A Standardized Methodology for FAIRness Assessment and Multi-Dimensional Scoring in Agrosystem Research Data Infrastructures
- 1Forschungszentrum Juellich
Abstract
The NFDI-consortium FAIRagro has established a systematic framework for evaluating the FAIRness of Research Data Infrastructures (RDI) within the German agrosystem research landscape. While FAIR principles are widely accepted, their practical implementation by RDIs remains challenging. By operationalizing the FAIR principles into a reproducible multi-dimensional 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 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-dimensional 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.
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BibTeX
@misc{haleem2026standardized,
title = {{A Standardized Methodology for FAIRness Assessment and Multi-Dimensional Scoring in Agrosystem Research Data Infrastructures}},
author = {A. U. Haleem and D. Arend and J. R. Etukala and E. R. Mazon and M. Schmidt and J. Jung and D. Martini and B. Usadel and C. Neidiger and R. Ulrich and M. Lange},
howpublished = {bioRxiv},
year = {2026},
month = jul,
doi = {10.64898/2026.07.17.738915},
url = {https://www.biorxiv.org/content/10.64898/2026.07.17.738915v2},
note = {CC BY},
}
RIS
TY - UNPB
TI - A Standardized Methodology for FAIRness Assessment and Multi-Dimensional Scoring in Agrosystem Research Data Infrastructures
AU - A. U. Haleem
AU - D. Arend
AU - J. R. Etukala
AU - E. R. Mazon
AU - M. Schmidt
AU - J. Jung
AU - D. Martini
AU - B. Usadel
AU - C. Neidiger
AU - R. Ulrich
AU - M. Lange
PY - 2026
DA - 2026/07/17
PB - bioRxiv
DO - 10.64898/2026.07.17.738915
UR - https://www.biorxiv.org/content/10.64898/2026.07.17.738915v2
AB - The NFDI-consortium FAIRagro has established a systematic framework for evaluating the FAIRness of Research Data Infrastructures (RDI) within the German agrosystem research landscape. While FAIR principles are widely accepted, their practical implementation by RDIs remains challenging. By operationalizing the FAIR principles into a reproducible multi-dimensional 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 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-dimensional 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.
ER -