Deliverable D7.1 PARC Data Management Plan V1.0 (DMP) WP 7.1

Deliverable/Additional deliverable template EUROPEAN PARTNERSHIP This partnership has received funding from the European Union’s Horizon Europe research and innovation programme under Grant Agreement No 101057014 PARC — HORIZON-HLTH-2021-ENVHLTH-03 Contract No. 101057014 Partnership for the Assessment of Risks from Chemicals Deliverable D7.1 PARC Data Management Plan V1.0 (DMP) WP 7.1 D7.1: PARC Data Management Plan V1.0 Dissemination level: PU WP7: FAIR Data Version: 1.0 Main Authors: Fred van de Brug (TNO), Rob Stierum (TNO) Page: 2 EUROPEAN PARTNERSHIP This partnership has received funding from the European Union’s Horizon Europe research and innovation programme under Grant Agreement No 101057014 Technical References D7.1: PARC Data Management Plan V1.0 Dissemination level: PU WP7: FAIR Data Version: 1.0 Main Authors: Fred van de Brug (TNO), Rob Stierum (TNO) Page: 3 EUROPEAN PARTNERSHIP This partnership has received funding from the European Union’s Horizon Europe research and innovation programme under Grant Agreement No 101057014 Work Package WP 7 - FAIR Data Task T 7.1 - PARC FAIR Data Policy (PFDP) and DMP Dissemination level 1 PU Lead Beneficiary / Responsible AE Lead Beneficiary: RIVM / Responsible AE: TNO Contributing Participants EAA (AT), UG-PL (PL), VITO (BE), UBA (DE), UU-IRAS (NL), ANSES (FR), MU (CZ), ISS (IT), EV-ILVO (BE), UZIS (CZ), AUTH (EL), BRGM (FR), JSI (SI), UL-LACDR (NL), KWR (NL), IMM-KI (SE) Responsible author(s) Fred van de Brug / TNO / [email protected] Rob Stierum / TNO / [email protected] Co-authors Sandrine Fraize-Frontier / ANSES / [email protected] Olivier Frezot / BRGM / [email protected] Barbara Magagna / GFF / [email protected]oundation Erik Schultes / GFF / [email protected] Cecilia Bossa / ISS / [email protected] Penny Nymark / KI / [email protected] Tessa Pronk / KWR / Te[email protected] Katarína Řiháčková / MU / [email protected] Lucie Bielska / MU / [email protected] Richard Hulek / MU / [email protected] Jildau Bouwman / TNO / [email protected] Sylvia Le Dévédec / UL-LACDR / [email protected] Iseult Lynch / University of Birmingham / I.Lync[email protected].uk Ondřej Májek / UZIS / [email protected] Jan Theunis / VITO / [email protected] Sylvie Remy / VITO / [email protected] Peter Von der Ohe / UBA / [email protected] Ivana Huskova / NIVA / [email protected]o D7.1: PARC Data Management Plan V1.0 Dissemination level: PU WP7: FAIR Data Version: 1.0 Main Authors: Fred van de Brug (TNO), Rob Stierum (TNO) Page: 4 EUROPEAN PARTNERSHIP This partnership has received funding from the European Union’s Horizon Europe research and innovation programme under Grant Agreement No 101057014 1 PU = Public PP = Restricted to other programme participants (including the Commission Services) RE = Restricted to a group specified by the consortium (including the Commission Services) CO = Confidential, only for members of the consortium (including the Commission Services) Reviewers RIVIERE Gilles / anses / [email protected] SANDERS Pascal / anses / [email protected] nina.vogel / rivmnl / [email protected] Achilleas Karakoltzidis / auth / [email protected] Susana Pedraza-Diaz, ISCIII (Invitado) Emma Westerholm / kemi / [email protected] Ivana Huskova / niva / Ivana.[email protected] Uhl Maria / umweltbundesamt / [email protected] Kirsten Baken / vito / [email protected] Liese Gilles / vito / [email protected] Urban Boije af Gennäs / kemi / Urban.BoijeafG[email protected] Phillipp Schmidt (UBA) (Guest) Marlene Ågerstrand (Stockholm University) (Guest) Margaux RIOU / santepubliquefrance / [email protected] Ngo Ondřej Mgr. / mzcr / [email protected] Maria João Silva / insa / [email protected] ROUSSELLE Christophe / anses / [email protected] Anja Duffek (UBA) / Umweltbundesamt316 / Sónia Namorado / insa / sonia.[email protected]n-saude.pt Henriqueta Louro / insa / [email protected] Lina Wendt - Rasch / kemi / [email protected] Maria João Silva (Convidado) Nancy Georgiou (Guest) Due date of deliverable 31 10 2022 Actual submission date 19 10 2022 D7.1: PARC Data Management Plan V1.0 Dissemination level: PU WP7: FAIR Data Version: 1.0 Main Authors: Fred van de Brug (TNO), Rob Stierum (TNO) Page: 5 EUROPEAN PARTNERSHIP This partnership has received funding from the European Union’s Horizon Europe research and innovation programme under Grant Agreement No 101057014 Document history Version Date Reviewer name/institution Short description of changes 0.5 2022-09-16; 202209-23 Online workshops with WP71 partners, GFF. MS Teams participants are listed as coauthors in the Technical References table. Initial draft created by TNO. Changes: Elaboration of context (pre-amble), work processes, numerous refinements. 1.0 2022-09-30 Shared with WP leaders / MB on September 30, 2022 and online MS Teams meeting on October 10, 2022. MS Teams participants are listed as reviewers in the Technical References table. Comments communicated during the Teams meeting with MB (WP9 training support, elaboration on roles, definition of domain, one invalid link) are resolved on 2022-10-17. The new version has timestamp date in document name of 20221017. D7.1: PARC Data Management Plan V1.0 Dissemination level: PU WP7: FAIR Data Version: 1.0 Main Authors: Fred van de Brug (TNO), Rob Stierum (TNO) Page: 6 EUROPEAN PARTNERSHIP This partnership has received funding from the European Union’s Horizon Europe research and innovation programme under Grant Agreement No 101057014 Abstract PARC is the European partnership for the development of next generation chemical risk assessment methods. In the context of Open Science, the PARC objective OO10 is to implement FAIR data practices and enhance innovation in complex data analysis for chemicals risk assessment. PARC will develop a FAIR data culture enabling open science and provide the technical methods, tools and infrastructure for effective exchange of data and information. Innovation in risk assessment and risk management which is required for the transition to next generation risk assessment can only be achieved in an open and collaborative way. FAIR and open data sharing according to the ‘as open as possible, as closed as necessary’ principle will be the default for PARC and will be applied to all research outputs (including reports, data, software, guidelines, method descriptions, formats, templates, semantic artefacts, ontologies, vocabularies etc.). This deliverable presents version 1 of the PARC overarching data management plan (DMP). The domain specific guidance in version 1 is based on the analysis of DMP’s of a selection of projects covering research data domains PARC is concerned with: exposome research, biomonitoring, innovations in (eco-)toxicological hazard assessment, e.g., via in vitro new approach methodologies, nanomaterial safety and metabolism disrupting chemicals. The DMP contains guidance to support PARC researchers in multiple ways: by providing additional information; by providing generic texts and by suggesting possible domain specific or generic choices. Keywords Data management plan, FAIR, risk assessment, open science, exposome, biomonitoring, hazard assessment, nanomaterials, safety “Funded by the European Union. Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union or the Health and Digital Executive Agency. Neither the European Union nor the granting authority can be held responsible for them.” D7.1: PARC Data Management Plan V1.0 Dissemination level: PU WP7: FAIR Data Version: 1.0 Main Authors: Fred van de Brug (TNO), Rob Stierum (TNO) Page: 7 EUROPEAN PARTNERSHIP This partnership has received funding from the European Union’s Horizon Europe research and innovation programme under Grant Agreement No 101057014 Table of contents Technical References ......................................................................................................... 2 Document history ............................................................................................................. 5 Abstract ............................................................................................................................ 6 Key Words ........................................................................................................................ 6 Table of contents .............................................................................................................. 7 Glossary............................................................................................................................ 9 Preamble to PARC Initial Data Management Plan ........................................................... 12 Introduction .................................................................................................................... 13 1. Data Summary ........................................................................................................ 16 1.1 What is the purpose of the data collection/generation and its relation to the objectives of the project? ........................................................................................................................... 16 1.1.1 The overarching PARC DMP and the relationship with project-level DMPs ....................................... 17 1.2 What types and formats of data will the project use? ........................................................ 20 1.2 Which data(sets) will the project generate or collect? ........................................................ 21 1.3 Will you re-use any existing data and how? What is the origin of the data? ........................ 22 1.4 What is the expected size of the data? .............................................................................. 22 1.5 To whom might it be useful ('data utility')?........................................................................ 23 2. FAIR data .................................................................................................................... 24 2. 1. Making data findable, including provisions for metadata ................................................. 24 2.1.1 Are the data produced and/or used in the project discoverable with metadata, identifiable and locatable by means of a standard identification mechanism (e.g. persistent and unique identifiers such as Digital Object Identifiers)? ........................................................................................................................... 24 2.1.2 What naming conventions for files do you follow? ............................................................................ 24 2.1.3 Will search keywords be provided that optimize possibilities for re-use? ......................................... 25 2.1.4 Do you provide explicit machine-readable version numbers? ........................................................... 25 2.1.5 What metadata will be created? In case metadata standards do not exist in your discipline, please outline what type of metadata will be created and how. ........................................................................... 25 2.2. Making data openly accessible ......................................................................................... 27 2.2.1 Which data produced and/or used in the project will be made openly available as the default? ..... 27 2.2.2 How will the data be made accessible (e.g. by deposition in a repository)? ...................................... 28 2.2.3 What methods or software tools are needed to access the data? .................................................... 28 D7.1: PARC Data Management Plan V1.0 Dissemination level: PU WP7: FAIR Data Version: 1.0 Main Authors: Fred van de Brug (TNO), Rob Stierum (TNO) Page: 8 EUROPEAN PARTNERSHIP This partnership has received funding from the European Union’s Horizon Europe research and innovation programme under Grant Agreement No 101057014 2.2.4 Is documentation about the software needed to access the data included? .................................... 28 2.2.5 Is it possible to include the relevant software (e.g. in open source code)? ....................................... 28 2.2.6 Where will the data and associated metadata, documentation and code be deposited? ................. 28 2.2.6 Have you explored appropriate arrangements with the identified repository? ................................ 29 2.2.7 If there are restrictions on use, how will access be provided? ........................................................... 29 2.2.8 Is there a need for a data access committee? .................................................................................... 29 2.2.9 Are there well described conditions for access (i.e. a machine readable license)? ........................... 29 2.2.10 How will the identity of the person accessing the data be ascertained? ......................................... 29 2.3. Making data interoperable ....................................................................................... 30 2.3.1 Are the data produced in the project interoperable? ........................................................................ 30 2.3.2 What data and metadata vocabularies, standards or methodologies will you follow to make your data interoperable? ..................................................................................................................................... 30 2.3.4 Will you be using standard vocabularies for all data types present in your data set, to allow interdisciplinary interoperability? ....................................................................................................................... 32 2.3.5 In case it is unavoidable that you use uncommon or generate project specific ontologies or vocabularies, will you provide mappings to more commonly used ontologies? ......................................... 32 2.4. Increase data re-use (through clarifying licences)...................................................... 33 2.4.1 How will the data be licensed to permit the widest re-use possible? ................................................ 