Analysis and Synthesis of Metadata Goals for Scientific Data
The proliferation of discipline-specific metadata schemes contributes to artificial barriers that can impede interdisciplinary and transdisciplinary research. The authors considered this problem by examining the domains, objectives, and architectures of nine metadata schemes used to document scienti...
| Main Authors: | , , |
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| Format: | Journal Article |
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John Wiley & Sons Inc.
2012
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| Online Access: | http://hdl.handle.net/20.500.11937/77694 |
| _version_ | 1848763896801787904 |
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| author | White, Hollie Willis, Craig Greenberg, Jane |
| author_facet | White, Hollie Willis, Craig Greenberg, Jane |
| author_sort | White, Hollie |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | The proliferation of discipline-specific metadata schemes contributes to artificial barriers that can impede interdisciplinary and transdisciplinary research. The authors considered this problem by examining the domains, objectives, and architectures of nine metadata schemes used to document scientific data in the physical, life, and social sciences. They used a mixed-methods content analysis and Greenberg's () metadata objectives, principles, domains, and architectural layout (MODAL) framework, and derived 22 metadata-related goals from textual content describing each metadata scheme. Relationships are identified between the domains (e.g., scientific discipline and type of data) and the categories of scheme objectives. For each strong correlation (>0.6), a Fisher's exact test for nonparametric data was used to determine significance (p < .05).
Significant relationships were found between the domains and objectives of the schemes. Schemes describing observational data are more likely to have “scheme harmonization” (compatibility and interoperability with related schemes) as an objective; schemes with the objective “abstraction” (a conceptual model exists separate from the technical implementation) also have the objective “sufficiency” (the scheme defines a minimal amount of information to meet the needs of the community); and schemes with the objective “data publication” do not have the objective “element refinement.” The analysis indicates that many metadata-driven goals expressed by communities are independent of scientific discipline or the type of data, although they are constrained by historical community practices and workflows as well as the technological environment at the time of scheme creation. The analysis reveals 11 fundamental metadata goals for metadata documenting scientific data in support of sharing research data across disciplines and domains. The authors report these results and highlight the need for more metadata-related research, particularly in the context of recent funding agency policy changes. |
| first_indexed | 2025-11-14T11:10:45Z |
| format | Journal Article |
| id | curtin-20.500.11937-77694 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T11:10:45Z |
| publishDate | 2012 |
| publisher | John Wiley & Sons Inc. |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-776942020-04-22T06:53:02Z Analysis and Synthesis of Metadata Goals for Scientific Data White, Hollie Willis, Craig Greenberg, Jane The proliferation of discipline-specific metadata schemes contributes to artificial barriers that can impede interdisciplinary and transdisciplinary research. The authors considered this problem by examining the domains, objectives, and architectures of nine metadata schemes used to document scientific data in the physical, life, and social sciences. They used a mixed-methods content analysis and Greenberg's () metadata objectives, principles, domains, and architectural layout (MODAL) framework, and derived 22 metadata-related goals from textual content describing each metadata scheme. Relationships are identified between the domains (e.g., scientific discipline and type of data) and the categories of scheme objectives. For each strong correlation (>0.6), a Fisher's exact test for nonparametric data was used to determine significance (p < .05). Significant relationships were found between the domains and objectives of the schemes. Schemes describing observational data are more likely to have “scheme harmonization” (compatibility and interoperability with related schemes) as an objective; schemes with the objective “abstraction” (a conceptual model exists separate from the technical implementation) also have the objective “sufficiency” (the scheme defines a minimal amount of information to meet the needs of the community); and schemes with the objective “data publication” do not have the objective “element refinement.” The analysis indicates that many metadata-driven goals expressed by communities are independent of scientific discipline or the type of data, although they are constrained by historical community practices and workflows as well as the technological environment at the time of scheme creation. The analysis reveals 11 fundamental metadata goals for metadata documenting scientific data in support of sharing research data across disciplines and domains. The authors report these results and highlight the need for more metadata-related research, particularly in the context of recent funding agency policy changes. 2012 Journal Article http://hdl.handle.net/20.500.11937/77694 10.1002/asi.22683 John Wiley & Sons Inc. restricted |
| spellingShingle | White, Hollie Willis, Craig Greenberg, Jane Analysis and Synthesis of Metadata Goals for Scientific Data |
| title | Analysis and Synthesis of Metadata Goals for Scientific Data |
| title_full | Analysis and Synthesis of Metadata Goals for Scientific Data |
| title_fullStr | Analysis and Synthesis of Metadata Goals for Scientific Data |
| title_full_unstemmed | Analysis and Synthesis of Metadata Goals for Scientific Data |
| title_short | Analysis and Synthesis of Metadata Goals for Scientific Data |
| title_sort | analysis and synthesis of metadata goals for scientific data |
| url | http://hdl.handle.net/20.500.11937/77694 |