Achieving human and machine accessibility of cited data in scholarly publications

PeerJ Computer Science

Arthur Smith¹  John Ernest Kratz²  Joan Starr²  Robert R. Downs³  Simone Sacchi⁴  Amy Nurnberger⁴  Mike Taylor⁵  Lars Holm Nielsen⁶  Tim Clark⁷  Simon Hodson⁸  Eleni Castro⁹  Mercè Crosas⁹  Andreas Rauber^1,10  Ruth Duerr^1,11  Laurel L. Haak^1,12  Melissa Haendel^1,13  Jennifer Lin^1,14  Stefan Proell^1,15  Joe Hourclé^1,16  Michel Dumontier^1,17  Ivan Herman^1,18 

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

关键词: Data citation;    Machine accessibility;    Data archiving;    Data accessibility;   

DOI  :  10.7717/peerj-cs.1

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown