Difference between revisions of "Data Quality and Validation White Paper Kick-off, GSFC"
From Earth Science Information Partners (ESIP)
(Created page with "==White Paper on Data Quality and Validation Framework for RS data: Best practices and user perspective== # Objective: Capture, harmonize and provide useful data quality for RS d...") |
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− | ==White Paper on Data Quality and Validation Framework for RS data: Best practices and user perspective== | + | ==""White Paper on Data Quality and Validation Framework for RS data: Best practices and user perspective""== |
− | # Objective: | + | # "Objective": |
Capture, harmonize and provide useful data quality for RS data | Capture, harmonize and provide useful data quality for RS data | ||
− | # Current Status: | + | # "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 | ||
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* 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 | ||
− | # Suggested approach: | + | # "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""
- "Objective":
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
- 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
- "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