xFAIR: A Multi-Layer Approach to Data FAIRness Assessment and Data FAIRification

The FAIR (Findable, Accessible, Interoperable, and Reusable) data principles are crucial for data discoverability, sharing, and exploitation across diverse contexts, where evaluating data FAIRness reliably is essential for quality assessment and continuous improvement of data assets. Organizations can identify issues, implement targeted enhancements, and increase data value and trustworthiness by leveraging actionable insights bluefrom systematic data FAIRness assessment. However, this often requires heterogeneous metrics and guidelines, particularly when domain-specific challenges are involved. To bridge the gap between theoretical FAIR principles and their practical implementation, this article introduces xFAIR, a multi-layer platform architecture for assessing and enhancing data FAIRness, and for achieving data FAIRification in multiple domains. xFAIR incorporates modules for data acquisition, FAIRness evaluation, and ontology support. Its versatility is demonstrated via three real-world use cases: i. improving open data portals for Public Administrations, ii. extending FAIR assessment to multi-level European data portals, and iii. tackling metadata quality in news media by applying FAIR data principles to examine how source trustworthiness and metadata richness are linked. Each use case highlights specific aspects (e.g., domain-dependent metadata validation, or trust scores integration) to enhance quality assurance. Additional components support user feedback and media literacy. The obtained research outcomes underscore the importance of combining automated metadata validation with community-driven refinements, to achieve higher data quality and foster ongoing FAIRification actions across domains.