Haleem, A. U.; Arend, D.; Etukala, J. R.; Mazon, E. R.; Schmidt, M.; Jung, J.; Martini, D.; Usadel, B.; Neidiger, C.; Ulrich, R.; Lange, M.

The NFDI-consortium FAIRagro has established a systematic framework for evaluating the FAIRness of Research Data Infrastructures (RDIs) within the German agrosystem research landscape. While FAIR principles are widely accepted, their practical implementation by research data infrastructures (RDI) remains challenging. By operationalizing the FAIR principles into a reproducible multi-dimentional scoring methodology, this initiative addresses the critical need for a transparent and citable benchmark of RDIs that moves beyond simple compliance. This paper details the underlying assessment criteria, comprising 20 aggregated core metrics, the iterative community-driven validation process, and the integration of these metrics into the FAIRagro Search Hub. This framework evaluates RDIs, like repositories or databases, instead of sampling hosted data sets., across the four distinct categories of FAIR independently, yielding granular, pillar-specific ratings. Unlike aggregate scoring models, which can inadvertently mask technical deficiencies by averaging performance across categories, this multi-dimentional approach ensures that a repositorys distinct strengths and bottlenecks remain fully visible. Our findings demonstrate that standardized scoring not only clarifies data accessibility for users but also highlights specific operational gaps, allowing repository providers to identify precisely where the service implementation can be enhanced. By establishing this data-driven service in the agronomy domain, we provide a scalable template for the broader NFDI and EOSC ecosystems to foster a culture of excellence in research data stewardship.