existing suitable standards of the discipline. If these do not
exist, an outline on how and what metadata will be created.
</td> </tr>
<tr>
<td>
**Data sharing**
</td>
<td>
Description of how data will be shared, including access procedures, embargo
periods (if any), outlines of technical mechanisms for dissemination and
necessary software and other tools for enabling reuse, and definition of
whether access will be widely open or restricted to specific groups.
Identification of the repository where data will be stored, if already
existing and identified, indicating in particular the type of repository
(institutional, standard repository for the discipline, etc.).
</td> </tr>
<tr>
<td>
**Archiving and preservation (including storage and backup)**
</td>
<td>
Description of the procedure that will be put in place for long-term
preservation of the data. Indication of how long the data should be preserved,
what is its approximated end, volume, what the associated costs are and how
these are planned to be covered.
</td> </tr>
<tr>
<td>
**Additional Dataset explanation**
</td> </tr>
<tr>
<td>
**Discoverable**
</td>
<td>
Are the data and associated software produced and / or used in the project
discoverable (and readily located), identifiable by means of a standards
identification mechanism (e.g. Digital Object Identifier)?
</td> </tr>
<tr>
<td>
**Accessible**
</td>
<td>
Are the data and associated software produced and / or used in UNICORN
accessible and in what modalities, scope, licenses (e.g. licencing framework
for research and education, embargo periods, commercial exploitation, etc.)?
</td> </tr>
<tr>
<td>
**Assessible and intelligible**
</td>
<td>
Are the data and associated software produced and / or used in the project
assessable for and intelligible to third parties in contexts such as
scientific scrutiny and peer review (e.g. are the minimal datasets handled
together with scientific papers for the purpose of peer review, are data is
provided in a way that judgments can be made about their reliability and the
competence of those who created them)?
</td> </tr>
<tr>
<td>
**Usage beyond the original purpose for which it was collected**
</td>
<td>
Are the data and associated software produced and / or used in UNICORN useable
by third parties even long time after the collection of the data (e.g. is the
data safely stored in certified repositories for long term preservation and
curation; is it stored together with the minimum software, metadata and
documentation to make it useful,; is the data useful for the wider public
needs and usable of the likely purpose of nonspecialists.
</td> </tr>
<tr>
<td>
**Interoperable to specific quality standards**
</td>
<td>
Are the data and associated software produced and / or used in the project
interoperable allowing data exchange between researchers, institutions,
organisations, countries, etc. (e.g. adhering to standards for data
annotation, data exchange, compliant with available software applications, and
allowing re-combinations with different datasets from different origins)?
</td> </tr> </table>
**4.1 Publications**
The management of the dissemination and publication process will be performed
in WP 7 and will be reported in deliverables D7.1, D7.2, D7.3, D7.4 and D7.6.
Publications will be related to main research results that will be achieved
within UNICORN which will fall in the following research directions:
All scientific publications will be indexed, thus also obtaining a DOI, and
will be made available to: Zenodo,
* respective literature databases, such as Scopus and DBLP,
* research databases like ResearchGate and Academia.edu.
**4.2 Software**
WP7 will guide the management and exploitation of the software produced within
this project and the respective reporting on this will be reflected on D7.5
deliverable leaded by STW.
The distribution of software mainly depends on the intended business model of
the respective owning parties. In UNICORN, we foresee that there will be two
types of software distribution:
1. open-source software distributed under a public license and mainly related to research/academic institutions as well as organisations with open-source distribution policies;
2. licensed software distributed with an appropriate (e.g. commercial) license and mainly related to commercial organisations.
Licensed software will be announced according to the respective dissemination
channels of the commercial organisation as well as on UNICORN web site. Open-
source software will be made available both in the UNICORN web site and will
also be published in respective repositories like Zenodo and github. This will
enable also the further development and sustainability of the software even
beyond the lifetime of UNICORN. The publishing of the open-source software
will depend on the respective community targeted by the partner organisation.
Respective dissemination actions will be performed to also reach such a
community.
The repository on which software will be distributed will handle the creation
of respective meta-data, such as author and commit history. Dependencies over
third-party tools, operating systems, runtime environments will also be
included, along with respective versioning information. Usage-related material
like documentation and tutorials will also be developed and published to ease
the exploitation of the software.
Depending on the publication medium (e.g., Zenodo vs github), each partner in
UNICORN will use its own state of the art version management mechanisms to
store its source code contributions and will guarantee the proper back up of
the code on a regular basis.
