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Dataset card Data Studio Files
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Data Sharing
Are any restrictions on data sharing required?
Where necessary, interview transcripts will not be available in their entirety in order to protect the anonymity of participants. I will try to ensure that as much of the interview transcript as possible is made available.
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dmponline.dcc.ac.uk
Responsibilities and Resources
Who will be responsible for data management?
Will Butler
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dmponline.dcc.ac.uk
Signoff
I confirm that I have read the Bath Spa University Research Data Policy and any relevant policy for my researchfunder.
William Butler 31/05/24
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dmponline.dcc.ac.uk
0. Administrative questions
Name of data management support staff consulted during the preparation of this plan.
The research plan was submitted as part of the Research Methodologies course headed by Ines Uriol Balbin. The research plan, along with the data management techniques in it, were reviewed in that course
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dmponline.dcc.ac.uk
IV. Legal and ethical requirements, codes of conduct
How will ownership of the data and intellectual property rights to the data be managed?
For projects involving commercially-sensitive research or research involving third parties, seek advice of your Faculty Contract Manager when answering this question. If this is not the case, you can use the example below. The datasets will be anonymised and the results of the pilot behaviour will be published in the Tu Delft repository. Participants have the ability to request their data to be removed and not used at any time. At that point, the prinicpal researcher (Sheharyar Ali) will remove their data from the storage locations
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IV. Legal and ethical requirements, codes of conduct
Please list the categories of data subjects
TU Delft Students, TU Delft staff,
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IV. Legal and ethical requirements, codes of conduct
Will your study participants be asked for their consent for data sharing?
The final report will explain that some participants refused to consent to their data being used for processing. Their data will be deleted from the storage locations
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dmponline.dcc.ac.uk
VI. Data management responsibilities and resources
If you leave TU Delft (or are unavailable), who is going to be responsible for the data resulting from this project?
My supervisor, Olaf Stroosma ([email protected])
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dmponline.dcc.ac.uk
Data Collection
What data will you be collecting ?
This project will generate three main types of raw data: 1. Percentages of cell viability and qPCR markers expression (inflammation and cartilage degeneration molecular markers). 2. Images from confocal microscopy of immunocytochemistry samples. 3. Images from microscopy of histopathological samples obtained from the in vivo study. Quantitative data will be recorded in spreadsheets while images will be stored as TIFF or JPEG files not occupying more than 1TB.
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dmponline.dcc.ac.uk
Data Collection
Who will be involved in your data collection ?
Data collection will be performed by Mónica Paesa and Natalia Izquierdo who actively work in the laboratory. The data will be raw data, both quantitative and images obtained from confocal and optical microscopy, not occupying more than 1TB. The storage of the data will not involved additional costs. The data will be collected from fluorescence intensity data in the case of viability percentages, while qPCR results will be obtained from relative expression compared to the housekeeping gene expression in the samples. Images will be recorded in microscopes from the SCT Microscopía (IIS Aragón, Spain). The equipment is annually revised by qualified technicians. The files will be stored with the name of the project, the technique and the date. Mónica Paesa is the Data Manager so she will be in charge of this task, keeping and saving the files. The experiments will be performed thrice at least in triplicate to assure significant statistics results. The data derived from the project will be deposited in an open access repository (Zenodo) which is the one recommended by our institution (IIS Aragón, Spain).
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dmponline.dcc.ac.uk
Ethics
Give a description of your Ethics
Percentages of cell viability obtained from the interpolation of experimental groups (treated groups) with control data (not treated cells).
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dmponline.dcc.ac.uk
General Information
Registration number/corresponding
2023-02077
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dmponline.dcc.ac.uk
Description of data – reuse of existing data and/or production of new data
How will data be collected, created or reused?
Study subjects Nine Scandinavian cohorts constitute the sampling frame for the study. Four cohorts were recruited in Stockholm County: , with a total of more than 22 500 participants. The Malmö Diet and Cancer (MDC) study recruited 28 098 men and women living in the city of Malmö. The Danish Diet Cancer and Health (DCH) cohort enrolled 57 053 subjects from the greater Copenhagen or Aarhus areas. Finally, the Danish Nurse Cohort (DNC) included 28 731 female nurses from the whole of Denmark. Overall, enrollment focused on ages 35 to 99 years and occurred 1992-2004. The study subjects of these seven cohorts formed the NordSOUND project, and answered questionnaires at recruitment on lifestyle factors, health status and socioeconomic characteristics. Blood samples were obtained from participants in the Swedish cohorts. Anthropometric measurements were performed by trained nurses for all cohort participants, except SALT and DNC, which used self-reported data. In addition, two cohorts are included: The Swedish Mammography Cohort (SMC) with 13 680 women residing in the Uppsala area, who completed a self- administrated questionnaire concerning diet and alcohol intake as well as on risk factors for breast cancer. Questions on anthropometric markers were also included. Subsequent questionnaires every tenth year provided more detailed information on lifestyle factors as well as on medication use, sleep habits, family disease history, stress and social support. The Danish National Health Survey (DNHS) cohort included 177 639 subjects randomly selected across Denmark who answered a questionnaire on cohabiting status, education, occupation, smoking, alcohol consumption, diet, physical activity and anthropometric data. Information on age, sex, education and labor market affiliation was obtained from national registers. Exposure assessment Traffic noise: Transportation noise exposure is assessed based on well validated models. Road traffic and railway noise are modelled using the Nordic Prediction Method or an update of this method. For road traffic noise the input variables include geocodes, screening by terrain and buildings, and information on annual average daily traffic, distribution of light/heavy traffic, travel speed, and road type for all major road links. Railway noise is calculated for all addresses within a 1000 m buffer around all railway tracks. Input variables include geocodes, screening by terrain and buildings, and average number of trains per period (day/evening/night), train types, and travel speed. In addition, cities with trams and/or metro include these in the calculations. Aircraft noise is estimated using noise maps obtained from local or national authorities. Noise exposure from airports and airfields is modelled using the Danish Airport Noise Simulation Model or the Integrated Noise Model 7.0. Detailed noise assessments have been performed every fifth or tenth year and noise levels for the years between those with estimates are calculated based on linear interpolation or other approximation methods. For each participant, the time-weighted average noise exposure from each traffic noise source during follow- up is calculated, taking into account all addresses where the subject has lived, and considering the duration of residence at each address. In addition, combined exposure to multiple traffic noise sources is estimated. Air pollution: Levels of air pollution are estimated at all residential addresses during the