To illustrate three kinds of problems in obtaining correct author lists for Open Citation data from articles in the PubMed Central Open Access subset (OASS), I take three examples, the first of which is the result of a publication policy, the second due to mis-handling of an authorship attribution at the time of publication, and the third exemplifing errors introduced when handling non-English personal names.

The Open Citations Project has aimed to liberate bibliographic references from biomedical research literature as Open Linked Data, using as its starting corpus the Open Access Subset (OASS) of articles within PubMed Central. The greatest problem faced during this project, naively unanticipated before we started, was the extend of incompleteness, noise and errors of various sorts within the reference information extracted from the OASS articles.

PubMed, created by the US National Library of Medicine in DATE, holds bibliographic records and abstracts for essentially all journal articles published in the biomedical sciences. It currently records almost a million new entries each year! PubMed Central (PMC), created as an extension of PubMed, is designed to hold full text articles from among the PubMed entries.

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

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.