FAIR in action - a flexible framework to guide FAIRification.
Sci Data
; 10(1): 291, 2023 05 19.
Article
em En
| MEDLINE
| ID: mdl-37208349
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.
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Conjuntos de Dados como Assunto
/
COVID-19
Tipo de estudo:
Guideline
Limite:
Humans
Idioma:
En
Revista:
Sci Data
Ano de publicação:
2023
Tipo de documento:
Article
País de afiliação:
Luxemburgo