study period for the subjects in the nine cohorts, using validated high-resolution dispersion models. Air pollution exposure is represented by PM2.5, which is influenced by both local and long-range transport, and by NO2, primarily reflecting local emissions, such as from road traffic. Interpolation of air pollution levels between years with assessments as well as calculation of individual time-weighted exposures are done using similar methodology as for transportation noise. Occupational exposures: This is focused on noise and combustion particles, based on occupations of the study subjects combined with information from a job-exposure-matrix (JEM). Occupational noise exposure is estimated based on a JEM developed in Sweden. The JEM is based on occupational measurements and specifies the annual average of the daily 8-hour equivalent A-weighted sound pressure level in five exposure classes. The noise level is matched on time period since noise levels differ within an occupation across time. Occupational noise exposure at recruitment is used and, if available, exposure in certain time-windows during follow-up. Occupational exposure to combustion particles is handled in the same way as noise, but based on an adapted Finnish JEM. Covariates: Selection of covariates is done a priori, based on existing literature, biological plausibility, and availability of harmonizable variables across cohorts. Cohort participants filled in questionnaires at recruitment with dietary and lifestyle variables, including smoking status, smoking intensity, alcohol consumption and leisure-time physical activity. The questionnaires also provided information on sleep disturbances. Individual educational level and marital status are obtained from national registers or questionnaires, and area-level (small socioeconomically homogeneous areas with around 1000-2000 inhabitants) mean income from registers. Green areas are assessed from satellite images, primarily using the normalized difference vegetation index. Outcome assessment Incidence of type 2 diabetes (T2D): All relevant information collected within each cohort is used to identify prevalent cases of diabetes at baseline, who will be excluded from the longitudinal analyses, and incident cases during follow-up until 2020. This includes linking with the Patient and Prescribed Drug Registers as well as using self-reported diabetes in the questionnaires and biomarker measurements (primarily fasting glucose and HbA1c), both at baseline and during follow-up. The methodology for identification of T2D cases has already been used successfully for cohorts in our project. In Sweden the Patient Register has full coverage since 1987 but contains comprehensive outpatient data only since 2001 and the Prescribed Drug Register was started in 2005. In Denmark both the Patient and Prescribed Drug Registers have full coverage during virtually the whole follow-up period of their cohorts. In addition, there are national T2D registers in Sweden and Denmark, but they do not have comprehensive coverage during most of the follow-up period. Overall, some registry sources for identification of T2D cases are lacking, primarily for Swedish cohorts during the early part of the follow-up period. However, to the extent that this is unrelated to the exposures under study it will not affect the validity of the findings. Anthropometry: In a majority of the cohorts, measurements were performed by trained nurses of height, weight and waist circumference at recruitment, while corresponding information was self-reported in the remaining cohorts. In two cohorts repeated measurements were performed during the during the follow-up period, enabling longitudinal assessment of anthropometric characteristics. In the mediation analyses, overweight/obesity data based on anthropometric information at recruitment will be combined with incident T2D during follow-up. Clinical biomarkers and measurements: For five of the Swedish cohorts information at recruitment of study subjects is available on blood pressure based on measurements by trained nurses, as well as on clinical biomarkers, including serum glucose and lipids. In the SNAC-K cohort measurements of glycated hemoglobin (HbA1c) levels were performed every 3-6 years from 2001 to 2019. For SDPP participants oral glucose tolerance tests have been made at three different occasions during follow-up. The biomarker information enables accurate determination of T2D and prediabetes, with due consideration of treatment, and will also be used for validation of the registry and questionnaire-based information, primarily to determine the degree of underdiagnosis. Population attributable risks: Risk assessment is based on estimates of the population exposure to transportation noise and occupational noise in the catchment areas of the participating cohorts (Aarhus, Copenhagen, Denmark, Malmö, Stockholm and Uppsala) using the high-resolution modeling techniques described above. This will be combined with exposure-response functions obtained in the project to estimate the number of cases of T2D attributable to transportation and occupational noise. In particular, assessment is made of the number of cases related to interactions between traffic noise and air pollution as well as with occupational exposures.
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dmponline.dcc.ac.uk
Documentation and data quality
How will the material be documented and described, with associated metadata relating to structure, standards andformat for descriptions of the content, collection method, etc.?
All data used in the project will be documented with detailed metadata, encompassing variable descriptions, data formats, and the context of data collection. The documentation will include: • Study protocols: Detailed outlines of the study design and methodologies. • Codebooks: Comprehensive descriptions of all variables and their values. • Logbooks: Records of all data handling activities, ensuring transparency and reproducibility. • Program files and scripts: Documentation of all code used for data processing and analysis, stored in accessible formats such as Do-files for STATA or r-files for R. • Output files: Results from data analyses, with accompanying descriptions to facilitate interpretation.
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dmponline.dcc.ac.uk
Documentation and data quality
How will data quality be safeguarded and documented (for example repeated measurements, validation of data input,etc.)?
Data quality will be safeguarded through rigorous documentation and validation procedures, including: • Quality Assurance: All data handling and analysis activities will be documented using the KI ELN (Electronic Lab Notebook) or equivalent systems that meet KI's information safety standards. • Data Validation: Comprehensive validation checks will be performed to verify the accuracy of data input and processing steps.
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dmponline.dcc.ac.uk
Storage and backup
How is storage and backup of data and metadata safeguarded during the research process?
All data utilized in the project will be stored on the secure central file server of Statistics Denmark (DST). The platform ensures: • Regular Backups: Automated regular backups to prevent data loss. • Data Security: Compliance with high standards for data security, including encryption and secure access protocols. • Controlled Access: Access to data is strictly regulated, with permissions granted only to authorized personnel.
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dmponline.dcc.ac.uk
Storage and backup
How is data security and controlled access to data safeguarded, in relation to the handling of sensitive data andpersonal data, for example?
Data security and controlled access will be ensured through several measures: • Strict access control: Only authorized personnel will have access to the data. Permissions are managed to ensure that sensitive data are only accessible to those who need it. • Pseudonymization: Personal identifiers (person-keys) will be pseudonymized and stored separately from the personal data to enhance privacy. • Data encryption: Sensitive data will be encrypted to prevent unauthorized access. • Monitoring and logging: All data access and handling activities will be logged and monitored to promptly detect and address any security breaches.
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dmponline.dcc.ac.uk
Legal and ethical aspects
How is data handling according to legal requirements safeguarded, e.g. in terms of handling of personal data,confidentiality and intellectual property rights?
