From FAIR Principles to Practice: A Case Study of FAIRification in a Heritage Science Data Service

The FAIR principles have become a central framework for research data management and digital infrastructures, yet their implementation remains challenging within the long-tail of research. This paper examines how FAIR principles can be operationalized in practice through a case study on the FAIRification of the INFRA-ART Spectral Library, a specialized heritage science data service hosting multi-analytical spectral datasets related to art and archaeological materials. The FAIRification process was approached as an iterative and incremental workflow structured around three interconnected dimensions: technical interoperability, semantic alignment, and governance-oriented stewardship practices. Implementation activities included machine-actionable metadata exposure, semantic enrichment through ontology mappings and controlled vocabularies, interoperability-oriented infrastructure development, and the adoption of TRUST-aligned governance mechanisms. The results demonstrate substantial improvements in metadata quality, discoverability, interoperability, and repository transparency. At the same time, the FAIRification process highlighted persistent challenges related to fragmented semantic resources, evolving interoperability requirements, limited stewardship capacity, and dependence on project-based funding and institutional support. The study argues that effective FAIRification in long-tail data services depends on context-sensitive and incremental implementation approaches rather than rigid compliance models.