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0002_APPLICATE_727862.md	# EXECUTIVE SUMMARY This plan is based on the H2020 FAIR Data Management Plan (DMP) template designed to be applicable to any H2020 project that produces, collect or processes research data. This is the same plan as OpenAIRE is referring to in their guidance material. The purpose of the Data Management Plan is to describe the data that will be created and how, as well as the plans for sharing and preservation of the data generated. This plan is a living document that will be updated during the project. APPLICATE follows a metadata-driven approach where a physically distributed number of data centres are integrated using standardised discovery metadata and interoperability interfaces for metadata and data. The APPLICATE Data portal, providing a unified search interface to all APPLICATE will also be able to host data. APPLICATE promotes free and open access to data in line with the European Open Research Data Pilot (OpenAIRE). Within this plan an overview of the production chains for model simulations is provided as well as an initial outline of dissemination. This version of the plan is an update on the first version submitted in June 2017. A second update to the plan are scheduled for October 2019\. # Introduction ## Background and motivation The purpose of the data management plan is to document how the data generated by the project is handled during and after the project. It describes the basic principles for data management within the project. This includes standards and generation of discovery and use metadata, data sharing and preservation and life cycle management. This document is a living document that will be updated during the project in time with the periodic reports (project months 18, 36 and 48). APPLICATE is following the principles outlined by the Open Research Data Pilot and The FAIR Guiding Principles for scientific data management and stewardship (Wilkinson et al. 2016). ## Organisation of the plan This plan is based on the H2020 FAIR Data Management Plan (DMP) template 1 designed to be applicable to any H2020 project that produces, collect or processes research data. This is the same plan as OpenAIRE is referring to in their guidance material. # Administration details <table> <tr> <th> Project Name </th> <th> APPLICATE </th> </tr> <tr> <td> Funding </td> <td> EU HORIZON 2020 Research and Innovation Programme </td> </tr> <tr> <td> Partners </td> <td> Alfred Wegener Institute Helmholtz Centre for Polar and Marine Research (AWI) - Bremerhaven, Germany Barcelona Supercomputing Center - Barcelona, Spain European Centre for Medium-Range Weather Forecasts (ECMWF) - Reading, United Kingdom University of Bergen (UiB) - Bergen, Norway Uni Research AS - Bergen, Norway Norwegian Meteorological Institute (MET Norway) - Oslo, Norway Met Office - Exeter, United Kingdom Catholic University of Louvain (UCL) - Louvain-la-Neuve, Belgium The University of Reading (UREAD) - Reading, United Kingdom Stockholm University (SU) - Stockholm, Sweden National Center for Scientific Research (CNRS-GAME) - Paris, France (with contributions from Météo France) European Centre for Research and Advanced Training in Scientific Calculation (CERFACS) - Toulose, France Arctic Portal - Akureyri, Iceland University of Tromsø (UiT) - Tromsø, Norway P.P. Shirshov Institute of Oceanology, Russian Academy of Sciences (IORAS) - Moscow, Russia Federal State Budgetary Institution Voeikov Main Geophysical Observatory (MGO) - St. Petersburg, Russia </td> </tr> </table> # Data summary The overarching mission of APPLICATE is _To develop enhanced predictive1 capacity for weather and climate in the Arctic and beyond, and to determine the influence of Arctic climate change on Northern Hemisphere midlatitudes, for the benefit of policy makers, businesses and society_ . Therefore, APPLICATE is primarily a project in which numerical models (for weather and climate prediction) are used. As such it depends on observations (e.g. for model evaluation and initialization), but the data generated by the project is primarily gridded output from the numerical simulations. The APPLICATE data management system will be used to collect information of relevant third-party datasets that the APPLICATE community could benefit from, and to share and preserve the datasets APPLICATE is generating, both internally and externally. A full overview of the datasets to be generated is yet not fully known, but there is an overview of the production chains. This was prepared in the proposal and is provided in Tables 1–3 below. ## Data overview ### Types and formats of data generated/collected APPLICATE will primarily generate gridded output resulting from numerical simulations and metrics based on these core datasets. The models used produce a number of output formats which is not known in detail, but specific requirements apply for data sharing and preservation (see below). Self-explaining file formats (e.g. NetCDF, HDF/HDF5) combined with semantic and structural standards like the Climate and Forecast Convention will be used. The default format for APPLICATE datasets are NetCDF following the Climate and Forecast Convention (feature types grid, timeseries, profiles and trajectories if applicable). This includes the Coupled Model Intercomparison Project (CMIP) requirements. The NetCDF files must be created using the NetCDF Classic Model (i.e. compression is allowed, but not groups and compound data types). The ESGF CMOR is recommended for conversion of model output. Some datasets may be made available as WMO GRIB or BUFR. Where no clear standard is identified initially, dedicated work will be attributed to identifying a common approach for those data. APPLICATE will exploit existing data in the region. In particular operational meteorological data made available through WMO Gobal Telecommunication System will be important for the model experiments. No full overview of third party data that will be used is currently available, but since the start of the project SYNOP data from WMO GTS have been available to the APPLICATE community. Work is in proigress for more data from GTS. If necessary (required by the scientific community in APPLICATE) metadata describing relevant thirdparty observations will be harvested and ingested in the data management system and through this simplifying the data discovery process for APPLICATE scientists. There is however no plan initially to harvest the data. Furthermore, model data produced in the context of CMIP5 and CMIP6 will be used as a baseline against which model improvements will be tested. ### Origin of the data Data will be generated by a suite of numerical models, including operational weather prediction and climate models. A preliminary list was provided in the proposal and is included below. APPLICATE is primarily a project in which numerical models are used. As such it depends on observations (e.g. for model evaluation and initialization), but the data generated by the project is primarily gridded output from numerical simulations. A summary of the numerical models to be used is provided in Tables 1-3. Table 1: List of climate models. <table> <tr> <th> Model </th> <th> AWI-CM </th> <th> EC-Earth CNRM-CM </th> <th> NorESM </th> <th> HadGEM </th> </tr> <tr> <td> Partner </td> <td> AWI </td> <td> BSC, UCL, SU CNRSGAME, CERFACS </td> <td> UiB, UR, Met.no </td> <td> MO, UREAD </td> </tr> <tr> <td> Atmosphere </td> <td> ECHAM6 T127 L95 </td> <td> IFS ARPEGE-Climat T255/T511 L91 T127/T359 L91 </td> <td> CAM-OSLO 1o×1o L32 / L46 </td> <td> MetUM N216/N96 L85 </td> </tr> <tr> <td> Ocean </td> <td> FESOM Unstruct. mesh 15-100 km L41 4.5-80 km L41 </td> <td> NEMO NEMO 1o , 0.25o L75 1o, 0.25 o L75 </td> <td> NorESM-O (extended MICOM) 1o, 0.25o L75 </td> <td> NEMO 1o×1o L75 0.25o×0.25o L75 </td> </tr> <tr> <td> Sea ice </td> <td> FESIM </td> <td> LIM3 GELATO </td> <td> CICE </td> <td> CICE </td> </tr> <tr> <td> Surface </td> <td> JSBACH </td> <td> HTESSEL SURFEX </td> <td> SURFEX </td> <td> JULES </td> </tr> <tr> <td> CMIP6 </td> <td> Yes </td> <td> Yes Yes </td> <td> Yes </td> <td> Yes </td> </tr> </table> Table 2: List of subseasonal to seasonal prediction systems. <table> <tr> <th> Model </th> <th> EC-Earth </th> <th> CNRM-CM </th> <th> IFS </th> <th> HadGEM/GloSea </th> </tr> <tr> <td> Partner </td> <td> BSC, UCL, AWI </td> <td> CNRS-GAME </td> <td> ECMWF </td> <td> MO, UREAD </td> </tr> <tr> <td> Atmosphere </td> <td> IFS T255/T511 L91 </td> <td> ARPEGE Climat T255/T359 L91 </td> <td> IFS T511-T319 L91 </td> <td> MetUM N216 L85 </td> </tr> <tr> <td> Ocean </td> <td> NEMO 1°/0.25° L75 </td> <td> NEMO 1°/0.25°, L75 </td> <td> NEMO 1°, L75 </td> <td> NEMO 0.25o×0.25o L75 </td> </tr> <tr> <td> Sea ice </td> <td> LIM3 </td> <td> GELATO </td> <td> LIM2/3 </td> <td> CICE </td> </tr> <tr> <td> Land </td> <td> HTESSEL </td> <td> SURFEX </td> <td> HTESSEL </td> <td> JULES </td> </tr> <tr> <td> Data assimilation </td> <td> Ensemble Kalman filter </td> <td> Extended Kalman Filter SAM2 </td> <td> 4D-Var </td> <td> 4D-Var, NEMOVAR 3D-Var FGAT </td> </tr> </table> Table 3: Numerical weather prediction systems. <table> <tr> <th> Model </th> <th> ARPEGE </th> <th> AROME </th> <th> IFS </th> <th> AROME-Arctic </th> </tr> <tr> <td> Partner </td> <td> CNRS-GAME </td> <td> CNRS-GAME </td> <td> ECMWF </td> <td> Met.no </td> </tr> <tr> <td> Atmosphere </td> <td> ARPEGE T1198, stretched HR </td> <td> AROME 1.3km / 500m, 90 vertical </td> <td> IFS T1279 L137 </td> <td> AROME 2.5 km L65 </td> </tr> <tr> <td> </td> <td> (7.5km on grid pole), L105 </td> <td> levels </td> <td> </td> <td> </td> </tr> <tr> <td> Ocean </td> <td> N/A </td> <td> N/A </td> <td> N/A </td> <td> N/A </td> </tr> <tr> <td> Sea ice </td> <td> GELATO </td> <td> GELATO </td> <td> N/A </td> <td> SICE </td> </tr> <tr> <td> Land </td> <td> SURFEX </td> <td> SURFEX </td> <td> HTESSEL </td> <td> SURFEX </td> </tr> <tr> <td> Data assimilation </td> <td> 4D-Var </td> <td> dynamical adaptation </td> <td> 4D-Var </td> <td> 3D-Var </td> </tr> </table> In the original version of this data management plan, the total amount of data was not known. This is still not known in detail, but some information on the expected volumes for publication is known (this is a consequence of the “partial dissemination” term used in Table 4). The ECMWF YOPP dataset is excluded from this overview currently. The major volumes to be disseminated through the data management system are the ECMWF YOPP dataset, and seasonal forecasts and potentially climate forecasts from WP5. Preliminary estimates (maximum values) of the volumes (Tb) planned for dissemination are currently: * ECMWF YOPP dataset ◦ Analysis and forecast dataset, including process tendencies, amounting to a total volume of 300 Tb. * WP 5 Seasonal forecasts ◦ Three different models, each producing in total approximately 20 Tb throughout the project. In total approximately 60 Tb. * WP 5 Climate change simulations ◦ One model in standard resolution approximately 20 Tb. ◦ One model in high resolution as well, approximately 685 Tb, would be available for dissemination. However, in practice only a subset of the data will be useful to the wider community, and hence significant data volume reduction is being considered for dissemination. ### ECMWF YOPP data Within APPLICATE, ECMWF has begun to generate an extended two-year global dataset to support the World Meteorological Organization’s Year of Polar Prediction (YOPP). The start of production was timed to coincide with the official launch of YOPP in Geneva, Switzerland, on 15 May. The dataset is intended to support YOPP’s goal of boosting polar forecasting capacity. In addition to the usual forecast data stored at ECMWF, it will include additional parameters for research purposes. These include ‘tendencies’ in physical processes modelled in ECMWF’s Integrated Forecasting System (IFS). More information on the ECMWF YOPP dataset is available from ECMWF. The actual data is available through the ECMWF YOPP Data Portal. This are discoverable through APPLICATE Data Portal as well as the YOPP Data Portal. ## Making data findable, including provisions for metadata [fair data] APPLICATE is following a metadata driven approach, utilizing internationally accepted standards and protocols for documentation and exchange of discovery and use metadata. This ensures interoperability at the discovery level with international systems and frameworks, including WMO Information System (WIS), Year of Polar Prediction (YOPP), and many national and international Arctic and marine data centers (e.g. Svalbard Integrated Arctic Earth Observing System). APPLICATE data management is distributed in nature, relying on a number of data centres with a long-term mandate. This ensures preservation of the scientific legacy. The approach chosen is in line with lessons learned from the International Polar Year, and the ongoing efforts by the combined SAON/IASC Arctic Data Committee to establish an Arctic data ecosystem. APPLICATE promotes the implementation of Persistent Identifiers at each contributing data centre. Some have this in place, while others are in the process of establishing this. Although application of globally resolvable Persistent Identifiers (e.g. Digital Object Identifiers) is not required, it is promoted by the APPLICATE data management system. However, each contributing data centre has to support locally unique and persistent identifiers if Digital Object Identifiers or similar are not supported. Concerning naming conventions, APPLICATE requires that controlled vocabularies are used both at the discovery level and the data level to describe the content. Discovery level metadata must identify the convention used and the convention has to be available in machine readable form (preferably through Simple Knowledge Organisation System). The fallback solution for controlled vocabularies is the Global Change Master Directory vocabularies. The search model of the data management system is based on GCMD Science Keywords for parameter identification through discovery metadata. At the data level the Climate and Forecast Convention is used for all NetCDF files. For data encoded using WMO standards, GRIB and BUFR, the standard approach at the host institute is followed. All discovery metadata records are required to include GCMD Science Keywords. Furthermore, CMOR standards will be employed for some of the climate model simulations, especially those contributing to CMIP6. Versioning of data is required for the data published in the data management system. Details on requirements for how to define a new version of a dataset is to be agreed, but the general principles include that a new version of a model dataset is defined if the physical basis for the model has changed (e.g. modification of spatial and temporal resolution, number of vertical levels and internal dynamics or physics). Integration of datasets (e.g. to create a long time series) is encouraged, but these datasets must be clearly documented. The APPLICATE data management system can consume and expose discovery metadata provided in GCMD DIF and ISO19115. If ISO19115 is used, GCMD keywords must be used to describe physical and dynamical parameters. Support for more formats is being considered. More specifications will be identified early in the project. As ISO19115 is a container that can be used in many contexts, APPLICATE promotes the application of the WMO Profile for discovery metadata. This is based on ISO19115. APPLICATE will be more pragmatic than WMO accepting records that not fully qualify in all aspects. The dialogue on what is required will be aligned with the ongoing efforts of the combined SAON /IASC Arctic Data Committee to ensure integration with relevant scientific communities. APPLICATE will integrate with the YOPP Data Portal to make sure that APPLICATE datasets are discoverable through the YOPP Data Portal. This will be implemented letting the YOPP Data Portal harvest the relevant discovery metadata from the APPLICATE data catalogue. ## Making data openly accessible [fair data] All discovery metadata will be available through a web based search interface available through the central project website (applicate.met.no 2 ). Some data may have temporal access restrictions (embargo period). These will be handled accordingly. Valid reasons for an embargo period on data are primarily for educational reasons, allowing Ph.D. students to prepare and publish their work. Even if data constrained in the embargo period, data will be shared internally in the project. Any disagreements on access to data or misuse of data internally are to be settled by the APPLICATE Executive Board. Data in the central repository will be made available through a THREDDS Data Server, activating OPeNDAP support for all datasets and OGC Web Map Service for visualisation of gridded datasets. Standardisation of data access interfaces and linkage to the Common Data Model through OPeNDAP 3 is promoted for all data centres contributing to APPLICATE. This enables direct access of data within analysis tools like Matlab, Excel 4 and R. Activation of these interfaces to data are recommended for other contributing data centres as well. Metadata and data for the datasets are maintained by the responsible data centres (including the central data repository). Metadata supporting unified search is harvested and ingested in the central node (through applicate.met.no) where it will be made available through human (web interface) and machine interfaces (OAI-PMH, support for OpenSearch is considered). Datasets with restrictions are initially handled by the responsible data centre. Generally, the metadata will be searchable and contain information on how to request access to the dataset. An example of a dataset with access restrictions is the ECMWF YOPP dataset where user registration is required. Access to information about the dataset does however not require registration ## Making data interoperable [fair data] In order to be able to reuse data, standardisation is important. This implies both standardisation of the encoding/documentation, as well as the interfaces to the data. Further up in the document, it is referred to documentation standards widely used by the modelling communities. This includes encoding model output as NetCDF files, following the Climate and Forecast convention or the WMO GRIB format. The WMN formats are table driven formats where the tables identify the content and makes it interoperable. NetCDF files following the CF convention is self-describing and interoperable. Application of the CF conventions implies requirements on the structure and semantic annotation of data (e.g. through identification of variables/parameters through CF standard names). Furthermore, it requires encoding of missing values etc. To simplify the process of accessing data, APPLICATE recommends all data centres to support OPeNDAP. OPeNDAP allows streaming of data and access without downloading the data as physical files. If OPeNDAP is not supported, straightforward HTTP access must be supported. In order to ensure consistency between discovery level and use level metadata, a system for translation of discovery metadata keywords (i.e. GCMD Science keywords) to CF Standard names is under development. This implies that e.g. controlled vocabularies used in the documentation of data may be mapped on the fly to vocabularies used by other communities. This is in line with current activities in the SAON/IASC Arctic Data Committee. ## Increase data re-use (through clarifying licenses) [fair data] APPLICATE promotes free and open data sharing in line with the Open Research Data Pilot. Each dataset needs a license attached. The recommendation in APPLICATE is to use Creative Commons attribution license for data. See https://creativecommons.org/licenses/by/3.0/ for details. APPLICATE data should be delivered in a timely manner meaning without un-due delay. Any delay, due or un-due, shall not be longer than one year after the dataset is finished. Discovery metadata shall be delivered immediately. APPLICATE is promoting free and open access to data. Some data may have constraints (e.g. on access or dissemination) and may be available to members only initially. Furthermore, some of the data will be used for modelling development purposes and are thus of limited interest to the broader community; these data will not be made publicly available. A draft dissemination plan was outlined in the proposal and is provided in Table 4. This will be updated as the project progresses. Table 4: Draft dissemination plan. <table> <tr> <th> Purpose </th> <th> Model systems </th> <th> Experimental design </th> <th> Data </th> </tr> <tr> <td> Determine the impact of model enhancements on process representation and systematic model error (WP2) </td> <td> * AWI-CM * EC-Earth * CNRM-CM * NorESM * HadGEM </td> <td> Baseline data: CMIP6-DECK experiments Implement the model changes suggested in WP2 in coupled models: • 200-yr pre- industrial control experiments • CMIP6 historical experiments • 1% CO 2 increase experiments </td> <td> Partial Dissemination </td> </tr> <tr> <td> Determine Arctic- lower latitude linkages in atmosphere and ocean (WP3) </td> <td> Coupled models * AWI-CM * EC-Earth * CNRM-CM * NorESM * HadGEM </td> <td> Large ensembles (50-100 members) of 12-months experiments starting June 1st with sea ice constrained to observed and projected sea ice fields Multi- decadal experiments with and without artificially reduced Arctic sea ice (enhanced downwelling LW radiation over sea ice); use of tracers for the ocean Repeat with enhanced models </td> <td> Full Dissemination </td> </tr> <tr> <td> Atmospheric models * ECHAM6 * IFS * ARPEGE- Climat * CAM-OSLO * MetUM </td> <td> Large ensembles (50-100 members) of 12-months experiments starting June 1st with sea ice constrained to observed and projected sea ice fields Various corresponding sensitivity experiments to explore the role of the background flow, and the prescribed sea ice pattern Repeat with enhanced models </td> <td> Full Dissemination </td> </tr> </table> <table> <tr> <th> </th> <th> Seasonal prediction systems • EC-Earth • CNRM-CM </th> <th> Seasonal prediction experiments with and without relaxation of the Arctic atmosphere towards ERA-Interim reanalysis data: 9-member ensemble forecasts with members initialized on Nov 1st, Feb 1st, May 1st and Aug 1st for the years 1979-2016 and 19932016 for EC-Earth and CNRM-CM, respectively. </th> <th> Full Dissemination </th> </tr> <tr> <td> Arctic observing system development (WP4) </td> <td> Atmospheric model • IFS </td> <td> Data denial experiments with the IFS for key observations (snow, surface pressure, wind, moisture) and different seasons. </td> <td> Partial dissemination </td> </tr> <tr> <td> Seasonal prediction * EC-Earth * HadGEM * GloSea </td> <td> \- Perfect model experiments to characterize basic sensitivity of forecasts to initial conditions. - Different configurations of initial conditions using reanalyses, new observations, ocean reruns forced by atmospheric reanalyses. - Experiments focused on sea-ice thickness, snow and spatial data sampling </td> <td> Partial dissemination </td> </tr> <tr> <td> Determine the impact of APPLI- CATE model enhancements on weather and climate prediction (WP5) </td> <td> Atmospheric model * ARPEGE * AROME * IFS * AROME-Arctic </td> <td> Test recommendations for model enhancements made in WP2 in pre- operational configurations Explore the impact of nesting, driving model and resolution </td> <td> Partial dissemination </td> </tr> <tr> <td> Seasonal prediction * EC-Earth * CNRM-CM * HadGEM </td> <td> Test recommendations for model enhancements made in WP2 in pre- operational configurations </td> <td> Partial dissemination </td> </tr> <tr> <td> </td> <td> Climate change * AWI-CM * EC-Earth * NorESM * AWI-CM </td> <td> Establish the impact of model enhancements developed in WP2 on climate sensitivity by carrying out experiments using the same initial conditions and time period (1950—2050) employed in HiResMIP climate sensitivity by carrying out experiments using the same initial 2050) employed in HiResMIP climate sensitivity by carrying out experiments using the same initial conditions and time period (1950—2050) employed in HiResMIP </td> <td> Partial dissemination </td> </tr> </table> The quality of each dataset is the responsibility of the Principal Investigator. The Data Management System will ensure the quality of the discovery metadata and that datasets are delivered according to the format specifications. Numerical simulations and analysed products will be preserved for at least 10 years after publication. # Allocation of resources In the current situation, it is not possible to estimate the cost for making APPLICATE data FAIR. Part of the reason is that this work is relying on existing functionality at the contributing data centres and that this functionality has been developed over years. The cost of preparing the data in accordance with the specifications and initial sharing is covered by the project. Maintenance of this over time is covered by the business models of the data centres. A preliminary list of data centres involved is given in Table 5. Table 5: As of autumn 2018, the following data centres are contributing to the APPLICATE project. <table> <tr> <th> Data centre </th> <th> URL </th> <th> Contact </th> <th> Comment </th> </tr> <tr> <td> BSC </td> <td> https://www.bsc.es/ </td> <td> Pierre-Antoine Bretonniére </td> <td> </td> </tr> <tr> <td> ECMWF </td> <td> https://www.ecmwf.int </td> <td> Manuel Fuentes </td> <td> </td> </tr> <tr> <td> DKRZ </td> <td> http://www.dkrz.de </td> <td> Thomas Jung </td> <td> </td> </tr> <tr> <td> Norwegian Meteoro- logical Institute/Arctic Data Centre </td> <td> https://applicate.met.no/ </td> <td> Øystein Godøy </td> <td> This subsystem will provide a unified search interface to all the data APPLICATE is generating. It will also host data not being hosted by other data centres contributing to APPLICATE. Metadata interfaces are available, data interoperability supported using OGC WMS and OPeNDAP. Will integrate relevant data from WMO GTS. </td> </tr> </table> Each data centre is responsible for accepting, managing, sharing and preserving the relevant datasets. Concerning interoperability interfaces the following interfaces are required: 1. Metadata 1. OAI-PMH serving either CCMD DIF or the ISO19115 minimum profile with GCMD Science Keywords. Dedicated sets should be available to identify APPLICATE data in large data collections. 2. Data (will also use whatever is available and deliver this in original form, for those data no synthesis products are possible without an extensive effort) 1. OGC WMS (actual visual representation, not data) 2. OPeNDAP In the current situation, long-term preservation of 50 Tb for 10 years is covered. Volumes to be preserved are still somewhat uncertain and the storage costs for some of the data produced in the project are covered by other projects/activities, e.g. the CMIP6 data and operational models. For some of these data only preservation of minor datasets is required by APPLICATE. All data that will contribute to CMIP6 will be stored in data centres contributing to the Earth System Grid Federation (ESGF). APPLICATE data centres contributing to this will be shown in the table above. For APPLICATE, the experiments contributing to the Polar Amplification Model Intercomparison Project (PA-MIP) will be managed in a ESGF data centre. # Data security Data security relies on the existing mechanisms of the contributing data centres. APPLICATE recommends ensuring the communication between data centres and users with secure HTTP. Concerning the internal security of the data centre, APPLICATE recommends the best practises from OAIS. The technical solution will vary between data centres, but most data centres have solutions using automated check sums and replication. The central node relies on secure HTTP, but not all contributing data centres support this yet. # Ethical aspects APPLICATE is not concerned with ethical sensitive data and follows the guidance of the IASC Statement of Principles and Practises for Arctic Data Management. # Other APPLICATE is linked to WMO’s Year of Polar Prediction activity. In this context APPLICATE is relating to the WMO principles for data management identified through the WMO Information System.	https://phaidra.univie.ac.at/o:1140797	Horizon 2020
0005_Made4You_780298.md	Introduction The Made4You project is committed to high quality output and responsible research and innovation. Thus, this document defines a set of procedures that the consortium is committed to adhere to and to improve in the course of the project. Openness and transparency are two of the guiding principles that the reader will see reflected in the different processes and methods described. At the same time there is a strong awareness within the consortium related to privacy and data protection of individual citizens. These core principles underlying the research work in Made4You correspond with the practices related to Responsible Research and Innovation (RRI). Section 2 below describes the management structures, including the nominees for the various boards. Section 3 is dedicated to specific quality management procedures, including communication structures and tools, the peer reviewing process for high quality deliverables as well as risk management, SWOT and other quality assurance means. In Section 4 the technical infrastructure for communication and collaboration is presented. Section 5 presents the RRI policies and identifies the most relevant aspects for Made4You. It includes the ethical approach and guidelines that the project is following (together with deliverables D8.1 and D8.2). In Section 6 the consortium’s strategy towards openness is described and relates to open source in terms of software as well as open access in terms of publications and other project results. Finally, Section 7 draws the conclusions that are relevant for a high quality implementation of the project. The appendix includes examples of templates mentioned throughout the document. 2 Management structure Both the Grant Agreement (GA) and the Consortium Agreement (CA) specify a number of bodies for the management of the project. Though the GA and CA, being legal documents, take precedence over this handbook, the following sections specify the operational view of these bodies. Made4You is a large-scale innovation action aiming at a wide community on a global scale. Therefore, the management structure and procedures work in a flexible manner in order to: * Achieve integration of all consortium members and to mobilise their expertise, knowledge and networks in every stage of the project * Efficiently coordinate the processing of the work plan in a collaborative environment * Continuously involve contextual expertise and knowledge of relevant stakeholders (patients, families, healthcare professionals, makers) and their networks Our approach is a combination of integration and decentralisation strategies. _Integration_ is achieved through the composition of a consortium with complementary skills and knowledge, the development of a joint framework, the agreement on common guidelines for co-design activities, the joint work on the platform and community development, and project workshops and meetings. The resources of all partners will be mobilised by _decentralisation of responsibilities_ through the assignment of leadership for work packages and defined work package tasks with a clear task sharing based on the different competence fields of the partners. Figure 1: Made4You – Management Structure: responsible roles in management The management structure defines the basic roles and responsibilities. The Coordinator (Dr. Barbara Kieslinger, ZSI) is responsible for the overall line of actions and the day-to-day management carried out by the project. Additional ZSI staff is providing financial and administrative support to the coordinator. The Project Coordinator is supported by the WP leaders in the strategic coordination of the innovation action. In addition, the Community Manager, who is also coordinating the dissemination and exploitation WP, is responsible for the coordination of the Made4You extended network. In close cooperation with the project manager the community manager will take care of the broad visibility of the project, amongst specific stakeholder groups and will have a special interest in the exploitation and transferability of the project results. ## 2.1 Work Package (WP) The work package (WP) is the building block of the project. The WP leader * organizes the WP and coordinates the different tasks, * prepares and chairs WP meetings, * organizes the production of the results of the WP, • represents the WP in the Project Management Board. Each work package has been appointed a Work Package Leader who is responsible for the progress within the work package and who is supported by task leaders and other members of the consortium involved in each of the WPs. Clear responsibilities (based on the competences of each partner) are described in the Work Package Description. Current WP leaders are shown in Table 1. <table> <tr> <th> Workpackage </th> <th> Lead partner </th> <th> Name </th> </tr> <tr> <td> WP1 Engagement & Community Growth </td> <td> WAAG </td> <td> Jurre Ongering </td> </tr> <tr> <td> WP2 Pilot Open Solutions </td> <td> MAKEA </td> <td> Daniel Heltzel </td> </tr> <tr> <td> WP3 Platform & Tooling </td> <td> OPEN </td> <td> Enrico Bassi </td> </tr> <tr> <td> WP4 Evaluation & Impact Assessment </td> <td> ZSI </td> <td> Teresa Schäfer </td> </tr> <tr> <td> WP5 Dissemination & Outreach </td> <td> GIG </td> <td> Sandra Mamitzsch </td> </tr> <tr> <td> WP6 Lethal & Ethical Aspects </td> <td> KUL </td> <td> Erik Kamenjasevic </td> </tr> <tr> <td> WP7 Project Management </td> <td> ZSI </td> <td> Barbara Kieslinger </td> </tr> <tr> <td> WP8 Ethical requirements </td> <td> ZSI </td> <td> Barbara Kieslinger </td> </tr> </table> Table 1 Current WP leaders ## 2.2 Project Management Board (PMB) The project is managed through the Project Management Board (PMB). It provides the overall direction for the project, both strategic and operational. The PMB maintains the project directions and obtains advice from the Work Package Leaders, to ensure that the project meets its stated and implied goals. The PMB ultimately supervises all project management processes, including initiation, planning, execution, control, and closure of project phases. Within this framework, the Work Package Leaders coordinate the detailed planning, execution and control of the technical tasks to meet the project’s scientific and technical objectives relevant to their work packages. The Project Management Board is responsible for the proper execution and implementation of the decisions of the General Assembly and makes suggestions to the General Assembly on pending decision such as: * Accept or reject changes to the work plan, changes in the Grant Agreement and amendments to the Consortium Agreement * Make changes in the Project Management structure The PMB is chaired by the Project Coordinator and composed of the Work Package Leaders plus a representative from partners not leading a work package. The PMB is currently composed of the persons listed in Table 2 below. <table> <tr> <th> Partner </th> <th> Partner manager </th> </tr> <tr> <td> ZSI </td> <td> Barbara Kieslinger </td> </tr> <tr> <td> WAAG </td> <td> Jurre Ongering </td> </tr> <tr> <td> OPEN </td> <td> Enrico Bassi </td> </tr> <tr> <td> GIG </td> <td> Sandra Mamitzsch </td> </tr> <tr> <td> MAKEA </td> <td> Daniel Heltzel </td> </tr> <tr> <td> WEV </td> <td> Richard Hulskes </td> </tr> <tr> <td> KUL </td> <td> Erik Kamenjasevic </td> </tr> <tr> <td> TOG </td> <td> Chiara Nizzola </td> </tr> </table> Table 2: Partner managers ## 2.3 General Assembly (GA) The General Assembly is the ultimate decision-making body of the consortium and functions as highest authority, as last resort of all relevant project decisions. The body consists of one representative per partner. A face-to-face general assembly comprising all project consortium partners will take place at least once a year, to coordinate overall project work. Additional extraordinary meetings can be held at any time upon request of the PMB or 1/3 of the members of the GA. Within the project, the general assembly will function as highest authority, as last resort of all relevant project decisions. Decisions taken by the General Assembly include the content, e.g. changes in the Description of Action (DoA), finances and intellectual property rights. This body also has the right to decide on the evolution of the partnership (e.g. entry of new partner), and the project as such (e.g. termination of the project). ## 2.4 Made4You Advisory Board The Made4You Advisory Board (MAB) is a group of persons from outside the project. The MAB will be consulted for important decisions that affect the direction of research and/or are related to adoption of the results from the Made4You project. The MAB members are listed in Table 3. <table> <tr> <th> MAB member </th> <th> Affiliation </th> </tr> <tr> <td> Sherry Lassiter </td> <td> President of Fab Foundation, MIT </td> </tr> <tr> <td> John Schull </td> <td> Co-founder eNABLE </td> </tr> <tr> <td> David Ott </td> <td> Global Humanitarian Lab </td> </tr> <tr> <td> Raul Krauthausen </td> <td> Sozialhelden </td> </tr> </table> Table 3: MAB members During the Kick-off meeting it was decided that this group of external experts can still be expanded with 2-3 persons for strategic purpose. ## 2.5 Consortium Agreement (CA) Before the start of the project a consortium agreement has been signed by all partners. It defines the specific operational procedures for the different project bodies described above. This includes, amongst other aspects, the responsibilities of the parties and their liabilities towards each other as well as the governance structure, financial provision and Intellectual Property Rights (IPR) issues. The consortium agreement also describes the decision making structures and defines the General Assembly as the ultimate decision making body. # 3 Quality procedures and Code of Conduct Quality assurance is of high priority in collaborative research, such as Made4You, and the consortium is committed to a set of quality procedures to guarantee high quality project output. Measures to ensure good quality include e.g. the definition of internal communication structures, regular internal reflections on risks and a proper SWOT analysis (Strengths, Weaknesses, Opportunities and Threats Analysis) as well as a defined peer review process for any project deliverable. The detailed procedures will be described in more detail in the following sections. ## 3.1 Internal communication structures & procedures Internal communication is first and foremost based on the concept of openness and transparency. An active communication strategy is implemented to establish a strong project identity in order to obtain maximum transparency for all partners involved, and to increase synergy in cooperation. Daily communication among the WPs, the partners, etc. is established mainly through . e-mails and a central mailing list including all project partners, . a Slack group for quick communication across teams and partners, . a project space hosted at the ZSI (Nextcloud) for internal exchange and online storage of all documents as well as offline communication; https://wolke1.zsi.at/, . web-conferencing (Skype) for regular online meetings, . face-to-face communication (during physical project meetings). The consortium partners meet approximately every three to four months face-to- face (at synchronisation points) to coordinate the progress.Each month, at least one virtual consortium meeting takes place via video conferencing, currently Skype. These meetings ensure the internal communication among partners, allow the WP Leaders/thematic leaders to coordinate the various tasks, and report the progress of work to the team members. During the meeting “live minutes” will be produced and are made accessible to all partners, to view at a later time. Each team reports about latest updates before a meeting in a shared document, all participants are invited to get an update before the meeting starts and the most relevant issues are then discussed during the meetings. The minutes are available on the Nextcloud. In addition to these virtual consortium meetings, thematic groups (similar to WPs, but overlapping in some cases) have started to emerge during the kick-off meeting and virtual meetings are organised by these working groups. Similar to the consortium meetings notes and recordings are available on the Nextcloud and each member of the consortium is invited to attend any of these meetings. ## 3.2 External communication structures & procedures The communication strategy also aims to effectively communicate with parties outside the consortium, especially since Made4You is an innovation action that aims at reaching and engaging a broad audience to create impact. Stakeholders will be addressed via the community engagement and communications strategy, which is coordinated in a collaborative effort by WP1 and WP5. The “ComCom” working group is elaborating the details for the external communication in terms of procedures and material. Basically different communication options will be elaborated for the different target groups. Most importantly, it should be mentioned that Made4You decided to promote the name of the platform, Careables (http://www.careables.org) , and to use the project name Made4You mainly for administrative purposes. ## 3.3 Quality of (non-)deliverables and peer review A peer-review process for the Made4You project is set up in order to obtain and guarantee the quality of the deliverables (documentation, reports, prototypes, etc.) that will be produced during the course of the project and delivered to the European Commission, offered to the Made4You stakeholders and more globally to the general public. This section describes standards for the Made4You deliverables and presents the peer-review procedure. A checklist for the deliverables and a template for peer-review reports are given in Appendices to this document. ### 3.3.1 Deliverables Made4You deliverables serve different purposes. They are a communication means within the consortium and communication with other people outside the consortium. They are aimed at transferring the know-how, to exploit the results and knowledge generated by the project. Deliverables should be written with their target readers in mind. They should be concise and easy to follow. The readability of a document is a vital ingredient for its success. The following general structure should be followed and is as such provided in the deliverable template of the project: * Cover page * Amendment History * List of Authors/Contributors * Table of Contents * Abbreviations/Acronyms * Executive summary * Introductory part * Core part * References * Annexes (optional) Annex I includes a checklist that should serve as a guideline when preparing a deliverable. A Made4You deliverable may be comprised of one or more volumes and may consist of the following parts: * The _Main part_ is the part that summarises the results for high-level executives, technical managers and experts with decision-making competence. It is typically one document and may contain Appendices * _Annexes_ are optional and have detailed technical information for experts and implementers. They are added to the main part at the end of the document Project deliverables may be classified according to different confidentiality levels, such as public (PU), restricted (RE) or confidential (CO). Following an open access strategy, which the project partners are committed to, all Made4You deliverables have been classified as PU regarding their dissemination level in the DoA. PU means completely public access and thus, all deliverables will be made available on the project website and/or specific open repositories (see data management plan further below. In the case consortium members want to change the level of confidentiality of any of the deliverables this requires a decision by the General Assembly and needs to be convincingly argued. In the following, steps to be taken for publishing a deliverable are listed: 1. These parts form the basis for the deliverable * Title and description of the project deliverables * The name(s) of the deliverables editor(s) * The deliverable history including names(s) of contributors and internal reviewer(s) in charge of the peer review for the deliverable 2. The people appointed to generate parts of the Deliverable – the authors – provide their contribution to the editor. 3. The editor(s) prepare draft 0.1 of the Deliverable by assembling and integrating all contributions. This draft is discussed with all authors. It is recommended to involve the internal reviewers already at this stage. 4. When the editors and the authors are satisfied with the results achieved, the editor issues draft 1.0 and puts it on the Made4You Nextcloud and sends a note to the consortium. 5. They inform the internal reviewers and ask for a quality check, opinions and constructive comments within a defined deadline (normally one week). 6. The editor deals with all the comments and problems raised, if necessary with the help of the authors. This is a critical phase due to the many interactions involved. It may be necessary to have a meeting (physical, audio- or video conference) in order to speed up the process for reaching a consensus on the amendments. 7. The editor prepares draft 2.0, puts it on the Made4You Nextcloud and informs the project manager (Dr. Barbara Kieslinger) and the whole consortium that the deliverable has reached final status and can be submitted to the EC and the reviewers. 8. The deliverable is sent to the PO and the EC reviewers only by the project manager. ### 3.3.2 Peer review process One of the feasible means to enhance the quality of the project deliverables is an internal peer review system. Made4You deliverables shall be evaluated by 2-3 reviewers so as to gather diversified and balanced viewpoints. Deliverables can be reviewed by members of the core project team or colleagues from the partner institutions as well invited external experts, for example Advisory Board members. Peer reviewers should be nominated by the editor(s) at least 3 weeks before the due date of the deliverable and communicated to the consortium. Nominated peer reviewers can turn down the invitation with clear justification (e.g. lack of expertise) and would thus be requested to nominate another candidate. Consented peer reviewers are required to produce a peer review feedback within 7-10 days after receiving the deliverable from the editor. In case of any expected delay, peer reviewers should notify the editor and the project manager immediately. During the review process, peer reviewers are encouraged to discuss the problems identified in the deliverable with the main author/editor. Peer reviewers are advised to pay particular attention to the following points: * Is the deliverable aligned with the objectives of the project and relevant work packages? * Does the deliverable make a significant contribution to the project or not? * Is the content of the deliverable focused on the intended purpose? Is the content of the deliverable presented in a precise and to-the-point manner? * Is the length of the deliverable justified? Are there superfluous or irrelevant parts that should be deleted? Are there overlong parts that should be shortened? Are there any parts that are written in flowery language and/or that are unspecific or redundant? * Are there many grammatical errors and/or typographical errors and/or incomprehensive sentences? Specifically, clear annotations indicating errors and suggested corrections are very helpful for the authors of the deliverable. The annotated deliverable may be sent back to the editor/authors via email together with the peer review report. * Does the deliverable require substantial revision or rewriting? If yes, it will facilitate the revision process if some concrete suggestions on how to improve the deliverable are given. Peer review results are described in a peer review report/e-mail (see Annex III), which contains the following information: * Basic information about the deliverable, author and peer reviewer * Comments on the length and content of the deliverable * Major strengths and weaknesses of the deliverable * Review summary If minor or substantial revisions are necessary, authors of the deliverable should make changes and produce the final version of the deliverable before due submission date. The final responsibility for the content of the deliverable remains with the editor and authors and it is thus their final decision about how to address and integrate the feedback from the peer reviewers. The review reports will be made available internally for the consortium only. Figure 2 : Peer review process ### 3.3.3 Non-deliverables For non-deliverables, such as publications and dissemination material, the procedure for deliverables will be used where applicable and with a timeline that fits the material. Since there are many types of material, this handbook cannot provide details for all cases. We distinguish the following broad categories of material. * Dissemination material (flyer, website, leaflets, popular science publications, etc.) Default reviewer is the communication manager, supported by project manager. * Scientific publication or conference presentation Reviewed by one or more team members according to focus and contributions ## 3.4 Internal surveys Made4You is committed to a continuous improvement process on the project management level. In addition to open and transparent communication and decision-making, the project management uses anonymous surveys for specific input on process management, risks and critical issues. These surveys are kept brief to ensure broad participation by each project member. The survey is distributed according to needs (no pre-defined schedule), but at least once a year (ideally before a GA meeting) to cover the following: * _Project management._ In this section, participants are asked to share their positive and negative observations about the project management processes. * _Current topics._ The second section focuses on topics that are currently important within the project. This can range from collaboration infrastructure, to satisfaction about certain results, or specific WP-level topics. A recurring topic will be questions regarding Responsible Research and Innovation (RRI) in order to sensitise project partners for the most relevant aspects of RRI for Made4you. * _Expectations and perceived risks_ . The third section focuses on the future and asks participants to share their perception about risks and expectations. An essential element of this survey process is that the results are discussed and reflected upon in the consortium, preferably during a face-to-face meeting. This allows for reacting to arising issues quickly and addressing them collaboratively, e.g., by adapting the agenda. ## 3.5 Risk management As stated above, internal surveys and discussions are used to check perceived concerns and risks by all consortium partners. In addition, the quarterly reports that each partner submits online (on Nextcloud) also include a section on possible risks, deviations or corrective actions to be reported to the project management. The basic risk management methodology to be followed in the project consists of four subsequent steps: * Risk identification – areas of potential risk are identified and classified. * Risk quantification – the probability of events is determined and the consequences associated with their occurrence are examined. * Risk response – methods are produced to reduce or control the risk, e.g. switch to alternative technologies. * Risk control and report – lessons learnt are documented. Risks with medium or high probability and severe impact are handled with particular caution during the project. At this point, it is expected that the project safely achieves its expected results. This is also supported by the preliminary risk analysis. Normal project risks are managed via “good- practice” project management and rely on the experience from the successful research projects that the partners have been performing. The close supervision and tight control both by the project management and by the various Boards ensure that results are available in time and with adequate quality. At the kick-off meeting a first risk analysis was performed for each of the work packages. Before the kick-off, all partners were asked to reflect on “dreams” and “fears” that they would associate with the work packages. The following two images summarise on the one hand the dreams and expectations and on the other hand the fears and risks associated with each of the work packages. Work package leaders will follow up on these aspects and reflect on contingencies should any of the identified risks, or emerging risks, start having an influence on the activities progress. In the course of the project, management is responsible for close monitoring of the overall progress and risk identification. Risk identification is however also collaboratively encouraged as part of reflective sessions during the project meetings. Early communication of risks is encouraged as well as discussions, in order to achieve a profound understanding of risks. The project management promotes an open communication culture to openly discuss any issues arising. ## 3.6 SWOT A mid-term analysis of strengths, weaknesses, opportunities and threats (SWOT) will be performed on the team and the project. This will be done during a plenary meeting and is to be used to refocus, if needed, the project in the second half of the project. The SWOT analysis is a structured planning method to evaluate the Strengths, Weaknesses Opportunities and Threats of a particular undertaking, be it for a policy or programme, a project or product or for an organization or individual. It is generally considered to be a simple and useful tool for analysing project objectives by identifying the internal and external factors that are favourable and unfavourable to achieving that objective. Strengths and weaknesses are regarded internal to the project while opportunities and threats generally relate to external factors. Strengths can be seen as characteristics of the project that give it an advantage over others while weaknesses are regarded as characteristics that place the team at a disadvantage relative to others. Opportunities comprise elements that the project could exploit to its advantage whilst threats include elements in the environment that could cause trouble for the project. Question to be answered during the SWOT analysis comprise: _Strengths (S):_ * What do we do well? What are our assets? * What advantages does the project have? What do we do better than anyone else? What unique resources can we draw upon that others can't? * What are our core competencies? What is the Unique Selling Proposition (USP)? * What do other people see as our strengths? _Weaknesses (W)_ : * What could we improve? What can we do better? * What should we avoid? * Where do we lack resources? * Which factors minimise the outcome? * What are external people likely to see as weaknesses? _Opportunities (O)_ : * Which good opportunities can we spot? What are the emerging political and social opportunities? * What interesting trends are we aware of? What are the economic trends that benefit us? * What new needs of PES and other future users could we meet? _Threats (T):_ * What obstacles do we face? * Where are we vulnerable? * Could any of our weaknesses seriously threaten our results? What are the negative political and social trends? To develop strategies that take into account the SWOT profile, a matrix can be constructed. The SWOT matrix (see below) includes strategies that make best use of strengths and opportunities and minimise weaknesses and threats. SO- Strategies pursue opportunities that are a good fit to the strengths. WO- Strategies overcome weaknesses to pursue opportunities. ST-Strategies identify ways in which the project can use its strengths to reduce its vulnerability to external threats. WT-Strategies establish a defensive plan to prevent the weaknesses from making it highly susceptible to external threats. <table> <tr> <th> SWOT Matrix </th> <th> Strengths </th> <th> Weaknesses </th> </tr> <tr> <td> Opportunities </td> <td> SO-Strategies </td> <td> WO-Strategies </td> </tr> <tr> <td> Threats </td> <td> ST-Strategies </td> <td> WT-Strategies </td> </tr> </table> Figure 5: SWOT Matrix After the first matrix has been drawn from the answers by the consortium, the following questions should be answered during the discussion and establishment of the project strategy: * How to make best use of strengths and opportunities? * How to best minimise weaknesses by making best use of opportunities? * How to make best use of strengths by reducing risk of threats? * How to best minimise weaknesses even with the expected threats? While SWOT can be a good complementary tool for analysing the project and redefining strategy, it has also several blind spots. These comprise, for instance that SWOT is a linear analysis and an expert's or group’s monophonic analysis. In the case of the Made4You project some external view, e.g. from the Advisory Board would give an important complementary interpretation of the project development. Overall, SWOT is an easy usable tool that provides quick access to the positive and negative aspects of a project and its environment and seems appropriate for the Made4You project to be performed mid-term. ## 3.7 Project templates Made4You intends to use a consistent ‘project style’. This is implemented by providing templates for deliverables and reports, presentations, posters and other dissemination and communication material. More project style templates can be produced by the communication and outreach team when needed. At the kick-off meeting the consortium decided to name the central platform of the project “Careables”. Thus, the main message in any promotional material will focus on the advertising Careables (http://www.careables.org). All available project style templates are available on the shared workspace on Nextcloud. # 4 Tools and collaboration infrastructure While the previous section was concerned with the processes of communication and collaboration there is also a technical side to this and a number of technical tools are used to provide the Made4You collaboration infrastructure. It consists of several pieces: * Made4You mailing list is used for project-wide asynchronous communication. The address of the mailing list is: [email protected]_ * Slack for ad-hoc communication to the whole team as well as to different subgroups and individual team members; * Skype is used for regular web conferencing (monthly meetings) * Nextcloud (https://wolke1.zsi.at/) is used for sharing files and for real-time cocreation of documents * e-mail and telephone are used for bilateral communication * Careables Website (http://www.careables.org) is the main portal for sharing open healthcare solutions and also used for presenting our work to the public The choice for this collaboration structures has been made taking into consideration practical aspects as well as privacy and data protection issues related to the EU General Data Protection Regulation (GDPR). Figure 6 : Nextcloud Workspace for Made4You 5 Responsible research and innovation (RRI) 5.1 What is RRI? Responsible Research and Innovation (RRI) has been formulated and widely promoted as guiding principle and policy concept by the European Commission to better align science with society and to meet the so called grand challenges 1 . It has been promoted as a cross-cutting issue within the H2020 research programme. A widely accepted definition describes RRI as “a transparent, interactive process by which societal actors and innovators become mutually responsive to each other with a view on the (ethical) acceptability, sustainability and societal desirability of the innovation process and its marketable products” (Schomberg, 2013). Others’ definitions of RRI (c.f. Jacob et al., 2013; Owen et al., 2013) might slightly differ from Von Schomberg’s but as described by Wickson & Carey (2014) the overall common accordance is that responsible research and innovation should (1) address significant socio-ecological needs and challenges, (2) actively engage different stakeholders, (3) anticipate potential problems and assess available alternatives and reflect on underlying values and beliefs and (4) to adapt according to these ideas. Generally speaking, RRI is doing science and innovation with and for society by re- imaging the science-society relationship. According to the European Commission (Jacob et al., 2013), RRI comprises the following key dimensions 2 : 1. Governance : Governance of policymakers to prevent harmful or unethical developments in research and innovation 2. Open Access : Open access to research results and publications to boost innovation and increase the use of scientific results 3. Ethics : Research must respect ethical standards and fundamental rights to respond to societal challenges 4. Gender : Gender equality and in a wider sense diversity 5. Public Engagement : Engagement of all societal actors (researchers, industry, policy makers, civil society) in a reflective research process 6. Science education : Enhancement of current education processes to better equip future researchers and society as a whole with the necessary competences to participate in research processes In addition to these key dimensions, which are reflected in the European policy agendas, RRI can also be defined with regards to its process requirements which include openness and transparency, anticipation and reflection, responsiveness and adaptive change and diversity and inclusion. Figure 7, which stems from the RRI-Tools project 3 where the ZSI has been a core partner, shows an integrative view on these tow perspectives, which complement each other. 2 A different operationalisation is described by Wickson and Carew (2014) who describe RRI from a process perspective with the following quality criteria: 1. Socially relevant and Solution oriented; 2. Sustainability centered and Future scanning; 3. Diverse and Deliberative; 4. Reflexive and Responsive; 5. Rigorous and Robust; 6. Creative and Elegant; and 7. Honest and Accountable 3 http://www.rri-tools.eu Figure 7: Overview of key dimensions and process requirements of RRI according to RRI-Tools project In the following, we briefly describe the six key dimensions and how they related to Made4You. ## 5.2 Governance Among the six key dimensions of RRI, governance has a slightly different function compared to the others, as it is rather an organising and steering principle that determinates the success of all other RRI dimensions. In other words, RRI relies on good governing structures for the promotion of RRI. Governance methods range from foresight techniques (scenario studies, value sensitive design, etc.), assessment (ethical committees, needs assessment, technology assessment, etc.), agenda setting (consultation, co-creation, etc.) to regulation (code of conduct, policies, funding guidelines, etc.). Currently, governance of RRI is rarely seen on a project level; it is rather applied on funding level or within organisations, e.g. to call for organisation-wide RRI guidelines and policies. The Made4You project can be perceived as an attempt to tackle RRI on a project level. However, comprehensive RRI guidelines for projects are still missing and thus this handbook together with the deliverables D8.1 and D8.2 will aim at meeting this need. Also, it has to be acknowledged that governance structures need to be at least on institutional level in order to be sustainable. On a project level however, it makes sense to break down what RRI in the specific context means and how it can be adapted to the project particularities. ## 5.3 Open access In the narrower sense, open access is about enabling or giving access to research results and publications to the public. It addresses only the final stage of research activity, the publication and dissemination phase. With the launch of Horizon 2020 it has become mandatory to follow open access publication strategies (European Commission, 2012) . Open access, in the narrow sense, is different from open science, open innovation and open data, although there are obvious overlaps. For instance, in contrast to open access, open science implies opening up the whole science process in real time to the public, from choosing areas to investigate in, formulating the research questions to choosing the methods, collecting data and finally discussing the results. Open science means democratising science and research, usually through ICT. When talking about open access in the context of Made4You we refer to open access in the narrower sense. Our project will basically follow an open access publication strategy, but will also make data available to the public at an earlier stage where suitable (c.f. chapter 6). ## 5.4 Ethics The European Commission defines ethics as key dimension of RRI as follows: _“European society is based on shared values. In order to adequately respond to societal challenges, research and innovation must respect fundamental rights and the highest ethical standards. Beyond the mandatory legal aspects, this aims to ensure increased societal relevance and acceptability of research and innovation outcomes. Ethics should not be perceived as a constraint to research and innovation, but rather as a way of ensuring high quality results.” (p.4)_ 2 Ethics thereby shall not be perceived as a constraint but rather as a guiding principle to help ensure high quality outcomes and to justify decisions. This is also the case for Made4You. A specific work package (WP6) is dedicated to legal and ethical aspects. We will deal with the three main aspects of ethics as defined by the European Commission (2015), namely 1) Research integrity and good research practice, 2) Research ethics for the protection of research objects, and 3) Societal relevance and ethical acceptability of research and innovation outcomes. Ethics further implies social justice and inclusion aspects: The widest range of societal actors and civil society shall benefit from research and innovation outcomes. In other words, products and services as a result of Research & Innovation (R&I) activities shall be acceptable and affordable for different social groups, which is also a special goal of Made4You. Ethics is an integral part of responsible research, from the conceptual phase to the publication of research results. The consortium of Made4You is clearly committed to show appreciation of potential ethical issues that may arise during the course of the project and has as such defined a set of procedures on how to deal with ethics in a responsible way. The main aspects the project is dealing with in regards to ethics are the protection of identity, privacy, obtaining informed consent and communicating benefits and risks to the involved target groups. The activities performed in Made4You may include data collection from individuals and organisations remotely as well as on site. In the following, we outline the basic processes of ethical compliance of the project with a general view on the scientific data collection. Complementary, there is also Deliverable D8.2, which describes in more detail how the patient data collection, processing and storing on the Made4You platform, called “Careables”, is compliant with the GDPR. ### Data protection and privacy During any data collection process, data protection issues with regards to handling personal data will be addressed by the following strategies: Participants, who volunteer to being enrolled in our activities, will be exhaustively informed so that they are able to autonomously decide whether they consent to participate or not. The purposes of the research, the procedures as well as the handling of their data (protection, storage) will be explained. For online interviews these explanations will be a part of the initial briefing of interviewees. For face-to-face interventions, informed consent (provided in D8.1) shall be agreed and signed by both, the study participants as well as the respective research partner. The data exploitation will be in line with the respective national data protection acts. Since data privacy is under threat when data are traced back to individuals – they may become identifiable and the data may be abused – to mitigate this risk, we will anonymise all data. Data gathered through questionnaires, interviews, observational studies, focus groups, workshops and other possible data gathering methods during this research will be anonymised and therefore the data cannot be traced back to the individual. Data will be stored only in anonymous forms so the identities of the participants will only be known by the research partners involved. Raw data like interview protocols and audio files will be shared within the consortium partners only after having signed the confidentially agreement (See Annex I). Reports based on interviews, focus group and other data gathering methods will be based on aggregated information and will comprise anonymous quotations respectively. The collected data will be stored on password-protected servers at the partner institution responsible for data collection and analysis. The data will be used only within the project and will not be made accessible for any third party, unless anonymised. Sensitive data or personal will not be stored after the end of the project (incl. the time for final publications) unless required by specific national legislation. The stored data do not contain the names or addresses of participants and will be edited for full anonymity before being processed (e.g. in project reports). ### Communication strategy Study participants will be made aware of the potential benefits and identified risks of participating in the project at all times. The main means of communicating benefits and risks to the individual is the informed consent (see Deliverable D8.1). Prior to consent, each individual participant in any of the studies in MADE4YOU will be clearly informed of its goals, its possible adverse events, and the possibility to refuse to enter or to retract at any time with no consequences. This will be done through a project information sheet or the informed consent form and it will be reinforced verbally. In order to make sure that participants are able to recall what they agree upon when signing, the informed consent forms will be provided in the native language of the participants. In addition, the consortium partners will make sure that the informed consent is written in a language suitable for the target group(s). Different informed consents will be made available, e.g. consent of adult participants, parental consent, informed assent for children/minors. For media material (e.g. photos, videos) produced during any of the Made4You events a media waiver will be distributed to participants to make sure that participants are aware of this and agree/disagree to the production and use of such material by the project partners. A template for the waiver is provided in the Annex IV of this document. ### Informed consent/informed assent As stated above informed consent/assent will be collected from all participants involved in Made4You studies. The declaration of consent forms is provided in the deliverable D8.1. _Relevant regulations and scientific standards _ The consortium is following European regulations and scientific standards to perform ethical research. The following lists some of the basic regulations and guidelines. The Made4You project will fully respect the citizens’ rights as reported by EGE and as proclaimed in the Charter of Fundamental Rights of the European Union (2000/C 364/01), having as its main goal to enhance and to foster the participation of European citizens to education, regardless of cultural, linguistic or social backgrounds. Regarding the personal data collected during the research the project will make every effort to heed the rules for the protection of personal data as described in Directive 95/46/EC 3 . In addition, the consortium is following the following European Regulations and Guidelines: * The Charter of Fundamental Rights of the European Union: * European Convention on Human Rights http://www.echr.coe.int/Documents/Convention_ENG.pdf * Horizon 2020 ethics self-assessment http://ec.europa.eu/research/participants/portal/doc/call/h2020/h2020-msca-itn- 2015/1620147-h2020_-_guidance_ethics_self_assess_en.pdf * EU Code of Ethics: * European Textbook on Ethics in Research https://ec.europa.eu/research/sciencesociety/document_library/pdf_06/textbook-on-ethics-report_en.pdf * European data protection legislation * RESPECT Code of Practice for Socio-Economic Research * Code of Ethics of the International Sociological Association (ISA) ### National and Local Regulations and Standards In addition to the more general and EU-wide guidelines, project partners have to adhere to, and respect, national regulations and laws as well as to research organisational ethical approval as requested by the own institutions. All partners are aware of their responsibilities in that respect and will follow the respective guidelines. ## 5.5 Gender Gender equality generally means equal rights, opportunities, and responsibilities for both genders so that individuals can exploit and realise their full potentials independently from their sex. Gender equality as key dimension of RRI comprises two main aspects (European Commission, 2015), namely to strive for gender balanced teams in research and innovation (at operational as well as at decision making level) and the inclusion and integration of gender perspectives in research and innovation content and process. Gender analysis and gender monitoring throughout the project shall aim at looking at both aspects of gender equality, at the human capital dimension (where possible, apart from institutional conditions) and the research aspect of gender (Föger et al., 2016). In Made4You gender is mostly relevant when it comes to internal processes, such as the composition of project teams, of work package leaders, of advisory group, the use of gender sensitive language and the awareness of producing gender sensitive content. We are aware of the current imbalance in the advisory board and we will consider gender specifically in any new allocations. In line with the Toolkit on Gender in EU-funded research (European Commission, 2009) Made4You will strive at doing gender-sensitive innovation. Particularly in the following project steps gender has to be addressed and taken into account: * Project design and methodology: we will make sure that for any of our approaches in co-design and other engagement activities, we will aim at representative data in the sense that different gender perspectives will be described, where relevant. * Project implementation: Data-collection tools such as questionnaire, interview guidelines, etc. need to be gender sensitive and use gender-neutral language and have to allow for differentiation between gender perspectives. In the evaluation data analysis we will particularly pay attention whether there are differences between males and females, for instance, in terms of artefacts that are produced, in terms of communicating and sharing, etc. * Dissemination phase – reporting of data: We will use gender-neutral language in our publications. Furthermore, we will sensitively decide which visual materials to use. In addition, we will aim at publishing gender specific results. ### Science Education Science education under the RRI umbrella is meant to meet several objectives (European Commission, 2015; Föger et al., 2016): 1. To empower the society to critically reflect and to improve on their skills to be able to challenge research, thus to make them “science-literate” (in this sense, there is a great overlap with the key dimension of public engagement) 2. To enhance future researchers and other societal actors to become good RRI actors 3. To make science attractive to children and teenagers with the purpose to promote science careers, especially in STEM (Science, Technology, Engineering, and Mathematics) 4. To close the gap between science and education. There is still a significant distance between the two areas. Co-design is regarded as a possible empowering tool for science education as it enables participants to shape the development of certain technologies or services according to the RRITools project. In Made4You we plan to include children in the co-design process for certain cases. In addition, educational activities and student engagement are part of the WP1 activities. They are targeted at students in the field of medicine, paramedical professions, design & arts, biomedical engineering and Fab Academy and aim to familiarise them with co-design processes. Also, the maker spaces involved in the Made4You project regularly offer educational activities to young people and schools as the maker movement has started to get attention from schools and educational authorities. ## 5.6 Public Engagement In recent years, science communication has moved from the one-way- communication approach to basically inform the general public towards public engagement, which means more elaborate and active involvement of citizens leading to collaboration and empowerment. There is a vast range of tools and methods with different levels of participation available, e.g. public consultations, public deliberations for decision making, public participation in R&I processes, Citizen Science, etc. The goal by opening up research and innovation processes to the public is to better meet the values, needs and expectations of society and thus to improve R&I and to find solutions to the so called grand challenges that society is facing (Cagnin, Amanatidou, & Keenan, 2012). Thus, realising this key dimension of RRI is an important goal in Made4You and two work packages are jointly working together to reach high quality public engagement. WP1 and WP5 are closely working on a joint strategy and have created a working team at the kick-off meeting to jointly define and execute the engagement and communication strategy of the project. ## 5.7 RRI management in Made4You The notion of Responsible Research and Innovation does not offer a checklist or one universal guideline how to do RRI. It is also not in the spirit of RRI to have such set of measures, as RRI is rather perceived as a process that requires continuous questioning and reflection. Thus, mechanisms have to be installed and embedded in the project by work package 6 and 7 to stimulate reflection of the consortium and to keep these alive throughout the lifetime of the project. We would like to point out that not all key dimensions are equally relevant for Made4You as can be inferred from the discussion above. In the following we will therefore concentrate on these key dimensions which will be dealt with in more detail. However, also the remaining dimensions shall remain in our mind- sets as we would like so continuously stimulate reflection and discussion on RRI. In order to stimulate reflection and deliberation on Responsible research and innovation and to keep these alive we have foreseen several instruments: * Ethical and legal questionnaire : a questionnaire addressing specific ethical and legal aspects has been distributed to all project partners at the beginning of the project. Questions range from the data that is being stored at the Careables platform to the data being collected to the compliance of the platform with the GDPR as well as data subjects’ rights. This questionnaire especially informs the deliverables D8.1 and D8.2 and partially also this handbook. * RRI Self-Reflection-Tool : The RRI-Tools project has developed the so called “RRI SelfReflection-Tool”. It is an online tool for different stakeholder groups and for people with different levels of knowledge on RRI. The tool is meant to comprise food for thought and to sensitise for RRI and to stimulate reflection on RRI key dimensions and process requirements. Participants can choose which questions they would like to reflect upon (since not all of them will be relevant) and receive suggestions at the end how to further improve in terms of RRI. Further resources such as best practice examples, tools or literature will be recommended. In Made4You we will invite the project partners to regularly make use of the SelfReflection –Tool. * Legal and ethics workshop : At selected consortium meetings WP6 is running a legal and ethics workshop to discuss relevant topics based on the results of the questionnaire and the experiences made by the consortium. To summarise, the main instruments for implementing RRI are the following: * ethical guidelines, including forms for informed consent and confidentiality agreement * open data management plan * RRI self-assessment tool * RRI-related legal and ethics workshops # 6 Open access and open research data The project firmly believes in openness to be a major factor for innovation and this was also one of the main motivations for Made4You, which promotes openness in healthcare. Openness has many facets. The most important ones for the Made4You consortium are, following Carlos Moedas’s (European Commissioner for Research, Science and Innovation) strategy of the 3 Os, Open Science, Open Innovation and Open Data 4 : * Open project collaboration. All partners are committed to developing (working) relationships with external partners for mutual benefit. Making contacts with similar projects and establishing collaboration is considered beneficial for all. Open collaboration in Made4You is understood in a trans-disciplinary way, opening innovation processes to the wider public and allowing new form of collaboration as intended in the co-design activities of the project. * Open source technology. From a technology perspective, the project fosters the sharing of open healthcare solutions to be shared on the Careables platform. A main aim is to share the co-designed technological artefacts with the community. Business models and exploitation strategies are not based on locking down access to project results, but on providing added value through services. * Open access to scientific results. From a scientific perspective, the consortium clearly favours open access to its scientific output, which is supported by several project members’ internal policies of supporting open access in general. * Open access to research data. Made4You is part of a pilot action on open access to research data and is thus committed to providing access not only to project results and processes, but also to data collected during that process. Although Made4You is an innovation action according to the work programme definition and not a research action, some research related data will be collected, mainly from an evaluation perspective. Although the general policy of the Made4You project is to apply “open by default” to its research data, we have to handle privacy issues with special care. Legal rules on anonymity, as described above (chapter 6), are thus highly relevant and need to be agreed with each of the participants. In case of a doubt, data privacy of our participants always prevails over open data policy. Made4You is part of the H2020 Open Research Data Pilot (ORDP), a pilot action on open access to research data, which requires projects to define and execute a data management plan. This deliverable includes the open data management plan for Made4You. The open access strategy will be detailed in the following sections. ## 6.1 Open access strategy for publications In line with the EC policy initiative on open access 5 , which refers to the practice of granting free Internet access to research articles, the project is committed to follow a publication strategy considering a mix of both 'Green open access' (immediate or delayed open access that is provided through self- archiving) and 'Gold open access' (immediate open access that is provided by a publisher) as far as possible. All deliverables labelled as “public” will be made accessible via the Made4You website (careables.org). The publications stemming from the project work will also be made available on the website as far as it does not infringe the publishers rights as well as on the OpenAIRE platform . All outcomes of the project labelled as “public” will be distributed under specific free/open license, where the authors retain the authors’ rights but the users can redistribute the content freely. The following are a few relevant sources for deciding on the specific license for each outcome: * Data: ◦ A definition of Open Data: ◦ Licenses: * Software: ◦ Free Software ▪ The definition: ▪ Licenses: ◦ Open Source Software: ▪ The definition: ▪ Licenses: * Reports, publications, media: ◦ Creative Commons ▪ Explanation: ▪ Licenses: ▪ Choose a license: ◦ Sharing publications on the project website and via OpenAIRE ## 6.2 Data management plan (DMP) This is a first version of the DMP for Made4You, which provides an analysis of the main aspects to be followed by the project’s data management policy. The DMP evolves in the course of the project and will be updated accordingly as data is collected. However, we would like to stress once more that Made4You is an innovation action and large collection of research data is thus not the focus of the project. This data management plan refers mainly to the data collected for the achievement of the project objectives, namely co-designing a platform for sharing open healthcare. Complementary to this data management plan, Deliverable D8.2. (POPD) refers to the handling of personal (sensitive) patient data. Please not that this is not addressed here. At the time of writing it is expected that the project will produce the following data: * WP1: secondary data from stakeholders. e.g. other open healthcare initiatives, associations, healthcare providers, etc. * WP2: secondary and primary data from pilot participants, e.g. demographic data * WP3: platform usage data from Careables.org * WP4: feedback data from participants in activities of other WPs, interview and questionnaire data, log data from the Careables platform, social media data and observational analysis * WP5: data from other open healthcare projects regarding Dissemination, Exploitation, Communication of the Made4You project * WP6: feedback data from participants in activities of other WPs, interview and questionnaire data This initial list includes primary (empirical) and secondary (desk-top, aggregated) data. For the currently identifiable primary research data sets, that the project will produce, we follow the requested template description as define by the European Commission 6 : <table> <tr> <th> Data set reference & name </th> <th> Data set description </th> <th> Standards & metadata </th> <th> Data sharing </th> <th> Archiving & preservation </th> </tr> <tr> <td> DOI_1 Made4You_Co-design_X </td> <td> Feedback documented directly during co-design sessions regarding the codesign process itself as well as documentation standards </td> <td> As indexed on the sharing platform e.g. Zenodo, it will have publication data, Digital Object Identifier (DOI), keywords, collections, license, uploaded by </td> <td> Shared on Zenodo, open digital repository; license will be most probably: Creative Commons Attribution Share- Alike </td> <td> Zenodo is developed by under the EU FP7 project (grant agreement no. 283595); the service is free of charge for those without ready access to an organized data centre; if this policy changes Made4You will provide the data accessible via its website for the duration of at least 5 years after project end. </td> </tr> <tr> <td> DOI_2 Made4You_Survey_X </td> <td> Survey data being collected across the pilot participants and external stakeholders; the data will be anonymised and will refer to aspects of evaluation (e.g. usability and usefulness, process feed-back, etc.) and sustainability (e.g. interest in sharing open healthcare, etc.) </td> <td> As indexed on the sharing platform e.g. Zenodo, it will have publication data, DOI, keywords, collections, license, uploaded by </td> <td> Shared on Zenodo, open digital repository; license will be most probably: Creative Commons Attribution Share- Alike </td> <td> Zenodo is developed by under the EU FP7 project (grant agreement no. 283595); the service is free of charge for those without ready access to an organized data centre; if this policy changes Made4You will provide the data accessible via its website for the duration of </td> </tr> </table> <table> <tr> <th> </th> <th> </th> <th> </th> <th> </th> <th> at least 5 years after project end. </th> </tr> <tr> <td> DOI_3 Made4You_Interview_X </td> <td> Interviews conducted with individuals being associated to any of the pilots needs to be stored anonymously; sometimes only in aggregated from, if too many details would allow to deduced a specific person. The data may be in the following format (depending on the interviews and the specific cases): * audio files * transcripts * aggregated files * interview guidelines </td> <td> As indexed on the sharing platform e.g. Zenodo, it will have publication data, DOI, keywords, collections, license, uploaded by </td> <td> Shared on Zenodo, open digital repository; license will be most probably: Creative Commons Attribution Share- Alike </td> <td> Zenodo is developed by under the EU FP7 project (grant agreement no. 283595); the service is free of charge for those without ready access to an organized data centre; if this policy changes Made4You will provide the data accessible via its website for the duration of at least 5 years after project end. </td> </tr> <tr> <td> DOI_5 Made4You_PlatformUsage_X </td> <td> Platform usage data from Careables.org (anonymous data); the data includes: Communication pattern, usage patterns, uploads, downloads, etc. </td> <td> As indexed on the sharing platform e.g. Zenodo, it will have publication data, DOI, keywords, collections, license, uploaded by </td> <td> Shared on Zenodo, open digital repository; license will be most probably: Creative Commons Attribution Share- Alike </td> <td> Zenodo is developed by under the EU FP7 project (grant agreement no. 283595); the service is free of charge for those without ready access to an organized data centre; if this policy changes </td> </tr> </table> <table> <tr> <th> </th> <th> </th> <th> </th> <th> </th> <th> Made4You will provide the data accessible via its website for the duration of at least 5 years after project end. </th> </tr> </table> D7.1 Data management plan & handbook To summarise, the main open access points for Made4You data, publications, and innovation are: * The project website: * Zenodo: * OpenAIRE for depositing publications and research data ## 6.1 Open access and open data handling process The internal procedures to grant open access to any publication, research data or other innovation stemming from the Made4You project (e.g. technology), are following a lightweight structure, while respecting ethical issues at all time. The main workflow starts at the WP level, where each team is responsible for respecting ethical procedures at all times during the data gathering and processing steps. The WP/working team members are also responsible for any data anonymization, if applicable. Agreement has to be reached within the team for making any outcome openly available; the final approval is done by the Project Management Board (see Figure 8): Figure 8 Open Access work flow : Collecting data Preparing p ublication Ethical guidelines, informed consent Approval by Project Management Board Developing standards Open Repository & www.careables.org Approval within team Anonymization … . Due to the nature of the project, the Data Management Plan may have to be revised during the course of project activities. As the co-design approach is a rather dynamic methodology it is not possible to clearly specify all data sources and collected outcomes from the beginning. Conclusions This handbook describes the main procedures of the Made4You project to operate successfully and effectively in order to achieve high quality project results following a responsible research and innovation (RRI) approach. Open access, ethics, and engagement of all societal actors are amongst the key elements of the European RRI framework (European Union, 2012). Made4You is clearly committed to respond to societal challenges in a responsible way by itself, given its main objective of open healthcare, and by the way the actions in the project are conducted. While this handbook is provided in the form of a report and deliverable, it is a living document in the sense of being updated and challenged by the consortium in the course of the project. The processes described in here are implemented in the daily work of the consortium and most of the elements (e.g. the forms for informed consent, data management plan, etc.) are separately available on the collaboration infrastructure such as Nextcloud. D7.1 Data management plan & handbook The management reports will include updates on any crucial changes in the handbook as well as on the results of specific measures such as the SWOT analysis or any additional elements added to the project structure related to high quality responsible research.	https://phaidra.univie.ac.at/o:1140797	Horizon 2020
0008_GREEN-WIN_642018.md	# Background and purpose of a Data Management Plan The GREEN-WIN Project participates in the Open Research Data Pilot , which aims to improve and maximize access to research data generated by the project. The project consortium has decided to participate in this pilot on a voluntary basis, as stated in the article 29.3 of the Grant Agreement (p. 48). The GREEN-WIN Project will therefore be monitored and receive specific support as a participant in the pilot. As stated in the “ _Guidelines on Open Access to scientific publication and research data in_ _Horizon 2020_ ”, the Open Research Data Pilot applies to two types of data: 1. the data needed to validate the results presented in scientific publications; 2\. other data as specified within the data management plan (DMP) . A DMP is a document outlining _how the research data collected or generated will be handled_ during a research project, and after the project is completed. The DMP describes _what data will be collected / 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_ . The “ _Guidelines on Data Management in Horizon 2020_ ” states that a first version of the DMP must be provided within six months of the project, but that the DMP is not a fixed document; it evolves and gains more precision and substance during the lifespan of the project. The DMP would need to 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 from the start. # Data set description ## Type of data generated According to the Grant Agreement Annex 1 (Description of Action) Part B (p. 33), the following data will be collected and generated during the GREEN-WIN project: * Qualitative data on relevant policies, policy processes and institutions, including description of actors, interests and actor-networks at national (WP2) and regional to local levels (WP4, WP5-7). * Data on business models described through a standard protocol template associated with estimated capital needs (WP4, WP5-7). * Quantitative data on financial flows, international trade and flows of goods. * Quantitative data on key macroeconomic indicators, environmental and social impacts (mitigation pathways of WP3). Public output papers of Green-Win project will include: * Project deliverables and milestones * Scientific publications * Conference and workshop presentations * Policy briefs * Newsletters * Posters/Flyers * Blogs ## Methods of data collection Both primary and secondary data will be collected. * Primary data on win-win solutions, green business models and enabling environments will be collected via interviews, workshops and participatory observation in field trips and business collaborations/partnerships, e.g. in the case studies of WP5, 6 & WP7. This includes both quantitative and qualitative data. * Primary data will also be generated by the macroeconomic modelling of WP3. * Secondary data will be obtained via databases (Bloomberg, Thompson Reuters, etc.) and the review of policy documents and scientific literature. Furthermore, both quantitative and qualitative data will be collected on win- win solutions and green business models via tailor-made templates (WP4). All work-package leaders are responsible for data management within their work-package. The overall process is overseen by the project coordinator (Jochen Hinkel, [email protected]) and the project administrator (Daria Korsun, [email protected]). ## Formats Qualitative data of interview and workshop may be collected through audio recordings or concurrent note-taking. * Text documents - .doc, .docx, .pdf or .odt files * Images - .png, .jpg and .tif files * Audio - .mp3, .flac and .wav files * Video – .mp4 files Quantitative data will be stored as * Tabular data - .csv .xls, .xlsx or .csv files. # Standards and metadata ## Metadata used and metadata standard Data will be documented following the common standards provided by Horizon 2020 guidance. * The terms “European Union (EU)” and “Horizon 2020 (H2020)” * The name of the action, acronym and the grant number * The publication date, and the length of embargo period of applicable * The authors * A persistent identifier It also includes a description of the document’s content (summary or blog) and key words (tags) for search. Scientific publications deposited in a repository will include bibliographic metadata required by the publisher and use a standard identification mechanism such as Digital Object Identifiers (DOI). Internally, all Green-Win partners use a version numbers format as such: V0, V1, V2, etc for submission of the project papers. # Data sharing ## Ownership The ownership policy is described in the Consortium Agreement section 8 (p. 15) and in the Grant Agreement Article 26 (pp. 43-45). In particular: * “Results are owned by the beneficiary who generates them” (art. 26.1). * Detailed procedure for joint-ownership is stipulated in article 26.2. * Article 26.3 describes the procedure to follow when results obtained by a beneficiary are generated by a third party (transfer of rights, licenses…). * Beneficiaries have the obligation to protect results generated during the project that could be commercially exploited, and when protection is possible, reasonable and justified (Article 27.1). In the case that the beneficiary intends not to protect the results, the Agency may take over ownership under specific conditions described in article 26.4. The ownership of specific results might be protected using Creative Common Attribution Licenses (CC-BY or ODB-By), as stated in the Grant Agreement Annex 1 (Description of Action) Part B, p. 33. ## Access to data generated/collected Datasets will be made available either attached to a published article or published in existing data repositories (cf. table in section 5). Internally, the data is stored on OwnCloud platform accessible to all consortium partners. Externally, the data is accessible on the Green-Win website (green-win- project.eu/deliverables and green-win-project.eu/publications) and on GGKP platform (greengrowthknowledge.org/resources). Data concerning topics of climate change mitigation is also stored on Climate Change Mitigation Platform (climatechangemitigation.eu/about/related-eu-projects/green-win/). The data is available in one of the formats specified in 2.3 Format section. No special software tools are needed. We will thereby follow the requirements of publishers concerning the accessibility of datasets underlying a research article. Data collected/generated but not yet published will remain inaccessible to the public. Furthermore, certain types of data will remain unavailable to the public including: - Data originating from proprietary databases or under license, - Confidential, private or personal data (following section 4.3). ## Specifics regarding anonymity For data collected in interviews and workshops, data handling may need to adhere to practices that ensure the anonymity of research participants is maintained. Whether anonymity needs to be maintained is determined by choice of the research participant and recorded in the GREEN-WIN Informed Consent Forms, which must be completed by all research participants prior to participating in the project (See D9.1 and D9.2). Maintaining the anonymity of participants, when this has been requested, will take precedence over the requirement to make data publicly available. The procedures for ensuring anonymity of those research participants who have elected to remain anonymous have been described in D9.5. # Archiving and preservation ## Data storage Copies of datasets are stored: * On the internal website, * On local computers (of the data producer and of the PMO). The internal website is backed up on a server and synchronized to local computers everyday. We use an Owncloud server to store and archive the data, which is passwordprotected and encrypted (https). ## Data preservation The Green-Win project website will be accessible a year after the end of the project (December 2018). At the end of the project all the output papers will be stored on the GGKP platform as well. The OwnCloud server storing the data will be kept up and running for one year after the end of the project. Afterwards, all data and files on the server will be archived on the GCF file server for 5 years. Some partners will also store the datasets on their own servers, which will also be publicly available (Table 1). _GREEN-WIN Project 642018 RIA; Data Management Plan_ # Summary of data management plan <table> <tr> <th> WP </th> <th> Type of data produced </th> <th> Qualitative/ Quantitative </th> <th> Anonymity measures to be applied (§3.3) </th> <th> Dissemination </th> <th> Data storage </th> <th> Publicly available </th> </tr> <tr> <td> WP1 </td> <td> Narratives from dialogue workshops </td> <td> Qualitative </td> <td> No </td> <td> Peer-review publications solutions potentially featured on: _http://climatechangem_ _itigation.eu_ </td> <td> GCF (personal computers and/or internal servers), internal website: http://green-winproject.eu </td> <td> Yes, documented in a report of the second dialogue workshop Will be presented on Final Green-Win conference in Barcelona in March 2018 </td> </tr> <tr> <td> WP2 </td> <td> Interview transcripts, financial data to populate models </td> <td> Qualitative & quantitative </td> <td> Yes </td> <td> Peer-review publication </td> <td> UCL (personal computers and/or internal servers), </td> <td> Not publicly available (team members) </td> </tr> <tr> <td> WP3 </td> <td> Model results </td> <td> Quantitative </td> <td> No </td> <td> Report, Peer -review publications </td> <td> E3M (personal computers and/or internal servers), internal website: http://green-winproject.eu </td> <td> Yes, available on project website </td> </tr> <tr> <td> WP4 </td> <td> Socioeconomic, technical and organisational information of WinWin strategies and GBMs </td> <td> Qualitative & Quantitative </td> <td> Yes </td> <td> Ground_Up Association (Nonprofit) internal database </td> <td> _http://survey.grounduppr oject.org/_ </td> <td> Not public. Only GREENWIN project partners can access it upon approval of Ground_Up. </td> </tr> <tr> <td> WP4 </td> <td> Interview transcripts & minutes from workshops with investors </td> <td> Qualitative </td> <td> Yes </td> <td> Peer-review publication </td> <td> IASS (personal computers and/or internal servers) </td> <td> Not publicly available (team members) </td> </tr> <tr> <td> WP5 </td> <td> Interview transcripts </td> <td> Qualitative </td> <td> Yes </td> <td> Peer-review publication </td> <td> Deltares (personal computers and/or internal servers) </td> <td> Not publicly available (team members) </td> </tr> <tr> <td> WP6 </td> <td> Interview transcripts & questionnaire responses </td> <td> Qualitative </td> <td> Yes </td> <td> Peer-review publication </td> <td> Available upon request to the authors </td> <td> Not publicly available (team members) </td> </tr> <tr> <td> WP7 </td> <td> Interview recordings & questionnaire responses </td> <td> Qualitative </td> <td> Yes </td> <td> Peer-review publication </td> <td> Available upon request to the authors </td> <td> Not publicly available (team members) </td> </tr> <tr> <td> WP8 </td> <td> Socioeconomic, technical and organisational information of GBMs. Data is introduced and shared on the platform directly by the entrepreneurs/GBM leaders upon registration. </td> <td> Qualitative & Quantitative </td> <td> Yes </td> <td> Ground_Up Project (Company) platform 7 </td> <td> _http://groundupproject.ne t/_ </td> <td> Only basic company information and contact is public and only for those who register on the platform (investors, entrepreneurs, service providers). GBM description is confidential to all users and becomes available to investors upon request of entrepreneurs. </td> </tr> </table>	https://phaidra.univie.ac.at/o:1140797	Horizon 2020
0009_SAFEWAY_769255.md	# Executive Summary This document describes the Data Management Plan (DMP) for the SAFEWAY project. The DMP provides an analysis of the main elements of the data management policy that will be used throughout the SAFEWAY project by the project partners, with regard to all the datasets that will be generated by the project. The documentation of this plan is a precursor to the WP1 Management. The format of the plan follows the Horizon 2020 template “Guidelines on Data Management in Horizon 2020” 1 . # Glossary of Terms <table> <tr> <th> DMP </th> <th> Data Management Plan </th> </tr> <tr> <td> E&BP </td> <td> Exploitation and Business Plan </td> </tr> <tr> <td> GDPR </td> <td> General Data Protection Regulation </td> </tr> <tr> <td> GFS </td> <td> Global Forecast System </td> </tr> <tr> <td> GIS </td> <td> Geographic Information System </td> </tr> <tr> <td> IMS </td> <td> Information Management System </td> </tr> <tr> <td> INEA </td> <td> Innovation and Networks Executive Agency </td> </tr> <tr> <td> IPMA </td> <td> Instituto Português do Mar e da Atmosfera </td> </tr> <tr> <td> IPR </td> <td> Intellectual Property Rights </td> </tr> <tr> <td> MMS </td> <td> Mobile Mapping System </td> </tr> <tr> <td> WP </td> <td> Work Package </td> </tr> <tr> <td> </td> <td> </td> </tr> <tr> <td> </td> <td> </td> </tr> <tr> <td> </td> <td> </td> </tr> <tr> <td> </td> <td> </td> </tr> <tr> <td> </td> <td> </td> </tr> <tr> <td> </td> <td> </td> </tr> <tr> <td> </td> <td> </td> </tr> <tr> <td> </td> <td> </td> </tr> <tr> <td> </td> <td> </td> </tr> <tr> <td> </td> <td> </td> </tr> <tr> <td> </td> <td> </td> </tr> <tr> <td> </td> <td> </td> </tr> <tr> <td> </td> <td> </td> </tr> </table> # Introduction The elaboration of the Data Management Plan (DMP) will allow SAFEWAY partners to address all issues related with data management. Due to the importance of research data in the support of the publications, it is necessary to define a data management policy. This document introduces the first version of the project Data Management Plan where the different datasets that will be produced within SAFEWAY project are identified. The document also includes the main exploitation perspectives for each of those datasets and major management principles the project will implement to handle those datasets. Although the DMP is a Deliverable to be submitted in Month 6 (D.1.3), this is also a live document throughout the life of the project. This initial version will evolve during the project according to the progress of project activities. Table 1: Planed calendar for submission of the DMP and its updates <table> <tr> <th> Deliverable Number </th> <th> Deliverable Title </th> <th> Due date </th> </tr> <tr> <td> D1.3 </td> <td> Data Management Plan (DMP) V1 </td> <td> M6 </td> </tr> <tr> <td> D1.5 </td> <td> Data Management Plan (DMP) V2 </td> <td> M18 </td> </tr> <tr> <td> D1.7 </td> <td> Data Management Plan (DMP) V3 </td> <td> M30 </td> </tr> <tr> <td> D1.9 </td> <td> Data Management Plan (DMP) V4 </td> <td> M42 </td> </tr> </table> The DMP will cover the complete data life cycle as shown in Figure 1\. Figure 1. Data life cycle # General Principles ## Pilot on Open Research Data The SAFEWAY Project is fully aware of the open access to scientific publications article (Article 29.2 of the H2020 Grant Agreement), as well as to the open access to research data article (Article 29.3 of the H2020 Grant Agreement). However, project partners have opted to be out of the Open Research Data due to a possible conflict with protecting results; SAFEWAY results will be close to market and results’ disclosures should be taken with care and always considering exploitation/commercialization possibilities. ## IPR management and security The SAFEWAY project strategy for knowledge management and protection considers a complete range of elements leading to the optimal visibility of the project and its results, increasing the likelihood of market uptake of the provided solution and ensuring a smooth handling of the individual intellectual property rights of the involved partners in view or paving the way to knowledge transfer: IPR protection and IPR strategy activities will be managed by Laura TORDERA from FERROVIAL (leader of WP10) as Innovation and Exploitation Manager with the support of the H2020 IPR Helpdesk. The overall IPR strategy of the project is to ensure that partners are free to benefit from their complementarities and to fully exploit their market position. Hence, the project has a policy of patenting where possible. An IPR Plan will be included in the Exploitation & Business Plans (D10.4). Regarding Background IP (tangible and intangible input held by each partner prior to the project needed to the execution of the project and/or exploiting the results) it will be detailed in the Consortium Agreement, defining any royalty payments necessary for access to this IP. Regarding Foreground IP (results generated under the project) they will belong to the partner who has generated them. Each partner will take appropriate measures to properly manage ownership issues. When several beneficiaries had jointly carried out generating results and where their respective share of work cannot be ascertained, they will have joint ownership of such results. They will stablish an agreement regarding the allocation of terms of exercising the joint ownership, including definition of the conditions for grating licenses to third parties. ## Allocation of resources The Project Technical Committee (PTC) will be responsible of collecting the knowledge generated and defining protection strategy and the necessary access rights for results exploitation, as well as propose fair solutions to any possible conflict related to IPR. Complementarily, the PTC through the Exploitation & Innovation Manager (E&IM) will keep a permanent surveillance activity on the blocking IP or new IP generated elsewhere in the EU landscape to ensure SAFEWAY freedom to operate. The output of this activity will be included in the Exploitation and Business Plan (E&BP), which will be updated during the project time frame. ## Personal data protection For some of the activities to be carried out by the project, it may be necessary to collect basic personal data (e.g. full name, contact details, background), even though the project will avoid collecting such data unless deemed necessary. Such data will be protected in compliance with the EU's General Data Protection Regulation, Regulation (EU) 2016/679. National legislations applicable to the project will also be strictly followed. All data collected by the project will be done after giving data subjects full details on the experiments to be conducted, and after obtaining signed informed consent forms. Such forms, provided in the previous deliverable D11.2 POPD – Requirement No 2, are included in Appendix 1 of this document. Additionally, the overall information about procedures for data collection, processing, storage, retention and destruction were also provided in D11.2, which are annexed to the present DMP in Appendix 2. ## Data security SAFEWAY shall take the following technical and organizational security measures to protect personal data: 1. Organizational management and dedicated staff responsible for the development, implementation, and maintenance of SAFEWAY’s information security program. 2. Audit and risk assessment procedures for the purposes of periodic review, monitoring and maintaining compliance with SAFEWAY policies and procedures, and reporting the condition of its information security and compliance to senior internal management. 3. Maintain Information security policies and make sure that policies and measures are regularly reviewed and where necessary, improve them. 4. Password controls designed to manage and control password strength, and usage including prohibiting users from sharing passwords. 5. Security and communication protocols, following Big Data analytics, will be developed as required. SAFEWAY solutions will anticipate security not only technically, but also regarding Data Protection Regulation 2016/679 changes in the Data Protection Regime as of May 2018. 6. SAFEWAY solutions will not centralise all the native data in a common database, but instead will retrieve data with values for the platform functionalities on demand. The services layer of the platform includes communication application proceeding information disclosure. 7. Operational procedures and controls to provide for configuration, monitoring, and maintenance of technology and information systems according to prescribed internal and adopted industry standards, including secure disposal of systems and media to render all information or data contained therein as undecipherable or unrecoverable prior to final disposal. 8. Change management procedures and tracking mechanisms designed to test, approve and monitor all changes to SAFEWAY technology and information assets. 9. Incident management procedures designed to investigate, respond to, mitigate and notify of events related to SAFEWAY technology and information assets. 10.Vulnerability assessment, patch management, and threat protection technologies and scheduled monitoring procedures designed to identify, assess, mitigate and protect against identified security threats, viruses and other malicious code. 11.Data could wherever be processed in anonymised or pseudonymised form. 12.Data will be processed ONLY if it is really adequate, relevant and limited to what is necessary for the research (‘data minimisation principle’). 1. Personal data will be adequate, relevant and limited to what is necessary in relation to the purposes for which they are processed. 2. The minimum amount of personal data necessary to fulfil the purpose of SAFEWAY will be identified. 3. No more personal data than necessary for the purpose of SAFEWAY will be achieved and stored. 4. Whenever it is necessary to process certain particular information about certain individuals, it will be collected only for those individuals. 5. Personal data will not be collected if it could be useful in the future. These guidelines will be of special application for INNOVACTORY and TØI CENTRE, the two project partners who are more intensive in the use of personal data. In the Deliverable D11.1-Ethics Requirements are annexed the exact treatment of the data made by these two entities. ## Ethical aspects An ethical approach will be adopted and maintained throughout the fieldwork process. The Ethics Mentor 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 according to the respective EU Directives for Data Management and ethics. The consortium will ensure the participants’ right to privacy and confidentiality of data in the surveys, by providing participants to the survey with the Informed Consent Procedures: \- for those participating in the surveys being carried out within Task 4.3, by the Institute of Transport Economics-Norwegian Center for Transport Research These documents will be sent electronically and will provide information about how the answers will be used and what is the purpose of the survey. Participants will be assured that their answers, or personal data, will be used only for the purposes of the specific survey. The voluntary character of participation will be stated explicitly in the Consent Form. As it is established in Deliverable D11.3, an Ethics Mentor is appointed to advise the project participants on ethics issues relevant to protection of personal data. The Ethics Mentor will advise and supervise the following aspects of the Project: * _Data protection by design and default_ . The Project will require data control to implement appropriate technical and organisational measures to give effect to the GDPR’s core data-protection principles. * _Informed consent to data processing_ . Whenever any personal data is collected directly from research participants, their informed consent will be seek by means of a procedure that meets the standards of the GDPR. * _Use of previously collected data (‘secondary use’)_ . If personal data is processed in the Project without the express consent of the data subjects, it will be explained how those data are obtained, and their use in the Project will be justified. * _Data protection impact assessments (DPIA)_ . If the Project involves operations likely to result in a high risk to the rights and freedoms of natural persons, this document will be conducted. * _Profiling, tracking, surveillance, automated decision-making and big data_ . If the Project involves these techniques, a detailed analysis will be provided of the ethics issues raised by this methodology. It will comprise an overview of all planned data collection and processing operations; identification and analysis of the ethics issues that these raise, and an explanation of how these issues will be addressed to mitigate them in practice. * _Data security_ . Both ethical and legal measures will be conducted to ensure that participants’ information is properly protected. These may include the pseudonymisation and encryption of personal data, as well as policies and procedures to ensure the confidentiality, integrity, availability and resilience of processing systems * _Deletion and archiving of data_ . Finally, the collected personal data will be kept only as long as it is necessary for the purposes for which they were collected, or in accordance with the established auditing, archiving or retention provisions for the Project. These must be explained to your research participants in accordance with informed consent procedures. # Data Set Description SAFEWAY is committed to adopt whenever possible the FAIR principles for research data; this is, data should be findable, accessible, interoperable and re-usable. SAFEWAY partners have identified the datasets that will be produced during the different phases of the project. The list is provided below, while the nature and details for each dataset are given in Section 4\. This list is indicative and allows estimating the data that SAFEWAY will produce – it may be adapted (addition/removal of datasets) in the next versions of the DMP to take into consideration the project developments. Table 2: SAFEWAY Dataset overview <table> <tr> <th> No </th> <th> Dataset name </th> <th> Responsibl e partner </th> <th> Related Task </th> </tr> <tr> <td> 1 </td> <td> Mobile Mapping System (MMS) data </td> <td> UVIGO </td> <td> T3.2 </td> <td> </td> </tr> <tr> <td> 2 </td> <td> Historic weather dataset </td> <td> UVIGO </td> <td> T3.1 T3.3 </td> <td> & </td> </tr> <tr> <td> 3 </td> <td> Global Forecasting System (GFS) data </td> <td> UVIGO </td> <td> T3.1 T3.3 </td> <td> & </td> </tr> <tr> <td> 4 </td> <td> Satellite data </td> <td> PNK </td> <td> T3.2 </td> <td> </td> </tr> <tr> <td> 5 </td> <td> Experts interviews </td> <td> TØI </td> <td> T4.3 </td> <td> </td> </tr> <tr> <td> 6 </td> <td> Data on risk tolerance </td> <td> TØI </td> <td> T4.3 </td> <td> </td> </tr> <tr> <td> 7 </td> <td> Sociotechnical system analysis </td> <td> TØI </td> <td> T4.3 </td> <td> </td> </tr> <tr> <td> 8 </td> <td> Infrastructure assets data </td> <td> UMINHO </td> <td> T5.1 </td> <td> </td> </tr> <tr> <td> 9 </td> <td> Information on the value system </td> <td> IMC </td> <td> T6.1 </td> <td> </td> </tr> <tr> <td> 10 </td> <td> Stakeholder contacts collection </td> <td> UVIGO </td> <td> WP10 </td> </tr> <tr> <td> 11 </td> <td> Workshops data </td> <td> FERROVIAL </td> <td> T10.3 </td> </tr> </table> Table 3: Datasets description and purpose <table> <tr> <th> No </th> <th> Dataset name </th> <th> Description </th> <th> Purpose </th> </tr> <tr> <td> 1 </td> <td> MMS data </td> <td> Data from the different sensors equipped in the Mobile Mapping System (MMS) employed for the monitoring of the infrastructures, including data from some or all the following sources: LiDAR sensors, RGB cameras, thermographic cameras, and Ground Penetrating Radar. </td> <td> Inspection of the infrastructure critical assets to quantify condition. From this data, the input information for predictive models (WP5) and SAFEWAY IMS (WP7) will be extracted. </td> </tr> <tr> <td> 2 </td> <td> Historic weather dataset </td> <td> Observational quantitative meteorological data measured with hourly (or less) temporal frequency over the Instituto Português do Mar e da Atmosfera (IPMA) weather stations network. Relevant variables are air temperature, atmospheric pressure, wind speed and direction, maximum wind gusts speed and direction, relative air humidity, instant rain and solar radiation. </td> <td> Main source of observational info for meteorological data interpolation and short-term prediction systems. Base dataset for meteorological activities on WP3. </td> </tr> <tr> <td> 3 </td> <td> Global Forecast System (GFS) data </td> <td> Predictive quantitative meteorological data calculated with hourly temporal frequency over a planetarywide ~11 km horizontal spatial resolution by the National Oceanic and Atmospheric Administration Global Forecast System (GFS) numerical model. Relevant variables are those most analogous to the Historic weather dataset ones. </td> <td> Complementary source of observational info for meteorological data interpolation and short-term prediction systems. Used on the same way than the Historic weather dataset. </td> </tr> <tr> <td> 4 </td> <td> Satellite data </td> <td> Sentinel-1 satellite imagery from Copernicus Open Access Hub, to optimize the Rethicus® displacement </td> <td> Geospatial information acquired from satellite are key to detect and </td> </tr> </table> <table> <tr> <th> No </th> <th> Dataset name </th> <th> Description </th> <th> Purpose </th> </tr> <tr> <td> </td> <td> </td> <td> service based on MTInSAR algorithms. </td> <td> quantify terrain displacement and deformation (e.g. landslides, subsidence, etc.) </td> </tr> <tr> <td> 5 </td> <td> Experts interviews </td> <td> The data contain transcriptions and notes from expert interviews with researchers and policy makers. They will be either conducted personally, on the phone (or skype) or they can also be conducted in written form. Include findings from completed/ongoing EU projects </td> <td> The aim is to identify and collect sources of knowledge on how the different users think/act in extreme situations, as well as their level of preparedness and risk tolerance, and identify case studies for analysis of risk tolerance </td> </tr> <tr> <td> 6 </td> <td> Data on risk tolerance </td> <td> This includes the evaluation of risk tolerance of different actors and scheduling for use in focus groups, and follow-up surveys with different user representatives. </td> <td> To make findings on varying levels of risk tolerance and preparedness for a range of short- and long-term extreme events, among the user groups </td> </tr> <tr> <td> 7 </td> <td> Sociotechnical system analysis </td> <td> Selected cases will be documented to represent a range of event types occurring in Europe. Interviews and template analysis will be conducted with people both managing and caught up in the extreme events studied. </td> <td> These analyses along with established sociotechnical system principles will inform on optimal social and technical arrangements for IMS. </td> </tr> <tr> <td> 8 </td> <td> Infrastructure assets data </td> <td> Database of infrastructures with identification, conservation state, inspections and structural detailing </td> <td> Databased needed to define the input data to the development of predictive models. </td> </tr> <tr> <td> 9 </td> <td> Information on the value system </td> <td> The information on the value systems, decision making processes and key performance indicators that </td> <td> The monetized direct and indirect consequences of inadequate </td> </tr> <tr> <td> No </td> <td> Dataset name </td> <td> Description </td> <td> Purpose </td> </tr> <tr> <td> </td> <td> </td> <td> transportation infrastructure agencies and stakeholders within the project use in management of their assets. </td> <td> infrastructure performance is needed as input to develop the value system that will allow to prioritize the intervention of stakeholders related to transport infrastructure. </td> </tr> <tr> <td> 10 </td> <td> Stakeholder contacts collection </td> <td> The data contain information on the main stakeholders of SAFEWAY along the major stakeholder groups. They include infrastructure managers, operators, public administrations, researchers, practitioners, policy makers. The contact information that is collected includes the name, institutional affiliation, position, email address, phone number and office address. </td> <td> The collection will be used for contacting the respondents for the validation of the project outcomes. It also provides the basis for the dissemination of the project and for promoting the SAFEWAY IT solutions. </td> </tr> <tr> <td> 11 </td> <td> Workshops data </td> <td> The data contain protocols, written notes and summaries that were done at the three workshops, which are organized in different countries. The workshops aim at developers and providers of technical solutions. This dataset also includes the collection of contact information of attendees that includes the name, institutional affiliation, position, email address, phone number and office address. </td> <td> The information gathered at the workshops will support the development of the SAFEWAY methodologies and tools. </td> </tr> </table> # SAFEWAY Datasets ## Dataset No 1: MMS data <table> <tr> <th> Mobile Mapping System (MMS) data </th> </tr> <tr> <td> Data identification </td> </tr> <tr> <td> Dataset description </td> <td> This dataset comprises all the data collected by the mapping technologies proposed by UVIGO in WP3. Therefore, it contains data from the different sensors equipped in the Mobile Mapping System (MMS) employed for the monitoring of the infrastructures, including data from some or all the following sources: LiDAR sensors, RGB cameras, thermographic cameras, and Ground Penetrating Radar. Data from different LiDAR sensors (Terrestrial or Aerial) that may be employed for the fulfilment of the different monitoring tasks will be comprised in this dataset as well. </td> </tr> <tr> <td> Source </td> <td> Sensor data gathered from the Mobile Mapping System (MMS) owned by UVIGO. </td> </tr> <tr> <td> Partners activities and responsibilities </td> </tr> <tr> <td> Partner owner of the data; copyright holder (if applicable) </td> <td> UVIGO; N/A </td> </tr> <tr> <td> Partner in charge of the data collection </td> <td> UVIGO </td> </tr> <tr> <td> Partner in charge of the data analysis </td> <td> UVIGO </td> </tr> <tr> <td> Partner in charge of the data storage </td> <td> UVIGO </td> </tr> <tr> <td> Related WP(s) and task(s) </td> <td> WP3: -Task 3.1 (Data acquisition). -Task 3.2 (Data pre-processing). -Task 3.3 (Data processing and automation of monitoring) </td> </tr> <tr> <td> Standards </td> </tr> <tr> <td> Info about metadata (production and storage dates, places) and documentation? </td> <td> Point cloud data from LiDAR sensors will be produced in real time when the monitoring of the infrastructures is carried out. The metadata of that information, stored in ‘.las’ format, has its documentation in _http://www.asprs.org/wp-_ _content/uploads/2019/03/LAS_1_4_r14.pdf_ </td> </tr> </table> <table> <tr> <th> Mobile Mapping System (MMS) data </th> </tr> <tr> <td> </td> <td> Imagery will be produced together with the point cloud data, and the metadata will have the specifications of the correspondent image file format. </td> </tr> <tr> <td> Standards, format, estimated volume of data </td> <td> Data recorded from the different sensors of the MMS dataset will be stored in standard formats: * Point cloud data obtained from the LiDAR sensors will be stored either in standard binarized format (.las) or (less likely) as plain text (.txt). * Imagery will be stored in standard image file formats (.jpg, .tiff…) </td> </tr> <tr> <td> Data exploitation and sharing </td> </tr> <tr> <td> Data exploitation (purpose/use of the data analysis) </td> <td> The recorded data will be used for the monitoring of the infrastructures within the case studies of the project. The raw data acquired by the set of sensors equipped in the monitoring system will be processed to extract meaningful information about the infrastructure that can feed different attributes of the Infrastructure Information Model that is being developed in Task 3.3, and also for three-dimensional visualization of the monitored infrastructure. </td> </tr> <tr> <td> Data access policy / Dissemination level: confidential (only for members of the Consortium and the Commission Services) or Public </td> <td> Only the partner in charge of the data collection will have access to the raw data of the dataset. The results of the data processing tasks (mainly attribute fields required by the Infrastructure Information Model) will be shared with other members as they will be integrated into the SAFEWAY database. Any relevant threedimensional visualization of the data could be made public for presenting final results. </td> </tr> <tr> <td> Data sharing, re-use, distribution, publication (How?) </td> <td> Data sharing and re-use at the end of the project will be subjected to the permission of the infrastructure owners. Nevertheless, data will be available for research purposes (development of future data processing algorithms) provided that datasets are fully anonymized in such a way they cannot be associated to real structures. </td> </tr> <tr> <td> Embargo periods (if any) </td> <td> N/A </td> </tr> <tr> <td> Personal data protection: are there personal data? If so, have you gained (written) consent from data subjects to collect this information? </td> <td> Data collected from this dataset will not intentionally include any personal data. In the event of an identifiable individual within the imagery part of the dataset, these data </td> </tr> <tr> <td> Mobile Mapping System (MMS) data </td> </tr> <tr> <td> </td> <td> will be pre-processed to ensure that it is anonymised or pseudonymised. </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 secured servers of the partner in charge of the dataset, where only research members will be granted access to the information within the dataset. The Consortium will take into account that for the purposes of the SAFEWAY project the retention period is the one used in the relevant field, by analogy to the administrative and financial issues this will be 5 years. </td> </tr> <tr> <td> Data destruction. How is data destruction handled? Compliance with EU / national legislation. </td> <td> Data destruction will always comply with EU and national legislation, and will be retained five years after the project ends. </td> </tr> </table> ## Dataset No 2: Historic weather dataset <table> <tr> <th> Historic weather dataset </th> </tr> <tr> <td> Data identification </td> </tr> <tr> <td> Dataset description </td> <td> IPMA’s Portugal Weather Dataset. </td> </tr> <tr> <td> Source </td> <td> Instituto Português do Mar e da Atmosfera. Web: _http://www.ipma.pt/pt/index.html_ </td> </tr> <tr> <td> Partners activities and responsibilities </td> </tr> <tr> <td> Partner owner of the data; copyright holder (if applicable) </td> <td> IPMA. </td> </tr> <tr> <td> Partner in charge of the data collection </td> <td> IP. </td> </tr> <tr> <td> Partner in charge of the data analysis </td> <td> UVIGO. </td> </tr> <tr> <td> Partner in charge of the data storage </td> <td> UVIGO. </td> </tr> <tr> <td> Related WP(s) and task(s) </td> <td> WP3, tasks 3.1, 3.3. </td> </tr> <tr> <td> Standards </td> </tr> <tr> <td> Info about metadata (production and storage dates, places) and documentation? </td> <td> Observation weather data is continuously generated by the automated meteorological stations belonging to the </td> </tr> <tr> <td> Historic weather dataset </td> </tr> <tr> <td> </td> <td> IPMA’s network with a 1 hour (or 10 minutes) frequency. IPMA will provide a subset of such data, limited to the requested variables, for the considered stations and timespan. </td> </tr> <tr> <td> Standards, format, estimated volume of data </td> <td> JSON, XML or SQL formats for storing meteorological data. Hour-interval numeric values for each of the 9 required meteorological variables (air temperature, atmospheric pressure, wind speed and direction, maximum wind gusts speed and direction, relative air humidity, instant rain and solar radiation), for each of the provided observation weather stations (number between 30 and 100), during the Portuguese meteorological case study time lapse. </td> </tr> <tr> <td> Data exploitation and sharing </td> </tr> <tr> <td> Data exploitation (purpose/use of the data analysis) </td> <td> Input for interpolation and short-term prediction algorithms used in WP3. </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> Collected data will potentially be used in future scientific research papers. </td> </tr> <tr> <td> Embargo periods (if any) </td> <td> N/A </td> </tr> <tr> <td> Personal data protection: are there personal data? If so, have you gained (written) consent from data subjects to collect this information? </td> <td> No personal data. </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 permanently stored in UVIGO computer facilities for the duration of the SAFEWAY project. </td> </tr> <tr> <td> Data destruction. How is data destruction handled? Compliance with EU / national legislation. </td> <td> Data will be stored indefinitely, with no planned destruction. </td> </tr> </table> ## Dataset No 3: GFS data <table> <tr> <th> Global Forecast System (GFS) data </th> </tr> <tr> <td> Data identification </td> </tr> <tr> <td> Dataset description </td> <td> GFS Portugal Weather Dataset. </td> </tr> <tr> <td> Source </td> <td> National Oceanic and Atmospheric Administration’s Global Forecast System weather forecast model. Web: _https://www.ncdc.noaa.gov/dataaccess/model-data/model- datasets/globalforcast-system-gfs_ </td> </tr> <tr> <td> Partners activities and responsibilities </td> </tr> <tr> <td> Partner owner of the data; copyright holder (if applicable) </td> <td> NOAA. </td> </tr> <tr> <td> Partner in charge of the data collection </td> <td> UVIGO. </td> </tr> <tr> <td> Partner in charge of the data analysis </td> <td> UVIGO. </td> </tr> <tr> <td> Partner in charge of the data storage </td> <td> UVIGO. </td> </tr> <tr> <td> Related WP(s) and task(s) </td> <td> WP3, tasks 3.1, 3.3. </td> </tr> <tr> <td> Standards </td> </tr> <tr> <td> Info about metadata (production and storage dates, places) and documentation? </td> <td> Forecast weather data is generated during the 4 cycle daily executions of the GFS model, with an hourly temporal resolution, for a global grid with ~11 km horizontal spatial resolution. UVIGO will gather a subset of such data, limited to the requested variables, for the considered geographic area and timespan. </td> </tr> <tr> <td> Standards, format, estimated volume of data </td> <td> SQL formats for storing meteorological data. Hour-interval numeric values for each of the 9 required meteorological variables (air temperature, atmospheric pressure, wind speed and direction, maximum wind gusts speed and direction, relative air humidity, instant rain and solar radiation), for each of the considered grid points (number 1000-2000) during the Portuguese meteorological case study time lapse. </td> </tr> <tr> <td> Data exploitation and sharing </td> </tr> <tr> <td> Data exploitation (purpose/use of the data analysis) </td> <td> Input for interpolation and short-term prediction algorithms used in WP3. </td> </tr> <tr> <td> Global Forecast System (GFS) data </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> Collected data will potentially be used in future scientific research papers. </td> </tr> <tr> <td> Embargo periods (if any) </td> <td> N/A </td> </tr> <tr> <td> Personal data protection: are there personal data? If so, have you gained (written) consent from data subjects to collect this information? </td> <td> No personal data. </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 permanently stored in UVIGO computer facilities for the duration of the SAFEWAY project. </td> </tr> <tr> <td> Data destruction. How is data destruction handled? Compliance with EU / national legislation. </td> <td> Data will be stored indefinitely, with no planned destruction. </td> </tr> </table> ## Dataset No 4: Satellite data <table> <tr> <th> Satellite data </th> </tr> <tr> <td> Data identification </td> </tr> <tr> <td> Dataset description </td> <td> Sentinel-1 images </td> </tr> <tr> <td> Source </td> <td> Copernicus Open Access Hub </td> </tr> <tr> <td> Partners activities and responsibilities </td> </tr> <tr> <td> Partner owner of the data; copyright holder (if applicable) </td> <td> Any Sentinel data available through the Sentinel Data Hub will be governed by the Legal Notice on the use of Copernicus Sentinel Data and Service Information. </td> </tr> <tr> <td> Partner in charge of the data collection </td> <td> Planetek Italia </td> </tr> <tr> <td> Partner in charge of the data analysis </td> <td> Planetek Italia </td> </tr> <tr> <td> Partner in charge of the data storage </td> <td> Planetek Italia </td> </tr> <tr> <td> Related WP(s) and task(s) </td> <td> WP3 – Displacement monitoring of infrastructures (roads and railways) </td> </tr> <tr> <td> Satellite data </td> </tr> <tr> <td> Standards </td> </tr> <tr> <td> Info about metadata (production and storage dates, places) and documentation? </td> <td> The metadata information are stored within a product.xml file </td> </tr> <tr> <td> Standards, format, estimated volume of data </td> <td> OGC standard format. Volume: about TB. </td> </tr> <tr> <td> Data exploitation and sharing </td> </tr> <tr> <td> Data exploitation (purpose/use of the data analysis) </td> <td> The Sentinel-1 images will be exploited using the Multi-Temporal Interferometry algorithm through the Rheticus ® platform. </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> Access through the Rheticus ® platform protected by Username and Password. </td> </tr> <tr> <td> Embargo periods (if any) </td> <td> N/A </td> </tr> <tr> <td> Personal data protection: are there personal data? If so, have you gained (written) consent from data subjects to collect this information? </td> <td> No personal data </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 will be stored within the cloud service platform Rheticus ® owned by Planetek Italia for the entire duration of the project. </td> </tr> <tr> <td> Data destruction. How is data destruction handled? Compliance with EU / national legislation. </td> <td> The data will be deleted in the cloud platform Rheticus ® five years after the end of the project. </td> </tr> </table> ## Dataset No 5: Experts interviews <table> <tr> <th> EXPERTS INTERVIEWS </th> </tr> <tr> <td> Data identification </td> </tr> <tr> <td> Dataset description </td> <td> The data contain transcriptions and notes from expert interviews with researchers and policy makers. They will be either conducted personally, on the phone (or skype) or they can also be conducted in written form. Include findings from completed/ongoing EU projects </td> </tr> <tr> <td> Source </td> <td> Interviews with experts </td> </tr> <tr> <td> Partners activities and responsibilities </td> </tr> <tr> <td> Partner owner of the data; copyright holder (if applicable) </td> <td> N/A </td> </tr> <tr> <td> Partner in charge of the data collection </td> <td> TØI </td> </tr> <tr> <td> Partner in charge of the data analysis </td> <td> TØI </td> </tr> <tr> <td> Partner in charge of the data storage </td> <td> TØI </td> </tr> <tr> <td> Related WP(s) and task(s) </td> <td> WP4 and 6 </td> </tr> <tr> <td> Standards </td> </tr> <tr> <td> Info about metadata (production and storage dates, places) and documentation? </td> <td> Production August 2019, anonymised data stored on secure server </td> </tr> <tr> <td> Standards, format, estimated volume of data </td> <td> Word documents </td> </tr> <tr> <td> Data exploitation and sharing </td> </tr> <tr> <td> Data exploitation (purpose/use of the data analysis) </td> <td> Gather state-of-the-art knowledge on risk tolerance, aspects of psychology and behaviour of different user groups. </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> Scientific articles </td> </tr> <tr> <td> Embargo periods (if any) </td> <td> N/A </td> </tr> <tr> <td> EXPERTS INTERVIEWS </td> </tr> <tr> <td> Personal data protection: are there personal data? If so, have you gained (written) consent from data subjects to collect this information? </td> <td> Will do </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 secured servers of the partner in charge of the dataset, where only research members will be granted access to the information within the dataset. The Consortium will take into account that for the purposes of the SAFEWAY project the retention period is the one used in the relevant field, by analogy to the administrative and financial issues this will be 5 years. </td> </tr> <tr> <td> Data destruction. How is data destruction handled? Compliance with EU / national legislation. </td> <td> Data destruction will always comply with EU and national legislation. </td> </tr> </table> ## Dataset No 6: Data on risk tolerance <table> <tr> <th> DATA ON RISK TOLERANCE </th> </tr> <tr> <td> Data identification </td> </tr> <tr> <td> Dataset description </td> <td> This includes the evaluation of risk tolerance of different actors and scheduling for use in focus groups, and follow-up surveys with different user representatives. </td> </tr> <tr> <td> Source </td> <td> Focus groups and surveys </td> </tr> <tr> <td> Partners activities and responsibilities </td> </tr> <tr> <td> Partner owner of the data; copyright holder (if applicable) </td> <td> TØI </td> </tr> <tr> <td> Partner in charge of the data collection </td> <td> TØI </td> </tr> <tr> <td> Partner in charge of the data analysis </td> <td> TØI </td> </tr> <tr> <td> Partner in charge of the data storage </td> <td> TØI </td> </tr> <tr> <td> Related WP(s) and task(s) </td> <td> WP4, 6 </td> </tr> <tr> <td> DATA ON RISK TOLERANCE </td> </tr> <tr> <td> Standards </td> </tr> <tr> <td> Info about metadata (production and storage dates, places) and documentation? </td> <td> Production circa Jan 2020, anonymised data stored on secure server </td> </tr> <tr> <td> Standards, format, estimated volume of data </td> <td> Word documents </td> </tr> <tr> <td> Data exploitation and sharing </td> </tr> <tr> <td> Data exploitation (purpose/use of the data analysis) </td> <td> Gather knowledge on risk tolerance, aspects of psychology and behaviour of different user groups. </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> Scientific articles </td> </tr> <tr> <td> Embargo periods (if any) </td> <td> N/A </td> </tr> <tr> <td> Personal data protection: are there personal data? If so, have you gained (written) consent from data subjects to collect this information? </td> <td> Will do </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 secured servers of the partner in charge of the dataset, where only research members will be granted access to the information within the dataset. The Consortium will take into account that for the purposes of the SAFEWAY project the retention period is the one used in the relevant field, by analogy to the administrative and financial issues this will be 5 years. </td> </tr> <tr> <td> Data destruction. How is data destruction handled? Compliance with EU / national legislation. </td> <td> Data destruction will always comply with EU and national legislation. </td> </tr> </table> ## Dataset No 7: Sociotechnical system analysis <table> <tr> <th> SOCIOTECHNICAL SYSTEM ANALYSIS </th> </tr> <tr> <td> Data identification </td> </tr> <tr> <td> Dataset description </td> <td> Selected cases will be documented to represent a range of event types occurring in Europe. Interviews and template analysis will be conducted with people both managing and caught up in the extreme events studied. </td> </tr> <tr> <td> Source </td> <td> Document analyses </td> </tr> <tr> <td> Partners activities and responsibilities </td> </tr> <tr> <td> Partner owner of the data; copyright holder (if applicable) </td> <td> TØI </td> </tr> <tr> <td> Partner in charge of the data collection </td> <td> TØI </td> </tr> <tr> <td> Partner in charge of the data analysis </td> <td> TØI </td> </tr> <tr> <td> Partner in charge of the data storage </td> <td> TØI </td> </tr> <tr> <td> Related WP(s) and task(s) </td> <td> WP4 and 6 </td> </tr> <tr> <td> Standards </td> </tr> <tr> <td> Info about metadata (production and storage dates, places) and documentation? </td> <td> Production circa June 2020, anonymised data stored on secure server </td> </tr> <tr> <td> Standards, format, estimated volume of data </td> <td> Word documents </td> </tr> <tr> <td> Data exploitation and sharing </td> </tr> <tr> <td> Data exploitation (purpose/use of the data analysis) </td> <td> Gather knowledge on risk tolerance, aspects of psychology and behaviour of different user groups. </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> Scientific articles, report </td> </tr> <tr> <td> Embargo periods (if any) </td> <td> N/A </td> </tr> <tr> <td> SOCIOTECHNICAL SYSTEM ANALYSIS </td> </tr> <tr> <td> Personal data protection: are there personal data? If so, have you gained (written) consent from data subjects to collect this information? </td> <td> N/A </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 secured servers of the partner in charge of the dataset, where only research members will be granted access to the information within the dataset. The Consortium will take into account that for the purposes of the SAFEWAY project the retention period is the one used in the relevant field, by analogy to the administrative and financial issues this will be 5 years. </td> </tr> <tr> <td> Data destruction. How is data destruction handled? Compliance with EU / national legislation. </td> <td> Data destruction will always comply with EU and national legislation. </td> </tr> </table> ## Dataset No 8: Infrastructure assets data <table> <tr> <th> INFRASTRUCTURE ASSETS DATA </th> </tr> <tr> <td> Data identification </td> </tr> <tr> <td> Dataset description </td> <td> Database of infrastructures with identification, conservation state, inspections and structural detailing </td> </tr> <tr> <td> Source </td> <td> Infraestruturas de Portugal; Ferrovial; Network Rails </td> </tr> <tr> <td> Partners activities and responsibilities </td> </tr> <tr> <td> Partner owner of the data; copyright holder (if applicable) </td> <td> Infraestruturas de Portugal; Ferrovial; Network Rails </td> </tr> <tr> <td> Partner in charge of the data collection </td> <td> University of Minho; University of Cambridge; Infrastructure Management Consultants Gmbh </td> </tr> <tr> <td> Partner in charge of the data analysis </td> <td> University of Minho; University of Cambridge; Infrastructure Management Consultants Gmbh </td> </tr> <tr> <td> Partner in charge of the data storage </td> <td> University of Minho </td> </tr> <tr> <td> INFRASTRUCTURE ASSETS DATA </td> </tr> <tr> <td> Related WP(s) and task(s) </td> <td> WP5 – Task 5.1 </td> </tr> <tr> <td> Standards </td> </tr> <tr> <td> Info about metadata (production and storage dates, places) and documentation? </td> <td> TBD </td> </tr> <tr> <td> Standards, format, estimated volume of data </td> <td> TBD </td> </tr> <tr> <td> Data exploitation and sharing </td> </tr> <tr> <td> Data exploitation (purpose/use of the data analysis) </td> <td> Development of predictive models for projecting risks of future infrastructure damage, shutdown and deterioration. Based on the database, and analytical and stochastic/probabilistic approaches, the most suitable models for risk and impact projections will be selected. </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> Database is to be used by members of the Consortium and the derived results are to be reviewed by the partner owner of data prior to publication </td> </tr> <tr> <td> Embargo periods (if any) </td> <td> Not applicable. </td> </tr> <tr> <td> Personal data protection: are there personal data? If so, have you gained (written) consent from data subjects to collect this information? </td> <td> There is no personal data </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 a physical external disk for storage during the duration of the project. A copy will also be accessible on a restricted online server for the partners involved in Task 5.1. </td> </tr> <tr> <td> Data destruction. How is data destruction handled? Compliance with EU / national legislation. </td> <td> Data will be retained five years after the project ends, and will be always destroyed complying with EU and national legislation. </td> </tr> </table> ## Dataset No 9: Information on the value systems <table> <tr> <th> INFORMATION ON THE VALUE SYSTEM </th> </tr> <tr> <td> Data identification </td> </tr> <tr> <td> Dataset description </td> <td> The information on the value systems, decision making processes and key performance indicators that transportation infrastructure agencies and stakeholders within the project use in management of their assets. </td> </tr> <tr> <td> Source </td> <td> On-line survey developed on a freeware software platform. </td> </tr> <tr> <td> Partners activities and responsibilities </td> </tr> <tr> <td> Partner owner of the data; copyright holder (if applicable) </td> <td> IMC </td> </tr> <tr> <td> Partner in charge of the data collection </td> <td> IMC </td> </tr> <tr> <td> Partner in charge of the data analysis </td> <td> IMC </td> </tr> <tr> <td> Partner in charge of the data storage </td> <td> IMC </td> </tr> <tr> <td> Related WP(s) and task(s) </td> <td> WP6, Task 6.1 </td> </tr> <tr> <td> Standards </td> </tr> <tr> <td> Info about metadata (production and storage dates, places) and documentation? </td> <td> None. </td> </tr> <tr> <td> Standards, format, estimated volume of data </td> <td> .xls (MS Excel format). </td> </tr> <tr> <td> Data exploitation and sharing </td> </tr> <tr> <td> Data exploitation (purpose/use of the data analysis) </td> <td> The data will be used in WP6 – for development of a robust decision support framework for short and medium to longterm maintenance planning. </td> </tr> <tr> <td> Data access policy / Dissemination level: confidential (only for members of the Consortium and the Commission Services) or Public </td> <td> Currently confidential. Perhaps public after the project completion. </td> </tr> <tr> <td> Data sharing, re-use, distribution, publication (How?) </td> <td> See under data access policy. </td> </tr> <tr> <td> Embargo periods (if any) </td> <td> See under data access policy. </td> </tr> <tr> <td> INFORMATION ON THE VALUE SYSTEM </td> </tr> <tr> <td> Personal data protection: are there personal data? If so, have you gained (written) consent from data subjects to collect this information? </td> <td> Yes, there are. It is planned to include related consent as a part of the survey, so subjects may comply. </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 secured servers of the partner in charge of the dataset, where only research members will be granted access to the information within the dataset. The Consortium will take into account that for the purposes of the SAFEWAY project the retention period is the one used in the relevant field, by analogy to the administrative and financial issues this will be 5 years. </td> </tr> <tr> <td> Data destruction. How is data destruction handled? Compliance with EU / national legislation. </td> <td> Data destruction will always comply with EU and national legislation. </td> </tr> </table> ## Dataset No 10: Stakeholders contact collection <table> <tr> <th> STAKEHOLDERS CONTACT COLLECTION </th> </tr> <tr> <td> Data identification </td> </tr> <tr> <td> Dataset description </td> <td> The data contain information on the main stakeholders of SAFEWAY along the major stakeholder groups. They include infrastructure managers, operators, public administrations, researchers, practitioners, policy makers. The contact information that is collected includes the name, institutional affiliation, position, email address, phone number and office address. </td> </tr> <tr> <td> Source </td> <td> Archives of SAFEWAY partners. </td> </tr> <tr> <td> Partners activities and responsibilities </td> </tr> <tr> <td> Partner owner of the data; copyright holder (if applicable) </td> <td> UVIGO; N/A </td> </tr> <tr> <td> Partner in charge of the data collection </td> <td> UVIGO </td> </tr> <tr> <td> Partner in charge of the data analysis </td> <td> UVIGO </td> </tr> </table> <table> <tr> <th> STAKEHOLDERS CONTACT COLLECTION </th> </tr> <tr> <td> Partner in charge of the data storage </td> <td> UVIGO </td> </tr> <tr> <td> Related WP(s) and task(s) </td> <td> WP10: -Task 10.1 (Dissemination, communication and IP management). -Task 10.2 (Standardization activities) -Task 10.3 (Technology transfer activities) -Task 10.4 (Collaboration and clustering) </td> </tr> <tr> <td> Standards </td> </tr> <tr> <td> Info about metadata (production and storage dates, places) and documentation? </td> <td> N/A </td> </tr> <tr> <td> Standards, format, estimated volume of data </td> <td> This dataset can be imported from, and exported to a CSV, TXT or Excel file. </td> </tr> <tr> <td> Data exploitation and sharing </td> </tr> <tr> <td> Data exploitation (purpose/use of the data analysis) </td> <td> This dataset is only used to disseminate the results obtained through SAFEWAY project. </td> </tr> <tr> <td> Data access policy / Dissemination level: confidential (only for members of the Consortium and the Commission Services) or Public </td> <td> As this dataset can contain personal data, only the partner in charge of the data collection will have access to the raw data. Data that is publicly available will be share among consortium partners. </td> </tr> <tr> <td> Data sharing, re-use, distribution, publication (How?) </td> <td> None </td> </tr> <tr> <td> Embargo periods (if any) </td> <td> N/A </td> </tr> <tr> <td> Personal data protection: are there personal data? If so, have you gained (written) consent from data subjects to collect this information? </td> <td> This dataset can include some personal data. Before collecting any personal data that is not publicly available, informed consents from subjects will be gained. </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 secured servers of the partner in charge of the dataset, where only research members will be granted access to the information within the dataset. The Consortium will take into account that for the purposes of the SAFEWAY project the retention period is the one used in the relevant field, by analogy to the </td> </tr> <tr> <td> STAKEHOLDERS CONTACT COLLECTION </td> </tr> <tr> <td> </td> <td> administrative and financial issues this will be 5 years. </td> </tr> <tr> <td> Data destruction. How is data destruction handled? Compliance with EU / national legislation. </td> <td> Data destruction will always comply with EU and national legislation. </td> </tr> </table> ## Dataset No 11: Workshop data <table> <tr> <th> STAKEHOLDERS DATA COLLECTION </th> </tr> <tr> <td> Data identification </td> </tr> <tr> <td> Dataset description </td> <td> The data contain contact information of SAFEWAY workshops attendees, provided during their registration in the event. The contact information that is collected includes the name, institutional affiliation, position, email address, phone number and office address. </td> </tr> <tr> <td> Source </td> <td> Archives of SAFEWAY partners. </td> </tr> <tr> <td> Partners activities and responsibilities </td> </tr> <tr> <td> Partner owner of the data; copyright holder (if applicable) </td> <td> UVIGO; N/A </td> </tr> <tr> <td> Partner in charge of the data collection </td> <td> UVIGO </td> </tr> <tr> <td> Partner in charge of the data analysis </td> <td> UVIGO </td> </tr> <tr> <td> Partner in charge of the data storage </td> <td> UVIGO </td> </tr> <tr> <td> Related WP(s) and task(s) </td> <td> WP10: -Task 10.3 (Technology transfer activities) </td> </tr> <tr> <td> Standards </td> </tr> <tr> <td> Info about metadata (production and storage dates, places) and documentation? </td> <td> N/A </td> </tr> <tr> <td> Standards, format, estimated volume of data </td> <td> This dataset can be imported from, and exported to a CSV, TXT or Excel file. </td> </tr> <tr> <td> Data exploitation and sharing </td> </tr> <tr> <td> STAKEHOLDERS DATA COLLECTION </td> </tr> <tr> <td> Data exploitation (purpose/use of the data analysis) </td> <td> This dataset is only used to disseminate the results obtained through SAFEWAY project. </td> </tr> <tr> <td> Data access policy / Dissemination level: confidential (only for members of the Consortium and the Commission Services) or Public </td> <td> As this dataset can contain personal data, only the partner in charge of the data collection will have access to the raw data. Data that is publicly available will be share among consortium partners. </td> </tr> <tr> <td> Data sharing, re-use, distribution, publication (How?) </td> <td> None </td> </tr> <tr> <td> Embargo periods (if any) </td> <td> N/A </td> </tr> <tr> <td> Personal data protection: are there personal data? If so, have you gained (written) consent from data subjects to collect this information? </td> <td> This dataset can include some personal data. Before collecting any personal data that is not publicly available, informed consents from subjects will be gained. </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 secured servers of the partner in charge of the dataset, where only research members will be granted access to the information within the dataset. The Consortium will take into account that for the purposes of the SAFEWAY project the retention period is the one used in the relevant field, by analogy to the administrative and financial issues this will be 5 years. </td> </tr> <tr> <td> Data destruction. How is data destruction handled? Compliance with EU / national legislation. </td> <td> Data destruction will always comply with EU and national legislation. </td> </tr> </table> # Outlook Towards Next DMP As stated in Table 1 of the Introduction, the next iteration of the DMP will be prepared in month 18 of the project, just after WP2 finishes. Also, every working package and their tasks (with the exception of WP9 – demonstrative pilots) will be underway. Several questions that remain unanswered in this DMP will be addressed in future stages of the project as its different activities are carried out. Therefore, the upcoming DMP will provide updates regarding the following topics: Table 4: Planed updated in upcoming DMP versions <table> <tr> <th> Category </th> <th> Updates in upcoming DMP </th> </tr> <tr> <td> Data interoperability </td> <td> * Information regarding data exchange between researchers and organizations. * Standards employed for allowing data exchange. </td> </tr> <tr> <td> Data re-use </td> <td> * Data licensing to permit re-use. * Data availability. Will the data be available for reuse? Will be an embargo to give time to publish or seek patents? * Can the data be used by third parties at the end of the project? Will be any restriction? * How long will the data be re-usable? * Have been (or will be) data quality assurance processes described? </td> </tr> <tr> <td> Data allocation </td> <td> * Where (and how) is existent data being stored? What is its cost and potential value? * Where (and how) will data still not acquired be stored? </td> </tr> <tr> <td> Data security </td> <td> \- What procedures have been conducted regarding data security (data recovery, data storage and transference). </td> </tr> <tr> <td> Other aspects </td> <td> \- Any other procedure regarding data management which has not been listed. </td> </tr> </table> # Update of the Ethical Aspects At this stage of the project, two are the main ethical aspects to review. In first place the outcome of the continuous monitoring process on ethical aspects, in particular regarding vehicle data crowdsourcing and interviews or surveys carried out during the development of WP4. Then, the report of the Ethics Mentor. ## Ongoing monitoring The ongoing monitoring regarding SAFEWAY ethical aspects has focused, in a first place, in identifying those tasks with relevance for data protection within the different activities of the project. It was concluded that the data protection risk posed by SAFEWAY is fairly limited, as the only task that might involve personal data collection is related to dissemination activities in workshops and meetings; and an explicit and verifiable consent will be obtained prior to any data collection, as required by the GDPR. Procedures for collection, processing, storage, retention and destruction of data have been defined to ensure its compliance with the legislative framework. Furthermore, for those activities that require it (interviews and surveys) an informed consent form together with an information sheet about the research study were defined (see Appendices). ## Report of the Ethics Mentor Throughout the duration of the project, the Ethics Mentor will organize the internal monitoring of the implementation of the ethical protocol by the consortium. This section of the Data Management Plan will include a report from the Ethics Mentor to be updated, according to the Grant Agreement-Annex 1b-section 5.1.2, in M18, M30, M42. # Acknowledgements This deliverable was carried out in the framework of the GIS-Based Infrastructure Management System for Optimized Response to Extreme Events of Terrestrial Transport Networks (SAFEWAY) project, which has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 769255. SAFEWAY GIS-BASED INFRASTRUCTURE MANAGEMENT SYSTEM FOR OPTIMIZED RESPONSE TO EXTREME EVENTS OF TERRESTRIAL TRANSPORT NETWORKS Grant Agreement No. 769255 Data Management Plan (DMP) V1 \- Appendices WP 1 Overall project coordination <table> <tr> <th> Deliverable ID </th> <th> D1.3 </th> </tr> <tr> <td> Deliverable name </td> <td> Data Management Plan (DMP) V1 </td> </tr> <tr> <td> Lead partner </td> <td> UVIGO </td> </tr> <tr> <td> Contributors </td> <td> DEMO, PNK, UMINHO, IMC </td> </tr> </table> PUBLIC PROPRIETARY RIGHTS STATEMENT This document contains information, which is proprietary to the SAFEWAY Consortium. Neither this document nor the information contained herein shall be used, duplicated or communicated by any means to any third party, in whole or in parts, except with prior written consent of the SAFEWAY Consortium. Appendices Contents #  Appendix 1: Informed Consent Form #  Appendix 2: Protection of Personal Data within SAFEWAY LEGAL NOTICE The sole responsibility for the content of this publication lies with the authors. It does not necessarily reflect the opinion of the European Union. Neither the Innovation and Networks Executive Agency (INEA) nor the European Commission are responsible for any use that may be made of the information contained therein. <table> <tr> <th> Appendix 1. </th> <th> Informed Consent Form </th> </tr> </table> GIS-Based Infrastructure Management System for Optimized Response to Extreme Events of Terrestrial Transport Networks ### INFORMED CONSENT FORM <table> <tr> <th> Project acronym </th> <th> SAFEWAY </th> </tr> <tr> <td> Project name </td> <td> GIS-BASED INFRASTRUCTURE MANAGEMENT SYSTEM FOR OPTIMIZED RESPONSE TO EXTREME EVENTS OF TERRESTRIAL TRANSPORT NETWORKS </td> </tr> <tr> <td> Grant Agreement no. </td> <td> 769255 </td> </tr> <tr> <td> Project type </td> <td> Research and Innovation Action </td> </tr> <tr> <td> Start date of the project </td> <td> 01/09/2018 </td> </tr> <tr> <td> Duration in months </td> <td> 42 </td> </tr> <tr> <td> This project has received funding from the European Union’s Horizon 2020 research and innovation programme under Grant Agreement No 769255\. </td> </tr> <tr> <td> Disclaimer: This document reflects only the views of the author(s). Neither the Innovation and Networks Executive Agency (INEA) nor the European Commission is in any way responsible for any use that may be made of the information it contains. </td> </tr> </table> SAFEWAY event: Date: Location: General Data Protection Regulation (GDPR) Compliance Data that is collected and processed for the purposes of facilitating and administering SAFEWAY workshops and events is subjected to GDPR to the EU General Data Protection Regulation (GDPR) which became applicable from the 25th of May 2018. Please see the document “POPD SAFEWAY.pdf” for further guidance on our data management policies. To process your application, we require your consent to the following (please check each box as appropriate). <table> <tr> <th> Please circle as necessary </th> </tr> <tr> <td> I give my consent for all personal information provided by registering to the SAFEWAY ( _workshop/event name_ ) to be stored and processed by relevant SAFEWAY project partners for Data Management Purposes. </td> <td> Yes </td> <td> No </td> </tr> <tr> <td> I give my consent for all personal information provided by registering to the SAFEWAY ( _workshop/event name_ ) to be stored and processed by SAFEWAY partners for the purpose of administering the SAFEWAY ( _workshop/event name_ ). </td> <td> Yes </td> <td> No </td> </tr> <tr> <td> I give my consent for all personal information provided by registering to the SAFEWAY ( _workshop/event name_ ) to be processed by the SAFEWAY ( _workshop/event name_ ) organizers to evaluate and decide on my application where workshop places are limited. </td> <td> Yes </td> <td> No </td> </tr> <tr> <td> I give my consent for all personal information provided by registering to the SAFEWAY ( _workshop/event name_ ) to be stored and processed by UVIGO for the purpose of overall coordination of the SAFEWAY project. </td> <td> Yes </td> <td> No </td> </tr> <tr> <td> I give my consent for all personal information provided by registering to the SAFEWAY ( _workshop/event name_ ) to be passed to UVIGO and FERROVIAL for storage and processing for the purposes of supporting exploitation and dissemination of workshop related information. </td> <td> Yes </td> <td> No </td> </tr> <tr> <td> I give my consent for the following personal information to be passed on to the European Commission in case my workshop application is approved: name, surname, title, organization, position, email address, phone number. </td> <td> Yes </td> <td> No </td> </tr> <tr> <td> I give my consent for the following personal information to be published on the Internet and elsewhere for the purposes of project transparency: name, surname and organisation affiliation. </td> <td> Yes </td> <td> No </td> </tr> <tr> <td> I give my consent for my e-mail address to be published on the Internet or elsewhere to assist others to contact me (optional). </td> <td> Yes </td> <td> No </td> </tr> </table> _PARTICIPANT CERTIFICATION_ I have read the _PROTECTION OF PERSONAL DATA WITHIN SAFEWAY_ and answered to all the questions on the table above. I have had the opportunity to ask, and I have received answers to, any questions I had regarding the protection of my personal data. By my signature I affirm that I am at least 18 years old and that I have received a copy of this Consent and Authorization form. ………………………………………………………………………………………………… Name and surname of participant ………………………………………………………………………………………………… Place, date and signature of participant NB: Attach this completed form to your SAFEWAY _(workshop/event name)_ application. Further information: for any additional information or clarification please contact SAFEWAY coordinators at UVIGO ( [email protected]_ ). This consent form does not remove any of your rights under GDPR but provides us with the necessary permissions to process your application and manage SAFEWAY workshops and events. <table> <tr> <th> Appendix 2. </th> <th> Protection of Personal Data Within SAFEWAY </th> </tr> </table> GIS-Based Infrastructure Management System for Optimized Response to Extreme Events of Terrestrial Transport Networks ### PROTECTION OF PERSONAL DATA WITHIN SAFEWAY <table> <tr> <th> Project acronym </th> <th> SAFEWAY </th> </tr> <tr> <td> Project name </td> <td> GIS-BASED INFRASTRUCTURE MANAGEMENT SYSTEM FOR OPTIMIZED RESPONSE TO EXTREME EVENTS OF TERRESTRIAL TRANSPORT NETWORKS </td> </tr> <tr> <td> Grant Agreement no. </td> <td> 769255 </td> </tr> <tr> <td> Project type </td> <td> Research and Innovation Action </td> </tr> <tr> <td> Start date of the project </td> <td> 01/09/2018 </td> </tr> <tr> <td> Duration in months </td> <td> 42 </td> </tr> <tr> <td> This project has received funding from the European Union’s Horizon 2020 research and innovation programme under Grant Agreement No 769255\. </td> </tr> <tr> <td> Disclaimer: This document reflects only the views of the author(s). Neither the Innovation and Networks Executive Agency (INEA) nor the European Commission is in any way responsible for any use that may be made of the information it contains. </td> </tr> </table> ### PROTECTION OF PERSONAL DATA WITHIN SAFEWAY _INTRODUCTION_ The SAFEWAY project assumes the responsibility of complying with current legislation on data protection, guaranteeing the protection of personal information in a lawful and transparent manner in accordance with Regulation (EU) 2016/679 of the European Parliament and of the Council of April 27, 2016, regarding the protection of individuals with regard to the processing of personal data and their free circulation (GDPR) and with the national regulations regarding the protection of personal data. This document informs in detail the circumstances and conditions of the processing of personal data and the rights that assist the interested persons. As coordinator of the action, the University of Vigo is the data controller for all personal data being collected for workshops and other communication and dissemination events. The University of Vigo has appointed as Data Protection Officer to: Pintos & Salgado Abogados S.C.P. with address at: Avda. de Arteixo, 10, 1.o izq., 15004 A Coruña ( [email protected]_ ). _PURPOSE:_ SAFEWAY partners will only collect the personal data strictly necessary in relation to the purposes for which they are treated, in accordance with the principles set in Article 5 of the GDPR. The information necessary to guarantee a fair and transparent treatment will be provided to the interested persons at the moment of collection, in accordance with the provisions of articles 13 and 14 of the GDPR. The data collected by SAFEWAY for the dissemination activities aims to reach the widest audience to disseminate SAFEWAY project outcomes and to communicate the knowledge gained by its partners during the duration of the project. The workshops or meetings with stakeholder are focused to present and discuss all project results, not only among project partners but also open to stakeholders and other target groups. The events will be targeted to technology innovators on infrastructure management, including end-users, materials and technology suppliers, the research community, regulatory agency, standardization bodies and all the potential players interested in fields associated to innovative resilience of transport infrastructure with special focus on their application in railway and roads. _PROCESSING OF PERSONAL DATA:_ Your Personal Data is freely provided. Where it is specified in the registration form, the provision of Personal Data is necessary to provide you with the services expected from the dissemination event, and the access to SAFEWY project results. if you refuse to communicate these Data, it may be impossible for the Data Controller to fulfil your request. On the contrary, with reference to Personal Data not marked as mandatory, you can refuse to communicate them and this refusal shall not have any consequence for your participation and attendance to SAFEWAY dissemination activities. The provision of your Personal Data for publication of your contact details on the Internet or elsewhere for networking implemented by the Data Controller is optional, consequently you can freely decide whether or not give your consent, or withdraw it at any time. Therefore, if you decide not to give your consent, SAFEWAY dissemination responsible will not be able to carry out the aforementioned activities. SAFEWAY will never collect any special categories of Personal Data (personal data revealing racial or ethnic origin, political opinions, religious or philosophical beliefs, or trade union membership, genetic data, biometric data, data concerning health or data concerning a natural person’s sex life or sexual orientation – Art. 9 of GDPR). SAFEWAY asks you expressly to avoid providing these categories of Data. In the event in which you voluntarily choose to give us these Data, the Company may decide not to process them or to process them only with your specific consent or, in any event, in compliance with the applicable law. In the event of accidental processing of third party Personal Data is communicated to SAFEWAY, you become an autonomous Data Controller and assume all the related obligations and responsibilities provided by the law. In this regard, SAFEWAY is exempt from any liability arising from any claims or requests made by third parties, whose Data have been processed by us because of your spontaneous communication of them to us, in violation of the law on the protection of Personal Data. In any event, if you provide or process third party Personal Data, you must guarantee as of now, assuming any related responsibility, that this particular hypothesis of processing is based on a legal basis pursuant to Art. 6 of GDPR. _DATA STORAGE AND RETENTION:_ The personal data provided will be kept for the time necessary to fulfill the purpose for which they are requested and to determine the possible liabilities that could derive from the same purpose, in addition to the periods established in the regulations on files and documentation. Unless otherwise stated, the data will be retained for a period of five years after the end of the project as this data can support the report of some of the implemented activities. During this period, the data will be stored in a secured area with access by a limited number of researchers. SAFEWAY data managers will apply appropriate technical and organizational measures to guarantee a level of safety appropriate to the risk and in accordance with the provisions of article 32 of the GDPR. The system also allows tracking of use of data. Five years after the end of the project, the data will be destructed at the surveillance of the Data Protection Officer at University of Vigo, as coordinating organization of SAFEWAY. _RIGHTS OF THE DATA SUBJECT:_ Any person, as the holder of personal data, has the following rights recognized in the terms and under the conditions indicated in articles 15-22 of the GDPR: * Right of Access: obtain from the controller confirmation as to whether or not personal data concerning you are being processed, more information on the processing and copy of the personal data processed. * Right to Rectification obtain from the controller, without undue delay, the rectification of inaccurate personal data concerning you and the right to have incomplete personal data competed. * Right to Erasure: obtain from the controller, without undue delay, the erasure of personal data concerning you. * Right to Restriction of Processing: obtain the restriction of the processing in the event you assume that your data are incorrect, the processing is illegal or if these data are necessary for the establishment of legal claims. * Right to Data Portability: receive the personal data concerning you, which you have provided to a controller, in a structured, commonly used and machine-readable format, in order to transfer these data to another Controller. * Right to Object: Object, on grounds relating to your particular situation, to the processing of personal data concerning you, unless the controller demonstrates compelling legitimate grounds for the processing. You can also object to processing your data where them are processed for direct marketing purposes. * Right to withdraw the Consent: withdraw the consent at any time. The withdrawal of consent shall not affect the lawfulness of processing based on consent before its withdrawal. The subject may exercise their rights without any cost and will have the right to receive a response, within the deadlines established by current legislation on data protection, by contacting SAFEWAY project coordinators at: [email protected]_ , or by contacting the Data Protection Officer at: [email protected]_ . _CONTACT PERSON_ For any additional information or clarification please contact SAFEWAY coordinators at UVIGO ( [email protected]_ ). This consent form does not remove any of your rights under GDPR but provides us with the necessary permissions to process your application and manage SAFEWAY workshops and events.	https://phaidra.univie.ac.at/o:1140797	Horizon 2020
0010_SlideWiki_688095.md	1. Introduction This deliverable outlines the strategy for data management to be followed throughout the course of the SlideWiki project by formulating a Data Management Plan for the datasets used within the context of the project. Moreover, this plan includes the descriptions of dataset lifecycle, stakeholder behaviours, and best practices for data management, data management guidelines, and templates for data management used in the SlideWiki project. This plan will be updated at every milestone cycle. Based on the Guidelines for FAIR Data management in H2020 1 , Data Management in H2020 2 and Linked Data Life Cycle (LDLC) 3 , we present the data management guideline for SlideWiki as follows: 1. Data Reference Name – a naming policy for datasets. 2. Dataset Content, Provenance and Value – general descriptions of a dataset, indicating whether it is aggregated or transformed from existing datasets, or original datasets from data publishers. 3. Standards and Metadata – descriptions about the format and underlying standards, under which the metadata shall be provided to enable machine-processable descriptions of dataset (supporting data transformation of Any2RDF and RDF2Any). 4. Data Access and Sharing – it is envisaged that all datasets are freely accessed under the Open Data Commons Open Database License (ODbL). Exceptions shall be stated clearly. 5. Archiving, Maintenance and Preservation – locations of physical repository of datasets shall be listed for each dataset. This deliverable will be updated at the completion of every milestone cycle, in case significant changes have been made, aiming thus to take into account any additional decisions or newly identified best practices. Briefly stated, the Data Management Plan (DMP) outlines the datasets that will be generated or collected during the project's lifetime highlighting the following information: 1. How datasets will be exploited/shared/licensed. For those that cannot be shared, the reasons why are explained. 2. Which standards are followed for publishing datasets. 3. Which strategies are used for curation and archiving of datasets. The first version of this deliverable outlines the a strategy for data management to be followed throughout the course of the project, in terms of data management guidelines and a template that can be instantiated for all datasets corresponding to project outputs. This deliverable will be periodically updated to take account of additional decisions or best practices adopted during the project lifetime. At the end of the project, it will include individual Data Management Plans for the ensuing datasets (or groups of related datasets). The plan addresses a number of questions related to hosting the data (persistence), appropriately describing the data (data value, relevant audience for re-use, discoverability), access and sharing (rights, privacy, limitations) and information about the human and physical resources expected to carry out the plan. 1.1 Purpose and Scope A Data Management Plan (DMP) is a formal document that specifies ways of managing data throughout a project, as well as after the project is completed. The purpose of DMP is to support the life cycle of data management, for all data that is/will be collected, processed or generated by the project. A DMP is not a fixed document, but evolves during the lifecycle of the project. SlideWiki aims to increase the efficiency, effectiveness and quality of education in Europe by enabling the creation, dissemination and use of widely available, accessible, multilingual, timely, engaging and high-quality educational material (i.e., OpenCourseWare). More specifically, the open- source SlideWiki platform (available at SlideWiki.org) will enable the creation, translation and evolution of highly-structured remixable OCW can be widely shared (i.e. crowdsourced). Similarly to Wikipedia for encyclopaedic content, SlideWiki allows 1. to collaboratively create comprehensive OCW (curricula, slide presentations, selfassessment tests, illustrations etc.) online in a crowdsourcing manner, 2. to semi-automatically translate this content into more than 50 different languages and to improve the translations in a collaborative manner and 3. to support engagement and social networking of educators and learners around that content. SlideWiki is already used by hundreds of educators, thousands of learners. Several hundred comprehensive course materials are available in SlideWiki in dozens of languages. The major block of these aims is the heterogenic nature of data formats used by various educational institutions, which vary extensively. Examples of the most popular formats used include CSV, XLS, XML, PDF, and RDB and presentation files, such as PowerPoint files (ppt), OpenDocumentPresentation (odp), PDF, or ePub, etc. By applying DCAT-AP standard for dataset descriptions and making them publicly available, the SlideWiki DMP covers the 5 key aspects (dataset reference name, dataset description, standards and metadata, access, sharing, and re-use, archiving and preservation), following the guidelines on Data Management of H2020 4 . While the collaborative authoring of engaging, inclusive, standard-compliant and multilingual OpenCourseWare content is deemed a crucial and still neglected component of advancing educational technology, there is a plethora of systems covering other stages of educational value chains, such as Learning Management (e.g. ILIAS, Moodle , OLAT), Learning Content, Learning Delivery (e.g., OpenCast, FutureLearn), Learning Analytics Systems and social networks. Instead of trying to incorporate as much functionality as possible in a single system, SlideWiki will facilitate the exchange of learning content and educational data between different systems in order to establish sustainable educational value chains and learning eco-systems. Open (learning metadata) standards such as SCORM are first steps in this direction, but truly engaging, inclusive and multi-lingual value chains can be only realized if we take the content structuring to the next level and employ techniques such as interactive HTML5 (which can be presented meanwhile on almost all devices) and fine-grained semantic structuring and annotation of OpenCourseWare. In order to implement this concept, a relational multi-versioning data structure will be employed, which is dynamically mapped to an ontology, following the MVC paradigm and exhibiting Linked Data. 1.2 Structure of the Deliverable The rest of this deliverable is structured as follows: Section 2 presents the data life-cycle of SlideWiki, the related stakeholders and 13 best practices for data management. Section 3 describes basic information required for datasets of the Slide Wiki project, and relevant guidelines. Section 4 presents DMP templates for data management. Each dataset has a unique reference name. Each data source and each of the transformed form will be described with metadata, which includes technical descriptions about procedures and tools used for the transformation, and common-sense descriptions for external users to better understand the published data. The Open Data Commons Open Database License (ODBL) is taken as the default data access, sharing, and re-use policies of the datasets used within the context of SlideWiki. Physical location of datasets shall be provided. 2. Data Lifecycle The SlideWiki platform is a Linked Data platform, whose data ingestion and management follow the Linked Data Life Cycle (LDLC). The LDLC describes the technical process required to create datasets and manage their quality. To ease the process, best practices are described to guide dataset contributors in the SlideWiki platform. Formerly, data management was executed by a single person or a working group that would also take responsibility for data management. With the popularity of the Web and the widely distributed data sources, data management has shifted to a service of a large stakeholder ecosystem. 2.1 Stakeholders For the SlideWiki platform, the stakeholders who influence the data management belong to the following categories: 1. Data Source Publisher/Owner: This category refers to organisations providing datasets to the SlideWiki platform. The communication between SlideWiki and DSPO is limited to two cases: SlideWiki downloads data from DSPO, and DSPO uploads data to SlideWiki. 2. Data End-User: This category refers to persons and organisations who use the SlideWiki platform in order to access, view and share OpenCourseWare (OCW). 3. Data Wrangler: This category refers to persons who integrate heterogenic datasets into the SlideWiki platform. They are able to understand both the terminology used in the datasets and the SlideWiki data model, and their role is to ensure that the data integration is semantically correct. 4. Data Analyser: This category refers to persons who provide query results to endusers of SlideWiki. They may need to use data mining software. 5. System Administrator and Platform Developer: This category refers to persons responsible for developing and maintaining the SlideWiki platform. #### 2.2 The Generic SlideWiki Data Value Chain Within the context of SlideWiki, we structure the generic data value chain as follows: 1. _Discover_ . An end-user query can require data to be collected from many datasets located within different entities and potentially also distributed in different countries. Datasets hence need to be located and evaluated. For SlideWiki, the evaluation of datasets results in dataset metadata, which is one of the main best practices in the Linked Data community. DCAT-AP is used as the metadata vocabulary. 2. _Ingest and make the data machine processable_ . In order to realise the value creation stage (integration, analyse, and enrich), datasets in different formats are transformed into a machine processable format. In the case of SlideWiki, it is the RDF format. The conversion pipeline from heterogenic datasets into an RDF dataset is fundamental. A Data Wrangler is responsible for the conversion process. For CSV datasets, additional contextual information is required to make the semantics of the dataset explicit. 3. _Persist_ . Persistence of datasets happens throughout the whole data management process. When a new dataset comes into the SlideWiki platform, the first data persistence is to backup this dataset and the ingestion result of this dataset. Later data persistence is largely determined by the data analysis process. Two strategies used in data persistence are (a) keeping local copy – copy the dataset from DSPO to the SlideWiki platform; (b) caching, to enhance data locality to increase the efficiency of data management. 4. _Integrate, analyse, enrich_ . One of the data management tasks is to combine a variety of datasets and find out new insights. Data integration needs both domain knowledge and technical knowhow. This is achieved by using a Linked Data approach enriched with a shared ontology. The life cycle of Linked Data ETL process starts from the extraction of RDF triples from heterogenic datasets, and storing the extracted RDF data into a storage, that is available for SPARQL querying. The RDF storage can be manually updated. Then, the interlinking and data fusion is carried out, which use ontologies in several public Linked Data sources and creates the Web of Data. In contrast to a relational data warehouse, the Web of Data is a distributed knowledge graph. Based on Linked Data technologies, new RDF triples can be derived, and new enrichment is possible. Evaluation is necessary to control the quality of new knowledge, which further results in searching more data sources, and performing data extraction. 5. _Expose_ . The result of data analysis will be exposed to end-users in a clear, salient, and simple way. The SlideWiki platform is a Linked Data platform, whose outcomes include (a) metadata description about the results; (b) a SPARQL endpoint for the metadata; (c) a SPARQL endpoint for the resulting datasets; (d) a user-friendly interface for the above results. #### 2.3 Best Practices The SlideWiki platform is a Linked Data platform. Considering the best practices for publishing Linked Data, the following 13 stages are recommended in order to publish a standalone dataset, 6 of them are vital (marked as must). 1. _Provide descriptive metadata with locale parameters:_ Metadata must be provided for both human users and computer applications. Metadata provides DEU with information to better understand the meaning of data. Providing metadata is a fundamental requirement when publishing data on the Web, because DSPO and DEU may be unknown to each other. Then, it is essential to provide information that helps DEU – both human users and software systems, to understand the data, as well as other aspects of the dataset. Metadata should include the following overall features of a dataset: The title and a description of the dataset; the keywords describing the dataset; the date of publication of the dataset.; the entity responsible (publisher) for making the dataset available; the contact point of the dataset; the spatial coverage of the dataset; the temporal period that the dataset covers; the themes/categories covered by a dataset. Locale parameters metadata should include the following information: the language of the dataset; the formats used for numeric values, dates and time. 2. _Provide structural metadata:_ Information about the internal structure of a distribution must be described as metadata, for this information is necessary for understanding the meaning of the data and for querying the dataset. 3. _Provide data license information:_ License information is essential for DEU to assess data. Data re-use is more likely to happen, if the dataset has a clear open data license. 4. _Provide data provenance information_ : Data provenance describes data origin and history. Provenance becomes particularly important when data is shared between collaborators who might not have direct contact with one another. 5. _Provide data quality information_ : Data quality is commonly defined as “fitness for use” for a specific application or use case. The machine readable version of the dataset quality metadata may be provided according to the vocabulary that is being developed by the DWBP working group, i.e., the Data Quality and Granularity vocabulary. 6. _Provide versioning information_ : Version information makes a dataset uniquely identifiable. The uniqueness enables data consumers to determine how data has changed over time and to identify specifically which version of a dataset they are working with. 7. _Use persistent URIs as identifiers_ : Datasets must be identified by a persistent URI. Adopting a common identification system enables basic data identification and comparison processes by any stakeholder in a reliable way. They are an essential precondition for proper data management and re-use. 8. _Use machine-readable standardised data formats_ : Data must be available in a machine-readable standardised data format that is adequate for its intended or potential use. 9. _Data Vocabulary_ : Standardised terms should be used to provide metadata, Vocabularies should be clearly documented, shared in an open way, and include versioning information. Existing reference vocabularies should be re-used where possible. 10. _Data Access:_ Providing easy access to data on the Web enables both humans and machines to take advantage of the benefits of sharing data using the Web infrastructure. Data should be available for bulk download. APIs for accessing data should follow REST (REpresentational State Transfer) architectural approaches. When data is produced in real-time, it should be available on the Web in real-time. Data must be available in an up-to-date manner and the update frequency made explicit. If data is made available through an API, the API itself should be versioned separately from the data. Old versions should continue to be available. 11. _Data Preservation:_ Data depositors willing to send a data dump for long term preservation must use a well-established serialisation. Preserved datasets should be linked with their "live" counterparts. 12. _Feedback_ : Data publishers should provide a means for consumers to offer feedback. 13. _Data Enrichment_ : Data should be enriched whenever possible, generating richer metadata to represent and describe it. 3. Data Management Plan Guidelines In this section, we describe guidelines of the DMP of SlideWiki. In order to enable the export of SlideWiki content on Data Web, as a proof -of-concept the RDB2RDF mapping tool Triplify 5 is employed in order to map SlideWiki content to RDF and publish the resulting data on the Data Web. The SlideWiki Triplify Linked Data interface will soon be available. With regard to Social Networking, at the current stage, SlideWiki supports limited social networking activities. In the future, it is envisaged that SlideWiki users will be able follow other users, slides and decks, they can discuss and comment on slides and decks, login/register to system using their Facebook account and share slides/decks on popular social networks (e.g. Facebook, LinkedIn, G+, Twitter). 3.1 Privacy and Security It is a fact that educational data mining needs to cope with large unstructured (live) data which needs to be handled, transferred and translated into interpretable structured datasets 6 . Analog to other data sensitive domains there is the critical question of privacy and (learning) data protection. Also the irresolution of which data is important from a pedagogical/technical point of view is still a complex intent and open question, taking the complex and individual learning process into account. It is mandatory for the collection of such data that the involved learner is using campus tools and platforms that support tracking of learning action. These analytics remain an immature field that has yet to be implemented broadly across a range of institutional types, student populations and learning technologies. So-called Learning Record Stores are the next generation tracking and reporting repositories that support ideas like the Tin Can protocol and the successor xAPI. Open analytic solutions as provided by the Open Academic Analytics Initiative 6 (OAAI), which is already fostering the collection of and meaningful interpretation of data across learning institutes. Given that the central aim of this consortium is to provide benefit to the European community, the project will prefer open data and free, open tools and provide the resources developed in the project under the Creative Commons Attribution 4.0 License (CC-BY) 7 . This license allows the learning material to be shared and adapted for any purpose, even commercially. The only restriction is attribution: linking to the source and indicating the changes made. Released in November 2013, CC-BY 4.0 improves its predecessor CC-BY 3.0, as it is an international license and includes databases. In order to prevent data loss and to ensure SlideWiki users' privacy, a sophisticated backup and archiving strategy, guaranteeing data security will be implemented and developed within the context of WP1. Within the context of T1.3 Privacy, Data Security, Backup and Archiving, all OpenCourseWare content stored in SlideWiki, be it slides, presentations, questionnaires, diagrams, images, user data etc.), is regularly backed-up and archived. In SlideWiki all content (also versioning histories and prior revisions) will be made available via Linked Data, SPARQL interfaces, APIs and data dumps. Incremental updates will be published, so that the interested parties (e.g. large universities, school authorities) can host their own synchronized SlideWiki mirrors (similar to services such as arXiv.org or DBLP), while ensuring that all privacy and data security regulations are enforced. In D1.8, privacy and data security report (M28), it is outlined how SlideWiki implements all relevant privacy and data security regulations and best practices. Moreover, at the SlideWiki website 8 , the Statement of Data Protection Conditions can be accessed, and it provides the following info with regard to personal data: “The Fraunhofer-Gesellschaft zur Förderung der angewandten Forschung e.V. (FraunhoferGesellschaft) takes the protection of your personal data very seriously. When we process the personal data that is collected during your visits to our Web site, we always observe the rules laid down in the applicable data protection laws. Your data will not be disclosed publicly by us, nor transferred to any third parties without your consent.” In the following sections, we explain what types of data we record when you visit our Web site, and precisely how they are used: 3.1.1 Recording and processing of data in connection with access over the Internet When you visit our Web site, our Web server makes a temporary record of each access and stores it in a log file. The following data are recorded, and stored until an automatic deletion date: 1. IP address of the requesting processor 2. Date and time of access 3. Name and URL of the downloaded file 4. Volume of data transmitted 5. Indication whether download was successful 6. Data identifying the browser software and operating system 7. Web site from which our site was accessed 8. Name of your Internet service provider The purpose of recording these data is to allow use of the Web site (connection setup), for system security, for technical administration of the network infrastructure and in order to optimize our Internet service. The IP address is only evaluated in the event of fraudulent access to the network infrastructure of the Fraunhofer-Gesellschaft. Apart from the special cases cited above, we do not process personal data without first obtaining your explicit consent to do so. Pseudonymous user profiles can be created as stated under web analysis (see below). #### 3.1.2 Orders If you order information material or other goods via our website, we will use the address data provided only for the purpose of processing your order. 3.1.3 Use and transfer of personal data All use of your personal data is confined to the purposes stated above, and is only undertaken to the extent necessary for these purposes. Your data is not disclosed to third parties. Personal data will not be transferred to government bodies or public authorities except in order to comply with mandatory national legislation or if the transfer of such data should be necessary in order to take legal action in cases of fraudulent access to our network infrastructure. Personal data will not be transferred for any other purpose. 3.1.4 Consent to use data in other contexts The use of certain services on our website, such as newsletters or discussion forums, may require prior registration and involves a more substantial processing of personal data, such as longer-term storage of email addresses, user IDs and passwords. We use such data only insofar as it has been sent to us by you in person and you have given us your express prior consent for this use. For example, we request your consent separately in the following cases: 3.1.4.1 Newsletters and press distribution In order to register for a newsletter service provided by the Fraunhofer- Gesellschaft, we need at least your e-mail address so that we know where to send the newsletter. All other information you supply is on a voluntary basis, and will be only if you give your consent, for example to contact you directly or clear up questions concerning your e-mail address. If you request delivery by post, we need your postal address. If you ask to be included on a press distribution list, we need to know which publication you work for, to allow us to check whether specific publications are actually receiving our press material. As a general rule, we employ the double opt-in method for the registration. In other words, after you have registered for the service and informed us of your e-mail address, you will receive an e-mail in return from us, containing a link that you must use to confirm your registration. Your registration and confirmation will be recorded. The newsletter will not be sent until this has been done. This procedure is used to ensure that only you yourself can register with the newsletter service under the specified e-mail address. You must confirm your registration as soon as possible after receiving our e-mail, otherwise your registration and email address will be erased from our database. Until we receive your confirmation, our newsletter service will refuse to accept any other registration requests using this e-mail address. You can cancel subscriptions to our newsletters at any time. To do so, either send us an email or follow the link at the end of the newsletter. ##### 3.1.4.2 Visitors’ books and forums If you wish to sign up for an Internet forum run by the Fraunhofer- Gesellschaft, we need at least a user ID, a password, and your e-mail address. For your own protection, the registration procedure for this type of service, like that for the newsletters, involves you confirming your request using the link contained in the e-mail we send you and you giving your consent to the use of further personal data where this is necessary to use the forum. You can cancel your registration for this type of service at any time, by sending us an e-mail via the Web page offering the service. As a general rule, the content of visitors’ books and forums is not subject to any form of monitoring by the Fraunhofer-Gesellschaft. Nevertheless, we reserve the right to delete posted contributions and to prohibit users from further use of the service at our own discretion, especially in cases where posted content contravenes the law or is deemed incompatible with the objectives of the Fraunhofer-Gesellschaft. 3.1.5 Cookies We do not normally use cookies on our Web site, but in certain exceptional cases we may use cookies which place technical session-control data in your browser’s memory. These data are automatically erased at the latest when you close your browser. If, exceptionally, one of our applications requires the storage of personal data in a cookie, for instance a user ID, we will point out you to it. Of course, it is perfectly possible to consult our Web site without the use of cookies. Please note, however, that most browsers are programmed to accept cookies in their default configuration. You can prevent this by changing the appropriate setting in the browser options. If you set the browser to refuse all cookies, this may restrict your use of certain functions on our Web site. 3.1.6 Security The Fraunhofer-Gesellschaft implements technical and organizational security measures to safeguard stored personal data against inadvertent or deliberate manipulation, loss or destruction and against access by unauthorized persons. Our security measures are continuously improved in line with technological progress. 3.1.7 Links to Web sites operated by other providers Our Web pages may contain links to other providers’ Web pages. We would like to point out that this statement of data protection conditions applies exclusively to the Web pages managed by the Fraunhofer-Gesellschaft. We have no way of influencing the practices of other providers with respect to data protection, nor do we carry out any checks to ensure that they conform to the relevant legislation. #### 3.1.8 Right to information and contact data You have a legal right to inspect any stored data concerning your person, and also the right to demand their correction or deletion, and to withdraw your consent for their further use. In some cases, if you are a registered user of certain services provided by the FraunhoferGesellschaft, we offer you the possibility of inspecting these data online, and even of deleting or modifying the data yourself, via a user account. 3.1.9 Acceptance, validity and modification of data protection conditions By using our Web site, you implicitly agree to accept the use of your personal data as specified above. This present statement of data protection conditions came into effect on October 1st, 2013. As our Web site evolves, and new technologies come into use, it may become necessary to amend the statement of data protection conditions. The FraunhoferGesellschaft reserves the right to modify its data protection conditions at any time, with effect as of a future date. We recommend that you re-read the latest version from time to time. 3.2 Dataset Content, Provenance and Value 3.2.1 What dataset will be collected or created? i. Used Datasets: 1. Continuously generated Web Server logs and (Google) analytics of project’s website access; 2. Continuously generated Social Media engagement data. ii. Produced Datasets: 1. Aggregated analytics of the courses developed within the framework; 2. Aggregated statistics of networking and engagement data produced as part of D10.4 and D10.5 reporting, usage statistics of the framework. 3.2.2 What is its value for others? This will ensure flexibility, adaptability and an improved user experience (UX). 3.3 Standards and Metadata 3.3.1 Which data standards will the data conform to? SlideWiki aims to be a long-lasting open-standard based incubator for the collaborative creation of OpenCourseWare in Europe. The evaluation of the quality of use, based on standards like ISO 25010, will also produce recommendations to improve SlideWiki user interfaces, novel interaction paradigms, information architecture components, etc. Furthermore, the distribution of the learning material to the following educational platforms * Massive Open Online Courses (MOOCs) * Learning Management Systems (LMSs) * Interactive eBooks * Social Networks will be facilitated by SlideWiki’s standard compliant HTML5, RDF/Linked Data and SCORM compatible content model, within the context of WP5. In the context of WP6, Secondary Education Trial, gold standards for the reconciliation of different open data sources of the city and of external organisations, such as the Spanish National Library or DBpedia will be generated. This activity is being also transferred into other cities, such as Madrid, and a similar expansion such as that of CoderDojo activities is expected. 3.3.2 What documentation and metadata will accompany the data? Following the best practices for data on the web, the technical and user documentation of the platform will be constantly updated (MS3). In T1.4, Semantic search, an intuitive search facility for content, structure, metadata, provenance and revision history of the educational material will be designed and implemented. Within the context of the Semantic representation of SlideWiki (D2.1), existing ontologies and vocabularies for semantic representation of OpenCourseWare material and enhancement of these for capturing SlideWiki representations will be reviewed and the resulting vocabulary will support representation of content, structure, metadata, provenance, and revision history. For D4.2, SlideWiki SEO plans and appropriate strategies such as integrating embedded and structured metadata into SlideWiki pages as well as using smart URLs will be implemented in order to increase the visibility of SlideWiki content among popular search engines. With regard to T4.4, Search engine optimization, embedded and structured metadata will be integrated into SlideWiki pages following vocabularies recognized by the main search engines like Schema.org so that its visibility in search engines and results pages of SlideWiki is improved. Another strategy is providing mechanisms like RDF2HTML converter for SEO in RDFaware search engines. Smart URLs will be implemented as more user-friendly URLS (e.g., using _http://slidewiki.org/semantic-web/_ to refer to a deck about Semantic Web). SlideWiki uses Ajax for client-side interactions and one problem we are dealing with is how to facilitate indexing of dynamic Ajax pages by search engines. To resolve this issue we will define suitable URL patterns and SEO strategies for making dynamically loaded content fragments more visible to search engines. 3.4 Data Access and Sharing 3.4.1 Which data is open, re-usable and what licenses are applicable? The SlideWiki project aims at creating widely available, accessible, multilingual, timely, engaging and high-quality educational material (i.e., OpenCourseWare). In particular, the Open Data Commons Open Database License (ODbL) to open datasets is adopted as a project's best practice. Suitable applicable licenses (such as ODBL), anonymization of personal data, possibility and suitability for reuse, and the long term management of the data resources in compliance with the LOD lifecycle and best practices will be implemented where applicable. Overall only 28 out of the 100 courses have a truly open license, the vast majority (i.e. 57 out of 100) are restricting reuse to non-commercial scenarios (i.e. CC-BY-NC-SA), which is not open licensing according to the Open Definition. Often, for example, if courses are offered with a fee or the training organization is a for-profit organization, the non-commercial restriction thus prevents reuse. With regard to content acquisition, an inventory of existing material (e.g. PowerPoint presentations, PDFs, images etc.), which can be used for the creation of new OCW will be created. Particular attention will be given to license clearance, so that the content can be published under the least restrictive conditions. Furthermore, new opportunities are emerging in online education (technology- enhanced learning), largely driven by the availability of high quality online learning materials, also known as Open Educational Resources (OERs). OERs can be described as teaching, learning and research resources that reside in the public domain or have been released under an intellectual property license that permits their free use or repurposing by others depending on which Creative Commons license is used 9 . SlideWiki aims to provide solutions for the very limited OCW availability, the fragmented educational content, the restrictive licenses (e.g. non-commercial) and the lack of inclusiveness or accessibility of educational content. This will be achieved by establishing an Open Educational Content and an educational ecosystem with focus on accessibility, which will be further supported by multilingualism. The created content itself will be published in an open manner without usage restrictions or license costs. However the content itself shall keep records with regard to authorship, modifications and possibly also its usage. The SlideWiki consortium aims to benefit the European community. Therefore, open data and free, open tools, such as the Creative Commons Attribution 4.0 License (CC-BY) 10 will be preferred. The Creative Commons Attribution 4.0 License will allow the learning material to be shared and adapted for any purpose, even commercially. The only restriction is attribution: linking to the source and indicating the changes made. Released in November 2013, CC-BY 4.0 improves its predecessor CC-BY 3.0, as it is an international license and includes databases. With regard to open data, where possible, the project will make use of existing open source libraries and make its efforts highly visible and open to external input aiming thus to attract collaboration rather than competition. During the trials, innovative approaches will be implemented, such as the use of crowdsourcing techniques among the participants and the collaboration with key stakeholders, such as university researchers, in generating gold standards for the reconciliation of different open data sources. The course material will be completely open to the community that is represented by the organisation, with the intention of incorporating additional materials from additional potential users that are not members of the organisation, and with the objective of making the course materials a reference for such domain. 3.4.2 How will open data be accessible and how will such access be maintained? Data should be available for bulk download. APIs for accessing data should follow REST architectural approaches. Real-time data should be available on the Web in real-time. Data must be available in an up-to-date manner, with explicitly demonstrated update frequency. For data available through an API, the API itself should be versioned separately from the data. Old versions should continue to be available. 3.5 Data Archiving, Maintenance and Preservation 3.5.1 Where will each dataset be physically stored? Datasets will be initially stored in a repository hosted by the SlideWiki server, or one of participating consortium partners. Depending on its nature, a dataset may be moved to an external repository, e.g. European Open Data Portal, or the LOD2 project's PublicData.eu. 3.5.2 Where will the data be processed? Datasets will be processed locally at the project partners. Later, datasets will be processed on the SlideWiki server, using cloud services. ### 3.5.3 What physical resources are required to carry out the plan? Hosting, persistence, and access will be managed by the SlideWiki project partners. They will identify virtual machines, cloud services for long term maintenance of the datasets and data processing clusters. ### 3.5.4 What are the physical security protection features? For open accessible datasets, security will be taken to ensure that the datasets are protected from any unwanted tampering, to guarantee the validity. ### 3.5.5 How will each dataset be preserved to ensure long-term value? Since the SlideWiki datasets will follow Linked Data principles, the consortium will follow the best practices for supporting the life cycle of Linked Data, as defined in the EU-FP7 LOD2 project. This includes curation, reparation, and evolution. ### 3.5.6 Who is responsible for the delivery of the plan? Members of each WP should enrich this plan from their own aspect. # Data Management Plan Template The following template will be used to establish plans for each dataset aggregated or produced during the project. ## Data Reference Name A data reference name is an identifier for the dataset to be produced [1]. <table> <tr> <th> Description </th> <th> A dataset should have a standard name within SlideWiki, which can reveal its content, provenance, format, related stakeholders, etc. </th> </tr> <tr> <td> Metadata </td> <td> Interpretation, guideline, and software tools shall be given, provided, or indicated for generating, interpreting data reference names. </td> </tr> </table> Table 1 - Template for Data Reference Name ## Dataset Content, Provenance and Value When completing this section, please refer to questions and answers 1-2 in Section 3.1 <table> <tr> <th> Description </th> <th> A general description of the dataset, indicating whether it has been: aggregated from existing source(s) created from scratch transformed from existing data in other formats generated via (a series of) other operations on existing dataset The description should include reasons leading to the dataset, information about its nature and size and links to scientific reports or publications that refer to the dataset. </th> </tr> <tr> <td> Provenance </td> <td> Links and credits to original data sources </td> </tr> <tr> <td> Operations performed </td> <td> If the dataset is a result of transformation or other operations (including queries, inference, etc.) over existing datasets, this information will be retained. </td> </tr> <tr> <td> Value in Reuse </td> <td> Information about the perceived value and potential candidates for exploiting and reusing the dataset. Including references to datasets that can be integrated for added value. </td> </tr> </table> Table 2 - Template for Dataset Content, Provenance and Value ## Standards and Metadata When completing this section, please refer to questions and answers 3-4 in Section 3.2 <table> <tr> <th> Format </th> <th> Identification of the format used and underlying standards. In case the DMP refers to a collection of related datasets, indicate all of them. </th> </tr> <tr> <td> Metadata </td> <td> Specify what metadata has been provided to enable machine-processable descriptions of dataset. Include a link if a DCAT-AP representation for the dataset has been published. </td> </tr> </table> Table 3 - Template for Standards and Metadata ## Data Access and Sharing When completing this section, please refer to questions and answers 5-6 in Section 2.3 <table> <tr> <th> Data Access and Sharing Policy </th> <th> It is envisaged that all datasets in the SlideWiki project should be freely accessed, in particular, under the Open Data Commons Open Database License (OdbL). When an access is restricted, justifications will be cited (ethical, personal data, intellectual property, commercial, privacy-related, security-related) </th> </tr> <tr> <td> Copyright and IPR </td> <td> Where relevant, specific information regarding copyrights and intellectual property should be provided. </td> </tr> <tr> <td> Access Procedures </td> <td> To specify how and in which manner can the data be accessed, retrieved, queried, visualised, etc. </td> </tr> <tr> <td> Dissemination and reuse Procedures </td> <td> To outline technical mechanisms for dissemination and re-use, including special software, services, APIs, or other tools. </td> </tr> </table> Table 4 - Template for Data Access and Sharing ## Archiving, Maintenance and Preservation When completing this section, please refer to questions and answers 6-12 in Section 3.4 <table> <tr> <th> Storage </th> <th> Physical repository where data will be stored and made available for access (if relevant) and indication of type: * SlideWiki partner owned * societal challenge domain repository * open repository * other </th> </tr> <tr> <td> Preservation </td> <td> Procedures for guaranteed long-term data preservation and backup. Target length of preservation. </td> </tr> <tr> <td> Physical Resources </td> <td> Resources and infrastructures required to carry out the plan, especially regarding long-term access and persistence. Information about access mechanism including physical security features. </td> </tr> <tr> <td> Expected Costs </td> <td> Approximate hosting, access, maintenance costs for the expected end volume, and a strategy to cover them. </td> </tr> <tr> <td> Responsibilities </td> <td> Individual and/or entities are responsible for ensuring that the DMP is adhered to the data resource. </td> </tr> </table> Table 5 - Template for Archiving, Maintenance and Preservation # Storage of the Datasets All project-related datasets are stored on either GitHub or our SlideWiki servers for public access. The so-called Learning Record Stores are the next generation tracking and reporting repositories that support ideas like the Tin Can protocol and the successor xAPI. Open analytic solutions as provided by the Open Academic Analytics Initiative (OAAI) already fostering the collection of and meaningful interpretation of data across learning institutes. The Learning Locker data repository stores learning activity data generated by xAPIcompliant (Tin Can) learning activities. A Learning Activity Database and the mechanism for logging and collecting all activity data in the platform will be developed. It will seamlessly track user actions, associate them with semantically rich events of the activity data model and store them in the Learning Activity Database. The mechanism will be implemented on top of state- of-the-art open source big data logging and ingestion tools (such as Apache Flume and Apache Kafka) such that it can exploit the dynamic scale-out infrastructure of WP1 and achieve efficient data ingestion for large volume and rate of user events. The Consortium will make sure, that all OpenCourseWare content stored in SlideWiki, be it slides, presentations, questionnaires, diagrams, images, user data etc., is regularly backedup and archived. # FAIR Data Management Principles The SlideWiki consortium monitors the application of the FAIR Data management principles, also listed here below. ## 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)? What naming conventions do you follow? Will search keywords be provided that optimize possibilities for re-use? Do you provide clear version numbers? 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. ## 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. 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. How will the data be made accessible (e.g. by deposition in a repository)? 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)? 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. Have you explored appropriate arrangements with the identified repository? If there are restrictions on use, how will access be provided? Is there a need for a data access committee? 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? ## Making Data Interoperable Are the data produced in the project interoperable, that is allowing data exchange and reuse 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)? What data and metadata vocabularies, standards or methodologies will you follow to make your data interoperable? 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? ## Increase Data Re-Use (Through Clarifying Licences) How will the data be licensed to permit the widest re-use possible? 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. 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. How long is it intended that the data remains re-usable? Are data quality assurance processes described? ## Allocation of Resources What are the costs for making data FAIR in your project? 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). Who will be responsible for data management in your project? 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)? ## Data Security What provisions are in place for data security (including data recovery as well as secure storage and transfer of sensitive data)? Is the data safely stored in certified repositories for long term preservation and curation? ## 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). Is informed consent for data sharing and long term preservation included in questionnaires dealing with personal data? ## Other Issues Do you make use of other national/funder/sectorial/departmental procedures for data management? If yes, which ones? # Conclusion This deliverable outlines the guidelines and strategies for data management within the context of the SlideWiki and will be fine-tuned and extended throughout the course of the project. Following the guidelines on FAIR Data Management in H2020 11 , Data Management in H2020 12 , we described the purpose and scope of datasets of SlideWiki, and specified the datasets management for the SlideWiki project. Five kinds of stakeholders related to the SlideWiki data management plan are identified and described: original data producer, data wrangler, data analyser, system administrator/developer, and data end-user; generic data flow chain of SlideWiki is listed and explained: data discover, data ingest, data persist, data analyse, and data expose. Following the best practices of Linked Data Publishing, we specified the 13 steps of best practices for the SlideWiki dataset management. Based on the above, we present DMP guidelines for SlideWiki, and DMP templates for data management process during the lifetime of the SlideWiki project.	https://phaidra.univie.ac.at/o:1140797	Horizon 2020
0013_COROMA_723853.md	# 1\. DATA SUMMARY ## 1.1 OBJECTIVE This document constitutes the first version of the Data Management Plan (D1.4) of COROMA project, elaborated under Task 1.4 Data management plan for Open Research Data Pilot of Work Package 1 Requirements definition. The objective of this deliverable is to give an overview of the data that will be collected during the runtime of the COROMA project. This document will define processes how data will be stored, published, and distributed amongst the consortium partners. COROMA is a large project with many partners which makes a good data management strategy to share and archive documents inevitable. Project partners will collect data in order to ensure that all conducted experiments are reproducible and to share knowledge between partners within the project and with other researchers. A key objective of the COROMA project is to develop the robot into an autonomous system. Autonomous systems rely more and more on experiences and collected data therefore data can be used to learn and improve various cognitive components of the robot as well as for validation of approaches and reproduction of results. ## 1.2 TYPES AND FORMATS Several types of data will be produced during the project runtime. These are for example: * Experimental data (e.g. sensor data) * Equipment data and environment data (e.g. CAD files) * Configuration files for software and hardware * Illustrations and videos * Documents, e.g. dissemination and communication material (publications, posters, presentations, etc.), deliverables * Software It is important that data is provided in standard formats that are easily accessible with standard software which should ideally be free of charge to simplify sharing the data among partners or to the public. Examples of preferable formats are * Documents (e.g. publications, presentations): PDF * Illustrations: PDF, SVG * Images: high quality, e.g. TIFF, BMP, JPEG 2000 * CAD: ISO-10303-21 (STEP file) * Dummy part data / process related data: SciLab, GNU Octave, CSV Process related data such as force measurements, distance measurements, etc. must be provided in SI. ## 1.3 OVERVIEW This Data Management Plan will briefly describe the general categories of data that will be generated during the project and in some cases it will already define specific details about the data that will be produced in this section. However, everything is subject to change. This part of the data management plan will be updated as soon as we know more details and it will form the basis of deliverable 8.5 “Data Compilation Open Research Report”. <table> <tr> <th> Partner </th> <th> Data </th> <th> Details in Section </th> <th> Type </th> <th> Shared with </th> </tr> <tr> <td> ITR </td> <td> Models of parts </td> <td> 1.3.1 </td> <td> CAD models </td> <td> Consortium </td> </tr> <tr> <td> ITR </td> <td> Semantic dataset </td> <td> 1.3.2 </td> <td> Sensor measurements </td> <td> Public </td> </tr> <tr> <td> SRC </td> <td> CORO-hand </td> <td> 1.3.3 </td> <td> CAD / simulation models and metadata </td> <td> Consortium </td> </tr> <tr> <td> SRC </td> <td> Experimental Data (SRC) </td> <td> 1.3.4 </td> <td> Sensor measurements </td> <td> Specific partners </td> </tr> <tr> <td> IDK </td> <td> Experimental Data (IDK) </td> <td> 1.3.5 </td> <td> Sensor measurements </td> <td> Public </td> </tr> <tr> <td> BEN </td> <td> Demonstration data (BEN) </td> <td> 1.3.6 </td> <td> Sensor measurements and metadata </td> <td> Public </td> </tr> <tr> <td> ACI </td> <td> Demonstration data (ACI) </td> <td> 1.3.7 </td> <td> Sensor measurements and metadata </td> <td> Consortium and public (after approval) </td> </tr> <tr> <td> ENSA </td> <td> Demonstration data (ENSA) </td> <td> 1.3.8 </td> <td> Sensor measurements and metadata </td> <td> Consortium or public (after approval) </td> </tr> <tr> <td> UNA </td> <td> Experimental data (UNA) </td> <td> 1.3.9 </td> <td> Sensor measurements and metadata </td> <td> Specific partners </td> </tr> <tr> <td> All </td> <td> Publications </td> <td> 1.3.10 </td> <td> Documents </td> <td> Public </td> </tr> <tr> <td> All </td> <td> Internal documents </td> <td> 1.3.11 </td> <td> Documents </td> <td> Consortium </td> </tr> <tr> <td> All </td> <td> Deliverables </td> <td> 1.3.12 </td> <td> Documents </td> <td> Public or consortium </td> </tr> <tr> <td> DFKI </td> <td> Machine learning datasets </td> <td> 1.3.13 </td> <td> Datasets </td> <td> Unknown </td> </tr> </table> ### 1.3.1 PART MODELS Responsible Partner: ITR Description: CAD models of products Purpose: Evaluation of visual scanning methods or procedures Type / Format: CAD, STL (Stereo Lithography Interface format) Metadata: \- Required Software: STL files can be opened with e.g. FreeCAD or Blender Data Collection: CAD models will be provided by partners that are involved in the demonstration Linked to Publications: \- License / Access: Shared with the consortium ### 1.3.2 SEMANTIC DATASET Responsible Partner: ITR Description: Dataset for Semantic Segmentation Purpose: Training set for new algorithms, can be used for benchmarking Type / Format: Dataset for machine learning, ROS Bags (ROS logfiles), PCD (point cloud library document), XML Metadata: \- Required Software: ROS Bags can be opened with the robot operating system (rosbag, rqt_bag), PCD can be opened with pcd_viewer Data Collection: Dataset will be acquired using 3D sensors in experimental setups Linked to Publications: \- License / Access: Shared publicly ### 1.3.3 CORO-HAND (SRC) Responsible Partner: SRC Description: (1) CAD and simulation data for mechanical parts, schematics and CAD for electronics, (2) videos, images Purpose: (1) used to prototype, design and verify the design of the CORO- hand, (2) dissemination and communication activities Type / Format: CAD data: STEP (ISO 10303), simulation models: URDF 1 Metadata: \- Required Software: URDF files can be used and visualized with the robot operating system (ROS), ROS supports multiple operating systems but Ubuntu is preferred and has several tools to visualize data (e.g. rviz), STEP files can be opened by any 3D CAD package (e.g. Solidworks, AutoCAD, Blender) Data Collection: SRC designs and provides the CORO-hand models Linked to Publications: \- License / Access: Design files shall not be made publicly available. Any CAD and simulation models of the CORO-hand that are made available to the consortium shall be regarded as confidential and may be simplified models which have been de-featured, e.g. external geometries only. ### 1.3.4 EXPERIMENTAL DATA (SRC) Responsible Partner: SRC Description: (1) Sensor data (joint angles, joint torques, motor temperature/voltage/current, tactile sensors if they are used), (2) control data (controller status/commands, driver status/commands) Purpose: Data is used to monitor the health of the CORO-hand, debugging and performance characterization Type / Format: ROS Bags (ROS logfiles) Metadata: Data shall be associated to which objects are being grasped Required Software: ROS Bags can be used and visualized with the robot operating system (ROS), ROS supports multiple operating systems but Ubuntu is preferred and has several tools to visualize data (e.g. rviz) Data Collection: (1,2) shall be recorded either live from using the CORO- hand or during simulation Linked to Publications: \- License / Access: Sensor data coupled with simulation models might be useful to consortium members, but shall remain private unless specifically requested. ### 1.3.5 EXPERIMENTAL DATA (IDK) Responsible Partner: IDK Description: Robot dynamics dataset Purpose: Monitoring of the dynamics of the robot during the machining process it is involved (as a fixture) Type / Format: Accelerometer signal, time signals and/or FFT signals, force signal, time signal, Matlab MAT (.mat) file 2 Metadata:* * Estimated acceleration measurement frequency: 5000 Hz * Acceleration unit: m/s² * Force unit: Newton Required Software: Matlab files can be opened with Matlab, SciLab, GNU Octave, etc. Data Collection: * Ingesys IC3 real time control system * Industrial or laboratory accelerometer * Force signal: force sensing plate, robot tool tip 1 or 6 axis force sensor Linked to Publications: Linked to two peer reviewed scientific papers, one about using the robot as a mobile fixturing system, one about robotic drilling License / Access: Shared publicly ### 1.3.6 DEMONSTRATION DATA (BEN) Responsible Partner: BEN Description: Technical data recorded during the demonstration at the facilities of BEN and corresponding metadata Purpose: Technical documentation and analysis, reproduction of results in a paper Type / Format: Video, images (JPEG 2000), logfiles Metadata: \- Required Software: \- Data Collection: Data will be logged and recorded during the demonstration Linked to Publications: Linked to a publication about the demonstration License / Access: Shared publicly ### 1.3.7 DEMONSTRATION DATA (ACI) Responsible Partner: ACI Description: Technical data recorded during the demonstration at the facilities of ACI and corresponding metadata Purpose: Technical documentation and analysis, reproduction of results in a paper Type / Format: Video, images (JPEG 2000), logfiles Metadata: \- Required Software: \- Data Collection: Data will be logged and recorded during the demonstration Linked to Publications: \- License / Access: Data related to the process itself, images, or videos without produced parts can be shared with the consortium and publicly after approval. Images and videos of the produced parts are property of the customers and cannot be shared. ### 1.3.8 DEMONSTRATION DATA (ENSA) Responsible Partner: ENSA Description: Technical data recorded during the demonstration at the facilities of ENSA and corresponding metadata Purpose: Technical documentation and analysis, reproduction of results in a paper Type / Format: Video, images (JPEG 2000), logfiles Metadata: \- Required Software: \- Data Collection: Data will be logged and recorded during the demonstration Linked to Publications: \- License / Access: Videos or pictures must be recorded by ENSA personnel. Logged and recorded data must be approved by ENSA and its customers before released to public. Customer information must not be published at all. ### 1.3.9 EXPERIMENTAL DATA (UNA) Responsible Partner: UNA Description: Experimental data (process monitoring, environment monitoring), equipment data, and configuration files Purpose: Benchmarking, evaluation of a method or procedure, reproduction of results in papers Type / Format: Equipment data (part file, path file equipment model file, environment model file), configuration file (robot position file, process conditions), videos, images (high quality, e.g. PNG, TIFF, BMP, JPEG 2000), CAD (STEP file, CATPART, IGS, STL, IFC, …), process related data (SciLab, GNU Octave, Matlab, Excel, PsiConsole (AGV)), force measurements (CSV) Metadata: \- Required Software: * Required licenses: CATIA V5, Matlab, Autodesk / Powermill, MS Word * Dependencies: Matlab robotic tool box, Windows 7 Data Collection: \- Linked to Publications: Possibly License / Access: Data will be shared according to WP distribution and limited to collaboration 1.3.10 PUBLICATIONS Responsible Partner: UNA, IDK, BEN, … Description: Scientific or industrial publications Purpose: Dissemination of project results, making results reproducible Type / Format: Publication, MS Word, LaTeX, PDF Metadata: \- Required Software: MS Word, PDF viewer Data Collection: \- Linked to Publications: \- License / Access: Scientific publications must be open access ### 1.3.11 INTERNAL DOCUMENTS Responsible Partner: all Description: Internal documents, confidential documents or temporary documents Purpose: Documents that are required to communicate among the partners to achieve the project goals Type / Format: Documents, PDF, DOCX, XLSX, PPTX Metadata: \- Required Software: MS Office, Adobe Acrobat Reader Data Collection: \- Linked to Publications: \- License / Access: Shared within the consortium ### 1.3.12 DELIVERABLES Responsible Partner: all Description: Deliverables Purpose: Documentation and planning of the project COROMA Type / Format: PDF Metadata: \- Required Software: PDF viewer Data Collection: \- Linked to Publications: \- License / Access: Public or confidential, as defined in the GA (annex 1, part A, pp 6-10) ### 1.3.13 MACHINE LEARNING DATASETS Responsible Partner: DFKI Description: Datasets that will be preprocessed and used for machine learning Purpose: Benchmarking, reproduction of results in papers Type / Format: \- Metadata: \- Required Software: \- Data Collection: Data will be collected by partners in the project COROMA Linked to Publications: Probably License / Access: Depends on the owner of the original data # 2\. FAIR DATA In COROMA, three different kinds of data will be aggregated and shared within the project: 1. Public data and documents 2. Restricted data 3. Internal documents Task leader DFKI will rely on the platform Zenodo 3 for data that has to be made publicly available (1) and DFKI will maintain an overview of publicly shared data and documents at the COROMA website 4 . Note that as COROMA is an H2020 project, all publications must be open access. For data that will be shared with a selected audience (e.g. the project consortium), DFKI _recommends_ to use Zenodo as a platform which supports detailed access control (2). It enables interested researchers to request access to restricted data directly from the owner of the data and it allows the owner of the data to define and revoke access rights per request. For internal documents (3) such as minutes, confidential deliverables, internal presentations, etc. the internal project website 5 will be used to share these documents among the project consortium. ## 2.1 MAKING DATA FINDABLE Publicly shared data produced in the project COROMA must be findable. Zenodo provides search functionality which will guarantee that COROMA datasets can be found. The search functionality will for example allow filtering by keywords and name of the data repository. Each responsible partner must provide appropriate keywords during the data publication process. These keywords should assist people that search for the data at Zenodo. Each upload receives a digital object identifier (DOI) which will make it easily findable even though the URL to the dataset might have changed. In addition, we will provide an overview of publicly available datasets and documents at our project website. All partners are encouraged to upload restricted data to Zenodo. Zenodo allows restricted access for confidential data. Note that the data is still findable although it is not visible if the access is restricted. The internal COROMA website provides a section to store documents that have been produced within the project. It will be used mostly to share confidential data between partners of the project. Documents will be findable through the hierarchical directory structure (e.g. ordered by categories like deliverables, meetings, general information, work packages, etc.). The project website will be available at least as long as the project COROMA is running. ## 2.2 MAKING DATA OPENLY ACCESSIBLE Due to the industrial character of the COROMA project and the participation of industrial partners, not all data generated within COROMA can be made public according to the Consortium Agreement. However, important scientific data that can be used to reproduce and validate results must be made openly accessible and publications must be published open access. The data will be published in forms that are specified at the beginning of Section 2. The data publication process is described in Section 2.5. Only free and standard software _should_ be required to use the data. Specific formats are described in Section 1 of this document. Some proprietary tools produce data in formats that can only be used by these tools. These tools must either be standard tools that are used by experts that are interested in the data or there must be a way to extract relevant information with free software. If neither the first of the second case is applied, there is no reason to release the data. All partners must be aware of these issues and there must be a good reason to violate these guidelines (e.g. unreasonable effort to convert a proprietary format). Access to restricted data will be controlled by the responsible partners. Zenodo allows searching for restricted datasets and it allows requesting access to these datasets over the platform. The responsible partner has to decide on each individual case whether the access will be granted. ## 2.3 MAKING DATA INTEROPERABLE Data must be interoperable. Each dataset that is released will have sufficient documentation included that describes the process of reading and using the data. Sufficient means that professionals will easily be able to make use of the data. For example, in the case of a publication in PDF format, no further documentation would be required. The documentation should be plain text or PDF that describes the required tools and procedures to make use of the data. It might be required to provide a short example script in a free programming language that loads and visualizes the data or metadata like information about the columns of a CSV (e.g. measured quantity, units). The format won’t be specified any further because the produced datasets and the systems used in the project are so diverse that further constraints could make the cost of releasing datasets unreasonably high. A good example of an interoperable public dataset is the MNIST dataset 6 which has been used in the machine learning community as a benchmark dataset for more than 15 years. Although the format of the data is very unusual, the website contains a short and sufficient description of the format which makes it transferable in almost any programming language. The website explains the purpose of the dataset, how it is related to other datasets, and it includes a comparison of various methods that have been evaluated with the dataset. ## 2.4 INCREASE DATA RE-USE Data and documents have to be published with a corresponding license. The responsible partner must make several decisions that affect the choice of an appropriate license, for example: * Is commercial use permitted? * Is modification permitted?  Is distribution permitted?  Is private use granted? * Is redistribution mandatory? Possible licenses for published data and documents are * Creative Commons Attribution 4.0, CC BY 4.0, _https://creativecommons.org/licenses/by/4.0/_ : allows copying and redistribution, allows adaption for any purpose, cannot be revoked by the author, users must give appropriate credit to the author * Creative Commons Attribution-ShareAlike 4.0, CC BY-SA 4.0, _https://creativecommons.org/licenses/by-sa/4.0/_ : similar to CC BY 4.0 with the additional obligation to distribute adaptions under the same license * Creative Commons Attribution-NonCommercial 4.0, CC BY-NC 4.0, _https://creativecommons.org/licenses/by-nc/4.0/_ : similar to CC BY 4.0 with the restriction that commercial use is not possible * Creative Commons Attribution-NoDerivatives 4.0, CC BY-ND 4.0, _https://creativecommons.org/licenses/by-nd/4.0/_ : similar to CC BY 4.0 with the restriction adaptions must not be shared * Any other Creative Commons license: _https://creativecommons.org/licenses/_ Other types of licenses are available for software. The choice of license depends on the specific requirements of the copyright owner. In case software should be released as open source partners can generally choose between licenses that allow commercial use (e.g. BSD) and those that do not (e.g. GNU General Public License, GPL). There are various platforms that help to select an appropriate license. 7 Public data will be made available as soon as it has been produced and documented, the corresponding publication has been published, or at the latest at the end of M36. DFKI is responsible for checking whether published data is findable, interoperable, and reusable. The publication process is described in Section 2.5. By relying mostly on standard formats and requiring a documentation for each published dataset, COROMA consortium will make the data reusable as long as possible. No further guarantees are given. ## 2.5 DATA PUBLICATION PROCESS We will briefly describe the process to publish public and restricted data and documents at the platform Zenodo. The responsible partner will prepare the data for publication. The preparation includes: * Definition of appropriate keywords that make the dataset findable for potential users of the dataset or document * Definition of access rights (open access, embargoed access and embargo date, restricted access) * Selection of license (see Section 2.4 for details) * Description of dataset, must be sufficient for experts in the field to make use of the data and for non-experts to understand for which purpose the data can be used * Optional: implementation of an example that loads the data and demonstrates how it can be used, e.g. by visualization After the preparation, the responsible partner uploads the data and generated metadata to the COROMA community at the Zenodo platform. 8 DFKI is responsible for checking whether all of the criteria mentioned above have been fulfilled and will either contact the responsible partner to complete the process or directly accept the dataset for the community. After the dataset has been accepted for the Zenodo community, DFKI will contact IDK to add the dataset to the COROMA website. # 3\. ALLOCATION OF RESSOURCES Collected data will be prepared for publication in T8.3. Datasets that require documentation and quality assurance are prepared and released by DFKI, IDK, UOS, UNA, and ITR. Other documents that will be used for communication or dissemination will be released in T8.1 and T8.2 respectively and usually do not require much further preparation for release apart from the work that is required to generate these documents (e.g. articles, posters). Each partner is responsible for his datasets as described in Section 1\. Each responsible partner must guarantee that the data is in a format that can be published, documented so that an expert is able to use the data, and can be read with free or standard software. The release process is supervised by DFKI. Details are described in Section 2.5. Each responsible partner must communicate about the release of public data or dissemination documents with DFKI. The release of communication documents must be communicated to IDK. There are no costs for long-term preservation of public data. The platform Zenodo is free and we cannot give any guarantees beyond the lifetime of Zenodo and its successors. # 4\. DATA SECURITY Publicly shared data is stored at the platform Zenodo. Zenodo guarantees to retain the data for the lifetime of the platform which is at least 20 years. Zenodo further guarantees that data is backed up nightly and stored in multiple online replicas. We rely on the platform’s security in terms of data recovery, secure storage of restricted data and transfer of sensitive data. # 5\. SUMMARY COROMA project partners have completed the data management plan. After analysing the planned data generating activities and the requirements of each partner, a plan has been produced to give an overview of these datasets and give guidelines for data management. In particular DFKI will focus on the process to make data available to the public and how the datasets have to be prepared. This data management plan is intended to be a living document that will be extended during the project and will finally result in the deliverable 8.5 “Data Compilation Open Research Report”.	https://phaidra.univie.ac.at/o:1140797	Horizon 2020
0014_CALIPSOplus_730872.md	The implementation of a Data Policy can start only with the availability of a metadata catalogue software. Such a software manages the metadata of raw and derived data taken at experiment facilities (i.e. partners of CALIPSOplus). In a metadata catalogue different types of metadata are saved: 1) _administrative metadata:_ data management lifecycle, ownership, file catalog and 2) _scientific metadata:_ describing the sample, the beamline and experiment as well as parameters relevant for data analysis. A data catalogue software enables management of the data lifecycle, i.e. from data aquisition to data analysis and eventual deletion of the data. The data can be linked to proposals and samples, to publications (DOI, PID) and can be migrated to and from longterm storage on tape. A metadata catalogue helps keeping track of the data provenance (i.e. the steps leading to the final results) and it allows to check scientific integrity (checksum of data). It allows to find data based on the metadata (i.e. the users own data and handles open access to data). In the long term: metadata catalogues will help to automate standardised analysis workflows and support the standardisation of data formats. _Has your facility some e-infrastructures like metadata catalogue software in place?_ <table> <tr> <th> </th> <th> CALIPSOplus partners </th> <th> Country </th> <th> Has your facility some e-infrastructures like Metadata Catagogue Software in place? </th> </tr> <tr> <td> 1 </td> <td> HELMHOLTZ-ZENTRUM DRESDENROSSENDORF EV Germany </td> <td> Germany </td> <td> Not planned yet </td> </tr> <tr> <td> 2 </td> <td> ANKARA UNIVERSITESI </td> <td> Turkey </td> <td> Not planned yet </td> </tr> <tr> <td> 3 </td> <td> AARHUS UNIVERSITET </td> <td> Denmark </td> <td> Not planned yet </td> </tr> <tr> <td> 4 </td> <td> ALBA - CONSORCIO PARA LA CONSTRUCCION EQUIPAMIENTO Y EXPLOTACION DEL LABORATORIO DE LUZ DE SINCROTRON </td> <td> Spain </td> <td> Alba offers as well a portal for remote access to data from the experimental team authenticated with the proposal ID. Alba is working on the implementation of ICAT metadata catalogues for the beamlines. This will be progressively implemented as well as the whole data policy on the beamlines. The plan is to have the first prototype working on a beamline by 2018. In parallel ALBA is also working in other specific macromolecular metadata laboratory information management systems (ISPyB) </td> </tr> <tr> <td> 5 </td> <td> CENTRE NATIONAL DE LA RECHERCHE SCIENTIFIQUE </td> <td> France </td> <td> Not planned yet </td> </tr> <tr> <td> 6 </td> <td> STIFTUNG DEUTSCHES ELEKTRONENSYNCHROTRON DESY </td> <td> Germany </td> <td> ICAT metadata catalogue </td> </tr> <tr> <td> 7 </td> <td> DIAMOND LIGHT SOURCE LIMITED </td> <td> United Kingdom </td> <td> ICAT metadata catalogue </td> </tr> <tr> <td> 8 </td> <td> ELETTRA - SINCROTRONE TRIESTE SCPA </td> <td> Italy </td> <td> ICAT metadata catalogue planned * We collect different type of data. Each beamline has its own data acquisition system. * Most of the beamline acquisition systems are implemented using the Tango control system. Data acquired are saved in the storage system in an area called scratch. * Tango, Labview </td> </tr> <tr> <td> 9 </td> <td> EUXFEL - EUROPEAN X-RAY FREE- ELECTRON LASER FACILITY GMBH </td> <td> Germany </td> <td> Planned </td> </tr> <tr> <td> 10 </td> <td> HELMHOLTZ-ZENTRUM BERLIN FUR MATERIALIEN UND ENERGIE GMBH </td> <td> Germany </td> <td> We use a hierarchical storage management system for the central storage and long time archival of the data. The bulk of the data will be on tape. We use ICAT as the metadata catalogue and user portal for the access to the data. </td> </tr> <tr> <td> 11 </td> <td> ISTITUTO NAZIONALE DI FISICA NUCLEARE </td> <td> Italy </td> <td> Not yet in place </td> </tr> <tr> <td> 12 </td> <td> ESRF - INSTALLATION EUROPEENNE DE RAYONNEMENT SYNCHROTRON </td> <td> France </td> <td> ICAT metadata catalogue </td> </tr> <tr> <td> 13 </td> <td> KIT - KARLSRUHER INSTITUT FUER TECHNOLOGIE </td> <td> Germany </td> <td> Not planned yet </td> </tr> <tr> <td> 14 </td> <td> LUNDS UNIVERSITET </td> <td> Sweden </td> <td> MELANI </td> </tr> <tr> <td> 15 </td> <td> PSI - PAUL SCHERRER INSTITUT </td> <td> Switzerland </td> <td> MELANI </td> </tr> <tr> <td> 16 </td> <td> FELIX - STICHTING KATHOLIEKE UNIVERSITEIT </td> <td> Netherlands </td> <td> Not yet in place </td> </tr> <tr> <td> 17 </td> <td> SESAME - SYNCHROTRON-LIGHT FOR EXPERIMENTAL SCIENCE AND APPLICATIONS IN THE MIDDLE EAST </td> <td> Jordan </td> <td> Not in user operation yet </td> </tr> <tr> <td> 18 </td> <td> Société Civile Synchrotron SOLEIL </td> <td> France </td> <td> Not yet in place </td> </tr> <tr> <td> 19 </td> <td> SOLARIS - UNIWERSYTET JAGIELLONSKI </td> <td> Poland </td> <td> Not yet in place </td> </tr> </table> Table 3. Implementation of metadata catalogue software _How data curation is regulated at your facility?_ <table> <tr> <th> </th> <th> CALIPSOplus partners </th> <th> Country </th> <th> How data curation is regulated at your facility? </th> </tr> <tr> <td> 1 </td> <td> HELMHOLTZ-ZENTRUM DRESDENROSSENDORF EV Germany </td> <td> Germany </td> <td> \- </td> </tr> </table> <table> <tr> <th> 2 </th> <th> ANKARA UNIVERSITESI </th> <th> Turkey </th> <th> \- </th> </tr> <tr> <td> 3 </td> <td> AARHUS UNIVERSITET </td> <td> Denmark </td> <td> The curation of data is carried out by the beam line scientists. All of the original data files are kept here at the facility, but users are given copies of all their data for analysis and publication. </td> </tr> <tr> <td> 4 </td> <td> ALBA - CONSORCIO PARA LA CONSTRUCCION EQUIPAMIENTO Y EXPLOTACION DEL LABORATORIO DE LUZ DE SINCROTRON </td> <td> Spain </td> <td> Access to raw data and the associated metadata obtained from a public access experiment is restricted to the experimental team for 3 years. After this embargo period, the data can be made publicly available. These data are remotely accessible by the research group. Data preprocessing and analysis is depending on the Beamline partially done at the Alba premises and completed by the researchers at their home institutes. This means that the data repository contains always raw data and in some cases processed and curated data. </td> </tr> <tr> <td> 5 </td> <td> CENTRE NATIONAL DE LA RECHERCHE SCIENTIFIQUE </td> <td> France </td> <td> \- </td> </tr> <tr> <td> 6 </td> <td> STIFTUNG DEUTSCHES ELEKTRONENSYNCHROTRON DESY </td> <td> Germany </td> <td> Long term archiving in tape library (up to 10 years) </td> </tr> <tr> <td> 7 </td> <td> DIAMOND LIGHT SOURCE LIMITED </td> <td> United Kingdom </td> <td> \- </td> </tr> <tr> <td> 8 </td> <td> ELETTRA - SINCROTRONE TRIESTE SCPA </td> <td> Italy </td> <td> * The users decide if the data acquired are of good quality and if the case they transform them into datasets. Datasets are saved in an area of the storage system called online where they can be processed, accessed. In the long term the idea is to move the data from the online area to the offline data that in principle can be remote. * Preferred data format is HDF5. </td> </tr> <tr> <td> 9 </td> <td> EUXFEL - EUROPEAN X-RAY FREE- ELECTRON LASER FACILITY GMBH </td> <td> Germany </td> <td> Long term archiving in tape library (up to 10 years) • Preferred data format is HDF5. </td> </tr> <tr> <td> 10 </td> <td> HELMHOLTZ-ZENTRUM BERLIN FUR MATERIALIEN UND ENERGIE GMBH </td> <td> Germany </td> <td> We are currently implementing the Data Policy. Data curation is still work in progress. </td> </tr> <tr> <td> 11 </td> <td> ISTITUTO NAZIONALE DI FISICA NUCLEARE </td> <td> Italy </td> <td> We backup data on dedicated external Hard Drives </td> </tr> <tr> <td> 12 </td> <td> ESRF - INSTALLATION EUROPEENNE DE RAYONNEMENT SYNCHROTRON </td> <td> France </td> <td> The deadline for implementing the data policy on all ESRF beamlines is in 2020. At the moment 11 beamlines are connected to the metadata catalogue (6 are inprogress), data archiving is work in progress at 17 beamlines. This is long term archiving in tape library (data are being archived for 10 years in tape archive) </td> </tr> <tr> <td> 13 </td> <td> KIT - KARLSRUHER INSTITUT FUER TECHNOLOGIE </td> <td> Germany </td> <td> \- </td> </tr> <tr> <td> 14 </td> <td> LUNDS UNIVERSITET </td> <td> Sweden </td> <td> Not yet in place </td> </tr> <tr> <td> 15 </td> <td> PSI - PAUL SCHERRER INSTITUT </td> <td> Switzerland </td> <td> * Data Analysis as a service DaaS project: focusses on offline data analysis and large offline disk storage. Finishes end of October 2017 * Petabyte Archive: focuses on enabling the data flows to and from a longterm data storage at HPC CSCS/Lugano. Finishes end of 2017 (data storage up to 10 years) * Data Curation Project, collaboration with ESS, focusses on data catalog and data analysis automation. Started 2017 and will last until end of 2019. Enabled to add dedicated manpower for data curation tasks. </td> </tr> <tr> <td> 16 </td> <td> FELIX - STICHTING KATHOLIEKE UNIVERSITEIT </td> <td> Netherlands </td> <td> The experimental data are stored on a facility server; maintainenance and back-up is organized centrally by the Radboud University IT department. We are currently exploring and evaluating the possibilities to use local (Radboud) and a national (e.g. DANS) repositories. </td> </tr> <tr> <td> 17 </td> <td> SESAME - SYNCHROTRON-LIGHT FOR EXPERIMENTAL SCIENCE AND APPLICATIONS IN THE MIDDLE EAST </td> <td> Jordan </td> <td> Not in user operation yet </td> </tr> <tr> <td> 18 </td> <td> Société Civile Synchrotron SOLEIL </td> <td> France </td> <td> Not yet in place </td> </tr> <tr> <td> 19 </td> <td> SOLARIS - UNIWERSYTET JAGIELLONSKI </td> <td> Poland </td> <td> Not yet in place </td> </tr> </table> Table 4. Data curation at different facilities. Implementation of Data Management Plan for CALIPSOplus As is clear from the survey 11 of 19 partners, by the end of 2018 will have a data policy in place based on the PaNdata data policy (Deliverable D2.1. of PaNdata Europe FP7 project in 2011). The PaNData data policy frame work defines (long term) goals concerning data storage, life cycle management, data access and ownership. Implementation of PSI data policy needs a metadata catalogue. This implies that the implementation of the data policies can only start with the availability of metadata catalogue software. Some facilities use the iCAT software (see Table 2) that was developed within the PaNdata ODI FP7 project, others are currently developing a new metadata catalogue software called MELANI (PSI, ESS, MAXIV) that is developed within the Data Analysis as A Service (DAAS) project of PSI. Implementation of the Data Policies is done step by step (i.e. role out from beamline to beamline) at all facilities. This stepwise process implies that a DMP will only be complete once these processes at the different facilities have been finished. FAIR Data management at a glance: DMP components to be covered 1. Data summary: Within this project data are collected during the experiments at the facilities. The data are collected by users that received transnational access money from the CALIPSOplus project. Most data are collected in the HDF5 or Nexus format . Data are open access after the embargo period of 3-5 years and can be reused by third parties. Data origin is from the experimental station of the CALIPSoplus partner large scale facilities. 2. The two types of metadata catalogues planned to be used by the CALIPSOplus partners (ICAT and MELANI; see Table 3) make data findable, accessable, interoparable and reusable (FAIR data), with standards for metadata creation. 2. 2.The timeline in which data become accessible is defined by the embargo period (3-5 years) in the specific facility data policy (see example of data policy in ANNEX 1). 2.3 The interoperability of the data is guaranteed by the metadata catalogue softwares in place. 3. The implementation of the data policies at the different facilities and with this the putting in place of metadata catalogue software is a just started and an ongoing process. Therefore the allocation of recources , i.e. the cost of making our data FAIR and the costs for long term preservation of data will be descibed in detail in the next update of our DMP which is due in month 19 of the project. Conclusion The stepwise implementation of the Data Policies (i.e. role out from beamline to beamline) at all facilities implies that a DMP will only be complete once these processes at the different facilities have been finished. The present version of the DMP reflects the status as it is now for the different partners of CALIPSOplus. In the next update of the DMP in month 19, a clear progress in role out of this process will be visible.	https://phaidra.univie.ac.at/o:1140797	Horizon 2020
0016_MURAB_688188.md	# General information ## MRI and ultrasound Robotic Assisted Biopsy (MURAB) The MURAB project has the ambition to revolutionise the way cancer screening and muscle diseases are researched for patients and has the potential to save lives by early detection and treatment. The project intends to create a new paradigm in which, the precision of great medical imaging modalities like MRI and Ultrasound are combined with the precision of robotics in order to target the right place in the body. This will be achieved by identifying a target using Magnetic Resonance Imaging (MRI) and then use a robot with an ultrasound (US) probe to match the images and navigate to the right location. The project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 688188. Partner organisations include the University of Twente (UT), Zorggroep Twente (ZGT), RadboudUMC, University of Verona (UV), Medical University of Vienna (MUW), KUKA, and Siemens. The project will be performed during four years with start date on January 1 st , 2016\. All institutions are involved in data management, but the main responsibilities for data management are supervised by the consortium leader from the UT, as shown in Table 1. Table 1: Institutions and their main responsibilities for data management <table> <tr> <th> Institution </th> <th> Responsibility </th> </tr> <tr> <td> UT </td> <td> Vincent Groenhuis, Françoise Siepel – DMP document authors Different members of MURAB team of Robotics and Mechatronics (RaM) – DMP implementation, server setup. </td> </tr> <tr> <td> Verona university </td> <td> Research and Dissemination Marta Capiluppi – Dissemination coordinator MURAB team of Verona – Monitoring DMP implementation. </td> </tr> <tr> <td> ZGT, RadboudUMC, Medical University of Vienna </td> <td> Medical investigations; responsible for anonymization of data collected at hospitals and transferring medical data to researchers according to this DMP. </td> </tr> <tr> <td> SIEMENS </td> <td> Support and business advisor during the scope of this project. </td> </tr> <tr> <td> KUKA </td> <td> Technical and business support during the scope of the project. </td> </tr> <tr> <td> All partners </td> <td> make use of DMP plan, sharing data through server etc. </td> </tr> </table> ## Laws, policies, contracts and agreements to comply with The MURAB data management Plan needs to comply with the following laws, policies, contracts and agreements: EU: * Horizon2020 project, participating in Open Access Data pilot. Guidelines: http://ec.europa.eu/research/participants/data/ref/h2020/grants_manual/hi/oa_pilot/h2020-hioa- data-mgt_en.pdf * Grant Agreement number 688188, available in the Horizon2020 Participant Portal under ‘Document library’. * Consortium agreement related to this Grant Agreement, available at each individual partner’s document library. Dutch: * Law protection personal data (Wet bescherming persoonsgegevens): https://autoriteitpersoonsgegevens.nl/nl/over-privacy/wetten/wet-bescherming- persoonsgegevens * FMWV Code of conduct for medical research of the Dutch biomedical research community (Gedragscode gezondheidsonderzoek van de Nederlandse biomedische onderzoeksgemeenschap): http://www.giantt.nl/gedragscode%20gezondheidsonderzoek.pdf ▪ UT data policy and research datamanagement: https://www.utwente.nl/ub/dienstverlening/MAIN/onderzoeksdatabeheer/ # Data Collection ## Data description There are several types of data that will be collected and shown in Table 2. At baseline all research data, as described in the table below, will form a single dataset. The name of this dataset is “MURAB research data”. Table 2: MURAB research data <table> <tr> <th> Type of data </th> <th> Format </th> <th> Software </th> <th> Data size/growth </th> <th> Specific character </th> </tr> <tr> <td> New MRI scans taken at various locations (UT and hospitals), from dummies, phantoms, and patients. </td> <td> DICOM (as produced by MRI equipment). </td> <td> The MRI computer is used to generate the DICOM data. DICOM software (available on WWW) can read and display the datasets, for example 3DSlicer or 3DimViewer. </td> <td> One slice with a size of 1MB on average (range 0.1-50MB); one scan contains around 100 slices (100MB av- erage); about 10 scans are taken per session (one session consists of scanning one patient multiple times on one daypart), resulting in 1GB of data per session on average. Scanning 100 sessions : 100GB of DICOM data on average, with maximum size of 5TB. </td> <td> Phantom models (exvivo) and possibly patients (in-vivo). Target: breast cancer diagnosis, muscle diseases. Patients: personal data is collected in hospitals (see below for anonymization procedure). </td> </tr> <tr> <td> Existing MRI and/or ultrasound datasets of several patients, retrieved from hospitals and/or databases. </td> <td> MRI: DICOM Ultrasound: depends on dataset, could be DICOM or a generic image format like jpg/ png. </td> <td> DICOM: See above. Ultrasound: depends on format; 3DSlicer can handle many formats. </td> <td> About 1GB per session. 10 to 100 sessions, so 10-100 GB total </td> <td> Anonymized breast scans of patients, all ages, retrieved from hospital’s databases and/or other data sources. </td> </tr> <tr> <td> Ultrasound scans with probe position data, and additional sensory data of phantoms and patients. </td> <td> Ultrasound: a raw data is always processed first. Processed data format could be DICOM or other format depending on hardware and processing software used in the different project stages. Coordinate and sensor data: coupled to scans, either embedded as metadata in DICOM or in separate data file(s) linked together. Coordinate data stored in comma- separated values (CSV) format. </td> <td> Ultrasound data recorded by own software (in case of research hardware), or pro- cessed by off-the-shelf system. Self-written software to record and process sensory data. </td> <td> Continuous ultrasound scanning results in multiple frames per second, each frame has multiple KB’s of data so one session may end up in file sizes in the order of 10 GB’s per case. Single image ultrasound scanning: order of 10 MB per session. </td> <td> Ultrasound scans of breast phantoms and patients, linked to probe position and orientation data from the robot arm. Personal data is collected in hospitals (see below for anonymization procedure). </td> </tr> <tr> <td> Segmentation data of MRI/Ultrasound scans. </td> <td> List of features in a text file, exact format depends on process. TBD </td> <td> MATLAB, Mevislab, 3D slicer, 20-sim, Modelica, own software and/or other software packages (TBD) </td> <td> In the order of 1-10 MB per session. </td> <td> Segmentation data mainly consists of landmark positions that can be used to combine MRI and US together. </td> </tr> <tr> <td> 3D volume modeling data with elastography information, generated from MRI and tracked ultrasound datasets. </td> <td> TBD </td> <td> See above </td> <td> Around 0.1GB per case, depending on level of detail </td> <td> Coupled with MRI/US/Elastography data of the same phantom or patient. Elastography generated from combinations of tracked ultrasound scans and pressure sensor. </td> </tr> <tr> <td> Biopsy needle insertion accuracy/deformation measurements. </td> <td> TBD </td> <td> See above </td> <td> 1-100 MB per case </td> <td> Coupled to a 3D volume model, with preoperative path planning, this processed data shows the effect of needle insertion in the phantom or patient, in terms of qualitative tissue deformations </td> </tr> </table> A special subgroup of data consists of design files, software code, organizational documents, pictures and videos for informative purposes. These are not research data in the sense of observational, experimental, simulation, derived or compiled data (Table 3). Still, such data has to be stored and back-upped to be useful during the project, so this is described below. The name of this dataset is “MURAB nonresearch data”. Table 3: MURAB non research data <table> <tr> <th> Type of data </th> <th> Format </th> <th> Software </th> <th> Data size/growth </th> <th> Specific character </th> </tr> <tr> <td> Design files, program code </td> <td> Depends on software, e.g. C++ for program code, Solidworks / FreeCAD for 3d de- sign files </td> <td> Development and CAD software </td> <td> 1GB total </td> <td> </td> </tr> <tr> <td> Organizational files </td> <td> MS Office, Google docs </td> <td> </td> <td> 1GB total </td> <td> Documents, spreadsheets Some partners cannot access Google docs </td> </tr> <tr> <td> Multimedia </td> <td> Common video and audio formats, such as JPG, PNG, MP4 etc. </td> <td> Generic audio/video editing software, like Windows photo viewer </td> <td> 1-10GB per video, 0.11GB per photo album. With 10 videos and 20 albums: up to 140GB needed. </td> <td> Video and photo collections about various parts of MURAB research. </td> </tr> </table> ## Procedures for anonymization of patient data at hospitals All data files are coded by using random numbers as subject codes. Linkage information connecting names to codes, will be stored separately from the coded data in a secured cabinet and will be destroyed ten years after completion of the study. Pseudonymization protocols will be applied, and all data will be stored at a secured server. The UT anonymization procedures are already covered by pseudonymization: \- deleting or masking _personal identifiers_ , such as _name_ and _social security number_ , - suppressing or generalizing _quasi-identifiers_ , such as _date of birth_ and _zip code_ The location of privacy-sensitive data will be restricted to the hospital and its access is managed by existing policies of the respective hospitals in The Netherlands (ZGT, RadboudUMC) and Austria (MUW). All privacy-sensitive data is anonymized before sending to researchers according to existing procedures, after which the data is basically safe for publication and patient identity will not be traceable. ## Anonymization protocols for each hospital Specific hospital guidelines are described below. ### RadboudUMC data protocol All data is collected and processed into Castor EDC by the investigator. Sources of data are electronic patient system for patient, demographic, laboratory, pathological, and follow-up data. Data collected regarding the biopsy procedure will be collected during the procedure on paper CRF's. The paper CRF will be dated, signed and scanned. They will be kept as a pdf file in the data folder. This data will be entered in the Castor database. Statistical procedures will be performed in SPSS. The used data will be imported from CASTOR EDC and locked by storing it on CD ROM. The statistical analyses and lists of codes will be locked after completion by storing it on CD ROM. Data is securely saved on the Castor server. The scanned paper CRF's together with the statistical analysis files are kept in the data folder on the server of the Radboud UMC, on the account of the investigator. The paper CRF's are stored in the locked cabin of the investigator. The code lists are kept on the server of the Radboud UMC, on the account of the investigator. Upon archiving, the paper CRFs will be archived in the secured archive facilities of Radboud UMC. All CD-roms be kept in the secured cabin in the PI's office. All data will be removed from folder in the investigators drive and from Castor EDC database. The data is coded: The first letter of the prenom combined with the 2 first letters of the last name are the code used for study patients. All study patients will also be assigned a study number. Access to the code list folder is limited to the creator of the folder (data manager), the PI and the investigator. ### ZGT data protocol The regulations of the “FMWV Gedragcode Gezondheidsonderzoek,” as authorized by the College Bescherming Persoonsgegevens, will be adhered to. In particular: medical data will be anonymized by ZGT before transferring them to the researchers. Researchers working with this data, also have to follow the “FMWV Gedragcode Gezondheidsonderzoek”. Researchers are not allowed to perform operations on data that may reveal personal identities. ### MUW data protocol All data are stored in a PACS. Externals have no access. If data is needed for research purposes, then it will be anonymized first before sending it to the researchers. Also, a data transfer agreement will be set up which has to be signed be partners. # Data Storage and Back-up ## Storage and back-up In this section, the data storage methods and back-up systems are described. This applies for both “MURAB research data” and “MURAB non-research data” (see table 4). Table 4: Type of data and storage <table> <tr> <th> Data </th> <th> Storage medium and location </th> <th> Backup location, frequency, RPO and RTO </th> </tr> <tr> <td> Raw data generated by different research groups within the consortium in experiments on phantoms </td> <td> Data is initially collected on a particular research computer within the research group. Useful data is then copied to a local server according to existing research group policies, and/or copied to the consortium’s main server located in the RaM lab of the University of Twente, Enschede, The Netherlands. Only authorized persons will have access to the RAM main server. After copying, the data on the local computer will be deleted. </td> <td> Research groups follow existing policies for data backup. The consortium’s main server will have three copies: the server itself (original), an on-site backup drive (back-upped weekly) and an off-line, off- site drive (back-upped monthly). All drives have a size in the order of one TB, with the intention that it can store all data (original and processed) within the project. If at some point in the project the allocated storage size appears to be insufficient, then the storage size will be increased. In present times, threats not only come from possible hardware damage and theft which can destroy local data only, but also from crypto locker infections (ransomware) which can encrypt all data accessible by the infected computer’s filesystem, including network shares and cloud storage. Therefore, at least one backup drive will be logically disconnected from the server. </td> </tr> <tr> <td> Raw data at hospital </td> <td> After anonymization, the data will be transferred to researchers. One option is via secured USB devices as this is the default way of sharing DICOM. </td> <td> When raw medical data (e.g. DICOM data) is copied from secured USB device to a researcher’s computer (and possibly network drive or cloud), the original secured USB device serves as back-up. When the raw data are stored in the network drive and/or cloud, backup procedures can follow the same one as for raw data at research group (see above). </td> </tr> <tr> <td> Processed data </td> <td> During development, data is processed at researcher’s computers and initially also stored there. Useful datasets (collection of processed MRI, ultrasound coupled with probe position, segmentation data, elastography, 3d volume model, simulation data of the same phantom) are put on the group’s project server (network drive etc.) and later moved to the consortium’s main server. As the data is no longer needed on the researchers computer, the data will be deleted. </td> <td> Processed datasets belong to the partner that generated/processed it; data which is relevant for other partners are shared with them via the consortium’s server. At end of the project, each partner itself decides which part(s) of their data is selected for publication under Open Access. These parts will be collected, filtered, documented and uploaded to a permanent storage location that is accessible for everyone. </td> </tr> <tr> <td> “MURAB non-research data”: files that are not research data, such as design files, organizational files etc. </td> <td> Design files and software source code are not research data and thus technically outside the scope of this document. It is included here only because these files need back-up and version management as well. Design files and source code are developed on researcher’s computers. When multiple people are working on same software, then a protocol like SVN, Git, Mercurial, effectively used by software </td> <td> Organizational files that are relevant for all partners, are also put on the consortium’s main server. </td> </tr> <tr> <td> </td> <td> like gitlab (self-hosted) will be used, operating from a group’s server. Otherwise, models and code are shared over the network drive, and versioning is managed by file naming conventions. </td> <td> </td> </tr> <tr> <td> Informed consents (include names and signatures of informed participants). </td> <td> Patient related research will include IRB approval and informed consent. We will use existing procedures at hospitals and its data stays at the hospitals. Consent forms will be kept in a locked cabinet in the office of the project manager who is the only person with a key to the cabinet. Privacy-sensitive data will never leave the hospital’s boundary. </td> <td> Hospitals already have existing infrastructure (and associated policies) for backup and storage. </td> </tr> </table> ## Consortium’s main server The University of Twente will provide the consortium’s main server on which master copies of all (anonymized) data can be stored and accessed by all partners. For the permanent server solution two options were considered: 1. Use of a virtual server provided by ICTS, a service by the University of Twente. This service includes scalable storage size and backups (both on-site and off-site). 2. Use a physical computer, server or network-attached storage (NAS) located in the Robotics and Mechatronics (RaM) lab. The management and maintenance part will be performed by technicians. Option b will be chosen and further implemented by the summer of 2016\. The services (data storage and project management) are protected by authentication via Lightweight Directory Access Protocol (LDAP). All researchers within the consortium will receive credentials to make use of the server functionality. No people outside the consortium will gain access to research data on the server. ## Length of time regarding use of data, storage and destruction of data The data (anonymised) will be kept indefinitely, but the linking data will be destroyed after completion of the research. The consent forms will be destroyed after ten years. # Data Documentation ## Description documents In each dataset, a master document describes all the data contained in that dataset. It describes the overall folder hierarchy, and links to other documents and spreadsheets that describe the different parts of the data. Publication restrictions are described here as well (open access or protected). For phantom and anonymized patient examinations, data is grouped into sessions. A session is a sequence of examinations and procedures on the same phantom or patient. The session log at least contains the date, investigator, types of examinations, analytical and procedural information and all links to the associated examination and/or processed data, such as folder locations containing the examination files. An examination can be a MRI scan, an ultrasound scanning sequence, a partial or full MURAB procedure with all associated data, or an entirely different research associated with the project. Processed data could be a 3d elastography model, pre-operative path plan, in-biopsy deformation, postbiopsy path analysis etc. All session logs are documented; either into a single document or spreadsheet, or distributed over a collection of documents/spreadsheets linked by the master document. Each type of examination or processed data is also documented separately in general. For MRI data, this documentation at least states that the format is DICOM and that it can be viewed in most DICOM viewers such as 3D Slicer or 3DimViewer. For ultrasound, the document exactly describes the data format(s) and how it can be accessed. Likewise, data types related to other examinations and MURAB procedure stages are documented in a similar fashion. For primary researchers (researchers within the consortium), all data is documented and made available by default. For secondary researchers, the open-access part of the data and its documentation (specific for the open-access dataset) is uploaded to a databank. ## Folder policy and data linking When data is stored on the network drive / cloud storage, there is a general folder policy that has to be adhered to whenever possible: * In every folder there is a readme.txt file describing the contents of the folder; in case of research data it describes which kind of data is found (e.g. MRI, ultrasound, camera), who created the data, on which date etc. * Folders and subfolders must be organized following one of these logical structures: * Option 1) Different types of data of the same session (e.g. MRI and ultrasound data from the same breast phantom, and its derived data) are put in different subfolders of the same folder. * Option 2) Put all MRI data in one folder hierarchy, all ultrasound data in another folder hierarchy, and other type of data in yet another hierarchy. In this case, a spreadsheet will be used to link the data together. The spreadsheets contain the relative folder locations for the different types of data belonging to the same patient. Which is the best option is yet to be seen; in any case the data should be logically organized so that datasets of the same type (or the same case) can be easily found in the folder hierarchy. Therefore, we have to work consistent and keep all data in the same hierarchy, avoiding fragmentation. ## File naming convention Default naming convention of files (folders): YYYYMMDD_Name_v1.0_AuthorI.ext. If files in a folder do not follow this convention, then at least one parent folder in the hierarchy should do. For example, a folder with DICOM data consists of an IMAGES directory with files named like IM1234. These names are part of the DICOM specification and the names cannot be changed, so the parent folder of the IMAGES directory should follow the default naming convention. ## Identifiers Each dataset will be given a unique and persistent identifier. These will be defined when the dataset itself is being generated and/or published. When a dataset is uploaded to a databank such as 3TU.Datacentrum, then a DOI is automatically assigned to the dataset. # Data Access Legal issues considering IP (intellectual property) of anything generated within the project, including research data, will be covered in the Grant Agreement, supplemented by the Consortium Agreement. In particular, research data is owned by the partner(s) that generated it. ## Access right for the consortium members During the project, all consortium members have the rights to use the data generated by any partner in the project, if this is needed for executing the tasks as defined in the Grant Agreement. The primary method for accessing the data is by using the consortium server. All data on the server is made available to all partners by default, in principle it is never needed to grant access data to specific partners only. All medical data on that server is anonymized, so a basic level of security such as single-factor authentication, suffices. 5.2 Access rights for external users During the project, external users have no access to the research data. # Data Sharing and Reuse MURAB project participate in the Horizon 2020 Pilot on Open Research Data and, therefore, is committed to make public the research data that does not belong to IP. MURAB will define all suitable measures to enable third parties to access, mine, exploit, reproduce, and disseminate (free of charge for any user) its research data. ## Research data being used in scientific publications After publication of a paper, the original data files are made available by using open-access. Other researchers may want to validate the results from the project’s papers, requiring access to the data after publication of the paper. ## Non-IP data after end of project At end of the project, the MURAB research data that is not part of IP, will also be made open-access. The subset of the data which is non-IP will be discussed in a consortium meeting around July 2019 (six months before end of the project). Also, the embargo period will be decided here. Several types of data might be useful for other researchers that also work on registration of different imaging modalities where deformable tissue is involved. In MURAB’s case, the researchers record raw MRI and tracked ultrasound data for position of the robotic arm on which other groups can try their algorithms for registration and 3d volume reconstruction. This processed data (reconstructed 3d volume with elastography information, needle insertion path planning etc.) might be used as a reference point to quantitatively evaluate the performance of their algorithms. Raw data using one specific modality (e.g. MRI) from patients could also be useful for researchers working on a different kind of study who need MRI scans of different patients. MURAB will publish datasets in public-available databases alongside with publications and at the end of the project, for example in 3TU.Datacentrum or in a subject-based/thematic repository, and coupled with a Digital Object Identifier. A Creative Commons Licence will be applied to the MURAB project research data. The MURAB website will probably also refer to these datasets. ## IP data after end of project IP data stays by its respective owner(s). If a partner wants to commercialize project results and needs IP of other partners, they have to gain access to these rights taking normal market conditions into account. # Data Preservation and Archiving After the end of the project, linkage information connecting patient names to codes shall be destroyed. Part of the data is placed in an Open Access dataset. Only data that is deemed useful for other researchers and does not belong to intellectual property of any partner of the consortium, is included in this dataset. Data that contain no scientific value (testing data, incomplete cases, failed experiments etc.) or data containing IP (especially models and code) are omitted from the dataset. This way, high research data quality of the Open Access dataset is ensured, without disclosing IP. After the end of the project, the University of Twente is no longer obliged to maintain the consortium server. All partners can make a copy of all data for themselves, taking IP in mind. What will happen with the data on the consortium server, will be discussed near the end of the project. If the RaM group decides to keep the server (e.g. if there is a continuation of the project), the server with all data can be transferred to the RaM group, taking IP rights into account. Another option is to store all data for longterm, protected storage in the 3TU.Datacentrum or other digital repository. # MURABproject.eu	https://phaidra.univie.ac.at/o:1140797	Horizon 2020
0017_BREAKBEN_686865.md	# 1 Introduction This document outlines the principles and processes for data collection, annotation, analysis and distribution as well as storage, security and final destroying of data within the Breaking the Nonuniqueness Barrier in Electromagnetic Neuroimaging (BREAKBEN) project. The procedures will be adopted by all project partners and third parties 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. BREAKBEN shall participate in the Open Data Pilot according to the Grant Agreement and Research Commitment. This document details the practices and solutions regarding the storage and re-usability of the research data, which will be made accessible for other researchers and the public for further use and analyses. One of the most important aspects of this document is to ensure that the data are not opened if this would violate privacy, safety, security, terms of project agreements or legitimate concerns of private partners. The Grant Agreement of the BREAKBEN project as an Open Data Pilot participant obligates the project to: 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: _(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', i.e. this document._ 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). However, the obligation to deposit research data in a databank does not change the obligation to protect results, take care of the confidentiality and the security obligations or the obligations to protect personal data. The Data Management Plan addresses these topics and details how the seemingly contradicting commitments to share and protect are implemented within the project. The Grant Agreement also 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 jeopardized. In such case, the Data Management Plan must contain the reasons for not giving access. The 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 BREAKBEN project consortium. The production of the 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. This Data Management Plan is a living document that will be submitted at the end of June 2016 (M6) and will be updated and complemented as the project evolves. When creating the BREAKBEN Open Data policy the following principles should be considered: ## 1\. Discoverability The location of research data and the necessary software to access the data are known, using a standard international identification mechanism. ## 2\. Accessibility Research data and the necessary software to access the data shall be easily accessible, an embargo period can be agreed upon, in order to achieve strategic advantage for the creator. The licenses that may be considered for research data are Creative Commons Attribution 4.0 International (CC BY 4.0), for metadata CC0 1.0 Universal (CC0 1.0) Public Domain Dedication and for software the MIT License. ## 3\. Assessability and intelligibility The research data and the necessary software to access the data shall be assessable for and intelligible to third parties for scientific scrutiny and peer review. They shall be published together with related scientific publications. ## 4\. Usability The research data and the necessary software to access the data shall also be usable for purposes beyond the original project. The research data chosen for long-term preservation shall be safely stored and cured. ## 5\. Interoperability Allows data exchange between researcher groups, higher education institutions and research institutions in different countries. Interoperability will also allow for the re-combination of different datasets from different origins. # 2 Data Types For each partner of the BREAKBEN consortium, there is a different profile of data produced within the project. The main outcome of the project, however, is not data, as the aims are focused towards developing methods, techniques and instrumentation for research and clinical use. Therefore, a large share of the results are in the documents and publications that describe the technological advances, as well as in the prototype instrumentation. Nevertheless, some phases of the project generate experimental data that may be able to produce additional value under further analysis or be relevant to the reproducibility of the science. From the scientific reproducibility point of view, computational results are typically best reproduced by reapplying the described theory and algorithm and by analyzing the validity of the model used. Data and documents in project BREAKBEN are described in terms of six basic types: 1. Reports and publications ; e.g., deliverables, presentations, articles 2. Project data ; e.g., agreements, financial statements 3. Design data ; e.g., MRI–MEG equipment structures 4. Research data ; e.g., time series of physical quantities 5. Analyzed research data ; e.g., results of statistical analysis of research data 6. Software ; e.g., computer-programmed data-analysis pipelines Reports and publications includes deliverables, presentations and for example journal articles. This data type also refers to the contents of the BREAKBEN project website. For deliverables, a simple filename metadata scheme is used within the consortium. Drafts and final deliverable documents will be named according to the format (X = work package, Y = deliverable number): BREAKBEN-DX.Y_Deliverable-Name_More-info_And-more.docx It is also advised within the consortium that any email communication regarding deliverables will have BREAKBEN-DX.Y in the subject line to help manage the email traffic related to deliverables. When dates are added to file names, they should follow the international standard for date format (ISO 8601) with the form YYYY-MM-DD. When this date format is inserted at the beginning of a file name, or at a fixed position (such as after “Deliverable- Name” for deliverables), automatic file name sorting will sort the files in chronological order. It is further recommended to include the project name BREAKBEN in the Subject line of email conversations, when the communication is specific to the BREAKBEN project. In order to ease the version handling of project deliverables and to diminish the need of sending them via email, the project workspace Eduuni will be used. Eduuni is a password-protected workspace, administrated by Aalto University. There, the deliverables will be uploaded under the appropriate Work Package. Eduuni allows the simultaneous editing of the document. Project data include administrative and financial project data, including contracts, partner information and reports, as well as accumulated data on project meetings, teleconferences and other internal materials. These data are confidential to the project consortium. Project data include mainly MS Office documents, in English, which ensures ease of access and efficiency for project management and reporting. Project data are stored in Eduuni workspace, administrated by Aalto University. There are differences in produced data types between different partners, as described below: ## AALTO The main role of Aalto University in the project is building expertise and developing ULF MRI and MEG methods and instrumentation. However, also data are produced as part of that development. Design data : descriptions of designs, written documents and/or schematic drawings; data types including LaTeX-generated pdf, Scalable Vector Graphics and CAD formats; metadata written inline, in format-specific metadata fields or in the file name. Research data : stored neuroimaging or phantom data that are recorded within the project; FIFF data format; metadata in format-specific fields or additionally in separate text files using JSON or other suitable structuring where applicable. Analyzed research data : results from data-analysis pipelines; various data formats, depending on the kind of analysis, sometimes in the form of figure or table. Software : software written to produce results or automate procedures, including data-analysis pipelines; e.g., written in Python 3.5 (file type .py or .ipynb, using the Jupyter Notebook for describing the recipe for reproducing the results, including documentation). ## ELEKTA Elekta is a medical technology company with a quality system that complies with ISO 9001 (2015), ISO 13485 (2015), and FDA QSR. The role of Elekta in the BREAKBEN project is not to collect research data, but to provide knowledge of commercial exploitation of medical technology and regulatory requirements, and to provide technical expertise in writing software and building instrumentation. Table 1 lists and describes the data types generated by Elekta in the project. <table> <tr> <th> Description </th> <th> Data type </th> <th> Tools </th> <th> Volume </th> <th> Metadata </th> </tr> <tr> <td> Project data </td> <td> Text files </td> <td> MS office or equivalent </td> <td> TBD </td> <td> TBD </td> </tr> <tr> <td> Reports and communication data </td> <td> Text files </td> <td> MS office or equivalent </td> <td> TBD </td> <td> TBD </td> </tr> <tr> <td> Design data </td> <td> Text files, CAD files </td> <td> MS office or equivalent, CAD programs </td> <td> TBD </td> <td> As required by Elekta document </td> </tr> <tr> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> <td> management system </td> </tr> <tr> <td> Software </td> <td> Source binaries </td> <td> code, </td> <td> SW development tools </td> <td> TBD </td> <td> As required by Elekta software development tools </td> </tr> </table> Table 1. In the BREAKBEN project, there are four basic types of data generated by Elekta: project data, reports and communication data, design data, and software. No research data are collected by Elekta in the project. Project data and Reports and communication data is described above. Design data includes all data created in the development process of the software and hardware modules developed in the project. Software includes source codes and binaries of the software generated in the project. The Metadata concerning design data and software will be handled as required by Elekta document management system. HUS ## Recorded data Data types: MEG, MRI and MEG-MRI signals of 5 healthy subjects and 2 patients with epilepsy. MRI images of four additional healthy subjects and 3 tumor patients. MEG : approximately 5 files of spontaneous MEG per subject/patient MRI : One MRI image stack from each subject and patient MEG-MRI approximately 5 files of spontaneous MEG per subject/patient Analyzed research data: The sources of somatosensory evoked oscillatory activity will be analyzed with standard commercial MEG methods. The occurrence of fast oscillatory activity in MEG of patients with epilepsy will be screened in a manner routine for clinical evaluation. MRIs of the healthy subjects will be analyzed by a neuroradiologist. The MRI images of the patients will be analyzed in connection with their clinical workup. ## PTB PTB will design, construct and operate ULF MR hardware for NCI and DCI. In addition to “Reports and Publication” and “Project data”, further data types will be generated. Design data: CAD files generated by Autodesk Inventor, Text files. Research data: These primary data include digitised voltages and personal information on subjects. For subject data, personal data will be saved and protected from unauthorized access by a third party. Analysed research data: These constitute the measurement data and records which are part of a measurement, are the result of the execution of a measurement or can be derived from a measurement. These include post-processed and/or analysed research data obtained on phantoms and subjects within the project. ## UDA The main role of UDA is to quantitatively assess the impact on the large scale connectivity results induced by the new device built in the project. This assessment will be carried out at the in-silico, phantom, and invivo levels. This will result in the production of a small set of real data and connectivity maps. Additionally, scripts to generate surrogate data and to evaluate the impact on the large scale connectivity will be produced. Research data: Time series containing MEG data will be stored in a proprietary format recognized by several software tools freely available (e.g., FieldTrip, Brainstorm). MEG data will be collected using the MEG device operating at ITAB-UDA during rest and the auditory protocols that will be specified along the project. Average data set size is 1 GB. Surrogate data generated simulating real data acquisition will be also produced. In the latter case, data will not be stored as individual files; as an alternative, the generating algorithm will be given. Analyzed research data : Results from real data-analysis pipelines (e.g., ERF, source activity, connectivity) and surrogate data results (e.g., connectivity values) will be saved using different data formats, depending on the kind of analysis, including figures (e.g., png). Software: software written to produce results from real and simulated data from the data-analysis pipelines; e.g., written in Matlab and/or Python. Documentation will be provided in the native editors of the programming environments. ## VTT The role of VTT is design and manufacture of superconducting sensors and their front-end interface electronics. Sensors are fabricated in the VTT Micronova clean room, whose operations are ISO9001:2008 certified. Design data : The raw design data will be in various CAD and engineering formats, primarily (i) GDS for the detector layout, (ii) PADS for electronics design, and (iii) AWR APLAC for behavioural models. Because raw designs will combine BREAKBEN-funded results with other results, both internally-funded and from earlier externally funded projects, the raw engineering files will not be made available. The publishable files will be edited to remove details which may infringe company secrets, other agreements, or may reveal other proprietary information, and get converted into MS-Office –compatible formats, SVG, and GDS. Research data: Detector and electronics performance data will be collected by (i) commercial measurement instruments taken from VTT instrument inventory, and (ii) home-built data acquisition setups. Commercial instruments have proprietary data formats, whose variety is too wide to be listed here. Data acquisition setups have not been designed or constructed yet, hence their data formats cannot be described yet. This information will be updated later in the document. For the sake of making the data accessible, they will be converted into the CSV format or MS Office compatible formats. Analyzed research data: Will be made available in MS Office compatible formats. # 3 Collecting the data Different data collection principles apply in different institutions, and not all partners collect research data. Since the project is developing new technologies, many kinds of data collection do not have any applicable standards. Therefore, the data are often collected using the most applicable methods available or most convenient to implement. On the other hand, it is among the aims of the project to move towards more standardized data handling (e.g., Deliverable 4.1: Software architecture). Details are given regarding each partner institution below. Good scientific practices will be applied according to the guidelines used by each institution. ## AALTO Data will be collected using in-house techniques fully or partially automated using Python scripts and Jupyter Notebooks. Data will be collected from phantoms, human subjects and non-imaging studies. Low-quality data are rejected in-house by automatic or semiautomatic procedures. ## HUS The MEG data are collected in BioMag Laboratory in standardized commercial MEG recording setup. The MRI data of epilepsy and tumor patients are collected in HUS in conjunction with their standard clinical examinations. The quality control standards are those used for clinical MEG and MRI recordings in HUS. No pre-existing data are used for this project. ## PTB Personal data will be obtained via a questionnaire. Standards and protection are described in the section 4.2. Raw research data will be obtained with data acquisition cards, DAQs, and other electronic equipment and stored in a proprietary format. PTB follows its in-house quality management system with procedural requirements for data handling and an additional directive for data storage. For testing purposes, surrogate data will be generated, simulating real data acquisition. ## TUIL Phantom data collection will be obtained according to: Tenner,U., Haueisen,J., Nowak,H., Leder,U., Brauer,H.: Source Localization in an Inhomogeneous Physical Thorax Phantom. Physics in Medicine and Biology, 44, 1969 - 1981, 1999. No existing data will be used. Quality assurance processes will be applied according to the DFG guidelines for good scientific practice. ## UDA Raw data on human subjects will be collected as time series using in-house techniques and stored in a proprietary format. Personal metadata consisting of consensus and questionnaire will be stored together with the raw data. To test the impact of the project achievements on connectivity, surrogate data will be generated simulating real data acquisition. Quality assurance processes will be assessed according to national ethical committee guidelines. ## VTT Due to the innovative and ground-breaking nature of the BREAKBEN project, steps in the detector development depend strongly on the results obtained in the previous step and on the difficulties encountered along the way. Data will be collected whenever a new generation of detectors or electronics is finished and its/their performance needs to be assessed. The data collection will take place in ad hoc manner, following the general principles of scientific inquiry and the craftsmanship of long-time practitioners of the art. A large part of the data from the measurement instruments or data acquisition systems will be a part of debugging work spent on the system. Only the data obtained with devices in a proper working condition is counted as publishable here. # 4 Levels of Confidentiality and Flow of the data ## 4.1 Confidentiality Overall, there are three basic levels of confidentiality, namely Public, Confidential to consortium (including Commission Services), and Confidential to the Partner / Subcontractor. Each data type is treated differently with regard to the level of confidentiality, e.g., the untreated research data such as personal data of patients and research subjects, whereas most project deliverables are actively disseminated. Some of the data falls under the EU and national laws on data protection and for this reason the project is obliged to seek necessary authorizations and to fulfill notification requirements. The project will assume the principle of using commonly used data formats for the sake of compatibility, efficiency and access. The preferred data types are in MS Office compatible formats, where applicable. Figure 1. Data types displayed in three levels of confidentiality, as applicable in most cases. There are exceptions to this confidentiality classification. Software and design data are not displayed, because their confidentiality may be in any of the three levels. See section 4 for more information. Figure 1 displays how the previously mentioned data types are positioned in the level of confidentiality context. Only one data type – (untreated or raw) research data – is totally situated in one level of confidentiality, which means that it solely remains with the partner or third party responsible for collecting it. Three types (project data, analyzed data and reports and communication) contain data of two different confidentiality levels. The remaining two types, software and design data, may belong to any of the three levels depending on the contents of the data and on potential restrictions due to contracts and other policies. In general, there are exceptions to the given confidentiality levels. For instance, there may be research data for which public distribution for scientific purposes is possible and desirable. More information is found later in this section. The data flow in most cases starts at the bottom of the confidentiality levels (Figure 1) and as the data moves up in levels, it is ensured that no data are included that cannot be placed in the next level. This may involve reduction of the data or anonymization, as necessary. However, anonymization is not always possible, which may restrict the possibilities of changing confidentiality level. Similarly, other privacy requirements may prevent publishing data, including analyzed data (see below). ## AALTO/HUS Confidentiality: The data from the control subjects and patients are available in anonymized form for the members of the research consortium. The averaged data and anonymized MRI images can be used in scientific publications provided that the individuals taking part into the research cannot be identified from them. ## ELEKTA Again, there are three basic levels of confidentiality (Figure 2), namely public, confidential to consortium, and confidential to the Partner (Elekta). Figure 2. Data types at Elekta displayed in three levels of confidentiality Project data include agreements and financial data, and, therefore, are confidential either to the project consortium or to the partner, depending on the scope and content of the data. Reports and other communication data are either public or confidential to the project consortium, depending on the scope and content of the data. Design data include information on Elekta’s development process and background IP, and, therefore, are confidential either to the project consortium or to the partner depending on the scope and content of the data. Software include information on Elekta’s development process and background IP, and, therefore, are confidential either to the project consortium or to the partner depending on the scope and content of the data. Figure 3. Elekta data flows within and between the three levels of confidentiality ## PTB Research data and analyzed research data obtained by performing experiments on humans cannot be opened to the public but to consortium level of confidentiality. The subjects are recruited within PTB, Institute Berlin. PTB intends to use maximum 50 subjects out of the about 400 strong workforce. There is a high risk of violation of data privacy laws as the use of metadata is very likely to allow the identification of the subject. Research data obtained on phantoms are open to the consortium level of confidentiality. Design data are confidential to the partner level only and software is open to the consortium level of confidentiality. ## UDA Raw MEG and MRI recordings are available in anonymized form for the members of the research consortium only upon request. Processed data are publicly available after scientific publication. Reports are either public or confidential to the project consortium, depending on the specific report as indicated in the Grant Agreement. ## VTT Unedited design data will be kept confidential. The edited design data which contains the findings obtained within the BREAKBEN project will be made Consortium confidential or Public, depending on the scope or content of the data. Editing involves hiding or removing those design items which are results of internallyfunded development work or which may infringe earlier confidentiality agreements. Research data and analyzed research data will be made public. ### 4.2 Data Storage and Protection Each partner has its own policies in storing and protecting data. As general project guidelines, it is recommended that git is used for version control where applicable and that the data are pushed into a repository with regular backups. One such repository is the GitLab service maintained by Aalto University IT Services, to which also personnel of non-Aalto partners can be given password-protected access. It is also possible to turn a GitLab data repository into a public repository when appropriate according to the data flow. For documents that do not support proper version control, such as Microsoft Office documents, the Eduuni workspace, is often the proper space for storage and sharing. For deliverables, and other consortiumwide written documents, Eduuni is always used. When date information is included in file and folder names, it is recommended that the date is positioned In the beginning of the name according to the ISO 8601 standard, YYYY-MM-DD, followed by an underscore (_) if other information is appended before the filename extension. However, policies at individual partner organizations may override these general guidelines. ## AALTO Data will be stored in regularly backed up storage maintained by the Aalto University IT Services. In addition, the department GitLab service is used for versioned data, which is also maintained by the local IT services. Data will be anonymized, when applicable, before publishing. Most data will be either versioned with git or saved under folder and/or file names beginning with an ISO 8601 date stamp, or both. ## ELEKTA All the data will be generated according to standard Elekta procedures, and stored to Elekta’s archives. The archives are backed up regularly. The retention time and disposal of the data follow the guidelines of Elekta’s procedure _EOYbms00015 Management of quality records._ The data will be copied to consortium members or made public when applicable (see 4.1 Confidentiality above). ## HUS The MEG data are stored in the data storage system of the BioMag Laboratory. The data are coded in recording phase and are protected by a user name and password. The code key is kept in locked space separate from the data. The MRI data of healthy subjects are stored in the Science PACS of HUS Medical Imaging Center. The MRIs of the patients are stored in the Clinical PACS system of HUS. The data storage capacity for MEG and MRI images are adequate. No additional services are needed. Automatic tape backup for MEG data is used. The MRI data are backed up in the usual hospital procedure. No new hardware or software is required. No charges are required for data storage. The MEG-MRI data are stored by Aalto University. ## PTB For human experiments, the names of the subjects will be pseudonymised using an algorithm and the data saved only under pseudonames. Contact data will be separately securely stored. All data will be saved on the PTB internal server. Such data will be protected in compliance with the EU's Data Protection Directive 95/46/EC aiming at protecting personal data. Compliance with privacy rules as stated by the local ethics committee and national laws will be ensured. Analyzed research data that will be accessible at consortium and at public level will be stored into BREAKBEN repository in the Aalto University’s GitLab data repository. PTB has a quality management system with procedural requirements for data handling (QM-VA-22) and an additional directive for data storage. Both directives clarify the responsibility of divisions for data storage and central data backup at PTB. These directives are based on the rules specified by Bundesamt für Sicherheit in der Informationstechnik (BSI). PTB data handling is governed by the DFG rules for good scientific practice and in accordance with the in-house quality management system. In particular, primary data will be archived for 10 years after acquisition. Archiving will be password protected and anonymous. ## TUIL Local TUIL repositories are used with no anonymization. The data will be preserved on local computers. Useful conventions will be chosen for folder structure and file naming. Versioning needs are minor and dealt with by labeling data according to the date of the recording. The central service of the computing center of TUIL takes care of backups. The TUIL staff is at expert level. No hardware or software is required which is additional or exceptional to existing institutional provision, nor will charges be applied by data repositories. ## UDA Anonymization of data: For some of the activities to be carried out by the project, it may be necessary to collect basic personal data (e.g., full name, contact details, background), even though the project will avoid collecting such data unless deemed necessary. Such data will be protected in compliance with the EU's Data Protection Directive 95/46/EC aiming at protecting personal data. Compliance with privacy rules as stated by the local Ethics Committee and National laws will be ensured. Preserving and archiving data: Data will be stored in two different repositories, according to level of confidentiality. Raw data that are open only at Partner Level will be stored in a local data storage server using RAID-6 technology. Analyzed research data that are accessible at consortium and at public level will be stored into a different local data server (using ISO 8601 standard) and in the Aalto University GitLab service. ## VTT VTT will obtain an account in the commercial GitHub repository for the duration of the project. The longer persistence of the data cannot be guaranteed at the present date, as it is not clear which mechanisms could be taken advantage of after the Breakben funding ends. Technical details of the data storage are those provided by the GitHub service. # 5 Opening the Data As stated in previous sections, the project aims at providing open data where applicable, although the nature of the data, or other requirements may prevent publication. All parties have signed/accessed to the project Grant Agreement and Consortium Agreement, which 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 BREAKBEN project is participating in the Open Research Data Pilot, which is an expression of the larger Open Access initiative of the European Commission. 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 obligated to protect personal data and results. The instructions to download and open the research data, together with description of the contents of the files containing the data will be uploaded in the same directory as the data themselves. A readme file describing the data will be placed in the same repository as the data and identify the data at the partner and consortium level. In most cases, publicly available software and a desktop computer will be sufficient for validating the results. To help potential users find the published data, the availability of the data will be posted on the project website and the distribution of data will be mentioned/cited in publications. The data will either be available to anyone or only on a case-by-case basis with applicable conditions. Privacy requirements may sometimes prohibit publication altogether, and a data sharing agreement may be required in special cases. Data storage updates and backups are managed by the system administrators or maintainers in order to prolong the lifetime of the data. Below are the policies designed for each participating organization. ## AALTO/ VTT Regarding the time of data publication, the aim is for article-related data to be public at the time of publication of the article. The data types used are selected so that they have specifications available so that opening the data is possible also in the future if the same software is no longer available. However, for some data, pieces of software may even be added as metadata along with the data itself to help others handle the data. Sometimes, it may be sufficient to refer to the software or specifications provided elsewhere. Where applicable, data will be shared in a public repository accessible over the internet. When (analyzed) research data are published for further scientific analysis, a DOI may be assigned using a service such as Zenodo. Licensing and conditions for such data will be determined based on the purpose of the data. The data are released under a Creative Commons Attribution license, unless the purpose or nature of the data requires otherwise. If the situation is unclear regarding risks related to data security or privacy, the partner will not publish data. To keep private data secure, it is accessed over a secured SSL connection when accessing from outside networks. ## ELEKTA The project data will be stored in Elekta’s document archives and, when applicable, copied to the project password protected Eduuni workspace (confidential to the project consortium). When shared, the data will be made available either according to external schedule or when requested. Reports and other communication data include mainly MS Office documents, in English. Public data will be published, for example, via the BREAKBEN website. Design data include MS Office documents and CAD files and Software data include the source code and the binaries of the software. These data will be stored in Elekta’s document management system and when applicable, shared, for example, via password protected Druva. When shared, the data will be made available either according to external schedule or when requested. ## HUS The present Finnish legislation enables data banks of patient samples (such as blood or tissue samples) but forbids the use of signals such as MEG or MRI data from individuals by parties not defined in the ethical permission of the project. The averaged data and anonymized MRI images can be used in scientific publications provided that the individuals taking part into the research cannot be identified from them. The data collected in HUS will be owned by HUS. The collected data can be used by the members of the consortium as specified in the data management plan and in the ethical permission. ## TUIL Data will be made available at the end of the publication process in formats that enable sharing and longterm access to the data. Web page Typo3 will be used for sharing the data, also beyond the original purpose of the data. Open data will be shared with no conditions. Documentation will provide the information needed for reading and interpreting the data in the future. In addition, other imaging phantoms may be needed to validate the results. Regarding managing risks to data security, no unacceptable risk is expected, although normal backup policies are applied. Potential users (anyone) will find out about data via a link in publications, and no data sharing agreement or equivalent will be required. ## UDA/PTB Data sharing and long-term access is then regulated as follows: * Partner level: research data format: compatible with in-house or freeware tools made available to all the partner participants; shared through standard commercial programs (e.g. Office, Matlab). An internal repository available within the first deliverable will enable the sharing of the data. * Consortium level: analyzed research data format will enable sharing through standard commercial software or software tools (e.g. scripts) made available to the Consortium members. A free of charge cloud system will be used to share the data. * Public level: research result formats will enable sharing through standard commercial software or software tools (e.g. scripts) placed in the same repository as the results accessible via a free of charge repository. Beyond the original purpose the data can be shared as follows: * Partner level: Research data will be shared between researchers involved in specific tasks to ensure fulfillment of the project tasks and to internally discuss intermediate results; * Consortium level: Analyzed research data will be shared between partners as required by specific project tasks, involving this and other partners. Conditions on shared data and minimizing restrictions are: * Third parties level: Access to research and analyzed data is restricted to users belonging to research entities. A formal request must be submitted by these users’ PIs. Specifically, re-use re-distribution, creation and publication of derivatives of the data with a third party can be granted within a cooperation after personal data protection/data privacy is guaranteed within the cooperation agreement. # 6 Data ownership At Aalto and VTT, the ownership of data will be will be handled according to applicable policies depending on the situation and/or the guidelines presented in the section 3 of the grant agreement. Attribution will mainly be received via citations to the work. While no research data are collected or produced by Elekta, the data types presented in section 2 will be handled according to the guidelines presented in the section 3 of the grant agreement. Regarding PTB, data are owned by the research institution collecting the data (PTB). At TUIL, the owner of collected data will be TUIL. Re-use of the data will be permitted with no conditions. Redistribution of the data as well as creation and publication of derivatives from the data are permitted either with or without conditions. Others will also be permitted to use the data to develop commercial products or in ways that produce a financial benefit for themselves, either with or without conditions. The people who generated the data sets will receive attribution for their work via citations. At UDA, research data will be owned by UDA. Re-use of the data, re- distribution of the data as well as creation and publication of derivatives from the data will be permitted either with or without conditions. The people who generated the data sets will receive attribution for their work via citations.	https://phaidra.univie.ac.at/o:1140797	Horizon 2020
0022_INTERACT_730938.md	# 1\. Data Management Principles and Guidelines ## 1.1 Introduction The Rif Field Station (RFS: Annex A) has a responsibility to promote and ensure the proper management of data and information resulting from activities conducted at RFS. Effective data stewardship is essential to ensure that valuable data resources are accessible now, and in the future, to advance our knowledge and understanding, promote public awareness, and support informed decision making. In addition, accurate and retrievable data are an essential component of research and are necessary to verify and defend, when required, the process and outcomes of research. This Document describes the principles and guidelines for management of data and information generated through monitoring and research conducted at RFS. These principals and guidelines support the long-term preservation of and timely access to important Arctic datasets and information. The Document has been developed through a collaboration between Polar Knowledge Canada; Greenland Ecosystem Monitoring and Zackenberg Research Station; The Conservation of Arctic Flora and Fauna and RFS. This Document is informed by: * The Management Principles and Guidelines for Polar Research and Monitoring in Canada (POLAR 2017) * Zackenberg Research Station Data Management Plan (2018) * The International Arctic Science Committee’s (IASC) Statement of Principles and Practices for Arctic Data Management (IASC 2013); * Management planning for arctic and northern alpine research stations – Examples of good practices (INTERACT 2014); * The Circumpolar Terrestrial Biodiversity Monitoring Plan (Christensen et al 2013); * The Circumpolar Freshwater Biodiversity Monitoring Plan (Culp et al 2013); and * Circumpolar Biodiversity Monitoring Program (CBMP) Strategic Plan 2018-2021 (CAFF 2018). This Data Management Plan (DMP) will be revised after 1 year to determine if it needs to be revised based upon lessons learned and feedback from users. ## 1.2 Goals and Objectives The goal of this DMP is to ensure that there is comprehensive inventory of the projects conducted at RFS and the data they produce. Metadata records are intended to provide a comprehensive searchable and publicly accessible inventory of these projects and datasets. This Document serves as a guide to assist RFS and those conducting research at RFS in applying consistent approaches to data management, and to clarify roles and responsibilities of researchers and collaborators. ## 1.3 Principles of Data Management RFS seeks to ensure long-term preservation of and access to data through application of the following principles: * Data are preserved by collecting, storing, and retaining data using formats that preserve the data beyond the duration of the original research project; * Data are discoverable by applying commonly accepted standards and reporting protocols in the use of metadata; * Data are accessible by supporting full, free, and open access with minimal delay, using a secure and curated repository or other platforms; and * Data are ethically managed by respecting legal and ethical obligations, including consent, privacy, and confidentiality; secondary use of data; and data linkage. This Document will be reviewed periodically by RFS to ensure the principles and guidelines herein remain relevant. ## 1.4 Types of Data and Definition ### 1.4.1 Data and Metadata Considerations RFS in collaboration with the international Arctic data management community, seek to promote the highest standards in the stewardship of data and metadata resources resulting from Arctic research and monitoring activities. ### 1.4.2 Definition of Data These principles and guidelines take a very broad approach to the concept of data, recognizing that it may take many forms, and, depending on the field of research or monitoring, can mean different things. This includes but is not limited to: survey results, written observations, software, interview transcripts, photographs, automatic measurements, hand-drawn maps, stories and video footage (FAO 2018). Thus, this Document’s definition of data incorporates Western/academic and local knowledge. There are five primary categories or sources of data: * Institutional Data: Data systematically collected or produced as part of baseline monitoring conducted at RFS. * Funded Data: Data collected or produced by funded projects at RFS. * External Data: Data from external repositories or data providers, including existing operational data streams and historical sources, industry, international institutions, or others, as relevant. * Rescued Data: Data retrieved from unpublished sources, e.g., field notebooks, records on outdated storage media, or photographic records, which are often at risk of loss. * Local Knowledge (LK): is the knowledge that people in a given community has developed over time and continue to develop. ### 1.4.3 Definition of Metadata Metadata provides the information about a dataset, specifically the _what, where, how, when,_ _by whom_ it was collected, its current location, and any access information. Metadata facilitates the understanding, use, and management of data and is a means for networking and collaboration. Standardized metadata records consist of a defined set of information fields that must be completed to allow automatic sharing of records via interoperability between metadata management facilities and data portals. Metadata submitted to RFS should conform to the Darwin core (TDWG 2009). ### 1.4.4 Physical Samples as Research Data The products of research and monitoring activities _may_ also include physical samples, preserved and living biological specimens including microbiological cultures, and other non-digital material. Researchers are responsible for the preservation, documentation, and ethical use of these physical samples according to existing standards relevant to the type of sample collected. Researchers are expected to allow scientific sharing and investigation in accordance with relevant standards and other guidance from a museum, research, or other applicable community. Such non-digital holdings should be described in a metadata record submitted to RFS. ### 1.4.5 Ethically _Open_ Access In order to support open access practices to maximize the benefit of the efforts put into proper stewardship of data, the RFS, through this Document, requires data contributors to make research and monitoring data available fully, freely, and openly, with minimal delay. The only exceptions to the requirement of full, free, open, and permanent access are * Where human subjects are involved or in situations where small sample sizes may compromise anonymity, confidentiality shall be protected as appropriate and guided by the principles of informed consent and the legal rights of affected individuals; * Where LK is concerned, rights of the knowledge holders shall not be compromised; * Where data release may cause harm or compromise security or safety, specific aspects of the data may need to be protected (for example, locations of nests of endangered birds); * Where pre-existing data are subject to access restrictions, access to data or information using this pre-existing data may be partially or completely restricted; and * Where disclosure of information is not in accordance would be in conflict with the mandate of the organization in question or other unforeseen circumstance which require action from the RFS. ## 1.5 Roles and Responsibilities ### 1.5.1 General Responsibilities The RFS metadata repository (Arctic Biodiversity Data Service (ABDS, Annex B)), data contributors, project sponsors, and external collaborators (Annex C) will work in partnership to implement good practices and meet relevant requirements. ### 1.5.2 Responsibilities of RFS * RFS in partnership with ABDS will provide advice to facilitate efficient and accurate metadata and data entry. * Archiving and access requirements of all metadata records, datasets, or other research products involving LK will be considered on a case-by-case basis. ### 1.5.3 Responsibilities of Those Conducting Research and Monitoring Activities at RFS Compliance with the requirements in this Document including: * Providing metadata that is accurate, complete and reliable; * Submitting metadata detailing the data products they produce to RFS, as early in the project as possible, typically within the year of collection and ensure any revisions needed so that it accurately describes the final state of the data; * Ensuring that their data are accessible to the general public, consistent with appropriate ethical, data sharing, and open access principles; * Providing a persistent locator, for data collected at RFS. If possible provided in the form of a unique digital object identifier (DOI). This recognizes the intellectual work required to create a useful dataset and allows the dataset to be recognized and cited through formal publication activities, including formal publication of the data itself; * Data creators acknowledge RFS where appropriate, in relevant presentations and publications; and * Those conducting research and monitoring activities at RFS requiring a repository to archive their data, can contact the ABDS for advice. # 2 Data Handling and Data Products This document details how metadata and data from research conducted at RFS are to be managed and submitted throughout a research project lifecycle (Fig 1). ## 2.1 Research Project Lifecycle In order to properly frame the responsibilities of researchers operating in the RFS’s extensive and intensive monitoring areas (Fig 2), a common research project lifecycle is presented, and specific responsibilities at each stage are noted. ### 2.1.1 Research Project Inception When an individual or organization wishes to conduct research at RFS, an application is made to the RFS. At this time, the applicant is responsible for providing the following: * Information about the Principal Investigator (PI) * A description of the project * An inventory of expected project outcomes and deliverables * Logistical requirements * An overview of potential impacts, and plans for impact mitigation * How many participants expected to travel to RFS These data are stored as a metadata record in the metadata catalogue, and the application is passed to RFS for consideration. ### 2.1.2 Research Project Consideration, Review, and Subsequent Approval or Rejection When an application has been made to the RFS, it undergoes review and is either accepted or rejected: * If a project is rejected, the applicant will receive an explanation of the justifications resulting in rejection. At this point, the applicant may revise their application and resubmit, however there is no obligation to do so. * There are no data or metadata responsibilities for either the applicant or RFS at this stage. * If a project is accepted, the applicant proceeds to the project initiation phase. Fig 1: RFS Project lifecycle and data overview Fig 2: Extensive Monitoring Area and Intensive Monitoring Areas ### 2.1.3 Project Initiation When an application is accepted, the RFS will provide the following: * The RFS Monitoring Plan * The RFS Data Management Plan * Field Safety Guides * Informative background data pertinent to the RFS * Logistical assistance * A preliminary metadata record to provide an overview of the project * The RFS project metadata excel tool At the same time, the applicant is responsible for understanding and complying with the regulations and conditions described in the DMP and RFS Monitoring plan ### 2.1.4 Annual Project Update At the end of each field season of active research, the applicant is responsible for: * Updating the project metadata to properly represent data in production, or published by the research project; * Visiting the published, revised (public facing) project metadata to validate the revision; and * Pushing versioned, intermediate data to a repository if the preliminary data is deemed of immediate value to the scientific community, or if desired by the principal investigator. ### 2.1.5 Project Closure At the conclusion of a research project, the applicant is responsible for: * Updating the project metadata record to properly represent the final data product(s) of the research project; * Visiting the published, revised (public facing) project metadata to validate the revision; and * Pushing final quality assured data to a public repository. If a researcher requires a repository to archive datasets, this can be accommodated in the ABDS; and  Ensuring a separate detailed metadata entry is present for each dataset. ## 2.2 Metadata and Data Standards, Requirements, and Best Practices ### 2.2.1 Detailed Metadata Metadata is essential for users to understand how the data can be used to determine the accuracy and validity of the initiative. To ensure accuracy and accessibility of both project data and metadata, researchers are responsible for ensuring the guidelines below are met when submitting metadata and publishing data: * Metadata collected should be consistent with metadata requirements as stated in this DMP (See Annex C). * The metadata must clearly describe the datasets, their contents and all relevant information about the monitoring conducted including methods used, monitoring location and date, monitors and their skill level, etc. * Best practices in the documentation of data collection procedures should be followed. Methodologies used must be included in the metadata, along with any discrepancies in applied methodologies. To ensure metadata is accurate, accessible, and ingestible to the Arctic Biodiversity Data Service (ABDS) the metadata is to be provided by completing the RFS detailed metadata form, which is provided by RFS as an Excel document. See Annex D for a list of the minimum mandatory elements required for metadata. ### 2.2.2 Data standards and requirements Data recording and data quality standards are the responsibility of the researcher. RFS encourages data generators at RFS to comply with IPY Data Policy on the delivery of free biodiversity data to the public and equivalent legislation in the European Union for spatial information, such as the INSPIRE Directive. Data formats should adhere to the Darwin Core. Acknowledgement is mandatory when publications utilize data collected at RFS. ## 2.3 Contact Information Questions arising from this document can be addressed to: Rif Field Station (RFS), +354 856 9500, [email protected]	https://phaidra.univie.ac.at/o:1140797	Horizon 2020
0027_FISSAC_642154.md	# 1 Introduction This document constitutes the first issue of Data Management Plan (DMP) in the EU framework of the project FISSAC under Grant Agreement No 642154\. The objective of the DMP is to establish the measures for promoting the findings during the project’s life. 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 is delivered at M6; later the DMP will be monitored and updated in parallel with the different versions of Dissemination and Exploitation Plans (the progress of the implementation of DMP will be included in the Project Progress Reports, at M18 and M36. It is acknowledged that not all data types will be available at the start of the project. However and whenever important, if any changes occur to the FISSAC project due to inclusion of new data sets, changes in consortium policies or external factors, the DMP will be updated as well in order to fine-tune it to the actual data generated and the user requirements as identified by the FISSAC consortium participants. FISSAC project comprises seven technical work packages (WP) as follows: * WP1 - FROM CURRENT MODELS OF INDUSTRIAL SYMBIOSIS TO A NEW MODEL * WP2 - CLOSED LOOP RECYCLING PROCESSES TO TRANSFORM WASTE INTO SECONDARY RAW MATERIALS * WP3 - PRODUCT ECO-DESIGN AND CERTIFICATION * WP4 - PRE-INDUSTRIAL SCALE DEMONSTRATION OF THE RECYCLING PROCESSES AND ECOINNOVATIVE PRODUCTS * WP5 - INDUSTRIAL PRODUCTION & REAL SCALE DEMONSTRATION * WP6 - FISSAC MODEL FOR INDUSTRIAL SYMBIOSIS * WP7 - INDUSTRIAL SYMBIOSIS REPLICABILITY AND SOCIAL ISSUES To facilitate the technical work there are three transversal work packages to provide, structure, coordination, integration and communications across all the work packages. * WP8 - EXPLOITATION AND BUSINESS MODELS FOR INDUSTRIAL SYMBIOSIS * WP9 - DISSEMINATION * WP10 - MANAGEMENT This document has been prepared to describe the data management life cycle for all data sets that will be collected, processed or generated by FISSAC project. It is a document outlining how research data will be handled during FISSAC 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. # 2 Open Access and Open Research Data Pilot 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 research and innovation, 'scientific information' can refer to: (i) peer-reviewed scientific research articles (published in scholarly journals) or (ii) research data (data underlying publications, curated data and/or raw data). The EC capitalises 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 (innovation in) a knowledgedriven 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. ## 2.1. Dissemination, Communication and Open Access For the implementation of FISSAC project, there is a complete dissemination and communication set of activities scheduled, with the objectives of raising awareness among non-expert citizens, but potential next users of the FISSAC knowledge and solutions. For instance, e-newsletters, e-brochures, poster or events, are foreseen for the dissemination of FISSAC to key groups potentially related to the project results’ exploitation. Likewise, FISSAC website, webinars, press releases or short 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 D9.1 “Dissemination Plan”. Open Access (OA) to scientific information is a complementary element to dissemination and communication, and how this issue is specifically tackled by FISSAC project is described in the present document. ## 2.2. 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” section 2 of FISSAC Grant Agreement (FISSAC, Research & Innovation action, 2014) establishes the obligation to ensure open access to all peer-reviewed articles produced by FISSAC. 29.2 Open access to scientific publications 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. (c) 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, and - a persistent identifier. ## 2.3. Open Access to research data Research data is the second type of scientific information that OA is planned for, besides the publications. '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. Open Research Data Pilot is a novelty in Horizon 2020 aiming to improve and maximise access to and re-use of research data generated by projects (European Commission, 9 December 2013). Particularly FISSAC is participating in this Open Research Data Pilot programme as issued in Article 29 article 3: ### 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 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); 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 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 to third parties. ### Consortium Agreement - Access Rights The Parties have identified and agreed on the Background for the Project and have also, where relevant, informed each other that Access to specific Background is subject to legal restrictions or limitations. Anything which has not been identified in the Consortium Agreement shall not be the object of Access Right obligations regarding Background. Any Party can propose to the General Assembly to modify its Background in the Consortium Agreement. Each Party shall implement its tasks in accordance with the Consortium Plan and shall bear sole responsibility for ensuring that its acts within the Project do not knowingly infringe third party property rights. * Any Access Rights granted expressly will exclude any rights to sublicense unless expressly stated otherwise. * Access Rights shall be free of any administrative transfer costs. * Access Rights are granted on a non-exclusive basis. * Results and Background shall be used only for the purposes for which Access Rights to it have been granted. * All requests for Access Rights shall be made in writing. * The granting of Access Rights may be made conditional on the acceptance of specific conditions aimed at ensuring that these rights will be used only for the intended purpose and that appropriate confidentiality obligations are in place. * The requesting Party must show that the Access Rights are needed. Access Rights to Results and Background Needed for the performance of the own work of a Party under the Project shall be granted on a royalty-free basis, unless otherwise agreed for Background in Consortium Agreement. Access Rights to Results if needed for Exploitation of a Party's own Results shall be granted on Fair and Reasonable conditions to be agreed in writing among the Parties concerned. Access rights to Results for internal non-commercial research activities shall be granted on a royalty-free basis. Access Rights to Background if Needed for Exploitation of a Party's own Results, including for research on behalf of a third party listed in Attachment 3, shall be granted on Fair and Reasonable conditions. A request for Access Rights may be made up to twelve months after the end of the Project or after the termination of the requesting Party’s participation in the Project. Affiliated Entities have Access Rights under the conditions of the Grant Agreement if they are identified in attachment “Identified Affiliated Entities” to this Consortium Agreement. Such Access Rights must be requested by the Affiliated Entity from the Party that holds the Background or Results. Alternatively, the Party granting the Access Rights may individually agree with the requesting Party to have the Access Rights include the right to sublicense to the latter's Affiliated Entities. Access Rights to Affiliated Entities shall be granted on Fair and Reasonable conditions and upon written bilateral agreement. Affiliated Entities which obtain Access Rights in return they should fulfil all confidentiality and other obligations accepted by the Parties under the Grant Agreement or this Consortium Agreement as if such Affiliated Entities were Parties. Access Rights may be refused to Affiliated Entities if such granting is contrary to the legitimate interests of the Party which owns the Background or the Results. Access Rights granted to any Affiliated Entity are subject to the continuation of the Access Rights of the Party to which it is affiliated, and shall automatically terminate upon termination of the Access Rights granted to such Party. Upon termination of the status as an Affiliated Entity, any Access Rights granted to such former Affiliated Entity shall lapse. Further arrangements with Affiliated Entities may be negotiated in separate agreements. # 3 DMP Objective The purpose of FISSAC 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 FISSAC project participants with regard to all the dataset types that will be generated by the FISSAC 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 FISSAC project. It also describes how data will be collected, processed, stored and managed holistically from the perspective of external accessibility and long term archiving. All aspects of procedures that are associated with the quality control of data management internal to the project is the subject of a separate deliverable, D10.2 “Quality Assurance Plan”. 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 1 Data Management Plan overview # 4 Information Management and Policy The information available to different stakeholders will be managed and stored in a Content Management System (CMS) taking advantage of existing information management open sources that could be adaptable to project data dissemination needs. CMS offers different levels of accessibility depending on the degree of confidentiality of the information. It includes both, Publications and Repository of other research data. Open access to research data refers to right to access and re-use digital research data under the terms and conditions set out in the Grant Agreement. Content Management System A content management system is a computer application that allows publishing, editing, modifying, organizing, deleting, and maintaining content from a central interface. Such systems of content management provide procedures to manage workflow in a collaborative environment. These procedures can be manual steps or an automated cascade. CMSs have been available since the late 1990s. The function of CMS is to store and organize files, and provide version- controlled access to their data. CMS features vary widely. Simple systems showcase a handful of features, while other releases, notably enterprise systems, offer more complex and powerful functions. Most CMSs include Web- based publishing, format management, (version control), indexing, search, and retrieval. The CMS increases the version number when new updates are added to an already-existing file. Some content management systems also support the separation of content and presentation. A CMS may serve as a digital asset management system containing documents, movies, pictures, phone numbers, scientific data. CMSs can be used for storing, controlling, revising, semantically enriching and publishing documentation. Distinguishing between the basic concepts of user and content. The CMS has two elements: * Content Management Application (CMA) is the front-end user interface that allows a user, even with limited expertise, to add, modify and remove content from a Web site without the intervention of a Webmaster. * Content Delivery Application (CDA) compiles that information and updates the Web site. Information Management Information Management (IM) is the collection and management of information from one or more sources and the distribution of that information to one or more audiences. This sometimes involves those who have a stake in, or a right to that information. Management means the organization of and control over the structure, processing and delivery of information. Information includes both electronic and physical information. The organizational structure must be capable of managing this information throughout the information lifecycle regardless of source or format (data, paper documents, electronic documents, audio, social business, video, etc.) for delivery through multiple channels that may include cell phones and web interfaces. The focus of IM is the ability of organizations to capture, manage, preserve, store and deliver the right information to the right people at the right time. Information management environments are comprised of legacy information resident in line of business applications, Enterprise Content Management (ECM), Electronic Records Management (ERM), Business Process Management (BPM), Taxonomy and Metadata, Knowledge Management (KM), Web Content Management (WCM), Document Management (DM) and Social Media Governance technology solutions and best practices. _Figure 2: Information Management_ FISSAC project website Project website will be used for storing both public and private documents related to project and dissemination, the website is meant to be live for the whole project duration and minimum 2 years after the project ends. * Public section of the project website: public deliverables, brochure, poster, presentations, scientific papers, videos, etc. * Private section of the project website: confidential deliverables, work packages related documentation, etc. The website _www.fissacproject.eu_ was launched on 15 th of January 2016. The website was designed by a subcontractor and will be managed by ACR+. It will be dynamic and interactive 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 will be regularly updated with news and articles. It will also provide access to the FISSAC platform and FISSAC model, once they are online. All partners are responsible for feeding the project website with news and relevant information. The website will remain at least two years after the end of the project (February 2020). The website will be available in English and in the languages of the project partners (Czech, French, German, Hungarian, Italian, Spanish, Swedish and Turkish). However, the information will be selectively translated where needed in the various languages of the partnership, specifically for hosting regional workshops, webinars and for disseminating local news. _Figure 3: FISSAC website_ # 5 DMP Implementation The organizational structure of the FISSAC project was created in order to address an effective project direction and management through the communication flow and methods for reporting, monitoring, management of intellectual properties, background and foreground generated among the project. Moreover, according to Project Quality Assurance Plan to be developed (see WP 10 management), communication aspects and information generated in the project will be monitored taking also into consideration management of gender equality and risks analysis regarding financial, legal, administrative and technical co-ordination and mitigation actions aspects. If new risks appear along the project, new mitigation actions will be launched. The FISSAC project is partly coordinated by the Scientific and Technical Committee and Innovation Management Committee. The project has a structured governance and management framework that controls and directs decisions during the project. This is organised as shown in Figure 4 below. The DMP is issued as project deliverable D10.3 under the work package 10 and will be administrated by the Technical Coordination as shown in Figure 3 below. _Table 1: FISSAC project partners and their roles_ <table> <tr> <th> Partner short name </th> <th> Partner legal name </th> <th> Partner role in FISSAC project </th> </tr> <tr> <td> 1\. ACC </td> <td> ACCIONA INFRAESTRUCTURAS S.A. </td> <td> Project coordinator, participating in the development and demonstration of FISSAC implemented technologies and FISSAC model. </td> </tr> <tr> <td> 2\. ACR+ </td> <td> ASSOCIATION DES CITES ET DES REGIONS POUR LE RECYCLAGE ET LA GESTION DURABLE DES RESSOURCES </td> <td> Dissemination leader, Stakeholders network, analysis of IS model and social aspects. </td> </tr> <tr> <td> 3\. AEN </td> <td> ASOCIACION ESPAÑOLA DE NORMALIZACION Y CERTIFICACION </td> <td> Standardization tasks </td> </tr> <tr> <td> 4\. CSIC </td> <td> AGENCIA ESTATAL CONSEJO SUPERIOR DE INVESTIGACIONES CIENTIFICAS </td> <td> Re-formulation of ceramic tiles composition and determination of measurable reduction of raw materials consumption by introducing waste in the ceramic tiles composition formula, participation in the design of new materials able to provide practical demonstration of FISSAC implemented technologies and FISSAC model. </td> </tr> <tr> <td> 5\. AKG </td> <td> AKG GAZBETON ISLETMELERI SANAYI VETICARETCARET AS </td> <td> Participation in the development of new products based on secondary raw materials and demonstration of FISSAC implemented technologies and products. </td> </tr> <tr> <td> 6\. BEF </td> <td> BEFESA SALZCHALACKE GMBH </td> <td> Active industrial partner as secondary raw material supplier. </td> </tr> <tr> <td> 7\. BGM </td> <td> BRITISH GLASS MANUFACTURERS CONFEDERATION LIMITED </td> <td> Contribution to IS replicability activities and social issues. </td> </tr> <tr> <td> 8\. CBI </td> <td> CBI Betonginstitutet AB </td> <td> Contribution in pre-industrial demonstration and real scale demonstration. </td> </tr> <tr> <td> 9\. CSM </td> <td> CENTRO SVILUPPO MATERIALI SPA </td> <td> Contribution in eco-design and certification activities. </td> </tr> <tr> <td> 10\. DAP </td> <td> D'APPOLONIA SPA </td> <td> Participation in development of the software platform, FISSAC methodology and business model for IS, and will lead demonstration of the replication of FISSAC model. </td> </tr> <tr> <td> 11\. EKO </td> <td> EKODENGE MUHENDISLIK MIMARLIK DANISMANLIK TICARET ANONIM SIRKETI </td> <td> Development of the software platform tool. </td> </tr> <tr> <td> 12\. FAB </td> <td> FUNDACION AGUSTIN DE BETANCOURT </td> <td> Participation in the development and demonstration of FISSAC implemented technologies and products. </td> </tr> <tr> <td> 13\. FEN </td> <td> FENIX TNT SRO </td> <td> Exploitation leader, business modelling, IPR management, Data Management. </td> </tr> <tr> <td> 14\. FER </td> <td> FERALPI SIDERURGICA S.p.A. </td> <td> Active industrial partner as secondary raw material supplier. </td> </tr> <tr> <td> 15\. GEO </td> <td> GEONARDO ENVIRONMENTAL TECHNOLOGIES LTD </td> <td> Participation in developing the software platform tool. </td> </tr> <tr> <td> 16\. GTS </td> <td> GLASS TECHNOLOGY SERVICES LIMITED </td> <td> Active R&D partner as secondary raw material supplier. </td> </tr> <tr> <td> 17\. TRI </td> <td> INGENIEURBUERO TRINIUS GMBH </td> <td> Eco-design and certification activities. </td> </tr> <tr> <td> 18\. HIF </td> <td> HIFAB AB </td> <td> Contribution in the demonstration of the replication of FISSAC model, exploitation & business model for IS. </td> </tr> <tr> <td> 19\. KER </td> <td> KERABEN GRUPO SA </td> <td> Participation in the development of new products based on secondary raw materials and demonstration of FISSAC implemented technologies and products. </td> </tr> <tr> <td> 20\. OVA </td> <td> OPENBARE VLAAMSE AFVALSTOFFENMAATSCHAPPIJ </td> <td> Member of ACR+. As a competent (regional) government body with experience in the development and follow-up of policies, business models, partnerships offers insight and steering during the research process. </td> </tr> <tr> <td> 21\. RIN </td> <td> RINA SERVICES SPA </td> <td> Contribute in Environmental Technology Verification tasks. </td> </tr> <tr> <td> 22\. SP </td> <td> SP SVERIGES TEKNISKA FORSKNINGSINSTITUT AB </td> <td> Eco-design and certification activities leader, LCA and LCC methods, responsible for ecological and economic evaluation of the developed processes. Evaluation of non-technical opportunities and obstacles for different business models in order to create better instruments and development towards greater sustainability. Contribution with the analysis of circular business models. </td> </tr> <tr> <td> 23\. SYM </td> <td> SIMBIOSY SIMBIOSI INDUSTRIAL SL </td> <td> Demonstration of the replication of FISSAC model, exploitation & business model for IS, IS model trends. </td> </tr> <tr> <td> 24\. TCM </td> <td> TURKIYE CIMENTO MUSTAHSILLERI BIRLIGI </td> <td> Participation in the development of new products based on secondary raw materials and demonstration of FISSAC implemented technologies and products. </td> </tr> <tr> <td> 25\. TEC </td> <td> FUNDACION TECNALIA RESEARCH & INNOVATION </td> <td> Active R&D partner participating in setting the basis for the IS concerning innovative solutions for the use of by-products of steel and ceramic industries in environmental-friendly products and efficient applications for the construction sector. Validation at preindustrial scale to demonstrate the efficiency of the solutions and products. </td> </tr> <tr> <td> 26\. VAN </td> <td> VANNPLASTIC LTD </td> <td> Participation in the development of new products based on secondary raw materials and demonstration of FISSAC implemented technologies and products. </td> </tr> </table> # 6 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. ## 6.1. Characteristics for datasets produced in the project 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 produced and/or used in the project should be interoperable allowing data exchange between researchers, institutions, organisations, countries, etc. # 7 FISSAC Data Sets ## 7.1 Collection and Management of FISSAC Data Sets #### Types of data The types of data to be included within the scope of the FISSAC Data Management Plan shall as a minimum cover the types of data that is considered complementary to material already contained within declared project deliverables. #### Data Collection & Definition The responsibility to define and describe all non-generic data sets specific to an individual work package shall be with the WP leader. The WP leader shall formally review and update the data sets related to his WP on a six-monthly basis. All modifications/ additions to the data sets shall be provided to the FISSAC Coordinator (ACCIONA) for inclusion in the DMP, and shall be prepared in accordance with the metadata capture table template contained in Appendix 2. ##### Table 2: Forecast of FISSAC datasets related to each WP <table> <tr> <th> WP num. </th> <th> WP name </th> <th> WP leader </th> <th> Dataset reference </th> <th> Dataset name </th> </tr> <tr> <td> WP1 </td> <td> FROM CURRENT MODELS OF INDUSTRIAL SYMBIOSIS TO A NEW MODEL </td> <td> ACC </td> <td> FISSAC_WP1 </td> <td> INDUSTRIAL SYMBIOSIS </td> </tr> <tr> <td> WP2 </td> <td> CLOSED LOOP RECYCLING PROCESSES TO TRANSFORM WASTE INTO SECONDARY RAW MATERIALS </td> <td> ACC </td> <td> FISSAC_WP2 </td> <td> RECYCLING PROCESSES </td> </tr> <tr> <td> WP3 </td> <td> PRODUCT ECO-DESIGN AND CERTIFICATION </td> <td> SP </td> <td> FISSAC_WP3 </td> <td> ECO-DESIGN </td> </tr> <tr> <td> WP4 </td> <td> PRE-INDUSTRIAL SCALE DEMONSTRATION OF THE RECYCLING </td> <td> TEC </td> <td> FISSAC_WP4 </td> <td> PREINDUSTRIAL DEMO </td> </tr> <tr> <td> WP5 </td> <td> INDUSTRIAL PRODUCTION & REAL SCALE DEMONSTRATION </td> <td> ACC </td> <td> FISSAC_WP5 </td> <td> REAL SCALE DEMO </td> </tr> <tr> <td> WP6 </td> <td> FISSAC MODEL FOR INDUSTRIAL SYMBIOSIS </td> <td> EKO </td> <td> FISSAC_WP6 </td> <td> FISSAC MODEL </td> </tr> <tr> <td> WP7 </td> <td> INDUSTRIAL SYMBIOSIS REPLICABILITY AND SOCIAL ISSUES </td> <td> DAPP </td> <td> FISSAC_WP7 </td> <td> REPLICABILITY </td> </tr> <tr> <td> WP8 </td> <td> EXPLOITATION AND BUSINESS MODELS FOR INDUSTRIAL SYMBIOSIS </td> <td> FEN </td> <td> FISSAC_WP8 </td> <td> EXPLOITATION </td> </tr> <tr> <td> WP9 </td> <td> DISSEMINATION </td> <td> ACR+ </td> <td> FISSAC_WP9 </td> <td> DISSEMINATION </td> </tr> <tr> <td> WP10 </td> <td> MANAGEMENT </td> <td> ACC </td> <td> FISSAC_WP10 </td> <td> MANAGEMENT </td> </tr> </table> Data set reference and name All data sets within this DMP have been given a unique field identifier and are listed in the table contained in Appendix 1. #### 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 FISSAC project. For each data set, the characteristics of the data set have been captured in a tabular format as enclosed in Appendix 1. #### 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”. This is especially relevant in the distributed data network environment that exists within FISSAC. Meta Data shall be considered as the formal means by which data is defined and by which the meaning of information is established. All data-sets generated within the project shall be defined such that “data about data” is specified. 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. More details about these standards can be found in Annex 3. #### 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 as described in the FISSAC Quality Assurance Plan (D10.2). 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 FISSAC Grant Agreement. PU: Public (data can be shared outside the consortium without restriction) CO: Confidential, only for members of the consortium (including the Commission Services) CI: Classified, as referred to in Commission Decision 2001/844/EC The three above levels are linked to the “Dissemination Level” specified for all FISSAC deliverables. All material designated with a PU dissemination level shall be deemed uncontrolled. Data will be shared when the related deliverable or paper has been made available at an open access 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. 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 FISSAC Website Administrator (ACR+) by the Lead Author. _Figure 5: OpenAIRE website_ _Figure 6: ZENODO repository_ #### 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 FISSAC 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. 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 8). The process of archiving will be based on a data extract performed within 12 weeks of the formal closure of the FISSAC 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 as listed in Appendix 1. This index file shall be used as an inventory record of the extracted files, and shall be validated by the associated WP leader. # 8 Data Sets Technical Requirements ## 8.1 General 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. ## 8.2 Prohibited file types The use of file compression utilities, such as “WinZip” is prohibited. No data files shall be encrypted. ## 8.3 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 FISSAC activity and do not breach IPR of any third parties. #### Image file formats Image file formats are the standardised means of organising and storing digital images. Image files are composed of digital data and can consist 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 rasterised, 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 FISSAC project shall only use raster based image files of one of the two formats described below and shall be selected based on the technical needs and the format characteristics described below. The two allowable static image file formats are JPEG and PNG (detailed description in Annex 4). #### Image file sizes & file compression 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. Considering different compressions, it is common for two images of the same number of pixels and colour depth to have a very different compressed file size. With some compression formats, images that are less complex may result in smaller compressed file sizes. This characteristic sometimes results in a smaller file size for some lossless formats than lossy formats. The use of compression tools shall not be used unless absolutely necessary. A digitally stored image has no inherent physical dimensions. Some digital file formats record a DPI value, or more commonly a PPI (pixels per inch) value, which is to be used when printing the image. This number provides information to establish the printed image size, or in the case of scanned images, the size of the original scanned object. Resolution refers to the number of pixels in an image. Resolution can be expressed by the width and height of the image as well as the number of pixels in the image. For example, an image that is 2048 pixels wide and 1536 pixels high (2048X1536) contains 3,145,728 pixels. As the megapixels in the pickup device increases so does the possible maximum size image that can be produced. File size is determined by the number of pixels. The image default sizes and resolution shall be as shown in Table 1. The image default size shall be A4. ##### Table 3: Image default sizes and resolution <table> <tr> <th> PPI </th> <th> Pixels </th> <th> mm </th> <th> Paper size </th> <th> Size (Greyscale) </th> <th> Size (RGB) </th> </tr> <tr> <td> 300 </td> <td> 11114x14008 </td> <td> 840x1186 </td> <td> A0 </td> <td> 155.7MB </td> <td> 467MB </td> </tr> <tr> <td> 300 </td> <td> 7016x11114 </td> <td> 594x840 </td> <td> A1 </td> <td> 78MB </td> <td> 234MB </td> </tr> <tr> <td> 300 </td> <td> 4961x7016 </td> <td> 420x594 </td> <td> A2 </td> <td> 34.8M </td> <td> 104.4MB </td> </tr> <tr> <td> 300 </td> <td> 3508x4961 </td> <td> 297x420 </td> <td> A3 </td> <td> 17.4MB </td> <td> 52.2MB </td> </tr> <tr> <td> 300 </td> <td> 2480x3508 </td> <td> 210x297 </td> <td> A4 </td> <td> 8.7MB </td> <td> 26.1MB </td> </tr> <tr> <td> 300 </td> <td> 1748x2480 </td> <td> 148x210 </td> <td> A5 </td> <td> 4.3MB </td> <td> 13MB </td> </tr> <tr> <td> 300 </td> <td> 1240x1748 </td> <td> 105x148 </td> <td> A6 </td> <td> 2.2MB </td> <td> 6.5MB </td> </tr> <tr> <td> 300 </td> <td> 874x1240 </td> <td> 74x105 </td> <td> A7 </td> <td> 1.08MB </td> <td> 3.25MB </td> </tr> <tr> <td> 300 </td> <td> 614x874 </td> <td> 52x74 </td> <td> A8 </td> <td> 0.54MB </td> <td> 1.6MB </td> </tr> </table> ## 8.4 Animated graphical image 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 two allowable animated graphical image file formats are AVI and MPEG (detailed description in Annex 4). The WP leader shall determine the most suitable choice of format based on equipment availability and any other factors. ## 8.5 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. Detailed description of audio data types is in Annex 4\. ## 8.6 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. Detailed description of textual data types is in Annex 4. ## 8.7 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. ## 8.8 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. FISSAC innovation development shall make use of this file type for system testing. ## 8.9 Microsoft Office Application Suite FISSAC participants shall use the currently MS supported operating system and convert from any previous obsolete releases. #### Microsoft Office Application Data files 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 8. #### Microsoft Office Configuration 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 Figure 4. <table> <tr> <th> </th> <th> </th> </tr> <tr> <td> Title: </td> <td> Duplication of the name used for the data file name </td> </tr> <tr> <td> Subject: </td> <td> Identifier for FISSAC work package discrimination and shall be of the following format FISSAC_WPxx where xx is the work package number in the range 01 to 10. </td> </tr> <tr> <td> Author: </td> <td> Name of the person creating the document and be formatted to have the surname stated first as follows: surname_firstname_secondname </td> </tr> <tr> <td> Manager: </td> <td> Name of the author’s immediate line manager and be formatted to have the surname stated first as follows: surname_firstname_secondname </td> </tr> <tr> <td> Company: </td> <td> Company name of the author to be stated as follows: companyname_FISSAC participant number </td> </tr> <tr> <td> Keywords: </td> <td> 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 </td> </tr> </table> 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 Naming Convention All files irrespective of the data type shall be named in accordance with the following document file naming convention: FISSAC_Dx.x_Deliverable short title_Px_yyyymmdd_Status “Dx.x”: Deliverable number according to the DoA “Px”: Lead beneficiary number “yyyymmdd”: Year/month/day “Status”: Short name of the last reviewer (beneficiary short name) _Example: FISSAC_D10.2_Quality Plan_P1_20150511_Acc_ Appendix files will be referred to the main document according to the following rule: FISSAC_Dx.x_Deliverable short title_Appx_Px_yyyymmdd_Status Where “Appx” is the Appendix letter _Example: FISSAC_D10.2_Quality Plan_AppA_P1_20150511_Acc_ When the document has been approved by the EC, the status in the file name will be changed to “Final” while a copy of the file in PDF format will be uploaded on the webpage. The file naming convention contains the 7 following sections: [PROJECT]_[WORKPACKAGE]_[TASK]_[TITLE]_[VERSION]_[DISSEMINATIONCLASS]_[ARCHIVE] Where: * [PROJECT] is FISSAC for all document types; * [WORKPACKAGE] is the FISSAC project work package number, with WP as a prefix; * [TASK] is the FISSAC project task number, this is two numbers where numbers less than 10 have a leading zero; * [TITLE] represents the description of the data item contents excluding capitalisation and punctuation characters; * [VERSION] is the version number consisting of integer numbers only without leading zeros, prefixed with V; * [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 ; # 10 Conclusions This report contains the first release of the Data Management Plan (DMP) and represents the status of the mandatory quality requirements at the time of deliverable D10.3. This first version of the DMP establishes the measures for promoting the findings during the project’s life. 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 is delivered at M6; later the DMP will be monitored and updated in parallel with the different versions of Dissemination and Exploitation Plans (the progress of the implementation of DMP will be included in the Project Progress Reports, at M18 and M36. This report should be read in association with all the referenced documents, appendix material and including the EC Grant /Consortium Agreement, annexes and guidelines.	https://phaidra.univie.ac.at/o:1140797	Horizon 2020
