We are excited that our paper, Achieving Human and Machine Accessibility of Cited Data in Scholarly Publications, has just been published and we are honored that it is ARTICLE #1 in the newly-launched open-access journal PeerJ Computer Science.
This article was the joint work of the multi-institution IDMETA team, led by Joan Starr (California Digital Library) as part of the Force11 Data Citation Implementation Group. It outlines a detailed set of guidelines for implementing data citations that are accessible both to humans via Web browsers, and to computers and software via Web services.
It contains specific technical recommendations on how to implement the Joint Declaration of Data Citation Principles, including how archived data should be cited, archived, and identified; how identifiers should resolve; which identifier schemes are recommended; which kinds of web services provide best accessibility; and how landing pages should be organized to provide maximal human and machine accessibility of the cited data.
Together with the new NISO JATS revisions to support direct data citation – a complementary effort led by Jo McEntyre (European Bioinformatics Institute) – this article provides robust working technical guidelines that publishers and archivists can follow to implement the JDDCP. As we note in the article:
"The recommendations outlined here were developed as part of a community process by participants representing a wide variety of scholarly organizations, hosted by the FORCE11 Data Citation Implementation Group (DCIG) (https://www.force11.org/data-citation-implementation-group/). This work was conducted over a period of approximately one year beginning in early 2014 as a follow-on activity to the completed JDDCP.
I would like to specifically mention what a great experience working with the PeerJ CS reviewers and production team was. Total time from submission to first review was two weeks. Harry Hocheiser the Academic Editor for this article, and Jackie Thai, Head of Publishing Operations, were outstanding to work with.
We hope that this article along with the new NISO JATS revision will help accelerate the wide adoption of data citation in scholarly literature, to better enable open transparency for validation, reuse and extension of results.
Transparency, validation, reuse & extension imply robustness of results. Robust findings are reproducible.
Harvard Medical School & Massachusetts General Hospital