FAIR EVA for EUCAIM

FAIR EVA for EUCAIM assesses the FAIRness of digital objects to quantify compliance with the Findable, Accessible, Interoperable, and Reusable (FAIR) principles for research data management.

Key Features:

• Evaluator, Validator & Advisor: Assesses, validates, and provides advice on the FAIRness of digital objects given an object identifier (preferably persistent and unique) and the repository.
• Generic and Agnostic Approach: Applies a repository-agnostic methodology usable across different repositories and data portals.
• Customization and Scalability: Supports customization and automated, scalable assessment across disciplines and repository software.
• Targeted Development: Developed within IFCA-Advanced-Computing and oriented to data management systems such as open repositories; addresses limitations of high-level indicators proposed by the Research Data Alliance.
• Application in Multidisciplinary Repositories: Demonstrated applicability to multidisciplinary institutional repositories, including DIGITAL.CSIC.

Scientific Applications:

• Open Science Compliance: Evaluates dataset management against the FAIR principles to support Open Science practices.
• Data Quality and Reproducibility: Provides evaluations and guidance to improve dataset FAIRness, quality, and reproducibility.
• Repository Assessment: Informs evaluation and benchmarking of institutional and multidisciplinary repositories to guide data management policies.

Methodology:

Assesses digital objects using a set of indicators that evaluate compliance with the FAIR Principles, interprets those indicators within specific repository environments, and takes an object identifier and repository as inputs while adapting to different repository software and disciplines.