0031_Residue2Heat_654650.md	# Introduction ## Scope The _Residue2Heat_ Data Management Plan (DMP) constitutes one of the outputs of the work package dissemination, communication and exploitation, dedicated to raising awareness and promoting the project and its related results, achievements. The present deliverable is prepared at an early project stage (Month 12), in order to commence on a strategy on data management from the project onset. It is also envisaged that the Data Management Plan will be implemented during the entire project lifetime and updated on a yearly basis. The main focus of the _Residue2Heat_ data management framework is to ensure that the project’s generated and gathered data can be preserved, exploited and shared for verification or reuse in a consistent manner. The main purpose of the 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. The definition of Research data is defined in the “Guidelines on Open Access to Scientific Publication and Research Data in Horizon 2020” (2015) as: “ _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." According to the EC provided documentation 1 for data management in H2020 aspects like research data access, sharing and security should also be addressed in the DMP. This document has been produced following these guidelines and aims to provide a policy for the project partners to follow. ## Objectives The generated and gathered research data need to preserved in-line with the EC requirements. They play a crucial role in exploitation, verification of the research results and should be effectively managed. This Data Management plan (DMP) aims at providing a timely insight into facilities and expertise necessary for data management both during and after the project is finished, to be used by all _Residue2Heat_ . The most important reasons for setting up this DMP are: * Embedding the _Residue2Heat_ project in the EU policy on data management. The rationale is that the Horizon 2020 grant consists of public money and therefore the data should be accessible to other researchers;. * Enabling verification of the research results of the _Residue2Heat_ project; * Stimulating the reuse of _Residue2Heat_ data by other researchers; * Enabling the sustainable and secure storage of _Residue2Heat_ data in repositories; This second version of the Data Management plan is submitted to the EU in December 2016. It is important to note however that the document will evolve and further develop during the project’s life cycle. It can be identified by a version number and a date. Updated versions will be uploaded by project partner OWI, which is the primary responsible for data management. # Findable, accessible interoperable and reusable (FAIR) data This document takes into account the latest “Guidelines on FAIR Data Management in Horizon 2020”. The _Residue2Heat_ project partners should make their research data findable, accessible, interoperable and reusable ( FAIR ) and ensure that is soundly managed. Good research data management is not a goal in itself, but rather the key conduit leading to knowledge discovery and innovation, and to subsequent data and knowledge integration and reuse 1 . ## Data Management Plan Data Management Plans (DMPs) are a key element of good data management. A DMP describes the data management life cycle for the data to be collected, processed and/or 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 2 : * 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;  how data will be curated and preserved. # Residue2Heat implementation of FAIR Data ## Data Summary It is a well-known phenomenon that the amount of data is increasing while the use and re-use of data to derive new scientific findings is more or less stable. This does not imply, that the data currently unused are useless - they can be of great value in the future. The prerequisite for meaningful use, re- use or recombination of data is that they are well documented according to accepted and trusted standards. Those 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 have to be defined. Strategies have to be fixed to preserve and store the data over a defined period of time in order to ensure their availability and re-usability after the end of the _Residue2Heat_ project. Data considered for open access would include items as fuel properties, energy flows and balances, modelling calculations etc. For example, the consortium expects that the following data will be obtained and made available: * Physico-chemical characterization of FPBO from different biomass resources (WP3); * Data underpinning the mass- and energy balances for fast pyrolysis (WP6); * Emission data and actual measurements obtained during combustion of FPBO (WP5); * Data on combustion reaction mechanism modelling (WP4); * Data on spray modelling (WP4); * Background data on screening LCA calculations (WP6). The data will be documented in 4 types of datasets: 1. Core datasets – datasets related to the main project activities. 2. Produced datasets – datasets resulting from _Residue2Heat_ applications, e.g. sensor data. 3. Project related datasets – datasets resulting from the documentation of the progress of the _Residue2Heat_ project. They are a collection of deliverables, dissemination material, training material and scientific publications. 4. Software related datasets – datasets resulting from the development of the combustion reaction mechanisms. These can be used for various purposes in the combustion area including research tasks or the development of new appliances. Generally, the datasets which be stored in file formats which have a high chance of remaining usable in the far future (see Annex 1). Especially the datasets which will be available for open access will be stored in these selected file formats. In principle the OpenAIRE 3 platform is selected to insure open access of the datasets, persistent identifiers, data discovery and preservation of data for a long term. The open access data is useful for different stakeholder groups from the scientific community, industry as well as socioeconomic actors. For example: * Industry and potential end users of the residential heating systems. To implement FPBO residential heaters in society, the potential end‐users need to be aware of their options. The end users will have certain demands, such as cost and comfort levels, which the industry needs to accommodate. This will be addressed by the datasets generated in WP6 and WP7. * Social and Environmental impacts of the _Residue2Heat_ value chain to the population. The proposed value chain has the potential to influence the daily life of many EUresidents, not only in heating their home, but also in terms of environmental impact, social aspects such as job security and the economic development of rural communities. The positive (and if present negative) effects will be documented in WP6. * Social and Environmental impacts of the _Residue2Heat_ value chain to the Regulatory Framework. To allow commercial use of FPBO in residential heating systems, both the fuel as well as the heating systems need to comply with numerous regulations. Examples are CE certification of the heating system (EU), EN standard for FPBO, Emission limits for both FPBO production as well as the heating system (National) and local development plans need to accommodate construction and operation of the FPBO production plant (Regional). In WP6 the regulatory framework on the different levels will be documented. ## FAIR Data ### Making data findable, including provisions for metadata In order to support the discoverability of data the OpenAIRE platform has been selected. This platform support multiple using unique identifiers (doi, arxiv, isbn, issn, etc) which are persistent for a long time. Currently this platform is being tested how it can support the Residue2Heat project in optimal form. This needs additional documentation of best practices with respect to: * the discoverability of data (metadata provision); * the identifiability of data and refer to standard identification mechanism. Do you make use of persistent and unique identifiers such as Digital Object Identifiers? * the naming conventions used; * the approach towards search keyword; * the approach for clear versioning; * specification of standards for metadata creation (if any). If there are no relevant standards available a documentation of what type of metadata will be created and how it is being created will be performed. ### Making data openly accessible The consortium will store the research data in a format which is suited for long-time preservation and accessibility. To prevent file format obsolescence, some precautions have been taken. One such measure is to select file formats which have a high chance of remaining usable in the far future (see Annex 1). Furthermore, in a future update of this deliverable the following issues will be addressed: * Specification of the data which will be made openly available? If some data is kept closed provide a rationale for doing so will be given; * Specification where the data and associated metadata, documentation and code are deposited; * Specification how access will be provided in case there are any restrictions. ### Making data interoperable In order to support the interoperability of the Residue2Heat project data a list of standard and metadata vocabularies need to be defined. Additionally it will be checked whether the data types present in our data set allow inter- disciplinary interoperability. If necessary mapping to more commonly ontologies will become available. The present version of the Data Management plan does not include the actual metadata about the data being produced in the _Residue2Heat_ project. Access to this project’s related metadata will be provided in an updated version of the DMP. ### Increase data re-use In order to support the data re-use the data will be a proper licence to permit the widest re-use possible. Most likely the best licenses to publish will be the Creative Commons license 4 . Other items which have to be addressed are: * when data will become available for re-use. If applicable, it is mentioned whether a data embargo is necessary; * the data produced and/or used in the project which is useable by third parties, in particular after the end of the project is listed. If the re-use of some data is restricted, it is explained why this is necessary; * data quality assurance processes; * the length of time for which the data will remain re-usable. ## Allocation of resources Lead for this data management task will be with OWI, co-lead with RWTH, though all partners are involved in the compliance of the DMP. The partners deliver datasets and metadata produced or collected in Residue2Heat according to the rules described in the Annex 1. The project coordinator and in particular the Technical coordinator are central players in the implementation of the DMP and will track the compliance of the rules as documented in this DMP. The Residue2Heat project partners have covered the costs for data FAIR in their budget estimations. The long term preservation of datasets has been secured via our internal communication platform EMDESK for up to eight years after the project is finished. ## Data security In this project various types of experimental and numerical data will be generated. The raw data will be stored by each partner according to their own standard procedures minimum for ten years after ending of the project. The processed data will become available in the form of project reports and open access publications. This data will be further exploited in webinars, articles in professional journals, and by conference presentations. The OpenAIRE platform 5 has been selected for secure long term storage and access of these datasets. The research data used for communication, dissemination and exploitation will be stored also on internal communication platform EMDESK ( _http://www.emdesk.com_ ) for up to 8 years after the project is finished. This internal platform is only accessible for the project partners. Access to research data which is not marked as confidential will be granted via a repository. ### Rights to access and re-use research data Open access to research data 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. Building on the proposed Consortium Agreement of the _Residue2Heat_ partnership the present data management plan is setup. The Consortium Agreement described general rules how data will be shared and/or made open, and how it will be curated, preserved and the proper licenses to publish, e.g. Creative Commons license. In an updated version of this DMP the right to access and re-use of research data will be documented in detail. ## Ethical aspects and Legal Compliance The legal compliance related to copyright, intellectual property rights and exploitation has been agreed on in the Consortium Agreement, which is also applicable to access to research data. It is unlikely that the _Residue2Heat_ project will produce research which is sensitive to personal and ethical concerns. # Conclusions This Data Management Plan (DMP) is focussed on the support of use and re-use of research data to validate or derive new scientific findings. The prerequisite for meaningful use, re-use or recombination of data is that they are well documented according to accepted and trusted standards. Those standards form a key pillar of science because they enable the recognition of suitable data. To ensure this, agreements on standards, accessibility and sharing practices have been defined. Strategies have to be fixed to preserve and store the data over a defined period of time in order to ensure their availability and re-usability after the end of _Residue2Heat_ . Especially, the metadata vocabularies and licences to permit the widest reuse possible need to be addressed more in detail in a future update of this deliverable.	https://phaidra.univie.ac.at/o:1140797	Horizon 2020
0032_MARISA_740698.md	# 1\. Introduction This document is developed as part of MARISA (MARitime Integrated Surveillance Awareness) project, which has received funding from the European Union’s Horizon 2020 Research and Innovation program, under the Grant Agreement number 740698. The Project Management, Quality and Risk Plan corresponds to Deliverable 1.1 of Work Package 1 (WP1) – Project Management & Coordination. WP1 will ensure an optimal coordination and management of MARISA Project, guaranteeing the effective implementation of the project activities in respect of technical progress, finance, contracts and administration. The specific objectives of WP1 include: * create the effective management structure on the basis of the principles included in the Grant and Consortium Agreements, for the whole duration of the project (committees, quality plans, procedures, risk register, project management tools, etc.); * ensure that the procedures are followed and changed if required during the project lifetime in order to ensure successful completion of tasks, deliverables and achievement of the milestones; * oversee the progress of work packages, monitor deliverables, and resolve any risks and unforeseen issues; * maintain contacts with the European Commission; * chair the Executive Board, Technical Board and the Advisory Board; * manage the innovation in MARISA ensuring the coherence between technology and exploitation dimensions. ## 1.1. Purpose of the document The D1.1 Project Management, Quality and Risk Plan provides an organized and harmonized set of practical guidelines, procedures and support documents that shall be used for optimizing the project implementation. It will be kept up to date as needed throughout the project lifecycle. This document is to be used by all partners to efficiently develop their individual and collective activities and contribute to the global objective of the project. ## 1.2. Reference documents [GA] Grant Agreement-740698-MARISA.pdf The Grant Agreement is the contract concluded between the EC (representing the EU) and the beneficiaries under which the parties receive the rights and obligations (e.g. the right of the Union's financial contribution and the obligation to carry out the research and development work). The Grant Agreement consists of the basic text and annexes, including Annex 1– Description of the action (DoA) - part A and part B. The DoA (Annex 1 part A) is also a key document to be taken into account given that it compiles a specific description of the tasks that will be carried out along the project and the expected results, deliverables and milestones to be obtained. This D1.1 Project Management, Quality and Risk Plan is a supporting document to the [GA] and the DoA (Annex 1 part A) and contains extracts from both the documents where appropriate to ensure a full definition of the management processes and procedures to ensure that the project is delivered successfully. [CA] Consortium Agreement The Consortium Agreement is the internal agreement signed between the members of the consortium establishing their rights and obligations with respect to the implementation of the action in compliance with the grant agreement. This D1.1 Project Management, Quality and Risk Plan is a supporting document to the [CA] intended to provide more detailed processes and procedures to ensure that the project is delivered successfully. ## 1.3. Applicability The D1.1 Project Management, Quality and Risk Plan is a reference document in the MARISA project. From the start of the project to its end, it is applicable to all partners, and is expected to remain stable. However, any changes will be agreed by the Executive Board (EB) and included in a revised version. In the unlikely event of a conflict between the D1.1 Project Management, Quality and Risk Plan and other documents such as the Description of Work or the Grant Agreement, they will prevail in the following order: 1. [GA] Grant Agreement including all Annexes; 2. [CA] Consortium Agreement; 3. [D1.1] D1.1 Project Management, Quality and Risk Plan (this Document). The latter documents will have to be modified to remain consistent with the former. This is especially mandatory for issues regulated by either the [GA] or [CA] documents. ## 1.4. Definitions In the following table, in order to clarify some concept tied to certain words used in the document, some overall definitions have been reported. <table> <tr> <th> </th> <th> DEFINITIONS </th> </tr> <tr> <td> EUCISE2020 </td> <td> EUropean test bed for the maritime Common Information Sharing Environment in the 2020 perspective. EUCISE2020 is a Security Research project of the European Seventh Framework Program; it aims at achieving the pre-operational Information Sharing between the maritime authorities of the European States. </td> </tr> <tr> <td> DEFINITIONS </td> </tr> <tr> <td> CISE </td> <td> CISE is the Common Information Sharing Environment for the Maritime Domain. It will integrate existing surveillance systems and networks and give to all the relevant authorities (EU and national authorities responsible for different aspects of surveillance) concerned access to the information they need for their missions at sea. The CISE will make different systems interoperable so that data and other information can be exchanged easily through the use of modern technologies. </td> </tr> <tr> <td> MARISA Toolkit </td> <td> In order to fostering faster detection of new events, better informed decision making and achievement of a joint understanding of a situation across borders, the MARISA toolkit it will be able to provide a suite of services to correlate and fuse various heterogeneous and homogeneous data and information from different sources, including Internet and social networks. </td> </tr> <tr> <td> Data Fusion </td> <td> The process of integrating multiple data sources to produce more consistent, accurate, and useful information than that provided by any individual data source. Is analogous to the ongoing cognitive process used by humans to integrate data continually from their senses to make inferences about the external world. </td> </tr> <tr> <td> Legacy Systems </td> <td> The previously existing Maritime Surveillance systems in the National/Regional Coordination Centers or Coastal Stations to which MARISA Toolkit must establish some kind of communications. </td> </tr> <tr> <td> Maritime Surveillance </td> <td> The set of activities aimed to understand, prevent wherever applicable and manage in a comprehensive way all the events and actions relative to the maritime domain which could impact the areas of maritime safety and security, law enforcement, defense, border control, protection of the maritime environment, fisheries control, trade and economic interest of the EU. </td> </tr> </table> Table 1: Definitions ## 1.5. Structure of the document The document is structured as follows: * Chapter 1 Introduction : it gives an introduction to the overall documents providing also useful information about the applicability of the present document. * Chapter 2 System Overview : in this chapter an overview of the MARISA system that will be implemented as objective of the project. * Chapter 3 Project Organization : this chapter presents the project management structure and the associated roles. * Chapter 4 Project Control and Monitoring : in this chapter processes set up for scheduling and schedule control, management of reviews and meetings, collaboration tools, are described. * Chapter 5 Configuration Management : this chapter presents the project rules for configuration management, data management. * Chapter 6 Quality Plan : it presents the quality control aspects. * Chapter 7 Risk Management Plan : in this chapter it is presented the risks and the opportunities management rules. # 2\. System Overview One of the main MARISA objective is to foster faster detection of new events, better informed decision making and achievement of a joint understanding of a situation across borders and allowing seamless cooperation between operating authorities and on-site / at sea / in air intervention forces. The proposed solution is a toolkit that provides a suite of services to correlate and fuse various heterogeneous and homogeneous data and information from different sources, including Internet and social networks. MARISA also aims to build on the huge opportunity that comes from using the open access to “big data” for maritime surveillance: the availability of large to very large amounts of data, acquired from various sources ranging from sensors, satellites, open source, internal sources and of extracting from these amounts through advanced correlation improves knowledge. The MARISA toolkit provides new means for the exploitation of the bulky information silos data, leveraging on the fusion of heterogeneous sector data and taking benefit of a seamless semantic interoperability with the existing legacy solutions available across Europe. In this regard the CISE data model and the EUCISE2020 services will be exploited, combining with the expertise of consortium members in creating security intelligence knowledge from a wide variety of sources. # 3\. Project Organization ## 3.1. Consortium Members <table> <tr> <th> No. </th> <th> Participant Organization Name </th> <th> Country </th> </tr> <tr> <td> 1 </td> <td> Leonardo S.p.A. (LDO) </td> <td> IT </td> </tr> <tr> <td> 2 </td> <td> Engineering Ingegneria Informatica S.p.A. (ENG) </td> <td> IT </td> </tr> <tr> <td> 3 </td> <td> GMV Aerospace & Defence S.A.U. (GMV) </td> <td> ES </td> </tr> <tr> <td> 4 </td> <td> Airbus Defence and Space SAS (ADS) </td> <td> FR </td> </tr> <tr> <td> 5 </td> <td> e-GEOS S.p.A (EG) </td> <td> IT </td> </tr> <tr> <td> 6 </td> <td> PLATH GmbH (PLA) </td> <td> DE </td> </tr> <tr> <td> 7 </td> <td> SATWAYS - Proionta Kai Ypiresies Tilematikis Diktyakon Kai Tilepikinoniakon Efarmogon Etairia Periorismenis Efthinis Epe (STW) </td> <td> GR </td> </tr> <tr> <td> 8 </td> <td> Inovaworks II Command and Control (IW) </td> <td> PT </td> </tr> <tr> <td> 9 </td> <td> Aster S.p.A. (AST) </td> <td> IT </td> </tr> <tr> <td> 10 </td> <td> Luciad NV (LUC) </td> <td> BE </td> </tr> <tr> <td> 11 </td> <td> INOV Inesc Inovação (INOV) </td> <td> PT </td> </tr> <tr> <td> 12 </td> <td> Nederlandse Organisatie voor toegepast-natuurwetenschappelijk onderzoek (TNO) </td> <td> NL </td> </tr> <tr> <td> 13 </td> <td> Fraunhofer Gesellschaft zur Förderung der angewandten Forschung e.V. (IOSB) </td> <td> DE </td> </tr> <tr> <td> 14 </td> <td> NATO STO – Centre for Maritime Research and Experimentation (CMRE) </td> <td> BE </td> </tr> <tr> <td> 15 </td> <td> Toulon Var Technologies (PMM) </td> <td> FR </td> </tr> <tr> <td> 16 </td> <td> Laurea University of Applied Sciences (LAU) </td> <td> FI </td> </tr> <tr> <td> 17 </td> <td> Alma Mater Studiorum University of Bologna (UNIBO) </td> <td> IT </td> </tr> <tr> <td> 18 </td> <td> Ministry of National Defence Greece (HMOD) </td> <td> GR </td> </tr> <tr> <td> 19 </td> <td> Netherlands Coastguard (NCG) </td> <td> NL </td> </tr> <tr> <td> 20 </td> <td> Guardia Civil (GUCI) </td> <td> ES </td> </tr> <tr> <td> 21 </td> <td> Italian Navy (ITN) </td> <td> IT </td> </tr> <tr> <td> 22 </td> <td> Portuguese Navy (PN) </td> <td> PT </td> </tr> </table> Table 3: MARISA Consortium ## 3.2. Management Structure Although MARISA is a project with a large number of partners, we have opted for a lean and mean organization since the majority of the partners have already worked closely together in other successful EU funded projects and joint research activities. An overview of the proposed organisational structure is given and shown in the Figure 1\. Figure 1: MARISA Organization Chart ## 3.3. Project Bodies and Main Roles ### 3.3.1. Project Management and Coordination The Project Coordinator (PC) [Francesco Cazzato, LDO], is the operative coordinator of the project and the intermediary between the consortium and the European Commission; PC coordinates Executive Board (EB) and Technical Board (TB) and the Advisory Board (AB). PC is responsible for the overall contractual, ethical, financial and administrative management of the project. This includes also the supervision of the technical activities of the work package leaders (WPLs). The PC is the sole contact person for the project with the EC and will ensure the punctual delivery of reports and deliverables; the liaison with the EndUsers/Advisory Board and with the partners; the chair of the EB and of the TB. He promotes the participation of female researchers in the project, approves all publications and deliverables. The Project Manager (PM), [Valeria Fontana, LDO], with related experience in managing research project, supports the PC and EB with the following tasks: * prepares the meeting agendas, organizes locations, schedules, and all activities related to helping the PC with the organization of the project; * monitors deadlines on time and budget and issues early warnings in case certain limits are reached; * monitors of all the deliverables and reports quality aspects; * provide appropriate reporting and addressing questions, problems, issues to be solved inside the EB meetings; * manage the risk plan. The Financial Officer (FO), [Alessandro Ambrosetti, LDO], supports the PC and EB with the following tasks: * distributes the payments received from the Commission to the Partners, according to the rules defined in the [GA] and [CA]; * receives the Partners’ reports on the person efforts and expenditures related to each six-month period; * support to the Project coordinator for any amendment may occur to the [GA] and [CA]; * produces and monitors the Annual Financial Statements. ### 3.3.2. Executive Board Executive Board (EB): is the decision making body and has the highest level of authority in the project. The EB is chaired by the Project Coordinator. The EB can authorize the PC to defer to the Commission for specific decisions in case a specific question cannot be resolved internally. Decisions within the EB will be taken ideally by consensus, but if necessary, by a two-thirds majority vote. In general, the EB meets in person at least once every six months. In addition to face-to-face meetings, the EB may hold teleconference meetings to discuss project progress and to make decisions and take action where appropriate. Specific tasks of the EB are: * Being responsible for all the aspects of the project, including to review progress against the defined deliverables and timetables and propose corrective actions whenever necessary; * Deciding upon the eventual reallocation of the projects’ budget by WPs and reviewing and proposing to the contractors budget transfers to include in the project plan. * Making proposals to the partners for the review and/or amendment of the terms of the [GA] and/or the [CA]. * Deciding upon major changes in work, particularly termination, creation, or reallocation of activities. * Deciding to suspend all or part of the project or to terminate all or part of the partners, or to request the Commission to terminate the participation of one or more partners. * Deciding upon the entering into the [GA] and the [CA] of new partners. * Agreeing procedures and policies in accordance with the Commission contractual rules, for the knowledge and Intellectual Property Rights (IPR) management. ### 3.3.3. Technical Board The Technical Board (TB) is the technical body which has technical authority in the project; it is chaired by the Project Coordinator (PC) and it is composed by the Innovation Manager (IM) and WP Leaders (WPLs). The TB supports the PC and the Executive Board (EB) in the following tasks: * guarantees that the engineering tasks are carried out on schedule and in accordance with the tasks and functions determined by the [GA]; * understand the plan and scope of the project by developing the technical breakdown structure, schedule and workflow diagrams, resource requirements, and constraints; * define interfaces as for instance understanding potential incompatibilities across project components; * control the configuration, help in milestone review and assessment; * understand biases, assumptions and other technical details that affect the results; * place any data regarding the user’s expectations, requirements, design, etc. under version control. ### 3.3.4. User Community The MARISA development methodology relies on the User Community that will include “end user practitioners”, partners, associates, maritime surveillance experts to explore and exploit the human capital in Member States and their institutions identifying operational needs, operational scenarios, existing gaps, acceptability issues and societal impacts that the proposed solutions may entail. The design of MARISA therefore integrates the users’ experience and design-development related R&D, trust building and cocreativity in a collective-authentic manner. The User Community will also provide guidance to the partners and enable interactions in the consortium for the implementation of the data fusion technologies. The current composition of the MARISA User Community comprehends all the partners including the “end users practitioners” that have joined MARISA as full partners: Italian Navy, Guardia Civil, Netherlands Coast guard, Hellenic MoD and Portuguese Navy. Moreover, the User Community also includes the associated partners that have already expressed interest and support to the objectives of the project. However the consortium is always open to accept other members during the execution of the project. ### 3.3.5. Advisory Board The MARISA Consortium has also foreseen an Advisory Board (AB). This additional body meets three times throughout the project (M10, M20 and M30) and consists of selected European and International organisations not directly involved in the project as full partners. The AB supports and advises project partners with experience and know-how throughout the project duration. Their valuable feedback to the strategic and technical process of the project brings many benefits for the MARISA project. Members of the AB will provide an external view. The AB will advise on strategic directions of the project in terms of detailed technical goals and impact, standardisation, ethical and societal issues. To achieve high quality results within the MARISA project, a strong cooperation with the AB members will actively be pursued and facilitated by frequent interaction. The current composition of the Advisory Board is reported in Table 4: <table> <tr> <th> Nation </th> <th> Org. Name/Dep. </th> <th> Member of the Advisory Board </th> </tr> <tr> <td> FI </td> <td> Finnish Border Guard </td> <td> _Commander Ari Laaksonen_ </td> </tr> <tr> <td> IT </td> <td> Italian Ministry of Interior - Dipartimento della Pubblica Sicurezza – Direzione Centrale dell’Immigrazione e della Polizia delle Frontiere </td> <td> _Dott.ssa Rosa Maria Preteroti_ </td> </tr> <tr> <td> SP </td> <td> Qi Europe - Security and Defence </td> <td> _Javier Warleta Alcina_ </td> </tr> <tr> <td> MA-USA </td> <td> Massachusetts Institute of Technology </td> <td> _Pierre F.J. Lermusiaux_ </td> </tr> <tr> <td> IT </td> <td> Italian Space Agency - Maritime Surveillance Projects </td> <td> _Dott.ssa Carolina Matarazzi_ </td> </tr> <tr> <td> NO </td> <td> Peace Research Institute Oslo </td> <td> _Maria Gabrielsen Jumbert_ </td> </tr> <tr> <td> FR </td> <td> État-Major de la Marine - ICT systems in CEPN </td> <td> _Captain of Frigate Benoit Stephan_ </td> </tr> <tr> <td> IT </td> <td> Leonardo S.p.A </td> <td> _Andrea Biraghi_ </td> </tr> </table> Table 4: MARISA Advisory Board ### 3.3.6. Security Board Security Advisory Board (SAB) will review all potential EU classified information, throughout the project life, coordinated by the MARISA Project Security Officer and report at the Executive Board meetings. The Security Advisory Board consists of representatives from the Consortium as reported in the Table 5: <table> <tr> <th> Nation </th> <th> Org. Name </th> <th> Project Security Officer </th> <th> E-Mail </th> </tr> <tr> <td> IT </td> <td> LDO </td> <td> _Francesco Moliterni_ </td> <td> [email protected] </td> </tr> <tr> <td> Nation </td> <td> Org. Name </td> <td> Member of the AB </td> <td> </td> </tr> <tr> <td> IT </td> <td> ENG </td> <td> _Fabio Barba_ </td> <td> [email protected] </td> </tr> <tr> <td> ES </td> <td> GMV </td> <td> _Oscar Pablo Tejedor Zorita_ </td> <td> [email protected] </td> </tr> <tr> <td> FR </td> <td> ADS </td> <td> _Philippe Chrobocinski_ </td> <td> [email protected] </td> </tr> <tr> <td> GR </td> <td> STW </td> <td> _Antonis Kostaridis_ </td> <td> [email protected] </td> </tr> <tr> <td> BE </td> <td> CMRE </td> <td> _Amleto Gabellone_ </td> <td> [email protected] </td> </tr> <tr> <td> ES </td> <td> GUCI </td> <td> _Luis Antonio Santiago Marín_ </td> <td> [email protected] </td> </tr> <tr> <td> ES </td> <td> GUCI </td> <td> _Carlos Díaz Martín_ </td> <td> [email protected] </td> </tr> </table> Table 5: MARISA Security Advisory Board All the SAB representatives have wide experience in handling security issues. As detailed in the summary CVs (reported in [GA]), many of them have acted as Project Security Officer in a number of H2020 projects or are responsible of Security Management in the respective companies. ### 3.3.7. Other Project Roles The Innovation Manager (IM), [Véronique Pevtschin, ENG] ensures the coordination of all activities connected to technology alignment to user needs, technology transfer, management of innovation, including IPR, detection of market opportunities and channels. IM supports the PC and the EB in aligning innovation to user needs, in fostering the exploitation of innovation and in connecting with other suppliers to promote the openness of the approach. In 4.4, it is reported a more detailed explanation about how the Innovation Management will be carried out in MARISA. The Ethics Manager (EM), [Sari Sarlio-Siintola, LAU], with an extensive expertise for the ethics work, a) identifies and analyses ethical and societal framework; b) monitors the ethical concerns in the project, b) ensures that the project will comply with the Research Ethics taking all relevant international ethical aspects into consideration, prior to the execution of the operational trials, c) translates and implements the ethical requirements to the various deliverables in the project, d) provides advice on assessments on ethics in the ethical and societal reports, e) facilitates collaboration with all the project actors. The Communication Manager (CM), [Eric den Breejen, TNO] ensures the quality and execution of the communication (and dissemination) plan of the project. CM supports the PC and EB with the following tasks: a) preparation of the communication and dissemination plan for activities in relation to the general media in conjunction with the innovation manager for the specifics of industrial dissemination; b) Monitoring of media and other means of dissemination for the duration of the project; c) definition and review of dissemination material. The User Community Leader (UCL), [Rauno Pirinen, LAU] with vast experience in applying user-driven methodology in the definition of user needs, design and validation of information systems, leads and animates the User Community. # 4\. Project Control and Monitoring ## 4.1. Work Breakdown Structure The project’s organisation regarding the overall work spread and allocation is based on a systematic approach: everything produced throughout and by the project corresponds with a Work Package task. The work division will therefore be articulated on a two level basis: * Level 1: Work Packages that gather group of single tasks, all with the same assigned objective. Each Work Package carries out its tasks, has autonomous control over internal issues and delivers research and development results in accordance with the Project Work Programme and within the allocated budget. * Level 2: Single Tasks, embedded within the Work Packages, which are linked to a sole and defined action, like the production of a Deliverable. <table> <tr> <th> # </th> <th> TITLE </th> <th> BRIEF DESCRIPTION </th> </tr> <tr> <td> WP1 </td> <td> Project Management & Coordination </td> <td> Overall management and coordination on the project in respect of technical progress, finance, contracts and administration. </td> </tr> <tr> <td> T1.1 </td> <td> Project Coordination </td> <td> Coordination of the overall administration and finances of the project. </td> </tr> <tr> <td> T1.2 </td> <td> Project Management, Quality Control and Risk Management </td> <td> Day-to-day planning and work supporting the Project Coordination during the entire lifecycle of the project. </td> </tr> <tr> <td> T1.3 </td> <td> Innovation Management </td> <td> Innovation management of MARISA reflects the strategy of MARISA deliver technology aligned to market needs, European strategies and operational contexts. </td> </tr> <tr> <td> T1.4 </td> <td> Ethics Management </td> <td> Guidance and steering on legal, ethical and societal issues of the proposed MARISA solution. </td> </tr> <tr> <td> WP2 </td> <td> User Needs and Operational Scenarios </td> <td> Animation of the MARISA User Community to foster pro-active involvement of stakeholders following a user-centric approach in the MARISA design and validation. </td> </tr> <tr> <td> T2.1 </td> <td> User community animation </td> <td> Animation of the MARISA User Community focused on the goal of “innovation”, delivering the benefits of data fusion to maritime surveillance through the MARISA toolkit of services. </td> </tr> <tr> <td> T2.2 </td> <td> Addressing user needs through MARISA services </td> <td> This task will build on results of previous R&D and cooperative projects and the input of the users’ community to elaborate the relevant requirements of data fusion functionalities, the organisation of these functionalities into actionable services. </td> </tr> <tr> <td> T2.3 </td> <td> Adoption model for MARISA </td> <td> The adoption path will be applied to drive the incremental alignment of the innovative services to the user needs and evaluate the implementations with respect to the needs. </td> </tr> <tr> <td> T2.4 </td> <td> Interaction with Existing/Legacy Systems and the CISE environment </td> <td> This task focuses on identifying the legacy systems which MARISA will interact with, during the project trials but also beyond, to address their capabilities to exchange data and required agreements. </td> </tr> <tr> <td> T2.5 </td> <td> Innovative use of additional data sources </td> <td> This task focus on designing a MARISA toolkit that can easily integrate new data sources and support new services beyond the MARISA project community and duration. </td> </tr> </table> <table> <tr> <th> # </th> <th> TITLE </th> <th> BRIEF DESCRIPTION </th> </tr> <tr> <td> T2.6 </td> <td> Legal and Ethical Context Analysis </td> <td> This task starts at the very beginning of the project by identifying and analysing legal and ethical regulation framework in which MARISA operates linked to the MARISA functionalities for data fusion. </td> </tr> <tr> <td> T2.7 </td> <td> Operational Scenarios & Trials Definition </td> <td> In this task, the implementation and trial results will be traced back to initial requirements, according to the two-phase approach of the work plan. </td> </tr> <tr> <td> WP3 </td> <td> MARISA Toolkit Design </td> <td> Delivery of the high level architecture of the MARISA Toolkit and the associated services based on requirements and scenarios previously defined. </td> </tr> <tr> <td> T3.1 </td> <td> Overall Toolkit Design </td> <td> Definition of the MARISA Toolkit main building blocks and interfaces among them; identification of services producers and consumers and main system components (internal and external, including legacy systems). </td> </tr> <tr> <td> T3.2 </td> <td> Data Fusion Module and resulting services </td> <td> Definition of the data fusion module architecture including the identification of the resulting services which shall be classified. </td> </tr> <tr> <td> T3.3 </td> <td> External and internal Interfaces and gateways design </td> <td> Based on the services behaviour, the required capabilities and the flow of information previously defined, the external and internal interfaces shall be identified. </td> </tr> <tr> <td> T3.4 </td> <td> Data models definition </td> <td> Elaboration of a common data model for data exchange. </td> </tr> <tr> <td> T3.5 </td> <td> User interactions (HCI) </td> <td> Development of human-computer-interface modules. </td> </tr> <tr> <td> WP4 </td> <td> Data analysis and fusion </td> <td> Identification of all the Data Fusion computing capabilities of Marisa Toolkit providing methods and algorithms to extract value added information from the available external data sources. </td> </tr> <tr> <td> T4.1 </td> <td> Data Fusion Level 1 products </td> <td> This task addresses the aspect of “Observation of elements in the environment” to provide a more accurate awareness of objects in the maritime environment. </td> </tr> <tr> <td> T4.2 </td> <td> Data Fusion Level 2 products </td> <td> This task addresses the aspect of “Comprehension of the current situation” to provide useful information among the relationships of level 1 objects in the maritime environment. </td> </tr> <tr> <td> T4.3 </td> <td> Data Fusion Level 3 products </td> <td> This task addresses the aspect of “Projection of future states”, to predict the evolution of a maritime situation, in support of rapid decision making and action. </td> </tr> <tr> <td> WP5 </td> <td> Supporting capabilities and infrastructure </td> <td> Definition and development of the supporting infrastructure for the MARISA toolkit. </td> </tr> <tr> <td> T5.1 </td> <td> Big Data Infrastructure set-up </td> <td> Definition, implementation and set-up of a scalable Big Data management and analysis infrastructure with the aim to support and integrate the analysis, fusion, and analytics modules. </td> </tr> <tr> <td> T5.2 </td> <td> Interfaces to external data sources (include gateways) </td> <td> Implementation of the adapter modules and gateways for the integration of the required external data sources and legacy Systems. </td> </tr> <tr> <td> T5.3 </td> <td> User interactions </td> <td> Implementation of the front-end components of the toolkit. </td> </tr> <tr> <td> T5.4 </td> <td> Data fusion distribution services </td> <td> A set of system-to-system interfaces will be made available to be used by the end user operational systems or other external systems. </td> </tr> <tr> <td> T5.5 </td> <td> Information and Assurance services </td> <td> Implementation of the identity and access management services providing MARISA toolkit the capability to identify (authentication) and grant and access privileges (authorization) to all users, systems and devices connecting to MARISA. </td> </tr> <tr> <td> WP6 </td> <td> MARISA Toolkit integration and validation </td> <td> Integration of the various components of the system and test and validation of the configurations to provide WP7 with qualified systems to support the trials. </td> </tr> </table> <table> <tr> <th> # </th> <th> TITLE </th> <th> BRIEF DESCRIPTION </th> </tr> <tr> <td> T6.1 </td> <td> General principles of test architecture and data integration </td> <td> Definition of the generic principles of the test architecture based on specifications provided by users or developers of the tools and components that will be used. </td> </tr> <tr> <td> T6.2 </td> <td> Definition and implementation of integration platforms </td> <td> Definition and implementation of integration platforms (at PMM premises), able to test all components in different configurations and interfaces to local legacy systems. </td> </tr> <tr> <td> T6.3 </td> <td> Definition of system configurations for each trial </td> <td> The outcome of the task will be the definition files of each campaign configuration, the definition of the data sets needed for each campaign and the test files that shall be used to validate each configuration. </td> </tr> <tr> <td> T6.4 </td> <td> Factory integration of campaign configurations </td> <td> The task will integrate all components developed in WP4 and WP5 into the whole chain, physically in a first step and functionally in a second step. The system configurations will be qualified through the test files and validated on the basis of the specifications prepared in WP2 and WP3. </td> </tr> <tr> <td> T6.5 </td> <td> Validation of campaign configurations with simulation/emulation tools </td> <td> Factory scenarios will be defined to test and validate the MARISA toolkit in the different trial configurations, with the aim to provide validated configurations for the trials execution. </td> </tr> <tr> <td> WP7 </td> <td> Verification in Operational Trials </td> <td> Definition of the overall approach to be applied to the operational trials, execution of the operational trials, collection and analysis of trial results. </td> </tr> <tr> <td> T7.1 </td> <td> Operational Trials Approach </td> <td> This task will define the approach and plans to operational trials to be conducted in Phase 1 and Phase 2 considering the preparatory work performed by the User Community in WP2. </td> </tr> <tr> <td> T7.2 </td> <td> Iberian Trial </td> <td> This task includes activities for the detailed organization, logistics, setting-up and execution of the Iberian Trial. </td> </tr> <tr> <td> T7.3 </td> <td> Northern Sea Trial </td> <td> This task includes activities for the detailed organization, logistics, setting-up and execution of the North Sea Trial. </td> </tr> <tr> <td> T7.4 </td> <td> Ionian Trial </td> <td> This task includes activities for the detailed organization, logistics, setting-up and execution of the Ionian Sea trial. </td> </tr> <tr> <td> T7.5 </td> <td> Aegean Trial </td> <td> This task include activities for the detailed organization, logistics, setting-up and execution of the Aegean Sea Trial. </td> </tr> <tr> <td> T7.6 </td> <td> Strait of Bonifacio Trial </td> <td> This task include activities for the detailed organization, logistics, setting-up and execution of the Strait of Bonifacio Trial. </td> </tr> <tr> <td> T7.7 </td> <td> Verification Results Consolidation </td> <td> This task will a) assess the results of each trial against the objective, b) verify the KPIs defined in Section 1.2, c) provide recommendations and define a roadmap for the subsequent project phases. </td> </tr> <tr> <td> WP8 </td> <td> Dissemination and Exploitation </td> <td> Dissemination, exploitation and standardization activities of the MARISA project. </td> </tr> <tr> <td> T8.1 </td> <td> Communication and Dissemination Strategy and Plan </td> <td> This task defines the complete communication and dissemination strategy for the MARISA project ensuring that the appropriate MARISA results are conveyed to the right audience in the right time. </td> </tr> <tr> <td> T8.2 </td> <td> MARISA Web Site and dissemination materials </td> <td> The MARISA web site will be set-up and maintained. Official MARISA project leaflets, brochures, posters, videos and workshops material will be prepared in this task. </td> </tr> <tr> <td> T8.3 </td> <td> MARISA Workshops Coordination </td> <td> This task will be dedicated to the preparation, organisation, management and coordination of 3 workshops to present and promote the MARISA results to the MARISA Consortium partners, end-users, user community and other relevant stakeholders. </td> </tr> <tr> <td> T8.4 </td> <td> MARISA Training toolkit support </td> <td> This task delivers the training material in a format adapted to users to support them through trials in phase 1 and in phase 2. </td> </tr> <tr> <td> # </td> <td> TITLE </td> <td> BRIEF DESCRIPTION </td> </tr> <tr> <td> T8.5 </td> <td> IPR Management and Exploitation plan </td> <td> This task focuses on creating and refining business models, supported by adoption paths and including proposed IPR models, oriented both to existing partners and external organisations interested in delivering new data fusion services through the MARISA toolkit. </td> </tr> <tr> <td> T8.6 </td> <td> Standardization </td> <td> This task will address standardizations of MARISA approach, methods and results. </td> </tr> <tr> <td> WP9 </td> <td> Ethics Requirements </td> <td> This work package sets out the 'ethics requirements' that the project must comply with. </td> </tr> </table> Table 6: MARISA WBS The Work Package Leaders (WPL) direct the day-to-day technical planning and execution of work and escalate issues to the EB as required. The WPLs are responsible for monitoring progress in their respective work package and for coordinating the activities and compiling the responses. They collaborate with partners on the tasks of each work package in order to assure the quality of work and the present the results in reports according to the project description. Specific activities of the Work Package Leader are: * planning of the Work Package’s activities; * coordination of the Task Leaders within the Work Package; * liaison with the Project Coordinator (technical follow-up and information on IPR issues in connection with the Work Package); * deadline management, and implementation of the Project Work Programme at the Work Package level, in particular the Work Package Leader has to inform the Coordinator and the other Work Package Leaders whenever a timeline might not be achieved so that the necessary contingency plans can be implemented; * quality control and performance assessment of the Tasks associated to the Work Package; * in case of conflict between contributors, the Work Package Leader tries to find a solution (corrective action) and if needed will inform the Coordinator and the EB; * responsible for security issues of the deliverables in their WP. The Work Package Leader is responsible for the respect of the stipulated deadlines, and if necessary the execution of the relevant part of the contingency plan. The Task Leaders (TLs) are responsible of all aspects of the Task’s execution. A Task consists of a clearly identified simple objective (develop a specified tool or provide a deliverable). Specific activities of the Task Leaders are: * contribute to the elaboration of the Work Package’s planning; * coordination and management of the Task team and the Contributors; * liaison with the Work Package Leader (technical follow-up and information on IPR issues in connection with the Work Package); * deliver milestones and deliverables in accordance with the Project Work Programme; * inform the Work Package Leader on all relevant events and activities related to the Task; * propose and implement corrective actions in case of malfunctions; * provide cost statements, information and data (financial and other) necessary for the mid-term and final review. The Task Leader is responsible for the respect of the stipulated deadlines, and if necessary the execution of the relevant part of the contingency plan. <table> <tr> <th> # </th> <th> TITLE </th> <th> LEADER </th> </tr> <tr> <td> WP1 </td> <td> Project Management & Coordination </td> <td> LDO </td> </tr> <tr> <td> T1.1 </td> <td> Project Coordination </td> <td> LDO </td> </tr> <tr> <td> T1.2 </td> <td> Project Management, Quality Control and Risk Management </td> <td> LDO </td> </tr> <tr> <td> T1.3 </td> <td> Innovation Management </td> <td> ENG </td> </tr> <tr> <td> T1.4 </td> <td> Ethics Management </td> <td> LAU </td> </tr> <tr> <td> WP2 </td> <td> User Needs and Operational Scenarios </td> <td> LAU </td> </tr> <tr> <td> T2.1 </td> <td> User community animation </td> <td> LAU </td> </tr> <tr> <td> T2.2 </td> <td> Addressing user needs through MARISA services </td> <td> LDO </td> </tr> <tr> <td> T2.3 </td> <td> Adoption model for MARISA </td> <td> ENG </td> </tr> <tr> <td> T2.4 </td> <td> Interaction with Existing/Legacy Systems and the CISE environment </td> <td> GMV </td> </tr> <tr> <td> T2.5 </td> <td> Innovative use of additional data sources </td> <td> IOSB </td> </tr> <tr> <td> T2.6 </td> <td> Legal and Ethical Context Analysis </td> <td> LAU </td> </tr> <tr> <td> T2.7 </td> <td> Operational Scenarios & Trials Definition </td> <td> AST </td> </tr> <tr> <td> WP3 </td> <td> MARISA Toolkit Design </td> <td> GMV </td> </tr> <tr> <td> T3.1 </td> <td> Overall Toolkit Design </td> <td> GMV </td> </tr> <tr> <td> T3.2 </td> <td> Data Fusion Module and resulting services </td> <td> LDO </td> </tr> <tr> <td> T3.3 </td> <td> External and internal Interfaces and gateways design </td> <td> STW </td> </tr> <tr> <td> T3.4 </td> <td> Data models definition </td> <td> ENG </td> </tr> <tr> <td> T3.5 </td> <td> User interactions (HCI) </td> <td> LUC </td> </tr> <tr> <td> WP4 </td> <td> Data analysis and fusion </td> <td> LDO </td> </tr> <tr> <td> T4.1 </td> <td> Data Fusion Level 1 products </td> <td> LDO </td> </tr> <tr> <td> T4.2 </td> <td> Data Fusion Level 2 products </td> <td> IOSB </td> </tr> <tr> <td> T4.3 </td> <td> Data Fusion Level 3 products </td> <td> TNO </td> </tr> <tr> <td> WP5 </td> <td> Supporting capabilities and infrastructure </td> <td> ENG </td> </tr> <tr> <td> T5.1 </td> <td> Big Data Infrastructure set-up </td> <td> ENG </td> </tr> <tr> <td> T5.2 </td> <td> Interfaces to external data sources (include gateways) </td> <td> GMV </td> </tr> <tr> <td> # </td> <td> TITLE </td> <td> LEADER </td> </tr> <tr> <td> T5.3 </td> <td> User interactions </td> <td> LUC </td> </tr> <tr> <td> T5.4 </td> <td> Data fusion distribution services </td> <td> LDO </td> </tr> <tr> <td> T5.5 </td> <td> Information and Assurance services </td> <td> ADS </td> </tr> <tr> <td> WP6 </td> <td> MARISA Toolkit integration and validation </td> <td> ADS </td> </tr> <tr> <td> T6.1 </td> <td> General principles of test architecture and data integration </td> <td> ADS </td> </tr> <tr> <td> T6.2 </td> <td> Definition and implementation of integration platforms </td> <td> PMM </td> </tr> <tr> <td> T6.3 </td> <td> Definition of system configurations for each trial </td> <td> ADS </td> </tr> <tr> <td> T6.4 </td> <td> Factory integration of campaign configurations </td> <td> ADS </td> </tr> <tr> <td> T6.5 </td> <td> Validation of campaign configurations with simulation/emulation tools </td> <td> ADS </td> </tr> <tr> <td> WP7 </td> <td> Verification in Operational Trials </td> <td> STW </td> </tr> <tr> <td> T7.1 </td> <td> Operational Trials Approach </td> <td> STW </td> </tr> <tr> <td> T7.2 </td> <td> Iberian Trial </td> <td> GMV </td> </tr> <tr> <td> T7.3 </td> <td> Northern Sea Trial </td> <td> TNO </td> </tr> <tr> <td> T7.4 </td> <td> Ionian Trial </td> <td> LDO </td> </tr> <tr> <td> T7.5 </td> <td> Aegean Trial </td> <td> STW </td> </tr> <tr> <td> T7.6 </td> <td> Strait of Bonifacio Trial </td> <td> ADS </td> </tr> <tr> <td> T7.7 </td> <td> Verification Results Consolidation </td> <td> STW </td> </tr> <tr> <td> WP8 </td> <td> Dissemination and Exploitation </td> <td> TNO </td> </tr> <tr> <td> T8.1 </td> <td> Communication and Dissemination Strategy and Plan </td> <td> TNO </td> </tr> <tr> <td> T8.2 </td> <td> MARISA Web Site and dissemination materials </td> <td> AST </td> </tr> <tr> <td> T8.3 </td> <td> MARISA Workshops Coordination </td> <td> TNO </td> </tr> <tr> <td> T8.4 </td> <td> MARISA Training toolkit support </td> <td> AST </td> </tr> <tr> <td> T8.5 </td> <td> IPR Management and Exploitation plan </td> <td> ENG </td> </tr> <tr> <td> T8.6 </td> <td> Standardization </td> <td> LDO </td> </tr> <tr> <td> WP9 </td> <td> Ethics Requirements </td> <td> LDO </td> </tr> </table> Table 7: MARISA WPs and Tasks Responsibilities ## 4.2. Development Methodology The MARISA overall development methodology is depicted in Figure 2. The following principles have been followed. Strong involvement of the user community to capture the relevant operational needs and validate the results. The MARISA development methodology relies on the User Community that will include “end user practitioners”, partners, associates, maritime surveillance experts to explore and exploit the human capital in Member States and their institutions identifying operational needs, operational scenarios, existing gaps, acceptability issues and societal impacts that the proposed solutions may entail. Compliance with European Maritime Security Strategy and CISE Data Model to maximize interoperability with other MSA community of interest already existing and operating. The MARISA toolkit will be designed to streamline the integration with the current and future MSA operational systems, to allow different configurations and levels of exploitation, to ensure full compatibility with the CISE data model and with the overall European policy that facilitates the interagency interoperability and cooperation and allows each Member State to decide how, when and whether additional data sources are of relevance to its operations. Attention to reuse capabilities and results coming from other European programs , but ready to introduce state-of-the-art technologies when appropriate. Great attention will be devoted to the previous European and national projects since it is the MARISA Consortium intention to build on results of these projects. Since all the MARISA participants have been involved in EU funded R&D and cooperative projects, their deep knowledge of those projects will allow to move from the current achievements and introduce improvements and further innovation. Protection of Data Fusion Products based on the “need-to-share” approach , to guarantee access and distribution of data fusion results among relevant stakeholders. MARISA on the one hand will process a great amount of raw data of different types, on the other hand will produce a relevant number of data fusion products. Adoption of the Agile Software Development Methodology supported by a continuous integration environment to assure an evolutionary development of MARISA services, ease the collaboration and ownership among geographically distributed partners. Validation of MARISA in specific operational trials . A large part of the effort will be dedicated to verify and validate the MARISA toolkit in a variety of operational trials. Indeed, MARISA will be a trial based project. Trials will be real life exercises, not a guided tour on pre-packed demos. Each specific trial is characterized by a geographic area, where MARISA services will be tested, by the Use Cases and hence the system functionalities will be verified in that trial by the project partners and practitioners involved in that trial, with its own assets and/or output data coming from their own systems and needed for the trial execution. Two Phase Approach . The project is organized in two main phases, each phase will include a complete MARISA life cycle iteration from statement and finalization of user needs, MARISA toolkit design, development, integration and setting-up, to trial sessions to validate MARISA through selected scenarios. This two-phases approach allows to initially construct the concepts and methods of the MARISA toolkit, deliver and operationally validate a subset of MARISA initial services. Successively, based on the feedback of the first phase, MARISA concepts and methods will be revised and enhanced, additional services will be included and the complete MARISA toolkit validated again in operational scenarios. Figure 2: MARISA Development Methodology ## 4.3. Work Logic MARISA is structured in 9 work packages, over a duration of 30-months. The work logic is shown in Figure 3\. Figure 3: MARISA Work Logic MARISA end users practitioners are involved from the start in WP2, contributing to the analysis of the context and the definition of the trials. Users also play a key role in refining the operational scenarios that drive the trials definition and in feeding the detailed analysis to be done of how the legacy systems are exercised during the trials. Users also play a key role in refining the operational scenarios that drive the trials definition and in feeding the detailed analysis to be done of how the legacy systems are exercised during the trials. This involvement is consolidated into user needs, which are then prioritised to create the functional, interface operational and security requirements as input to the MARISA Toolkit Design phase performed in WP3. The MARISA software development, integration and test activities performed in (WP4, WP5 and WP6) will exploit Agile software development processes supported by a continuous integration environment that eases the management of multiple contributions provided by the project partners in a geographically distributed productive scenario. A relevant integration and validation environment will be provided in WP6 to minimize the risk of toolkit deployment. Operational scenarios will be tested in advance in WP6 using simulated and real data. The MARISA Toolkit will be operationally validated in WP7 according to selected scenarios. The validation will also include an assessment of the metrics and KPIs. ## 4.4. Innovation Management The Figure 4 shows the approach to Innovation Management in the MARISA project. Figure 4: MARISA Innovation Management The User Needs analysis will focus on the organization of “brainstorming meetings”, identification and analysis of the assets through the use of value proposition canvas methodology (who is the customer/s, brainstorming on customer’s needs). Output will be value proposition canvas, preliminary input to business models, input to WP3, WP8 The Competitors and market analysis will involve the Innovation Team as well as WP8 Leader in order to come out with a competitor-feature matrix and an analysis (or re-analysis) of main competitors (value proposition, featured offered, etc.). The Technical improvements analysis will be carried out together with the technical partners and it will deal with active participation and animation of the technical meeting, planning of technical improvements providing also roadmaps for the MARISA solution as well as provision of new version of the assets. Check point meetings will represent moments in the project lifecycle, involving team of technical and nontechnical people with a set of heterogeneous skills, in which to have demo of improved asset, discussion on results of previous activities and refined value proposition and unique selling point (updates, refinement). The Innovation Manager (IM), [Véronique Pevtschin, ENG] ensures the coordination of all Innovation Management activities. Main foreseen tasks of the Innovation Manager are: * Links and refines user needs to monitor and ensure alignment of technology innovation to user needs. * Prepares the exploitation plan taking into account the inputs from the AB and the EB. * Manages the execution of the overall exploitation plan of the project and supports the partners in setting up their individual business plans, in order to exploit the results. * Manages the knowledge produced during the project lifecycle and assesses the opportunity for applying for patents or declaring copyrights, through maintaining all innovations descriptions, screening and ownership. * Supports the individual partners’ legal departments in their drafting of the legal and contractual agreements with respect to the IPR of the project either internally or externally (the IM does not provide the legal consultancy, as this knowledge is part of the internal legal departments, but acts as a support to fully explain the features of the MARISA approach and their requirements on issues such as data exchange, interconnection of solutions with different IPRs and owners etc). MARISA Tasks and Deliverables affected by the Innovation Management activities are reported in the Figure 5. Figure 5: Innovation Managements in the Project Tasks and Deliverables ## 4.5. Schedule and Milestones The Figure 6 shows the master schedule of the different work packages. The whole project duration is 30 months. The project is organized in two main phases each phase ending with trial sessions that validate MARISA through selected operational scenarios. * Phase-1 focuses on the initial construction of the concepts and methods needed for the MARISA Toolkit and delivers and validate a subset of services. * Phase-2 evolves the concepts and methods based on the feedback gained from the first phase, delivers and validate the complete MARISA toolkit. Phase-1 will be completed in 20 months (M1-M20), subsequent Phase-2 will be completed in 10 months (M21-M30). Figure 6: MARISA Gantt Chart The following table includes the Project Milestones, with a short description of their scope and verification means. <table> <tr> <th> ID </th> <th> Milestone name </th> <th> Related work package(s) </th> <th> Estimated date </th> <th> Means of verification </th> </tr> <tr> <td> MS1 </td> <td> Project Start </td> <td> All </td> <td> M01 </td> <td> 1\. Kick-Off meeting. </td> </tr> </table> <table> <tr> <th> ID </th> <th> Milestone name </th> <th> Related work package(s) </th> <th> Estimated date </th> <th> </th> <th> Means of verification </th> </tr> <tr> <td> MS2 </td> <td> Initial Definition of User community needs, Operational trial scenarios and strategy </td> <td> WP2, WP6, WP7, WP8 </td> <td> M05 </td> <td> 1\. 2. 3\. 4\. </td> <td> User Community established. Initial User needs and operational trial scenarios strategy and definition achieved (initial version of D2.2 to D2.7 submitted). Availability of project management, risks, quality plans (D1.1). Establishment of communication and dissemination strategy (D8.1). </td> </tr> <tr> <td> MS3 </td> <td> MARISA Toolkit Initial Design </td> <td> WP3, WP6 </td> <td> M10 </td> <td> 1\. 2\. </td> <td> Initial definition of MARISA toolkit design achieved (initial version of D3.1 to D3.5 submitted). Definition of integration test platform and integration plans achieved (D6.1, D6.2, D6.4 submitted). </td> </tr> <tr> <td> </td> <td> </td> <td> </td> <td> </td> <td> 3\. </td> <td> Detailed plans for operational scenarios achieved (D7.1). </td> </tr> <tr> <td> </td> <td> </td> <td> </td> <td> </td> <td> 4\. </td> <td> Web Site established and populated with dissemination material (D8.2, D8.3). </td> </tr> <tr> <td> </td> <td> </td> <td> </td> <td> </td> <td> 5\. </td> <td> Initial Project Report produced (D1.2 submitted). </td> </tr> <tr> <td> </td> <td> </td> <td> </td> <td> </td> <td> 6\. </td> <td> Initial Societal Ethical Report available (D1.5). </td> </tr> <tr> <td> MS4 </td> <td> MARISA Toolkit completed for Phase-1 Operational Trials </td> <td> WP4, WP5, WP6, WP7 </td> <td> M15 </td> <td> 1\. 2\. </td> <td> MARISA Toolkit completed for Phase 1 (Initial version of D4.1 to D4.3 and D5.1 to D5.5 produced) and in factory validated for Phase-1 Operational Trials (initial version of D6.3, D6.5, D6.6 submitted). Training Kit available (initial version of D8.5). </td> </tr> <tr> <td> MS5 </td> <td> Mid-Term Review </td> <td> WP1, WP2, WP7, WP8 </td> <td> M20 </td> <td> 1\. </td> <td> Phase-1 Operational Trials completed and results available (initial version of D7.2 submitted). </td> </tr> <tr> <td> </td> <td> </td> <td> </td> <td> </td> <td> 2\. </td> <td> Analysis of MARISA Phase-1 achievements and assessment from the User Community available (Initial version of D2.1 produced). </td> </tr> <tr> <td> </td> <td> </td> <td> </td> <td> </td> <td> 3\. </td> <td> Updated Web Site and dissemination material available (D8.2, D8.3 final). </td> </tr> <tr> <td> </td> <td> </td> <td> </td> <td> </td> <td> 4\. </td> <td> First workshop achieved (D8.4), Exploitation plan achieved (D8.6). </td> </tr> <tr> <td> </td> <td> </td> <td> </td> <td> </td> <td> 5\. </td> <td> First standardization report available (D8.8). </td> </tr> <tr> <td> </td> <td> </td> <td> </td> <td> </td> <td> 6\. </td> <td> Final user and operational needs for Phase-2 achieved. (Final version of D2.2 to 2.7 submitted). </td> </tr> <tr> <td> </td> <td> </td> <td> </td> <td> </td> <td> 7\. </td> <td> Intermediate Project and Societal Ethical Reports Available (D1.3, D1.6). </td> </tr> <tr> <td> ID </td> <td> Milestone name </td> <td> Related work package(s) </td> <td> Estimated date </td> <td> </td> <td> Means of verification </td> </tr> <tr> <td> MS6 </td> <td> MARISA Toolkit Completed for Phase-2 Operational Trials </td> <td> WP4, WP5, WP6, WP7 </td> <td> M26 </td> <td> 1\. 2\. </td> <td> Final MARISA toolkit implementation achieved (final version of D3.1 to D3.5, D4.1 to D4.3, D5.1 to D5.4, D6.3 to D6.6 submitted). Detailed plans for phase-2 operational scenarios achieved (final D7.1 produced). </td> </tr> <tr> <td> </td> <td> </td> <td> </td> <td> </td> <td> 3\. </td> <td> Final version of Training Kit available (final D8.5 produced) </td> </tr> <tr> <td> MS7 </td> <td> MARISA Project Completion </td> <td> WP1, WP2, WP7, WP8 </td> <td> M30 </td> <td> 1\. 2\. </td> <td> Phase-2 Operational Trials completed and results available (final version of D7.2). Assessments of MARISA Phase-2 achievements available (final version of D2.1 produced). </td> </tr> <tr> <td> </td> <td> </td> <td> </td> <td> </td> <td> 3\. </td> <td> Exploitation Uptake actions established (D8.7). </td> </tr> <tr> <td> </td> <td> </td> <td> </td> <td> </td> <td> 4\. </td> <td> Standardization reports available (final D8.8). </td> </tr> <tr> <td> </td> <td> </td> <td> </td> <td> </td> <td> 5\. </td> <td> Final Project and Societal Ethical Reports available (D1.3, D1.7). </td> </tr> <tr> <td> </td> <td> </td> <td> </td> <td> </td> <td> 6\. </td> <td> All action deliverables completed and submitted. </td> </tr> </table> Table 8: MARISA Milestones ### 4.5.1. Activity Network Figure 7 shows inter WP dependencies among the nine work packages. WP2 provides the user requirements to WP3 and WP7 for the design of the MARISA toolkit and operational trials. WP7 provides feedback to WP3-6 after the first project phase is finished, which will be used to fine-tune the outcome of the corresponding WPs during the second phase of the project. Figure 7: MARISA Pert-like diagram showing deliverables and inter-WP links ### 4.5.2. Project Review Plan The MARISA Project is split into two reporting periods: * P1: Month 1 – Month 18 (M1 – M18); * P2: Month 19 – Month 30 (M19 – M30); At the end of each period a Project Review Meeting will be held with the participation of the EC Project Officer and independent reviewers appointed by the EC. <table> <tr> <th> Review number </th> <th> Tentative timing </th> <th> Planned Venue of Review </th> </tr> <tr> <td> RV1 </td> <td> 18 </td> <td> Rome </td> </tr> <tr> <td> RV2 </td> <td> 30 </td> <td> Rome </td> </tr> </table> Table 9: Project Reviews ### 4.5.3. List of Deliverables The following table provides the list of the deliverables throughout the project lifecycle. <table> <tr> <th> Del. Num. </th> <th> Deliverable name </th> <th> WP </th> <th> Lead </th> <th> Type </th> <th> Dissem. level </th> <th> Deliv. date </th> </tr> <tr> <td> D1.1 </td> <td> Project Management, Quality and Risk Plan </td> <td> WP1 </td> <td> LDO </td> <td> R </td> <td> PU </td> <td> M5 </td> </tr> <tr> <td> D1.2 </td> <td> MARISA Project Initial Report </td> <td> WP1 </td> <td> LDO </td> <td> R </td> <td> PU </td> <td> M10 </td> </tr> <tr> <td> D1.3 </td> <td> MARISA Project Intermediate Report </td> <td> WP1 </td> <td> LDO </td> <td> R </td> <td> PU </td> <td> M20 </td> </tr> <tr> <td> D1.4 </td> <td> MARISA Project Final Report </td> <td> WP1 </td> <td> LDO </td> <td> R </td> <td> PU </td> <td> M30 </td> </tr> <tr> <td> D1.5 </td> <td> MARISA Societal Ethical Initial Report </td> <td> WP1 </td> <td> LDO </td> <td> R </td> <td> PU </td> <td> M10 </td> </tr> <tr> <td> D1.6 </td> <td> MARISA Societal Ethical Intermediate Report </td> <td> WP1 </td> <td> LDO </td> <td> R </td> <td> PU </td> <td> M20 </td> </tr> <tr> <td> D1.7 </td> <td> MARISA Societal Ethical Final Report </td> <td> WP1 </td> <td> LDO </td> <td> R </td> <td> PU </td> <td> M30 </td> </tr> <tr> <td> D1.8 </td> <td> MARISA Data Management Plan </td> <td> WP1 </td> <td> LDO </td> <td> ORDP </td> <td> CO </td> <td> M6 </td> </tr> <tr> <td> D1.9 </td> <td> MARISA Data Management Plan (final) </td> <td> WP1 </td> <td> LDO </td> <td> ORDP </td> <td> CO </td> <td> M24 </td> </tr> <tr> <td> D2.1 </td> <td> MARISA User Community Report </td> <td> WP2 </td> <td> LAU </td> <td> R </td> <td> PU </td> <td> M20 </td> </tr> <tr> <td> D2.2 </td> <td> MARISA User Requirements </td> <td> WP2 </td> <td> LDO </td> <td> R </td> <td> PU </td> <td> M5 </td> </tr> <tr> <td> D2.3 </td> <td> MARISA Adoption Models </td> <td> WP2 </td> <td> ENG </td> <td> R </td> <td> PU </td> <td> M5 </td> </tr> <tr> <td> D2.4 </td> <td> MARISA Interaction with existing/legacy systems </td> <td> WP2 </td> <td> GMV </td> <td> R </td> <td> CO </td> <td> M5 </td> </tr> <tr> <td> D2.5 </td> <td> MARISA Usage of Additional Data Sources </td> <td> WP2 </td> <td> IOSB </td> <td> R </td> <td> CO </td> <td> M5 </td> </tr> <tr> <td> D2.6 </td> <td> Legal, ethical and societal aspects of MARISA </td> <td> WP2 </td> <td> LAU </td> <td> R </td> <td> PU </td> <td> M5 </td> </tr> <tr> <td> D2.7 </td> <td> MARISA Operational Scenarios and Trials </td> <td> WP2 </td> <td> AST </td> <td> R </td> <td> PU </td> <td> M5 </td> </tr> <tr> <td> D2.8 </td> <td> MARISA User Community Report (final) </td> <td> WP2 </td> <td> LAU </td> <td> R </td> <td> PU </td> <td> M30 </td> </tr> <tr> <td> D2.9 </td> <td> MARISA User Requirements (final) </td> <td> WP2 </td> <td> LDO </td> <td> R </td> <td> PU </td> <td> M20 </td> </tr> <tr> <td> D2.10 </td> <td> MARISA Adoption Models (final) </td> <td> WP2 </td> <td> ENG </td> <td> R </td> <td> PU </td> <td> M20 </td> </tr> <tr> <td> D2.11 </td> <td> MARISA Interaction with existing/legacy systems (final) </td> <td> WP2 </td> <td> GMV </td> <td> R </td> <td> CO </td> <td> M20 </td> </tr> <tr> <td> D2.12 </td> <td> MARISA Usage of Additional Data Sources (final) </td> <td> WP2 </td> <td> IOSB </td> <td> R </td> <td> CO </td> <td> M20 </td> </tr> <tr> <td> D2.13 </td> <td> Legal, ethical and societal aspects of MARISA (final) </td> <td> WP2 </td> <td> LAU </td> <td> R </td> <td> PU </td> <td> M20 </td> </tr> <tr> <td> D2.14 </td> <td> MARISA Operational Scenarios and Trials (final) </td> <td> WP2 </td> <td> AST </td> <td> R </td> <td> PU </td> <td> M20 </td> </tr> <tr> <td> D3.1 </td> <td> MARISA Toolkit Design </td> <td> WP3 </td> <td> GMV </td> <td> R </td> <td> PU </td> <td> M10 </td> </tr> <tr> <td> D3.2 </td> <td> MARISA Services Description Document </td> <td> WP3 </td> <td> LDO </td> <td> R </td> <td> PU </td> <td> M10 </td> </tr> <tr> <td> D3.3 </td> <td> MARISA Interfaces description Document </td> <td> WP3 </td> <td> STW </td> <td> R </td> <td> CO </td> <td> M10 </td> </tr> <tr> <td> D3.4 </td> <td> MARISA data model description </td> <td> WP3 </td> <td> ENG </td> <td> R </td> <td> PU </td> <td> M10 </td> </tr> <tr> <td> D3.5 </td> <td> MARISA Human machine best practices and design document </td> <td> WP3 </td> <td> LUC </td> <td> R </td> <td> PU </td> <td> M10 </td> </tr> <tr> <td> D3.6 </td> <td> MARISA Toolkit Design (final) </td> <td> WP3 </td> <td> GMV </td> <td> R </td> <td> PU </td> <td> M22 </td> </tr> <tr> <td> D3.7 </td> <td> MARISA Services Description Document (final) </td> <td> WP3 </td> <td> LDO </td> <td> R </td> <td> PU </td> <td> M22 </td> </tr> <tr> <td> D3.8 </td> <td> MARISA Interfaces description Document (final) </td> <td> WP3 </td> <td> STW </td> <td> R </td> <td> CO </td> <td> M22 </td> </tr> </table> <table> <tr> <th> Del. Num. </th> <th> Deliverable name </th> <th> WP </th> <th> Lead </th> <th> Type </th> <th> Dissem. level </th> <th> Deliv. date </th> </tr> <tr> <td> D3.9 </td> <td> MARISA data model description (final) </td> <td> WP3 </td> <td> ENG </td> <td> R </td> <td> PU </td> <td> M22 </td> </tr> <tr> <td> D3.10 </td> <td> MARISA Human machine best practices and design document (final) </td> <td> WP3 </td> <td> LUC </td> <td> R </td> <td> PU </td> <td> M22 </td> </tr> <tr> <td> D4.1 </td> <td> MARISA Level 1 Data Fusion Services Description </td> <td> WP4 </td> <td> LDO </td> <td> R </td> <td> PU </td> <td> M12 </td> </tr> <tr> <td> D4.2 </td> <td> MARISA Level 2 Data Fusion Services Description </td> <td> WP4 </td> <td> IOSB </td> <td> R </td> <td> PU </td> <td> M12 </td> </tr> <tr> <td> D4.3 </td> <td> MARISA Level 3 Data Fusion Services Description </td> <td> WP4 </td> <td> TNO </td> <td> R </td> <td> PU </td> <td> M12 </td> </tr> <tr> <td> D4.4 </td> <td> MARISA Level 1 Data Fusion Services Description (final) </td> <td> WP4 </td> <td> LDO </td> <td> R </td> <td> PU </td> <td> M24 </td> </tr> <tr> <td> D4.5 </td> <td> MARISA Level 2 Data Fusion Services Description (final) </td> <td> WP4 </td> <td> IOSB </td> <td> R </td> <td> PU </td> <td> M24 </td> </tr> <tr> <td> D4.6 </td> <td> MARISA Level 3 Data Fusion Services Description (final) </td> <td> WP4 </td> <td> TNO </td> <td> R </td> <td> PU </td> <td> M24 </td> </tr> <tr> <td> D5.1 </td> <td> MARISA Big data Infrastructure </td> <td> WP5 </td> <td> ENG </td> <td> R </td> <td> PU </td> <td> M12 </td> </tr> <tr> <td> D5.2 </td> <td> MARISA Interfaces to External data sources </td> <td> WP5 </td> <td> GMV </td> <td> R </td> <td> CO </td> <td> M12 </td> </tr> <tr> <td> D5.3 </td> <td> MARISA User Interfaces </td> <td> WP5 </td> <td> LUC </td> <td> R </td> <td> PU </td> <td> M12 </td> </tr> <tr> <td> D5.4 </td> <td> MARISA Data Fusion Distribution Services </td> <td> WP5 </td> <td> LDO </td> <td> R </td> <td> PU </td> <td> M12 </td> </tr> <tr> <td> D5.5 </td> <td> MARISA Access Control Services </td> <td> WP5 </td> <td> ADS </td> <td> R </td> <td> CO </td> <td> M12 </td> </tr> <tr> <td> D5.6 </td> <td> MARISA Big data Infrastructure (final) </td> <td> WP5 </td> <td> ENG </td> <td> R </td> <td> PU </td> <td> M24 </td> </tr> <tr> <td> D5.7 </td> <td> MARISA Interfaces to External data sources (final) </td> <td> WP5 </td> <td> GMV </td> <td> R </td> <td> CO </td> <td> M24 </td> </tr> <tr> <td> D5.8 </td> <td> MARISA User Interfaces (final) </td> <td> WP5 </td> <td> LUC </td> <td> R </td> <td> PU </td> <td> M24 </td> </tr> <tr> <td> D5.9 </td> <td> MARISA Data Fusion Distribution Services (final) </td> <td> WP5 </td> <td> LDO </td> <td> R </td> <td> PU </td> <td> M24 </td> </tr> <tr> <td> D5.10 </td> <td> MARISA Access Control Services (final) </td> <td> WP5 </td> <td> ADS </td> <td> R </td> <td> CO </td> <td> M24 </td> </tr> <tr> <td> D6.1 </td> <td> General principles of MARISA test architecture and data integration </td> <td> WP6 </td> <td> ADS </td> <td> R </td> <td> PU </td> <td> M10 </td> </tr> <tr> <td> D6.2 </td> <td> Definition of MARISA integration platforms </td> <td> WP6 </td> <td> PMM </td> <td> R </td> <td> PU </td> <td> M10 </td> </tr> <tr> <td> D6.3 </td> <td> Definition of MARISA Trial Configurations </td> <td> WP6 </td> <td> ADS </td> <td> R/O </td> <td> CO </td> <td> M15 </td> </tr> <tr> <td> D6.4 </td> <td> MARISA Toolkit Integration and validation Test Plan </td> <td> WP6 </td> <td> ADS </td> <td> R </td> <td> CO </td> <td> M10 </td> </tr> <tr> <td> D6.5 </td> <td> MARISA Toolkit Integration and validation test report </td> <td> WP6 </td> <td> ADS </td> <td> R </td> <td> PU </td> <td> M15 </td> </tr> <tr> <td> D6.6 </td> <td> MARISA Toolkit </td> <td> WP6 </td> <td> ADS </td> <td> DEM </td> <td> PU </td> <td> M15 </td> </tr> <tr> <td> D6.7 </td> <td> Definition of MARISA Trial Configurations (final) </td> <td> WP6 </td> <td> ADS </td> <td> R/O </td> <td> CO </td> <td> M26 </td> </tr> <tr> <td> D6.8 </td> <td> MARISA Toolkit Integration and validation Test Plan (final) </td> <td> WP6 </td> <td> ADS </td> <td> R </td> <td> CO </td> <td> M22 </td> </tr> <tr> <td> D6.9 </td> <td> MARISA Toolkit Integration and validation test report (final) </td> <td> WP6 </td> <td> ADS </td> <td> R </td> <td> PU </td> <td> M26 </td> </tr> <tr> <td> Del. Num. </td> <td> Deliverable name </td> <td> WP </td> <td> Lead </td> <td> Type </td> <td> Dissem. level </td> <td> Deliv. date </td> </tr> <tr> <td> D6.10 </td> <td> MARISA Toolkit (final) </td> <td> WP6 </td> <td> ADS </td> <td> DEM </td> <td> PU </td> <td> M26 </td> </tr> <tr> <td> D7.1 </td> <td> MARISA Validation in operational trial approach and plan </td> <td> WP7 </td> <td> STW </td> <td> R </td> <td> PU </td> <td> M10 </td> </tr> <tr> <td> D7.2 </td> <td> MARISA Validation in operational trial approach and plan - Appendix </td> <td> WP7 </td> <td> STW </td> <td> R </td> <td> RE </td> <td> M10 </td> </tr> <tr> <td> D7.3 </td> <td> MARISA Operational trials results Report and Lesson Learnt </td> <td> WP7 </td> <td> STW </td> <td> R </td> <td> CO </td> <td> M20 </td> </tr> <tr> <td> D7.4 </td> <td> MARISA Operational trials results Report and Lesson Learnt - Appendix </td> <td> WP7 </td> <td> STW </td> <td> R </td> <td> RE </td> <td> M20 </td> </tr> <tr> <td> D7.5 </td> <td> MARISA Validation in operational trial approach and plan (final) </td> <td> WP7 </td> <td> STW </td> <td> R </td> <td> PU </td> <td> M20 </td> </tr> <tr> <td> D7.6 </td> <td> MARISA Validation in operational trial approach and plan (final) - Appendix </td> <td> WP7 </td> <td> STW </td> <td> R </td> <td> RE </td> <td> M20 </td> </tr> <tr> <td> D7.7 </td> <td> MARISA Operational trials results Report and Lesson Learnt (final) </td> <td> WP7 </td> <td> STW </td> <td> R </td> <td> CO </td> <td> M30 </td> </tr> <tr> <td> D7.8 </td> <td> MARISA Operational trials results Report and Lesson Learnt (final) – Appendix </td> <td> WP7 </td> <td> STW </td> <td> R </td> <td> RE </td> <td> M30 </td> </tr> <tr> <td> D8.1 </td> <td> MARISA Communication and Dissemination Strategy and Plan </td> <td> WP8 </td> <td> TNO </td> <td> R </td> <td> PU </td> <td> M5 </td> </tr> <tr> <td> D8.2 </td> <td> MARISA Dissemination Material </td> <td> WP8 </td> <td> AST </td> <td> R </td> <td> PU </td> <td> M10 </td> </tr> <tr> <td> D8.3 </td> <td> MARISA WEB Site </td> <td> WP8 </td> <td> AST </td> <td> DEC </td> <td> PU </td> <td> M10 </td> </tr> <tr> <td> D8.4 </td> <td> MARISA Workshop Organization and Results </td> <td> WP8 </td> <td> TNO </td> <td> R/O </td> <td> PU </td> <td> M20 </td> </tr> <tr> <td> D8.5 </td> <td> MARISA Training Kit </td> <td> WP8 </td> <td> AST </td> <td> O </td> <td> PU </td> <td> M15 </td> </tr> <tr> <td> D8.6 </td> <td> MARISA Exploitation Plan </td> <td> WP8 </td> <td> ENG </td> <td> R </td> <td> PU </td> <td> M20 </td> </tr> <tr> <td> D8.7 </td> <td> MARISA Exploitation and Uptake mechanisms </td> <td> WP8 </td> <td> ENG </td> <td> R </td> <td> CO </td> <td> M30 </td> </tr> <tr> <td> D8.8 </td> <td> MARISA Services Standardization Report </td> <td> WP8 </td> <td> LDO </td> <td> R </td> <td> PU </td> <td> M20 </td> </tr> <tr> <td> D8.9 </td> <td> MARISA Dissemination Material (final) </td> <td> WP8 </td> <td> AST </td> <td> R </td> <td> PU </td> <td> M20 </td> </tr> <tr> <td> D8.10 </td> <td> MARISA WEB Site (intermediate) </td> <td> WP8 </td> <td> AST </td> <td> DEC </td> <td> PU </td> <td> M20 </td> </tr> <tr> <td> D8.11 </td> <td> MARISA Workshop Organization and Results (final) </td> <td> WP8 </td> <td> TNO </td> <td> R/O </td> <td> PU </td> <td> M30 </td> </tr> <tr> <td> D8.12 </td> <td> MARISA Training Kit (final) </td> <td> WP8 </td> <td> AST </td> <td> O </td> <td> PU </td> <td> M26 </td> </tr> <tr> <td> D8.13 </td> <td> MARISA Services Standardization Report (final) </td> <td> WP8 </td> <td> LDO </td> <td> R </td> <td> PU </td> <td> M30 </td> </tr> <tr> <td> D8.14 </td> <td> MARISA WEB Site (final) </td> <td> WP8 </td> <td> AST </td> <td> DEC </td> <td> PU </td> <td> M30 </td> </tr> <tr> <td> D9.1 </td> <td> H – Requirement No.4 </td> <td> WP9 </td> <td> LDO </td> <td> ETH </td> <td> CO </td> <td> M9 </td> </tr> <tr> <td> D9.2 </td> <td> POPD – Requirement No.6 </td> <td> WP9 </td> <td> LDO </td> <td> ETH </td> <td> CO </td> <td> M9 </td> </tr> <tr> <td> D9.3 </td> <td> POPD – Requirement No.8 </td> <td> WP9 </td> <td> LDO </td> <td> ETH </td> <td> CO </td> <td> M9 </td> </tr> <tr> <td> D9.4 </td> <td> POPD – Requirement No.9 </td> <td> _WP9_ </td> <td> _LDO_ </td> <td> ETH </td> <td> CO </td> <td> M9 </td> </tr> </table> Table 10: MARISA Deliverables List ## 4.6. Collaboration among partners Due to the nature of the project, an efficient and effective communication and knowledge-flow among the partners is very important. A deliverable describing the complete communication and dissemination strategy for the MARISA project is going to be delivered in M5. Regarding collaboration among partners, the following arrangements will be made to ensure optimum sharing of knowledge within the consortium. ### 4.6.1. Collaboration Tools #### _4.6.1.1. Web Site_ A fully accessible project portal will be developed, with users’ customized view and public as well as consortium-internal areas. The project web site will support the communication among the project members, as well as maintenance of a common repository of the documentation. #### _4.6.1.2. Slack_ Slack is a cloud-based set of team collaboration tools and services. Slack teams allow communities, groups, or teams to join through a specific URL or invitation sent by a team admin or owner. A Slack Environment has been already opened in the context of WP2 activities, with a specific focus on the MARISA User Requirements gathering phase, in order to support “dynamics” between participants (EndUsers and Practitioners). At this purpose, it has been created one slack channel per Trial involving End-users and providers, encouraging discussions as well as exchanging of documentation among participants. ### 4.6.2. Project Meetings * Internal project meetings will serve as the main forum for interactions between all groups and for reviews preparation. Project internal meetings will be more telephone conferences instead of physical meetings. * Telephone conferences will be held at least every months and physical meetings will be held at least every 3 months. WP internal/cross WP meetings will be held when requested via telephone or physical if needed. Video conference may be used, although some partners may not have the facility, and may not be suitable if more than two partners need to be involved. * Integration meetings are also foreseen at the end of integration activities to assess the readiness of the MARISA Toolkit to operational trials. * We plan three Advisory Board meetings to take place together with the achievement of main project milestones: MS3 (M10) toolkit design, MS5 (M20) Mid-Term Review, MS7 (M30) Final Review. * Innovation meetings are planned every 3 months in the first year of the project (4 in total). * Technical Board meetings are planned about every 6 month. * Executive Board meetings are planned at least every 6 months or on request. ##### 4.6.2.1. Meetings Plan The following figure provides an overall view of the project meetings plan. This plan does not include the Formal Project reviews, that have been described in the related paragraph 4.5.2. Figure 8: Project Meetings Master Schedule ## 4.7. Involvement of end-users external to the Consortium Community mechanisms will be created (as part of the MARISA Web Site implemented in WP8) to foster interactions, leading to knowledge co-created through social interactions, competence sharing and collective service development. The User Community will be set-up involving user partners as well as external end users invited to join the initiative. In this context, MARISA aims to be as inclusive as possible, building also on the links of individual consortium partners of the MARISA consortium to past and on-going initiatives in the domain (CLOSEYE, EUCISE2020, SeaBILLA, PERSEUS, CoopP etc.) to speed up the process. The User Community includes partners, practitioners involved as full partners, all the associated partners (the organizations that have already expressed interest and are supporting the objectives of the project maritime domain experts, external end users). However the consortium is always open to accept other members during the execution of the project. It addresses user needs, operational requirements, potential ethical and societal issues. It is coordinated and animated by the User Community Leader. # 5\. Configuration Management Configuration Management deals with the overall project consistency, identification and tracking of changes related to all project results including the deliverables, documents, testing procedures and any other related activity. ## 5.1. Document Configuration Management Document configuration management will be ensured through the tracking of the versions and history of changes of the various project documents (deliverables, meeting minutes, reviewed documents, etc.). Document history will be tracked in each deliverable in a separate table describing the different versions of the document and the reasons of change/updates on it. Each deliverable main author will be responsible for updating this. ## 5.2. Software Configuration Management The software components monitoring, will be done using a software version configuration tool (CVS, SVN etc.), which will be installed on a central Server. This will ensure that all necessary components of the MARISA Toolkit will be available for the distributed development teams. ## 5.3. E-Mail Conventions E-mail will be an important means to exchange information in the MARISA project. All E-mail subject headings must start with the text “[MARISA]”. Additional tags can be added to specify relevant work packages, tasks, and deliverables where appropriate, and if deemed useful. The tags should never contain spaces within the square brackets. Some examples of email subject headings are: * [MARISA] [WP8] Title * [MARISA] [WP1] [Task1.2] [D1.4] Title…….. document * [MARISA] [WP2] Title * [MARISA] [WP4] [Task4.3] Title # 6\. Quality Management ## 6.1. Quality Assurance Process Quality control is responsibility of everybody involved in the each project activity. The quality control task performed by the Coordinator at project level will not substitute for internal quality control used in the various partner organisations for their internal work. All partner organisations should ensure that their existing internal quality control procedures are applied to MARISA project tasks. However, as part of their role, the Project Coordinator, the Project Manager, the Innovation Manager and the Technical Board will act as Project Quality Assurance Team. Objectives of the Project Quality Assurance Team are: * to ensure appropriate application of the procedures in MARISA; * to control the main outputs (mainly documents) of the Project/Work Packages & organising reviews. With reference to Project Deliverables : each project deliverable is assigned to one leading responsible partner. This partner takes the responsibility that the deliverable is of high quality and timely delivered. The responsible partner assures that the content of a deliverable is consistent with the team-workings of the deliverable and that the particular objectives related to the goals of the project are met. Any issues related to deliverables, endangering the success of the work package or the project, have to be reported by the WP leader immediately to the Project Management and discussed within the Coordination team. ### 6.1.1. Reviews for Documentation/Deliverables A Reviews Process involving each partner and selected reviewers is adopted in the Consortium to ensure the quality of deliverables and of any other external publication with regard to the technical content, the objectives of the project and to adhere to formal requirements established in the Grant and Consortium Agreements. Review process ensures that publications and deliverables comply with IPR of the partners. For external publications as well as for project deliverables, the review process will involve all Consortium partners and requires the approval of the Project Quality Assurance Team. Project documentation will be reviewed against the following criteria regarding form as well as content of the document: * Format of the document according to the document templates. * Identification and correction of typing mistakes, etc. * Check of consistency: * with the overall scope of the document (e.g. it contains the right information, avoiding unnecessary information, etc.); * with previous relevant documentation (e.g. technical specifications vs requirements definition, no redundancy with other documents, etc.). * Technical aspects of the documentation will be reviewed also by the Project Quality Assurance Team in order to ensure that the document meets the technical goals of the project, and that all technical information is advancing the current state of the art and the recent technological research level. The procedures and timeline for the review project documentation are described hereafter. * The partner responsible for preparing the deliverable, drafts a Table of Contents (ToC), assigns tasks to all involved partners and sets the respective deadlines (considering also time needed for quality review). * Involved partners provide their feedback within the deadlines and the responsible partner prepares the first draft of the document. * This draft is sent to the entire consortium for comments and improvements/additions. The feedback period for project partners should last at least five working days. Feedback is sent directly to the responsible partner who revises the document and prepares the semi-final version. * The Quality Control Process begins based on the semi-final version of the deliverable. This version has to be ready no later than 20 working days before the final deadline. At least two Internal Reviewers have been assigned in advance (refer to the reviewers table). * The Internal Reviewers send their comments (by five working days) to the Project Quality Assurance Team who consolidates and checks the reports and sends them to the partner responsible. * This partner responsible for preparing the deliverable then improves the document based on received comments. In case the comments/suggestions cannot be realised, the reasons for this must be documented. If necessary (i.e. if there are too many comments on the first round), another round of comments from the Internal Reviewers takes place. * The partner responsible addresses them appropriately and prepares the final version of the document, which is sent to the Project Coordinator (at least five days before the final deadline). * The Project Coordinator then submits the document to the EC. Figure 9: MARISA Deliverable Preparation and Quality Review Process - Flow Figure 10: MARISA Deliverable Preparation and Quality Review Process \- Timeline ## 6.2. Deliverables Item List and Internal Reviewers <table> <tr> <th> Del. Num. </th> <th> Deliverable name </th> <th> Lead </th> <th> Type </th> <th> Int. Rev/er #1 </th> <th> Int. Rev/er #2 </th> <th> Delivery date </th> </tr> <tr> <td> D1.1 </td> <td> Project Management, Quality and Risk Plan </td> <td> LDO </td> <td> R </td> <td> ENG </td> <td> GMV </td> <td> M5 </td> </tr> </table> <table> <tr> <th> Del. Num. </th> <th> Deliverable name </th> <th> Lead </th> <th> Type </th> <th> Int. Rev/er #1 </th> <th> Int. Rev/er #2 </th> <th> Delivery date </th> </tr> <tr> <td> D1.2 </td> <td> MARISA Project Initial Report </td> <td> LDO </td> <td> R </td> <td> STW </td> <td> ENG </td> <td> M10 </td> </tr> <tr> <td> D1.3 </td> <td> MARISA Project Intermediate Report </td> <td> LDO </td> <td> R </td> <td> GMV </td> <td> ADS </td> <td> M20 </td> </tr> <tr> <td> D1.4 </td> <td> MARISA Project Final Report </td> <td> LDO </td> <td> R </td> <td> ADS </td> <td> STW </td> <td> M30 </td> </tr> <tr> <td> D1.5 </td> <td> MARISA Societal Ethical Initial Report </td> <td> LDO </td> <td> R </td> <td> LAU </td> <td> ENG </td> <td> M10 </td> </tr> <tr> <td> D1.6 </td> <td> MARISA Societal Ethical Intermediate Report </td> <td> LDO </td> <td> R </td> <td> LAU </td> <td> ENG </td> <td> M20 </td> </tr> <tr> <td> D1.7 </td> <td> MARISA Societal Ethical Final Report </td> <td> LDO </td> <td> R </td> <td> LAU </td> <td> ENG </td> <td> M30 </td> </tr> <tr> <td> D1.8, D1.9 </td> <td> MARISA Data Management Plan (initial, final) </td> <td> LDO </td> <td> ORDP </td> <td> ENG </td> <td> GMV </td> <td> M6, M24 </td> </tr> <tr> <td> D2.1, D2.8 </td> <td> MARISA User Community Report (initial, final) </td> <td> LAU </td> <td> R </td> <td> ADS </td> <td> INOV </td> <td> M20, M30 </td> </tr> <tr> <td> D2.2, D2.9 </td> <td> MARISA User Requirements (initial, final) </td> <td> LDO </td> <td> R </td> <td> CMRE </td> <td> IW </td> <td> M5, M20 </td> </tr> <tr> <td> D2.3, D2.10 </td> <td> MARISA Adoption Models (initial, final) </td> <td> ENG </td> <td> R </td> <td> LUC </td> <td> TNO </td> <td> M5, M20 </td> </tr> <tr> <td> D2.4, D2.11 </td> <td> MARISA Interaction with existing/legacy systems (initial, final) </td> <td> GMV </td> <td> R </td> <td> EG </td> <td> ADS </td> <td> M5, M20 </td> </tr> <tr> <td> D2.5, D2.12 </td> <td> MARISA Usage of Additional Data Sources (initial, final) </td> <td> IOSB </td> <td> R </td> <td> TNO </td> <td> STW </td> <td> M5, M20 </td> </tr> <tr> <td> D2.6, D2.13 </td> <td> Legal, ethical and societal aspects of MARISA (initial, final) </td> <td> LAU </td> <td> R </td> <td> IW </td> <td> PLA </td> <td> M5, M20 </td> </tr> <tr> <td> D2.7, D2.14 </td> <td> MARISA Operational Scenarios and Trials (initial, final) </td> <td> AST </td> <td> R </td> <td> STW </td> <td> PMM </td> <td> M5, M20 </td> </tr> <tr> <td> D3.1, D3.6 </td> <td> MARISA Toolkit Design (initial, final) </td> <td> GMV </td> <td> R </td> <td> TNO </td> <td> INOV </td> <td> M10, M22 </td> </tr> <tr> <td> D3.2, D3.7 </td> <td> MARISA Services Description Document (initial, final) </td> <td> LDO </td> <td> R </td> <td> ADS </td> <td> TNO </td> <td> M10, M22 </td> </tr> <tr> <td> D3.3, D3.8 </td> <td> MARISA Interfaces description Document (initial, final) </td> <td> STW </td> <td> R </td> <td> IOSB </td> <td> LAU </td> <td> M10, M22 </td> </tr> <tr> <td> D3.4, D3.9 </td> <td> MARISA data model description (initial, final) </td> <td> ENG </td> <td> R </td> <td> IW </td> <td> ADS </td> <td> M10, M22 </td> </tr> <tr> <td> D3.5, D3.10 </td> <td> MARISA Human machine best practices and design document (initial, final) </td> <td> LUC </td> <td> R </td> <td> LAU </td> <td> IW </td> <td> M10, M22 </td> </tr> <tr> <td> D4.1, D4.4 </td> <td> MARISA Level 1 Data Fusion Services Description (initial, final) </td> <td> LDO </td> <td> R </td> <td> AST </td> <td> EG </td> <td> M12, M24 </td> </tr> <tr> <td> D4.2, D4.5 </td> <td> MARISA Level 2 Data Fusion Services Description (initial, final) </td> <td> IOSB </td> <td> R </td> <td> ENG </td> <td> UNIBO </td> <td> M12, M24 </td> </tr> <tr> <td> D4.3, D4.6 </td> <td> MARISA Level 3 Data Fusion Services Description (initial, final) </td> <td> TNO </td> <td> R </td> <td> ADS </td> <td> CMRE </td> <td> M12, M24 </td> </tr> <tr> <td> D5.1, D5.6 </td> <td> MARISA Big data Infrastructure (initial, final) </td> <td> ENG </td> <td> R </td> <td> STW </td> <td> EG </td> <td> M12, M24 </td> </tr> <tr> <td> D5.2, D5.7 </td> <td> MARISA Interfaces to External data sources (initial, final) </td> <td> GMV </td> <td> R </td> <td> TNO </td> <td> INOV </td> <td> M12, M24 </td> </tr> </table> <table> <tr> <th> Del. Num. </th> <th> Deliverable name </th> <th> Lead </th> <th> Type </th> <th> Int. Rev/er #1 </th> <th> Int. Rev/er #2 </th> <th> Delivery date </th> </tr> <tr> <td> D5.3, D5.8 </td> <td> MARISA User Interfaces (initial, final) </td> <td> LUC </td> <td> R </td> <td> IOSB </td> <td> LAU </td> <td> M12, M24 </td> </tr> <tr> <td> D5.4, D5.9 </td> <td> MARISA Data Fusion Distribution Services (initial, final) </td> <td> LDO </td> <td> R </td> <td> PMM </td> <td> STW </td> <td> M12, M24 </td> </tr> <tr> <td> D5.5, D5.10 </td> <td> MARISA Access Control Services (initial, final) </td> <td> ADS </td> <td> R </td> <td> EG </td> <td> TNO </td> <td> M12, M24 </td> </tr> <tr> <td> D6.1 </td> <td> General principles of MARISA test architecture and data integration </td> <td> ADS </td> <td> R </td> <td> LDO </td> <td> GMV </td> <td> M10 </td> </tr> <tr> <td> D6.2 </td> <td> Definition of MARISA integration platforms </td> <td> PMM </td> <td> R </td> <td> ENG </td> <td> STW </td> <td> M10 </td> </tr> <tr> <td> D6.3, D6.7 </td> <td> Definition of Trial Configurations (initial, final) </td> <td> ADS </td> <td> R/O </td> <td> IOSB </td> <td> IW </td> <td> M15, M26 </td> </tr> <tr> <td> D6.4, D6.8 </td> <td> MARISA Toolkit Integration and validation Test Plan ( initial, final) </td> <td> ADS </td> <td> R </td> <td> GMV </td> <td> ENG </td> <td> M10, M22 </td> </tr> <tr> <td> D6.5, D6.9 </td> <td> MARISA Toolkit Integration and validation test report (initial, final) </td> <td> ADS </td> <td> R </td> <td> STW </td> <td> LDO </td> <td> M15, M26 </td> </tr> <tr> <td> D6.6, D6.10 </td> <td> MARISA Toolkit </td> <td> ADS </td> <td> DEM </td> <td> IW </td> <td> LUC </td> <td> M15, M26 </td> </tr> <tr> <td> D7.1, D7.5 </td> <td> MARISA Validation in operational trial approach and plan (initial, final) </td> <td> STW </td> <td> R </td> <td> LDO </td> <td> GMV </td> <td> M10, M20 </td> </tr> <tr> <td> D7.2, D7.6 </td> <td> MARISA Validation in operational trial approach and plan – Appendix (initial, final) </td> <td> STW </td> <td> R </td> <td> LDO </td> <td> GMV </td> <td> M10, M20 </td> </tr> <tr> <td> D7.3, D7.7 </td> <td> MARISA Operational trials results Report and Lesson Learnt (initial, final) </td> <td> STW </td> <td> R </td> <td> ENG </td> <td> ADS </td> <td> M20, M30 </td> </tr> <tr> <td> D7.4, D7.8 </td> <td> MARISA Operational trials results Report and Lesson Learnt – Appendix (initial, final) </td> <td> STW </td> <td> R </td> <td> ENG </td> <td> ADS </td> <td> M20, M30 </td> </tr> <tr> <td> D8.1 </td> <td> MARISA Communication and Dissemination Strategy and Plan </td> <td> TNO </td> <td> R </td> <td> GMV </td> <td> PLA </td> <td> M5 </td> </tr> <tr> <td> D8.2, D8.9 </td> <td> MARISA Dissemination Material (initial, final) </td> <td> AST </td> <td> R </td> <td> EG </td> <td> INOV </td> <td> M10, M20 </td> </tr> <tr> <td> D8.3, D8.10, D8.14 </td> <td> MARISA WEB Site (initial, intermediate, final) </td> <td> AST </td> <td> DEC </td> <td> LUC </td> <td> UNIBO </td> <td> M10, M20, M30 </td> </tr> <tr> <td> D8.4, D8.11 </td> <td> MARISA Workshop Organization and Results </td> <td> TNO </td> <td> R/O </td> <td> CMRE </td> <td> EG </td> <td> M20, M30 </td> </tr> <tr> <td> D8.5, D8.12 </td> <td> MARISA Training Kit (initial, final) </td> <td> AST </td> <td> O </td> <td> INOV </td> <td> LAU </td> <td> M15, M26 </td> </tr> <tr> <td> D8.6 </td> <td> MARISA Exploitation Plan </td> <td> ENG </td> <td> R </td> <td> STW </td> <td> GMV </td> <td> M20 </td> </tr> <tr> <td> D8.7 </td> <td> MARISA Exploitation and Uptake mechanisms </td> <td> ENG </td> <td> R </td> <td> PLA </td> <td> CMRE </td> <td> M30 </td> </tr> <tr> <td> D8.8, D8.13 </td> <td> MARISA Services Standardization Report </td> <td> LDO </td> <td> R </td> <td> UNIBO </td> <td> STW </td> <td> M20, M30 </td> </tr> <tr> <td> D9.1 </td> <td> H – Requirement No.4 </td> <td> LDO </td> <td> ETH </td> <td> LAU </td> <td> N/A </td> <td> M9 </td> </tr> <tr> <td> Del. Num. </td> <td> Deliverable name </td> <td> Lead </td> <td> Type </td> <td> Int. Rev/er #1 </td> <td> Int. Rev/er #2 </td> <td> Delivery date </td> </tr> <tr> <td> D9.2 </td> <td> POPD – Requirement No.6 </td> <td> LDO </td> <td> ETH </td> <td> LAU </td> <td> N/A </td> <td> M9 </td> </tr> <tr> <td> D9.3 </td> <td> POPD – Requirement No.8 </td> <td> LDO </td> <td> ETH </td> <td> LAU </td> <td> N/A </td> <td> M9 </td> </tr> <tr> <td> D9.4 </td> <td> POPD – Requirement No.9 </td> <td> LDO </td> <td> ETH </td> <td> LAU </td> <td> N/A </td> <td> M9 </td> </tr> </table> Table 11: Deliverables Item Reviewers # 7\. Risk Management ## 7.1. Risk Management Process The Risk Management Process has started during the proposal preparation and has been assessed at Project Kick-Off. The following steps are foreseen: * initial brainstorming and preliminary risk identification; * preparation of the Project Risk Register. Every entry of the Risk register provides the evaluation (High, Medium, Low), of the likeliness of the risk (“L.” column), and the impact of the consequences (“C.” column); * for each Risk Item in the Risk register, a mitigation action will be identified; * every three months an assessment of the Risk register will be performed, which will result in the update of the Risk register; * every six months the Risk Analysis will be performed during the Executive Board; * if, during the Risk register assessment (i.e. every three months), a given Risk Item will present a high like hood or a high consequence, and if the mitigation action did not produce any results, a specific meeting will be called to discuss the risk item and update the mitigation action as needed. Several risks have been already identified. The following table presents the current risk identification for various work packages. For each item, it is included the Description of the Risk, the Mitigation Action, The Like hood and Consequence. <table> <tr> <th> Task/WP </th> <th> Description of risk </th> <th> Mitigation action </th> <th> L </th> <th> C </th> </tr> <tr> <td> WP1, Management </td> <td> General project management risk: insufficient resources and personnel committed to the project. Partner being in difficulties (company reorganization), partner withdrawal </td> <td> Raise the issue urgently with higher level management in partner organization, ask EB to proposed solutions, in case of withdrawal replace partner. </td> <td> L </td> <td> H </td> </tr> <tr> <td> WP2, WP3 Requirement definition and standards </td> <td> If the requirements are not precise, there is risk that the implementation of the components suffers a general slack that would compromise effectiveness of the toolkit. </td> <td> This risk is mitigated by the SE approach adopted for the MARISA design and development, that makes use of a Model Based Systems Engineering and of a “Architectural Framework” as well by the adoption of the Agile development with the practitioners involved as full partners </td> <td> M </td> <td> M </td> </tr> <tr> <td> WP3, Interfaces </td> <td> Interaction with operational border surveillance systems could pose two difficulties: a) security restrictions can prevent a seamless integration with the network envisaged in MARISA, b) daily operations (planned or unplanned) of existing civil and military systems could delay the tests envisaged in MARISA </td> <td> Mitigation action is twofold: a) early engagement with the final users responsible of the current border surveillance operational systems to assess the security restrictions. It is highlighted that these practitioners are already involved in MARISA as full partners, b) flexible contingence plan: in case one of these systems (for operational reasons) becomes unavailable during a certain period of the demonstration an alternative time slot is envisaged </td> <td> H </td> <td> M </td> </tr> </table> <table> <tr> <th> Task/WP </th> <th> Description of risk </th> <th> Mitigation action </th> <th> L </th> <th> C </th> </tr> <tr> <td> WP3, WP4 and WP5, Technical solution </td> <td> There is technical risk in the design phase of MARISA solution that shall be only detected at verification/validation time. </td> <td> Trade-off studies planned during the development phase (e.g. prototypes, mock- ups, simulations) These help in mitigating the risk by analyzing early the potential technical problems. Moreover the iterative approach will further mitigate this risk. </td> <td> M </td> <td> M </td> </tr> <tr> <td> WP5, risk in software development </td> <td> Delay in testing and validation of the software </td> <td> MARISA relies on software components that are already existing and largely tested, integrated with new software development. Then, the complexity of the software to be developed is reduced. Delays will affect individual components and not the system as a whole. </td> <td> L </td> <td> H </td> </tr> <tr> <td> WP6, integration </td> <td> The major risk at the integration phase is clearly that a number of components do not match the agreed specification, functionalities or simply interfaces. </td> <td> Thanks to SE methods, the complexity of the development would be kept at manageable level. The possibility to test in advance the MARISA toolkit in synthetic environment reduces the risk to have troubles during the physical trials. </td> <td> M </td> <td> M </td> </tr> <tr> <td> WP6, integration </td> <td> Delays for components provided by WP4 and 5 </td> <td> The integration is made in an incremental way, starting by an initial version to test the interfaces. Any time a version of the components is stabilized, it is tested in the integration platform to avoid bottlenecks and identify the problems early. </td> <td> M </td> <td> L </td> </tr> <tr> <td> WP6, integration </td> <td> Components not stabilized or not mature for integration </td> <td> All development WP will test and qualify the components prior to delivery to WP6. </td> <td> L </td> <td> M </td> </tr> <tr> <td> WP7, WP2 </td> <td> Risk in the availability of real sensible data set to sustain the trials impact </td> <td> Joint definition of trials during WP2, reduces the risk for misalignment between trials objectives and practitioners data availability </td> <td> M </td> <td> M </td> </tr> <tr> <td> WP7, WP2 </td> <td> Demonstrated solution not fully in line with end user constraints </td> <td> End users involvement in all project phases. High number of end users with dedicated budget ensures high average level of consultation in due time. WP7 will be performed in two phases, 1) with the toolkit providing a subset of services, 2) with the toolkit in its final configuration. The feedback of the first phase will be analyzed and incorporated in the final MARISA toolkit. </td> <td> L </td> <td> H </td> </tr> <tr> <td> WP7 </td> <td> System failure during trials </td> <td> The components will be tested in laboratories and in a controlled environment by technical partners to ensure a minimal operation of the system at the start of each trial phase. More, the trials performance in 2 phases permits possible malfunctions to be identified at early stage. </td> <td> M </td> <td> M </td> </tr> <tr> <td> Task/WP </td> <td> Description of risk </td> <td> Mitigation action </td> <td> L </td> <td> C </td> </tr> <tr> <td> WP8, dissemination </td> <td> Delay in dissemination and exploitation of the results due to a disagreement about IPR </td> <td> MARISA has nominated an Innovation Manager in the management structure in order to advise about all IP issues and propose fair solution </td> <td> M </td> <td> M </td> </tr> </table> Table 12: Risk Identification ## 7.2. Risk Areas The following Risk Areas will be taken into account as reference during the Risk Analysis. <table> <tr> <th> Risk Areas </th> <th> Low </th> <th> Medium </th> <th> High </th> </tr> <tr> <th> 1 </th> <th> 2 </th> <th> 3 </th> </tr> <tr> <td> _Technology _ </td> <td> Developed & used in other projects in maritime or other sectors </td> <td> Technology qualified not in use. </td> <td> Only investigation work, new technology </td> </tr> <tr> <td> _Standards _ </td> <td> Developed & used in other projects in maritime or other sectors </td> <td> Experience in use of applicable Standards not consolidated </td> <td> Only investigation work, no application, new Standard </td> </tr> <tr> <td> _Requirements _ </td> <td> Well defined, no modification risks or user uncertainty. Some minor areas needs requirements definition </td> <td> For some critical areas the requirements are not complete and aligned </td> <td> Critical requirements imprecise or unreachable </td> </tr> <tr> <td> _External Interface Definition _ </td> <td> Well-established External Interfaces definition, with only minor areas to be defined </td> <td> The External Interfaces definition is not fully consolidated, with many areas to be defined </td> <td> The External Interfaces definition needs to be largely established during the project </td> </tr> <tr> <td> _Complexity of Integration and _ _validation _ </td> <td> Early definition and baseline of Requirements and External Interface. Low coupling between S/W component. Availability of Integration/ validation Platform </td> <td> Requirements and External Interface baseline with some areas to be defined. Medium coupling between S/W component. Availability of Integration/ validation Platform </td> <td> No Requirements and External ICD baseline. High coupling between S/W component. Insufficient availability of Integration/Validation Platform </td> </tr> <tr> <td> _Parallel development _ </td> <td> Build approach with no parallel development between builds and adequate time allocated to development </td> <td> Build approach with low parallel development between builds and adequate time allocated </td> <td> Build approach with high parallel development between build with no margin on schedule (all activities are on critical path) </td> </tr> <tr> <td> Risk Areas </td> <td> Low </td> <td> Medium </td> <td> High </td> </tr> <tr> <td> 1 </td> <td> 2 </td> <td> 3 </td> </tr> <tr> <td> _People Motivation & Commitment _ </td> <td> All resources required for project are motivated an committed for the project success </td> <td> Limited Motivation and commitment </td> <td> Team completely non motivated and committed </td> </tr> <tr> <td> _Schedule _ _Constraints _ </td> <td> Schedule with critical path, no particular schedule shifting forecast </td> <td> Schedule under control with precise critical areas, some criticality may create schedule impacts </td> <td> Schedule very critical and/or unfeasible based S/W size and complexity </td> </tr> <tr> <td> _Supplier _ </td> <td> Supplier already successfully involved in previous project </td> <td> Supplier not involved in previous project </td> <td> Supplier not involved in previous project or that in previous project had major criticality in schedule and/or in performances </td> </tr> <tr> <td> _Project Staffing _ </td> <td> Project understaffed in number or in expertise </td> <td> Project staffing very insufficient in expertise or in number </td> <td> Project staffing very insufficient both in number and in expertise </td> </tr> <tr> <td> _SW/HW _ _Availability _ </td> <td> Insufficient number of SW licenses or work station </td> <td> Medium sharing of SW licenses or work station </td> <td> Total lack of SW licenses or work station </td> </tr> <tr> <td> _Communication _ </td> <td> Low lack of communication inside the project team </td> <td> Medium lack of communication inside the project team </td> <td> Total lack of communication inside the project team </td> </tr> </table> Table 13: Risk Areas # 8\. Data Management The MARISA Data Management Plan will describe the data management life cycle for the data to be collected, processed and/or generated by the MARISA project. Information about 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) will be described in the D1.8 Data Management Plan due in M6.	https://phaidra.univie.ac.at/o:1140797	Horizon 2020
0035_INTERACT_730938.md	# Introduction ## Background and motivation INTERACT research stations generate data as a result of long-term environmental monitoring programmes and shorter term research projects. Currently more than 75 research stations located throughout arctic and northern alpine areas are part of the INTERACT network (Figure 1). Among the scientific disciplines practiced in the network are climatology, geoscience, biology, ecology, cryospheric science, and to some extent anthropology. These activities can be organised by the station itself or by external scientists. In addition, research stations often archive relevant data from external sources (usually meteorological observations, photos, reports, maps). Such heterogeneous data generating activities, combined with lacking structured data management practices at the stations, result in data archived at multiple locations for individual stations. Current INTERACT data repositories include research stations’ archives, local archives (e.g. municipal authorities), national archives (e.g. meteorological institutes), archives of international, single discipline networks (e.g. CALM), EU repositories (e.g. SIOS Knowledge Centre), pan-Arctic/regional repositories (e.g. SAON), and global repositories (e.g. Pangaea, GTN-P). Far too often, research project data stays with the research project leader and is not shared according to SAON/IASC, EU, OECD, WMO and GEOSS recommendations. Furthermore, most stations lack the interoperability interfaces necessary to actively engage in national and international data exchange and management activities coordinated through international programmes (e.g. EU, WMO, ICSU, GEO etc.). Also, no unified interface for INTERACT datasets curretly exists that could help INTERACT achieve a domain data repository role. The consequence is underutilisation of existing and future monitoring capabilities, as well as INTERACT as a contribution to the scientific toolbox. However, through the application of accepted documentation and exchange standards, INTERACT can become a valuable asset in network gap analysis performed in various communities (e.g. WMO Observation Systems Capability Analysis and Review tool (OSCAR) surface supporting GAW and GCW). The improvements to data management practices would make INTERACT data FAIR : findable, accessible, interoperable, and re- useable. The data management work package of INTERACT aims to increase the data interoperability among stations and towards external data consumers by defining needs for generating common standards and data dissemination strategies. The benefit of such a process is increased visibility and potentially impact for INTERACT stations. The purpose of the data management plan is to describe the basic principles how the data generated by the project is handled during and after the project. This includes standards and generation of discovery and use metadata, data sharing and preservation and life cycle management; i.e. by following the principles outlined by the _Open Research Data Pilot_ and _The FAIR Guiding Principles for scientific data management and_ _stewardship_ (Wilkinson et al. 2016). However, INTERACT is a heterogeneous community and full implementation of data management at stations is not in the budget. Thus the primary objectives of this Data Management Plan is to initiate a process that at some time will lead to a unified view of the INTERACT data. This is achieved through dialogue with station managers, description of best practises and linking stations and data centres where stations do not want to manage data themselves. This document is a living document that will be updated during the project. _1.2.Organisation of the plan _ This plan is based on the _template_ provided by the UK Digital Curation Centre (DMP Online). This approach is recommended by _OpenAIRE_ _guidelines_ . # Admin details <table> <tr> <th> Project Name </th> <th> INTERACT </th> </tr> <tr> <td> Funding </td> <td> EU HORIZON 2020 Research and Innovation Programme </td> </tr> <tr> <td> Partners </td> <td> * Lund University (LU) SE * University of Sheffield (USFD) UK * University of Copenhagen (UCPH) DK * University of Oulu (UOULU) FI * Aarhus University (AU) DK * CLU srl (CLU) IT * Alfred Wegener Institute for Polar and Marine Research (AWI) DE * Norwegian Polar Institute (NPI) NO * Natural Environment Research Council (NERC) UK * Tomsk State University (TSU) RU * University of South Bohemia in Ceske Budejovice (USB) CZ * Swedish Polar Research Secretariat (SPRS) SE * Norwegian Institute for Agricultural and Environ. Research (NIBIO) NO * Stockholm University (SU) SE  University of Helsinki (UH) FI * Greenland Institute of Natural Resources (GINR) GL * Polish Academy of Sciences - Geophysics dept. IGF-PAS PL * University of Turku (UTU) FI * University of Oslo (UiO) NO * Natural Resources Institute Finland (LUKE) FI * Russian Academy of Sciences - Siberian Branch (IBPC) RU * M V Lomonosov Moscow State University (MSU) RU  Swedish University of Agricultural Sciences (SLU) SE * Zentralanstalt für Meteorologie und Geodynamik (ZAMG) AT * University of Innsbruck (LFU) AT * Yugra State University (YSU) RU * Faroe Islands Nature Investigation (JF) FO * Northeast Iceland Nature Center (RFS) IS * Centre for Northern Studies (CEN) CA * Polish Academy of Sciences - Geography Dept. (IGSO-PAS) PL * Consiglio Nazionale delle Ricerche (CNR) IT * University of Alaska Fairbanks (UAF) US * Sudurnes Science and Learning Center (SSLC) IS * Finnish Meteorological Institute (FMI) FI * CAFF International Secretariat (CAFF) IS * APECS - University of Tromsoe (UiT) NO * Aurora College - The Western Arctic Research Centre (AC) CA * Arctic Institute of North America (AINA) CA * Umbilical Design (UD-AB) SE * ÅF Technology AB (AF) SE * Norwegian Meteorological Institute (METNO) NO </td> </tr> <tr> <td> </td> <td>  </td> <td> Agricultural University of Iceland (AUI) IS </td> </tr> <tr> <td> </td> <td>  </td> <td> University of Groningen (UoG-AC) NL </td> </tr> <tr> <td> </td> <td>  </td> <td> International Polar Foundation (IPF) BE </td> </tr> <tr> <td> </td> <td>  </td> <td> Mapillary (MAP) SE </td> </tr> <tr> <td> </td> <td>  </td> <td> University Centre in Svalbard (UNIS) NO </td> </tr> <tr> <td> </td> <td>  </td> <td> The International Centre for Reindeer Husbandry (ICR) NO </td> </tr> </table> # Data summary The INTERACT Data Management Plan addresses data describing >75 research stations (Figure 1) in cold regions of the Northern Hemisphere. A listing of the stations involved is provided in the proposal Section 4 and on the projects website ( _http://www.eu-interact.org_ ) . These stations obtain baseline- and monitoring data on a multitude of scientific disciplines practiced within the network. Through the integration of the independent research stations’ data through a unified approach, a comprehensive coordinated view on the Arctic is achieved. Multitudes of stakeholders, scientists, modellers, government agencies, educators, and to some extent private citizens have a vested interest in accessing the various kinds of data collected at the stations that can provide historical records, serve in model validation, and provide critical indicators across the disciplines covered within the network. The main objective of INTERACT is to build capacity for identifying, understanding, predicting and responding to diverse environmental changes throughout the wide environmental and land-use envelopes of the Arctic . A prerequisite to achieve this is to coordinate the data collected at INTERACT stations and to make them available. Thus, INTERACT data management aims to integrate datasets in a unified system, simplifying discovery, access and utilisation of data for various stakeholders in the scientific community, as well as in operational communities (e.g. scientists, national and local decision makers, etc.). INTERACT is truly interdisciplinary. With this perspective and as this activity on coordinated data management has just begun, no full overview of data types exist. Concerning the encoding of data, self-explaining file formats (e.g. NetCDF, HDF/HDF5) combined with semantic and structural standards like the Climate and Forecast Convention are required to ensure interoperability at the data level. Implementation of this is however a time consuming process and will be done gradually. Eventually, data can be integrated from different data centres with this approach. The default format for INTERACT datasets is NetCDF following the Climate and Forecast Convention (feature types grid, timeseries, profiles and trajectories if applicable). However, not all data handled at INTERACT stations are covered by the Climate and Forecast Convention for standard names. INTERACT is currently in a process of analysing the data collected and potential ways for handling these data. This work must be based on external activities within the disciplines and in Arctic data management in general. INTERACT has a huge legacy of data. Within this phase of INTERACT, an effort to identify legacy datasets and plan future handling of these will be initiated. Data are generated by permanent instrumentation (monitoring) and field work (projects) at the INTERACT research stations. The total amount of data is yet not known in detail currently. As the project progress, better understanding of the full capacity of INTERACT will be achieved. _Figure 1: More than 75 research stations are participating in INTERACT_ INTERACT data are useful for all users of INTERACT research stations, as well as projects, programmes and individual scientists undertaking scientific or monitoring work in the Arctic. Establishing a unified view on the data produced by INTERACT stations will improve the impact of INTERACT and the individual stations through promotion of the their capacity for various data consumers, ranging from individual scientists to regional or global monitoring programmes (e.g. _AMAP_ , _GCW_ and _GAW_ ) . ## Making data findable, provisions for metadata [FAIR data] Improving the ability of internal and external data consumers to find and understand the data INTERACT stations are producing is essential to increase the impact of INTERACT, individual stations and researchers. Through exposure of the data produced by INTERACT in relevant discipline specific, regional and global catalogues, the knowledge and interest in INTERACT is increased. This can be done both individually by each station or by the INTERACT community. INTERACT is following a metadata driven approach. This means that by utilizing internationally accepted standards and protocols for documentation and exchange of discovery and use metadata, interoperability with international systems and frameworks, including WMO’s systems, _Year of Polar Prediction_ (YOPP), _WMO Global Cryosphere Watch_ (GCW) and many national and international Arctic and marine data centers (e.g. _Svalbard Integrated Arctic Earth Observing System_ ) is ensured. INTERACT data management is distributed in nature, relying on a number of data centres with a long term mandate. This ensures preservation of the scientific legacy. While defining the approach of INTERACT data management, INTERACT is aligning efforts with _SAON/IASC Arctic Data_ _Committee_ . This implies documenting all datasets with standardised discovery metadata using either the _Global Change Master_ _Directory_ _Directory Interchange Format_ or _ISO19115_ standards. INTERACT promotes and encourages the implementation of globally resolvable Persistent Identifiers (e.g. Digital Object Identifiers - DOI) at each contributing data centre. Some have this in place, while others are in the process of establishing this. If DOIs are not supported, a local persistent identifier must be supported. Concerning naming conventions, INTERACT requires that controlled vocabularies are used both at the discovery level and the data level to describe the content. Discovery level metadata must identify the convention used and the convention has to be available in machine readable form (preferably through Simple Knowledge Organisation System). The fallback solution for controlled vocabularies is the _Global Change_ _Master Directory vocabularies_ . The search model of the data management system is based on _GCMD Science Keywords_ for parameter identification through discovery metadata. Versioning of data is required for the data published in the data management system. Details on requirements for how to define a new version of a dataset is to be agreed upon by the Data Forum. The central node can consume and expose discovery metadata as GCMD DIF and ISO19115 records (using GCMD keywords for physical/dynamical parameters). Support for more formats is considered. For use metadata the Climate and Forecast convention is promoted. More specifications will be identified early in the project. ## Making data openly accessible [FAIR data] Being able to find relevant data is only the first step. Most data consumers are interested in the actual data. The requirements of data consumers vary. While ad hoc consumers (usually scientists) frequently consume whatever is found from a network of stations, consumers concerned with monitoring, or calibration and validation of numerical models, or remote sensing products will usually require harmonisation of the data to a common form before they invest in integration. In order to address this standardisation of file formats (encoding) and data access mechanisms is required. The discovery metadata that can be collected will be made available through a web based search interface at _https://interact.met.no_ . Some data may have temporal access restrictions (embargo period). An embargo period on data may be requested for different reasons, e.g. allowing Ph.D. students to prepare their work, or while data is used in the preparation of a publication. Even if data are constrained in the embargo period, data will be shared internally in the project. Any disagreements on access to data or misuse of data internally are to be settled by the INTERACT Steering Board. A central data repository supporting the demonstrator will be made available. Within this demonstrator, data are made accessible using interoperability protocols using a THREDDS Data Server. This will support OpeNDAP, OGC Web Map Service for visualisation of gridded datasets, and direct HTTP download of full files. Standardisation of data access interfaces and linkage to the Common Data Model through OPeNDAP 1 is promoted for all data centres contributing to INTERACT. This enables direct access of data within analysis tools like Matlab, Excel 2 and R. The purpose of this demonstrator is to show how data may be shared in standard manner using Open Source Software. Most of the INTERACT data will however be managed by the stations or data centres the stations make agreements with. The purpose of the demonstrator is to increase the knowledge among stations on metadata and data interoperability and to encourage stations not sharing data today to start exploring possibilities. Metadata and data for the datasets are maintained by the stations and responsible data centres, metadata supporting unified search is harvested and ingested in the demonstrator hosted by central node. ## Making data interoperable [FAIR data] Interoperability at the data level will be facilitated by following best practises within international data management and relevant standardisation efforts. This includes application of self explaining file formats utilising discipline specific controlled vocabularies for data annotation. Data will be made available through _OPeNDAP_ with use metadata following the _Climate and Forecast conventions_ e.g. for geophysical data. However, exceptions will occur due to the diversity of INTERACT data. Some of the disciplines covered by INTERACT, e.g. meteorology, are advanced in the context of use metadata, while others are lacking a unified, discipline specific approach. INTERACT must rely on discipline specific activities and larger network activities (e.g. GTN-P, GAW, GCW) to avoid duplication of efforts and reuse the solutions developed. In order to address this aspect, the Data Forum is established to promote the understanding of emerging data management requirements. Implementation within INTERACT will be a long and stepwise process. Initially _GCMD Science keywords_ will be used, mapping between GCMD Science keywords and _CF_ _standard names_ is supported (but needs to be updated). Other vocabularies are included (e.g. _GBIF_ ) as they are available and considered mature. In the current situation, interaction with the stations is needed to fully get an overview of the relevant standards and controlled vocabularies. ## Increase data re-use (through clarifying licenses) [FAIR data] The INTERACT data policy is not written yet, but INTERACT promotes free and open data sharing in line with the _Open Research Data Pilot_ . Each dataset requires a license attached. The recommendation in INTERACT is to use _Creative Commons_ _attribution license_ for data (see _https://creativecommons.org/licenses/by/3.0/_ for details). However, INTERACT is spanning many nations and a more careful examination of the business models for various stations and funding regimes is required. INTERACT data should be delivered in a timely manner, meaning without undue delay. Any delay, due or undue, shall not be longer than one year after the dataset is finished. Discovery metadata shall be delivered immediately. INTERACT is promoting free and open access to data. Some data may have access constraints. Details will be evaluated during the project. The quality of each dataset is the responsibility of the Principal Investigator. The Data Management System will ensure that information on the quality of the data is available in the discovery metadata. INTERACT is primarily concerned with observational data. These data cannot be reproduced and must be reusable in the undefined future. # Allocation of resources In the current situation it is not possible to estimate the cost for making INTERACT data FAIR. Part of the reason is that this work is relying on existing functionality at the contributing data centres and that this functionality has been developed over years. The project is also still in the process of establishing an overview of the current situation among the 79 research stations involved. Within the first period of INTERACT, a questionnaire has been filed to stations asking for details on existing data management. This is still being analysed, but preliminary results indicate challenges establishing a preliminary data management system as a demonstrator for INTERACT. Over 50 % of the stations surveyed indicated established data management routines. Thus, instead of starting with stations, INTERACT will start with selected data centres that host data for INTERACT stations. Most of these data centres are active in relevant data management activities. The following data centres are so far identified: Page Not all contact points identified above are directly involved in INTERACT, but their institutions are and the data centres are handling INTERACT data. For some archives, contact points are to be identified. This table will be further developed and is only to be considered as a preliminary version. 4. Will be available August 2017. 5. _Data available through Polar Data Catalogoue which has interoper_ ability interfaces for metadata. Page Once INTERACT data management is fully established, each data centre is responsible for accepting, managing, sharing and preserving relevant datasets. Concerning interoperability interfaces the following interfaces are required for the first version of the system: 1\. Metadata 1. _OAI-PMH_ serving either _CCMD DIF_ or the _ISO19115_ minimum profile with _GCMD Science_ _Keywords_ . 2. Data (will also use whatever is available and deliver this in original form, for those data no synthesis products are possible without an extensive effort) 1. OGC WMS (actual visual representation, not data) 2\. _OPeNDAP_ for data streaming/download, including format conversion However, it should be understood that this is a best effort basis to show the benefit for the INTERACT community, at least initially. Thus, the activities are aligned with the efforts of the _SAON/IASC Arctic Data_ _Committee_ . In the current situation there is no overview of the costs of long term preservation of data as this is the responsibility of the contributing data centres and the business model for these differs. This information will be updated. # Data security Data security relies on the existing mechanisms of the contributing data centres. INTERACT recommends to ensure the communication between data centres and users with secure HTTP. Concerning the internal security of the data centre, INTERACT recommends the best practises from _OAIS_ . The central node relies on secure HTTP, but not all contributing data centres support this yet. As this effort is for demonstration initially, this section will be addressed following discussions in the Data Forum. # Ethical aspects INTERACT is handling a wide variety of data. Some data may be ethically sensitive. In the _IASC context_ this is especially related to humans and resources (e.g. fisheries, birds and mammals). As the INTERACT Data Policy still is under development, this will be addressed in later versions of the document. # Other This is not applicable in the current situation, but other considerations (e.g. funder, institutional, departmental or group policies on data management, data sharing and data security) may become applicable in later versions of the plan. Page	https://phaidra.univie.ac.at/o:1140797	Horizon 2020
0039_DataBio_732064.md	# Introduction ## 1.1 Project Summary The data intensive target sector on which the DataBio project focuses is the Data-Driven Bioeconomy . DataBio focuses on utilizing Big Data to contribute to the production of the best possible raw materials from agriculture, forestry and fishery (aquaculture) for the bioeconomy industry, as well as their further processing into food, energy and biomaterials, while taking into account various accountability and sustainability issues. DataBio will deploy state-of-the-art big data technologies and existing partners’ infrastructure and solutions, linked together through the DataBio Platform . These will aggregate Big Data from the three identified sectors ( agriculture, forestry and fishery ), intelligently process them and allow the three sectors to selectively utilize numerous platform components, according to their requirements. The execution will be through continuous cooperation of end user and technology provider companies, bioeconomy and technology research institutes, and stakeholders from the big data value PPP programme. DataBio is driven by the development, use and evaluation of a large number of pilots in the three identified sectors, where associated partners and additional stakeholders are also involved. The selected pilot concepts will be transformed to pilot implementations utilizing co-innovative methods and tools. The pilots select and utilize the best suitable market-ready or almost market-ready ICT, Big Data and Earth Observation methods, technologies, tools and services to be integrated to the common DataBio Platform. Based on the pilot results and the new DataBio Platform, new solutions and new business opportunities are expected to emerge. DataBio will organize a series of trainings and hackathons to support its uptake and to enable developers outside the consortium to design and develop new tools, services and applications based on and for the DataBio Platform. The DataBio consortium is listed in Table 1. For more information about the project see [REF01]. #### _Table 1: The DataBio consortium partners_ <table> <tr> <th> Number </th> <th> Name </th> <th> Short name </th> <th> Country </th> </tr> <tr> <td> 1 (CO) </td> <td> INTRASOFT INTERNATIONAL SA </td> <td> INTRASOFT </td> <td> Belgium </td> </tr> </table> <table> <tr> <th> 2 </th> <th> LESPROJEKT SLUZBY SRO </th> <th> LESPRO </th> <th> Czech Republic </th> </tr> <tr> <td> 3 </td> <td> ZAPADOCESKA UNIVERZITA V PLZNI </td> <td> UWB </td> <td> Czech Republic </td> </tr> <tr> <td> 4 </td> <td> FRAUNHOFER GESELLSCHAFT ZUR FOERDERUNG DER ANGEWANDTEN FORSCHUNG E.V. </td> <td> Fraunhofer </td> <td> Germany </td> </tr> <tr> <td> 5 </td> <td> ATOS SPAIN SA </td> <td> ATOS </td> <td> Spain </td> </tr> <tr> <td> 6 </td> <td> STIFTELSEN SINTEF </td> <td> SINTEF ICT </td> <td> Norway </td> </tr> <tr> <td> 7 </td> <td> SPACEBEL SA </td> <td> SPACEBEL </td> <td> Belgium </td> </tr> <tr> <td> 8 </td> <td> VLAAMSE INSTELLING VOOR TECHNOLOGISCH ONDERZOEK N.V. </td> <td> VITO </td> <td> Belgium </td> </tr> <tr> <td> 9 </td> <td> INSTYTUT CHEMII BIOORGANICZNEJ POLSKIEJ AKADEMII NAUK </td> <td> PSNC </td> <td> Poland </td> </tr> <tr> <td> 10 </td> <td> CIAOTECH Srl </td> <td> CiaoT </td> <td> Italy </td> </tr> <tr> <td> 11 </td> <td> EMPRESA DE TRANSFORMACION AGRARIA SA </td> <td> TRAGSA </td> <td> Spain </td> </tr> <tr> <td> 12 </td> <td> INSTITUT FUR ANGEWANDTE INFORMATIK (INFAI) EV </td> <td> INFAI </td> <td> Germany </td> </tr> <tr> <td> 13 </td> <td> NEUROPUBLIC AE PLIROFORIKIS & EPIKOINONION </td> <td> NP </td> <td> Greece </td> </tr> <tr> <td> 14 </td> <td> Ústav pro hospodářskou úpravu lesů Brandýs nad Labem </td> <td> UHUL FMI </td> <td> Czech Republic </td> </tr> <tr> <td> 15 </td> <td> INNOVATION ENGINEERING SRL </td> <td> InnoE </td> <td> Italy </td> </tr> <tr> <td> 16 </td> <td> Teknologian tutkimuskeskus VTT Oy </td> <td> VTT </td> <td> Finland </td> </tr> <tr> <td> 17 </td> <td> SINTEF FISKERI OG HAVBRUK AS </td> <td> SINTEF Fishery </td> <td> Norway </td> </tr> <tr> <td> 18 </td> <td> SUOMEN METSAKESKUS-FINLANDS SKOGSCENTRAL </td> <td> METSAK </td> <td> Finland </td> </tr> <tr> <td> 19 </td> <td> IBM ISRAEL - SCIENCE AND TECHNOLOGY LTD </td> <td> IBM </td> <td> Israel </td> </tr> <tr> <td> 20 </td> <td> MHG SYSTEMS OY - MHGS </td> <td> MHGS </td> <td> Finland </td> </tr> <tr> <td> 21 </td> <td> NB ADVIES BV </td> <td> NB Advies </td> <td> Netherlands </td> </tr> <tr> <td> 22 </td> <td> CONSIGLIO PER LA RICERCA IN AGRICOLTURA E L'ANALISI DELL'ECONOMIA AGRARIA </td> <td> CREA </td> <td> Italy </td> </tr> <tr> <td> 23 </td> <td> FUNDACION AZTI - AZTI FUNDAZIOA </td> <td> AZTI </td> <td> Spain </td> </tr> <tr> <td> 24 </td> <td> KINGS BAY AS </td> <td> KingsBay </td> <td> Norway </td> </tr> </table> <table> <tr> <th> 25 </th> <th> EROS AS </th> <th> Eros </th> <th> Norway </th> </tr> <tr> <td> 26 </td> <td> ERVIK & SAEVIK AS </td> <td> ESAS </td> <td> Norway </td> </tr> <tr> <td> 27 </td> <td> LIEGRUPPEN FISKERI AS </td> <td> LiegFi </td> <td> Norway </td> </tr> <tr> <td> 28 </td> <td> E-GEOS SPA </td> <td> e-geos </td> <td> Italy </td> </tr> <tr> <td> 29 </td> <td> DANMARKS TEKNISKE UNIVERSITET </td> <td> DTU </td> <td> Denmark </td> </tr> <tr> <td> 30 </td> <td> FEDERUNACOMA SRL UNIPERSONALE </td> <td> Federu </td> <td> Italy </td> </tr> <tr> <td> 31 </td> <td> CSEM CENTRE SUISSE D'ELECTRONIQUE ET DE MICROTECHNIQUE SA - RECHERCHE ET DEVELOPPEMENT </td> <td> CSEM </td> <td> Switzerland </td> </tr> <tr> <td> 32 </td> <td> UNIVERSITAET ST. GALLEN </td> <td> UStG </td> <td> Switzerland </td> </tr> <tr> <td> 33 </td> <td> NORGES SILDESALGSLAG SA </td> <td> Sildes </td> <td> Norway </td> </tr> <tr> <td> 34 </td> <td> EXUS SOFTWARE LTD </td> <td> EXUS </td> <td> United Kingdom </td> </tr> <tr> <td> 35 </td> <td> CYBERNETICA AS </td> <td> CYBER </td> <td> Estonia </td> </tr> <tr> <td> 36 </td> <td> GAIA EPICHEIREIN ANONYMI ETAIREIA PSIFIAKON YPIRESION </td> <td> GAIA </td> <td> Greece </td> </tr> <tr> <td> 37 </td> <td> SOFTEAM </td> <td> Softeam </td> <td> France </td> </tr> <tr> <td> 38 </td> <td> FUNDACION CITOLIVA, CENTRO DE INNOVACION Y TECNOLOGIA DEL OLIVAR Y DEL ACEITE </td> <td> CITOLIVA </td> <td> Spain </td> </tr> <tr> <td> 39 </td> <td> TERRASIGNA SRL </td> <td> TerraS </td> <td> Romania </td> </tr> <tr> <td> 40 </td> <td> ETHNIKO KENTRO EREVNAS KAI TECHNOLOGIKIS ANAPTYXIS </td> <td> CERTH </td> <td> Greece </td> </tr> <tr> <td> 41 </td> <td> METEOROLOGICAL AND ENVIRONMENTAL EARTH OBSERVATION SRL </td> <td> MEEO </td> <td> Italy </td> </tr> <tr> <td> 42 </td> <td> ECHEBASTAR FLEET SOCIEDAD LIMITADA </td> <td> ECHEBF </td> <td> Spain </td> </tr> <tr> <td> 43 </td> <td> NOVAMONT SPA </td> <td> Novam </td> <td> Italy </td> </tr> <tr> <td> 44 </td> <td> SENOP OY </td> <td> Senop </td> <td> Finland </td> </tr> <tr> <td> 45 </td> <td> UNIVERSIDAD DEL PAIS VASCO/ EUSKAL HERRIKO UNIBERTSITATEA </td> <td> EHU/UPV </td> <td> Spain </td> </tr> <tr> <td> 46 </td> <td> OPEN GEOSPATIAL CONSORTIUM (EUROPE) LIMITED LBG </td> <td> OGCE </td> <td> United Kingdom </td> </tr> <tr> <td> 47 </td> <td> ZETOR TRACTORS AS </td> <td> ZETOR </td> <td> Czech Republic </td> </tr> <tr> <td> 48 </td> <td> COOPERATIVA AGRICOLA CESENATE SOCIETA COOPERATIVA AGRICOLA </td> <td> CAC </td> <td> Italy </td> </tr> </table> ## 1.2 Document Scope This document outlines DataBio’s data management plan (DMP), formally documenting how data will be handled both during the implementation and upon natural termination of the project. Many DMP aspects will be considered including metadata generation, data preservation, data security and ethics, accounting for the FAIR (Findable, Accessible, Interoperable, Re-usable) data principle. DataBio, Data-driven Bioeconomy project, is an innovation big data intensive action involving public private partnership to promote productivity on EU companies in three of the major bioeconomy sectors namely, Agriculture, forestry and fishery. Experiences from US show that bioeconomy can get a significant boost from Big Data. In Europe, this sector has until now attracted few large ICT vendors. A central goal of DataBio is to increase participation of European ICT industry in the development of Big Data systems for boosting the lagging bioeconomy productivity. As a good case in point, European agriculture, forestry and fishery can benefit greatly from the European Copernicus space program which has currently launched its third Sentinel satellite, telemetry IoT, UAVs, etc. Farm and forestry machinery, and fishing vessels in use today collect large quantities of data in unprecedented pattern. Remote and proximal sensors and imagery, and many other technologies, are all working together to give details about crop and soil properties, marine environment, weeds and pests, sunlight and shade, and many other primary production relevant variables. Deploying big data analytics in these data can help the farmers, foresters and fishers to adjust and improve the productivity of their business operations. On the other hand, large data sets such as those coming from the Copernicus earth monitoring infrastructure, are increasingly available on different levels of granularity, but they are heterogeneous, at times also unstructured, hard to analyze and distributed across various sectors and different providers. It is here that data management plan comes in. It is anticipated that DataBio will provide a solution which will assume that datasets will be distributed among different infrastructures and that their accessibility could be complex, needing to have mechanisms which facilitate data retrieval, processing, manipulation and visualization as seamlessly as possible. The infrastructure will open new possibilities for ICT sector, including SMEs to develop new Bioeconomy 4.0 and will also open new possibilities for companies from the Earth Observation sector. This DMP will be updated over the course of DataBio project whenever significant changes arise. The updates of this document will increasingly provide in-depths on DataBio DMP strategies with particular interest on the aspects of findability, accessibility, interoperability and reusability of the Big Data the project produces. At least two updates will be prepared, on Month 18 and Month 36 of the project. ## 1.3 Document Structure This document is comprised of the following chapters: Chapter 1 presents an introduction to the project and the document. Chapter 2 presents the data summary including the purpose of data collection, data size, type and format, historical data reuse and data beneficiaries. Chapter 3 outlines DataBio’s FAIR data strategies. Chapter 4 describes data management support. Chapter 5 describes data security. Chapter 6 describes ethical issues. Chapter 7 presents the concluding remarks. Appendix A presents the managed data sets. # Data Summary ## 2.1 Purpose of data collection During the lifecycle of the DataBio project, big data will be collected that is, very large data sets (multi-terabyte or larger) consisting of a wide range of data types (relational, text, multistructured data, etc.) from numerous sources, including relatively new generation big data (machines, sensors, genomics, etc.). The ultimate purpose of data collection is to use the data as a source of information in the implementation of a variety of big data analytics algorithms, services and applications DataBio will deploy to create a value, new business facts and insights with a particular focus on the bioeconomy industry. The big datasets are part of the building blocks of the DataBio’s big data technology platform (Figure 1) that was designed to help European companies increase productivity. Big Data experts provide common analytic technology support for the main common and typical Bioeconomy applications/analytics that are now emerging through the pilots in the project. Data from the past will be managed and analyzed, including many different kind of data sources: i.e., descriptive analytics and classical query/reporting (in need of variety management - and handling and analysis of all of the data from the past, including performance data, transactional data, attitudinal data, descriptive data, behavioural data, location-related data, interactional data, from many different sources). Big data from the present time will be harnessed in the process of monitoring and real-time analytics - pilot services (in need of velocity processing - and handling of real-time data from the present) - trigging alarms, actuators etc. Harnessing big data for the future time include forecasting, prediction and recommendation analytics - pilot services (in need of volume processing - and processing of large amounts of data combining knowledge from the past and present, and from models, to provide insight for the future). _Figure 1: DataBio’s analytics and big data value approach_ Specifically: * Forestry: Big Data methods are expected to bring the possibility to both increase the value of the forests as well as to decrease the costs within sustainability limits set by natural growth and ecological aspects. The key technology is to gather more and more accurate information about the trees from a host of sensors including new generation of satellites, UAV images, laser scanning, mobile devices through crowdsourcing and machines operating in the forests. * Agriculture: Big Data in Agriculture is currently a hot topic. The DataBio intention is to build a European vision of Big Data for agriculture. This vision is to offer solutions which will increase the role of Big Data role in Agri Food chains in Europe: a perspective, which will prepare recommendation for future big data development in Europe. * Fisheries: the ambition is to herald and promote the use of Big Data analytical tools within fisheries applications by initiating several pilots which will demonstrate benefits of using Big Data in an analytical way for the fisheries, such as improved analysis of operational data, tools for planning and operational choices, crowdsourcing methods for fish stock estimation. * The use of Big data analytics will bring about innovation. It will generate significant economic value, extend the relevant market sectors, and herald novel business/organizational models. The cross-cutting character of the geo-spatial Big Data solutions allows the straightforward extension of the scope of applications beyond the bio-economy sectors. Such extensions of the market for the Big Data technologies are foreseen in economic sectors, such as: Urban planning, Water quality, Public safety (incl. technological and natural hazards), Protection of critical infrastructures, Waste management. On the other hand, the Big Data technologies revolutionize the business approach in the geospatial market and foster the emergence of innovative business/organizational models; indeed, to achieve the cost effectiveness of the services to the customers, it is necessary to organize the offer to the market on a territorial/local basis, as the users share the same geospatial sources of data and are best served by local players (service providers). This can be illustrated by a network of European services providers, developing proximity relationships with their customers and sharing their knowledge through the network. ## 2.2 Data types and formats The DataBio specific data types, formats and sources are listed in detail in Appendix A; below are described key features of the data used in the project. ### 2.2.1 Structured data Structured data refers to any data that resides in a fixed field within a record or file. This includes data contained in relational databases, spreadsheets, and data in forms of events such as sensor data. Structured data first depends on creating a data model – a model of the types of business data that will be recorded and how they will be stored, processed and accessed. This includes defining what fields of data will be stored and how that data will be stored: data type (numeric, currency, alphabetic, name, date, address) and any restrictions on the data input (number of characters; restricted to certain terms such as Mr., Ms. or Dr.; M or F). ### 2.2.2 Semi-structured data Semi-structured data is a cross between structured and unstructured data. It is a type of structured data, but lacks the strict data model structure. With semi-structured data, tags or other types of markers are used to identify certain elements within the data, but the data doesn't have a rigid structure. For example, word processing software now can include metadata showing the author's name and the date created, with the bulk of the document just being unstructured text. Emails have the sender, recipient, date, time and other fixed fields added to the unstructured data of the email message content and any attachments. Photos or other graphics can be tagged with keywords such as the creator, date, location and keywords, making it possible to organize and locate graphics. XML and other markup languages are often used to manage semi- structured data. Semi-structured data is therefore a form of structured data that does not conform with the formal structure of data models associated with relational databases or other forms of data tables, but nonetheless contains tags or other markers to separate semantic elements and enforce hierarchies of records and fields within the data. Therefore, it is also known as self- describing structure. In semistructured data, the entities belonging to the same class may have different attributes even though they are grouped together, and the attributes' order is not important. Semi-structured data are increasingly occurring since the advent of the Internet where full-text documents and databases are not the only forms of data anymore, and different applications need a medium for exchanging information. In object-oriented databases, one often finds semistructured data. XML and other markup languages, email, and EDI are all forms of semi- structured data. OEM (Object Exchange Model) was created prior to XML as a means of self-describing a data structure. XML has been popularized by web services that are developed utilizing SOAP principles. Some types of data described here as "semi-structured", especially XML, suffer from the impression that they are incapable of structural rigor at the same functional level as Relational Tables and Rows. Indeed, the view of XML as inherently semi-structured (previously, it was referred to as "unstructured") has handicapped its use for a widening range of data-centric applications. Even documents, normally thought of as the epitome of semistructure, can be designed with virtually the same rigor as database schema, enforced by the XML schema and processed by both commercial and custom software programs without reducing their usability by human readers. In view of this fact, XML might be referred to as having "flexible structure" capable of humancentric flow and hierarchy as well as highly rigorous element structure and data typing. The concept of XML as "human-readable", however, can only be taken so far. Some implementations/dialects of XML, such as the XML representation of the contents of a Microsoft Word document, as implemented in Office 2007 and later versions, utilize dozens or even hundreds of different kinds of tags that reflect a particular problem domain - in Word's case, formatting at the character and paragraph and document level, definitions of styles, inclusion of citations, etc. - which are nested within each other in complex ways. Understanding even a portion of such an XML document by reading it, let alone catching errors in its structure, is impossible without a very deep prior understanding of the specific XML implementation, along with assistance by software that understands the XML schema that has been employed. Such text is not "human-understandable" any more than a book written in Swahili (which uses the Latin alphabet) would be to an American or Western European who does not know a word of that language: the tags are symbols that are meaningless to a person unfamiliar with the domain. JSON or JavaScript Object Notation, is an open standard format that uses human-readable text to transmit data objects consisting of attribute–value pairs. It is used primarily to transmit data between a server and web application, as an alternative to XML. JSON has been popularized by web services developed utilizing REST principles. There is a new breed of databases such as MongoDB and Couchbase that store data natively in JSON format, leveraging the pros of semi-structured data architecture. ### 2.2.3 Unstructured data Unstructured data (or unstructured information) refers to information that either does not have a pre-defined data model or is not organized in a pre- defined manner. This results in irregularities and ambiguities that make it difficult to understand using traditional programs as compared to data stored in “field” form in databases or annotated (semantically tagged) in documents. Unstructured data can't be so readily classified and fit into a neat box: photos and graphic images, videos, streaming instrument data, webpages, PDF files, PowerPoint presentations, emails, blog entries, wikis and word processing documents. In 1998, Merrill Lynch cited a rule of thumb that somewhere around 80-90% of all potentially usable business information may originate in unstructured form. This rule of thumb is not based on primary or any quantitative research, but nonetheless is accepted by some. IDC and EMC project that data will grow to 40 zettabytes by 2020, resulting in a 50-fold growth from the beginning of 2010. Computer World states that unstructured information might account for more than 70%–80% of all data in organizations. Software that creates machine-processable structure can utilize the linguistic, auditory, and visual structure that exist in all forms of human communication. Algorithms can infer this inherent structure from text, for instance, by examining word morphology, sentence syntax, and other small- and large-scale patterns. Unstructured information can then be enriched and tagged to address ambiguities and relevancy-based techniques then used to facilitate search and discovery. Examples of "unstructured data" may include books, journals, documents, metadata, health records, audio, video, analog data, images, files, and unstructured text such as the body of an e-mail message, Web page, or word-processor document. While the main content being conveyed does not have a defined structure, it generally comes packaged in objects (e.g. in files or documents, …) that themselves have structure and are thus a mix of structured and unstructured data, but collectively this is still referred to as "unstructured data". ### 2.2.4 New generation big data The new generation big data is in particular focusing on semi-structured and unstructured data, often in combination with structured data. In the BDVA reference model for big data technologies a distinction is done between 6 different big data types. ##### 2.2.4.1 Sensor data Within the Databio pilots, several key parameters will be monitored through sensorial platforms and sensor data will be collected along the way to support the project activities. Two types of sensor data have been already identified and namely, a) IoT data from in-situ sensors and telemetric stations, b) imagery data from unmanned aerial sensing platforms (drones), c) imagery from hand-held or mounted optical sensors. ###### 2.2.4.1.1 Internet of Things data The IoT data are a major subgroup of sensor data involved in multiple pilot activities in the Databio project. IoT data are sent via TCP/UDP protocol in various formats (e.g. txt with time series data, json strings) and can be further divided into the following categories: • Agro-climatic/Field telemetry stations which contribute with raw data (numerical values) related to several parameters. As different pilots focus on different application scenarios, the following table summarizes several IoT- based monitoring approaches to be followed. ##### Table 2: Sensor data tools, resolution and spatial density <table> <tr> <th> Pilot </th> <th> Mission, instrument </th> <th> Data resolution and spatial density </th> </tr> <tr> <td> A1.1, B1.2, C1.1, C2.2 </td> <td> NP’s GAIAtrons, which are telemetry IoT stations with modular/expandable design will be used to monitor ambient temperature, humidity, solar radiation, leaf wetness, rainfall volume, wind speed and direction, barometric pressure (GAIAtron atmo), soil temperature and humidity (multi-depth) (GAIAtron soil) </td> <td> Time step for data collection every 10 minutes. One station per microclimate zone (300ha - 1100 ha for atmo, 300ha - 3300ha for soil) </td> </tr> <tr> <td> A1.2, B1.3 </td> <td> Field bound sensors will be used to monitor air temperature, air moisture, solar radiation, leaf wetness, rainfall, wind speed and direction, soil moisture, soil temperature, soil EC/salinity, PAR, and barometric pressure. These sensors consist in technology platform of retriever and pups wireless sensor network and SpecConnect, a cloud based crop data management solution. </td> <td> Time step for data collection is customizable from 1 to 60 minutes; Field sensors will be used to monitor 5 tandemly located sites at a density: a) Air temperature, air moisture, rainfall, wind data and solar radiation: one bloc of sensors per 5 ha 2. Leaf wetness: two sensors per ha 3. Soil moisture, soil temperature and soil EC/salinity: one combined sensor per ha </td> </tr> <tr> <td> A2.1 </td> <td> Environmental indoor: air temperature, air relative humidity, solar radiation, crop leaf temperature (remotely and in contact), soil/substrate water content. Environmental outdoor: wind speed and direction, evaporation, rain, UVA, UVB </td> <td> To be determined </td> </tr> <tr> <td> B1.1 </td> <td> Agro-climatic IoT stations monitoring temperature, relative and absolute humidity, wind parameters </td> <td> To be determined </td> </tr> </table> * Control data in the parcels/fields measuring sprinklers, drippers, metering devices, valves, alarm settings, heating, pumping state, pressure switches, etc. * Contact sensing data that determine problems with great precision, speeding up the use of techniques which help to solve problems * Vessel and buoy-based stations which contribute with raw data (numerical values), typically hydro acoustic and machinery data ###### 2.2.4.1.2 Drone data A specific subset of sensor data generated and processed within DataBio project is images produced by cameras on-board drones or RPAS (Remotely Piloted Aircraft Systems). In particular, some DataBio pilots will use optical (RGB), thermal or multispectral images and 3D point-clouds acquired from RPAS. The information generated by drone-airborne cameras is usually Image Data (JPEG or JPEG2000). A general description of the workflow is provided below. __Data acquired by the RGB sensor_ _ The RGB sensor acquires individual pictures in .JPG format, together with their ‘geotag’ files, which are downloaded from the RPAS and processed into: * .LAS files: 3D point clouds (x, y, z), which are then processed to produce Digital Models (Terrain- DTM, Surface-DSM, Elevation-DEM, Vegetation-DVM) * .TIF files: which are then processed into an orthorectified mosaic. In order to obtain smaller files, mosaics are usually exported to compressed .ECW format. __Data acquired by the thermal sensor_ _ The Thermal sensor acquires a video file which is downloaded from the RPAS and: * split into frames in .TIF format (pixels contain Digital Numbers: 0-255) * 1 of every 10 frames is selected (with an overlap of about 80%, so as not to process an excessive amount of information) __Data acquired by the multispectral sensor_ _ The multispectral sensor acquires individual pictures from the 6 spectral channels in .RAW format, which are downloaded from the RPAS and processed into: * .TIF files (16 bits), which are then processed to produce a 6-bands .TIF mosaic (pixels contain Digital Numbers: 0-255) ###### 2.2.4.1.3 Data from hand-held or mounted optical sensors Images from hand-held or mounted cameras will be collected using truck-held or hand held full Range / high resolution UV-VIS-NIR-SWIR Spectroradiometer. ###### _2.2.4.2 Machine-generated data_ Machine-generated data in the DataBio project are data produced by ships, boats and machinery used in agriculture and in forestry (such as tractors). These data will serve for further analysis and optimisation of processes in the bio-economy sector. For illustration purposes, examples of data collected by tractors in agriculture are described. Tractors are equipped by the following units: * Control units for data control, data collection and analyses including dashboards, transmission control unit, hydrostatic or hydrodynamic system control unit, engine control unit. * Global Positioning System (GPS) units or Global System for Mobile Communications (GSM) units for tractor tracking. * Unit for displaying characteristics of field/soil characteristics including area, quality, boundaries and yields. These units generate the following data: * Identification of tractor + identification of driver by code or by RFID module. * Identification of the current operation status. * Time identification by the date and the current time. * Precise tractor location tracking (daily route, starts, stops, speed). * Tractor hours - monitoring working hours in time and place. * Information from tachometer [Σ km] and [Σ working hrs and min]. * Identification of the current maintenance status. * Tractor diagnostic: failure modes or failure codes * Information about the date of the last calibration of each tractor systems + information about setting, information about SW version, last update, etc. * The amount of fuel in the fuel tank [L]. * Online information about sudden loss of fuel in the fuel tank. * Fuel consumption per trip / per time period / per kilometer (monitoring of fuel consumption in various dependencies e.g. motor load). * Total fuel consumption per day [L/day]. * Engine speed [run/min]. * Possibility to online setup engine speed in range [run/min from - to], signaling when limits are exceeding. * Current position of accelerator pedal [% from scale 0-100 %]. * Charging level of the main battery [V]. * Current temperature of the cooling weather [C ͦ or F ͦ ]. * Current temperature of the motor oil [C ͦ or F ͦ ]. * Current temperature of after treatment [C ͦ or F ͦ ]. * Current temperature of the transmission oil [C ͦ or F ͦ ]. * Diagnosis gear shift [grades backward and forward]. * Current engine load [% from scale 0-100 %] ###### _2.2.4.3 Geospatial data_ The DataBio pilots will collect earth observation (EO) data from a number of sources which will be refined during the project. Currently, it is confirmed that the following EO data will be collected and used as input data: ##### Table 3: Geospatial data tools, format and origin <table> <tr> <th> Mission, instrument </th> <th> Format </th> <th> Origin </th> </tr> <tr> <td> Sentinel-1, C-SAR </td> <td> SLC, GRD </td> <td> Copernicus Open Access Hub (https://scihub.copernicus.eu/) </td> </tr> <tr> <td> Sentinel-2, MSI </td> <td> L1C </td> <td> Copernicus Open Access Hub (https://scihub.copernicus.eu/) </td> </tr> </table> Information about the expected sizes will be added, when the information becomes available. In addition to EO data, DataBio will utilise other geospatial data from EU, national, local, private and open repositories including Land Parcel Identification System data, cadastral data, Open Land Use map ( _http://sdi4apps.eu/open_land_use/_ ) , Urban Atlas and Corine Land Cover, Proba-V data ( _www.vito-eodata.be_ ) . The meteo-data will be collected mainly from EO systems based and will be collected from European data sources such as COPERNICUS products, EUMETSAT H-SAF products, but also other EO data sources such as VIIRS and MODIS and ASTER will be considered. As complementary data sources, the weather forecast models output (ECMWF) and the regional weather services output usually based on ground weather stations can be considered according to the specific target areas of the pilots." ###### _2.2.4.4 Genomics data_ Within the DataBio Pilot 1.1.2 different data will be collected and produced. Three categories of data have been already identified for the Pilot and namely, a) in-situ sensors (including image capture) and farm data, b) genomic data from plant breeding efforts in Green Houses produced using Next Generation Sequencers (NGS), c) biochemical data of tomato fruits produced by chromatographs (LC/MS/MS, GS/MS, HPLC). In-situ sensors/Environmental outdoor: Wind speed and direction, Evaporation, Rain, Light intensity, UVA, UVB. In-situ sensors/Environmental indoor: Air temperature, Air relative humidity, Crop leaf temperature (remotely and in contact), Soil/substrate water content, crop type, etc.). Farm Data: * In-Situ measurements: Soil nutritional status. * Farm logs (work calendar, technical practices at farm level, irrigation information,). * Farm profile (Static farm information, such as size ##### Table 4: Genomic, biochemical and metabolomic data tools, description and acquisition <table> <tr> <th> Pilot A1.1.2 </th> <th> Mission, Instrument </th> <th> Data description and acquisition </th> </tr> <tr> <td> Genomic data </td> <td> To characterize the genetic diversity of local tomato varieties used for breeding. To use the genetic- genomic information to guide the breeding efforts (as a selection tool for higher performance) and develop a model to predict the final breeding result in order to achieve rapidly and with less financial burden varieties of higher performance. Data will be produced using two Illumina NGS Macchines. </td> <td> Data produced from Illumina machines stored in compressed text files (fastq). Data will be produced from plant biological samples (leaf and fruit). Collection will be done in 2 different plant stages (plantlets and mature plants). Genomic data will be produced using standard and customized protocols at CERTH. Genomic data, although plait text in format, are bigvolume data and pose challenges in their storage, handling and processing. Preliminary analysis will be performed using the local HPC computational facility. </td> </tr> <tr> <td> Biochemical, metabolomic data </td> <td> To characterize the biochemical profile of fruits from tomato varieties used for breeding. Data will be produced from different chromatographs and mass spectrometers </td> <td> Data will be mainly proprietary binary based archives converted to XML or other open formats. Data will be acquired from biological samples of tomato fruits. </td> </tr> </table> While genomic data are stored in raw format as files, environmental data, which are generated using a network of sensors, will be stored in a database along with the time information and will be processed as time series data. ## 2.3 Historical data In the context of doing machine learning and predictive and prescriptive analytics it is important to be able to use historical data for training and validation purposes. Machine learning algorithms will use existing historical data as training data both for supervised and unsupervised learning. Information about datasets and the time periods concerned with historical datasets to be used for DataBio can be found in Appendix A. Historical data can also serve as training complex event processing applications. In this case, historical data is injected as “happening in real-time” therefore serving as testing the complex event driven application in hand before running it in real-environment. ## 2.4 Expected data size and velocity The big data “V” characteristics of Volume and Velocity is being described for each of the identified data sets in the DataBio projects - typically with measurements of total historical volumes and new/additional data per time unit. The DataBio-specific Data Volumes and velocities (or injection rates) can be found in Appendix A. ## 2.5 Data beneficiaries In this section, this document analyses the key data beneficiaries who will benefit from the use of big data in several fields as analytics, data sets, business value, sales or marketing. This section will consider both tangibles and intangibles concepts. In examining the value of big data, it is necessary to evaluate who is affected by them and their usage. In some cases, the individual whose data is processed directly receives a benefit. Nevertheless, regarding Data Driven Bio-Economy, the benefit to the individual can be considered as indirect. In other cases, the relevant individual receives no benefit attributable, with big data value reaped by business, government, or society at large. Concerning General Community, the collection and use of an individual’s data benefits not only that individual, but also members of a proximate class, such as users of a similar product or residents of a geographical area. In the case of organizations, Big Data analysis often benefits those organizations that collect and harness the data. Data-driven profits may be viewed as enhancing allocative efficiency by facilitating the free economy. The emergence, expansion, and widespread use of innovative products and services at decreasing marginal costs have revolutionized global economies and societal structures, facilitating access to technology and knowledge and fomenting social change. With more data, businesses can optimize distribution methods, efficiently allocate credit, and robustly combat fraud, benefitting consumers as a whole. On the other hand, big data analysis can provide a direct benefit to those individuals whose information is being used. However, DataBio project is not directly involved on those specific cases (see chapter6 about ethical issues). Regarding general benefits, big data is creating enormous value for the global economy, driving innovation, productivity, efficiency, and growth. Data has become the driving force behind almost every interaction between individuals, businesses, and governments. The uses of big data can be transformative and are sometimes difficult to anticipate at the time of initial collection. This section does not provide a comprehensive taxonomy of big data benefits. It would be pretentious to do so, ranking the relative importance of weighty social goals. Rather it posits that such benefits must be accounted for by rigorous analysis considering the priorities of a nation, society, or economy. Only then, can benefits be assessed within an economic framework. Besides those general concepts on Big Data Beneficiaries, it is possible to analyse the impact of DataBio project results regarding the final users of the different technologies, tools and services to be developed. Using this approach, and taking into account that more detailed information is available at Deliverables D1.1, D2.1 and D3.1 regarding Agricultural, Forestry and Fishery pilots definition, the main beneficiaries of big data are described in the following sections. ### 2.5.1 Agricultural Sector One of the proposed agricultural pilots is about the use of tractor units able to online send information regarding current operations to the driver or farmer. The prototypes will be equipped with units for tracking and tracing (GPS - Global Positioning System or GSM - Global System for Mobile Communications) and the unit for displaying characteristics of soil units. The proposed solution will meet Farmers requests on cost reduction and improved productivity in order to increase their economic benefits following, also, sustainable agriculture practices. In other case, Smart farming services provided as irrigation through flexible mechanisms and UIs (web, mobile, tablet compatible) will promote the adoption of technological tools (IoT, data analytics) and collaboration with certified professionals to optimize farm productivity. Therefore, Farming Cooperatives will obtain, again, cost reduction and improved productivity migrating from standard to sustainable smart-agriculture practices. As a summary, main beneficiaries of DataBio will be Farming cooperatives, farmers and land owners. ### 2.5.2 Forestry Sector Data sharing and a collaborative environment enable improved tools for sustainable forest management decisions and operations. Forest management services make data accessible for forest owners, and other end users, and integrate this data for e-contracting, online purchase and sales of timber and biomass. Higher data volumes and better data accessibility increase the probability that the data will be updated and maintained. DataBio WP2 will develop and pilot standardized procedures for collecting and transferring Big Data based on DataBio WP4 platform from silvicultural activities executed in the forest. As a summary, the Big Data beneficiaries related to WP2 – Forestry Pilots activities will be: * Forest owners (private, public, timberland investors) * Forest authority experts * Forest companies * Contractors and service providers ### 2.5.3 Fishery Sector Regarding WP3 – Fisheries Pilot, in Pilot A2: Small pelagic fisheries immediate operational choices, the main users and beneficiaries of this pilot will be the ship owners and masters on board small pelagic vessels. Modern pelagic vessels are equipped with increasingly complex machinery systems for propulsion, manoeuvring and power generation. Due to that, the vessel is always in an operational state, but the configuration of the vessel systems imposes constraints on operation. The captain is tasked with safe operation of the vessel, while the efficiency of the vessel systems may be increased if the captain is informed about the actual operational state, potential for improvement and expected results of available actions. The goal of the pilot B2: Oceanic tuna fisheries planning is to create tools that aid in trip planning by presenting historical catch data as well as attempting to forecast where the fish might be in the near future. The forecast model will be constructed from historical data of catches with the data available by the skippers at that moment (oceanographical data, buoys data etc). In that case, the main beneficiary of DataBio development will be tuna fisheries companies. Therefore, as a summary, DataBio WP3 beneficiaries will be the broad range of fisheries stakeholders from companies, captains and vessels owners. ### 2.5.4 Technical Staff Adoption rates aside, the potential benefits of utilising big data and related technologies are significant both in scale and scope and include, for example: better/more targeted marketing activities, improved business decision making, cost reduction and generation of operational efficiencies, enhanced planning and strategic decision making and increased business agility, fraud detection, waste reduction and customer retention to name but a few. Obviously, the ability of firms to realize business benefits will be dependent on company characteristics such as size, data dependency and nature of business activity. A core concern voiced by many of those participating in big data focused studies is the ability of employers to find and attract the talent needed for both a) the successful implementation of big data solutions and b) the subsequent realisation of associated business benefits. Although ‘Data Scientist’ may currently be the most requested profile in big data, the recruitment of Data Scientists (in volume terms at least) appears relatively low down the wish list of recruiters. Instead, the openings most commonly arising in the big data field (as is the case for IT recruitment) are development positions ~~.~~ ### 2.5.5 ICT sector ##### 2.5.5.1 Developers The generic title of developer is normally employed together with a detailed description of the specific technical related skills required for the post and it is this description that defines the specific type of development activity undertaken. The technical skills most often cited by recruiters in adverts for big data Developers are: NoSQL (MongoDB in particular), Java, SQL, JavaScript, MySQL, Linux, Oracle, Hadoop (especially Cassandra), HTML and Spring. ##### 2.5.5.2 Architects More specifically, however, applicants for these positions are required to hold skills in a range of technical disciplines including: Oracle (in particular, BI EE), Java, SQL, Hadoop and SQL Server, whilst the main generic areas of technical Knowledge and competence required were: Data Modelling, ETL, and Enterprise Architecture, Open Source and Analytics. ##### 2.5.5.3 Analysts Particular process/methodological skills required from applicants for analyst positions were primarily in respect of: Data Modelling, ETL, Analytics and Data. ##### 2.5.5.4 Administrators In general, the technical skills most often requested by employers from big data Administrators at that time were: Linux, MySQL and Puppet, Hadoop and Oracle, whilst the process and methodological competences most often requested were in the areas of Configuration Management, Disaster Recovery, Clustering and ETL. ##### 2.5.5.5 Project Managers The specific types of Project Manager most often required by big data recruiters are Oracle Project Managers, Technical Project Managers and Business Intelligence Project Managers. Aside from Oracle (and in particular BI EE, EBS and EBS R12), which was specified in over twothirds of all adverts for big data related Project Management posts, other technical skills often needed by applicants for this type of position were: Netezza, Business Objects and Hyperion. Process and methodological skills commonly required included ETL and Agile Software Development together with a range of more ‘business focused’ skills, i.e. PRINCE2 and Stakeholder Management. ##### 2.5.5.6 Data Designers The most commonly requested technical skills associated with these posts to have been Oracle (particularly BIEE) and SQL followed by Netezza, SQL Server, MySQL and UNIX. Common process and methodological skills needed were: ETL, Data Modelling, Analytics, CSS, Unit Testing, Data Integration and Data Mining, whilst more general knowledge requirements related to the need for experience and understanding of Business Intelligence, Data Warehouse, Big Data, Migration and Middleware. ##### 2.5.5.7 Data Scientists The core technical skills needed to secure a position as a Data Scientist are found to be: Hadoop, Java, NoSQL and C++. As was the case for other big data positions, adverts for Data Scientists often made reference to a need for various process and methodological skills and competences. Interestingly however, in this case, such references were found to be much more commonplace and (perhaps as would be expected) most often focused upon data and/or statistical themes, i.e. Statistics, Analytics and Mathematics. ### 2.5.6 Research and education Researchers, scientists and academics are one of the largest groups for data reuse. DataBio data published as open data will be used for further research and for educational purposes (e.g. thesis). ### 2.5.7 Policy making bodies The DataBio data and results will serve as a basis for decision making bodies, especially for policy evaluation and feedback on policy implementation. This includes mainly the European Commission, national and regional public authorities. # FAIR Data The FAIR principle ensures that data can be discovered through catalogs or search engines, is accessible through open interfaces, is compliant to standards to interoperable processing of that data, and therefore can be easily being reused. ## 3.1 Data findability ### 3.1.1 Data discoverability and metadata provision Metadata is, as its name implies, data about data. It describes the properties of a dataset. Metadata can cover various types of information. Descriptive metadata includes elements such as the title, abstract, author and keywords, and is mostly used to discover and identify a dataset. Another type is administrative metadata with elements such as the license, intellectual property rights, when and how the dataset was created, who has access to it, etc. The datasets on the DataBio Infrastructure are either added locally, by a user, harvested from existing data portals, or fetched from operational systems or IoT ecosystems. In DataBio, the definition of a set of metadata elements is necessary in order to allow identification of the vast amount information resources managed for which metadata is created, its classification and identification of its geographic location and temporal reference, quality and validity, conformity with implementing rules on the interoperability of spatial data sets and services, constraints related to access and use, and organization responsible for the resource. In addition, metadata elements related to the metadata record itself are also necessary to monitor that the metadata created are kept up to date, and for identifying the organization responsible for the creation and maintenance of the metadata. Such minimum set of metadata elements is also necessary to comply with Directive 2007/2/EC and does not preclude the possibility for organizations to document the information resources more extensively with additional elements derived from international standards or working practices in their community of interest. Metadata referred to datasets and dataset series (particularly relevant for DataBio will be the EO products derived from satellite imagery) should adhere to the profile originating from the INSPIRE Metadata regulation with added theme-specific metadata elements for the agriculture, forestry and fishery domains if necessary. This approach will ensure that metadata created for the datasets, dataset series and services will be compliant with the INSPIRE requirements as well international standards ISO EN 19115 (Geographic Information – Metadata; with special emphasis in ISO 19115-2:2009 Geographic information -- Metadata -- Part 2: Extensions for imagery and gridded data), ISO EN 19119 (Geographic Information – Services), ISO EN 19139 (Geographic Information – Metadata – Metadata XML Schema) and ISO EN ISO 19156 (Earth Observation Metadata profile of Observations & Measurements). Besides, INSPIRE conformant metadata may be expressed also through the DCAT Application Profile 1 , which defines a minimum set of metadata elements to ensure cross-domain and cross-border interoperability between metadata schemas used in European data portals. If adopted by DataBio, such a mapping could support the inclusion of INSPIRE metadata in the Pan-European Open Data Portal for wider discovery across sectors beyond the geospatial domain. A Distribution represents a way in which the data is made available. DCAT is a rather small vocabulary, but deliberately leaves many details open. It welcomes “application profiles”: more specific specifications built on top of DCAT resp GeoDCAT – AP as geospatial extension. For sensors we will focused on SensorML. SensorML can be used to describe a wide range of sensors, including both dynamic and stationary platforms and both in-situ and remote sensors. Other possibility is Semantic Sensor Net Ontology which describes sensors and observations, and related concepts. It does not describe domain concepts, time, locations, etc. these are intended to be included from other ontologies via OWL imports. This ontology is developed by the W3C Semantic Sensor Networks Incubator Group (SSN-XG). In DataBio, there is a need for metadata harmonization of the spatial and non- spatial datasets and services. GeoDCAT-AP was an obvious choice due to the strong focus on geographic datasets. The main advantage is that it enables users to query all datasets in a uniform way. GeoDCAT-AP is still very new, and the implementation of the new standard within EUXDAT can provide feedback to OGC, W3C & JRC from both technical and end user point of view. Several software components are available in the DataBio architecture that have varying support for GeoDCAT-AP, being Micka 2 , CKAN 3 and GeoNetwork 4 . For the DataBio purposes we will need also integrate Semantic Sensor Net Ontology and SensorML. For enabling compatibility with COPERNICUS, INSPIRE and GEOSS, the DataBio project will make three extensions: i) Module for extended harvesting INSPIRE metadata to DCAT, based on XSLT and easy configuration; ii)Module for user friendly visualisation of INSPIRE metadata in CKAN; and iii)Module to output metadata in GeoDCAT-AP resp SensorDCAT. We plan use Micka and CKAN systems. 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 the management of spatial data and services metadata, and other sources (documents, websites, etc.). CKAN supports DCAT to import or export its datasets. CKAN enables harvesting data from OGC:CSW catalogues, but not all mandatory INSPIRE metadata elements are supported. Unfortunately, the DCAT output does not fulfil all INSPIRE requirements, nor is GeoDCAT-AP fully supported. An ongoing programme of spatial data infrastructure projects, undertaken with academic and commercial partners, enables DataBio to contribute to the creation of standard data specifications and policies. This ensures their databases remain of high quality, compatible and can interact with one another to deliver data which provides practical and tangible benefits for European society. The network’s mission is to provide and disseminate statistical information which has to be objective, independent and of high quality. Federal statistics are available to everybody: politicians, authorities, businesses and citizens. ### 3.1.2 Data identification, naming mechanisms and search keyword approaches For data identification, naming and search keywords we will use INSPIRE data registry. The INSPIRE infrastructure involves a number of items, which require clear descriptions and the possibility to be referenced through unique identifiers. Examples for such items include INSPIRE themes, code lists, application schemas or discovery services. Registers provide a means to assign identifiers to items and their labels, definitions and descriptions (in different languages). The INSPIRE Registry is a service giving access to INSPIRE semantic assets (e.g. application schemas, meta/data codelists, themes), and assigning to each of them a persistent URI. As such, this service can be considered also as a metadata directory/catalogue for INSPIRE, as well as a registry for the INSPIRE "terminology". Starting from June 2013, when the INSPIRE Registry was first published, a number of version have been released, implementing new features based on the community's feedback. Now, recently, a new version of the INSPIRE Registry has been published, which, among other features, makes available its content also in RDF/XML: _http://inspire.ec.europa.eu/registry/_ 5 The INSPIRE registry provides a central access point to a number of centrally managed INSPIRE registers 6 . INSPIRE registry include: * _INSPIRE application schema register_ * _INSPIRE code list register_ * _INSPIRE enumeration register_ * _INSPIRE feature concept dictionary_ * _INSPIRE glossary_ * _INSPIRE layer register_ * _INSPIRE media-types register_ * _INSPIRE metadata code list register_ * _INSPIRE reference document register_ * _INSPIRE theme register_ Most relevant for naming in metadata is INSPIRE metadata code list register, which contains the code lists and their values, as defined in the INSPIRE implementing rules on metadata. 7 ### 3.1.3 Data lineage Data lineage refers to the sources of information, such as entities and processes, involved in producing or delivering an artifact. Data lineage records the derivation history of a data product. The history could include the algorithms used, the process steps taken, the computing environment run, data sources input to the processes, the organization/person responsible for the product, etc. Provenance provides important information to data users for them to determine the usability and reliability of the product. In the science domain, the data provenance is especially important since scientists need to use the information to determine the scientific validity of a data product and to decide if such a product can be used as the basis for further scientific analysis. The provenance of information is crucial to making determinations about whether information is trusted, how to integrate diverse information sources, and how to give credit to originators when reusing information [REF-02]. In an open and inclusive environment such as the Web, users find information that is often contradictory or questionable. Reasoners in the Semantic Web will need explicit representations of provenance information in order to make trust judgments about the information they use. With the arrival of massive amounts of Semantic Web data (eg, via the Linked Open Data community) information about the origin of that data, ie, provenance, becomes an important factor in developing new Semantic Web applications. Therefore, a crucial enabler of the Semantic Web deployment is the explicit representation of provenance information that is accessible to machines, not just to humans. Data provenance as the information about how data was derived. Both are critical to the ability to interpret a particular data item. Provenance is often conflated with metadata and trust. Metadata is used to represent properties of objects. Many of those properties have to do with provenance, so the two are often equated. Trust is derived from provenance information, and typically is a subjective judgment that depends on context and use [REF-03]. W3C PROV Family of Documents defines a model, corresponding serializations and other supporting definitions to enable the interoperable interchange of provenance information in heterogeneous environments such as the Web [REF-04]. Current standards include [REF-05]: PROV-DM: The PROV Data Model [REF-06] - PROV-DM is a core data model for provenance for building representations of the entities, people and processes involved in producing a piece of data or thing in the world. PROV-DM is domain-agnostic, but with well-defined extensibility points allowing further domain-specific and application-specific extensions to be defined. It is accompanied by PROV-ASN, a technology-independent abstract syntax notation, which allows serializations of PROV-DM instances to be created for human consumption, which facilitates its mapping to concrete syntax, and which is used as the basis for a formal semantics. PROV-O: The PROV Ontology [REF-07] - This specification defines the PROV Ontology as the normative representation of the PROV Data Model using the Web Ontology Language (OWL2). This document is part of a set of specifications being created to address the issue of provenance interchange in Web applications. Constraints of the PROV Data Model [REF-08] - PROV-DM, the PROV data model, is a data model for provenance that describes the entities, people and activities involved in producing a piece of data or thing. PROV-DM is structured in six components, dealing with: (1) entities and activities, and the time at which they were created, used, or ended; (2) agents bearing responsibility for entities that were generated and activities that happened; (3) derivations of entities from entities; (4) properties to link entities that refer to a same thing; (5) collections forming a logical structure for its members; (6) a simple annotation mechanism. PROV-N: The Provenance Notation [REF-09] - PROV-DM, the PROV data model, is a data model for provenance that describes the entities, people and activities involved in producing a piece of data or thing. PROV-DM is structured in six components, dealing with: (1) entities and activities, and the time at which they were created, used, or ended; (2) agents bearing responsibility for entities that were generated and activities that happened; (3) derivations of entities from entities; (4) properties to link entities that refer to the same thing; (5) collections forming a logical structure for its members; (6) a simple annotation mechanism. Figure 2 [REF-10] is a generic data lifecycle in the context of a data processing environment where data are first discovered by the user with the help of metadata and provenance catalogues. During the data processing phase, data replica information may be entered in replica catalogues (which contain metadata about the data location), data may be transferred between storage and execution sites, and software components may be staged to the execution sites as well. While data are being processed, provenance information can be automatically captured and then stored in a provenance store. The resulting derived data products (both intermediate and final) can also be stored in an archive, with metadata about them stored in a metadata catalogue and location information stored in a replica catalogue. Data Provenance is also addressed in W3C DCAT Metadata model [REF-11]. dcat:CatalogRecord describes a dataset entry in the catalog. It is used to capture provenance information about dataset entries in a catalog. This class is optional and not all catalogs will use it. It exists for catalogs where a distinction is made between metadata about a dataset and metadata about the dataset's entry in the catalog. For example, the publication date property of the dataset reflects the date when the information was originally made available by the publishing agency, while the publication date of the catalog record is the date when the dataset was added to the catalog. In cases where both dates differ, or where only the latter is known, the publication date should only be specified for the catalog record. W3C PROV Ontology [prov-o] allows describing further provenance information such as the details of the process and the agent involved in a particular change to a dataset. Detailed specification of data provenance is also additional requirements for DCAT – AP specification effort [REF-12]. ## 3.2 Data accessibility Through DataBio experiments with a large number of tools and technologies identified in WP4 and WP5, a common data access pattern shall be developed. Ideally, this pattern is based on internationally adopted standards, such as OGC WFS for feature data, OGC WCS for coverage data, OGC WMS for maps, or OGC SOS for sensor data. ### 3.2.1 Open data and closed data Everyone from citizens to civil servants, researchers and entrepreneurs can benefit from open data. In this respect, the aim is to make effective use of Open Data. This data is already available in public domains and is not within the control of the DataBio project. All data rests on a scale between closed and open because there are variances in how information is shared between the two points in the continuum. Closed data might be shared with specific individuals within a corporate setting. Open data may require attribution to the contributing source, but still be completely available to the end user. Generally, open data differs from closed data in three key ways 8 : 1. Open data is accessible, usually via a data warehouse on the internet. 2. It is available in a readable format. 3. It’s licensed as open source, which allows anyone to use the data or share it for noncommercial or commercial gain. Closed data restricts access to the information in several potential ways: 1. It is only available to certain individuals within an organization. 2. The data is patented or proprietary. 3. The data is semi-restricted to certain groups. 4. Data that is open to the public through a licensure fee or other prerequisite. 5. Data that is difficult to access, such as paper records that haven’t been digitized. The perfect example of closed data could be information that requires a security clearance; health-related information collected by a hospital or insurance carrier; or, on a smaller scale, your own personal tax returns. There are also other datasets used for the pilots, like e.g. cartography, 3D or land use data but those are stored in databases which are not available through the Open Data portals. Once the use case specification and requirements have been completed these data may also be needed for the processing and visualisation within the DataBio applications. However, this data – in its raw format – may not be made available to external stakeholders for further use due to licensing and/or privacy issues. Therefore, at this stage, the data management plan will not cover these datasets. ### 3.2.2 Data access mechanisms, software and tools Data access is the process of entering a database to store or retrieve data. Data Access Tools are end user oriented tools that allow users to build structured query language (SQL) queries by pointing and clicking on the list of table and fields in the data warehouse. Thorough computing history, there have been different methods and languages already that were used for data access and these varied depending on the type of data warehouse. The data warehouse contains a rich repository of data pertaining to organizational business rules, policies, events and histories and these warehouses store data in different and incompatible formats so several data access tools have been developed to overcome problems of data incompatibilities. Recent advancement in information technology has brought about new and innovative software applications that have more standardized languages, format, and methods to serve as interface among different data formats. Some of these more popular standards include SQL, OBDC, ADO.NET, JDBC, XML, XPath, XQuery and Web Services. ### 3.2.3 Big data warehouse architectures and database management systems Depending on the project needs, there are different possibilities to store data: ##### 3.2.3.1 Relational Database This is a digital database whose organization is based on the relational model of data. The various software systems used to maintain relational databases are known as a relational database management system (RDBMS). Virtually all relational database systems use SQL (Structured Query Language) as the language for querying and maintaining the database. A relational database has the important advantage of being easy to extend. After the original database creation, a new data category can be added without requiring that all existing applications be modified. This model organizes data into one or more tables (or "relations") of columns and rows, with a unique key identifying each row. Rows are also called records or tuples. Generally, each table/relation represents one "entity type" (such as customer or product). The rows represent instances of that type of entity and the columns representing values attributed to that instance. The definition of a relational database results in a table of metadata or formal descriptions of the tables, columns, domains, and constraints. When creating a relational database, the domain of possible values can be defined in a data column and further constraints that may apply to that data value can be described. For example, a domain of possible customers could allow up to ten possible customer names but be constrained in one table to allowing only three of these customer names to be specifiable. An example of a relational database management system is the Microsoft SQL Server, developed by Microsoft. As a database server, it is a software product with the primary function of storing and retrieving data as requested by other software applications—which may run either on the same computer or on another computer across a network (including the Internet). Microsoft makes SQL Server available in multiple editions, with different feature sets and targeting different users. _PostgreSQL – for specific domains_ : PostgreSQL, often simply Postgres, is an object-relational database management system (ORDBMS) with an emphasis on extensibility and standards compliance. As a database server, its primary functions are to store data securely and return that data in response to requests from other software applications. It can handle workloads ranging from small single-machine applications to large Internet-facing applications (or for data warehousing) with many concurrent users; on macOS Server, PostgreSQL is the default database. It is also available for Microsoft Windows and Linux. PostgreSQL is developed by the PostgreSQL Global Development Group, a diverse group of many companies and individual contributors. It is free and open- source, released under the terms of the PostgreSQL License, a permissive software license. Furthermore, it is ACIDcompliant and transactional. PostgreSQL has updatable views and materialized views, triggers, foreign keys; supports functions and stored procedures, and other expandability. ##### 3.2.3.2 Big Data storage solutions A NoSQL (originally referring to "non-SQL", "non-relational" or "not only SQL") database provides a mechanism for storage and retrieval of data which is modeled in means other than the tabular relations used in relational databases. Such databases have existed since the late 1960s, but did not obtain the "NoSQL" moniker until a surge of popularity in the early twentyfirst century, triggered by the needs of Web 2.0 companies such as Facebook, Google, and Amazon.com. NoSQL databases are increasingly used in big data and real-time web applications. NoSQL systems are also sometimes called "Not only SQL" to emphasize that they may support SQL-like query languages. Motivations for this approach include: simplicity of design, simpler "horizontal" scaling to clusters of machines (which is a problem for relational databases), and finer control over availability. The data structures used by NoSQL databases (e.g. key-value, wide column, graph, or document) are different from those used by default in relational databases, making some operations faster in NoSQL. The particular suitability of a given NoSQL database depends on the problem it must solve. Sometimes the data structures used by NoSQL databases are also viewed as "more flexible" than relational database tables. _MongoDB_ : MongoDB (from humongous) is a free and open-source cross-platform documentoriented database program. Classified as a NoSQL database program, MongoDB uses JSONlike documents with schemas. MongoDB is developed by MongoDB Inc. and is free and opensource, published under a combination of the GNU Affero General Public License and the Apache License. MongoDB supports field, range queries, regular expression searches. Queries can return specific fields of documents and also include user-defined JavaScript functions. Queries can also be configured to return a random sample of results of a given size. MongoDB can be used as a file system with load balancing and data replication features over multiple machines for storing files. This function, called Grid File System, is included with MongoDB drivers. MongoDB exposes functions for file manipulation and content to developers. GridFS is used in plugins for NGINX and lighttpd. GridFS divides a file into parts, or chunks, and stores each of those chunks as a separate document. MongoDB based (but not restricted to) is _GeoRocket_ , developed by Fraunhofer IGD. It provides high-performance data storage and is schema agnostic and format preserving. For more information please refer to D4.1 which describes the components applied in the DataBio project. ## 3.3 Data interoperability Data can be made available in many different formats implementing different information models. The heterogeneity of these models reduces the level of interoperability that can be achieved. In principle, the combination of a standardized data access interface, a standardized transport protocol, and a standardized data model ensure seamless integration of data across platforms, tools, domains, or communities. When the amount of data grows, mechanisms have to be explored to ensure interoperability while handling large volumes of data. Currently, the amount of data can still be handled using OGC models and data exchange services. We will need to review this element during the course of the project. For now, data interoperability is envisioned to be ensured through compliance with internationally adopted standards. Eventually, interoperability requires different phenotypes when being applied in various “disciplinary” settings. The following figure illustrates that concept (source: Wyborn 2017). _Figure 3: The “disciplinary data integration platform: where do you ssit? (source: Wyborn)_ The intra-disciplinary type remains within a single discipline. The level of standardization needs to cover the discipline needs, but little attention is usually paid to cross-discipline standards. The multi-disciplinary situation has many people from different domains working together, but eventually they all remain within their silos and data exchange is limited to the bare minimum. The cross-disciplinary setting is what we are experiencing at the beginning of DataBio. All disciplines are interfacing and reformatting their data to make it fit. The model works as long as data exchange is minor, but does not scale, as it requires bilateral agreements between various parties. The interdisciplinary approach is targeted in DataBio. The goal here is to adhere to a minimum set of standards. Ideally, the specific characteristics are standardized between all partners upfront. This model adds minimum overhead to all parties, as a single mapping needs to be implemented per party (or, even better, the new model is used natively from now on). The transdisciplinary approach starts with data already provided as linked data with links across the various disciplines, well-defined vocabularies, and a set of mapping rules to ensure usability of data generated in arbitrary disciplines. ### 3.3.1 Interoperability mechanisms Key to interoperable data exchange are standardized interfaces. Currently, the amount of data processing and exchange tools is extremely large. We expect a consolidation of the number of tools during the first 15 months of the project. We will revise the requirements set by the various pilots and the data sets made available regularly to ensure that proper recommendations can be given at any time. ### 3.3.2 Inter-discipline interoperability and ontologies A key element to interoperability within and across disciplines are shared semantics, but the Semantic Web is still in its infancy and it is not clear to which extent it will become widely accepted within data intensive communities in the near future. It requires graph-structures for data and/or metadata, well defined vocabularies and ontologies, and lacks both the necessary tools to get DataBio data operational within reasonable amounts of time. Therefore, at this stage it is mainly recommended to observe the topic of vocabularies and ontologies, but concentrate on initial base-vocabularies and their governance to ensure that at least base parameters are well defined. ## 3.4 Promoting data reuse The reuse of data is a key component in FAIR. It ensures that data can be reused for purposes other than it was initially created for. This reuse improves the cost-balance of the initial data production and allows cross- fertilization across communities. DataBio will advertise all the data produced to ensure that they are known to wider audience. In combination with standardized models and interfaces as described above and complemented with metadata and a catalog system that allows proper discovery, DataBio can serve as valuable input outside of the project. At this stage, it is not clear what licensing models need to be applied for the various data products produced in DataBio. Generally, the focus shall be on public domain attribution and open licenses that maximize reusability in other contexts. All data products produced by DataBio will be reviewed for FAIR principles once a year by the data producing organization. on the other hand, DataBio is open to any third-party data and process provisioning. Data quality is a key component for data reuse. Without proper quality parameters, data cannot be integrated in external processes, as the level of uncertainty of the remote processes becomes undefined. DataBio will review its data products for quality information provided as part of the metadata. Currently, ISO quality flags are envisioned to be used. # Data management support ## 4.1 FAIR data costs The DataBio consortium will handle both the open data and data with restricted access. These data will be used by the project and the project pilots to demonstrate the power of big data. These data will be published through the DataBio infrastructure. The current list of datasets and their details are described in Appendix A. All data are either open data or data with restricted access provided for free to the consortium partners for project purposes. DataBio does not foresee to purchase any data. The consortium has the knowledge and tools to make data FAIR, i.e. findable, accessible, interoperable and reusable. To make data FAIR is one of the project objectives and appropriate resources were allocated by each partner to cover costs for data harmonisation, integration and publication. The DataBio project has allocated appropriate resources to the sustainability of the project results. This includes the sustainability of FAIR data that are in the scope of the project. To satisfy the dataset reusability requirement, DataBio anticipated several strategies for data storage and preservation. Dataset storage and preservation plan will include but not limited to disk drives, solid-state drives, in- memory functions and off-premises storage. Insofar as security concerns are not an issue, DataBio partners will be encouraged to store data in the publicly available certified data repositories. ## 4.2 Big data managers Managing Big Data also includes a specific structure or role-system, which means in fact types of people how manage or use Big Data in a specific way. Following chapter will describe the team structures for Big Data Management in DataBio. DataBio will employ a two-layer approach for the management of the data used. On the first layer, the management of data provided in any of the participating institutions is done locally. On the second layer, data used in the context of DataBio and needed in the context of data exchange or integration across organizations will be subject to the methodologies described within this document. These are enforced by the roles described below. ### 4.2.1 Project manager DataBio includes a diverse group of talented professionals, which have to be led. Beside the complex pilot-driven management structure, Intrasoft can be called the main project manager. ### 4.2.2 Business Analysts Business analysts are business-oriented domain experts, which are comfortable with data handling. They have deep insights in business requirements and logics and make sure that big data applications and platforms are capable to them. Business analysts are the connection between “non-technical” business user and technical developers. This includes technoeconomic analysis as well as advanced visualisation services. DataBio has five Business analysts from five different organizations: Lesprojekt, ATOS, CIAOTECH, IBM and CREA. ### 4.2.3 Data Scientists Data scientists represent the data experts and analysis within the DataBio consortium. They are able to turn raw data into purified insights and value with data science methods, techniques and tools. They have strong programming skills and can handle big data as well as linked data (incl. metadata). Furthermore, they are able to identify datasets for different requirements and develop solutions with regard to common standards. They are also able to visualise eloquently the results and findings. Within the DataBio consortium following partner are data scientists: Lesprojekt, UWB, Fraunhofer IGD, SINTEF, InfAI, INNOVATION ENGINEERING SRL, OGC, VITO. ##### 4.2.3.1 Data Scientists: Machine Learning Experts One of the most important parts of DataBio is making sense and value of data in different bioeconomic sectors. In order to do so, methods, techniques and tools of machine learning are necessary to handle the huge amount of data. The DataBio project has several partner which are capable machine learning experts with different specialisations. These are: PSNC, InfAI, INNOVATION ENGINEERING SRL, VTT, IBM, CREA, DTU, CSEM, EXUS, Terrasigna, CERTH ### 4.2.4 Data Engineer / Architect Data Engineers or Architects are data professionals who prepare the big data to be ready for analysis. This includes data discovery, data integration, data processing (and pre-processing) extraction and exchange as well as the quality control. Furthermore, they focus on design and architecture. DataBio have thirteen partners who fulfil this important role: UWB, ATOS, SpaceBel, VITO, IBM, InfAI, MHG, CREA, e-GEOS, DTU, Cybernetica, CERTH and Rikola. ### 4.2.5 Platform architects The data platform and its architecture is one of the most important part of DataBio. In order to ensure a valid platform design, systems integration and platform development, high experienced platform architects are needed. This role will taken by Intrasoft, ATOS, Fraunhofer IGD, SINTEF and VTT. ### 4.2.6 IT/Operation manager Some of the realized pilots will be very processing intensive, which requires a very good infrastructure. In order to provide and manage this infrastructure specific operation manager are needed. This function will be fulfilled by PSNC and Softeam. ### 4.2.7 Consultant Big Data Consultant are responsible for support, guidance and help within all design and implementation phases. That includes high knowledge and practice in design big data solutions as well as develop data pipelines that leverage structured and unstructured data from multiple sources. The DataBio consortium have several partners which fulfil this role, including SpaceBel, CIAOTECH, InfAI, FMI, Federunacoma, University of St. Gallen, CITOLIVA and OGC ### 4.2.8 Business User Business users are direct (business) beneficiaries of the developed DataBio solutions. Further, they are important to specify detailed domain requirements and implement the solutions. These partners are TRAGSA, Neuropublic, Finnish Forest Centre, MHG, LIMETRI, Kings Bay, Eros, Ervik & Saevik, Liegruppen Fiskeri, Norges Sildesalgslag SA, GAIA, MEEO, Echebastar, Novamont, Rikola, UPV/EHU, ZETOR and CAC ### 4.2.9 Pilot experts In order to specify and prioritize requirements as well as manage the different pilots, finding synergies and connecting the different experts into the pilot, domain experts are needed. These are Lesprojekt, FMI, VTT, SINTEF, Finnish Forest Centre and AZTI. _Figure 4: DataBio’s data managers_ # Data security ## 5.1 Introduction In order to be able to address data security properly, one has to identify the various phases of data lifecycle, from their creation, to their use, sharing, archive and deletion. Handling project data securely throughout their lifecycle lays the foundations of a sensitive data protection strategy. In this context, the project consortium will determine specific security controls to apply in each phase, evaluating during the course of the project their level of compliance. Those data lifecycle phases are featured in the image below and are summarized as follows: 1. Phase 1: Create This first phase includes the creation of structured or unstructured (raw) data. For the needs of the DataBio project, those sensitive data are classified in the following categories: a) Enterprise Data (commercially sensitive data), b) Personal Data (personal sensitive data) and c) other data that are not applicable in one of the previous categories. Especially for the enterprise data, upon the creation phase already, security classification occurs based on an enterprise data security policy. 2. Phase 2: Store Once data is created and included in a file, then it is stored somewhere. What needs to be ensured is that stored data is protected and the necessary data security controls have been implemented, so as to secure and minimize risk of information leak, ensuring efficient data privacy. More information about this phase is found in sections 5.2 about data recovery and 5.3 about secure storage . 3. Phase 3: Use During this phase when data is viewed, processed, modified and saved, security controls are directly applied to data, with a focus on monitoring user activity and applying security controls to ensure data leak prevention. 4. Phase 4: Share Data is constantly being shared between employees, customers and partners, necessitating a strategy that continuously monitors data stores and users. Data move among a variety of public and private storage locations, applications and operating environments, and are accessed by various data owners from different devices and platforms. That can happen at any stage of the data security lifecycle, which is why it’s important to apply the right security controls at the right time. 5. Phase 5: Archive In the case of data leaving active use but still needed to be available, they should be securely archived in appropriate storages, normally of low cost and performance, sometimes offline. This may cover also version control where older versions of original (raw) data files and data source processing programs are maintained in archive storages, per case. These backups are then stored and can be brought back online within a reasonable timeframe that will ensure that there is no detrimental effect of the data being lost or corrupted. 6. Phase 6: Destroy In the case of data no longer needed, this data should be deleted securely so as to avoid any data leakage. ## 5.2 Data recovery Data recovery strategy (also called disaster recovery plan) is not only a plan, but also ongoing process of minimizing a risk of data loss that can be a consequence of different random events. Since DataBio is a project dealing with Big Data scenarios, the context of data recovery is focused mostly on management procedures of data centers that are able to store and process significant amount of data. The disasters that can occur can be classified into two categories: * Natural disasters (floods, hurricanes, tornadoes or earthquakes): because they cannot be avoided it is possible to minimize their effects on IT infrastructure (distributed backups) * Man-made disasters (infrastructure failure, software bugs, hackers attacks): besides minimizing the effect it is possible to prevent them in different ways (regular software updates, good, active protection mechanisms, regular testing procedures) The most important elements of Data recovery plan are: * Backup management: well-designed automatic procedures for regular storing copies of datasets on separate machines or even geographically distributed places * Replication of data to an off-site location, which overcomes the need to restore the data (only the systems then need to be restored or synchronized), often making use of storage area network (SAN) technology * Private Cloud solutions that replicate the management data (VMs, Templates and disks) into the storage domains that are part of the private cloud setup. * Hybrid Cloud solutions that replicate both on-site and to off-site data centers. These solutions provide the ability to instantly fail-over to local on-site hardware, but in the event of a physical disaster, servers can be brought up in the cloud data centers as well. * The use of high availability systems which keep both the data and system replicated off-site, enabling continuous access to systems and data, even after a disaster (often associated with cloud storage) Several partners in the project are infrastructure providers. They ensure high quality in terms of reliability and scalability. ## 5.3 Privacy and sensitive data management ### 5.3.1 Introduction With regards to privacy and sensitive data management, it is confirmed that these activities will be rigorously implemented in compliance to the privacy and data collection rules and regulations as they are applied nationally and in the EU, as well as with the H2020 rules. The next sections include more specific information regarding those activities, rules and measures based on the classification of data made in the introduction of this section (5.1). ### 5.3.2 Enterprise Data (commercial sensitive data) This category of data includes the (raw) data coming from specific sensor nodes and other similar data management systems and sources from the various project partners in each pilot case. They also include data about technologies and other assets protected by IPR and are considered to be highly-commercially sensitive, belonging to the partner that provides them for the various research and pilot activities within DataBio project. Therefore, access to those data will be controlled and exchanges normally take place between specific end users and partners involved in their use and management within each pilot case for DataBio related activities. Following also project GA and CA, each partner who provides or otherwise makes available to any other project partner shared information represents that: (i) it has the authority to disclose this shared information, (ii) where legally required and relevant, it has obtained appropriate informed consents from all 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 project partner from using this shared information for the purpose of DataBio project and the exploitation thereof. The abovementioned rules are also applied to any new data stemming from the project activities. This data will be also anonymised and protected and only based on the above rules our partners will be able to make data available to external industry stakeholders to utilise them for their own purposes. Related publications will be released and disseminated through the project dissemination and exploitation channels to make these parties aware of the project as well as appropriate access to any data (see Appendix A for DataBio specific data). On a technical level, data are protected by IPRs are often accessed as a service, with specific access rights given under specific terms. Alternatively, they are shared encrypted or similarly protected with the keys provided under specific terms. ### 5.3.3 Personal Data According to the Grant Agreement, it has been agreed by all partners 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 shall not include personal data . Accordingly, each Party agreed 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. de-identify) to the other Parties prior to providing the Shared Information. Therefore, no personal sensitive data are included in data exchanged between partners within DataBio. Data created within project activities, e.g. some pilot activities, could initially involve personal and/or sensitive data from human participants, like location and id, DataBio will apply specific security measures for their informed consent and data protection in line with the legislation and regulations in force in the countries where the research will be carried out, with most relevant rules to the project being the following: * The Charter of Fundamental Rights of the EU, specifically the article concerning the protection of personal data * Council Directive 83/570/EEC of 26 October 1983 amending Directives 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. Regarding the procedure that is required in order to be able to participate in any DataBio activities, we foresee that all potential participants will have to read and sign an informed consent form before starting the participation. This form aims to fully inform the participants about the study procedure and goals in order to guarantee that they have basic information in order to make the decision about whether to participate or not in the project activity. It shall include a summary and schedule of the study, the objectives and descriptions of the DataBio system and its components. All participants have the right to receive a copy of the documents of this form. Participants will receive a generic user ID to identify them in the system and to anonymise their identities. No full names will be stored anywhere electronically. All gathered personal data shall be password protected and encrypted. Users’ personal data will be safeguarded from other people not involved in the project. No adults unable to give informed consent will be involved. It should be stated that the protection of the privacy of participants is a responsibility of all persons involved in research with human participants. Privacy means, that the participant can control the access to personal information and is able to decide who has access to the collected data in the future. Due to the principle of autonomy, the participants will be asked for their agreement before private and personal information is collected. It will be ensured that all persons involved in the project activities understand and respect the requirement for confidentiality. The participants will be informed about the confidentiality policy that is used in this research project. ## 5.4 General privacy concerns Other privacy concerns will be addressed as following: * External experts: Any external experts that will be involved in the project shall be required to sign an appropriate non-disclosure agreement prior to participating in any project related meeting, decision or activity. * Publications: Hints to or identifiable personal information of any participant in (scientific) publications should be omitted. It is avoided to reveal the identity of participants in research deliberately or inadvertently, without the expressed permission of the participants. * Dissemination: Dissemination of data between partners. This relates to access to data, data formats, and methods of archiving (electronic and paper), including data handling, data analyses, and research communications. Access to private information will be granted only to DataBio partners for purposes of evaluation of the system and only in an anonymised form, i.e. any personally identifiable information such as name, phone number, location, address, etc. will be omitted. * Protection: The lead project partner of every pilot case is responsible for the protection of the participants’ privacy throughout the whole project, including procedures such as communications, data exchange, presentation of findings, etc. * Control: The responsible project partners are not allowed to circulate information without anonymisation. This means that only relevant attributes, i.e. gender, age, etc. are retained. * Information: As already mentioned above, the protection of the confidentiality implies informing the participants about what may be done with their data (i.e. data sharing). Individuals that participate in any study must have the right to request and obtain free of charge information on his/her personal data subjected to processing, on the origin of such data and on their communication or intended communication. # Ethical issues In line with the Consortium’s commitment in the DATABIO proposal, the ethics and responsibility work in the project is guided by the principles of responsible research and innovation in the information society ( _http://renevonschomberg.wordpress.com/implementing-responsible-research-_ _andinnovation/_ ) , by the guidelines of European Group on Ethics ( _http://ec.europa.eu/bepa/european-groupethics_ ) . Since the research activities do not include any human trial, animal intervention or acquisition of tissues thereof, there are no ethical concerns. Remote sensing of fields, forests or fish stocks does not cause any ethical concerns. The Partners agreed 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 Partners 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 Partner 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. de-identify) to any other Party prior to providing the Shared Information to such other Party. Each Partner who provides or otherwise make available to any other Partner Shared Information (“Contributor”) represents that: (i) it has the authority to disclose the Shared Information, if any, which it provides to the Partner; (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 Partner from using the Shared Information for the purpose of the DATABIO Action and the exploitation thereof. Any Advisory Board member or external expert shall be required to sign an appropriate nondisclosure agreement prior to participating in any project related meeting, decision or activity. # Conclusions The DataBio project is an EU lighthouse project with eighteen pilots running from hundreds of piloting sites across Europe in the three main bioeconomy sectors, agriculture, forestry, and fishery. During the lifecycle of the DataBio project, big data will be collected consisting of very large data sets including a wide range of data types from numerous sources. Most data will come from farm and forestry machinery, fishing vessels, remote and proximal sensors and imagery, and many other technologies. In this document, DataBio’s D6.2 deliverable “Data Management Plan” was presented as the key element of good data management. As DataBio participates in the European Commission H2020 Program’s extended ORD pilot, a DMP is required and as a consequence, DataBio project’s datasets will be as open as possible and as closed as necessary, focusing on sound big data management for the sake of best research practice, and in order to create value, and foster knowledge and technology out of big datasets for the good of man. The data management life cycle for the data to be collected, processed and/or generated by DataBio project was described, accounting also for the necessity to make research data findable, accessible, interoperable and re-usable, without compromising the security and ethics requirements. As a part of the project implementation, DataBio’s partners will be encouraged to adhere to sound data management to ensure that data are well-managed, archived and preserved. This is the first version of DataBio DMP; it will be updated over the course of the project as warranted by significant changes arising during the project implementation, and the project consortium. The scheduled advanced releases of this document will particularly include information on the repositories where the data will be preserved, the security measures, and several other FAIR aspects.	https://phaidra.univie.ac.at/o:1140797	Horizon 2020
0040_COMPACT_740712.md	# 1\. Introduction This deliverable will set out the first version of the data management plan (DMP) for the COMPACT project. A DMP is a key element of good data management, which is especially important in the COMPACT context, as all Horizon 2020-funded projects from 2017 onward are required to contain a DMP. 1 This DMP is based on the European Commission’s Guidelines on FAIR Data Management in Horizon 2020 2 and the COMPACT Grant Agreement. 3 It reflects the consortium’s comprehensive approach towards data management. It is a living document, which will be updated in months 18 and 24 (DMP version 2, and the final version, respectively), due to the possible significant changes, including but not limited to: * Use of new data, * Changes in consortium policies (e.g. new innovation potential, decision to file for a patent, etc.), * Changes in consortium composition and external factors (e.g. new consortium members joining or existing members leaving). This deliverable will contribute towards legal and ethical compliance regarding data protection, alongside the Deliverable 2.5 ‘S.E.L.P. Framework’. While the latter focuses specifically on legal and ethical aspects of principles and minimum requirements of procedures, necessary for proper data collection, this document will serve as a project management tool, implementing those requirements in terms of data management. In order to implement the open data principle, the DMP sets out the following information: * 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). Sections 2 to 7 of this document will cover the different DMP components, based on the outline suggested in the Guidelines. They are based on input from the following partners: AIT, CINI, INOV and KUL, as indicated in the relevant sections. # 2\. Data summary ## 2.1. AIT What is the purpose of the data collection/generation and its relation to the objectives of the project? For AIT the purpose of collecting user data is to understand user behaviour on an analytical basis. What types and formats of data will the project generate/collect? AIT will record audio and video of test participants. In addition we will save log-files within the prototypes. We will also collect data via online surveys. Will you re-use any existing data and how? AIT will not re-use any existing data. What is the origin of the data? AIT will collect data by observing users during technology interaction and asking them (either in real time or via online surveys). What is the expected size of the data? 1 TB (which will mainly be video recordings of end-user interaction behaviour) To whom might it be useful ('data utility')? Recordings of end-users (besides being the basis for end-user-studies in the project) are – due to their heavy context dependence – not useful to third parties. It would also create a privacy problem for end-users if the recordings would be public. Hence they are closed. ## 2.2. CINI What is the purpose of the data collection/generation and its relation to the objectives of the project? Within COMPACT Project, CINI is in charge of developing an advanced Security Information and Event Management (SIEM) system endowing LPAs’ organisation with real-time monitoring capabilities. SIEM services receive log files, which represent records of the events occurring within an organization’s systems and networks when a user attempts to authenticate into the system or a system event occurs (such as starting a service or shutting down the system, etc.). The content/records of these log files related to computer security information are then analysed for investigating malicious activities. A particular alarm or event is generated in relation to the particular detected attack. What types and formats of data will the project generate/collect? SIEM systems come with a number of adapters for receiving data/events from a wide variety of sources, such as Operating System (OS) log files (in proprietary or open formats) or Commercial Off The Shelf (COTS) products for logical and physical security monitoring, including: Wireshark, Nessus, Nikto, Snort, Ossec, Argus, Cain & Abel, OpenNMS, Nagios, CENTEROS, Ganglia, Milestone, openHAB, IDenticard, FieldAware, and CIMPLICITY. In terms of data generated, a format has not been defined yet. However, any XMLcompliant format (such as for example the “Json” (JavaScript Object Notation) format) can represent a valuable solution for alarms/events generated by the SIEM system. Will you re-use any existing data and how? It is not definitive in this phase, since the datasets are not currently specified. However, in a first phase of the SIEM service implementation, we foresee to use existing anonymized data present within the archives of some of the LPAs involved in the project. What is the origin of the data? Data collected and analysed by the SIEM system will be originated by testing activities carried out at a pilot sites (LPAs involved within the project) and will be relied on the collection and analysis of log files of the LPAs participating in the COMPACT project. What is the expected size of the data? At this stage of the project, it is not possible to predict the size of the data that will be processed, but we can hypothesize that they will be more than a terabyte. To whom might it be useful ('data utility')? Data collected by the SIEM system can be useful to the other technical partners in charge of developing COMPACT’s tools and services, such as risk assessment tool or personalization of training courses for LPAs’ employees, etc.. They could be useful to other research groups working on similar research, as well as for testing alternative SIEM solutions. ## 2.3. INOV What is the purpose of the data collection/generation and its relation to the objectives of the project? During the COMPACT project, INOV will collect data to test and demonstrate its Business process intrusion detection system (BP-IDS). This data collection is related with the project objective “SO3: Lower the entry barrier to timely detection and reaction to cyber-threats”, and may occur during the tasks: “Task 4.3 Threat intelligence and monitoring Component”; “Task 4.5 Integration of solutions in a unified platform”; “Task 5.1 Validation and Demonstration scenarios”; “Task 5.2 Trials Setup”; and “Task 5.3 Pilot execution and demonstration”. What types and formats of data will the project generate/collect? It is not definitive in this phase, since the datasets are not currently specified. However, it is expected that BP-IDS collects data from multiple sources of data, such as: network traffic generated during the communications of the monitored hosts; or by inspecting specific files present in the file- system of the monitored hosts. Will you re-use any existing data and how? It is not definitive in this phase, since the datasets are not currently specified. But it is expected that all the data collected is self-contained in the dataset used, and not re-used existing data. What is the origin of the data? It has not been decided yet, the datasets need to be specified first in order to respond to this question. What is the expected size of the data? It is difficult to estimate the size of the data at this stage, because the size of the data highly varies on the network protocols or the files monitored. To whom might it be useful ('data utility')? The principal benefactor of this dataset will be CMA that will use the tools developed to monitor threats against their infrastructure. INOV will use it for adapting BP-IDS for LPAs. Besides INOV this dataset might be useful to technical partners in the COMPACT project, that require live data to adapt their technical solutions to LPA environments. # 3\. FAIR data Under Horizon 2020’s principle of open access to data, research data must be FAIR: findable, accessible, interoperable and reusable. This will contribute to the use of data in future research. 4 In order to be Findable : * F1. (meta)data are assigned a globally unique and eternally persistent identifier. * F2. data are described with rich metadata. * F3. (meta)data are registered or indexed in a searchable resource. * F4. metadata specify the data identifier. In order to be Accessible : * A.1. (meta)data are retrievable by their identifier using a standardized communications protocol. * A1.1. the protocol is open, free, and universally implementable. * A1.2. the protocol allows for an authentication and authorization procedure, where necessary. * A2. metadata are accessible, even when the data are no longer available. In order to be Interoperable : * I1. (meta)data use a formal, accessible, shared, and broadly applicable language for knowledge representation. * I2. (meta)data use vocabularies that follow FAIR principles. * I3. (meta)data include qualified references to other (meta)data. In order to be Re-usable : * R1. meta(data) have a plurality of accurate and relevant attributes. * R1.1. (meta)data are released with a clear and accessible data usage license. * R1.2. (meta)data are associated with their provenance. * R1.3. (meta)data meet domain-relevant community standards. Answering the following questions will contribute towards compliance with the FAIR data standards. The answers are provided in a comprehensive manner, not on a yes/no basis. ## 3.1. Making data findable, including provisions for metadata ### 3.1.1. AIT 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)? The scientific publications from AIT will end up with a DOI when accepted at a conference. What naming conventions do you follow? None. Will search keywords be provided that optimize possibilities for re-use? Yes. Do you provide clear version numbers? Yes, versioning is already implemented in the document templates. 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. AIT TX will use standard HCI classifiers from ACM. ### 3.1.2. CINI 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 defined yet. However, if the Zenodo Repository will be adopted for data storing and sharing, the persistent identification through DOIs for sharing research result will be adopted. What naming conventions do you follow? We refer to the “Glossary of Key Information Security Terms” provided by NIST 5 or, in turn, to the SANS Glossary of Security Terms 6 . Will search keywords be provided that optimize possibilities for re-use? At this stage we have not yet planned to provide keywords for optimizing re- use. Do you provide clear version numbers? Not defined yet 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. Not defined yet ### 3.1.3. INOV 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 defined yet What naming conventions do you follow? Not defined yet Will search keywords be provided that optimize possibilities for re-use? Not defined yet Do you provide clear version numbers? Not defined yet 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. Not defined yet ## 3.2. Making data openly accessible ### 3.2.1. AIT What methods or software tools are needed to access the data? The COMPACT project strives to make data available in a format, which can be read by free tools (also) to not force people to buy software only to read through the COMPACT outcomes. Is documentation about the software needed to access the data included? No. ### 3.2.2. CINI 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. There has been no opt-out in the COMPACT project yet. Data produced and/or used in the project will be made openly available by default only after a pseudonymisation and/or anonymization process in order to prevent that the data can be attributed to a specific person. How will the data be made accessible (e.g. by deposition in a repository)? Data will be made accessible through a research data repository. The consortium will take measures to enable third parties to access, mine, exploit, reproduce, and disseminate the data free of charge. What methods or software tools are needed to access the data? The best candidate tool for data sharing – at the time of this writing – is ZENODO, an OpenAIRE/CERN compliant repository. 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, <table> <tr> <th> </th> <th> including text, spreadsheets, audio, video, and images across all fields of science. </th> </tr> <tr> <td>  </td> <td> display the 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. </td> </tr> <tr> <td>  </td> <td> easily access and reuse shared research results. </td> </tr> </table> Is documentation about the software needed to access the data included? Yes it is. Is it possible to include the relevant software (e.g. in open source code)? It is possible, but not decided yet if open source code will be included. 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. The consortium plans to deposit data in an OpenAIRE compliant research data repository. Have you explored appropriate arrangements with the identified repository? Not defined yet If there are restrictions on use, how will access be provided? Not defined yet Is there a need for a data access committee? Not defined yet Are there well described conditions for access (i.e. a machine-readable license)? Not defined yet How will the identity of the person accessing the data be ascertained? Not defined yet ### 3.2.3. INOV 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. There has been no opt-out in the COMPACT project yet. Not defined yet How will the data be made accessible (e.g. by deposition in a repository)? Not defined yet What methods or software tools are needed to access the data? Not defined yet Is documentation about the software needed to access the data included? Not defined yet Is it possible to include the relevant software (e.g. in open source code)? Not defined yet 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. Not defined yet Have you explored appropriate arrangements with the identified repository? Not defined yet If there are restrictions on use, how will access be provided? Not defined yet Is there a need for a data access committee? Not defined yet Are there well described conditions for access (i.e. a machine-readable license)? Not defined yet How will the identity of the person accessing the data be ascertained? Not defined yet ## 3.3. Making data interoperable ### 3.3.1. AIT 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)? AIT will rely on XML when publishing HCI-patterns, which guarantees data exchange with existing HCI-pattern providers. What data and metadata vocabularies, standards or methodologies will you follow to make your data interoperable? PLML (pattern language mark-up language) Will you be using standard vocabularies for all data types present in your data set, to allow inter-disciplinary interoperability? No. 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? Yes. ### 3.3.2. CINI 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 What data and metadata vocabularies, standards or methodologies will you follow to make your data interoperable? Not defined yet Will you be using standard vocabularies for all data types present in your data set, to allow inter-disciplinary interoperability? Not defined yet 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 defined yet ### 3.3.3. INOV 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)? Not defined yet What data and metadata vocabularies, standards or methodologies will you follow to make your data interoperable? Not defined yet Will you be using standard vocabularies for all data types present in your data set, to allow inter-disciplinary interoperability? Not defined yet 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 defined yet ## 3.4. Increase data re-use (through clarifying licenses) ### 3.4.1. CINI How will the data be licensed to permit the widest re-use possible? Not defined yet 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. At this stage of the project it is not possible to predict the date for making re-use available. 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. Anonymised data will be usable by third parties. How long is it intended that the data remains re-usable? We intend to store data and make it re-usable for an appropriate period of time according to the key guidelines established by the following regulations: Directive (EU) 2016/1148 of the European Parliament and of the Council of 6 July 2016 concerning measures for a high common level of security of network and information systems across the Union (NIS Directive) 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) Are data quality assurance processes described? Not defined yet ### 3.4.2. INOV How will the data be licensed to permit the widest re-use possible? Not defined yet 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. Not defined yet 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. Not foreseeable at this stage How long is it intended that the data remains re-usable? For the duration of the project. Are data quality assurance processes described? Not defined yet # 4\. Allocation of resources – the whole consortium According to the Horizon 2020 rules, costs related to open access to research data are eligible for reimbursement during the duration of the project under the conditions defined in the COMPACT Grant Agreement, in particular Articles 6 and 6.2.D.3. 7 These are direct costs, related to subcontracting of project tasks, such as subcontracting the open access to data. What are the costs for making data FAIR in your project? According to the budget, AIT has planned to address 10000 EUR for making data FAIR in the project. 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 defined yet Who will be responsible for data management in your project? A specific role has been foreseen in the project, the Data Controller (DC). Salvatore D’Antonio from CINI has been appointed as Data Controller and will be responsible for data management. 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)? Not defined yet # 5\. Data security ### 5.1.1. AIT What provisions are in place for data security (including data recovery as well as secure storage and transfer of sensitive data)? Standard data at AIT is stored on a state-of-the-art secured storage. For sensible data encrypted file storages are created on demand with extremely restricted access. Is the data safely stored in certified repositories for long term preservation and curation? AIT runs regular backups on all data. This ensures preservation. ### 5.1.2. CINI What provisions are in place for data security (including data recovery as well as secure storage and transfer of sensitive data)? Regarding security, all the data collected will be stored on a database only accessible to authenticated users on the partner premises. Regarding the data recovery, database backups will be stored on premises and only accessible to CINI. Is the data safely stored in certified repositories for long term preservation and curation? It is not definitive in this phase. ### 5.1.3. INOV What provisions are in place for data security (including data recovery as well as secure storage and transfer of sensitive data)? Regarding security, all the data collected will be stored on a database only accessible to authenticated users on the partner premises. Regarding the data recovery, database backups will be stored on premises and only accessible to INOV. Is the data safely stored in certified repositories for long term preservation and curation? It is not definitive in this phase, but it is not expected to store collected data in a repository. # 6\. Ethical and legal 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). Two types of data will be used in the COMPACT research: personal data and anonymised data. Personal data is defined in the General Data Protection Regulation (GDPR) as ‘any information relating to an identified or identifiable natural person (‘data subject’); an identifiable natural person is one who can be identified, directly or indirectly, in particular by reference to an identifier such as a name, an identification number, location data, an online identifier or to one or more factors specific to the physical, physiological, genetic, mental, economic, cultural or social identity of that natural person’. When processing personal data, data protection legislation applies. Until May 25 th 2018, this is the Data Protection Directive (DPD) 8 and the relevant legislation, transposing the DPD into national law, and after that date, the General Data Protection Regulation (GDPR). 9 Deliverable D1.2 ‘S.E.L.P. Management Plan’ sets out the management procedures, enabling the consortium to comply with legal requirements. Ethics requirements are addressed in WP8, Deliverables D8.1-8.3. Anonymised data, on the other hand, are not subject to such requirements. 10 This is because once data have been successfully anonymised, their subjects can no longer be (re-)identified. Therefore, there are no specific data protection-relevant provisions in EU law, which hinder dissemination or further use of anonymised data. Data must be anonymised in a manner that absolutely prevents the data subject from being reidentified. While there are no specific provisions in the Open Data Research Pilot requiring the participants to anonymise data, the open research data from the COMPACT project will be anonymised before it is made publically available. Anonymisation is a data processing operation, so the GDPR requirements apply before and while it is being carried out, 11 especially the basic principles such as data minimisation and purpose limitation. The procedure for carrying out a GDPR-compliant anonymisation procedure is described in Deliverable D2.5, ‘S.E.L.P. Framework'. Regarding possible intellectual property (IP) restrictions on the use of research data, these are dealt with in the Consortium Agreement. Research data qualifies as ‘results’, which are defined as any (tangible or intangible) output of the action such as data, knowledge or information – whatever its form or nature, whether it can be protected or not – that is generated in the action, as well as any rights attached to it, including intellectual property rights. Research results are owned by the partner which produced them. Regarding access to such results, partners will conclude individual agreements with end-users. Is informed consent for data sharing and long term preservation included in questionnaires dealing with personal data? All participants will give their free and informed consent before any personal data is obtained from them. In order to do so, they will be provided with Informed Consent Forms and Information Sheets, which set out the purposes and means of data collection and their relevance in the COMPACT project. They take into account the principles of data minimisation and purpose limitation. Data minimisation refers to processing on the data, which are adequate, limited and necessary for the research purposes, including time limitation on storage and amount of data. Purpose limitation means that processing will be carried out for a specific, explicit and legitimate purpose, i.e. research for the purposes of the COMPACT project, as well as storage for potential further research, which is explained in the Informed Consent Form and Information Sheet. # 7\. Other Do you make use of other national/funder/sectorial/departmental procedures for data management? If yes, which ones? Not defined yet (AIT, CINI, INOV).	https://phaidra.univie.ac.at/o:1140797	Horizon 2020
0044_INNOPATHS_730403.md	# 1\. Introduction This Data Management Plan presents a summary of the key data that will be used and generated by the INNOPATHS project, and how this data will be managed to ensure that it is FAIR – Findable, Accessible, Interoperable and Re-usable. This is intended to be a ‘living’ document. The information presented below will evolve and become more specific (or change) over time, as the INNOPATHS project progresses and as details, practicalities and feedback from project partners and key stakeholders emerges. # 2\. Data Summary The INNOPATHS project involves both primary and secondary data collection, and the generation of new data. Primary data collection is planned by activities under Tasks 1.3, 1.4, 1.5, 2.1, 2.2, 2.3, 2.4, 2.5, 3.1, 3.6 and 4.4. Such data collection will largely take the form of semistructured interviews with officials, analysts, technical experts, academics and members of the INNOPATHS ‘Co-Design Group’ (CDG). The collection, handling and storage of personal data will comply with the procedures outlined in Deliverables 8.1 and 8.2. Secondary data will be collected under a wide range of activities, tasks and subtasks throughout the project. Such data will be largely sourced from the scientific, government and grey literature and associated databases, along with proprietary datasets. Specific examples of activities and secondary data sources employed include: * Key characteristics (e.g. cost, performance, spillover rates and associated uncertainty ranges) related to key technologies (both historic and characteristics projected for the future according to existing EU scenario, roadmap and transition studies), across the power, ICT, industry, buildings, transport and agriculture sectors, for the EU and Member States (T1.1 – T1.3, & T1.6) * Energy-related investments by households, using data from the German Mobility Panel (MOP), possibly in conjunction with the German Socio-Economic Panel (SOEP) and the survey Mobility in Germany 2008. Analysis may also be extended to Denmark or Norway, using similar datasets (T1.4). * Effect of environmental legislation on sectoral environmental and economic performance, skill composition of the workforce, and wage premia to different occupational groups, with data required to conduct such analysis sourced from varied public databases, including WIOD, EU-KLEMS and ONET (T1.4). * Current and historic flows of finance to both high- and low-carbon technologies, and financial instruments employed. Data will be sourced from both publically available and proprietary databases (e.g. IEA/OECD, BNEF, Thompson Reuters) (T1.5). These datasets are already available to project partners, and will not incur additional cost. * Identify significant technological advancements and associated spillover effects for key energy-related technologies using patent analysis. Patent data will be purchased to allow this analysis, with a budget of €10,000 allocated for this purpose (T2.2). * Industrial process technologies and their characteristics will be updated in the PRIMES industrial sector model, using the European Commission’s Best Available Technique database (T3.6) * Effect on the labour market and industrial competitiveness from changes in energy prices (a proxy of climate policies) using French firm-level data. In particular, three data sources available from the French statistical office will be used: EACAI (on energy expenditures), DADS (on employment and skills) and FARE/FICUS (on productivity and balance sheets). Access to this data is given after presenting a project and paying an annual subscription fee (paid by project partner SPO, outside of the INNOPATHS project budget). These data are protected by the statistical confidentiality directly at the source, and access to is granted only remotely using fingerprint identification (T4.1). * The effect of income (in)equality on the ability to transition to a low-carbon economy, using cross-country emissions data linked with the various freely available datasets on inequality (i.e. Milanovic all gini, the world wealth and income database (http://wid.world/) (T4.5). * The impact of new low-carbon transition pathways on air pollution, security of supply resources, materials agriculture and land, drawing on publically-available databases including GAINS and EXIOPOL (T4.6). Other primary and secondary data may be collected throughout the project, as the need or opportunity arises. The purpose for such primary and secondary data collection is multifaceted. Such data is required to (a) understand how existing studies have treated different aspects of technology, the environment, economy and society, to draw lessons for the analysis to be conducted in INNOPATHS, (b) allow analysis to address specific questions posed by the INNOPATHS project, using the most relevant and best data available, (c) improve the quality of data inputs to, and the characterisation and detail of, different models to be used to produce new low-carbon transition pathways, and (d) to analyse the environmental and socio-economic impacts of such pathways, beyond those assessed by the models that created them. The data generated by the project in the tasks outlined above (along with those from other activities and tasks) will be made available principally through three publically accessible ‘online tools’: * Technology Matrix – This will consist of graphical representations of underlying data on different parameters regarding different energy-related technologies. The user would be able to select different cost and performance (including environmental performance) variables for the x/y axes and to show the results in a wide range of units. There would also be the functionality for cross-country and cross-technology comparisons (when data at that level is available), a search function (by keywords), and a menu structure (or equivalent). It will also be possible to select future-looking cost and performance estimates for the period 2030-2050 (with uncertainty ranges). A perspective on ‘Technology Readiness Level’ (TRL) progression would also be provided. * Policy Assessment Framework - This tool will be designed to exploit to the fullest the research carried out under WP2, which is dedicated to the creation of a framework, which will build on official EU criteria, to set out what we know about the outcomes of a range of energy policy instruments. The tool will allow the user to select a policy or a group of policies, and then visualize what the state of the art knowledge is on the different types of impact that policy has had. This knowledge will come from a wide range of literatures and countries and from the project itself. The indication of impacts would include the source of the impact, as well as the level of confidence in the results. * Low Carbon Pathways Platform - This Platform will allow stakeholders to assess the socio-economic implications of medium- to long-term decarbonisation pathways, including their associated costs, benefit and risks, view and interact with the energy-economy pathways for the 21st century modeled in WP3, and extract the information relevant for their decision-making. They will be able to access the important variables (energy, emissions, technology deployment, prices) for the different models, sectors, scenarios, and countries (and combinations thereof). The user-friendly interface will provide quick introductions and how-to-recipes to facilitate information access by first-time users. These tools, and the information and data contained and presented therein, will be useful for a wide range of stakeholders, including academics and researchers (not least those involved with INNOPATHS, in order to address the key research questions posed by the project), national and local policy makers, industry representatives, and the wider public. A forth online tool will also be developed and employed by the project: \- Interactive Decarbonisation Simulator – This tool has the goal of giving policy makers, the general public and industry associates a more intuitive understanding of different decarbonisation strategies, and what different choices and targets in the various sectors and MS entails for the other sectors and MS. It is a tool that is fully interactive; policymakers can increase the mitigation effort in one area and decrease it in another, and the simulator will give them a rough idea of the expected effect. The tool invites users to interact with it - by designing a few different decarbonisation strategies, one can quickly get an intuitive understanding of which measures have a large effect and which don't, and where potential bottlenecks might lie. However, the Interactive Decarbonisation Simulator will draw on scenarios and data generated by existing studies by project partner E3M, rather than those generated by the INNOPATHS project. In addition, although it will be accessible to the public through the INNOPATHS website alongside the three online tools described above, it will be produced and hosted by E3M (with project partner N&S applying INNOPATHS style and branding). As such, the sections below refer only to the first thee online tools described above (except for Section 2.1, which is applicable to all four tools). # 3\. FAIR Data ## 1.1 Making data findable & Metadata ### Discoverability and Metadata For each online tool webpage/portal, we will adhere to search engine optimisation (SEO) best practices. This means they will be easily discoverable by search engines, using HTML metatags to describe the content of the tools in clear and easily understandable manner. The presence and location of the online tools will be heavily advertised by INNOPATHS Deliverables, publications and various dissemination channels (including the project flyer, social media and events). Once a user has navigated to the online tool, specific data may be found through a verity of means, depending on the tool in question and data of interest. For example, the Policy Assessment Framework (PAF) tool will allow users to access underlying information (e.g. effectiveness, cost-efficiency) about different policy instruments and mixes at different levels of granularity (at least two), including outcomes, level of confidence, and source material. Different categories of data will be presented in different ways. For example, the context for a particular policy may be displayed in the form of a comment, the level of confidence on a particular outcome may be shown as a number with a clear description (translation) of what that number represents, and in some cases the ‘impact’ may be measured with a number or a range. It is likely that each online tool will have a keyword search, dropdown filtering functionality and/or structured menus to help narrow down the user’s selection and more easily navigate the tools to identify the data of interest. Specific keyword and metadata terms and conventions will be defined as the data is produced and online tools are created. However, for the Low Carbon Pathways Platform (LCCP) the metadata will follow the Integrated Assessment Modelling Consortium (IAMC) convention 1 of scenario and variable metadata, thus containing information on the scenario definitions, the models used, their version number, regional resolution. ### Versioning To make it clear when an update has been made to either the functionality of the online tools, or the data contained in and presented by the online tools, a version number will be added to the relevant page, along with the date that the tool was last updated. ## 1.2 Making data openly accessible ### Accessibility for Project Partners During the construction of the online tools and their initial population with data, all partner institutions will have access to the tools via a web interface. In addition, for the Low Carbon Pathways Platform (LCPP), for the purpose of convenient scenario data analysis, consolidated snapshots of scenario data will be available to partner institutions. Selected stakeholders (i.e. co-designers who will test and provide feedback to the design of the tools) will also have access, under strict confidentiality arrangements. The specific protocol for access and data use during this period will depend on the specific tool. For example, for the LCPP, when submitting data to the INNOPATHS scenario database for subsequent display on the LCPP tool, modelling teams will agree to the internal use of their scenario data by all INNOPATHS partner institutions. However, the team producing the scenario data, in order to account for the iterative process of scenario calculation and quality control, must approve publication of research based on such data by project partners. Individual modelling teams participating in the INNOPATHS project shall retain control of their preliminary scenario data. Use of preliminary scenario data by other partners than the modelling team generating this data must be through explicit permission from the modelling team concerned. When the online tools are made available to the general public, differentiated access permissions will be defined. Accessibility for the Public All online tools will be fully accessible to the public. The datasets contained within and presented by each of the tools will be viewable directly within the tools themselves, or extractable to comma separated value (CSV) file format for further analysis. The CSV format is a widely used format applicable to most data analysis software. To preserve transparency, the code for each of the online tools will be made open source and accessible to the public through GitHub (the Interactive Decarbonisation Simulator will also be open source). ## 1.3 Making data interoperable The form in which the data is presented will depend on the specific tool and specific data of interest (e.g. numerical value, text, hyperlink, etc.). All efforts will be made to ensure the data presented is easily understood and easy to use for further analysis. This will include, for example, the use of the IAMC variable template for the data presented by the LCPP. Efforts will also be made to link to the common scenario databases and scenario visualisations of the IAMC Working Group on scenarios and the Horizon 2020 project CD-LINKS. As discussed above, all data presented by the online tools will be downloadable into CSV format, for ease of interpretation and analysis. ## 1.4 Increasing data reuse Any license conditions for the use of data presented by the online tools will be clearly displayed, both on the online viewing platform and accompanying any downloaded data. The data for each online tool will be made available to the public once related scientific work has been made available for publication. This ensures that the data is subject to the additional quality control from the review process. After the re-use embargo is lifted, the data may be freely used by third parties for non-commercial and educational purposes. Efforts will be made to preserve the online tools for public use for as long as possible after the conclusion of INNOPATHS. Specific timeframes will be clarified as the project progresses. Subject to future funding, updates to data presented by the tools may be updated. # 4\. Allocation of Resources The costs for making the data generated by the project ‘FAIR’ will be minor, and are included as part of the budget assigned to the project partners responsible for producing the data for the online tools (AU, UCAM, E3M and PIK), and the project partner responsible for producing the tools themselves (N&S). Responsibilities for data management rest in the first instance with the project partners responsible for generating and collating them (as above), and then with UCL, who will host the data and the tools through the UCL Research Data Service (see below). Preserving long-term access to this data, through the online tools, will be highly valuable to INNOPATHS stakeholders (e.g. policy makers, industrial groups, NGOs), and is achievable at minimal, and perhaps zero cost (specific value to be determined). # 4\. Data Security The online tools, and data generated by the project for use by these tools, will be curated by the UCL Research Data Service 2 . UCL’s Research Data Services (RDS) has the capability to store and access very large volumes of electronic research data and data products, to support coordinated end-to-end research workflows encompassing the use of both data storage and computational resources (e.g. UCL’s high performance computing services), and to protect and preserve digital data assets, including for future re-use and exploitation. The project’s data storage strategy will consist of three components: private web server storage, a secure short-term backup facility, and a long-term archive. Consortium partners are able to upload and exchange data using the private server. Web server storage is flexible, backed up, and can be readily expanded if necessary. The long-term archive will ensure that data and the online tools are preserved once the project comes on an end (for a timeframe yet to be determined). # 4\. Ethical Aspects For an assessment and management of the ethical aspects of data collection and use for the INNOPATHs project, please see Deliverables 8.1 and 8.2.	https://phaidra.univie.ac.at/o:1140797	Horizon 2020
0046_InterFlex_731289.md	# 1\. INTRODUCTION & PROJECT BACKGROUND ## 1.1. Scope of the document This report presents the data management life cycle for the data to be collected, processed and/or generated by the InterFlex project. As part of making research data findable, accessible, interoperable and re- usable (FAIR), the deliverable will include information on: * The handling of research data during & after the end of the project * What data will be collected, processed and/or generated * Which methodology & standards will be applied * Whether data will be shared/made open access and * How data will be curated & preserved (including after the end of the project). Within the InterFlex project, it may be necessary to limit access to certain information, in accordance with the article 12 of the Electricity Directive by guaranteeing that commercially sensitive information obtained in the course of carrying out their business shall remain confidential, and that information disclosed regarding their activities, which may be commercially advantageous, shall be made available in a non-discriminatory manner. The document will be updated over the course of the project whenever significant changes arise, such as (but not limited to) 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). The DMP will be updated as a minimum in time with the periodic evaluation/assessment of the project: M18 and M36. ## 1.2. Notations, abbreviations and acronyms The table below provides an overview of the notations, abbreviations and acronyms used in the document. ## 1.3. EU Expectations from InterFlex InterFlex is a response to the Horizon 2020 Call for proposals LCE-02-2016 (“Demonstration of smart grid, storage and system integration technologies with increasing share of renewables: distribution system”). This Call addresses the challenges of the distribution system operators in modernizing their systems and business models in order to be able to support the integration of distributed renewable energy sources into the energy mix. Within this context, the LCE-02-2016 Call promotes the development of technologies with a high TRL (technology readiness level) into a higher one. InterFlex explores pathways to adapt and modernize the electric distribution system in line with the objectives of the 2020 and 2030 climate-energy packages of the European Commission. Six demonstration projects are conducted in five EU Member States (Czech Republic, France, Germany, The Netherlands and Sweden) in order to provide deep insights into the market and development potential of the orientations that were given by the call for proposals, i.e., demand-response, smart grid, storage and energy system integration. With Enedis as the global coordinator and ČEZ Distribuce as the technical director, InterFlex relies on a set of innovative use cases. Six industry-scale demonstrators are being set up in the participating European countries: Through the different demonstration projects, InterFlex will assess how the integration of the new solutions can lead to a local energy optimisation. Technically speaking, the success of these demonstrations requires that some of the new solutions, which are today at TRLs 57, are further developed reaching TRLs 7-9 to be deployed in real-life conditions. This allows new business models and contractual relationships to be evaluated between the DSOs and the market players. Environment : Through the optimisation of the local energy system, the project generates benefits in terms of increased energy efficiency (load shifts to off peak hours; optimized selfconsumption in case of prosumers, increased awareness leading to active DSM and reduced electricity consumption), power generation optimization (peak shaving, avoiding electricity generation from carbonized peak load generation units) and increased share of renewables (optimized integration of intermittent renewable energy sources), resulting in the overall reduction of GHG emissions. Socio-economic : The project stimulates the development of new services for end-customers allowing for instance the development of demand response service packages for small and large consumers as well as prosumers. The provision of community storage solutions or the optimal use of multiple source flexibilities should help to decrease the electricity bill without any noticeable impact on the supply quality. Policy : The Use cases of the project will help to * Formulate recommendations for micro grid operation (control schemes and observability), * Elaborate an appropriate regulatory framework for self- consumption and storage solutions (community or individual residential storage) * Provide guidelines on the participation of distributed resources in DSO operations (modifications of grid codes). _Figure 1: InterFlex Demo Map_ # 2\. DATA SUMMARY ## 2.1. Purpose of the data collection The goal of the data collection is to design a structure for data classification and define level of confidentiality and access rights for each subcategory: * To evaluate the technical and financial performance of the 6 demonstrators and the InterFlex project * To communicate properly on the demonstrators and the results of the InterFlex project * To make sure the 6 demonstrators methodology and results are exploitable and replicable * Without affecting the confidentiality of some data 1 2 Data Subcategories 1 Level of confidentiality Owner of data Recipient 2 Perime ter of access rights Dissemination perimeter of data subcategories: For each subcategory and each applicant recipien t, owner can choose between: Sharing detailed data Sharing aggregated or equivalent data No sharing _Figure 2 : Level of confidentiality and access rights_ Detailed system data need to be transformed before being exchanged with non- authorized recipient: * Detailed data : Raw data * Aggregated data : Aggregated data: Data based on detailed data that are aggregated at a sufficient level so that raw data can’t be identified(statistical law) with respect to competition laws * Equivalent data : Data based on detailed data that are in an anonymous form or with modified values so that raw data can’t be identified ## 2.2. Types and formats of generated or collected data Different types of data are collected or generated within the InterFlex Project: Table 2: Data classification ## 2.3. Data Structure and utility The data structure has been defined gathering and compiling all project data and processes. It may evolve and be updated during the life of the project. The actual structure for Actors and Data for the InterFlex project are listed in the tables below: <table> <tr> <th> _Categories_ </th> <th> _Type_ </th> <th> _Subcategories_ </th> <th> _Definition and utility_ </th> <th> _Example_ </th> </tr> <tr> <td> Actors </td> <td> Role </td> <td> DSO </td> <td> Responsible for operating, ensuring the maintenance of and, if necessary, developing the distribution system in a given area </td> <td> Avacon, CEZ, E.ON, Enexis, Enedis </td> </tr> <tr> <td> Actors </td> <td> Role </td> <td> Industrial partner </td> <td> All industrial partners involved in Interflex project at a DEMO level </td> <td> * GE * Siemens * Schneider … </td> </tr> <tr> <td> Actors </td> <td> Role </td> <td> University and research partner </td> <td> All university or research partners involved in Interflex project at a DEMO level </td> <td> * RWTH * AIT etc </td> </tr> <tr> <td> Actors </td> <td> Role </td> <td> Retailer </td> <td> Licensed supplier of electricity to an end-user </td> <td> \- EDF - Engie... </td> </tr> <tr> <td> Actors </td> <td> Role </td> <td> Legal Client </td> <td> A legal client of a DSO that is involved at Demo scale </td> <td> * Company producer * Municipalities * Tertiary service providers </td> </tr> <tr> <td> Actors </td> <td> Role </td> <td> Physical client </td> <td> A physical client of a DSO that is involved at Demo scale </td> <td> \- Residential client </td> </tr> <tr> <td> Actors </td> <td> System </td> <td> Charging facilities </td> <td> Facilities to charge electrical vehicles </td> <td> \- Charging facilities </td> </tr> <tr> <td> Actors </td> <td> System </td> <td> DER installation </td> <td> Power plant that use renewable technology and are owned by a legal person </td> <td> * Photovoltaics panels * Biomass farm * Wind power, … </td> </tr> <tr> <td> Actors </td> <td> System </td> <td> In house device </td> <td> All devices working on electricity that can be find in a customer's dwelling. </td> <td> * Heater * Meter * Local display * Customer's battery </td> </tr> <tr> <td> Actors </td> <td> System </td> <td> Communication infrastructure </td> <td> All the infrastructure that are used for communication at all level (from customer's place to power command) </td> <td> \- Modem - Routers </td> </tr> <tr> <td> Actors </td> <td> System </td> <td> Network device </td> <td> All devices placed on MV/LV network for monitoring or gathering information on grid's situation or electrical parameters values. It also include the IS associated </td> <td> * Secondary Substation control infrastructure * RTU : Remote terminal units * Circuits breakers * sensors </td> </tr> <tr> <td> Actors </td> <td> System </td> <td> IS IT </td> <td> All the hardware and software associated, used at power command to control and monitor the network </td> <td> * SCADA * Central database * Control operation center </td> </tr> <tr> <td> Actors </td> <td> System </td> <td> Interactive communication device </td> <td> All device used to interact with customers in order to involved him in the Demo </td> <td> * Web portal * Display used for communication </td> </tr> </table> _Table 3: List of actors_ <table> <tr> <th> _Categories_ </th> <th> _Type_ </th> <th> _Subcategories_ </th> <th> _Definition and utility_ </th> <th> _Example_ </th> </tr> <tr> <td> Data </td> <td> Document </td> <td> Internal document </td> <td> All the documentation made by Demo to run operation, to monitor and conduct the project's good development </td> <td> * Meeting minutes * Report on the cost's impact of selected flexibility plans </td> </tr> <tr> <td> Data </td> <td> Document </td> <td> Interflex deliverable </td> <td> All the deliverables that Demo have to produce during the project's time as agreed in the DOW </td> <td> * Risk analysis * Documentation on KPI * Detailed use case - Report on technical experimentation, market research, … </td> </tr> <tr> <td> Data </td> <td> Document </td> <td> Communication material </td> <td> All the documentation that describe the project to the public and can be put on the future website </td> <td> * Purpose of the DEMO (leaflet) * Brief description of use case * Location of use case </td> </tr> <tr> <td> Data </td> <td> Financial data </td> <td> Project financial data </td> <td> All the financial data that are produced during the project and that are used to make financial report for European Commission and internal report </td> <td> * Invoices * Cost and time imputation </td> </tr> <tr> <td> Data </td> <td> Financial data </td> <td> Solution cost and selling price </td> <td> All the financial data that can be made concerning estimation prices of solution for replication </td> <td> * Unit product cost of hardware developed by Demo * Sell price of the solution develop (software,…) </td> </tr> <tr> <td> Data </td> <td> Parameter </td> <td> Condition parameter </td> <td> All the external parameters that may influence the success of the use case </td> <td> * Weather * Time of day * Day of week … </td> </tr> <tr> <td> Data </td> <td> Parameter </td> <td> Scenario assumption </td> <td> All the stated parameters that are necessary to determinate a scenario for the use case </td> <td> * Location of islanding * Experiment's location </td> </tr> <tr> <td> Data </td> <td> Parameter </td> <td> Electrical parameter </td> <td> All the electrical parameters that are used to supervise the network and its good state </td> <td> * Intensity * Voltage * Frequency * Quality </td> </tr> </table> <table> <tr> <th> Data </th> <th> Parameter </th> <th> Algorithm, formula, rule, specific model </th> <th> All the intellectual data that are created during the project to made software's contents </th> <th> * Algorithm to optimize flexibility plan * Simulation to determine location of circuit breaker * Voltage regulation algorithm </th> </tr> <tr> <td> Data </td> <td> Parameter </td> <td> Optimized value </td> <td> Values of parameters that optimized the use case or the demo‘s performance </td> <td> \- Optimization time of islanding </td> </tr> <tr> <td> Data </td> <td> Parameter </td> <td> Forecast data </td> <td> All the data used to forecast consumption or production of customer </td> <td> \- Forecast customer's consumption - Forecast photovoltaic panels' production </td> </tr> <tr> <td> Data </td> <td> Facility data </td> <td> Network topology </td> <td> All information on network devices and their location and interaction, mainly coming from GIS (Geographic Information System) </td> <td> * Map of the network * Substations location * All the other data found in the GIS (Geographical Information System) </td> </tr> <tr> <td> Data </td> <td> Facility data </td> <td> Network state </td> <td> All information concerning the network's status (global or local) at a precise moment useful to monitor the network </td> <td> * Feeding situation in a distribution area * State of network regarding Limit value violation * Location of constraint * Flexibility needs of DSO </td> </tr> <tr> <td> Data </td> <td> Facility data </td> <td> Customer's meter state and output </td> <td> All the information concerning customer’s meter state and outputs information </td> <td> \- Customer’s consumption or production </td> </tr> <tr> <td> Data </td> <td> Facility data </td> <td> Other device state and output </td> <td> All the information concerning device's state and outputs information </td> <td> * State of charge of batteries * Consumption data coming from meter * Production data coming from meter * State of charge of storage components </td> </tr> <tr> <td> Data </td> <td> Parameter </td> <td> Information exchanged between IS or sent to device </td> <td> All automated information sent between facilities in order to send information or order for monitoring </td> <td> * Order sent to breaker devices (open, close,…) * Information on local network status coming from sensors - Order and roadmap sent to network devices (batteries, aggregator,…) </td> </tr> <tr> <td> Data </td> <td> Parameter </td> <td> Detailed specification on devices </td> <td> All detailed information (reference components, specification, process,…) useful to build the devices </td> <td> * Detailed specification of the telecommunication infrastructure * Detailed specification of interactive sensor network </td> </tr> <tr> <td> Data </td> <td> Network data </td> <td> Network topology </td> <td> All information on network devices and their location and interaction, mainly coming from GIS (Geographic Information System) </td> <td> * Map of the network * Substations location * All the other data found in the GIS (Geographical Information System) </td> </tr> <tr> <td> Data </td> <td> Network data </td> <td> Network state </td> <td> All information concerning the network's status (global or local) at a precise moment useful to monitor the network </td> <td> * Feeding situation in a distribution area * State of network regarding Limit value violation * Location of constraint * Flexibility needs of DSO </td> </tr> <tr> <td> Data </td> <td> KPI </td> <td> Data for KPI (input raw data) </td> <td> All raw data that are used to calculate the final KPI </td> <td> * Duration of experiment * Customer response to DSO's demand * Electrical parameter used for KPI </td> </tr> <tr> <td> Data </td> <td> KPI </td> <td> KPI (KPI values) </td> <td> All the KPI values and the way to calculate them </td> <td> * Economic KPI * System Efficiency KPI </td> </tr> <tr> <td> Data </td> <td> Customer data </td> <td> Customer contract’s data </td> <td> All the data in customer's contact that are used for contact or make payment </td> <td> * Address * Phone number * Bank account details </td> </tr> <tr> <td> Data </td> <td> Customer data </td> <td> Information sent to /received from customer </td> <td> All the information and data that are exchanged between the DEMO and the customer in order to involve customer in the experiment </td> <td> * Customer's response to DSO's request to reduce consumption - Information and data available to customer in order to visualize its consumption * Advices and encouragement sent to encourage a smart consumption </td> </tr> <tr> <td> Data </td> <td> Customer data </td> <td> Customer analysis (profile analysis, studies on client reactivity) </td> <td> All the data that are produced in order to better understand the customer's behaviour regarding the possibility to adopt smarter habits in their electricity consumption </td> <td> * Customer's typology and behaviour patterns * Analysis on customer's response to DSO's request </td> </tr> </table> _Table 4: List of data_ # 3\. FAIR DATA ## 3.1. Making data findable, including provisions for metadata In order to make data findable and usable, regarding the level of access rights, rules have been defined to identify the data. ### KPIs #### Data concerned * All demo KPIs * Common KPIs #### Characteristics * KPI values were created in order to be shared and published outside the Interflex project * KPIs reflect the results of the demos and the InterFlex Project and are one of the main tools of the Technical Management * For each KPI, the level of dissemination has been defined in the deliverable ‘D2.2 MinimalSetOfKPIs_CEZd_InterFlex_V1.0’ #### Rules/ Identification/Versioning * Decision has been taken that only calculated values will be put inside the data clearing house located on the Project Intranet. The data collection frequency and responsibilities for data collection are defined for each KPI in the deliverable ‘D2.2 MinimalSetOfKPIs_CEZd_InterFlex_V1.0’ o Data name * Data ID * Methodology for Data collection o Source/tools/Instruments for Data collection o Location of Data collection o Frequency of data collection o Data responsible data collection o KPI ID o KPI Name * Each responsible for WP KPI collection is the WPL who collects the different KPIs in the database stored on the InterFlex Intranet, so the Technical Director can assess the project thanks to the KPI collection. * Each Raw data and calculated value has an Object Identifier defined in deliverable ‘D2.2 MinimalSetOfKPIs_CEZd_InterFlex_V1.0’. ### InterFlex Deliverables _Data concerned_ \- List of deliverables defined in the Grant Agreement (or its amended version) #### Characteristics \- Depending on the deliverable, perimeter of dissemination can be different - A level of dissemination is already pre-defined in the Grant Agreement #### Rules/ Identification/Versioning * Level of dissemination is chosen by the author and Technical committee validates the choice. Main deliverables and appendices may have different levels of confidentiality, especially if appendices are more detailed * The deliverables are available on the Project Intranet and on the website for public audience * Versioning and nomenclatures are defined in the deliverable ‘D10.1 ProjectManagementPlan_Enedis_InterFlex_V2.0’ ### Demo’s local Data #### Data concerned * Internal documents * Electrical parameters * Forecast data * Device state and outputs and information exchanged between facilities * Customer analyses (profile analyses, studies on client reactivity…) #### Characteristics * Data used to run each of the Demos on a daily basis * Data are with low added value outside source Demo, as Demos are running separately without overlap #### Rules to be applied * Data should stay at Demo level and be used only by Demo partners for the achievement of their activity * If another partner outside Demo needs these data, a written request and explanation should be provided about the way he is going to use the data (Agreement form) > Sample data in an anonymous format can be sent only for illustration > > Providing aggregated data should be the rule * A global description will be integrated inside deliverable ### Project Financial Data #### Data concerned * Invoices * Time spendings/imputations * Cost/price * Company internal financial documents #### Characteristics * Data can be at different levels of details * Detailed data (cost by unit) are extremely sensitive * Demos need to send financial data to Coordinator who will aggregates data to present a global cost statement covering all Demos and general expenses of each partner (for internal financial report) broken down to individual WPs #### Rules to be applied * Detailed data should stay in partner's accounting system * Data sent to the Coordinator should be in the detailed level described in the template provided by Coordinator * All data sent to Coordinator must be kept strictly confidential and must not be disseminated * Coordinator aggregates data to present to the consortium * Company internal financial information is not shared ### Network Data #### Data concerned * Network topology * Network state #### Characteristics * Network topology GIS information and Network state are highly confidential and sensitive * Network state can be sensitive information as it can reveal grid's weakness and vulnerability #### Rules to be applied * Network topology with GIS information must not be shared except between DSOs * If another partner needs network data, a written request and explanation should be provided about the way he is going to use the data (Agreement form) > Data in an anonymous format : equivalent data without indication of location > can be sent > Providing aggregated data should be the rule ### Demo Customer Data _Data concerned_ * Customer's meter data * Customer's contract data _Characteristics_ * All this data are strictly under personal data protection (European and national laws) * Detailed identification data (address, phone number,…) are sensitive information and must be secured _Rules to be applied_ * Customer's contract must never be sent * As stated in laws : > Customer must have an access to this data > Data must be protected and traceability of the use must be made > Data can't be disclosed to anyone without the full consent of customers on > usage and access rights * All this information has to be clearly stated in the customer's contract that is signed to enter project * in order to send information to other partners if the need is clearly established, these data have to be delivered in an anonymous format (equivalent data) or aggregated format * The DSO must ensure to record these data in compliance with their national data protection regulations ## 3.2. Making data openly accessible Data that are used to manage the project within the consortium are stored on the Project Intranet with a private login and password. This intranet is used as a working tool for the sharing of documents related to InterFlex and consists of a private area, accessible online to the project partners. It allows the safe access to project information and reports, circulation of preparatory and internal work, online exchanges and virtual communication tools such as shared Agenda, Instant Messaging etc. Groups and access roles have been defined in order to assure clearly identified access rights. _Figure 3: Tree view of the access rights of the Intranet_ Data that are public are accessible on the project website. Key words and search tool are available in order to make the data more accessible. Confidential data are identified with a confidential tag in order to protect them. The access rights per actors per data is described in the table below: D2.4 Da t a Management Plan Interflex – GA N°731289 Page 20 Table 5: Data classification ## 3.3. Making data interoperable In order to identify and aggregate the data in an interoperable way the InterFlex project uses the SGAM framework/ Use case methodology approach and also the IEC PAS 62559 based template to describe in detail the Use Cases. See deliverable D2.1. The SGAM framework and its methodology are intended to present the design of Smart Grid use cases in an architectural but solution and technology-neutral manner. The SGAM framework consists of five layers representing business objectives and processes, functions, information exchange and models, communication protocols and components. These five layers represent an abstract and condensed version of the GWAC interoperability categories. Each layer covers the smart grid plane, which is spanned by electrical domains and information management zones. The intention of this model is to represent on which zones of information management interactions between domains take place. It allows the presentation of the current state of implementations in the electrical grid, but furthermore to depict the evolution to future smart grid scenarios by supporting the principles universality, localization, consistency, flexibility and interoperability InterFlex aims to get information on: 1. Description of the Use Case 2. Diagrams of the Use Case 3. Technical data - Actors 4. Step by Step Analysis of Use Case (can be extended by detailed info on “information exchanged”) 5. Information exchanged Moreover, the WP3 focuses on defining an interoperable API for IT systems involved in the flexibility transactions with cybersecurity constraints (D3.6), Interoperability and interchangeability validation results (D3.7), and Scalability and replicability analyses for all the use cases (D3.8). ## 3.4. Increase data re-use (through clarifying licenses) In order to enable and promote data re-use, all data provided need to take the following questions in a reasonable way into account and must be specified in the Exploitation Plan of the project (D4.7, D4.8, i.e., 1 st and 2 nd version of the Exploitation Plan of the project results): * How will the data be licensed to permit the widest possible re-use? * 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. * 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. * How long is it intended that the data remains re-usable? * Are data quality assurance processes described? # 4\. ALLOCATION OF RESOURCES Enedis as Project Coordinator of the InterFlex project is responsible for data management of the project. All relevant data such as KPI results, publication and deliverables must be accessible at least 5 years after the end of the project. As such, the Intranet and Internet platforms will be available and maintained by Enedis during this period from 2020 until 2024. The corresponding estimated expenses are shown hereunder: \- Intranet licences: 96€/year/licences. 1 licence for each of the 20 partners (TBC) - Internet Hosting and maintenance: 40€/month <table> <tr> <th> Cost category </th> <th> Year 2020 </th> <th> Year 2021 </th> <th> Year 2022 </th> <th> Year 2023 </th> <th> Year 2024 </th> <th> Total </th> </tr> <tr> <td> Intranet </td> <td> 1 920 € </td> <td> 1 920 € </td> <td> 1 920 € </td> <td> 1 920 € </td> <td> 1 920 € </td> <td> 9 600 € </td> </tr> <tr> <td> Internet </td> <td> 480 € </td> <td> 480 € </td> <td> 480 € </td> <td> 480 € </td> <td> 480 € </td> <td> 2 400 € </td> </tr> <tr> <td> Total direct costs </td> <td> 2 400 € </td> <td> 2 400 € </td> <td> 2 400 € </td> <td> 2 400 € </td> <td> 2 400 € </td> <td> 12 000 € </td> </tr> </table> _Table 6 : Direct data management costs excluding HR after the end of the project_ # 5\. DATA SECURITY AND ETHICAL ASPECTS ## 5.1. Data security Based on common works and agreements among GWP and Demo leaders, each subcategory of data was assessed with three levels of confidentiality in order to ensure data security. Depending on the constraints applying to these types of data (laws, internal rules…), it is possible to apply a level of confidentiality as followed: \- Level 0: * Detailed data can never be shared * Aggregated data or equivalent data can be shared - Level 1: * Detailed data can be shared with some partners upon request and dedicated agreement * No restriction on aggregated data or equivalent data - Level 2: * No restriction whatsoever. ## 5.2. Ethical aspects Ethics requirements in the protection of personal data must be taken into account. Indeed, within the context of Interflex demos and exploitation of related results, partners will be collecting or processing personal data. As such, the D1.1 Ethics POPD deliverable (protection of personal data) specifies for each partner: * Certification by their competent Data Protection Authority of compliance with applicable local and European laws, * Detailed information on the procedures that will be implemented for data collection, storage, protection, retention and destruction * where applicable, providing templates of consent forms to be given out to customers whose personal data may collected and used # 6\. APPENDIX List of InterFlex Common KPIs <table> <tr> <th> Interflex Project KPI </th> <th> KPI ID </th> <th> KPI TYPE </th> <th> KPI Description </th> </tr> <tr> <td> Flexibility </td> <td> WP2.2_KPI_1 </td> <td> Technical </td> <td> Flexible power that can be used for balancing specific grid segment. </td> </tr> <tr> <td> Hosting capacity </td> <td> WP2.2_KPI_2 </td> <td> Technical </td> <td> Percentage increase of network hosting capacity for DER. </td> </tr> <tr> <td> Islanding </td> <td> WP2.2_KPI_3 </td> <td> Technical </td> <td> Capacity of the energy system to switch to islanding whilst keeping the power quality requirement. </td> </tr> <tr> <td> Customer recruitment </td> <td> WP2.2_KPI_4 </td> <td> Social </td> <td> Measure whether demos are managing to recruit enough customer bases in order to attain demo objectives. </td> </tr> <tr> <td> Active participation </td> <td> WP2.2_KPI_5 </td> <td> Social </td> <td> Reflects how versatile the demos are in leveraging flexibility from different technologies. </td> </tr> </table>	https://phaidra.univie.ac.at/o:1140797	Horizon 2020
0049_RURITAGE_776465.md	# 1\. Introduction The RURITAGE project participates in the Pilot on Open Research Data launched by the European Commission (EC) along with the H2020 programme. The aim of the programme is to allow access to research data generated in H2020 projects. A FAIR (findable, accessible, interoperable and re-usable) Data Management Plan (DMP) is required a deliverable for all projects participating in the Open Research Data Pilot. Open access is defined as the practice of providing free of charge on-line access to scientific information that is reusable. Scientific information may be defined as research data and scientific peer-reviewed journal publications. In the context of RURITAGE, research data will concern Key Performance Indicators developed through the project and will include socio-economic, environmental and cultural heritage related data. Spatial data will also be collected and stored within the RURITAGE ATLAS (output from project). In addition stakeholder’s perception of change will be collected via the Cult- Rural Toolkit and stored in anonymous fashion and displayed in the ATLAS. Personal data of participants in the project activities will also collected and stored securely. The Consortium believes in the concepts of open science and in the benefits that can be drawn from allowing the reuse of data at a larger scale. Furthermore, there is a need to gather experience and knowledge relating to innovative use of heritage for rural regeneration. In fact, the majority of European heritage is found in rural areas, however there is a much longer tradition of heritage promotion in the urban context. Hence, most rural areas are facing chronic economic, social and environmental problems, resulting in unemployment, disengagement, depopulation, marginalisation or loss of cultural, biological and landscape diversity. In most cases, tangible and intangible Cultural Heritage is threatened. This project proposes to remove this condition by demonstrating the heritage potential for sustainable growth. Around Europe and in Third countries, numerous examples of good practices show how CNH is emerging as a driver of development and competitiveness through the introduction of sustainable and environmentally innovative solutions and the application of novel business models. The project will for the first time provide open access, high quality data relating to the innovative use of heritage for rural regeneration. Although the project embraces open access data there will be legitimate situations where access to data will be restricted due to commercial exploitation reasons. However processes will be developed to limit restrictions, for example anonymising data or limited embargos on datasets. ## 1.2 Purpose of Data Management Plan 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 the project research data. The DMP covers the complete research data life cycle (figure 1). It describes the types of research data that will be generated or 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. It also reflects the current state of the Consortium agreements on data management and must be consistent with exploitation and IPR requirements. Figure 1.1: Research Data Cycle. (Source: University of Plymouth (2018). _Research data cycle_ . Available online: _https://plymouth.libguides.com/ld.php?content_id=31431849_ ) The DMP is not a fixed document, but will develop during the lifecycle of the project, particularly whenever significant changes arise such as dataset updates or changes in Consortium policies. This document is the first version of the DMP, delivered in Month 6 (D8.3) of the project. It includes an overview of the datasets to be produced by the project, and the specific conditions that are attached to them. The next versions of the DMP will get into more detail and describe the practical data management procedures implemented by the project with reference with the IT tools developed in WP4. At a minimum, the DMP will be updated in Month 24 and Month 48 respectively. This document has been produced following the EC guidelines for project participating in this pilot. # 2\. Data Summary The dataset types that have been identified in the initial DMP are focused on the description of the action and on results obtained in the first months of the project. Therefore, Table 1.1 reports a list of initial types of research data identified. The list may have datasets added or removed in later versions of the DMP as the project develops. Datasets relating to the ATLAS, Cult-Rural Toolkit and replicators baseline data will be developed within the next period of the project and therefore will be added to the next version of the DMP. Details for each of the current dataset have been included in the following section. ## 2.1 Dataset <table> <tr> <th> NAME of DATASET </th> <th> RESPONSIBLE PARTNER </th> <th> WP </th> </tr> <tr> <td> Role Model BP </td> <td> TECNALIA </td> <td> WP1 </td> </tr> <tr> <td> Participatory Managment </td> <td> UNESCO </td> <td> WP2 </td> </tr> <tr> <td> Stakeholder Participants Information </td> <td> UNIBO </td> <td> WP3 </td> </tr> <tr> <td> KPI Evidence </td> <td> CARTIF </td> <td> WP4 </td> </tr> <tr> <td> DSS Knowledge </td> <td> POLITO </td> <td> WP5 </td> </tr> <tr> <td> Dissemination Information </td> <td> ICLEI </td> <td> WP7/WP6 </td> </tr> </table> Table 1.1 DataSet Types ### 2.1.1 Dataset 1 Role_Model_BP <table> <tr> <th> DATASET REFERENCE AND NAME: Dataset 1: Role_Model_BP </th> </tr> <tr> <td> DATASET DESCRIPTION: The data collected in this dataset relates to best practices and lesson learnt from RMs. It contains contextual data, description of the process, description of the stakeholders and resources involved. It would include also a description of the actions that can be replicated. The data is in an excel sheet format, with a workbook for each RM. </td> </tr> <tr> <td> ORIGIN, NATURE AND SCALE OF DATA: The data is gathered from the RMs via a questionnaires campaign. The scale of the data relates to the number of RMs . </td> </tr> <tr> <td> STANDARDS AND METADATA: Specific keywords will be attached to document to make them findable. </td> </tr> <tr> <td> DATA SHARING: During the lifecycle of the project all raw data will the stored on the RURITAGE SharePoint site which is backup via University of Plymouth IT systems. Sensitive data will be shared between partners via SharePoint folders with limit access to data. Sensitive data will not be shared outside the EU </td> </tr> <tr> <td> ARCHIVING AND PRESERVATION: Research datasets that can be shared will be uploaded to ZENODO repository with detailed metadata to insure discoverability. In additional to ZENODO, a metadata record for each dataset will be added to the University of Plymouth’s “PEARL” open publication/data repository ( _https://pearl.plymouth.ac.uk/_ ) in order to improve discoverability. </td> </tr> </table> 2.1.2 Dataset 2 Participatory_Management <table> <tr> <th> DATASET REFERENCE AND NAME: Dataset 2: Participatory_Managment </th> </tr> <tr> <td> DATASET DESCRIPTION: The data within this dataset relates to the theoretical and methodological approach to the participatory process into the Rural Heritage Hubs. The Methodology has been divided in three phase: 1) Setting up the Hub (identifying hub coordinator, hub space, hub stakeholders and multi-use of the hub); 2) Activities to be implemented in the Hub (includes a detailed individual calendar with activities to be implemented for each of the RMs and Rs); 3) Monitoring the Hub (i.e. processes and indicators). A document detailing the methodology will be complied and will contain several annexes including contact details of Hub coordinators. A Serious Game kit which will be part of the RURITAGE Replicator Tool Box (WP5) will be produced and be free to download by other institutions interested in being trained and in using the game (DIY approach). A word documents/pdfs containing (‘match-making agreement’, schedule for the visits, required inputs, expected outputs will also be produced. This is likely to contain contact details. Video recordings of presentation will be produced and placed on YouTube channel. The video recordings will not include personal data. </td> </tr> <tr> <td> ORIGIN, NATURE AND SCALE OF DATA: This dataset is a document that builds on research about participatory process. It also includes a calendar of planned events and explains different techniques that can be used in hub activities. Deliverable of the RURITAGE project (D2.2 – public): Serious Game kit which will be part of the RURITAGE Replicator Tool Box (WP5) The scale of the data will relate to the number of RMs and Rs and future participatory activity. Recordings of online presentation (min. 9 videos) to be uploaded on YouTube will be produced. </td> </tr> <tr> <td> STANDARDS AND METADATA: Specific keywords will be attached to document to make them findable and keywords and digital object identifiers will be to the game kit and YouTube videos. </td> </tr> <tr> <td> DATA SHARING: Videos will be shared on YouTube channel that will be created for the project. These videos will also be part of WP5 Platform and the project web page. The documents containing the methodology and approach will be shared via webpage and events. However Annex III, which contains the contact details will not be shared. Any raw data will the stored on the RURITAGE SharePoint site which is backup via University of Plymouth IT systems. Sensitive data will </td> </tr> <tr> <td> be shared between partners via SharePoint folders with limit access to data. Sensitive data will not be shared outside the EU </td> </tr> <tr> <td> ARCHIVING AND PRESERVATION: Research datasets that can be shared will be uploaded to ZENODO repository with detailed metadata to insure discoverability. In additional to ZENODO, a metadata record for each dataset will be added to the University of Plymouth’s “PEARL” open publication/data repository ( _https://pearl.plymouth.ac.uk/_ ) in order to improve discoverability. The documents outlining the methodology will be upload to repository and open access papers will be produced to share the method. In order to insure longevity of video recordings as suitable format for archiving will be explored. </td> </tr> </table> 2.1.3 Dataset 3 Stakeholder_Participants_Information <table> <tr> <th> DATASET REFERENCE AND NAME: Dataset 3: Stakeholder_Participants_Information </th> </tr> <tr> <td> DATASET DESCRIPTION: This dataset will include the stakeholders’ databases coming from the 13 RMs and the 6 Rs. A Total of 38 databases will then be produced. Indeed, each RM and each R will have to compile two different databases: * One including the organizations involved as stakeholders in the process of the RHH * One including details (anonymised personal information) of the citizens that will also participate into the RHH </td> </tr> <tr> <td> ORIGIN, NATURE AND SCALE OF DATA: The databases will come from the RMs and Rs that will compile those also including the personal information and contact details of the participants. The databases will be fulfilled and transmitted in an .xlsx format. _Organizations’ database_ The database that will be transmitted from RMs and Rs to responsible project partners (CE and UNIBO) will include the following information: * Name of the Organisation * Organisation level * Organisation Form * Value chain (for profit organizations) * Sector of activity (for non-profit organizations) * Brief description of the organisation * Organisation address * Website * Generic organisation email * Twitter/Facebook handle of the organisation (if applicable) * Additional comments * Topic of interest: Pilgrimage, Sustainable local Food production, migration, art and festival, integrated landscape management, resilience </td> </tr> <tr> <td> The following information will be collected by the partners but will not be transmitted to project partners and thus won’t be included in the databases: * Name * Role * Residence * Gender * Age (range of age) * Disability * Email * Telephone _Organizations’ database_ The database that will be transmitted from RMs and Rs to responsible project partners (CE and UNIBO) will include the following information: * Residence * Gender * Age (Range of age) * Disability * Topic of interest: Pilgrimage, Sustainable local Food production, migration, art and festival, integrated landscape management, resilience The following information will be collected by the partners but will not be transmitted to project partners and thus won’t be included in the databases: * Name * Email * Telephone </td> </tr> <tr> <td> STANDARDS AND METADATA: Specific keywords will be attached to document to make them findable, however this is only for internal use as information is confidential. </td> </tr> <tr> <td> DATA SHARING: The template with the full contact and personal details will be kept, together with their relevant signed consent form and information sheets, a secure folder on the project SharePoint site that is only accessible to the responsible partners and won’t be shared with any other project partners. The anonymized databases will be included in Del 3.2 that will be available to all project partners and to the commission services. The deliverable is expected to be submitted at M10 of the project implementation (End of March 2019). Deliverable 3.2 will in any case be kept confidential, meaning that it will not be shared with a wider audience. </td> </tr> <tr> <td> ARCHIVING AND PRESERVATION: Research datasets that can be shared will be uploaded to ZENODO repository with detailed metadata to insure discoverability. In additional to ZENODO, a metadata record for each dataset will be added to the University of Plymouth’s “PEARL” open publication/data repository ( _https://pearl.plymouth.ac.uk/_ ) in order to improve discoverability. Datasets that are confidential will be archived within the University of Plymouth system with restricted access for 5 years to support any queries. </td> </tr> </table> ### 2.1.4 Dataset 4 KPI_Evidence <table> <tr> <th> DATASET REFERENCE AND NAME: Dataset 4: KPI_Evidence </th> </tr> <tr> <td> DATASET DESCRIPTION: Dataset will provide quantifiable evidences of the potential role of CNH as a driver for sustainable growth. WP4 will monitor the performance of the deployed regeneration schemes in the 6 Rs through selected Key Performance Indicators (KPIs). </td> </tr> <tr> <td> ORIGIN, NATURE AND SCALE OF DATA: * Origin: Data are provided mainly by (Rs), or obtained from official statistics (Eurostat or the like). * Nature: Most of data are quantitative, either absolute values or percentages. - Scale: Considering the worst case: 6 Rs + 6 additional Res = 12 cases Less than 100 KPIs 3 data gathering campaigns (Baseline + intermediate + final) TOTAL = 12 x 100 x 3 = 3,600 data fields (approx.) It should be taking into account the project exploitation and upscaling after project’s end, so scale could be 3 or 4 times the estimated value (i.e. 10,800 – 14,400 data fields). Optionally, some KPIs could also be collected from (RM). In that case, there will be no monitoring, i.e. no gathering campaigns, so only baseline data will be collected. 13 RM + 8 additional RM = 21 cases Less than 100 KPIs TOTAL = 2,100 data fields TOTAL = 14,400 + 2,100 = 16,500 data fields (aprox.) Detailed information on data types and formats can be found in Deliverable D4.1 ‘KPIs definition and evaluation procedures’. </td> </tr> <tr> <td> STANDARDS AND METADATA: Data will be stored in a database (PostgreSQL, MySQL, or similar), preferably open source database, as INTEGER, FLOAT or STRING data type. Minimum metadata will be data source, timestamp, validity, … Data field names and table names will be auto-descriptive, avoiding the use of abbreviations. All names will be lowercase, except for the first letter between two consecutive words, e.g. tableName. Variables must include a letter at the beginning of the name illustrating data type of the variable (‘i’ for integer, ‘f’ for float and ‘s’ for string data), e.g. iAge, fAverageTemperature, sAddress. Versioning will be formed by a major version number, followed by a dot and a minor version number, starting from ‘0.1’. Including new data in the Monitoring database does not mean a new version. Minor changes in the database, as modifying or including a new data field, will suppose increasing minor version number. Major changes, as removing or including a new table, will suppose increasing major version number. </td> </tr> <tr> <td> DATA SHARING: Data will be open access. No personal data is stored, so no issues regarding GDPR are expected. Data will be accessible through the RURITAGE platform. The only tool necessary to access the data is a web browser, but some database management system (DBMS) as PostgreSQL or MySQL and some database administration tools like phpPgAdmin or phpMyAdmin, could be necessary to manage the dataset properly. This will be documented accordingly to enable future reuse. Licence conditions of official statistics obtained will be checked to insure they do not impact on sharing of data outputs. </td> </tr> <tr> <td> ARCHIVING AND PRESERVATION: Research datasets will be uploaded to ZENODO repository with detailed metadata to insure discoverability. In additional to ZENODO, a metadata record for each dataset will be added to the University of Plymouth’s “PEARL” open publication/data repository ( _https://pearl.plymouth.ac.uk/_ ) in order to improve discoverability. </td> </tr> </table> 2.1.5 Dataset 5 DSS_Knowledge <table> <tr> <th> DATASET REFERENCE AND NAME: Dataset 5: DSS_Knowledge </th> </tr> <tr> <td> DATASET DESCRIPTION: This dataset relates to the data generated from the DSS. The DSS will use data from other WPs and inbuilt rules to generate suggestions relating to regeneration policies that Rs can use. The results were be in a textual form. </td> </tr> <tr> <td> ORIGIN, NATURE AND SCALE OF DATA: Data are generated by the DSS using as input other datasets from WP1 and WP5. The scale of data relates to the number of Rs (currently 6 increasing to 12 over life of project) involved but will expand when the project is up scaled. </td> </tr> <tr> <td> STANDARDS AND METADATA: There are no standards, however all data stored/produced will be documented and tagged with keywords </td> </tr> <tr> <td> DATA SHARING: During the lifecycle of the project all raw data will the stored on the RURITAGE SharePoint site which is backup via University of Plymouth IT systems. Sensitive data will be shared between partners via SharePoint folders with limit access to data. Sensitive data will not be shared outside the EU. Data will be also be accessible through the RURITAGE platform. </td> </tr> <tr> <td> ARCHIVING AND PRESERVATION: Research datasets that can be shared will be uploaded to ZENODO repository with detailed metadata to insure discoverability. In additional to ZENODO, a metadata record for each dataset will be added to the University of Plymouth’s “PEARL” open publication/data repository ( _https://pearl.plymouth.ac.uk/_ ) in order to improve discoverability. Data will also be archived in the ARCHES database </td> </tr> </table> 2.1.6 Dataset 6 Dissemination_Information <table> <tr> <th> DATASET REFERENCE AND NAME: Dataset 6: Dissemination_Information </th> </tr> <tr> <td> DATASET DESCRIPTION: During the WP7 Dissemination and Communication Activity the following information might be temporary requested from participants: * Name of participant * Name of organisation represented * City where the organisation is located * Work Emails Address * Work phone number </td> </tr> <tr> <td> ORIGIN, NATURE AND SCALE OF DATA: The events that are likely to request this information are: * EU and International cooperation with other similar projects. Information might be necessary for communication between our project and other organisations. * Community events - Information might be necessary for registering and informing participants about the event. * Photo Contest and Troubadour activities - Information might be necessary for registering and informing participants about the contest results, as well as for the troubadour diary. * Public events, workshops (e.g. Dialogue Breakfast and joint meetings) and conferences: - Information might be necessary for registering and informing participants about the contest results. * Newsletters - Information might be necessary for subscribing. * Summer Schools and Master Course – Information will be necessary for registering on courses. The scale of the data is indicated in the D7.1 Communication and Dissemination Plan (and its regular updates). </td> </tr> <tr> <td> STANDARDS AND METADATA: Due to the fact that information will not be shared additional descriptive metadata is not required. </td> </tr> <tr> <td> DATA SHARING: The information will not be shared externally. The information is just needed and used for the practical organisation and functioning of the cooperation, events, contest and newsletter. The data presented, will be accessed just by the core team (involved in the development that particular event). The data will the stored on the RURITAGE SharePoint site which is backup via University of Plymouth IT systems. Data will be stored in restricted access folders on the site. </td> </tr> <tr> <td> ARCHIVING AND PRESERVATION: All the data will respect carefully the GDPR requirements and will be responsibly administrated and managed. </td> </tr> </table> # 3\. Open access to publication The RURITAGE consortium is an open data pilot project within H2020 programme; this means that all the publications deriving from the project should be published in open access. To ensure open access to scientific journal publications, appropriate project budget has been allocated and will be available to all partners producing scientific journal publications. Hence all scientific journal publication will follow a “Gold” route and be open access on the day of publication. The University of Plymouth also manages and maintains “PEARL” (https://pearl.plymouth.ac.uk/) an open publication repository, therefore all publications (“postprint” version) will be deposited in the repository within 20 days of acceptance. All “postprint” version will then be replaced by final versions when copyright allows. RURITAGE intends to publish at least 10 publications. # 4\. Open access to raw data ## 4.1 Data Storage, Security, back up and repository All RURITAGE project datasets will be stored on the project SharePoint. The site is hosted in the Microsoft Cloud (which sit on a secure EU server). The site will be automatically backed up every 12 hours by the service provider (Microsoft). SharePoint Online (part of Office 365) provides encryption during data storage and transfer. It has been approved by the University Security Architects for the storage of valuable research data. (See security certifications: _https://technet.microsoft.com/en- GB/library/office-365-compliance.aspx_ ) The PI can use the site to share documents with the partners, reducing duplication and the risks associated with emailing documents. The PI can set up different access permission levels to fit in with confidentiality requirements.” When appropriate research datasets will be uploaded to ZENODO repository with detailed metadata to insure discoverability. In additional to ZENODO, a metadata record for each dataset will be added to the University of Plymouth’s “PEARL” open publication/data repository ( _https://pearl.plymouth.ac.uk/_ ) in order to improve discoverability. This will ensure wide dissemination of relevant scientific outputs. Links will also be created to openAIRE platform. A dedicated dissemination plan will also ensure that materials are shared with relevant groups and stakeholders. # 5\. Ethics and Data protection As defined in the relevant datasets personal data protection will be ensured in all the step of the project. In particular RURITAGE comply with the H2020 Ethical standard and rules according to the following procedures: * A signed consent form will be collected from all research participants who are participating in the research before any data collection takes place. An information sheet will also be provided to all research participants. The following information will be included in the information sheet: o Details of what the study is about, who is undertaking the study and why it is being conducted; o Link to the web page containing the Privacy Notice for Research Participants. * Clear details of what participation would involve- i.e. What they would be asked to do, where, for how long etc.; * The advantages/disadvantages of taking part; o Who is funding the study; o Who has reviewed/authorised the study; * The researcher’s contact details; o Another named person, besides the researcher, whom people can contact (e.g. With any questions / complaints); * What will happen to the data collected- how it will be stored, for how long and with whom / how it will be shared, whether it will be anonymised, how it will be published also including whether any automated decision-making will apply, as well as the significance and the envisaged consequences of such processing for the participant; * How long it will be retained, security measures in place; o The categories of personal data collected; o What safeguards are in place in relation to personal data shared with other parties and/or transferred out of Europe; * The source of the personal data if not from the participant or if additional to them, for example whether it came from publicly accessible sources (e.g. Online, social media, NHS) * Participation is voluntary; people are free to withdraw at any time without giving a reason and with no negative consequences; any timescales for withdrawal; * Eligibility criteria; * A GDPR and Information Security online training course hosted on the Project SharePoint site has been developed and all partners responsible for collecting data will undertake the training. This will insure that all researchers have a good understanding of GDPR regulations and that the study is complying with GDPR regulation concerning the collection, processing and storage of personal data. # 6\. Other Data Management issues 6.1 Responsibilities and Resource Allocation for Data ## Management Each WP has been allocated appropriate resources to manage the planning, collection, processing, publication to open access repositories and archiving of data produced within their WP. Each partner will respect the processes identified in the DMP and support the WP leaders. Resource has also been allocated to develop and maintain the project SharePoint site that will store data in a secure fashion. The SharePoint site will be maintained for the life of the project and beyond. Both the Coordinator and University of Plymouth have appropriate resources allocated to their project budgets to manage the overall data management plan and provide suitable training and guidance to all partners as and when required. ### 6.2 Intellectual Property Rights In line with the Grant Agreement, the Consortium has a policy of protecting the project’s results, whenever results are expected to be commercially exploitable and whenever this protection is possible, reasonable and justified. Where this is applicable, the necessary steps to protect the associated IP will be included in the Action Plan for the relevant project results. IPR will be managed through the detailed internal IP Protection Plan that will be developed in WP8. The ownership of results is strictly controlled by the Consortium Agreement (CA) - Section 8, which includes all provisions related to the Ownership of Results, Joint Ownership of Results, Use of Results and Transfer of Results. Specifically relating to jointly owned results which could be commercially exploited, including but not limited to RURITAGE Systematic Innovation Areas (SIAs), RURITAGE branding, RURITAGE Decision Support System (DSS), Serious Game Kit, RURITAGE Atlas, My Cult-Rural Toolkit; these will be the subject of discussion among the parties that participate in the development of such results. The parties shall make their best efforts to negotiate in good faith and finalise joint ownership agreements before the end of the project.	https://phaidra.univie.ac.at/o:1140797	Horizon 2020
0053_ReFreeDrive_770143.md	# Executive Summary This document, D9.2 Open Data Management Plan (ODMP) main objetive is to collect, analyse and share open motor design, characterization and testing data and experience to validate and de-risk future industrial innovations. This objective has been addresed in this document and there have been no deviations in content or time from the deliverable objectives set out in the ReFreeDrive Grant Agreement. The data gathering and management will be a continuous action throughout the duration of the project. The Consortium strongly believes in the concepts of open science, and in the benefits that the European innovation ecosystem and economy can draw from allowing the reuse of data at a larger scale. Besides, the share and reuse of research data to the electric machine design research community will eliminate barriers and enforece an innovation culture. The purpose of the ODMP is to provide an analysis of the main elements of the data management policy that will be used by the Consortium with regard to the project open research data. The ODMP covers the complete research data life cycle. It describes the types of research data that will be generated or collected during the project, how the research data will be preserved and what parts of the datasets will be shared for verification or reuse. Research data linked to exploitable results will not be put into the open domain if they compromise its commercialisation prospects or have inadequate protection, which is a H2020 obligation. The rest of research data will be deposited in an open access repository. The ODMP is not a fixed document; on the contrary it will evolve during the lifespan of the project. This first version of the ODMP includes an overview of the datasets to be produced by the project, and the specific conditions that are attached to them. The next versions of the ODMP will get into more detail and describe the practical data management procedures implemented by the ReFreeDrive project. The expected types of research data that will be collected or generated along the project will be discussed following the project work package structure. # 1 Introduction Open access is 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 research and innovation, scientific information can refer to peer-reviewed scientific research articles or 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. Nevertheless, data sharing in the open domain can be restricted as a legitimate reason to protect results that can reasonably be expected to be commercially or industrially exploited. Strategies to limit such restrictions will include anonymising or aggregating data, agreeing on a limited embargo period or publishing selected datasets. It must be duly noted that the automotive industry is highly competitive and ReFreeDrive project aims at providing its industrial partners with added value innovation, which would not be such if made public. ## _1.1 Purpose_ The purpose of the ODMP is to provide an analysis of the main elements of the data management policy that will be used by the Consortium with regard to the project research data. The ODMP covers the complete research data life cycle. It describes the types of research data that will be generated or 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. Figure 1 shows the research data life cycle, taken from [1], which has been used as guideline for this deliverable. The ODMP is not a fixed document, but will evolve during the lifespan of the project, particularly whenever significant changes arise such as dataset updates or changes in Consortium policies. This document is the first version of the ODMP, delivered in Month 6 of the project. It includes an overview of the datasets to be produced by the project. The ODMP will be updated in month 18 if needed and again at the end of the project. This document has been produced following the EC guidelines for project participating in this pilot and additional consideration described in ANNEX I: KEY PRINCIPLES FOR OPEN ACCESS TO RESEARCH DATA. Figure 1. Reserach Data Life Cycle ## _1.2 Research data types_ For this first release of ODMP, the data types that will be produced during the project are focused on the Description of the Action (DoA) and on the results obtained in the first months of the project. According to such consideration, Table 1 reports a list of indicative types of research data that each of the ReFreeDrive work packages will produce. This list may be adapted with the addition or removal of datasets in the next versions of the ODMP to take into consideration the project developments. A detailed description of each dataset is given in the following sections of this document. Table 1. Work pacakges and expected datasets of the ReFreeDrive project <table> <tr> <th> # </th> <th> Work Package </th> <th> Lead Partner </th> <th> Expected Datasets </th> </tr> <tr> <td> 1 </td> <td> Project Management </td> <td> CIDAUT </td> <td> None </td> </tr> <tr> <td> 2 </td> <td> Boundary Conditions </td> <td> PRIVÉ </td> <td> KPIs, driving cycles and boundary conditions values </td> </tr> <tr> <td> 3 </td> <td> Induction Machine Design </td> <td> MDL </td> <td> Design Simulation results </td> </tr> <tr> <td> 4 </td> <td> Synchronous Reluctance Machine Design </td> <td> IFPEN </td> <td> Design Simulation results </td> </tr> <tr> <td> 5 </td> <td> e-Drive Design </td> <td> PRIVÉ </td> <td> Control Simulation results </td> </tr> <tr> <td> 6 </td> <td> Prototype Manufacturing </td> <td> UAQ </td> <td> Prototype Pictures </td> </tr> <tr> <td> 7 </td> <td> Powertrain Testing, Vehicle integration and Validation </td> <td> CIDAUT </td> <td> Test Results </td> </tr> <tr> <td> 8 </td> <td> Techno Economic Evaluation and Exploitation </td> <td> JLR </td> <td> Environmental assessment (LCA results) </td> </tr> <tr> <td> 9 </td> <td> Dissemination Communication </td> <td> and </td> <td> UAQ </td> <td> None </td> </tr> </table> Specific datasets may be associated to scientific publications (i.e. underlying data), public project reports and other raw data or curated data not directly attributable to a publication. The policy for open access are summarised in the following Figure 2. Figure 2 . ReFreeDrive timing of different options ReFreeDrive Results Research Data Exploitable? Deposit Data Linked to Publication? Gold Open Access Green Open Access YES NO YES NO ≤ Project End ≤ Publication Date \+ 6 Months = Publication Date Research data linked to exploitable results will not be put into the open domain if they compromise its commercialization prospects or have inadequate protection, which is a H2020 obligation. The rest of research data will be deposited in an open access repository. When the research data is linked to a scientific publication, the provisions outlined in the Grant and Consortium agreements will be followed. Research data needed to validate the results presented in the publication should be deposited at the same time for “Gold” Open Access 1 or before the end of the embargo period for “Green” Open Access 2 . Underlying research data will consist of selected parts of the general datasets generated, and for which the decision of making that part public has been made. Other datasets will be related to any public report or be useful for the research community. They will be selected parts of the general datasets generated or full datasets and be published as soon as possible. ## _1.3 Responsibilities_ Each ReFreeDrive partner has to respect the policies set out in this ODMP. Datasets have to be created, managed and stored appropriately and in line with applicable legislation. The Project Coordinator has a particular responsibility to ensure that data shared through the ReFreeDrive website are easily available, but also that backups are performed and that proprietary data are secured. CIDAUT, will ensure dataset integrity and compatibility for its use during the project lifetime by different partners. Validation and registration of datasets and metadata will be done by CIDAUT in close collaboration with the Work Package Leader generating the respective datasets. Metadata constitutes an underlying definition or description of the datasets, and facilitate finding and working with particular instances of data. Backing up data for sharing through open access repositories will be done by CIDAUT. Quality control of these data is the responsibility of the relevant WP leader, supported by the Project Coordinator. If datasets are updated, the partner that possesses the data has the responsibility to manage the different versions and to make sure that the latest version is available in the case of publically available data. WP1 will provide naming and version conventions. Last but not least, all partners must consult the concerned partner(s) before publishing data in the open domain that can be associated to an exploitable result, as outlined in the Grant and Consortium Agreements of this project. # 2 Data Sharing Relevant datasets will be stored in ZENODO [2] , which is the open access repository of the Open Access Infrastructure for Research in Europe, OpenAIRE [3]. ZENODO builds and operates a simple and innovative service that enables researchers, scientists, EU projects and institutions to share 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 get credited by making the research results citable and integrate them into existing reporting lines to funding agencies like the European Commission. easily access and reuse shared research results. Data access policy will be unrestricted since no confidentiality or Intellectual Property Rights (IPR) issues are expected regarding the environmental monitoring datasets. All collected datasets will be disseminated without an embargo period unless linked to a green open access publication. Data objects will be deposited in ZENODO under: * Open access to data files and metadata and data files provided over standard protocols such as HTTP and Open Archive Initiative-Protocol for Metadata Harvesting (OAI-PMH). * Use and reuse of data permitted. * Privacy of its users protected. ## _2.1 Findable, Accessible, Interoperable, Reusable (FAIR) Principles_ FAIR Principles definition as referenced from FAIR principles description [4]. 2.1.1 To be Findable: * F1 : (meta)data are assigned a globally unique and persistent identifier o A Digital Object Identified (DOI) is issued to every published record on Zenodo. * F2 : data are described with rich metadata (defined by Reusable R1 principle below) o Zenodo's metadata is compliant with DataCite's Metadata Schema minimum and recommended terms, with a few additional enrichements. The DataCite Metadata Schema is a list of core metadata properties chosen for an accurate and consistent identification of a resource for citation and retrieval purposes, along with recommended use instructions. * F3 : metadata clearly and explicitly include the identifier of the data it describes o The DOI is a top-level and a mandatory field in the metadata of each record. * F4 : (meta)data are registered or indexed in a searchable resource o Metadata of each record is indexed and searchable directly in Zenodo's search engine immediately after publishing. o Metadata of each record is sent to DataCite servers during DOI registration and indexed there. 2.1.2 To be Accessible: * A1 : (meta)data are retrievable by their identifier using a standardized communications protocol o Metadata for individual records as well as record collections are harvestable using the OAI-PMH protocol by the record identifier and the collection name. o Metadata is also retrievable through the public Representational state transfer (REST) Application Programming Interface (API) API. * A1.1 : the protocol is open, free, and universally implementable o See point A1. OAI-PMH and REST are open, free and univesal protocols for information retrieval on the web. * A1.2 : the protocol allows for an authentication and authorization procedure, where necessary o Metadata are publicly accessible and licensed under public domain. No authorization is ever necessary to retrieve it. * A2 : metadata are accessible, even when the data are no longer available o Data and metadta will be retained for the lifetime of the repository. This is currently the lifetime of the host laboratory CERN, which currently has an experimental programme defined for the next 20 years at least. * Metadata are stored in high-availability database servers at CERN, which are separate to the data itself. 2.1.3 To be Interoperable: * I1 : (meta)data use a formal, accessible, shared, and broadly applicable language for knowledge representation. o Zenodo uses JavaScript Object Notation (JSON) Schema as internal representation of metadata and offers export to other popular formats such as Dublin Core or MARCXML. The Dublin Core Schema is a small set of vocabulary terms that can be used to describe digital resources (video, images, web pages, etc.), as well as physical resources such as books or CDs, and objects like artworks. The full set of Dublin Core metadata terms can be found on the Dublin Core Metadata Initiative website. MARCXML is an XML schema based on the common MARC21 standards. MARCXML was developed by the Library of Congress and adopted by it and others as a means of facilitating the sharing of, and networked access to, bibliographic information. Being easy to parse by various systems allows it to be used as an aggregation format, as it is in software packages. * I2 : (meta)data use vocabularies that follow FAIR principles o For certain terms Zenodo refers to open, external vocabularies, e.g.: license (Open Definition), funders (FundRef) and grants (OpenAIRE). * I3 : (meta)data include qualified references to other (meta)data o Each referrenced external piece of metadata is qualified by a resolvable URL. 2.1.4 To be Reusable: * R1 : (meta)data are richly described with a plurality of accurate and relevant attributes o Each record contains a minimum of DataCite's mandatory terms, with optionally additional DataCite recommended terms and Zenodo's enrichments. * R1.1 : (meta)data are released with a clear and accessible data usage license o License is one of the mandatory terms in Zenodo's metadata, and is referring to a Open Definition license. * Data downloaded by the users is subject to the license specified in the metadata by the uploader. * R1.2 : (meta)data are associated with detailed provenance o All data and metadata uploaded is tracable to a registered Zenodo user. * Metadata can optionally describe the original authors of the published work. * R1.3 : (meta)data meet domain-relevant community standards o Zenodo is not a domain-specific repository, yet through compliance with DataCite's Metadata Schema, metadata meets one of the broadest cross-domain standards available. ## _2.2 Archiving and Preservation_ Zenodo is hosted by CERN which has existed since 1954 and currently has an experimental programme defined for the next 20+ years. CERN is a memory institution for High Energy Physics and renowned for its pioneering work in Open Access. Organisationally Zenodo is embedded in the IT Department, Collaboration Devices and Applications Group, Digital Repositories Section (IT-CDADR). Zenodo is offered by CERN as part of its mission to make available the results of its work (CERN Convention, Article II, §1 [5]). Data files and metadata are backed up nightly and replicated into multiple copies in the online system. 2.2.1 Data storage All files uploaded to Zenodo are stored in CERN’s EOS service 3 in an 18 petabytes disk cluster. Each file copy has two replicas located on different disk servers. For each file Zenodo stores two independent MD5 4 checksums. One checksum is stored by Invenio 5 [6], and used to detect changes to files made from outside of Invenio. The other checksum is stored by EOS, and used for automatic detection and recovery of file corruption on disks. Zenodo may, depending on access patterns in the future, move the archival and/or the online copy to The CERN Advanced STORage manager (CASTOR) [7] in order to minimize long-term storage costs. EOS is the primary low latency storage infrastructure for physics data from the Large Hadron Collider 6 (LHC) [ 8 ] and CERN currently operates multiple instances totalling 150+ petabytes of data with expected growth rates of 30-50 petabytes per year. CERN’s CASTOR system currently manages 100+ petabytes of LHC data which are regularly checked for data corruption. Invenio provides an object store like file management layer on top of EOS which is in charge of e.g. version changes to files. # 3 Datasets Description The Table 2 refers to each of the datasets that will be produced during the project, their description and importance to the project. Table 2. Datasets generated by the ReFreeDrive Project <table> <tr> <th> Who (WPs generating the dataset) </th> <th> What (Dataset description) </th> <th> Why (Importance of this dataset) </th> <th> How (use of this dataset in the project) </th> </tr> <tr> <td> WP2 </td> <td> KPIs: Project targets at vehicle levels </td> <td> These figures set the design space for the project electric motors </td> <td> These values will drive the different designs </td> </tr> <tr> <td> WP2 </td> <td> Full Subsystems Technical specifications </td> <td> Assigned boundary conditions at the subsystem level </td> <td> These values will drive the different designs and lead the in vehicle integration activities </td> </tr> <tr> <td> WP3 </td> <td> Simulation Results: Electromagnetic, mechanical, thermal, Noise Vibration, Harshness (NVH) </td> <td> Induction Machine design expected result will help comparisons with other technologies </td> <td> This dataset will be the basis for at least one scientific publication. </td> </tr> <tr> <td> WP4 </td> <td> Simulation Results: Electromagnetic, mechanical, thermal, NVH </td> <td> Synchronous Reluctance design expected results will help comparisons with other technologies </td> <td> This dataset will be the basis for at least one scientific publication. </td> </tr> <tr> <td> WP3 & WP4 </td> <td> Material characterization values </td> <td> Grain oriented and non grain oriented materials magnetic and mechanical performance will help other designers reuse this knowledge </td> <td> This information will be used for design purposes. This dataset will be the basis for at least one scientific publication. </td> </tr> <tr> <td> WP5 </td> <td> Control algorithm design results </td> <td> Implementation of control strategies or innovation in control strategies </td> <td> This dataset will be the basis for at least one scientific publication. It will drive the power electronic configuration. </td> </tr> <tr> <td> WP6 </td> <td> Prototype Manufacuring Pictures </td> <td> Comparison with other technologies, technology demonstration feasibility </td> <td> The project will use these pictures for communication purposes </td> </tr> <tr> <td> WP7 </td> <td> Motor Test Results: test results for the integrated e-Drive </td> <td> These data will enable a comparison with other technologies and help designers set ambitious targets in future designs </td> <td> This dataset will be the basis for at least one scientific publication. The project will use these data for the techno economic evaluation and exploitation strategies </td> </tr> <tr> <td> WP7 </td> <td> Powertrain test results: results in the powertrain test </td> <td> These data will enable a comparison with other technologies and help </td> <td> This dataset will be the basis for at least one scientific </td> </tr> <tr> <td> </td> <td> bench </td> <td> designers set ambitious targets in future designs </td> <td> publication. The project will use these data for the techno economic evaluation and exploitation strategies </td> </tr> <tr> <td> WP7 </td> <td> Vehicle driving test results </td> <td> These data will enable a comparison with other technologies at the vehicle level </td> <td> This dataset will be the basis for at least one scientific publication. The project will use these data for the techno economic evaluation and exploitation strategies </td> </tr> <tr> <td> WP7 </td> <td> Vehicle integration pictures </td> <td> Technology demonstration feasibility </td> <td> The project will use these pictures for communication purposes </td> </tr> <tr> <td> WP8 </td> <td> Life Cycle Analysis (LCA) results: environmental assessment and comparatives of the studied technologies </td> <td> LCA data are used throughout the electric vehicle market for marketing, communication, and new designs comparative evaluations. </td> <td> LCA will be key to demonstrate the rare earth free environmental advantages poised by the ReFreeDrive technologies </td> </tr> </table>	https://phaidra.univie.ac.at/o:1140797	Horizon 2020
0054_RINGO_730944.md	# 1\. INTRODUCTION This initial Data Management Plan describes the existing and planned data management, data access and data security policies of ICOS RI. The structure of this report consists of a general overview on the data management of the RINGO project as a whole, as well as a more detailed description of data management. The mission of the European Research Infrastructure ‘Integrated Carbon Observatory System‘ (ICOS RI) is to enable research to understand the greenhouse gas (GHG) budgets and perturbations. The ICOS RI is a distributed research infrastructure that provides the long-term observations required to understand the present state and predict future behaviour of the global carbon cycle and GHG emissions. ICOS RI ensures the continuous, high-precision and long- term greenhouse gas measurements in Europe and adjacent key regions of Africa and Eurasia. The backbone of ICOS RI are the three measurement stations networks: the ICOS atmospheric, ecosystem and ocean networks. Together they are organized within national measurement networks. Technological developments and implementations, related to GHGs, will be promoted by the linking of research, education and innovation. ICOS Central Facilities (ICOS CF), which are Atmospheric Thematic Centre (ATC), Ecosystem Thematic Centre (ETC), Ocean Thematic Centre (OTC) and the Central Analytical Laboratories (CAL) have the specific tasks of collecting and processing the data and samples (e.g. flask or radiocarbon samples in Atmosphere or soil and plant tissue samples in Ecosystem observational network) received from the national measurement networks. ICOS ERIC is the legal entity of ICOS RI established to coordinate the operations and the data of ICOS distributed research infrastructure, and to develop, monitor and integrate the activities and the data of ICOS RI. The ICOS Carbon Portal is the ICOS data centre from where ICOS data and ancillary data sets will be published and be accessible for the users. Carbon Portal is responsible for handling and providing ICOS data products. Carbon Portal is being designed and envisioned as the single access point for environmental scientists to discover, obtain, visualise and track observation measurements produced from the observation stations as quickly as possible. The design of the overall ICOS RI data management is challenged by the complicated requirements of dataflow from the distributed acquisition via centralized processing and quality assessment to the publication of the data products. The ICOS national observation stations are highly distributed; data are semantically diverse, organisational features of the National Networks are different from country to country, observational measurements and resulting data life cycles are varying between observational networks. Data definitions, transfers and responsibilities have been discussed within ICOS RI for several years. These discussions have been documented in numerous documents. # 2\. Data summary This section specifies the purpose of the data, the formats and origin, the size and to whom it will be useful. There are different kinds of data generated in the project. First of all, there are experiments where new observational technologies are developed and tested, either in the lab or in the field. The data is used to evaluate the performance of the instrumentation and/or methods. Data and metadata are essential for documentation and publishing of the results in the literature. Data formats will be very similar to the current standard data formats used in ICOS, except for new instrument specific raw data formats that are often proprietary. Order of magnitude of the data generated is several Gigabytes. In several work packages methods are developed for new or existing measurement strategies. For this existing (ICOS or pre-ICOS) measurements and/or model simulations are used. The model data will be generated using existing models. The data will be used to evaluate the different alternatives for measurement strategies. Data and metadata are essential for documentation and publishing of the results in the literature. Data formats will be very similar to the current standard data formats used in ICOS and mainly consist of netcdf files. Order of magnitude of the data is tens of Gigabytes. In WP5 the historical data sets (pre-ICOS) will be re-evaluated for a limited number of stations by going back to the original raw data and a re-analysis using methodologies as close as possible to the current data processing standards and strategies of ICOS RI, including evaluation of the uncertainties in the individual measurements. The data will be used and offered to the users as improved, quality controlled and recalibrated data sets extending the ICOS dataset to the pre-ICOS period. This dataset will be essential for inverse modelling experiments to complement the ICOS dataset with at least 10 years of historical data. Order of magnitude of the datasize is one gigabyte. This data will be published through the ICOS Carbon Portal following the ICOS data license (CC4BY) and data policy. 3\. FAIR data # 3.1 Data findability, including provisions for metadata This section outlines the discoverability and identifiability of the data and the use of persistent and unique identifiers. All data will be curated using standard EUDAT B2 services, making sure that all data is discoverable through B2FIND. All final (Level 2) datasets will be shared through the ICOS Carbon Portal, which will be fully implementing the FAIR principles. All EUDAT and ICOS CP services make use of ePIC handle for identification of all data objects. (Collections of) Level 2 products will also be minted doi identifiers based on Datacite. All ICOS data object metadata is shred with the GEOSS portal. Naming conventions are not relevant due to the use of persistent identifiers and the linked machine-readable description through metadata complying with INSPIRE and ISO19115 as a subset. New versions of data objects receive of course their own unique persistent identifier and in the metadata the appropriate link to the older version is added. Same for the reference of an updated version in the newer version. Keywords are part of the metadata, following the appropriate (community) standards. # 3.2 Data accessibility This section specifies the extent of open access, how the data is made available, what methods and tools are used. Experiment and model data might but will be openly accessible only after the end of the projects as soon as the results have been published. All publications will be open access. Whenever possible data will be openly accessible following the ICOS CC4BY license. Through the EUDAT B2 services of B2FIND, B2DROP and B2SHARE and the ICOS Carbon portal all metadata and data can be found and accessed. Where relevant and possible with regards to property rights developed software will be made available through the open source repository Github or similar using a GPL license. # 3.3 Data interoperability This section covers what data and metadata vocabularies, standards and methodologies are used to facilitate interoperability. All RINGO and ICOS data and metadata are designed for interoperability and in all cases follow and in some cases even form de-factor (community) standards. All metadata will available in INSPIRE compliant form. All ICOS Carbon Portal data is available as linked open data and through an open SPARQL endpoint. The RINGO project specific data will be available through the EUDAT B2 services following the same standards. # 3.4 Data re-use This section specifies data licencing, availability and length of time for re- use. Wherever possible the data will be shared right after production following the Creative Commons 4.0 International License with Attribution (CC4BY). Experimental data test data will in some cases only become available after the end of the project or publication of the results, whatever comes first, and will be shared used the same CC4BY license. The CC4BY licenses guarantees maximum re-use (and redistribution) while maintaining the traceability of the use and credit to the data providers and their sponsors. Data quality assurance and control is central and the raison d'étre of ICOS and the RINGO project. About 80% of the efforts spent in the ICOS Thematic Centres is directed at data quality assurance. ICOS RI has a time horizon of at least 20 years, the data will remain useful and usable beyond that period. For example, now the time-series generated since 1957 of CO 2 concentrations at Mauna Loa are still being used. ## 4\. Allocation of resources The costs of making data fair can be estimated to be 100% of the effort by ICOS Carbon Portal and 25% of the operational costs of the ICOS Thematic Centres. About 10% of the RINGO budget is used for improvement of the interoperability of the ICOS metadata. The cost of long term preservation of the data is at this moment impossible to estimate. On the long-term the costs of storage are foreseen to go down tremendously. At this moment, the storage costs for ICOS are foreseen to be in the order of 50 k€ per year. ## 5\. Data security ICOS and RINGO produce non-sensitive data. Personal information is processes and stored according to the ICOS privacy policy. For secure storage ICOS relies on the European e-Infrastructure of EUDAT and EGI. 6\. Ethical aspects Not relevant for the RINGO data. ## 7\. Other There are several documents that are related to the data management plan developed in RINGO. These are ICOS Data Policy document, ICOS Data lifecycle document, ICOS Data License, and ICOS measurement protocol documentation for Atmosphere, Ocean and Ecosystem community.	https://phaidra.univie.ac.at/o:1140797	Horizon 2020
0057_Net4Society5_838335.md	# Executive Summary This document is a deliverable of the Net4Society5 project, which is funded by the European Union’s Framework Programme for Research and Innovation, Horizon 2020, under Grant Agreement # 838335. This data management describes the data which will be used by the project, in particular how this data will be collected, the activities for which it will be utilized, and how and where this data will be stored. Subsequent versions of this data management plan will outline how the data used in Net4Society will be shared and preserved. # Introduction This Data Management Plan (DMP, D5.2) is a deliverable (month 6) of the Net4Society5 project, which is funded by the European Union’s Horizon 2020 Programme under Grant Agreement # 838335. Net4Society5 is the transnational network for National Contact Points (NCPs) working in Societal Challenge 6 – “Europe in a changing world: inclusive, innovative and reflective Societies” of Horizon 2020. The main aims of the project focus on providing NCPs with professional and tailor-made services designed to help them support their research communities in their efforts to secure EU-funding. Also incorporated in these aims is the objective of improving and strengthening the integration of SSH research and researchers throughout the whole of Horizon 2020 as a way of fostering interdisciplinarity. These aspects, along with promotion the outcomes of social scientific and humanities research and its impact on society, comprise the general scope of the project. This document constitutes a first version of Net4Society’s Data Management Plan. The purpose of this plan is to describe the main sort of activities and type of data used by the project, and the policy for data management to be followed by the consortium. In this first version, focus rests on a description of the various datasets generated and used by Net4Society5. Because this DMP is a living document, subsequent versions of the plan will go into further detail on the specifics concerning actual data management, as well as reflect any changes made to management procedures. The plan will thus evolve and cover the entire project lifecycle, including where, how, and with which standards data is collected and used in the project. The following section provides an overview of the specific datasets which will be generated by Net4Society5. In addition, this section describes the origins of the datasets and the work packages to which they belong. This section is then followed by a general overview of Net4Society5’s participation in the ongoing Pilot on Open Research Data and approach to personal data protection. The remainder of the plan then presents the datasets and concludes with a brief outlook toward the next DMP update. # Data Summary The following table presents the different datasets that will be generated and used during Net4Society5. The list provided here presents an overview of the sets. As such, it is an indicative list and will be adapted (either addition or removal of datasets) as the project develops. Any changes will be taken into account in subsequent versions of the DMP. <table> <tr> <th> # </th> <th> Data type </th> <th> Description & Purpose </th> <th> Utility </th> </tr> <tr> <td> 1 </td> <td> Net4Society NCP mailing list </td> <td> _Description_ : This dataset contains contact information of the main target group of the Net4Society project— Societal Challenge 6 (SC6) National Contact Points (NCPs). NCPs are individuals who have been officially nominated by their national bodies to provide assistance in the form of information on all matters related to securing EU funding under the H2020 </td> <td> This data could be useful for research related to better understanding NCP needs, as well as for future projects which access researchers in SSH disciplines. </td> </tr> </table> Table 1. Datasets overview <table> <tr> <th> # </th> <th> Dataset (DS) name </th> <th> Origin </th> <th> WP # </th> <th> Format </th> </tr> <tr> <td> 1 </td> <td> DS1_Subscribers_Net4Society_NCP_mailing_list </td> <td> Publically available data </td> <td> 1,2,4,5 </td> <td> .csv </td> </tr> <tr> <td> 2 </td> <td> DS2_Net4Society_External Newsletter_Subscriber </td> <td> Publically available data </td> <td> 4 </td> <td> .csv </td> </tr> <tr> <td> 3 </td> <td> DS2_Net4Society_Internal Newsletter_Subscriber </td> <td> Publically available data </td> <td> 4 </td> <td> .csv </td> </tr> <tr> <td> 4 </td> <td> DS4_SSH Opportunities Document </td> <td> Primary data </td> <td> 3 </td> <td> .docx; .pdf </td> </tr> <tr> <td> 5 </td> <td> DS5_SSH Integration Monitoring Report_ESR Analysis </td> <td> European Commission </td> <td> 3 </td> <td> .xls </td> </tr> <tr> <td> 6 </td> <td> DS6_Net4Society_Internal_Website </td> <td> publically available data </td> <td> 4 </td> <td> .html </td> </tr> <tr> <td> 7 </td> <td> DS7_Research Directory and Partner Search Tool </td> <td> publically available data </td> <td> 2 </td> <td> .csv </td> </tr> <tr> <td> 8 </td> <td> DS8_Institutional Portraits </td> <td> publically available data </td> <td> 2 </td> <td> .xls </td> </tr> <tr> <td> 9 </td> <td> DS9_Surveys </td> <td> primary data </td> <td> 2,3,5 </td> <td> .xls </td> </tr> </table> Table 2 below describes the dataset and the purpose of the generation and collection of data in relation to the project’s objectives. The table also explains the utility of this data collection and generation and for whom it might be useful. Table 2. Datasets description, purpose, and utility <table> <tr> <th> </th> <th> </th> <th> Programme. Information included in this dataset is the full name, organizations, countries of origin, and email addresses of the SC6 NCPs. _Purpose_ : This information is collected in order to keep SC6 NCPs up- to-date on news and developments related to H2020 that is necessary for their daily work. This mailing list also provides the basis for dissemination of information related to Net4Society project activities. </th> <th> </th> </tr> <tr> <td> 2 </td> <td> DS2_Net4Society_External Newsletter_Subscriber </td> <td> _Description_ : This dataset is a mailing list all contact information (full name, email address, organization, country of origin) of non-NCP stakeholders within the SC6 community. These stakeholders include researchers, policy makers, research managers, and members of civil society organizations. _Purpose_ : To be able to send Net4Society5’s external newsletter and emagazine, ISSUES, to subscribers. </td> <td> This data is useful for disseminating information related to EU-based research and funding. </td> </tr> <tr> <td> 3 </td> <td> DS2_Net4Society_Internal Newsletter_Subscriber </td> <td> _Description_ : This dataset is a mailing list of all contact information (full name, email address, organization, country of origin) of SC6 NCPs who voluntarily sign-up to be part of the network. _Purpose_ : To be able to send updates on project activities (past, up- </td> <td> This data is useful for disseminating information related to Net4Society organized events, EU-level events (DG-RTD) EU-based research and funding, and relevant policy updates. </td> </tr> </table> <table> <tr> <th> </th> <th> </th> <th> coming)to SC6 NCPs </th> <th> </th> </tr> <tr> <td> 4 </td> <td> SSH Opportunities Document </td> <td> _Description_ : The data collected here is a compiled list of 2020 SSH- flagged topics on all H2020 Work Programmes. _Purpose_ : This dataset will be used to produce the 2019 edition of the “Opportunities document for researchers from the Socio-economic sciences and Humanities in H2020”. </td> <td> This data is useful for providing potential applicants to Horizon 2020 funding with key information for where they can apply. It is extremely well-received and great value and use by researchers at all career levels, and in all countries within Europe and the world (where applicable). </td> </tr> <tr> <td> 5 </td> <td> DS5_SSH Integration Monitoring Report_ESR Analysis </td> <td> _Description_ : “This dataset includes statistical information for all projects funded under SSH-flagged topics (Evaluation Summary Reports, Anonymised Parts A & B of Grant Agreements (without project participant names), project acronyms, numbers, project participant organizations, LE country code, LE participant description) in 2018.”” _Purpose_ : This data is used to develop a second set of data which will be used in the production of the data analysis for the European Commission publication _5th Monitoring Report on SSH-flagged topics funded in 2018 under Societal Challenges and Industrial Leadership priorities. Integration of Social Sciences and_ _Humanities in Horizon_ _2020: Participants,_ _Budget and Disciplines_ . </td> <td> This data is useful for evaluating the success of efforts to strengthen the integration of SSH throughout the whole of H2020. The statistics can be useful for research managers and funding organizations at local, national, and EU- levels seeking to understanding areas where social scientific and humanities researchers have been successful in EU Framework Programmes. </td> </tr> <tr> <td> 6 </td> <td> Research Directory and Partner Search Tool </td> <td> _Description_ : The dataset </td> <td> This data is useful for </td> </tr> </table> <table> <tr> <th> </th> <th> </th> <th> contains full names, organizational affiliations, research interests and areas of expertise, email addresses, and phone numbers of researchers in an online database located. The tool is accessible via the Net4Society project website. The tool is updated with new calls for proposals each time a new SC6 Work Programme is published. _Purpose_ : The data is collected to support researchers seeking partners for consortiumbuilding purposes in response to calls announced in the various Horizon 2020 SC6 Work Programmes. This data is voluntarily entered by interested researchers, primarily coordinators, and stored in a repeatedly accessible online database. </th> <th> any researcher looking to make professional connections, and in particular for the purpose of building consortia to write and submit research proposals in Horizon 2020. The tool is always accessible, not only when new calls are open, meaning that interested parties always have an opportunity to search the database and reach out to potential partners. </th> </tr> <tr> <td> 7 </td> <td> Institutional Profiles </td> <td> _Description_ : The data collected here are the full names of researchers, their organizational affiliations, professional profiles, email addresses, and phone numbers. This information is associated with researchers of excellence located in EU member states which are underperforming in Horizon 2020. _Purpose_ : This data is collected in order to comprise and published profiles of researchers </td> <td> The data collected can be useful for public authorities at the EUlevel interested in strengthening the visibility and awareness of researchers in nontraditionally strong countries within EU Framework Programmes. </td> </tr> </table> <table> <tr> <th> </th> <th> </th> <th> and institutions of excellence in underperforming EU member states. The aim is to boost the chances of these researchers to become part of winning consortia, as well as to become coordinators of projects. An additional aim is to increase the success rate of researchers within H2020 who do not stem from a traditional powerhouse country (i.e. Germany, France, England). </th> <th> </th> </tr> <tr> <td> 8 </td> <td> Surveys </td> <td> _Description_ : This dataset contains answers from feedback forms and other surveys aimed at participants of Net4Society events. Feedback forms collect data from NCPs, whereas other surveys include quantitative surveys targeting researchers who have taken part in Net4Society-organized brokerage events. _Purpose_ : Both types of surveys serve the purpose of providing Net4Society5 insight into the nature of the experiences event participants have had and to gather information about what sort of specific services they need and or seeking. The idea is to gain insight into areas where services and tools provided by the project can be enhanced, modified, or newly created to better address project </td> <td> This data is useful to Net4Society, as it helps the project better strategically plan its activities and consider which groups to address. This information can also be of interest to public bodies interested in understanding the needs and interests of stakeholders within the SC6 community. </td> </tr> <tr> <td> </td> <td> </td> <td> stakeholder needs. </td> <td> </td> </tr> </table> # FAIR Data _Participation in the Pilot on Open Research Data_ Although Net4Society5 as a project does not actively generate research data, it does intend to make use of data of a personal nature in many of its activities. Because of this reliance on personal data, the project has opted to participate in the ongoing Pilot on Open Research Data. The consortium of this project takes the protection of data very seriously, and it is for this reason that it has chosen to produce this data management plan. _Personal Data Protection_ For many of its planned activities, Net4Society5 will need to collect personal data. The personal data to be collected will be of a basic nature (i.e. full name, professional background, email address, phone number, organization, country of origin). In collecting and using this data, Net4Society5 will work in compliance with the EU’s General Data Protection Regulation (GDPR), as well as all relevant national regulations. The principle of informed consent will be followed to further ensure the proper handling of personal data, especially in the administration of planned surveys. In such cases, data subjects who are asked, for example, to respond to either event feedback forms, or satisfaction surveys, will be explicitly informed over the use and purpose of the personal data to be collected. To further protect the personal data collected, Net4Society 5 will make use of secure folders which will be placed on secure servers. Access to these folders will only be possible for those individuals specifically assigned to the task. No other project members will have access. # Outlook toward next DMP The next version of the DMP will be prepared for month 19, which is the final month of Net4Society’s runtime. In the next version, updates will be made related to how the data will be made interoperable, where and how they will be stored.	https://phaidra.univie.ac.at/o:1140797	Horizon 2020
