| 5 | | 2. User wants to know what related data parameters are likely to be available for this dataset. |
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| | 5 | In the first situation, the user wants to use the knowledge about the instrument to better evaluate or process the data. For example, the user may want to know expected accuracy and resolution of the instrument's measurements, or the appropriateness of the calibration status that has been provided. |
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| | 6 | |
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| | 7 | In the second situation, the user wants to know what related data parameters are likely to be available for this dataset. One way to discover additional metadata is to find what other data can be collected by the instrument that collected this data. (Another way, learning what other devices were deployed with this one, is outside the scope of this project.) |
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| | 8 | |
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| | 9 | The workflow is something like this: |
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| | 10 | |
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| | 11 | 1. User has data set of interest. |
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| | 12 | 2. User goes to provider of data set and looks up the data set and its metadata. |
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| | 13 | 3. User finds metadata describing the metadata used to collect the metadata. |
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| | 14 | 4a. User obtains device metadata directly from the metadata description, or |
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| | 15 | 4b. User learns the type of device (manufacturer, model, and in some cases serial number is needed) used to collect the data, and does additional research on that device to learn the necessary information. |
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