33 2.4.2 When will the data be made available for re-use? If an embargo is sought to give time to publish or seek patents, specify why and how long this will apply, bearing in mind that research data should be made available as soon as possible. ............................................................................................................ 33 2.4.3 Are the data produced and/or used in the project useable by third parties, in particular after the end of the project? If the re-use of some data is restricted, explain why. .................................................. 33 2.4.4 How long is it intended that the data remains re-usable? ................................................................. 34 2.4.5 Are data quality assurance processes described? .............................................................................. 34 3. Allocation of resources ................................................................................................ 35 3.1 What are the costs for making data FAIR in your project? .................................................................... 35 3.2 How will these be covered? Note that costs related to open access to research data are eligible as part of the Horizon 2020 grant (if compliant with the Grant Agreement conditions). ............................... 35 3.3 Who will be responsible for data management in your project? .......................................................... 35 3.4 Are the resources for long term preservation discussed (costs and potential value, who decides and how what data will be kept and for how long)? .......................................................................................... 35 4. Data security ............................................................................................................... 36 4.1 What provisions are in place for data security (including data recovery as well as secure storage and transfer of sensitive data)? .......................................................................................................................... 36 4.2 Is the data safely stored in certified repositories for long term preservation and curation? ............... 37 5. Ethical aspects ............................................................................................................ 38 5.1 Are there any ethical or legal issues that can have an impact on data sharing or data visiting? .......... 38 5.2 Is informed consent for data sharing and long term preservation included in questionnaires dealing with personal data? ..................................................................................................................................... 38 D7.1: PARC Data Management Plan V1.0 Dissemination level: PU WP7: FAIR Data Version: 1.0 Main Authors: Fred van de Brug (TNO), Rob Stierum (TNO) Page: 9 EUROPEAN PARTNERSHIP This partnership has received funding from the European Union’s Horizon Europe research and innovation programme under Grant Agreement No 101057014 6. Other issues ................................................................................................................ 40 6.1 Do you make use of other national/funder/sectorial/departmental procedures for data management? If yes, which ones? ....................................................................................................................................... 40 Glossary COMPSAFENANO CompSafeNano drives the development of integrated and universally applicable nanoinformatics models, with broad domains of applicability across nanomaterials compositions and forms, that are directly usable by industry, especially SMEs, regulators for risk assessment and decision making (COMPSAFENANO). Data Champion Data Champions are appointed by each PARC project to “champion” the need for proactive data management and feed issues that arise in the data management back to WP7. FAIR data champions are scientific experts and are handson in the field of FAIR data. The Champions work as FAIR ambassadors, sharing FAIR implementation stories, enhancing synergies, contributing to training activities and webinars, and encouraging crossdomain engagement with FAIR (see also section 1.1.1 in this document and the WP7 Role description). Data Liaison Data Liaisons from within WP7 have overarching data management knowledge (and receive additional training) and preferably domainspecific knowledge that are appointed to “support” the PARC projects and Data Champions in developing their data management plans. The data liaison is a key person in use case identification based on the PARC project descriptions of the WPs 4,5,6 and 8 (see also section 1.1.1 in this document and the WP7 Role description). Data Steward The research data steward, positioned at the research institution, supports and works in close collaboration with the main data producers and users in academia: the researchers, ranging from undergraduate students to full professors. The data steward advises researchers, makes sure data is handled in a manner compliant with the institute’s policy and may also perform hands-on work in a project (ELIXIR). Domain The term domain refers to the scientific domain of activity, e.g., environmental monitoring versus human biomonitoring, toxicity / ecotoxicity, etc. If projects are clustered per scientific domain, then the term domain closely aligns with a cluster of projects. D7.1: PARC Data Management Plan V1.0 Dissemination level: PU WP7: FAIR Data Version: 1.0 Main Authors: Fred van de Brug (TNO), Rob Stierum (TNO) Page: 16 EUROPEAN PARTNERSHIP This partnership has received funding from the European Union’s Horizon Europe research and innovation programme under Grant Agreement No 101057014 1. Data Summary Points to be addressed Provide a summary of the data addressing the following issues: • State the purpose of the data collection/generation • Explain the relation to the objectives of the project • Specify the types and formats of data generated/collected • Specify the existing data that is being re-used • Specify the origin of the data • State the expected size of the data (if known) • Outline the data utility: to whom will it be useful • Metadata preservation policy as requested by Principle A2 (not mentioned below)?! 