Data handling will comply with all relevant legal requirements, including: • Regulations compliance: All project staff are trained and informed about the legal requirements and institutional policies at KI and other participating institutions regarding personal data handling. • Confidentiality agreements: All personnel will sign necessary agreements according to policies of the participating institutions and DST.dk to ensure the protection of personal data. • Intellectual property rights: Data usage will hold to the intellectual property policies of the participating institutions and the terms outlined in the project's data management plan.
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Legal and ethical aspects
How is correct data handling according to ethical aspects safeguarded?
The project has received all required ethical permissions. All scientists and staff involved in the project are informed about the conditions for data handling according to the ethical permissions.
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dmponline.dcc.ac.uk
Accessibility and long-term storage
How, when and where will research data or information about data (metadata) be made accessible? Are there anyconditions, embargoes and limitations on the access to and reuse of data to be considered?
A Disclosure Agreement (the "Agreement") has been signed between the Institute of Environmental Medicine, Karolinska Institutet ("Provider") and the Danish Cancer Society ("Recipient"), collectively referred to as the "Parties" and individually as a "Party”, regarding data accessibility and storage: The Parties are obligated to assess the risks to the rights and freedoms of natural persons posed by each Party's processing and to implement measures to address these risks. Depending on their relevance, this may include: a) pseudonymization and encryption of personal data; b) the ability to ensure the continued confidentiality, integrity, availability and resilience of processing systems and services; c) the ability to restore the availability of and access to personal data in a timely manner in the event of a physical or technical incident; d) a procedure for regular testing, assessment and evaluation of the effectiveness of the technical and organizational measures to ensure secure processing. Each Party must implement appropriate technical and organizational measures, taking into account the current state of the art, implementation costs and the nature, scope, context and purposes of the processing, as well as the risks of varying likelihood and severity to the rights and freedoms of natural persons, in order to ensure a level of security appropriate to those risks and in accordance with Article 32 of the General Data Protection Regulation. Provider guarantees that Provider has the necessary legal basis to disclose the data to Recipient. Recipient guarantees that a) Recipient has the necessary legal basis to collect data from Provider and process the collected data; b) data will be processed solely for statistical or scientific purposes and will not be disclosed for processing for any other purpose; c) the data is necessary for statistical or scientific processing. Data which, after collection, prove to be unnecessary for statistical or scientific processing must be deleted, destroyed or returned as soon as possible; d) disclosure by Provider is made only to persons of Recipient who are authorised by Recipient to access the data concerned. Recipient has only authorised persons handling the personal data. Individuals are not authorized for uses for which they have no need; e) necessary instructions are given to the employees of Recipient who have access to the personal data. In this context, the employees are informed that the personal data may solely be processed for statistical or scientific purposes and that the personal data may not later be processed for purposes other than scientific or statistical ones; f) the dissemination of the results of Recipient's data processing is done in such a way that it is not possible to identify individuals; g) at the end of the examination, or when it is not relevant to examine further, the data will either (a) be deleted, anonymised, destroyed or returned in such a way that it subsequently is not possible to identify natural persons from the data or in combination with other data, or (b) be transferred for archiving in accordance with the legislation on archives; h) appropriate security measures are implemented. The guarantee is given from the time of the transfer and is maintained until termination of the Agreement. As indicated above, all access to data in sever files is strictly regulated. Only authorized persons have access to data. Sensitive personal data such as person-key are only available to 2-3 database managers. Access to data is determined by PIs for the different cohorts involved in the project together with the project PI. This also relates to future use of the data generated within the project. Some metadata will be available and can be shared, also post publication. However, unique data possibly revealing identity, such as address information or geocodes, will not be available. This also relates to individual health data.
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dmponline.dcc.ac.uk
Accessibility and long-term storage
In what way is long-term storage safeguarded, and by whom? How will the selection of data for long-term storage bemade?
Long-term storage will be managed as follows: • Decision making: The selection of data for long-term storage will be determined by the Principal Investigators (PIs) of the involved cohorts, in consultation with the project PI. • Storage location: Data will be securely stored on the central file server at Statistics Denmark. • Future use: Decisions regarding future use and accessibility of the stored data will be made by the PIs, ensuring compliance with all ethical and legal requirements.
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dmponline.dcc.ac.uk
Accessibility and long-term storage
Will specific systems, software, source code or other types of services be necessary in order to understand, partake ofor use/analyse data in the long term?
For long-term data usability, the following will be ensured: • Analysis documentation: All analysis information will be stored as (syntax code, Stata or R) in accessible text formats to facilitate future use and replication of analyses. • System compatibility: Documentation will be maintained using the KI ELN or comparable systems that ensure long-term accessibility and compliance with data standards.
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Accessibility and long-term storage
How will the use of unique and persistent identifiers, such as a Digital Object Identifier (DOI), be safeguarded?
DOIs will be generated and maintained in collaboration with KI library, also when data sets are stored in data repositories.
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Responsibility and resources
Who is responsible for data management and (possibly) supports the work with this while the research project is inprogress? Who is responsible for data management, ongoing management and long-term storage after the researchproject has ended?
Göran Pershagen ([email protected]) is responsible for the overall data management structure, while Jessica Edstorp ([email protected]) handles the day-to-day data mangement activities. Since Göran Pershagen has retired, but continue to work (50%) as professor at KI during the project period, the group leader Petter Ljungman ([email protected]) will be responsible for long-term storage of data from the project.
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dmponline.dcc.ac.uk
Responsibility and resources
What resources (costs, labour input or other) will be required for data management (including storage, back-up,provision of access and processing for long-term storage)? What resources will be needed to ensure that data fulfilthe FAIR principles?
The Swedish Research Council granted only 40% of the total costs applied for in the project. Consequently, a discussion is ongoing on prioritization of the project activities. However, ample resources will be secured for data management and storage.
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Assessment of existing data
Provide an explanation of the existing data sources that will be used by the research project, with references
The project will be generating new data as there are no suitable existing data sources for this purpose for re-use (determined through preliminary work and an early literature review).
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Assessment of existing data
Provide an analysis of the gaps identified between the currently available and required data for the research
New data needs to be generated to build and test a maturity framework for implementing and sustaining AI-led innovation in dental healthcare organisations. Currently there is no such data available.
