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The main objective of this deliverable is to provide resources that can assist life sciences researchers and data stewards in creating reference Data Management Plans (DMPs) for their research projects. The resources provided in this deliverable are intended to promote good research data management practices across the EU research landscape. To achieve this objective, a range of activities were undertaken, with a particular focus on the needs of the different domains covered by the demonstrator use-cases. In order to create these resources, the RDMkit pages were extended to include domain-specific information, which can be used as a reference when developing DMPs for different research projects. These pages fall under the "Your Domain" category and provide specific information on the data management needs and considerations for each domain. They also highlight challenges that are specific to each domain, such as data types, species, or areas, and offer solutions and considerations to overcome these challenges. In this deliverable, the RDMkit page for the Toxicology data demonstrator use-case was completed and added to the existing RDMkit pages for the other demonstrator use-cases. Additionally, a new Tool Assembly was added to the RDMkit corresponding to the Plant Sciences demonstrator use-case, covering the entire life cycle of experimental plant phenotyping data. In addition, the general Knowledge Models (KMs) of the Data Stewardship Wizard (DSW) were adapted to address the specific DMP questions needed for each demonstrator use-case. The DSW is a collaborative tool that enables data stewards and researchers to efficiently create DMPs for their research projects and it is designed with a hierarchical KM that guides users through the creation of DMPs. Since the relevant information for DMPs can vary across different domains, these KMs can be modified to contain the information relevant for each demonstrator use-case. For this deliverable, special focus was put on two of the demonstrator use-cases, namely Toxicology and Epitranscriptomics data. Additionally, related to the Human Data use-case, separate efforts are underway to enhance the sensitive data section of the KM system to ensure the proper management of such data. The improvements and new question suggestions that were found during these sessions were incorporated into the DSW KM by the DSW team. Furthermore, DMP templates were created in DSW for the demonstrator use-case using two standard approaches: creating a KM or a project template (PT). When creating a PT, a set of answers is saved and can be used to generate a partially pre-filled questionnaire for a new project. In the ideal case scenario, the two methods can be used together to provide domain-specific recommendations by answering questions that better reflect a scientific domain, such as metadata standards. In conclusion, this deliverable provides several valuable resources for life sciences researchers and data stewards, including extended RDMkit pages, customised DSW KMs, domain-specific DMP templates, and a new KM for creating DPIAs. These resources are designed to encourage good research data management practices across the EU research landscape, ensuring that valuable research data is effectively managed before, during, and after a project.

The notion that data should be Findable, Accessible, Interoperable and Reusable, according to the FAIR Principles, has become a global norm for good data stewardship and a prerequisite for reproducibility. Nowadays, FAIR guides data policy actions and professional practices in the public and private sectors. Despite such global endorsements, however, the FAIR Principles are aspirational, remaining elusive at best, and intimidating at worst. To address the lack of practical guidance, and help with capability gaps, we developed the FAIR Cookbook, an open, online resource of hands-on recipes for “FAIR doers” in the Life Sciences. Created by researchers and data managers professionals in academia, (bio)pharmaceutical companies and information service industries, the FAIR Cookbook covers the key steps in a FAIRification journey, the levels and indicators of FAIRness, the maturity model, the technologies, the tools and the standards available, as well as the skills required, and the challenges to achieve and improve data FAIRness. Part of the ELIXIR ecosystem, and recommended by funders, the FAIR Cookbook is open to contributions of new recipes. We thank all book dash participants and recipe authors, as well as the FAIRplus fellows, all partners, and the members of the FAIRplus Scientific Advisory Board, and the management team. In particular we acknowledge a number of colleagues for their role in the FAIRplus project, in particular: Ebitsam Alharbi (0000-0002-3887-3857), Oya Deniz Beyan (0000-0001-7611-3501), Ola Engkvist (0000-0003-4970-6461), Laura Furlong (0000-0002-9383-528X), Carole Goble (0000-0003-1219-2137), Mark Ibberson (0000-0003-3152-5670), Manfred Kohler, Nick Lynch (0000-0002-8997-5298), Scott Lusher (0000-0003-2401-4223), Jean-Marc Neefs, George Papadotas, Manuela Pruess (0000-0002-6857-5543), Ratnesh Sahay, Rudi Verbeeck (0000-0001-5445-6095), Bryn Williams-Jones, and Gesa Witt (0000-0003-2344-706X). This work and the authors were primarily funded by FAIRplus (IMI 802750). PRS and SAS also acknowledge contributions from the following grants (the FAIR Cookbook is also embedded in or connected to): ELIXIR Interoperability Platform, EOSC-Life (H2020-EU 824087), FAIRsharing (Wellcome 212930/Z/18/Z), NIH CFDE Coordinating Center (NIH Common Fund OT3OD025459-01), Precision Toxicology (H2020-EU 965406), UKRI DASH grant (MR/V038966/1), BY-COVID (Horizon-EU 101046203), AgroServ (Horizon-EU 101058020). Peer Reviewed "Article signat per 33 autors/es: Philippe Rocca-Serra, Wei Gu, Vassilios Ioannidis, Tooba Abbassi-Daloii, Salvador Capella-Gutierrez, Ishwar Chandramouliswaran, Andrea Splendiani, Tony Burdett, Robert T. Giessmann, David Henderson, Dominique Batista, Ibrahim Emam, Yojana Gadiya, Lucas Giovanni, Egon Willighagen, Chris Evelo, Alasdair J. G. Gray, Philip Gribbon, Nick Juty, Danielle Welter, Karsten Quast, Paul Peeters, Tom Plasterer, Colin Wood, Eelke van der Horst, Dorothy Reilly, Herman van Vlijmen, Serena Scollen, Allyson Lister, Milo Thurston, Ramon Granell, the FAIR Cookbook Contributors & Susanna-Assunta Sansone"

