FAIR in Practice: evaluación de los principios FAIR para datasets de Investigación

Abstract

This content explores the practical implementation of FAIR principles—ensuring research data is findable, accessible, interoperable, and reusable. It outlines different levels of FAIR maturity and highlights the importance of metadata quality, licensing, and repository standards. A range of tools and platforms are presented to help researchers assess and improve the FAIRness of their datasets, including automated evaluation services and self-assessment resources. It also includes templates and guidelines for describing research data to facilitate better sharing and reuse across scientific communities.