0067_M-CUBE_736937.md	2\. DATA SET DESCRIPTION # A. DESCRIPTION, NATURE AND SCALE OF THE DATA COLLECTED The data generated by the M-CUBE project are related to the collection, characterization and standardization of calculation codes for measurements, creation of antennas and to produce various kinds of improved MRI images. The data will be collected through web interfaces on the intranet website of the project and will serve to build up a web-based catalogue (the M-CUBE repository) accessible on the MCUBE public website. Hence, it will help disseminating worldwide data generated through the project. # B. ORIGINS OF COLLECTED DATA The origins of the collected data are the following: 1. UNIVERSITE D’AIX-MARSEILLE (AMU) 1a) Institut Fresnel 1b) Centre de Résonance Magnétique Biologique et Médicale 2. COMMISSARIAT A L’ENERGIE ATOMIQUE ET AUX ENERGIES ALTERNATIVES (CEA) 3. UNIVERSITE CATHOLIQUE DE LOUVAIN (UCL) 4. CENTRE NATIONAL DE LA RECHERCHE SCIENTIFIQUE (CNRS) 5. UNIVERSITAIR MEDISCH CENTRUM UTRECHT (UMC UTRECHT) 6. AALTO-KORKEAKOULUSAATIO (AALTO) 7. SAINT PETERSBURG NATIONAL RESEARCH UNIVERSITY OF INFORMATION TECHNOLOGIES, MECHANICS AND OPTICS (ITMO) 8. THE AUSTRALIAN NATIONAL UNIVERSITY (ANU) 9. MULTIWAVE TECHNOLOGIES AG (Multiwave) 10. MR COILS BV (MR Coils BV) C. TO WHOM CAN THESE DATA BE USEFUL? The aim of collecting these data is to organise the M-CUBE collection and to propose a web based catalogue to the scientific communities. It will constitute a readily accessible repository at the European and world level. \- It helps: The Scientific community of physicists in accessing high quality data and tools for improving the next generation of MRI images The health authorities and doctors in facilitating detection of diseases thanks to better images 3\. How will the data be created and collected? Each M-CUBE project partner generates the data which help the creation of new kind of antenna and which facilitate the radically improvement of spatial and temporal resolutions of MRI images. These data are collected into a database, through the M-CUBE website, thanks to formatted forms allowing easy filling in. All M-CUBE partners have provided details on what type of data they are going to generate, to provide a short description of the data, their formats, if they wish to open these sets of data and how they are going to archive and preserve them. The updated sets of data generated by M-CUBE partners are listed below. ## A. AMU – FRESNEL <table> <tr> <th> </th> <th> AMU/FRESNEL </th> <th> </th> <th> </th> </tr> <tr> <td> Calculation codes </td> <td> Analytical model codes. To calculate the resonant frequency of coils. Volumic coils (birdcage ex.) and surface coils (dipole loops) </td> <td> MATLAB </td> <td> YES. Post deliverable </td> <td> Fresnel Data center and M-CUBE website </td> </tr> <tr> <td> Modelisation results </td> <td> Field maps, SNR maps, scattering matrix (Sij) </td> <td> MATLAB, .TXT, JPEG + others. </td> <td> YES. Post deliverable </td> <td> Fresnel Data center and M-CUBE website </td> </tr> <tr> <td> 3D antenna models </td> <td> Architecture design, material & composition </td> <td> CST, HFSS, COMSOL </td> <td> YES. Post deliverable </td> <td> Fresnel Data center and M-CUBE website </td> </tr> <tr> <td> Simulation results </td> <td> Field maps, SNR maps, scattering matrix (Sij) </td> <td> MATLAB, .TXT, JPEG + others. </td> <td> YES. Post deliverable </td> <td> Fresnel Data center and M-CUBE website </td> </tr> <tr> <td> Electric caracterization </td> <td> Field maps, Electromagnetic compatibility </td> <td> MATLAB, .TXT, JPEG + others. </td> <td> NO. Ethics reasons. </td> <td> Fresnel Data center and M-CUBE website </td> </tr> <tr> <td> Electronic caraterization </td> <td> Field maps, Electromagnetic compatibility </td> <td> MATLAB, .TXT, JPEG + others. </td> <td> NO. Ethics reasons. </td> <td> Fresnel Data center and M-CUBE website </td> </tr> </table> ## B. AMU – CRMBM <table> <tr> <th> AMU/CRMBM </th> <th> </th> </tr> <tr> <td> Data generated </td> <td> Data set description </td> <td> Formats </td> <td> Open? </td> <td> Data preservation </td> </tr> <tr> <td> coil specifications </td> <td> dimensions </td> <td> _Value (cm)_ </td> <td> YES </td> <td> on lab data server </td> </tr> <tr> <td> coil specifications </td> <td> Maximum power </td> <td> _Value (kW)_ </td> <td> YES </td> <td> on lab data server </td> </tr> <tr> <td> coil specifications </td> <td> homogeneity </td> <td> _Values (FOV (cm)_ _and percent_ _variation over_ _DSV area (cm))_ </td> <td> YES </td> <td> on lab data server </td> </tr> <tr> <td> coil specifications </td> <td> Signal to noise ratio in reference image obtained on phantom with reference sequence </td> <td> _Value (unitless)_ </td> <td> YES </td> <td> on lab data server </td> </tr> <tr> <td> coil specifications </td> <td> homogeneity Q factor of coils </td> <td> dB - Hz - Ohm </td> <td> YES </td> <td> on lab data server </td> </tr> <tr> <td> coil evaluation </td> <td> Reference voltage on reference phantom </td> <td> _Value (V)_ </td> <td> YES </td> <td> on lab data server </td> </tr> <tr> <td> B1 RF maps (measurements) </td> <td> maps </td> <td> </td> <td> YES </td> <td> on lab data server </td> </tr> <tr> <td> MRI measurements on small animals </td> <td> MRI and MRS data </td> <td> dicom </td> <td> YES </td> <td> on lab data server </td> </tr> <tr> <td> MRI measurements on phantoms </td> <td> MRI and MRS data </td> <td> dicom </td> <td> YES </td> <td> on lab data server </td> </tr> <tr> <td> MRI measurements on humans </td> <td> MRI and MRS data </td> <td> dicom </td> <td> YES </td> <td> on lab data server </td> </tr> <tr> <td> 3D printer structure manufacturing </td> <td> dimensions of antenna </td> <td> ACSI SAT </td> <td> YES </td> <td> on lab data server </td> </tr> <tr> <td> 3D printer structure manufacturing </td> <td> dimensions of antenna support for 3D printer </td> <td> .STL </td> <td> YES </td> <td> on lab data server </td> </tr> </table> ### C. CEA-NEUROSPIN <table> <tr> <th> </th> <th> CEA/NEUROSPIN </th> <th> </th> <th> </th> </tr> <tr> <td> Data generated </td> <td> Data set description </td> <td> Formats </td> <td> Open ? </td> <td> Data preservation </td> </tr> <tr> <td> __Types of Data_ _ </td> <td> __Description of the data_ _ </td> <td> __Standards and_ _ __Metadata_ _ </td> <td> __Data_ _ __Sharing_ _ </td> <td> __Archive and_ _ __Preservation_ _ </td> </tr> <tr> <td> Calculation codes </td> <td> m-file </td> <td> Matlab </td> <td> No </td> <td> No </td> </tr> <tr> <td> Modelisation results </td> <td> m-file </td> <td> Matlab </td> <td> Yes </td> <td> Yes </td> </tr> <tr> <td> 3D antenna models </td> <td> \- </td> <td> CST, HFSS, COMSOL </td> <td> No </td> <td> No </td> </tr> <tr> <td> Simulation results </td> <td> \- </td> <td> CST, HFSS, COMSOL </td> <td> Yes </td> <td> No </td> </tr> <tr> <td> MRI measures on small animals </td> <td> quantitative or weighted MR images </td> <td> Raw data &DICOM </td> <td> Yes </td> <td> Yes </td> </tr> <tr> <td> MRI measures on fantoms </td> <td> quantitative or weighted MR images </td> <td> DICOM </td> <td> Yes </td> <td> Yes </td> </tr> <tr> <td> MRI measures on men </td> <td> quantitative or weighted MR images </td> <td> DICOM </td> <td> Yes </td> <td> Yes </td> </tr> </table> ### D. UCL <table> <tr> <th> </th> <th> UCL </th> <th> </th> <th> </th> </tr> <tr> <td> Data generated </td> <td> Data set description </td> <td> Formats </td> <td> Open or private? </td> <td> Data preservation </td> </tr> <tr> <td> __Types of Data_ _ </td> <td> __Description of the data_ _ </td> <td> __Standards and_ _ __Metadata_ _ </td> <td> __Data Sharing_ _ </td> <td> __Archive and_ _ __Preservation_ _ </td> </tr> <tr> <td> Calculation codes </td> <td> Codes regarding E and H interactions; Codes regarding array scanning method </td> <td> executables and Matlab routines </td> <td> Shared among partners in collaborative framework </td> <td> Archive internal to consortium </td> </tr> <tr> <td> Modelisation results </td> <td> Effects of impedance surfaces, eigenmode analysis, active impedance studies, etc </td> <td> presentations, papers </td> <td> presenations limited to consortium; papers public </td> <td> Archive all </td> </tr> <tr> <td> 3D antenna models </td> <td> Wire or strip-type metal in dielectic volume (e.g. Teflon) </td> <td> GMSH files or STEP files </td> <td> Shared among partners </td> <td> \- </td> </tr> <tr> <td> Simulation results </td> <td> Effects of impedance surfaces, eigenmode analysis, active impedance studies, etc </td> <td> Text and Matlab data files </td> <td> Limited to Consortium, except for papers </td> <td> Archive all </td> </tr> <tr> <td> CAD MECA files </td> <td> GMSH files for geometry </td> <td> .msh files </td> <td> Open to all Consortium members </td> <td> Archive all </td> </tr> <tr> <td> Electric caracterization </td> <td> Near-field patterns of metamaterial antennas obtaine with probes </td> <td> Formats offered by VNA (Vector Network Analyzer), with proper scaling rule </td> <td> Open to all Consortium members </td> <td> Archive all </td> </tr> <tr> <td> Electronic caraterization </td> <td> Scattering matrix of N-port MRI coils/birdcages </td> <td> Formats offered by VNA </td> <td> Limited to Consortium, except for papers </td> <td> Archive all </td> </tr> </table> ## E. UMC UTRECHT <table> <tr> <th> </th> <th> UMC UTRECHT* </th> <th> </th> </tr> <tr> <td> Data generated </td> <td> Data set description </td> <td> Formats </td> <td> Open? </td> <td> Data preservation </td> </tr> <tr> <td> Calculation codes </td> <td> Scripts and functions to process simulated and measured data </td> <td> Plain text (matlab, C++ or Python) </td> <td> YES, after finalizing project </td> <td> Archived </td> </tr> <tr> <td> Simulation input files </td> <td> Input file for EM simulation (e.g. Sim4Life) that contains simulation geometry (=antenna design) and simulation settings </td> <td> CST or Sim4Life format </td> <td> Private </td> <td> Archived </td> </tr> <tr> <td> 3D antenna models </td> <td> Antenna design with electronics, geometry and materials. </td> <td> Powerpoint, CAD </td> <td> Private </td> <td> Archived </td> </tr> <tr> <td> Simulation results </td> <td> Resulting simulated field distributions </td> <td> CST or Sim4Life format </td> <td> private; data not stored longterm </td> <td> The data is bulky and can always be reproduced with the simulation input files. The data is therefore not stored long-term. </td> </tr> <tr> <td> MRI measures on fantoms </td> <td> 2D or 3D images representing MRI measurements on phantoms </td> <td> DICOM </td> <td> YES, after finalizing project </td> <td> Archived </td> </tr> <tr> <td> Electric caracterization </td> <td> S11 and S12 response from bench measurements of antennas and metamaterial structures </td> <td> Touchstone </td> <td> YES, after finalizing project </td> <td> Archived </td> </tr> </table> ## F. ITMO <table> <tr> <th> </th> <th> ITMO </th> <th> </th> <th> </th> </tr> <tr> <td> Data generated </td> <td> Data set description </td> <td> Formats </td> <td> Open? </td> <td> Data preservation </td> </tr> <tr> <td> Calculation codes </td> <td> Codes for experimental data post-processing </td> <td> Matlab, Python </td> <td> To be defined </td> <td> ITMO data center </td> </tr> <tr> <td> 3D antenna models </td> <td> Optimized coil and metasurface structures as project files in commercial software pachages (CST, HFSS, Sim4Life) </td> <td> CST, HFSS, COMSOL </td> <td> private </td> <td> ITMO data center </td> </tr> <tr> <td> Simulation results </td> <td> Calculated RF-field distributions of RF-coils (magnetic, electric fields, SAR, SNR, B1+,B1-) </td> <td> CSV data files </td> <td> YES </td> <td> M-CUBE website </td> </tr> <tr> <td> CAD MECA files </td> <td> CAD models of RF-coils exported from simulation tools (3D geometry) </td> <td> IGES, STEP, DXF, STL </td> <td> YES </td> <td> M-CUBE website </td> </tr> <tr> <td> MRI measures on fantoms </td> <td> Images obtained using metasurface-based coils </td> <td> DICOM </td> <td> YES </td> <td> M-CUBE website </td> </tr> <tr> <td> MRI measures on men </td> <td> Images obtained using metasurface-based coils </td> <td> DICOM </td> <td> YES </td> <td> M-CUBE website </td> </tr> <tr> <td> Electronic caraterization </td> <td> Schematic models </td> <td> P-cad </td> <td> private </td> <td> ITMO data center </td> </tr> </table> ## G. MULTIWAVE <table> <tr> <th> </th> <th> MULTIWAVE </th> <th> </th> <th> </th> </tr> <tr> <td> Data generated </td> <td> Data set description </td> <td> Formats of the data </td> <td> Open ? </td> <td> Data preservation </td> </tr> <tr> <td> __Types of_ _ __Data_ _ </td> <td> __Description of the data_ _ </td> <td> __Standards and_ _ __Metadata_ _ </td> <td> __Data_ _ __Sharing_ _ </td> <td> __Archive and Preservation_ _ </td> </tr> <tr> <td> Calculation codes </td> <td> Spectral Element Solver </td> <td> Python source code </td> <td> Private </td> <td> Multiwave data center </td> </tr> <tr> <td> Modeling results </td> <td> Mathematical modeling of antennas (equivalent circuits) </td> <td> Python source code </td> <td> Yes, partially </td> <td> M-CUBE website </td> </tr> <tr> <td> 3D antenna models </td> <td> CAD models and Meshes </td> <td> CST, HFSS, COMSOL </td> <td> Yes </td> <td> M-CUBE website </td> </tr> <tr> <td> Simulation results </td> <td> Post processing of IBVP solutions </td> <td> ascii, txt, csv, hdf5 </td> <td> Yes </td> <td> M-CUBE website </td> </tr> <tr> <td> CAD MECA files </td> <td> CAD files for geometry or mesh description </td> <td> .nii, .stl, .nastran </td> <td> Open </td> <td> Multiwave data center </td> </tr> </table> H. MR COILS <table> <tr> <th> </th> <th> </th> <th> MR COILS </th> <th> </th> </tr> <tr> <td> Data generated </td> <td> Data set description </td> <td> Formats </td> <td> Open? </td> <td> Data preservation </td> </tr> <tr> <td> __Types of Data_ _ </td> <td> __Description of the_ _data_ _ </td> <td> __Standards and_ _ __Metadata_ _ </td> <td> __Data Sharing_ _ </td> <td> __Archive and Preservation_ _ </td> </tr> <tr> <td> MRI measures on fantoms </td> <td> B1 maps, SNR maps, g-factor maps </td> <td> Dicom and matlab </td> <td> Private, but open upon request </td> <td> Synergy storage (at MRCoils) </td> </tr> <tr> <td> Electric caracterization </td> <td> test results checklist </td> <td> Word or PDF </td> <td> Private, but open upon request </td> <td> Synergy storage (at MRCoils) </td> </tr> </table> ### I. CNRS - ESPCI <table> <tr> <th> </th> <th> </th> <th> CNRS </th> <th> </th> <th> </th> </tr> <tr> <td> Data generated </td> <td> Data set description </td> <td> Formats of the data </td> <td> Open ? </td> <td> Data preservation </td> </tr> <tr> <td> Calculation codes </td> <td> Codes for computing eigenmodes from analytical models of metamaterial structure </td> <td> Matlab, python, C </td> <td> YES, after publication </td> <td> CNRS-ESPCI data center and M-CUBE website </td> </tr> <tr> <td> Modelisation results </td> <td> Output of the calculation codes. </td> <td> CSV, binary </td> <td> YES, after publication </td> <td> CNRS-ESPCI data center and M-CUBE website </td> </tr> <tr> <td> 3D antenna models </td> <td> CAD file with electromagnetic properties </td> <td> CST, HFSS, COMSOL </td> <td> Private </td> <td> CNRS-ESPCI data center </td> </tr> <tr> <td> Simulation results </td> <td> E and H fields, S parameters </td> <td> CSV </td> <td> YES, after publication </td> <td> CNRS-ESPCI data center and M-CUBE website </td> </tr> <tr> <td> Electric caracterization </td> <td> S parameters </td> <td> CSV </td> <td> YES, after publication </td> <td> CNRS-ESPCI data center and M-CUBE website </td> </tr> </table> 4\. DATA SHARING # A. DATA SHARING AND DISSEMINATION The data will be made available worldwide through the M-CUBE Website thanks to dedicated interfaces that will list the M-CUBE data files available and enable the users to access all data and place enquiries on data of interest. Access to the data is not restricted, except for adding and editing permissions that are restricted to the M-CUBE partners. The M-CUBE data will be widely open to any user except for intellectual property reasons and/or unmet quality criteria, and will respect the following disclosure levels set: * Consortium: the data are made available to consortium partners only. * Public: the data are listed on the public web interface and any user can place an enquiry about a data of interest. # B. DATA REPOSITORY The data will be deposited on the M-CUBE database through the M-CUBE website that is hosted on servers of the AMU-Institut Fresnel partner laboratory. ## C. DATA DISCOVERY The data generated by the project correspond to the description of the calculation codes, modelisation results, simulation results, coil specifications and MRI measures that will be generated by M-CUBE partners, laboratories and SMEs, in their own infrastructure. These data are discoverable by the users simply, indirectly by using any web search engines (e.g: google, yahoo, bing...), or directly by connecting to the M-CUBE website's internal search engine. LINK : _http://www.mcube-project.eu/opendata/_ ## D. REUSING DATA The data are generated by the all the M-CUBE partners. The collected data are used by endusers to create their own calculation codes, their own coils and to improve their own measures. There is no limitation on the way the data can be reused and practically can serve as reference, even after the project ends.	https://phaidra.univie.ac.at/o:1140797	Horizon 2020
0074_IoF2020_731884.md	# EXECUTIVE SUMMARY Data or Big Data rapidly has become a new resource or asset in the current economy, also in the agricultural sector. This development leads to several issues that have to be addressed such as data availability, quality, access, security, responsibility, liability, ownership, privacy, costs and business models. In Europe several initiatives have already been established to work on this in a general context (e.g. the EU open data policy, General Data Protection Regulation) and more specifically in agriculture (e.g. GODAN, COPA- COGECA). IoF2020 has to address these issues by developing a Data Management Plan (DMP). This document (D1.4) provides a first version of a DMP containing a general overview of relevant developments and a first inventory of needs and issues that play a role in the use cases. This has resulted in a number of general guidelines for data management in IoF2020: * Research papers derived from the project should be published under open access policy * Research data should be stored in a central repository according to the FAIR principles: findable, accessible, interoperable and reliable * The use cases in IoF2020 should be clearly aligned to the European Data Economy policy and more specifically in line with the principles and guidelines provided by the stakeholder community _i.e._ COPA-COGECA. * All IoF2020 use cases should be GDPR-compliant. A number of follow-up actions were identified to establish open, transparent data management in IoF2020: * Investigate what research data is involved in the use cases and other project activities and define how they should be treated according to the open data policy. * Participate actively in the debate and developments of the European Data Economy and data sharing in agriculture. * Analyze and explore the use cases in a deeper way in order to identify which data management issues potentially play a role and define plans how to deal with them. * Concerning collaboration with other projects, a more systematic and structural approach should be explored in order to maximize the benefits and impact of the mutual activities on data management. * A feasible, but sufficient, plan has to be developed to make the use cases GDPR-compliant. As a result the DMP will be continuously adapted and updated during the project’s period. ## 1 INTRODUCTION ### 1.1 CONTEXT AND BACKGROUND Big Data is becoming a new resource, a new asset, also in the agricultural sector. Big Data in the agricultural sector includes enterprise data from operational systems, farm field sensor data (e.g. temperature, rainfall, sunlight), farm equipment sensor data (from tractors, harvesters, milking robots, feeding robots), data from wearable animal sensors (neck tag, leg tag), harvested goods and livestock delivery vehicles sensor data (from farms to processing facilities) etc. Increasingly, Big Data applications, Big Data initiatives and Big Data projects are implemented and carried out, aiming for improving the farm and chain performance (e.g., profitability and sustainability) and support associated farm management decision making. In the IoF2020 different use cases are taking place in which data also plays a key role involving farm companies that share their (big) farm data with enterprises and organisations who strive to add value to that data. This implies that the data from one party is combined with data from other parties in the chain, and then analysed and translated into advices, knowledge, or information for farmers. In this way Big Data becomes an asset in supporting farmers to further improve their business performance (e.g., higher yield, better quality, higher efficiency). These data-driven developments often involve collaborations between agri-IT companies, farmers’ cooperatives and other companies in the food supply chain. These business-to-business initiatives and interactions are increasingly conducted through inter- organisational coordination hubs, in which standardised IT-based platforms provide data and business process interoperability for interactions among the organisations. In Figure 1 an example of such a network of collaborating organisations is provided. It involves four stakeholders around the farmer (i) sperm supplier, (ii) milk processor, (iii) feed supplier and (iv) milking robot supplier. Multiple farms can be involved, and for each stakeholder relation data are collected from the farm and collected in a data platform. All stakeholders can then receive multiple datasets back from this platform, depending on authorization settings. This has to be arranged and governed by some form of network administrative organization. It can be expected that the IoF2020 use cases can be modelled into a similar picture. C loud DATA platform Farmer Supplier C Supplier A Supplier B Customer X feed sperm milk milking robot data data data data data data data data data data data data data Network Administrative Organization _Figure 1 Example of a data sharing network in dairy farming in the agri-food sector_ There are multiple ways of expanding and intensifying such a Big Data network. For instance, at the end of the market chain, consumers may play a role in future. They could be interested in Big Data as a quality check for products, and they could in principle provide consumer based data to the platform. Moreover, the role of government can be relevant. While governments seem to be interested in open data possibilities, this may cause uncomfortable privacy concerns for suppliers in the network, who prefer to keep business models away from the public. The management of Big Data initiatives comprises challenges of privacy and security, which impact discouragements and distrust among famers. Trust is considered to be a starting point in increasing Big Data applications. Many involved companies are refraining from sharing data because of the fear of issues such as data security, privacy and liability. Corresponding to the different stages of the data value chain (data capture, data storage, data transfer, data transformation, data analytics and data marketing), several key issues of Big Data applications have been identified and can be summarised as data availability, data quality, access to data, security, responsibility, liability, data ownership, privacy, costs, and business models. Ownership and value of data appear to be important issues in discussions on the governance of Big Data driven inter-organisational applications. A growing number of Big Data initiatives address privacy and security concerns. Also Big Data applications raise power-related issues that sometimes can lead to potential abuses of data. And many companies expect to develop new business models with data but are in a kind of deadlock and afraid of taking the first step. So the challenge is how companies could or should deal with these inter- organisational governance issues as they are considered as (the most) hindering factors for fulfilling the promises and opportunities of Big Data in agri-food sector. Against this background, the network structure raises multiple questions in the scope of data management. For instance, how is the communication among the different actors? On what conditions do farmers take part, and how easy is it to enter or leave the network? What is the role of a network administrative organization and how openly do they perform together with partners of the network? ### 1.2 OBJECTIVE OF THIS DOCUMENT Task 1.4 ‘Development Data Management Plan’ is meant to address these questions by developing a plan and specific guidelines for the use cases and the project as a whole concerning data management. The Data Management Plan (DMP) will be developed, outlining: * how (research) data will be collected, processed or generated within the project; * what methodology and standards will be adopted; * whether and how this data will be shared and/or made open; * and how this data will be curated and preserved during and after the project. The DMP aims to ensure that IoF2020 activities are compliant with the H2020 Open Access policy and the recommendations of the Open Research Data pilot. The DMP will furthermore explain how the project will be connected with other past and on-going initiatives such as EIP-Agri, agINFRA and global channels, such as OpenAIRE, CIARD and GODAN. Under this task an Open Access Support Pack will be developed translating the generic H2020 requirements and recommendations into specific guidelines and advice that can be applied in the project. This document (D1.4) will provide first version of Data Management Plan containing a general overview of relevant developments and a first inventory of needs and issues that play a role in the use cases. The application of the DMP by all IoF2020 partners will be continuously monitored under this task and an updated version of the DMP including more detailed specific support packs for the use cases (D1.5) will be delivered in Month 36. ### 1.3 OUTLINE The remainder of this document is organized as follows. Chapter 2 will describe the approach how the results were found. This results in an overview of general developments on data management, described in Chapter 3. Chapter 4 will provide a short overview of relevant past and on-going initiatives and projects on data management in agriculture. Then a first inventory of the needs and potential issues of the use cases will be described. Based on the findings in these Chapters, a concrete Data Management Plan will be provided in Chapter 6, followed by some general conclusions in Chapter 7. ## 2 APPROACH The approach that was followed to generate this report consists of the following steps (see Figure 2): 1. Identification and description of relevant external developments in the field of data management in general and more specific for agriculture (see Chapter 3). 2. Identification and description of relevant initiatives and/or projects (in the recent past of ongoing) in the field of (agricultural) data management (see Chapter 4). 3. Based on the results of the previous two steps a quick scan of the needs and issues in the IoF2020 use cases is made (see Chapter 5). 4. Based on the results of all previous steps a preliminary version of the IoF2020 Data Management is defined that provides general guidelines for the use cases (see Chapter 6). _Figure 2 Steps that were taken to achieve a first version of the Data Management Plan for IoF2020_ After these steps the data management plan will be further refined and tailored into a specific support pack for the use cases, but this is outside the scope of this deliverable. The results of this deliverable will be iteratively updated and refined as a living document but consolidated in a next deliverable in Month 36 (D1.5). ## 3 EXTERNAL DEVELOPMENTS In this chapter we will highlight and briefly describe some data management developments that are most relevant to a H2020 innovation action project such as IoF2020 in Section 3.1. In Section 3.2 we will then describe the possible consequences for IoF2020 of these developments. ### 3.1 DATA MANAGEMENT DEVELOPMENTS #### 3.1.1 H2020 Open Access Policy 1 Open access (OA) can be defined as the practice of providing on-line access to scientific information that is free of charge to the user and that is re- usable. In the context of R&D, open access to 'scientific information' refers to two main categories: * Peer-reviewed scientific publications (primarily research articles published in academic journals) * Scientific research data: data underlying publications and/or other data (such as curated but unpublished datasets or raw data) It is now widely recognised that making research results more accessible to all societal actors contributes to better and more efficient science, and to innovation in the public and private sectors The Commission therefore supports open access at the European level (in its framework programmes), at the Member States level and internationally. #### Peer-reviewed scientific publications All projects receiving Horizon 2020 funding are required to make sure that any peer-reviewed journal article they publish is openly accessible, free of charge (article 29.2. Model Grant Agreement). #### Research data The Commission is running a pilot on open access to research data in Horizon 2020: the Open Research Data (ORD) pilot. This pilot takes into account the need to balance openness with the protection of scientific information, commercialisation and Intellectual Property Rights (IPR), privacy concerns, and security, as well as questions of data management and preservation. The pilot applies to research data underlying publications but beneficiaries can also voluntarily make other datasets open. Participating projects are required to develop a Data Management Plan, in which they will specify what data will be open. In previous work programmes, the ORD Pilot was limited to some specific areas of Horizon 2020. Starting with the 2017 work programme, however, the ORD pilot was extended to cover all thematic areas of Horizon 2020, thus realising the Commission's ambition of "open research data per default" (but allowing for opt-outs). #### More information For details of how open access applies to beneficiaries in projects funded under Horizon 2020, please see the _Guidelines_ _on_ _Open_ _Access_ _to_ _Scientific_ _Publications_ _and_ _Research_ _Data_ and/or the _Guidelines_ _on_ _data_ _management_ . Also: * _Participants_ _Portal_ * _OpenAIRE_ * _Open_ _access_ _in_ _FP7_ ##### 3.1.2 Open Research Data pilot 2 _What is the open research data pilot?_ Open data is data that is free to access, reuse, 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. If your Horizon 2020 project is part of the pilot, and your data meets certain conditions, you must deposit your data in a research data repository where they will be findable and accessible for others. Don’t panic - you are not expected to share sensitive data or breach any IPR agreements with industrial partners. You do not need to deposit all the data you generate during the project either – only that which underpins published research findings and/or has longer-term value. In addition to supporting your research’s integrity, openness has many other benefits. Improved visibility means your research will reach more people and have a greater impact – for science, society and your own career. Recent studies have shown that citations increase when data is made available alongside the publication; these papers also have a longer shelf-life. _Which H2020 strands are required to participate?_ Projects starting from January 2017 are by default part of the Open Data Pilot. If your project started before earlier and stems from one of these Horizon 2020 areas, you are automatically part of the pilot as well:: * Future and Emerging Technologies * Research infrastructures (including e-Infrastructures) * Leadership in enabling and industrial technologies – Information and Communication Technologies * Nanotechnologies, Advanced Materials, Advanced Manufacturing and Processing, and Biotechnology: ‘nanosafety’ and ‘modelling’ topics * Societal Challenge: Food security, sustainable agriculture and forestry, marine and maritime and inland water research and the bioeconomy - selected topics in the calls H2020-SFS2016/2017, H2020-BG-2016/2017, H2020-RUR-2016/2017 and H2020-BB-2016/2017, as specified in the work programme * Societal Challenge: Climate Action, Environment, Resource Efficiency and Raw materials – except raw materials * Societal Challenge: Europe in a changing world – inclusive, innovative and reflective Societies * Science with and for Society * Cross-cutting activities - focus areas – part Smart and Sustainable Cities. Maybe data sharing is not appropriate for your project; the _EC’s Guide on Open Access_ _Scientific_ _Publications and Research Data_ lists conditions that would allow or require you to opt out of the pilot. In that case please consider if a partial opt-out is possible. _What is a data management plan (DMP)?_ To help you optimise the potential for future sharing and reuse, a Data Management Plan (DMP) can help you to consider any problems or challenges that may be encountered and helps you to identify ways to overcome these. A DMP should be thought of as a “living” document outlining how the research data collected or generated will be handled during and after a research project. Remember, the plan should be realistic and based around the resources available to you and your project partners. There is no point in writing a gold plated plan if it cannot be implemented! It should describe: * The data set: What kind of data will the project collect or generate, and to whom might they be useful later on? The pilot applies to (1) the data and metadata needed to validate results in scientific publications and (2) other curated and/or raw data and metadata that may be required for validation purposes or with reuse value. * Standards and metadata: What disciplinary norms will you adopt in the project? What is the data about? Who created it and why? In what forms it is available? Metadata answers such questions to enable data to be found and understood, ideally according to the particular standards of your scientific discipline. Metadata, documentation and standards help to make your data Findable, Accessible, Interoperable and Re-usable or FAIR for short. * Data sharing: By default as much of the resulting data as possible should be archived as Open Access. Therefore legitimate reasons for not sharing resulting data should be explained in the DMP. Remember, no one expects you to compromise data protection or breach any IPR agreements. Data sharing should be done responsibly. The DMP Guidelines therefore ask you to describe any ethical or legal issues that can have an impact on data sharing. Furthermore, * Archiving and preservation: Funding bodies are keen to ensure that publicly funded research outputs can have a positive impact on future research, for policy development, and for societal change. They recognise that impact can take quite a long time to be realised and, accordingly, expect the data to be available for a suitable period beyond the life of the project. Remember, it is not simply enough to ensure that the bits are stored in a research data repository, but also consider the usability of your data. In this respect, you should consider preserving 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. The DMP is not a fixed document. The first version of the DMP is expected to be delivered within the first 6 months of your project, but you don’t have to provide detailed answers to all the questions yet. The DMP needs to be updated over the course of the project whenever significant changes arise, such as new data or changes in the consortium policies or consortium composition. The DMP should be updated at least in time with the periodic evaluation or assessment of the project as well as in time for the final review. Consider reviewing your DMP at regular intervals in the project and consider how you might make use of scheduled WP and/or project staff meetings to facilitate this review. _What practical steps should you take?_ 1. When your project is part of the pilot, you should _create a Data Management Plan_ . Your institution may offer Research Data Management support to help you planning. 2. Also, you should _select a data repository_ that will preserve your data, metadata and possibly tools in the long term. It is advisable to contact the repository of your choice when writing the first version of your DMP. Repositories may offer guidelines for sustainable data formats and metadata standards, as well as support for dealing with sensitive data and licensing. 3. As noted earlier, you do not need to keep everything. Curating data requires time and effort so you want to make sure that you are putting your effort into the outputs that really matter. Select what data you’ll need to retain to support validation of your finding but also consider any data outputs that may have longer term value as well – for you and for others. #### Links * EC’s Guide on Open Access to Scientific Publications and Research Data in Horizon 2020 (updated August 25, 2016) _http://ec.europa.eu/research/participants/data/ref/h2020/grants_manual/hi/oa_pilot/_ _h2020-hi-oa-pilot-guide_en.pdf_ * EC’s Guidelines on Data Management in Horizon 2020 (updated July 26, 2016): _http://ec.europa.eu/research/participants/data/ref/h2020/grants_manual/hi/oa_pilot/_ _h2020-hi-oa-data-mgt_en.pdf_ * EC’s Agenda on Open Science: _https://ec.europa.eu/digital-agenda/en/open-science_ * DMPonline tool: _https://dmponline.dcc.ac.uk/_ * DCC How to Write a DMP guide: _http://www.dcc.ac.uk/resources/how-guides/develop-dataplan_ * DCC How to Select What Data to Keep guide: _http://www.dcc.ac.uk/resources/howguides/five-steps-decide-what-data-keep_ * DCC How to Licence Research Data guide: _http://www.dcc.ac.uk/resources/howguides/license-research-data_ * RDNL video The what, why and how of data management planning: _http://datasupport.researchdata.nl/en/start-de-cursus/iiplanfase/datamanagementplanning/_ * Software Sustainability Institute’s Software Management Plan: _https://www.software.ac.uk/sites/default/files/images/content/SMP_Checklist_2016__ _v0.1.pdf_ ##### 3.1.3 European Data Economy Building a European data economy is part of the Digital Single Market strategy. The initiative aims at fostering the best possible use of the potential of digital data to benefit the economy and society. It addresses the barriers that impede the free flow of data to achieve a European single market. #### General need for action _Digital data_ is an essential resource for economic growth, competitiveness, innovation, job creation and societal progress in general. The EU needs to ensure that data flows across borders and sectors. This data should be accessible and reusable by most stakeholders in an optimal way. A coordinated European approach is essential for the development of the data economy, as part of the _Digital Single_ _Market strategy_ . The European Commission adopted a _Communication_ on "Building a European Data Economy", accompanied by a _Staff Working Document_ on January 2017, where it: * looks at the rules and regulations impeding the free flow of data and present options to remove unjustified or disproportionate data location restrictions, and * outlines legal issues regarding access to and transfer of data, data portability and liability of non-personal, machine-generated digital data. The European Commission has launched a _public consultation_ and _dialogue with stakeholders_ on these topics to gather further evidence. This process will help identify future policy or legislative measures that will unleash Europe's data economy. The development of the European Data economy is one of the three emerging challenges identified in the _mid-term review_ . The actions to be implemented are: * to prepare a legislative proposal on the EU free flow of data cooperation framework (autumn 2017) * to prepare an initiative on accessibility and re-use of public and publicly funded (spring 2018) In addition, the Commission will continue its work on liability and other emerging data issues. For more details, read the _Communication_ . #### Facing the challenge - removing data localisation restrictions: the free flow of data Free flow of data means the freedom to process and store data in electronic format anywhere within the EU. It is necessary for the development and use of innovative data technologies and services. In order to achieve the free flow of data, the European Commission will collect more evidence on data location restrictions and assess their impacts on businesses, especially SMEs and start-ups, and public sector organisations. The Commission will also discuss the justifications for and proportionality of those data location restrictions with Member States and other stakeholders. It will then take justified and appropriate follow-up actions, in line with _better regulation principles_ , to address the issue. #### Exploring the emerging issues relating to the data economy The European Commission is currently defining, scoping and articulating the following issues in order to trigger and frame a dialogue with stakeholders: * Non-personal machine-generated data need to be tradable to allow innovative business models to flourish, new market entrants to propose new ideas and start-ups to have a fair chance to compete. * Data-driven technologies are transforming our economy and society, resulting in the production of ever-increasing amounts of data. This phenomenon leads to innovative ways of collecting, acquiring, processing and using data which can pose a challenge to the current legal framework. * Access to and transfer of non-personal data, data liability, as well as portability of nonpersonal data, interoperability and standards are complex legal issues. This _consultation_ process will contribute to the policy choices taken by the European Commission in the future. #### Useful links * Have a look at the _workshops_ organised on how to build a European data economy. * _Press release and MEMO_ _-_ _Q &A _ * _Communication_ on Building a European Data Economy * _Staff Working Document_ on Building a European Data Economy * _Factsheet_ on Building a European Data Economy * Study on _Measuring the economic impact of cloud computing in Europe_ * Study on _Facilitating cross border data flow in the DSM_ * Intermediary study on _Cross-border data flow in the Digital Single Market: data location_ _restrictions_ * _Speech from Commissioner Oettinger_ at the Conference "Building European Data Economy" (17 October 2016). * _Speech from Vice-President Ansip_ at the Digital Assembly 2016, "Europe should not be afraid of data" (29 September 2016). ##### 3.1.4 Agricultural Data developments In view of the technical changes brought forth by Big Data and Smart Farming, we seek to understand the consequences for the stakeholder network and governance structure around the farm in this section. The literature suggests major shifts in roles of and power relations among different players in existing agri-food chains. We observed the changing roles of old and new software suppliers in relation to Big Data and farming and emerging landscape of data-driven initiatives with prominent role of big tech and data companies like Google and IBM. In Figure 3, the current landscape of data-driven initiatives is visualized. The stakeholder networks exhibits a high degree of dynamics with new players taking over the roles played by other players and the incumbents assuming new roles in relation to agricultural Big Data. As opportunities for Big Data have surfaced in the agribusiness sector, big agriculture companies such as Monsanto and John Deere have spent hundreds of millions of dollars on technologies that use detailed data on soil type, seed variety, and weather to help farmers cut costs and increase yields. Other players include various accelerators, incubators, venture capital firms, and corporate venture funds (Monsanto, DuPont, Syngenta, Bayer, DOW etc.). _Figure 3 The landscape of the Big Data network with business players._ Monsanto has been pushing big-data analytics across all its business lines, from climate prediction to genetic engineering. It is trying to persuade more farmers to adopt its cloud services. Monsanto says farmers benefit most when they allow the company to analyse their data - along with that of other farmers - to help them find the best solutions for each patch of land. While corporates are very much engaged with Big Data and agriculture, start- ups are at the heart of action, providing solutions across the value chain, from infrastructure and sensors all the way down to software that manages the many streams of data from across the farm. As the ag-tech space heats up, an increasing number of small tech start-ups are launching products giving their bigger counterparts a run for their money. In the USA, start-ups like FarmLogs, FarmLink and 640 Labs challenge agribusiness giants like Monsanto, Deere, DuPont Pioneer. One observes a swarm of dataservice start-ups such as FarmBot (an integrated open-source precision agriculture system) and Climate Corporation. Their products are powered by many of the same data sources, particularly those that are freely available such as from weather services and Google Maps. They can also access data gathered by farm machines and transferred wirelessly to the cloud. Traditional agri-IT firms such as NEC and Dacom are active with a precision farming trial in Romania using environmental sensors and Big Data analytics software to maximize yields. Venture capital firms are now keen on investing in agriculture technology companies such as Blue River Technology, a business focusing on the use of computer vision and robotics in agriculture. The new players to Smart Farming are tech companies that were traditionally not active in agriculture. For example, Japanese technology firms such as Fujitsu are helping farmers with their cloud based farming systems. Fujitsu collects data (rainfall, humidity, soil temperatures) from a network of cameras and sensors across the country to help farmers in Japan better manage its crops and expenses. Data processing specialists are likely to become partners of producers as Big Data delivers on its promise to fundamentally change the competitiveness of producers. Beside business players such as corporates and start-ups, there are many public institutions (e.g., universities, USDA, the American Farm Bureau Federation, GODAN) that are actively influencing Big Data applications in farming through their advocacy on open data and data-driven innovation or their emphasis on governance issues concerning data ownership and privacy issues. Well-known examples are the Big Data Coalition, Open Agriculture Data Alliance (OADA) and AgGateway. Public institutions like the USDA, for example, want to harness the power of agricultural data points created by connected farming equipment, drones, and even satellites to enable precision agriculture for policy objectives like food security and sustainability. Precision farming is considered to be the “holy grail” because it is the means by which the food supply and demand imbalance will be solved. To achieve that precision, farmers need a lot of data to inform their planting strategies. That is why USDA is investing in big, open data projects. It is expected that open data and Big Data will be combined together to provide farmers and consumers just the right kind of information to make the best decisions. Data ownership is an important issue in discussions on the governance of agricultural Big Data generated by smart machinery such as tractors from John Deere. In particular, value and ownership of precision agricultural data have received much attention in business media. It has become a common practice to sign Big Data agreements on ownership and control data between farmers and agriculture technology providers. Such agreements address questions such as: How can farmers make use of Big Data? Where does the data come from? How much data can we collect? Where is it stored? How do we make use of it? Who owns this data? Which companies are involved in data processing? There is also a growing number of initiatives to address or ease privacy and security concerns. For example, the Big Data Coalition, a coalition of major farm organizations and agricultural technology providers in the USA, has set principles on data ownership, data collection, notice, third-party access and use, transparency and consistency, choice, portability, data availability, market speculation, liability and security safeguards 3 . And AgGateway, a non-profit organization with more than 200 member companies in the USA, have drawn a white paper that presents ways to incorporate data privacy and standards 4 . It provides users of farm data and their customers with issues to consider when establishing policies, procedures, and agreements on using that data instead of setting principles and privacy norms. The European farmers and agri-cooperatives association COPA- COGECA has recently also published their ‘Main principles underpinning the collection, use and exchange of agricultural data’ 5 . The principles concern: * _Ownership of farm data_ – data produced on the farm or during farming operations should be owned by the farmers themselves. If this data is used, also indirectly through combined services, the farmer should be somehow compensated for this. * _Ownership of the underlying rights to derived data_ \- In any case, it should be clear when farm data is used and for what purpose; the farmer should be in full control of his/her data. When farm data is used by third parties anonymization and security are of utmost importance. * _Duration, suspension and termination of supply_ – farmers must be provided with the possibility to opt out of a contract on data use with the right to delete all historical data. * _Guarantee of compliance with laws and regulation_ – data collection should not be in conflict with general laws (e.g. on privacy) and data should not be used for unlawful purposes * _Liability_ – in contracts on data use intellectual property rights of farmers and agri-cooperatives must be protected and liabilities must be clearly described. It is not always possible to go into all possible details so there should be a good balance between what is written in the contract and trust between partners. Based on these type of principles several codes of conduct/practice with concrete models for contracts have been developed such as the New Zealand Farm Data Code of Practice 6 , the Dutch BO-Akkerbouw Code of Conduct 7 , and Ag Data Transparency has established a certifying procedure 8 . More similar codes and model contracts will pop-up in the near future. The ‘Ownership Principle’ of the Big Data Coalition states that “We believe farmers own information generated on their farming operations. However, it is the responsibility of the farmer to agree upon data use and sharing with the other stakeholders (...).” While having concerns about data ownership, farmers also see how much companies are investing in Big Data. In 2013, Monsanto paid nearly 1 billion US dollars to acquire The Climate Corporation, and more industry consolidation is expected. Farmers want to make sure they reap the profits from Big Data, too. Such change of thinking may lead to new business models that allow shared harvesting of value from data. In conclusion, Big data applications in Smart Farming will potentially raise many power-related issues. There might be companies emerging that gain much power because they get all the data. In the agrifood chain these could be input suppliers or commodity traders, leading to a further power shift in market positions. This power shift can also lead to potential abuses of data e.g. by the GMO lobby or agricultural commodity markets or manipulation of companies. Initially, these threats might not be obvious because for many applications small start-up companies with hardly any power are involved. However, it is a common business practice that these are acquired by bigger companies if they are successful and in this way the data still gets concentrated in the hands of one big player. It can be concluded that Big Data is both a huge opportunity as a potential threat for farmers. ##### 3.1.5 General Data Protection Regulation 9 Security and privacy of personal data increasingly have become a public concern which has led to the General Data Protection Regulation (GDPR) imposed by the EU in 2018. The aim of GDPR is that: * It allows European Union citizens to better control their personal data. It also modernises and unifies rules allowing businesses to reduce red tape and to benefit from greater consumer trust. * The GDPR is part of the _EU data protection reform package_ , along with the _data protection_ _directive for police and criminal justice authorities_ . _Key points:_ #### Citizens’ rights The GDPR strengthens existing rights, provides for new rights and gives citizens more control over their personal data. These include: * easier access to their data — including providing more information on how that data is processed and ensuring that that information is available in a clear and understandable way; * a newright to data portability — making it easier to transmit personal data between service providers; * a clearer right to erasure (‘right to be forgotten’) — when an individual no longer wants their data processed and there is no legitimate reason to keep it, the data will be deleted; * right to know when their personal data has been hacked — companies and organisations will have to inform individuals promptly of serious data breaches. They will also have to notify the relevant data protection supervisory authority. #### Rules for businesses The GDPR is designed to create business opportunities and stimulate innovation through a number of steps including: * a single set of EU-wide rules — a single EU-wide law for data protection is estimated to make savings of €2.3 billion per year; * a data protection officer, responsible for data protection, will be designated by public authorities and by businesses which process data on a large scale; * one-stop-shop — businesses only have to deal with one single supervisory authority (in the EU country in which they are mainly based); * EU rules for non-EU companies — companies based outside the EU must apply the same rules when offering services or goods, or monitoring behaviour of individuals within the EU; * innovation-friendly rules — a guarantee that data protection safeguards are built into products and services from the earliest stage of development (data protection by design and by default); * privacy-friendly techniques such as pseudonymisation (when identifying fields within a data record are replaced by one or more artificial identifiers) and encryption (when data is coded in such a way that only authorised parties can read it); * removal of notifications — the new data protection rules will scrap most notification obligations and the costs associated with these. One of the aims of the data protection regulation is to remove obstacles to free flow of personal data within the EU. This will make it easier for businesses to expand; * impact assessments — businesses will have to carry out impact assessments when data processing may result in a high risk for the rights and freedoms of individuals; * record-keeping — SMEs are not required to keep records of processing activities, unless the processing is regular or likely to result in a risk to the rights and freedoms of the person whose data is being processed. The _European Commission_ must submit a report on the evaluation and review of the regulation by 25 May 2020. The GDPR will apply as of 25 May 2018\. For more information, see: * _Press release_ ( _European Commission_ ) * _‘2018 reform of EU data protection rules’_ ( _European Commission_ ). ### 3.2 CONSEQUENCES FOR IOF2020 The following consequences for IoF2020 could be derived from the developments that were described in the previous section: 1. Any peer-reviewed journal article should be published openly accessible 2. Research data should be stored in a central repository where it is findable and accessible by others 1. Since IoF2020 is an Innovation Action type of project, the first question is whether _research_ data is actually involved; this has to be identified first 2. It should be further explored whether data sharing is appropriate in IoF2020 and if a partial opt-out for the Open Research Data pilot is appropriate 3. A data management plan needs to be written for IoF2020 according to the guidelines provided by the ORD Pilot 4. It should be identified if and to what extent the data-driven technologies in the IoF2020 use cases are aligned with the European Data Economy policy. 1. Since this policy is also under debate, IoF2020 should actively participate in this debate and development of this Data Economy. 5. It should be explored which issues around agricultural data sharing (e.g. data ownership/control, rights to use data, etc.) are (potentially) playing a role and if principles and guidelines, especially provided by IoF2020 partner COPA-COGECA, are applicable. And if yes, what type of contracts should be set-up? 6. The IoF2020 use cases should be analysed for GDPR compliance and take measures on this where necessary. ## 4 RELEVANT INITIATIVES AND PROJECTS In this chapter several projects and initiatives are described that are dealing with (Agricultural) Data and are potentially relevant for IoF2020. ### 4.1 PROJECTS AND INITIATIVES #### 4.1.1 GODAN 10 GODAN supports the proactive sharing of open data to make information about agriculture and nutrition available, accessible and usable to deal with the urgent challenge of ensuring world food security. It is a rapidly growing group, currently with over 584 partners from national governments, non- governmental, international and private sector organisations that have committed to a joint _Statement of Purpose._ The initiative focuses on building high-level support among governments, policymakers, international organizations and business. GODAN promotes collaboration to harness the growing volume of data generated by new technologies to solve long-standing problems and to benefit farmers and the health of consumers. GODAN encourages collaboration and cooperation between stakeholders in the sector. The GODAN initiative was launched At the 2012 G-8 Summit, G-8 leaders committed to the New Alliance for Food Security and Nutrition, the next phase of a shared commitment to achieving global food security. As part of this commitment, they agreed to “share relevant agricultural data available from G-8 countries with African partners and convene an international conference on Open Data for Agriculture, to develop options for the establishment of a global platform to make reliable agricultural and related information available to African farmers, researchers and policymakers, taking into account existing agricultural data systems.” In April 2013, the commitment to convene an international conference on Open Data for Agriculture was fulfilled when the G8 International Conference on Open Data for Agriculture took place. This conference worked to ‘obtain commitment and action from nations and relevant stakeholders to promote policies and invest in projects that open access to publicly funded global agriculturally relevant data streams, making such data readily accessible to users in Africa and world-wide, and ultimately supporting a sustainable increase in food security in developed and developing countries. The GODAN initiative was a by-product of this conference and was announced at the Open Government Partnership Conference in October 2013\. Any organization that supports open access to agriculture and nutrition data can become member of GODAN. Partners include government, donors, international and not-for-profit organizations and businesses. GODAN partners support the shared principles based on GODAN’s Statement of Purpose: * Agricultural and nutritional data to be available, accessible, usable and unrestricted * Partners aim to build high level policy and private sector support for open data * Encourage collaboration and cooperation across existing agriculture, nutrition and open data activities and stakeholders to solve long-standing global problems GODAN partners commit to: * Host regular conversations with our peer multilateral and local organisations to identify and share best practices and determine how to more effectively share data and provide useable analysis for local application * Recruit new partners to GODAN GODAN activities and its Secretariat are financially supported by the US Government, the UK Department for International Development (DFID), the Government of the Netherlands, FAO, Technical Centre for Agricultural and Rural Cooperation (CTA), GFAR, The Open Data Institute (ODI), the CGIAR and CABI. _More information:_ * _Statement of Purpose_ * _Theory of Change_ * _Ownership of Open Data: Governance Options for Agriculture and Nutrition_ * _A Global Data Ecosystem for Agriculture and Food_ #### 4.1.2 EIP-Agri 11 The agricultural European Innovation Partnership (EIP-AGRI) works to foster competitive and sustainable farming and forestry that 'achieves more and better from less'. It contributes to ensuring a steady supply of food, feed and biomaterials, developing its work in harmony with the essential natural resources on which farming depends. The European Innovation Partnership for Agricultural productivity and Sustainability (EIP-AGRI) has been launched in 2012 to contribute to the European Union's strategy 'Europe 2020' for smart, sustainable and inclusive growth. This strategy sets the strengthening of research and innovation as one of its five main objectives and supports a new interactive approach to innovation: _European Innovation Partnerships._ The EIP-AGRI pools funding streams to boost interactive innovation. Having an idea is one thing, turning it into an innovation action is another. Different types of available funding sources can help get an agricultural innovation project started, such as the European Rural Development policy or the EU's research and innovation programme Horizon 2020 . The EIP-AGRI contributes to integrating different funding streams so that they contribute together to a same goal and duplicate results. Rural Development will in particular support Operational Groups and Innovation Support Services within a country or region. Horizon 2020 will fund multi- actor projects and thematic networks involving partners from at least three EU countries. Other policies may offer additional opportunities. The EIP-AGRI brings together innovation actors (farmers, advisers, researchers, businesses, NGOs and others) at EU level and within the rural development programmes (RDPs). Together they form an EU-wide EIP network. EIP Operational Groups can be funded under the RDPs, are project-based and tackle a certain (practical) problem or opportunity which may lead to an innovation. The Operational Group approach makes the best use of different types of knowledge (practical, scientific, technical, organisational, etc.) in an interactive way. An Operational Group is composed of those key actors that are in the best position to realise the project's goals, to share implementation experiences and to disseminate the outcomes broadly. The first Operational Groups are currently being set up in several EU countries and regions. The Rural Networks' Assembly, which was launched in January 2015, coordinates two networks - the EIP-AGRI Network and the European Network for Rural Development (ENRD). The Assembly includes several subgroups, one of them being the permanent Subgroup on Innovation for agricultural productivity and sustainability. This Subgroup on Innovation will support the EIP-AGRI Network. The EIP-AGRI website has exciting and interactive features. All visitors can voice their research needs, discover funding opportunities for innovation projects and look for partners to connect with. Through the website's interactive functions, users can share innovative project ideas and practices, information about research and innovation projects , including projects' results, by filling in the available easy-to-use e-forms. Various EIP-AGRI-related publications are available for download on the website, providing visitors with information on a wide range of interesting topics. Future functionalities will be developed for Operational Groups and European funds managing authorities once the programmes start. Through this collaborative effort, the EIP-AGRI website will become a one-stop-shop for agricultural innovation in Europe. The EIP-AGRI network is run by the European Commission (DG Agriculture and Rural Development) with the help of the EIP-AGRI Service Point . The Service Point offers a wide range of tools and services which can help you further your ideas and projects. It also facilitates networking activities; enhancing communication, knowledge sharing and exchange through conferences, Focus Groups , workshops, seminars and publications. A recent series of workshops on Agricultural Data is particularly relevant: * ‘Data revolution: emerging new data-driven business models in the agri-food sector’, Sophia, Bulgaria, 22-23 June 2016. _https://ec.europa.eu/eip/agriculture/en/event/eip-agri-seminar%E2%80%98data- revolution-emerging-new_ * ‘Data Sharing: ensuring fair sharing of digitisation benefits in agriculture’ Tuesday 4-5 April 2017, Bratislava, Slovakia. _https://ec.europa.eu/eip/agriculture/en/event/eip-agri-workshopdata-sharing_ * ‘Digital Innovation Hubs: mainstreaming digital agriculture’, 1-2 June 2017, Kilkenny, Ireland. _https://ec.europa.eu/eip/agriculture/en/event/eip-agri-seminar-digital- innovation-hubs_ _More about EIP-AGRI:_ * _Brochure: EIP-AGRI network_ * _Brochure: EIP-AGRI Service Point_ #### 4.1.3 AgInfra+ 12 AGINFRA+ aims to exploit core e-infrastructures such as _EGI.eu_ , _OpenAIRE_ , _EUDAT_ and _D4Science_ , towards the evolution of the AGINFRA data infrastructure, so as to provide a sustainable channel addressing adjacent but not fully connected user communities around Agriculture and Food. To this end, the project will develop and provide the necessary specifications and components for allowing the rapid and intuitive development of variegating data analysis workflows, where the functionalities for data storage and indexing, algorithm execution, results visualization and deployment are provided by specialized services utilizing cloud based infrastructure(s). Furthermore, AGINFRA+ aspires to establish a framework facilitating the transparent documentation and exploitation and publication of research assets (datasets, mathematical models, software components results and publications) within AGINFRA, in order to enable their reuse and repurposing from the wider research community. AGINFRA is the European research hub and thematic aggregator that catalogues and makes discoverable publications, data sets and software services developed by Horizon 2020 research projects on topics related to agriculture, food and the environment. It is part of the broader vision of the European research e-infrastructure “European Open Science Cloud”, a synergy between _OpenAIRE_ , _EUDAT_ , _GEANT_ , _EGI_ , _LIBER_ . With the integration of big data processing components from projects like the Horizon 2020 BigDataEurope and the FP7 _SemaGrow_ , _AGINFRA_ evolves into a big data analytics capable einfrastructure for agri-food, to respond to the needs of three (3) adjacent yet not fully connected user communities: * _H2020 SC1 _ Health * _H2020 SC2 _ Food security and sustainable agriculture * _H2020 SC5 _ Climate action and environment AGINFRA+ addresses the challenge of supporting user-driven design and prototyping of innovative einfrastructure services and applications. It particularly tries to meet the needs of the scientific and technological communities that work on the multi-disciplinary and multi-domain problems related to agriculture and food. It will use, adapt and evolve existing open e-infrastructure resources and services, in order to demonstrate how fast prototyping and development of innovative data- and computingintensive applications can take place. This project builds upon the extensive experience and work of its partners, who are key stakeholders in the e-infrastructures ecosystem. It also implements part of a strategic vision shared between Agroknow , the National Agronomic Research Institute of France ( INRA ), the Alterra Institute of the Wageningen University & Research Center ( ALTERRA ), the National Institute for Risk Assessment of Germany ( BfR ), and the Food and Agriculture Organization ( FAO ) of the United Nations - the latter one, not participating as a funded beneficiary, but supporting the project and its activities. These stakeholders are part of a core group of internationally recognised players (including the Chinese Academy of Agricultural Sciences) aiming to put in place a free global data infrastructure for research and innovation in agriculture, food and environmental science. This data infrastructure will become an incubator of the large infrastructure investments that global donors (including the European Commission) make in the field of agricultural research around the world. AGINFRA+ will evolve and develop further the resources and services of the AGINFRA data infrastructure, which has been developed in the context of the FP7 _agINFRA_ p roject. The new project will build upon core components of AGINFRA, such as: * the federated data and software registry of _CIARD RING_ , * the _AGINFRA API_ gateway for indexing and hosting executable software components for advanced data processing & analysis, * the open source software stack for data analysis, indexing, publication and querying developed by projects such as FP7 _SemaGrow_ and H2020 _Big Data Europe_ , * the semantic backbone of the Global Agricultural Concept Scheme (GACS1) that has been based upon the alignment of FAO’s AGROVOC with the USDA’s National Agricultural Library Thesaurus and CABI’s Thesaurus, * the advanced research data set processing & indexing demonstrators developed within FP7 SemaGrow for specific scientific communities such as _Trees4Futures_ and _gMIP_ . The envisaged pilots will focus on three societal challenges that are of primary importance for our planet and for humanity: * Food safety risk assessment and risk monitoring, addressing H2020 SC1 Health, demographic change and well-being. * Plant phenotyping for food security, addressing H2020 SC2 Food security, sustainable agriculture and forestry, marine and maritime and inland water research, and the Bioeconomy. * Agro-climatic and Economic Modelling, addressing H2020 SC5 Climate action, environment, resource efficiency and raw materials. In order to realize its vision, AGINFRA+ will achieve the following objectives: * identify the requirements of the specific scientific and technical communities working in the targeted areas, abstracting (wherever possible) to new AGINFRA services that can serve all users; * design and implement components that serve such requirements, by exploiting, adapting and extending existing open e-infrastructures (namely, OpenAIRE, EUDAT, EGI, and D4Science), where required; * define or extend standards facilitating interoperability, reuse, and repurposing of components in the wider context of AGINFRA; * establish mechanisms for documenting and sharing data, mathematical models, methods and components for the selected application areas, in ways that allow their discovery and reuse within and across AGINFRA and served software applications; * increase the number of stakeholders, innovators and SMEs aware of AGINFRA services through domain specific demonstration and dissemination activities. The development of fully defined demonstrator applications in each of the three application areas will allow to showcase and evaluate the AGINFRA components in the context of specific end-user requirements from different scientific areas. #### 4.1.4 BigDataEurope 13 Big Data Europe will undertake the foundational work for enabling European companies to build innovative multilingual products and services based on semantically interoperable, large-scale, multilingual data assets and knowledge, available under a variety of licenses and business models. Big Data Europe aims to: * Collect requirements for the ICT infrastructure needed by data-intensive science practitioners tackling a wide range of societal challenges; covering all aspects of publishing and consuming semantically interoperable, large-scale, multi-lingual data assets and knowledge. * Design and implement an architecture for an infrastructure that meets requirements, minimizes the disruption to current workflows, and maximizes the opportunities to take advantage of the latest European RTD developments, including multilingual data harvesting, data analytics, and data visualization. Societal challenges and their Big Data focus areas are: * Health - heterogeneous data linking and integration, biomedical semantic indexing * Food & Agriculture - large-scale distributed data integration * Energy - real-time monitoring, stream processing, data analytics, decision support * Transport - streaming sensor network and geospatial data integration * Climate - real-time monitoring, stream processing and data analytics * Social Sciences - statistical and research data linking and integration * Security - real-time monitoring, stream processing and data analytics, image data analysis The Food and Agriculture pilot (SC2) within BDE is focusing on viticulture. The problem of discovery and linking of information is present in every major area of agricultural research and agriculture in general. This is especially true in viticulture where different research methodologies produce a great amount of heterogeneous data from diverse sources; scientists need to be able to find all this information so as to analyse and correlate it to provide integrated solutions to the emerging problems in the European and global vineyard. These problems arise largely because of the impact of climate change and therefore the exploitation of the appropriate grapevine varieties is very important. Factors to bear in mind include the intensity of diseases, the intensification of the cultivation, the proper implementation of precision viticulture systems that affect the quality of viticultural products and their role in human health. The overall goal of the SC2 Pilot is to demonstrate the ability of Big Data technologies to complement existing community-driven systems (e.g. _VITIS_ for the Viticulture Research Community) with efficient large-scale back-end processing workflows.The pilot deployment is organised in three Cycles with different targeted objectives: * Pilot Cycle 1 (SC2 Pilot Pitch-Deck) \- The goal of this Pilot Cycle is to showcase a largescale processing workflow that automatically annotates scientific publications relevant to Viticulture. The focus of the first demonstrator cycle is on the Big Data aspects of such a workflow (i.e. storage, messaging and failure management) and not on the specificities of the NLP modules/tools used in this demonstrator. * Pilot Cycle 2 (SC2 Pilot Maturity / Functionality Expansion) \- The goal of this Pilot Cycle is to showcase the ability of scalable processing workflows to handle a variety of data types (beyond bibliographic data) relevant to Viticulture. * Pilot Cycle 3 (Lowering SC2 Community Boundaries) \- The goal of this Pilot Cycle is to provide an engaging, intuitive graphical web interface addressing key data-oriented questions relevant to the Viticulture Research Community, and if possible, intuitive interfaces for endusers for sharing and linking their on-the-field generated data. In SC2 Pilot Cycle 1, content mainly refers to open scientific publications relevant to Viticulture, available at FAO/AGRIS and NCBI/PubMed in PDF format (about 26K and 7K publications respectively). In Cycle 2, the content pool has been extended to include: * Weather Data, available via publicly available APIs (e.g. OpenWeatherMap, Weather Underground, AccuWeather etc.) * User-generated data, e.g. geotagged photos from leaves, young shoots and grape clusters, ampelographic data, SSR-marker data etc. Additional data sources include: * Sensor Data, measuring temperature, humidity and luminosity retrieved from sensors installed in selected experimental vineyards, * ESA Copernicus Sentinel 2 Data, for selected experimental vineyards. The goal of the inclusion of these data is to complement the existing SC2 Pilot Demonstrator Knowledge Base so as to support complex real-life research questions, based on the correlation of environmental conditions with real observations on crop production and quality. #### 4.1.5 DATABIO 14 he Data-Driven Bioeconomy project (DataBio) focuses on the production of best possible raw materials from agriculture, forestry and fishery for the bioeconomy industry to produce food, energy and biomaterials taking into account responsibility and sustainability In order to meet the above objectives, DataBio is controlling and putting to use the innovative ICTs and information flows centered mostly around the use of proximal and remote sensors, in order to provide a streamlined Big Data Infrastructure for data discovery, retrieval, processing and visualizing, in support to decisions in bioeconomy business operations. The main goal of the DataBio project is to show the benefits of Big Data technologies in the raw material production from agriculture , forestry and fishery/aquaculture for the bioeconomy industry to produce food , energy and biomaterials responsibly and sustainably. DataBio proposes to deploy a state of the art, big data platform on top of the existing partners’ infrastructure and solutions – the Big DATABIO Platform . The work will be continuous cooperation of experts from end user and technology provider companies, from bioeconomy and technology research institutes, and of other partners. DataBio is working on pilots in three areas: Agriculture, Fishery and Forestry. The Agicultural pilots are divided into: * Precision Horticulture including vine and olives o A1. Precision agriculture in olives, fruits, grapes and vegetables o A2. Big Data management in greenhouse eco-systems * B. Arable Precision Farming o B1. Cereals and biomass crops o B2. Machinery management and environmental issue * C. Subsidies and insurance o C1. Insurance o C2. CAP support In the pilots also associated partners and other stakeholders will be actively involved. The selected pilots and concepts will be transformed into pilot implementations using co-innovative approaches and tools where the bioeconomy sector end users, experts and other stakeholders will give input to the user and sector domain understanding for the requirement specifications for ICT, Big Data and Earth Observation experts and for other solution providers in the consortium. Based on the preparation and requirement specifications work, the pilots are implemented using and selecting the best suitable market ready or almost market ready Big Data and Earth Observation methods, technologies, tools and services to be integrated to the common Big DATABIO Platform . During the pilots the close cooperation continues and feedback from the bioeconomy sector user companies will be harnessed in the technical and methodological upgrades for pilot implementations. Based on the pilot results and the new solutions also new business opportunities are expected. In addition during the pilots the end users are participating in trainings to learn how to use the solutions and developers also outside the consortium will be active in the Hackathons to design and develop new tools, services and application for the platform. Databio’s expected achievements include but are not limited to: * Demonstrate increase of productivity in bioeconomy * Increase of market share of Big Data technology providers in the bioeconomy sector * More than double the use of Big Data technology in bioeconomy  Leveraging additional target sector investments by a factor of >5 * More than 100 organizations in demonstrations * Liaison with other Big Data actions * Closely working with BDVA ### 4.2 RELATIONSHIP WITH IOF2020 Through overlapping partners between the mentioned initiatives and projects and IoF2020, there is already quite a natural ground for collaboration and knowledge exchange on data management. Especially IoF2020’s coordinator Wageningen University & Research is involved in most relevant initiatives and projects. However since we are dealing with relatively large initiatives, projects and organizations, knowledge exchange and collaboration it is not automatically guaranteed. Specific attention needs to be paid to this. Work on very similar specific pilot areas (e.g. viticulture, agricultural machinery data, etc.) would be a good starting point for this. Since the beginning of the IoF2020 project there were already several occasions (conferences, workshops, etc.) where explicit connections were made with these projects and initiatives. It was decided to setup an explicit collaboration with the DataBio project. ## 5 INVENTORY OF USE CASES Based on the issues that are identified and described in the previous chapters, a first scan of the IoF2020 use cases was made. A quick scan for the constraints to data privacy and security was conducted by a questionnaire at use case level. The results are listed in Table 1 . Table 1 Potential constraints to data privacy and security for each use case. <table> <tr> <th> Trial and use cases </th> <th> Data privacy & security constraints </th> </tr> <tr> <td> Arable Trial </td> </tr> <tr> <td> UC 1.1. Within-field management zoning </td> <td> Farmer’s data can only be used outside his company only if he agrees. Other partners provide proprietary software tools and information. </td> </tr> <tr> <td> UC 1.2 Precision Crop Management </td> <td> The data collected by the Bosch systems are intended to remain confidential and will be the property of the farmer who has acquired a system. The Bosch cloud must be able to guarantee this confidentiality and the security of its data. The API-AGRO interface, which is an API management platform operated by a consortium represented in the Use Case by Arvalis, allows to add a layer of management of the confidentiality and possible opening to other actors of those data. While remaining under the control of the owner of the systems, it will be possible to communicate the acquired data according to conditions which still have to be defined while guaranteeing a control by the owner of the data of the destination that he wants to give to his data. The Arvalis models and agro-climatic references that will be used for DSS are the property of Arvalis. </td> </tr> <tr> <td> UC1.3 Soya Protein Management </td> <td> No constraints so far about data privacy and security in UC 1.3. </td> </tr> <tr> <td> UC1.4 Farm Machine Interoperability </td> <td> The constraints will be dependent on the agreements achieved with the partners and farmers involved in the UCs that we collaborate with, i.e. UC 1.1 and UC 1.3. UC 1.4 members will have to sign agreements with the farmers for the access to their data, but also NDAs for specific software elements and equipment components with the other use case participants. </td> </tr> <tr> <td> Dairy Trial </td> </tr> </table> <table> <tr> <th> UC2.1 Grazing Cow Monitor </th> <th> No specific data protection issues arise </th> </tr> <tr> <td> UC2.2 Happy Cow </td> <td> The platform is secured to prevent access to cow/farm data by default. Login opens the data associated to the user. Communication is encrypted. </td> </tr> <tr> <td> UC2.3 Silent Herdsman </td> <td> An agreement on the use of the data acquired on each trial site has to be established. The principle is that the data will be owned by the farmer and will be made available for the purposes of the project. Any public reporting of the key findings regarding the trial farms must be anonymised and cleared by the data owners. </td> </tr> <tr> <td> UC2.4 Remote Milk Quality </td> <td> Up to our present knowledge, there are no constraints. There will be no data exchanged. Data of users will be monitored and handled but not used. Only alerts and checks are exchanged </td> </tr> <tr> <td> Fruit Trial </td> </tr> <tr> <td> UC3.1 Fresh table grapes chain </td> <td> No issues mentioned. </td> </tr> <tr> <td> UC3.2 Big wine optimization </td> <td> None till the edition of this document. However, the following considerations will be taken into account during the implementation phase, others could come according to the discover needs:  All the communications are expected to be carried out over DTLS (Datagram Transport Layer Security) using DTLS 1.2 version, it means ECC asymmetric cryptography for the key negotiation and authentication, in conjunction with AES symmetric cryptography for the efficient and optimal data exchange.  Since the protocol, expected to be used, is OMA LwM2M, it includes all the security from this protocol for defining an access control for the list of servers that can interact with the devices and also the definition of a bootstrap server and commissioning service to guarantee the secure provisioning of all the credentials and configuration details. </td> </tr> </table> <table> <tr> <th> UC3.3 Automated olive chain </th> <th> Farmers involved in the action would like to remain privacy their personal data. </th> </tr> <tr> <td> UC3.4 Intelligent fruit logistics </td> <td> Collection of data on customer sites using our IoT-RTI: * Who owns the data collected on premise by the IoT-enabled RTI? * Who can do what with the data? * Which data can be shown to whom (datadriven business models!)? * Encryption of data Questions regarding security of IoT-Technology used in the pilot: * Is there a danger of hacking, data capturing by others, interference with communication? * Would it be possible to manipulate collected data? * Can the central database be accessed by external parties? * To which security level is it possible to encrypt the data? </td> </tr> <tr> <td> Vegetable Trial </td> </tr> <tr> <td> UC4.1 City farming leafy vegetables </td> <td> The data generated in a city farm, in general, are related to plant growth and the operation of a city farm. Data related to persons are out of scope. The data are owned by the owner of the city farm. The data collected in a city farm represent a value from an economic point of view. Access to the data in general only takes place with the consent of the owner. Data access should be protected. Also, any data link should be secured. Measures need to be in place against unauthorized or unlawful access or processing of data as well as against accidental loss or damage of data. Data access rights management should be in place. </td> </tr> <tr> <td> UC4.2 Chain-integrated greenhouse production </td> <td> Data should be owned by farmers and agri-business, and privacy issues should be respected according to European regulation. As for Intellectual Property, a </td> </tr> </table> <table> <tr> <th> </th> <th> consortium Agreement (CA) will be negotiated between all partners, settling among other things the internal organization of the consortium, reflecting what has been described about the project management structure. With respect to this Use Case 4.3, Background IP remains with the partner(s) who created it and Foreground IP (if any) goes to the partner(s) that developed it. </th> </tr> <tr> <td> UC4.3 Added value weeding data </td> <td> Yes, farmers need to provide access to their machine data and crop data. </td> </tr> <tr> <td> UC4.4 Enhanced quality certification system </td> <td> No issues mentioned </td> </tr> <tr> <td> Meat Trial </td> <td> </td> </tr> <tr> <td> UC5.1 Pig farm management </td> <td> </td> <td> Raw pig farm data is confidential and owned by the farmer – and must only be accessed in aggregated fashion. Aggregated feed data must be kept in the farm and only accessed by Cloud Service component when actually needed. All accesses should be logged so that farmer can be informed about what is being read by whom. The same is true for slaughterhouse data. </td> </tr> <tr> <td> UC5.2 Poultry chain management </td> <td> </td> <td> Privacy: * Delivering data to platforms (who owns data…) * The data remain the ownership of the provider – since SADA is an integrated company, all raw data is property of SADA * Support on legal and ethical issues on workers manipulation model Security: * Authentication of the data delivered to the platform </td> </tr> <tr> <td> UC5.3 Meat Transparency and Traceability </td> <td> Data privacy and security must be handled within this project. Farms may provide information which can result in detailed insights on their internal processes. Data access restriction is provided by the access layer. This layer implements a set of static and dynamic access rules for EPCIS events, supporting different </td> </tr> <tr> <td> </td> <td> roles and actors. Data providers (farmer) stay owner of their data and decide which supply chain partner can access what kind of information. </td> </tr> </table> From this table it can be generally concluded that there is quite some difference between the several use cases in thinking about data management issues. Some use cases have thought about this in much detail and are very aware of the various issues that might arise. Other use cases say that they don’t see any issue or only have thought about in a general way. The results from this scan will be shared and discussed between the use cases and it is expected that they can learn from each other. This will result in a more generic list of data management issues that in the end again can be made specific for each use case. For all trials and use cases together an inventory was made on what type of data is expected as is presented in Table 2. _Table 2 Data that is expected to be generated by all use case and trials_ <table> <tr> <th> Type </th> <th> Origin </th> <th> Format </th> <th> Estimated size </th> </tr> <tr> <td> Dissemination material: * press releases, * leaflets, * audio-visual material, * posters, * images/photos </td> <td> IoF2020 consortia generated </td> <td> Adobe Photoshop (.psd) MS PowerPoint (.ppt, pptx) JPEG (.jpg, .jpeg) MS Word (.doc, .docx) Adobe Acrobat Reader (.pdf) Adobe Illustrator (.ai) Hard copy </td> <td> 0.5 Gb </td> </tr> <tr> <td> Demographic and personal data of third parties interviewed </td> <td> Interviews, questionnaires, cooperation agreements, invitation letters to participate in the pilots, application forms, informed consent forms </td> <td> Google forms MS Excel (.xls, .xlsx) MS Word (.doc, .docx) Adobe Acrobat Reader (.pdf) Comma Separated Values (.csv) </td> <td> 0.5 Gb </td> </tr> <tr> <td> Demographic and personal data of partners within Use Cases </td> <td> Survey, questionnaires </td> <td> Google forms MS Excel (.xls, .xlsx) MS Word (.doc, .docx) </td> <td> 0.5 Gb </td> </tr> </table> D1.4 Data Management Plan <table> <tr> <th> Type </th> <th> Origin </th> <th> Format </th> <th> Estimated size </th> </tr> <tr> <td> </td> <td> </td> <td> Adobe Acrobat Reader (.pdf) Comma Separated Values (.csv) </td> <td> </td> </tr> <tr> <td> Project reports/deliverables with internal reviewing process </td> <td> WP2 team generated </td> <td> MS Excel (.xls, .xlsx) MS Word (.doc, .docx) </td> <td> 0.5 Gb </td> </tr> <tr> <td> Project reports/deliverables with external reviewing process </td> <td> WP2 team generated </td> <td> MS Excel (.xls, .xlsx) MS Word (.doc, .docx) </td> <td> 0.5 Gb </td> </tr> <tr> <td> Contact details of project partners and advisory/scientific board(s) (Name, Email, Phone, Skype ID…) </td> <td> Survey, questionnaires </td> <td> MS Excel (.xls, .xlsx) MS Word (.doc, .docx) Adobe Acrobat Reader (.pdf) </td> <td> 0.5 Gb </td> </tr> <tr> <td> Guidelines for consortium members </td> <td> WP2 team generated </td> <td> MS Word (.doc, .docx) Adobe Acrobat Reader (.pdf) MS PowerPoint (.ppt, pptx) </td> <td> 0.5 Gb </td> </tr> <tr> <td> Outputs generated at project events </td> <td> Agendas and meeting minutes, Attendance sheets </td> <td> MS Word (.doc, .docx) Adobe Acrobat Reader (.pdf) MS PowerPoint (.ppt, pptx) </td> <td> 0.2 Gb </td> </tr> <tr> <td> Dataset produced by aggregating data during project implementation </td> <td> Interview </td> <td> MS Word (.doc, .docx) </td> <td> 1 Gb </td> </tr> <tr> <td> Type </td> <td> </td> <td> Origin </td> <td> Format </td> <td> Estimated size </td> </tr> <tr> <td> </td> <td> </td> <td> Focus group discussion Observation Survey </td> <td> Adobe Acrobat Reader (.pdf) MS PowerPoint (.ppt, pptx) </td> <td> </td> </tr> <tr> <td> A dataset documenting and providing evidence for either a report or a publication produced in the context of project activities </td> <td> Peerreviewed scientific publications </td> <td> Interview Focus group discussion Observation Survey Desk research etc. </td> <td> MS Word (.doc, .docx) Adobe Acrobat Reader (.pdf) </td> <td> 0.5 Gb </td> </tr> <tr> <td> Scientific publications with internal reviewing process </td> </tr> </table> ## 6 DATA MANAGEMENT PLAN ### 6.1 GENERAL GUIDELINES FOR DATA MANAGEMENT IN IOF2020 From the previous Chapters in this document the following general guidelines for data management in IoF2020 can be derived: * Research papers that are derived from the project should be published according to the open access policy * Research data should be stored in a central repository according to the FAIR principles: findable, accessible, interoperable and reliable * The use cases in IoF2020 should be clearly aligned to the European Data Economy policy and more specifically in line with the principles and guidelines provided by the stakeholder community _i.e._ COPA-COGECA. * All IoF2020 use cases should be GDPR-compliant. ### 6.2 FURTHER ACTIONS AND RECOMMENDATIONS The following steps should be taken to further develop data management in IoF2020: * Investigate what research data is involved in the use cases and other project activities and define how they should be treated according to the open data policy. * Participate actively in the debate and developments of the European Data Economy and data sharing in agriculture. * Analyze and explore the use cases in a deeper way in order to identify which data management issues potentially play a role and define plans how to deal with them. * Although many collaborative actions with other relevant projects and initiatives are already taking place and there are many natural connections through the project partners, a more systematic and structural approach should be explored in order to maximize the benefits and impact of the mutual activities on data management. * Collect information concerning GDPR from all IoF2020 use cases based on questionnaires that can be provided by several partners that are already GDPR-compliant. * Prepare for each use case a Data Protection Policy, Privacy Policy Statement, Consent Forms and a Data Breach Notification Procedure For the latter two points, an IoF2020 GDPR package has already been developed containing several tools and templates (see a separate file ‘ _IoF2020_ _GDPR package.zip_ ’) . However, a full implementation of this package is expected to be a too heavy load for all IoF2020 use cases. Through an iterative approach with the use cases and advise from internal and external experts we will search for an appropriate approach to have a light, but sufficient implementation. This Data Management Plan and its future updates will be actively communicated with all partners in IoF2020 and the use cases in particular. It is planned to have a workshop on this issue, possibly combined with other similar issues such as ‘Ethics’ during the annual project meeting in Spring 2018 organized by WP4. ## 7 CONCLUSIONS Data or Big Data rapidly has become a new resource or asset in the current economy, also in the agricultural sector. This development leads to several issues that have to be addressed such as data availability, quality, access, security, responsibility, liability, ownership, privacy, costs and business models. In Europe several initiatives and projects have already been established to work on this in a general context (e.g. the EU open data policy) and more specifically in agriculture (e.g. GODAN, COPA-COGECA). The consequences for IoF2020 are partly mandatory (open access publishing, open research data and GDPR) and for the other part guiding principles that also have to be further explored. In this document we have presented a first version of a Data Management Plan with concrete actions to be taken in order to establish open, transparent data management in IoF2020.	https://phaidra.univie.ac.at/o:1140797	Horizon 2020
0082_bIoTope_688203.md	Executive Summary The bIoTope project lays the foundation for creating open innovation ecosystems supporting the Internet of Things (IoT) by providing a platform that enables companies to easily create new IoT systems and to rapidly harness available information using advanced Systems-of-Systems (SoS) capabilities for connected smart objects. The project will develop three large scale pilots in smart cities to provide social, technical and business proofs-of-concept of the bIoTope enabled SoS ecosystems. This document describes the initial Data Management Plan of the bIoTope project and the initial data sets that have been identified for the large scale pilots. The data sets are described in this report in accordance with the European Commission guidelines addressing key attributes of the data type, format, metadata, use of standards and sharing modalities. At this early stage of the project, benchmarks and other facilities to be used in the development of the bIoTope components and their resultant data have not yet been fully identified. These data sets will be described in future Data Management Plan updates as the technology development tasks progress and relevant data sets are developed or adopted. 1\. Introduction The bIoTope project participates to the Open Data pilot under the Horizon 2020 Programme and, in the interest of supporting the bIoTope ecosystems seeks, whenever feasible, to share open data by making data sets used and created within the project publicly available. This will apply both to data sets used for the large scale pilots later in the project, as well as data used for validation of the work carried out under the research and development tasks, which may not be included as part of the formal reporting from the project. This deliverable outlines the initial Data Management Plan (DMP) for bIoTope, in line with the H2020 guidelines for data management plan creation 1 . It identifies the initial classes of datasets that the project foresees to utilise and create primarily with respect to the smart cities pilots and target communities providing an outline of the type, format, metadata and sharing modalities for the initial data sets identified in the early stages of the project. The purpose of this DMP is to provide a description of the main elements of the data management policy that will be used by the consortium with regard to all the data sets that will be generated or adopted by the project. The DMP is not a fixed document and is intended to evolve during the operation of the bIoTope project. This initial version of the DMP includes an overview of the data sets to be produced by the project, and the specific conditions that are attached to them. Updated versions of the DMP will provide more details and describe the practical data management procedures that have been implemented by the bIoTope project. ### 1.1. Data lifecycle The data management planning for the bIoTope project is intended to evolve over the project duration to eventually cover the entire data life cycle for the data created or adopted by the project. Figure 1 depicts the typical lifecycle for a given data set. Data lifecycle support is an important aspect related to creating a sustainable bIoTope ecosystem where evolving data sets can help sustain the ecosystems and create opportunities for new applications and services that further exploit the bIoTope technologies. Supporting policies and procedures concerning bIoTope related data sets will be further analysed and outlined in later deliverables addressing bIoTope ecosystem management and business modelling. Figure 1: Data Lifecycle Elements 2 Figure 1 depicts a typical lifecycle and there can be many variations. For example, some Internet of Things applications may utilise or share raw data from Data Collection sources without any Data Analysis. Key elements to be considered in managing data within the data lifecycle are the following: * File formats * Organisation and naming conventions * Quality control * Access modalities * Persistence and recovery * Metadata and data conversion * Sharing and preservation These elements are addressed for the initial data sets described in this plan in accordance with the European Commission guidelines. At this early stage of the bIoTope project some of the data set information provided should be considered preliminary and subject to change. ### 1.2. Intended audience This deliverable is intended for use both internally within the bIoTope project and externally to create awareness of the initial data sets that have been identified for use in the bIoTope ecosystem and the planning that is already taking place within the project concerning access and preservation of data. This report provides initial guidance on data management to the project partners and is particularly relevant for partners responsible for data collection and large scale pilots. It should be considered a snapshot at this stage, which will evolve throughout the project as further details concerning the technologies and pilots are specified and further procedures and potential infrastructures are created or modified for storing and managing project related data. ### 1.3. Structure of this deliverable This deliverable has been structured with two main sections addressing the different aspects of data management within the bIoTope project as follows: * Section 2 provides an overview of the initial data policies established for the project * Section 3 describes the data sets already identified for use in the large scale pilots, some of which exist and others will be created during the project operation A final section with conclusions is also included. 2\. Project data policies ### 2.1. Participation in the Pilot on Open Research Data The bIoTope project participates in the Pilot on Open Research Data launched by the European Commission along with the Horizon 2020 Programme. The consortium strongly believes in the concepts of open research and development, and in the benefits that the European Internet of Things community can draw from allowing reuse of data on much larger scales across Europe. Therefore, whenever feasible, data produced by the project can potentially be published under open access procedures – though this objective may be constrained in view of other principles related to IPR and security as described below. ### 2.2. IPR management and security Many project partners have or will have Intellectual Property Rights (IPR) on the project technologies and data, which for some partners are essential for economic sustainability. The bIoTope project consortium will therefore have an obligation to protect these data and to publish data only if the concerned partner(s) have granted explicit authorisation. Another effect of IPR management is that some of the data collected through the bIoTope project may be of high value for application and service providers and therefore due consideration of the business models and ecosystem should be taken in advance of open access decisions for project data sets. All measures should therefore be taken to prevent data from being leaked or hacked, which could potentially undermine the ecosystem planning for the project or the commercial opportunities for bIoTope project partners. Repositories used by the project for data that have potential commercial value will be secured until decisions are taken concerning open access by the respective partner(s), and in view of the planned business models within the bIoTope ecosystem. For sensitive data a holistic security approach will be undertaken to protect the three mains pillars of information security: confidentiality, integrity, and availability. The security approach will consist of an assessment of security risks for each data set followed by an impact analysis. This analysis will be performed on the information and data processed by the bIoTope system, their flows and any risk associated to their processing. Particular assessment attention will be placed on any data sets containing personally identifiable information. ### 2.3. Personal data protection For some of the activities to be carried out by the project, it may be necessary to collect basic personal data (e.g. full name, contact details, background) for use in the large scale pilots, even though the project will avoid collecting such data unless deemed necessary. Such data will be protected in compliance with the EU's Data Protection Directive 95/46/EC1 aiming at protecting personal data. National legislations in Belgium, Finland and France applicable to the project will also be strictly followed. All personal data collected by the project will be done after giving data subjects full details on the pilot experiments to be conducted, and after providing options to opt out of collection of any personal data. 3\. Initial bIoTope data sets The different data sets that will be gathered and processed by the bIoTope project are described in the following subsections. The descriptions follow the guidelines provided by the European Commission with respect to data set characteristics to be described. These initial data sets will be updated and extended with additional data sets by the project partners responsible for the different pilots to be conducted later in the project, as well as by partners involved in technology development of the core technology components where benchmark and other relevant data sets may be generated or adopted during development. Table 1 provides an overview of the initial datasets identified for the project. Table 1: Summary of initial bIoTope data sets <table> <tr> <th> No. </th> <th> Data set name </th> <th> Description </th> </tr> <tr> <td> 1 </td> <td> Lyon Bottle Banks </td> <td> Point data representing the location of the metropolis bottle banks. </td> </tr> <tr> <td> 2 </td> <td> Lyon Bottle Banks status </td> <td> One or several data sets will be created to store the following measures (including historical data) coming from each bottle bank sensor </td> </tr> <tr> <td> 3 </td> <td> Lyon Real-time traffic conditions </td> <td> Traffic density on the road sections, refreshed every minute </td> </tr> </table> <table> <tr> <th> No. </th> <th> Data set name </th> <th> Description </th> </tr> <tr> <td> 4 </td> <td> Lyon Road real-time event </td> <td> Point data representing a road perturbation. </td> </tr> <tr> <td> 5 </td> <td> Lyon Temperatures and humidity measures </td> <td> Point data supporting temperature and humidity. </td> </tr> <tr> <td> 6 </td> <td> Lyon Trees evapotranspiration </td> <td> The data set stores, for each tree which is monitored, the evapotranspiration rate. </td> </tr> <tr> <td> 7 </td> <td> Brussels Schools location </td> <td> Coordinates and details of schools along with their location. </td> </tr> <tr> <td> 8 </td> <td> Brussels Entry points of school </td> <td> Likely to be geo coordinates for entry points of schools in the Brussels Capital region </td> </tr> <tr> <td> 9 </td> <td> Brussels Green spaces </td> <td> Geo localization of all the green spaces in the Brussels Capital region </td> </tr> <tr> <td> 10 </td> <td> Brussels Waterflows </td> <td> Geo localization of all the public water installation (fountains, etc.) in the Brussels Capital region </td> </tr> <tr> <td> 11 </td> <td> Brussels Stops of trams, metro, bus </td> <td> Geo localization of all the stops of the public transport the Brussels Capital region </td> </tr> <tr> <td> 12 </td> <td> Brussels Itineraries of tram, metro, bus </td> <td> Geo localization of all the routes of the public transport in the Brussels Capital region </td> </tr> <tr> <td> 13 </td> <td> Brussels Details of the stops of tram, metro, bus </td> <td> Geo localization of all the additional information of the stops of the public transport in the Brussels Capital region </td> </tr> <tr> <td> 14 </td> <td> Brussels Timetable of tram, metro, bus </td> <td> Timetable of the stops of the public transport in the Brussels Capital region </td> </tr> <tr> <td> 15 </td> <td> Brussels Real-time travel time of tram, metro, bus </td> <td> Realtime travel timing of the public transport in the Brussels Capital region </td> </tr> <tr> <td> 16 </td> <td> Brussels Cyclist routes </td> <td> Geo localization of the cyclist routes in the Brussels Capital region </td> </tr> <tr> <td> 17 </td> <td> Brussels RER Bicycle </td> <td> Geo localization of the cyclist routes in the Brussels Capital region </td> </tr> <tr> <td> 18 </td> <td> Brussels Parking for 3 Bikes </td> <td> Geo localization of the parking for 3 Bikes in the Brussels Capital region </td> </tr> <tr> <td> 19 </td> <td> Brussels Free-service bike stations </td> <td> Geo localization of the free-service bike stations in the Brussels Capital region </td> </tr> <tr> <td> 20 </td> <td> Brussels Free-service bike stations tariffs </td> <td> Tariffs of the free-service bike stations in the Brussels Capital region </td> </tr> <tr> <td> 21 </td> <td> Brussels Real-time flow of cyclists </td> <td> Geo localization of the flows of cyclists in the Brussels Capital region </td> </tr> <tr> <td> 22 </td> <td> Brussels Drive directions </td> <td> General driving directions for emergency services in the Brussels Capital region </td> </tr> <tr> <td> 23 </td> <td> Brussels Zone 30 </td> <td> Geo localization of the Zones 30 Brussels Capital region </td> </tr> <tr> <td> 24 </td> <td> Brussels Crossroads with red lights </td> <td> Geo localization of crossroads and red lights in the Brussels </td> </tr> </table> <table> <tr> <th> No. </th> <th> Data set name </th> <th> Description </th> </tr> <tr> <td> </td> <td> </td> <td> Capital region </td> </tr> <tr> <td> 25 </td> <td> Brussels Public on-street parking </td> <td> Geo localization of the public parking spots in the Brussels Capital region </td> </tr> <tr> <td> 26 </td> <td> Brussels Realtime flow of cars </td> <td> Geo localization of the car traffic flows in the Brussels Capital region </td> </tr> <tr> <td> 27 </td> <td> Brussels Road works and events </td> <td> Geo localization of the public road works and events (weekly markets, etc.) in the Brussels Capital region </td> </tr> <tr> <td> 28 </td> <td> Brussels Congestion of public roads </td> <td> General congestion status of the public roads in the Brussels Capital region </td> </tr> <tr> <td> 29 </td> <td> Brussels Sidewalks </td> <td> Geo localization of the public sidewalks in the Brussels Capital region </td> </tr> <tr> <td> 30 </td> <td> Brussels Pedestrian crossroads </td> <td> Geo localization of the pedestrian crossroads in the Brussels Capital region </td> </tr> <tr> <td> 31 </td> <td> Brussels Dangerous traffic points </td> <td> Geo localization of the dangerous traffic points in the Brussels Capital region </td> </tr> <tr> <td> 32 </td> <td> Brussels Realtime flow of pedestrians </td> <td> Geo localization of the realtime flow of pedestrians in the Brussels Capital region </td> </tr> <tr> <td> 33 </td> <td> Brussels Traffic signs </td> <td> Geo localization of the traffic signs in the Brussels Capital region </td> </tr> <tr> <td> 34 </td> <td> Brussels Fire station localization </td> <td> Geo localization of the fire stations in the Brussels Capital region </td> </tr> <tr> <td> 35 </td> <td> Brussels Hospitals localization </td> <td> Geo localization of the hospitals in the Brussels Capital region </td> </tr> <tr> <td> 36 </td> <td> Brussels Garbage trucks localization </td> <td> Geo localization of garbage trucks in the Brussels Capital region </td> </tr> <tr> <td> 37 </td> <td> Brussels Extra Long Busses localization </td> <td> Geo localization of Extra Long Busses in the Brussels Capital region </td> </tr> <tr> <td> 38 </td> <td> Brussels Reserved Traffic lanes </td> <td> Geo localization of the reserved traffic lanes in the Brussels Capital region </td> </tr> <tr> <td> 39 </td> <td> Brussels Fire trucks localization </td> <td> Geo localization of the fire trucks in the Brussels Capital region </td> </tr> <tr> <td> 40 </td> <td> Brussels Hydrants </td> <td> Geo localization of the hydrants in the Brussels Capital region </td> </tr> <tr> <td> 41 </td> <td> Helsinki KNX data </td> <td> Water, electricity, heating consumption data from apartments. </td> </tr> <tr> <td> 42 </td> <td> Helsinki Presence data </td> <td> Data to identify if a person is at home or not, using some personalised service or combination of them (e.g. GPS location of mobile phone, personal calendar, home away button). </td> </tr> <tr> <td> No. </td> <td> Data set name </td> <td> Description </td> </tr> <tr> <td> 43 </td> <td> Open Charging Station Vocabulary </td> <td> A linked open vocabulary covering charging stations services for e-Mobility will be created. </td> </tr> </table> The descriptions of each data set follows the European Commission provided template and includes the following elements: * Data Set Name – used to keep track of different data sets. * Contributor(s) – organisations that are responsible for the data. This can be a project partner, but also external organisations can also be contributors. * Description – briefly summarises the type of data elements that exist, or will be created, and the format. * Standards – any standards the data set might follow in the way elements are described or structured. * Quality Assurance – procedures that might be in place to ensure quality is maintained such as consistency or reliability of the data. * Access – indication if the existing data set or data to be created in the bIoTope project will be publicly accessible or restricted. * Archiving and preservation – indicate if facilities for preserving the data are provided by the project partner, third party or the bIoTope project will need to create facilities (e.g. project website). The characteristics of each of the identified data sets are summarized in the following sections. 3.1. Lyon Bottle Banks <table> <tr> <th> Data set name or reference </th> <th> Bottle banks [ Silos à verre] </th> </tr> <tr> <td> Contributor(s) </td> <td> Lyon metropolis - Cleanliness Department [Métropole de Lyon / Direction de la propreté (DP)] </td> </tr> <tr> <td> Data set description and format </td> <td> Point data representing the location of the metropolis bottle banks. Formats : WMS WFS KML geo-json Shape-zip json </td> </tr> <tr> <td> Standards (if any) </td> <td> System of coordinates : EPSG::RGF93 / CC46 (EPSG:3946) </td> </tr> <tr> <td> Quality assurance (if any) </td> <td> No </td> </tr> <tr> <td> Access </td> <td> ☒Public access ☐Restricted access ☐Other (please describe): </td> </tr> <tr> <td> Archiving and preservation responsibility </td> <td> ☒Project partner ☐External party ☐bIoTope project ☐Other (please describe): </td> </tr> </table> ### 3.2. Lyon Bottle Banks Status <table> <tr> <th> Data set name or reference </th> <th> Bottle banks status (to be created) </th> </tr> <tr> <td> Contributor(s) </td> <td> Lyon metropolis </td> </tr> <tr> <td> Data set description </td> <td> One or several data sets will be created to store the following measures </td> </tr> <tr> <td> and format </td> <td> (including historical data) coming from each bottle bank sensor : * Filling rate * Internal temperature * Location (GPS) * Acceleration (as an event : when bottle bank is emptied) - (timestamp) Format : to be defined </td> </tr> <tr> <td> Standards (if any) </td> <td> No </td> </tr> <tr> <td> Quality assurance (if any) </td> <td> No </td> </tr> <tr> <td> Access </td> <td> ☐Public access ☒Restricted access ☐Other (please describe): </td> </tr> <tr> <td> Archiving and preservation responsibility </td> <td> ☒Project partner ☐External party ☐bIoTope project ☐Other (please describe): </td> </tr> </table> 3.3. Lyon Real-time traffic conditions <table> <tr> <th> Data set name or reference </th> <th> Real-time traffic conditions [Etat du trafic temps reel] </th> </tr> <tr> <td> Contributor(s) </td> <td> Lyon metropolis – Roads and mobility department / [Métropole de Lyon / Direction de la voirie (DV)] </td> </tr> <tr> <td> Data set description and format </td> <td> Traffic density on the road sections, refreshed every minute Formats : WMS WFS KML geo-json Shape-zip json xml </td> </tr> <tr> <td> Standards (if any) </td> <td> To be determined </td> </tr> <tr> <td> Quality assurance (if any) </td> <td> To be determined </td> </tr> <tr> <td> Access </td> <td> ☐Public access ☐Restricted access ☐Other (please describe): </td> </tr> <tr> <td> Archiving and preservation responsibility </td> <td> ☐Project partner ☐External party ☐bIoTope project ☐Other (please describe): </td> </tr> </table> ### 3.4. Lyon Road real-time event <table> <tr> <th> Data set name or reference </th> <th> Road real-time event [Evènement routier temps reel] </th> </tr> <tr> <td> Contributor(s) </td> <td> Lyon metropolis – Roads and mobility department / [Métropole de Lyon / Direction de la voirie (DV)] </td> </tr> <tr> <td> Data set description and format </td> <td> Point data representing a road perturbation. Formats : WMS WFS KML geo-json Shape-zip json xml </td> </tr> <tr> <td> Standards (if any) </td> <td> Datex 2 descriptive informations (type of perturbation, start date, end date) </td> </tr> <tr> <td> Quality assurance (if any) </td> <td> No </td> </tr> <tr> <td> Access </td> <td> ☐Public access ☒Restricted access ☐Other (please describe): </td> </tr> <tr> <td> Archiving and preservation responsibility </td> <td> ☒Project partner ☐External party ☐bIoTope project ☐Other (please describe): </td> </tr> </table> 3.5. Lyon Temperatures and humidity measures <table> <tr> <th> Data set name or reference </th> <th> Temperatures and humidity measures (to be created) </th> </tr> <tr> <td> Contributor(s) </td> <td> Lyon metropolis – Climate plan unit / Citizens (crowdsourcing) / External partners </td> </tr> <tr> <td> Data set description and format </td> <td> Point data supporting temperature and humidity. Formats : to be defined. </td> <td> </td> </tr> <tr> <td> Standards (if any) </td> <td> To be determined </td> <td> </td> </tr> <tr> <td> Quality assurance (if any) </td> <td> No </td> <td> </td> </tr> <tr> <td> Access </td> <td> ☒Public access ☐Restricted access ☐Other (please describe): </td> <td> </td> </tr> <tr> <td> Archiving and preservation responsibility </td> <td> ☒Project partner ☒External party ☐Other (please describe): </td> <td> ☐bIoTope project </td> </tr> </table> ### 3.6. Lyon Trees evapotranspiration <table> <tr> <th> Data set name or reference </th> <th> Trees evapotranspiration </th> </tr> <tr> <td> Contributor(s) </td> <td> Lyon metropolis – Climate plan unit </td> </tr> <tr> <td> Data set description and format </td> <td> The data set stores, for each tree which is monitored, the evapotranspiration rate. Formats : to be defined. </td> </tr> <tr> <td> Standards (if any) </td> <td> To be determined </td> </tr> <tr> <td> Quality assurance (if any) </td> <td> No </td> </tr> <tr> <td> Access </td> <td> ☒Public access ☐Restricted access ☐Other (please describe): </td> </tr> <tr> <td> Archiving and preservation responsibility </td> <td> ☒Project partner ☐Other (please describe): </td> <td> ☐External party </td> <td> ☐bIoTope project </td> </tr> </table> ### 3.7. Brussels Schools location <table> <tr> <th> Data set name or reference </th> <th> Schools location (3.1.4.1) </th> </tr> <tr> <td> Contributor(s) </td> <td> CIRB (or Others) </td> </tr> <tr> <td> Data set description and format </td> <td> To be determined, but likely to be similar to the following: <table> <tr> <th> Nom </th> </tr> <tr> <td> Language </td> </tr> <tr> <td> NameFr </td> </tr> <tr> <td> NameNl </td> </tr> <tr> <td> AdressFr Street Number PostalCode City </td> </tr> <tr> <td> AdressNl </td> </tr> <tr> <td> Phone </td> </tr> <tr> <td> Fax </td> </tr> <tr> <td> eMail </td> </tr> <tr> <td> WebSite </td> </tr> <tr> <td> Director </td> </tr> <tr> <td> Coordinates </td> </tr> </table> </td> <td> </td> </tr> <tr> <td> Standards (if any) </td> <td> To be determined </td> <td> </td> </tr> <tr> <td> Quality assurance (if any) </td> <td> No </td> <td> </td> </tr> <tr> <td> Access </td> <td> ☒Public access ☐Restricted access ☐Other (please describe): </td> <td> </td> </tr> <tr> <td> Archiving and preservation responsibility </td> <td> ☒Project partner ☐External party ☐Other (please describe): </td> <td> ☐bIoTope project </td> </tr> <tr> <td> Use cases </td> <td> ☒1 School ☒2 Emergency ☐ 3 Bikes </td> <td> </td> </tr> </table> 3.8. Brussels Entry points of school <table> <tr> <th> Data set name or reference </th> <th> Entry points of school (3.1.4.2) </th> </tr> <tr> <td> Contributor(s) </td> <td> CIRB (or Others) </td> <td> </td> </tr> <tr> <td> Data set description and format </td> <td> Coordinates for entry points of schools in the area </td> <td> </td> </tr> <tr> <td> Standards (if any) </td> <td> To be determined </td> <td> </td> </tr> <tr> <td> Quality assurance (if any) </td> <td> No </td> <td> </td> </tr> <tr> <td> Access </td> <td> ☐Public access ☒Restricted access ☐Other (please describe): </td> <td> </td> </tr> <tr> <td> Archiving and preservation responsibility </td> <td> ☒Project partner ☐External party ☐Other (please describe): </td> <td> ☐bIoTope project </td> </tr> <tr> <td> Use cases </td> <td> ☒1 School ☒2 Emergency ☐ 3 Bikes </td> <td> </td> </tr> </table> ### 3.9. Brussels Green spaces <table> <tr> <th> Data set name or reference </th> <th> Green spaces (3.1.4.3) </th> </tr> <tr> <td> Contributor(s) </td> <td> CIRb / IBGE </td> </tr> <tr> <td> Data set description and format </td> <td> Geo localisation of all the green spaces in the Brussels Capital region </td> </tr> <tr> <td> Standards (if any) </td> <td> GeoJSON </td> </tr> <tr> <td> Quality assurance (if any) </td> <td> No </td> </tr> <tr> <td> Access </td> <td> ☒Public access ☐Restricted access ☐Other (please describe): </td> </tr> <tr> <td> Archiving and preservation responsibility </td> <td> ☐Project partner ☒External party ☐bIoTope project ☐Other (please describe): </td> </tr> <tr> <td> Use cases </td> <td> ☒1 School ☐2 Emergency ☐ 3 Bikes </td> </tr> </table> 3.10. Brussels Waterflows <table> <tr> <th> Data set name or reference </th> <th> Waterflows (3.1.4.4) </th> </tr> <tr> <td> Contributor(s) </td> <td> IBGE </td> </tr> <tr> <td> Data set description and format </td> <td> Geo localization of all the public water installation (fountains, etc.) in the Brussels Capital region </td> </tr> <tr> <td> Standards (if any) </td> <td> GML 3.2.1 </td> </tr> <tr> <td> Quality assurance (if any) </td> <td> No </td> <td> </td> </tr> <tr> <td> Access </td> <td> ☒Public access ☐Restricted access ☐Other (please describe): </td> <td> </td> </tr> <tr> <td> Archiving and preservation responsibility </td> <td> ☐Project partner ☒External party ☐Other (please describe): </td> <td> ☐bIoTope project </td> </tr> <tr> <td> Use cases </td> <td> ☒1 School ☒2 Emergency ☐ 3 Bikes </td> <td> </td> </tr> </table> ### 3.11. Brussels Stops of trams, metro, bus <table> <tr> <th> Data set name or reference </th> <th> Stops of trams, métro, bus (3.1.4.5) </th> </tr> <tr> <td> Contributor(s) </td> <td> STIB </td> </tr> <tr> <td> Data set description and format </td> <td> Geo localization of all the stops of the public transport the Brussels Capital region </td> </tr> <tr> <td> Standards (if any) </td> <td> GTFS </td> <td> </td> </tr> <tr> <td> Quality assurance (if any) </td> <td> No </td> <td> </td> </tr> <tr> <td> Access </td> <td> ☒Public access ☐Restricted access ☐Other (please describe): </td> <td> </td> </tr> <tr> <td> Archiving and preservation responsibility </td> <td> ☐Project partner ☒External party ☐Other (please describe): </td> <td> ☐bIoTope project </td> </tr> <tr> <td> Use cases </td> <td> ☒1 School ☒2 Emergency ☐ 3 Bikes </td> <td> </td> </tr> </table> 3.12. Brussels Itineraries of tram, metro, bus <table> <tr> <th> Data set name or reference </th> <th> Itineraries of tram, métro, bus (3.1.4.6) </th> </tr> <tr> <td> Contributor(s) </td> <td> STIB </td> </tr> <tr> <td> Data set description and format </td> <td> Geo localization of all the routes of the public transport in the Brussels Capital region </td> </tr> <tr> <td> Standards (if any) </td> <td> GTFS </td> </tr> <tr> <td> Quality assurance (if any) </td> <td> No </td> </tr> <tr> <td> Access </td> <td> ☐Public access ☒Restricted access ☐Other (please describe): </td> </tr> <tr> <td> Archiving and preservation responsibility </td> <td> ☐Project partner ☒External party ☐Other (please describe): </td> <td> ☐bIoTope project </td> </tr> <tr> <td> Use cases </td> <td> ☒1 School ☐2 Emergency ☐ 3 Bikes </td> <td> </td> </tr> </table> 3.13. Brussels Details of the stops of tram, metro, bus <table> <tr> <th> Data set name or reference </th> <th> Details of the stops of tram, métro, bus (3.1.4.7) </th> </tr> <tr> <td> Contributor(s) </td> <td> STIB </td> </tr> <tr> <td> Data set description and format </td> <td> Geo localization of all the additional information of the stops of the public transport in the Brussels Capital region </td> </tr> <tr> <td> Standards (if any) </td> <td> GTFS </td> <td> </td> </tr> <tr> <td> Quality assurance (if any) </td> <td> No </td> <td> </td> </tr> <tr> <td> Access </td> <td> ☐Public access ☒Restricted access ☐Other (please describe): </td> <td> </td> </tr> <tr> <td> Archiving and preservation responsibility </td> <td> ☐Project partner ☒External party ☐Other (please describe): </td> <td> ☐bIoTope project </td> </tr> <tr> <td> Use cases </td> <td> ☒1 School ☐2 Emergency ☐ 3 Bikes </td> <td> </td> </tr> </table> ### 3.14. Brussels Timetable of tram, metro, bus <table> <tr> <th> Data set name or reference </th> <th> Timetable of tram, métro, bus (3.1.4.8) </th> </tr> <tr> <td> Contributor(s) </td> <td> STIB </td> </tr> <tr> <td> Data set description and format </td> <td> Timetable of the stops of the public transport in the Brussels Capital region </td> </tr> <tr> <td> Standards (if any) </td> <td> GTFS </td> </tr> <tr> <td> Quality assurance (if any) </td> <td> No </td> </tr> <tr> <td> Access </td> <td> ☐Public access ☒Restricted access ☐Other (please describe): </td> </tr> <tr> <td> Archiving and preservation responsibility </td> <td> ☐Project partner ☒External party ☐bIoTope project ☐Other (please describe): </td> </tr> <tr> <td> Use cases </td> <td> ☒1 School ☐2 Emergency ☐ 3 Bikes </td> </tr> </table> 3.15. Brussels Real-time travel time of tram, metro, bus <table> <tr> <th> Data set name or reference </th> <th> Real-time travel time of tram, métro, bus (3.1.4.9) </th> </tr> <tr> <td> Contributor(s) </td> <td> STIB </td> </tr> <tr> <td> Data set description and format </td> <td> Realtime travel timing of the public transport in the Brussels Capital region </td> </tr> <tr> <td> Standards (if any) </td> <td> GTFS </td> </tr> <tr> <td> Quality assurance (if any) </td> <td> No </td> </tr> <tr> <td> Access </td> <td> ☐Public access ☒Restricted access ☐Other (please describe): </td> </tr> <tr> <td> Archiving and preservation responsibility </td> <td> ☐Project partner ☒External party ☐bIoTope project ☐Other (please describe): </td> </tr> <tr> <td> Use cases </td> <td> ☒1 School ☐2 Emergency ☐ 3 Bikes </td> </tr> </table> ### 3.16. Brussels Cyclist routes <table> <tr> <th> Data set name or reference </th> <th> Cyclist routes (3.1.4.10) </th> </tr> <tr> <td> Contributor(s) </td> <td> Brussels Mobility </td> </tr> <tr> <td> Data set description and format </td> <td> Geo localization of the cyclist routes in the Brussels Capital region </td> </tr> <tr> <td> Standards (if any) </td> <td> GeoJSON EPSG </td> </tr> <tr> <td> Quality assurance (if any) </td> <td> No </td> </tr> <tr> <td> Access </td> <td> ☒Public access ☐Restricted access ☐Other (please describe): </td> </tr> <tr> <td> Archiving and preservation responsibility </td> <td> ☐Project partner ☒External party ☐bIoTope project ☐Other (please describe): </td> </tr> <tr> <td> Use cases </td> <td> ☒1 School ☐2 Emergency ☐ 3 Bikes </td> </tr> </table> ### 3.17. Brussels RER Bicycle <table> <tr> <th> Data set name or reference </th> <th> RER Bicycle (3.1.4.11) </th> </tr> <tr> <td> Contributor(s) </td> <td> Brussels Mobility </td> </tr> <tr> <td> Data set description and format </td> <td> Geo localization of the cyclist routes in the Brussels Capital region </td> </tr> <tr> <td> Standards (if any) </td> <td> GeoJSON </td> <td> </td> </tr> <tr> <td> Quality assurance (if any) </td> <td> No </td> <td> </td> </tr> <tr> <td> Access </td> <td> ☒Public access ☐Restricted access ☐Other (please describe): </td> <td> </td> </tr> <tr> <td> Archiving and preservation responsibility </td> <td> ☐Project partner ☒External party ☐Other (please describe): </td> <td> ☐bIoTope project </td> </tr> <tr> <td> Use cases </td> <td> ☒1 School ☐2 Emergency ☐ 3 Bikes </td> <td> </td> </tr> </table> ### 3.18. Brussels Parking for 3 Bikes <table> <tr> <th> Data set name or reference </th> <th> Parking for 3 Bikes (3.1.4.12) </th> </tr> <tr> <td> Contributor(s) </td> <td> Brussels Mobility </td> </tr> <tr> <td> Data set description and format </td> <td> Geo localization of the parking for 3 Bikes in the Brussels Capital region </td> </tr> <tr> <td> Standards (if any) </td> <td> GeoJSON ShapeFile KML GeoJSON </td> </tr> <tr> <td> Quality assurance (if any) </td> <td> No </td> </tr> <tr> <td> Access </td> <td> ☒Public access ☐Restricted access ☐Other (please describe): </td> </tr> <tr> <td> Archiving and preservation responsibility </td> <td> ☐Project partner ☒External party ☐bIoTope project ☐Other (please describe): </td> </tr> <tr> <td> Use cases </td> <td> ☒1 School ☐2 Emergency ☐ 3 Bikes </td> </tr> </table> 3.19. Brussels Free-service bike stations <table> <tr> <th> Data set name or reference </th> <th> Free-service bike stations (3.1.4.13) </th> </tr> <tr> <td> Contributor(s) </td> <td> JCDecaux </td> </tr> <tr> <td> Data set description and format </td> <td> Geo localization of the free-service bike stations in the Brussels Capital region </td> </tr> <tr> <td> Standards (if any) </td> <td> To be determined </td> </tr> <tr> <td> Quality assurance (if </td> <td> No </td> </tr> <tr> <td> any) </td> <td> </td> <td> </td> </tr> <tr> <td> Access </td> <td> ☒Public access ☐Restricted access ☐Other (please describe): </td> <td> </td> </tr> <tr> <td> Archiving and preservation responsibility </td> <td> ☐Project partner ☒External party ☐Other (please describe): </td> <td> ☐bIoTope project </td> </tr> <tr> <td> Use cases </td> <td> ☒1 School ☐2 Emergency ☐ 3 Bikes </td> <td> </td> </tr> </table> 3.20. Brussels Free-service bike stations tariffs <table> <tr> <th> Data set name or reference </th> <th> Free-service bike stations tariffs (3.1.4.14) </th> </tr> <tr> <td> Contributor(s) </td> <td> JCDecaux </td> </tr> <tr> <td> Data set description and format </td> <td> Tariffs of the free-service bike stations in the Brussels Capital region </td> </tr> <tr> <td> Standards (if any) </td> <td> To be determined </td> </tr> <tr> <td> Quality assurance (if any) </td> <td> No </td> </tr> <tr> <td> Access </td> <td> ☒Public access ☐Restricted access ☐Other (please describe): </td> </tr> <tr> <td> Archiving and preservation responsibility </td> <td> ☐Project partner ☒External party ☐bIoTope project ☐Other (please describe): </td> </tr> <tr> <td> Use cases </td> <td> ☒1 School ☐2 Emergency ☐ 3 Bikes </td> </tr> </table> ### 3.21. Brussels Real-time flow of cyclists <table> <tr> <th> Data set name or reference </th> <th> Real-time flow of cyclists (3.1.4.15) </th> </tr> <tr> <td> Contributor(s) </td> <td> Orange BE </td> </tr> <tr> <td> Data set description and format </td> <td> Geo localization of the flows of cyclists in the Brussels Capital region </td> </tr> <tr> <td> Standards (if any) </td> <td> To be determined </td> </tr> <tr> <td> Quality assurance (if any) </td> <td> No </td> </tr> <tr> <td> Access </td> <td> ☐Public access ☒Restricted access ☐Other (please describe): </td> </tr> <tr> <td> Archiving and preservation </td> <td> ☒Project partner ☐External party ☐bIoTope project </td> </tr> <tr> <td> responsibility </td> <td> ☐Other (please describe): </td> </tr> <tr> <td> Use cases </td> <td> ☒1 School ☐2 Emergency ☐ 3 Bikes </td> </tr> </table> 3.22. Brussels Drive directions <table> <tr> <th> Data set name or reference </th> <th> Drive directions (3.1.4.16) </th> </tr> <tr> <td> Contributor(s) </td> <td> SIAMU </td> </tr> <tr> <td> Data set description and format </td> <td> General driving directions for emergency services in the Brussels Capital region </td> </tr> <tr> <td> Standards (if any) </td> <td> To be determined </td> <td> </td> </tr> <tr> <td> Quality assurance (if any) </td> <td> No </td> <td> </td> </tr> <tr> <td> Access </td> <td> ☒Public access ☐Restricted access ☐Other (please describe): </td> <td> </td> </tr> <tr> <td> Archiving and preservation responsibility </td> <td> ☐Project partner ☒External party ☐Other (please describe): </td> <td> ☐bIoTope project </td> </tr> <tr> <td> Use cases </td> <td> ☒1 School ☒2 Emergency ☐ 3 Bikes </td> <td> </td> </tr> </table> ### 3.23. Brussels Zone 30 <table> <tr> <th> Data set name or reference </th> <th> Zone 30 (3.1.4.17) </th> </tr> <tr> <td> Contributor(s) </td> <td> Brussels Mobility </td> </tr> <tr> <td> Data set description and format </td> <td> Geo localization of the Zones 30 Brussels Capital region </td> </tr> <tr> <td> Standards (if any) </td> <td> EPSG </td> </tr> <tr> <td> Quality assurance (if any) </td> <td> No </td> </tr> <tr> <td> Access </td> <td> ☒Public access ☐Restricted access ☐Other (please describe): </td> </tr> <tr> <td> Archiving and preservation responsibility </td> <td> ☒Project partner ☐External party ☐bIoTope project ☐Other (please describe): </td> </tr> <tr> <td> Use cases </td> <td> ☒1 School ☐2 Emergency ☐ 3 Bikes </td> </tr> </table> 3.24. Brussels Crossroads with red lights <table> <tr> <th> Data set name or reference </th> <th> Crossroads with red lights (3.1.4.18) </th> </tr> <tr> <td> Contributor(s) </td> <td> Brussels Mobility </td> </tr> <tr> <td> Data set description and format </td> <td> Geo localization of crossroads and red lights in the Brussels Capital region </td> </tr> <tr> <td> Standards (if any) </td> <td> To be determined </td> </tr> <tr> <td> Quality assurance (if any) </td> <td> No </td> </tr> <tr> <td> Access </td> <td> ☐Public access ☒Restricted access ☐Other (please describe): </td> </tr> <tr> <td> Archiving and preservation responsibility </td> <td> ☒Project partner ☐External party ☐bIoTope project ☐Other (please describe): </td> </tr> <tr> <td> Use cases </td> <td> ☒1 School ☒2 Emergency ☐ 3 Bikes </td> </tr> </table> ### 3.25. Brussels Public on-street parking <table> <tr> <th> Data set name or reference </th> <th> Public on-street parking (3.1.4.19) </th> </tr> <tr> <td> Contributor(s) </td> <td> Parking Brussels </td> </tr> <tr> <td> Data set description and format </td> <td> Geo localization of the public parking spots in the Brussels Capital region </td> </tr> <tr> <td> Standards (if any) </td> <td> GeoJSON EPSG </td> </tr> <tr> <td> Quality assurance (if any) </td> <td> No </td> </tr> <tr> <td> Access </td> <td> ☒Public access ☐Restricted access ☐Other (please describe): </td> </tr> <tr> <td> Archiving and preservation responsibility </td> <td> ☒Project partner ☐External party ☐bIoTope project ☐Other (please describe): </td> </tr> <tr> <td> Use cases </td> <td> ☒1 School ☐2 Emergency ☐ 3 Bikes </td> </tr> </table> ### 3.26. Brussels Realtime flow of cars <table> <tr> <th> Data set name or reference </th> <th> Realtime flow of cars (3.1.4.20) </th> </tr> <tr> <td> Contributor(s) </td> <td> Orange BE </td> </tr> <tr> <td> Data set description and format </td> <td> Geo localization of the car traffic flows in the Brussels Capital region </td> </tr> <tr> <td> Standards (if any) </td> <td> To be determined </td> <td> </td> </tr> <tr> <td> Quality assurance (if any) </td> <td> No </td> <td> </td> </tr> <tr> <td> Access </td> <td> ☐Public access ☒Restricted access ☐Other (please describe): </td> <td> </td> </tr> <tr> <td> Archiving and preservation responsibility </td> <td> ☒Project partner ☐External party ☐Other (please describe): </td> <td> ☐bIoTope project </td> </tr> <tr> <td> Use cases </td> <td> ☒1 School ☒2 Emergency ☐ 3 Bikes </td> <td> </td> </tr> </table> ### 3.27. Brussels Road works and events <table> <tr> <th> Data set name or reference </th> <th> Road works and events (3.1.4.21) </th> </tr> <tr> <td> Contributor(s) </td> <td> Brussels Mobility </td> </tr> <tr> <td> Data set description and format </td> <td> Geo localization of the public road works and events (weekly markets, etc.) in the Brussels Capital region </td> </tr> <tr> <td> Standards (if any) </td> <td> GeoJSON </td> <td> </td> </tr> <tr> <td> Quality assurance (if any) </td> <td> No </td> <td> </td> </tr> <tr> <td> Access </td> <td> ☒Public access ☐Restricted access ☐Other (please describe): </td> <td> </td> </tr> <tr> <td> Archiving and preservation responsibility </td> <td> ☒Project partner ☐External party ☐Other (please describe): </td> <td> ☐bIoTope project </td> </tr> <tr> <td> Use cases </td> <td> ☒1 School ☒2 Emergency ☐ 3 Bikes </td> <td> </td> </tr> </table> 3.28. Brussels Congestion of public roads <table> <tr> <th> Data set name or reference </th> <th> Congestion of public roads (3.1.4.22) </th> </tr> <tr> <td> Contributor(s) </td> <td> WAZE </td> </tr> <tr> <td> Data set description and format </td> <td> General congestion status of the public roads in the Brussels Capital region </td> </tr> <tr> <td> Standards (if any) </td> <td> To be determined </td> </tr> <tr> <td> Quality assurance (if </td> <td> No </td> </tr> <tr> <td> any) </td> <td> </td> <td> </td> </tr> <tr> <td> Access </td> <td> ☐Public access ☒Restricted access ☐Other (please describe): </td> <td> </td> </tr> <tr> <td> Archiving and preservation responsibility </td> <td> ☐Project partner ☒External party ☐Other (please describe): </td> <td> ☐bIoTope project </td> </tr> <tr> <td> Use cases </td> <td> ☒1 School ☒2 Emergency ☐ 3 Bikes </td> <td> </td> </tr> </table> ### 3.29. Brussels Sidewalks <table> <tr> <th> Data set name or reference </th> <th> Sidewalks (3.1.4.23) </th> </tr> <tr> <td> Contributor(s) </td> <td> IBGE </td> </tr> <tr> <td> Data set description and format </td> <td> Geo localization of the public sidewalks in the Brussels Capital region </td> </tr> <tr> <td> Standards (if any) </td> <td> EPSG </td> </tr> <tr> <td> Quality assurance (if any) </td> <td> No </td> </tr> <tr> <td> Access </td> <td> ☒Public access ☐Restricted access ☐Other (please describe): </td> </tr> <tr> <td> Archiving and preservation responsibility </td> <td> ☐Project partner ☒External party ☐bIoTope project ☐Other (please describe): </td> </tr> <tr> <td> Use cases </td> <td> ☒1 School ☐2 Emergency ☐ 3 Bikes </td> </tr> </table> 3.30. Brussels Pedestrian crossroads <table> <tr> <th> Data set name or reference </th> <th> Pedestrian crossroads (3.1.4.24) </th> </tr> <tr> <td> Contributor(s) </td> <td> Brussels Mobility </td> </tr> <tr> <td> Data set description and format </td> <td> Geo localization of the pedestrian crossroads in the Brussels Capital region </td> </tr> <tr> <td> Standards (if any) </td> <td> To be determined </td> </tr> <tr> <td> Quality assurance (if any) </td> <td> No </td> </tr> <tr> <td> Access </td> <td> ☐Public access ☒Restricted access ☐Other (please describe): </td> </tr> <tr> <td> Archiving and preservation </td> <td> ☒Project partner ☐External party ☐bIoTope project </td> </tr> <tr> <td> responsibility </td> <td> ☐Other (please describe): </td> </tr> <tr> <td> Use cases </td> <td> ☒1 School ☐2 Emergency ☐ 3 Bikes </td> </tr> </table> ### 3.31. Brussels Dangerous traffic points <table> <tr> <th> Data set name or reference </th> <th> Dangerous traffic points (3.1.4.25) </th> </tr> <tr> <td> Contributor(s) </td> <td> Brussels Mobility </td> </tr> <tr> <td> Data set description and format </td> <td> Geo localization of the dangerous traffic points in the Brussels Capital region </td> </tr> <tr> <td> Standards (if any) </td> <td> To be determined </td> <td> </td> </tr> <tr> <td> Quality assurance (if any) </td> <td> No </td> <td> </td> </tr> <tr> <td> Access </td> <td> ☐Public access ☒Restricted access ☐Other (please describe): </td> <td> </td> </tr> <tr> <td> Archiving and preservation responsibility </td> <td> ☒Project partner ☐External party ☐Other (please describe): </td> <td> ☐bIoTope project </td> </tr> <tr> <td> Use cases </td> <td> ☒1 School ☐2 Emergency ☐ 3 Bikes </td> <td> </td> </tr> </table> 3.32. Brussels Realtime flow of pedestrians <table> <tr> <th> Data set name or reference </th> <th> Realtime flow of pedestrians (3.1.4.26) </th> </tr> <tr> <td> Contributor(s) </td> <td> Orange BE </td> </tr> <tr> <td> Data set description and format </td> <td> Geo localization of the realtime flow of pedestrians in the Brussels Capital region </td> </tr> <tr> <td> Standards (if any) </td> <td> To be determined </td> <td> </td> </tr> <tr> <td> Quality assurance (if any) </td> <td> No </td> <td> </td> </tr> <tr> <td> Access </td> <td> ☐Public access ☒Restricted access ☐Other (please describe): </td> <td> </td> </tr> <tr> <td> Archiving and preservation responsibility </td> <td> ☒Project partner ☐External party ☐Other (please describe): </td> <td> ☐bIoTope project </td> </tr> <tr> <td> Use cases </td> <td> ☒1 School ☐2 Emergency ☐ 3 Bikes </td> <td> </td> </tr> </table> ### 3.33. Brussels Traffic signs <table> <tr> <th> Data set name or reference </th> <th> Traffic signs </th> </tr> <tr> <td> Contributor(s) </td> <td> Orange BE </td> </tr> <tr> <td> Data set description and format </td> <td> Geo localization of the traffic signs in the Brussels Capital region </td> </tr> <tr> <td> Standards (if any) </td> <td> To be determined </td> <td> </td> </tr> <tr> <td> Quality assurance (if any) </td> <td> No </td> <td> </td> </tr> <tr> <td> Access </td> <td> ☒Public access ☐Restricted access ☐Other (please describe): </td> <td> </td> </tr> <tr> <td> Archiving and preservation responsibility </td> <td> ☐Project partner ☒External party ☐Other (please describe): </td> <td> ☐bIoTope project </td> </tr> <tr> <td> Use cases </td> <td> ☒1 School ☐2 Emergency ☐ 3 Bikes </td> <td> </td> </tr> </table> ## 3.34. Brussels Fire station localization <table> <tr> <th> Data set name or reference </th> <th> Fire station localization </th> </tr> <tr> <td> Contributor(s) </td> <td> SIAMU </td> </tr> <tr> <td> Data set description and format </td> <td> Geo localization of the fire stations in the Brussels Capital region </td> </tr> <tr> <td> Standards (if any) </td> <td> GeoJson </td> </tr> <tr> <td> Quality assurance (if any) </td> <td> No </td> </tr> <tr> <td> Access </td> <td> ☒Public access ☐Restricted access ☐Other (please describe): </td> </tr> <tr> <td> Archiving and preservation responsibility </td> <td> ☐Project partner ☒External party ☐bIoTope project ☐Other (please describe): </td> </tr> <tr> <td> Use cases </td> <td> ☐1 School ☒2 Emergency ☐ 3 Bikes </td> </tr> </table> ## 3.35. Brussels Hospitals localization <table> <tr> <th> Data set name or reference </th> <th> Hospitals localization </th> </tr> <tr> <td> Contributor(s) </td> <td> SIAMU </td> </tr> <tr> <td> Data set description and format </td> <td> Geo localization of the hospitals in the Brussels Capital region </td> </tr> <tr> <td> Standards (if any) </td> <td> GeoJson </td> <td> </td> </tr> <tr> <td> Quality assurance (if any) </td> <td> No </td> <td> </td> </tr> <tr> <td> Access </td> <td> ☒Public access ☐Restricted access ☐Other (please describe): </td> <td> </td> </tr> <tr> <td> Archiving and preservation responsibility </td> <td> ☐Project partner ☒External party ☐Other (please describe): </td> <td> ☐bIoTope project </td> </tr> <tr> <td> Use cases </td> <td> ☐1 School ☒2 Emergency ☐ 3 Bikes </td> <td> </td> </tr> </table> ## 3.36. Brussels Garbage trucks localization <table> <tr> <th> Data set name or reference </th> <th> Garbage trucks localization (RealTime) </th> </tr> <tr> <td> Contributor(s) </td> <td> Cleanness Brussels </td> </tr> <tr> <td> Data set description and format </td> <td> Geo localization of garbage trucks in the Brussels Capital region </td> </tr> <tr> <td> Standards (if any) </td> <td> GeoJson </td> </tr> <tr> <td> Quality assurance (if any) </td> <td> No </td> </tr> <tr> <td> Access </td> <td> ☐Public access ☒Restricted access ☐Other (please describe): </td> </tr> <tr> <td> Archiving and preservation responsibility </td> <td> ☐Project partner ☒External party ☐bIoTope project ☐Other (please describe): </td> </tr> <tr> <td> Use cases </td> <td> ☐1 School ☒2 Emergency ☐ 3 Bikes </td> </tr> </table> ## 3.37. Brussels Extra Long Busses localization <table> <tr> <th> Data set name or reference </th> <th> Extra Long Busses localization (RealTime) </th> </tr> <tr> <td> Contributor(s) </td> <td> STIB </td> </tr> <tr> <td> Data set description and format </td> <td> Geo localization of Extra Long Busses in the Brussels Capital region </td> </tr> <tr> <td> Standards (if any) </td> <td> GeoJson </td> </tr> <tr> <td> Quality assurance (if </td> <td> No </td> </tr> <tr> <td> any) </td> <td> </td> <td> </td> </tr> <tr> <td> Access </td> <td> ☐Public access ☒Restricted access ☐Other (please describe): </td> <td> </td> </tr> <tr> <td> Archiving and preservation responsibility </td> <td> ☐Project partner ☒External party ☐Other (please describe): </td> <td> ☐bIoTope project </td> </tr> <tr> <td> Use cases </td> <td> ☐1 School ☒2 Emergency ☐ 3 Bikes </td> <td> </td> </tr> </table> ## 3.38. Brussels Reserved Traffic lanes <table> <tr> <th> Data set name or reference </th> <th> Reserved Traffic lanes </th> </tr> <tr> <td> Contributor(s) </td> <td> Brussels Mobility / STIB </td> </tr> <tr> <td> Data set description and format </td> <td> Geo localization of the reserved traffic lanes in the Brussels Capital region </td> </tr> <tr> <td> Standards (if any) </td> <td> GeoJson </td> </tr> <tr> <td> Quality assurance (if any) </td> <td> No </td> </tr> <tr> <td> Access </td> <td> ☒Public access ☐Restricted access ☐Other (please describe): </td> </tr> <tr> <td> Archiving and preservation responsibility </td> <td> ☐Project partner ☒External party ☐bIoTope project ☐Other (please describe): </td> </tr> <tr> <td> Use cases </td> <td> ☐1 School ☒2 Emergency ☐ 3 Bikes </td> </tr> </table> ## 3.39. Brussels Fire trucks localization <table> <tr> <th> Data set name or reference </th> <th> Fire trucks localization (RealTime) </th> </tr> <tr> <td> Contributor(s) </td> <td> SIAMU </td> </tr> <tr> <td> Data set description and format </td> <td> Geo localization of the fire trucks in the Brussels Capital region </td> </tr> <tr> <td> Standards (if any) </td> <td> GeoJson </td> </tr> <tr> <td> Quality assurance (if any) </td> <td> No </td> </tr> <tr> <td> Access </td> <td> ☐Public access ☒Restricted access ☐Other (please describe): </td> </tr> <tr> <td> Archiving and preservation </td> <td> ☐Project partner ☒External party ☐bIoTope project </td> </tr> <tr> <td> responsibility </td> <td> ☐Other (please describe): </td> </tr> <tr> <td> Use cases </td> <td> ☐1 School ☒2 Emergency ☐ 3 Bikes </td> </tr> </table> ## 3.40. Brussels Hydrants <table> <tr> <th> Data set name or reference </th> <th> Hydrants </th> </tr> <tr> <td> Contributor(s) </td> <td> SIAMU </td> </tr> <tr> <td> Data set description and format </td> <td> Geo localization of the hydrants in the Brussels Capital region </td> </tr> <tr> <td> Standards (if any) </td> <td> GeoJson </td> </tr> <tr> <td> Quality assurance (if any) </td> <td> No </td> </tr> <tr> <td> Access </td> <td> ☐Public access ☒Restricted access ☐Other (please describe): </td> </tr> <tr> <td> Archiving and preservation responsibility </td> <td> ☐Project partner ☒External party ☐bIoTope project ☐Other (please describe): </td> </tr> <tr> <td> Use cases </td> <td> ☐1 School ☒2 Emergency ☐ 3 Bikes </td> </tr> </table> ## 3.41. Helsinki KNX data <table> <tr> <th> Data set name or reference </th> <th> KNX data </th> </tr> <tr> <td> Contributor(s) </td> <td> The facility management company of Fiskars and Fregatti buidings in Kalasatama, the residents, and the service providers (companies ABB, Helen) </td> </tr> <tr> <td> Data set description and format </td> <td> Water, electricity, heating consumption data from the apartments. Formats : not known yet </td> </tr> <tr> <td> Standards (if any) </td> <td> KNX </td> </tr> <tr> <td> Quality assurance (if any) </td> <td> No </td> </tr> <tr> <td> Access </td> <td> ☐Public access ☒Restricted access ☐Other (please describe): </td> </tr> <tr> <td> Archiving and preservation responsibility </td> <td> ☐Project partner ☒External party ☐bIoTope project ☐Other (please describe): </td> </tr> </table> ## 3.42. Helsinki Presence data <table> <tr> <th> Data set name or reference </th> <th> Presence data </th> </tr> <tr> <td> Contributor(s) </td> <td> Residents of Fiskary, Fregatti houses </td> </tr> <tr> <td> Data set description and format </td> <td> Data to identify if the person is at home or not, using some personalised service or combination of them. For example, GPS location of mobile phone, personal calendar, home away button. Format : to be decided </td> </tr> <tr> <td> Standards (if any) </td> <td> No </td> </tr> <tr> <td> Quality assurance (if any) </td> <td> No </td> </tr> <tr> <td> Access </td> <td> ☐Public access ☒Restricted access ☐Other (please describe): </td> </tr> <tr> <td> Archiving and preservation responsibility </td> <td> ☐Project partner ☒External party ☐bIoTope project ☐Other (please describe): </td> </tr> </table> ## 3.43. Open Charging Station Vocabulary <table> <tr> <th> Data set name or reference </th> <th> Open Charging Station Vocabulary (MobiVoc) \- planned </th> </tr> <tr> <td> Contributor(s) </td> <td> eccenca, Fraunhofer IAI, University of Bonn and everybody who is interested </td> </tr> <tr> <td> Data set description and format </td> <td> A linked open vocabulary covering charging stations services for e-Mobility will be created. RDF. </td> </tr> <tr> <td> Standards (if any) </td> <td> Semantic Web Standards (W3C) </td> </tr> <tr> <td> Quality assurance (if any) </td> <td> No </td> </tr> <tr> <td> Access </td> <td> ☒Public access ☐Restricted access ☐Other (please describe): </td> </tr> <tr> <td> Archiving and preservation responsibility </td> <td> ☐Project partner ☐External party ☐bIoTope project ☒Other (please describe): not decided yet </td> </tr> </table> # Conclusion This DMP provides an overview of the data that bIoTope project will produce or adopt together with related challenges and constraints that need to be taken into consideration. The analysis contained in this report supports the procedures and infrastructures to be implemented by the bIoTope project to efficiently manage the existing data that will be used, as well as data that will be produced within the project. It is too early in the project to have a complete identification of the data sets that will be used or created by the project as the initial user requirements have only just been completed and substantial design of the bIoTope technologies and identification of required data sets is underway. Some of the data that will need to be collected is not sufficiently clear to be detailed with the required level of specification to be included in this preliminary plan, and others will be identified later in the project. This first version of the DMP should therefore be considered an initial view that will be updated periodically as the project tasks progress. By project completion, many bIoTope project partners will be owners or producers of relevant data, in particular those associated with the large scale pilots. This implies specific responsibilities and this initial version of the DMP is intended to create awareness among the project partners regarding the importance of appropriate procedures with respect to collection, publication in the case of open access data sets, and use of metadata to increase the value of data, as well as persistence of all the information necessary for the optimal use and reuse of bIoTope related data sets to support the bIoTope ecosystems. Specific attention will be given to ensuring that the data made public breaks neither partner IPR rules, nor regulations and good practices related to personal data protection. For this latter point, procedures such as systematic anonymisation of personal data should be anticipated whenever data created within the bIoTope project has potential for misuse or disclosure of personally identifiable information unless specific security measures have been taken.	https://phaidra.univie.ac.at/o:1140797	Horizon 2020
0083_BRAIN-IoT_780089.md	Introduction </th> </tr> </table> The purpose of this document is to present the initial Data Management Plan (DMP) of the BRAIN-IoT project and to provide the guidelines for maintaining the DMP during the project. The Data Management Plan methodology approach adopted for the compilation of D6.1 has been based on the updated version of the “Guidelines on FAIR Data Management in Horizon 2020 version 3.0 released on 26 July 2016 by the European Commission Directorate – General for Research & Innovation” [1]. It defines how data in general and research data in particular will be handled during the research project and will make suggestions for the after-project time. It describes what data will be collected, processed or generated within the scope of the project, what methodologies and standards shall be followed during the collection process, whether and how these data shall be shared and/or made open for the evaluation needs, and how they shall be curated and preserved. All BRAIN-IoT data will be handled according to EU Data protection and Privacy regulation and the General Data Protection Regulation (GDPR) [2]. The BRAIN-IoT DMP addresses the following issues: * Data Summary * FAIR data * Making data findable, including provisions for metadata * Making data openly accessible * Making data interoperable * Increase data re-use * Allocation of resources * Data security * Ethical aspects * Other issues According to EU’s guidelines regarding the DMP, the document will be updated – whenever the Project Board considers it necessary to be updated - during the project lifetime (in the form of deliverables). BRAIN-IoT will be deployed in two pilot sites in Coruna and Valencia, Spain, with the aim to be replicated in several other places and domains. More specifically, BRAIN-IoT is envisaged to be evaluated also within the context of use cases identified in running Large Scale Pilot (LSP). Currently (M5 of the project), the exact definition, deployment and usage of BRAIN-IoT functionalities are not yet completely defined. Therefore, we will need to update the DMP with the data that is being collected/created at each pilot site according to their usage and whether they can be published as Open Data. # 1.1 Scope This document is generated by WP6 ”Test, Demonstration and Evaluation”, and more specifically by task T6.1 ”Integration and Lab-scale Evaluation”. The scope of the DMP is to describe the data management life cycle for all data sets to be collected, processed or generated in all Work Packages during the 36 months of the Brain-IoT project. FAIR Data Management is highly promoted by the Commission and since Brain-IoT deals with several kinds of data, relevant attention has been given to this task. However, the Data Management Plan is going to be updated throughout the course of the project and more specifically, extended information on data and data management will be included in the upcoming deliverables D6.3 – “ _Phase 1 Integration and Evaluation Framework_ ”, due on M16, and D6.5 - “ _Phase 2 Integration and Evaluation Framework”_ , due on M28. # 1.2 Methodology The DMP [1] concerns all the data sets that will be collected, processed and/or generated, shared, and deleted when not needed anymore, within the project. The methodology the consortium follows to create and maintain the project DMP is hereafter outlined: 1. Create a data management policy. 1. Using the elements that the EC guidelines [1] proposes to address for each data set. 2. Adding the strategy that the consortium uses to address each of the elements. 2. Create a DMP template that will be used in the project for each of the collected data sets, see Section 5 - DMP dataset description template. 3. Creating and maintaining DMPs 1. If a data set is collected, processed and/or generated within a work package, a DMP should be filled in. For instance, training data sets, example collections etc. 2. For each of the pilots, when it is known which data will be collected, the DMP for that pilot should be filled in. 4. The filled DMPs should be added to the upcoming D6.3 and D6.5, describing which data are collected within the project as well as how it is managed. 5. Towards the end of the project, an assessment will be made about which data is valuable to be kept as Open Data after the end of the project. 1. For the data that is considered to be valuable an assessment of how the data can be maintained and the cost involved will be made. The Consortium will also evaluate the possibility to share data, or a subset, under an Open Data Commons Open Database License (ODbL). The deliverable is organized as following: Chapter 2 outlines a data overview in the BRAIN-IoT project. It details BRAIN-IoT data categories, data types and metadata. Chapter 3 outlines the data management policy in BRAIN-IoT about dataset naming and collection, giving also an insight about the Open Research Data Pilot under H2020 guidelines and FAIR Data principle, as well as how to achieve it. Chapter 4 presents the identified approach to be used in order to describe the set of data generated and collected by the project. # 1.3 Related documents <table> <tr> <th> ID </th> <th> Title </th> <th> Reference </th> <th> Version </th> <th> Date </th> </tr> <tr> <td> DoA </td> <td> Description of Action/ Grant Agreement </td> <td> ISMB </td> <td> 1.0 </td> <td> 2017-10-09 </td> </tr> <tr> <td> D1.1 </td> <td> Project Handbook, Quality & Risk Management Plan </td> <td> IM </td> <td> 1.1 </td> <td> 2018-02-19 </td> </tr> <tr> <td> D2.1 </td> <td> Initial Visions, Scenarios and Use Cases </td> <td> UGA </td> <td> 1.0 </td> <td> 2018-06-23 </td> </tr> </table> <table> <tr> <th> 2 </th> <th> Data Management and the GDPR </th> </tr> </table> The EU General Data Protection Regulation (GDPR) brings revolutionary changes to European data protection laws. Some principles found from the GDPR are defined to correspond to both the technological developments happened in recent years, and to better answer the requirements for privacy protection in the digitized world of today and tomorrow. The principles relating to the personal data management are set out in GDPR’s Article 5(1) * Lawfulness, fairness and transparency: personal data shall be processed lawfully, fairly and in a transparent manner in relation to the data subject. To be more transparent while managing and processing data, making privacy policies more user friendly and promoting the rights of users could be considered. * Purpose limitation: personal data shall be collected for specified, explicit and legitimate purposes and not further processed in a manner that is incompatible with those purposes. * Data minimization: personal data shall be adequate, relevant and limited to what is necessary in relation to the purposes for which they are processed. Considering the purpose, only necessary data is managed and processed. Data minimization is strongly related to purpose limitation, since enough data should be collected to achieve the purpose, but only the strictly amount needed. * Accuracy: personal data shall be accurate and, where necessary, kept up to date. The erasure or rectification of inaccurate personal data must be implemented without delay. * Storage limitation: personal data shall be kept 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. * Integrity and confidentiality: personal data shall be processed in a manner that ensures appropriate security of the personal data, including protection against unauthorized or unlawful processing and against accidental loss, destruction or damage, using appropriate technical or organizational measures. Article 5(2) provides for the perhaps most important principle of all: the principle of accountability, which sets an obligation on data controllers to be responsible for and to be able to demonstrate compliance with the GDPR. It complements the GDPR’s transparency requirements; data controllers must not only comply with the GDPR but must also be able to demonstrate it by e.g. documenting their decisions while managing and processing data. In May 2018 the GDPR has been officially released. This means all partners within the consortium have to follow the new rules and principles. The novelty of the new regulamentation implies the consortium tools and partner specific guidelines for data management are not yet fully available. This chapter addresses how the founding principles of the GDPR will be followed in the BRAIN-IoT project. # 2.1 Lawfulness, fairness and transparency BRAIN-IoT project describes all handling of personal data in its Data Management Plan. Some of the answers requested cannot be provided at the moment of writing this report. Therefore, updates to the plan will be provided in the next deliverables. Meanwhile, the project Wiki tool (see D1.1 – “ _Project Handbook, Quality & Risk Management Plan _ ”), used as a logger of all the ongoing activities related to the project, will be used as a working tool to log also information related to DMP and will be updated accordingly as soon as new information about data sets become available. The collected information will be lately afterwards officially reported within upcoming deliverables D6.3 – “ _Phase 1 Integration and Evaluation Framework_ ”, due on M16, and D6.5 - “ _Phase 2 Integration and Evaluation Framework”_ , due on M28. All data gathering from individuals will require informed consent of the subjects who are engaged in the project. Informed consent requests will consist of an information letter and a consent form. This will state the specific causes for the experiment, or other activity, how the data will be handled, stored, and shared. The request will also inform the subjects of their rights to have data updated or removed, and the project’s policies on how these rights are managed. As far as possible, BRAIN-IoT project will anonymise the personal data. Whenever considered necessary, further consent will be asked to use the data for open research purposes, this includes presentations at conferences, publications in journals as well as depositing a data set in an open repository at the end of the project. The consortium tries to be as transparent as possible in their collection of personal data: while collecting data, information leaflet and consent form will describe the kind of information, the manner in which it will be collected and processed, if, how, and for which purpose it will be disseminated and if and how it will be made open access. Finally, the subjects will have the possibility to request what kind of information has been stored about them and they can request to be removed from the results. # 2.2 Purpose limitation BRAIN-IoT project will not collect any data that is outside the scope of the project. Each partner will only collect data which is needed within the scope of their specific work package. # 2.3 Data minimisation BRAIN-IoT will collect only data that is relevant for the project’s research questions and demonstration. However, while testing the system in an environment including the interaction with human beings, it could be possible to collect indirect data related to the personal behaviours of the involved individuals. Since this data can be highly personal, it will be treated according to all guidelines on personal data and won’t be shared without anonymization or explicit consent of the involved persons. # 2.4 Accuracy All data collected will be checked for consistency. Since all data is gathered within a specific timeframe, we chose not to keep the data up to date, since it would hinder our research. However, we will try to capture the data as accurately as possible, for example “warehouse map” could be stored as “warehouse map in June 2018”. This will remove the necessity of keeping this information up to date. # 2.5 Storage limitation All data that will no longer be used for research purposes will be deleted as soon as possible. All personal data or data that can be reconducted to personal information or behaviours will be made anonymous as soon as possible. At the end of the project, if the data has been anonymised, the data set could be considered to be released as open dataset. If data cannot be made anonymous, it will be pseudonymised as much as possible and stored according the archiving rules of the partner institutions who was responsible for the management of the specific data to be stored. # 2.6 Integrity and confidentiality All personal data will be handled with appropriate security measures applied. Each partner who is responsible for the management of specific data will store or share data through means and channels that comply with the GDPR. # 2.7 Accountability Within the scope of the project, the project and quality management is responsible for the correct data management within the project. Whether the partners follow the GDPR principles will be regularly checked during the project the project lifetime. For each data set, a responsible person has been appointed at partner level, who will be held accountable for the specific data set. <table> <tr> <th> 3 </th> <th> Data in BRAIN-IoT: an Overview </th> </tr> </table> BRAIN-IoT project will deal with a large amount of raw data to measure the benefit of IoT and federation of IoT platforms within the two selected scenarios, i.e. Service Robotics and Water Critical Infrastructure Management, and also in other scenarios to be selected from the ones identified in Large Scale Pilot (LSP) projects, i.e. AUTOPILOT, MONICA, ACTIVAGE, IoF2020 and SynchroniCity. From raw data, a large amount of derived data can be produced to address multiple research needs and enable Smart Behaviours. Some processing, such as cleaning, verification, conversion, aggregation, summarization or reduction could also be applied to raw data according to specific needs derived from the use cases. In any case, data must be well documented in order to facilitate and foster sharing, to enable validity assessments and to enable its usage in an efficient way. Thus, each data must be described using additional information called metadata. The latter must provide information about the data source, the data transformation and the conditions in which the data has been produced. # 3.1 Data sets Categories The BRAIN-IoT project will produce different categories of data sets: * _Context data_ : data that describe the context of an experiment. * _Acquired and derived data_ : data that contain all the collected information related to an experiment. * _Aggregated data_ : data summary obtained by reduction of acquired data and generally used for data analysis. ## 3.1.1 Context Data Context data is any information that helps to explain observation during a measurement campaign. Context data can be collected, generated or retrieved from existing data. For example, it contains information such as presence of humans or presence of obstacles in the robot-path, quality of water, etc. ## 3.1.2 Acquired and Derived Data Acquired data are all data collected during the course of the study for the sole purpose of the analysis. . Derived data is created by different type of transformation including data fusion, filtering, and classification. Derived data are usually required according to specific needs from the use cases. Derived data may contain derived measures and performance indicators referring to a time period when specific conditions are met. This category includes measures from sensors coming from robotic platforms or IoT devices and subjective data collected from either the users or the environment. The following list outlines the data types and sources that will be collected: Service Robotics: The service robotics scenario identified three different interactions of robots with external world (see D2.1): * Robot-thing interaction (e.g. robot needs of crossing door or using lifts, interactions with conveyor belts etc.) * Robot-environment interaction in order to have environment context information (e.g. alarm system failure/errors, obstacles or humans in the way, detection of beacons etc.) * Robot-robot interaction to enable self-organization and collaborative features (such as map generation and shared resources) These use cases allow to roughly outline an initial set of types of data to be dealt with: * robots involved in the scenario will be endowed with capabilities to scan and navigate the entire area of the warehouse and share the acquired information to update the knowledge base (e.g., map) on the go. * robots will also be used for collecting additional information implicitly e.g. room temperature, presence of humans, also paying special attention to their privacy and any image recorded during operation, detection of in-path obstacles, other IoT devices etc. * robots can also collect context information such as the presence of alarm system, layout, number of items to interact with, loads per day, which may also be an indicator of the performance, productivity of the factory , detection of beacons etc. Critical Water Management Infrastructure scenario: Currently a part of the systems that helps us develop the business processes are implemented in a platform called SICA. Relevant data for this scenario concern the urban water domain. More specifically, in D2.1 the following domains have been identified, which outline an initial set of types of data to be dealt with: _RESOURCE_ * Connection of a multiparameter probe to measure the water quality control parameters in reserve water (surface waters). * Connection with the gaugin station to measure the circulating flow of the river (entry and exists of the reserve). * Pluviometry and temperature. _TREATMENT_ * Headstock deposits levels. * Volume * Cl/pH levels * Pump systems at the plant. * Pump from the headstock to treatment. o Pump of treated water. _DISTRIBUTION_ * Distribution deposits levels. * Volume * Cl/pH/turbidity • Pump and repump systems. * Section control systems. * Flows o Cl/pH/turbidity * Tele-read meters: * Domestic. Control sections: ABERING platform. o Commercial. Sectors. iEcoCity platform. * Large clients. Complex multi-sensor remote systems. * Control of green zone irrigation. * Irrigation programme. Connection with automatons. o Meteorological control: rain forecast. ## 3.1.3 Aggregated data Aggregated data contains a specific part of the acquired or derived data (raw data). Its smaller size allows a simple storage in e.g. database tables and an easy usage suitable for data analysis. To obtain aggregated data, several data reduction processes are performed. The reduction process summarizes the most important aspects in the data into a list of relevant parameters or events, through one or all of the following processes: validation, curation, conversion, annotation. Aggregated data is generally created in order to answer different research question. They are supposed to be verified and cleaned, thus facilitating their usage for analysis purposes. # 3.2 Metadata This section provides the first recommendations regarding the description of the data provided by BRAIN-IoT project. As the project will collect several data categories and several data types, several metadata descriptions must be provided to describe the characteristics of each measure or component and also the origin on how the data was produced and collected. BRAIN-IoT project will follow and adapt the metadata type recommendation provided by the FOT-Net Data project ( http://fot-net.eu/ ). This project identifies in its Data Sharing Framework several metadata types that can be applied to BRAIN-IoT. The following list provides a first version of the metadata that may be managed by the project and their content in the upcoming months. A more detailed version will be provided in the upcoming deliverables D6.3 – “ _Phase 1 Integration and Evaluation Framework_ ”, due on M16, and D6.5 - “ _Phase 2 Integration and Evaluation Framework”_ , due on M28. 3.2.1 Metadata attributes of time-history data: Time-history data corresponds to the history of a measurement over the time. Time-history data can be collected by legacy instrument, by IoT devices or IoT platforms. Time-history data stores a variation over the time of single or complex physical value. To enable their re-use, each dataset provides a metadata description that includes the following descriptive attributes: * Precision (accuracy) * Unit of measure * Sample rate (frequency of the measure) * Filtering (low-pass, interpolation, etc.) * Origin (data source) * Type (Integer, Float, String) * Error codes (full description of error codes) * Quality (Quality measure related to this measure) * Enumeration specification (Defines how to convert constant to correct value, e.g.: 1 means Left, 2 means Right) • ## 3.2.2 Metadata attributes of aggregated data As aggregated data varies depending on the purpose of the experiment, it can be described as time history measures or as time segment. Time segment is a sub-set of data parameters or measures generated by data summarization or data reduction. This metadata type should include the following descriptive attributes: * Description (Purpose of the aggregated data) * Definition (Algorithm applied on the aggregated measures) * Origin (Measures used to calculate the aggregated data) * Unit (Unit of output value) ## 3.2.3 Metadata attributes of self-reported data Self-reported data corresponds to interviews, surveys or questionnaires. This metadata type should include the following descriptive attributes: * Description (Purpose of the questionnaire) * Instructions (way how the collection process was executed) * Type (Free text, single or multiple choices, etc.) * Options (description of possible alternatives) <table> <tr> <th> 4 </th> <th> BRAIN-IoT Data Management Policy </th> </tr> </table> The responsible party for creating and maintaining the DMP for a data set is the partner that creates/collects such data. If a data set is collected, processed and/or generated within a work package, a DMP should be created. Before each pilot execution, it should be clear which data set is collected/created in the pilot and how the data will be managed, i.e. the DMPs for the pilot data must be ready and accepted. This will be done individually for each of the pilots because of the difference between the pilots being in different domains and of different types of data and events. # 4.1 Naming and identification of the Data set To have a mechanism for easily identifying the different collected/generated data, we will use a naming scheme. The naming scheme for BRAIN-IoT datasets will be a simple hierarchical scheme including country, pilot, creating or collecting partner and a describing data set name. This name should be used as the identification of the data set when it is published as Open Data in different open data portals. The structure of the naming of the dataset will be as follows: BRAINIOT_{Country+Area Code or WP}_{Pilot Site or WP}_{Responsible Partner}_{Description}_{Data Set Sub Index} Figure 1: GOEASY Data Set Naming Scheme Figure 1: BRAIN-IoT Data Set Naming Scheme The parts are defined as follows: * BRAINIOT: Static for all data sets and is used for identifying the project. * Country+Area Code: The two letter ISO 3166-1 country code for the pilot where data has been collected or generated plus the numeric routing code that identifies each geographic area in the telephone numbering plan, e.g. ES96. * WP: the work package label along with the work package number, e.g., WP6. * Pilot Site: The name of the pilot site where the data was collected, without spaces with CamelCaps in case of multiple words, e.g. ServiceRobotics etc. * Responsible Partner: The partner that is responsible for managing the collected data, i.e. creates and maintains the Data Management plan for the data set. Using the acronyms from D1.1, e.g. ISMB * Description: Short name for the data set, without spaces with CamelCaps in case of multiple words, e.g., WarehouseMap, WaterPollution, etc. * Data Set Sub Index: Optional numerical index starting from 1. The purpose of the dataset sub index is that data sets created/collected at different times can be distinguished and have their individual meta data. BRAINIOT_ES96_ServiceRobotics_ROB_Warehouse_1 Figure 2: Figure 2: BRAINBRAIN--IoTIoT Data Set Naming Example Data Set Naming Example In the example shown in Figure 2, the Data set is created within BRAIN-IoT project in Valencia city, Spain, at Service Robotics pilot site. Robotnik is responsible for the relevant Data Management plan for the dataset. The dataset contains location data and it is the first of a series of data sets collected at different times. There can be situations where the data needs to be anonymised with regards to the location the data has been collected, for instance at some pilots it might not be allowed to publish people count data with the actual event location for security reasons. In these cases, the Country and Pilot Site will be replaced by string UNKNOWN when it is made available as Open Data. For data sets that are not connected to a specific pilot site the Pilot Site should be replaced with the prefix WP followed by the Work Package number that creates and maintains the Data Management plan for the dataset, e.g., WP6. The same applies to the Country part which also should be replaced with the prefix WP followed by the Work Package number in the cases where the data set is not geographically dependent, such as pure simulations or statistics. # 4.2 Data Summary / Data set description The data collected/created needs to be described including the following information: * 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) o Provide the identification of the re-used data, i.e. BRAIN-IoT identifier or pointer to external data, if possible. * Specify the origin of the data * State the expected data size (if known) * Outline the data utility: to whom will it be useful # 4.3 Fair Data FAIR data management means in general terms, that research data should be “FAIR” (Findable, Accessible, Interoperable and Re-usable). These principles precede implementation choices and do not necessarily suggest any specific technology, standard, or implementation solution. ## 4.3.1 Making data findable, including provisions for metadata This point addresses the following issues: * Outline the discoverability of data (metadata provision) * Outline the identifiability of data and refer to standard identification mechanism. * Outline the naming conventions used. * Outline the approach towards search keywords. * Outline the approach for clear versioning. * Specify standards for metadata creation (if any). As far as the metadata are concerned, the way the consortium will capture and store information should be described. For instance, for data records stored in a database with links to each item, metadata can pinpoint their description and location. There are various disciplinary metadata standards, however the BRAIN-IoT consortium has identified a number of available best practices and guidelines for working with Open Data, mostly by organisations or institutions that support and promote Open Data initiatives, and will be taken into account. These include: * FOT-Net Data project * Open Data Foundation * Open Knowledge Foundation * Open Government Standards Furthermore, data should be interoperable and compliant with respect to data annotation and data exchange. 4.3.2 Making data openly accessible The objectives of this aspect address the following issues: * Specify which data will be made openly available and, in case some data is kept closed, explain the reason why. * Specify how data will be made available. * Will the data be added to any Open Data registries? * Specify what methods or software tools are needed to access such data, if a documentation is necessary about the software and if it is possible to include the relevant software (e.g. in open source code). * Specify where data and associated metadata, documentation and code are deposited. * Data that will be considered safe in terms of privacy, and useful for release, could be made available for download under the ODbL License. * Specify how access will be provided in case there are restrictions. ## 4.3.3 Making data interoperable This aspect refers to the assessment of the data interoperability specifying which data and metadata vocabularies, standards or methodologies will be followed in order to facilitate interoperability. Moreover, it will address whether standard vocabulary will be used for all data types present in the data set in order to allow inter-disciplinary interoperability. In the framework of the BRAIN-IoT project, we will deal with many different types of data coming from very different sources, but in order to promote interoperability we use of the following guidelines: * OGC SensorThings API model for time series data [4], such as environmental readings etc. * If the data is part of a domain with well-known open formats that are in common use, this should be selected. * If the data does not fall in the previous categories, an open and easily machine-readable format should be selected. ## 4.3.4 Increase Data Re-use This aspect addresses the following issues: * Specify how the data will be licensed to permit the widest reuse possible. o Tool to help selecting license: https://www.europeandataportal.eu/en/content/show-license o If a restrictive license has been selected, explain the reasons behind it. * Specify when data will be made available for re-use. * Specify if the data produced and/or used in the project is useable by third parties, especially, after the end of the project. * Provide a data quality assurance process description, if any. * Specify the length of time for which the data will remain re-usable. In order to maximize the reusability of data, ODbL licence could be considered in some cases as a good candidate to distribute datasets. ODbL allows to: * to copy, distribute and use the database; * to produce works from the database; * to modify, transform and build upon the database; as long as you: * must attribute any public use of the database, or works produced from the database, in the manner specified in the ODbL. For any use or redistribution of the database, or works produced from it, you must make clear to others the license of the database and keep intact any notices on the original database. * publicly use any adapted version of this database, or works produced from an adapted database, you must also offer that adapted database under the ODbL. * redistribute the database, or an adapted version of it, then you may use technological measures that restrict the work (such as DRM) as long as you also redistribute a version without such measures. # 4.4 Allocation of Resources This aspect addresses the following issues: * Estimate the costs for making the data FAIR and describe the method of covering these costs. o This includes, if applicable, the cost for anonymising data. * Identify responsibilities for data management in the project. * Describe costs and potential value of long-term preservation. # 4.5 Data security and Privacy Based on the self-assessment performed by the BRAIN-IoT consortium, no major ethics issues are foreseen to be relevant for project activities. Nevertheless, the consortium recognizes the potential risks that the deployment of IoT technology developed in BRAIN IoT could generate. In fact, the project has a dedicated WP i.e. WP5 “End-to-end Security, Privacy and Trust Enablers” that is specifically conceived to mitigate these risks and focuses on: * Threat Modelling and Assessment (Task 5.1); * Decentralized Authorization, Authentication and Trust (Task 5.2); * Privacy awareness and control (Task 5.3); * End-to-end data security and provenance (Task 5.4). The project consortium is committed to conducting responsible research and innovation and will respect careful experimentation methodologies whenever end users are present in experimentations: * end users will get a complete briefing on the project, the experimentation and any potential risks as part of their training. * the project will ensure that any end user involved understands and consent to the experiment. In addition to the above approach that will be adopted during the project implementation and beyond, BRAINIoT has realized at proposal stage an ethic self-assessment of risks and identified two main points that can be of concerns: * the involvements of end users in the experiments run on the test-sites. * the potential collection and handling of personal data. Such evaluation has also been performed taking into consideration possible links of BRAIN-IoT project to IoT Large Scale Pilots. However, it is worth observing that, in those cases, BRAIN-IoT will act as technology solution provider and will not take care of the data and user involvement aspects (that will remain within the scope of the Large Scale Pilots projects). In the following, rules defined for handling data are presented. More specifically, the data collected will be treated as confidential and security processes and techniques will be applied to ensure their confidentiality. Overall the following general principles will be used regarding any data collection: * Transparency of usage of the data: User – data subject in the European Union (EU) parlance - shall give explicit consent of usage of data. * Collected Data shall be adequate, relevant and not excessive: The data shall be collected on “need to know” principle. This principle is also known as “Data Minimization”. The principle also helps to setup the user contract, to fulfil the data storage regulation and enhance the “Trust” paradigm. * Collector shall use data for explicit purpose: Data shall be collected for legitimate reasons and shall be deleted (or anonymize) as soon as data is no longer relevant. * Collector shall protect data at communication level: The Integrity of the information is important because modification of received information could have serious consequence for the overall system availability. User has accepted to disclose information to a specific system, not all the systems. The required level of protection depends on the data to be protected according the cost of the protection and the consequence of data disclosure to unauthorized systems. * Collector shall protect collected data at data storage: User has accepted to disclose information to a specific system, not all the systems. It also could be mandatory to get infrastructure certification. The required level of protection depends on the data to be protected according the cost of the protection and the consequence of data disclosure to unauthorized systems. As example, user financial information can be used to perform automatic billing. Such data shall be carefully protected. Security keys at device side and server side are very exposed and shall be properly protected against hardware attacks. * Collector shall allow user to access / remove Personal Data: Personal Data may be considered as a property of the user. User shall be able to verify correctness of the data and ask – if necessary – correction. Dynamic Personal Data – for instance home electricity consumption – shall also be available to the user for consultation. For static user identity, this principle is simply the application of current European regulations according access to user profile. # 4.6 Ethical aspects Some of the most mature tests and demonstrations of the project will be run in “live” environment (city) where ordinary citizens are present. The development of new human behaviours is an important impact of ICT that can be felt by end users as positive, neutral or negative. It is indeed a task of the project to perform a user-center evaluation of the BRAINIoT solution (task 6.2). An additional task that could be possibly performed beyond the duration of the project, aims to ensure that end user involvement is done in condition as good as possible for the end users. This will involve the following activities: * Engaging with end user only on an informed way: making sure they are aware of the presence of experiments and that relevant documentation, in understandable format (language and avoidance of technical jargon) is available. * Gathering end user consent as a prerequisite for interaction and any data collection * Providing a complaint procedure with a neutral third party * Ensuring that end-users are free to refuse the experiment at any moment, including after it is started, without any prejudice or disadvantage. # 4.7 Other issues Other issues will refer to other national/ funder/ sectorial/ departmental procedures for data management that are used. <table> <tr> <th> 5 </th> <th> DMP dataset description template </th> </tr> </table> During the course of the project, each work package will analyse which DMP components are relevant for its activities. When the pilots definitions will be ready with regards to which data is collected and how data is used, DMPs for the pilots need to be created. This table is a template that shall be used to describe the datasets. Table 1: BRAIN-IoT Template for DMP <table> <tr> <th> DMP Element Issues to be addressed </th> </tr> <tr> <td> Identifier </td> <td> Brain-IoT_WPX_TX.X_{Responsible Partner}_{Description}_{Data Set Sub Index } </td> </tr> <tr> <td> Revision History </td> <td> Partner Name Description of change ISMB Xu Tao Created initial DMP </td> </tr> <tr> <td> Dataset Description </td> <td> Each data set 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> Findability </td> <td> 1\. </td> <td> Outline the discoverability of data (metadata provision). </td> </tr> <tr> <td> </td> <td> 2\. </td> <td> Outline the identifiability of data and refer to standard identification mechanism. </td> </tr> <tr> <td> </td> <td> 3\. </td> <td> Outline the naming conventions used. </td> </tr> <tr> <td> </td> <td> 4\. </td> <td> Outline the approach towards search keywords. </td> </tr> <tr> <td> </td> <td> 5\. </td> <td> Outline the approach for clear versioning. </td> </tr> <tr> <td> </td> <td> 6\. </td> <td> Specify standards for metadata creation (if any). </td> </tr> <tr> <td> Accessibility </td> <td> 1\. </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> 2\. </td> <td> Specify how the data will be made available. </td> </tr> <tr> <td> </td> <td> 3\. </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> 4\. </td> <td> Specify where the data and associated metadata, documentation and code are deposited. </td> </tr> <tr> <td> </td> <td> 5\. </td> <td> Specify how access will be provided in case there are any restrictions. </td> </tr> <tr> <td> Interoperability </td> <td> 1\. </td> <td> Assess the interoperability of your data. Specify what data and metadata vocabularies, standards or methodologies you will follow to facilitate interoperability. </td> </tr> <tr> <td> </td> <td> 2\. </td> <td> 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> Reusability </td> <td> 1\. </td> <td> Specify how the data will be licenced to permit the widest reuse possible. </td> </tr> <tr> <td> </td> <td> 2\. </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> 3\. </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> 4\. </td> <td> Describe data quality assurance processes </td> </tr> <tr> <td> </td> <td> 5\. </td> <td> Specify the length of time for which the data will remain re-usable. </td> </tr> <tr> <td> Data Sharing </td> <td> 1\. </td> <td> Explanation of the sharing policies related to the data set between the next options: </td> </tr> <tr> <td> </td> <td> 2\. </td> <td> Open: Open for public disposal </td> </tr> <tr> <td> </td> <td> 3\. </td> <td> 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> </td> <td> 4\. </td> <td> Each data set must have its distribution license. </td> </tr> <tr> <td> </td> <td> 5\. </td> <td> Provide information about personal data and mention if the data is anonymized or not. Tell if the dataset entails personal data and how this issue is taken into account. </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 …) </td> </tr> </table> <table> <tr> <th> 6 </th> <th> Resource allocation </th> </tr> </table> Costs for establishing and maintaining the HBM4EU data repository are covered by the financial budget of BRAIN-IoT. While the repository in itself is not maintained after the end of the project, all files stored within the BRAIN-IoT repository shall be stored after the project to meet the requirements of good scientific practice. A strategy for storage of the files after the project is being developed and will be included in the DMP later. The responsibility for data management during and after the end of the project is up to the owners of the scenarios which are also the providers of the data sources and the organizations who are mainly interested to the semantic value of the data itself. <table> <tr> <th> 7 </th> <th> Conclusions </th> </tr> </table> This deliverable provides a planning overview of the data that BRAIN-IoT project is going to deal with, together with related data processes and requirements that need to be taken into consideration. The descriptions of the data sets will be incrementally enriched along the project lifetime. These descriptions include a detailed description, standards, methodologies, sharing and storage methods. The Data Management Plan has been outlined within this deliverable and is going to be updated and further detailed in the upcoming deliverables D6.3 – “ _Phase 1 Integration and Evaluation Framework_ ”, due on M16, and D6.5 - “ _Phase 2 Integration and Evaluation Framework”_ , due on M28.	https://phaidra.univie.ac.at/o:1140797	Horizon 2020
0084_HRadio_761813.md	EXECUTIVE SUMMARY This deliverable is focusing on the data management planning within the HRADIO project. To create the data management plan, we applied the ‘Guidelines on FAIR Data Management in Horizon 2020 1 , which helps to make the research data findable, accessible, interoperable and reusable. In this deliverable we will discuss the different types of data that will be gathered, the processing and storing of the data and the data handling principles. Data within the HRADIO project will be gathered for 3 objectives, namely technical integration, service harmonization and user engagement. Personal, technological and documents repository data will be collected. These data will be handled as partner specific data, consortium confidential data or open data. This is a living document, meaning that the document will be updated throughout the project’s lifetime, when new data is gathered or when specific issues concerning data management arise. In this first version, the general principles (FAIR) for the data management will be discussed in section 2, followed by the allocation of sources (section 3), data security (section 4) and ethical aspects (section 5). In addition to this deliverable, deliverable 7.1 about the ethical requirements is submitted. In the latter, the ethical clearance took place through the Vrije Universiteit Brussel Humanities Ethical Board. In addition, an independent ethical expert is appointed. # DATA SUMMARY In this chapter, the purpose of data collection and the related objectives are outlined. Also, the different types and formats of the gathered data are discussed. Furthermore, the reuse of existing data is explained. Finally, the origin, expected size of the data and the data utility are cited. ## PURPOSE OF THE DATA COLLECTION AND GENERATION In the HRADIO study, the purpose of the data collection is related to three main objectives for radio in the digital era as mentioned in the Grant Agreement (page 4-5, part B) [1]: * Technical integration: Today’s radio devices, although providing different reception technologies, lack integration. It’s often up to the listener to make the decision which technology currently will deliver the best and most cost-effective user experience. Application developers for mobile platforms need to be enabled to gain a comfortable access to embedded tuner hardware in order to integrate broadcast and broadband seamlessly into the applications. * Service harmonization: The permanent availability of return channels paired with the versatility of applications on mobile devices, forces broadcasters into a competition with sophisticated services such as music streaming, on-demand content and general information services. In order not to be perceived as the “old” radio service, broadcasters must combine their traditional linear services together with their IP-based on-demand content in order to provide an integrated service for the listener, which matches the expectations of end-users. * User engagement: Radio applications on mobile platforms enable broadcasters to get in direct contact with their listeners and increase audience engagement. Besides enabling more interactive features, such as personalization, targeted advertising, games and voting. This also opens up the possibility of measuring exactly the number of people currently listening to the program and analyzing their behavior, as each stream is sent out individually to the end user.” The collected data within the project will be linked to these three main objectives. To create a service harmonization and enhance user engagement, personal data from radio audiences will need to be collected, as well as specific feedback on user behavior, user experiences and technical performance of the system. The specific types of data we will collect within the HRADIO project is discussed in the next section. ## TYPES AND FORMATS OF DATA GENERATED/COLLECTED This section gives a description of the different data types we will collect during the HRADIO project, how open the data will be, and in which format the data can appear. A summary table is given, followed by a detailed explanation of data types, data handling and data formats. Table 1 - Data types and formats <table> <tr> <th> Data types </th> <th> Data openness </th> <th> Data formats </th> </tr> <tr> <td> Personal data: * Personal profile data * Sensitive personal data * Behavioral data </td> <td> Partner specific datasets </td> <td> Textual, numerical, audio, pictures, video </td> </tr> <tr> <td> Technical data </td> <td> Consortium confidential datasets </td> <td> Logging data, user statistics,... </td> </tr> <tr> <td> Other datasets </td> <td> Open datasets </td> <td> Logos, templates, audit documents, meeting, reports,,... </td> </tr> </table> ### Data types #### Personal data Three specific types of personal data will be gathered within the HRADIO project. * The first one is _personal profile data . _ This contains sociodemographic data (for example: first name, surname, sex, place of birth, phone-number, email address, profession,...), radio usage data (user patterns via logging data or self-reported behavior), user feedback (in oral or written form, in response to interview and/or survey questions), pictures or videos (taken during workshops or by respondents (for example where they listen to radio). Contact details (physical address, phone number, email address) are only used to contact the participant for the research activity. These data will not be shared with other project partners who are not directly involved in the research activity. The data will also not be saved, nor distributed outside the project. Registration of participants to mailing lists will only happen when they give their explicit permission to do so. All users are ensured to be anonymized when used in project reports. * A second specific type of data is _sensitive personal data _ . This type of dataset will be gathered when voice control is used as a function of the hybrid radio. This type of data will never be open data, as it can contain data about sexual life, political opinions, mental health,... Therefore, sensitive data will be treated in the same way as personal profile data. * A third type of collected data is _behavioral data _ . This includes user patterns of the HRADIO services, for example how long the radio user is listening to a radio program. These data will also be anonymized before being made publicly available. #### Technical data Within the HRADIO project, also technical data will be gathered, such as access logs for the quality assurance of the technical solution, user logging data etc. Depending on the specific nature of the data that is gathered, the data will be consortium confidential or open. The nature of the technical data will become clear with the development of the pilots. #### Other Datasets: documents repository The repository of documents is used for the purpose to share and store all documentation related to the execution of the project. The repository within the HRADIO project for the consortium partners is imec’s MyMinds communication platform to store and share all project related documents (public, consortium confidential and partner specific). For public documents, we will also use the Zenodo repository as well as the project website (hradio.eu) to allow open access. In what follows we describe the different types of documents that can be archived in the repository: * User feedback (interview transcripts, survey responses etc.). These data will be made public after anonymization of the data. * Video and images of workshops and other research activities. Pictures will only be used after informed consent of the respondents. Videos of the workshops will not be made public, as these will not be anonymized. * Deliverables : MyMinds will contain the repository of deliverables of the project available to all the partners. It includes public as well as confidential deliverables. Relevant public deliverables are also published on the website. * Reports : reports that are public will be made available for third parties. * Meeting documents : will contain documents presented or generated during plenary meetings, work package meetings and conference calls (such as meeting minutes and supporting materials). Depending on the specific meeting topic, meeting minutes can be made public outside the consortium. * Audit documents : material exchanged among partners for audits preparation. * Work packages documents : the documentation, specific to that WP will be accessible for all the partners including activities description, APs and others. * Templates and logos : in this category reference documents and guides to generate standard documents for the project have been uploaded as well as logos and visual materials for media dissemination. * Dissemination materials : papers, articles, blogs resulting from the project tasks,.. This dissemination material will also be made available on the project website. ### Data openness There are different ways of data handling, depending on the specific type of data (personal, sensitive, behavioral, technical data or other data). A of 33 distinction has to be made between partner specific, consortium confidential, open and other datasets, as not all the data can be made public, and the consortium is simultaneously bound to regulations stemming from a) EC open access policies, b) EC and national data protection regulations, c) Grant Agreement stipulations and d) Consortium Agreement stipulations. #### Partner specific datasets Partner specific datasets cannot be shared with other partners of the consortium. These data are generated by one specific partner, for example: e-mail addresses or other personal data of respondents that engage in research activities. These data will only be used by the partner that generated these data and are thus confidential data. #### Consortium confidential datasets These datasets cannot be made public because of intellectual property or data privacy constraints, but these datasets can be shared among the consortium members for research purposes. Examples of consortium confidential datasets are user patterns or user behavior that cannot be completely anonymized, or business intelligence gathered for innovation management purposes. #### Open datasets Open and other datasets will be made public as much as possible, to the extent possible, on the condition that these data are anonymized so there will be no recognizable patterns. Examples of openly available data are logos, station names, descriptions and bearers, dissemination material etc. Most of the open datasets can be reused but these data will be anonymized and thus could not be linked to specific participants. This means third parties have the opportunity to freely use these data as the data will be available to everyone. ### Data formats The formats of these gathered data will either be textual (i.e. questionnaires, transcriptions,...), numerical (access logs, logging data,..) audio or pictures/videos. In the table below, we indicate if the data is confidential or public, where the data will be shared and if the data is personal or technical data. Table 2 - Data confidentiality and sharing location <table> <tr> <th> Consortium confidential (C), partner specific (PS) or public (P) data </th> <th> MyMinds </th> <th> Project Zenodo website </th> </tr> <tr> <td> Personal profile data </td> <td> PS </td> <td> </td> <td> </td> <td> </td> </tr> <tr> <td> Sensitive personal data </td> <td> PS </td> <td> </td> <td> </td> <td> </td> </tr> <tr> <td> Behavioral data </td> <td> PS </td> <td> </td> <td> </td> <td> </td> </tr> <tr> <td> Technical data </td> <td> C or P </td> <td> x </td> <td> </td> <td> </td> </tr> <tr> <td> Dissemination materials </td> <td> P </td> <td> x </td> <td> x </td> <td> x </td> </tr> <tr> <td> Deliverables </td> <td> C or P </td> <td> x </td> <td> x </td> <td> x (only P) </td> </tr> <tr> <td> Reports </td> <td> C or P </td> <td> x </td> <td> x (only P) </td> <td> x (only P) </td> </tr> <tr> <td> Meeting documents </td> <td> C or P </td> <td> x </td> <td> </td> <td> </td> </tr> <tr> <td> Templates and logos </td> <td> P </td> <td> x </td> <td> </td> <td> x </td> </tr> <tr> <td> Audit documents </td> <td> C </td> <td> x </td> <td> </td> <td> </td> </tr> <tr> <td> Workpackages documents </td> <td> C </td> <td> x </td> <td> </td> <td> </td> </tr> <tr> <td> User feedback </td> <td> P (after anonymization) </td> <td> x </td> <td> </td> <td> x </td> </tr> <tr> <td> Video and images </td> <td> C or P (after consent) </td> <td> x </td> <td> </td> <td> x </td> </tr> </table> ## REUSE OF EXISTING DATA For piloting activities, it is possible that existing profile data is used. For the Belgian pilot, Digimeter 2 profiles are used to sample and recruit potential participants for the pilot activities. In this case, we will reuse data from these panels when participants accept to participate in the HRADIO project. Reusing data from these participants is only possible when the panel manager provides the data from the participants. These data will always be considered confidential data. Also, for the German pilot, RBB can rely to some extent on approved user groups that might be recruited and complemented for the pilot activities. RBB will reuse data if participants accept to participate in the HRADIO project. Also, in this case, the personal data will be considered confidential. Also, for the technical provision of radio centric metadata such as service names, service descriptions, logos, links to web streams, podcasts or slideshow images, existing data from the broadcast partners will be reused in the HRADIO project. To assure technical interoperability, this data will be provided by applying the RadioDNS/WorldDAB set of ETSI specifications e.g. ETSI 102 818 and ETSI 103 270. These data will be public. ## ORIGIN OF THE DATA All the data gathered within the HRADIO project, will be the result of research and piloting activities. Piloting activities including workshops and user tests will take place in Belgium and Germany, as well as in the UK. Besides user feedback on the developed scenarios and HRADIO services, also technical data from the HRADIO client device will be collected and monitored (e.g. logging data). ## EXPECTED SIZE OF THE DATA (IF KNOWN) Currently the expected data size is unknown. ## DATA UTILITY Not all the data gathered within the context of the HRADIO project will be made public (see supra), but some data will be publicly available. In this case, third parties or other researchers and developers could have an insight in specific developed services and user feedback on these services. This can be useful for other research projects working on radio, for broadcasters, for radio manufacturers etc. # FAIR DATA FAIR data 3 4 stands for findable, accessible, interoperable and reusable data and is used to ensure good research data management. Below, these four key elements are discussed more in-depth. ## MAKING DATA FINDABLE, INVLUDING PROVISIONS FOR METADATA ### The discoverability of data (metadata provision) Metadata is provided within the HRADIO project to aid discovery of data in Zenodo, the platform we will use to share our public project data. ### Identifiability and locatability of data We will use the Zenodo archiving system to share all open data generated within the project. Articles, uploaded on Zenodo, will have a DOI-number. ### Used naming conventions Metadata will be used to improve discoverability of the data. We will use existing vocabularies to describe the metadata via the Data Catalogue Vocabulary (DCAT) 5 . “ _Naming conventions, once adopted, help people converge on a shared vocabulary and then make it easier to use the terms in that vocabulary_ ” [2]. ### Approach towards search keyword Undefined at this moment. ### Outline the approach for clear versioning The deliverables will have a clear version numbering, which is also indicated at the beginning of each deliverable in a version table. Each deliverable starts with version 0.1. And consequent versions are numbered 0.2 etc. The final version is version 1.0. In the table, the added content and revisions in each version is also indicated. ### Standards for metadata creation Personal and technical data produced by HRADIO and intended for publication will be modelled following linked data principles, so that the data may be augmented with metadata using dedicated vocabularies. Data formats are described in section 1.2.3. To complete these formats where needed and appropriate for data that the project will publish: 1. Provenance metadata will follow the PROV ontology 6 , a W3C standard 2. Time-related metadata will follow the Time in OWL ontology 7 , a W3C standard 3. Geolocation metadata will follow the W3C Spatial Data on the Web Best Practices 8 4\. Metadata to describe published datasets will follow the Data Catalogue Vocabulary (DCAT) 9 ## MAKING DATA OPENLY ACCESSIBLE ### Openly available data and rationale for closed data If possible, data will be made publicly available (see supra). This means that open and other datasets will be made public to a certain extent, on the condition that these data are anonymized so there will be no recognizable patterns. For example, the participant’s names can be changed in aliases or codes (i.e. P01, P02,...). Also, radio service and program metadata will be openly available as the default. Broad search statistics (number of users that searched for genre x or for keyword y) are shared across search instances in the federated search architecture. However detailed user search statistics and usage statistics are collected by search instances but kept closed since privacy should be preserved as much as possible. Some partner and consortium datasets are kept closed because of intellectual property rights and data protection. For example, broadcasters expect their stream URLs to be kept private (because mis-use of streams by unauthorized third parties, costs broadcaster’s money in wasted bandwidth bills) so for the project partners offering radio streams, the streams will only be available to authorized users. Other private data concerns personal information such as names and addresses of respondents (see supra) will be anonymized. ### Format data availability To comply with the guidelines for open data availability, we will make use of Zenodo to store and share all open project information free of charge. Also, the project website will be used as a communication tool. * The stored data will include the data, including associated metadata. * Information 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). ### Methods or software tools needed to access the data All the transcriptions of the interviews and all data files are analyzed with dedicated software, like SPSS or NVivo, which are commercial available software tools. All the files that will become publicly available will be accessible for third parties, for example by transforming the files in a .txt or .csv format. ### Deposition of data, metadata, documentation and code The data will be made available via Zenodo. ### Provision of access in case of restrictions The provision of access depends on how open the dataset is. Open data will be publicly available via Zenodo. For project specific data, most data is available via the internal project platform (MyMinds). For data that is partner specific, each partner controls the access and sharing settings. ## MAKING DATA INTEROPERABLE ### Data interoperability Data produced and stored by the metadata dissemination and communication platform will be fully interoperable. On a syntactic level, this will be ensured by using standard data description languages like XML and JSON. On a semantic level, the description of radio service and program metadata will follow the RadioDNS ETSI standard as defined in ETSI TS 103 270 (Hybrid lookup for radio services) as well as ETSI TS 102 818 (DAB EPG). Publicly available interfaces of the platform will follow well known RESTful service principles for full interoperability with external systems. ### Vocabulary for inter-disciplinary interoperability The HRADIO platform will use standard vocabularies for data description wherever they exist (eg. RadioDNS service and program description) in order to ensure interdisciplinary interoperability. The offered external interfaces are fully described, open and extensible. More specifically, the project will use a linked data approach, for instance by providing the vocabulary context to turn a JSON document into a JSONLD document. Where possible and useful, e.g. to improve public content indexing by search engines, the project will also align data with or provide mappings to the schema.org vocabulary. If - during development or after initial release - commonly used ontologies emerge, the system can be extended or changed accordingly while still ensuring backward compatibility. Other standard vocabularies used within the project, are the TVAnytime 10 specification. ## INCREASE DATA REUSE ### Data licensing to permit the widest reuse possible It has been agreed that the project results (research results, software, business models, etc.), which will be provided as open-source components will be protected under open source licenses (e.g. the EUPL, the LGPL or another open source license). LGPL is a free software license which allows developers to integrate software under the LGPL even into proprietary code 11 . ### Data embargo This does not apply. ### Reuse of data by third parties after end of project The open data will also remain available after the project duration. See section 1.2.2 for an overview of open data. ### Length of time for which the data will remain reusable In the Grant Agreement it is stated that the EU may — with the consent of the beneficiary concerned — assume ownership of results to protect them, if a beneficiary intends — up to four years after the period set out in Article 3 (see grant agreement)— to stop protecting them or not to seek an extension of protection, except in any of the following cases: 1. the protection is stopped because of a lack of potential for commercial or industrial exploitation; 2. an extension would not be justified given the circumstances. A beneficiary that intends to stop protecting results or not seek an extension must — unless any of the cases above under Points (a) or (b) applies — formally notify the Commission at least 60 days before the protection lapses or its extension is no longer possible and at the same time inform it of any reasons for refusing consent. The beneficiary may refuse consent only if it can show that its legitimate interests would suffer significant harm [3]. We will comply with these guidelines. ### Data quality assurance processes #### Personal data In response to ethical considerations, attention is given to the gathering of data by using an informed consent each time personal data is collected from respondents (see annex 1 for a draft version of an informed consent). Besides ethical considerations, all data within the project is gathered based on scientifically validated methods. Reliability and validity criteria associated with these methods are also taken into account, ensuring the data quality. #### Broadcast content For UKRP, data quality is assured for broadcasters which are members of Radioplayer, since UKRP is able to help them provide metadata at a high standard. VRT data has been broadcasted in the past, and thus already underwent a quality check in the media production process. RBB plans to use audio in the pilots, which have already undergone the normal production processes. This audio will be further adapted to ensure suitability for HRADIO. In addition to the audio, we want to use different types of metadata, ranging from SI data, to data already provided by RadioDNS, to newly processed or accumulated data. #### Technical data For the technical activities within the project, the platform defines the following data quality assurance processes: * Search index data exchanged between two instances of the federated search system is checked by the receiving system on two levels: Syntactically, only well-formed documents are accepted. Semantically, only content signed by certified federation partners is accepted and further filtered using a plausibility analysis. * Service metadata that was entered manually using the corresponding external interface is checked syntactically and semantically as well (same for service metadata coming from the RadioDNS crawler). * User statistics for the broad search mechanism are distributed amongst federation partners. Incoming data is checked syntactically and for plausibility. In addition, only signed content is accepted. * Detailed user data collected by the user statistics module is checked syntactically and filtered based on a plausibility analysis. # ALLOCATION OF RESOURCES ## COST ESTIMATION FOR MAKING YOUR DATA FAIR Making service and program metadata generated within the project openly accessible is associated with costs for purchase and maintenance of server infrastructure for search instances. Since the architecture is based on a federated approach, costs are shared across participating service providers dependent on the chosen level of engagement. In addition, the publication of different types of data will be free or at a reasonable cost, for example when Zenodo will be used. Also, different quality checks have been done and standards are used within the HRADIO project. At this moment, it is not possible yet to estimate the specific cost for making FAIR datasets, however we expect the cost to be reasonable. ## DATA MANAGEMENT RESPONSIBILITY Imec will be responsible for the overall data management in the HRADIO project, but for the data that is considered partner specific (see supra), every partner is responsible for following the guidelines agreed within the project and described in this data management plan (see section 1.2.2. Data openness). ## COSTS AND POTENTIAL VALUE OF LONG TERM PRESERVATION The project lead and work package lead decide on the opportunity of long term preservation through a discussion within the management board meeting. Data will be kept during implementation of the action and for four years after the period set out in article 3 of the Grant Agreement [4], the parties must keep confidential any data, documents or other material (in any form) that is identified as confidential at the time it is disclosed. If a beneficiary requests, the Commission may agree to keep such information confidential for an additional period beyond the initial four years (see supra). Long term preservation of service and program metadata would allow for deep analysis of radio program changes over long periods of time. For user data, longterm analysis could be as well of value for service providers and academic research but issues of data protection and data sovereignty must be considered as well. Costs depend on the chosen preservation strategy (Refreshing, Migration, Replication, Emulation, Encapsulation, ...). Other project documents, including deliverables can provide interesting benchmark material for future projects. # DATA RECOVERY, STORAGE AND TRANSFER The International Standard ISO/IEC 17799 covers data security under the topic of information security, and one of its main principles is that all stored information, i.e. data, should be “owned”, so that it is clear whose responsibility it is to protect and control access to that data, to keep it safe from corruption and involuntary disclosure outside the European Economic Area. Any evidence of natural persons’ identity and sensitive data collected during the research will be destroyed at the end of it. For all collected data, a responsible project partner is appointed that ensures the data is stored and managed in a secure way. For internal sharing of project data, we will use the password protected MyMinds platform. This system has an adequate user administration system, including individual rights assignment to project participants and back-up systems to ensure data preservation. For the open sharing of documents, we will make use of the Zenodo system, which is provided by CERN with support of the EC. In this platform, only open project data will be shared. # ETHICAL ASPECTS Users will be informed about what data is collected/shared and the purpose of the collection/sharing, so they can make an informed decision whether they consent or not. If data would be shared to third parties, this data will be anonymized. Requests for informed consents for data sharing and long term preservation are included in all research activities (e.g. interviews, user workshops, surveys,...) dealing with personal data. Within work package 7 of the HRADIO project about the ethical requirements (see deliverable 7.1), the ethical clearance took place through the Vrije Universiteit Brussel Humanities Ethical Board. In addition, an independent ethical expert is appointed. # OTHER PROCEDURES Each project partner that is responsible for piloting activities, will ensure compliance with their national data protection regulations. The project was also assessed and approved by the ethical board of Vrije Universiteit Brussel. # CONCLUSIONS In this deliverable, we have set out the data management plan for HRADIO. To start with, we provided a data summary that explained the purpose and objectives of data collection, the different types and formats of data, how we plan to reuse existing data, and the origin, expected size and utility of data that is gathered in the project. Secondly, we described how HRADIO will strive to follow the FAIR data guidelines. This document presents our provisions to make sure that HRADIO data is findable, accessible, interoperable and reusable. Next, we illustrated the allocation of resources in the HRADIO project and the arrangements that we have made in order to ensure data security. We provided insight into the ethical aspects and legal issues that can have an impact on data sharing within the project. To conclude, we explained other procedures for data management which ensure the compliance of the HRADIO consortium partners with national/funder/sectorial/departmental regulations. As stated in the executive summary, this data management plan is a living document. This means that the next step is to update the document throughout the project’s lifetime when new data is gathered or when specific issues concerning data management arise. The updates of the data management plan will be incorporated in the quarterly and periodic management reports (deliverables D1.4 and D1.5).	https://phaidra.univie.ac.at/o:1140797	Horizon 2020
0085_scMODULES_778609.md	# 1\. Introduction This document (Deliverable 3.1 or D3.1) describes the project’s Dissemination and Communication strategy and the activities carried out to reach the objectives. All data management aspects will be analysed in detail in D3.2 and D5.1 ## Scope of the document The Dissemination and Communication Plan is the core document outlining the project’s dissemination and communication activities. This plan is fundamental for a good coordination of all initiatives and also for defining the messages which should be targeted enhance the visibility of the project results. This Dissemination and Communication Plan aims concretely to: * Outline the main objectives of the dissemination actions; * Identify the target audiences for each communications objective; * Define the tools and channels to be used and the activities required to reach targeted audiences; * Identify the dissemination KPIs, useful to measure the effectiveness and efficiency of the activities conducted; * Explain how the dissemination activities will support the exploitation activity. # 2\. Communication plan: goals The communication strategy of the project seeks to achieve the following objectives: G.1. Publicize the initiative among the target audience of the project and attract participants to their pilots G.2. Publicize among museums audiences the published contents as a result of this project, to encourage its use, analyze how it is used and measure the success of the project G.3. Show the value added by the platform to participants in the project to convert them into customers, attract new clients, and position the platform among future clients and partners as the reference solution: 1. for the publication of digitized collections of museums 2. for the creation of new digital experiences based on them # 3\. How to achieve these goals To achieve these objectives, a combination of communication and dissemination actions has been defined through both offline and online channels, which we believe are the most appropriate for this purpose. Offline dissemination * Professional event attendance * PR / Press Releases * Organization of presentation events Online dissemination * Content creation and publication on different channels (combined with SEO actions): 1. Website (blog) ○ Social Media: Facebook, Twitter, Linkedin (professional forums and museum’s associations) * Emailing and newsletters: 1. email campaigns (emails and newsletters) based on our network of contacts and on existing databases, focused on impacting potential candidates and generating leads ● SEM and SMM campaigns: ○ targeted campaigns in search and display networks, and in social networks (Google AdWords, Facebook Ads, Twitter Ads, ...) <table> <tr> <th> </th> <th> G.1 </th> <th> G.2 </th> <th> G.3 </th> </tr> <tr> <td> Events attendance </td> <td> √ </td> <td> x </td> <td> √ </td> </tr> <tr> <td> PR / Press releases </td> <td> √ </td> <td> √ </td> <td> √ </td> </tr> <tr> <td> Events organization </td> <td> √ </td> <td> x </td> <td> √ </td> </tr> <tr> <td> Content creation and publication </td> <td> √ </td> <td> x </td> <td> √ </td> </tr> <tr> <td> Email / Newsletters </td> <td> √ </td> <td> x </td> <td> √ </td> </tr> <tr> <td> SEM and SMM </td> <td> √ </td> <td> √ </td> <td> x </td> </tr> </table> # 4\. Target audience Three main types of target audiences have been established to which communication and marketing actions are directed: * Potential customers (and/or channels to reach them) 1. The potential market for scMODULES is basically composed by museums and of other institutions managing collections and art/cultural heritage (GLAM sector: Galleries, Libraries, Archives and Museums). Nonetheless, Madpixel will concentrate on medium-sized and small museums specialised in pictorial art for which scMODULES bring the highest differential USP. Therefore, our main target are directors and responsible for the digital area and user experience of this kind of institutions. Additional channels: ■ Sectorial associations/groups that represent potential customers, such as Network of European Museum Organisations (NEMO), the European Museums Academy (EMA), American Alliance of Museums, The European Group on Museum Statistics (EGMUS), or their national equivalents in target countries. ■ Highly reputed and influencers professionals from the sector such as art exhibition curators, photographers, consulting companies and individuals that work for museums. ■ Local stakeholders and evangelizers that have a high degree of influence * End users 1. Museum Visitors / Audience ○ Schools, universities and other educational centers ○ Consumers of other digital content * Potential partners 1. Educational publishers. ○ Digital multimedia services agencies specialized in museums, photographers, consulting companies and individuals that work for museums. ## 4.1. Geographical location According to the company exploitation strategy described in our project plan, our target countries for this phase (and until 2020) are UE countries (with special focus on Spain, UK, France, Germany, Netherlands, and Italy) and USA. In later phases we will approach LATAM (specially Mexico and Brazil), Canada and other European countries, and finally, China, Japan and Russia. For communication actions during the project we will mainly focus on UE, and USA although we also consider to so some tests in Latam (specially in Mexico), due to the potential of this market. ## 4.2. Segmentation of actions and value proposition <table> <tr> <th> Target </th> <th> Actions </th> <th> Timeline </th> <th> Value proposition </th> </tr> <tr> <td> GLAM Sector </td> <td> Events attendance PR / Press releases Events organization Content creation and publication Email / Newsletters SEM and SMM </td> <td> See timeline at point 7.1 </td> <td> The best way to “go digital”. Affordable premium solution to get the most of digitized collections, opening new opportunities to: * Create engaging and innovative experiences * Disseminate collections * Reach new audiences * Generate new revenue streams Easy-to-use (no technical/design skills required) Multi-language Multiple publishing options </td> </tr> <tr> <td> Sectorial Associations / groups </td> </tr> <tr> <td> Professionals from the sector </td> </tr> <tr> <td> Local stakeholders and evangelizers </td> <td> Events organization Content creation and publication Email / Newsletters </td> </tr> <tr> <td> Museum visitors / audience </td> <td> PR / Press releases SEM and SMM </td> <td> See timeline at point 7.1 </td> <td> New tool multi-device (mobiles, tablets, computers) to explore museums collections: * Innovative complement to the physical visit * Remote visit * Visit preparation </td> </tr> <tr> <td> Schools, universities and other educational centers </td> <td> Events attendance PR / Press releases Content creation and publication Email / Newsletters </td> <td> New educational asset, based on big art repository: * High quality and resolution * Multimedia and interactive * Many possibilities of activities for students based on it </td> </tr> <tr> <td> Consumers of other digital content </td> <td> PR / Press releases SEM and SMM </td> <td> Innovative audiovisual and multimedia experience based on art </td> </tr> <tr> <td> Educational publishers </td> <td> Events attendance Content creation and publication Email / Newsletters </td> <td> See timeline at point 7.1 </td> <td> New business opportunities based on innovative EdTech tool offering: * Exclusive content * Possibilities to create tailor made experiences for schools and students * New revenue streams </td> </tr> <tr> <td> Digital multimedia services agencies specialized in museums, photographers, consulting companies and individuals that work for museums </td> <td> New business opportunities with museums: * New category of projects * Complementing other services (web/apps designing, photography, …) </td> </tr> </table> # 5\. Main actions ## G.1: Publicize the initiative among the target audience of the project and attract participants to their pilots ### Target audience ● Phase I: ○ Directors of institutions, and responsible for the digital area and user experience of the GLAM sector (Galleries, Libraries, Archives and Museums) and of other institutions managing collections and art/cultural heritage. ○ Sectorial associations/groups that represent potential customers, such as Network of European Museum Organisations (NEMO), the European Museums Academy (EMA), American Alliance of Museums, The European Group on Museum Statistics (EGMUS), or their national equivalents in target countries. ○ Highly reputed and influencers professionals from the sector such as art exhibition curators, photographers, consulting companies and individuals that work for museums. ○ Local stakeholders and evangelizers that have a high degree of influence ● Fase II: ○ Educational publishers. ○ Digital multimedia services agencies specialized in museums. ### List of actions Offline dissemination * Assistance to professional events: attendance at conferences of the sector and participation with stands and sessions (workshops, others) * PR: Press releases in general and professional media to publicize relevant project milestones: ○ Open call for participation in pilot projects ○ Relevant museums and institutions participating * Organization of events: project presentation sessions, in collaboration with the first 10 participating museums and at their own facilities, focused on informing other museums of the same region about it Online dissemination * Content creation and publication * Emailing and newsletters * SEM y SMM campaigns: targeted campaigns in search and display networks, and in social networks (Google AdWords, Facebook Ads, Twitter Ads, ...), focused on impacting potential candidates and on generating leads ## G.2. Publicize among museums audiences the published contents as a result of this project, to encourage its use, analyze how it is used and measure the success of the project ### Target audience * Museum Visitors * Schools, universities and other educational centers * Consumers of other digital content ### List of actions Offline dissemination: * PR: Press releases in general media to publicize relevant project milestones: ○ Relevant museums and institutions participating ○ Offer of digitized content (cultural / artistic heritage) and of experiences based on it, available as a result of the project Online dissemination: * SEM y SMM campaigns: targeted campaigns in search and display networks, and in social networks (mainly Facebook Ads), focused on impacting end users and on generating visits and downloads (apps) of the published content. ## G.3. Show the value added by the platform to participants in the project to convert them into customers, attract new clients, and position the platform among future clients and partners as the reference solution for digitized content ### Target audience * Directors of institutions, and responsible for the digital area and user experience of the GLAM sector (Galleries, Libraries, Archives and Museums) and of other institutions managing collections and art/cultural heritage. * Sectorial associations/groups that represent potential customers, such as Network of European Museum Organisations (NEMO), the European Museums Academy (EMA), American Alliance of Museums, The European Group on Museum Statistics (EGMUS), or their national equivalents in target countries. * Highly reputed and influencers professionals from the sector such as art exhibition curators, photographers, consulting companies and individuals that work for museums. ● Local stakeholders and evangelizers that have a high degree of influence ● Educational publishers. * Digital multimedia services agencies specialized in museums. ### List of actions Offline dissemination * Assistance to professional events: attendance at conferences of the sector and participation with stands and sessions (workshops, others) * PR: Press releases in general and professional media, to publicize project results * Events of presentation of the project results, for participants and potential clients, in different chosen locations. Online dissemination * Content creation and publication: focused on positioning Second Canvas as the reference solution for digitized content * Emailing and newsletters * SEM / SMM campaigns: targeted campaigns focused on impacting potential candidates and on generating leads # 6\. KPIs <table> <tr> <th> Action </th> <th> </th> <th> KPI </th> <th> </th> </tr> <tr> <td> Professional events attendance </td> <td> ● ● </td> <td> # of events with company’s presence # of potential leads </td> <td> 10 50 </td> </tr> <tr> <td> PR / Press Releases </td> <td> ● ● </td> <td> # of press releases launched # of target media publishing our press releases </td> <td> 5 25 </td> </tr> <tr> <td> Organization of presentation events </td> <td> ● ● ● </td> <td> # of events organized # of attendees # of potential leads </td> <td> 5 250 50 </td> </tr> <tr> <td> Content creation and publication on different channels (combined with SEO actions) </td> <td> ● ○ ○ ○ ● ○ ○ ○ ● ○ ○ ○ ● ○ ○ ○ ○ ● ○ ○ ○ ○ ● ○ ○ ○ ● ○ ○ ○ </td> <td> Company’s website # of visitors # of pages viewed Conversion rate scModules # of visitors Average Session Duration Conversion rate Blog # of posts published # of visitors Average Session Duration Facebook # of subscribers # of posts published # of interactions # click through rate Twitter # of followers # of tweets # of interactions # click through rate Linkedin # of followers # of posts published # of impressions Instagram # of followers # of posts published # of interactions </td> <td> 15.000 30.000 4% 8.000 0:04:00 4% 20 2.000 0:05:00 500 followers 180 posts 7.000 1.000 900 followers 480 tweets 8.000 4.000 200 100 posts 5.000 400 followers 130 posts 5.200 </td> </tr> <tr> <td> SMM </td> <td> ● ● ● </td> <td> # of impressions CTR Conversion rate (downloads) </td> <td> 248.000 0,90 % 0,7 % </td> </tr> <tr> <td> Emailing and newsletters </td> <td> ● </td> <td> Emailing campaigns </td> <td> </td> </tr> <tr> <td> </td> <td> ○ ○ ○ ● ○ </td> <td> # emails Open-rate Conversion rate Direct emails # emails </td> <td> 80 26% 20% 150 </td> </tr> </table> 11 <table> <tr> <th> </th> <th> ○ ○ ● ○ ○ ○ ○ </th> <th> Open-rate Conversion rate Newsletter # of newsletters sent # of subscribers Open-rate Conversion rate </th> <th> 80% 50% 12 130 26% 20% </th> </tr> </table> 12 7\. Timeline / Calendar ## 7.1. Timeline <table> <tr> <th> Action </th> <th> Goals </th> <th> Timeline </th> </tr> <tr> <td> Professional event attendance </td> <td> G1 </td> <td> 2017/Nov: MCN (Pittsburgh) - Confirmed: stand and workshop </td> </tr> <tr> <td> G1 </td> <td> 2017/Nov: UAM (México DF) - Confirmed: stand and session </td> </tr> <tr> <td> G1 </td> <td> 2017/Nov: Sharing is caring (Aarhus) - Confirmed: session </td> </tr> <tr> <td> G1 </td> <td> 2018/Jan: SITEM (Paris) - TBC </td> </tr> <tr> <td> G1 </td> <td> 2018/Apr: Museums and the web (Vancouver) - TBC </td> </tr> <tr> <td> G1 </td> <td> 2018/Apr: We Are Museums - TBC </td> </tr> <tr> <td> G1 </td> <td> 2018/May: AAM (Phoenix) - TBC </td> </tr> <tr> <td> G1, G3 </td> <td> 2018/Jun: Museum Next (London) - TBC </td> </tr> <tr> <td> G1, G3 </td> <td> 2018/Jun: Art#Connexion (Paris - June 2018) - TBC </td> </tr> <tr> <td> G3 </td> <td> 2018/Nov: MCN (Denver) - TBC </td> </tr> <tr> <td> PR / Press Releases </td> <td> G1 </td> <td> 2017/Aug: Launching of the project </td> </tr> <tr> <td> G1 </td> <td> 2017/Nov: Open call for participation in pilot projects </td> </tr> <tr> <td> G1, G3 </td> <td> 2018/Jan: Relevant museums and institutions participating </td> </tr> <tr> <td> G2, G3 </td> <td> 2018/May: Offer of digitized content (cultural / artistic heritage) and of experiences based on it, available as a result of the project </td> </tr> <tr> <td> G3 </td> <td> 2018/Sep: Project results </td> </tr> <tr> <td> Organization of presentation events </td> <td> G1 </td> <td> 2018/Jan: Museum TBD </td> </tr> <tr> <td> G1 </td> <td> 2018/Apr: Museum TBD </td> </tr> <tr> <td> G1 </td> <td> 2018/Jun: Museum TBD </td> </tr> <tr> <td> G3 </td> <td> 2018/Sep: Museum TBD </td> </tr> <tr> <td> G3 </td> <td> 2018/Nov: Museum TBD </td> </tr> <tr> <td> Content marketing </td> <td> </td> <td> </td> </tr> <tr> <td> \- Company’s website </td> <td> G1, G3 </td> <td> 2017/Nov: Launching </td> </tr> <tr> <td> \- scModules website </td> <td> G1 </td> <td> 2017/Nov: Launching </td> </tr> <tr> <td> \- Blog </td> <td> G1, G3 </td> <td> 2017/Nov: Launching </td> </tr> <tr> <td> 2017/Dec - 2018/March: 1 post / month </td> </tr> <tr> <td> 2018/Apr - 2019/Jan: 2 posts / month </td> </tr> <tr> <td> \- Facebook </td> <td> G1, G2 </td> <td> 2018/Feb - 2019/Jan: 15 posts / month </td> </tr> <tr> <td> \- Twitter </td> <td> G1, G3 </td> <td> 2018/Feb - 2019/Jan: 40 tweets / month </td> </tr> <tr> <td> \- Instagram </td> <td> G1, G2 </td> <td> 2018/Mar - 2019/Apr: 10 posts / month </td> </tr> <tr> <td> 2018/Apr - 2019/Jan: 12 posts / month </td> </tr> <tr> <td> \- Linkedin </td> <td> G1, G3 </td> <td> 2018/Apr - 2019/Jan: 10 posts / month </td> </tr> </table> <table> <tr> <th> SMM </th> <th> G2 </th> <th> 2018/Feb - 2019/Jan: Facebook promoted posts (included as a part of the Direct Marketing </td> <td> </td> <td> </td> </tr> <tr> <td> Email campaigns </td> <td> G1 </td> <td> 2018/Feb-Mar and 2018/Oct-Nov </td> </tr> <tr> <td> Direct emailing </td> <td> G1 </td> <td> 2017/Aug - 2018/Mar </td> </tr> <tr> <td> Newsletter </td> <td> G1 </td> <td> 2018/Feb - 2019/Jan: 1 newsletter / month </td> </tr> </table> ## 7.2. Calendar <table> <tr> <th> Year </th> <th> 2017 </th> <th> 2018 </th> <th> 2019 </th> </tr> <tr> <td> Month </td> <td> Aug </td> <td> Sep </td> <td> Oct </td> <td> Nov </td> <td> Dec </td> <td> Jan </td> <td> Feb </td> <td> </td> <td> Mar </td> <td> </td> <td> Apr </td> <td> May </td> <td> </td> <td> Jun </td> <td> </td> <td> Jul </td> <td> Aug </td> <td> Sep </td> <td> </td> <td> Oct </td> <td> Nov </td> <td> Dec </td> <td> Jan </td> </tr> <tr> <td> Week </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> </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> </td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> </tr> <tr> <td> _Events attendance _ </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> </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> </td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> </tr> <tr> <td> _PR / Press releases _ </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> </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> </td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> </tr> <tr> <td> _Events organization _ </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> </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> </td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> </tr> <tr> <td> _Company’s website _ </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> </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> </td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> </tr> <tr> <td> _scModules website _ </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> </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> </td> <td> </td> <td> </td> <td> </td> <td> </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> <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> <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> <td> </td> <td> </td> <td> </td> <td> </td> </tr> <tr> <td> _Facebook _ </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> </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> </td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> </tr> <tr> <td> _Twitter _ </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> </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> </td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> </tr> <tr> <td> _Instagram _ </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> </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> </td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> </tr> <tr> <td> _Linkedin _ </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> </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> </td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> </tr> <tr> <td> _SMM _ </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> </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> </td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> </tr> <tr> <td> _Email campaigns _ </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> </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> </td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> </tr> <tr> <td> _Direct emailing _ </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> </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> </td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> </tr> <tr> <td> _Newsletter _ </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> </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> </td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> </tr> </table> # 8\. Resources The project requires the hiring of a dedicated team, in charge of designing, executing and analyzing the results of the different actions to be implemented. Profiles to be contracted are the following: * Responsible for online marketing * Community manager * Contents marketer Our proposal already includes a budget for communication activities, events attendance, branding, project marketing and product positioning, which will cover the actions planned. <table> <tr> <th> Travels </th> <th> </th> </tr> <tr> <td> _Communication activities, events attendance and project management related meetings_ </td> <td> </td> </tr> <tr> <td> Concept </td> <td> # </td> <td> Cost (estimate) </td> <td> People </td> <td> Days </td> <td> Sbtl </td> </tr> <tr> <td> Flights </td> <td> 10 </td> <td> 800.00€ </td> <td> 2.5 </td> <td> </td> <td> 20,000.00€ </td> </tr> <tr> <td> Hotels </td> <td> 10 </td> <td> 100.00€ </td> <td> 2.5 </td> <td> 3 </td> <td> 7,500.00€ </td> </tr> <tr> <td> Meals, others </td> <td> 10 </td> <td> 60.00€ </td> <td> 2.5 </td> <td> 3 </td> <td> 4,500.00€ </td> </tr> <tr> <td> </td> <td> </td> <td> </td> <td> Total cost (estimate) </td> <td> 32,000.00€ </td> </tr> <tr> <td> </td> <td> </td> </tr> </table> <table> <tr> <th> Other goods and services </th> <th> </th> <th> </th> <th> </th> <th> </th> <th> </th> </tr> <tr> <td> _Design, branding, project marketing and product positioning_ </td> <td> </td> <td> </td> </tr> <tr> <td> Concept </td> <td> # </td> <td> Cost (estimate) </td> <td> Days </td> <td> Sbtl </td> </tr> <tr> <td> Website, landing page and blog design </td> <td> 3 </td> <td> 1,500.00€ </td> <td> </td> <td> 4,500.00€ </td> </tr> <tr> <td> Online Marketing Campaigns (monthly budget) </td> <td> 12 </td> <td> 250.00€ </td> <td> </td> <td> 3,000.00€ </td> </tr> <tr> <td> Press releases </td> <td> 5 </td> <td> 1,500.00€ </td> <td> </td> <td> 7,500.00€ </td> </tr> <tr> <td> IP protection related actions </td> <td> </td> <td> 18,000.00€ </td> <td> </td> <td> 18,000.00€ </td> </tr> <tr> <td> Conferences attendance </td> <td> </td> <td> </td> <td> </td> <td> </td> </tr> <tr> <td> _\- Stand_ </td> <td> 6 </td> <td> 2,500.00€ </td> <td> </td> <td> 15,000.00€ </td> </tr> <tr> <td> _\- AV equipment (renting)_ </td> <td> 6 </td> <td> 1,500.00€ </td> <td> 2 </td> <td> 18,000.00€ </td> </tr> <tr> <td> _\- Other (posters, flyers, roll-ons, others)_ </td> <td> 6 </td> <td> 500.00€ </td> <td> </td> <td> 3,000.00€ </td> </tr> <tr> <td> </td> <td> </td> <td> Total cost (estimate) </td> <td> 69,000.00€ </td> </tr> </table>	https://phaidra.univie.ac.at/o:1140797	Horizon 2020
0086_microSPIRE_766955.md	# Introduction This document is a deliverable of the µSPIRE project which is funded by the European Union’s H2020 Programme under Grant Agreement No. 766955\. This first version of the Data Management Plan (DMP) describes the main elements of the data management policy that will be used by the members of the Consortium with regard to the data generated throughout the duration of the project and after its completion. The µSPIRE consortium shall implement procedures that are in line with national legislation of each consortium partner and in line with the European Union standards. This DMP will apply to all data under µSPIRE consortium control. If we shall strive to make data open, we cannot overrule limitations that partner institutions put on data that they contributed to generate (see e.g. the grant agreement). This first version of the Data Management Plan (DMP), based on the knowledge available and developed at the moment of the deliverable submission, describes the main elements of the data management policy that will be used by the members of the Consortium with regard to the data generated throughout the duration of the project and after its completion. The DMP will be regularly updated as far as necessary during the development of the project activities. The next editions of the DMP will provide additional details. New versions will be relased at months M18 and M36. The DMP is released in compliance with the H2020 FAIR [1] (making data Findable, Accessible, Interoperable and Reusable). # Administrative data Project name: micro-crystals Single Photon InfraREd detectors - µSPIRE Grant reference number: H2020-FETOPEN-1-2016-2017 – ID: 766955 Project description: µSPIRE aims at establishing a technological platform for homo- and hetero- structure based photonic and electronic devices using the self-assembling of epitaxial crystals on patterned Si substrates. Emerging micro-electronic and photonic devices strongly require the integration on Si of a variety of semiconducting materials such as Ge, GaAs, GaN and SiC, in order to add novel functionalities to the Si platform. µSPIRE pursues this goal employing a novel deposition approach, which we termed vertical hetero- epitaxy (VHE). VHE exploits the patterning of conventional Si substrates, in combination with epitaxial deposition, to attain the self-assembly of arrays of Ge and GaAs epitaxial micro-crystals elongated in the vertical direction, featuring structural and electronic properties unparalleled by “conventional” epitaxial growth. As a concrete demonstration of VHE potentialities, we will deliver a complete set of novel photon counting detectors: VHE micro-crystals will be used as the elementary microcells for single-photon detectors with performances far beyond those of current state-of-the-art devices. Consortium members: 1. Politecnico di Milano – Polimi (coordinator) 2. Università degli Studi Milano Bicocca – Unimib 3. University of Glasgow – UGLA 4. Philipps-Universität Marburg – UMR 5. Technische Universität Dresden – TDU 6. Micro Photon Devices Srl.- MPD Project data contact: Giovanni Isella Politecnico di Milano - Polo Territoriale di Como Via Anzani 42 , 22100 Como Phone: +39 0313327303 e-mail: [email protected] # Dataset description and generation/collection The data generated in µSPIRE will mainly be: 1. Design Data 2. Protocol Data 3. Experimental and Characterization Data 4. Computational and Modeling Data A comprehensive description of such data is given in the tables here below: ## Design data _Table 1: Design of substrate pattern_ <table> <tr> <th> Type of study </th> <th> </th> <th> Schematics representing the geometry and dimensions of the silicon (Si) pillars etched in the Si wafer. </th> </tr> <tr> <td> Data set reference name </td> <td> and </td> <td> DEV<progressive number> (substrates meant for device fabrication) DIS<progressive number> (substrates meant for dislocation expulsion studies) </td> </tr> <tr> <td> Provenance of data </td> <td> </td> <td> Original data produced by: Ugla, Unimib, Polimi. </td> </tr> <tr> <td> Type of data </td> <td> </td> <td> Relevant dimensions of the pillars etched into the Si substrate: pillar shape (square, round) orientation with respect to crystallographic directions, pillar height, lateral dimension and spacing between adjacent pillars. </td> </tr> <tr> <td> Nature and formats </td> <td> </td> <td> Text document(PDF). Images (PNG). Autocad file (gds). </td> </tr> <tr> <td> Amount of data </td> <td> Based on previous studies, the amount of resulting data is estimated around 500MB per year. Some text format data files are also required for post- processing in the laboratory and are anticipated to be around 5MB per year. </td> </tr> <tr> <td> Requirements for software and hardware </td> <td> Substrate patterns are typically designed by using proprietary software such as AutoCAD. Free viewers for gds files are available and all relevant information can be exported as text document (PDF) or images (PNG). </td> </tr> </table> 3.2. Protocol data _Table 2: Protocol Data_ <table> <tr> <th> Type of study </th> <th> </th> <th> Relevant parameters used during low-energy plasma-enhanced chemical vapour deposition (LEPECVD) and molecular beam epitaxy (MBE) growth (substrate temperature, plasma current, gas flow, pressure and growth rate) and microfabrication. </th> </tr> <tr> <td> Data set reference name </td> <td> and </td> <td> LG<number of deposition system (1 or 2)>_<progressive number identifying the epitaxial growth> MBE_<progressive number identifying the epitaxial growth> </td> </tr> <tr> <td> Provenance of data </td> <td> </td> <td> Original data produced by: Polimi and Unimib. </td> </tr> <tr> <td> Type of data </td> <td> </td> <td> Intended profile of the epilayer stack: silicon (Si), germanium (Ge) and aluminum-gallium-arsenide (AlGaAs) concentration and thickness. Relevant growth parameters: deposition temperature, deposition rate, plasma parameters. Substrate pattern as described in Table 1. </td> </tr> <tr> <td> Nature and formats </td> <td> </td> <td> Text document (TXT) organized in columns with headers indicating the quantity measured and unit of measure used. </td> </tr> <tr> <td> Amount of data </td> <td> </td> <td> We expect a total of 1GB. </td> </tr> <tr> <td> Requirements for software and hardware </td> <td> Any text editor. </td> </tr> </table> 3.3. Experimental and characterization data _Table 3: Experimental and Characterization Data_ <table> <tr> <th> Type of study </th> <th> </th> <th> Morphological data (scanning electron microscopy – SEM, trasmission electron microscopy – TEM and high-resolution X-ray diffraction – HRXRD), optical/spectroscopical data (photoluminescence – PL, µPL, Raman and µRaman) and optoelectronic data (current-voltage characteristics, photoresponse). </th> </tr> <tr> <td> Data set reference name </td> <td> and </td> <td> PL_<progressive number> identifying the epitaxial growth as indicated in Table 2 and Error! Reference source not found. > TEM_<progressive number> AFM_<progressive number> IV_<progressive number> </td> </tr> <tr> <td> Provenance of data </td> <td> </td> <td> Original data produced by: Unimib (PL), Umar (TEM and SEM), Polimi (SEM, AFM and electrical measurements). </td> </tr> <tr> <td> Type of data </td> <td> </td> <td> Photoluminescence response of the sample. TEM, SEM and AFM images showing crystal defects with atomic resolution and micro-crystals morphology. Electro-optical characterization of µSPIRE’s devices. </td> </tr> <tr> <td> Nature and formats </td> <td> </td> <td> PL and electrical measurement data: text document (TXT) organized in columns with headers indicating the quantity measured and unit of measure used. TEM data: images (bmp, tiff, jpeg…) Post processed images can be visualized with free software. SEM data: images (bmp, tiff, jpeg…). AFM data: raw data are in flt format and can exported in an image format (bmp, tiff, jpeg…). Accessing raw data is possible by means of freely available software such as Gwyddion. Electrical measurements: text document (TXT) organized in columns with headers indicating the quantity measured and unit of measure used. </td> </tr> <tr> <td> Amount of data </td> <td> </td> <td> 1TB </td> </tr> <tr> <td> Requirements for software and hardware </td> <td> TXT file can be accessed with several free software. Image file are also freely accessible. Accessing .flt raw data is possible by means of freely available software such as Gwyddion. </td> </tr> </table> 3.4. Computational and modeling data _Table 4: Computational and Modeling Data_ <table> <tr> <th> Type of study </th> <th> </th> <th> Modeling of the morphological properties of Si, Ge and AlGaAs micro-crystals as a function of substrate patterning and growth conditions. Electronic design of µSPIRE devices: material (Si, Ge or GaAs), doping profile and micro-crystals shape/dimensions. Modeling of the micro-crystals bandstructure. </th> </tr> <tr> <td> Data set reference name </td> <td> and </td> <td> MOD_<progressive number identifying the morphological modeling> ELDES<progressive number identifying the electronic modeling> BAND_<progressive number identifying the bandstructure calculation> </td> </tr> <tr> <td> Provenance of data </td> <td> </td> <td> Original data produced by: Polimi, TUD and Unimib </td> </tr> <tr> <td> Type of data </td> <td> </td> <td> Morphological modeling: phase field calculation of micro-crystals morphology. Electronic modelling: drift-diffusion Poisson calculations of optoelectronic response of µSPIRE devices. Bandstructure calculations: K-dot-P or effective mass calculation. </td> </tr> <tr> <td> Nature and formats </td> <td> </td> <td> Morphological modeling: images (bmp, tiff, jpeg). Electronic modeling: such calculations are performed using the proprietary software Synopsis Sentaurus TCAD. Selected image can be exported in almost any image format. Bandstructure calculationsSuch calculations are performed using the proprietary software Nextnano. Results are exported as TXT files. </td> </tr> <tr> <td> Amount of data </td> <td> </td> <td> 1TB </td> </tr> <tr> <td> Requirements for software and hardware </td> <td> Any image viewer or TXT editor </td> </tr> </table> # Data management documentation and curation This section describes the processes and actions that will be implemented during the course of the research for data management, documentation, sharing between partners and curation (saving and preservation). _Table 5: File naming convention_ <table> <tr> <th> Convention </th> <th> _[time_stamp]_microSPIRE_[data type]_[Partner]_[Version].[file format] _ </th> <th> </th> </tr> <tr> <td> </td> <td> Time Stamp </td> <td> Data Type </td> <td> Partner </td> <td> Version </td> <td> File format </td> </tr> <tr> <td> YYYY_MM_DD </td> <td> Data set reference and name as describe in tables 1 to 4 </td> <td> Polimi, Unimib, Ugla Umar TDU MPD </td> <td> V1 </td> <td> According to software: txt csv odt ods pdf </td> </tr> <tr> <td> Examples </td> <td> 2018_04_30_LEPECVD_10001_Polimi_v1.txt </td> <td> </td> </tr> </table> ## Data Managing: access, storage and back-up µSPIRE data are mainly created _ex novo_ as a result of the research activities listed from 1 to 4 in Section 3. Common rules will be used for file naming in order to favour data sharing and accessibility. The proposed naming convention is described in Table 5. The data managing cycle is composed of three steps: 1. data production and storage by each partner; 2. data sharing and storage on a repository accessible only to the project participants; 3. data open-access on a public repository. ### Data production and storage by each partner The first responsible for each data set is the Partner ho generated it, therefore a copy of the data set will be stored and maintained on the Partner servers according to its internal practices and regulation described in Table 6. _Table 6_ <table> <tr> <th> Polimi </th> <th> All data is stored on a local server, Dr. Chrastina is the server administrator and provides access and read/write permissions to the different members of the group. Data transmission can be accessed through secure file transfer protocol. A back-up procedure on an external hard disk is performed weekly. </th> </tr> <tr> <td> Unimib </td> <td> All local data is stored on a local server. Access and permission is granted to the project members by the server administrator, Dr. Bergamaschini. The access to the data is possible through secure file transfer protocol and network file system export on local computers (password protected). The server is configured as RAID-5 and a weekly backup is done to local hard drives. </td> </tr> <tr> <td> Ugla </td> <td> All data is stored on a raid server which is backed up every evening to a set of secondary discs. The server is also backed up to a tape system every month. The server is administered by the School’s IT department and user access is controlled by Prof. Paul. The server is accessed through secure file transfer or secure https protocols. </td> </tr> <tr> <td> Umar </td> <td> All data is stored on a local server, Dr. Beyer is the server administrator and provides access and read/write permissions to the different members of the group. Data transmission can be accessed through secure file transfer protocol. The server is integrated in the daily backup scheme of the university´s computer centre. </td> </tr> <tr> <td> TUD </td> <td> All challenges to store, process and manage data at TUD, are addressed according to the ``Guidelines on the Handling of Research Data at TUD". TUD closely cooperates with the Saxon State and University Library (SLUB) and started in 2018 an institutional repository and longterm preservation infrastructure for research data (Project OpARA). This system will be used as the central base for data management. </td> </tr> <tr> <td> MPD </td> <td> All data is stored on external servers administered by our secure cloud infrastructure provider. Mr Sandro Rizzetto is our internal server administrator and provides access with individual read/write permissions to the different MPD employees. Local MPD data can be accessed through a secure VPN connection. A back-up procedure is daily performed by our external IT infrastructure provider. </td> </tr> </table> ### Data sharing and storage on a repository accessible only to the project participants Each Partner will transfer data which are considered relevant for the project on a server set-up and maintained by personnel of the ICT services of the Politecnico di Milano (ASICT Polimi). The system operates as a Git server ( _http://gitlab.polimi.it/_ ) . The access is granted by means of username/password provided by ASICT. Gitlab allows for access control: different users can be given different access permit. Each Partner will be allowed to add data to the repository. Only the Coordinator will be allowed to delete data from the repository. Versioning and profiling are implemented, it is therefore possible to trace which changes have been made to the database and who made them. Back-ups are performed daily and kept for 30 days (i.e. each given day it is possible to recovery the data as they were in any of the 30 days before). A full back-up is performed every 12 months and kept for 12 months. The gitlab repository will ensure preservation of the data also after the duration of the project for a time of at least 5 years. We do not envision any need to transfer data to a different server during the project. The costs are covered by the Polimi budget and consists of 200 € for the gitlab set-up and 360 €/year for its maintenance. ### Data open access Data underpinning published papers will be made available through ZENODO ( _https://zenodo.org/_ ) , an open access repository for all fields of science that allows uploading any kind of data file formats. ZENODO is recommended by the Open Access Infrastructure for Research in Europe (OpenAIRE). ZENODO assign a persistent digital identifier to all published records. ZENODO will also be used for the long term preservation of data, even those not shared, after completion of the project. ## Metadata standards and data documentation Different information will be stored as metadata and associated to the different types of data set, from 1 to 4, described in Section 3. Some examples are given in Table 7. _Table 7: Metadata_ <table> <tr> <th> Metadata associated to Design Data </th> <th> Units of measurement (in case they are not specified in the TXT file headers), software required to make the data usable. </th> </tr> <tr> <td> Metadata associated to Protocol Data </td> <td> Information allowing for the correct interpretation of the growth parameters used: units of measurement of the relevant physical quantities (pressure, gas flow, temperature, shutters pneumatic valves staus). </td> </tr> <tr> <td> Metadata associated to Experimental and characterization data </td> <td> Equipment used for the measurements, measuring condition (temperature), sample preparation, Information on data analysis procedures, software required for making data readable. </td> </tr> <tr> <td> Computational and modelling data </td> <td> Equipment used for the measurements, measuring condition (temperature), sample preparation, Information on data analysis procedures, software required for making data readable. </td> </tr> </table> # Data sharing µSPIRE DMP is inspired by the FAIR data Principles i.e. making data findable, accessible, interoperable and reusable[1]. Therefore, µSPIRE will consider the following approaches as far as applicable for providing the open access to research data: * data appearing or underpinning publications will be available on the web trough the ZENODO platform under a Creative Commons license of the CC BY-NC-SA (non commercial share alike) type (https://creativecommons.org/); * open-source formats and softwares (e.g. CSV instead of Excel) will be preferred over their commercial counterpart; * interoperability will be enforced by using Metadata to specify the data type (as highlighted in Table 1 to 4), and the software required to analyze and process data. The Metadata file (typically in the form of a README.txt file) will be filled by the authors in order to summarize the characteristics of each data set to give a quick understanding of the content of the data to anyone that reads it. The policy for open-access to the research data in µSPIRE will follow two core principles: 1. The generated research data should generally be made as widely accessible as possible in a timely and responsible manner; 2. The research process should not be impaired or damaged by the inappropriate release of such data. Data sets containing key information that could be patented for commercial or industrial exploitation will be excluded for public distribution and data sets containing key information that could be used by the research team for publications will not be shared until the embargo period applied by the publisher is over. µSPIRE will follow the Open Access mandate for its publications (art. 29.2 Grant agreement). Each publication, including associated metadata, will be deposited in Individual partner repositories (institutional or subject). The institutional repository of Politecnico of Milano, compliant with OpenAIRE rules, is _https://re.public.polimi.it/_ . # Ethical Aspects In the µSPIRE project, there are no ethical or legal issues present which impairs the data managing. The research does not create, process and store personal data.	https://phaidra.univie.ac.at/o:1140797	Horizon 2020
0087_CIRC-PACK_730423.md	INTRODUCTION This is the first version of DMP to be revised during the course of the project within Task 1.4 Data Management Plan, including new data, changes in consortium policies regarding innovation potential or decision to file a patent, and changes in the consortium composition and external factors. This plan will establish the measures for promoting the findings during CIRC- PACK’s lifecycle and will set the procedures for the sharing of data of the project. Addressing FAIR principle for research data (Findable, Accessible, Interoperable and Re-usable) CIRC-PACK DMP will consider: * Data set reference and name * Data set description * Standards and metadata * Data sharing and handling during and after the end of the project * Archiving and preservation (including after the end of the project) # DATA SUMMARY The data that will be managed in CIRC-PACK is included under the umbrella of the following categories. * End-user personal data : During baseline definition and preliminary assessment of packaging value chain, surveys will be implemented in different countries in order to identify public perception and expectations about plastics and plastic packaging value chain, as well as to collect interesting info to be considered during project development regarding products design, waste management measures and dissemination activities. Besides, the exploitation and commercialization of knowledge and technical results achieved will be also addressed. This data set is private and will be managed only by the organization hosting the Data Repository where is allocated. This collection will be not shared unless the data are previously anonymized to remove any possible personal link. In those cases where data is shared this process will be according European regulation and requesting users permission for doing it. Data protection and privacy in conjunction with market surveys. * Processes information of stages involved in demo cases: Processes information will be required during demo cases execution and to carry out Life Cycle Assessment and Life Cycle Cost analysis (LCA/LCC) of the plastic packaging value chain (before and after the implementation of project innovations). These analyses will consider data such as flow diagrams, materials (input and output), energy consumption, transport of materials, storage conditions, raw materials, materials sorting, waste management, assets value and lifespan, investment and turnover, etc. Certain datasets may not be shared (or will need restrictions), legal and contractual reasons will be explained in that case. * Sensor Data : this collection comprises all information collected from sensors involved in waste sorting facilities. This collection is public, as it is not related to any specific person and therefore it will be open. * Derived Data : this comprises the result of applying Data Analytics techniques to the data steaming from sensors. At this stage of the project, the vision is that the data in this collection will not serve as means to identify any person, therefore they are considered as public. Regarding the nature of the data, in order to fulfil the required security and privacy requirements in this project, which are set by the Data Protection Directive (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), the project assumes the differentiation set in this Directive between Personal and No Personal data. Data are considered as personal data “when someone is able to link the information to a person, even if the person holding the data cannot make this link”. Any data susceptible of being considered as Personal Data will be managed according to this Directive. # STANDARDS AND METADATA There are several domains considered in CIRC-PACK, each of them follow different rules and recommendations. This is a very early stage identification of standards: * Microsoft Word 2010 for text based documents (or any other compatible version). * MP3 or WAV for audio files. * Quicktime Movie or Windows Media Video for video files. * Quantitative data analysis will be stored in SAV file format (used by SPSS) from which data can be extracted using the open-source spread Perl script. These file formats have been chosen because they are accepted standards and in widespread use. Files will be converted to open file formats where possible for long-term storage. Metadata will be comprised of two formats – contextual information about the data in a text based document and ISO 19115 standard metadata in an xml file. These two formats for metadadata are chosen to provide a full explanation of the data (text format) and to ensure compatibility with international standards (xml format). # ALLOCATION OF RESOURCES CIRCE will be responsible for data management in CIRC-PACK project. Costs related to open access to research data are eligible as part of the Horizon 2020 grant (if compliant with the Grant Agreement conditions). Resources for long term preservation, associated costs and potential value, as well as how data will be kept beyond the project and how long, will be discussed by the whole consortium during General Assembly meetings. # DATA SHARING, ACCESS AND PRESERVATION The digital data created by the project will be diversely curated depending on the sharing policies attached to it. For both open and non-open data, the aim is to preserve the data and make it readily available to the interested parties for the whole duration of the project and beyond. A public API will be provided to registered users allowing them the access to the platform. The database compliance aims to ensure the correct implementation of the security policy on the databases verifying vulnerability and incorrect data. The target is to identify excessive rights granted to users, too simple passwords (or even the lack of password) and finally to perform an analysis of the entire database. At this point, we can assure that at least the following measures will be considered for assuring a proper management of data: 1. Dataset minimisation. The minimum amount of data needed will be stored so as to prevent potential risks. 2. Access control list for user and data authentication. Depending on the dissemination level of the information an Access Control List will be implemented reflecting there for each user the data sets that can be accessed. 3. Monitoring and Log of activity. The activity of each user in the project platform, including the data sets accessed is registered in order to track and detect harmful behaviour of users with access to the platform. 4. Implementation of an alert system that informs in real time of violation of procedures or about hacking attempts. 5. Liability. Identification of a person who is responsible of keeping safe the information stored, 6. When possible, the information will be also made available in the initiative that the EC has launched for open data sharing from research, which is ZENODO.ORG. The mechanisms explained in this document aim at reducing to the maximum the risks related with data storage. However due to the activities that are going to be carried out in the project, it is still not defined the amount of time that data will be stored in the platform since Big Data analysis and services run data analytics procedures and depending of the accuracy of results based on the size of the sets considered. ## Non-Open research data The non-open research data will be archived and stored long-term in the EMDESK portal administered by CIRCE. The CIRCE platform is currently being employed to coordinate the project's activities and to store all the digital material connected to CIRC-PACK. If certain datasets cannot be shared (or need restrictions), legal and contractual reasons will be explained. ## Open research data The open research data will be archived on the Zenodo platform (http://www.zenodo. org). Zenodo is a EU-backed portal based on the well established GIT version control system (https://git-scm.com) and the Digital Object Identifier (DOI) system (http://www.doi.org). The portal's aims are inspired by the same principles that the EU sets for the pilot; Zenodo represents thus a very suitable and natural choice in this context. The repository services offered by Zenodo are free of charge and enable peers to share and preserve research data and other research outputs in any size and format: datasets, images, presentations, publications and software. The digital data and the associated meta-data is preserved through well- established practices such as mirroring and periodic backups. Each uploaded data-set is assigned a unique DOI rendering each submission uniquely identifiable and thus traceable and referenceable. # ETHIC ASPECTS ## Personal data collected through the interviews and surveys imported from Turkey to the EU ## partners / exported from EU partners to Turkey Personal data of users will be collected during the social acceptance and participation in eco-design. Regarding personal data from Kartal (non-EU couontry), KARTALMUN will collect information at local level through digital media (tablets, computers, etc.). These data will be collected and included in an excel file by an external Turkish entity. This excel file will show only aggregated data and will be analysed by OCU EDICIONES, it will not show identity data and will be processed, together with the rest of countries information, in an aggregated way. All surveys will be processed anonymously and no personal data will be exported from EU partners to a non-EU country. Procedures to be implemented in Turkey to manage personal data in Turkey, will be established by KARTALMUN and the external entity, according to national regulation. ## Collection, storage and protection of personal data EU countries surveys will be based on a combined self-administered postal and online sampling and data collection approach. Necessary measures will be taken for guaranteeing optimal anonymization of collected, analysed and stored data. Respondents will be comprehensibly informed about it. Whenever necessary, survey respondents will be requested for their explicit consent (e.g. in case of follow-up/second-layer contacts & questionnaires). Collected data will be used exclusively within the context and for the purpose of the CIRCPACK project. No data will be transmitted to any person, company or organization not being involved on the CIRC-PACK project. Collected data will not be used for commercial purposes. All personal data will be collected, stored and destructed only with and accordingly to the consent of the personal data holder. The research will comply with: * ethical principles * applicable international, EU and national law (in particular, EU Directive 95/46/EC). Data collection, storage, protection, retention and destruction will be carried out through the intranet system of the project: EMDESK. Interviewees/beneficiaries/recipients will be informed about data security, anonymity and use of data as well as asked for accordance. Participation happens on a voluntary basis. # LIST OF THE DATA-SETS This section will list the data-sets produced within the CIRC-PACK project. # TIMETABLE FOR UPDATES As established in the Grant Agreement, at the end of the project, a new version of DMP will be provided. Nevertheless, after each Steering Committee meeting, an updating of the document will be performed, if required. This is the current Steering Committee calendar: <table> <tr> <th> Meeting </th> <th> Month </th> <th> Dates </th> <th> Country </th> <th> Host Partner </th> </tr> <tr> <td> I SC </td> <td> month 7 </td> <td> 16-17 November 2017 </td> <td> Italy </td> <td> RINA </td> </tr> <tr> <td> I GA, II SC </td> <td> month 13 </td> <td> may-18 </td> <td> Italy </td> <td> NOVAMONT </td> </tr> <tr> <td> III SC </td> <td> month 19 </td> <td> November 2018 </td> <td> The Netherlands </td> <td> BUMAGA BV </td> </tr> <tr> <td> II GA, IV SC </td> <td> month 25 </td> <td> may-19 </td> <td> Spain </td> <td> AITIIP </td> </tr> <tr> <td> V SC </td> <td> month 31 </td> <td> November 2019 </td> <td> Croatia </td> <td> MI-PLAST </td> </tr> <tr> <td> III GA, VI SC, Final meeting </td> <td> month 36 </td> <td> April 2020 </td> <td> Belgium </td> <td> CIRCE </td> </tr> </table>	https://phaidra.univie.ac.at/o:1140797	Horizon 2020
0088_PlastiCircle_730292.md	# 1\. Overview of the PlastiCircle Project ## 1.1 Objectives The main objective of PlastiCircle is to improve the Circular Economy of Plastics (Closure of the European Plastic Loop 1 ). In order to achieve this purpose, the PlastiCircle concept is centred on closing the plastic waste chain in several ways: Collection Transport Sorting Recovery 1. Increasing the amount of plastic waste collected. 2. Reducing the costs of recovering plastic waste. 3. Increasing the quality of collected plastic waste. 4. Developing new value-added applications. The combination of these measures will promote the recovery of the most important plastic fraction in Europe (i.e. plastic packaging). The PlastiCircle approach is based on innovation in the four stages associated with plastic packaging treatment: collection, transport, sorting and recovery in value-added products. ## 1.2 The PlastiCircle Consortium A key factor will be the integration of the project results in a global process to be implemented throughout Europe. The consortium has been designed with this objective in mind as subsequently explained (see Figure 1). Collection, transport, sorting and recycling improvements will be developed by experienced RTDs and companies (ITENE, PICVISA, AXION, SINTEF and PROPLAST) and then tested in _Figure 1. PlastiCircle concept and consortium._ three pilots in Valencia- Spain, Alba Iulia-Romania and Utrecht-The Netherlands, being finally results exploited, disseminated and communicated in a EU level (PROPLAST, KIMbcn, ICLEI, PLAST-EU and ECOEMBES). It should be also remarked the participation of the Municipality of Velenje (MOV) as a follower city, with a view to learn from the 3 pilots, to disseminate results in Balkan countries and Central Europe, as well as to ensure and facilitate the incorporation of the PlastiCircle approach in the medium and long term in European cities and regions (To assure PlastiCircle replicability after the project). # 2\. Scope of the DMP ## 2.1 Related Policies The present Data Management Plan (DMP) complies with and has been developed according to the following EU policies regarding Research Data and Data Protection: 1. The Open Research Data Pilot (“ORD pilot”) 2. The General Data Protection Regulation ### 2.1.1 Open Research Data Pilot (ORD pilot) 2 The European Commission is running a flexible pilot under Horizon 2020 called the Open Research Data Pilot (ORD pilot). 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. According to this 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. Further details are provided in the Guidelines on FAIR Data Management in Horizon 2020 (v.3, 26 July 2016). ### 2.1.2 General Data Protection Regulation (GDPR) (3) 3 The General Data Protection Regulation (GDPR) (Regulation (EU) 2016/679) is a regulation by which the European Parliament, the European Council and the European Commission intend to strengthen and unify data protection for all individuals within the European Union (EU). It also addresses the export of personal data outside the EU. The primary objectives of the GDPR are to give citizens and residents back control of their personal data and to simplify the regulatory environment for international business by unifying the regulation within the EU. When the GDPR takes effect, it will replace the data protection directive (officially Directive 95/46/EC) from 1995. The regulation was adopted on 27/4/2016. It applies from 25/5/2018 after a two-year transition period and, unlike a directive, it does not require any enabling legislation to be passed by national governments. Under GDPR it will not be necessary to submit notifications / registrations to each local DPA of data processing activities, nor will it be a requirement to notify / obtain approval for transfers based on the Model Contract Clauses (MCCs). Instead, there will be internal record keeping requirements and DPO appointment will be mandatory only for those controllers and processors whose core activities consist of processing operations which require regular and systematic monitoring of data subjects on a large scale or of special categories of data or data relating to criminal convictions and offences (which is not the case of PlastiCircle Project). # 3\. Data Summary ## 3.1 Purpose of the data The data (databases, datasets, etc.) that are required for PlastiCircle will be used to: 1. Develop, test and evaluate the plastic value chain: * Collection. Using smart containers provided with a user identification, identifiable labels and money compensation procedure (WP2). * Transport. Based on the compaction of plastic, both in container and in the trucks; measuring container filling levels; optimizing collection routes and efficient driving (WP3). * Sorting. With an innovative technology will be based on a new filmstabilizing conveyor for plastic sorter able to achieve an excellent performance on films. A special focus will be offered to the stages of material feeding, identification and ejection. The system will be based on Near- Infra-Red-Hyperspectral-Imaging (NIR-HSI), THz (TeraHertz) imaging, and hyperspectral whiskbroom/pushbroom shooting along with a spectral shifting.) (WP4). 2. Demonstrate the potential to obtain added-value and innovative recovered products from the fractions previously sorted (circular economy approach) (WP5) 3. Elaborate the pilot tests in the cities of Valencia, Utrecht and Alba Iulia (WP6) 4. Integrate and validate the PlastiCircle approach from a technical, environmental, economic and social point view (WP7 and WP8). ## 3.2 Types and formats of data The data to be collected, processed and stored in PlastiCircle project can be categorized as follows: 1. Type of data based on its content: * Citizen data * Waste quantity and composition data * Containers filling data * Collection routes data * Plastic identification * Economic, social and environmental data 2. Type of data based on its collection time: * Real-time data * Historical (archived) data 3. Type of data, based on its sources: * Crowdsourced data * Open data * Proprietary data * Artificially generated data 4. Processes related to the abovementioned data are: * Data collection and storage * Data manipulation and management * Data analysis ## 3.3 Data Formats The following data formats are PlastiCircles’ preferred choices to enable sharing and long-term validity of the data: * JSON: JSON is a simple file format that is very easy for any programming language to read. Its simplicity means that it is generally easier for computers to process than others, such as XML. * XML: XML is a widely-used format for data exchange because it gives good opportunities to keep the structure in the data and the way files are built on, and allows developers to write parts of the documentation in with the data without interfering with the reading of them. * Spreadsheets: Many authorities have information left in the spreadsheet, for example Microsoft Excel. This data can often be used immediately with the correct descriptions of what the different columns mean. However, in some cases there can be macros and formulas in spreadsheets, which may be somewhat more cumbersome to handle. It is therefore advisable to document such calculations next to the spreadsheet, since it is generally more accessible for users to read. * Comma Separated Values (CSV): CSV files can be a very useful format because it is compact and thus suitable to transfer large sets of data with the same structure. However, the format is so spartan that data are often useless without documentation since it can be almost impossible to guess the significance of the different columns. It is therefore particularly important for the comma-separated formats that documentation or metainformation of the individual fields is provided and is sufficient and accurate. * Text Documents: Classic documents in formats like RTF, ODF, OOXML, or PDF are sufficient to show certain kinds of documents, e.g., deliverables, reports, etc. Templates may be used whenever possible, so that displayed data can be re-used. * Plain Text (TXT): Plain text documents (.txt) are chosen because they are very easy to read and process via plain text parsers. They generally exclude structural metadata. * HTML: Nowadays much data is available in HTML format on various sites. This may well be sufficient if the data is very stable and limited in scope. In some cases, it could be preferable to have data in a form easier to download and manipulate, but as it is cheap and easy to refer to a page on a website, it might be a good starting point in the display of data. Typically, it would be most appropriate to use tables in HTML documents to hold data, and then it is important that the various data fields are displayed and are given IDs which make it easy to find and manipulate data. * Web Services: For data that changes frequently, and where each pull is of limited size, it is very relevant to expose data through web services. There are several ways to create a web service, but some of the most used is SOAP and REST. * Proprietary formats: Some dedicated systems, etc. have their own data formats that they can save or export data in. It can sometimes be enough to expose data in such a format - especially if it is expected that further use would be in a similar system as that which they come from. The following Table contains guidance on file formats recommended for data sharing, reuse and preservation. _Table 1. File formats recommended for data sharing, reuse and preservation_ <table> <tr> <th> Type of data </th> <th> Recommended formats </th> </tr> <tr> <td> Quantitative tabular data with extensive metadata. A dataset with variable labels, code labels, and defined missing values, in addition to the matrix of data </td> <td> SPSS portable format (.por) delimited text and command ('setup') file (SPSS, Stata, SAS, etc.) structured text or mark-up file of metadata information, e.g. DDI XML file </td> </tr> <tr> <td> Quantitative tabular data with minimal metadata A matrix of data with or without column headings or variable names, but no other metadata or labelling </td> <td> Comma-separated values (.csv) tab-delimited file (.tab) including delimited text of given character set with SQL data definition statements where appropriate </td> </tr> <tr> <td> Geospatial data vector and raster data </td> <td> ESRI Shapefile (.shp, .shx, .dbf, .prj, .sbx, .sbn optional) geo-referenced TIFF (.tif, .tfw) CAD data (.dwg) tabular GIS attribute data, geojson Geography Markup Language (.gml) </td> </tr> <tr> <td> Quantitative data textual </td> <td> Rich Text Format (.rtf) plain text, ASCII (.txt) JSON eXtensible Mark-up Language (.xml) text according to an appropriate Document Type Definition (DTD) or schema </td> </tr> <tr> <td> Digital image data </td> <td> TIFF 6.0 uncompressed (.tif) PNG JPEG </td> </tr> <tr> <td> Digital audio data </td> <td> Free Lossless Audio Codec (FLAC) (.flac) Wav Mp3 </td> </tr> <tr> <td> Digital video data </td> <td> MPEG-4 (.mp4) OGG video (.ogv, .ogg) motion JPEG 2000 (.mj2) </td> </tr> <tr> <td> Documentation </td> <td> Rich Text Format (.rtf) PDF/UA, PDF/A or PDF (.pdf) XHTML or HTML (.xhtml, .htm) OpenDocument Text (.odt) Doc, docx, xls, xlsx </td> </tr> </table> [Modified from source: _Managing and Sharing Research Data: A Guide to Good Practice_ ] ## 3.4 Existing Data During its lifetime, the PlastiCircle project will make use of existing data that are in the possession of various partners as well as other data sources (open data). The existing data (or background) is defined as “data, know-how or information (…) that is needed to implement the action or exploit the results” It includes inventions, experience and databases. Background that partners will bring to the project has been defined in the Consortium Agreement (CA). The background that each partner brings to the project is defined below: _Table 2. Background provided by each partner_ <table> <tr> <th> 1\. INSTITUTO TECNOLÓGICO DE ENVASE TRANSPORTE Y LOGÍSTICA </th> </tr> <tr> <td> Describe Background </td> <td> Specific limitations and/or conditions for implementation (Article 25.2 Grant Agreement) </td> <td> Specific limitations and/or conditions for Exploitation (Article 25.3 Grant Agreement) </td> </tr> <tr> <td> 1)Collection: Characterization protocols for evaluating the segregation quality of Municipal Solid Waste(MSW) fractions and specifically packaging, commingled MSW, organic. 2)Transport: -Improved algorithms for route optimization tested in urban vehicles, with an interface developed for the routing management, being able to help decision-making and recalculate routes in real time. - Hardware device for vehicle data adquisition from CAN-BUS port (speed, gps position, level of fuel, brake use, etc.), connected to a database where data are stored and can be post-treated. - Knowledge in the deployment and use of traceability technologies, such as RFID or QR systems, and sensorization devices (i.e. for detecting filling levels). 3)Sorting: Evaluation of possibilities to sort new packaging materials by Near </td> <td> INSTITUTO TECNOLOGICO DEL EMBALAJE, TRANSPORTE Y LOGISTICA shall grant access to Background that is, or will be found to be, necessary for the implementation of the Project royalty free to the Party or Parties that Need access to implement their work in the Project. Provided that the access to background do not contravene non-disclosure agreements or exploitation agreements with third parties. ITENE shall not be obliged to grant Access Rights (i) to Background not owned by ITENE and/ or (ii) to Background for which ITENE is not able to grant Access Rights, due to third party rights and/ or (iii) to Background for which ITENE is not able to grant Access Rights without paying compensation to third parties and/or (iv) to information that was not </td> <td> INSTITUTO TECNOLOGICO DEL EMBALAJE, TRANSPORTE Y LOGISTICA shall grant access to Background needed to use the Results of the Project under fair and reasonable conditions to be agreed with the Party or Parties that Need access to use the Results of the Project. ITENE shall not be obliged to grant Access Rights (i) to Background not owned by ITENE and/ or (ii) to Background for which ITENE is not able to grant Access Rights, due to third party rights and/ or (iii) to Background for which ITENE is not able to grant Access Rights without paying compensation to third parties and/or (iv) to </td> </tr> </table> <table> <tr> <th> InfraRed (NIR); Possibility to add markerts in packaging materials to be then sorted 4)Recovery and recycling: Evaluation of recyclability of new packaging materials and specifically polymers incorporating nanomaterials and TIC technologies by injection, extrusion and compression moulding. 5)Sustainability assessment: Environmental, economic and social LCA (Life Cycle Assessment) of new packaging materials (biomaterials, nanoreinforced materials, smart packaging) </th> <th> held by ITENE before they acceded to the Grant Agreement </th> <th> information that was not held by ITENE before they acceded to the Grant Agreement </th> </tr> <tr> <td> 2\. STIFTELSEN SINTEF (SINTEF) </td> </tr> <tr> <td> Describe Background </td> <td> Specific limitations and/or conditions for implementation (Article 25.2 Grant Agreement) </td> <td> Specific limitations and/or conditions for Exploitation (Article 25.3 Grant Agreement) </td> </tr> <tr> <td> 1. Transport: Software modules for predicting vehicle speed profiles and energy consumption. 2. Sustainability assessment: Environmental and Economic LCA (Life Cycle Assessment) of new packaging materials (biomaterials, nano-reinforced materials, smart packaging) </td> <td> SINTEF shall grant access to Background that is necessary for the implementation of the Project royalty free to the Party or Parties that Need access to implement their work in the Project. SINTEF shall not be obliged to grant Access Rights (i) to Background not owned by SINTEF and/ or (ii) to Background for which SINTEF is not able to grant Access Rights, due to third party rights and/ or (iii) to Background for which SINTEF is not able to grant Access Rights without paying compensation to third parties and/or (iv) to information that was not held by SINTEF before they acceded to the Grant </td> <td> SINTEF shall grant access to Background needed to use the Results of the Project under fair and reasonable conditions to be agreed with the Party or Parties that Need access to use the Results of the Project. SINTEF shall not be obliged to grant Access Rights (i) to Background not owned by SINTEF and/ or (ii) to Background for which SINTEF is not able to grant Access Rights, due to third party rights and/ or (iii) to Background for which SINTEF is not </td> </tr> </table> <table> <tr> <th> </th> <th> Agreement </th> <th> able to grant Access Rights without paying compensation to third parties and/or (iv) to information that was not held by SINTEF before they acceded to the Grant Agreement </th> </tr> <tr> <td> 3\. PICVISA </td> </tr> <tr> <td> Describe Background </td> <td> Specific limitations and/or conditions for implementation (Article 25.2 Grant Agreement) </td> <td> Specific limitations and/or conditions for Exploitation (Article 25.3 Grant Agreement) </td> </tr> <tr> <td> 1)Sorting of post-consumer plastics: Evaluation and optimisation of mechanical and optical sorting of post-consumer plastics. Evaluation and optimisation of marker/tracer technology to sort plastics including technologies such as fluorescent markers and digital watermarking. Specific knowledge on the sorting of flexible packaging and the effect of packaging design on optical sorting. </td> <td> PICVISA shall grant access to Background that is necessary for the implementation of the Project royalty free to the Party or Parties that Need access to implement their work in the Project. </td> <td> PICVISA shall grant access to Background needed to use the Results of the Project under fair and reasonable conditions to be agreed with the Party or Parties that Need access to use the Results of the Project. </td> </tr> <tr> <td> 4\. AXION RECYCLING </td> </tr> <tr> <td> Describe Background </td> <td> Specific limitations and/or conditions for implementation (Article 25.2 Grant Agreement) </td> <td> Specific limitations and/or conditions for Exploitation (Article 25.3 Grant Agreement) </td> </tr> <tr> <td> 1)Sorting of post-consumer plastics: Evaluation and optimisation of mechanical and optical sorting of post-consumer plastics. Evaluation and optimisation of marker/tracer technology to sort plastics including </td> <td> AXION RECYCLING shall grant access to Background that is, or will be found to be, necessary for the implementation of the Project royalty free to the Party or Parties that Need access to implement their </td> <td> AXION shall grant access to Background needed to use the Results of the Project under fair and reasonable conditions to be </td> </tr> </table> <table> <tr> <th> technologies such as fluorescent markers and digital watermarking. Specific knowledge on the sorting of flexible packaging and the effect of packaging design on optical sorting. 2)Washing of post-consumer plastics: Evaluation and optimisation of washing technologies to remove contamination from packaging. Knowledge on packaging design and the effect on the washing/cleaning process. 3)Conversion of post-consumer plastics to secondary raw material Evaluation and optimisation of extrusion and upgrading technologies to produce secondary raw materials from post-consumer plastics. Knowledge on the impact of different materials on the quality recycled. 4) Testing of recycled plastics: Evaluation of recycled polymers to assess the physical properties and determine the suitability for different applications. </th> <th> work in the Project. Provided that the access to background do not contravene non-disclosure agreements or exploitation agreements with third parties. AXION shall not be obliged to grant Access Rights (i) to Background not owned by AXION and/ or (ii) to Background for which AXION is not able to grant Access Rights, due to third party rights and/ or (iii) to Background for which AXION is not able to grant Access Rights without paying compensation to third parties and/or (iv) to information that was not held by AXION before they acceded to the Grant Agreement </th> <th> agreed with the Party or Parties that Need access to use the Results of the Project. AXION shall not be obliged to grant Access Rights (i) to Background not owned by AXION and/ or (ii) to Background for which AXION is not able to grant Access Rights, due to third party rights and/ or (iii) to Background for which AXION is not able to grant Access Rights without paying compensation to third parties and/or (iv) to information that was not held by AXION before they acceded to the Grant Agreement </th> </tr> <tr> <td> 5\. CENTRO RICERCHE FIAT (CRF) </td> </tr> <tr> <td> No data, know-how or information of CENTRO RICERCHE FIAT shall be Needed by another Party for implementation of the Project (Article 25.2 Grant Agreement) or Exploitation of that other Party’s Results (Article 25.3 Grant Agreement). </td> </tr> <tr> <td> 6\. GEMEENTE UTRECHT (UTRECHT) </td> </tr> <tr> <td> No data, know-how or information of GEMEENTE UTRECHT shall be Needed by another Party for implementation of the Project (Article 25.2 Grant Agreement) or Exploitation of that other Party’s Results (Article 25.3 Grant Agreement). </td> </tr> </table> 7. FUNDACION DE LA COMUNITAT VALENCIANA PARA LA PROMOCION ESTRATEGICA EL DESARROLLO Y LA INNOVACION URBANA (INNDEA) No data, know-how or information of FUNDACION DE LA COMUNITAT VALENCIANA PARA LA PROMOCION ESTRATEGICA EL DESARROLLO Y LA INNOVACION URBANA (LAS NAVES shall be Needed by another Party for implementation of the Project (Article 25.2 Grant Agreement) or Exploitation of that other Party’s Results (Article 25.3 Grant Agreement). 8. PRIMARIA MUNICIPIULUI ALBA IULIA (ALBA) Specific Specific limitations limitations and/or and/or conditions for conditions for Describe Background Exploitation implementation (Article 25.2 Grant Agreement) (Article 25.3 Grant Agreement) 1)Collection: data and information about the existing waste collection system and collection infrastructure at local level; expertize and information necessary for the design of the compensation policies 2) Transport: data and information about the existing truck fleet in Alba Iulia (it will be necessary to design the guidance and best practices report on truch traceabiity and driving behaviour guidance) 3) Communication: procedures, expertize and best practices that will be needed in the regional case study workshop and in all the external communication activities where PRIMARIA MUNICIPIULUI ALBA IULIA. PRIMARIA MUNICIPIULUI ALBA IULIA shall grant access to Background that is, or will be found to be, necessary for the implementation of the Project royalty free to the Party or Parties that Need access to implement their work in the Project. Provided that the access to background do not contravene non- disclosure agreements or exploitation agreements with third parties. PRIMARIA MUNICIPIULUI ALBA IULIA shall not be obliged to grant Access Rights (i) to Background not owned by PRIMARIA MUNICIPIULUI ALBA IULIA and/ or (ii) to Background for which PRIMARIA MUNICIPIULUI ALBA IULIA is not able to grant Access Rights, due to third party rights and/ or (iii) to Background for which PRIMARIA MUNICIPIULUI ALBA IULIA is not able to grant Access Rights PRIMARIA MUNICIPIULUI ALBA IULIA shall grant access to Background needed to use the Results of the Project under fair and reasonable conditions to be agreed with the Party or Parties that Need access to use the Results of the Project. PRIMARIA MUNICIPIULUI ALBA IULIA shall not be obliged to grant Access Rights (i) to Background not owned by PRIMARIA MUNICIPIULUI ALBA IULIA and/ or (ii) to <table> <tr> <th> <table> <tr> <th> </th> <th> without paying compensation to third parties and/or (iv) to information that was not held by PRIMARIA MUNICIPIULUI ALBA IULIA before they acceded to the Grant agreement </th> <th> to Background for which PRIMARIA MUNICIPIULUI ALBA IULIA is not able to grant Access Rights without paying compensation to third parties and/or (iv) to information that was not held by PRIMARIA MUNICIPIULUI ALBA IULIA before they acceded to the Grant Agreement </th> </tr> <tr> <td> 9\. MESTNA OBCINA VELENJE (MOV) </td> </tr> <tr> <td> </td> </tr> </table> No data, know-how or information of MESTNA OBCINA VELENJE shall be Needed by another Party for implementation of the Project (Article 25.2 Grant Agreement) or Exploitation of that other Party’s Results (Article 25.3 Grant Agreement). 10\. SOCIEDAD ANONIMA AGRICULTORES DE LAVEGA DE VALENCIA (SAV) Specific Specific limitations limitations and/or and/or conditions for conditions for Describe Background Exploitation implementation (Article 25.2 Grant Agreement) (Article 25.3 Grant Agreement) </th> </tr> </table> Background for which PRIMARIA MUNICIPIULUI ALBA IULIA is not able to grant Access Rights, due to third party rights and/ or (iii) 1) Collection: -Knowledge in monitoring of the filling of waste containers by sensors and external communication from the sensors to a cloud platform. -Knowledge and experience in the use of the best systems for the collection of solid urban waste. 2) Transport: -Experience in data acquisition from the CAN-BUS SAV shall grant access to Background that is, or will be found to be, necessary for the implementation of the Project royalty free to the Party or Parties that Need access to implement their work in the Project. Provided that the access to background do not contravene non-disclosure agreements or exploitation agreements with third parties. SAV shall not be obliged to grant Access Rights (i) to Background SAV shall grant access to Background needed to use the Results of the Project under fair and reasonable conditions to be agreed with the Party or Parties that Need access to use the Results of the Project. POLARIS M HOLDING shall not be obliged to grant Access Rights (i) to Background not <table> <tr> <th> port of vehicles. Data taken from more than 200 vehicles during 5 years. -Knowledge and experience in the more efficient driving model system in function of the loading system of the recollector (rear-loading, lateral-loading, etc…). And in the in the field of fuel consumption in function of the time of the power take-off. </th> <th> not owned by SAV and/ or (ii) to Background for which SAV is not able to grant Access Rights, due to third party rights and/ or (iii) to Background for which SAV is not able to grant Access Rights without paying compensation to third parties and/or (iv) to information that was not held by ITENE before they acceded to the Grant Agreement </th> <th> owned by POLARIS M HOLDING and/ or (ii) to Background for which POLARIS M HOLDING is not able to grant Access Rights, due to third party rights and/ or (iii) to Background for which POLARIS M HOLDING is not able to grant Access Rights without paying compensation to third parties and/or (iv) to information that was not held by POLARIS M HOLDING before they acceded to the Grant Agreement </th> </tr> <tr> <td> </td> <td> 11\. POLARIS M HOLDING (POLARIS) </td> </tr> <tr> <td> Describe Background </td> <td> Specific limitations and/or conditions for implementation (Article 25.2 Grant Agreement) </td> <td> Specific limitations and/or conditions for Exploitation (Article 25.3 Grant Agreement) </td> </tr> <tr> <td> 1)Collection: data and information about the existing waste collection system and collection infrastructure at local level; 2. Transport: data and information about the existing truck fleet in Alba Iulia (it will be necessary to design the guidance and best practices report on truck traceability and driving behaviour guidance) 3. Sorting: data and information about the existing sorting solutions at local level </td> <td> POLARIS M HOLDING shall grant access to Background that is, or will be found to be, necessary for the implementation of the Project royalty free to the Party or Parties that Need access to implement their work in the Project. Provided that the access to background do not contravene non-disclosure agreements or exploitation agreements with third parties. POLARIS M HOLDING shall not be obliged to grant </td> <td> POLARIS M HOLDING shall grant access to Background needed to use the Results of the Project under fair and reasonable conditions to be agreed with the Party or Parties that Need access to use the Results of the Project. POLARIS M HOLDING shall not be obliged to grant Access Rights (i) to Background not owned by POLARIS M HOLDING and/ or (ii) </td> </tr> </table> <table> <tr> <th> </th> <th> Access Rights (i) to Background not owned by POLARIS M HOLDING and/ or (ii) to Background for which POLARIS M HOLDING is not able to grant Access Rights, due to third party rights and/ or (iii) to Background for which POLARIS M HOLDING is not able to grant Access Rights without paying compensation to third parties and/or (iv) to information that was not held by POLARIS M HOLDING before they acceded to the Grant Agreement </th> <th> to Background for which POLARIS M HOLDING is not able to grant Access Rights, due to third party rights and/ or (iii) to Background for which POLARIS M HOLDING is not able to grant Access Rights without paying compensation to third parties and/or (iv) to information that was not held by POLARIS M HOLDING before they acceded to the Grant Agreement </th> </tr> <tr> <td> </td> <td> 12\. INDUSTRIAS TERMOPLÁSTICAS VALENCIANAS (INTERVAL) </td> </tr> <tr> <td> </td> </tr> <tr> <td> Describe Background </td> <td> Specific limitations and/or conditions for implementation (Article 25.2 Grant Agreement) </td> <td> Specific limitations and/or conditions for Exploitation (Article 25.3 Grant Agreement) </td> </tr> <tr> <td> INTERVAL, is a plastic bags manufacture that is working with recycled materials for more than 30 years, It has a broad knowledge on the behavior of the polyethilene in film applications. Nowadays INTERVAL is working its products with plastics from agricultural or industrials waste. </td> <td> INTERVAL shall grant access to Background that is necessary for the implementation of the Project royalty free to the Party or Parties that Need access to implement their work in the Project. </td> <td> INTERVAL shall grant access to Background needed to use the Results of the Project under fair and reasonable conditions to be agreed with the Party or Parties that Need access to use the Results of the Project. </td> </tr> <tr> <td> 13\. ARMACELL Benelux S.A. (ARMACELL) </td> </tr> <tr> <td> No data, know-how or information of ARMACELL Benelux S.A. shall be needed by another Party for implementation of the Project (Article 25.2 Grant Agreement) or Exploitation of that other Party’s Results (Article 25.3 Grant Agreement). </td> </tr> </table> 14\. DERBIGUM / IMPERBEL (DERBIGUM) Specific limitations Specific limitations and/or and/or conditions for conditions for Describe Background Exploitation (Article implementation (Article 25.2 25.3 Grant Grant Agreement) Agreement) Derbigum owns the knowhow Derbigum shall grant access Derbigum shall grant and technology to produce to Background that is access to Background bituminous roofing products necessary for the needed to use the based on the combination of implementation of the Results of the Project bitumen and polymers. Project royalty free to the under fair and These polymers are mainly PP Party or Parties that Need reasonable conditions and SBS based. access to implement their to be agreed with the Derbigum has the knowhow work in the Project. Party or Parties that and technology to use Need access to use recycled polymers within its the Results of the blends to produce the roofing Project. products. It also has developed a technology to reuse old roofing membranes without loss within their new membranes. Derbigum has several patents to protect its technology. #### 15\. CONSORZIO PER LA PROMOZIONE DELLA CULTURA PLASTICA PROPLAST (PROPLAST) Specific limitations Specific limitations and/or and/or conditions for conditions for Describe Background Exploitation (Article implementation (Article 25.2 25.3 Grant Grant Agreement) Agreement) 1)Sorting: Experience in sorting recycled plastic by analytical techniques, such as for example Fourier Transform Infrared spectroscopy (FTIR), Thermal gravimetric analysis (TGA), Differential Scanning Calorimetry (DSC). 2)Recovery and recycling: Development and characterization of high added value plastic formulations based on recycled plastics <table> <tr> <th> <table> <tr> <th> </th> <th> background for which Proplast is not able to grant access rights, due to third party rights and/ or (iii) to background for which Proplast is not able to grant access rights without paying compensation to third parties and/or (iv) to information that was not held by Proplast before they acceded to the Grant Agreement. </th> <th> Proplast is not able to grant access rights, due to third party rights and/ or (iii) to background for which Proplast is not able to grant access rights without paying compensation to third parties and/or (iv) to information that was not held by Proplast before they acceded to the Grant Agreement </th> </tr> <tr> <td> 16\. HAHN PLASTICS Ltd (HAHN) </td> </tr> <tr> <td> </td> </tr> </table> No data, know-how or information of Hahn Plastics Ltd shall be Needed by another Party for implementation of the Project (Article 25.2 Grant Agreement) or Exploitation of that other Party’s Results (Article 25.3 Grant Agreement). 17\. ECOEMBALAJES ESPAÑA S.A. (ECOEMBES) Specific limitations Specific limitations and/or and/or conditions for conditions for Describe Background Exploitation (Article implementation (Article 25.2 25.3 Grant Grant Agreement) Agreement) </th> </tr> </table> Proplast will grant access – on a royalty-free basis – to background that is necessary for the implementation of the project to the party or parties that need access for implementing their work in the project, provided that the access to background do not contravene nondisclosure agreements or exploitation agreements with third parties. Proplast shall not be obliged to grant access rights (i) to background not owned by Proplast and/ or (ii) to Proplast shall grant access to background needed to use the results of the project under fair and reasonable conditions to be agreed with the party or parties that need access for using the results of the project. Proplast shall not be obliged to grant Access Rights (i) to background not owned by Proplast and/ or (ii) to background for which Sustainability assessment: Environmental LCA (Life Cycle Assessment) of SCRAP model. FENIX database (specific database of the recovery, sorting, recycling stages of the household packaging waste management ECOEMBES shall grant access to Background that is, or will be found to be, necessary for the implementation of the Project royalty free to the Party or Parties that Need access to implement their work in the Project. Provided that the access to background do not contravene non-disclosure agreements or exploitation agreements with third parties. ECOEMBES shall not be obliged to grant Access Rights (i) to Background not owned by ECOEMBES and/ or (ii) to Background for which ECOEMBES is not able to grant Access Rights, due ECOEMBES shall grant access to Background needed to use the Results of the Project under fair and reasonable conditions to be agreed with the Party or Parties that Need access to use the Results of the Project. ECOEMBES shall not be obliged to grant Access Rights (i) to Background not owned by ECOEMBES and/ or (ii) to Background for which ECOEMBES is not able to grant Access Rights, due to third party rights and/ or (iii) <table> <tr> <th> </th> <th> to third party rights and/ or (iii) to Background for which ECOEMBES is not able to grant Access Rights without paying compensation to third parties and/or (iv) to information that was not held by ECOEMBES before they acceded to the Grant Agreement </th> <th> to Background for which ECOEMBES is not able to grant Access Rights without paying compensation to third parties and/or (iv) to information that was not held by ECOEMBES before they acceded to the Grant Agreement </th> </tr> <tr> <td> 18\. FUNDACIÓ KNOWLEDGE INNOVATION MARKET BARCELONA (KIMbcn) </td> </tr> <tr> <td> </td> </tr> </table> No data, know-how or information of FUNDACIÓ KNOWLEDGE INNOVATION MARKET BARCELONA shall be Needed by another Party for implementation of the Project (Article 25.2 Grant Agreement) or Exploitation of that other Party’s Results (Article 25.3 Grant Agreement). 19\. PLASTICSEUROPE (PLAST-EU) Specific limitations Specific limitations and/or and/or conditions for conditions for Describe Background Exploitation (Article implementation (Article 25.2 25.3 Grant Grant Agreement) Agreement) PlasticsEurope should grant access to Background needed to use the Results of the Project under fair and reasonable conditions to be agreed with the Party or Parties that Need access to use the Results of the Project. PlasticsEurope shall not be obliged to grant Access Rights (i) to Background not owned by PlasticsEurope and/ or (ii) to Background for which PlasticsEurope is not able to grant Access Rights, due to third party rights and/ or (iii) to Background for which PlasticsEurope is not PlasticsEurope is one of the leading European trade associations with centres in Brussels, Frankfurt, London, Madrid, Milan and Paris. We are networking with European and national plastics associations and have more than 100-member companies, producing over 90% of all polymers across the EU28 member states plus Norway, Switzerland and Turkey. Since the early 1990s the association of European Plastics manufacturers has been committed and prepared to contribute to the enhancement of plastics waste management schemes. Today, we call for a landfill ban of all recyclable and recoverable post- consumer waste by 2025 and the establishment of recoveryPlasticsEurope should grant access to Background that is, or will be found to be, necessary for the implementation of the Project royalty free to the Party or Parties that Need access to implement their work in the Project, once the data is public and has been internally approved. Provided that the access to background do not contravene non-disclosure agreements or exploitation agreements with third parties. PlasticsEurope shall not be obliged to grant Access Rights (i) to Background not owned by PlasticsEurope and/ or (ii) to Background for which PlasticsEurope is not able to grant Access Rights, due to third party oriented collection schemes. These will need to be aligned with modern sorting inf rastructur e and improved recycling and recovery in order exploit the fullest potential of this precious resource. Furthermore, with a focus on high quality and market standards this will stimulate markets for the more resource efficient use of end \- of \- life plastics t hroughout Europe. Our actions are based on: •Specific know \- how and expertise compiled via studies and thorough evaluations of practices in high \- performing member states, •An open dialogue with all relevant stakeholders •Analysis of data on amounts of pl astics waste, recycling and recovery in Europe regularly compiled and made available to the broader public All PlasticsEurope publications are available at: http://www.plasticseurope.org /information \- centre/publications.aspx The Unknown life of Plastics, 20 16 Plastics \- the Facts 2015 Plastic Packaging: Born to protect, 2012 The impact of plastic packaging on energy consumption and GHG emissions, 2011 The impact of plastics on life cycle energy consumption and greenhouse gas emissions in Europe, 2010 rights and/ or (iii) to Background for which PlasticsEurope is not able to grant Access Rights without paying compensation to third parties and/ or (iv) to information that was not held by PlasticsEurope before they acceded to the Grant Agreement able to grant Access Rights without paying compensation to third parties and/or (iv) to information that was not held by PlasticsEurope before they acceded to the G rant Agreement 20. ICLEI EUROPEAN SECRETARIAT GMBH (ICLEI) No data, know-how or information of ICLEI EUROPEAN SECRETARIAT GMBH shall be Needed by another Party for implementation of the Project (Article 25.2 Grant Agreement) or Exploitation of that other Party’s Results (Article 25.3 Grant Agreement). 21. CALAF INDUSTRIAL No data, know-how or information of CALAF INDUSTRIAL shall be Needed by another Party for implementation of the Project (Article 25.2 Grant Agreement) or Exploitation of that other Party’s Results (Article 25.3 Grant Agreement). ## 3.5 Origin of data Each step of the Plastic Value Chain will generate its own data. This means production of data in the collection, transport, sorting and recovery of the plastic. Furthermore, data analysis will be generated to evaluate the whole project approach, as well as, disseminate the project achievements. _Figure 2. PlastiCircle data origin and fluxes_ ### 3.5.1 Collection of plastic waste The origin of the data from the collection of plastic waste will be produced using smart containers. These smart containers are an innovative collection system that will allow to monitor the pilot tests developed in the cities of Valencia, Utrecht and Alba Iulia. These containers will be provided with a user identification system, label expending functionalities, anti-fraud measures, garbage level detection, and IoT communication protocols. For the user identification, the system will be provided with a reading system of “citizen cards” (i.e. unique and smart identification system based on NFC or QR). Citizens will stick a label on the garbage bag before depositing it in the container, which will be provided by the container. This label, which will also be designed in the project, will match the garbage bag with the citizen. Therefore, it will be possible to know how many bags have been deposited by each user and check if the separation of the recycling material has been done properly or if there is unwanted material. With respect to garbage level detection, the filling level of containers will be measured in real time using ultrasonic and/or optic sensors that will provide information from the bins where these sensors are embedded. The data from the sensors in the smart containers will be transmitted by SigFox (radio technology UNB Ultra Narrow Band) that allows the coverage of several kilometers, or LoRA (Low-power wireless protocol for the Internet of Things (IoT). LoRa is likely the better option when bidirectionality is needed because of the symmetric link. Thus, when command-and-control functionality is required. With SigFox, it is possible to use bidirectional command-and-control functionality, but to work appropriately, network density would need to be higher (due to the asymmetric link). Therefore, it is better for applications that send only small and infrequent bursts of data. ### 3.5.2 Transport The transport associated to the collection process of packaging waste will be monitored in order to optimize it. This includes a software platform to gather all data, a truck traceability system, algorithms for route optimization, and guidelines for efficient driving. Furthermore, this platform will allow to integrate all information received from the different sources of data (components of the smart container and truck solution). It will be an IoT (Internet of Things) web-based platform which will let to: create new users; check the segregation performance of each user, the filling status of each container, the remaining labels in each container and the truck position; define driving behaviour guidance and establish and assess current recollection route, as well as to introduce alarms when containers are full. In this sense, the system will allow to define the optimal collection routes based on the position of the containers and their filling levels. This optimization process will reduce the empty travels, allowing to increase the efficiency of the global system. On the other hand, the transport monitoring process will use a system of sensors connected to the CAN-Bus of each vehicle of the waste collector fleet. These sensors will measure the key parameters to optimize the performance of the vehicles and the behaviour of the drivers. Electronic devices with GPRS communication will be connected to the CAN of each vehicle. These devices will parameterize among others the following data: time of use of the power take-off (PTO) of the waste collectors, excess of speed, RPM excess, acceleration, sudden braking, fuel consumption and excessive idling. Reducing the time of use of the PTO in the process of emptying the containers and making the collectors to work at the optimum RPM (i.e., in the optimum power curve and consumption) will significantly reduce the fuel consumption. The data from the sensors will be sent via GPRS periodically and saved in a digital cloud platform. In case of GPRS coverage not being available, the information will be stored in the onboard computer, and ready to be sent when it is restored. After analysing for example, the curves of maximum RPM, PTO or idling time, the hardware installed in the waste collectors will be programmed with the optimal operating values. A traceability system will be defined and developed with GPS, GPRS, and CANdata capabilities. The information generated will be communicated with the PlastiCircle platform, and defined in a way that it is portable and easily adaptable for different trucks. The system will be tested in real applications, and optimized. All participants will help in the testing, giving input for optimization. CITIES and WASTE MANAGERS will also provide data about their own fleet. ### 3.5.3 Sorting Data from sorting of plastic packaging will also be collected and analysed. This will allow to develop, integrate and validate innovative technologies to sort valuable plastic fractions within the packaging waste. This innovative technology will be based on a new film-stabilizing conveyor for plastic sorter able to achieve an excellent performance on films. A special focus will be offered to the stages of material feeding, identification and ejection. The system will be based on Near- Infra-Red-Hyperspectral-Imaging (NIR-HSI), THz (Tera-Hertz) imaging, and hyperspectral whiskbroom/pushbroom shooting along with a spectral shifting. ### 3.5.4 Recovery Once the plastic packaging has been sorted, data from the recovery and recycling of these plastics will be collected and analysed. This information will allow to develop and validate added-value applications and products from the plastic packaging waste previously sorted. The parameters considered to meet the technical requirements of recycled polymers for the new products will be: format (i.e. washed flake, extruded pellet, sorted packaging etc.), maximum contamination level (i.e. % of PVC, PS, bioplastics, metals, paper, etc.), as well as mechanical (e.g. tensile/tear strength), optical (e.g. whiteness, yellowness index and opacity) and processing properties (e.g. melt flow index) of the recycled material (raw material for the applications). In parallel to that, a characterization of all sorted fractions will be carried out (contamination level, mechanical and processing properties) with a view to determine if they are aligned with the technical requirements stabilized by the industries. It should be noted that this characterization will be made both before the pilot (sorted fractions currently available in the market) and during/after the pilots (PlastiCircle sorted fractions). Finally, an economic validation will be fulfilled. For this purpose, the data on production costs of each products will be collected, integrated and analysed; this information will be provided by the 5 industries partners in the project: DERBIGUM, HAHN, INTERVAL, ARMACELL and CRF. ### 3.5.5 Sustainable assessment: The whole PlastiCircle system will be evaluated from a sustainability point of view. The analysis will be centred on the three pillars of sustainability (society, environment and economy) making use of a Life Cycle Assessment approach. In order to achieve this analysis, data from the current plastic waste collection, transport, sorting and treatment in the three test cities (Valencia, Alba Iulia and Utrecht), as well as, data from their integration in the PlastiCircle project will be needed. All the partners will collaborate to collect this information. ### 3.5.6 Data collection & dissemination plan The Communication and Dissemination Manager (ICLEI) supported by the consortium will formulate the dissemination strategy. Key elements of the strategy will include: articulation of the project identity (branding); identification of target audiences (public and private figures); specification of channels for connecting with audiences (events and media platforms); cross- integration of dissemination output (print, electronic and face-to-face). The strategy will also propose ways of developing synergies with existing projects in relevant thematic areas. A dedicated web site will produce an extensive record of all publications and communications originated during the course of the project. This website will ensure a rapid exchange and circulation of information between partners and other stakeholders. All partners will be invited to publish an article in magazines. At least one article per PlastiCircle result will be published in specialised magazines, either at national or European level. Scientific publications will follow an open-access model according to a green model by ITENE, Sintef, Axion, Proplast, PICVISA and SAV. Part of the life cycle data generated will be disseminated according the European Life Cycle Data Network guidelines. Training activities: In order to increase the participation of citizens, industry and waste managers during and after the project, a training plan will be developed. The table below shows the description of training activities: _Table 3. Training activities of PlastiCircle project_ <table> <tr> <th> Target Audience </th> <th> Purpose </th> <th> Result </th> <th> Medium </th> <th> Volume </th> <th> Location </th> <th> Date </th> <th> Partners </th> </tr> <tr> <td> Cities and manufactu rers of smart containers </td> <td> Synergies of PlastiCircle solution with other smart containers </td> <td> 8-12 attendee s </td> <td> Workshop, panel expert </td> <td> 1 </td> <td> Belgium </td> <td> M4 </td> <td> SAV </td> </tr> <tr> <td> Citizens </td> <td> Involve citizens on the design of the containers </td> <td> Adapt container s to citizen needs </td> <td> Workshop on cocreation methodolo gy </td> <td> 1 </td> <td> Valencia </td> <td> M5 </td> <td> INNDEA </td> </tr> <tr> <td> Citizens </td> <td> Proper use of containers. </td> <td> Improve quality of waste to be sorted </td> <td> Workshops and videos on how to use smart containers </td> <td> 10,000 brochur es 1 video </td> <td> Valencia Alba Iulia Utrecht </td> <td> M25 M30 M34 </td> <td> INNDEA ALBA UTRECHT </td> </tr> <tr> <td> Waste managers </td> <td> Proper use of our guidelines </td> <td> Stakehol ders trained </td> <td> Training event: segregatio n quality </td> <td> 1 per country </td> <td> Valencia Alba Iulia Utrecht </td> <td> M36 </td> <td> SAV, ITENE, ECOEMBES </td> </tr> <tr> <td> Waste managers, drivers </td> <td> Proper use of our guidelines </td> <td> Stakehol ders trained </td> <td> Training event: efficient driving </td> <td> 1 per country </td> <td> Valencia Alba Iulia Utrecht </td> <td> M36 </td> <td> ITENE SINTEF PLAST-EU </td> </tr> <tr> <td> Waste managers, sorting plants </td> <td> Proper use of our guidelines </td> <td> Stakehol ders trained </td> <td> Training event: Optimal sorting </td> <td> 1 per country </td> <td> Valencia Alba Iulia Utrecht </td> <td> M36 </td> <td> PICVISA </td> </tr> </table> Workshops: Co-design work is foreseen in order to adapt the PlastiCircle approach to the needs of stakeholders. Co-design will be mainly focussed on a Distributed Participatory Design (DPD) and Mass Participatory design (MPD). The application of both methodologies will have as a main objective the collection and incorporation of the input from all stakeholders in the final design of the PlastiCircle approach. Distributed Participatory Design (DPD) will be based on the realization of meetings in UTRECHT, INNDEA and ALBA IULIA in which the initial project approach will be explained to stakeholders (specially citizens will be invited but also associations and companies). Stakeholders will be asked to give comments and suggestions in these meetings on how to improve and adapt PlastiCircle approach to the specific needs of the cities in study. Mass Participatory design (MPD) will be based on the integration in the webpage of a platform to compile comments/suggestions from stakeholders. INNDEA will prepare the material needed to incorporate this platform in the webpage by ICLEI. This platform will work as a social network in which stakeholders will be able to give comments. Tasks and results of each partner will be also presented in the platform, giving the stakeholders the possibility to directly contact them to give suggestions on the improvement of the whole system. In order to boost the participation of stakeholders in the platform, visits to the waste management plants in Utrecht, Valencia and Alba Iulia will be raffled among participants. UTRECHT, INNDEA and ALBA IULIA will attach sticks to containers used in pilots in order to inform citizens about the possibility to give comments in the platform. Individual visits: Moreover, visits to citizens are foreseen during the initial stage of each pilot in the three cities. These visits will be conducted by UTRECHT, INNDEA, and ALBA IULIA. These visits will be used to inform the citizens about the pilot but also to collect inputs from them through questionnaires. Questionnaires: General Questionnaires will be prepared by INNDEA and SINTEF in English and then adapted and translated to the local languages by the three cities. Comments registered in the platform will be collected by ICLEI and input in questionnaires/visits/meetings respectively by INNDEA, UTRECHT and ALBA IULIA. All these comments will be sent to INNDEA, which will analyse and present them in the PSC meetings. All partners will analyse these results, taking decisions for the following stages of the project in order to align the design to the requirements of stakeholders. Communication campaigns. Three types of communication activities are expected: Raising-awareness campaigns among citizens to boost their understanding of the new systems before being implemented and to encourage participation in pilots (one per city pilot). Waste management campaign to inform waste managers about the implementation and use of the solutions and technologies in each pilot demonstrator. Recycled plastic campaign will take place in each participating country to show the expected impact and benefits of using recycled plastic from packaging waste. Communication Material: Poster, roll-up, business cards, flyers. A series of three posters are planned to promote the project and will be available in the project languages. The posters will be displayed at relevant events related to circular Economy, within the beneficiaries’ organizations, at conferences and exhibitions. In addition, a minimum of 5.000 flyers or postcards will be distributed at events and sent to relevant organizations from the European polymer industries, waste managers and public authorities. Participation in external congresses/conferences/fairs. The dissemination of the project will be boosted by the participation of all the partners to congresses/conferences/fairs in which the project outcomes will be underlined and presented to the other participants. ## 3.6 Data Volumes & Storage ### 3.6.1 PlastiCircle IoT cloud Platform SAV will collaborate to setup the project’s IoT cloud Platform. The location, architecture and structure of the repository and the related security processes will be decided and documented. The IoT cloud Platform is the official data repository of the PlastiCircle project in addition to the project’s web-site. All project data, public and private, will be stored in the IoT cloud Platform, consistently and concurrently. The IoT cloud Platform must support secure access to its storage facilities, based on SSL/TLS certificates. ITENE and SAV will closely collaborate to maintain the IoT cloud Platform. ### 3.6.2 Data Volumes Expected (approximate) data volumes of existing datasets (i.e., those made available to PlastiCircle) will be provided in the updated version of the DMP in M30. ### 3.6.3 Data Storage The existing project datasets are stored at the premises of the owning partners / organizations. Copies of them will be delivered to PlastiCircle storage after anonymization. Datasets of relatively small volumes, such as surveys, spreadsheets, interviews, focus-group data, along with accompanying consent forms, will also be uploaded to and maintained at the project’s SharePoint space. The minimum available storage for data storage in the IoT cloud Platform should be 1 TByte, extendible to 3 TBytes. This storage capacity is considered sufficient for the scope and duration of PlastiCircle. However, if necessary, it can be increased with very little cost. ### 3.6.4 Managed Access Procedures The IoT cloud Platform will store all project data that are potentially of large volume, typically comprising smart containers and transport data. There will be password controlled access to the IoT cloud Platform and all partners will be able to access the private and shared project data through passwords that will be provided to them in a secure way. Data will be uploaded to the IoT cloud Platform by any PlastiCircle partner and downloaded by them via secure FTP. Typical secure ftp applications are sftp, WinSCP, Filezilla, Cyberduck, etc. Data-sharing agreements between partners will not be necessary, because the data on the repository will be anonymous. Each partner is responsible to provide its own metadata and data descriptions. Quality control on the metadata and data descriptions will be enforced by the IoT cloud Platform manager (SAV) and provisions will be made to enable metadata searching capability. ### 3.6.5 Third-party Data During the project all the results, technologies and techniques will be analysed in order to establish if any of the results can be exploited by licensing them to a third part, according to the market interest of the partners. The main target stakeholders to succeed in this exploitation strategy will be public administrations that can adopt the solutions and transfer it to their services providers or recycling and waste management companies, mostly environmental engineering that want to acquirer the right to exploit the solution in order to boost their competitiveness. The strategy will be mainly driven by the aim to boost the European economy as a whole. For this reason, the exploitation rights conceded to the entities interested will be not-exclusive in order to widen the positive effects of PlastiCircle project the most. # 4\. FAIR data In general terms, PlastiCircles’ research data should be “F.A.I.R.” that is Findable, Accessible, Interoperable and Re-usable. These principles precede implementation choices and do not necessarily suggest any specific technology, standard or implementation-solution. It should be noted that, participating in the ORD Pilot does not necessarily imply opening-up all PlastiCircle research data. Rather, the ORD pilot follows the principle "as open as possible, as closed as necessary" and focuses on encouraging sound data management as an essential part of research best practice. The European Commission recognizes that there are good reasons to keep some or even all research data generated in a project closed. The DMP explains which data can be shared and under what terms and conditions. ## 4.1 Making data findable, including provisions for metadata ### 4.1.1 Provisions for Findability Provisions and actions that are to be taken to ensure the discoverability of PlastiCircle data include: * Accompanying datasets with properly structured and accurate metadata * Providing proper documentation identifying their content and potential uses * Making data identifiable by using standard identification mechanisms and persistent and unique identifiers (e.g. Digital Object Identifiers (DOI), where applicable * Advertising them in global search engines * Publishing papers and reports with references to them * Providing consistent naming conventions (e.g., using descriptive filenames, in English, with version numbers, etc.) * Ensuring accessibility of the hosting infrastructure (at least 99.9%) ### 4.1.2 Metadata and documentation Data should be documented and organized in order to be accessible. Good data documentation includes information on: * the context of data collection: aims, objectives and hypotheses * data collection methods: data collection protocol, sampling design, instruments, hardware and software used, data scale and resolution, temporal coverage and geographic coverage * dataset structure of data files, cases, relationships between files * data sources used * data validation, checking, proofing, cleaning and other quality assurance procedures carried out * modifications made to data over time since their original creation and identification of different versions of datasets * information on data confidentiality, access and use conditions, where applicable At data-level, datasets should also be documented with: * names, labels and descriptions for variables, records and their values * explanation of codes and classification schemes used * codes of, and reasons for, missing values * derived data created after collection, with code, algorithm or command file used to create them * weighting and grossing variables created data listing with descriptions for cases, individuals or items studied ## 4.2 Making data openly accessible The IoT cloud Platform will store all large volumes of data that are not appropriate for storage on SharePoint (e.g. filling levels, ID data, efficiency driving, collection routes, plastic characteristics, etc.). Following the H2020 open access strategy for scientific publications, all the publishable results will follow an open-access model according to a green model. Previously, an IPR assessment will be done to achieve the best exploitation strategy for each partner and avoid incompatibilities. The “green model or self-archiving” will be the preferential way. However, when the magazine does not allow the deposit of items in the repository, two strategies will be followed: * Post the article in "gold model” open access, on payment of fees, or considering the possibility to publish it in open access journals subscription. * If gold or green open access is not possible, other relevant journals will be chosen. In order to comply with green model requirements, beneficiaries will, at the very least, ensure that their publications, if any, can be read online, downloaded and printed. Each beneficiary will deposit a machine readable electronic copy of the published version or final peer-reviewed manuscript accepted for publication in a repository for scientific publications. This step will be followed even in case of open access publishing ('gold' open access) in order to ensure long-term preservation of the article. The repository for scientific publications is an online archive. Open Access Infrastructure for Research in Europe (OpenAIRE) will be the entry point for researchers to determine what repository to choose (http://www.openaire.eu). After depositing publications and, where possible, underlying data, beneficiaries will ensure open access to the deposited publication via the chosen repository. On the other hand, a pre-selection of journals and publications of interest for the dissemination of the project results has been made: _Table 4. Pre-selected Journals for Open Access dissemination strategy_ <table> <tr> <th> Pre-selected Journals for open access dissemination strategy </th> </tr> <tr> <td> Advances in Recycling & Waste Management </td> <td> Advances in Materials Science and Engineering </td> </tr> <tr> <td> International Journal of Integrated Engineering </td> <td> Open Environmental Engineering Journal </td> </tr> <tr> <td> Sustainability: Science, Practice and Policy </td> <td> Journal of Management and Science </td> </tr> <tr> <td> Environmental Research, Engineering and Management </td> <td> Journal of Waste Management </td> </tr> </table> ### 4.2.1 Methods for Data Sharing Methods for data sharing include: * Secure FTP access (compressed files) * Web-site (hyperlink based) file access * Web-services (SOAP, REST) through database access * API-based access (for application programmers) Potential data users include public administrations that can adopt the solutions and transfer it to their services providers or recycling and waste management companies, mostly environmental engineering that want to acquirer the right to exploit the solution in order to boost their competitiveness. All shareable data will be released for general access as soon as the dataset is complete, its quality is assured, and it is sufficiently annotated to be widely useful. Before general release, the adequacy of data storage and access procedures will be tested first by project personnel, then by selected colleagues external to the project. Publications describing the data collected and conclusions drawn from them would be submitted soon thereafter. Other data will more appropriately be made generally available at the time publications reporting on them are accepted. Archived data will be made available initially as just described and are intended to be available indefinitely or until judged no longer useful. The project data are intended to be available indefinitely beyond the term of the grant, or for as long as their hosting infrastructure (repositories, etc.) are accessible. The development of data-analysis tools is anticipated as outcomes of the PlastiCircle project. Those tools that will be openly offered will be made available through the project’s website and/or the IoT cloud Platform with sufficient tutorial to allow future users to use them tools without undue difficulty. ### 4.2.2 Data Repositories As mentioned above, all project data will be stored (maintained and archived) in the PlastiCircle’s IoT cloud Platform. However, public information (i.e. public deliverables) will be made open access through Open Data Repositories as the project runs and after its end. This will ensure that PlastiCircle open data will be done more persistent, even when the IoT cloud Platform will not be available and maintained for several years after the end of the project. An international list of global data repositories is available via _Re3data_ ( _http://www.re3data.org_ ) . Journal articles will be made available on an Open Access basis. Outputs deposited on such repositories will be discoverable via search engines such as Google scholar, increasing visibility, increased likelihood of citation and raising of research profiles. Open access facilitates broader knowledge transfer and open science as it ensures that non-academic organisations such as small and medium-sized enterprises and charities who have limited access to journal outputs are able to freely access published research via the Internet. ## 4.3 Making data interoperable Interoperability refers to allowing data exchange and re-use between researchers, institutions, organizations, countries, etc. (i.e. adhering to standards for formats, as much as possible compliant with available (open) software applications, and particularly facilitating re-combinations with different datasets from different origins. * Interdisciplinary interoperability of PlastiCircle data is ensured by: * Having the data accessible via global and well-known data repositories * Using consistent and standard metadata vocabularies * Using standard and re-usable data formats * Providing well-defined and standard access methods * Providing open tools and open software for processing the data * Adhering to open data standards as much as possible * Using licensing types that facilitate broad use and open-access of the data ## 4.4 Increase data re-use Publicly available data from the PlastiCircle project will be available in open formats and with as least-restrictive licenses as possible, to allow the widest reuse possible. ### 4.4.1 Data Sharing Public (shareable) datasets from the PlastiCircle project (along with accompanying metadata) will be shared in a timely fashion. It is generally expected that timely release should be no later than 3 months after publication of the main findings and that the data will remain available and re-usable until at least 3 years after the end of the project. Shareable data will have properly defined and specific (open) formats and nonrestrictive licensing descriptions, to facilitate easy re-usability and interoperability by third parties. Data licensing will respect the owner’s IPRs (requiring proper attribution), but will permit the re-use of the data for non-commercial purposes. The public datasets will be complete and consistent and a quality assurance process will be enforced on them before they are made available to ensure that they are usable and of acceptable quality. User feedback will be used to further improve the quality of the data and increase their re-usability. ### 4.4.2 Expected Data Re-use Interested parties in PlastiCircle data include: * Stakeholders, including plastic producers/converters, waste managers, equipment firms, * Consumers/citizens * Public organizations * Researchers and academics conducting research in the field of plastic recycling and converting. Improving competitiveness in Europe: The project will create new business opportunities in the plastic sector in terms of producers, converters, waste management, equipment and software. They will manufacture and exploit eco-innovative solutions on collection, transport, sorting and recovery of plastic waste, which will be launched on the European and global markets. ### 4.4.3 IPR Ownership and Licensing The definition of IPR policies and knowledge management for knowledge protection in PlastiCircle will be based on the Consortium Agreement, in addition to the IPR provisions in the Grant Agreement and Rules for Participation of Horizon 2020. IPR and Innovation management will be described in a Strategy Document (D. 8.4) and will result in the Exploitation and Business Plan (D. 8.3) where each partner’s need will be identified. WP leaders will be responsible within their WP for the identification, monitoring and issue of project outcomes. They will fill in an IPR Chapter in their periodic reporting, which will be added in the Exploitation and Business Plan. In the General Assembly the IPR issues will be exposed while decisions are taken jointly concerning the strategies of and provisions for protection of IPR in the specific cases. The main effort here will be devoted to the implementation of the IPR related decisions of the Project Steering Committee. It should be noted that the Project Steering Committee (formed by one representative of each partners) will be formed during the first six months of the project, having the first meeting in Month 6. For every dataset deposited to the PlastiCircle the IoT cloud Platform the following information will have to be declared: * Data description * Data volume and format * Ownership of data (i.e., who produced and who owns the data) ##### Licensing type (“Creative Commons” type, or other) The above requirement applies to both local project data, produced by PlastiCircle partners during the lifetime of the project, as well as for existing and third-party data that will be used during PlastiCircle. Open License (Legal Openness) In most jurisdictions, there are intellectual property rights in data that prevent third-parties from using, reusing and redistributing data without explicit permission. Even in places where the existence of rights is uncertain, it is important to apply a license simply for the sake of clarity. Thus, if you are planning to make your data available you should put a license on it – and if you want your data to be open this is even more important. For open data one of the licenses conformant with the Open Definition and marked as suitable for data can be used. This list (along with instructions for usage) can be found at: _http://opendefinition.org/licenses_ A short instruction guide to applying an open data license can be found on the Open Data Commons website: _http://opendatacommons.org/guide_ Creative Commons (CC) licensing is described in: _https://creativecommons.org/licenses/_ _https://en.wikipedia.org/wiki/Creative_Commons_ _https://en.wikipedia.org/wiki/Creative_Commons_license_ # 5\. Allocation of resources ## 5.1 Allocation of Responsibilities People/groups involved in Data Management in PlastiCircle are the members of the PSC: _Table 5. Members of the PSC: People involved in the Data Management Plan_ <table> <tr> <th> </th> <th> Partner </th> <th> Person </th> <th> </th> <th> Partner </th> <th> Person </th> </tr> <tr> <td> 1 </td> <td> ITENE </td> <td> César Aliaga </td> <td> 11 </td> <td> POLARIS </td> <td> Tarsosaga IonutNicolae </td> </tr> <tr> <td> 2 </td> <td> SINTEF </td> <td> Dr. Einar Hinrichsen </td> <td> 12 </td> <td> INTERVAL </td> <td> Eva García </td> </tr> <tr> <td> 3 </td> <td> PICVISA </td> <td> Luis Seguí </td> <td> 13 </td> <td> ARMACELL </td> <td> Sven Hendriks </td> </tr> <tr> <td> 4 </td> <td> AXION </td> <td> Richard McKinlay </td> <td> 14 </td> <td> DERBIGUM </td> <td> Hans Aerts </td> </tr> <tr> <td> 5 </td> <td> CRF </td> <td> Vito Lambertini </td> <td> 15 </td> <td> PROPLAST </td> <td> Marco Monti </td> </tr> <tr> <td> 6 </td> <td> UTRECHT </td> <td> Jan Bloemheuvel </td> <td> 16 </td> <td> HAHN </td> <td> Howard Waghorn </td> </tr> <tr> <td> 7 </td> <td> INNDEA </td> <td> Julian Torralba </td> <td> 17 </td> <td> ECOEMBES </td> <td> Ana Rivas </td> </tr> <tr> <td> 8 </td> <td> ALBA </td> <td> Valentin Voinica </td> <td> 18 </td> <td> KIMbcn </td> <td> Jordi Gasset </td> </tr> <tr> <td> 9 </td> <td> MOV </td> <td> Mirjam Britovšek </td> <td> 19 </td> <td> PLAST-EU </td> <td> Irene Mora </td> </tr> <tr> <td> 10 </td> <td> SAV </td> <td> Jerónimo Franco </td> <td> 20 </td> <td> ICLEI </td> <td> Kelly Cotel </td> </tr> <tr> <td> </td> <td> </td> <td> </td> <td> 21 </td> <td> CALAF INDUSTRIAL (THIRD PARTY) </td> <td> Rodrigo Verbal </td> </tr> </table> ## 5.2 Additional Resources & Costing The cost of Data Management regarding PlastiCircle’s role and responsibilities has been included in the project’s budget and no additional resources (i.e. with extra costs) will be charged to the project. # 6\. Data security ## 6.1 Data Security Policies ### 6.1.1 Confidentiality, Integrity, Availability ITENE’s Information Security Management System (ISMS) is certificated by AENOR, according to ISO 27001 4 . ISMS are the most effective means of minimising risks, ensuring that assets and risks are identified, considering the impact for the organisation, and that the most effective controls and procedures are adopted in line with business strategy. The policies, procedures, human and machine resources, which constitute the ISMS of the PlastiCircle project ensures an effective management of information according to the “CIA” triad — Confidentiality, Integrity and Availability. * confidentiality, ensuring that only those who are authorised can access the information, * integrity, ensuring that the information and its processing methods are accurate and complete, and * availability, ensuring that authorised users have access to the information and to related assets when they need it. Physical security, network security and security of computer systems and files are all considered to ensure security of data and prevent unauthorised access, changes to data, disclosure or destruction of data. The certification of Information Security Management by AENOR, in accordance with the UNE-ISO/IEC 27001:2014, helps to promote activities of data protection, thus improving their image and generating confidence with respect to third parties. ### 6.1.2 Data Anonymization / Pseudonymization According to the GDPR, “pseudonymization” refers to the processing of personal data in such a manner that the personal data can no longer be attributed to a specific data subject without the use of additional information, provided that such additional information is kept separately and is subject to technical and organisational measures to ensure that the personal data are not attributed to an identified or identifiable natural person. However, the explicit introduction of pseudonymization is not sufficient by itself to preclude any other measures of data protection. Therefore, security policies for data protections should always be enforced as strictly as possible. The controllers of the PlastiCircle IoT cloud Platform should require that data pseudonymization (or anonymization) is enforced before any dataset is uploaded to the IoT cloud Platform. ### 6.1.3 Encrypted Communications Even if pseudonymization is done at the device, the user's privacy can still be compromised because its data is typically communicated through WiFi networks, and thus may be eavesdropped and/or be victimized by man-in-the- middle type of attacks. For this reason, strong end-to-end encryption must be enforced from the device (one end) to the central server (the other end). On the server's end, the uploaded data should be decrypted, validated and stored, stripped from the (hashed) device_ID. [Note: for statistical purposes, we may store the hashed device_IDs, without linking them with their associated data.] The hashing and encryption keys should comply with symmetric/asymmetric cryptography standards and techniques. Strong symmetric encryption (AES-256) and strong asymmetric encryption (e.g. RSA-2048) should be used to provide the strongest possible encryption available today. Server digital certificates are protected on the server's side. Shared keys are exchanged with the client devices using asymmetric cryptography and are applied in the symmetric encryption/decryption of large data volumes (for efficiency reasons). ## 6.2 Storage & Backups Data storage, in the context of Data Security, must be done in such a way to ensure the privacy and integrity of data and prevent unauthorised access, changes to data, disclosure or destruction of data. Transmitting (uploading or downloading) sensitive or personal data between locations or within research teams must always be done using data encryption, e.g. using secure FTP or other secure data transfer protocol, to ensure data privacy and prevent unauthorized access of data (e.g. eavesdropping). Access to data repositories should be password protected and access logs should be maintained. Archived data of personal or sensitive nature should be stored encrypted, with strong encryption. Before the PlastiCircle project is completed, the partners will decide which data will have to be destroyed and which data will be maintained (and for how long). To ensure data integrity, avoid loss of data and maintain storage consistency, regular data backups should be performed on a weekly and monthly basis, either incremental or full. Data backups should be accompanied with appropriate and corresponding data recovery procedures. # 7\. Ethical aspects An “Ethics Handbook” has been developed, as Deliverable D10.1, D10.2, D10.3 and D10.4 of Work Package 10, which addresses the ethics requirements of the PlastiCircle project. # 8\. Conclusion The Data Management Plan (DMP) will be the guide document for project’s data treatment and management. As has been seen, the DMP describes which and how data is collected, processed or generated, but also outlining the methodology and standards used. Furthermore, the DMP explains whether and how this data is shared and/or made open, and how it is curated and preserved. Finally, it should be taken into account that the DMP evolves during the lifespan of the project. Thus, this initial version will be updated in M30 to reflect the status of the PlastiCircle project with respect to its data management.	https://phaidra.univie.ac.at/o:1140797	Horizon 2020
