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Questions of granularity – Dryad’s use of DataCite DOIs for data citation, and the Annotation Ontology

DataCite is an international organisation, founded in 2009, which promotes the use of DOIs (Digital Object Identifiers) for published datasets, in order to establish easier access to research data, to increase acceptance of research data as legitimate contributions in the scholarly record, and to support data archiving to permit results to be verified and re-purposed for future study.

Its founding members were the British Library; the Technical Information Center of Denmark; TU Delft Library; the National Research Council’s Canada Institute for Scientific and Technical Information (NRC-CISTI); California Digital Library; Purdue University; and the German National Library of Science and Technology. Since its foundation, it has been joined by several other leading organisations from around the world, and it therefore provides a stable basis for the ongoing use of DOIs for data.

This recent availability of DOIs from DataCite for the identification of data entities has made all the difference to data repositories wishing to give unique global identifiers to their data holdings, since DOIs are widely recognised and respected throughout the academic world, because of their widespread prior use for identifying journal articles, made possible by CrossRef.

However, in their recent discussion paper Data Citation and Linking, published on 8th June 2011, Alex Ball and Monica Duke of UKOLN at the University of Bath ask:

“At what granularity should data be made citable? If single datasets are given identifiers, what about collections of datasets, or subsets of data?”

Individual data files and metadata documents will, of course, have their own unique internal identifiers within any data repository, but may not have externally resolvable identifiers such as DOIs.  Practice varies.

This post is to explain how DOIs are employed in the Dryad Data Repository, that specializes in publishing data linked to peer-reviewed biological journal articles, since it is both elegant and addresses at least some of the issues raised by Alex and Monica.

The Dryad DOI usage policy is described at https://www.nescent.org/wg_dryad/DOI_Usage, and involves assigning unique DOIs to each version of every data package, and to each version of every data file, in a principled and easy-to-understand manner. In summary:

  • Each data package is given a DataCite DOI, which can be versioned by adding “.2”, “.3”, etc. after the original DOI to create new DOIs for new versions of the same data package.
  • Within each data package, each data file has a unique DOI defines by suffixing the data package DOI with “/1”, “/2”. etc., with versions indicated as for data packages.

Thus the third version of the second data file in the second version of a Dryad data package would have a DOI of the form doi:10.5061/dryad.1234.2/2.3.

One might argue that it would result in an awfully large number of DOIs if a single data package was made up of thousands of data files. True, but numbers themselves are limitless and free, and the cost of a DataCite DOI is small relative to the cost of data creation and preservation. The real problem at present is lack of identifiable, citable data entities within repositories – to have so many that the cost of DOIs becomes an issue should be regarded as an achievement, not a problem!

Dryad does not have a mechanism for assigning identifiers to a portion of a data file (“a subset of data”), and DOIs are probably not the correct identifiers for that purpose, since they are primarily designed for citation and resource discovery.

A more appropriate method for identifying portions of a data file, or of any other digital object or document, is to use the Annotation Ontology (AO) developed by Paolo Ciccarese of Harvard University, described at http://code.google.com/p/annotation-ontology/wiki/Homepage. AO can be used to identify and annotate portions of a wide variety of resources such as HTML, PDF, Word, Excel, XML documents, images, videos, databases, web services, experimental data and metadata files. Paolo is currently working with a group in Harvard that focuses on biodiversity, who are using OA to address databases and data, and he anticipates publishing version 2.0 of AO in September.

DataCite2RDF – Mapping DataCite Metadata Scheme Terms to ontologies

The DataCite Metadata Kernel version 2.0 [1] specifies the minimal metadata, and optional metadata, that should accompany a DataCite DOI for the identification of a published data entity. Within the Metadata Kernel document there is an XML mapping of these metadata terms, using DCMI Metadata Terms, and an example encoded in XML.

Silvio Peroni and I recently published a mapping of the DataCite metadata elements to RDF using ontology terms [2], in order to enable data repositories to publish DataCite metadata in RDF as Open Linked Data, enabling these metadata to be understood programmatically and integrated automatically with similar data from elsewhere.

Our mapping covers all the main terms, and the Relation Type sub-properties that describe the relationship of the related resource to the resource being registered, but does not address DataCite sub-terms, e.g. 2.2.1 nameIdentifierScheme.

Wherever possible, commonly used Dublin Core Elements, DCMI (Dublin Core Metadata Initiative) Metadata Terms, FOAF (Friend of a Friend Vocabulary) and PRISM (Publishing Requirements for Industry Standard Metadata) terms have been used.

These have been supplemented, as appropriate, by terms:

from FRBR (Functional Requirements for Bibliographic Records),

from the following SPAR (Semantic Publishing and Referencing) Ontologies:

                CiTO, Citation Typing Ontology

                FaBiO, FRBR-aligned Bibliographic Ontology, and

CiTO4Data, an extension of CiTO for datasets that provides the properties cito4data:compiles and cito4data:isCompiledBy that the DataCite Metadata Kernel requires;

and from a new DataCite Ontology (http://purl.org/spar/datacite/) that we created to provide the following four object properties lacking in other Ontologies:

                    datacite:hasPrimaryIdentifier

                    datacite:hasAlternateIdentifier

                    datacite:hasRelatedIdentifier

                    datacite:hasPersonalIdentifier

Use of DCMI Metadata Terms in RDF

An object property has a class or a URI as its object, while a data property has a literal (e.g. text, number, date) as its object, and may have a W3C XML Schema Definition Language (XSD) datatype qualifier, e.g. ^^xsd:date. (See http://www.w3.org/TR/xmlschema11-2/).

Many Dublin Core properties are not formally specified to be one or another, leading to potential confusion. In the following mapping, Dublin Core Elements are always used as data properties, while Dublin Core Metadata Initiative Metadata Terms are used either as data properties or as object properties, as helpfully specified by the Max Planck Digital Library in their document entitled How to use DCMI Metadata as linked data.  

In our DataCite2RDF mapping document [2], alternative mappings are given where appropriate, separated by semi-colons.  Both dc: and dcterms: properties are listed.  Preferred terms are shown bold.  RDF statements are given in Turtle notation.

Accompanying this DataCite2RDF mapping document, we published as Google docs both an RDF mapping of the DataCite XML example, and an RDF mapping of the metadata for a Dryad repository holding, showing how DataCite2RDF can be used for real data.

We welcome feedback on these documents: <david.shotton@zoo.ox.ac.uk> and <speroni@cs.unibo.it>.

[1]    The DataCite Metadata Kernel version 2.0 (2011). http://datacite.org/schema/DataCite-MetadataKernel_v2.0.pdf.

[2]     David Shotton and Silvio Peroni (2011). Mapping DataCite Metadata Scheme Terms (v2.0) to ontologies (DataCite2RDF). Google docs. https://docs.google.com/document/d/1paJgvmCMu3pbM4in6PjWAKO0gP-6ultii3DWQslygq4/edit?authkey=CMeV3tgF&hl=en_GB.