Harmonizing quality measures of FAIRness assessment towards machine-actionable quality information

Abstract

FAIR Principles are a set of high-level guidelines for sharing digital resources. The growing global adoption of the FAIR Principles by policymakers, funders, and organizations compels data professionals, projects, and repositories to demonstrate the level of FAIR-compliance (referred to as FAIRness) of their digital data, metadata, and infrastructures. Because the FAIR Principles offer general objectives rather than specific implementation instructions, discrepancies exist due to different interpretations, domain-specific requirements, and intended applications. These discrepancies hinder direct comparisons and integration of assessment outcomes. To address this issue, we propose a novel framework, including a consolidated FAIR vocabulary. This framework establishes quality measures upfront in FAIRness assessment workflows to surpass the intricacies arising from the aforementioned dependencies. The established quality measures encapsulate the distinctive core concepts inherent in individual FAIR principles and can serve as common, fundamental pillars of holistic FAIRness assessment workflows. Building upon this fundamental set of the quality measures, we introduce a FAIRness quality maturity matrix (FAIR-QMM) as a structured, tiered, and progressive approach for evaluating and reporting the degree of FAIR-compliance. The FAIR-QMM can be used as a FAIRness assessment tool independently and/or as a translator between other FAIRness assessment tools or models.

Keywords

• FAIR
• quality measure
• FAIRness assessment
• scientific data
• FAIRness quality maturity matrix