Is Data Publication the Right Metaphor? is an essay by Mark Parsons and Peter Fox to be published in the Data Science Journal, for which a preprint has been provided for open pre-publication community peer review at http://mp-datamatters.blogspot.com/2011/12/seeking-open-review-of-provocative-data.html.

In biology, the fields of macromolecular structural biology and sequence bioinformatics have, since the 1970s, had established international databases for the deposition of data, and journal policies mandating such deposition prior to acceptance for publication of manuscripts describing the data.  Similar good practices have developed more recently in other disciplines, notably astronomy.

In the previous post, I outlined reasons why researchers don’t publish data, presented as evidence to the Royal Society’s Policy Study “Science as a Public Enterprise” Call for Evidence.

Evidence submitted by David Shotton in response to the Royal Society’s Policy Study “Science as a Public Enterprise” Call for Evidence, addressing the following two topics raised by that call: Getting Researcher buy-in. How do we get researchers to be more willing to share data? What is there to be learned from disciplines such as genomics which have norms which favour wide sharing of data?

Alistair Miles, of SKOS fame, who formerly worked in our research group, spent yesterday afternoon catching up with us, and has written a nice blog post on the MalariaGEN Informatics Blog describing our current activities, including our work on the Open Citations Corpus, and how they might intersect with the data management activities of the MalariaGEN, the Malaria Genome Epidemiology Network for which he now works.

In a recent blog post, Heather Piwowar, in discussing the advantages of citing datasets in the reference list of the article, said “No journals have standardized on this approach so far”. However, Pensoft Journals, a publisher that specializes in publishing biodiversity and biological systematics papers, and that has taken the lead in promoting the publication of datasets with DOIs, has exactly such a policy.

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.