
This report (FAIRagro Milestone M3.3.3) defines exemplary data-fitness elements that can be reused to build FAIRness measurement templates, with “data fitness” understood as how well data meet the needs of a specific scientific/analytical use case. It proposes a reusable, explicitly non-operational method that bridges (1) producer-side, standards-based data quality concepts grounded in ISO 19157 and (2) user-side, fitness-for-purpose requirements extracted from domain literature. The result is an integrated, machine-readable conceptual framework (illustrated via a digital soil mapping use case) intended to support subsequent FAIRagro work on operationalization, community refinement, and implementation of application-specific data quality queries, without prescribing metrics or running assessments itself.
M3.3, FAIR principles, FAIRness, Data-Fitness-For-Purpose (DFFP), FAIRagro Community Summit 2026, data quality, Digital Soil Mapping
M3.3, FAIR principles, FAIRness, Data-Fitness-For-Purpose (DFFP), FAIRagro Community Summit 2026, data quality, Digital Soil Mapping
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