Published October 7, 2022
| Version v1
Preprint
Open
FAIR in action - a flexible framework to guide FAIRification
Authors/Creators
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Welter, Danielle1
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Juty, Nick2
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Rocca-Serra, Philippe3
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Xu, Fuqi4
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Henderson, David5
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Gu, Wei1
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Strubel, Jolanda6
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Giessmann, Robert T.5
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Emam, Ibrahim7
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Gadiya, Yojana8
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Abbassi-Daloii, Tooba9
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Alharbi, Ebtisam10
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Gray, Alasdair J.G.11
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Courtot, Melanie4
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Gribbon, Philip8
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Ioannidis, Vassilios12
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Reilly, Dorothy S.13
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Lynch, Nick14
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Boiten, Jan-Willem15
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Satagopam, Venkata1
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Goble, Carole2
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Sansone, Susanna-Assunta3
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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.
Files
Welter-FAIRificationFramework-2022.pdf
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