학술논문
FAIR in action - a flexible framework to guide FAIRification
Document Type
Original Paper
Author
Welter, Danielle; Juty, Nick; Rocca-Serra, Philippe; Xu, Fuqi; Henderson, David; Gu, Wei; Strubel, Jolanda; Giessmann, Robert T.; Emam, Ibrahim; Gadiya, Yojana; Abbassi-Daloii, Tooba; Alharbi, Ebtisam; Gray, Alasdair J. G.; Courtot, Melanie; Gribbon, Philip; Ioannidis, Vassilios; Reilly, Dorothy S.; Lynch, Nick; Boiten, Jan-Willem; Satagopam, Venkata; Goble, Carole; Sansone, Susanna-Assunta; Burdett, Tony
Source
Scientific Data. 10(1)
Subject
Language
English
ISSN
2052-4463
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.