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Information on new data
Provide information on the data that will be produced or accessed by the research project
Participatory Action Research methodology is chosen to prioritise the voices and experiences of those directly affected by the research topic. It is a collaborative and empowering method of inquiry that will enable participants to reflect on how their processes need to change whilst providing space for the co-creation of the innovation maturity framework, ensuring it's grounded in the realities of the dental healthcare context. Data will be collected in the course of two collaborative, in-person, knowledge-exchange workshops: a) the first aims to test the outcomes of WP2 (factors and suggested model derived from the review) through using design thinking and techniques like mind mapping and sketching, to spur creative thinking and ideation. Workflow diagrams and storyboards are to be used to generate ideas for AI applications in dental care and create prototypes of improving patient experience through technology. This is key in gathering the right requirements for the successful implementation of the mobile software application; b) After the tool is developed and sent to the partners for use, a second workshop is to be carried out to gather user feedback and to collect views on policy changes and improvements needed in terms of successfully adopting new technology, improving innovation, and leveraging AI. This will help to identify patterns and commonalities in the responses to guide further development and refinement of the framework. Workshops are to be held in Sheffield and Salford respectively to maximise audience reach, impact and coverage. They are designed to run as full-day events (10:00-16:00) to support stakeholder engagement and promote knowledge exchange and dissemination. Data will be created in the following forms: 1) Written notes including quotes and key discussion points from workshop discussions and interviews; 2) Audio/video recording of workshop activities and interviews with the consent of all participants (e.g. using an encrypted audio/video recorder); 3) Source code written in programming language. The formats of digital data are expected to be in mp4 and .doc, and the approximate volume of the data around 22.5GB.
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dmponline.dcc.ac.uk
Quality assurance of data
Describe the procedures for quality assurance that will be carried out on the data collected at the time of datacollection, data entry, digitisation and data checking.
Workshop notes will be transcribed with identifying initials of speakers. These notes, along with the code produced and any identifiable data such as consent forms / pseudonymisation key will be stored on the University X: drive, where access is restricted to specified individuals. The recording device used will be checked before workshops, and recordings will be transferred to a University drive as soon as possible, then checked before being deleted from the recorder.
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dmponline.dcc.ac.uk
Backup and security of data
Describe the data security and backup procedures you will adopt to ensure the data and metadata are securely storedduring the lifetime of the project.
In regard to storage of data during the project, the University Google Drive will be used for de-identified data and the University X: drive for identifiable data.
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dmponline.dcc.ac.uk
Management and curation of data
Outline your plans for preparing, organising and documenting data.
Data will be stored in a logical file structure with appropriate metadata. Consideration is given to include details of our file structure and methodology in a README file with our data, and also to provide descriptive metadata with the data made available in ORDA. Personal data (e.g., workshop notes with initialised attribution of quotes and/or discussion points) will not be shared externally. Any notes and code will be archived for 3 years after publication/dissemination of project outputs. As this is a co-creation project, the team will work with the workshop participants and project partners to establish the best approach to distribute anonymised versions of workshop notes. At minimum, the journal article to be produced, which will summarise key takeaways from workshop discussions, will be freely deposited in ORDA. Materials placed in ORDA are assigned a DOI, which we can include in a data availability statement in publications. We will also make articles available in the White Rose Research Online Open Access repository, through the DOI link associated with each submission.
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dmponline.dcc.ac.uk
Difficulties in data sharing and measures to overcome these
Identify any potential obstacles to sharing your data, explain which and the possible measures you can apply toovercome these.
Personal data will not be shared to protect anonymity and participants’ identities.
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dmponline.dcc.ac.uk
Consent, anonymisation and strategies to enable further re-use of data
Make explicit mention of the planned procedures to handle consent for data sharing for data obtained from humanparticipants, and/or how to anonymise data, to make sure that data can be made available and accessible for futurescientific research.
Data created may be beneficial for public benefit or other research purposes. The anonymised data sets from this study will be made available and accessible for future scientific research through the outputs produced. All partners are committed to open access and open data strategies, making research publications freely available online. Participant consent will be covering inclusion of quotes and ideas in openly shared outputs. Identifiable data, such as recordings, will be deleted by the end of the project.
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dmponline.dcc.ac.uk
Copyright and intellectual property ownership
State who will own the copyright and IPR of any new data that you will generate.
The copyright and IPR will be held by the University of Sheffield.
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Responsibilities
Outline responsibilities for data management within research teams at all partner institutions
The Project Lead (PL) and Project Co-Leads (PcLs) will take responsibility for the datasets, creation of metadata and placing data in ORDA. The PL will have overall responsibility for the quality of the data acquired, and its storage. The PL and PcLs will jointly collaborate over peer review and quality assurance of data.
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dmponline.dcc.ac.uk
Preparation of data for sharing and archiving
Are the plans for preparing and documenting data for sharing and archiving with the UK Data Service appropriate?
Anonymised versions of discussion notes will be stored on the institutional Google Drive space; based on content of notes the research team will explore opportunities for further public sharing of anonymised notes via the University of Sheffield data repository ORDA with the project partners and workshop participants. Project outputs, including a self-assessment guide for practitioners summarising the workshop process and main discussion points, will be posted on ORDA for long-term archival storage. The code and associated training material and documentation will also be made freely available. No long-term storage options are anticipated to incur financial costs. Details of the data in ORDA will be provided to the UK Data Service as per ESRC's requirements.
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dmponline.dcc.ac.uk
Preparation of data for sharing and archiving
Is there evidence that data will be well documented during research to provide highquality contextual informationand/or structured metadata for secondary users?
The methodology and process of data collection will be well-documented to provide high quality contextual information for secondary users.
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1. General Information
DMP - Contact Person
Ivã Guidini Lopes, [email protected]; Department of Biosystems and Technology, SLU Alnarp; https://orcid.org/0000-0003- 0381-7537
caa2e718615d4978eb158edabcebad4b
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1. General Information
Project - Project Description
The treatment of organic waste streams with black soldier fly larvae ( Hermetia illucens, BSFL) is a fast-growing technology spread worldwide. Two products are obtained when treating waste: a larval biomass that can be used for feeding animals and an organic fertilizer (frass), ensuring the return of waste-derived nutrients back into the food production loop. Due to the very rapid waste conversion process (10-12 days), frass ends up being phytotoxic, due to the presence of ammonia and other compounds, thus it is advocated that post-treatments (e.g. composting) must be applied to ensure its safety for cultivating plants. In this proposal, we aim to validate a novel method for improving frass quality that was recently proposed, which is recirculating frass back into the bioconversion process. This has demonstrated a great improvement of the process itself by yielding higher larval biomass per unit of waste input as well as the larval quality (protein accumulation and fat reduction) and subsequent frass quality (concentration of nutrients and higher stability, maturity and safety). This method was tested with a single waste stream (food waste) and in small scale. Thus, its validation is herein proposed for other waste streams, mimicking a large-scale setting.