1.1 What is the purpose of the data collection/generation and its relation to the objectives of the project? PARC’s general objective is to consolidate and strengthen the EU's R&I capacity for chemical RA to protect from impacts on human health, biodiversity and human and natural environments at large. Linked to this general are three specific objectives (SO), around which the 9 Work Packages (WP) of PARC are structured and for which 13 realistic, measurable, achievable and verifiable operational objectives (OO) are developed. The WPs and OOs are further described in Part B of the Project Proposal. PARC’s first specific objective (SO1) is that EU and national risk assessors and regulatory entities come together with the scientific community in a cross-disciplinary network to set priorities for R&I in chemical RA. The implementation of FAIR data and services as directed under this DMP can foster these discussions. Specific objective 2 (SO2) is that European and national RA entities and their scientific networks carry out a joint R&I programme to respond to the agreed priorities in chemicals RA. In this programme, various types of data are being generated, from existing and novel technologies (e.g., on hazard assessment/toxicology, exposure assessment, risk assessment, see in detail 1.2). The purpose of this data collection is to help the development of innovative methods for risk assessment that can be ultimately applied for regulatory purposes. This will allow to speed up the risk assessment process, and to potentially use less animal data. Also, some datatypes are generated to fill in data gaps, e.g., conventional in vivo toxicology data. Specific objective 3 (SO3) is that European risk assessors, their scientific network and the wider stakeholder community have access to the R&I capacities required to implement innovative chemical RA. The implementation of collected data compliant with the PARC DMP will result in an increase in the FAIRness of chemical risk assessment related data, thus the potential access to and interoperability among the data generated within PARC. D7.1: PARC Data Management Plan V1.0 Dissemination level: PU WP7: FAIR Data Version: 1.0 Main Authors: Fred van de Brug (TNO), Rob Stierum (TNO) Page: 17 EUROPEAN PARTNERSHIP This partnership has received funding from the European Union’s Horizon Europe research and innovation programme under Grant Agreement No 101057014 1.1.1 The overarching PARC DMP and the relationship with project-level DMPs Given the size and scale of PARC, much of the research will be performed with smaller units of fixed duration, referred to as projects. These are developed within (or across) WPs and reviewed for the fit to the overall PARC regulatory remit by WP2 and then supported in the development and implementation of their project-level DMPs. These DMPs are specific to the type and scale of data being generated in the project, and aligned to the domain-specific norms and standards (where these exist). A key aspect of the initial PARC DMP (this deliverable) is to lay out the process for integration and alignment of Data Management into the projects. Here we lay out the details of this process (see Figure 1 and Figure 2), the roles and responsibilities within WP7 and with the PARC projects, and the initial process agreed for development, review, updating and alignment of project-level DMPs and the overarching PARC DMP. Figure 1: Summary of the process for development, review, approval and implantation of PARC projects, and the involvement of WP7 in the development, review and updating of the project-level DMP. The roles of Data Champion and Data Liaison are described briefly in the glossary, and a fuller description is presented below. D7.1: PARC Data Management Plan V1.0 Dissemination level: PU WP7: FAIR Data Version: 1.0 Main Authors: Fred van de Brug (TNO), Rob Stierum (TNO) Page: 18 EUROPEAN PARTNERSHIP This partnership has received funding from the European Union’s Horizon Europe research and innovation programme under Grant Agreement No 101057014 Figure 2: Schematic illustration of the relationship between the PARC Data Policy, the overarching PARC DMP and the project-level DMPs. We define also a set of Domains of relevance to PARC, e.g., environmental and human exposure, environmental and human hazard, chemical properties, omics data, computational data etc., and the clustering of projects within and across these domains to help ensure harmonisation of the project-level and PARC DMPs. Additionally, we foresee the development of Fair Implementation Profiles (FIPs) initially at the domain level, and for the Use-cases that will be defined within WP7. Specifically, in an interactive process (Figure 3), PARC project leaders are required to describe the data management section of the project initiation template, and to communicate in an early stage to WP7 for all projects for which data will be generated or used, if reuse is foreseen (for input into 1.3), what the expected data size is (for input into 1.4) and to whom the data may be useful (for input into 1.5). WP7 assigns a Data Liaison to the project, and PARC project leaders (preliminarily) assign a Project Data Responsible (“Data champion”). This person organizes an exploratory meeting with the Data Liaison (for which we can use (parts of) the actual data management template as guide for the discussion) and includes conclusions of this exploratory meeting in the Project Initiation Template. These will serve the input towards this DMP. Figure 3: The iterative process by which PARC projects leads communicate on data types, data formats, data reuse, data size and data utility within their project / task /WP and with WP7. D7.1: PARC Data Management Plan V1.0 Dissemination level: PU WP7: FAIR Data Version: 1.0 Main Authors: Fred van de Brug (TNO), Rob Stierum (TNO) Page: 19 EUROPEAN PARTNERSHIP This partnership has received funding from the European Union’s Horizon Europe research and innovation programme under Grant Agreement No 101057014 The roles and responsibilities of the Data Champion, Data Liaison and Fair Implementation Task group are defined, and the processes for how they are working is being defined. (Please see the roles and responsibilities via the link for now, as WP7 partners are still refining this – the polished version will be dropped in here when ready). Figure 4 describes the tasks, as currently defined, for the Data Champion, Data Liaison and FAIR Implementation Taskgroup (FIT). A key aspect of each of the 3 data support roles in PARC is that they will receive FAIR awareness training and be upskilled in data management. WP9, task 9.4, will support the organisation of training regarding data (re)use (WP7). Figure 4: This figure describes the tasks, as currently defined, for the Data Champion, Data Liaison and FAIR Implementation Taskgroup (FIT). The GO FAIR Foundation (GFF) has been subcontracted by PARC to provide training for the Data Champions, Data Liaisons and to establish the FAIR Implementation Task group. GFF have developed a Three-point FAIRification Framework that begins with a local data producer (e.g., university, hospital) deciding on a range of data policy issues and metadata descriptions needed to ensure FAIRness. These metadata are then rendered machine-actionable in (M4M) workshops. The reusable metadata schemata produced in the M4M compose part of the larger FAIR Implementation Profile (FIP), which in turn guides the configuration of the FAIR Data Point (see Figure 5). D7.1: PARC Data Management Plan V1.0 Dissemination level: PU WP7: FAIR Data Version: 1.0 Main Authors: Fred van de Brug (TNO), Rob Stierum (TNO) Page: 20 EUROPEAN PARTNERSHIP This partnership has received funding from the European Union’s Horizon Europe research and innovation programme under Grant Agreement No 101057014 Figure 5: Schematic illustration of the Go FAIR Foundation 3-point implementation that will be applied in PARC through training of the FAIR Implementation Task group members (who can also be Data Liaisons or Data Champions) via the M4M workshops and through the development of FAIR Implementation Plans (FIPs) for each domain involved in PARC and for each of the WP7 use cases. Data liaisons and data champions are key in making data FAIR. Data will be made FAIR at the source whenever possible, described by FAIR metadata, and associated with a persistent identifier. Specific use cases will enable this FAIRification process to be developed gradually and pragmatically. Not all use cases have been identified yet. The process of use case identification (Tasks 7.2.3 & 7.1.3) starts from the project planning phase by analysing and clustering the project initiation plans to the description of use cases. The data management sections in the project plans will point the attention to different data management aspects related to data generated in PARC as well as to use of data from external sources. The use cases will be formalised as PARC projects. 