Status of discussions around the topic of data standard formats for plant sciences and their interoperability at the BioHackathon Europe 2022 in Paris.

EUbOPEN is an international consortium of 22 partners from academia and industry, funded by IMI/IHI. The goal of EUbOPEN is to create a library of compounds binding to 1,000 proteins. These ~5,000 compounds will be well characterized for their ability to interact with human proteins within their native environment, the cell. EUbOPEN is fully committed to Open Science and thus aims to publish all its generated data open and fully accessible to everyone. Therefore, the EUbOPEN project has been working with experts from the FAIRplus project from the start. The IMI FAIRplus project aims to develop tools and guidelines for making life science data FAIR (Findable, Accessible, Interoperable, Reusable). In the past year, the so-called ‘squad teams’ from FAIRplus, consisting of experts working in universities and pharmaceutical companies, have been actively working to FAIRify data sets from large IMI projects such as EUbOPEN, APPROACH, eTOX, and COMBINE. The developed tools and methods are subsequently added as ‘recipes’ to the FAIR Cookbook, enabling projects and companies with similar FAIR data challenges to apply this consolidated know-how to increase the FAIRness of their data.

The discoverability of datasets resulting from the diverse range of translational and biomedical projects remains sporadic. It is especially difficult for datasets emerging from pre-competitive projects, often due to the legal constraints of data-sharing agreements, and the different priorities of the private and public sectors. The Translational Data Catalog is a single discovery point for the projects and datasets produced by a number of major research programmes funded by the European Commission. Funded by and rooted in a number of these European private-public partnership projects, the Data Catalog is built on FAIR-enabling community standards, and its mission is to ensure that datasets are findable and accessible by machines. Here we present its creation, content, value and adoption, as well as the next steps for sustainability within the ELIXIR ecosystem.

FAIRsharinghas been selected by WP1 as the project’s registry of data resources and standards that are part of and used by research infrastructures in EOSC-Life. This deliverable summarises the work of WP6 to deliver a FAIRsharing Collection of these data resources (and associated standards) to ensure these resources are Findable (e.g. discoverable and citable via a DOI), Accessible (e.g. access methods to their data is clearly described), Interoperable (e.g. standards implemented are declared) and Re-usable (e.g. terms of use of the data are made explicit to users). This work is described in detail in D1.2and D1.3, and the EOSC-Life FAIRsharing Collection of 133 records (as of Aug 2022) is publicly available. As this is a live view of the EOSCLife resource ecosystem, a variety of stages of the resource life cycle are displayed. Indevelopment, Ready and Deprecated resources are marked as such allowing users to assess their relevance. In Jan 2022 a newly improved FAIRsharing backend and frontend was also released by the UOXF partner, who also runs and curates the registry. This new version is set to improve the discoverability of and the relationships among the resources, and also offers new RI-centric pages (e.g. ELIXIRand MIRRI). These pages enable attribution at the level of the RIs and the responsible individuals themselves. Each RI page in FAIRsharing showcases its associated repositories, standards and collections and identifies and attributes (via ORCID) related users. The records in the EOSC-Life FAIRsharing Collection are a part of the wider FAIRsharing registry, which is computationally accessible to a wide variety of third-party tools, and which is interoperable with pan-EOSC activities via the FAIRsharing collaboration with OpenAIRE.

FAIRplus has produced four types of technical solutions: FAIRification processes, and adoption of a FAIRification framework FAIR assessment through adoption of the FAIR-DSM maturity model FAIR Cookbook and application of recipes FAIR tooling and data hosting This deliverable report describes how the FAIRplus approach to FAIRification, through a co-production model utilising “squad teams” that run in 3 month cycles and provides frequent checkpoints for evaluating technical feasibility, has ensured technical feasibility of project outputs in each of these four impact areas. This deliverable report described, for each of the four key impact areas, a brief description of the creation of technical solutions, their application to IMI projects and an overview of their evaluation. Based on this evaluation, we quantify the ease with which solutions can be applied and any requirements that may need to be in place prior to adoption of technical solutions from FAIRplus in future projects.

