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FAIR in action - a flexible framework to guide FAIRification

Abstract:
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 several major public-private partnership projects, demonstrating and delivering improvements across all aspects of FAIR and across a variety of datasets and their contexts. We therefore managed to establish the reproducibility and far-reaching applicability of our approach to FAIRification tasks.
Publication status:
Published
Peer review status:
Peer reviewed

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Publisher copy:
10.1038/s41597-023-02167-2

Authors

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Role:
Author
ORCID:
0000-0003-1058-2668
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Role:
Author
ORCID:
0000-0002-2036-8350
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Institution:
University of Oxford
Division:
MPLS
Department:
Engineering Science
Sub department:
Oxford e-Research Centre
Role:
Author
ORCID:
0000-0001-9853-5668
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Role:
Author
ORCID:
0000-0002-5923-3859
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Role:
Author
ORCID:
0000-0002-6433-200X


Publisher:
Springer Nature
Journal:
Scientific Data More from this journal
Volume:
10
Issue:
1
Article number:
291
Place of publication:
England
Publication date:
2023-05-19
Acceptance date:
2023-03-28
DOI:
ISSN:
2052-4463
Pmid:
37208349


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