0089_IL TROVATORE_740415.md	# Introduction to DMP development IL TROVATORE contributes to the H2020 Open Research Data (ORD) Pilot, which ‘aims to make the research data generated by H2020 projects accessible with as few restrictions as possible, while at the same time protecting sensitive data from inappropriate use’ [1]. The IL TROVATORE participation to the ORD Pilot calls for the development of a Data Management Plan (DMP), the 1 st version of which is presented herein; regularly updated versions of the DMP, which is a ‘living document’, will be submitted at the end of each reporting period or in an _ad hoc_ manner when properly motivated. Typically, a DMP is a document that describes the way the data will be treated during the project lifetime and the fate of these data after the end of the project [2]. DMPs may cover fully or partially the data life cycle, i.e., from data production, selection/collection and organisation (e.g., databases), through quality assurance & quality control, documentation (e.g., data types, lab methods) and use of the data, to data preservation and sharing (e.g., dissemination strategies) [2]. Michener et al. [2] have summarised the following 10 simple rules that govern the development of an efficient DMP: 1. Determine the research sponsor requirements: sponsors usually provide DMP requirements in either the public request/call for proposals or in an online grant proposal guide. The European Commission’s expectations from the DMPs produced within the ORD Pilot may be found in the Participant Portal H2020 Online Manual [3], which also provides a DMP template in ODT format (i.e., an OpenDocument format) [4]. This template was used to produce the IL TROVATORE DMP. 2. Identify the data to be collected: a good DMP plan includes sufficient information to understand the collected data, i.e., the data types, sources and volume, as well as the data and file formats. Since not all data-related information is known for the IL TROVATORE project at this stage, the DMP will be iteratively updated in the project lifetime. 3. Define how the data will be organised: defining the best approach to organise and manage the produced data can only happen when the types and volume of data are known or can at least be predicted with a reasonable level of accuracy. Therefore, the appropriate software tools for data organisation will be defined in one of the updated DMP versions that will be produced later in the project lifetime. The IL TROVATORE DMP proposes naming conventions for important documents (e.g., Deliverables, Milestones, etc.), while it considers the use of persistent unique identifiers (e.g., Digital Object Identifiers – DOIs) and versioning control (e.g., software and data products) whenever appropriate. 4. Explain how the data will be documented: this refers to the use of metadata that can allow data and files to be discovered, used, and properly cited. Metadata provide details on what, where, when, why and how the data were collected, processed and interpreted. Metadata also describe how data and files are named, physically structured, and stored; they also provide details about the experiments, analytical methods, and research context in which they were acquired. The metadata completeness and comprehensiveness can be directly associated with the data utility and longevity. A successful documentation strategy is based on 3 steps: (a) identify the type of information that must be captured so as to enable data discovery, access, interpretation, use and citation; (b) determine whether there is a community-based metadata schema or standard (i.e., preferred sets of metadata elements) that can be easily adopted. Often, a data-repository-specific content standard is recommended by the target data repository, archive, or domain professional organisation; (c) identify software tools that can be used to create and manage metadata content. A well-informed documentation strategy will only be defined for the IL TROVATORE metadata at a later stage of the project, i.e., when there is more information on the nature of the data (i.e., type, volume, format, etc.) to be collected in the project lifetime. 5. Describe how data quality will be assured: quality assurance and quality control (QA/QC) refer to the processes employed to measure, assess, and improve the quality of products (e.g., data, software). Since the 1 st version of the IL TROVATORE DMP is produced at an early stage of the project lifetime, where the optimum material testing methods (e.g., for joining materials, SiC/SiC composites, etc.) have not yet been identified, it is impossible to describe the QA/QC measures (e.g., instrument calibration and verification tests, statistical and visualisation approaches to detect errors, training activities, etc.) that will be employed in the project. The appropriate QA/QC measures will be described in later DMP versions, when the project beneficiaries have had the opportunity to make well-informed decisions on the most appropriate, activity-specific measures. 6. Present a sound data storage and preservation strategy: data storage and preservation are key to a good DMP, so as to ensure that data remain available for use by both their originators and others. The data storage and preservation strategy must consider the following 3 questions: (1) how long will the data be accessible, (2) how will the data be stored and protected over the duration of the project, and (3) how will the data be preserved and made available for future use. It has already been decided that the data will be first stored in the password-protected SharePoint area of the IL TROVATORE website ( _http://www.iltrovatore-h2020.eu/_ ) . The openly-accessible data sets, codes, etc., will be preserved in the Zenodo repository ( _https://zenodo.org/_ ) , which has been developed in the framework of the OpenAIRE project ( _https://www.openaire.eu/_ ) in order to share, curate and publish data produced by EC-funded research. 7. Define the project’s data policies: a good DMP should include explicit policy statements on data management and data sharing. Such policy statements could touch upon issues, such as licensing or sharing arrangements of pre-existing (background) data as well as plans for retaining, licensing, sharing and embargoing data, codes, etc., produced in the project framework (foreground data). Developing a sound data policy that does not regard sensitive data with legal/ethical restrictions typically comprises 2 steps: (a) identify and describe relevant licensing and sharing arrangements by considering proprietary and intellectual property rights (IPR) laws as well as export control regulations on the research products (e.g., data, codes, software, etc.); and (b) explain how and when the data/research products will be made available (e.g., describe embargo/delay periods associated with publications or patenting, possible non-standard licenses and waivers, etc.). The IL TROVATORE IP management approach has been described in detail in the project Consortium Agreement (CA) document, which has been signed by the 30 beneficiaries in February 2018 [5]. The 1 st version of the DMP also discusses publication embargo issues associated with open access publishing in the framework of the project. 8. Describe how the data will be disseminated: the DMP must also address how and when the data products will be disseminated both within and beyond the Consortium boundaries. Dissemination can be ensured using both passive and active approaches. In the IL TROVATORE project, passive dissemination involves placing the data in the password-protected area (SharePoint) of the website, while active dissemination involves publishing the data in the Zenodo repository; moreover, open access will be ensured for all peer-reviewed scientific publications on the data produced within the framework of the project. Other means of active data dissemination may include (a) submitting the data (or data subsets) as appendices or supplementary materials in peer-reviewed Journal articles, and (b) publishing the data, metadata and relevant codes as “data papers” [6]. It is important to note that many Journals and data repositories provide guidelines on the appropriate citation of data by others, including the use of DOIs and recommended citation formats. Moreover, the data will be more usable and interpretable by all interested parties, provided they are disseminated using standard, non-proprietary approaches and when they are accompanied by metadata and associated codes used for data processing. 9. Assign roles and responsibilities: a good DMP describes with clarity the roles and responsibilities of every organisation involved in the project. These roles may include data collection, data entry, QA/QC measures, metadata creation and management, backup, data preparation and submission to an archive/repository, and systems administration. Time allocation and needed staff level of expertise must be carefully considered. A large-scale, multi-investigator, multidisciplinary project, such as IL TROVATORE, should consider a staff member dedicated to data management, probably within the premises of the project coordinator (SCK•CEN). As already mentioned, this 1 st DMP version is considered a ‘living document’ that will be updated at the end of each reporting period (m18, m36, m54), until a mature data management policy is established. It is also recommended to revisit the DMP frequently (e.g., on a quarterly basis) so as to reflect the evolution in policies and protocols. The DMP revision history will also be tracked, registering the dates when changes were made along with the person who made them. In order to describe the principles of data management after the end of the project, it is advisable to describe the policies and procedures of the data repository (i.e., Zenodo in the case of IL TROVATORE) where the data will be stored. 10. Prepare a realistic budget: data management is time-consuming and costly in terms of personnel, software, and hardware. At present, data management is entrusted to SCK•CEN, in association with the planned activities on website maintenance. However, it is clear that data management activities will grow as data production evolves in the project lifetime. Since IL TROVATORE brings together academic partners, who are interested in publishing, with industrial partners, who are keen on patenting and transferring the produced innovation to market, data management is an activity that cannot be taken lightly in this project. The budget/personnel that will be dedicated to data management at SCK•CEN is currently under consideration. The potential disagreement between entities that traditionally strive for publishing (i.e., research organisations, ROs) and entities that strive for patenting (i.e., industries) has already been discussed by the European IPR Helpdesk ( _https://www.iprhelpdesk.eu/_ ) in a dedicated report/fact sheet [7]; in the same fact sheet, the idea of bridging that commonly encountered ‘gap’ has been explored and alternative dissemination routes (e.g., defensive publication, open access, etc.) have been proposed. Fig. 1 is a graphical representation of the relation between research and data life cycles that provides reference to the 10 rules governing DMP development [2]. Figure 1. Graphical representation of the link between research and data life cycles; the red numbers refer to the 10 rules governing DMP development. The research life cycle involves (1) formulation of ideas & hypotheses, (2) data acquisition, (3) data analysis, visualisation & interpretation, and (4) data publication or alternative dissemination (e.g., Conference presentations, etc.). The data life cycle involves (1) DMP development, (2) discovery of existing data, (3) collection & organisation of new data, (4) data quality assurance, (5) data description (e.g., ascribing metadata), (6) use of data, (7) data preservation, and (8) data sharing. Figure adapted from Ref. [2]. As already mentioned, the DMP is a ‘living document’ that aims to describe all stages in the data life cycle, from data generation and collection to data preservation and sharing, providing guidelines on data management both during the project lifetime and after the project ends. The herein presented 1 st version of the DMP gives a tentative overview of important data management aspects, such as the types and formats of data that will be generated/collected during the project, the expected origin and reuse of data, how to make data FAIR (findable, accessible, interoperable, and reusable), etc. The next (updated) DMP version is scheduled for the end of the 1 st reporting period (m18) and will contain more information on appropriate metadata & keywords, QA/QC measures, strategy to make data interoperable, resources that will be dedicated to data management, etc. # Data Summary ## Data collection/generation with respect to the project objectives IL TROVATORE is a Research & Innovation Action (RIA) dedicated to the improvement of nuclear energy safety on a global scale by validating select accident-tolerant fuel (ATF) cladding material concepts in an industrially relevant environment (i.e., via neutron irradiation in PWR-like water). The first 2 years in the project lifetime are dedicated to the optimisation of the candidate ATF cladding concepts (SiC/SiC composites, MAX phase- and oxide- coated clads, GESA surface-alloyed clads and ODS-FeCrAl alloys) and the assessment of their performance with respect to the cladding material property requirements imposed by the ATF application. Years 3-4 of the project are dedicated to the neutron irradiation of well-performing materials in PWR-like water in the BR2 research reactor. The R&D activities will be concluded by the post-irradiation examination (PIE) of neutron-irradiated ATF cladding materials; it should be noted that the collection of PIE data will start much before m48 (end of the total 2-years irradiation in BR2) by analysing the early-sampled materials (1-3 dpa). The PERT chart of the IL TROVATORE workflow is shown in Fig. 2, indicating that the successful implementation of this project relies on the feedback loops (i.e., data exchange) between foreseen activities. Figure 2. PERT chart of the workflow in the IL TROVATORE project. IL TROVATORE strives to deliver proof-of-concept cladding materials (TRL 5) that are designed to address the stringent requirements of the ATF application. In all phases of the project, applicationdriven material design exchanges info/data with material production and material performance assessment, in a conscious effort towards accelerated materials development (AMD), as dictated by the dire global societal and industrial demand for safer nuclear energy. A schematic representation of the AMD principle is given in Fig. 3. Meeting the ambitious S&T objectives of IL TROVATORE means that a continuous flow of high-quality data must be ensured between the different stages in ATF cladding material development. For example, the development of accurate models that can predict the in-service degradation of the candidate ATF clads relies on reliable input data from processing (WP1-WP3), performance evaluation (WP4-WP6) and validation (WP7-WP8). AMD also involves the development of high-throughput screening tools, such as ion/proton irradiation to recreate, in a fast and relatively inexpensive way, material-specific defect microstructures similar to those induced by neutron irradiation. The successful employment of ion/proton irradiation to emulate neutron-induced defect microstructures in innovative nuclear materials, such as the ones studied in IL TROVATORE, relies on a robust data management policy. In accordance with Fig. 1, the first step involves the labour-intensive collection and evaluation of existing ion/proton/neutron irradiation data for all IL TROVATORE ATF cladding materials & constituents thereof; needless to say, the critical evaluation of the quality and relevance of the existing irradiation data requires the involvement of reviewers with background in Radiation Materials Science. This has been already achieved for the majority of the candidate ATF cladding materials in the 1 st edition of the report accompanying Milestone 4 – Mining of existing irradiation data [8]. The second step implies the collection and organization of new ion/proton irradiation data; the new data can be divided in two data subsets: (a) the data that are needed to validate the material-specific ion/proton irradiation approach, which will be used in the project to assess the radiation tolerance of the candidate ATF clads prior to the BR2 neutron irradiation; and (b) the actual data that will provide key information on the radiation tolerance of the new materials and on the possible ways to improve it (feedback to WP1-WP3). The robustness of the employed ion/proton irradiation approach is linked to the quality of the ion/proton irradiation data (step 4 in Fig. 1); hence, it is quite important to dedicate a part of the ion/proton irradiation campaigns in the project to develop and validate the proposed irradiation approaches. Once the quality of the irradiation data produced in the project is ensured, their further description, use , preservation and sharing (steps 58 in Fig. 1) are guaranteed. Apart from the value of the actual irradiation data, proposing a robust approach of using ions & protons to assess the radiation tolerance of innovative nuclear materials is a major step forward for the conservative nuclear sector. The data subsets involved in establishing such an approach will be openly published in the framework of Deliverable 6.3 – Best-practice guidelines on the use of ion/proton irradiation to facilitate AMD of nuclear materials (m54), as well as in an accompanying “gold open access” Journal article. The data management involved in the establishment of a material- specific, best-practice approach for ion/proton irradiation is a single indication of the anticipated complexity of the mature (end-of-project) DMP of the IL TROVATORE project. Fig ure . 3 AMD principle in IL TROVATORE. Exploitation of Innovative ATF Material Concepts Material Design Material Assessment ## Types and formats of collected data The research data that are expected to be collected in the framework of the IL TROVATORE project include: data in numerical and graphical format (e.g., facts/numbers; graphs of the evolution of material properties with processing/testing conditions); images (e.g., microstructural information on all scales, from the microscale to the nanoscale); documents (e.g., reports analysing/processing data subsets; peer-reviewed Journal articles with data in appendix or supplement; patents; standards); presentations (e.g., Conference presentations); etc. Moreover, various documents related with the project management/progress will be produced in the course of the project. These documents include: Deliverables; Milestone reports; administrative documents (e.g., templates); progress reports (e.g., BEBRs, IARs, PARs); minutes of progress meetings; financial documents; presentations; etc. Documents associated with contractual (Grant Agreement) obligations (e.g., Deliverables, Milestones, etc.) will be uploaded to the ECAS portal and the password-protected area (SharePoint) of the website ( _http://www.iltrovatore-h2020.eu/_ ) . These documents will always be uploaded in PDF format. Dissemination-oriented documents include: peer-reviewed Journal articles, articles in Conference proceedings, educational/training notes/modules, special Journal editions, etc. Communication-oriented documents/materials comprise: brochures & leaflets, video, website, etc. The tentative list of datasets to be collected in the course of the IL TROVATORE project is summarised in tabular form in the __Archive Plan_ _ (see Annex 1 and IL TROVATORE Archive Plan_31032018.xlsx). The 1 st version of the DMP recommends the use of the following tentative data formats: For numerical/tabular data: * delimited text (.txt) with characters not present in data used as delimiters * widely-used formats: MS Excel (.xls/.xlsx), MS Access (.mdb/.accdb), dBase (.dbf), OpenDocument Spreadsheet (.ods) For textual data: * Rich Text Format (.rtf) * PDF/UA, PDF/A * plain text, ASCII (.txt) * widely-used formats: MS Word (.doc/.docx) For images: * TIFF 6.0 uncompressed * TIFF other versions (.tif, .tiff) * PEG (.jpeg, .jpg, .jp2), if the original is created in this format * GIF (.gif) * RAW image format (.raw) * Photoshop files (.psd) * BMP (.bmp) * PNG (.png) * Adobe Portable Document Format (PDF/A, PDF) (.pdf) As explained in section 2.1 of this document, the success of the IL TROVATORE project relies on the efficient flow of information between tasks (represented by the feedback loops/interconnectivity arrows in Fig. 2). It is essential, therefore, that the contributors to a specific task agree as soon as possible on the type, format and typical size of data, the way to describe them (metadata) and share them, as well as on any other aspect related to data production and management (e.g., used standards, advanced test methods developed as part of pre-normative research, etc.) ## Reuse and origin of existing data The development of the ATF cladding material concepts considered in IL TROVATORE does not start from zero (i.e., TRL 1 – basic principles observed). All considered material concepts have already reached a satisfactory level of technological maturity (TRL4 – technology validated in the lab) or have, at least, demonstrated an adequate manufacturing feasibility (TRL 3 – experimental proof-ofconcept). For truly innovative materials, such as the MAX phase-coated clads, the processes required for their manufacturing (e.g., magnetron sputtering, cathodic arc deposition, cold spraying, etc.) have already demonstrated a technological maturity and industrial scalability that bodes well for reaching the targeted TRL 5 for these materials in the lifetime of the project. Hence, a large pool of data is already available with respect to the manufacturing and properties/performance of the envisaged materials & constituents thereof within the IL TROVATORE Consortium. Some of these data are proprietary (background IP) and protected by the technology drivers (WH, MuroranIT, CEA/EDF, etc.); such data primarily pertain to material manufacturing and cannot affect the progress of the project if they do not become openly available to all members of the Consortium. In fact, such eventuality is undesirable, as it would jeopardize the commercial exploitation of the considered innovation, undermining one of the main objectives of the project, which is to help the involved industries to reach the market as fast as possible, thus enhancing the nuclear energy safety worldwide. Data pertaining to the performance of the state-of-the-art materials in the project (e.g., data from cladding/coolant interaction tests, ion/proton/neutron irradiation data on specific material concepts or their constituents, etc.) will be openly shared between the data originators and the researchers involved in the study of a particular aspect in material performance. These data will be reused in order to achieve the S&T objectives of the project; their possible sharing in an open context will be addressed on a case-by-case basis. It is the aspiration of the project coordinator, Dr. K. Lambrinou, to facilitate the publication of key articles based on the invaluable pool of data that are available within the Consortium, reflecting the state-of-the-art progress in ATF cladding material development for Gen-II/III LWRs. The data/innovation produced in the framework of the project (i.e., foreground IP) primarily belongs to the partners producing them. Again, open publication vs. patenting/protecting of these data will be decided on a case-by-case basis. It should be emphasised, however, that all Consortium members like the prospect of maximising the reuse of data produced with European tax payers’ money by making them FAIR (i.e., findable, accessible, interoperable and reusable), as long as this does not jeopardise the industrial exploitability of produced innovation. This _modus operandi_ is in agreement with the principle “as open as possible, as closed as necessary”. The provenance of the data that will be produced/used/reused in the framework of IL TROVATORE – apart from relevant data that may be found in open literature – is mainly expected to be identified amongst the members of the international IL TROVATORE Consortium (30 partners: 28 from Europe, 1 for the USA, and 1 from Japan). Moreover, relevant data are expected to be provided by experts involved in the 3 expert advisory committees of the project, i.e., the Scientific Advisory Committee (SAC), the End Users Group (EUG), and the Standardization Advisory Committee (STC). ## Data utility The data produced in the framework of the IL TROVATORE project will be primarily used by the Consortium members, in order to achieve the project S&T objectives. Therefore, the project data can be used by the Gen-II/III LWR community , both for scientific publications, test method/standard development, etc., but also for the core industrial purpose of expedited commercialisation; with respect to the latter, the results of the BR2 irradiation in PWR-like water and especially the collection of ‘high dpa’ data (i.e., 6-7 dpa after 2 full years following the standard BR2 operational schedule of 6 cycles/year; potentially extendable to 8-9 dpa for 8 cycles/year) is of high value for the technology drivers, so as to validate their materials in an industrially relevant environment or identify points of improvement for their preferred material concept(s). The strong cross-cutting character of the IL TROVATORE R&D activities bodes well for the use of the project data by various future-generation nuclear systems, such as Gen-IV systems (e.g., LFRs, GFRs) and fusion . Moreover, the investigated material solutions and the manufacturing processes needed for application-driven performance optimisation are expected to produce data that could potentially be used in concentrated solar power (CSP), aerospace and other industrial sectors requiring the use of materials in harsh/extreme environments. Last but not least, the educational & training activities (i.e., Workshops, Summer School) planned in the course of the project is expected to contribute to the formation of skilled scientific/technical personnel that is likely to be needed in implementing the new cutting-edge technologies in diverse industrial sectors. The overall project activities are expected to forge strong ties between academia and industries in the common quest towards safer nuclear energy, while the produced know-how is expected to increase the competitiveness of European industries by providing skilled personnel. # FAIR Data The H2020 Open Research Data (ORD) Pilot encourages the beneficiaries of H2020 projects, such as IL TROVATORE, to make their research data findable , accessible , interoperable and reusable ( FAIR ) [9]. Working towards an enhanced reuse of scholarly data is desired by various stakeholders, such as academia, funding agencies, industries and scholarly publishers, and a concise set of guidelines known as the FAIR Guiding Principles may be used as guideline towards an enhanced data reusability. The FAIR Guiding Principles may be summarised, according to Wilkinson et al. [10], as follows: To be Findable: F1. (meta)data are assigned a globally unique and persistent identifier F2. data are described with rich metadata (defined by R1 below) F3. metadata clearly and explicitly include the identifier of the data it describes F4. (meta)data are registered or indexed in a searchable resource To be Accessible: A1. (meta)data are retrievable by their identifier using a standardised communications protocol A1.1. the protocol is open, free, and universally implementable A1.2. the protocol allows for an authentication and authorization procedure, where necessary A2. metadata are accessible, even when the data are no longer available To be Interoperable: I1. (meta)data use a formal, accessible, shared, and broadly applicable language for knowledge representation I2. (meta)data use vocabularies that follow FAIR principles I3. (meta)data include qualified references to other (meta)data To be Reusable: R1. (meta)data are richly described with a plurality of accurate and relevant attributes R1.1. (meta)data are released with a clear and accessible data usage license R1.2. (meta)data are associated with detailed provenance R1.3. (meta)data meet domain-relevant community standards The FAIR Guiding Principles presented herein are the elaborated version of the principles defined at the meeting held by the FORCE11 community [11] in Leiden, The Netherlands, in 2014. FORCE11, i.e., The Future of Research Communications and e-Scholarship, is a community – initiated in 2011 – of scholars, librarians, archivists, publishers and research funders that has arisen originally to help facilitate the change towards improved knowledge creation and sharing [10]. This section describes the basic approach that will be adopted in the IL TROVATORE project in order to make the produced data FAIR, i.e., findable, accessible, interoperable and reusable. This approach is tentative and generic and does not suggest specific technologies, standards, or implementation solutions. The suggested approach in this 1 st version of the DMP will be updated, based on the return of experience, for the first time at the end of the 1 st Reporting Period, i.e., in month 18. ## Making data findable, including provisions for metadata The success of IL TROVATORE depends on the smooth exchange and retrieval of information/data. Updated information and peer-reviewed (high quality) data are uploaded to the password-protected (SharePoint) area of the website, thus becoming available to the persons involved in the project in a secure manner. As explained in Deliverable D13.1 – Project website and logo [12], only WP and Task leaders have read & write access rights to the SharePoint, so as to ensure that only high-quality, peer-reviewed data are uploaded to the ‘members only’ website section. As mentioned in section 2.2 of this document, an __Archive Plan_ _ was created to manage, classify and archive information/data produced in electronic format during the IL TROVATORE project. The __Archive Plan_ _ may be seen in Annex 1, and is also submitted together with this document as supplementary material (Excel file: IL TROVATORE Archive Plan_31032018.xlsx). The __Archive Plan_ _ provides an inventory of type and subtype of information (document/data) produced/used in the project; for each information type/subtype, the following is specified: * parent domain (domain in which the information was produced) * dissemination level (public, restricted, confidential) * storing place * archiving/retention period * action after the retention period ### Parent domain In IL TROVATORE, all datasets will originate from one of the 4 project domains (DMs): Domain 1 (DM1) – Processing optimization and joining of ATF cladding materials WP1 – Processing of engineered bulk materials WP2 – Coating deposition & surface modification of clads & joints WP3 – Joining of ATF clads & testing of joint/welds Domain 2 (DM2) – Evaluation and pre-screening of ATF clads & joints WP4 – Advanced characterization & testing of ATF clads & joints WP5 – Coolant/cladding/fuel interaction tests WP6 – Ion/proton irradiation & PIE of ATF clads & joints Domain 3 (DM3) – In-service validation of ATF clads & joints WP7 – Neutron irradiation of ATF clads & joints WP8 – PIE of neutron- irradiated ATF clads & joints WP9 – Predictive modelling activities Domain 4 (DM4) – Access to end users WP10 – Standardisation WP11 – Exploitation of results WP12 – Dissemination & Communication ### Dissemination level Participation to the ORD Pilot should not jeopardize the commercialisation potential of the project results and must avoid to offend the interests of the industrial partners that are prepared to invest in bringing the achieved innovation to market. Therefore, the datasets that are granted permission to become openly accessible will be selected based on the principle “as open as possible, as closed as necessary”, taking into account the project policy for protection of foreground IPR, as defined in the CA [5]. Datasets that cannot be disclosed in view of activities leading to patenting or commercial exploitation are assigned a Restricted (R) dissemination level (DL); this means that these data are open only to select partners (i.e., the IP owners who want to proceed with the commercial valorisation of the particular IP). Datasets produced in the framework of the project, which cannot become openly accessible but are available to the whole Consortium, are assigned a Confidential (CO) dissemination level. These datasets are likely being analysed, processed and/or interpreted prior to becoming published. Datasets that are published, either as appendix or supplement of scientific publications or are placed on an openly accessible data repository (e.g., Zenodo) in support of publications produced within the project, are assigned a Public (PU) dissemination level. Documents associated with _contractual obligations_ (e.g., Deliverables, Milestones, etc.) are assigned either a PU dissemination level (most Deliverables) or a CO dissemination level (select Deliverables and all Milestone reports). All _publications_ (Journal articles, Conference Proceeding articles, etc.) are assigned a PU dissemination level. All _other documents_ (material associated with education/training, standards and pre- normative test methods/procedures, etc.) are assigned a dissemination level on a case-by-case basis. ### Naming convention & metadata The __Archive Plan_ _ introduces a tentative nomenclature so as to be able to access efficiently datasets (documents/data/other) produced in the project. This nomenclature will be tested in the next phase of the project and, if needed, will be updated at the end of the 1 st reporting period (m18), based on the return of experience. The proposed nomenclature takes into account the following aspects: * Type of dataset (document/data/other): * Deliverable o Milestone report o Report (EEBR, IAR, PAR, etc.) * Patent o Standard o Journal/Conference Proceedings article o Educational/training material for Workshops, Summer School, etc. o Leaflets/Brochures o Video * Status of the dataset: * D raft (as-produced by author(s)) * R eviewed by peers (usually within the institute(s) producing the dataset) o A pproved (by authors, reviewers, project coordinator) o F inal (published/submitted to the EC) • Parent domain: DM 1-4 (see also section 3.1.1) * Dissemination level (see also section 3.1.2): * PU – Public (openly published, openly accessible) o CO – Confidential (accessible only by the Consortium members) o R – Restricted (accessible only by select Consortium members) * Version: standard numbering (v1, v2, v3,…) Most dataset types (documents, reports, Deliverables, etc.) are assigned a naming convention that provides information on all above aspects. Particular datasets will be assigned a DOI (digital object identifier) or a URL (uniform resource locator). A DOI is a unique alphanumeric string assigned by a registration agency (i.e., the International DOI Foundation) to identify content and provide a persistent link to its location on the internet. For example, the publisher assigns a DOI when a Journal article is published and made available electronically [13]. A URL provides a way to locate a resource on the web and contains the name of the protocol to be used to access the resource, as well as the resource name. The 1 st URL part identifies what protocol to use, while the 2 nd part identifies the IP address or domain name where the resource is located. For example, URL protocols include HTTP (hypertext transfer protocol) and HTTPS (HTTP secure) for web resources [14]. The tentative naming convention for each type of dataset may be found in the __Archive Plan_ _ (Excel file: IL TROVATORE Archive Plan_31032018.xlsx). Two examples are provided herein for Deliverables and Milestone reports; the version number (v#) drops out when the document reaches its final status and is ready to be submitted to the EC (i.e., uploaded to the ECAS portal): _Deliverable_ : ILTROVATORE_DM#_D##.#-Title_DL_v# _Milestone report_ : ILTROVATORE_DM#_WP##_MS##-Title_CO_v# In the above: (a) the WP number (WP##) is not needed for Deliverables, as this is implied in the name of the Deliverable (i.e., D12.2 – Data Management Plan is the 2 nd Deliverable in WP12); this is not the case for Milestone reports (MS1-MS15 relate to all WPs, and the MS number does not correspond to the WP with the same number; MS12 is a special case, because it refers to WP1-WP6, thus requiring a special naming convention, i.e., ILTROVATORE_DM1-2_W1-6_MS12-Title_CO_v#); and (b) DL is the dissemination level (PU or CO for Deliverables, CO for Milestone reports). It is too early in the project lifetime to ascribe metadata to each type of dataset mentioned in the __Archive Plan_ _ . It is advisable to attempt doing so towards the end of the 1 st reporting period, when the first ‘critical mass’ of data has been produced and a trend has been established with respect to data type, size, format, etc. At that time, it is also advisable to define the first set of keywords to ensure that the data produced in the project will be optimally reused. Finding the right keywords is indeed essential in retrieving the right type of information/data (step 2 – discover existing data, in Fig. 1) needed for a particular project activity. This became apparent during the preparation of the report accompanying Milestone 4 – Mining of existing irradiation data, which also describes the data mining methodology (Section 1.3 – Strategy of literature survey, in that report) used to achieve that particular Milestone [8]. It is, therefore, very important to define dataset-specific keywords early in the project, so as to ensure maximum data reuse as well as optimal citation of the data provenance. ### Storage & retention period Datasets (e.g., documents, data, graphs, codes, simulations, etc.) produced in the framework of the project will be stored in the SharePoint platform of the website ( _http://www.iltrovatore-h2020.eu/_ ) , for a period of 10 years. As already explained in Deliverable 13.1 – Project Website and Logo [12], the SharePoint platform is a secure environment accessible via password by the persons contributing to the project. This platform is used to share information/data generated in the project between the Consortium members as well as with experts involved in the 3 expert advisory committees (STC, EUG, STC) of the project (all these experts have signed non-disclosure agreements (NDAs) with SCK•CEN); the latter surely applies to the members of the EEAB (External Expert Advisory Board) that will be called to peer-review the project achievements at the mid-term and end of the project (see Deliverable D13.2 – Mid-term review report of the EEAB & Deliverable D13.3 – Final review report of the EEAB in Annex 1 of the IL TROVATORE Grant Agreement [15]). The structure on the SharePoint follows the project DMP __Archive Plan_ _ , in order to make data storage consistent and user-friendly. The first level of the SharePoint platform is structured as follows: * Calendar (meetings & events) * Deliverables, Milestones and Reports * Project documents * Meetings * Work packages – Documents & Data * Practical information (guidelines, templates) * Education & Training * External Communication Openly-accessible datasets, codes, publications (after the embargo period for ‘green’ open access articles), etc., will be stored in the Zenodo repository _. _ The __Archive plan_ _ also distinguishes between documents that can be eliminated (deleted) after the retention period and those that can be stored for a longer time due to their merit (scientific or other). ## Making data openly accessible IL TROVATORE supports fully the EC initiative towards open access & open data movement in Europe. This is not only reflected in the IL TROVATORE participation to the H2020 ORD Pilot (‘open access to research data’), but also in the commitment to ensure open access to all peer-reviewed scientific publications stemming from the results of the project (‘open access to peer-reviewed scientific research articles’). According to the Participant Portal H2020 Online Manual, “open access can be defined as the practice of providing online access to scientific information that is free of charge to the reader” [3]. Both self-archiving ('green' open access) and open access publishing ('gold' open access) options will be explored in IL TROVATORE, according to the following basic guidelines: * In self-archiving ('green' open access) , also called ‘parallel publishing’, an embargo period may apply. The maximum permitted embargo period is 6 months from the initial publication date. * In open access publishing ('gold' open access) , the publications become immediately openly available online. ‘Gold’ open access is typically covered by the authors by paying an article processing charge (APC) to the Journal. The Journal may either be “gold” (i.e., all papers have a ‘gold’ open access status) or “hybrid” (i.e., subscription Journals with the option to pay for individual papers). In IL TROVATORE, all partners have reserved a part of their budget to ensure open access to peer-reviewed scientific publications containing project results. Even though open access fees are eligible costs in H2020 projects, such as IL TROVATORE, each partner has a limited budget, which cannot cover ‘gold’ open access fees for all Journal articles. A reasonable approach to this issue could be the following: * Articles that are expected to have high impact on the scientific community should be published in ‘gold’ open access. Indicative APCs are as follows: _Nature Communications_ – 3700 EUR; _Advanced Materials_ – 4375 EUR; American Chemical Society (ACS) Journals (e.g., _ACS Nano_ , _Journal American Chemical Society_ , etc.) – 1500-2000 USD for ACS members; etc. * Regular articles (i.e., with average impact on the scientific community) could be primarily published in ‘green’ open access with select articles in ‘gold’ open access. When preparing to submit an article to a peer-reviewed Journal in one of the open access options, one should take the following facts into account: 1\. Some institutions/countries have agreements with publishers, so that the ‘gold’ open access fees are paid by the institution or the country’s library organization and not by the authors. This is an option that merits consideration whenever possible. Relevant information is provided below: * _Wiley_ : _https://authorservices.wiley.com/author-resources/Journal-Authors/licensing-openaccess/open-access/institutional-funder-payments.html_ * _Institute of Physics (IOP)_ : IOP has agreements with select Universities and/or countries (e.g., Sweden, Norway, Austria, UK) for free ‘gold’ open access to its _subscription Journals_ : _https://publishingsupport.iopscience.iop.org/questions/paying-for-open-access/_ * _Royal Society of Chemistry_ : Authors from institutions with full Journal subscriptions may claim vouchers (NB: limited number, on a first-come, first-served basis) for free ‘gold’ open access to their _subscription Journals_ . It is recommended to contact your library on this item. * _Springer_ : Springer has agreements with some Universities and/or countries (e.g., Sweden) for free ‘gold’ open access to their _subscription Journals_ . 2. Publishing in ‘gold’ open access Journals with low-to-moderate APCs: fully open access publishers (PLOS, MDPI) and fully open access Journals from “traditional” publishers should be considered. Examples are MDPI (Multidisciplinary Digital Publishing Institute) Journals like _Materials_ , _Coatings_ , etc. (850-1500 CHF), _RSC Advances_ (500 GBP during 2018, 750 GBP afterwards), _Materials Research Letters_ (500 USD), and mega-Journals, such as _ACS Omega_ (750 USD), _AIP Advances_ (1350 USD), _Scientific Reports_ (1370 EUR), etc. 3. Publishing in ‘green’ open access: authors could post/upload the accepted manuscript version (i.e., final version after review, but before any typesetting/production) to a repository such as Zenodo. Making a scientific publication openly accessible may entail an embargo period. The maximum permitted embargo period is 6 months from the initial date of publication. Some publishers require longer embargo periods (e.g., 12 or 24 months) for ‘green’ open access; however, publishing under those terms is not permitted. Useful information on Journal-specific embargo periods is given below: * _Nature_ (subscription) Journals allow parallel publishing with 6 months embargo. * _American Institute of Physics (AIP)_ Journals (e.g., _Applied Physics Letters_ , _Journal of Applied Physics_ ) allow parallel publishing without embargo. * _American Physical Society (APS)_ (e.g., _Physical Review_ Journals) allows parallel publishing of the published Journal version (not only the accepted manuscript) without embargo. * _Elsevier_ allows posting a preprint of the manuscript on the preprint server ArXiv, and updating the preprint with the accepted manuscript without embargo. This is sufficient to meet the open access requirements, even though it only applies to ArXiv. For other repositories, longer embargo periods apply (often up to 24 months). All peer-reviewed Journal articles containing data/findings produced in the project lifetime will be deposited on the Zenodo repository ( _https://zenodo.org/_ ) . As already mentioned, Zenodo has been developed in the framework of the OpenAIRE project ( _https://www.openaire.eu/_ ) , which was commissioned by the EC to support the nascent Open Data policy by providing a catch-all repository for EC-funded research. CERN, an OpenAIRE partner and pioneer in open source, open access and open data, made a major contribution to the launching of Zenodo in May 2013 [16]. The name Zenodo is derived from Zenodotus, the first librarian of the Ancient Library of Alexandria and father of the first recorded use of metadata, a landmark in library history [16]. Apart from the peer-reviewed Journal articles, openly-accessible datasets, codes, etc. , will also be preserved in the Zenodo repository. These datasets are firstly the datasets used to produce the peerreviewed Journal articles deposited in Zenodo, especially in case the used datasets have not been published as appendices or supplementary material(s) in the original articles. Other datasets, not necessarily associated with scientific articles, will also become openly accessible in Zenodo during the project lifetime. Deciding the datasets that will become openly accessible and the timeframe this will happen is to occur on a case-by-case basis. These datasets should not jeopardise either the future industrial exploitation of the project findings (patenting, licensing, etc.) or their prospect of getting published in peer-reviewed Journals. As already mentioned, IL TROVATORE is prepared to support the EC initiative towards open access and open data movement in Europe, while simultaneously respecting the principle “as open as possible, as closed as necessary”. ## Making data interoperable The success of the IL TROVATORE project relies on the continuous exchange of information and data between partners (Fig. 2), which implies that the produced datasets must be interoperable . Datasets belonging to members of the Consortium (i.e., ATF technology drivers; experts in different project aspects, e.g., specific manufacturing processes and materials or constituents thereof; etc.) at the project outset (background IP) are reused, mostly as input of follow-up R&D activities. Datasets will be systematically exchanged between researchers and institutions/organisations in the international project setting (Europe, USA, Japan) in order to achieve its S&T objectives. The SharePoint of the project website is selected as platform for data exchange between partners since the early stages in the project lifetime; datasets that are ready to become openly accessible will be uploaded to the Zenodo repository (see section 3.2). Metadata will be ascribed to the produced datasets later in the project lifetime, once the project data landscape has become more familiar. Moreover, existing and under development standards related to the project activities have already been identified and are listed in Deliverable 10.1 – Standardisation roadmap, together with standardised vocabularies for specific types of materials [17]. The diversity of datasets (i.e., types, formats, provenance) might be accompanied by an unavoidable level of incompatibility that could hinder full data interoperability at the initial stages of the project; however, with careful alignment between the data originators (e.g., opting for similar/compatible data formats, open/widely used software applications, etc.) and elaboration of a good strategy (vocabularies, standards, methodologies), it is expected to make data fully interoperable within a short timeframe. Some tentative recommendations towards data interoperability are given in Table 1. Table 1. Tentative recommendations to make data interoperable in IL TROVATORE. <table> <tr> <th> No. </th> <th> Recommendation to make data interoperable </th> </tr> <tr> <td> 1 </td> <td> When specific datasets will be the input of subsequent tests/analysis/processing performed by others, expectations in terms of data type & format, etc., should be clearly defined. </td> </tr> <tr> <td> 2 </td> <td> The __Archive Plan_ _ concerning the nomenclature, dissemination level, storing and archiving of each dataset type and sub-type should be followed or collectively updated/improved. </td> </tr> <tr> <td> 3 </td> <td> Documents that constitute contractual obligations (Deliverables, Milestone reports, etc.) must be reviewed (in the original file format) and their final version (in PDF format) must be uploaded by the project coordinator to the SharePoint platform and the ECAS portal. </td> </tr> <tr> <td> 4 </td> <td> Use same data and metadata vocabularies to the extent possible; if possible, use standard vocabularies for all data types in a dataset so as to allow interdisciplinary interoperability; if needed, generate jointly new vocabularies and use them during the project </td> </tr> <tr> <td> 5 </td> <td> Maximise data reuse within the project premises by uploading to the SharePoint platform peer-reviewed, high-quality datasets as soon as possible. Maximise data reuse beyond the project community by making them openly accessible, i.e., by storing them in the Zenodo repository. Whenever possible, assign a DOI to the openly accessible datasets. </td> </tr> <tr> <td> 6 </td> <td> In scientific publications, strive for publishing the datasets used to produce the publications as appendices or supplements; if this is not always possible, upload the data together with the scientific publication to the Zenodo repository according to the guidelines given in section 3.2. Also describe the used standards, lab methods, processes, codes and software. </td> </tr> <tr> <td> 7 </td> <td> The deposition of the openly accessible datasets in other repositories, such as institutional repositories, is allowed and/or encouraged so as to maximise data reuse. </td> </tr> </table> ## Increase data re-use (through clarifying licenses) The data produced in the framework of the IL TROVATORE project could potentially benefit/interest various industrial sectors beyond the Gen-II/III LWR community (e.g., Gen-IV LFRs/GFRs, fusion, CSP, aerospace, etc.). Hence, the widest reuse of the project data could indeed be very beneficial for the society, in terms of fostering diverse entrepreneurial initiatives on European ground and beyond. It is quite early in the project lifetime, however, to decide the data licensing approach so as to achieve the widest possible reuse, especially in view of the fact that the value and possible impact of the data cannot be estimated before the data have actually been produced. It is also premature to define the timeframe in which the data will be made available for reuse beyond the project community. Within the project community, the data are either first protected (e.g., patented) and then made available for use by the partners that need them as input via the SharePoint platform or they become directly available on the SharePoint platform (once peer-reviewed with respect to their quality). As already mentioned in section 3.1.4, the intended archiving period for the project data is 10 years or even longer for specific datasets; the openly accessible data in the project data pool will be available for reuse in the same period of time. # Allocation of Resources As mentioned in section 3.2, all partners have reserved a part of their budget to ensure open access to peer-reviewed scientific publications containing project results/data by paying the APCs required by certain Journals. The recommended approach for open access publications in IL TROVATORE has also been described in section 3.2. Apart from the resources associated with open access publishing, the resources (budget/personnel) that will be needed for data management in a multi-investigator, and multidisciplinary project, such as IL TROVATORE, might actually be not negligible. This is a point of current consideration at SCK•CEN, which is entrusted with the maintenance of the project website and the management of the SharePoint, i.e., the platform that will be used throughout the project lifetime for data exchange between partners. The decision of the SCK•CEN management on this item will be described in the next DMP version. It is important to emphasise that each partner is responsible for the data quality (including the data interoperability) generated and provided to other partners for further R&D activities during the project. Moreover, each partner is also expected to contribute to the overarching effort to make the project data FAIR by doing so for the datasets that originate from that particular partner. Last but not least, the Dissemination Manager (Prof. P. Eklund, LiU) will overlook all activities associated with knowledge management & protection, such as the contribution to the ORD Pilot and the continuous update of the DMP. For this purpose, the Dissemination Manager will constantly be in consultation with the Executive Board (EB) and the project coordinator. Moreover, the Dissemination Manager will support the Consortium members with the registration of publications in the Zenodo repository. # Data Security Data security relies on the infrastructural security of each Consortium member and SCK•CEN, which is responsible for the construction and maintenance of the project website ( _http://www.iltrovatoreh2020.eu/_ ) . As explained in D13.1 – Project Website and Logo [12], the website is divided in 2 areas: * Public website area (SiteCore): the public (openly accessible) part of the website provides non-sensitive information (i.e., project scope & participants, generic description of WPs, events, useful links, list of publications, contact information). * Secure website area (SharePoint): the SharePoint is the password-protected part of the website that is only accessible by members of the Consortium. All contributors have been provided with username & password and have _read access rights_ to the SharePoint. Only WP and Task leaders have _read & write access rights _ to the SharePoint. Passwords can only be made by the ICT Department of SCK•CEN. Therefore, password requests should be sent to the IL TROVATORE project office (e-mail address: [email protected]_ ). Publications and openly accessible datasets will be stored in the Zenodo repository, following the procedure described in section 3.2. # Ethical Aspects No ethical/legal issues with an impact on data sharing have so far been identified. As confirmed in D14.1 – GEN-NEC-Requirement No. 1 [18], both non-European partners (Drexel University, DU, and Kyoto University, KU) will ensure that all ethical standards and guidelines of H2020 will be rigorously applied in the course of the IL TROVATORE project. The fact that both DU and KU are willing to respect the H2020 ethical standards and guidelines implies that both partners are willing (in fact, they are already doing so) to share data related with the project R&D activities and to support the EC initiative for open access to publications & open access to research data (ORD Pilot).	https://phaidra.univie.ac.at/o:1140797	Horizon 2020
0090_LOGISTAR_769142.md	# Executive Summary This document describes the Data Management Plan (DMP) for the LOGISTAR project. The DMP provides an analysis of the main elements of the data management policy that will be used throughout the project by the project partners, with regard to all the datasets that will be generated, harvested and/or used by the project. Documentation of this plan is a precursor to the trials and pilot activities. The format of the plan follows the Horizon 2020 template [1]. _In more detail this document explains and describes:_ 1. the LOGISTAR data identification and collection approach, 2. the LOGISTAR overall dataset structure, including an overview of identified data sources and datasets 3. the LOGISTAR overall data management plan and policy including 1. the policies for dataset reference and naming, 2. the dataset description (metadata scheme), 3. relevant standards and metadata, 4. guidelines for (secure) data sharing and 5. information security guidelines, 4. the approach for data archiving and preservation, and finally 5. Ethical aspects in regard to data management in the LOGISTAR project. As data management is an ongoing process along the duration of the LOGISTAR project and data management in the project is taking place in a dynamic environment, this document on hand is seen as a living document, this means that the document will be developed and maintained continuously over time. # 1\. Data Collection Procedure This Data Management Plan (DMP) has been prepared by taking into account the template of the “Guidelines on Data Management in Horizon 2020” 1 . Elaboration of the DMP will allow LOGISTAR partners to address all issues related to management of data collected during the project as well as ethics. DMP is planned as a deliverable for M6. However, it is a living document which will be updated throughout the project based on the project progress. The consortium will comply with the requirements of Regulation (EU) 2016/679 of the European Parliament and of the Council of 27 April 2016 (General Data Protection Regulation) on the protection of individuals with regard to the processing of personal data and on the free movement of such data. Type of data, storage, recruitment process, confidentiality, ownership, management of intellectual property and access: The Grant Agreement and the Consortium Agreement are to be referred to for these aspects. The procedures that will be implemented for data collection, storage, and 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. The Steering Committee of the project will also ensure that EU standards are followed. Informed consent will be provided to all participants in the project trials and pilots. All collection of sensitive data will be done with full consideration of data protection principles and industry standards, and will satisfy data protection requirements in accordance with EU and non-EU directives and national implementations thereof. Due to nature of services it is NOT likely that personal data will be captured and processed. In case that there will be sensitive/ personal data, collection and processing will be done according to the applicable data protection provisions, such as Regulation (EU) 2016/679 on the protection of individuals with regard to the processing of personal data and on the free movement of such data including article 29 working group 8/2010 opinion and Directive 2002/58 on Privacy and Electronic Communications. For this reason, in case of personal data collection and processing, only anonymous user data will be collected and securely stored. Anonymous identification of user-provided information will be leveraged only to confirm the authenticity of users interacting with the system and to prevent malicious behaviour. No need to personally identify users through their information is envisioned nor to include sensitive data. The collected data will be treated anonymously and additionally a various set of measures will be put in place in order to protect user privacy and its data security, by embedding privacy by design principles from the early stage of the project technical start. Where needed, a prompt Privacy Impact Assessment (PIA) exercise will be performed. The type, quality and quantity of accessed data will be regulated, by designing and implementing adequate PIR (Privacy Information Retrieval) and PPQ (Privacy Preserving Query) mechanisms. By referring to the proposed work plan, it is worth noticing that all such measures will be considered at all levels of the technical project development, starting from WP1 (Market research: interviews, user needs functional requirements analysis and network data collection), from WP2 where data gathering and harmonization will be done (overall data storage and data processing) up to WP3, WP4 and WP5 where data will be used to build different algorithms and services. While new required and relevant technologies will be developed as part of the project. LOGISTAR is already aware of the following existing technological measures necessary to minimize associated privacy risks such as: * use of secure data storage, encrypted transfer of data over the capturing channels, controlled and auditable access for different classes of data; * obscuring/removing user identities at the source of field trial data generation to prevent direct user tracing; * obscuring personal location data through indirect or delayed routing to prevent individual localization as much as possible and limit user tracking through correlation of depersonalized data based on its location. The procedure for data identification and collection in LOGISTAR has been specified as follows, taking into account the specifics of the project: 1. Evaluation of the overall requirements elicitated in WP1 (Market research: interviews, user needs functional requirements analysis and network data collection) 2. Evaluation of the available requirements specification of WP7 (Use Cases and Living Labs) 3. Development of a metadata schema for LOGISTAR (to manage data monitoring for the project along a unique schema), based on DCAT (Data Catalogue Vocabulary) [2] 2 4. Data monitoring and data identification for the LOGISTAR project (along the ODI Data Spectrum 3 ), means open – shared – closed data. Thereby identification of data sources and datasets and collection of respective metadata of these datasets to provide overview and search & browse mechanisms over the LOGISTAR data. 5. Setting up a data catalogue containing metadata (no data!) of the above identified and collected datasets 6. Development of data- and information security guidelines to ensure trusted and secure data sharing between partners and third parties 7. Data acquisition and harvesting by making use of the WP2 data storage layer and harvesting mechanisms. Plus continuous ingestion of new data, as well as updates and maintenance of existing data. 8. The metadata and data stores will be used for data analysis and visualisation (in WPs 3,4,5,7). As pointed out above the data collection of LOGISTAR follows the ODI Data Spectrum that includes data and information as follows: _Fig.001: ODI Data Spectrum, https://theodi.org/about-the-odi/the-data- spectrum/ _ The overall LOGISTAR Data Management approach in LOGISTAR follows the (Linked) Data Lifecycle as follows: _Fig. 002 (Linked) Data Life Cycle_ # 2\. Overall dataset structure The Data Management Plan will present in details the procedures for creating ‘primary data’ as well as its management. Separate datasets will be created for each stakeholder, providing the same structure, in accordance with the guide of Horizon 2020 for the Data Management Plan. Data gathered during validation of LOGISTAR and functionality as preparation for the pilots or for the purpose of scientific publications will be included in this dataset as well. The consortium will decide and describe the specific procedures that will be used in order to ensure long-term preservation of the data sets. This field will provide information regarding the duration of the data preservation, the approximate end volume, the associated costs and the plans of the consortium to cover the costs. ### 2.1. Purpose of data management The main objective of the LOGISTAR project is to allow effective planning and optimizing of transport operations in the supply chain by taking advantage of horizontal collaboration , relaying on the increasingly real time available data gathered from the interconnected environment. For this, a real-time decision making support tool and a real-time visualization tool of freight transport will be developed , with the purpose of delivering information and services to the various agents involved in the supply chain, i.e. freight transport operators, their clients, industries and other stakeholders such as warehouse or infrastructure managers. The data management activities and guidelines in LOGISTAR are built on top of this main project objectives – aligned with WP2 (Data Gathering and Harmonisation) where the major objectives are as follows: * the identification of broad, open and IoT data for the project as well as relevant stakeholder and stakeholders partner data (closed data on i.e. goods), * the provision of the data acquisition layer of LOGISTAR (broad & open & IoT data) and * the provision of the metadata / semantic layer of LOGISTAR, and finally * the provision of the overall data storage layer of LOGISTAR including 3 stores: (i) event store (ii) big data store and (iii) metadata store Thereby the ultimate goal is to prepare a managed collection of actionable data to be used in other WPs. What includes strategies and mechanisms of secure data storage and sharing so data can be used easily and secure for analytics and visualisation et al. ### 2.2. Sources, Types and Formats of Data The following sources of relevant data have been identified as relevant data sets for the LOGISTAR project: * Closed Data / shared data o Data from Use Case partners (types of data see below) coming from their transport management systems (TMS) * 3 rd party data (external use case partners) providing data from TMS and/or specific datasets about routes, prices, et al. * Simulated transport data from project partners * Open Data o EU Data Portal, _https://www.europeandataportal.eu/de/homepage_ o EC Open Data Portal, _http://data.europa.eu/euodp/en/home_ * Lighthouse Project: Transforming Transport, _https://data.transformingtransport.eu/_ o Other Transport H2020 projects in place * EU Intelligence Transport Systems, e.g. safe & secure t ruck p arking, _https://ec.europa.eu/transport/themes/its/road/action_plan/intelligent-truckparking_en_ * Standards et al (e.g. GS1, ISO, W3C…) * Weather and traffic information data from relevant countries: UK, Italy, Europe The following types of data have been identified as being relevant for the LOGISTAR project. This list will be maintained and expanded over time along the LOGISTAR project. <table> <tr> <th> Data Type </th> <th> Data </th> </tr> <tr> <td> Products ordered </td> <td> Product Code </td> </tr> <tr> <td> </td> <td> Order Number </td> </tr> <tr> <td> </td> <td> Quantity </td> </tr> </table> <table> <tr> <th> Data Type </th> <th> Data </th> </tr> <tr> <td> Order Data </td> <td> Order Number </td> </tr> <tr> <td> </td> <td> Facility Picking Order (this could be Facility code) </td> </tr> <tr> <td> </td> <td> Date & time order placed </td> </tr> <tr> <td> </td> <td> Date & time order ready for despatch </td> </tr> <tr> <td> </td> <td> Date & time required for delivery </td> </tr> <tr> <td> </td> <td> Delivery location of order (This could also be a code) </td> </tr> <tr> <td> </td> <td> Special delivery requests </td> </tr> <tr> <td> </td> <td> Orders to take from A to B </td> </tr> </table> <table> <tr> <th> Data Type </th> <th> Data </th> </tr> <tr> <td> Customer data </td> <td> Customer Code </td> </tr> <tr> <td> </td> <td> Location of customer </td> </tr> <tr> <td> </td> <td> Vehicle access constraints </td> </tr> <tr> <td> </td> <td> Opening Hours </td> </tr> <tr> <td> </td> <td> Typical delivery drop times </td> </tr> <tr> <td> </td> <td> </td> </tr> <tr> <td> Data Type </td> <td> Data </td> </tr> <tr> <td> Vehicle data </td> <td> Tractor ID </td> </tr> <tr> <td> </td> <td> Trailer ID </td> </tr> <tr> <td> </td> <td> Vehicle departed from location </td> </tr> <tr> <td> </td> <td> Date & time of departure </td> </tr> <tr> <td> </td> <td> Current location </td> </tr> <tr> <td> </td> <td> Date & time expected at destination </td> </tr> <tr> <td> </td> <td> Order IDs on vehicle </td> </tr> <tr> <td> </td> <td> Truck type </td> </tr> <tr> <td> </td> <td> Truck features (characteristics) </td> </tr> <tr> <td> </td> <td> Position of vehicles </td> </tr> </table> <table> <tr> <th> Data Type </th> <th> Data </th> </tr> <tr> <td> Tractor data </td> <td> Tractor ID </td> </tr> </table> <table> <tr> <th> Data Type </th> <th> Data </th> </tr> <tr> <td> Facility data </td> <td> Facility Code </td> </tr> <tr> <td> (These could be supplier/factory/warehouse) </td> <td> Location of facility </td> </tr> <tr> <td> Vehicle access constraints </td> </tr> <tr> <td> Opening Hours </td> </tr> <tr> <td> </td> <td> Typical load/unload times </td> </tr> </table> <table> <tr> <th> Data Type </th> <th> Data </th> </tr> <tr> <td> Product Profile </td> <td> Product code </td> </tr> <tr> <td> </td> <td> Ambient / chill / frozen / hazardous </td> </tr> <tr> <td> </td> <td> Dimensions </td> </tr> <tr> <td> </td> <td> Weight </td> </tr> <tr> <td> </td> <td> Stackability </td> </tr> <tr> <td> </td> <td> Contamination data </td> </tr> <tr> <td> </td> <td> Danger classes </td> </tr> <tr> <td> </td> <td> Moving of goods (location of goods) </td> </tr> <tr> <td> </td> <td> Pallet type </td> </tr> <tr> <td> </td> <td> Cases or quantity per pallet </td> </tr> <tr> <td> </td> <td> Cases or quantity per layer </td> </tr> </table> <table> <tr> <th> Data Type </th> <th> Data </th> </tr> <tr> <td> Costs </td> <td> Transportation costs </td> </tr> <tr> <td> </td> <td> calculation of costs (metrics) </td> </tr> <tr> <td> </td> <td> Rates negotiated </td> </tr> </table> <table> <tr> <th> Data Type </th> <th> Data </th> </tr> <tr> <td> Directives </td> <td> Chemical directives </td> </tr> <tr> <td> </td> <td> EU mobility directives </td> </tr> <tr> <td> </td> <td> EU transport directives </td> </tr> <tr> <td> </td> <td> National directives </td> </tr> <tr> <td> </td> <td> EU Environmental Directives </td> </tr> </table> <table> <tr> <th> Data Type </th> <th> Data </th> </tr> <tr> <td> Geo Information </td> <td> Regions </td> </tr> <tr> <td> </td> <td> Countries </td> </tr> <tr> <td> </td> <td> Addresses </td> </tr> <tr> <td> </td> <td> Routes </td> </tr> </table> <table> <tr> <th> Data Type </th> <th> Data </th> </tr> <tr> <td> Standards </td> <td> Article codes </td> </tr> <tr> <td> </td> <td> GS1 for retail </td> </tr> <tr> <td> </td> <td> Global location numbers </td> </tr> <tr> <td> </td> <td> Industry sectors (e.g. NACE codes) </td> </tr> <tr> <td> </td> <td> Country Codes (e.g. ISO) </td> </tr> <tr> <td> </td> <td> Languages (e.g. ISO 2 or 3 digits) </td> </tr> <tr> <td> </td> <td> City Codes (e.g. IATA 3 digit) </td> </tr> <tr> <td> </td> <td> Existing logistics taxonomies and ontologies </td> </tr> </table> <table> <tr> <th> Data Type </th> <th> Data </th> </tr> <tr> <td> Other data </td> <td> Vehicle filled </td> </tr> <tr> <td> </td> <td> Empty miles </td> </tr> <tr> <td> </td> <td> Types of containers </td> </tr> <tr> <td> </td> <td> CO2 emission / carbon footprint calculation </td> </tr> <tr> <td> </td> <td> miles empty </td> </tr> <tr> <td> </td> <td> events of interest (to be specified) </td> </tr> <tr> <td> </td> <td> weather data </td> </tr> <tr> <td> </td> <td> traffic information </td> </tr> <tr> <td> </td> <td> news articles (relevant) </td> </tr> <tr> <td> </td> <td> pollution level / location </td> </tr> </table> <table> <tr> <th> Data Type </th> <th> Data </th> </tr> <tr> <td> Facilities </td> <td> Addresses </td> </tr> <tr> <td> </td> <td> Opening hours </td> </tr> <tr> <td> </td> <td> Vehicle access (restrictions) </td> </tr> </table> <table> <tr> <th> Data Type </th> <th> Data </th> </tr> <tr> <td> Driver data </td> <td> availability of drivers </td> </tr> <tr> <td> </td> <td> time already worked / allowed to work </td> </tr> <tr> <td> </td> <td> working on the day </td> </tr> <tr> <td> </td> <td> driver schedules </td> </tr> </table> <table> <tr> <th> Data Type </th> <th> Data </th> </tr> <tr> <td> Rail data </td> <td> Train & schedules </td> </tr> <tr> <td> </td> <td> Capacity </td> </tr> <tr> <td> </td> <td> Rail operator </td> </tr> </table> <table> <tr> <th> Data Type </th> <th> Data </th> </tr> <tr> <td> Services </td> <td> Schedules </td> </tr> <tr> <td> </td> <td> Real time information (to be specified) </td> </tr> <tr> <td> </td> <td> Service Level constraints </td> </tr> </table> In regards of formats of dat a the project has to deal with a big variety of data – means data from several sources and in several formats. The data and information will be used in unstructured format (e.g. documents), as well as in semi-structured and structured format (e.g. tabular data like CSV files). Some data will be harvested via APIs and the format such data is being received needs to be specified in the course of the technical requirements specification and architecture work in WP6. _A preliminary list of data formats is as follows:_ * API data (output in several formats available) * XML * RDF * CSV * Relational DBs * Json (LD) * Documents (MS Word, XLS, PDFs et al) * Etc. In regards to re-use of existing data the LOGISTAR project has a strong focus on making use of existing data like standards (ISO, W3C, et al) in the form of for example models, taxonomies, ontologies or controlled vocabularies (like code lists), furthermore the use of open and broad data (e.g. in the area of weather data, traffic information or environmental data) wherever possible. The size of data is still not specified at the moment of the creation of this deliverable but it can be said, that LOGISTAR data has the following 3 main attributes: (i) big data (volume) and (ii) velocity data (real time data / streaming data) and (iii) high variety (different sources, different formats) of data. # 3\. Management plans and policy This section reflects the current status of the primary data envisioned in the project. Being in line with the EU’s guidelines regarding the DMP (European Commission, 2016 4 5 ), this document should address for each data set collected, processed and/or generated in the project the following elements: 1. Data set reference and name 2. Data set description 3. Standards and metadata 4. Data sharing 5. Archiving and preservation To this end, the consortium develops a number of strategies that will be followed in order to address the above elements. In this section, we provide a detailed description of these elements in order to ensure their understanding by the partners of the consortium. For each element, we also describe the strategy that will be used to address it. Wherever possible the LOGISTAR project will follow the EC Guidelines for Open Access 6 as well as the principles of FAIR data 7 , this means: FAIR data are data which meet standards of _findability_ , _accessibility_ , _interoperability_ , and _reusability_ . Remark: as LOGISTAR is also working with sensitive industry data from partners and 3 rd parties (e.g. data from transport management system from project partners) such data cannot be made publicly available – BUT the listed principles can be applied for data sharing between the partners and inside the consortium, where applicable and necessary) ### 3.1. Data set reference and name Unique identification of datasets is ensured by following provisioned unique naming convention drafted for the purpose of the LOGISTAR project. The convention for the dataset naming is as follows: 1\. Each data set name consists of 5 different parts separated with a “:”, e.g. PartnerName:EntityGroup:EnityType:VarcharId , 1. PartnerName represents the name (or the short name) of the organisation (e.g. data owner, data custodian) associated with the dataset: i. UDEUSTO - UNIVERSIDAD DE LA IGLESIA DE DEUSTO ENTIDAD RELIGIOSA ii. UCC - UNIVERSITY COLLEGE CORK - NATIONAL UNIVERSITY OF IRELAND, CORK iii. CSIC - AGENCIA ESTATAL CONSEJO SUPERIOR DEINVESTIGACIONES CIENTIFICAS iv. DNET - DRUSTVO ZA KONSALTING, RAZVOJ I IMPLEMENTACIJU INFORMACIONIH I KOMUNIKACIONIH TEHNOLOGIJA DUNAVNET DOO NOVI SAD 5. SWC - SEMANTIC WEB COMPANY GMBH 6. PRESTON - PRESTON SOLUTIONS LIMITED 7. MDST - MDS TRANSMODAL LIMITED 8. SAG - SOFTWARE AG 9. DBH - dbh Logistics IT AG 10. GENEGIS - GENEGIS GI SRL 11. AGLERS - AHLERS BELGIUM NV 12. ZAILOG - CONSORZIO ZAILOG 13. NESTLE - NESTLE UK LTD 14. PLADIS - UNITED BISCUITS (UK) LIMITED 15. CODOGNOTTO - CODOGNOTTO ITALIA SPA 2. EntityGroup – represents the category of data source, such as carrier name of the load 3. EntityType – represents the type of data source category 4. VarcharId – in systems there is a chance that context already have being assigned with the ID. In some cases, certain data context IDs in databases will be automatically iterated. For both, this suffix will be used as a final part of ID. It can be text and or numerical. An example of one dataset name generated used above provide convention would be: Nestle:Group1:VehicleSpeed:0001 ### 3.2. Data set description, Standards and Metadata Data collected, processed or generated within the project will have its description to explain dataset in more details (the metadata, MD). This data will be provided by the data owner/producer and/or other stakeholders. Information gathered during the LOGISTAR will be accompanied by context information (location, date, time) as well as publicly available information such as weather from the online local measurement stations. The metadata schema (as follows) has been created by taking into account the DCAT vocabulary 8 (a W3C recommendation, that is used for e.g. metadata for open data across Europe and/or for the European Data Portal) but has been slightly adapted to the needs of LOGISTAR. The description will provide information as given in the table below. <table> <tr> <th> Title </th> <th> Title of the dataset </th> </tr> <tr> <td> Type of data </td> <td> The type of data, e.g. driver data, vehicle data, geo information </td> </tr> <tr> <td> Data provider </td> <td> Provider of the data, not necessary owner </td> </tr> <tr> <td> Dataset owner </td> <td> Owner of the data not necessary provider </td> </tr> <tr> <td> Description </td> <td> Brief description of the data features and the purpose of the data </td> </tr> <tr> <td> Format (Media type) </td> <td> Doc, pdf, api, json, xml </td> </tr> <tr> <td> License </td> <td> The license and terms under data can be used </td> </tr> <tr> <td> Language </td> <td> ISO code of language </td> </tr> <tr> <td> Metadata </td> <td> Yes, no, if available provide URI reference schema </td> </tr> <tr> <td> Static / Dynamic dataset </td> <td> Information about the dataset if it is static (e.g. dataset as a cvs file) or dynamic data (e.g. real time data via API) </td> </tr> <tr> <td> Data Type along the ODI Data Spectrum </td> <td> Controlled vocabulary (CV) to describe the type of data: open, shared, closed </td> </tr> <tr> <td> Classification of sensitivity of data </td> <td> CV to describe of a dataset is sensible (attributes: Yes / No). If a dataset is marked as sensible: Yes then the Information & Data Security Checks and Guidelines (see below) need to be taken into account. </td> </tr> </table> Potentially this LOGISTAR MD schema can be adapted and expanded over time if necessary, for example the following additional fields could be useful for metadata in LOGISTAR: * Data purpose (what is this data used for?) * Temporal Coverage (of Data; e.g. year 2017 or June 2016) * Geographical Coverage of data (e.g. UK or Scandinavia) * Language [use ISO like EN please] * Size (this should be given approximately in MB. The goal is to know if the dataset is too large to plan how to handle it, e.g. if we need to import file couple of hundred of MB) Finally, the MD schema will be used for data monitoring and identification and the LOGISTAR data catalogue (a catalogue of metadata of identified datasets for LOGISTAR). In regard to the use of standards and the re-use of models (e.g. controlled vocabularies, taxonomies and / or ontologies) the relevant standards and models will be screened and identified along the requirements elicitation and the use case specification in the LOGISTAR project – sources are as follows: * Standards o GS1 * _https://www.gs1.ch/en/home/topics/master-data-based-on-gs1_ * _https://www.gs1.org/standards_ * _https://www.gs1.org/standards/gdsn_ o ISO (logistic related standardsm but also terminology and metadata and information security related standards as follows) <table> <tr> <th> ▪ </th> <th> ISO 28000:2007: Specification for security management systems for the supply chain </th> </tr> <tr> <td> ▪ </td> <td> ISO 704, Terminology work </td> </tr> </table> * ISO 1087-1, Vocabularies * ISO 11179, MDR * ISO 20943-1 MDR (consistency) * ISO 25964-1 Thesauri & Interoperability * ISO 27001 - Information Security (Certified end 2018) o W3C, _https://www.w3.org/_ * Resource for Vocabularies: _https://bartoc.org/_ * EC ISA2 (Core) Vocabularies: _https://ec.europa.eu/isa2/solutions/core-vocabularies_en_ * EU Data Portal, _https://www.europeandataportal.eu/de/homepage_ * Lighthouse Project: Transforming Transport, _https://data.transformingtransport.eu/_ The principle approach is to make use of existing standards and wherever necessary to interlink and/or map, or to adapt such to the LOGISTAR requirements. ### 3.3. Secure Data sharing & Information Security Guidelines The LOGISTAR project will define how data will be shared and more specifically the access procedures, the embargo periods, the necessary software and other tools for enabling re-use, for all datasets that will be collected, generated, or processed in the project. In case the dataset cannot be shared, the reasons for this will be mentioned (e.g. ethical, rules of personal data, intellectual property, and commercial, privacy-related, security-related). In addition, beneficiaries do not have to ensure open access to specific parts of the research data if the achievement of the action's main objective, as described in Annex 1 of the DoW, would be jeopardised by making those specific parts of the research data openly accessible. In this case, the data management will present the reasons for not giving access. _More concrete the following mechanisms has been specified:_ _Remark_ : such mechanisms will be adapted over time following the dynamic requirements of the LOGISTAR project. * Datasets that are identified as ‘sensible / closed’ data in the course of the data monitoring activities of LOGISTAR will be part of a data security check and the specified LOGISTAR data / information security guidelines (see as follows) * Between the partners a clear Non Disclosure Agreement / NDA will be executed (in addition to the Consortium Agreement) that includes mechanisms and agreements for secure data sharing between the parties o This NDA will be adapted to be used also for agreements for data sharing with 3 rd parties in the course of the use case development in the project * The NDA mentioned above will include – in the form of an Annex – specific Information and Data Security Guidelines that apply to LOGISTAR secure data sharing. The attributes for this are as follows: * Store data (into the LOGISTAR store) from each data provider separately (physically) & ensure secure data transfer o No unnecessary data transfer (e.g. as of federated systems) * Aggregation / anonymisation of data (decision on dataset by dataset basis) if necessary and useful * For data analytics / prediction etc data needs to be integrated, thereby the people making use of such data need to be specified and listed (remark: data sharing can increase data quality (e.g. address data)) * Establish mechanisms of TRUST (LOGISTAR operator = data stewardship) need to be specified and implemented * Compliance in (i) GDPR and (ii) other data regulations, including: * NO export of any data outside of the EU * Any data breach, data loss or similar to be reported in e.g. 72 hours * Data will be deleted on request in specified duration (plus written confirmation) * Create and maintain a list of team members with access / data / organisation * Data processing agreements for PII (personal identifiable information) between partners to be executed * Any data processing, storage et al must follow Industry standards * Data sharing only on a ‘need to know basis to data & any use is for LOGISTAR purpose only To ensure a stable and efficient solution LOGISTAR will take into account best practises of other projects and initiatives for secure data sharing like for instance: NextTrust ( _http://www.nextrustproject.eu/_ ) or iShare ( _https://cordis.europa.eu/project/rcn/208159_de.html_ ) . # 4\. Archiving and preservation The data sharing procedures will be different across the datasets depending of license and will be in accordance with the Grant Agreement. Raw data will be converted to non-talking identifiers (coded identification of the vehicle) with the use of one-way thickening algorithms using random values (different for each tracked data set – salt cryptography) while the original data will be discarded and the coded identification of the vehicle will be stored for maximum of 24 hours for the specific needs of the system. Appropriate technical and organizational measures will be taken against unauthorised or unlawful processing of personal data in order to ensure that the individual cannot be identified from the captured data and furthermore we will ensure that data that could eventually lead to subsequent determination of the individual’s path (where, when, how fast) will be stored for maximum of 24 hours. This way the possibility of identification of Individual is sufficiently minimalized The system will aggregate the data collected in short intervals which will ensure the anonymity of the data. # 5\. Ethical aspects As in LOGISTAR sensible data will be harvested, stored and processed as well as potentially also personal data will be processed the project has established a separate workpackage (WP10 - Ethics requirements) to tackle ethical issues. The 'ethics requirements' that the project must comply with are included as deliverables in this work package, see as follows: D10.1: NEC - Requirement No. 1 [7] (Month 1) The applicants must ensure that the research conducted outside the EU is legal in at least one EU Member State. D10.2: H - Requirement No. 2 [8] (Month 1) The informed consent procedures that will be implemented for the participation of humans must be submitted as a deliverable. Templates of the informed consent/assent forms and information sheets (in language and terms intelligible to the participants) must be kept on file. D10.3: POPD - Requirement No. 3 [9] (Month 1) Detailed information on the procedures for data collection, storage, protection, retention, and destruction, and confirmation that they comply with national and EU legislation must be included in the Data Management Plan. However, relevant information that pertain to the interviewing/surveying activities performed before the delivery of the Data Management Plan in M6 must also be provided by the start of these activities. In case personal data are transferred from/to a non-EU country or international organisation, confirmation that this complies with national and EU legislation, together with the necessary authorisations, must be kept on file. Detailed information on the informed consent procedures in regard to the collection, storage, and protection of personal data must be submitted as a deliverable. Templates of the informed consent forms and information sheets (in language and terms intelligible to the participants) must be kept on file. In case of further processing of previously collected personal data, relevant authorisations must be kept on file. D10.4: GEN - Requirement No. 4 [10] (Month 6) An ethics mentor must be appointed to advise the project participants on ethics issues relevant to the protection of personal data. A report on the activities of the ethics mentor must be submitted with the Data Management Plan. ### 5.1. Activities of the ethics mentor The Ethics mentor appointed for LOGISTAR project is Dr Pedro Manuel Sasia who is lecturer and researcher at the University of Deusto. During these 6 first months of the project (from June 2018 to November 2018), the activities of the ethics mentor have been focused on stablishing the adequate procedures to fulfil the ethical requirements that are relevant to the project, namely: * Research out of Europe Definition of Procedures to be followed for analysing the collection and processing of personal data obtained or handled by LOGISTAR project in Serbia. Data transfer agreement to comply with GDPR requirements. (Detailed in Deliverable 10.1 [7]) * Informed consent Definition of Procedures implemented for the participation of humans in LOGISTAR’s research activities both in interviewing activities and for the testing and validation of the system. Informed consent templates (Detailed in Deliverable 10.2 [8]) * Data management Definition of procedures in relation with data collection, storage, protection, retention and destruction in order to comply with the applicable national and EU legislation. Particularly, data handling procedures to be implemented related to interviewing activities that have taken place at the beginning of the project (Detailed in Deliverable 10.3 [9]) The ethics mentor has been in close contact with coordinator via direct mail, phone and regular meeting and has had access to all the information about the activities of the project that could imply ethically relevant aspects via email and accessing the common repository of LOGISTAR. # 6\. Conclusions The LOGISTAR project makes use of data along the whole ODI Data Spectrum, means closed – shared – open data with main attributes: volume, velocity and variety. Data comes from different sources like open data sources, consortium members and also 3 rd parties in the course of the use case realisation. Parts of the data are sensible data and potentially even personal data and thereby secure data management / sharing is an important issue to be tackled by the project. This will be taken into account from a technical as well as from an organisational viewpoint! This Data Management Plan on hand is created as a living document that is maintained over time following the dynamic requirements of the LOGISTAR project and it acts as a guideline for the whole consortium in regards of any data management in the project.	https://phaidra.univie.ac.at/o:1140797	Horizon 2020
0092_EnDurCrete_760639.md	1. Introduction This document constitutes the first issue of Data Management Plan (DMP) foreseen in the EU framework of the EnDurCrete project under Grant Agreement No. 760639. 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 aligns The Dissemination, Communication and Networking Plan. The first version of the DMP is delivered at month 8, later the DMP will be monitored and regularly updated up to the release of the Final Data Management Plan. 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 EnDurCrete 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 EnDurCrete consortium participants. The EnDurCrete project aims to develop a new cost-effective sustainable reinforced concrete for long lasting and added value applications. The concept is based on the integration of novel lowclinker cement including high-value industrial by-products, new nano and micro technologies and hybrid systems ensuring enhanced durability of sustainable concrete structures with high mechanical properties, self-healing and self-monitoring capacities. EnDurCrete project comprises seven technical work packages as follows: * WP1 Design requirements for structures exposed to aggressive environment * WP2 Development and characterisation of new green and low-cost cementitious materials * WP3 Innovative concrete technologies, including nano/microfillers, coatings and reinforcement * WP4 Multifunctional and multiscale modelling and simulations of materials, components and structures * WP5 Lab-scale performance testing and development of monitoring tools for concrete components & structures * WP6 Prototyping, demonstration and solutions performance validation * WP7 Life cycle assessment and economic evaluation, standardization and health and safety aspects Two non-technical work packages ensure the facilitation of the technical work and coordination of all the work packages, dissemination and communication of the project results. These work packages consist of the following: * WP8 Training, dissemination and exploitation * WP9 Project Management This document has been prepared to describe the data management life cycle for all data sets that will be collected, processed or generated by the EnDurCrete 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. 2. Open Access Open access can be defined as the practice of providing online 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), or  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 including creation and dissemination of knowledge, acceleration of innovation, foster collaboration and reduction of the effort duplication, involvement of citizens and society, contribution to returns on investment in R&D etc. Possibilities to access and share scientific information Faster growth Fost er collaboration Involve citizens and society Build on previous research results OPEN ACCESS Accelerated innovation Increased efficiency Improved quality of results Improved transparency Figure 1 - Open Access benefits The EC capitalizes on open access and open science as it lowers barriers to accessing publiclyfunded 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 easily accessible scientific information for the scientific community itself, but also for innovative small businesses. 2.1 Open Access to peer-reviewed scientific publications Open access to scientific peer-reviewed publications (also known as Open Access Mandate) 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. Non-compliance can lead, amongst other measures, to a grant reduction. More specifically, Article 29 of the EnDurCrete GA: “Dissemination of results - Open Access - Visibility of EU Funding” establishes the obligation to ensure open access to all peer-reviewed articles relating to the EnDurCrete project. _Article 29.2 EnDurCrete GA: Open access to scientific publications_ “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.” 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. 2. Gold Open Access The gold open access is also 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. 2.2 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 EnDurCrete GA: 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 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 the EnDurCrete 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.” 2.3 Dissemination & Communication and Open Access For the implementation of the EnDurCrete 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 the EnDurCrete to key groups potentially related to the project results’ exploitation). Likewise, the EnDurCrete 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, Networking and Dissemination Plan. The Data Management Plan and the actions derived are part of the overall EnDurCrete dissemination and communication strategy, which is included in the abovementioned deliverable. 3 Objectives of Data Management Plan The purpose of the EnDurCrete Data Management Plan is to provide a management assurance framework and processes that fulfil the data management policy that will be used by the EnDurCrete project partners regarding all the dataset types that will be generated by the EnDurCrete project. The aim of the DMP is to control and ensure quality of project activities, and to manage the material/data generated within the EnDurCrete project effectively and efficiently. 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 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. EnDurCrete Data Management Plan Communication, Networking and Dissemination Plan Exploitation Plan Publication Repository of research data IPR management Business models Open access to scientific publication Open access to research data IPR strategy Business Plan 4 EnDurCrete Project Website - storage and access EnDurCrete 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. Reserved Area 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.endurcrete.eu_ was launched during the early Project stage, 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 EnDurCrete Project, press releases, magazine articles, scientific papers, etc. The website is available in English. 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 EnDurCrete project website itself already has its own backup procedures. The Project Coordinator (HC) of the EnDurCrete along with the Dissemination and Exploitation Leader (FENIX) will be in charge for data management and all the relevant issues. 5 Data Management Plan implementation The organisational structure of the EnDurCrete project matches the complexity of the Project and is in accordance with the recommended management structure of the DESCA model Consortium Agreement. The organisational structure of the Project is shown in the figure below. project The general and technical management of the Project is handled by the Project Coordination Group (PCG). The PCG administers the project, acts as a single point of contact between the EnDurCrete consortium and the Commission. It provides the general direction to the project by regularly reporting to the General Assembly (GA). The PCG comprises the Project Coordinator (PC), the Chief Scientific-Technical Office (CSO), the Chief Financial Officer (CFO), and the Chief Administrative Officer. Responsibilities of the PCG include: * financial control, * contractual issues, * communication, * IPR issues, and * reporting to the Commission The R&D work in the Project is divided in seven technical work packages and two non-technical work packages. Each work package is managed by Work Package Leader (WP Leader). WP Leaders are responsible for managing their work package as a self-contained entity. Tasks and responsibilities of the WP Leaders include, among others, following: * Coordination of the technical work in the WPs, including contribution to reporting * Assessment of the WP progress to ensure the output performance, costs and timeline are met * Identification of IPR issues and opportunities * Organisation of the WP meetings * Contribution to the dissemination activities * Initiation of all actions necessary for reaching a solution or decision in consultation with the researchers involved and the PMs In the case of technical problems at WP level, the WP Leader should be notified as soon as possible. In addition, each WP is further subdivided into its large components tasks, which are allocated to a Task Leader responsible for their coordination. In the organisation structure following management bodies are identified: * General Assembly (GA): GA consists of one representative for each partner institution. Each representative is responsible for the proper utilization of the contractor’s resources allocated to the project and for the attainment of the objectives assigned to his institution. Each representative further names a deputy who has the necessary knowledge and authorization to represent its institution in the framework of the EnDurCrete project. * Dissemination, Exploitation and Standardisation Board (DESB): DESB forms a project body that shall assist and support the GA as far as concerns issues on the exploitation of results and disagreement resolutions. It constitutes the central office coordinating all the contacts towards stakeholder communities and other dissemination and communication target audiences. The DESB is also responsible for the performance of the innovation management activities. * Demonstration Board (DB): DB coordinates the demonstration activities. The DB shall manage the activities performed in different locations with a common systemic approach. * Technical Board (TB): TB is responsible for the technical activities of the Work Packages (WPs) and consists of all the WPs Leaders (WPLs). The TB directly refers to the GA and is responsible for providing technical updates on the on-going activities. The TB is also an essential tool to keep the whole consortium informed about any criticism, problem, and deviation from original plan that may arise when carrying out the technical activities. The GA is supported by the Advisory Board (AB) consisting of the number of external experts that will be selected on the basis of their profound and long-lasting expertise in the field of research, innovation and industrialisation. Partners of the EnDurCrete project demonstrate relevant management capabilities necessary to support and provide major contribution to all the activities envisaged in the Project work. All partners and their roles in the EnDurCrete project are listed in the following table. Table 1 - EnDurCrete partners and their role in the project <table> <tr> <th> No. </th> <th> Partner short name </th> <th> Partner legal name </th> <th> Partner role in the EnDurCrete project </th> <th> </th> </tr> <tr> <th> 1 </th> <th> HC </th> <th> HEIDELBERGCEMENT AG </th> <th> HC is a Project coordinator and leader of Development and characterisation of new green and low-cost cementitious materials. HC brings key knowledge on the development of new environmentally friendly low-clinker binders and of concrete mixes integrating novel additive technologies. In addition, HC is responsible for the Project Management. </th> </tr> <tr> <th> 2 </th> <th> RINA-C </th> <th> RINA CONSULTING SPA </th> <th> RINA-C develops requirements for structures exposed to harsh environmental conditions, designs and optimises smart textile selfmonitoring reinforcing system, performs modelling simulation activities, calibrates monitoring tools and performs structural health monitoring activities. Additionally, RINA-C develops EnDurCrete business models and contributes to exploitation. RINA-C has also small contributions for LCA and safety related aspects. </th> </tr> <tr> <th> 3 </th> <th> CEA </th> <th> COMMISSARIAT A L ENERGIE ATOMIQUE ET AUX ENERGIES ALTERNATIVES </th> <th> CEA leads Multifunctional and multiscale modelling and simulations of materials, components and structures, being involved in modelling and simulations. CEA also contributes to Lab-scale performance testing and development of monitoring tools for concrete components and structures. CEA is responsible for the assessment of the exposure likelihood of the new nano- modified EnDurCrete products. </th> </tr> <tr> <th> 4 </th> <th> ACCIONA </th> <th> ACCIONA CONSTRUCCION SA </th> <th> ACCIONA provides its expertise to the demonstration and performance validation activities in the demonstration sites located in Spain. ACCIONA also collaborates in the definition of requirements for concrete design mix and additives to be used and develops concrete mix designs integrating new designed durability technologies and prepares concrete specimens for later analysis. ACCIONA also participates with NDT technologies NT492 and electrical resistivity measurements to asset </th> </tr> </table> <table> <tr> <th> </th> <th> </th> <th> </th> <th> corrosion in laboratory specimens. </th> <th> </th> </tr> <tr> <th> 5 </th> <th> KVAERNER </th> <th> KVAERNER AS </th> <th> KVAERNER is primarily in charge to write the requirements for offshore platforms within Requirements and conceptual design of new components and structures and contributes in Multifunctional and multiscale modelling and simulation of materials, components and structures. KVAERNER also performs testing at Stord shipyard to simulate North Sea water condition. </th> </tr> <tr> <th> 6 </th> <th> SIKA </th> <th> SIKA TECHNOLOGY AG </th> <th> SIKA is a leader of Innovative concrete technologies, including nano/microfillers, coatings and reinforcement and coordinates the design and development of new durable concrete systems incorporating innovative technologies. SIKA is in charge of evaluating the compatibility of the novel additives developed by other partners with common additives in use in current concrete technology. </th> </tr> <tr> <th> 7 </th> <th> ZAG </th> <th> ZAVOD ZA GRADBENISTVO SLOVENIJE </th> <th> ZAG’s main contribution to the project deals with lab-scale performance testing, the demonstration in a real environment (Croatia), performance validation (as far as concerns corrosion monitoring) and the promotion of standardisation activities. </th> </tr> <tr> <th> 8 </th> <th> VITO </th> <th> VLAAMSE INSTELLING VOOR TECHNOLOGISCH ONDERZOEK N.V. </th> <th> VITO contributes customized sustainable supplementary cementitious materials to the project and collaborates in the development of the low impact binder with minimal Portland cement content. In addition, VITO contributes to the environmental assessment and high-grade recyclability of the end products. In particular VITO will establish second life reuse potential of the developed concrete products. </th> </tr> <tr> <th> 9 </th> <th> NTNU </th> <th> NORGES TEKNISKNATURVITENSKAPELIGE UNIVERSITET NTNU </th> <th> NTNU leads the characterization of the novel cementitious materials and contributes to modelling of the phase assemblage of the novel binders. In addition, NTNU contributes to the simulations of the experimental laboratory tests by providing experimental data and a critical review of the simulation results. NTNU performs purpose-build tests to validate or estimate durability parameters required for the numerical models. </th> </tr> <tr> <th> 10 </th> <th> UNIVPM </th> <th> UNIVERSITA POLITECNICA DELLE MARCHE </th> <th> UNIVPM is an academic leader of Lab-scale performance testing and development of monitoring tools for concrete components and structures. UNIVPM develops and optimizes </th> </tr> </table> <table> <tr> <th> </th> <th> </th> <th> </th> <th> novel self-sensing cement based mixtures manufactured with green micro-fillers and it contributes to their durability assessment. UNIVPM manages the calibration and testing of the self-sensing/monitoring properties of the new concrete. UNIVPM will develop advanced non-destructive testing tools for non- intrusive in-field inspection, which will be used in selected demos. </th> </tr> <tr> <td> 11 </td> <td> FENIX </td> <td> FENIX TNT SRO </td> <td> FENIX is in charge of training, dissemination and exploitation activities. </td> </tr> <tr> <td> 12 </td> <td> GEO </td> <td> GEONARDO ENVIRONMENTAL TECHNOLOGIES LTD </td> <td> GEO leads Life cycle assessment and economic evaluation, standardization and health and safety aspects and brings its expertise to address environmental and economic sustainability (LCA and LCC) and standardisation aspects. It also performs training activities on sustainable concrete products. </td> </tr> <tr> <td> 13 </td> <td> AMSolution </td> <td> PROIGMENES EREVNITIKES & DIAHIRISTIKES EFARMOGES </td> <td> The main role of AMSolution is to develop and optimise new multi-functional protective coatings. AMSolution is responsible for development of multi- functional coating formulation with self-healing as well as solar/UV reflection, hydrophobicity, antimolding and self-cleaning properties; investigation and optimization of encapsulation technique for the achievement of desired healing efficiency in final coating formulation and finally, execution of variety tests for the confirmation of the full compatibility of the investigated materials. </td> </tr> <tr> <td> 14 </td> <td> NTS </td> <td> NUOVA TESI SYSTEM SRL </td> <td> NTS brings expertise in precasting process and performance evaluation. Additionally, NTS will manufacture the prototypes used for the demonstrations. NTS is also a recipient of scope visits for adequate safety assessment and management. </td> </tr> <tr> <td> 15 </td> <td> IBOX </td> <td> I-BOX CREATE S.L. </td> <td> The main contribution of IBOX concerns the development and optimisation of smart corrosion inhibitors, based on nano-modified clays. </td> </tr> <tr> <td> 16 </td> <td> INFRA PLAN </td> <td> INFRA PLAN KONZALTNIG JDOO ZA USLUGE </td> <td> The main role of INFRA PLAN is to lead the demonstration activity on the Krk bridge and contribute to ND monitoring activities. INFRA PLAN leads the Prototyping, demonstration and performance validation in a bridge in Croatia, by providing planning and the execution of the monitoring project. </td> </tr> </table> 6. 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: * _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). * _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. * _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). * _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 duration; 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). * _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, artefacts, 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. 7. Data sets of the EnDurCrete project Projects under Horizon 2020 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. 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 EnDurCrete 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 EnDurCrete project detailing the kind of results and how it will be managed. The responsibility to define and describe all non-generic 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 10.1 (List of the EnDurCrete project data sets 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 EnDurCrete project. For each data set, the characteristics of the data set have been captured in a tabular format as enclosed in table 4 (List of the EnDurCrete project data sets and sharing strategy). _Standards & Metadata _ Metadata is defined as “data about data”. It refers to 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 character “dissemination classification” (i.e. public, or confidential) 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 EnDurCrete Grant Agreement. The above levels are linked to the “Dissemination Level” specified for all EnDurCrete deliverables as follows: * PU Public * CO Confidential, only for members of the consortium (including the Commission Services) * EU-RES Classified Information: RESTREINT UE (Commission Decision 2005/444/EC) * EU-CON Classified Information: CONFIDENTIEL UE (Commission Decision 2005/444/EC) * EU-SEC Classified Information: SECRET UE (Commission Decision 2005/444/EC) 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 with FENIX assistance 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. A link to the OpenAIRE entry will then be submitted to the EnDurCrete Website Administrator (FENIX) by the Lead Author. OpenAIRE is an EC/funded initiative designated to promote the open access policies of the EC and help researchers, research officers and project coordinators comply with them. OpenAIRE 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. Each EC project has its own page on OpenAIRE, featuring project information, related project publications and data sets, and a statistics section. In case of any questions regarding the Open Access policy of the EC the representatives of the National Open Access Desk for OpenAIRE should be contacted. _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 EnDurCrete 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 (Project 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. The process of archiving will be based on a data extract performed within 12 weeks of the formal closure of the EnDUrCrete 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. 8. Technical requirements of data sets 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 Grant Agreement 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. 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. 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 EnDurCrete 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 rasterised 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 EnDurCrete 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. 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 EnDurCrete 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> <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> 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 EnDurCrete 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> <th> MIDI </th> <th> .midi .mid </th> <th> MIDI (Musical Instrument Digital Interface). Main format for all electronic music devices like synthesizers and PC sound </th> </tr> <tr> <td> </td> <td> </td> <td> 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> </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> </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> </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> </td> <td> .ogg </td> <td> Theora Ogg. Developed by the Xiph.Org Foundation. Supported by HTML5. </td> </tr> <tr> <td> MP3 </td> <td> </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 MP4 </td> <td> or </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> 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". 8.6 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. 8.7 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. EnDurCrete innovation development shall make use of this file type for system testing. 8.8 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. 9 GDPR compliance At every stage, the EnDurCrete project management and Project Consortium will ensure that the Data Management Plan is in line with the norms of the EU and Commission [as expressed in the General Data Protection Regulation (GDPR) (Regulation (EU) 2016/679)] and will promote best practice in Data Management. The GDPR comes into force on 25 May 2018. The responsibility of protection and use of personal data 2 is on the Project partner collecting data. The questionnaire answers shall be anonymized in as early stage of the process, and data making it possible to connect the answers to individual persons shall be destroyed. The consent of the questionnaire participant will be asked in all questionnaires conducted within the EnDurCrete project. This will include a description how and why the data is to be used. The consent must be clear and distinguishable from other matters and provided in an intelligible and easily accessible form, using clear and plain language. It must be as easy to withdraw consent as it is to give it. The questionnaire participants will not include children or other groups requiring a supervisor. Also when asking for somebody’s contact information, the asking party shall explain why this information is asked and for what purposes it will be used. _Controller and Processor_ Controller means the natural or legal person, public authority, agency or other body which, alone or jointly with others, determines the purposes and means of the processing of personal data. Processor refers to a natural or legal person, public authority, agency or other body which processes personal data on behalf of the controller. _Data Protection Officer_ The Data Protection Officer (DPO) is responsible for overseeing data protection strategy and implementation to ensure compliance with GDPR requirements. Under the GDPR, there are three main scenarios where the appointment of a DPO by a controller or processor is mandatory: * The processing is carried out by a public authority * The core activities of the controller or processor of processing operations which require regular and systematic processing of data subjects on a large scale; or * The core activities of the controller or processor consist of processing on a large scale of sensitive data or data relating to criminal convictions / offences. Each EnDurCrete partner shall assess its own data processing activities to understand whether they fall within the scope of the requirements set out above. If they do, then it will be important to either fulfil the DPO position internally or from an external source. For those organisations to whom the requirements do not apply, they may still choose to appoint a DPO. If they choose not to appoint a DPO, then it is recommended to document the reasoning behind that decision. _Data protection_ European citizens have a fundamental right to privacy. In order to protect this right of individual data subject, the anonymisation and pseudonymisation can be used. Anonymisation refers to personal data processing with the aim of irreversibly preventing the identification of the individual to whom it relates. For the anonymized types of data, the GDPR does not apply, as long as the data subject cannot be re-identified, even by matching his/her data with other information held by third parties. Pseudonymisation refers to the personal data processing in such a manner that the data can no longer be attributed to a specific data subject without the use of additional information. 3 To pseudonymize a data set, the additional information must be kept separately and subject to technical and organizational measures to ensure non/attribution to an identified or identifiable person. In other words, the pseudonymized data constitutes the basic privacy-preserving level allowing for some data sharing, and represent data where direct identifiers (e.g. names) or quasiidentifiers (e.g. unique combinations of date and zip codes) are removed and data is mismatched with a substitution algorithm, impeding correlation of readily associated data to the individual’s identity. For such data, GDPR applies and appropriate compliance must be ensured. Due to the limited amount and less harmful nature of the personal data that is collected within the EnDurCrete project, neither pseudonymisation nor anonymisation will be used. Other means of data security will be used to protect data collected in the framework of the Project. _Breach Notification_ Under the GDPR, breach notification will become mandatory in all member states where a data breach is likely to “result in a risk for the rights and freedoms of individuals”. This must be done within 72 hours of first having become aware of the breach. Data processors will also be required to notify the data subjects and the controllers, “without undue delay” after first becoming aware of a data breach. _Right to be Forgotten_ Also known as Data Erasure, the right to be forgotten entitles the data subject to have the data controller erase his/her personal data, cease further dissemination of the data, and potentially have third parties halt processing of the data. The conditions for erasure include the data no longer being relevant to original purposes for processing, or a data subjects withdrawing consent. It should also be noted that this right requires controllers to compare the subjects' rights to "the public interest in the availability of the data" when considering such requests. If a data subject wants his/her personal data to be removed from a questionnaire, the non-personal data shall remain in the analysis of the questionnaire. _Data portability_ GDPR introduces data portability which refers to the right for a data subject to receive the personal data concerning them, which they have previously provided in a 'commonly use and machine readable format' and have the right to transmit that data to another controller. The personal data collected within EnDurCrete project will be in electronic form, mostly in Microsoft Excel file forms .xls or .xlsx. In case the data subject requests to transmit his/her data to another controller there should be no technical limitations for providing them. _Privacy by design and by default_ Privacy by design refers to the obligation of the controller to implement appropriate technical and organisational measures, such as pseudonymisation, which are designed to implement data protection principles, such as data minimisation, in an effective manner and to integrate the necessary safeguards into the processing. Privacy by default means that the controller shall implement appropriate technical and organisational measures for ensuring that only personal data which are necessary for each specific purpose of the processing are processed. That obligation applies to: * the amount of personal data collected, * the extent of personal data processing,  the period of personal data storage, and  the accessibility of personal data. In particular, such measures shall ensure that by default personal data are not made accessible without the individual’s intervention to an indefinite number of natural persons. 4 Personal data collected during the EnDurCrete project will be used only by project partners, including linked third parties, and only for purposes needed for the implementation of the project. Also within the EnDurCrete project, if someone of the project consortium asks for personal data, the partner holding the data should consider whether that data is needed for the implementation of the Project. If personal data is provided, the data shall not be distributed further within or outside the Project. _Records of processing activities_ Records of data processing and plans for the use of data will be kept by the WP Leaders of those work packages that collect personal data. 10 Expected research data of the EnDurCrete project Expected research data of the EnDurCrete project is listed below. The table template will be circulated periodically in order to monitor the data sets and set the strategy for their sharing. Table 4 - List of the EnDurCrete project data sets 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 5 </th> </tr> <tr> <td> WP1 Design requirements for structures exposed to aggressive environment </td> <td> RINA \- C </td> <td> Task 1.1: Requirements and design process for marine environment </td> <td> M1-M5 </td> <td> RINA-C </td> <td> List of technical directives, surveys, standards and regulations for concrete materials in the target applications </td> <td> Report describing the development of guideline documents for concrete structures exposed to the different aggressive environments (marine, continental and offshore). </td> <td> .pdf </td> <td> PU </td> </tr> <tr> <td> Design requirements for concrete structures exposed to marine environment </td> <td> Report reviewing actual technical directives, surveys, standards and regulations applying to concrete materials for harbours and maritime construction at European level, as well as some key national documents. </td> <td> .pdf </td> <td> CO </td> </tr> <tr> <td> Task 1.2: Requirements and design process for continental environment (road </td> <td> M1-M6 </td> <td> RINA-C </td> <td> List of technical directives, surveys, standards and regulations for concrete materials in </td> <td> Report describing the development of guideline documents for concrete structures exposed to the different aggressive </td> <td> .pdf </td> <td> PU </td> </tr> </table> <table> <tr> <th> </th> <th> </th> <th> infrastructures) </th> <th> </th> <th> </th> <th> the target applications </th> <th> environments (marine, continental and offshore). </th> <th> </th> <th> </th> </tr> <tr> <th> Design requirements for concrete structures exposed to continental environment (road infrastructures) </th> <th> Report reviewing actual technical directives, surveys, standards and regulations applying to concrete materials for continental construction at European level, as well as some key national documents. </th> <th> .pdf </th> <th> CO </th> </tr> <tr> <th> Task 1.3: Requirements and design process for offshore platforms </th> <th> M1-M5 </th> <th> KVAERNER </th> <th> List of technical directives, surveys, standards and regulations for concrete materials in the target applications </th> <th> Report describing the development of guideline documents for concrete structures exposed to the different aggressive environments (marine, continental and offshore). </th> <th> .pdf </th> <th> PU </th> </tr> <tr> <th> Offshore design requirements </th> <th> Design loads, design process, design requirements, environmental exposure scenario, concrete constituencies and composition, references </th> <th> .pdf </th> <th> CO </th> </tr> <tr> <td> Data Shari </td> <td> ng </td> <td> * Confidential data: Reserved Area on the EnDurCrete website * Public data: EnDurCrete website </td> <td> Data Archiving and preservation </td> <td> EnDurCrete website and RINA-C server </td> <td> Data management Responsibilities </td> <td> Eriselda Lirza </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 Development and characterisation of new green and low-cost cementitious materials </td> <td> HC </td> <td> Task 2.1: Optimisation of a novel Portland Composite Cement, including sustainable supplementary cementitious materials </td> <td> M2-M7 </td> <td> HC </td> <td> D2.1 Report on in depth characterisation of Portland Composite Cement components </td> <td> Report </td> <td> .pdf </td> <td> CO </td> </tr> <tr> <td> Results of the characterisation of individual cement components </td> <td> Raw data and results of the various experiments carried out within T2.1 (TGA, XRD, calorimetry, PSD, trace elements and heavy metals, SEMimages) </td> <td> .xlsx .png </td> <td> CO </td> </tr> <tr> <td> Task 2.2: Development of customised separate grinding technology for each PCC component </td> <td> M5-M8 </td> <td> HC </td> <td> D2.2 Report on optimization of most promising mixes to be further investigated </td> <td> Report </td> <td> .pdf </td> <td> CO </td> </tr> <tr> <td> Results of the cement development </td> <td> Raw data and results of the tested cements within T2.2 (strength, workability, PSD) </td> <td> .xlsx </td> <td> CO </td> </tr> <tr> <td> Task 2.3: Characterization of the novel cementitious materials </td> <td> M5-M8 </td> <td> NTNU </td> <td> Results of the hydration study phase assemblage and reaction degree of the hydrated novel binders </td> <td> Raw data and results of the various experiments performed within T2.3 (TGA, XRD, calorimetry, chemical shrinkage, rheological measurements, SEM-EDS, MIP) </td> <td> .xlsx .docx .tif .jpg </td> <td> CO </td> </tr> <tr> <td> D2.3 Report on rheological </td> <td> This report describes the results of the rheological </td> <td> .docx .pdf </td> <td> CO </td> </tr> </table> <table> <tr> <th> </th> <th> </th> <th> </th> <th> </th> <th> </th> <th> measurements and packing </th> <th> tests performed in T2.3 </th> <th> </th> <th> </th> </tr> <tr> <th> D2.4 Report on the hydration study </th> <th> This report describes the results of the hydration tests performed in T2.3 </th> <th> .docx .pdf </th> <th> CO </th> </tr> <tr> <td> Data Sharing </td> <td> * Confidential data: Reserved Area on the EnDurCrete website and Heidelberg Cement server * Raw data and results: Heidelberg Cement server * Public data: EnDurCrete website </td> <td> Data Archiving and preservation </td> <td> EnDurCrete website and Heidelberg Cement server </td> <td> Data management Responsibilities </td> <td> Gerd Bolte Arnaud Muller </td> </tr> <tr> <td> WP number and name </td> <td> WP lead </td> <td> Task number and name </td> <td> Duration </td> <td> Task lead </td> <td> Dataset name </td> <td> Dataset description </td> <td> Format </td> <td> Level </td> </tr> <tr> <td> WP3 Innovative concrete technologies, including nano/ microfillers, coatings and reinforcement </td> <td> SIKA </td> <td> Task 3.1: Development and optimization of smart corrosion inhibitors, based on nano-modified clays </td> <td> M1-M15 </td> <td> IBOX </td> <td> Protocols with the synthesis description </td> <td> Files with the description of the different steps to follow for the development and optimization of smart corrosion inhibitors, based on nano-modified clays </td> <td> .pdf </td> <td> CO </td> </tr> <tr> <td> Characterization graphs </td> <td> graphs with the characterization of the synthesized products applying different techniques, such as, X-ray </td> <td> .pdf .jpeg </td> <td> CO </td> </tr> </table> <table> <tr> <th> </th> <th> </th> <th> </th> <th> </th> <th> </th> <th> </th> <th> diffraction, thermogravimetric analysis, infrared or ultra violet spectroscopy </th> <th> </th> <th> </th> </tr> <tr> <th> Task 3.2: Development and optimization of novel self-sensing carbon-based green micro-fillers </th> <th> M1-M15 </th> <th> UNIVPM </th> <th> Analysis of carbon based green microfillers behaviour in cement </th> <th> Composition and properties, graphs </th> <th> .xlsx; .pdf; .docx </th> <th> CO </th> </tr> <tr> <th> Test data </th> <th> Data of tests performed during the characterization of the self-sensing properties </th> <th> .jpg; .xlsx; .pdf </th> <th> CO </th> </tr> <tr> <th> Task 3.3: Development and optimization of new multi-functional protective coatings </th> <th> M1-M15 </th> <th> AM \- SOLUTIONS </th> <th> Development of selfhealing coatings </th> <th> Images coming from microscopy techniques for the evaluation of microcapsules and coatings. </th> <th> .png </th> <th> CO </th> </tr> <tr> <th> Development of self- cleaning coatings </th> <th> Contact angle measurements for the evaluation of selfcleaning performance of particles and coatings. </th> <th> .xlsx </th> <th> CO </th> </tr> <tr> <th> Development of antimoulding coatings </th> <th> Measurements and photos of anti-moulding properties of particles and coatings. </th> <th> .xlsx, .png </th> <th> CO </th> </tr> <tr> <th> Development of lightreflective coatings </th> <th> Measurements of thermal behaviour of the coatings under IR lamps. </th> <th> .xlsx </th> <th> CO </th> </tr> <tr> <th> D3.6 Development and evaluation of new multi-functional </th> <th> Report on self-healing agents for developed EnDurCrete coatings </th> <th> .pdf </th> <th> CO </th> </tr> </table> <table> <tr> <th> </th> <th> </th> <th> </th> <th> </th> <th> </th> <th> protective coatings </th> <th> </th> <th> </th> <th> </th> </tr> <tr> <th> Task 3.4: Evaluation of compatibility of additives in concrete </th> <th> M4-M12 </th> <th> SIKA </th> <th> \- </th> <th> \- </th> <th> \- </th> <th> \- </th> </tr> <tr> <th> \- </th> <th> \- </th> <th> \- </th> <th> \- </th> </tr> <tr> <th> Sub Task 3.5.1: Development of mix designs according to requirements defined in WP1 </th> <th> M3-M9 </th> <th> HC </th> <th> D3.1 Report on optimized mix designs using novel binders </th> <th> Report </th> <th> .pdf </th> <th> CO </th> </tr> <tr> <th> Results of the concrete development </th> <th> Raw data and results of concrete development (mix design/concrete composition, strength, workability and durability results) </th> <th> .xlsx </th> <th> CO </th> </tr> <tr> <th> Sub Task 3.5.2: Implementation of novel additives </th> <th> M6-M12 </th> <th> HC </th> <th> Implementation of novel additives in concrete </th> <th> Raw data and results of the impact of the novel additive technologies (mix design/concrete composition, resulting concrete properties) </th> <th> .xlsx </th> <th> CO </th> </tr> <tr> <th> Sub Task 3.5.3: Preparation of concrete specimens for lab-scale testing </th> <th> M9-M15 </th> <th> ACCIONA </th> <th> \- </th> <th> \- </th> <th> \- </th> <th> \- </th> </tr> <tr> <th> Sub Task 3.5.4: Validation, final tuning and roll out to WP6 </th> <th> M20- M22 </th> <th> HC </th> <th> D3.9 Report on optimized mix designs using novel binders and additives ready for upscaling in WP6 </th> <th> Report </th> <th> .pdf </th> <th> CO </th> </tr> <tr> <th> Optimized mix design </th> <th> Raw data and results of </th> <th> .xlsx </th> <th> CO </th> </tr> </table> <table> <tr> <th> </th> <th> </th> <th> </th> <th> </th> <th> </th> <th> using additives </th> <th> concrete development with additives (mix design/concrete composition, resulting concrete properties) </th> <th> </th> <th> </th> </tr> <tr> <th> Task 3.6 Design and integration of the multifunctional selfmonitoring reinforcing system </th> <th> M1-M15 </th> <th> RINA-C </th> <th> Textiles datasheets </th> <th> Datasheets of textiles selected as candidates for the application </th> <th> .pdf </th> <th> CO </th> </tr> <tr> <th> Optical fiber sensors datasheets </th> <th> Datasheets of optical fiber sensors selected as candidates for the application </th> <th> .pdf </th> <th> CO </th> </tr> <tr> <th> Test images </th> <th> Pictures related to the technological embedding tests </th> <th> .jpg </th> <th> CO </th> </tr> <tr> <th> Test videos </th> <th> Videos of the technological embedding tests </th> <th> video </th> <th> CO </th> </tr> <tr> <th> Sub Task 3.6.1: Design of multifunctional selfmonitoring reinforcing system </th> <th> M1-M12 </th> <th> RINA-C </th> <th> Textiles datasheets </th> <th> Datasheets of textiles selected as candidates for the application </th> <th> .pdf </th> <th> CO </th> </tr> <tr> <th> Optical fiber sensors datasheets </th> <th> Datasheets of optical fiber sensors selected as candidates for the application </th> <th> .pdf </th> <th> CO </th> </tr> <tr> <th> Subtask 3.6.2: Integration of the multifunctional selfmonitoring reinforcing system </th> <th> M6-M15 </th> <th> NTS </th> <th> Test images </th> <th> Pictures related to the technological embedding tests </th> <th> .jpg </th> <th> CO </th> </tr> <tr> <th> Test videos </th> <th> Videos of the technological embedding tests </th> <th> video </th> <th> CO </th> </tr> </table> <table> <tr> <th> Data Sharing </th> <th> * Confidential data: Reserved Area on the EnDurCrete website * Public data: EnDurCrete website, Zenodo </th> <th> Data Archiving and preservation </th> <th> EnDurCrete website and servers of the respective partners </th> <th> Data management Responsibilities </th> <th> TBD </th> </tr> <tr> <td> WP number and name </td> <td> WP lead </td> <td> Task number and name </td> <td> Duration </td> <td> Task lead </td> <td> Dataset name </td> <td> Dataset description </td> <td> Format </td> <td> Level </td> </tr> <tr> <td> WP4 Multifunctional and multiscale modelling and simulations of materials, components and structures </td> <td> CEA </td> <td> Task 4.1: Completed EnDurCrete MODA template </td> <td> M1-M41 </td> <td> RINA-C </td> <td> EnDurCrete MODA </td> <td> Modelling work flow and description of the single modelling steps </td> <td> .docx .ppt </td> <td> PU </td> </tr> <tr> <td> Task 4.2: Multiscale modelling of the ageing mechanical and diffusive properties of the new materials due to hydration and degradation </td> <td> M3-M30 </td> <td> CEA </td> <td> \- </td> <td> \- </td> <td> \- </td> <td> \- </td> </tr> <tr> <td> \- </td> <td> \- </td> <td> \- </td> <td> \- </td> </tr> <tr> <td> Sub task 4.2.1: Simulation of the phase assemblage of the novel binders </td> <td> M3-M24 </td> <td> NTNU </td> <td> Results of the modelling of the phase assemblage </td> <td> Input data, modelling code, and results of the modelling of the phase assemblage performed within T4.2.1 </td> <td> .xlsx .docx </td> <td> CO </td> </tr> <tr> <td> D4.1 Report on modelling of the </td> <td> This report presents the results of the modelling </td> <td> .docx </td> <td> CO </td> </tr> </table> <table> <tr> <th> </th> <th> </th> <th> </th> <th> </th> <th> </th> <th> phase assemblage of the novel binders </th> <th> activities performed in T4.2.1, which constitutes inputs for T4.2.2 (D4.2) </th> <th> .pdf </th> <th> </th> </tr> <tr> <th> Subtask 4.2.2: Multiscale modelling of the material mechanical and diffusive properties at the cement paste, mortar and concrete scale </th> <th> M3-M30 </th> <th> CEA </th> <th> D4.2 Report on multiscale analytical modelling at the cement paste, mortar and concrete scale </th> <th> Report describes modelling methods and results </th> <th> .pdf </th> <th> CO </th> </tr> <tr> <th> Results of the multiscale analytical modelling </th> <th> Input data and results of the modelling (evolution of the effective properties as a function of phase assemblage) </th> <th> .xlsx </th> <th> CO </th> </tr> <tr> <th> Task 4.3: Computational analyses of micromesostructures for model testing and corrosion and cracking investigations </th> <th> M9-M36 </th> <th> CEA </th> <th> D4.3 Report on computational analyses of micromesostructures </th> <th> Report describes modelling and simulations at micromeso scale </th> <th> .pdf </th> <th> CO </th> </tr> <tr> <th> Results of the computational analyses of micromesostructures </th> <th> Results of the computational analyses: evolution of effective properties, degradation (carbonation), cracking (carbonation-induced corrosion) </th> <th> .xlsx, .png .jpg .gif </th> <th> CO </th> </tr> <tr> <th> Task 4.4: Computational analyses of macrostructures for service life estimation </th> <th> M30- M39 </th> <th> RINA-C </th> <th> Report on computational analyses of macrostructures for service life estimation, including corrosion phenomena and critical </th> <th> This report describes macro modelling and simulations, aiming ultimately at service life prediction of critical infrastructures. </th> <th> .pdf </th> <th> CO </th> </tr> </table> <table> <tr> <th> </th> <th> </th> <th> </th> <th> </th> <th> </th> <th> </th> <th> environments </th> <th> </th> <th> </th> <th> </th> </tr> <tr> <td> Data Sharing </td> <td> </td> <td> * Confidential data: Reserved Area on the EnDurCrete website * Public data: EnDurCrete website, Zenodo </td> <td> Data Archiving and preservation </td> <td> EnDurCrete website and servers of the respective partners </td> <td> Data management Responsibilities </td> <td> Benoît Bary </td> </tr> <tr> <td> WP number and name </td> <td> WP lead </td> <td> Task number and name </td> <td> Duration </td> <td> Task lead </td> <td> Dataset name </td> <td> Dataset description </td> <td> Format </td> <td> Level </td> </tr> <tr> <td> WP5 Lab-scale performance testing and development of monitoring tools for concrete components & structures </td> <td> UNIVPM </td> <td> Task 5.1: Lab-scale performance testing </td> <td> M12- M20 </td> <td> ZAG </td> <td> Lab-scale development of selfhealing coatings </td> <td> Images coming from microscopy techniques for the evaluation of the coatings. </td> <td> .png </td> <td> CO </td> </tr> <tr> <td> Reports on air permeability tests </td> <td> Reports </td> <td> .pdf </td> <td> CO </td> </tr> <tr> <td> Reports on carbonation tests </td> <td> Reports </td> <td> .pdf </td> <td> CO </td> </tr> <tr> <td> Reports on chloride diffusion tests </td> <td> Reports </td> <td> .pdf </td> <td> CO </td> </tr> <tr> <td> Reports on water absorption and penetration of water tests </td> <td> Reports </td> <td> .pdf </td> <td> CO </td> </tr> <tr> <td> Reports on porosity tests </td> <td> Reports </td> <td> .pdf </td> <td> CO </td> </tr> <tr> <td> Reports on FT and FTS tests </td> <td> Reports </td> <td> .pdf </td> <td> CO </td> </tr> <tr> <td> Reports on corrosion </td> <td> Reports </td> <td> .pdf </td> <td> CO </td> </tr> </table> <table> <tr> <th> </th> <th> </th> <th> </th> <th> </th> <th> </th> <th> tests </th> <th> </th> <th> </th> <th> </th> </tr> <tr> <th> D5.1 Report on durability testing </th> <th> Report on the durability tests results performed in T5.1, with the goal of assessing the durability of novel concrete EnDurCrete solutions (against several benchmarks) and giving inputs for computational model calibration. </th> <th> .pdf </th> <th> CO </th> </tr> <tr> <th> Task 5.2: Calibration and laboratory testing of self- sensing/monitoring properties </th> <th> M12- M21 </th> <th> UNIVPM </th> <th> Test data </th> <th> Data of tests performed during the metrological characterization of the self- sensing/monitoring properties </th> <th> mat; json; xls; pdf. </th> <th> CO </th> </tr> <tr> <th> Task 5.3: Advanced NDT tools for nonintrusive in-field inspection </th> <th> M12- M30 </th> <th> UNIVPM </th> <th> Test data </th> <th> Data of tests performed during the metrological characterization of the self- sensing/monitoring properties </th> <th> mat; json; xls; pdf. </th> <th> CO </th> </tr> <tr> <th> Sub Task 5.3.1: NDT solutions for cracks/sub-surface damages and moisture </th> <th> M12- M30 </th> <th> UNIVPM </th> <th> Test data </th> <th> Data of tests performed during the metrological characterization of the self- sensing/monitoring properties </th> <th> jpg; mat; json; xls; pdf. </th> <th> CO </th> </tr> <tr> <th> Test data </th> <th> Data of tests performed during the metrological characterization of the self- sensing/monitoring properties </th> <th> mat; json; xls; pdf. </th> <th> CO </th> </tr> </table> <table> <tr> <th> </th> <th> </th> <th> Sub Task 5.3.2: Ion migration under Electrical field </th> <th> M12- M30 </th> <th> CEA </th> <th> Test data </th> <th> Data of tests performed during the ion migration under electrical field measurement </th> <th> .jpg .txt .xlsx .mphbin .mphtxt </th> <th> CO </th> </tr> <tr> <th> Ion migration under electrical field study </th> <th> Report on the feasibility of Ion migration under electrical field measurement as NDT solutions </th> <th> .doc .pdf </th> <th> CO </th> </tr> <tr> <th> Sub Task 5.3.3: Electrical resistivity measurement </th> <th> M12- M30 </th> <th> ACCIONA </th> <th> Electrical resistivity measurement </th> <th> Evaluation of the electrical resistivity in EnDurCrete concretes </th> <th> .docx </th> <th> CO </th> </tr> <tr> <td> Data Sharing </td> <td> EnDurCrete project website in Reserved Area </td> <td> Data Archiving and preservation </td> <td> Regular backup of data on server, managed by IT departments and EnDurCrete website </td> <td> Data management Responsibilities </td> <td> Gian Marco Revel; Paolo Chiariotti </td> </tr> <tr> <td> WP number and name </td> <td> WP lead </td> <td> </td> <td> Task number and name </td> <td> </td> <td> Duration </td> <td> Task lead </td> <td> Dataset name </td> <td> Dataset description </td> <td> Format </td> <td> Level </td> </tr> <tr> <td> WP6 Prototyping, demonstration and solutions performance validation </td> <td> ACCIONA </td> <td> </td> <td> Task 6.1: Demonstration and Validation Plan </td> <td> </td> <td> M17-M22 </td> <td> UNIVPM </td> <td> \- </td> <td> \- </td> <td> \- </td> <td> \- </td> </tr> <tr> <td> Task 6.2: Prototyping, demonstration and </td> <td> </td> <td> M22-M40 </td> <td> ACCIONA </td> <td> Evaluation of coatings performance </td> <td> Images coming from optical observation </td> <td> .png </td> <td> CO </td> </tr> </table> <table> <tr> <th> </th> <th> </th> <th> performance validation in a maritime port in Spain </th> <th> </th> <th> </th> <th> </th> <th> (including microscopy techniques) for the evaluation of the coatings. </th> <th> </th> <th> </th> </tr> <tr> <th> Evaluation of EnDurCrete concretes and additives in a maritime port </th> <th> Strength, porosity, permeability, chloride content, electrical conductivity, and SEM results </th> <th> .docx </th> <th> CO </th> </tr> <tr> <th> Task 6.3: Prototyping, demonstration and performance validation in a tunnel in Spain </th> <th> M22-M40 </th> <th> ACCIONA </th> <th> Evaluation of coatings performance </th> <th> Images coming from optical observation (including microscopy techniques) for the evaluation of the coatings. </th> <th> .png </th> <th> CO </th> </tr> <tr> <th> Evaluation of EnDurCrete concretes and additives in a tunnel </th> <th> Strength, porosity, permeability, chloride content, electrical conductivity, and SEM results </th> <th> .docx </th> <th> CO </th> </tr> <tr> <th> Task 6.4: Prototyping, demonstration and performance validation in an offshore structure in Norway </th> <th> M22-M40 </th> <th> KVAERNER </th> <th> Evaluation of coatings performance </th> <th> Images coming from optical observation (including microscopy techniques) for the evaluation of the coatings. </th> <th> .png </th> <th> CO </th> </tr> <tr> <th> Task 6.5: Prototyping, demonstration and performance validation in a bridge in Croatia </th> <th> M22-M40 </th> <th> INFRA PLAN </th> <th> Evaluation of coatings performance </th> <th> Images coming from optical observation (including microscopy techniques) for the evaluation of the coatings. </th> <th> .png </th> <th> CO </th> </tr> </table> <table> <tr> <th> </th> <th> </th> <th> </th> <th> </th> <th> </th> <th> D6.3 Ready-mix concrete prototypes ready for demonstration </th> <th> Report on prototypes for the bridge demo. </th> <th> .pdf </th> <th> CO </th> </tr> <tr> <th> D6.6 Pilot deployment report on the Adriatic coast bridge demo site </th> <th> Report describing the bridge pilot, the installation made, including the sensors and monitoring equipment. </th> <th> .pdf </th> <th> CO </th> </tr> <tr> <th> Task 6.6: Analysis of the results and validation of EnDurCrete solutions </th> <th> M27-M40 </th> <th> RINA-C </th> <th> Evaluation of coatings performance </th> <th> Images coming from optical observation (including microscopy techniques) for the evaluation of the coatings. </th> <th> .png </th> <th> CO </th> </tr> <tr> <th> Demo data analysis 2 </th> <th> Data related to physical and mechanical parameters (corrosion progress, mechanical strength, porosity, water permeability, Chloride content, electrical conductivity etc.) </th> <th> .xls .doc </th> <th> CO </th> </tr> <tr> <td> Data Sharing </td> <td>  Confidential data: Reserved Area on the EnDurCrete website </td> <td> Data Archiving and preservation </td> <td> EnDurCrete website and partners’ servers </td> <td> Data management Responsibilities </td> <td> Rosa Lample </td> </tr> <tr> <td> WP number and name </td> <td> WP lead </td> <td> Task number and name </td> <td> Duration </td> <td> Task lead </td> <td> Dataset name </td> <td> Dataset description </td> <td> Format </td> <td> Level </td> </tr> <tr> <td> WP7 </td> <td> G E O </td> <td> Task 7.1: Environmental </td> <td> M2-M42 </td> <td> GEO </td> <td> Life cycle inventories </td> <td> The inventory of all </td> <td> .xls </td> <td> CO </td> </tr> </table> <table> <tr> <th> Life cycle assessment and economic evaluation, standardization and health and safety aspects </th> <th> </th> <th> and economic viability of the novel products based on LCA and LCCA </th> <th> </th> <th> </th> <th> </th> <th> inputs (material, energy etc.) related to all considered (sub)products </th> <th> </th> <th> </th> </tr> <tr> <th> D7.1 Sustainability Life Cycle Assessment of the new product types </th> <th> Short report providing overview of the key factors influencing the sustainability of the novel products </th> <th> .pdf </th> <th> PU </th> </tr> <tr> <th> D7.2 Life Cycle Analysis at material level </th> <th> Intermediate report on LCA of the new materials, cradle-to-gate </th> <th> .pdf </th> <th> CO </th> </tr> <tr> <th> D7.5 Report on environmental and economic viability of the novel products based on the findings of the LCA and LCCA </th> <th> Final report including LCA on the product level ( cradle-to-grave) and life cycle cost analysis </th> <th> .pdf </th> <th> PU </th> </tr> <tr> <th> Task 7.2: Standardisation </th> <th> M6-M42 </th> <th> ZAG </th> <th> D7.4 Recommendations for updates of current European standards and national technical requirements </th> <th> Report on technical recommendations collected during the projects for updates of existing standards or for future standards </th> <th> .pdf </th> <th> PU </th> </tr> <tr> <th> Recommendations for updates of current European standards and national technical requirements </th> <th> Presentation on technical recommendations collected during the projects for updates of existing standards or for future standards </th> <th> .pdf </th> <th> For relevan t CEN TCs </th> </tr> <tr> <th> Task 7.3: Assessment of the exposure likelihood of the new nano- </th> <th> M1-M42 </th> <th> CEA </th> <th> Samples pictures </th> <th> Pictures of the different samples tested (before/after) to illustrate </th> <th> .jpg </th> <th> CO </th> </tr> </table> <table> <tr> <th> </th> <th> </th> <th> modified EnDurCrete products </th> <th> </th> <th> </th> <th> </th> <th> the final report </th> <th> </th> <th> </th> </tr> <tr> <th> Mechanicals tests and artificial protocols </th> <th> Description of the mechanical tests and the climatic ageing done on samples and also of the standards used to performed its </th> <th> .docx .pdf </th> <th> CO </th> </tr> <tr> <th> Real-time measurements raw data </th> <th> Data obtained by CPC, FMPS, OPC during mechanical tests on noaged and aged samples </th> <th> .txt .xlsx </th> <th> CO </th> </tr> <tr> <th> Off-line measurements raw data </th> <th> SEM/TEM pictures, EDS spectra, XPS data on samples collected during the mechanical tests </th> <th> .jpg .tif </th> <th> CO </th> </tr> <tr> <th> Report on assessment of nanomaterial exposure likelihood </th> <th> Global report on the evaluation of the general exposure likely to occur and the value chain and the life cycle of the new “Endurcrete” concrete materials developed in WP3. The report is based on data obtained by CEA, IBOX, NTS and DAPP </th> <th> .pdf </th> <th> CO </th> </tr> <tr> <th> Task 7.4: Health, safety and risk assessment and management activities </th> <th> M6-M42 </th> <th> CEA </th> <th> Questionnaire for scoping visit </th> <th> Questionnaire addressed to Endurcrete partners who handled or synthetized nanoparticles to plan scoping visit and performed in second time measurement campaign </th> <th> .doc </th> <th> CO </th> </tr> </table> <table> <tr> <th> </th> <th> </th> <th> </th> <th> </th> <th> </th> <th> </th> <th> (exposure assessment) </th> <th> </th> <th> </th> </tr> <tr> <th> Raw data obtain during measurement campaign (real-time and off-lines) </th> <th> Real-time measurement and off-lines characterisations obtained during measurement campaign performed in Endurcrete partners facilities where nanoparticles are used (handling or synthetised) </th> <th> .txt .jpg .tif .xlsx .doc </th> <th> CO </th> </tr> <tr> <th> Report on health and safety assessment and management measures </th> <th> Global report in two parts, 1 st regarding the occupational exposure assessment and management, 2 nd regarding the risk assessment and management The report is based on data obtain by CEA, IBOX, NTS and DAPP </th> <th> .pdf </th> <th> CO </th> </tr> <tr> <td> Data Sharing </td> <td> * Confidential data: Reserved Area on the EnDurCrete website * Public data: EnDurCrete website, Zenodo </td> <td> Data Archiving and preservation </td> <td> EnDurCrete website and servers of the respective partners </td> <td> Data management Responsibilities </td> <td> Jakub Heller </td> </tr> <tr> <td> WP number and name </td> <td> WP lead </td> <td> Task number and name </td> <td> Duration </td> <td> Task lead </td> <td> Dataset name </td> <td> Dataset description </td> <td> Format </td> <td> Level </td> </tr> <tr> <td> WP8 Training, </td> <td> FENI X </td> <td> Task 8.1: Dissemination, Communication and </td> <td> M1-M42 </td> <td> FENIX </td> <td> D8.1 Project Website </td> <td> Report describing the project website, including </td> <td> .pdf </td> <td> PU </td> </tr> </table> <table> <tr> <th> dissemination and exploitation </th> <th> </th> <th> Networking </th> <th> </th> <th> </th> <th> </th> <th> public and private area </th> <th> </th> <th> </th> </tr> <tr> <th> D8.2 Communication, Networking and Dissemination plan </th> <th> Report identifying target audiences, key messages, communication channels, roles and timelines </th> <th> .pdf </th> <th> CO </th> </tr> <tr> <th> D8.3 Promo material design </th> <th> 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 </th> <th> .eps, .jpeg, .png, .mpeg, .avi, .mp4, .pdf </th> <th> PU </th> </tr> <tr> <th> D8.4 Initial Data Management Plan </th> <th> Initial data management plan analysing the main data uses and restrictions, with focus on open access publication </th> <th> .pdf </th> <th> PU </th> </tr> <tr> <th> D8.7 Progress report on dissemination and networking activities and awareness campaign </th> <th> Progress report on performed dissemination and networking activities and activities towards spreading project awareness among stakeholders and public workshop organization </th> <th> .pdf </th> <th> PU </th> </tr> <tr> <th> D8.9 Final Data Management Plan </th> <th> Final data management plan, including references to open </th> <th> .pdf </th> <th> PU </th> </tr> </table> <table> <tr> <th> </th> <th> </th> <th> </th> <th> </th> <th> </th> <th> </th> <th> access publication developed by the Consortium </th> <th> </th> <th> </th> </tr> <tr> <th> D8.11 Final report on dissemination and networking activities and awareness campaign </th> <th> Final report on performed dissemination and networking activities and activities towards spreading project awareness among stakeholders and public workshop organization </th> <th> .pdf </th> <th> PU </th> </tr> <tr> <th> Task 8.2: Exploitation and IPR management </th> <th> M3-M42 </th> <th> FENIX </th> <th> D8.5 Market Assessment </th> <th> Preliminary market assessment mapping concrete market and other relevant sectors information </th> <th> .pdf </th> <th> CO </th> </tr> <tr> <th> D8.6 Initial Exploitation Plan </th> <th> Initial identification of the key project exploitable results, characterization of each result and its expected use, individual partners’ exploitation plans and identification of potential risks </th> <th> .pdf </th> <th> CO </th> </tr> <tr> <th> D8.10 Final Exploitation Plan </th> <th> Report on final version of the exploitation plan, consolidating comprehensive exploitation strategy </th> <th> .pdf </th> <th> CO </th> </tr> <tr> <th> Task 8.3: Business models </th> <th> M12-M42 </th> <th> RINA-C </th> <th> D8.8 Business models </th> <th> Business models for the new technologies, paving the way for future </th> <th> .pdf </th> <th> CO </th> </tr> </table> <table> <tr> <th> </th> <th> </th> <th> </th> <th> </th> <th> </th> <th> </th> <th> market uptake </th> <th> </th> <th> </th> </tr> <tr> <th> Task 8.4: Training Activities </th> <th> M24-M42 </th> <th> GEO </th> <th> D8.12 Report on training activities and guidelines </th> <th> Report on training activities and guidelines and webinars for easy installation, use and disassembly of the new solution </th> <th> .pdf </th> <th> PU </th> </tr> <tr> <td> Data Sharing </td> <td> * Confidential data: Reserved Area on the EnDurCrete website * Promo material (PU): EnDurCrete website, social network profiles, videos on YouTube, thematic portals  Public data: EnDurCrete website </td> <td> Data Archiving and preservation </td> <td> EnDurCrete website and FENIX server </td> <td> Data management Responsibilities </td> <td> Petra Colantonio </td> </tr> <tr> <td> WP number and name </td> <td> WP lead </td> <td> Task number and name </td> <td> Duration </td> <td> Task lead </td> <td> Dataset name </td> <td> Dataset description </td> <td> Format </td> <td> Level </td> </tr> <tr> <td> WP9 Project Management </td> <td> HC </td> <td> Task 9.1: Project Coordination </td> <td> M1-M42 </td> <td> HC </td> <td> \- </td> <td> \- </td> <td> \- </td> <td> \- </td> </tr> <tr> <td> \- </td> <td> \- </td> <td> \- </td> <td> \- </td> </tr> <tr> <td> Task 9.2: Consortium Management </td> <td> M1-M42 </td> <td> HC </td> <td> D9.1: Periodic and final reports </td> <td> Report </td> <td> .pdf </td> <td> CO </td> </tr> <tr> <td> \- </td> <td> \- </td> <td> \- </td> <td> \- </td> </tr> <tr> <td> Task 9.3: Administrative and Financial Management </td> <td> M1-M42 </td> <td> HC </td> <td> \- </td> <td> \- </td> <td> \- </td> <td> \- </td> </tr> <tr> <td> \- </td> <td> \- </td> <td> \- </td> <td> \- </td> </tr> <tr> <td> Data Sharing </td> <td>  Confidential data: Reserved Area on the EnDurCrete website and Heidelberg Cement server </td> <td> Data Archiving and preservation </td> <td> EnDurCrete website Heidelberg Cement server </td> <td> Data management Responsibilities </td> <td> Arnaud Muller </td> </tr> </table> Publication The EnDurCrete Consortium is willing to submit papers for scientific/industrial publication during the course of the EnDurCrete project. In the framework of the The Dissemination, Communication and Networking Plan agreed by the GA, project partners are responsible for the preparation of the scientific publications. As a general approach, the project 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 a version of their published work into a subject-based repository or an institutional repository, Gold Open Access: alternatively, researcher can publish in an open access journal, where the publisher of a scholarly journal provides free online access). After the paper is published and license for open access is obtained, project partner will contact the leader of the Training, dissemination and exploitation (FENIX), who is responsible for EnDurCrete data management, and FENIX will upload the publication into project website and deposit in the OpenAIRE repository ZENODO indicating the project it belongs to in the metadata. Dedicated pages per project are visible on the OpenAIRE portal. 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), Innovation Action (IA), EnDurCrete acronym, GA N° 760639 * 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): Persistent identifier, 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_ . 57 Conclusions This deliverable contains the first release of the Data Management Plan for EnDurCrete project and it provides preliminary guidelines for the management of the project results during the project and beyond. The Data Management related to the data generation, storage and sharing has been addressed. The report will be subject to revisions as required to meet the needs of the EnDurCrete project and will be formally reviewed every six months and at the end of the project to ensure ongoing fitness to the purpose. 58	https://phaidra.univie.ac.at/o:1140797	Horizon 2020
0096_QROWD_732194.md	# EXECUTIVE SUMMARY The Data Management Plan (DMP) describes QROWD's data management life cycle for the data to be collected, processed and/or generated, as part of making its research data findable, accessible, interoperable and re-usable (FAIR), following the guidelines for FAIR Data Management 1 . FAIR data management guarantees that the advancements and results developed on top of these data can be replicated and exploited by future EU-funded initiatives and the community in general. This document provides an update on our processes after the end of the project. The key target readers of the DMP are the warrants of the Open Research Data Pilot program that will check the compliance of the project with H2020 guidelines, and members of the research community interested in replicating and/or reproducing the results of QROWD's research and development. Technical partners of the consortium, that will use it as a guide for publishing and maintaining data associated to bespoke research. In this deliverable we describe the processes, policies and tools the QROWD consortium used to ensure FAIR data management, that can be summarized as follows: * Findability: Datasets produced by research in the project will be assigned a DOI and deposited in Zenodo. Datasets meant to be part of the open source * Accessibility: Research datasets will be published in Zenodo and linked to OpenAire * Interoperability: In addition to the descriptions used for accessibility, we will select the most appropriate standards to format the data. We will pay particular attention to those issued from the OASC initiative. * Re-usability: All research data outputs will be published with an open license with the exception of those corresponding to the TomTom use case. We also describe the measures we took regarding data protection and privacy with personal data. The deliverable is complemented by the data catalog (D4.1), that was also updated to reflect changes in the second half of the project. # SUMMARY OF UPDATES * On Data Summary was updated to reflect updates on the Data Catalog (D4.1), that now features the list of datasets that were used during the whole project (It was previously up to M12) * On Data Summary, update on the non-publication of datasets including GPS coordinates of citizens, as deemed personal data with publication outside of the scope of consent given * On FAIR, we removed mentions to DCAT-AP extension for scientific datasets to describe our datasets, as it stayed only as draft. We added mentions to our usage of ML-Schema, and crowd-voc (described in D3.4) * On FAIR, we now state our choice of Fiware Data Models to increase interoperability with cities that have adopted OASC standards. * Throughout, we added Zenodo as the main archive for data and software outputs. * Added a section on Data Protection, describing the assessment of the risks and measures we took for processing the personal data required for the modal split use case. * Add mention to data we did not publish due to Data Protection considerations: GPS stream and trip confirmation data. # DATA SUMMARY Data in QROWD is divided in three groups, according to its use and its relation with other WPs: 1. Data collected and generated for testing and improving analytics and hybrid discovery capabilities of the QROWD's platform. This comprises the input data in RDF format on which the analysis/discovery processes will be run, the "ground truth" data used to assess the effectiveness of the newly developed approaches, and the crowdsourced data used either as part of the "ground truth", or to improve the analysis/discovery processes. All these data will be made openly accessible. 2. Data collected and generated for the Trento municipality use case (cf. D2.2). This comprises data transformed from municipality's data sources (static datasets and sensor data) and data collected from citizens as a complement to sensor data. Transformations from Open Data will remain open. In the initial version of this document, we expected that an appropriately curated and anonymized subset of the crowdsourced data that allows the reproduction of experiments conducted in connection with this use case will be published, provided that data subjects have agreed with the release of their particular GPS traces. However, GPS traces were considered sensitive data by the Data Protection Officer, and consent was only granted for a limited time (up to the end of the project). Therefore, we decided to not make GPS traces openly available. Only 3. Data collected and generated for the TomTom use case (cf D1.1). This use case re-uses some data from the Trento municipality, but also includes data proprietary to TomTom that cannot be released due to being core to TomTom's business model. During the project, Data will be under the custody of the research/industrial partner leading the work package, in the case of WP2, UniTN and MT will be joint controllers. In the case of WP1, the custody is shared between TomTom and InfAI. During this phase, access to the data will be restricted to consortium partners. The full list of data used and collected in the project is in the Data Catalog deliverable (c.f. D4.1)). Most of it is data already considered as open and without any GDPR implications, except for a combination of identifying fields, and sensor streams collected through the i-Log application, namely * Name, email, gender, age range, number of vehicles available, number of members of the household, preferred vehicle * GPS stream * Accelerometer stream * Gyroscope stream * User feedback from inferred trips * User input for missing trips Section 5 describes the data protection measures we took to guarantee a seamless and GDPR compliant workflow. # FAIR DATA ## FINDABILITY Each dataset that we consider important for research purposes developed during the project will be assigned a Digital Object Identifier through the Zenodo service. In the initial version of this document we considered the use of the DCAT-AP extension for scientific datasets recently proposed by the European Commission Joint Research Centre 2 . However, as the DCAT-AP extension for scientific datasets did not proceed further than unofficial draft, we ultimately decided to only use Zenodo. Datasets acquired and transformed through the data acquisition framework (D4.2) will be findable through the internal CKAN infrastructure of the framework for use by the project consortium. Internal versioning will follow the incremental 0.x format until publication of the linked scientific contribution, point from which the numeration will follow the 1.x format for minor corrections or improvements. ## ACCESSIBILITY Data corresponding to research conducted for the Trento municipality use case considered as anonymous and or securely pseudonymised will be released (c.f. section 5). Data corresponding to the TomTom use case will not be publicly available due to the reliance of the business model of TomTom on it. The possibility of releasing a subset of the data is currently being discussed internally. This plan will be updated according to the final decision made. Code corresponding to research conducted for WP5 and WP6 will be released to the community with an open license. While research is being undertaken, accessibility to datasets will be limited to the members of the consortium. During this stage, partners leading the specific research will be in charge of the storage and accessibility for the rest of the partners that require it. Following the directives of OpenAire, all research outputs that could be released with open licenses are deposited in Zenodo 3 . This includes releases from QROWD's Github repository corresponding to the latest versions of code used in the project. Concerning the tools required to access QROWD’s research data. At the time of the first version of this plan, we initially expected that all datasets will be available in RDF format. Any RDF Graph-Store and SPARQL engine can be used to load them and query them. As a result of the re-use of other established data models and formats (e.g. those used by OASC), only a fraction of the produced datasets are in RDF. ## INTEROPERABILITY To ensure inter-operability, we aligned our datasets to the FiWare data models for transportation and parking 4 , as it is mostly used by the OASC organisation. We also re-used the ML-Schema vocabulary to describe datasets resulting from We also developed the crowd-voc 5 vocabulary for describing datasets produced with Crowdsourcing. ## RE-USABILITY Datasets connected to the Trento Municipality use-case (WP2) will be re-usable according to one of the following schemes 1. Data transformed from existing data sources (curated or not) will have the same license as the original source. 2. Data collected and generated from the project that is connected to a scientific publication will be made available with a CreativeCommons license. In the case of data coming from crowdsourcing, appropriate anonymisation processes will be apply before release (cf. D11.1). Datasets connected to the TomTom use-case (WP1) will not be re-usable. # RESOURCE ALLOCATION The archiving infrastructure and resources will be provided by Southampton and UniTN. Successive updates of the DMP will be led by Soton, as coordinating partner. 3 4 _https://zenodo.org/search?page=1 &size=20&q=qrowd _ _https://www.fiware.org/developers/data-models_ _/_ 5 https://doi.org/10.5281/zenodo.3373397 # DATA SECURITY Southampton, has a secure enterprise scale coherent storage solution for active research data. The data stored within this facility is regularly backed up and a copy of the back-up, regularly off-sited to a secure location for disaster recovery purposes. The research data storage platform is solely for the storage of research data. Final versions of datasets will be deposited in Zenodo. UniTN’s infrastructure abides to the European Commission Recommendation on access to and preservation of scientific information (July 17th 2012) the H2020/ERC Model Grant Agreement, that has all the security measures to avoid data loss and intrusion. Data for the TomTom business case will be hold by TomTom, following their industry-grade security measures. Concerning location-identifying data, we cite the following excerpt from TomTom's policy: “ _Within_ _24 hours of you shutting down your device or app, TomTom automatically and irreversibly destroys the data that would allow you or your device to be identified from the location data we received._ _For Traffic, SpeedCameras, Danger Zones and Weather we delete the information within 20 minutes after you have stopped using the service by shutting down your device or app. We do not know where you have been and cannot tell anyone else, even if we somehow were forced to._ _This, now anonymous, information is used to improve TomTom's products and services, such as TomTom maps, Traffic, products based on traffic patterns and average speeds driven, and for search queries to inform businesses how well- received their information is. These products and services are also used by government agencies and businesses.”_ TomTom data used in the project is always aggregated or stripped of its identifiers. # DATA PROTECTION QROWD processes personal data of citizens as part of the modal split estimation of WP2. In this section, we detail the measures we took for ensuring GDPR compliance, on the light of the need for several partners of the consortium to process data. Under the advice and assistant of the Data Protection Officer of the University of Trento, we carried out a checklist to assess data protection. The original document, in Italian, is annexed to this document. We summarize the key details as follows. Roles were established as follows: * Joint Data Controllers: Municipality of Trento and University of Trento * Data Processors: University of Southampton, InfAI The following table summarizes the data collected and who processed it. <table> <tr> <th> Data field (P = Personal) </th> <th> Purpose </th> <th> Processed by </th> </tr> <tr> <td> Name (P) </td> <td> To address citizen </td> <td> Controllers </td> </tr> <tr> <td> Email (P) </td> <td> Contact citizen </td> <td> Controllers </td> </tr> <tr> <td> Gender (P) </td> <td> Enable aggregations by gender </td> <td> Controllers </td> </tr> <tr> <td> Age range </td> <td> Enable aggregations by age range </td> <td> Controllers </td> </tr> <tr> <td> Vehicles available and preferred </td> <td> Enable aggregations </td> <td> Controllers </td> </tr> <tr> <td> GPS Trace (P) </td> <td> Automatic detection of trips and changes of transport mode. </td> <td> Controllers and processors </td> </tr> <tr> <td> Accelerometer </td> <td> Automatic detection of trips and changes of transport mode. </td> <td> Controllers and processors </td> </tr> <tr> <td> Gyroscope </td> <td> Automatic detection of trips and changes of transport mode. </td> <td> Controllers and processors </td> </tr> <tr> <td> Manual confirmation and input of trips (P) </td> <td> Validate automated detection of trips and changes of transport mode. Collect trip data </td> <td> Controllers and processors </td> </tr> <tr> <td> Preferred time to receive questions </td> <td> Sets time to send questions to users. </td> <td> Controllers and processors </td> </tr> </table> The evaluation revealed that none of the conditions of article 35.3 apply to our collection and processing, namely * No decisions with legal effect will be taken based on the collected data * No special categories of personal data involved * No large scale monitoring of public areas Inline with the principle of minimisation. a pseudonymous was generated by controllers to identify traces belonging to the same user, so the identifying fields name and email would not need to be accessible to data processors. We also designed the API calls available to data processors to avoid any data leakage with respect to demographic information, returning only aggregated information. All identified risks on confidentiality, integrity, and availability, were evaluated as "Low" or "Very Low". The table below shows the risk log. <table> <tr> <th> Risk # </th> <th> Description </th> <th> Probability </th> <th> Impact </th> <th> Mitigation </th> </tr> <tr> <td> 1 </td> <td> Lost of device containing personal data </td> <td> Low </td> <td> High </td> <td> Minimize the number of devices where personal data is stored </td> </tr> <tr> <td> 2 </td> <td> Personal data wrongly sent to unauthorized party </td> <td> Low </td> <td> Medium </td> <td> Check for record integrity before sending personal data back to participants </td> </tr> <tr> <td> 3 </td> <td> Web server/service misconfiguration leaks personal data </td> <td> Very low </td> <td> High </td> <td> Audit server configurations before experiments </td> </tr> <tr> <td> 4 </td> <td> Information (e.g. Google accounts) of a participant needed for providing a service is modified </td> <td> Low </td> <td> Low </td> <td> Instruct participants to keep records stable during the experiments </td> </tr> <tr> <td> 5 </td> <td> A linking record is lost. </td> <td> Low </td> <td> Low </td> <td> Correct backup management </td> </tr> <tr> <td> 6 </td> <td> Lost of data of a participant </td> <td> Low </td> <td> Medium </td> <td> Correct backup management </td> </tr> <tr> <td> 7 </td> <td> A critical service is down </td> <td> Very low </td> <td> Medium </td> <td> Infrastructure test. </td> </tr> </table>	https://phaidra.univie.ac.at/o:1140797	Horizon 2020
0102_LAY2FORM_768710.md	# Executive summary This deliverable provides the first version of the Data Management Plan for project LAY2FORM. This deliverable presents a preliminary description of how research data collected and generated within the scope of the project will be handled during and after the end of LAY2FORM activities, namely concerning the standards and sharing approaches. The Data Management will be continuously reviewed and updated in months 24 and 48 as planned in LAY2FORM deliverables list. This document follows the template provided by the European Commission 1 . # Introduction LAY2FORM project participates in the Open Research Data Pilot (ORD pilot) according to Articles 29.2 and 29.3 of the GAM. This participation entails the sharing and reuse of research data generated by H2020 programme funded projects to improve and maximize their impact. Notwithstanding this, the ORD Pilot addresses the balance required between openness and protection of scientific knowledge for the sake of commercialization, privacy and security purposes, following the principle “as open as possible, as closed as necessary”. The main purpose of this DMP is to describe the data management policies to be followed by LAY2FORM consortium. More specifically, this document presents an overview of the types of datasets to be generated and collected during the project, the data standards and how data will be shared and preserved for later reuse. This DMP reflects the Consortium Agreement established by the partners and currently in force. This DMP is also consistent with exploitation and IPR requirements of the project. Within the scope of the project, any research data linked or potentially linked to results that can be exploited by any consortium partner will not be considered into the open domain to protect commercialization interests. Any other research data not linked to exploitable results will be deposited in an open access repository. This DMP is a document to be continuously reviewed during the course of the project. The first version of the DMP presented in this document will be updated in M24 (September-2019) and M48 (September-2021) of the project as formal deliverables, with more detail on the procedures for data management. # Summary of data types With the framework of the project LAY2FORM research data, including datasets with data, metrics and procedures, will be open for benchmarking purposes, as well as technical data needed to validate the results presented in the deposited scientific publications. A summary of the data types, their formats and standards to be generated and collected during the project are provided in Table 1 . During the project and in the planned future issues of the Data Management Plan, this list may be updated if necessary. _Table 1 - Summary of data types, formats and standards_ <table> <tr> <th> Types of Data </th> <th> Data formats and standards </th> </tr> <tr> <td> Experimental/ observation-derived data. </td> <td> Microsoft Office (docx, xlsx, pptx,…) and Adobe Acrobat (pdf) will be the reference file formats. LaTeX may be used for the production of scientific and technical documentation. </td> </tr> <tr> <td> Models and representations (Product, system and process) </td> </tr> <tr> <td> Project and WP management documents (reports, presentations,…). </td> </tr> <tr> <td> Scientific and technical publications </td> </tr> <tr> <td> FEM structural simulation studies (Product, system and tooling) </td> <td> FEM simulation results will be stored in the ERF format (based on the HDF5 file format) ISO-STEP - according to ISO 10303 and ISO 14649 10-17 - will be the standard for CAx data; non-applicable “ab-origine” data is stored in original SW tool used and exported in ISO-STEP. Some data linked to DSS will be stored under an ASCII file format </td> </tr> <tr> <td> FEM process simulation studies (hotforming) </td> </tr> <tr> <td> Software and algorithms (CAx, Decision Support System) </td> </tr> <tr> <td> Measurements raw data obtained during system/process/part characterization </td> <td> Data will be filed in original format - sensor/application specific - then exported to ASCII file using TXT and CSV format. </td> </tr> <tr> <td> Images </td> <td> JPEG compressed format or equivalent. TIFF uncompressed for shearography and thermography. </td> </tr> <tr> <td> Videos (short movies, animations, …) </td> <td> MPEG codec / AVI format. </td> </tr> </table> The manufacturing process pilot to be developed within the scope of LAY2FORM has been mapped and its several sub-processes were identified. In addition to this, several parameters, both for the process control and process defect have been analysed. This is perceived as a critical stage in terms of data management, in the way that a preliminary overview of several parameters have been defined for each sub-process. Table 2 presents shows some of these input parameters and quality criteria evaluated. Most of the data collected will be used for controlling the manufacturing process through the Self-adaptive system and the Decision Support System. The datasets collected will serve as a basis to the creation of a historical dataset. _Table 2 - Summary of main manufacturing steps with some associated control / defect parameters._ <table> <tr> <th> Manufacturing stage </th> <th> Process control data </th> <th> Process defect data* </th> </tr> <tr> <td> Texturing </td> <td> \- </td> <td> Surface roughness </td> </tr> <tr> <td> Tape feeding </td> <td> Feeding velocity, tape alignment </td> <td> \- </td> </tr> <tr> <td> Tape cutting </td> <td> Laser frequency, displacement speed. </td> <td> Heat affected zone (laser cutting), metal bent, fibre dragging </td> </tr> <tr> <td> US spot bonding </td> <td> US frequency, US holding time, US pressure, number of welded spots </td> <td> Local resin degradation, welding quality. </td> </tr> <tr> <td> Stacked layup </td> <td> \- </td> <td> Size of lap and gaps, local fibre damage, tape misalignment </td> </tr> <tr> <td> Stack heating </td> <td> Consolidation temperature </td> <td> Blank under or over heated. Material degradation. </td> </tr> <tr> <td> Stack compaction </td> <td> Loading rate (press ramp speed), consolidation pressure/vacuum, holding time </td> <td> Intimate contact, thermoplastic overflow, fibre drag, fibre misalignment, nonhomogeneous fibre volume fraction </td> </tr> <tr> <td> Cooling </td> <td> Cooling rate </td> <td> Warpage </td> </tr> <tr> <td> Consolidated blank </td> <td> \- </td> <td> Intimate contact, void distribution, void size, void content, thermoplastic overflow, fibre drag, fibre misalignment, non homogeneous fibre volume fraction. </td> </tr> <tr> <td> Holding consolidated blank </td> <td> Blank holder gripping force </td> <td> \- </td> </tr> <tr> <td> Heating </td> <td> Blank temperature </td> <td> Blank under or over heated, matrix degradation. </td> </tr> <tr> <td> In-place tension control </td> <td> Blank in-plane stress </td> <td> Material slip / fibre breakage </td> </tr> <tr> <td> Transfer to press position </td> <td> Transfer time, transfer speed, trajectory: speed and position </td> <td> Transfer time out of process window </td> </tr> <tr> <td> Molten matrix consolidated blank (shearography) </td> <td> \- </td> <td> Shear wrinkling, sagging, nonuniform temperature, matrix degradation, adhesive degradation, temperature loss, transfer-time mismatch, void content, position placement </td> </tr> <tr> <td> Forming </td> <td> Mould temperature, loading rate, press trajectory: speed, position and time, moulding pressurization: time, force and speed, moulding de- pressurization: time and speed, die temperature </td> <td> Parallelism, Wrinkles, fibre misalignment, insert misalignment, fibre breakage. </td> </tr> <tr> <td> Force control (blank holder) </td> <td> Clamp force, blank in-plane stress </td> <td> Material slip </td> </tr> <tr> <td> Consolidation </td> <td> Consolidation time </td> <td> \- </td> </tr> <tr> <td> Molten formed sub product </td> <td> \- </td> <td> Intra-ply shear, fibre wrinkling, fibre buckling, fibre waviness, air entrapment, fibre damage. </td> </tr> <tr> <td> Cooling </td> <td> Cooling rate, clamp force </td> <td> Premature solidification, nonuniform surface temperature, transverse cracking, metal/composite adhesion </td> </tr> <tr> <td> Demoulding </td> <td> Cooling rate </td> <td> \- </td> </tr> <tr> <td> Formed sub product </td> <td> \- </td> <td> Residual stress/warpage/spring-in, transvers cracking, adhesion metal/composite, crystallization </td> </tr> <tr> <td> Edge trimming </td> <td> Laser type, laser power, trajectory </td> <td> \- </td> </tr> <tr> <td> Final product </td> <td> \- </td> <td> Geometry, delamination, mechanical properties, matrix degradation </td> </tr> </table> * The process defect data mentioned in the table will only be generated provided that the defect detection technology contemplated within the framework of the Lay2Form project are relevant for each type of defect. This will be assessed during the course of the project. # FAIR data 3.1 Data findability Appropriate provisions will be implemented within the scope of LAY2FORM to make data generated in the project findable in repositories. Sets of metadata attributes will be created through a machine-readable index in a searchable resource to facilitate the findability of the data. At the current state, the metadata requirements for the different data sets to be generated are under development. Notwithstanding this, and with respect to scientific publications to be deposited in repositories, the project will comply with minimum metadata format requirements, namely: 1) the terms ['European Union (EU)' and 'Horizon 2020'] ['Euratom' and 'Euratom research and training programme 2014-2018']; 2) the name of the action, acronym and grant number; 3) the publication date and length of embargo period (if applicable); 4) a persistent identifier (such as a Digital Object Identifier or DOI). 3.2 Data accessibility There are three access levels for the data generated by LAY2FORM: * _Public_ : access and download are permitted only to registered users in the project website ( _http://lay2form-project.eu/_ ) , against password to login to the system. The list of files open to the public for dissemination will be maintained available and accessible by download via the website. Interested users need to request a login and a password to the official project e-mail. The purpose is to track data use and build a contact database for the project. * _Confidential_ : accessible only to the subscriber of the Grant Agreement, namely their beneficiaries and linked third parties. Confidential data exchanged between consortium partners is made accessible through a repository in an internal secured server hosted by the PC. Data is available to partners through a webFTP site from which access is granted via a login and password provided by the PC. * _Open access_ : peer review scientific publications and experimental data needed to validate results will be deposited in a repository using the “gold model” as preferential way, meaning that such data will be available for both subscribers and the wider public, with permitted reuse. Furthermore, the LAY2FORM consortium will seek for opportunities to provide open access to non-peer reviewed scientific publications, such as monographs or conference proceedings. Some of the data generated during the manufacturing process, or by the self- adaptive system, or coming from the process simulation, will most likely be stored on a cloud service hosted by AWS. The storage conditions are currently under study and have not yet been implemented in the project. These data will also be available to the project partners. In order to promote exchange and raise awareness on the project results with other researchers, stimulating concurrent research to accelerate the uptake and further development of breakthrough LAY2FORM technology concepts, research data will be deposited in a public research data repository, namely Zenodo. Both public and confidential data will be maintained accessible to authorized users for at least 5 years after the end of the project. 3. Data interoperability Data interoperability, or the ability for data to be processed by different systems, will be ensured through standardised terms or controlled vocabularies with qualified references to other metadata, so that can be machine readable, following the FAIR principles At the present moment, metadata requirements are under study by the consortium. Metadata templates and generators such as may be used to support this task. The Dublin Core Schema, developed by the Dublin Core Metadata Initiative is a set of vocabulary terms for describing digital and physical resources, and may be used for this purpose. 4. Data re-use (through clarifying licences) Data re-use, or the ability for data to be understood by humans and machines through sets of precise and relevant metadata attributes, will be ensured by a clarifying data usage licence. In the scope of project LAY2FORM, a set of rights will be retained by the copyrights holders, and a Creative Commons will be used for this purpose. By observing the different rights granted by the six licences available from Creative Commons, a licence CC BY-NC-ND 4.0 will be used for the data generated in the project. The following attributes of this licence should be noted: * BY : stands for attributions. A user of the data must give appropriate credit, a link to the licence, and indicate if changes were made. * NC : stands for non-commercial. A licence with an NC modifier cannot be used for a commercial purpose, such as being sold or used in an advertisement. You may not use the material for commercial purposes. A commercial use is one primarily intended for commercial advantage or monetary compensation. * ND : stands for no derivatives. This limits the creation of derivative works based upon the original, such as rewriting or translations. A user of the data must not distribute modified, remixed, transformed or built upon material. # Allocation of resources Data management will be assumed by the project coordinator – INEGI - taking the responsibility for the communication activities under the frame of WP1, and the associated costs eligible for this purpose. The costs related to open access publications were already considered in the estimated budget of the project. # Data security Multiple levels of security to access, transfer, store and back-up data files (encryption methods: Secure Sockets Layer (SSL) will be the implemented to the PC server, the project website and on the cloud infrastructure Amazon Web Services (AWS), which is currently under study. Regarding AWS, the data will be stored on S3 and Glacier services and used by any AWS service (SageMaker, Redshift, …) located in the European region (Ireland, Frankfurt, London or Paris). To prevent disaster recovery, all data available on AWS will be replicated within an AWS Region across multiple Availability Zones AWS provides specific features and services which customers can leverage as they seek to comply with the GDPR ( _https://aws.amazon.com/compliance/gdprcenter/?nc1=h_ls_ ) . # Ethical aspects The ethical framework of the data management within project LAY2FORM is currently being assed. In this regards, the General Data Protection Regulation (GDPR), which entered into force on the 25 th of May 2018, is under study in order to ensure compliance with the newest legislation.	https://phaidra.univie.ac.at/o:1140797	Horizon 2020
0104_Pret-a-LLOD_825182.md	# Introduction 1.1. Scope This document contains the initial version of the Prêt-à-LLOD Data Management Plan (DMP). The DMP is a living document and will be regularly updated. Succesive stable versions of the DMP will be published in M24 and M36. This document is complemented by “D5.2 Policy-based language Data Management” (due in M24) and it is related to “D7.1 Ethics Requirements I” (delivered in M3). The Data Management Plan adheres to and complies with the “H2020 Data Management Plan – General Definition” given by the European Commission (EC) online 1 , where the DMP is described as follows: “ _A DMP describes the data management life cycle for the data to be collected, processed and/or generated by a Horizon 2020 project. As part of making research data findable, accessible, interoperable and reusable (FAIR), a DMP should 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)”_ Prêt-à-LLOD adopts policies compliant with the official FAIR guidelines [1] (findable, accessible, interoperable and re-usable), as mandated by the EC. Also, Prêt-à-LLOD participates in the Open Research Data Pilot (ORDP 2 ) and is obliged to deposit the produced research data in a research data repository, as per Art. 29.3 of the Grant Agreement. This Section 1 concludes with the presentation of preliminary concepts; Section 2 is the Data Management Plan itself and follows the template proposed by the EC 3 . 1.2. Preliminary concepts Zenodo Zenodo 4 5 is a general-purpose open-access repository much used for publishing5 deliverables and data of H2020 projects. Zenodo exposes the data to OpenAIRE , a network of Open Access repositories to support the EC publication policies. Resources in Zenodo (consequently also in OpenAire) are identified with a Document Object Identifier (DOI), they can be versioned, and there are good chances that they will enjoy long term preservation. Moreover, common search engines such as Google Scholar or Microsoft Research are aware of the assets hosted at Zenodo and they enjoy high visibility. A Prêt-à-LLOD community has been created in the Zenodo portal. https://zenodo.org/communities/pret-a-llod/ Both deliverables and research data will be published in this Zenodo community. Prêt-à-LLOD Data Portal The “ Prêt-à-LLOD Data Portal ” will be the data portal of the project and will host the description of relevant datasets (metadata). The data portal will also be used to host newly created language resources as long as their size is manageable. This data portal will be open to the general public and users will be able to search for datasets, visualize their description and eventually download the resource itself. The Prêt-à-LLOD Data Portal will be built using the CKAN 6 technology (a standard software package for data portals) and Linghub, a Linked Data based portal already describing language resources [3]. Due to many internet users being already familiar with CKAN, their visual appearence will be respected, only being customized for the needs of this project and using the corporate image of Prêt-à-LLOD. A CKAN data access API will be exposed to offer infomation on the datasets metadata. # Data Management Plan The sections of this document and the questions hereinafter are taken from the _Horizon_ _2020 FAIR Data Management Plan (DMP) template._ The use of the template is recommended by the EU commission. 2.1 Data summary <table> <tr> <th> 1\. Data summary </th> </tr> <tr> <td> a) What is the purpose of the data collection / generation and its relation to the objectives of the project? </td> </tr> </table> <table> <tr> <th> </th> <th> The declared objectives of this project are: * to support the exchange of multilingual cross-sectoral data * to develop interoperable language technology services and language data * to favour the sustainability of language technologies and language resources Consequently, the collection and generation of data are core activities for this project, and its purpose can be summarized as ‘prepare linguistic data so that it can power multilingual applications in a digital single market’. An initial list of 52 processing activities is documented in annex A of “D7.1. Ethic Requirements I” [2]. </th> </tr> <tr> <td> b) What types and formats of data will the project generate / collect? </td> </tr> <tr> <td> </td> <td> The vast number of formats that will be handled by this project does not allow a preliminary enumeration. Data in different formats will be collected and eventually transformed. The preferred type for the generated data is the one which most favours interoperability ― this will be RDF (Resource Description Framework 7 ) in its different serializations. The types of data in Table 1 have been identified, with respect to their meaning: <table> <tr> <th> Name </th> <th> Description </th> </tr> <tr> <td> Catalogue metadata </td> <td> Description of existing data resources </td> </tr> <tr> <td> Open linguistic data </td> <td> Existing open linguistic data already available prior to Prêt-à-LLOD. </td> </tr> <tr> <td> New linguistic data </td> <td> Transformation of existing resources or creation of new resources in the LLOD (Linguistic Linked Open Data cloud [8]) by the Prêt-à-LLOD project. These assets are considered results of this project. </td> </tr> <tr> <td> Experiment-related data </td> <td> Data produced in the course of reports generations, execution of experiments (e.g. experiments for automated linking), etc., often related to research publications. </td> </tr> </table> _Table 1. Types of data generated or collected by Prêt-à-LLOD according to their meaning_ The following types of data have been identified, according to their openness. <table> <tr> <th> Name </th> <th> Description </th> </tr> <tr> <td> Private to partners </td> <td> Available to the partner who owns it </td> </tr> <tr> <td> Available to partners </td> <td> Not public, only available to the partners. No Non-Disclosure Agreements (NDAs) are not necessary and the Consortium Agreement suffices. </td> </tr> <tr> <td> Published as Open Data </td> <td> Both public and available with an open license. </td> </tr> </table> _Table 2. Types of data in Prêt-à-LLOD, according to their openness._ </td> </tr> <tr> <td> c) Will you re-use any existing data and how? </td> </tr> <tr> <td> </td> <td> This project will extensively reuse linguistic resources, eventually republishing them possibly after a transformation. </td> </tr> <tr> <td> d) What is the origin of the data? </td> </tr> </table> <table> <tr> <th> </th> <th> Datasets available in the LLOD cloud and resources available in other data catalogues (OLAC 8 , LRE Map 9 , META-SHARE 10 , Clarin 11 , Retele 12 ), and private data resources that will not be exposed. </th> </tr> <tr> <td> e) What is the expected size of the data? </td> </tr> <tr> <td> </td> <td> The size of the data is broken down per data type: Catalogue metadata: ~1Gb Open linguistic data: not to be stored by Prêt-à-LLOD New linguistic data: ~100Gb Experiment-related data: ~10Gb These figures have been estimated considering the experience of some of the Prêt-à-LLOD partners in the past FP7-funded LIDER project 13 . </td> </tr> <tr> <td> f) To whom might the data be useful ('data utility')? </td> </tr> <tr> <td> </td> <td> Two large communities are identified: (i) the community of researchers and practitioners of linguistics and social sciences and (ii) the community of computer scientists and developers with interests in natural language processing. </td> </tr> </table> 2.2. FAIR data <table> <tr> <th> 2\. FAIR data </th> </tr> <tr> <td> 2.1 Making data findable, including provisions for metadata </td> </tr> <tr> <td> a) Are the data produced and / or used in the project discoverable and identifiable? </td> </tr> <tr> <td> </td> <td> Catalogue metadata will be available at the Prêt-à-LLOD Data Portal . Data will be discoverable because each dataset will have a description using the standard DCAT vocabulary 14 (see Figure 1) --in particular, DCAT-AP: the "DCAT application profile for European data portals", developed in the framework of the EU ISA Programme 15 , which has become a de-facto standard. New linguistic data produced by this project will also be offered through the Prêt-à-LLOD Data Portal. Identifiability will be supported because each data in all datasets will have a unique identifier (IRI 16 ) accessible through the Web. Experiment-related data will be published in Zenodo, in turn connected with OpenAIRE and every major indexer of scientific documents. Eventually, small pieces of data will also be available from source code repositories (e.g. a Gitlab instance hosted in the premises of the coordinating institution in Ireland). </td> </tr> </table> <table> <tr> <th> </th> <th> _Figure 1. Metadata elements in the DCAT specification_ </th> </tr> <tr> <td> b) What naming conventions do you follow? </td> </tr> <tr> <td> </td> <td> We defined the following two policies: 1. Identification of datasets. Datasets are identified by an slug (a user friendly and URL valid name of a resource). 2. URI minting policy, to be decided at a later stage of the project. </td> </tr> <tr> <td> c) Will search keywords be provided that optimize possibilities for re-use? </td> </tr> <tr> <td> </td> <td> The use of keywords is natural in the Prêt-à-LLOD Data Portal and in Zenodo. Zenodo’s commitment with FAIR policies is made explicit 17 . </td> </tr> <tr> <td> d) Do you provide clear version numbers? </td> </tr> <tr> <td> </td> <td> The use of a semantic versioning is inherent to Zenodo. The Prêt-à-LLOD Data Portal will also provide versioning and provenance mechanisms. </td> </tr> <tr> <td> e) What metadata will be created? </td> </tr> <tr> <td> </td> <td> The stored data are described by using the standard metadata schema Qualified Dublin Core and DCAT. Zenodo's metadata is compliant with DataCite's Metadata Schema minimum and recommended terms, with a few additional enrichments 18 . </td> </tr> <tr> <td> 2.2 Making data openly accessible </td> </tr> <tr> <td> a) Which data produced and / or used in the project will be made openly available as the default? </td> </tr> </table> <table> <tr> <th> </th> <th> By default, all metadata in Zenodo and Prêt-à-LLOD Data Portal are openly available as soon as the record is published. </th> </tr> <tr> <td> b) </td> <td> How will the data be made accessible (e.g. by deposition in a repository)? </td> </tr> <tr> <td> </td> <td> All data are stored in Zenodo and the Prêt-à-LLOD Data Portal. All metadata in Zenodo and the Prêt-à-LLOD Data Portal are publicly available in an Open Access modality. Eventually, language resources created by Prêt-à-LLOD will be introduced in the well-known language resources catalogues (OLAC, LRE Map, META-SHARE, Clarin, Retele). </td> </tr> <tr> <td> c) </td> <td> What methods or software tools are needed to access the data? </td> </tr> <tr> <td> </td> <td> The extensive use of open specifications and consolidated standards grants that there is no need for special software tools to access the data. Eventually, experiment-related data may require of additional software (e.g. GATE 19 ). </td> </tr> <tr> <td> d) </td> <td> Is documentation about the software needed to access the data included? </td> </tr> <tr> <td> </td> <td> Not necessary for the time being. </td> </tr> <tr> <td> e) </td> <td> Is it possible to include the relevant software (e.g. in open source code)? </td> </tr> <tr> <td> </td> <td> Not necessary for the time being. </td> </tr> <tr> <td> f) </td> <td> Where will the data and associated metadata, documentation and code be deposited? </td> </tr> <tr> <td> </td> <td> The following data stores are foreseen: ― The Prêt-à-LLOD Data Portal store defined in Section 1.1, hosted in Ireland, for catalogue data and some newly generated resources. ― A Gitlab instance, hosted in Ireland, for small datasets. ― Zenodo for research-related data. Data will also be mirrored, whenever possible, in projects with whom liaisons will be established. In particular, relevant data will be also passed to the ELG (European Language Grid 20 ) project, “Towards the Primary Platform for Language Technologies in Europe”. </td> </tr> <tr> <td> g) </td> <td> Have you explored appropriate arrangements with the identified repository? </td> </tr> <tr> <td> </td> <td> The aforementioned repositories are either self-managed by Prêt-à-LLOD partners, or they are already deemed for these purposes. Formal arrangements with ELG are pending to be done. </td> </tr> <tr> <td> h) </td> <td> If there are restrictions on use, how will access be provided? </td> </tr> <tr> <td> </td> <td> No restrictions have been identified at this stage, but the commercial interest of the partners may lead to the creation of private data. </td> </tr> </table> <table> <tr> <th> i) Is there a need for a data access committee? </th> </tr> <tr> <td> </td> <td> No. Rules that concern governing of data access of the partner institutions will be followed and implemented, together with the FAIR principles followed by this Plan. </td> </tr> <tr> <td> j) Are there well described conditions for access (i.e. a machine readable license)? </td> </tr> <tr> <td> </td> <td> Licenses in Prêt-à-LLOD Data Portal are represented in a machine readable form, using the most common metadata descriptor (dct:license, see Figure 1) pointing to standard URL licenses’. Whenever linked data is published, standard practices will be followed to publish the rights information [5]. Moreover, in some cases, a fully machine readable representation of the licenses is given using the Open Digital Rights Management Language (ODRL) 21 . Licenses from the RDFLicense dataset are also used [4]. </td> </tr> <tr> <td> k) How will the identity of the person accessing the data be ascertained? </td> </tr> <tr> <td> </td> <td> Not necessary for the time being. </td> </tr> <tr> <td> 2.3 Making data interoperable </td> </tr> <tr> <td> a) Are the data produced in the project interoperable? </td> </tr> <tr> <td> </td> <td> Both Zenodo and the Prêt-á-LLOD Data Portal use standard interfaces, protocols and metadata, etc. Using standard metadata schemas in Zenodo, metadata can easily be converted into other metadata schemas. </td> </tr> <tr> <td> b) What data and metadata vocabularies, standards or methodologies will you follow to make your data interoperable? </td> </tr> <tr> <td> </td> <td> DCAT (described above) and the CKAN schema 22 based on it. Linghub currently makes use of the META-SHARE OWL ontology [6]. </td> </tr> <tr> <td> c) Will you be using standard vocabularies for all data types present in your data set, to allow inter-disciplinary interoperability? </td> </tr> <tr> <td> </td> <td> Yes, see above (2.3.b). </td> </tr> <tr> <td> d) 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> </td> <td> The use of RDF as the meta-format grants the easy definition of links between equivalent metadata elements. </td> </tr> <tr> <td> 2.4 Increase data re-use (through clarifying licences) </td> </tr> <tr> <td> a) How will the data be licensed to permit the widest re-use possible? </td> </tr> <tr> <td> </td> <td> Open by 23default, using the CC-BY license (Creative Commons 4.0 Attribution International ) unless this hampers the business model of our partners. </td> </tr> </table> <table> <tr> <th> b) When will the data be made available for re-use? </th> </tr> <tr> <td> </td> <td> Data will be made available as soon as it is created and no data embargoes are foreseen. </td> </tr> <tr> <td> c) 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> </td> <td> Making data ready to use is the motto of this project, and every possible measure will be taken to maximize its usability. </td> </tr> <tr> <td> d) How long is it intended that the data remains re-usable? </td> </tr> <tr> <td> </td> <td> Data in the Prêt-à-LLOD Data Portal may not be supported after the end of the project, but because it will be mirrored in the ELG, long time preservation will be possible. Research data will enjoy long term preservation as it will be uploaded to Zenodo. </td> </tr> <tr> <td> e) Are data quality assurance processes described? </td> </tr> <tr> <td> </td> <td> No. Future versions of this DMP may include a definition of such process. </td> </tr> </table> 2.3. Allocation of resources <table> <tr> <th> 3 Allocation of resources </th> </tr> <tr> <td> a) What are the costs for making data FAIR in your project? </td> </tr> <tr> <td> </td> <td> None that is not foreseen in the Grant Agreement: making data FAIR is an explicit objective of this project. </td> </tr> <tr> <td> b) How will these be covered? </td> </tr> <tr> <td> </td> <td> Not applicable. </td> </tr> <tr> <td> c) Who will be responsible for data management in your project? </td> </tr> <tr> <td> </td> <td> Víctor Rodríguez Doncel (UPM) will be the responsible for the management of open data in this project. The management of private data will be responsibility of the partners having produced it. </td> </tr> <tr> <td> d) Are the resources for long term preservation discussed? </td> </tr> <tr> <td> </td> <td> The cooperation agreements with the ELG project are headed towards long term preservation. </td> </tr> </table> 2.4. Data security <table> <tr> <th> 4 Data security </th> </tr> <tr> <td> a) Is the data safely stored in certified repositories for long term preservation and curation? </td> </tr> <tr> <td> </td> <td> Most data (catalogue data, newly created resources) will contain data to be published under an open license. This data does not need any security measure whatsoever. For the case partners generate privative data with personal information, security measures </td> </tr> <tr> <td> </td> <td> will have to be adopted to comply with the General Data Protection Regulation (GDPR, Regulation (EU) 2016/679). </td> </tr> <tr> <td> b) What provisions are in place for data security? </td> </tr> <tr> <td> </td> <td> Not yet described. </td> </tr> </table> 2.5. Ethical aspects <table> <tr> <th> 5 Ethical aspects </th> </tr> <tr> <td> a) Are there any ethical or legal issues that can have an impact on data sharing? </td> </tr> <tr> <td> </td> <td> Ethical aspects have been extensively documented in Prêt-à-LLOD deliverables “D7.1 Ethics Requirements I” and in “D7.4 Ethics Requirements 4”. </td> </tr> <tr> <td> b) Is informed consent for data sharing and long term preservation included in questionnaires dealing with personal data? </td> </tr> <tr> <td> </td> <td> See Prêt-à-LLOD deliverable “D7.1. Ethics Requirements I”. </td> </tr> </table> 2.6. Other issues <table> <tr> <th> 6 Other issues </th> </tr> <tr> <td> a) Do you make use of other national / funder / sectorial / departmental procedures for data management? </td> </tr> <tr> <td> </td> <td> ― National University of Ireland Galway (NUIG) is subject to a _“Insight Open Source Release Process”_ procedure. _―_ Universidad Politécnica de Madrid (UPM) is subject to “ _Normativa sobre protección de resultados de investigación de la Universidad Politécnica de_ _Madrid_ ” and “ _Reglamento del comité de ética de actividades i+d+i de la Universidad Politécnica de Madri_ d” These procedures are compatible with the provisions made in this data management plan. </td> </tr> </table>	https://phaidra.univie.ac.at/o:1140797	Horizon 2020
0107_MyoChip_801423.md	# Data Management Plan Overview This document constitutes the Deliverable 3.2 “Data Management Plan” of the project “Building a 3D innervated and irrigated muscle on a chip.”, hereinafter referred to as MyoChip, funded by the European Union’s H2020-FETOPEN-2016-2017, under the Grant Agreement number 801423\. According to the European Commission suggested guidelines, participating projects are required to develop a Data Management Plan (DMP). This Data Management Plan describes the types of data that will be generated during the project, standards that will be used to generate and store data, the ways data will be shared, reused and preserved. It is very important to note that the DMP is not a closed document, on the contrary, it will be adjusted/corrected throughout the duration of the project. # Open Research Data Pilot The Open Research Data Pilot is part of the Open Access to Scientific Publications and Research Data Program in H2020. It aims to improve and maximize access to data generated by Horizon 2020 as well as to promote its reutilization. As mentioned in article 29.3 of the grant agreement this pilot also takes into account the need to carefully balance openness with protection of scientific information. Taking all of this into consideration MyoChip consortium will decide what information will be made public in a case to case basis, always making a careful analysis of potential conflicts against commercialization, intellectual property rights protection, etc. Effective Dissemination and exploitation of MyoChip results depends on proper management of data and its intellectual property. The terms and conditions pertaining to ownership, access rights, exploitations of background and dissemination of results, are defined in the Consortium Agreement signed by all the partners. Some examples of sections on our Consortium Agreement are: 8. Section: Results 1. Ownership of Results Results are owned by the Party that generates them. Where Results are generated from work carried out jointly by two or more Parties and it is not possible for the purpose of applying for, obtaining and/or maintaining the relevant patent protection or any other intellectual property right, to separate the contributions made by the respective Parties to such Results, those Parties shall have joint ownership of such Results. 2. 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 Joint ownership agreements should be formalized between the specific partners, before the first exploitation act of the Joint Results concerned, to regulate the share, protection and commercial exploitation of project results. 3. Transfer of Results 8.3.1 Each Party may transfer ownership of its own Results following the procedures of the Grant Agreement Article °30. 8.3.2 It may identify specific third parties it intends to transfer the ownership of its Results to in Attachment (3) to this Consortium Agreement. The other Parties hereby waive their right to prior notice and their right to object to a transfer to listed third parties according to the Grant Agreement Article 30.1. 8.3.3 The transferring Party shall, however, at the time of the transfer, inform the other Parties of such transfer and shall ensure that the rights of the other Parties will not be affected by such transfer. Any addition to Attachment (3) after signature of this Agreement requires a decision of the General Assembly. 8.3.4 The Parties recognize that in the framework of a merger or an acquisition of an important part of its assets, it may be impossible under applicable EU and national laws on mergers and acquisitions for a Party to give the full 45 calendar days prior notice for the transfer as foreseen in the Grant Agreement. 8.3.5 The obligations above apply only for as long as other Parties still have - or still may request - Access Rights to the Results. 8.4 Dissemination 8.4.1 For the avoidance of doubt, nothing in this Section 8.4 has impact on the confidentiality obligations set out in Section 10. 8.4.2 Dissemination of own Results 8.4.2.1 During the Project and for a period of 2 year after the end of the Project, the dissemination of own Results by one or several Parties including but not restricted to publications, presentations, data and related metadata, shall be governed by the procedure of Article 29.1 of the Grant Agreement subject to the following provisions. Prior notice of any planned publication shall be given to the other Parties at least 45 calendar days before the publication. Any objection to the planned publication shall be made in accordance with the Grant Agreement in writing to the Coordinator and to the Party or Parties proposing the dissemination within 30 calendar days after receipt of the notice. If no objection is made within the time limit stated above, the publication is permitted. 8.4.2.2 An objection is justified if (a) the protection of the objecting Party's Results or Background would be adversely affected (b) the objecting Party's legitimate interests in relation to the Results or Background would be significantly harmed. The objection has to include a precise request for necessary modifications. 8.4.2.3 If an objection has been raised the involved Parties shall discuss how to overcome the justified grounds for the objection on a timely basis (for example by amendment to the planned publication and/or by protecting information before publication) and the objecting Party shall not unreasonably continue the opposition if appropriate measures are taken following the discussion. 8.5 The objecting Party can request a publication delay of not more than 90 calendar days from the time it raises such an objection. After 90 calendar days the publication is permitted. 8.5.1 Dissemination of another Party’s unpublished Results or Background A Party shall not include in any dissemination activity another Party's Results or Background without obtaining the owning Party's prior written approval, unless they are already made public. 8.5.2 Cooperation obligations The Parties undertake to cooperate to allow the timely submission, examination, publication and defence of any dissertation or thesis for a degree that includes their Results or Background subject to the confidentiality and publication provisions agreed in this Consortium Agreement. 8.5.3 Use of names, logos or trademarks Nothing in this Consortium Agreement shall be construed as conferring rights to use in advertising, publicity or otherwise the name of the Parties or any of their logos or trademarks without their prior written approval. 9. Section: Access Rights 9.1 Background included 9.1.1 In Attachment 1, the Parties have identified and agreed on the Background for the Project and have also, where relevant, informed each other that Access to specific Background is subject to legal restrictions or limits. Anything not identified in Attachment 1 shall not be the object of Access Right obligations regarding Background. 9.1.2 Any Party may add further own Background to Attachment 1 during the Project by written notice to the other Parties. However, approval of the General Assembly is needed should a Party wish to modify or withdraw its Background in Attachment 1. 9.2 General Principles 9.2.1 Each Party shall implement its tasks in accordance with the Consortium Plan and shall bear sole responsibility for ensuring that its acts within the Project do not knowingly infringe third party property rights. 9.2.2 Any Access Rights granted expressly exclude any rights to sublicense unless expressly stated otherwise. 9.2.3 Access Rights are granted on a non-exclusive basis. 9.2.4 Results and Background shall be used only for the purposes for which Access Rights to it have been granted. 9.2.5 Requests for Access Rights shall be made, and Access Rights granted, in writing. The granting of Access Rights may be made conditional on the acceptance of specific conditions aimed at ensuring that these rights will be used only for the intended purpose and that appropriate confidentiality obligations are in place. 9.2.6 The requesting Party must show that the Access Rights are Needed. 9.3 Access Rights for implementation 9.3.1 Access Rights to Results and Background Needed for the performance of the own work of a Party under the Project shall be granted on a royalty-free basis, unless otherwise provided for in Attachment 1. 9.3.2 If the Background or Results take the form of non-consumable materials, including but not limited to instruments, databases, software, protocols, said background or Results will be returned to the providing Party by the receiving Party at the end of the project, and eventual copies of said materials will be destroyed by the receiving party. Notwithstanding the above provisions, the Receiving and the providing party may negotiate conditions in which said materials will be kept by the receiving Party, at fair and reasonable conditions 9.4 Access Rights for Exploitation 9.4.1 Access Rights to Results Access Rights to Results if Needed for commercial Exploitation of a Party's own Results shall be granted on Fair and Reasonable conditions. Provided the Party(ies) (co) owner(s) of the Results concerned gives its prior written consent, Access rights to Results for internal and/or non-commercial collaborative research activities shall be granted on a royalty-free basis for academic Parties and on financial conditions for industrial Parties. 9.4.2 Access Rights to Background if Needed for Exploitation of a Party’s own Results, including for research on behalf of a third party, shall be granted on Fair and Reasonable conditions. 9.4.3 A request for Access Rights may be made up to twelve months after the end of the Project or, in the case of Section 9.7.2.1.2, after the termination of the requesting Party’s participation in the Project. 9.4.4 A Member which can show that its own liabilities, intellectual property rights or other legitimate interests would be severely affected by the granting of such access right, or that granting such access right would infringe its legal obligations, will have the right to refuse such access right to Results or Background. The Member that wishes to exercise the veto must identify the exact rights that will be affected by the decision, quantify the damages and identify the moment when the damages will actually occur. 9.5 Access Rights for Affiliated Entities Affiliated Entities have Access Rights under the conditions of the Grant Agreement Articles 25.4 and 31.4. if they are identified in Attachment 4 (Identified Affiliated Entities) to this Consortium Agreement. Such Access Rights must be requested directly by the Affiliated Entity from the Party that holds the Background or Results. Alternatively, the Party granting the Access Rights may individually agree with the Party requesting the Access Rights to have the Access Rights include the right to sublicense to the latter's Affiliated Entities listed in Attachment 4. Access Rights to Affiliated Entities shall be granted on Fair and Reasonable conditions and upon written bilateral agreement. Affiliated Entities which obtain Access Rights in return fulfil all confidentiality and other obligations accepted by the Parties under the Grant Agreement or this Consortium Agreement as if such Affiliated Entities were Parties. Access Rights may be refused to Affiliated Entities if such granting is contrary to the legitimate interests of the Party which owns the Background or the Results. Access Rights granted to any Affiliated Entity are subject to the continuation of the Access Rights of the Party to which it is affiliated, and shall automatically terminate upon termination of the Access Rights granted to such Party. Upon cessation of the status as an Affiliated Entity, any Access Rights granted to such former Affiliated Entity shall lapse. Further arrangements with Affiliated Entities may be negotiated in separate agreements. 9.6 Additional Access Rights The Parties agree to negotiate in good faith any additional Access Rights to Results as might be asked for by any Party, upon adequate financial conditions to be agreed. 9.7 Access Rights for Parties entering or leaving the consortium 9.7.1 New Parties entering the consortium As regards Results developed before the accession of the new Party, the new Party will be granted Access Rights on the conditions applying for Access Rights to Background. 9.7.2 Parties leaving the consortium 9.7.2.1 Access Rights granted to a leaving Party 9.7.2.1.1 Defaulting Party Access Rights granted to a Defaulting Party and such Party's right to request Access Rights shall cease immediately upon receipt by the Defaulting Party of the formal notice of the decision of the General Assembly to terminate its participation in the consortium. 9.7.2.1.2 Non-defaulting Party A non-defaulting Party leaving voluntarily and with the other Parties' consent shall have Access Rights to the Results developed until the date of the termination of its participation. It may request Access Rights within the period of time specified in Section 9.4.3. 9.7.2.2 Access Rights to be granted by any leaving Party Any Party leaving the Project shall continue to grant Access Rights pursuant to the Grant Agreement and this Consortium Agreement as if it had remained a Party for the whole duration of the Project. 9.8 Specific Provisions for Access Rights to Software For the avoidance of doubt, the general provisions for Access Rights provided for in this Section 9 are applicable also to Software. Parties’ Access Rights to Software do not include any right to receive source code or object code ported to a certain hardware platform or any right to receive respective Software documentation in any particular form or detail, but only as available from the Party granting the Access Rights. 9.8.1 Definitions relating to Software “Application Programming Interface” means the application programming interface materials and related documentation containing all data and information to allow skilled Software developers to create Software interfaces that interface or interact with other specified Software. "Controlled Licence Terms" means terms in any licence that require that the use, copying, modification and/or distribution of Software or another work (“Work”) and/or of any work that is a modified version of or is a derivative work of such Work (in each case, “Derivative Work”) be subject, in whole or in part, to one or more of the following: 1. (where the Work or Derivative Work is Software) that the Source Code or 2. other formats preferred for modification be made available as of right to any third party on request, whether royaltyfree or not; 3. that permission to create modified versions or derivative works of the Work or Derivative Work be granted to any third party; 4. that a royalty-free licence relating to the Work or Derivative Work be granted to any third party. For the avoidance of doubt, any Software licence that merely permits (¬but does not require any of) the things mentioned in (a) to (c) is not a Controlled Licence (and so is an Uncontrolled Licence). “Object Code” means software in machine-readable, compiled and/or executable form including, but not limited to, byte code form and in form of machine- readable libraries used for linking procedures and functions to other software. “Software Documentation” means software information, being technical information used, or useful in, or relating to the design, development, use or maintenance of any version of a software programme. “Source Code” means software in human readable form normally used to make modifications to it including, but not limited to, comments and procedural code such as job control language and scripts to control compilation and installation. 9.8.2 General principles For the avoidance of doubt, the general provisions for Access Rights provided for in this Section 9 are applicable also to Software as far as not modified by this Section 9.8. Parties’ Access Rights to Software do not include any right to receive Source Code or Object Code ported to a certain hardware platform or any right to receive Source Code, Object Code or respective Software Documentation in any particular form or detail, but only as available from the Party granting the Access Rights. The intended introduction of Intellectual Property (including, but not limited to Software) under Controlled Licence Terms in the Project requires the approval of the General Assembly to implement such introduction into the Consortium Plan. 9.8.3 Access to Software Access Rights to Software that is Results shall comprise: Access to the Object Code; and, where normal use of such an Object Code requires an Application Programming Interface (hereafter API), Access to the Object Code and such an API; and, if a Party can show that the execution of its tasks under the Project or the Exploitation of its own Results is technically or legally impossible without Access to the Source Code, Access to the Source Code to the extent necessary. Background shall only be provided in Object Code unless otherwise agreed between the Parties concerned. 9.8.4 Software licence and sublicensing rights 9.8.4.1 Object Code 9.8.4.1.1 Results - Rights of a Party Where a Party has Access Rights to Object Code and/or API that is Results for Exploitation, such Access shall, in addition to the Access for Exploitation foreseen in Section 9.4, as far as Needed for the Exploitation of the Party’s own Results, comprise the right: to make an unlimited number of copies of Object Code and API; and to distribute, make available, market, sell and offer for sale such Object Code and API as part of or in connection with products or services of the Party having the Access Rights; provided however that any product, process or service has been developed by the Party having the Access Rights in accordance with its rights to exploit Object Code and API for its own Results. If it is intended to use the services of a third party for the purposes of this Section 9.8.4.1.1, the Parties concerned shall agree on the terms thereof with due observance of the interests of the Party granting the Access Rights as set out in Section 9.2 of this Consortium Agreement. 9.8.4.1.2 Results - Rights to grant sublicenses to end-users In addition, Access Rights to Object Code shall, as far as Needed for the Exploitation of the Party’s own Results, comprise the right to grant in the normal course of the relevant trade to end-user customers buying/using the product/services, a sublicense to the extent as necessary for the normal use of the relevant product or service to use the Object Code as part of or in connection with or integrated into products and services of the Party having the Access Rights and, as far as technically essential: * to maintain such product/service; * to create for its own end-use interacting interoperable software in accordance with the Directive 2009/24/EC of the European Parliament and of the Council of 23 April 2009 on the legal protection of computer programs 9.8.4.1.3 Background For the avoidance of doubt, where a Party has Access Rights to Object Code and/or API that is Background for Exploitation, Access Rights exclude the right to sublicense. Such sublicensing rights may, however, be negotiated between the Parties. 9.8.4.2 Source Code 9.8.4.2.1 Results - Rights of a Party Where, in accordance with Section 9.8.3, a Party has Access Rights to Source Code that is Results for Exploitation, Access Rights to such Source Code, as far as Needed for the Exploitation of the Party’s own Results, shall comprise a worldwide right to use, to make copies, to modify, to develop, to create/market a product/process and to create/provide a service. If it is intended to use the services of a third party for the purposes of this Section 9.8.4.2.1, the Parties shall agree on the terms thereof, with due observance of the interests of the Party granting the Access Rights as set out in Section 9.2 of this Consortium Agreement. 9.8.4.2.2 Results – Rights to grant sublicenses to end-users In addition, Access Rights, as far as Needed for the Exploitation of the Party’s own Results, shall comprise the right to sublicense such Source Code, but solely for purpose of adaptation, error correction, maintenance and/or support of the Software. Further sublicensing of Source Code is explicitly excluded. 9.8.4.2.3 Background For the avoidance of doubt, where a Party has Access Rights to Source Code that is Background for Exploitation, Access Rights exclude the right to sublicense. Such sublicensing rights may, however, be negotiated between the Parties. 9.8.5 Specific formalities Each sublicense granted according to the provisions of Section 9.8.4 shall be made by a traceable agreement specifying and protecting the proprietary rights of the Party or Parties concerned. # FAIR Data Beneficiaries must ensure that their research data respect FAIR principles, i.e. they are Findable, Accessible, Interoperable and Reusable (FAIR). ## Findable Partners agree to label all data with name, date and keywords to facilitate data search & find. Also, all metadata files should be kept together with raw data. We are currently working with iMM’s Communication and IT teams to create the new MyoChip website. In the website there will be a web platform that will allow access to data stored in a server protected by login and password for each partner. ## Accessible Data will be made "as open as possible, as closed as necessary". Open data will be made accessible by publishing and/or deposited on repository such as Zenodo (https://zenodo.org) Decisions on specific identification of closed data, or data subject to embargo related to IP policies will be defined at due time. ## Interoperable All partners will use vocabularies that follow FAIR principles, being accessible and broadly applicable. Data/Metadata files should include clear references to other Data/Metadata files. ## Re-usable Licensing policies will be defined when the general dissemination, IP protection and exploitation policies will be more clearly drawn. Usually, after acceptance of relevant publications, a 6-12 months’ embargo can be considered. Data will be made available and reusable through open data repositories for periods of up to 10 years. # MyoChip Data Set Description The MyoChip project will generate mainly electronic data, however some data records can also be found handwritten as lab books for example. MyoChip project will ensure that all electronic files follow the FAIR policy as explained earlier. Expected size of data among all partners could amount close to 100TB during the entire project. The majority of data will come from software use for experimental setup, equipment and data analysis software. All partners have identified the datasets that will most likely be produced during the different phases of the project. These will be updated when necessary in the next versions of the DMP. All types of data are listed in the table below. <table> <tr> <th> Type of Data </th> <th> Formats used on MyoChip </th> <th> Source </th> </tr> <tr> <td> Documents </td> <td> .docx, .doc,.xlsx, .xls, .pptx, .ppt, .pdf, .txt </td> <td> * Protocols elaborated by the partners * Project meetings (minutes, presentations, other supporting documents), exchange of ideas * Group meeting discussions * Literature review: references in an Zotero, Endnote, Mendeley or other database; * Word documents with search details (databases, strategies, results) and reviews </td> </tr> <tr> <td> Video files </td> <td> .gif, .avi, .mov ,.mp4, .m4p, .mpe, .czi </td> <td> Different microscopes and analysis software. </td> </tr> <tr> <td> Digital images </td> <td> .tif, .tiff, .gif, .jpeg, jpg, .jif, .jfif, .jp2, .jpx, .j2k, .j2c, .fpx, .pcd, .png, .pdf, .czi. ,.sld, .ai, .lsm, </td> <td> Different microscopes and analysis software. </td> </tr> <tr> <td> CAD files </td> <td> .dwg, .dxf, .gds, .cif, .stl, .step </td> <td> Computer Assisted Design (CAD) files </td> </tr> <tr> <td> Code </td> <td> </td> <td> Development of software </td> </tr> <tr> <td> Database files </td> <td> .sqlite, .enl, .data </td> <td> Literature reference software such as Zotero, Endnote or Mendeley </td> </tr> <tr> <td> Raw data </td> <td> .csv </td> <td> Measurements / sensor outputs </td> </tr> </table> <table> <tr> <th> Reusing and Sharing </th> <th> Archiving and preserving (including storage and backup) </th> </tr> <tr> <td> All types of data are accessible to all the partners on demand. Partners share and reuse data. Files will be shared at meetings. </td> <td> The data will be stored by the partner collecting it (on their own computers and/or institutional servers). </td> </tr> </table>	https://phaidra.univie.ac.at/o:1140797	Horizon 2020
0108_GENE-SWitCH_817998.md	1 DATA MANAGEMENT PLAN Project 1 Number: 817998 Project Acronym: GENE-SWitCH Project title: The regulatory GENomE of SWine and CHicken: functional annotation during development Author : Peter Harrison EMBL-EBI Version : 1.1 Disclaimer The information in this document is provided as is and no guarantee or warranty is given that the information is fit for any particular purpose. The user thereof uses the information at its sole risk and liability. History of changes <table> <tr> <th> VERSION </th> <th> PUBLICATION DATE </th> <th> CHANGES </th> </tr> <tr> <td> 1.0 </td> <td> 06.12.2019 </td> <td> Initial version from WP3 </td> </tr> <tr> <td> 1.1 </td> <td> 23.12.2019 </td> <td> Minor revisions from consortium comments </td> </tr> </table> # 1\. Data Summary The GENE-SWitCH project aims to deliver to the livestock community two functionally mapped monogastric genomes, for chicken and pig. These functional maps and associated data collections will enable immediate translation into the pig and poultry sectors for developments in sustainable production. For example, within the project the datasets will be employed to evaluate the effect of maternal diet on the epigenome of pig foetuses. A key aspect of the generated datasets will be the extensive associated rich and controlled metadata, information that is key for the development of phenome to genome resources. The project will utilise existing pig and chicken datasets from the FAANG collection and wider community from the public archives as test datasets for the development of its openly developed bioinformatic pipelines and to enrich its new functional maps; it will also use the existing reference genomes from the community as a starting point for its own improved mapped genomes. The tissue samples for sequencing are being collected for pig from the INRA experimental unit GenESI in Lusignan, France and chicken samples from the UK National Avian Research Facility (www.narf.ac.uk). In total the project will generate data from 14 different assays, including both the core FAANG assays and additionally DNA methylation, Hi-C and Whole Genome Sequencing (Table 1). The Data Management Plan will be periodically updated throughout the project to reflect changes in the data produced by the project and any changes in storage and release. A future update will include the sizes of the datasets produced by the project. Table 1. The number of GENE-SWitCH assays and samples across developmental stages. All processed data generated in the project will be shared using standard bioinformatic data file formats (i.e. FASTQ, FASTA, SAM/BAM, GFF/GTF, BED, and VCF). The project will make extensive use of open access existing legacy datasets and of additional datasets generated during the lifetime of the project identified and accessed from EMBL-EBI public archives. Overall the project contributes to the global FAANG coordinated effort with i) the deliverance of high quality functional genome maps for the pig and the chicken, ii) demonstrable impact of these new data resources on developments in the breeding industry, and iii) the production of cutting edge bioinformatic pipelines and experimental techniques that will be of wide benefit to the scientific research community and breeding industries. # 2\. FAIR data ## 2\. 1. Making data findable, including provisions for metadata The proposed data deposition of GENE-SWitCH data through FAANG to the EMBL-EBI public archives will ensure the generated data is highly discoverable. GENE- SWitCH utilises the FAANG metadata standards ( _https://data.faang.org/ruleset/samples#standard_ ) . All data submissions will be validated through the FAANG validation and submission tools, that are in fact being updated as part of the GENE- SWitCH project and are accessible at ( _https://data.faang.org/validation/samples_ ). The deposition in the public archives gives every data file a unique accession. These accessions are globally recognised by the comparable archives at the National Center for Biotechnology Information (NCBI; _https://www.ncbi.nlm.nih.gov/_ ) and DNA Databank of Japan (DDBJ; _https://www.ddbj.nig.ac.jp/index-e.html)/_ ) . Different assay files are linked through the inclusion of the BioSamples identifier in all data submissions so that all of the datasets generated on each sample can be easily grouped and accessed from downstream presentation resources. GENE-SWitCH will conform with the FAANG record naming conventions. The FAANG data portal utilises ElasticSearch to ensure that all ontology validated metadata fields are keyword searchable using its predictive simultaneous search across samples, reads and analyses ( _https://data.faang.org/search_ ) . It will be possible to search for GENE-SWitCH data as part of an all search or pre-limit the search specifically to only return GENESWitCH project data results. The data portal utilises the rich ontology supported metadata to provide filters that allow a user to explore the GENE-SWitCH data based on species, technology, breeds, sex, material, organism part, cell type, assay type, archive, and sequencing instrument. All software will be appropriately versioned using an agreed versioning structure from its coding standards document of work package 2\. ### 2.2. Making data openly accessible All samples and `omics data will be deposited in the EMBL-EBI public archives that includes BioSamples, the European Nucleotide Archive, the European Variation Archive, PRIDE and BioImage archive. These are widely recognised and approved repositories for the long-term storage of biological data and the deposition routes are established with the FAANG Data Coordination Centre (DCC), that itself is based within the Molecular Archives cluster at EMBL-EBI. Apart from the reserved right of first publication stipulation set out in the FAANG Data Sharing statement ( _https://www.faang.org/data-shareprinciple_ ) , there are no restrictions on use of the data, no data access committee is required and apart from anonymous usage analytics no tracking of individual data use will be made. The following data sharing statement is available both via the websites and Application Programmatic Interfaces (machine readable) of the public archives and FAANG data portal. _" This study is part of the FAANG project, promoting rapid prepublication of data to support the research community. These data are released under Fort Lauderdale principles, as confirmed in the Toronto Statement (Toronto International Data Release Workshop. Birney et al. 2009. Pre-publication data sharing. Nature 461:168-170). Any use of this dataset must abide by the FAANG data sharing principles. Data producers reserve the right to make the first publication of a global analysis of this data. If you are unsure if you are allowed to publish on this dataset, please contact the FAANG Consortium ([email protected]) to enquire. The full guidelines can be found at _ _ _http://www.faang.org/data-share-principle_ . " _ The EMBL-EBI public archives are fully aware and accepting of incoming FAANG data including the data of the GENE-SWitCH project. Whilst the FAANG metadata is fully machine readable and the license is available to both web and programmatic users, further improvements will be investigated to further improve the machine readability, in collaboration with the requirements of the other H2020 SFS30 projects. The FAANG DCC will investigate specific license API endpoints, html embedding of license links and license structure formatting to improve machine-based access, a key component of FAIR compliance. The submission model for GENE-SWitCH will make the data available for direct download from both the FAANG data portal that utilises the underlying public archives infrastructure and from the public archives themselves. This provides by default a range of data access methods including web browser download, FTP, Aspera, Globus and API access to give flexibility to data consumers. All of these download options are open source and the archives have extensive documentation on the various data access options. The FAANG data portal collates the files from the various underlying archives to a single access point. The FAANG API provides programmatic users with the access FTP addresses to make a secondary call to download the data files themselves. All GENE-SWitCH software will be publicly developed on the FAANG GitHub repository, so that the development process is open to community input and available pre-publication. All of the GENESWitCH repositories will be given the prefix ‘proj-gs-‘ within the FAANG GitHub repository ( _https://github.com/FAANG_ ) . GENE-SWitCH will ensure that in the FAANG data portal the bioinformatic pipeline that was used to generate the analysis file is linked from the analysis file results page. This ensures that the analysis file, the raw data that generated it, the protocols and the bioinformatic pipelines are all downloadable from the same location. The software will include complete documentation, nextflow workflow management and be containerised in Docker. No specific tools are required to access the data from the data portals or the FAANG data portal, as they will use standard accepted file formats of the public archives. The FAANG data portal will provide a GENE-SWitCH project slice that will allow the data portal and programmatic access interface to provide a bulk download of all GENE-SWitCH data at once, this will be available at _http://data.faang.org/projects/gene- switch_ . ### 2.3. Making data interoperable GENE-SWitCH data will be submitted through the FAANG DCC that will ensure the data is interoperable with other FAANG datasets and highly reusable by the wider livestock community. To ensure interoperability with all other FAANG datasets, including the other three H2020 SFS30 projects, GENESWitCH will employ the latest version of the FAANG metadata standards (and utilise all future updates to these standards), currently version 3.8 ( _https://github.com/FAANG/dccmetadata/tree/master/rulesets_ ) , and in a more readable form at _https://data.faang.org/ruleset/samples#standard_ . It will ensure its compliance with these standards by running all data through the FAANG validation software prior to submission to the public archives. GENE- SWitCH will develop coding standards to ensure that all pipelines developed by the consortium are easily utilised, they will be containerised to ease installation and reuse. For its pipelines it will utilise open software applications, that will be implemented with a nextflow workflow manager and containerised using Docker to ensure consistent reuse across the project and by downstream users. To ensure interdisciplinary interoperability GENE-SWitCH will utilise the recommended ontologies of the FAANG metadata standards as set by the FAANG Metadata and Data Sharing Committee. A specific action of the project will be through the coordination of the FAANG DCC to improve the coverage and quality of ontologies for use in livestock metadata recording, and the consortium will publish a manuscript on the state of the art and usage of ontologies. Wherever an ontology is not possible we will employ controlled lists to prevent erroneous metadata recording. The ontologies that will be utilised in the project will be: OBI https://www.ebi.ac.uk/ols/ontologies/obi NCBI Taxonomy https://www.ebi.ac.uk/ols/ontologies/ncbitaxon EFO https://www.ebi.ac.uk/ols/ontologies/efo LBO https://www.ebi.ac.uk/ols/ontologies/lbo PATO https://www.ebi.ac.uk/ols/ontologies/pato VT https://www.ebi.ac.uk/ols/ontologies/vt <table> <tr> <th> ATOL </th> <th> https://www.ebi.ac.uk/ols/ontologies/atol </th> </tr> <tr> <td> EOL </td> <td> https://www.ebi.ac.uk/ols/ontologies/eol </td> </tr> <tr> <td> UBERON </td> <td> https://www.ebi.ac.uk/ols/ontologies/uberon </td> </tr> <tr> <td> CL </td> <td> https://www.ebi.ac.uk/ols/ontologies/cl </td> </tr> <tr> <td> BTO </td> <td> https://www.ebi.ac.uk/ols/ontologies/bto </td> </tr> <tr> <td> CLO </td> <td> https://www.ebi.ac.uk/ols/ontologies/clo </td> </tr> <tr> <td> SO </td> <td> _https://www.ebi.ac.uk/ols/ontologies/so_ </td> </tr> <tr> <td> GO </td> <td> _https://www.ebi.ac.uk/ols/ontologies/go_ </td> </tr> <tr> <td> NCIT </td> <td> https://www.ebi.ac.uk/ols/ontologies/ncit </td> </tr> <tr> <td> CHEBI </td> <td> https://www.ebi.ac.uk/ols/ontologies/chebi </td> </tr> </table> ### 2.4. Increase data re-use (through clarifying licences) GENE-SWitCH data will be publicly released in the EMBL-EBI archives at the earliest opportunity and pre-publication. This will be submitted to the archives without embargo so that it is immediately released to the public. This is in accordance with the FAANG data sharing principles ( _https://www.faang.org/data-share-principle_ ) , that is based upon the principles of the Toronto ( _https://www.nature.com/articles/461168a_ ) and Fort Lauderdale ( _https://www.genome.gov/Pages/Research/WellcomeReport0303.pdf_ ) agreements. This reserves the right for GENE-SWitCH to make the first publication with the data, whether a dataset has an associated publication is tracked clearly in the FAANG data portal ( _https://data.faang.org/home_ ) . All datasets will be clearly labelled with these data sharing principles, with the following statement: _" This study is part of the FAANG project, promoting rapid prepublication of data to support the research community. These data are released under Fort Lauderdale principles, as confirmed in the Toronto Statement (Toronto International Data Release Workshop. Birney et al. 2009. Pre-publication data sharing. Nature 461:168-170). Any use of this dataset must abide by the FAANG data sharing principles. Data producers reserve the right to make the first publication of a global analysis of this data. If you are unsure if you are allowed to publish on this dataset, please contact the FAANG Consortium ([email protected]) to enquire. The full guidelines can be found at _ _ _http://www.faang.org/data-share-principle_ . " _ This enables the wider community to immediately make use of the data that GENE-SWitCH produces to provide maximal value to researchers. All software developed by the consortium will be openly licensed for reuse, an example of this is in the GENE-SWitCH RNA-Seq pipeline ( _https://github.com/FAANG/proj- gs-rna-seq/blob/master/LICENSE_ ) . In accordance with GENE-SWitCH coding standards, this license file will be displayed in the root folder of all repositories. Data quality assurance processes and metrics will be investigated and implemented by work package 2 as part of the pipeline development process. It is intended that through the accurate recording of metadata, associated protocols and analysis software, and deposition in public archives that the data will remain available for long after the project grant ends, for the lifetime of the underlying public archives. The data will therefore be reusable by any party, at some point the datasets may be superseded by those produced on newer technologies. There will be no restriction on third party use of the data. The data generation work packages will apply the latest recommended community standards for data quality, comply with any standards set by FAANG working groups or the public archives, and for the generation and execution of bioinformatics analysis will utilise the latest open source, published and recognised analysis software for the construction of its pipelines. # 3\. Allocation of resources GENE-SWitCH directly funds the activity of the FAANG Data Coordination centre (DCC) to conduct data management and coordination for the project. The proposal has specific tasks and deliverables that will ensure the data generated in the project will conform to FAIR data principles. This in particular enhances the existing FAANG metadata standards, archival support tools, data portal discovery and data visualisations to improve findability, accessibility, interoperability and reusability of GENE-SWitCH data. These enhancements will also benefit the entire FAANG community as improvements will apply to all FAANG data. Thus the costs associated with ensuring GENE-SWitCH data is FAIR have been fully factored into the costs provided to EMBL-EBI in work package 3. All work packages that generate and analyse data have appropriate funding for the accurate recording and provision of metadata, through the validation and submissions software provided by the DCC. Data management is the responsibility of the FAANG Data Coordination Centre at EMBL-EBI that is operated by Peter Harrison and Guy Cochrane. GENE-SWitCH will use the EMBL-EBI public archives for the long-term preservation of its generated data, these resources have separate long term funding that will persist the data long after the grant ends. The inclusion of the data within the FAANG consortium data portal ( _https://data.faang.org/home_ ) and Ensembl browser ( _https://www.ensembl.org/index.html_ ) also ensures the functional annotation of genomes will remain accessible by the community in the long term, as these are likely to continue to receive separate funding. # 4\. Data security GENE-SWitCH will at the earliest opportunity submit all data to the public archives at EMBL-EBI. Intermediate results and ongoing analyses will be conducted and stored on the EMBL-EBI embassy cloud platform that is located in the same data centre as the public archives. Access to the GENESWitCH embassy cloud analysis platform is controlled by user specific ssh keys only issued to consortium members. As soon as an analysis is finished it will be submitted to the relevant EMBL-EBI archive for immediate public release without embargo. The EMBL-EBI archives are internationally recognised repositories for the long-term secure storage of scientific data. The EMBL-EBI archives are recognised Core Elixir data resources ( _https://elixireurope.org/platforms/data/core-data-resources_ ) . All data will be assigned a unique identifier for long term identification and preservation of the datasets. The EMBL-EBI data centres that host the public archives providing the long-term data storage, and the embassy cloud platform for the analysis and intermediate processing of GENE-SWitCH data are state of the art. EMBL-EBI uses three discrete Tier III plus data centres in different geographical locations to ensure long-term security. Research data is also replicated through the International Nucleotide Sequence Database Collaboration (INSDC; _http://www.insdc.org/_ ) agreements that sees the data replicated at the National Center for Biotechnology Information (NCBI; _https://www.ncbi.nlm.nih.gov/_ ) and DNA Databank of Japan (DDBJ; _https://www.ddbj.nig.ac.jp/index-e.html_ ) that agree to recognise each other centres accessioned datasets. EMBL-EBI commits to store the data for the lifetime that the archives remain active, this will be far beyond when the GENE-SWitCH grant ends, ensuring this data remains available to the scientific community for years to come. # 5\. Ethical aspects The proposed GENE-SWitCH data management plan complies fully with all international, EU and national legal and ethical requirements. GENE-SWitCH data sharing and long-term preservation is not subject to informed consent. GENE-SWitCH will fully comply with General Data Protection Regulations for its activities and web services. # 6\. Other issues As well as complying with H2020 procedures for data management, the GENE- SWitCH project will abide by the data sharing policy of the Functional Annotation of Animal Genomes (FAANG) coordinated action ( _https://www.faang.org/data-share-principle_ ) . This statement outlines the expectations of all FAANG projects that contribute to the coordinated action in terms of data recording, archiving and sharing. The statement includes the principles of the Toronto ( _https://www.nature.com/articles/461168a_ ) and Fort Lauderdale ( _https://www.genome.gov/Pages/Research/WellcomeReport0303.pdf_ ) agreements. The requirements set out in the FAANG data sharing principles do not conflict with those imposed by the EU H2020 data management principles. 5.2 FAANG Data Sharing Statement This document describes the principles of data sharing held by the FAANG consortium. This document is subject to approval by the FAANG steering committee. Any queries about this document should be sent to [email protected]_ . <table> <tr> <th> Definitions Archive means one of the archives hosted at the EBI, NCBI or DDBJ. These include the ENA, Genbank, ArrayExpress and Geo. A full list of the FAANG recommended archives is available as part of the FAANG metadata recommendations. Submission means data and metadata submission to one of the FAANG recommended Archives. FAANG member means an individual who has signed up to the FAANG consortium through the FAANG website and agreed to the FAANG core principles. Data means any assay or metadata generated for or associated with FAANG experiments. Analysis means any computational process where raw assay data is aligned, transformed or combined to produce a new product. Internal means data that is only accessible via the FAANG private shared storage. Private shared storage means a storage space hosted at EMBL-EBI that has password access via FTP, aspera and Globus Grid FTP technologies. Public means all data available through the FAANG public FTP site, which has no password and is accessible to everyone. </th> </tr> </table> FAANG recognizes that quickly sharing the data generated by the consortium with the wider community is a priority. Rapid data sharing before publication ensures that everyone can benefit from the data created by FAANG and can take advantage of improved understanding of the functional elements in these animal genomes to aid their own research. All raw data produced for a FAANG associated project will be submitted to the archives without any hold until publication date, thus allowing the data to be publicly available immediately after successful archive submission and useful to the community as soon as possible. The FAANG analysis group will turn the raw data into primary and integrated analysis results. Primary analysis results consistent of sample level analysis such as alignment to a reference genome or quantification of signal in the assay. Integrated analysis results represent analyses which drawn together data from multiple samples and/or experiments such as genome segmentation or differential analysis results. The majority of these analysis results will not be archived before publication but FAANG recognizes the need to share them both within the consortium and with the community. Initially all files that are not archived will be shared between FAANG members in private shared storage hosted at the EMBL-EBI. Any individual who signs up to FAANG and agrees to _the Toronto principles_ 1 will be allowed access to this. There will be metadata files in the private data sharing area, which make credit for different datasets as clear as possible. FAANG expects to make multiple releases each year. A data release will involve declaring a data freeze and copying all files associated with that data freeze from the private shared storage to the public FTP site. In the first instance these data freezes will contain the primary analysis results. As FAANG's analyses progress, the data freeze will be expanded to include integrative analysis too. The data freeze process will be coordinated by the FAANG Data Coordination Centre and will be based on consultation with FAANG members. FAANG will also aim to release all data associated with a paper before publication even if it lies outside this standard freeze cycle. The public data will be available to the whole community. All FAANG public data is released under _Fort Lauderdale principles_ 2 . The FAANG website, data portal and FTP site will all have clear data reuse statements on them. When considering internal FAANG data, if one FAANG member wishes to publish using data generated by another FAANG member they should first contact the data generator and clarify the member's publication strategy. Collaboration is for everyone's benefit and is strongly encouraged. The FAANG Steering Committee commits to report to journal editors and the laboratories involved any event that disregards the rights of data creators (including biological measurements as well as analysis of such measurements). All members of FAANG can and will continue to do experimental and analysis work outside of FAANG and the other data generated is not required to meet the same data sharing expectations. Only FAANG data can be distributed through the private storage and public FTP site.	https://phaidra.univie.ac.at/o:1140797	Horizon 2020
0112_i-GRAPE_825521.md	# Executive Summary i-GRAPE project aims to develop a new generation of analytical methods for stand-alone, onthe-field control of the grape maturation phase and vine hydric stress, based on highly integrated photonics components and modules. The objective of this document is to establish the framework for managing the data generated and collected throughout the project. This will consider: 1. Handling of data collected before, during, and after the project, 2. Identification of the major data sources, 3. Methodology and standards for data management, 4. Data sharing policy, 5. Data curation and preservation. Following the EU’s guidelines regarding the Data Management Plan, this document may be updated - if appropriate - during the project lifetime. # Introduction The present document is the deliverable D6.3 Data Management Plan (D 6.3). It establishes the framework and guidelines for managing the data generated and collected during the project. The target audience of the document, being a public deliverable, is the i-GRAPE consortium, the European Commission and the general public. The document is composed by the sections below with the following rationale: * Section 2- definitions, * Section 3- data sources, * Section 4- data storage and backup, - Section 5- data management principles, - Section 6- conclusion and revision cycle. # Definitions Data Management Plan (DMP): a working document which outlines how all available datasets will be handled both during the active project phase and after its end. Data Owners: are the individuals or groups of individuals who are held accountable for a dataset and who have legal ownership rights to a dataset even though that dataset may have been collected, collated or disseminated by another party. Dataset: a collection of data created in the course of i-GRAPE project or secondary data that can be published with due permission of the user. Datasets are the fundamental unit of data management. Metadata: Information about datasets stored in a repository. Metadata is the term used to describe the summary information or characteristics of a set of data. In general terms, this means the What, Who, Where, When and How of the data. For example, in the particular case of data generated by i-GRAPE sensors, this can include the area of geographic information, the grape variety, or the temperature. Primary data: original data that has been uploaded by the user especially for the purpose in mind. Secondary data: data that has been captured for another purpose than the purpose in mind. Secondary data that is being reused, usually in a different context. # Data sources Regarding the multidisciplinary character of the project, there are several complementary sources of data that will be generated and collected during the project. Therefore, it can be identified three major data sources: Historical datasets: collection of already existing time series data that target vine hydric stress and grape maturation parameters relevant to the development of the project. These datasets are owned and supplied by the project’s end-user of i-GRAPE’s technology (Sogrape). Field datasets: systematic collection of data generated during the monitoring of grapes and / or vine plants during a season. This includes data acquired by i-GRAPE sensors, reference instrumentation (e.g. benchtop and portable optical instrumentation such as spectrophotometers or fluorometers), and information generated by wet chemistry assays. Experimental datasets : collection of data generated during the experimental activities related to the project (e.g. simulations of electronic circuits, simulations of optical nanostructures, optical data collected with reference materials for standardization). # Data storage and backup The data to be produced within i-GRAPE project will be securely stored and backups made regularly. The legal principles related to data storage and backup will be stated on the i-GRAPE Data Policy as described below on section 5.2. # Data management principles This section provides information on i-GRAPE project principles for data management considering the benefits, drivers, principles and mechanisms needed for data acquisition, storage, security, retrieval, dissemination archiving, and disposal. The i-GRAPE key principles for data management are described under the sub-sections below. ## Data lifecycle control The whole datasets of i-GRAPE will be managed in order to ensure that the data is usable and securely stored in i-GRAPE database. ## Data policy The datasets will be acquired by all partners during the activities of the project. Each partner is responsible to add the datasets to the database provided by INL. Datasets will be managed by the i-GRAPE consortium under the procedures established by this plan. Datasets will be periodically maintained in order to make them usable in the long-term. Intellectual Property Rights (IPR) management will specify any restrictions on the use of the data. All data that is considered not essential for IPR protection will be made accessible to the public. ## Metadata All datasets produced during i-GRAPE project will contain compiled metadata that will summarize the major characteristics of the dataset. The i-GRAPE consortium will set the guidelines for establishing a consistent metadata among the different datasets generated. This metadata record will allow a correct identification and suitable reuse / reprocessing of the dataset. ## Data access and dissemination Access and dissemination of the data will comply with the following principles: * The right to use or provide access to data can be passed to a third party and subject to dissemination policies. * IPR management will define the access level of the data. * Open Access will be applied to research data, upon assessing that: * IPR, Copyright, and data ownership of the consortium and/or third party data are respected; * No sensitive or confidential information are disclosed. * The potential for re-use and exploitation of data will be considered. * Public access to data available under i-GRAPE platform will be provided in compliance with the General Data Protection Regulation. ## Data audit The i-GRAPE consortium will perform periodically audits to the datasets in order to verify the compliance with the policies of the present document. # Conclusions and revision cycles The Data Management Plan is currently under implementation and includes all datasets identified in section 3. The i-GRAPE consortium is responsible for verifying the application of rules stated in the present document. This document should be reviewed according to the quality and quantity of data generated throughout the project. The revisions cycles should coincide with the consortium meetings.	https://phaidra.univie.ac.at/o:1140797	Horizon 2020
0113_SACOC_831977.md	# DATASETS At this stage of the project, the following datasets are envisaged: ## REFERENCE DESIGN The reference design (v0) consists of a flat plate with no fins or any other heat exchanger geometry and is intended to validate flow conditions inside the wind tunnel and the heat transmission. ### Experimental data: IDs data_v0_exp_UPV and data_v0_exp_Purdue Table 1 _metadata for dataset Data_v0_exp_UPV_ #### FIELD VALUE <table> <tr> <th> Title </th> <th> Experimental data for the reference design </th> </tr> <tr> <td> Creator </td> <td> UPV </td> </tr> <tr> <td> Subject </td> <td> Reference design v0 (flat plate) </td> </tr> <tr> <td> Description </td> <td> This dataset will include all the experimental data of the reference design gathered at UPV with the purpose of providing realistic conditions to create and validate the CFD model </td> </tr> <tr> <td> Publisher </td> <td> TBD </td> </tr> <tr> <td> Contributor </td> <td> TBD </td> </tr> <tr> <td> Date </td> <td> TBD </td> </tr> <tr> <td> Type </td> <td> Experimental data </td> </tr> <tr> <td> Format </td> <td> ASCII text, MAT file, Excel workbook </td> </tr> <tr> <td> Identifier </td> <td> Data_v0_exp_UPV </td> </tr> <tr> <td> Source </td> <td> UPV wind tunnel </td> </tr> <tr> <td> Relation </td> <td> Data_v0_CFD, Data_v0_exp_Purdue </td> </tr> <tr> <td> Rights </td> <td> TBD </td> </tr> </table> Table 2 _metadata for dataset Data_v0_exp_Purdue_ #### FIELD VALUE <table> <tr> <th> Title </th> <th> Validation data for the reference design </th> </tr> <tr> <td> Creator </td> <td> Purdue </td> </tr> <tr> <td> Subject </td> <td> Reference design v0 (flat plate) </td> </tr> <tr> <td> Description </td> <td> This dataset will include all the experimental data of the reference design gathered at Purdue with the purpose of validating both CFD simulations and initial experimental measurements </td> </tr> <tr> <td> Publisher </td> <td> TBD </td> </tr> <tr> <td> Contributor </td> <td> TBD </td> </tr> <tr> <td> Date </td> <td> TBD </td> </tr> <tr> <td> Type </td> <td> Experimental data </td> </tr> <tr> <td> Format </td> <td> ASCII text, MAT file, Excel workbook </td> </tr> <tr> <td> Identifier </td> <td> Data_v0_exp_Purdue </td> </tr> <tr> <td> Source </td> <td> Purdue wind tunnel </td> </tr> <tr> <td> Relation </td> <td> Data_v0_CFD, Data_v0_exp_UPV </td> </tr> <tr> <td> Rights </td> <td> TBD </td> </tr> </table> ### Numerical data: ID data_v0_cfd Table 2 _metadata for dataset Data_v0_cfd_ #### FIELD VALUE <table> <tr> <th> Title </th> <th> CFD results for the reference design </th> </tr> <tr> <td> Creator </td> <td> UPM </td> </tr> <tr> <td> Subject </td> <td> Reference design (flat plate) </td> </tr> <tr> <td> Description </td> <td> This dataset will include all the numerical results from the CFD calculations carried out by UPM on the v0 design geometry </td> </tr> <tr> <td> Publisher </td> <td> TBD </td> </tr> <tr> <td> Contributor </td> <td> TBD </td> </tr> <tr> <td> Date </td> <td> TBD </td> </tr> <tr> <td> Type </td> <td> Numerical simulation data </td> </tr> <tr> <td> Format </td> <td> ASCII text, HDF5, proprietary CFD format </td> </tr> <tr> <td> Identifier </td> <td> Data_v0_cfd </td> </tr> <tr> <td> Source </td> <td> UPM calculations </td> </tr> <tr> <td> Relation </td> <td> Data_v0_exp_UPV, Data_v0_exp_Purdue </td> </tr> <tr> <td> Rights </td> <td> TBD </td> </tr> </table> ## STANDARD DESIGN The standard design (v1) will include typical heat exchanger fins and will represent current state of the art approaches for SACOCs design. ### Experimental data: IDs data_v1_exp_UPV and data_v1_exp_Purdue Table 4 _metadata for dataset Data_v1_exp_UPV_ #### FIELD VALUE <table> <tr> <th> Title </th> <th> Experimental data for the reference design </th> </tr> <tr> <td> Creator </td> <td> UPV </td> </tr> <tr> <td> Subject </td> <td> Design v1 (standard fins) </td> </tr> <tr> <td> Description </td> <td> This dataset will include all the experimental data of the v1 design gathered at UPV with the purpose of providing realistic conditions to cre- ate and validate the CFD model </td> </tr> <tr> <td> Publisher </td> <td> TBD </td> </tr> <tr> <td> Contributor </td> <td> TBD </td> </tr> <tr> <td> Date </td> <td> TBD </td> </tr> <tr> <td> Type </td> <td> Experimental data </td> </tr> <tr> <td> Format </td> <td> ASCII text, MAT file, Excel workbook </td> </tr> <tr> <td> Identifier </td> <td> Data_v1_exp_UPV </td> </tr> <tr> <td> Source </td> <td> UPV wind tunnel </td> </tr> <tr> <td> Relation </td> <td> Data_v1_CFD, Data_v1_exp_Purdue </td> </tr> <tr> <td> Rights </td> <td> TBD </td> </tr> </table> Table 5 _metadata for dataset Data_v1_exp_Purdue_ #### FIELD VALUE <table> <tr> <th> Title </th> <th> Experimental data for the standard design </th> </tr> <tr> <td> Creator </td> <td> Purdue </td> </tr> <tr> <td> Subject </td> <td> Design v1 (standard fins) </td> </tr> <tr> <td> Description </td> <td> This dataset will include all the experimental data of the v1 design gathered at UPV with the purpose of providing realistic conditions to cre- ate and validate the CFD model </td> </tr> <tr> <td> Publisher </td> <td> TBD </td> </tr> <tr> <td> Contributor </td> <td> TBD </td> </tr> <tr> <td> Date </td> <td> TBD </td> </tr> <tr> <td> Type </td> <td> Experimental data </td> </tr> <tr> <td> Format </td> <td> ASCII text, MAT file, Excel workbook </td> </tr> <tr> <td> Identifier </td> <td> Data_v1_exp_Purdue </td> </tr> <tr> <td> Source </td> <td> Purdue wind tunnel </td> </tr> <tr> <td> Relation </td> <td> Data_v1_CFD, Data_v1_exp_UPV </td> </tr> <tr> <td> Rights </td> <td> TBD </td> </tr> </table> ### Numerical data: dataset ID data_v1_cfd Table 6 _metadata for dataset Data_v1_cfd_ #### FIELD VALUE <table> <tr> <th> Title </th> <th> CFD results for the standard design </th> </tr> <tr> <td> Creator </td> <td> UPM </td> </tr> <tr> <td> Subject </td> <td> Design v1 (standard fins) </td> </tr> <tr> <td> Description </td> <td> This dataset will include all the numerical results from the CFD calculations carried out by UPM on </td> </tr> <tr> <td> </td> <td> the v1 design geometry </td> </tr> <tr> <td> Publisher </td> <td> </td> <td> TBD </td> </tr> <tr> <td> Contributor </td> <td> </td> <td> TBD </td> </tr> <tr> <td> Date </td> <td> </td> <td> TBD </td> </tr> <tr> <td> Type </td> <td> </td> <td> Numerical simulation data </td> </tr> <tr> <td> Format </td> <td> </td> <td> ASCII text, HDF5, proprietary CFD format </td> </tr> <tr> <td> Identifier </td> <td> </td> <td> Data_v1_cfd </td> </tr> <tr> <td> Source </td> <td> </td> <td> UPM calculations </td> </tr> <tr> <td> Relation </td> <td> </td> <td> Data_v1_exp_UPV, Data_v1_exp_Purdue </td> </tr> <tr> <td> Rights </td> <td> </td> <td> TBD </td> </tr> <tr> <td> </td> <td> </td> <td> </td> </tr> </table> ## ADVANCED DESIGN 1 The next iteration (v2) will feature an innovative design that departs from the current approach of fin-type SACOCs. ### Experimental data: IDs data_v2_exp_UPV and data_v2_exp_Purdue Table 7 _metadata for dataset Data_v2_exp_UPV_ #### FIELD VALUE <table> <tr> <th> Title </th> <th> Experimental data for the advanced design 1 </th> </tr> <tr> <td> Creator </td> <td> UPV </td> </tr> <tr> <td> Subject </td> <td> Design v2 (advanced design 1) </td> </tr> <tr> <td> Description </td> <td> This dataset will include all the experimental data of the v2 design gathered at UPV with the purpose of providing realistic conditions to cre- ate and validate the CFD model </td> </tr> <tr> <td> Publisher </td> <td> TBD </td> </tr> <tr> <td> Contributor </td> <td> TBD </td> </tr> <tr> <td> Date </td> <td> TBD </td> </tr> <tr> <td> Type </td> <td> Experimental data </td> </tr> <tr> <td> Format </td> <td> ASCII text, MAT file, Excel workbook </td> </tr> <tr> <td> Identifier </td> <td> Data_v2_exp_UPV </td> </tr> <tr> <td> Source </td> <td> UPV wind tunnel </td> </tr> <tr> <td> Relation </td> <td> Data_v2_CFD, Data_v2_exp_Purdue </td> </tr> <tr> <td> Rights </td> <td> TBD </td> </tr> </table> Table 8 _metadata for dataset Data_v2_exp_Purdue_ #### FIELD VALUE <table> <tr> <th> Title </th> <th> Experimental data for the advanced design 1 </th> </tr> <tr> <td> Creator </td> <td> Purdue </td> </tr> <tr> <td> Subject </td> <td> Design v2 (advanced design 1) </td> </tr> <tr> <td> Description </td> <td> This dataset will include all the experimental data of the v1 design gathered at UPV with the purpose of providing realistic conditions to cre- ate and validate the CFD model </td> </tr> <tr> <td> Publisher </td> <td> TBD </td> </tr> <tr> <td> Contributor </td> <td> TBD </td> </tr> <tr> <td> Date </td> <td> TBD </td> </tr> <tr> <td> Type </td> <td> Experimental data </td> </tr> <tr> <td> Format </td> <td> ASCII text, MAT file, Excel workbook </td> </tr> <tr> <td> Identifier </td> <td> Data_v2_exp_Purdue </td> </tr> <tr> <td> Source </td> <td> Purdue wind tunnel </td> </tr> <tr> <td> Relation </td> <td> Data_v2_CFD, Data_v2_exp_UPV </td> </tr> <tr> <td> Rights </td> <td> TBD </td> </tr> </table> ### Numerical data: dataset ID data_v2_cfd Table 9 _metadata for dataset Data_v2_cfd_ #### FIELD VALUE <table> <tr> <th> Title </th> <th> CFD results for the advanced design 1 </th> </tr> <tr> <td> Creator </td> <td> UPM </td> </tr> <tr> <td> Subject </td> <td> Design v2 (advanced design 1) </td> </tr> <tr> <td> Description </td> <td> This dataset will include all the numerical results from the CFD calculations carried out by UPM on the v2 design geometry </td> </tr> <tr> <td> Publisher </td> <td> TBD </td> </tr> <tr> <td> Contributor </td> <td> TBD </td> </tr> <tr> <td> Date </td> <td> TBD </td> </tr> <tr> <td> Type </td> <td> Numerical simulation data </td> </tr> <tr> <td> Format </td> <td> ASCII text, HDF5, proprietary CFD format </td> </tr> <tr> <td> Identifier </td> <td> Data_v2_cfd </td> </tr> <tr> <td> Source </td> <td> UPM calculations </td> </tr> <tr> <td> Relation </td> <td> Data_v2_exp_UPV, Data_v2_exp_Purdue </td> </tr> <tr> <td> Rights </td> <td> TBD </td> </tr> </table> ## ADVANCED DESIGN 2 The final iteration (v3) will feature a different innovative design for the SACOC geometry. ### Experimental data: IDs data_v3_exp_UPV and data_v3_exp_Purdue Table 10 _metadata for dataset Data_v3_exp_UPV_ #### FIELD VALUE <table> <tr> <th> Title </th> <th> Experimental data for the advanced design 2 </th> </tr> <tr> <td> Creator </td> <td> UPV </td> </tr> <tr> <td> Subject </td> <td> Design v3 (advanced design 2) </td> </tr> <tr> <td> Description </td> <td> This dataset will include all the experimental data of the v3 design gathered at UPV with the purpose of providing realistic conditions to cre- ate and validate the CFD model </td> </tr> <tr> <td> Publisher </td> <td> TBD </td> </tr> <tr> <td> Contributor </td> <td> TBD </td> </tr> <tr> <td> Date </td> <td> TBD </td> </tr> <tr> <td> Type </td> <td> Experimental data </td> </tr> <tr> <td> Format </td> <td> ASCII text, MAT file, Excel workbook </td> </tr> <tr> <td> Identifier </td> <td> Data_v3_exp_UPV </td> </tr> <tr> <td> Source </td> <td> UPV wind tunnel </td> </tr> <tr> <td> Relation </td> <td> Data_v3_CFD, Data_v3_exp_Purdue </td> </tr> <tr> <td> Rights </td> <td> TBD </td> </tr> </table> Table 11 _metadata for dataset Data_v3_exp_Purdue_ #### FIELD VALUE <table> <tr> <th> Title </th> <th> Experimental data for the advanced design 2 </th> </tr> <tr> <td> Creator </td> <td> Purdue </td> </tr> <tr> <td> Subject </td> <td> Design v3 (advanced design 2) </td> </tr> <tr> <td> Description </td> <td> This dataset will include all the experimental data of the v3 design gathered at UPV with the purpose of providing realistic conditions to cre- ate and validate the CFD model </td> </tr> <tr> <td> Publisher </td> <td> TBD </td> </tr> <tr> <td> Contributor </td> <td> TBD </td> </tr> <tr> <td> Date </td> <td> TBD </td> </tr> <tr> <td> Type </td> <td> Experimental data </td> </tr> <tr> <td> Format </td> <td> ASCII text, MAT file, Excel workbook </td> </tr> <tr> <td> Identifier </td> <td> Data_v3_exp_Purdue </td> </tr> <tr> <td> Source </td> <td> Purdue wind tunnel </td> </tr> <tr> <td> Relation </td> <td> Data_v3_CFD, Data_v3_exp_UPV </td> </tr> <tr> <td> Rights </td> <td> TBD </td> </tr> </table> ### Numerical data: dataset ID data_v3_cfd Table 12 _metadata for dataset Data_v3_cfd_ #### FIELD VALUE <table> <tr> <th> Title </th> <th> CFD results for the advanced design 2 </th> </tr> <tr> <td> Creator </td> <td> UPM </td> </tr> <tr> <td> Subject </td> <td> Design v3 (advanced design 2) </td> </tr> <tr> <td> Description </td> <td> This dataset will include all the numerical results from the CFD calculations carried out by UPM on the v3 design geometry </td> </tr> <tr> <td> Publisher </td> <td> TBD </td> </tr> <tr> <td> Contributor </td> <td> TBD </td> </tr> <tr> <td> Date </td> <td> TBD </td> </tr> <tr> <td> Type </td> <td> Numerical simulation data </td> </tr> <tr> <td> Format </td> <td> ASCII text, HDF5, proprietary CFD format </td> </tr> <tr> <td> Identifier </td> <td> Data_v3_cfd </td> </tr> <tr> <td> Source </td> <td> UPM calculations </td> </tr> <tr> <td> Relation </td> <td> Data_v3_exp_UPV Data_v3_exp_Purdue </td> </tr> <tr> <td> Rights </td> <td> TBD </td> </tr> </table> # FAIR DATA ## Making data findable, including provisions for metadata The data produced in the project and described in the aforementioned tables 1-12 will be issued with DOI identifiers once it has reached an approved level of maturity for consumption by interested parties. It is envisaged that the DOIs will be assigned by the final, public data repository, which at this point it is expected to be Zenodo. The metadata described in the aforementioned tables follows the Dublin Core standard and will be uploaded to the repository alongside the data and indexed, thus making it discoverable. ## Naming conventions The dataset ID will be Data_[design iteration]_[experimental/CFD]_[Facility (if experimental)]; inside the dataset the variables will be named according to habitual aerospace engineering vocabulary. It is envisaged that both static and total thermodynamic will be provided. This will be updated with the precise structure of the dataset once the data becomes available. ## Search keywords Adequate keywords will be provided alongside the data to maximize the re-use potential. This will be updated with such keywords once the data is uploaded. ## Version numbers Presently the following overall version numbers are envisaged. * v0 Reference design: a flat plane with no special heat exchanger geometry * v1 Standard design: SACOC equipped with standard fins * v2 Advanced design 1: an innovative heat exchange geometry (TBD) * v3 Advanced design 2: another innovative heat exchange geometry (TBD) Sub-versions will be added to this document if required. _Searchable metadata_ The metadata will be added to the repository search mechanism. ## Standardized formats It is envisaged that standardised data formats will be used, such as: * ASCII text (.txt, .csv, .dat) * Level 5 MAT-file format (MATLAB) (.mat) * Microsoft Excel Workbook (.xls) * Portable Document Format (.pdf) * HDF5 (.hdf) This section will be updated if additional data formats are used. ## Open file formats Some of the formats may require the use of proprietary tools. The intention however is to provide copies of the data in openly accessible formats for all the data. ## Open source tools The objective is that all data be made accessible through open source tools, such as Python, Octave, Calc, etc. for the experimental data, and ParaView, VTK, etc. for the simulation data. # MAKING DATA OPENLY ACCESSIBLE ## Openly available data All data underlying the scholarly publications produced by the SACOC project will be made openly available. it is envisaged at this point that data will cover the thermodynamic variables describing the flow state in the aforementioned designs. ## Data location The data will be included at first in the internal project repository with a short description of the test case represented by the data and the information contained in the data (limited metadata). Once that the data has reached appropriate maturity, it will be uploaded to a public repository such as Zenodo where it will be available free of charge for any user. ## Methods or software tools The data will be accessible through standard software such as EXCEL, MATLAB, etc. It is expected that all data will be accessible through open source tools such as Python, Octave, ParaView, etc. ## Software documentation The data should be easily ingested into the software as it will be provided in standardized file formats. It is possible that examples for popular applications may be included. ## Included software Links will be included to open source software that can be used to access the data. Custom software written by the consortium members to process the data may be included. This document will be updated to reflect such decision. ## Location of data and associated metadata, documentation and code During the project, and for each WP, data will be stored at partner’s datacenters and replicated in the shared SACOC repository provided by SAFRAN. Open data will be deposited in an open access repository. At the moment Zenodo has been identified as the most likely candidate. ## Special arrangements with the identified repository At the moment it is not envisaged that the processed, scientific data surpasses the standard Zenodo quota per dataset. If that was the case, special arrangements will be sought. ## Restrictions on use and access provision At this stage, it is envisaged that access to the data will be open. Restrictions on use will be defined by the relevant license. It is expected that this license will be Creative Commons’ AttributionNonCommercial- ShareAlike 4.0 International (CC-BY-NC-SA 4.0). This document will be updated to reflect changes in these decisions. _Data access committee_ Not expected _Conditions for access_ The acceptance of the license, will be included in a machine-readable format. _Identification_ Data will be public access without identification. # MAKING DATA INTEROPERABLE It will be an objective that all data produced in the project shall be as interoperable as possible, thus allowing data exchange and re-use between researchers, institutions, organisations, countries, etc. In particular, participants will adhere to the aforementioned standards for formats that will be compatible with open source software applications thereby facilitating re- combinations with different datasets from different origins. This is especially important when comparing experimental datasets with numerical results. Care will be put in defining a common and frame of reference for all variables. These variables will be described by stablished engineering vocabulary in order to maximize the interoperability of the data. # INCREASE DATA RE-USE (THROUGH CLARIFYING LICENCES) ## Data licensing It is envisaged that the Creative Commons’ Attribution-NonCommercial- ShareAlike 4.0 International (CC-BY-NC-SA 4.0) license will be used for the public datasets. ## Time framework for data availability within the project Data will be made available for re-use immediately upon publication of the accompanying article. There will be no embargo period for the data. ## Re-use after the end of the project As the data will be deposited in Zenodo, it is expected that re-use will continue after the end of the project. However, no permanent support, storage facility or point of contact person for the general public is at this point expected after the closing of the project budget. _Time framework for data availability after the project conclusion_ Data will remain in Zenodo for as long as the repository operators allow. # ALLOCATION OF RESOURCES _Costs for making data FAIR in your project_ Will be updated on next DMP versions. _Covering of FAIR costs_ Will be updated on next DMP versions. _Responsible for data management_ The coordinator will be responsible for data management in SACOC. _Resources for long term preservation_ Will be decided in agreement with the Topic Manager on next DMP versions. _National or institutional repositories_ At this stage no depositing in national or institutional depositories is envisaged. # DATA SECURITY ## Provisions in place for data security Partners’ data is stored in the respective datacenters of each institution, which are secured and backed up by different means. Additionally, a secure, managed shared repository has been provided by SAFRAN for use by all partners and to secure the data transfers. ## Certified repositories for long term preservation and curation Long term preservation is expected to be carried out by depositing the scientific data in the Zenodo repository. Raw and auxiliary data will be kept for a period to be decided and then deleted, as no budget can be allocated for data preservation or curation after the end of the project. # ETHICAL ASPECTS The participants have not identified any ethical issue regarding the data, as no experiments or data concerns living organisms whatsoever, nor is it expected any impact on the environment as the tests will be carried out in a closed, appropriate facility. # OTHER ISSUES At this stage, the data will not be subject to any additional data management procedures, as this DMP will be used as a common framework that supersedes the individual procedures of each member of the consortium.	https://phaidra.univie.ac.at/o:1140797	Horizon 2020
0114_eFactory_825075.md	Executive Summary This deliverable provides the plan for the management of data in the eFactory project. It describes the methods applied to making data findable, openly accessible, interoperable, re-useable and secure. Furthermore, the legal framework, risks and measures associated to ethical aspects, mechanisms for data protection as well as governance and trust are addressed. In order to realise the FAIR principle along the eFactory project, this deliverable describes the following main mechanisms: Document management: The project team set-up common procedures and practices that are used for handling documents within eFactory: the common WebDAV repository “OwnCloud”, the usage of the Microsoft OneDrive cloud storage, internal templates with its document metadata that support them as well as the eFactory glossary. Data management: The eFactory Marketplace framework provides the ground for interlinking multiple marketplaces from different platforms. Within this context, primarily the exchange of user data for single-sign-on, (meta-) data related to third-party offerings and accountancy services (for tracking the user journeys) are implemented. To access the different marketplaces, the eFactory Marketplace framework accesses each external marketplace through a central component called Data Spine. Data accessibility: The Data Spine provides an open, platform-independent and secure communication and interoperability infrastructure with interfaces for the loosely coupled platforms, tools and services (e.g. third-party marketplaces). According to the current design, the security framework associated with Data Spine stores user data, i.e. username, password (and most probably the email address and/or phone number for password recovery). Data interoperability: Considering overall interoperability, the Data Spine is the gluing mechanism in the context of connecting multiple tools, services and platforms to realise a federated platform and ecosystem. Based on the identification of common standards and abstractions, the APIs, connectors and interfaces that need to be implemented for the tools, systems and platforms federated through the Data Spine are defined and realised within the project. Besides the eFactory Data Spine being in the centre of the interoperability towards platforms, external interoperabilit _y_ is also fostered by means of open experiments of smart factory tools and solutions as well as the related data within the federated eFactory ecosystem. When it comes to Data Security and Privacy , the eFactory project carefully analyses the implications of, and compliance with, the relevant regulations on data management and consumption. This includes ensuring compliance with GDPR (General Data Protection Regulation) and NIS Direction (Directive on Security of Network and Information Systems). Besides the fact that the eFactory Consortium Agreement explicitly states that the project partners are GDPR compliant based on the requirements of the regulation, the following security controls are addressed within eFactory in the context of data integrity and quality: * Data input validation * Data and metadata protection * Data protection at rest * Data protection in shared resources * Notification of data integrity violations * Informed consent by design In addition, the eFactory project defines and implements Data Governance and Trust mechanisms , covering information governance, a policy-based control of information to meet all legal, regulatory, risk and business demands as well as data governance, involving processes and controls to ensure that information at the data level is true, accurate, and unique (not redundant). It involves data cleansing to strip out corrupted, inaccurate, or extraneous data and de-duplication, to eliminate redundant occurrences of data. Considering Ethical Aspects , eFactory does not introduce any critical issues or problems. However, several considerations typical to ICT and on-site industrial trials, where employees are also involved in the demonstration and evaluation stages, are considered. Here, the consortium is fully aware of these and has the necessary experience to address them seamlessly by being compliant with the relevant international and national law, regulations as well as directives, e.g. * 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 Despite the far-reaching provisions implemented, Potential Risks and Related Mitigation Activities in the context of data management are continuously analysed by the eFactory team, including the following domains: Data security, storage and process of personal as well as confidentiality, privacy control, and transparency. # 0 Introduction ## 0.1 eFactory Project Overview eFactory – European Connected Factory Platform for Agile Manufacturing – is a project funded by the H2020 Framework Programme of the European Commission under Grant Agreement 825075 and conducted from January 2019 until December 2022. It engages 30 partners (Users, Technology Providers, Consultants, and Research Institutes) from 11 countries with a total budget of circa 16M€. Further information can be found at eFactoryproject.eu. In order to foster the growth of a pan-European platform ecosystem that enables the transition from “analogue-first” mass production, to “digital twins” and lot-size-one manufacturing, the eFactory project will design, build and operate a federated digital manufacturing platform. The platform will be bootstrapped by interlinking four base platforms from FoF-11-2016 cluster funded by the European Commission, early on. This will inform the design of the eFactory Data Spine and the associated toolsets to fully connect the existing user communities of the 4 base platforms. The federated eFactory platform will also be offered to new users through a unified Portal with value-added features such as single sign-on (SSO), user access management functionalities to hide the complexity of dealing with different platform and solution providers. ## 0.2 Deliverable Purpose and Scope The purpose of this deliverable “D11.10 Data Management Plan” is to document the framework for the management of all generated data in the project with a special focus on the FAIR data approach. Data management refers to all aspects of creating, housing, delivering, maintaining, archiving and preserving data; It is one of the essential areas of responsible conduct of research. ## 0.3 Target Audience The deliverable at hand is of public nature, providing the eFactory project team the fundament for handling data generated and managed in the eFactory project. ## 0.4 Deliverable Context This document is one of the cornerstones for achieving the project aims. Its relationship to other documents is as follows: * Description of Action (DOA): Provides the foundation for the actual research and technological content of eFactory. Importantly, the Description of Action includes a description of the overall project work plan * Project Handbook (D1.1) : Provides the foundation for the practical work in the project throughout its duration and helps to ensure that the project partners follow the same well-defined procedures and practices also in terms of information sharing ## 0.5 Document Structure This deliverable is broken down into the following sections: * Section 0 Introduction: An introduction to this deliverable including a general overview of the project, an outline of the purpose, scope, context, status, and target audience of the deliverable at hand. * Section 1 Data Summary: Provides an overview on data used and generated in the eFactory project as well as related parameters. * Section 2 FAIR Data: Describes the ways applied to make data findable, openly accessible, interoperable and re-useable. * Section 3 Allocation of Resources: Outlines the efforts towards the realisation of the FAIR data approach. * Section 4 Data Security: Presents details about relevant regulations, data integrity and quality, data storage, data privacy, federated identity management and a blockchain approach. * Section 5 Ethical Aspects: Provides information on relevant legal frameworks as well as potential data management risks and related mitigation measures. * Section 6 Other Issues: Outlines project activities related to data protection, governance and trust. * Annexes: * Annex A : Document History ## 0.6 Document Status This document is listed in the Description of Action as “public”. ## 0.7 Document Dependencies This document has no preceding documents or further iterations. ## 0.8 Glossary and Abbreviations A definition of common terms related to eFactory, as well as a list of abbreviations, is available at: _https://www.efactory-project.eu/glossary_ ## 0.9 External Annexes and Supporting Documents Annexes and Supporting Documents: • None ## 0.10 Reading Notes • None # 1 Data Summary The following table summarises the data generated and/or managed within the eFactory project as well as its fundamental parameters. <table> <tr> <th> eFactory Context </th> <th> Internal Documents </th> </tr> <tr> <td> Description </td> <td> Documents set-up and updated during the preparation and execution of the eFactory project. They include the Consortium Agreement (CA), Description of Action (DoA), document templates meeting minutes, working documents and the eFactory deliverables. The handling of eFactory related documents is done based on OwnCloud, a solution for document management and storage. </td> </tr> <tr> <td> Purpose </td> <td> Provision of all information to successfully perform the eFactory project tasks </td> </tr> <tr> <td> Formats </td> <td> .docx, .pptx, .xlsx, .pdf, .txt </td> </tr> <tr> <td> Origins </td> <td> eFactory partners </td> </tr> <tr> <td> Size </td> <td> Typically <20MB </td> </tr> <tr> <td> Utility </td> <td> Depending on the dissemination level the interested public, eFactory partners and/or the EC </td> </tr> </table> <table> <tr> <th> eFactory Context </th> <th> Marketplace </th> </tr> <tr> <td> Description </td> <td> The eFactory Marketplace framework provides the ground for interlinking of multiple marketplaces from different platforms. Within this context primarily the exchange of user data for single-sign-on, (meta) data related to third-party offerings (such as applications and services) and accountancy services (for tracking the user journeys) are implemented. </td> </tr> <tr> <td> Purpose </td> <td> Exchange and administration of all data to provide the user of the eFactory Marketplace a state-of-the-art service interaction and to enable user tracking and affiliate revenue models in the eFactory ecosystem </td> </tr> <tr> <td> Formats </td> <td> Database entries </td> </tr> <tr> <td> Origins </td> <td> eFactory partners </td> </tr> <tr> <td> Size </td> <td> Depending on registered users </td> </tr> <tr> <td> Utility </td> <td> eFactory partners including the eFF and third-party organisation, marketplaces and platforms that aim to make use of developed applications and services </td> </tr> </table> <table> <tr> <th> eFactory Context </th> <th> Data Spine </th> </tr> <tr> <td> Description </td> <td> The Data Spine provides an open, platform-independent and secure communication infrastructure with interfaces for the loosely coupled platforms, tools and services (e.g. third-party marketplaces). According to the current design, the security framework associated with the Data Spine may store user data for authorisation and authentication purposes. It is not envisioned to store any other data. </td> </tr> <tr> <td> Purpose </td> <td> Management of user data (username, password, email address and/or phone number for password recovery) to offer the authorisation, authentication and user- management services such as those associated with the user single-sign-on functionality </td> </tr> <tr> <td> Formats </td> <td> Database entries and Data Spine source code (open-source) and related specification documents </td> </tr> <tr> <td> Origins </td> <td> eFactory partners, user communities of marketplaces, platforms and generally the users in the eFactory ecosystem </td> </tr> <tr> <td> Size </td> <td> Depending on registered users </td> </tr> <tr> <td> Utility </td> <td> eFactory partners including the eFF, third-party marketplaces and platforms along with their user-communities </td> </tr> </table> <table> <tr> <th> eFactory Context </th> <th> Dissemination and Promotion </th> </tr> <tr> <td> Description </td> <td> Dissemination material generated and provided by the eFactory consortium includes presentations, contributions and publications at domain-specific conferences and journals, software not covered by IPR as well as research data not affected by IPR or data privacy. </td> </tr> <tr> <td> Purpose </td> <td> Gain maximum awareness towards the eFactory project and its results as well as the eFactory ecosystem, including the eFactory Foundation </td> </tr> <tr> <td> Formats </td> <td> .docx, .pptx, .xlsx, .csv, .pdf Source code (open-source) and related specification documents </td> </tr> <tr> <td> Origins </td> <td> eFactory partners </td> </tr> <tr> <td> Size </td> <td> Typically <100MB </td> </tr> <tr> <td> Utility </td> <td> Interested public, eFactory partners, and/or the EC </td> </tr> </table> # 2 FAIR Data ## 2.1 Making Data Findable, Including Provisions for Metadata ### 2.1.1 Document Management This section introduces common procedures and practices that are used for handling various kinds of documents within eFactory common WebDAV repository “ownCloud”, the usage of the Microsoft OneDrive cloud storage, internal templates with its document metadata that supports them as well as the eFactory glossary. #### OwnCloud The eFactory document management approach aims at reducing the burden for project partners to synchronise, store, and locate documents. For this, the ownCloud solution for document management and storage is used, it is also referred to as synchronised file storage using the WebDAV protocol. It is similar in operation to the well-known Dropbox solution except that is self- hosted. This is convenient since it avoids issues associated with the geo- location of confidential material. OwnCloud is used within eFactory for the exchange and transfer of documents in progress and documents extensively used by all partners, e.g. the current version of the DOA or the eFactory templates. The ownCloud software is installed on servers of the eFactory project partner ASC, who is located in Germany. Access to ownCloud is personalised via a dedicated username and password. If it is necessary to share the ownCloud folder with further colleagues, the ICE Project Office needs to be contacted. A sample ownCloud folder structure is shown in the following figure. It follows a hierarchical approach , grouping horizontal documents like the Consortium Agreement, the Description of Action and templates as well as current and historical versions of work package related contents (subfolders such as “[Working]” and “[Final]” for the according documents). Figure 1: Sample ownCloud Folder Structure The following list briefly describes the intended content of each key folder: * CRITICAL: Critical documents for the project as mentioned above * Admin: Source versions of previous and current EU Contract (including DOA) and CA * Marketing and Templates: Logos, Graphics, Brochures, etc. and their sources * Meetings: Resources and results primarily for physical meetings such as plenaries * Reference Information: Important non-project document such as the Annotated Model Grant Agreement (AMGA) * Pictures and Fun: Pictures from eFactory related events * Work Packages: Contains subfolders for each work package and then within each subfolder, each task, and within each task there are subfolders for each deliverable. In addition to the access management on a solution level mentioned above, ownCloud does not offer an access rights model for individual folders. #### OneDrive Although ownCloud provides distributed sharing and allows offline editing in common office tools in a latency-free way, it does not support multiparty editing and the dealing of conflicts. Thus, a two-part solution is taken by eFactory, using Microsoft OneDrive cloud office solution based on Microsoft Excel spreadsheets for the recording of common financial or survey information. Each project partner is given a coded link to this repository and maintains the link securely such that only partners can access it. #### Document Templates In eFactory, Microsoft Word, Excel, and PowerPoint, as part of the Microsoft Office suite, are used for most documents. For Microsoft Word and PowerPoint, templates have been created and are available in ownCloud. To make sure that documents can be easily exchanged, all partners need to make use of at least Microsoft Office 2013. For all formal deliverables, and informal ones that are submitted to the EC, the Microsoft Word template is applied (file: “ eFactory Document Template xxx ”). Within eFactory it is also mandatory to make use of the eFactory Microsoft PowerPoint template for external presentations regarding eFactory – i.e. at non eFactory events and reviews meetings. It is preferred to use this for internal meetings as well. If eFactory is only a minor part of a presentation, e.g. to show the different projects a partner is involved in, it is _not_ mandatory to make use of the eFactory Microsoft PowerPoint template, but it should be considered (file: “ eFactory Presentation Template ”). ### 2.1.2 Document Metadata #### Deliverable Cover Page and Footer The Word deliverable template cover page defines certain styles that are then referenced via field codes in other parts of the document – e.g. the status information on page 2 and in page footers of this deliverable. This allows information to be entered once and automatically referenced correctly throughout the document. This includes information for WP/Deliverable ID, name status, etc. #### Deliverable Status Information The following states are used for deliverables: * Draft : The working versions of a deliverable, i.e. work in progress which is not ready for review yet * For EU Approval (implying Consortium Approved): A deliverable which has been accepted by the project-internal reviewers and is therefore sent to the EC (for approval) * EU Approved: A deliverable accepted by the EC and therefore ready for publication at the eFactory Website #### Naming Conventions and Versioning In general, file names need to be meaningful and unique, and they should include the word ‘eFactory’ at the start to distinguish from other projects. For deliverables, this means that the file name indicates the deliverable number, its version, and any further specific information: * Example: “EU-ID D104 – eFactory-ID D1.3.1a – Periodic Report (M6) – Draft - v0.9.0 - ICE” * General Format: “EU-ID D[N] – eFactory-ID D[N].[N][a] – [AAA][(Mx)] – [BBB] - v[M].[M].[M] [- CCC]” * The spaces (“ “) and hyphens (“-“) are critical parts of the structural format and must be used * EU-ID D[N] 🡪”EU-ID D104” The [N] represents the sequential number of the deliverable and which is used by the EU. This number can be found in the Budget XLS on “ownCloud/_CRITICAL…” During the drafting process, the editor should already include this ID * eFactory-ID D[N].[N][a] 🡪 “eFactory-D1.3.1a” is the first (“a”) formal release of deliverable D1.3.1. Note that the [a] indicator is only used if there are multiple versions of the same deliverable. Typically, these [a] versions are related to living or period deliverables * [AAA][Mx] 🡪 “Periodic Report (M6)” i.e. the name of the document and for iterative deliverables the Month of the deliverable * [BBB] 🡪 * “Draft” - Labelled draft until the document is ready for reviewer 1 * “Reviewer1[a]”: The first (“a”) version ready for the internal Reviewer1 of the deliverable * “Reviewer 2[a]” _:_ The first (“a”) version ready for the internal Reviewer2 of the deliverable * “For EU Approval”: The version of the document, which is submitted to the EC. For this (and subsequent versions) the version number is deleted * “Accepted” _:_ The version of the deliverable that has been accepted by the EC. It is published at the project Website if the deliverable is marked as public * v[M].[M].[M] 🡪 “v0.9.0” is the 9th major draft version 0.9.0 of the deliverable. It is better to number below “1.0” so that the final output of the consortium can be identified as “1.0” * [- CCC] 🡪 “- ICE” indicates a branch of the deliverable typically signified by a partner (e.g. ICE) or an individual’s acronym (UW). Branches should only be temporal documents, e.g. to decrease the risk of version conflicts as different partners may work on various parts of a document in parallel If generating a PDF (for example for the definitive version to the EC), e.g. from a Word document it should have the same filename as the original document except for the file extension (e.g. “pdf”). #### Microsoft Office Metadata Microsoft Office allows metadata properties for each document to be entered. In eFactory, the fields “Author” and “Title” are used. Usually, the author information is filled in automatically, provided the author (deliverable lead) stated the full name in the Word personalisation properties. The title needs to be filled in manually and should be the same as on the first page of a document. #### Deliverable Confidentiality Information (Dissemination Levels) There are two different dissemination levels for eFactory project deliverables: Public (PU) deliverables, which are potentially available to everybody and Confidential (CO) deliverables, which are available only for the members of the eFactory consortium. The dissemination levels of all eFactory deliverables have been defined within Table 3.2c of the eFactory DOA. Data Management Information regarding the dissemination levels must be marked in each deliverable as defined in the eFactory template. Furthermore, a brief description of the dissemination level and the logic for it needs to be given in section 0.6 (Document Status) of each deliverable. #### 2.1.3 Data Management eFactory Marketplace The extensible eFactory Marketplace framework offers the interlinking of multiple marketplaces from different platforms (i.e. NIMBLE, COMPOSITION, DIGICOR and vf-OS). The framework will provide components, which can easily be integrated in each platform to enable the access to external marketplaces. Furthermore, in order to enable an integrated affiliate model for supporting sustainable eFactory business models, an Accountancy Service (AS) is intended to gather tracking data from user journeys; The figure below provides a representative example: Figure 2: User journey example in the eFactory Marketplace Rather than creating a centralised marketplace from scratch, the marketplace framework in eFactory interlinks existing marketplaces, enabling users to access offered tools and services through a unified interface, which will be embedded in the eFactory Portal and other platform’s marketplaces. #### Standards and open / reusable metadata To access the different marketplaces, the eFactory Marketplace framework will access each external marketplace through the central component Data Spine. As each external marketplace provides different data structures, the Data Spine will provide a mechanism to implement the necessary metadata as well as the conversion logic between data models of different marketplaces. As a central aim in this context, the marketplace framework needs to handle minimum complexity in dealing with the offerings of multiple marketplaces as possible. ## 2.2 Making Data Openly Accessible ### 2.2.1 eFactory Data Spine The realisation of the eFactory Data Spine is envisioned to be based on an open-source technology . According to the current design, the security framework associated with the Data Spine will store user data, i.e. username, password (and most probably the email address for password recovery). It is not envisioned to store any other transactional data therefore the Data Spine does not provide any components for data storage and data management. Note: The eFactory Platform could provide components that allow data storage and management, however, the utilisation of these components (e.g. for analysis or decision support) will be solely on the discretion of the eFactory users. The Data Spine provides an open, platform-independent and secure communication and data exchange infrastructure with interfaces for the loosely coupled platforms, tools and services. This enables, for example, the analysis and fusion of real-time data to securely capture multi-tier supply chain intelligence. Clustering and propagation of business and supply chain intelligence will also be possible through the Data Spine. It will improve the competitiveness of the networked partner companies and increase the possibility for collaborations between organisations from different domains; allowing companies to share best practices and address dynamic market needs. The high-level architecture of the Data Spine, reflecting the approach described, is schematically shown in the following figure. Figure 3: eFactory Data Spine Architecture To give this ecosystem a maximum of flexibility, the tools and services interlinked within the eFactory platform are offered as far as possible as open-source resources under an Apache Licence (Version 2.0). This is already the IPR and licencing basis preferred in the four base platforms in eFactory. The Apache Licence only requires preservation of the copyright notice (attribution) but is otherwise permissive as it allows further use of the tools for any purpose, to distribute them, to modify them, and to distribute modified versions of the tools, under the terms of the license, without concern for royalties. The open-source nature of the eFactory tools also supports the strategic goal of co-creation of smart factory technologies. The adoption of permissive open-source licensing allows users to utilise the eFactory tools as standalone or combined/integrated functionalities. In addition, the eFactory platform provides open interfaces to the interoperable Data Spine, allowing the interconnectivity of eFactory platform with external platforms, tools and services. Moreover, a platform level SDK is developed – building upon the SDK from (EU H2020) vf-OS to enable the development, customisation and integration of smart factory applications. The SDK (in Task 5.5 of eFactory work program) provides a Studio environment with intuitive interfaces, integrated libraries, execution environment and connectors to industrial systems and data sources to enable prototyping, application development and testing. ### 2.2.2 Software Versioning and Revision Control System An eFactory instance of the open-source tool GitLab 1 has been installed and is to be used for all development activities. GitLab covers the full software development lifecycle from source code management over integrated bug tracking mechanisms and continuous integration support. As GitLab provides many optional modules covering all DevOps activities, during the project runtime it will be decided if additional functionality will be added to the eFactory GitLab instance. The source code of the open-source components (e.g. Data Spine) will be accessible in the project GitLab repository. Eventually, the open-source components to be developed during the eFactory project will be hosted in a publicly accessible software/code repository. ### 2.2.3 Dissemination of Results What concerns the dissemination of project results, the eFactory partners are fully aware about the open access policy that applies to scientific publications as stated in the Article 29.2 of the H2020 Grant Agreement Open Access to Scientific Publications. In this sense, all peer review publications arising from eFactory, will be made freely and openly available via an online repository and the project website. The actions which will be taken by the project are: * All presentations, contributions and publications even partially funded by the project will include the project logo, as well as the meta-data prescribed by the EC i.e. the acknowledgement of the grant agreement number, the term EU Horizon 2020, the name of the project, publication date and a persistent identifier * The publications funded by the project will be uploaded to some social network such as ResearchGate as well as open-access repository such as OpenAire (https://openaire.eu) and Zenodo (https://zenodo.org), and no later than 6 months after its original date of publication * Software not covered by IPR will be open source licensed and openly distributed (e.g. via Source Forge or GitHub) community * The open access to research data article (GA Article 29.3) will also be of application to eFactory. This will allow the consortium to: * Deposit all the data generated in the project (specially data used in scientific publications) not affected by IPR or data privacy issues in an open repository such as FIWARE-LAB or the relevant open data initiatives of the partners involved in the proposal * Provide information available at the repository about tools and instruments at the disposal of the beneficiaries and necessary for validating the eFactory results Moreover, appropriate presentation materials will be published at the project web site under a Creative Commons license. Some of the important industrial fairs in Europe are already being used (e.g. participation in AIX Expo, Paris Airshow and the IDSA Summit) to present the project results to a broad public. Partners will provide appropriate data and information to contribute towards project dissemination activities, which will be made visible through the project website and social media channels. ## 2.3 Making Data Interoperable ### 2.3.1 Overall Interoperability The rapid growth of smart manufacturing enterprises and digital manufacturing platforms around Europe raises challenges of interoperability and questions regarding the suitability of existing platforms to support agile collaborations needed for lot-size-one production, particularly in cross- sectorial scenarios. What concerns industrial data acquisition and processing, advancements in CPS and IoT technologies have resulted in the proliferation of new communication mechanisms and protocols that add to the complexity of handling real time data exchange and analysis. The use of proprietary technology for data transfer and the lack of adherence to standard protocols can hinder the realisation and smooth operations of connected factories. In its very centre, the eFactory project realises a federated smart factory ecosystem by initially interlinking four smart factory platforms, from the FoF-11-2016 cluster, through an open and interoperable Data Spine (see also Chapter 2.2.1). The federation of the four base platforms is complemented by industrial platforms, collaboration tools and smart factory systems, specifically selected to support connected factories in lot-size-one manufacturing. The figure below schematically shows the information/data flow achieved by the interoperation of available and emerging smart factory tools and services. Starting at the bottom layers, there are groups of manufacturing firms registered with the four base platforms or – as would be expected from an open ecosystem – firms associated with another, similarly targeted platform. Each of the four base platforms offer communication with external entities via open APIs that are not homogenized yet. Furthermore, as the case of further external platforms illustrates, there will not be a standardised cross- platform interoperation layer for some time to come. This brings us to the first important technical innovation of eFactory, the Data Spine: Figure 4: Technical Concept of eFactory The Data Spine interlinks the APIs of the participating platforms so that each platform’s functionality is visible and accessible at the level of eFactory. Overarching this, interoperable security features will give eFactory a layer of tools that can be used transparently at the platform and marketplace. The functionality of the eFactory Platform and Marketplace is thus composed of: * Selected services offered by each of the original component platforms present in eFactory * Services offered by any further platform that is willing to expose its API for alignment via the Data Spine * Third party apps that are offered directly via eFactory either as free or paid services * Dedicated management facilities to manage the governance, security and cloud deployment, etc. For the ecosystem of eFactory, many engagement options arise from the federated nature of the system: Manufacturers may * Connect directly to the eFactory platform * Develop new tools and services using the eFactory SDK * Use the marketplace to transparently use underlying services that may come from any of the participating platforms, with internal cross-billing managed by eFactory, in the case of commercial offerings ### 2.3.2 Platform Interoperability The interoperable Data Spine is the gluing mechanism that connects multiple tools, services and platforms to realise an integrated platform. Based on the identification of common standards and abstractions, the APIs, connectors and interfaces that need to be implemented for the tools, systems and platforms federated through the Data Spine are defined and realised within the project. The implementation of the eFactory Data Spine through open-source technologies will interlink and establish interoperability between - initially the existing deployments of four base platforms (COMPOSITION, DIGICOR, NIMBLE and vf-OS) along with their respective tools and services as schematically shown in the following figure. Figure 5: Federation of the 4 base platforms The interconnectivity of the four base platforms will be followed by the integration of other platforms (such as ValueChain’s iQluster, Siemens’s Mind Works, Fortiss’s Future Factory, C2K’s Industreweb) and standalone tools brought forward by the eFactory partners. Here, the Data Spine will enable the integration of third-party platforms through a modular plugin system. Data model conversions between two or more platforms will have to be handled by so called “Processing Flows” that have to be implemented in order to make the data interoperable between the platforms. The related generic flow of data is schematically shown below: Figure 6: Generic data flow between eFactory-connected platforms ### 2.3.3 External Interoperability The basic building blocks of the eFactory ecosystem are the individual tools, systems and platforms that are provided by different partners (and external entities) to the eFactory project. These tools, systems and platforms are interlinked through the Data Spine. In this respect, the tools, systems and platforms need to be able to communicate through the Data Spine technology, which means relevant interfaces and APIs to handle heterogeneous data will be defined during the eFactory project. What concerns the interoperability of smart factory tools and solutions are the open experiments performed (through funded call) with the focus on the enhancement of the eFactory platform e.g. through the integration of innovative solutions in the federation. The open experimentation in eFactory will include: * Experiments that integrate a 3rd party application in the eFactory platform, providing a validation scenario to demonstrate the seamless access and utilisation of the 3rd party system/application by eFactory services and users * Experiments that focus on the integration of 3rd party platforms with eFactory through agreements on security framework (e.g. single-sign-on, user authorisation, rights management etc) with the emphasis to provide eFactory users with wider access to Industry4.0 and digital manufacturing solutions ### 2.3.4 Message Interoperability In the context of eFactory Data Spine, data model interoperability corresponds to the ability to share information among partner messages and processes, as well as to trigger appropriate actions based on the events received from existing eFactory platforms. Considering the interoperability guidelines designed and developed in Task 3.2, the data model interoperability task aligns the data models of the federated platforms to support meaningful message exchange and viable business processes that spread across two or more of the existing eFactory platforms. The task utilises the proven methods and opensource tools for data-model alignment to establish synergies and resolve overlaps and conflicts between different data models. ### 2.3.5 Data Analytics eFactory enhances the data handling, analytics and interoperability modules from the four base platforms to (a) use/integrate them within the eFactory platform in a way to make them accessible to wider use-base in cross-platform scenarios, and (b) exposes them as analytic services that can be used in the pilots/ experiments in on-demand basis. The TRL of the existing analytic toolset (such as COMPOSITION’s Deep Learning toolkit and vf-OS’s Data Analytic services) is enhanced with the aim of capturing in-factory implicit data knowledge and providing the analytics that can help optimise the manufacturing processes. By deploying the analytic services as untrained plug-and-play applications, the eFactory platform will provide the means to analyse heterogeneous datasets and propagate meaningful information to dashboards and HMIs. The handling of the data by the analytic services will be the responsibility of the service providers – as typical in a federated ecosystem. The eFactory project will provide secure data storage service, if needed by the analytic services, to temporarily store the raw or analysed data from processes, shop-floor and manufacturing systems – see Section 4.3. However, no handling or analytics of sensitive data (e.g. personal details or data of high business value) is envisioned in the project. ## 2.4 Increase Data Re-Use In the eFactory project, the sharing and re-use of data for research and experimentation purposes will be determined by the data owner i.e. the entity that has the data under its jurisdiction. It is necessary to take into account that the data owner and data provider may not be the same entity. In line with EC’s interests, the eFactory project supports the exchange, sharing and re-use of non-personalised data through the Data Spine and other eFactory solution with the fair use policy that the data is used with consent of the owner. The data used for the validation of eFactory tools will be made available for use in further experimentation (e.g. open-calls) though an open-access repository. It is important to note that the eFactory project does not include any purely technological solutions to prevent the mis-use of data during or after the project lifetime. However, it supports these important aspects by putting in place the necessary authentication and authorisation checks that govern the access and (to certain extent) the utilisation of data stored in the eFactory platform. Furthermore, the project supports the development of collaborative solutions (within the project or through open-calls) and provides an appropriate technology infrastructure to address the data sovereignty and data protection issues. # 3 Allocation of Resources The management of data in the eFactory project is carried out through the provisioning of relevant tools and systems, as described in Section 2.1.1. These systems (such as OwnCloud) provide the required level of fairness towards data sharing, security and privacy. During the eFactory project, the data management systems (described in Section 2.1.1) are provided by the project partners as part of their commitment towards the project. The management of the data in the eFactory project is a collective activity of all partners, where the project manager takes the lead role of establishing the procedures and monitoring the utilisation of available infrastructure. The underlying infrastructure is maintained by the respective owners e.g. ASC is the owner of the OwnCloud document management system and therefore responsible for ensuring the continuous provisioning and quality of service of OwnCloud system. Similarly, the ownership of the other infrastructure e.g. Data Spine, Marketplace etc. will be defined during the course of project. The management of data is the responsibility of data owners who decide which data to share, with whom, for what purpose and under what conditions. The provisioning of data for research purposes is ensured by putting in place the relevant procedures (based on H2020 guidelines) and by using open-access repositories. This data will be limited to the purpose of the research and prototyping activities conducted within the scope of this project, in accordance with the data minimisation principle. If processing activities of the personal data is needed, an explicit confirmation will be put in place to make explicit that the beneficiary has lawful basis for the data processing and that the appropriate technical and organisational measures are in place to safeguard the rights of the data subjects. # 4 Data Security In Task 5.3, the eFactory team defines and implements data governance mechanisms, covering the following aspects (for more information see Section 6.2): * Information governance, a policy-based control of information to meet all legal, regulatory, risk, and business demands * Data governance, involving processes and controls to ensure that information at the data level is true, accurate, and unique (not redundant). It involves data cleansing to strip out corrupted, inaccurate, or extraneous data and de-duplication, to eliminate redundant occurrences of data For the security analytics in Task 6.2, some of the following open datasets will be considered: * https://github.com/defcom17/NSL_KDD * http://www.shubhamsaini.com/datasets.html * https://web.archive.org/web/20150205070216/http://nsl.cs.unb.ca/NSL-KDD/ ## 4.1 Regulation The project carefully analyses the implications of, and compliance with, the relevant regulations on data management and consumption. This includes ensuring compliance with GDPR (General Data Protection Regulation) 2 and NIS Direction (Directive on Security of Network and Information Systems) 3 . The tasks responsible for data storage (T4.3) and security framework (#T6.2) are the core activities concerned with the management of data and ensuring the compliance with relevant data security and privacy regulations. Furthermore, the eFactory Consortium Agreement explicitly states that the project partners are GDPR compliant. ## 4.2 Data Integrity and Quality Based on GDPR requirements, the following security controls are addressed within eFactory in the context of data integrity and quality. * Data input validation : Controls over various factors like predictable behaviour, manual override, timing, etc. corresponding to the Data Quality Principle and the GDPR requirement for verifying sensitive data for its accuracy, completeness and for being up-to-date * Data and metadata protection : Protection against unauthorised access and manipulation, automated restricted access and cryptographic protection for supporting subject’s requests to access personal data and deletion of personal data and/or personal data modification * Data protection at rest : Cryptographic protection and off-line storage (GDPR requirement for deletion and/or modification of personal data by the data subject) * Data protection in shared resources : Cryptographic protection (GDPR requirement for deletion of personal data and/or personal data modification by the data subject) * Notification of data integrity violations : Monitoring services for detecting, reporting and investigating personal data breaches as well as for reviewing existing privacy notices and keeping them up-to-date * Informed consent by design : User must have an informed consent on the data usage, which prevents the use of data in a way that is not according to the user wish (GDPR requirement for implementing privacy procedures for seeking, recording, and managing user’s consent) ## 4.3 Data Storage Data gathered from shop-floors (Task 4.1) and analysed data (Task 4.2) is stored in a secure data-store that will be made available as docker containers, allowing users to deploy the container on the cloud or deploy on premise. Access to the data storage is secured such that only authenticated (using the single-sign on credentials) and authorised persons within the federation are granted access. These data protection mechanisms ensure fine- grained access control based upon the User Managed Access (UMA) standard, where the data owners can themselves control who can use the data (even when this is stored in the cloud). Privacy enforcing mechanisms are utilised to ensure that stored personal data (if any) complies with privacy regulations (in particular, GDPR), e.g., access to any personal data in the store must follow informed consent. The data storage may also store and disclose personal data in pseudonymised data sets – the data store provides support to developers to convert data sets to a pseudonymised format (where personal data is involved). Moreover, tools are also created to evaluate the extent sensitive personal data is at risk of disclosure using the chosen form of pseudonymisation and it is ensured that cross federation security and privacy is achieved in a holistic end-to-end manner. ## 4.4 Data Privacy The eFactory project pays specific emphasis on data privacy by putting in place procedures where parties attempting to access information must be authenticated (confirming their identity) and authorised (confirming they have permission from the data owner for access). During the project data confidentiality is maintained, whereby access to data is revealed only to authorised parties. Within Task 4.3 (Secure Data Storage Solution), data owners can configure access to stored data using the User Managed Access (UMA) protocol standard, which works in conjunction with OAuth to authentication user identities. For this purpose, a holistic (platform level) framework for security, privacy and management of data, as well as users on the eFactory platform, is developed within the eFactory project Task 6.2. In terms of data and information security, the framework specifies and implements the protocols that ensure eFactory’s (i.e. interlinked platform, systems and tools) compliance with relevant cybersecurity and privacy mechanisms. This includes mechanisms and standards related to data security (e.g. encryption, cryptography) and privacy (e.g. GDPR and NIS). In terms of user management, the framework ensures that the eFactory users have seamless access to the integrated resources while satisfying the security and privacy concerns of users are satisfied. A preliminary study of the 4 base platforms identified a common set of security protocols and standards (e.g. OpenID Connect, OAuth2.0 and SAML 2.0) that are being used across them. The open-source solutions KeyCloak and WSO2 have been identified as extensible solutions that implement those open protocols and standards to provide delegated identity management and role-based access management. These technology implementations provide foundations for centralised access and security infrastructure for the eFactory platform. In addition, the standardised data encryption and cryptography techniques used in base platforms are tuned to work in conjunction to ensure security and privacy of data exchanged through eFactory. Moreover, during the eFactory project continuous checks are done so that the interconnected platforms, systems and tools in the federation adhere to the holistic security and privacy concepts. ## 4.5 Federated Identity Management To access the administrative environment and for the separation of duties, eFactory uses the Federated Identity Management, which includes: * Single Sign On (SSO): It replaces various passwords with a single set of enterprise credentials and provides a consistent authentication experience * Access security: It centralises access control with a policy-driven security layer for all apps and APIs When it comes to the integrating of diverse platforms the priority is given to standardised and modern technologies for identification, authorisation and authentication methods. Furthermore, the registration is clearly separated from the access to resources and backup authentication methods are put in place. The following figure shows the outline of the Security, Privacy & User Management framework in eFactory: Figure 7: Security, Privacy & User Management Framework ## 4.6 Blockchain Approach for Secure Data Exchange The blockchain approach is currently being tested in several application domains, including financial services, eHealth and supply chain management. For traceability in supply chains, blockchain can be used to provide an audit trail for products and their associated manufacturing and supply chain data. Blockchain and Distributed Ledger Technologies (DLTs) stand to offer an end- to-end accountancy mechanism that can facilitate product data integration, services interoperability, cost-effectiveness and increased trust in supply and value chain management. Towards this goal, companies such as IBM, Oracle, and SAP are building their blockchain platforms on Hyperledger, a blockchain technology more suitable to building business applications. Microsoft Azure, Amazon AWS and IBM have all started offering blockchain as a service to streamline the adoption of the technology and its applicability in several fields. While the most prominent use of blockchain is in cryptocurrencies, such as Bitcoin, it can be used for in several applications such as fulfilment, agreements/contracts, tracking and, of course, payments. The value it offers is inherent to the technology, which is essentially a distributed ledger of transactions kept on cryptographically protected blocks. As such transactions across multiple parties, protected by security and privacy layer, are immutable offering transparency and trust in supply chain management. eFactory leverages blockchain technology to ensure trust, security and automated exchange of supply chain data among all authorised actors. The goal is to ensure the origin, quality, compliance and appropriate handling of data/documents tracked throughout connected factories, while supporting interoperability and product traceability. The eFactory blockchain service realised in Task 5.4 is sectoral agnostic to serve cross-sectorial stakeholders (production, distribution, customers, etc.). As a federation level solution, no single entity owns the process of Blockchain but all stakeholders can access and use Blockchain as a Service Platform. # 5 Ethical Aspects eFactory does not introduce any critical ethical issues or problems. However, several considerations typical to ICT and on-site industrial trials, where employees are also involved in the demonstration and evaluation stages, shall be considered. The consortium is fully aware of these and has the necessary experience to address them seamlessly as summarised below. ## 5.1 Legal Framework eFactory proposed solutions do 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 the industrial pilots planned in Greece, Germany, and Spain. Furthermore, the eFactory consortium considers during the project lifetime 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 are given all due attention. eFactory addresses any ethical and other privacy issues in Task 1.4 for 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. Besides these general conditions, the consortium is aware that a number of privacy and data protection issues could be raised by the activities (i.e. in all pilots planned in WP9 activities) to be performed in the scope of the project. The project involves the carrying out of data collection in all industrial pilots and trials in order to assess the technology and effectiveness of the proposed smart factory and digital manufacturing solutions. For this reason, if any human participants are needed to be involved in certain aspects of the project, then it will be done in full compliance of any European and national legislation and directives relevant to the country where the data collections are taking place (International/European). The eFactory partners found the following regulations to be relevant and considered when dealing with personal data: * The Universal Declaration of Human Rights 4 and the Convention 108 5 for the Protection of Individuals with Regard to Automatic Processing of Personal Data * Directive 95/46/EC 6 & Directive 2002/58/EC 7 of the European parliament regarding issues with privacy and protection of personal data and the free movement of such data. Specifically, when dealing with personal data the eFactory partners will observe the following guidelines: * Unnecessary personal data collection is avoided (for example, unless it is absolutely required for security or it constitutes the nature of a research study, there is no collection of personal details, identities, bio-identification data at registration to eFactory software systems foreseen, i.e. nick names can used instead of real names whenever possible) * The personal data needed for statistical analysis is collected anonymously, i.e. without association with the names of individuals; * Any personal data is collected only with the explicit permission of the individuals in question * The personal data collected is treated confidentially and carefully (taking proper technical means of information protection, e.g. storing general and personal data separately, using encryption for personal data and identities, deleting personal data when it becomes unnecessary) * Individuals are given the right to access their personal data and the analysis and user models made based on it To further ensure that the fundamental human rights and privacy needs of participants are met whilst they take part in the project, in the evaluation plans a dedicated section will be delivered for providing ethical and privacy guidelines for the execution of the industrial trials. In order to protect the privacy rights of participants, a number of best practice principles are followed. They include: * Data is not 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 is sold or used for any purposes other than the current project * A data minimisation policy is applied at all levels of the project and is supervised by each Industrial Pilot Demonstration responsible. This ensures that no data which is not strictly necessary to the completion of the current study is collected * Any shadow (ancillary) personal data obtained during the course of the research is immediately deleted. However, the ultimate plan is to minimise this kind of ancillary data as much as possible. Special attention is also paid to comply with the Council of Europe Committee of Ministers Recommendation R(87)15 on regulating the use personal data in the police sector, 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 is prohibited and is not done within the project * 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 is paid to avoid any form of unfair inducement * If employees of partner organisations 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 The pilot implementation activities (Task 9.1 –Task 9.4) are performed in three European countries under the leadership of the pilot coordinating partner. Below the relevant national legislation for the countries involved in the pilot is outlined: Greek Pilot (Kleemann, ELDIA, MilOil): * 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/ German Pilot (Airbus, Innovint Aircraft Interior GmbH, Walter Otto Müller GmbH & Co.KG, AM Allied Maintenance GmbH): * Federal Commissioner for Data Protection and Freedom of Information (https://www.bfdi.bund.de/DE/Home/home_node.html) * Data protection authorities for its various states (https://www.ldi.nrw.de/mainmenu_Service/submenu_Links/Inhalt2/Aufsichtsbehoerd en/Aufsichtsbehoerden.php ) Spain Pilot (AIDIMME, LAGRAMA): * Organic Law 3/2018, of December 5 th , of Personal Data Protection and guarantee of the digital rights (https://www.boe.es/eli/es/lo/2018/12/05/3) * Law 34/2002, of July 11 th , of services of the information society and electronic commerce (https://www.boe.es/eli/es/l/2002/07/11/34/con) * Law 9/2014, of May 9 th , General on Telecommunications. (https://www.boe.es/eli/es/l/2014/05/09/9/con) In addition to the relevant national legislation, the main EU and international policy documents that are relevant to eFactory are listed below: * Charter of Fundamental Rights of the European Union * European Convention for the Protection of Human Rights and Fundamental Freedoms * 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 * Directive 2000/58/EC concerning the processing of personal data and the protection of privacy in the electronic communications sector (Directive on privacy and electronic communications) * Directive 2002/58/EC on data protection ## 5.2 Risks and Related Measures The table below summarises the ethical risks identified related to eFactory activities. Within WP1 (Task 1.4), such risks are further elaborated prior the execution of the industrial pilots and results will be included in the corresponding reports of WP9. <table> <tr> <th> No. </th> <th> Ethical Risk </th> <th> Description of Risk </th> <th> Foreseen Risk Management Measures </th> </tr> <tr> <td> 1 </td> <td> Data Security </td> <td> Difficulty in ensuring the security of shared personal data in the trials. </td> <td> Special attention will be given to ensure confidentiality and for incorporating privacy enhancing technologies (pseudoanonymisation, etc.) to ensure protection from data breaches. eFactory partners have the capacity and the experience to cope with the delivery of security mechanisms, if needed. </td> </tr> <tr> <td> 2 </td> <td> Storage and process of personal data, Confidentiality </td> <td> Measurements from various sensors will be transmitted wirelessly. Difficulty in ensuring the security of privacy-related data collected before and/or during the execution of the trials. </td> <td> CERTH have the expertise and the know-how from similar past and ongoing research projects, towards providing the necessary ethical guidelines that should be adopted during the execution of the trials. Local ethical committee (and the National committee, if needed) will be informed towards getting an official permission for the execution of the selected trials. </td> </tr> <tr> <td> 3 </td> <td> Loss of Privacy Control </td> <td> Storage and process of privacy-related data towards the validation of </td> <td> For activities related to the factory optimisation, existing data will be initially categorised and only those that are not exposing privacy or ethical issues will be </td> </tr> <tr> <td> </td> <td> </td> <td> the eFactory integrated tools in the selected trials. </td> <td> utilised. In any case, if needed for conducting the research activities of the project, records or data dealing with privacy will anonymised and will be totally destroyed after the research study. Always, the data management policy will take care that such activities are not forbidden by law of the country in which the information was collected, stored and analysed. </td> </tr> <tr> <td> 4 </td> <td> Delegation of Control Privacy Incidental Findings </td> <td> Need to notify proper trial authorities. </td> <td> Within Task 1.4, a sub-activity has been included to address local and European legislation. In that context, all the pilots will be performed according to them and relevant data protection authorities will be informed on time. </td> </tr> <tr> <td> 5 </td> <td> Lack of Transparency </td> <td> Work of professionals (Workers, Employees in selected trials, etc.). </td> <td> An ethics manual will be delivered for each of the trials towards all activities performed to be in compliance with National and European legislation. Prior the execution of the pilots the local ethical committees will be informed for any data analysis or collection needed, as part of the eFactory Evaluation and the necessary documents will be created by the respective Industrial Pilot Responsible in order to get an ethical approval. </td> </tr> </table> Summarising, privacy-related issues within the eFactory project are related to: * Concerns arising from the project’s activities and fields of implementation (use of existing data or newly collected information through the shop floor involving human activities or confidential information dealing with enterprise performance) * Privacy protection and confidentiality of volunteers for the shop-floor data analysis and potential new collection during the industrial trials. Here, special guidelines will be delivered in the ethics manual of eFactory and informed consent will be created for the implied data utilisation by requesting all involved persons to read, be informed and sign the appropriate forms # 6 Other Issues ## 6.1 Data Protection In the course of the entire project, the fundamental rights of data protection and the right to privacy of the volunteer research participants will be strictly followed. Furthermore, the developments and tests performed within eFactory project life will observe the Charter of Fundamental Rights of the European Union 11 (2000/C 364/01). The following articles of this Charter apply directly to this project: * Article 1: Human dignity is inviolable. It must be respected and protected * Article 7: Everyone has the right to respect for his or her private and family life, home and communications * Article 8.1: Everyone has the right to the protection of personal data concerning him or her * Article 8.2: 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. Everyone has the right of access to data, which has been collected concerning him or her, and the right to have it rectified * Article 8.3: Compliance with these rules shall be subject to control by an independent authority – in this case this responsibility lies with the eFactory Project Manager (ICE) * Article 23: Equality between men and women must be ensured in all areas, including employment, work and pay. The principle of equality shall not prevent the maintenance or adoption of measures providing for specific advantages in favour of the underrepresented sex ## 6.2 Governance Rules and Trust Mechanisms Task 5.3 of the eFactory project sets-up a formal model of distributed collaborative activities where each activity is defined by its contribution to the overall goal in a recursive approach. Formal contracts gather the way in which companies are given responsibility for activities, and ensure the results of the activity conform to the relevant regulations. The contracting framework also covers the process used by a company to implement its activity, ensuring compliance at process and at results level. Using this theoretical model, the requirements for relevant regulations, smart contracting mechanisms, secure message exchange, company sourcing, monitoring protocols and coordination mechanisms are developed, ensuring support for regulation compliance and trusted distributed and coordination of activities. Based on these activities, the following governance rules and trust mechanisms are implemented. ### Information level • Policy based control of information to meet legal, regulatory and business demands ### IT level • Aligning IT efforts with the business objectives of eFactory ### Data level * Ensuring that data are accurate and true * Eliminating corrupted and inaccurate data (data cleansing) * Eliminating redundant data (de-duplication) * Ensuring security controls for data integrity and quality	https://phaidra.univie.ac.at/o:1140797	Horizon 2020
0118_StarFormMapper_687528.md	Introduction # 1.1 Scope This document is the deliverable # D7.5 – “Data Management Plan - Update” for the EU H2020 (COMPET-5-2015–Space) project “ A Gaia and Herschel Study of the Density Distribution and Evolution of Young Massive Star Clusters ” (Grant Agreement Number: 687528 ), acronym: StarFormMapper (SFM) project. 2\. Description of Work WP 7, “Data Management and Curation”, is aimed at the provision of central storage for data associated with the project, together with its public access. In addition, the documentation and metadata required for full access will be properly described. # 2.1 Server Status Update At the time of the last update to the Data Management Plan (hereafer DMP) contained in the deliverable D7.5 (submitted in September 2018), our intention was still to follow the scheme outlined in our initial DMP (deliverable D7.1). That scheme outlined: “ _The project has allowed for separate servers at the Leeds and Madrid nodes, which are now fully installed and functional. These will provide backup to each other.”_ The intention was that these two servers would serve any data gathered by the project, as well as acting as the gateway to any online resources developed during the project. We still intend that the servers will carry out the former task. However, the success of Quasar as a company , and the development by them of their DEAVI server/client architecture, and in particular its approval as a suitable Added Value Interface to sit near to the actual ESA archives at ESAC, somewhat negates the need for the latter. We will deal with this in detail below. First, we consider the current state of the servers and outline exactly what we will provide on them. In the last period, we noted that: _The Leeds "data repository/backup" is functioning but due to changes in IT staffing and management is not yet available as an external facing resource. We cannot at the moment give a specific timing for this to happen, as the staff required to set it up are beyond our control, as are the specific details as to how this will be provided._ The last review requested that we provide an appropriate backup plan in the event that this situation did not improve. The approved scheme was that the Leeds server be transferred to Cardiff, who would provide the capability to install the sofware required to provide this external facing service. The transfer has now occurred (though as with all things related to Leeds IT currently, rather late since this occurred in early Sep 19) and the required setup is underway. It is still our intention to make publically available all data gathered for the project, together with appropriate descriptions and metadata. We can now be clearer about what these data encompass. First, all of the simulations developed at Cardiff will be provided to the community through the web, using a detailed front end that provides filtering according to type of simulation and the initial parameters etc. Secondly, we have acquired data for the star formation region NGC2264 with both the JVLA and CFHT. The first of these datasets will be analysed before the end of the project and the reduced data products made available from this server as well. The second of these datasets is unlikely to be fully analysed but we will provide it “as is” at the project completion, and update this in time afer the project end. We note that we still promise to provide these data to the community as a resource until ten years afer the project inception (see 2.3 below). The Madrid servers are fully functional and have been providing simulation data to the consortium for over a year now. These data are also now public, as described in D3.4. # 2.2 Archive Data Analysis Update The Quasar servers are running the Docker s/w that allows us to interface with their developing toolset. This is part of the final adopted access protocol for the project which will eventually become public. Testing of this has proved the basic methodology. It was our original intention that these services would be provided through our own servers going forward. We no longer feel that there is adequate time to do this with the server transferred to Cardiff, as it will need to be set up again from scratch. However, since our last update, Quasar have been successful in fully testing and deploying their client/server architecture (described in deliverables WP 4.3, 4.4, 4.10 and 4.12), both locally to them but more impressively as an add-on to ESA’s archive services through their GAVIP platform. In addition, Quasar as a company have been successful in expanding, and have a much clearer long term future than at the start of the project. This therefore opens up the option of a new route to our goal of a server that can be used to apply our sofware to the ESA archives. * First, Quasar are now able to commit themselves to being part of the long term data access solution, since the framework they have developed is used for other projects they are working on. * Second, the success of the GAVIP trial allows for the possibility that the sofware can also be run there, nearer to the archive, which was one of our original goals. We cannot commit ESA to supporting GAVIP obviously, so our primary supported access will be through Quasar. We aim to demonstrate fully the state of both the GAVIP and Quasar service before we submit the deliverable D7.2 which has been delayed due to the IT issues at Leeds. Our intention is to submit this by the end of this calendar year. # 2.3 Fair Data Update There are no changes to the availability, openness, re-use provisions or the requirement for making data findable. The only modification is on the item: _“In particular, the University of Leeds will commit to hosting the server mentioned in Section 2 for a period of at least 10 years.”_ Obviously this requirement now devolves to Cardiff. # 2.4 Data Security Update Now that we have a feasible plan for supporting the server in Cardiff, longer term viability for the servers at Quasar, and for deploying our algorithms through GAVIP, we can be confident on the longer term data security. Both Quasar SR and the University of Cardiff will now work together to ensure that as a minimum first step the server transferred to Cardiff provides a backup facility to the data stored in Madrid, whether simulation data, observational data, or project records, sofware, webpages etc. This is within the control of the project.	https://phaidra.univie.ac.at/o:1140797	Horizon 2020
0122_A-WEAR_813278.md	1. Executive Summary This document comprises deliverable D1.2 Data Management and Quality Assurance Plan of WP1 Management Working Package. The document is prepared according to “H2020 templates: Data management plan v1.0 – 13.10.2016”. In general terms, the research data is aimed to follow the 'FAIR' principle that refers to f indable, a ccessible, i nteroperable, and r e-usable data. A-WEAR will participate in the Open Research Data Pilot of Horizon 2020 and hence will make the research data publicly available. This document is a living document in which information can be made available on a finer level of granularity through updates as the implementation of the A-WEAR project progresses and when significant changes occur. This document would be updated in the context of the periodic evaluation/assessment of the project if any changes appear. 2. Partners This section provides a list of A-WEAR partners and corresponding abbreviations. We remark that the abbreviations are used here only for the sake of compactness, but they may not be affiliated with or reflect company’s official abbreviation. Table 2-1 A-WEAR Beneficiaries <table> <tr> <th> Consortium Member </th> <th> Legal Entity Short Name </th> <th> Country </th> </tr> <tr> <td> TAU Tampere University (formerly Tampere University of Technology) </td> <td> TAU (formerly TUT) </td> <td> Finland </td> </tr> <tr> <td> UJI Universitat Jaume I de Castellon </td> <td> UJI </td> <td> Spain </td> </tr> <tr> <td> BUT Brno University of Technology </td> <td> BUT </td> <td> Czech Republic </td> </tr> <tr> <td> UPB University “Politehnica” of Bucharest </td> <td> UPB </td> <td> Romania </td> </tr> <tr> <td> URC Universita Mediterranea di Reggio Calabria </td> <td> UNIRC </td> <td> Italy </td> </tr> </table> # Table 2-2 A-WEAR partner organizations <table> <tr> <th> Partner Organization </th> <th> Legal Entity Short Name </th> <th> Country </th> </tr> <tr> <td> NET Netcope technologies </td> <td> Netcope </td> <td> Czech Republic </td> </tr> <tr> <td> CIT CITST </td> <td> CITST </td> <td> Romania </td> </tr> <tr> <td> NXP NXP Semiconductors </td> <td> NXP </td> <td> Romania </td> </tr> <tr> <td> WPS Wirepas </td> <td> Wirepas </td> <td> Finland </td> </tr> <tr> <td> DLI Digital Living International Oy </td> <td> DLI </td> <td> Finland </td> </tr> <tr> <td> BEIA Beia Consult International </td> <td> BEIA </td> <td> Romania </td> </tr> <tr> <td> S2G S2 Grupo </td> <td> S2 GRUPO </td> <td> Spain </td> </tr> <tr> <td> ERI Ericsson </td> <td> Ericsson </td> <td> Finland </td> </tr> <tr> <td> CPD City of Castellón, police department </td> <td> \- </td> <td> Spain </td> </tr> <tr> <td> IDOM IDOM Consulting, Engineering, Architecture S.A.U. </td> <td> IDOM </td> <td> Spain </td> </tr> <tr> <td> SWO Sewio Networks </td> <td> SEWIO </td> <td> Czech Republic </td> </tr> <tr> <td> T6E T6 Ecosystems </td> <td> T6-ECO </td> <td> Italy </td> </tr> </table> The Working Package (WP) structure of the project is illustrated below. Figure 1 Working Packages in A-WEAR 3. Data Management Plan A-WEAR is part of a flexible pilot under Horizon 2020 called the Open Research Data Pilot (ORD pilot). The ORD pilot aims to improve and maximize access to and re-use of research data generated by Horizon 2020 projects and it 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. 1. Data Summary 1. Data Purpose All technical WPs, namely WP2-WP5 (see Figure 1) will rely on various data types (simulated, measurements-based, etc.) in order to analyze, verify, test, and improve the developed algorithms and methods. 2. Data Types and Formats The data will consist of software data developed by the (e.g., based on Matlab, C/C++, Python, VHDL, Java, Android OS, Wear OS, etc.), raw data measurements from the field experiments and testbed campaigns, mathematical and statistical models, and channel traces and context-awareness metrics in time, frequency or space domains. Data types can include multidimensional time-series, structured data, and unstructured data – such as image analysis, video analysis, audio analysis and machine generated data analysis. The data types specific to eHealth studies in WP3 will be electronic health records, clinical data based on HL7 standard, DICOM files. 3. Data Re-using Open-access datasets and other open-access data might also be used for the scope of A-WEAR research. The main open-access repositories that we plan to use are: 1. EU Zenodo repository ( _www.zenodo.org_ ) : it contains research papers, datasets with measurements, software tools (Matlab, Python, etc.), etc. 2. EU OpenAIRE ( _https://www.openaire.eu/_ ) is the EU emerging repository for open science. We remark that the metadata records of the published data sets on Zenodo can be easily loaded into the OpenAIRE platform. 3. EU Open Data Portal ( _https://data.europa.eu/euodp/en/home_ ) : a repository of an expanding range of data from the European Union institutions and other EU bodies 4. Github repository ( _https://github.com/_ ) : it mostly contains software tools, but also reduced-scope datasets with measurements are available. Github and and Zenodo are tightly coupled and publishing majors releases of data in Zenodo from GitHub is trivial (almost automated). 5. Crawdad ( _https://crawdad.org/_ ) is an archive for wireless datasets at Dartmouth university 6. ArXiv ( _https://arxiv.org/_ ) is an archive of pre-print publications and unpublished work by research community in various fields (ICT, physics, mathematics, etc.) 7. Stanford Large Network Dataset Collection ( _https://snap.stanford.edu/data/#email_ ) is a library of relevant datasets for research on large social and information networks 8. Kaggle ( _https://www.kaggle.com/datasets_ ) : it provides a large collection of datasets and models in various areas, such as health domain (e.g., physiological parameters), demographics, data visualization tools, etc. 9. CodeOcean ( _https://codeocean.com_ ) is a collection of scientific codes (software) associated to papers published in IEEE venues, with the target of making the research results reproductible and reusable 10. UC Irvine machine learning repository ( _https://archive.ics.uci.edu/ml/index.php)_ 11. Finnish open data ( _https://www.avoindata.fi/en_ ) is a repository of open data sets from Finnish R&D units, covering all vertical industries, such as smart cities, agriculture, energy, health, etc. 12. US government open data ( _https://www.data.gov/_ ) is a huge repository of data, software tools, and other resources with the purpose to help the research in various areas and to develop web and mobile applications 13. Romanian government open data ( _http://data.gov.ro/_ ) is a Romanian repository of open-access data collected from public administration institutions in Romania All A-WEAR researchers will be highly encouraged to add all their publications (and other relevant material) on Zenodo, in addition to other institutional or personal repositories. In addition to the above-mentioned open-access repositories, an open health data repository will be available soon (managed by the Finnish National Institute for health and Welfare), as an “Act on the Secondary Use of Health and Social Data” has come to effect in Finland 1 since 1 May 2019. The available open-source datasets can be used in various manners, such as: 14. benchmark data to test the developed algorithms in A-WEAR; 15. benchmark unlabeled (blind) data to organize competitions in A-WEAR or other events (e.g., at IPIN annual events); 16. benchmark software codes to compare the developed algorithms with existing state-of-the-art from scientific literature; 17. benchmark calibration scenarios and parameters for the purposes of cross-validation; 18. PhysioNet offers free web access to large collections of recorded physiologic signals (PhysioBank) and related open-source software (PhysioToolkit). 4. Data Origin 3.1.5. Data origin at Beneficiaries The data will be collected utilizing the hardware (HW) existing in the A-WEAR team, such as Arduino, Raspberry Pi-s, wireless sensor/actuator devices of various nature, RFID systems, Intel Galileo dedicated to medical devices, brain computer interface systems, and Artix-7 development boards, as well as software (SW) tools available on mobile devices (e.g. via Google play for Android devices) or developed by the AWEAR team, software tools for network traffic analyses (such as Wireshark or similar), external open source software tools available online. In order to attain A-WEAR objectives, large or massive wearable data might be collected through crowdsensing approaches for the purpose of social and consumer applications, such as eHealth and public safety (e.g., for studies in WP3). Crowdsensed data can be either stored/processed in the aggregated manner, or represented in a user-tailored form according to the target application. The eHealth data used in our studies come from PhysioBank that offers a variety of medical signals and data. Also, some data come from different paraclinical investigation obtained from emergency hospitals of Bucharest based on signed protocols. During our future experiments, data collection will also contain information coming from the wearable healthcare monitoring systems that may consist of various types of biosensors. These sensors are measuring significant physiological parameters such as blood pressure, electrocardiogram (ECG), muscle electromyography (EMG), oxygen in the blood, body temperature and patient position. Sensors found or planned on top android devices will be: Accelerometer, Gyroscope, Magnetomete, Barometric pressure sensor, ambient temperature, Heart rate monitor, Oxymetry sensor, Skin conductance sensor, skin temperature sensor, blood glucose, wrist force sensors, ECG. Some sports sensors (bicycle) will also be considered: speed, cadence, power, heart rate. The cloud context-aware platform will be based on the UJI’s experience in open sensor platforms for the smart city context. Crowdsourcing positioning data will be collected and used for reduced training efforts and for the study of the device-free localization methods (e.g., addressed in WP4). Data origin for WP5 studies will rely on the different laboratory as well as field measurement campaigns, where the data will be collected via a variety of personal wearable devices (e.g. AR/VR glasses, smart watches, smart wristband, etc.) as well as industrial sensors (e.g. electricity meters, environmental sensors, etc.). The research will have to tackle the analysis of various data types including multidimensional timeseries, structured data, unstructured data – such as image analysis, video analysis, audio analysis and machine generated data analysis. 3.1.6. Data origin at Partner Organisations During the secondment the researcher might have access to data sets which are provided for commercial purposes and property of the partner organisations or the third parties. In these cases, the access rights and the use of the data for scientific purposes will be agreed separate. In any case, opening the data sets and supporting the researchers’ possibilities to research and graduate will be targeted, as well as extend their skills for advanced career possibilities. Always, when possible these data sets will be opened to allow the maximum exploitation of the outcome of the action as well for further scientific purposes beyond the action as for the commercialization by the industry. 3.1.7. Estimated Data Sizes The data sizes are expected to be on average below 100 GB of data/year), however occasionally huge amount of data may be captured via USRPs measurements with high sampling frequencies and such data can easily reach few snapshots of 100GB or more in size (e.g, hours of data collected at high sampling frequencies which might be relevant for the 5G studies of ESR4 may require in excess of several hundred GB of storage, also smaprtphone data such as gyroscope data, accelerometer data, WiFi and BLE data, etc. typically can reach about 150 MB/hour in uncompressed form). 3.1.8. Data Utility The main bulk of generated data to be made available will be SW and measurements data. Sharing platform for the consortium data is the Microsoft OneDrive and the main sharing platforms for the open-source data to be created in A-WEAR are the EU Zenodo and GitHub. As parts of the university infrastructure at TAU, the vital components of hosting and sharing the data are expected to stay in place in the long term (i.e., minimum15 years after the project’s end), thus ensuring continued access to the collected data. 3.1.9. Data sharing across the consortium Data not containing any personal information and not raising any ethical concerns (e.g., such as simulated data) will be shared between the Consortium units on-a-need basis. The sharing of any other data types across the consortium will be based on anonymity of data. Where partners require access to data to enable a synthesis of findings across studies, this will be provided in strongly anonymized form only. We will comply with the EC Data Protection Directive 2 and its newer amendments 3 at all steps in our project and after its end. 3.2. FAIR data 3.2.1. Making data findable, including provisions for metadata A-WEAR project will follow the EU FAIR data guiding principles in order to make it Findable, Accessible, Interoperable, and Reusable. A-WEAR goals are to follow FORCE11 FAIR data principles 4 where possible: Metadata to be created within A-WEAR refers to any summaries and documentations about data to be produced within the project (measurements, SW, simulated data, analytical/mathematical model, etc.), publications, conference slides, workshop presentations, etc. Most metadata will be available through the project deliverables and open-access publications, as well as through the project dissemination channels (webpage, blog, Twitter, Youtube channels, etc.). We will be guided by the following FAIR principles: In order to make the data findable in A-WEAR, the following procedures will be followed: 19. Clear version numbers will be used for the project deliverables. 20. The target publication venues of A-WEAR are open-access peer-reviewed journals and conferences, due to wide dissemination opportunities. Those are easy to access online and offer a simple keyword, author or DOI (digital object identifier) search through their homepages or publication search engines such as “sciencedirect.com” or “scopus.com”. 21. standard metadata generated for publications (Springer, SCOPUS, ISI, etc). 22. standard metadata for software on GIT. 3.2.2. Making data openly accessible 27 out of 35 A-WEAR deliverables are to be open-access deliverables. Relevant measurement data and simulation-based data generated within A-WEAR project will also be made available at least on Zenodo open-access repository, and possibly to the Beneficiaries’ relevant webpages. Relevant software codes developed with AWEAR will be made available at least on GitHub repository, and possibly to the Beneficiaries’ webpages. The relevance will be agreed upon by discussions within the Advisory Board of AWEAR, by considering the following aspects: 23. the open-access data must be useful for the research community at large, e.g., by providing valuable benchmark solutions (SW, measurements, etc.). 24. the open-data might have a reasonable tradeoff between its size and its potential usefulness (e.g., huge datasets of radio frequency (RF) or intermediate frequency (IF) samples at high sampling frequency may be not relevant enough to be shared directly with the community or might not fit into the current upper size limits of existing repositories, but there might be made available on request to interested researchers). The main target groups of the dissemination are the scientific community, the industrial stakeholders in wearables and IoT, the authorities and bodies responsible for development national and EU knowledge societies and digital economy, potential end users (including all population in contact with a wearable device, primary end users, public service personnel, etc.), high- school pupils (as the future users of wearables and current users of Internet), and persons developing multinational PhD and cross-sector trainings. Several activities will be considered in A-WEAR to ensure that there is a clear way of communication between the ESRs and both the scientific and general public the target groups. The main goal of these activities will be to share results and more in general to create awareness of the importance of A-WEAR research themes to society and to raise awareness of the MSCA Actions aiming to follow FAIR principles. A-WEAR will use 10 dissemination and outreach activities listed in Table 3-1 and conference and journal publications and workshop participation. # Table 3-1 The 10-step involvement in social media in A-WEAR, in addition to the project webpage <table> <tr> <th> Additional dissemination activities besides webpage, scientific publications, conference & workshop participation, and patents. All ESRs will be involved in all these activities. One or two ESRs/task will lead the efforts </th> <th> Lead ESRs </th> </tr> <tr> <td> Webropol survey active all through the EJD where users and stakeholders will be free to share their concerns and challenges regarding the technology (on one hand) and applications (on the other hand) of wearables </td> <td> 1,9 </td> </tr> <tr> <td> Facebook open group for A-WEAR public awareness </td> <td> 10 </td> </tr> <tr> <td> LinkedIn open group regarding discussions in the areas of A-WEAR with blog posts on LinkedIn, including ESRs’ blog inputs on their experiences within the EJD (technical, social, experiences associated to mobility in other country, lesson learnt and </td> <td> 4,5 </td> </tr> <tr> <td> best practices) with at least two posts/quarter </td> <td> </td> </tr> <tr> <td> Adding A-WEAR open-source measurement data on open repositories, such as EU Zenodo, GitHub or Bitbucket – Fellows 3 and 13 will be in charge with finding out the distribution terms for the open repositories, informing the other fellows of those and regularly reminding each of them to distribute their open measurement data through those repositories </td> <td> 3,13 </td> </tr> <tr> <td> ESRs will maintain a youtube channel with video clips and fellows testimonies related to the main topic of the project, providing lessons and general-purpose talks, to spread the relevance of the activities carried out in the network </td> <td> 6,14 </td> </tr> <tr> <td> Twitter 140-character postings with links to to results and elevator pitches </td> <td> 8 </td> </tr> <tr> <td> ESRs will attempt contact with local mass-media to spread the activities of the consortium, the Marie Curie Actions, and of individual activities </td> <td> 2,12 </td> </tr> <tr> <td> Each ESR will post his/her publications (at least the abstract) on ResearchGate and participate in the ResearchGate discussions related to A-WEAR topics </td> <td> 7 </td> </tr> <tr> <td> ESRs from each beneficiary will organize a A-WEAR Open Day (one per beneficiary) where general audience will be invited to visit the host facilities and create attraction to the conducted research activities & doctoral studies </td> <td> 11 </td> </tr> <tr> <td> Each ESR will commit to act as Marie Curie Ambassadors and visit local schools and universities , as well as local councils , exposing the activities and results of the network. They will give at least 2 public presentations per ESR within the 36 months of contract. The specific election of places to give the talk will be left for decision of the ESRs with the support of the nominated supervisors. </td> <td> 15 </td> </tr> </table> Research papers will be published as open-access by taking up self-archiving rights for journals and conferences that have them, or if necessary paying the open-access fees where self-archiving and or free open access is not possible. Also, online pre-publication in ArXiv will be recommended to fellows. In addition, related to data accessibility, participants to the conferences where A-WEAR fellows will present their work will have a direct access to the information through oral presentations, posters and conference proceedings. The A-WEAR webpage is hosted at TAU. The project will have a dedicated webpage aims to promote the ESRs’ skills and progress in their career in order to be available for the best possible employment opportunities, the training network, disseminate the results achieved and announce the events organized within this project. This website will be supported by a set of static content pages (institutional content) and will integrate a more dynamic area, eventually adding a blog and making easy to any participant in the network to collaboratively update and create new content. A-WEAR beneficiaries commit to make their results available in open–access as much as possible, through at least the followings: a) majority of deliverables in public access; b) publications via open-access option in IEEE and other publication forums; c) dissemination of results on open-forums such as ResearchGate, personal webpages, and open library pages (e.g., TUT has own open-access portfolios: Dpub and TUTCRIS, UJI publish the papers also on their webpages, etc.); d) less sensitive and privacy-preserving measurement data to be provided in open access. The next European Researchers’ Night will be organized on 27 September 2019 together with Europe and 11 other locations in Finland. During one day and night, visitors from all walks of life can take part in different kinds of workshops, panel discussions and exhibitions as researchers open their doors to the public. The A-WEAR researchers will participate the events yearly either as soon as possible talking about their work or visiting the relevant scientist. All events have been earlier very popular in Finland. They are organized in collaboration between the universities and research organisations and are free of charge. The webpages of the partner organisations will be linked to the project webpage. The partner organisations are encouraged to refer the A-WEAR action in their professional or commercial occasions always when possible. For cryptography-related papers free repository ( _https://www.iacr.org/eprint/_ ) will be used. 3.2.3. Making data interoperable In order to make the data interoperable in A-WEAR, standard open formats will be used for storage. Additional metadata will be described and clarified. Proprietary software and language-dependent formats will be avoided where possible (e.g., during industrial secondments). 3.2.4. Making data re-usable (through clarifying licenses) In order to make the data re-usable in A-WEAR, the following procedures will be followed: 25. The datasets will be typically shared under Creative Commons Attribution-NonCommercial 4.0 International License. Commercial licenses will be available upon request. Data is expected to be available as long as the service used for sharing data is operational. Data published in scientific journals in form of a journal article would be in open access. 26. We will encourage the use of the collected data in open challenges at dedicated conferences (e.g. IPIN annual open challenge on indoor localization). 27. We will include information how data were created and will describe experiment details. A-WEAR team recognizes the importance of software licensing from the outset of the project, but given the uncertainty we have now of the potential value of such tools in the future, the exact licenses to be used in case-by-case situation will be refined through the project. This is because some of the created data may only be demonstrators or proof of concepts. Other type of created data, on the contrary, may be valuable tools to launch marketable ideas via starts-ups. Once we will have a clearer idea of what type of data will be produced by the ESRs during the project (e.g., only open sources tools, mix of proprietary and software tools, etc.), the DMP will be updated with relevant information. At this point, we are exploring the spectrum of licenses as a preliminary step based on the information found at _https://choosealicense.com/_ . Regarding the WP3 studies (eHealth domain), we mention that the interoperability of medical data is a key concept for the electronic health records by measuring the communication and cooperation capacity between different healthcare entities that allow the exchange of information through electronic health records or other medical information systems. The interoperability of medical data is realized by means of HL7 standard that assure automated conversion of information into structured data. Interoperability of medical data also mean interoperability with medical devices that capture (generates) medical information from medical sensors and different devices like Holter, ECG, MRI, ECOGRAF, etc., interoperability with emergency support systems, with other systems that can quickly and efficiently deliver the medicines needed for patients unable to move and interoperability of medical information by creating medical social media portals for physicians, where they can access studies, updated medical guides and patients records. 3.2.5. Data – related procedures at A-WEAR units 3.2.5.1. TAU The public datasets will be archived and shared through the Research data storage IDA ( _https://openscience.fi/ida_ ) and Research data finder ETSIN ( _https://openscience.fi/etsin_ ) services provided by the Finnish IT Centre for Science (SCS) and endorsed by the Finnish Ministry of Education and Culture. In addition, the data will be promoted on the project web site, through relevant publications (with related DOIs and keywords) and through presentation in scientific and public events. All the data collected during the subjective experiments will be anonymized and aggregated. The created test databases will be made available through web sites where applicable. 3.2.5.2. UJI The public datasets will be archived published in Zenodo and the IndoorLoc platform ( _http://indoorloc.uji.es_ ) provided by Universitat Jaume I. In addition, the data will be promoted on the project web site, through relevant publications (with related DOIs and keywords) and through presentation in scientific and public events. All the data collected during the subjective experiments will be anonymized and aggregated. The created test databases will be made available through web sites where applicable. 3.2.5.3. BUT We will publish the data sets in Open data of the Czech Republic, _https://opendata.gov.cz/_ . In addition, the project outputs (generic data, software, etc.) will be promoted on the project web site, through relevant publications (with related DOIs and keywords) and through presentation in scientific and public events. All the data collected during the subjective experiments will be anonymized and aggregated. The created test databases will be made available through web sites where applicable. 3.2.5.4. UPB UPB will have a local site linked to _http://cs.pub.ro_ on which the collected data will be available. The data will be also promoted on the project web site, through relevant publications. Regarding ESR8 research, the collected data will be non-personal data (e.g. radio measurements). 3.2.5.5. URC After the execution of any test, the resulting collected are analyzed for modelling, model verification or contribution purposes. The data collected during any subjective experiment will be anonymized and aggregated. Data is then kept in personal computers with password security and open access for only the people involved in the study or co-authors of the relevant article for scientific purposes. The created test databases will be made available through web sites where applicable. The collected data will be disseminated through relevant publications (with related DOIs and keywords) and through presentation in scientific and public events. Publications will be available on the project web site and through other channels. Should experiment involve external subjects supplying data (such as position, for example), although these data are anonymized, subjects are also informed to have rights to have their collected results destroyed if they wish by supplying the nick name they chose or the number they are assigned with. 3.3. Allocation of resources The data will be prepared during regular working hours of the ESR. The data will be stored on OneDrive provided by TAU. No additional cost to the project is expected for the OneDrive repository. The costs for publications in Open Access journals can be between 1000 and 4000 EUR per paper and this will be covered by A-WEAR project allocated resources. 3.4. Data security All data will be stored on secured password protected computers. Data will not be stored on unencrypted flash drives. For possible vulnerable data (such as the data regarding anonymous user traces and operator collected data) to be commonly used by several Consortium partners, a password-protected space on the project web server will be created and data will be stored in encrypted form. The sharing platform for the consortium data is the Microsoft OneDrive. As parts of the university infrastructure at TAU, the vital components of hosting and sharing the data are expected to stay in place in the long term, thus ensuring continued access to the collected data in a secure way. Raw data collected from volunteers will only be retained for the lifetime of the research project and stored on OneDrive and password-protected computers according to the participants’ information security policy. It will be stored unless explicit permission is requested and given by the research participants for an extension period (which may necessitate an appropriate consent form amendment). All research participants will be informed of the nature and limits of confidentiality in accordance with the data protection and privacy legislation in the jurisdiction where the research is to be carried out. The web surveys to be organized within A-WEAR will be done with volunteers and fully consenting to fill in the web surveys and the data will be collected anonymously, and without storing the IP of the respondent (e.g., Webropol survey tool to be used in Dissemination activities has such an option). No data will be collected from children or vulnerable adults or any other person deemed unable to express his/her full and free consent. 3.5. Ethical aspects related to data management Detailed description of the ethical aspects is given in deliverable D1.3 Collection of ethical clearance procedure and forms available at each beneficiary. 3.5.1. Wearables data of individuals Making data anonymous and implementing privacy-enhancing mechanisms will require access to nonanonymized or weakly anonymized data at some point. In A-WEAR, all data sets used will come from informed and volunteer individuals and approved databases that will only be used for training purposes. Anonymity techniques will be implemented as soon as it is feasible and databases will not be shared between institutions. Ethical assessment is a key component for the adoption of new medical technologies. Ethical problems resulting from the inherent risks of Internet enabled devices can appear due to the sensitivity of healthrelated data, and their impact on the delivery of healthcare. These issues can also come from that fact that devices range from single-sensor wearable devices to complex spatial networks capable of measuring and health-related behaviors for management of health and well-being. When talking about ethical issues concerning eHealth wearables, we call the ethics of devices and data. eHealth wearables are generally carried by the user at home, at residential care, workplace or public spaces. In each case, a door into private life is created, enabling the collection of data about the user’s health and behaviors and the analysis. The lives of users can be digitized, recorded, and analyzed, creating opportunities for data sharing, processing and mining. That is why the privacy should be respected and ethical forms will be signed with all the individuals that will be involved in any experiments and medical reports. 3.5.2. Assessing privacy intrusion In order to assess the acceptability and privacy intrusion of specific privacy-enhancing solutions, partners may choose to conduct surveys. For web surveys, the information will be collected anonymously and with full consent of the participants, on a volunteer basis. For in-person surveys, if any, information sheets and consent forms will be provided, storage of personal data will be avoided whenever possible and data with potential for re- identification will be safely deleted as soon as it is feasible. 3.5.3. Webropol surveys If qualitative methodologies are used, participants will be duly informed in writing of the nature of the research and their involvement, their rights during and after their participation and the final goal of the study. Data will be collected and stored anonymously, as Webropol survey tool has an option for fully anonymous data collection. 3.6. Other aspects 3.6.1. Issue register The “Issue Register” is a log of any issue which arises during the course of the project, and will be maintained by the Project Coordinator on a separate folder on OneDrive. The Issue Register will collate any issue as it arises, issues will then be analyzed and escalated or dealt with accordingly. An issue could be anything which is of concern to an ESR, beneficiary, partner, supervisor, etc., this could be deviations from the project plan, identification of new risks or just concerns. For example it may be an issue such as Partner X is not responding to emails, this may be a precursor to larger problems which may impact on A-WEAR progress, therefore, as an issue is raised it may cause a trigger which causes a new risk to be identified and added to the risk register. This register will also allow an additional tool for keeping track on the risks and means for responding immediately with mitigating activities. 3.6.2. Lessons Learnt Log A lessons log will also be maintained by the Project Coordinator to record lessons generated out of the “Issues Register” and any other lessons from the project. The lessons log will categories the lessons for their significance to different parties and the stage in future projects at which the lesson log should be reviewed. The A-WEAR project lessons log will be available to all of the consortium members for ensuring that lessons learnt in this project may be applied to future projects. As well the lessons Learnt Log will provide information relevant for realizing the significant results * linked to dissemination, exploitation and impact potential of the outcome overall and the management and usage of data in particular, and * with significant immediate or potential impact in science or industry. 4\. Quality Assurance Plan Internal quality assurance (QA) of all deliverables will be carried out prior to submission to the Commission. First of all, each deliverable gets assigned of up to two internal reviewers. To that purpose, a draft copy will be delivered to the internal reviewers one month before its due date for comments on technical as well as formal quality. The reviewer has specific responsibility for providing feedback to the lead authors on more detailed quality assurance in terms of presentation, quality of writing, consistency, clarity, etc. Reviewing will be done by using the reviewing form, provided in Annex 1, in order to ensure consistency in the reviewing process. At the same time, draft copies of deliverables will be circulated electronically to all partners for additional comments. Review forms and comments are to be sent in the agreed way within a maximum of two weeks to those responsible for deliverables, which gives the latter one week correction time before the final version is delivered to the PC team and submitted. External quality assurance is to be gained from the various contacts within the advisory board, during the dissemination moments, and with the various other contacts that will be established, including any ongoing and future academic and industrial collaborations. For the appointed external advisory board member, one week of reviewing time will also be granted, and one week of revision time for the deliverable authors. In addition, as ESRs will publish in peer-reviewed open-access publication channels, the peer reviewed papers are a reliable quality check. The impact from the representatives from industry during meetings or public/professional presentations, etc. or the possible implementation of the outcome as a part of the product development of the partner organisations will also prove of the good quality of the outcome 4.1. Project Coordinator Involvement Project coordinator (PC) team is officially responsible for sending all deliverables to the European Commission for the evaluation process. It cooperates with the deliverable leaders and Training and Project Manager on all relevant matters to ensure the quality of the project’s deliverables. PC receives (in cc) the deliverables for peer reviewing from the respective responsible party followed by the results of peer reviewing from each assigned reviewer. Finally, PC receives the confirmation of the satisfactory implementation of the recommendations. PC team should send reminders and alerts in due time to the responsible parties in order to remind them the deadlines for the delivery submission and the procedure to be followed within the quality assurance phase. PC team receives the deliverables for peer reviewing from the respective responsible partner and organizes the quality assurance procedure. If necessary, OD team is also in charge with 28. Compiling the related peer review reports with recommendations or, if necessary, sending the deliverable to another partner who will be in charge for peer reviewing. 29. Delivering the results of peer reviewing to the deliverable author and other beneficiaries. 30. Verifying the satisfactory implementation of the recommendations of the peer review report, in cooperation with the responsible partner. PC team is also responsible for keeping track of the reviewer’s assignments and storage of the related data. 4.2. Review responsibilities and process A deliverable is sent by the responsible person 15 days prior the deadline for any comments and revision. The compiled peer review report (if any major comments) is sent to the relevant author(s) within 7 calendar days. If no major comments, the minor comments are provided via telco or email to the responsible person. The Author(s) of the deliverable, in cooperation with the other partners (if applicable), carries out the required improvements with the highest priority, and sends it back to the project coordinator team within a further 5 calendar days. 4.3. Simplified quality assurance procedure In order to achieve the QA, each reviewer will provide his comments either in a free form or with track changes on the deliverables or by filling the form given in Annex 1. The reviewers will keep in mind the following questions when reviewing a deliverable: 1. are the objectives/goals of the deliverable clearly presented? 2. does the work include references to relevant material and literature? (if applicable) 3. is there sufficient detail in all areas? 4. is the information technically sound? 5. are the findings clear and well argued? 6. does the deliverable provide inputs as expected for the subsequent work? 7. is the information clearly presented? 8. is the work cohesive and consistent? 9. is the writing style appropriate? 10. is the graphical content appropriate? 4.4. Risk management TAU as coordinator is in charge with the risk management procedure. Each partner has the responsibility to report immediately to their respective WP leader and to the PC any risky situation that may arise and may affect the successful completion of A-WEAR objectives. In case of problems or delays, the AB will be consulted and it may set up task forces in order to take the necessary actions. In case there is no resolution, the PC together with AB will establish mitigation plans to reduce the impact of risk occurring. Table 3.2c shows the implementation risk analysis. # Table 4-1 Implementation risks and mitigation procedures <table> <tr> <th> Type </th> <th> Risk No. </th> <th> Description of Risk </th> <th> Probabilit y / Impact </th> <th> WP No </th> <th> Proposed mitigation measures </th> </tr> <tr> <td> Technical </td> <td> R1 </td> <td> Input measurement data is unavailable in research literature at the time when mathematical modelling work has to start </td> <td> High/Low </td> <td> 2 </td> <td> Conduct own measurements in BUT-TAU LTE test network similar to how it was done in _http://wislab.cz/our-work/lte-assisted-wifidirect_ </td> </tr> <tr> <td> R2 </td> <td> Scarce availability of off-the-shelf devices for implementing& testing real use cases </td> <td> Low/ Medium </td> <td> 2 </td> <td> A-WEAR team has a wide expertise in the implementation of simulators and testbeds in order to overcome the considered issues </td> </tr> <tr> <td> R3 </td> <td> Insufficient crowdsensed data in WP3 studies </td> <td> Low/ Medium </td> <td> 3 </td> <td> Collecting data through all A-WEAR units as much as possible; using analytical models & existing open-source data to supplement the missing measurements </td> </tr> <tr> <td> R4 </td> <td> Standardization efforts in wearables is highly dynamic; new emerging standards may rely on privacy assumptions we have not considered </td> <td> High/ Low </td> <td> 4 </td> <td> Actively following the standardization efforts in wearables, IoT and future wireless communications in in order to adjust the hypotheses and project work accordingly. </td> </tr> <tr> <td> R5 </td> <td> Noisy mmWave and industrial data or inappropriate data format, not suitable for machine learning analysis </td> <td> Low/ Medium </td> <td> 5 </td> <td> Collecting data through all A-WEAR units in both supervised and unsupervised modes from very beginning of the project; supplementing unavailable data with statistical models; discussing with industrial partners for finding out suitable/standardized formats </td> </tr> <tr> <td> R6 </td> <td> Some of the envisioned tasks may require collaboration with experts in other fields (user experience, control theory, SW engineering, etc.) </td> <td> Medium/ Medium </td> <td> 5 </td> <td> Utilize the rich contact network of the consortium units to seek prompt advice in complex matters related to other fields of knowledge; proactive role of AB in providing timely feedback on tasks planning and completion </td> </tr> <tr> <td> Administrative </td> <td> R7 </td> <td> Integration problems in building the SW and HW platforms </td> <td> Low/ Medium </td> <td> 2-5 </td> <td> A-WEAR team has a wide expertise in SW, HW and SoC and active discussions and feedback from AB will help to overcome the problems. </td> </tr> <tr> <td> R8 </td> <td> Delays in recruitment process </td> <td> Medium/ Low </td> <td> 1-7 </td> <td> The positions will be actively advertised through various channels, in addition to the joint network links of all partners </td> </tr> <tr> <td> R9 </td> <td> More than 36 months needed to complete the double/joint PhD degree </td> <td> High/ Medium </td> <td> 1-7 </td> <td> Each beneficiary commits to ensure all needed resources in terms of funding & supervision to allow the ESRs to finish their joint/double degree. </td> </tr> <tr> <td> R10 </td> <td> Potential problems in leading a consortium of 17 partners </td> <td> Low/ High </td> <td> </td> <td> PC has worked successfully before (projects, publications,…) with 47% of the 17 A-WEAR units; PC has experience in leading large national Consortia and she gets strong support from TAU Research Services (having great experience in ITNs and EU projects) to address promptly any issues that might appear </td> </tr> <tr> <td> R12 </td> <td> Scientific misconduct </td> <td> Low/ Medium </td> <td> 1-5 </td> <td> Termination of contract and recruitment of replacement </td> </tr> <tr> <td> R13 </td> <td> Some industrial partner going bankrupt </td> <td> Low/ Low </td> <td> 1-5 </td> <td> Replacing the industrial secondment unit with new industrial partners, suitable to the addressed objectives. </td> </tr> <tr> <td> R14 </td> <td> Topic divergences from the scheduled A-WEAR network events in table 1.2b </td> <td> Medium/ Low </td> <td> 7 </td> <td> If some of the forecast lecturers are not available, we invite new lecturers to cover in a comprehensive non-overlapping manner the core topics </td> </tr> </table>	https://phaidra.univie.ac.at/o:1140797	Horizon 2020
