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Examples
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Two key challenges seem to stand out in this work: (1) the lack of shared ontology between scientific sub-communities who want to share datasets, and (2) the inevitable separation of data from commentaries on that data (which includes problems of knowledge provenance, and meta-data management).
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The data is messy, the meta-data standards are not in place, the resources to curate this data are not in place.
steve | Serendipity 2010
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Two key challenges seem to stand out in this work: (1) the lack of shared ontology between scientific sub-communities who want to share datasets, and (2) the inevitable separation of data from commentaries on that data (which includes problems of knowledge provenance, and meta-data management).
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The data is messy, the meta-data standards are not in place, the resources to curate this data are not in place.
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The data is messy, the meta-data standards are not in place, the resources to curate this data are not in place.
Serendipity: What has software engineering got to do with climate change? 2010
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Of course, an even better solution is to capture all information about a particular run of the model (including subsequent commentary on the results) as meta-data in the configuration file, so that no wiki pages are needed: lab notebook pages are just user-friendly views of the configuration file.
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Of course, an even better solution is to capture all information about a particular run of the model (including subsequent commentary on the results) as meta-data in the configuration file, so that no wiki pages are needed: lab notebook pages are just user-friendly views of the configuration file.
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What this means, if I'm understanding the technology correctly, is that Google's recipe search will not just scan a Web page looking for words like "sweet" and "sour" and "chicken," but will actually read embedded meta-data that will assure browsers that the resulting recipes actually include the searched-for terms.
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The data is messy, the meta-data standards are not in place, the resources to curate this data are not in place.
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The data is messy, the meta-data standards are not in place, the resources to curate this data are not in place.
If we can’t make mistakes, then we can’t do science | Serendipity 2010
nathan.marcel commented on the word meta-data
Seems to mean: "resources to curate the data" Perhaps self-referential extra associations in context.
May 8, 2012