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| - !Session 2: Enabling capabilities of a shared observational data model |
| + !!Session 2: Capabilities enabled by a shared observational data model |
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| - The need for a shared data model for scientific observations is motivated by the capabilities that such a model would support, and these capabilities will determine the degree of detail and formalization required in the model. Many different uses of a shared observational data model can be envisioned. The goal of this session is to identify needed capabilities, resolve the importance and potential for these capabilities, and to clarify the modeling requirements needed to enable these capabilities. Examples of capabilities that an observational data model could facilitate include: improved discovery of relevant observations across highly heterogeneous collections of data; clarification of semantic relationships among observations that might have been taken under varying data collection protocols or as part of differing experimental regimes; and the integration of observational data to facilitate new scientific analyses. |
| + !Breakout-Group Products |
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| + * __Create a list of possible capabilities.__ |
| + * __For each capability, include the importance (e.g., generally applicable or relevant for only specific projects/disciplines) and corresponding features needed to support the capability in a shared observation data model.__ |
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| + !Background |
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| + The need for a shared data model for environmental observations is motivated by the capabilities that such a model would support, and these capabilities will determine the degree of detail and formalization required in the model. Many different uses of a shared observational data model can be envisioned. The goal of this session is to identify needed capabilities, resolve the importance and potential for these capabilities, and to clarify the modeling requirements needed to enable these capabilities. Examples of capabilities that an observational data model could facilitate include: improved discovery of relevant observations across highly heterogeneous collections of data; clarification of semantic relationships among observations that might have been taken under varying data collection protocols or as part of differing experimental regimes; and the integration of observational data to facilitate new scientific analyses. |
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