Michener-institutional and subject-specific data repositories-nfdp13
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Presentation by Bill Michener asking whether Institutional and Subject-Specific Data Repositories can Co-Exist given as a 'provocation' in the final panel session at the Now and Future of Data Publishing Symposium, 22 May 2013, Oxford, UK
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Michener-institutional and subject-specific data repositories-nfdp13
- 1. Can Institutional and Subject-Specific Data Repositories Co-Exist? William Michener University Libraries University of New Mexico 22 May 2013
- 2. 2 The Long Tail of Orphan Data Volume Rank frequency of datatype Well-curated/-preserved Orphan data (B. Heidorn) 2 Characteristics Big Science Large Volume Automated sensors Well described Well curated Easily Discovered • Small Science • Small Volume • Poorly described • Rarely Indexed • Invisible to scientists • Rarely Used • Dark Data • High spatial resolution • Process based • Theory Development • Model Development • Benchmarking Characteristics
- 3. 3 The Long Tail of Orphan DataVolume Rank frequency of datatype Subject repositories Institutional repositories (B. Heidorn) 3 No repositories
- 4. 4
- 5. 5 DataONE: Federating Data Providing universal access to data about life on earth and the environment that sustains it 1. Building community 2. Developing sustainable data discovery and interoperability solutions 3. Enabling science through tools and services
- 6. 6 Metadata Interoperability KNB LTER ORNL DAAC Internal Metadata Index CDL Coordinating Nodes MetadataExtraction • Virtual Portals • Numerous search capabilities • Metadata has link to data, which reside at Member Nodes USGS CSAS D-Space, I-Rods … EML, ISO FGDC FGDC, ISO EML FGDC Dublin Core Darwin Core … FGDC, ISO Member Nodes *Others
Editor's Notes
- There is widely used infrastructure for certain well-defined “easy” biological datatypes like DNA sequences and protein structures. But these repositories are not adequate to capture all those many datasets that requires more context to be reusable. Our civilization is not wealthy to ever support the variety specialized repositories that would be needed, and the curation that would be needed to standardize these data. Big science: large volume data sets from sensors (NEON, Remote sensing, Small science: orphan data, dark data higher resolution, poorly described
- There is widely used infrastructure for certain well-defined “easy” biological datatypes like DNA sequences and protein structures. But these repositories are not adequate to capture all those many datasets that requires more context to be reusable. Our civilization is not wealthy to ever support the variety specialized repositories that would be needed, and the curation that would be needed to standardize these data. Big science: large volume data sets from sensors (NEON, Remote sensing, Small science: orphan data, dark data higher resolution, poorly described