Sprecher
Beschreibung
Agricultural decision support systems—from crop insurance to yield forecasting—depend critically on trustworthy geodata metadata. However, quality information is typically scattered across documentation, leaving uncertainty and fitness-for-purpose reasoning opaque to users.
We present a best practice example for enriching weather index metadata that operationalizes quality, uncertainty, and decision context as machine-actionable FAIR Digital Objects. Using Germany-wide phenology and precipitation data (1 km resolution, 1993–2022), we integrate three metadata components:
- Standardized quality metrics: ISO 19157-1–compliant accuracy measures ($R^2$, $RMSE$) for each crop, phenological phase, and year.
- Spatial uncertainty quantification: Uncertainty layers quantifying local interpolation error for all phenological predictions, enabling site-specific accuracy assessment.
- Fitness-for-purpose matrices: Structured metadata capturing validated use contexts, limitations, and application-specific quality requirements—extracted and formalized using LLM-assisted workflows.
Rather than treating metadata as static compliance artifacts, enriched metadata travels with data products as ARC containers, enabling users to query "where are data sufficiently accurate and validated for this specific decision context?"
By embedding ISO-compliant quality metrics, uncertainty products, and formalized fitness-for-purpose knowledge into operational workflows, enriched metadata transforms research data into trusted decision support infrastructure. This framework provides a transferable template for domains where metadata quality directly influences decision reliability and stakeholder trust.