FAIRness at all costs? Practical limitations in applying the FAIR principles to plant genetic resources data.

Modern scientific infrastructure increasingly depends on standardized data governance frameworks, with FAIR (Findable, Accessible, Interoperable, Reusable) principles now widely adopted across research domains. This study investigates how FAIR principles are implemented in plant genetic resources (PGR) information systems by assessing the European Search Catalogue for Plant Genetic Resources (EURISCO) against the Research Data Alliance (RDA) FAIR Data Maturity Model indicators. As a large, federated catalogue containing data on 2.1 million accessions from 43 countries, EURISCO provides a strong empirical basis for evaluating FAIR implementation in distributed biological data systems. The assessment reveals uneven compliance across FAIR dimensions. Human-oriented findability and manual accessibility are comparatively strong, supported by a consolidated catalogue interface, open web access, and standardized passport data using Multi-Crop Passport Descriptors (MCPD v2.1). Persistent identification remains partial because the Digital Object Identifiers (DOI) currently cover only a subset of accessions and primarily identify physical germplasm instead of versioned EURISCO data record. Machine-actionability and semantic interoperability exhibit the most pronounced gaps, reflecting limited use of machine-readable formats, formal vocabularies and explicit links to external knowledge resources. The assessment also highlights domain-specific constraints, including tight coupling of physical collections and digital records, nationally distributed governance, heterogeneous institutional capacities, and long-standing legacy data integration requirements. Evaluation across ex situ passport, in situ crop wild relative, and phenotypic data domains indicates implementation gradients aligned with standardization maturity and documentation complexity. Standardized FAIR metrics only partially capture these constraints inherent to PGR information systems. Effective data governance must therefore balance FAIR ambitions with practical limits, rather than assume that generic frameworks can be applied uniformly across all contexts. Domain-calibrated approaches that prioritize scientific utility and fitness-for-purpose over exhaustive metric-based compliance may ultimately better support complex biological data systems underpinning global agricultural research.