Deliverable: 7.2 Title: Data Management Plan

D7.2 DMP I page 1 of 25 Deliverable: 7.2 Title: Data Management Plan Project information Project Title Networking for excellence in the development of innovative, consumer-oriented horticultural food products using the Living Lab approach Acronym: HortiFoodTrends Grant agreement: 101159293 Topic HORIZON-WIDERA-2023-ACCESS-02-01 - Twinning Bottom-Up Type of action Horizon - Coordination and Support Action Granting authority European Research Executive Agency Start date of the project 1 June 2024 Duration 36 months Project Coordinator Monika Mieszczakowska-Frąc Project Website www.hortifoodtrends.eu D7.2 DMP I page 2 of 25 Deliverable information Deliverable title Data Management Plan Deliverable number 7.2 Deliverable version 1.0 Work Package WP7, Project Management Contractual date of delivery M5 – 31/10/2024 Actual date of delivery M6 – 28/11/2024 Deliverable type: R – Document, report Responsible Partner InHort Authoring & Approval Authors Katarzyna Hołownicka Reviewers Adam Kaźmierczak, Monika Mieszczakowska-Frąc, Michael Bom Frøst Approved for submission Monika Mieszczakowska-Frąc Dissemination level PU Public, fully open, e.g. web (Deliverables flagged as public will be automatically published in CORDIS project’s page) X SEN Sensitive, limited under the conditions of the Grant Agreement Classified R-UE/EU-R EU RESTRICTED under the Commission Decision No2015/444 Classified C-UE/EU-C EU CONFIDENTIAL under the Commission Decision No2015/444 Classified S-UE/EU-S UE/EU-S – EU SECRET under the Commission Decision No2015/444 Document History Version Date Description of Change V0.1 03/10/2024 First draft V0.2 08/11/2024 Second draft version incorporating review comments V0.3 27/11/2024 Third draft version incorporating review comments V0.4 28/11/2024 Final draft send to Coo V1.0 28/11/2024 Final version submitted to EC by the Coo Disclaimer This project has received funding from the European Union’s Horizon Europe Innovation programme under grant agreement No 101159293. Disclaimer: This report only reflects the views of the author(s) and do not necessarily reflect the official opinion of the European Union. The European Commission is not liable for any use that may be made of the information contained therein. Configuration Management: Document Location The latest version of this controlled document is stored in D7.2 Data Management Plan. D7.2 DMP I page 3 of 25 TABLE OF CONTENTS EXECUTIVE SUMMARY ............................................................................................................................. 4 TABLE OF ABBREVIATIONS AND ACRONYMS ............................................................................................ 5 1 INTRODUCTION ............................................................................................................................... 6 2 DATA SUMMARY............................................................................................................................. 7 2.1 Purpose of the data collection .............................................................................................................. 7 2.1 Data types, formats and size ................................................................................................................. 7 2.1.1 Data categories ..................................................................................................................... 8 2.2 Re-use of data........................................................................................................................................ 9 2.3 Origin/provenance of the data ............................................................................................................ 10 2.4 Data utility ........................................................................................................................................... 10 3 FAIR DATA ..................................................................................................................................... 14 3.1 Making data findable, including provisions for metadata ................................................................... 14 3.1.1 Globally unique persistent identifier .................................................................................. 14 3.1.2 Rich Metadata .................................................................................................................... 14 3.1.3 File naming ......................................................................................................................... 15 3.2 Making data openly accessible ............................................................................................................ 16 3.2.1 Repository: ......................................................................................................................... 16 3.2.2 Data: ................................................................................................................................... 16 3.2.3 Metadata: ........................................................................................................................... 18 3.2.4 Open Access to Scientific Publications ............................................................................... 19 3.3 Making data interoperable .................................................................................................................. 19 3.4 Increase data re-use ............................................................................................................................ 20 4 OTHER RESEARCH OUTPUS ........................................................................................................... 21 5 ALLOCATION OF RESOURCES ......................................................................................................... 21 6 DATA SECURITY ............................................................................................................................. 