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1. General Information
Project - Project ID1.9 Project - Project Leader
Ivã Guidini Lopes, [email protected]; Department of Biosystems and Technology, SLU Alnarp; https://orcid.org/0000-0003- 0381-7537
caa2e718615d4978eb158edabcebad4b
dmponline.dcc.ac.uk
1. General Information
Project - Contributor
Cecilia Lalander; [email protected] Jean Yong; [email protected]
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2. Data Description and Collection or Reuse of Existing Data
Will newly collected/produced data, already existing data, both, or neither be used in the project? (multipleanswers are allowed)
New data Several new data are predicted to be collected in this project, on the topics, including waste bioconversion with BSFL; microbial abundance data on frass fertilizers; presence and quantification of plant biostimulants in frass fertilizers; frass toxicity-related data.
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dmponline.dcc.ac.uk
2. Data Description and Collection or Reuse of Existing Data
In what format will the data collected/produced and/or reused come in?
Larval growth and process parameters: data will be collected in paper sheets and immediately transferred to the cloud as XLS or CSV files, in the applicant group's Dropbox; Microbial community data: data will be generated in an outsourced laboratory (InPP in Portugal) and stored at the nxcloud server as interactive reports and XLS files, being shared with the applicant; Plant biostimulants data will be collected by an outsourced laboratory (tbd) and shared with the applicant in the form of a PDF report.
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dmponline.dcc.ac.uk
4. Storage and Backup during the Research Process
How will data, metadata, and other documentation be stored and backed up during the project?
Data will be initially stored in paper sheets, and immediately put on the cloud (Dropbox folders). A backup will be saved in the applicant's computer
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dmponline.dcc.ac.uk
5. Legal and Ethical Requirements, Codes of Conduct
How will compliance with legislation on personal data and on security be ensured? (multiple answers are allowed)
Not valid
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dmponline.dcc.ac.uk
5. Legal and Ethical Requirements, Codes of Conduct
How will other legal issues, such as intellectual property rights and ownership, be managed? What legislation isapplicable?
The data generated in this research project will be handled and controlled by the applicant Ivã Guidini Lopes. Data will be used for writing one or two scientific manuscripts, which will be published in Open Access format by journals, which will then have the copyrights of the published data
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dmponline.dcc.ac.uk
5. Legal and Ethical Requirements, Codes of Conduct
What ethical issues and codes of conduct are there, and how will they be taken into account?
Not valid
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dmponline.dcc.ac.uk
6. Data Sharing and Long-Term Preservation
How and when will data (or metadata) be shared (i.e., made publicly available)? Are there possible restrictions todata sharing and embargo reasons?
We will share selected data in open access databases, such as Mendeley, after revising the data and agreeing with all the participants of the project
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dmponline.dcc.ac.uk
6. Data Sharing and Long-Term Preservation
How will data for preservation be selected, and where will data be preserved long-term (e.g., a data repository orarchive)?
In case all project participants agree with sharing some of the data generated in this research project, these will be shared in a data repository, such as Mendeley
caa2e718615d4978eb158edabcebad4b
dmponline.dcc.ac.uk
6. Data Sharing and Long-Term Preservation
What methods/systems, software tools, source code or other types of services are needed to understand, access,and use the data?
Data will simply be saved as .csv; therefore, it will be possible to access it in Excel
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dmponline.dcc.ac.uk
7. Data Management Responsibilities and Resources
Who (e.g., role, position, and institution) will be responsible for data management?
Ivã Guidini Lopes, postdoctoral researcher at SLU
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dmponline.dcc.ac.uk
7. Data Management Responsibilities and Resources
What resources (e.g., costs and time) will be dedicated to data management?
Data management is included in the timetable of the project. It was foreseen that 2h will be dedicated to data management every time an experiment is concluded
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dmponline.dcc.ac.uk
7. Data Management Responsibilities and Resources
What resources (e.g., costs and time) will be dedicated to ensuring that data will be FAIR (Findable, Accessible,Interoperable, Reusable)?
We expect to gather the first dataset that will refer to the process parameters of waste bioconversion with frass as part of the larvae's diet. This will be composed by growth data, material reduction, bioconversion efficiency and survival data.
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dmponline.dcc.ac.uk
Data
If you are re-using existing data, what licences or terms of use will you have to comply with?
No existing data.
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dmponline.dcc.ac.uk
Data
How will new data build on and relate to existing data?
No existing data.
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dmponline.dcc.ac.uk
Data
What types of new data will you create and in what format?
At the scoping phase, we will create interviews and qualitative data collected from semi-structured questionnaires stored as text documents initially then collated into a SQL database. In the intervention design phase, we will use surveys and lab-in-the-field methods to measure the preferences of participants over different types of intervention. This will be collected using tablets and stored as encrypted quantitative data on secure servers located in the EU. We will collect similar survey data on behaviour, economic outcomes, beliefs about climate shocks, and adaptation behaviours using tablets stored on the same secure servers.
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dmponline.dcc.ac.uk
Data
Can you estimate the size of the data you will create?
At the scoping phase this will be quite small as we are considering less than 50 interviews, certainly under 50mb. For the main experiment, we are expecting one round of intervention design with 500 participants, and a further 3 rounds of survey data with 1500 participants. This data will also be quite small as it will be quantitatively coded survey data, and will likely be under 100mb.
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dmponline.dcc.ac.uk
Data
What methods will you use to capture your data and how will these ensure that your data are high quality?
For scoping, semi-structured interviews conducted by an experienced researcher. For the main experiment, we will use a survey coded on tablets and translated into Swahili and Ngaturkan to ensure all participants can understand and answer accurately. We will train enumerators for a minimum of two days in using the surveys to ensure they understand how to ask each question, implement the economics lab-in-the-field measurements and interpret answers of respondents. We will also pilot these instruments with a sample similar to the main respondents to ensure the surveys are properly understood and the incentivized measures behind lab-in-the-field measures are understood and explained in a trustworthy way. We will follow standard best practice in survey design based on the knowledge compiled by the J-PAL Poverty Action Lab and the World Bank. We will also use measures appropriate to this population, similar to Burlig et al (2024).
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dmponline.dcc.ac.uk
Documentation and description
What contextual information is needed for you or someone else to understand your data?
As this data will be in the form of semi-structured interviews, the only contextual information required will be basic understanding of the climate risks faced by pastoralist communities in Northern Kenya. For the survey data, we will provide the original survey instruments and instructions to enumerators to provide the necessary context to interpret data.
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dmponline.dcc.ac.uk
Documentation and description
How will you capture contextual information?