Software published in github will obtain a URL via which it can then be
accessed and exploited. It can also be made discoverable via respective web-
based search engines, like Google. If software is also published in Zenodo, a
proper DOI will be assigned to it.
**4.3 Research Data**
**4.3.1 Template Example**
# Table 3: Dataset template example
<table>
<tr>
<th>
**Initial Dataset Template**
</th>
<th>
</th> </tr>
<tr>
<td>
**Dataset reference name**
</td>
<td>
Dataset XXX.
</td> </tr>
<tr>
<td>
**Dataset description**
</td>
<td>
The dataset covers the description of ...
</td> </tr>
<tr>
<td>
**Standards and metadata**
</td>
<td>
Dataset description was performed in standard language X.
</td> </tr>
<tr>
<td>
**Data sharing**
</td>
<td>
Dataset will be published in Zenodo to be made freely available for everyone.
</td> </tr>
<tr>
<td>
**Archiving and preservation**
</td>
<td>
This is mainly responsibility of the open-access repository (e.g., Zenodo).
</td> </tr>
<tr>
<td>
**Additional Explanation**
</td>
<td>
</td> </tr>
<tr>
<td>
**Discoverable**
</td>
<td>
The dataset will be assigned a DOI via which it can be discoverable.
</td> </tr>
<tr>
<td>
**Accessible**
</td>
<td>
A respective public licence will be associated to this dataset to govern its
accessing.
</td> </tr>
<tr>
<td>
**Accessible and Intelligible**
</td>
<td>
N.A
</td> </tr>
<tr>
<td>
**Usage beyond original collection purpose**
</td>
<td>
Dataset can be exploited during and even beyond the course of UNICORN.
</td> </tr>
<tr>
<td>
**Interoperable to specific quality standards**
</td>
<td>
Respective methodologies, best practices and standards
</td> </tr> </table>
**4.3.2 More Concrete Example - Anonymous User Statistics**
# Table 4: Anonymous User Statistics example
<table>
<tr>
<th>
**Initial Dataset Template**
</th>
<th>
</th> </tr>
<tr>
<td>
**Dataset reference name**
</td>
<td>
Anonymous User Statistics.
</td> </tr>
<tr>
<td>
**Dataset description**
</td>
<td>
UNICORN platform will provide online services not only to developers but also
to customers (end-users). The idea of UNICORN includes social collaboration
and anonymous customer profiles that help developers to recruit potential
users for a project or to customize software for a market. After UNICORN
platform has gone online, real users will interact with our system. The
behaviour of these users will be studied and used for improving our platform.
Any results that are published will be reduced to anonymous user statistics.
The details of this data set will be decided during the implementation and
updated accordingly in the following versions of the data management plan.
</td> </tr>
<tr>
<td>
**Standards and metadata**
</td>
<td>
The data is stored in a platform-independent format (e.g. XML). Any names or
features that may be used for single user identification (e.g. e-mail address,
phone number, date of birth) will be removed and summarized in a more abstract
way (e.g. 300 users in the age of 30-
40).
</td> </tr>
<tr>
<td>
**Data sharing**
</td>
<td>
User-related data that may lead to single user identification will not be
published. Only abstract user statistics that do not allow for single user
identification will be made openly accessible, but only if it contributes to
the results of a scientific publication. Any anonymous user data (e.g.
statistics) will be published openly using either existing institutional
</td> </tr>
<tr>
<td>
</td>
<td>
repositories or Zenodo. The access is free for everyone and without
restrictions.
</td> </tr>
<tr>
<td>
**Archiving and preservation**
</td>
<td>
Any user-related data is stored in project-related databases, which are hosted
by UNICORN partners. Each partner restricts access to private information by
implementing a security policy. Databases are backed on a regular basis by
each partner. All public user statistics will be added to Zenodo for long term
preservation during or at the end of UNICORN.
</td> </tr>
<tr>
<td>
**Additional Explanation**
</td>
<td>
</td> </tr>
<tr>
<td>
**Discoverable**
</td>
<td>
The dataset will be assigned a DOI via which it can be discoverable.
</td> </tr>
<tr>
<td>
**Accessible**
</td>
<td>
A respective public licence will be associated to this dataset to govern its
accessing.
