Achieving human and machine accessibility of cited data in scholarly publications

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1 California Digital Library, University of California, Office of the President, Oakland, CA, USA
2 Institute of Quantitative Social Science, Harvard University, Cambridge, MA, USA
3 Stanford Center for Biomedical Informatics Research, School of Medicine, Stanford University, Palo Alto, CA, USA
4 Center for International Earth Science Information Network (CIESIN), Columbia University, Palisades, NY, USA
5 University of Colorado at Boulder, National Snow and Ice Data Center, Boulder, CO, USA
6 ORCID, Inc., Bethesda, MD, USA
7 Oregon Health and Science University, Portland, OR, USA
8 W3C/CWI, Amsterdam, The Netherlands
9 CODATA (ICSU Committee on Data for Science and Technology), Paris, FR
10 Solar Data Analysis Center, NASA Goddard Space Flight Center, Greenbelt, MD, USA
11 Public Library of Science, San Francisco, CA, USA
12 European Organization for Nuclear Research (CERN), Geneva, Switzerland
13 Columbia University Libraries/Information Services, New York, NY, USA
14 SBA Research, Vienna, AT
15 Institute of Software Technology and Interactive Systems, Vienna University of Technology / TU Wien, Vienna, AT
16 Journal Information Systems, American Physical Society, Ridge, NY, USA
17 Elsevier, Oxford, UK
18 Department of Neurology, Harvard Medical School, Boston, United States
DOI
10.7287/peerj.preprints.697v2
Published
Accepted
Subject Areas
Science Policy, Human-Computer Interaction, Computational Science
Keywords
data accessibility, data citation, machine accessibility, data archiving, data accessibility, data citation, data archiving, machine accessibility
Copyright
© 2014 Starr et al.
Licence
This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ PrePrints) and either DOI or URL of the article must be cited.
Cite this article
Starr J, Castro E, Crosas M, Dumontier M, Downs RR, Duerr R, Haak L, Haendel M, Herman I, Hodson S, Hourclé J, Kratz JE, Lin J, Nielsen LH, Nurnberger A, Pröll S, Rauber A, Sacchi S, Smith AP, Taylor M, Clark T. 2014. Achieving human and machine accessibility of cited data in scholarly publications. PeerJ PrePrints 2:e697v2

Abstract

This short article provides operational guidance on implementing scholarly data citation and data deposition, in conformance with the Joint Declaration of Data Citation Principles (JDDCP, http://force11.org/datacitation) to help achieve widespread, uniform human and machine accessibility of deposited data. The JDDCP is the outcome of a cross-domain effort to establish core principles around cited data in scholarly publications. It deals with important issues in identification, deposition, description, accessibility, persistence, and evidential status of cited data. Eighty-five scholarly, governmental, and funding institutions have now endorsed the JDDCP. The purpose of this article is to provide the necessary guidance for JDDCP-endorsing organizations to implement these principles and to achieve their widespread adoption.

Author Comment

This version adds an important reference on the evolution of data citation (Altman & Crosas 2013) and makes some language changes for a better flow of the article. Metadata has been corrected to remove checkbox references to "conducting experiments", which is not a task applicable to this article.