Published March 6, 2023 | Version 1.1
Preprint Open

FAIR in action - a flexible framework to guide FAIRification

Authors/Creators

  • 1. University of Luxembourg
  • 2. University of Manchester
  • 3. University of Oxford
  • 4. EMBL-EBI
  • 5. Bayer
  • 6. The Hyve
  • 7. Imperial College London
  • 8. Fraunhofer
  • 9. Maastricht University
  • 10. Umm Al-Qura University
  • 11. Heriot-Watt University
  • 12. Swiss Institute of Bioinformatics
  • 13. Novartis
  • 14. OpenPHACTS
  • 15. Lygature

Description

The COVID-19 pandemic has highlighted the need for FAIR (Findable, Accessible, Interoperable, and Reusable) data more than any other scientific challenge to date. We developed a flexible, multi-level, domain-agnostic FAIRification framework, providing practical guidance to improve the FAIRness for both existing and future clinical and molecular datasets. We validated the framework in collaboration with a wide range of public-private partnership projects, demonstrating and implementing improvements across all aspects of FAIR, using a variety of datasets, to demonstrate the reproducibility and wide-ranging applicability of this framework for intra-project FAIRification.

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Welter-FAIRificationFramework-full_v2.pdf

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Additional details

Funding

European Commission
FAIRplus - FAIRplus 802750