22 7 ETHICS ........................................................................................................................................... 24 8 CONCLUSIONS ............................................................................................................................... 25 D7.2 DMP I page 4 of 25 EXECUTIVE SUMMARY This deliverable D7.2 describes the first version of the HortiFoodTrends Data Management Plan (DMP) and is based on the Guidelines on the HORIZON EUROPE Data Management Plan Template Version 1.0 1 . The DMP specifies how data collected or generated in the course of the project will be handled during the HortiFoodTrends action and how it will be stored, published, cited and made Findable, Accessible, Interoperable, and Reusable (FAIR) beyond the project life. Moreover, the DMP details the standards and methodology for data collection and generation will be followed that will be used, how the research data will be preserved and what data sets will be published for verification or reuse as open access. This DMP is the first version of the plan, and it will therefore be a “living document” that will be updated periodically as project activities progress. The DMP will establish consistent practices between partners to enhance the efficiency and robustness of data handling during the delivery of the project. 1 https://ec.europa.eu/info/funding-tenders/opportunities/docs/2021-2027/horizon/temp-form/report/data-management-plan_he_en.docx D7.2 DMP I page 5 of 25 TABLE OF ABBREVIATIONS AND ACRONYMS Abbreviation Meaning CA Consortium Agreement CC Creative Commons Coo Project Coordinator DMP Data Management Plan DOI Digital Object Identifier GDPR General Data Protection Regulation FAIR Findable, Accessible, Interoperable and Reusable PID Persistent Identifiers WP Work Package D7.2 DMP I page 6 of 25 1 INTRODUCTION The Horizon Europe Model Grant Agreement requires that a data management plan (DMP) is established and regularly updated. This DMP is based on Data Management Template of European Commission Horizon Europe, addressing the requirements for research data management of Horizon Europe as described in article 17 of the Grant Agreement, it complies with the General Data Protection Regulation (GDPR), the FAIR principles and the motto to share data ‘as open as possible, as closed as necessary’. The purpose of this DMP is to set general data management practices within HortiFoodTrends project, specifically: • to provide relevant information on the nature of data to be collected and/or generated (purpose, type, format, expected volume, and origin); • to describe the management procedures to collect and/or generate data in HortiFoodTrends during and after the end of the project, serving as the key element of good data management; • To define how the collected and/or generated data will be made ‘FAIR’ (Findable, Accessible, Interoperable and Reusable. The deliverable is structured in 8 Chapters, as follows: • Chapter 1 introduces the objectives and structure of the DMP. • Chapter 2 describes the nature of the data (purpose, format, origin, expected size). • Chapter 3 explains how FAIR principles will be followed. • Chapter 4 presents other research outputs generated within the workplan of the project. • Chapter 5 defines the allocation of resources for the data management activities. • Chapter 6 refers to data security. • Chapter 7 describes the relation of the data management with the potential ethical aspects • Chapter 8 provides a summary of the report. The DMP will be updated in the middle (M17) and at the end of the project (M36). The DMP has been compiled collectively in response to consultation and iterative dialogue with the project partners with respect to all project activities and outputs. Each partner in the specific project is responsible for implementing the project DMP steps and for ensuring that their data is managed in accordance with the project-level DMP. D7.2 DMP I page 7 of 25 2 DATA SUMMARY The Data Summary section deals with the purpose of the data to be collected and generated by the project HortiFoodTrends and its relationship with the project objectives, the types and formats of data that will be generated, the re-utilisation of existing data and how it will be implemented, the origin of the data, and to whom it may be useful (data utility). 2.1 Purpose of the data collection The purpose of data collection is strongly linked with the objectives and the planned outputs of the HortiFoodTrends project. The general purpose of collecting data is to develop and optimize products based on berries, fruits and vegetables with input from consumers in the development process. New data on end user's expectations in the form of survey questionnaires containing indicator data and descriptive data will be developed in WP4 (Task 4.1). These data will be exploited in Task 4.2 to develop innovative products based on berries, fruits and vegetables. The developed products will generate new data from the chemical and sensory analyses performed. Quantitative and qualitative characteristics of nutrients (including protein, fats, sugars, fibre, minerals) and bioactive components (vitamins, phenolic compounds including anthocyanins and other antioxidants) and sensory quality indicators. WP4 will also produce data correlated with processing (process parameters, technological methods, critical control points, etc. Qualitative data will influence the selection of products for consumer evaluation in 3 countries: Denmark, France and Poland. Consumer research data will be key to developing communication procedures with the industry and determining commercialization and economic value. The purpose of collecting data from consumers is to understand consumers preferences and differences between segments of consumers (e.g. gender, age, country and/or region). WP5 will upscale WP4 results defining future of the implemented innovations. Financial and economic incentives will be analysed as a part of the value proposition. WP5 creates a strategy based on the Living-lab approach and in