As an approach towards developing best practice for data citation, I recently wrote a Data Citation Best Practice Discussion Document that is available on Google Docs, and that I have now slightly revised to Version 2 [1]. In that document, I first compared what is recommended by DataCite [2] and by Altman and King [3] with what currently practised by the Dryad Data Repository and what presently occurs ‘in the wild’ in a

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 meaning of the word “dataset” is ambiguous, changing with context.

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.

In addition to using CiTO and CiTO4Data to describe relationships of relevance to data entities, as discussed in the previous blog post, FaBiO, the FRBR aligned Bibliographic Ontology described elsewhere, another member of the suite of SPAR (Semantic Publishing and Referencing) Ontologies, also has a number of classes and properties specifically designed for addressing data, software, metadata and other non-bibliographic entities.

This is the first of a series of blog posts on the Open Citations blog that address the problem of citing data entities, for example a data package in a data repository, rather than bibliographic entities such as journal articles. For these purposes, the existence of DataCite to assign DOIs to datasets, and extensions to the SPAR (Semantic Publishing and Referencing) Ontologies to handle data items, are both important.

In the previous blog post, I made a textual comparison between BIBO v1.3, the Bibliographic Ontology developed by Bruce D’Arcus and Frédérick Giasson, and FaBiO, the FRBR-aligned Bibliographic Ontology.

BIBO v1.3, the Bibliographic Ontology developed by Bruce D’Arcus and Frédérick Giasson [1], was the first OWL ontology dedicated to describing bibliographic entities, and has attracted a wide group of users. It provided the much-needed ability to describe the nature of cited works in RDF to a high degree of granularity, in terms of Title, Abstract, Journal, Volume, Pages, ISSN, DOI, dataCopyrighted, editor, etc.

One of the most important need for a publisher is to categorising each bibliographic entity it produces by adding free-text keywords and/or specific terms structured according to recognised classification systems and/or thesauri specific for certain academic disciplines. Academics have the same need when annotating bibliographic references.

FRBR, the Functional Requirements for Bibliographic Records [1] is a general model, proposed by the International Federation of Library Association (IFLA), for describing bibliographic documents. It works for both physical and digital resources and has proved to be very flexible and powerful. One of the most important aspect of FRBR is the fact that it is not associated with a particular metadata schema or implementation.