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Functional and Architectural Requirements for Metadata: Supporting Discovery and Management of Scientific Data
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Functional and Architectural Requirements for Metadata: Supporting Discovery and Management of Scientific Data

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The tremendous growth in digital data has led to an increase in metadata initiatives for different types of scientific data, as evident in Ball’s survey (2009). Although individual communities have …

The tremendous growth in digital data has led to an increase in metadata initiatives for different types of scientific data, as evident in Ball’s survey (2009). Although individual communities have specific needs, there are shared goals that need to be recognized if systems are to effectively support data sharing within and across all domains. This paper considers this need, and explores systems requirements that are essential for metadata supporting the discovery and management of scientific data. The paper begins with an introduction and a review of selected research specific to metadata modeling in the sciences. Next, the paper’s goals are stated, followed by the presentation of valuable systems requirements. The results include a base-model with three chief principles: principle of least effort, infrastructure service, and portability. The principles are intended to support “data user” tasks. Results also include a set of defined user tasks and functions, and applications scenarios.

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  • 1. Functional and Architectural Requirements for Metadata: Supporting Discovery and Management of Scientific Data Jian Qin Alex Ball Jane GreenbergSchool of Information Digital Curation Centre School of Information and Studies UKOLN Library Science Syracuse University University of Bath University of North Caroline USA UK Chapel Hill, USA International Conference of Dublin Core Metadata Kuching, Malaysia, September 5, 2012
  • 2. Metadata standards for scientific data Ecological Metadata Language (EML)CSDGM Access to Biological Collections Data – ABCD Darwin Core
  • 3. Many tools have been developed…
  • 4. Many tools have been developed…
  • 5. Many tools have been developed…
  • 6. Motivation for adoption• Standardize the format and terminology of metadata• Enable fast and effective discovery of datasets across different data repositories• Enable data sharing, reuse, and preservation• Provide information for obtaining datasets from the data owners
  • 7. Hindering factors• large numbers of • steep learning curve elements • difficult to automate• many layers in structure metadata generation – “Unwieldy to apply” • unnecessary duplicate• been created for data entry manual data entry, • high costs in time, rather than for resource, and automatic generation personnel expertise
  • 8. Same entity data repeated in the same record…Seamless Daily Precipitation for the Conterminous United StatesMetadata:Identification_InformationData_Quality_InformationSpatial_Data_Organization_InformationSpatial_Reference_InformationEntity_and_Attribute_InformationDistribution_InformationMetadata_Reference_Information
  • 9. …and they are already in…
  • 10. Research questions• What functions do metadata standards for scientific data serve?• How should metadata standards for scientific data be modeled to support these functions by meeting the associated requirements?
  • 11. Functions expected• Resource discovery and use,• Data interoperability,• Automatic and semi-automatic metadata generation,• Linking of publications and underlying datasets,• Data/metadata quality control, and• Data security.
  • 12. Metadata requirements for scientific data
  • 13. Architectural view
  • 14. Identity metadata• Person: researcherID, • Name repositories URI • Linked data architecture• Institution: ORCID, URI • Customizable research• Data object: DOI, group/community Handle, URI member name lists• Associated publication: DOI
  • 15. Semantic metadata• Large semantic resources available in linked data format, but usually not suitable for representing scientific data because they are designed for publications, especially books and journals (containers)• Format is contemporary but the content is far from it Smaller, specialized semanticresources are necessary for automatic semantic metadata generation
  • 16. Contextual metadata• Provenance Provenance data model (W3C, http://www.w3.org/TR/prov-primer/) Provenance data represent the origins of digital objects and describe the entities and activities involved in producing and delivering or otherwise influencing a given object.
  • 17. Geospatial metadata Biological sciences Climate Ecological Darwin Metadata Biological Core NetCDF Data Profile Language (DwC) (EML) Climate and Forecast (CF) Georeferencing Metadata Shoreline elements Conventions Metadata Profile FGDC Georeferencing elements AstronomyCSDGM Profiles CSDGM Astronomy Visualization Metadata ISO 19115: 2003 Standard Geographic information - Metadata. 18
  • 18. Temporal metadata• Mean solar time • Different measurement• Civil time systems result in• GPS time different units and format• Terrestrial time • Conversion between• Atomic time systems• Geologic time
  • 19. • The least effort principle• The infrastructure service principle• The portable principle
  • 20. TABLE 1. Mapping data user tasks with metadata functions and architectural building blocksData Metadata function Architectural building blockusertasksDiscover Descriptive metadata Identity and semantic metadataIdentify Descriptive metadata Identity metadataSelect Descriptive, technical Identity, semantic, scientific metadata context, geospatial, temporal, miscellany metadataObtain Descriptive metadata Identify metadataVerify Descriptive metadata Scientific context metadataAnalyze Scientific context, geospatial, and temporal metadataManage Descriptive, Identify, semantic, scientific administrative, context, geospatial, temporal, structural, and miscellany metadata technical metadataArchive Descriptive, Identify, semantic, scientific administrative, context, geospatial, temporal, structural, and miscellany metadata technical metadataPublish Descriptive metadata Identity, semantic, scientific context, geospatial, and temporal metadataCite Descriptive metadata Identify metadata
  • 21. Development potentials• An infrastructure of metadata services – Entities as linked data – Tools for “slicing” members by research group, community, or institution to customize the entity set – Tools for grabbing entity data from existing resources through interoperability protocols
  • 22. Application scenarios• Cross-domain discovery and verification• Automatically populating entity information from customized slices of entities into metadata records• And more…
  • 23. Conclusion• Scientific data are inherently complex and diverse• Functional metadata requirements should be translated into an effective and efficient architecture – Three principles for modeling metadata for scientific data• Metadata for scientific data (or other domains at large) should adopt an infrastructure service approach• Much to be explored, experimented, and evaluated
  • 24. Thank you!Questions?

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