FAIR EVA: Bringing institutional multidisciplinary repositories into the FAIR picture
Fernando Aguilar G\'omez, Isabel Bernal

TL;DR
FAIR EVA is a scalable, adaptable tool designed to evaluate and enhance the FAIRness of digital objects in multidisciplinary repositories, addressing the limitations of high-level indicators.
Contribution
It introduces FAIR EVA, a customizable, automatic tool tailored for specific data management systems within the European Open Science Cloud.
Findings
Successfully applied to DIGITAL.CSIC repository
Addresses variability in repository environments
Enhances FAIRness assessment accuracy
Abstract
The FAIR Principles are a set of good practices to improve the reproducibility and quality of data in an Open Science context. Different sets of indicators have been proposed to evaluate the FAIRness of digital objects, including datasets that are usually stored in repositories or data portals. However, indicators like those proposed by the Research Data Alliance are provided from a high-level perspective that can be interpreted and they are not always realistic to particular environments like multidisciplinary repositories. This paper describes FAIR EVA, a new tool developed within the European Open Science Cloud context that is oriented to particular data management systems like open repositories, which can be customized to a specific case in a scalable and automatic environment. It aims to be adaptive enough to work for different environments, repository software and disciplines,…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Code & Models
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsScientific Computing and Data Management · Research Data Management Practices · Semantic Web and Ontologies