particular, develops a road map for effective communication with the end-users. In WP6 we will collect contact data from stakeholders with the purpose of developing the network of producers and other relevant stakeholders. Also in WP6 we collect data from end-users in focus group with consumers (reaching the target of 40% of women) to explore their level of acceptance the new products. Lastly, expert interviews will be conducted to facilitate decisions of human, social and material aspects of responsible innovation. Videos and pictures (in the sense of non-research data) are generated and used to communicate and disseminate the project and its results via the projects’ media channels and to illustrate methods for internal protocols. Personal data are not collected to use them for a research purpose but are needed to contact persons (e.g. researchers, members of the Multi-actor forum) and ask certain questions in frame of questionnaires. Personal data will be used according the GDPR and only if given consent for their use by the persons. In short, persons are informed on the use of the data and have the right to see what has been collected and stored in relation to their personal data. They can ask for deletion and this request will be followed. Information about this is included in section 6. 2.1 Data types, formats and size HortiFoodTrends project follows multi-actor approach by co-creating of the project activities, which are deeply interconnected with businesses and end-users’ needs. Thus HortiFoodTrends will generate data of different nature, including measurements summarising the results of sensory and consumer tests and food D7.2 DMP I page 8 of 25 processing experiments (WP4), questionnaires from consumer research, laboratory and process analysis measurements (WP4). Data regarding stakeholder evaluation of innovation implementation will be collected from stakeholders (WP5, WP6). End-user product expectations from focus groups (WP6). 2.1.1 Data categories Datasets that will be collected/generated during HortiFoodTrends project are categorized into four main categories: Confidential Dataset: includes confidential data that cannot be shared due to its sensitive nature. Access to this data is restricted to authorized personnel only, and it is kept securely to ensure confidentiality. Please refer to section Błąd! Nie można odnaleźć źródła odwołania. Błąd! Nie można odnaleźć źródła odwołania. for more information on how GDPR and Ethics guidelines are applied in the project, as well as an initial Consent Form procedure. Core Dataset: includes the project’s deliverables, dissemination materials, and training materials that are essential for the project. These datasets are mandatory for running and reporting on the project as they are the backbone of its activities and outcomes. Stakeholder Dataset: includes data collected from SMEs and other related stakeholders (such as national and international bodies/ institutions/ organizations related to HortiFoodTrends) in the food sector through various means such as surveys, interviews, visits, online and onsite meetings and questionnaires. Apart from those, all the data received from third parties will be considered SD. The data in this category is essential for identifying the challenges and developing effective solutions. Produced and Collected Dataset: includes the academic publications and lab results that are generated by the HortiFoodTrends staff. The data in this category is crucial for demonstrating the project’s research capacity and potential impact on the food industry. A schematic table questionnaire was submitted to the partners to ask what kind of data will be produced. The complete list of data types is reported as Table 2: Table Data Overview. Data types and formats are included in Table 2 (columns 4 and 8). These formats are compatible with many computer programs and should be used by all partners. Other data formats might be used by individual partners but need to be converted to the standard formats before uploading to allow proper exchange of data. Table 1: The recommended and acceptable formats for various types of data Type of data Recommended formats Acceptable formats Tabular data with extensive metadata variable labels, code labels, and defined missing values • 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 • proprietary formats of statistical packages: SPSS (.sav), Stata (.dta), MS Access (.mdb/.accdb) Tabular data with minimal metadata column headings, variable names • comma-separated values (.csv) • tab-delimited file (.tab) • delimited text with SQL data definition statements • 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) D7.2 DMP I page 9 of 25 Textual data • Rich Text Format (.rtf) • plain text, ASCII (.txt) • eXtensible Mark-up Language (.xml) text according to an appropriate Document Type Definition (DTD) or schema • Hypertext Mark-up Language (.html) • widely-used formats: MS Word (.doc/.docx) • some software-specific formats: NUD*IST, NVivo and ATLAS.ti Image data • TIFF 6.0 uncompressed (.tif) • JPEG (.jpeg, .jpg, .jp2) if original created in this format • GIF (.gif) • TIFF other versions (.tif, .tiff) • RAW image format (.raw) • Photoshop files (.psd) • BMP (.bmp) • PNG (.png) • Adobe Portable Document Format (PDF/A, PDF) (.pdf) Audio data • Free Lossless Audio Codec (FLAC) (.flac) • MPEG-1 Audio Layer 3 (.mp3) if original created in this format • Audio Interchange File Format (.aif) • Waveform Audio Format (.wav) Video data • MPEG-4 (.mp4) • OGG video (.ogv, .ogg) • motion JPEG 2000 (.mj2) • AVCHD video (.avchd) Documentation and scripts • Rich Text Format (.rtf) • PDF/UA, PDF/A or PDF (.pdf) • XHTML or HTML (.xhtml, .htm) • OpenDocument Text (.odt) • plain text (.txt) • widely-used formats: MS Word (.doc/.docx), MS Excel (.xls/.xlsx) • XML marked-up text (.xml) according to an appropriate DTD or schema, e.g. XHMTL 1.0 Source: https://www.openaire.eu/data-formats-for-preservation/ Data sizes are included in Table 2 (column 9). However, it is not possible to estimate the size of some of the items included. The information will be completed with project advancement. Relatively many datasets will be produced, but none will be overly big, which will allow them to be collected on spreadsheets for easy accessibility. Just raw data derived from consumer analyses of prototype berry products conducted in 3 countries will need high storage space for and statics and informatics. 