1.2 What types and formats of data will the project use? The FAIR Principles require to provide both the data and its metadata. Ideally both should be rendered machine-actionable, but metadata should describe the format of the datasets in whatever form they take, whether they are machine-ready or not. Examples of most commonly used data/file formats can be found here (https://en.wikipedia.org/wiki/List_of_open_file_formats) and here: data formats in bioinformatics (Lapatas et al, 2015). During the PARC project, data champions, data liaisons and data stewards should try to identify/determine in which specific domains standards are lacking and where there is a need for convergence to standards. The various forms or categories of data to be generated and collected include: raw or experimental data, derived (processed, computed, or computational) data and data associated with formal publications. Data types can be: cohort data, molecular data, occurrence data (including suspect and non-targeted screening data), occupational exposure data, hazard effect data ((eco-)toxicological endpoints for risk assessment), D7.1: PARC Data Management Plan V1.0 Dissemination level: PU WP7: FAIR Data Version: 1.0 Main Authors: Fred van de Brug (TNO), Rob Stierum (TNO) Page: 21 EUROPEAN PARTNERSHIP This partnership has received funding from the European Union’s Horizon Europe research and innovation programme under Grant Agreement No 101057014 modelling data (data to develop and validate in silico models, model output data), biological measurement data (including omics data), clinical data, health outcome data, data from questionnaires, pathway data, additional data (e.g., from grey literature, research paper). In terms of size and heterogeneity of the data generated, PARC is extensive. It is expected that various biomonitoring and environmental and occupational exposure data (WP4), hazard data (WP5) (in vitro, in vivo, in silico) and data emerging from novel risk assessment models (WP6) will be produced. Right now, it is not possible to fully describe all data in full detail, as PARC projects, in which these data will be generated, are just being conceived, but we are implementing a mechanism in WP7 to achieve the required level of detail on the types of data being used. For illustration, exemplary data types and potential details, are listed in Table 1, including the reuse of information from existing DMPs. Type of data Give details on category of data (e.g., database, excel file, etc.), data format (e.g., csv, xls, etc.), and how the data will be generated/collected. Data of measurements in biological matrices Raw data (measurement data) output files from instruments will be MS Excel compatible file formats (CSV, txt and proprietary software files (for suspect screening data)) (HBM4EU DMP) Sensor based data for chemical occurrence Expected to be mainly CSV compatible. (EPHOR DMP, NORMAN DCT) Data from in vitro assay experiments where a biological sample (e.g., cell line) is exposed to a chemical Omics data in proprietary format as well as CSV compatible (RiskHunt3r DMP). Table 1: Examples of data types and data formats , the method by which data are generated. More details will follow from the projects DMPs. 1.2 Which data(sets) will the project generate or collect? Please specify here which datasets you are going to use, either based on existing data or those that are being created in PARC. Type of data Specific datasets Description of data Data of measurements in biological matrices <not known yet> Measurements in urine, sputum, blood, hair from human subjects D7.1: PARC Data Management Plan V1.0 Dissemination level: PU WP7: FAIR Data Version: 1.0 Main Authors: Fred van de Brug (TNO), Rob Stierum (TNO) Page: 22 EUROPEAN PARTNERSHIP This partnership has received funding from the European Union’s Horizon Europe research and innovation programme under Grant Agreement No 101057014 Sensor based data for chemical occurrence WFD surface water Monitoring data - 2017 (France) Surface water measurements Data from in vitro assay experiments where a biological sample (e.g., cell line) is exposed to a chemical List of endpoints for PPP Hazard assessment to fill in gaps Table 2: Example of datasets that the project will generate or collect. 1.3 Will you re-use any existing data and how? What is the origin of the data? If applicable, state any constraint with reason on the re-use of existing data. During the PARC projects these details will emerge and will be used to populate the table. Explain why re-use of existing data is not feasible and data needs to be generated in this project. Type of data Dataset (Origin of the data) Give details on any constraints. If applicable, explain why re-use of data is not possible and the project will generate the data. Personal exposure data GDPR Data at ECHA uploaded by companies Confidential data Data from unfinished research Data related to concept documents which are not yet published Chemical occurrence data WFD surface water Monitoring data - 2017 (France) Publicly available Table 3: Examples of data types, their origin and description of the constraints associated with the data sets. 1.4 What is the expected size of the data? Type of Data Size estimation (if known) D7.1: PARC Data Management Plan V1.0 Dissemination level: PU WP7: FAIR Data Version: 1.0 Main Authors: Fred van de Brug (TNO), Rob Stierum (TNO) Page: 23 EUROPEAN PARTNERSHIP This partnership has received funding from the European Union’s Horizon Europe research and innovation programme under Grant Agreement No 101057014 Data of measurements in biological matrices The expected size the crude data size will be about 2 GB per study Sensor based data for chemical occurrence An indication: for chemical concentrations from passive sampling wrist band: 100-500 GB Data from in vitro assay experiments where a biological sample (e.g., cell line) is exposed to a chemical About 1-1.5 GB Table 4: Examples of data types and the estimation of the size if this can be known in advance. 