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How to describe a dataset. Interoperability issues

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Presented by Valeria Pesce during the pre-meeting of the Agricultural Data Interoperability Interest Group (IGAD) of the Research Data Alliance (RDA), held on 21 and 22 September 2015 in Paris at INRA.

Publicada em: Tecnologia
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How to describe a dataset. Interoperability issues

  1. 1. How to describe a dataset. Interoperability issues Valeria Pesce Global Forum on Agricultural Research
  2. 2. Definition of “dataset” The term “dataset” has been defined in several ways, all of which further specify or extend the basic concept of “a collection of data”. Definition given by the W3C Government Linked Data Working Group: A dataset is “a collection of data, published or curated by a single source, and available for access or download in one or more formats” The “instances” of the dataset “available for access or download in one or more formats” are called “distributions”. A dataset can have many distributions. Examples of distributions include a downloadable CSV file, an API or an RSS feed.
  3. 3. Definition of “interoperability” “Data interoperability is a feature of datasets - and of information services that give access to datasets - whereby data can easily be retrieved, processed, re-used, and re-packaged (“operated”) by other systems.” Interim Proceedings of International Expert Consultation on “Building the CIARD Framework for Data and Information Sharing”, CIARD (2011) software applications datasets have to be machine-readable
  4. 4. What applications need Besides information common to any type of resource (name, author / owner, date…), applications have to find enough metadata about datasets to understand: 1. the specific coverage of the dataset (type of data, thematic coverage, geographic coverage) 2. the necessary technical specifications to retrieve and parse a distribution of the dataset (format, protocol etc.) 3. the conditions for re-use (rights, licenses) 4. the “dimensions” covered by the dataset (e.g. temperature, time, salinity, gene, coordinates) 5. the semantics of the dimensions (units of measure, time granularity, syntax, reference taxonomies)
  5. 5. Partial answers in existing vocabularies • DCAT vocabulary – RDF vocabulary for describing any dataset – Datasets can be standalone or part of a “catalog” – Datasets are accessible through several “distributions” – “Other, complementary vocabularies may be used together with DCAT to provide more detailed format-specific information. For example, properties from the VoID vocabulary can be used if that dataset is in RDF format.” • VOID vocabulary – RDF vocabulary for expressing metadata about RDF datasets • (SDMX ) DataCube vocabulary – RDF vocabulary for describing statistical datasets – Useful for attaching metadata about the “data structure” to any dataset that doesn’t follow a known published standard
  6. 6. Coverage of a dataset • This can be handled by common Dublin Core properties like subject and coverage. • DCAT re-uses these DC properties. Issue 1: No specific property for the type of data covered in a dataset The values of these properties have to be understood by machines: - The value should be standardized, possibly a URI - The URI should be de-referenceable to a thing - The thing should be part of an authority list / taxonomy Issue 3: There is no authority vocabulary for types of data Issue 1 Issue 2
  7. 7. Conditions for re-use • DCAT re-uses the license DC property at the level of distributions • DCAT re-uses the rights DC property at bith the level of dataset and the level of distribution dc:license > dc:LicenseDocument dc:rights > dc:RightsStatement
  8. 8. W3C DCAT > DCAT AP
  9. 9. DCAT core
  10. 10. Technical properties The necessary technical specifications to retrieve and parse a distribution of a dataset (format, protocol etc.) • DCAT re-uses the DC format property; Issue No property for protocol The values of these properties have to be understood by machines, possibly URIs: Issue2 No comprehensive RDF authority lists for these values (partial: DC Types; non-RDF: IANA types) Issue 1 Issue 2
  11. 11. VOID VOID can help with the protocol metadata but only for RDF datasets: - Property for data dump: dataDump - Property for SPARQL endpoint: sparqlEndpoint
  12. 12. “Dimensions” and their semantics DCAT does not describe the dimensions of a dataset, except for a reference to a standard if the dataset dimensions can be defined by a formalized standard (e.g. an XML schema or an RDF vocabulary or an ISO standard) dc:conformsTo > dc:Standard Statistical vocabularies can help with the description of the dimensions
  13. 13. SDMX: data structure and dimensions SDMX: Statistical Data and Metadata Exchange The data structure definition is a description of all the metadata needed to understand the data set structure. This includes: • identification of the dimensions (Dimension) according to standard statistical terminology, • the key structure (KeyDescriptor), • the code-lists (CodeList) that enumerate valid values for each dimension • coded attribute (CodedAttribute), information about whether attributes are required or optional and coded or free text. Given the metadata in the data structure definition, all of the data in the data set becomes meaningful.
  14. 14. DataCube: simplified SDMX in RDF
  15. 15. DataCube: simplified SDMX in RDF Reference to a concept scheme
  16. 16. DataCube: simplified SDMX in RDF “Semantic role” of the property
  17. 17. DataCube: simplified SDMX in RDF “Semantic role” of
  18. 18. Combining different vocabularies Name URL Owner Content type Topic(s) Language Metadata set(s) Data structure Distribution(s) […] DATASET Name Protocol Endpoint URL Media type Format Size DISTRIBUTION DCAT model Dimensions Attributes Measures Value lists DATA STRUCTURE DataCube model Catalog: the directory Vocabulary(ies) SPARQL endpoint Data dump Serialization format Number of triples RDF dataset info VOID properties If one or more known published metadata sets are used, just fill “metadata set(s)”, otherwise link to a “data structure” with custom “dimensions” IF media type has RDF or SPARQL response
  19. 19. Tools for managing dataset metadata • CKAN maintained by the Open Knowledge Foundation Uses most of DCAT. Doesn’t describe dimensions. Also provides a global dataset hub called the Datahub • Dataverse created by Harvard University Uses a custom vocabulary. Doesn’t describe dimensions. • Commercial solutions • Repositories and catalogs: OpenAIRE, DataCite (using re3data to search repositories) and Dryad use their own vocabularies. • CIARD RING Uses full DCAT AP with some extended properties (protocol, data type) and local taxonomies with URIs mapped when possible to authorities. Next steps: adding DataCube properties for dimensions.
  20. 20. Major outstanding issues • Some missing properties in existing vocabularies:  approach vocabulary owners OR extend vocabularies • Missing vocabularies for protocols, formats  approach standardizing bodies?  perhaps specific dataset formats? • Need for more standardized semantics for dimensions:  Joint discussions with the RDA Data Type Registries WG? • Lack of interoperability metadata in existing tools
  21. 21. References • W3C DCAT: http://www.w3.org/TR/vocab-dcat/ • DCAT AP: https://joinup.ec.europa.eu/asset/dcat_application_profile/asset_release/dcat- application-profile-data-portals-europe-final • DataCube: http://purl.org/linked-data/cube# • VOID: http://rdfs.org/ns/void-guide • VIVO Datastar: http://sourceforge.net/projects/vivo/files/Datastar%20ontology/ • CERIF for datasets: https://cerif4datasets.wordpress.com/c4d-deliverables/ • CKAN: http://ckan.org/ • Datahub: http://datahub.io/ • DataCite: http://search.datacite.org/ui?q=subject%3Aagriculture • Re3data: http://www.re3data.org • Dryad: http://datadryad.org/ • OpenAIRE: https://www.openaire.eu/
  22. 22. Thank you Valeria Pesce Global Forum on Agricultural Research