2.2 Re-use of data Data will also derive from collection of existing databases (e.g. from USDA database, EFSA Food consumption data, EFSA Database of health claims), outcomes from previous and ongoing European and national projects (e.g., OPTIFEL, ISAFRUIT, etc.), literature review (sources: Zenodo, PubMed, SCOPUS, Web of Science, ScienceDirect, etc.), as well as reports and technical documents. They will be re-used to: • develop and implement innovative products and technologies that reduce production costs and improve product quality, thus maintaining the high competitiveness of Polish producers (WP4). • increasing awareness of the general public about the importance of healthy eating - which is facilitated by increased consumption of fruit and vegetables, especially super fruits - regarding improving the health of citizens and reducing the problem of obesity (WP4, WP5, WP6); and • horizontal mapping review of existing European, national and regional policy incentives and nudges contributing to supporting and promoting good practice (WP5). D7.1 DMP I page 16 of 25 [Status] Draft, Final, Public, Restricted, Confidential [Free] Free text field for internal communication purposes, at the end of the file name, immediately before the extension (e.g., initials of reviewer). This field should not be included in the name of published files. [Extention] File extention These minima metadata schemas can be extended by arbitrary from a taxonomy or controlled vocabulary as described in the Zenodo API documentation. 3.2 Making data openly accessible 3.2.1 Repository: All datasets supporting publications will be made openly and publicly available on the OpenAire repository Zenodo upon publication of manuscripts. For this, the Community “HortiFoodTrends” will be created. Zenodo is always available, and no particular arrangements must take place. Zenodo assigns DOI (when the resource does not have a DOI) for every published record in the repository. Only data gathered by partners outside the project work plan and protected by IPR, or inside the work plan but containing confidential information (e.g. related to personal interviews, stakeholder’s contact information), will be kept closed for privacy reasons. Data will be shared among members of work packages continuously as they are being generated, by using the project SharePoint. Furthermore, when a dataset turns openly accessible, it will be announced on the web page of the HortiFoodTrends project, where a link to the dataset on Zenodo will be available. 3.2.2 Data: The HortiFoodTrends dataset will be both public (data access policy unrestricted) and accessible via: • Project website • Partners database (personal computer, or on institutional secure server of the data owners) • HortiFoodTrends Community Zenodo • Open access journals • Other platform if needed All data deposited on Zenodo will be accessible without restriction for public. For other data, potential users must contact the Coordinator or the data owner to gain access. If necessary, appropriate procedure (such as non-disclosure agreement) will be used, including the conditions of use, criteria for access, and acknowledgements, will ensure a proper data-sharing. In general, until the dataset is fully finalised and ready for publication, the project's default repository, the FIZZ programme database, nextCloud.groupe-esa.com and the SharePoint platform, will be used to exchange data between project partners. Accessibility status will be specified in the Data Register including any reason why a specific dataset is not being made available to download e.g., ethical; containing personal data; intellectual property protection; commercially sensitive information; privacy related; and/or security related. Also, transparent accessing and use conditions will be made available. D7.1 DMP I page 17 of 25 Private, sensitive data will be restricted and partially available after applying aggregation and anonymization procedures. The project will guarantee the anonymity of respondents through an informed consent form that will be described in Ethics and Gender Guidelines (D7.4). If anonymised interview data were to be made public via open access, each respondent would have to sign a second informed consent for this step. Key information will be collected in Data Register for each dataset by the lead beneficiary responsible for the dataset, using the template provided in the Table 3. Table 3: Data Register TOPIC DESCRIPTION Dataset identifier HFT_[ShortTitle]_ v[Version]_[Type] Dataset name Dataset description Dataset DOI Dataset version history Key contact (Partner) Dataset file format and size Associated WPs/Tasks/ Deliverables/ Milestone Accessibility [Not yet considered – with who is responsible for the decision[ [Open – with reasoning, date of decision and who is responsible for the decision] [No Open – with reasoning, date of decision and who is responsible for the decision] Repository(-ies) Keywords Licence [Default: CC BY-4.0] Useful for whom? Key data sources The HortiFoodTrends consortium will make a great effort, where possible, to make research data available as open data or through open services. However, it is important to note that because of the low maturity of this document and some existing uncertainties about the data collected in the project. As the project progresses, further information on making data openly accessible will be provided in subsequent versions of the DMP. In particular, information on the methods or software tools needed to access the data and how access will be provided in the event of restrictions. The data will be available as soon as the publishable