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.
- 1. Functional and Architectural Requirements for
Metadata:Supporting Discovery and Management of Scientific DataJian
QinAlex BallJane GreenbergSchool of Information Digital Curation
CentreSchool of Information and Studies UKOLNLibrary Science
Syracuse UniversityUniversity of BathUniversity of North Caroline
USA UKChapel Hill, USAInternational Conference of Dublin Core
MetadataKuching, Malaysia, September 5, 2012
2. Metadata standards for scientific data Ecological Metadata
Language (EML)CSDGMAccess to BiologicalCollections Data ABCDDarwin
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 ofmetadata Enable
fast and effective discovery of datasetsacross different data
repositories Enable data sharing, reuse, and preservation Provide
information for obtaining datasetsfrom the data owners 7. Hindering
factors large numbers of steep learning curveelements difficult to
automate many layers in structure metadata generation Unwieldy to
apply unnecessary duplicate been created for data entrymanual data
entry, high costs in time,rather than forresource, andautomatic
generation personnel expertise 8. Same entity data repeated in the
same recordSeamless 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 forscientific data serve? How should metadata
standards for scientificdata be modeled to support these
functionsby meeting the associated requirements? 11. Functions
expected Resource discovery and use, Data interoperability,
Automatic and semi-automatic metadatageneration, Linking of
publications and underlyingdatasets, Data/metadata quality control,
and Data security. 12. Metadata requirements for scientific data
13. Architectural view 14. Identity metadata Person: researcherID,
Name repositoriesURI Linked data architecture Institution: ORCID,
URI Customizable research Data object: DOI, group/communityHandle,
URI member name lists Associated publication:DOI 15. Semantic
metadata Large semantic resources available in linked data format,
butusually not suitable for representing scientific data
becausethey are designed for publications, especially books
andjournals (containers) Format is contemporary but the content is
far from it Smaller, specialized semanticresources are necessary
for automaticsemantic 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
sciencesClimateEcological Darwin MetadataBiologicalCore NetCDF Data
Profile Language (DwC)(EML) Climate andForecast (CF)Georeferencing
MetadataShoreline elementsConventionsMetadata ProfileFGDC
Georeferencing elements AstronomyCSDGM Profiles CSDGM
AstronomyVisualization Metadata ISO 19115: 2003StandardGeographic
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 timesystems 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 blocksDataMetadata function Architectural
building blockusertasksDiscoverDescriptive metadata Identity and
semantic metadataIdentifyDescriptive metadata Identity
metadataSelectDescriptive, technical Identity, semantic,
scientificmetadata context, geospatial, temporal, miscellany
metadataObtainDescriptive metadata Identify
metadataVerifyDescriptive metadata Scientific context
metadataAnalyzeScientific context, geospatial, and temporal
metadataManageDescriptive, Identify, semantic,
scientificadministrative,context, geospatial, temporal,structural,
andmiscellany metadatatechnical metadataArchive Descriptive,
Identify, semantic, scientificadministrative,context, geospatial,
temporal,structural, andmiscellany metadatatechnical
metadataPublish Descriptive metadata Identity, semantic, scientific
context, geospatial, and temporal metadataCiteDescriptive 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
entityset Tools for grabbing entity data from existingresources
through interoperability protocols 22. Application scenarios
Cross-domain discovery and verification Automatically populating
entity informationfrom customized slices of entities intometadata
records And more 23. Conclusion Scientific data are inherently
complex and diverse Functional metadata requirements should
betranslated into an effective and efficient architecture Three
principles for modeling metadata for scientific data Metadata for
scientific data (or other domains atlarge) should adopt an
infrastructure service approach Much to be explored, experimented,
and evaluated 24. Thank you!Questions?
