Data Quality and Validation White Paper Kick-off, GSFC
From Earth Science Information Partners (ESIP)
""White Paper on Data Quality and Validation Framework for RS data: Best practices and user perspective""
Capture, harmonize and provide useful data quality for RS data
- "Current Status":
- No coordinate approach ... besides QA4EO, WGCV and some discipline-specific efforts
- Duplication of efforts across missions - inefficient utilization of funding
- L2: validate in some areas and extrapolate globally. Issues: filtering by QC flag doesn't necessarily lead to good product
- L3: what is L3 validation?
- Fitness for purpose: quality need should depend on data usage but the current quality is based on validation that has the specific purpose of validating instrument and retrieval algorithms
- "Suggested approach":
- Collect best practices from various communities and known efforts
- Develop consistent terminology and metrics, e.g., completeness, consistency, representativeness
- Identify main purpose classes and establish a high-level Q-metrics levels for different purposes