</td> </tr>
<tr>
<td>
**Accessible and Intelligible**
</td>
<td>
N.A
</td> </tr>
<tr>
<td>
**Usage beyond original collection purpose**
</td>
<td>
Dataset can be exploited during and even beyond the course of UNICORN.
</td> </tr>
<tr>
<td>
**Interoperable to specific quality standards**
</td>
<td>
Respective methodologies, best practices and standards
</td> </tr> </table>
|
https://phaidra.univie.ac.at/o:1140797
|
Horizon 2020
|
1214_SOLUS_731877.md
|
# FAIR DATA
3.1. Making data findable, including provisions for metadata:
* **Outline the discoverability of data (metadata provision)**
* **Outline the identifiability of data and refer to standard identification mechanism. Do you make use of persistent and unique identifiers such as Digital Object Identifiers?**
* **Outline naming conventions used**
* **Outline the approach towards search keyword**
* **Outline the approach for clear versioning**
* **Specify standards for metadata creation (if any). If there are no standards in your discipline describe what metadata will be created and how**
The two datasets will be identified using two unique identifiers (DOI) by
uploading them onto a public repository. Data discoverability will be
facilitated by adding a data description with keywords related to potential
users (e.g. developers of new analysis tools), as described above.
For the Phantom dataset, different updated measurement sessions are possible
depending on updated versions of the prototype. Conversely, for the Clinical
dataset a single measurement session is foreseen, since there is no provision
to recall back the same patient. Different versions of analysis are possible,
depending on the update of the analysis tools. Therefore, the versioning will
foresee a first number for the raw data acquisition (only for phantoms) and a
second number for the analysis.
Apart from clinical images (e.g. US images) for which the DICOM standard is
usually adopted, there are no specific standards for optical data. In general,
we will create metadata files in XML, embedding large binary data in XML with
Base91 encoding.
The software developed for the SOLUS project generates data from experiments.
These data are the results of a sequence that acquires data on an instrument
and then performs signal processing on them. An experiment takes place in a
context that must be attached to the result to allow for a proper data
management. All these data are categorized into eight classes:
<table>
<tr>
<th>
**Class**
</th>
<th>
**Description**
</th>
<th>
**Example**
</th> </tr>
<tr>
<td>
Project
</td>
<td>
Name of the project or the sub-project for which the experiment is done
</td>
<td>
SOLUS, SOLUS_characterization, SOLUS_clinical
</td> </tr>
<tr>
<td>
User
</td>
<td>
Name of the operator during the experiment
</td>
<td>
Researcher during the characterization, radiologist during the clinical tests
</td> </tr>
<tr>
<td>
Subject
</td>
<td>
The acquisitions are performed on the subject
</td>
<td>
Multimodal phantoms during the characterization, patient during the clinical
tests
</td> </tr>
<tr>
<td>
Instrument
</td>
<td>
Equipment used to acquire the data
</td>
<td>
Prototype #1
</td> </tr>
<tr>
<td>
</td>
<td>
</td>
<td>
</td> </tr>
<tr>
<td>
Device
</td>
<td>
Subsystems of an instrument
</td>
<td>
Bimodal probe #1, bimodal probe #2
</td> </tr>
<tr>
<td>
Sequence
</td>
<td>
Set of instructions mixing acquisition and data processing
</td>
<td>
Sequence for phantom characterization, sequence for clinical tests
</td> </tr>
<tr>
<td>
Processing
</td>
<td>
Algorithm for signal analysis developed during the project and available in
the sequence
</td>
<td>
Ultrasound image segmentation, optical parameter estimation
</td> </tr>
<tr>
<td>
Results of experiments
</td>
<td>
Set of acquisitions and processed signals
</td>
<td>
Results for characterization of phantom #3, results for patient ID31
</td> </tr> </table>
They are all organized as records (i.e. a container for data), which are
stored in the eight tables (i.e. a container for records) of a database. In
order to avoid a dependency to a third-party software, to make the data easy
to read from an external program and to ease the data recovery as well as the
custom use of the database, a dedicated solution has been developed. The
database is written on the hard drive as a hierarchy of folders and files. It
has the following structure:
<table>
<tr>
<th>
</th>
<th>
**Box A:** (top folder) installation folder
**Box B:** (subfolder of A) root folder for the database named `SOLUS' (prefix
DBROOT_)
**Box C:** (subfolders of B) set of tables required for the software (prefix
TABLE_)
**Box D:** (subfolders of C) set of records for the selected table (prefix
RECORD_)
</th> </tr> </table>
Each table stores one of the eight type of data. The folder of a table has the
prefix _TABLE__ . It contains the records and the file _metadata.xml_ that
describes the configuration.