version is available. Nevertheless, this aspect is to be assessed based on datasets that are to be produced during project implementation. The HortiFoodTrends Consortium Agreement states that prior notice of any planned publication shall be given to the consortium parties at least 30 calendar days before the submission for publication. This time is allocated to assess whether the protection of the objecting party's results or background would be adversely affected, or the objecting party's legitimate interests in relation to its results or background would be significantly harmed, or the proposed publication includes confidential information of the objecting party. A publication delay of maximally 90 days can be requested in case of an objection. D7.1 DMP I page 18 of 25 All HortiFoodTrends data used and/or generated will be accessed based on an open (accessed without barriers), free (no costs added), standardized communication protocols (TCP/IP, HTTP) for individuals and machines. Using standardised communication protocols contributes to better accessibility of the data (with authorisation and authentication procedures when necessary). During the project completion different levels of access will be defined according to the accessibility levels (user profiles) to assure the potential to work on data and open access to aggregated data with relevant permissions. At the end of the project all data (with permission from project partners) are publicly shared. For restricted access data, a well-documented authorisation procedure has to be established to access the data (define who grants access to the data). Upon authorisation, the second step is authentication, which allows secure access (ex., registration through username & password). The procedure will be valid during and after the end of the project. Ascertaining the identity of the person accessing the data is related to the access protocol. There is a comprehensive plan to address data security concerns at both the database and platform levels. In summary, users will be categorized as 'internal' or 'external.' External users, typical platform visitors, will have limited data access. Internal users, integral to our project, will be assigned a unique project partner ID, granting them broader data access based on our three-tiered data protection strategy. There is a data protection protocol in place (included as Annex). Small excerpt: the data protection encompasses three layers that vary depending on the user type. These layers include: • Public (Option 1): The data is publicly available. This option applies to most HortiFoodTrends (final) products. • Private - Confidential to partner (Option 2): This option applies when data are shared only between specific partners in the consortium (e.g., data involved in collaborative work, sharing of intermediate or incomplete products, data expected to be included in patent applications). • Private - Confidential to consortium (Option 3): This option applies for data underlying publications that may not yet published in peer reviewed scientific papers or that are planned to be published before becoming publicly available. For the moment, there is no need for a data access committee since there is no personal/sensitive data in the project database. In some work packages partners generate sensitive data, but that data is anonymised before it is shared with the project database. 3.2.3 Metadata: The metadata of deposited publications will be licenced under Creative Common Public Domain Dedication (CC 0) or equivalent (to the extent legitimate interests or constraints are safeguarded), in line with the FAIR principles (in particular machine-actionable) and provide information at least about the following: • datasets (description, date of deposit, author(s), venue and embargo), • Horizon Europe funding, • grant project name, acronym and number, • licensing terms, • persistent identifiers for the dataset, • the authors involved in the action and, • if possible, for their organisations and the grant. D7.1 DMP I page 19 of 25 Where applicable, the metadata will include persistent identifiers for any research output or any other tools and instruments needed to validate the conclusions of the publication. The metadata will be collected and included in the database (and data platform) to make the data searchable. There may be restrictions on licensed secondary data and private company data. The availability of the data and metadata is ensured through the HortiFoodTrends project website (they remain reusable for as long as the project's resources and infrastructure allow) in the Zenodo open repository indefinitely. However, the availability of data and metadata needs to be further assessed as the project progresses. The metadata will include the reference to the software needed to access and read the data. 3.2.4 Open Access to Scientific Publications Open science HortiFoodTrends practices will include sharing research data, publishing under Gold Open Access (OA) with full pdfs freely available on the HortiFoodTrends Community of the OpenAire repository Zenodo, on project website and social media, to reach a wider target audience including citizens. The HortiFoodTrends partners must ensure open access to peer-reviewed scientific publications relating to their results. In particular, they must ensure that: • at the latest at the time of publication, a machine-readable electronic copy of the published version or the final peer-reviewed manuscript accepted for publication, is deposited in a Zenodo • immediate open access is provided to the deposited publication via the repository, under the latest available version of the Creative Commons Attribution International Public Licence (CC BY) or a licence with equivalent rights; for monographs and other long-text formats, the licence may exclude commercial uses and derivative works (e.g. CC BY-NC, CC BY-ND) and • information is given via the repository about any research output or any other tools and instruments needed to validate the conclusions of the scientific publication. Beneficiaries (or authors) must retain sufficient intellectual property rights to comply with the open access requirements. Only publication fees in full open access venues for peer-reviewed scientific publications are eligible for reimbursement. 