0123_SUITCEYES_780814.md	# Executive Summary This Data Management Plan (DMP) defines effective _governance_ and _management_ of research data generated and or used with the SUITCEYES project. It addresses issues of data generation, ownership, storage, access, exchange, use, openness, protection, preservation and destruction. This document (and its subsequent updated versions) will act as a guideline and provides an overview of research-data-related procedures within the project. It aims to facilitate collaboration and help avoid unnecessary duplications of work and data creations. It further defines procedures and routines for easy and effective information sharing during the project time and beyond. It is also a useful tool in ensuring continuity and bridging gaps even in the cases of additional new members in the project. After the introductory parts, Chapter 3 of the document presents an adaptation of five (5) Data Governance Domains of _Data principles_ , _Data quality_ , _Metadata_ , _Data access_ , and _Data lifecycle_ which aim at defining the decision-making structures that govern data-related issues within the project. Following these, chapter 4 provides further description of routines for data management. The approach of reaching informed consent and other agreements with users taking part in requirements, user studies, video-recordings and other R&D activities has also been included. The document is concluded with a short summary and two appendices. # Introduction and Rationale This deliverable – Data Management Plan (D8.14) – incorporates both Data Governance and Data Management and accordingly defines the principals, procedures, and routines that are put in place for the management of research data within the SUITCEYES project. As such, this document and its subsequent versions, act as a guide to help form an up-to-date overview of the project-related data and related procedures. This DMP is produced with the aim of facilitating information sharing and collaboration, while avoiding unnecessary duplications of work. It also defines the basis for various choices and is meant to help the members to make sound and appropriate decisions when needed. This DMP is also meant to create continuity even in potential cases of membership change. This document presents the data governance approach adopted, and describes the research data that will be collected, generated and or used. It outlines the related data types and the ways in which the data will be handled both during and after the project. Furthermore, it will describe which data will be made available openly and which date will be kept protected, and the reasons why. This DMP will remain a living document and will be updated when needed as the project progresses. This deliverable is licensed under the Creative Commons License CC BY-NC-SA (AttributionNonCommercial-ShareAlike). ## SUITCEYES and Open Data SUITCEYES is a three-year long (2018-2020) Horizon 2020 RIA project with a focus on facilitating communication in cases of deafblindness through a smart haptic interface. The project will address three challenges of (a) improved perception of the physical surroundings, (b) improved exchanges of semantic contents, and (c) enjoyable learning through gamification. The project involves many areas of research including disability studies, user studies, psychophysics, sensor technologies, face and object recognition, semantics and knowledge management, social media studies, gamification and affective computing, and smart textiles. As such, much data will be accessed used and or generated during the project. Open data, typically refers to online free distribution of research data and results for access and reuse by third parties towards benefiting both future research and society. With the advances in the “open” movement, a growing demand for open and interoperable research data has emerged. The view held in SUITCEYES is that all research, and especially those funded by public money, should benefit the whole society, be instrumental for progress, and act as a stepping stone for further research. We will therefore make the research results available through different channels and strive to provide open access to the project data, as far as possible. However, not all data generated and used within SUITCEYES are suitable or appropriate for sharing and reuse, and hence, SUITCEYES has chosen not to participate in the Open Research Data Pilot. The decision to opt out has been based on two factors, the potential for exploitation of results by some partners, but more importantly, the vulnerability of some of the project’s study participants and the sensitivity of the data that will be generated in the project. SUITCEYES involves user-studies (including interviews and observations) of sensitive nature. The participants, due to the small population and specific circumstances of each participant, are potentially easily recognizable. Although we anonymize user-study-generated data soon after collection and before sharing among the project members, this data is still not suitable for making openly available for wider access and use. This decision is supported by the results of multiple studies 1 2 3 that have shown the ease of deanonymisation even in areas where stringent efforts have been made in removing identifying data. ## Data Governance and Data Management In this document we have broadened the scope to also include data governance based on the structural features outlined by Khatri and Brown (2010) 4 where in their state-of-the-art contribution they have defined five data governance domains. Through the use of data governance, there is a distinction made between governance of data and management of data. Management concerns making and implementing decisions. Governance is concerned with the creation of a structure that allows for a decision-making structure. As they exemplify, “governance includes establishing who in the organization holds decision rights for determining standards for data quality. Management involves determining the actual metrics employed for data quality” (Khatri & Brown, 2010: 148). By extending the scope of this document to go beyond data management issues and to also include data governance a more comprehensive approach is adopted. # Data Governance Khatri and Brown (2010) have identified five (5) decision domains for data governance comprising of (i) _Data principles_ , (ii) _Data quality_ , (iii) _Metadata_ , (iv) _Data access_ and (v) _Data life cycle_ . Khatri and Brown (2010) propose that there is a need for creating a decision-making structure for each of these domains, where the full table with more detail is provided in Appendix 1. In the following subsections, we outline the plan for SUITCEYES data governance according to these five domains. ## Data principles Data principles concern the overarching ideas about the kind of decisions that are to be made relative to the four other domains. These principles also introduce boundary requirements for the use of data as well as standards for data quality. The role of this domain is to clarify the role of data as an asset. For the current DMP the following data principles have been established as presented in Table 1. Table 1: Adaptation of Khatri and Brown (2010)’s Domain of Data Principles in SUITCEYES <table> <tr> <th> Domain Decisions </th> <th> Potential Roles or Locus of Accountability </th> </tr> <tr> <td> * In SUITCEYES data is used for multiple purposes, including forming an understanding of the user needs, preferences and aspirations; forming an informed overview of the related policies; experimentations and conduct of research towards project goals and production of haptic, intelligent, personalized interface (HIPI). * The various uses of data are communicated continuously at regular meetings which are held at various levels and in different formats. Furthermore, written documentations and an information sharing tool are further mechanisms for communicating uses of the data. * Datasets are seen as assets and are therefore valuable and should be managed accordingly. * Ownership implies responsibility and accountability for keeping data assets securely stored. * As stipulated in related agreements, some of the data generated in SUITCEYES is of sensitive nature the use of which is regulated by the projects internal operational and ethical guidelines. * Such data assets should be handled through principles of privacy by design and data minimization. * All practices should be compliant to General Data Protection Regulation (GDPR). * The current DMP should be used to guide all handling of data assets. It should also be revised regularly to accommodate the </td> <td> * The Project Management Board (PMB) is the ultimate decision-making body within SUITCEYES, and it is responsible to oversee the existence of appropriate data governance structures within the project. * The PMB is responsible for decisions made on data management issues. * The PMB should refer difficult issues of data management to the Ethical Advisory Board (EAB) for advice. * The project DMP (the text at hand and its subsequent updates) will also be reviewed by the project’s EAB. * The default structure for ownership is that the partner creating the data also owns it as defined in Grant Agreement (GA) article 26 where some data may be subject to JOINT ownership governed by GA Article 26.2 with the addition of further stipulated in PCA section 8. * Data assets and their ownerships are clearly defined in a related tool share with all project members. * Securely stored refers both to protection against breach and to instances of force majeure, i.e. fire, data crash etc. </td> </tr> <tr> <td> development of new insights, challenges and problems. </td> <td> </td> </tr> </table> ## Data quality Data quality is connected to accurate, complete and trustworthy data being available for various research tasks in a timely fashion. Lack of data quality is a fundamental problem for most data intensive work and one of the core issues that can be attended to through the DMP. There are multiple dimensions involved in data quality which will be presented with the help of the following table (Table 2). The role of data quality domain is to establish requirements of intended uses of data. Table 2: Adaptation of Khatri and Brown (2010)’s Domain of Data Quality in SUITCEYES <table> <tr> <th> Domain Decisions </th> <th> Potential Roles or Locus of Accountability </th> </tr> <tr> <td> * Accuracy refers to the correlation between recorded value, actual value and the kind of value needed for the research task. A number of crucial questions emerge regarding the user study data. For example: Will the interview transcripts reflect correctly the responses of the participants? Do the local conventions vary at different partner countries? How will the principles of data minimization and privacy by design affect the data sharing and interpretation as cross referenced in different countries of the studies? These accuracy-concerns are addressed by regular meeting and joint analyses among the researchers participating in user studies and collaboration with the User-Data Working Group. For the technical aspects of the project, experimental data, ontologies and so on, there will be other questions asked, and the accuracy of that data will be verified based on domain specific scientific measures and in collaboration with an Analytical-Data Working Group. * Timeliness refers to up-to-date values being available at the right time. Timeliness is typically a challenge in complex projects with many dependencies and potential bottle necks. It is crucial for the project members across the board to be aware of the relationships between the different tasks and deadlines to ensure smooth and timely deliverance of results that are needed for the next phase in the project in their own and other work packages in the project. * Completeness defines the need for the data to be as detailed, deep and broad as necessary for the research tasks. Related to the user studies, the ambitions for adherence to GDPR legislation and the principles of data minimization and privacy by design will be carefully balanced with the need to capture the data that is necessary for the design of the HIPI and an informed understanding of user needs and preferences. For Analytical data, while collection of broad data may not be bound by the same concerns, a challenge in cases of machine learning may be the lack of enough data. Such a challenge is carefully considered and potential solutions are investigated. * Credibility refers to the need that the sources of data assets must be trustworthy. In the user studies utmost care is taken to ensure that most relevant participants are recruited and </td> <td> * The PMB will develop and assign responsibilities to a _User-Data_ _Working Group_ (UDWG) to oversee accuracy , timeliness , completeness and credibility of user data. * The PMB will develop and assign responsibilities to an _AnalyticalData Working Group_ (ADWG) to oversee accuracy, timeliness, completeness and credibility of analytical and technical data. * There are a number of guidelines and tools devised and routines put in place in the project in order to ensure the timely conduct of all project tasks. The project PCA defines the members’ responsibility towards one another, the need for timely deliverance and measures to ensure compliance. * The project has established a large network of contacts to allow collection of rich set of user data which will promote data completeness and credibility. * Similarly, continued environ- mental scanning keeps the project members informed of emerging data sources and recent research to promote access to most relevant and appropriate set of data for conduct of research tasks within </td> </tr> <tr> <td> best user-study practices are put in place to ensure high levels of trustworthiness in the results. Regarding the technical and analytical data, in general Best Available Technology (BAT) and evaluation methods will be utilized. ▪ A set of measures defined in task and work package meetings in collaboration with UDWG and ADWG will be used for evaluation of data quality and associated data collection procedures. </td> <td> SUITCEYES. ▪ Measures for data quality will be discussed, set, and documented in regular task and work package related meetings. These measures will be promoted and followed up by the two data working groups UDWG and ADWG. </td> </tr> </table> ## Metadata Metadata includes descriptions of data assets. Proper use of metadata facilitates findability and, in the long run, quality of research. The role of the metadata domain is given as Establishing the semantics or “content” of data so that it is interpretable by the users. Khatri and Brown (2010) make a distinction between three types of metadata. These will be reviewed below according to the way that they are seen as relevant for the project. Table 3: Adaptation of Khatri and Brown (2010)’s Domain of Metadata in SUITCEYES <table> <tr> <th> Domain Decisions </th> <th> Potential Roles or Locus of Accountability </th> </tr> <tr> <td> * For describing and documenting different datasets a tool in the form of a project-wide spreadsheet has been devised with multiple columns that each describe an attribute of the dataset at hand. This tool generally includes the following sub-categories of information. * Content metadata describes the contents of different datasets, including whether data has been generated through user studies, policy studies, or technical experimental research streams. The list of related attributes includes (but is not limited to) dataset identifier, data description, source and mode of creation. It also describes whether the data can be shared openly or is of sensitive nature and special care is required. * Storage metadata involves information about means of data storage. This involves the choice of local and cloud-based as well as level of cryptology necessary for different kinds of data. For each dataset and based on the level of sensitivity (whether it can be openly shared or not), ownership and data type, the appropriate means of storage is defined. * User metadata relates to various annotations that different project members may associate with various data assets. This can involve notations on usage, findability, preferences and user history. * General metadata refers to all the other attributes and information recorded about each dataset, these include area of use, ownership, date of creation, history of change, the standards used, general technical format of data, compatibility level with different analysis tools, the procedure for metadata and data update, and more. </td> <td> * The UDWG and the ADWG in collaboration with different instances in the project will develop a plan for the types of data used and create and provide guidelines related to appropriate storage procedure for each set of data. * UDWG and ADWG oversee that the metadata tool is kept updated (as potentially new types and sets of data may emerge) and includes sufficient details to provide appropriate information towards effective further data generation, use, and potential reuse, storage and long-term preservation. * The members collaborate closely with the two data work groups to facilitate their task of overseeing the upkeep of metadata information. </td> </tr> </table> ## Data access Building upon compliance with GDPR as well as principles of data minimization and privacy by design, there needs to be a clear plan for data access in place. The role of this domain is to specify access requirement of data. The UDWG and the ADWG will be tasked with development of a plan for data rights to various data assets. The PMB will monitor development of this plan. The plan will also be evaluated by the EAB as elaborated in the following table. Table 4: Adaptation of Khatri and Brown (2010)’s Domain of Data access in SUITCEYES <table> <tr> <th> Domain Decisions </th> <th> Potential Roles or Locus of Accountability </th> </tr> <tr> <td> * Risk assessment related to data value and sensitivity will conducted on regular basis. * Data access related to sensitive material within the project is based on a clearly defined need for purposes of research as monitored and decided upon by the PMB. * Mechanisms for sharing such data should be through cryptology technology . * Other data assets can be shared publicly ( Open Data ) but decisions on such initiatives will be taken at a later stage of the project when all of the research needs of the project are clearly understood. * Appropriate naming conventions as well as use of standards for data sets should be adopted to ensure interoperability within the project. </td> <td> * Sharing of sensitive data will be monitored and decided upon by the PMB. * UDWG and ADWG will be tasked to oversee the procedures for information security and alert of deviations and potential risks. * Continued dialogue with partner organisation IT support centres will take place to ensure being kept updated on security and preservation issues. </td> </tr> </table> ## Data lifecycle All data moves through various lifecycle stages and this DMP is designed with an awareness of this. The role of this domain is to determine the definition, production, retention and retirement of data. Informed decisions related to each stage of data lifecycle has gained increased importance in the light of compliance with the GDPR principles of data minimization and privacy by design. Some of the measures and related decisions are outlined in Table 5. Table 5: Adaptation of Khatri and Brown (2010)’s Domain of data lifecycle in SUITCEYES <table> <tr> <th> Domain Decisions </th> <th> Potential Roles or Locus of Accountability </th> </tr> <tr> <td> * Data inventory will be conducted on multiple occasions during the project. * Data lifecycle plan will be defined as part of the metadata tool, mentioned above. That is, at the time of data creation, not only the data will be defined, but plans will be put in place for data lifecycle, including long-term retention and even future destruction if appropriate. * Towards compliance with legislations and other regulations, some data and records are required to be kept for given periods of times. An overview of such regulation is formed through access to related guidelines and in collaboration with partner organisations’ archival departments. * At the time of project conclusion, the sensitive data will be securely archived based on the timelines specified in the metadata tool or made publicly available through different channels. </td> <td> * During the project, the UDWG and the ADWG will be tasked with recommendations for data life cycle management. * The UDWG and the ADWG will also supply recommendations on data that might at some stage be made available as open data. * The UDWG and the ADWG might also make recommendations of changing physical storage and alternating practices of metadata during various stages of data lifecycles. * Formal decisions related to the recommendations made by the UDWG and the ADWG will be made by the PMB. </td> </tr> </table> # Data Management Based on the data governance structures described above, the following sections describe the project-related datasets and actual measures and steps employed to ensure effective production, access, use, reuse, storage and preservation of data within SUITCEYES making it FAIR – findable, accessible, interoperable and re-usable. ## Data collected/generated within SUITCEYES There are a number of different data types and datasets either collected or generated within SUITCEYES. Some of this data is defined as sensitive and not suitable for sharing or openly making available to third parties. Others will be deemed as intellectual assets of partners and intended for future exploitation or are subject to other copyright issues and will not be shared openly or not at least before adequate measures have been taken. A third group of data will be made available under the open data principals for use and reuse by third parties. The main datasets in SUITCEYES contain data resulting from user-studies, policy studies, bibliographic searches, collections of semantic vocabularies, algorithms, technical experiments in the project. For administrative purposes we have grouped these data under two broad categories of User Data and Analytical Data. All sensitive data that will include personal information or result from interviews and observations are placed in the first group. All the other data- sets (although some not related to experiments) fall in the same category. Table 6 provides a summary of these data and its main categories. The separate group of Social Data is also provided in the table as a data about the potential interest of different groups, social media data and disseminated information about the project. Table 6: Main categories of data and the methods of its collection/generation <table> <tr> <th> Category </th> <th> Type of Study </th> <th> Methods of collection/ generation </th> <th> Data </th> </tr> <tr> <td> User-Data </td> <td> User studies </td> <td> Interviews, observations, reaction tracking, audio visual recording, qualitative data analysis tools </td> <td> Transcripts, psychophysical data and informed consent forms </td> </tr> <tr> <td> User-Data </td> <td> Policy studies </td> <td> Interviews with decision makers </td> <td> Transcripts </td> </tr> <tr> <td> AnalyticalData </td> <td> Policy studies </td> <td> Policy documents collection </td> <td> Policy documents </td> </tr> <tr> <td> AnalyticalData </td> <td> Literature studies </td> <td> Searches in bibliographic databases (e.g. Web of Science). </td> <td> Bibliographic metadata, collections of articles and other publications </td> </tr> <tr> <td> AnalyticalData </td> <td> Semantics </td> <td> Searches for and collection of a set of sign language vocabularies </td> <td> Sets of sign language vocabularies </td> </tr> <tr> <td> AnalyticalData </td> <td> Semantics </td> <td> Searches for collections of social haptic signals </td> <td> Sets of social haptic signals </td> </tr> <tr> <td> AnalyticalData </td> <td> Deploying visual understanding algorithms </td> <td> Wearable RGB-D (Red, Green, Blue and Depth) cameras or RGB and depth sensors </td> <td> Benchmark datasets from wearable cameras for activity recognition, object detection, face and hands detections, </td> </tr> <tr> <td> </td> <td> </td> <td> </td> <td> navigation </td> </tr> <tr> <td> AnalyticalData </td> <td> User studies </td> <td> Scientific instruments (temperature loggers, am-meters, thermography, optical microscopy, video, tensiometers, martindale etc.) </td> <td> Technical measurement data (temperature, time, vibration amplitudes, frequencies) </td> </tr> <tr> <td> Social-Data </td> <td> Social interactions </td> <td> Appropriate accounts on the social media </td> <td> Data about potential interest of groups, disseminated information about the project </td> </tr> </table> ## Standards for collection, creation, and reuse There are a set of standards and guidelines related to each type of data collected or generated within the project. For example, there is a detailed interview protocol that defines clearly the aim of the interviews; the procedure for the interview, analysis, collaboration between the researchers who conduct the user-studies; instruction about how the interviews are to be conducted, the instructions for ensuring informed consent by the participants; the interview questions and more. In other cases, for example for bibliographic studies, the information scientist within the project will apply best practice for data collection, pre-processing, analysis and visualizations. Similarly, each of the researchers in the project will apply their field expertise to ensure compliance with standards and data quality. Furthermore, information is recorded about each sets of data, in a descriptive accompanying document. The type of information included may vary from one descriptive overview document to the next, but in general the types of information that are included may comprise of information about the contents, the source, means of data collection or generation, privacy level, storage details, retention instructions, member(s) involved in data creation, notes, dates of creation, use, versions, tools involved, methods used, and so on. Additionally, a select set of such information is uniformly captured in a Metadata Tool (Appendix 2), which for each dataset within the project defines the dataset’s unique id and name, data type, description, ownership, purpose, area of use, size, level of data sensitivity, depository, duration of preservation, reuse instructions, accompanying metadata, required tools and methods, data quality assurance process, and pertinent ethical considerations. The information provided in the Metadata Tools therefore provides guidelines as to whether each specific set of data can be shared for reuse or will be of sensitive nature and would need to be protected and not shared. ## Ownership and responsibilities In SUITCEYES, some data will be generated and other data may be captured for further analysis and use within the project. The ownership of the collected data will remain with the original owner. For the rest, the default structure for data ownership in SUITCEYES is that the partner creating the data also owns it as defined in GA article 26. Where some data is subject to JOINT ownership, this is governed by Grant Agreement Article 26.2. Further stipulated are listed in the project’s consortium agreement, section 8. For the sake of clarity and future reference, the name(s) of data generator(s) and owner(s) is (are) clearly stated in the Metadata Tool. The data owners are responsible to provide the required information that informs the project of the level of data sensitivity and means of storage and retention. The PMB is the ultimate decision-making body within SUITCEYES and as such it is also responsible for decisions made on data management issues. The PMB is also responsible to oversee the existence of appropriate data governance structures within the project. Towards this, the PMB will develop and assign responsibilities to two Data Working Groups one for User-Data and one for Analytical-Data. These working groups will collaborate with project members and will oversee accuracy, timeliness, completeness and credibility of data. ## Dataset labelling convention Each dataset will be assigned a unique identifying number (each dataset receiving the next consecutive available number as indicated on the Metadata Tool). To facilitate labelling of datasets, much of the identifying information about each dataset will be provided in the Meta-Data Tool and potential accompanying data descriptive file. Therefore, the actual labelling of the dataset will be as follows: #### SC-DS_ID_Name Where SC-DS is indicative of SuitCeyes-DataSet. ID refers to the dataset’s unique identifying number. Finally, Name is to be formed in a way to provide immediate meaningful information about the content of the dataset. Example: SC-DS_1_WoS-809 items-deafblindness-2018-02-02 ## Storage and sharing (during and after the project) Currently, the main depository for SUITCEYES data is BOX, which is a data storage and sharing solution, procured nationally within Sweden for use by Swedish universities and their collaborators. This storage facility has been approved as meeting the standards set by the GDPR requirements. Some data may be stored locally at partner organisations in accordance to secure GDPR- compliant guidelines. The consortium identifies the level of security; sensitiveness; storage requirements; retention instructions; sharing routines; and the specifics of archiving, preservation, and or destruction for each set of data as they emerge and as the project progresses. These parameters will define how the data within the project are to be handled. ## Data quality and evaluation The consortium has defined guidelines, procedures, and routines to ensure a general level of quality of data and research work by the means of the structures that are put in place. In addition to this, each member of the project is competent in their his or her specific area of research and well familiar with related guidelines and best practices to ensure quality of work and to apply appropriate evaluations measures. Furthermore, the internal review structures and collaborative feedback by colleagues is a further means for ensuring quality of work, data and research. ## Ethical and legal compliance As mentioned earlier, SUITCEYES involves user studies. The partners involved in these studies either hold or will seek and obtain ethics approval from their national ethics review boards. Based on the procedures described above, the sensitive data generated or used within the project will be subject to guidelines and related best practices. After assigning codes for future cross- referencing purposes, personal identification information will be removed from the interview and observation data, soon after data collection and before sharing (only if needed) with other project members. The consortium partners are also aware of the GDPR (EU) 2016/679 5 which is a regulation in EU law on data protection and privacy for all individuals within the European Union. It also addresses the export of personal data outside the EU. The GDPR aims primarily to give control over personal data and to simplify the regulatory environment for international business by unifying the regulation within the EU. It also involves decisions regarding which datatypes can be characterized as containing sensitive information and how such information is stored and shared. Each partner is aware to examine the possibility of protecting its results and must adequately protect them within the project, and after its finishing. It is especially important if the results can reasonably be expected to be commercially or industrially exploited and protecting them is possible, reasonable, and justified. The consortium considers its own legitimate interests and the legitimate interests (especially commercial) of the other partners. Already on the stage of submitting the project application the consortium decided to opt out of the Pilot on Open Research Data in Horizon 2020. It is connected with: * allowing the protection of results (e.g. patenting) * incompatibility with privacy/data protection Where possible within the SUITCEYES project, the partners are using healthy adults, so that one can minimise the burden on the target user group. In other words, people with deafblindness are only being called upon where their “expert user” perspective is required. For healthy users, ethical risks are low – there is no sensitive data being recorded, so the only concerns are around data protection and health and safety. The partners will only work with those people with deafblindness who have the capacity to consent, and who have the communication skills to carry out an interview. Interviews will be conducted via a caregiver, so that the participant is with someone familiar and with someone skilled in acting as an intermediary. Interviews will be conducted in a location of the participants' choice – in this way they will be in a familiar environment. Naturally, informed consent forms will always be applied. ## The approach and format of reaching informed consent The approach to be used to reach informed consent, or any other agreement with end users taking part in requirements and other user studies, has been proposed and performed from the beginning of the project. This approach and the format of reaching informed consent for this target group is of usage also for other R&D activities and are made available / published as an important output (the templated used by the consortium from the beginning of the project are shown in Appendix 3). Moreover non-sensitive data are stored in a Google drive folder (according to D1.1 Quality Assurance Plan). The YouTube videos and other dissemination materials involving the presence of users and outside participants require reaching informed consent for the target group presented on the video, materials etc. In case the consortium will decide to video- record the interviews, iteration of the agile process for WP7 or other activities and applications involving persons outside the consortium, the DMP requires to use the consent form that includes reference on how to treat these materials (please check the forms in Appendix 3). At the start, the consortium informs that giving consent to process any personal data collected in the context of SUITCEYES is entirely voluntary and that the consortium commits to protect personal data, and process it only according to applicable laws and regulations such as the GDPR. The consent might be withdrawn at any time, however withdrawing it might not effect immediate stopping of using the material and that it does usually not affect material that has already been made public. One is informed that the material collected will be used in internal training of project members and/or published in which media channels, e.g. billboards, newspapers, website, TV programs, Twitter, YouTube, LinkedIn etc. It is important to note that the consortium informs that published material probably reaches large audience and that the consortium is not able to control other use of the material. It is also mentioned that publishing the material on social media means that the material is transferred to companies based in the United States. These companies are members of the "Privacy Shield"- agreement and are thus considered to ensure an adequate level of protection of personal data. SUITCEYES ensures transparency with processing personal data. There is a possibility of receiving information about the way of processing and copying personal data. It will be received in a structured, commonly used and machine- readable format. The consortium can rectify or supplement personal data that is inaccurate or incomplete. It is possible to erase personal data under certain circumstances, however personal data that has been already made public, e.g. published on social media is usually not affected by a withdrawn consent. Because of legal provisions we may also be prevented from immediately erasing personal data. Lodging a complaint to the supervisory authority is also possible. Therefore, the privacy is an important issue for SUITCEYES consortium and we do the best to protect personal data of internal and external members of the project. Various formats of reaching informed consent have been used in SUITCEYES from the beginning of the project, e.g.: * The consent forms for the interviews with the users within WP2 in Greek, German, Swedish, Dutch and English * Non-Disclosure Agreements that were signed with the advisors, symposia participants, and e.g. persons who helped in transcribing some parts of the interviews * The informed consent forms used for experiments in the Netherlands (in Dutch) * The informed consent form (in English) used by HB for taking photos, filming and publishing (for various university applications, also used in the project) * A letter of consent regarding video/audio recording or photos in German * Universal consent form created in the second year of project in accordance with the project identity. The templates of these informed consent forms can be found in Appendix 3. # Summary This DMP incorporates both Data Governance and Data Management and accordingly defines the principals, procedures, and routines that are put in place for the management of research data within the SUITCEYES project. The SUITCEYES partners will make the research results available through different channels and strive to provide open access to the project data, as far as possible. However, not all data generated and used within the project are suitable or appropriate for sharing and reuse, and hence, SUITCEYES has chosen not to participate in the Open Research Data Pilot. The main datasets in SUITCEYES contain data resulting from user-studies, policy studies, bibliographic searches, collections of semantic vocabularies, algorithms, technical experiments in the project. For administrative purposes these data has been grouped under two broad categories of User Data and Analytical Data. For the sake of clarity and future reference, the name(s) of data generator(s) and owner(s) is (are) clearly stated in the Metadata Tool. The data owners are responsible to provide the required information that informs the project of the level of data sensitivity and means of storage and retention (relevant informed consent forms are also used). The PMB as the ultimate decisionmaking body within SUITCEYES is also responsible for decisions made on data management issues. Each dataset is assigned a unique identifying number (each dataset receiving the next consecutive available number as indicated on the Metadata Tool) and the main depository for SUITCEYES data is BOX, which is a data storage and sharing solution. This storage facility has been approved as meeting the standards set by the GDPR requirements. Some data may be stored locally at partner organisations in accordance to secure GDPR-compliant guidelines. The DMP will be updated over the course of the project whenever significant changes arise, such as (but not limited to): * 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). # Appendices ### Appendix 1 Framework for data decision domains (Khatri & Brown, 2010: 149) ### Appendix 2 SUITCEYES Metadata Tool The following information is recorded for each set of data collected or generated within the project. (One dataset item is included here to exemplify.) 78081416 ### Appendix 3 Informed consent forms used in SUITCEYES The consent form used for the interviews with the users within WP2 (in Greek) The consent form used for the interviews with the users within WP2 (in German) The consent form (last page) used for the interviews with the users within WP2 (in Swedish) The consent form used for the interviews with the users within WP2 (in Dutch) The consent form used for the interviews with the users within WP2 (in English) The form of Non-Disclosure Agreements that were signed with the advisors, symposia participants, and e.g. persons who helped in transcribing some parts of the interviews The informed consent forms used for experiments in the Netherlands (in Dutch) The informed consent form (in English) used by HB for taking photos, filming and publishing (for various university applications, also used in the project) Letter of consent regarding video/audio recording or photos in German Universal consent form created in the second year of project in accordance with the project identity	https://phaidra.univie.ac.at/o:1140797	Horizon 2020
0126_SEA-TITAN_764014.md	# INTRODUCTION SEA TITAN project participates in the Pilot on Open Research Data (ORD) launched by the European Commission (EC) along with the H2020 programme [1]. This pilot is part of the Open Access to Scientific Publications and Research Data programme in H2020. The goal of the programme is to foster access to research data generated in H2020 projects. The use of a Data Management Plan (DMP) is required for all projects participating in the Open Research Data Pilot. Open access is 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 research and innovation, scientific information can refer to peer-reviewed scientific research articles or 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. The Consortium strongly believes in the concepts of open science, and in the benefits that the European innovation ecosystem and economy can draw from allowing the reuse of data at a larger scale. Furthermore, there is a need to gather experience in wave technology, especially power performance and operating data. In fact, there has been very limited experience in wave energy, which is essential in order to fully understand the challenges in device performance and reliability. The limited data and experience that currently exists are rarely shared, as testing is partly private-sponsored. This project proposes to remove this roadblock by delivering for the first time, open access, highquality power take-off (PTO) performance, reliability and operational data to the wave energy development community. Nevertheless, data sharing in the open domain can be restricted as a legitimate reason to protect results that can reasonably be expected to be commercially or industrially exploited. Strategies to limit such restrictions will include anonymizing or aggregating data, agreeing on a limited embargo period or publishing selected datasets. ## Purpose of the Data Management Plan 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 the project research data. The DMP covers the complete research data life cycle. It describes the types of research data that will be generated or 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. It also reflects the current state of the Consortium agreements on data management and must be consistent with exploitation and IPR requirements. The DMP is not a fixed document, but will evolve during the lifespan of the project, particularly whenever significant changes arise such as dataset updates or changes in Consortium policies. This document is the first version of the DMP, delivered in Month 3 of the project. It includes an overview of the datasets to be produced by the project, and the specific conditions that are attached to them. The next versions of the DMP will get into more detail and describe the practical data management procedures implemented by the SEA TITAN. At a minimum, the DMP will be updated in Month 18 (D8.6) and Month 36 (D8.7) respectively. This document has been prepared by taking into account the “Template horizon 2020 data management plan (DMP)” [Version 1.0. of 10 October 2016] and additional consideration described in ANNEX I: KEY PRINCIPLES FOR OPEN ACCESS TO RESEARCH DATA. ## Research Data Types in SEA TITAN For this first release, the DMP highlights the data types expected to be produced during SEA TITAN project life span, these datasets will be revised on next iterations of the document if found redundant or insufficient. According to such consideration, Table 1 reports a list of indicative types of research data that SEA TITAN will produce. This list may be adapted with the addition or removal of datasets in the next versions of the DMP to take into consideration the project developments. A detailed description of each dataset is given in the following sections of this document. <table> <tr> <th> # </th> <th> Dataset reference </th> <th> Lead partner </th> <th> Related WP(s) </th> </tr> <tr> <td> 1 </td> <td> DS_AMSRM_Performance </td> <td> CIEMAT </td> <td> WP2, WP3, WP4, WP5 </td> </tr> <tr> <td> 2 </td> <td> DS_AMSRM_Feasibility </td> <td> CIEMAT </td> <td> WP2, WP3, WP4, WP5 </td> </tr> <tr> <td> 3 </td> <td> DS_Cooling_System_performance </td> <td> CIEMAT </td> <td> WP6 </td> </tr> </table> ### Table 1. SEA TITAN types of data Specific datasets may be associated to scientific publications (i.e. underlying data), public project reports and other raw data or curated data not directly attributable to a publication. The policy for open access are summarized in the following picture. Research data linked to exploitable results will not be put into the open domain if they compromise its commercialization prospects or have inadequate protection, which is a H2020 obligation. The rest of research data will be deposited in an open access repository. When the research data is linked to a scientific publication, the provisions described in ANNEX II: SCIENTIFIC PUBLICATIONS will be followed. Research data needed to validate the results presented in the publication should be deposited at the same time for “Gold” Open Access ( _Authors make a one-off payment to the publisher so that the scientific publication is immediately published in open access mode_ ) or before the end of the embargo period for “Green” Open Access ( _Due to the contractual conditions of the publisher, the scientific publication can undergo an embargo period up to six months since publication date before the author can deposit the published article or the final peer-reviewed manuscript in open access mode_ ). Underlying research data will consist of selected parts of the general datasets generated, and for which the decision of making that part public has been made. Other datasets will be related to any public report or be useful for the research community. They will be selected parts of the general datasets generated or full dataset and be published as soon as possible. ## Responsibilities Each SEA TITAN partner has to respect the policies set out in this DMP. Datasets have to be created, managed and stored appropriately and in line with applicable legislation. The Project Coordinator has a particular responsibility to ensure that data shared through the SEA TITAN website are easily available, but also that backups are performed and that proprietary data are secured. WEDGE GLOBAL, as WP1 leader, will ensure dataset integrity and compatibility for its use during the project lifetime by different partners. Validation and registration of datasets and metadata is the responsibility of the partner that generates the data in the WP. Metadata constitutes an underlying definition or description of the datasets and facilitate finding and working with particular instances of data. Backing up data for sharing through open access repositories is the responsibility of the partner possessing the data. Quality control of these data is the responsibility of the relevant WP leader, supported by the Project Coordinator. If datasets are updated, the partner that possesses the data has the responsibility to manage the different versions and to make sure that the latest version is available in the case of publicly available data. WP1 will provide naming and version conventions. Last but not least, all partners must consult the concerned partner(s) before publishing data in the open domain that can be associated to an exploitable result. # CHANGELOG This document has been reviewed and no modifications are required so far. # DATASETS DESCRIPTION ## DS_AMSRM_PERFORMANCE Along the AMSRM development, the representative variables to be obtained during the different design and testing procedures are separated in the different stages: calculation of specifications and experimental tests performance. Calculation of the specifications of the PTO During the simulation of the system, corresponding to WP2, the data obtained to define and place the linear generator in the different WEC technologies will be: * Available space. (Length, width, height) * Maximum stroke * Maximum velocity * Maximum force After evaluating the WECs in different scenarios proposed for each WEC technology, different values of: force, velocity and stroke will be obtained. This data will be private, only shared internally for the project partners, since they are sensible data corresponding to the involved technologies. Experimental tests performance Finally, during the laboratory test performance, accomplished in WP5, a set of data will be collected, for each of the scenarios tested, corresponding to one type of WEC technology and a certain sea location, reproducing a certain sea state: * Force values as a function of the current applied to the generator phases, for different velocities and current levels. * Output power, supplied to the grid as a function of the force and velocity. Mechanical power will be also calculated, obtaining a complete global efficiency map. This data will be mostly public, since it is considered they are part of the results obtained from the project and part of the dissemination plan. ## DS_AMSRM_FEASIBILITY The data set are obtained as a result of the design stage of the PTO solution. Based on that solution, a PTO module will be defined to develop a prototype. During the design of both the linear generator, the power converters and the control platform, corresponding to WP3, different variables will be defined as a result of the calculations: * Based on Finite Elements Method (FEM) analysis, force map depending on the position, velocity and the current level. Force validation will demonstrate the feasibility of the proposed solution. * Losses provided by the losses model, depending on the position, velocity and current level. * Expected efficiency map depending on the position, velocity and current level. The losses model and efficiency map will allow to develop an energy matrix to explore the economic feasibility of the system when it is applied to the different WEC technologies. * Thermal behaviour will be analysed along the different operation situations defined, validating the feasibility of the system. This data will be private, only some of these data will be shared internally for the project partners, since they are sensible data corresponding to the know-how of the machine. ## DS_COOLING_SYSTEM_PERFORMANCE Related to the thermal behaviour of the system, considering that PTO will be evaluated for different WEC technologies and sea states, it will be analised in those scenarios the time evolution of temperature in the following points: \- At the linear generator: temperature at the machine coils (at least two measurements), translator magnetic circuit and bearings (at least two measurements) - At the power electronic converters: IGBT case, water cooling fluid, ambient. Related to the SLSG, since only calculation and preliminary design is accomplished during the project, no thermal data will be provided. However, the superconducting solution requires, as one of the main results of the solution definition, a cryostat, being the system in charge of taking the system to the required low temperature. Anyway, only a engineering solution will be defined, no results or data set. # STANDARDS AND METADATA This aspect will be defined as part of task 7.3 Standardization activities, identification and analysis of related existing standards and the contribution to the ongoing and future standardization developments from the results of the project. The participation of a Standardization Body (UNE) provides the relevance, knowledge and experience in the standardization system and its internal procedures. Other project partners will provide the technical support to the development of this task. It is expected to fulfill an analysis of the applicable standardization landscape by M6 and to define in detail the contribution to the ongoing and future standardization developments by M36. As so this part of the document will be updated as soon as more information is available for the consortium. # DATA SHARING During the lifecycle of the SEA-TITAN project datasets will be stored and systematically organized in a database. An online data query tool will be operational by Month 18 and for open dissemination by Month 24. The database schema and the quarriable fields, will be also publicly available to the database users as a way to better understand the database itself. In addition to the project database, relevant datasets will be also stored in ZENODO [5], which is the open access repository of the Open Access Infrastructure for Research in Europe, OpenAIRE [4]. Data access policy will be unrestricted if no confidentiality or IPR issues are expected by the relevant Work Package leader in consensus with the Project Coordinator. All collected datasets will be disseminated without an embargo period unless linked to a green open access publication. Otherwise, in order to protect the commercial and industrial prospects of exploitable results aggregated data will be used in order to limit this restriction. The aggregated dataset will be disseminated as soon as possible. In the case of the underlying data of a publication this might imply an embargo period for green open access publications. Data objects will be deposited in ZENODO under: * Open access to data files and metadata and data files provided over standard protocols such as HTTP and OAI-PMH. * Use and reuse of data permitted. * Privacy of its users protected. # ARCHIVING AND PRESERVATION The SEA-TITAN project database will be designed to remain operational for at least 5 years after project end. By the end of the project, the final dataset will be transferred to the ZENODO repository, which ensures sustainable archiving of the final research data. Items deposited in ZENODO will be retained for the lifetime of the repository, which is currently the lifetime of the host laboratory CERN and has an experimental programme defined for at least the next 20 years. Metadata and persistent identifiers in Zenodo are stored in a PostgreSQL instance operated on CERN’s Database on Demand infrastructure with 12-hourly backup cycle with one backup sent to tape storage once a week. Metadata is in addition indexed in an Elasticsearch cluster for fast and powerful searching. Metadata is stored in JSON format in PostgreSQL in a structure described by versioned JSONSchemas. All changes to metadata records on Zenodo are versioned and happening inside database transactions. In addition to the metadata and data storage, Zenodo relies on Redis for caching and RabbitMQ and python Celery for distributed background jobs.	https://phaidra.univie.ac.at/o:1140797	Horizon 2020
0127_PLANMAP_776276.md	# Executive Summary The updated PLANMAP Data Management Plan (DMP) is provided. Map-wide metadata structure and fields for PLANMAP mapping products are described and exemplified. The long-term data repository venues for PLANMAP products are listed and specified. Delivery file formats are updated, as reflected by deliverables. Described aspects in this DMP include: beneficiaries producing data, adherence to FAIR principles, data types, formats and standards, metadata, documentation, intellectual property and data storage, archiving and curation during and after the project. # Introduction PLANMAP will both use and produce data. Different data categories can be distinguished in the framework of PLANMAP: Base mapping data: * A) Individual higher-level data products derived from raw experiment data (which are archived on PDS or PSA), e.g. map-projected individual images or custom calibrated cubes (e.g. CTX, OMEGA, CRISM) * B) Custom processed or mosaicked data from multiple data products (i.e. derived hyperspectral summary products, multi-image mosaics) available in public archives or repositories (e.g. PSA, PDS, USGS) * C) Individual higher-level data products already produced by experiment teams and available from PDS/PSA archives (e.g. HRSC) * D ) Custom processed or mosaicked data with from multiple data products (i.e. derived hyperspectral summary products, multi-image mosaics) produced by the consortium Integrated mapping products: * Intermediate temporary mapping products (for scientific discussion and sharing within the consortium) o Raster imagery/data o Vector mapping data * finished geological maps (See _D2.1 (Mapping Standards)_ , Rothery et al, 2018, _D2.2-public_ (Morphostratigraphic maps, Rothery et al., 2019) o Standard USGS-like geological maps * Integrated geo-spectral and geo-stratigraphic maps o Geo-structural maps o Geo-modelling maps o Landing site and traverse maps o In-situ integrated maps o Digital outcrop models o Subsurface models * 3D models for Virtual Reality environments (from one or more of the above categories) The fate of datasets and data products belonging to these categories is different. Individual data products (A B, C) are preserved for the long-term in appropriate archives. Their eventual reduction and reprocessing is reproducible, with well-known open source tools, and supported for the long term by robust institutions and agencies (e.g. USGS/NASA). Intermediate mapping products are instrumental to producing final, released and/or published PLANMAP digital mapping products (see section on Data Storage and Management during the project) and their long termstorage is not planned, but documentation, in the form of wiki or individual documents is going to be preserved during the course of the project, and significant summaries and excerpts will be included as deliverable text and annexes, and can also be used as ancillary material attached to scientific publications. The current long-term archiving and availability of PLANMAP data is as follows (please refer to relevant subsections below): * All Raster, vector and layout (pdf) mapping data o Short-term: on the PLANMAP data archive on _https://data.planmap.eu/_ o Long-term: on the ESA PSA DOI-granting guest storage facility on _https://www.cosmos.esa.int/web/psa/psa_gsf_ * Additional ancillary geologic models and specific 3D products o Short-term: on the PLANMAP data archive on _https://data.planmap.eu/_ o Long-term on the Univeristy of Padova DOI-granting data repository on _http://researchdata.cab.unipd.it_ * Additional ancillary specific compositional products o Short-term: on the PLANMAP data archive on _https://data.planmap.eu/_ o Long-term on the INAF DOI-granting data repository # Scope of the document The present document updates the type of data, their characteristics and their use, archiving and preservation plans throughout the PLANMAP project. Intellectual property rights are also clarified, as well as specific per- partner data use and responsibilities. The document outlines the basic data management directions that are going to be updated throughout the project and issued at discrete steps. # Beneficiaries using data All beneficiaries will use data, in either individual or - in most cases - combined forms. Data access to archived (NASA/ESA) mission data is free for anyone. Some data will have temporary team-only access (during an embargo of up to several months), such as mapping data used by PLANMAP researchers (see section on Data Storage and Management during the project) # Beneficiaries producing data All beneficiaries will produce either derived data (higher-level data products) or new data derived by both human and computer/algorithm-assisted mapping. In particular, beneficiaries are set to produce these data categories (see Annex A, B): * UNIPD: * mosaics and higher level products derived from planetary archives o vector mapping products (geologic/geomorphologic maps) o 3D models * (subject to increase/expansion) * OU * mosaics and higher level products derived from planetary archives o vector mapping products (geologic/geomorphologic maps) o (subject to increase/expansion) * WWU * mosaics and higher level products derived from planetary archives o vector mapping products (geologic/geomorphologic maps) o (subject to increase/expansion) * INAF o mosaics and higher level products derived from planetary archives o (subject to increase/expansion) * CNRS o 3D and virtual reality models o digital outcrop maps * vector mapping products (geologic/geomorphologic maps and models) o (subject to increase/expansion) * JacobsUni o mosaics and higher level products derived from planetary archives * vector mapping products (geologic/geomorphologic maps) o (subject to increase/expansion) # Adherence to FAIR principles Data produced by PLANMAP will impact future robotic and space exploration, mainly through mature, finished, published mapping products. Underlying data and special mapping products will be of scientific use also before and beyond that. Accessibility to the data will be provided in three different forms: Findable data: * Longer-term discoverability will be guaranteed via connected Institutional repositories (ESA, UNIPD, INAF), VESPA sharing and inclusion in planetary data archives that are accessible and commonly used by the community. * Shorter-term discoverability will be supported by the PLANMAP web-map and data access Accessible data: * Geological mapping products will have multiple level of accessibility, with variable scale and complexity, from individual units to finished products and thematic maps Interoperable data: * OGC standards for CRS and formats will be adopted * Data discovery interoperability will be granted via the use of state-of-theart VESPA EPN-TAP (Virtual European Solar and Planetary Access EuroPlanet Table Access Protocol) for data search and query. Re-usable data: * Raw data will be used and processed/reduced, with embedded re-usability upstream with respect to PLANMAP * Custom base-map data (e.g. mosaics) and partial mapping products and processed/derived datasets underlying geological mapping products (standard, non-standard, integrated, etc.) will be usable by others, also in the future, regardless of the final geological mapping products. * Integrated and/or final mapping products will be re-usable directly or indirectly, with access to combined information content or individual layers (See _D2.1 (Mapping Standards),_ _D2.2-public_ ) with relevant topologies (units, contacts, etc.). # Data types, formats and standards PLANMAP uses existing datasets and data products and creates new products deriving from combination or derivation of existing, processed data products, as well as from completely new mapping (e.g. units), see _D2.1 (Mapping Standards)_ (Rothery et al., 2018). # Data Raw data Planetary archives, PDS3, PDS4 imagery and cubes. ## Base mapping data OGC-compliant data already available from external entities (e.g. USGS) or base mapping data produced by PLANMAP partners, some in PDS standards/formats. ## Integrated mapping products Integrated mapping products with individual layers are being produced in OGCcompliant formats, both raster and vector, as well as with suitable 3D formats (See Annex A). All individual layers/components of maps are in geospatial format and with CRS suitable for the specific mapping project: in- situ, local (mostly non-standard, see _D2.1 (Mapping Standards)_ ), regional or global (both standard and non-standard). # Metadata The aim of including metadata is to allow reproducibility by providing information about the processing steps performed. Map-wide metadata including both geometric and bibliographic information are provided for each map (e.g. as accessible on _https://data.planmap.eu/_ ). ## Raw data Metadata from processed raw data are the same as those from archived data. SPICE kernel version and software used (e.g. USGS ISIS) should be recorded. Isis Cube labels (i.e. recording cumulative processing steps and used ancillary data, metadata, CRS and alike). The information is going to be recorded in processing labels and as temporary output in ASCII format. ## Base mapping data Projection, cubes and images used, type of control network used, and relevant additional information available from original derived data producers (e.g. USGS, ESA, academic institutions or local PLANMAP base mapping data producers or groups) will be recorded. ## Integrated mapping products Metadata for integrated mapping products will be both map-related and sub-map (i.e. geological unit)-related. Map-related metadata include, as a minimum: * Used datasets and products * Mapping individuals * CRS * Summary of used tools and documented workflow Unit-related data/metadata include, as a minimum, recorded and updated during the mapping processes, see also _D2.1 (Mapping Standards)_ * Individual products and layers used to determine unit extent and contacts * Eventual interpolation/extrapolation of data underlying mapped unit outline * Qualitative assessment on uncertainties involved in the unit determination Authors of the maps, programs, processing and basic info to allow reproducibility of the underlying workflow is included and added to the documentation. Also, geocoding of units (i.e. associating toponyms to locations and mapped surface units) will be produced in order to ease search of at least individual maps as well as individual units and their occurrence within maps (see e.g. _http://geometrics.jacobsuniversity.de/_ , Rossi et al, 2018). ## Map types PLANMAP map-types, as per DoA, include: * S = Stratigraphic * C = Compositional * M = Morphologic * G = Geo-structural * I = Integrated * D = Additional DOM-specific mapping products for individual or multiple lander/rover-imaged outcrops can be included ## Map-level metadata Complementing metadata related to individual units, each map of PLANMAP include several map-wide field, exemplified below: <table> <tr> <th> Field </th> <th> Field description (and example entries) </th> </tr> <tr> <td> Map name (PM_ID) </td> <td> PM-MER-MS-H02_3cc_01 </td> </tr> <tr> <td> Target body </td> <td> Mercury </td> </tr> <tr> <td> Title of map </td> <td> Geologic Map of the Victoria Quadrangle (H02), Mercury </td> </tr> <tr> <td> Bounding box - Min Lat </td> <td> -22.5° </td> </tr> <tr> <td> Bounding box - Max Lat </td> <td> 65° </td> </tr> </table> <table> <tr> <th> Bounding box - Min Lon (0-360) </th> <th> 270° </th> </tr> <tr> <td> Bounding box - Max Lon (0-360) </td> <td> 360° </td> </tr> <tr> <td> Author(s) </td> <td> Valentina Galluzzi; Laura Guzzetta; Luigi Ferranti; Gaetano di Achille; David A. Rothery; Pasquale Palumbo </td> </tr> <tr> <td> Type </td> <td> Released </td> </tr> <tr> <td> Output scale </td> <td> 1:3M </td> </tr> <tr> <td> Original Coordinate Reference System </td> <td> Lambert conformal conic Center longitude: 315° Standard parallel 1: 30° Standard parallel 2: 58° Datum: 2440 km (non-IAU, MESSENGER team datum) </td> </tr> </table> <table> <tr> <th> Data used </th> <th> MESSENGER MDIS BDR v0 uncontrolled basemap (166 m/pixel) MESSENGER MDIS 2013 complete uncontrolled basemap (250 m/pixel) MESSENGER MDIS uncontrolled mosaics v6, v7, v8 (250 m/pixel) MESSENGER MDIS partial mosaic (USGS) (200 mpp) MESSENGER MDIS 2011 albedo partial mosaic (USGS) (200 m/pixel) Mariner 10 + MESSENGER flyby uncontrolled basemap (USGS) (500 m/pixel) MESSENGER MLA DTM (665 m) MESSENGER MDIS M2 flyby stereo-DTM (DLR) (1000 m) </th> </tr> <tr> <td> Standards adhered to </td> <td> Mapping scale: Tobler (1987); Output scale: USGS; Symbology: USGS FGDC and other new symbols </td> </tr> <tr> <td> DOI </td> <td> 10.1080/17445647.2016.1193777 </td> </tr> <tr> <td> Aims </td> <td> Morpho-stratigraphic analysis of Mercury's units and BepiColombo target selection. </td> </tr> </table> <table> <tr> <th> Short description </th> <th> Mercury’s quadrangle H02 ‘Victoria’ is located in the planet’s northern hemisphere and lies between latitudes 22.5° N and 65° N, and between longitudes 270° E and 360° E. This quadrangle covers 6.5% of the planet’s surface with a total area of almost 5 million km2. Our 1:3,000,000-scale geologic map of the quadrangle was produced by photo- interpretation of remotely sensed orbital images captured by the MESSENGER spacecraft. Geologic contacts were drawn between 1:300,000 and 1:600,000 mapping scale and constitute the boundaries of intercrater, intermediate and smooth plains units; in addition, three morpho-stratigraphic classes of craters larger than 20 km were mapped. The geologic map reveals that this area is dominated by Intercrater Plains encompassing some almost-coeval, probably younger, Intermediate Plains patches and interrupted to the northwest, north- east and east by the Calorian Northern Smooth Plains. This map represents the first complete geologic survey of the Victoria quadrangle at this scale, and an improvement of the existing 1:5,000,000 Mariner 10-based map, which covers only 36% of the quadrangle. </th> </tr> <tr> <td> Related products </td> <td> Geologic Map of the Hokusai Quadrangle (H05), Mercury Geologic Map of the Shakespeare Quadrangle (H03), Mercury (pre-Planmap) Geologic Map of the Kuiper Quadrangle (H06), Mercury (prePlanmap) </td> </tr> </table> <table> <tr> <th> Units Definition (polygon styling) </th> <th> Smooth Plains, sp, 255-190-190 Northern Smooth Plains, spn, 245-162-122 Intermediate Plains, imp, 245-122-122 Intercrater Plains, icp, 137-90-68 Crater material-well preserved, c3, 255-255-115 Crater material-degraded, c2, 92-137-68 Crater material-heavily degraded, c1, 115-0-0 Crater floor material-smooth, cfs, 255-255-175 Crater floor material-hummocky, cfh, 205-170-102 </th> </tr> <tr> <td> Stratigraphic info </td> <td> This map has an associated database of craters larger than 5 km used for basic crater frequency analysis for N(5), N(10), and N(20). </td> </tr> <tr> <td> Other comments </td> <td> Since the mapping scale (~1:400k) was much higher than the output scale (1:3M) the polylines of the map were not smoothed. This map is currently being updated to fit the new controlled MESSENGER's end- of-mission basemaps. A post-release boundary merging was done with the H03 and H05 quadrangles. This map uses a legend also for feature labels. </td> </tr> <tr> <td> Heritage used </td> <td> former Mariner 10 map by McGill and King (1983) </td> </tr> <tr> <td> Link to other repositories </td> <td> (crater database link) (shapefiles database link) </td> </tr> <tr> <td> Acknowledgements beyond Planmap </td> <td> This research was supported by the Italian Space Agency (ASI) within the SIMBIOSYS project [ASI-INAF agreement number I/022/10/0]. Rothery was funded by the UK Space Agency (UKSA) and STFC. </td> </tr> </table> Table 1: Exemplary map-wide metadata for PLANMAP products, implemented for a PLANMAP in-kind contribution (Geologic map by Galluzzi et al., 2016) see _https://data.planmap.eu/pub/mercury/PM-MER-MS-H02_3cc_01/_ ## Documentation Documentation of PLANMAP will be available on the project wiki space ( _https://wiki.planmap.eu/display/planmap)_ , which will be kept functional after project-end based on best-effort and availability of resources. The internal wiki space is used for both internal project coordination and technical, scientific documentation. The latter, in evolved form, will be also shared via the project public wiki space ( _https://wiki.planmap.eu/display/public_ ). The types of documentation in the PLANMAP wiki include: * Summary of relevant activities per WP * Procedures and workflows * Mapping use case description * Best practices and recommendations * Tutorials on data handling and mapping * Other documents # Software The software used to access and analyze PLANMAP data will be based on Open Standards, in particular OGC standards. Both Open Source and proprietary software (such as QGIS, ArcGis, and such like) will therefore be suitable for accessing PLANMAP data. A particular case is constituted by the software that is employed for 3d geological modeling, for which open source alternatives rarely exist. For the choice of the software package two criteria will be considered: a) the feasibility for the task that will be undertaken b) the academic licensing scheme that is adopted. Under the same feasibility conditions, software packages granting low-cost/affordable licensing schemes for academic purposes will be favored. The consortium will use a wide range of publicly available Open Source and commercial tools to work and perform mapping tasks. Additionally algorithmic and programmatic methods that add value to interactive human-computer mapping will also use, as far as possible, Open Source tools, packages and libraries. Software, tools and scripts or snippets developed throughout the project will be shared both internally and externally via the PLANMAP GitHub organization and relevant repositories ( _https://github.com/planmap-eu_ ). Some repositories might be private, with access restricted to beneficiaries, during the early phases of the project. Ultimately, all will be made public and will be made available indefinitely after the end of PLANAMP. Data exploitation, accessibility and intellectual property Intellectual property rights on individual science outputs will be held by the scientific collaborators and publishing venue/journal (e.g. individual papers). Data and maps published on the PLANMAP data archive (ESA Guest Storage facility, INAF, UNIPD or other institutional data repositories) and their long-term evolution are cited either via their dataset DOI or via relevant linked publications. # Data and metadata Produced base mapping data are provided as CC-BY (attribution). Published maps (of any kind) are going to be provided, free to use, with CC-BY (attribution). Acknowledgment of the PLANMAP EC H2020 Space project is requested from those using PLANMAP-derived data. A relevant acknowledgement message will be included in the documentation provided to ESA, as well as within the global metadata of VESPA-shared datasets. # Documentation Documentation licensing will follow Creative Commons CC-BY-4.0 ( _https://creativecommons.org/licenses/by/4.0/_ ). Documentation will be also available, complementing or copying information on the public wiki space, on GitHub and possibly other public repositories. At the end of the project the entire body of documentation will be consolidated and available both on the PLANMAP public ( _https://wiki.planmap.eu/display/public_ ) wiki and Github ( _https://github.com/planmap-eu_ ). # Software Software developed by PLANMAP partners is going to be open source, with the possible exception of specific software involving SMEs (e.g subcontracted within virtual-reality tasks). GPLv3 is recommended, or any other license covered by the Open Source initiative ( _https://opensource.org/licenses/category_ ). Specific licensing for WP involving SMEs and potential exploitation beyond the project of pre-existing or specific technological aspects WP5 will be established and documented. Software, tools and scripts produced by PLANMAP will be available as soon as they are considered usable, on the public GitHub organisation ( _https://github.com/planmap-eu_ ). Private repositories will be used during the course of the project, but will cease to exist at its end and all will be made public. # Data/Software citation Archived data used that comes from mission archives will follow the custom of quoting experiment description papers and eventual relevant follow-up papers (e.g. Malin et al., 2007; McEwen et al., 2007; Neukum et al., 2004; Jaumann et al., 2007). Datasets from NASA/ESA archives (PDS, PSA) follow the citation requirements of those archives. In the case of NASA public domain data, the experiment- description papers (e.g. Malin et al., 2007) would be cited in scientific publications. ESA data follow similar citation styles (suggested citations are included in the PSA entry pages). Datasets produced by the PLANMAP consortium will be possibly quoted via: * Relevant peer-reviewed publications or published maps indicated in the dataset metadata (similar to PSA/VESPA) * Dataset-specific DOI, i.e via OpenAIRE/Zenodo/GitHub for relevant datasets * Eventual additional DOI-generating data services that might become available during the project lifetime # Data storage and management during project During day-to-day operations and technical/scientific activities of the consortium, data will be stored on each partner's premises as well as, when relevant, on shared network resources (such as cloud, FTP and web mapping data access services). In principle, data will be made publicly available as soon as possible during the project, respecting publication embargoes. # Data curation, archiving, preservation and security Data curation Base data and maps (See Annex A) undergo archiving review by the archive maintainers (PDS, PSA). If any issue is encountered (e.g. missing or problematic labels, metadata and/or eventual problems with data themselves) the PLANMAP consortium will share those information with respective archive data publishers (PDS, PSA). Mapping is an iterative, interactive process that will go through a few levels of interactive, informal and formal scientific review within the PLANMAP partners and consortium. Before a final map is produced (and its related scientific publication is submitted), preliminary versions will be shared on the PLANMAP web page, wiki and web-mapping data access page ( _https://maps.planmap.eu/_ and its file-based access site on _https://data.planmap.eu/_ ). In case underlying base mapping data require or are subject to improvements that will affect the mapping, newer versions will be used and posted and metadata updated. # Data preservation Input data (based data and maps) are preserved by the respective archives and not under PLANMAP responsibility. Custom higher-level data imagery, cubes, virtual environments and 3D models produced by PLANMAP partners during the course of the project will be preserved on PLANMAP storage services. After the project data will be shared (See Data Sharing subsection), optimally with some redundancy and in different geographic locations, for longer-term availability. # Data security The PLANMAP data processed, produced and analysed are not sensitive. No specific security measures are planned. Data recovery, in case of storage failures will be optimised by the use of central backup and local copies across PLANMAP partner institutions. # Data sharing Data sharing will be performed via 4 possible channels: * Individual partners. E.g., on own web site or repositories using industry standards for geodata (e.g. web-GIS) * PLANMAP consortium, via web-gis and data-access web page, linked form the PLANMAP web page * ESA PSA, upon delivery of data and mapping products * VESPA, via distributed VO-compliant systems for integrated mapping products and, in the future, potentially sub-map, mapping unilt-level access (e.g. individual mapping units). ## EPN-TAP VESPA-based sharing on premise VESPA-shared data contain data-product-level metadata pointing to actual data sources. The release of data (see DoA) is planned in steps, to conclude by the end of the project. Exemplar metadata for mapping products to be released via VESPA: A set of mandatory, documented metadata for VESPA services exists ( _https://voparisconfluence.obspm.fr/display/VES/Implementing+a+VESPA+service_ ), plus optional ones. Those are mostly related to data-products (in PDS sense) with some datasetwide. Individual unit granularity is not yet covered by VESPA technical capabilities. New developments of VESPA (currently not implemented, but envisaged for future VESPA developments within the lifetime of PLANMAP) should allow for metadatabased discovery and search that could extend the geographic data search and experiment metadata search with feature/unit data/metadata search. ## ESA PSA data deliveries Individual used data products already existing in planetary archives (PDS, PSA) will not be released to PSA (already in PDS in either raw or processed form). Exemplary metadata for mapping products to be released via PSA: Release to PSA of non-PDS data geological mapping data, with relevant documentation of a minimum of: * target body * geographic extent (bounding box) of mapping product * CRS * additional fields as described in the Map-wide metadata table in the above sections. Data exchange formats for archived data will include: * For raster data = preferentially Geotiff * For vector data = preferentially OGC/Geopackage * Additional files or the same version of release raster files also provided in different formats might include eg. ISIS3 cube (.cub) format, Envi (.img + .hdr) or alike. A copy of the data in either geotiff (raster) and Geopackage (vector) will be provided in any case, where relevant.	https://phaidra.univie.ac.at/o:1140797	Horizon 2020
0130_ReMAP_769288.md	# Introduction ## Project Summary ReMAP “Real-time Condition-based Maintenance for adaptive Aircraft Maintenance Planning” (hereinafter also referred as “ReMAP” or “the project”), is a European project started on the 1 st of June 2018 and has a duration of four years. The project addresses the specific challenge to take a step forward into the adoption of Condition-Based Maintenance in the aviation sector. In order to achieve this, a datadriven approach will be implemented, based on hybrid machine learning & physics-based algorithms for systems, and data- driven probabilistic algorithms for systems and structures. A similar approach will be followed to develop a maintenance management optimisation solution, capable of adapting to real-time health conditions of the aircraft fleet. These algorithms will run on an open-source IT platform, for adaptive fleet maintenance management. The proposed Condition-Based Maintenance solution will be evaluated according to a safety risk assessment, ensuring its reliable implementation and promoting an informed discussion on regulatory challenges and concrete actions towards the certification of Condition-Based Maintenance. ## Purpose of this Document Deliverable D9.3 Data Management Plan (DMP) addresses the way research data is managed in the ReMAP project within the Open Research Data Pilot (ORD Pilot). The ORD pilot aims to improve and maximise access and re-use of research data generated by Horizon 2020 projects, considering the need to balance openness and protection of sensitive information, commercialisation and Intellectual Property Rights (IPR), privacy concerns, as well as data management and preservation of questions. DMPs are a key element for good data management, as they describe the management of the data to be collected, processed and published during a research project, creating awareness about research data management topics such data storage, backup, data access, data sharing, archiving and licensing. ReMAP hereby states the adherence to the FAIR 1 data principles, whereby research data is made Findable, Accessible, Interoperable and Re-usable for the community, responsibly considering possible data restrictions on public sharing. ## Context This Data Management Plan is closely linked to the report on D10.1 Ethics Requirements – POPD, submitted to the European Commission at the end of October 2018, whereby in section 4, a general overview of ReMAP data management strategy regarding interview data was described. In the following chapters, we enfold the ReMAP DMP making use of the UK Digital Curation Centre template for _Initial DMP_ . It is acknowledged that a DMP is a living document and, therefore, as the implementation of the project progresses and significant changes occur, we will update this plan accordingly on a finer level of granularity at the end of each project period (M18, M36 and M48) using the templates for the _Detailed DMP_ and _Final Review DMP_ . It is important to mention that, at the outset of the project, we have engaged the Data Steward of the Faculty of Aerospace Engineering at Delft University of Technology (TUD), Dr. Heather Andrews ([email protected]). TUD has appointed dedicated Data Stewards 2 at every faculty with the goal of improving awareness of good research data management practices in a disciplinary manner. Data Stewards are then the first point of contact for research data management advice. The input and guidance of the faculty’s Data Steward are at the basis of this plan. # Data summary ### Purpose The aim of ReMAP is to develop a maintenance management optimization solution to monitor the real-time health conditions of aircrafts. The algorithms resulting from this project will run on an open-source IT platform built by the consortium. The data collected, stored, protected and analysed throughout this project consist of: * personal data from interviews and workshop participants (see D10.1 Ethics Requirements – POPD deliverable) and, * technical data (KLM operations, aircraft sensors and structural laboratory tests). Personal data from interviews and workshop participants will be used for both dissemination purposes and research purposes. Data collection, storage, protection and analysis procedures regarding personal data used for dissemination purposes has been already presented in the D10.1 Ethics Requirements – POPD deliverable. In this DMP, only the management of interviews data used for research purposes will be discussed. Regarding the technical data, this consists of: i) data provided by KLM on aircraft operations, health monitoring and flight information; ii) laboratory test data on aircraft structural elements; iii) programming algorithms (code) to analyse and model different aspects of the maintenance management of aircrafts; iv) technology design and assessment results; and v) external data collected from multiple sources relevant for the project (e.g., EUROCONTROL Monoradar Weather Data or EASA Air quality data repository). _Table 1. Research activities to be done per partner_ lists a description of the research that will be carried out by each collaborating partner, and its purpose, focusing on the technical data mentioned above. <table> <tr> <th> ATOS </th> <th> _IT Platform for Integrated Fleet Health Management solution (IFHM)_ Development of an IT platform to collect data from systems and sensors, and provide it to the different algorithms and decission support solutions. Collaborating partners: ENSAM , IPN, KLM, TUD </th> </tr> <tr> <td> CTEC & STEC </td> <td> _Development of sensor technology for Structural Health Management (SHM)_ Procurement, development and integration of promising sensor technologies for damage monitoring in aeronautical composite structures. Collaborating partners: ENSAM, UPAT </td> </tr> <tr> <td> ENSAM </td> <td> _Damage monitoring of complex aeronautic structures by means of Ultrasonic Lamb Waves_ Develop hardware and software systems able to monitor damages in composite structures by means of Lamb waves emitted and received by piezoelectric elements. Collaborating partners: CTEC </td> </tr> <tr> <td> EMB </td> <td> _Development of predictive algorithms for aircraft systems Prognostics & Health Monitoring (PHM) _ Develop algorithms for predicting the Remaining Useful Life (RUL) of aircraft, based on data from aircraft models provided by KLM (KLC). Collaborating partners: ATOS, KLM, ONERA, UTRCI, UC </td> </tr> <tr> <td> KLM </td> <td> _Development, verification and test of an IFHM_ KLM will provide the data to the rest of the partners. Data provided by KLM consist on operations data, aircraft health monitoring data, and flight data for different aircraft models. These datasets are commercially and safety- sensitive data. Thus, they are subject to strict institutional and national rules and protocols (see Annex A). Collaborating partners: ATOS, EMB, ONERA, TUD, UTRCI, UC </td> </tr> <tr> <td> ONERA </td> <td> _Safety risk assessment of the IFHM_ Identification of hazards and safety barriers related with CBM technologies. Future CBM regulations and industrial processes discussion. Collaborating partners: EMB, KLM, ONERA, TUD </td> </tr> <tr> <td> OPT </td> <td> _Design and manufacture of aircraft structure cupons and study of ReMAP’s impact on aircraft weight_ Design, test and manufacture components for experimental tests (WP4). Study of the impact of a Condition-based Maintenance (CBM) approach in weight reduction of aircraft structures. </td> </tr> <tr> <td> </td> <td> Collaborating partners: -EMB, TUD, UPAT </td> </tr> <tr> <td> TUD </td> <td> _Development of predictive algorithms for aircraft structures and systems & maintenance scheduling decision support _ _tool & safety risk assessment _ Develop validated multi-disciplinary SHM system methodologies towards remaining useful life estimation (prognosis) in the presence of adverse conditions during flight. Several sensing technologies are going to be used along with an ambitious extended test campaign. This campaign will result in a massive SHM database upon which the diagnostic and prognostic methodologies are going to be developed and validated. Development of the adaptive aircraft fleet maintenance schedule solution, including the definition of an uncertainty mapping. Model development for safety assessment of CBM technologies included in the _IFHM._ Collaborating partners: CTEC, EMB, ENSAM, OPT, STEC, TUD </td> </tr> <tr> <td> UTRCI </td> <td> _Development of system level Prognostics & Health Monitoring (PHM) & Condition-Based Monitoring (CBM) _ _technologies_ Develop PHM models to predict and detect degradation and failures in aircraft systems and components by using data on aircrafts, weather conditions, component removals, among others. Collaborating partners: ATOS, EMB, KLM, ONERA, UC </td> </tr> <tr> <td> UC </td> <td> _Development of system level Prognostics & Health Monitoring (PHM) & maintenance planning decision support tool _ Enabling edge computing and Actionable information extraction and visualization for optimal maintenance Development of efficient machine learning algorithms for optimal maintenance. Development of a user interface for the maintenance planning decision support tool. Development of an adaptive plan and uncertainty mapping. Collaborating partners: ATOS, EMB, IPN, KLM, ONERA, TUD </td> </tr> <tr> <td> UPAT </td> <td> _Structural Health Management: Diagnostics & Remaining Useful Life (RUL) Prognostics _ Develop validated multi-disciplinary SHM system methodologies towards remaining useful life estimation (prognosis) in the presence of adverse conditions during flight. Several sensing technologies are going to be used along with an ambitious extended test campaign. This campaign will result in a massive SHM database upon which the diagnostic and prognostic methodologies are going to be developed and validated. Collaborating partners: CTEC, EMB, ENSAM, OPT, STEC, TUD </td> </tr> </table> Table 1. Research activities to be done per partner ### Data Types and Formats As explained above, there are two main sets of data in this project: technical data (KLM data, laboratory data, programming algorithms, design and assessment data, external data) and personal data. _Technical data:_ The IFHM solution resulting from ReMAP will be developed, validated and demonstrated based on KLM’s operational data. KLM will follow internal protocols to anonymize all data before sharing it with the consortium partners, meaning that the research team will not have access to personal data. The KLM operational data includes: — Aircraft Health Monitoring data, used as input and/or validation data for to-be-developed model for assessing condition and prognosis of individual aircraft systems. This data is owned by KLM and it is restricted data under governmental and company regulations. — Aircraft Maintenance data, used to support the development of the maintenance schedule solution and as validation data for tobe-developed model for assessing condition and prognosis of individual aircraft system. This data is owned by KLM and it is restricted data under governmental and company regulations. — Risk assessment, used to map and mitigate operation, technical, commercial, economical and health & safety risks that are associated with aircraft maintenance. This data is owned by KLM and it is restricted data under governmental and company regulations. Most of these datasets correspond to log files, reporting documents and tabular data. Health monitoring data and flight data from aircraft models are observational data from sensor measurements. KLM will provide the monitoring data to partners in .csv format, while log files and reports will be provided in .pdf format. It is important to mention no personal information (e.g. ground staff, flight crew, etc.) will be disclosed by KLM. No information will be disclosed that can, directly or indirectly, be linked to KLM staff either. Aside KLM’s data, there will be experimental data from laboratory tests on aircraft structural elements and composite generic elements and subcomponents, typically found in modern commercial aircraft. This type of data will be generated during the project at UPAT and TUD premises. The data correspond to sensor recordings obtained during the tests, as well as Finite Element Analysis outputs from simulation endeavors. Various file formats will be involved depending on the monitoring technique and the associated software that is utilized to record the data. However, all data will be converted to .txt, .csv, or .dat files after some raw data processing to increase data interoperability. The codes that will be generated throughout the project will be mainly in MATLAB, Python and R languages. For their development and validation, researchers will also make use of external data (e.g. on weather conditions, pollution information, etc.) collected from multiple sources, which shall include public repositories and other existing data gathering channels available to partners (e.g., EUROCONTROL Monoradar Weather Data or EASA Air quality data repository). Finally, the information generated with the design and development of sensor technology and aircraft structures will be produced during the project, together with data related with the assessment of the performance of the multiple technologies developed for the IFHM solution proposed. _Personal Data:_ As mentioned in Section 3 of D10.1 Ethics Requirements – POPD deliverable, the project will carry out interviews to team members and external workshop participants. In order to collect, store and use the personal data from interviews, the consortium shall seek the informed consent of each individual, following the policy of the EU for Data Protection (see D10.1 Ethics Requirements – POPD deliverable). The individual subjects will be informed about all aspects of the research in which they are being asked to participate and the future use of the data they might provide. The interviews will be recorded as audio-visual footage. The recording of each interview will be stored in the work laptop/computer of the IPN researcher in charge of the interview. The data will be saved in a private password- protected folder accessed only by the respective IPN researcher. The IPN team or another partner will anonymise and transcribe the data (e.g., into .docx files). The transcriptions will then be shared with relevant consortium researchers via SURFdrive ( _https://www.surfdrive.nl_ ) , which is a password protected cloud storage service. The raw interview data will be then transferred to a private repository in the DataverseNL environment ( _ https://dataverse.nl/) . _ This environment is expected to be safe enough for the recorded material. In case the raw interview data contains highly sensitive information, then the data will be saved in a Project Data drive at TUD. This is a drive maintained by IT TUD and it is meant for confidential data. The folder containing the raw data will be managed by Dimitrios Zarouchas ( [email protected]_ ) from TUD, and accessible to the project coordinator Bruno Santos ( [email protected]_ ) and to Mónica Ferreira ( [email protected]_ ), the IPN coordinator for WP9. If it becomes pertinent for the research purpose of the project, the interview data might be released to the public. This will happen only if the respective interviewee agrees on it via email, reacting to a consent request sent by either IPN (WP9 leader) or TUD (project coordinator) explaining the context, purpose, content to be made public and the right to reject this request, which is assumed by default. If any of the interviewees request not to keep the recordings but for getting notes, they will be deleted from the repositories and only the notes will be circulated among the consortium. ### Data Size The estimated size of the data delivered by KLM to partners is about 2 TB in total, taking as a reference data from 30 aircraft operating during a time frame of 3 years. Included in these 2 TB is also the external data (e.g., weather conditions and pollution information) taken from public repositories and/or data gathering channels available to partners. The estimated size of the laboratory data is expected to be in the order of 100 GB. The processing of the data and its use for algorithm development might be on the order of 1 TB. The data regarding the design and development of sensor technologies and aircraft technology, together with the data resulting from the technology assessment, should be in the order of few tens GB. The estimated size of the interview data considering the audio-visual footage is undetermined, but it might be in the order of few hundred GBs (considering they will mainly be audio files and the transcribed files into .docx documents). ### Data Utility The final outcome of ReMAP will be useful for aircraft manufacturers (OEMs), maintenance service providers and airlines around the world. The data generated throughout the project will be useful for: — The data from the laboratory tests will be useful to structural scientists, OEMs and airline researchers, for the future development and training of SHM prognostics and diagnosis data-driven algorithms. — The sensor technology design and reliability performance data will be useful to sensor companies and OEMs, for the analysis and development of sensor solutions for SHM in future aircraft. — The safety risk assessment data will be essential for the development of the common roadmap towards the implementation of CBM in practice. In particular, it will be useful for the discussion with EASA about continuing airworthiness regulations part-M and part-145, and with the Maintenance Steering Group-3 (or MSG-3) regarding aircraft maintenance procedures and they could be adapted to CBM. — The IFHM validation and test data will also be essential for the development of the common roadmap. In this case, regarding the involvement of maintenance service providers and airlines in a common solution to the implementation of CBM. The results will test the overall capability of the multiple CBM technologies developed, including the adaptive aircraft maintenance schedule approach proposed. # FAIR data ## Making data findable, including provisions for metadata #### Metadata During the research project, KLM will deliver the data to partners in a structured way with proper documentation (README file) indicating (at least): 1. Data origin and collection methodology 2. Structure and organisation of data files 3. Data manipulations applied prior to sharing 4. Variable names and descriptors (if applicable) 5. Definitions of terminology and acronyms At the moment there are no metadata standards defined, but KLM might select a metadata standard at a later stage for partners to use when describing the data. Even though KLM data will not be disclosed to the public (because of safety and commercial reasons; see Annex A), such standard would be the one used for other publishable datasets for consistency within the project. If no discipline specific standard is used, then Dublin Core ( _www.dublincore.org_ ) metadata standard will be adopted (and further information on the data will be delivered in file headers or in separate documentation files). This last statement also applies to documentation files (e.g. reports) and other types of data including experimental data and input/output tabular data for code training, testing and validation. Regarding codes, these will be developed and managed mainly via Gitlab (partners may use their account or use an account provided by ATOS) and Subversion (SVN), where both allow easy metadata attachment. The metadata standard to be adopted for all technology codes will be discussed as part of the IT platform requirements and specifications (WP2). As stated above, if no discipline specific standard is used, then Dublin Core metadata standard will be adopted (and further information on the data will be delivered in file headers or in separate documentation files). Once scientific journal publications are published (in Open Access), publishable data (according to the Consortium Aggrement) will be publicly archived for the long term via the 4TU.Centre for Research Data archive (documentation, experimental data and tabular data; _https://researchdata.4tu.nl/en/home/_ ) , or similar online archives, following their metadata standards (Dublin Core). TUD researchers can, nowadays, upload up to 1 TB of data per year free of charge. This should suffice for the data that will be archived for the long term. The 4TU.Centre for Research Data Archive ensures data will be well-preserved and findable in the long term (each uploaded dataset is given a unique persistent digital identifier). In order to allow for responsible public reuse of the data, datasets will be publicly released under an open content license (CC-BY). More specific, metadata of the files in the dataset will be given in XML format (if necessary) following the standards agreed upon during the project. #### Version Control and File Naming Conventions Partners working on programming algorithms will use Gitlab repositories to collaboratively work with their research team members. Gitlab allows for clear code version management, and it is already available to the respective partners. Subversion tool will also be used by some partners to keep track of versioning for documents related to other technical data files. Reports and other types of data will be managed manually for which file naming conventions will be followed as the project progresses. This will described in D1.1 Project Handbook. ## Making data openly accessible: The data provided by KLM cannot be open to the public for safety and commercial reasons (see Annex A for the provisions for KLM Operational Data use). Processed and/or analysed data might be released to the public via the 4TU.Centre for Research Data Archive after proper discussion with KLM (KLC). Codes and auxiliary scripts built upon the processed data provided by KLM might be released via GitLab after proper discussion with all partners. During the project, laboratory data will be accessible only to consortium partners. Some processed data might be subject to Intellectual Property Rights of the respective partner(s) that generate the data, and thus, will be restricted for use within the consortium only. Whenever a journal article is ready to be published (in Open Access), the laboratory data related to the journal article will be released openly to the public via the 4TU.Centre for Research Data Archive. This data includes all data necessary for the re-use of the results, as well as the data needed to validate them. The final technological outcome of ReMAP will be an open IT cloud platform where the finalized algorithms can work in an interoperable manner. This IT platform will be open source. The platform will be built in a modular way, following an architecture that will allow the integration of third-party data analytics and maintenance management solutions or the exploitation of solutions developed by the ReMAP consortium (these ones, not necessarily open). Regarding the interviews to team members and workshop participants, the raw interview data (audio visual footage) will not be released to the public. It will only be accessible to relevant partners for a maximum period of 10 years and erased afterwards. The data will be kept in the long term for recording and auditing of the project and so it can be used for future research and learning, unless the interviewee does not grant such a permission. Nonetheless, as described, the participants have the “Right to be forgotten”. They can request at any time the elimination of their personal data (such as names, emails, contact details, information from interviews) stored in the project data storage services and IPN servers. If requested by the participants, when given consent for the interview, only anonymized transcripts of the interviews might be used for research purposes. The anonymized transcripts will be available only to relevant consortium partners during the project. If the transcripts are used as material for a journal article, then anonymized transcripts will be published via the 4TU.Centre for Research Data Archive at the same time the journal publication is released. The public outcomes of workshops that can be made public will be shared via DataverseNL, with reference to it via the project’s website. Unless an informed consent is given, the public outcomes from these workshops will be free of any personal reference. The workshop coordinator will be responsible for preparing these results and anonymize the information when necessary. #### Documentation and Software for Data Access As all openly publishable material will be made available via the 4TU.Centre for Research Data Archive, DataverseNL or GitLab, the datasets’ models will be easily downloadable from these platforms with the respective metadata. Open and standard formats will be preferred for archived data files (e.g., .csv, .txt) and code (e.g., python). Proper documentation files will be delivered together with the datasets in order to facilitate reuse of data. In some cases, MATLAB files (.mat) will also be released, as MATLAB will be used to record, process and visualize data in some research lines of ReMAP. MATLAB is a licensed software, widely used in the engineering community, and it is already available to all partners. CATIA software will also be used for computer-aided engineering. This is a licensed software already available to partners. Output design files of CATIA software will be converted to other formats whenever the data can be open to the public, to facilitate reuse. ## Making data interoperable: As mentioned above, all publishable data will be delivered in open and standard data formats. Discipline specific metadata is currently under discussion. If applicable, metadata will be delivered in XML/JSON format together with the data (depending on the chosen format). Proper documentation (README) files will be delivered accordingly. Tabular data and codes (auxiliary scripts) will be archived with informative and explanatory headers to facilitate data re-use and interoperability. All code will be managed via Gitlab and/or SVN, which are interoperable with one another, and are platforms that encourage interoperability between different workflows. The final IFHM solution will gather different algorithms that will be interoperable with each other. The open IT platform over which the algorithms will be implemented, will have proper documentation and manuals for researchers to use. ## Increase data re-use (through clarifying licenses): The data provided by KLM may never be disclosed because of safety and commercial reasons, not after the end of the Project and not after the end of the 4-year non-disclosure term stated in the Consortium Agreement (see Annex A). All other data that cannot be disclosed (except KLM data) will be kept at the respective institutional server for the long term (at least 4 years after the end of the project); accessed only by team members within the institution, for auditing and validation purposes. It is also acknowledged that, for some of the outcomes, copyright and IPR rules as stated in the Consortium Agreement may apply. This will include some of the _Prognostics & Health _ _Monitoring (PHM) solutions to be developed_ . The following release of this data will be determined based on an internal review and decision from the Steering Committee, supported by the Project Board. Since the results from this project will make a strong impact in the aviation sector (airlines, manufactures, maintenance service providers, etc.), we find it is extremely important to share the data responsibly. Hence datasets that will be open to the public will be released along the journal scientific publications after proper discussion with partners. The datasets will be published via repositories such as GitLab (algorithms, code, auxiliary scripts) and the 4TU.Centre for Research Data Archive (documentation, images, tabular data, etc.) under open content licenses in order to increase data re- use (e.g., CC-BY license for documentation and MIT license for software and code). In the same way, and in order to motivate re-use of data, the journal articles associated to these datasets will be published in open access and/or selfarchived in ReMAP’s website and subject repositories, following the publisher’s self-archiving policies. Regarding the final IT platform, this will be distributed, deployed, and explored according to an open source license. The platform itself, and the users may integrate their data analytics solutions or use ReMAP’s proposed solutions (both the ones made available in the public domain, and the ones copyrighted subjected to usage fees). # Allocation of resources ### Costs In principle no costs are expected for the archiving of the publishable data via the 4TU.Centre for Research Data Archive nor via Github. TUD researchers can upload nowadays up to 1 TB of data to the 4TU.Centre for Research Data Archive (per year) free of charge. TUD researchers also have free of charge access to the DataverseNL environment. Also, most of the software used for version control and data processing are already available at each institution, as well as the storage capacity and privately accessed drives managed by each institution’s IT department. The IT platform will be developed and implemented in ATOS infrastructure, using Cloud services with rental costs included in the budget of the project (ATOS ‘other goods and services’ budget). ### Responsibilities The following table specifies the team members who will be in charge of the management of the data, within each research line of study. It is important to mention that each institution has support staff that can provide advice whenever data management issues arise, and who will be contacted if it is necessary. <table> <tr> <th> Partner </th> <th> Name </th> <th> Email address </th> </tr> <tr> <td> TUD </td> <td> Dimitrios Zarouchas </td> <td> [email protected] </td> </tr> <tr> <td> ATOS </td> <td> Javier García Hernández </td> <td> [email protected] </td> </tr> <tr> <td> ENSAM </td> <td> Nazih Mechbal </td> <td> [email protected] </td> </tr> <tr> <td> CTEC </td> <td> Frank Claeyssen </td> <td> [email protected] </td> </tr> <tr> <td> EMB </td> <td> Rúben Menezes </td> <td> [email protected] </td> </tr> <tr> <td> KLM </td> <td> Floris Freeman </td> <td> [email protected] </td> </tr> <tr> <td> OPT </td> <td> Nicole Cruz </td> <td> [email protected] </td> </tr> <tr> <td> UTRCI </td> <td> Anarta Ghosh </td> <td> [email protected] </td> </tr> <tr> <td> UC </td> <td> Bernardete Ribeiro </td> <td> [email protected] </td> </tr> <tr> <td> UPAT </td> <td> Theodoros Loutas </td> <td> [email protected] </td> </tr> </table> In case any of the above team members is unavailable, other research team members (within one institution) will have access to the data, as all data will be stored on the respective institutional servers, with provided access only to team members. # Data security KLM will take care of anonymizing the data before sharing it with partners. Servers that are set-up by KLM will have a redundancy scheme (e.g. RAID5 or alternative) or backup plan to mitigate risks of disk failure. KLM internal servers will keep a copy of the original raw data, in case the processed data is no longer available. Also, KLM will monitor ReMAP’s process through an internal knowledge management system (e.g. Confluence). All data that KLM staff generated for ReMAP will be stored on an Office365 or similar environment, such that other KLM staff can pick up work in case of long-term illness or unforeseen staff changes. Some data will be processed in work laptops of research team members only when allowed (given the sensitivity of the data). Master copies will be kept at the drives of each respective institution. The IT departments of each institution will maintain the data regarding backups (redundancy) and secure storage (protected access to only team members). Only team members within each institution will have access to the data during the research project. Such data access will be set up by the respective IT departments of each institution. The data that will remain close to the public will be archived at each partner’s servers for at least 4 years after the end of the project. Surfdrive will be used for temporal data storage and for data sharing among different partners coordinated by TUD (coordinator). Google Drive might also be used for the sharing and temporal storage of non-sensitive data. # Ethical aspects Please refer to D10.1 Ethics Requirements – Protection of Personal Data (POPD) deliverable for the management of personal data information for both communicational and research purposes. It is important to mention, in case there are ethics-related questions or issues arising throughout the project, these will be reported to Bruno Santos ( [email protected]_ ) and will be discussed accordingly among team members. Extra advise can be discussed with the Human Research Ethics Committee of TUD (at [email protected]_ ). # Other ReMAP will make use of the TUD Research Data Framework Policy which can be found following: _https://www.tudelft.nl/en/2018/library/researchdatamanagement/tu-delft- research-data-framework-policy-published/_	https://phaidra.univie.ac.at/o:1140797	Horizon 2020
0131_SySTEM 2020_788317.md	# Executive Summary This deliverable is the first version of the SySTEM 2020 Data Management Plan (DMP). It has been written in detail so as to be a useful support tool for the project consortium in how to manage data collected during the duration of the project. The DMP presents a data summary, describes the provisions for sharing FAIR data, and addresses data security. It also addresses the allocation of resources and data management roles and responsibilities. In addition, an appendix is included containing the Grant Agreement and Consortium Agreement Provisions. The DMP will be constantly updated throughout the life of the project, including before the first assessment (M15, July 2019) and at the end of the project before the final review (M36, June 2021). It will also be reviewed if there are any significant changes that affect the project, such as changes in relevant policies, necessary adaptations to research methodologies, or any other developments that occur that affect data management. This deliverable includes a guide for the data collection, storage and the ongoing and future activities of handling data, during and even after the project is completed. Detailed information included in the DMP are: data management lifecycle for all data sets that will be collected, processed or generated by the research project. Further, the methodology and standards are outlined and it is explained whether and how this data will be shared and/or made open, and how it will be curated and preserved. # Introduction SySTEM 2020 will focus on science learning outside the classroom, mapping the field across Europe, evaluating a number of transdisciplinary programmes to design best principles for educators in this field, and also examining individual learning ecologies by piloting self-evaluation tools for learners which will document science learning outside of the classroom. This study will map practices in 18 EU countries and Israel, including indepth studies in 8 of these countries, covering learners between 9–20 years from various backgrounds including those from geographically remote, socio-economically disadvantaged, minority and/or migrant communities. This document (Deliverable 1.5 “Data Management Plan”) describes the plan for data management for the duration of the SySTEM 2020 project, and how it will be made available after the end of the project (M36, July 2021). Data is collected for the duration of the project using the following data collection methods: * A longitudinal questionnaire which surveys young people within the ages of 9-20 years old in all of the 19 participating countries on their individual learning ecologies in and outside of the school setting. A consent sheet for the guardians of minor respondents as well as for the respondents themselves is set up. * Experimental sampling method (ESM) is used to implement a further, smaller case survey which is going to be answered by a subset of the young learners participating in the longitudinal questionnaire using an app run on their own smartphone. Alongside this, a smaller subset will also be involved in creating Learning Portfolios and Self-Monitoring Tools. * A large part of the project is the creation of an online data visualisation of STE(A)M initiatives outside the classroom across Europe and beyond. Requirements for the data acquisition, - access and - publishing for this visualisation can be found in detail in D2.2 _User requirements and parameters for the map_ . This rest of this document is structured as follows: * Section 2 is a brief overview of FAIR data, the legal framework, including the EU regulation on personal data protection (GDPR), the H2020 provisions for open access to research data. * Section 3 discusses the different data usage scenarios and the key issues to be examined in relation to each scenario. These issues include decisions on e.g. data anonymization, privacy and security protection measures, licensing etc; * Section 4 is the conclusion, with a description of how the document will be maintained in the future. # FAIR data ## Overview A good DMP under H2020 should comply with the FAIR Data Handling Principles. Sharing data in line with the FAIR principles requires that the data is Findable, Accessible, Interoperable and Reusable (FAIR). The European Commission (2016) considers the FAIR principles fulfilled if a DMP includes the following information: 1. “The handling of research data during and after the end of the project” 2. “What data will be collected, processed and/or generated” 3. “Which methodology and standards will be applied” 4. “Whether data will be shared/made open access”, and 5. “How data will be curated and preserved (including after the end of the project)”. The above information is provided in Section 3 of this document: 1. Data summary (typologies and contents of data collected and produced) 2. Data collection (which procedures for collecting which data) 3. Data processing (which procedures for processing which data) 4. Data storage (data preservation and archiving during and after the project) 5. Data sharing (including provisions for open access) ## Legal framework This section gives a brief overview of the key references in regards to making up the DMP external context. The next paragraphs respectively deal with: 1. The General Data Protection Regulation, which came into force in May 2018; 2. The terms of the H2020 Open Research Data Pilot (ORDP) the SySTEM 2020 consortium has adhered to; 3. The resulting, relevant provisions of both the Grant and the Consortium Agreements; ## The EU Personal Data Protection Regulation (GDPR) Regulation (EU) 2016/679 sets out the new General Data Protection Regulation (GDPR) framework in the EU, notably concerning the processing of personal data belonging to EU citizens by individuals, companies or public sector/non- government organisations, irrespective of their localization. It is therefore important that the SySTEM 2020 consortium takes GDPR into consideration in its data management. GDPR was adopted on 27 th of April 2016 and became enforceable on 25 th of May 2018 after a two-year transition period. The regulation has replaced the previous Data Protection Directive (95/46/EC) and its national implementations. Being a regulation, not a directive, GDPR does not require Member States to pass any enabling legislation but is directly binding and applicable. The GDPR text is available on the Eur-Lex website. The GDPR provisions do not apply to the processing of personal data of deceased persons or of legal entities. They do not apply either to data processed by an individual for purely personal reasons, or activities carried out at home, provided there is no connection to a professional or commercial activity. When an individual uses personal data outside the personal sphere, for socio- cultural or financial activities, for example, then the data protection law has to be respected. On the other hand, the legislative definition of personal data is quite broad, as it includes any information relating to an individual, whether it relates to his or her private, professional or public life. It can be anything from a name, a home address, a photo, an email address, bank details, posts on social networking websites, medical information, or a computer’s IP address. It is worth noting that the specific requirements of GDPR for privacy and security will be separately dealt with in other SySTEM 2020 Deliverables [such as D8.1 (H - Requirement No. 1); D8.2 (POPD - Requirement No. 2); D8.3 (OEI - Requirement No. 3); D8.4 (OEI - Requirement No. 4); D8.5 (H - POPD – Requirement No. 6)]. ## Open Access in Horizon 2020 The European Commission (EC) has launched in H2020 a flexible pilot for open access to research data (ORDP), aiming to improve and maximise access to and reuse of research data generated by funded Research & Development (R&D) projects, while at the same time taking into account the need to balance openness with privacy and security concerns, protection of scientific information, commercialisation and intellectual property rights (IPR). This latter need is aided by an opt-out rule, according to which it is possible at any stage - before or after the GA signature - to withdraw from the pilot, but legitimate reasons must be given, such as IPR/privacy/data protection or national security concerns. With the Work Programme 2017 the ORDP has been extended to cover all H2020 thematic areas by default. This has particularly generated the obligation for all consortia to deliver a Data Management Plan (DMP), in which they specify what data the project will generate, whether or not It will be freely disclosed, how it will be made accessible for verification and reuse, and how it will be curated and preserved. The ORDP applies primarily to the data needed to validate the results presented in scientific publications. Other data can however be provided by the beneficiaries of H2020 projects on a voluntary basis. The costs associated with the Gold Open Access rule, as well as the creation of the DMP, can be claimed as eligible in any H2020 grant. The SySTEM 2020 consortium has decided to adhere to the Green and Gold Open Access rule. # SySTEM 2020 Data Management Plan In this section, the different data usage scenarios will be discussed in detail, in relation to data summary, data collection, data processing, data storage, data sharing, and finally data security. In this way, the data management lifecycle of the SySTEM 2020 project will be presented in full. The three scenarios that make up the SySTEM 2020 data management lifecycle are: 1. Original data produced by the SySTEM 2020 consortium and/or individual members of it (e.g. the questionnaires with young people, the credentialisation tool and the population of the map); 2. Existing data already in possession of the SySTEM 2020 consortium and/or individual members of it prior to the beginning of the project (see Appendix 1); 3. Existing data sourced/procured by the SySTEM 2020 consortium and/or individual members of it during the project’s timeline. It is also important to note that the datasets handled within the three above scenarios can belong to either of these three categories: • Confidential data (for business and/or privacy protection); • Anonymised and Public data (these two aspects go hand in hand); • Non anonymised data (the residual category). ## Data Summary The following table summarizes the typologies and contents of data collected and produced during the project timeline. <table> <tr> <th> </th> <th> </th> <th> TYPES OF DATASETS </th> <th> </th> </tr> <tr> <td> DATA USAGE SCENARIOS </td> <td> Confidential </td> <td> Anonymised, Pseudonymised and Public </td> <td> Non anonymised </td> </tr> <tr> <td> Original data produced by the SySTEM 2020 consortium </td> <td> Any possible sensitive data from the questionnaires, the map, learning portfolios, experience sampling method, codesign meeting and credentialisation tool Personal data from young people doing the surveys and their parents, such as email and mail addresses and phone numbers New contacts established </td> <td> Summaries of questionnaires/interviews/ learning portfolios Photos/videos of learners shot during the activities will now include names End user data, stakeholders and policy makers data on public display Contact data within deliverables </td> <td> Photos/videos shot of adults during public events and project workshops Audio recordings (e.g. Skype) Data in the project internal repositories </td> </tr> <tr> <td> Existing data sourced/procured by the SySTEM 2020 consortium and/or partners </td> <td> Data embedded in some of the Background knowledge (see Appendix A) Contact databases </td> <td> Data embedded in some of the Background knowledge (see Appendix A) Data embedded in case studies materials </td> <td> N/A </td> </tr> <tr> <td> Existing data already in possession of the SySTEM 2020 consortium and/or partners </td> <td> Raw data in possession of the pilots or of any third party involved in the pilots </td> <td> Free and open data (including from scientific and statistical publications) </td> <td> N/A </td> </tr> </table> Table 1 : Summary of relevant data for the SySTEM 2020 research agenda * For any photos/videos shot during the project internal events and meetings, as well as public events related to the project, it is crucial to collect an informed consent form from all the participants, with an explicit disclaimer in case of intended publication of those personal images on e.g. newspapers, internet sites, or social media groups. This will bring the data back into the Confidential category, where it is legitimate to store and/or process it for legitimate reasons. When sharing photos/videos of learners, no names will be provided ensuring greater privacy. * For any audio recordings stored, e.g. in the project’s official repository (currently Team Drive) or in individual partners’ repositories, care must be taken of the risk of involuntary disclosure and/or the consequences of misuse for any unauthorized purpose. Same goes for the personal data of each partner in the consortium. * Informed consent forms must be signed (also electronically) by all participants taking part in questionnaires, learning portfolios and interviews. Detailed procedures on informed consent and their storage are reported in the deliverables for WP8. * Informed consent is also required when using available contacts (be they pre-existing to the project or created through it) to disseminate information via e.g. newsletters or dedicated emails. In this respect, the GDPR provisions are particularly binding and must be carefully considered. * As a general rule, access conferred to Background knowledge on a royalty free basis during a project execution does not involve the right to sublicense. Therefore, attention must be paid by each partner of SySTEM 2020 to ensure the respect of licensing conditions at all times and by every member of the team. * This also applies to any dataset sourced or procured from third parties during the SySTEM 2020 project’s lifetime. The following table describes how the DMP is most relevant for each WP: <table> <tr> <th> Work Package </th> <th> The DMP is most relevant for... </th> </tr> <tr> <td> WP1: MANAGE </td> <td> How the DMP will be used as a document to define the how the project data will be collected and stored </td> </tr> <tr> <td> WP2: MAP </td> <td> How the data collected in the map will be stored, how map participants will be informed that the data is being collected and opt in to be contacted </td> </tr> <tr> <td> WP3: EXAMINE </td> <td> How the data from the questionnaires will be collected and stored, analysed and then the results made public with responses anonymised </td> </tr> <tr> <td> WP4: IDENTIFY & CO-DESIGN </td> <td> How the data collected during the contextual inquiry and co-design workshop will be stored, analysed and shared – taking into account any needs to anonymise results based on the sensitivity of the data. </td> </tr> <tr> <td> WP5: DEVELOP & EXECUTE </td> <td> How the data collected through the self-evaluation tools for learners, facilitators and organisers will be collected, stored and analysed, How the Learning Portfolios will be stored, and anonymised, or pseudonymised where appropriate, before being shared publicly. </td> </tr> <tr> <td> WP6: EVALUATE </td> <td> How the data collected through experience sampling method and further evaluation and self-reflection techniques can be collected, stored and analysed according to this DMP. In accordance with the required consent, collected data will be made available as open-access dataset. </td> </tr> <tr> <td> WP7: SHARE </td> <td> How the results from the project will be shared via open access and made public, while sensitive data still being anonymised. </td> </tr> <tr> <td> WP8: ETHICS </td> <td> How data management will be referenced in the consent forms, and GDPR followed. </td> </tr> </table> Table 2 : Description of relevance of DMP for each WP in the SySTEM 2020 project ## Data Collection The following table summarizes the procedures for collecting project related data. <table> <tr> <th> </th> <th> TYPES OF DATASETS </th> <th> </th> </tr> <tr> <td> DATA USAGE SCENARIOS </td> <td> Confidential </td> <td> Anonymised and Public </td> <td> Non anonymised </td> </tr> <tr> <td> Original data produced by the SySTEM 2020 consortium </td> <td> Questionnaires, experience sampling method, interviews, Learning Profiles, workshops, meeting with stakeholders, co-design sessions, evaluation sessions </td> <td> Newsletters Publications Open Access repositories </td> <td> Events coverage – directly or via specialised agencies A/V conferencing systems Internal repositories </td> </tr> <tr> <td> Existing data sourced/procured by the SySTEM 2020 consortium and/or partners </td> <td> Seamless access and use during project execution </td> <td> Seamless access and use during project execution </td> <td> N/A </td> </tr> <tr> <td> Existing data already in possession of the SySTEM 2020 consortium and/or partners </td> <td> Licensed access and use during project execution </td> <td> Free, open access and use during project execution </td> <td> N/A </td> </tr> </table> Table 3 : Summary of SySTEM 2020 data collection procedures Data will be collected in both paper and digital forms (CSV, PDF, Word, xls spreadsheets and textual documents will be the prevalent formats). For the data collected via paper (e.g. the questionnaires), these documents will be scanned by the partners and third parties and stored digitally. Original copies will be destroyed within 2 months of being collected. In case of audio/video recordings and images, the most appropriate standards will be chosen and adopted (such as .gif, .jpg, .png, .mp3, .mp4, .mov and .flv). Website pages can be created in .html and/or .xml formats. Research data in the SySTEM 2020 project is primarily generated by the members of the consortium. This data mainly takes the form of questionnaires, Learning Portfolios, and data collected from the map. Research data generated throughout the project includes primarily qualitative material, as well as some quantitative information, e.g. information about the questionnaire participants. Curated and anonymised materials will be made publicly available. This includes the following: * Data gathering and analysis templates; * Templates and guidelines for evaluation data gathering and analysis; * Completed templates from each pilot containing evaluation material from workshops and other activities/events carried out in the co-creation labs; * Questionnaire responses (originally Microsoft Excel documents); * Intermediate evaluation analysis outputs based on data gathered in workshops and * other activities/events carried out in the co-creation labs. ## Data Processing The following table summarizes the procedures for processing SySTEM 2020 related data that can be envisaged at this project’s stage. <table> <tr> <th> </th> <th> </th> <th> TYPES OF DATASETS </th> <th> </th> </tr> <tr> <td> DATA USAGE SCENARIOS </td> <td> Confidential </td> <td> Anonymised and Public </td> <td> Non anonymised </td> </tr> <tr> <td> Original data produced by the SySTEM 2020 consortium </td> <td> Anonymisation Analyses/ Visualisation </td> <td> Qualitative and quantitative evaluation Analyses/ Visualisation </td> <td> Selection/ destruction Blurring of identities </td> </tr> <tr> <td> Existing data sourced/procured by the SySTEM 2020 consortium and/or partners </td> <td> Anonymisation Statistical evaluation </td> <td> Analyses/ Visualisation Qualitative and quantitative evaluation </td> <td> N/A </td> </tr> <tr> <td> Existing data already in possession of the SySTEM 2020 consortium and/or partners </td> <td> Anonymisation Statistical evaluation </td> <td> Analyses/ Visualisation Qualitative and quantitative evaluation </td> <td> N/A </td> </tr> </table> Table 4 : Summary of SySTEM 2020 data processing procedures State of the art tools will be used to process/visualise the data used or generated during the project. Typically, the partners are left free to adopt their preferred suite (such as Microsoft Office TM for PC or Mac, Apple’s iWork TM and OpenOffice TM or equivalent). However, the following tools are the ones mainly used by the consortium: * Google’s shared productivity tools (so-called G-Suite TM ) are used for the cocreation of outputs by multiple, not co-located authors; * Adobe Acrobat TM or equivalent software is used to visualise/create the PDF files; * Photoshop TM or equivalent software are used to manipulate images; * State of the art browsers (such as Mozilla Firefox TM , Google Chrome TM , Apple Safari TM and Microsoft Internet Explorer TM ) are used to navigate and modify the Internet pages, including the management and maintenance of social media groups; * Google hangouts or Skype TM (depending on the number of participants) are the selected tools for audio/video conferencing, which may also serve to manage public webinars; * Tools like Limesurvey and Google Forms are used for the administration of online surveys with remotely located participants; * Dedicated YouTube TM channels can help broadcast the video clips produced by the consortium to a wider international audience, in addition to the project website; * Mailchimp TM or equivalent software is helpful to create, distribute and administer project newsletters and the underlying mailing lists; * At the moment email only is used for the consortium internal communication flow; 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 of where data was created. ## Data Storage The following table summarizes the procedures for storing project related data, during and after the SySTEM 2020 lifetime, and the most frequently used repositories. <table> <tr> <th> </th> <th> </th> <th> TYPES OF DATASETS </th> </tr> <tr> <td> DATA USAGE SCENARIOS </td> <td> Confidential </td> <td> Anonymised and Public </td> <td> Non anonymised </td> </tr> <tr> <td> Original data produced by the SySTEM 2020 consortium </td> <td> Individual partner repositories Common project repository (the current one is Team Drive) </td> <td> Project website </td> <td> Individual partner repositories Common project repository </td> </tr> <tr> <td> Existing data sourced/procured by the SySTEM 2020 consortium and/or partners </td> <td> Individual partner repositories Specific software Repositories </td> <td> Project website </td> <td> N/A </td> </tr> <tr> <td> Existing data already in possession of the SySTEM 2020 consortium and/or partners </td> <td> Individual partner repositories Third party repositories Cloud repositories </td> <td> Project website </td> <td> N/A </td> </tr> </table> Table 5 : Summary of SySTEM 2020 data storage procedures Google Team Drive is the selected tool for SySTEM 2020’s data and information repository since it is GDPR compliant. This includes both the project deliverables (including relevant references utilised for their production or generated from them as project publications, e.g. journal articles, conference papers, e-books, manuals, guidelines, policy briefs, white papers etc.) and any other related information, including relevant datasets. Additionally, the project coordinator will make sure that the official project repository periodically generates back-up files of all data, in case anything may get lost, corrupted or become unusable at a later stage (including after the end of the project). The same responsibility goes to each partner for the local repositories utilised by them (in some cases, these are handled by large organisations such as Universities; in others, by small organisations or even personal servers or laptops). As the license that the consortium establishes for final datasets will still have to be determined, their intermediate versions will be deemed as business confidential , and restricted to circulation only within the consortium. Finally, each digital object identified as a R&D result, including their associated metadata, will be stored in a dedicated open access repository managed by SGD, to the purpose of both preserving that evidence and making it more visible and accessible to the scientific, academic and corporate world. In addition to the SGD open access server, other datasets may be stored on the following repositories: * The SySTEM 2020 website (with links on/to the Social Media profiles); * Individual Partner websites and the social media groups they are part of; * The portals of the academic publishers where scientific publications will be accepted; * Other official sources such as OpenAIRE/Zenodo and maybe EUDAT ## Data Sharing Data sharing will be a very important aspect of the SySTEM 2020 project, however it needs to be ensured that it is done in a useful and legitimate manner. When sharing, it is of utmost importance to keep in mind, not only the prescriptions and recommendations of extant rules and norms (including this DMP), as far as confidentiality and personal data protection are concerned, but also the risk of voluntary or involuntary transfer of data from the inside to the outside of the European Economic Area (EEA). In fact, while the GDPR applies also to the management of EU citizens personal data (for business or research purposes) outside the EU, not all the countries worldwide are subject to bilateral agreements with the EU as far as personal data protection is concerned. For instance, the US based organisations are bound by the so-called EU-U.S. Privacy Shield Framework, which concerns the collection, use, and retention of personal information transferred from the EEA to the US. This makes the transfer of data from the partners to any US based organisation relatively exempt from legal risks. This may not be the same in other countries worldwide, however, and the risk in question is less hypothetical than one may think, if we consider the case of personal sharing of raw data with e.g. academic colleagues being abroad for the purpose of attending a conference. It is also for this reason that the sharing of non- anonymized data is discouraged for whatever reason, as shown in the table. It must be kept in mind that one of the SySTEM 2020 partners is based in Israel, outwith the EU. However, it is one of the countries associated to Horizon 2020. “ _The association to Horizon 2020 is governed by Article 7 of the Horizon 2020 Regulation._ _Legal entities from Associated Countries can participate under the same conditions as legal entities from the Member States. Association to Horizon 2020 takes place through the conclusion of an International Agreement._ ” <table> <tr> <th> </th> <th> TYPES OF DATASETS </th> </tr> <tr> <td> DATA USAGE SCENARIOS </td> <td> Confidential </td> <td> Anonymised and Public </td> <td> Non anonymised </td> </tr> <tr> <td> Original data produced by the SySTEM 2020 consortium </td> <td> Personal email communication Shared repositories </td> <td> Project website Open access repository </td> <td> N/A </td> </tr> <tr> <td> Existing data sourced/procured by the SySTEM 2020 consortium and/or partners </td> <td> Personal email communication Shared access to software repositories </td> <td> Project website Open access repository </td> <td> N/A </td> </tr> <tr> <td> Existing data already in possession of the SySTEM 2020 consortium and/or partners </td> <td> Personal email communication Shared repositories </td> <td> Project website Open access repository </td> <td> N/A </td> </tr> </table> Table 6 : Summary of SySTEM 2020 data sharing procedures We intend to make curated research data from the questionnaires, Learning Portfolios and map accessible, as well as cross-cutting 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 SySTEM 2020 model for researching science learning outside the classroom. 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. In regards to how long is the intention that the data remains re-usable, we will adhere to the standard of the chosen repositories for the project. The curated research data related to the activities conducted in the questionnaires will be made available at M32 (after the collection and analysis is finished). By the end of the project at M36 all data that is not affected by embargo will be made available through the appropriate repositories. ## Data Security Research data is shared between project partners and stored in a collaborative online working platform during the project’s lifetime Google Team Drive: _https://gsuite.google.com/learning-center/products/drive/get-started-team- drive/#!/_ Team Drive is provided by Science Gallery Dublin as project coordinator. Science Gallery Dublin is using the GSuite for Education licence, which is provided at no charge by Google. Team Drive is ISO 27017 certified for Cloud Security and is fully compliant with GDPR regulations. Uncurated and unanalysed material created during the project is stored locally by the SySTEM 2020 partners according to their institutional data management and storage guidelines (see D8.1 and D8.2). All final versions of evaluation data collected in the project and analysis outputs of this material will be saved in a standardised filing system with dedicated naming conventions. Consent forms will be kept beyond the end of the SySTEM 2020 project as detailed in D8.1 and D8.3. 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. # Conclusions and Future Work ## Conclusions This document is the fundamental deliverable concerning the SySTEM 2020 Data Management Plan (DMP) in fulfilment of the requirements of WP1. The main reason for planning an early version of the DMP is so it can be the most useful for the lifetime of the project in being a guideline for the activities to follow beginning in M9 with the campaigning of the map and the questionnaires. However, to ensure the right balance between the current data management framework and the real data that will come from the questionnaires, the Experience-Sampling Method, Learning Portfolios and the map, the project coordinators will check with the Executive Board every six months, if any changes need to be made to the DMP. The DMP has gathered together all the information regarding legal frameworks and GDPR, and has described how the SySTEM 2020 will align with the principles of FAIR data handling according to the EC requirements (and that the SySTEM 2020 consortium and individual partners and third parties are bound to respect). It has also summarized the key aspects of data collection, processing, storage and sharing (the typical contents of a DMP) within the proposed data lifecycle elements and particularly highlighting - first and foremost, to the attention of the partners - some key aspects of data management that go beyond the operational link with open access and interfere with privacy and security policies as well as with the way background knowledge. It is hoped that the DMP will enable all partners and third parties to understand the different actions required when handling data of different natures, and how to store and share it securely, in keeping with the EC requirements. ## Maintenance of Data Management Plan ### Responsibilities of Data Management The responsibilities for the data management are distributed and controlled as follows: _Data collection, storage and backup:_ The partners and third parties who collect data (e.g. questionnaires and Learning Portfolios). The responsibilities also include taking care for data security and personal data protection. Project partners who receive data from data collectors for project evaluation (AALTO and ZSI) have the same obligations. _Metadata production in view of depositing the research data:_ The partners and third parties based on guidelines provided by the project coordinators (SGD). They will be provided with templates for the tasks involving data, which they will be required to fill in This will ease and ensure consistency of the data provision, which will be controlled by the project coordinators. _Data deposition and sharing:_ The project coordinators (SGD), involving individual partners where further information or clarification is required (e.g. with regard to content authors or contributors, related material). _Maintenance and updates of the Data Management Plan:_ The DMP will be maintained and updated by the project coordinators in consultation with the Executive Board. Updates will be done according to the planned schedule and when needed due to changes in consortium policies, research methodology or other significant developments which affect the DMP. _Responsibilities in general:_ Each partner or third party is obliged to respect and follow the rules of the Grant Agreement and the Guidelines to the Rules on Open Access to Scientific Publications and Open Access to Research Data in Horizon 2020 (European Commission 2017). Support by the project coordinator or another partner in following the rules does not transfer these obligations to the supporting partner. ### Data Management Plan Maintenance The SySTEM 2020 Data Management Plan will be maintained and updated by the project coordinators of the project (SGD) in consultation with the Executive Board. The DMP is a “living document” that will be updated at least before the first assessment of the project (M15, December 2018) and at the end of the project lifecycle, before the final review (M36, September 2020). Furthermore the plan will be updated if needed due to changes in consortium policies, research methodology or other significant developments which affect the DMP. ### Contacts for the Data Management Plan Kali Dunne, Science Gallery Dublin (Project Manager): [email protected]_ Joanna Crispell, Science Gallery Dublin (European Projects Researcher): [email protected]_ Derek Williams, Science Gallery Dublin (Technical Manager): [email protected]_	https://phaidra.univie.ac.at/o:1140797	Horizon 2020
0133_WorkingAge_826232.md	# Executive Summary The Data Management Plan describes all the data management processes related to the WorkingAge project. First of all, the document will define some general principles about data management policy and scientific publications in research context. Then, the Data Management Plan will define all the procedures to collect, manage and store data with the priority to be GDPR compliant. The document will define also technical procedures, such as pseudonymization and data encryption, directly related to the GDPR compliance. Finally, the Data Management Plan will describe official figures already stated by the GDPR, such as Data Controller, who will deal with Data Management and Data Protection issues. # 1\. Introduction This document is Version 1 of the Data Management Plan (DMP), presenting an overview of data management processes, as agreed among WorkingAge (WA) project’s partners. This DMP will first establish some general principles in terms of data management and Open Access. Subsequently, it will be structured as proposed by the European Commission in H2020 Programme – Guidelines on FAIR Data Management in Horizon 2020, covering the following aspects: * Data Summary; * FAIR Data; * Allocation of resources; * Data security; * Ethical aspects; The DMP is a “living” document outlining how the research data collected or generated will be handled during and after the WorkingAge project. The DMP is updated over the course of the project whenever significant changes arise. # 2 General principles for data management ## 2.1 Data collected and personal data protection Within the WorkingAge (WA) project, partners collect and process research data and data for general project management purposes, according to their respective internal data management procedures and in compliance with applicable regulations . Data collected for general purposes may include contact details of the partners, their employees, consultants and subcontractors and contact details of third parties (both persons and organisations) for coordination, evaluation, communication, dissemination and exploitation activities. Research data are collected and processed in relation with the research pilots. During the project lifetime, data are kept on computers dedicated to this purpose, which are securely located within the premises of the project partners. Data archiving, preservation, storage and access, is undertaken in accordance with the corresponding ethical standards and procedures of the partner institution where the data is captured, processed or stored. The data is preserved for a minimum of 10 years (unless otherwise specified). All data susceptible of data protection are subject to standard anonymization and stored securely (with password protection). The costs for this are covered by the partner organization concerned. Confirmation that the aforementioned processes comply with national and EU legislation is provided by each partner and verified by each Data Controller. # 3 Research data and Open Access The WorkingAge project is part of the H2020 Open Research Data Pilot (ORDP) and publication of the scientific results is chosen as a mean of dissemination. In this framework, open access is granted to publications and research data and this process is carried out in line with the Guidelines on Open Access to Scientific Publications and Research Data in Horizon 2020. The strategy to apply Open Access for the project’s scientific results is revised, step by step, according to personal data protection regulations, the results of the ethical approval process of the research protocols and the provisions of the Consortium Agreement. If needed, it will be possible to “opt out” from this open access strategy for specific and well-defined subsets of data. ## 3.1 Scientific publications Open access is applicable to different types of scientific publication related to the research results, including its bibliographic metadata, such as: * journal articles; * monographs and books; * conference proceedings, abstract and presentations;  grey literature (informally published written material). Grey literature includes also reports and deliverables of the projects related to the research, whose Dissemination level is marked as Public. Open access is granted as follows: * Step 1 – Depositing a machine-readable electronic copy of a version accepted for publication in repositories for scientific publications (before or upon publication). * Step 2 – Providing open access to the publication via the chosen repository. For access to publications, a hybrid approach is considered (both green OA and gold OA), depending on the item and the dissemination channels that will be available: * Green OA (self-archiving) – depositing the published article or the final peer-reviewed manuscript in repository of choice and ensure open access within at most 6 months (12 months for publications in the social sciences and humanities). * Gold OA (open access publishing) – publishing directly in open access mode/journal. ## 3.2 Data Management Policy The Data Management Policy will address the points below and will detail the current status of reflection within the consortium regarding the data that is being produced. According to ORDP requirements, the WorkingAge DMP observes FAIR (Findable, Accessible, Interoperable and Reusable) Data Management Protocols. In order to better proceed with Data Management and Data Protection issues, all partners which deal with data require the figure of the Data Controller, this role will be achieved by the legal representative of each partner managing (i.e. generating and/or processing) data, as stated by the Article 4 and the Article 24 of the GDPR. Each partner might nominate a Local Data Manager, who will manage data on behalf of Data Controllers as a result of a relationship that links them. Local Data Managers will be responsible for contacting the users in order to provide the information sheet for the WA project and obtain consent forms. ## 3.3 Research data In addition, open access is granted also to underlying research data (data needed to validate results presented in publication) and their associated metadata, any other data (not directly attributable to the publication and raw data) and information on the tools needed to validate the data and, if possible, access to these tools (code, software, protocols etc.). Open access is granted as following. * Step 1 – Depositing the research data in a research data repository. A repository is an online database service, an archive that manages the long-term storage and preservation of digital resources and provides a catalogue for discovery and access. * Step 2 – Enabling access and usage free of charge for any user (as far as possible). The consortium will try to publish as much research data as possible, but this will be decided on a case-by-case basis, in order to be compliant with the GDPR in terms of publishing non-sensitive and personal data. ## 3.4 Other project’s outcomes As per any other outcomes of the project, they are disseminated accordingly to the Dissemination level indicated in the Description of Action and they are also subject to protection in accordance with the Consortium Agreement and in reference to Access Rights. # 4 FAIR Data management plan ## 4.1 Data summary The Data Summary provides an overview of the purpose and the nature of data collection and generation, and its relation to the objective of the WorkingAge (WA) project. ### 4.1.1 Objectives of the project and research The WA project as a complex research requires careful planning, management and administration in its development and implementation. Work has been structured in ten work packages: six covering Research and Innovation work, two for test (Integration and User tests), one for exploitation and dissemination, one for the definition of all specifications and one management work package. WP1 will include all management issues. Prior to the main research cycle, the consortium will participate in WP2: this WP intends to set the bases for all ulterior research including the selection of tools and participants, optimizing the time for the tests. Research cycles (Expected at least two) will start with the definition of the interventions for the users (WP3) at work and in daily life process. This work will be the base for WP4 (HCI platform), WP5 (IOT infrastructure and services), WP6 (Data Analysis) which occur in parallel (and sharing produced knowledge). Ethics and security domain performed during WP7. Deployment and Integration (WP8) in which the final prototypes and the optimization of the research cycles will be adapted for the tests. The intervention models and measurement prototypes will be then integrated and used to collect data in the WP9 (Test Performance). In WP10 Standardization and Business Development, Commercialization and IPR Management will be worked considering the future the market release of the studied solution. This WP will also summarize all the actions proposed for dissemination. ### 4.1.2 Purpose of the data collection during the project WorkingAge will use innovative Human-Computer-Interaction (HCI) methods (augmented reality, virtual reality, gesture/voice recognition and eye tracking, neurometrics) to measure the user’s cognitive and emotional states and create communication paths. At the same time with the use of Internet of Things (IoT) sensors will be able to detect environmental conditions. The purpose is to promote healthy habits of users in their working environment and daily living activities in order to improve their working and living conditions. ### 4.1.3 Relation to the objectives of the project By studying the profile of the >50 (year old) workers and the working place requirements in three different working environments (Office, Driving and Manufacturing), both profiles (user and environment) will be considered. Information obtained will be used for the creation of interventions that will lead to healthy ageing inside and outside the working environment. WorkingAge will test and validate an integrated solution that will learn the user’s behaviour, health data and preferences and through continue data collection and analysis will interact naturally with the user. This innovative system will provide workers assistance in their everyday routine in the form of reminders, risks avoidance and recommendations. In this way the WorkingAge project will create a sustainable and scalable product that will empower their user's easing their life by attenuating the impact of aging in their autonomy, work conditions, health and well-being. ### 4.1.4 Processing of the data and consent form Processing of all the WorkingAge project data will take place in several countries, complying with GDPR and other local legislation. The consent form in paper format will be stored in the correspondent country in which they are generated. ### 4.1.5 The types and formats of data generated/collected All the data are stored in digital way and the different types are defined as follows: #### 4.1.5.1 Raw data Raw data is data produced by all the devices used in the measurements: EEG (Electroencephalography), ECG (Electrocardiography), GSR (Galvanic Skin Response), Camera (video and images), Voice Recognition, Movement and Pose Recognition. #### 4.1.5.2 Online pre-processing Online pre-processing is the action of checking the quality and/or decoding and/or modifying raw-data before storing or uploading them (e.g. by filtering techniques). #### 4.1.5.3 Online markers / annotation / indicators calculation Thanks to sensors data and online calculations one can identify specific events (e.g. interruptions during the working task, etc.) and/or compute various indicators (e.g. performance indicators). #### 4.1.5.4 Offline markers / annotation / indicators calculation Thanks to observers and/or offline calculations one can retrieve contextual information and/or identify specific events (e.g. unsafe situations, events complex to automatically identify online), and/or various indicators. Such process can be used to enrich and/or manually/automatically annotate or mark information in the database. #### 4.1.5.5 Offline data acquisition Some data are acquired using either questionnaires or interviews that are potentially supported by markers and/or annotation data. Such data (sometimes called subjective data) can be stored in raw form (audio-visual recording or scanned documents), or encoded form. Moreover, it is possible that data will be acquired offline, deriving from service providers (e.g. weather forecast). #### 4.1.5.6 WorkingAge database The WorkingAge database will consist of data collected during studies (online / offline), plus markers / annotations / indicators (online / offline). The project database will be organized in directories: each partner could use a directory in which store the different data acquired types. All the data stored in the database will be encrypted and each dataset will be accessible only to the partner who acquired the dataset and to other partners who own the secondary decryption key. ### 4.1.6 Data sources In this section, all the sources of data are briefly discussed. The data will be generated by two types of test ( _in-LAB_ phase, and _in-COMPANY_ phase) in three different time-scales (single test, week test, long-term test). It is noted that upcoming D2.6 “Study Protocols for the Test” will describe the tests with more detail, which may introduce some modifications. #### 4.1.6.1 In-LAB Phase ##### 4.1.6.1.1 Single Tests Experiments will be performed with one individual in a single occasion. Single tests will run in two sessions and two series of these tests will be implemented. These tests will be performed with up to 90 individuals. They will involve the analysis of in-depth aspects and validity of the intervention focusing on user expectation, usability and validity. Users of these tests will not have age requirements (only gender and/or health requirements) and will not be rewarded. In-LAB tests will be performed at the end of the development of three modules of the WA system, namely WP4, WP5 and WP6. The aim of these tests is to verify the functionality of each element of the WA system, irrespective of its effectiveness, which will be tested in the second set of tests described hereinafter. Researchers and students from the responsible partners will be involved for testing the measure, teach and adapt modules, whereas tests for the middleware will consist mainly in software tests to verify the communication among the elements of the WA system. Preliminary in-lab tests aim at verifying that the different modules are able to detect mental strain and monitor user’s interaction with the system and the validation of the technical functions and the identification of software bugs of the offline and online training system. Therefore, users will be asked to carry out representative tasks with the developed training system. Three types of activities will be included in this type of tests: “Offline” assessment, consisting in questionnaires regarding demographic questions, or tests for perceptive, cognitive or motoric capabilities. Moreover, skills and constitutional characteristics will be queried by questioning. Real-time measurements consist in measuring physiological indicators for mental strain, such as pupil diameter, blinking rate, skin conductance, cerebral activity, body temperature, and heart rate. Other measurements to create HCI interactions will also be tested. Performance indicators will be tracked, e.g. time for decisions, executions step for the task, mistakes, and redundancies. For these tests, users recruited by each organization developing a module will interact with the system: UCAM for facial expression analysis and recognition, EXO for gesture recognition, RWTH for eye tracking, ITCL for body pose recognition, AUD and POLIMI for voice and BS for EEG, ECG. #### 4.1.6.2 In-Company Phase At the moment of writing, the pilot tests are planned to be performed in Spain and Greece, by end-user’s organizations Grupo Antolin, Piraeus Bank in Greece and FirstAid ambulance company in Greece, led by INTRAS. This multi site design will allow the evaluation of the WA system in different social and cultural contexts. The variety of partners’ profiles will allow the consortium to test the WA solution in heterogeneous environments: some tests will be more focused on the company dimension involving occupational health and safety professionals or human resources managers, while others will address the worker’s environments with the support of ICT or organizational departments. Pilot application will consist of the following phases: (i) protocol design, (ii) analysis of the pilot study with sample size considerations, (iii) pilot applications, (iv) assessment of results. The research body of the consortium will focus this pilot application to seek the potential mechanism of efficacy for a new intervention and investigate those indicators that are triggering the aforementioned intervention. The selection of the sample sizes for the WA project includes judgement- and aims-specific considerations, but also practical feasibility that leads to proper conclusions and interventions. The inclusion criteria for the tests will be being healthy age 50+ and exclusion criteria having neuropsychiatric disorders or addiction problems. ##### 4.1.6.2.1 Single Tests Experiments will be performed with one individual in a single occasion. These tests will be used to fine-tune the subsystems for the users that will perform the week and long-term tests. These tests will aim to assess reliably the psychological, physical, cognitive and social health status in the presence of an occupational health specialist by means of the HCI services. This experiment should be done with a large group of 30 subjects (10 for each use case) from the total 90, and would be preceded by development of the different assessment methodologies. These will include information from existing renowned tests, such as: quality of life (WHOQOL-BREF) and activities of daily living (ADCS-MCI-ADL), reduction in health resource consumption (EQ-5D), Mini-Nutritional Assessment (MNA); Health-related quality of life (HRQOL), Life’s Simple 7 metric., EQ5, ESM, PHQ, GAD score, test of executive functioning, PAST, fluency tests, long-term memory tests working memory and Lubben Social Network Scale. These methodologies will be tested with questionnaires, speech, or AR interaction. Other tests may be added, according to the decisions of the specialists. Performance should be compared with questionnaires and other tests managed by a trained specialist (external validity). Additionally, test-retest reliability should be assessed. ##### 4.1.6.2.2 Week Tests Experiments will run for several sessions and involving up to 45 individuals from the final 90 individuals that will test the system. These experiments will assess unmonitored interaction, track occupational related parameters for analysis, and test if the system detects a health risk and delivers an intervention. It will also be used for the assessment of the ability of WA to self-improve. Objective (Are they still using it after three weeks?) and subjective reactions (what did they like best? what didn’t they like?) will also be tracked. The scenarios considered in the tests, called evaluation scenarios, will be defined for the three use cases. These specify what tasks the study participants will be asked to perform while the effectiveness of the WA system is measured. As part of the evaluation scenarios, the most frequent errors likely to occur when using the traditional interaction systems will be found. The goal will be to improve the interaction approach and, hence, decrease the occurrence of such errors. These tests (of the week category) will be comprised by a whole series of experiments, with a total amount of 45 users involved (15 per use case) from the 90 users. The interventions could take many forms. In order to test their effectiveness in all WA aspects (physical, psychosocial, working and health) they must be activated one-at-a-time and not all at once. ##### 4.1.6.2.3 Long-Term Tests This test type will assess the ultimate goal of prevention and monitoring on a long-range time scale, Up to 90 individuals will be monitored for about a year towards the end of the project. Such a last testing session will also investigate issues and benefits that may arise with long-term usage without the interference of more controlled testing conditions and at the same time test adherence and compliance. Good predictions and compile advice on how to further pursue this objective in future research and development will be included in the final report. There will also be a follow-up after 6 months to see whether the technology is still used (reflecting sustainability). Users will be rewarded with the equipment needed for the experiment. These users will include the week tests users and new users performing the questionnaires of the single tests to fine tune the system. In order to test the adherence rate of the solution 90 users (30 per use case) will have to use the complete solution for a long period of time (1 year). Participants’ average weekly compliance rate will be calculated. Tests for Dropout and Compliance; TAM will also be considered for evaluation. The following Key-Point Indicators will be covered: i) reported average weekly compliance of the indications; ii) Use of the networks to report improvement, iii) attrition rate. A compendium of tools series of testing for the assessment of the Physical, Psychosocial, working and Health wellbeing of the worker in the context of primary prevention will take place during the year. The evaluation methodology will be user centred and will describe a series of Key Performance Indicators (KPI) supported by the knowledge and input of the parallel research. This evaluation will be supported on the evidence exposed during the validation with real users. ## 4.2 FAIR Data In general terms, research data generated in the WorkingAge project are – in as far as possible – “FAIR”, that is findable, accessible, interoperable and reusable. ### 4.2.1 Findability - Making data findable, including provisions for ### metadata Publications are provided with bibliographic metadata (in accordance with the guidelines). Unique and persistent identifiers are used (such as Digital Object Identifiers - DOI), when possible also applying existing standards (such as ORCID for contributor identifiers). As per the European Commission guidelines, bibliographic metadata that identify the deposited publication are in a standard format and include the following: * The terms ["European Union (EU)" & "Horizon 2020"]. * The name of the action, acronym and grant number. * The publication date, the length of the embargo period (if applicable) and a persistent identifier. Datasets are provided with appropriate machine-readable metadata (see paragraph 4.2.3) and keywords are provided for all type of data. #### 4.2.1.1 Naming conventions and versioning Files are named according to their content to ease their identification with the project, following this format: * Country code, e.g. 00 for Italy, 01 for Spain, 02 for UK (eventually we could include also the partner code). * Dominant hand, R for right handed or L for left handed. * Gender, M for male or F for female.  Participant order. * Age. * Protocol code, e.g. LBA for In-LAB Acceptability. * Data Type, e.g. A for EEG data, B for ECG data, C for GSR data. Each partner will keep this pseudo-ID on his side, in particular it will be stored by the Data Controller. BrainSigns, as Data Manager of the project, will receive only the anonymous data label containing the partner’s name and the participant’s order. An example is reported below: <table> <tr> <th> Mapping on Data Controller’s Side </th> <th> Label uploaded on server (BrainSigns) </th> </tr> <tr> <td> 03RM0001045LBAE </td> <td> EX001 </td> <td> EX001 </td> </tr> </table> ### 4.2.2 Accessibility – Making data openly accessible Data and related documentation are made available depositing them in the repository of choice (Zenodo), together with the publications, and are accessible free of charge for any user . Zenodo is a repository built by CERN, within the OpenAIRE project, with the aim of supporting the EC’s Open Data policy by providing a set of tools for funded research . Zenodo provides tools to deposit publications and related data and to link them. Any needed restriction in access to the data is evaluated before final publication, in accordance with ethical aspects (conducting research with humans and children) and with protection of personal data. All the consent forms related to the WorkingAge activities will explicitly indicate that the pseudonymized dataset will be published on a public repository. In case of privacy issues, Zenodo repository allows the publisher to restrict the data access, asking for the data owner approval before downloading them. ### 4.2.3 Interoperability - Making data interoperable Metadata models were evaluated among the ones available in the Metadata Standards Directory. Dublin Core standard (Table 1) was selected to add metadata to each of the datasets identified in sub-section 4.1. Table 1 – DC Metadata Element Set <table> <tr> <th> Term name </th> <th> contributor </th> </tr> <tr> <td> URL </td> <td> http://purl.org/dc/elements/1.1/contributor </td> </tr> <tr> <td> Label </td> <td> Contributor </td> </tr> <tr> <td> Definition </td> <td> An entity responsible for making contributions to the resource </td> </tr> <tr> <td> Comment </td> <td> Examples of a Contributor include a person, an organization, or a service. Typically, the name of a Contributor should be used to indicate the entity </td> </tr> <tr> <td> Term name </td> <td> coverage </td> </tr> <tr> <td> URL </td> <td> http://purl.org/dc/elements/1.1/coverage </td> </tr> <tr> <td> Label </td> <td> Coverage </td> </tr> <tr> <td> Definition </td> <td> The spatial or temporal topic of the resource, the spatial </td> </tr> </table> <table> <tr> <th> </th> <th> applicability of the resource, or the jurisdiction under which the resource is relevant </th> </tr> <tr> <td> Comment </td> <td> Spatial topic and spatial applicability may be a named place or a location specified by its geographic coordinates. Temporal topic may be a named period, date, or date range. A jurisdiction may be a named administrative entity or a geographic place to which the resource applies. Recommended best practice is to use a controlled vocabulary such as the Thesaurus of Geographic Names [TGN]. Where appropriate, named places or time periods can be used in preference to numeric identifiers such as sets of coordinates or date ranges </td> </tr> <tr> <td> References </td> <td> http://www.getty.edu/research/tools/vocabulary/tgn/index.html </td> </tr> <tr> <td> Term name </td> <td> creator </td> </tr> <tr> <td> URL </td> <td> http://purl.org/dc/elements/1.1/creator </td> </tr> <tr> <td> Label </td> <td> Creator </td> </tr> <tr> <td> Definition </td> <td> An entity primarily responsible for making the resource </td> </tr> <tr> <td> Comment </td> <td> Examples of a Creator include a person, an organization, or a service. Typically, the name of a Creator should be used to indicate the entity </td> </tr> <tr> <td> Term name </td> <td> date </td> </tr> <tr> <td> URL </td> <td> http://purl.org/dc/elements/1.1/date </td> </tr> <tr> <td> Label </td> <td> Date </td> </tr> <tr> <td> Definition </td> <td> A point or period of time associated with an event in the lifecycle of the resource </td> </tr> <tr> <td> Comment </td> <td> Date may be used to express temporal information at any level of granularity. Recommended best practice is to use an encoding scheme, such as the W3CDTF profile of ISO 8601 [W3CDTF] </td> </tr> <tr> <td> References </td> <td> http://www.w3.org/TR/NOTE-datetime </td> </tr> <tr> <td> Term name </td> <td> description </td> </tr> <tr> <td> URL </td> <td> http://purl.org/dc/elements/1.1/description </td> </tr> <tr> <td> Label </td> <td> Description </td> </tr> <tr> <td> Definition </td> <td> An account of the resource </td> </tr> <tr> <td> Comment </td> <td> Description may include but is not limited to: an abstract, a table of contents, a graphical representation, or a free-text account of the resource </td> </tr> <tr> <td> Term name </td> <td> format </td> </tr> <tr> <td> URL </td> <td> http://purl.org/dc/elements/1.1/format </td> </tr> <tr> <td> Label </td> <td> Format </td> </tr> <tr> <td> Definition </td> <td> The file format, physical medium, or dimensions of the resource </td> </tr> <tr> <td> Comment </td> <td> Examples of dimensions include size and duration. Recommended best practice is to use a controlled vocabulary such as the list of Internet Media Types [MIME] </td> </tr> <tr> <td> References </td> <td> http://www.iana.org/assignments/media-types/ </td> </tr> <tr> <td> Term name </td> <td> identifier </td> </tr> <tr> <td> URL </td> <td> http://purl.org/dc/elements/1.1/identifier </td> </tr> <tr> <td> Label </td> <td> Identifier </td> </tr> <tr> <td> Definition </td> <td> An unambiguous reference to the resource within a given </td> </tr> </table> <table> <tr> <th> </th> <th> context </th> </tr> <tr> <td> Comment </td> <td> Recommended best practice is to identify the resource by means of a string conforming to a formal identification system </td> </tr> <tr> <td> Term name </td> <td> language </td> </tr> <tr> <td> URL </td> <td> http://purl.org/dc/elements/1.1/language </td> </tr> <tr> <td> Label </td> <td> Language </td> </tr> <tr> <td> Definition </td> <td> A language of the resource. </td> </tr> <tr> <td> Comment </td> <td> Recommended best practice is to use a controlled vocabulary such as RFC 4646 [RFC4646]. </td> </tr> <tr> <td> References </td> <td> http://www.ietf.org/rfc/rfc4646.txt </td> </tr> <tr> <td> Term name </td> <td> publisher </td> </tr> <tr> <td> URL </td> <td> http://purl.org/dc/elements/1.1/publisher </td> </tr> <tr> <td> Label </td> <td> Publisher </td> </tr> <tr> <td> Definition </td> <td> An entity responsible for making the resource available. </td> </tr> <tr> <td> Comment </td> <td> Examples of a Publisher include a person, an organization, or a service. Typically, the name of a Publisher should be used to indicate the entity. </td> </tr> <tr> <td> Term name </td> <td> relation </td> </tr> <tr> <td> URL </td> <td> http://purl.org/dc/elements/1.1/relation </td> </tr> <tr> <td> Label </td> <td> Relation </td> </tr> <tr> <td> Definition </td> <td> A related resource. </td> </tr> <tr> <td> Comment </td> <td> Recommended best practice is to identify the related resource by means of a string conforming to a formal identification system. </td> </tr> <tr> <td> Term name </td> <td> rights </td> </tr> <tr> <td> URL </td> <td> http://purl.org/dc/elements/1.1/rights </td> </tr> <tr> <td> Label </td> <td> Rights </td> </tr> <tr> <td> Definition </td> <td> Information about rights held in and over the resource. </td> </tr> <tr> <td> Comment </td> <td> Typically, rights information includes a statement about various property rights associated with the resource, including intellectual property rights. </td> </tr> <tr> <td> Term name </td> <td> source </td> </tr> <tr> <td> URL </td> <td> http://purl.org/dc/elements/1.1/source </td> </tr> <tr> <td> Label </td> <td> Source </td> </tr> <tr> <td> Definition </td> <td> A related resource from which the described resource is derived. </td> </tr> <tr> <td> Comment </td> <td> The described resource may be derived from the related resource in whole or in part. Recommended best practice is to identify the related resource by means of a string conforming to a formal identification system. </td> </tr> <tr> <td> Term name </td> <td> subject </td> </tr> <tr> <td> URL </td> <td> http://purl.org/dc/elements/1.1/subject </td> </tr> <tr> <td> Label </td> <td> Subject </td> </tr> <tr> <td> Definition </td> <td> The topic of the resource. </td> </tr> <tr> <td> Comment </td> <td> Typically, the subject will be represented using keywords, key phrases, or classification codes. Recommended best practice is to use a controlled vocabulary. </td> </tr> <tr> <td> Term name </td> <td> title </td> </tr> <tr> <td> URL </td> <td> http://purl.org/dc/elements/1.1/title </td> </tr> <tr> <td> Label </td> <td> Title </td> </tr> <tr> <td> Definition </td> <td> A name given to the resource. </td> </tr> <tr> <td> Comment </td> <td> Typically, a Title will be a name by which the resource is formally known. </td> </tr> <tr> <td> Term name </td> <td> type </td> </tr> <tr> <td> URL </td> <td> http://purl.org/dc/elements/1.1/type </td> </tr> <tr> <td> Label </td> <td> Type </td> </tr> <tr> <td> Definition </td> <td> The nature or genre of the resource. </td> </tr> <tr> <td> Comment </td> <td> Recommended best practice is to use a controlled vocabulary such as the DCMI Type Vocabulary [DCMITYPE]. To describe the file format, physical medium, or dimensions of the resource, use the Format element. </td> </tr> <tr> <td> References </td> <td> http://dublincore.org/documents/dcmi-type-vocabulary/ </td> </tr> </table> ### 4.2.4 Data re-use and licensing Publications and underlined data will be made available at the end of each experimental phase, once all data are collected and analysed. All the data indicated as Open Data will be made available for re-use after the end of the project. The licences for publications and related data will be defined in the final version of this plan, based on the final data, in order to verify compliance with personal data protection regulations and the ethical approval process results. ## 4.3 Allocation of resources Costs related to open-access to research data in Horizon 2020 are eligible for reimbursement under the conditions defined in the H2020 Grant Agreement [6.2 D.3], but also other articles relevant for the cost category chosen. Costs cannot be claimed retrospectively. Project beneficiaries will be responsible for applying for reimbursement for costs related to making data accessible to others beyond the consortium. ## 4.4 Data Security All research data produced during WorkingAge will be stored in dedicated hard drive and in separated Network Attached Storage (NAS), and for backup purpose. All the partners will transfer only pseudonymized data. Each transfer will be protected by end-to-end encryption. The data transfer support will be provided by the sharing platform called freeNAS, physically placed in BrainSigns and exposed through HyperText Transfer Protocol over Secure Socket Layer (HTTPS). All the data stored in the server will be encrypted. The software used for the encryption and decryption procedure will be GnuPG. Each partner will own the key to decrypt only the data that he acquired. BrainSigns will not have access to the other partner’s datasets. If any partner needs to access other partner’s research data, the data owner will encrypt the data and will provide the secondary decryption key only to the partner who needs the access. The data owner will keep a register of all recipients of decryption keys. ### 4.4.1 Pseudonymization Process at Local level Data Results in the platform are not associated with user’s identity. The name of the research participant appears on the consent forms. All data in the platform is pseudonymized by assigning an anonymized user code to each participant. Information of the association between platform user and participant of each experimental location is transmitted to each Local Data Manager in Excel format and with the specific data. The Excel sheet is secured through 256-bit AES (Advanced Encryption Security) codification and password. The Data Controller is responsible for the Excel sheet security. Participant’s data and platform’s users’ conversion of the experimental location are stored by the centre following the legal requirements of the country. All data collected during the study through the platform is associated to the platform user. That means that all shared reports, results, internal communications and external publications do not contain any personal data of the participant. ### 4.4.2 Data maintenance and storage at central WA level #### 4.4.2.1 Data access in freeNAS platform Research and research-related personal data collected are encrypted and stored in the systems of the organization where the data were produced. Personal Data is only accessible by Data Controller of each organization. Access is restricted to each participant, under their fictional pseudo- identity, and to the members of the Data Controller organization and WorkingAge research team. Each access to the research data is properly logged with the information of the authorized user who requests access to the data. #### 4.4.2.2 Process of backups of freeNAS platform Each partner will send the pseudonymized and encrypted dataset to the server through the freeNAS platform, after the experimental session conclusion. Each partner will not transmit the primary decryption key. The partner’s Data Controller will be responsible for the decryption key security. ## 4.5 Ethical aspects The project will conform to privacy and confidentiality guidance from the EU guidance notes, “Data protection and privacy ethical guidelines” and to the Data Protection Directive (Directive 95/46/EC, http://ec.europa.eu/justice/data-protection/index_en.htm). The following ethical issues have been considered in the WA project and will be explained in this point related to each country involved: * Notification/Authorizations of the Tests * Data processing in the Cloud * Data Controllers * Video recording The ethical aspects of the research generating the scientific data of the project are covered in the following deliverables, also taking into consideration the European Commission Ethics Summary Report for the project. * D 7.1 - Ethical and Legal report. * D 7.2 - Security and Privacy Model. The correspondence between the participant’s code, described in paragraph 4.2.1, and the participant’s identity is held in a suitably encrypted table held on a secure computer at the Data Controller’s premises. No reference about the participant’s code will be written on the respective consent form. The ethics committee will include one member for each partner of the consortium. This member will act also as the contact point for data privacy issues and compliance to the data management plan. Contacts of DPOs of data collectors will be included in the consent forms as per the Grant Agreement.	https://phaidra.univie.ac.at/o:1140797	Horizon 2020
0134_INFORM_693537.md	# FAIR DATA ## 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)?_ _What naming conventions do you follow?_ _Will search keywords be provided that optimize possibilities for re-use?_ Data will be encoded using the conventions for Sociology specified in the DDI Codebook. Data will initially be made available through the INFORM project website ( _http://www.formal-informal.eu/en/_ ) . After the end of the project data will be held and made available through the UK Data Archive (UKDA). _Do you provide clear version numbers?_ The Management Board has decided that only final versions of research data will be made publicly available, and that raw data and working versions will be circulated only internally. _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._ Categories and keywords are the standard referents employing the benchmarks developed through Data Documentation Initiative (DDI, version 3.2). The principal referent will be the metadata standards for Sociology, although as the project is multidisciplinary additional keywords may be taken from the standards for other disciplines (Anthropology, Economics, Political Science). All of these are available through the DDI Codebook. ## 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._ _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._ All processed quantitative data as well as interview summaries will be made openly available. Unshared data will remain at the discretion of the party that has obtained it. _How will the data be made accessible (e.g. by deposition in a repository)?_ In the first instance data will be made available through the INFORM website. After the project is completed data will be deposited in the UK Data Archive (UKDA). The UKDA will assign a PID. _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)?_ Text files will be accessible using any of the widely used software packages available for reading and manipulation of text. They will be downloadable and can be analysed using any type of QCA software. Survey data will be downloadable and suitable for analysis using any of the standard statistical software packages currently in use. No special software that is not commonly in use is required to access the data. _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._ After the project is completed data will be deposited in the UK Data Archive (UKDA). _Have you explored appropriate arrangements with the identified repository?_ This will be done in the final period of the INFORM project. _If there are restrictions on use, how will access be provided?_ There are no restrictions on the use of publicly shared INFORM project data. _Is there a need for a data access committee?_ The INFORM Management Board carries responsibility for issues related to data access. After the end of the project period responsibility for issues related to data access rests with the Project Coordinator. _Are there well described conditions for access (i.e. a machine readable license)?_ Having in mind that the data will be made available under a Creative Commons licence we will be using the machine-readable codes which will be obtained using the CC license chooser tool. _How will the identity of the person accessing the data be ascertained?_ It is not necessary to ascertain the identities of people accessing the publicly shared data. ## 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)?_ _What data and metadata vocabularies, standards or methodologies will you follow to make your data interoperable?_ _Will you be using standard vocabularies for all data types present in your data set, to allow inter-disciplinary interoperability?_ Categories and keywords are the standard referents employing the benchmarks developed through Data Documentation Initiative (DDI, version 3.2). The principal referent will be the metadata standards for Sociology, although as the project is multidisciplinary additional keywords may be taken from the standards for other disciplines (Anthropology, Economics, Political Science). All of these are available through the DDI Codebook. Text files will be accessible using any of the widely used software packages available for reading and manipulation of text. They will be downloadable and can be analysed using any type of QCA software. Survey data will be downloadable and suitable for analysis using any of the standard statistical software packages currently in use. No special software that is not commonly in use is required to access the data. _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?_ We will not be using uncommon, specific ontologies or vocabularies, but standard ones defined in DDI codebook for Social Sciences. In fact, we have chosen to follow standards defined by Data Documentation Initiative (DDI) in order to make our data interoperable. ## Increase data re-use (through clarifying licences) _How will the data be licensed to permit the widest re-use possible?_ _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._ Data will be made available under a Creative Commons licence accessible to all users under the condition that INFORM is acknowledged as the source of the data in case of publication. The embargo period applies to the period before participants in the INFORM project release the first publications of project data. _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 publicly shared data will be made available for use by third parties. No restrictions are applied. _How long is it intended that the data remains re-usable?_ We expect data to be preserved in perpetuity in the UK Data Archive (UKDA). _Are data quality assurance processes described?_ As part of descriptive metadata we will describe, for quantitative data, the procedures of sampling, data collection, testing of logical consistency of data, ways of coding of data (including refusals to answer, no responses and missing data); in the case of interviews ways of sampling, interview guidelines and interview procedures, while in the case of ethnographic field reports procedures for observation and documentation will be thoroughly described, enabling assessment of their accuracy and overall data quality. Further to the FAIR principles, DMPs should also address: # ALLOCATION OF RESOURCES _What are the costs for making data FAIR in your project?_ _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)._ Open Access costs are budgeted in the INFORM grant. If necessary it will be possible to apply for coverage of additional costs through the UCL Research Office. _Who will be responsible for data management in your project?_ The INFORM Management Board carries overall responsibility for data management. Within the Management Board the individuals carrying principal responsibility are Eric Gordy (Project Coordinator), Predrag Cvetičanin (Research Coordinator) and Klavs Sedlenieks (Outreach 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)?_ As already stated the decisions on how what data will be kept and for how long will be made by the Management Board of the INFORM project, but resources for long term data preservation are not yet determined (these will be determined during the final year of the project). After the expiry of the project period, responsibility for data preservation resides with the Project Coordinator. # DATA SECURITY _What provisions are in place for data security (including data recovery as well as secure storage and transfer of sensitive data)?_ All publicly shared data, in addition to being available through the INFORM website and appropriate repositories, are also held by the coordinating institution (UCL) and the institution coordinating the field research (CESK). In the event of data loss or corruption this form of triangulation allows for recovery of data. The publicly shared project data includes no sensitive data calling for special measures related to security. Data that may contain sensitive information (ethnographic field data and qualitative interview data) will be stored in an encrypted form in password- protected environments. Procedures will be devised to ascertain maximum separation of any identifiers and the data. _Is the data safely stored in certified repositories for long term preservation and curation?_ The publicly shared data will be deposited in the UK Data Archive (UKDA) for long term preservation and curation # 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)._ The research proposal is being reviewed by UCL Research Ethics Committee. We have identified the main area in which particular sensitivity is required as the safety/privacy of those people subject to the research. We will not be working with people who would be regarded as vulnerable by any standard definition, such as children. Most of the ethnographic portions of the study involve the collection of data based on the knowledge, attitudes and practices of adults that, while not necessarily personal, may be sensitive in the sense that people are persecuted for their political views in many parts of the world. This requires a critical commitment to the preservation of anonymity, not only in the final presentations of our data but also in the storage of this data prior to and after publication. The data made available to researchers will be anonymised data. Raw data will not be included in the project archive. In practical terms, this requires ensuring that all fieldworkers are trained in methods of keeping and storing their field notes in formats (electronic or otherwise) that would not allow a third party to identify persons either from names or other distinguishing features such as job titles. We will work together with UCL Research Ethics Committee to ensure that we consistently abide by data protection concerns with respect to the safe storage of personal data. The sample of research sites includes non-EU countries, some of which are lower-income countries. Although standard schemes of benefitsharing do not apply, consciousness of inequalities in the relationship between international researchers and domestic publics forms an essential element of the ethos of ethnographic research. The experienced researchers on the project will be sensitive to power differentials inherent in this type of international research. Our research plan involves the engagement of domestic academics in the research as members of the project advisory board and as local advisors to the field researchers (as described above). We also intend to seek internal university funding for workshops in the host countries of the research at which findings will be presented and shared with the local academic and policy communities. Because the project consortium consists of researchers from a range of countries, communication and coordination of ethical standards will be essential. Guidelines in compliance with EU standards and UCL procedures will be distributed to all researchers as part of the project coordination. _Is informed consent for data sharing and long term preservation included in questionnaires dealing with personal data?_ The survey and interview instruments include information to respondents regarding the anonymisation of data and data preservation. # OTHER ISSUES _Do you make use of other national/funder/sectorial/departmental procedures for data management? If yes, which ones?_ In the final instance policies will be determined in compliance with the guidelines provided by the coordinating institution. The overseeing office is UCL Research Data Services ( _https://www.ucl.ac.uk/research- itservices/research-data-service_ ) , which assures compliance with procedures outlined by the UCL Research Data Policy ( _https://www.ucl.ac.uk/library/research-support/researchdata/policies_ ) .	https://phaidra.univie.ac.at/o:1140797	Horizon 2020
0135_GetReal Initiative_807012.md	# Introduction and aim The ultimate goal of the GetReal Initiative is to drive the sustainable adoption of tools, methodologies and best practices from IMI GetReal and thereby increase the quality of real-world evidence (RWE) generation in drug development and regulatory/Health Technology Assessment (HTA) processes across Europe. In this way, the project is committed to maximizing the societal value of public and private investments in the IMI GetReal project. The GetReal Initiative Description of Action (DoA) includes a Data Management Plan (DMP) as deliverables 3.13, 3.14 and 3.15, as part of WP3. Together with the GetReal Initiative Consortium Agreement, the DMP provides a general framework regarding data management, data protection, data ownership, accessibility and sustainability requirements. Overall, the DMP provides a description of the data management, regarding generated research data, that will be applied during the GetReal Initiative project including: * A description of the data repositories, who is able to access the data, and who owns the data. * The main DMP elements for each of the studies contributing (or sharing data) to GetReal Initiative and its tools. * The time period for which data will be stored * The standards for data collection, validation evaluation. * The possibilities of and conditions for sharing data. * The implementation of data protection requirements. The DMP is an evolving documents, therefore, some aspects may updates and/or updated in later versions of the documents. An updated version of the document will be uploaded as deliverables 3.14 and 3.15 in M12 and M24 respectively. In summary, the GetReal Initiative DMP gives guidance and provides an oversight of general data management, while each study needs to provide specific data management information including, but not limited to, data capture systems, data analysis systems, data protection and data privacy measures, including description of de-identification of data sets and access rules. In cases where the research results are not open access a justification needs to be provided. The following descriptions regarding research data and personal data are used: Research data 1 _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._ Personal data 2 _Personal data is any information that relates to an identified or identifiable living individual. Different pieces of information, which collected together can lead to the identification of a particular person, also constitute personal data. Personal data that has been de-identified, encrypted or pseudonymised but can be used to reidentify a person remains personal data and falls within the scope of the law._ _Personal data that has been rendered anonymous in such a way that the individual is not or no longer identifiable is _no longer_ considered personal data. For data to be truly anonymised, the anonymisation must be irreversible. _ # General principles This is the first version of the DMP for GetReal Initiative. The DMP is a working document and will evolve during the course of the project. The document will regularly be updated to reflect the project progress. Table 1 lists the deliverables related to the multiple versions of the DPM for GetReal Initiative. _Table 1 GetReal Initiative DMP deliverables_ <table> <tr> <th> Deliverable no.* </th> <th> Deliverable name </th> <th> WP no. </th> <th> Short name of lead participant </th> <th> Type </th> <th> Dissemination level </th> <th> Delivery date </th> </tr> <tr> <td> 3.13 </td> <td> Data Management Plan (M6) </td> <td> 3 </td> <td> UMCU </td> <td> R </td> <td> PU </td> <td> November 2018 </td> </tr> <tr> <td> 3.14 </td> <td> Data Management Plan (M12) </td> <td> 3 </td> <td> UMCU </td> <td> R </td> <td> PU </td> <td> June 2019 </td> </tr> <tr> <td> 3.15 </td> <td> Data Management Plan (M24) </td> <td> 3 </td> <td> UMCU </td> <td> R </td> <td> PU </td> <td> May 2020 </td> </tr> </table> _DMP = Data Management Plan; WP = Work Package; R = Document, Report; PU = public_ _Accordingly to the Description of Action for GetReal Initiative, page 37_ The DMP follows the ‘FAIR data principle’, i.e. data should be findable, accessible, interoperable and reusable 3 . The general principles on access rules are defined in the GetReal Initiative Consortium Agreement (Section 8 Intellectual property – Access rights). GetReal Initiative makes use of one information exchange platform, the GetReal Initiative member area. The member area is an password secured web space were consortium member can store and exchange reports and documents. The platform is not meant to share patient research datasets. The member area is hosted by UMCU, contact person: Florian van der Nolle ( _f.l.vandernolle- [email protected]_ ). # Overview of data types generated and collected in GetReal Initiative The GetReal Initiative will generate and collect the following types of data: user surveys, interview reports, usage data, website analytics, market data, and recruitment data. No personal data will be transferred from/to a non-EU country or international organisations. Interview report are most prone to contain confidential information. The individual research projects and their main researcher will ensure appropriate data storage and protection. Therefore they are asked to complete a small dataset specific DMP table, as described in this DMP (chapter 4). The processes will be worked out and implemented between M6 and M24, in collaboration between WP3 Project management and the main researchers. A summary of the data generated in the project can be found in Table 2, this table will be updated along the course of the project. All generated data is expected to be useful for the GetReal Initiative project, especially for the sustainability of the tools, methodologies and best practices generated in IMI GetReal. _Table 2 Summary of data generated in GetReal Initiative_ <table> <tr> <th> Group* </th> <th> Task </th> <th> Objective </th> <th> Design </th> <th> Format </th> </tr> <tr> <td> Task Force A </td> <td> 1.2.3 </td> <td> Evaluation and maximization of the value/use of the PragMagic tool </td> <td> User data </td> <td> Numerical + textual </td> </tr> <tr> <td> Task Force A </td> <td> 1.2.2 </td> <td> Identify reasons for (not) conducting pragmatic trials, as well as describing encountered challenges in the consideration, planning, conduct and evaluation of pragmatic trials. </td> <td> Survey </td> <td> Textual </td> </tr> <tr> <td> Task Force A </td> <td> 1.2.2 </td> <td> Substantiate results from surveys and provide more in-depth insights into specific challenges and their possible solutions </td> <td> Interviews </td> <td> Multimedia + textual </td> </tr> <tr> <td> Task Force B </td> <td> 1.3.3/ 1.3.3 </td> <td> Determine requirements for ADDIS uptake and implement/test improvements to the ADDIS tool </td> <td> User data </td> <td> Numerical + textual </td> </tr> <tr> <td> Task Force B </td> <td> 1.3.3 </td> <td> Determine requirements for ADDIS uptake </td> <td> Interviews </td> <td> Multimedia + textual </td> </tr> <tr> <td> Work Package 2 </td> <td> 2.1/2.3 </td> <td> Conducting a market research and establishing the value of the GetReal brand and the tools </td> <td> Interviews </td> <td> Multimedia + textual </td> </tr> <tr> <td> Work Package 3 </td> <td> 3.6 </td> <td> Maintaining the public website </td> <td> User data </td> <td> Numerical + textual </td> </tr> </table> # Operational data management requirements for GetReal Initiative research projects All individual studies within the project will need to complete the study- specific DMP table (Table 3). The table will be shared on the member area. The data owners are responsible for the completion of the table. Thereafter, the completed table shall be shared with the data management team in WP3. They will review the table for completeness, compliance with the DMP and the CA. Furthermore, the completed tables will be added to the annex of future versions of the DMP. The data owners of the respective dataset are responsible to comply with all legal and ethical requirements for data collection, handling, protection and storage. This includes adherence to regulations, guidelines such as (but not limited to) the EU clinical trial directive 2001/20/EC, Good clinical practice (GCP) and Good Pharmacoepidemiology Practice (GPP), as applicable. _Table 3 study specific DMP table (adapted from the Data Management General Guidance of the DMP Tool_ 4 _)_ <table> <tr> <th> General Overview </th> <th> </th> </tr> <tr> <td> Title </td> <td> _Name of the dataset or research project that produced it_ </td> </tr> <tr> <td> Task </td> <td> _GetReal Initiative task/subtask where dataset was generated_ </td> </tr> <tr> <td> Data owner </td> <td> _Name(s) and address(es) of the organizations or people who own the data_ </td> </tr> <tr> <td> Start and end date </td> <td> _Start and end date of the study_ </td> </tr> <tr> <td> Methods </td> <td> _Explain how data is generated and analysed, listing equipment and software used_ </td> </tr> <tr> <td> Type of data </td> <td> _User data or Interview data;_ _Does the dataset contain personal data?_ </td> </tr> <tr> <td> Processing </td> <td> _How is the data altered or processed (e.g. normalized), including de- identification procedures_ </td> </tr> <tr> <td> Sources </td> <td> </td> </tr> <tr> <td> Source </td> <td> _E.g. Citations to data derived from other sources, including details of where the source data is held and how it was accessed._ </td> </tr> <tr> <td> Funder </td> <td> _Information regarding financial support such as research grants, or indicate that the data owner funds the study._ </td> </tr> <tr> <td> Content description </td> <td> </td> </tr> <tr> <td> Subject </td> <td> _Describe the subjects or content of the data_ </td> </tr> <tr> <td> Language </td> <td> _All languages used in the dataset_ </td> </tr> <tr> <td> Variable list and codebook </td> <td> _List all variables in the data file_ </td> </tr> <tr> <td> Data quality </td> <td> _Description of data quality standards and procedures to assure data quality_ </td> </tr> <tr> <td> Technical description </td> <td> </td> </tr> <tr> <td> File inventory </td> <td> _Files associated with the project, including extensions_ </td> </tr> <tr> <td> File formats </td> <td> _Format of the files_ </td> </tr> <tr> <td> File structure </td> <td> _Organization of the data file(s)_ </td> </tr> <tr> <td> Checksum _(if applicable)_ </td> <td> _A digest value computed of reach file that can be used to detect changes_ </td> </tr> <tr> <td> Necessary software </td> <td> _Names of any special-purpose software packages required to create, view, analyse, or otherwise use the data_ </td> </tr> <tr> <td> Access </td> <td> </td> </tr> <tr> <td> Rights </td> <td> _Any known intellectual property rights, statutory rights, licenses, or restrictions on use of the data_ </td> </tr> <tr> <td> Access information </td> <td> _Where and how your data can be accessed by other researchers_ </td> </tr> <tr> <td> Data sharing </td> <td> _Description of how data will be shared, including access procedures_ </td> </tr> <tr> <td> Ethical and legal issues </td> <td> _Description of any ethics and legal issues associated with the dataset, if any_ </td> </tr> <tr> <td> Archiving and preservation </td> <td> _Description of how and to what extent long-term preservation of the data is assured. This includes information on how this long-term preservation is supported._ </td> </tr> </table> # Sharing and secondary use of data generated within GetReal Initiative The information collected from the completed study specific DMP tables will be uploaded on the member area. This will enable easy identification of the available datasets and their respective data owners by consortium members. The data owners are responsible for appropriate findability outside the consortium. To achieve the objectives of GetReal Initiative, it is imperative to follow the collaborative approach the partners agreed on when signing the consortium agreement. This includes the necessity to share data from the individual studies for the implementation of the project, while respecting data protection and intellectual property of the partners’ work. For those individual studies within the project that need to use data generated in another task, the metadata will contain the data owner contact details to whom a requester can reach out if they need to access the results. All data that will be generated within the project will be made accessible for verification and re-use for subsequent research in due time, taking into account intellectual property rights. When third parties want to use the data that was generated or collected as part of the GetReal Initiative project, the consortium PMO office should be contacted via Florian van der Nolle ( [email protected]_ ). Giving access to external parties will be considered by the Coordinating Team (CT) and the data owner. Decisions are made on a case by case basis. A separate procedure for accessing consortium data after the end of the project will be described in the final version of the Data Management Plan (D3.15, M24). # Personal data The collection, handling storage and exchange of personal data will be conducted in a secure manner, through secure channels. In addition, this will happen under the applicable international, IMI and national laws and regulations. Only data of relevance for the proposed research will be collected, no excess data will be stored. GetReal Initiative researchers commit to the highest standers of data security and protection in order to preserve the personal rights and interests of study participants. They will adhere to the provisions set out in the: * Regulation (EU) 2016/679 - General Data Protection Regulation (GDPR) 5 * Directive 2002/58/EC of the European Parliament and of the Council of 12 July 2002 concerning the processing of personal data and the protection of privacy in the electronic communications sector (Directive on privacy and electronic communications) 6 # Ethical aspects The partners of GetReal Initiative and the associated partners are required to adhere to all relevant international, IMI, and national legislation and guidelines relating to the conduct of studies. ‘Ethics requirements’ are set out in more detail by Work package 4 in Deliverable 4.1-4.4.	https://phaidra.univie.ac.at/o:1140797	Horizon 2020
0137_EPRISE_732695.md	1 INTRODUCTION To boost the benefits of public investment in research funded under H2020, the European Commission wants to improve access to scientific information, including both publications and research data. H2020 already mandates open access to all scientific publications and from 2017 is running the Research Data Pilot (ODR). The ODR pilot’s aim is to enable and maximise access to and re-use of research data generated by Horizon 2020 projects. Specifically, open access following the FAIR principle is encouraged, i.e. that research data should be Findable, Accessible, Interoperable and Reusable. The pilot applies across all thematic areas of the H2020 work programme, including Research and Innovation Actions (RIA), Innovation Actions (IA) and Coordination and Support Actions (CSA). As a CSA, peer-reviewed scientific publications do not fall within the scope of the EPRISE project. However, the deliverables containing the most relevant results will be made available on the project website as well as all the dissemination material. The project will produce research data and this deliverable describes the types of data and how it will be collected, processed and/or generated, which methodology and standards will be applied, which data and with which tools data will be shared and preserved. This Data Management Plan is not a static document and it will be updated before the project reviews. The EPRISE project aims to coordinate regional and European strategies and financial resources. It also looks to support SMEs working in the photonics industry to overcome barriers in four markets: Medical Technologies, Pharmaceuticals, Agriculture and Food. Thus, it will produce two categories of data, namely data about regions and data about markets. A selection of this data will be made open access by means of an online database during and after the end of the project. # DESCRIPTION AND PURPOSE Research data will be organised into three datasets. For each dataset, a brief description is provided, including the type of data that will be collected or generated, its purpose and relation to the objectives of the project, the data utility. ## Regional Photonics Dataset This dataset arises from the activities of the work package WP2 “Regional Grounds and Opportunities”. It contains information on: * Regional photonics ecosystem, namely companies, universities, research technology organisations (RTOs), clusters and networks; * Regional Research and Innovation Smart Specialization Strategy (RIS3) and previous regional/national/European investments in the four target markets. Data is both qualitative (description of organisations’ core activity, investigation into RIS3) and quantitative (company size, investment amount). The purpose of this data collection is: * Mapping photonics activities and actors in the majority of regions of the eight countries covered by the EPRISE consortium. This relates to the project objective of raising regional authorities' awareness about the potential of photonics-based technologies and their applications on the four target markets; * Writing success stories with the aim of highlighting regions’ photonics sector; * Producing case studies to profile European/regional co-funding scenarios in relation to the project objective of promoting co-funding initiatives and coordinating regional photonics strategies within Europe. The dataset has a twofold utility. Throughout the project, generated data (success stories) will be useful to public authorities and policy makers to showcase their regional photonics ecosystem to peers and potential partner regions with the aim of developing new collaborations. After the project, the open access database issued from the dataset will provide information useful for the whole photonics community. ## Market Data Information on Medical Technologies, Pharmaceuticals, Agriculture and Food sectors includes data related to the access to these markets for photonics companies, data about specific market experts, data about system integrators and end-users. The aim is to cover the whole value chain. This data will be collected and generated through the WP3 (“Go to Market services”) and WP4 (“Photonics SMEs networking”) activities and will be organised into two separate datasets. ### Go-to-Market Dataset This dataset consists of qualitative data, namely the results of a survey of the market barriers that companies developing photonics-based products face in the four target markets. The survey also contains information about the potential interest of companies, integrators and end-users in participating in the events organised in the framework of EPRISE (“European Photonics Roadshow”). The purpose of the dataset is: * identifying a list of Go-to-Market challenges that companies encounter with the aim of organising events tailored to their needs and expectations; * producing a list of companies, integrators and end-users to pre-arrange B2B meetings for the Roadshow, because one of the project objectives is to boost collaboration along the whole value chain. As a result, the dataset will be useful to both SMEs and integrators. It will encourage business development in the form of testing or adaptation of new or existing photonics technologies on the basis of the end-users’ feedback. ### Dataset of Experts In order to set-up a network of market specific experts in the chosen target markets, WP3 will create a dedicated dataset, including their contact details and area of expertise. The purpose of this qualitative dataset is the organisation of Go-to-Market sessions during the European Photonics Roadshow. By matching the list of Go- to-Market challenges (Go-to-Market Dataset) to the suited expert, SMEs will be provided with concrete solutions on how to overcome market barriers. Also, project partners will be able to search the dataset for an expert to help answer requests which come in from SMEs during the project. This fits with the project aim of assisting SMEs in accessing the four target markets through qualified advice tailored to their needs. Thanks to this dataset, partner clusters will rely on a network of experts when providing their members with business support as well as when organising future events based on a wellestablished format. Additionally, companies will enhance their business skills, while experts will benefit of increased visibility. # DATA COLLECTION This section describes how the data will be collected and generated, the origin of the data, how the data will be organised during the project, including naming conventions, version control and folder structure. It outlines how the consistency and quality of the data collection will be controlled and documented to help secondary users to understand and re-use it. Finally, it details how data will be stored and backed up during the research. ## Methodology The methodology for the gathering of data about regional photonics ecosystems was established by WP2’s leader and co-leader in the first two months of the project. The dataset will be built-up by merging information (basically name of the company/research institute, country, postal and website address) from the already published Photonics 21, EPIC (European Photonics Industry Consortium) and OASIS (Open the Access to Life Science Infrastructures for SMEs) databases. This data will be checked, updated and improved by including additional information such as home region of photonics companies, universities, networks and clusters, contact details when available, company size, target markets, keywords or description of the company core activity. Companies will be classified as follows: * Companies fabricating and developing photonics components or products; * Companies manufacturing photonics-enabled products/systems; * Industries dependent on photonics for product manufacturing. Each partner will collect this information for as many regions as possible in their country. New data will be derived from partner clusters’ databases, cluster fieldwork and, as detailed in Section 4.2, from direct online submission by organisations on the project website. Financial data and information about a company’s evolution and maturity will be extracted from additional existing national databases (for example “Allabolag” in Sweden or “Corporama” in France). Regarding regional RIS3s and information on previous regional and European investments and funding, data published by the regions themselves (calls for bids, announcements, reports, roadmaps) or results of other EU funded projects will be exploited. The latter will be obtained via the CORDIS portal or by establishing collaborations with other H2020 project, such as Europho21. Data for the Go-to-Market Dataset will be collected via interviews with companies, integrators and end-users. The interviews will be conducted by partners during the first year of the project. An electronic template to guide the interviews has been produced by the WP4’s and WP3’s leaders. It will be used by every partner during the regional events and their organisation and follow-up. The questionnaire contains questions that are common to all interviewees (for example their interest in participating in the Roadshow), others concerning end-users only (their awareness of photonics) or photonics companies only (questions about Go-to-Market barriers and time to market). Finally, the Datasets of Experts will be based on the EPRISE consortium fieldwork and previous partners' contacts. ## Standards enabling data re-use The management procedures enabling future use of data are described below: * _Data formats_ : to facilitate data re-use standard file formats such as Excel and Word files (xls and docx extensions) will be used; * _Metadata provision:_ The web-based tool developed for data sharing is described in the Section 4.2. No metadata is needed to re-use data imported from this online database. * _Documentation provision_ : For online submission of data (see Section 4.2), procedural information will be provided on the dedicated webpage, namely how to fill in the data form, how to extract data, how gathered information will be used and how confidentiality aspects will be managed. A brief description of the dataset and its purpose will be included as well. * _Naming conventions_ : Files will be named according to their content for easy identification. The name of the file version uploaded to the web-based tool chosen for data storage and internal sharing (see subsection 3.3) will be preceded by the prefix “EPRISE_GANo732695”. Partners will attach the suffix “_initials” to the original filename when editing files. * _Version handling, folder structure_ : If multiple versions of a file are kept, the version number will be specified after the filename. In the internal shared space data will be stored using a folder structure following WP organisation. Once the files are uploaded, versioning will be automatically managed by the web-based tool in the case of further uploads or editing and co-editing performed directly in the shared space. * _Quality assurance:_ project management structure, decentralized responsibility and crosscheck of the results (see deliverable D1.1, “Project management Guide”) will ensure data quality. ## Storage Data collected and generated during the project as well as deliverables and dissemination materials will be stored in an online space shared by partners. A Microsoft Sharepoint site was set-up during the first months of the project in the framework of the WP5 (“Dissemination and communication activities”). This server-based web-application enables internal communication and exchange of data and documents. The site is hosted by the WP5 leader on a server located at his Sedgefield site in the UK. Stored files are regularly backed-up (daily and weekly with the backups held off-site) and it is possible to track their history, compare different file versions and restore previous versions. It is accessible to all team members of each partner working on the project via a personal account. # DATA SHARING This section focuses on data sharing, including which data will be retained, how it will be made accessible and how it will be preserved during the project and beyond the lifetime of the grant. It determines whether access will be public or restricted to specific groups and how ethics and legal compliance will be managed. The tools set-up for enabling re-use, procedures for accessing existing data and submitting new data are also described. Finally, a long-term preservation plan for the data is presented. ## Data Selection and Confidentiality _Regional Photonics Dataset_ Information contained in the Regional Photonics Dataset will be partially shared. The adopted selection criteria for data on photonics actors are the following: * Name of the organisation (company, RTO, network, cluster), country, region, postal and website addresses, target markets, and keywords/description will be open access; * Company size and internal company classification will be used only within the consortium. Company classification could affect the data organisation on the online database described in Section 4.2; * Contact details will be kept confidential (GPDR compliant) as they are personal data which is not publicly available. The contact details of the related cluster will be provided instead. Confidential details about company activity and strategy or trade secrets of new data issued from partner clusters’ databases will not be included in the company description. Additionally, organisations will be informed about which data will be made publicly visible and about the possibility of obtaining a correction of inaccurate data or opting out at any moment. Companies submitting data directly on the website will be informed about the applied confidentiality policy when completing the form via dedicated documentation (see Sections 3.3 and 4.2). Other regional data, namely information on RIS3, success stories and case studies will be published only in the form of public deliverables or dissemination material. _Market Datasets_ : The use of the Go-to-Market dataset will be restricted to the consortium. Information will be exploited to organise business events and to write market booklets for dissemination. As a general rule, questionnaire results published in market booklets will be made anonymous. In case of publication of information susceptible to disclosing confidential data (testimony, Go-to- Market session outcomes) formal consent will be asked. Regarding the Dataset of Experts, a mid-term (Month 15) version will be published in the form of confidential deliverable made accessible only to partners to organise the events and to the European Commission. An updated version will be made open access in the last project period. Experts’ contact details will be published in the online database only after having received their permission. Finally, depending on the questionnaire outcomes it will be decided if it’s worth adding some of the data about integrators and end-users included in the Go-to-Market Dataset to the Regional Photonics Dataset. The personal data protection rules mentioned previously for the Regional Photonics Dataset will be applied. ## Accessibility The Regional Photonics Dataset and the Dataset of Experts will be shared via an online database that will be published on the project website ( _https://eprise.eu/_ ) . The former will be made available at the end of the first year of the project (Month 12), the latter at the end of the second year (Month 24). Online data will be regularly updated as the activities of the WP2 and WP3 progress. The template used by partners for interviews with companies, integrators and end-users (see Section 3.1) will be adapted and made available on the website as an online questionnaire for external users from the second year of the project. A registration page has been designed on the website to allow organisations or experts to directly provide their information to the EPRISE project with permission to use it for the purposes described in this document (Sections 2.1 and 2.2). After they register with the site, they will able to log in and create or modify their own profile at any time by filling in a web form. All data collected online will be stored encrypted in a secure area of the server which is hosting the website. The data will be exported securely at regular intervals and then saved on the Sharepoint site for partners to access and use it. Data stored in the Sharepoint site will be processed following the selection procedures described in Section 4.1 and exported to the online database. Data is stored in either csv or xls format and the standard language for relational database management systems Structured Query Language (SQL) is used. Website users will be able to search with different criteria depending on the database section (section dedicated to the Regional Photonics Dataset or section dedicated to the Dataset of Experts). Search results will be displayed on a results webpage. ## Long-term preservation In addition to the setting-up of the online database, public deliverables and dissemination materials will be published on the project website. At the end of the project, a Smart Book based on market data, including Go-to-Market session outcomes, will be uploaded on the website in the form of an ebook. This will ensure that the data will be preserved as long as the website exists. At the end of the project, the Smart Book will be also distributed as a long-lasting publication with an associated ISBN code (international standard ISO 2108) through a service provided by the CNR partner. The Photonics Dataset and the Dataset of Experts have long-term value and will be preserved and curated beyond the lifetime of the project. The EPRISE consortium is discussing with Photonics21 about the possibility of transferring Regional Photonics Dataset information to their website for longterm preservation. Photonics21 is renewing its website, therefore more details about this collaboration will be available in October 2017. The information contained in the Dataset of Experts will be included in partner clusters’ databases for further exploitation in business event organisation and business support to their members. Partners are considering transferring data about experts to the Photonics21 website as well. Further details will be provided in the updated version of this Data Management Plan. # RESPONSABILITIES AND RESOURCES WP leaders (WP2, WP3 and WP4) are in charge of the management of the data collected and generated through the activities of their own work package. This includes data storage in the space shared by partners and related procedures (folder structure, naming, versioning, documentation provision) as well as data selection and the protection of confidential data (anonymization, consent request). As the WP5 deals with dissemination and communication activities, its leader will manage the technical aspects such as development and maintenance of the web-based tool developed for gathering and sharing information (database) and related infrastructures (website, webserver). He will also be in charge of the preservation of any digital content, including data storage (Sharepoint site set-up) and the back-up of the data. The project Manager will coordinate all the activities related to data management. During the project, the costs related to the Sharepoint site and the website will be covered by the WP5 leader’s budget. 25 user licences for this Sharepoint site have been purchased. Those licences are not time-limited. Sharepoint site will be maintained open for six months after the end of the project to allow partners to download copies of any of the information they want to keep. After that, the site will be closed and the data archived by the WP5 leader. The project website is being hosted by a web-hosting company under hosting fees and for ownership of the domain name. The partners will decide commercially which partner(s) will host the site after the end of the project. The data will be then transferred to that partner’s preferred web host just prior to the end of the project. Smart Book publication will be free of charge.	https://phaidra.univie.ac.at/o:1140797	Horizon 2020
0138_SHIP2FAIR_792276.md	# 1\. INTRODUCTION The SHIP2FAIR Data Management Plan (DMP) gives an overview of the data and information collected throughout the project and shows the interaction and interrelation of the data collecting activities within and between the work packages. The DMP will also link these activities to the SHIP2FAIR partners and discuss their responsibilities with respect to all aspects of data handling. Furthermore, the SHIP2FAIR DMP will lay out the procedure for data collection, consent procedure, storage, protection, retention and destruction of data, and confirmation that they comply with national and EU legislation. The DMP will ensure that the exchange of data of companies and industries is in full compliance with the participating companies and industries internal data protection strategies. This DMP aims at providing an effective framework to ensure comprehensive collecting and handling of the data used in the project. Thereby and wherever trade secrets of the participating companies and industries are not violated, SHIP2FAIR strives to comply with the open access policy of Horizon 2020. The DMP is intended to be a living document which will be adjusted to the specific needs of SHIP2FAIR throughout the project’s runtime and will be adapted whenever appropriate. This is the first version of DMP to be revised during the course of the project within Task 1.1 Consortium Management, including new data, changes in consortium policies regarding innovation potential or decision to file a patent, and changes in the consortium composition and external factors. This plan will establish the measures for promoting the findings during SHIP2FAIR’s lifecycle and will set the procedures for the sharing of data of the project. Addressing FAIR principle for research data (Findable, Accessible, Interoperable and Re-usable) SHIP2FAIR DMP will consider: * Data set reference and name * Data set description * Standards and metadata * Data sharing and handling during and after the end of the project * Archiving and preservation (including after the end of the project) The following document made use of the HORIZON 2020 FAIR DATA MANAGEMENT PLAN TEMPLATE and was written with reference to the Guidelines to FAIR data management in Horizon 2020 [1] and the GDPR (Regulation (EU) 2016/679). # 2 SHIP2FAIR DATA SUMMARY Being in line with the EU’s guidelines regarding the DMP, this document should address for each data set collected, processed and/or generated in the project the following characteristics: dataset description, reference and name, standards and metadata, data sharing, archiving and preservation. At this point in time, an estimation of the size of the data cannot be given. To this end, the consortium develops a number of strategies that will be followed in order to address the above elements. This section, shall be provided a detailed description of these elements in order to ensure their understanding by the partners of the consortium. For each element, we also describe the strategy that will be used to address it. ## 2.1 Data set description, reference and name In order to be able to distinguish and easily identify data sets, each data set will be assigned with a unique name. This name can also be used as the identifier of the data sets. All data files produced, including emails, include the term “SHIP2FAIR”, followed by file name which briefly describes its content, followed by a version number (or the term “FINAL”), followed by the short name of the organisation which prepared the document (if relevant). Each data set that will be collected, processed or generated within the project will be accompanied by a brief description. ### 2.2 Standards and metadata This version of the SHIP2FAIR DMP does not include a compilation of all the metadata about the data being produced in SHIP2FAIR project, but there are already several domains considered in the project which follows different rules and recommendations. This is a very early stage identification of standards: * Microsoft Office 2010 for text based documents (or any other compatible version) .doc, .docx, .xls, .xlsx, .ppt, .pptx. Also, especially where larger datasets need to be dealt with, .csv and .txt file formats will be used. All finished and approved documents will also be made available as .pdf documents. * Illustrations and graphic design will make use of Microsoft Visio (Format: .vsd), Photoshop (Format: different types possible, mostly .png), and will be made available as .jpg, .psd, .tiff and .ai files. * PFDs, PIDs and layouts will preferentially use inkscape.org, an open source software for vector graphics. (Format: .svg), and will be made available as .png, .jpg and .pdf files. * MP3 or WAV for audio files. * Quicktime Movie or Windows Media Video for video files. These file formats have been chosen because they are accepted standards and in widespread use. Files will be converted to open file formats where possible for long-term storage. Metadata will be comprised of two formats – contextual information about the data in a text based document and ISO 19115 standard metadata in an xml file. These two formats for metadata are chosen to provide a full explanation of the data (text format) and to ensure compatibility with international standards (xml format). ### 2.3 Data sharing, access and preservation The digital data created by the project will be diversely curated depending on the sharing policies attached to it. For both open and non-open data, the aim is to preserve the data and make it readily available to the interested parties for the whole duration of the project and beyond. A public Application Programing Interface (API) will be provided to registered users allowing them the access to the platform. The database compliance aims to ensure the correct implementation of the security policy on the databases verifying vulnerability and incorrect data. The target is to identify excessive rights granted to users, too simple passwords (or even the lack of password) and finally to perform an analysis of the entire database. At this point, we can assure that at least the following measures will be considered for assuring a proper management of data: * Dataset minimisation. The minimum amount of data needed will be stored so as to prevent potential risks. * Access control list for user and data authentication. Depending on the dissemination level of the information an Access Control List will be implemented reflecting there for each user the data sets that can be accessed. * Monitoring and Log of activity. The activity of each user in the project platform, including the data sets accessed, is registered in order to track and detect harmful behaviour of users with access to the platform. * Implementation of an alert system that informs in real time of the violation of procedures or about hacking attempts. * Liability. Identification of a person who is responsible for keeping safe the information stored, * When possible, the information will be also made available in the initiative that the EC has launched for open data sharing from research, which is ZENODO.ORG [2]. The mechanisms explained in this document aim at reducing to the maximum the risks related to data storage. #### 2.3.1 Non-Open research data The non-open research data will be archived and stored long-term in the EMDESK portal administered by CIRCE. The CIRCE platform is currently being employed to coordinate the project's activities and to store all the digital material connected to SHIP2FAIR. If certain datasets cannot be shared (or need restrictions), legal and contractual reasons will be explained. #### 2.3.2 Open research data The open research data will be archived on the Zenodo platform ( _http://zenodo.org_ ) . Zenodo is a EU-backed portal based on the well- established GIT version control system ( _https://git-scm.com_ ) [3] and the Digital Object Identifier (DOI) system ( _http://www.doi.org_ ) [4]. The portal's aims are inspired by the same principles that the EU sets for the pilot; Zenodo represents thus a very suitable and natural choice in this context. The repository services offered by Zenodo are free of charge and enable peers to share and preserve research data and other research outputs in any size and format: datasets, images, presentations, publications and software. The digital data and the associated meta-data is preserved through well-established practices such as mirroring and periodic backups. Each uploaded data-set is assigned a unique DOI rendering each submission uniquely identifiable and thus traceable and referenceable. 3. ALLOCATION OF RESOURCES Data management in SHIP2FAIR will be done as part of the WP1 and CIRCE, as project coordinator, will be responsible for data management in SHIP2FAIR project. CIRCE has allocated a part of the overall WP1 budget and person months to these activities. For the time being, the project coordinator is responsible for FAIR data management. Costs related to open access to research data are eligible as part of the Horizon 2020 grant (if compliant with the Grant Agreement conditions). Resources for long term preservation, associated costs and potential value, as well as how data will be kept beyond the project and how long, will be discussed by the whole consortium during General Assembly (GA) meetings. 4. DATA SECURITY For the duration of the project, datasets will be stored on the responsible partner’s storage system. Every partner is responsible to ensure that the data are stored safely and securely and in full compliance with European Union data protection laws. After the completion of the project, all the responsibilities concerning data recovery and secure storage will go to the repository storing the dataset. All data files will be transferred via secure connections and in encrypted and password-protected form (for example with the open source 7-zip tool providing full AES-256 encryption: http://www.7-zip.org/ or the encryption options implemented in MS Windows or MS Excel). Passwords will not be exchanged via e-mail but in personal communication between the partners. # 5 ETHICAL ASPECTS This section deals with ethical and legal compliance issues, like the consent for data preservation and sharing, protection of the identity of individuals and companies and how sensitive data will be handled to ensure it is stored and transferred securely. Data protection and good research ethics are major topics for the consortium of this project. Good research ethics meet all actions to take great care and prevent any situation where sensitive information could get misused. This is what the consortium wants to guarantee for this project. Research data which contains personal data will just be disseminated for the purpose for which it was specified by the consortium. Furthermore, all processes of data generation and data sharing have to be documented and approved by the consortium to guarantee highest standards of data protection. SHIP2FAIR partners have to comply with the ethical principles as set out in Article 34 of the Grant Agreement, which states that all activities 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 including, in particular, avoiding fabrication, falsification, plagiarism or other research misconduct) and * applicable international, EU and national law (in particular, EU Directive 95/46/EC). ### 5.1 Informed Consent <table> <tr> <th> </th> <th> Document: Author: Reference: </th> <th> D1.4. Project Management Plan </th> <th> </th> <th> </th> </tr> <tr> <th> CIRCE </th> <th> Version: Date: </th> <th> 1 </th> </tr> <tr> <th> D1.4 SHIP2FAIR ID GA 792276 </th> <th> 1/10/18 </th> </tr> </table> An Informed Consent Form will be handed out to any individual participating in SHIP2FAIR interviews, workshops or other activities which may lead to the collection of data which will subsequently be used in the project. An example of the Informed Consent Form is shown in the Annex of this document. ### 5.2 Confidentiality SHIP2FAIR partners must retain any data, documents or other material as confidential during the implementation for the project. Further details on confidentiality can be found in Article 36 of the Grant Agreement along with the obligation to protect results in Article 27\. ### 5.3 Involvement of non-EU countries SHIP2FAIR non-EU partner (TVP) has confirmed that the ethical standards and guidelines of Horizon2020 will be rigorously applied, regardless of the country in which the research is carried out. Activities carried out outside the EU will be executed in compliance with the legal obligations in the country where they are carried out, with an extra condition that the activities must also be allowed in at least one EU Member State. In SHIP2FAIR data will be transferred between the named non-EU country (Switzerland) and countries in the European Union to allow for joined analyses and storage of all data in the common database. All data transferred between project partners (within or outside the EU) will be restricted to pseudonymized or anonymized data and transfer will only be made in encrypted form via secured channels. ### 5.4 Management of ethical issues Personal data which will be collected within this project, will only be stored, analysed and used anonymously. The individuals will be informed comprehensively about the intent use of the information collected from them and have to agree to the data collection for this scientific purpose with their active approval in form of a written consent. The identity of any individual interviewed or other wisely engaged in the project (e.g. by email correspondence) will be protected by this anonymization of the data. The anonymization process guarantees that no particular individual can be identified anymore. Statistics and tables of quantitative research will be published in a manner such that it will not be possible to identify any person. The legal experts of this project will guarantee that this process, including the information for the individuals about data protection issues, fully complies with national and EU laws. Data collection, storage, protection, retention and destruction will be carried out through the intranet system of the project: EMDESK. Interviewees/beneficiaries/recipients will be informed about data security, anonymity and use of data as well as asked for accordance. Participation happens on a voluntary basis. # 6 TIMETABLE FOR UPDATES After each Steering Committee meeting, an updating of the document will be performed, if required. This is the current Steering Committee calendar: # 7\. LIST OF DATA SETS This section will list the data-sets produced within the SHIP2FAIR project. For each partner involved in the collection or generation of research data a short technical description is given stating the context in which the data has been created.	https://phaidra.univie.ac.at/o:1140797	Horizon 2020
0139_MUSA_644429.md	# Executive summary This document describes the Data Management Plan (DMP) for the _Multi-cloud Security Applications_ (MUSA) Project (see Appendix A). This is the second release of the DMP, during the project life cycle DMP will be updated as described in Section 1. This second version supersedes the previous deliverable D6.3, which content is partially replicated in this document. D6.3 is _Obsolete_ after the release date of this document. This document describes the policy adopted for the management of data produced during the project activity. It describes the types of data the project will generate/collect, which standards will be used, how and in which cases the data will be exploited, shared and/or made accessible to others, and how the data will be curated and preserved, even after the project duration. The document is structured as follows: the introductory Section 1 describes the DMP life cycle and explains the context of the document. Then, Section 2 gives an overview of the expected type of data to be managed. Each of the following sections (Section 3 and Section 4) is devoted to a type of data, describing the policies adopted for their management. # Introduction ## Purpose of the document This document describes the _Multi-cloud Security Applications_ (MUSA) Project Data Management Plans (DMPs), as introduced in the Horizon 2020 Work Programme for 2014-15: _“A further new element in Horizon 2020 is the use of Data Management Plans (DMPs) detailing 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 use of a Data Management Plan is required for projects participating in the Open Research Data Pilot. Other projects are invited to submit a Data Management Plan if relevant for their planned research.”_ The MUSA DMP is a live document, updated during the project as illustrated in Figure 1, which assumes three incremental releases of the DMPs, at months M6, M18, and M36 (end of project) respectively. The DMP addresses the management procedures for each type of data generated in the project. Similarly, the DMP description document, as also introduced in Section 1.2, will contain a section for reporting each type of data produced during the project, as per the H2020 reporting guidelines. DMP Initial Release (M6) DMP Second Release (M18) DMP Final release (M36) Any new version of DMP will include all the information of the previous release, which will be considered obsolete from the release date of the new DMP, i.e., the DMP released at M18 will contain all the section of DMP release at M6. Note that if DMP released at M18 contains corrections to sections in common with M6, the policies described in the DMP released at M18 are valid for the remainder of the project. Each release of the DMP, included the initial release, will report the management policies only for the data actually produced at the release date of the DMP. The first section after the introduction will report the description of all the types of data that the MUSA project is expected to produce. ## Structure of the document The DMP contains an initial Section 2 that outlines the possible types of data produced by the project. For each type of data, a dedicated section describes the management policies; this release contains Section 3, devoted to Scientific Publications and Section 4, which describes Public Reports. Each section devoted to a type of data contains: 1. a description of the type of data; 2. a description of the standards adopted for that data and/or a description of their format (metadata); 3. a description of the way in which such data are shared; 4. a description of how to access to such data; 5. a description of how to discover such data; 6. a description of the mechanisms used in the MUSA project to archive and preserve such data. The document includes in Appendix A the overview of MUSA motivation and background, common to all MUSA deliverables. ## Relationships with other deliverables All deliverables indirectly affect this document, due to the data they contain. According to Section 1.2, this deliverable contains a section for each type of data produced by the project. ## Contributors All partners contributed to the definition of the policies adopted for the data management plan, CeRICT and Tecnalia are the main contributors of the deliverable. The following documents are directly related to D6.7: * D6.3 _Data Management Plan_ (M6) contains the initial version of the DMP, delivered at month 6\. * D6.9 _Final data management report_ (M36) will contain the final version of the DMP for MUSA project, to be delivered at the end of the project in month 36. # Expected Types of Data in MUSA In order to collect the data types that will be produced during the project, for this second release of DMP, we focused on the description of the work and on the results obtained in the first 18 months of the project. According to such consideration, this section reports the data type produced during the first months of the project. Table 1 reports a very brief description for each of them and few considerations related to the policies to be applied for each type of data. A complete section of DMP is dedicated for each data type reported in Table 1. ### Table 1: MUSA types of data available at M18 <table> <tr> <th> Data Type </th> <th> Description </th> <th> Notes </th> </tr> <tr> <td> Scientific Publications </td> <td> Publications containing results of the project. </td> <td> Scientific publications are subject to copyrights, depending on the editorial form they assume. DMP policies have to take into account both the need for large diffusion and the need for a wellevaluated editorial collocation. </td> </tr> <tr> <td> Public Reports </td> <td> MUSA public deliverables and eventual internal reports and whitepapers. </td> <td> Eventual internal reports and whitepapers could be produced during the project. DMP rules outline how they are made publicly available. </td> </tr> </table> According to the work done in the first eighteen months of the project, we already identified a set of possible data types that will be made available in the next releases of DMP. The following Table 2 reports such data types together with few considerations for them. Note that we removed the Multi-cloud application scenarios collected during the first year of the project as data set, because we considered they are useful for the MUSA framework definition, but they are already included in Deliverable D1.1. Moreover, we added the Cloud Threat Catalogue to the data set. Both Security Metric Catalogue and Cloud Threat Catalogue are not publicly available yet as they are still under work. ### Table 2: MUSA expected types of data <table> <tr> <th> Data Type </th> <th> Description Notes </th> </tr> <tr> <td> Research Data </td> <td> Data, which supports Scientific Publications and/or Public Reports for validation of results. </td> <td> Annotated data of a corresponding type dependant on the context where data was captured (e.g., different types of logs, configuration files, etc.). </td> </tr> <tr> <td> Open Source Software </td> <td> Software produced during the project under Open Source licenses. </td> <td> The Consortium Agreement describes the ownership rules for the code. DMP policies should only describe how the code is made publicly available if there is such an interest. </td> </tr> <tr> <td> Security Metrics Catalogue </td> <td> The complete set of Security Metrics used in the project. </td> <td> The MUSA framework focuses on supporting security aspects for multicloud application development and operation. Security metrics are a known </td> </tr> </table> <table> <tr> <th> </th> <th> </th> <th> research topic and any contribution to collect standard quantifiable metrics is of interest for the project. </th> </tr> <tr> <td> Cloud Threats Catalogue </td> <td> Catalogue of security Threats and risks in Cloud. </td> <td> The MUSA framework supports tools that simplify risk analysis in the cloud, in order to generate Security SLAs. Cloud Security Threats, together with detailed information that helps to identify when such threats apply are being collected and made available to the community. Any contribution to enlarge such Threats Catalogue is of interest for the project. </td> </tr> </table> # Scientific Publications ## Scientific Publications Data Set Description This data set will contain all the Scientific Publications developed in the project for the promotion of all the MUSA results. In the first 18 months of the project, the following Scientific Publications have been developed: * “Towards Self-Protective Multi-Cloud Applications MUSA – a Holistic Framework to Support the Security-Intelligent Lifecycle Management of Multi-Cloud Applications”. Written by Erkuden Rios, Eider Iturbe, Leire Orue-Echevarria, Massimiliano Rak and Valentina Casola. Presented in CLOSER 2015 “5th International Conference on Cloud Computing and Services Science”. * “Security in Cloud-based Cyber-physical Systems”. Written by Juha Puttonen, Samuel Olaiya Afolaranmi, Luis Gonzalez Moctezuma, Andrei Lobov, Jose L. Martinez Lastra. Presented in SecureSysComm2015. * “Self-protecting multi-cloud applications”. Written by Antonio M. Ortiz, Erkuden Rios, Wissam Mallouli, Eider Iturbe, Edgardo Montes de Oca. Presented in IEEE Security and Privacy in the Cloud (SPC) 2015\. * “Methodology to obtain security controls in Multi-cloud applications” Written by Samuel Olaiya Afolaranmi, Luis Gonzalez Moctezuma, Massimiliano Rak, Valentina Casola, Erkuden Rios and Jose L. Martinez Lastra. Presented in CLOSER 2016 “6h International Conference on Cloud Computing and Services Science”. * “Enhancing Security in Cloud-based Cyber-physical Systems” Written by Juha Puttonen, Samuel Olaiya Afolaranmi, Luis Gonzalez Moctezuma, Andrei Lobov, Jose L. Martinez Lastra. Journal of Cloud Computing Research. * “SLA-driven Monitoring of Multi-Cloud Application Components using the MUSA framework”. Written by Erkuden Rios, Wissam Mallouli, Massimiliano Rak, Valentina Casola and Antonio M. Ortiz. Presented in STAM 2016. * “Per-service Security SLA: a New Model for Security Management in Clouds”. Written by V. Casola, A. De Benedictis, J. Modic, M. Rak, U. Villano. Presented in WETICE 2016. * A Security SLA-driven Methodology to Set-up Security Capabilities on Top of Cloud Services”. Written by Valentina Casola, Alessandra De Benedictis, Madalina Erascu, Massimiliano Rak and Umberto Villano, (to be presented in SWISM 2016). * “Scoring Cloud Services through Digital Ecosystem Community Analysis”. Written by Jaume Ferrarons Llagostera, Smrati Gupta, Victor Muntés-Mulero, Josep-Lluis Larriba-Pey, Peter Matthews. Presented in EC-Web 2016 (DEXA 2016 Conference). ## Standards and Metadata Each MUSA Scientific Publication will follow the template that is asked in the publication procedures of the different conferences, books or publications where the publications will be presented. ## Data Sharing MUSA project will support the open access approach to Scientific Publication (as defined in article 29.2 of the Grant Agreement). Scientific Publication covered by an editorial copyright will be made available internally to the partners and shared publicly through references to the copyright owners web sites. Whenever is possible, a Scientific Publication, as soon as possible and at the latest six months after the publication time, will be deposited in 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 should aim at depositing at the same time the research data needed to validate the results presented in the deposited scientific publications. TECNALIA has just finalised the development of its own repository, which is accessible by RECOLECTA [3] (a platform that gathers all scientific repositories at Spanish national level) and OpenAire [4] (a new platform aimed at gathering a H2020 EU funded-projects’ scientific publications). The repository fulfils international interoperability standards and protocols to gain longterm sustainability. All scientific publications of the MUSA project are intended to be available through OpenAire repository and the potential delayed access (‘embargo periods’) required by specific publishers and magazines will be negotiated in a case-by-case basis. ## Access to MUSA Scientific Publications MUSA Scientific Publications will have 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. ## Discover the MUSA Scientific Publications For MUSA Scientific Publications, it will be ensured 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, and  A persistent identifier. ## Archiving and Preservation Scientific publications repositories increase visibility (and therefore the impact) of the work of the authors and the organisations to which they belong, using standardized international protocols that guarantee the visibility of documents in the search engines. These same protocols allow metadata of the repository and files within can be collected by external systems (collectors) to offer new services (e.g., search across multiple repositories, etc.). TECNALIA owns the _TECNALIA Publications_ repository, which is an open access repository accessible by RECOLECTA [3] and OpenAire [4] as explained before. The _TECNALIA Publications_ repository is visible through Google and fulfils international interoperability standards and protocols to gain long-term sustainability. The aim of the consortium is that all scientific publications of the MUSA project will be available through the OpenAire repository, which allows searching publications per project. The potential delayed access (‘embargo periods’) required by specific publishers and magazines will be negotiated in a case-by-case basis. # Public Reports MUSA produces, as an open set of data, a number of reports, which summarize the main project activities and deliverables, marked as public. The project deliverables will be publicly released, when it is prescribed in the description of the work, only after the acceptance from the European Commission. Internal reports and whitepapers will be made publicly available according to an agreement among the report authors. ## Public Report Data Set Description The following table shows the Public Deliverables at month 18 of the project. It is worth noticing that all the deliverables in the list are already delivered, but not yet publicly available, waiting for EC approval. #### Table 3: Public deliverables at M18 <table> <tr> <th> Deliverable (number) </th> <th> Deliverable name </th> <th> Work package number </th> </tr> <tr> <td> D1.1 </td> <td> Initial MUSA framework specification. </td> <td> WP1 </td> </tr> <tr> <td> D1.2 </td> <td> Guide to security management in multi-cloud applications lifecycle. </td> <td> WP1 </td> </tr> <tr> <td> D2.1 </td> <td> Initial SbD methods for multi-cloud applications. </td> <td> WP2 </td> </tr> <tr> <td> D5.1 </td> <td> MUSA case studies work plan </td> <td> WP5 </td> </tr> <tr> <td> D6.1 </td> <td> MUSA brochure and public website </td> <td> WP6 </td> </tr> <tr> <td> D6.2 </td> <td> Dissemination Strategy </td> <td> WP6 </td> </tr> <tr> <td> D6.3 </td> <td> Data Management Plan </td> <td> WP6 </td> </tr> <tr> <td> D6.4 </td> <td> Communication Plan </td> <td> WP6 </td> </tr> <tr> <td> D6.5 </td> <td> Networking plan </td> <td> WP6 </td> </tr> <tr> <td> D6.6 </td> <td> Dissemination, communication and networking report </td> <td> WP6 </td> </tr> <tr> <td> D6.7 </td> <td> Data management report </td> <td> WP6 </td> </tr> <tr> <td> D7.1 </td> <td> Initial market study, trends, segmentation and requirements </td> <td> WP7 </td> </tr> <tr> <td> D7.2 </td> <td> Business scenarios analysis </td> <td> WP7 </td> </tr> <tr> <td> D7.5 </td> <td> Standards analysis and strategy plan </td> <td> WP7 </td> </tr> <tr> <td> D7.6 </td> <td> Revised standards strategy plan </td> <td> WP7 </td> </tr> </table> ## Standards and MetaData MUSA Public Deliverables have a standard template available on the internal document management system (https://intranet.musa-project.eu). The Executive summary, at the beginning of the document is a brief summary of the deliverable content. All the information about the document is reported in Section 1 (Introduction). All Introduction sections contain: * A description of the purpose of the deliverable (section 1.1). * A description of the structure of the deliverable (section 1.2). * A description of relationships with other deliverables (section 1.3). * A list of contributors (section 1.4). * A section devoted to summarize acronyms and abbreviations (section 1.5). * A section that reports the revision history (section 1.6). * A section that describes the changes applied in different versions after evaluation of the Commission (section 1.7) - optional ## Data Sharing All public report/deliverables will be published through the MUSA website [1]. In the website of the MUSA project there is a section where all the MUSA Public Results will be published and made available for free to the general public. ## Access to MUSA Public Deliverables The access to the public repository can be done through the Public Results section of the MUSA website. For accessing to these public reports no identification is required. ## Discover the MUSA Public Deliverables The MUSA website will be made as visible as possible and discovering should be possible through any web search engine. ## Archiving and Preservation All final versions of the deliverables are maintained on the internal document management system ( _https://intranet.musa-project.eu_ ) , based on Alfresco. All reports available on the web site are archived together with web site infrastructure (see D6.1 _MUSA brochure and public website_ ).	https://phaidra.univie.ac.at/o:1140797	Horizon 2020
0140_StarFormMapper_687528.md	# Introduction ## 1.1 Scope This document is the deliverable # D7.5 – “Data Management Plan - Update” for the EU H2020 (COMPET-5-2015–Space) project “ A Gaia and Herschel Study of the Density Distribution and Evolution of Young Massive Star Clusters ” (Grant Agreement Number: 687528 ), acronym: StarFormMapper (SFM) project. # Description of Work WP 7, “Data Management and Curation”, is aimed at the provision of central sotrage for data associated with the project, together with its public access. In addition, the documentation and metadata required for full access will be properly described. ## Update Our original intention on moving to annual reporting for the Data Management Plan was that these would be scheduled yearly after the last report submitted in Period 1 (which would have been May 2018 and 2019). Inadvertently this became November 2017 and 2018 (ie the anniversary of the initial report). No full report was submitted in November 2017 because of this oversight. Instead, this document provides a brief overview of the position at the end of period 2. _This project is being funded by the European Union’s Horizon 2020 research and innovation actions_ _(_ _RIA) programme under the grant agreement No 687528._ The initial Data Management Plan (D7.1) was submitted and approved during period 1. This document will deal only with updates to the plan. Quotes in italics are taken from the relevant section of the original plan. It is still our intention to make publically available all data gathered for the project, together with appropriate descriptions and metadata. The relevant descriptions will be developed in the time before the next review. ## Data Summary Update “ _The project has allowed for separate servers at the Leeds and Madrid nodes, which are now fully installed and functional. These will provide backup to each other.”_ The separate site servers are fully functional – those in Madrid are also capable of serving data through public access although none is as yet offered (private access is provided). The server in Leeds is not as yet capable of acting as a web host. The data currently provided are the simulations generated by the project. No other restricted access data exists as yet for the project. The Quasar servers are running the Docker s/w that allows us to interface with their developing toolset. This is part of the final adopted access protocol for the project which will eventually become public. Testing of this has proved the basic methodology. Page 5 of 11 The Leeds "data repository/backup" is functioning but due to changes in IT staffing and management is not yet available as an external facing resource. We cannot at the moment give a specific timing for this to happen, as the staff required to set it up are beyond our control, as are the specific details as to how this will be provided. The specific issue in question is the location of any external facing facility, and the limits that may be placed on us with regard to the capability of such a facility. The minimum capability that will certainly be offered is public data access in an archive sense. We stress that this affects only our ability to serve data - the website for the project is otherwise fully functional at Leeds. This situation will be resolved before the next update. ## Fair Data Update There are no changes to the availability, openness, re-use provisions or the requirement for making data findable. The only modification is on the item: _This project is being funded by the European Union’s Horizon 2020 research and innovation actions_ _(_ _RIA) programme under the grant agreement No 687528._ _“In particular, the University of Leeds will commit to hosting the server mentioned in Section 2 for a period of at least 10 years.”_ Obviously this required the actions outlines in 2.2 to be taken first. ## Data Security Update A fuller description of the plan for ongoing data security, particularly given the aim stated above in 2.3, will be provided at the next update. Other options may be viable if the actual data repository size is relatively limited, as it is at the moment. Both Quasar SR and the University of Leeds will work together to ensure that as a minimum first step the Leeds node provides a backup facility to the data stored in Madrid. This is within the control of the project.	https://phaidra.univie.ac.at/o:1140797	Horizon 2020
0143_BRISK II_731101.md	# Introduction The BRISK2 project will consolidate the creation of a centre of excellence in the field of 2 nd and 3 rd generation biofuels through the coordination of leading European research infrastructures. Via an integrated approach the entire value chain of biomass conversion is covered, from the preparation of the biomass feedstock, to conversion, then treatment and finally through to efficient utilization. Beyond conventional biomass sources and thermochemical conversion (tackled in the first BRISK project), BRISK2 also includes novel biogenic sources such as green and marine biomass. Moreover, the scope of biomass conversion is broadened to include biochemical conversion and new biorefinery approaches. BRISK2 will ultimately improve the success of the implementation of biofuels in Europe by helping to consolidate bioenergy expertise and knowledge, providing opportunities for international collaboration, fostering a culture of cooperation and leading to new bioenergy research activities across Europe. Sharing knowledge on biofuel production is the core of the project activities, and this philosophy will be closely linked to the management and dissemination of research data. With this background, the present document reports the Data Management Plan (DMP) that will be implemented within the BRISK2 project. The DMP contains all the data-related activities that will be performed throughout the entire data cycle (including data generation, processing and analysis, storage, sharing and preservation). Given the strong focus of BRISK2 on cooperation on research infrastructure to promote innovation in biofuels production, the vast majority of the generated data will be made publicly accessible in order to maximize the impact of the use of the shared biofuel infrastructure offered within the project. BRISK2 will be automatically part of the H2020 Open Data Pilot being a research infrastructure project that has started in May 2017\. With the ultimate objective of producing FAIR data (findable, accessible, interoperable and reusable), the H2020 Open Data Pilot crucially influences the development of the DMP, which will be described in detail in Chapter 2. # Data management plan The Data Management Plan (DMP) that will be implemented in BRISK2 is based on the Annex 1 of the "Horizon 2020 DMP" template [1] and the “Guidelines to the rules on open access to scientific publications and Open Access to Research Data in Horizon 2020” [2] prepared by the European Commission, the OpenAIRE guidelines [1][3][4], and the derived guidelines of the UK Digital Curation Centre (DCC) [5][6][7][8][9]. All these sources are based on the FAIR principle (ensuring findable, accessible, interoperable and re-usable data). As part of the H2020 Open Research Data Pilot, the data generated within BRISK2 must be stored in a research data repository and made accessible for the public. The most relevant questions related to the handling of data throughout the project are addressed in the sections below. ## Decision tree for the dissemination of preservation of the data According to the Grant Agreement of BRISK2, article 29.1 (pp. 48-50), “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 (…). Each beneficiary must ensure open access (free of charge online access for any user) to all peer-reviewed scientific publications relating to its results”. Figure 1 summarizes the decision process within the BRISK2 project to determine whether the generated research data should be disseminated or preserved as confidential. In consistency with the Grant Agreement, taking into account the nature and objective of this project (sharing European research infrastructure on biofuel production and promoting exchange between academy, research and industry), and given the fact that BRISK2 participates in the H2020 Open Access Program, it is expected that the vast majority of the produced data will be made publicly available in form of databases (e.g. Phyllis-2 database and others), project deliverables (all of them with public dissemination level), open-access publications, other dissemination material (posters, presentations, etc.) or in form of datasets. All this material will be placed in a number of data repositories with public access. All peer- reviewed publications derived from the project will be published as gold open access and will be also accessible at (some of) the repositories. In the unlikely event that a partner considers that the data should be kept confidential, the partner should justify this to the project coordinator, who in turn will decide whether the data should remain confidential or be published. However, the data will be kept restricted to public access only under exceptional circumstances. In case the data are part of a publication (all of which will be released as open-access), a data embargo of maximum 6 months might applied before the release of data in the repository. FIGURE 1. DECISION TREE WITHIN BRISK2 FOR THE PRESERVATION OR DISSEMINATION OF DATA. ## Data generated within the project The ultimate objective of the data produced within BRISK2 is to contribute to the reinforcement of the European biofuel sector by sharing knowledge and promoting cooperation in the use of strategic research infrastructure. As part of the H2020 Open Data Pilot and in consistency with its nature of promoting research collaboration, virtually all the research data produced within BRISK2 will be made publicly accessible. Table 1 presents an overview of the data generated or received/collected by the work packages involved in the generation, management and dissemination of research data within the project, and an outline of the basic measures for the management of these data. As can be seen, intense interaction and collaboration is required between the different work packages: WP4 will act as focal point and coordinator of the research data generated in WP5-WP8 (joint research activities, JRA) as well as the TA work packages (WP9-WP23). WP4 will in turn assist and collaborate with WP5-WP8 for the establishment of measurement protocols and standards to ensure the delivery of goodquality data. TABLE 1. OVERVIEW OF DATA PRODUCED OR RECEIVED BY DIFFERENT WORK PACKAGES OF BRISK2. <table> <tr> <th> WP </th> <th> Data generated </th> <th> Data collected </th> <th> Data management actions </th> </tr> <tr> <td> WP3. Promotion and dissemination </td> <td> \- </td> <td> Description of experimental facilities for TA </td> <td> Public website </td> </tr> <tr> <td> Internal project documents: agendas, minutes, templates, etc. </td> <td> Internal partner area in website </td> </tr> <tr> <td> WP4. Protocols, databases and benchmarking </td> <td> \- </td> <td> Data from JRA and TA actions:  Characterization data * Protocols/methodologies * Results of round robin tests and measurement campaigns. * Techno-economic evaluation and LCA of biorefinery chain. </td> <td> Phyllis-2 database Project website BRISK2 public repositories (Zenodo, Research Gate) Other repositories (e.g. Gas Analysis wiki) </td> </tr> <tr> <td> WP5-WP8 WP9-WP23 </td> <td> * Characterization data: marine and green biomass, biorefinery streams, solid biofuels, etc. * Description of TA experimental facilities. * Protocols/procedures for data determination. * Results of round robin tests, measurement campaigns and TA activities. * Techno-economic evaluation and LCA of biorefinery chain. </td> <td> \- </td> <td> Preparation of data according to format requirements agreed in advance Supply of data to WP4 Publications/ deliverables </td> </tr> </table> The results of the project collected both within JRA work packages WP5-WP8 and within TA work packages (WP9-WP23) will be supplied to WP4 in a pre-agreed format for dissemination in form of databases (Phyllis-2) and benchmarking activities. The data will be collected, pooled and benchmarked within WP4 and disseminated in close collaboration with WP3. As a second step, a selection has to be made of the data generated within the project that will be part of the open access datasets. For this task, the systematic data appraisal procedure of the Digital Curation Centre described in “Five steps to decide what data to keep” [6] has been followed. The data appraisal can be found in Appendix A. ## FAIR data – datasets generated in the project Once the possible input research data are identified (See Section 2.3), a systematic method based on the Data Curation Centre guidelines has been applied (see Appendix A) to select which data will be included in datasets in public access tools (Zenodo date repository, Phyllis-2 database, Research Gate). Based on the type of data expected, a preliminary list of datasets (which in the course of the project will be updated) is listed in Table 2. TABLE 2. OVERVIEW OF DATASETS GENERATED IN BRISK2. <table> <tr> <th> Area </th> <th> Dataset </th> <th> WP </th> </tr> <tr> <td> All areas </td> <td> Development of protocols </td> <td> WP5, WP6, WP7 </td> </tr> <tr> <td> Biomass characterization </td> <td> Characterization of marine biomass and biorefinery streams </td> <td> WP5, WP6, WP7 </td> </tr> <tr> <td> Solid biofuels (torrefied biomass, biochars, ash behavior) </td> <td> WP7 </td> </tr> <tr> <td> Thermochemical conversion </td> <td> TGA round robin: Pyrolysis, torrefaction, char oxidation and char gasification </td> <td> WP5 </td> </tr> <tr> <td> Round robin pyrolysis </td> <td> WP5 </td> </tr> <tr> <td> Round robin gasification/combustion </td> <td> WP5 </td> </tr> <tr> <td> Measurement of trace compounds in gasification producer gas </td> <td> WP6, WP7 </td> </tr> <tr> <td> Sampling method for pyrolysis and gasification plants </td> <td> WP6 </td> </tr> <tr> <td> Biochemical conversion </td> <td> High-throughput characterization techniques/ ATR-FTIR and online measurement for characterization & pretreatment </td> <td> WP6, WP7 </td> </tr> <tr> <td> Biomass pretreatment </td> <td> WP6, WP7 </td> </tr> <tr> <td> Market analysis, techno-economic analysis and LCA of biorefinery chain </td> <td> WP8 </td> </tr> </table> A particular type of dataset produced in BRISK2 is the Phyllis-2 database, where the results of the characterization of new and advanced biomass resources will be also collected and made public. The management of the Phyllis-2 database will be described in detail in Section 2.5. ## Phyllis-2 database Within WP4 of BRISK2, the Phyllis-2 database ( _https://www.ecn.nl/phyllis2/_ ) , which currently contains more than 3000 data records of biomass and waste composition and properties, will be extended and upgraded. Phyllis-2 can be considered a special part of the data management plan of BRISK2. Specific plans for the improvement of Phyllis-2 within the project include the following: * Extension of the number of datasets by including compositional data and properties of green and marine biomass, as well as products, residues and intermediates of biorefinery processing. * Creation of standard file formats for more efficient submission and uploading of datasets. * Inclusion of the standard file template(s) at the Phyllis-2 site to promote and encourage the submission of data from external parties, thus increasing the impact of the database. * Get journal editors involved by requesting that the data submitted as supplementary information is delivered using the standard file format of Phyllis-2. * Implementation of algorithms for the automatization of the protocols for submission, delivery, check and upload of data records to the database. * Merging and interlinking with existing databases (e.g. Zeewier portal, Atlas seaweed database, etc.). * Interlinking of Phyllis-2 with the rest of BRISK2 public access tools (Zenodo repository, project website, Research Gate). * Optionally, upgrading of the current layout to mobile-friendly. Partner ECN (WP4 leader) will be responsible for the update, upgrading and maintenance of Phyllis2 in the course of the BRISK2 project. ECN will also keep Phyllis-2 as a heritage product after the project is finalized. ## Project website In the project website _www.brisk2.eu_ , there will be a link to the ‘partner area’, a password-protected area of the site only accessible to partners, which will protect any project confidential information. This area will provide both a document drop and a partners’ forum where documents can be stored such as: * Restricted project documents. * Templates for deliverables, etc. * Agendas and minutes of project meetings and WP meetings. * Completed application forms * Conference posters and presentations * User guides * Any other necessary documentation Thus, the BRISK2 website will be used as a private administrative repository for minutes, agendas, deliverable drafts, and miscellaneous documents and items to be shared with partners only, as described in the agreement. The project website will also include a publically accessible area for documents, whereby articles can be uploaded, published, tagged and categorised as required. The project website will also act as signpost of the other public access tools of BRISK2 (Zenodo, Phyllis-2, Research Gate). The WP3 leader (Aston University) will be the responsible partner for the coordination of the document management and public dissemination through the BRISK2 website. ## Open access data repository As mandated by the H2020 Open Access Pilot [1][2], the datasets, articles, reports and other grey literature generated in the course of the project will be stored in a data repository for public access. This will ensure the re-use of data according to the FAIR principle. As stated by OpenAIRE guidelines [3][4], the BRISK2 repository will provide a landing page for each dataset, with metadata that will make the data findable, with clear identification and easy access to promote the reuse of the data. Within BRISK2, besides Phyllis-2 (see Section 2.5) and the project website (Section 2.6), data will be stored at the Zenodo repository site ( _www.zenodo.org_ ) . Zenodo allows the uploaded files to get a DOI, while it ensures a better possibility for data curation after the completion of the project. In this case, the data repository will be updated and maintained by WP4 partners at least for the duration of BRISK2. Optionally, a BRISK2 community might be created. LNEG (WP4.1 task leader) will be responsible for the update of the data related to protocols, whereas partner CENER (WP4.3 leader) will be responsible for the benchmarking activities of the project. ECN (WP4 leader) will be the ultimate responsible for the proper management of data in Phyllis-2, Zenodo and Research Gate repositories, whereas Aston University (WP3 leader) will be the responsible of the data management in the project website. The BRISK2 website will act as a signposting facility to the Zenodo repository, directing the direct web traffic from the project site in that direction. The data repository will be regularly updated with new contributions to the data sets. The users of the BRISK2 data repository will thus comply to the conditions of use of Zenodo. Besides Zenodo, a BRISK2 community will be created in Research Gate, where all public deliverables and datasets will be uploaded for public access. The combined implementation of the website, Phyllis-2, Zenodo and Research Gate will maximize the impact of the project. The different platforms will be interlinked. ## Metadata The metadata associated to the different datasets will specify, on the one hand, the information about the generation of the dataset (project, organization, date, dataset type, scientific keywords). On the other hand, it will address the list of parameters (and associated units) included in the file. According to this, a format and structure for the datasets generated within BRISK2 that will be placed for open access in the repository site is proposed below. Each dataset (which will be identified with keywords and a unique DOI number to ensure the findability and interoperability of data according to FAIR principle) will include the following information (following the example from [11]): 1. General * Dataset title (see Section 2.4) * Project acronym: BRISK2 * Type: collection * Authors: acronyms of the partner(s) participating in the dataset  Date of creation * Date of latest modification 2. Description of dataset * Description of dataset * Field of research * Keywords * Related publications/ websites/ data/ services 3. Team * Authors/contributors to dataset * Dataset manager * Contact person 4. Terms of use * Access Rights/conditions * Rights 5. Data file(s). 6. Citation and DOI. ## Stakeholders interested in the results of BRISK2 The addressed audience of the BRISK2 project results includes: 1. The scientific and applied R&D community, in particular those in the areas of biofuel production, thermochemical conversion, biochemical conversion, biorefinery platform, marine biomass production. 2. Technology suppliers in these areas. 3. Seaweed cultivators and investors in cultivation of marine biomass. 4. Biomass producers. 5. Biofuel producers. 6. Transport sector and engine manufacturers. 7. Public entities (definition of policies and legal framework to increase the competitiveness of the European biofuel industry): associations, technology platforms, Biobased Industries Consortia, etc. ## Roles and responsibilities and internal information flow Data management issues fall within the competencies of WP3 and WP4. The required resources (PM effort, software), which have already been estimated during the setting up of the project, are covered by the project funding. The main responsibilities related to data management in BRISK2 (which might be updated if necessary in the course of the project) are distributed as follows: * The project partners will approve the selection of the project data repositories (Zenodo, website, Research Gate). * The partners involved in the generation of each dataset will be responsible for the preparation of standard file templates for the delivery of results and the agreement on methodologies. The respective WP leader (WP5-WP8) to which the dataset belongs as well as WP4 partners will assist in the preparation of these templates and the methodology/protocols. The use of these templates are crucial for a proper information flow within the project. * WP5-WP8 leaders will be responsible for the first quality-check and the submission to WP4 of the research data generated in each dataset (see Table 2) according to the agreed format throughout the duration of the project. * TA hosts (WP9-WP23 leaders) will be responsible for the first quality-check and the submission to WP4 of the research data generated in TA activities according to the agreed format throughout the duration of the project. * The WP4 leader will receive and do a second quality-check on the research input data received from WP5-WP8 and TA work packages, and will in turn distribute the data to Task leaders 4.1 (protocols) and 4.3 (benchmarking). * ECN (Task 4.2 leader) will be responsible for the upgrading and maintenance of the Phyllis 2 database. * LNEG (Task 4.1 leader) will be responsible for the maintenance of data related to protocols/methodology. * CENER (Task 4.3 leader) will be responsible for the maintenance of data related to benchmarking activities. * Aston University (WP3 leader) will be responsible for the launching and maintenance of the project website. * ECN (WP4 leader) will be the responsible for the update and maintenance of the repository sites at Zenodo and Research Gate. * The leaders of WP3 (dissemination) and WP4 (protocols, database and benchmarking) will collaborate with each other for the interlinking of the different data platforms (website, Phyllis2, Zenodo, Research Gate). * Regular meetings will be held between the WP4 partners (if required, also with representation of WP5-WP8) to update the data management activities. The frequency will vary depending on the work load, but at least 3 WP4 meetings are envisioned. Complementary, the WP4 leader might participate in other WP meetings as representative for data management activities. * The DMP will be accordingly updated and adapted by the WP4 leader based on the outcome of the project (lessons learned, best practices). The internal information flow within BRISK2 is schematically depicted in Figure 2 (for the sake of clarity, only JRA activities in WP5-WP8 are included in the graph). Project partners involved in each dataset (small green circles) will supply the data (according to a pre-agreed format) to the partners coordinating each dataset (DS). The datasets coordinators will in turn supply the data to the respective WP leader (WP5-WP8), who will perform a first quality-check of the data and will supply it to the WP4 leader. The WP4 leader will in turn distribute the data to the corresponding task leader: protocols (Task 4.1), characterization data for Phyllis-2 database (Task 4.2) and benchmarking (Task 4.3). WP4 partners will in turn assist the WP5-WP8 leaders and dataset coordinators (dashed arrows) in the establishment of protocols and the feedback on best practices from benchmarking. WP3 and WP4 will collaborate with each other in the coordination of the different public access tools. In the case of Transnational Access work packages (WP9-WP23), the data generated will be firstly quality-checked by the corresponding host, who will then submit the data to the WP4 leader. FIGURE 2. PROPOSED INTERNAL INFORMATION FLOW WITHIN BRISK2 PROJECT. The action list of for the implementation of the DMP is shown in Appendix B.	https://phaidra.univie.ac.at/o:1140797	Horizon 2020
0144_UnCoVerCPS_643921.md	# Introduction The Data Management Plan (DMP) is based on the information (models, tools, and data) used in each tasks. UnCoVerCPS has an open access policy; the DMP has been written following the Guidelines on Open Access to Scientific Publications and Research Data in Horizon 2020 and the Guidelines on Data Management in Horizon 2020. The required information was collected among all the partners following Annex 1 provided by the European Commission in the Guidelines on Data Management in Horizon 2020\. The template covers the following points: * Identification; * Description; * Standards and metadata; * Sharing policy; * Archiving and preservation. The aim of the consortium is to implement structures that ensure open-access of scientific results, software tools, and benchmark examples. # Elements of the UnCoVerCPS data management policy These tables below summarize the data, models, and tools that have been produced. The shared information establishes the basis for the validation of each use case of the project. It should be noted that the scale of each element may not directly correspond to its end volume, as the latter depends on the format of data collected. ## Technische Universit¨at Mu¨nchen ### Element No. 1 Reference _TUM MP_ 1 Name Annotated motion primitives Origin Generated from MATLAB Nature Data points and sets Scale Medium Interested users People performing motion planning Underpins scientific publications Yes Existence of similar data No Integration and/ or reuse Can be integrated in most motion planners Standards and Metadata Not existing Access procedures Download from website or request from authors Embargo period N/a Dissemination method Website Software/tools to enable re-use Not required Dissemination Level Open access Repository UnCoVerCPS website Storing time 12/01/2022 Approximated end volume 100 MB Associated costs None Costs coverage N/a Table 1: _TUM MP_ 1 2 <table> <tr> <th> Reference Name </th> </tr> </table> _MT_ 1 Manipulator trajectories <table> <tr> <th> Origin Nature Scale Interested users Underpins scientific publications Existence of similar data Integration and/ or reuse Standards and Metadata </th> </tr> </table> Recorded from experiments with a robotic manipulator for safe human-robot interaction Joint angles and velocities over time Medium People researching in human-robot collaboration No Yes Data can be compared, but not integrated Not existing <table> <tr> <th> Access procedures Embargo period Dissemination Software/tools to enable re-use Dissemination Level Repository Storing time Approximated end volume Associated costs Costs coverage </th> </tr> </table> Download from website or request from authors N/a Website Not required Open access UnCoVerCPS website 12/01/2022 1 GB None N/a Table 2: _TUM MT_ 1 3 <table> <tr> <th> Reference Name </th> </tr> </table> _CORA_ 1 CORA <table> <tr> <th> Origin Nature Scale Interested users Underpins scientific publications Existence of similar data Integration and/ or reuse Standards and Metadata </th> </tr> </table> N/a (software tool) Software N/a (software tool) People performing formal verification of CPSs Yes N/a (software tool) Integrated in MATLAB Not existing <table> <tr> <th> Access procedures Embargo period Dissemination Software/tools to enable re-use Dissemination Level Repository Storing time Approximated end volume Associated costs Costs coverage </th> </tr> </table> Download from website or request from authors N/a Website CORA is already a tool Open access Bitbucket 12/01/2022 10 MB None N/a Table 3: _TUM CORA_ 1 3 <table> <tr> <th> Reference Name </th> </tr> </table> _CommonRoad_ 1 Traffic scenarios for trajectory planning of automated vehicles <table> <tr> <th> Origin Nature Scale Interested users Underpins scientific publications Existence of similar data Integration and/ or reuse Standards and Metadata </th> </tr> </table> Various Complete traffic situations medium People performing motion planning Yes No Platform independent (XML format) Created by ourselves <table> <tr> <th> Access procedures Embargo period Dissemination Software/tools to enable re-use Dissemination Level Repository Storing time Approximated end volume Associated costs Costs coverage </th> </tr> </table> Website: commonroad.in.tum.de N/a Website Not required Open access GitLab 12/01/2030 1 GB None N/a Table 4: _TUM CORA_ 1 ## Universit´e Joseph Fourier Grenoble 1 ### Element No. 1 Reference _UJF SX_ 1 Name SpaceEx Origin N/a (software tool) Nature Software Scale N/a (software tool) Interested users Academia, researchers Underpins scientific publications Yes Existence of similar data N/a (software tool) Integration and/ or reuse N/a Standards and Metadata Not existing Access procedures Available at spaceex.imag.fr Embargo period None Dissemination method Website Software/tools to enable re-use None Dissemination Level Open access Repository Institutional (forge.imag.fr) Storing time 31/12/2020 Approximated end volume 50 MB Associated costs None Costs coverage N/a Table 5: _UJF SX_ 1 ## Universit¨at Kassel ### Element No. 2 Reference _UKS Con_ 1 Name Control Strategies Origin Generated from MATLAB Nature Algorithm Scale Scalable Interested users Partners using control algorithms Underpins scientific publications Yes Existence of similar data No Integration and/ or reuse Implemented in MATLAB Standards and Metadata Not existing Access procedures Will be made available on website Embargo period Available after publication Dissemination method E-mail Software/tools to enable re-use MATLAB Dissemination Level Restricted to project partners until publication Repository N/a Storing time 31.12.2020 Approximated end volume _ < _ 10 _MB_ Associated costs None Costs coverage N/a Table 6: _UKS Con_ 1 ## Politecnico di Milano ### Element No. 1 Reference _PoliMi MG_ 1 Name Microgrid data Origin Measured and generated from MATLAB Nature Data points Scale Medium Interested users Researchers working on microgrid energy management Underpins scientific publications Yes Existence of similar data No Integration and/ or reuse Can be integrated in larger microgrid units Standards and Metadata Not existing Access procedures Download from website or request from authors Embargo period N/a Dissemination method UnCoVerCPS website Software/tools to enable re-use Not required Dissemination Level Open access Repository UnCoVerCPS website Storing time 12/01/2022 Approximated end volume 6 MB Associated costs None Costs coverage N/a Table 7: _PoliMi MG_ 1 ## GE Global Research Europe ### Element No. 1 Reference _GEGR Model_ 1 Name MATLAB/Simulink model of wind turbine dynamics Origin Designed in MATLAB/Simulink Nature MATLAB/Simulink Model Scale Small Interested users All project partners working on verification Underpins scientific publications Yes Existence of similar data Yes, but existing models are typically more complex Integration and/ or reuse Can be reused with verification tools accepting MAT- LAB/Simulink models Standards and Metadata N/a Access procedures Made available to project partners upon request Embargo period N/a Dissemination method Limited to consortium partners Software/tools to enable re-use MATLAB/Simulink Dissemination Level Limited to consortium partners Repository GE-internal repository Storing time December 2019 Approximated end volume 1 MB Associated costs N/a Costs coverage N/a Table 8: _GEGR Model_ 1 ### Element No. 2 Reference _GEGR Data_ 1 Name Wind turbine load data Origin Generated in MATLAB/Simulink Nature Data on wind, turbine power, turbine speed, turbine loads Scale Medium Interested users All project partners working on verification Underpins scientific publications Yes Existence of similar data Yes, but typically based on more complex models Integration and/ or reuse Reuse in verification tools Standards and Metadata N/a <table> <tr> <th> Access procedures Embargo period Dissemination method Software/tools to enable re-use Dissemination Level Repository Storing time Approximated end volume Associated costs Costs coverage </th> </tr> </table> Made available to project partners upon request N/a Limited to consortium partners MATLAB/Simulink Limited to consortium partners GE-internal repository December 2019 100 MB N/a N/a Table 9: _GEGR Data_ 1 ## Robert Bosch GmbH ### Element No. 1 Reference _BOSCH Model_ 1 Name Simulink Model of an Electro-Mechanical Brake Origin Designed in Simulink Nature Simulink Model Scale Small Interested users People working on (simulation-based) verification Underpins scientific publications Yes Existence of similar data No Integration and/ or reuse Can be used with verification tools accepting Simulink models Standards and Metadata Not existing Access procedures Download from ARCH website Embargo period N/a Dissemination method Website Software/tools to enable re-use Mathworks Simulink Dissemination Level Open access Repository ARCH website (linked from UnCoVerCPS) Storing time 12/01/2022 Approximated end volume 1 MB Associated costs None Costs coverage N/a Table 10: _BOSCH Model_ 1 <table> <tr> <th> Element No. </th> </tr> </table> 2 <table> <tr> <th> Reference Name </th> </tr> </table> _BOSCH Model_ 2 SpaceEx Models of an Electro-Mechanical Brake with Conformance Monitor <table> <tr> <th> Origin Nature Scale Interested users Underpins scientific publications Existence of similar data Integration and/ or reuse Standards and Metadata </th> </tr> </table> Designed in SpaceEx SpaceEx Model Small People working on formal verification Yes No Can be used with verification tools accepting SpaceEx models Not existing <table> <tr> <th> Access procedures Embargo period Dissemination method Software/tools to enable re-use Dissemination Level Repository Storing time Approximated end volume Associated costs Costs coverage </th> </tr> </table> Download from UnCoVerCPS website N/a Website SpaceEx Open access (for distribution refer to LICENSE.txt) UnCoVerCPS website 12/01/2022 6 KB None N/a Table 11: _BOSCH Model_ 2 ### Element No. 3 Reference _BOSCH Tests_ 1 Name Parametric test case instances Origin Generated with MATLAB Nature MATLAB mat files Scale Medium Interested users People working on test case generation for confor- mance testing Underpins scientific publications Yes Existence of similar data No Integration and/ or reuse The instances from the parametric test cases for con- formance testing in the automated driving use case can be loaded using MATLAB. For more details and associated models see [2] and deliverable D1.3 [1] Standards and Metadata Not existing <table> <tr> <th> Access procedures Embargo period Dissemination method Software/tools to enable re-use Dissemination Level Repository Storing time Approximated end volume Associated costs Costs coverage </th> </tr> </table> Download from UnCoVerCPS website N/a Website MATLAB Open access (for distribution refer to LICENSE.txt) UnCoVerCPS website 12/01/2022 6 KB None N/a Table 12: _BOSCH Tests_ 1 ## Esterel Technologies ### Element No. 1 Reference _ET SCADE_ Name SCADE Origin N/a (software tool) Nature Software Scale N/a (software tool) Interested users People working on code generation Underpins scientific publications Yes Existence of similar data N/a (software tool) Integration and/ or reuse API access to models Standards and Metadata Scade Access procedures Licensing, academic access Embargo period N/a Dissemination method Website Software/tools to enable re-use SCADE Dissemination Level Commercial access or Academics programs Repository Proprietary Storing time _ > _ 20 _years_ Approximated end volume N/a Associated costs N/a Costs coverage N/a Table 13: _ET SCADE_ ### Element No. 2 Reference _ET SCADE HY BRID_ Name Scade Hybrid Origin N/a (software tool) Nature Software Scale N/a (software tool) To whom it could be useful Code generation Underpins scientific publications yes Existence of similar data N/a (software tool) Possibilities for integration and/or API access to models reuse Standards and Metadata Scade Hybrid Access procedures Licensing, academic access Embargo periods n/a Technical mechanisms for dissemi- ftp, email nation Software and other tools to enable SCADE re-use Access widely open or restricted to Commercial access or Academics programs specific groups Repository Proprietary Data set will not be shared n/a How and for how long data should _ > _ 20 _years_ be stored Approximated end volume n/a Associated costs n/a Costs coverage n/a Table 14: _ET SCADE HY BRID_ ### Element No. 3 Reference _ET SX_ 2 _SH_ Name sx2sh Origin N/a (software tool) Nature Software Scale N/a (software tool) To whom it could be useful Code generation Underpins scientific publications yes Existence of similar data N/a (software tool) Possibilities for integration and/or SpaceEx format reuse Standards and Metadata SpaceEx / Scade Hybrid Access procedures Licensing, academic access Embargo periods n/a Technical mechanisms for dissemi- ftp, email nation Software and other tools to enable SpaceEx / SCADE re-use Access widely open or restricted to Academics programs specific groups Repository Proprietary Data set will not be shared n/a How and for how long data should _ > _ 20 years be stored Approximated end volume n/a Associated costs n/a Costs coverage n/a Table 15: _ET SX_ 2 _SH_ ## Deutsches Zentrum fu¨r Luft- und Raumfahrt ### Element No. 1 Reference _DLR MA_ 1 Name Maneuver Automata Origin Generated from MATLAB Nature Datapoints, sets and graph structures Scale Big Interested users People researching in motion planning Underpins scientific publications Yes Existence of similar data No Integration and/ or reuse Low probability of reuse Standards and Metadata Not existing Access procedures Request from author Embargo period N/a Dissemination method Reduced version will be placed on UnCoVerCPS web- site Software/tools to enable re-use MATLAB Dissemination Level Open access Repository UnCoVerCPS website, DLR SVN Storing time 12/01/2022 Approximated end volume 10 GB Associated costs None Costs coverage N/a Table 16: _DLR MA_ 1 ### Element No. 2 Reference _DLR TEST_ 1 Name Vehicle Trajectories Origin Recorded during testdrives with one or two vehicles Nature Datapoints Scale Medium Interested users People researching in driver assistance systems, vehicle automation, vehicle cooperation, Car2X Underpins scientific publications Yes Existence of similar data Yes Integration and/ or reuse Data can be compared, but not integrated Standards and Metadata Not existing <table> <tr> <th> Access procedures Embargo period Dissemination method Software/tools to enable re-use Dissemination Level Repository Storing time Approximated end volume Associated costs Costs coverage </th> </tr> </table> Download from website or request from author N/a UnCoVerCPS website MATLAB Open access UnCoVerCPS website, DLR SVN 12/01/2022 5 GB None N/a Table 17: _DLR TEST_ 1 ### Element No. 3 Reference _DLR TEST_ 2 Name Communication Messages Origin Recorded during testdrives with one or two vehicles Nature Sent and received messages of Car2Car- Communication/Vehicle cooperation Scale Medium Interested users People researching in driver assistance systems, vehicle automation, vehicle cooperation, Car2X Underpins scientific publications Yes Existence of similar data Yes Integration and/ or reuse Data can be compared, but not integrated Standards and Metadata Not existing Access procedures Download from website or request from author Embargo period N/a Dissemination method UnCoVerCPS website Software/tools to enable re-use MATLAB Dissemination Level Open access Repository UnCoVerCPS website, DLR SVN Storing time 12/01/2022 Approximated end volume 1 GB Associated costs None Costs coverage N/a Table 18: _DLR TEST_ 2 ## Fundacion Tecnalia Research & Innovation ### Element No. 1 Reference _TCNL TV D_ Name Twizy Vehicle Data Origin Recorded from experiments with TCNL’s automated vehicle Nature Vehicle’s trajectory, accelerations (lateral, longitudi- nal), speed, yaw as well as control commands leading to these values. Recorded from vehicle’s CAN bus, DGPS and IMU. Scale Medium Interested users Research group in automated vehicles Underpins scientific publications No Existence of similar data Yes Integration and/ or reuse Data was used in the vehicle identification Standards and Metadata Not existing Access procedures Download from website or request from authors Embargo period N/a Dissemination method UnCoVerCPS website Software/tools to enable re-use Matlab/Simulink Dissemination Level Open access Repository UnCoVerCPS website Storing time 12/01/2022 Approximated end volume 10 MB Associated costs None Costs coverage N/a Table 19: _TCNL TV D_ ### Element No. 2 Reference _TCNL DTCD_ Name Dynacar Trace Conformance Data Origin Recorded from Multi-body Dynacar Simulator. Data used in the trace conformance testing of the Tecnalia vehicle Nature The reference data is based on high fidelity simulations of a multi-body vehicle model. Scale Medium Interested users Conformance and validation test researchers Underpins scientific publications No Existence of similar data No Integration and/ or reuse Data can be compared Standards and Metadata Not existing Access procedures Download from website or request from authors Embargo period N/a Dissemination method UnCoVerCPS website Software/tools to enable re-use Matlab/Simulink and Dynacar Dissemination Level Open access Repository UnCoVerCPS website Storing time 12/01/2022 Approximated end volume 15 MB Associated costs None Costs coverage N/a Table 20: _TCNL DTCD_ ### Element No. 3 Reference _TCNL DCLCV_ Name Dynacar Closed Loop Controller Validation Origin Recorded from our Automated driving testing tool, based on the Dynacar Simulator. Data used to validate different controllers. Nature The reference data is based on high fidelity simulations and real vehicles for the Automated Driving Use Case. Scale Medium Interested users Control research groups Underpins scientific publications Yes Existence of similar data Yes Integration and/ or reuse Data can be compared Standards and Metadata Not existing Access procedures Download from website or request from authors Embargo period N/a Dissemination method UnCoVerCPS website Software/tools to enable re-use Matlab/Simulink and Dynacar Dissemination Level Open access Repository UnCoVerCPS website Storing time 12/01/2022 Approximated end volume 10 MB Associated costs None Costs coverage N/a Table 21: _TCNL DCLCV_ 3 CONCLUSIONS AND FUTURE DEVELOPMENTS ## R.U. Robots Ltd <table> <tr> <th> Element No. </th> </tr> </table> 1 <table> <tr> <th> Reference Name </th> </tr> </table> _RUR SS_ 1 Safety System for Human-Robot Colaboration Test Bed <table> <tr> <th> Origin Nature Scale Interested users Underpins scientific publications Existence of similar data Integration and/ or reuse Standards and Metadata </th> </tr> </table> N/a (software tool) Software N/a (software tool) People performing formal verification of CPSs Yes N/a (software tool) High possibility for reuse in other control systems Not existing <table> <tr> <th> Access procedures Embargo period Dissemination method Software/tools to enable re-use Dissemination Level Repository Storing time Approximated end volume Associated costs Costs coverage </th> </tr> </table> Download from website or request from authors N/a Website Compiler for appropriate programming language Open access Not know at this stage 12/01/2022 10 MB - estimated None N/a Table 22: _RUR SS_ 1 # Conclusions and future developments The tables above display the current practice proposed by the consortium regarding the management of data sets, models and software tools. As UnCoVerCPS has not collected huge amounts of data during its lifespan, partners decided to include other elements apart from data sets in the data management plan. The consortium will continue to provide open access to the models and tools employed beyond the funding period of UnCoVerCPS. REFERENCES	https://phaidra.univie.ac.at/o:1140797	Horizon 2020
0148_UTILE_733266.md	# The Data Management Plan (DMP) This DMP provides details regarding all the (research) data collected and generated within the UTILE project. In particular, it explains the way data is handled, organized and made openly available to the public, and how it will be preserved after the project is completed. This DMP also provides justifications when versions or parts of the project data cannot be openly shared due to third-party copyright issues, confidentiality or personal data protection requirements or when open dissemination could jeopardize the project’s achievements. This DMP reflects the current state of the art of the UTILE project. The details and the final number of the project data sets may vary during the course this project. The variations will be recorded in updated versions of this DMP. 2.1 Data summary The overall objective of UTILE is to defragment and actively bring together both Innovation Providers and Innovation Developers, by setting-up an innovative ICT Marketplace as a valorisation one-stop-shop, and additionally to develop a value adding strategy ensuring 1. to facilitate and catalyse innovation to effective valorisation, 2. to develop a viable business case ensuring sustainability of this initiative. UTILE will evaluate all FP7 and H2020 Health projects present in CORDIS at the effective date of 1 April 2017, which will be conducted by health focused TTOs with direct engagement of actual market end-users (biotech, pharma, investors) in Europe and the USA, to identify the research results with the highest potential for translation and exploitation. The data used in UTILE are derived from the following sources of information, 1. EU project reports, provided by the EU (CORDIS) 1 ; 2. EU project questionnaires 2 , requested from former project coordinators; 3. Evaluation methods, consisting of first evaluation round (III.A) 3 and second evaluation round (III.B) 4 , developed by UTILE partners in WP2. The EU project reports (I) are (final and/or intermediate) reports which are publicly available and published in CORDIS. Within UTILE these project reports will be used by the TTOs for the evaluation of the projects. Within UTILE a questionnaire is developed (II). The questionnaire is a web based version on the UTILE website (Marketplace) 5 . The EU will send former project coordinators of all projects the request to complete this questionnaire. Within the questionnaire specific project information is requested that act as complementary information to the (final) reports, to improve the evaluation process of the TTOs. The questionnaire will explicitly request for non-confidential information and information which can be published publicly. This is requested since a submitted questionnaire will be uploaded directly into the backend of the Marketplace. With a response rate of 1020% it is expected to receive around 120-240 completed questionnaires. For evaluation of the projects, UTILE will developed and implement an evaluation method (III). This evaluation will assist the matchmaking process for the linking of Innovation Providers with Innovation Developers. The evaluation method is a two-round evaluation method. The first evaluation round (III.A), also known as basic assessment, will be done based upon the EU project reports (I). To ensure consistency among the TTOs, a Basic Assessment Form (BAF) was developed. To ensure there is consistency between TTOs, in a pilot a number of projects was assessed by all TTOs and results were comparable. The result of this evaluation will be a web based information sheet in the backend of the Marketplace. All projects will go through the first evaluation round. After the selection of projects from the first evaluation round (based on either a positive rank in the BAF or a positive questionnaire), the second evaluation round (III.B) will take place. This evaluation round is also known as deep dive assessment. It will be an extended assessment of which the exact method is still under development. It will be a thorough evaluation, which will at least include the information from the first evaluation round and a completed questionnaire if available. The main goal of the deep dive is to verify whether research results selected in the first evaluation round are promising enough for further valorisation. Similar to the first evaluation round, the result will be an evaluation report in the form of a web based information form. The evaluation method (III) developed in UTILE can be of use for other evaluators. The evaluation method (III) will be made public. Information from the evaluation methods (III) will be published in the Marketplace for other users to get information about the projects or a scientific area. 2.2 Fair Data This DMP follows the EU guidelines 6 and describes the data management procedures according to the FAIR principles 7 . The acronym FAIR identifies the main features that the project data must have in order be findable, accessible, interoperable and re-useable. The data used and collected in this project are: the project reports (I), questionnaires (II) and assessments (III.A & III.B). Since the project reports (I) are already public data and made FAIR by the European Commission, the questionnaires (II) and the evaluation rounds reports (III.A & III.B) need to be made FAIR by UTILE. ## Findable data - Metadata To make the data (questionnaires and evaluation reports) findable, unique data identifiers are used. All this information will be stored into a database, each entity will be a single table linked (related) to other entities by external key. Due to these unique data identifiers and relations between data entities, the data will be searchable on keywords like: “project ID, date-time of upload or project title”. Due to these function the data will be easy findable for project partners and externals. Next to this, data not created for the functioning of Marketplace (i.e. the deliverables) will be saved using specified conventions (i.e. “73326_D _#_ _[ _file description]_ ”) on the backend of the website of UTILE. The deliverable 3.1: “Technical documentation” 8 describes the design and architecture of the UTILE website and Marketplace as well. ## Accessible & Interoperable data The database and Java code will reside into the subnet of INNEN and will not be accessible from outside. Only developers with secured connection will have access to the database interface. Project information, incl. project reports (I), questionnaires (II) and evaluation reports (III.A & III.B) will be accessible for registered users as explained in the technical documentation of Marketplace Error! Bookmark not defined. . The project information, which is accessible after login, will be exposed using html & JavaScript interface for users. Marketplace will display the project information as text files, e.g. pdf format, which will all be in English. Since Marketplace uses html & JavaScript interface no additional proprietary software is needed. ## Reusable data The data collected in this project will be reusable for others, since no other software is needed to download the information and the information can easily be used as a source of information to other users. The evaluation method (III) developed within UTILE exists of several steps and decisions which will lead to the evaluation and ranking of the projects. It will be published on the website as a deliverable and the UTILE website. Since this is public information the method will be reusable. 2.3 Allocation of Resources For the hosting and maintenance of the UTILE website there is a small allocation of resources, but this is minimal and accounted for in WP3. The data will be saved on the website which will be hosted by a primary world leading service with high-level physical and IT security. INNEN will be responsible for the data and will make backups of the information and provide maintenance where necessary. After the project the platform will be made sustainable for example by making use of paid users for the maintenance of the platform. The knowledge gathered within the project will be preserved on the Marketplace website. The sustainability of the project will be investigate in WP6, as part of the exploitation plan. 2.4 Data Security The data security will mainly be dependent on the security of the website, since all data will be located and transferred here. INNEN will be responsible for this security and for providing back-ups were necessary. Since all data will be transferred within the website, security issues with physical transferable disks is not applicable. In addition mailing is only used for communication purposes. The UTILE website will be totally under secure connection using SSL (Secure Sockets Layer) protocol. The application will stand on a dedicated machine not accessible from other applications or domains. The dedicated machine will be hosted by a primary world leading service with high-level physical and IT security. A backup of the entire system will be done daily with a complete snapshot of the Linux virtual machine. Application backups (database, files, etc.) will be executed with 6-12 hours frequency. Backup will be stored on a separate storage disks provided by the hosting service. Since there will only be one up-to-date version, there will not be any version specifications. The back-up will have coded versions for security and maintenance reasons. The security of the public website part (front-end) will use SSL protocol for logged in pages. For the back-end of the website, restricted access is provided to the project partners via registered login, due to which only logged in user by secure connection will be allowed in the backend of the website. ## 2.5 Ethical Aspects There are some ethical aspects which may play a role in the implementation of the project: 1. Tracking of search behaviour To obtain information about the interest of the market, it is interesting to track search behaviour of users of the marketplace. It could provide policymakers, like the European Commission, relevant data on market interest and thus may give direction to policy making. The tracking of search behaviour might become an ethical aspect which need to be monitored. For anonymous frontend user UTILE will probably use google analytics (analytics.google.com). For registered user search tracking UTILE will store, the timestamp of the search action and the parameters used for the search. We know that all other information details (e.g. account name, username, etc.) have ethical issues and thus will be avoided. Only an admin account will have access to that information for recovery of registration problems. 2. Permission form, terms and conditions, disclaimer For requesting and requiring information from the questionnaire, the questionnaire will contain a permission form (terms and conditions, disclaimer, under development by the UTILE Ethics Board) that states that UTILE can use the provided information for the use of the execution of the project. It needs to state the project for which this questionnaire will be conducted, what kind of information is requested, what will be done with this information and how this will be stored and protected. It will be emphasized only public, non-confidential data should be shared through the questionnaire. Conclusions Within this DMP all the types and sources of information are described. It provides an overview of how the data within UTILE will be used and handled.	https://phaidra.univie.ac.at/o:1140797	Horizon 2020
0150_StarFormMapper_687528.md	# 1\. Introduction 1.1. Scope This document is the deliverable “D7.1 Data Management Plan v1” for the EU H2020 (COMPET-5-2015 – Space) project “A Gaia and Herschel Study of the Density Distribution and Evolution of Young Massive Star Clusters” (Grant Agreement Number: 687528), with abbreviated code name StarFormMapper (SFM) project. The structure and content is based on [AD1]. 1.2. Applicable documents The following documents contain requirements and references, which are applicable to the work: \- [AD1] SFM proposal: A Gaia and Herschel Study of the Density Distribution and Evolution of Young Massive Star Clusters. Reference 687528\. Reference: Space2015- StarFormMapper-Parts1-3, Issue: 1.1, Date: 04/08/2015. # 2\. Data Summary The project relies on the analysis of images and catalogued properties of objects related to star formation regions. The catalogues are in standard database format for astronomy (VO compatible XML or FITS tables), the images will be FITS files. This allows for long term curation of the data. Any simulations present will either be stored in appropriate generic form (we are considering how to provide these as HDF5 files) or suitable s/w to read them provided through our own dedicated servers. The exact solution here is still to be determined. There are no restrictions on the use of these data which are all public. Any other data acquired for the project (e.g. from ground based telescopes) will have their final products made available after the nominal telescope proprietary period ends (usually 12 months). The GAIA and Herschel data will be taken from the ESA Science Archive (eventually may be fed directly from there into the project server). Any other observational data will be taken from other relevant science archives but will require local hosting as those archives do not have the same feed in capability. The original metadata will be added to as appropriate for the project. We estimate the final data size for the project that will be stored locally to be modest (since we are only interested in relatively small areas of the sky). Our initial DMP is scoped on a final dataset of up to 8TB. The project has allowed for separate servers at the Leeds and Madrid nodes, which are now fully installed and functional. These will provide backup to each other. In addition, off-server local backups (to local NAS) will also be made up to the 8TB limit noted above. These facilities are allowed for within the cost of the project. The initial project datasets will be limited to simulation data which are estimated to require ~300MB each. # 3\. Fair Data 3.1. Making data findable, including provisions for metadata: Both FITS and the IVOA formats are standard, and the metadata requirements are established. The Madrid node will be responsible for creating and documenting all data standards required for the project. They have prior experience with large ESA archives which will be adopted by this project. 3.2. Making data openly accessible Data will be open access, and made available through the dual public access servers at Leeds and Madrid. The exact format of the interface has not yet been decided, but will be addressed in a series of documents to be prepared by Quasar. 3.3. Making data interoperable: We will use standard IVOA metadata and file types. 3.4. Increase data re-use (through clarifying licenses): * Quasar Science Resources will gather from public archives the data you will need to run your algorithms and the derived products will be accessible from the consortium. At the end of the H2020 project, the consortium will decide what to do with these data. * The idea is to have the data available as soon as possible, but keeping in mind the release of the premature data could be unproductive. According to this, the consortium will decide when to make data public. * There are no thoughts to restrict any kind of data. It should be all available at the end of the project. * QSR will provide the interface to run the scientific validations. As the project progresses, the data quality test, a kind of scientific validation, will be developed by scientific teams. * There are no restrictions about this point. As long as there are resources the data will remain re-usable. In particular, the University of Leeds will commit to hosting the server mentioned in Section 2 for a period of at least 10 years. # 4\. Allocation of Resources We have adopted open standards from the outset, so there are limited additional costs in ensuring fair access to all data gathered. The scientific validation of simulations and data products will be developed by the scientific teams and therefore will be their responsibility. Data products, meta-accessibility of data and SW interfaces to scientific algorithms will be the responsibility of QSR. Overall data management is the responsibility of the management group, with day-to-day leadership of the DM being led by Leeds. # 5\. Data Security There are no sensitive data held by this project. Immediate storage will be provided on the servers at Leeds and Madrid paid for by the project. Off-line backup in Madrid will be provided by local NAS also paid for by the project. Off-line backup in Leeds will be provided within space already purchased by the Astrophysics Group for general data curation. Medium term storage of key products is envisaged to continue through these two routes. Longer term storage may evolve, particularly in line with the University of Leeds data storage policies. 6\. Ethical Aspects There are no ethical implications for this project. # 7\. Other All of our policies at Leeds are also consistent with the DMP requirements of STFC, which the IT support team are already familiar with, and which is aligned with the EU's policy on open access. The local IT team at Leeds will at all times also follow institutional guidance. Quasar will adopt similar policies.	https://phaidra.univie.ac.at/o:1140797	Horizon 2020
0151_Co-ReSyF_687289.md	# Introduction The Co-ReSyF project will deploy a dedicated data access and processing infrastructure and user portal, with automated tools, methods and standards to support research applications using Earth Observation (EO) data for monitoring of Coastal Waters, leveraging system components deployed as part of the SenSyF project (www.sensyf.eu). The main objective is to facilitate the access to Earth Observation data and processing tools for the Coastal research community, aiming at the provision of new Coastal Water services based on EO data. Through Co-ReSyF‘s collaborative front end, even unexperienced researchers in EO will be able to upload their applications to the Cloud Platform, in order to compose and configure processing chains, for easy deployment and exploitation on a cloud computing infrastructure. Users will be able to accelerate their development of high-performing applications taking full advantage of the scalability of resources available from Terradue Cloud Platform’s Application integration service. The system’s facilities and tools, optimized for distributed processing, include EO data access catalogues, discovery and retrieval tools, as well as a number of preprocessing tools and toolboxes for manipulating EO data. Advanced users will also be able to go further and take full control of the processing chains and algorithms by having access to dedicated cloud back-end services, and to further optimize their applications for fast deployment addressing big data access and processing. The Co-ReSyF capabilities will be supported and initially demonstrated by a series of early adopters who will develop new research applications for the coastal domain, guide the definition of requirements and serve as system beta testers. Following this, a competitive call will be issued within the project to further demonstrate and promote the usage of the Co-ReSyF release. These pioneering researchers will be given access not only to the Cloud Platform itself, but also to extensive training material on the system and on Coastal Waters research themes, as well as to the project's events, including the Summer School and Final Workshop. ## Purpose and Scope 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 Co-ReSyF project 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. ## Document Structure The structure of the document is as follows: x Chapter 2 : Description of the datasets to be produced x Chapter 3: Description of the standards and metadata used x Chapter 4: Description of the method for sharing the data x Chapter 5: Solutions for the archiving and preservation # Data Sets Description ## SAR_BATHYMETRY_DEM Digital Elevation model of the sea bed surface derived from a collection of SAR images of the area of interest. The derived bathymetry data will be applicable to the region of the sea bed from the coastline ranging from depths lower than 200m and greater than 10m. The data may allow monitoring the coastal bathymetry evolution from multiple images (at multiple times). Also in rapidly morphologically changing conditions, such as during coastal storms or tsunamis, if the SAR-image conditions are valid, then one can obtain pre and postdisaster bathymetries, which are extremely useful for disaster/risk management and coastal management in general. The bathymetry evolution would also be of extreme importance for data assimilation studies with the numerical morphodynamical or storm-surge models. Other uses of this product are: coastal engineering studies, coastal morphological evolution studies, coastal wave and current numerical modelling, etc... ## OPT_BATHYMETRY_DEM Digital Elevation model of the sea bed surface derived from a collection of HR optical images of the area of interest. The derived bathymetry data will be applicable to the region of the sea bed from the coastline ranging from depths from 0m to 10m (shallow waters). This data will be complementary to the SAR_BATHYMETRY_DEM, covering the shallow waters region that cannot be covered by the SAR technique. The usage of the data will be the same as for the SAR_BATHYMETRY_DEM (refer to Section 2.1). ## WAT_BENTHIC_CLASS Data containing the classification of the sea floor by its class of sea bottom albedo type. The derived data will be applicable to the region of the sea bed from the coastline ranging from depths from 0m to 10m (shallow waters). For shallow ocean waters, knowledge of the optical bottom albedo is necessary to model the underwater and above-water light field, to enhance underwater object detection or imaging, and to correct for bottom effects in the optical remote sensing of water depth or inherent optical properties (IOP’s). Measurements of the albedo can also help one identify the bottomsediment composition, determine the distribution of benthic algal or coral communities, and detect objects embedded in the sea floor. ## WAT_QUALITY_PROP Data containing the water quality properties (Chlorophyll-a concentration, particulates backscattering coefficient, and absorption of Coloured dissolved organic materials), for a region of interest. The derived data will be applicable to the region of the sea bed from the coastline ranging from depths from 0m to 10m (shallow waters). Knowledge of the water quality properties can be used for environmental analysis of the quality of the coastal waters and their evolution with the growing of the coastal city areas. Knowledge of the Chlorophyll-a concentration (biomass) is one of the most useful measurements in limnology and oceanography. The biomass can be used for studying phytoplankton community structure, the size frequency distribution of the algal cells and seasonal shifts within the plankton community. Phytoplankton abundance is related to natural cycles in nutrient availability and to the input of phosphate and nitrate. Excess phosphate and nitrate can come from groundwater or water treatment plants and sewer overflow (nitrate and phosphate are not removed in most sewage treatment plants). Excess nutrients can cause blooms of phytoplankton, which can contribute to bottom water anoxia under stratified conditions. ## VESSEL_DETECTION_TESTS Data containing the position of the detected vessels for a time interval and region of interest, and the real position of vessels from historical AIS databases for the same time interval and region of interest. The data may be used for researchers to analyse the performance of their detection algorithms with respect to the number of false alarms and true positives for the ships detection. Ship detection plays an important role in monitoring illegal ships activities and controlling the country’s borders. It can also be used for statistics for marine traffic in order to identify the areas with intensive ship routes. ## OIL_SPILL_DETECTION_TESTS Data containing the position of the detected oil spills for a time interval and region of interest, and the real position of the oil spills from historical databases for the same time interval and region of interest. The data may be used for researchers to analyse the performance of their detection algorithms with respect to the number of false alarms and true positives for the oil spills detection. Oil spill detection is used to monitor the illegal dumping of oil from vessels and maintaining the environmental integrity of the coastline. The identification of oil spills may also play an important role in case of environmental disasters in order to assist in the clean-up procedures of the environmental agencies. ## WAT_BOUNDARY_MAPS The data is composed of two ocean boundary map for the period and region of interest in question. One map is delineating the boundaries between different pixels exhibiting different seasonalities, where prime zones for water mass mixing can be found. And another map is providing a randomised SST/Chl boundary output, to determine whether the patterns evident in the true boundary data, are indeed patterns worth basing scientific research on. Water mass convergence zones are critically important to maintaining our ocean’s fisheries and ecological systems, frequently representing areas where colder, nutrient rich water mixes with warmer waters. This interaction fuels increased plant growth at the base of the ocean food chain, feeding the system that produces the overwhelming majority of the fish we eat. As ocean surface phytoplankton grows, sickens, and dies, their photosynthetic activity fluctuates accordingly. Satellite-derived measurements of chlorophyll activity provide a measurable estimate of this photosynthetic activity. Furthermore, with near-daily repeat times over fixed points, some optical datasets currently extend over a continuous 15 years. ## COAST_ALTIMETRY_TRACKS Data containing the SSH, SLA, SWH and Wind Speed data, and its respective geographical coordinates and time stamp, derived from the ALES retracker algorithm for the region of interest and the selected time period with a sampling frequency of 20Hz. In addition an extra dataset with the range and the applied corrections in order to derive the main parameters will also be part of the data. Sea level rise is an important indicator of climate change and one of its greatest impacts on society. Due to sea level rise many regions of the world’s coasts will be at much increased risk of flooding during the course of the 21st century and hundreds of millions of people currently living just above may have to be relocated, and coastal infrastructure to be moved or rebuilt. The rise rate shows huge regional variations so it is essential for coastal planners to have regional observations of sea level rise rate, which in combination with regional models will lead to regional projections. Tide gauges measure sea level variation but are affected by local vertical land movement, for instance due to subsidence. Altimetry, and coastal altimetry in particular, provide complementary measurements that are not affected by vertical land variation. In essence the coastal planners need the integration of both types of measurement. # Standards and Metadata The Co-ReSyF catalogue uses the OGC® OpenSearch Geo and Time extensions standard to expose data + metadata. The baseline of the standard is the OpenSearch v1.1 specification (A9.com, Inc, n.d.). The Geo and Time extensions provide with the main queryables to manage geospatial data products, and the specification is standardized at the Open Geospatial Consortium (OGC, n.d.). According to the selected standard baseline and extensions, the data catalogue queryable elements are as follows: x count={count?} x startPage={startPage?} x startIndex={startIndex?} x sort={sru:sortKeys?} x q={searchTerms?} x start={time:start?} x stop={time:end?} x timerelation={time:relation?} x name={geo:name?} x uid={geo:uid?} x bbox={geo:box?} x geometry={geo:geometry?} x submitted={dct:dateSubmitted?} # Data Sharing The Cloud Platform will be the privileged storage and cataloguing resource for managing the information layers produced by the project and described above. The Data Agency hosted by Terradue is the dedicated Cloud Platform service in charge of this type of operations. The catalogue entries of the Data Agency are open and web accessible. The data products referenced by a catalogue entry might require user authentication to allow download from a storage repository, depending on the policies (e.g. embargo period before full public release) applied by the producer organization. User registration procedures shall be described on the Co-ReSyF portal and be simple to follow by registrants (base don a single signon principle). To query the Data Agency catalogue from within application scripts, users can take advantage of the opensearch-client tool (Terradue, n.d.). Here are the available keywords to receive direct results: x wkt -> retrieve the product geometry in Well Known Text format x enclosure -> the URL to download the product x startdate -> Product Start Datetime x enddate -> Product End Datetime x identifier -> Product ID # Archiving and preservation While Terradue Cloud Platform provides the Data management and operations backbone from where data products can be directly accessed, the Cloud Platform supports distributed computing protocols allowing managing different storage locations for a same dataset. Long term data preservation of the produced datasets is foreseen to follow two main tracks: 1. Management of product copies onto the European Science Cloud, with as of today, physical resources being provided by the organization EGI (European Grid Infrastructure). 2. Management of products copies onto partners own storages offering an access point to the Platform to perform the data staging operations. Option 1 is the default option for long-term preservation of the product copies and will be assured as long as the Co-ReSyF platform is operational with no additional cost to the partners for storing the data. Option 2 is an alternative option in case the generator of the dataset prefers to keep control of the data. This option is not foreseen to be used for the datasets described above and in case it is used it is the responsibility of the owner of the repository to arrange for financial support for ensuring the preservation of the data. # Reference Documents x Co-ReSyF. (2016). _GRANT AGREEMENT-687289_ . European Commission, Research Executive Agency. x Terradue (n.d.). opensearch-client. Terradue Developer Cloud Sandbox. Retrieved June 22 nd , 2016, from _http://docs.terradue.com/developersandbox/reference/man/bash_commands_functions/catalogue/opensearchclient.html?highlight=opensearch_ x A9.com, Inc (n.d.). OpenSearch Specifications 1.1. OpenSearch.org. Retrieved June 22 nd , 2016, from _http://www.opensearch.org/Specifications/OpenSearch_ x OGC (n.d.). OpenSearch Geo and Time Extensions. Opengeospatial.org. Retrieved June 22 nd , 2016, from _http://www.opengeospatial.org/standards/opensearchgeo_ _PUBLIC_ Page ## END OF DOCUMENT _PUBLIC_ Page	https://phaidra.univie.ac.at/o:1140797	Horizon 2020
0157_SUPERTED_800923.md	2\. Theory data: Theoretical predictions about different quantities of interest, typically obtained from a derivation underlying a sequence of assumptions and approximations, and a subsequent numerical (or in some cases analytical) solution of the resulting equations. Here the data can mean (i) the numerical code used to obtain those results, or (ii) the curves produced in the numerical simulations. The purposes of the data management within the SUPERTED project are: 1. Takes care of the integrity of the data, its proper storage, and documentation. 2. Efficient communication of intermediate data among the project members. 3. Identifying three categories of data: publishable data (PU), supporting data (SU), and data that may require IPR protection (IPR). 4. In the spirit of open data initiatives, attempting to publish as much data as possible along with the research publications based on that data along with metadata helping to search that data. # Data collection Data collection takes place in work packages 1-4 as part of the research targeting the SUPERTED goals. We identify slightly different ways of data collection for the experiments and theory work. 1. Experimental data: All experimental partners of the project maintain electronic lab books on their experimental activity. These form the basis for collecting experimental data. Based on these lab books, we gather regularly a set of relevant information regarding each project on the SUPERTED wiki site. This site is open only to the SUPERTED project partners. To ensure useful representation of data, gathering the information into the wiki site requires some selection by the researchers. This is because the way experimental data is gathered in our field of science exhibits some level of uncertainty: a large part of data turns out to be not relevant for the general outcome because of some trivial or uninteresting reason - for example, that the sample under study was not of the intended type, or in case of equipment failure. The selected set of information is presented in the wiki site under the following generic form, structured within the different work packages/project deliverables in the wiki site. Acronyms: PU=publishable as such after relevant paper has been submitted, PE=publishable after submitting the relevant paper, but needs editing, CO=confidential, do not publish, IPR=possibly relevant for patenting PA - part of the published manuscript, SU - supplementary information, EX - published separately Overall aim: 1\. First aim 2. Second aim Specific aims: 1. First specific aim 2. Second specific aim Process description - for example, different sample batches: (For each item, add the month/year, a reference to your lab book, and your name and institute) * Month/year * Some overall description of this set of data * Link to a summary of the data with pictures/descriptions - lab book reference - (name, institute) Conclusions (update during the process): * Conclusion for the process, or overall conclusions from the research Open questions: * Possible new open questions whose solutions would help carry out the process The main purpose of this website is to serve the efficient communication within the project partners. However, at the same time it is possible to directly identify parts of data that can be used either as part of publication, part of supplementary information material, or part of an external set of data to be published on an archival site. In addition, we will actively aim at identifying data that needs to stay confidential for future research projects or because it is linked to protecting intellectual property rights. The different types of data will be identified via the acronyms and colour codes listed above. 2. Theory data: Theoretical physics work does not produce lab books. However, it may support presenting the experimental data by producing predictions of expected behaviour of the experimentally studied observables. This is often not as such new publishable theory, but rather using existing theory within the parameter regime of the experiment. Such predictions often arise from some generic numerical codes, and can be presented in a graphical form as curves. SUPERTED project will include such curves and codes from the theory work within the wiki site as parts of the experimental project. This set of data then includes identification related to their publishing capability similarly as with the experimental data. In addition, SUPERTED includes separate theory collaboration projects among two of the partners. For such projects, we will create separate wiki site pages, with the following format: Acronyms: PU=publishable as such after relevant paper has been submitted, PE=publishable after submitting the relevant paper, but needs editing, CO=confidential, do not publish, IPR=possibly relevant for patenting PA - part of the published manuscript, SU - supplementary information, EX - published separately Overall aim: 1\. First aim 2. Second aim Specific aims: 1. First specific aim 2. Second specific aim Intermediate results: (For each item, add a title of the subproject, month/year, and your name and institute) * Title of the subproject (month/year) * Some overall description * Link to a summary of the data with pictures/descriptions - (name, institute) ⮚ Link to codes (matlab/python/C++/Mathematica) producing this behavior Conclusions (update during the process): * Conclusion for the process, or overall conclusions from the research Open questions: * Possible new open questions whose solutions would help carry out the process # Data storage, preservation and data sharing The SUPERTED wiki site will act as an intermediate-stage repository for the data gathered during the project, in addition to the lab books of the participating experimental groups. Access of the participants to this wiki site will be maintained until at least 3 years after the end of the project. The wiki site is provided by the University of Jyväskylä for this project. Figure 1: Main page of SUPERTED project intranet or wiki site. Confluence wiki is used in this project. In addition, access to the lab books will be maintained until at least 3 years after the project. The data format within the wiki site is in terms of description (ascii text) and pictures (png/jpg/pdf and other often-readable formats). For the data chosen to be part of published scientific papers (identified with acronyms PU/PE and/or PA/SU/EX), we will use one or more of the following way to ensure data sharing and open access: ## PA: part of the published manuscript or SU: supplementary information If the data is part of the published manuscript or the supplementary information published together with the manuscript, it typically does not need further efforts in terms of preservation as we will publish our results in well-known journals that have long-term storage of the information. In addition, if not forbidden by the journal, we will submit the manuscripts to the arXiv repository (https://arxiv.org) 3 . _Self-archived at JYU._ Research conducted at the JYU is self-archived (parallel published) in the JYX publication archive. In JYU, researcher submits a research article (both the final PDF and the final draft version) using the TUTKA form in connection of registering the publication data for the University Library. (Final draft = Author’s Accepted Manuscript (AAM) = the authors' version = post-print = post-review = the version after peer-review changes but before copy editing and formatting by the publisher.) The University Library verifies the publication permission of the article, checks the archived version and possible embargo and saves the article in JYX. ## EX: published separately Besides supplementary information material to the published works, in certain cases we will opt to move part of the background content of the project wiki page directly into a public data repository (for example, University of Jyväskylä JYX publication archive or the zenodo.org service provided by the OpenAIRE project) for long-time storage and with open access. This data is then linked to and from the published scientific articles. # Documentation and metadata The following table indicates an example structure of the metadata included in the beginning of the self-archived datasets. It is compatible with the DataCite 4.0 scheme 3 <table> <tr> <th> Project and GA number </th> <th> SUPERTED 800923 </th> </tr> <tr> <td> Identifier </td> <td> Superted.xxx (xxx is a running number) </td> </tr> <tr> <td> Creator </td> <td> Name and institute (+ contact details) </td> </tr> <tr> <td> Title </td> <td> Title of the data </td> </tr> <tr> <td> Publisher </td> <td> Name and institute (+ contact details) </td> </tr> <tr> <td> Publication Year </td> <td> Year </td> </tr> <tr> <td> Resource type </td> <td> Experimental/simulation/code </td> </tr> <tr> <td> Description </td> <td> Short description of the presented data </td> </tr> <tr> <td> Data source </td> <td> E.g. experiment performed in Pisa on date </td> </tr> <tr> <td> Version </td> <td> Possible version information (number) </td> </tr> <tr> <td> Rights </td> <td> Licence (if any) or use constraints </td> </tr> </table> The SUPERTED project maintains up-to-date documentation and metadata to ensure that the research data produced in project will be 'FAIR', that is findable, accessible, interoperable and reusable 5 . # Intellectual property rights The Intellectual Property Rights (IPR) Management Plan ( _Separate Deliverable 4.1_ ) of SUPERTED project has been elaborated as a set of rules and protocols. In SUPERTED project, we follow three main principles: 1. Both the recognition of knowledge brought in (background) and generated within the project (foreground) by each partner has been assigned by default, based in the information contained in the Grant and Consortium Agreements. This will allow ensuring respect of partners’ rights without causing administrative burden. 2. There is a dedicated Task ( _Deliverable 4.1_ carried on by partners JYU and BIHUR) who will actively monitor the IPR generation and propose paths to exploit and disseminate the results avoiding conflicts. This will allow to systematically dealing with all knowledge generation while allowing partners to focus onto the technical activity. 3. Management of IPR (including conflicts) will be in the first instance carried out by the teams involved in the project. This is to take the most of the technical knowledge of the teams for discussing the issue and finding solutions. # Responsibilities and resources All SUPERTED partners are responsible for complying with the DMP and its procedures for data collection, handling and preservation. JYU is responsible for overseeing this implementation, along with ensuring that the plan is reviewed and revised during the project. This DMP is a plan, and its actual implementation may reveal better ways to operate. We hence leave the possibility of improving the policies during the project. Any changes in DMP will however require approval by the SUPERTED board, either in a common Skype meeting, or in an annual project meeting. The contact person for communication: Tero Heikkilä, [email protected] ## Bibliography 1. European Commission, Data management in Horizon 2020 Online Manual Available from: http://ec.europa.eu/research/participants/docs/h2020-funding-guide/cross- cuttingissues/open-access-data-management/data-management_en.htm#A1-template 2. University of Jyväskylä, Open Science and Research, Available from: https://openscience.jyu.fi/en/open-science-jyu 3. arXiv ®, Cornell University, Available from: https://arxiv.org <table> <tr> <th> DataCite, https://schema.datacite.org/meta/kernel-4.0/doc/DataCite- </th> </tr> <tr> <td> MetadataKernel_v4.0.pdf </td> <td> </td> </tr> </table> 4\. 5\. European Commission. Guidelines on FAIR Data Management in Horizon 2020, July 2016. Available from: http://ec.europa.eu/research/participants/data/ref/h2020/grants_manual/hi/ oa_pilot/h2020-hi-oa-data-mgt_en.pdf	https://phaidra.univie.ac.at/o:1140797	Horizon 2020
