Difference between revisions of "AQ 2007 10 31 Discussion"
From Earth Science Information Partners (ESIP)
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*Special considerations for hiding and introducing complexity | *Special considerations for hiding and introducing complexity | ||
==Data lineage tracking== | ==Data lineage tracking== | ||
− | *Do we need to coordinate | + | *Do we need to coordinate conventions for tracking provenance, even in readme files,? |
*How do we track sources and magnitude of variance within and across datasets throughout the processing chain? | *How do we track sources and magnitude of variance within and across datasets throughout the processing chain? | ||
**Take as an example the case x and y variance are not the same. | **Take as an example the case x and y variance are not the same. | ||
− | *Can we list sources of variance that must be taken into consideration as we visualize or composite datasets? | + | **Which processing tools compound error? How do we account for it? |
+ | *Can we list spurious sources of variance that must be taken into consideration as we visualize or composite datasets? | ||
**Cloud cover | **Cloud cover | ||
**Registration issues | **Registration issues | ||
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***resolution | ***resolution | ||
***interference (NOx) | ***interference (NOx) | ||
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==Data quality considerations== | ==Data quality considerations== | ||
*What is the best way to determine "best available evidence"? | *What is the best way to determine "best available evidence"? |
Revision as of 12:21, October 31, 2007
back to 2007-10-31 Workshop page
Data/tool Decision Tree
- wonder if we could set up table comparing products
- might do by datasource, visualization tool, processing tool, etc.
Usage considerations
Legal
- EPA always has to defend its judgments in court
- Court needs "preponderance of evidence" or "beyond a reasonable doubt"
- This is quite different from 0.95% certainty.
Science
- Needs detailed information about sources and models used in "correcting" data
Education
- Special considerations for "real-time" data
- Special considerations for hiding and introducing complexity
Data lineage tracking
- Do we need to coordinate conventions for tracking provenance, even in readme files,?
- How do we track sources and magnitude of variance within and across datasets throughout the processing chain?
- Take as an example the case x and y variance are not the same.
- Which processing tools compound error? How do we account for it?
- Can we list spurious sources of variance that must be taken into consideration as we visualize or composite datasets?
- Cloud cover
- Registration issues
- Instrument issues
- resolution
- interference (NOx)
Data quality considerations
- What is the best way to determine "best available evidence"?
- How do we know if a remote sensing product has been verified with ground data and in situations comparable to use?
- How and why might we tag a dataset "bad data"?
Use Case
How much NO2 is man made?
- How would we provide data to justify the statement, "Man-made emissions of nitrogen oxides dominate total emissions"?