1.5 To whom might it be useful ('data utility')? Generic: Scientists will be enabled to use and enhance the data, methods and models in innovative RA research. Regulators (e.g. ECHA, EMA, EEA, EFSA), policy makers and risk assessors within the EU will use the data for the development of evidence-based policies and risk advice. Given PARC’s remit to support regulatory risk assessment of chemicals, key stakeholders or end-users for the PARC data and models are the EU regulatory agencies (ECHA, EFSA etc.) and the member state regulatory organisation, including those involved in the PARC project. Scientists (toxicologists, ecotoxicologists, modellers, risk assessors etc.) within and beyond PARC will also be major users of the datasets generated in, and harmonised by PARC. D7.1: PARC Data Management Plan V1.0 Dissemination level: PU WP7: FAIR Data Version: 1.0 Main Authors: Fred van de Brug (TNO), Rob Stierum (TNO) Page: 24 EUROPEAN PARTNERSHIP This partnership has received funding from the European Union’s Horizon Europe research and innovation programme under Grant Agreement No 101057014 2. FAIR data Points to be addressed In general terms, your research data should be 'FAIR', i.e. that it is findable, accessible, interoperable and re-usable by humans and also by machines. These principles precede implementation choices and do not necessarily suggest any specific technology, standard or implementation-solution. Note that, in general, the FAIR elements in “F”,”A”,”R” provide benefits beyond the project scope, whereas the elements in “I” can also directly enhance data integration benefits across PARC projects. 2. 1. Making data findable, including provisions for metadata Metadata are values or texts that provide a description of data of interest. As one use, metadata can describe a complete dataset (e.g. datatype, author, period, subject). As another use, metadata can describe individual data points or samples for data selection and interpretation. Points to be addressed. • Outline the discoverability of data (metadata provision) • Outline the identifiability of data and refer to standard identification mechanism. Do you make use of persistent and unique identifiers such as Digital Object Identifiers? • Outline naming conventions used • Outline the approach towards search keyword • Outline the approach for clear versioning • Specify standards for metadata creation (if any). If there are no standards in your discipline describe what metadata will be created and how 2.1.1 Are the data produced and/or used in the project discoverable with metadata, identifiable and locatable by means of a standard identification mechanism (e.g. persistent and unique identifiers such as Digital Object Identifiers)? Generic: Data sets are stored with metadata and DOIs (via available PID services, e.g. ePIC, or via trusted data repositories, publishers), which will be available after publication of the paper. If possible, metadata are published preceding the publication of the actual data. The data related to the publications are findable by users via the repository website via a metadata search and/or as outlined in a Data Availability Statement. Entities, such as chemical names and gene names, will be used with their standard global identifier. In case chemicals or metabolites are used which do not have a global identifier, the European Registry of Materials, which was introduced by the NanoCommons project, may be a solution to assign a unique registry number. 2.1.2 What naming conventions for files do you follow? Generic: The general advise is: files should be named consistently per project. File names should be short but descriptive (<25 characters). Use capitals and underscores instead of periods or spaces or slashes, no special characters or spaces. Use date format: YYYYMMDD as suffix. A version number or the term “FINAL” in the suffix will be included. Build filenames from general elements to specific. D7.1: PARC Data Management Plan V1.0 Dissemination level: PU WP7: FAIR Data Version: 1.0 Main Authors: Fred van de Brug (TNO), Rob Stierum (TNO) Page: 25 EUROPEAN PARTNERSHIP This partnership has received funding from the European Union’s Horizon Europe research and innovation programme under Grant Agreement No 101057014 A possible format can be: PARC_WPn_Short_name_Version_YYYYMMDD. The file names containing the key towards pseudonymisation should not contain personal information. Concerning the hierarchical file folder structure (as in Windows), it is advised not to extend these towards too deeply nested structures as this may impair the retrieval of files with too long path names. If possible, semantic file systems are used for information persistence. 2.1.3 Will search keywords be provided that optimize possibilities for re-use? Generic: Keywords will be used as a standard. Aside free text, researchers will be encouraged to use as much as possible keyword terms listed within controlled vocabularies and metadata. Researchers should also know that increasingly research outputs in repositories are indexed automatically by assigning keywords. 2.1.4 Do you provide explicit machine-readable version numbers? Explain how the versioning is done, depending on the research environment (e.g. SharePoint, MySQL, iRODS\YODA, other such as GitHub). Generic: The following versions of the data will be recoded as a new version: 1. The raw data as collected 2. The pre-processed data as the basis for analyses (clean data). There can be more versions here. 3. The processed data used for a publication (for example peer-reviewed or report) 2.1.5 What metadata will be created? In case metadata standards do not exist in your discipline, please outline what type of metadata will be created and how. Per PARC domain specific choices will be made. Projects are suggested the following. The Digital Curation Centre and RDA maintain an overview of metadata standards covering a wide range of research domains and general purposes. Here below a list with generic and PARC specific examples which will be updated during the PARC project. The inventory of relevant metadata schema’s will also evolve from the interaction with the PARC projects. In addition to currently established metadata standards, there are initiatives towards developing standards for specific purposes such as nanomaterials. Metadata schema Purpose Examples of use DataCite Metadata Generic metadata schema. A set of mandatory metadata that must be registered with the DataCite Metadata Store when minting a DOI persistent identifier for a dataset. For citation and retrieval purposes. OpenAire, Zenodo, iRODS D7.1: PARC Data Management Plan V1.0 Dissemination level: PU WP7: FAIR Data Version: 1.0 Main Authors: Fred van de Brug (TNO), Rob Stierum (TNO) Page: 32 EUROPEAN PARTNERSHIP This partnership has received funding from the European Union’s Horizon Europe research and innovation programme under Grant Agreement No 101057014 2.3.4 Will you be using standard vocabularies for all data types present in your data set, to allow inter-disciplinary interoperability? Generic: The project will use standard controlled vocabularies when available (e.g., Gene ontology, MESH, International Classification of Disease, Exposure Science Ontology ExO). At present, no specific data and metadata vocabularies are available for the field of Human Biomonitoring. 