0158_DESTINATIONS_689031.md	# Executive Summary Throughout the whole work programme, the CIVITAS DESTINATIONS project embeds the process of data management, and the procedure of compliance to ethical/privacy rules set out in the Ethics Compliance Report (D1.1). The data management procedures within the CIVITAS 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. D1.7 represents the third edition of the Project Data Management Plan (PDMP) related to the data collected, handled and processed by the CIVITAS DESTINATIONS project in the horizontal (HZ) WPs until February 2019\. According to the guidelines and indications defined in the Ethics Compliance Report (D1.1), the overall approach to data management issues adopted by the CIVITAS DESTINATIONS project is described in section 2.2. The Project Data Management Plan is structured as follows: * Section 2 provides the introduction to the role of the Project Data Management Plan (PDMP) in the project. * Section 3 identifies the different typologies of data managed by the whole CIVITAS DESTINATIONS project (the data described are cumulated since the beginning of the project until M30). * On the basis of the data typologies identified in section 3, section 4 details the specific data collected and generated by CIVITAS DESTINATIONS (the data described are cumulated since the beginning of the project until M30). * Section 5 focuses on Horizontal (HZ) WPs and it specifies the data managed/processed and the procedures adopted (when applicable) at this level. # Introduction ## Objectives of the CIVITAS DESTINATIONS project The CIVITAS 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 the CIVITAS 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 stakeholder 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 the SUMP (WP8); * Development of guidelines to sites for the design, contracting and operation of ITS (WP8); * Evaluation of results both at the project level and at site level (WP9); * Cross-fertilization of knowledge and best practice replication including cooperation with Chinese partners (WP10); * Communication and Dissemination (WP11). ## Role of PDMP and LDMP in CIVITAS DESTINATIONS The role and the positioning of the PDMP within the whole CIVITAS DESTINATIONS project (in particular with the Ethics Compliance Report, D1.1) is detailed in the following: * The PDMP specifies the project data typologies managed in CIVITAS DESTINATIONS; * Based on the identified data typologies, the PDMP details the data which are collected, handled, accessed, and made openly available/published (eventually). The PDMP provides the structure (template) for the entire Data Management reporting both at Horizontal (WP8, WP9, WP10) and Vertical (from WP2 to WP7) level; * The LDMP (D1.10) describes the procedures for data management implemented at site level. ## PDMP lifecycle The CIVITAS DESTINATIONS project includes a wide range of activities spanning from users’ needs analysis of the demonstration measures, including SUMP/SULP (survey for data collection, assessment of the current mobility offer which could include the management of data coming from previous surveys and existing data sources, personal interviews/questionnaires, collection of requirements through focus groups and coparticipative events, etc.) to the 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.) and to data collection for ex-ante evaluation to ex-post evaluation. Data can be grouped in some main categories, but the details vary from WP to WP (in particular for the demonstration ones) and from site to site. 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 the PDMP and LDMP is to restrict the data collection in each six-monthly period: this will also allow the project partners, in particular Site Managers, to keep track of and control the data to be provided. This version of the PDMP covers the period of project activities until February 2019. # Data collected and processed in CIVITAS DESTINATIONS The CIVITAS DESTINATIONS project covers different activities (identified in section 2.1) and deals with an extended range of possible data to be considered. The term “data” can be related to different kinds/sets of information (connected to the wide range of actions taking place during the project). A specification of the “data” collected/processed 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 required to set up the institutional background to support SUMP-SULP elaboration, design and operation of demo measures; * 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 the service, validation, transactions/payment, points for green credits, etc.); * Data collected to carry out the ex-ante and ex-post evaluation; * Data required to develop guidelines supporting the design/operation of demo measures; * Data used for knowledge exchange and transferability; * Data used for dissemination. Data collected by the CIVITAS DESTINATIONS project are mainly related to local activities of the demonstration measures design, setup and implementation. This process deals mostly with responsibilities of Site Managers. This is reflected in the production of the LDMP for which each site provides its contribution. Data processed/produced by the project are mainly: * SUMP/SULP; * Demonstration measures in the six pilot sites; * Outputs coming from WP8 (business principles and scenarios, ITS contracting documents, etc.), WP9 (evaluation) and WP10 (transferability). Regarding this data, the data management process deals mostly with responsibilities of Horizontal WP Leaders/Task Leaders and they are described in this Deliverable. The activities which have taken place since the beginning of the CIVITAS DESTINATIONS project are the following (here the reporting is restricted to the activities of interest for the data management process): * WP2 – collection of information on SUMP baseline * WP3 , WP4 , WP6 , WP7 – User needs analysis, design and implementation of demonstration of services and measures, operation of demo services and measures * WP5: collection of information on SULP baseline. User needs analysis design and implementation of services and measures, operation of demo services and measures ### • WP8 * Task 8.1 – Stakeholder mapping exercise detailing the organisations in each of the six sites which have differing levels of power and interest in the site measures. This included the collection of the names and email addresses of key individual contacts in these organisations and phone numbers. Development of guidelines in how to engage the identified stakeholder. * Task 8.2 – Elaboration of the documents for the call for tender for subcontracting professional expertise on business model trainings and coaching activities to be provided to the project sites. Launch of the tender, collection of participants offers, evaluation of the offers and awarding of the tender to META Group srl. Coordination of sub-contracting activities by ISINNOVA. * Task 8.3 – Provision of guidelines for the design of ITS supporting demo measures, provision of guidelines for tendering/contracting ITS, provision of guidelines for ITS testing. ### • WP9 * Task 9.1 and 9.3: Identification of indicator categories for ex-ante/ex-post evaluation. Continuous coordination activity in order to support LEMs (Local Evaluation Managers) and discuss the definition of their measures impact indicators (in accordance with the guidelines distributed in December 2016), the preparation of the local Gantt charts and the setting of the ex-ante impact evaluations. Close and continuous cooperation with the SATELLITE project. * Task 9.2: Preparation and delivering of the draft evaluation report (delivered 4 th of July 2017) * Data collection through MER (Measure Evaluation Report) and PER (Process Evaluation Report) ### • WP10 o Participation to ITB-China 2017 o Urban Mobility Management Workshop in Beijing o On-site Technical Visits in Beijing and Shenzhen o Launch of platform of followers # Detail of data categories In the following the typologies of “sensible” data produced, handled or managed by these activities are identified. The description of the data management procedure is provided in section 5 (for Horizontal WPs) and in D1.10 (for demo WPs and site activities). ### 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; * Data on network and traffic flow (speed, occupancy, incidents, etc.); * Emissions/Energy consumption; * Pollution; * Questionnaires on travel behaviour, attitudes, perceptions 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; * Passenger counting. ### WP3 __Task 3.2 User 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 paths, intersections, crossing points, traffic lights; * Traffic data (combined with WP2), * Road safety statistics (number of incidents on the network, etc.) combined with WP2; * Emissions/Energy consumption (combined with WP2); * 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 4.2 User 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 for electrical vehicles and recharge points; * Data on the offer of electrical vehicles and recharge points; * Survey on users’ needs and expectations; * Reports coming from stakeholder and target users focus group. __Task 4.4/Task 4.5/Task 4.6 Demonstration of demo services_ _ Data collection during service demonstration * Registered service users and related info; * Data collected during the service operation; * User satisfaction analysis. ### WP5 __Task 5.2 Logistics context and user needs analysis for piloting services on freight logistics_ _ Data/collection surveys for SULP elaboration: * Network/traffic data (combined with WP2); * Data on shops, supply process, logistics operators, etc.; * Energy/emissions consumption (combined with WP2); * 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 * Data related to the used cooked oil collection process currently adopted; * Survey on users’ needs and expectations; * Reports coming from stakeholder and target users focus group. __Task 5.6/Task 5.7 Demonstration of demo services_ _ Data collection during service demonstration * Registered service users and related info; * Data collected during the service operation; * User satisfaction analysis. ### WP6 __Task 6.2 User needs analysis, requirements and design_ _ Data/collection for the design of demo measures for increasing awareness on sustainable mobility: * Network/traffic data (combined with WP2); * Energy/emissions consumption (combined with WP2); * Data on mobility and tourist “green services”, green labelling initiatives and 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. __Task 6.4/Task 6.5/Task 6.6 Demonstration of demo measures_ _ Data collection during service demonstration * Registered service users and related info; * Data collected during the service operation; * User satisfaction analysis. ### WP7 __Task 7.2 User 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. __Task 7.4/Task 7.5/Task 7.6 Demonstration of demo measures_ _ Data collection during service demonstration * Registered service users and related info; * Data collected during the service operation; * User satisfaction analysis. ### WP8 __Task 8.1_ _ * Data on stakeholders: * Contact names of individuals working at the stakeholder organisations; o Email addresses of the individuals; o Phone numbers of the stakeholder organisations. __Task 8.2_ _ * Information provided by tender participants in their offer: * General information of the tender participants (contact details and address, authorized signature and subcontracting, declarations); * Information to prove the professional and technical capability to carry out the activities requested in the tender (description of proposed methodology, curriculum vitae of the experts). * Information supporting CANVAS development for relevant measures in the sites __Task 8.3_ _ 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. ### WP9 __Task 9.2 – Task 9.3 – Task 9.4 Evaluation Plan, Ex-ante/Ex-post evaluation_ _ • Baseline (BAU): baselines are calculated in different ways, including surveys and according to the measures the baselines refer to. The used data are highlighted below: * Economic impacts (operating revenues, investment costs, operating costs); o Energy consumption (fuel consumption, energy resources); o Environmental impacts (air quality, emissions, noise); o Sustainable mobility (modal split, traffic level, congestion level, vehicle occupancy, parking, public transport reliability and availability, opportunity for walking, opportunity for cycling, bike/car sharing availability, road safety, personal safety, freight movements); * Societal impacts (user acceptance, awareness and satisfaction, physical accessibility towards transport, car availability, bike availability); * Health impacts. ### WP10 __Task 10.4 – Cross-fertilisation among consortium members and beyond_ _ * Information provided by tender participants in their offer; * Management of personal data required to register to the platform. __Task 10.5 – International cooperation in research and innovation in China_ _ * Data to prepare a collective brochure in Mandarin per site (as detailed below); * Contacts collected by visitors in China cooperation events. ### 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 The Data Management Plan for the demonstration measures (WP2-WP7) is detailed in Deliverable D1.10 – Local Data Management Plan (LDMP) – third edition (M19-M30). ## WP8 For each of the data categories identified in section 4, the following table describes the management procedures. <table> <tr> <th> WP8 – Task 8.1 </th> </tr> <tr> <td> Stakeholder mapping </td> </tr> <tr> <td> Data management and storing procedures </td> </tr> <tr> <td> 8.1.1 </td> <td> How data collected by sites are stored? </td> <td> Data has been inputted by the six cities into proforma Excel files, issued by Vectos (electronic format) </td> </tr> <tr> <td> 8.1.2 </td> <td> Please detail where the data are stored and in which modality/format (if applicable) </td> <td> Information provided by the six cities is stored in Vectos internal server in electronic format. </td> </tr> <tr> <td> 8.1.3 </td> <td> How data are used (restricted use/public use)? Are they made publicly available? </td> <td> Email addresses and individuals’ names are restricted and are only for the use of the sites when liaising with stakeholders. </td> </tr> <tr> <td> 8.1.4 </td> <td> Who is organization responsible for data storing management? </td> <td> the and </td> <td> Vectos, Paul Curtis is overall responsible for the collation of the data and storing centrally on the Vectos server. The six site managers are responsible for the storing of their respective stakeholder data, with the following variances: * Andreia Quintal, HF (individual names, individual email addresses stored internally by Vectos) * Antonio Artiles Del Toro, GUAGUAS (organisation phone numbers and individual email addresses stored internally by Vectos) * Maria Stylianou, LTC (data stored internally only) * Alexandra Ellul, TM (individual names, individual email addresses stored internally by Vectos) * Stavroula Tournaki, TUC – (data held internally only) * Renato Belllini, Elba – (data held internally only) </td> </tr> <tr> <td> 8.1.5 </td> <td> By (organization, responsible) data are accessible? </td> <td> whom </td> <td> Data is accessible to Vectos via the internal server. It is also accessible by each site partner - who provided the details via their servers. </td> </tr> </table> Table 1: Description of WP8 (Task 8.1) data management procedures (stakeholders mapping) <table> <tr> <th> WP8 – Task 8.2 </th> </tr> <tr> <td> Management of the call for tender for the selection of expert support for business development for the more relevant site measures </td> </tr> <tr> <td> Data management and storing procedures </td> </tr> <tr> <td> 8.2.1 </td> <td> How data collected by tender participants are stored? </td> <td> Tender participants have sent their offer in electronic format. </td> </tr> <tr> <td> 8.2.2 </td> <td> Please detail where the data are stored and in which modality/format (if applicable) </td> <td> Information provided by the participants are stored in ISINNOVA archive in electronic format. Details of awarded participant (META Group srl) have been also forwarded to ISINNOVA accounting system for the management of payment procedures. </td> </tr> <tr> <td> 8.2.3 </td> <td> How data are used (restricted use/public use)? Are they made publicly available? </td> <td> Information are restricted and they are managed in accordance with confidentiality rules required for tender management. Information will not be made publicly available </td> </tr> <tr> <td> 8.2.4 </td> <td> Who is the organization responsible for data storing and management? </td> <td> ISINNOVA, Ms. Loredana MARMORA </td> </tr> <tr> <td> 8.2.5 </td> <td> By whom (organization, responsible) data are accessible? </td> <td> Data have been accessed by ISINNOVA team involved in the tender management and awarding and by the members of the evaluation board (3 people from ISINNOVA and 2 people from Madeira). Data related to the awarded participant (META Group srl) are also available to ISINNOVA accounting staff for payment management </td> </tr> </table> Table 2: Description of WP8 (Task 8.2) data management procedures – call for tender ## WP9 For each of the data categories identified in section 4, the following table describes the management procedures. <table> <tr> <th> WP9 </th> </tr> <tr> <td> Data management and storing procedures </td> </tr> <tr> <td> 9.7.1 </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. Information provided by sites through MER and PER templates </td> </tr> <tr> <td> 9.7.2 </td> <td> Please detail where the data are stored and in which modality/format (if applicable) </td> </tr> <tr> <td> 9.7.3 </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> 9.7.4 </td> <td> Who is the organization responsible for data storing and management? </td> <td> ISINNOVA </td> </tr> <tr> <td> 9.7.5 </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 3: Description of WP9 data management procedures ## WP10 For each of the data categories identified in section 4, the following table describes the management procedures. <table> <tr> <th> WP10 </th> </tr> <tr> <td> Participation to ITB China 2017 </td> </tr> <tr> <td> Data management and storing procedures </td> </tr> <tr> <td> 10.1.1 </td> <td> How data collected are stored? </td> <td> Data collected from the sites are included in a promotional brochure in Mandarin. The business cards collected by GV21 during the ITB-China trade fair (and collateral events) have been used to send a follow-up email and allow to identify follow-up actions that could be conducted by the sites (possibly outside the project as no budget for ITB China 2017’s follow-up actions allocated in the DESTINATIONS project). A specific archive has been created to store those business cards’ data. </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 4: Description of WP10 data management procedures (Participation to ITB China 2017) <table> <tr> <th> Urban Mobility Management Workshop in Beijing (June 2018) On-site Technical Visits in Beijing and Shenzhen </th> </tr> <tr> <td> Data management and storing procedures </td> </tr> <tr> <td> 10.2.1 </td> <td> How data collected are stored? </td> <td> Names and coordinates from attendees and people met at the technical visit have been collected by GV21 and put in an electronic format. The file is stored in a specific archive (see 10.1.1). The data have been stored for any future promotion activities but not used right now. </td> </tr> <tr> <td> 10.2.2 </td> <td> Please detail where the data are stored and in which modality/format (if applicable) </td> </tr> <tr> <td> 10.2.3 </td> <td> How will be data used? </td> </tr> <tr> <td> 10.2.4 </td> <td> Who is the organization responsible for data storing and management? </td> <td> GV21 </td> </tr> <tr> <td> 10.2.5 </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 5: Description of WP10 data management procedures (On Site Technical Visits, Urban Mobility Management Workshop in Beijing) <table> <tr> <th> WP10 </th> </tr> <tr> <td> Management of the call for tender for the selection of IT provider in charge of the setup of the platform of followers </td> </tr> <tr> <td> Data management and storing procedures </td> </tr> <tr> <td> 10.3.1 </td> <td> How data collected by tender participants are stored? </td> <td> Tender participants have sent their offer in electronic format </td> </tr> <tr> <td> 10.3.2 </td> <td> Please detail where the data are stored and in which modality/format (if applicable) </td> <td> Information provided by the participants are stored by the Project Dissemination Manager (PDM) and the CPMR Financial services. Data are stored in electronic format on the CPMR server. </td> </tr> <tr> <td> 10.3.3 </td> <td> How data are used (restricted use/public use)? Are they made publicly available? </td> <td> The data stored were used to evaluate and select the successful bidder. They are available in case of INEA audit. </td> </tr> <tr> <td> 10.3.4 </td> <td> Who is the organization responsible for data storing and management? </td> <td> CPMR </td> </tr> <tr> <td> 10.3.5 </td> <td> By whom (organization, responsible) data are accessible? </td> <td> Data are accessible by CPMR (Mr. Panos Coroyannakis) and his colleagues. The offers have been shared with the Project PCO & PM teams via emails. </td> </tr> </table> Table 6: Description of WP10 data management procedures (tender for platform for followers) <table> <tr> <th> WP10 </th> </tr> <tr> <td> Follower registration to the DESTINATIONS platform </td> </tr> <tr> <td> Data management and storing procedures </td> </tr> <tr> <td> 10.4.1 </td> <td> How data collected are stored? </td> <td> The data are collected and stored on the platform’s administration site. The site is on the server of the platform designer INEVOL The data will only be used to invite the followers to join the platform by sending a password to qualified followers. </td> </tr> <tr> <td> 10.4.2 </td> <td> Please detail where the data are stored and in which modality/format (if applicable) </td> </tr> <tr> <td> 10.4.3 </td> <td> How will be data used? </td> </tr> <tr> <td> 10.4.4 </td> <td> Who is the organization responsible for data storing and management? </td> <td> CPMR The PLATFORM designer organisation INEVOL </td> </tr> <tr> <td> 10.4.5 </td> <td> By whom (organization, responsible) data are accessible? </td> <td> Data are accessible by CPMR personnel Mr. Panos Coroyannakis, Mr. Stavros Kalognomos and the platform designers INEVOL. </td> </tr> </table> Table 7: Description of WP10 data management procedures (operation of platform for followers)	https://phaidra.univie.ac.at/o:1140797	Horizon 2020
0160_NUTRIMAN_818470.md	# 2\. Data Management Plan (DMP) Applied Guiding Principles According to the requirements, the NUTRIMAN DMP observes FAIR (Findable, Accessible, Interoperable and Reusable) Data Management Protocols. This Data Management Plan of NUTRIMAN is coordinated by Work Package 7, and is articulated around the following key points: I. This Data Management Plan (DMP) has been prepared by taking into account the template of the Guideline on "Open access & Data management": * http://ec.europa.eu/research/participants/docs/h2020-funding-guide/cross-cuttingissues/open-access-data-management/data-management_en.htm and * http://ec.europa.eu/research/participants/data/ref/h2020/grants_manual/hi/oa_pilot/h2 020-hi-oa-data-mgt_en.pdf). The elaboration of the DMP will allow NUTRIMAN partners to address all issues related with IP protection and data. The NUTRIMAN network Data Management Plans “DMP” describes the data management life cycle for the data to be collected, processed and/or generated by the project, that are findable, accessible, interoperable and re-usable, while consistent with exploitation and Intellectual Property Rights requirements. The NUTRIMAN DMP is an official project Deliverable (D7.2) due in Month 5 (28 February 2019), but it will be a live document throughout the project. NUTRIMAN 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. A clear version number will be added for each NUTRIMAN DMPs updates. This initial first version will evolve depending on significant changes arising and periodic reviews at reporting stages of the project. This NUTRIMAN DMP will be updated over the course of the project whenever significant changes arise, such as(but not limited to): * 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). As a minimum, the NUTRIMAN DMP will be updated in the context of the periodic evaluation/assessment of the project. II: Ethics requirements: For some of the activities to be carried out by the project, it may be necessary to collect basic personal data (e.g. full name, contact details, background), even though the project will avoid collecting such data unless deemed necessary. The consortium will comply with the requirements of Regulation (EU) 2016/679 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). The ethics related issues of NUTRIMAN is a seperated official project deliverables (D1.1.H-Requirements No. 1 and D1.2.PODPRequirements No. 2) submitted on December 2018. All personnel data collected by the project will be done after giving data subjects full details on the experiments to be conducted, and after obtaining signed informed consent forms. III: Type of data, storage, confidentiality, ownership, management of intellectual property and access: Procedures that will be implemented for data collection, storage, access, sharing policies, protection, retention and destruction will be in line with EU standards as described in the Grant Agreement and the Consortium Agreement, particularly Articles 18, Keeping Records — Supporting Documentation; Article 23, Management of Intellectual Property; Article 24 Agreement on background; Article 25, Access Rights to Background; Article 26, Ownership of Results; Article 27, Protection of Results — Visibility of EU funding; Article 30,Transfer and Licensing of Results; Article 31, Access Rights to Results; Article 36, Confidentiality; Article 37 Security-related Obligations; Article 39 Processing of Personal Data; Article 52, Communication between the parties, and “Annex I – Description of Work” of the Grant Agreement. NUTRIMAN DMP is including 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). # 3\. Data Summary ## The purpose of the data collection/generation and its relation to the objectives of the project NUTRIMAN is a Nitrogen and Phosphorus Thematic network compiling knowledge for recovered bio-based fertiliser products, technologies, applications and practices while connecting the market competitive and commercially “ready for practice” innovative results from high research maturity applied scientific programmes and common industrial practice, for the interest and benefit of agricultural practitioners. The NUTRIMAN general objective is to improve the exploitation of the N/P nutrient management and recovery potential for the ready for practice cases not sufficiently known by practitioners and make integrated coherence in this strategic priority. NUTRIMAN is focusing on compiling knowledge and knowledge exchange of best practices and methodologies for innovative organic and low input farming in particular cost efficient and safe recovered N/P innovative fertilizer supply. NUTRIMAN is supporting the collection, provision and EU- wide efficient delivery of easily accessible multi lingual practice oriented abstracts and training materials in the thematic area of N/P nutrient recycling. NUTRIMAN is targeting to provide cross-border knowledge exchange through disseminating matured FP7/H2020/LIFE/OGs innovative research results, that are near to be put into practice, but not sufficiently known by practitioners, using the EIP common format for abstracts. NUTRIMAN is targeting to deliver a substantial number of practice abstracts in the common EIP-AGRI format and training materials in the thematic area of nutrient recovery. The practice abstracts and training materials will remain easily and open available in the long term, beyond the project period on the NUTRIMAN Multi lingual SME practice oriented and maintained web platform, managed by the coordinator. Relevant WPs for data collection: * WP2: the Specific collection and provision of practice-oriented knowledge for Nitrogen and Phosphorus recovery innovative TECHNOLOGIES and PRODUCTS ready for practice * WP3: practice abstracts in the common EIP-AGRI format and training material. The purpose and specific objectives of the NUTRIMAN data collection: * Inventory of matured FP7/H2020/LIFE/ Operational Groups (OGs) innovative research results from the field of Nitrogen and Phosphorus recovery EU28 technologies, which are near to be put into practice, but not sufficiently known by large industrial agricultural practitioners. (WP2) * Inventory of matured FP7/H2020/LIFE/OGs innovative research results from the field of Nitrogen and Phosphorus recovery EU28 products, which are near to be put into practice, but not sufficiently known by SME small and medium scales users and agricultural practitioners. (WP2) * Evaluation of technologies, products and practices, both by experts and by the potential end-users. (WP2) * Collection of Practice abstracts in common EIP-AGRI format (https://ec.europa.eu/eip/agriculture/en/eip-agri-common-format). The EIP common format consists of a set of basic elements characterising the given project. (WP3) As the NUTRIMAN project progresses and data is identified and collected, further information on the specific datasets will be outlined in subsequent versions of the DMP. Additional datasets may be identified and added to future versions of the NUTRIMAN DMP as necessary. ## Types and formats of data that will the project generate/collect The organization of data collection and most convenient format will be the responsibility of the relevant task leader and will be integrated in a database hosted on the project internal database. * We are expecting to collect >100 filled questionnaires (WP2) in a form of Word documentum with a size of ~500KB per each questionnaire. * We are expecting to collect 100 of practice abstracts in the common EIP-AGRI format (Excel file) with a size of ~500KB per practice abstract. ## The origin of the data The NUTRIMAN network is collecting open data that is free to access, reuse, repurpose, and redistribute, where transparency is implemented. The project will not generate/collect any protected sensitive research data, confidential technical and business information. Concerns in particular in relation to privacy, trade secrets, national security, legitimate commercial interests and to intellectual property rights and copyright shall be duly taken into account. The following origins are used for data collection: * NUTRIMAN consortium partners projects. A database of 100 projects (47 EU/25 national/25 linked projects) * Biorefine Cluster Europe (BCE; www.biorefine.eu, managed by UGent) connects projects and professionals to the NUTRIMAN in the field of nutrient and energy recovery and recycling. * CORDIS database (https://cordis.europa.eu/): EUFP7/H2020 projects * LIFE project database (https://ec.europa.eu/easme/en/life) * Database of Ministry of Agriculture and Rural Development, Chamber of Agriculture: AKIS, Operational Groups projects * Collecting from the vendors/owners of the innovative technologies/products via NUTRIMAN Questionnaire. A specific project questionnaire has been set up and placed on the NUTRIMAN web page by P1 TERRA. https://nutriman.net/questionnaire. NUTRIMAN is not collecting protected sensitive data (confidential technical and business information). * Collecting from the vendors/owners of the Practice abstracts in common EIPAGRI format. The EIP common format used for reporting on projects and disseminate the results. This common format consists of a set of basic elements characterising the project and includes one or more "practice abstract"(s). ## The expected size of the data * We are expecting to collect 100 filled questionnaires in a form of Word documentum with a size of ~500KB per each questionnaire. The expected total size is 50MB * We are expecting to collect 100 of practice abstracts in the common EIP-AGRI format (Excel file) with a size of ~500KB per practice abstract. The expected total size is 50MB ## Data utility to whom might it be useful Multi-lingual NUTRIMAN practice oriented master web platform (https://www.nutriman.eu) will be used to spread the collected practice abstracts and knowledge towards farmers and agricultural practitioners about the insufficiently exploited P and N recovery innovative research results (technologies, products and practices). NURTIMAN will use the wellestablished networks of the project partners at national and regional level, which will significantly boost local knowledge transfer to reach the largest possible number of farmers. The following stakeholders might use the collected data both at national/regional and EU28 levels. * Individual farmers and farmer groups * Agricultural networks and organisations: farmer associations and cooperatives, chambers of Agriculture, producers Organisations * Agri practicioners * Regulators and Policymaker * Representatives of the European Commision, DG-AGRI, DG-Grow. At International level the relevant international organisations, such as FAO and other international organisations. ## _Detailed NUTRIMAN Data inventory table_ <table> <tr> <th> Dataset No: DS1 </th> <th> Dataset name: Practice-oriented knowledge for Nitrogen and Phosphorus recovery innovative TECHNOLOGIES </th> </tr> <tr> <td> Data identification </td> </tr> <tr> <td> Dataset description: </td> <td> Collection of practice-oriented knowledge for Nitrogen and Phosphorus recovery innovative TECHNOLOGIES </td> </tr> <tr> <td> Data Subject: </td> <td> Type of data </td> </tr> <tr> <td> Qualified and quantified technology description: </td> <td> * processing aim, process conditions , feed flexibility and description: * energy/water use: * added value innovative technical content: * location: * emissions and environmental/climate impacts (as of EU/MS regulations) </td> </tr> <tr> <td> INPUT tons/year and tons/hour </td> <td> * input material(s) specs, input material availability in economical industrial scale, logistics and cost/ton • at minimum economical industrial scale * scale up options: </td> </tr> <tr> <td> OUTPUT tons/year and tons/hour </td> <td> * at minimum economical industrial scale: * N/P nutrient concentration % and plant availability: * Fertilizing product category selection </td> </tr> <tr> <td> Maturity and status description: </td> <td> • TRL/IRL level or beyond </td> </tr> <tr> <td> CAPEX in EURO </td> <td> • Capital Expenditure for economical industrial scale: </td> </tr> <tr> <td> OPEX in EURO </td> <td> • Operational Expenditure for economical industrial scale: </td> </tr> <tr> <td> IPR status: </td> <td> • Intellectual property rights, such as industrial properties (industrial designs, models, know-how, patents) and copyrights, trademarks. </td> </tr> <tr> <td> EC/MS Authority permits: </td> <td> * Permit number: * Issuing Authority: * Permit area: * Permit validity : </td> </tr> <tr> <td> New/Existing data: </td> <td> Existing </td> </tr> <tr> <td> Source: </td> <td> * NUTRIMAN consortium partners projects. A database of 100 projects (47 EU/25 national/25 linked projects) * Biorefine Cluster Europe (BCE; www.biorefine.eu, managed by UGent) connects projects and professionals to the NUTRIMAN in the field of nutrient and energy recovery and recycling. * CORDIS database (https://cordis.europa.eu/): EUFP7/H2020 projects • LIFE project database (https://ec.europa.eu/easme/en/life) * Database of Ministry of Agriculture and Rural Development, Chamber of Agriculture: AKIS, Operational Groups projects * Collecting from the vendors/owners of the innovative technologies/products via NUTRIMAN Questionnaire. A specific project questionnaire has been set up and placed on the NUTRIMAN web page by P1 TERRA. https://nutriman.net/questionnaire. NUTRIMAN is not collecting protected sensitive data (confidential technical and business information). </td> </tr> <tr> <td> Related WP(s) and task(s) </td> <td> WP2: Specific collection and provision of practice-oriented knowledge for Nitrogen and Phosphorus recovery innovative TECHNOLOGIES and PRODUCTS ready for practice T2.1. Collection of matured FP7/H2020/LIFE/OGs/national innovative </td> </tr> <tr> <td> </td> <td> research results from the field of Nitrogen and Phosphorus recovery EU28 technologies and products, which are near to be put into practice, but not sufficiently known by practitioners. </td> </tr> <tr> <td> Partners activities and responsibilities </td> </tr> <tr> <td> Partner owner of the data; copyright holder (if applicable) </td> <td> N/A </td> </tr> <tr> <td> Partner in charge of the data collection </td> <td> P5 UGent </td> </tr> <tr> <td> Partner in charge of the data analysis </td> <td> P5 UGent </td> </tr> <tr> <td> Partner in charge of the data storage </td> <td> P1 TERRA </td> </tr> <tr> <td> Involving partners: </td> <td> All NUTRIMAN consortium partners and linked Third Parties. </td> </tr> <tr> <td> Method of capture/Standards </td> </tr> <tr> <td> Method of Data capture </td> <td> European Nutrient Recycling Contest Questionnaire https://nutriman.net/questionnaire </td> </tr> <tr> <td> Format of data capture and expected size: </td> <td> We are expecting to collect 100 filled questionnaires in a form of Word documentum with a size of ~500KB per each questionnaire. The expected total size is 50MB. </td> </tr> <tr> <td> Info about metadata (production and storage dates, places) and documentation? </td> <td> N/A </td> </tr> <tr> <td> Data exploitation and sharing </td> </tr> <tr> <td> Data utility. Who outside of the consortium might use the data? </td> <td> Restricted data not used outside the consortium. </td> </tr> <tr> <td> Data sharing, re-use, distribution, publication </td> <td> No data sharing and/or publication. </td> </tr> <tr> <td> Data access policy. Type of access: Restricted (only for members of the Consortium and the Commission Services) or Public </td> <td> Restricted only for the members of the Consortium, Farmers Advisory Board and the Commission Services. </td> </tr> <tr> <td> Ethical issue Y/N Personal data protection </td> <td> Yes. During this data collection it is necessary to collect basic personal data (e.g. full name, contact details) which comply with the requirements of Regulation (EU) 2016/679 and of the Council of 27 April 2016 (General Data Protection Regulation). All personnel data collected will only be done after giving data subjects full details on the experiments to be conducted, and after obtaining signed informed consent forms. Information sheet and consent form are provided together with the Questionnaire. </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> This DS1 dataset will be preserved in the coordinator TERRA HUMANA infrastructure. +10 years after the closure of the project. </td> </tr> </table> <table> <tr> <th> Dataset No: DS2 </th> <th> Dataset name: Practice-oriented knowledge for Nitrogen and Phosphorus recovery innovative PRODUCTS </th> </tr> <tr> <td> Data identification </td> </tr> <tr> <td> Dataset description: </td> <td> Collection of practice-oriented knowledge for Nitrogen and Phosphorus recovery innovative PRODUCTS </td> </tr> <tr> <td> Data Subject: </td> <td> Type of data: </td> </tr> <tr> <td> Fertilizing product category selection as of EC Fertilizers Regulation revision COM (2016) 157 </td> <td> • Category of fertilizing product </td> </tr> <tr> <td> Status of the product development incl. TRL/IRL level </td> <td> • TRL/IRL level or beyond </td> </tr> <tr> <td> Input material(s) specification: </td> <td> • Specification of the input material </td> </tr> <tr> <td> Quality characterization (Nutrients) as of EC Fertilizers Regulation revision COM (2016) 157 </td> <td> * Overall texture of the product (granulometry, moistureI) • Organic carbon content (% of dry matter by weight): * Total carbon content (% of dry matter by weight): * Total Nitrogen content % dry matter: * Phosphorus content mg/kg dry matter: * Other macro and micro elements (mg/kg dry matter): * Plant available nutrient content % (e.g water soluble, citric acid soluble nutrient content): * Dry matter content: * Particle density (g cm-3): * pH </td> </tr> <tr> <td> Product safety as of EC Fertilizers Regulation revision COM (2016) 157 </td> <td> * Metals/metalloids: As, Cd, Cr, Cu, Hg, Ni, Pb, Zn (mg/kg dry matter): * PAH16 or PAH19 (mg/kg dry matter): * PCB6 (mg/kg dry matter): * PCDD/F (ng WHO Toxicity equivalents/kg dry matter): </td> </tr> <tr> <td> Product testing condition </td> <td> * Countries * Condition </td> </tr> <tr> <td> Product economics, EXW whole sale:€cost/ton: </td> <td> • EXWorks product availability at manufacturers location for professional large scale users </td> </tr> <tr> <td> User recommendations </td> <td> • incl. doses/ha, application conditions, formulations, asoI) </td> </tr> <tr> <td> EC/MS Authority permits for product use: </td> <td> * Permit number: * Issuing Authority: * Permit area: * Permit validity (crops): </td> </tr> <tr> <td> New/Existing data: </td> <td> Existing </td> </tr> <tr> <td> Source: </td> <td> * NUTRIMAN consortium partners projects. A database of 100 projects (47 EU/25 national/25 linked projects) * Biorefine Cluster Europe (BCE; www.biorefine.eu, managed by UGent) connects projects and professionals to the NUTRIMAN in the field of nutrient and energy recovery and recycling. * CORDIS database (https://cordis.europa.eu/): EUFP7/H2020 projects • LIFE project database (https://ec.europa.eu/easme/en/life) * Database of Ministry of Agriculture and Rural Development, Chamber of Agriculture: AKIS, Operational Groups projects * Collecting from the vendors/owners of the innovative technologies/products via NUTRIMAN Questionnaire. A specific </td> </tr> </table> <table> <tr> <th> </th> <th> project questionnaire has been set up and placed on the NUTRIMAN web page by P1 TERRA. https://nutriman.net/questionnaire. NUTRIMAN is not collecting protected sensitive data (confidential technical and business information). </th> </tr> <tr> <td> Related WP(s) and task(s) </td> <td> WP2: Specific collection and provision of practice-oriented knowledge for Nitrogen and Phosphorus recovery innovative TECHNOLOGIES and PRODUCTS ready for practice. T2.1. Collection of matured FP7/H2020/LIFE/OGs/national innovative research results from the field of Nitrogen and Phosphorus recovery EU28 technologies and products, which are near to be put into practice, but not sufficiently known by practitioners. </td> </tr> <tr> <td> Partners activities and responsibilities </td> </tr> <tr> <td> Partner owner of the data; copyright holder (if applicable) </td> <td> N/A </td> </tr> <tr> <td> Partner in charge of the data collection </td> <td> P5 UGent </td> </tr> <tr> <td> Partner in charge of the data analysis </td> <td> P5 UGent </td> </tr> <tr> <td> Partner in charge of the data storage </td> <td> P1 TERRA </td> </tr> <tr> <td> Involving partners: </td> <td> All NUTRIMAN partners and linked Third Parties. </td> </tr> <tr> <td> Method of capture/Standards </td> </tr> <tr> <td> Method of Data capture </td> <td> European Nutrient Recycling Contest Questionnaire https://nutriman.net/questionnaire </td> </tr> <tr> <td> Format of data capture and expected size: </td> <td> We are expecting to collect 100 filled questionnaires in a form of Word documentum with a size of ~500KB per each questionnaire. The expected total size is 50MB. </td> </tr> <tr> <td> Info about metadata and documentation </td> <td> N/A </td> </tr> <tr> <td> Data exploitation and sharing </td> </tr> <tr> <td> Data utility. Who outside of the consortium might use the data? </td> <td> Restricted data not used outside the consortium. </td> </tr> <tr> <td> Data sharing, re-use, distribution, publication </td> <td> No data sharing and/or publication. </td> </tr> <tr> <td> Data access policy. Type of access: Restricted (only for members of the Consortium and the Commission Services) or Public </td> <td> Restricted only for the members of the Consortium, Farmers Advisory Board and the Commission Services. </td> </tr> <tr> <td> Ethical issue Y/N Personal data protection </td> <td> Yes. During this data collection it is necessary to collect basic personal data (e.g. full name, contact details) which comply with the requirements of Regulation (EU) 2016/679 and of the Council of 27 April 2016 (General Data Protection Regulation). All personnel data collected will only be done after giving data subjects full details on the experiments to be conducted, and after obtaining signed informed consent forms. Information sheet and consent form are provided together with the Questionnaire. </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> This DS2 dataset will be preserved in the coordinator TERRA HUMANA infrastructure. +10 years after the closure of the project. </td> </tr> </table> <table> <tr> <th> Dataset No: DS3 </th> <th> Dataset name: PRACTICE ABSTRACTS in the common EIP-AGRI format </th> </tr> <tr> <td> Data identification </td> </tr> <tr> <td> Dataset description: </td> <td> Development of practice abstracts in the common EIP-AGRI format (https://ec.europa.eu/eip/agriculture/en/eip-agri-common-format) The NUTRIMAN practice abstracts are synthesizes of simple and lesstechnical language practice abstract collection of FP7, H2020 projects on the nutrient recovery and innovative fertilizers and easily accessible enduser materials developed and managed in substantial number and feed into the European Innovation Partnership (EIP) 'Agricultural Productivity and Sustainability' </td> </tr> <tr> <td> Data Subject: </td> <td> Type of data: </td> </tr> <tr> <td> Project information </td> <td> * Title * Geographical location * Project period * Project status * Project coordinator contact data * Project period and project status * website hosting information on the project results and audiovisual materials </td> </tr> <tr> <td> Project Partners </td> <td> • contact data </td> </tr> <tr> <td> Practice abstracts </td> <td> • Short summary for practitioners </td> </tr> <tr> <td> New/Existing data: </td> <td> Existing </td> </tr> <tr> <td> Source: </td> <td> • Collecting from the vendors/owners of the innovative technologies/products. </td> </tr> <tr> <td> Related WP(s) and task(s) </td> <td> WP3: PRACTICE ABSTRACTS in the common EIP-AGRI format and training materials T3.1. Development of practice abstracts in the common EIP-AGRI format. T3.2. Translation of selected 25 best practice abstracts into partner’s native languages </td> </tr> <tr> <td> Partners activities and responsibilities </td> </tr> <tr> <td> Partner owner of the data; copyright holder (if applicable) </td> <td> N/A </td> </tr> <tr> <td> Partner in charge of the data collection </td> <td> P10 UNITO </td> </tr> <tr> <td> Partner in charge of the data analysis </td> <td> P10 UNITO </td> </tr> <tr> <td> Partner in charge of the data storage </td> <td> P1 TERRA </td> </tr> <tr> <td> Involving partners: </td> <td> All NUTRIMAN consortium partners and linked Third Parties. </td> </tr> <tr> <td> Method of capture/Standards </td> </tr> <tr> <td> Method of Data capture </td> <td> Collecting from the vendors/owners of the Practice abstracts in common EIP- AGRI format. The EIP common format used for reporting on projects and disseminate the results. This common format consists of a set of basic elements characterising the project and includes one or more "practice abstract"(s). </td> </tr> <tr> <td> Format of data capture and expected size: </td> <td> We are expecting to collect 100 of practice </td> </tr> <tr> <td> </td> <td> abstracts in the common EIP-AGRI format (Excel file) with a size of ~500KB per practice abstract. The expected total size is 50MB </td> </tr> <tr> <td> Info about metadata (production and storage dates, places) and documentation? </td> <td> N/A </td> </tr> <tr> <td> Data exploitation and sharing </td> </tr> <tr> <td> Data utility. Who outside of the consortium might use the data? </td> <td> The following stakeholders might use the collected data both at national/regional and EU28 levels. * Individual farmers and farmer groups * Agricultural networks and organisations: farmer associations and cooperatives, chambers of Agriculture, producers Organisations * Agri practicioners * Regulators and Policymaker * Representatives of the European Commision, DG-AGRI, DG-Grow. At International level the relevant international organisations, such as FAO and other international organisations. </td> </tr> <tr> <td> Data sharing, re-use, distribution, publication </td> <td> A multi lingual interactive practice oriented master NUTRIMAN farmer web platform will be developed for provision of easily accessible practice- oriented knowledge on the Nutrient Recovery and Nutrient Recycling. The generated practice abstracts and training materials will also be shared through the EIP network. Best practice abstract booklet will be published for the selected 25 best practice abstracts in the common EIP-AGRI format translated into partner’s native language. </td> </tr> <tr> <td> Data access policy. Type of access: Restricted (only for members of the Consortium and the Commission Services) or Public </td> <td> Public </td> </tr> <tr> <td> Ethical issue Y/N Personal data protection </td> <td> Yes. During this data collection it is necessary to collect basic personal data (e.g. full name, contact details of the project coordinator) which comply with the requirements of Regulation (EU) 2016/679 and of the Council of 27 April 2016 (General Data Protection Regulation). All personnel data collected will only be done after giving data subjects full details on the experiments to be conducted, and after obtaining signed informed consent forms. </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> This DS3 dataset will be preserved in the coordinator TERRA HUMANA infrastructure. +10 years after the closure of the project. The multi languages interactive SME practice oriented NUTRIMAN master web platform will be remain open in the long term (10 years) beyond the project period and maintained by the coordinator P1 TERRA. </td> </tr> </table> # 4\. FAIR data NUTRIMAN will work to ensure that its data will be ’FAIR’, that is findable, accessible, interoperable and re-usable, according to the points below in line with H2020 Guideline on FAIR Data Management in Horizon 2020. http://ec.europa.eu/research/participants/data/ref/h2020/grants_manual/hi/oa_pilot/h2020-hioa- data-mgt_en.pdf ## 4\. 1. Making data findable, including provisions for metadata PROTOCOL – Storing NUTRIMAN data and making it ’Findable’ NUTRIMAN developing open-access practice abstract database for public information, gathering from the technology and product developers/owners. All and any data, information and knowledge streams to and through the NUTRIMAN will be documented, including written confirmations from the legal information provider, e.g. the legal owner and manager of the data, information and knowledge. Protecting intellectual property rights, copyrights innovative ideas and dissemination activities may be an issue and need to be considered already at early stage planning of the NUTRIMAN activities. The NUTRIMAN is developing open-access practice database for public information (practice abstract in the common EIP-AGRI format) in a language easily understandable for agricultural practitioners, gathering the main outputs and results for the interest and benefits of the farmers. Confidential information streams, if any, to be carefully managed and legally documented. The collection and provision of easily accessible practice-oriented data- informationknowledge on the NUTRIMAN thematic area remains available in the long term, at least ten years beyond the project period on the project website (https://www.nutriman.net), which is maintained by the coordinator. ### 4.2. Making data openly accessible The NUTRIMAN is developing open-access practice oriented database will be available on the NUTRIMAN website (https://www.nutriman.net) for public information (practice abstract in the standard common EIP-AGRI format) in a language easily understandable for agricultural practitioners, gathering the main outputs and results for the interest and benefits of the farmers. Confidential information streams, if any, to be carefully managed and legally documented. ### 4.3. Making data interoperable The collected practice abstracts in the common EIP-AGRI format is multi lingual and interoperable, that is allowing data exchange and re-use between researchers, institutions, organisations, countries, and farmers. The EIP-AGRI common format facilitates knowledge flows on innovative and practice-oriented projects in the thematic area of N/P nutrient recycling. The use of this EIP- AGRI format also enables farmers, advisers, researchers and all other actors across the EU to contact each other. The database of practice abstracts will be collected and published in a standard form of EIP-AGRI common format (https://ec.europa.eu/eip/agriculture/en/eip-agri-common-format). ### 4.4. Increase data re-use (through clarifying licences) The collected practice abstracts in the common EIP-AGRI format will be placed on open NUTRIMAN website (https://www.nutriman.net) where the widest accessible and re-use will be permitted. The collected and published practice abstracts in the common EIP-AGRI format will remain re-usable for 10 years after the closure of the project. # 5\. Allocation of resources Responsible person for data management of the project: Edward Someus/Terra Humana Ltd. # 6\. Data security All research data underpinning publications will be made available for verification and re-use unless there are justified reasons for keeping specific datasets confidential. The main elements when considering confidentiality of datasets are: * Protection of intellectual property regarding new processes, products and technologies where the data could be used to derive sensitive information that would impact the competitive advantage of the consortium or its members, * Commercial agreements as part of the procurements of components or materials that might foresee the confidentiality of data. * Personal data that might have been collected in the project where sharing them is not allowed by the national and European legislation. # 6\. Ethical aspects The NUTRIMAN consortium comply with the requirements of Regulation (EU) 2016/679 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). The ethics related issues of NUTRIMAN is a separated official project deliverables (D1.1.H-Requirements No. 1 and D1.2.PODPRequirements No. 2) submitted on December 2018. NUTRIMAN has a dedicated work package (WP1) to ensure that ethical requirements are met for all personal data processing undertaken in the project in compliance with H2020 ethical standards and Regulation (EU) 2016/679. All NUTRIMAN partners will assure that the EU standards regarding ethics and data management are fulfilled. _The NUTRIMAN project is complying with H - Requirement No. 1. (D1.1.):_ * Details on the procedures and criteria that will be used to identify/recruit research participants has been developed and submitted as a deliverable (D1.1). * Detailed information on the informed consent procedures that will be implemented for the participation of humans (stakeholders such as agricultural participants, growers, farmers, advisers) and in regard to data processing has been developed and submitted as a deliverable (D1.1.) * Templates of the informed consent forms and information sheets covering the voluntary participation, data protection and data preservation issues (in language and terms intelligible to the participants) has been developed and the English version has been submitted as a deliverable (D1.1.). ## _The NUTRIMAN project is complying with POPD - Requirement No. 2 (D1.2.)_ * NUTRIMAN beneficiaries confirmed that it has appointed a Data Protection Officer (DPO) and the contact details of the DPO are made available to all data subjects involved in the research. For beneficiaries not required to appoint a DPO under the General Data Protection Regulation 2016/679 (GDPR) a detailed NUTRIMAN Data Protection Policy for the project has been developed and submitted as a deliverables (D1.2.). * A description of the technical and organisational measures that will be implemented to safeguard the rights and freedoms of the data subjects/research participants has been submitted as a deliverable (D1.2.).	https://phaidra.univie.ac.at/o:1140797	Horizon 2020
0161_WASP_825213.md	# Executive Summary WASP is a research project aims to bring changes in flexible and wearable electronics by developing new printing technology for the definition of electronic devices and circuits on paper. The main goals of the projects are: 1. To demonstrate electronic functionalities and emerging electronic applications enabled by nanomaterials on paper. 2. To demonstrate a sustainable technology for low-cost and flexible electronics. 3. To demonstrate a wearable paper-based technology, including sensing and communication functionalities for health care applications. 4. To demonstrate multi-scale modeling of paper based devices and complete design tool chain for printed electronis circuits and systems. During the development of the project, WASP will generate data from the experimental and theoretical research activities. In this sense, and as a project participating in the Open Research Data Pilot (ORDP) in Horizon 2020, WASP will make its research data findable, accessible, interoperable and reusable (FAIR). The present document corresponds to the deliverable D1.6 “Data Management Plan” of the WASP project and was produced as part of the WP1 “Management and Dissemination”. It contains information about the main elements of the Data Management policy that will be used by the Consortium with regard to the project research data. It will include information about the production and management of the research data along the project and the conditions and aspects related to them. This is the first version of the DMP document. It will be systematically reviewed and more details and description of the data management procedures implemented by the WASP project will be included. This deliverable will include the analysis of the most relevant aspects of the data management policy. The document is divided into seven sections, corresponding to the highlighted points of the Data Management Plan (DMP) scheme: a) a general introduction describing the framework of the DMP; b) Data Summary; c) FAIR data; d) Allocation of resources; e) Data security; f) Ethical aspects and g) Other issues. Every section will include information about the research data generated/collected, standards that will be used, preservation of the data and the datasets will be shared for verification or reuse. # Introduction The deliverable D1.6 – Data Management Plan (DMP) of the WASP project provides the strategy for managing data generated and collected during the project. It consists on a document that will include information about how the WASP research data obtained will be handle during and after the project, so it will be reviewed and updated as the project evolves. The DMP include that data handling, the types and format of the data generated and collected, the methodologies applied and how the data will be shared and preserved. The use of a DMP is required for all projects participating in the Open Research Data Pilot. The document has been prepared taken into account the template of the “Guidelines on Data Management in Horizon 2020” ( _https://ec.europa.eu/research/participants/data/ref/h2020/grants_manual/hi/oa_pilot/h2020-hi- oadata-mgt_en.pdf_ ). The purpose of the DMP is to provide an analysis of the main elements of data management policy. In the particular case of the WASP project, the expected types of research datasets that will be collected or generated along the project lie in the following categories: i) Materials/devices modelling and circuit design; ii) Materials and device fabrication and characterization; iii) System integration and demonstration; iv) Process scalability, susteintability and exploitation. From the mentioned categories, WASP project will generate several datasets, including experimental measurements data, codes developed in different programming languages, data coming from the atomistic simulations and scientific articles. The data will be processed and analysed and they will be preserved using appropriated naming rules and metadata schemes. This information will be then organized in a DMP, considering open science resources that are interoperable and trusted. This document is the first version of the DMP, delivered in Month 6. It includes an overview of the datasets to be produced by the project, and the specific conditions related with it. The document will be updated in regular intervals, including more details about the procedures implemented within the WASP project. _Figure 1: Research data life-cycle _ # Data Summary <table> <tr> <th> </th> <th> </th> </tr> <tr> <td> Data collection/generation purpose, based on the project objectives. </td> <td> The generation and collection of data has several purposes, in particular the reproducibility of the performed results (either experimental and theoretical), the dissemination (through articles), as well as the training of researchers (either PhDs or post-docs), who will benefit from a detailed description of the method/results obtained within the project. </td> </tr> <tr> <td> Data types and formats generated/ collected </td> <td> In the WASP project several datasets will be generated, that can be classified and identify schematically as follow: Dataset1 : Materials formulation for solution processing deposition techniques Dataset2 : Development of a procedure to planarize/functionalize the paper substrates Dataset3 : Sensing components Dataset4 : Energy generation and storage Dataset5 : Transponder design Dataset6 : Device models At each dataset will be assigned a specific name/number. Inside these datasets, the formats for the data generated/collected will correspond to: 1. Raw data in ASCII format (.txt, .xls, .xlsx) 2. Open-source codes developed in different programming languages (Fortran, C and python) 3. Text based documents related with the description of the methodologies implemented in the project and publication in scientific journals, in (.doc, .tex, .pdf). 4. Illustrations, graphics and presentations (.png, .jpg, .tiff, .eps, .ppt, .pptx, .pdf). Further information about changes or inclusion of additional datasets during the progress of the project will be included in the subsequent versions of the DMP. If specialized software is used then information about free readers will be provided. </td> </tr> <tr> <td> Origin of the data </td> <td> Based on the nature of the data generated, the origin of the data is related with two principal sources: from the simulations activity, coming especially from ab initio and atomistic simulations and the data obtained from the experimental measurements of the devices. Moreover, another origin is the development of open-source codes, that allow a complete transparency of the obtained results. </td> </tr> <tr> <td> Size of the data </td> <td> The size of the files varies depending on the data generated. From the simulation activity we will get raw data in ASCII format of the order of tens/hundreds of Gigabyte coming especially from ab initio and atomistic simulations. From the experimental measurements, the amount of data will be reduced, expecting a collection of data </td> </tr> </table> <table> <tr> <th> </th> <th> of the order of hundreds of Megabytes at most. With respect to the documents size, it is expected files of the order of Megabytes. </th> </tr> <tr> <td> Re-use of existing data </td> <td> Some database will be used, for instance 2D Materials database ( _https://cmr.fysik.dtu.dk/c2db/c2db.html_ ) to get information about computational simulations parameters to be used to model the devices. Moreover, the DFT pseudopotentials included in Quantum Espresso package ( _https://www.quantum-_ _espresso.org/pseudopotentials_ ) will be used in order to describe the interaction among the atoms in the atomistic simulations. </td> </tr> <tr> <td> Data utility </td> <td> A repository of the generated data and implemented methodologies will be fundamental for the training of new researchers in the field and also to those joining the research group, so to reduce the learning curve and to allow a faster integration within the WASP project activity. The data obtained from experimental measurements and simulations can be used by theoretical groups as input for theoretical modelling or as a reference for future comparison and for the reproducibility of published results. The data will be also important for the private sector, for commercial applications and for other research groups working in the field. The data will be suitable for use by other research groups working on the following topics: nanomaterials, flexible electronics, sensors, multi-scale modelling. </td> </tr> </table> # FAIR Data 3.1 Making data findable, including provisions for metadata <table> <tr> <th> </th> <th> </th> </tr> <tr> <td> Data discoverable with metadata, identifiable and locatable by means of a standard identification mechanism (e.g. DOI) </td> <td> WASP has chosen ZENODO platform ( _http://zenodo.org_ ) as repository data archive. It is based on facilities and management FAIR data principles, on GIT version control system and the Digital Object Identifier (DOI) system. ZENODO is part of OpenAIRE collaboration and allows researchers to locate data and publications, and assigning a persistent identifier and data citations in order to link to them. The guidelines provided by ZENODO will be used by WASP to ensure the right format of data uploaded to comply with FAIR principles. </td> </tr> <tr> <td> Naming conventions </td> <td> The research data, documents and other product obtained in the WASP project will be identified, collected and structured by using a name convention, consisting of project and dataset name and an identification number related with the dataset. Partners will be informed about the specific information and metadata parameters that will support FAIR data management. </td> </tr> <tr> <td> Re-use through keywords provided </td> <td> Keywords will be provided and updated with the project advancement. </td> </tr> <tr> <td> Version numbers used </td> <td> Individual file names will contain version numbers that will be incremented at each version. </td> </tr> <tr> <td> Metadata </td> <td> Metadata is a descriptive information that help other researchers to find the data in an online repository. Detailed metadata will give to the other researchers the information to determine whether the dataset is important and useful for its own research. The metadata created for all of the project’s datasets will fulfil the respository’s (Zenodo) requirement for a minimum set of metadata. </td> </tr> </table> 3.2 Making data openly accesible <table> <tr> <th> </th> <th> </th> </tr> <tr> <td> Data openly available and data shared under restrictions. </td> <td> The generated data from the research activity (simulations and experimental measurements, codes, etc) will be included in repositories chosen for the project (ZENODO platform) and it will be made openly available unless there is a specific reason not to publish it. With respect the multi-scale simulations, it will be availabile the website of open-source code NanoTCAD ViDES (an open- source simulation framework), which is representing a benchmark for two- dimensional materials based devices _http://vides.nanotcad.com_ , exploiting ViDES as simulations tool, as well as the specific website of the WASP project ( _https://www.wasp-project.eu_ ). The beneficiaries must give each other access (under reasonable conditions) to background needed for exploiting their own results. </td> </tr> <tr> <td> Accessibility of the data (e.g. deposition in a repository) </td> <td> About the datasets, they will be transferred to the ZENODO, repository, through open access to data files and metadata and use and reuse of data permitted. Additional data storage will be ensured by individual partner institution’s data repositories. Data will also included in HPC resources of University of Pisa, and made publicly available, in order to allow availability, well beyond the timespan of the project. For what concerns the published articles, and in particular regarding those published in journals which do not offer the possibility to the reader to download freely, we will upload in public repositories as Arxiv.org the final version of the draft of the article, as well as in the publication section of the WASP website, where we will also attach the tarball of the raw data included in the published figures. Within the timespan of the project, we will consider the possibility of including the generated codes in repositories (as for example github or sourceforge). </td> </tr> <tr> <td> Methods or software to access the data </td> <td> The data deposited on ZENODO will be accessible to the public without restrictions. The access to the data will be through standard available softwares. In the case specific software tools will be developed, a text document including the information about such software and how to use it will be provided. </td> </tr> <tr> <td> Relevant software (open source code) </td> <td> Most of the softwares needed inside the project are available. In the particular case of specific software developed in the project, the source will be deposited in the repository. In particular, the link to the open source softwares used to carry out the modelling and simulations (Quantum Espresso and related codes therein for atomistic simulations and NanoTCAD Vides for nanoscale devices simulation) will be included. </td> </tr> <tr> <td> Data, metadata, documentation and code repositories </td> <td> WASP project has chosen ZENODO platform ( _http://zenodo.org_ ) as repository data archive. It is based on facilities and management FAIR data principles. ZENODO is part of OpenAIRE collaboration. The guidelines provided by ZENODO will be used by WASP to ensure the right format of data is uploaded to comply with FAIR principles. </td> </tr> <tr> <td> Access under restriction on use </td> <td> There are no restrictions on the use of the published data, but users will be required to acknowledge the Consortium and the source of the data in any resulting publications. </td> </tr> <tr> <td> Data access committee </td> <td> There won’t be need of data access committee. </td> </tr> <tr> <td> Conditions for access </td> <td> Zenodo provides well described conditions for access. </td> </tr> <tr> <td> Identity of the users accesing the data </td> <td> Zenodo does not need any especial permissions or registration in order to access to the repository files. In order to upload the data, users are required to register. </td> </tr> </table> 3.3 Making data interoperable <table> <tr> <th> </th> <th> </th> </tr> <tr> <td> Interoperability of the data produced, data exchange and re-use between researchers, institutions, organisations, countries, etc. </td> <td> The generated and shared data will consist on simple raw data (ASCII files in multicolumn structures). These data can be opened by any user with a simple text editor. It is possible also to release the data directly embedded in the file exploited to generate the figures of the articles. The programs used to get and visualise data are open source (for example, Xmgrace or Python). </td> </tr> <tr> <td> Data, metadata, vocabularies, standards or methodologies for interoperability </td> <td> Vocabularies will be used in metadata fields in order to support consistent, accurate and quick indexing and retrieval of relevant data. Keywords will be used for indexing and subject headings of the data and metadata. Vocabularies and keywords will be the standards used by OpenAIRE and will be updated along the project execution, in order to increase the interopaerability of the project’s data and metadata. </td> </tr> <tr> <td> Inter-disciplinary interoperability by standard vocabularies for data types </td> <td> Standard vocabularies will be used for all datasets in order to ensure inter- disciplinary interoperability and re-use. All datasets will use the same standard vocabularies for data and metadata capture/creation. </td> </tr> <tr> <td> Mappings to ontologies if unavoidable uncommon or specific ontologies or vocabularies </td> <td> The compability of our project specific ontologies and vocabularies will be guaranteed through appropriate mapping to more commonly used onrtologies. </td> </tr> </table> 3.4 Increase data re-use (through clarifying licenses) <table> <tr> <th> </th> <th> </th> </tr> <tr> <td> License for the re-use </td> <td> The data will be openly available under CC0 Creative Commons license. All open content is openly accessible through open APIs. Metadata is exported via OAI-PMH and can be harvested. </td> </tr> <tr> <td> Availability of the data for the re-use. Application of eventual embargo. </td> <td> Data will be fully available in the repositories (both data and codes), except in those cases we believe that a disclosure could be detrimental for the competitive advantage gained with respect to other groups or if we decide to secure the intellectual property with patents. If needed, datasets could be deposited under embargo status and the repository will restrict access to the data until the end of the embargo period, becoming available automatically and the end of such period. </td> </tr> <tr> <td> Third parties use of the data produced. Restrictions on the re-use of data. </td> <td> The data produced and/or used along the project will be deposited on a public repository (in our case ZENODO) and the access to it will be unlimited by third parties. </td> </tr> <tr> <td> Duration of the re-usable data </td> <td> There won’t be a limiting time for the re-usability from a thirdparty of the data/info. </td> </tr> <tr> <td> Description of the data quality assurance </td> <td> Repetion and comparation of the measurements, adherence to standards for data recording, the use of specific vocabularies and terminology, characterisation of the measurement set-ups and validation of the data collected will assure the data quality. </td> </tr> </table> Allocation of resources <table> <tr> <th> </th> <th> </th> </tr> <tr> <td> Costs of data FAIR </td> <td> The repository services offered by ZENODO are free of charge and enable peers to share and preserve research data in several sizes and formats: datasets, images, presentations, publications and softwares. * Data archiving at ZENODO: free of charge, including the DOI assigned to each dataset. * Copyright licensing with Creative Commons: free of charge • Cost of the name domain: 12 EUR/yr The eventual costs have been kept to a minimum by making only relevant data FAIR. </td> </tr> <tr> <td> How will these be covered? </td> <td> Costs related to open access to research data are eligible as part of the Horizon 2020 grant (if compliant with the Grant Agreement conditions). Resources for long term preservation, associated costs and potential value, as well as how data will be kept beyond the project and how long, will be discussed by the whole consortium during General Assembly (GA) meetings. </td> </tr> <tr> <td> Data management responsible </td> <td> The PI and the project manager will lead the coordination of the updates to the data management plan. Project manager will be responsible for organising data backup and storage, data archiving and for depositing the data within the repository (ZENODO). </td> </tr> <tr> <td> Resourses for long term preservation, costs, value, availability of the data. </td> <td> The value of the preservation will be determined during the progress of the project. The associated costs for dataset preparation for archiving will be covered by the project itself. Long-term preservation will be provided and associated costs covered by a selected disciplinary repository. There are no costs associated with the long-term preservation of the data. </td> </tr> </table> Data security <table> <tr> <th> </th> <th> </th> </tr> <tr> <td> Data security provisions, data security, data storage and transfer of sensitive data. </td> <td> In order to ensure the security of the data, some actions will be taken, for instance: store data in at least two separate locations, lable files in systematic and structured way in order to keep the coherence of the final dataset. Deposition in the Zenodo public repository will provide additional security as it has multiple replicas in a distributed file system which is backed up on a nightly basis. </td> </tr> <tr> <td> Storage of the data </td> <td> The data will be safely stored in the ZENODO open access repository. CERN is working towards ISO certification of the organisational end technical infrastructure which ZENODO relies on for long-term preservation. Every partner will be responsible of the data produced and will ensure that the data will be stored safely and securely and in agreement with the EU data protection laws. At the end of the project the repository chosen to store the dataset will have the responsibility of the data recovery and secure storage. </td> </tr> </table> Ethical aspects <table> <tr> <th> </th> <th> </th> </tr> <tr> <td> Ethical or legal issues on data sharing </td> <td> WASP partners involved in the project will follow the EU and national standards regarding ethics and data management. They must comply with the ethical principles and confidentiality (according with the Article 34 of the Grant Agreement). WASP partners must retain any data, documents or other material as confidential during the implementation for the project. </td> </tr> <tr> <td> Consent for data sharing and long term preservation. </td> <td> Research data which contains personal data will just be disseminated for the purpose for which it was specified by the consortium. Moreover, the data generated and shared have to be documented and approved by the consortium to guarantee highest standards of data protection. </td> </tr> </table> Other issues <table> <tr> <th> </th> <th> </th> <th> </th> </tr> <tr> <td> Use of departmental management </td> <td> national/funder/sectorial/ procedures for data </td> <td> Data management will be compliant with the research data policy of H2020 Horizon and European laws. </td> </tr> </table>	https://phaidra.univie.ac.at/o:1140797	Horizon 2020
0164_DESTINATIONS_689031.md	# Executive Summary This document represents the third edition of Local Data Management Plan (LDMP) relating to the modalities of involvement of human participants and the data under collection/collected, handled and processed by CIVITAS DESTINATIONS sites over the period M1-M30 (until February 2019). This document is updated on a yearly basis in order to integrate the different data typologies the project will manage in its progress. The collection of data is carried out over a six-month period to allow Site Managers to easily cope with this task. This document follows the methodological approach adopted by CIVITAS DESTINATIONS project and described in D1.7 (PDMP – third edition) according to the guidelines defined in the Ethics Compliance Report (D1.1). 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 CIVITAS DESTINATIONS project and the cross-relations with Project Data Management Plan (PDMP); * Section 3 details the modalities of involvement of human participants and summarizes the sensitive data collected/handled * The specific data collected and generated by DESTINATIONS sites in the period M1M30 (until February 2019) is detailed in the Annex 1. The Annex is organized per site with tables for the data collected with reference to each demo WP (WP2-WP7 and WP9). # Role of Project and Local DMPs in DESTINATIONS PDMP – third edition (D1.7) 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 by CIVITAS DESTINATIONS sites over the period M1- M30 (until February 2019). Data has been collected through the contribution of Site Managers (SM) according to the template defined in PDMP – first edition (D1.2). LDMP can be considered an integration of Project Data Management Plan – third edition (D1.7) which sets the framework for approaching data management in CIVITAS DESTINATIONS project. # Local Data Management Plan In the following sections the DESTINATIONS Local Data Management Plans are presented. In order to improve the readability, this section focuses on the main topics: involvement of human participants and identification if/how sensitive data have been collected by the sites during the design and the operation of demonstration measures. Detailed specifications of data and description of the collection, management and storing procedures is provided in the following Annex (per site and per WP). <table> <tr> <th> FUNCHAL (MAD) </th> </tr> <tr> <td> Details of involvement modalities of research participants </td> </tr> <tr> <td> 1.1 </td> <td> In the case data collection processes involve human participants, please describe the selection process </td> <td> The procedures and criteria used to identify target participants to the collection processes, are carried out under a fair and random method, assuring a representative sample. This participants’ sampling process arises as essential to ensure that a full cross-section of individuals is surveyed (nationalities, students, etc.). The sample is random and of a size that can be analysed with the ability to make statistical inference for the overall sample. The selection of the participants also guarantees the non-discrimination and non-exclusion principles. The data collection process assures above all the accuracy and integrity of the research (the travel patterns, attitudes and socio-demographic characteristics of the respondents) and will not code specific people or households (anonymous data). The implementation of the data collection process, occurs in predefined places, seen as core locations to meet the target groups (Schools - students, airport - tourists, etc.) and the best opportunity to evaluate the measures’ effects accordingly. Data is collected mostly through questionnaires, applied voluntarily and randomly to the participants, assuring a representative sample related to each CIVITAS measure in place. </td> </tr> </table> <table> <tr> <th> FUNCHAL (MAD) </th> </tr> <tr> <td> Details of involvement modalities of research participants </td> </tr> <tr> <td> 1.1.1 </td> <td> Which kind of inclusion/exclusion criteria have been adopted? </td> <td> No inclusion/exclusion criteria were adopted. </td> </tr> <tr> <td> 1.1.2 </td> <td> Have participants been included on a volunteer basis? </td> <td> Yes. Questionnaires are filled out on a voluntary basis by the participants. </td> </tr> <tr> <td> 1.1.3 </td> <td> Please confirm that the Informed Consent has been requested. Please keep copy of the Informed Consent form adopted. Please provide enclosed with this document a copy of one Informed Consent sheet (in original language) together with a very brief text in English describing in which data collection procedure the Consent has been asked and which information have been given to the participants </td> <td> Yes (when applicable). To all tourists willing to participate in the Focus Group dynamic, a Data Protection and Privacy Note is given to read and sign (who wants to join the tourist panel). Consent forms are stored in HF office. </td> </tr> <tr> <td> 1.1.4 </td> <td> Have persons not able to provide Informed Consent included as research participants? In this case which procedures to get Informed Consent have been adopted? And/or to ensure that they have not been subjected to any coercion? </td> <td> No. </td> </tr> <tr> <td> 1.1.5 </td> <td> Have participants been selected among any vulnerable group? In this case please detail the motivations and the ethical rules applied </td> <td> No. Random participants selected. </td> </tr> <tr> <td> 1.1.6 </td> <td> Please specify which kind of personal data have been handled in the operation of the local measures? </td> <td> Yes. Name, phone number and e-mail, as described in Annex (row 2.1.2.1) </td> </tr> <tr> <td> FUNCHAL (MAD) </td> </tr> <tr> <td> Details of involvement modalities of research participants </td> </tr> <tr> <td> 1.1.7 </td> <td> Which kind of actions has been put into practice in order to manage this data (i.e. procedures for anonymising the data, database protection, allocation of access rights, etc) </td> <td> Personal data will be managed (collection, storing and access) in accordance with EU GDPR regulation. The analysis of data will not reveal specific respondents to questionnaires. The respondents will be anonymous codes and the codes will be used to mark specific individuals in order to track their responses before and after a CIVITAS measure and then used in the ‘panel analysis’. Following the analysis, the codes will be erased and the data stored as anonymous. (described in Annex, row 2.1.2.2). As described in Annex (row 2.1.3.1), a separate Excel database was created to store the personal data provided, which is protected by a strong password, file stored on a PC only and where access to it is prohibited to any other person. The participants will be anonymous codes to prevent tracking. </td> </tr> </table> Table 1: Description of involvement modalities for research participants in Madeira <table> <tr> <th> RETHYMNO (RET) </th> </tr> <tr> <td> Details of involvement modalities of research participants </td> </tr> <tr> <td> 1.2 </td> <td> In the case data collection processes involve human participants, please describe the selection process </td> <td> According to the research methodology applied by the Municipality and the assigned subcontractor, the human participants involved were selected randomly, while in order to acquire a more precise sample, the stratified sampling selection of the final filled forms was followed </td> </tr> <tr> <td> 1.2.1 </td> <td> Which kind of inclusion/exclusion criteria have been adopted? </td> <td> Inclusion / exclusion criteria were not adopted; the sample was selected randomly </td> </tr> <tr> <td> 1.2.2 </td> <td> Have participants been included on a volunteer basis? </td> <td> Yes </td> </tr> </table> <table> <tr> <th> RETHYMNO (RET) </th> </tr> <tr> <td> Details of involvement modalities of research participants </td> </tr> <tr> <td> 1.2.3 </td> <td> Please confirm that the Informed Consent has been requested? Please keep copy of the Informed Consent form adopted. Please provide enclosed with this document a copy of one Informed Consent sheet (in original language) together with a very brief text in English describing in which data collection procedure the Consent has been asked and which information have been given to the participants </td> <td> The questionnaires were anonymous. No personal data were collected and all participants were included on a volunteer basis Therefore, no informed consent forms needed to be used </td> </tr> <tr> <td> 1.2.4 </td> <td> Have persons not able to provide Informed Consent included as research participants? In this case which procedures to get Informed Consent have been adopted? And/or to ensure that they have not been subjected to any coercion? </td> <td> All participants were informed about the procedure and type of data collected by the researchers and were included on a volunteer basis As noted in 1.2.3, due to the surveys set up (anonymous, no personal data), procedures to get Informed Consent forms have not taken place </td> </tr> <tr> <td> 1.2.5 </td> <td> Have participants been selected among any vulnerable group? In this case please details the motivations and the ethical rules applied </td> <td> No. Random sampling was used from people passing by, from selected public spaces </td> </tr> <tr> <td> RETHYMNO (RET) </td> </tr> <tr> <td> Details of involvement modalities of research participants </td> </tr> <tr> <td> 1.2.6 </td> <td> Please specify which kind of personal data have been handled in the operation of the local measures? </td> <td> The questionnaires were anonymous and no personal data were collected or handled from Municipality of Rethymno </td> </tr> <tr> <td> 1.2.7 </td> <td> Which kind of actions has been put into practice in order to manage this data (i.e. procedures for anonymising the data, database protection, allocation of access rights, etc.) </td> <td> N/A </td> </tr> </table> Table 2: Description of involvement modalities for research participants in Rethymno <table> <tr> <th> LIMASSOL (LIM) </th> </tr> <tr> <td> Details of involvement modalities of research participants </td> </tr> <tr> <td> 1.3 </td> <td> In the case data collection processes involve human participants, please describe the selection process </td> <td> The sampled data will be random through the distribution of questionnaires in Limassol region. The survey will involve randomly selected tourists and local citizens for questions. </td> </tr> <tr> <td> 1.3.1 </td> <td> Which kind of inclusion/exclusion criteria have been adopted? </td> <td> * Include local citizens and tourists over 18 years old * The questions and answers will take place at the same time </td> </tr> <tr> <td> 1.3.2 </td> <td> Have participants been included on a volunteer basis? </td> <td> Yes </td> </tr> </table> <table> <tr> <th> LIMASSOL (LIM) </th> </tr> <tr> <td> Details of involvement modalities of research participants </td> </tr> <tr> <td> 1.3.3 </td> <td> Please confirm that the Informed Consent has been requested. Please keep copy of the Informed Consent form adopted. Please provide enclosed with this document a copy of one Informed Consent sheet (in original language) together with a very brief text in English describing in which data collection procedure the Consent has been asked and which information have been given to the participants </td> <td> Questionnaires have been randomly distributed to tourists and local citizens around the city centre of Limassol, and the questions will be orally based. </td> </tr> <tr> <td> 1.3.4 </td> <td> Have persons not able to provide Informed Consent included as research participants? In this case which procedures to get Informed Consent have been adopted? And/or to ensure that they have not been subjected to any coercion? </td> <td> Answering the questions was considered voluntary work, and participants have not been subjected to any coercion. </td> </tr> <tr> <td> 1.3.5 </td> <td> Have participants been selected among any vulnerable group? In this case please details the motivations and the ethical rules applied </td> <td> No </td> </tr> <tr> <td> LIMASSOL (LIM) </td> </tr> <tr> <td> Details of involvement modalities of research participants </td> </tr> <tr> <td> 1.3.6 </td> <td> Please specify which kind of personal data have been handled in the operation of the local measures? </td> <td> Questionnaires have been randomly distributed to citizens/tourists in order to give real information about the mobility situation of Limassol city centre. </td> </tr> <tr> <td> 1.3.7 </td> <td> Which kind of actions has been put into practice in order to manage this data (i.e. procedures for anonymising the data, database protection, allocation of access rights, etc.) </td> <td> Questionnaires were anonymous and will be securely stored in our data files. </td> </tr> </table> Table 3: Description of involvement modalities for research participants in Limassol <table> <tr> <th> ELBA (ELB) </th> </tr> <tr> <td> Details of involvement modalities of research participants </td> </tr> <tr> <td> 1.4 </td> <td> In the case data collection processes involve human participants, please describe the selection process </td> <td> Tourists for the dedicated survey on travel behavior, attitudes and opinions were selected randomly. The survey on travel needs, attitudes, opinions and level of satisfaction of TPL users were carried out on the bus and at the information office of the local Public Transport Company (CTT Nord). The survey regarding opinion and level of satisfaction for the additional TPL service by boat (Chicchero) was targeted to passengers (tourists and residents) selected randomly. The survey regarding the initiative of the e-bikes long-term rental service and the customer satisfaction was targeted to tourists and to participant hoteliers. </td> </tr> <tr> <td> 1.4.1 </td> <td> Which kind of inclusion/exclusion criteria have been adopted? </td> <td> Considering the above criteria, the only criterion of exclusion was the willingness not to answer. </td> </tr> <tr> <td> 1.4.2 </td> <td> Have participants been included on a volunteer basis? </td> <td> Yes </td> </tr> </table> <table> <tr> <th> ELBA (ELB) </th> </tr> <tr> <td> Details of involvement modalities of research participants </td> </tr> <tr> <td> 1.4.3 </td> <td> Please confirm that the Informed Consent has been requested? Please keep copy of the Informed Consent form adopted. Please provide enclosed with this document a copy of one Informed Consent sheet (in original language) together with a very brief text in English describing in which data collection procedure the Consent has been asked and which information have been given to the participants </td> <td> Respondents have been informed that data would have been collected anonymously and for statistical analysis only so the statistical confidentiality will be guaranteed. For this reason, there was no need to collect a formal Informed Consent but we received a verbal consent for the interview. </td> </tr> <tr> <td> 1.4.4 </td> <td> Have persons not able to provide Informed Consent included as research participants? In this case which procedures to get Informed Consent have been adopted? And/or to ensure that they have not been subjected to any coercion? </td> <td> All the involved participants have been able to provide a verbal consent for the interview. </td> </tr> <tr> <td> 1.4.5 </td> <td> Have participants been selected among any vulnerable group? In this case please details the motivations and the ethical rules applied. </td> <td> The selection was/will be random and no selection of specific vulnerable group will be adopted. </td> </tr> <tr> <td> ELBA (ELB) </td> </tr> <tr> <td> Details of involvement modalities of research participants </td> </tr> <tr> <td> 1.4.6 </td> <td> Please specify which kind of personal data have been handled in the operation of the local measures? </td> <td> No personal data has been handled. </td> </tr> <tr> <td> 1.4.7 </td> <td> Which kind of actions has been put into practice in order to manage this data (i.e. procedures for anonymising the data, database protection, allocation of access rights, etc.) </td> <td> Not applicable </td> </tr> </table> Table 4: Description of involvement modalities for research participants in Elba <table> <tr> <th> MALTA (MAL) </th> </tr> <tr> <td> Details of involvement modalities of research participants </td> </tr> <tr> <td> 1.5 </td> <td> In the case data collection processes involve human participants, please describe the selection process </td> <td> Participants to the telephone surveys with local residents under MAL4.1, MAL6.2 and MAL7.1 were selected following a stratified random sampling strategy using the telephone directory of one of the main national telephony providers. Participants to in-person surveys with local residents and tourists under MAL6.3 and MAL7.1 were randomly selected for participation at the airport, ferry terminal and cruise line terminal in the case of MAL6.3, and while waiting to board the ferry or whilst on the ferry for MAL7.1. </td> </tr> <tr> <td> 1.5.1 </td> <td> Which kind of inclusion/exclusion criteria have been adopted? </td> <td> Respondents under the age of 18 were excluded. </td> </tr> <tr> <td> 1.5.2 </td> <td> Have participants been included on a volunteer basis? </td> <td> Yes. The respondents were asked whether they would like to participate in the research. During the introduction, the interviewer explained that it is on a voluntary basis. </td> </tr> </table> <table> <tr> <th> MALTA (MAL) </th> </tr> <tr> <td> Details of involvement modalities of research participants </td> </tr> <tr> <td> 1.5.3 </td> <td> Please confirm that the Informed Consent has been requested? Please keep copy of the Informed Consent form adopted. Please provide enclosed with this document a copy of one Informed Consent sheet (in original language) together with a very brief text in English describing in which data collection procedure the Consent has been asked and which information have been given to the participants </td> <td> Consent has been requested verbally during the telephone survey, as well as during in-person surveys. The respondent was also able to stop during the interview process should he/she wished to do so. We do not have copies of the Informed Consent form as the research was done over the telephone or in-person. Such Consent is not required since there is no follow-up following the research. </td> </tr> <tr> <td> 1.5.4 </td> <td> Have persons not able to provide Informed Consent included as research participants? In this case which procedures to get Informed Consent have been adopted? And/or to ensure that they have not been subjected to any coercion? </td> <td> People who declined participation were not included as research participants. In the telephone survey, a larger sample than required was extracted to compensate for non-response or refusal to participate. None of the participants have been subjected to coercion to participate. </td> </tr> <tr> <td> 1.5.5 </td> <td> Have participants been selected among any vulnerable group? In this case please details the motivations and the ethical rules applied </td> <td> Elderly people have been included in the surveys in order to ensure a representative sample. </td> </tr> <tr> <td> MALTA (MAL) </td> </tr> <tr> <td> Details of involvement modalities of research participants </td> </tr> <tr> <td> 1.5.6 </td> <td> Please specify which kind of personal data have been handled in the operation of the local measures? </td> <td> No personal data has been collected </td> </tr> <tr> <td> 1.5.7 </td> <td> Which kind of actions has been put into practice in order to manage this data (i.e. procedures for anonymising the data, database protection, allocation of access rights, etc.) </td> <td> No personal data has been collected </td> </tr> </table> Table 5: Description of involvement modalities for research participants in Malta <table> <tr> <th> LAS PALMAS DE GRAN CANARIA (LPA) </th> </tr> <tr> <td> Details of involvement modalities of research participants </td> </tr> <tr> <td> 1.6 </td> <td> In the case data collection processes involve human participants, please describe the selection process </td> <td> The interviews for the mobility survey carried out in LPA3.1 were made by using Computer Assisted Telephone Interview (CATI) software. This software automatically selects the people to interview based on criteria in order to reach a representative sample. </td> </tr> <tr> <td> 1.6.1 </td> <td> Which kind of inclusion/exclusion criteria have been adopted? </td> <td> The criteria adopted was to reach a proportional sample to the whole universe (inhabitants of Las Palmas de Gran Canaria and the whole island of Gran Canaria) based on age, gender, employment status, etc. </td> </tr> <tr> <td> 1.6.2 </td> <td> Have participants been included on a volunteer basis? </td> <td> Once the CATI software dialled the phone numbers the interviewer asked the interviewee his/her consent to get his/her answers recorded. </td> </tr> </table> <table> <tr> <th> LAS PALMAS DE GRAN CANARIA (LPA) </th> </tr> <tr> <td> Details of involvement modalities of research participants </td> </tr> <tr> <td> 1.6.3 </td> <td> Please confirm that the Informed Consent has been requested? Please keep copy of the Informed Consent form adopted. Please provide enclosed with this document a copy of one Informed Consent sheet (in original language) together with a very brief text in English describing in which data collection procedure the Consent has been asked and which information have been given to the participants </td> <td> There is an Informed Consent for each interview that we carried out for the mobility survey. However, the Informed Consent of each participant has not been merged in a single document (audio or transcribed in a sheet document). </td> </tr> <tr> <td> 1.6.4 </td> <td> Have persons not able to provide Informed Consent included as research participants? In this case which procedures to get Informed Consent have been adopted? And/or to ensure that they have not been subjected to any coercion? </td> <td> No persons without Informed Consent were included in the survey. </td> </tr> <tr> <td> 1.6.5 </td> <td> Have participants been selected among any vulnerable group? In this case please details the motivations and the ethical rules applied </td> <td> No. </td> </tr> <tr> <td> LAS PALMAS DE GRAN CANARIA (LPA) </td> </tr> <tr> <td> Details of involvement modalities of research participants </td> </tr> <tr> <td> 1.6.6 </td> <td> Please specify which kind of personal data have been handled in the operation of the local measures? </td> <td> Please see WP3 description (LPA3.1). </td> </tr> <tr> <td> 1.6.7 </td> <td> Which kind of actions has been put into practice in order to manage this data (i.e. procedures for anonymising the data, database protection, allocation of access rights, etc.) </td> <td> Please see WP3 description (LPA3.1). </td> </tr> </table> Table 6: Description of involvement modalities for research participants in Las Palmas # Conclusions Summarizing the information provided in the previous section 3: * Human participation to the mobility measures demonstrated in CIVITAS Destinations is mainly related to questionnaires/interviews/survey carried out for the assessment of local needs (design of the measures) and the assessment of impacts/level of satisfaction (evaluation of the measures). The selection of participants has been carried out randomly, the participants have been always able to provide the informed consent and free to decline participation. The Informed Consent has been asked in different way (written/verbally). The purpose for collecting the Informed Consent varies case by case: in a large number of cases, the Informed Consent focused mainly on informing the participants why the data has been collected (when the data collected are not sensitive) and sometime to specify the procedures for data storing and handling (when sensitive data are collected) * A procedure for collecting examples of Informed Consents adopted in the sites has been already established: this action fosters also the exchange of practices among the sites * Data has been collected mostly in an anonymous and aggregated way. Most of them have been accessed from public sources. In a few cases where personal data has been collected appropriate procedures for Informed Consent and handling of data have been established * In general, the data collected by the sites are made available for dissemination purposes in an aggregated way or as an extraction, not in a publicly accessible “open” data format.	https://phaidra.univie.ac.at/o:1140797	Horizon 2020
0167_SENSITIVE_801347.md	# DATA COLLECTION Describe for all the data that will be collected the collection procedure in as much detail as possible including where possible pictures images etc. ## • Mouse RNA-seq data Mouse tissue from the esophagus and the large intestine will be isolated from healthy and diseased mice and will be processed for the generation of RNA, which will be used from the generation of sequencing libraries to be run in the Illumina NextSeq 500 sequencer within the Greek genome Center at BRFAA. Raw data will be processed with the use of appropriate algorithms and comparison will be performed between healthy tissues and diseased tissues at various stages of the disease. Molecular biomarkers that discriminate between the healthy and diseased state will be identified. ## • Optical data from mice and human tissues Mouse tissue from the esophagus and the large intestine from healthy and diseased mice and human specimen collected at UMCG will be imaged with the hybrid Raman/Scattering microscope (see Fig. 1 for the designs of the microscope), as well as the existing SHG, THG, and OA microscope at HMGU. Specific acquisition protocols will ensure imaging of the same tissue regions with both microscopes. Raw data will be processed with the use of appropriate algorithms and comparison will be performed between healthy tissues and diseased tissues at various stages of the disease. Molecular biomarkers that discriminate between the healthy and diseased state will be identified in combination with the RNA-seq analysis. Fig. 1. Designs of the (a) Raman and (b) scattering modules as coupled in the hybrid microscope. ## • Clinical validation of the endoscope on patients _Adenoma trial in high risk colorectal cancer Lynch syndrome (LS) patients_ : We will perform SRS/Scattering endoscopy of the rectum to identify patients with adenomas and the accompanying field cancerization fingerprints with guidance of standard surveillance colonoscopy. Results will be compared with the amount of detected adenomas in these patients and degree of standard histopathology. On average, 30-50% of the Lynch patients will have 1 or more polyps during colonoscopy. The patient without adenomas will serve as controls. Control biopsies of normal tissue will be collected for ex vivo control. _Barrett trial in patients with early stage esophageal cancer_ : SRS/Scattering endoscopy will be performed on patients with dysplasia or early stage esophageal cancer patients, who are scheduled for endoscopic mucosal resection (EMR). As control, we will include Barrett patients undergoing surveillance endoscopy without dysplastic lesions. Field cancerization alterations will be assessed in the normal adjacent Barrett epithelium. Control biopsies of normal tissue will be collected for ex vivo control. The hybrid SRS/Scattering endoscope will consist of two polarization- maintaining fibers for scattering (illumination/collection) and one excitation and a number of collection fibers for SRS spectroscopy. Before in vivo human application, the endoscope will undergo constancy and mechanical integrity tests against repeat standard clinical washing procedures. The number, type, and distribution of fibers will be determined during the design and validation of the test probes and will be based on the findings from the multimodal microscope, as well as on the clinical needs. Finally, the developed endoscope will follow the “motherdaughter” approach, where it will be passed through the working channel of a normal endoscope (figure 2) and thus be used during normal surveillance colonoscopy/endoscopy procedures. Figure 2 Design of standard gastrointestinal endoscope (GIF-H190) that is currently used as a standard endoscope in the UMCG. Images adapted from EVIS EXERA III gastrovideoscope brochure. # DATA MANAGEMENT SENSITIVE participates in the Open Research Data Pilot (ORDP) which aims to improve access to and re-use of research data generated by Horizon 2020 projects and applies primarily to the data needed to validate the results presented in scientific publications. To support the FAIR principles, SENSITIVE is oriented towards the Zenodo solution. Zenodo is built and developed by researchers, for Open Science. The OpenAIRE project, for open access and open data movements in Europe was commissioned by the EC to support their nascent Open Data policy by providing a catch-all repository for EC funded research. CERN, an OpenAIRE partner and pioneer in open source, open access and open data, provided this capability and Zenodo was launched 2013. In support of its research programme CERN has developed tools for Big Data management and extended Digital Library capabilities for Open Data. Through Zenodo these Big Science tools could be effectively shared with the long-tail of research. For findable data, Zenodo provides a Digital Object Identifier (DOI), which is issued to every published record. Zenodo's metadata is compliant with DataCite's Metadata Schema m inimum and recommended terms, with a few additional enrichements. The DOI is a top-level and a mandatory field in the metadata of each record. Metadata of each record is indexed and searchable directly in Zenodo's search engine immediately after publishing. Metadata of each record is sent to DataCite servers during DOI registration and indexed there. For making data openly accessible Zenodo provides metadata for individual records as well as record collections are harvestable using the OAI- PMH protocol by the record identifier and the collection name. Metadata is also retrievable through the public REST API. OAI-PMH and REST are open, free and universal protocols for information retrieval on the web. Metadata are publicly accessible and licensed under public domain. No authorization is ever necessary to retrieve it. Data and metadata will be retained for the lifetime of the repository. For making data interoperable Zenodo provides a formal, accessible, shared, and broadly applicable meta(data) language. Zenodo uses JSON Schema as internal representation of metadata and offers export to other popular formats such as Dublin Core or MARCXML. Each referenced external piece of metadata is qualified by a resolvable URL. For making data re-useable in Zenodo each record contains a minimum of DataCite's mandatory terms, with optionally additional DataCite recommended terms and Zenodo's enrichments. License is one of the mandatory terms in Zenodo's metadata and is referring to an Open Definition license. Data downloaded by the users is subject to the license specified in the metadata by the uploader. All data and metadata uploaded is tracable to a registered Zenodo user. Metadata can optionally describe the original authors of the published work. Zenodo is not a domain-specific repository, yet through compliance with DataCite's Metadata Schema, metadata meets one of the broadest crossdomain standards available. # DATA SECURITY / SHARING Guidelines for data security and personal data protection will be followed. To protect data storage and processing, the following safety measures will be undertaken: * Compliance with the General Data Protection Regulation – (EU) 2016/679 (EU GDPR ) * Reporting of data security incidents including personal data breach to data controller of the related dataset within two working days after becoming aware of it. The datasets should be shared with all partners once all relevant legal procedures (informed consent, CDA, MTA, ethical approval) are in place. The informed consent of study participants will cover the sharing of data collected directly from the study participant and/or based on the measurement of the derived bio-samples from the study participant. Even though every effort will be made to keep data confidential, there is a possibility that information can be linked to the individual. We will inform the research subject about this possibility and respect his decision, if he/she wishes to completely anonymise his/her data. # ETHICAL ASPECTS All research activities performed for the collection, use, transfer and protection of patient data, including biological samples will follow the ethical standards and guidelines of Horizon 2020 including the Charter of Fundamental Rights of the European Union and the European Convention on Human Rights. Patients’ data and biological samples will be lawfully collected and processed. Medical research in human subjects will follow the procedures described in the World Medical Association’s Declaration of Helsinki and the Oviedo Bioethics Convention (Convention on Human Rights and Biomedicine). In addition, all procedures will comply with National law and the European Union’s General Data Protection Regulation (GDPR) . Collection, use, storage and otherwise processing of human genetic data, human proteomic data and of the biological samples from which they are derived will comply with UNESCO's Universal Declaration on the Human Genome and Human Rights and International Declaration on Human Genetic Data. All SENSITIVE partners confirm that the ethical standards and guidelines of Horizon 2020 will be rigorously applied, regardless of the country in which the research is carried out. No studies will commence before the approval of relevant ethics committee has been obtained where such an approval is required. Biomedical research will comply with international and EU conventions and declarations. Appropriate informed consent form is a prerequisite for obtaining approval. Informed consent procedures and data processing, including data/sample collection, and use, data transfers, storage and security will comply with the new EU’s General Data Protection Regulation. All data related processes, from collection and sharing to data research and sustainability will be in compliance with the legal requirements established by GDPR (General Data Protection Regulation). # CONCLUSION This is the first version of the SENSITIVE Data Management Plan. The plan follows the Horizon 2020guidelines for findable, accessible, interoperable and reusable (FAIR) data and will address the EU’s General Data Protection Regulation (GDPR). The project participates in the Open Research Data Pilot (ORDP), which aims to improve access to and re-use of research data generated by Horizon 2020 projects and applies primarily to the data needed to validate the results presented in scientific publications. The SENSITIVE DMP is intended to be a “living document” and will be updated in the context of the periodic reporting of the project.	https://phaidra.univie.ac.at/o:1140797	Horizon 2020
0168_HARMONIC_847707.md	# Data summary Whereas the HARMONIC project involves the collection and processing of sensitive personal data on medical and radiological history (all data, regardless of genetic, biometric and/or health data, are subject to medical confidentiality and data protection), we will ensure that, whenever possible, our data is Findable, Accessible, Interoperable and Reusable. Protocols are developed for each of the four scientific WP (as shown in Figure 2 below): — WP2: protocol to build the cohort of cancer patients — WP3: protocol to build the cohort of cardiac patients — WP4: dose reconstruction protocols (one protocol dedicated to each cohort) — WP5: biology protocol FIGURE 2 : Organisation of the project ## DATA PURPOSE The HARMONIC project combines retrospective and prospective data collection in two paediatric populations: paediatric patients treated with ionising radiation for cancer or for cardiac defects. Data collection will be performed in the participating hospitals after local, regional/national ethics approvals are obtained. Our study will not affect treatment. Data collected and processed within HARMONIC will be relevant and limited to the purpose of the research project in accordance with the `data minimisation´ principle. To fulfil the objectives of HARMONIC in _evaluating specific outcomes, reconstructing_ _doses and investigating radiation-induced cellular responses and biomarkers of_ _sensitivity_ , data collection will include: — In WP2, extracting data from medical records and hospital databases such as clinical outcomes, lab test results, specific prescriptions, collecting data from electronic and paper records from the radiotherapy departments (Treatment planning, DICOM RT data), obtaining neurovascular/MRI imaging and cardiac echography data, collecting blood and saliva samples and interviewing participants via questionnaires. Linkage with regional/national registries or healthcare databases is anticipated. — In WP3, collecting data from electronic and paper records from the radiology and cardiology departments (DICOM data). Linkage with cancer, mortality, other local/national registries and with health insurance databases will be performed. Retrospective data collection will be performed in all hospitals participating in WP3 and three centers participating in WP2 (UZ Leuven (Belgium), Gustave Roussy (France) and University Hospital Essen (Germany)). Linkage with regional/national registries or healthcare databases will be done at the national level, following the regulation in place in each participating country. All personal identifiers necessary for linkage will be dissociated with the link between the study ID and personal information stored in password protects computer files either in the study centre or in appropriate authority departments, depending on the country. Access to these files will be strictly limited to authorised project personnel. Only pseudonymised data will be included in the study database and transferred to participating centres for processing and analysis Prospective data collection is anticipated in all medical centres participating in WP2 (KU Leuven (Belgium), DC Aarhus University hospital (Denmark), Centre François Baclesse (France), Gustave Roussy (France), University Hospital Essen (Germany)) and in at least two Italian cardiology centres (Genova, Bergamo) of WP3. Prospective data collection will be performed under informed consent signed with the clinicians in the participating hospitals. For that, adapted documents on the study description and procedures will be prepared to explain the project to the participant and its legal representative and to guarantee their comprehension. Material will be prepared specifically for the assent to understand and consent to participation in the study. Informed consent will include — In WP2 and WP3, collection of human cells and tissues (blood and saliva): * A safe amount of blood samples will be collected. * Saliva collection is a non-invasive procedure; it is performed in dedicated tubes. — Specifically, in WP2, prolonged MRI acquisition of 7 to 14 minutes per scan (but no additional MRI) resulting in minimal discomfort. ## DATA TRANSFER The HARMONIC project is collaborative by nature with main objective to pool data and build European cohorts of pediatric patients. Transfer of data or material between partners (or with linked third parties) is therefore essential and will be performed after Data/Material Transfer Agreements are signed. Templates of these agreements are included in the Consortium Agreement and are provided in Annex-1 of this document. It is reminded that only pseudonymised data will transferred, through secured servers, to the central databases for analyses. ## DATA COLLECTION Data collection will be done independently for the two cohorts. For preservation and longterm access, data collection will be accompanied with proper documentation and associated metadata. Files will include the data itself, documentation files with a description of how the data was collected, and metadata that administer each behavioural task. ### 1\. CANCER PATIENTS A detailed table of the cohort database is provided in Annex-2, with data type, coding system and time point of collection (assuming prospective data collection could be pursued in the last year of the project in parallel to analyses). The database is structured with a core dataset including data which should be available, for all patients, to allow building the cohort and reconstruct doses (Task 4.2 in WP4). It also includes task-specific datasets to performed WP2 tasks and subtasks on: — Endocrine dysfunctions — Cardiovascular diseases — Neurovascular damages — Second cancer — QoL and societal impact All data are classified in mandatory or optional as follows: — Level I (Task 2.1, all centres): Mandatory for all patients — Level II (Task 2.1, all centres): Optional for all patients / if information is available — Level III * a) (Additional data, Tasks 2.2. to 2.6, in participating centres): Mandatory for patients included in the specific Task * b) (Additional data, Tasks 2.2. to 2.6, in participating centres): Optional for patients included in the specific Task Optional information will be used to conduct specific sub-analyses within each task. The database of the project (HARMONIC-RT) will be developed at INSERM (partner 2): an experienced data manager (permanent staff) will be responsible for the development of the CRFs, the setting-up of the database and supervising the maintenance of this database. A budget has been secured to hire a data manager (half-time for 4 years) to ensure the daily maintenance of the database and data quality control. Beyond the resources directly allocated to the project, INSERM can benefit from the support of its IT and data management permanent staff, who is experienced to setting-up and maintaining databases for large- scale epidemiological studies. INSERM will develop the structure of the database and the CRFs, in close collaboration with Essen and with the help of all investigators. An overview of the circuit of data is provided in Figure 3 below. The CRFs and the database will be developed by an experienced data manager. The use of RedCap ( _https://www.project-redcap.org/_ ) is currently investigated as potential secure web application to build and manage the database. A significant proportion of the included patients will come from Essen, which already has a local registry. Automatically data transfer from this database to the HARMONIC-RT centralized database will be explored. Data collection will be performed in participating centres: — Investigators from each participating centre will have access rights to their own structured data at any time, but only to their own data except if they contribute to a task group. Task group investigators should have access to core data + taskspecific data that are provided by all centres contributing to that task — Linkage with external registries or databases (e.g. national cancer registries, health insurance data) should be made at the centre or sponsor level, because personal identifiers are needed for this purposes, and only pseudonymized data will go to the centralized database — Results of WP2, WP4 and WP5 tasks and subtasks (e.g. estimated radiation doses, measured biomarkers) will be transferred to the centralized database FIGURE 3 : Overview of the circuit of data _Reconstruction of doses and optimization_ (task 4.2 of WP4). Data mandatory for dose reconstruction are height, weight and sex of the patient, DICOM files including CT images and treatment planning data and delivered dose (see Annex-2 Radiotherapy table). These data will be used together with characteristics of the machine (beam commissioning data: Linac, Beam data and detectors) to estimate doses to the main organs and structures (whether there are in or out of the radiation field) (See Annex 3 for details). Pseudonymised DICOM files will be transferred between treatment centre and partners involved in dosimetry activities via a secured dedicated platform (several options are being investigated considering technical specifications and compliance with GDPR). Contouring of the structures of interest will be provided by the clinical centres to the dosimetry team (WPE, SCK●CEN, CEA and UZH) so that doses could be estimated based on exiting analytical models, available phantom library and Monte-Carlo simulation. A whole-body representation of the patient will be created automatically by matching the patient structures acquired for treatment planning through imaging with a computational human phantom. Based on data collected for organ dose estimation, we will refine current dosimetry systems and further develop and validate dosimetric tools, in particular developing tools for estimation of dose in the clinic from secondary neutrons in PBT, to provide the medical community with means to improve real-time patient specific dosimetry, investigate the overall radiation burden in radiotherapy patients, including contribution from CT imaging for therapy planning and re-planning. ### 2\. CARDIAC PATIENTS Individual epidemiological data for study subjects included in the study will be collected by participating national centres. Database will be organised in a similar way in all participating countries with a relational database structure similar to the one presented in Annex-4. Data will be stored locally and standards procedures will be implemented to send the data to centralized database which will be localised at ISGlobal on a password protected SQL server with restricted access to the data manager. The preferred mode of transferring the data and the type of files will be defined in collaboration with the responsible data base manager of national study centre. Each data transfer should be accompanied by a description of the data: list of datasets, number of rows in each file, the coding system used. The anticipated size of the database (incl. dose data) is approximately 500Go. National data managers will be in direct contact with data manager at ISGlobal, they will send data samples to test data quality, validity and compatibility; feedback on the data received will be provided. ISGlobal (following agreement with national centres) will append national datasets into one international dataset and transfer it to the centre responsible for the specific analysis task (Statistical analyses) as defined in Annex I (Description of work) of the Grant agreement. This will be done in close collaboration with national teams and the centre responsible for specific analysis task, adjusting for the needs of each specific data analyses task. Basic data will be collected mainly from hospital electronic records concerning the patient and cardiac procedure. Health insurance databases will also be used where possible. Basic data which will be transferred will include: — Study ID (link with patient ID will remain at national level in a password protected file with limited access to allow linkage with external registries or databases (e.g. national cancer registries, health insurance data) — Name/code of the cardiology department — Date of cardiac catheterisation, — Machine type if available — Name of procedure according to the classification built within the project. — Height and weight if available — Dosimetric data: radiation dose structured reports (RDSRs) will be obtained when available. The information from these RDSRs will be used to estimate exposure parameters (see Annex 5). Data on potential confounding will also be collected, where possible. It will include data on transplant, confounding by indication/predisposing syndroms and socio-economic status. Also, since these patients might have been subjected to other types of medical (diagnostic) procedures, efforts will be made to reconstruct, where possible, the personal radiological history of the patients, including history of CT examinations. _Reconstruction of doses and optimization (WP4)_ Detailed dose reconstructions will be performed for examinations in which RDSRs can be obtained. The use of a dedicated software for data collection is currently under investigation. The information from these RDSRs will be used to estimate exposure parameters (beam angle, x-ray energy, etc.) for examinations in which less detailed data were recorded. Dose estimation will be performed using computer modelling which will be validated using physical measurements in tissue-equivalent anthropomorphic phantoms. Target organs for dose reconstruction will include breast (right and left), bone marrow, brain, thyroid, heart, lens, oesophagus and lungs. Estimated organ doses will be linked to the patient study ID. Optimization of interventional procedures is difficult, as doses depend largely on factors including difficulty of case, experience of operator, type of procedure. Our data will provide a unique set of data on the trends in radiation doses, patient characteristics and procedure types over time. This information will be useful in the setting of indicationspecific diagnostic reference levels (DRLs) for radiation protection purposes. The dosimetry system developed during the project, linking organ doses to DAP or K A,R as a function of a discrete set of variables, will be of use to hospitals and researchers in radiation dose audits and dose reduction research. To further contribute to optimization of doses in interventional cardiology, we propose to develop, in collaboration with 2 to 3 pilot sites, an innovative Augmented Reality (AR) Computer System that is able to support the operator and assess the procedure. The operator will be supported with AR information (wearing a dedicated headset) to increase image quality. The tool can then be presented in dedicated training sessions to improve the realization of future procedures. ### 3\. BIOMARKERS ANALYSES All patients, radiotherapy and interventional radiotherapy will receive a consent form and short invitation from wp2 and wp3 including the purpose and aims of the research in a concise and clear manner. For each patient who agreed to participate blood and saliva samples will be collected. Other relevant patients’ data for biomarker and mechanistic studies include demographics and treatment data: Age at first cancer diagnosis, sex, other vascular and cardiotoxic treatment (e.g. cumulative doses of anthracyclines, alkylating agents and other chemotherapy, including start date and end date), radiation dose to different organ and substructures of interest performed by WP4, follow-up data e.g., health events including; vascular and cardiac damages, hypertension etc. Estimated doses of interest for WP5 activities are doses to the heart, brain and large/middle vessels (mediastinal carotids, willis polygon; cerebral arteries). Blood and saliva samples from 300 patients will be collected in WP2 and WP3. Blood will be collected at 3 time points: 1- Before start of radiotherapy/interventional cardiology 2- day of finishing radiotherapy/interventional cardiology or anytime up to three months after finishing therapy 3- one year after finishing radiotherapy/interventional cardiology. Biological samples will receive a unique identification number prior to any transfer of material (link between the ID of the patient and the code will be kept at the collection centre in password protected files). Centralization of material will be performed at SU (Sweden) where material will be sent in boxes containing dried ice to keep the samples (blood, serum, plasma) frozen during the transport. The biomarkers which will be investigated are associated with changes induced by radiation at the level of the transcriptome (miRNA), the proteome (plasma and saliva protein profiling; and RPPA) and the epigenome (gene expression regulation and protein modification) as well as inflammation and oxidative stress levels (see Deliverable 5.1 for specific details) ## DATABASE STRUCTURE VALIDATION The CRFs will be reviewed against the database specifications documents as well as the protocols to ensure the following: — There is no duplication of data being collected. — The forms are in the correct order. — All associated code lists are correct. — Data required for the statistical analysis have been included. After completing the above, test data will be entered into the database to ensure that all the specifications for each field have been programmed correctly, including: — Tab order of the fields. — Sufficient field length. — Entering of valid vs invalid data. These checks ensure that when the database goes live, the data management system works as expected. Any changes that are required to the CRF during the study (assuming they are approved by relevant ethical committees), will follow the above validation and verification procedures before going into the production database. ## DATA CLEANING We assume that data will be checked and validated by providers. A list of logical validation checks will be generated and shared with all contributing centres (a list of basic checks is provided in Annex 6). The validations will be performed at the level of the centre (or national level in WP3). Additional validation and cleaning will be performed at entry in centralized database and prior to analyses for specific tasks. # Data sharing Intellectual property and data generated by this project will be managed in agreement with the guidelines of EC and based on the Consortium Agreement and Publication Policy. HARMONIC beneficiaries will disclose all inventions developed within the project and such inventions will be reported and managed according to the e.g. EC guidelines. Integration and re-use of the data relies on the data being well organized and adequately documented. The shared data are expected to be of interest to the scientific and more specifically the radiation protection community, the medical community and the general public. The type of data and tools to be produced in HARMONIC include (but are not restricted to) background demographic information, results from computational model simulations, software….. Data and data products will be made available with as few restrictions as possible. The publication of the research data or tools may take place during the project or at the end of the project in accordance with normal scientific practices. Access to the project tools will be made available for educational, research and non-profit purposes. This access may be provided via web-based applications. Research data documenting, supporting and validating research results will be made available after the main findings of the final research dataset have been accepted for publication. This research data will be processed to prevent the disclosure of personal data. Reported study results will pertain to analyses of aggregate data. No individual’s name will be associated with any published or unpublished report of this study. Results of the HARMONIC project will be disseminated mainly through open access (free of charge online access for any user) peer-reviewed scientific publications. Most beneficiaries have secured a budget for manuscript publication. Each beneficiary publishing results will: — 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; the repository of University Pompeu Fabra, Barcelona, is used by the coordinator (ISGlobal); — deposit at the same time, the research data needed to validate the results presented in the deposited scientific publications and/or publish data papers. — deposit — via the repository — the bibliographic metadata that identify the deposited publication — The bibliographic metadata shall include all of the following: * the terms “Euratom” and “Euratom research and training programme 20142018”; * the Health effects of cArdiac fluoRoscopy and MOderN radIotherapy in paediatriCs, HARMONIC and grant number 847707; * the publication date, and length of embargo period if applicable, and * a persistent identifier or digital object identifier (DOI) for the submitted data set(s). # Ethical Requirements (POPD 4 & 5) As described in Deliverable 1.1 - Ethics and Data Protection Requirements, general procedures to be included in the research protocol to safeguard the privacy of study subjects are: — Pseudonymization will be implemented as a general standard meaning that all material obtained in the framework of the project (questionnaires, diagnostic images and imaging data, detailed information on treatment if available) will be identified through a code, the name and/or other personal data that could allow the identification of the participant will never be indicated. This unique identifier will link all basic data required for the study. — The master key file linking the centre’s study numbers with personal identifiers will be maintained in a password protected file with limited access. — All files containing personal data will be stored in encrypted and/or passwordlocked files. Access to these files will be limited to authorized project personnel; — Written consent to use personal data will be obtained from the participants consenting to be involved in specific parts of the project. The contact details of the DPO will be made available to the participants. — The patient will be free to withdraw his or her consent to the study at any time — Separate age-graded information and consent forms will be available for minors/assents. — If, according to the project requirements, it is necessary to transfer personal data, participants will be properly informed in the consent form and measures to ensure personal data protection will be implemented. Transfer of anonymized data will be done according to the current legislation. — Reported study results will pertain to analyses of aggregate data. No individual’s name will be associated with any published or unpublished report of this study. — All project personnel will be trained in the importance of confidentiality of individual records and required to sign a confidentiality agreement. — All data transfers will be completed using secured servers. ## PSEUDONYMISATION TECHNIQUES The codification of a study subject consists of a country code and a study subject code. This entire code is called the “Study ID of study subject” throughout this document. A Study ID should not be any number, such as social security or health insurance number, which could allow the identification of the subject. Study ID of the study subjects are therefore created as a 9-digit number — Country code (2-digit number) — Study subject code (7-digit number) As an example: the subject with national ID=5689 in the country coded as 13 would have the ID=130005689. Keeping the country code inside the ID allows rapid grouping of subjects in analysis. If the country code cannot be set for any raison, the code “99-unknown” will be used. However, each patient should belong to specific national cohort. Pseudonymisation of DICOM data will be performed under the following principle _(1,2)_ to ensure that there are no reasonably likely means to identify the patient from the use of this data: — All DICOM tags associated with patient ID (example tag 0010,0020) will be systematically replaced by Study ID or any other relevant general term. — A code will be automatically generated (or Study ID will be entered) as a new field. — Where required for de-identification, date of birth (month/year to be kept) and/or date of examination will be removed — Link between Patient ID and code will be kept on a password protected file in the centre providing data. “To ascertain whether means are reasonably likely to be used to identify the natural person, account should be taken of all objective factors, such as the costs of and the amount of time required for identification, taking into consideration the available technology at the time of the processing and technological developments.” ## TRANSFER OF DATA TO NON EU COUNTRIES Transfer of pseudonymised data is anticipated from and to Switzerland (for dose reconstruction purposes). The European Commission has recognised that Switzerland is providing adequate protection (Adequacy decision). The effect of such a decision is that personal data can flow from the EU (and Norway, Liechtenstein and Iceland) to Switzerland without any further safeguard being necessary. In others words, transfers to the country in question will be assimilated to intra-EU transmissions of data. # Data security, storage and archiving ## DATA SECURITY AND INTEGRITY Data must never be sent via public networks or external wireless (including email), and it should be encoded (or protected by any other encryption method) that guarantees the information is not intelligible or manipulated by third parties. ### 1\. CANCER PATIENTS All data sent to INSERM is stored on the Institution’s server, within an internal network duly protected to guarantee the integrity and security of the information. Access is restricted to users with unique, personal and non- transferrable authorisation. The REDCAP application used at CESP for data collection is hosted on a virtualized infrastructure (VMWARE). Access to the application is possible from outside the CESP network, through a web proxy. The application is protected by a double authentication (a first authentication at the level of the proxy and a second at the level of the application itself). The identifiers are different and nominative. Communication between the application and the remote stations is encrypted with the implementation of the https protocol. In terms of network communications, security is provided by STORMSHIELD firewall to ensure the filtering between the web proxy and the virtual server. Daily backups are performed on disks, which are then outsourced on tapes (fireproof box). ### 2\. CARDIAC PATIENTS All data sent to ISGlobal is stored on the Institution’s server, within an internal network duly protected by the _IT Resources Service_ to guarantee the integrity and security of the information. Access is restricted to users with unique, personal and non-transferrable authorisation. All information housed on the ISGlobal servers is of a confidential nature. All the computer equipment of the Institution is equipped with anti-virus software, connected to a central database where all the virus definitions are up to date, and there are periodically scheduled scans. Also, all the files coming from an external source (usb devices, e-mail...) are scanned in real-time to avoid accidental infections. The data backup and storage infrastructure comprises six NAS server devices. Of these, five are in the PRBB 1 data centre and are used to manage the storage of all the institution’s data (administration and research projects) and backup tasks corresponding to said data. A sixth Synology is located in a nearby data centre, to have a physical device outside the main headquarters as an external backup. There are four kinds of backup copies with two levels (1-internal, 2-external) to be able to restore the institution’s data and systems in different scenarios (see below): * Internal backup of data * Internal backup of Server * External backup of data * External backup of server The back-up strategy in place is meant to prevent accidental loss of data, to allow reset of the service to its previous, fully functional status, in such diverse situations as corruption of the operating system, configuration errors or those caused by an IT attack, to restore all the centre’s data and data infrastructure in the event of a critical incident at the institution’s data centre. <table> <tr> <th> </th> <th> </th> <th> DATA </th> <th> SERVER </th> <th> </th> </tr> <tr> <td> INTERNAL </td> <td> PROTOCOL </td> <td> Hyperbackup & Active Backup for Server </td> <td> Snapshots </td> <td> </td> </tr> <tr> <td> FREQUENCY </td> <td> daily </td> <td> 3 times a week </td> <td> </td> </tr> <tr> <td> RETENTION </td> <td> 30 days </td> <td> 15 days </td> <td> </td> </tr> <tr> <td> EXTERNAL </td> <td> PROTOCOL </td> <td> Hyperbackup </td> <td> Hyperbackup Snapshots </td> <td> and </td> </tr> <tr> <td> FREQUENCY </td> <td> Twice a week </td> <td> Twice a week </td> <td> </td> </tr> <tr> <td> RETENTION </td> <td> 15 days </td> <td> 15 days </td> <td> </td> </tr> </table> ## STORAGE AND ARCHIVING Parties shall negotiate, toward the end of the project, an agreement to provide guidance on future use of the databases including the following provisions: * The access policy for the Parties, Affiliated entities to the databases * The modalities for third parties to access databases * The modalities to maintain the integrity of the datasets, the storage place for the databases and modalities to keep copies on remote storage * The legal framework ### 1\. CANCER PATIENTS The HARMONIC-RT database is meant to initiate a pan-European registry of particle beam therapy (and photon therapy at a later stage) in children and adolescents. It is anticipated that toward the end of the project, agreements will be signed for data to constitute such a sustainable registry. Complete documentation for long-term data use and preservation will be provided. ### 2\. CARDIAC PATIENTS ISGlobal Data Manager will follow ISGlobal guidelines to provide accurate and complete documentation for data preservation. It will ensure that the data are curated in a relevant long-term archive and ensure data will be available after project funding has ended. We will create metadata for long-term data preservation. If no agreements are signed, the data will be returned to the providing centre, in the format used for analysis and it will be destroyed from the centralized database. ### 3\. BIOLOGICAL SAMPLES Samples will be kept at 20°C for short-term storage (6 months) and -80 °C for long-term storage. The plan is to analyse the samples within 6 months and the left-over samples will be sent to the original centres upon request. Otherwise, the samples will be decoded and used for new method development or discarded after the results are published.	https://phaidra.univie.ac.at/o:1140797	Horizon 2020
0169_ICO2CHEM_768543.md	# Data Summary ICO2CHEM aims at developing a new production concept for converting waste CO 2 to valueadded chemicals. The focus is on the production of white oils and high molecular weight aliphatic waxes. The technological core of the project consists in the combination of a Reverse Water Gas Shift (RWGS) reactor coupled with an innovative modular Fischer-Tropsch (FT) reactor. The aim of the project is to demonstrate the production of white oils and aliphatic high molecular weight waxes from industrial CO 2 and H 2 streams at Industrial Park Höchst in Frankfurt am Main, Germany. During the project, the experimental data is generated related to development of catalysts, reactors, product analysis and demonstration runs. In addition, market, techno-economic and life cycle analysis will be produced. Those analyses require experimental data obtained during the project. Data generated will be mainly numerical data (e.g. analytical data, test results) and graphical data (e.g. process flow charts, P&I diagrams) in electronic format. The project will also generate project management documents, such as minutes of the meetings, deliverables and progress reports. # FAIR data 2.1 Making data findable According to practices established in other EU projects coordinated by VTT, the official data (i.e. consortium agreement, reports, deliverables, minutes, meeting presentations, publications, publication permissions, etc) will be stored centrally on VTT’s External SharePoint server, where all partners have an access to the data. Raw data will be stored in accordance to internal rules of each partner. However, when needed the raw data can be also shared between the partners via SharePoint. 2.2 Making data openly accessible During the project, the data will be used by the consortium members. After the project is closed, all requests for further use of data will be considered carefully and whenever possible approved by the Coordinator and by the General Assembly. Permission for data use will be granted providing there are no IPR or confidentiality issues involved or any direct overlap of research questions with the primary research. All essential results will be documented as deliverable reports. Written deliverables will be stored on the VTT’s external SharePoint server. All partners have an access to all documents stored on the SharePoint server. Most of the deliverables produced during the project are confidential. With the approval of partners, the results will be disseminated through several routes described in DoA and D8.2 Dissemination plan. 2018/05/04 Version 1, 4 Open access publication will be ensured to all peer-reviewed scientific publications. All publications are under the permission policy described in the CA: Prior notice of any planned publication and patent application shall be given to the other Parties at least 30 calendar days before the publication. Any objection to the planned publication shall be made in accordance with the Grant Agreement in writing to the Coordinator and to the Party or Parties proposing the dissemination within 21 calendar days after receipt of the notice. If no objection is made within the time limit stated above, the publication is permitted. In case of objection, it has to include a precise request for necessary modifications. The objecting Party can request a publication delay of not more than 90 calendar days from the time it raises such an objection. After 90 calendar days, the publication is permitted provided that confidential Information of the objecting Party has been removed from the Publication as indicated by the objecting Party. 2.3 Increase data re-use (through clarifying licences) The publication permission procedure described in Section 2.2.2 must be followed during the Project, and one year after the project. Permission for data use will be granted providing there are no IPR or confidentiality issues involved. # Allocation of resources The Coordinator is responsible for data management in the project. Data management is included in Task 8.2 Intellectual property rights (IPR) and exploitation. Costs related to open access publishing are included in partners’ budgets. # Data security SharePoint data backups are taken every day. The SharePoint workspace will be maintained 20 years after the end of the project. It will also guarantee the availability of SharePoint documents for the same period. VTT is responsible for the maintenance of the SharePoint workspace. # Ethical aspects The project does not raise any ethical issues mentioned in the administrative proposal form. All participants in this project are committed to the responsible engineering principles and will conform to the current legislation and regulations in countries where the research will be carried out. Ethical standards and guidelines of Horizon2020 will be rigorously applied regardless of the country in which the research is carried out. The ethical principles of the research integrity will be respected as set out in the European Code of Conduct for Research Integrity 1 . 1 http://ec.europa.eu/research/participants/data/ref/h2020/other/hi/h2020-ethics_code- of-conduct_en.pdf 2018/05/04 Version 1, 5	https://phaidra.univie.ac.at/o:1140797	Horizon 2020
0170_ROMSOC_765374.md	_1\. Introduction_ # Introduction This document describes the data management life cycle for the data to be collected, processed an/or generated within the ROMSOC project. Carefully managing research data is an essential part of good research practice and starts with adequate planning. According to the Open Research Data Pilot open access to research data that is needed to validate the results presented in scientific publications has to be guaranteed. Moreover, open access to scientific peer-reviewed publications is obligatory in the Horizon 2020 programme. The purpose of this document is to help to make the research data findable, accessible, interoperable and reusable (FAIR). It specifies how data will be handled both during and after the research project and reflects on data collection, data storage, data security and data retrieval. The Data Management Plan presented herein has been prepared by taking into account the template of the Version 3.0 of the “Guidelines on FAIR Data Management in Horizon 2020”. 1 The ROMSOC Data Management Plan (DMP) is an evolving document that will be edited and updated throughout the project. This initial version of the DMP will be officially delivered in project-month 6 (February 2018). It will be updated over the course of the project and resubmitted to the European Commission (EC) whenever significant changes arise (e.g. new data, changes in consortium or consortium composition), and as a minimum as part of the Progress Report in project-month 13 (September 2018) and as part of the Periodic Reports in project-months 24 (August 2019) and 48 (August 2021). # Data Summary The ROMSOC network will produce mainly two kinds of research data: models to describe real-world systems and processes, and algorithms for simulation and optimization in the form of sophisticated software. A collection of benchmarks for model hierarchies will be created and these benchmarks will be open access. Besides, another type of research data are scientific publications (e.g., technical reports, peer-reviewed publications, software manuals) and related consolidated simulation results. For text-based documents the data format PDF/A is binding. ZIP files are used for sets of related files. In this case an associated publication is saved independently, not as the content of the ZIP file, so that it can be indexed and the content can be found via full- text search. # FAIR data In 2014, the Technische Universitat Berlin (TU Berlin) established a research data infrastructure. It was devel-¨ oped by the Service Center Research Data and Publications (SZF); a virtual organization, where the University Library, the IT-Service-Center tubIT and the Research Department of TU Berlin cooperate to support the researchers of TU Berlin in all questions concerning their research data (https://www.szf.tuberlin.de/). The research data infrastructure complies with the requirements of the funding organizations, e.g. the Deutsche Forschungsgemeinschaft (DFG) or the European Commission (EC). The technical core of the research data infrastructure at TU Berlin is the institutional repository DepositOnce. The repository DepositOnce is based on the open source repository software DSpace. In DepositOnce consolidated research data and all information necessary to verify or reuse the data (e.g. scripts, calculations, software etc.) as well as publications can be stored. DepositOnce provides a workflow for the description and upload of the data files. There is a record for each data set (research data as well as publications). Each record has a persistent identifier (Digital Object Identifier (DOI)). Research data may be linked to the corresponding publications and vice versa via their DOIs. Other core functions of DepositOnce are versioning and an embargo function (i.e. a blocking period for the publication of full texts and research data until a certain date). _3.1 Making data findable, including provisions for metadata_ ## Making data findable, including provisions for metadata DepositOnce automatically assigns a DOI to every submitted record and each of its versions for a persistent identification of the data. To get DOIs, the University Library (as head of SZF) has concluded a contract with the DOI registration agency DataCite. DepositOnce uses the standard metadata schema Qualified Dublin Core to describe the stored data. To meet the requirements of its service partners, e.g. the DOI registration agency DataCite or Open Access initiatives like OpenAIRE, some additional qualifiers have been added. Based on the principle of using standard metadata schemes in DepositOnce, metadata can easily be converted into other metadata schemes. All metadata in DepositOnce are made publicly available in the sense of Open Access. To enable search engines and service providers to index contents stored in DepositOnce, all reasonable steps are taken: like generating sitemaps, offering an OAI-PMH interface etc. DepositOnce is included in common search engines, e.g. Google Scholar, BASE – Bielefeld Academic Search Engine and others. Furthermore the DOI registration agency DataCite itself acts as a data provider: While registering a DOI all important metadata are sent to DataCite. Both nationally and internationally defined standards and interfaces – such as the Dublin Core Metadata Initiative, the regulations of the Open Archives Initiative (OAI) or the German National Library’s xMetaDissPlus format – are used for the formal and content-related indexing of the digital objects. Metadata is captured by means of an upload form into which the authors enter the data. In doing so, the authors attribute a class according to the Dewey Decimal Classification (DDC) as well as free keywords in German and English. According to the principle of ”Good Scientific Practice” (e.g. in order to guarantee correct citation) the data file and the descriptive metadata cannot be changed after they have been published in DepositOnce. One of the core functions of DepositOnce is versioning, which allows new versions of published records while previous versions are kept available. To every new version, a new persistent identifier (DOI) is assigned. Previous and new versions are linked to each other automatically. ## Making data openly accessible DepositOnce is committed to the open access concept and is one of the tools for implementing open access objectives in line with the _Berlin Declaration on Open Access to Knowledge in the Sciences and Humanities_ : * The metadata of digital objects stored on DepositOnce is freely accessible on the Internet and may be obtained, saved and made accessible to third parties via open interfaces that are accessible to anyone. The metadata is subject to the CC0 license. * After publication, the digital objects are normally freely accessible. This applies unconditionally to publications. Access protection may be arranged for research data; to this end, DepositOnce has a management system for access rights. In order to ensure permanent access to the digital objects, the latter are assigned quotable, lasting indicators in the form of Digital Object Identifiers, which are registered and administered at DataCite. Once published, digital objects cannot be changed. DepositOnce provides a versioning feature to document new findings. A search feature for both the bibliographic metadata and the full texts is available on the pages of DepositOnce. Digital objects can be researched in local, regional and transregional library catalogs and search engines as well as via the DataCite Metadata Store and the academic search engine BASE. To increase the visibility of its service, DepositOnce is registered with the Registry of Open Access Repositories (ROAR), the Directory of Open Access Repositories (OpenDOAR) and the Registry of Research Data Repositories (re3Data). As a registered OAI Data Provider, DepositOnce fulfills the requirements of OAI-PMH Version 2.0. The base URL is https://depositonce.tu-berlin.de/oai/request. ## Making data interoperable The production of high quality, portable, and easy-to-use scientific software must heavily rely on rigorous implementation and documentation standards. To ensure such high quality demands, the Working Group on _3.4 Increase data re-use (through clarifying licences)_ Software (WGS) produced in 1990 the Implementation and Documentation Standards [1] for the SLICOT 2 library (Subroutine Library in Control and Systems Theory), a library of widely used control system design algorithms. All software that is develop within the ROMSOC project will follow these implementation and documentation standards. The collection of benchmarks for model hierarchies that will form the basis for interdisciplinary research and for the training program in the ROMSOC project will follow the standards set by the SLICOT Benchmark Collection [2] and the collection of benchmark examples for model reduction of linear time invariant dynamical systems [3]. By following these software documentation and implementation standards a uniform, understandable user interface is ensured that is fundamental to user acceptance of the developed software routines and necessary to ensure portability, reliability, and ease of maintenance of the software itself. ## Increase data re-use (through clarifying licences) Any publication at DepositOnce requires that the author permanently transfers to the Operator (i.e. the SZF) the non-exclusive right to reproduce the publication and make it publicly accessible. Any printed or electronic publication of the research results – with amendments or in excerpts, if applicable –prior to, or after their publication at DepositOnce remains at the absolute discretion of the author. The transfer of the non-exclusive right of use entitles the Operator of DepositOnce to permanently * make accessible to the public electronic copies of the digital object (upon expiry of the news embargo, if any) and, if need be, to modify the digital object (with regard to its form) in order to enable its display on future computer systems; * announce and transmit digital objects to third parties, for instance, as part of the libraries’ national collection mandates, particularly for the purpose of long-term archiving; * transfer agreed rights and obligations to another repository (for instance, any repository succeeding DepositOnce). This also entitles DepositOnce to assign to a third party the right to supply the digital object to the public (e.g., through a facility specializing in enabling the long-term availability of such objects). In addition, beyond the German copyrights, authors may transfer certain rights of use to the general public by means of suitable open-content licenses (for instance a Creative Commons license or software licenses such as GPL, BSD, MIT or Apache licenses). For publications the Creative Commons license ‘Attribution CC BY’ will be used. # Allocation of resources The service of DepositeOnce itself is free of charge. Costs may arise for additional storage space. According to the policy for safeguarding good scientific practice of TU Berlin, SZF as the service provider of DepositOnce guarantees the storage of the research data for at least 10 years. In cooperation with other partners and institutions (e.g. the Zuse Institute Berlin and the Kooperativer Bibliotheksverbund Berlin-Brandenburg); SZF will develop a concept for long term preservation. According to the concept of the research data infrastructure the task-sharing between the stakeholders is as following: * The researchers are responsible for the quality check of the research results: After having collected a large amount of data during the project they select those data that shall be preserved. In the submission process to DepositOnce they describe these data and upload the data files. Researchers are also responsible to create new versions of published submissions if necessary. * SZF is responsible for the formal check of the submitted research data: The uploaded data files are not published immediately but stored in an intermediate store. SZF checks e.g. whether the metadata fields are filled properly, whether PDFs can be opened, etc. If there are any questions, the SZF-Team contacts the submitter of the data. Accepted submissions are published by SZF and stored in DepositOnce. _5\. Data security_ * tubIT is responsible for the IT infrastructure which includes safe storage and the accessibility of all data. # Data security The DepositOnce servers that store the research data and their metadata are part of the security concept of tubIT. Every new IT service at TU Berlin has to go through an approval procedure that is conducted by the Data Protection Officer of TU Berlin and the Staff Council. DepositOnce has successfully passed this procedure. DepositOnce uses the virtual server infrastructure of tubIT that is secured by several firewalls. All servers, networks and backup services are maintained by tubIT. The security concept strictly restricts any physical access to the data center and any remote access to the servers. The metadata as well as the research data are backed-up at least once a day in form of database dumps and files. A cronjob verifies file checksums once a week and ensures the data integrity. The administrators at the University Library are responsible for the further enhancement and development of the software. They are also responsible for the recovery of the DepositOnce services out of the backup. Digital objects are stored for the long term, that is, in accordance with the recommendations of the German Research Foundation, for at least ten years (see _Guidelines for Safeguarding Good Academic Practice_ at TU Berlin). In cooperation with a suitable facility specialized in long-term archiving, the Operator aims to digitally preserve the digital objects stored in DepositOnce. The digital preservation of publications is ensured through the long-term archiving system of the German National Library. # Ethical aspects In order to protect the participants’ identity all contents that are stored in DepositOnce have to be anonymized according to the policy for safeguarding good scientific practice of TU Berlin. When submitting a data file in DepositOnce the submitter has to confirm that the research data which has been submitted do not contain any personal data. If personal data are contained they must be anonymized completely according to canonical standards and the human subjects must have consented to the data collection as well as to the publication of the (anonymized) data. Ownership of results as well as access rights to Background and Software are regulated in the Consortium Agreement (Section 8 and 9). Any ethical or legal issues that can have an impact on data sharing will be discussed in the context of the ethical reports (in project- month 12, 24 and at the end of the project). # Other issues We follow the _Guidelines on FAIR Data Management in Horizon 2020_ , i.e., research data should be findable, accessible, interoperable and reusable. TU Berlin doesn’t have a Research Data Management policy. However, since 2002 TU Berlin has a directive for safeguarding good scientific practice: _”Richtlinien zur Sicherung guter wissenschaftlicher Praxis an der TU Berlin”_ . We also respect the research data policy of Friedrich-AlexanderUniversitat Erlangen-N¨ urnberg (FAU).¨ 3	https://phaidra.univie.ac.at/o:1140797	Horizon 2020
0171_TAHYA_779644.md	# 1\. INTRODUCTION TAHYA is part of the Horizon 2020 Open Research Data Pilot, the Pilot project of the European Commission which aims to improve and maximize access to and reuse of research data generated by projects. The focus of the Pilot is on encouraging good data management as an essential element of research best practice. The Deliverable D8.6 Data Management Plan (DMP), represents the first version of the DMP of the TAHYA project. TAHYA is a Research and Innovation Action project funded under the Fuel Cells and Hydrogen 2 Joint Undertaking that will last 36 months. As such, TAHYA participates in ORD Pilot, and, therefore, is providing, as requested, the current deliverable six months after the beginning of the project (M6, June 2018). The DMP is not a fixed document, but it is likely to evolve during the whole lifespan of the project, serving as a working document. This document will be updated as needed during the Project General Assemblies. st version Data Management Plan of the TAHYA The purpose of the current deliverable is to present the 1 project. The deliverable has been compiled with the collaborative work among the coordinator and the consortium partners who were involved in data collection, production and processing. It includes detailed descriptions of all datasets that will be collected, processed or generated in all Work Packages during the course of the 36 months of TAHYA project. The deliverable is submitted six months after project start as required by the European Commission (EC) through the latest guidelines: The Open Research Data Pilot (ORD Pilot). For the methodological part, the latest EC guidelines 1 have been adopted for the current deliverable. The deliverable is structured in the following sections: 1. An introduction to the deliverable and a brief description on how Data Management is approached in Horizon 2020 (H2020) program along with the importance of it. 2. A description of the methodology used, an analysis of the chapters of the provided template and last the methodological steps followed in TAHYA. 3. A description of the datasets to be used in TAHYA reflected on the template provided by the EC. 4. A summary table with all the datasets included in 1 st TAHYA DMP. # 2\. DATA MANAGEMENT IN H2020 PROGRAM According to the latest Guidelines on FAIR Data Management in Horizon 2020 released by the EC DirectorateGeneral for Research & Innovation on the 26th of July 2016 “ _beneficiaries must make their research data findable, accessible, interoperable and reusable (FAIR) ensuring it is soundly managed_ ”. FAIR data management is part of the ORD Pilot promoted by the European Commission. The purpose of the ORD is to improve and maximize access to and re-use of research data generated by H2020 projects and to take 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 issues. The inclusion of a DMP is a key element for FAIR data management in a H2020 project. In a DMP, the data management life cycle for the data to be collected, processed and/or generated by a H2020 project is described and analysed. The DMP should also include information on (a) the handling of research data during & after the end of the project, (b) what data will be collected, processed and/or generated, (c) which methodology & standards will be applied, (d) whether data will be shared/made open access and (e) how data will be curated & preserved (including after the end of the project). # 3\. METHODOLOGY ## a. DMP Template In order to assist the beneficiaries with the completion of the DMP, the EC produced and provided a template that act as a basis for data description. The template contains a set of questions that beneficiaries should answer with a level of detail appropriate to the project. If no related information is available for a given dataset, then the phrase “ _Non-applicable_ ” or N/A will be used. In the following paragraphs, the main sections and proposed contents of the template are listed and presented, along with the way TAHYA reflects to these sections. ## b. Data summary In this section, beneficiaries are asked to describe (a) the purpose of the data collection or generation and how this purpose reflects to the objectives set in the project as a whole, (b) the types and formats of data that will be generated or collected, (c) the origin of the data, (d) the expected size of the data, and also (e) whether existing data will be reused and (f) the usefulness of the described datasets. ## c. FAIR data ### a) Making data findable, including provisions for metadata This section includes a description of metadata and related standards, the naming and keywords to be used. In the context of TAHYA the following naming convention will be used for all the datasets of the project. First the work package number will be placed, then the serial number of the dataset within this work package and last the dataset title, all separated with underscore (Data_<WPno>_<serial number of dataset>_<dataset title>). An example can be the following Data_WP2_1_specifications_data. However, it has to be noted that this naming convention describes only the general dataset that can contain files of different size and format. The naming of each separate file follows a different naming convention that is proposed by the partners who creates the files. The use of a standard identification mechanism in for the datasets of TAHYA will be decided by the project consortium. If it turns out to be necessary, the use of the Guidelines and standards provided by the International DOI Foundation (IDF) and the DOI system and ISO 26324 2 will be considered. ### b) Making data openly accessible This section includes a description of the data that will be made accessible and how. It also explains why some datasets cannot be made open due to possible, legal, contractual or ethical issues. It is possible that some beneficiaries have decided to keep their data closed. A description of the potential data repositories is also included along with the potential software tools required to access the data. In the context of TAHYA, the following options for open repositories of data, metadata, documentation or code will be considered: (a) The Registry of Research Data Repositories 3 , (b) Zenodo 3 , (c) OpenAIRE 4 , In the context of the TAHYA DMP, not any arrangements have been made with an identified repository. This will be discussed by the consortium during the upcoming plenary meeting. Currently the data are collected and preserved on a private platform: Project Netboard 5 . ### c) Making data interoperable In this section, data interoperability is detailed for every dataset of TAHYA. Issues such as the allowing of data exchange between researchers, institutions or even countries are covered along with all the technicalities including standards for formats, metadata vocabularies or ontologies of vocabularies. The issue of interoperability will be discussed among the consortium members in the upcoming project plenary meeting. ### d) Increase data re-use (through clarifying licenses) This section describes the licenses, if any, under which data will be re-used in TAHYA. It includes provisions regarding the period when data will be available for reuse and if third parties will have the option to use the data and when. ### e) Allocation of resources FAIR data management in TAHYA project is under WP9 –Dissemination and Exploitation strategy lead by Partner N°6 Absiskey, in close collaboration with the Coordinator. Within the project budget, a specific amount of person months has been dedicated for these activities. All costs related to FAIR data management that will occur during project implementation will be covered by the project budget. Any other cost that may relate to long term data preservation will be discussed among consortium members. ### f) Data security Data security is of major importance in the TAHYA project. Special attention will be given to the security of sensitive data. The protection of data will be ensured through procedures and appropriate technologies, on Project NetBoard like the use of HTTPS protocol for the encryption of all internet transactions and appropriate European and Internet security standards from ISO, ITU, W3C, IETF and ETSI. If data will be kept in a certified repository, then the security standards of that repository will apply. ### g) Ethical aspects With respect to the H2020 ethics self-assessment, the TAHYA proposal and the use case scenarios to be defined will not be concerned with any ethical issue. ### h) Other issues In this section, other issues can be covered not included above such as the use of other national/funder/sectorial/departmental procedures for data management. ## d. Methodological steps in TAHYA For the 1 st version of TAHYA DMP, the following methodological steps were followed: 1. Absiskey and the Coordinator, responsible for the implementation WP9 – Dissemination and Exploitation strategy - sent to all partners, well in advance, an email notifying them about the upcoming deliverable. Contribution was asked from all partners that were involved in any data collection in each task of the WPs. They were asked to answer a questionnaire on which data they were expecting to produce and collect during the project. 2. In parallel, the latest guidelines from the EC regarding data management were sent to all partners to be informed. Sufficient time was given to send their input. 3. The project team collaborated efficiently and contributed with the needed information. The first version of the TAHYA DMP is intended to provide an initial screening of the data to be collected, processed and produced within TAHYA. It is also the first attempt to collect the vision and input from all the partners involved in any data management option. During the upcoming Project Steering Boards in October 2018 special attention will be given to data management in order to provide further clarifications and conclusions on data management. # 4\. DATASETS ## a. WP1 – Project Decision making and innovation management N/A ## b. WP2 – End-users' specifications, product, safety & service definition N/A Specifications are highly confidential results which cannot be share and held by VW. ## c. WP3 – Design and Prototyping Design and developments for liner, composite and OTV are highly confidential results which cannot be share to the scientific community and held by industrial partners (Optimum, Raigi and Anleg). Only one task in this WP could lead to the open access to data: Task 3.5 optimisation and filling and venting process. <table> <tr> <th> DMP component </th> <th> WP3_1_Simulation assumptions_data </th> </tr> <tr> <td> 1\. Data summary </td> <td> Purpose: Simulation data Data formats : .xlsx, .docx, .pdf, .pptx Will you re-use any existing data and how? * yes What is the origin of the data? * simulation, * experimental protocols, measurement conditions What is the expected size of the data? * The total file of this dataset will be approximately 0,2 Gb. To whom might it be useful ('data utility')? * scientific community </td> </tr> <tr> <td> 2\. FAIR Data </td> <td> Findable, Accessible, Interoperable, Re-usable </td> </tr> <tr> <td> 2.1 Making data findable, including provisions for metadata </td> <td> Description of the data: \- WP3_1_Simulation assumptions_data </td> </tr> <tr> <td> 2.2 Making data openly accessible </td> <td> \- assumption data on gas and Temperature distribution to members of a consortium on project, members of an international scientific community for research purposes. The data will be stored on Project Netboard a secure platform and TUC servers. </td> </tr> <tr> <td> 2.3 Making data interoperable </td> <td> \- the data produced in the project are interoperable, that is allowing data exchange and re-use between researchers, institutions, organisations </td> </tr> <tr> <td> 2.4. Increase data re-use (through clarifying licences) </td> <td> Specify the licenses and the conditions for sharing and reusing the data: \- No specific conditions </td> </tr> <tr> <td> 3\. Allocation of resources </td> <td> All costs related to the data collection and processing are covered by the project budget with dedicated person months under WP9. </td> </tr> <tr> <td> 4\. Data security </td> <td> Audio, doc and xls files will be deposited in ABSISKEY servers and will be protected with the ABSISKEY server’s security protocol: PNB security is provided by OVH. OVH is currently the number 3 web hosting provider worldwide. OVH condition of security: https://www.ovh.co.uk/aboutus/security.xml and https://www.ovh.co.uk/aboutus/datacentres.xml In addition to this security, a complete database backup is performed each day and stored during one week. Each week a secured backup is stored on CD. Finally, every connection to Project NetBoard is made using the https protocol to login to the platform _._ </td> </tr> <tr> <td> 5\. Ethical aspects </td> <td> There are no ethical issues regarding the project. </td> </tr> <tr> <td> 6\. Other </td> <td> N/A </td> </tr> </table> ## d. WP4 – Design verification phase To be defined later on in the project according to IP management of development realised in WP3. ## e. WP5 – System validation phase and safety aspects <table> <tr> <th> DMP component </th> <th> WP5_1_performance results_data </th> </tr> <tr> <td> 1\. Data summary </td> <td> Purpose: Results of the hydraulic performance and fire resistance tests. Data formats : .xlsx , .docx, .pdf , .pptx, .mp4, .asc , .opj Will you re-use any existing data and how?* * N/A What is the origin of the data? * Experimental measurement data recorded with universal measurement amplifiers What is the expected size of the data? * The total size of all data will be approx. less than 2 Gb. To whom might it be useful ('data utility')? </td> </tr> </table> <table> <tr> <th> </th> <th> \- Project partners, scientific community, notified bodies, competent authority </th> </tr> <tr> <td> 2\. FAIR Data </td> <td> Findable, Accessible, Interoperable, Re-usable </td> </tr> <tr> <td> 2.1 Making data findable, including provisions for metadata </td> <td> Description of the data: \- The following naming for the dataset will be WP5_1_BAM_performance results_data </td> </tr> <tr> <td> 2.2 Making data openly accessible </td> <td> * mainly results of normalised data needed for publications (national/international scientific community and standards committee) * raw measurement data only to members of the consortium * Shared documents/data uploaded to Project Netboard All Data will be stored on company internal shared directory with limited number of users </td> </tr> <tr> <td> 2.3 Making data interoperable </td> <td> All published data are generated with common programs and stored within common file formats and therefore they are interoperable and can be re-use between researchers, institutions, companies etc. </td> </tr> <tr> <td> 2.4. Increase data re-use (through clarifying licences) </td> <td> Specify the licenses and the conditions for sharing and reusing the data: \- Creative Commons: (CC BY-ND 3.0 DE or CC BY-NC-ND 3.0 DE) https://creativecommons.org/ </td> </tr> <tr> <td> 3\. Allocation of resources </td> <td> All costs related to the data collection and processing are covered by the project budget with dedicated person months under WP9. </td> </tr> <tr> <td> 4\. Data security </td> <td> Files will be deposited in ABSISKEY servers and will be protected with the ABSISKEY server’s security protocol: PNB security is provided by OVH. OVH is currently the number 3 web hosting provider worldwide. OVH condition of security: _https://www.ovh.co.uk/aboutus/security.xml_ and _https://www.ovh.co.uk/aboutus/datacentres.xml_ In addition to this security, a complete database backup is performed each day and stored during one week. Each week a secured backup is stored on CD. Finally, every connection to Project NetBoard is made using the https protocol to login to the platform _._ </td> </tr> <tr> <td> 5\. Ethical aspects </td> <td> There are no ethical issues regarding the project. </td> </tr> <tr> <td> 6\. Other </td> <td> N/A </td> </tr> </table> ## f. WP6 – Manufacturing process N/A according to IP definition and management. ## g. WP7 – Economical aspects and implementation strategy N/A This topic and associated results are highly confidential and cannot be shared by industrial partners. ## h. WP8 – RCS standardisation work <table> <tr> <th> DMP component </th> <th> WP8_1_safety levels composite cylinders_data </th> </tr> <tr> <td> 1\. Data summary </td> <td> Purpose: Identify and improve existing safety level of composite cylinders and standards. Data formats : .xlsx, .docx, .pdf, .pptx Will you re-use any existing data and how? * N/A What is the origin of the data? * Experimental test results What is the expected size of the data? * The total size of all data will be approx. less than 0,2 Gb. To whom might it be useful ('data utility')? * Project partners, scientific community, notified bodies, competent authority </td> </tr> <tr> <td> 2\. FAIR Data </td> <td> Findable, Accessible, Interoperable, Re-usable </td> </tr> <tr> <td> 2.1 Making data findable, including provisions for metadata </td> <td> Description of the data: \- The following naming for the dataset will be WP8_1_BAM_safety levels composite cylinders_data </td> </tr> <tr> <td> 2.2 Making data openly accessible </td> <td> * mainly results of normalised data needed for publications (national/international scientific community and standards committee) * Shared documents/data uploaded to Project Netboard All Data will be stored on company internal shared directory with limited number of users </td> </tr> <tr> <td> 2.3 Making data interoperable </td> <td> All published data are generated with common programs and stored within common file formats and therefore they are interoperable and can be re-use between researchers, institutions, companies etc. </td> </tr> <tr> <td> 2.4. Increase data re-use (through clarifying licences) </td> <td> Specify the licenses and the conditions for sharing and reusing the data: \- Creative Commons: (CC BY-ND 3.0 DE or CC BY-NC-ND 3.0 DE) https://creativecommons.org/ </td> </tr> <tr> <td> 3\. Allocation of resources </td> <td> All costs related to the data collection and processing are covered by the project budget with dedicated person months under WP9. </td> </tr> <tr> <td> 4\. Data security </td> <td> Audio, doc and xls files will be deposited in ABSISKEY servers and will be protected with the ABSISKEY server’s security protocol: PNB security is provided by OVH. OVH is currently the number 3 web hosting provider worldwide. OVH condition of security: _https://www.ovh.co.uk/aboutus/security.xml_ and _https://www.ovh.co.uk/aboutus/datacentres.xml_ In addition to this security, a complete database backup is performed each day and stored during one week. Each week a secured backup is stored on CD. Finally, every connection to Project NetBoard is made using the https protocol to login to the platform _._ </td> </tr> <tr> <td> 5\. Ethical aspects </td> <td> There are no ethical issues regarding the project. </td> </tr> <tr> <td> 6\. Other </td> <td> N/A </td> </tr> </table> _i. WP9 – Dissemination and exploitation strategy _ NA <table> <tr> <th> WP / Task </th> <th> Responsible partner </th> <th> Dataset name </th> <th> File types </th> <th> Findable </th> <th> Accessible </th> <th> Interoper able </th> <th> Reusable </th> <th> Size </th> <th> Security </th> <th> Ethics </th> </tr> <tr> <td> WP3 T3.5 </td> <td> TUC </td> <td> WP3_1_Simulation assumptions_data </td> <td> .xlsx, .docx, .pdf, .pptx </td> <td> WP3_1_Simulation assumptions_data </td> <td> Results published (papers, presentation etc.) \- depository: project netboard </td> <td> Yes </td> <td> Yes </td> <td> <0,2 GB </td> <td> Kept in AK /TUC servers </td> <td> N/A </td> </tr> <tr> <td> WP8 / T08.01, T08.02, T08.03. </td> <td> BAM </td> <td> WP8_1_safety levels composite cylinders_data </td> <td> .xlsx, .docx, .pdf, .pptx </td> <td> WP8_1_BAM_safe ty levels composite cylinders_data </td> <td> * Results published (papers, presentation etc.) * depository: project netboard and company internal </td> <td> Yes </td> <td> License publications CC BY-ND 3.0 DE or CC BY-NC-ND 3.0 DE </td> <td> <0,2 GB </td> <td> Kept in AK /BAM servers </td> <td> N/A </td> </tr> </table> D1.4__TAHYA-P6_AK_190115 Page 13 / 16 5. SUMMARY TABLE 6.	https://phaidra.univie.ac.at/o:1140797	Horizon 2020
0172_SKILLFUL_723989.md	# Executive Summary This report constitutes the Deliverable 5.2 (Data Management Plan) of the SKILLFUL project, part of the WP5 (Pilots). SKILLFUL project aims to identify the skills and competences needed by the Transport workforce of the future (2020, 2030 and 2050 respectively) and define the training methods and tools to meet them. Within this context SKILLFUL have developed new training schemes, which have been adapted to the particular needs of the transportation professionals of the present and the future. These training schemes have been validated through the realisation of Pilots in 27 Pilot sites in 12 different countries (namely, Brazil, Denmark, Finland, France, Germany, Greece, Ireland, Italy, Lithuania, Portugal, Slovakia and Spain). The Data Management Plan (DMP) has been prepared (and also updated), following the regulations of the Pilot action on Open Access to Research Data of Horizon 2020. In this 3 rd and final version of the Deliverable the necessary aspects of the Data Management process, mainly related to the SKILLFUL pilots’ realisation are being described. # Introduction SKILLFUL ( _http://skillfulproject.eu/_ ) is dealing with one of the main challenge for the transportation sector, which is the ability to attract new employees, as well as equip the existing ones with the competences required for addressing the needs of the constantly changing and developing transportation sector. Its vision is to identify the skills and competences needed by the Transport workforce of the future (2020, 2030 and 2050 respectively) and define the training methods and tools to meet them. Within this context, the project objectives can be described as following: * to critically review the existing, emerging and future knowledge and skills requirements of workers at all levels in the transportation sector, with emphasis on competences required by important game changers and paradigm shifters (such as electrification and greening of transport, automation, MaaS, etc.); * to structure the key specifications and components of the curricula and training courses that will be needed to meet these competence requirements optimally, with emphasis on multidisciplinary education and training programmes; * to identify and propose new business roles in the education and training chain, in particular those of “knowledge aggregator”, “training certifier” and “training promoter”, in order to achieve European wide competence development and take-up in a sustainable way. For the aforementioned objectives to be achieved, the whole project process has been structured that way that it can be divided into three major categories/ steps: * Step 1 : Identification of Future Trends/ Needs & Best Practices * Step 2 : Development of Training Schemes & Definition of Profiles and Competences * Step 3 : Verification and Optimization of training schemes During the third step of the SKILLFUL project and the procedure of the training schemes piloting and verification, data have been collected during the realisation of the 27 pilots that were organised during the years 2018 and 2019. The Data Management Plan is a deliverable directly connected to evaluation and pilot plans for each of the pilot sites. This final version of the deliverable (Month 36) includes description of the datasets developed and used for the pilots’ analysis, following the regulations of the Pilot action on Open Access to Research Data of Horizon 2020 [1]. ## Interrelations Data Management aspects are closely related to: 1. Ethics issues in SKILLFUL, especially in the context of collecting, managing and processing data from real-life users (including Pilots’ participants), 2. Legal issues related to personal data (including sensitive personal data), security and privacy. Therefore, this document has been updated as the work evolved and in close synergy with the work of Activity 7.4 “A7.4 Quality Assurance, Ethics, Equity and Gender issues”. # Data processes during the SKILLFUL Pilots The Guidelines on Data Management in Horizon 2020 document has been taken under consideration, used in order to identify and define the data management procedures, which the SKILLFUL project has followed. Data collection, storing, accessing, and sharing abides to the international legislation (Data Protection 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” and EU general data protection regulation 2016/679 (GDPR), which has taken effect in May 25 2018) and guidelines [2]. This final update (M36) contains descriptions of dataset structures of data contained and used for the evaluation of the SKILLFUL pilots. Subjective data have been collected during several types of qualitative surveys of the project (i.e. within WP1, WP2 and WP4), workshops (i.e. within WP1, WP2) and, of course during the realisation of the pilots (WP5). This data is collected, managed and processed by SKILFUL partners and it has been anonymised in all cases. In the case of Pilots, subjective data deal mostly with the evaluation and assessment of the piloted SKILLFUL courses, as well as satisfaction/perceived quality and acceptance (perceived/rated by users) of these courses, according to the pilot participants, namely the following: * Trainers * Trainees * Organisers * Stakeholders Apart from data, metadata have been collected to define the characteristics and in many cases to facilitate processing, storing, and, finally, understanding the data collected during the pilots. Metadata definitions range from quality descriptions of datasets when they are used by analysts who did not participate in the data collection, thus, it is important for them to understand as much as possible about the related processes and procedures to aggregation of data to something different. For the evaluation phase of the SKILLFUL Pilots, an online questionnaire was developed ( _https://www.soscisurvey.de/skillfulTCE/_ ) . Web link to the questionnaire was distributed to all survey participant groups (4 different types mentioned above). The following types of data have been logged, managed and processed in this SKILLFUL system during performance and evaluation of SKILLFUL Pilots: * Personalisation data: Data concerning each survey respondent’s profile (each pilot’s participant as listed above) have been collected by the evaluation questionnaires provided. More specifically, the following information is being required: o Gender information o Curriculum background (only for trainees) o Years of professional experience (only for trainees) All information acquired in anonymised. * User feedback data: Through the second part of each questionnaire (for all 4 types of participants) an upper level (subjective) evaluation of each pilot course examined has been obtained from the participants. The feedback concerns various aspects of the course, such as (indicatively) its content and usefulness, appropriateness, its potential of providing new skills, its learning outcomes and how they could improve the job opportunities of the individual participants. Feedback is provided also on the organisation of the pilot course (i.e. resources, functionality of classroom, technical equipment, timetable, teaching and learning methods, the trainers’ background knowledge and skills, etc.). At the end of each questionnaire, users were asked to indicate their overall rating concerning the examined Pilot course, while also provide general comments and suggestions for improvement. All information has been acquired in anonymised form. Figure 1: Example of the anonymisation of the data obtained during the SKILLFUL pilots ## Data storage and back up The collection of the data obtained by the SKILLFUL were regularly securely stored and backed-up. Sometimes multiple copies were made as the data were initially collected by CERTH, which was also responsible for the monitoring and optimisation of the online questionnaires and periodically (biweekly), copies of the data were send to VTT, the partner that was responsible for the analysis of the Pilots information and the consolidation of their results (processor). The following data storage options have been used: * External hard drives/USB sticks: used in long-trials (WP5) and local evaluations. They have served as backups and intermediate storage units before transferring data to a permanent/longterm storage place. * Personal computers and laptops: Similarly they have mainly served as a short-term options and for transferring data after the evaluation sessions to a selected storage place. * Network/fileservers: large data sets are being stored and they will serve as the long-term storage solution. Regular backups ensure data are not lost or corrupted. ## Data ownership and preservation Any data gathered during the lifetime of the project are the ownership of the beneficiary or the beneficiaries (joint ownership) that produce them according to subsection 3, Art. 26 of the signed Grant Agreement (723989-SKILLFUL). Data will be preserved after the end of the project (for a period of two years) only for complete datasets that partners have agreed to share them with other researchers (if any). However, since the data obtained by the participants during the SKILLFUL project and especially during the Pilots, concerned mainly personal data describing features that have to do with the training and the professional skills of the participants, and also due to the fact that the pools of the data are small they are not intended to be reused or shared with third parties. The data collected do not include physiological measurements and information, so their nature make them not appropriate for algorithmic applications. # SKILLFUL data privacy policy Participants’ personal data have been being used in strictly confidential terms and have been published only as statistics (anonymously). The stored data only refer to users’ gender, professional background and nationality (no other identifier was collected). Nevertheless, stored date relate only to users’ activities related to their specific position and job, not to a person’s beliefs or political or sexual preferences. Moreover, it is very important to also mention that any data related to the performance of each pilot participant/ user to their job/ position duties (“incidental findings”) it is not part of the SKILLFUL research and thus will not be taken into account and no relevant information will be disclosed to any 3 rd party; including the trainees’ colleagues and management. Any of the following data have _not_ be stored: * Name, address, telephone, fax, e-mail, photo, etc. of the user (any direct or indirect link to user ID). * User location (retrieved every time dynamically by the system, but not stored). * Any other preferences/actions by the users, except the ones motioned explicitly above. * To whom they communicates, their frequent contacts, etc. ## During pilots During the SKILLFUL Pilot tests: 1. In cases that any personal data (i.e. names, address, and contact details) from transport professionals participating in the pilots were required, these were provided only to a single person in each pilot site, to be stored in a protected local database (to contact them and arrange for the tests). None of these persons participated in the evaluation process and the analysis of the data. Each participant was registered in the database through an anonymous ID. 2. This personal data have been 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 have not been communicated to any other partner or even person in each pilot site. Once the pilots were completed, any relevant information has been deleted. 3. Since personal data have been deleted, no follow-up studies with the same people will be feasible. The partners of the consortium agree and declare that personal data have been used in strictly confidential terms and been published only as statistics (anonymously). # Conclusions This deliverable (D5.2) contains and monitors since the beginning of the SKILLFUL project the Data Management processes that have been structured and followed, regarding mainly the analysis of the data provided by real users (i.e. through questionnaire’s, dissemination events and workshops and during the project’s pilot tests), emphasizing the proper management of personal data and the protection of participants. This deliverable has acted as s a reference document, since ethical considerations, especially about data protection, privacy and security have elaborated in it, in cooperation also with the SKILLFUL Ethics Board.	https://phaidra.univie.ac.at/o:1140797	Horizon 2020
0173_OPERA_654444.md	# 1\. INTRODUCTION ## 1.1 OPERA MOTIVATION The OPERA project participates in the Pilot on Open Research Data launched by the European Commission (EC) along with the H2020 programme. This pilot is part of the Open Access to Scientific Publications and Research Data programme in H2020. The goal of the programme is to foster access to research data generated in H2020 projects. The use of a Data Management Plan (DMP) is required for all projects participating in the Open Research Data Pilot. Open access is 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 research and innovation, scientific information can refer to peer-reviewed scientific research articles or 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. The Consortium strongly believes in the concepts of open science, and in the benefits that the European innovation ecosystem and economy can draw from allowing the reuse of data at a larger scale. Furthermore, there is a need to gather experience in open sea operating conditions, structural and power performance and operating data in wave energy. In fact, there has been very limited open sea experience in wave energy, which is essential in order to fully understand the challenges in device performance, survivability and reliability. The limited operating data and experience that currently exists are rarely shared, as testing is partly private-sponsored. This project proposes to remove this roadblock by delivering for the first time, open access, high-quality open sea operating data to the wave energy development community. Nevertheless, data sharing in the open domain can be restricted as a legitimate reason to protect results that can reasonably be expected to be commercially or industrially exploited [1] . Strategies to limit such restrictions will include anonymising or aggregating data, agreeing on a limited embargo period or publishing selected datasets. ## 1.2 PURPOSE OF THE DATA MANAGEMENT PLAN 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 the project research data. The DMP covers the complete research data life cycle. It describes the types of research data that will be generated or 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. It also reflects the current state of the Consortium agreements on data management and must be consistent with exploitation and IPR requirements. The DMP is not a fixed document, but will evolve during the lifespan of the project, particularly whenever significant changes arise such as dataset updates or changes in Consortium policies. This document is the final version of the DMP which was first delivered in Month 6 of the project (D8.5) and later updated (D8.6). This document has been produced following the EC guidelines for project participating in this pilot and additional consideration described in ANNEX I: KEY PRINCIPLES FOR OPEN ACCESS TO RESEARCH DATA. ## 1.3 RESEARCH DATA TYPES IN OPERA The data types that will be produced during the project are focused on the Description of the Action (DoA) and their results. According to such consideration, Table 1.1 reports a list of categories of research data that OPERA will produce. These research data types have been mainly defined in WP1, including data structures, sampling and processing requirements, as well as relevant standards. This list may be adapted with the addition or removal of datasets in the next versions of the DMP to take into consideration the project developments. A detailed description of each dataset is given in the following sections of this document. #### TABLE 1.1: OPERA TYPES OF DATA <table> <tr> <th> # </th> <th> Dataset category </th> <th> Lead partner </th> <th> Related WP(s) </th> </tr> <tr> <td> 1 </td> <td> Environmental monitoring </td> <td> TECNALIA </td> <td> WP1 </td> </tr> <tr> <td> 2 </td> <td> Mooring performance </td> <td> UNEXE </td> <td> WP1, WP2, WP5 </td> </tr> <tr> <td> 3 </td> <td> Bi-radial performance </td> <td> IST </td> <td> WP1, WP3 </td> </tr> <tr> <td> 4 </td> <td> Power output </td> <td> OCEANTEC </td> <td> WP1, WP4, WP5 </td> </tr> <tr> <td> 5 </td> <td> Power quality </td> <td> UCC </td> <td> WP1, WP5 </td> </tr> <tr> <td> 6 </td> <td> Offshore operations </td> <td> TECNALIA </td> <td> WP6, WP7 </td> </tr> </table> Specific datasets may be associated to scientific publications (i.e. underlying data), public project reports and other raw data or curated data not directly attributable to a publication. Datasets can be both collected, unprocessed data as well as analysed, generated data. The policy for open access are summarised in the following picture. Research data linked to exploitable results will not be put into the open domain if they compromise its commercialisation prospects or have inadequate protection, which is a H2020 obligation. The rest of research data will be deposited in an open access repository. When the research data is linked to a scientific publication, the provisions described in ANNEX II: SCIENTIFIC PUBLICATIONS will be followed. Research data needed to validate the results presented in the publication should be deposited at the same time for “Gold” Open Access 1 or before the end of the embargo period for “Green” Open Access 2 . Underlying research data will consist of selected parts of the general datasets generated, and for which the decision of making that part public has been made. Other datasets will be related to any public report or be useful for the research community. They will be selected parts of the general datasets generated or full datasets (e.g. up to 2 years of key operating data) and be published as soon as they become available. ## 1.4 ROLES AND RESPONSIBILITIES Each OPERA partner has to respect the policies set out in this DMP. Datasets have to be created, managed and stored appropriately and in line with applicable legislation. The Project Coordinator has a particular responsibility to ensure that data shared through the OPERA website are easily available, but also that backups are performed and that proprietary data are secured. OCEANTEC, as WP1 leader, will ensure dataset integrity and compatibility for its use during the project lifetime by different partners. Validation and registration of datasets and metadata is the responsibility of the partner that generates the data in the WP. Metadata constitutes an underlying definition or description of the datasets, and facilitate finding and working with particular instances of data. Backing up data for sharing through open access repositories is the responsibility of the partner possessing the data. Quality control of these data is the responsibility of the relevant WP leader, supported by the Project Coordinator. If datasets are updated, the partner that possesses the data has the responsibility to manage the different versions and to make sure that the latest version is available in the case of publicly available data. WP1 will provide naming and version conventions. Last but not least, all partners must consult the concerned partner(s) before publishing data in the open domain that can be associated to an exploitable result. # 2\. DATA COLLECTION, STORAGE AND BACK-UP The OPERA project will generate data resulting from instrumentation recordings during the lab testing and open-sea testing. In addition to the raw, uncorrected sensor data, converted and corrected data, as well as several other forms of derived data will be produced. Instrumentation, data acquisition and logging systems are thoroughly described in D1.1 [2] . A database management system will be used in the project to create, read, update and delete data from a database. The software platform being used is MySQL 5.7.9. A SCADA system will allows partners to access monitoring information locally and remotely. The following sections describe the different datasets that will be produced in the course of the project. ## 2.1 ENVIRONMENTAL MONITORING DATA Environmental monitoring data will be collected at two locations, namely the Mutriku shoreline plant and the open sea test site BiMEP. These numeric datasets will be directly obtained through observations, and derived using statistical parameters and models. In general, environmental monitoring datasets will be useful for further research activities beyond the scope of OPERA objectives. Metocean observations are common practice for different uses. Dataset could be integrated and reused, particularly for the characterisation of wave resource and the estimation of device performance. They will be also valuable for technology developers who plan to test their devices at either Mutriku or BiMEP. Although the raw datasets are useful by themselves, it is the objective of the OPERA project to use the data as a basis for at least one scientific publication. A short description of the environmental monitoring datasets is given next. At present two datasets have been made public through ZENODO as indicated in the tables. TABLE 2.1: WAVE RESOURCE AT MUTRIKU <table> <tr> <th> Reference/Name </th> <th> • DS_Wave_Mutriku </th> </tr> <tr> <td> Description </td> <td> * Wave resource data 200 m off the shoreline plant. * Main data are the pressure fluctuations over time. </td> </tr> <tr> <td> Source </td> <td> • RBR & Isurki Pressure Gauges </td> </tr> <tr> <td> Type </td> <td> • Observational and derived </td> </tr> <tr> <td> Format </td> <td> * MySQL database for real-time data * TXT for instrument recordings and derived data </td> </tr> <tr> <td> Software </td> <td> * Scilab program to transform pressure into wave height and period. * Spectral analysis software. </td> </tr> <tr> <td> Estimated size </td> <td> • 2 GB (6 months @ 2 Hz sampling frequency) </td> </tr> <tr> <td> Storage </td> <td> • Internal USB memory stick, on-site database server, and real-time replication onto cloud-hosted database server </td> </tr> <tr> <td> Back-up </td> <td> • Daily back-ups on both local and cloud-hosted servers. 15-day retention period for incremental backups in the latter. </td> </tr> <tr> <td> Link </td> <td> • _https://zenodo.org/record/832847#.WpPuDLNG0kI_ (version 1.0) </td> </tr> </table> TABLE 2.2: WAVE RESOURCE AT BIMEP <table> <tr> <th> Reference/Name </th> <th> • DS_Wave_BiMEP </th> </tr> <tr> <td> Description </td> <td> * Wave resource at 300 m up-wave of the WEC. * Datasets mainly consist of wave parameters such as wave H s , T p , direction and spreading. </td> </tr> <tr> <td> Source </td> <td> • TRIAXYS surface following buoy </td> </tr> <tr> <td> Type </td> <td> • Observational and derived </td> </tr> <tr> <td> Format </td> <td> * MySQL database for real-time data * TXT for instrument recordings * MS Excel for derived and filtered data </td> </tr> <tr> <td> Software </td> <td> • Spectral analysis software </td> </tr> <tr> <td> Estimated size </td> <td> * 150 MB of statistical data (20 min x 2 years) * 1 GB of real-time data (20 min x 2 Hz sampling frequency when real-time communications activated) * 8 GB (2 years @ 2 Hz sampling frequency) </td> </tr> <tr> <td> Storage </td> <td> • Internal USB memory stick, on-site database server and real-time replication onto cloud-hosted database server </td> </tr> <tr> <td> Back-up </td> <td> * Daily back-ups on both local and cloud-hosted servers. * 15-day retention period for incremental backups in the latter. </td> </tr> <tr> <td> Link </td> <td> • _https://zenodo.org/record/1311593#.W1Xp55N9hPY_ (version 2.1) </td> </tr> </table> ## 2.2 MOORING PERFORMANCE DATA Experimental data will be collected at the DMAC facility in UNEXE [4] . Besides, field tests will be conducted at the open sea test site at BiMEP. Datasets consists of mooring performance data will be both experimental and observational, raw and derived (using statistical parameters and models). The mooring performance dataset will be useful to inform technology compliance, survivability and reliability as well as economical improvements. They will be also valuable for the certification processes of other technology developers. These data will be the basis for at least one scientific publication on the comparison and validation of dynamic response and mooring loads from field measurements. A short description of the mooring performance datasets is given below. TABLE 2.3: TETHER LOADS AT DMAC <table> <tr> <th> Reference/Name </th> <th> • DS_Tethers_Lab </th> </tr> <tr> <td> Description </td> <td> • Characterisation of design load behaviour, fatigue and durability of several elastomeric tether specimens. </td> </tr> <tr> <td> Source </td> <td> • DMAC facility </td> </tr> <tr> <td> Type </td> <td> • Experimental </td> </tr> <tr> <td> Format </td> <td> • CSV (processed data) </td> </tr> <tr> <td> Software </td> <td> • Labview, Optitrack Motive and Matlab </td> </tr> <tr> <td> Estimated size </td> <td> • 8.7 GB (50 Hz sampling frequency) </td> </tr> <tr> <td> Storage </td> <td> • Network storage </td> </tr> <tr> <td> Back-up </td> <td> • Network drive is backed up daily with two-disk fault tolerance (i.e. backups are safe even if two disks fail). Backups are stored in a different building and protected by a dedicated UPS. </td> </tr> </table> #### TABLE 2.4: MOORING LOADS AT BIMEP <table> <tr> <th> Reference/Name </th> <th> • DS_Mooring_BiMEP </th> </tr> <tr> <td> Description </td> <td> * Extreme loads and motion response to different sea states will be monitored. * The loading data will be combined with the environmental monitoring dataset to derive the final mooring performance dataset. * Comparison between the polyester lines and the elastomeric mooring tethers. </td> </tr> <tr> <td> Source </td> <td> * MARMOK-A-5 prototype. * A mooring condition monitoring has been implemented for the project consisting of 4 load shackles deployed in two mooring nodes of the prototype. </td> </tr> <tr> <td> Type </td> <td> • Observational and derived </td> </tr> <tr> <td> Format </td> <td> * MySQL database for real-time data * TXT for raw instrument recordings * MS Excel for derived and filtered data </td> </tr> <tr> <td> Software </td> <td> • Statistical and spectral analysis software </td> </tr> <tr> <td> Estimated size </td> <td> • ≤ 400 GB (2.5 years recording x 16 measurements @ 20 Hz) </td> </tr> <tr> <td> Storage </td> <td> • On-site database server and real-time replication onto cloudhosted database server </td> </tr> <tr> <td> Back-up </td> <td> * Daily back-ups on both local and cloud-hosted servers. * 15-day retention period for incremental backups in the latter. </td> </tr> </table> ## 2.3 BIRADIAL TURBINE PERFORMANCE DATA Experimental data will be collected at existing IST Turbomachinery Laboratory (Dry Lab) for tests in varying unidirectional flow. Also, field tests will be conducted both at Mutriku shoreline plant and the BiMEP open sea test site. Bi-radial turbine performance data will be both experimental and observational, raw and derived (using statistical parameters and models). The bi-radial turbine performance dataset will be useful to assess turbine efficiency and reliability. The loading data will be combined with the environmental monitoring dataset to derive the final bi-radial turbine performance dataset. This dataset will be the basis for at least one scientific publication on the description of the biradial turbine dry tests performed at the IST turbomachinery test rig. A short description of the bi-radial turbine performance datasets is given below. #### TABLE 2.5: BI-RADIAL TURBINE PERFORMANCE AT DRY LAB FACILITY <table> <tr> <th> Reference/Name </th> <th> • DS_Biradial_Turbine_Lab </th> </tr> <tr> <td> Description </td> <td> • Assess turbine performance though unidirectional steady-state and alternating flow. </td> </tr> <tr> <td> Source </td> <td> * IST Turbomachinery Laboratory. * Sensor data acquired at a frequency of 1kHz for turbine pressure head, plenum temperature and humidity, turbine rotational speed, turbine flow rate and the instantaneous position of the flow control valve. * The voltage and the current of the three AC phases at the input and output of the power electronics were acquired at a frequency of 62.5kHz. </td> </tr> <tr> <td> Type </td> <td> • Experimental </td> </tr> <tr> <td> Format </td> <td> • Matlab “mat” files and comma separated value “csv” text files. </td> </tr> <tr> <td> Software </td> <td> * Matlab (Experimental data acquisition) * A special purpose parallelized C++ software (Data filtering), </td> </tr> <tr> <td> </td> <td> • A software package written in the Julia language (Computation of the instantaneous and time-averaged turbine shaft power, electrical power and available pneumatic power) </td> </tr> <tr> <td> Estimated size </td> <td> • 320 GB </td> </tr> <tr> <td> Storage </td> <td> • Local PC storage </td> </tr> <tr> <td> Back-up </td> <td> • Static data stored at three computers </td> </tr> </table> TABLE 2.6: BI-RADIAL TURBINE PERFORMANCE AT MUTRIKU <table> <tr> <th> Reference/Name </th> <th> • DS_Biradial_Turbine_Mutriku </th> </tr> <tr> <td> Description </td> <td> • Assess turbine performance and collect extensive data on drivers of components fatigue such as high rpm and accelerations; electrical, temperature and pressure load cycles; humidity in the cabinet (which exacerbates electrical stress damage); rate of salt accumulation and corrosion. </td> </tr> <tr> <td> Source </td> <td> * Mutriku Wave Power Plant. * Bi-radial turbine-generator set and chamber #9 have been instrumented for the project. </td> </tr> <tr> <td> Type </td> <td> • Observational and derived </td> </tr> <tr> <td> Format </td> <td> * MySQL database for real-time data * MS Excel for derived and filtered data </td> </tr> <tr> <td> Software </td> <td> • Statistical and spectral analysis software </td> </tr> <tr> <td> Estimated size </td> <td> • ≤ 50 GB (6-month recording x 150 measurements @ 4 Hz) </td> </tr> <tr> <td> Storage </td> <td> • On-site database server and real-time replication onto cloudhosted database server </td> </tr> <tr> <td> Back-up </td> <td> * Daily back-ups on both local and cloud-hosted servers. * 15-day retention period for incremental backups in the latter. </td> </tr> </table> TABLE 2.7: BI-RADIAL TURBINE PERFORMANCE AT BIMEP <table> <tr> <th> Reference/Name </th> <th> • DS_Biradial_Turbine_BiMEP </th> </tr> <tr> <td> Description </td> <td> • Internal water level, chamber pressure/temperature/humidity, rotation speed and torque to assess turbine efficiency in response to different sea states to compare turbine performance drivers of components fatigue </td> </tr> <tr> <td> Source </td> <td> * MARMOK-A-5 prototype. * Bi-radial turbine-generator set and hull structure have been instrumented for the project. </td> </tr> <tr> <td> Type </td> <td> • Observational and derived </td> </tr> <tr> <td> Format </td> <td> • MySQL database for real-time dataMS Excel for derived and filtered data </td> </tr> <tr> <td> Software </td> <td> • Statistical and spectral analysis software </td> </tr> <tr> <td> Estimated size </td> <td> • ≤ 100 GB (12-month recording x 150 measurements at 4 Hz) </td> </tr> <tr> <td> Storage </td> <td> • On-site database server and real-time replication onto cloudhosted database server </td> </tr> <tr> <td> Back-up </td> <td> * Daily back-ups on both local and cloud-hosted servers. * 15-day retention period for incremental backups in the latter. </td> </tr> </table> ## 2.4 POWER OUTPUT DATA Experimental data will be collected at electrical test rigs of UCC [5] and TECNALIA [6] . Besides, field tests data will be collected at Mutriku shoreline plant and at the BiMEP open sea test site. Numerical models will be also used to extend the dataset beyond sea-trials data. In the latter, specialist software may be needed for further processing the data. Selected parts of the generated datasets generated will be made public. Power output data will be both experimental and observational, raw and derived such as mean, standard deviation, minimum and maximum values. Power output data will be useful to identify sources of uncertainty in power performance prediction and for the certification processes of other technology developers. This dataset will be the basis for at least one scientific publication. At the time of writing this deliverable, there is a publication in preparation comparing operational data from Control Strategies applied to the biradial turbine in the Mutriku Wave Power Plant. This publication desn not require BiMEP experimental data. Nontheless, a short description of all possible power output datasets is given below. #### TABLE 2.8: POWER OUTPUT AT ELECTRICAL TEST RIG <table> <tr> <th> Reference/Name </th> <th> • DS_Power_Output_Lab </th> </tr> <tr> <td> Description </td> <td> • Generator speed, voltage, frequency and electric power. </td> </tr> <tr> <td> Source </td> <td> • Electrical test rigs of UCC and TECNALIA </td> </tr> <tr> <td> Type </td> <td> • Experimental and Simulation </td> </tr> <tr> <td> Format </td> <td> • MS Excel </td> </tr> <tr> <td> Software </td> <td> • MATLAB numerical model of the Mutriku Wave Power Plant </td> </tr> <tr> <td> Estimated size </td> <td> • 20 GB (7 CLs x approx. 300 MB) </td> </tr> <tr> <td> Storage </td> <td> • Network storage </td> </tr> <tr> <td> Back-up </td> <td> • Daily back-ups </td> </tr> </table> #### TABLE 2.9: POWER OUTPUT AT MUTRIKU <table> <tr> <th> Reference/Name </th> <th> • DS_Power_Output_Mutriku </th> </tr> <tr> <td> Description </td> <td> • Generator speed, voltage, frequency and electric power, including phase voltages & currents. </td> </tr> <tr> <td> Source </td> <td> * Mutriku Wave Power Plant. * Bi-radial turbine-generator set and chamber #9 have been instrumented for the project. </td> </tr> <tr> <td> Type </td> <td> • Observational and derived </td> </tr> <tr> <td> Format </td> <td> * MySQL database for real-time data * MS Excel for derived and filtered data </td> </tr> <tr> <td> Software </td> <td> • Statistical and spectral analysis software </td> </tr> <tr> <td> Estimated size </td> <td> • ≤ 50 GB (6-month recording x 150 measurements @ 4 Hz) </td> </tr> <tr> <td> Storage </td> <td> • On-site database server and real-time replication onto cloudhosted database server </td> </tr> <tr> <td> Back-up </td> <td> * Daily back-ups on both local and cloud-hosted servers. * 15-day retention period for incremental backups in the latter. </td> </tr> </table> #### TABLE 2.10: POWER OUTPUT AT BIMEP <table> <tr> <th> Reference/Name </th> <th> • DS_Power_Output_BiMEP </th> </tr> <tr> <td> Description </td> <td> • Generator speed, voltage, frequency and electric power, including phase voltages & currents. </td> </tr> <tr> <td> Source </td> <td> • MARMOK-A-5 prototype. Bi-radial turbine-generator set and hull structure have been instrumented for the project. </td> </tr> <tr> <td> Type </td> <td> • Observational and derived </td> </tr> <tr> <td> Format </td> <td> * MySQL database for real-time data * MS Excel for derived and filtered data </td> </tr> <tr> <td> Software </td> <td> • Statistical and spectral analysis software </td> </tr> <tr> <td> Estimated size </td> <td> • ≤ 100 GB (12-month recording x 150 measurements at 4 Hz) </td> </tr> <tr> <td> Storage </td> <td> • On-site database server and real-time replication onto cloudhosted database server </td> </tr> <tr> <td> Back-up </td> <td> * Daily back-ups on both local and cloud-hosted servers. * 15-day retention period for incremental backups in the latter. </td> </tr> </table> ## 2.5 POWER QUALITY DATA Experimental data will be collected at electrical test rig of UCC [10]. Also, field tests data will be collected at the Mutriku shoreline plant. Simulated models may be used to assess the power quality for other operating conditions, such as varying control algorithms, resource conditions, grid strengths, and control using a dry-lab to create a wider profile for the WEC. Power quality data will be both experimental and observational, raw and derived (using statistical parameters and models). Selected parts of the experimental datasets generated will be made public. Power quality data will be useful to identify sources of uncertainty in assessing the impact of the wave energy converter on the performance of the grid. They will be also valuable for the certification processes of other technology developers. This dataset will be the basis for at least one scientific publication on the approach and results of Power Quality monitoring of OWC devices and the fault response of WEC on small grid. A short description of the power quality datasets is given next. #### TABLE 2.11: POWER QUALITY AT ELECTRICAL TEST RIG <table> <tr> <th> Reference/Name </th> <th> • DS_Power_Quality_Lab </th> </tr> <tr> <td> Description </td> <td> • Current, voltage, power quality characteristic parameters (such as voltage fluctuations, harmonics, inter-harmonics, active/reactive power, and flicker). </td> </tr> <tr> <td> Source </td> <td> • Electrical test rig at UCC </td> </tr> <tr> <td> Type </td> <td> • Experimental and Simulation </td> </tr> <tr> <td> Format </td> <td> • MS Excel </td> </tr> <tr> <td> Software </td> <td> • MATLAB Simulink numerical model of the Mutriku Wave Power Plant </td> </tr> <tr> <td> Estimated size </td> <td> * Maximum 1.2 GB per 10-minute test (at 20 kHz sampling frequency). * 4 signals at 20 kHz for 10 minutes per test </td> </tr> <tr> <td> Storage </td> <td> • Network storage </td> </tr> <tr> <td> Back-up </td> <td> • Daily back-ups </td> </tr> </table> #### TABLE 2.12: POWER QUALITY AT MUTRIKU <table> <tr> <th> Reference/Name </th> <th> • DS_Power_Quality_Mutriku </th> </tr> <tr> <td> Description </td> <td> • Data will be collected from both a single turbine and the plant as a whole, obtaining valuable conclusions about how aggregation of multiple turbines affects the power quality. </td> </tr> <tr> <td> Source </td> <td> • Mutriku Wave Power Plant. </td> </tr> <tr> <td> Type </td> <td> • Observational and derived </td> </tr> <tr> <td> Format </td> <td> * MySQL database for real-time data * MS Excel for derived and filtered data </td> </tr> <tr> <td> Software </td> <td> * LabView * Statistical and spectral analysis software </td> </tr> <tr> <td> Estimated size </td> <td> * > 200 GB (12-month recording x 12 measurements @ 20 kHz). * Given the large data storage requirements, the measurements will be triggered, and not carried out continuously. After sufficient power quality analysis has been carried out at 20 kHz, the sampling rate will then be reduced (to approximately 12 kHz, and 10 kHz). </td> </tr> <tr> <td> Storage </td> <td> • On-site database server and real-time replication onto cloudhosted database server </td> </tr> <tr> <td> Back-up </td> <td> * Daily back-ups on both local and cloud-hosted servers. * 15-day retention period for incremental backups in the latter. </td> </tr> </table> Power quality at Mutriku should be used in the case of the approach and results of Power Quality monitoring of OWC, whereas, the Electrical test rig will address the fault response of WEC on small grid. ## 2.6 OFFSHORE OPERATIONS DATA Field tests will be conducted at the BiMEP open sea test site. The offshore operations data will be combined with the environmental monitoring dataset to derive the final dataset. Offshore operations data will be observational and derived. Offshore operations data will be useful to reduce the uncertainty on the determination of risk and cost of offshore operations, and to optimise these activities. The offshore logistics experience can be extrapolated to different scenarios of larger deployment with a view to more accurately assess the economies of scale and identify logistics bottlenecks when deployed in large arrays. Collected datasets will be used for the global modelling of costs, namely the integration of real sea operating data in an economic model. These datasets alone are however not expected to be sufficient for a scientific publication. Therefore, no underlying data is foreseen regarding Offshore Operations Data. Nevertheless, a short description of the offshore operations datasets is given below. TABLE 2.13: OFFSHORE OPERATIONS <table> <tr> <th> Reference/Name </th> <th> • DS_Offshore_Operations </th> </tr> <tr> <td> Description </td> <td> • Failures, type of maintenance, offshore resources (such as vessels, equipment, personnel, parts and consumables), health & safety, and activity log. </td> </tr> <tr> <td> Source </td> <td> • Unlike the previous datasets, these are not based on process instrumentation and therefore will not be stored in the WP1 database. </td> </tr> <tr> <td> Type </td> <td> • Observational </td> </tr> <tr> <td> Format </td> <td> • MS Excel </td> </tr> <tr> <td> Software </td> <td> • n/a </td> </tr> <tr> <td> Estimated size </td> <td> • 10 MB </td> </tr> <tr> <td> Storage </td> <td> • Network storage </td> </tr> <tr> <td> Back-up </td> <td> • Daily back-ups on a separate server </td> </tr> </table> # 3\. DATA STANDARDS AND METADATA The following standards should be used for data documentation: * Ocean Data Standards Project [7] : it contains an extensive number of references on Oceanographic Data Management and Exchange Standards. It includes references on Metadata, Date and Time, Lat/Lon/Alt, Country names, Platform instances, Platform types, Science Words, Instruments, Units, Projects, Institutions, Parameters, Quality Assurance and Quality Control. * ISO 19156:2011 [8] : it defines a conceptual schema for observations, and for features involved in sampling when making observations. These provide models for the exchange of information describing observation acts and their results, both within and between different scientific and technical communities. * IEC TS 62600-101 [9] : technical specification for wave energy resource assessment and characterisation. * DNVGL-OS-E301 [10] : it contains criteria, technical requirements and guidelines on design and construction of position mooring systems. The objective of this standard is to give a uniform level of safety for mooring systems, consisting of chain, steel wire ropes and fibre rope. * IEC TS 62600-10 [11] : technical specification for assessment of mooring system for Marine Energy Converters (MECs). * IEC TS 62600-100 [12] : technical specification on power performance assessment of electricity producing wave energy converters * IEC TS 62600-102 [13] : technical specification on wave energy converter power performance assessment at a second location using measured assessment data * IEC TS 62600-30 [14] : technical specification on electrical power quality requirements for wave, tidal and other water current energy converters * IEC 61000-4-7:2002 [15] : further instructions on processing harmonic current components are given in for power supply systems and equipment connected thereto. 🞂 FRACAS [16] Failure the Reporting, Analysis and Corrective Action System * ISO 14224:2006 [17] : collection and exchange of reliability and maintenance data for equipment. Metadata records will accompany the data files in order to describe, contextualise and facilitate external users to understand and reuse the data. OPERA will adopt the DataCite Metadata Schema [18] , a domain agnostic metadata schema, as the basis for harvesting and importing metadata about datasets from data archives. The core mission of DataCite is to build and maintain a sustainable framework that makes it possible to cite data through the use of persistent identifiers. The following metadata should be created to identify datasets: * Identifier: A unique string that identifies the dataset * Author/Creator: The main researchers involved in producing the data in priority order * Title: A name or title by which a data is known * Publisher: The name of the entity that holds, archives, publishes prints, distributes, releases, issues, or produces the data. * Publication Year: The year when the data was or will be made publicly available 🞂 Subject: Subject, keyword, classification code, or key phrase describing the resource. * Contributor: Name of the funding entity (i.e. "European Union" & "Horizon 2020") * Size: Unstructured size information about the dataset (in GBs) * Format: Technical format of the dataset (e.g. cvs, txt, xml, ...) * Version: The version number of the dataset * Access rights: Provide a rights management statement for the dataset. Include embargo information if applicable * Geo-location: Spatial region or named place where the data was gathered # 4\. DATA SHARING AND REUSE During the life cycle of the OPERA project datasets will be stored and systematically organised in a database tailored to comply with the requirements of WP1 (for more details on the database architecture, please see D1.1 Process instrumentation definition [2] ). An online data query tool was operational in Month 12, and available for open dissemination by Month 18. The database schema and the queryable fields, will be also publicly available to the database users as a way to better understand the database itself. In addition to the project database, relevant datasets will be also stored in ZENODO [19] [10] , which is the open access repository of the Open Access Infrastructure for Research in Europe, OpenAIRE [20] All collected datasets will be disseminated without an embargo period unless linked to a green open access publication. Data objects will be deposited in ZENODO under: * Open access to data files and metadata and data files provided over standard protocols such as HTTP and OAI-PMH. * Use and reuse of data permitted. 🞂 Privacy of its users protected. Data access policy is summarised in the following table. ### TABLE 4.1: DATA ACCESS POLICY <table> <tr> <th> Dataset </th> <th> Data access policy </th> </tr> <tr> <td> DS_Wave_Mutriku </td> <td> * Unrestricted since no confidentiality or IPR issues are expected regarding the environmental monitoring datasets * Licence: CC-BY </td> </tr> <tr> <td> DS_Wave_BiMEP </td> </tr> <tr> <td> DS_Tethers_Lab </td> <td> * Restricted to WP2 participants, in order to protect the commercial and industrial prospects of exploitable results (KER1 and KER3). * Samples of aggregated data (e.g. load averages or extreme load ranges) will be shared in the open domain for the most relevant sea states. * Licence: CC-BY-ND </td> </tr> <tr> <td> DS_Mooring_BiMEP </td> </tr> <tr> <td> DS_Biradial_Turbine_Lab </td> <td> * Restricted to WP3 participants, in order to protect the commercial and industrial prospects of exploitable results (KER1 and KER2). * Samples of aggregated data (e.g. chamber pressure, air flow, mechanical power) will be shared in the open domain. * Licence: CC-BY-ND </td> </tr> <tr> <td> DS_Biradial_Turbine_Mutriku </td> </tr> <tr> <td> DS_Biradial_Turbine_BiMEP </td> </tr> <tr> <td> DS_Power_Output_Lab </td> <td> * Restricted to WP4 and WP5 participants, in order to protect the commercial and industrial prospects of exploitable results (KER1, KER4 and KER6). * Samples of aggregated data (e.g. electric power for the different control laws) will be shared in the open domain. * Licence: CC-BY-ND </td> </tr> <tr> <td> DS_Power_Output_Mutriku </td> </tr> <tr> <td> DS_Power_Output_BiMEP </td> </tr> <tr> <td> DS_Power_Quality_Lab </td> <td> * Restricted to WP5 participants, in order to protect the commercial and industrial prospects of exploitable results (KER1, KER4 and KER6). * Samples of aggregated data (e.g. active, reactive power and power factor) will be shared in the open domain. * Licence: CC-BY-ND </td> </tr> <tr> <td> DS_Power_Quality_Mutriku </td> </tr> <tr> <td> DS_Offshore_Operations </td> <td> • Not to be shared in the open domain in order to protect the commercial and industrial prospects of partners. </td> </tr> </table> # 5\. DATA ARCHIVING AND PRESERVATION The OPERA project database will be designed to remain operational for 5 years after project end. By the end of the project, the final dataset will be transferred to the ZENODO repository, which ensures sustainable archiving of the final research data. Items deposited in ZENODO will be retained for the lifetime of the repository, which is currently the lifetime of the host laboratory CERN and has an experimental programme defined for the at least next 20 years. Data files and metadata are backed up on a nightly basis, as well as replicated in multiple copies in the online system. All data files are stored along with a MD5 checksum of the file content. Regular checks of files against their checksums are made.	https://phaidra.univie.ac.at/o:1140797	Horizon 2020
0179_OPERA_654444.md	# 1\. INTRODUCTION ## 1.1 OPERA MOTIVATION The OPERA project participates in the Pilot on Open Research Data launched by the European Commission (EC) along with the H2020 programme. This pilot is part of the Open Access to Scientific Publications and Research Data programme in H2020. The goal of the programme is to foster access to research data generated in H2020 projects. The use of a Data Management Plan (DMP) is required for all projects participating in the Open Research Data Pilot. Open access is 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 research and innovation, scientific information can refer to peer-reviewed scientific research articles or 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. The Consortium strongly believes in the concepts of open science, and in the benefits that the European innovation ecosystem and economy can draw from allowing the reuse of data at a larger scale. Furthermore, there is a need to gather experience in open sea operating conditions, structural and power performance and operating data in wave energy. In fact, there has been very limited open sea experience in wave energy, which is essential in order to fully understand the challenges in device performance, survivability and reliability. The limited operating data and experience that currently exists are rarely shared, as testing is partly private-sponsored. This project proposes to remove this roadblock by delivering for the first time, open access, high-quality open sea operating data to the wave energy development community. Nevertheless, data sharing in the open domain can be restricted as a legitimate reason to protect results that can reasonably be expected to be commercially or industrially exploited. Strategies to limit such restrictions will include anonymising or aggregating data, agreeing on a limited embargo period or publishing selected datasets. ## 1.2 PURPOSE OF THE DATA MANAGEMENT PLAN 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 the project research data. The DMP covers the complete research data life cycle. It describes the types of research data that will be generated or 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. It also reflects the current state of the Consortium agreements on data management and must be consistent with exploitation and IPR requirements. FIGURE 1.1: RESEARCH DATA LIFE CYCLE (ADAPTED FROM UK DATA ARCHIVE [1] ) The DMP is not a fixed document, but will evolve during the lifespan of the project, particularly whenever significant changes arise such as dataset updates or changes in Consortium policies. This document is the first version of the DMP, delivered in Month 6 of the project. It includes an overview of the datasets to be produced by the project, and the specific conditions that are attached to them. The next versions of the DMP will get into more detail and describe the practical data management procedures implemented by the OPERA project with reference with the IT tools developed in WP1. At a minimum, the DMP will be updated in Month 18 (D8.6) and Month 30 (D8.7) respectively. This document has been produced following the EC guidelines for project participating in this pilot and additional consideration described in ANNEX I: KEY PRINCIPLES FOR OPEN ACCESS TO RESEARCH DATA. ## 1.3 RESEARCH DATA TYPES IN OPERA For this first release of DMP, the data types that will be produced during the project are focused on the Description of the Action (DoA) and on the results obtained in the first months of the project. According to such consideration, Table 1.1 reports a list of indicative types of research data that OPERA will produce. These research data types have been mainly defined in WP1, including data structures, sampling and processing requirements, as well as relevant standards. This list may be adapted with the addition or removal of datasets in the next versions of the DMP to take into consideration the project developments. A detailed description of each dataset is given in the following sections of this document. TABLE 1.1: OPERA TYPES OF DATA <table> <tr> <th> # </th> <th> Dataset </th> <th> Lead partner </th> <th> Related WP(s) </th> </tr> <tr> <td> 1 </td> <td> Environmental monitoring </td> <td> TECNALIA </td> <td> WP1 </td> </tr> <tr> <td> 2 </td> <td> Mooring performance </td> <td> UNEXE </td> <td> WP1, WP2, WP5 </td> </tr> <tr> <td> 3 </td> <td> Biradial performance </td> <td> IST </td> <td> WP1, WP3 </td> </tr> <tr> <td> 4 </td> <td> Power output </td> <td> OCEANTEC </td> <td> WP1, WP4, WP5 </td> </tr> <tr> <td> 5 </td> <td> Power quality </td> <td> UCC </td> <td> WP1, WP5 </td> </tr> <tr> <td> 6 </td> <td> Offshore operations </td> <td> TECNALIA </td> <td> WP6 </td> </tr> </table> Specific datasets may be associated to scientific publications (i.e. underlying data), public project reports and other raw data or curated data not directly attributable to a publication. The policy for open access are summarised in the following picture. FIGURE 1.2: RESEARCH DATA OPTIONS AND TIMING Research data linked to exploitable results will not be put into the open domain if they compromise its commercialisation prospects or have inadequate protection, which is a H2020 obligation. The rest of research data will be deposited in an open access repository. When the research data is linked to a scientific publication, the provisions described in ANNEX II: SCIENTIFIC PUBLICATIONS will be followed. Research data needed to validate the results presented in the publication should be deposited at the same time for “Gold” Open Access 1 or before the end of the embargo period for “Green” Open Access 2 . Underlying research data will consist of selected parts of the general datasets generated, and for which the decision of making that part public has been made. Other datasets will be related to any public report or be useful for the research community. They will be selected parts of the general datasets generated or full datasets (i.e. up to 2 years of key operating data), and be published as soon as possible. 1.4 RESPONSIBIL ITIES Each OPERA partner has to respect the policies set out in this DMP. Datasets have to be created, managed and stored appropriately and in line with applicable legislation. The Project Coordinator has a particular responsibility to ensure that data shared through the OPERA website are easily available, but also that backups are performed and that proprietary data are secured. OCEANTEC, as WP1 leader, will ensure dataset integrity and compatibility for its use during the project lifetime by different partners. Validation and registration of datasets and metadata is the responsibility of the partner that generates the data in the WP. Metadata constitutes an underlying definition or description of the datasets, and facilitate finding and working with particular instances of data. Backing up data for sharing through open access repositories is the responsibility of the partner possessing the data. Quality control of these data is the responsibility of the relevant WP leader, supported by the Project Coordinator. If datasets are updated, the partner that possesses the data has the responsibility to manage the different versions and to make sure that the latest version is available in the case of publically available data. WP1 will provide naming and version conventions. Last but not least, all partners must consult the concerned partner(s) before publishing data in the open domain that can be associated to an exploitable result. # 2\. ENVIRONMENTAL MONITORING ## 2.1 DATASET REFERENCE AND NAME DS_Environmental_Monitoring 2.2 DATASET DESCRIPTION The DS_Environmental_Monitoring datasets mainly consists of several wave parameters (such as wave Hs, Tp, direction and spreading), but may also include wind, tide, current and temperature parameters. These numeric datasets will be directly obtained through observations, and derived using statistical parameters and models. In the latter, specialist software may be needed for further processing of data. Environmental monitoring data will be collected at two locations, namely the Mutriku shoreline plant and the open sea test site BiMEP. Currently at Mutriku, the single environmental parameter collected is the wave elevation in the Oscillating Water Column. In order to increase the characterisation of the wave resource, a new wave instrument will be installed about 300 m off the shoreline for approximately 6 months. It will be of the bottom mounted pressure gauge type. The BiMEP reference buoy is the main wave instrument for the test site Fugro- OCEANOR Wavescan buoy, deployed in March 2009, recording almost continuously except for a significant gap from January 2013 to April 2014. This wave instrument records 17 minutes heave, pitch and roll time series, from which omnidirectional and directional spectra can be estimated, as well as standard sea-state parameters. It is located at about 1.1 km to the WSW of the prototype deployment. Additionally, a surface following buoy will be installed close to the prototype for the research activities of the project at BiMEP for approximately two years. In general, environmental monitoring datasets will be useful for further research activities beyond the scope of OPERA objectives. Metocean observations are common practice for different uses. Dataset could be integrated and reused, particularly for the characterisation of wave resource and the estimation of device performance. They will be also valuable for technology developers who plan to test their devices at either Mutriku or BiMEP. Although the raw datasets are useful by themselves, it is the objective of the OPERA project to use the dataset as a basis for at least one scientific publication. ## 2.3 STANDARDS AND METADATA There have been many discussions for processing data and information in oceanography. Many useful ideas have been developed and put into practice, but there have been few successful attempts to develop and implement international standards in managing data. The Ocean Data Standards Project [2] contains an extensive number of references on Oceanographic Data Management and Exchange Standards. It includes references on Metadata, Date and Time, Lat/Lon/Alt, Country names, Platform instances, Platform types, Science Words, Instruments, Units, Projects, Institutions, Parameters, Quality Assurance and Quality Control. The ISO 19156:2011 defines a conceptual schema for observations, and for features involved in sampling when making observations. These provide models for the exchange of information describing observation acts and their results, both within and between different scientific and technical communities. Additionally, regarding the wave energy application, the relevant standard is the technical specification for wave energy resource assessment and characterization IEC TS 62600-101 [4] . The environmental monitoring system will be integrated in the existing IT infrastructure at Mutriku and BiMEP. A SCADA system will be developed that allows partners to access monitoring information locally and remotely. TECNALIA will be responsible for version control and validation dataset of datasets to be shared open access. 2.4 DATA SHARING During the lifecycle of the OPERA project datasets will be stored and systematically organised in a database tailored to comply with the requirements of WP1 (for more details on the database architecture, please see D1.1 Process instrumentation definition). An online data query tool will be operational by Month 12 and for open dissemination by Month 18. The database schema and the queryable fields, will be also publicly available to the database users as a way to better understand the database itself. In addition to the project database, relevant datasets will be also stored in ZENODO [5] , which is the open access repository of the Open Access Infrastructure for Research in Europe, OpenAIRE [6] . Data access policy will be unrestricted since no confidentiality or IPR issues are expected regarding the environmental monitoring datasets. All collected datasets will be disseminated without an embargo period unless linked to a green open access publication. Data objects will be deposited in ZENODO under: * Open access to data files and metadata and data files provided over standard protocols such as HTTP and OAI-PMH. * Use and reuse of data permitted.  Privacy of its users protected. ## 2.5 ARCHIVING AND PRESERVATION The OPERA project database will be designed to remain operational for 5 years after project end. By the end of the project, the final dataset will be transferred to the ZENODO repository, which ensures sustainable archiving of the final research data. Items deposited in ZENODO will be retained for the lifetime of the repository, which is currently the lifetime of the host laboratory CERN and has an experimental programme defined for the at least next 20 years. Data files and metadata are backed up on a nightly basis, as well as replicated in multiple copies in the online system. All data files are stored along with a MD5 checksum of the file content. Regular checks of files against their checksums are made. # 3\. MOORING PERFORMANCE ## 3.1 DATASET REFERENCE AND NAME DS_Mooring_Performance 3.2 DATASET DESCRIPTION The DS_Mooring_Performance datasets consists of extreme loads and motion response (6 DOF) to different sea states for the mooring lines. Mooring performance data will be both experimental and observational, raw and derived (using statistical parameters and models). Experimental data will be collected at the DMAC facility in UNEXE [7] . These tests will be focused on the characterisation of design load behaviour, fatigue and durability of several elastomeric tether specimens. Raw data will be used. Selected parts of the experimental datasets generated will be made public. Field tests will be conducted at the open sea test site at BiMEP. A mooring condition monitoring will be implemented for the project consisting of 4 load shackles deployed in two mooring nodes of the prototype. Extreme loads and motion response to different sea states will be monitored. The loading data will be combined with the environmental monitoring dataset to derive the final mooring performance dataset. Selected parts of the field test datasets generated will be made public. The mooring performance dataset will be useful to inform technology compliance, survivability and reliability as well as economical improvements. They will be also valuable for the certification processes of other technology developers. This dataset will be the basis for at least one scientific publication. ## 3.3 STANDARDS AND METADATA In order to ensure the required compatibility, this dataset will use the same ocean data standards than the previous environmental monitoring dataset for data and metadata capture/creation. The offshore standard DNVGL-OS-E301 [8] contains criteria, technical requirements and guidelines on design and construction of position mooring systems. The objective of this standard shall give a uniform level of safety for mooring systems, consisting of chain, steel wire ropes and fibre rope. Besides, regarding the wave energy application, the relevant standard is the technical specification for assessment of mooring system for Marine Energy Converters (MECs) IEC TS 62600-10 [9] . During the OPERA project, a SCADA system will be developed that allows partners to access monitoring information locally and remotely. UNEXE will be responsible for version control and validation dataset of datasets to be shared open access. 3.4 DATA SHARING As it has been described before, during the lifecycle of the OPERA project datasets will be stored and systematically organised in a database tailored to comply with the requirements of WP1. An online data query tool will be operational by Month 12 and for open dissemination by Month 18. The database schema and the queryable fields will be also publicly available to the database users as a way to better understand the database itself. Full data access policy will be restricted to WP2 participants, in order to protect the commercial and industrial prospects of exploitable results (ER1 and ER3). However, aggregated data will be used in order to limit this restriction. The aggregated dataset will be disseminated as soon as possible. In the case of the underlying data of a publication this might imply an embargo period for green open access publications. 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. ## 3.5 ARCHIVING AND PRESERVATION As it has been described before, the OPERA project database will be designed to remain operational for 5 years after project end. By the end of the project, the final dataset will be transferred to the ZENODO repository, which ensures sustainable archiving of the final research data. # 4\. BIRADIAL TURBINE PERFORMANCE ## 4.1 DATASET REFERENCE AND NAME DS_Biradial_Turbine_Performance 4.2 DATASET DESCRIPTION The DS_Biradial_Turbine_Performance datasets mainly consists of internal water level, chamber pressure/temperature/humidity, rotation speed and torque to assess turbine efficiency in response to different sea states. Biradial turbine performance data will be both experimental and observational, raw and derived (using statistical parameters and models). Experimental data will be collected at existing rig in IST Turbomachinery Laboratory for tests in varying unidirectional flow. Built-in sensors will measure rpm, pressure differential across rotor, vibration and generator temperature, voltage and current. Raw data will be used. Selected parts of the experimental datasets generated will be made public. Field tests will be conducted both at Mutriku shoreline plant and the BiMEP open sea test site. Testing at Mutriku will assess turbine performance and collect extensive data on drivers of components fatigue such as high rpm and accelerations; electrical, temperature and pressure load cycles; humidity in the cabinet (which exacerbates electrical stress damages); rate of salt accumulation and corrosion. Similar data will be collected at BiMEP and results will be compared. Additionally, lowfrequency accelerometers will assess loads on the rotor and bearings. The loading data will be combined with the environmental monitoring dataset to derive the final biradial turbine performance dataset. Non-dimensional values, aggregated data and selected parts of the field test datasets generated will be made public. The biradial turbine performance dataset will be useful to assess turbine efficiency and reliability. This dataset will be the basis for at least one scientific publication. ## 4.3 STANDARDS AND METADATA In order to ensure the required compatibility, this dataset will use the same ocean data standards than the previous environmental monitoring dataset for data and metadata capture/creation. DNV GL will advise on applicable rules and standards to ensure appropriate design and data capture for open ocean operating conditions. During the OPERA project, a SCADA system will be developed that allows partners to access monitoring information locally and remotely. IST will be responsible for version control and validation dataset of datasets to be shared open access. 4.4 DATA SHARING OPERA project datasets will be stored and systematically organised in a database tailored to comply with the requirements of WP1. An online data query tool will be operational by Month 12 and for open dissemination by Month 18. The database schema and the queryable fields, will be also publicly available to the database users as a way to better understand the database itself. Full data access policy will be restricted to WP3 participants, in order to protect the commercial and industrial prospects of exploitable results (ER1 and ER2). However, aggregated data will be used in order to limit this restriction. The aggregated dataset will be disseminated as soon as possible. In the case of the underlying data of a publication this might imply an embargo period for green open access publications. 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. ## 4.5 ARCHIVING AND PRESERVATION As it has been described before, the OPERA project database will be designed to remain operational for 5 years after project end. By the end of the project, the final dataset will be transferred to the ZENODO repository, which ensures sustainable archiving of the final research data. # 5\. POWER OUTPUT ## 5.1 DATASET REFERENCE AN DS_Power_Output 5.2 DATASET DESCRIPTION The DS_Power_Output datasets mainly consists of generator speed, voltage, frequency and electric power. Power output data will be both experimental and observational, raw and derived such as mean, standard deviation, minimum and maximum values. Experimental data will be collected at electrical test rigs of UCC [10] and TECNALIA [11] . Field tests data will be collected at Mutriku shoreline plant and at the BiMEP open sea test site. Numerical models will be also used to extend the dataset beyond sea-trials data. In the latter, specialist software may be needed for further processing the data. Selected parts of the generated datasets generated will be made public. Power output data will be useful to identify sources of uncertainty in power performance prediction. They will be also valuable for the certification processes of other technology developers. This dataset will be the basis for at least one scientific publication. ## 5.3 STANDARDS AND METADATA In order to ensure the required compatibility, this dataset will use the same ocean data standards than the previous environmental monitoring dataset for data and metadata capture/creation. Additionally, regarding the wave energy application, the relevant standards are the technical specification on power performance assessment of electricity producing wave energy converters IEC TS 62600-100 [12] , and the technical specification on wave energy converter power performance assessment at a second location using measured assessment data IEC TS 62600-102 [13] . As indicated in the technical specifications, the datasets shall provide a record of sea state and electrical power production over time. Each aggregated data record shall be date and time stamped using ISO 8601\. The records shall be annotated with quality control flags giving the results of the quality control checks carried out during recording and analysis. A SCADA system will be developed that allows partners to access monitoring information locally and remotely. OCEANTEC will be responsible for version control and validation dataset of datasets to be shared open access. 5.4 DATA SHARING During the lifecycle of the OPERA project datasets will be stored and systematically organised in a database tailored to comply with the requirements of WP1. An online data query tool will be operational by Month 12 and for open dissemination by Month 18. The database schema and the queryable fields, will be also publicly available to the database users as a way to better understand the database itself. Full data access policy will be restricted to WP4 and WP5 participants, in order to protect the commercial and industrial prospects of exploitable results (ER1, ER4 and ER6). However, aggregated data will be used in order to limit this restriction. The aggregated dataset will be disseminated as soon as possible. In the case of the underlying data of a publication this might imply an embargo period for green open access publications. 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. ## 5.5 ARCHIVING AND PRESERVATION As it has been described before, the OPERA project database will be designed to remain operational for 5 years after project end. By the end of the project, the final dataset will be transferred to the ZENODO repository, which ensures sustainable archiving of the final research data. # 6\. POWER QUALITY ## 6.1 DATASET REFERENCE AN DS_Power_Quality 6.2 DATASET DESCRIPTION The DS_Power_Quality datasets consists of current, voltage, power quality characteristic parameters (such as voltage fluctuations, harmonics, inter- harmonics, active/reactive power, and flicker). Power quality data will be both experimental and observational, raw and derived (using statistical parameters and models). Experimental data will be collected at electrical test rig of UCC [10]. Field tests data will be collected at the Mutriku shoreline plant. Simulated models may be used to assess the power quality for other operating conditions, such as varying control algorithms, resource conditions, grid strengths, and control using a dry-lab to create a wider profile for the WEC. In the latter, specialist software may be needed for further processing the data. Selected parts of the experimental datasets generated will be made public. In Mutriku, data will be collected from both a single turbine and the plant as a whole, obtaining valuable conclusions about how aggregation of multiple turbines affects the power quality. Non-dimensional values, aggregated data and selected parts of the Mutriku field test datasets generated will be made public. Power quality data will be useful to identify sources of uncertainty in power performance prediction. They will be also valuable for the certification processes of other technology developers. This dataset will be the basis for at least one scientific publication. ## 6.3 STANDARDS AND METADATA In order to ensure the required compatibility, this dataset will use the same ocean data standards than the previous environmental monitoring dataset for data and metadata capture/creation. Additionally, regarding the wave energy application, the relevant standards are the technical specification on electrical power quality requirements for wave, tidal and other water current energy converters IEC TS 62600-30 [14] . Further instructions on processing harmonic current components are given in IEC 61000-47:2002 [15] , for power supply systems and equipment connected thereto. A SCADA system will be developed that allows partners to access monitoring information locally and remotely. UCC will be responsible for version control and validation dataset of datasets to be shared open access. Additional attention must be given to integrating and combining the power quality datasets with others due to the largely varying timescales. 6.4 DATA SHARING Datasets will be stored and systematically organised in a database tailored to comply with the requirements of WP1. An online data query tool will be operational by Month 12 and for open dissemination by Month 18\. The database schema and the queryable fields, will be also publicly available to the database users as a way to better understand the database itself. Full data access policy will be restricted to WP5 participants, in order to protect the commercial and industrial prospects of exploitable results (ER1, ER4 and ER6). However, aggregated data will be used in order to limit this restriction. The aggregated dataset will be disseminated as soon as possible. In the case of the underlying data of a publication this might imply an embargo period for green open access publications. 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. ## 6.5 ARCHIVING AND PRESERVATION The OPERA project database will be designed to remain operational for 5 years after project end. By the end of the project, the final dataset will be transferred to the ZENODO repository, which ensures sustainable archiving of the final research data. # 7\. OFFSHORE OPERATIONS ## 7.1 DATASET REFERENCE AN DS_Offsohre_Operations 7.2 DATASET DESCRIPTION The DS_Offsohre_Operations datasets consists of failures, type of maintenance, offshore resources (such as vessels, equipment, personnel, parts and consumables), health & safety, and activity log. Offshore Operations data will be observational and derived. Field tests will be conducted at the BiMEP open sea test site. The offshore operations data will be combined with the environmental monitoring dataset to derive the final dataset. Full datasets will be made public. Offshore operations data will be useful to reduce the uncertainty on the determination of risk and cost of offshore operations, and to optimise these activities. The offshore logistics experience can be extrapolated to different scenarios of larger deployment with a view to more accurately assess the economies of scale and identify logistics bottlenecks when deployed in large arrays. Although the raw datasets are useful by themselves, it is the objective of the OPERA project to use the dataset as a basis for at least one scientific publication. ## 7.3 STANDARDS AND METADATA Unlike the previous datasets, these are not based on process instrumentation and therefore will not be stored in the WP1 database. This dataset can be imported from, and exported to a CSV, TXT or Excel file. Failure data will be reported according to Failure the Reporting, Analysis and Corrective Action System (FRACAS) [16] and the ISO 14224:2006 Collection and exchange of reliability and maintenance data for equipment [17] . The DataCite Metadata Schema [18] will be used for publication of the offshore operations datasets. DataCite is a domain-agnostic list of core metadata properties chosen for the accurate and consistent identification of data for citation and retrieval purposes. TECNALIA will be responsible for version control and validation dataset of datasets to be shared open access. 7.4 DATA SHARING As it has been described before, the datasets will be organised in files tailored to comply with the requirements of WP6. The file structure will be also publicly available to the data users as a way to better understand the file itself. The aggregated dataset will be disseminated in order to protect the commercial and industrial prospects of exploitable results (ER1 and ER8). In the case of the underlying data of a publication this might imply an embargo period for green open access publications. 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. ## 7.5 ARCHIVING AND PRESERVATION As it has been described before, the OPERA project database will be designed to remain operational for 5 years after project end. By the end of the project, the final dataset will be transferred to the ZENODO repository, which ensures sustainable archiving of the final research data.	https://phaidra.univie.ac.at/o:1140797	Horizon 2020
0180_OPERA_654444.md	# 1\. INTRODUCTION ## 1.1 OPERA MOTIVATION The OPERA project participates in the Pilot on Open Research Data launched by the European Commission (EC) along with the H2020 programme. This pilot is part of the Open Access to Scientific Publications and Research Data programme in H2020. The goal of the programme is to foster access to research data generated in H2020 projects. The use of a Data Management Plan (DMP) is required for all projects participating in the Open Research Data Pilot. Open access is 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 research and innovation, scientific information can refer to peer-reviewed scientific research articles or 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. The Consortium strongly believes in the concepts of open science, and in the benefits that the European innovation ecosystem and economy can draw from allowing the reuse of data at a larger scale. Furthermore, there is a need to gather experience in open sea operating conditions, structural and power performance and operating data in wave energy. In fact, there has been very limited open sea experience in wave energy, which is essential in order to fully understand the challenges in device performance, survivability and reliability. The limited operating data and experience that currently exists are rarely shared, as testing is partly private-sponsored. This project proposes to remove this roadblock by delivering for the first time, open access, high-quality open sea operating data to the wave energy development community. Nevertheless, data sharing in the open domain can be restricted as a legitimate reason to protect results that can reasonably be expected to be commercially or industrially exploited [1] . Strategies to limit such restrictions will include anonymising or aggregating data, agreeing on a limited embargo period or publishing selected datasets. ## 1.2 PURPOSE OF THE DATA MANAGEMENT PLAN 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 the project research data. The DMP covers the complete research data life cycle. It describes the types of research data that will be generated or 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. It also reflects the current state of the Consortium agreements on data management and must be consistent with exploitation and IPR requirements. The DMP is not a fixed document, but will evolve during the lifespan of the project, particularly whenever significant changes arise such as dataset updates or changes in Consortium policies. This document is an update of the DMP which was delivered in Month 6 of the project (D8.5). It included an overview of the datasets to be produced by the project, and the specific conditions that are attached to them. The current version of the DMP gets into more detail and describes the practical data management procedures implemented by the OPERA project with reference with the IT tools developed in WP1. The final version of the DMP will be delivered in Month 30 (D8.7). This document has been produced following the EC guidelines for project participating in this pilot and additional consideration described in ANNEX I: KEY PRINCIPLES FOR OPEN ACCESS TO RESEARCH DATA. ## 1.3 RESEARCH DATA TYPES IN OPERA The data types that will be produced during the project are focused on the Description of the Action (DoA) and their results. According to such consideration, Table 1.1 reports a list of categories of research data that OPERA will produce. These research data types have been mainly defined in WP1, including data structures, sampling and processing requirements, as well as relevant standards. This list may be adapted with the addition or removal of datasets in the next versions of the DMP to take into consideration the project developments. A detailed description of each dataset is given in the following sections of this document. ### TABLE 1.1: OPERA TYPES OF DATA <table> <tr> <th> # </th> <th> Dataset category </th> <th> Lead partner </th> <th> Related WP(s) </th> </tr> <tr> <td> 1 </td> <td> Environmental monitoring </td> <td> TECNALIA </td> <td> WP1 </td> </tr> <tr> <td> 2 </td> <td> Mooring performance </td> <td> UNEXE </td> <td> WP1, WP2, WP5 </td> </tr> <tr> <td> 3 </td> <td> Bi-radial performance </td> <td> IST </td> <td> WP1, WP3 </td> </tr> <tr> <td> 4 </td> <td> Power output </td> <td> OCEANTEC </td> <td> WP1, WP4, WP5 </td> </tr> <tr> <td> 5 </td> <td> Power quality </td> <td> UCC </td> <td> WP1, WP5 </td> </tr> <tr> <td> 6 </td> <td> Offshore operations </td> <td> TECNALIA </td> <td> WP6 </td> </tr> </table> Specific datasets may be associated to scientific publications (i.e. underlying data), public project reports and other raw data or curated data not directly attributable to a publication. Datasets can be both collected, unprocessed data as well as analysed, generated data. The policy for open access are summarised in the following picture. Research data linked to exploitable results will not be put into the open domain if they compromise its commercialisation prospects or have inadequate protection, which is a H2020 obligation. The rest of research data will be deposited in an open access repository. When the research data is linked to a scientific publication, the provisions described in ANNEX II: SCIENTIFIC PUBLICATIONS will be followed. Research data needed to validate the results presented in the publication should be deposited at the same time for “Gold” Open Access 1 or before the end of the embargo period for “Green” Open Access 2 . Underlying research data will consist of selected parts of the general datasets generated, and for which the decision of making that part public has been made. Other datasets will be related to any public report or be useful for the research community. They will be selected parts of the general datasets generated or full datasets (e.g. up to 2 years of key operating data) and be published as soon as they become available. ## 1.4 ROLES AND RESPONSIBILITIES Each OPERA partner has to respect the policies set out in this DMP. Datasets have to be created, managed and stored appropriately and in line with applicable legislation. The Project Coordinator has a particular responsibility to ensure that data shared through the OPERA website are easily available, but also that backups are performed and that proprietary data are secured. OCEANTEC, as WP1 leader, will ensure dataset integrity and compatibility for its use during the project lifetime by different partners. Validation and registration of datasets and metadata is the responsibility of the partner that generates the data in the WP. Metadata constitutes an underlying definition or description of the datasets, and facilitate finding and working with particular instances of data. Backing up data for sharing through open access repositories is the responsibility of the partner possessing the data. Quality control of these data is the responsibility of the relevant WP leader, supported by the Project Coordinator. If datasets are updated, the partner that possesses the data has the responsibility to manage the different versions and to make sure that the latest version is available in the case of publicly available data. WP1 will provide naming and version conventions. Last but not least, all partners must consult the concerned partner(s) before publishing data in the open domain that can be associated to an exploitable result. # 2\. DATA COLLECTION, STORAGE AND BACK-UP The OPERA project will generate data resulting from instrumentation recordings during the lab testing and open-sea testing. In addition to the raw, uncorrected sensor data, converted and corrected data, as well as several other forms of derived data will be produced. Instrumentation, data acquisition and logging systems are thoroughly described in D1.1 [2] . A database management system will be used in the project to create, read, update and delete data from a database. The software platform being used is MySQL 5.7.9. A SCADA system will allows partners to access monitoring information locally and remotely. The following sections describe the different datasets that will be produced in the course of the project. ## 2.1 ENVIRONMENTAL MONITORING DATA Environmental monitoring data will be collected at two locations, namely the Mutriku shoreline plant and the open sea test site BiMEP. These numeric datasets will be directly obtained through observations, and derived using statistical parameters and models. In general, environmental monitoring datasets will be useful for further research activities beyond the scope of OPERA objectives. Metocean observations are common practice for different uses. Dataset could be integrated and reused, particularly for the characterisation of wave resource and the estimation of device performance. They will be also valuable for technology developers who plan to test their devices at either Mutriku or BiMEP. Although the raw datasets are useful by themselves, it is the objective of the OPERA project to use the data as a basis for at least one scientific publication. A short description of the environmental monitoring datasets is given next. ### TABLE 2.1: WAVE RESOURCE AT MUTRIKU <table> <tr> <th> Reference/Name </th> <th>  DS_Wave_Mutriku </th> </tr> <tr> <td> Description </td> <td>  Wave resource data 200 m off the shoreline plant.  Main data are the pressure fluctuations over time. </td> </tr> <tr> <td> Source </td> <td>  RBR & Isurki Pressure Gauges </td> </tr> <tr> <td> Type </td> <td>  Observational and derived </td> </tr> <tr> <td> Format </td> <td> * MySQL database for real-time data * TXT for instrument recordings and derived data </td> </tr> <tr> <td> Software </td> <td>  Scilab program to transform pressure into wave height and period.  Spectral analysis software. </td> </tr> <tr> <td> Estimated size </td> <td>  2 GB (6 months @ 2 Hz sampling frequency) </td> </tr> <tr> <td> Storage </td> <td>  Internal USB memory stick, on-site database server, and real-time replication onto cloud-hosted database server </td> </tr> <tr> <td> Back-up </td> <td>  Daily back-ups on both local and cloud-hosted servers. 15-day retention period for incremental backups in the latter. </td> </tr> </table> ### TABLE 2.2: WAVE RESOURCE AT BIMEP <table> <tr> <th> Reference/Name </th> <th>  DS_Wave_BiMEP </th> </tr> <tr> <td> Description </td> <td> * Wave resource at 300 m up-wave of the WEC. * Datasets mainly consist of wave parameters such as wave H s , T p , direction and spreading. </td> </tr> <tr> <td> Source </td> <td>  TRIAXYS surface following buoy </td> </tr> <tr> <td> Type </td> <td>  Observational and derived </td> </tr> <tr> <td> Format </td> <td> * MySQL database for real-time data * TXT for instrument recordings * MS Excel for derived and filtered data </td> </tr> <tr> <td> Software </td> <td>  Spectral analysis software </td> </tr> <tr> <td> Estimated size </td> <td> * 150 MB of statistical data (20 min x 2 years) * 1 GB of real-time data (20 min x 2 Hz sampling frequency when real-time communications activated) * 8 GB (2 years @ 2 Hz sampling frequency) </td> </tr> <tr> <td> Storage </td> <td>  Internal USB memory stick, on-site database server and real-time replication onto cloud-hosted database server </td> </tr> <tr> <td> Back-up </td> <td> * Daily back-ups on both local and cloud-hosted servers. * 15-day retention period for incremental backups in the latter. </td> </tr> </table> ## 2.2 MOORING PERFORMANCE DATA Experimental data will be collected at the DMAC facility in UNEXE [4] . Besides, field tests will be conducted at the open sea test site at BiMEP. Datasets consists of mooring performance data will be both experimental and observational, raw and derived (using statistical parameters and models). The mooring performance dataset will be useful to inform technology compliance, survivability and reliability as well as economical improvements. They will be also valuable for the certification processes of other technology developers. These data will be the basis for at least one scientific publication. A short description of the mooring performance datasets is given below. TABLE 2.3: TETHER LOADS AT DMAC <table> <tr> <th> Reference/Name </th> <th>  DS_Tethers_Lab </th> </tr> <tr> <td> Description </td> <td>  Characterisation of design load behaviour, fatigue and durability of several elastomeric tether specimens. </td> </tr> <tr> <td> Source </td> <td>  DMAC facility </td> </tr> <tr> <td> Type </td> <td>  Experimental </td> </tr> <tr> <td> Format </td> <td>  CSV (processed data) </td> </tr> <tr> <td> Software </td> <td>  Labview, Optitrack Motive and Matlab </td> </tr> <tr> <td> Estimated size </td> <td>  8.7 GB (50 Hz sampling frequency) </td> </tr> <tr> <td> Storage </td> <td>  Network storage </td> </tr> <tr> <td> Back-up </td> <td>  Network drive is backed up daily with two-disk fault tolerance (i.e. backups are safe even if two disks fail). Backups are stored in a different building and protected by a dedicated UPS. </td> </tr> </table> ### TABLE 2.4: MOORING LOADS AT BIMEP <table> <tr> <th> Reference/Name </th> <th>  DS_Mooring_BiMEP </th> </tr> <tr> <td> Description </td> <td> * Extreme loads and motion response to different sea states will be monitored. * The loading data will be combined with the environmental monitoring dataset to derive the final mooring performance dataset. * Comparison between the polyester lines and the elastomeric mooring tethers. </td> </tr> <tr> <td> Source </td> <td> * MARMOK-A-5 prototype. * A mooring condition monitoring has been implemented for the project consisting of 4 load shackles deployed in two mooring nodes of the prototype. </td> </tr> <tr> <td> Type </td> <td>  Observational and derived </td> </tr> <tr> <td> Format </td> <td> * MySQL database for real-time data * TXT for raw instrument recordings * MS Excel for derived and filtered data </td> </tr> <tr> <td> Software </td> <td>  Statistical and spectral analysis software </td> </tr> <tr> <td> Estimated size </td> <td>  ≤ 400 GB (2.5 years recording x 16 measurements @ 20 Hz) </td> </tr> <tr> <td> Storage </td> <td>  On-site database server and real-time replication onto cloudhosted database server </td> </tr> <tr> <td> Back-up </td> <td> * Daily back-ups on both local and cloud-hosted servers. * 15-day retention period for incremental backups in the latter. </td> </tr> </table> ## 2.3 BIRADIAL TURBINE PERFORMANCE DATA Experimental data will be collected at existing IST Turbomachinery Laboratory (Dry Lab) for tests in varying unidirectional flow. Also, field tests will be conducted both at Mutriku shoreline plant and the BiMEP open sea test site. Bi-radial turbine performance data will be both experimental and observational, raw and derived (using statistical parameters and models). The bi-radial turbine performance dataset will be useful to assess turbine efficiency and reliability. The loading data will be combined with the environmental monitoring dataset to derive the final bi-radial turbine performance dataset. This dataset will be the basis for at least one scientific publication. A short description of the bi-radial turbine performance datasets is given below. ### TABLE 2.5: BI-RADIAL TURBINE PERFORMANCE AT DRY LAB FACILITY <table> <tr> <th> Reference/Name </th> <th>  DS_Biradial_Turbine_Lab </th> </tr> <tr> <td> Description </td> <td>  Assess turbine performance though unidirectional steady-state and alternating flow. </td> </tr> <tr> <td> Source </td> <td> * IST Turbomachinery Laboratory. * Sensor data acquired at a frequency of 1kHz for turbine pressure head, plenum temperature and humidity, turbine rotational speed, turbine flow rate and the instantaneous position of the flow control valve. * The voltage and the current of the three AC phases at the input and output of the power electronics were acquired at a frequency of 62.5kHz. </td> </tr> <tr> <td> Type </td> <td>  Experimental </td> </tr> <tr> <td> Format </td> <td>  Matlab “mat” files and comma separated value “csv” text files. </td> </tr> <tr> <td> Software </td> <td> * Matlab (Experimental data acquisition) * A special purpose parallelized C++ software (Data filtering), * A software package written in the Julia language (Computation of the instantaneous and time-averaged turbine shaft power, electrical power and available pneumatic power) </td> </tr> <tr> <td> Estimated size </td> <td>  320 GB </td> </tr> <tr> <td> Storage </td> <td>  Local PC storage </td> </tr> <tr> <td> Back-up </td> <td>  Static data stored at three computers </td> </tr> </table> TABLE 2.6: BI-RADIAL TURBINE PERFORMANCE AT MUTRIKU <table> <tr> <th> Reference/Name </th> <th>  DS_Biradial_Turbine_Mutriku </th> </tr> <tr> <td> Description </td> <td>  Assess turbine performance and collect extensive data on drivers of components fatigue such as high rpm and accelerations; electrical, temperature and pressure load cycles; humidity in the cabinet (which exacerbates electrical stress damage); rate of salt accumulation and corrosion. </td> </tr> <tr> <td> Source </td> <td> * Mutriku Wave Power Plant. * Bi-radial turbine-generator set and chamber #9 have been instrumented for the project. </td> </tr> <tr> <td> Type </td> <td>  Observational and derived </td> </tr> <tr> <td> Format </td> <td> * MySQL database for real-time data * MS Excel for derived and filtered data </td> </tr> <tr> <td> Software </td> <td>  Statistical and spectral analysis software </td> </tr> <tr> <td> Estimated size </td> <td>  ≤ 50 GB (6-month recording x 150 measurements @ 4 Hz) </td> </tr> <tr> <td> Storage </td> <td>  On-site database server and real-time replication onto cloudhosted database server </td> </tr> <tr> <td> Back-up </td> <td> * Daily back-ups on both local and cloud-hosted servers. * 15-day retention period for incremental backups in the latter. </td> </tr> </table> TABLE 2.7: BI-RADIAL TURBINE PERFORMANCE AT BIMEP <table> <tr> <th> Reference/Name </th> <th>  DS_Biradial_Turbine_BiMEP </th> </tr> <tr> <td> Description </td> <td>  Internal water level, chamber pressure/temperature/humidity, rotation speed and torque to assess turbine efficiency in response to different sea states to compare turbine performance drivers of components fatigue </td> </tr> <tr> <td> Source </td> <td> * MARMOK-A-5 prototype. * Bi-radial turbine-generator set and hull structure have been instrumented for the project. </td> </tr> <tr> <td> Type </td> <td>  Observational and derived </td> </tr> <tr> <td> Format </td> <td>  MySQL database for real-time dataMS Excel for derived and filtered data </td> </tr> <tr> <td> Software </td> <td>  Statistical and spectral analysis software </td> </tr> <tr> <td> Estimated size </td> <td>  ≤ 100 GB (12-month recording x 150 measurements at 4 Hz) </td> </tr> <tr> <td> Storage </td> <td>  On-site database server and real-time replication onto cloudhosted database server </td> </tr> <tr> <td> Back-up </td> <td> * Daily back-ups on both local and cloud-hosted servers. * 15-day retention period for incremental backups in the latter. </td> </tr> </table> 2.4 POWER OUTPUT DATA Experimental data will be collected at electrical test rigs of UCC [5] and TECNALIA [6] . Besides, field tests data will be collected at Mutriku shoreline plant and at the BiMEP open sea test site. Numerical models will be also used to extend the dataset beyond sea-trials data. In the latter, specialist software may be needed for further processing the data. Selected parts of the generated datasets generated will be made public. Power output data will be both experimental and observational, raw and derived such as mean, standard deviation, minimum and maximum values. Power output data will be useful to identify sources of uncertainty in power performance prediction and for the certification processes of other technology developers. This dataset will be the basis for at least one scientific publication. A short description of the power output datasets is given below. ### TABLE 2.8: POWER OUTPUT AT ELECTRICAL TEST RIG <table> <tr> <th> Reference/Name </th> <th>  DS_Power_Output_Lab </th> </tr> <tr> <td> Description </td> <td>  Generator speed, voltage, frequency and electric power. </td> </tr> <tr> <td> Source </td> <td>  Electrical test rigs of UCC and TECNALIA </td> </tr> <tr> <td> Type </td> <td>  Experimental and Simulation </td> </tr> <tr> <td> Format </td> <td>  MS Excel </td> </tr> <tr> <td> Software </td> <td>  MATLAB numerical model of the Mutriku Wave Power Plant </td> </tr> <tr> <td> Estimated size </td> <td>  20 GB (7 CLs x approx. 300 MB) </td> </tr> <tr> <td> Storage </td> <td>  Network storage </td> </tr> <tr> <td> Back-up </td> <td>  Daily back-ups </td> </tr> </table> ### TABLE 2.9: POWER OUTPUT AT MUTRIKU <table> <tr> <th> Reference/Name </th> <th>  DS_Power_Output_Mutriku </th> </tr> <tr> <td> Description </td> <td>  Generator speed, voltage, frequency and electric power, including phase voltages & currents. </td> </tr> <tr> <td> Source </td> <td> * Mutriku Wave Power Plant. * Bi-radial turbine-generator set and chamber #9 have been instrumented for the project. </td> </tr> <tr> <td> Type </td> <td>  Observational and derived </td> </tr> <tr> <td> Format </td> <td> * MySQL database for real-time data * MS Excel for derived and filtered data </td> </tr> <tr> <td> Software </td> <td>  Statistical and spectral analysis software </td> </tr> <tr> <td> Estimated size </td> <td>  ≤ 50 GB (6-month recording x 150 measurements @ 4 Hz) </td> </tr> <tr> <td> Storage </td> <td>  On-site database server and real-time replication onto cloudhosted database server </td> </tr> <tr> <td> Back-up </td> <td> * Daily back-ups on both local and cloud-hosted servers. * 15-day retention period for incremental backups in the latter. </td> </tr> </table> ### TABLE 2.10: POWER OUTPUT AT BIMEP <table> <tr> <th> Reference/Name </th> <th>  DS_Power_Output_BiMEP </th> </tr> <tr> <td> Description </td> <td>  Generator speed, voltage, frequency and electric power, including phase voltages & currents. </td> </tr> <tr> <td> Source </td> <td>  MARMOK-A-5 prototype. Bi-radial turbine-generator set and hull structure have been instrumented for the project. </td> </tr> <tr> <td> Type </td> <td>  Observational and derived </td> </tr> <tr> <td> Format </td> <td> * MySQL database for real-time data * MS Excel for derived and filtered data </td> </tr> <tr> <td> Software </td> <td>  Statistical and spectral analysis software </td> </tr> <tr> <td> Estimated size </td> <td>  ≤ 100 GB (12-month recording x 150 measurements at 4 Hz) </td> </tr> <tr> <td> Storage </td> <td>  On-site database server and real-time replication onto cloudhosted database server </td> </tr> <tr> <td> Back-up </td> <td> * Daily back-ups on both local and cloud-hosted servers. * 15-day retention period for incremental backups in the latter. </td> </tr> </table> ## 2.5 POWER QUALITY DATA Experimental data will be collected at electrical test rig of UCC [10]. Also, field tests data will be collected at the Mutriku shoreline plant. Simulated models may be used to assess the power quality for other operating conditions, such as varying control algorithms, resource conditions, grid strengths, and control using a dry-lab to create a wider profile for the WEC. Power quality data will be both experimental and observational, raw and derived (using statistical parameters and models). Selected parts of the experimental datasets generated will be made public. Power quality data will be useful to identify sources of uncertainty in assessing the impact of the wave energy converter on the performance of the grid. They will be also valuable for the certification processes of other technology developers. This dataset will be the basis for at least one scientific publication. A short description of the power quality datasets is given next. TABLE 2.11: POWER QUALITY AT ELECTRICAL TEST RIG <table> <tr> <th> Reference/Name </th> <th>  DS_Power_Quality_Lab </th> </tr> <tr> <td> Description </td> <td>  Current, voltage, power quality characteristic parameters (such as voltage fluctuations, harmonics, inter-harmonics, active/reactive power, and flicker). </td> </tr> <tr> <td> Source </td> <td>  Electrical test rig at UCC </td> </tr> <tr> <td> Type </td> <td>  Experimental and Simulation </td> </tr> <tr> <td> Format </td> <td>  MS Excel </td> </tr> <tr> <td> Software </td> <td>  MATLAB Simulink numerical model of the Mutriku Wave Power Plant </td> </tr> <tr> <td> Estimated size </td> <td> * Maximum 1.2 GB per 10-minute test (at 20 kHz sampling frequency). * 4 signals at 20 kHz for 10 minutes per test </td> </tr> <tr> <td> Storage </td> <td>  Network storage </td> </tr> <tr> <td> Back-up </td> <td>  Daily back-ups </td> </tr> </table> TABLE 2.12: POWER QUALITY AT MUTRIKU <table> <tr> <th> Reference/Name </th> <th>  DS_Power_Quality_Mutriku </th> </tr> <tr> <td> Description </td> <td>  Data will be collected from both a single turbine and the plant as a whole, obtaining valuable conclusions about how aggregation of multiple turbines affects the power quality. </td> </tr> <tr> <td> Source </td> <td>  Mutriku Wave Power Plant. </td> </tr> <tr> <td> Type </td> <td>  Observational and derived </td> </tr> <tr> <td> Format </td> <td> * MySQL database for real-time data * MS Excel for derived and filtered data </td> </tr> <tr> <td> Software </td> <td> * LabView * Statistical and spectral analysis software </td> </tr> <tr> <td> Estimated size </td> <td> * > 200 GB (12-month recording x 12 measurements @ 20 kHz). * Given the large data storage requirements, the measurements will be triggered, and not carried out continuously. After sufficient power quality analysis has been carried out at 20 kHz, the sampling rate will then be reduced (to approximately 12 kHz, and 10 kHz). </td> </tr> <tr> <td> Storage </td> <td>  On-site database server and real-time replication onto cloudhosted database server </td> </tr> <tr> <td> Back-up </td> <td> * Daily back-ups on both local and cloud-hosted servers. * 15-day retention period for incremental backups in the latter. </td> </tr> </table> ## 2.6 OFFSHORE OPERATIONS DATA Field tests will be conducted at the BiMEP open sea test site. The offshore operations data will be combined with the environmental monitoring dataset to derive the final dataset. Collected datasets will be made public. Offshore operations data will be observational and derived. Offshore operations data will be useful to reduce the uncertainty on the determination of risk and cost of offshore operations, and to optimise these activities. The offshore logistics experience can be extrapolated to different scenarios of larger deployment with a view to more accurately assess the economies of scale and identify logistics bottlenecks when deployed in large arrays. Although the raw datasets are useful by themselves, it is the objective of the OPERA project to use the dataset as a basis for at least one scientific publication. A short description of the offshore operations datasets is given below. TABLE 2.13: OFFSHORE OPERATIONS <table> <tr> <th> Reference/Name </th> <th>  DS_Offshore_Operations </th> </tr> <tr> <td> Description </td> <td>  Failures, type of maintenance, offshore resources (such as vessels, equipment, personnel, parts and consumables), health & safety, and activity log. </td> </tr> <tr> <td> Source </td> <td>  Unlike the previous datasets, these are not based on process instrumentation and therefore will not be stored in the WP1 database. </td> </tr> <tr> <td> Type </td> <td>  Observational </td> </tr> <tr> <td> Format </td> <td>  MS Excel </td> </tr> <tr> <td> Software </td> <td>  n/a </td> </tr> <tr> <td> Estimated size </td> <td>  10 MB </td> </tr> <tr> <td> Storage </td> <td>  Network storage </td> </tr> <tr> <td> Back-up </td> <td>  Daily back-ups on a separate server </td> </tr> </table> # 3\. DATA STANDARDS AND METADATA The following standards should be used for data documentation: * Ocean Data Standards Project [7] : it contains an extensive number of references on Oceanographic Data Management and Exchange Standards. It includes references on Metadata, Date and Time, Lat/Lon/Alt, Country names, Platform instances, Platform types, Science Words, Instruments, Units, Projects, Institutions, Parameters, Quality Assurance and Quality Control. * ISO 19156:2011 [8] : it defines a conceptual schema for observations, and for features involved in sampling when making observations. These provide models for the exchange of information describing observation acts and their results, both within and between different scientific and technical communities. * IEC TS 62600-101 [9] : technical specification for wave energy resource assessment and characterisation. * DNVGL-OS-E301 [10] : it contains criteria, technical requirements and guidelines on design and construction of position mooring systems. The objective of this standard is to give a uniform level of safety for mooring systems, consisting of chain, steel wire ropes and fibre rope. * IEC TS 62600-10 [11] : technical specification for assessment of mooring system for Marine Energy Converters (MECs). * IEC TS 62600-100 [12] technical specification on power performance assessment of electricity producing wave energy converters * IEC TS 62600-102 [13] technical specification on wave energy converter power performance assessment at a second location using measured assessment data * IEC TS 62600-30 [14] technical specification on electrical power quality requirements for wave, tidal and other water current energy converters * IEC 61000-4-7:2002 [15] further instructions on processing harmonic current components are given in for power supply systems and equipment connected thereto. 🞂 FRACAS [16] Failure the Reporting, Analysis and Corrective Action System * ISO 14224:2006 [17] collection and exchange of reliability and maintenance data for equipment. Metadata records will accompany the data files in order to describe, contextualise and facilitate external users to understand and reuse the data. OPERA will adopt the DataCite Metadata Schema [18] , a domain agnostic metadata schema, as the basis for harvesting and importing metadata about datasets from data archives. The core mission of DataCite is to build and maintain a sustainable framework that makes it possible to cite data through the use of persistent identifiers. The following metadata should be created to identify datasets: * Identifier: A unique string that identifies the dataset * Author/Creator: The main researchers involved in producing the data in priority order * Title: A name or title by which a data is known * Publisher: The name of the entity that holds, archives, publishes prints, distributes, releases, issues, or produces the data. * Publication Year: The year when the data was or will be made publicly available 🞂 Subject: Subject, keyword, classification code, or key phrase describing the resource. * Contributor: Name of the funding entity (i.e. "European Union" & "Horizon 2020") * Size: Unstructured size information about the dataset (in GBs) * Format: Technical format of the dataset (e.g. cvs, txt, xml, ...) * Version: The version number of the dataset * Access rights: Provide a rights management statement for the dataset. Include embargo information if applicable * Geo-location: Spatial region or named place where the data was gathered # 4\. DATA SHARING AND REUSE During the life cycle of the OPERA project datasets will be stored and systematically organised in a database tailored to comply with the requirements of WP1 (for more details on the database architecture, please see D1.1 Process instrumentation definition [2] ). An online data query tool was operational in Month 12, and available for open dissemination by Month 18. The database schema and the queryable fields, will be also publicly available to the database users as a way to better understand the database itself. In addition to the project database, relevant datasets will be also stored in ZENODO [19] [10] , which is the open access repository of the Open Access Infrastructure for Research in Europe, OpenAIRE [20] All collected datasets will be disseminated without an embargo period unless linked to a green open access publication. Data objects will be deposited in ZENODO under: * Open access to data files and metadata and data files provided over standard protocols such as HTTP and OAI-PMH. * Use and reuse of data permitted. 🞂 Privacy of its users protected. Data access policy is summarised in the following table. ## TABLE 4.1: DATA ACCESS POLICY <table> <tr> <th> Dataset </th> <th> Data access policy </th> </tr> <tr> <td> DS_Wave_Mutriku </td> <td> * Unrestricted since no confidentiality or IPR issues are expected regarding the environmental monitoring datasets * Licence: CC-BY </td> </tr> <tr> <td> DS_Wave_BiMEP </td> </tr> <tr> <td> DS_Tethers_Lab </td> <td> * Restricted to WP2 participants, in order to protect the commercial and industrial prospects of exploitable results (KER1 and KER3). * Samples of aggregated data (e.g. load averages or extreme load ranges) will be shared in the open domain for the most relevant sea states.  Licence: CC-BY-ND </td> </tr> <tr> <td> DS_Mooring_BiMEP </td> </tr> <tr> <td> DS_Biradial_Turbine_Lab </td> <td> * Restricted to WP3 participants, in order to protect the commercial and industrial prospects of exploitable results (KER1 and KER2). * Samples of aggregated data (e.g. chamber pressure, air flow, mechanical power) will be shared in the open domain. * Licence: CC-BY-ND </td> </tr> <tr> <td> DS_Biradial_Turbine_Mutriku </td> </tr> <tr> <td> DS_Biradial_Turbine_BiMEP </td> </tr> <tr> <td> DS_Power_Output_Lab </td> <td> * Restricted to WP4 and WP5 participants, in order to protect the commercial and industrial prospects of exploitable results (KER1, KER4 and KER6). * Samples of aggregated data (e.g. electric power for the different control laws) will be shared in the open domain. * Licence: CC-BY-ND </td> </tr> <tr> <td> DS_Power_Output_Mutriku </td> </tr> <tr> <td> DS_Power_Output_BiMEP </td> </tr> <tr> <td> DS_Power_Quality_Lab </td> <td> * Restricted to WP5 participants, in order to protect the commercial and industrial prospects of exploitable results (KER1, KER4 and KER6). * Samples of aggregated data (e.g. active, reactive power and power factor) will be shared in the open domain. * Licence: CC-BY-ND </td> </tr> <tr> <td> DS_Power_Quality_Mutriku </td> </tr> <tr> <td> DS_Offshore_Operations </td> <td> * The aggregated dataset (e.g. operation time, forecast vs recorded wave conditions) will just be shared in the open domain in order to protect the commercial and industrial prospects of exploitable results (KER1 and KER8). * Licence: CC-NC-BY-ND </td> </tr> </table> # 5\. DATA ARCHIVING AND PRESERVATION The OPERA project database will be designed to remain operational for 5 years after project end. By the end of the project, the final dataset will be transferred to the ZENODO repository, which ensures sustainable archiving of the final research data. Items deposited in ZENODO will be retained for the lifetime of the repository, which is currently the lifetime of the host laboratory CERN and has an experimental programme defined for the at least next 20 years. Data files and metadata are backed up on a nightly basis, as well as replicated in multiple copies in the online system. All data files are stored along with a MD5 checksum of the file content. Regular checks of files against their checksums are made.	https://phaidra.univie.ac.at/o:1140797	Horizon 2020
0181_MIND_661880.md	3 MIND Data-sets The MIND Data Management Plan (DMP) has three main purposes: 1. To support the project partners when publishing the datasets generated by the project. 2. Support the Coordinator in assuring the availability of the data generated by the project. 3. Assist external parties in analysing the work performed and accessing the data generated by the project. To ensure that all three purposes are fullfilled we have standardized the information that needs to be given regarding each of the datasets generated by the project. The project partner responsible for producing a dataset will fill out the two tables show below which will be added to this dokument as individula sub- chapters to chapter 3 in this document (3.1, 3.2, 3.3 etc.). Assitance in doing this will be available through both the work package managers and the coordinators. <table> <tr> <th> Metadata </th> <th> Description </th> </tr> <tr> <td> Dataset reference and/or name </td> <td> Unique name identifying the dataset. Identifier should start with EU- MIND2020-WPx where “x” is the relevant work package number followed by a three digit number. Example: “ _EU-MIND2020-WP4-001_ ” </td> </tr> <tr> <td> MIND Datatype </td> <td> Choose one or more of the relevant data types: Experimental Data, Observational Data, Raw Data, Derived Data, Physical Data (samples), Models, Images and Protocols. Alternatives are further described in Appendix 1. </td> </tr> <tr> <td> Source </td> <td> Source of the data. Reference should include work package number, task number and the main project partner or laboratory which produced the data. </td> </tr> </table> Dataset description Provides a brief description of the data set along with the purpose of the data and whether it underpins a scientific publication. This should allow potential users to determine if the data set is useful for their needs. <table> <tr> <th> Standards and metadata </th> <th> </th> <th> Provides a brief description of the relevant standards used and list relevant metadata in accordance with the description in Appendix 1. The usage of the Directory Interchange Format is optional. </th> </tr> </table> Science Keywords List relevant scientific key words to ensure that the data can be efficiently indexed so others may locate the data. <table> <tr> <th> Data sharing </th> <th> </th> <th> Description of how data will be shared both during and after the MIND2020 project. Include 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. Information should include a reference to the repository where data will be stored. 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). </th> </tr> </table> Archiving and Description of the procedures that will be put in place for long-term preservation of preservation the data. # 3.1 “Dataset one” <table> <tr> <th> Metadata </th> <th> Description </th> </tr> <tr> <td> Dataset reference and/or name </td> <td> … </td> </tr> <tr> <td> Source </td> <td> … </td> </tr> <tr> <td> Dataset 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 </td> <td> … </td> </tr> </table> # 3.2 “Dataset two” <table> <tr> <th> Metadata </th> <th> Description </th> </tr> <tr> <td> Dataset reference and/or name </td> <td> … </td> </tr> <tr> <td> Source </td> <td> … </td> </tr> <tr> <td> Dataset 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 </td> <td> … </td> </tr> </table> # APPENDIX A: MIND2020 DATA TYPES ## 1 Experimental Data ### 1.1 Dataset description The experimental data originate from measurements performed in a laboratory environment, be it _in situ_ or _ex situ_ . The data comprise point or continuous numerical measurements (e.g. pH, temperature), microbial counts, dose measurements) The data will be collected either on a sample basis (sampling an experiment at a certain point in time) or on an experiment scale (without sampling the experimental set-up). Data can be derived from either destructive of preservative analyses. Experimental data collection can occur automatically or manually, and will be available in a digital or a hard copy format. In the case of the latter, experimental data will first be copied to e.g. a lab book and then digitized. Experimental data are supposed to be unique, in the way that new experiments will be set-up, producing fresh data. In some cases, similar data will be available from previous/other experiments within the project, within the partners’ institution or from overlapping projects, allowing comparison and integration of the newly obtained data. Experimental data will be used in downstream statistical analyses (hypothesis testing, correlations, etc.), interpretations, quantifications and modelling approaches. ### 1.2 Standards and metadata Experimental data are obtained using standardized laboratory techniques which are calibrated when applicable. Positive and negative controls are used and standards, internal or external, are introduced. Metadata (summary information) can optionally be provided according to a Directory Interchange Format (DIF). A DIF allows users of data to understand the contents of a dataset and contains those fields which are necessary for users to decide whether a particular dataset would be useful for their needs. ## 2 Observational Data ### 2.1 Dataset description Observational research (or field research) is a type of correlational (i.e., non-experimental) research in which a researcher observes ongoing behaviour. ### 2.2 Standards and metadata The metadata for observational data should include any standards used and the necessary information so that an external researcher has the possibility to analyse how the data was gathered. ## 3 Raw Data ### 3.1 Dataset description _Raw data_ are primary data collected from a source, not subjected to processing or any other manipulation. Raw data are derived from a source, including analysis devices like a sequencer, spectrometer, chromatograph etc. In most cases, raw data are digitally available. In some cases (e.g. sequencing), the raw data will be very extensive datasets. Raw data has the potential to become information after extraction, organization, analysis and/or formatting. It is therefore used as input for further processing. ### 3.2 Standards and metadata Raw data are obtained using standardized laboratory techniques which are calibrated when applicable. Positive and negative controls are used and standards, internal or external, are introduced. Metadata should at least include standards, techniques and devices used. Metadata can optionally be provided according to a DIF. A DIF allows users of data to understand the contents of a dataset and contains those fields which are necessary for users to decide whether a particular dataset would be useful for their needs. ## 4 Derived Data ### 4.1 Dataset description Derived data are the output of the processing or manipulation of raw data. Derived data originate from the extraction, organization, analysis and/or formatting or raw data, in order to derive information from the latter. In most cases, derived data are digitally available, as are the raw data. Derived data will allow for the interpretation of laboratory experiments, e.g. through statistical analysis or bioinformatics processing. ### 4.2 Standards and metadata Manipulation of data will be performed using a ‘scientific code of conduct’, i.e. maintaining scientific integrity and therefore not falsifying the output or its representation. Metadata should include any standard or method or best practice used in the analysis. Metadata can optionally be provided according to a Directory Interchange Format. A DIF allows users of data to understand the contents of a dataset and contains those fields which are necessary for users to decide whether a particular dataset would be useful for their needs. ## 5 Physical Data (samples) ### 5.1 Dataset description Physical data are samples that have been produced by an experiment or taken from a given environment. Sampling of an environment or experiment is performed in order to obtain information through analyses. As such, experimental, raw and or derived data will be obtained from physical data. When the analyses are destructive, the samples cannot be stored for later use. When the analyses are preservative, samples can be stored for later use, but only for a limited time. Environmental samples will primarily be samples from the Underground Research Facilities and analogue sites. ### 5.2 Standards and metadata When sampling an environment or experiment, blank samples are taken as well, as a reference. In case of microbiological samples, a blank can be a non- inoculated experiment. Metadata should include description of the origin of the sample, age, processing, storage conditions and expected viability of the sample (as some sets of samples can only be stored for a limited time, due to their nature). ## 6 Models ### 6.1 Dataset description Representation or simplified version of a concept, phenomenon, relationship, structure or system used for facilitating understanding by eliminating unnecessary components. ### 6.2 Standards and metadata References and metadata should include existing standards of the discipline used, tools used in the modelling and focus of the modelling. ## 7 Images ### 7.1 Dataset description Imaging data are optical semblances of physical objects. Objects of macro- and microscopic scale can be imaged in a variety of ways (e.g. photography, electron microscopy), enabling the optical appearance to be captured for later use or for sharing. When required, the optical appearance can be magnified (e.g. microscopy) and manipulated to enable the interpretation of the objects (mostly samples from an environment or experiment). Imaging data support the interpretation of other data, like experimental data. Some imaging data will be raw data (3.3), which need to be derived through image processing to enable interpretation. ### 7.2 Standards and metadata Advanced imaging devices are calibrated to ensure prospering visualization. Metadata which are provided are time of imaging, device settings and magnification/scale when appropriate. In addition, metadata will be provided about the object that is being imaged. ## 8 Protocols ### 8.1 Dataset description A protocol is a predefined written procedural method in the design and implementation of experiments or sampling. In addition to detailed procedures and lists of required equipment and instruments, protocols often include information on safety precautions, the calculation of results and reporting standards, including statistical analysis and rules for predefining and documenting excluded data to avoid bias. ### 8.2 Standards and metadata Protocols enable standardization of a laboratory method to ensure successful replication of results by others in the same laboratory or by partners’ laboratories. Metadata for Protocols should include the purpose of the protocols, references to standards and literature.	https://phaidra.univie.ac.at/o:1140797	Horizon 2020
0182_OpenAIRE2020_643410.md	# OPENAIRE2020 INITIAL DATA MANAGEMENT PLAN 2.1 Content collected from providers Data set Data set description Standards and Data sharing Archiving and ## reference and metadata preservation name metadata publications Records are collected from Dublin Core Not public at this stage of No need to (XML records) institutional/thematic repositories, processing; OpenAIRE preserve the journals or aggregators of these. The must keep synchronized data, which can records match their original data models with data providers and be recollected and are therefore heterogeneous. respect their will of any time from the visibility (i.e. all records in original data datasets (XML Records are collected from data DataCite OpenAIRE must be sources. records) repositories or aggregators of these. The available from one of the records match their original data models data providers). The data and are therefore heterogeneous. is not shared as it is not in projects (XML Records are collected from so-called proprietary the mission of OpenAIRE records) entity registries, which are data sources to do so; the same records providing and maintaining and can be collected from the authoritative list of project entities (e.g. original data sources. CORDA, WellcomeTrust, FCT Portugal). The records match their original data models and are therefore heterogeneous. CRIS metadata Records are collected from CRIS CERIF-XML (XML records) systems and regard publications, OpenAIRE datasets, persons, and projects. The records match their original data models and are therefore heterogeneous. files of publications full-texts (files of Files of publications relative to metadata PDFs, XML, HTML Files are used for the Regular backups publications records collected in OpenAIRE. Files are purpose of mining and only. relative to collected only if the data sources are never distributed to metadata records giving the permission to do so. OpenAIRE end-users or collected in third-party services: OpenAIRE) agreement with content providers. Files are not shared due to agreements with the data sources, which provide the files for the purpose of mining/inference, but want to remain the reference for downloads by users or services world-wide ("get <table> <tr> <th> 2.2 Content collected from OpenAIRE users </th> <th> the hits"). </th> </tr> </table> Data set Data set description Standards and Data sharing Archiving and reference and metadata preservation name Claims Relationships between The metadata (relationships publications/datasets and created are between projects/datasets/publications. Such relationships publications/data relationships are provided by authorized between sets and (logged-in) end- users and are meant to publications and projects/datasets enrich or fix the OpenAIRE information projects, datasets /publications) space (aggregated information, and projects, enhanced by inference processing). publications and dataset. The relationships are XML records, following an internally defined schema. Not public at this stage of Backup of the processingRelationships DB that contains alone do not make any the relationships sense, they will be openly are kept on a shared and accessed regular basis. once integrated in the The OpenAIRE OpenAIRE information infrastructure is space. the keeper of this information and must preserve it over time. Preservation is ensured by ICM data centre preservation policies. 2.3 Generated and aggregated contents ## Data set reference and Data set description Standards and name metadata Disambiguated metadata generated for internal Proprietary internal (Similarity relationships purposes representation between publication, author and organization objects) Data produced by the Origin: All this data is The data conforms to OpenAIRE mining system ingested from internal schemas developed IIS: (1) Available at least OpenAIRE databases internally. The in the upcoming version (Information Space) and schemas are of the beta instance of eventually lands in internal described using Avro the portal databases (Information Interface Description (new.openaire.eu): Space). Nature: The data Language.The citation links between consists of information system enriches documents,similarity extracted from scientific existing metadata of relationships between documents. Scale: the documents documents,classification Terabytes of data. Existence available in labels attached to of similar data: Similar data OpenAIRE's documents (labels such is provided by other systems Information Space as “chemistry”, that allow for exploring with inferred “medicine”, artefacts of scholarly information (e.g. “electrochemistry”, communication: Google citation links between “legal”),links from Scholar, Microsoft Academic documents, documents to projects Search, ArnetMiner, classification labels) that founded these CiteSeerX. documents, links from documents to their socalled EGI contexts, links from documents to data sets cited by these documents. (2) Available inside IIS but not integrated with the rest of OpenAIRE system yet: affiliations of the authors of scientific documents, references from documents to Protein Database entries corresponding to proteins mentioned in these documents. (3) Planned: references to some other biomedical databases. Information Space generated for internal The Information (Object/knowledge graph purposes Space assumes of metadata, formed by several aggregating content manifestations: collected form providers - HBASE internal and from users and representation enriching it with content - XML files on HDFS we generated) - XML files on OAI- PMH Publisher \- Statistics in a relational database Data sharing Archiving and preservation Not public at this These data can stage of be regenerated. processing. The data is System's ingested philosophy is not internally by to store any data. other OpenAIRE It ingests data systems and from OpenAIRE eventually Information presented to the Space, user through processes it, and portal produces data openaire.eu or that is then through some exported back to machine- OpenAIRE readable APIs. Information Space. The volume of produced data is of the order of terabytes of data. Publicly and These data can openly available be regenerated. from:- web portal (www.openaire.e u)- APIs (api.openaire.eu) : OAI-PMH, REST search, LOD (to be implemented) 2.4 Content published openly through the portal and the API Data set Data set description Standards and Data sharing Archiving and reference metadata preservation ## and name metadata (original and enriched by cleaning and mining) publications Origin, nature and scale: (XML, JSON, CSV, Publicly and openly The portal and the APIs The content we publish in the TSV, HTML) available from: are available systems datasets portals and APIs, is the content https://www.openair - web portal for searching and that we collect from providers, e.eu/schema/0.2/oa (www.openaire.eu) accessing publications' from users, and the one we f-result-0.2.xsd - APIs (api.openaire.eu): metadata. The projects process and generate. These XML, JSON, CSV, OAI-PMH, HTTP API The management of the are described in details above. TSV, HTML oaf schemata are internally metadata is part of the So the origin, nature and scale https://www.openair developed and are for the underlying systems. are directly connected to the e.eu/schema/0.2/oa XML records. JSON origin, nature and scale of that f-project-0.2.xsd records are simple content. interpretations of the XML people It is useful to users who are HTML format to JSON format. interested in the scientific results https://www.openair CSV, TSV and HTML of funded research (e.g. e.eu/schema/0.2/oa records contain subsets of researchers, project officers, f-person-0.2.xsd the metadata of the XML funders...). It also promotes the records. We have a special organizations OA initiative. XML, HTML schema between The data can be reused through https://www.openair OpenAIRE and the the API and it is already used e.eu/schema/0.2/oa European Commission that datasources from the EC Participant Portal, f-org-0.2.xsdHTML is for XML publications and EC CORDIS portal, and various https://www.openair project records; these also projects to present their e.eu/schema/0.2/oa contain a subset of the publications in their pages, etc. f-datasource- metadata of the oaf for more information consult 0.2.xsd schemata. https://www.openaire.eu/index.p hp? option=com_content&view=articl e&id=719:api&catid=61:newslett er-items users This information comes directly As the portal is User personal data are not The OpenAIRE from the users, upon signing up based on Joomla disclosed to Third Party platform uses the to the OpenAIRE portal and CMS, the users are Entities and are only provided user data for through the editing possibility of stored following the aggregated and used authentication and their personal data. schema that within the context of the offering controlled Joomla has defined Openaire2020 Project. Any access to the provided . personal data published by online services. Backup the user voluntarily on the of the DB that contains web application will be the user metadata are visible to other users of the kept on a regular basis. web application.User Access to the DB that activity is monitored and contains user metadata collected for internal usage information is open only statistics and evaluation of to the administrators of the portal services. the platform. Passwords are kept encrypted for security reasons and are secret to all users, including administrators. The OpenAIRE infrastructure is the keeper of this information and must preserve it over time. Preservation is ensured by ICM data centre preservation policies. articles The articles are authored by HTML. As the portal Publicly and openly Backups of the DB that registered users with special is based on Joomla available from web portal contains the articles are role. These articles contain CMS, the articles (www.openaire.eu). kept on a regular basis. information related to Open are stored in the DB Possibly shared on third The OpenAIRE Access and OpenAIRE2020 that is provided by party social sites infrastructure is the projects related topics Joomla, following (Facebook, Twitter, etc) keeper of this the schema that through social sharing. information and must Joomla has defined preserve it over time. for articles. Preservation is ensured by ICM data centre preservation policies. ## Data Management Plan	https://phaidra.univie.ac.at/o:1140797	Horizon 2020
0183_LIBRA_665937.md	# Data sharing 1. For Survey monkey staff data, are securely stored at CRG professional dropbox and access is regulated through ID and passwords. Due to feedback from the evaluator during the first review meeting, we decided that we will not make the collected data publically available. The criticism was towards the survey questions and the potential ambiguity of collected answers. 2. Project Implicit pre-analysed the data and shared them with the CRG Project Manager. Project Implicit is only allowed to store and pre-analyse data. Further analysis and utilisation of these data is responsibility of LIBRA. The LIBRA coordinator is collaborating with José GarcíaMontalvo (UPF) to analyse the data in detail. An agreement for sharing the data has been signed between CRG and UPF. Once the data analysis is published the underlying data will be made publically available in Zenodo (unfortunately after the end of the project). Raw data sets cannot be entirely shared to the public in the original format since in some cases individuals could be identified through the combination of profile data (e.g. female PI researcher in an institute where there is only few female PIs). Thus, data will be accessed carefully before made public, always guarantying anonymity. 3. Data from each LIBRA IO will be analysed by ASDO and reports based on the data are shared with the consortium. # Data Archiving and Preservation The raw data will be archived 20 years and the intermediate data will be preserved for at least 2 years more after the end of the project at CRG’s data infrastructure. There are no associated costs for archiving the raw and intermediate data at the CRG infrastructure. # Data Management Policy at ASDO for institutional data from LIBRA In the framework of WP1 (Initial assessment) and WP7 (Monitoring and evaluation), data have been collected and managed as follows. ## 1\. Data origin Data used for WP1 and WP7 pertaining to the implementing organisations (IOs) came from the following sources: * Data from scientific and policy literature * Official IO documents * Institutional IO websites * Internal documents provided by the IOs * Direct participation in meetings and events organised by the IOs * Monitoring sessions through at distance interviews with members of the LIBRA Team * Monitoring sessions carried out through face to face meetings with members of the LIBRA Team * Focus groups conducted with representatives of the IOs * Interviews with representatives of the IOs ### 1.2. Data collection purposes Data have been gathered with the aims of: * As for WP1 (Initial assessment) * Developing the basis for the Gender Equality Plans (GEPs) of the 10 IOs * Developing a picture of gender arrangements at each IO * Providing the basis of higher-level comparisons and benchmarking * Providing an information basis for designing and implementing WPs 3–6 * As for WP7 (Monitoring and evaluation) * Overseeing the flow of actions in the GEPs (to verify progress, see how well it aligns with the expected results and impacts, and to monitor any problems arising) * Controlling the compliance with GEP deadlines * Determining the main obstacles during implementation and facilitate their mitigation. ### 1.3. Data types, utility and public availability All the collected data have been used for developing two deliverables, i.e., D1.3 “Diagnostic report of the IOs, including relevant resources” and D7.3 “Mid-term report on monitoring and assessment at IO and WP level”. As for the types of data , apart from written sources already publicly available or made available by the IOs, they include: * Electronic questionnaires filled by representatives of the IOs where information (also of statistical nature) were asked; * Audio-recordings of face-to-face activities (focus groups, monitoring sessions, part of the interviews conducted with representatives of IOs), stored in MP3 format * Audio-recordings of the at-distance monitoring sessions, stored in MP3 format * Anonymised transcriptions (text files) of the audio recordings * Anonymised transcriptions (text files) of handwritten notes of interviews. As for the utility of these data, they have been used only for the purposes described above and exclusively in the framework of the project. As for the public availability of these data, the following issues are to be considered: * Both D1.3 and D7.3 are confidential and cannot be (and will be not) made publicly available * In both reports, personal data and opinions have been anonymised, and any personal features of the interviewee/participant are not mentioned when it could lead to their identification; this is not the case of GEP team leaders, who have been identified as such, even though their names have not been mentioned * Original recordings and their transcription, presently stored by ASDO, are not and will not be made publicly available due to privacy and data protection reasons * Original recordings will be deleted after five years from the final payment of the project, when the obligation of keeping all project documentation to the aim of a possible audit will be expired. # Data storage security at Survey Monkey In this Annex we address the question about “What arrangements or assurances have been made or provided from Survey Monkey for the data that is stored on their servers. Are they in compliance with EU law in this area?” As described in deliverable D9.2 (Data Management Plan), LIBRA used the Survey Monkey platfrom to run the survey about staff perception about gender equality realted topics. The deliverable D9.2 was submitted in April 2016 and approved during the first REA organised project evaluation in Februar 2017 to be compliant with the European Data Protection Directive (Directive 95/46/EC on the protection of individuals with regard to the processing of personal data). We run the surevy during the first half of the year of 2016 on the institutional Survey Monkey account owned by the BI team. The account holder is responsible for the management of the collected data. After closing the survey, BI downloaded the data from the Survey Monkey server to the BI servers. All data were deleted from the Survey Monkey servers . All raw data were transferred to the CRG servers to be archived as foreseen in the Data Managemnt Plan and deleted on the BI servers. Also after the enforcement of the General Data Protection Regulation (EU) 2016/679 (GDPR) in in May 2018 Survey Monkey keeps to be compliant the EU law _as stated on their website_ ( _https://help.surveymonkey.com/articles/en_US/kb/surveymonkey-gdpr#requests_ ) . Cited from the website: _Under GDPR, EU data subjects are entitled to exercise the following rights:_ _Right of Access: Find out what kind of personal information is held about you and get a copy of this information._ _Right of Rectification: Ask for your information to be updated or corrected._ _Right to Data Portability: Receive a copy of the information you've provided under contract so you can provide it to another organization._ _Right to Restrict Use: Ask for your personal information to stop being used in certain cases, including if you believe that the personal information about you is incorrect or the use is unlawful._ _Right to Object: Object to use of your information where a party is processing it on legitimate interest basis, and object to have your personal information deleted._ _Right to Erasure (also known as Right to be Forgotten): Request that your personal information be deleted in certain cases._ Personal Data requests (such as right to access, right to erase, right to be forgotten, or others) can be submitetd by e.g. account holder or respondents: _https://help.surveymonkey.com/contact?form=GDPR_	https://phaidra.univie.ac.at/o:1140797	Horizon 2020
0184_Lynx_780602.md	# INTRODUCTION This document is the Data Management Plan (DMP) of the project. The final version of this document will be available as “D2.8 Final report of the data management activities” in M36. This document is complemented by “D7.2 IPR and Data Protection Management”, which was delivered in M6. The Data Management Plan adheres to and complies with the _H2020 Data Management Plan – General Definition_ given by the EC online, where the DMP is described as follows: _“A DMP describes the data management life cycle for the data to be collected, processed and/or generated by a Horizon 2020 project. As part of making research data findable, accessible, interoperable and reusable (FAIR), a DMP should 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)”_ Section 2 follows the template proposed by the EC 1 . Lynx adopts policies compliant with the official FAIR guidelines [1] (findable, accessible, interoperable and re-usable). Lynx participates Open Research Data Pilot (ORDP) and is obliged to deposit the produced research data in a research data repository. For such effect, the Zenodo repository has been chosen, which exposes the data to OpenAIRE (a European project supporting Open Science) granting its long term preservation. The description of the most relevant datasets for compliance have been published in a Lynx Data Portal, using the open source data portal CKAN software 2 . Metadata is provided for every relevant dataset, and data is selectively provided whenever it can be republished without license restrictions and relevance for the project is high. This deliverable also describes a catalogue of relevant legal and regulatory data models and a strategy for the homogenisation of the data sources. Finally, the document describes the _Multilingual Legal Knowledge Graph_ for Compliance, or Legal Knowledge Graph for short (Section 6), which is the backbone on when the Lynx services rest (Figure 1). European Directives General legal goals for every European Member State National Legislation Every Member State has different national and regional legislation in force European Regulations Legislative act binding in every Industry standards Technical documents in occasions necessary to achieve certification Case law Judgements, sentences European Member State Figure 1. Schematic description of the Multilingual Legal Knowledge Graph for Compliance # DATA MANAGEMENT PLAN This Section is the Data Management Plan as of M18. It follows the template proposed by the EC and is applicable to the data used in or generated by Lynx, with the sole exception of pilot-specific data, whose management may be further specified in per-pilot DMPs. If the implementation of the pilots required a different DMP, either new DMP documents or new additions to this document shall be defined by the pilot leaders and the resulting work included in future versions of this document. The EC promotes the access to and reuse of research data generated by Horizon 2020 projects through the Open Research Data Pilot. This project commit to the rules 2 on open access to scientific peer reviewed publications and research data that beneficiaries have to follow in projects funded or cofunded under Horizon 2020 [33]. In particular: ― Lynx has developed and maintains keep up-to-date a Data Management Plan (this version is a snapshot of a continuously evolving document). ― Lynx has deposited the data in a research data repository –Zenodo. Lynx has a community in Zenodo, and CKAN provides a stable repository for data results. The data outcomes of the project live in CKAN. ― Lynx makes sure third parties can freely access, mine, exploit, reproduce and disseminate it – where applicable and not in conflict with any IPR considerations. ― Lynx has made clear what tools will be needed to use the raw data to validate research results – standard formats have been used for data at every moment. The next sections and the questions are taken from the Horizon 2020 FAIR DMP template, which is recommended by the EU commission but voluntary. ## DATA SUMMARY <table> <tr> <th> 1\. Data summary </th> </tr> <tr> <td> a) What is the purpose of the data collection / generation and its relation to the objectives of the project? </td> </tr> <tr> <td> </td> <td> The main objective of Lynx is “to create an ecosystem of smart cloud services to better manage compliance, based on a legal knowledge graph (LKG) which integrates and links heterogeneous compliance data sources including legislation, case law, standards and other aspects”. In order to deliver these smart services, data is collected and integrated into a Legal Knowledge Graph, described in more detail in Section 6. </td> </tr> <tr> <td> b) What types and formats of data will the project generate / collect? </td> </tr> <tr> <td> </td> <td> The very nature of this project makes the number of formats too high as to be foreseen in advance. However, the project will be keen on gathering data in RDF format or producing RDF data itself. RDF will be the format of choice for the meta model, using standard vocabularies and ontologies as data models. More details on the initially considered data models are given in Section 4. </td> </tr> <tr> <td> c) Will you re-use any existing data and how? </td> </tr> <tr> <td> </td> <td> The core part of the LKG is created by reusing existing datasets, either copying them into the consortium servers (only if strictly needed) or using them directly from the sources. </td> </tr> <tr> <td> d) What is the origin of the data? </td> </tr> <tr> <td> </td> <td> Although Lynx is greedy in gathering and linking as much compliance-related data as possible from any possible source, it can be foreseen that the Eur-Lex portal will become the principal data source. Users of the Pilots may contribute their own data (e.g. private contracts, paid standards), which will be neither included into the LKG nor made publicly available. </td> </tr> <tr> <td> e) What is the expected size of the data? </td> </tr> <tr> <td> </td> <td> The strong reliance of Lynx in external open data sources minimizes the amount of data that Lynx will have to physically store. No massive data storage infrastructure is foreseen. </td> </tr> <tr> <td> f) To whom might the data be useful ('data utility')? </td> </tr> <tr> <td> </td> <td> Data will be useful for SMEs and EU citizens alike through different portals. </td> </tr> </table> ## FAIR DATA <table> <tr> <th> 2\. FAIR data </th> </tr> <tr> <td> 2.1 Making data findable, including provisions for metadata </td> </tr> <tr> <td> a) Are the data produced and / or used in the project discoverable and identifiable? </td> </tr> <tr> <td> </td> <td> Data is discoverable through a dedicated data portal (http://data.lynx- project.eu), further described in Section 3. Data assets will be identified with a harmonized policy to be defined in the forthcoming months. Research data may be linked to the corresponding publications and vice versa via their DOIs. </td> </tr> <tr> <td> b) What naming conventions do you follow? </td> </tr> <tr> <td> </td> <td> A specific URI minting policy has been defined in Section 5 to identify data assets. </td> </tr> <tr> <td> c) Will search keywords be provided that optimize possibilities for re-use? </td> </tr> <tr> <td> </td> <td> Open datasets described in the Lynx data portal are findable through standard forms including keyword search. </td> </tr> <tr> <td> d) Do you provide clear version numbers? </td> </tr> <tr> <td> </td> <td> Zenodo supports DOI versioning. </td> </tr> <tr> <td> e) What metadata will be created? </td> </tr> <tr> <td> </td> <td> Metadata records describing each dataset is downloadable as DCAT-AP entries in the CKAN. Assets in Zenodo have also metadata records. </td> </tr> <tr> <td> 2.2 Making data openly accessible </td> </tr> <tr> <td> a) Which data produced and / or used in the project will be made openly available as the default? </td> </tr> <tr> <td> </td> <td> Open data : data in the LKG . The adopted approach is “as open as possible, as closed as necessary”. Data assets produced during the project will preferably be published as open data. Nevertheless, during the project some datasets will be created from existing private resources (e.g. dictionaries by KDictionaries), whose publication would irremediable damage their business model. These datasets will not be released as open data. </td> </tr> </table> <table> <tr> <th> </th> <th> Datasets in the LKG will be in any case published along with a license. This license will be specified as a metadata record in the data catalog, which can also be exported as RDF using the appropriate vocabulary terms (dtc:license) and eventually using machine readable licenses. Open data: research data. In December 2013, the EC announced their commitment to open data through the Pilot on Open Research Data, as part of the Horizon 2020 Research and Innovation Programme. The Pilot’s aim is to “improve and maximise access to and reuse of research data generated by projects for the benefit of society and the economy”. In the frame of this Pilot on Open Research Data, results of publicly-funded research should be disseminated more broadly and faster, for the benefit of researchers, innovative industry and citizens. The Lynx project chose to participate in the Open Research Data Pilot (ORDP). Consequently, publishing as “open” the digital research data generated during the project is a contractual obligation (GA Art. 29.3). This provision does not include the pieces of data which are derivative of private data of the partners. Their openness would endanger their economic viability and jeopardize the Lynx project itself (which is sufficient reason not to open the data as per GA Art. 29.3). Every Lynx partner will ensure Open Access to all peer-reviewed scientific publications relating to its results. Lynx uses Zenodo as the online repository (https://zenodo.org/communities/lynx/) to upload public deliverables and possibly part of the scientific production. Zenodo is a research data repository created by OpenAIRE to share data from research projects. Records are indexed immediately in OpenAIRE, which is specifically aimed to support the implementation of the EC and ERC Open Access policies. Nevertheless, in order to avoid fragmentation, the Lynx webpage will act as the central information node. The following categories of outputs require Open Access to be provided free of charge by Lynx partners, to related datasets, in order to fulfil the H2020 requirements of making it possible for third parties to access, mine, exploit, reproduce and disseminate the results contained therein: * _Public deliverables_ will be available both at Zenodo and the Lynx website at http://lynxproject.eu/publications/deliverables. See Figure 2 and Figure 3. * _Conference and Workshop presentations_ may be published at Slideshare under the account https://www.slideshare.net/LynxProject. * _Conference and Workshop papers and articles for specialist magazines_ may be also reproduced at: http://lynx-project.eu/publications/articles. * _Research data and metadata_ are also available. Metadata and selected data is available in the CKAN data portal, http://data.lynx-project.eu, produced research data at Zenodo. Information will be also given about tools and instruments at the disposal of the beneficiaries and necessary for validating the results. </th> </tr> </table> <table> <tr> <th> </th> <th> Figure 2. Lynx public deliverable at Zenodo. Figure 3. Deliverables on the Lynx website </th> </tr> <tr> <td> b) How will the data be made accessible (e.g. by deposition in a repository)? </td> </tr> </table> <table> <tr> <th> </th> <th> Data descriptions (metadata) are accessible through a dedicated data portal, hosted in Madrid and available under http://data.lynx-project.eu. Data from small datasets is also available from the web server –where _small_ means a file size that does not compromise the web server availability. Eventually the metadata descriptions will be uploaded into other repositories, such as Retele 3 resources in Spanish language, ELRC-SHARE 4 in general and others to be identified. In addition, the cooperation with the CEF eTranslation 5 TermBank project will be considered, in view of sharing terminological domain- specific resources. </th> </tr> <tr> <td> c) What methods or software tools are needed to access the data? </td> </tr> <tr> <td> </td> <td> Relevant datasets whose license is liberal is available as downloadable files. Eventually, a SPARQL endpoint will be set in place for those dataset in RDF form. Also, the CKAN technology in which the portal is based on, offers an API using standard JSON structures to access the data. The CKAN platform provides the documentation on how to use the API (http://docs.ckan.org/en/ckan2.7.3/api/). </td> </tr> <tr> <td> d) Is documentation about the software needed to access the data included? </td> </tr> <tr> <td> </td> <td> Yes, tools to visualize RDF and JSON are given. </td> </tr> <tr> <td> e) Is it possible to include the relevant software (e.g. in open source code)? </td> </tr> <tr> <td> </td> <td> Some of the software to be developed in Lynx is expected to be published as Open Source. Other software to be developed in Lynx will be derived from private or non-open source code and, thus, not be made publicly accessible. </td> </tr> <tr> <td> f) Where will the data and associated metadata, documentation and code be deposited? </td> </tr> <tr> <td> </td> <td> Lynx uses a private source code repository (https://gitlab.com/superlynx). Open data is deposited in the Lynx open data portal; consortium-internal data within the project intranet. The choice of Nextcloud is justified as the information resides within UPM secured servers in Madrid, avoiding third parties and granting the privacy and confidentiality of the data. Gitlab, as a major provider and host of code repositories, is a common choice among developers but if necessary code might be also hosted at UPM. </td> </tr> <tr> <td> g) Have you explored appropriate arrangements with the identified repository? </td> </tr> <tr> <td> </td> <td> Zenodo already foresees the existence of H2020 consortiums. </td> </tr> <tr> <td> h) If there are restrictions on use, how will access be provided? </td> </tr> <tr> <td> </td> <td> All metadata in Zenodo are openly accessible as soon as the record is published, even if there are restrictions like an embargo on the publications or research data themselves. In this way, it is always possible to contact the author of the data to ask for individual agreements on accessing the data, even if there are general restrictions. </td> </tr> <tr> <td> i) Is there a need for a data access committee? </td> </tr> </table> <table> <tr> <th> </th> <th> As of today, there is no need for a Data Access Committee 6 . </th> </tr> <tr> <td> j) Are there well described conditions for access (i.e. a machine readable license)? </td> </tr> <tr> <td> </td> <td> Description of data assets include a link to well-known licenses, for which machine readable versions exist. Either Creative Commons Attribution International 4.0 (CC-BY) or Creative Commons Attribution Share-Alike International 4.0 (CC-BY-SA) will be the recommended licenses. </td> </tr> <tr> <td> k) How will the identity of the person accessing the data be ascertained? </td> </tr> <tr> <td> </td> <td> The Lynx intranet (Nextcloud) provides standard access control functionalities. The servers are located in a secured data centre at UPM. The access point is https://delicias.dia.fi.upm.es/lynxnextcloud/. Access is secured by asymmetric keys or passwords and communications use SSL </td> </tr> <tr> <td> 2.3 Making data interoperable </td> </tr> <tr> <td> a) Are the data produced in the project interoperable? </td> </tr> <tr> <td> The LKG preferred format is RDF, granting interoperability between institutions, organisations and countries. This choice optimally facilitates re-combinations with different datasets from different origins. Zenodo uses standard interfaces, protocols, metadata, etc. CKAN implements standard api access. </td> </tr> <tr> <td> b) What data and metadata vocabularies, standards or methodologies will you follow to make your data interoperable? </td> </tr> <tr> <td> </td> <td> Specific data and metadata vocabularies will be defined throughout the entire project. An initial collection has already been edited and has been published at http://lynx-project.eu/data2/datamodels (see also Figure 4). </td> </tr> <tr> <td> c) Will you be using standard vocabularies for all data types present in your data set, to allow interdisciplinary interoperability? </td> </tr> <tr> <td> </td> <td> Standard vocabularies will be used inasmuch as possible, like the ECLI ontology, the Ontolex model and other vocabularies similarly spread. These choices grant inter-disciplinary collaboration. For example, Ontolex 7 is standard in the language resources and technologies communities, whereas the ELI ontology 8 (European Law Identifier) is standard in the European legal community. </td> </tr> <tr> <td> d) 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> </table> <table> <tr> <th> </th> <th> If vocabularies or ontologies are further defined, they will be published online, documented and mapped to other standard ontologies. Figure 4 illustrates a possible visualization for the data models. Figure 4. A catalogue of relevant ontologies and vocabularies </th> </tr> <tr> <td> 2.4 </td> <td> Increase data re-use (through clarifying licences) </td> </tr> <tr> <td> a) </td> <td> How will the data be licensed to permit the widest re-use possible? </td> </tr> <tr> <td> </td> <td> Data in Zenodo is openly licensed. </td> </tr> <tr> <td> b) </td> <td> When will the data be made available for re-use? </td> </tr> <tr> <td> _G _ </td> <td> _uidance: If an embargo is sought to give time to publish or seek patents, specify why and how long this will _ </td> </tr> <tr> <td> _ap_ </td> <td> _ply, bearing in mind that research data should be made available as soon as possible._ </td> </tr> <tr> <td> </td> <td> No data embargoes are foreseen. Public data is published as soon as possible, but private data will remain private as long as the interested parties, rightsholders of the data, decide. </td> </tr> <tr> <td> c) </td> <td> Are the data produced and / or used in the project useable by third parties, in particular after the end of </td> </tr> <tr> <td> th </td> <td> e project? </td> </tr> <tr> <td> </td> <td> Lynx aims at building a LKG towards compliance. In the long term, the LKG may be repurposed and the data portal may become a reference entry point to find open, linguistic legal information as RDF. </td> </tr> <tr> <td> d) </td> <td> How long is it intended that the data remains re-usable? </td> </tr> <tr> <td> </td> <td> Some of the datasets require maintenance (e.g. legislation and case law must be kept up to date). Whereas a core of information may still be of interest even with no maintenance, those datasets directly used by services under exploitation will be maintained. In any case, metadata records describing the datasets will include a field informing on the last modification date. </td> </tr> <tr> <td> e) </td> <td> Are data quality assurance processes described? </td> </tr> <tr> <td> </td> <td> Only formal aspects of data quality are expected to be assured. In particular, the 5-stars 9 paradigm is considered, and the data portal describes this quality level in due time. </td> </tr> </table> ## ALLOCATION OF RESOURCES <table> <tr> <th> 3 Allocation of resources </th> </tr> <tr> <td> a) What are the costs for making data FAIR in your project? </td> </tr> <tr> <td> </td> <td> The cost of publishing FAIR data includes (a) maintenance of the physical servers; (b) time devoted to the data generation and (c) long term preservation of the data. Zenodo is free. Maintaining the hosting for CKAN costs money, but this has been foreseen in the budget. </td> </tr> <tr> <td> b) How will these be covered? </td> </tr> <tr> <td> </td> <td> Resources to maintain and generate data are covered by the project. Long term preservation of data is free by uploading the research data at Zenodo. </td> </tr> <tr> <td> c) Who will be responsible for data management in your project? </td> </tr> <tr> <td> </td> <td> UPM is responsible for managing data in the data portal, and for managing private data in the intranet. UPM is not responsible for keeping personal data collected to provide the pilot services but the directly involved partners (openlaws, Cuatrecasas, DNV GL). UPM is responsible for the Zenodo account, and must approve ( _curate_ ) every upload. </td> </tr> <tr> <td> d) Are the resources for long term preservation discussed? </td> </tr> <tr> <td> </td> <td> Public deliverables and research data are being uploaded to Zenodo, which grants the long term preservation. A specific community has been created in Zenodo 10 . Alternatively, if difficulties are found with Zenodo, datasets may also be uploaded to Figshare 11 or B2Share 12 where a permanent DOI is retrieved. Other sites such as META-SHARE, ELRC-SHARE or the European Language Grid may be considered in addition to grant long term preservation and maximize the impact and dissemination. </td> </tr> </table> ## DATA SECURITY <table> <tr> <th> 4 Data security </th> </tr> <tr> <td> a) Is the data safely stored in certified repositories for long term preservation and curation? </td> </tr> <tr> <td> </td> <td> UPM is physically storing data on their servers: webpage, files and data in the Nextcloud system, the CKAN data catalogue and mailing lists. Source code is hosted at Gitlab on a Dutch data center. These pieces of data are both digitally and physically secured in a data centre. Backups are made of these systems, to external hard disks or other machines. In principle, no personal data will be kept at UPM, and the pilot leaders will define specific DMP with specific data protection provisions and specific data security details. </td> </tr> <tr> <td> b) What provisions are in place for data security? </td> </tr> </table> Relevant data which is open, shall be uploaded to Zenodo. In addition, relevant language datasets produced in the course of Lynx will be uploaded to catalogues of language resources. ## LEGAL, ETHICAL AND SOCIETAL ASPECTS <table> <tr> <th> 5 Ethical aspects </th> </tr> <tr> <td> a) Are there any ethical or legal issues that can have an impact on data sharing? </td> </tr> <tr> <td> </td> <td> Legal framework EU citizens are granted the rights of privacy and data protection by the Charter of Fundamental rights of the EU. In particular, Art. 7 states that “ _everyone has the right respect for private and family life, home and communications_ ”, whereas Art. 8 regulates that “ _everyone has the right to the protection of personal data concerning him or her_ ” and that processing of such data must be “ _on the basis of the consent of the person concerned or some other legitimate basis laid down by law_ .” These rights are developed in detail by the General Data Protection Regulation (GDPR), Regulation 2016/679/EC, which is in force in every Member State since 25 th May 2018. This regulation imposes obligations to the Lynx consortium, which is also reminded by Art. 39 of the Lynx Grant Agreement (GA): “ _the beneficiaries must process personal data under the Agreement in compliance with applicable EU and national law on data protection_ ” The same GA also reminds that beneficiaries “ _may grant their personnel access only to data that is strictly necessary for implementing, managing and monitoring the Agreement_ ” (GA Art. 39.2). _Personal data_ is, according to GDPR art. 4.1 “ _any information relating to an identified or identifiable natural person (‘data subject’); an identifiable natural person is one who can be identified, directly or indirectly, in particular by reference to an identifier such as a name, an identification number, location data, an online identifier or to one or more factors specific to the physical, physiological, genetic, mental, economic, cultural or social identity of that natural person_ ”, whereas _data processing_ is (art. 4.2): “ _any operation or set of operations which is performed on personal data or on sets of personal data, whether or not by automated means, such as collection, recording, organisation, structuring, storage, adaptation or alteration, retrieval, consultation, use, disclosure by transmission, dissemination or otherwise making available, alignment or combination, restriction, erasure or destruction_ ”. With these definitions, Pilot 1 will most likely have to collect and process personal data, and possibly other Pilots as well. The purposes for which personal data will be collected are justified in compliance with art.5.b, and the processing of personal data is legitimate in compliance with art. 6. The implementation of the Pilot 1 and other pilots processing personal data will have to implement the necessary legal provisions to respect the rights of the data subjects. Several internal communication channels have been established for Lynx: mailing lists, a website and an intranet. The three servers are hosted at UPM and comply with the Spanish legislation. The Lynx web site (http://lynx-project.eu) is compliant regarding the management of cookies with _Ley 34/2002, de 11 de julio, de servicios de la sociedad de la información y de comercio electrónico_ . Lynx will most likely handle datasets with personal data (Pilot 1), as users will be registered in the Lynx platform to enjoy personalised services and to upload contracts with personal data. The consortium will adopt any measure to comply with the current legislation. Ethical and societal aspects The ethical aspect of greatest interest is the processing of personal data. The processing of personal data may become a possibility in the framework of Pilot 1. GA Article 34 “Ethics and research integrity” is binding and shall be respected. Ethical and privacy related concerns are fully addressed in Section 3.2 of Deliverable 7.2 “ _IPR and Data Protection management documents_ ”. Besides, the ethics issues identified are already being handled by the pilot organisations during their </td> </tr> <tr> <td> </td> <td> daily operation activities, as they confront with national laws and EU directives regarding the use of information in their daily services, as clearance for the processing, storing methods, data destruction, etc. has been provided to such organisation a priori and is not case specific. The research to be done during Lynx does not raise any other issues, and the project will make sure that it will follow the same patterns and rules used by the pilot organisations, that will guarantee the proper handling of ethical issues and the adherence to national, EU wide and international law and directives that do not violate the terms of the programme. The societal impact of this project is expected to be positive, enhancing the access of EU citizens to legislation and contributing towards a fairer Europe. In addition to the best effort made by the project partners, members of the Advisory Board may be requested to issue a statement on the ethical and societal impact of the Lynx project. An more detailed internal assessment of the Legal, Ethical and Societal impact of this project is made in Section 2.6. Finally, the Lynx websites will try to comply with the W3C recommendations on accessibility, such as the Web Content Accessibility Guidelines (WCAG) 2.0 –which covers a wide range of recommendations for making Web content more accessible. </td> </tr> <tr> <td> b) Is informed consent for data sharing and long term preservation included in questionnaires dealing with personal data? </td> </tr> <tr> <td> </td> <td> Whenever the operation of the piltos start, pilot leaders will report these consent documents. </td> </tr> </table> ## ASSESSMENT OF LEGAL, ETHICAL AND SOCIETAL IMPACT ASPECTS ### Lynx methodology for the impact assessment The Lynx strategy for dealing with legal, ethical and societal aspects was initially included in _D2.1 Initial Data Management Plan_ and _D7.2 IPR and Data Protection Management Documents._ The main issue identified as posing potential risks in terms of ethical, legal and societal impact was the potential affection of some Human Rights, and in particular, the right to privacy and data protection. To manage this risks the Consortium put in place a series of measures as a result of the Initial Recommendations. At this stage of the project, as part of the Ongoing Monitoring devised in Paragraph 3.3.4 of D7.2, the UAB partner has proceeded to review the status of implementation of the risk management strategy. Furthermore an ethical and societal impact assessment has been conducted to verify that no other issues have arisen now that the project has advanced in the development of the Lynx solution. Paragraph 2.2 below contains the Ethical and Societal impact assessment. This assessment has been conducted following the methodology developed by the H2020 e-SIDES project. 13 In particular, Deliverable 2.2. of the e-SIDES project contains a list of ethical, legal societal and economic issues of Big Data technologies. This list has been verified against the Lynx project, explaining how Lynx deals with avoiding each one of the issues on the lists. Paragraph 2.3 presents the review of the status of implementation of the Initial Recommendations. The original strategy for the management of privacy and data protection presented in _D7.2 IPR and Data Protection Management Documents_ , included a two-fold perspective: recommendations for the requirements elicitation techniques to be deployed in Tasks 1.1. and 4.1., and recommendations for the Lynx Solution. Since then, Tasks 1.1. and 4.1. have finished and been reported in the corresponding deliverables ( _D1.1 Functional requirements analysis report and D4.1 Pilots requirements analysis report_ ). Therefore, an update is necessary only in relation to the recommendations for the Lynx solution. Below we have included a review of the status of implementation of each of this recommendations at this stage of the project, as well as the indication of whether there are still some concerns related to some of them, in the form of mid-term recommendations. ### General ethical and societal aspects: Ethical and societal impact assessment #### Ethical impact assessment * Human welfare: Discrimination of humans by big data-mediated prejudice can occur. Detrimental implications can emerge in the contexts of employment, schooling or travelling by various forms of big data-mediated unfair treatment of citizens. Lynx : Personal data is not the type of data relevant for Lynx. Lynx integrates and links heterogeneous compliance data sources including legislation, case law, standards and other private documents such as contracts. Within this sources personal data may be contained. However, personal data _per se_ is not analysed or processed in order to extract patterns, trends, decisions or connexions related to humans and human behaviour. Therefore Lynx will not impact in human welfare. * Autonomy: Big data-driven profiling practices can limit free will, free choice and be manipulative in raising awareness about, for instance, news, culture, politics and consumption. Lynx : Lynx does not entail automated decision making nor profiling, therefore autonomy is preserved. * Non-maleficence: Non-transparent data reuse in the world of big data are vast and could have diverse detrimental effects for citizens. This puts non-maleficence as a value under pressure. Lynx : The only foreseen reuse is that of personal data contained in case- law. However, this is openly available data and therefore can be used as part of the legal documents to provide compliance services. The reuse is therefore transparent and there is no risk of maleficence. * Justice (incl. equality, non-discrimination, digital inclusion): Systematic unfairness can emerge, for instance, by generating false positives during preventative law enforcement practices or false negatives during biometric identification processes. (Such instances put constant pressure on the value of justice.) Lynx : Lynx does not entail automated decision making nor profiling. The aim of Lynx is not to identify, characterize or give access to services to individuals. * Accountability (incl. Transparency): For instance, in the healthcare domain patients or in the marketing domain consumers often do not know what it means and who to turn to when their data is shared via surveys for research and marketing purposes. Lynx : As part of their Data Protection Policy, users of the Lynx technology should disclose to their clients that their personal data may be processed by the Lynx technology. * Trustworthiness (including honesty and underpinning also security): Citizens often do not know how to tackle a big data-based calculation about them or how to refute their digital profile, in case there are falsely accused, e.g.: false negatives during biometric identification, false positives during profiling practices. Their trust is then undermined. The technology operators trust at the same time lies too much in the system. Lynx : Lynx does not entail automated decision making nor profiling. It does not generate any type of conclusion on individuals or individual’s behaviours. * Privacy: Simply the myriad of correlations between personal data in big data schemes allows for easy identifiability, this can lead to many instances for privacy intrusion. Lynx : Privacy and data protection implication of Lynx are described in further detail in the List of legal issues. * Dignity: For instance, when revealing too much about a user, principles of data minimization and design requirements of encryption appear to be insufficient. Adverse consequences of algorithmic profiling, such as discrimination or stigmatization also demonstrate that dignity is fragile in many contexts of big data. Lynx : Lynx does not entail automated decision making nor profiling, therefore autonomy is preserved. * Solidarity: Big data-based calculations in which commercial interests are prioritized rather than nonprofit- led interests, are examples of situations in which solidarity is under pressure. For instance, immigrants are screened by big data-based technologies, they may not have the legal position to defend themselves from potential false accusations resulting from digital profiling which can be seen as a non-solidary treatment. Lynx : Lynx does not entail automated decision making nor profiling, therefore autonomy is preserved. * Environmental welfare : Big data has rather indirect effects on the environment. But for instance, lithium mining for batteries is such. (But extending the life-expectancy of batteries and, for instance, using more sun-energy for longer-lasting batteries could be helpful.) #### Societal impact assessment * Unequal access : People are not in the same starting position with respect to data and data-related technologies. Certain skills are needed to find one’s way in the data era. Privacy policies are usually long and difficult to understand. Moreover, people are usually not able to keep their data out of the hands of parties they don’t want to have them. Lynx : Lynx technologies are foreseen to be used by experienced, trained professionals. No personal data will be processed other than that contained in case law (openly available data) and private documents such as contracts (consent and privacy policy of user). The users of the Lynx technologies will make sure that their clients understand when their personal data may be processed by the Lynx technologies. However, it is important to remember that personal data per se will not be analysed or processed in order to extract patterns, trends, decisions or connexions related to humans and human behaviour. * Normalisation: The services offered to people are selected on the basis of comparisons of their preferences and the preferences of people considered similar to them. People are put into categories whose characteristics are determined by what is most common. There is pressure toward conformity: the breadth of choices is restricted, and pluralism and individuality are pushed back. Lynx : Lynx does not collect nor process any data on preferences and or characteristics of individuals. It is important to remember that personal data per se will not be analysed or processed in order to extract patterns, trends, decisions or connexions related to humans and human behaviour. * Discrimination: People are treated differently based on different individual characteristics or their affiliation to a group. The possibility to reproach people with things they did years ago or to hold people accountable for things they may do in the future affects people’s behaviour. The data as well as the algorithms may be incorrect or unreliable, though. Lynx : Lynx does not process any data on characteristics of individuals or behaviours. It is important to remember that personal data per se will not be analysed or processed in order to extract patterns, trends, decisions or connexions related to humans and human behaviour. * Dependency: People depend on governmental policy for security and privacy purposes. It is considered a misconception that people can be self-governing in a digital universe defined by big data. People choosing not to disclose personal information may be denied critical information, social support, convenience or selection. People also depend on the availability of services provided by companies. It is considered a risk if there are no alternatives to services that are based on the collection or disclosure of personal data. Lynx : Lynx does not determine access to public services. As for private companies Lynx adds value to the service provided by their users to their clients. If a client rejects the processing of his/her personal data by the Lynx technologies the company will provide the service nonetheless, just without the improvement in efficiency * Intrusiveness: Big data has integrated itself into nearly every part of people’s online life and to some extent also in their offline experience. There is a strong sentiment that levels of data surveillance are too intimate but nevertheless many press ‘agree’ to the countless number of ‘terms and conditions’ agreements presented to them. Lynx : Lynx does not request personal data from its users or third parties. It does not intrude individual’s private lives. * Non-transparency: Algorithms are often like black boxes to people, they are not only opaque but also mostly unregulated and thus perceived as incontestable. People usually cannot be sure who is collecting, processing or sharing which data. Moreover, there are limited means for people to check if a company has taken suitable measures to protect sensitive data. Lynx : Lynx users will make sure that their privacy policy includes all the relevant information on the Lynx platform, the processing of personal data, the data controller and processors, etc. More information on this can be found in the list of legal issues. * Abusiveness: Even with privacy regulations in place, large-scale collection and storage of personal data make the respective data stores attractive to many parties including criminals. Simply anonymised data sets can be easily attacked in terms of privacy. The risk of abuse is not limited to unauthorised actors alone but also to an overexpansion of the purposes of data use by authorised actors (e.g. law enforcement, social security). Lynx : Lynx does not entail large-scale collection and storage of personal data. Minor amounts of personal data may be processed as part of some of the sources used by Lynx, namely case-law and private documents such as contracts. # CATALOGUE OF DATASETS This section describes a catalogue of relevant legal, regulatory and linguistic datasets. Datasets in the Legal Knowledge Graph are those necessary to provide compliance related services that also meet the requirement of being published as linked data. The purpose of Lynx Task 2.1 is twofold: 1. Identify as many as possible open dataset possibly relevant to the problem in question (either in RDF or not) 2. Build the Legal Knowledge Graph by identifying existing linked data resources or by transforming existing datasets into linked data whenever necessary Figure 5 represents the Legal Knowledge Graph as a collection of dataset published as linked data. The LKG lies amidst another cloud of datasets, in various formats either structured or not (such as PDF, XLS or XML). The section contains: (a) the methodology followed to describe datasets of interest; (b) the methodology to transform existing resources into LKG datasets; (c) a description of the Lynx data portal and the related technology and (d) an initial list of relevant datasets. Legal Knowledge Graph ( RDF ) Other datasets of interest ( PDF, XLS, XML… ) Figure 5. Datasets in the LKG and out of it ## METHODOLOGY FOR CATALOGUING DATASETS Data assets potentially relevant to the Lynx project are those that might help providing multilingual compliance services. They might be referenced by datasets in the LKG as external references. The identification and description of these datasets is being made during the project in a cooperative way, during the entire project lifespan. The methodology has consisted of the following steps: 1. _Identification of datasets of possible interest_  Identification of relevant datasets by the partners;  Discovery of relevant datasets by browsing data portals, reviewing literature and making general searches; 2. _Description of resources_  Description of the resources identified in Step 1 using an agreed template (spreadsheet) with metadata records (see Section 3.1.1). 3. _Publication of dataset descriptions_  Publication of the dataset description in the CKAN Open Data Portal via CKAN form  Transformation of the metadata records to RDF using the vocabulary DCAT-AP (to be an automated task from the spreadsheet) This process is being iteratively carried out throughout the project. ### Template for data description Every Lynx partner, within their domain of expertise, has described an initial list of data sources of interest for the project. In order to homogeneously describe the data assets, a template with metadata records has been created with the due consensus among the partners. The template for data description contains two main blocks: one with general information about the dataset and another with information about the resource. Within this context, “dataset” makes reference to the whole asset, while “resource” defines each one of the different formats in which the dataset is published. For instance, the UNESCO thesaurus is a single dataset which can be found as two different resources: as a SPARQL Endpoint and as a downloadable file in RDF. Thereby, the metadata records in Table 1 describe information about the dataset as a whole. As the project progressed, it was required to add a new property to the first metadata selection reported in the D2.1, _Initial Data Management Plan_ . At this stage of the project, Lynx Data Portal collets a wide amount of resources; however, not all of them are included in the Legal Knowledge Graph. Such external resources are present in the portal since they can be useful in further processes. Therefore, a classification between those datasets in the LKG and the external resources is performed by the use of the Boolean parameter “Directly LKG Link”. <table> <tr> <th> Field </th> <th> Description </th> </tr> <tr> <td> Title </td> <td> the name of the dataset given by the author or institution that publishes it. </td> </tr> <tr> <td> URI </td> <td> identifier pointing to the dataset. </td> </tr> </table> Type in the LKG type of dataset in the legal knowledge graph (language, data, etc.). <table> <tr> <th> Type </th> <th> type of dataset (term bank, glossary, vocabulary, corpus, etc.). </th> </tr> <tr> <td> Domain </td> <td> topic covered by the dataset (law, education, culture, government, etc.). </td> </tr> <tr> <td> Identifiers </td> <td> other type of identifiers assigned to the dataset (ISRN, DOI, Standard ID, etc.). </td> </tr> </table> Description a brief description of the content of the dataset. <table> <tr> <th> Availability </th> <th> if the dataset is available online, upon request or not available. </th> </tr> <tr> <td> Languages </td> <td> languages in which the content of the dataset are available. </td> </tr> <tr> <td> Creator </td> <td> author or institution that created the dataset. </td> </tr> <tr> <td> Publisher </td> <td> institution publishing the dataset. </td> </tr> <tr> <td> License </td> <td> license of the dataset (Creative Commons, or others). </td> </tr> <tr> <td> Other rights </td> <td> if the dataset contains personal information. </td> </tr> <tr> <td> Jurisdiction </td> <td> jurisdiction where the dataset applies (if necessary). </td> </tr> <tr> <td> Date of this entry </td> <td> date of registration of the dataset in the CKAN. </td> </tr> <tr> <td> Proposed by </td> <td> Lynx partner or Lynx organisation proposing the dataset. </td> </tr> <tr> <td> Number of entries </td> <td> number of terms, triplets or entries that the dataset contains. </td> </tr> <tr> <td> Last update </td> <td> date in which the last modification of the dataset took place. </td> </tr> <tr> <td> Dataset organisation </td> <td> name of the Lynx organisation registering the dataset. </td> </tr> <tr> <td> Direct LKG Link [NEW] </td> <td> indicates whether a dataset is directly represented in the LKG or if it is a external resource. </td> </tr> </table> Table 1. Fields describing a data asset The second set of metadata records, listed in Table 2, gives additional information about the resource in which the metadata can be accessed. This section is repeated as many times as needed (depending on the number of formats of the metadata). <table> <tr> <th> Field </th> <th> Description </th> </tr> <tr> <td> Description </td> <td> description of the type of resource (i.e. downloadable file, SPARQL endpoint, website search application, etc.). </td> </tr> <tr> <td> Data format </td> <td> the format of the resource (RDF, XML, SKOS, CSV, etc.). </td> </tr> <tr> <td> Data access </td> <td> technology used to expose the resource (relational database, API, linked data, etc.). </td> </tr> <tr> <td> Open format </td> <td> if the format of the resource is open or not. </td> </tr> <tr> <td> URI </td> <td> the URI pointing to the different resources. </td> </tr> </table> Table 2. Fields describing a resource associated to a data asset The template was materialized as a spreadsheet distributed among the partners. ### Lynx Data Portal With the aim of publishing the metadata of the harvested datasets, a data portal has been made available under http://data.lynx-project.eu. This data portal uses the technology of CKAN. The Comprehensive Knowledge Archive Network (CKAN) is a web-based management system for the storage and distribution of open data. The system is open source 14 , and it has been deployed on the UPM servers using containerization technologies –Rancher 15 , a leading solution to deploy Docker containers in a Platform as a Service (PaaS). The CKAN open data portal gives access to the resources gathered by all the members of the Lynx project. In the same way, members are able to register and describe their harvested resources to jointly create the Lynx Open Data Portal. To correctly display the relevant information about the datasets, CKAN application uses the metadata described in Section 4.2.1. As a result, each dataset presents the interface as shown by Figure 6 . Figure 6. Screenshot of the Lynx Data Portal The “Data and Resources” section corresponds to the “Resource information” metadata block and “Additional Info” contains the metadata of the “Dataset information” table. The CKAN data portal allows faceted browsing, with filters such as language, format and jurisdiction. At this moment, there are 67 datasets classified in the CKAN, but this number will grow. For the metadata records to be correctly displayed on the website, it was required to establish a correspondence between the metadata in the spreadsheet and the structure in the JSON file that gives shape to the CKAN platform. In the Lynx Data Portal, each dataset can be accessed through their own URI, that is built by using the ID of each resource. Datasets IDs are shown in Table 3, contained in the next section. As a result, dataset URIs look like the example below, where the ID would be unesco-thesaurus: http://data.lynx-project.eu/dataset/unesco-thesaurus The CKAN API enables a direct access to the metadata records. The API is intended for developers who want to write code that interacts with CKAN sites and their data, and it is documented online 16 . For example, the REST GET method: http://data.lynx-project.eu/api/rest/dataset/unesco-thesaurus will return the following answer: {"license_title": null, "maintainer": null, "private": false, "maintainer_email": null, "num_tags": 0, "id": "efaf72c9-f8da-4257-b77e-c1f90952d71a", "metadata_created": "2018-04-11T08:35:41.813169", "relationships": [], "license": null, "metadata_modified": "2018-04-11T08:39:59.429186", "author": null, "author_email": null, "download_url": "http://skos.um.es/sparql/", "state": "active", "version": null, "creator_user_id": "3b131ddc-4bbf- 42ff-9c33-ee1c4f7adb5c", "type": "dataset", "resources": [{"Distribuciones": "SPARQL endpoint", "hash": "", "description": "SPARQL endpoint", "format": "SKOS", "package_id": "efaf72c9-f8da-4257-b77e-c1f90952d71a", "mimetype_inner": null, "url_type": null, "formatoabierto": "", "id": "2a610dc8-15cd-4f17-aee0-149201c427cd", "size": null, "mimetype": null, "cache_url": null, "name": "SPARQL endpoint", "created": "2018-04- 11T08:39:13.979840", "url": "http://skos.um.es/sparql/", "cache_last_updated": null, "last_modified": null, "position": 0, "resource_type": null}, {"Distribuciones": "Downloadable files", "hash": "", "description": "Downloadable files in RDF and Turtle.", "format": "RDF", "package_id": "efaf72c9-f8da-4257-b77e-c1f90952d71a", "mimetype_inner": null, "url_type": null, "formatoabierto": "", "id": "81ddd071-4018-4850-b5d8-04b4f5badd7d", "size": null, "mimetype": null, "cache_url": null, "name": "Downloadable files", "created": "2018-04- 11T08:39:59.170137", "url": "http://skos.um.es/unescothes/downloads.php", "cache_last_updated": null, "last_modified": null, "position": 1, "resource_type": null}], "num_resources": 2, "tags": [], "groups": [], "license_id": null, "organization": {"description": "", "title": "OEG", "created": "2018-04-05T08:10:35.821305", "approval_status": "approved", "is_organization": true, "state": "active", "image_url": "", "revision_id": "66f3c9c3-9bdf-4ebe-8ed2-54b4aea30375", "type": "organization", "id": "d4250a6e-d1d4-4a2d-8e40-b663271d8404", "name": "oeg"}, "name": "unesco- thesaurus", "isopen": false, "notes_rendered": "<p>The UNESCO Thesaurus is a controlled and structured list of terms used in subject analysis and retrieval of documents and publications in several fields.</p>", "url": null, "ckan_url": "http://data.lynx-project.eu/dataset/unesco-thesaurus", "notes": "The UNESCO Thesaurus is a controlled and structured list of terms used in subject analysis and retrieval of documents and publications in several fields.\r\n", "owner_org": "d4250a6e-d1d4-4a2d-8e40-b663271d8404", "ratings_average": null, "extras": {"lkg_type": "language", "domain": "Education, Science, Culture, Politics, Countries, Information", "total_number": "4408 (skos concepts)", "language": "en, es, fr, ru", "creator": "Research group of Information Technology (University of Murcia)", "publisher": "UNESCO", "jurisdiction": "", "other_rights": "no", "last_update": "2015", "licence": "Creative Commons 3.0, https://creativecommons.org/licenses/by-ncsa/3.0/deed.es_ES", "date": "11/04/18", "partner": "UPM", "identifier": "", "availability": "online"}, "ratings_count": 0, "title": "UNESCO Thesaurus", "revision_id": "67553ea8-aa13-4dfe-905d-eb499d2d78e9"} ## TRANSFORMATION OF RESOURCES The minimum content of the LKG is the collection of datasets necessary for the execution of the Lynx pilots that are published as linked data. Whereas transformation of resources to linked data is not a central activity of Lynx, the project foresees that some resources will exist but not as linked data, and a transformation process will be necessary. The cycle of activities usually made when publishing linked data is shown in Figure 7. Figure 7. Usual activities for publishing linked data. Figure taken from [25]. Whereas the specification is derived from the pilots and the use case needs, the modelling process leans on existing data models, to be harmonized as described in Section 4.2. The generation of linked data is the transformation of existing resources. These transformation will be different depending on the source format:  From unstructured text, extraction tools (PoolParty, OpenCalais, SketchEngine etc.) and dedicated harvesters to create resources in the LKG.  From relational databases, technologies such as R2RML exist and its use is foreseen, but as of M18 no use of them has been made.  For tabular data, Open Refine and similar tools have been used. ## CATALOGUE OF DATASETS This section contains the datasets catalogued as of M18. ### Datasets in the regulatory domain Within the initial version of this document (D2.1), three datasets in the regulatory domain were identified:  Eur-Lex: Database of legal information containing: EU law (EU treaties, directives, regulations, decisions, consolidated legislation, etc.) preparatory acts (legislative proposals, reports, green and white papers, etc.), EU case-law (judgments, orders, etc.), international agreements, etc. A huge database updated daily with some texts dating back to 1951.  Openlaws: Austrian laws (federal laws and of the 9 regions) and rulings (from 10 different courts), German federal laws, European laws (regulations, directives) and rulings (general court, European Court of Justice). It includes Eur-Lex, 11k national acts and 300k national cases in a neo4j graph.  DNV-GL: Standards, regulations and guidelines to the public, usually in PDF. As the project has progressed, many other datasets have been collected and a new structure has been accordingly defined. Pilot 1 changed the focus from “Data Protection” into “Contracts”. Therefore, harvested legal corpora has been organised accordingly: Contracts, Labour Law and Industrial Standards. Regarding Pilot 1, contract corpora is provided by openlaws. Most of documents are in Austrian German containing personal data that is to be disclosed. Thus, this kind of files are private and not published in the Data Portal. Nevertheless, openlaws will provide more contracts in future stages and some of them are expected to be bilingual, combining German and English information. Hence, they will need to be processed ad hoc. Since Labour Law, Pilot 2, is a huge field itself, these specific corpora is, in turn, divided into three subtopics:  Collective agreements, official documents about conditions of work for a specific sector at the same level as ordinary laws.  Judgements, case law related to labour law in the different jurisdictions.  Legislation, at European Union level and Member State Level. Finally, each corpus is accordingly separated as per the four languages of the project: English, German, Spanish and Dutch. See Figure 8 to get a clear idea of the structure of Lynx datasets in the regulatory domain. Finally, since Pilot 3, Industrial Standards, is led by DNV, most of the documents cover Dutch language. However, a few of them are also in English. Just like Pilot 1, at this moment, Industrial Standards corpus is for private use only. ### Datasets in the language domain Using the methodology described in Section 3.1, several sites and repositories have been surveyed. One of the sources of most interest for linguistic open data is the Linked Open Data Cloud 17 or LOD cloud, due to its open nature and its adequate format as linked data or RDF. In particular, the Linguistic Linked Open Data Cloud 18 is a subset of the LOD cloud which provides exclusively linguistic resources sorted by typology. Different types of datasets in the Linguistic Linked Open Data Cloud are:  Corpora  Terminology, thesauri and Knowledge Bases  Lexicons and Dictionaries  Linguistic Resource Metadata  Linguistic Data Categories  Typological Databases Within this project, the three first types of resources have been shortlisted as the most useful. Besides consuming linked data or RDF in general, other valuable non-RDF resources can be included in the graph, possibly once converted to RDF. Many non-RDF resources of interest in this context can be found in data portals like the European Data Portal, the Library of Congress or the Termcoord public portal, which is of particular interest for the multilingual glossaries in the domain of law. Due to the huge amount of information and open data available nowadays, it is essential to establish these limits to gather only the relevant resources. In the case that more types of datasets are required, they will be harvested at a later stage. Thus, some of the resources already published as linked data and that have been identified as of interest for Lynx are listed below:  STW Thesaurus for Economics: a thesaurus that provides a vocabulary on any economic subject. It also contains terms used in law, sociology and politics (monolingual in English) [30].  Copyright Termbank: a multilingual term bank of copyright-related terms that has been published connecting WIPO definitions, IATE terms and definitions from Creative Commons licenses (multilingual) .  EuroVoc: a multilingual and multidisciplinary thesaurus covering the activities of the EU. It is not specifically legal, but it contains pertinent information about the EU and their politics and law (multilingual).  AGROVOC: a controlled vocabulary covering all the fields of the Food and Agriculture Organization (FAO) of the United Nations. It contains general information and it has been selected since it shares many structures with other important resources (multilingual).  IATE: a terminological database developed by the EU which is constantly being updated by translators and terminologists. Amongst other domains, the terms are related with law and EU governments (multilingual). A transformation to RDF was made in 2015. Resources published in other formats have been considered as well. Structured formats include TBX (used for term bases), CSV and XLS. Exceptionally, resources published in non-machine-readable formats might be considered. Consequently, the following resources published by the EU have also been listed as usable, although they are not included in the Linguistic Linked Open Data Cloud:  INSPIRE Glossary: a term base developed by the INSPIRE Knowledge Base of the European Union. Although this project is related with the field of spatial information, the glossary contains general terms and definitions that specify the common terminology used in the INSPIRE Directive and in the INSPIRE Implementing Regulations (monolingual, en).  EUGO Glossary: a term base addressed to companies and entrepreneurs that need to comply with administrative or professional requirements to perform a remunerated economic activity in Spain. This glossary is part of a European project and contains terms about regulations that are valuable for Lynx purpose (monolingual in Spanish).  GEMET: a general thesaurus, conceived to define a common general language to serve as the core of general terminology for the environment. This glossary is available in RDF and it shares terms and structures with EuroVoc (multilingual).  Termcoord: a portal supported by the European Union that contains glossaries developed by the different institutions. These glossaries cover several fields including law, international relations and government. Although the resources are available in PDF, at some point these documents could be treated and transformed into RDF if necessary (multilingual). In the same way, the United Nations also counts with consolidated terminological resources. Given their intergovernmental domain, the following resources have been selected:  UNESCO Thesaurus: a controlled list of terms intended for the subject analysis of texts and document retrieval. The thesaurus contains terms on several domains such as education, politics, culture and social sciences. It has been published as a SKOS thesaurus and can be accessed through a SPARQL endpoint (multilingual).  InforMEA Glossary: a term bank developed by the United Nations and supported by the European Union with the aim of gathering terms on Environmental Law and Agreements. It is available as RDF and it will be upgraded to a thesaurus during the following months (multilingual).  International Monetary Fund Glossary: a terminology list containing terms on economics and public finances related with the European Union. It is available as a PDF downloadable file; however, it may be transformed as a future work (multilingual). On the other hand, other linguistic resources (not supported by the EU nor the UN) have been spotted. Some of them are already converted into RDF:  Termcat (Terminologia Oberta): a set of terminological databases supported by the government of Catalonia. They contain term equivalents in several languages. Part of these terminological databases were converted into RDF previously and are part of the TerminotecaRDF project. They can be accessed through a SPARQL endpoint (multilingual).  German Labour Law Thesaurus: a thesaurus that covers all main areas of labour law, such as the roles of employee and employer; legal aspects around labour contracts. It is available through a SPARQL endpoint and as RDF downloadable files (monolingual, de).  Jurivoc: a juridical thesaurus developed by the Federal Supreme Court of Switzerland in cooperation with Swiss legal libraries. It contains juridical terms arranged in a monohierarchic structure (multilingual).  SAIJ Thesaurus: a thesaurus that organises legal knowledge through a list of controlled terms which represent concepts. It is available in RDF and intended to ease users’ access information related to the argentine legal system that can be found in a file or in a documentation centre (monolingual, es).  CaLaThe: a thesaurus for the domain of cadastre and land administration that provides a controlled vocabulary. It is interesting because it shares structures and terms with AGROVOC and the GEMET thesaurus, and it can be downloaded as an RDF file (monolingual, en).  CDISC Glossary: a glossary contains definitions of terms and abbreviations that can be relevant for medical laws and agreements It is available in several formats, including OWL (monolingual, en). Finally, one last resource available in other PDF has also been considered due to different facts:  Connecticut Glossary: a glossary that contains legal terms published by the Judicial Branch of the State of Connecticut. It can be transformed into a machine-readable format and from there into RDF since it provides with equivalences of legal terms from English into Spanish (bilingual). Table 3 lists all the resources as a review of the information presented above. On the other hand, the set of the identified linguistic resources has also been represented in an interactive graph, in which each dataset is coloured as per the domain it covers (Figure 9). ID Name Description Language <table> <tr> <th> iate </th> <th> IATE </th> <th> EU terminological database. </th> <th> EU languages </th> </tr> <tr> <td> eurovoc </td> <td> Eurovoc </td> <td> EU multilingual thesaurus. </td> <td> EU languages </td> </tr> <tr> <td> eur-lex </td> <td> EUR-Lex </td> <td> EU legal corpora portal. </td> <td> EU languages </td> </tr> <tr> <td> conneticutlegal-glossary </td> <td> Conneticut Legal Glossary </td> <td> Bilingual legal glossary. </td> <td> en, es </td> </tr> <tr> <td> unescothesaurus </td> <td> UNESCO Thesaurus </td> <td> Multilingual multidisciplinary thesaurus. </td> <td> en, es, fr, ru </td> </tr> <tr> <td> library-ofcongress </td> <td> Library of Congress </td> <td> Legal corpora portal. </td> <td> en </td> </tr> <tr> <td> imf </td> <td> International Monetary Fund </td> <td> Economic multilingual terminology. </td> <td> en, de, es </td> </tr> <tr> <td> eugo-glossary </td> <td> EUGO Glossary </td> <td> Business monolingual dictionary. </td> <td> es </td> </tr> <tr> <td> cdisc-glossary </td> <td> CDISC Glossary </td> <td> Clinical monolingual </td> <td> en </td> </tr> <tr> <td> stw </td> <td> STW Thesaurus for Economics </td> <td> Economic monolingual thesaurus. </td> <td> en </td> </tr> <tr> <td> edp </td> <td> European Data Portal </td> <td> EU datasets. </td> <td> EUlanguages </td> </tr> <tr> <td> inspire </td> <td> INSPIRE Glossary (EU) </td> <td> General terms and definitions in English. </td> <td> en </td> </tr> <tr> <td> saij </td> <td> SAIJ Thesaurus </td> <td> Controlled list of legal terms. </td> <td> es </td> </tr> <tr> <td> calathe </td> <td> CaLaThe </td> <td> Cadastral vocabulary </td> <td> en </td> </tr> <tr> <td> gemet </td> <td> GEMET </td> <td> General multilingual thesauri. </td> <td> en, de, es, it </td> </tr> <tr> <td> informea </td> <td> InforMEA Glossary (UNESCO) </td> <td> Monolingual glossary on environmental law. </td> <td> en </td> </tr> <tr> <td> copyrighttermbank </td> <td> Copyright Termbank </td> <td> Multi-lingual term bank of copyrightrelated terms </td> <td> en, es, fr, pt </td> </tr> <tr> <td> gllt </td> <td> German labour law thesaurus </td> <td> Thesaurus with labour law terms. </td> <td> de </td> </tr> <tr> <td> jurivoc </td> <td> Jurivoc </td> <td> Juridical terms from Switzerland. </td> <td> de, it, fr </td> </tr> <tr> <td> termcat </td> <td> Termcat </td> <td> Terms from several fields including law. </td> <td> ca, en, es, de, fr, it </td> </tr> <tr> <td> termcoord </td> <td> Termcoord </td> <td> Glossaries from EU institutions and bodies. </td> <td> EU languages </td> </tr> </table> agrovoc Agrovoc Controlled general vocabulary. 29 languages Table 3. Initial set of resources gathered. Figure 9. Datasets represented by domain. # DATA MODELS ## INTRODUCTION ### Existing data models in the regulatory domain A number of vocabularies and ontologies for documents in the legal domain has been published in the last few years. Núria Casellas surveyed 52 legal ontologies in 2011 [18], and in the meantime many other new ontologies have appeared, but in practice, only a few of them have direct interest for the LKG, as not every published legal ontology is created with the intention of supporting data models. Some ontologies had the intent of formalizing abstract conceptualizations. For example, ontology design patterns in the legal domain have been explored [17] –but these works have little interest for supporting data publication. The XML schema Akoma Ntoso 19 was initially funded by the United Nations to become some years later an OASIS specification as Legal RuleML 20 . MetaLex [12] was an XML vocabulary for the encoding of the structure and content of legislative documents, which included in newer versions functionality related to timekeeping and version management. The European Committee for Standardization (CEN) adopted MetaLex and evolved the schema to an OWL ontology. MetaLex was extended in the context of the FP6 ESTRELLA project (2006-2008) which developed a network of ontologies known as Legal Knowledge Interchange Format (LKIF). The LKIF ontologies are still available and a reference in the area 21 [14]. Licenses used for the publication of copyrighted work have been modelled with the ODRL (Open Digital Rights Language) language [27]. The European Legislation Identifier (ELI) is a system to make legislation available online in a standardised format, so that it can be accessed, exchanged and reused across border [13]. ELI describes a new common framework to unify and link national legislation with European legislation. ELI, as a framework, proposes a URI template for the identification of legal resources on the web and it also provides an OWL ontology for supporting the representation of metadata of legal events and documents. The European Case Law Identifier (ECLI), much like ELI, was introduced recently for modelling case laws. The BO-ECLI project, funded under the Justice Programme of the European Union (2015-2017), aimed to broaden the use of ECLI and to further improve the accessibility of case law. ### Data models in the linguistic domain Similarly, a large amount of language resources can already be found across the Semantic Web. Such datasets are represented with various schemas, depending on given factors such as the inner structure of the dataset, language, content or the objective of its publication, to mention but a few. _Simple Knowledge Organization System_ ( _SKOS_ ) is aimed to represent the structure of organization systems such as thesauri and taxonomies, since they share many similarities. It is widely used within the Semantic Web context, since it provides an intuitive language and can be combined with formal representation languages such as the Web Ontology Language (OWL). _SKOS XL_ works as an extension of SKOS to represent lexical information [23]. With regard to multilingualism in ontologies, _Linguistic Information Repository_ ( _LIR_ ) was proposed as model for ontology localisation: it grants the localisation of the ontology terminological layer, without modifying the ontology conceptualisation. LIR allows enriching ontology entities with the linguistic information necessary for the localisation and cultural adaptation of the ontology [24]. Another model intended for the representation of linguistic descriptions associated to ontology concepts is _Lexinfo_ [20]. It contains a complete collection of linguistic categories. Currently, it is used in combination with other models such as Ontolex (described in the next paragraph), to describe the properties of the linguistic objects that describe ontology entities. Other repositories of linguistic categories are ISOcat 22 , OLiA 23 or GOLD 24 . The _Lexicon Model for Ontologies_ or _lemon_ [26] was especially created to represent lexical information in the Semantic Web, covering some needs that previous models did not. This model has evolved in the context of a W3C Community Group into _lemon-Ontolex_ first, now better known as _Ontolex_ 25 . In this model, linguistic descriptions are as well separated from the ontology, and point to the corresponding concept in the ontology. The structure of this model is divided into a core set of classes and different modules containing various types of linguistic information that range from morpho-syntactic properties of lexical entries, lexical and terminological variation and translation, decomposition of phrase structures, syntactic frames and mappings to the ontological predicates, and morphological decomposition of lexical forms. Linguistic annotations such as data categories and linguistic descriptors are not captured in the model but referred to by pointing to models that contain them (see LexInfo model above). ## LYNX DATA MODELS ### Strategy for the harmonisation of data models Users of the LKG need a uniform collection of data models in order to integrate heterogeneous resources, which is initially provided in this Deliverable but which will be in constant maintenance until the end of the project. In order to select the data models, a simultaneous top down and bottom up approaches has been conducted, as illustrated by Figure 10. A parallel work has been carried out, where in the one hand a top down approach has been conducted, extracting a list of formats, vocabularies and ontologies which can be chosen to satisfy the functional requirements of the pilots, whereas in the other hand a bottom up approach has been followed, exploring every possible format, vocabulary or ontology of interest, with special attention to the most widely spread ones. Identification of vocabularies and ontologies in the domain Generation of minimal metadata description Publication in the Lynx web as a catalogue of vocabularies Analysis of functional requirements Analysis of technical requirements Identification of vocabularies and formats necessary Selection of vocabularies and ontologies Top down approach An analysis of the functional and technical requirements of the pilots determines a list of vocabularies and ontologies of choice Bottom up approach A survey of ontologies and vocabularies tries to comprehensively identify the most widely spread formats Figure 10. Strategy for the selection of data models in Lynx ### Definition of Lynx Documents The added value of the Lynx services revolves around a better processing of heterogenous, multilingual documents in the legal domain. Hence, the most important data structure is the _Lynx Document_ . Lynx Documents may be grouped in _Collections_ , and may be enriched with _Annotations_ . The main entities to deal with can be defined as follows: * Lynx Documents are the basic information units in Lynx: identified pieces of text, possibly with structure, metadata and annotations. A Lynx Document Part is a part of Lynx documents. * Collections are groups of Lynx Documents with any logical relation. There may be one collection per use case, per jurisdiction, etc. * Annotations are enrichments of Lynx Documents, such as summaries, translation, recognized entities, etc. Because most of AI algorithms dealing with documents focus on text -manipulation of images, videos or tables is less developed-, the essence of a Lynx Document is its text version. Thus, the key element in a Lynx Document is an identified piece of text. This document can be annotated with an arbitrary number of metadata elements (creation date, author, etc.), and eventually structured for a minimally attractive visual representation. Original documents are transformed as represented in Figure 11: first, they are acquired by harvesters from their heterogeneous sources and formats, being structured and represented in a uniform manner. Then, they are enriched with annotations (such as named entities like persons, organisations, etc.). Original document Harvester LynxDocument id text metadata parts Enrichment workflows LynxDocumentAnnotated id text metadata parts annotations Figure 11 Original documents and Lynx Documents The elements in a complete Lynx Document, with annotations, are depicted in Figure 12. Metadata is defined as a list of pairs attribute-values. Parts are defined as text fragments delimited by two offsets, possibly with a title and a parent, so that they can be nested. Annotations also refer to text fragments delimited by two offsets, and describe in different manners such a fragment (e.g. ‘it refers to a Location which is Madrid, Spain’). LynxDocument id text metadata prop1: value1a,value1b... prop2: value2, value2b... ... parts part: id, ini, end, title, parent part: id, ini, end, title,parent ... annotations annotation: ini, end, anchorOf, classReference, id... annotation: ini, end, anchorOf, classReference, id... ... Figure 12 Elements in a Lynx Document Lynx Documents can be serialized as RDF documents. Explicit support is given to its serialization as JSONLD version 1.0, and a JSON-LD context is available at: http://lynx-project.eu/doc/jsonld/lynxdocument.json The format of a Lynx Document is shared among the three pilots and is valid for every type of documents. Refinements of this schema are possible –for example, even if an initial table of metadata records is described, new fields can be added as they become necessary for the pilot implementation. ### Lynx Documents with metadata The simplest possible Lynx Document as a JSON file is shown in the listing below. { "@context": "http://lynx-project.eu/doc/jsonld/lynxdocument.json", "@id": "doc001", "@type": "http://lynx-project.eu/def/lkg/LynxDocument", "text" : "This is the first Lynx document, a piece of identified text." } The first line declares the context (@context), which describes how to interpret the rest of the JSON LD document. It references an external file. The second one (@id) declares the identifier of the element. The complete URI to identify the document is created from this string and also from the @base declared in the context. The @type declares what is the type of the document, and finally the text element represents the text of the document. The text is not repeated in the fragments, in order to save space. Alternative transformations of this JSON structure are possible and recommended for every specific implementation need (e.g. OLS in Pilot 1). The JSON-LD version can, however, be automatically converted into other RDF syntaxes. For example, the Turtle version of the same document follows. @prefix rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#> . @prefix xsd: <http://www.w3.org/2001/XMLSchema#> . <http://lkg.lynx-project.eu/res/doc001> a <http://lynx- project.eu/def/lkg/LynxDocument> ; rdf:value "This is the first Lynx document, a piece of identified text." . Metadata is a collection of pairs property-list of values. This is better illustrated with the example below. { "@context": "http://lynx-project.eu/doc/jsonld/lynxdocument.json", "@id": "doc002", "@type": "http://lynx-project.eu/def/lkg/LynxDocument", "text" : "This is the second Lynx document.", "metadata" : { "title": ["Second Document"], "subject": ["testing", "documents"] } } Which is rendered as RDF Turtle in the next listing. <table> <tr> <th> @prefix lkg: <http://lkg.lynx-project.eu/def/lkg/> . @prefix dc: <http://purl.org/dc/terms/> . @prefix rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#> . <http://lkg.lynx-project.eu/res/doc002> a <http://lynx-project.eu/def/lkg/LynxDocument> ; lkg:metadata [ dc:subject "testing", "documents"; dc:title "Second Document" ] ; rdf:value "This is the second Lynx document." . </th> </tr> </table> The language tag can be defined with the @language JSON-LD element, as an additional context element. This will make strings (RDF literals) to have the language tag set to Spanish. <table> <tr> <th> { "@context": ["http://lynx-project.eu/doc/jsonld/lynxdocument.json", {"@language": "es"}], "@id": "doc003", "@type": "http://lynx-project.eu/def/lkg/LynxDocument", "text" : "Un documento en español." } </th> </tr> </table> Figure 17 Example of Lynx Document with language tag (JSON-LD) ### Lynx Documents with structuring information Parts and structuring information can be included as shown in the next example. Parts are defined by the offset (begin and final character of the excerpt). They can be nested because they have a parent property and they can be possibly identified. Fragment identifiers can be built as described in the NIF specification 26 . The example below shows an example of nested fragments, as Art. 2.1 { "@context": "http://lynx-project.eu/doc/jsonld/lynxdocument.json", <table> <tr> <th> "@id": "doc004", "@type": "http://lynx-project.eu/doc/lkg/LynxDocument", "text": "Art.1 This is the fourth Lynx document. Art.2 This is the fourth Lynx document. Art 2.1. Empty.", "metadata": { "title": ["A document with parts."] }, "parts": [ { "offset_ini": 0, "offset_end": 39, "title": "Art.1" }, { "@id": "http://lkg.lynx-project.eu/res/doc004/#offset_41_94", "offset_ini": 41, "offset_end": 94, "title": "Art.2" }, { "offset_ini": 80, "offset_end": 94, "title": "Art.2.1", "parent": { "@id": "http://lkg.lynx-project.eu/res/doc004/#offset_41_94" } } ] } </th> </tr> </table> Figure 18 Example of Lynx Document with structure (JSON-LD) In the following example, the Turtle RDF version is shown. <table> <tr> <th> @prefix eli: <http://data.europa.eu/eli/ontology#> . @prefix nif: <http://persistence.unileipzig.org/nlp2rdf/ontologies/nif-core#> . @prefix dc: <http://purl.org/dc/terms/> . @prefix lkg: <http://lkg.lynx-project.eu/def/lkg/> . @prefix rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#> . <http://lkg.lynx-project.eu/res/doc004> a <http://lynx-project.eu/doc/lkg/LynxDocument> ; eli:has_part [ nif:beginIndex 0 ; nif:endIndex 39 ; dc:title "Art.1" ], <http://lkg.lynx-project.eu/res/doc004/#offset_41_94>, [ lkg:parent <http://lkg.lynx-project.eu/res/doc004/#offset_41_94> ; nif:beginIndex 80 ; nif:endIndex 94 ; dc:title "Art.2.1" ] ; lkg:metadata [ dc:title "A document with parts." ] ; rdf:value "Art.1 This is the fourth Lynx document. Art.2 This is the fourth Lynx document. Art 2.1. E mpty."^^. <http://lkg.lynx-project.eu/res/doc004/#offset_41_94> nif:beginIndex 41 ; nif:endIndex 94 ; dc:title "Art.2" . </th> </tr> </table> Figure 19 Simple example of Lynx Document (Turtle) Two classes suffice for representing Lynx Documents without annotations as UML objects (See Figure 20). Figure 20 UML class diagram representation of Lynx document and Lynx document part. ### Lynx document with annotations Annotations are represented using NIF. The next example shows a Lynx Document with one annotation, highlighting the existence of a reference to London, which is a Location. <table> <tr> <th> { "@context": "http://lynx-project.eu/doc/jsonld/lynxdocument.json", "@id": "doc005", "@type": "http://lynx-project.eu/doc/lkg/LynxDocument", "text": "I was born in London long time ago.", "metadata": { "title": [ "An annotated document" ] }, "annotations": { "annotation": [ { "@id": "http://lynx-project.eu/res/id000#offset_29_35", "@type": [ "nif:String", "nif:RFC5147String" ], "anchorOf": "London", "offset_ini": "14", "offset_end": "20", "referenceContext": "http://lkg.lynx-project.eu/res/doc005", "taClassRef": "http://dbpedia.org/ontology/Location", "taIdentRef": "http://dbpedia.org/resource/London" } ] } } </th> </tr> </table> Figure 21 Annotated Lynx Document (JSON LD). The equivalent RDF Turtle excerpt follows, with the prefixes as above. <table> <tr> <th> <http://lkg.lynx-project.eu/res/doc005> a <http://lynx-project.eu/doc/lkg/LynxDocument> ; lkg:metadata [ dc:title "An annotated document" ] ; lkg:annotations [ lkg:annotation <http://lynx- project.eu/res/id000#offset_29_35> ] ; rdf:value "I was born in London long time ago." . <http://lynx-project.eu/res/id000#offset_29_35> a nif:String, nif:RFC5147String ; nif:anchorOf "London" ; nif:beginIndex 14 ; nif:endIndex 20 ; nif:referenceContext <http://lkg.lynx-project.eu/res/doc005> ; </th> </tr> </table> itsrdf:taClassRef <http://dbpedia.org/ontology/Location> ; itsrdf:taIdentRef <http://dbpedia.org/resource/London> . Figure 22 Annotated Lynx Document (Turtle). The use of nif:annotationUnit is optional, but useful for avoiding colliding annotations. The last line should be replaced then by the following excerpt. See more details on NIF on Table 6. nif:annotationUnit [ itsrdf:taIdentRef <http://vocabulary.semantic- web.at/CBeurovoc/C8553> . ] . ### List of recommended metadata fields and their representation <table> <tr> <th> Group Property Usage </th> <th> RDF property </th> </tr> <tr> <td> basic elements </td> <td> id </td> <td> Lynx identifier of the document </td> <td> dct:identifier </td> </tr> <tr> <td> text </td> <td> Text of the document </td> <td> rdf:value </td> </tr> <tr> <td> parts </td> <td> Parts of the document </td> <td> eli:has_part </td> </tr> <tr> <td> general </td> <td> type </td> <td> Type of document (legislation, case law, etc.) </td> <td> dct:type </td> </tr> <tr> <td> rank </td> <td> Sub-type of document (constitution, law, etc.) </td> <td> eli:type_document </td> </tr> <tr> <td> language </td> <td> Language of the document </td> <td> dct:language </td> </tr> <tr> <td> jurisdiction </td> <td> Jurisdiction using ISO </td> <td> eli:jurisdiction </td> </tr> <tr> <td> wasDerivedFrom </td> <td> Original URL if the document was extracted from the web </td> <td> prov-o:wasDerivedFrom </td> </tr> <tr> <td> title </td> <td> Title of the document </td> <td> dct:title </td> </tr> <tr> <td> hasAuthority </td> <td> Authority issuing the document </td> <td> lkg:hasAuthority </td> </tr> <tr> <td> nick </td> <td> Alternative names of the document </td> <td> foaf:nick </td> </tr> <tr> <td> version </td> <td> Consolidated, draft or bulletin </td> <td> eli:version </td> </tr> <tr> <td> subject </td> <td> Subjects or keywords of the document </td> <td> dtc:subject </td> </tr> <tr> <td> identifier s </td> <td> id_local </td> <td> Local identifier (e.g. BOE-A-20191234) </td> <td> eli:id_local </td> </tr> <tr> <td> identifier </td> <td> Official identifier (e.g. ELI etc.) </td> <td> dct:identifier </td> </tr> <tr> <td> dates </td> <td> first_date_entry_in_force </td> <td> Date when enters into force </td> <td> eli:first_date_entry_in_force </td> </tr> <tr> <td> date_no_longer_in_force </td> <td> Date when repealed / expired </td> <td> eli:date_no_longer_in_force </td> </tr> <tr> <td> version_date </td> <td> Date of publication of the document </td> <td> eli:version_date </td> </tr> <tr> <td> mappings </td> <td> hasEli </td> <td> Official identifier (ELI, ECLI or equivalent) </td> <td> lkg:hasEli </td> </tr> <tr> <td> hasPDF </td> <td> Link to the PDF version </td> <td> lkg:hasPDF </td> </tr> <tr> <td> hasDbpedia </td> <td> Link to the equivalent dbpedia version </td> <td> lkg:hasDbpedia </td> </tr> <tr> <td> hasWikipedia </td> <td> Link to the equivalent wikipedia version </td> <td> lkg:hasWikipedia </td> </tr> <tr> <td> sameAs </td> <td> Equivalent document </td> <td> owl:sameAs </td> </tr> <tr> <td> seeAlso </td> <td> Related documents </td> <td> rdfs:seeAlso </td> </tr> <tr> <td> Internal </td> <td> creator </td> <td> Creators of the documents in Lynx (person or software) </td> <td> dct:creator </td> </tr> <tr> <td> created </td> <td> Date when created in Lynx (internal) </td> <td> dct:created </td> </tr> </table> ##### Table 4 List of recommended metadata fields and their representation Table 4 lists the recommended metadata fields and their representation and. <table> <tr> <th> Element </th> <th> Meaning </th> <th> Values / example </th> </tr> <tr> <td> itsrdf:taClassRef </td> <td> Class of the annotated context </td> <td> dbo:Person, dbo:Location, dbo:Organization, dbo:TemporalExpression </td> </tr> <tr> <td> itsrdf:taIdentRef </td> <td> URL from external resource, such as DBPedia, Wikidata, Geonames, etc. </td> <td> http://dbpedia.org/resource/London </td> </tr> <tr> <td> itsrdf:taConfidence </td> <td> Confidence </td> <td> [0..1] </td> </tr> <tr> <td> nif:summary </td> <td> Summary </td> <td> text </td> </tr> </table> Table 5 List of some NIF-related properties and their values Table 6 lists the prefixes used in this section. <table> <tr> <th> Vocabulary </th> <th> Prefix </th> <th> URL </th> </tr> <tr> <td> LKG Ontology </td> <td> lkg </td> <td> http://lkg.lynx-project.eu/def/ </td> </tr> <tr> <td> Dublin Core </td> <td> dct </td> <td> http://purl.org/dc/terms/ </td> </tr> <tr> <td> RDF </td> <td> rdf </td> <td> http://www.w3.org/1999/02/22-rdf-syntax-ns# </td> </tr> <tr> <td> European Legislation Ontology </td> <td> eli </td> <td> http://data.europa.eu/eli/ontology# </td> </tr> <tr> <td> W3C Provenance Ontology </td> <td> prov-o </td> <td> https://www.w3.org/TR/prov-o/ </td> </tr> <tr> <td> Friend of a Friend Ontology </td> <td> foaf </td> <td> http://xmlns.com/foaf/spec/ </td> </tr> <tr> <td> NLP Interchange Format </td> <td> nif </td> <td> http://persistence.uni- leipzig.org/nlp2rdf/ontologies/nif-core# </td> </tr> <tr> <td> ITS 2.0 / RDF Ontology </td> <td> itsrdf </td> <td> http://www.w3.org/2005/11/its/rdf# </td> </tr> </table> Table 6 Prefixes used in this document # URI MINTING POLICY ## BACKGROUND This section highlights the importance of choosing a good URI naming strategy. URIs (or IRIs to be more precise, as per RFC 3987 on Internationalized Resource Identifiers) are the natural identifiers for resources in Lynx. An IRI is a sequence of Unicode characters (Unicode/ISO 10646) that can be used to mint identifiers that use a wider set of characters than the one defined for the URIs in RFC3986. Choosing good IRIs are key at least for the following reasons: ― Make humans easier to understand what is the resource in question. URIs with information on the identified resource and its nature (e.g. class) are easier for humans to remember and understand. URIs play the role of documenting ontologies and RDF resources in natural language. This is a misuse of URIs, and hardens the operation of resources in multilingual environments [32], but it is a common practice. ― Make easier the execution of automated tasks, such as resource mapping [34], information extraction [35] or natural language generation. The W3C Consortium does not provide a normative recommendation on how to mint URIs. However, it was Tim Berners-Lee himself who as early as 1998 wrote in his article _Cool URIs don’t change_ 27 a list of good practices. Berners-Lee introduced the concept of _URI design_ , which has proven to be a challenge for the Semantic Web community. A second reference is the W3C Note _Common HTTP Implementation problems_ 28 , issued in the context of the Technical Architecture Group. This note elaborates the ideas of Berners-Lee’s article, specifying some rules for choosing URIs: (i) Use short URIs as much as possible, (ii) Choose a case policy, (iii) Avoid URIs in mixed case, and (iv) As a case policy choose either “all lowercase” or “first letter uppercase”. More recently, the _Best Practices for Publishing Linked Data_ 29 specification issued by a W3C Working Group only recommended: ‘ _A URI structure will not contain anything that could change_ ’ and that URIs shall be constructed ‘ _to ensure ease of use during development_ ’. However, no more precise rules are given by W3C. Some recommend using hyphens, other claim a camel case policy for the local names suffices. ## ALTERNATIVE URI MINTING STRATEGIES Given that technically there is no clear recommendation on how to choose sets of URIs, two alternatives can be considered: either they are meaningful conveying information on the resource and its structure or they are meaningless because URIs should not be semantically interpreted. For example, given a certain sentence (judgment), one might consider including in the URI either: ― the title of the judgment ― the unique reference number for the judgment ― the internal record number in the Lynx databases This section describes the pros and cons of these alternatives. ### Structured, non-opaque URIs Once the semantic web has grown mature and widely accepted, public institutions have also issued guides on URI minting, all of them leaning towards structured, non-opaque URIs. Most notably, the UK Cabinet Office published the recommendation “Designing URIs for the public sector”, the government in Netherlands issued a similar document 30 and the Spanish one issued the Norma Técnica de Interoperabilidad contains a chapter for that “ _Definición de un esquema de URI_ ” 31 . Finally, the European Commission published in 2014 the document _Towards a common approach for the management of persistent HTTP URIs by EU Institutions_ to be used in the EU portals. These documents specify the path structure for URIs, establishing a clear separation of different types of data (a bus line is not a police office) and defining naming conventions. These conventions emphasize the need of stability and scalability and specifically address the problem of managing large amounts of data on the Web. Spanish case . For example, the Spanish norm defines the following URI pattern: http://{base}/{carácter}[/{sector}][/{dominio}][.{ext}][#{concepto}] If this strategy was applied to Lynx, _Base_ would be lynx-project.eu; _character_ would be either def (for ontologies and vocabularies), kos (for dictionary data, thesauri, taxonomies and other knowledge organization data), cat (for catalogue information) or res (for resources, such as a document); _sector_ would be one word describing the domain sector (economy, justice-legislation, etc.). For Lynx this might be (standards/legislation/caselaw/doctrine/others); _dominio_ would be the specific data type (e.g. Judgment) and _concept_ would be the id of the resource (ext being the extension). An example of URI using the Spanish recommendation would be: http://lynx-project.eu/res/caselaw/judgment/C23987 UK case . The UK recommendation is a well detailed document, which proposes the following URI pattern for documents: http://{domain}/doc/{concept}/{reference}. This would mean, applied to Lynx, having this URI for the same: http://lynx-project.eu/doc/judgment/C23987 Holland case . The Dutch administration has adopted the URI pattern: http://{domain}/{type}/{concept}/{reference}. The example for Lynx would read: http://lynx-project.eu/id/judgment/C23987 ### Opaque URIs In the _Architecture of the World Wide Web 33 _ , which is a W3C Recommendation, we read “ _Agents making use of URIs should not attempt to infer properties of the referenced resource_ ”. This recommendation is directly opposed to the strategies mentioned in the section before, and leads to enabling opaque URIs or at least with less semantics in it. For example, Tim Berners-Lee (1998) recommended not to put too much semantics in the URI, and not to bind URIs to some classification or topic (as one change the point of view). Opaque URIs can be generated automatically, are easier to manage and do not convey character encoding problems –in a project intrinsically multilingual such as Lynx, there should not be a cultural bias against languages with accents and other local characters (such as the Spanish Ñ). An examples of opaque URI would be one chosen from the Spanish National Library (BNE) to identify the writer Miguel de Cervantes: http://datos.bne.es/ persona /XX1718747 From this URI, it can be inferred that it refers to a person, but no clue is given on which person. On the contrary, the dbpedia policy for cervantes hides the type of entity, but makes clear who is the referred writer: http://es.dbpedia.org/resource/ Miguel_de_Cervantes ## LYNX URI MINTING STRATEGY Considering the advantages and disadvantages examined in the previous section, Lynx has chosen the URI patterns as described in Table 7. <table> <tr> <th> Type of resource </th> <th> URI pattern </th> </tr> <tr> <td> Ontology _Example_ </td> <td> http:// lkg.lynx-project.eu/def/{onto_id} _http://_ lkg. _lynx- project.eu/def/core_ </td> </tr> <tr> <td> Ontology element _Example_ </td> <td> http://lkg.lynx-project.eu/def/{onto_id}/{element} _http://_ lkg. _lynx- project.eu/def/core/Document_ </td> </tr> <tr> <td> KOS (thesauri, terminologies) _Example_ </td> <td> http://lkg.lynx-project.eu/kos/{kos_id}/{id} _http://_ lkg. _lynx- project.eu/kos/contracts_terms/24232_ </td> </tr> <tr> <td> Resource Example </td> <td> http://lkg.lynx-project.eu/res/{id} _http://_ lkg. _lynx-project.eu/res/23983_ </td> </tr> </table> Table 7. URI patterns for different resources Advantages of this choice are: ― Problems derived from character encoding are solved ― Automatic generation of ids is possible, avoiding auto-increment derived problems ― Freedom of choice of ids for the different implementors ― No collision between resources sharing a name ― Relatively short URIs ― Easy scalability (no types of resources are predefined) ― Lynx URIs do not compete with official ones such as ELI or ECLI. # THE MULTILINGUAL LEGAL KNOWLEDGE GRAPH As stated in the introduction, a secondary goal of this document is to define the Legal Knowledge Graph that will be developed during the Lynx project with a linguistic regulatory Linked Open Data Cloud. ## SCOPE OF THE LEGAL KNOWLEDGE GRAPH The amount of legal data made accessible either in open or under payment modalities by legal information providers can be hardly imagined. Lexis Nexis claimed 32 to have 30 Terabytes of content, WestLaw accounted for more than 40,000 _databases_ . Their value can be roughly estimated: as of 2012, the four big players (WestLaw, Lexis Nexis, Wolters Kluwer and Bloomberg Legal) totalled about $10,000M in revenues. Language data (e.g. resources with any kind of linguistic information) belongs to a much smaller domain, but still, unmanageable as a whole. The Lynx project is interested in a small fraction of the information belonging to these domains. In particular, Lynx is in principle interested only in using the data necessary to provide the compliance services described in the pilots. Data of interest is regulatory data (legal and standards- related) and language data (to cover the multilingual aspects of the services). The intersection of these domains is of the utmost interest and Lynx will try to comprehensively identify every possible open dataset in this core category. These ideas are represented in Figure 23. Language data Legal data Legal data for compliance in the Lynx pilots Language data for compliance in the Lynx pilots Core Data Lynx Multlingual LKG Corpora TerminologIcal databases Thesauri, glossaries Lexicons and dictionaries Linguistic resource metadata Typological databases Law Case law Opinions, recommendations Doctrine, books, journals Standards, technical norms Sectorial good practices Figure 23. Scope of the multilingual Legal Knowledge Graph The definitions of both _language data_ and _regulatory data_ are indeed fuzzy, but flexible as to introduce data of many different kinds whenever necessary (geographical data, user information, etc.). Because data in the Semantic Web is inseparable from the data models, and data models are accessed in the same manner as data is, ontologies and vocabularies are part of the LKG as well. Moreover, any kind of metadata (describing documents, standards etc.) is also part of the LKG, as well as the description of the entities producing the documents (courts, users, jurisdictions). In order to provide the compliance services, and with different degree of interest, both primary and secondary law are of use, and any relevant document in a wide sense may become part of the Legal Knowledge Graph. This is illustrated in Figure 25. Lynx Multilingual LKG Multilingual LKG Resources whose IRI is within the lynx-project.eu domain Resources whose IRI is out of the lynx-project.eu do- main but are directly linked Figure 24 Lynx LKG and LKGs We may define the Lynx Multilingual LKG as the set of entities and relations whose IRIs are within the http://lynx-project.eu top level domain. However, the resources in it are connected to other resources published by other entities, which constitute a wider LKG. Figure 24 represents this idea, together with the notion of private resources, which are only accessible to the authorized users (e.g. contracts only visible for the parties). Figure 25. Types of information in the Legal Knowledge Graph ## KNOWLEDGE GRAPHS In the realm of Artificial Intelligence, a knowledge graph is a data structure to represent information, where entities are represented as nodes, their attributes as node labels and the relationship between entities are represented as edges. Knowledge graphs such as Google’s 33 , Freebase [2] and WordNet [3] turn data into knowledge, and they have become important resources for many AI and NLP applications such as information search, data integration, data analytics, question answering or context-sensitive recommendations. Large knowledge graphs include millions of concepts and billions of relationships. For example, DBpedia describes about 30M entities connected through 10,000M relationships. Entities belong to classes described in ontologies. There are different manners of representing knowledge graphs, not the least important being the one using W3C specifications of the Semantic Web: RDF, RDFS, OWL. RDF data is accessible online in different forms: as file dumps, through a SPARQL endpoints or dedicated APIs or simply published online as Linked Data [4]. ### Legal Knowledge Graphs In the last few years, a number of Legal Knowledge Graphs have been created in different applications. The MetaLex Document Server offers legal documents as versioned Linked Data [10], including Dutch national regulations. Finnish [9] and Greek [8] legislation are also offered as Linked Data. The Publications Office of the EU maintains the central content and metadata CELLAR repository for storing official publications and bibliographic resources produced by the institutions of the EU [11]. The content of CELLAR, which includes EU legislation, is made publicly available by the Eur-Lex service and it offers also an SPARQL endpoint. The FP7 EUCases project (2013-2015) offered European and national case law and legislation linked in an open data stack (http://eucases.eu). Finally, Openlaws offers a platform based on linked open data, open source software and open innovation processes [5][6][7]. Lynx will benefit from the expertise of Openlaws, which will be the preferred source for the data models, methods and algorithms. New H2020 projects in the area of data protection are also using semantic web technologies, such as the H2020 Special 34 , devoted to ease the collection of user consents and represent policies as RDF or the H2020 Mirel 35 (2016-2019), with a network of experts to define a formal framework and to develop tools for mining and reasoning with legal texts, or e-Compliance, an FP7 project (2013-2016), focused on using semantic web technologies for regulatory compliance in the maritime domain. ### Linguistic Knowledge Graphs In the last few years, the language technology community has shaped the Linguistic Linked Open Data Cloud: the graph with those language resources available in RDF and published as Linked Data [16]. The graph represented in Figure 26, resembles the one of the Linked Data Cloud, but limited to the language domain. Figure 26. Linguistic Linked Open Data Cloud 36 A major resource contained in this graph is _DBpedia_ , a vast network that structures data from Wikipedia and links them with other datasets available on the Web [3]. The result is published as Open Data available for the consumption of both humans and machines. Different versions of DBpedia exist for different languages. Another core resource in the LOD Cloud is _BabelNet_ [15], a huge multilingual semantic network, generated automatically from various resources and integrating the lexicographical information of _WordNet_ and the encyclopaedic knowledge of Wikipedia. BabelNet also applies Machine Translation to get information from several languages. As a result, BabelNet is considered an encyclopaedic dictionary that contains concepts and named entities connected thanks to a great amount of semantic relations. _Wordnet_ , is one of the best known Linguistic Knowledge Graphs, since it is a large online lexical database that contains nouns, verbs, adjectives and adverbs in English [3]. These words are organised in sets of synonyms that represent concepts, known as _synsets_ . WordNet uses these synonyms to represent word senses; thus, synonymy is WordNet’s most important relation. Four additional relations are also used by this network: antonymy (opposing- name), hyponymy (sub-name), meronymy (part-name), troponymy (manner-name) and entailment relations. Other resources equivalent to WordNet have been published for different languages, such as EuroWordNet [29]. However, there are other semantic networks (considered linguistic knowledge graphs) that do not appear in the LOD Cloud but are also worth to mention. This is the case of _ConceptNet_ [28], a semantic network designed to represent common sense and support textual reasoning about documents in the real word. It represents part of human experiences and tries to share this common-sense knowledge with machines. ConceptNet is often integrated with natural language processing applications to speed up the enrichment of AI systems with common sense [4]. ### The Lynx Multilingual Legal Knowledge Graph Building on these previous experiences, we are in the position to define the Lynx Multilingual Legal Knowledge Graph. The Lynx Multilingual Legal Knowledge Graph (LKG) is a knowledge graph using W3C specifications with the necessary information to provide multilingual compliance services. The Lynx LKG builds on previous initiatives reusing open data and will evolve adding new resources whenever needed to provide compliance services. The LKG preferred form of publication is Linked Data, although other access mechanisms will be provided.	https://phaidra.univie.ac.at/o:1140797	Horizon 2020
