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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.

SUBMITTER: Welter D

PROVIDER: S-EPMC10199076 | biostudies-literature | 2023 May

REPOSITORIES: biostudies-literature

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FAIR in action - a flexible framework to guide FAIRification.

Welter Danielle D Juty Nick N Rocca-Serra Philippe P Xu Fuqi F Henderson David D Gu Wei W Strubel Jolanda J Giessmann Robert T RT Emam Ibrahim I Gadiya Yojana Y Abbassi-Daloii Tooba T Alharbi Ebtisam E Gray Alasdair J G AJG Courtot Melanie M Gribbon Philip P Ioannidis Vassilios V Reilly Dorothy S DS Lynch Nick N Boiten Jan-Willem JW Satagopam Venkata V Goble Carole C Sansone Susanna-Assunta SA Burdett Tony T

Scientific data 20230519 1

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 a ...[more]

PMID: 37208349

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