For qualitative scoping work, where any important contextual information is required, the person conducting the interview will include this as endnotes to the interview transcript. We will capture contextual information for the main experiment by providing documentation and survey manuals provided to enumerators as well as survey instruments. We will create a readme file to explain all of these components.
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dmponline.dcc.ac.uk
Documentation and description
Will you use any metadata standards?
Not necessary for scoping data. For the main experiment, we will use a readme file to ensure all the different piece of data and documentation are properly stored and understandable.
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dmponline.dcc.ac.uk
Data Protection
Where will you store your data and how will you ensure that they are backed up? Will you use University-manageddata storage or will you need to set up your own back-up procedures?
All data will be stored on a secure Sharepoint site that has been created for this project on the University of Exeter Sharepoint site. We will also archive this data separately on the Uni Exeter Research Data Storage system. Only relevant members of the research team will be able to access this data to ensure personally identifiable information is protected. Analysis will be performed on cleaned, anonymized, data. Survey data will be encrypted when stored on the ONA Data servers.
5415872b53273f8a6e134fe27c9307b6
dmponline.dcc.ac.uk
Data Protection
How will you secure your data? What methods will you use to restrict access to your sensitive data? Will you encrypthardware when working off campus?
At the scoping stage, sensitive data will not be collected, and all data we collect will be stored on a secure Sharepoint site, with any physical copies of transcripts destroyed after interviews are transcribed and uploaded. Access to raw interview transcripts with identifiable information will be limited to research team members. For the main experiment, all personally identifiable data will be stored on password protected Sharepoint sites, and should data need to be stored temporarily on portable devices they will be encrypted. We will securely share data among collaborators by managing access to the project Sharepoint page and managing access to the data on the ONA Data server.
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dmponline.dcc.ac.uk
Data Protection
How will you protect your research participants? Will you obtain informed consent for data retention and sharing?How will you anonymise data to safeguard the privacy of your participants?
We will obtained informed consent including details on data retention and sharing. All information shared as part of this data will have names and any other identifiable information removed before being shared. We will anonymise data by removing all personal identifiers and obscuring geographic information to an appropriate level where individual households cannot be identified.
5415872b53273f8a6e134fe27c9307b6
dmponline.dcc.ac.uk
Retention and preservation
Which subsets of your data will you keep at the end of your project? Will you retain anonymised versions but destroypersonal data and identification keys? Will you retain all of the raw data or is a processed version more suitable topreserve? Do you need to keep all intermediary files or would you only need to refer back to input files or a finalversion?
At the end of the scoping phase of this project we will only keep the anonymous sections of interview transcripts we include in research publications or other outputs. We will only keep fully-anonymous data with all identifiable information removed. No intermediate or original files would need to be kept. For the main experiment, we will keep the survey data, retaining anonymised information without identification keys beyond the end of the project. We will keep anonymised intermediary files for completeness.
5415872b53273f8a6e134fe27c9307b6
dmponline.dcc.ac.uk
Retention and preservation
How will you prepare your data for long-term preservation? Are you able to convert your data to open file formats?What contextual information do you need to retain so that your data remain understandable and usable?
Interview transcripts will be stored in open file formats and will not require any contextual information beyond the notes included by the enumerator. We will delete all personally identifiable data from the dataset and store it in standard CSV and STATA-dta formats on the PI's website and on the journal website when it is published. We will retain the survey manuals and questionnaires to ensure it is understandable and usable.
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dmponline.dcc.ac.uk
Retention and preservation
Where will you archive your data to ensure that they are preserved and sustained for several years after your projectends? Will you submit your data to a specialist data repository/centre and if so, have you consulted them about yourrequirements?
We will use the University of Exeter's institutional repository and the Research Data Services system for secure long-term storage.
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dmponline.dcc.ac.uk
Retention and preservation
How big will your final dataset be and will there be any costs associated with archiving them, such as data depositcharges?
The dataset will be quite small as it is only a small number of interview transcripts and will not require any costs to archive, and a larger number of quantitative surveys that are not particularly large. We do not anticipate any costs to archiving this data.
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dmponline.dcc.ac.uk
Data sharing
Can you demonstrate that you'll plan ahead to maximise data sharing? For example, will you only share a subset ofthe data where informed consent was granted for data sharing?
We will only share identifiable information within the research team, and only fully-anonymised portions of the interviews will be included in any external communication where participants have consented to this. We will include data sharing as part of the consent process for all observations to demonstrate our commitment to open data.
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dmponline.dcc.ac.uk
Data sharing
Are there any reasons why you would not be able to share some of your data? Would they be covered by dataprotection legislation, licence restrictions, or contractual confidentiality clauses? Are there ethical reasons why yourdata should not be released?
There are no reasons not to release the fully-anonymous interview data from scoping phase. We will not share the exact geolocations of households from surveys, which will be helpful for analysis as it will allow us to compute distances to various key items of interest. We will not share exact geolocations for privacy and ethics concerns, and will instead share obscured geographic data that cannot be used to identify households.
5415872b53273f8a6e134fe27c9307b6
dmponline.dcc.ac.uk
Data sharing
When will you share your data? Will data be made available upon first publication of findings or within a limitedperiod after the end of the project? Do you need to delay publication to allow for commercialisation or patentapplications? Will you embargo your data to allow for a limited period of exclusive use?
We will share the relevant segments of the interviews as quoted within the research publications. We will not need to delay sharing of this. We will share data upon first publication of findings, with no reason to delay beyond this point. We will not embargo the data beyond the date when the relevant data is used in publication.
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dmponline.dcc.ac.uk
Data sharing
How will you disseminate your research? Will you include a data access statement in published articles? Does yourchosen method of data preservation provide a persistent identifier such as a Digital Object Identifier? What licenceswill you assign to your data?
We will disseminate research via academic publication. Data access statements and DOIs will not apply to the segments of interviews included in publications and grant applications. The institutional repositories we will use at the University of Exeter will have a DOI that can be shared.
5415872b53273f8a6e134fe27c9307b6
dmponline.dcc.ac.uk
Data Protection Impact Assessment
What do you require this personal data for? What is the purpose of using the personal data?
For scoping work, we will not keep this personal data as part of the data beyond identifying key demographic characteristics. Personal data will not be used in the analysis or stored or shared. For the main experiment, we will need personal data to track participants in order to implement treatment and find them for follow- up surveys.
5415872b53273f8a6e134fe27c9307b6
dmponline.dcc.ac.uk
Data Protection Impact Assessment
How are you making people aware of how their personal data is being used? Do you need to update your privacynotice?
Participants will be informed that their personal data will not be shared and all information shared beyond the research team will be anonymous.