3.3 Making data interoperable The depositors will strive to use commonly and internationally used metadata in their field of research, since no widely accepted specific metadata vocabularies exist. Data variable names and units, commonly accepted by the international scientific community and International System of Units, will be used. The vast majority of the datasets will be made available as spreadsheets, .xlsx format, and to a lesser extent as R files, FIZZ files, geotiff files, and .img, .asc extensions, so that they can be fully used by potential users. In addition, the Data Management Team will ensure that data held on the HortiFoodTrends data repository are provided in commonly used data format and file types (Table 1 and 2). These formats have been used as they are widely accepted standards and the ease of access to the software needed to handle them. The files will be converted to open file formats where possible for long-term storage. Maximum effort will be taken to use the available standard vocabularies for all data types present to maintain interdisciplinary interoperability. However, when suitable standard vocabularies are not available, suitable controlled vocabularies will be created. Connections to already existing ontologies and public databases will be assured. D7.1 DMP I page 20 of 25 A qualified reference is a cross-reference that explains its intent. For example, X is regulator of Y is a much more qualified reference than X is associated with Y, or X see also Y. The goal therefore is to create as many meaningful links as possible between (meta)data resources to enrich the contextual knowledge about the data (Source: https://www.go-fair.org/fair-principles/i3-metadata-include-qualified-referencesmetadata/). This will be assessed at a later stage of the project. 3.4 Increase data re-use Partners are encouraged to publish research data as supporting material with their publications to facilitate data archiving for future re-use by other projects or research initiatives. New data will be announced on the HortiFoodTrends website and the different profiles in the social media, with links to the page with the dataset on Zenodo. Internally, all partners will be informed by email about the availability of new datasets, including the link to Zenodo. The metadata will be richly described, containing, at minimum, the mandatory terms of DataCite (but strongly recommended to include other recommended terms) and Zenodo enrichments. The project intention is to make as much data as possible re-useable for third parties. Restriction will apply only when privacy, IPR or other exploitations ground are in play. Where this is going to be the case, agreements will be made based on the individual data sets. Requests for the use of the data by externals will be approved by the project consortium. When possible, the data set will be licensed under an Open Access license. Specifically, the project will make use of the CC-BY 4.0, free cultural works license. The particular license applicable to each dataset will be decided on an individual basis, though the recommendation provided by either CC-BY or CC-BY-SA. The data produced in the HortiFoodTrends project will be usable under the selected license (CC0, CC-BY, CC BYNC, CC BY-ND) and uploaded in the open repository Zenodo (ensuring their availability after the project). Dataset published on Zenodo will be retained for the lifetime of the repository, which is currently indicated as indefinitely. Uploaded data files and metadata are backed up on a 12-hourly basis, as well as replicates in multiple copied in the online system. Where relevant, dataset will be made available through the HortiFoodTrends website, which will remain online at least 5 years after the end of the project. The metadata includes information about the studysuch as its purpose, methods, results, and conclusions; title and abstract of research; authors and contributors; keywords, research design, results (findings, statistical analysis, trends, patterns), conclusions, funding sources, data availability, and access. In addition to these types of metadata, it will include the software or tools used, relevant citations, or details about the data processing and analysis and will be based on FAIR metadata standards. 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 done according to the procedure set in point 4.3 of D7.1 (Project Management Guidelines). Data quality in the HortiFoodTrends project will be ensured throughout the lifecycle of the data, including (where applicable): • Accuracy; • Relevance, data meeting the requirements for its intended use; • Completeness, no missing values or records; • Timeless, updated data; • Consistency, data format, and cross-referenceable. The data quality assurance includes three moments: D7.1 DMP I page 21 of 25 • Before data collection takes place – in this stage, the researcher must ensure the data collection process is correct, well understood and appropriate collection methods adopted. • During the data collection process, the researcher must make sure that the process goes smoothly, have a shared understanding and make sure no fake information is provided. • After data collection - in this stage, data cleaning (removing null values, special characters, replacing abbreviation with full words or using the same word, formatting) and storage (the original data). 4 OTHER RESEARCH OUTPUS This section of the plan will be further developed as the project progresses and due consideration will be given depending on the type of output generated, always in line with FAIR principles. 