 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 FA verification 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 FAI verification tasks. This article was authored by Danielle Welter, Nick Dutty, Philippe Rocacera, and others.