5415872b53273f8a6e134fe27c9307b6
dmponline.dcc.ac.uk
Data Protection Impact Assessment
How are you ensuring that personal data obtained from individuals or other organisations is accurate? How will youkeep it updated?
We will be collecting information directly from participants, and there is no need to ensure accuracy beyond this. We will not need to keep it updated for this stage of the project.
5415872b53273f8a6e134fe27c9307b6
dmponline.dcc.ac.uk
Data Protection Impact Assessment
How long will you keep the data and how will you dispose of it? Are the retention periods on the University RetentionSchedule?
We will dispose of identifiable data after project completion. We will store anonymised data long-term in keeping with University of Exeter policy.
5415872b53273f8a6e134fe27c9307b6
dmponline.dcc.ac.uk
Data Protection Impact Assessment
Where will the data be stored? If storage is in the cloud, where is the physical server? Will you need to transfer thedata outside the EEA? If yes, how will you ensure adequate protection?
All data will be stored securely on Sharepoint during analysis, with a backup on the Research Data Services system, before being archived on the Uni. Exeter's ORE repository.
5415872b53273f8a6e134fe27c9307b6
dmponline.dcc.ac.uk
Data Protection Impact Assessment
Please briefly document below any risks with the use of personal data and how you will control such risks. Includetechnical controls (IT security, encryption etc), physical controls (location, locked room etc), personnel controls(training, access control etc), and procedural controls (contract, polices etc).
We will control these risks by storing all data on a secure Sharepoint site that we regularly monitor to maintain secure access by team members only. We will encrypt all survey data when it is being transferred to the ONA Data survey server platform and delete data from the tablets on which it is collected after upload. We will only store data temporarily in encrypted form on portable devices, which will be kept in locked drawers in locked officers when not in use.
5415872b53273f8a6e134fe27c9307b6
dmponline.dcc.ac.uk
0. Proposal name
Enter the proposal name
Health of young Looked After Children in Scotland
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dmponline.dcc.ac.uk
1. Description of Data.
Format and scale of the data
~400,000 review records stored electronically and processed on NHS National Services Scotland National Safe Haven.
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dmponline.dcc.ac.uk
2. Data collection / generation
Methodologies for data collection / generation
Secondary data collected as part of routine NHS Scotland work.
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dmponline.dcc.ac.uk
2. Data collection / generation
Data quality and standards
N/A - no control over data collection standards.
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dmponline.dcc.ac.uk
3. Data management, documentation and curation
Managing, storing and curating data
Data stored electronically and processed on Public Health Scotland National Safe Haven.
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dmponline.dcc.ac.uk
3. Data management, documentation and curation
Data preservation strategy and standards
Records stored electronically Public Health Scotland National Safe Haven with plan to retain until 2036.
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dmponline.dcc.ac.uk
4. Data security and confidentiality of potentially disclosive personal information
Formal information/data security standards
Project will follow Public Health Scotland guidelines and standards for data processing and disclosure. See https://www.publichealthscotland.scot/media/2707/public-health-scotland-statistical-disclosure-control-protocol.pdf
c76b74505ef0da3ab90b2b589cecf2b7
dmponline.dcc.ac.uk
4. Data security and confidentiality of potentially disclosive personal information
Main risks to data security
Although data will be anonymized by the Public Health Scotland eletronic Data and Research Innovation Service (eDRIS) there is a risk that individuals will become identifiable through small cell sizes in results tables etc. However, results cannot be taken off the National Safe Haven without explicit permission of an eDRIS Research Coordinator (eRC). Project members submit a request to use results and the eRC checks the request against statistical disclosure protocols which are designed to avoid any individual becoming identifiable.
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dmponline.dcc.ac.uk
5. Data sharing and access
Suitability for sharing
Data is not suitable for sharing and responsibility for data storage and access lies with Public Health Scotland electronic Data Research and Innovation Service (eDRIS). It contains health records of young children as well as data about ethnicity. Any use of NHS data must be approved by an appropriate panel (e.g., Public Benefit Privacy Panel). New research team members must be approved by eDRIS.
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dmponline.dcc.ac.uk
7. Relevant policies
Relevant institutional, departmental or study policies on data sharing and data security
Project will follow Public Health Scotland guidelines and standards for data processing and disclosure. See https://www.publichealthscotland.scot/media/2707/public-health-scotland-statistical-disclosure-control-protocol.pdf
c76b74505ef0da3ab90b2b589cecf2b7
dmponline.dcc.ac.uk
8. Author and contact details
Author of this Data Management Plan (Name) and, if different to that of the Principal Investigator, their telephone& email contact details
Main applicant: Daniel Bradford - PhD student in MRC/CSO Social and Public Health Sciences Unit [email protected] Project team: Denise Brown - PhD Supervisor/Epidemiologist/Statistician in MRC/CSO Social and Public Health Sciences Unit [email protected] Mirjam Allik - PhD Supervisor and Research Fellow in MRC/CSO Social and Public Health Sciences Unit [email protected] Alex McMahon - PhD Supervisor and Reader in University of Glasgow Dental School [email protected]
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dmponline.dcc.ac.uk
General Information
Registration number at the Swedish Research Council
2020-02009
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dmponline.dcc.ac.uk
Description of data – reuse of existing data and/or production of new data
How will data be collected, created or reused?
Data collection will be performed in 2020/2021 at age 62 and at age 65 2023/2024 of the human longitudinal cohort study SPAF- 1958. At baseline, in 1974, six geographic areas in Sweden were systematically selected based on reflecting climate and population density representative of Sweden as a whole. In each area, one upper secondary school was randomly selected, from which 429 (224 male, and 205 female) pupils in the lowest grade level were randomly selected to be included in the study. The entire cohort was invited for questionnaire and testing at age 16, 34 and 52. The blood samples that were collected at age 52 are available for further analyses. A smaller group (n=83) contributed with muscle biopsies at age 16 and 27. In the current project a third extended follow-up is planned almost 50 years after the first baseline measurements, when the participants are 62 years of age. At baseline, consent to a muscle biopsy and extra testing was given by 116 of the participants (69 boys and 47 girls). Eleven years later, at the age of 27, this subgroup was invited to a follow-up and a total of 83 participants (72%) (55 men and 28 women) consented to take a new muscle biopsy also at the age of 27. Biopsy materials from both age 16 and 27 are still available. Importantly, no differences have been identified between the biopsy subgroup and the remaining members of the cohort with respect to body dimensions, blood pressure, resting heart rate or most of the measures of physical capacity at 16, 34 or 52 years of age. Three hundred and eighty-one participants from the baseline cohort (90%) have been identified (i.e. being alive and with a known address and personal identity number). All subjects will be contacted during spring-summer 2020 and asked to participate in some or all of: . Those who have developed signs of neuropathy at age 65 will be added to the group with an extended test battery (EMG, MRI, tissue biopsies).