The name of the folder of a record is _RECORD_key_ . The key is a unique
identifier that is automatically generated at the record creation (refer to
java UUID for more information about the identifier). There are three types of
records depending on data to save and policy for history:
<table>
<tr>
<th>
</th>
<th>
**Single**
</th>
<th>
**Version**
</th>
<th>
**Bag of data**
</th> </tr>
<tr>
<td>
Number of data
</td>
<td>
One
</td>
<td>
One
</td>
<td>
On purpose
</td> </tr>
<tr>
<td>
History
</td>
<td>
No
</td>
<td>
Yes
</td>
<td>
No
</td> </tr>
<tr>
<td>
Tables
</td>
<td>
User, project, device, processing
</td>
<td>
Instrument, sequence, subject
</td>
<td>
Result
</td> </tr>
<tr>
<td>
Common files
</td>
<td>
* **metadata.xml:** short description of the record
* **format.xml:** definition the object that is saved
* **misc:** file(s) or folder(s) containing the data (examples below: definition.xml, sequence.mat, data.mat)
</td> </tr>
<tr>
<td>
Thumbnail
</td>
<td>
One
</td>
<td>
One per version
</td>
<td>
One
</td> </tr>
<tr>
<td>
Specific folders
</td>
<td>
None
</td>
<td>
**VERSION_###** contains the data for version number ###
</td>
<td>
**DATA_LINK_*** contains a pointer to another record of the database
**DATA_x_out_y** contains the data of output named y that are
generated by the item named x
**ITEM_#####** is a subfolder of DATA_*. It contains the data for iteration
#####
</td> </tr> </table>
<table>
<tr>
<th>
Structure
</th>
<th>
</th>
<th>
</th>
<th>
</th> </tr> </table>
The open data will be organized closely to this internal software structure.
However, the exact content of the open database as well as the release of
software tools to ease the reading of these data is still subject to
discussion.
3.2. Making data openly accessible:
* **Specify which data will be made openly available? If some data is kept closed provide rationale for doing so**
* **Specify how the data will be made available**
* **Specify what methods or software tools are needed to access the data? Is documentation about the software needed to access the data included? Is it possible to include the relevant software (e.g. in open source code)?**
* **Specify where the data and associated metadata, documentation and code are deposited**
* **Specify how access will be provided in case there are any restrictions**
Data will be made "as open as possible, as closed as necessary". In this
respect, all data described above will be made open apart from:
* algorithms for data analysis, which could be considered for IP protection
* personal data subject to privacy protection, accordingly to GDPR Regulation (EU) 2016/679, and ethical provisions.
* SW elastography images.
In particular, clinical data retrieved at Ospedale San Raffaele (OSR) will be
directly saved without any personal data. As defined in Deliverable
D5.1(“Definition of the clinical protocol”) the following data will be saved
for scientific purposes:
* age
* menopausal status
* pathology results
* results from color Doppler
* BI-RADS score
Due to the fact that the possibility to share SW images as open-access is
still ongoing, more details will be given in the final version of the DMP, due
by month 48.
The software will automatically assign a numerical ID to each patient for data
analysis, as described in section 3.1.
All specifications required to access the data will be inserted in the data
repository. The segmentation of US images, and in general the extraction of
optical properties for suspect lesions/inhomogeneities require advanced
analysis tools, generally pertaining to the methods of inverse problems in
diffuse optics. If already published or not involved in IP protections, the
algorithms will be described in detail to permit replications. Inclusion of
software tools for data processing will be considered if not causing
significant overburden distracting important energies from the fulfilment of
the project aims.
A three-phase process for data storage is foreseen. Initially, data will be
collected by the SOLUS prototype and stored locally on the instruments, while
other information will be gathered by clinicians and recorded on paper (as
described in Deliverable D5.1). In the second phase, all collected data will
be stored at POLIMI data warehouse, apart from protected clinical information
which will be retained at OSR. This will permit construction of the database
and initial tests on analysis. In the third phase, when data acquisition is
complete, data will be uploaded on an open repository. At present, the choice
is for Zenodo, because of perfect match with requirements, and increased
interest in the International community. Still final decision will be made
close to the actual deposition (not earlier than month 36) to take into
account the updated status of public repositories.
