Fostering FAIR Data Practices in Europe

Projektinformationen

FAIRsFAIR

ID Finanzhilfevereinbarung: 831558

DOI

10.3030/831558

Projekt abgeschlossen

EK-Unterschriftsdatum 16 April 2019

Startdatum 1 März 2019

Enddatum 28 Februar 2022

Finanziert unter

• EXCELLENT SCIENCE - Research Infrastructures

Gesamtkosten

€ 9 998 848,75

EU-Beitrag

€ 9 998 848,75

Koordiniert durch

KONINKLIJKE NEDERLANDSE AKADEMIE VAN WETENSCHAPPEN - KNAW
Netherlands

Periodic Reporting for period 2 - FAIRsFAIR (Fostering FAIR Data Practices in Europe)

Berichtszeitraum: 2020-09-01 bis 2022-02-28

Zusammenfassung vom Kontext und den Gesamtzielen des Projekts

FAIRsFAIR aimed to supply practical solutions for the use of the FAIR data principles throughout the research data life cycle with an emphasis on fostering FAIR data culture and the uptake of good practices in making data FAIR, in particular in the context of the European Open Science Cloud (EOSC). FAIRsFAIR delivered and supported recommendations on data policy and practice, provides solutions and support for semantic interoperability, supported certification of trustworthy data repositories and data assessment, as well as developed a FAIR enabling competence centre and framework for higher education.
Our overall objective was to accelerate the realization of the goals of the EOSC by opening up and sharing knowledge, expertise, guidelines, implementations, new trajectories, courses and education on FAIR matters. FAIRsFAIR seeked to establish a level playing field for European member states (and beyond) when it comes to contributing data to scientific and scholarly communities and re-using data from elsewhere. In this way it contributed to the culture change required for the wide adoption of FAIR practices. We employed the help of many stakeholders to accomplish this.
FAIRsFAIR produced a wide range of guidance, tools and training for different stakeholder groups: researchers, data stewards, data repositories, research performing organisations, as well as policy makers and funders. We are currently witnessing substantive interest in, and adoption and implementation of these products by our stakeholders in the project and beyond, not only in Europe but also elsewhere.

Arbeit, die ab Beginn des Projekts bis zum Ende des durch den Bericht erfassten Berichtszeitraums geleistet wurde, und die wichtigsten bis dahin erzielten Ergebnisse

During the initial stages of the project landscaping activities have been a core activity. As part of these cross-package landscaping activities a series of surveys were fielded. The programmes for the pilot Data Steward strand and the support for FAIR-enabling repositories got underway. The FAIRsFAIR Synchronisation Force (SF) held the first two of three planned workshops.
In the later phases of the project there has been a focus on sustainability activities. Planning for the long-term sustainability of project outputs was central to the whole project. The approach consisted of developing various measures and activities during the lifetime of the project as well as preparing the ground for the period after the end of the project.
The third SF workshop which was restructured as a series of eight online sessions running in 2021. The workshops were used to measure the progress towards implementing the recommendations outlined in the Turning FAIR into Reality report.
There have been third iterations of both the work on Persistence and Interoperability, as well as the recommendations on FAIR Semantics. A hackathon was organized for repositories to test and further develop implementation of the FAIRdata Point to support semantic interoperability and testing it against the F-UJI tool. The Framework for FAIR assessment of services was published and presented.
To support the implementation of recommendations for policy makers, research communities and repositories, a programme of targeted support was delivered, and a suite of resources was developed.
The CoreTrustSeal+FAIRenabling Capability and Maturity model, the Repository Finder, as well as the latest versions of the FAIRsFAIR metrics and the subsequent FAIR data assessment tools FAIR-Aware and F-UJI were released. FAIR-Aware and F-UJI were heavily tested, presented, and promoted to successfully foster uptake in the research community. The Repository Support Programme was successfully carried out and wrapped-up. 2021 saw the continued development of the FAIRdata forum, and the competence centre activities in WP6 including the development of the training library of resources, a new self paced online data stewards training course (in collaboration with EOSC Synergy), and a proof of concept materials browser providing a practical demonstration of Terms4FAIRSkills terminology. The CODATA-RDA Data Science School was moved to a 10 week virtual school and the team delivered a fully revised data stewardship instructor train-the-trainer programme.
Three practical tools to support universities in integrating FAIR data-related skills and competences in their programmes at the bachelor, master and doctoral levels, were finalized: the FAIR Competence Framework for Higher Education, the FAIR Adoption Handbook for Universities and the Good Practices in FAIR Competence Education.
All of the activities mentioned above were aimed to create practical solutions for the use of the FAIR data principles throughout the research data life cycle. We produced guidance, tools and training for a large range of stakeholders. In total FAIRsFAIR has completed 54 deliverables, and achieved 59 project milestones. We published our deliverables, scientific publications, and relevant outputs to ZENODO. A dedicated community (holding 233 outputs) was created for this: https://zenodo.org/communities/fairsfair.
The main outputs of the project are:
In the category FAIR guidance: FAIR implementation stories and guidance, FAIR adoption handbook for higher education institutions, FAIR data policy checklist and structured policy description template, Framework for assessing FAIR-enabling services, Recommendations for FAIRness of research software, FAIR competence framework and Recommendations on FAIR semantics.
In the category FAIR tools: Capability model for FAIR-enabling organizations (ACME-FAIR), FAIR-Aware, F-UJI, FAIR-enabling repository finder, FAIR-enabling repository finder, FAIRdata forum, CoreTrustSeal + FAIR certification, European network of FAIR-enabling trusted digital repositories.
In the category FAIR training: Training materials, Good practices in FAIR education, Data steward Schools.

Fortschritte, die über den aktuellen Stand der Technik hinausgehen und voraussichtliche potenzielle Auswirkungen (einschließlich der bis dato erzielten sozioökonomischen Auswirkungen und weiter gefassten gesellschaftlichen Auswirkungen des Projekts)

FAIRsFAIR has created an impact beyond the boundaries of the project through its important role as an ambassador for the FAIR principles both inside and outside of the EOSC ecosystem. We actively seeked out and amplified the best existing practices and policies for FAIR. A good example is the involvement of project partners as part of the global FAIR community in the rapid response to the COVID-19 pandemic.
FAIRsFAIR produced a wide range of guidance, tools and training for different stakeholder groups. We are currently witnessing substantive interest in, and adoption and implementation of these products by our stakeholders in the project and beyond, not only in Europe but also elsewhere.
This means that FAIRsFAIR contributed to the removal of technical and organisational barriers to ensure FAIR research data. The project enhanced the uptake of FAIR data, contributed to harmonisation of related policies in Europe and facilitated alignment with international initiatives on research data sharing.
The success of a project like FAIRsFAIR is difficult to measure and quantify. In the State of Open Data Report 2019 less than 20% of the international group of researchers surveyed stated that they were familiar with the FAIR principles (https://doi.org/10.6084/m9.figshare.9980783.v2 ). In the same survey two years later over 30% of the researchers claimed that their data comply to a certain extend with the FAIR principles and over 20% even claimed that their data comply very much with the principles (https://doi.org/10.6084/m9.figshare.17061347.v1). These figures show a real improvement. And it would be wonderful if some of this progress is the result of our work in FAIRsFAIR.

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