0186_MERCES_689518.md	Results or Background (including thesis dissertations), and the use of names, logos and trademarks will be regulated by the MERCES Consortium Agreement, which will assure the suitable results protection measures and legitimate interests of all parties involved in the project. To ensure the widest access possible to science produced by MERCES, all datasets and scientific publications will be deposited in the dedicated Zenodo community, in the account already created for MERCES project (i.e., “MERCES project” community). Zenodo has been selected as repository platform since it is free and it allows Horizon 2020 grant support since the data are automatically exported to OpenAIRE. Furthermore, it allows to have Digital Object Identifier (DOI, both for datasets and scientific publications), as well as restricted usage in case of need for embargo periods (both for datasets and scientific publications). All partners will strive to publish articles in open access. This will enhance the transparency of MERCES research results, ensuring at the same time immediate access to data and results by policy and/or business, by stakeholders, end-users and scientists. It is also envisaged that the partners will be able to produce approximately 100 contributions to international scientific symposia and business conferences. Moreover, it has been envisaged that MERCES will coordinate at least 3 special sessions organized in the framework of international symposia. 3\. General characteristics and typology of datasets MERCES will collect and collate data spanning from scientific and environmental to socio-economic contexts, mainly in numerical and textual formats. All different data sets will be allocated in the internal (web site private area) and public (Zenodo) repositories in order to be: 1. discoverable (e.g. assigning each data set to a Digital Object Identifier, DOI); 2. accessible (e.g., providing each data set with clear information about their scope, licences, rule for commercial exploitation and re-usage); 3. measurable and intelligible (ready for scientific scrutiny and peer review); 4. useable beyond their original purpose (e.g. ensuring data preservation during the project’s after life); 5. interoperable to specific quality standards (e.g. adhering to standards for data annotation and exchange, compliant with multiple software applications, and allowing recombination with different datasets from different sources). # 3.1 Datasets from literature Datasets created from literature review (e.g. metadata on existing habitats, census of impacted deep sea ecosystems), as datasets foreseen in WP1, will be deposited on Zenodo, as well as made available in open international portals, under the responsibility of WP1 leaders (HCMR and NUIG). Appropriate metadata created for each dataset will be based on the Infrastructure for Spatial Information in the European Community (INSPIRE) specifications (listed at _http://inspire.jrc.ec.europa.eu/_ , _http://inspire-geoportal.ec.europa.eu/_ ) and will be compatible with EMODNET/EuSeaMap/MARATLAS portals requirements. Where appropriate the integration of mapping exercises and knowledge related to habitats and ecosystem services changes will be based on the MAES process (Mapping and Assessment of Ecosystems and their Services, _http://biodiversity.europa.eu/maes_ of the BISE: Biodiversity Information System for Europe). MERCES will interact with MAES for typology, standardization, classification and visualization. # 3.2 Datasets on environmental-, biological/communities-, ecosystem services-, legal and policy-, socio-economic- and business-related data Datasets created in all WP1-8 will be deposited in Zenodo, in accessible formats (e.g., excel, access, etc.) depending on the typology of the data and the partner responsible for each dataset. Once in Zenodo, each dataset will include all raw data as well as all the info related to the data (e.g., area, environment, ecosystem typology, in which the data have been collected). Moreover, the datasets will be accompanied by the name of the data’s owner, a brief description and a DOI. In the cases in which a period of embargo will be necessary, the datasets will be temporary protected by a password. Once the embargo will finish, the datasets will be immediately made available. This kind of datasets will be made available for internal usage among MERCES partners, by depositing them also on the restricted area of MERCES web site, with the associated link to Zenodo repository. This will enhance the transparency of MERCES research results, ensuring at the same time immediate access to data and results by policy and/or business, by stakeholders, end- users and scientists. # 3.3 Scientific publications MERCES foresees the publication of top-level and specialized papers in excellent quality journals (e.g. Restoration Ecology, Ecological Engineering) highlighting the outputs of the project. All partners will strive to publish articles in open access (“gold open access”). To ensure the widest access possible to the science produced by MERCES, all scientific publications will be deposited in the dedicated Zenodo community. All the papers, including the related datasets and associated metadata, will be deposited into a research repository (i.e., Zenodo) and where possible, the Creative Commons Licence (CC-BY or CC0 tool) will be attached to the papers. In the cases in which a period of embargo will be necessary, the papers and datasets will be temporary protected by a password. Once the embargo will finish, the papers will be immediately made available. This kind of datasets will be made available for internal usage among MERCES partners, by depositing them also on the restricted area of MERCES web site, with the associated link to Zenodo repository. In those cases in which the scientific papers do not have the full open access (“gold open access”) to the version of the paper edited by the Publisher, several options can be considered. Following the Publishers’ indications, the papers could be made available under the “green open access” (as example see _https://www.elsevier.com/about/open-science/open-access_ ), or by the self- archiving (publishing online the pdf version of the post-print of the article, in which will be added all the information requested by the specific Publisher, as example see _http://olabout.wiley.com/WileyCDA/Section/id-406071.html_ ), or by the personal web page of the main author. In each case, the options made by the Publisher will be followed. At the same time the paper will be added in the MERCES community in Zenodo, following one of the abovementioned options or a combination of them. Once on Zenodo, the paper will be accompanied by all the information requested by the Horizon2020 funding program, a Digital Object Identifier (DOI), the all related metadata, and by the link to the Journal web page or to the personal web page of the main author. The pdf file upload on Zenodo will be available or protected by a password, depending on the options given by the Publisher. In the case by which the Publisher will not allow to make available the pdf file, the link to the personal web page of the main author will be added, where the pdf file (post-print version, according to the Publisher option) will be uploaded. In the case by which the main author does not have a personal web page, it will be created on the MERCES web site. In the MERCES web site will be also created a dedicated page, with the entire list of scientific publications, with the DOI and the direct links to Zenodo products. # Data sharing All the datasets and publications will be deposited in the MERCES community on Zenodo, in this way all products will be immediately identified as MERCES outputs. The use of citable and permanent DOIs for all datasets and scientific publications archived in Zenodo ensures the long term availability of MERCES data. If necessary, the access to datasets and the scientific publications will be protected by a password during an embargo period. Short embargo periods for datasets or scientific publications could be required by the editorial policies of scientific journals. Once the embargo will finish, the open access will be immediately ensured. For each product, a brief description and a DOI will be provided, in order to make each dataset and publication identifiable and univocally citable. For the internal (among partners) and external (stakeholders, general public) usage and sharing of datasets, specific sections for the allocation of datasets in the MERCES website have been created. In particular, in the restricted area there will be the datasets files as well as their link to Zenodo repository, whereas in the public area of the web site, there will be the links to Zenodo. For each dataset, also a brief description of the data and the DOI will be provided. # Archiving and preservation (including storage and backup) Long-term archiving (more than 5 years) and a backup of these datasets will be guaranteed by the institutes responsible for the Data Management Plan (ECOREACH and UNIVPM). Each dataset will be identified by its own DOI.	https://phaidra.univie.ac.at/o:1140797	Horizon 2020
0189_SerIoT_780139.md	# Executive Summary This report describes the initial Data Management Plan for the SerIoT project, funded by the EU’s Horizon 2020 Programme under Grant Agreement number 780139. The purpose is to set out the main elements of the SerIoT consortium data management policy for the datasets generated by the project. The DMP presents the procedure for the management of datasets created during the lifetime of the project and describes the key data management principles. Specifically, the DMP describes the data management life cycle for all the datasets to be collected, processed and/or generated by a research project, including the following processes: * handling of research data during and after the project implementation, * what data will be collected, processed or generated, * what methodology and standards will be applied, * whether data will be shared/made open, and  how data will be curated and preserved. The methodology for the DMP is as follows: 1. Create a general data management policy (the strategy that will be used by the consortium to address all the datasets); 2. A DMP template will be created and sent to the partners of the consortium in order to be filled with information for each relative data set; 3. Analyze the completed by the project’s partners DMP templates. 4\. Creating an updated version of SerIoT project DMP. The current document formulates the general data management policy. The project’s partners provided preliminary information. DMP Template was created (see Appendix 1). As the detailed assumptions regarding Use Cases will be formulated by M12, the DMP template will be sent for complement/ revised and the DMP document will be updated accordingly. The initial version of the SerIoT DMP was developed according to guidance by EUROPEAN COMMISSION (HORIZON 2020): HORIZON 2020 DMP [1]. The structure of the document is as follows: Section 1 provides the initial assumptions of the datasets generated during the lifetime of the project, including assumed types and formats of data, the expected size of the datasets and the data utility. The specific description of how SerIoT will make this research data findable, accessible, interoperable and reusable (FAIR) is outlined in Section 2. Sections 3 to 6 outline the policy in relation to data resources, security and ethics. Section 6 contains the conclusions. # Project Participants <table> <tr> <th> </th> <th> Instytut Informatyki Teoretycznej i Stosowanej Polskiej Akademii Nauk (IITiS, Coordinator, Poland) </th> </tr> <tr> <td> </td> <td> Centre for Research and Technology Hellas, Information Technologies Institute (CERTH, Quality Manager, Greece) </td> </tr> <tr> <td> </td> <td> Joint Research Centre – European Commission (JRC, Belgium) </td> </tr> <tr> <td> </td> <td> Technische Universität Berlin (TUB, Germany) </td> </tr> <tr> <td> </td> <td> Deutsche Telekom AG (DT, Germany) </td> </tr> <tr> <td> </td> <td> Hispasec Sistemas S.L. (HIS, Spain) </td> </tr> <tr> <td> </td> <td> HOP UBIQUITOUS SL (HOPU, Spain) </td> </tr> <tr> <td> </td> <td> Organismos Astikon Sygkoinonion Athinon (OASA, Greece) </td> </tr> <tr> <td> </td> <td> ATOS SPAIN S.A. (ATOS, Spain) </td> </tr> <tr> <td> </td> <td> University of Essex (UESSEX, UK) </td> </tr> <tr> <td> </td> <td> Institute of Communication and Computer Systems (ICCS, Greece) </td> </tr> <tr> <td> </td> <td> Fundacion TECHNALIA Research & Innovation (TECNALIA, Spain) </td> </tr> <tr> <td> </td> <td> AUSTRIATECH - GESELLSCHAFT DES BUNDES FUR TECHNOLOGIEPOLITISCHE MASSNAHMEN GMBH (AUSTRIATECH, Austria) </td> </tr> <tr> <td> </td> <td> Grupo de Ventas Hortofrutícolas (GRUVENTA, Spain) </td> </tr> <tr> <td> </td> <td> HIT Hypertech Innovations LTD (HIT, Cyprus) </td> </tr> </table> # Data Summary ## Purpose of the data collection and generation The main purpose of data generation and collection is to introduce the prototype implementation of IoT platform across all IoT domains (e.g., embedded mobile devices, smart homes/cities, security & surveillance, etc.) and to optimize the information security in IoT networks in a holistic, cross- layered manner (i.e., IoT platforms and devices, Honeypots, Fog networking nodes, SDN routers and operator’s controller). SerIoT will produce a number of datasets/databases during the lifetime of the project. The data will be both analyzed using a range of methodological perspectives for project development and scientific purposes, and will be available in a variety of accessible data formats. The data sources are IoT devices and IoT systems deployed in the use cases locations, created in cooperation with project’s four industrial partners: OASA, AustriaTech, HOPU and Tecnalia (see Fig.1). According to that on general level four separate datasets categories will be created. For example:  Intelligent Transport Systems in Smart Cities: Partner AustriaTech, which will provide data from the Road Side stations within the development phase of the SerIoT’s monitoring, will include several data sets with C-ITS messages. Fig. 1. SerIoT global acquisition architecture. The main goal of work in the project is designing and implementing the SerIoT system and its components. Specifically, the data generated within WP1 and WP2 will have impact on architectural formal modelling, analysis, and synthesis, verification of the SDN-Controller and Secure Router Design as well as automated penetration testing. Two types of data will be generated in WP2. The first type will be models, in a selected model checker language. The second type of data generated in this WP will be the security, safety and quantitative properties of the IoT communication architecture. Such security properties are in accordance with rules of confidentiality, integrity, availability, authentication, authorization and non-repudiation. Quantitative properties could be for example, "what is the probability of a failure causing the system to shut down within 4 hours?", "what is the probability of the protocol terminating in error state, over all possible initial configurations?". Furthermore, for task T2.3, formal verification will work at the property level (a group of output points make up a property). A common misconception, which should be avoided, is that formal verification ensures a 100% bug-free design. Simulation evaluations are not as effective in detecting all the potential issues within today’s complex chips, despite the big progress that has been achieved in stimulus generation. Besides, extracted netlists of modern designs are in most cases too large for (re-)simulation, which creates a gap in the verification flow. On the other hand, given a property, formal verification exhaustively searches all possible input and state conditions for failures. Therefore, if the set of properties formally verified, collectively constitutes the specifications, it can be assumed that a design meets its specifications. Formal verification can be classified further into two categories, equivalence checking which determines whether two implementations are functionally equivalent and property checking that takes in a design and a property which is a partial specification of the design and proves or disproves that the design has the property. According to results of WP1 and WP2, the SDN network, which is the core of SerIoT network system, will be implemented. WP3 partners will develop and implement software algorithms and methods described in WP1 and WP2, as well as algorithms and methods developed within work of WP4 partners. The outcome of the work will be the source code of prototype modules, extending capabilities of SDN switch and SDN controller, as well as capabilities of existing fog architecture elements. The source code will be programmed in e.g. C, C++, Java, Python, etc., accompanied by files enabling their compilation and deployment in devices used for testing - project files, makefiles etc., and split into programming projects. Thus, team members will be able to download, compile and deploy the code for testing, bug fixing or further development. The safe and reliable way of depositing the source code is using the VCS repositories. Repositories of VCS may be stored locally on partners' servers or on external servers. Part of solutions will be shared with the community as Open Source projects. In that case, public repositories like GitHub or GitLab will be used. The source code of software implementing new methods developed in the SerIoT project will be created during work in mainly WP3, WP4, WP5 and WP6. The code development may concern also WP2 (e.g., extensions to model verification software), WP7 (e.g., software enabling integration of solutions developed by partners) and WP8 (e.g., test scripts). The largest volume of projects’ data will be obtained from test sites. Corresponding WP4, (IoT Monitoring Security and Mitigation) will deal with the research and development of a crosslayer data collection infrastructure, as well as the actual data generated by IoT devices. More specifically, the data will be delivered by IoT data collection infrastructure and will include key measurement mechanisms in order to deal with the effective management of information related to the IoT threat landscapes. Moreover, a sophisticated multi-layer anomaly detection framework will run on the datasets and will detect in early stages malicious attacks at peripheral devices, honeypots and core network nodes. Real-time data will be processed in order to extract important IoT system features for anomaly detection monitoring and generate design-driven security monitors. The reason is to detect non-consistent IoT behaviors utilizing IoT design artefacts such as requirements, architecture and behavioral models. Effective and resource- efficient cross-layered mitigation techniques deployed on the data, will tackle with emerging vulnerabilities in the IoT landscape. Finally, the data processed through a robust cross-layer Decision Support framework will assist in the identification of malicious activities, attacks and root cause analysis related to the IoT-enabled ecosystem. ## Types and formats of datasets Data produced by SerIoT includes the following categories: experimental data (related to data from test sites), models, simulations and software. At the current, early stage of the project implementation, the final list of datasets, formats and access rules cannot be predicted in detail. Most of the project data will be related to testing in test sites - real IoT environments (according to list of assumed use cases and scenarios). The general SerIoT data types are presented in Table 1\. Table 1. Dataset generic description fields. <table> <tr> <th> # </th> <th> Datasets </th> <th> Related WP </th> </tr> <tr> <td> 1 </td> <td> Models, system design, specifications </td> <td> WP1, WP2 </td> </tr> <tr> <td> 2 </td> <td> Repository of codes, code documentation </td> <td> WP2, WP3, WP5, WP6 </td> </tr> <tr> <td> 3 </td> <td> UC1: Surveillance </td> <td> WP7, WP8 </td> </tr> <tr> <td> 4 </td> <td> UC2: Intelligent Transport Systems in Smart Cities </td> <td> WP7, WP8 </td> </tr> <tr> <td> 5 </td> <td> UC3: Flexible Manufacturing Systems </td> <td> WP7, WP8 </td> </tr> <tr> <td> 6 </td> <td> UC4: Food Chain </td> <td> WP7, WP8 </td> </tr> </table> According to each pilot use case, types and formats of anonymized data, collected for SerIoT will differ. For example, HOPU will provide data regarding food chain supplies such as temperature or humidity while AustriaTech will provide data regarding vehicle traffic or emergencies on the road. The related datasets will consist of C-ITS messages, captured by a test vehicle with the use of dedicated application. These datasets will be provided as Wireshark PCAPs files with the same capture protocol stack: ETH/GN/BTP/<target>, “target” being <CAM\|DENM\|IVI\|SPAT\|MAP> with GeoNetworking/BTP stack transport type SingleHop Broadcasting (SHB). All this data will be collected by the data acquisition platform (WAPI server) in order to be processed then by the different modules. Gruventa will provide data collected from the vehicle (track), and will contain tracks’ information (Vehicle ID, total Km covered by the vehicle, partial Km, GPS status, GPS position, date, time, dashboard alerts, dashboard light status, on board temperature, outdoor temperature, insight temperature, Humidity, VOC level). In order to perform the evaluation in the Automated Driving Scenario (TECNALIA) different performance indicators will be assessed using a range of measures that will be monitored and logged. For that, both, sensors and questionnaires, will be used depending on the nature of the performance indicator. The required measures to calculate and evaluate the performance indicators will be defined in a validation matrix. Raw data will be acquired through sensors, intended a sensor as any method to obtain relevant data in the tests. This information will be post-processed obtaining the derived measures from raw data, and also synchronizing the data coming from different data loggers in order to have coherent global registers from TECNALIA´s pilot site. Then data will be logged to a local data base following a data format and table structure agreed by project partners. TECNALIA will store also the logging files in their own local server. After storing all logged data in the local server, these files will be sent to the SerIoT Central Data Repository using ftp communications. This repository is nowadays available and there is a directory for each pilot site with enough space to store all data to be logged for the project. This will allow partners (within the evaluations in WP8) to compile early reports and also to provide a backup service for the pilot sites. Detailed information of the use cases and application scenarios are currently formulated (more detailed assumptions will be made in second version of D1.2, that will be issued by M12) and the detailed analysis will be finished with the second issues of D1.2 deliverable. Thus, the first version of the document presents the basic assumptions. ## Origin of data In SerIoT, the assumed origins of data are: * Honeypots (WP5) * SDN router packet inspection (WP3, WP5) * SDN router high-level communications (WP3) * Different IoT traffic (devices, vehicular IoT etc). (WP4, WP7, WP8) The honeypots will provide data dynamically to the detector algorithms, to detect anomalies and malware installed on the device. Different IoT traffic is the traffic collected form test sites (data collected from IoT devices, sensors, actuators and additional modules installed). SDN router packet inspection data results from analysis of flows of data that are analyzed, collected and sent to controllers by network nodes. SDN router high-level communications are collected by higher layers of the SerIoT framework e.g. related to timely information to/from analytics module, root causes analysis & mitigation engine, multi-level visualization engine. ## Re-use of existing data In specific cases datasets already exist, e.g., obtained by industrial partners from existing IoT environments. For example, for the Smart City Use Case (with key partner AustriaTech) some of C-ITS data already exist and will be used to develop the monitoring application of SerIoT. This can be used to improve recognition of incorrect information and be able to therefore monitor the incoming as well as outgoing communications of the Road Side Stations. Those data were previously captured during testing and evaluation of C-ITS projects which use the ETSI standardized C-ITS specifications. ## Expected size of the data Dataset size might vary, depending on the pilot and the amount of information sent to the data collection infrastructure by each IoT sensor. The dataset size corresponds also to the amount of messages collected during the operation and the needs of the monitoring device. The expected size of the produced Use Cases datasets will be between 5MB and 5GB. The other datasets (related to WP1-3) are code repositories, model descriptions, modeling and simulation results. The expected sizes will be relatively small of about 1GB. The information about expected and actual sizes of the data will be updated. ## Data utility Except the internal needs to use the dataset (in order to develop SerIoT component e.g. monitoring application for C-ITS Road Side Stations, and test them), the data may be useful for research purposes in future projects, which have interest in IoT devices and Cyber-security. Moreover, the dataset will include data related to automated transport, and will be useful to researchers in automated transport more focused on secure communications for safety. # FAIR data ## Data management policy In general, being in line with the EU’s guidelines regarding the DMP [1], each dataset collected, processed and/or generated in the project comprise of/ includes the following elements: 1. Dataset reference and name 2. Dataset description 3. Standards and metadata 4. Data sharing 5. Archiving and preservation All datasets in project repositories (public and confidential) will be supplemented with additional metadata, identifiers, keywords as described in the following subsection, where, we provide a generic description of datasets elements in order to ensure their understanding by the partners of the consortium. ## Making data findable, including provisions for metadata At first, databases are created and used by corresponding WPs and maintained in local repositories of responsible partners. At this stage, datasets will be confidential and only the members participating in the deployment of WPs or the consortium members will have access to them. Then, the selected data will be made accessible through the data repository (See 2.3.1). The more detailed specification of the datasets that will be available to the public will be presented in the updated version of DMP. A DOI is assumed to be assigned to key datasets (assumed at least for the central repository) for effective and persistent citation. DOI will be assigned when a dataset is uploaded to the repository. This DOI can be used in any relevant publications to direct readers to the underlying dataset. ### Data identification SerIoT will follow the minimum Data Cite metadata standards [2] in order to make data infrastructure easy to cite, as a key element in the process of research and academic discourse. Recommended DataCite format for data citation is relatively simple and follows the format: _Creator (PublicationYear). Title. Publisher. Identifier_ It may also be desirable to include information about two optional properties, Version and Resource Type. If so, the recommended form is as follows: _Creator (PublicationYear). Title. Version. Publisher. ResourceType. Identifier_ E.g. Organisation for Economic Co-operation and Development (OECD) (2018-04-06). Main Economic Indicators (MEI): Finance \| Country: Argentina \| Indicator ID: CCUS, 01/1959 - 12/2017. Data Planet™ Statistical Datasets: A SAGE Publishing Resource [Dataset]. Dataset-ID: 062-003-004. https://doi.org/10.6068/DP163F9ED671E6 ### Naming convention SerIoT naming convention for project datasets will comprise of the following: 1. A prefix "SerIoT" indicating a SerIoT dataset. 2. A unique chronological number of the dataset 3. The title of the dataset 4. For each new version of a dataset it will be allocated with a version number which will, for example, start at v1_0. 5. A unique identification number linking e.g. with the dataset work package and/or deliverable/task, e.g., "WP4_D4.3". E.g. SerIoT.11234.serSDN_edge_node_traffic.v1_12.WP3_T2 ### Version number On general level simple version numbering is assumed. Version number consists of Version/ subversion (e.g. mesurements_1.12). For specific cases selected database versioning best practices are recommended and applied (e.g. for integration of source code with external databased in [3]). For the WP2 purposes (IoT Architectural Analysis & Synthesis) two stages of formal modelling and analysis are assumed. Therefore, there will be two version numbers. The first stage is preliminary, counting from M1 to M12. Within this stage, the infrastructure will be set up and studied. Moreover, formal modelling in architectural and high performance level will be conducted. The second stage, counting from M13 will perform formal modelling and verification in code level. More specifically, scripts will be run in order to observe if particular parameters or constraints are being verified. For the code versioning compilation number will be included to the version number. The code versioning system will also be adopted (e.g. SVN). Tutorial and examples can be found e.g. in SVN Tutorial [5]. ### Metadata The specific metadata content is presented in table below. The content (See ) is preliminary, contains generic data and will be further defined in future versions of the DMP. The assumed file format for metadata is XML. The detailed metadata structures to describe specific content will be developed and presented in an updated version of DMP. Additionally, content specific metadata are linked in the generic description. Table 2. Dataset generic description fields. <table> <tr> <th> Dataset Name </th> <th> Name according to naming conventions </th> </tr> <tr> <td> Title </td> <td> The specific title of the dataset </td> </tr> <tr> <td> Keywords </td> <td> The keywords associated with the dataset </td> </tr> <tr> <td> Work Package </td> <td> Related Work Package of the project </td> </tr> <tr> <td> Dataset Description </td> <td> A brief description of the dataset </td> </tr> <tr> <td> Dataset Benefit </td> <td> The benefits of the dataset </td> </tr> <tr> <td> Type </td> <td> Type of dataset (XML, JSON, XLSX, PDF, JPEG, TIFF, PPT) </td> </tr> <tr> <td> Expected Size </td> <td> The approximate size of the dataset </td> </tr> <tr> <td> Source </td> <td> How/why was the dataset generated </td> </tr> <tr> <td> Repository </td> <td> Expected repository to be submitted </td> </tr> <tr> <td> DOI (if set) </td> <td> The DOI assigned (if valid) when dataset has been deposited in the repository </td> </tr> <tr> <td> Date of Submission </td> <td> The date of submission to the repository once it has been submitted </td> </tr> <tr> <td> Date of Update </td> <td> The date of update </td> </tr> <tr> <td> Publisher/Responsible partner </td> <td> Lead partners responsible for the creation of the dataset </td> </tr> <tr> <td> Version </td> <td> Version/ subversion number (to keep track of changes to the datasets) </td> </tr> <tr> <td> Link to additional metadata </td> <td> Link to content specific metadata (will be defined in next versions of DMP) </td> </tr> </table> ### Dataset description There are not formal requirements for the dataset description formulated yet. In particular, the description will depend on the type of a dataset. However, it is recommended to publishers of dataset to provide following information: * The nature of the dataset (scope of data) * The scale of the dataset (amount of data) * To whom could the dataset be useful * Whether the dataset underpins a scientific publication (and which publications) * Information on the existence (or not) of similar datasets * Possibilities for integration with other datasets and reuse It is also possible that the description will have additional internal structure (XML). ### Keywords Search keywords will be provided when the dataset is uploaded to the repository, which will optimize possibilities for reuse of the data. Keywords will be part of a general metadata structure. ## Making data openly accessible In general, research data is owned by the partner who generates the data. Each partner has to disseminate its results as soon as possible, unless there is a legitimate interest to protect the results. WP leaders will propose the access procedure to their developed datasets, conditions for making datasets public (if applicable) and specify the embargo periods for all the datasets that will be collected, generated, or processed in the project. In case the dataset cannot be shared, the reasons for this will be mentioned (e.g., ethical, rules of personal data, intellectual property, commercial, privacy-related, security-related, etc.). A partner that intends to disseminate its scientific results has to give an advance notice to the other partners (at least 45 days) together with sufficient information on the results it will disseminate (according to Grant Agreement). Research data that underpins scientific publications should be by default deposited in the SerIoT data repository as soon as possible, unless a decision has been taken to protect results. Specifically, research data needed to validate the results in the scientific publications should be deposited in the data repository at the same time as publication. ### Data repository Data and the associated metadata, documentation and code will be initially deposited in the repositories created by each SerIoT pilot. For example, AustriaTech will provide credentials to WP4 partners that need access to their datasets. HOPU will store data securely on a platform on the FIWARE and MongoDB architecture. Then, the dataset could be transferred to the SerIoT data repository (https://opendata.iti.gr/seriot) , which is established by Partner CERTH (see Fig. 2). Fig. 2. SerIoT central repository (screenshot of main page). The datasets will be confidential and only the task / consortium members will have access to them. If a dataset or specific portions of it (e.g., metadata, statistics, etc.) is decided to become of widely open access, it will be uploaded to the SerIoT open data platform. This data will be anonymized, in order to avoid any potential ethical issues with their publication and dissemination. ### Methods or software tools are needed to access the data Two ways of accessing the data repository exist. Firstly, (email and password) credentials are needed in order to have administrator privileges. Such privileges are online editing of the datasets, adding new datasets or downloading public datasets. On the other hand, a single button tagged as “ACCESS”, gives anonymized access to the public datasets only for the purpose of downloading them To process data stored in a form of XML or JSON files there are available libraries to process the data. For some scientific data MATLAB or OCTAVE have to be used. To use and compile data in code repositories the related software platform has to be used (Linux with JAVA, Python, C++, etc.). The information about platforms will be included to content specific metadata. ### Data access committee The Grant Agreement (GA) does not describe the existence of a data access committee. The access policies will be determined by the owners of the data in agreement with the coordinator and related WP leaders/ partners. ### Identity of the person accessing the data Confidential datasets are stored in each responsible partner’s local repository, accessed by credentials. When the datasets are agreed to become public, only then the data is uploaded to the SerIoT open access repository. Each pilot use case stores its datasets into local repositories at its premises. The partner provides credentials to other partners that need access to their confidential datasets, and the accessing person is identified. And only when pilots agree, together with the rest consortium, the datasets become anonymized and are uploaded to central open access SerIoT repository (see 2.3.1). The public part is open accessed and no identification of the person accessing the data is assumed. Valid credentials to identify accessing person are required for editing or uploading new data. ## Making data interoperable ### Interoperability of data The data produced in the SerIoT project are interoperable, allowing data exchange and re-use only inside the SerIoT consortium. Thus, since the SerIoT consortium is composed of fifteen partners from eight different countries, data exchange and re-use will be accomplished. The use cases data will be first collected by the data acquisition platform, which consists of a WAPI server. Then, the WAPI server will be responsible for the data distribution amongst the different modules, such as the Analytics and the Decision Support System (DSS) module or the Mitigation Engine module. This way the data is made interoperable, allowing re-use between the SerIoT modules (for the use cases data modules developed within WP4). ### Metadata vocabularies, standards or methodologies for interoperability A metadata schema which defines constraints about metadata records is a fundamental resource for metadata interoperability. Existing metadata schemas are assumed to be used, to develop a new schema in order to minimize newly defined metadata vocabularies [6] Key concepts considered are DSP as a formal basis of metadata schema and LOD, as a framework to connect metadata schema resources. We assume to study and apply two approaches: * search metadata terms and description set profiles using resources registered at schema registries and the like, * search metadata terms using metadata instances included in a LOD dataset. ## Increase data re-use (through clarifying licenses) ### Licensing and data sharing policies In general, the coordinator partner (IITIS) along with all work package leaders, will define how data will be shared. WP leaders will propose the access procedure to developed datasets, set conditions for making it public (if applicable), set the embargo periods, the necessary accompanied software and other tools for enabling re-use, for all datasets that will be collected, generated, or processed in the project. In case the dataset cannot be shared, the reasons for this will be mentioned (e.g., ethical, rules of personal data, intellectual property, commercial, privacy-related, security-related, etc.). The plan will be prepared in advance and will be presented in updated version of DMM. Detailed data sharing policies have not been decided yet but European Union Public License (EUPL) V. 1.1 is considered [4] as a license that has been created and approved by the European Commission. ### Data availability for re-use The time for making the data available for re-use, has not been decided yet. It has also not been decided yet for how long the data will remain re-usable. # Allocation of resources The data repository has been created by the responsible partner (CERTH) to the extent of making data ‘FAIR’. In order to access the public datasets stored in the repository for editing or uploading new ones, valid credentials are required. Whereas, only downloading the data does not require any credentials. Furthermore, the repository will use the HTTPS protocol, which helps in the authentication of the accessed repository and protection of the privacy and integrity of the exchanged data while in transit. The coordinator partner (IITIS) is responsible for the data management. ## Long term data preservation Resources for long-term data preservation are intended to be discussed in future meetings of the SerIoT project. The details will be presented in the updated version of DMP. The long-term preservation of open to public datasets is assumed, by archiving them for at least 5 years after the end of the project. The partners will decide and describe the procedures that will be used in order to ensure long- term preservation of the remaining data sets. # Data security Pilot/use cases data in the first period of the project will be stored in use cases partners’ repositories. In terms of WP4 data, the CIA triad principles will be followed. The CIA includes the principles confidentiality, integrity, and availability, which are the heart of information security. In other words, confidentiality is the property, that datasets are not made available or disclosed to unauthorized individuals, entities, or processes. Integrity stands for maintaining and assuring the accuracy and completeness of data over its entire lifecycle. Lastly, the meaning of availability is to ensure that the data is available at all times when it is needed. A central repository (with valid HTTPS certificate) created by CERTH will be maintained for long-term preservation. In this repository, the portion of the dataset that is not restricted by intellectual property rights will be decided to become of open access, whereas the other will remain confidential and will not be uploaded to this repository. The repository will be periodically backed up. # Ethical aspects The SerIoT project has taken into account ethical and legal issues that can have an impact on data sharing, and has dedicated a WP (WP11: Ethics requirements) to ensure compatibility of the activities carried out with ethical standards and regulations. Under this WP, the relevant complex, legal and ethics issues will be tackled. Moreover, deliverable D11.1 (title “ _H - Requirement No. 1_ ”) is pointing out ethical issues, including informed consent for data sharing and long-term preservation rules (included in questionnaires concerning personal data). In order to make the widest re-use possible, the data will be anonymized, to avoid any potential ethical issues with their further distribution. Since the datasets in most cases will not contain personal information (name, surname) data sharing can be spread amongst third parties. In case of confidential datasets containing sensitive information, the re-use of the data will be possible by third parties in order to avoid any potential ethical issues. # Other issues ## National and EU regulations Regulations based on the country of origin of the dataset together with the regulations of the country where the data will be processed, will be followed. More specifically, D11.1 points out all national and EU regulations to be followed. # Conclusions The purpose of this document was to provide the initial plan for managing the data generated and collected during the SerIoT project. Specifically, the DMP described the data management life cycle for all datasets to be collected, processed and/or generated by a research project. Following the EU’s guidelines regarding the DMP, this document will be updated. Current version was created in early state of the project (M6) and details regarding data that will be produced by use cases has not formulated yet. Dataset from test sites will be supplemented with the ID, metadata and (if applicable) with the related software and documentation. It is assumed to provide at least one dataset of each scenario to the public, available through central SerIoT repository. Finally, datasets will be preserved after the end of the project on the web server.	https://phaidra.univie.ac.at/o:1140797	Horizon 2020
0191_LIBRA_665937.md	# Data sharing In general all data produced by LIBRA will be securely stored at the CRG by the Bioinformatics Unit (Dr. Ponomarenko, Head of the Unit). Nevertheless, depending on which type of data we process the sharing plans change. Each LIBRA WP should have access to the data through ID and passwords. 1. For Survey monkey staff data, CRG will securely store data and give access through ID and passwords first to P7, P5 and P6 for them to analyse it. Once the data are analysed it will be shared to all LIBRA partners most likely via LIBRA Professional Dropbox. 2. For Project Implicit data, CRG will have access to the data recorded and stored by Project Implicit at the US using a password protected web account. Project Implicit will analyse the data and give access to the CRG Project Manager. Project Implicit is only allowed to store and analyse data. Further analysis and utilisation of these data is responsibility of LIBRA. After a detailed second analysis of the data it will be shared through ID and passwords to the LIBRA partners. 3. Data from each LIBRA IO will be analysed by ASDO and after that shared to all LIBRA WP though ID and passwords. Data sets cannot be entirely shared to the public in the original format so no data can be traced back in some way (e.g. female PI researcher in an institute where there is only few female PIs). Thus, data made public through publications, news on the website and similar will be in formats that protect anonymity. For example, research data made public can be visualised as histograms, thus informing but preserving anonymity. # Data Archiving and Preservation The raw data will be archived 20 years and the intermediate data will be preserved for at least 2 years more after the end of the project at CRG’s data repository. Raw and intermediate data stored at the UPF – Open Access Repository will be previously selected depending on its output and always guarantying anonymity. There are no associated costs for archiving the intermediate data at the infrastructure website of CRG since the amount of data is not big enough. The costs for archiving data results at the open access repository are also included in the ordinary fees CRG pays to the UPF library. The cost for storing and managing data by Dr. Hermoso and Dr. Ponomarenko (Bioinformatics Unit) still have to be determine once the amount of data and data sets we are dealing with are clear. These costs are all eligible since we are participating in the ORD pilot experiment. 4	https://phaidra.univie.ac.at/o:1140797	Horizon 2020
0195_SponGES_679849.md	2. Initial Data Management Plan Project Data Contact: Amelie Driemel ( [email protected]_ ) Specification of the expected datasets created during SponGES: 2.1 Baseline environmental data Data set description: During various campaigns with research vessel G.O. Sars (Norway) and probably other vessels, various baseline datasets will be assembled: * CTD/Rosette (=>water chemistry profiles), * Multibeam/Synthetic aperture sonars (=>bathymetry), * Remotely Operated Vehicle (=>sea-bottom pictures) and * Sediment cores (=> historic sponge spicule abundance, sediment chemistry, stratigraphic data) These datasets will serve as a baseline set for all Work Packages and define the environmental context of the project. Due to the standard instrumentation used, the datasets will be directly comparable to datasets from other regions/areas (e.g. _https://doi.pangaea.de/10.1594/PANGAEA.753658_ . Standards and metadata: Standard oceanographic and geoscientific instrumentation will be used (CTD/Rosettes, ROVs, gravity cores etc.) to obtain the data. The analysis of ocean water and sediment will take place in the research institutes of the consortium. Raw data will be marked differently to quality controlled data and the latter will be submitted to PANGAEA, data publisher for georeferenced data from earth system research ( _www.pangaea.de_ ). The scientist will retain his work-file until he is confident that the data quality is sufficient for archiving. After that the data will be submitted to PANGAEA and an independent quality check will be performed by the data curator of PANGAEA (units correct? parameter names unique? metadata sufficient?). The data and metadata will then be archived in PANGAEAs relational database. Metadata supplied to PANGAEA are: Latitude/longitude, depth, date/time of sample, the PI, the authors of the dataset, dataset title, methodology, link to article where data has been used, specific comments if needed. PANGAEA supports several metadata standards such as ISO19xxx series, DIF, Dublin Core, Darwin Core etc. and supports existing metadata portals (e.g. OAIster, ScientificCommons) to disseminate its data/metadata by using the OAI- PMH and other standards. Data sharing: The use of citable and permanent DOIs for all datasets archived in PANGAEA ensures the longterm availability of SponGES data. The data will be access restricted during a moratorium period (password protected datasets), but will already be archived in PANGAEA and will have the project label "SponGES" for the fast identification of and search for project data. For the baseline datasets a project access will be created in PANGAEA, so that all project members can access and use (and cite) the same version for their own work. At the latest after the moratorium the data will be freely accessible, citable (DOI) and directly downloadable in tab format (at _www.pangaea.de_ ). All data will be shared by the Creative Commons Attribution 3.0 Licence (CCBY). Archiving and preservation (including storage and backup): Long-term archiving (>10 years) and a backup of these datasets (and all costs hereof) are guaranteed by the institutes operating PANGAEA (Alfred-Wegener Institut, Bremerhaven and Center for Marine Environmental Sciences, Bremen), see also information above. Each dataset will have a unique and persistent DOI. 4 Sponge genetic, metabolic and microbiological data Data set description: The genome of selected sponge species will be analyzed (=> gene code data). Furthermore, an analysis of the sponge-associated microbes/symbionts (16S amplicon sequence data) will be conducted. Other expected data include: Secondary metabolite gene clusters (nucleotide sequences) of for example PKS, NRPS, saccharides, terpenes, siderophores, lantipeptides other biotechnologically relevant secondary metabolite gene clusters (by ways of metagenomics, metatranscriptomics) and enzyme-encoding genes (nucleotide sequences) of for example halogenases and bioluminescent enzymes and other biotechnologically relevant enzymes (by ways of metagenomics, metatranscriptomics). Standards and metadata: State-of-the-art high-throughput sequencing facilities and the expertise available in the consortium will be used to obtain the data. Gene codes will be archived in a standard gene code repository such as GenBank. Other associated data will be submitted to PANGAEA, data publisher for georeferenced data from earth system research ( _www.pangaea.de_ ). The link to the respective GenBank entry will be added to the datasets stored in PANGAEA (example: _https://doi.pangaea.de/10.1594/PANGAEA.855513_ ). Metadata supplied to PANGAEA are: Latitude/longitude, water depth, date/time of sample, the PI, the authors of the dataset, dataset title, methodology, link to article where data has been used, specific comments if needed. PANGAEA supports several metadata standards such as ISO19xxx series, DIF, Dublin Core, Darwin Core etc. and supports existing metadata portals (e.g. OAIster, ScientificCommons) to disseminate its data/metadata by using the OAI- PMH and other standards. Data sharing: The use of citable and permanent DOIs for all datasets archived in PANGAEA ensures the longterm availability of SponGES data. The data will be access restricted during a moratorium period (password protected datasets), but will already be archived in PANGAEA (or GenBank). At the latest after the moratorium the data will be freely accessible, citable and directly downloadable in tab format. All data will be shared by the Creative Commons Attribution 3.0 Licence (CC- BY). (Exceptions could be datasets which are relevant to the biotechnological potential of sponges, in which case the legal grounds have to be determined by the institutes first). Archiving and preservation (including storage and backup): Long-term archiving (>10 years) and a backup of PANGAEA datasets (and all costs hereof) are guaranteed by the institutes operating PANGAEA (Alfred- Wegener Institut, Bremerhaven and Center for Marine Environmental Sciences, Bremen), see also information above. Each dataset will have a unique and persistent DOI. GenBank also is an open access database aimed at the long-term availability of nucleotide sequence data ( _http://www.ncbi.nlm.nih.gov/genbank/_ ). Each dataset here has a unique GenBank Identifier. 5 Element flux data Data set description: In in-situ and ex-situ experiments the element fluxes and budgets (sources/sinks) of carbon, nitrogen and silicon through deep-sea sponge grounds will be determined. Standards and metadata: Standard instrumentation will be used (e.g. benthic flux chambers, VacuSip system for in-situ sampling) to obtain the data. The analysis of the water and sediment samples will take place in the research institutes of the consortium. Raw data will be marked differently to quality controlled data and the latter will be submitted to PANGAEA, data publisher for georeferenced data from earth system research. The scientist will retain his work-file until he is confident, that the data quality is sufficient for archiving. After that the data will be submitted to PANGAEA and an independent quality check will be performed by the data curator of PANGAEA (units correct? parameter names unique? metadata sufficient?). The data and metadata will then be archived in PANGAEAs relational database ( _www.pangaea.de_ ). Metadata supplied to PANGAEA are (for ex-situ experiments some do not apply): Latitude/longitude, water depth, date/time of sample, the PI, the authors of the dataset, dataset title, methodology, link to article where data has been used, specific comments if needed. PANGAEA supports several metadata standards such as ISO19xxx series, DIF, Dublin Core, Darwin Core etc. and supports existing metadata portals (e.g. OAIster, ScientificCommons) to disseminate its data/metadata by using the OAI- PMH and other standards. Data sharing: The use of citable and permanent DOIs for all datasets archived in PANGAEA ensures the longterm availability of SponGES data. The data will be access restricted during a moratorium period (password protected datasets), but will already be archived in PANGAEA and will have the project label "SponGES" for the fast identification of and search for project data. At the latest after the moratorium the data will be freely accessible, citable (DOI) and directly downloadable in tab format (at www.pangaea.de). All data will be shared by the Creative Commons Attribution 3.0 Licence (CC-BY). Archiving and preservation (including storage and backup): Long-term archiving (>10 years) and a backup of these datasets (and all costs hereof) are guaranteed by the institutes operating PANGAEA (Alfred-Wegener Institut, Bremerhaven and Center for Marine Environmental Sciences, Bremen), see also information above. Each dataset will have a unique and persistent DOI. 6 Model data Data set description: Various environmental and ecological models (generic, dynamic) will be developed and applied on SponGES baseline data and on data obtained from the literature (e.g. bycatch statistics, historic distribution of sponges, physiology/biomass information). Resulting datasets will be e.g.: * Predictions of fishing impacts on sponge grounds * Sponge recovery trajectories following significant disturbance scenarios * Present and future species distribution models (maps of likely distribution) * Dynamic food-web and biogeochemical model (to assess sponge-ecosystem functioning) The datatypes will either be maps, raster/shape files, or complete sets (zips files) of model codes and underlying data tables (e.g. as in: _https://doi.pangaea.de/10.1594/PANGAEA.842757_ ). Standards and metadata: The documentation of the model used and the algorithms applied will be stored together with the model output. Other metadata stored with the model data are the PI, the authors of the dataset, dataset title, methodology, and the link to the article where model data has been used (if applicable). Data sharing: The use of citable and permanent DOIs for all datasets archived in PANGAEA ensures the longterm availability of SponGES data. The model data will be access restricted during a moratorium period (password protected datasets), but will already be archived in PANGAEA and will have the project label "SponGES" for the fast identification of and search for project data. For those model outputs needed for other work packages a project access will be created in PANGAEA, so that all project members can access and use (and cite) the same version for their work. At the latest after the moratorium the data will be freely accessible, citable (DOI) and directly downloadable in tab format (at _www.pangaea.de_ ). All data will be shared by the Creative Commons Attribution 3.0 Licence (CC-BY). Archiving and preservation (including storage and backup): Long-term archiving (>10 years) and a backup of these datasets (and all costs hereof) are guaranteed by the institutes operating PANGAEA (Alfred-Wegener Institut, Bremerhaven and Center for Marine Environmental Sciences, Bremen), see also information above. Each dataset will have a unique and persistent DOI. 7	https://phaidra.univie.ac.at/o:1140797	Horizon 2020
0196_Lynx_780602.md	# 1 INTRODUCTION This document contains the initial version of the Data Management Plan (DMP). The final version of this document will be available as “D2.4 Data Management Plan” in M18. This document is complemented by “D7.2 IPR and Data Protection Management”, to be delivered by M6 as well. The Data Management Plan adheres to and complies with the _H2020 Data Management Plan – General Definition_ given by the EC online, where the DMP is described as follows: “A DMP describes the data management life cycle for the data to be collected, processed and/or generated by a Horizon 2020 project. As part of making research data findable, accessible, interoperable and reusable (FAIR), a DMP should 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)” Section 2 follows the template proposed by the EC 1 . Lynx adopts policies compliant with the official FAIR guidelines [1] (findable, accessible, interoperable and re-usable). Lynx participates Open Research Data Pilot (ORDP) and is obliged to deposit the produced research data in a research data repository. For such effect, the Zenodo repository has been chosen, which exposes the data to OpenAIRE granting its long term preservation. The description of the most relevant datasets for compliance have been published in a Lynx Data Portal, using CKAN technology. Metadata is provided for every relevant dataset, and data is selectively provided whenever it can be republished without license restrictions and relevance for the project is high. This deliverable also describes a catalogue of relevant legal and regulatory data models and a strategy for the homogenisation of the data sources. Finally, the document describes the concept of a Multilingual Legal Knowledge Graph for Compliance, or Legal Knowledge Graph for short (Section 5), which is the backbone on when the Lynx services will rest (Figure 1). European Directives General legal goals for every European Member State National Legislation Every Member State has different national and regional legislation in force European Regulations Legislative act binding in every Industry standards Technical documents in occasions necessary to achieve certification Case law Judgements, sentences European Member State Figure 1. Schematic description of the Multilingual Legal Knowledge Graph for Compliance # 2 DATA MANAGEMENT PLAN This Section is the Initial Data Management Plan. It follows the template proposed by the EC and is applicable to the data used in or generated by Lynx, with the sole exception of pilot-specific data, whose management may be further specified in per-pilot DMPs. If the implementation of the pilots required a different DMP, either new DMP documents or new additions to this document shall be defined by the pilot leaders and the resulting work included in D2.4. ## 2.1 DATA SUMMARY Purpose . The main objective of Lynx is “to create an ecosystem of smart cloud services to better manage compliance, based on a legal knowledge graph (LKG) which integrates and links heterogeneous compliance data sources including legislation, case law, standards and other aspects”. In order to deliver these smart services, data will be collected and integrated into a Legal Knowledge Graph, to be described in more detail in Section 3. Formats . The very nature of this project makes the number of formats too high as to be foreseen in advance. However, the project will be keen on gathering data in RDF format or producing RDF data itself. RDF will be the format of choice for the meta model, using standard vocabularies and ontologies as data models. More details on the initially considered data models are given in Section 5. Data reuse . The core part of the LKG will be created by reusing existing datasets, either copying them into the consortium servers (only if strictly needed) or using them directly from the sources. Data origin . Although Lynx will be greedy in gathering and linking as much compliance-related data as possible from any possible source, it can be foreseen that the Eur-Lex portal will be the principal data source. Users of the Pilots may contribute their own data (e.g. private contracts, paid standards), which will be neither included into the LKG nor made publicly available. Data size . The strong reliance of Lynx in external open data sources minimizes the amount of data that Lynx will have to physically store. No massive data storage infrastructure is foreseen. Data utility . Data will be useful for SMEs and EU citizens alike through different portals. ## 2.2 FAIR DATA ### 2.2.1 Making data findable, including provisions for metadata Discoverability . Data will be discoverable through a dedicated data portal (http://data.lynxproject.eu), further described in Section 4. Data assets will be identified with a harmonized policy to be defined in the forthcoming months. Naming convention . A specific URI minting policy will be used to identify data assets. The policy will be specified in the forthcoming months after the publication of this deliverable. Search keywords . Open datasets described in the Lynx data portal are findable through standard forms including keyword search. Versioning . Versioning is an intrinsic part of the URI strategy to be devised. Metadata . Metadata records describing each dataset will be downloadable as DCAT-AP entries. ### 2.2.2 Making data openly accessible Open data : data in the LKG . The adopted approach is “as open as possible, as closed as necessary”. Data assets produced during the project will preferably be published as open data. Nevertheless, during the project some datasets will be created from existing private resources (e.g. dictionaries by KDictionaries), whose publication would irremediable damage their business model. These datasets will not be released as open data. Datasets in the LKG will be in any case published along with a license. This license will be specified as a metadata record in the data catalog, which can also be exported as RDF using the appropriate vocabulary terms (dtc:license) and eventually using machine readable licenses. Open data: research data. In December 2013, the EC announced their commitment to open data through the Pilot on Open Research Data, as part of the Horizon 2020 Research and Innovation Programme. The Pilot’s aim is to “improve and maximise access to and reuse of research data generated by projects for the benefit of society and the economy”. In the frame of this Pilot on Open Research Data, results of publicly-funded research should be disseminated more broadly and faster, for the benefit of researchers, innovative industry and citizens. The Lynx project chose to participate in the Open Research Data Pilot (ORDP). Consequently, publishing as “open” the digital research data generated during the project is a contractual obligation (GA Art. 29.3). This provision does not include the pieces of data which are derivative of private data of the partners. Their openness would endanger their economic viability and jeopardize the Lynx project itself (which is sufficient reason not to open the data as per GA Art. 29.3). Every Lynx partner will ensure Open Access to all peer-reviewed scientific publications relating to its results. Lynx will use Zenodo as the online repository (https://zenodo.org/communities/lynx/) to upload public deliverables and possibly part of the scientific production. Zenodo is a research data repository created by OpenAIRE to share data from research projects. Records are indexed immediately in OpenAIRE, which is specifically aimed to support the implementation of the EC and ERC Open Access policies. Nevertheless, in order to avoid fragmentation, the Lynx webpage will act as the central information node. The following categories of outputs require Open Access to be provided free of charge by Lynx partners, to related datasets, in order to fulfil the H2020 requirements of making it possible for third parties to access, mine, exploit, reproduce and disseminate the results contained therein: * _Public deliverables_ will be available both at Zenodo and the Lynx website at http://lynxproject.eu/publications/deliverables. See Figure 1 and Figure 2. * _Conference and Workshop presentations_ may be published at Slideshare under the account https://www.slideshare.net/LynxProject. * _Conference and Workshop papers and articles for specialist magazines_ may be also reproduced at: http://lynx-project.eu/publications/articles. * _Research data and metadata_ are also available. Metadata and selected data is available in the CKAN data portal, http://data.lynx-project.eu, produced research data at Zenodo. Information will be also given about tools and instruments at the disposal of the beneficiaries and necessary for validating the results. Figure 2. Lynx public deliverable at Zenodo. Figure 3. Deliverables on the Lynx website Accessibility . Data descriptions (metadata) will be accessible through a dedicated data portal, hosted in Madrid and available under http://data.lynx- project.eu. Eventually, data from small datasets will be also made available from the web server –where _small_ means a file size that does not compromise the web server availability. Eventually the metadata descriptions will be uploaded into other repositories, such as Retele 2 resources in Spanish language, ELRC-SHARE 3 in general and others to be identified. In addition, the cooperation with the CEF eTranslation 4 TermBank project will be considered, in view of sharing terminological domain-specific resources. Necessary methods and tools to access the data and its documentation . Relevant datasets whose license is liberal will be available as downloadable files. Eventually, a SPARQL endpoint will be set in place for those dataset in RDF form. Also, the CKAN technology in which the portal is based on, offers an API using standard JSON structures to access the data. The CKAN platform provides the documentation on how to use the API (http://docs.ckan.org/en/ckan-2.7.3/api/). Publication of software . Some of the software to be developed in Lynx is expected to be published as Open Source. Other software to be developed in Lynx will be derived from private or non-open source code and, thus, not be made publicly accessible. Data and code repositories and arrangement . Lynx uses a private source code repository (https://gitlab.com/superlynx). Open data will be deposited in the Lynx open data portal; consortiuminternal data within the project intranet. The choice of Nextcloud is justified as the information resides within UPM secured servers in Madrid, avoiding third parties and granting the privacy and confidentiality of the data. Gitlab, as a major provider and host of code repositories, is a common choice among developers but if necessary code might be also hosted at UPM. Data Access Committee . As of today, there is no need for a Data Access Committee 5 . Conditions for access . Description of data assets include a link to well- known licenses, for which machine readable versions exist. Either Creative Commons Attribution International 4.0 (CC-BY) or Creative Commons Attribution Share-Alike International 4.0 (CC-BY-SA) will be the recommended licenses. Access control . The Lynx intranet (Nextcloud) provides standard access control functionalities. The servers are located in a secured data centre at UPM. The access point is https://delicias.dia.fi.upm.es/lynx-nextcloud/. Access is secured by asymmetric keys or passwords and communications use SSL. ### 2.2.3 Making data interoperable Interoperability . The LKG preferred format is RDF, granting interoperability between institutions, organisations and countries. This choice optimally facilitates re-combinations with different datasets from different origins. Data and metadata vocabularies . Specific data and metadata vocabularies will be defined throughout the entire project. An initial collection has already been edited and will be soon published at http://lynx- project.eu/data/data-models (see also Figure 3). Standard vocabularies and inter-disciplinarity . Standard vocabularies will be used inasmuch as possible, like the ECLI ontology, the Ontolex model and other vocabularies similarly spread. These choices grant inter- disciplinary collaboration. For example, Ontolex 6 is standard in the language resources and technologies communities, whereas the ELI ontology 7 (European Law Identifier) is standard in the European legal community. Mappings of vocabularies and ontologies developed by Lynx . If vocabularies or ontologies are further defined, they will be published online, documented and mapped to other standard ontologies. Figure 3 illustrates a possible visualization for the data models. Figure 4. A catalogue of relevant ontologies and vocabularies ### 2.2.4 Increase data reuse Embargoes . No data embargoes are foreseen. Public data will be published as soon as possible, but private data will remain private as long as the interested parties, rightsholders of the data, decide. Data after the project . Lynx aims at building a LKG towards compliance. In the long term, the LKG may be repurposed and the data portal may become a reference entry point to find open, linguistic legal information as RDF. Data validity after time . Some of the datasets require maintenance (e.g. legislation and case law must be kept up to date). Whereas a core of information may still be of interest even with no maintenance, those datasets directly used by services under exploitation will be maintained. In any case, metadata records describing the datasets will include a field informing on the last modification date. Data quality assurance . Only formal aspects of data quality are expected to be assured. In particular, the 5-stars 8 will be considered, and the data portal will describe this quality level in due time. ## 2.3 ALLOCATION OF RESOURCES Costs . The cost of publishing FAIR data includes (a) maintenance of the physical servers; (b) time devoted to the data generation and (c) long term preservation of the data. Coverage of the costs . Resources to maintain and generate data are covered by the project. Long term preservation of data is free by uploading the research data at Zenodo. Responsibility of the Data Management . UPM is responsible for managing data in the data portal, and for managing private data in the intranet. UPM is not responsible of keeping personal data collected to provide the pilot services but the directly involved partners (openlaws, Cuatrecasas, DNV GL). Long term preservation . Public deliverables and research data will be uploaded to Zenodo, which grants the long term preservation. A specific community has been created in Zenodo 9 . Alternatively, if difficulties are found with Zenodo, datasets may also be uploaded to Figshare 10 or B2Share 11 where a permanent DOI is retrieved. Other sites such as META-SHARE, ELRC- SHARE or the European Language Grid may be considered in addition to grant long term preservation and maximize the impact and dissemination. ## 2.4 DATA SECURITY ### 2.4.1 Data Security Data security . UPM is physically storing data on their servers: webpage, files and data in the Nextcloud system, the CKAN data catalogue and mailing lists. These pieces of data are both digitally and physically secured in a data centre. Backups are made of these systems, to external hard disks or other machines. In principle, no personal data will be kept at UPM, and the pilot leaders will define specific DMP with specific data protection provisions and specific data security details. Long term preservation . Relevant data which is open, shall be uploaded to Zenodo. In addition, relevant language datasets produced in the course of Lynx will be uploaded to catalogues of language resources. ## 2.5 LEGAL, ETHICAL AND SOCIETAL ASPECTS ### 2.5.1 Legal framework EU citizens are granted the rights of privacy and data protection by the Charter of Fundamental rights of the EU. In particular, Art. 7 states that “ _everyone has the right respect for private and family life, home and communications_ ”, whereas Art. 8 regulates that “ _everyone has the right to the protection of personal data concerning him or her_ ” and that processing of such data must be “ _on the basis of the consent of the person concerned or some other legitimate basis laid down by law_ .” These rights are developed in detail by the General Data Protection Regulation (GDPR), Regulation 2016/679/EC, which is in force in every Member State since 25 th of May of 2018. This regulation imposes obligations to the Lynx consortium, which is also reminded by Art. 39 of the Lynx Grant Agreement (GA): “ _the beneficiaries must process personal data under the Agreement in compliance with applicable EU and national law on data protection_ ” The same GA also reminds that beneficiaries “ _may grant their personnel access only to data that is strictly necessary for implementing, managing and monitoring the Agreement_ ” (GA Art. 39.2). _Personal data_ is, according to GDPR art. 4.1 “ _any information relating to an identified or identifiable natural person (‘data subject’); an identifiable natural person is one who can be identified, directly or indirectly, in particular by reference to an identifier such as a name, an identification number, location data, an online identifier or to one or more factors specific to the physical, physiological, genetic, mental, economic, cultural or social identity of that natural person_ ”, whereas _data processing_ is (art. 4.2): “ _any operation or set of operations which is performed on personal data or on sets of personal data, whether or not by automated means, such as collection, recording, organisation, structuring, storage, adaptation or alteration, retrieval, consultation, use, disclosure by transmission, dissemination or otherwise making available, alignment or combination, restriction, erasure or destruction_ ”. With these definitions, Pilot 1 (Compliance Assurance Services in Data Protection) will most likely have to collect and process personal data, and possibly other Pilots as well. The purposes for which personal data will be collected are justified in compliance with art.5.b, and the processing of personal data is legitimate in compliance with art. 6. The implementation of the Pilot 1 and other pilots processing personal data will have to implement the necessary legal provisions to respect the rights of the data subjects. Several internal communication channels have been established for Lynx: mailing lists, a website and an intranet. The three servers are hosted at UPM and comply with the Spanish legislation. The Lynx web site (http://lynx-project.eu) is compliant regarding the management of cookies with _Ley 34/2002, de 11 de julio, de servicios de la sociedad de la información y de comercio electrónico_ . Lynx will most likely handle datasets with personal data (Pilot 1), as users will be registered in the Lynx platform to enjoy personalised services and to upload contracts with personal data. The consortium will adopt any measure to comply with the current legislation. ### 2.5.2 Ethical aspects The ethical aspect of greatest interest is the processing of personal data. The processing of personal data may become a possibility in the framework of Pilot 1. GA Article 34 “Ethics and research integrity” is binding and shall be respected. Ethical and privacy related concerns are fully addressed in Section 3.2 of Deliverable 7.2 “ _IPR and Data Protection management documents_ ”. Besides, the ethics issues identified are already being handled by the pilot organisations during their daily operation activities, as they confront with national laws and EU directives regarding the use of information in their daily services, as clearance for the processing, storing methods, data destruction, etc. has been provided to such organisation a priori and is not case specific. The research to be done during Lynx does not raise any other issues, and the project will make sure that it will follow the same patterns and rules used by the pilot organisations, that will guarantee the proper handling of ethical issues and the adherence to national, EU wide and international law and directives that do not violate the terms of the programme. ### 2.5.3 Societal impact The societal impact of this project is expected to be positive, enhancing the access of EU citizens to legislation and contributing towards a fairer Europe. In addition to the best effort made by the project partners, members of the Advisory Board may be requested to issue a statement on the ethical and societal impact of the Lynx project. # 3 CATALOGUE OF DATASETS This section describes a catalogue of relevant legal, regulatory and linguistic datasets. Datasets in the Legal Knowledge Graph are those necessary to provide compliance related services that also meet the requirement of being published as linked data. The purpose of Lynx Task 2.1 is twofold: 1. Identify as many as possible open dataset possibly relevant to the problem in question (either in RDF or not) 2. Build the Legal Knowledge Graph by identifying existing linked data resources or by transforming existing datasets into linked data whenever necessary Figure 5 represents the Legal Knowledge Graph as a collection of dataset published as linked data. The LKG lies amidst another cloud of datasets, in various formats either structured or not (such as PDF, XLS or XML). The section contains: (a) the methodology followed to describe datasets of interest; (b) the methodology to transform existing resources into LKG datasets; (c) a description of the Lynx data portal and the related technology and (d) an initial list of relevant datasets. Legal Knowledge Graph ( RDF ) Other datasets of interest ( PDF, XLS, XML… ) Figure 5. Datasets in the LKG and out of it ## 3.1 METHODOLOGY FOR CATALOGUING DATASETS Data assets potentially relevant to the Lynx project are those that might help providing multilingual compliance services. They might be referenced by datasets in the LKG as external references. The identification and description of these datasets is being made during the project in a cooperative way, during the entire project lifespan. The methodology has consisted of the following steps: 1. _Identification of datasets of possible interest_  Identification of relevant datasets by the partners;  Discovery of relevant datasets by browsing data portals, reviewing literature and making general searches; 2. _Description of resources_  Description of the resources identified in Step 1 using an agreed template (spreadsheet) with metadata records (see Section 4.2.1). 3. _Publication of dataset descriptions_  Publication of the dataset description in the CKAN Open Data Portal via CKAN form  Transformation of the metadata records to RDF using the vocabulary DCAT-AP (to be an automated task from the spreadsheet) This process is being iteratively carried out throughout the project. ### 3.1.1 Template for data description Every partner of Lynx, within their domain of expertise, has described an initial list of data sources of interest for the project. In order to homogeneously describe the data assets, a template with metadata records has been created with the due consensus among the partners. The template for data description contains two main blocks: one with general information about the dataset and another with information about the resource. Within this context, “dataset” makes reference to the whole asset, while “resource” defines each one of the different formats in which the dataset is published. For instance, the UNESCO thesaurus is a single dataset which can be found as two different resources: as a SPARQL Endpoint and as a downloadable file in RDF. Thereby, the metadata records in Table 1 describe information about the dataset as a whole. <table> <tr> <th> Field Description </th> </tr> <tr> <td> Title </td> <td> the name of the dataset given by the author or institution that publishes it. </td> </tr> <tr> <td> URI </td> <td> identifier pointing to the dataset. </td> </tr> <tr> <td> Type in the LKG </td> <td> type of dataset in the legal knowledge graph (language, data, etc.). </td> </tr> <tr> <td> Type </td> <td> type of dataset (term bank, glossary, vocabulary, corpus, etc.). </td> </tr> <tr> <td> Domain </td> <td> topic covered by the dataset (law, education, culture, government, etc.). </td> </tr> <tr> <td> Identifiers </td> <td> other type of identifiers assigned to the dataset (ISRN, DOI, Standard ID, etc.). </td> </tr> <tr> <td> Description </td> <td> a brief description of the content of the dataset. </td> </tr> <tr> <td> Availability </td> <td> if the dataset is available online, upon request or not available. </td> </tr> <tr> <td> Languages </td> <td> languages in which the content of the dataset are available. </td> </tr> <tr> <td> Creator </td> <td> author or institution that created the dataset. </td> </tr> <tr> <td> Publisher </td> <td> institution publishing the dataset. </td> </tr> <tr> <td> License </td> <td> license of the dataset (Creative Commons, or others). </td> </tr> <tr> <td> Other rights </td> <td> if the dataset contains personal information. </td> </tr> <tr> <td> Jurisdiction </td> <td> jurisdiction where the dataset applies (if necessary). </td> </tr> <tr> <td> Date of this entry </td> <td> date of registration of the dataset in the CKAN. </td> </tr> <tr> <td> Proposed by </td> <td> Lynx partner or Lynx organisation proposing the dataset. </td> </tr> <tr> <td> Number of entries </td> <td> number of terms, triplets or entries that the dataset contains. </td> </tr> <tr> <td> Last update </td> <td> date in which the last modification of the dataset took place. </td> </tr> <tr> <td> Dataset organisation </td> <td> </td> </tr> <tr> <td> name of the Lynx organisation registering the dataset. </td> </tr> </table> Table 1. Fields describing a data asset The second block of metadata (whose fields are listed in Table 2) gives additional information about the resource in which the metadata can be accessed. This section is repeated as many times as needed (depending on the number of formats of the metadata). <table> <tr> <th> Field </th> <th> Description </th> </tr> <tr> <td> Description </td> <td> description of the type of resource (i.e. downloadable file, SPARQL endpoint, website search application, etc.). </td> </tr> <tr> <td> Data format </td> <td> the format of the resource (RDF, XML, SKOS, CSV, etc.). </td> </tr> <tr> <td> </td> <td> technology used to expose the resource (relational database, API, linked data, etc.). </td> </tr> <tr> <td> Data access </td> </tr> <tr> <td> Open format </td> <td> if the format of the resource is open or not. </td> </tr> <tr> <td> URI </td> <td> the URI pointing to the different resources. </td> </tr> </table> Table 2. Fields describing a resource associated to a data asset The template was materialized as a spreadsheet distributed among the partners. ### 3.1.2 Lynx Data Portal With the aim of publishing the metadata of the harvested datasets, a data portal has been made available under http://data.lynx-project.eu. This data portal uses the technology of CKAN. The Comprehensive Knowledge Archive Network (CKAN) is a web-based management system for the storage and distribution of open data. The system is open source 12 , and it has been deployed on the UPM servers using containerization technologies –Rancher 13 , a leading solution to deploy Docker containers in a Platform as a Service (PaaS). The CKAN open data portal gives access to the resources gathered by all the members of the Lynx project. In the same way, members are able to register and describe their harvested resources to jointly create the Lynx Open Data Portal. To correctly display the relevant information about the datasets, CKAN application uses the metadata described in Section 4.2.1. As a result, each dataset presents the interface as shown by Figure 5 . Figure 6. Screenshot of the Lynx Data Portal The “Data and Resources” section corresponds to the “Resource information” metadata block and “Additional Info” contains the metadata of the “Dataset information” table. The CKAN data portal allows faceted browsing, with filters such as language, format and jurisdiction. At this moment, there are 26 datasets classified in the CKAN, but this number will grow. For the metadata records to be correctly displayed on the website, it was required to establish a correspondence between the metadata in the spreadsheet and the structure in the JSON file that gives shape to the CKAN platform. In the Lynx Data Portal, each dataset can be accessed through their own URI, that is built by using the ID of each resource. Datasets IDs are shown in Table 3, contained in the next section. As a result, dataset URIs look like the example below, where the ID would be unesco-thesaurus: http://data.lynx-project.eu/dataset/unesco-thesaurus The CKAN API enables a direct access to the metadata records. The API is intended for developers who want to write code that interacts with CKAN sites and their data, and it is documented online 14 . For example, the method: http://data.lynx-project.eu/api/rest/dataset/unesco-thesaurus will return the following answer: {"license_title": null, "maintainer": null, "private": false, "maintainer_email": null, "num_tags": 0, "id": "efaf72c9-f8da-4257-b77e-c1f90952d71a", "metadata_created": "2018-04-11T08:35:41.813169", "relationships": [], "license": null, "metadata_modified": "2018-04-11T08:39:59.429186", "author": null, "author_email": null, "download_url": "http://skos.um.es/sparql/", "state": "active", "version": null, "creator_user_id": "3b131ddc-4bbf- 42ff-9c33-ee1c4f7adb5c", "type": "dataset", "resources": [{"Distribuciones": "SPARQL endpoint", "hash": "", "description": "SPARQL endpoint", "format": "SKOS", "package_id": "efaf72c9-f8da-4257-b77e-c1f90952d71a", "mimetype_inner": null, "url_type": null, "formatoabierto": "", "id": "2a610dc8-15cd-4f17-aee0-149201c427cd", "size": null, "mimetype": null, "cache_url": null, "name": "SPARQL endpoint", "created": "2018-04- 11T08:39:13.979840", "url": "http://skos.um.es/sparql/", "cache_last_updated": null, "last_modified": null, "position": 0, "resource_type": null}, {"Distribuciones": "Downloadable files", "hash": "", "description": "Downloadable files in RDF and Turtle.", "format": "RDF", "package_id": "efaf72c9-f8da-4257-b77e-c1f90952d71a", "mimetype_inner": null, "url_type": null, "formatoabierto": "", "id": "81ddd071-4018-4850-b5d8-04b4f5badd7d", "size": null, "mimetype": null, "cache_url": null, "name": "Downloadable files", "created": "2018-04- 11T08:39:59.170137", "url": "http://skos.um.es/unescothes/downloads.php", "cache_last_updated": null, "last_modified": null, "position": 1, "resource_type": null}], "num_resources": 2, "tags": [], "groups": [], "license_id": null, "organization": {"description": "", "title": "OEG", "created": "2018-04-05T08:10:35.821305", "approval_status": "approved", "is_organization": true, "state": "active", "image_url": "", "revision_id": "66f3c9c3-9bdf-4ebe-8ed2-54b4aea30375", "type": "organization", "id": "d4250a6e-d1d4-4a2d-8e40-b663271d8404", "name": "oeg"}, "name": "unesco- thesaurus", "isopen": false, "notes_rendered": "<p>The UNESCO Thesaurus is a controlled and structured list of terms used in subject analysis and retrieval of documents and publications in several fields.</p>", "url": null, "ckan_url": "http://data.lynx-project.eu/dataset/unesco-thesaurus", "notes": "The UNESCO Thesaurus is a controlled and structured list of terms used in subject analysis and retrieval of documents and publications in several fields.\r\n", "owner_org": "d4250a6e-d1d4-4a2d-8e40-b663271d8404", "ratings_average": null, "extras": {"lkg_type": "language", "domain": "Education, Science, Culture, Politics, Countries, Information", "total_number": "4408 (skos concepts)", "language": "en, es, fr, ru", "creator": "Research group of Information Technology (University of Murcia)", "publisher": "UNESCO", "jurisdiction": "", "other_rights": "no", "last_update": "2015", "licence": "Creative Commons 3.0, https://creativecommons.org/licenses/by-nc-sa/3.0/deed.es_ES", "date": "11/04/18", "partner": "UPM", "identifier": "", "availability": "online"}, "ratings_count": 0, "title": "UNESCO Thesaurus", "revision_id": "67553ea8-aa13-4dfe-905d-eb499d2d78e9"} ## 3.2 TRANSFORMATION OF RESOURCES The minimum content of the LKG is the collection of datasets necessary for the execution of the Lynx pilots that are published as linked data. Whereas transformation of resrouces to linked data is not a central activity of Lynx, the project foresees that some resources will exist but not as linked data, and a transformation process will be necessary. The cycle of activities usually made when publishing linked data (see Figure 7) is Figure 7. Usual activities for publishing linked data. Figure taken from [25]. Whereas the specification is derived from the pilots and the use case needs, the modelling process will lean on existing data models, to be harmonized as described in Section 4.2. The generation of linked data will be the transformation of existing resources. These transformation will be different depending on the source format:  From unstructured text, extraction tools (PoolParty, OpenCalais, SketchEngine etc.) will be used, before creating the entities.  From relational databases, technologies such as R2RML exist, but no relation database is expected to be necessary.  For tabular data, Open Refine and similar tools will be used. The publication means is to be decided but it will be made using either PoolParty or Open Link Virtuoso in local servers. ## 3.3 INITIAL CATALOGUE OF DATASETS This section contains only preliminary information, and it will be completed by M18 with D2.4. ### 3.3.1 Datasets in the regulatory domain These are the initially identified datasets in the regulatory domain:  Eur-Lex: Database of legal information containing: EU law (EU treaties, directives, regulations, decisions, consolidated legislation, etc.) preparatory acts (legislative proposals, reports, green and white papers, etc.), EU case-law (judgments, orders, etc.), international agreements, etc. A huge database updated daily with some texts dating back to 1951.  Openlaws: Austrian laws (federal laws and of the 9 regions) and rulings (from 10 different courts), German federal laws, European laws (regulations, directives) and rulings (general court, European Court of Justice). It includes Eur-Lex, 11k national acts and 300k national cases in a neo4j graph.  DNV-GL: Standards, regulations and guidelines to the public, usually in PDF. ### 3.3.2 Datasets in the language domain Using the methodology described in Section 4.2, several sites and repositories have been surveyed. One of the sources of most interest for linguistic open data is the Linked Open Data Cloud 15 or LOD cloud, due to its open nature and its adequate format as linked data or RDF. In particular, the Linguistic Linked Open Data Cloud 16 is a subset of the LOD cloud which provides exclusively linguistic resources sorted by typology. Different types of datasets in the Linguistic Linked Open Data Cloud are:  Corpora  Terminology, thesauri and Knowledge Bases  Lexicons and Dictionaries  Linguistic Resource Metadata  Linguistic Data Categories  Typological Databases Within this project, the three first types of resources have been shortlisted as the most useful. Besides consuming linked data or RDF in general, other valuable non-RDF resources can be included in the graph, possibly once converted to RDF. Many non-RDF resources of interest in this context can be found in data portals like the European Data Portal, the Library of Congress or the Termcoord public portal, which is of particular interest for the multilingual glossaries in the domain of law. Due to the huge amount of information and open data available nowadays, it is essential to establish these limits to gather only the relevant resources. In the case that more types of datasets are required, they will be harvested at a later stage. Thus, some of the resources already published as linked data and that have been identified as of interest for Lynx are listed below:  STW Thesaurus for Economics: a thesaurus that provides a vocabulary on any economic subject. It also contains terms used in law, sociology and politics (monolingual in English) [30].  Copyright Termbank: a multilingual term bank of copyright-related terms that has been published connecting WIPO definitions, IATE terms and definitions from Creative Commons licenses (multilingual) .  EuroVoc: a multilingual and multidisciplinary thesaurus covering the activities of the EU. It is not specifically legal, but it contains pertinent information about the EU and their politics and law (multilingual).  AGROVOC: a controlled vocabulary covering all the fields of the Food and Agriculture Organization (FAO) of the United Nations. It contains general information and it has been selected since it shares many structures with other important resources (multilingual).  IATE: a terminological database developed by the EU which is constantly being updated by translators and terminologists. Amongst other domains, the terms are related with law and EU governments (multilingual). A transformation to RDF was made in 2015. Resources published in other formats have been considered as well. Structured formats include TBX (used for term bases), CSV and XLS. Exceptionally, resources published in non-machine-readable formats might be considered. Consequently, the following resources published by the EU have also been listed as usable, although they are not included in the Linguistic Linked Open Data Cloud:  INSPIRE Glossary: a term base developed by the INSPIRE Knowledge Base of the European Union. Although this project is related with the field of spatial information, the glossary contains general terms and definitions that specify the common terminology used in the INSPIRE Directive and in the INSPIRE Implementing Regulations (monolingual, en).  EUGO Glossary: a term base addressed to companies and entrepreneurs that need to comply with administrative or professional requirements to perform a remunerated economic activity in Spain. This glossary is part of a European project and contains terms about regulations that are valuable for Lynx purpose (monolingual in Spanish).  GEMET: a general thesaurus, conceived to define a common general language to serve as the core of general terminology for the environment. This glossary is available in RDF and it shares terms and structures with EuroVoc (multilingual).  Termcoord: a portal supported by the European Union that contains glossaries developed by the different institutions. These glossaries cover several fields including law, international relations and government. Although the resources are available in PDF, at some point these documents could be treated and transformed into RDF if necessary (multilingual). In the same way, the United Nations also counts with consolidated terminological resources. Given their intergovernmental domain, the following resources have been selected:  UNESCO Thesaurus: a controlled list of terms intended for the subject analysis of texts and document retrieval. The thesaurus contains terms on several domains such as education, politics, culture and social sciences. It has been published as a SKOS thesaurus and can be accessed through a SPARQL endpoint (multilingual).  InforMEA Glossary: a term bank developed by the United Nations and supported by the European Union with the aim of gathering terms on Environmental Law and Agreements. It is available as RDF and it will be upgraded to a thesaurus during the following months (multilingual).  International Monetary Fund Glossary: a terminology list containing terms on economics and public finances related with the European Union. It is available as a PDF downloadable file; however, it may be transformed as a future work (multilingual). On the other hand, other linguistic resources (not supported by the EU nor the UN) have been spotted. Some of them are already converted into RDF:  Termcat (Terminologia Oberta): a set of terminological databases supported by the government of Catalonia. They contain term equivalents in several languages. Part of these terminological databases were converted into RDF previously and are part of the TerminotecaRDF project. They can be accessed through a SPARQL endpoint (multilingual).  German Labour Law Thesaurus: a thesaurus that covers all main areas of labour law, such as the roles of employee and employer; legal aspects around labour contracts. It is available through a SPARQL endpoint and as RDF downloadable files (monolingual, de).  Jurivoc: a juridical thesaurus developed by the Federal Supreme Court of Switzerland in cooperation with Swiss legal libraries. It contains juridical terms arranged in a monohierarchic structure (multilingual).  SAIJ Thesaurus: a thesaurus that organises legal knowledge through a list of controlled terms which represent concepts. It is available in RDF and intended to ease users’ access information related to the argentine legal system that can be found in a file or in a documentation centre (monolingual, es).  CaLaThe: a thesaurus for the domain of cadastre and land administration that provides a controlled vocabulary. It is interesting because it shares structures and terms with AGROVOC and the GEMET thesaurus, and it can be downloaded as an RDF file (monolingual, en).  CDISC Glossary: a glossary contains definitions of terms and abbreviations that can be relevant for medical laws and agreements It is available in several formats, including OWL (monolingual, en). Finally, one last resource available in other PDF has also been considered due to different facts:  Connecticut Glossary: a glossary that contains legal terms published by the Judicial Branch of the State of Connecticut. It can be transformed into a machine-readable format and from there into RDF since it provides with equivalences of legal terms from English into Spanish (bilingual). Table 3 lists all the resources as a review of the information presented above. On the other hand, the set of the identified linguistic resources has also been represented in an interactive graph, in which each dataset is coloured as per the domain it covers (Figure 8). A second version of the graph has also been created in order to make a distinction between those datasets in RDF (green) and those in different formats (grey) (Figure 9). The graph also represents the relations between each asset, since most of those in RDF share structures and terms. ID Name Description Language <table> <tr> <th> iate </th> <th> IATE </th> <th> EU terminological database. </th> <th> EU languages </th> </tr> <tr> <td> eurovoc </td> <td> Eurovoc </td> <td> EU multilingual thesaurus. </td> <td> EU languages </td> </tr> <tr> <td> eur-lex </td> <td> EUR-Lex </td> <td> EU legal corpora portal. </td> <td> EU languages </td> </tr> <tr> <td> conneticutlegal-glossary </td> <td> Conneticut Legal Glossary </td> <td> Bilingual legal glossary. </td> <td> en, es </td> </tr> <tr> <td> unescothesaurus </td> <td> UNESCO Thesaurus </td> <td> Multilingual multidisciplinary thesaurus. </td> <td> en, es, fr, ru </td> </tr> <tr> <td> library-ofcongress </td> <td> Library of Congress </td> <td> Legal corpora portal. </td> <td> en </td> </tr> <tr> <td> imf </td> <td> International Monetary Fund </td> <td> Economic multilingual terminology. </td> <td> en, de, es </td> </tr> <tr> <td> eugo-glossary </td> <td> EUGO Glossary </td> <td> Business monolingual dictionary. </td> <td> es </td> </tr> <tr> <td> cdisc-glossary </td> <td> CDISC Glossary </td> <td> Clinical monolingual </td> <td> en </td> </tr> <tr> <td> stw </td> <td> STW Thesaurus for Economics </td> <td> Economic monolingual thesaurus. </td> <td> en </td> </tr> <tr> <td> edp </td> <td> European Data Portal </td> <td> EU datasets. </td> <td> EUlanguages </td> </tr> <tr> <td> inspire </td> <td> INSPIRE Glossary (EU) </td> <td> General terms and definitions in English. </td> <td> en </td> </tr> <tr> <td> saij </td> <td> SAIJ Thesaurus </td> <td> Controlled list of legal terms. </td> <td> es </td> </tr> <tr> <td> calathe </td> <td> CaLaThe </td> <td> Cadastral vocabulary </td> <td> en </td> </tr> <tr> <td> gemet </td> <td> GEMET </td> <td> General multilingual thesauri. </td> <td> en, de, es, it </td> </tr> <tr> <td> informea </td> <td> InforMEA Glossary (UNESCO) </td> <td> Monolingual glossary on environmental law. </td> <td> en </td> </tr> <tr> <td> copyrighttermbank </td> <td> Copyright Termbank </td> <td> Multi-lingual term bank of copyrightrelated terms </td> <td> en, es, fr, pt </td> </tr> <tr> <td> gllt </td> <td> German labour law thesaurus </td> <td> Thesaurus with labour law terms. </td> <td> de </td> </tr> <tr> <td> jurivoc </td> <td> Jurivoc </td> <td> Juridical terms from Switzerland. </td> <td> de, it, fr </td> </tr> <tr> <td> termcat </td> <td> Termcat </td> <td> Terms from several fields including law. </td> <td> ca, en, es, de, fr, it </td> </tr> <tr> <td> termcoord </td> <td> Termcoord </td> <td> Glossaries from EU institutions and bodies. </td> <td> EU languages </td> </tr> </table> agrovoc Agrovoc Controlled general vocabulary. 29 languages Table 3. Initial set of resources gathered. Figure 8. Datasets represented by domain. Figure 9. Datasets represented by format. # 4 DATA MODELS ## 4.1 INTRODUCTION ### 4.1.1 Data models in the regulatory domain A number of vocabularies and ontologies for documents in the legal domain has been published in the last few years. Núria Casellas surveyed 52 legal ontologies in 2011 [18], and in the meantime many other new ontologies have appeared, but in practice, only a few of them have direct interest for the LKG, as not every published legal ontology is created with the intention of supporting data models. Some ontologies had the intent of formalizing abstract conceptualizations. For example, ontology design patterns in the legal domain have been explored [17] –but these works have little interest for supporting data publication. The XML schema Akoma Ntoso 17 was initially funded by the United Nations to become some years later an OASIS specification as Legal RuleML 18 . MetaLex [12] was an XML vocabulary for the encoding of the structure and content of legislative documents, which included in newer versions functionality related to timekeeping and version management. The European Committee for Standardization (CEN) adopted MetaLex and evolved the schema to an OWL ontology. MetaLex was extended in the context of the FP6 ESTRELLA project (2006-2008) which developed a network of ontologies known as Legal Knowledge Interchange Format (LKIF). The LKIF ontologies are still available and a reference in the area 19 [14]. Licenses used for the publication of copyrighted work have been modelled with the ODRL (Open Digital Rights Language) language [27]. The European Legislation Identifier (ELI) is a system to make legislation available online in a standardised format, so that it can be accessed, exchanged and reused across border [13]. ELI describes a new common framework to unify and link national legislation with European legislation. ELI, as a framework, proposes a URI template for the identification of legal resources on the web and it also provides an OWL ontology for supporting the representation of metadata of legal events and documents. The European Case Law Identifier (ECLI), much like ELI, was introduced recently for modelling case laws. The BO-ECLI project, funded under the Justice Programme of the European Union (2015-2017), aimed to broaden the use of ECLI and to further improve the accessibility of case law. ### 4.1.2 Data models in the linguistic domain Similarly, a large amount of language resources can already be found across the Semantic Web. Such datasets are represented with various schemas, depending on given factors such as the inner structure of the dataset, language, content or the objective of its publication, to mention but a few. _Simple Knowledge Organization System_ ( _SKOS_ ) is aimed to represent the structure of organization systems such as thesauri and taxonomies, since they share many similarities. It is widely used within the Semantic Web context, since it provides an intuitive language and can be combined with formal representation languages such as the Web Ontology Language (OWL). _SKOS XL_ works as an extension of SKOS to represent lexical information [23]. With regard to multilingualism in ontologies, _Linguistic Information Repository_ ( _LIR_ ) was proposed as model for ontology localisation: it grants the localisation of the ontology terminological layer, without modifying the ontology conceptualisation. LIR allows enriching ontology entities with the linguistic information necessary for the localisation and cultural adaptation of the ontology [24]. Another model intended for the representation of linguistic descriptions associated to ontology concepts is _Lexinfo_ [20]. It contains a complete collection of linguistic categories. Currently, it is used in combination with other models such as Ontolex (described in the next paragraph), to describe the properties of the linguistic objects that describe ontology entities. Other repositories of linguistic categories are ISOcat 20 , OLiA 21 or GOLD 22 . The _Lexicon Model for Ontologies_ or _lemon_ [26] was especially created to represent lexical information in the Semantic Web, covering some needs that previous models did not. This model has evolved in the context of a W3C Community Group into _lemon-Ontolex_ first, now better known as _Ontolex_ 23 . In this model, linguistic descriptions are as well separated from the ontology, and point to the corresponding concept in the ontology. The structure of this model is divided into a core set of classes and different modules containing various types of linguistic information that range from morpho-syntactic properties of lexical entries, lexical and terminological variation and translation, decomposition of phrase structures, syntactic frames and mappings to the ontological predicates, and morphological decomposition of lexical forms. Linguistic annotations such as data categories and linguistic descriptors are not captured in the model but referred to by pointing to models that contain them (see LexInfo model above). ## 4.2 STRATEGY FOR THE HARMONISATION OF DATA MODELS IN LYNX The LKG needs a uniform collection of data models in order to integrate heterogeneous resources. The definition of these data models will be provided in Deliverable 2.4. In order to select the data models, a simultaneous top down and bottom up approaches will be conducted, as illustrated by Figure 11. A parallel work is carried out, where in the one hand a top down approach is conducted, extracting a list of formats, vocabularies and ontologies which can be chosen to satisfy the functional requirements of the pilots, whereas in the other hand a bottom up approach is followed, exploring every possible format, vocabulary or ontology of interest, with special attention to the most widely spread ones. Identification of vocabularies and ontologies in the domain Generation of minimal metadata description Publication in the Lynx web as a catalogue of vocabularies Analysis of functional requirements Analysis of technical requirements Identification of vocabularies and formats necessary Selection of vocabularies and ontologies Top down approach An analysis of the functional and technical requirements of the pilots determines a list of vocabularies and ontologies of choice Bottom up approach A survey of ontologies and vocabularies tries to comprehensively identify the most widely spread formats Figure 10. Strategy for the selection of data models in Lynx # 5 THE MULTILINGUAL LEGAL KNOWLEDGE GRAPH As stated in the introduction, a secondary goal of this document is to define the Legal Knowledge Graph that will be developed during the Lynx project with a linguistic regulatory Linked Open Data Cloud. ## 5.1 SCOPE OF THE LEGAL KNOWLEDGE GRAPH The amount of legal data made accessible either in open or under payment modalities by legal information providers can be hardly imagined. Lexis Nexis claimed 24 to have 30 Terabytes of content, WestLaw accounted for more than 40,000 _databases_ . Their value can be roughly estimated: as of 2012, the four big players (WestLaw, Lexis Nexis, Wolters Kluwer and Bloomberg Legal) totalled about $10,000M in revenues. Language data (e.g. resources with any kind of linguistic information) belongs to a much smaller domain, but still, unmanageable as a whole. The Lynx project is interested in a small fraction of the information belonging to these domains. In particular, Lynx is in principle interested only in using the data necessary to provide the compliance services described in the pilots. Data of interest is regulatory data (legal and standards- related) and language data (to cover the multilingual aspects of the services). The intersection of these domains is of the utmost interest and Lynx will try to comprehensively identify every possible open dataset in this core category. These ideas are represented in Figure 4. Language data Legal data Legal data for compliance in the Lynx pilots Language data for compliance in the Lynx pilots Lynx core linguistic legal data. Corpora TerminologIcal databases Thesauri, glossaries Lexicons and dictionaries Linguistic resource metadata Typological databases Law Case law Opinions, recommendations Doctrine, books, journals Standards, technical norms Sectorial good practices Figure 11. Scope of the multilingual Legal Knowledge Graph The definitions of both _language data_ and _regulatory data_ are indeed fuzzy, but flexible as to introduce data of many different kinds whenever necessary (geographical data, user information, etc.). Because data in the Semantic Web is indissociable from the data models, and data models are accessed in the same manner as data is, ontologies and vocabularies are part of the LKG as well. Moreover, any kind of metadata (describing documents, standards etc.) is also part of the LKG, as well as the description of the entities producing the documents (courts, users, jurisdictions). In order to provide the compliance services, and with different degree of interest, both primary and secondary law are of use, and any relevant document in a wide sense may become part of the Legal Knowledge Graph. This is illustrated in Figure 5. Figure 12. Types of information in the Legal Knowledge Graph ## 5.1 KNOWLEDGE GRAPHS In the realm of Artificial Intelligence, a knowledge graph is a data structure to represent information, where entities are represented as nodes, their attributes as node labels and the relationship between entities are represented as edges. Knowledge graphs such as Google’s 25 , Freebase [2] and WordNet [3] turn data into knowledge, and they have become important resources for many AI and NLP applications such as information search, data integration, data analytics, question answering or context-sensitive recommendations. Large knowledge graphs include millions of concepts and billions of relationships. For example, DBpedia describes about 30M entities connected through 10,000M relationships. Entities belong to classes described in ontologies. There are different manners of representing knowledge graphs, not the least important being the one using W3C specifications of the Semantic Web: RDF, RDFS, OWL. RDF data is accessible online in different forms: as file dumps, through a SPARQL endpoints or dedicated APIs or simply published online as Linked Data [4]. ### 5.1.1 Legal Knowledge Graphs In the last few years, a number of Legal Knowledge Graphs have been created in different applications. The MetaLex Document Server offers legal documents as versioned Linked Data [10], including Dutch national regulations. Finnish [9] and Greek [8] legislation are also offered as Linked Data. The Publications Office of the EU maintains the central content and metadata CELLAR repository for storing official publications and bibliographic resources produced by the institutions of the EU [11]. The content of CELLAR, which includes EU legislation, is made publicly available by the Eur-Lex service and it offers also an SPARQL endpoint. The FP7 EUCases project (2013-2015) offered European and national case law and legislation linked in an open data stack (http://eucases.eu). Finally, Openlaws offers a platform based on linked open data, open source software and open innovation processes [5][6][7]. Lynx will benefit from the expertise of Openlaws, which will be the preferred source for the data models, methods and algorithms. New H2020 projects in the area of data protection are also using semantic web technologies, such as the H2020 Special 26 , devoted to ease the collection of user consents and represent policies as RDF or the H2020 Mirel 27 (2016-2019), with a network of experts to define a formal framework and to develop tools for mining and reasoning with legal texts, or e-Compliance, an FP7 project (2013-2016), focused on using semantic web technologies for regulatory compliance in the maritime domain. ### 5.1.2 Linguistic Knowledge Graphs In the last few years, the language technology community has shaped the Linguistic Linked Open Data Cloud: the graph with those language resources available in RDF and published as Linked Data [16]. The graph represented in Figure 6, resembles the one of the Linked Data Cloud, but limited to the language domain. Figure 13. Linguistic Linked Open Data Cloud 28 A major resource contained in this graph is _DBpedia_ , a vast network that structures data from Wikipedia and links them with other datasets available on the Web [3]. The result is published as Open Data available for the consumption of both humans and machines. Different versions of DBpedia exist for different languages. Another core resource in the LOD Cloud is _BabelNet_ [15], a huge multilingual semantic network, generated automatically from various resources and integrating the lexicographical information of _WordNet_ and the encyclopaedic knowledge of Wikipedia. BabelNet also applies Machine Translation to get information from several languages. As a result, BabelNet is considered an encyclopaedic dictionary that contains concepts and named entities connected thanks to a great amount of semantic relations. _Wordnet_ , is one of the best known Linguistic Knowledge Graphs, since it is a large online lexical database that contains nouns, verbs, adjectives and adverbs in English [3]. These words are organised in sets of synonyms that represent concepts, known as _synsets_ . WordNet uses these synonyms to represent word senses; thus, synonymy is WordNet’s most important relation. Four additional relations are also used by this network: antonymy (opposing- name), hyponymy (sub-name), meronymy (part-name), troponymy (manner-name) and entailment relations. Other resources equivalent to WordNet have been published for different languages, such as EuroWordNet [29]. However, there are other semantic networks (considered linguistic knowledge graphs) that do not appear in the LOD Cloud but are also worth to mention. This is the case of _ConceptNet_ [28], a semantic network designed to represent common sense and support textual reasoning about documents in the real word. It represents part of human experiences and tries to share this common-sense knowledge with machines. ConceptNet is often integrated with natural language processing applications to speed up the enrichment of AI systems with common sense [4]. ### 5.1.3 The Lynx Multilingual Legal Knowledge Graph Building on these previous experiences, we are in the position to define the Lynx Multilingual Legal Knowledge Graph. The Lynx Multilingual Legal Knowledge Graph (LKG) is a knowledge graph using W3C specifications with the necessary information to provide multilingual compliance services. The Lynx LKG builds on previous initiatives reusing open data and will evolve adding new resources whenever needed to provide compliance services. The LKG preferred form of publication is Linked Data, although other access mechanisms will be provided.	https://phaidra.univie.ac.at/o:1140797	Horizon 2020
0197_MELOA_776825.md	# INTRODUCTION ## Project Overview The MELOA project proposes to develop a low-cost, easy-to-handle, wave resilient, multi-purpose, multi-sensor, extra light surface drifter for use in all water environments, ranging from deep-sea to inland waters, including coastal areas, river plumes and surf zones. The device will be developed as an upgrade to the WAVY drifter conceived by the Faculty of Engineering of the University of Porto, which was used to measure the surface circulation forced by wave breaking, including detailed structure of rifts and the littoral drift current. The philosophy of the WAVY drifter will essentially be respected: * a small-size sphere with just enough room to accommodate power source, GNSSreceiver, communications modules, antennae, sensors and data processor; * optimised buoyancy to prevent the drifter trajectory responding to the wind instead of the current, while providing just enough exposure of the antennae to ensure acquisition of the GNSS signal at the required rate and reliable near real-time communications. Given the low influence of wind upon the drifters’ displacements, MELOA will provide a cheap effective way to monitor surface currents and surface dynamic features anywhere in the World Ocean. Through equipping the drifters with thermistors at two different levels, the possibility is open for monitoring “near-skin temperature” and nearsurface vertical temperature gradients, which will be invaluable for calibration/validation of satellite derived SST fields. <table> <tr> <th> General Information </th> </tr> <tr> <td> Project Title </td> <td> Multi-purpose/Multi-sensor Extra Light Oceanography Apparatus </td> </tr> <tr> <td> Starting Date </td> <td> 1st December 2017 </td> </tr> <tr> <td> Duration in Months </td> <td> 39 </td> </tr> <tr> <td> Call (part) Identifier </td> <td> H2020-SC5-2017-OneStageB </td> </tr> <tr> <td> Topic </td> <td> SC5-18-2017 Novel in-situ observation systems </td> </tr> <tr> <td> Fixed EC Keywords </td> <td> Market development, Earth Observation / Services and applications, Technological innovation, In-Situ Instruments / sensors </td> </tr> <tr> <td> Free Keywords </td> <td> Novel measurements; Cost reduction </td> </tr> </table> This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 776825\. ## Scope The Data Management Plan (DMP) will detailing 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 purpose of the DMP is to support the data management life cycle for all data that will be collected, processed or generated by the project. ## Responsibilities The table below provides information on who's contributed to this document and to which sections. Table 3 Document Responsibilities <table> <tr> <th> Name </th> <th> Institution </th> <th> Responsibilities </th> </tr> <tr> <td> Diego Lozano García </td> <td> DMS </td> <td> All sections </td> </tr> <tr> <td> Nuno Almeida </td> <td> DME </td> <td> Revision </td> </tr> <tr> <td> Félix Pedrera García </td> <td> DMS </td> <td> Revision </td> </tr> <tr> <td> Jorge Silva </td> <td> IH </td> <td> Revision </td> </tr> <tr> <td> Joaquin del Rio </td> <td> UPC </td> <td> Revision </td> </tr> </table> ## Document Structure This document is structured as following: * Section 1 provides a project overview and then goes on to describes the scope, responsibilities and structure of this deliverable. * Section 2 will describe the datasets generated in the project. * Section 3 will analyse each of the aspects of the FAIR data: Findable, Accessible, Interoperable and Re-use * Section 4 will present the resources allocation * Section 5 will deal with data security # Data Summary MELOA will develop a family of five versions of a low-cost, light-weight multiparameter drifters. These WAVY drifters will be very easy to carry around and to deploy. In contrast and in spite of that, the data they produce have far-reaching applications, directly providing valuable information that will help to derive answers to diverse scientific, environmental and societal needs and achieving multiple objectives, from complementing observational gaps in ocean observation, to delivering validation datasets to satellite ground- truthing, along with the real possibility of their effective use by the common citizen. The data generated in the MELOA project will be acquired in the test campaign and demonstrations of the WAVY drifters. A data set in MELOA is the collection of data samples acquired by a WAVY during a campaign. The contents of the data samples depend on the type of WAVY drifter. The common information for all the types is the GNSS (Time, position, velocity and direction) and the battery power. The table below presents the contents of the data samples for each type of WAVY drifter and the approximate size. Table 4 WAVY dataset contents <table> <tr> <th> WAVY type </th> <th> Sensors </th> <th> Data sample contents </th> <th> Sample size (approx. in CSV) </th> </tr> <tr> <td> WAVY basic </td> <td> GNSS (1Hz) Thermistor (0.17Hz) </td> <td> Timestamp, Position, velocity, direction, n. satellites, HDOP (76 bytes) 1x temperature (7 bytes) Battery power (7 bytes) </td> <td> 90 bytes </td> </tr> <tr> <td> WAVY littoral </td> <td> GNSS (1Hz) IMU (20Hz) </td> <td> Timestamp, Position, velocity, direction, n. satellites, HDOP (76 bytes) wave parameters (Wavelength, Amplitude, Period & Speed) + 5 fourier coefficients (120 bytes) battery power (7 bytes) </td> <td> 203 bytes </td> </tr> <tr> <td> WAVY ocean </td> <td> GNSS (1Hz) 2xThermistors (0.17Hz) IMU (20Hz) </td> <td> Timestamp, Position, velocity, direction, n. satellites, HDOP (76 bytes) wave parameters (Wavelength, Amplitude, Period & Speed) + 5 fourier coefficients (120 bytes) 2x temperatures (14 bytes) battery power (7 bytes) </td> <td> 217 bytes </td> </tr> <tr> <td> WAVY ocean plus </td> <td> GNSS (1Hz) 2xThermistors (0.17Hz) IMU (20Hz) </td> <td> Timestamp, Position, velocity, direction, n. satellites, HDOP (76 bytes) wave parameters (Wavelength, Amplitude, Period & Speed) + 5 fourier coefficients (120 bytes) 2x temperatures (14 bytes) battery power (7 bytes) </td> <td> 217 bytes </td> </tr> <tr> <td> WAVY ocean atmo </td> <td> GNSS (1Hz) 2xThermistors (0.17Hz) IMU (20Hz) 1xAir pressure gauge (0.17Hz) 2xThermistors (0.17Hz) </td> <td> Timestamp, Position, velocity, direction, n. satellites, HDOP (76 bytes) wave parameters (Wavelength, Amplitude, Period & Speed) + 5 fourier coefficients (120 bytes) 2x temperatures (14 bytes) 1x air pressure value (7 bytes) 2x air temperatures (7 bytes) battery power (7 bytes) </td> <td> 238 bytes </td> </tr> </table> The format of the data set is equivalent to the L1 Product format, which is defined in the deliverables WAVY L1 Product Specifications (V1 D4.04, V2 D4.12). In the current version, it basically consists of a CSV file containing the data samples acquired by a WAVY and a metadata JSON file specifying the WAVY id, campaign, time period and location. In further iterations during the project, other data formats like O&M JSON and GeoJSON may be supported. The size of a data set depends on the WAVY type, the sampling rate and the duration of the WAVY activity during the campaign. The sampling rate varies according to the transmission channel used to receive the WAVY data: some minutes with ARGOS Satellite for WAVY ocean, a few seconds with GPRS for WAVY littoral and 1 second with WIFI (sampling rate of the GNSS). For instance, the size of a data set of a WAVY littoral during a day (assuming a sampling rate of 1Hz) will be around 17 MB in CSV format. Note that the raw data from the IMU (recorded at 20Hz) are used to calculate the wave parameters that are the ones stored (with lower rate) in the data sets offered to the user. The number of datasets obtained in a campaign depends on the duration of the campaign, the type and number of Wavys. The data obtained from a WAVY could be divided into several data sets for different time intervals. The Field Test campaigns will be defined in the deliverable Field Tests Campaigns Plan (V1 D6.01, V2 D6.02). The lists of MELOA data sets cannot be identified yet and will be described in the next version of the document, after the validation campaigns. The Field Test Campaigns will be divided into groups by open ocean, Argos V4 (Mediterranean sea) and coast (Portuguese, Irish and Spain). Each of these groups will have a dedicated Test Report with two versions, one for each Test period of the project, they will summarise the obtained results and the validity of the data sets for each test campaign: * Portuguese Coast Field Tests Report (D6.03 and D6.04) * Irish Coast Field Tests Report (D6.05 and D6.06) * Spanish Coast Field Tests Report (D6.07 and D6.08) * Argos V4 Field Tests Report (D6.09 and D6.10) * Open Ocean Field Tests Report (D6.11 and D6.12) # FAIR data ## Making data findable, including provisions for metadata The MELOA Catalogue solution is based on CKAN, a tool used by national and local governments, research institutions, and other organisations who manage and publish lots of data collections. Once the data sets are published, users can use its faceted search features to browse and find the data set they need, and preview it using maps, graphs and tables. Each data set is associated to a campaign (CKAN group) and an organisation, so that the user can easily browse among the data sets belonging to a campaign or an organisation. The MELOA catalogue stores the WAVY data sets with metadata following the OGC Observation&Measurement profile and formatted in JSON. Also, there will be a dictionary of the metadata compliant with INSPIRE (ISO 19115). The metadata shall include the following information: * a title and description for the data acquisition campaign * the unique ID of the WAVY * the location and the date of the launch of the campaign * all the configurations of all the sensors in the WAVY As introduced above, the format and naming convention of the data sets is defined in the deliverables WAVY L1 Product Specifications (V1 D4.04, V2 D4.12). The filename of a data set includes the version number that makes unique the data set product. ## Making data openly accessible The data sets are mainly acquired in the Field Test Campaigns during the MELOA project as described in the section 2. The raw data obtained by the WAVY drifters in the campaigns are revised (e.g. cleaning some spurious samples) and in some cases postprocessed (e.g. calculating wave parameters) in order to obtain the final data sets that will be offered to the users. The MELOA data sets will be openly accessible after their revision and sometimes after their publication. So far, no access restriction has been identified to the data sets that will be generated in MELOA. In the case that certain data sets will require any access restrictions, they will be clearly identified and explained in the second version of the Data Management Plan. To have access to the MELOA data sets, a user has to register in the MELOA web portal. The registration will be free and it will allow the users to use the MELOA web applications, in particular the Catalogue and the Geo portal. The Catalogue allows users browsing and searching the data sets collected by the WAVYs for downloading them or previewing them using maps, graphs and tables. It also provides APIs for the access to the data sets by the MELOA Geo portal and other tools such as federated Catalogues. The Geo portal provides the capability of visualisation of the MELOA data sets in a way that is easy to find and interpret by the general public. The Geo portal retrieves the data sets from the MELOA Catalogue and offers links to the Catalogue for downloading them. The SW user manuals, version 1 and 2, correspond to the deliverables D4.01/D4.02 for the Catalogue and D4.06/D4.07 for the Geo portal. These manuals will be available online in the knowledge base of the Helpdesk in the MELOA web portal and also as a link in the web applications. The MELOA catalogue and Geo portal will be online accesible in the respective URLs: * _http://catalogue.ec-meloa.eu_ * _http://geoportal.ec-meloa.eu_ In order to get closer to relevant user communities, the metadata of the MELOA data sets will be federated with data hubs such as GEOSS and Copernicus. There will be a metadata link between the WAVY data catalogue and the nextGEOSS catalogue. Furthermore, the link to the Copernicus programme will be assured by linking to the CORDA portal and the EuroGOOS, with the provision of data services based on WMS layers (to be provided by the geoportal in V2). Also, WAVYs data sets will be accesible in the FIWARE catalogue in order to use them by the FIWARE community in the scope of the FIWARE Lab to test integration of the FIWARE SW components with devices such as the WAVYs. FIWARE is an open source community that generates open data and open source code [R-6]. Opening the WAVY data sets to such communities will open new opportunities of data exploitation to the market. By the time being, we are not planning to deposite the Wavy's data sets in other repositories. However, the possibility will be analysed further taken into account certified repositories from the registry of Research Data repositories (https://www.re3data.org/), in special the ones supported by the openAIR. ## Making data interoperable For WAVYs data sets, interoperability with other similar platforms will be achieved by using standard implementations from the Open Geospatial Consortium such as the Observation&Measurements profile, Web Map Service, Web Feature Service and Sensor Observation Service. The metadata of the data sets will be compliant with the OGC Observation&Measurements profile. The OGC WMS/WMTS/WFS will be used to export data sets of L1 WAVYs datasets and added-value products to Copernicus data hubs. The API to connect with the FIWARE platform will be supported by the MELOA Data services. These standards and methodologies are useful to federate the data sets with other data hubs and catalogues (FIWARE, NextGEOSS) that are used by other user communities, organisations or institutions. It will be considered the use of certified repositories that are supported by openAIR, in that case its requirements shall be taken into account [R-1]. The actual data in the MELOA data sets are formatted in CSV files that are easily readable by many standard tools (e.g. Open Office). The CSV format simply allows sharing the MELOA data sets with other users and researchers. Moreover, data from other sources can be translated to CSV files and combine with the WAVY data using commonly used SW applications. Other formats will be evaluated and eventually implemented during the project, such as O&M JSON or GeoJSON. ## Increase data re-use (through clarifying licences) The MELOA data sets will have an open license that will require the reusers to give attribution to the source of the data (MELOA project). It is still to be decided if the open license will require that the derived data must be shared with the same license (called share-alike). Instead of creating our own license, we may select an existing open license, possible candidates are: Creative Commons (CC-by [R-2], CC-by-sa [R-3]) or Open Data Commons (ODC-by [R-4], ODbL [R-5]). The license will be indicated in the MELOA web portal. The data sets offered to the users in the MELOA Catalogue are revised to guarantee their quality and validity before they are published with open access. In general, they will be analysed to check that they satisfy the test objectives and are valid. The time to perform the analysis may include the creation of the report for the associated Field test campaign in which the conclusions of the test are agreed. The MELOA web portal will be kept operational during the lifetime of the MELOA project, although it will be available later as long as the EGI infrastructure keeps the resources. After the project lifetime, the data sets downloaded from the catalogue will remain reusable in accordance with the terms of MELOA open license. # Allocation of resources The activities for making data FAIR are covered by the tasks in the WP4, in particular by: * T4.1 Development of catalogue and data storage component: it implements the discovery and access to the MELOA data sets as well as the connectors to FIWARE and the links to other data hubs (Copernicus and NextGEOSS). * T4.2 Development data processing component (Level 1): this task defines the metadata and formats of the data sets, which are generated by the data processing component implemented in this task. * T4.3 Development of Level 1 Data visualisation portal: the component developed in this task is provisioning of OGC WMS/WMTS/WFS layers for external applications. In this way, the cost of making data FAIR in MELOA was already covered by the estimations done for these tasks and it represents a small part of it. The resources for the long term preservation have not been discussed yet in the project. # Data security The EGI infrastructure provides redundancy of the HW storage with an availability of 99.4%. A backup policy is defined to store all the servers data (including MELOA data sets and Catalogue databases) in a online secure cloud storage (amazon S3). In case of dramatic lost of data in the MELOA web portal, it will be restored from this backup. The backup may include WM images and periodic snapshots to facilitate the recovery procedure. For the long term, we have not planned yet to store the data sets in a certified repository. They will be available in the MELOA web portal as long as the EGI infrastructure keeps the resources for the project. END OF DOCUMENT	https://phaidra.univie.ac.at/o:1140797	Horizon 2020
0198_5G-Xcast_761498.md	# Data summary _The template is a set of questions that you should answer with a level of detail appropriate to the project._ _It is not required to provide detailed answers to all the questions in the first version of the DMP that needs to be submitted by month 6 of the project. Rather, 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. Therefore, DMPs should have a clear version number and include a timetable for updates. As a minimum, the DMP should be updated in the context of the periodic evaluation/assessment of the project. If there are no other periodic reviews envisaged within the grant agreement, an update needs to be made in time for the final review at the latest. _ The Data Management Plan (DMP) describes the management life cycle for the data to be collected, processed and generated by this Horizon 2020 project, 5G-Xcast. This DMP represents a first version of the final document. It is intended to be updated, making it available on a finer level of granularity through updates as the implementation of the project progresses and when significant changes occur. The DMP will be updated in the context of the periodic evaluation of the project. The next updates are planned to be submitted to the official project website: _Table 1. Timetable for updates of the data management plan._ <table> <tr> <th> Version </th> <th> Date </th> </tr> <tr> <td> First version </td> <td> M3 </td> </tr> <tr> <td> Revision for first periodic evaluation </td> <td> M12 </td> </tr> <tr> <td> Revision for final evaluation </td> <td> M23 </td> </tr> </table> ## Purpose of data collection _What is the purpose of the data collection/generation and its relation to the objectives of the project?_ Throughout the project, partners of the consortium will naturally generate data in the form of results and presentations whilst carrying out research activities related to respective project objectives. The collection and sharing of this information within the project is essential to allow the effective coordination of research tasks among the task contributors. Data will be shared internally through an internal repository, accessible only to project partners. In addition to the internal data sharing activities, a series of public deliverables and presentations are planned for open publication. This will make the more key research discoveries available to the wider research community and industry, including other 5GPPP phase-2 projects. 5G-Xcast will coordinate with other 5G projects related to broadcast and media to maximize the exploitation of findings. This includes sharing project deliverables and exploring possible exchange of results with other projects; i.e. where appropriate, 5G-Xcast results could be used in other projects and vice versa. The sharing of and building upon knowledge is the foundation of research and discovery. The project will ensure the technical and scientific relevance of the results as well as promote the image of the consortium by supervision of the look & feel quality of its output. The 5G-Xcast project aims at providing its main results and ideas through the official website. This public website will be also the central hub for the dissemination activities. Open access to scientific publications will be ensured by publishing submitted paper in compliance with IEEE rules. A periodic newsletter will include information on the latest achievements of the project and links to recent public deliverables and forthcoming events. ## Types of data _What types and formats of data will the project generate/collect?_ During the project, different types and formats of open access data will be generated and collected to be shared in the public website of the project: * Deliverables : during the project, a series of confidential and public open Deliverables (D) will be developed related to specific tasks. All documents will be shared with 5G-PPP projects for inter-project cooperation. Public deliverables will be released to the open public. To monitor the work progress of the tasks, a first draft version will be released several months before the last and final version. * Presentations : main presentations summarising global results of the different working packages (WP) will be shared as well through the official website of the project. Organisation of workshops will be proposed to relevant conferences. In order to have the maximum impact, the presentations made in these events will be also accessible to the open public. * Results: during the project, specific results are expected to be shared. Examples of these results would be Matlab data files, coverage maps in terms of signal strength level, or some information about field trials such as Global Position System (GPS) position, signal level, interference, etc. * Standardization technical contributions from project partners will be also shared in the official webpage. Examples of standardization forums are 3GPP or DVB. Currently, project members do not expect to make public any code related to simulation platforms or specific tools. All types of data mentioned above will be also shared internally among the members of the consortium throughout the drafting phase. By making use of the EBU repository and email lists dedicated to this purpose, project partners can collaborate, jointly building the deliverables with shared access to all data. In addition, project partners will share privately (allowing access to project partners) other types of data, in order to ensure that the objectives of the project are fulfilled: * Software: during the project, some simulation platforms and software tools will be shared among the partners. Different repositories will be used, depending on the nature of the simulation tool. For instance, a common air interface simulator will be developed among several partners by making use of the Git system. Git is a free and open source distributed version control system designed to handle very large projects with speed and efficiency. * Preliminary results in the form of presentations, spreadsheets, figures, etc., will be shared among partners through the internal repository. * Preliminary research ideas presented in teleconferences will be shared among partners through the internal repository as well. * Standardization technical documents: partners submitting contributions to standards developing organisations (SDOs) will make the contribution public if the SDOs documents are public (for example 3GPP). For others SDOs that have documents restricted to members (for examples DVB) partners will check with the chairman and aim to make the documents public whenever possible. ## Re-use of existing data _Will you re-use any existing data and how? What is the origin of the existing data?_ 5G-Xcast will make use of existing data developed and validated by partners outside the framework of this project. Public specifications coming from DTT (Digital Terrestrial Television) committees such as DVB (Digital Video Broadcasting) or ATSC (Advanced Television Systems Committee), as well as 3GPP (3rd Generation Partnership Project ) technical specifications (TS) and technical reports (TR), will be used as starting point for the further development of required data. 5G-Xcast will not develop technologies entirely from scratch; partners will build upon concepts already developed in 3GPP and 5G-PPP phase-1 projects, for unicast PTP transmissions. Baseline data such as simulators and tools based on current specifications will be considered as a benchmark for the 5G-Xcast technology solutions developed within the project. Likewise, scientific journal and conference publications, technical reports, white papers and workshops will be also considered for calibration and cross- checking of the technological data deployed. ## Expected size _What is the expected size of the project data?_ The size of the data will depend on the outcomes of the project research tasks. The 5GXcast project is expected to provide: * At least one presentation per WP summarizing the main findings of the work. * Specific results such as field trials, figures or data files. * Several specification technical contributions. * Eight confidential deliverables . * Nineteen open deliverables. * Several videos about demonstrations and showcases could be produced and release. This could be one or more of the following events: * Demonstration in International Broadcasting Convention (IBC), in 2018 o Demonstration in either the Mobile World Congress (MWC) o Demonstration at the European Conference on Networks and Communications (EuCNC), in 2019. * Showcase at the European Championships of 2018. * Dissemination activities: * 40 journal papers, whitepapers and conference papers. o 10 filed patents. o 15 standard contributions. o 10 keynotes and panels. * 10 participations in 5G or broadcast events and forums. o 8 workshops in major IEEE conferences. o 4 summer schools/trainings. ## Data utility _To whom might it be useful ('data utility')?_ 5G-Xcast data will be available for the Research and Development industry, the European creative media industry and the telecom industry. The project will facilitate the exploitation of the outcomes into future products and services and provides good knowledge for faster deployment of 5G networks in Europe. In addition, the different types of data facilitated will be useful not only to the industry, but also to universities, research institutes, scientific magazines and specialised press. Concerning the press, contacts will be established with the relevant trade press in order to extend the utility and reach of communication activities. # Findable, accessible, interoperable and re-usable (FAIR) data ## 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)?_ Internally, documents (presentations, figures, deliverables, etc.) will use a specific format for tracking all internal sharing per WP, within the project internal repository. Externally, several metadata tracking methods will be used, depending on the type of data. Publications derived from the projects will follow the DOI (Digital Object Identifiers) mechanisms already established in the scientific research community. These publications will also include keyword metadata as well as descriptive titling. As such, these will become indexed and searchable by any academic or research search tool (including IEEEXplore, Research Gate, as well as most public search engines such as Google, Bing, etc.). Patents follow a well-established form of description via metadata as well as possessing unique identifiers, which vary depending on country filed and patent type. Once again searchable via patent indexing services and most search engines. _What naming conventions do you follow? Do you provide clear version numbers?_ The naming conventions are explained in deliverable D1.1[2] and are summarized below. Deliverables have unique IDs and always are presented with their full title. This will make them accessible via search engines and easily through the project website. Project partners will also use an internal versioning following naming convention. The nomenclature fixed throughout the 5G-Xcast project is as follows: 1. Working documents in the repository will have names _5G-Xcast_DZ.T_ Draft_vX.Y.docx for the draft version. Once the document is reviewed and ready to be released, the document will be made available to the EC and in the project website and the naming will be changed. _ 2. Final versions Deliverables have name _5G-Xcast_DZ.T_Title_vX.Y.docx _ , where _DZ.T_ denotes the deliverable number and _vX.Y_ is the version number. * Version numbering shall only arrive at _v_ 1.0 once the document is ready to be sent to the EC. * Different versions may be differentiated by using _v_ 0\. _Y_ with _Y_ being integer numbers between 0 and 9. * The version being transmitted to the EC will be labelled _v_ 1\. If the EC requests modifications, the updated version will be labelled _v_ 2\. The intermediate versions will be labelled _v_ 1.1, _v_ 1.2, etc. 3. Internal documents will be stored in the repository and might be used as a basis for public deliverables. Internal documents of WP will also have the following names: 5G-Xcast_WPn_Title_VX.Y.ext (the extension ext depends on the type of document) 4. For the case of the Quarterly Reports (QR) , the nomenclature will be: * Partner QR will be named _5G-Xcast_QRY_Partner.docx _ where _Partner_ is the acronym of the project partner and Y is the quarter number (for example, _5GXcast_QR1_UPV.docx_ ). * A WP quarterly report will be prepared by the WP leader and should be named _5G-Xcast_WPX_QRY.docx _ , where _X_ is the WP number ( _for example, 5GXcast_WP1_QR3.docx_ ). * Quarterly Management Reports (QMR) will be prepared by the project manager and should be named _5G-Xcast_QMRY.docx _ . 5. For journals, articles, conference papers, standard contributions the naming standard is as follows: _ <Event>_<yyyy>_<Authors>_<Title> _ * _ <Event> _ : indicates the journal, conference, standardization body (e.g. VTC, IEEECommMag, 3GPP, etc.) * _ <yyyy> _ : Year of the publication * _ <Authors> _ : indicates the first three letters of the last name of the author(s). In case of several authors, only the first three letters of the last name of the main author will be indicated, appending ‘ _etal_ ’. * _ <Title> _ : indicates the title of the document. Only two meaningful words indicating the contents of the document will be used. Titles will be kept shorter than 10 letters by using abbreviations. _Will search keywords be provided that optimize possibilities for re-use?_ Keywords are also provided in all public deliverables to optimize possibilities for re-use. ## 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._ As a result of the 5G-Xcast project, many results will be generated and produced in order to fulfil the objectives and tasks planned. Some of the results produced will be shared (or shared under certain restrictions) to the open public. Currently, particular data results to be shared are not defined. The specific results to be released from each project partner will be specified in future versions of this document. Open deliverables produced and used within the project will be made openly available as the default as well. The closed data due to legal and contractual reasons will not be shared. This specific type of data that may have a very sensitive commercial/technological value to some partners will be shared only to the level and the number of partners required for the execution of the specific project tasks, such as demonstrations and showcases. _How will the data be made accessible (e.g. by deposition in a repository)?_ The collection and sharing of information within the project is essential to allow the effective coordination of research tasks among the task contributors. Data will be shared internally through a workspace powered by the European Broadcasting Union (EBU), current partner of the project consortium. Data will be only accessible to project partners. The 5G-Xcast project will also provide its main results, thoughts and ideas by making use of the official project website ( _http://5g-xcast.eu/_ ) . The website is open and accessible to the general public. In order to ensure the largest possible exposure of the project, different social media and networking tools are used (LinkedIn and Twitter). A YouTube channel is used as well to capture presentations from e.g. industry forum demonstrations, workshops, and test-bed trials. _What methods or software tools are needed to access the data?_ Specific software tools will be required for the correct access to the data generated. Open deliverables and presentations (dissemination activities, workshops, WP summaries, etc.) will be uploaded using the Portable Document Format (PDF). Microsoft Office or equivalents will also be required as a basic tool to open DOC, XLS and PPT documents. _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)?_ Currently, there is no information about software to be shared or published in the website. The same applies to documentation related to tests and field trials. In the future as field trials become more specific partners may wish to share some of the data. This will be updated in upcoming version. _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._ Open access data, associated metadata and documentation will be deposited in the File Download Area of the official project website ( _http://5g-xcast.eu/documents/_ ) . Internally, all data and associated metadata will be deposited in the repository folder created for the associated work package. _Have you explored appropriate arrangements with the identified repository?_ All project partners and contributors fulfil the appropriate arrangements with the internal repository. Note that this repository is implemented within a workspace that belongs to a consortium member, i.e. the European Broadcasting Union. This repository is a customised implementation of the Atlassian Confluence software. For more information, please see _https://www.atlassian.com/software/confluence_ . _If there are restrictions on use, how will access be provided? How will the identity of the person accessing the data be ascertained?_ Individual member registration is required to access the internal repository and ascertain the identity of the person accessing the data. Although anyone can sign up as new user, specific content such as the 5G-Xcast project repository is restricted to member organizations and individual partners. On the other hand, no registration is required to visit the different tags or access the data provided in the project webpage. Documents are open and accessible to the general public without any restriction. ## 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)?_ Data produced in the project will be interoperable. The project will allow to the extent possible data exchange and re-use of published results between open public i.e. individual researchers, institutions, organisations, etc. All reports, deliverables and public presentations will be presented (or where not able, translations provided) in English. Final versions of such documents will also be provided in PDF format, offering wide support and readability on a wide range of devices and platforms with many free and open source readers available. Where this is not possible, the project will endeavour to provide data in formats which are open, widely accepted and are accessible to the wider public through open source utilities. ## Increase data re-use _How will the data be licensed to permit the widest re-use possible?_ Public project outputs such as public deliverables, papers, presentations and project results will be available on the project website and can be reused by other projects. Some of the contributions of the project will also be available in different standard developing organisations (SDOs). Some of these organisations allow open access to the general public (such 3GPP) while some other allow access only to members (Such DVB, IEEE). An indirect access to some of project results will be possible via these standard and technical organisations. However, specific rules apply for each organisation (E.g. 3GPP allows access to results but they cannot be reproduced / used without permission) Specific confidential material will require direct licensing from the originating company. _How long is it intended that the data remains re-usable?_ Data produced within the 5G-Xcast framework and openly published in the website will be useable by third parties, during and after the end of the project. On use, there is a requirement for appropriate attribution back to the 5G-Xcast project. Any modifications to the original data or results must be indicated clearly. Data will remain accessible for as long as the project website is kept open. Data obtained will remain useable indefinitely. _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._ Note that this DMP represents a first version of the document, released on M3 of the project. Therefore, information about embargos or when the data will be available for reuse is still unknown. This information will be specified in future versions of the deliverable. _Are data quality assurance processes described?_ The data quality including all the review process and risk mitigation for all project outputs is described in the project proposal [1] and project guidelines [2] documents. # Allocation of resources _What are the costs for making data FAIR in your project?_ The project has as entire Work Package, WP7, dedicated to dissemination, standardisation and exploitation of the data and research produced within the project. This acts as a focused resource in which a key responsibility is ensuring data is findable, accessible, interoperable and reusable. WP7 Objectives: 1. Promote the project, its objectives, results and the concepts developed. 2. Influence standardisation bodies to adopt the concepts developed in 5G-Xcast. 3. Coordinate with other 5G European projects for maximum synergy. 4. Build awareness on the use of broadcast with its different applications and how 5G-Xcast helps in this regards. 5. Maximize the exploitation of the project results by consortium members. 6. Maximize the innovation impact of the project. To best achieve this, WP7 will work closely with all other WP leaders to ensure key research output and accompanying insights are shared to a wider audience, promoting the project, expanding knowledge in the field and promoting further research in the field of 5G broadcast. WP7 has resources dedicated throughout the project, starting in month 1 and ending in month 24. In total this forms 69 Person Months of the total project budget and a minimum of 1 person months have been assigned to each project partner (with most partners getting 2 or more). The full breakdown is provided in Table 2. This allows for time by each partner to be dedicated to interfacing with the external world and making their research available to the wider academic, scientific and industrial community. This encourages establishing and continuation of communication with different parties, making data available through the public project website, maximising scientific visibility through publication in major conferences and high impact journals (IEEE etc). _Table 2. WP7 Resource Allocation per Participant_ <table> <tr> <th> Participant number </th> <th> 1 </th> <th> 2/3 </th> <th> 4 </th> <th> 5 </th> <th> 6 </th> <th> 7 </th> <th> 8 </th> <th> 9 </th> <th> 10 </th> <th> 11 </th> <th> 12 </th> <th> 13 </th> <th> 14 </th> <th> 15 </th> <th> 16 </th> <th> 17 </th> <th> 18 </th> </tr> <tr> <td> Short name of participant </td> <td> UPV </td> <td> NOK </td> <td> BBC </td> <td> BT </td> <td> BPK </td> <td> BLB </td> <td> EXP </td> <td> FS </td> <td> IRT </td> <td> LU </td> <td> NOM </td> <td> O2M </td> <td> SEUK </td> <td> TIM </td> <td> TUAS </td> <td> EBU </td> <td> UNIS </td> </tr> <tr> <td> Person months per participant: </td> <td> 12 </td> <td> 8 </td> <td> 1 </td> <td> 2 </td> <td> 2 </td> <td> 2 </td> <td> 3 </td> <td> 4 </td> <td> 2 </td> <td> 3 </td> <td> 4 </td> <td> 1 </td> <td> 13 </td> <td> 2 </td> <td> 2 </td> <td> 6 </td> <td> 2 </td> </tr> </table> Note that apart from this, some additional costs may be covered in this WP, e.g. website costs. However, additional costs will depend on the data and results shared during and after the project. _Who will be responsible for data management in your project?_ The person responsible for data management within the 5G-Xcast project will be Dr Belkacem Mouhouche, from Samsung Electronics R&D UK (SEUK). Dr Mouhouche is the Innovation Manager of the project, and leader of this WP7. _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)?_ Data is intended to be long-term preserved, after the end of the project. Internal and confidential reports, as well as results, presentations and all types of data are expected to be available in the EBU internal repository in a static copy for at least 5 years.. UPV could keep the public website open for a minimum of two years following the end date of the project. Note that this is an early version of the DMP, and no commitment has been done in this regard. More information will be given in future versions of this deliverable. # Data security _What provisions are in place for data security (including data recovery as well as secure storage and transfer of sensitive data)?_ ## Shared project data The sharing of all non-public data within the project is carried out through a team collaboration platform provided by the EBU. Access to the platform requires each individual to generate a personal username and password. Passwords are encrypted and only known to the individual herself/himself, i.e. neither the EBU nor the platform provider has access to passwords. Each individual must then be associated to the project space by the EBU administrators in agreement with the project management team. Only once this association has been made is access to the project space enabled to the user. The platform provided by the EBU is part of the company information infrastructure and is protected by the state-of-the-art security systems. It employs enterprise strength encryption and an enterprise level backup plan implemented in the event of system failure (i.e. daily back-up on the entire content of the repository). ## Data within each partner institution The consortium is comprised of established and respected institutions, each of which is expected to have measures in place to protect and preserve data, as well as relevant policies to ensure compliance. Furthermore, each partner has agreed to comply with the consortium agreement, requiring observation of obligations under the EU data Protection Directive 95/46/EC [1]. For the duration of the project, the data generated by each partner whilst carrying out their respective research activities is subject to their own internal measures of safety and security. Partners are requires to provide updates and share the outcomes of this research on a regular basis through the project, at which point documentation will be uploaded to the EBU collaboration platform and be subject to the storage and security levels outlines above.	https://phaidra.univie.ac.at/o:1140797	Horizon 2020
0203_BETTER_776280.md	# 1.Introduction ## 1.1 Project Overview The main objective of BETTER is to implement an EO Big Data intermediate service layer devoted to harnessing the potential of the Copernicus and Sentinel European EO data directly from the needs of the users. BETTER aims to go beyond the implementation of generic EO data tools and incorporate those tools with user experience, expertise and resources to deliver an integrated EO intermediate service layer. This layer will deliver customized solutions denominated Data Pipelines for large volume EO and non-EO datasets access, retrieval, processing, analysis and visualisation. The BETTER solutions will focus in addressing the full data lifecycle needs associated with EO Big Data to bring more downstream users to the EO market and maximise exploitation of the current and future Copernicus data and information services. BETTER developments will be driven by a large number of Data Challenges to be set forward by the users deeply involved in addressing the Key Societal Challenges. The World Food Programme, the European Union Satellite Centre and the Swiss Federal Institute of Technology- Zurich working in the areas of Food Security, Secure Societies and GeoHazards will be the challenge promoters. During the project each promoter will introduce 9 challenges, 3 in each project year, with an additional nine brought by the “Extending the market” task, in a total of 36 challenges. The Data Pipelines will be deployed on top of a mature EO data and service support ecosystem which has been under consolidation from previous R&D activities. The ecosystem and its further development in the scope of BETTER rely on the experience and versatility of the consortium team responsible for service/tool development from DEIMOS and Terradue. This is complemented by Fraunhofer Institute’s experience in Big Data systems, which brings to the consortium transversal knowledge extraction technologies and tools that will help bridge the current gap between the EO and ICT sectors. <table> <tr> <th> General Information </th> </tr> <tr> <td> Project Title </td> <td> Big-data Earth observation Technology and Tools Enhancing Research and development </td> </tr> <tr> <td> Starting Date </td> <td> 1st November 2017 </td> </tr> <tr> <td> Duration in Months </td> <td> 36 </td> </tr> <tr> <td> Call (part) Identifier </td> <td> H2020-EO-2017 </td> </tr> <tr> <td> Topic </td> <td> EO-2-2017 EO Big Data Shift </td> </tr> <tr> <td> Fixed EC Keywords </td> <td> Visual techniques / Visual analytics / Intelligent data understanding, Earth Observation / Services and applications, Space data exploitation, Data mining and searching techniques, Downstream industry </td> </tr> <tr> <td> Free Keywords </td> <td> Data Challenges, Data Pipelines </td> </tr> </table> This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 776280\. ## 1.2 Scope This document defines the Data Management approach for the datasets used and generated in the project. ## 1.3 Responsibilities The table below provides information on who contributed to this document and to which sections. Table 3 Document Responsibilities <table> <tr> <th> Name </th> <th> Institution </th> <th> Responsibilities </th> </tr> <tr> <td> Diego Lozano García </td> <td> DMS </td> <td> All sections </td> </tr> <tr> <td> Nuno Grosso </td> <td> DME </td> <td> Section 2.1 and Revision </td> </tr> <tr> <td> Fabrice Brito </td> <td> TDUE </td> <td> Section 2.2, 3.1, 3.2 and 3.3 </td> </tr> <tr> <td> Pedro Gonçalves </td> <td> TDUE </td> <td> Revision </td> </tr> <tr> <td> Koushik Panda </td> <td> DME </td> <td> Revision </td> </tr> <tr> <td> Simon Scerri </td> <td> IAIS </td> <td> Revision </td> </tr> <tr> <td> Thomas Filippa </td> <td> DMS </td> <td> Section 2.1 </td> </tr> </table> ## 1.4 Document Structure This document is structured as following: * Section One provides a project overview and then goes on to describes the scope, responsibilities and structure of this deliverable. * Section Two will describe the datasets used/generated by the data pipelines. * Section Three will analyse each of the aspects of the FAIR data: Findable, Accessible, Interoperable and Re-use * Section Four will present the resources allocation and deal with data security # Data Summary ## Datasets used/generated by the Data pipelines The tables below provide the main information related to the data sets management: the Data format, Preferential Data source, Access restrictions, area of interest, the Expected data Volume and the long-term data preservation policy. The data sets generated by the BETTER data processing pipelines are raster files in GeoTIFF format. Currently, the tables below provide the list of input and output datasets used/generated by the Data pipelines defined in the first and second cycle of the project. They will be updated in the next version of the document in order to include the datasets of the last cycle (no-defined yet) and modify some fields partially defined yet. <table> <tr> <th> Input datasets included in all first cycle challenges </th> </tr> <tr> <td> Name </td> <td> Challenges involved </td> <td> Data format </td> <td> Preferential Data source </td> <td> Access restrictions </td> <td> Area of interest </td> <td> Expected data Volume </td> <td> Long-term data preservation </td> </tr> <tr> <td> Sentinel-1 GRD acquisitions of matching orbit directions </td> <td> WFP-01-01 - Hazards and Change detection using Sentinel- 1 SAR data </td> <td> SAFE </td> <td> Sentinel Open Data Hub </td> <td> \- </td> <td> AOI 1 NW - South Sudan POLYGON((26.832 9.5136, 28.6843 9.5136, 28.6843 7.8009, 26.832 7.8009, 26.832 9.5136)) AOI 2 Renk - Blue Nile POLYGON((32.0572 12.4549, 33.9087 12.4549, 33.9087 10.7344, 32.0572 10.7344, 32.0572 12.4549)) AOI 3 Niger Delta, Mali (updated after D2.2 delivered to EC) POLYGON((-5.5 17.26, -1.08 17.26, -1.08 13.5, -5.5 13.5, 5.17 17.26)) AOI 4 NE - Nigeria POLYGON((12.9415 13.7579, 14.6731 13.7579, 14.6731 12.0093, 12.9415 12.0093, 12.9415 13.7579)) </td> <td> ~ 40 Sentinel1A/B / 12 days ~ 63 GB / 12 days (1.58 GB / product) </td> <td> \- </td> </tr> <tr> <td> SRTM 30m/90m for Terrain Correction </td> <td> Geotiff </td> <td> NASA LTA </td> <td> \- </td> <td> \- </td> <td> \- </td> </tr> <tr> <td> S1 precise orbit file(s) </td> <td> SAFE </td> <td> Sentinel Open Data Hub </td> <td> \- </td> <td> \- </td> <td> \- </td> </tr> <tr> <td> Sentinel-1 GRD </td> <td> WFP-01-02 - </td> <td> SAFE </td> <td> Sentinel Open </td> <td> \- </td> <td> AOI 1 Niger </td> <td> ~ 40 </td> <td> \- </td> </tr> </table> This project has received funding from the European Union’s Horizon 2020 Research and Innovation Programme under grant agreement no 776280 <table> <tr> <th> </th> <th> Land cover changes and inter-annual vegetation performance </th> <th> </th> <th> Data Hub </th> <th> </th> <th> POLYGON((6.4788 14.5973, 7.5577 14.5973, 7.5577 13.6328, 6.4788 13.6328, 6.4788 14.5973)) AOI 2 Tajikistan POLYGON((67.7116 37.9032, 68.791 37.9032, 68.791 36.9211, 67.7116 36.9211, 67.7116 37.9032)) AOI 3 Mali POLYGON((-10.3668 15.3471, - 9.3518 15.3471, -9.3518 14.3406, -10.3668 14.3406, 10.3668 15.3471)) AOI 4 Afghanistan POLYGON((67.6243 36.7228, 68.116 36.7228, 68.116 35.6923, 67.6243 35.6923, 67.6243 36.7228)) </th> <th> Sentinel1A/B / 12 days ~ 63 GB / 12 days (1.58 GB / product) </th> <th> </th> </tr> <tr> <th> Sentinel-2 L1C </th> <th> WFP-01-02 - Land cover changes and inter-annual vegetation performance </th> <th> SAFE </th> <th> Sentinel Open Data Hub </th> <th> \- </th> <th> ~ 75 Sentinel2A/B / 10 days ~ 75 GB / 10 days (~1 GB / product) </th> <th> \- </th> </tr> <tr> <th> Landsat 8 L1C Reflectances </th> <th> WFP-01-02 - Land cover changes and inter-annual vegetation performance </th> <th> Geotiff </th> <th> EarthExplorer </th> <th> \- </th> <th> ~ 15 Landsat 8 / 16 days ~ 15 GB / 16 days (~1 GB / product) </th> <th> \- </th> </tr> <tr> <td> Copernicus (VGTProbaV) 1Km LAI time series. NRT product (RT0, RT2 and RT6) </td> <td> WFP-01-03 EO indicators for global Early Warning, Seasonal Monitoring and Climatology Studies </td> <td> netCDF </td> <td> Copernicus Land Monitoring Service </td> <td> \- </td> <td> All WFP regions </td> <td> 1 product LAI: ~310MB, </td> <td> \- </td> </tr> <tr> <td> Copernicus (VGTProbaV) 1Km fAPAR time </td> <td> WFP-01-03 EO indicators for global Early </td> <td> netCDF </td> <td> Copernicus Land Monitoring Service </td> <td> \- </td> <td> All WFP regions </td> <td> 1 product FAPAR: ~380 MB; </td> <td> \- </td> </tr> </table> <table> <tr> <th> series. NRT product (RT0, RT2 and RT6) </th> <th> Warning, Seasonal Monitoring and Climatology Studies </th> <th> </th> <th> </th> <th> </th> <th> </th> <th> </th> <th> </th> </tr> <tr> <td> Time series of MODIS MOD11C2 LST 2000-present </td> <td> WFP-01-03 EO indicators for global Early Warning, Seasonal Monitoring and Climatology Studies </td> <td> HDF </td> <td> NASA LPDAAC </td> <td> \- </td> <td> All WFP regions </td> <td> \- </td> <td> \- </td> </tr> <tr> <td> CHIRPS RFE 5Km resolution daily data </td> <td> WFP-01-03 EO indicators for global Early Warning, Seasonal Monitoring and Climatology Studies </td> <td> Geotiff </td> <td> Climate Hazards Group InfraRed Precipitation with Station data </td> <td> \- </td> <td> All WFP regions </td> <td> 1.77 GB / 1 month (CHIRPS: ~58 MB per product) </td> <td> \- </td> </tr> <tr> <td> Sentinel-2 L1C </td> <td> SATCEN-0101 - Thematic Indexes for Land Use Indentification </td> <td> SAFE </td> <td> Sentinel Open Data Hub </td> <td> \- </td> <td> AOI 1 Columbia - Cauca-Narino 2°44'38.51"N 78°19'27.30"W, 2° 5'57.77"N 77°14'51.12"W, 0°55'38.70"N 78° 1'49.27"W, 1°34'56.24"N 79° 0'39.68"W AOI 2 Albania border with Greece </td> <td> ~ 220 Sentinel2A/B L2A / 10 days ~ 220 GB / 10 days ( ~1 GB / product) </td> <td> \- </td> </tr> </table> <table> <tr> <th> </th> <th> </th> <th> </th> <th> </th> <th> </th> <th> 41° 0'57.81"N 20°44'47.45"E, 40°51'0.67"N 21°17'1.40"E, 39°34'1.65"N 20°22'47.57"E, 39°52'46.66"N 19°53'54.20"E AOI 3 Afghanistan - Helmand - Nad-e-Ali 73°22'24.90"W 64°44'54.92"E,32° 0'17.86"N 65° 0'7.32"E, 30°25'32.61"N 63°57'57.16"E, 31°31'55.09"N 63°55'9.38"E AOI 4 Serbia 45°56'55.10"N 18°42'39.05"E, 45°57'44.72"N 18°59'43.44"E, 45° 7'2.00"N 19°40'15.66"E, 44°48'47.50"N 18°55'57.11"E </th> <th> </th> <th> </th> </tr> <tr> <td> Sentinel-1 A/B 1 SLC StripMap </td> <td> SATCEN-0102 - Change Detection based on SAR single look complex (SLC) data </td> <td> SAFE </td> <td> Sentinel Open Data Hub </td> <td> \- </td> <td> AOI 1 - 15°18'50.37"N, 8°22'52.84"E, 15°28'49.28"N, 10° 2'15.33"E, 12°17'43.44"N, 10°38'54.40"E, 12° 9'54.62"N, 9° 2'22.33"E AOI 2 - 23° 8'15.91"N, 12°55'57.41"E, 23°27'11.02"N, 14°28'12.27"E, 19° 6'4.65"N, 15°24'34.73"E, 18°52'42.78"N, 13°50'31.39"E AOI 3 20° 6'22.43"N, 5°20'58.79"E, 20°13'57.72"N, 6° 3'52.23"E, 18°30'49.60"N, 6°25'48.99"E, 18°23'13.00"N, 5°42'55.45"E </td> <td> ~ 380 Sentinel1 A/B / YEAR ~ 3 TB / YEAR ( ~8 GB / product) </td> <td> \- </td> </tr> </table> <table> <tr> <th> Sentinel-2 L2A </th> <th> SATCEN-01- 03 - Illicit Crop Monitoring with Optical data </th> <th> SAFE </th> <th> Sentinel Open Data Hub </th> <th> \- </th> <th> AOI 1 Columbia - Cauca-Narino 2°44'38.51"N 78°19'27.30"W, 2° 5'57.77"N 77°14'51.12"W, 0°55'38.70"N 78° 1'49.27"W, 1°34'56.24"N 79° 0'39.68"W AOI 2 Albania border with Greece 41° 0'57.81"N 20°44'47.45"E, 40°51'0.67"N 21°17'1.40"E, 39°34'1.65"N 20°22'47.57"E, 39°52'46.66"N 19°53'54.20"E AOI 3 Afghanistan - Helmand - Nad-e-Ali 73°22'24.90"W 64°44'54.92"E,32° 0'17.86"N 65° 0'7.32"E, 30°25'32.61"N 63°57'57.16"E, 31°31'55.09"N 63°55'9.38"E </th> <th> ~ 220 Sentinel2A/B L2A / 10 days ~ 220 GB / 10 days ( ~1 GB / product) </th> <th> \- </th> </tr> <tr> <td> Envisat ASAR dataset, observation period 2002-2010 </td> <td> ETHZ-01-01 Global catalogue of co-seismic deformation </td> <td> N1 </td> <td> EOLI </td> <td> Depending on processing level ESA might need to approve the pre-procesing of image </td> <td> Coordinates of the Earthquake epicenters with magnitude higher than 5 (10 km box around them) </td> <td> ~ 4 ENVISAT ASAR / event ~ 2.3 GB / days ( ~ 600 MB / product) </td> <td> \- </td> </tr> <tr> <td> SRTM-1 (30m) </td> <td> ETHZ-01-01 Global catalogue of co-seismic deformation </td> <td> Geotiff </td> <td> NASA LTA </td> <td> \- </td> <td> \- </td> </tr> <tr> <td> DLR systematic </td> <td> ETHZ-01-02 - </td> <td> SAFE </td> <td> DLR </td> <td> \- </td> <td> ~ InSAR </td> <td> \- </td> </tr> <tr> <td> Sentinel-1 interferograms </td> <td> Exploitation of differential SAR interferograms </td> <td> </td> <td> </td> <td> </td> <td> </td> <td> Browse MedRes output / event ~ 200 MB / event ( ~ 33 MB / product)- </td> <td> </td> </tr> <tr> <td> ShakeMap from USGS </td> <td> ETHZ-01-02 - Exploitation of differential SAR interferograms </td> <td> Geotiff </td> <td> USGS </td> <td> \- </td> <td> \- </td> </tr> <tr> <td> Sentinel 1 product for generating interferograms </td> <td> ETHZ-01-03 Automated detection of changes due to earthquakes </td> <td> SAFE </td> <td> Sentinel Open Data Hub </td> <td> \- </td> <td> ~ 4 Sentinel1 A/B / event ~ 22.8 GB / event ( ~5.7 GB / product)- </td> <td> \- </td> </tr> <tr> <td> USGS Earthquake catalogue </td> <td> ETHZ-01-0 Automated detection of changes due to earthquakes </td> <td> Geotiff </td> <td> USGS </td> <td> \- </td> <td> \- </td> </tr> </table> <table> <tr> <th> </th> <th> </th> <th> </th> <th> Output datasets included in all first cycle challenges </th> <th> </th> <th> </th> </tr> <tr> <td> Name </td> <td> Challenges involved </td> <td> Data format </td> <td> Preferential Data source </td> <td> Access restrictions </td> <td> Area of interest </td> <td> Expected data Volume </td> <td> Long-term data preservation </td> </tr> <tr> <td> Sentinel-1 SLC image pair, repeat pass 6 or </td> <td> WFP-01-01 - Hazards and Change </td> <td> Geotiff </td> <td> From Data Pipeline to be developed in </td> <td> \- </td> <td> AOI 1 NW - South Sudan POLYGON((26.832 9.5136, 28.6843 9.5136, 28.6843 </td> <td> Sentinel-1 Backscatter Time Series ~ </td> <td> </td> </tr> </table> <table> <tr> <th> 12 days </th> <th> detection using Sentinel1 SAR data </th> <th> </th> <th> BETTER </th> <th> </th> <th> 7.8009, 26.832 7.8009, 26.832 9.5136)) AOI 2 Renk - Blue Nile POLYGON((32.0572 12.4549, 33.9087 12.4549, 33.9087 10.7344, 32.0572 10.7344, 32.0572 12.4549)) AOI 3 Niger Delta, Mali (updated after D2.2 delivered to EC) POLYGON((-5.5 17.26, -1.08 17.26, -1.08 13.5, -5.5 13.5, 5.17 17.26)) AOI 4 NE - Nigeria POLYGON((12.9415 13.7579, 14.6731 13.7579, 14.6731 12.0093, 12.9415 12.0093, 12.9415 13.7579)) </th> <th> 210 GB /12 days (~5.27 GB / product) TOTAL SPACE FORESEEN: ~18.5 TB Sentinel-1 Coherence Time Series ~ 135 GB /12 days (~3 GB / product) TOTAL SPACE FORESEEN ~12 TB </th> <th> </th> </tr> <tr> <td> Sentinel-2 L1C derived NDVI, NDWI, MNDWI, NDBI Indices </td> <td> WFP-01-02 - Land cover changes and inter-annual vegetation performance </td> <td> Geotiff </td> <td> From Data Pipeline to be developed in BETTER </td> <td> \- </td> <td> AOI 1 Niger POLYGON((6.4788 14.5973, 7.5577 14.5973, 7.5577 13.6328, 6.4788 13.6328, 6.4788 14.5973)) AOI 2 Tajikistan POLYGON((67.7116 37.9032, 68.791 37.9032, 68.791 36.9211, 67.7116 36.9211, 67.7116 37.9032)) AOI 3 Mali POLYGON((-10.3668 15.3471, - 9.3518 15.3471, -9.3518 14.3406, -10.3668 14.3406, - </td> <td> ~ 44 GB /10 days (~600 MB / product) TOTAL SPACE FORESEEN ~4.7 TB </td> <td> </td> </tr> <tr> <td> Sentinel 2 derived NDVI, NDWI, MNDWI, NDBI smoothed indices </td> <td> WFP-01-02 - Land cover changes and inter-annual vegetation performance </td> <td> Geotiff </td> <td> From Data Pipeline to be developed in BETTER </td> <td> \- </td> <td> </td> </tr> <tr> <td> Landsat-8 L1C derived NDVI, </td> <td> WFP-01-02 - Land cover </td> <td> Geotiff </td> <td> From Data Pipeline to be </td> <td> \- </td> <td> ~ 55 GB / 16 days (~3.66 GB / </td> <td> </td> </tr> </table> <table> <tr> <th> NDWI, MNDWI, NDBI Indices </th> <th> changes and inter-annual vegetation performance </th> <th> </th> <th> developed in BETTER </th> <th> </th> <th> 10.3668 15.3471)) AOI 4 Afghanistan POLYGON((67.6243 36.7228, 68.116 36.7228, 68.116 35.6923, 67.6243 35.6923, 67.6243 36.7228)) </th> <th> product) TOTAL SPACE FORESEEN ~3.7 TB </th> <th> </th> </tr> <tr> <th> Landsat-8 derived NDVI, NDWI, MNDWI, NDBI smoothed indices </th> <th> WFP-01-02 - Land cover changes and inter-annual vegetation performance </th> <th> Geotiff </th> <th> From Data Pipeline to be developed in BETTER </th> <th> \- </th> <th> </th> </tr> <tr> <td> Copernicus (VGTProbaV) 1Km LAI time series smoothed and gap-filled </td> <td> WFP-01-03 - EO indicators for global Early Warning, Seasonal Monitoring and Climatology Studies </td> <td> Geotiff </td> <td> From Data Pipeline to be developed in BETTER </td> <td> \- </td> <td> All WFP regions </td> <td> ~268 MB / 1 month (~ 67 MB / product) TOTAL SPACE FORESEEN ~9.5 GB </td> <td> </td> </tr> <tr> <td> Copernicus (VGTProbaV) 1Km fAPAR time series smoothed and gap-filled </td> <td> WFP-01-03 - EO indicators for global Early Warning, Seasonal Monitoring and Climatology Studies </td> <td> Geotiff </td> <td> From Data Pipeline to be developed in BETTER </td> <td> \- </td> <td> </td> </tr> <tr> <td> Time series of Copernicus (VGT- </td> <td> WFP-01-03 - EO indicators </td> <td> Geotiff </td> <td> From Data Pipeline to be </td> <td> \- </td> <td> </td> </tr> </table> <table> <tr> <th> ProbaV) LAI temporally aggregated </th> <th> for global Early Warning, Seasonal Monitoring and Climatology Studies </th> <th> </th> <th> developed in BETTER </th> <th> </th> <th> </th> <th> </th> <th> </th> </tr> <tr> <th> Time series of Copernicus (VGTProbaV) fAPAR temporally aggregated </th> <th> WFP-01-03 - EO indicators for global Early Warning, Seasonal Monitoring and Climatology Studies </th> <th> Geotiff </th> <th> From Data Pipeline to be developed in BETTER </th> <th> \- </th> <th> </th> </tr> <tr> <th> Long term averages of Copernicus (VGT-ProbaV) fAPAR (2 indicators * 2 aggregation functions (max and avg) * 9 aggregation time windows = 36 LTA sets to be generated) </th> <th> WFP-01-03 - EO indicators for global Early Warning, Seasonal Monitoring and Climatology Studies </th> <th> Geotiff </th> <th> From Data Pipeline to be developed in BETTER </th> <th> \- </th> <th> </th> </tr> <tr> <th> Time series of Copernicus (VGT- </th> <th> WFP-01-03 - EO indicators </th> <th> Geotiff </th> <th> From Data Pipeline to be </th> <th> \- </th> <th> </th> </tr> </table> <table> <tr> <th> ProbaV) fAPAR anomalies at a variety of temporal aggregations </th> <th> for global Early Warning, Seasonal Monitoring and Climatology Studies </th> <th> </th> <th> developed in BETTER </th> <th> </th> <th> </th> <th> </th> <th> </th> </tr> <tr> <th> Long term averages of Copernicus (VGT-ProbaV) LAI (2 indicators * 2 aggregation functions (max and avg) * 9 aggregation time windows = 36 LTA sets to be generated) </th> <th> WFP-01-03 - EO indicators for global Early Warning, Seasonal Monitoring and Climatology Studies </th> <th> Geotiff </th> <th> From Data Pipeline to be developed in BETTER </th> <th> \- </th> <th> </th> </tr> <tr> <th> Time series of Copernicus (VGTProbaV) LAI anomalies at a variety of temporal aggregations </th> <th> WFP-01-03 - EO indicators for global Early Warning, Seasonal Monitoring and Climatology Studies </th> <th> Geotiff </th> <th> From Data Pipeline to be developed in BETTER </th> <th> \- </th> <th> </th> </tr> <tr> <th> Time series of MODIS MOD11C2 LST temporally </th> <th> WFP-01-03 - EO indicators for global Early </th> <th> Geotiff </th> <th> From Data Pipeline to be developed in </th> <th> \- </th> <th> ~ 408 MB / 1 month for 3 years </th> <th> </th> </tr> </table> <table> <tr> <th> aggregated values (N = 1 (no aggregation), 3, 6, 9, 12, 15, 18, 27, 36) </th> <th> Warning, Seasonal Monitoring and Climatology Studies </th> <th> </th> <th> BETTER </th> <th> </th> <th> </th> <th> WFP-01-03-01 aggregations (~ 34 MB/ product) ~ 9 MB / 1 month for 3 years WFP-01-03-02 aggregations (~ 1 MB / product) </th> <th> </th> </tr> <tr> <th> Long term averages of MODIS MOD11C2 LST (2 indicators * 2 aggregation functions (max and avg) * 9 aggregation time windows = 36 LTA sets to be generated) </th> <th> WFP-01-03 - EO indicators for global Early Warning, Seasonal Monitoring and Climatology Studies </th> <th> Geotiff </th> <th> From Data Pipeline to be developed in BETTER </th> <th> \- </th> <th> </th> </tr> <tr> <th> Time series of MODIS MOD11C2 LST 2000-present anomalies </th> <th> WFP-01-03 - EO indicators for global Early Warning, Seasonal Monitoring and Climatology Studies </th> <th> Geotiff </th> <th> From Data Pipeline to be developed in BETTER </th> <th> \- </th> <th> </th> </tr> <tr> <th> Time series of aggregated CHIRPS RFE 1981present (N = 10, </th> <th> WFP-01-03 - EO indicators for global Early Warning, </th> <th> Geotiff </th> <th> From Data Pipeline to be developed in BETTER </th> <th> \- </th> <th> ~ 12 MB / 1 month (~2 MB / product) TOTAL SPACE </th> <th> </th> </tr> </table> <table> <tr> <th> 30, 60, 90, 120, 150, 180, 270, 365 days) </th> <th> Seasonal Monitoring and Climatology Studies </th> <th> </th> <th> </th> <th> </th> <th> </th> <th> FORESEEN ~432 MB </th> <th> </th> </tr> <tr> <th> Long term averages of CHIRPS RFE 1981present (2 indicators * 2 aggregation functions (max and avg) * 9 aggregation time windows = 36 LTA sets to be generated) </th> <th> WFP-01-03 - EO indicators for global Early Warning, Seasonal Monitoring and Climatology Studies </th> <th> Geotiff </th> <th> From Data Pipeline to be developed in BETTER </th> <th> \- </th> <th> </th> </tr> <tr> <th> Time series of anomalies of aggregated CHIRPS RFE 1981present and current anomalies </th> <th> WFP-01-03 - EO indicators for global Early Warning, Seasonal Monitoring and Climatology Studies </th> <th> Geotiff </th> <th> From Data Pipeline to be developed in BETTER </th> <th> \- </th> <th> </th> </tr> <tr> <td> Sentinel-2 A/B L1C NDVI and NDWI indices </td> <td> SATCEN-0101 - Thematic Indexes for Land Use Identification </td> <td> DIM </td> <td> From Data Pipeline to be developed in BETTER </td> <td> \- </td> <td> AOI 1 Columbia - Cauca-Narino 2°44'38.51"N 78°19'27.30"W, 2° 5'57.77"N 77°14'51.12"W, 0°55'38.70"N 78° 1'49.27"W, 1°34'56.24"N 79° 0'39.68"W </td> <td> ~ 6.8 GB / 12 days (~400 MB / product) TOTAL SPACE FORESEEN ~620 </td> <td> </td> </tr> </table> <table> <tr> <th> </th> <th> </th> <th> </th> <th> </th> <th> </th> <th> AOI 2 Albania border with Greece 41° 0'57.81"N 20°44'47.45"E, 40°51'0.67"N 21°17'1.40"E, 39°34'1.65"N 20°22'47.57"E, 39°52'46.66"N 19°53'54.20"E AOI 3 Afghanistan - Helmand - Nad-e-Ali 73°22'24.90"W 64°44'54.92"E,32° 0'17.86"N 65° 0'7.32"E, 30°25'32.61"N 63°57'57.16"E, 31°31'55.09"N 63°55'9.38"E AOI 4 Serbia 45°56'55.10"N 18°42'39.05"E, 45°57'44.72"N 18°59'43.44"E, 45° 7'2.00"N 19°40'15.66"E, 44°48'47.50"N 18°55'57.11"E </th> <th> GB </th> <th> </th> </tr> <tr> <th> Co-registered stack of Sentinel2 A/B L1C </th> <th> SATCEN-0101 - Thematic Indexes for Land Use Identification </th> <th> DIM </th> <th> From Data Pipeline to be developed in BETTER </th> <th> \- </th> <th> ~3.7 TB / YEAR (~10 GB / product) TOTAL SPACE FORESEEN ~11.1 TB </th> <th> </th> </tr> <tr> <td> Multi-temporal stack of Sentinel1 A/B 1 SLC StripMap </td> <td> SATCEN-0102 - Change Detection based on SAR single look complex (SLC) data </td> <td> DIM </td> <td> From Data Pipeline to be developed in BETTER </td> <td> \- </td> <td> AOI 1 - 15°18'50.37"N, 8°22'52.84"E, 15°28'49.28"N, 10° 2'15.33"E, 12°17'43.44"N, 10°38'54.40"E, 12° 9'54.62"N, 9° 2'22.33"E AOI 2 - 23° 8'15.91"N, 12°55'57.41"E, 23°27'11.02"N, 14°28'12.27"E, 19° 6'4.65"N, 15°24'34.73"E, 18°52'42.78"N, 13°50'31.39"E AOI 3 20° 6'22.43"N, 5°20'58.79"E, 20°13'57.72"N, 6° 3'52.23"E, 18°30'49.60"N, </td> <td> ~9.3 TB / YEAR (~ 25 GB / product) TOTAL SPACE FORESEEN ~27.9 TB </td> <td> </td> </tr> <tr> <td> Multi-temporal stack of Coherence products derived from Multitemporal stack of </td> <td> SATCEN-0102 - Change Detection based on SAR single look complex (SLC) </td> <td> DIM </td> <td> From Data Pipeline to be developed in BETTER </td> <td> \- </td> <td> ~3.7 TB / YEAR (~10 GB / product) TOTAL SPACE FORESEEN ~11.1 TB </td> <td> </td> </tr> </table> <table> <tr> <th> Sentinel-1 A/B 1 SLC StripMap </th> <th> data </th> <th> </th> <th> </th> <th> </th> <th> 6°25'48.99"E, 18°23'13.00"N, 5°42'55.45"E </th> <th> </th> <th> </th> </tr> <tr> <td> Sentinel-2 A/B L1C DVI Maps </td> <td> SATCEN-01- 03 - Illicit Crop Monitoring with Optical data </td> <td> Geotiff DIM </td> <td> From Data Pipeline to be developed in BETTER </td> <td> \- </td> <td> AOI 1 Columbia - Cauca-Narino 2°44'38.51"N 78°19'27.30"W, 2° 5'57.77"N 77°14'51.12"W, 0°55'38.70"N 78° 1'49.27"W, 1°34'56.24"N 79° 0'39.68"W AOI 2 Albania border with Greece 41° 0'57.81"N 20°44'47.45"E, 40°51'0.67"N 21°17'1.40"E, 39°34'1.65"N 20°22'47.57"E, 39°52'46.66"N 19°53'54.20"E AOI 3 Afghanistan - Helmand - Nad-e-Ali 73°22'24.90"W 64°44'54.92"E,32° 0'17.86"N 65° 0'7.32"E, 30°25'32.61"N 63°57'57.16"E, 31°31'55.09"N 63°55'9.38"E </td> <td> ~1 TB / 10 days (~5 GB / product) TOTAL SPACE FORESEEN ~109.5 TB-- </td> <td> </td> </tr> <tr> <td> Co-registered stack of Sentinel2 A/B L1C </td> <td> SATCEN-01- 03 - Illicit Crop Monitoring with Optical data </td> <td> Geotiff DIM </td> <td> From Data Pipeline to be developed in BETTER </td> <td> \- </td> <td> </td> </tr> <tr> <td> Color composites of co-registered stack of Sentinel2 A/B L1C </td> <td> SATCEN-01- 03 - Illicit Crop Monitoring with Optical data </td> <td> Geotiff DIM </td> <td> From Data Pipeline to be developed in BETTER </td> <td> \- </td> <td> </td> </tr> <tr> <td> Co-seismic deformation maps from Envisat ASAR dataset </td> <td> ETHZ-01-01 Global catalogue of co-seismic deformation </td> <td> Geotiff </td> <td> From Data Pipeline to be developed in BETTER </td> <td> \- </td> <td> Coordinates of the Earthquake epicenters with magnitude higher than 5 (10 km box around them) </td> <td> ~ 200 MB / event (~ 100 MB/ output set) </td> <td> </td> </tr> <tr> <td> Map of areas with SAR coherence decrease after earthquakes M>5 derived from Envisat ASAR </td> <td> ETHZ-01-01 Global catalogue of co-seismic deformation </td> <td> Geotiff </td> <td> From Data Pipeline to be developed in BETTER </td> <td> \- </td> <td> </td> </tr> </table> <table> <tr> <th> dataset </th> <th> </th> <th> </th> <th> </th> <th> </th> <th> </th> <th> </th> <th> </th> </tr> <tr> <th> Composite map including areas with SAR coherence decrease larger than a defined threshold & coseismic deformation larger than a defined threshold after earthquakes with M>5 + USGS shakemap derived from Envisat ASAR dataset </th> <th> ETHZ-01-01 Global catalogue of co-seismic deformation </th> <th> Geotiff </th> <th> From Data Pipeline to be developed in BETTER </th> <th> \- </th> <th> </th> </tr> <tr> <th> Filtered DInSAR interferograms derived from DLR systematic Sentinel-1 interferograms </th> <th> ETHZ-01-02 - Exploitation of differential SAR interferograms </th> <th> Geotiff </th> <th> From Data Pipeline to be developed in BETTER </th> <th> \- </th> <th> ~ 200 MB / event (~ 33 MB / product) TOTAL SPACE FORESEEN ~ 57 GB ( current 292 events ) </th> <th> </th> </tr> <tr> <th> Co-seismic deformation maps derived from Sentinel-1 </th> <th> ETHZ-01-03 Automated detection of changes due to earthquakes </th> <th> Geotiff </th> <th> From Data Pipeline to be developed in BETTER </th> <th> \- </th> <th> ~ 12 GB / event (~ 6 GB/ output set) </th> <th> </th> </tr> <tr> <td> Coherence changes map (areas with SAR coherence decrease after earthquakes M>5) derived from Sentinel-1 </td> <td> ETHZ-01-03 Automated detection of changes due to earthquakes </td> <td> Geotiff </td> <td> From Data Pipeline to be developed in BETTER </td> <td> \- </td> <td> </td> <td> </td> <td> </td> </tr> <tr> <td> Composite map including areas with SAR coherence decrease larger than a defined threshold & coseismic deformation larger than a defined threshold after earthquakes with M>5 + USGS shakemap derived from Sentinel-1 </td> <td> ETHZ-01-03 Automated detection of changes due to earthquakes </td> <td> Geotiff </td> <td> From Data Pipeline to be developed in BETTER </td> <td> \- </td> <td> </td> </tr> </table> <table> <tr> <th> Input Datasets included in all second cycle challenges </th> </tr> <tr> <td> Name </td> <td> Challenges involved </td> <td> Data format </td> <td> Preferential Data source </td> <td> Access restrictions </td> <td> Area of interest </td> <td> Expected data Volume </td> <td> Long-term data preservation </td> </tr> <tr> <td> _LST MODIS_ _MOD11C2 - 5.6Km_ _resolution 8 day_ _data_ </td> <td> WFP-02-01 - MODIS TERRA Land Surface Temperature (LST) - Aggregations and Anomalies </td> <td> GEOTIFF </td> <td> </td> <td> \- </td> <td> Global For validation: Years 2015, 2016 and 2017 For production: Full archive processing of the data (2000 - present) </td> <td> \- </td> <td> </td> </tr> </table> <table> <tr> <th> MODIS Aqua/Terra Snow Cover 8-Day L3 Global 500m Grid, Version 6. </th> <th> WFP-02-01 - MODIS TERRA/AQUA Snow Cover - Aggregations and Anomalies </th> <th> GEOTIFF </th> <th> </th> <th> \- </th> <th> Central Asia - POLYGON((25.0 48.0, 94.0 48.0, 94.0 23.1, 25.0 23.1, 25.0 48.0)) For validation: Years 2015, 2016 and 2017 for winter season (August - July) For production: Full archive processing of the data (2000 - present) for winter season (August - July) </th> <th> \- </th> <th> </th> </tr> </table> <table> <tr> <th> _Sentinel-2 A/B L1C_ </th> <th> WFP-02-03 - Smooth & gap- filled Sentinel- 2 </th> <th> GeoTiff </th> <th> </th> <th> \- </th> <th> For validation: Gambella (S2 tiles: 36PXQ, 36PYQ, 36NXP, 36NYP) Marchfeld (S2 tiles: 33UXP) For production: AOI defined by Analyst triggering the pipeline </th> <th> \- </th> <th> </th> </tr> <tr> <td> Product 1: * Two Radar images, Image 1 and Image 2  Sensor: Sentinel-1 A/B * Processing level: L1 GRD * Acquisition mode: Interferometric Wide Mode * Pass: same pass for all the stack (e.g. DESCENDING) * Orbit : same orbit throughout the stack </td> <td> SATCEN-02-01 \- Change Detection and Characterizatio n 2 (SAR Change Detection with GRD data) </td> <td> GEOTIFF </td> <td> </td> <td> \- </td> <td> Peru's Madre de Dios region UL 70.5659 W 12.4567 S BR 69.1411 W 13.0922 S For validation Two images, covering 99 % of the area, (S1 IW Descending mode) above the area on dates 2018-08-12 and 2018-09-05 Image 1 S1B_IW_GRDH_1SDV_20180812T 101414_ 20180812T101439_012228_0168 7D_BCA9 Image 2 S1B_IW_GRDH_1SDV_20180905T 101415_ 20180905T101440_012578_0173 58_3259 For Production images from April 2019 to October 2019 Frequency: when a new image, acquired in the same interferometric conditions, is </td> <td> \- </td> <td> </td> </tr> <tr> <td> Product 2: * DEM Model * DEM image </td> <td> GEOTIFF </td> <td> </td> <td> \- </td> <td> \- </td> <td> </td> </tr> </table> <table> <tr> <th> should be used for the Terrain Correction (often automatically downloaded by specific software)  Orbit file should be used for Orbital Correction (often automatically downloaded by specific software) </th> <th> </th> <th> </th> <th> </th> <th> </th> <th> available </th> <th> </th> <th> </th> </tr> <tr> <td> Sensor: Sentinel-2 A/B Processing Level: Level2A (level 1B for the test in 2018) Bands: All 12 spectral bands (in fact, only bands 2,4, 11 and 12 are required) and the cloud mask </td> <td> SATCEN-02-02 \- Thematic Indexes 2 (mineral indexes with Optical data </td> <td> GEOTIFF </td> <td> </td> <td> \- </td> <td> Peru's Madre de Dios region UL 70.5659 W 12.4567 S BR 69.1411 W 13.0922 S </td> <td> \- </td> <td> </td> </tr> <tr> <td> Sensor: Sentinel-2 A/B </td> <td> SATCEN-02-03 - Land </td> <td> GEOTIFF </td> <td> </td> <td> </td> <td> Peru's Madre de Dios region UL 70.5659 W 12.4567 S BR </td> <td> \- </td> <td> </td> </tr> </table> <table> <tr> <th> Processing Level: Level2A (level 1B for the test in 2018) Bands: All 12 spectral bands (in fact, only bands 11,8, 4 and 2 are required) and the cloud mask </th> <th> Use/Land Cover (Illegal deforestation </th> <th> </th> <th> </th> <th> </th> <th> 69.1411 W 13.0922 </th> <th> </th> <th> </th> </tr> <tr> <td> * 1.Weekly report on active volcanoes _https_ _://volcano.si.ed_ _u/reports_wee_ _kly.cfm#_ ; * 2\. Sentinel-1 imagery; * 3\. SRTM-1 (30m </td> <td> ETHZ-02-01 - Radar interferometry in active volcanic regions </td> <td> GEOTIFF </td> <td> </td> <td> </td> <td> Volcanoes with ongoing activity and with new activity (from WVAR) </td> <td> \- </td> <td> </td> </tr> <tr> <td> :S1 GRD high resolution imagery; </td> <td> ETHZ-02-02 - Large surface displacements measured with feature tracking </td> <td> GEOTIFF </td> <td> </td> <td> </td> <td> Galapagos Islands (Isabela and Fernandina); Great Aletsch Area, Switzerland; Slumgullion landslide, Colorado. </td> <td> \- </td> <td> </td> </tr> <tr> <td> Wrapped or unwrapped interferogram </td> <td> ETHZ-02-03 - Systematic modeling of </td> <td> GEOTIFF </td> <td> </td> <td> </td> <td> All volcanoes systematically monitored by DLR with S1 </td> <td> \- </td> <td> </td> </tr> </table> <table> <tr> <th> </th> <th> surface deformation at active volcanoes </th> <th> </th> <th> </th> <th> </th> <th> </th> <th> </th> <th> </th> </tr> <tr> <td> Sensor: Sentinel-1 A/B * Processing level: L1 GRD * Acquisition mode: IW * Pass: same pass for all the stack (ASCENDING / DESCENDING) * Orbit : same orbit throughout the stack </td> <td> EXT-02-01 - Change Detection in Rural Localities for SemiAutomated Border Surveillance Applied to Insecure Areas in Lake Chad Basin </td> <td> GEOTIFF </td> <td> </td> <td> </td> <td> (ideally areas where there is already ground truth) For Validation AOI2 (border with Burkina Faso) - 5m Xmin= 1.47, Xmax= 2.17¡ Ymin=,12.56 Ymax= 13.65 AOI1 (Diffa/Geidam Region - Lake Chad) - 10m Xmin=11, Xmax=15 Ymin=12.5, Ymax=15 For production AO3 (border with Nigeria - region of Zinder) - 5m Xmin=8.39, Xmax=10.65 Ymin=12.17, Ymax= 15.49 Polygon corner coordinates X1=8.94, Y1=12.64 X2=9.86, Y2=12.37 X3=10.54, Y3=12.73 X4=10.50, Y4=15.05 X5=10.06, Y5=15.43 X6=8.40, Y6=15.31 X7=8.94, Y7=12.64 AOI4 (Markoye/Teguey Region) - 10m Xmin=-0.4, Xmax=1 </td> <td> \- </td> <td> </td> </tr> <tr> <td> DEM Model. DEM image should be used for the Terrain Correction (often automatically downloaded by specific software). Orbit file should be used for Orbital Correction (often automatically downloaded by specific software) </td> <td> </td> <td> </td> <td> </td> <td> </td> </tr> </table> <table> <tr> <th> </th> <th> </th> <th> </th> <th> </th> <th> </th> <th> Ymin=13.5, Ymax=15. AOI5 (Agadez Region) - 10 m Xmin=4, Xmax=8.5 Ymin = 16.8, Ymax=19.45 Polygon corner coordinates X1=0.76, Y1=11.74 X2=2.46, Y2=12.24 X3=1.99, Y3=14.40 X4=1.02, Y4=13.97 X5=0.76, Y5=11.74 </th> <th> </th> <th> </th> </tr> <tr> <td> Sentinel 1 (A or B) Mode: IW (Interferometric Wide Swath) Product Level: GRD Polarization: VV or VV+VH </td> <td> EXT-02-02 - Satellite observations of oil sheen of natural oil seepage in Disko Bay, West Greenland </td> <td> GEOTIFF </td> <td> Sentinel Open Data Hub </td> <td> </td> <td> Location of known seepage in Disko – Nuussuq – Svartenhuk Halvø region along the West coast of Greenland. Map from Bojesen-Koefoed et al., 2007. Petroleum seepages at Asuk Disko, West Greenland: Implications for regionals petroleum exploration. Journal of Petroleum Geology 30, 219–236. doi:10.1111/j.17475457.2007.00219.x </td> <td> </td> <td> </td> </tr> <tr> <td> Landsat-7 and -8 Level 2A (atmospherically corrected, includes cloud and cloud shadow mask) </td> <td> EXT-02-03 - Crop Loss Detection using NDVI anomalies </td> <td> GEOTIFF </td> <td> EarthExplorer </td> <td> </td> <td> For validation: one of the events identified by period for the parcels affected by that event For production: All events/parcel coordinates in Portugal to be provided by Fidelidade </td> <td> </td> <td> </td> </tr> </table> <table> <tr> <th> L1T ASTER, L1T LANDSAT8 (TIRS), L1 SLSTR Sentinel 3, L1C Sentinel2. </th> <th> EXT-02-04 - Surface temperature map evolution </th> <th> GEOTIFF </th> <th> EarthExplorer </th> <th> </th> <th> Etna, Vesuvio, Campi Flegrei, Stromboli </th> <th> </th> <th> </th> </tr> </table> <table> <tr> <th> </th> <th> </th> <th> Output Datasets included in all second cycle challenges </th> </tr> <tr> <td> Name </td> <td> Challenges involved </td> <td> Data format </td> <td> </td> <td> Preferential Data source </td> <td> Access restrictions </td> <td> Area of interest </td> <td> Expected data Volume </td> <td> long-term data preservation </td> </tr> <tr> <td> Time series of MODIS MOD11C2 LST 2000present gap-filled, smoothed and interpolated to 10 days </td> <td> WFP-02-01 - MODIS TERRA Land Surface Temperature (LST) - Aggregations and Anomalies </td> <td> Geotiff </td> <td> </td> <td> \- </td> <td> \- </td> <td> \- Global For validation: Years 2015, 2016 and 2017 For production: Full archive processing of the data (2000 - present) </td> <td> \- </td> <td> </td> </tr> <tr> <td> Time series of LST temporally aggregated values </td> <td> Geotiff </td> <td> </td> <td> \- </td> <td> \- </td> <td> \- </td> <td> </td> </tr> <tr> <td> Land Surface Temperature Time Series products for a reference number of years </td> <td> Geotiff </td> <td> </td> <td> \- </td> <td> </td> <td> </td> <td> </td> </tr> <tr> <td> Time series of LST </td> <td> Geotiff </td> <td> </td> <td> \- </td> <td> \- </td> <td> \- </td> <td> </td> </tr> </table> <table> <tr> <th> anomalies </th> <th> </th> <th> </th> <th> </th> <th> </th> <th> </th> <th> </th> <th> </th> </tr> <tr> <td> Snow cover characterization time series start date = 8 cons. snow days finish date= 8 cons. no snow days </td> <td> WFP-02-01 - MODIS TERRA/AQUA Snow Cover - Aggregations and Anomalies </td> <td> Geotiff </td> <td> \- </td> <td> \- </td> <td> Central Asia - POLYGON((25.0 48.0, 94.0 48.0, 94.0 23.1, 25.0 23.1, 25.0 48.0) For validation: Years 2015, 2016 and 2017 for winter season (August - July) For production: Full archive processing of the data (2000 - present) for winter season (August - July) </td> <td> \- </td> <td> </td> </tr> <tr> <td> Long Term snow season characterization </td> <td> Geotiff </td> <td> \- </td> <td> </td> <td> </td> <td> </td> </tr> <tr> <td> Difference between annual snow cover characterization maps and the reference snow cover climatology </td> <td> Geotiff </td> <td> \- </td> <td> </td> <td> </td> <td> </td> </tr> <tr> <td> Time series of temporally smoothed and gap- filled Sentinel-2 NDVIs </td> <td> WFP-02-03 - Smooth & gap- filled Sentinel- 2 </td> <td> Geotiff </td> <td> \- </td> <td> </td> <td> For validation: Gambella (S2 tiles: 36PXQ, 36PYQ, 36NXP, 36NYP) Marchfeld (S2 tiles: 33UXP) For production: AOI defined by Analyst triggering the pipeline </td> <td> </td> <td> </td> </tr> <tr> <td> time series of temporally smoothed and gap- filled Sentinel-2 reflectances. </td> <td> Geotiff </td> <td> \- </td> <td> </td> <td> </td> <td> </td> </tr> <tr> <td> Raster with three bands: the two intensities (after </td> <td> SATCEN-02-01 \- Change Detection and </td> <td> Geotiff </td> <td> \- </td> <td> </td> <td> Peru's Madre de Dios region UL 70.5659 W 12.4567 S BR 69.1411 W 13.0922 S </td> <td> </td> <td> </td> </tr> </table> <table> <tr> <th> the pre-processing) and the map of changes. </th> <th> Characterizatio n 2 (SAR Change Detection with GRD data) </th> <th> </th> <th> </th> <th> </th> <th> For validation Two images, covering 99 % of the area, (S1 IW Descending mode) above the area on dates 2018-08-12 and 2018-09-05 Image 1 S1B_IW_GRDH_1SDV_20180812T 101414_ 20180812T101439_012228_0168 7D_BCA9 Image 2 S1B_IW_GRDH_1SDV_20180905T 101415_ 20180905T101440_012578_0173 58_3259 For Production images from April 2019 to October 2019 Frequency: when a new image, acquired in the same interferometric conditions, is available </th> <th> </th> <th> </th> </tr> <tr> <td> RGB time series of the indexes above and cloud mask </td> <td> SATCEN-02-02 \- Thematic Indexes 2 (mineral indexes with Optical data </td> <td> GEOTIFF </td> <td> \- </td> <td> </td> <td> Peru's Madre de Dios region UL 70.5659 W 12.4567 S BR 69.1411 W 13.0922 S </td> <td> </td> <td> </td> </tr> <tr> <td> NDVI, Bare Soil Index, vegetation </td> <td> SATCEN-02-03 - Land </td> <td> GEOTIFF </td> <td> \- </td> <td> </td> <td> Peru's Madre de Dios region UL 70.5659 W 12.4567 S BR </td> <td> </td> <td> </td> </tr> </table> <table> <tr> <th> mask and cloud mask </th> <th> Use/Land Cover (Illegal deforestation </th> <th> </th> <th> </th> <th> </th> <th> 69.1411 W 13.0922 </th> <th> </th> <th> </th> </tr> <tr> <td> Surface displacements </td> <td> ETHZ-02-01 - Radar interferometry in active volcanic regions </td> <td> GEOTIFF </td> <td> \- </td> <td> </td> <td> Volcanoes with ongoing activity and with new activity (from WVAR) </td> <td> </td> <td> </td> </tr> <tr> <td> Surface displacements </td> <td> ETHZ-02-02 - Large surface displacements measured with feature tracking </td> <td> GEOTIFF </td> <td> \- </td> <td> </td> <td> Galapagos Islands (Isabela and Fernandina); Great Aletsch Area, Switzerland; Slumgullion landslide, Colorado. </td> <td> </td> <td> </td> </tr> <tr> <td> Surface displacement (synthetic) in LOS </td> <td> ETHZ-02-03 - Systematic modeling of surface deformation at active volcanoes </td> <td> GEOTIFF </td> <td> \- </td> <td> </td> <td> All volcanoes systematically monitored by DLR with S1 </td> <td> </td> <td> </td> </tr> <tr> <td> Difference between surface displacement (synthetic) and measured (InSAR )in LOS </td> <td> \- </td> <td> </td> <td> </td> <td> </td> </tr> <tr> <td> Change Detection Map at 5m (Vector with polygons covering the changes between two </td> <td> </td> <td> </td> <td> \- </td> <td> </td> <td> (ideally areas where there is already ground truth) For Validation AOI2 (border with Burkina Faso) - 5m Xmin= 1.47, Xmax= 2.17¡ </td> <td> </td> <td> </td> </tr> </table> <table> <tr> <th> periods and one attribute: No change = 0; Change =1) </th> <th> EXT-02-01 - Change Detection in Rural Localities for SemiAutomated Border Surveillance Applied to Insecure Areas in Lake Chad Basin </th> <th> SHP </th> <th> </th> <th> </th> <th> Ymin=,12.56 Ymax= 13.65 AOI1 (Diffa/Geidam Region - Lake Chad) - 10m Xmin=11, Xmax=15 Ymin=12.5, Ymax=15 For production AO3 (border with Nigeria - region of Zinder) - 5m Xmin=8.39, Xmax=10.65 Ymin=12.17, Ymax= 15.49 Polygon corner coordinates X1=8.94, Y1=12.64 X2=9.86, Y2=12.37 X3=10.54, Y3=12.73 X4=10.50, Y4=15.05 X5=10.06, Y5=15.43 X6=8.40, Y6=15.31 X7=8.94, Y7=12.64 AOI4 (Markoye/Teguey Region) - 10m Xmin=-0.4, Xmax=1 Ymin=13.5, Ymax=15. AOI5 (Agadez Region) - 10 m Xmin=4, Xmax=8.5 Ymin = 16.8, Ymax=19.45 Polygon corner coordinates X1=0.76, Y1=11.74 X2=2.46, Y2=12.24 X3=1.99, Y3=14.40 X4=1.02, Y4=13.97 X5=0.76, Y5=11.74 </th> <th> </th> <th> </th> </tr> <tr> <th> Change Detection Map at 10 m (Vector with polygons covering the changes between two periods and one attribute: No change = 0; Change =1) </th> <th> \- </th> <th> </th> <th> </th> <th> </th> </tr> </table> <table> <tr> <th> Oil sheen location map (0-1 data with locations of oil sheen) </th> <th> EXT-02-02 - Satellite observations of oil sheen of natural oil seepage in Disko Bay, West Greenland </th> <th> GEOTIFF </th> <th> \- </th> <th> </th> <th> Location of known seepage in Disko – Nuussuq – Svartenhuk Halvø region along the West coast of Greenland. Map from Bojesen-Koefoed et al., 2007. Petroleum seepages at Asuk, Disko, West Greenland: Implications for regionals petroleum exploration. Journal of Petroleum Geology 30, 219–236. doi:10.1111/j.17475457.2007.00219.x </th> <th> </th> <th> </th> </tr> <tr> <td> NDVI values </td> <td> EXT-02-03 - Crop Loss Detection using NDVI anomalies </td> <td> GEOTIFF </td> <td> \- </td> <td> </td> <td> For validation: one of the events identified by Fidelidade </td> <td> </td> <td> </td> </tr> <tr> <td> NDVI statistics values for entire growing season for each year </td> <td> \- </td> <td> </td> <td> </td> <td> </td> </tr> <tr> <td> NDVI statistics values for entire growing season for each year </td> <td> \- </td> <td> </td> <td> </td> <td> </td> </tr> <tr> <td> NDVI statistics values for entire growing season for each year </td> <td> \- </td> <td> </td> <td> </td> <td> </td> </tr> <tr> <td> NDVI statistics values for entire growing season for each year </td> <td> \- </td> <td> </td> <td> </td> <td> </td> </tr> <tr> <td> Long term averages </td> <td> \- </td> <td> </td> <td> </td> <td> </td> </tr> </table> <table> <tr> <th> of NDVI statistics values for entire growing season for each year </th> <th> </th> <th> </th> <th> </th> <th> </th> <th> the reference time period for the parcels affected by that event For production : All events/parcel coordinates in Portugal to be provided by Fidelidade </th> <th> </th> <th> </th> </tr> <tr> <th> NDVI statistics values for entire growing season for each year </th> <th> \- </th> <th> </th> <th> </th> <th> </th> </tr> <tr> <th> Long term averages of NDVI statistics values for entire growing season for each year </th> <th> \- </th> <th> </th> <th> </th> <th> </th> </tr> <tr> <th> NDVI statistics values for entire growing season for each year </th> <th> \- </th> <th> </th> <th> </th> <th> </th> </tr> <tr> <th> Long term averages of NDVI statistics values for entire growing season for each year </th> <th> \- </th> <th> </th> <th> </th> <th> </th> </tr> <tr> <th> NDVI statistics values for entire growing season for each year </th> <th> \- </th> <th> </th> <th> </th> <th> </th> </tr> <tr> <th> Long term averages of NDVI statistics values for entire growing season for </th> <th> \- </th> <th> </th> <th> </th> <th> </th> </tr> </table> <table> <tr> <th> each year </th> <th> </th> <th> </th> <th> </th> <th> </th> <th> </th> <th> </th> <th> </th> </tr> <tr> <th> NDVI statistics values for entire growing season for each year </th> <th> \- </th> <th> </th> <th> </th> <th> </th> </tr> <tr> <td> Delineation map </td> <td> EXT-02-04 - Surface temperature map evolution </td> <td> GEOTIFF </td> <td> \- </td> <td> </td> <td> Etna, Vesuvio, Campi Flegrei, Stromboli </td> <td> </td> <td> </td> </tr> <tr> <td> Temperature map (in °C) </td> <td> \- </td> <td> </td> <td> </td> <td> </td> </tr> </table> * source-err: a series for tracking the systematic processing where the identifiers not successfully processed are inserted * results: a series for the thematic data where the generated products metadata and enclosures are stored An entry in the tracking index (and associated series) is an OWS Context document that contains at least: * An identifier (the identifier of the input product) * A title * A date/time coverage * A spatial coverage * A via link pointing to the input resource (required for recovery scenarios) * A published date (the date of the source-in stage) * A category expressing the processing stage (source-queue, source-in, sourceout, source-err) 2.2 Catalogue and Metadata of the BETTER data pipelines The systematic processing of a BETTER data pipeline is supported by a tracking catalogue index and its associated tracking series. A BETTER pipeline has an index in the catalog. This concept applied to the Word Food Programme data processor for the WFP \- 01 \- 01 \- 01 Sentinel \- 1 backscatter timeseries gives us the index: _https://catalog.terradue.com/bette_ _r_ _-_ _wf_ _p_ _-_ _0000_ _1_ This index has five series, accessible at the URL _htt_ _p_ _s://catalog.terradue.com/bette_ _r_ _-_ _wf_ _p_ _-_ _0000_ _1_ / series/search :  source \- queue: a series for tracking the identifiers in the queue  source \- in: a series for tracking the systematic processing where the identifiers being processed are inserted  source \- out: a series for tracking the systematic processing where the identifiers successfully processed are inserted * The generator as version of the processing * The output opensearch offerings limited by the geo:uid element * The processing offering * The URLs for the WPS GetCapabilities and Describe Process GET requests * The URL for the WPS GetStatus GET request * The URL for the WPS Execute POST request * The WPS POST request The model can be extended with elements from the OGC EO Metadata profile of O&M (a.k.a EOP) [EOP_OM] or the ISO-19115 metadata model. This project has received funding from the European Union’s Horizon 2020 Research and Innovation Programme under grant agreement no 776280 # FAIR data ## Making data findable, including provisions for metadata As introduced in the section above, the catalogue containing the BETTER data pipeline uses the OGC® OpenSearch Geo and Time extensions standard as a possible metadata model to provide discovery functions to all the elements of a data pipeline. The baseline of the standard is the OpenSearch v1.1 specification ( _A9.com_ , Inc, n.d.) [OS]. The Geo and Time extensions provide with the main queryables to manage geospatial data products, and the specification is standardised at the Open Geospatial Consortium (OGC, n.d.). The data generated by the BETTER data processing pipelines have an entry in a data catalogue that includes metadata with at least the geo and time attributes. This minimum set of metadata ensures this data can be discovered using geographical and time search criteria. The discoverability of data generated by the BETTER data processing pipelines is guaranteed with the data catalogue OpenSearch search engine that exposes an OpenSearch Description document (OSDD) describing the search template and returns several search output formats such as Atom or JSON. The response includes the enclosure to the data file (or files). The OSDD will have a DOI assigned using the ZENODO platform, which is associated to results of a data pipeline. The process requires to upload the OSDD XML document to Zenodo and add all the content needed for the dataset landing page. The DOI will thus resolve to item in Zenodo that contains the OSSD XML document and the description of the dataset. The landing page will include the following: * Info necessary to create citations in various different citation standards ( I.e title, authors, data created etc) * provisions for machine readability * Long term preservation - if the data is no longer available, the landing page should remain to explain why * Links to the data - where possible, these should be links to the data itself, but it may also be contact information to coordinate having the data sent on physical media for extremely large datasets. The landing page will be hosted by Zenodo. According to the selected standard baseline and extensions, the data catalogue queryable elements are discovered by requesting the OpenSearch description document containing the URL templates and response formats. For each of the associated data pipeline series, the OpenSearch description document can be retrieved with the URL: _https://catalog.terradue.com/ < _ d ata pipeline identifier>/series/<stage>/description Where * <data pipeline identifier> is the data pipeline unique identifier that can be traced in the BETTER deliverable D3.1. Example better-wfp-00001 for the WFP-01-01-01 Sentinel-1 backscatter timeseries data pipeline.  <stage> is one of: o source-queue: to search the messages in the data pipeline queue o source-in: to search the messages of the data pipeline being processed o source-out: to search the messages of the data pipeline successfully processed o source-err: to search the messages of the data pipeline not successfully processed o results: to search for the products generated by the data pipeline The policy for the discovery functions is open. The OpenSearch description document obtaining from the previous URL contains the list of parameters to do a query against the metadata, such as: * start: start of the temporal interval (RFC-3339) * stop: stop of the temporal interval (RFC-3339) * bbox: Rectangular bounding box ## Making data openly accessible The Terradue Ellip Cloud Platform is the privileged storage and cataloguing resource for managing the dataset produced by the BETTER data pipelines and described above in the section of Data summary. In principle, all these datasets would be make open accessible if not access or size restrictions are declared. As mandatory, the datasets used in publications will be open accessible and available at openAIRE. As declared in the previous section, the discovery of a given BETTER data pipeline is open and accessible via the OpenSearch mechanism (available on the Web). The data access requires software tools allowing to do GET requests to the data enclosure pointing to the storage where the data is hosted. The data access can be done with the same set of tools as for the data discovery. The metadata associated to the the data generated by the BETTER data processing pipelines is deposited on the data catalog accessible at the URL _https://catalog.terradue.com._ One must know the BETTER data processing pipeline catalog index to access the OSDD URL. The data generated by the BETTER data processing pipelines is hosted on the data storage accessible at the URL _https://store.terradue.com_ . The metadata associated to the the data generated by the BETTER data processing pipelines contains the enclosure. The access to a given BETTER data pipeline results may: * Be subject to an authentication/authorization process according to an agreed policy with the BETTER Challenge Promoter (e.g. embargo period before full public release). * Be open for download without any authentication or authorization Even when the results are subject to an authentication process, there will be an registration process for getting a user credentials freely. If any access policy is applied to a data set, it is described in the column 'access restrictions' in the table of the section 2.1. ## Making data interoperable As introduced above, the results produced by the BETTER data pipelines are discoverable using OpenSearch [OS]. The response of an opensearch query is a list of records containing an OWS Context document [OWS_C]. The contents of the OWS Context document are described in the section 2.2 and they can be extended with elements from the OGC EO Metadata profile of O&M [EOP_OM]. As can be found in the column Format in the table of the section 2.1, the results themselves are formatted in general in well known formats: geotiff, netcdf, DIM or SAFE. Most of these formats are supported by standard tools. Furthermore, the outputs of a data pipeline can be used as an input to another data pipeline. ## Increase data re-use (through clarifying licences) The data is available and catalogued as soon as the data pipeline generated it. The owner of the data will be the BETTER Challenge Promoter of the corresponding challenge. They are responsible for setting the right policies of use of the generated data. If a data usage license is applied to any data it should be compatible with the FAIR data principles. The BETTER Promoters have not defined any license for the re-use of the data generated at the moment. If they are identified in the future, they will be reported in the column 'access restrictions in the updates of the table in section 2.1. The data retention policy of the BETTER data pipelines results is to agree and define with the BETTER Challenge Promoter a period of time (retention time) in which this data is stored and replicated on the Ellip platform. After that period, the results are eliminated in a FIFO approach. At the moment, we have not defined yet measures for the long term preservation of the data generated in the project such as publishing the data in other platforms or repositories. The data in the Ellip platform will not persist even during the duration of the project. The long term preservation of the data will be addressed in the updates of the table in section 2.1. # Other issues ## Allocation of resources The cost for making data FAIR is covered by the tasks in the WP3 of the project. The Ellip Platform, provided in Task 3.1, supports the catalogue opensearch for the data pipelines. The data ingestion modules of the data pipelines are developed in the task 3.3, they will format the input and output data adding the associated metadata for their cataloguing. Some effort is allocated to the Challenge Promoters to define the access and use policy of the data generated by the data pipelines. The resources for the long term preservation have not been discussed yet, they will be addressed in the next version of this document. ## Data security The Ellip platform ensures the safety of the data during the configured retention time. The data is protected at different levels of the platform storage system: * At the service level, the storage is a “distributed-replicated” type meaning that “distributed-replicated” volumes distribute files across replicated servers for the same volume. When replicating, servers are organised in pairs. Therefore, it’s possible to lose up to one entire server for each pair of servers constituting the storage service. * At the server level, the storage is configured with RAID 6 using a hardware-RAID Controller and 2 HotSpace disks. This configuration ensures double-parity RAID and thus resilient up to 4 disks failures within the RAID set before any data is lost. For the long term preservation of the data, as said in previous sections, the measurements to take with the data are still to be defined. END OF DOCUMENT	https://phaidra.univie.ac.at/o:1140797	Horizon 2020
0204_SEMIoTICS_780315.md	# EXECUTIVE SUMMARY This deliverable shows how quality, innovation and data management aspects are considered in a variety of processes and activities within the SEMIoTICS project. The interrelated quality/innovation/data management processes namely quality management, quality control and quality assurance, innovation management, and data management have impact on the project work from the requirements & architectural definition to the project’s implementation in 3 different usage scenarios. In chapter 3, Quality Management Plan refers to reporting procedures, the definition of roles and responsibilities, quality control & assurance policies for deliverables and publications with a well-defined internal review process and implementation throughout the project’s duration. In chapter 4, Innovation Management Plan provides detailed plan for activities and processes for identifying internal and external opportunities for and realising innovation. In chapter 5, Data Management Plan provides details about how to manage the data generated within technical work-packages within the consortium. The processes and criteria described in this document may also be updated if additional needs arise during the execution of the project in regular WP1 deliverables (Yearly project reports and project plan updates). # INTRODUCTION Global networks like IoT create an enormous potential for new generations of IoT applications, by leveraging synergies arising through the convergence of consumer, business and industrial Internet, and creating open, global networks connecting people, data, and “things”. A series of innovations across the IoT landscape have converged to make IoT products, platforms and devices technically and economically feasible. However, despite these advancements the realization of the IoT potential requires overcoming significant business and technical hurdles, e.g. Dynamicity, Scalability, Heterogeneity, End-to-end Security and Privacy. To address these challenges, SEMIoTICS aims “ _To develop a pattern-driven framework, built upon existing IoT platforms, to enable and guarantee secure and dependable actuation and semi-autonomic behaviour in IoT/IIoT applications. The SEMIoTICS framework will support cross-layer intelligent dynamic adaptation, including heterogeneous smart objects, networks and clouds. To address the complexity and scalability needs within horizontal and vertical domains, SEMIoTICS will develop and integrate smart programmable networking and semantic interoperability mechanisms. The above will be validated by industry, using three diverse usage scenarios in the areas of renewable energy, healthcare, and smart sensing and will be offered through an open_ _API._ ” An essential measure to reach the highly ambitious and challenging aim and subsequent objectives of SEMIoTICS project is to set the appropriate quality management processes and criteria. These processes and criteria are described in this document in order to successfully ensure high quality project outcomes. This deliverable is addressed to any interested public reader. It will be practically useful for the consortium members who can use it as a basis for the general management of all project activities. Guidelines/criteria given in this deliverable (Chapter 3) will ensure that all kind or project reports and publications will follow high quality standards and will give all consortium members a comprehensive overview on the SEMIoTICS procedures for publication and reporting. Chapter 4 provides details about Innovation Management plan and project’s different tasks which will lead to innovation and its assessment. Chapter 5 gives details about project’s Data Management Plan (DMP) which for the consortium internal usage. This document refers to: 1. the Description of Action (part B) [1] 2. the Consortium Agreement [2] 3. the Grant Agreement [3] # QUALITY MANAGEMENT PLAN (QMP) ## Roles and Responsibilities Roles and responsibilities for maintaining and updating deliverables/plans are linked to roles within SEMIoTICS. In case new personnel is assigned to a relevant role, responsibilities with respect to previously assigned tasks are also taken over. The project management roles and the structure of SEMIoTICS are described, and the following table gives a quick reference to the detailed description of each role: <table> <tr> <th> Project Coordinator (PC) </th> <th> The Coordinator will consolidate the input and will do the continuous reporting (online). </th> </tr> <tr> <td> Technical Project Manager (TPM) </td> <td> TPM, also known as Scientific, Technical and Innovation Project Manager, is responsible of the technical and innovation management; and transparently communicates innovation management related issues within PCC and PTC. </td> </tr> <tr> <td> Project Coordination Committee (PCC) </td> <td> PCC approval is required for all disclosure of confidential project results outside the consortium. PCC and PTC are responsible to take appropriate actions according to the rules on innovation management and intellectual property creation. PCC constitutes all the named personnel in CA [2] of SEMIoTICS </td> </tr> <tr> <td> Project Technical Committee (PTC) </td> <td> PTC has a strong focus on development and protection of intellectual property and its efforts will be supported by TPM and PCC in order to create a solid base for industrial and commercial exploitation. PTC constitutes all the WP leads of SEMIoTICS </td> </tr> <tr> <td> Work Package Leaders </td> <td> Work package leaders are responsible for quality control measures within their work package and will monitor that this quality management plan is followed. </td> </tr> <tr> <td> Task Leaders </td> <td> Task leaders have to give work package leader support in effectively monitoring the QMP implementation. Work package leaders are responsible to report incidents of the QMP not being followed to the PTC </td> </tr> <tr> <td> Reviewer </td> <td> Reviewer is assigned by WP Leader/PTC who reviews the deliverable/other material based on respective quality criteria </td> </tr> <tr> <td> Advisory Committee (AC) </td> <td> Advisory Committee (AC) consisting of relevant external stakeholders from research, academia and industry. The AC will follow the project development and will provide necessary feedback in order to ensure that the scientific and technological evolution of the project is in the direction to fulfill its goals and provide an external global viewpoint. </td> </tr> </table> _TABLE 1: PROJECT MANAGEMENT ROLES _ ## Management Bodies SEMIoTICS has a lean management structure supporting an effective project execution including the tasks and activities related to innovation and innovation management. A clear definition of roles is also given there and can be summarized as follows: * During project execution, the TPM will be responsible of driving the technical and innovation management and transparently communicate innovation management related issues within PCC and PTC. * PCC and PTC are responsible to take appropriate actions according to the rules on innovation management and intellectual property creation. * Partners of SEMIoTICS have departments for maintaining the intellectual property portfolio and existing interfaces of consortium partners will be used to protect intellectual property developed within the project according to local law * SEMIoTICS consortium partners have experience in the collaboration of their own legal / IP departments and projects and existing processes will support creation of intellectual property within the project (e.g., use of tools like internal/external patent databases). * The PTC will have a strong focus on development and protection of intellectual property and its efforts will be supported by TPM and PCC in order to create a solid base for industrial and commercial exploitation. * Different exploitation strategies were already discussed during the proposal phase and Task 6.2 and 6.1 has allocated resources for work on exploitation and impact creation. * PCC approval is required for all disclosure of confidential project results outside the consortium and decisions will be taken according to this deliverable. * SEMIoTICS has an interface established to interact with other projects within IOT-EPI programme in order to drive the collaborative exercise of all partners. * Consortium partners contributing expertise on business, technologies, application domain, and research which enable innovation aligned to the business activities of the partners, and thus, will lead to either the development of a product, a service, or future research. * Legal aspects of innovation, intellectual property being created within the project, joint ownership of results (if applicable), joint exploitation strategies, and all related confidentiality issues are clarified in the consortium agreement [2]. ## Quality Management ### REPORTING PROCEDURES All project reporting procedures have to follow the terms and conditions as stated in the Grant Agreement. In general, SEMIoTICS facilitates quality management by regular project reporting of all partners being used as input for the project reports for the EC and the Project Officer. SEMIoTICS will use continuous reporting to the EC via the web-based project management portal. Therefore, WP leads have to give short reports on WP related activities and achievements to the Coordinators at the end of each quarter (March, June, September, and December). The Coordinator will consolidate the input and will do the continuous reporting (online). Reporting includes progress report (against baseline), achievements, resources, and risks. Content for the reporting deliverables, namely yearly reports (D1.3, D1.4, and D1.5) is created based on information from continuous reporting, as well as specific information on closed, active and upcoming WPs directly given by corresponding WP leads. Detailed technical content and detailed progress information of each WP is reported from Task leads towards WP lead and to Project Technical Committee (PTC) via WP lead. Monthly PTC meetings/calls are used to review progress, review/update the risk register, updates of DMP, and dissemination plan, inter-WP collaboration, as well as for reporting towards PCC. PTC call minutes will be forwarded to PCC within 5 working days after each PTC call. ### QUALITY CONTROL – GENERAL Work package leaders are responsible for quality control measures within their work package and will monitor that this quality management plan is followed. Task leaders have to give them support in effectively monitoring the QMP implementation. Work package leaders are responsible to report incidents of the QMP not being followed to the PTC. PTC will decide on mitigation actions, if possible. In case mitigation is not possible through PTC, PTC will inform PCC for further actions. Detailed quality control measures for different types of results are described in the following sections. ## Quality control for publications ### RULES FOR PUBLICATION The following procedure ensures a high quality of joint publications related to SEMIoTICS and take care that IPR of other parties is not infringed. Timing is aligned so that each project partner can also complete mandatory internal approval procedures. <table> <tr> <th> At least 4 weeks before deadline </th> <th> Venue is registered in the Dissemination Plan. Author will send notification to Task 6.1 lead and PTC will start tracking the status of that publication. This includes planned authors, title, abstract, and planned venue. </th> </tr> <tr> <td> At least 3 weeks before deadline </td> <td> Outline of presentation is ready, and authors start partner internal approval processes. </td> </tr> <tr> <td> At least 1 week before deadline </td> <td> Work package internal (authors / subject matter experts) review started, authors are responsible to integrate / discuss comments with reviewers. </td> </tr> <tr> <td> Before submission </td> <td> Authors declare successful (internal) review and all authors agree to the submission of that final version (e.g., via email). PTC will store that information on the repository together with the submitted version. </td> </tr> <tr> <td> After submission / dissemination / acceptance of publication </td> <td> Authors ensure that status of publication is tracked and updated in Dissemination Plan (update notifications to Task 6.1 lead). </td> </tr> </table> #### TABLE 2A: PROCEDURE FOR JOINT PUBLICATION For publications with only one partner being involved, the procedure is simplified: <table> <tr> <th> At least 4 weeks before deadline </th> <th> Venue is registered in the Dissemination Plan and PTC will start tracking the status of publication and submission. This includes planned authors, title, abstract/outline, and planned venue. </th> </tr> <tr> <td> After submission / dissemination / acceptance of publication </td> <td> Authors ensure that status of publication is tracked and updated in Dissemination Plan (update notifications to Task 6.1 lead). </td> </tr> </table> _TABLE 2B: PROCEDURE FOR PUBLICATION OF ONE PARTNER _ Objections will be handled according to the procedures given in the Grant Agreement. ### ACKNOWLEDGEMENT Acknowledgement to the EC for its funding must be clearly indicated on every publication and presentation for which project funding will be claimed. Typical text is as follows: This [paper/presentation/...] has received funding from the European Union's Horizon 2020 research and innovation programme H2020-IOT-2016-2017/H2020-IOT-2017, under grant agreement No. 780315\. ### DISCLAIMER It is recommended to include a disclaimer on every publication and presentation. Typical text is as follows: This [paper/presentation/...] reflects only the authors' views and the European Commission is not responsible for any use that may be made of the information it contains. ## Quality control for deliverables The project coordinator, together with PCC and Technical Manager will closely coordinate technical quality checks for all deliverables. All deliverables will be subject to a review within the work package before forwarding them to PCC for final review and approval. Where necessary, the Project Coordinator could request further work of the partners on a deliverable, to ensure that it complies with the project’s contractual requirements. All deliverables will include the names of the editor (responsible person), the authors of the content, the reviewers, as well as the approvers. After PCC approval, deliverables are submitted to the EC by the coordinator. Escalations in case of quality concerns will follow the procedures given in the “Conflict Resolution” of Grant Agreement [3]. To ensure that this process can be followed through, the following time plan has been agreed: <table> <tr> <th> Start of tasks contributing to a deliverable </th> <th> ## Quality control for other material ### DISSEMINATION ACTIVITIES (INCL. IOT-EPI PROGRAMME LEVEL TOPICS) Detailed procedures on dissemination activities are given in the Dissemination Plan (e.g., event description, activity description, and timing). Quality control related actions for dissemination activities are: * Dissemination activities have to be coordinated with the WP6 lead * For “standard” dissemination activities, a SEMIoTICS project presentation slide deck is available in the project repository. * For all additional technical content in dissemination activities a project internal peer review is foreseen, and PCC has to give approval for dissemination after peer review is done. Peer review is initiated by the author by sending a request to PTC. PTC will assign a reviewer. After review is finished PTC will give a recommendation for approval to PCC. For publications to go to public, prior written notice with abstract of any planned publication (irrespective if for scientific journals, conferences, online publications, or the like) needs to be given to the consortium members at least forty-five (45) days before the planned publication date ((i.e. the day the journal will be published, the day the conference is scheduled for, etc.). Any objection from PTC/PCC members as specified in consortium agreement to the planned publication shall be made in writing to all the consortium members within thirty (30) days after receipt of the written notice. If no objection is made within the time limit stated above, the publication is permitted. The internal approval process is mentioned earlier in Table 1A and Table 1B. ### DEMONSTRATION ACTIVITIES (INCL. IOT-EPI PROGRAMME LEVEL TOPICS) Demonstration activities will follow the same procedure as dissemination activities with technical content (e.g., peer review of technical content). All demonstration activities have to be coordinated with PTC well in time - due to extended visibility, partners might have to follow internal approval procedures which need lead time. Demonstrations will be coordinated by dedicated (internal) workgroups and these workgroups will directly report to PTC. Quality assurance will follow the procedures given in the Grant Agreement [3]. ## Quality assurance Quality assurance will follow the procedures given in the Grant Agreement, namely: Quality assurance will be performed in all project phases through WP1 by PCC that will undertake to secure SEMIoTICS quality and relevant documentation at all development stages of the project. SEMIoTICS is adopting the Plan-Do-CheckAct (PDCA) principle to achieve proper monitoring of all project activities. With PDCA all work done within the WPs and tasks will be closely monitored on a continuous basis resulting in PCC/PTC initiated corrective actions and changes to the project plan when necessary. ### QA FOR DELIVERABLES QA recommends and expects certain Quality levels / Quality Assurance levels for project deliverables, as well as the criteria and processes for assessing them, including responsible project internal stakeholders (Task leader/Editor, Contributor, Reviewer, WP Leader, PTC, TPM, PCC, PC). The title page of the SEMIoTICS deliverables clearly identifies authors, reviewers, and approvers. This gives a transparent view on the persons involved in quality control and deliverables can only be released after the quality assurance levels (e.g., internal reviews/processes) are successfully passed. Each Task leader/editor will follow the criteria such as: 1. The deliverable Table of Contents are prepared according to PERT chart (see section 6.5) of the project. 2. Incorporating suggestions from PTC, TPM, PCC and PC for continuous improvement of the deliverable 3. Making sure timely inputs from the contributors of the task. 4. Checking and managing the flow of information in the technical contents as per task objectives fulfilling the WP objectives. Each contributor of the deliverable will follow the criteria such as: 1. Timely contribution of the technical contents to the Editor of the deliverable. 2. Checking the flow of information in the technical contents. Each WP lead will follow the criteria such as: 1. Monitor technical progress w.r.t. WP objectives in comparison to resource consumption each of its tasks within WP. 2. Check each of WP’s deliverable as per PERT chart (see section 6.5) of the project and ask for corrections from the Editor, if necessary. 3. Assign a reviewer/s which will check the consistency of the deliverable from the information flow point of view. The deliverable reviewer assigned by WP lead will follow the criteria such as: 1. Check whether each deliverable starting from high level concepts, and then presents technologies and details in separate sections. 2. Check for English grammatical errors, broken links. 3. Check the layout of the deliverables w.r.t. the reporting template After the deliverable is properly reviewed internal to the WP, TPM sends it to PTC for their review and checking for any obvious artifacts which may affect other tasks in their respective WPs. After their approval TPM sends it to PCC for final approval. After PCC approval, PC uploads the deliverable on EC portal. ### QA FOR PUBLICATIONS All SEMIoTICS publication activities will be captured in the Dissemination Plans of WP6 and also information quality control gates are recorded there. This includes proper documentation that the quality control gates are successfully passed, as well as the status of external peer reviews (e.g., publication submitted to double blind peer review and was accepted for publication on the conference). ### QA FOR OTHER MATERIAL All internal reviews for other SEMIoTICS dissemination material will be documented in the dissemination plan (per dissemination activity). This includes information on authors, reviewers, and approvers. ## Risk Management The overall risks, considering the project to be technically too broad and ambitious, is controlled by the excellence and experience of the consortium partners. It is important that the consortium identify the risks that may originate from the approaches used to achieve the project goals, as well as the measures that the consortium could take to minimize them. The potential project risks can be classified into the following groups: i. Execution risks, 1. Partner related, 2. Planning problems 3. Consortium Collaboration Issues ii. Technological risks, iii. External risks. SEMIoTICS’s major risks, including description, possible impacts, and related contingency plans are described in the Risk register which is regularly updated in monthly PTC meetings. In case a serious issue arises in any deliverable, deliverable responsible person/reviewer/PTC/Technical Project Manager/PCC/Project Coordinator can raise that issue directly with the involved persons in the project directly or indirectly through Technical Project Manager/Project Coordinator. ## Deliverable tracking process Transparency of roles and responsibilities has a big impact on the project success. Uncertainty can dramatically affect individual, organisational as well as the consortium performance. Therefore, responsible persons for each organisation and per WP were defined. The tables below show initial assignments of the Deliverables, its reviewers and Milestones of the project; and detailed tacking incl. reasons for delay (if any) will be done throughout the WP1 activities. While Deliverable leading organisations were already defined within the DoA [1], the concrete editor responsible for requesting and guiding partner inputs towards a punctual and high-quality submission, were named at the project start till Milestone 2 (MS2). The later assignments will be mentioned in subsequent deliverables of WP1 (Yearly and half-yearly project plan updates). In line with the quality control process for deliverables (described in chapter 3.4) at least one specific internal reviewer for each deliverable was defined and clear deadlines for the draft version, internal review as well as for the pre-final version and final version submission were established. Note: For simplicity, Table 4A is filled till Milestone MS2 and Table 4B is filled till Milestone MS1. In the forthcoming WP1 deliverables this table will be updated continuously. This table is linked to file “Deliverables_tracking_Editors and reviewers.xlsx” _https://overseer1.erlm.siemens.de/repository/Document/downloadWithName/Deliverables_tracking_ _Editors%20and%20reviewers.xlsx?reqCode=downloadWithName&id=24675185 <table> <tr> <th> Deliverables for Project 780315 </th> </tr> <tr> <td> Deliverables </td> </tr> <tr> <td> WP No </td> <td> Del No </td> <td> Title </td> <td> Editor </td> <td> Reviwer/s </td> <td> Comments </td> </tr> <tr> <td> WP1 </td> <td> D1.1 </td> <td> Project web site and internal communication platform </td> <td> Andreas Miaoudakis (FORTH) </td> <td> Nikolaos Petroulakis (FORTH), Vivek Kulkarni (SIEMENS) </td> <td> </td> </tr> <tr> <td> WP2 </td> <td> D2.1 </td> <td> Analysis of IoT value drivers </td> <td> Prof. Georgios Spanoudakis (Sphynx) </td> <td> Vivek Kulkarni (SIEMENS) </td> <td> </td> </tr> <tr> <td> WP2 </td> <td> D2.2 </td> <td> SEMIoTICS usage scenarios and requirements </td> <td> Vivek Kulkarni (SIEMENS) </td> <td> Use case wise peer-reviewers </td> <td> </td> </tr> <tr> <td> WP1 </td> <td> D1.2 </td> <td> Initial Quality, Innovation and Data Management Plan </td> <td> Volkmar Döricht (SIEMENS) </td> <td> Nikolaos Petroulakis (FORTH) </td> <td> </td> </tr> <tr> <td> WP1 </td> <td> D1.7 </td> <td> Periodic project plan updates (M6) </td> <td> Nikolaos Petroulakis (FORTH) </td> <td> Vivek Kulkarni (SIEMENS) </td> <td> </td> </tr> <tr> <td> WP2 </td> <td> D2.3 </td> <td> Requirements specification of SEMIoTICS framework </td> <td> Mirko Falchetto (ST-I) </td> <td> Peer-reviewers of each section </td> <td> </td> </tr> <tr> <td> WP6 </td> <td> D6.1 </td> <td> Impact creation, dissemination and exploitation plan </td> <td> Christos Verikoukis (CTTC) </td> <td> Nikolaos Petroulakis (FORTH) </td> <td> </td> </tr> <tr> <td> MS1 </td> <td> </td> <td> Finalization of the Requirements </td> <td> Danilo Pau (ST-I) </td> <td> Nikolaos Petroulakis (FORTH) </td> <td> Approxmiate 1 month delay in completion of the milestone was agreed with the PO at that time. </td> </tr> <tr> <td> WP1 </td> <td> D1.3 </td> <td> Year 1 project report and project plan updates </td> <td> Vivek Kulkarni (SIEMENS) </td> <td> Nikolaos Petroulakis (FORTH) </td> <td> </td> </tr> <tr> <td> WP3 </td> <td> D3.5 </td> <td> Field-level middleware & networking toolbox (first draft) </td> <td> Prodromos Vasileios Mekikis (IQUADRAT) </td> <td> Ermin Sakic (SIEMENS) </td> <td> </td> </tr> <tr> <td> WP2 </td> <td> D2.4 </td> <td> SEMIoTICS high level architecture (first draft) </td> <td> Mirko Falchetto (ST-I) </td> <td> Łukasz Ciechomski (BlueSoft) </td> <td> </td> </tr> <tr> <td> WP3 </td> <td> D3.1 </td> <td> Software defined programmibilty for IoT devices (first draft) </td> <td> Ermin Sakic (SIEMENS) </td> <td> Nikolaos Petroulakis (FORTH) </td> <td> </td> </tr> <tr> <td> WP3 </td> <td> D3.2 </td> <td> Network Functions Virtualization for IoT (1st draft) </td> <td> Luis Sanabria Russo (CTTC) </td> <td> Ermin Sakic (SIEMENS) </td> <td> </td> </tr> <tr> <td> WP3 </td> <td> D3.3 </td> <td> Bootstrapping and interfacing SEMIoTICS field level devices (1st draft) </td> <td> Darko Anicic (SIEMENS) </td> <td> Kostas Ramantas (IQUADRAT) </td> <td> </td> </tr> <tr> <td> WP3 </td> <td> D3.4 </td> <td> Network-level Semantic Interoperability (first draft) </td> <td> Iason Somarakis (Sphynx) </td> <td> Ermin Sakic (SIEMENS) </td> <td> </td> </tr> <tr> <td> WP4 </td> <td> D4.1 </td> <td> SEMIoTICS SPDI Patterns (first draft) </td> <td> Konstantinos Fysarakis (Sphynx) </td> <td> Nikolaos Petroulakis (FORTH) </td> <td> </td> </tr> <tr> <td> MS2 </td> <td> </td> <td> First version of SEMIoTICS architecture, End of 1st Field and Network level mechanisms development cycle </td> <td> SIEMENS </td> <td> Nikolaos Petroulakis (FORTH) </td> <td> </td> </tr> <tr> <td> WP4 </td> <td> D4.2 </td> <td> SEMIoTICS Monitoring, prediction and diagnosis mechanisms (first draft) </td> <td> ENG </td> <td> </td> <td> </td> </tr> <tr> <td> WP4 </td> <td> D4.3 </td> <td> Embedded Intelligence and local analytics (first draft) </td> <td> ST-I </td> <td> </td> <td> </td> </tr> <tr> <td> WP4 </td> <td> D4.4 </td> <td> Semantic interoperability mechanisms for IoT (first draft) </td> <td> FORTH </td> <td> </td> <td> </td> </tr> <tr> <td> WP4 </td> <td> D4.5 </td> <td> SEMIoTICS Security and privacy mechanisms (first draft) </td> <td> UNI PASSAU </td> <td> </td> <td> </td> </tr> <tr> <td> WP4 </td> <td> D4.6 </td> <td> Implementation of SEMIoTICS BackEnd API (Cycle 1) </td> <td> BlueSoft </td> <td> </td> <td> </td> </tr> <tr> <td> MS3 </td> <td> </td> <td> End of 1st Pattern-driven smart behavior of IIoT mechanisms development cycle and the 1st backend implementation cycle </td> <td> Sphynx </td> <td> Nikolaos Petroulakis (FORTH) </td> <td> </td> </tr> <tr> <td> WP1 </td> <td> D1.8 </td> <td> Periodic project plan updates (M18) </td> <td> FORTH </td> <td> </td> <td> </td> </tr> <tr> <td> WP6 </td> <td> D6.2 </td> <td> Interim report on impact creation, dissemination activities </td> <td> CTTC </td> <td> </td> <td> </td> </tr> <tr> <td> WP6 </td> <td> D6.3 </td> <td> Interim report on exploitation activities (M18) </td> <td> ENG </td> <td> </td> <td> </td> </tr> <tr> <td> WP6 </td> <td> D6.4 </td> <td> Interim report on standardization activities </td> <td> SIEMENS </td> <td> </td> <td> </td> </tr> <tr> <td> WP3 </td> <td> D3.6 </td> <td> Field-level middleware & networking toolbox (second draft) </td> <td> IQUADRAT </td> <td> </td> <td> </td> </tr> <tr> <td> WP5 </td> <td> D5.1 </td> <td> SEMIoTICS KPIs and Evaluation Methodology </td> <td> UNI PASSAU </td> <td> </td> <td> </td> </tr> <tr> <td> WP2 </td> <td> D2.5 </td> <td> SEMIoTICS high level architecture (final) </td> <td> BlueSoft </td> <td> </td> <td> </td> </tr> <tr> <td> WP4 </td> <td> D4.7 </td> <td> Implementation of SEMIoTICS BackEnd API (Cycle 2) </td> <td> BlueSoft </td> <td> </td> <td> </td> </tr> <tr> <td> WP5 </td> <td> D5.2 </td> <td> Software system integration (Cycle 1) </td> <td> BlueSoft </td> <td> </td> <td> </td> </tr> <tr> <td> WP5 </td> <td> D5.3 </td> <td> IIoT Infrastructure set-up and testing (Cycle 1) </td> <td> IQUADRAT </td> <td> </td> <td> </td> </tr> <tr> <td> MS4 </td> <td> </td> <td> Final version of SEMIoTICS architecture, end of 2nd implem.cycle, End of setup and testing 1st cycle, Evaluation methodology defined </td> <td> FORTH </td> <td> Vivek Kulkarni (SIEMENS) </td> <td> </td> </tr> </table> ### TABLE 4A: DELIVERABLE INITIAL ASSIGNMENTS (EDITOR/RESPONSIBLE PERSON, REVIEWER) <table> <tr> <th> Deliverables for Project 780315 </th> </tr> <tr> <td> Deliverables </td> </tr> <tr> <td> WP No </td> <td> Del No </td> <td> Title </td> <td> Lead Beneficiary </td> <td> Nature </td> <td> Dissemination Level </td> <td> Est. Del. Date (annex I) </td> <td> Receipt Date </td> <td> Status </td> <td> Reasons for delay, if any </td> </tr> <tr> <td> WP1 </td> <td> D1.1 </td> <td> Project web site and internal communication platform </td> <td> FORTH </td> <td> Report </td> <td> Confidential </td> <td> 30-Jan-18 </td> <td> 28-May-18 </td> <td> Submitted </td> <td> Project start happened 1 month earlier than consortium's expected start, Web site was already operational and internal communication was in operation. The delay was agreed with the PO at that time. </td> </tr> <tr> <td> WP2 </td> <td> D2.1 </td> <td> Analysis of IoT value drivers </td> <td> Sphynx </td> <td> Report </td> <td> Public </td> <td> 31-Mar-18 </td> <td> 03-May-18 </td> <td> Submitted </td> <td> The deliverable submission delay was agreed with the PO at that time. </td> </tr> <tr> <td> WP2 </td> <td> D2.2 </td> <td> SEMIoTICS usage scenarios and requirements </td> <td> SIEMENS </td> <td> Report </td> <td> Public </td> <td> 30-Apr-18 </td> <td> 14-Jun-18 </td> <td> Submitted </td> <td> The deliverable submission delay was agreed with the PO at that time. </td> </tr> <tr> <td> WP1 </td> <td> D1.2 </td> <td> Initial Quality, Innovation and Data Management Plan </td> <td> SIEMENS </td> <td> Report </td> <td> Public </td> <td> 30-Jun-18 </td> <td> 23-Jul-18 </td> <td> Submitted </td> <td> The deliverable submission delay was agreed with the PO at that time. </td> </tr> <tr> <td> WP1 </td> <td> D1.7 </td> <td> Periodic project plan updates (M6) </td> <td> FORTH </td> <td> Report </td> <td> Confidential </td> <td> 30-Jun-18 </td> <td> 02-Aug-18 </td> <td> Submitted </td> <td> The previously delayed deliverables brought additional delay. </td> </tr> <tr> <td> WP2 </td> <td> D2.3 </td> <td> Requirements specification of SEMIoTICS framework </td> <td> ST-I </td> <td> Report </td> <td> Public </td> <td> 30-Jun-18 </td> <td> 07-Aug-18 </td> <td> Submitted </td> <td> The previously delayed deliverables brought additional delay. </td> </tr> <tr> <td> WP6 </td> <td> D6.1 </td> <td> Impact creation, dissemination and exploitation plan </td> <td> CTTC </td> <td> Report </td> <td> Public </td> <td> 30-Jun-18 </td> <td> 03-Jul-18 </td> <td> Submitted </td> <td> The previously delayed deliverable esp. D2.1 and D2.2 brought additional delay. </td> </tr> <tr> <td> MS1 </td> <td> </td> <td> Finalization of the Requirements </td> <td> ST-I </td> <td> </td> <td> </td> <td> 30-Jun-18 </td> <td> 07-Aug-18 </td> <td> Submitted </td> <td> Approxmiate 1 month delay in completion of the milestone was agreed with the PO at that time. </td> </tr> <tr> <td> WP1 </td> <td> D1.3 </td> <td> Year 1 project report and project plan updates </td> <td> SIEMENS </td> <td> Report </td> <td> Public </td> <td> 31-Dec-18 </td> <td> </td> <td> </td> <td> </td> </tr> <tr> <td> WP3 </td> <td> D3.5 </td> <td> Field-level middleware & networking toolbox (first draft) </td> <td> IQUADRAT </td> <td> Other </td> <td> Confidential </td> <td> 31-Dec-18 </td> <td> </td> <td> </td> <td> </td> </tr> <tr> <td> WP2 </td> <td> D2.4 </td> <td> SEMIoTICS high level architecture (first draft) </td> <td> ST-I </td> <td> Report </td> <td> Public </td> <td> 28-Feb-19 </td> <td> </td> <td> </td> <td> </td> </tr> <tr> <td> WP3 </td> <td> D3.1 </td> <td> Software defined programmibilty for IoT devices (first draft) </td> <td> SIEMENS </td> <td> Report </td> <td> Public </td> <td> 28-Feb-19 </td> <td> </td> <td> </td> <td> </td> </tr> <tr> <td> WP3 </td> <td> D3.2 </td> <td> Network Functions Virtualization for IoT (1st draft) </td> <td> CTTC </td> <td> Report </td> <td> Public </td> <td> 28-Feb-19 </td> <td> </td> <td> </td> <td> </td> </tr> <tr> <td> WP3 </td> <td> D3.3 </td> <td> Bootstrapping and interfacing SEMIoTICS field level devices (1st draft) </td> <td> SIEMENS </td> <td> Report </td> <td> Public </td> <td> 28-Feb-19 </td> <td> </td> <td> </td> <td> </td> </tr> <tr> <td> WP3 </td> <td> D3.4 </td> <td> Network-level Semantic Interoperability (first draft) </td> <td> Sphynx </td> <td> Report </td> <td> Public </td> <td> 28-Feb-19 </td> <td> </td> <td> </td> <td> </td> </tr> <tr> <td> WP4 </td> <td> D4.1 </td> <td> SEMIoTICS SPDI Patterns (first draft) </td> <td> Sphynx </td> <td> Report </td> <td> Public </td> <td> 28-Feb-19 </td> <td> </td> <td> </td> <td> </td> </tr> <tr> <td> MS2 </td> <td> </td> <td> First version of SEMIoTICS architecture, End of 1st Field and Network level mechanisms development cycle </td> <td> SIEMENS </td> <td> </td> <td> </td> <td> 28-Feb-19 </td> <td> </td> <td> </td> <td> </td> </tr> <tr> <td> WP4 </td> <td> D4.2 </td> <td> SEMIoTICS Monitoring, prediction and diagnosis mechanisms (first dr </td> <td> a ENG </td> <td> Report </td> <td> Public </td> <td> 31-May-19 </td> <td> </td> <td> </td> <td> </td> </tr> <tr> <td> WP4 </td> <td> D4.3 </td> <td> Embedded Intelligence and local analytics (first draft) </td> <td> ST-I </td> <td> Report </td> <td> Public </td> <td> 31-May-19 </td> <td> </td> <td> </td> <td> </td> </tr> <tr> <td> WP4 </td> <td> D4.4 </td> <td> Semantic interoperability mechanisms for IoT (first draft) </td> <td> FORTH </td> <td> Report </td> <td> Public </td> <td> 31-May-19 </td> <td> </td> <td> </td> <td> </td> </tr> <tr> <td> WP4 </td> <td> D4.5 </td> <td> SEMIoTICS Security and privacy mechanisms (first draft) </td> <td> UNI PASSAU </td> <td> Report </td> <td> Public </td> <td> 31-May-19 </td> <td> </td> <td> </td> <td> </td> </tr> <tr> <td> WP4 </td> <td> D4.6 </td> <td> Implementation of SEMIoTICS BackEnd API (Cycle 1) </td> <td> BlueSoft </td> <td> Other </td> <td> Confidential </td> <td> 31-May-19 </td> <td> </td> <td> </td> <td> </td> </tr> <tr> <td> MS3 </td> <td> </td> <td> End of 1st Pattern-driven smart behavior of IIoT mechanisms development cycle and the 1st backend implementation cycle </td> <td> Sphynx </td> <td> </td> <td> </td> <td> 31-May-19 </td> <td> </td> <td> </td> <td> </td> </tr> <tr> <td> WP1 </td> <td> D1.8 </td> <td> Periodic project plan updates (M18) </td> <td> FORTH </td> <td> Report </td> <td> Confidential </td> <td> 30-Jun-19 </td> <td> </td> <td> </td> <td> </td> </tr> <tr> <td> WP6 </td> <td> D6.2 </td> <td> Interim report on impact creation, dissemination activities </td> <td> CTTC </td> <td> Report </td> <td> Public </td> <td> 30-Jun-19 </td> <td> </td> <td> </td> <td> </td> </tr> <tr> <td> WP6 </td> <td> D6.3 </td> <td> Interim report on exploitation activities (M18) </td> <td> ENG </td> <td> Report </td> <td> Public </td> <td> 30-Jun-19 </td> <td> </td> <td> </td> <td> </td> </tr> <tr> <td> WP6 </td> <td> D6.4 </td> <td> Interim report on standardization activities </td> <td> SIEMENS </td> <td> Report </td> <td> Public </td> <td> 30-Jun-19 </td> <td> </td> <td> </td> <td> </td> </tr> <tr> <td> WP3 </td> <td> D3.6 </td> <td> Field-level middleware & networking toolbox (second draft) </td> <td> IQUADRAT </td> <td> Other </td> <td> Confidential </td> <td> 30-Sep-19 </td> <td> </td> <td> </td> <td> </td> </tr> <tr> <td> WP5 </td> <td> D5.1 </td> <td> SEMIoTICS KPIs and Evaluation Methodology </td> <td> UNI PASSAU </td> <td> Report </td> <td> Public </td> <td> 31-Oct-19 </td> <td> </td> <td> </td> <td> </td> </tr> <tr> <td> WP2 </td> <td> D2.5 </td> <td> SEMIoTICS high level architecture (final) </td> <td> BlueSoft </td> <td> Report </td> <td> Public </td> <td> 30-Nov-19 </td> <td> </td> <td> </td> <td> </td> </tr> <tr> <td> WP4 </td> <td> D4.7 </td> <td> Implementation of SEMIoTICS BackEnd API (Cycle 2) </td> <td> BlueSoft </td> <td> Other </td> <td> Confidential </td> <td> 30-Nov-19 </td> <td> </td> <td> </td> <td> </td> </tr> <tr> <td> WP5 </td> <td> D5.2 </td> <td> Software system integration (Cycle 1) </td> <td> BlueSoft </td> <td> Report </td> <td> Public </td> <td> 30-Nov-19 </td> <td> </td> <td> </td> <td> </td> </tr> <tr> <td> WP5 </td> <td> D5.3 </td> <td> IIoT Infrastructure set-up and testing (Cycle 1) </td> <td> IQUADRAT </td> <td> Report </td> <td> Public </td> <td> 30-Nov-19 </td> <td> </td> <td> </td> <td> </td> </tr> <tr> <td> MS4 </td> <td> </td> <td> Final version of SEMIoTICS architecture, end of 2nd implem.cycle, End of setup and testing 1st cycle, Evaluation methodology defined </td> <td> FORTH </td> <td> </td> <td> </td> <td> 30-Nov-19 </td> <td> </td> <td> </td> <td> </td> </tr> <tr> <td> WP1 </td> <td> D1.4 </td> <td> Year 2 project report and project plan updates </td> <td> SIEMENS </td> <td> Report </td> <td> Public </td> <td> 31-Dec-19 </td> <td> </td> <td> </td> <td> </td> </tr> <tr> <td> WP6 </td> <td> D6.8 </td> <td> Interim report on exploitation activities (M24) </td> <td> ENG </td> <td> Report </td> <td> Public </td> <td> 31-Dec-19 </td> <td> </td> <td> </td> <td> </td> </tr> <tr> <td> WP3 </td> <td> D3.7 </td> <td> Software defined programmability for IoT devices (final) </td> <td> SIEMENS </td> <td> Report </td> <td> Public </td> <td> 29-Feb-20 </td> <td> </td> <td> </td> <td> </td> </tr> <tr> <td> WP3 </td> <td> D3.8 </td> <td> Network Functions Virtualization for IoT (final) </td> <td> CTTC </td> <td> Report </td> <td> Public </td> <td> 29-Feb-20 </td> <td> </td> <td> </td> <td> </td> </tr> <tr> <td> WP3 </td> <td> D3.9 </td> <td> Bootstrapping and interfacing SEMIoTICS field level devices (final) </td> <td> SIEMENS </td> <td> Report </td> <td> Public </td> <td> 29-Feb-20 </td> <td> </td> <td> </td> <td> </td> </tr> <tr> <td> WP3 </td> <td> D3.10 </td> <td> Network-level Semantic Interoperability (final) </td> <td> Sphynx </td> <td> Report </td> <td> Public </td> <td> 29-Feb-20 </td> <td> </td> <td> </td> <td> </td> </tr> <tr> <td> WP3 </td> <td> D3.11 </td> <td> Field-level middleware & networking toolbox (final) </td> <td> IQUADRAT </td> <td> Other </td> <td> Confidential </td> <td> 30-Apr-20 </td> <td> </td> <td> </td> <td> </td> </tr> <tr> <td> WP4 </td> <td> D4.8 </td> <td> SEMIoTICS SPDI Patterns (final) </td> <td> Sphynx </td> <td> Report </td> <td> Public </td> <td> 30-Apr-20 </td> <td> </td> <td> </td> <td> </td> </tr> <tr> <td> WP4 </td> <td> D4.9 </td> <td> SEMIoTICS Monitoring, prediction and diagnosis mechanisms (final) </td> <td> ENG </td> <td> Report </td> <td> Public </td> <td> 30-Apr-20 </td> <td> </td> <td> </td> <td> </td> </tr> <tr> <td> WP4 </td> <td> D4.10 </td> <td> Embedded Intelligence and local analytics (final) </td> <td> ST-I </td> <td> Report </td> <td> Public </td> <td> 30-Apr-20 </td> <td> </td> <td> </td> <td> </td> </tr> <tr> <td> WP4 </td> <td> D4.11 </td> <td> Semantic interoperability mechanisms for IoT (final) </td> <td> FORTH </td> <td> Report </td> <td> Public </td> <td> 30-Apr-20 </td> <td> </td> <td> </td> <td> </td> </tr> <tr> <td> WP4 </td> <td> D4.12 </td> <td> SEMIoTICS Security and privacy mechanisms (final) </td> <td> UNI PASSAU </td> <td> Report </td> <td> Public </td> <td> 30-Apr-20 </td> <td> </td> <td> </td> <td> </td> </tr> <tr> <td> WP1 </td> <td> D1.9 </td> <td> Periodic project plan updates (M30) </td> <td> FORTH </td> <td> Report </td> <td> Confidential </td> <td> 30-Jun-20 </td> <td> </td> <td> </td> <td> </td> </tr> <tr> <td> WP4 </td> <td> D4.13 </td> <td> Implementation of SEMIoTICS BackEnd API (Final Cycle) </td> <td> BlueSoft </td> <td> Other </td> <td> Confidential </td> <td> 30-Jun-20 </td> <td> </td> <td> </td> <td> </td> </tr> <tr> <td> WP6 </td> <td> D6.9 </td> <td> Interim report on exploitation activities (M30) </td> <td> ENG </td> <td> Report </td> <td> Public </td> <td> 30-Jun-20 </td> <td> </td> <td> </td> <td> </td> </tr> <tr> <td> MS5 </td> <td> </td> <td> End of all development and imlementation </td> <td> BlueSoft </td> <td> </td> <td> </td> <td> 30-Jun-20 </td> <td> </td> <td> </td> <td> </td> </tr> <tr> <td> MS8 </td> <td> </td> <td> Feedback from local ethical board on ethical guidlelines </td> <td> ENG </td> <td> </td> <td> </td> <td> 30-Jun-20 </td> <td> </td> <td> </td> <td> </td> </tr> <tr> <td> WP5 </td> <td> D5.4 </td> <td> Demonstration and validation of IWPC-Energy (Cycle 1) </td> <td> SIEMENS </td> <td> Demonstrator </td> <td> Public </td> <td> 31-Jul-20 </td> <td> </td> <td> </td> <td> </td> </tr> <tr> <td> WP5 </td> <td> D5.5 </td> <td> Demonstration and validation of SARA-Health (Cycle 1) </td> <td> ENG </td> <td> Demonstrator </td> <td> Public </td> <td> 31-Jul-20 </td> <td> </td> <td> </td> <td> </td> </tr> <tr> <td> WP5 </td> <td> D5.6 </td> <td> Demonstration and validation of IHES-Generic IoT (Cycle 1) </td> <td> ST-I </td> <td> Demonstrator </td> <td> Public </td> <td> 31-Jul-20 </td> <td> </td> <td> </td> <td> </td> </tr> <tr> <td> WP5 </td> <td> D5.7 </td> <td> Software system integration (Cycle 2) </td> <td> BlueSoft </td> <td> Report </td> <td> Public </td> <td> 31-Aug-20 </td> <td> </td> <td> </td> <td> </td> </tr> <tr> <td> WP5 </td> <td> D5.8 </td> <td> IIoT Infrastructure set-up and testing (Cycle 2) </td> <td> IQUADRAT </td> <td> Report </td> <td> Public </td> <td> 31-Aug-20 </td> <td> </td> <td> </td> <td> </td> </tr> <tr> <td> MS6 </td> <td> </td> <td> End of 2nd setup and testing cycle, End of 1st demonstation cycle </td> <td> IQUADRAT </td> <td> </td> <td> </td> <td> 31-Aug-20 </td> <td> </td> <td> </td> <td> </td> </tr> <tr> <td> WP1 </td> <td> D1.5 </td> <td> Year 3 project report and project plan updates </td> <td> SIEMENS </td> <td> Report </td> <td> Public </td> <td> 31-Dec-20 </td> <td> </td> <td> </td> <td> </td> </tr> <tr> <td> WP1 </td> <td> D1.6 </td> <td> Final project report </td> <td> SIEMENS </td> <td> Report </td> <td> Confidential </td> <td> 31-Dec-20 </td> <td> </td> <td> </td> <td> </td> </tr> <tr> <td> WP5 </td> <td> D5.9 </td> <td> Demonstration and validation of IWPC-Energy (Cycle 2) </td> <td> SIEMENS </td> <td> Demonstrator </td> <td> Public </td> <td> 31-Dec-20 </td> <td> </td> <td> </td> <td> </td> </tr> <tr> <td> WP5 </td> <td> D5.10 </td> <td> Demonstration and validation of SARA-Health (Cycle 2) </td> <td> ENG </td> <td> Demonstrator </td> <td> Public </td> <td> 31-Dec-20 </td> <td> </td> <td> </td> <td> </td> </tr> <tr> <td> WP5 </td> <td> D5.11 </td> <td> Demonstration and validation of IHES-Generic IoT (Cycle 2) </td> <td> ST-I </td> <td> Demonstrator </td> <td> Public </td> <td> 31-Dec-20 </td> <td> </td> <td> </td> <td> </td> </tr> <tr> <td> WP6 </td> <td> D6.5 </td> <td> Final report on impact creation, dissemination activities </td> <td> CTTC </td> <td> Report </td> <td> Public </td> <td> 31-Dec-20 </td> <td> </td> <td> </td> <td> </td> </tr> <tr> <td> WP6 </td> <td> D6.6 </td> <td> Final report on exploitation activities </td> <td> ENG </td> <td> Report </td> <td> Public </td> <td> 31-Dec-20 </td> <td> </td> <td> </td> <td> </td> </tr> <tr> <td> WP6 </td> <td> D6.7 </td> <td> Final report on standardization activities </td> <td> SIEMENS </td> <td> Report </td> <td> Public </td> <td> 31-Dec-20 </td> <td> </td> <td> </td> <td> </td> </tr> <tr> <td> MS7 </td> <td> </td> <td> Completion of Demonstration and Evaluation </td> <td> SIEMENS </td> <td> </td> <td> </td> <td> 31-Dec-20 </td> <td> </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> </table> MS9 Selection and consent of Users ENG 30-Dec-20 TABLE 4B: DELIVERABLE DETAILED TRACKING # INNOVATION MANAGEMENT PLAN (IMP) ## Innovation Management SEMIoTICS has a consortium agreement [2] in place which covers ownership, and access / usage rights of foreground and background IP, during and after project. High-profile academic partners, as well as strong involvement of the IP departments of industry partners will ensure that IP is identified and appropriately protected. The Description of Action (Section 3.2.3) [1] also defines the implementation of Innovation Management in SEMIoTICS as described below: Beyond the advancements to the state of the art in individual technology and scientific areas, the overarching groundbreaking objective of SEMIoTICS is to deliver a framework, available through an open API that will enable and guarantee secure and dependable actuation and intelligent semiautonomous adaptation of IIoT and IoT applications involving heterogeneous smart objects. SEMIoTICS approach is characterized by three pillars: i. SPDI patterns-based development of IIoT/IoT application to ensure SPDI properties [WP4 deliverables] ii. Semi-autonomic behaviour and evolution based on cross-layer embedded intelligence [WP3, WP4 and WP5 deliverables] and iii. Supporting interoperability across heterogeneous IIoT/IoT platforms and vertical domains [WP3 (esp. Task 3.3, Task 3.4), WP4 (esp. Task 4.4) and WP5 deliverables]. which during the course of the project may lead to following innovations: 1. Platform innovation stemming from security driven design foundations empowering SEMIoTICS to become a basis for building secure and trustworthy IoT ecosystems. 2. Application and Systems innovation stemming from the development of a SPDI-centric layered infrastructure involving network, software and device innovations integrated to form the SEMIoTICS framework. 3. Service innovation stemming from the deployment of the SEMIoTICS framework in lab trials, the procedures and applications developed through this process as well as lessons learned from the lab trials of SEMIoTICS. Innovation Management will be effectively performed by the leader of Task 6.2 Exploitation of results with the support of the Technical Project Manager (Scientific and Technical Project Manager (STIPM)) and ultimately of the Project Coordination Committee (PCC). The innovation collection process will be managed by the consortium in the context of relevant WPs and will be integrated in the overall project results. The Task 6.2 leader will assist the WP leaders and the Technical Project Manager in handling all matters concerning Intellectual Property protection for the produced innovations as well as their inclusion in the project’s exploitation plan in D6.1. ### INNOVATION SUPPORTING TASKS AND PROCESSES Driving Research and Innovation is an integral part of SEMIoTICS and deeply anchored inside the work structure. Following tasks and deliverables are directly related to support, enable, or drive innovation in SEMIoTICS (for detailed descriptions of tasks and deliverables see [6]): Task 6.1: Impact Creation and Dissemination Task 6.2: Exploitation of results Task 6.3: Standardization <table> <tr> <th> Related Task </th> <th> Created Output </th> </tr> <tr> <td> Task 2.2: Specification of use case scenarios & applications and their requirements Task 2.3: Specification of infrastructure requirements Task 2.4: SEMIoTICS architecture design </td> <td> D2.3: Requirements specification of SEMIoTICS framework (M6) D2.5: SEMIoTICS high level architecture (M26) </td> </tr> <tr> <td> Task 3.3: Semantics-based bootstrapping & interfacing Task 3.4: Network-level semantic Interoperability Task 3.5: Implementation of Field-level middleware & networking toolbox. </td> <td> D3.9: Bootstrapping and interfacing SEMIoTICS field level devices (final) (M26) D3.10: Network-level Semantic Interoperability (final) (M26) D3.11: Field-level middleware & networking toolbox (final) (M28) </td> </tr> <tr> <td> Task 4.1: Architectural SPDI patterns Task 4.2: Monitoring, prediction and diagnosis Task 4.3: Embedded Intelligence and local analytics Task 4.4: End-to-End Semantic Interoperability Task 4.5: End-to-End Security and Privacy Task 4.6: Implementation of SEMIoTICS backend API </td> <td> D4.8: SEMIoTICS SPDI Patterns (final) (M28) D4.9: SEMIoTICS Monitoring, prediction and diagnosis mechanisms (final) (M28) D4.10: Embedded Intelligence and local analytics (final) (M28) D4.11: Semantic interoperability mechanisms for IoT (final) (M28) D4.12: SEMIoTICS Security and privacy mechanisms (final) (M28) D4.13: Implementation of SEMIoTICS BackEnd API (final) (M30) </td> </tr> <tr> <td> Task 5.3: IIoT Infrastructure set-up and testing Task 5.4: Demonstration and validation of IWPC- Energy scenario Task 5.5: Demonstration and validation of SARAHealth scenario Task 5.6: Demonstration and validation of IHES- Generic IoT scenario </td> <td> D5.4/5.9; D5.5/5.10; D5.6/5.11: Demonstration and Validation of respective usage scenarios (M31/M36) </td> </tr> <tr> <td> Task 6.1: Impact Creation and Dissemination Task 6.2: Exploitation of results Task 6.3: Standardization </td> <td> D6.1: Impact creation, dissemination and exploitation plan (M6) D6.3: Interim report on exploitation activities (M18) D6.4: Interim report on standardization activities (M18) D6.8: Interim report on exploitation activities(M24) D6.9: Interim report on exploitation activities (M30) D6.6: Final report on exploitation activities (M36) D6.7: Final report on standardization activities (M36) </td> </tr> </table> #### TABLE 5: INNOVATION RELATED TASK AND OUTPUT After M28, SEMIoTICS will be in a position to understand both market and technical problems at hand, with a goal of successfully implementing appropriate creative ideas demonstrated by WP3 and WP4. Following classification shows different phases of Innovation management as per (Specht, 2002). _Source: Classification of technology, R &D and innovation management (Specht, 2002) _ Being a RIA project, SEMIoTICS focusses on Technology management in the technical work packages WP2, WP3, WP4 and WP5. In Task 6.2 of WP6, SEMIoTICS consortium plans to come up with leaner business model canvas for SEMIoTICS depicting a new or improved product or service or process from each partner perspective. This may empower SEMIoTICS consortium to respond to an external (e.g. testing Business Models in different scenarios) or internal opportunity (e.g. use SEMIoTICS for internal product development). This will be checked/pursued by the consortium members from time to time during the project or even after the project end. The exact proceedings and decisions will be taken directly in Task 6.2 of WP6. ## Exploitation Management SEMIoTICS presented already a draft exploitation strategy at proposal stage (see Section 2.2.3 in [1]) and elaborations of further details on the exploitation plan are subject to WP6. Each partner delivered a high-level exploitation plan (Section 2.2.3) [1] at proposal stage and these exploitation plans will be detailed further within work of WP6. Task 6.2 will address exploitation of the project results via e.g. commercial and scientific exploitation strategies, plans and implementation. SEMIoTICS will investigate different routes to be used for exploitation (e.g. use for further research, developing and selling own products/services, spin-off activities, and standardization activities/new standards/ongoing procedures) Exploitation of project results will be a topic on the agenda of consortium meetings in order to support exploitation of results on consortium level. Results are documented in Deliverables D6.3, D6.8, D6.9 and D6.6. ## Communication / Dissemination Management All aspects of communication and dissemination management are captured in the Dissemination Plan [5] and will not be covered here. ## Capturing and handling IPR IPR handling is covered in SEMIoTICS consortium agreement [2] (CA) and allows each partner the exploitation of their own results. Also joint IPR is covered by the CA and will ensure proper exploitation of the project results. The SEMIoTICS consortium is composed in a way that each partner has strong expertise in a certain technical/business domain and is responsible for driving this domain forward. This ensures that the essential elements of successful Innovation management are contributed by experts in the field (good research practice by high profile academic partners, technologies, industrial research, business aspects, and industry domain knowledge by leading industry partners). Capturing of IPR is supported by close interaction of technical and exploitation tasks throughout the project. Awareness of proper IPR capturing and handling will be raised by regular sessions on this topic at all SEMIoTICS consortium meetings and PTC meetings. For avoiding additional overhead in the project, PTC meeting is serving as Intellectual Property Rights Committee (IPRC) to deal with intellectual property that either is introduced to the project by a partner or produced as a work package outcome. IPRC will be responsible for the definition of access rights and licensing (if required so) of the project results as guided by section 9.4 of consortium agreement [2] (CA), which provide details on access rights for exploitation especially regarding exploitation of project outcomes as a whole. The table in the annex will give the overview of IPR filed within SEMIoTICS and each partner is responsible to keep that list up to date (see also terms in [2]). Data recorded in that table will also be used to update information for the periodic reports in the EC project management web portal. ## Standards & Regulations SEMIoTICS has a dedicated task on Standardization (Task 6.3) with resources of leading industry partners being allocated. This task will use the “Standardization and Open Source Engagement” namely, AIOTI, W3C, ETSI, IEEE, ISO as baseline and will continuously refine and update involvement of SEMIoTICS. Task 6.3 is also used to monitor progress and activities or related these standardization activities, as well as for creating input for standardization bodies if possible. More details and a summary on related standardization activities will be given in the deliverables D6.4 and D6.7. ## Innovation Assessment Assessment of SEMIoTICS result will be done on three levels. 1. Project level objectives: SEMIoTICS has a clear definition of its objectives (see Section 1.1.2. in [1]). These definitions include a description of the objective and the corresponding measures of success. 2. SEMIoTICS as Research and Innovation Action (RIA) is clearly targeting only lab trials which corresponds to a TRL 4-6 (according to definition by EC) as stated in section 1.3.2 of [1]. 3. Lab Trial evaluation on technical level is foreseen in WP5 and uses defined requirements and KPIs as a result from respective D2.3 and D5.1 deliverable. In addition, SEMIoTICS also establishes an advisory committee during runtime in order to get external views and assessment results of the achievements as well. # DATA MANAGEMENT PLAN (DMP) ## Expected data SEMIoTICS is a three-year project and will produce a number of technical results relevant for IoT networks, specifically in WP5. This includes data created in lab experiments and demos for Wind turbine use case, healthcare use case and Generic IoT use case. Wind energy use case related data used in SEMIoTICS will be related to critical infrastructures data and therefore will not be publicly accessible. For the healthcare use case, there will be no person-related data which will be generated/stored/transferred by that use case. Moreover, based on the GA 29.3 Open access to research data "Not applicable" for SEMIoTICS. Following sections provide detailed description of the specific data sets in 3 different use cases which will be handled only within the consortium and the data will be classified as per use case. ## Data formats and Metadata ### DATA FORMATS Following table gives initial version of the data formats in the consortium- level DMP which will be handled only within the consortium. There are no real- world field trials in SEMIoTICS’s 3 use cases. The data generated in all the 3 use cases will only be in lab-environment. 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> SEMIoTICS_UC1 </td> <td> Test Data </td> <td> Test Data </td> <td> NA </td> <td> confidential </td> <td> _Link_ </td> <td> Ermin Sakic </td> </tr> <tr> <td> SEMIoTICS_UC2 </td> <td> Test Data </td> <td> Test Data </td> <td> NA </td> <td> confidential </td> <td> _Link_ </td> <td> Domenico Presenza </td> </tr> <tr> <td> SEMIoTICS_UC3 </td> <td> Test Data </td> <td> Test Data </td> <td> NA </td> <td> confidential </td> <td> _Link_ </td> <td> Mirko Falchetto </td> </tr> </table> #### TABLE 6: DATA FORMATS Note: This table is linked to file “SEMIoTICS DMP.xlsx” _https://overseer1.erlm.siemens.de/repository/Document/downloadWithName/SEMIoTICS%20DMP.xlsx_ _?reqCode=downloadWithName &id=12740979 _ The following table gives a detailed description of the fields used in the data formats table of Section 5.2.1. <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> The dataset cannot be shared publicly as GA 29.3 - Open access to research data is not applicable to SEMIoTICS </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> _TABLE 7: FIELDS USED IN THE DATA FORMATS _ ### METADATA SEMIoTICS 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. ## Data sharing and access Project related documents (release version of document – not raw format) with dissemination level “public” will be accessible via the project website _http://www.SEMIoTICS-project.eu_ . Registration (free of charge) is required to get access. The dissemination level is initially proposed by the corresponding author and will be reviewed and approved by PTC and 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 ARXIV/OpenAIRE or other means. In case embargo periods (e.g., given by publishers) have to be considered, open access will be given after the embargo period expires. In particular, an open-access policy is foreseen for all scientific publications in the context of SEMIoTICS, which is in-line with the EC open access policy (see: _https://www.openaire.eu/how-do-i-make-my- publicationsopen-access-to-comply-with-the-ec-s-open-access-policy_ ) . ## 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 lifetime. 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. The Data Management Plan is maintained by the Project Coordination Committee (PTC). Although SEMIoTICS is not liable for “Open Access to Research Data” as Article 29.3 is not applicable to the project, PTC members reviews of the DMP are a regular agenda item of PTC meetings, conference calls, and work package (WP) results will be checked with respect to relevant information for the DMP. The sole purpose of the DMP is how to handle the research data within the consortium. WP leads (WP3, WP4 and WP5) 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 DMP is updated as soon as data is created within their WP (for details on the update procedure see Section 7.2). Updates of the tables in DMP are communicated from the Project Technical Committee (PTC) to the PCC together with the minutes of the monthly PTC calls (see also section on update procedures). In order to ensure that this DMP is implemented and followed, reviews (by PCC and/or PTC) 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. # CONCLUSION This deliverable presented the initial quality management plan of SEMIoTICS where the different roles and bodies are presented. Moreover, the different levels of quality controls and assurance are also described. In addition, the different innovation plans regarding the exploitation plans and innovation, standards and the innovation management are also detailed. Furthermore, the data management plan is also provided by analyzing the different expected data, data formats and sharing strategies, Finally, the deliverable provides, in the Annex, links on the different procedures followed and the IPR as well as the PERT diagram with interaction between the Tasks.	https://phaidra.univie.ac.at/o:1140797	Horizon 2020
0206_CHIC_760891.md	# 1 Summary This document outlines the data management strategies that will be implemented throughout the CHIC research data lifecycle. In particular, it describes (i) the type, format and volume of the generated data, (ii) the metadata and documentation provided to make it findable, interoperable and reusable, (iii) the long-term preservation plan, (iv) how data will be shared and licensed for re-use, (v) the resources that need to be allocated to data management, (vi) data storage and back up policies during the active phase of the project, and (vii) the handling of personal data. As stipulated in the Guidelines on FAIR Data Management in Horizon 2020, this DMP will be updated when important changes to the project occur and, at least, as part of the periodic reviews and at the end of the project. The data management plans of the CHIC project and of the NEWCOTIANA project (grant agreement 760331) have been generated in close collaboration between these two projects. Both projects will generate datasets on technical performance, safety assessment, socio-economic and stakeholder interactions related to the use of NPBT for the development of multipurpose crops for molecular farming. Aligning and standardising the data management between these projects will facilitate data reuse and data interoperability. In addition, no reporting and metadata standards are currently available for NPBTs. The CHIC and the NEWCOTIANA projects will together contribute to the development of reporting requirements for datasets related to NPBTs. # 2 CHIC Data Summary CHIC aims to develop new chicory varieties with improved dietary fiber characteristics and improved terpene composition. Additionally, we will address the self-incompatibility which hampers the breeding efforts for this crop. This goal will be achieved by new plant breeding techniques NPBTs. More precisely in CHIC we will develop and apply gene editing approaches all based on the CRISP/Cas technology. We will use stable agrobacteriummediated gene editing, transient gene editing techniques and the application of ribonucleoproteins to edit the genome DNA in the chicory protoplasts. In the CHIC project data to assess the technological performance of these different methods will be collected. Additionally, the data related to the risk assessment of different NPBT techniques used, such as the off-target effects, will be generated. In improved chicory lines data about the dietary fiber and terpene composition and bioactivity will be evaluated. Economic feasibility and socio-economic impact of the newly produced chicory varieties will be evaluated. The data generated will contribute to evidence-based informed decisions on the legal status and regulation of NPBT crops. To accomplish this a series of datasets will be generated: * Improved genome assembly of _C. Intybus_ * RNAseq data on _C. intybus_ * gRNA inventory and gene editing efficiencies * genetic part and construct designs * Dietary fiber characterisation * Terpene characterisation * Data on regulatory networks for secondary metabolite biosynthesis in _C. intybus_ \- Bioactivity data for dietary fiber / terpenes * Phenotypic and agricultural parameters of newly developed _C. intybus_ varieties * Safety assessment, including untargeted effects, of different NPBT applications * Socio-economic impacts * Broader societal impacts - Stakeholder views Table 1 provides a list of research data categories that will be produced in CHIC and the expected data volume for each of them. CHIC will re-use the constructs, RNAseq data and available genome data as well as established protocols for tissue-culture cultivation, protoplast transformation and regeneration of chicory that is available at different partners to maximize the use of resources. Stakeholder views on commercial cultivation and use of GE chicory will be collected in order to clarify possible hurdles and facilitating factors for chicory innovation using GE techniques. Stakeholder views will be collected in the course of document reviews, interviews, questionnaires, workshops and focus groups. Data will be gathered as audio recordings, transcripts, interviews, and workshop notes. Only data gathered in the course of the CHIC project will be used These data will not only serve to meet the objectives of the current project, but will also be useful for stakeholders including the scientific community, plant breeders, farmers, industry, legislators and regulators, and the general public. Thus, the scientific community will benefit from the development of the NPBT techniques for chicory. The improved gene editing and knowledge on the off- target effects can be applied broader for gene editing of (the asteraceous) crops. The project will create added value for chicory farmers, by providing improved dietary fibre yield and quality and terpene yields. Additionally, the work on chicory incompatibility and the development of NPBTs will benefit chicory breeders. Finally, the generated data on the utility, efficiency and safety of NPBTS as well as the generated communication materials will help EU and National legislators and regulators and the general public make informed decisions on the regulation and public acceptance of NPBTs. To enhance the usability of the data, open or otherwise widely-used file formats will be the preferred option for data collection (see Table 1). Formats that are open and/or in widespread use stand the best change to be readable in the future; on the contrary, proprietary formats used only by a particular software are prone to becoming obsolete. In those cases in which the laboratory instrument used to perform the measurement outputs the data in an instrument specific proprietary format, a converted version of the output file to an open data format will be shared together with the original file thus fostering data interoperability. Table 1. CHIC foreseen data types, size and selected file formats. <table> <tr> <th> Research data </th> <th> </th> </tr> <tr> <td> Data Type </td> <td> File Format </td> </tr> <tr> <td> Genome sequence data (raw and processed data) </td> <td> bam, fastq </td> </tr> <tr> <td> DNA parts and constructs </td> <td> genbank, fasta plain text, ASCII (.txt), Ab1 (.ab1), </td> </tr> <tr> <td> qPCR data </td> <td> Raw data, comma-separated values (.csv), text (tab delimited) (.txt) </td> </tr> <tr> <td> RNA-Seq data (raw and processed data) </td> <td> .ffn </td> </tr> <tr> <td> Metabolomics data (raw and processed data) </td> <td> mzML, netCDF (.cdf), comma-separated values (.csv), text (tab delimited) (.txt) </td> </tr> <tr> <td> Images (e.g. microscopy, immunoblots) </td> <td> TIFF (.tiff), png (.png), jpeg (.jpg) </td> </tr> <tr> <td> Tabular data (e.g. ELISA tests, metabolite yield, purity and functionality) </td> <td> comma-separated values (.csv), text (tab delimited) (.txt), MS excel (.xlsx) </td> </tr> <tr> <td> Plant phenotypic data (contained and field conditions) </td> <td> text (tab delimited) (.txt), comma-separated values (.csv), MS excel (.xlsx) </td> </tr> <tr> <td> Plant genotypic descriptions </td> <td> text (tab delimited) (.txt), comma-separated values (.csv), MS excel (.xlsx) </td> </tr> <tr> <td> Stakeholder views : audio recordings, transcripts, interview and workshop notes, questionnaires </td> <td> audio recordings (mp3), MS Word (.docx), MS excel (.xlsx), comma-separated values (.csv). </td> </tr> <tr> <td> Standard operating procedures, protocols </td> <td> pdf (.pdf), MS word (.docx) </td> </tr> <tr> <td> Scientific publications </td> <td> pdf (.pdf), MS word (.docx) </td> </tr> <tr> <td> Project reports </td> <td> pdf (.pdf), MS word (.docx) </td> </tr> </table> # 2\. FAIR Data ## 2.1. Making data findable, including provisions for metadata The provision of adequate metadata (a description of the key attributes and properties of each dataset) is fundamental to enable the finding, understanding and reusability of the data, as well as the validation of research results. Descriptive metadata in particular, aims to provide searchable information that makes data discovery and identification possible. CHIC will adopt the DataCite Metadata Schema, one of the broadest crossdomain standards available, as the basis for dataset description. The minimum set of descriptors established for a CHIC dataset include: Type: a description of the resource. Recommended best practice: use of a controlled vocabulary such as the DCMI Type Vocabulary). * Identifier: a unique string that identifies a resource. Provided by repository where the dataset is stored. Preferred option: digital object identifier (DOI); also accepted URL, URN, Handle, PURL, ARK. * Publication date: date when the data was or will be made publicly available. Format: YYYY-MM-DD * Title: a name by which a resource is known (free text). * Authors: the main researcher(s) involved in producing the data, or the authors of the publication, in priority order and affiliation. Recommended inclusion of a name identifier (e.g. ORCID) Personal name format: family, given. Affiliation format: free text * Description: additional information that does not fit in any of the other categories. Example: publication abstract. Format: open. * Version: the version number of the resource. Format: track major_version.minor_version. Examples: 1.0, 2.1 * Language: primary language of the resource * Rights: information about rights held in and over the resource Values: openAccess, embargoedAccess, restrictedAccess, closedAccess. * Licence: information about the type of licence applying to the dataset * Contributors: institution or person responsible for collecting, managing, distributing, or otherwise contributing to the development of the resource. This property must also be used to allow unique and persistent identification of the funder. Values: European Commission (EU), H2020, Research and Innovation action, CHIC, Grant Agreement Number 760891 . * Subject: subject, keywords, classification code, or key phrase describing the resource (free text). Additionally, metadata elements and documentation providing specific information about the data collection processes, methodology, data analysis procedures, variable definitions, or relationships between the different files of a dataset will be compiled to ensure data interpretability and reusability. These metadata elements will be covered in section 2.3. The relevant metadata categories mentioned above will also be applied for data related to stakeholder interactions. ## 2.2. Making data openly accessible CHIC project results will be made openly accessible provided that open publication does not interfere with the obligation to protect and exploit the results or the protection of personal data. Regarding protection of results, to ensure that dissemination of the CHIC research outputs does not jeopardize their exploitation potential, project results will be subject to evaluation prior to any dissemination activity. CHIC IPR management and dissemination strategies are described in document D7.1 – PEDR. Results approved for dissemination will be made accessible through a variety of channels including project webpage (www.chicproject.com) social-media, scientific conferences, scientific publications in peer-reviewed journals, and data repositories, among others. Regarding the protection of personal data, stakeholder views will be either audio recorded or documented in writing as interview or workshop notes. A restricted access policy will be implemented for stakeholder consultation data in order to insure confidentiality of personal data. These raw data will be only be handled and analysed by the teams conducting the respective research tasks. Summaries of stakeholder views will be presented in project reports which will be made publicly available on the project website and in open- access repositories. In these reports stakeholder views will be presented in a pseudonymised way. No reference will be made to individual stakeholder representatives or individual stakeholder organisations. Being part of the Open Research Data Pilot (ORDP), the CHIC consortium is committed to provide Open Access (free-of-charge access) to all scientific publications and associated research data. The Open Access policy implementation is described in D7.1 – PEDR. Open Access (OA) to CHIC peer reviewed scientific publications will be mostly granted through "Gold" OA, although "Green" OA will be also be considered if "Gold" OA is not provided by the selected journal. Final versions of articles accepted for publication and their associated metadata (see section 2.1 and below) will be deposited in Zenodo, an interdisciplinary open data repository service created through the European Commission’s OpenAIRE project and hosted at CERN, and will be made openly accessible at the time of publication ("Gold" OA) or with a maximum of 6 months embargo (for "Green" OA). Zenodo is compliant with the FAIR principles: it assigns a DOI to each deposited object, supports DOI versioning, is compliant with the DataCite Metadata Schema, is searchable, provides clear and flexible licensing, and provides secure back-up (see section 4). In addition to the scientific publication, OA will also be provided to the research data required to validate the published results. Although Zenodo allows the deposit of data as well as publications, the use of disciplinespecific repositories is often a more convenient option since (i) they have been developed to cover the subject specific needs and (ii) being widely used by the community, facilitate integration with other datasets. At present time, several discipline-specific repositories are under consideration for the deposit of CHIC datasets. These include: * Metabolights: a metabolomics cross-platform and cross-species repository maintained by the European Bioinformatics Institute (EMBL-EBI). Metabolights supports the Core Information for Metabolomics Reporting (CIMR) metadata standard and submission of datasets follows the ISA-Tab format, a general purpose framework with which to collect and communicate complex metadata used by a growing number a repositories and publishers. * Gene Expression Omnibus (GEO), Sequence Read Archive (SRA): two public data repositories at the US National Center for Biotechnology Information (NCBI) suitable for the deposit of RNA-Seq data (GEO) and highthroughput sequencing data (SRA) which are compliant with the Minimum Information about a highthroughput SEQuencing Experiment (MINSEQE) standard. All data deposited in a discipline-specific repository will also have a record in Zenodo for the associated publication with a link to the externally deposited data files. Additionally, Zenodo will be the repository of choice for those data types for which a disciplinary repository is not available. The deposited dataset will include all the information needed to interpret and re- use the data following reporting standards when available (see section 2.3). These will include: publication file, raw and processed data files (in open or widely used formats), detailed protocols with information on instruments and settings used, a codebook for the variables used, and a readme file describing the files that compose the dataset and the relation between them. As already mentioned, open or widely used file formats that can be accessed with open software (or software that is in widespread use) will be the preferred option for data collection. When the use of proprietary formats is necessary, the name and version of the software used to generate the file will be indicated in a readme.txt file included in the dataset. All data deposited in a repository will be made openly accessible under no access restrictions other than the embargo period for "Green" OA publications mentioned above. ## 2.3. Making data interoperable Promoting data exchange and integration to its full potential requires the use of standardised data formats, metadata elements, and ontologies that ensure the reusability of the underlying data. As discussed in section 1, open or otherwise widely used file formats will be used to collect and share the data derived from CHIC research activities, thus facilitating data retrieval and analysis by other users. With regard to metadata, likewise discipline-specific repositories, discipline specific metadata schemes broadly accepted by the scientific community should be the preferred alternative since they have been developed to cover subject specific needs. Accordingly, disciplinary repositories often show compliance with such specific metadata standards in combination with (recommended) controlled vocabularies. Metadata standards and ontologies that will be used to document datasets generated within the CHIC project include: * Core Information for Metabolomics Reporting (CIMR) (metabolomics data) * Minimum Information about a high-throughput SEQuencing Experiment (MINSEQE) (RNA-Seq and genome sequence data) * Minimum Information about a Plant Phenotyping Experiment (MIAPPE) (plant phenotypic data) * Minimum Information about a Proteomics Experiment (MIAPE) (protein mass spectrometry data) * Minimum Information for Publication of Quantitative Real-Time PCR Experiments (MIQE) (qPCR data) • Data Documentation Initiative (DDI) (survey data) * Plant Ontology * Gene ontology * OBI ontology * NCBI taxonomy There is currently no available reporting standard for CRISPR experiment metadata. To cover this need, the NIST Genome Editing Consortium works on the development of suggested minimal information reporting for public studies and the generation of a common lexicon for genome editing. CHIC will follow the progress of the Genome Editing Consortium on the development of a standard CRISPR metadata. At the same time, NEWCOTIANA and CHIC partners have initiated a common dialogue to define the metadata elements that should be collected for each genome editing experiment in order to facilitate sharing, validation, and interpretability of the results. A first draft metadata checklist (see Annex 1) covering the whole genome editing workflow has been assembled as a result of this work. This draft will continue to be refined in future working discussions involving both projects. ## 2.4. Increase data re-use (through clarifying licences) Re-use is one of the pillars of FAIR data. Data re-use increases the impact and visibility of research, maximises transparency and accountability, promotes the improvement and validation of research methods, stimulates innovation through new data uses, and saves resources avoiding unnecessary replications. For data to be reusable it should be in an open or widely-used file format, well described with rich metadata that meet domainrelevant community standards, and released under a clear data usage licence. The way CHIC will approach the first two points has already been discussed in section 1 (file formats) and sections 2.1 and 2.3 (metadata). Regarding licensing, as a default standard CHIC will share scientific publications and the associated research data under a Creative Commons Attribution Licence CC-BY whenever possible. CC-BY does not impose any restriction on access and reuse of the data; it allows users to copy, distribute, transmit, adapt and make commercial use of the data with the sole condition that the creator is appropriately credited. Most data repositories as well as most open access and hybrid publishers support the use of CC-BY licence. Data quality check is the responsibility of the partners involved in the generation of the dataset and will be supported by a peer-review process at publication. Should errors be detected in already published data, these will be corrected and adequately documented in a new version of the dataset. # 4\. Allocation of resources Adequate data management is an integral part of good research practice and as such it concerns every person involved in the research process. All CHIC partners have agreed to the general guidelines set up in this DMP and it is the responsibility of the group leaders to ensure that they are known and implemented by all members of their research group. For each dataset, the partner that generates the data is accountable for registering and storing all data and metadata according to the guidelines of this DMP, applying adequate back up policies, and sharing all public data through the selected open access repository. The project coordinator is in addition responsible for the maintenance of the project website and the Sharepoint hosting service (see Section 4) for the sharing and storing of CHIC main documents during the active phase of the project. As indicated in section 2.2, "Gold" OA publication will be chosen as the preferred publication option. Article processing charges for OA publishing were budgeted at the proposal stage and will be covered by the main partner of the publication out of their allocated funds. The estimated costs of applying open access publication is 2500€. It is not possible at this stage to determine the number of publications that will be produced. Resources for data storage and back up during the active phase of the project will be provided by the respective partner’s institutions (costs included in standard indirect costs). No direct costs for data sharing and long term preservation are anticipated given that all the considered data repositories are free of charge. # 5\. Data security All CHIC partners have adequate storage capability and back up policies at their respective institutions that guarantee the safe storage of the generated research data during the active phase of the project. Additionally, a variety of platforms are being used for internal data sharing, which also serve to the purpose of backup storage. All project documents (grant and consortium agreements, deliverables, meeting minutes, project reports and presentations, scientific manuscripts) are stored in a shared folder in Sharepoint, an Wageningen University and Research hosted sharing platform administered by WR that supports control access back-up and file version control. Sustainable long-term preservation of the data beyond project completion is guaranteed by the use of trustworthy repositories such as Zenodo. Zenodo accessibility principles guarantee that deposited data and metadata will be retained for the lifetime of the repository, which is currently the lifetime of the host laboratory CERN, with an experimental programme defined for the next 20 years at least. Data files and metadata are backed up nightly and replicated into multiple copies in the online system ensuring file preservation. Finally, to preserve data authenticity and integrity, all files are stored along with a MD5 checksum of the file content and are regularly checked against their checksums to assure that file content remains constant. Audio recordings and written notes of stakeholder views, as well as internal reports will be stored on passwordprotected servers only accessible for the partner conducting the research tasks. # 6\. Ethical aspects In order to comply with the EU General Data Protection Regulation (GDPR) stakeholders participating in the project will be informed about the purpose, method, storage, processing, and publication of personal stakeholder data and data containing stakeholder views and asked for their permission. Stakeholder data collected in the course of the CHIC project will not include any sensitive personal data in the meaning of the GDPR. In publicly available reports stakeholder views will be presented in a anonymized way. There are no other ethical or legal issues relevant to data sharing of stakeholder data nor on the ethics Deliverables.	https://phaidra.univie.ac.at/o:1140797	Horizon 2020
0209_CARBAFIN_761030.md	# 2.2 Making data openly accessible Underlying data of scientific publications produced in the project will be made openly available as the default. However, we will still keep the possibility to partially opt-out for the individual datasets. Datasets from life cycle assessment and economic analysis cannot be shared (or need to be shared under restrictions) as they have a major impact on development of business plans for our industrial beneficiaries. The underlying data of scientific publications and associated metadata will be made accessible by deposition in the research data and publication repository Zenodo. Zenodo is a certified repository which supports open access but also enables closed access. Access to datasets shared under restriction will be discussed in more detail during the second data management plan. Zenodo accepts data under a variety of licenses in order to be inclusive. Software tools that can read CSV files (spreadsheet) and SCF files (DNA sequence viewer) are needed to access our data. We follow the file format guide currently supported by the Sequence Read Archives (SRA) at NCBI, EBI, and DDBJ for gene and protein sequence format. Therefore, documentation about the software is not needed to access the data included. # 2.3 Making data interoperable The underlying data of scientific publications produced in our project will be interoperable, that is allowing data exchange and re-use between researchers, institutions, organisations, countries, etc. The data will adhere to standards for formats, as much as possible compliant with available (open) software applications. According to the DCC homepage ( _http://www.dcc.ac.uk/resources/metadata- standards_ ) we will follow data and metadata vocabularies, standards or methodologies from Biology (in particular from Synthetic Biology, Molecular Biology, Biochemistry, Biotechnology and Bioprocess engineering) to make our data interoperable. We will use the STRENDA Guidelines, registered in _FAIRsharing.org_ , as reference for metadata and standards within our discipline ( _http://www.beilstein-institut.de/en/projects/strenda/guidelines_ ). _FAIRsharing.org_ is a web portal that collects interrelated data standards, databases, and policies in the life, environmental and biomedical sciences. We will be using standard vocabularies for all data types present in our datasets, to allow interdisciplinary interoperability. In case it is unavoidable that we use uncommon or generate project specific vocabularies, we will provide mappings to more commonly used ontologies. # 2.4 Increase data re-use (through clarifying licences) To permit the widest re-use possible, we will license the data by Creative Commons Attribution CC-BY 4.0. CC-BY 4.0 permits unrestricted use, distribution and reproduction in any medium provided that the original document is properly cited. It is a machine readable license available free of charge from _creativecommons.org_ . The underlying data of scientific publications will be made available for re- use once the publication is accepted. Zenodo offers the function “Reserve DOI”, so we can already use the right DOI when writing the publication. A text field will display the DOI that our record will have once it is published. This will not register the DOI yet, nor will it publish our record. Next to open access publications, Zenodo offers the possibility to upload embargoed, restricted or closed access publications. When we publish with “green” open access we will do self-archiving of the publication in Zenodo. We will consider embargo periods imposed by the Journal and link it to our publications. According to Zenodo the data remains re-usable forever. Our data is stored in CERN Data Center. Both data files and metadata are kept in multiple online and independent replicas. CERN has considerable knowledge and experience in building and operating large scale digital repositories and a commitment to maintain this data centre to collect and store 100s of PBs of LHC data as it grows over the next 20 years. In the highly unlikely event that Zenodo will have to close operations, they guarantee that they will migrate all content to other suitable repositories, and since all uploads have DOIs, all citations and links to Zenodo resources (such as our data) will not be affected. According to the statement above the underlying data of scientific publications produced in our project will be usable by third parties also after the end of our project using CC-BY 4.0 # Allocation of resources According to Zenodo`s Terms-of-Use, content may be uploaded free of charge by those without ready access to an organized data centre. As we do not have an organized data centre available within our consortium we assume that the costs for making data FAIR in our project are limited to the costs for open access publishing (gold open access). Anyway, costs for open access publishing as well as costs related to open access to research data are eligible for reimbursement during the duration of the project as part of the Horizon 2020 grant. The project manager together with the General Assembly members will be responsible for data management in our project. The resources for long term preservation are going to be discussed for the second data management plan. Discussion will include questions on costs and potential value, who decides and how what data will be kept and for how long. # Data security We will follow provisions of _help.zenodo.org/features_ for data security (including data recovery as well as secure storage and transfer of sensitive data). At Zenodo the research output is stored safely for the future in the same cloud infrastructure as research data from CERN's Large Hadron Collider. They are using CERN's battle-tested repository software Invenio, which is used by some of the world's largest repositories such as INSPIRE HEP and CERN Document Server. The underlying data of scientific publications as well as the publication itself will be safely stored in the certified research data repository Zenodo for long term preservation and curation. # Ethical aspects Concerning underlying data of scientific publications we do not see any ethical or legal issues that can have an impact on data sharing. For ethics reviews see Deliverables D8.1 and D8.2. If we do questionnaires dealing with personal data we will include an informed consent for data sharing and long term preservation. # Other issues We do not make use of other national/funder/sectorial/departmental procedures for data management at the moment. Literature 1. Gardossi, L., Poulsen, P.B., Ballesteros, A., Hult, K., Švedas, V.K., Vasić-Rački, Đ., Carrea, G., Magnusson, A., Schmid, A., Wohlgemuth, R., and Halling, P.J. (2010) Guidelines for reporting of biocatalytic reactions. _Trends in Biotechnology_ , 28 (4): 171-180. 2. Tipton, K.F., Armstrong, R.N., Bakker, B.M., Bairoch, A., Cornish-Bowden, A., Halling, P.J., Hofmeyr, J.-H., Leyh, T.S., Kettner, C., Raushel, F.M., Rohwer, J., Schomburg, D., and Steinbeck, C. (2014) Standards for Reporting Enzyme Data: The STRENDA Consortium: What it aims to do and why it should be helpful. _Perspectives in Science_ , 1 (1): 131-137. 3. _STRENDA GUIDELINES - LIST LEVEL 1A. Data required for a complete Description of an Experiment._ 2016, doi:10.3762/strenda.17 4. _STRENDA GUIDELINES LIST LEVEL 1B. Description of Enzyme Activity Data._ 2016, doi:10.3762/strenda.27	https://phaidra.univie.ac.at/o:1140797	Horizon 2020
0212_XILforEV_824333.md	# EXECUTIVE SUMMARY This Deliverable describes the technical and organisational measures to be implemented within the XILforEV project for the data management of the project results and assets. These results and assets are subject of both internal access by the consortium participants and open access in selected cases. The document introduces general information about relevant data, standards, and quality assurance methods. Detailed data specifications are formulated for four use cases, which are being investigated in the XILforEV project. Particular attention is given to procedures for data archiving and preservation as well as data repository. _Attainment of the objectives and explanation of deviations:_ The project works related to this Deliverable are being carried out in full compliance with the XILforEV objectives. There are no deviations from the actions set in the Grant Agreement. KEYWORDS: Data management, Open Access, Research data. # GENERAL INFORMATION ## Background The Data Management Plan, established in the XILforEV project, is based on internal practices of the consortium organisations and also uses outcomes of relevant measures, which are efficiently realised by the consortium coordinator (TUIL) in previous Horizon 2020 projects EVE 1 and ITEAM 2 . The objectives of the data management are: * Realise a proper access to project results for both consortium participants and target audiences outside of the consortium; * Ensure easy public search and access to publications, which are directly arising from the research funded by the EU; * Allow reuse of research results, produced in the project, to enhance value of the project outcomes to all potential stakeholders; • Avoid unnecessary duplication of research activities; * Guarantee transparency of research process. ## Data Categories The research and development works of the project will produce three general categories of analytical and experimental data: * Publishable raw data; * Publishable analysed data; * Data not selected for publication. ### Publishable Raw Data The digitised data of parameters, which are recorded during the experiments, are allocated to the category of publishable raw data. The recorded parameters can include: * Signals of sensors installed on test rigs, driving simulators and vehicle systems used in experiments; * Data describing the testing environments, i.e. ambient temperature and moisture; * State signals from communication and measuring devices used on test equipment. The publishable raw data are stored in a digital format, which is determined by corresponding data processing and acquisition systems. For instance, most common data formats in this case are MATLAB data files and Microsoft Excel spreadsheet files. The stored data will be supplemented with “readme” file describing the raw content and the procedures of collecting raw data. ### Publishable Analysed Data The publishable analysed data may include figures, tables, charts, video recordings and other relevant visual objects, which are created during the processing and analysis of raw data. These data can be used for the project reporting as well as for publications, presentations and other related dissemination and communication activities. ### Data not Selected for Publication Some raw and analysed data can be tagged by the consortium as unpublished. First of all, such a tagging can be applied in the cases predefined by IP/IPR management procedures. Another tagging case is when the data have intermediate character and used for preliminary works. Nevertheless, the data from this category will be screened for quality and available upon request for potential external users. ## Relevant Regulatory Documents The consortium uses instructions and recommendations from corresponding regulatory documents to fulfil data management cycle during the project life. It concerns detailing the character of data generated in the project and linked metadata, exploitation, sharing, curation and preservation of these data. All these actions are being performed in a strong compliance with the following documents: * ISO/IEC JTC 1/SC 32 - Data management and interchange; * ISO 9001:2008 - Quality management systems; * ISO 27001:2013 - Information Security Management Systems; * Regulation (EU) 2016/679 of the European Parliament and of the Council of 27 April 2016 on the protection of individuals with regard to the processing of personal data and on the free movement of such data. ## Quality Assurance and Control The quality assurance and control measures, related to the data management, are under responsibility of all project beneficiaries and includes supervision of the project coordinator. The relevant measures cover three groups identified next. _Measures for quality assurance before data collection: _ * Definition of the standard(s) for measurements and recording prior to the data collection; * Definition of the digital format for the data to be collected; * Specification of units of measurement; * Definition of required metadata; * Assignment of responsibility to a person over quality assurance for each test series; * Design of Experiments (DoE) for each test series; * Design of a data storage system with sufficient performance; * Design of a purpose-built database structure for data organization. _Measures for quality assurance and control during data collection and entry: _ * Calibration of sensors, measuring devices and other relevant instruments to check the precision, bias and scale of measurements; * Taking multiple measurements and observations in accordance with the established DoE; * Setting up validation rules and input masks in data entry software; * Unambiguous labelling of variable and record names; * Implementation of double entry rule – ensuring that two persons, performing the tests, can independently enter the data; * Use of reference mechanisms (a relational database) to minimize the number of times the data need to be entered. _Measures for quality control during data checking: _ * Documentation of any modifications to the dataset to avoid duplicate error checking; * Checking the dataset for missing or irregular data entries to ensure the data completeness; * Performing statistical summaries with checking for outliers by using graphical methods as probability and regression plots, scatterplots et al.; * Verifying random samples of the digital data against the original data; * Ensuring the data peer review both by scientific and technical criteria. # CONTENT OF DATA SETS Next sections specifies the content of expected data sets for each use case defined in the XILforEV project description. ## Use Case 1 – Brake Blending Data sets: * Documentation to testing environment, incl. specification of software, hardware and communication components; * Documentation to the hardware-in-the-loop brake test rig, the powertrain test rig, the brake dynamometer, and the brake robot; * Vehicle dynamics model incl. a full vehicle, multi-body dynamics model of the target vehicle and real-time versions of the models for the use in dSPACE environment; * Programme code of brake blending controller and related control functions; * Results of validation and testing of models and brake blending controller in XILforEV testing environment, incl. recorded signals of sensors and measurement techniques on test rigs / setups. Features of data sets: * Online storage on the project SharePoint server; * Data collected by the operators of test rigs / setups; * No personal data and no ethical issues are identified; * Use in documentation, reports and publications; * Selected parts of data sets can be shared with external partners or provided for open access; * Preservation in the archive. ## Use Case 2 – Ride Blending Data sets: * Documentation to testing environment, incl. specification of software, hardware and communication components; * Documentation to the driving simulator and suspension test rig; * Vehicle dynamics model incl. a full vehicle, multi-body dynamics model of the target vehicle, tyre model, and real-time versions of the models for the use in dSPACE environment; * Programme code of ride blending controller and related control functions; * Results of validation and testing of models and ride blending controller in XILforEV testing environment, incl. recorded signals of sensors and measurement techniques on test rigs / setups. Features of data sets: * Online storage on the project SharePoint server; * Data collected by the operators of test rigs / setups; * Ethical issues can be related to the test persons operating the driving simulator that will be handled in accordance with the procedures stated in the project Deliverables 8.1-8.3; * Use in documentation, reports and publications; * Selected parts of data sets can be shared with external partners or provided for open access; * Preservation in the archive. ## Use Case 3 – Integrated Chassis Control Data sets: * Documentation to testing environment, incl. specification of software, hardware and communication components; * Documentation to the driving simulator, hardware-in-the-loop brake test rig, the powertrain test rig, and suspension test rig; * Vehicle dynamics model incl. a full vehicle, multi-body dynamics model of the target vehicle, tyre model, and real-time versions of the models for the use in dSPACE environment; * Programme code of integrated chassis controller and related control functions; * Results of validation and testing of models and integrated chassis controller in XILforEV testing environment, incl. recorded signals of sensors and measurement techniques on test rigs / setups. Features of data sets: * Online storage on the project SharePoint server; * Data collected by the operators of test rigs / setups; * Ethical issues can be related to the test persons operating the driving simulator that will be handled in accordance with the procedures stated in the project Deliverables 8.1-8.3; * Use in documentation, reports and publications; * Selected parts of data sets can be shared with external partners or provided for open access; * Preservation in the archive. ## Use Case 4 – Fail-safe and Robustness Study Data sets: * Documentation to testing environment, incl. specification of software, hardware and communication components; * Documentation to the powertrain and chassis component test rigs; * Models of vehicle subsystems and operational environments, and real-time versions of the models for the use in dSPACE environment; * Programme code of fail-safe controllers of powertrain and chassis subsystems as well as related control functions; * Results of validation and testing of models and fail-safe controllers in XILforEV testing environment, incl. recorded signals of sensors and measurement techniques on test rigs / setups. Features of data sets: * Online storage on the project SharePoint server; * Data collected by the operators of test rigs / setups; * No personal data and no ethical issues are identified; * Use in documentation, reports and publications; * Selected parts of data sets can be shared with external partners or provided for open access; * Preservation in the archive. # HANDLING OF DATA SETS ## Archiving and Preservation All data sets will be centrally stored on the secured server established by the project coordinator TUIL. This server is linked to the project webpage under the address _HTTPS://SHAREPOINT.TU-ILMENAU.DE/WEBSITES/KFT/PROJEKTE/XILFOREV/_ , Figure 1. It can be seen on Figure 1 the corresponding folders “Use Case #”, where the corresponding data sets are being stored. Figure 1 – Screenshot of the secured consortium area of the XILforEV project. The access to this secure consortium area is organized for the persons involved in the project through individual login names and passwords. The login names and passwords are issued by the secure server administrator from TUIL. The data sets will be stored on this server at least for five years following the project end date. The access for this time frame will be ensured for all registered users as well. The consortium has no restrictions for participating beneficiaries to keep archives of data sets on their institutional servers under conditions that all required data management actions will be properly handled. ## Data Sharing The knowledge sharing outside of the consortium will be realized through two main instruments: * The consortium will define a set of documents and reports with the analysis of the project results and assets that will be available for open access on the project website. Most of the project presentations delivered on professional events will be also published on the website for free download. * The consortium will aim at granting free access for all the scientific publication that will be prepared during the project activities. The planned publications will be subjected to the “green” open access model. In addition to this, presentation of program activities and relative results will be published on the consortium website. The publishable and analysed raw data can be reused upon request in exchange for authorship and/or establishment of a formal collaboration. Figure 2 – Screenshot of the XILforEV webpage with the publishable project results. The data with open access will be available through two channels: the project webpage and the project area on the ResearchGate portal. The data on the project webpage are available through the link < _HTTPS://XIL.CLOUD/RESULTS/_ > , Figure 2. The project area on the ResearchGate portal is created and accessible through the link < _HTTPS://WWW.RESEARCHGATE.NET/PROJECT/XILFOREV_ > , Figure 3. Figure 3 – Screenshot of the XILforEV project area on the ResearchGate portal. Individual consortium participants can share any type of data linked to the project, such as articles, conference papers, presentations, posters et al. Specifically for the XILforEV project area on the ResearchGate portal, there is the possibility to preserve the data by means of the DOI codification. In the “Digital Object Identifier” field it is either possible to assign a new DOI, automatically generated by the ResearchGate service, or to use the original code in order to allow the other users to easily and unambiguously cite the uploaded file. # CLOSING REMARKS The presented document regarding the data management plan in the XILforEV project has a living character and will be regularly updated to address eventual emergence of new data sets, changes in regulatory guidelines and other relevant issues. In the case of changes, the content of the data management plan will be adapted, and the applied actions will be reported in project dissemination reports (Deliverables 6.3 and 6.4).	https://phaidra.univie.ac.at/o:1140797	Horizon 2020