Until now, there has been no standard or generalisable process for the ‘FAIRification’ of any given dataset. As a consequence, over the course of the FAIRplus project, it was necessary to develop such a methodology so that a) any given dataset could be onboarded for FAIRification (IMI or EFPIA); b) passaged through a defined and repeatable process, and c) could be shown to be demonstrably more FAIR at the conclusion of such a process. Originally, we conceived of a process whereby a FAIRplus ‘FAIR-CMMI’ team would collaborate with IMI projects through a number of “Bring your own data” (BYOD) workshops. However, early in the project, we refined our strategy such that these teams were known as ‘squads’ and collaborated closely with IMI projects representatives on an ongoing basis, rather than in discrete one-off workshops, but otherwise executed precisely the same functions (see ‘Background’). We report here the development of a methodology, which has been iteratively refined over the lifetime of the project, to describe how the personnel responsible for the FAIRification will work, essentially providing a ‘user manual’ for those people engaged in the practical work. For the FAIRplus project we formed ‘squads’, inspired by but differing from ‘agile’ and ‘sprint’ practices. Here, we populated teams across project-organisational and reporting boundaries, based on required expertise for specific FAIRification tasks. Squads were fluid in population, with members switching as required for specific tasks, and tactically responsive, with additional teams ‘spun up’ or down as required. Squads worked in 3-monthly ‘release cycles’, targeting specific FAIRification tasks around which they would ‘swarm’. Accompanying the defined steps (e.g. inputs and outputs) of the FAIRification, we have developed a variety of checkpoints and templates to assist in developing a repeatable process. The squads have been an important mechanism in achieving FAIRplus project objectives, being heavily embedded in many major outputs, and acting as a point of contact with other FAIRification stakeholders such as RDMkit and Pistoia Alliance.

The FAIR Cookbook is an online resource with a focus on the Life Sciences with content (a collection of “recipes”) that help users to implement FAIR principles and/or improve the FAIRness of their data. It is a resource for ‘FAIR doers’ and its objectives are to: guide researchers and data stewards in their FAIRification journey, providing hands-on and technical step-by-step examples to follow; provide policy makers and trainers with practical examples to recommend in their guidance and use in their educational material; contribute to enabling the necessary culture change to deliver FAIR in organisations; filling the current gaps between high-level FAIR principles and actual implementation. The FAIR Cookbook currently serves over 70 citable recipes, which provide a broad and in-depth coverage of the technical operational processes allowing to deliver FAIR datasets and build FAIR capacity, as well a set of exemplar FAIRified datasets. The credited contributors include almost 100 researchers and data professionals across pharma and academia, including ELIXIR members not directly involved in FAIRplus. The FAIR Cookbook is offered as a JupyterBook, deployed and hosted on GitHub, with an ELIXIR sub-domain name. This infrastructure provides operational processes from community building to technical procedures such as deployment, identifier minting and editorial decision The FAIR Cookbook integrates the dataset maturity model developed as part of Deliverable D2.2 as well as the Pistoia Alliance’s FAIRtoolkit. Work is in progress to cross-reference recipes to other complementary resources in the ELIXIR ecosystems, such as FAIRsharing (for standards and databases), RDMkit (for high-level signposting of research data management resources). The FAIR Cookbook is an ELIXIR service provided by the UK and the Luxembourg Nodes. Other Nodes are working on adding this resource to their Service Delivery Plan, a step that will contribute towards the sustainability of the FAIR Cookbook as part of the ELIXIR FAIR Service Framework, part of the ELIXIR Interoperability Platform. The membership of the Editorial Board also reflects the role that ELIXIR Nodes play for the future of this resource.&nbsp

The IMI FAIR Data Catalog is being developed as a single point of entry to the diverse range of projects and datasets available in the Innovative Medicine Initiative (IMI), Horizon 2020 (H2020) and other biomedical research initiatives funded by the European Commission. It is built on a flexible community standard, the DAta Tag Suite (DATS) data model, which is able to accommodate a wide range of biomedical data types as well as being interoperable with other standards such as DCAT and Bioschemas. The current round of work included improvements on all aspects of FAIR, improving the user experience and expanding the Catalog content through active curation efforts.

This report provides an overview of the maintenance and review of the FAIRplus Project Handbook as well as the continued project monitoring activities between months 12-36 of the IMI2 JU FAIRplus project, including: Key Performance Indicators (KPIs), risk management, financial monitoring, project sustainability, and lessons learned.

A tremendous number of new chemical entities are discarded in drug development because candidate drugs prove to be toxic, even when potential drugs show excellent efficacy. Broadly sharing toxicology knowledge would: • Lower the attrition rate in drug development • Significantly reduce animal testing • Accelerate the development of novel drugs. The IMI FAIRplus project demonstrated how the eTOX treasure trove of toxicology data could be made more Findable, Accessible, Interoperable and Reusable (FAIR) for drug development, arrived at lessons learned and developed a generic ‘recipe’ for the underlying ontology mapping.