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PeerJ Comput Sci. 2015;1. pii: e1.

Achieving human and machine accessibility of cited data in scholarly publications.

Author information

1
California Digital Library, Oakland, CA, United States of America.
2
Institute of Quantitative Social Sciences, Harvard University, Cambridge, MA, United States of America.
3
Stanford University School of Medicine, Stanford, CA, United States of America.
4
Center for International Earth Science Information Network (CIESIN), Columbia University, Palisades, NY, United States of America.
5
National Snow and Ice Data Center, Boulder, CO, United States of America.
6
ORCID, Inc., Bethesda, MD, United States of America.
7
Oregon Health and Science University, Portland, OR, United States of America.
8
World Wide Web Consortium (W3C)/Centrum Wiskunde en Informatica (CWI), Amsterdam, Netherlands.
9
ICSU Committee on Data for Science and Technology (CODATA), Paris, France.
10
Solar Data Analysis Center, NASA Goddard Space Flight Center, Greenbelt, MD, United States of America.
11
Public Library of Science, San Francisco, CA, United States of America.
12
European Organization for Nuclear Research (CERN), Geneva, Switzerland.
13
Columbia University Libraries/Information Services, New York, NY, United States of America.
14
SBA Research, Vienna, Austria.
15
Institute of Software Technology and Interactive Systems, Vienna University of Technology/TU Wien, Austria.
16
American Physical Society, Ridge, NY, United States of America.
17
Elsevier, Oxford, United Kingdom.
18
Harvard Medical School, Boston, MA, United States of America.

Abstract

Reproducibility and reusability of research results is an important concern in scientific communication and science policy. A foundational element of reproducibility and reusability is the open and persistently available presentation of research data. However, many common approaches for primary data publication in use today do not achieve sufficient long-term robustness, openness, accessibility or uniformity. Nor do they permit comprehensive exploitation by modern Web technologies. This has led to several authoritative studies recommending uniform direct citation of data archived in persistent repositories. Data are to be considered as first-class scholarly objects, and treated similarly in many ways to cited and archived scientific and scholarly literature. Here we briefly review the most current and widely agreed set of principle-based recommendations for scholarly data citation, the Joint Declaration of Data Citation Principles (JDDCP). We then present a framework for operationalizing the JDDCP; and a set of initial recommendations on identifier schemes, identifier resolution behavior, required metadata elements, and best practices for realizing programmatic machine actionability of cited data. The main target audience for the common implementation guidelines in this article consists of publishers, scholarly organizations, and persistent data repositories, including technical staff members in these organizations. But ordinary researchers can also benefit from these recommendations. The guidance provided here is intended to help achieve widespread, uniform human and machine accessibility of deposited data, in support of significantly improved verification, validation, reproducibility and re-use of scholarly/scientific data.

KEYWORDS:

Data Science; Data accessibility; Data archiving; Data citation; Digital Libraries; Human–Computer Interaction; Machine accessibility; World Wide Web and Web Science

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