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dmponline.dcc.ac.uk
Description of data – reuse of existing data and/or production of new data
What types of data will be created and/or collected, in terms of data format and amount/volume of data?
Enkätsvar som samtliga deltagare. Uppskattat antal deltagare är 200 för blodprov och max 100 för biopsier Blod: ca 50 ml per deltagare per uppföljning Hudbiopsier: 1-3 biopsier per deltagare per uppföljning Muskelbiopsier: 1 per deltagare per uppföljning
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dmponline.dcc.ac.uk
Documentation and data quality
How will the material be documented and described, with associated metadata relating to structure, standards andformat for descriptions of the content, collection method, etc.?
Samtliga deltagare har sedan tidigare testning redan ett kodnummer. Under datainsamlingen kommer personens namn kontrolleras mot hens kodnummer vid respektive undersökning för att insamlade uppgifter ska registreras på rätt person. Kodlista med namn är tillgänglig för forskningsledarna under hela datainsamlingen för att kunna matcha rätt person med rätt kodnummer vid testningen. När alla insamlade uppgifter samlas i databas och statistikprogram anges endast kodnummer och kön. Kodnyckeln förvaras i låst utrymme i forskargruppen. Primärdata lagras i ELN samt för fysiska kopior i KI:s arkiv i minst 30 år. Kodnyckeln kommer sparas eftersom detta är en longitudinell studie med ytterligare planerade datainsamlingar. Enkäter skickas ut via post eller digitalt. Svaren i de insamlade enkäterna matas in i digitalt format. Datainsamlingen sker genom att fp kommer till undersökningslokalen och deltar i tester under en-två dagar. Blodproverna skickas för analys samt sparas i -80grader i biobank.Muskelbiopsierna snittas för analys av muskelfibertyp samt innervering och övrig morfologi. Prover fryses i flytande kväve/för-kyld isopentan och förvaras vid -80°C tills analys utförs. Samtliga prover förvaras i befintlig biobank vid Karolinska Universitetssjukhuset. Muskelsnitt analyseras exv. för fiberstorlek och fiberklustring, innervering, cellkärnor, satellitceller, kapillärisering enligt standardmetoder på vårt laboratorium. Muskelvävnad bearbetas för analys av genutryck, metabolism och olika aspekter av proteinhantering enligt sedvanliga metoder på vårt laboratorium. Nervfibertäthet: Biopsimaterialet fixeras i fixeringsmedel mellan 18–24 timmar. Därefter skickas hudvävnaden kyld till neuropatologen för immunohistokemisk hantering inkl. snittning och färgning. Immunohistokemisk analys av hudbiopsierna genomförs där nervfibrer färgas med efterföljande kvantifiering av nervfibertätheten (nervfibrer per mm hud); resultatet kan jämföras med publicerade normalmaterial samt lokal referensdatabas. Resultatet sparas och resterande vävnadsmaterial sparas för framtida forskning i biobank.
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dmponline.dcc.ac.uk
Documentation and data quality
How will data quality be safeguarded and documented (for example repeated measurements, validation of data input,etc.)?
Varje deltagare har ett kodnummer. Vid resultatbearbetning anges kodnumret och initialerna för varjedeltagare. Alla uppgifter matas in i en databas, som REDCap som är ett system via Karolinska insitutet. All laboratoriearbete dokumenteras fortlöpande i ELN som signeras av ansvariga forskare. Alla undersökningar utförs enligt faställda SOPs av ansvarig och för metoden med kompetens.
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dmponline.dcc.ac.uk
Storage and backup
How is storage and backup of data and metadata safeguarded during the research process?
Alla uppgifter matas in i en databas som REDCap, ett system via Karolinska insitutet. All datainsamling dokumenteras och arkiveras i ELN eller andra av KI godkända platser för arkivering.
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dmponline.dcc.ac.uk
Storage and backup
How is data security and controlled access to data safeguarded, in relation to the handling of sensitive data andpersonal data, for example?
Se ovan
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dmponline.dcc.ac.uk
Legal and ethical aspects
How is data handling according to legal requirements safeguarded, e.g. in terms of handling of personal data,confidentiality and intellectual property rights?
Samtliga deltagare har sedan tidigare testning redan ett kodnummer. Under datainsamlingen kommer personens namn kontrolleras mot hens kodnummer vid respektive undersökning för att insamlade uppgifter ska registreras på rätt person. Kodlista med namn är tillgänglig för forskningsledarna under hela datainsamlingen för att kunna matcha rätt person med rätt kodnummer vid testningen. När alla insamlade uppgifter samlas i databas och statistikprogram anges endast kodnummer och kön. Kodnyckeln förvaras i låst utrymme i forskargruppen. Primärdata lagras i ELN samt för fysiska kopior i KI:s arkiv i minst 30 år. Kodnyckeln kommer sparas eftersom detta är en longitudinell studie med ytterligare planerade datainsamlingar.
3bf7577f03a0337aca96862cf21e283d
dmponline.dcc.ac.uk
Legal and ethical aspects
How is correct data handling according to ethical aspects safeguarded?
Godkänd enligt GDPR samt samtycke inhämtad från samtliga som valt att ingå i studien. Dokumenterd process (enligt ovan) om hur datathantering, process samt lagring ska ske och i enlighet med gällande förordning
3bf7577f03a0337aca96862cf21e283d
dmponline.dcc.ac.uk
Accessibility and long-term storage
How, when and where will research data or information about data (metadata) be made accessible? Are there anyconditions, embargoes and limitations on the access to and reuse of data to be considered?
Aggregerade data kommer göras tillgängliga (i anonymiserad form) i samband med publisering i enlighet med creative commons framework.
3bf7577f03a0337aca96862cf21e283d
dmponline.dcc.ac.uk
Accessibility and long-term storage
In what way is long-term storage safeguarded, and by whom? How will the selection of data for long-term storage bemade?
All datainsamling dokumenteras och arkiveras i enlighet med KI regelverk. Primärdata lagras i ELN samt för fysiska kopior i KI:s arkiv i minst 30 år. Kodnyckeln kommer sparas eftersom detta är en longitudinell studie med ytterligare planerade datainsamlingar.
3bf7577f03a0337aca96862cf21e283d
dmponline.dcc.ac.uk