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- DCAT - for dataset description for discovery and selection
- DarwinCore - for species occurrence data
- Geo-Location standards: 19115, GeoJSON, GeoSPARQL ...
- LOINC – codes for identifying laboratories (would be applied internally to a dataset)
- QB/Data Cube, if transformation from GHO structure (aggregated data) is required
Strategies for how these standards can be applied to existing resources. Strategies of how they can be applied to new datasets such that the data can be integrated in real time. Explicitly includes business processes and upstream activity to improve the way datasets are annotated at collection.
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Importance of this would not just be for infectious disease. Also want to be useful for other related research topics: e.g. quality of medicines.
- Case 1 - non-overlapping data available from more than one source. Different organisations are doing similar work. These reports are useful but they are distributed, different data providers. Creating standards in this area, would be useful to integrate data on this topic in order to assemble a more complete
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- dataset on a particular theme.
- Case 2 - Multiple d For a particular
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- scenario, there is a science hypothesis about what the vectors and exacerbating factors are. On the basis of this model, we need a
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- specific selection of
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- datasets to test or make predictions. For a different model, a different set would be required. There may be a need for a pre-rolled set of required datasets to respond to each
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- scenario.
Do we have the classification system that will allow us to find the set of data sets needed for each scenario?
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