5 ALLOCATION OF RESOURCES Public project deliverables and datasets will be published on the Zenodo repository. These activities will be free of charge. The HortiFoodTrends website can host deliverables and data sets, or their links. Currently, the HortiFoodTrends website has been established for 3 years, and InHort covered the cost of the website. Before the end of the project, the web page will be extended for the forthcoming years. InHort will cover the cost arising from the extension of the period. Partners will use their own budgets to archive personal data in their own repositories, It should be noted that the cost of making the data FAIR includes the fees associated with the publication of scientific articles containing the project data in open access journals and the operation of the project website. The Grant Agreement (Annex 5, page 166) states that "only publication fees in full open access venues for peer-reviewed scientific publications are eligible for reimbursement." It is also highlighted to partners that the EC also offers a no-cost open access, after open science peer review, service. Publications can be submitted for no cost here: https://open-research-europe.ec.europa.eu/. Fees related to the open access scientific publication of the data will be the responsibility of the data partner in compliance with the Grant Agreement. The HortiFoodTrends project has a data management team to ensure project data is shared and stored effectively. It consists of at least 1 member selected by each partner (see Table 4). The project coordinator has the ultimate responsibility for the data management in the project. Data files quality will be checked by the Management Team who will be also responsible of submit them to the repositories. Each Work Package Leader is responsible for the data sets that are used for research within the project. This responsibility can be delegated to the Task leader or researcher responsible for data collection. Table 4: The data management team members Partner Data Management Team InHort Jan Zdulski UCPH Michael Bom Frøst ESA GROUPE Ronan Symoneaux LYFE Agnès Giboreau Long-term preservation of the data will be ensured through the following: • in the open-source repository Zenodo indefinitely (no costs added, all research data under open– access); D7.1 DMP I page 22 of 25 • in all partner's archives for at least 5 years after project completion (all research and non-research data and documents produced in the framework of the HortiFoodTrends project, costs beard by the partner organisations); • in the project website for the duration of the project (all research and non-research data and documents produced in the framework of the HortiFoodTrends project based on level of sensitivity/publicness). 6 DATA SECURITY Data protection is an essential topic for the HortiFoodTrends project consortium. All partners are individually responsible for data security. Encryption and backup are recommended to keep data consistent over the project's lifetime and beyond. If the file gets lost and/or corrupted, it will be replaced with the correct one. Data, metadata and documents will be shared among HortiFoodTrends project partners through Teams and SharePoint Online libraries associated with the O365 platform. Data protection services are provided by Microsoft to prevent the loss of data, within the institutional account of InHort. For any dataset produced, the responsible partner will provide for the measures to be adopted to ensure data security, privacy and ethical considerations. Table 5: Information about storage procedures at the individual partners Partner Procedure InHort relies on Sharepoint drive with an account-based permission system, which restricts access to only members of the consortium that need access to the data. InHort also stores data on the InHort’s intranet, which has access control for data, and automated data backup. UCPH All research data will be collected with digital means that is safe and in compliance with EU GDPR. The only person that will have access to collected data is the data collector Michael Bom Frøst. After collection data be transferred to a password-protected UCPH-Security-drive. Following pseudonymization all data will be stored on personal PC with a password set and will be safely stored so that no one other than these can access it. All collected research data will be shared according to the HortiFoodTrends guidelines after completion of the publication. ESA GROUPE All files are stored and managed on an internal server, "nextCloud.groupe-esa.com," based on the Nextcloud solution. An annual backup copy is made on an external hard drive dedicated to research project backups and stored in the laboratory's safe, accessible by code. For video files, they are stored on the Noldus server and on an external hard drive kept at ESA due to their large size. Personal data is collected for consumer tests and trained panels in accordance with the personal data policy of ESA, in compliance with the GDPR. Informed consent is provided in writing by participants when they register to take part in the test. Only the personal data necessary for the project will be collected and stored on the servers of ESA with secure access to the data protected by a username and password. D7.1 DMP I page 23 of 25 Files containing participants' contact information (name, first name, email, phone) will be collected in a recruitment file. This file will be kept separate from the test result data and will not be shared among project partners or externally. Participants will be identified by an alphanumeric code in the project data file, ensuring their data remains anonymous. Video data is stored on hard drives kept in secured facilities, accessible only by authorized personnel at ESA. LYFE All files are backed up daily, stored on a local server of Institut LYFE. An annual backup copy is made on an external hard drive dedicated to research project backups. Personal data is collected for consumer tests and trained panels in accordance with the personal data policy of LYFE, in compliance with the GDPR. Informed consent is provided in writing by participants when they register to take part in audiovisual recorded test. Only