Published March 6, 2023
| Version 1.1
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FAIR in action - a flexible framework to guide FAIRification
Creators
- Welter, Danielle1
- Juty, Nick2
- Rocca-Serra, Philippe3
- Xu, Fuqi4
- Henderson, David5
- Gu, Wei1
- Strubel, Jolanda6
- Giessmann, Robert T.5
- Emam, Ibrahim7
- Gadiya, Yojana8
- Abbassi-Daloii, Tooba9
- Alharbi, Ebtisam10
- Gray, Alasdair J.G.11
- Courtot, Melanie4
- Gribbon, Philip8
- Ioannidis, Vassilios12
- Reilly, Dorothy S.13
- Lynch, Nick14
- Boiten, Jan-Willem15
- Satagopam, Venkata1
- Goble, Carole2
- Sansone, Susanna-Assunta3
- Burdett, Tony4
- 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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