Difference between revisions of "Data Quality and Validation White Paper Kick-off, GSFC"
From Earth Science Information Partners (ESIP)
| Line 1: | Line 1: | ||
| − | == | + | ==White Paper on Data Quality and Validation Framework for RS data: Best practices and user perspective== |
| − | + | 1. "Objective": | |
Capture, harmonize and provide useful data quality for RS data | Capture, harmonize and provide useful data quality for RS data | ||
| − | + | 2. "Current Status": | |
* No coordinate approach ... besides QA4EO, WGCV and some discipline-specific efforts | * No coordinate approach ... besides QA4EO, WGCV and some discipline-specific efforts | ||
* Duplication of efforts across missions - inefficient utilization of funding | * Duplication of efforts across missions - inefficient utilization of funding | ||
| Line 11: | Line 11: | ||
* 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 | * 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 | ||
| − | + | 3. "Suggested approach": | |
* Collect best practices from various communities and known efforts | * Collect best practices from various communities and known efforts | ||
* Develop consistent terminology and metrics, e.g., completeness, consistency, representativeness | * 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 | * Identify main purpose classes and establish a high-level Q-metrics levels for different purposes | ||
* | * | ||
Revision as of 15:54, December 22, 2010
White Paper on Data Quality and Validation Framework for RS data: Best practices and user perspective[edit | edit source]
1. "Objective": Capture, harmonize and provide useful data quality for RS data
2. "Current Status":
- No coordinate approach ... besides QA4EO, WGCV and some discipline-specific efforts
- Duplication of efforts across missions - inefficient utilization of funding
- Validation:
- 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
3. "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
