| Source: |
Welter, D, Juty, N, Rocca-Serra, P, Xu, F, Henderson, D, Gu, W, Strubel, J, Giessmann, R T, Emam, I, Gadiya, Y, Abbassi-Daloii, T, Alharbi, E, Gray, A J G, Courtot, M, Gribbon, P, Ioannidis, V, Reilly, D S, Lynch, N, Boiten, J-W, Satagopam, V, Goble, C, Sansone, S-A & Burdett, T 2023, 'FAIR in action - a flexible framework to guide FAIRification', Scientific Data, vol. 10, no. 1, 291. https://doi.org/10.1038/s41597-023-02167-2 |
| 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 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. |