2026· Data Science Journal· Vol 25· 0 citations· 36 references
Computer Science
TL;DR
A semantic model that integrates established vocabularies to describe both FAIR metrics and their corresponding assessment outcomes is proposed and demonstrates how web standards can support holistic, machine-actionable analyses of FAIR assessments across different tools.
Abstract
This study proposes a web standards-based semantic model for representing FAIR (Findable, Accessible, Interoperable, Reusable) metrics and FAIR assessment results. Building on existing recommendations for the publication of data quality information on the Web, in particular the W3C Data Quality Vocabulary (DQV) and related ontologies, we examine to what extent these standards are applicable to FAIR assessments. Based on a comparative analysis of the output formats of existing FAIR assessment tools, we identify core requirements for a harmonized representation of FAIR metrics and results. On this basis, we propose a semantic model that integrates established vocabularies to describe both FAIR metrics and their corresponding assessment outcomes. The model is presented together with an exemplary implementation for the F-UJI assessment tool and demonstrates how web standards can support holistic, machine-actionable analyses of FAIR assessments across different tools.
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