the personal data necessary for the project will be collected, if needed and stored after pseudonymization on LYFE several with secure access to the data protected by a username and password. Raw data will be curated with appropriate meta information, statistically/bioinformatically analysed and maintained by relevant project partners in appropriate media (in-house servers or cloud-based), and regularly backed-up and recovered. Data, metadata and document sharing between HortiFoodTrends project partners & third parties will be done through non-disclosure agreements (NDA) & material transfer agreements (MTA) in the case of confidentiality issues during exchanging. All personal data obtained in the project will be held strictly confidential and will not be shared with or disclosed to third parties external to the consortium without the knowledge and permission of any concerned data subjects, unless the consortium is legally required to do so. (E.g. by the European Commission or for law enforcement purposes). Personal data will be stored on secured servers and will be only accessed by authorized and assigned members of the project. Each partner is responsible for implementing and executing safe practices for sensitive data. The HortiFoodTrends project recommends the following restrictions to the storage and transfer of sensitive data: • For information in digital format: o Must be stored only on hard drives with encryption, such as BitLocker. o Transfer of sensitive data to occur only via Microsoft SharePoint, by encrypted email, or equivalently safe transfer method. • For information in paper format: o To be stored in a locked cabinet or office, with only a restricted, known number of people have access to it. Additionally, HortiFoodTrends partners will ensure data security following common guidelines: • Store data in at least two different locations to prevent data loss: o Back up data in the secondary/alternative open repository. o Back up data in respective institution database (or space) and external hard disks. • Enabling firewalls in computers/laptops and regularly updating antivirus/malware software. • Limit the use of flash drives (i.e. USB). Label files systematically to ensure consistency of the final dataset. D7.1 DMP I page 24 of 25 All data (without restrictions, confidentiality and/or sensitive ones) will be stored safely in the open science data repository (Zenodo) and published in open-access journals. Research data stored in Zenodo is securely stored on CERN premises. Each file copy has two replicas located on different disk servers. 7 ETHICS The HortiFoodTrends project will adopt the Ethics and Gender Guidelines (D7.4) in M6, and the revised version of DMP will be aligned with its provisions. The Ethics and Gender Guidelines will include ethical and privacy protection provisions. However, no legal issues are foreseen. If during the research activity carried out during the project duration (and beyond) ethical issues arise, the Grant Agreement provisions and the Ethical and Gender Guidelines of the HortiFoodTrends project provisions will be respected. In addition, with research information gaining more structure and substance, any ethical and legal issues will be addressed in the revised in the first update of DMP (D7.5) in the 17th month of the project. The HortiFoodTrends project will request informed consent for collecting, sharing and re-using the data gathered throughout the planned research activity of all partners involved. Personal data processing by HortiFoodTrends project in the research activity will be conducted in respect of Article 15, "Data Protection" of the Grant Agreement. The informed consent will be described in Ethics and Gender Guidelines (D7.4), including participants' right to be informed and consent for participation in research projects (like in surveys, interviews etc.). In order to obtain informed consent for the collection of personal data, the consortium will provide potential participants with sufficient opportunity to consider whether or not to participate in the study by informing them in a timely and accurate manner about the research activities and the consequences (positive and possible risks) of participation. This will be done in such a way that participants are not subject to coercion or undue influence. Information (recruitment information, informed consent, information sheet and questionnaire) will be offered to participants in a language that they fully understand. Furthermore, the informed consent will: 1) Identify the responsible person of the research activity (questionnaire) and the purpose of the research project. Also, contact information (e-mail address, telephone number, and department) will be provided for participants to contact the responsible person for any inquiries or questions about the study or their participation. 2) Describe the aim, the methods, and implication of the research study, in specific details on the research activity in which the participants are involved. 3) Describe the nature of participation, as well as the benefits and possible risks that might follow from participations. 4) Clearly state that participants take part in the research activities on a voluntary basis, and that they have the right to withdraw from participation at any time, for any reason and without any consequences. The participants are encouraged to fill the questionnaire completely and in full truth. 5) Describe how the personal data will be collected, processed, protected during the project, and disposed after the project. Furthermore the information sheet will clearly state the confidentiality of the personal data. Copies of all versions of consent forms and information sheets will be kept on file. D7.1 DMP I page 25 of 25 8 CONCLUSIONS This Deliverable D7.2 presents the Data Management Plan (DMP) of the HortiFoodTrends project. Guidelines are provided for informing and supporting project partners to ensure that partners understand the need, and process to take to fulfil this DMP during their research.