2.3.5 In case it is unavoidable that you use uncommon or generate project specific ontologies or vocabularies, will you provide mappings to more commonly used ontologies? Generic: As for now, the project will not develop own ontologies or vocabularies. In case it is unavoidable to use uncommon or generate specific ontologies or vocabularies, mappings to more commonly used ontologies will be explored for feasibility. D7.1: PARC Data Management Plan V1.0 Dissemination level: PU WP7: FAIR Data Version: 1.0 Main Authors: Fred van de Brug (TNO), Rob Stierum (TNO) Page: 33 EUROPEAN PARTNERSHIP This partnership has received funding from the European Union’s Horizon Europe research and innovation programme under Grant Agreement No 101057014 2.4. Increase data re-use (through clarifying licences) Points to be addressed • Specify how the data will be licensed to permit the widest re-use possible • Specify when the data will be made available for re-use. If applicable, specify why and for what period a data embargo is needed • Specify whether the data produced and/or used in the project is useable by third parties, in particular after the end of the project? If the re-use of some data is restricted, explain why • Describe data quality assurance processes • Describe the data provenance or data lineage in the metadata or publication/documentation • Specify the length of time for which the data will remain re-usable 2.4.1 How will the data be licensed to permit the widest re-use possible? The EUDAT B2SHARE, Choose a license and Creative Commons License Chooser tools facilitate the selection of an adequate license for research data. Openaire and Elixir guide researchers on how to license research data. Generic: The Creative Commons license will be applied to all data. Ideally the CC-BY0 or CC-BY-SA - so that it can be re-used with the minimum limitations yet ensuring citation. All data will be made Open Access at the point of publication of the manuscripts or earlier where possible. Any open data and open-source tools and models will be complemented and compatible with the licenses agreed with the respective owners, and be indexed in OpenAIRE data. Similarly, any third-party data or tools will be reused with complete respect to their licensing requirements, the conditions specified by the respective owners and with appropriate attribution. 2.4.2 When will the data be made available for re-use? If an embargo is sought to give time to publish or seek patents, specify why and how long this will apply, bearing in mind that research data should be made available as soon as possible. Generic: Data will be made full available with a clear re-use license as soon as possible after generation. Alternatively, data will be made full available at the latest at the time of publication of the research. Data will be made available when no human identifying characteristics are ensured to be present in the disclosed data. 2.4.3 Are the data produced and/or used in the project useable by third parties, in particular after the end of the project? If the re-use of some data is restricted, explain why. Generic: The project data, methods and models will be made available to be used by others. In case of human data, restrictions for data use will be put in place. D7.1: PARC Data Management Plan V1.0 Dissemination level: PU WP7: FAIR Data Version: 1.0 Main Authors: Fred van de Brug (TNO), Rob Stierum (TNO) Page: 34 EUROPEAN PARTNERSHIP This partnership has received funding from the European Union’s Horizon Europe research and innovation programme under Grant Agreement No 101057014 2.4.4 How long is it intended that the data remains re-usable? Generic: The data is intended to be reusable for at least 10 years upon upload to the repository. 2.4.5 Are data quality assurance processes described? Per PARC domain specific choices will be made. Projects are informed on the examples of standards and recommendations to ensure data quality. If standards are lacking data stewards and data liaisons will provide input for the generic DMP. Standard or recommendation for data quality Domain Examples of use Good Laboratory Practice (GLP) General Testing of chemicals (OECD) Good Cell Culture Practice (GCCP) Starting of cell and tissue cultures Testing of chemicals (OECD) Good In Vitro Method Practices (GIVIMP) Developing and implementing celland tissue-based methods Evaluation of chemical safety (OECD) Table 9: Examples of standards and recommendations that ensure data quality, their domains of application and examples of their use. Generic: Data quality assurance processes and standards will be described in the protocols of the studies. The partners involved in this will follow the procedures and will also follow partner-specific guidance and procedures. Reproducibility of the data quality will be maintained by registering both the raw data and the curated data. The scripts used and other data handling steps will be described and documented comprehensively. D7.1: PARC Data Management Plan V1.0 Dissemination level: PU WP7: FAIR Data Version: 1.0 Main Authors: Fred van de Brug (TNO), Rob Stierum (TNO) Page: 35 EUROPEAN PARTNERSHIP This partnership has received funding from the European Union’s Horizon Europe research and innovation programme under Grant Agreement No 101057014 3. Allocation of resources Explain the allocation of resources, addressing the following issues: • Estimate the costs for making your data FAIR. Describe how you intend to cover these costs • Clearly identify responsibilities for data management in your project • Describe costs and potential value of long-term preservation 3.1 What are the costs for making data FAIR in your project? Generic: The costs for making the data FAIR are covered within the PARC project budget as allocated in the grant agreement. See guidance on cost estimation, for projects within PARC, of these Universities: TUDelft, Utrecht University. 3.2 How will these be covered? Note that costs related to open access to research data are eligible as part of the Horizon 2020 grant (if compliant with the Grant Agreement conditions). Generic: Per project, the project leaders and principal scientists are responsible for collection, managing, analysing the various data types. Each partner institute is responsible for storage, back-up, data archiving and sharing. 3.3 Who will be responsible for data management in your project? Generic: Each of partner institutes for each study is responsible for each of the following: data collection, data pre-processing, data publication, data analysis, data sharing, data interoperability. 