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