0214_SERENA_767561.md	# Executive Summary The SERENA project participates in the ORDP. As such, the current document describes the initial version of its Data Management Plan as this was developed through the first period M01-M06 of the project. The deliverable outlines the handling of research data that will be generated during and after the lifetime of SERENA . The possible ways of archiving and management of the data through available web-based platforms will be investigated. Furthermore, online databases for storing research data have been examined and the most suitable was selected to be used both by the consortium partners as well as from interested people/organizations from outside the project. <table> <tr> <th> 1 </th> <th> Introduction </th> </tr> </table> Computer applications have multiple data sources defined depending on the supported functionalities and their purpose. Source data constitute a valuable source of information. Data sources can be a database, a dataset, a spreadsheet or even hardcoded data. Although raw data, often mentioned as source data, have the potential to become information, meaning useful digital information for a specific application and purpose, it requires selective extraction, organization, analysis and formatting for presentation. Once processed data may reveal valuable information and characteristics of their origin or even enable certain predictive analytics forecasting, for example, future trends. Thus, it becomes clear that the acquisition, preservation and proper management of data may enable more efficient data-driven decision making approaches for companies, forecasting, analysis of their current practices, and identification of potential bottlenecks as well as the verification of scientific and commercial published research results. SERENA tackles with the acquisition of raw machine/sensor data and their analysis towards enabling predictive analytics aiming towards forecasting potential failures of the equipment. Such identification may result in appropriate predictive maintenance operations to take place, while an early failure identification may additionally result in a more effective scheduling of the production operation with respect to a predictive maintenance plan, thus, reducing the overall production cost. During the lifetime of the SERENA project, various types of raw data will be generated through the different pilot cases. These data will contain both machine and sensor data. In addition, datasets will be generated through the intermediate processing steps of the SERENA systems such as KPIs for machine’s condition evaluation and/or training datasets for the machine learning algorithms. ## 1.1 Purpose of the DMP A DMP typically contains information on how data are created outlining the steps for sharing and preserving them. In the context of the H2020, a DMP details what kind of data will the project generate, whether and how they will be exploited or made accessible for verification and reuse and how they will be managed and preserved [1]. This particular document has been created in order to present and analyze the first steps towards the creation of the SERENA project DMP. An investigation of the data needed for the various developed sub-systems within the project is ongoing and their formats and prerequisites are under examination. In addition, the deliverable focuses on the available web-based solutions for archiving, accessing and preserving Project’s data made publicly available. At this point, it should be stated that the data to be made available to the public audience will be first examined for confidentiality issues and if possible made anonymous. ## 1.2 Objectives and tasks of WP7 WP7 aims at the creation of impact through the dissemination of the project results as widely as possible making them known to all relevant stakeholders, maximizing at the same time the exploitation of the project’s results to the benefit of the SERENA partners. WP7 is appropriately structured into tasks that focus on achieving the above objectives: * Task 7.1: is the task that focuses on the establishment of the project’s web portal intended for the communication with the public, in order to effectively disseminate the project’s results. * Task 7.2: is the task that obtains the activities concerning the dissemination of projects results to scientific community and industry. * Task 7.3: focuses on the exploitation of the project’s results with respect to the background and foreground IPR policies and the respective articles of the Grant Agreement. The consortium of the SERENA project acknowledges that impact may be created through knowledge circulation and innovation. Making data publicly available is recognized by the members of the consortium as well as by the European Commission as an effective approach towards innovation in the public and private sectors. As a result, an approach for the DMP of the SERENA system that will be introduced and developed during the project, is presented in the following sections. The confidentiality of the data will be examined too, as well as the prerequisites for archiving, making them anonymous and preserving them. ## 1.3 Background of the DMP The DMP specifications are governed by the “Open access to research data” article (article 29.2) of the AGA [2]. As such, the guidelines and rules are defined on open access to scientific peer-reviewed publications and research data that all beneficiaries have to follow in projects funded or co-funded under Horizon 2020 programme. In the context of research and innovation, OA includes providing online access to scientific information free of charge and reusable [3]. Scientific information can be: 1. Research data, meaning data used in publications, curated data and/or raw machine/sensor data. 2. Peer-reviewed scientific articles which have been published in a journal. 1.3.1 Open access to peer-reviewed journal Open access provided by journals is called “gold” open access while open access delivered by repositories is called “green” open access. Both terms are used by the OA community focused on how OA is implemented. Gold stands for publications made available directly from the publisher while Green means that a version is available somewhere else, such as a repository. However, there are several dimensions in OA including the following:  Rea der rights * Reuse rights * Copyrights * Author posting rights * Machine readability * Publishing costs * Peer review In both cases, open access to publications and/or research data is a decision of the grant beneficiaries and not an obligation. The main points towards ensuring OA to research data and publications in the context of the SERENA project is Figure 1: SERENA OA approach for data sets and publications illustrated in Figure 1, which has been adapted from [3]. 1.3.2 Open access to research data Apart from publishing to an open access journal, self-archiving to an institutional repository such as INDIGO [4], or a repository supported by the EC, such as ZENODO [5], or other like the re3data repository [6], could be an option towards making something publicly available. In fact, making data publicly available is more related to making science open, which may enable the following benefits: 1. Effective scientific practices include a level communicating the evidence and validating the results. 2. Open data practices have enabled breakthroughs in certain areas of research such as crystallography, Earth observation, DNA sequencing, AI, especially when data could be reused. 3. As a result, open data may accelerate discovery through the reuse of data from the academic system and others. <table> <tr> <th> 2 </th> <th> Guiding principles </th> </tr> </table> This deliverable is a living document, which will be updated regularly during the lifetime of the project. The intention of the DMP is to describe numerical models and/or datasets collected or created within SERENA during the runtime of the project following the guiding principles of Annex 1 as well as of the FAIR original policies [7]. Due to the fact that the project started in October 2017, there is no dataset generated or collected by the time of the compilation of this deliverable. The datasets to be made publicly available will deliver information considering the following: * Dataset reference and name : Identifier for the data set to be produced. In order to be able to identify and distinguish each data set, unique object identifiers will be assigned. * Dataset description : Descriptions of the data that will be generated or collected, the description element includes its types (text, spreadsheets, software, models, images, movies, audio, etc.), source (human observation, laboratory, field instruments, experiments, simulations, compilations, etc.), volume (volume of data, number of files, etc.), data and file formats (non-proprietary formats, used within community). * Standards and metadata : Reference to existing suitable standards of the discipline, such as Dublin Core. If these do not exist, an outline on how and what metadata will be created. Metadata helps to categorize, understand and interpret data and may provide details about experimental setup as well as facilitate identification and discovery of new data. Metadata also tunes the data that is suggested to users. * 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 wide 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): 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. The information listed above reflects the current concept and design of the individual work packages. The information follows a specific template and will be updated by the project-partners responsible for the different datasets to be created. With respect to the FAIR data principles [8], an initial version of a dataset template to be used for making data FAIR in an automatic approach is included in Appendix A: XML template, while a description of each field is provided in the following section. <table> <tr> <th> 3 </th> <th> Data Management related to SERENA </th> </tr> </table> The SERENA consortium recognizing the importance of making research data available and easily reusable is participating in the Open Research Data Pilot in Horizon 2020. As such and with respect to section “2.2.5 Management of Data” of the DoA, observational data consisting of sensor and machine recordings have started to be collected, but are not available at the time of compiling this document. As a result, the data format is constrained to the raw data format of each source. After the collection of those datasets, from their source, the conversion to the suitable data format will be defined along with the appropriate sharing format. In order to facilitate the retrieval and reuse of the related dataset appropriate metadata values will be defined and integrated, after resolving any confidentiality issues that may be raised by the data provider. Towards this direction anonymization approaches will be considered. Datasets that will be decided to become publically available will be following the dataset format that is presented in section 3.2 of this document. Apart from sensor data, the consortium will evaluate during the development stage of the several SERENA components making publicly available through the channel described in the following section additional experimental data. ## 3.1 DMP Platforms introduction and documentation For the SERENA project, the Zenodo platform has been selected for the data which will be decided by the members of the consortium to become publicly available. All research outputs from the entire scientific field can be stored in the particular platform, such as publications, posters, presentations, images and videos/audio. A first trial account for SERENA project purposes was created in Zenodo. After the profile is registered and the account is activated the user can easily upload and manipulate his data files. The profile constitutes an example profile in order to serve the presentation of the platform installation to the needs of the project. A space or community for the SERENA project has been established, named SERENA Data under the following link: _https://zenodo.org/communities/serena/edit/_ . One of the main aspects that the platform offers is the creation of the aforementioned communities. Communities imply the dedicated storage space for a defined entity. This entity could be from research project to any other scientific procedure which demands data storage for archiving and reuse purposes Figure 2. Figure 2: SERENA project community creation in ZENODO After the creation of the community, the creator or administrator may access it and proceed to any of the following options: 1. view the uploaded contents, 2. manage them, and 3. export the datasets Moreover, any user with access to the community link may either search and download content or upload new datasets. In order to upload new datasets the creation of a new account is required or use of an existing one from GitHub or ORCID. In order to download pre-existing files, no registration is required. Furthermore, it provides the user with the option of uploading to establish the access rights of the files. Four types of access rights can be selected as it is depicted in, depending on the confidentiality of the data. License type can be configured in the relative tab as well as Figure 3: Log in screen, access rights and license options funding related information to be provided Figure 3\. Two example files have been uploaded to the community of the SERENA Data, for which the Creative Commons Attribution-Share – Alike 4.0 has been selected. The type of the license can be reconfigured depending on the terms of each suggested license and the confidentiality level of the data Figure 4. Figure 4: SERENA data community uploaded test files ## 3.2 Dataset template description As mentioned in section 2, an initial dataset template in the form of XML has been created for storing data. The XML format suggested can automate in the future the upload of data to ZENODO through a mechanism that will consume the XML and take all the required info from the XML elements. Such a mechanism can make the upload and manipulation of data a very efficient procedure and will be investigated in the future. A short description of the main data field elements included in the template is provided in the table below. Table 1: XML elements ### ELEMENT NAME PURPOSE <table> <tr> <th> SERENA_subject </th> <th> The root name of each datasets referring to the SERENA community </th> </tr> <tr> <td> datasetID </td> <td> A unique identifier of the dataset </td> </tr> <tr> <td> datasetDescription </td> <td> A textual description of the dataset </td> </tr> <tr> <td> sharingOptions </td> <td> It included the sharing options of the SERENA subject, embargo periods, licenses, etc. </td> </tr> <tr> <td> origin </td> <td> It defines the main source of the dataset, such as machine name </td> </tr> <tr> <td> volume </td> <td> It includes the size of the dataset in MBs or GBs </td> </tr> <tr> <td> Date </td> <td> The date element includes the initial upload and any modification date. Furthermore, it contains a reference element which can be further linked to any other element of the XML. </td> </tr> <tr> <td> contents </td> <td> Under the content element, multiple elements may be included such as images, videos, documents (doc, docx, docm, pdf, ppt, etc.) as well as raw data either as plain text or in another format such as odt. </td> </tr> <tr> <td> standards </td> <td> This field defines any incorporated mechanism for encoding the specific dataset, along with the organization and the description of the standard. </td> </tr> <tr> <td> metadata </td> <td> The metadata element contains additional information over the dataset including the total number of downloads, the times that the dataset has been parsed, the ranking of the SERENA subject as well as the last time it was updated. </td> </tr> </table> <table> <tr> <th> 4 </th> <th> Conclusions </th> </tr> </table> In conclusion, the requirements imposed on SERENA with regards to granting OA to research data have been discussed. The adopted online platform for archiving and preserving research data under the guiding principles of Annex 1 has been described. Additionally, the first steps towards populating the newly created SERENA data community has been made by means of two test files. The responsible partner for the archiving of the data in the proposed online platform will manage the in time update of the aforementioned tables in order for the consortium to be kept updated on the project outcomes. This DMP includes also an XML schema to upload in a formatted and structured approach the datasets that are intended to become publicly available. Last but not least, OA regarding publications have been discussed, however for the publications, papers or deliverables, as well as for the data that are not made anonymous or are confidential the project portal will be used. At this point the SERENA consortium has initiated the process of collecting data from the pilot cases. However this process also considering the confidentiality policies and data sharing restrictions of each company will require additional time. In the next stages of the project and under the decision of the responsible companies, datasets consisting primarily of sensor data may be uploaded and managed by the responsible partners according to the guidelines described in this document.	https://phaidra.univie.ac.at/o:1140797	Horizon 2020
0215_PROCESS_777533.md	### Executive Summary This report has been created to support all PROCESS team members in their daily work for PROCESS. The specific purpose of this document is to complement quality related aspects of the key contractual documents of the project: * The EC Grant Agreement, including the Annex 1 "Description of Action" (DoA) * PROCESS Consortium Agreement (GA) From the practical point of view, these documents establish primarily the contents of the project deliverables and the schedule for their delivery to the Commission (Annex 1), and the structure and the decision-making processes of the PROCESS project organisation. The quality plan complements these two documents by describing in a more detailed manner the processes that are utilised to ensure that the project outputs are of the highest possible quality. The quality in this context refers to to the outputs being accurate and fit for the purposes stated explicitly in the DoA or implied in the informal communications (such as presentations), and complying certain technical and formal characteristics (e.g. using document template). The quality assessment will thus provide foundations for successful dissemination and exploitation activities. This document contains the descriptions of the quality management processes and responsibilities of the different project organisations that provide additional guidance on the implementation of the clauses in the DoA and GA. These include: * Release procedure of deliverables and other project outputs carrying the PROCESS brand * Communication practices and tools * Software quality approach * Management of IPR-issues The Data Management Plan (DMP) will present the overall goals, background and constraints the project needs to take into account when generating and publishing reusable datasets. The multifaceted nature of the project and its stakeholders is discussed in some detail, before presenting the potential data assets the project will generate or be responsible for. The use cases of the project are largely based on providing tools aimed at making orders of magnitude improvements in the efficiency and convenience of extracting value from existing data assets. For this reason, the data management plan includes relative detailed description of the heuristics for determining whether the project should publish an actual derived datasets based on already published data sources or just publish the algorithm and tools for replicating the steps. The DMP will also include a short summary of the potential sources of reusable dataset emerging from each of the use cases. ### 1 Quality Plan #### 1.1 Summary of the quality-related processes in the DoA The project management structure is based on the following three components: * Strategic decisions: General Assembly (GA) with all partners represented * Innovation management, with innovation manager monitoring any issues arising from work packages WP2 (Motivation, Future Requirements, and Emerging Opportunities) and WP9 (Dissemination, Engagement and Exploitation) * Day-to-day coordination: Work package leaders coordinating the work within the work packages and exchanging information with the project coordinator and other WP leaders through the project executive committee. WP leaders will have autonomy in deciding on the approaches to be used within the work package, as long as they can support other WP leaders and the project coordinator in their tasks. All of these groups will use the project Wiki (Confluence tool discussed in the next chapter) for circulating agendas and to record the minutes of the project meetings. The meetings of these three management structures have the following cycle: * GA meetings: three times per year at the minimum, * Day-to-day coordination: weekly Project Executive Committee (PEC) teleconferences, * Innovation management: status review in the PEC meetings, in-depth analysis on demand and ad hoc contacts between WP2 and WP9 leaders when a need arises. #### 1.2 Summary of the quality-related processed in the Consortium Agreement The PROCESS Consortium Agreement is based on the DESCA model, and as such it does not have a direct bearing to the project quality management. It includes certain provisions related to timing of the meeting invitations and finalisation of agendas - both for the ordinary and the extraordinary meetings. Any partner may call for an extraordinary meeting. #### 1.3 Tools used by the project ##### 1.3.1 Collaborative online working spaces - Confluence and GitLab The project uses a _Confluence collaboration tool_ installed at LRZ as an online collaboration space. Confluence is a Wiki-style system that supports collaborative editing of pages and their relationships with each other. It provides fine-grained access control for the content and advanced collaboration mechanisms that allow users to subscribe to notifications (for example page edits), assign tasks and leave comments on the pages. Confluence is used as the main hub collecting links to all project internal tools, documentation and outputs. The deliverables are edited primarily using the confluence system to minimise the barriers for contributions and the additional workload needed for manual integration of contributions from multiple sources. The software developed by the project is stored in the GitLab installation provided by LRZ as a service for LMU ( _gitlab.lrz.de_ ) . This mature, widely-used version management system supports well-defined software development processes and facilitates uptake by the other developer communities using GitLab by providing familiar interface for the published software. The details of the software development process are described in some detail in the section _"Software Quality Assurance"_ in this document. ##### 1.3.2 Communication tools The communication tools range from mailing lists (one for each work package, WP leaders' list and list containing all members of any of the PROCESS list) to conference call systems. The mailing lists are listed in the Confluence system and can be added if need arises. The primary conference calling system used by PROCESS is Gotomeeting ( _https://www.gotomeeting.com/_ ) , which was deemed to provide the best balance of ease-ofuse, support of multiple platforms, features (e.g. screen sharing and chat functionalities that are not available on traditional conference call systems) while complying with the IT security policies of all of the partners. ##### 1.3.3 Meeting practices The basic schedule of the meetings is already defined in the Description of Action (DoA), Annex 1 of the Grant Agreement. The minutes of the meetings are stored on the confluence system, linked to a common "Meetings" page. This provides a common repository and a way to track any corrections made to the meeting minutes as changes are logged (timestamp and username). #### 1.4 Deliverable process The deliverable process was discussed in the kick-off meeting and resulted in a simple, straightforward approach documented on the Confluence page (Quality Assurance process). The process agreed is as follows: * Every official piece of paper (e.g. deliverable) will be sent to the Executive Board two weeks before official deadline ( [email protected]_ ) * Executive Board has one week to suggest changes/improvements * No reaction means silent consent * One week left in order to include changes or improve on document for authors * Final version has to be circled after being published to all project members ( [email protected]_ ) This basic mechanism can be refined during the project lifetime if needed (e.g. to accommodate urgent requests from parties project collaborates with). The main quality-related issue is the tacit approval of deliverables - in a multifaceted project consisting of platform development and highly autonomous use case pilots, waiting for explicit approval from all the members would increase risks of delays while not necessarily improving the coverage of the review process. #### 1.5 Software quality assurance The foundations of the software quality assurance are the guidelines developed and best practices documented by NLeSC in their internal software development guide (accessible at _https://nlesc.gitbooks.io/guide/content/_ ) . This approach is a natural choice since NLeSC is responsible for the WP8 (Validation) and has extensive experience in applying the guide in other NLeSC projects. The guide is also so called "live document" that is continuously updated and refined, making it easy to encompass lessons learned from the PROCESS work in a way that benefits automatically a larger group of projects. The scope of the guideline documentation extends beyond the software quality aspects, extending to areas covered by other PROCESS documents (e.g. publishing of the results) and covers some details that need to be adjusted (e.g. exact repository used for the software). In the process context other PROCESS documents will naturally have a precedence - and in case there is a danger of misunderstanding the exact approach is documented in the project Wiki (described earlier in this section). Some of the key principles stemming from the NLeSC guide are: 1. In case of doing proof-of-concept/prototyping work that doesn't comply with the software development process, state this explicitly in all the communications 2. Version control - apply consistent practices from the beginning of the project 3. Arrange formal code reviews as part of the development process 4. Automate testing as much as possible 5. Apply standards and language-specific implementation guides is available 6. Do an in-depth assessment of the IPR-related issues at least in two stages: 1. Finalising the design of the software 2. Before making software publicly available The implementation of these approaches will be reviewed in the executive board meetings, based on information collected by the WP8 leader. #### 1.6 Dissemination and exploitation quality issues ##### 1.6.1 DoA dissemination aspects The DOA includes a list of potential dissemination channels and KPIs that project partners identified at the time of writing the proposal. These will be reviewed and complemented during the project lifetime, with the first update documented in the deliverables D9.1 ("Initial DEP and market research Report"). The dissemination-related Key Performance Indicators (KPIs) are defined as follows: _Table 1 Dissemination-related Key Performance Indicators_ <table> <tr> <th> Target area </th> <th> Indicator </th> <th> Expected progress (cumulative numbers unless otherwise stated) </th> </tr> <tr> <th> After M12 </th> <th> After M24 </th> <th> After M36 </th> </tr> <tr> <td> Scientific </td> <td> Number of publications, talks, presentations in conferences and workshops </td> <td> 4 </td> <td> 12 </td> <td> 20 </td> </tr> <tr> <td> Scientific </td> <td> Number of lectures, courses or training events (including extreme scaling workshops) </td> <td> 2 </td> <td> 8 </td> <td> 16 </td> </tr> <tr> <td> Other projects </td> <td> Number of meetings with other project presence (either hosted or participated) </td> <td> 5 </td> <td> 12 </td> <td> 20 </td> </tr> <tr> <td> Website </td> <td> Unique monthly visitors (best threemonth average) </td> <td> 200 </td> <td> 400 </td> <td> 600 </td> </tr> <tr> <td> Website </td> <td> Returning monthly visitors (best threemonth average) </td> <td> 50 </td> <td> 100 </td> <td> 150 </td> </tr> <tr> <td> Press </td> <td> Number of mentions in paper press, online media, TV/Radio </td> <td> 4 </td> <td> 12 </td> <td> 30 </td> </tr> <tr> <td> Social media </td> <td> Number of followers/friends on social media networks (across all platforms) </td> <td> 80 </td> <td> 150 </td> <td> 450 </td> </tr> <tr> <td> Developers </td> <td> Monthly downloads of technical documentation: White papers, architecture descriptions or software releases (best three month average) </td> <td> 50 </td> <td> 150 </td> <td> 200 </td> </tr> </table> From the quality perspective, the scientific goals require balancing the type of output and its impact: for example a talk or a presentation in a small workshop that targets an ideal niche audience is of much higher value than a publication that is perhaps more prestigious on the surface but doesn't provide similar targeted audience. This concrete approaches to solve this challenge will be discussed in the D9.1 and in case they require changes to the overall quality processes of the project this deliverable will be updated to reflect the next practices. ##### 1.6.2 Internal guidelines The project has an internal guideline document complementing the plans presented in the DOA and providing rapid guidance to supplement the procedures and methods presented in the previous chapters. The document contains reflections, best practices and templates for identifying, refining and promoting project success stories. This covers both direct activities of the project team as well as activities that support dissemination and exploitation indirectly, such as forming alliances with entities that have synergies with PROCESS goals, building and managing communities and so one. #### 1.7 IPR-related quality assurance issues PROCESS needs to take IPR issues into account in several parts of its activities: * Publishing the software solutions through the GitLab installation * Integrating software components with other open source solutions * Publishing derived datasets (to ensure that the constraints of the original license dataset is published in respected) * Submitting publications to scientific journals (to ensure at least green open access) * Preparing presentation material (e.g. ensuring that photographs used as an illustration do not infringe licensing terms) * Dealing with potential infringement of IPR generated by the project Hence, the IPR issues form a part of Software Quality Assurance, Dissemination and exploitation activities as well as forming an integral part of the Data Management Plan of the project. Thus several groups and individuals are dealing with the issues and need to act with relatively high degree of autonomy. The overall coordination of the IPR issues is the responsibility of the Innovation Manager, who will ensure that the relevant IPR-related information is made available to everyone involved in the day-today IPR management. #### 1.8 Privacy issues While the datasets used by the use cases and pilots are not planned to include data that would have privacy issues, any changes to this practice need to be reviewed by the Innovation Manager. The work package leader of WP9 will ensure that any contact information collected will be used only for the purposes the consent was obtained for (and in a way that is compliant with GDPR). WP9 leader will report on the privacy issues in the PEC meetings at the minimum twice a year. ### 2 Data Management Plan #### 2.1 Introduction The requirements for the Data Management Plan (DMP) are laid out in grant agreement (GA) and supporting documentation provided by the EC. The GA states that: _"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:_ 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" (N.B. need to check the text hasn't changed in PROCESS GA before submitting the deliverable!)_ The prerequisites for complying with these requirements include: * Identifying the data generated that could form the basis of becoming a reusable data asset * Identifying and securing access to optimal repositories for long-term preservation of the data * Review and refine the metadata so that it provides information that is relevant and understandable also when separate from the PROCESS project context. This is important already for the project internal use as the data assets of the PROCESS project are multi-disciplinary in nature. * Map the data with publications made by the project * Having necessary _due diligence_ processes in place to ensure publication of data will not - directly or indirectly - raise any additional compliance issues. Fulfilling these requirements in a multifaceted project such as PROCESS requires a two-stage approach: creating an initial data management plan describing potential reusable data assets that project can generate during its lifetime, and common principles and approaches used to choose optimal approaches for making them reusable in the longer term. The initial data management plan will be refined during the project lifetime as the nature of the data assets generated will become clearer. However, it should be noted that due to the interdisciplinary nature of the project, it is likely that the project will generate several data management plans to match the specific requirements and community conventions of each of the disciplines involved. Maximising the potential for reuse will also depend on successful identification of the potential secondary user communities, as this is a prerequisite for successful reviewing and refining of metadata specifications and identification of the optimal repositories that are to be used for storing the data generated during the PROCESS lifetime. One of the common characteristics of the PROCESS use cases is that they do not do primary data collection themselves. Instead, they will generate data, either based on the publicly available datasets or (especially in case of the UC#4, see next section) provide simulation results based on statistical distributions of actual, private datasets that are used as background of the project work. #### 2.2 Background PROCESS is a project delivering a comprehensive set of mature services prototypes and tools specially developed to enable extreme scale data processing in both scientific research and advanced industry settings. These service prototypes are validated by the representatives of the communities around the five use cases: * UC#1: Exascale learning on medical image data * UC#2: Square Kilometre Array/LOFAR * UC#3: Supporting innovation based on global disaster risk data/UNISDR * UC#4: Ancillary pricing for airline revenue management * UC#5: Agricultural analysis based on Copernicus data From the data management perspective, each of these five use cases presents challenges that are complementary to each other, and with different potential for direct generation of exploitable data assets. This mapping is presented in the table below: # Table 2 Key challenges of use cases <table> <tr> <th> Use case </th> <th> Key challenge </th> <th> Type of reusable data asset </th> </tr> <tr> <td> UC#1 </td> <td> Machine learning using massive, public datasets; exploitation requires high degree of privacy </td> <td> More challenging datasets based on the published ones (e.g. with noise of artefacts simulating mistakes made during the scanning of a tissue slide, rotation of regions of interest etc.) </td> </tr> <tr> <td> UC#2 </td> <td> Extreme volume of data (LOFAR reduced data set 5-7PB per year, SKA centrally processed data rate: 160Gbps) </td> <td> For the most part the data assets will remain in LOFAR LTA (long term archive), however a disk copy of test observation could be useful for software testing and validation </td> </tr> <tr> <td> UC#3 </td> <td> Usability of extreme scale tools to support emerging big data user communities: The UNISDR Global Assessment Report (GAR) datasets have been made publicly available for non-commercial use since early 2017. The process to be used for the 2019 will be fundamentally different, with considerably larger group of experts with heterogeneous and evolving data curation practices involved in the data production and curation. </td> <td> The 2015 and 2017 GAR datasets and the results of the CIMA showcase. </td> </tr> <tr> <td> UC#4 </td> <td> Very large datasets, extreme responsiveness requirements, high financial risks/potential rewards; exploitation requires demonstrating high degree of security and auditability of the PROCESS solutions. </td> <td> Tools, documentation and parameter files for generating simulated transaction datasets </td> </tr> <tr> <td> UC#5 </td> <td> Support wide range of uses of a very large dataset of satellite images (growing at the rate of 7.5PB per month) </td> <td> Tools, documentation and parameter files for accessing Copernicus data, possibly specialised derived sets of data (e.g. time series of specific location) </td> </tr> </table> All these use cases have distinct communities, practices and documentation/metadata conventions, thus any component that can be used as part of all five demonstrators can be considered a proven, generalizable data management component with very high exploitation and uptake potential. #### 2.3 Potential Data assets The potential reusable datasets will emerge from the following primary sources: * The work focused on the use cases and supporting the communities around it * The work on the general purpose exascale data solution that supports the use cases. The use case-related data assets will almost certainly represent the majority of the assets that are used. The technical platform development may develop tools and technologies that are e.g. used for testing, benchmarking or validation of the PROCESS solution. The primary data management approach will be based on the software quality process, leveraging as much as possible metadata and repository structure used for the software releases and link the services storing the physical datasets to the PROCESS software repository. #### 2.4 Common approaches to data management As in all of the five use cases the foundation of the development is the use of existing datasets, the decision to store and publish a derived set is based on assessment of the potential value this derived dataset might represent for other users. The assessment is currently based on the following abstract "checklist" that will be refined and formalised during the project lifetime based on the experiences gained in its application: 1. Would publishing the dataset raise potential privacy issues (e.g. allowing deanonymising subjects)? 2. Does the license of the original dataset allow publishing a derived set (IPR)? 3. Does publishing dataset lead to potential savings (in terms of time, computational resources etc.) when compared to re-generating the derived set? 4. Does the project need to keep a derived dataset already for its internal use (e.g. for testing, benchmarking, validation)? a) Would these datasets be needed by third parties to fully validate correct behaviour of PROCESS tools 5. Can a suitable (i.e. a repository that would facilitate discovery of the data asset by its intended users), managed repository that would be willing to host the dataset be found? 6. If not, can the long-term commitments needed for formally publishing a dataset be met by the partners? Regarding the last point, LMU has secured storage space for 20TB of raw data at LRZ, with a minimum commitment of providing managed, high-availability access at least for three years after the end of the project. It is assumed that if the datasets will be used, this time period can be extended, or the dataset migrated to a community-specific data repository. Data Management Plan PROCESS will aim at complying with the FAIR principles 1 with all of its data publication activities. Any deviations from these principles will be documented, together with the reasons for them (e.g. constraints imposed by the practices of the specific community). #### 2.5 Use-case specific data management aspects The following sections will present _a priori_ assessment of potential reusable datasets generated in the context of each of the use cases. The details of the data processing workflows and requirements are presented in the deliverable D4.1, so this deliverable will present only very brief summary of the potential reusable data assets and the possible approaches their use could be supported. This section is expected to be refined considerably in the future editions of the PROCESS DMP. ##### 2.5.1 UC#1 _Background datasets_ The UC#1 uses the following published datasets as background material, each of them already published in repositories that are well known by the medical informatics community. # Table 3 Background datasets of use case 1 <table> <tr> <th> Dataset Name </th> <th> Estimated size </th> <th> Description </th> <th> Format </th> <th> Annotations </th> </tr> <tr> <td> Camelyon17 </td> <td> >3TB </td> <td> 1000 WSI, 100 patients </td> <td> BIGTIFF </td> <td> XML file </td> </tr> <tr> <td> Camelyon16 </td> <td> >1TB </td> <td> 400 WSI </td> <td> BIGTIFF </td> <td> XML file + Binary Mask </td> </tr> <tr> <td> TUPAC16 </td> <td> >3TB </td> <td> WSI </td> <td> BIGTIFF </td> <td> CSV file </td> </tr> <tr> <td> TCGA </td> <td> >3TB </td> <td> WSI </td> <td> BIGTIFF </td> <td> TXT file </td> </tr> <tr> <td> PubMed Central </td> <td> ~5 million images </td> <td> Low resolution </td> <td> Multiple formats </td> <td> NLP of image captions </td> </tr> <tr> <td> SKIPOGH </td> <td> >30TB </td> <td> WSI </td> <td> BIGTIFF </td> <td> </td> </tr> </table> _Data generated_ The UC#1 will generate two types of data assets of potential interest:  Derived datasets based on one of the published ones (Camelyon17, Camelyon16 ...) that support more comprehensive training of machine learning algorithms. The methods include rotation of images, adding noise or simulated processing artefacts (e.g. foreign bodies like hairs in the scanned tissue slide).  Actual neural networks trained by the datasets. _Publishing approach_ In case of the derived datasets, it is likely that in most cases it would be most appropriate to publish the method for generating the derived dataset (software and artefact images). A small sample of trained networks could also be of interest. Data Management Plan In the former case publishing the "recipe" for a derived dataset would ideally be done in the context of the original repository, whereas in the latter the software repository might be the most natural location. ##### 2.5.2 UC#2 _Background datasets_ The work in the UC#2 will rely on accessing the data from the LOFAR Long Term Archive (LTA - _https://lta.lofar.eu/_ ) . The publishable data assets would likely be a minimal set for validation testing of the software. ##### 2.5.3 UC#3 _Background datasets_ The work in the UC#3 is based on using the UNISDR community as a pilot community for advanced PROCESS tools. The project will support the data management of the community, however for now the work does not result in generation of publishable datasets by the project. ##### 2.5.4 UC#4 _Background datasets_ The background datasets are the private transaction records kept by LSY that are used as basis for generating statistically similar simulated datasets for testing the ancillary pricing mechanism. _Data generated_ The simulated data needs to be evaluated based on the checklist presented in the section "Common approaches to data management". It is likely that the value of large datasets is relatively low compared to publishing of the generation algorithm. ##### 2.5.5 UC#5 _Background datasets_ The datasets will be based on the Copernicus data service. _Data generated_ Tools, documentation and parameter files for accessing Copernicus data, possibly specialised derived sets of data (e.g. time series of specific location). _Publishing approach_ There are three potential channels that could be interested in the data assets generated in the UC#5 context 1. Users of the PROMET software 2. Broader agronomy research community interested in easy access to satellite data 3. Providers of generalised Copernicus access services Evaluating (based on the experiences of the first pilot versions) which of these channels will be most promising channels for the project data assets will be one of the key focus areas of the update of this DMP.	https://phaidra.univie.ac.at/o:1140797	Horizon 2020
0217_PROCESS_777533.md	# Executive Summary This updated Data Management Plan (DMP) presents an overview of the evolution of the goals, background and constraints the project needs to take into account when generating and publishing reusable datasets. The initial assessment of the role of the use cases being focused on providing tools aimed at making orders of magnitude improvements in the efficiency and convenience of extracting value from existing data assets has not changed, thus most of the new material in this updated DMP is stemming from the experiences gained in the work that is directly related to the use cases. For convenience, the detailed background analysis of the use cases presented in the deliverable D1.1 has been included in this document as an annex. # Updated Data Management Plan ## Introduction As stated in the Deliverable D1.1, the requirements of the PROCESS DMP stem from the EC Grant Agreement. These requirements need to be applied in the contexts of the five service pilots included in PROCESS, each of them with unique opportunities and constraints related to the reuse of the data generated. These foundations of the DMP have not changed during the first 18 project months, but for convenience the initial analysis of the situation from the deliverable D1.1 is included in the Annex 1 of this document. ## Potential Data assets The deliverable D1.1 identified two groups of sources for reusable datasets: * The work focused on the use cases and supporting the communities around it * The work on the general purpose exascale data solution that supports the use cases. The first group was assumed to contain the majority of the assets that are used, and the experiences of the first 18 months tend to support this initial assessment. It is possible that the validation step of the PROCESS solution will generate datasets that could be used as basis for more general benchmarks of extreme data applications, but this remains to be confirmed during the second half of the project. ## Common approaches to data management The initial assessment framework determining whether the project should publish a dataset or not is still valid: 1. Would publishing the dataset raise potential privacy issues (e.g. allowing deanonymising subjects)? 2. Does the license of the original dataset allow publishing a derived set (IPR)? 3. Does publishing dataset lead to potential savings (in terms of time, computational resources etc.) when compared to re-generating the derived set? 4. Does the project need to keep a derived dataset already for its internal use (e.g. for testing, benchmarking, validation)? a) Would these datasets be needed by third parties to fully validate correct behaviour of PROCESS tools? 5. Can a suitable (i.e. a repository that would facilitate discovery of the data asset by its intended users), managed repository that would be willing to host the dataset be found? 6. If not, can the long-term commitments needed for formally publishing a dataset be met by the partners? Applying this framework has produced following observations: * There are potential derived datasets stemming from use cases (UC) #1 and #4. However, at least in the case of UC#4, it is likely that publishing the algorithm would be more efficient way of allowing reuse. In case of UC#1, the project is using derived dataset internally. However, these data assets are deemed to be very specific to the project and not of interest to third parties. * The dataset used as a starting point for the UC#3 has certain licensing issues that need to be taken into account. However, the complementary datasets identified do not carry this limitation. * There are some promising – albeit early stage – discussions that indicate that the resource requirements of long-term preservation of datasets can possibly be met through collaborative arrangements with other projects. ## Use-case specific data management aspects The following sections will present an update of the potential reusable datasets generated in the context of each of the use cases. The details of the data processing workflows and requirements are presented in the deliverable D4.1. ### UC#1 _Background datasets_ The ongoing UC#1 activities are focused on the Camylyon17 and Camelyon16 datasets, with the other background datasets kept as candidates for further testing and validation at the end of the project. The project may also gain access to datasets collected and used by the ExaMode project 1 , in which case the use of other already published datasets will have a considerably lower priority. _Table 1 Updated background dataset summary of use case 1 – data sets under active study highlighted_ <table> <tr> <th> Dataset name </th> <th> Estimated size </th> <th> Description </th> <th> Format </th> <th> Annotations </th> </tr> <tr> <td> Camelyon17 </td> <td> >3TB </td> <td> 1000 WSI, 100 patients </td> <td> BIGTIFF </td> <td> XML file </td> </tr> <tr> <td> Camelyon16 </td> <td> >1TB </td> <td> 400 WSI </td> <td> BIGTIFF </td> <td> XML file + Binary Mask </td> </tr> <tr> <td> TUPAC16 </td> <td> >3TB </td> <td> WSI </td> <td> BIGTIFF </td> <td> CSV file </td> </tr> <tr> <td> TCGA </td> <td> >3TB </td> <td> WSI </td> <td> BIGTIFF </td> <td> TXT file </td> </tr> <tr> <td> PubMed Central </td> <td> ~5 million images </td> <td> Low resolution </td> <td> Multiple formats </td> <td> NLP of image captions </td> </tr> <tr> <td> SKIPOGH </td> <td> >30TB </td> <td> WSI </td> <td> BIGTIFF </td> <td> </td> </tr> <tr> <td> ExaMode </td> <td> Tens of TB </td> <td> _TBD_ </td> <td> _TBD_ </td> <td> _TBD_ </td> </tr> </table> _Data generated_ As described in the original DMP, the UC#1 will generate two types of data assets for the project internal use: * Derived datasets based on one of the published ones (Camelyon17, Camelyon16 ...). * Actual neural networks trained by the datasets. _Publishing approach_ As in the original DMP – the project will focus on documenting the processes used to develop derived datasets. ### UC#2 _Background datasets_ The work in the UC#2 will rely on accessing the data from the LOFAR Long Term Archive (LTA) 2 and produce tools that allow more efficient use of the LTA contents. Publishing datasets retrieved from LTA is not deemed necessary at this stage, as any actual analysis performed by third party users would need to access the official archive as an authoritative source of data. Furthermore, it is possible to validate the UC#2-related service pilot by using files filled with random values. ### UC#3 _Background datasets_ The work in the UC#3 was based on using the UNISDR community as a pilot community for advanced PROCESS tools. The original dataset consists of about 2TB of data and is openly accessible via http://unisdr.mnm-team.org. This resource is described in more detail in Annex 2 of this deliverable. As the new, community-based process used by UNISDR is still evolving, the project is at the moment considering enhancing the original datasets with other assets. The primary candidate for testing this approach is based on the datasets produced by the CliMex project 3 . The project has generated fifty climate simulation models for the time period of 1950 to 2100 covering Central Europe and North-Eastern North America. This data is used as an input for hydrological simulations to identify extreme flooding scenarios associated to climate change. The CliMex project will aim at publishing ~200TB dataset during 2019, with a suitable open license (details of the exact license still under review). The integration work to make these two datasets available through a single interface is ongoing. In parallel to this technical work, the project is investigating ways to benefit from the synergies with the LEXIS project 4 , that has two pilot activities dealing with disaster risk modelling (Earthquake and Tsunami, Weather and Climate). ### UC#4 _Background datasets_ The background datasets are the private transaction records kept by LSY that are used as basis for generating statistically similar simulated datasets for testing the ancillary pricing mechanism. _Data generated_ The simulated data needs to be evaluated based on the checklist presented in the section "Common approaches to data management". It is likely that the value of large datasets is relatively low, i.e. it wouldn’t add value to publishing of the generation algorithm. ### UC#5 The used datasets base on pre-processed data from the Copernicus Sentinel data. _Data generated_ Tools, documentation and parameter files for the pre-processed Copernicus data used in PROMET 5 and output generated with PROMET. _Publishing approach_ There are three potential channels that could be interested in the data assets generated in the UC#5 context 1. Users of the PROMET software 2. Broader agronomy research community interested in easy access to satellite data 3. Providers of generalised Copernicus access services Evaluating which of these channels are the best ones for promotion of third- party reuse is still ongoing. # introduction and background of the PROCESS DMP ## Introduction The requirements for the Data Management Plan (DMP) are laid out in grant agreement (GA) and supporting documentation provided by the EC. The GA states that: _"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:_ 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"_ The prerequisites for complying with these requirements include: * Identifying the data generated that could form the basis of becoming a reusable data asset * Identifying and securing access to optimal repositories for long-term preservation of the data * Review and refine the metadata so that it provides information that is relevant and understandable also when separate from the PROCESS project context. This is important already for the project internal use as the data assets of the PROCESS project are multi-disciplinary in nature. * Map the data with publications made by the project * Having necessary _due diligence_ processes in place to ensure publication of data will not - directly or indirectly - raise any additional compliance issues. Fulfilling these requirements in a multifaceted project such as PROCESS requires a two-stage approach: creating an initial data management plan describing potential reusable data asset that project can generate during its lifetime, and common principles and approaches used to choose optimal approaches for making them reusable in the longer term. The initial data management plan will be refined during the project lifetime as the nature of the data assets generated will become clearer. However, it should be noted that due to the interdisciplinary nature of the project, it is likely that the project will generate several data management plans to match the specific requirements and community conventions of each of the disciplines involved. Maximising the potential for reuse will also depend on successful identification of the potential secondary user communities, as this is a prerequisite for successful reviewing and refining of metadata specifications and identification of the optimal repositories that are to be used for storing the data generated during the PROCESS lifetime. One of the common characteristics of the PROCESS use cases is that they do not do primary data collection themselves. Instead, they will generate data, either based on the publicly available datasets or (especially in case of the UC#4, see next section) provide simulation results based on statistical distributions of actual, private datasets that are used as background of the project work. ## Background PROCESS is a project delivering a comprehensive set of mature services prototypes and tools specially developed to enable extreme scale data processing in both scientific research and advanced industry settings. These service prototypes are validated by the representatives of the communities around the five use cases: * UC#1: Exascale learning on medical image data * UC#2: Square Kilometre Array/LOFAR * UC#3: Supporting innovation based on global disaster risk data/UNISDR * UC#4: Ancillary pricing for airline revenue management * UC#5: Agricultural analysis based on Copernicus data From the data management perspective, each of these five use cases presents challenges that are complementary to each other, and with different potential for direct generation of exploitable data assets. This mapping is presented in the table below: _Table 2 Key challenges of use cases_ <table> <tr> <th> Use case </th> <th> Key challenge </th> <th> Type of reusable data asset </th> </tr> <tr> <td> UC#1 </td> <td> Machine learning using massive, public datasets; exploitation requires high degree of privacy </td> <td> More challenging datasets based on the published ones (e.g. with noise of artefacts simulating mistakes made during the scanning of a tissue slide, rotation of regions of interest etc.) </td> </tr> <tr> <td> UC#2 </td> <td> Extreme volume of data (LOFAR reduced data set 5-7PB per year, SKA centrally processed data rate: 160Gbps) </td> <td> For the most part the data assets will remain in LOFAR LTA (long term archive), however a disk copy of test observation could be useful for software testing and validation </td> </tr> <tr> <td> UC#3 </td> <td> Usability of extreme scale tools to support emerging big data user communities: The UNISDR Global Assessment Report (GAR) datasets have been made publicly available for non-commercial use since early 2017. The process to be used for the 2019 will be fundamentally different, with considerably larger group of experts with heterogeneous and evolving data curation practices involved in the data production and curation. </td> <td> The 2015 and 2017 GAR datasets and the results of the CIMA showcase. </td> </tr> <tr> <td> UC#4 </td> <td> Very large datasets, extreme responsiveness requirements, high financial risks/potential rewards; exploitation requires demonstrating high degree of security and auditability of the PROCESS solutions. </td> <td> Tools, documentation and parameter files for generating simulated transaction datasets </td> </tr> <tr> <td> UC#5 </td> <td> Support wide range of uses of a very large dataset of satellite images (growing at the rate of 7.5PB per month) </td> <td> Tools, documentation and parameter files for accessing Copernicus data, possibly specialised derived sets of data (e.g. time series of specific location) </td> </tr> </table> All these use cases have distinct communities, practices and documentation/metadata conventions, thus any component that can be used as part of all five demonstrators can be considered a proven, generalizable data management component with very high exploitation and uptake potential.	https://phaidra.univie.ac.at/o:1140797	Horizon 2020
0218_SYLFEED_745591.md	# Executive summary The overall objective of WP7 in SYLFEED is to “ _continuously monitor and provide means for the SYLFEED partners to share their knowledge within the consortium and to integrate the research activities as well as to exploit the developments, and/or communicate and disseminate the results to the scientific and industrial community and to the wider audience. The global objective is to prepare and encourage the use and wide acceptance of project outputs_ ”, as per the Description of Action (DoA). Additionally, internal communication is an integral part of the success of SYLFEED, as described in WP8, specifically Task 8.3. Deliverable 7.6, contributes towards these goals and to the success of SYLFEED by creating a "Data Management Plan (DMP)", as per the Horizon 2020 Open Research Data Pilot. The current deliverable contains two SYLFEED DataSets (SDS) detailing the content of datasets used within SYLFEED, how it will be preserved, and steps taken to make data publically available after the project end. Additional SDS will be created as the project progresses. This deliverable is set up by MATIS with the help of ARBIOM and both partners will update and add to the DMP. # Deliverable report SYLFEED Data Management Plan (DMP) under the H2020 Open Research Data Pilot. # Introduction Over the course of a research project, considerable amounts of data are gathered and generated. Often, these data are not preserved or made available for reuse later on, causing time and effort to be spent in other projects gathering similar data. The goal of the Horizon 2020 Open Research Data Pilot is to remedy this issue, by ensuring that research data generated through a project is made available for reuse after the project ends. # H2020 Open Research Data Pilot The H2020 Open Research Data Pilot is based on the principle of making data FAIR : * Findable * Accessible * Interoperable * Reusable # SYLFEED Data Management Plan As a way of managing the data used during a project lifetime, a Data Management Plan (DMP) must be created. The DMP-forms includes details 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 * how data will be curated and preserved (including after the end of the project) * ethical issues related to the data * estimated costs associated with data archiving/sharing The creation of the DMP is included in deliverable 7.6 (D7.6). As per the DoA, D7.6 will fulfill three requirements as a participant in the H2020 Open Research Data Pilot: * "Firstly, the collected research data should be deposited in data repository (the SYLFEED project will use the Zenodo repository), * Secondly, the project will have to take measures to enable third parties to access, mine, exploit, reproduce and disseminate this research data, * Finally, a Data Management Plan (DMP) has to be developed detailing what kind of data the project is expected to generate, whether and how it will be exploited or made accessible for verification and reuse, and how it will be curated and preserved". Two types of data will be generated during the project: * _Raw data:_ This data will be stored on partners secured server and is not intended to be shared with the consortium. Partners ensure that the server capacity is adequate to store the data of SYLFEED project. * _Consolidated data:_ This data will be shared within the consortium. It will be stored on Aymingsphere, the internal project website, further described below. In addition, consolidated data will be at least stored on the data’s owner secured server. The project data identified at this stage of the project, and that will be generated during the project life have been compiled in a table ; this table is confidential and is made available to the project partners on the project collaborative platform Aymingsphere. This table will be regularly updated during the project in order to include all new data that could be identified at a later stage. Data is classified by Work Package, and additional information regarding the database where the data will be stored, and the author is mentioned. # The SYLFEED DataBase and DataSets Currently the SYLFEED DataBase (SDB) includes several SYLFEED DataSets (SDS). These can be found in the appendix at the end of this document. However, during the later stages of the project and as the project progresses, relevant SDS will be uploaded to the SDB. At or near the project end, datasets will be uploaded from the SDB to OpenAire ( _openaire.eu/_ ) or other relevant and appropriate locations as agreed upon by the SYLFEED consortium. A part of the SDB are two webpages; SYLFEED public page ( _www.sylfeed.eu_ ) and AYMINGSPHERE, the internal page for consortium communication. <table> <tr> <th> Name of database </th> <th> SYLFEED website (WP7) </th> </tr> <tr> <td> Data summary </td> <td> Sylfeed project website is functional since end of November 2017. It highlights graphical elements such as logo, spiral, footer, and a common set of colors. The objective of the website is to present SYLFEED project, objectives, work packages, and inform interested parties about the latest news of the project. All data displayed on the SYLFEED website is open to the public. The website will be available throughout the lifetime of the project at the current domain, www.sylfeed.eu, and the content of the website will be available after the end of the project under the website of the coordinator, ARBIOM, through web page redirect from current address. </td> </tr> <tr> <td> Making data findable, including provisions for metadata </td> <td> The data is available on the project website and is open source. All data can be found through regular websearch and through a regular keyword search. Each website post or page is labelled with relevant metadata for easier access. </td> </tr> <tr> <td> Making data openly accessible </td> <td> All website data is open source, hosted within the WordPress Content Management system (CMS) </td> </tr> <tr> <td> Making data interoperable </td> <td> N/A </td> </tr> <tr> <td> Increase data re-use (through clarifying licences) </td> <td> Published data will be re-usable. Confidential data will be assessed on a case by case basis upon official request, and address by the consortium as a whole. </td> </tr> <tr> <td> Allocation of resources </td> <td> N/A </td> </tr> <tr> <td> Data security </td> <td> Data reside on a secure server (SSL) and is backed-up on a regularly basis. </td> </tr> <tr> <td> Ethical aspects </td> <td> N/A </td> </tr> <tr> <td> Other </td> <td> N/A </td> </tr> </table> <table> <tr> <th> Name of database </th> <th> Website for internal matters (AYMINGSPHERE / SharePoint; WP8) </th> </tr> <tr> <td> Data summary </td> <td> AYMINGSPHERE ensures that partners have access to adequate resources, monitoring and planning procedures for an efficient management of the whole technological work carried out in the framework of the SYLFEED project. Partners can exchange and share documents, participate in discussion forums… The structure is organized as follows: * Project records: includes Communication documents, quality documents, management documents, meetings documents, * Certified documents * Steering tools: action list, roadmap, issues, decisions, risks * Financial follow-up with subsections per partner - Work packages with a separate folder per WP. * Useful links </td> </tr> <tr> <td> Making data findable, including provisions for metadata </td> <td> Data on the AYMINGSPHERE website is for internal communication only and is not available for the public, unless otherwised determined by the SYLFEED consortium. </td> </tr> <tr> <td> Making data openly accessible </td> <td> Data is not available for the public, unless otherwised determined by the SYLFEED consortium. </td> </tr> <tr> <td> Making data interoperable </td> <td> N/A </td> </tr> <tr> <td> Increase data re-use (through clarifying licences) </td> <td> Published data will be re-usable by the consortium only, unless otherwised determined by the SYLFEED consortium. </td> </tr> <tr> <td> Allocation of resources </td> <td> N/A </td> </tr> <tr> <td> Data Security </td> <td> Data is stored on a SharePoint Content Management System. Data reside on a secure server and is backedup on a regularly basis. </td> </tr> <tr> <td> Ethical aspects </td> <td> N/A </td> </tr> <tr> <td> Other </td> <td> N/A </td> </tr> </table> # Conclusion The Appendix describes the content of the different datasets, the ways in which data will be stored and how/if it will be made available at the project end. Due to the project being at an early stage, and because different work packages are at a different time schedule, not all forms share the same level of detail or same level of dissemination. The DMP is intended to be a "living" document and will evolve as the project progresses. Periodic revisions of the DMP are planned as described in the DoA. Extra revisions might be scheduled should it be needed. The table on page 2 in this document "Document history" provides a summary of revisions carried out over the lifetime of this Data Management Plan. It provides a version number, the date of the latest revision, the editor, and a comment describing the change made. # Appendix: DataSets <table> <tr> <th> DataSet reference and name </th> <th> Demo plant equipment specifications & data sheet (WP1) </th> </tr> <tr> <td> DataSet summary </td> <td> * Brochures * Booklets </td> </tr> <tr> <td> Making data findable, including provisions for metadata </td> <td> Data is confidential, only for members of the consortium (including the Commission Services) </td> </tr> <tr> <td> Making data openly accessible </td> <td> Data is confidential, only for members of the consortium (including the Commission Services) </td> </tr> <tr> <td> Making data interoperable </td> <td> N/A </td> </tr> <tr> <td> Increase data re-use (through clarifying licences) </td> <td> Published data will be re-usable. Confidential data will be assessed on a case by case basis upon official request, and addressed by the consortium as a whole. </td> </tr> <tr> <td> Allocation of resources </td> <td> N/A </td> </tr> <tr> <td> Data security </td> <td> Data reside on a secure server (SSL) and is backedup on a regularly basis. </td> </tr> <tr> <td> Ethical aspects </td> <td> N/A </td> </tr> <tr> <td> Other </td> <td> Upon completion of the project, data will stored on a secure server at Arbiom. </td> </tr> </table> <table> <tr> <th> DataSet reference and name </th> <th> Demo plant drawings (WP1) </th> </tr> <tr> <td> DataSet summary </td> <td> * Drawings * Electronic documents </td> </tr> <tr> <td> Making data findable, including provisions for metadata </td> <td> Data is confidential, only for members of the consortium (including the Commission Services) </td> </tr> <tr> <td> Making data openly accessible </td> <td> Data is confidential, only for members of the consortium (including the Commission Services) </td> </tr> <tr> <td> Making data interoperable </td> <td> N/A </td> </tr> <tr> <td> Increase data re-use (through clarifying licences) </td> <td> Published data will be re-usable. Confidential data will be assessed on a case by case basis upon official request, and addressed by the consortium as a whole. </td> </tr> <tr> <td> Allocation of resources </td> <td> N/A </td> </tr> <tr> <td> Data security </td> <td> Data reside on a secure server (SSL) and is backedup on a regularly basis. </td> </tr> <tr> <td> Ethical aspects </td> <td> N/A </td> </tr> <tr> <td> Other </td> <td> Upon completion of the project, data will stored on a secure server at Arbiom. </td> </tr> </table> <table> <tr> <th> DataSet reference and name </th> <th> Demo plant SOP (WP1) </th> </tr> <tr> <td> DataSet summary </td> <td> * Brochures * Booklets </td> </tr> <tr> <td> Making data findable, including provisions for metadata </td> <td> Data is confidential, only for members of the consortium (including the Commission Services) </td> </tr> <tr> <td> Making data openly accessible </td> <td> Data is confidential, only for members of the consortium (including the Commission Services) </td> </tr> <tr> <td> Making data interoperable </td> <td> N/A </td> </tr> <tr> <td> Increase data re-use (through clarifying licences) </td> <td> Published data will be re-usable. Confidential data will be assessed on a case by case basis upon official request, and addressed by the consortium as a whole. </td> </tr> <tr> <td> Allocation of resources </td> <td> N/A </td> </tr> <tr> <td> Data security </td> <td> Data reside on a secure server (SSL) and is backedup on a regularly basis. </td> </tr> <tr> <td> Ethical aspects </td> <td> N/A </td> </tr> <tr> <td> Other </td> <td> Upon completion of the project, data will stored on a secure server at Arbiom. </td> </tr> </table> <table> <tr> <th> DataSet reference and name </th> <th> Demo plant operational outputs (T, P, flow rate, conc, …) (WP1) </th> </tr> <tr> <td> DataSet summary </td> <td>  Matrix of data </td> </tr> <tr> <td> Making data findable, including provisions for metadata </td> <td> Data is confidential, only for members of the consortium (including the Commission Services) </td> </tr> <tr> <td> Making data openly accessible </td> <td> Data is confidential, only for members of the consortium (including the Commission Services) </td> </tr> <tr> <td> Making data interoperable </td> <td> N/A </td> </tr> <tr> <td> Increase data re-use (through clarifying licences) </td> <td> Published data will be re-usable. Confidential data will be assessed on a case by case basis upon official request, and addressed by the consortium as a whole. </td> </tr> <tr> <td> Allocation of resources </td> <td> N/A </td> </tr> <tr> <td> Data security </td> <td> Data reside on a secure server (SSL) and is backedup on a regularly basis. </td> </tr> <tr> <td> Ethical aspects </td> <td> N/A </td> </tr> <tr> <td> Other </td> <td> Upon completion of the project, data will stored on a secure server at Arbiom. </td> </tr> </table> <table> <tr> <th> DataSet reference and name </th> <th> Demo plant sourcing </th> </tr> <tr> <td> DataSet summary </td> <td> * Report * LOIs * Contract and other commercial documents </td> </tr> <tr> <td> Making data findable, including provisions for metadata </td> <td> Data is confidential </td> </tr> <tr> <td> Making data openly accessible </td> <td> Data is confidential </td> </tr> <tr> <td> Making data interoperable </td> <td> N/A </td> </tr> <tr> <td> Increase data re-use (through clarifying licences) </td> <td> Published data will be re-usable. Confidential data will be assessed on a case by case basis upon official request, and addressed by the consortium as a whole. </td> </tr> <tr> <td> Allocation of resources </td> <td> N/A </td> </tr> <tr> <td> Data security </td> <td> Data reside on a secure server (SSL) and is backedup on a regularly basis. </td> </tr> <tr> <td> Ethical aspects </td> <td> N/A </td> </tr> <tr> <td> Other </td> <td> Upon completion of the project, data will stored on a secure server at Arbiom and NSG. </td> </tr> </table> <table> <tr> <th> DataSet reference and name </th> <th> Demo plant construction, commissioning and operation </th> </tr> <tr> <td> DataSet summary </td> <td>  Reports </td> </tr> <tr> <td> Making data findable, including provisions for metadata </td> <td> Data is confidential, only for members of the consortium (including the Commission Services) </td> </tr> <tr> <td> Making data openly accessible </td> <td> Data is confidential, only for members of the consortium (including the Commission Services) </td> </tr> <tr> <td> Making data interoperable </td> <td> N/A </td> </tr> <tr> <td> Increase data re-use (through clarifying licences) </td> <td> Published data will be re-usable. Confidential data will be assessed on a case by case basis upon official request, and addressed by the consortium as a whole. </td> </tr> <tr> <td> Allocation of resources </td> <td> N/A </td> </tr> <tr> <td> Data security </td> <td> Data reside on a secure server (SSL) and is backedup on a regularly basis. </td> </tr> <tr> <td> Ethical aspects </td> <td> N/A </td> </tr> <tr> <td> Other </td> <td> Upon completion of the project, data will stored on a secure server at Arbiom and NSG. </td> </tr> </table> <table> <tr> <th> DataSet reference and name </th> <th> Bioprocess data (WP2) </th> </tr> <tr> <td> DataSet summary </td> <td> * R&D Bioprocess Data, * Reports on SCP production at small scale * Reports on SCP production at small scale  Reports on optimisation of SCP production at small * Reports on optimisation of SCP production at small </td> </tr> <tr> <td> Making data findable, including provisions for metadata </td> <td> Data is confidential, only for members of the consortium (including the Commission Services) </td> </tr> <tr> <td> Making data openly accessible </td> <td> Data is confidential, only for members of the consortium (including the Commission Services) </td> </tr> <tr> <td> Making data interoperable </td> <td> N/A </td> </tr> <tr> <td> Increase data re-use (through clarifying licences) </td> <td> Published data will be re-usable. Confidential data will be assessed on a case by case basis upon official request, and addressed by the consortium as a whole. </td> </tr> <tr> <td> Allocation of resources </td> <td> N/A </td> </tr> <tr> <td> Data security </td> <td> Data reside on a secure server (SSL) and is backedup on a regularly basis. </td> </tr> <tr> <td> Ethical aspects </td> <td> N/A </td> </tr> <tr> <td> Other </td> <td> Upon completion of the project, data will stored on a secure server at Arbiom and SPP. </td> </tr> </table> <table> <tr> <th> DataSet reference and name </th> <th> Fish feed nutritional analysis (WP3) </th> </tr> <tr> <td> DataSet summary </td> <td> * Intermediary reports (3) on nutritional analysis, undesirable components and methods for optimization * Final report on nutritional analysis, undesirable components and methods for optimization </td> </tr> <tr> <td> Making data findable, including provisions for metadata </td> <td> Data is confidential, only for members of the consortium (including the Commission Services) </td> </tr> <tr> <td> Making data openly accessible </td> <td> Data is confidential, only for members of the consortium (including the Commission Services) </td> </tr> <tr> <td> Making data interoperable </td> <td> N/A </td> </tr> <tr> <td> Increase data re-use (through clarifying licences) </td> <td> Published data will be re-usable. Confidential data will be assessed on a case by case basis upon official request, and addressed by the consortium as a whole. </td> </tr> <tr> <td> Allocation of resources </td> <td> N/A </td> </tr> <tr> <td> Data security </td> <td> Data reside on a secure server (SSL) and is backedup on a regularly basis. </td> </tr> <tr> <td> Ethical aspects </td> <td> N/A </td> </tr> <tr> <td> Other </td> <td> Upon completion of the project, data will stored on a secure server at Matís and Arbiom. </td> </tr> </table> <table> <tr> <th> DataSet reference and name </th> <th> Feed formulation (WP3) </th> </tr> <tr> <td> DataSet summary </td> <td>  Report on feed formulation of commercial diets for carnivorous fish (Atlantic salmon) and omnivorous fish (Tilapia) </td> </tr> <tr> <td> Making data findable, including provisions for metadata </td> <td> Data is confidential, only for members of the consortium (including the Commission Services) </td> </tr> <tr> <td> Making data openly accessible </td> <td> Data is confidential, only for members of the consortium (including the Commission Services) </td> </tr> <tr> <td> Making data interoperable </td> <td> N/A </td> </tr> <tr> <td> Increase data re-use (through clarifying licences) </td> <td> Published data will be re-usable. Confidential data will be assessed on a case by case basis upon official request, and addressed by the consortium as a whole. </td> </tr> <tr> <td> Allocation of resources </td> <td> N/A </td> </tr> <tr> <td> Data security </td> <td> Data reside on a secure server (SSL) and is backedup on a regularly basis. </td> </tr> <tr> <td> Ethical aspects </td> <td> N/A </td> </tr> <tr> <td> Other </td> <td> Upon completion of the project, data will stored on a secure server at Matís and Arbiom. </td> </tr> </table> <table> <tr> <th> DataSet reference and name </th> <th> Small & large scale feed production (WP3) </th> </tr> <tr> <td> DataSet summary </td> <td> * Report on production methods and quality parameters for small scale feed production * Report on production methods and quality parameters fo large scale feed production </td> </tr> <tr> <td> Making data findable, including provisions for metadata </td> <td> Data is confidential, only for members of the consortium (including the Commission Services) </td> </tr> <tr> <td> Making data openly accessible </td> <td> Data is confidential, only for members of the consortium (including the Commission Services) </td> </tr> <tr> <td> Making data interoperable </td> <td> N/A </td> </tr> <tr> <td> Increase data re-use (through clarifying licences) </td> <td> Published data will be re-usable. Confidential data will be assessed on a case by case basis upon official request, and addressed by the consortium as a whole. </td> </tr> <tr> <td> Allocation of resources </td> <td> N/A </td> </tr> <tr> <td> Data security </td> <td> Data reside on a secure server (SSL) and is backedup on a regularly basis. </td> </tr> <tr> <td> Ethical aspects </td> <td> N/A </td> </tr> <tr> <td> Other </td> <td> Upon completion of the project, data will stored on a secure server at Matís and Arbiom. </td> </tr> </table> <table> <tr> <th> DataSet reference and name </th> <th> Small & large scale trials (WP3) </th> </tr> <tr> <td> DataSet summary </td> <td>  Report on Small scale feeding trials describing test setup, fish’s growth rate or deformations  Report on large scale feeding trials describing test setup, fish’s growth rate or deformations </td> </tr> <tr> <td> Making data findable, including provisions for metadata </td> <td> Data is confidential, only for members of the consortium (including the Commission Services) </td> </tr> <tr> <td> Making data openly accessible </td> <td> Data is confidential, only for members of the consortium (including the Commission Services) </td> </tr> <tr> <td> Making data interoperable </td> <td> N/A </td> </tr> <tr> <td> Increase data re-use (through clarifying licences) </td> <td> Published data will be re-usable. Confidential data will be assessed on a case by case basis upon official request, and addressed by the consortium as a whole. </td> </tr> <tr> <td> Allocation of resources </td> <td> N/A </td> </tr> <tr> <td> Data security </td> <td> Data reside on a secure server (SSL) and is backedup on a regularly basis. </td> </tr> <tr> <td> Ethical aspects </td> <td> N/A </td> </tr> <tr> <td> Other </td> <td> Upon completion of the project, data will stored on a secure server at Matís and Arbiom. </td> </tr> </table> <table> <tr> <th> DataSet reference and name </th> <th> Feed formulation from C5 and lignin streams (WP4) </th> </tr> <tr> <td> DataSet summary </td> <td> * Intermediary report on lignin incorporation in fish feed formulation * Final report on lignin incorporation in fish feed formulation * Intermediary report on C5 fermentation into SCP and for C5 use in fish feed formulation * Final report on C5 fermentation into SCP and for C5 use in fish feed formulation * Intermediary report on lignin incorporation in fish feed formulation * Final report on lignin incorporation in fish feed formulation * Intermediary report on C5 fermentation into SCP and for C5 use in fish feed formulation * Final report on C5 fermentation into SCP and for C5 use in fish feed formulation </td> </tr> <tr> <td> Making data findable, including provisions for metadata </td> <td> Data is confidential, only for members of the consortium (including the Commission Services) </td> </tr> <tr> <td> Making data openly accessible </td> <td> Data is confidential, only for members of the consortium (including the Commission Services) </td> </tr> <tr> <td> Making data interoperable </td> <td> N/A </td> </tr> <tr> <td> Increase data re-use (through clarifying licences) </td> <td> Published data will be re-usable. Confidential data will be assessed on a case by case basis upon official request, and addressed by the consortium as a whole. </td> </tr> <tr> <td> Allocation of resources </td> <td> N/A </td> </tr> <tr> <td> Data security </td> <td> Data reside on a secure server (SSL) and is backedup on a regularly basis. </td> </tr> <tr> <td> Ethical aspects </td> <td> N/A </td> </tr> <tr> <td> Other </td> <td> Upon completion of the project, data will stored on a secure server at Matís and Arbiom. </td> </tr> </table> <table> <tr> <th> DataSet reference and name </th> <th> Cost of ownership of SCP (WP5) </th> </tr> <tr> <td> DataSet summary </td> <td>  Techno economic evaluation of the cost of goods and cost of operations. calculations, market data </td> </tr> <tr> <td> Making data findable, including provisions for metadata </td> <td> Data is confidential, only for members of the consortium (including the Commission Services) </td> </tr> <tr> <td> Making data openly accessible </td> <td> Data is confidential, only for members of the consortium (including the Commission Services) </td> </tr> <tr> <td> Making data interoperable </td> <td> N/A </td> </tr> <tr> <td> Increase data re-use (through clarifying licences) </td> <td> Published data will be re-usable. Confidential data will be assessed on a case by case basis upon official request, and addressed by the consortium as a whole. </td> </tr> <tr> <td> Allocation of resources </td> <td> N/A </td> </tr> <tr> <td> Data security </td> <td> Data reside on a secure server (SSL) and is backedup on a regularly basis. </td> </tr> <tr> <td> Ethical aspects </td> <td> N/A </td> </tr> <tr> <td> Other </td> <td> Upon completion of the project, data will stored on a secure server at Arbiom. </td> </tr> </table> <table> <tr> <th> DataSet reference and name </th> <th> Business cases (WP5) </th> </tr> <tr> <td> DataSet summary </td> <td> * Presentations * Report * Market analysis  Projections </td> </tr> <tr> <td> Making data findable, including provisions for metadata </td> <td> Data is confidential, only for members of the consortium (including the Commission Services) </td> </tr> <tr> <td> Making data openly accessible </td> <td> Data is confidential, only for members of the consortium (including the Commission Services) </td> </tr> <tr> <td> Making data interoperable </td> <td> N/A </td> </tr> <tr> <td> Increase data re-use (through clarifying licences) </td> <td> Published data will be re-usable. Confidential data will be assessed on a case by case basis upon official request, and addressed by the consortium as a whole. </td> </tr> <tr> <td> Allocation of resources </td> <td> N/A </td> </tr> <tr> <td> Data security </td> <td> Data reside on a secure server (SSL) and is backedup on a regularly basis. </td> </tr> <tr> <td> Ethical aspects </td> <td> N/A </td> </tr> <tr> <td> Other </td> <td> Upon completion of the project, data will stored on a secure server at Arbiom. </td> </tr> </table> <table> <tr> <th> DataSet reference and name </th> <th> Report on LCA on production of proteins from lignocellulose based on ARBIOM’s process (WP6) </th> </tr> <tr> <td> DataSet summary </td> <td>  Report </td> </tr> <tr> <td> Making data findable, including provisions for metadata </td> <td> Data is confidential, only for members of the consortium (including the Commission Services) </td> </tr> <tr> <td> Making data openly accessible </td> <td> Data is confidential, only for members of the consortium (including the Commission Services) </td> </tr> <tr> <td> Making data interoperable </td> <td> N/A </td> </tr> <tr> <td> Increase data re-use (through clarifying licences) </td> <td> Published data will be re-usable. Confidential data will be assessed on a case by case basis upon official request, and addressed by the consortium as a whole. </td> </tr> <tr> <td> Allocation of resources </td> <td> N/A </td> </tr> <tr> <td> Data security </td> <td> Data reside on a secure server (SSL) and is backedup on a regularly basis. </td> </tr> <tr> <td> Ethical aspects </td> <td> N/A </td> </tr> <tr> <td> Other </td> <td> Upon completion of the project, data will stored on a secure server at Arbiom and Ostfold. </td> </tr> </table> <table> <tr> <th> DataSet reference and name </th> <th> List of protein sources to be compared (WP6) </th> </tr> <tr> <td> DataSet summary </td> <td>  Report </td> </tr> <tr> <td> Making data findable, including provisions for metadata </td> <td> Data is confidential, only for members of the consortium (including the Commission Services) </td> </tr> <tr> <td> Making data openly accessible </td> <td> Data is confidential, only for members of the consortium (including the Commission Services) </td> </tr> <tr> <td> Making data interoperable </td> <td> N/A </td> </tr> <tr> <td> Increase data re-use (through clarifying licences) </td> <td> Published data will be re-usable. Confidential data will be assessed on a case by case basis upon official request, and addressed by the consortium as a whole. </td> </tr> <tr> <td> Allocation of resources </td> <td> N/A </td> </tr> <tr> <td> Data security </td> <td> Data reside on a secure server (SSL) and is backedup on a regularly basis. </td> </tr> <tr> <td> Ethical aspects </td> <td> N/A </td> </tr> <tr> <td> Other </td> <td> Upon completion of the project, data will stored on a secure server at Arbiom and Ostfold. </td> </tr> </table> <table> <tr> <th> DataSet reference and name </th> <th> Report on existing LCA for other protein sources (WP6) </th> </tr> <tr> <td> DataSet summary </td> <td>  Report </td> </tr> <tr> <td> Making data findable, including provisions for metadata </td> <td> Data is confidential, only for members of the consortium (including the Commission Services) </td> </tr> <tr> <td> Making data openly accessible </td> <td> Data is confidential, only for members of the consortium (including the Commission Services) </td> </tr> <tr> <td> Making data interoperable </td> <td> N/A </td> </tr> <tr> <td> Increase data re-use (through clarifying licences) </td> <td> Published data will be re-usable. Confidential data will be assessed on a case by case basis upon official request, and addressed by the consortium as a whole. </td> </tr> <tr> <td> Allocation of resources </td> <td> N/A </td> </tr> <tr> <td> Data security </td> <td> Data reside on a secure server (SSL) and is backedup on a regularly basis. </td> </tr> <tr> <td> Ethical aspects </td> <td> N/A </td> </tr> <tr> <td> Other </td> <td> Upon completion of the project, data will stored on a secure server at Arbiom and Ostfold. </td> </tr> </table> <table> <tr> <th> DataSet reference and name </th> <th> Report on LCA of other protein sources (WP6) </th> </tr> <tr> <td> DataSet summary </td> <td>  Report </td> </tr> <tr> <td> Making data findable, including provisions for metadata </td> <td> Data is confidential, only for members of the consortium (including the Commission Services) </td> </tr> <tr> <td> Making data openly accessible </td> <td> Data is confidential, only for members of the consortium (including the Commission Services) </td> </tr> <tr> <td> Making data interoperable </td> <td> N/A </td> </tr> <tr> <td> Increase data re-use (through clarifying licences) </td> <td> Published data will be re-usable. Confidential data will be assessed on a case by case basis upon official request, and addressed by the consortium as a whole. </td> </tr> <tr> <td> Allocation of resources </td> <td> N/A </td> </tr> <tr> <td> Data security </td> <td> Data reside on a secure server (SSL) and is backedup on a regularly basis. </td> </tr> <tr> <td> Ethical aspects </td> <td> N/A </td> </tr> <tr> <td> Other </td> <td> Upon completion of the project, data will stored on a secure server at Arbiom and Ostfold. </td> </tr> </table> <table> <tr> <th> DataSet reference and name </th> <th> Report on LCA for all protein sources (WP6) </th> </tr> <tr> <td> DataSet summary </td> <td>  Report </td> </tr> <tr> <td> Making data findable, including provisions for metadata </td> <td> Data is confidential, only for members of the consortium (including the Commission Services) </td> </tr> <tr> <td> Making data openly accessible </td> <td> Data is confidential, only for members of the consortium (including the Commission Services) </td> </tr> <tr> <td> Making data interoperable </td> <td> N/A </td> </tr> <tr> <td> Increase data re-use (through clarifying licences) </td> <td> Published data will be re-usable. Confidential data will be assessed on a case by case basis upon official request, and addressed by the consortium as a whole. </td> </tr> <tr> <td> Allocation of resources </td> <td> N/A </td> </tr> <tr> <td> Data security </td> <td> Data reside on a secure server (SSL) and is backedup on a regularly basis. </td> </tr> <tr> <td> Ethical aspects </td> <td> N/A </td> </tr> <tr> <td> Other </td> <td> Upon completion of the project, data will stored on a secure server at Arbiom and Ostfold. </td> </tr> </table> <table> <tr> <th> DataSet reference and name </th> <th> LCA data sets (WP6) </th> </tr> <tr> <td> DataSet summary </td> <td>  Report </td> </tr> <tr> <td> Making data findable, including provisions for metadata </td> <td> Data is open access and is findable through research data repository. </td> </tr> <tr> <td> Making data openly accessible </td> <td> Data is open access. </td> </tr> <tr> <td> Making data interoperable </td> <td> N/A </td> </tr> <tr> <td> Increase data re-use (through clarifying licences) </td> <td> Published data will be re-usable. Confidential data will be assessed on a case by case basis upon official request, and addressed by the consortium as a whole. </td> </tr> <tr> <td> Allocation of resources </td> <td> N/A </td> </tr> <tr> <td> Data security </td> <td> Data reside on a secure server (SSL) and is backedup on a regularly basis. </td> </tr> <tr> <td> Ethical aspects </td> <td> N/A </td> </tr> <tr> <td> Other </td> <td> Upon completion of the project, data will stored on a secure server at Arbiom and Ostfold. </td> </tr> </table> <table> <tr> <th> DataSet reference and name </th> <th> Communiction/dissemination tools (WP7) </th> </tr> <tr> <td> DataSet summary </td> <td> * Brochures * Posters * Infographics including animations * Videos * Newsletters * Press releases * Social media accounts </td> </tr> <tr> <td> Making data findable, including provisions for metadata </td> <td> The data is available on the project website and is open source. All data can be found through regular websearch and through a regular keyword search. Each website post or page is labelled with relevant metadata for easier access. </td> </tr> <tr> <td> Making data openly accessible </td> <td> All website data is open source, hosted within the WordPress Content Management system (CMS)) </td> </tr> <tr> <td> Making data interoperable </td> <td> N/A </td> </tr> <tr> <td> Increase data re-use (through clarifying licences) </td> <td> Published data will be re-usable. Confidential data will be assessed on a case by case basis upon official request, and addressed by the consortium as a whole. </td> </tr> <tr> <td> Allocation of resources </td> <td> N/A </td> </tr> <tr> <td> Data security </td> <td> Data reside on a secure server (SSL) and is backedup on a regularly basis. </td> </tr> <tr> <td> Ethical aspects </td> <td> N/A </td> </tr> <tr> <td> Other </td> <td> N/A </td> </tr> </table> <table> <tr> <th> DataSet reference and name </th> <th> Registration of SCP (Single Cell Protein) (WP7) </th> </tr> <tr> <td> DataSet summary </td> <td>  Document submitted to the EU to use our SCP as fish feed ingredient/ Authorisation from the EU/FDA to use our product for feed applications </td> </tr> <tr> <td> Making data findable, including provisions for metadata </td> <td> Data is confidential, only for members of the consortium (including the Commission Services) </td> </tr> <tr> <td> Making data openly accessible </td> <td> Data is confidential, only for members of the consortium (including the Commission Services) </td> </tr> <tr> <td> Making data interoperable </td> <td> N/A </td> </tr> <tr> <td> Increase data re-use (through clarifying licences) </td> <td> Published data will be re-usable. Confidential data will be assessed on a case by case basis upon official request, and addressed by the consortium as a whole. </td> </tr> <tr> <td> Allocation of resources </td> <td> N/A </td> </tr> <tr> <td> Data security </td> <td> Data reside on a secure server (SSL) and is backedup on a regularly basis. </td> </tr> <tr> <td> Ethical aspects </td> <td> N/A </td> </tr> <tr> <td> Other </td> <td> Upon completion of the project, data will stored on a secure server at Arbiom. </td> </tr> </table> <table> <tr> <th> DataSet reference and name </th> <th> Exploitation data (WP7) </th> </tr> <tr> <td> DataSet summary </td> <td> * Project business cases * SCP based product competitive advantages  SYLFEED demonstration impact analysis  Update of business plans and identification of steps </td> </tr> <tr> <td> Making data findable, including provisions for metadata </td> <td> Data is confidential, only for members of the consortium (including the Commission Services) </td> </tr> <tr> <td> Making data openly accessible </td> <td> Data is confidential, only for members of the consortium (including the Commission Services) </td> </tr> <tr> <td> Making data interoperable </td> <td> N/A </td> </tr> <tr> <td> Increase data re-use (through clarifying licences) </td> <td> Published data will be re-usable. Confidential data will be assessed on a case by case basis upon official request, and addressed by the consortium as a whole. </td> </tr> <tr> <td> Allocation of resources </td> <td> N/A </td> </tr> <tr> <td> Data security </td> <td> Data reside on a secure server (SSL) and is backedup on a regularly basis. </td> </tr> <tr> <td> Ethical aspects </td> <td> N/A </td> </tr> <tr> <td> Other </td> <td> Upon completion of the project, data will stored on a secure server at Arbiom. </td> </tr> </table> <table> <tr> <th> DataSet reference and name </th> <th> Meeting minutes & reports (WP8) </th> </tr> <tr> <td> DataSet summary </td> <td> * Minutes from all SYLFEED meetings * Internal progress reports </td> </tr> <tr> <td> Making data findable, including provisions for metadata </td> <td> Data is confidential, only for members of the consortium (including the Commission Services) </td> </tr> <tr> <td> Making data openly accessible </td> <td> Data is confidential, only for members of the consortium (including the Commission Services) </td> </tr> <tr> <td> Making data interoperable </td> <td> N/A </td> </tr> <tr> <td> Increase data re-use (through clarifying licences) </td> <td> Published data will be re-usable. Confidential data will be assessed on a case by case basis upon official request, and addressed by the consortium as a whole. </td> </tr> <tr> <td> Allocation of resources </td> <td> N/A </td> </tr> <tr> <td> Data security </td> <td> Data reside on a secure server (SSL) and is backedup on a regularly basis. </td> </tr> <tr> <td> Ethical aspects </td> <td> N/A </td> </tr> <tr> <td> Other </td> <td> Upon completion of the project, data will stored on a secure server at Arbiom. </td> </tr> </table> <table> <tr> <th> DataSet reference and name </th> <th> Quality & Risk Management Plan (WP8) </th> </tr> <tr> <td> DataSet summary </td> <td>  reports </td> </tr> <tr> <td> Making data findable, including provisions for metadata </td> <td> Data is confidential, only for members of the consortium (including the Commission Services) </td> </tr> <tr> <td> Making data openly accessible </td> <td> Data is confidential, only for members of the consortium (including the Commission Services) </td> </tr> <tr> <td> Making data interoperable </td> <td> N/A </td> </tr> <tr> <td> Increase data re-use (through clarifying licences) </td> <td> Confidential data will be assessed on a case by case basis upon official request, and addressed by the consortium as a whole. </td> </tr> <tr> <td> Allocation of resources </td> <td> N/A </td> </tr> <tr> <td> Data security </td> <td> Data reside on a secure server (SSL) and is backedup on a regularly basis. </td> </tr> <tr> <td> Ethical aspects </td> <td> N/A </td> </tr> <tr> <td> Other </td> <td> Upon completion of the project, data will be stored on a secure server at Arbiom. </td> </tr> </table>	https://phaidra.univie.ac.at/o:1140797	Horizon 2020
0219_GHOST_770019.md	# Introduction ## Executive summary The Project Coordinator (CRF) is responsible for technical coordination and scientific quality assurance throughout the project. This task involves monitoring of the technical progress, coordinating data management, the various work packages and tasks, and risk monitoring. When necessary corrective actions will be taken including potential work reallocation which will be coordinated by the Project Coordinator and agreed by the Executive Board consisting of all WP leaders. An approval procedure was defined during the kick-off meeting and it has been agreed that the Executive Board is the body for quality assurance in line with the Consortium Agreement. D1.1 the Quality Assurance Guideline contains more information on the related internal roles, rules and procedures. This Deliverable report addresses both the risk and data management plans and includes definitions and details of the processes being implemented with respect to the GHOST project. The Risk Management Plan (RMP) is an on-going process which is continuously updated throughout the lifetime of the project to monitor known risks, identify emerging risks, and where necessary to respond to them. As part of the risk management plan, a risk register is compiled from risks identified in the GHOST Grant Agreement, Deliverables to date, and from risks which are identified during the course of the project. This register is monitored and discussed during the Executive Board meetings and adjusted when necessary. The Data Management Plan (DMP), which is also an on-going process, defines how different project data will be handled during the project and aims to ensure the appropriate level of access to and re-use of data generated by the GHOST project in particular with respect to the public domain Deliverable reports. The Project Coordinator (CRF) is responsible for the implementation of the DMP and its coordination is assisted by the Executive Board. Both the RMP and DMP should be considered to be complimentary with respect to the other Deliverable reports and in particular Deliverable D1.1 “Quality Assurance Guidelines” and D9.2 “Dissemination and communication plan”. Furthermore, all the partners have signed a Consortium Agreement, in which all relevant issues necessary for the proper execution of the project are described in detail including: the responsibilities (General Assembly, Project Coordinator and individual parties), liabilities, voting rules, intellectual property rights (IPR), knowledge management, rules for publishing information, conflict resolution, etc. # Overview of the GHOST Project ## Objectives and Scope The aim of the GHOST project is to develop an InteGrated and PHysically Optimised Battery System for Plug-in Vehicles Technologies. The overall objectives of the GHOST project, looking at both the existing Li- ion battery technologies and the future commercial post-lithium-ion ones, are: * Design of a novel and modular battery system with higher energy density (in weight) up to 20% based on the state- of-the-art of lithium-ion battery cell technologies through: Implementation of advanced light and functionalized battery system (BS) housing material; Innovative, modular, energy and cost efficient thermal management architectures & strategies; Optimal selection of the right battery cell technology for different applications and use-cases that will be demonstrated in the proposed project; * Increase of the energy density of the battery system up to 30% based on novel Dual Battery System concept based on new emerging battery technologies and high power lithium-ion battery; * Development of mass producible innovative and integrated design solutions to reduce the battery integration cost at least by 30% through smart design: starting from cell up to recycling, testing and modelling approaches; * Definition of new test methodologies and procedures to evaluate reliability, safety and lifetime of different Battery Systems; * Design of novel prototyping, manufacturing and dismantling techniques for next generation of lithium-ion BS; Evaluation of 2nd life battery potential, applications and markets starting from requirements and specifications; Demonstration of GHOST solutions in two demonstrators (BEV bus with ultrafast partial charge capability and P-HEV) and one lab demonstrator (module level) for the post Lithium-Ion technology. The aim is to achieve these key innovations at affordable cost in order to strengthen Europe’s competitive position in terms of Battery System, a crucial field for electrified vehicles. Technologies developed in the frame of the project will aim for first market introduction between 2023 and 2024. Importantly, the technology devised will have a strong impact on the electrically chargeable vehicles (BEVs and P-HEVs) performance increase (including range and related battery lifetime and reliability). <table> <tr> <th> Aim </th> <th> GHOST Objective </th> </tr> <tr> <td> Thermal, electrical and mechanical design of battery systems based on lithium and post lithium cells aiming at highly increased energy density and modularity </td> <td> Mechanical design: Replacement of bulky and heavy housings that are used in the current conventional BS by reliable and functionalized lightweight materials, contributing up to 30% weight reduction and provide improved safety by higher specific energies; Redesign of the BS by using novel materials at module level to reduce further the weight up to 20% and to maintain the required mechanical stability and safety. PCM with Al-foam Cell Cooling system Al plates Thermal design: Development of a module level novel and modular thermal management through advanced thermal concepts, which is independent of the cooling concept and selected media; Adaptive and smart active control of the cooling circuit pumps/fans; Thermal management design for bus and future BEVs fast charging (up to 350-450 kW). Electrical design: Development of a modular battery module architecture (i.e. 48V), which can be easily scaled up to 400-800 V with full commonalities in the field of used mechanical connections between the cells, fuses, safety, and battery controller unit concept; Novel Dual-Cell-Battery architecture </td> </tr> <tr> <td> Battery cost reduction </td> <td> Implementation of intelligent more integrated and simplified harness sensing and communications (i.e. for temperature and state of heath estimation) with high reliability; Complete redesign of the BS by taking into account the dismantling and recycling aspects (reduction of integration cost up to 20% and reduction time up to 30-40%); Standardized and innovative parameterization test protocols, models and state functions which can speed up the battery module and system development process by 20% compared to SoA contributing to reduce the integration cost as a consequence; Defining the needs to be taken into account to obtain a modular balancing concept solution suitable for automotive and second life applications; Improved modelling and simulation tools for BS improvement/development using virtual modelling approach, which is mainly based on the concept of Simulation-In-Loop (SIL), addressed through the application of the knowledge that will be generated in the project on thoroughly insight ageing mechanisms, SoH, SoC, SoF and electro-thermal modelling. </td> </tr> </table> <table> <tr> <th> Aim </th> <th> GHOST Objective </th> </tr> <tr> <td> Design for manufacturing, recycling and second use </td> <td> In the GHOST Project, efficient manufacturing processes will be applied for the BS that will be designed taking into account the best experiences in the field. In addition, the manufacturing will be processed in such way towards recycling in a cost efficient way thanks to the innovative solution of the physical integration of battery modules. </td> </tr> <tr> <td> Prototyping and mass-production technologies for battery systems </td> <td> New environmental friendly design of prototyping and manufacturing processes of BS for automotive will be considered and analysed; Identification of the cost efficient BS solution for mass-production. </td> </tr> <tr> <td> Demonstration of performance, lifetime and safety behaviour including lab testing and demonstration under real life conditions in vehicles </td> <td> Demonstration of GHOST BS solutions at lab level (TRL 5) and within 2 demonstrators (PHEV 500X and BEV bus) under real life conditions based on performances and operational/functional safety. </td> </tr> <tr> <td> Advanced physical integration technologies for high energy/power density battery packs should take into account safety and modularity </td> <td> Development of a Novel Dual-Cell-Battery architecture for next generation BS that comprises high-power (HP) battery and next generation high-energy battery technologies (HE) with highly integrated and efficient physical integration thanks to advanced DC/DC converter based on newest semiconductors technologies (Si and WBG technologies). This concept will be demonstrated at lab level (TRL 4). The integration, manufacturing and safety methodology aspects considered for lithium-ion technology will be transferred for the Dual-Cell-Battery architecture. </td> </tr> <tr> <td> Demonstration of performance, lifetime and safety behaviour including bench testing and demonstration under real life conditions in </td> <td> Test methodologies and procedures to evaluate the functional safety and lifetime of the battery from cells, modules to system levels: Advanced and reliable standardized test procedure focusing on lifetime, safety, reliability for Liion and post Li-ion as well. New technologies may need new accurate and reliable testing methods and evaluation procedures to reflect real-life scenarios, because different C-rates, operation temperatures, safety limits, etc. are required to verify the operational BS. More realistic test methods can decrease testing time and enhance safety and competitiveness; Moreover, the novel BS architecture will require devoted new procedures for the experimental evaluation to be applied in the validation phase; </td> </tr> <tr> <td> Aim </td> <td> GHOST Objective </td> </tr> <tr> <td> vehicles </td> <td> Reliability enhancement due to: BS with less external and internal connections (i.e. advanced harness sensing for temperature), cabling, components and also for the vehicle protection relays architecture; Innovative simplified connection methods between BS and battery controller unit. Safety improvement thanks to: In GHOST project, novel thermal-management architectures and strategies will be implemented to increase the safety and to guarantee insulation protection; Battery state function will be implemented to detect possible failure mechanisms; The battery system will be investigated in depth based on the functional safety during the verification, validation and integration within the vehicle. </td> </tr> </table> ## Workplan Overview * PHASE I: Define the specifications starting from the requirements and constraints (WP2); * PHASE II: Define the modular novel BS architecture for lithium based technology (WP3): New functionalized lightweight materials for reducing the weight/volume of the BS; Novel and cost efficient modular Thermal Management at battery module level that can be used for different types of vehicles (i.e. 400 or 800V) through refrigerant direct cooling or liquid cooling, depending of the application requirements; Advanced modular electric design which can guarantee higher level of safety and simplification of the battery system; Modular battery system approach for current and midterm lithium-ion battery technology; Recycle and reuse design approach for cost reduction; Eco-design analysis of the selected material. * PHASE III: Advanced prototyping and manufacturing of processing of BS and verification (WP3 & WP4): Integration of the developed state functions and harness sensing in the battery controller unit; Prototyping battery systems towards manufacturing processes; * PHASE IV: Evaluation of the safety requirements of the battery system at controlled environment (WP5); * PHASE V: Integration within the vehicle and demonstration in real environment (WP6); * PHASE VI: Analysis of recycling and second use of batteries at end of first life in the vehicle (WP4, WP7); Implementation and exploitation of the results and disseminate the findings (WP9). <table> <tr> <th> </th> </tr> <tr> <td> GHOST Work Breakdown Structure </td> </tr> </table> ## Expected Impacts of the GHOST Project <table> <tr> <th> Expected impact </th> <th> GHOST Contribution </th> <th> Related Deliverables </th> </tr> <tr> <td> Battery integra- tion costs (ex- cluding cell cost) reduced by 20 to 30% </td> <td> Implementation of intelligent harness sensing (i.e. for temperature and state of heath estimation) with high reliability and safety; Complete redesign of the battery system by taking into account the dismantling and recycling aspects (like reduction of integration cost up to 20% and dismantling time by 30-40%); Standardised and innovative parameterization test protocols and models that work with existing and next generation cell technologies (such as lithium sulphur or next generation) and which can speed up the battery model development process by 20% compared to state of the art models. Thus, the proposed solution will contribute to reduce the integration cost and the development time; Defining the needs to be taken into account in order to obtain a modular balancing concept solution suitable for automotive and second life applications; Improved modelling and simulation tools for battery system improve- ment/development thanks to the use of virtual modelling approach, which is mainly based on the concept of Simulation-in-Loop (SiL), addressed through the application of the knowledge that will be generated in the project on thoroughly insight ageing mechanisms, SoH, SoC, SoF and electro-thermal modelling. </td> <td> D3.4 D3.1 D4.1 D4.5 D4.5 </td> </tr> <tr> <td> Strengthening the EU value chain, from design and manufacturing to dismantling and recycling. </td> <td> Efficient manufacturing processes will be considered for the BS that will be designed taking into account the best experiences in the field. In addition, the manufacturing will be processed in such way towards recycling in a cost efficient way thanks to the innovative solution of the physical integration of battery modules; New design of prototyping and manufacturing processes of battery system for automotive will be considered and analysed; Identification of new cost-efficient BS for mass production. </td> <td> D3.6 D3.7 D8.2 </td> </tr> <tr> <td> Contributing to climate action and sustainable development objectives </td> <td> A modular battery system will be designed that targets weight reduction. The newly integrated battery system will not only be more efficient on pack level; the increased total energy density will also yield a significant improvement of the overall vehicular energy efficiency and the upstream CO2 emissions linked to the generation of the electricity used during charging of the vehicles. </td> <td> D3.5 D7.1 D7.2 </td> </tr> <tr> <td> Contributing to climate action and sustainable development objectives </td> <td> The GHOST proposal will investigate how reusing and refurbishing second-life batteries can be enabled (reducing barriers and gaining leverages) in order to meet specific sustainability goals. The dismantling process of a module will be simplified to decrease the handling cost for second life usage. The manufacturing of components and the mining of materials of the battery system have an important environmental and social impact. Optimizing the usage and need of these materials and components with a circular approach has the potential to reduce the environmental and social impacts significantly. </td> <td> D3.5 D7.1 D7.2 </td> </tr> </table> <table> <tr> <th> Expected Impact </th> <th> GHOST Contribution </th> <th> Related deliverables </th> </tr> <tr> <td> Energy density improvement of battery packs in the order of 15 to 20% </td> <td> Mechanical design: Replacement of bulky metallic housing of battery system that are used in the current or conventional battery systems (i.e. BMW i3 or TESLA) by reliable and lightweight materials, which will contribute up to 30% weight reduction; Redesign of battery system by using novel materials such as Al-foam at module level to reduce further the weight up to 20% and to maintain the required mechanical stability and safety according to the standards of on-road vehicles. Thermal design: Optimization of thermal management through efficient integration of phase change materials (PCM) into Al-foam to increase the thermal buffer on one hand and to simplify the thermal architecture on another hand. The incorporated PCM inside Al-foam will be coupled with refrigerant or liquid cooling at the bottom of the battery module to achieve an efficient cooling concept; Adaptive and smart active control of the cooling circuit pumps/fans; Design of thermal management applicable for fast charging opportunities. Electrical design: Development of a modular battery module architecture (i.e. 48V), which can be easily scaled up to 400-800V or high capacity applications with full commonalities in the field of used mechanical connections between the cells, fuses, safety, and opti- mised electrical integration and control thanks to battery control system (BCS); Novel Modular Dual Battery System architecture composed by one high-power (Li- Ion) and one high-energy (Li-Sulphur) battery modules, combined by a DC/DC converter with integrated control unit. </td> <td> D3.1 D3.2 D3.3 D3.4 D8.1 D8.2 D8.3 </td> </tr> </table> # Risk Management ## Introduction Risk is defined as an event that has a likelihood of occurring, and could have a negative impact on a project. A risk may have one or more causes and, if it occurs, one or more impacts. All projects assume some element of risk, and it’s through risk management to monitor and track those events that have the potential to impact the outcome of a project. Risk management has four stages: risk identification, analysis, evaluation and mitigation underpinned by continuous monitoring and control. These stages are described below in more detail as well as the roles and responsibilities connected to the risk management. Figure 1: Stages of risk management Risk management is an on-going process that continues throughout the life of a project. It includes processes for risk management planning, identification, analysis, monitoring and control. It is the objective of risk management to decrease the likelihood and impact of events averse to the project. On the other hand, any event that could have a positive impact should be exploited. ## Types of Risk Typically the risks in a technical research and innovation project of this type include: * Technological risks * Partnership risks * Market risks * Legal risks * Management risks * Environmental/regulation /safety risks The purpose of this document is to describe the procedures being implemented within the GHOST project for identifying and handling risks. This risk management plan applies to all partners in the project. ## Risk likelihood The risk likelihood is the chance that a risk will occur in the life time of the project. The following chart shows the risk likelihood definitions. For each of the identified risks the potential likelihood that a given risk will occur must be assessed, and appropriate risk likelihood is selected from the chart below. <table> <tr> <th> Likelihood Category </th> <th> Description </th> </tr> <tr> <td> Certain </td> <td> Risk event expected to occur </td> </tr> <tr> <td> Likely </td> <td> Risk event more likely than not to occur </td> </tr> <tr> <td> Moderate </td> <td> Risk event may or may not occur </td> </tr> <tr> <td> Unlikely </td> <td> Risk event less likely than not to occur </td> </tr> <tr> <td> Rare </td> <td> Risk event not expected to occur </td> </tr> </table> Table A: Risk Likelihood ## Risk impact The risk impact is the cause or effect of the risk in the project’s progress. It is classified in five levels: * Very serious: The risk would jeopardize the project’s continuity or would significantly affect the projects outcomes. A very serious impact would be that the project needs to stop. * Serious: The risk would jeopardize the project’s continuity or would significantly affect the project outcomes. Usually, when a serious impact risk occurs, there is a need of changing the project contract, eg. one of the partners abandons the project. * Moderate: the risk has a significant impact on the project, but it is perceived that the objectives will be still achieved eg. difficulties of defining RTD specifications lead to 6 months delay. * Slight: the effect on the project is minor e.g. a task leader drops out the project. * Low: the effect on the project is negligible, e.g. Shift of budget between partners. The complexity, the technical challenges and the size of GHOST require an adequate risk management. It is therefore necessary that potential risks are clearly identified, assessed, and that possible recovery actions be prepared. Potential risks can be related to delays, performance, collaboration and management. Risk is by definition the product of Probability and Impact. A preliminary analysis of GHOST risks associated with the work plan and using a Risk ProbabilityImpact Matrix approach is presented by a sequential step- approach: * Identify potential impacts of risks; * Evaluate Probability Impact Scores; * Prioritize Risks for Management Action; * Determine risk mitigation measures into actions. <table> <tr> <th> </th> <th> </th> <th> </th> <th> </th> <th> IMPACT </th> <th> </th> <th> </th> </tr> <tr> <th> TRIVIAL </th> <th> MINOR </th> <th> MODERATE </th> <th> MAJOR </th> <th> EXTREME </th> </tr> <tr> <td> PROBABILITY </td> <td> RARE </td> <td> LOW </td> <td> LOW </td> <td> LOW </td> <td> MEDIUM </td> <td> MEDIUM </td> </tr> <tr> <td> UNLIKELY </td> <td> LOW </td> <td> LOW </td> <td> MEDIUM </td> <td> MEDIUM </td> <td> MEDIUM </td> </tr> <tr> <td> MODERATE </td> <td> LOW </td> <td> MEDIUM </td> <td> MEDIUM </td> <td> MEDIUM </td> <td> HIGH </td> </tr> <tr> <td> LIKELY </td> <td> MEDIUM </td> <td> MEDIUM </td> <td> MEDIUM </td> <td> HIGH </td> <td> HIGH </td> </tr> <tr> <td> VERY LIKELY </td> <td> MEDIUM </td> <td> MEDIUM </td> <td> HIGH </td> <td> HIGH </td> <td> HIGH </td> </tr> </table> Table B: Probability-impact matrix ## Specific aspects of the GHOST Risk Management Plan ### Roles and responsibilities All project partners should identify the project risks and give input to the reports concerning those risks. The risks must be defined and reported through the progress report template, indicating its risk likelihood, impact, contingency plan, and responsible partner and in what period of the project the risk is valid and should be monitored. The Project Coordinator (CRF) with the support of the Back Office (VUB) will review and assess the identified risks. If necessary, feedback will be provided to the identified responsible partner. If the risk exposure is critical or there is a need for further discussion, the Project Coordinator is responsible for raising the issue during the Executive Board and Consortium meetings (i.e. General Assembly’s and/or Progress Meetings). In that case, the mitigation plan must be established through a consensus decision process, and it may require the involvement of the Project Officer and all the partners. ### Reporting and monitoring The risks must be identified and reported as part of the progress reports which are scheduled at nominally 6-month intervals in conjunction with the General Assembly meetings. All project partners are required to participate in the identification of the project risks and give input to the progress reports regarding those risks. <table> <tr> <th> Updates of the RMP </th> <th> </th> </tr> <tr> <td> Version number </td> <td> Project month </td> <td> Nature </td> </tr> <tr> <td> 1 </td> <td> M8 </td> <td> Original version </td> </tr> <tr> <td> 2 </td> <td> M12 </td> <td> Update </td> </tr> <tr> <td> 3 </td> <td> M18 – RP1 </td> <td> Update </td> </tr> <tr> <td> 4 </td> <td> M24 </td> <td> Update </td> </tr> <tr> <td> 5 </td> <td> M30 </td> <td> Update </td> </tr> <tr> <td> 6 </td> <td> M36 – RP2 </td> <td> Update </td> </tr> <tr> <td> 7 </td> <td> M42 – RP3 </td> <td> Update </td> </tr> </table> ### The Risk Management register The risks are registered in a repository that is presented below. The Risk Management register is available on EMDESK, on the online management platform used for GHOST. Work Package leaders are expected to collect feedback from the partners involved in their Work Packages, revise and if necessary update the register at the end of each bi-annual internal reporting period. Figure 2: Risk register on EMDESK ### Foreseen risks An overview on significant risks and associated contingency plans is given in the Table below. The project management approach provides mechanisms to identify and resolve potential risks, such as continuous controls of the project plan with its milestones and key deliverables. The progress and resource reporting will enable the Project Management team (i.e. Coordinator, Back-office and Executive Board) to be continuously aware of potential problems. Hence, the team can initiate countermeasures in a timely fashion before a problem becomes jeopardizing and fall-back solutions can be defined and implemented in time. <table> <tr> <th> No. </th> <th> Description of risk </th> <th> WP </th> <th> Risk-mitigation measures </th> <th> Probability </th> <th> Effect </th> </tr> <tr> <td> Delays </td> <td> </td> <td> </td> </tr> <tr> <td> 1 Delays in providing the All Track development progress and components in time for focus efforts especially in the most following WPs activities sensible components. </td> <td> Medium </td> <td> High </td> </tr> <tr> <td> Performance </td> <td> </td> <td> </td> </tr> <tr> <td> 2 </td> <td> Detail level of component models when relevant data is not being supplied by the partners </td> <td> All </td> <td> Setup 1:1 confidentiality agreements where needed. </td> <td> Medium </td> <td> Medium </td> </tr> <tr> <td> 3 </td> <td> Integration effort of the components higher than expected </td> <td> WP3, WP7 </td> <td> Upfront virtual design validation need to be applied where possible. </td> <td> Medium </td> <td> Medium </td> </tr> <tr> <td> 4 </td> <td> Final assessment does not show expected target </td> <td> WP3, WP7, WP8 </td> <td> Re-evaluation of specification and recommendations for future improvement. </td> <td> Medium </td> <td> Medium </td> </tr> <tr> <td> 5 </td> <td> Component failure during </td> <td> WP3, </td> <td> Since from the proposal phase, </td> <td> Medium </td> <td> Medium </td> </tr> <tr> <td> No. </td> <td> Description of risk </td> <td> WP </td> <td> Risk-mitigation measures </td> <td> Probability </td> <td> Effect </td> </tr> <tr> <td> </td> <td> testing </td> <td> WP4, WP7, WP8 </td> <td> the potential component failure critical pathways have been identified and the availability of a proper number of spare parts to avoid delays on the Project timing have been planned. </td> <td> </td> <td> </td> </tr> <tr> <td> 6 </td> <td> Bench devices failure during testing </td> <td> WP3, WP7, WP8 </td> <td> Back-up plan for alternative test capabilities. </td> <td> Low </td> <td> Low </td> </tr> <tr> <td> 7 </td> <td> Availability of high quality li-ion cells </td> <td> WP3, WP4 </td> <td> Back-up suppliers involvement. </td> <td> Low </td> <td> High </td> </tr> <tr> <td> 8 </td> <td> Availability of high quality Li-S cells proto </td> <td> WP8 </td> <td> Contact with different possible Projects, organisations able to supply proto cells. </td> <td> Medium </td> <td> Medium </td> </tr> <tr> <td> Collaboration </td> <td> </td> <td> </td> </tr> <tr> <td> 9 Poor cooperation ALL In the monthly WP leader phone between partners calls, the effectiveness of the partner interactions will be continuously monitored. If this problem happens, it will be managed identifying the reasons why and solving them at WP level or at Project level depending on which are the involved partners and the nature of the problem. </td> <td> Low </td> <td> High </td> </tr> <tr> <td> Management </td> <td> </td> <td> </td> </tr> <tr> <td> 10 </td> <td> Partners leave or Partners become insolvent </td> <td> ALL </td> <td> Back-up partners list or inside Consortium solution. </td> <td> Low </td> <td> High </td> </tr> </table> Table C: List of foreseen risks and associated mitigation measures ### Preliminary Identification of Risks As indicated in the Grant Agreement, the GHOST Project consortium has identified at a preliminary stage some of the main barriers, obstacles and framework conditions that may limit and/or reduce the level of achievement of the previously described expected impacts. The achievement of the potential impacts that will be demonstrated via the GHOST project is dependent upon the market adoption of the technologies. Therefore, anything putting at risk their cost effective realization can be considered as potential barrier or obstacle. In particular there are foreseen to be: * The lack of industrial and international standards for the investigated architecture solution to set certain technical specifications, dimensions, and mechanical, electrical, and communication interfaces. Without a standard, OEMs and suppliers will be cautious in their development programs because of the financial risks associated with the risk not to be able to achieve economy of scale advantages; * Uncertain deployment of the European Energy Taxation regulation (COM/2011/169 and COM/2011/168) and a modification of the directive 96/53/EC on weight and dimensions of commercial vehicles will result in planning uncertainty in the automotive supply chain for electrified commercial vehicles; * The lack of homogeneous, and in some case adequate, government incentives in the different European Member States in order to stimulate the deployment of, in particular, electrified vehicles. ### International Standards As is documented in the APPENDIX to this report, a preliminary survey of the type approval regulatory body has been performed in order to identify any possible hindrance to a future homologation of the developed enhanced vehicle, taking into account those specifications and requirements which might be affected by the introduced advanced technologies; special attention has been paid to the hazardous aspects of the changes planned to the battery pack with a particular focus on crashworthiness, and the protection of occupants and vulnerable road users. # Data Management ## Introduction According to the H2020 Programme Guidelines on FAIR Data Management in Horizon 2020: “Data Management Plans (DMPs) are a key element of good data management. A DMP describes the data management life cycle for the data to be collected, processed and/or generated by a Horizon 2020 project. As part of making research data findable, accessible, interoperable and reusable (FAIR), a DMP should 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). The DMP is a living document to be updated over the course of the project whenever significant changes arise and as a minimum in time with the periodic evaluation/assessment of the project. The consortium will, at the time of the bi-annual internal progress reporting assess whether the DMP needs to be updated. The aim of the initial version of the GHOST DMP is to provide a general overview of data types collected and/or created by different project partners and on the usage of such data in line with the project objectives. The Horizon 2020 FAIR DMP template has been used and as the project proceeds, more questions will be addressed in detail, if needed. <table> <tr> <th> Updates of the DMP </th> <th> </th> </tr> <tr> <td> Version number </td> <td> Project month </td> <td> Nature </td> </tr> <tr> <td> 1 </td> <td> M8 </td> <td> Original version </td> </tr> <tr> <td> 2 </td> <td> M12 </td> <td> Assessment whether update is needed </td> </tr> <tr> <td> 3 </td> <td> M18 – RP1 </td> <td> Update </td> </tr> <tr> <td> 4 </td> <td> M24 </td> <td> Assessment whether update is needed </td> </tr> <tr> <td> 5 </td> <td> M30 </td> <td> Assessment whether update is needed </td> </tr> <tr> <td> 6 </td> <td> M36 – RP2 </td> <td> Update </td> </tr> <tr> <td> 7 </td> <td> M42 – RP3 </td> <td> Update </td> </tr> </table> ## The GHOST DMP <table> <tr> <th> Component </th> <th> Issues to be addressed </th> <th> GHOST Project DMP </th> </tr> <tr> <td> 1\. Data summary </td> <td> State the purpose of the data collection/generation </td> <td> A wide variety of technical data will be collected, primarily via simulation and experimental testing during the activities of the project primarily to enable direct comparison, in order to validate the simulation models, and to evaluate the performance of the technical solutions developed. </td> </tr> <tr> <td> Explain the relation to the objectives of the project </td> <td> The generation of data through the testing and simulation activities will focus on evaluating the performance of the innovative battery systems developed during the project. In this context, the generation of data is directly relevant to the objectives of the project. </td> </tr> <tr> <td> Specify the types and formats of data generated/collected </td> <td> The exact types and formats of the data generated/collected, whether any existing data is being re-used, the origin of the data, and the expected size of specific databases, will be defined in due course as a result of the testing and simulation activities which are planned to be conducted throughout the project. </td> </tr> <tr> <td> Specify if existing data is being reused (if any) </td> </tr> <tr> <td> Specify the origin of the data </td> </tr> <tr> <td> State the expected size of the data (if known) </td> </tr> <tr> <td> Outline the data utility: to whom will it be useful </td> <td> The data will be primarily of use to the partners within the GHOST consortium, bearing in mind that the exchange of technical information is effectively one of the fundamental and essential elements of a collaborative technical research project. Furthermore key data generated within the project, and reported within the Public (PU) Domain Deliverable reports, are likely to be useful to other stakeholders external to the GHOST consortium working in fields related to the development and deployment of battery systems for (primarily) automotive applications. </td> </tr> </table> <table> <tr> <th> 2\. FAIR Data </th> <th> </th> <th> </th> </tr> <tr> <td> 2.1 Making data findable, including provisions for metadata </td> <td> Outline the discoverability of data (metadata provision) </td> <td> </td> </tr> <tr> <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> <td> With respect to the provision and sharing of data, particularly those data relating to Deliverable reports classified as PU, every effort will be made to ensure that the data are easily identifiable and that standard identification mechanisms will be used. </td> </tr> <tr> <td> Outline naming conventions used </td> <td> Together with the creation and making available of data generated within the project, the naming conventions used will be specified. At this stage, the approaches being adopted for keyword searching and versioning will be specified if appropriate. </td> </tr> <tr> <td> Outline the approach towards search keyword </td> </tr> <tr> <td> Outline the approach for clear versioning </td> </tr> <tr> <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> <td> In this context it is assumed that the term metadata refers to “data [information] that provides information about other data”. With respect to this specific project, no specific existing standards are known. Nevertheless, if and when metadata are created, the approach adopted will be specified. </td> </tr> <tr> <td> 2.2 Making data openly accessible </td> <td> Specify which data will be made openly available? If some data is kept closed provide rationale for doing so </td> <td> The data which will be made openly available will correspond to the Deliverables that are classified as PU and hence will be in the Public Domain. </td> </tr> <tr> <td> Specify how the data will be made </td> <td> The data will be made available using the EMDESK platform ( _https://www.emdesk.com/en/_ ) which is the instrument </td> </tr> </table> <table> <tr> <th> </th> <th> available </th> <th> selected for the storage and exchange of all key documents and information within the project. </th> </tr> <tr> <th> 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)? </th> <th> The EMDESK platform which will be used for data storage and exchange is a standard instrument which can be accessed directly. Should any specific instructions be required to facilitate data access, such as information relating to the nature and origin of the data, then these will be provided when the data is deposited. </th> </tr> <tr> <th> Specify where the data and associated metadata, documentation and code are deposited </th> <th> All relevant documentation, codes, etc. relating to the data to be made openly accessible will be made available also within the EMDESK platform via public links that are accesible by those external users with whom the URL has been shared. </th> </tr> <tr> <th> Specify how access will be provided in case there are any restrictions </th> <th> The data selected for open access relating to public-domain Deliverable reports will be made available without restrictions. Conversely data relating to Deliverables which are classified at Consortium-level will be stored within the EMDESK platform with access restricted to the partners in the consortium. </th> </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. </td> <td> Due to the nature of the data which will be generated, primarily from the simulation and testing of the battery systems to be developed in the project, the data will be strictly linked to the specific system or component under investigation. Full details of the respective system/component and test/simulation will be provided in the corresponding public-domain Deliverable report relating to the data made available. </td> </tr> <tr> <td> Specify whether you will be using standard vocabulary for all data types present in your data set, to allow inter- </td> <td> Standard and conventional vocabulary, nomenclature, measurement units will be used throughout the project. A full glossary of abbreviations and acronyms will also be provided if and when necessary. </td> </tr> </table> <table> <tr> <th> </th> <th> disciplinary interoperability? If not, will you provide mapping to more commonly used ontologies? </th> <th> </th> </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 </td> <td> Currently there are no plans to license the use of key data related to public- domain Deliverables, but instead to allow open access to permit wide re-use during and after the project. Should it instead become necessary to alter this policy, and license the use of data, this will be specified in subsequent version of the GHOST Data Management Plan. </td> </tr> <tr> <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> <td> In general, the data for open access will be made available for re-use once deposited on the EMDESK platform, the timing of which would nominally correspond to the delivery date of the public-domain Deliverable reports to which the data is related. Should it become necessary, during the course of the project, to alter this process, eg. delay the release of key data with respect to when the Deliverable report is deposited, then this will be specified in a subsequent release of the GHOST DMP. </td> </tr> <tr> <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> <td> The key data which will be made publically available and hence useable by third parties during the course of the project will be made available for between 12 months and 36 months following project completion. </td> </tr> <tr> <td> Describe data quality assurance processes </td> <td> The data quality assurance process is outlined below. </td> </tr> <tr> <td> Specify the length of time for which the data will remain reusable </td> <td> Typically data will remain usable for 24 months after project completion. </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 </td> <td> The task of making key data from the project FAIR was envisaged from the outset and are therefore covered by the project budget. </td> <td> </td> </tr> <tr> <td> Clearly identify responsibilities for data management in your project </td> <td> The responsibilities for data management are described below. </td> <td> </td> </tr> <tr> <td> Describe costs and potential value of long term preservation </td> <td> The cost of long-term preservation (ie. for longer than 36 months) will be estimated if and when the need become apparent. Currently long-term preservation is not planned since it is likely that data will become obsolete within a 2-3 year timeframe following completion of the project due to new technical developments and innovations. </td> <td> </td> </tr> <tr> <td> 4\. Data security </td> <td> Address data recovery as well as secure storage and transfer of sensitive data </td> <td> It is foreseen that none of the data to be stored and made available will be of a sensitive nature. </td> <td> </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> <td> No aspects which may be considered to be sensitive from an ethical perspective will be addressed during this project. </td> <td> </td> </tr> <tr> <td> 6\. Other </td> <td> Refer to other national/funder/secto rial/departmental procedures for data management that you are using (if any) </td> <td> References to other national/funder/sectorial/departmental procedures for data management will be made in due course if used during the project. </td> <td> </td> </tr> </table> Table D: Overview of the GHOST DMP at project launch ## Overview of individual WPs with respect to the generation of data WP2 (Requirements & Specifications) collects requirements from the application point of view, related to the vehicle demonstrators to define the Battery System complete specification both for PHEV and BEV applications. At the same time, the available cell technologies to target the most suitable for the application will be investigated in order to have the most promising tested within WP4. In parallel, in WP2 the test typologies need to assess the technology development of GHOST versus the requirements previously listed. In WP3 (Modular Battery Architecture, Design, Prototyping and Manufacturing), first an investigation and an evaluation of the best suited battery module architecture will be performed followed by a detailed design of the modular battery modules for the targeted P-HEV and BEV battery systems. The outcome will contain data relating to detailed mechanical, electrical and thermal design of two battery systems based on the same flexible and scalable battery modules that enable thermally optimized ultra-fast charging at up to 350kW (for the BEV system). WP4 (Battery Cell Characterization, Modelling and Testing for Automotive & Second Life Batteries) focuses on developing a good understanding of the behaviour of the battery cells considered in the project. In this work package the cells will be characterized experimentally for the development of electrical, thermal and lifetime models, hence generating data from measurements. Furthermore, WP4 will provide the required information and technical background regarding the thermal behaviour of the battery cells, which in WP3 will be extended to module and battery system level. In addition, in this Work Package dedicated analysis will be carried out on advanced battery technologies e.g. Li-S and on considered lithium-ion battery cells during second life. WP5 (System Safety Validation of the Advanced Battery System) a methodology to build-up a multiaspect system assurance case of the novel battery system will be developed in order to demonstrate system safety for different system environments (e.g. P-HEV, BEV) and different use cases (e.g. 1st life as battery in the vehicle, 2nd life as storage system). A multi-concern safety analysis approach will be then applied to perform the battery system safety analysis; the ISO 26262 will be considered as initial base to start the analysis, using methods, like the hazard analysis and risk assessment, suggested by ISO 26262. The outcomes of these analyses will provide the baseline data for performing appropriate safety analysis such as FMEA in order to identify the safety critical elements of the general concepts. The analysis will consider, in an integrated approach, electrical, thermal and functional safety aspects of the battery system. A comparative analysis and impact evaluation of installing the battery system in different system environments on achieving system safety in different contexts will be also performed. Once the multi-concern safety of the general concepts have been analysed, the defined safety measures will be verified under lab conditions but with realistic build-in and operational conditions. WP6 (Vehicle Integration, Testing and Demonstration) focuses on the in-vehicle integration of the previously developed and commissioned battery systems for Li-ion applications. In WP6, actual vehicle testing activities in real conditions, either on road or on rolling bench, will be performed and hence measurement data will be generated. Finally, a comprehensive assessment of the battery system versus the specifications defined in WP2 will take place, considering all the tests performed in WP4, WP5 and WP6 and also the outcomes of the second life testing and analysis performed in WP7. In WP7 (Dismantling and Second life use of batteries), dismantling studies on existing batteries will provide data regarding how to improve battery design of the new battery pack with the target to reduce efforts and cost at the EoL- management including getting the new packs optimized for remanufacturing, reparation, reuse and second life. This finally will be demonstrated by a design approval based on dismantling studies. Furthermore a future oriented concept for reuse and 2nd life application will be developed, technically and commercially evaluated to show beneficial solutions for the expanded usage of battery packs and/or valuable components after the first life. In WP8 (Dual Battery System Design, Prototyping and Bench Testing), high power Li-Ion and Li-S technology dual battery concept will be studied with the aim of incrementing the energy density and efficiency of current Li-Ion-based battery systems. The modular concept for Li-Ion technology developed in WP3 is used and adapted for the design of a dual battery system together with the development of a DC/DC converter that emerges in this novel system. A validation and assessment of the novel dual battery system will be investigated with a scaled-down prototype to carry out a quantitative comparison between the scope of the dual battery system concept and the Li-Ion modular one. <table> <tr> <th> Number </th> <th> Title </th> <th> Type </th> <th> WP </th> <th> Lead partner </th> <th> Due </th> </tr> <tr> <td> D1.2 </td> <td> Risk and data management plan </td> <td> Report </td> <td> WP1 </td> <td> CRF </td> <td> M6 </td> </tr> <tr> <td> D2.2 </td> <td> Cells specification report for battery system prototype </td> <td> Report </td> <td> WP2 </td> <td> VUB </td> <td> M6 </td> </tr> <tr> <td> D2.3 </td> <td> Concept validation plan report </td> <td> Report </td> <td> WP2 </td> <td> CRF </td> <td> M8 </td> </tr> <tr> <td> D3.3 </td> <td> Prototyping, commissioning and functional verification of the designed battery systems </td> <td> Demonstrator </td> <td> WP3 </td> <td> AVL </td> <td> M28 </td> </tr> <tr> <td> D4.1 </td> <td> Metholodology test, characterisation test and electro-thermal battery model report </td> <td> Report </td> <td> WP4 </td> <td> VUB </td> <td> M12 </td> </tr> <tr> <td> D8.2 </td> <td> Assembly, commissioning of the dual cell Li-ion, Li-S battery module and validation & assessment of the dual battery system concept </td> <td> Demonstrator </td> <td> WP8 </td> <td> IKERLAN </td> <td> M40 </td> </tr> <tr> <td> D9.1 </td> <td> Public website </td> <td> Website </td> <td> WP9 </td> <td> VUB </td> <td> M3 </td> </tr> <tr> <td> D9.2 </td> <td> Dissemination and communication plan </td> <td> Report </td> <td> WP9 </td> <td> VUB </td> <td> M6 </td> </tr> <tr> <td> D9.4 </td> <td> Report on liaison with ongoing relevant EU projects in the field of battery system development and testing </td> <td> Report </td> <td> WP9 </td> <td> VUB </td> <td> M42 </td> </tr> </table> Table E: List of GHOST deliverables with Public dissemination level ## Quality Assurance and Responsibilities ### Scientific and Technical Quality The quality of the overall outcome of the project is primarily dependent upon the quality of the execution of the innovation and demonstration activities. Formally, the quality of the work is monitored throughout the project by the General Assembly, the Executive Board and the Project Coordination team. Informally, each and every project team member, including the WP leaders and the Coordinator, has the responsibility to critically consider the quality of the work and strive for the best possible results. Potential deviations from the project plan must be anticipated and identified in a timely manner to allow mitigating actions to be developed and planned. In this way, quality can subsequently be maintained by taking suitable corrective actions to recover the deficiency in output or time delay. In this process, particular attention will be paid to monitoring, and supporting good communication and cooperation between work packages in order to avoid a fragmentation of the activities, which could lead to a mismatch between interrelated work packages. ### Quality of Results The formal deliverables of the GHOST project are the output of the research and innovation activities and, as such, should be high-quality representations of the activities undertaken. The quality of the deliverables will be managed through a straightforward review process, which was agreed during the Kick-off meeting, in the Consortium Agreement and described in D1.1. The quality assurance mainly aims at the quality of deliverables by ensuring smooth cooperation within the consortium and defining the process of decision making and knowledge sharing. The deliverables primarily shall deliver all the initially agreed information which is required by partners to carry out their own work and to fulfil their obligations in the project. Furthermore, the deliverables must be in line with the project targets. During the Kick-off Meeting it was agreed that the Executive Board is the main body for quality assurance. The procedure rests on the premise that the author of the deliverable is the technical expert on the topic of the deliverable and as such is responsible for the technical content. The work package leader should check the deliverable, with a focus on the check with the objectives of the work package and its fit with the overall work within the work package (consistency check). Furthermore, all deliverables are reviewed by at least one and ideally by two reviewer who is not directly involved in the preparation of the deliverable. The Project Coordinator performs the final review of the deliverable, focusing on general fit with the project objectives. If the deliverable serves as input for other work packages, focus should be put on whether the deliverable serves this purpose, both an a qualitative as well as quantitative level. Subsequently, the Project Coordinator is responsible for the delivery of the report to the EC. Another important aspect of quality management is the administrative processes of a project. The Project Coordinator, CRF, complies with the procedures indicated by the ISO 9001 certified Quality System. The Project Coordinator, strongly supported by VUB, will regularly monitor administrative processes in such matters as finances and any legal issues which may arise. Key points to be monitored such that the administration of the project is traceable and justifiable include, finances, risks and risk mitigation, changes which may affect the Grant Agreement, timing and reporting. The Project Coordinator, CRF, with the support of the Back-Office, VUB, coordinates the periodic management reports and the final report. In this task, the H2020 Tool, which was specifically designed for the collection of detailed use of resources and the technical status of projects, will be used. The Coordinator will collect, check and send to the EC the required cost statements, on the basis of the scheduled plan. The Project Coordinator with the support of the Back-Office will monitor progress through the nominally monthly Executive Board meetings, bi-annual Work Progress reports and annual General Assembly meetings. All finalised deliverables will be stored at the internal EMDESK platform for partners. All public deliverables with a public (PU) dissemination level will be made available on EMDESK via public sharing links also for external users and will also be placed on the GHOST public website. Instead, if the deliverable has a dissemination level of confidential (CO) only the publishable executive summary will be placed on the public website. Furthermore, the key data relating to the public deliverables which will be made available according to the GHOST DMP (see Table D) will be made available on the EMDESK platform and accessible for those who possesses the public link. EMDESK, the GHOST platform serves as central data storage and secure repository for document management and sharing. It allows the project partners to define specific permissions for folders or documents, to track document versions, share large documents or datasets, to share direct document link e.g. via email, to make documents accessible to public via public sharing link, to assign documents to related project items for easy and quick document retrieval and to receive email digests on activities in the document manager. Within the project, document templates have been developed for deliverables. These templates can be found on EMDESK. As outlined in D1.1, documents shall be named in a way that makes them easily identifiable and findable. Suggested process to be used for giving titles to project documents: [Project] - [WP] - [Document] - [Owner] - [Version]. For every deliverable the author will take the review comments into account. A motivated explanation is required if it is not possible to process the comments as requested by the reviewer, or if the author(s) disagree with the comments received. In case of remaining disagreement, the Project Coordinator will guide the process and will ensure a convergence of the process towards a final result. ## IPR: handling of intellectual property rights and data access All the partners have signed a Consortium Agreement. This agreement addresses the exploitation of the results and the patent and licensing issues as well as procedures with respect to the dissemination of results. A guiding rule is that partners investing in research should have an advantage compared to those who do not. This means that knowledge created during projects that offer commercial interest must be safeguarded and protected for exploitation by the owner. On the other hand, partners of this project need to come together in order to collaborate and benefit from their respective resources and competencies. Thus, added value through the sharing of knowledge and promoting exploitation represents a clear objective and driving forces of this collaborative project. This approach to knowledge management and IPR is detailed and regulated in the Consortium Agreement, which has been signed by all partners at the start of the project. Some of the major aspects covered are indicated below: * Background knowledge, specific limitations and/or conditions for implementation and for exploitation; Project Results, their (joint) ownership and the transfer of Results; * Access rights to Background and Results, for consortium partners and their affiliates; * Publications, procedures for dissemination of results and research data and open access hereto. Background (= existing know how or pre-existing intellectual property) of a specific partner shall be made available to the Partner (or Partners) within the consortium that needs this information for the proper execution of their tasks within the scope of the project. The use of Background is strictly limited for use to the achievement of the project goals and for the duration of the project. The receiving partner or partners will sign appropriate non- disclosure agreements with the providing partner. An overview of the Background was included as an annex to the consortium agreement. All partners shall be entitled to license their Background. Licensing of Background to third parties will be done on commercial conditions whereas licensing of Background to partners of the consortium will be done on fair and reasonable conditions. Results (e.g. results, including intellectual property generated in the project) shall be owned by the partner or partners who developed the results. Each partner is responsible for taking the appropriate steps for securing intellectual property of the knowledge or results created in the project (e.g. filing of patent applications). Each partner is obligated to fully inform the project coordinator of the filing of patent applications of knowledge or results created in the field of the project within two weeks of the date of filing. Each partner that owns a specific Result shall be free to exploit their Result as it sees fit. Appropriate joint ownership agreements will be drawn up. The participating research institutes/universities are entitled to use knowledge or results from the project, which either have been published or have been declassified, for research and teaching purposes. The project’s website will also contain an overview/archive of all published information and/or links hereto. Access Rights to Background and Results shall be free of charge to partners of the consortium for research and development purposes within the scope and the duration of the project. Access Rights to Background and/or Results that are owned by one or more of the partners shall be granted on fair and reasonable conditions and to the extent necessary to enable these partners to exploit their own results. For this purpose, the involved partners are entitled to conclude appropriate (bi, tri or multilateral) license, supply, product and/or service agreements. # APPENDIX Overview of Relevant International Safety Standards The international standards concerning type-approval requirements for the general safety of motor vehicles have been reviewed and analyzed with reference to the technical innovations in the framework of the GHOST project. Within the European Union, two systems of type approval are in force. The first is based on the European Commission directives and regulations, and targets the entire vehicle system as well the subsystems and components. The second is based on the UN regulation, and targets subsystems and components of the vehicle, but not the whole vehicle. The main directive for the vehicle type approval in the EU is the 2007/46/EC of the European parliament and of the council. The directive establishes a framework for the approval of motor vehicles and their trailers, and of systems, components and separate technical units intended for such vehicles. This Framework poses requirements for the different subsystem constituting the vehicle. Hence, to gain the whole approval of the vehicle, the various subsystems shall be verified against their compliance. The above mentioned directive provides a list of Regulatory acts for EC type- approval of vehicles produced in unlimited series and includes, for example: * General Safety Regulation (EC) No 661/2009 * Electric safety Regulation (EC) No 661/2009 UNECE Regulation No 100 The main directive for the vehicle type approval with respect to the General safety is the Regulation (EC) No 661/2009. It sets out requirements regarding both the general safety of motor vehicles and the environmental performance of tires. The subsystems reported in Annex 1 of the regulation are in scope of the requirements expressed in Article 5 (1) and (2), e.g.: 1. Manufacturers shall ensure that vehicles are designed, constructed and assembled so as to minimize the risk of injury to vehicle occupants and other road users, 2. Manufacturers shall ensure that vehicles, systems, components and separate technical units comply with the relevant requirements set out in this Regulation and its implementing measures, including among other the requirements relating to: * vehicle structure integrity, including impact tests; * systems to provide the driver with visibility and information on the state of the vehicle and the surrounding area, including glazing, mirrors and driver information systems; * electromagnetic compatibility; * heating system * electrical safety In general, pursuant with par. 7 of UNECE Regulation No. 94, any increase in mass of the vehicle greater than 8 per cent might imply a bigger testing effort in order to demonstrate compliance with the regulation provisions, depending on the judgement of the Type Approval Authority. Product certification is a fundamental precondition to establish a product at the market. The certification comprises the tests regarding required standards and robustness. Generally, depending on the mission profile the vehicle manufacturer specifies the requirements on the electrical and electronic equipment. The supplier of the Battery System is liable to observe these requirements and to perform the appropriate tests to ensure functionality, reliability and safety. The Battery System as an electric device have to undergo through various tests and likewise its electrical and electronic components have to meet various requirements and standards. Such components are for example, circuit boards, sensors, actuators, integrated circuits (ICs), semiconductors, active and passive components etc. An important standard for qualification of electrical and electronic equipment in road vehicles is the ISO 16750, Road vehicles - Environmental conditions and electrical testing for electrical and electronic equipment. The ISO 16750 [4-8] applies to electric and electronic systems and components for vehicles. It describes the potential environmental stresses and specifies tests and requirements recommended for the specific mounting location on or in the vehicle. The ISO 16750 consists of the following 5 parts: * ISO 16750-1: General * ISO 16750-2: Electrical loads * ISO 16750-3: Mechanical loads * ISO 16750-4: Climatic loads * ISO 16750-5: Chemical loads Similarly to device level, the electrical and electronic components that build up the Battery System have to undergo tests before assembling. In general, the responsible international standardization organisations for electrotechnical and electronic applications are: * IEC -International Electrotechnical Commission * CENELEC -European Committee for Electrotechnical Standardization * JEDEC Solid State Technology Association In the automotive field, the Automotive Electronics Council (AEC) based in the United States was established for the purpose of setting common part qualification and quality-system standards for the supply of components in the automotive electronics industry. The AEC Component Technical Committee is the standardization body for establishing standards for reliable, high quality electronic components. Components meeting these specifications are suitable for use in the harsh automotive environment without additional component-level qualification testing. The quantity, value and complexity of electronics in passenger vehicles continue to rise. Therefore, it is suggested to ensure to the vehicle manufacturer that the supplier of the meets the following standards of the AEC - Q100 norm which in turn refers to many JEDEC standards. JEDEC has been the global leader in developing open standards and publications for the microelectronics industry. JEDEC brings manufacturers and suppliers together to participate in more than 50 committees and subcommittees, with the mission to create standards to meet the diverse technical and developmental needs of the industry. JEDEC’s collaborative efforts ensure product interoperability, benefiting the industry and ultimately consumers by decreasing time-to-market and reducing product development costs. The JEDEC Automotive Electronics Forum is going to bring together experts from the worldwide automotive electronics industry to evaluate current standardization efforts and future industry needs. * AEC - Q101 - Failure mechanism based stress test qualification for discrete semiconductors * AEC - Q200 - Stress test qualification for passive components The AEC standard is well-known to customers of electrical and electronic components. The application and compliance with AEC standards, respectively, create clarity in reliability and standardization issues and save time in communication between supplier and customer. The signal integrity is also an important issue to consider regarding electrical requirements. Generally, signal integrity is closely related to the EMC. If a design was implemented under consideration of its physical requirements, both fields achieve best results. As far as the enquiry resulted in there is no particular standard respective to the signal integrity. The signal integrity is a set of measures describing the quality of an electric signal. Some of the main issues of concern for signal integrity are ringing, crosstalk, ground bounce, distortion, signal loss, and power supply noise. Today's transfer rates require a combination of simulation, modeling and measurement in order to already avoid signal integrity issues in the design. When integrating fast data lines, signal integrity is one of the most important parameters at all levels of electronics packaging and assembly (from internal connections of an IC, through the package, the printed circuit board (PCB), the backplane, and inter-system connections) because various effects can degrade the electrical signal to the point where errors occur and the system or device fails. The design of a new battery module with integrated thermal management requires that attention is given to the safety requirements regarding the vehicles equipped with electric power train in the event of frontal or lateral collision, pursuant UNECE Regulations No. 94 and No. 95; thus following the test conducted in accordance with the procedure defined in Annex 3 to Regulation No. 94 and in Annex 4 to Regulation No. 95, the electrical power train operating on high voltage, and the high voltage components and systems, which are galvanically connected to the high voltage bus of the electric power train, shall ensure that the vehicle passengers are not exposed to voltages higher than 30 VAC or 60 VDC, or alternatively the total energy (TE) on the high voltage buses shall be in the limits established in Annex 11 of UNECE Regulation No. 94 or Annex 9 of UNECE Regulation No. 95. Until 30 minutes after the impact no electrolyte from the Rechargeable Energy Storage Systems (REESS) shall spill into the passenger compartment and no more than 7 per cent of electrolyte shall spill from the REESS. REESS located inside the passenger compartment shall remain in the location in which they are installed and REESS components shall remain inside REESS boundaries. No part of any REESS that is located outside the passenger compartment for electric safety reasons shall enter the passenger compartment during or after the impact test. Complying also with regulation No. 100 Uniform provisions concerning the approval of vehicles with regard to specific requirements for the electric power train, the protection DEGREE IPXXD against direct contact with high voltage live parts should be provided and the resistance between all exposed conductive parts and the electrical chassis shall be lower than 0.1 ohm when there is current flow of at least 0.2 ampere. Isolation resistance should meet the prescriptions of regulation No. 100, besides those exposed in regulation 94, where it is measured as indicated in Annex 4A of such abovementioned regulation. The REESS shall overcome the tests established in Annex 8 to regulation No. 100, such as vibration test, thermal shock and cycling test; moreover, fire resistance shall also be granted complying with regulation No. 100. Besides, since the employment of battery cells other than those used in the current vehicle has been planned for the prototypal battery module, external short circuit protection, overcharge/overdischarge protection and over- temperature protection shall also be ensured pursuant regulation No. 100\.	https://phaidra.univie.ac.at/o:1140797	Horizon 2020
0222_FORCE_689157.md	# 1 INTRODUCTION A Data Management Plan (DMP) describes the data management life cycle for the data to be collected, processed and/or generated by a Horizon 2020 project 1 . As part of making research data findable, accessible, interoperable and reusable ('FAIR'), a DMP should 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). The due date of the first version of the DMP is month 6 (i.e. 28 February 2017). The first version of the DMP does not provide detailed answers to all the questions in Annex 1 of the guideline. The DMP is intended to be _a living document_ in which information can be made more detailed and specific through updates as the implementation of the project progresses and when significant changes occur. Thus, the DMP has a clear version number and includes a timetable for updates. The DMP must be updated, as a minimum, in time with the periodic evaluation/assessment of the project, or whenever significant changes arise, such as (but not limited to): * 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 first version of the DMP focuses mainly on data management in the four lead partnerships, and presents the initial thoughts on data collection and management in the 12 local partnerships. A more comprehensive description for the local partnerships will be included in the second version of the DMP in M18 (January 2018). The second version of the DMP will also include aspects of the General Data Protection Regulation 2 that shall apply from 25 May 2018, and the Directive 3 by 6 May 2018 . # 2 DATA SUMMARY ## 2.1 WP3: PLASTIC WASTE Plastic waste data collected in WP3 will support decision making regarding the transition towards a more sustainable use of plastic materials. The aim is to generate and collect data, which provides a more accurate image of the recycling potentials, environmental, social and economic impact of different types of plastic waste, with an emphasis on flexible plastics. The data generated in WP3 might be utilised to qualify the efficiency and quality of the three new collection schemes, which will be set up as part of task 3.1. Furthermore, it will be used to describe the challenges and possibilities that the industrial partners experience when recycling household plastic waste in new products as well as the specific requirements regarding waste composition, cleanliness, and uniformity that they set for their production processes. Thus, the data will be useful for waste handlers across the value chain and production companies with an interest in utilising more recycled plastics in their production. Furthermore, the data might be useful for other municipalities/local authorities aiming to increase their recycling rates of household plastic waste and improving their collaboration efforts with the private industry. Specifically, the following data collection is expected: For task 3.1 data will be collected to monitor the efficiency of the three collection schemes for household plastic waste, which will be set up. This includes: * Quantitative data on: * material quantities (kg) o time of collection (week no.) * composition (PET, HDPE, PP, flexible plastics, other plastics, contaminants) o NIR sorting efficiency (%) * colours o economic value (euro) * avoided CO 2 emissions from activities (tonnes)  Qualitative data from: * citizens (e.g. focus group interviews) * retailers The qualitative data will concern citizens perception and use of the sorting schemes as well as their suggestions as to improvement. * Other: data in the format of photos and videos of the waste materials and the collection and sorting equipment. We will reuse existing data from the City of Copenhagen for the drafting of the baseline analysis (D3.1). This includes data regarding collection rates and frequency, collection scheme coverage, and waste recycling. Furthermore, it includes data from the annual citizen satisfaction survey. The baseline analysis will also include reused data that originates from continuous surveys conducted by the City of Copenhagen and partners regarding the efficiency of existing waste collection schemes. For task 3.2 it is necessary to collect a number of data in order to identify 20 promissing applications. The data includes material demands for the different products and data for quality of obtained polymeric materials from processing of different types of collected and pre-sorted materials. For the 20 promising applications, sufficient data must be generated to select 10 product applications, which will be tested at production facilities and used to prepare business cases. The quantitative data with specification of amounts and composition/quality characterisation of materials. In connection with the 10 promising products end market potentials for three kinds of products will be estimated. The data will be based on data generated in the project for treatment of the collected materials supplemented with existing knowledge from the participating companies regarding their products and technologies. It is difficult to assess the size of the database generated through WP3. However, the aim is to generate data from the management of around 1,000 tonnes of plastic waste. ## Local partnerships on plastic waste In the table below, we have presented the initial thoughts on data collection and management for the three local partnerships on plastic waste. <table> <tr> <th> Partner </th> <th> Activity </th> <th> What </th> <th> How </th> <th> How to store the data </th> </tr> <tr> <td> SRH Hamburg </td> <td> Provision of collection infrastructure (Purchase of 20 grid boxes for 10 receipt stations, 1 open container, 1 additional press container) </td> <td> Number of filled grid boxes/time, ideally per recycling station Weight of filled boxes (tbd) </td> <td> Manual documentation at receipt stations (by staff) Digital documentation: unclear if data (weight) can be differentiated by receipt station </td> <td> Internally: Documentation in SAP ERP system / container delivery (not single boxes) Aggregate (anonymous) on Confluence </td> </tr> <tr> <td> Waste composition </td> <td> To be developed </td> <td> </td> <td> </td> </tr> <tr> <td> City of Lisbon </td> <td> Event exhibition with 10 urban pieces </td> <td> Amount of plastic (kilograms) </td> <td> Weight </td> <td> Municipal data server in data sheets </td> </tr> <tr> <td> CO 2 footprint </td> <td> CO 2 calculation (conversion indicators) </td> <td> Aggregated data in confluence </td> </tr> <tr> <td> Number of collaborating artists </td> <td> Counting </td> <td> </td> </tr> <tr> <td> City of Genoa </td> <td> To be developed </td> <td> </td> <td> </td> <td> </td> </tr> </table> ## 2.2 WP4: STRATEGIC METALS To gather information about the supply and demand of used electronic for the analysis of market for second hand equipment, we will collect data from online-shops (e.g. ebay, rebuy, momox, Shpock and NGO-advertisements) and local advertisements in newspapers. This will be done with Big Data Technology. Through a portal/app for collection points and repair services we will gather information about all registered repair facilities for electronic devices and geo-coordinate this data with suitable search parameters and be displayed on a public portal. All facilities will receive a possibility to update their entries constantly i.e. to generate a link to their direct websites. The Stadtreinigung Hamburg provides information on recycling areas, storage containers and other recycling possibilities. Websites which provide repair instructions will be connected also. The DST development in WP4 and WP7 is based on the same Big Data collection. The designated user and their user interface will be quite different and also the corresponding responses to the user: In WP4 and WP 7.5 in the context of DST two separate data streams are used: - Day to Day DST (only developed and applied in the context of WP4) 2 : * Data is collected in an ongoing process from internet sites such as Ebay. The regularly collected data is used for real-time analysis about specific EEE (e.g. used electronic goods) and their availability, selling prices and options for repair. The results of the analysis will be available for citizens and other actors via the App/Portal (to be developed in WP4). The App/Portal only provides information about the real-time situation. The Day to Day DST will not provide any data analysis over longer periods of time. * The App/Portal will have areas of limited access (i.e. only registered users can access certain areas): Registered users can provide individual information on e.g. disassembling of equipment, locations of repair shops and their offerings etc. for publication. A data base of personal repair instructions can be created. * The end product (App/Portal) can be useful to everyone (users, repair cafés, recycling companies etc.) to inform decisions on how to reuse, repair and/or recycle EEE. Users can adjust their behaviour according to the real-time information provided. \- DST (developed in WP7 for the waste streams EEE and wood): Provides specific analysis based on the sampled data from the Day to Day DST within a given timeframe, i.e. the realtime data gathered by the Day to Day DST is aggregated, stored and analyzed for longer time periods. Changes in waste generation, resale, reuse, repair and recycling can be analyzed over longer time periods. This information can be used to inform decision makers from the public and private sectors on how to steer processes in order to increase the reuse, repair and/or recycling of EEE. Data generated by the analysis function of DST can be used by other parties interested in such statistical information (e.g. decision makers in waste collection companies). The origin of the continuously collected data is the Internet (web sites such as Ebay). The offers and sales of specific EEE (or furniture) in internet based second-hand markets will be downloaded. Personal data, like names or e-mail addresses, will be excluded from sampling. All data will be stored in MongoDB in Xml, JSON formats. ## Local partnerships on strategic metals In the table below, we have presented the initial thoughts on data collection and management for the three local partnerships on strategic metals. <table> <tr> <th> Partner </th> <th> Activity </th> <th> What </th> <th> How </th> <th> How to store the data </th> </tr> <tr> <td> City of Copenhagen </td> <td> Measuring tests results </td> <td> Amount/volume Value Costs </td> <td> Collection vehicle or manual weighing Assessment by partners </td> <td> Placed in relevant City of Copenhagen set up for e-storage of documents (folder, eDoc) </td> </tr> <tr> <td> Setting up the partnership </td> <td> Contracting documents </td> <td> Documents for partnership agreement developed by CPH </td> </tr> <tr> <td> City of Lisbon </td> <td> Repair cafés </td> <td> Amount of reused equipment (kilograms) </td> <td> Weight </td> <td> Municipal data server in data sheets </td> </tr> <tr> <td> Aggregated data in confluence </td> </tr> <tr> <td> City of Genoa </td> <td> To be developed </td> <td> </td> <td> </td> <td> </td> </tr> </table> hand shops in the internet, paper based advertisements and other data. The outcome will be connected to an App/Portal open for all stakeholders (incl. individuals) along the value chain. ## 2.3 WP5: FOOD WASTE PREVENTION AND BIOWASTE Food waste data collected will support managing the transition process for recovering food waste and produce a more accurate image of the economic, social and environmental impact of food waste, through the cross-referencing with data sources from the waste collection and treatment processes. Data collected regarding food waste will comply with the _Food Waste Loss Accounting and Reporting Standard_ 3 in order to facilitate its validation for future research purposes. Types and formats for other datasets to be cross-referenced with food waste data will be defined at a later stage, as it is still not known at this point, what datasets it will be possible to import to the system. The parameters regarding food waste that has to be collected are:  Recovered food, measured in mass (kilograms)  Typology of recovered food (categorical): o Soup o Complements o Main dish o Uncooked foodstuff. * Geographical location of waste production and recovery namely regarding: o Coordinates according to the World Geodetic System (WGS 84) o Country o Municipality o Parish. * Date of food recovery (month and year) * Destination of recovered food * Number of beneficiaries of recovered food for social use  Number of participants of the food recovery supply chain. The known, generated data at this point are: * Economic generated value (in euro)  Avoided CO 2 emissions (in tonnes)  Avoided organic residue (in tonnes). As the tool manages the food recovery transitional process, the data above is recorded and generated through the transactions and registered in the ICT tool databases. The metrics might be changed as the process is adapted to the ICT. Other data might be added to this list, as it becomes more clear, which datasets on waste treatment and collection that can be successfully incorporated. Besides incorporating these datasets, the ICT platform with historical data sets from the Zero Waste Network in Lisbon will migrate its operations to the new platform. The expected size of the data cannot be asserted at this point as the data from waste treatment and collection has not been identified and categorized. Both the data collected and generated might be useful for: * Public administration as a monitoring tool for compliance of food waste prevention and waste management goals. * Food waste producers’/donor entities, as a source of data for historical analysis of food waste both at individual level and at a sectorial level as well as a metric for the social, economic and environmental value generated from the recovered food. * Receiving entities, for the social, economic and environmental value generated from the recovered food. * Academic and scientific purposes. ## Local partnerships on food waste and biowaste In the table below, we have presented the initial thoughts on data collection and management for the three local partnerships on food waste and biowaste. <table> <tr> <th> Partner </th> <th> Activity </th> <th> What </th> <th> How </th> <th> How to store the data </th> </tr> <tr> <td> City of Copenhagen </td> <td> Technology mapping for treatment of biowaste </td> <td> Waste composition and of digestate </td> <td> Collection of data from partners in waste management </td> <td> Data management in CPH complies with EU regulation on data management. </td> </tr> <tr> <td> Treatment technology data </td> <td> Collection of data from partners and previous works </td> </tr> <tr> <td> Survey in households about the collection of biowaste </td> <td> Citizens opinions about the waste management system </td> <td> Anonymous surveys </td> </tr> <tr> <td> Citizens suggestions for improvement of the collection of waste </td> <td> Anonymous surveys </td> </tr> <tr> <td> SRH Hamburg </td> <td> Implementation and test of 10 underground collection systems for biowaste disposal (for residents of apartment buildings) </td> <td> Perform surveys with residents in order to understand challenges and obstacles concerning biowaste disposal </td> <td> Survey with residents and stakeholders on acceptance of the tested collection system Weighing and review of quality of collected biowaste </td> <td> Stadtreinigung internal standard procedures in terms of data security and safety are followed </td> </tr> <tr> <td> City of Genoa </td> <td> To be developed </td> <td> </td> <td> </td> <td> </td> </tr> </table> ## 2.4 WP6: WOOD WASTE The purposes of the data collection/generation are: * to develop the Value Chain Based Partnership by identifying and involving all relevant stakeholders to re-engineer wood waste streams and collection schemes; * to implement wood collection schemes within a specific Urban Lab applied in a city district; * to promote research activities in order to develop technology solutions to close the wood chain loop; * to analyse and test market applications in terms of business model sustainability. Different types and formats of data will be generated/collected in WP6 over the project lifecycle, as for instance: * Quality and quantities of wood from contributors and re-manufacturers * Number and surface of beach resorts * Qualitative and quantitative data regarding market applications (e.g. market growth rate, size of the market, availability of raw materials, bargaining power, social acceptance for a product) and technology applications. Existing data will be used mainly to develop the Value Chain Based Partnership. To serve this purpose, it may be necessary to share some data, in the foreseen newsletter, local press information, communications or meetings and possibly through the partners’ communication channels (website, newsletters, social media, etc.). Other existing data include: * Amiu company’s data referring to the city (citizens’ waste disposal information (when available), including data from Fabbrica del Riciclo and EcoVan App, etc.) * Statistics at municipal level (population density and demographics) * Municipality Open Data Catalogue * Geoportal of the municipality (Genoa) * Data of the administrative districts such as list of local associations  GIS regional data on Fire Damaged Areas 2003 – 2013 * Regional data on forests. Data come from institutional databases (region, metropolitan city, municipality, submunicipalities) or from local partners, from scientific publication, public and private statistics and outcomes of previous projects (Silvamed, Robinwood). The expected size of data is not yet possible to evaluate. Data may be useful for: * the local level to improve the efficiency of collection schemes * the local and national level to share value chain data in terms of quantities and quality to promote market development * the local and EU level to establish a new governance model * EU, national and local level to assess new technology applications, feasibility and market attractiveness. Big Data and DST (see also WP 7.5): Similar to the collection of data about used EEE in WP4 but only for a period of about six months a Big Data application will collect data about the offerings of used furniture in the internet. The data analysis done with the DST may allow to identify patterns in citizen behaviour with regard to waste generation, reselling, reuse, recycling and repair and furniture (what used furniture is offered and where, what is bought with which price and where etc.). This information will be used to (re-)design approaches to citizen involvement and will be considered when developing recommendations on citizen involvement (task 7.3). ## Local partnerships on wood waste In the table below, we have presented the initial thoughts on data collection and management for the three local partnerships on wood waste. <table> <tr> <th> Partner </th> <th> Activity </th> <th> What </th> <th> How </th> <th> How to store the data </th> </tr> <tr> <td> City of Copenhagen </td> <td> Measuring tests results </td> <td> Amount/volume Value Costs </td> <td> Collection vehicle or manual weighing Assessment by partners </td> <td> Placed in relevant CPH set up for estorage of documents (folder, eDoc) </td> </tr> <tr> <td> Setting up the partnership </td> <td> Contracting documents </td> <td> Partnership agreement developed by CPH </td> </tr> <tr> <td> SRH Hamburg </td> <td> Introduction of new wood waste services (shredding, chimney wood production) </td> <td> Identification of suitable properties (property owners) for new „wood“ services => identification of properties with gardens, contact of property owners to offer/disseminate services </td> <td> Existing data from waste fee payers may be used to identify property owners Aerial photographs may be used to identify properties with gardens/ trees </td> <td> Stadtreinigung internal standard procedures in terms of data security and safety are followed Information about property owners will remain internal at SRH </td> </tr> <tr> <td> City of Lisbon </td> <td> Sorting and storage of wood waste </td> <td> Amount of wood reusable (kilograms) </td> <td> Weight </td> <td> Municipal data server in data sheets </td> </tr> <tr> <td> </td> <td> </td> <td> </td> <td> Aggregated data in confluence </td> </tr> </table> ## 2.5 WP7: GOVERNANCE AND DECISION SUPPORT The following sections present the data collection and use for tasks 7.1-7.5. ### 2.5.1 Development of governance models (tasks 7.1, 7.2 and 7.4) Within the process of developing the governance models, we will gather data in the four partner cities via expert interviews. Identification and selection of interviewees Interviews will be conducted with key stakeholders for the implementation of eco-innovative solutions and governance arrangements in the respective local contexts of the four cities. Key stakeholders will include project partners of the value chain partnerships implemented in WP3-6 as well as non-project partners. Interviewees will be identified based on the results of the analysis of local framework conditions and the stakeholder analysis, in coordination with the city cluster coordinators and the WP3-6 Leaders respectively. The identified stakeholders (public institutions, enterprises, intermediate organisations) will be contacted to identify interviewees within the respective organisations. Processing and protection of data gathered in interviews We will contact interviewees to arrange face-to-face interviews (if possible). Via interviews, we will gather qualitative data on the local situation, framework conditions and cooperation processes. Before conducting the interview, each interviewee will be informed about the purpose of the analysis and the further use of the interview material 4 . The involvement of research participants (interviewees) will follow the recommendations on good research practices of the European Science Foundation 5 and the proposals for safeguarding good scientific practice of the Commission on Professional Self-Regulation in Science of Deutsche Forschungsgemeinschaft 6 7 . The interviews will be recorded and transcribed (if permission by the interviewee is given). The interview transcripts form the basis for the qualitative analysis of the interviews (tasks 7.1 and 7.2). In order to ensure confidentiality, the full interview transcripts will not be published and not be shared with other persons (nor within the FORCE consortium). The interviewee will receive a brief summary of the interview for approval. During the interview analysis, the gathered data will be anonymised and aggregated. Only aggregated and anonymised analysis results will be published in project reports and scientific papers. ### 2.5.2 Evaluating citizen involvement (task 7.3) In the context of evaluating local citizen involvement tools, qualitative and/or quantitative data may be gathered from local citizens in the four cities. Details of how and to which extent this will be done during the project will be developed together with the project partners. Irrespective of the applied methods to gather information about the perspectives and/or behaviour of local citizens, the collected data will be anonymised before the start of the analysis. Participating citizens will be informed about the FORCE project, the purpose of the interview/questionnaire and the further use their data 8 . The involvement of citizens will follow the recommendations on good research practices of the European Science Foundation and the proposals for safeguarding good scientific practice of the Commission on Professional SelfRegulation in Science of Deutsche Forschungsgemeinschaft. Only aggregated and anonymised analysis results will be published in project reports and scientific papers. 2.5.3 Development of decision support tool(s) (task 7.5) See section 2.2. ## 2.6 WP9: EXPLOITATION, REPLICATION AND MARKET DEPLOYMENT ACTIVITIES ### 2.6.1 Exploitation Plan The preparation of the FORCE exploitation plan, which is a strategy on how to exploit the project results, is based on input provided in the Grant Agreement, the work programme and on literature review / desk research. Literature research has been done based on an internet research where open accessible files available for free use where checked. All references are marked in a reference list and citations in the text are marked recognizable and according to scientific standards. Project partners were asked to check and to contribute to the plan. The exploitation plan (D.9.1) is confidential and only for internal use of the FORCE consortium members including the Commission Services. ### 2.6.2 Stakeholder analysis In order to acquire the deployment perspectives for the results gathered in frame of the project a stakeholder analysis will be carried out. It will focus on the opportunities and obstacles with regard to the project results in the participating cities and beyond. As a first step, all FORCE city cluster coordinators will be asked to provide organisation names, contact data and website links of relevant value chain stakeholders on local, national and international level engaged in the four waste streams. All contact data received will be treated strictly confidential and will not be used for any other purposes other than the intended (stakeholder analysis; invitations to business workshops). Based on this information tailored questionnaires will be designed taking different waste streams and stakeholder levels into consideration. Questionnaires will be sent either by mail or personalized e-mail. Answers and information gathered in frame of the survey will be evaluated anonymously. Within the stakeholder analysis report, compiled data and information will also be presented in a way which ensures anonymity of personal information. The stakeholder analysis (D.9.2) is confidential and only for internal use of the FORCE consortium members including the Commission Services. It will serve as a baseline and additional information for project partners to prepare their project results according to the needs and including insights gathered from external relevant stakeholders. ### 2.6.3 Exploitation and market deployment strategy Project partners will be asked to identify their results and outcomes which have potential for further exploitation and which have been referenced to in the exploitation plan. Based on the identification of project results, an exploitation and market deployment strategy will be developed for the four waste streams (D.9.3). Relevant data for this strategy will be gathered through interviewing FORCE consortium members. Insights gained will be backed by further empirical, secondary data from additional scientific research. All research activities will follow the recommendations on good research practices of the European Science Foundation and the proposals for safeguarding good scientific practice of the Commission on Professional Self-Regulation in Science of Deutsche Forschungsgemeinschaft. The exploitation and market deployment strategy (D.9.3) is confidential and only for internal use of the FORCE consortium members including the Commission Services. It will serve as a baseline for project partners to further prepare and design the exploitation of their results. ### 2.6.4 Business modelling strategies Four business modelling strategies (D.9.4) will be produced based on the business cases, achievements and feasibility studies developed within the four waste streams. Data relevant for this task will be gathered by interviewing / using data provided by project partners. The level of detail of partner’s project data will be agreed with the project partners beforehand in order to ensure IPR. The strategies will be backed with latest market information including status quo analysis and prognosis. (Scientific guiding principles see above). The business modelling strategies be made available for public use via the project website. ### 2.6.5 Workshops with business stakeholders In order to raise awareness of the project and to enhance network activities and collaboration between business stakeholders and project partners for potential exploitation of project results, various workshops with business stakeholders will be carried out during the project. Stakeholders who will participate in the workshops will be invited by HAW and contacted by project partners through their value chain networks. Additional participants including corresponding contact details will be gathered by internet research. Participant lists will be prepared (including name of participants and organisations name) and shared during the workshops. Data protection standards will be respected. The same applies for photos to be taken during the workshops, i.e. only with prior agreement may these be used in the project context. # 3 WP3: PLASTIC WASTE ## 3.1 FAIR DATA ### 3.1.1 Making data findable, including provisions for metadata In order to ensure the comparability of data, standard naming conventions will be used whenever suitable. For the waste materials, the European list of waste 9 will be used as a standard nomenclature. The plastic resins resulting from sorting and reprocessing will be classified following the naming convention used in PlastEurope’s Plastics Exchange 11 . Due to the nature of the project, which focuses on development of new prototypes of plastic products based on innovative, new plastic resin mixtures and production processes, it is not expected that the use of standard naming conventions will always be suitable. In these cases, a suitable categorisation will be developed in collaboration with the experts involved in the project. In addition, some plastic resin mixtures might be considered as a trade secret by the involved companies. In this case, the project management will engage in productive dialogue with the companies about which data can and cannot be published. ### 3.1.2 Making data openly accessible The project aims to make all data openly accessible and exploitable, which might be relevant for the key stakeholders pointed out in the stakeholder analysis. However, it will not always be possible to publish data, due to contractual reasons. Data which is found to be of vulnerable nature to the involved stakeholders (e.g. trade secrets, process data) will not be made public without prior consent of these stakeholders. This data is contractually protected. Qualitative data resulting from interviews with citizens, stakeholders etc. will only be made public on an aggregated level, meaning that the overall conclusions and summaries will be published; not the individual results. ### 3.1.3 Making data interoperable The use of standardised nomenclature such as PlastEurope’s Plastics Exchange and the European List of Waste will ensure the interoperability, reusability and exchangeablity of the data generated. When possible, quantitative data will be collected, managed, and stored in Excel format, ensuring the interoperability with other possible users. ### 3.1.4 Increase data re-use (through clarifying licenses) Data licensing matters will be clarified by the project partners once the main datasets have been developed. At this point, it is too early to define the licensing issues. The increase of data-reuse is also dependant on the success of the dissemination and exploitation activities as described in the Communication and Dissemination Plan and the Exploitation Plan. Data will be available for reuse on a continuous basis as soon as the data is ready and cleared for publishing. All data, which is made public during the project period, is useable by third parties also after the end of the project period. Public data will be available via the project website, which will be online until one year after the end of the project period. After this, the public datasets will be available through direct contact with the project partners. Data quality assurance processes will be presented in the second version of the DMP. ### 3.2 ALLOCATION OF RESOURCES The City of Copenhagen is responsible for collection and storage the data generated in task 3.1, including data on waste sorting, quantities, polymer types etc. The Danish Technological Institute is responsible for collection and storage of data generated in task 3.2, including data related to regranulation and production of ten prototypes. Costs for making data FAIR are under consideration by the City of Copenhagen and the Danish Technological Institute. ### 3.3 DATA SECURITY All data is securely stored by the project partners, and will follow the respective security measures deployed in their organisations. This includes generation of data backups to ensure that all data can be recovered. Regarding the transfer of sensitive data relating to the project, the project partners will discuss how to best manage this issue. ### 3.4 ETHICAL ASPECTS Templates of the informed consent form and an information sheet about the FORCE project has been prepared as part of the deliverables in WP1 Ethics, D1.4: H – Requirement no 4. The informed consent form will be used when beneficiaries collect information via interviews, questionnaires, workshops and similar activities in the five work packages, WP3-WP7. The form includes a brief presentation of the project, a description of how participants will be involved, and how data will be used in the project, all in the native language. 3.5 OTHER All procedures followed comply with national and international legislation. # 4 WP4: STRATEGIC METALS ## 4.1 FAIR DATA The Day to Day DST (WP4) only provides information and data, if someone runs it. Who and under what conditions this will be done after the end of the project is part of the exploitation plan in WP9. WP7 Task 7.5 DST: The collected data from the internet in the mongoDB has to stay with Consist because the amount of data will be too much to hand it over and the e.g. ebay usage rights would not allow to do that either. However, the DST provides analysis using the collected data. These analysis (could be huge tables themselves) are open to further use. ### 4.1.1 Making data findable, including provisions for metadata EAN numbers will be used as metadata for the metal for the analysis of data generated by the Day to Day DST. Naming conventions and categorizations from WEEE regulations will be further used for specifying keywords. Analysis always refer to a given timeframe and region. ### 4.1.2 Making data openly accessible The Day to Day DST will be re-usable and freely accessible to the public beyond the end of the project if someone runs it. The analysis from the DST will be made openly accessible. All project related data, as well as the code and the documentation will be long term stored on the Consist server. ### 4.1.3 Making data interoperable The DST tool can be reused in other fields. The DST interface will be developed based on the specific needs of the metal (and wood chains), therefore certain adjustments might be needed for further usage. ### 4.1.4 Increase data re-use (through clarifying licences) The DST tool can be reused in other fields. The DST interface will be developed based on the specific needs of the metal (and wood chains), therefore certain adjustments might be needed for further usage. The statistical data generated by the DST in case of a metal chain will remain useful for e.g. time series analysis as long as the categorization in the WEEE has not changed. The DST will be made freely accessible to the public beyond the end of the project. This statement is valid for principle data categories and structures being used in the project as well. By that, it shall be possible to transfer, adapt and reuse the Big Data application as a base component in other cities or regions after the project has finished 10 . Data quality assurance processes will be presented in the second version of the DMP. ### 4.2 ALLOCATION OF RESOURCES All data management will be done under the surveillance of the data protection officer of Consist and according to the data protection rules of Consist. All employees have to agree to these rules and their statements are being recorded. (The DST for the wood chain will be also developed by Consist ITU, therefore the same regulations will be followed). ### 4.3 DATA SECURITY At Consist ITU, there are standard procedures to ensure data security of company’s projects. All data is stored on the separate servers in Kiel and Hamburg, as well as on the backup servers provided by third parties. The Data Protection Act of the Free and Hanseatic City of Hamburg (Hamburgisches Datenschutzgesetz) is followed. (The DST for the wood chain will be also developed by Consist ITU, therefore the same regulations will be followed). ### 4.4 ETHICAL ASPECTS Day to Day DST: Registered users can provide own information on e.g. disassembling of equipment, repair shop offerings, collections point etc. for publication. In the context of registration, users will be informed about the use of their data via the ‘Informed consent’ (in conformance to the rules of the respective country), all in the native language. The DST will not provide any personal information, therefore there is no need to consider ethical aspects here. ### 4.5 OTHER Consist ITU applies the following procedures for data management in WP4 (includes DST for WP6): * Standard procedures for data security at Consist ITU * Data Protection Act of the Free and Hanseatic City of Hamburg (Hamburgisches Datenschutzgesetz) * German User data protection regulations. # 5 WP5: FOOD AND BIOWASTE ## 5.1 FAIR DATA ### 5.1.1 Making data findable, including provisions for metadata Regarding naming convention, the _Food Waste Loss Accounting and Reporting Standard_ 11 will be followed when possible. If, and when, this standard fails to encompass a given subject further research will be done to find an adequate standard, if one is available, if not a convention will be defined in collaboration with experts in the given field. Matters related with metadata, identification mechanisms and versioning numbers are under consideration with the developer, Addapt Creative. ### 5.1.2 Making data openly accessible Raw Data collected through the transactional processing of food waste recovery cannot be made publicly available due to contractual reasons. The access to an individual donors or receptors food waste data might enable insights of operational, commercial or other value that must be protected to ensure their engagement with the project. As such, this data is contractually protected. On the other hand, aggregated data (e.g. sectoral data, geographical data) will be made public whenever the privacy of the donor or recipient is not at stake. This will be done online, through a public access webpage accessible by any browser. Access to the disaggregated data might be possible for academic purposes under a confidentiality agreement. Data can be supplied in CVS File format in order to be universally accessible. The need for a data access committee will be established once the datasets to be supplied regarding the collection and treatment process are determined. Until then it is difficult to analyse the full involvement of each partner in the decision process. When such is established conditions for access as well as the methodology to do so will be defined. The data and associated documentation will be deposited on the ICT tool itself. ### 5.1.3 Making data interoperable The use of the _Food Waste Loss Accounting and Reporting Standard_ will support the interoperability, reusability and exchangeability of the data gathered and generated through the ICT tool. Technical aspects of interoperability are under consideration by the developer, Addapt Creative. ### 5.1.4 Increase data re-use (through clarifying licences) Data licensing matters will be established once the datasets to be supplied regarding the collection and treatment process are determined. Until then it is difficult to analyse the full involvement of each partner in the decision process. When such is established conditions for access as well as the methodology to do so will be defined. Public data is made accessible as it is inserted in the system during the transitional process management. Disaggregated data will be accessible at the official launch of the ICT tool, after the trial period, under de conditions described above. The data will remain available after the end of the project, especially because is intended that the ICT tool remains active beyond 2020 collecting data. Currently, _Zero Desperdicio Network_ data quality assurance processes will be adapted to the ICT tool and included in the user manual. ### 5.2 ALLOCATION OF RESOURCES Data management is a co-responsibility for Addapt Creative, with responsibility for the technical aspects, and DARiACORDAR as a data curator. Long term preservation of data processes will be established once the datasets to be supplied regarding the collection and treatment process are determined. Until then it is difficult to analyse the full involvement of each partner in the decision process. The preservation of this data is essential has, at this time, there are no available time series for sectorial food waste recovery, among others. Costs for making data FAIR are under consideration by the developer, Addapt Creative. 5.3 DATA SECURITY Security provisions are under consideration by the developer, Addapt Creative. ### 5.4 ETHICAL ASPECTS The data collected and generated by the ICT tool does not include personal data. As such, there are no ethical issues that impact on data sharing beyond the contractual limitations pointed out in section 2.3. Still, any concern that might arise as well as all the foreseen data uses will be included in the terms of use of the ICT tools. For data collection via interviews, questionnaires, workshops and similar activities, templates of the informed consent form and an information sheet about the FORCE project has been prepared as part of the deliverables in WP1 Ethics, D1.4: H – Requirement no 4. The informed consent form includes a brief presentation of the project, a description of how participants will be involved, and how data will be used in the project, all in the native language. 5.5 OTHER All procedures followed comply with national and international legislation. # 6 WP6: WOOD WASTE ## 6.1 FAIR DATA ### 6.1.1 Making data findable, including provisions for metadata The data produced and used in the project will be discoverable with metadata, identifiable and locatable by means of the standard identification mechanism in use in the Municipality of Genoa that refers to different methodology (RNDT methodology, INSPIRE methodology. The software used for the metadata catalogue will be GeoNetwork (open source). The metadata available from by the Geoportal are _Inspire Compliance_ that is adhering to EU legislation 12 . The municipality of Genoa will make available all the considered/expected information collected by the Geoserver opensource platform of the Geoportal through interoperability services of WMS and WFS. An example of a metadata set that will be created by the Geonetwork application is: IDENTIFICATION INFORMATION  Date * Cited responsible party * Point of contact * Resource maintenance * Resource constraints * Equivalent scale * Topic category * Geographic bounding box DISTRIBUTION INFORMATION  Distributor REFERENCE SYSTEM INFORMATION DATA QUALITY INFO  Date METADATA  File identifier * Metadata language * Character set * Hierarchy level * Date stamp * Metadata standard name * Metadata standard version Data already openly available, is the Open Data portal of the Municipality of Genoa at the following link: _http://dati.comune.genova.it/_ licenses chosen are Creative Commons 3.X e 4.X. Issues regarding search keywords and version numbers will be decided upon during the project. ### 6.1.2 Making data openly accessible Aspects of data management will include open access and support protection of personal data to allow effective and secure exploitation. Public interests and the protection of intellectual property will be well-balanced (Grant Agreement p. 174). Shared data on existing platform: Municipality: Open data catalogue _http://dati.comune.genova.it/search/type/dataset_ The public geographic databases are accessible at: _http://mappe.comune.genova.it/geoserver/wms?service=wms &version=1.3.0&request=GetCapa _ _bilities_ Ticass and ActiveCells may manage some confidential information relative to the task 6.3 “Innovative applications” at the aim of the protection of IP necessary for to provide the incentives for private investment and exploitation of the results (according to the Grant Agreement, p. 174). ### 6.1.3 Making data interoperable Open access to the Geoserver opensource platform of the Geoportal would be provided through interoperability services of WMS and WFS (see above). ### 6.1.4 Increase data re-use (through clarifying licences) The data will be licensed to permit the widest re-use possible according to the Exploitation Plan and Communication and Dissemination Plan. In the case of WP6, Guidelines for scalability and replication they will be developed in general with licenses depending on the degree of product data openings. It will be defined in more detail in the next version of the DMP. The data produced and/or used in the project will be useable by third parties, in particular after the end of the project, according to WP6 Guidelines for scalability and replication and Italian Data Protection Code. In general, licenses will depend on the degree data access. It will be defined later on. For the time being, we expect data will remain re-usable during the project plus one year. Data quality assurance processes will be presented in the second version of the DMP. ### 6.2 ALLOCATION OF RESOURCES For data protection and system management the responsible office is the Informative Systems of the Municipality of Genoa. For data collection and data quality, the responsible office is the Environmental Department. The DST for the wood chain will be also developed by Consist ITU, so all data management will be done under the surveillance of the data protection officer of Consist and according to the data protection rules of Consist. All employees have to agree to these rules and their statements are being recorded. Costs for making data FAIR are under consideration by the City of Genoa. ### 6.3 DATA SECURITY Security provisions will be in accordance with Municipality of Genoa standards (e.g. security protocol and policy of disaster recovery). ### 6.4 ETHICAL ASPECTS D1.3: H – Requirement no 3: Details on the procedures and criteria that will be used to identify/recruit research participants for the interviews in WP6. For data collection via interviews, questionnaires, workshops and similar activities, templates of the informed consent form and an information sheet about the FORCE project has been prepared as part of the deliverables in WP1 Ethics, D1.4: H – Requirement no 4. The informed consent form includes a brief presentation of the project, a description of how participants will be involved, and how data will be used in the project, all in the native language. ### 6.5 OTHER We will use information on cross application founded by own municipality resource (the Geoportal) and regional resources (e.g. cartography 1:5000). More details will follow in the second version of the DMP. # 7 WP7: GOVERNANCE AND DECISION SUPPORT This section describes data management in task 7.1-7.4. Task 7.5 is presented together with strategic metals in sections 2.2 and 4. ## 7.1 FAIR DATA 7.1.1 Making data findable, including provisions for metadata Not applicable to the qualitative data gathered for implementing of tasks (cf. section 2.5). ### 7.1.2 Making data openly accessible In order to ensure confidentiality, the full interview transcripts from the expert interviews and the citizen involvement will not be published and not be shared with other persons (also not within the FORCE consortium). This allows research participants to freely express their opinions and thus improves the research results of the project. During the interview analysis, the gathered data will be anonymized and aggregated. Only aggregated and anonymised analysis results will be published. The data will be published in project reports and scientific journal articles, conference papers and (scientific) conference presentations. 7.1.3 Making data interoperable Not applicable to the qualitative data gathered for implementing tasks. 7.1.4 Increase data re-use (through clarifying licences) Not applicable to the qualitative data gathered for implementing tasks. ### 7.2 ALLOCATION OF RESOURCES At HCU, there are standard procedures in place for data management, which ensure data security (including data recovery, secure data storage and transfer) of data gathered and processed in research projects. These include the storage of on separate servers and networks with restricted access only for selected research staff of HCU. Data security standards are overseen by the responsible data protection officer. Moreover, the Data Protection Act of the Free and Hanseatic City of Hamburg (Hamburgisches Datenschutzgesetz) is followed. Since standard procedures are followed which are already in place and generally applied to all research data there will be no additional costs for managing project data. ### 7.3 DATA SECURITY At HCU, there are standard procedures in place to ensure data security (including data recovery, secure data storage and transfer) of data gathered and processed in research projects. These include data storage on separate servers and networks with restricted access only for selected research staff of HCU. Data security standards are overseen by the responsible data protection officer. Moreover, the Data Protection Act of the Free and Hanseatic City of Hamburg (Hamburgisches Datenschutzgesetz) is followed 13 . ### 7.4 ETHICAL ASPECTS Since the gathered data is anonymised and aggregated before publication there are no ethic issues that impact data use or sharing (see above for details). ### 7.5 OTHER HCU applies the following procedures for data management gathered in tasks: * Recommendations on good research practices of the European Science Foundation * Proposals for safeguarding good scientific practice of the Commission on Professional Self-Regulation in Science of Deutsche Forschungsgemeinschaft * Data Protection Act of the Free and Hanseatic City of Hamburg (Hamburgisches Datenschutzgesetz) * Standard procedures for data security at Hamburg’s universities. # 8 WP9: EXPLOITATION, REPLICATION AND MARKET DEPLOYMENT STRATEGIES ## 8.1 FAIR DATA 8.1.1 Making data findable, including provisions for metadata Not applicable to the data gathered for implementation of tasks in WP9. ### 8.1.2 Making data openly accessible Most of the data collected and used in frame of WP9 is relevant to draft information in an aggregated, not individualized manner (exploitation plan, stakeholder analysis, market deployment strategy). It is meant to serve project partners as background and guiding information when planning the exploitation of project results. According to the Grant Agreement, these documents are for internal (FORCE consortium and EC services) use only and will not be published openly. Nevertheless, data / information collected when preparing the stakeholder analysis will be handled anonymously and the corresponding reports for partners will not include any data that allows reference to a particular entity or person. Only aggregated and anonymised analysis results will be published. The business modeling strategies will also be drafted to serve as guiding documents for project partners in order to enable them plan their business cases/model for exploiting their results in a commercial way but the strategies will be made available and accessible via the project’s website. No special software tools will be necessary to access the data. ### 8.1.3 Making data interoperable Not applicable to the data gathered for implementing tasks in WP9. 8.1.4 Increase data re-use (through clarifying licences) Not applicable to the data gathered for implementing tasks in WP9. ### 8.2 ALLOCATION OF RESOURCES HAW has the same procedures for managing data as the HCU (section 7.2). Furthermore, HAW is also governed public law, and therefore the Data Protection Act of the City of Hamburg will also be followed. 8.3 DATA SECURITY Same applies for HAW Hamburg as presented in section 7.3. 8.4 ETHICAL ASPECTS The same applies for data gathered in WP9 as in section 7.4. ### 8.5 OTHER HAW applies the following procedures for data management gathered for tasks to be undertaken in WP9: * Recommendations on good research practices of the European Science Foundation * Proposals for safeguarding good scientific practice of the Commission on Professional Self-Regulation in Science of Deutsche Forschungsgemeinschaft * Data Protection Act of the Free and Hanseatic City of Hamburg (Hamburgisches Datenschutzgesetz) * Standard procedures for data security at Hamburg’s universities.	https://phaidra.univie.ac.at/o:1140797	Horizon 2020
0223_INVITE_763651.md	# Introduction The current document constitutes the interim version of the Data Management Plan (DMP) elaborated in the framework of INVITE, which has received funding from the European Union’s Horizon 2020 Research and Innovation programme under Grant Agreement No 763651. INVITE is set on co-creating a well-connected European Open Innovation (OI) ecosystem. It envisions an OI ecosystem in which knowledge meaningfully flows across borders and is translated into marketable innovations, bringing increased socio-economic benefits to EU citizens. To this end, INVITE will co- design, pilot and demonstrate a pan-European service platform, the Open Innovation 2.0 Lab , aiming to better link the currently fragmented innovation systems of the EU by facilitating meaningful cross-border knowledge flows; empower EU businesses with the skill-sets required to tap into Europe’s knowledge-base and turn it into value; and increase the participation of private investors in OI and collaborative innovation projects. The Open Innovation 2.0 (OI2) Lab will experiment with novel bottom-up collaborative models of innovation and leverage OI support services and ICT tools to stimulate and support OI across Europe, building a vibrant community of OI actors and stakeholders (including business, science and research, public authorities and civil society) along the way. The valuable knowledge, evidence and experiences gained through the experiments of the OI2 Lab will be diffused across the EU so as to fuel their replication and scale- up for the benefit of the European economy and society as a whole. To this end, INVITE brings together a well-balanced and complementary consortium , that consists of 9 partners across 5 different European countries , as presented in the following table. ## _Table 1: INVITE partners_ <table> <tr> <th> No </th> <th> Name </th> <th> Short name </th> <th> Country </th> </tr> <tr> <td> 1 </td> <td> Q-PLAN INTERNATIONAL ADVISORS </td> <td> Q-PLAN </td> <td> Greece </td> </tr> <tr> <td> 2 </td> <td> STEINBEIS INNOVATION </td> <td> SEZ </td> <td> Germany </td> </tr> <tr> <td> 3 </td> <td> EUROPE UNLIMITED </td> <td> E-UNLIMITED </td> <td> Belgium </td> </tr> <tr> <td> 4 </td> <td> RTC NORTH </td> <td> RTC NORTH </td> <td> United Kingdom </td> </tr> <tr> <td> 5 </td> <td> NINESIGMA EUROPE </td> <td> NINESIGMA </td> <td> Belgium </td> </tr> <tr> <td> 6 </td> <td> INTRASOFT INTERNATIONAL </td> <td> INTRASOFT </td> <td> Luxembourg </td> </tr> <tr> <td> 7 </td> <td> CENTRE FOR RESEARCH AND TECHNOLOGY HELLAS </td> <td> CERTH/ITI </td> <td> Greece </td> </tr> <tr> <td> 8 </td> <td> WIRTSCHAFTSFOERDERUNG REGION STUTTGART </td> <td> WRS </td> <td> Germany </td> </tr> <tr> <td> 9 </td> <td> NORTH EAST LOCAL ENTERPRISE PARTNERSHIP </td> <td> NELEP </td> <td> United Kingdom </td> </tr> </table> In this context, all partners of INVITE’s consortium adhere to sound data management in order to ensure that the meaningful data collected, processed and/or generated throughout the duration of the project is well-managed, archived and preserved, in line with the _Guidelines on Data Management in Horizon 2020_ . Along these lines, this interim version of the DMP builds upon and significantly enriches the initial version of INVITE’s DMP in order to achieve the following objectives : * Describe the data management lifecycle for the data to be collected and/or generated in the framework of INVITE, serving as a key element of good data management. * Outline the methodology employed to safeguard the sound management of the data collected, and/or generated as well as to make them Findable, Accessible, Interoperable and Re-usable (FAIR). * Provide information on the data that will be collected and/or generated and the way in which it will be handled during and after the end of the project along with the standards applied to this end. * Describe how the data will be made openly accessible and searchable to interested stakeholders as well as its curation and preservation. * Present an estimation of the resources allocated to make data FAIR, while also identifying the responsibilities pertaining to data management and addressing data security. In order to achieve its objectives, this interim version of the DMP takes into account and builds upon common best practices and guidelines for sharing the project’s data (such as the practices described by the _Consortium of European Social Science Data Archives tour guide on data management,_ _UK Data_ _Service_ , etc.) and facilitating open access (through the use of _openly accessible repositories_ ) to the data collected/generated while taking into account the recommendations provided by the European Commission (hereinafter referred to as Commission) based on the evaluation of INVITE’s initial DMP. With the above in mind, this interim version of the DMP is structured in 7 distinct chapters , as follows: * Chapter 1 provides introductory information about the DMP, the context in which it has been elaborated as well as about its objectives and structure. * Chapter 2 presents a summary of the data to be collected/generated during the activities of INVITE including the purpose of its collection/generation as well as its types and formats. Additionally, it outlines its origin, expected volume and the stakeholders that may find it useful. * Chapter 3 describes the methodology that is applied in the framework of INVITE in order to safeguard the effective management of data across their entire lifecycle, making it FAIR. * Chapter 4 estimates the resources required for making the project’s data FAIR, while also identifying data management responsibilities. * Chapter 5 outlines the data security strategy applied within the context of INVITE along with the respective secure storage solutions employed. * Chapter 6 addresses ethical aspects as well as other relevant considerations pertaining to the data collected/generated during the implementation of the project. * Chapter 7 concludes on the next steps foreseen in the framework of the project with respect to its data management plan. Finally, the Annex of this document includes the privacy policy adopted by the project’s Web Portal as well as an initial draft version of the privacy policy that will be employed in the framework of OI2 Lab. Moreover, templates for the informed consent form and information sheet used in the implementation of the project’s activities that collect/generate data are settled in the Annex of this document. The DMP is not a fixed document . It evolves during the lifespan of the project. More specifically, the DMP will be updated at least once more during INVITE (i.e. as D7.4 at M36) . Additional ad hoc updates may also be realised (if necessary), to include new data, better detail and/or reflect changes in the methodology or other aspects relevant to its management (such as costs for making data FAIR, size of data, etc.), changes in consortium policies and plans or other potential external factors. Q-PLAN is responsible for elaborating the DMP and with the support of all partners will update and enrich it when required. # Data summary INVITE will collect/generate meaningful non-sensitive data that do not fall into any special categories 1 of personal data as those are described within the General Data Protection Regulation 2 3 (GDPR). This data may be quantitative, qualitative or a blend of those in nature and will be analysed from a range of methodological perspectives with a view to producing insights that will successfully feed INVITE’s activities, enable us to deliver evidence-based results and ultimately achieve the objectives of the project. With that in mind, the second chapter of the Data Management Plan (DMP) starts by explaining the purpose for which this data will be collected/generated and how it relates with INVITE. It proceeds by describing the different types and formats of this data as well as its origin and expected volume, before concluding with an overview of potential stakeholders for whom it may prove useful for re-use. ## Purpose of data collection/generation and its relation to the project In order to successfully meet its objectives and ensure the production of evidence-based results, INVITE entails several activities during which data will be collected/generated. The purpose for which this data is collected/generated is interrelated with the objective of the activity during which it is produced. In particular, these activities along with their objectives in the framework of INVITE are as follows: * Analysis of successful European Open Innovation (OI) support service providers and platforms , which are interconnected with INVITE, in order to identify potential gaps and opportunities in the respective market targeted by the Open Innovation 2.0 (OI2) Lab and its value propositions. * Analysis of the needs and requirements of prospective users and stakeholders , aimed at shedding ample light on their views on and experiences with the European OI ecosystem and ultimately at fuelling the demand-driven design and development of INVITE’s pilots and OI2 Lab. * Analysis of ideas and feedback collected during the INVITE Co-Creation Workshop , with a view to informing the co-design of INVITE’s pilots and OI2 Lab as well as for elaborating recommendations on how they may be implemented and customised according the needs of users and stakeholders. * Monitoring, co-evaluation and validation of INVITE’s pilots and OI2 Lab , in order to improve and fine-tune their design and offers based on data and feedback collected/generated by users of the OI2 Lab and stakeholders participating in the project’s pilot activities. * Improvement and validation of the business models designed for the post project market rollout of the OI2 Lab aimed at producing a set of commercially viable and sustainable business models for the OI2 Lab taking into account the needs of potential users/customers as well as the interests and visions of INVITE’s consortium partners. * Monitoring and evaluation of the results produced by the project’s stakeholder engagement activities in order to effectively measure and report the progress of these activities towards building up a vibrant community of diverse OI stakeholders around the OI2 Lab. * Monitoring and assessment of the dissemination and communication results of the project with a view to measuring the impact of the project’s relevant activities, accordingly fine-tuning INVITE’s strategy in this respect as well as fulfilling its reporting requirements towards the Commission. On top of the aforementioned, data will be collected/generated by the applicants of the Open Innovation Competitions (OICs) to be launched in the framework of INVITE. The purpose of this data collection/generation is to select the award participants to be provided with financial support in the form of innovation vouchers for short-term virtual Human Capital Mobility. Still, neither the platform that will host the OICs nor the exact data to be collected/generated are determined from the consortium partners. With that in mind, this category of data will be further elaborated in the final version of the DMP (M36). Along these lines, consortium partners acknowledge the importance of this data, and thus they will handle it according to the national and European laws with respect to the processing of personal data. The following section provides further details on the different types and formats of data collected\generated during the project’s activities. ## Types and formats of collected/generated data During the activities of INVITE, data of different nature will be collected/generated. The types of this data can be described in many different ways depending on the source and physical format of the data. Nevertheless, data is often seen as how it is created/captured 4 . Examples include electronic text documents, spreadsheets, questionnaires and transcripts, among others. Another way to think about data is the format in which different data types (qualitative, quantitative, etc.) are stored. Along these lines, INVITE’s data will be available in easily accessible formats, such as post scripts (e.g. pdf, xps, etc.), machine readable formats (xml, html, etc.), spreadsheets (e.g. xlsx, csv, etc.), text documents (e.g. docx, rtf, etc.), compressed formats (e.g. rar, zip, etc.) or any other format required by the objectives and methodology of the activity within the frame of which it is produced. In this respect, special attention will be paid in using open formats 5 (such as csv, pdf, zip, etc.) and/or machine-readable formats 6 (such as xml, json, rdf, html, etc.) when possible in order to enhance the interoperability and re-use of INVITE’s data. In doing so, we will be providing data that is easily readable and freely usable in any software program employed by third-parties interested in utilizing the data. With that in mind, the type and formats of the data collected/generated in the framework of INVITE can be divided into 3 distinct categories , namely (i) data collected/generated by direct input methods and (ii) users of the OI2 Lab as well as (ii) data collected/generated from dissemination, communication and stakeholder engagement activities, as further described in the following subsections. ### _Data collected/generated through direct input methods_ In the framework of INVITE, direct input methods encompass methodologies for collecting data through interactions between consortium partners and external stakeholders, with the latter providing data to the former. Along these lines, external stakeholders assume the role of a data subject that is a natural person whose personal data is being processed 7 . In particular, the identification and selection of suitable data subjects are based on purposeful sampling according to which, external stakeholders are identified and selected by consortium partners based on their role within the OI ecosystem and the objectives of the respective activity for which data is collected 8 . In this context, quantitative and qualitative data will be collected/generated during the course of INVITE: * Quantitative data is numerical and acquired through counting or measuring 9 . Examples of quantitative data are the yearly turnovers of a business, the hourly compensation of a worker, the number of SMEs in Europe, etc. This data may be represented by ordinal, interval or ratio scales and lend themselves to statistical manipulation. * Qualitative data, sometimes referred to as categorical data, is data that can be arranged into categories based on physical traits, gender, colours or anything that does not have a number associated with it 10 . Moreover, written documents, interviews, and various forms of in-field observation are all sources of qualitative data. Examples of quantitative data are the preferences of learning, skillsets, country of origin, etc. With that in mind, further details with respect to the different types and formats of data that will be collected through direct input methods under the frame of INVITE are provided in the remainder of this subsection. #### Market gaps and opportunities This data has been collected in two phases, with a view to supporting the analysis of successful OI support service providers and platforms, which are interconnected with INVITE. The first phase involved a desk review of secondary data sources that are available in the public domain. The second phase was implemented through a series of semi-structured in-depth interviews with respondents who work for either the Enterprise Europe Network (EEN), NineSigma or Steinbeis. The data collected consists of a combination of information extracted from the secondary data sources and information provided by the respondents during the in-depth interviews. In both cases, the data collected are mainly of a qualitative nature and recorded in the form of interview transcripts, containing information regarding how the OI platforms/service providers under study currently operate in terms of their value propositions, target audiences, service offerings (both online and offline) and commercial models. #### User needs and requirements This data was collected during the qualitative interview-based survey that was conducted in the framework of INVITE, which aimed at revealing the needs and requirements of prospective users and stakeholders of the OI2 Lab. In particular, a stratified purposeful sampling methodology was employed in order to include diverse OI stakeholders of the quadruple helix (i.e. from businesses and investors in the private sector over to academia and public authorities as well as civil society) and across different regions in Europe. Data in the form of interview transcripts was collected by means of semi- structured interviews and encompass information on the different ways in which OI actors are currently engaged (or not) in OI, various enablers / barriers that appear to be fostering / hindering their participation in OI as well as insights into the perceived knowledge, skills and support that they may need in order to successfully adopt and apply OI. #### Ideas and feedback collected during the INVITE Co-Creation Workshop In the framework of the INVITE Co-Creation Workshop, a diverse group of OI stakeholders engaged in a series of co-creative brainstorming and ideation sessions to co-define demand-driven designs and features for INVITE’s pilots and OI2 Lab. In particular, the World Café process 11 was used during the workshop and six tables were set up to discuss six themes, each of which addressed key aspects of the pilots, services and tools to be deployed through the OI2 Lab. The discussions followed a semi-structured approach and were guided by six key questions, unique to each theme. Data of both qualitative and quantitative nature, as derived from the participants’ responses to each of these six questions per theme along with any additional ideas, were written up and collected by means of post-it notes 12 . #### Pilot monitoring, co-evaluation and validation data Users and stakeholders who will participate in the pilot activities of INVITE and its OI2 Lab will be required to provide feedback as part of an ongoing monitoring framework that will be established to keep track of, coevaluate and validate their performance, ultimately fuelling their demand-driven improvement. To this end, both qualitative and quantitative data will be collected from pilot participants by means of questionnaire-based surveys aimed at capturing customer experience metrics (e.g. a Net Promoter Score) as well as key general information relating to how users found out about the OI2 Lab, the main objectives that drove them to use it, the components and services used as well as to the experiences and outcomes derived from their participation in the pilots. Specific data on the impact of the pilots will also be collected, revolving around themes such as the degree of integration of OI in the users’ business model, external knowledge search and acquisition, collaboration with other stakeholders, occasional vs continuous engagement in OI activities, disruptive vs incremental innovation, internal innovation capability, time-to- market, level of proficiency gained in collaborative innovation, scale achieved in terms of outreach (volume, sectoral and geographical), fundraising capacity, staff impact, organizational impact and cost-benefit. #### Feedback on the OI2 Lab business models This data will be collected through a questionnaire-based survey of pilot participants as well as members of the project’s Advisory Board identified as potential early adopters / lead users, with a view to gaining meaningful feedback on how the business models of the OI2 Lab may be refined and further shaped to fit the needs of potential users and stakeholders. The data will be of both qualitative as well as quantitative nature addressing the appropriateness and acceptance of different elements of the different Business Model Canvases and Value Propositions (e.g. customer relationships, collaborators, revenue streams, cost structure, etc.) that will constitute the business models designed for the OI2 Lab. Data collected/generated through direct input methods will be stored in standard .docx as well as . xlsx formats . These kinds of formats allow the documentation of information coming from various files and documents so that it exists in a single location. By doing so, it is possible to circulate raw data from transcripts, as well as text, images and other objects from other files to one document file or multiple tabs of a single spreadsheet. Moreover, both formats can be immediately converted into open and machinereadable formats (such as .xml and .csv) boosting the interoperability and re-usability of the datasets produced during the implementation of INVITE. ### _Data collected/generated by users of the Open Innovation 2.0 Lab_ The OI2 Lab platform aims to facilitate the needs of potential users and stakeholders of an extended OI ecosystem. With that in mind, a suite of tailored ICT tools will be leveraged with a view to structuring a vibrant web- based community of OI actors and create value for all of them within the OI ecosystem. The ICT tools include: (i) an open multi-sided marketplace , (ii) an online collaboration space , (iii) an e-learning environmen t as well as (iv) a crowdfunding tool . Within this framework, users of various roles, such as SME representatives, advisors and mentors with field expertise and account managers are expected to utilize the functionalities offered by the ICT tools which in their turn will generate valuable data for the consortium partners. On another note, external data is expected to be sourced, reformatted and presented accordingly from other open innovation initiatives such as, but not limited, to the European Enterprise Network, NineSigma as well as data sourced by local actors such consortium members active in the OI sphere in their respective regions. Along these lines, data collected by the users of the OI2 Lab incorporates: * Data provided by OI2 Lab participants including personal details such as name, contact details, social media accounts, organisation, date of birth and other. Additionally, personal preferences will be collected with respect to fields of interest, expertise, activities as suggested directly by users that will help the OI2 Lab platform personalise the content delivered to users during the pilot phases. * Data based on tracking the user’s activity across the OI2 Lab platform and will be utilised towards further enhancing the personalisation of the user experience throughout the platform. The overarching goal will be to support: (a) a data driven reiteration and adaptation development process for the consortium in order to identify processes that need enhancements and/or (b) functionalities that are of low or no interest and could be deprecated, as they provide no additional value to participants. Activity data will be collected for all roles and stand to not only streamline processes, but also allow the consortium members to identify the most prominent features required and utilised by OI participants, which will subsequently support finalisation of business modelling and commercialisation efforts. Further details of data collected from the ICT tools are provided over the following subsections. #### Open multi-sided market place data One of the main scopes of the overall architecture includes bringing together all the ICT tools functionality under a well-designed Open multi-sided market place. The main feature of this process is a global user registration and profile creation, whereby potential users of the OI2 Lab will be able to access the functional elements and subsequently the value that will be offered by participating. As such, the registration process includes the following data collection process: * Required Data: This set of data will be required for registration and include personal data for profile creation including: * Full Name o City, country o Date of birth o Organisation/SME o Job Function o Notification settings (email, platform only or desktop, etc.) o Privacy settings (publicly available or private profile) * Optional Data: The second set of data to be collected will be optional but highly important in allowing the matchmaking algorithms to match users with relevant content throughout the platform and drive engagement and user experience. Such data will include among others: * Basic interests (e.g. Technology offer/request, Competitions, etc.) * Fields of interest/expertise (e.g. machine learning, renewable sources, e-health, etc.) In addition to personal profile data, the Open multi-sided marketplace will accommodate tracking of user activity to further inform the matchmaking engine included in prioritizing notification settings and matching OI2 Lab users with content or other users (via introductory notifications and search results adaptive sorting) with common interests and activity. All personally identifiable data will be tied with a unique userID per participant, which will be utilised across the ICT tools and processes. In the case, whereby users wish to withdraw their participation and delete their account, all personally identifiable data will be deleted and the userID will no longer be associated with a particular profile. The overall data collected will be stored in the OI2 Lab MySQL database under the required schema to facilitate process automation. #### Online collaboration space data In order to facilitate user interaction and collaboration the OI2 Lab will foster an online collaboration space. This will leverage user profiles and allow participants to: * Communicate directly with account managers assigned to them as part of the support process; * Ask questions in a Q&A format; * Create topics for discussion among platform users; * Create work groups on collaborative projects. As such, the platform will have to store messages, attachments and activity in an identifiable manner according to userID in the OI2 Lab MySQL database. Furthermore, any registered user can tag the collaborative spaces or discussions they initiated as private (i.e. among collaborating parties) or public allowing open participation. UserIDs will link back to user profiles, unless someone terminates its profile. As an extension to this, users apart from being disassociated from content contributed, they may also have the option to delete the relevant content they contributed to one of the communication and collaboration spaces they participated. #### E-Learning environment data One aspect that stands to accelerate open innovation participation and efficiency is access to learning material relevant to OI. The E-learning environment to be integrated to the OI2 Lab, will be based on the _Moodle_ open source e-learning environment adapted accordingly to facilitate the needs of the platform. Course creators will be able to upload content, append fields of relevance and all necessary details for final submission. Conversely, live webinars will be created including date and participant participation data that need to be circulated with the instructors and among other users depending on the level of anonymity required. As such the e-learning environment will not only include course or webinar specific data but also personal data in some cases. All data including personal preferences and activity will be stored in the OI2 Lab database and lend themselves for access by the matchmaking engine of the Open multi-sided marketplace to facilitate notifications, content placement and personalisation, ultimately supporting engagement and re-engagement of platform participants. Similarly, to the other use cases, personally identifiable data will only be accessible as long as users remain registered in the OI2 Lab platform environment. #### Crowdfunding tool data An important function of any open innovation platform in support of participating SMEs is access to financing options. Besides state or EU driven financing tools listed, the OI2 Lab will also provide a crowdfunding tool whereby participants will have a chance to showcase their projects in search of sourcing financial support from the general public or investors. In this respect, crowdfunding campaign creators will upload their proposed projects and relevant contents, stored in the platforms database, and decide whether the campaign should be publicly accessible or only visible to registered users. Following that, users (SMEs, individuals, investors) that want to participate will be able to pledge their support in an anonymised manner, whilst only the campaign creator will have access to their personal contact info. The matchmaking engine will have access to the campaign details as registered in the OI2 Lab database and subsequently match them with users with expressed or relevant interest with notifications and emails in order to drive engagement. Campaign data will also be conveyed to account managers to enhance support extending beyond the platform participants. Crowdfunding campaign creators will have full control over the timeframe of their campaign up to a designated time limit, inherently coded in the platform, and may proceed with editing or deleting their campaign and relevant content if they so choose. #### Web portal analytics The OI2 Lab will be supported by platform exclusive tracking and analytics software based on the _Matomo_ open source analytics platform. This implementation will make sure that the project maintains 100% data ownership, user privacy is protected and that user-centric insights can be generated and leveraged across the board. To track visitors, Matomo will be configured to use 1 st party cookies, set on the OI2 Lab domain. Cookies created by Matomo start with: _pk_ref, _pk_cvar, _pk_id, _pk_ses. Users that wish to be excluded from being tracked using the cookie method, will be allowed to do so via opting out which will create a cookie piwik_ignore set on the domain of the Matomo server hosted in the OI2 Lab server environment. In case of account deletion all first party cookies will be disabled from being set — for example for privacy reasons. First party cookies track among others the following data: * User IP address * Optional User ID * Date and time of the request * Title of the page being viewed (Page Title) * URL of the page being viewed (Page URL) * URL of the page that was viewed prior to the current page (Referrer URL) * Files that were clicked and downloaded (Download) * Links to an outside domain that were clicked (Outlink) * Pages generation time (the time it takes for webpages to be generated by the webserver and then downloaded by the user: Page speed) * Location of the user: country, region, city, approximate latitude and longitude (Geolocation) Other optional data or events may also be tracked in order to further enhance a data driven implementation plan along future iterations of the platform. These may include: (i) custom dimensions, (ii) custom variables, (iii) campaigns, (iv) site search, (v) goals, (vi) events, (vii) e-commerce, (viii) viewing and clicking on content. ### _Data collected/generated from dissemination, communication and stakeholder engagement activities_ #### Social media statistics (including Facebook, Twitter, LinkedIn, YouTube) This data will be collected/generated through a periodic monitoring of the project’s social media statistics (including Facebook, Twitter, LinkedIn and YouTube) with a view to measuring and assessing the performance and results of the project’s social media activity in terms of dissemination and communication. With that in mind, the data will be both qualitative as well as quantitative in nature addressing the metrics reached on each channel (e.g. followers, tweets impressions on twitter, friends, likes on Facebook etc.). Additionally, this data will be followed by an analysis of the results stemming from it and possible ways to improve the results so as to reach the project’s targets. All in all, the data will be stored in a Microsoft excel file (.xlsx) while at the same time the analysis of the results will be stored in a standard word document (.docx). _Data collected from project events (e.g. co-creation workshop, stakeholder engagement events, etc.) _ This data will be collected in 2 ways during the implementation of the project, that is: * The stakeholder engagement events organised by INVITE (such as the co-creation workshop, regional stakeholder engagement events, etc.) consisting of the participants lists that will enclose demographic information about the participants; * The participation of INVITE consortium partners in third party relevant events to reach out and engage stakeholders, thus including general information about the events attended and their outreach. Along these lines, this data is collected so as to keep track of the results of stakeholder engagement activities and provide the opportunity to project partners to report on these activities. Moreover, this data will be updated every time a partner attends an event, or a partner organises an event. Finally, the data will be both quantitative and qualitative in nature and will be stored in a standard spreadsheet (.xlsx). #### Newsletter subscriptions (e.g. contact details of subscribers) A subscription form hosted in the project’s _web portal_ will aid the collection of this data in which any interested stakeholder can freely provide his/her contact details in a dedicated sign-up form so as to receive the most up-to-date news and outcomes of the project. A newsletter will be sent to subscribers once per 4 months while a short version of it will be distributed every month via an e-mail message. With that in mind, this data will be collected so as interested stakeholders can be informed about the INVITE project as well as the OI2 Lab. Along these lines, the data will be comprised of a list of stakeholders along with their personal information. In this context, the data collected include the following information: (i) email address, (ii) first and last name, (iii) country, (iv) type of organisation, (v) region and (vi) gender. A copy of this contact list will be stored to MailChimp’s server ( _http://mailchimp.com_ ) , which is used for e-mail campaigns and newsletters distribution. All personal information included in this contact list will be used and protected according to MailChimp’s Privacy Policy. #### Data from dissemination and communication This data will be collected through a periodic monitoring of the project’s miscellaneous dissemination activities such as publications in relevant journals, posts in the blogs, etc. The data will consist of a list of publications and posts published by the consortium partners. The purpose of collecting this data is to assess the outreach and efficiency of the dissemination activities during the implementation of the project. For this purpose, a template has been shared with all partners to recommend activities to be performed and log the activities they performed. The template is provided also online so as the partners can directly update their input. Finally, all the data will be integrated in a single excel file (.xlsx). #### Data from monitoring stakeholder engagement This data is collected during the project’s stakeholder engagement activities with a view to effectively measuring and reporting the progress of these activities. With that in mind, a dedicated methodological tool has been designed and is being employed throughout the duration of INVITE, namely the Stakeholder Matrix. Each project partner sets-up an internal Stakeholder Matrix ensuring the confidentiality of the data included. In this respect, this Stakeholder matrix includes data about key stakeholder groups and individual stakeholders spanning across the quadrable helix innovation system. These are classified by organisation name, contact person (incl. gender, region/nation), contact details and activities in which they have been involved. At least 120 stakeholders shall be identified by each partner throughout the duration of the project in order to bring together at least 1000 members for the Open Innovation 2.0 Lab community. Finally, project partners must only send an anonymised Stakeholder Matrix (with only data on organization type, gender and region / country) to the project’s Innovation Manager, that is SEZ, for aggregating the data and updating the aggregated Stakeholder Matrix of the project at least on a semester basis as well as ad hoc when deemed necessary. The Stakeholder Matrix is stored in a standard excel file (.xlsx). ## Origin of data and re-use of pre-existing data In the context of INVITE, new data will be collected/generated by consortium partners as well as external stakeholders participating in the activities of the project and/or using the OI2 Lab. With that in mind and aside consortium partners, external groups of stakeholders from which new data will originate include : * Innovative entrepreneurs (social or not) as well as CEOs and (OI) managers of SMEs (including microfirms) as well as OI managers and practitioners in large enterprises. * Knowledge, technology and innovation solution providers (e.g. within academic institutions and their technology/knowledge transfer offices, non-university public research organisations, research and technology organisations, high-tech SMEs and large enterprises, etc.). * Policy designers and implementers at regional, national and EU level (e.g. in regional/national/EU authorities, development agencies, etc.). * Financers from the private funding sector in both mainstream finance markets (e.g. venture capital, business angels, etc.) as well as more alternative ones (e.g. award-based/equity-based crowdfunding, peer-to-peer consumer lending, etc.). * Staff members of non-governmental organisations as well as representatives of civil society groups active within the European OI ecosystem and aiming to address social challenges and needs; and * Other stakeholders (e.g. OI practitioners in cluster organisations and science parks, e-learning providers, etc.) including individual citizens (general public) that may be interested in the project’s results. Moreover, specific pre-existing data may be utilised within the context of the project as well. In particular, OI intermediaries, support networks and/or relevant development agencies as well as online collaboration networks and providers of OI, collective intelligence and knowledge platforms will be provided with the opportunity to integrate with and enhance the accessibility of their data-driven offers through the OI2 Lab (e.g. through its open multi- sided market place or e-learning environment). Prime example of such preexisting datasets is the sourcing of European Enterprise Network’s profiles, including technology offers/requests, business offers/requests and R&D requests. Other examples are inclusion of NineSigma’s competition or challenges, textual content and redirects to local, national or European wide financing options and lists of Venture Capital Funds throughout Europe. These pre-existing datasets will foster an already populated environment with engaging content that will resonate with the OI2 Lab’s targeted audience. ## Expected size of data INVITE entails a series of activities aiming at setting the stage for and ultimately facilitating the demanddriven evidence-based development, piloting, evaluation, validation and fine-tuning of its OI2 Lab and value propositions. With that in mind, the table that follows presents the different activities implemented during the course of the project in which data is collected/generated, the types and formats of the data as well as the expected size of the data. ### _Table 2: Expected size of data_ <table> <tr> <th> Νο </th> <th> Name of activity </th> <th> Data </th> <th> Type of data </th> <th> Format of data </th> <th> Expected size of data (KB)* </th> </tr> <tr> <td> 1 </td> <td> Analysis of European OI support service providers/platforms </td> <td> Market gaps and opportunities </td> <td> Interview transcripts </td> <td> .docx </td> <td> 207 </td> </tr> <tr> <td> 2 </td> <td> Analysis of needs and requirements of prospective users and stakeholders </td> <td> User needs and requirements </td> <td> Interview transcripts </td> <td> .docx </td> <td> 2,516.5 </td> </tr> <tr> <td> 3 </td> <td> Analysis of ideas and feedback collected during INVITE Co-Creation Workshop </td> <td> Ideas and feedback collected during the INVITE Co-creation Workshop </td> <td> Post-it notes </td> <td> .docx </td> <td> 107 ** </td> </tr> <tr> <td> 4 </td> <td> Monitoring, co-evaluation and validation of INVITE’s pilots and OI2 Lab </td> <td> Data collected through direct input methods </td> <td> Questionnaires </td> <td> .docx </td> <td> 100,000 * </td> </tr> <tr> <td> Open multi-side market place data </td> <td> Machine & user generated </td> <td> MySQL dB </td> <td> 80,000 * </td> </tr> <tr> <td> Online collaboration space data </td> <td> Machine & user generated </td> <td> MySQL dB </td> <td> 1,000,000 * </td> </tr> <tr> <td> E-learning environment data </td> <td> Machine & user generated </td> <td> MySQL dB </td> <td> 1,000,000 * </td> </tr> <tr> <td> Crowdfunding tool data </td> <td> Machine & user generated </td> <td> MySQL dB </td> <td> 100,000 * </td> </tr> <tr> <td> Web portal analytics </td> <td> Machine generated </td> <td> MySQL dB </td> <td> 20,000 * </td> </tr> <tr> <td> 5 </td> <td> Improvement and validation of the OI2 Lab’s Business Models </td> <td> Feedback on the OI2 Lab business models </td> <td> Questionnaires </td> <td> .xlsx </td> <td> 250 * </td> </tr> <tr> <td> 6 </td> <td> Monitoring and evaluation of the results produced by the project’s stakeholder engagement activities </td> <td> Data for monitoring stakeholder engagement </td> <td> Stakeholder Matrix </td> <td> .xlsx </td> <td> 61 * </td> </tr> <tr> <td> 7 </td> <td> Monitoring and assessment of the project’s dissemination and communication results </td> <td> Social media statistics </td> <td> Machine generated </td> <td> .xslx </td> <td> 150 * </td> </tr> <tr> <td> Project events data </td> <td> Spreadsheets </td> <td> .xlsx </td> <td> 150* </td> </tr> <tr> <td> Newsletter subscriptions </td> <td> Spreadsheets </td> <td> .xslx </td> <td> 300* </td> </tr> <tr> <td> Data for dissemination and communication reporting </td> <td> Spreadsheets </td> <td> .xlsx </td> <td> 150* </td> </tr> </table> * The estimated expected size of the data is based on the adjusted size of data generated via similar activities of project partners in the past unless otherwise indicated. The collection/generation of this data has already been completed and the size of the data represent real values (not estimations). ## Data utility The stakeholders that may find meaningful utility for the data to be collected/generated by the project (both within as well as outside of INVITE’s consortium) along with the benefits that could arise for them by utilizing this data, are concisely presented in the table that follows. ### _Table 3: Data utility_ <table> <tr> <th> _Stakeholder group _ </th> <th> _Data utility_ </th> </tr> <tr> <td> Researchers in the field of Open Innovation </td> <td> The field of OI, albeit having promising potential for generating sustainable innovations with great market and social value, appears to still be relatively under-researched and characterised by a limited evidence base of relevant efforts worldwide 13 . Under this light, INVITE’s data can provide researchers in the multi-disciplinary and cross-cutting field of OI with valuable insights into how OI is currently taken place in Europe as well as with empirical evidence generated from practical applications of OI and collaborative models of innovation. Interested researchers may re-use the data of INVITE as a basis to replicate similar studies within the same or different contexts as well as to design and launch new studies, generating comparable research findings to further advance the field and shed ample light on the inner workings of OI within a quadruple helix innovation model. </td> </tr> <tr> <td> Policy makers, implementers and funders </td> <td> Throughout its duration, INVITE is set on collecting and producing quantifiable evidence on the effectiveness and impact of the support mechanisms and measures to be piloted during the project (such as the innovation voucher scheme or the e-learning interventions deployed through the OI2 Lab), with a view to fostering their replication and scale-up beyond its completion. Data generated to this end, may find great utility in the hands of experts who design, implement and/or fund relevant innovation and business support policies. Indeed, data on what really changed (or not), for whom and why during the experiments conducted by the OI2 Lab, can provide them with reliable input to analyse the potential successes (and failures) generated under the pilot operation of the OI2 Lab. This can in turn help them gain a better understanding of what could drive successful OI in their own context, supporting them in facilitating knowledge flows from and to their respective nations/regions, while also fostering OI, especially amongst SMEs. </td> </tr> <tr> <td> Project partners </td> <td> The data collected/generated during INVITE is of paramount utility for project partners in order to produce evidence-based results and ultimately achieve the objectives of the project. Indeed, this data will enable the co-design, development, validation and fine-tuning of the project’s pilots and OI2 Lab. Moreover, the data will be used to design, improve and validate sustainable business models for the rollout of the OI2 Lab, while also fostering the replication and scale-up of its piloted solutions. At the same time, this data may hold meaningful utility for project partners beyond the end of the project as well, enabling them to build and capitalise upon interesting ideas and opportunities that may emerge to ensure the long-term sustainability of the OI2 Lab. </td> </tr> </table> # FAIR data The _Guidelines on Data Management in Horizon 2020_ of the Commission emphasise the importance of making the data produced by projects funded under Horizon 2020 Findable, Accessible, Interoperable as well as Reusable (FAIR)** , with a view to ensuring its sound management. This means using standards and metadata to make data discoverable, specifying data sharing procedures and which data will be open, allowing data exchange via open repositories as well as facilitating the reusability of the data. With that in mind, the following sections of the DMP lay out the methodology followed in the framework of INVITE with respect to making data findable, accessible and interoperable as well as ensuring their preservation and open access, with a view to increasing its re-use. ## Making data findable, including provisions for metadata ### _Data discoverability and identification mechanisms_ INVITE places special emphasis in enhancing the discoverability of the data collected/generated during the course of its activities. To this end, the project follows a metadata-driven approach so as to increase the searchability of the data, while also facilitating its understanding and re-use. Metadata is defined as “data about data” or “information about information” 14 . It is usually structured textual information that describes something about the creation, content, or context of a digital resource – be it a single file, part of a single file, or a collection of many files. Metadata is the glue which links information and data across the world wide web. It is the tool that helps people to discover, manage, describe, preserve and build relationships with and between digital resources 15 . In particular, three distinct types of metadata exist 16 , as presented below: * Descriptive metadata , used to identify and describe collections and related information resources. Descriptive metadata at the local level helps with searching and retrieving. In an online environment, descriptive metadata helps to discover resources. Most of the times includes information such as the title, author, date, description, identifier, etc. * Administrative metadata is used to facilitate the management of information resources. It is helpful for both short-term and long-term management and processing of data. This is information that will not usually be relevant to the public but will be essential for staff to manage collections internally. Such metadata may be location information, acquisition information, etc. * Structural metadata enables navigation and presentation of electronic resources. Its documents how the components of an item are organized. Examples of structural metadata could be the way in which pages are ordered to form chapters of a book, a photograph that is included in a manuscript or a scrapbook or the JPEG and TIF files that were created from the original photograph negative, linked together. With that in mind, data produced/used during INVITE is discoverable with metadata suitable to its content and format. To this end, the project employs metadata standards to produce rich and consistent metadata to support the long-term discovery, use and integrity of its data (see Subsection 3.1.5 for more details on the metadata standards adopted by INVITE). In parallel, to further increase data discoverability, the data produced by INVITE and deemed open for sharing and re-use, will be deposited to Zenodo ( _www.zenodo.org_ ) , an open data repository. This data repository, created by OpenAIRE and CERN, has been chosen to enable open access to the project’s open data free of charge. In fact, Zenodo builds and operates a simple service that enables researchers, scientists, EU projects and institutions, among others, to share and showcase research results (including data and publications) that are not part of the existing institutional or subject-based repositories of the research communities. It accepts any file format, promotes peer-reviewed openly accessible research, allows the creation of own collections and it is available free of charge both for INVITE to upload and share data as well as for other stakeholders to explore, download and re-use this data. Moreover, by employing this data repository, the data produced during the implementation of the project is locatable by means of a standard identification mechanism. Indeed, INVITE will be able to assign globally resolvable Persistent Identifiers (PIDs) on any data uploaded to Zenodo. An identifier is a unique identification code that is applied to a dataset, so that it can be unambiguously referenced 17 . For example, a catalogue number is an identifier for a particular specimen and an ISBN code is an identifier for a particular book. PIDs are simply maintainable identifiers that allow for permanent reference to a digital object. In other words, PIDs are a way of giving digital resources, such as documents, images and data records, a unique and persistent reference number. Moreover, as a digital repository, Zenodo registers Digital Object Identifiers (DOIs) for all submitted data through _DataCite_ , which is the leading global non-profit organisation that provides PIDs (and specifically DOIs) for research data, and preserves these submissions using the safe and trusted foundation of CERN’s data centre, alongside the biggest scientific dataset in the world, the LHC’s 100PB Big Data store 18 . This means that the data preserved in Zenodo will be accessible for years to come, and the DOIs will function as perpetual links to the resources. DOIs remain valuable since they are future proofed against Uniform Resource Locator (URL) or even protocol changes, through resolvers (such as _DOI)_ . With that in mind, an example of a DOI retrieved from this open repository follows the structure illustrated by Figure 1. _Figure _ _1 _ _: _ _Typical DOI created by Zenodo _ At the same time, datasets not uploaded to Zenodo will be deposited in a searchable resource (i.e. the web portal of the project) and utilise well- tailored identification mechanisms as well, in the form of standard naming conventions that will safeguard their consistency and make them easily locatable for project partners within the framework of the project. The following subsection provides further details in this respect. ### _Naming conventions_ Following a consistent set of naming conventions in the development of the project’s data files can greatly enhance their searchability. With that in mind, INVITE creates consistent data file names that provide clues to their content, status and versioning, while also increasing their discoverability. In doing so, project partners as well as interested stakeholders can easily identify a file as well as classify and sort them. According to the UK Data Archive ( _UK Data Service, 2017b_ ) , a best practice in naming convention is to create brief yet meaningful names for data files, that facilitate classification. The naming convention should avoid the utilisation of spaces, dots and special characters (such as & or !), whereas the use of underscores is endorsed, to separate elements in the data file name and make them understandable. At the same time, versioning should be a part of a naming convention to clearly identify the changes and edits in a file. With that in mind and to facilitate the reference of the datasets that will be produced during its implementation, INVITE employs a standard naming convention that integrates versioning and takes into account the possibility of creating multiple datasets during an activity that entails data collection/generation. Indeed, INVITE’s naming convention takes considers this issue and addresses it by employing a unique element that captures the number of datasets that are produced under the same activity. In particular, the naming convention employed by the project is described below . INVITE _ [Name of Study] _ [Number of dataset] _ [Issue Date] _ [Version number] * INVITE: The name of the project. * Name of Study: A short version of the name of the activity for which the dataset is created. * Number of dataset: An indication of the number assigned to the dataset. * Issue Date: The date on which the latest version of the dataset was modified (YYYY.MM.DD.). * Version number: The versioning number of a dataset. With the above in mind, some indicative examples to showcase the naming structure applied in the context of INVITE are provided below: * INVITE_NeedsAndRequirements_Dataset1_2017.10.31_v1 – The first dataset generated within the framework of the survey conducted to identify the needs and requirements of diverse OI stakeholders. This is the first version of the dataset that was last modified on the 31 st of October 2017 (31/10/2017). * INVITE_BMValidation_Dataset2_2018.02.01_v2 – The second dataset created in the process of validating and improving the business models developed for the OI2 Lab with a view to feeding the elaboration of the business plan that will guide its market rollout beyond the end of the project. The last modification of this dataset, which in this case produced the second version of the dataset, was on the 1 st of February 2018 (01/02/2018). ### _Search keywords_ The project’s data will be provided with search keywords with a view to optimizing its re-use by interested stakeholders during its entire lifetime. With that in mind, the metadata standards employed by INVITE provide opportunities for tagging the data collected/generated and its content with keywords. In general, keywords are a subset of metadata and include words and phrases used to name data. In the context of INVITE, keywords are used to add valuable information to the data collected/generated as well as to facilitate the description and interpretation of its content and value. Along these lines, the project’s strategy on keywords is underpinned by the following principles: * The who, the what, the when, the where, and the why should be covered. * Consistency among the different keyword tags needs to be ensured. * Relevant, understandable and clear keywording ought to be sought. In general, the keywords will comprise terms related to open innovation, co- creation, the quadruple helix as well as SMEs. The keywords will accurately reflect the content of the datasets and avoid words used only once or twice within them. ### _Versioning_ Versioning of information makes a revision of datasets uniquely identifiable and can be used to determine whether and how data changed over time and to define specifically which version the creators/editors are working with. Moreover, effective data versioning enables understanding if a newer version of a dataset is available and which are the changes between the different versions allowing for comparisons and preventing confusion. In this context, a clear version number indicator is used in the naming convention of every data file produced during the course of the INVITE in order to facilitate the identification of different versions. ### _Standards for metadata creation_ INVITE employs standards for creating metadata for the data collected/generated by the project, with a view to describing it with rich metadata and thus improving their discoverability and searchability. In result, effective searching, improved digital curation and easy sharing will be realized. In addition, the metadata standards applied enable the integration of metadata from a variety of sources into other technical systems. With that in mind, for INVITE’s openly available data the metadata standards provided by Zenodo will be used . Zenodo creates metadata to accompany the datasets that are uploaded to its repository, extending their reach to a wider audience of interested stakeholders. This metadata can be exported in several standard formats, including open and machine-readable ones (such as MARCXML, Dublin Core, and DataCite Metadata Schema), following the guidelines of OpenAIRE and are stored by Zenodo in JSON-format according to a defined JSON schema 19 . Project data not available for re-use, will also be annotated with open and machine-readable metadata following the Dublin Core Metadata standard . The Dublin Core Metadata element set (certified with the ISO Standard 15836) is a standard which can be easily understood and implemented and as such, is one of the best known metadata standards. It was originally developed as a core set of elements for describing the content of web pages and enabling their search and retrieval. Among the reasons for selecting this standard is also the fact that Zenodo is compatible with Dublin Core metadata formats and thus any initially closed data, that may become open at a later stage (e.g. due to a change in the consortium’s policy), will not lose its metadata. With that said, the Dublin Core metadata standard is a simple yet effective set for creating rich metadata that will describe a wide range of resources. The fifteen element "Dublin Core" described in this standard is part of a larger set of metadata vocabularies and technical specifications maintained by th e _Dublin_ _Core Metadata Initiative (DCMI)_ . The full set of vocabularies, also includes sets of resource classes, vocabulary encoding schemes, and syntax encoding schemes. An online metadata generator will be used to produce the different metadata elements required ( _dublincoregenerator.com_ ) . ## Making data openly accessible ### _Openly available and closed data_ INVITE is part of the H2020 Open Research Data Pilot (ORDP) that aims to “ _make the data collected/generated by selected projects openly available with as few restrictions as possible, while at the same time protecting sensitive data from inappropriate access_ ” 20 . Under this light, the project adopts the good practice encouraged by the ORDP, namely that of making data as open as possible and as closed as necessary 21 . This calls for project partners to disseminate the project’s data that have the potential to offer long-term value to external stakeholders and do not harm the confidentiality and privacy of the stakeholders that contributed in the collection/generation of this data, with a view to maximising the beneficial impact of INVITE. Only anonymised and aggregated data will be made open to ensure that data subjects cannot be identified in any reports, publications and/or datasets resulting from the project. The project partner serving as the data controller 22 in each case will undertake all the necessary anonymisation procedures to anonymise the data in such a way that the data subject is no longer identifiable (more details on data management responsibilities are provided in Section 4.2). To this end, it is important to keep in mind that during the process of data anonymisation, data identifiers need to be removed, generalised, aggregated or distorted. Moreover, anonymisation is different than pseudonymisation , which falls under a distinct category in the GDPR - anonymisation theoretically destroys any way of identifying the data subject, while pseudonymisation allows for the data subject to be reidentified with additional information. Along these lines, the table below provides a list of good practices for the anonymisation of quantitative and qualitative data derived from the tour guide on data management of the Consortium of European Social Science Data Archives (CESSDA). #### Table 4: Good practices for data anonymisation <table> <tr> <th> Type of data </th> <th> </th> <th> Good practices </th> </tr> <tr> <td> Quantitative data </td> <td> • • </td> <td> _Removing or aggregate variables or reduce the precision or detailed textual meaning of a variable._ _Aggregate or reduce the precision of a variable such as age or place of residence. As a general rule, report the lowest level of geo-referencing that will not potentially breach respondent confidentiality._ </td> </tr> <tr> <td> </td> <td> • </td> <td> _Generalise the meaning of a detailed text variable by replacing potentially disclosive free-text responses with more general text._ </td> </tr> <tr> <td> </td> <td> • </td> <td> _Restrict the upper or lower ranges of a continuous variable to hide outliers if the values for certain individuals are unusual or atypical within the wider group researched._ </td> </tr> <tr> <td> Qualitative data </td> <td> • • • </td> <td> _Use pseudonyms or generic descriptors to edit identifying information, rather than blanking-out that information;_ _Plan anonymisation at the time of transcription or initial write-up, (longitudinal studies may be an exception if relationships between waves of interviews need special attention for harmonised editing)._ _Use pseudonyms or replacements that are consistent within the research team and throughout the project. For example, using the same pseudonyms in publications and follow-up research;_ </td> </tr> <tr> <td> </td> <td> • </td> <td> _Use 'search and replace' techniques carefully so that unintended changes are not made, and misspelt words are not missed;_ </td> </tr> <tr> <td> </td> <td> • </td> <td> _Identify replacements in text clearly, for example with [brackets] or using XML tags such as <seg>word to be anonymised</seg>; _ </td> </tr> <tr> <td> </td> <td> • </td> <td> _Create an anonymisation log (also known as a de-anonymisation key) of all replacements, aggregations or removals made and store such a log securely and separately from the anonymised data files._ </td> </tr> </table> Source: Tour guide on data management of the CESSDA 23 With that in mind, the following table presents the data collected/generated during the course of the project that will be made openly available. In case certain data cannot be shared (or need to be shared under restrictions), a justification for that choice is provided. #### Table 5: Data availability <table> <tr> <th> Νο </th> <th> Data </th> <th> Availability </th> <th> Notes </th> </tr> <tr> <td> 1 </td> <td> Market gaps and opportunities </td> <td> Open </td> <td> \- </td> </tr> <tr> <td> 2 </td> <td> User needs and requirements </td> <td> Open </td> <td> \- </td> </tr> <tr> <td> 3 </td> <td> Ideas and feedback collected during the INVITE Co-creation Workshop </td> <td> Open </td> <td> \- </td> </tr> <tr> <td> 4 </td> <td> Pilot monitoring, co-evaluation and validation data collected through direct input methods </td> <td> Open </td> <td> \- </td> </tr> <tr> <td> 5 </td> <td> Open multi-side market place data </td> <td> Closed </td> <td> Data provided and/or produced via the interaction of users with the OI2 Lab will be closed and only accessible to platform account managers as they include personally identifiable data. Furthermore, registered users will be provided with options as to the privacy settings of their personal data. </td> </tr> <tr> <td> 6 </td> <td> Online collaboration space data </td> </tr> <tr> <td> 7 </td> <td> E-learning environment data </td> </tr> <tr> <td> 8 </td> <td> Crowdfunding tool data </td> </tr> <tr> <td> 9 </td> <td> Web portal analytics </td> <td> Open </td> <td> \- </td> </tr> <tr> <td> 10 </td> <td> Feedback on the OI2 Lab business models </td> <td> Closed </td> <td> The data will remain closed (accessible only to members of the INVITE consortium) so as to safeguard the commercial interests of project partners with respect to the market rollout of the OI2 Lab. </td> </tr> <tr> <td> Νο </td> <td> Data </td> <td> Availability </td> <td> Notes </td> </tr> <tr> <td> 11 </td> <td> Data for monitoring stakeholder engagement </td> <td> Open </td> <td> \- </td> </tr> <tr> <td> 12 </td> <td> Social media statistics </td> <td> Open </td> <td> \- </td> </tr> <tr> <td> 13 </td> <td> Project events data </td> <td> Closed </td> <td> This data will remain closed (accessible only to consortium members) as it is useful only for internal reporting purposes. On top of that, any anonymization will leave no data within the dataset. </td> </tr> <tr> <td> 14 </td> <td> Newsletter subscriptions </td> <td> Closed </td> <td> This data will remain closed (accessible only to consortium members) as it is useful only for internal reporting purposes. On top of that, any anonymization will leave no data within the dataset. </td> </tr> <tr> <td> 15 </td> <td> Data for dissemination and communication reporting </td> <td> Open </td> <td> \- </td> </tr> </table> It is important to note that all personal data collected / generated will be considered as closed data prior to their anonymisation and aggregation to safeguard the confidentiality of the data subjects. This data will be securely stored by the consortium partners that collected them to be preserved in their respective records only for as long as necessary for them to comply with their contractual obligations to INVITE’s funding authority, namely the Research Executive Agency (REA) of the Commission, and no longer than 5 years from the project’s completion . During this period the personal data will be accessible only by authorised individuals of INVITE’s consortium partner that collected this data and of the REA. After this period the personal data will be deleted from the respective consortium partner’s records. ### _Data accessibility and availability_ Public access to the open data will be made available through Zenodo, which will automatically link to OpenAIRE. The data will be fully accessible thanks to the included metadata and the search facility available on Zenodo. At the same time, closed data will be stored and shared amongst authorised members of the consortium through web portal of the project. With that in mind, the following table presents where data will be made accessible in the context of INVITE. #### Table 6: Data accessibility <table> <tr> <th> Νο </th> <th> Data </th> <th> Accessibility </th> </tr> <tr> <td> 1 </td> <td> Market gaps and opportunities </td> <td> Zenodo </td> </tr> <tr> <td> 2 </td> <td> User needs and requirements </td> <td> Zenodo </td> </tr> <tr> <td> 3 </td> <td> Ideas and feedback collected during the INVITE Co-creation Workshop </td> <td> Zenodo </td> </tr> <tr> <td> 4 </td> <td> Pilot monitoring, co-evaluation and validation data collected through direct input methods </td> <td> Zenodo </td> </tr> <tr> <td> 5 </td> <td> Open multi-side market place data </td> <td> \- </td> </tr> <tr> <td> 6 </td> <td> Online collaboration space data </td> <td> \- </td> </tr> <tr> <td> 7 </td> <td> E-learning environment data </td> <td> \- </td> </tr> <tr> <td> 8 </td> <td> Crowdfunding tool data </td> <td> \- </td> </tr> <tr> <td> 9 </td> <td> Web portal analytics </td> <td> Zenodo/Matomo * </td> </tr> <tr> <td> 10 </td> <td> Feedback on the OI2 Lab business models </td> <td> \- </td> </tr> <tr> <td> 11 </td> <td> Data for monitoring stakeholder engagement </td> <td> Zenodo </td> </tr> <tr> <td> 12 </td> <td> Social media statistics </td> <td> Zenodo </td> </tr> <tr> <td> 13 </td> <td> Project events data </td> <td> \- </td> </tr> <tr> <td> 14 </td> <td> Newsletter subscriptions </td> <td> \- </td> </tr> <tr> <td> 15 </td> <td> Data for dissemination and communication reporting </td> <td> Zenodo </td> </tr> </table> A subset of the web portal analytics relevant to the usage of the OI2 Lab tools and the overall user activity will be anonymised and (a) extracted in the form of .xlsx for upload to the Zenodo platform and (b) provided for open access via the Matomo analytics platform to interested stakeholders and the research community _._ ### _Methods, software tools and documentation to access the data_ INVITE emphasises the accessibility of the data collected/generated during the course of the project. With that in mind, no specialised method, software tool and/or documentation are needed* , at the moment, in order to access the data. Stakeholders can access the data by simply using their web browser (e.g. Mozilla, Google Chrome, Internet Explorer, Safari, etc.) through their computers (either desktop or laptop), smart phones and/or tablets. More specifically, they first need to access Zenodo through its webpage (following the lin k _https://zenodo.org/_ ) and utilise the search engine of the repository to search for interesting data. By typing the name of the project (or any other relevant keyword connected to the data) the search engine will direct the user to the project’s data, ready to be explored and re-used. Moreover, since the data will be available in open formats we will be ensuring that they can correctly be read by a range of different software programs that most of the people employ in their everyday lives. Closed data can be accessed only by authorised project partners through the respective member section of INVITE’s web portal. Again, no specialised method, software tool and/or documentation are needed to this end. Nevertheless, the member section of the web portal is accessible only with the provision of their unique username and password combination. Therefore, they first need to login the INVITE web portal (following the lin k _http://invite-project.eu/user_ ) through their digital device (e.g. computers, smartphones, tables, etc.) and provide their respective usernames and passwords. Then, they can find the uploaded data categorised under the file browser section. ### _Data, metadata, code and documentation repositories_ INVITE’s open data along with their linking metadata as well as any relevant code and documentation (if applicable) required to access this data, will be deposited to and securely stored by Zenodo. It is quite unlike that Zenodo will have to terminate its operation and stop providing its services, but in such a case all data, metadata, code and documentation uploaded by INVITE will be transferred and hosted to other suitable repositories 24 without undue delay. In this respect, it is important to note that, since all of INVITE’s openly available data will make use of PIDs (i.e. DOIs), the links to the data will not be affected. In parallel, INVITE’s data that will not be openly available for sharing will be deposited, together with their accompanying metadata, code and documentation (if necessary), to the web portal of the project. ### _Restrictions_ By utilising Zenodo for sharing the project’s openly available data, INVITE can apply different levels of accessibility for this data taking into account any relevant issues (such as ethical, rules of personal data, intellectual property, commercial, privacy-related, security-related, etc.). More specifically, Zenodo offers the following levels of data accessibility : * Open access : Data remains available for re-use. Nevertheless, the level in which this data can be reused is determined also by their accompanied licence for re-use (see subsection 3.4.1). * Embargoed status : Access to the data will be restricted until the end of the embargo period, at which time, the content will automatically become publicly available. * Restricted access : The data will not be made publicly available and sharing will be made possible only by the approval of the project partner that have the responsibility of the data. * Closed access : The data is protected against unauthorized access at all levels and only members of the consortium have the right to access it. Project partners will mainly use the open access level to disseminate the project’s data amongst the interested stakeholders. Nevertheless, in some cases embargo periods or restricted access may be used as described in Subsection 3.2.1. Data that will not be available for re-use will be accessible only by authorised partners of INVITE’s consortium and /or authorised personnel from the REA of the Commission. Moreover, INVITE will ensure open access to all peer-reviewed scientific publications that may be produced in the framework of the project. In particular, according to the Grant Agreement, INVITE will: * 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 as well as deposit, at the same time, the research data needed to validate the results presented in the deposited scientific publications. * 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. * Safeguard open access — via the repository — to the bibliographic metadata that identify the deposited publication. The bibliographic metadata shall be in a standard format and include the terms “European Union (EU)” and “Horizon 2020”; the name of the action, acronym and grant number; the publication date, and length of the embargo period if applicable; and a PID. Along these lines, this section has provided the methodology applied in the framework of INVITE so as to ensure that its data is as openly accessible as possible by any stakeholder that may find it interesting for reuse. In this context, INVITE also focuses on providing metadata standards and appropriate metadata vocabularies to increase its data interoperability. The following section provides further details in this respect. ## Making data interoperable Data interoperability refers to the ability of systems and services that create, exchange and use data to have clear, shared expectations for the contents, context and meaning of that data 25 . With that in mind, INVITE has adopted in its data management methodology the use of metadata vocabularies, standards and methods that will increase the interoperability of the data collected/generated through its activities. More specifically, the interoperability of the data that will not be publicly shared will be facilitated by the use of the Dublin Core Metadata standard. This standard is a small “metadata element set” which accounts for issues that must be resolved in order to ensure that data meet traditional standards for quality and consistency, while still remaining broadly interoperable with other data sources in the linked data environment. The fifteen elements of the standard provide a vocabulary of concepts with natural-language definitions (e.g. title, creator, author, etc.) that are instantly converted into open machine-readable formats (such as XML, HTML, etc.), enabling machine-processability. Each element is optional and may be repeated, while the standard itself offer ways exist for refining them, encouraging the use of encoding and vocabulary schemes. The vocabulary of the Dublin Core Metadata standard is presented by the following table 26 : ### _Table 7: Dublin Core Metadata standard vocabulary_ <table> <tr> <th> Νο </th> <th> Element </th> <th> Element definition </th> </tr> <tr> <td> 1 </td> <td> Title </td> <td> A name given to the resource. </td> </tr> <tr> <td> 2 </td> <td> Creator </td> <td> An entity primarily responsible for making the content of the resource. </td> </tr> <tr> <td> 3 </td> <td> Subject </td> <td> The topic of the content of the resource. </td> </tr> <tr> <td> 4 </td> <td> Description </td> <td> An account of the content of the resource. </td> </tr> <tr> <td> 5 </td> <td> Publisher </td> <td> An entity responsible for making the resource available. </td> </tr> <tr> <td> 6 </td> <td> Contributor </td> <td> An entity responsible for making contributions to the content of the resource. </td> </tr> <tr> <td> 7 </td> <td> Date </td> <td> A date associated with an event in the life cycle of the resource </td> </tr> <tr> <td> 8 </td> <td> Type </td> <td> The nature or genre of the content of the resource. </td> </tr> <tr> <td> 9 </td> <td> Format </td> <td> The physical or digital manifestation of the resource. </td> </tr> <tr> <td> 10 </td> <td> Identifier </td> <td> An unambiguous reference to the resource within a given context. </td> </tr> <tr> <td> 11 </td> <td> Source </td> <td> A reference to a resource from which the present resource is derived. </td> </tr> <tr> <td> 12 </td> <td> Language </td> <td> A language of the intellectual content of the resource. </td> </tr> <tr> <td> 13 </td> <td> Relation </td> <td> A reference to a related resource. </td> </tr> <tr> <td> 14 </td> <td> Coverage </td> <td> The extent or scope of the content of the resource. </td> </tr> <tr> <td> 15 </td> <td> Rights </td> <td> Information about rights held in and over the resource. </td> </tr> </table> Along similar lines, the interoperability of openly available data will be facilitated through Zenodo , since its metadata will be stored internally in JSON format according to a defined JSON schema. This encloses HTML microdata that allows machine-readable data to be embedded in HTML documents in the form of nested groups of name-value pairs. Moreover, the JSON schema provides a collection of shared vocabularies in microdata format that can be used to mark-up pages in ways that can be understood by the major search engines. Moreover, all metadata linked to the open data is exported via the Open Archives Initiative Protocol for Metadata Harvesting (OAI-PMH) and can be harvested. The OAI-PMH develops and promotes interoperability standards that facilitate the efficient dissemination of data amongst diverse communities 27 . ## Increase data re-use ### _License schemes to permit the widest use possible_ The application of a licence to INVITE’s open data is a simple way to ensure that any interested third-party can re-use it. In this context, licences are the instrument which permit a third-party to copy, distribute, display and/or modify the project’s data only for the purposes that are set by the licence. Licences typically grant permissions on condition that certain terms are met. While the precise details vary, three conditions are commonly found in licences which are the attribution, non-derivative, and non-commerciality. Along these lines, INVITE publishes its openly available data under the Creative Commons licencing scheme to foster their re-use and build an equitable and accessible environment for them. In fact, Zenodo provides INVITE the opportunity to publish its open data under five Creative Common licences as follows: * Creative commons Attribution-Share Alike 4.0 (CC BY-SA 4.0) according to which any third party can freely copy, distribute, display and modify the datasets for any purpose. Remix, transform, or built upon data, must be distributed under the same license as the original. Third parties must give appropriate credit, provide a link to the license, and indicate if changes were made. _Figure _ _2 _ _: _ _CC _ _BY _ _- _ _SA 4.0 _ _Figure _ _3 _ _: _ _CC BY 4.0 _ * Creative Commons Attribution 4.0 International (CC BY 4.0) according to which any third party can freely copy, distribute, display and modify the datasets for any purpose. Third parties must give appropriate credit, provide a link to the license, and indicate if changes were made. * Creative Commons Attribution-No Derivatives 4.0 International (CC BY-ND 4.0) during which any third party can freely copy, distribute, display and modify the datasets for any purpose. Remix, transform, or built upon data, however must not be distributed. Third parties must give appropriate credit, provide a link to the license, and indicate if changes were made. _Figure _ _4 _ _: _ _CC BY _ _- _ _ND 4.0 _ * Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) based on which third parties can copy, distribute, display and modify the datasets for any purpose other than commercial unless they get a permission by project partners first. Third parties must give appropriate credit, provide a link to the license, and indicate if changes were made. _Figure _ _6 _ _: _ _CC BY _ _- _ _NC 4.0 _ * Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) according to which third parties can copy, distribute, display and modify the datasets for any purpose other than commercial unless they get a permission by project partners first. Remix, transform, or built upon data, however, must not be distributed. Third parties must give appropriate credit, provide a link to the license, and indicate if changes were made. _Figure _ _5 _ _: _ _CC BY _ _- _ _NC _ _- _ _ND 4.0 _ With that in mind, the INVITE consortium considers that the CC BY-NC 4.0 is an appropriate licensing scheme to ensure the widest re-use of the data , while also taking into account the importance of recognising both the source and the authority of the data as well as safeguarding the commercial interests of the OI2 Lab. Nevertheless, different licensing schemes may be selected to better fit the need of INVITE’s open data ensuring not only their long-term preservation and re-use but also the interests of the consortium along with the rights of individuals for whom the data is about. In such a case, this subsection of the DMP will be updated accordingly. ### _Availability for re-use_ The re-use of data is a key component of INVITE’s methodology for making data FAIR. In fact, making data available for re-use ensures interested stakeholders, other that project partners, can benefit from this data, contributing towards maximising the impact of the project. Rich metadata created based on metadata standards that enable proper discovery as well as appropriate licensing schemes facilitate the re-use of INVITE’s open data , allowing them to find valuable utility. In principle, it is expected that data will become available for re-use no later than 120 days after the end of its processing in the framework of the project (i.e. collection, anonymisation, aggregation, etc.) to ensure that any additional data management activities required to this end do not compete with the timely delivery of the project’s planned outputs. Nevertheless, the data that have been already collected/generated will be uploaded to Zenodo immediately after the submission of the interim version of the Data Management Plan, that is the 1 st of September 2018. This data refers to the activities performed during the course of Task 1.1, Task 1.2 and Task 1.3. The period for which the data will remain available for re-use depends on the lifetime of their repository. In the case of data deposited to Zenodo, this is the lifetime of CERN’s relevant laboratory, which at the moment has an experimental programme defined for the next 20 years. In case Zenodo discontinuous the data, this will be transferred and hosted to other suitable repositories. With that in mind, the expected time that INVITE’s data will be made openly accessible and uploaded to Zenodo is indicatively provided in the following table: #### Table 8: Expected time that data will be made open through Zenodo 28 <table> <tr> <th> Νο </th> <th> Name of activity </th> <th> Expected time for making data open </th> <th> Notes </th> </tr> <tr> <td> 1 </td> <td> Market gaps and opportunities </td> <td> 01/09/2018 </td> <td> </td> </tr> <tr> <td> 2 </td> <td> User needs and requirements </td> <td> 01/09/2018 </td> <td> </td> </tr> <tr> <td> 3 </td> <td> Ideas and feedback collected during the INVITE Co-creation Workshop </td> <td> 01/09/2018 </td> <td> </td> </tr> <tr> <td> 4 </td> <td> Pilot monitoring, co-evaluation and validation data collected through direct input methods </td> <td> 1 st version: 31/01/2020 2 nd version: 31/10/2020 </td> <td> This data will be collected/generated during the iterative implementation of the project’s pilots. In this context, the respective dataset will be updated twice during the course of the project, once per each pilot round. Accordingly, the </td> </tr> <tr> <td> </td> <td> </td> </tr> <tr> <td> 5 </td> <td> Web portal analytics </td> <td> </td> <td> datasets will be made openly available no later than 120 days after the completion of each round. </td> </tr> <tr> <td> 6 </td> <td> Data for monitoring stakeholder engagement </td> <td> 1 st version: 31/12/2018 2 nd version: 31/10/2020 </td> <td> The data will be updated regularly during the course of INVITE following the monitoring of the project’s stakeholder engagement. An upto-date version of the respective dataset will be uploaded after the end of each of the 2 reporting periods of INVITE. </td> </tr> <tr> <td> 7 </td> <td> Social media statistics </td> <td> 1 st version: 31/12/2018 2 nd version: 31/10/2020 </td> <td> This data will be collected throughout the duration of INVITE as the dissemination and communication activities of the project run their course. An up-to-date version of the </td> </tr> <tr> <td> </td> <td> </td> </tr> <tr> <td> 8 </td> <td> Data for dissemination and communication reporting </td> <td> </td> <td> respective dataset will be uploaded after the end of each of the 2 reporting periods of INVITE. </td> </tr> </table> ### _Data quality assurance processes_ Quality Assurance (QA) and Quality Control (QC) activities are an integral part of INVITE’s data management methodology and are implemented prior to the publication of any data to Zenodo, safeguarding the transparency, consistency, comparability, completeness and accuracy of the data. QA is a planned system of review procedures conducted outside the framework of developing a dataset, by personnel not directly involved in the dataset development process 28 . In the context of INVITE, it takes the form of peer-reviews of methods and/or data summaries to assess the quality of the dataset and identify any need for improvement, ensures that the dataset correctly incorporates the scientific knowledge and data generated. QC is defined as a system of checks to assess and maintain the quality of the dataset being compiled 29 . The relevant procedures of INVITE are designed to provide routine technical checks as they measure and control data consistency, integrity, correctness and completeness as well as identify and address errors and omissions. In this context, QC checks cover everything from data acquisition and handling, application of approved procedures and methods, and documentation. Some of the general quality checks undertaken in the framework of the project include checking (i) for transcription errors in data input; (ii) that scale measures are within the range of acceptable values; and (iii) whether proper naming conversions are used. 28. 2006 IPCC Guidelines for National Greenhouse Gas Inventories, Vol. 1 General Guidance and Reporting, CHAPTER 6 Quality Assurance / Quality Control and Verification. 29. 2006 IPCC Guidelines for National Greenhouse Gas Inventories, Vol. 1 General Guidance and Reporting, CHAPTER 6 Quality Assurance / Quality Control and Verification. # Allocation of resources ## Estimated costs for making data FAIR The costs required for making the data collected/generated during the course of INVITE’s activities FAIR are integrated within the budget of the project. With that in mind, the table which follows provides an overview of the estimated costs of making data FAIR as well as their budget source within the framework of INVITE. ### _Table 9: Estimated costs for making data FAIR_ <table> <tr> <th> No </th> <th> Data Processing / Management Activity </th> <th> Budget source </th> <th> Total estimated effort in Person Months 29 </th> <th> Total estimated cost in Euro 30 </th> </tr> <tr> <td> 1 </td> <td> Collection </td> <td> Budget allocated to the WP under which the respective data are processed </td> <td> 25.60 </td> <td> 153,767.08 </td> </tr> <tr> <td> 2 </td> <td> Documentation </td> <td> Budget allocated to the WP under which the respective data are processed </td> <td> 1.45 </td> <td> 8,694.63 </td> </tr> <tr> <td> 3 </td> <td> Storage </td> <td> Budget allocated to the WP under which the respective data are processed </td> <td> 0.88 </td> <td> 5,261.79 </td> </tr> <tr> <td> 4 </td> <td> Access and security </td> <td> Budget allocated to the WP under which the respective data are processed </td> <td> 0.88 </td> <td> 5,261.79 </td> </tr> <tr> <td> 5 </td> <td> Preservation </td> <td> Budget allocated to the WP under which the respective data are processed </td> <td> 1.63 </td> <td> 9,816.50 </td> </tr> <tr> <td> 6 </td> <td> Availability and re-use </td> <td> Budget allocated to the WP under which the respective data are processed </td> <td> 2.57 </td> <td> 15,413.03 </td> </tr> <tr> <td> 7 </td> <td> Overall data management </td> <td> WP7 </td> <td> 4.00 </td> <td> 24,024.72 </td> </tr> <tr> <td> </td> <td> </td> <td> </td> <td> _Total _ </td> <td> _222,239.54 _ </td> </tr> </table> In order to produce the estimations of the costs for making data FAIR in the context of INVITE, a series of assumptions were made, taking into account the respective guidelines provided by the Research Data Management Support, a multidisciplinary network of data experts within Utrecht University 31 , as well as of the UK Data Service and its data management costing tool 32 . With that in mind, the estimated costs for making INVITE’s data FAIR cover data-related activities and resources across the data lifecycle , spanning from collection and documentation through storage and preservation over to sharing and re-use. In particular, costs for data collection cover activities necessary for acquiring external datasets (if required), gathering/generating new data, transcribing (if applicable), formatting and organising this data as well as acquiring informed consent from data subjects. This data processing activity reflects the majority of the costs required for making data FAIR as the majority of INVITE’s data constitutes new data collected/generated over the course of the project. At the same time, data documentation costs address the effort required for describing data (e.g. marking data with variable and value labels, code descriptions, etc.) as well as creating well-defined metadata along with a meaningful description of the context and methodology of how data was collected/generated and processed (where necessary). Costs for data storage include both the resources required for ensuring adequate storage space for the data as well as the effort necessary for conducting data back-ups, while data access and security costs encompass costs related to ensuring access to the data as well as for protecting it from unauthorised access or use or from disclosure. Given that the storage of INVITE’s data will not require the procurement of additional space (other than what is already available to project partners) as well as that no special measures or software are required to access and secure the data (other than the what is inherently built in to the repositories of INVITE’s data), such costs are kept to a minimum. Data preservation costs, on the other hand, are estimated relatively higher than data storage, access and security costs, as additional effort will be required in several cases in order to convert the collected/generated data from their original form (e.g. physical interview transcripts) to an open and/or machine readable format suitable for long-term preservation (e.g. to an .xlsx format.). Adequate effort for data availability and re-use costs is also foreseen to safeguard the appropriate digitisation and anonymisation of the data as well as cover any resources required for data sharing and cleaning. Along the same lines, appropriate effort is foreseen for overall data management as well, in order to cover the effort related with the operationalisation of data management in the framework of INVITE Finally, costs for long-term preservation in the framework of INVITE are assumed to be negligible, since the open data of the project will be hosted in the repository of Zenodo free of charge. ## Data management responsibilities For the effective, proper and secure handling of the data collected/generated in the framework of INVITE, specific data management roles have been established within the data management methodology and procedures of the project. These responsibilities are outlined in this section of the DMP and are as follows. Project Coordinator (PC) : The PC, Q-PLAN, is responsible for overall data management in the framework of INVITE, including the elaboration of the DMP and its updates (when necessary and with support of all partners). At the same time, the PC is responsible for the elaboration of proper templates for the informed consent form and information sheet to be appropriately adjusted and utilised by project partners during the relevant activities of the project. Finally, the PC coordinates with Work Package and Task Leaders to determine whether and how the data collected/generated by INVITE are shared and become available for re-use, contributes to its quality assurance and uploads the project’s openly available data to Zenodo. Work Package Leaders (WPL) : The WPL is responsible for coordinating the implementation of the data processing activities performed under the WPs they are leading. Moreover, they align with the PC and the respective Work Task Leader on whether and how the data gathered/produced under the tasks that fall within the WP they are leading will be shared and/or re-used. 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. Finally, the WPL are the main responsible for assuring the quality of the data stemming from the activities of the WP they are leading, including assessing their quality and indicating any need for improvement to the respective Work Task Leaders. Work Task Leaders (WTL) : The WTL act as data controllers 33 of the data collected/generated in the frame of the tasks that fall under their leadership, determining the purposes and means of processing this data as well as safeguarding its appropriate and timely processing. Moreover, they are responsible for properly adjusting the templates for the informed consent form and information sheet to the needs and specificities of the activities carried out in the task they are leading. Finally, they undertake any necessary actions to prepare the data collected/ generated through the tasks they are leading for sharing either within the consortium or openly (including the use of proper naming conventions, application of suitable anonymisation techniques, creation of appropriate metadata and documentation, etc.). Data processors : Data processors are project partners that are tasked to collect, digitise, anonymise, store, destroy and/or otherwise process data for the specific purpose of the activity in which it has been collected/generated within the framework of the project. They are responsible for appropriately collecting the necessary consent for processing data as well as for ensuring that the informed consent form and information sheet used to this end is properly adjusted to the needs of the activity they are participating and any particularities applicable to their organisation. Moreover, they are also responsible for managing the consents they have retrieved with a view to demonstrating their compliance with the relevant applicable EU and national regulation. Finally, they perform quality checks to assess and maintain the quality of the dataset(s) held within their records. Data repositories : Data repositories are tasked with the storage and long-term preservation of the project’s data. In this respect, Zenodo maintains and preserves the openly available data of INVITE, enabling its sharing and re-use. To this end, Zenodo assigns metadata and DOIs to the data, while also taking all the necessary measures to securely back-up the data and be in a position to restore it, safeguarding its long-term preservation. Accordingly, the Web Portal of INVITE shall securely store and preserve the project’s data available for sharing amongst authorised consortium members in the framework of the project. In this context, the following table illustrates the allocation of data management responsibilities amongst the members of the INVITE consortium per data collected/generated under each WP. _Table 10: Data management responsibilities of INVITE partners per data collected/generated under each WP _ <table> <tr> <th> WP </th> <th> WPL </th> <th> Data </th> <th> Tasks </th> <th> WTL _Data Controllers _ </th> <th> Data Processors </th> </tr> <tr> <td> WP1 </td> <td> CERTH/ITI </td> <td> Market gaps and opportunities </td> <td> Task 1.1 </td> <td> RTC NORTH </td> <td> RTC NORTH </td> </tr> <tr> <td> User needs and requirements </td> <td> Task 1.2 </td> <td> CERTH/ITI </td> <td> All partners </td> </tr> <tr> <td> Ideas and feedback collected during the INVITE Co-creation Workshop </td> <td> Task 1.3 </td> <td> RTC NORTH </td> <td> All partners </td> </tr> <tr> <td> WP3 </td> <td> RTC NORTH </td> <td> Pilot monitoring, co-evaluation and validation data collected through direct input methods </td> <td> Task 3.2 & Task 3.3 </td> <td> RTC NORTH & CERTH/ITI </td> <td> All partners </td> </tr> <tr> <td> Open multi-side market place data </td> <td> Task 3.2 & Task 3.3 </td> <td> RTC NORTH & CERTH/ITI </td> <td> INTRASOFT </td> </tr> <tr> <td> Online collaboration space data </td> <td> Task 3.2 & Task 3.3 </td> <td> RTC NORTH & CERTH/ITI </td> <td> INTRASOFT </td> </tr> <tr> <td> E-learning environment data </td> <td> Task 3.2 & Task 3.3 </td> <td> RTC NORTH & CERTH/ITI </td> <td> INTRASOFT </td> </tr> <tr> <td> Crowdfunding tool data </td> <td> Task 3.2 & Task 3.3 </td> <td> RTC NORTH & CERTH/ITI </td> <td> INTRASOFT </td> </tr> <tr> <td> Web portal analytics </td> <td> Task 3.2 & Task 3.3 </td> <td> RTC NORTH & CERTH/ITI </td> <td> INTRASOFT </td> </tr> <tr> <td> WP4 </td> <td> Q-PLAN </td> <td> Feedback on the OI2 Lab business models </td> <td> Task 4.2 </td> <td> Q-PLAN </td> <td> All partners </td> </tr> <tr> <td> WP5 </td> <td> SEZ </td> <td> Data for monitoring stakeholder engagement </td> <td> Task 5.1 </td> <td> SEZ </td> <td> All partners </td> </tr> <tr> <td> WP6 </td> <td> E-UNLIMITED </td> <td> Social media statistics </td> <td> Task 6.1 </td> <td> E-UNLIMITED </td> <td> E-UNLIMITED </td> </tr> <tr> <td> Project events data </td> <td> Task 6.1 </td> <td> E-UNLIMITED </td> <td> E-UNLIMITED, NINESIGMA, WRS and NELEP </td> </tr> <tr> <td> Newsletter subscriptions </td> <td> Task 6.1 </td> <td> E-UNLIMITED </td> <td> E-UNLIMITED </td> </tr> <tr> <td> Data for dissemination and communication reporting </td> <td> Task 6.1 </td> <td> E-UNLIMITED </td> <td> E-UNLIMITED </td> </tr> </table> # Data security INVITE will securely handle any collected/generated data throughout its entire lifecycle as it is essential to safeguard this data against accidental loss and/or unauthorised manipulation. Particularly, in case of personal data collection/generation it is crucial that this data can only be accessible by those authorised to do so . With that in mind, the project’s back-up and data recovery strategy aims at ensuring that no data loss will occur during the course and after the completion of INVITE, either from human error or hardware failure, as well as inhibit any unauthorised access. In particular, all project partners responsible for processing 34 data within their private servers will ensure that this data is protected and any necessary data security controls have been implemented , so as to minimize the risk of information leak and destruction. This case refers to the data that will be closed and therefore will not be shared and/or re-used within the framework of the project. In this case and to avoid data losses, the data will be backed up on a daily basis and the backed-up files will be stored in external hard disk drives so as to safeguard their preservation, while also enabling their recovery at any time. Moreover, integrity checks 35 will be carried out once a month (or more often, if deemed necessary) ensuring that the stored data has not been changed or corrupted. The tool that will support partners in undertaking integrity checks is the _MD5summer_ which generates and verifies MD5 checksums 36 . Access to closed data will only be permitted to authorised project partners. In case there is a personal data breach, project partners will notify, without undue delay and, where feasible, not later than 72 hours after having become aware of it, their competent national supervisory authorities (e.g. data protection authorities) as well as the data subject(s) that may be affected by the breach. Moreover, they will document any personal data breaches, including information such as the facts relevant to the breach, its effects and the remedial action(s) taken. With that in mind, identification and authentication access controls play an important role in the context of the project, as they help partners to protect the data collected/generated during the course of INVITE and especially personal data. To this end, each project partner is responsible for and committed to ensuring the application of appropriate access controls to the data they are processing within their private servers of their organisation. At the same time, technical access controls are built into the Web Portal and OI2 Lab of INVITE , setting out clear roles with access rights to the data stored there, so that only authorised personnel have access. Each project partner has been provided with unique accounts with one or more roles assigned to them enforcing role-based security when its staff processes the project’s data. These accounts are username/password protected maximising access control. Moreover, in order to safeguard the privacy of the users of the project’s Web Portal and OI2 Lab, dedicated privacy policies have been created that clearly state the way in which these online spaces collect, process and use personal data, the security procedures followed, the users’ rights as well as the cookies policy employed (see Annex I and II of this document). On another note, INVITE’s openly available data will be stored safely for long-term preservation on Zenodo , in the same cloud infrastructure as research data from CERN’s Large Hadron Collider, using CERN’s battletested repository software INVENIO, which is used by some of the world’s largest repositories (such as INSPIRE HEP and the CERN Document Server). Along these lines, data is stored and backed-up in CERN’s EOS service in an 18 petabytes disk cluster. Both data files and metadata are kept in multiple online replicas and independent replicas ensuring their long-term preservation as well as their recovery when necessary. Moreover, for each file two independent MD5 checksums are stored. One checksum is stored by INVENIO, used to detect changes to files made from outside of it whereas the other checksum is stored by EOS, and used for automatic detection and recovery of file corruption on disks. In this context, access control is applied by the different level of openness that Zenodo allows (i.e. open, embargoed, restricted and closed). # Ethical aspects and other procedures INVITE entails activities which involve the processing of data that does not fall into any special category of personal data 37 (i.e. non-sensitive data). The collection/generation of this data from individuals participating in the project’s activities is based upon a process of informed consent . In fact, any personal data collected/generated in the framework of INVITE is processed according to the principles laid out by the 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 which has entered into force in May 2018 aiming to protect individuals’ rights and freedoms in relation to the processing of their personal data, while also facilitating the free flow of such data within the European Union. Along these lines, data is collected/generated only for specified, explicit and legitimate purposes relative to project’s objectives. Moreover, all project partners tasked with processing data during the course of INVITE fully abide with their respective applicable national as well as EU regulations. Under this light, further details about the scope of the activities that entail data collection/generation in the frame of INVITE along with the procedures for identifying/recruiting suitable stakeholders to take part in them as well as for obtaining their informed consent are provided in “ D8.1: H - Requirement No. 1 ”. Moreover, evidence that all data handling procedures carried out by project partners are in line with relevant EU and national regulations are provided in “ D8.2: H - Requirement No. 2 ”. The templates for the Information Sheet and the Informed Consent Form, used in the implementation of the project’s activities, are compliant with the General Data Protection Regulation and annexed to this DMP (see Annex II). In this respect, it is important to highlight that each project partner is responsible for ensuring that the templates for the Information Sheet as well as the Informed Consent Form are appropriately adjusted according to (i) the needs of the activity for which they are being used by them as well as to (ii) the relevant regulations applicable to their respective countries and/or organisation. Moreover, all partners should keep records to demonstrate that an individual has consented to processing of his / her personal data and use consent management mechanisms that make it easy for individuals to withdraw their consent. Finally, no other national/funder/sectoral/departmental procedures for data management are currently used in the framework of INVITE. # Conclusions and way forward The interim version of the DMP builds upon its initial version to further elaborate on the methodology employed in the framework of INVITE. With that in mind, it safeguards the sound management of the data collected/generated during the course of the project’s activities across their entire lifecycle, while also making them FAIR. Moreover, this version of the DMP provides an estimation of the costs required for making data FAIR, outlines the provisions pertaining to their security as well as addresses the ethical aspects revolving around their collection/generation. The DMP is considered to be a living document in the framework of INVITE and is updated throughout the course of the project taking into account its latest developments and available results. In fact, the interim version of the DMP will be further elaborated and updated at least once more over the course of INVITE, namely on M36 of the project. Ad hoc updates, may also be realised when deemed necessary, with a view to delivering an accurate, up-to-date and comprehensive DMP before the completion of the project.	https://phaidra.univie.ac.at/o:1140797	Horizon 2020
0224_ExoplANETS A_776403.md	# INTRODUCTION In the framework of the ExoplANETS-A project, archival data from ESA Space Science archives (HST) combined with NASA Space Archives (Spitzer, Kepler) will be exploited with novel data calibration and spectral extraction tools, novel retrieval tools, to produce a homogeneous and reliable catalog of exoplanet atmosphere properties. In parallel, a coherent and uniform database of the relevant properties of host stars will be developed from ESA Space Science archives (XMM, Gaia, Herschel), combined with data from international space missions and ground-based telescopes. These exoplanet and host star catalogues will be accompanied/interpreted with models to assess the importance of star – planet interactions. The project gathers the expertise of seven laboratories: This document presents an initial version of the data management plan (DMP) of the project, the deliverable number 3 of the management workpackage (WP1) due 6 months after the start of the project. It follows the template given in Reference Document 1 (see below). It is a living document which will be updated as the implementation of the project progresses. # APPLICABLE DOCUMENTS (AD) <table> <tr> <th> AD-1 </th> <th> ExoplANETS-A Grant Agreement </th> <th> N° 776403 </th> </tr> <tr> <td> AD-2 </td> <td> ExoplANETS-A Consortium Agreement </td> <td> Version 3, 2017-12-22; DRF 0647_X30423 </td> </tr> </table> # REFERENCE DOCUMENTS (RD) <table> <tr> <th> RD-1 </th> <th> _http://ec.europa.eu/research/participant s/data/ref/h2020/gm/reporting/h2020tpl-oa-data-mgt-plan_en.docx_ </th> <th> Version 1.0 ; 13 October 2016 </th> </tr> </table> # DATA SUMMARY 4.1 PURPOSE OF THE DATA COLLECTION/GENERATION AND ITS RELATION TO THE OBJECTIVES OF THE PROJECT? The objectives of the project are : * To establish new knowledge on the atmosphere of exoplanets by exploiting archived space data (HST, Spitzer, Kepler) using novel data reduction methods, as well as improved techniques to retrieve atmospheric parameters from data. * To establish new insight on the influence of the star on the planet atmosphere by exploiting archived space data (GAIA, XMM, Chandra, Herschel, IUE, HST) on the host stars, as well as complementary ground-based data. * To disseminate new knowledge. Data are thus essential to the ExoplANETS-A project. Archival data are at the start of the project and new data sets with added scientific value are generated by the project and are made available to the community via a knowledge server. The global concept is shown below. Figure 1 : starting from archive data on exoplanets and their host star, the project will develop novel data reduction and retrieval techniques to get an homogeneous catalogues of a hundred of exoplanets; the data will be available from a knowledge server. 4.2 WHAT TYPES AND FORMATS OF DATA WILL THE PROJECT GENERATE/COLLECT? The science products of the project consist of spectra of exoplanet atmospheres (see Figure 2), exoplanets and host-star parameters (such as molecular content of exoplanet atmosphere see Figure 3), modelling and retrieval algorithms, tools to analyze the data, ab-initio models of sources… Figure 2. Example of transmission spectrum (left) and emission spectrum (right) of an exoplanet (WASP43 b), as observed with the WFC3 instrument on board of HST (white circle); the dark blue line show the best fit models from retrieval analysis. The feature observed around 1.4 microns is a water feature. The insert on the right image shows the photometric points from observations with the Spitzer Space Telescope. (L. Kreidberg et al. ApJL 793 , Issue 2, L27-32; arXiv:1410.2255) Figure 3: A) Examples of Tau-REx results for three simulated super-Earths HD 219134 b, GJ 1132 b and Kepler 78 b (planetary parameters from exoplanet.eu). Top: atmospheric spectra for varying compositions at Hubble/WFC3 wavelengths. Expected error bars for observations are also shown. Bottom: Tau-REx retrieved constraints of H2O abundance for the spectra shown above. B) Posterior distributions of complex likelihood functions encountered in spectral retrievals. Parameter spaces are often highly dimensional (>20D) with non-linear inter-parameter correlations. We will fully map these correlations and, using manifold learning, identify model degeneracies. C) Simulated observational data analysed by the Tau-REx framework. Multiple atmospheric components are shown visually. Our aim is to use, as much as possible, the Virtual Observatory standard or one of the standard formats in the astronomical community, i.e. the fits format. 4.3 WHAT IS THE ORIGIN OF THE DATA? The data will be of various origins: * We will use archival data from observations of exoplanet atmospheres with space observatories, as well as ground-based observatories. * Thanks to the development of novel data reduction, we will produce from the archival data, new calibrated data sets. * From this new set of data and thanks to the development of new retrieval techniques, we will derive parameters of exoplanets atmospheres, such as its molecular contain. * Data will also be generated from the modelling the atmosphere of exoplanets. * We will use archival data from observations of exoplanet host stars with space observatories, as well as ground-based observatories. When needed, we will submit observing proposal to complete information on some of the host stars of our target list. * From those data, we will derive, either directly or thanks to models, the star parameters: effective temperature, luminosity, gravity (also as an age estimate), metallicity, rotational period, variability, proper motion, multiplicity, magnetic field, topology of the field, wind … * From the parameters and thanks to star – planet interaction models, we will determine the importance of such interactions. 4. WHAT IS THE EXPECTED SIZE OF THE DATA? To be determined precisely; but not big; in the gigaoctets range. One of the end products will be a catalogue with the properties of the atmosphere of about 100 targets. <table> <tr> <th> Work Package number </th> <th> Deliverable type </th> <th> Data format </th> <th> Data size </th> </tr> <tr> <td> WP1 </td> <td> Documents </td> <td> PDF, excel </td> <td> At maximum in the 100 Moctets range </td> </tr> <tr> <td> WP2 </td> <td> Calibrated spectra of about 100 exoplanets Data reduction Codes Documents, Scientific papers </td> <td> Fits, PDF, excel </td> <td> At maximum in the 10 gigaoctets range </td> </tr> <tr> <td> WP3 </td> <td> Retrieved parameters for the atmosphere of about 100 exoplanets Retrieval codes Documents, Scientific papers </td> <td> Fits, PDF, Excel </td> <td> At maximum in the 10 gigaoctets range </td> </tr> <tr> <td> WP4 </td> <td> Catalogues of host stars Codes Documents, Scientific papers </td> <td> TBD, FITS, PDF, Excel </td> <td> At maximum in the 10 gigaoctets range </td> </tr> <tr> <td> WP5 </td> <td> Models Documents, Scientific papers </td> <td> TBD, FITS, PDF, Excel </td> <td> At maximum in the 10 gigaoctets range </td> </tr> <tr> <td> WP6 </td> <td> Science products Web site Videos MOOC – SpoC Documents </td> <td> TBD, FITS, PDF, Excel, Virtual Observatory standards, MP4, TXT, PNG, JPEG, EPS </td> <td> In the few 100 gigaoctets range </td> </tr> </table> 5. TO WHOM MIGHT IT BE USEFUL ('DATA UTILITY')? The data will be useful in first place to the scientific community working on exoplanets. It will also be of interest to students, as well as general public. # MAKING DATA FINABLE ACCESSIBLE INTEROPERABLE AND RE-USABLE (FAIR) The dissemination of knowledge is a key aspect of the project and a WorkPackage, WP6, is dedicated to this aspect (see Figure 4). Figure 4 : The data generated by the various WPs will be integrated to a knowledge server. The Knowledge Management WP aims at * Capturing knowledge produced by the other WPs (see Figure 4) within a knowledge base including all scientific products (data, models, tools and interpretation). * Providing open access to them through a knowledge displayer with two interfaces, one for the scientific community with a direct access to the science products, the second for the general public with educational resources based on the science products. * Getting feedback from the users Figure 5 describes the various aspects of knowledge management. Figure 5: Overview of the knowledge management of the science products 1. MAKING DATA FINDABLE AND OPENLY ACCESSIBLE The Definition, design and production of a Knowledge Base is scheduled from Month 6 to Month 36. The data produced in the framework of the project will be made findable by means of a standard identification mechanism. The data will be encapsulated with metadata. Metadata categories will be defined to follow the different types of science products (ex: Observation, Code…) or educational resources (ex: Video, Image…). For science products, we plan to follow the naming convention used for the Virtual Observatory. The architecture of the knowledge base (KB) that will capture, record and format the science products for dissemination will be defined first. Once defined collectively with the WorkPackages (WP) 2, 3, 4, 5, the KB will be produced by deploying a server. The data will be stored under a relational database. Indexes, Data Base (DB) engines and cache mechanism will be set up to ensure the scalability and efficiency of the overall data access. Regarding binary files, an indexed and versioning file system will be deployed to keep track of modifications without losing content. The KB will be hosted on a server (NodeJS or PHP according to the actual requirements identified during the specification phase) providing REST (REpresentational State Transfer) access to the data (i.e. a set of standardised Uniform Resource Identifier (URI) allowing to obtain the information in a JSON format). Specific routes will be defined to query the KB according to several criteria: hierarchy, semantic, historic, syntactic... This server will also provide a standardised export mechanism to export data to a user-friendly product (e.g. PDF files or JPG images). We expect a hundred of exoplanet atmospheres at the beginning for the Beta version; but the project will be dimensioned to be able to handle thousands of entries. The Definition and production of a Knowledge Displayer (KD) will start on Month 12. Once recorded in the knowledge base, the scientific community and general public will be granted open access to our science products using two dedicated interfaces. The Knowledge displayer will be in charge of displaying the KB to the end-user by accessing the Knowledge Base Server REST API’s (Application Programming Interfaces). All the data will be made openly available at the latest at the end of the project. 2. MAKING DATA INTEROPERABLE The data produced in the project will be interoperable throughout virtual Observatory standard for science products and Learning Tool Interoperability (LTI) standard for education resources. 3. INCREASE DATA RE-USE The data will be made available for re-use through the knowledge server following the deliverables plan of the project. There is no restriction in the re-use of the data generated by the project. The idea is to keep on feeding the data base after the end of the ExoplANETS-A project, for example in the framework of ARIEL; Ariel is the ESA space mission just selected for the M4 slot of the ESA cosmic vision 2015 – 2025 program and whose adoption is scheduled end of 2020. In any case, we can guarantee that the database will be designed to remain operational for at least 5 years after the project end (for example putting the data on the CERN ZENODO data base). # ALLOCATION OF RESOURCES The data management is one of the work packages of the project; its cost is included in the project cost; it is covered partly by EC and partly by the institutes participating in the project. 46 person.months have been attributed to the workpackage. There is a lead from CEA and a co-lead from INTA. # DATA SECURITY The data security (including data recovery as well as secure storage) will be taken into account in the design of the data base and of the knowledge server.	https://phaidra.univie.ac.at/o:1140797	Horizon 2020
0225_TAKEDOWN_700688.md	# Executive Summary Data management is a crucial issue for research and innovation projects and many mistakes were made in the past, when no one was actually thinking about what to do with the data and how to preserve them or make them available for other researchers too. This first Data Management Plan (DMP) shows that there are mainly eight data sets that will be produced as part of the project activities and that are relevant to be included in the DMP. These data sets cover the collected stakeholder contacts, public security services and digital security solutions. Furthermore, the empirical research will generate data from the quantitative survey, expert interviews, focus groups and workshops. Additionally, also the validation of the TAKEDOWN solutions will generate data. Due to privacy and security concerns related with the sample size, the qualitative research data will not be made openly accessible as primary data but in a processed form. Due to the scope of the research and the intended sample size, it is planned to make the data from the quantitative survey openly accessible on the data repository Zenodo. Furthermore, reports working with the qualitative data will also be accessible. The consortium will also aim at open access when publishing papers and articles. However, these steps will be done in accordance with the ethical guidelines elaborated in this report as well as in D2.2. The DMP is a living document and hence several issues will be updated and further questions will be answered in the second version, which will be finalized in month 18. # 1\. Introduction Research and innovation projects such as TAKEDOWN usually produce large sets of data. Depending on the discipline, the data could come for example from social science research, laboratory testing, field studies or observations. However, it often remains unclear and uncertain, what will happen with the data after they were analysed and the project was finished. Furthermore, a lot of data sets are potentially interesting also for other researchers, but due to the fact that they are either stored on a local serves or miss crucial meta-data (or both), their potential value cannot be exploited. Hence, researcher need to think about the data that they will produce at the beginning of the research – and this is exactly the purpose of the Data Management Plan (DMP). 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 in the TAKEDOWN project and by the consortium with regard to the project research data. The DMP covers the complete research data life cycle. It describes the types of research data that will be generated or 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 DMP is a living document, which will evolve during the lifespan of the project, particularly whenever significant changes arise such as dataset updates or changes in Consortium policies. This document is the first version of the DMP, delivered in Month 6 of the project. It includes an overview of the datasets to be produced by the project, and the specific conditions that are attached to them. Although this report already covers a broad range of aspects related to the TAKEDOWN data management, the upcoming versions will get into more detail on particular issues such as data interoperability and practical data management procedures implemented by the TAKEDOWN project consortium. The following section of the DMP provides an overview of the data sets, which will be produced in the TAKEDOWN project. It describes the origins of the data as well as the formats the allocation the particular WPs. Furthermore, it highlights the purpose of the collection as well as information on the utility. Section 3 clearly points out, which data will be made openly accessible and which won’t – including detailed justifications for the reasons. This is especially relevant for the primary data that will be collected as part of the empirical research. Furthermore, the section also provides details on the data repositories or other locations, where the data will be stored. Section 4 highlights main aspects related to the costs of the accessibility, whereas Section 5 discusses the main ethical issues. The final section provides an outlook on the open issues and questions to be addressed in the next DMP report. # 2\. Data summary In order to provide an overview of the different data sets that are currently and will be produced in the TAKEDOWN project, the following table shows the data type, the origin of the data, the related WP number and the format, in which the data will be presumably stored. <table> <tr> <th> # </th> <th> Data type </th> <th> Origin </th> <th> WP# </th> <th> Format </th> </tr> <tr> <td> 1 </td> <td> Stakeholder contacts collection </td> <td> Publicly available data </td> <td> 2 </td> <td> .xls </td> </tr> <tr> <td> 2 </td> <td> Public security services collection </td> <td> Publicly available data </td> <td> 2 </td> <td> .xls </td> </tr> <tr> <td> 3 </td> <td> Digital security solutions collection </td> <td> Publicly available data </td> <td> 2 </td> <td> .xls </td> </tr> <tr> <td> 4 </td> <td> Quantitative survey data </td> <td> Primary data </td> <td> 3 </td> <td> .xls +.csv </td> </tr> <tr> <td> 5 </td> <td> Expert interview data </td> <td> Primary data </td> <td> 3 </td> <td> .mp3 + .docx + .txt </td> </tr> <tr> <td> 6 </td> <td> Focus groups data </td> <td> Primary data </td> <td> 3 </td> <td> .docx + .txt </td> </tr> <tr> <td> 7 </td> <td> Workshops data </td> <td> Primary data </td> <td> 3 </td> <td> .docx + .txt </td> </tr> <tr> <td> 8 </td> <td> Validation cycles data </td> <td> Primary data </td> <td> 7 </td> <td> xls + .csv </td> </tr> </table> ## Table 1: Data sets overview Table 2 describes the data set and the purpose of the data collection of data generation in relation with the objectives of the project. Additionally, it shows the data utility for clarifying to whom the data might be useful. <table> <tr> <th> # </th> <th> Data type </th> <th> Description & Purpose </th> <th> Utility </th> </tr> <tr> <td> 1 </td> <td> Stakeholder contacts collection </td> <td> Description The data contain information on the main stakeholders of TAKEDOWN along the major stakeholder groups. They include researchers, practitioners, policy makers, law enforcement agencies, NGOs and other initiatives as well as security solutions providers. The contact information that is collected includes the name, institutional affiliation, position, email address, phone number and office address. Purpose The collection will be used for contacting the respondents of the empirical research as well as the validation of the project outcomes. It also provides the basis for the dissemination of the project and for promoting the TAKEDOWN solutions. </td> <td> The data could be on the one hand useful for research, as they comprise a large part of the ecosystem. Furthermore, the data might also be interesting for the private sector as target groups of their products. </td> </tr> <tr> <td> 2 </td> <td> Public security services collection </td> <td> Description The data set is a collection of public security services such as helplines, online reporting platforms or information sites. It covers most of the European countries and the entries are providing information on the name, the purpose or focus (OC or TN of both), the institution </td> <td> These data are on the one hand useful for initiatives working in the field of counter violent extremism (CVE) and against organized crime, because it allows them to get an overview on </td> </tr> </table> <table> <tr> <th> </th> <th> </th> <th> that is providing the service and the link or phone number etc. Purpose The collection is on the one hand crucial to get an overview of already existing public service. On the other hand, the collection will be made accessible on the TAKEDOWN open information hub. </th> <th> the available resources. On the other hand, also public authorities can make use of the data in order to see which countries have implemented which services. </th> </tr> <tr> <td> 3 </td> <td> Digital security solutions collection </td> <td> Description The data set structures and clusters digital security software and hardware from European companies along several main categories. They include the name of the solution, the field where it can be applied, the geographical scope and the status (laboratory, market-ready etc.). Additionally it includes a brief description of the solution, the operational language, the target group, the vendor, the country of the company and the link to the solution. Purpose The collection is used for getting an overview of existing software and hardware tools, which aim at fighting organized crime and terrorist networks. Additionally, it will be accessible for LEAs and professionals on the TAKEDOWN Solutions Platform. </td> <td> These data can be useful on the one hand for LEAs, which are looking for specific software or hardware against particular phenomena related to OC or TN. Furthermore, they can also be useful for private security companies working in one of these fields. The data might also be useful for solution developer in order to get a market overview as well as for investors, who are looking for future products to invest in. </td> </tr> <tr> <td> 4 </td> <td> Quantitative survey data </td> <td> Description This data set contains the data from the quantitative survey, which is conducted in the TAKEDOWN project. The target group of the survey is first-line practitioners (such as teachers, social workers, street workers, community police officers etc., who are working with people at risk of becoming involved in OC or TN. In addition to getting information on how they are dealing with these issues in their daily practice, the survey strongly aims at getting an understanding what they actually need in order to make their work easier and how toolkits need to be shaped in order to be a real support for them. The quantitative survey will be implemented as an online survey and aims at a minimum of 1.000 recipients. Purpose The outcomes of the survey will be used to develop the practitioner toolkits, the policy recommendations and the digital Open Information Hub. </td> <td> The large-scale survey, which will be implemented in TAKEDOWN, is the first one of this kind and of this scope. On the one hand, the outcomes will be crucial for understanding the needs and requirements of the first-line practitioners and for developing the toolkits etc. On the other hand, the data will be interesting for other researchers working in this field – either for (full or partial) secondary analysis, for a comparative analysis with other data or for a panel (longitudinal) survey. </td> </tr> <tr> <td> 5 </td> <td> Expert interview data </td> <td> Description The data contain of recordings, transcriptions and notes from about 40 qualitative expert interviews with </td> <td> The information provided in the data is not only crucial for TAKEDOWN, but it can </td> </tr> <tr> <td> </td> <td> </td> <td> researchers and policy makers. The interviews will be either conducted personally, on the phone (or skype) or they can also be conducted in written form. Purpose The aim of the qualitative interview is to get further insights on the obstacles related to current policies that are in place. They will therefore support the development of recommendations for future policies related to both OC and TN. </td> <td> also be useful for research as well as for policy making. Also practitioners and LEAs might benefit from it. </td> </tr> <tr> <td> 6 </td> <td> Focus groups data </td> <td> Description The dataset contains of protocols, written notes and summaries from the five focus groups that are held in different countries and attended by practitioner organizations and LEA representatives. Purpose The focus groups aim at getting in-depth insights on the challenges and obstacles that these stakeholders are facing related to OC and TN. The acquired knowledge will help the consortium to shape the toolkits, the open information hub and the digital solutions platform. </td> <td> The data are not only crucial for TAKEDOWN, but they are also useful for OC or TN research and policy making. Also practitioners and LEAs might benefit from it. </td> </tr> <tr> <td> 7 </td> <td> Workshops data </td> <td> Description The data contain protocols, written notes and summaries that were done at the three workshops, which are organized in different countries. The workshops aim at developers and providers of technical solutions. Purpose The information gathered at the workshops will support the development of the TAKEDOWN Solutions Platform by being able to take into account the requirements of the security industry. </td> <td> The information provided in the data is not only important for TAKEDOWN, but it can also be useful for research as well as for policy making. Also practitioners and LEAs might benefit from it. </td> </tr> <tr> <td> 8 </td> <td> Validation cycles data </td> <td> Description The data from the evaluation of the non-digital and the digital solutions shows how major stakeholder groups experience their usability and relevance. Purpose The validation provides the basis for improving and releasing the final solutions. </td> <td> The data from the validation of the non-digital and the digital solutions do mainly have an internal use for improving the solutions and for the lessons- learned. </td> </tr> </table> Table 2: Data sets description and utility # 3\. FAIR data ## Making data openly accessible The following table is highlighting A) which data that are produced and used in the project and B) will be made openly available. It also explains why several datasets cannot be shared because of particular reasons. For these cases, an alternative solution is provided. <table> <tr> <th> # </th> <th> Data type </th> <th> Data openly available (y/n) </th> <th> Justification </th> <th> Alternative solution </th> </tr> <tr> <td> 1 </td> <td> Stakeholder contacts collection </td> <td> No </td> <td> Although the contacts of the collection are professionals’ contacts that are available, the consortium can’t publish them due to potential misuse caused by automated Spam programs. </td> <td> publicly </td> <td> The statistical information on the stakeholder data (such as how many, from which countries, which professions etc.) will be integrated in the public report D2.6. In case an external institution is looking for contacts in a specific and the coordinator doesn’t see any privacy concerns, relevant contacts might be forwarded. </td> </tr> <tr> <td> 2 </td> <td> Public security services collection </td> <td> Yes </td> <td> (not relevant) </td> <td> </td> <td> (not relevant) </td> </tr> <tr> <td> 3 </td> <td> Digital security solutions collection </td> <td> Yes </td> </tr> <tr> <td> 4 </td> <td> Quantitative survey data </td> <td> Yes </td> </tr> <tr> <td> 5 </td> <td> Expert interview data </td> <td> No </td> <td> The data from the interviews (recordings, protocols </td> <td> expert </td> <td> </td> </tr> <tr> <td> </td> <td> </td> <td> </td> <td> and transcriptions) will not be published as primary data due to privacy and security concerns. Anonymization is not considered as an alternative, because the sample size allows drawing conclusions on the respondents. </td> <td> The categorization, analysis and interpretation of the primary data will be accessible in the public report D3.6 (and others) that can be accessed on the TAKEDOWN project website. Furthermore, the </td> </tr> <tr> <td> 6 </td> <td> Focus groups data </td> <td> No </td> <td> The data from the focus groups (recordings, protocols and transcriptions) will not be published as primary data due to privacy and security concerns. Anonymization is not considered </td> </tr> <tr> <td> </td> <td> </td> <td> </td> <td> as an alternative, because the sample size allows drawing conclusions on the respondents. </td> <td> outcomes will also be disseminated in scientific publications. </td> </tr> <tr> <td> 7 </td> <td> Workshops data </td> <td> No </td> <td> The data from the workshops (recordings, protocols and transcriptions) will not be published as primary data due to privacy and security concerns. Anonymization is not considered as an alternative, because the sample size allows drawing conclusions on the respondents. </td> </tr> <tr> <td> 8 </td> <td> Validation cycles data </td> <td> No </td> <td> The data from evaluation survey will not be published due to privacy and security concerns. Anonymization is not considered as an alternative, because the sample size allows drawing conclusions on the respondents. </td> <td> The deliverable D7.3 will report on the validation and the development of the final non-digital and digital solutions based on the validation. </td> </tr> </table> ### Table 3: Data sets accessibility As it was indicated above, the following data sets will be made openly accessible: Data type #2 (Public security services collection), #3 (Digital security solutions collection) and #4 (Quantitative survey data). The following table describes the accessibility details of these particular datasets. <table> <tr> <th> # </th> <th> Data type </th> <th> Location </th> <th> Level of acessibility </th> <th> Type of availability and required software tools </th> <th> Information on metadata and additional data information </th> </tr> <tr> <td> 2 </td> <td> Public Security Services </td> <td> TD Solutions Platform </td> <td> Public </td> <td> Filterable and searchable database; can be accessed with a state-ofthe-art webbrowser </td> <td> No metadata needed; additional information will be provided on the platform </td> </tr> <tr> <td> 3 </td> <td> Digital security solutions collection </td> <td> TD Solutions Platform </td> <td> Validated professionals </td> <td> Filterable and searchable database; can be accessed with a state-ofthe-art webbrowser </td> <td> No metadata needed; additional information will be provided on the platform </td> </tr> <tr> <td> 4 </td> <td> Quantitative survey data </td> <td> https://zenodo.org/ </td> <td> Registered ZENODO users </td> <td> Cleaned primary data; can be accessed with SPSS, Excel or open source </td> <td> Metadata as well as a codebook will be deposited in the data repository Zenodo </td> </tr> </table> data analysis software (such as PSPP etc.) ### Table 4: Details on accessible data sets As it is indicated in the table, especially dataset #4 (Quantitative survey data) can be accessed with commercial statistical programs such as SPSS or with open source programs such as PSPP. An account at the Zenodo repository was created by the TAKEDOWN coordinator and a TAKEDOWN community, where the dataset as well as papers, reports and presentations will be published, was installed. The consortium will follow the conditions, rules and regulations from the Zenodo repository – including the settings for accessing the dataset. # 4\. Allocation of resources and data security The consortium will use the free-of-charge Zenodo repository for making the dataset #4 (Primary data from the qualitative survey) accessible. Additionally, also the reports D2.6 and D3.6, which includes the analysis of the expert interview, the workshops and the focus groups, will be published there. This will ensure that the data are safely stored in this certified repository for long term preservation and curation. The handling of the Zenodo repository on behalf of TAKEDOWN as well as all data management issues related to the project fall in the responsibility of the coordinator. As for the publications, where the analyses of the empirical research data will be presented, the consortium will publish them in scientific journals that allow open access the costs related to open access will be claimed as part of the Horizon 2020 grant. # 5\. Ethical aspects In order to ensure that all ethical aspects are considered and that the TAKEDOWN project is compliant with all legal requirements and ethical issues, a general strategy has been designed by the Ethics leader (IDT-UAB). This strategy involves an ad hoc monitoring process of the project development by applying the privacy-by-design approach trough a methodological design based on a “Socio-legal Approach.” This is a risk approach to privacy and data protection issues in line with the new General Regulation for Data Protection. The complete strategy is included in Deliverable 2.2. This general strategy for the monitoring of the ethical and privacy implications of the TAKEDOWN project consists of the following four steps. * Knowledge acquisition: This task will include the study of the needs of the empirical research of the project. It will also include the study of all the stakeholders involved in the project and all their potential interactions with the Open Information Hub and the Solutions platform. * _Privacy-impact-assessment (PIA): _ a PIA (Wright & de Hert 2012) will be conducted to study of all the scenarios in which, during the project lifecycle, personal data rights can be at stake. Special attention will be paid to activities involving data collection from external participants. * _Risk mitigation strategy _ : Initial, mid-term and final recommendations, prepared by the Ethics leader (IDT-UAB), regarding compliance with the relevant ethical and legal provisions. * _Ongoing Monitoring _ : In order to ensure that all data collection, storage, protection, retention and destruction during the project are developed in full compliance with EU legislation and relevant national provisions, an Ethics Board has been included in the management structure. At this stage, a set of initial recommendations have been generated by the Ethics leader (IDT-UAB) for the three main domains of the project: (i) empirical research, (ii) Open Information Hub and, (iii) Solutions Platform. ## Initial Recommendations in relation to the empirical research task within the TAKEDOWN project The set of recommendations presented here suggest procedures, measures or strategies for conducting a proper and responsible empirical research according to the ethical and legal requirements previously identified. These recommendations are the results of the potential risks detected through: i) the EUROPRISE criteria, and ii) the application of the Privacy Impact Assessment methodology. The structure of these recommendations represents the different domains of the TAKEDOWN research that are relevant for the ethical and legal requirements and potential risks detected trough the previously stated methodologies. <table> <tr> <th> </th> <th> Data formats and software </th> </tr> <tr> <td> 1.R1 </td> <td> Online Survey : Detailed information on open source software tool for programming the European-wide online survey will be provided (SocialSci, ScoGosurvey, LimeSurvey or Survey Monkey). </td> </tr> <tr> <td> 1.R2 </td> <td> Expert interviews: Specific guidance to researchers will be provided about techniques and procedures to conduct expert interviews. Data formats and software to manage the information gathered through the interviews will be specified, specially taking into account the potential variety of research material that can be generated (interviews transcriptions, audio recordings, images, ethnographic diaries, written texts). </td> </tr> </table> <table> <tr> <th> 1.R3 </th> <th> Focus groups: Specific guidelines on procedures and content of the focus groups will be provided. Clear protocols on how to collect and store information during the development of the focus groups will be specified. Formats regarding the data gathered and different software to manage this information will be provided too. </th> </tr> <tr> <td> </td> <td> Processing quantitative data files </td> </tr> <tr> <td> 2A.R1 </td> <td> Questions regarding the recording of data matrix, variable names and labels, values and labels, recording variables, missing data, and weighting will be specified in a codebook. </td> </tr> <tr> <td> </td> <td> Processing qualitative data files </td> </tr> <tr> <td> 2B.R1 </td> <td> Provide guidance and methods for transcribing interviews </td> </tr> <tr> <td> 2B.R2 </td> <td> Provide guidance and specification on procedures to organize files </td> </tr> <tr> <td> 2B.R3 </td> <td> Provide guidance and specification on naming data files </td> </tr> <tr> <td> </td> <td> Physical data storage </td> </tr> <tr> <td> 3.R1 </td> <td> The Consortium will evaluate a secure storage system in addition to the repository. </td> </tr> <tr> <td> 3.R2 </td> <td> Primary and secondary research data will be stored in a secure and accessible form. </td> </tr> <tr> <td> 3.R3 </td> <td> It is necessary to define who, when and under which conditions data can be accessed (raw data/analyzed data). Definition of access rights for: folders and files, particularly when they are stored on a server instead of a single computer. </td> </tr> <tr> <td> 3.R4 </td> <td> Define procedures for backup and recovery of data (frequency and reliability). </td> </tr> <tr> <td> 3.R5 </td> <td> Specification of data security measures (physical security, software updates, virus protection). </td> </tr> <tr> <td> 3.R6 </td> <td> Data disposal (erasure of data). </td> </tr> <tr> <td> 3.R7 </td> <td> Specification of procedures for keeping data accessible in terms of migration (conversion of data files from older formats to newer ones) and refreshing (transfer of data from one storage tool to another). </td> </tr> <tr> <td> </td> <td> Anonymisation, confidentiality and personal data </td> </tr> <tr> <td> 4.R1 </td> <td> Online survey : Ensuring anonymity and confidentiality. </td> </tr> <tr> <td> 4.R2 </td> <td> Interviews : Anonymity and use of coded data - replacing personal names with pseudonyms or categories. (example: replace Maria by female subject or woman)- change or remove sensitive information (example: “I studied in Oxford” by “I studied in University”). </td> </tr> <tr> <td> 4.R3 </td> <td> Interviews: Each researcher is responsible for guaranteeing the confidentiality of the information gathered from the data subject. </td> </tr> <tr> <td> 4.R4 </td> <td> Focus groups and Workshops: Specific information must be provided to the participants in relation to the type of information to be collected: video, audio, transcripts. </td> </tr> <tr> <td> 4.R5 </td> <td> Storage of data collected: Data collected will be stored by each partner in local and protected with password or equivalent measures. Each partner will ensure that only authorized researchers and for the purpose of the TAKEDOWN research have access to the data. As this data is stored in local, each partner can be considered liable for the misuse of such data. </td> </tr> <tr> <td> 4.R6 </td> <td> Stakeholder’s database: Information collected will be restricted to professional information gathered from open sources. In case a researcher has private or private obtained information he/she should introduce only the “available upon request” phrase. Prior to the transmission of this private obtained information, as a result of a request by other researcher from the Consortium, consent must be obtained from the data subject. </td> </tr> <tr> <td> 4.R7 </td> <td> Stakeholder’s database: Professional information gathered from open sources can be considered covered by the exemption to the obligation to inform the data subject contained in article 14.5 (b) of the General Regulation. However, the safeguards referred to in article 89.1, in relation to the data minimization principle (as defined in article 5.1 (c), will be respected. </td> </tr> <tr> <td> 4.R7 </td> <td> Summary of empirical research: No personal data or sensitive information will be included in the summary. In order to ensure this, the summaries will be sent to the EAB leader for checking before sharing with the rest of the Consortium. </td> </tr> <tr> <td> </td> <td> Informing Research Participants </td> </tr> <tr> <td> 5.R1 </td> <td> The participation information sheet provided will include: contact information, subject and objectives of the research, data collection methods, voluntary nature of participation, confidentiality, and information about the potential reuse of data. </td> </tr> <tr> <td> 5.R2 </td> <td> The participant information sheet will be specific for each research activity: online survey, interviews, focus groups and workshops. </td> </tr> <tr> <td> 5.R3 </td> <td> The informed consent form has to include the information sheet and a certificate of consent. A model is provided by the Ethics Board. </td> </tr> <tr> <td> 5.R4 </td> <td> The informed consent form must specific for each type of data that will be collected, especially regarding video and audio recording. </td> </tr> <tr> <td> 5.R5 </td> <td> Research participants must be informed that they may withdraw from the project at any moment, without having to explain the reasons, and without any repercussion. </td> </tr> </table> Table 5: Initial informations for the Empirical Research ## Initial Recommendations in relation to the Open Information Hub within the TAKEDOWN project The set of Recommendations presented here suggest procedures, measures or strategies to be included in the design of the Open Information Hub. The structure of these recommendations represents the different domains of the Open Information Hub that are relevant for the ethical and legal requirements and potential risks detected trough the previously stated methodologies. <table> <tr> <th> Scope of information collected and purposes of collection </th> </tr> <tr> <td> 1 .R1 Data subjects will be informed according to articles 12 to 14 of the General Data Protection Regulation. </td> </tr> <tr> <td> Notice and rights of the individual </td> </tr> <tr> <td> 2.R1 </td> <td> When the information is obtained from the data subject the Open Information Hub will provide information according to article 13 and gather the subject consent. </td> </tr> <tr> <td> 2.R2 </td> <td> Regarding personal data in the Open Information Hub, only data obtained from the data subject will be processed. </td> </tr> <tr> <td> 2.R3 </td> <td> In case that the Open Information Hub provides the users with the possibility to include data, a disclaimer should be included stating that the user acknowledges that the inclusion of personal data from other subjects is not allowed. 1 </td> </tr> <tr> <td> 2.R4 </td> <td> Any natural person whose data is available in the Open Information Hub will have the right to access, modify and erase such data. </td> </tr> <tr> <td> Uses of the Open Information Hub and information collected </td> </tr> <tr> <td> 3.R1 </td> <td> More information is needed related to the digital reporting functionality. </td> </tr> <tr> <td> 3.R2 </td> <td> In case the information is used for the reporting of malicious/suspicious activities the exceptions concerning the use of personal data by competent authorities for the purposes of the prevention, investigation, detection or prosecution of criminal offences or the execution of criminal penalties will be taken into account. 2 </td> </tr> <tr> <td> 3.R3 </td> <td> In case that the digital reporting tool allows for personal data to be uploaded only relevant competent authorities, according to national legislation, will have access to this data. </td> </tr> <tr> <td> 3.R4 </td> <td> Access roles and permissions will be defined in the first steps of the designing and development process. </td> </tr> <tr> <td> </td> <td> Retention </td> </tr> <tr> <td> 4.R1 </td> <td> The Consortium will take into account that for the purposes of the TAKEDOWN project the retention period is the one used in the relevant field, by analogy to the administrative and financial issues this should be 5 years. (Grant agreement article 18) </td> </tr> <tr> <td> 4.R2 </td> <td> The Consortium will take into account that in case of a future exploitation of the Open Information Hub, different retention periods may apply, depending on the national legislations. </td> </tr> <tr> <td> </td> <td> Technical aspects and security </td> </tr> <tr> <td> 5.R1 </td> <td> When defining roles and permissions special attention will be paid to the possibility to track any interaction with the platform that entails access, modification and deletion of personal data. </td> </tr> </table> Table 6: Initial Recommendations for the Open Information Hub ## Initial Recommendations in relation to the Solutions Platform within the TAKEDOWN project The set of recommendations presented here suggest procedures, measures or strategies to be included in the design of the Solutions Platform. The structure of these recommendations represents the different domains of the Solution Platform that are relevant for the ethical and legal requirements and potential risks detected trough the previously stated methodologies. <table> <tr> <th> Scope of information collected and purposes of collection </th> </tr> <tr> <td> 1 .R1 Data subjects will be informed according to articles 12 to 14 of the General Data Protection Regulation. </td> </tr> <tr> <td> Notice and rights of the individual </td> </tr> <tr> <td> 2.R1 </td> <td> During the registration process, in case personal data is collected, the Solutions platform will provide the information listed in articles 13 and 14 of the General data protection regulation and collect the consent of the data subjects. </td> </tr> <tr> <td> 2.R2 </td> <td> Regarding personal data in the Solutions Platform, only data obtained from the data subject will be processed. </td> </tr> <tr> <td> 2.R3 </td> <td> In case that the Solutions Platform provides the users with the possibility to include data from other subjects, a disclaimer will be included stating that the user acknowledges that the inclusion is only allowed with the consent of that subject. 3 </td> </tr> <tr> <td> 2.R4 </td> <td> Any natural person whose data is available in the Open Information Hub must have the right to access, modify and erase such data. </td> </tr> <tr> <td> </td> <td> Retention </td> </tr> <tr> <td> 3.R1 </td> <td> The Consortium will take into account that for the purposes of the TAKEDOWN project the retention period is the one used in the relevant field, by analogy to the administrative and financial issues this will be 5 years. (Grant agreement article 18) </td> </tr> <tr> <td> 3.R2 </td> <td> The Consortium will take into account that in case of a future exploitation of the Solution platform, different retention periods may apply, depending on the national legislations. </td> </tr> <tr> <td> </td> <td> Technical aspects and security </td> </tr> <tr> <td> 4.R1 </td> <td> More information will be provided concerning the auditing mechanisms foreseen for the platform. </td> </tr> </table> ### Table 7: Initial Recommendations for the Solutions Platform Related with the generation of primary empirical data, it needs to be highlighted that, at this stage of the project, and realizing the importance of the empirical research being conducted, a specific and comprehensive set of guidelines have been provided to all partners in the Consortium by the Ethics leader (IDT-UAB): the _Ethical Guidelines for the processing of data in the context of the Empirical research for the TAKEDOWN project_ (see Annex). This document aims at offering specific guidance to all the partners of the Consortium for the performance of the different tasks and activities foreseen in WP3, concerning empirical research. In order to ensure that all partners are compliant with the requirements related to Research Ethics and particularly, Informed consent procedures, the Guidelines include: * a general introduction containing an explanation on the concept and meaning of Informed consent, in the context of research ethics and empirical research. * a set of guidelines for quantitative research (online survey) * a set of guidelines for qualitative research (interviews, focus groups, workshops) * legal notice to be included in the online survey * written informed consent form * oral consent script # 6\. Outlook towards next DMP The next DMP will be prepared in for month 18, which is after the finalization of WP3 (Empirical research). As it was emphasized in the introduction, the DMP is a living document and several questions can only be answered at a later stage of the project. Hence, the upcoming DMP will provide updates on the issues raised above and more information on the following questions: <table> <tr> <th> Category </th> <th> Underlying questions </th> </tr> <tr> <td> Making data interoperable </td> <td> * 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)? * What data and metadata vocabularies, standards or methodologies will you follow to make your data interoperable? * Will you be using standard vocabularies for all data types present in your data set, to allow inter-disciplinary interoperability? </td> </tr> <tr> <td> Increase data re-use (through clarifying licences) </td> <td> * How will the data be licensed to permit the widest re-use possible? * 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. * 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. * How long is it intended that the data remains re-usable? * Are data quality assurance processes described? </td> </tr> <tr> <td> Allocation of resources </td> <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)? * Do you make use of other national/ funder/ sectorial/ departmental procedures for data management? If yes, which ones? </td> </tr> <tr> <td> Data security </td> <td> \- What provisions are in place for data security (including data recovery as well as secure storage and transfer of sensitive data)? </td> </tr> <tr> <td> Other aspects </td> <td> * Do you make use of other national / funder / sectorial / departmental procedures for data management? * If yes, which ones? </td> </tr> </table> Table 8: Issues to be addressed in the next DMPs	https://phaidra.univie.ac.at/o:1140797	Horizon 2020

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