3.4 Are the resources for long term preservation discussed (costs and potential value, who decides and how what data will be kept and for how long)? Generic: Agreement amongst the partners is needed on long term preservation and the costs associated with this. This will be discussed during the course of the project. D7.1: PARC Data Management Plan V1.0 Dissemination level: PU WP7: FAIR Data Version: 1.0 Main Authors: Fred van de Brug (TNO), Rob Stierum (TNO) Page: 36 EUROPEAN PARTNERSHIP This partnership has received funding from the European Union’s Horizon Europe research and innovation programme under Grant Agreement No 101057014 4. Data security Address data recovery as well as secure storage and transfer of sensitive data 4.1 What provisions are in place for data security (including data recovery as well as secure storage and transfer of sensitive data)? Research performing organisations will have in place data secure research environments. Projects are advised to seek confirmation and describe the institutional data security provisions. When you need technical guidance regarding personal data, here are some links. Statistical packages may have tooling for anonymisation (e.g. aggregation), or consider to use ARX. Guidance to anonymisation and pseudonymisation techniques and best practices can be found at the EU ENISA, UK data service and CESSDA ERIC. This link, to EU ENISA, gives guidance to privacy-preserving computation, transfer and storage. You may describe here the digital/virtual research environment that you are using (for example: MS Sharepoint, iRODS). Microsoft Sharepoint works according to GDPR regulations. Describe where the data, including the key to pseudonymisation, at rest will be stored and back upped and also describe the backup schedule during research activities. It is recommended to store data in least at two separate geographic locations. Give preference to the use of robust, managed storage with automatic backup, such as provided by IT support services of the home institution. Explain how the data will be recovered in the event of an incident. Explain which institutional data protection policies (DPP) are in place. An institutional DPP is mostly an internal document. You may here provide a summary per institution. Explain who will have access to the data during the research and how access to data is controlled, especially in collaborative partnerships. If your data is sensitive for example containing personal data or otherwise sensitive: describe the main risks and how these will be managed. Describe how data in transit (data transfer) is secured by current secure file transfer protocols like the TLS 1.2 standard and/or SFTP. Ask your local security officer for advice. Also consider data visiting versus data sharing under FAIR, as ways to data visiting are being developed. As example: SPHN - Swiss Personalized Health Network (SPHN). D7.1: PARC Data Management Plan V1.0 Dissemination level: PU WP7: FAIR Data Version: 1.0 Main Authors: Fred van de Brug (TNO), Rob Stierum (TNO) Page: 37 EUROPEAN PARTNERSHIP This partnership has received funding from the European Union’s Horizon Europe research and innovation programme under Grant Agreement No 101057014 4.2 Is the data safely stored in certified repositories for long term preservation and curation? Per PARC domain specific choices for repositories will be made. For PARC projects it is advised to check the retention period of items at the repository and document this information in the project DMP. For example, the retention period of items in Zenodo is for the next 20 years at least. D7.1: PARC Data Management Plan V1.0 Dissemination level: PU WP7: FAIR Data Version: 1.0 Main Authors: Fred van de Brug (TNO), Rob Stierum (TNO) Page: 38 EUROPEAN PARTNERSHIP This partnership has received funding from the European Union’s Horizon Europe research and innovation programme under Grant Agreement No 101057014 5. Ethical aspects To be covered in the context of the ethics review, ethics section of DoA and ethics deliverables. Include references and related technical aspects if not covered by the former. A PARC ethics and data protection framework will be developed, within Task 1.4, in parallel and in coordination with the Data Management Plan under WP7 (Task 7.1 Data policy) to ensure that procedures fully respect data management and privacy legislation, and measures are in place to prevent malevolent use of research findings. Future versions of the DMP will refer to the ethics framework. Concerns human data, in vivo data and biological (cell) samples/models. The storage and transfer of data on human subjects is only considered when: • informed consents, • in-country ethical approval of the study and – when applicable – • approval by local data protection authorities cover the purpose that the data are envisaged to be used within PARC and thus allow storage and transfer of individual or aggregated data. All data that are transferred within PARC shall be either pseudonymised or completely anonymised (concerns direct and indirect personal identifiers). Pseudonymous data are considered by the GDPR as personal data while anonymous data are not. The supplying data controller is responsible for the anonymisation or pseudonymisation process and for ensuring that identifiable variables are not transferred to the receiving data controller. For project management purposes and to fulfil the ethics requirements of the project, ANSES or a dedicated PARC partner (to be confirmed) will keep a registry of the data exchanges and of data use. 5.1 Are there any ethical or legal issues that can have an impact on data sharing or data visiting? These can also be discussed in the context of the ethics review. If relevant, include references to ethics deliverables and ethics chapter in the Description of the Action (DoA) or a policy paper which address legal and ethical aspects. Generic: All ethical issues related to the project are described in the Description of Action. In summary, the personal data of any donors will be converted into anonymous research data files. It is therefore not possible to correlate any cells or any experimental data, including genetic information, to the original donor. 5.2 Is informed consent for data sharing and long-term preservation included in questionnaires dealing with personal data? Generic: Informed consents will cover data sharing and long-term preservation. D7.1: PARC Data Management Plan V1.0 Dissemination level: PU WP7: FAIR Data Version: 1.0 Main Authors: Fred van de Brug (TNO), Rob Stierum (TNO) Page: 39 EUROPEAN PARTNERSHIP This partnership has received funding from the European Union’s Horizon Europe research and innovation programme under Grant Agreement No 101057014 D7.1: PARC Data Management Plan V1.0 Dissemination level: PU WP7: FAIR Data Version: 1.0 Main Authors: Fred van de Brug (TNO), Rob Stierum (TNO) Page: 40 EUROPEAN PARTNERSHIP This partnership has received funding from the European Union’s Horizon Europe research and innovation programme under Grant Agreement No 101057014 6. Other issues Refer to other national/funder/sectorial/departmental procedures for data management that you are using (if any). 6.1 Do you make use of other national/funder/sectorial/departmental procedures for data management? If yes, which ones? The procedures for data sharing, and requesting access to use the data are described in the PARC FAIR data policy, which will be developed during the project.