Enhancing environmental indicator trustworthiness: A framework for user-specific quality assessment of spatial input data using data-fitness-for-purpose principles

The presentation introduces a framework for improving the trustworthiness of environmental indicators through user-specific quality assessments of spatial input data based on data-fitness-for-use (DFFU) and data-fitness-for-purpose (DFFP) principles. It distinguishes between general data suitability (DFFU) and context-dependent usability (DFFP), highlighting challenges in assessing input data quality when reference data are unavailable. Using case examples from biodiversity and extreme weather indicators, the study shows that standard accuracy metrics are insufficient for composite indicators lacking spatial accuracy information. It proposes extending geospatial quality standards like ISO 19157-1 by incorporating spatial uncertainty metrics to evaluate and communicate data usability. The framework aligns with FAIR principles, promoting data reusability and AI readiness through the structured packaging of research objects with quality metadata.