Evaluating FAIR maturity through a scalable, automated, community-governed framework

Area of Science:

• Data Science
• Digital Curation
• Information Science

Background:

• Increasing demand for transparent evaluation of digital resource FAIRness from diverse stakeholders.
• Need for standardized, automatable methods to assess FAIR principles across scientific domains.

Purpose of the Study:

• To propose a scalable and automatable framework for evaluating the FAIRness of digital resources.
• To facilitate community-driven, domain-specific FAIR assessments.

Main Methods:

• Development of a framework comprising Maturity Indicators (community-authored specifications), Compliance Tests (web apps for testing), and the Evaluator (a web application for assembly and reporting).
• Utilizing open-source tools and participation guidelines for a community-driven infrastructure.
• Focus on measurable indicators for automated assessment of FAIR behaviors.

Main Results:

• The framework provides a detailed report on how a machine perceives a digital resource.
• The Evaluator tool generates a roadmap for data stewards to incrementally improve resource FAIRness.
• Demonstration of a community-driven infrastructure for FAIR assessments.

Conclusions:

• The proposed framework offers a practical and realistic approach to enhancing the FAIRness of digital resources.
• Community involvement in defining FAIR assessment criteria is crucial for domain relevance and adoption.
• Automatable FAIR evaluations support scientific transparency and data management best practices.