3.3. Making data interoperable:
* **Assess the interoperability of your data. Specify what data and metadata vocabularies, standards or methodologies you will follow to facilitate interoperability.**
* **Specify whether you will be using standard vocabulary for all data types present in your data set, to allow inter-disciplinary interoperability? If not, will you provide mapping to more commonly used ontologies?**
The realm of clinical optical data is at present not covered by standards or
specific vocabularies. The numerosity of the clinical study limits its
potential use mainly to researchers and operators within the field. The
definition of metadata and in particular the fields in the XLM will match the
vocabularies most often covered by scientific publications in diffuse optics.
3.4. Increase data re-use (through clarifying licenses):
* **Specify how the data will be licenced to permit the widest reuse possible**
* **Specify when the data will be made available for re-use. If applicable, specify why and for what period a data embargo is needed**
* **Specify whether the data produced and/or used in the project is useable by third parties, in particular after the end of the project? If the re-use of some data is restricted, explain why**
* **Describe data quality assurance processes**
* **Specify the length of time for which the data will remain re-usable**
A 6-12 months embargo after acceptance of relevant publications will be
considered.
Data will be made available and reusable through open data repositories for
periods of 10 years.
# ALLOCATION OF RESOURCES
**Explain the allocation of resources, addressing the following issues:**
* **Estimate the costs for making your data FAIR. Describe how you intend to cover these costs**
* **Clearly identify responsibilities for data management in your project**
* **Describe costs and potential value of long term preservation**
Since data deposit in a local data warehouse and an external repository will
not start earlier than 2 years from now, cost estimate will be performed at
due time since policies and costs are rapidly changing under great internal
and external pressure on data preservation and sharing. In general terms, it
is highly probable that no extra-costs will be incurred for the storage of
data since the overall dimension of data will be handled by standard POLIMI
data facilities and fit in the free allowances of Zenodo repository.
Concerning the POLIMI warehouse, in September 2018 the Institution proposed to
each Department the possibility of a support for data storage related to
European projects. This data storage will be directly managed by the central
ICT staff of POLIMI, providing high-level services in terms of backup,
robustness, protection and restricted access. A request has been already sent
to get access to that space. The feasibility of that opportunity will be
definitely updated in the Final version of DMP due by month 48. Dr Andrea
Farina is responsible for the coordination of the overall data management.
# DATA SECURITY
**Address data recovery as well as secure storage and transfer of sensitive
data**
The second phase of data storage will be performed internally at a data
warehouse of POLIMI and at OSR for protected clinical information. No access
external to the consortium will be possible.
The actual data repository in force for the research group at POLIMI is
located in secure hard-drives provided by a redundant system (RAID 5) that is
backed up every week by an incremental back-up script (rsbackup) to other
external servers. The data servers are located in the basement of the Physics
Department of Politecnico di Milano in a restricted access area. The data
servers have an access controlled by passwords, and they are part of a VLAN
without access from outside the POLIMI institution. The VLAN at which not only
the data servers are connected but all the PCs used for this project is part
of an institutional network protected by a firewall. We note that the POLIMI
group has a proven track-record in long-term data storage and access going
back to the 80s.
In case POLIMI benefits of access to the institutional repository, the data
stored will inherit the high-level security of the overall institutional
network. More detail about the security level will be specified in the Final
version of the DMP (M48).
In the final phase, the public repository will be chosen to grant requirements
of long-term secure storage. The most probable choice - Zenodo - already
fulfils all requirements.
Sensitive data - mostly personal data of the clinical study - will not be
shared and will be stored only at OSR to comply with the privacy policies
foreseen in the clinical protocol.
# ETHICAL ASPECTS
**To be covered in the context of the ethics review, ethics section of DoA and
ethics deliverables. Include references and related technical aspects if not
covered by the former**
The Clinical protocol (Deliverable 5.1 - Definition of the clinical protocol -
produced at M3) and the ethical requirements in terms of protection of
personal data (Deliverable D5.2 - Approval of clinical protocol by ethical
committee - due at month 36) set specific requirements for anonymization of
data and protection of personal data of patients. These requirements will be
strictly followed and will prevent sharing of information.
All data stored at POLIMI data warehouse and deployed at public repository
will be completely anonymized.
The patient information and consent will follow the guidelines set forth in
ISO 14155 for patient information and informed consent, and will imply also
sharing of data excluding sensitive data.
# OTHER
**Refer to other national/funder/sectorial/departmental procedures for data
management that you are using (if any)**
At present, the main local procedures for data management are related to the
requirements of sensitive data protection described in the clinical protocol
(Deliverable D5.1) and operated by OSR. No other prescriptive procedures are
identified so far.
# CONCLUSIONS
In summary, we have shown that two independent datasets will be generated: i)
Phantom data and ii) Clinical data. These two datasets will be organized into
an XML-based database whose folders are saved on the hard drive of the
clinical machine. Every measurement will be associated to an ID number for
anonymization. A copy of this database will be placed in a local repository
hosted at the Physics Department of Politecnico di Milano with restricted
access to the consortium members. A request of space with restricted
permission on the recent institutional repository has been sent, whose result
will be discussed in the last version of the DMP, due by month 48.
The two datasets will be, eventually, uploaded for open-access on two
independent public repositories on Zenodo, except for:
* algorithms for data analysis, which could be considered for IP protection.
* personal data subject to privacy protection, accordingly to GDPR Regulation (EU) 2016/679, and ethical provisions.
* SW elastography images.
The policy on SW elastography images is still under discussion, thus updates
on that will be given at month 48 with the final version of the document.
|
https://phaidra.univie.ac.at/o:1140797
|
Horizon 2020
|
1215_InKreate_731885.md
|
# Executive summary
The Data Management Plan (DMP) of the InKreate project is designed according
the guidelines “Guidelines on FAIR Data Management in Horizon 2020”. Research
data should be findable, accessible, interoperable and reusable (FAIR).
The DMP is a living document. Current version includes the data that will be
collected according to the DoA (description of Action). The DMP will be
updated if any data not foreseen in the DoA is collected during the project.
The only partner who collects data during the project is the Institute of
Biomechanics of Valencia (IBV). IBV collects human shapes (3D scans) and
personal data according to ethical principles and legislation to scientific
research, as described in the Deliverable 9-1.
The collected data will be curated using interchangeable formats to facilitate
the access of third parties: STL for 3D data and CSV for personal data.
All data is deposited in Zenodo repository (https://www.zenodo.org) with
Creative Commons Licences CC-BY. This way, third parties are enabled to
access, mine, exploit, reproduce and disseminate (free of charge for any user)
the data collected during the project.
# Purpose of the data collected
Part of the developments that will be carried out during INKREATE will be
based on libraries of 3-D body shapes, and applications to generate models of
such shapes from user data and 2-D pictures. Those development activities will
require 3-D scans of human bodies. That collection will be done by IBV, in
experimental activities with external volunteers. This involves recording,
storing and managing personal data, including images of the participants’
bodies. All pictures and scans will be taken in the Laboratory of Human Shapes
of IBV. These activities will be done according to ethical principles and
legislation to scientific research, as described in the Deliverable 9-1.
Figure 1. 3-D body scanner.
# Data curation
3D scans will be stored in STL files. STL is a standard file format supported
by many software packages, and it is widely used for rapid prototyping, 3D
printing and computer-aided manufacturing. STL files describe the surface
geometry of a three-dimensional object. An STL file describes a raw
unstructured triangulated surface by the unit normal and vertices (ordered by
the right-hand rule) of the triangles using a three-dimensional Cartesian
coordinate system.
In addition, the following personal data will be recorded for each
participant:
* Gender
* Birth date
* Country of residence
Version 100 4 of 5 Date 05-06-2017
_Deliverable 8.4 Data Management Plan INKREATE_
* Weight
* Stature and other anthropometric measures
These data will be recorded in electronic format in using comma-separated
values (CSV) file. CSV files stores tabular data in plain text. CSV is a
common data exchange format that is widely supported by consumer, business,
and scientific applications (e.g. Microsoft EXCEL).
The data will be deposited in Zenodo repository ( _https://www.zenodo.org_ )
with Creative Commons Licences CC-BY. This way, third parties are enabled to
access, mine, exploit, reproduce and disseminate (free of charge for any user)
this research data.
# Conclusion
This report explains the Data Management Plan (DMP) of the InKreate project.
The DMP is designed according the guidelines “Guidelines on FAIR Data
Management in Horizon 2020”. Research data should be findable, accessible,
interoperable and reusable (FAIR).
Version 100 5 of 5 Date 05-06-2017
|
https://phaidra.univie.ac.at/o:1140797
|
Horizon 2020
|
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