Achieving human and machine accessibility of cited data in scholarly publications
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...
| Main Authors: | , , , , , , , , , , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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PeerJ Inc.
2015-05-01
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| Series: | PeerJ Computer Science |
| Subjects: | |
| Online Access: | https://peerj.com/articles/cs-1.pdf |
| _version_ | 1818356670274732032 |
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| author | Joan Starr Eleni Castro Mercè Crosas Michel Dumontier Robert R. Downs Ruth Duerr Laurel L. Haak Melissa Haendel Ivan Herman Simon Hodson Joe Hourclé John Ernest Kratz Jennifer Lin Lars Holm Nielsen Amy Nurnberger Stefan Proell Andreas Rauber Simone Sacchi Arthur Smith Mike Taylor Tim Clark |
| author_facet | Joan Starr Eleni Castro Mercè Crosas Michel Dumontier Robert R. Downs Ruth Duerr Laurel L. Haak Melissa Haendel Ivan Herman Simon Hodson Joe Hourclé John Ernest Kratz Jennifer Lin Lars Holm Nielsen Amy Nurnberger Stefan Proell Andreas Rauber Simone Sacchi Arthur Smith Mike Taylor Tim Clark |
| author_sort | Joan Starr |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-13T20:00:54Z |
| format | Article |
| id | doaj.art-cccb9a6fed394c4cbbc45a9cac4ea7b3 |
| institution | Directory Open Access Journal |
| issn | 2376-5992 |
| language | English |
| last_indexed | 2024-12-13T20:00:54Z |
| publishDate | 2015-05-01 |
| publisher | PeerJ Inc. |
| record_format | Article |
| series | PeerJ Computer Science |
| spelling | doaj.art-cccb9a6fed394c4cbbc45a9cac4ea7b32022-12-21T23:33:12ZengPeerJ Inc.PeerJ Computer Science2376-59922015-05-011e110.7717/peerj-cs.1Achieving human and machine accessibility of cited data in scholarly publicationsJoan Starr0Eleni Castro1Mercè Crosas2Michel Dumontier3Robert R. Downs4Ruth Duerr5Laurel L. Haak6Melissa Haendel7Ivan Herman8Simon Hodson9Joe Hourclé10John Ernest Kratz11Jennifer Lin12Lars Holm Nielsen13Amy Nurnberger14Stefan Proell15Andreas Rauber16Simone Sacchi17Arthur Smith18Mike Taylor19Tim Clark20California Digital Library, Oakland, CA, United States of AmericaInstitute of Quantitative Social Sciences, Harvard University, Cambridge, MA, United States of AmericaInstitute of Quantitative Social Sciences, Harvard University, Cambridge, MA, United States of AmericaStanford University School of Medicine, Stanford, CA, United States of AmericaCenter for International Earth Science Information Network (CIESIN), Columbia University, Palisades, NY, United States of AmericaNational Snow and Ice Data Center, Boulder, CO, United States of AmericaORCID, Inc., Bethesda, MD, United States of AmericaOregon Health and Science University, Portland, OR, United States of AmericaWorld Wide Web Consortium (W3C)/Centrum Wiskunde en Informatica (CWI), Amsterdam, NetherlandsICSU Committee on Data for Science and Technology (CODATA), Paris, FranceSolar Data Analysis Center, NASA Goddard Space Flight Center, Greenbelt, MD, United States of AmericaCalifornia Digital Library, Oakland, CA, United States of AmericaPublic Library of Science, San Francisco, CA, United States of AmericaEuropean Organization for Nuclear Research (CERN), Geneva, SwitzerlandColumbia University Libraries/Information Services, New York, NY, United States of AmericaSBA Research, Vienna, AustriaInstitute of Software Technology and Interactive Systems, Vienna University of Technology/TU Wien, AustriaColumbia University Libraries/Information Services, New York, NY, United States of AmericaAmerican Physical Society, Ridge, NY, United States of AmericaElsevier, Oxford, United KingdomHarvard Medical School, Boston, MA, United States of AmericaReproducibility 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.https://peerj.com/articles/cs-1.pdfData citationMachine accessibilityData archivingData accessibility |
| spellingShingle | Joan Starr Eleni Castro Mercè Crosas Michel Dumontier Robert R. Downs Ruth Duerr Laurel L. Haak Melissa Haendel Ivan Herman Simon Hodson Joe Hourclé John Ernest Kratz Jennifer Lin Lars Holm Nielsen Amy Nurnberger Stefan Proell Andreas Rauber Simone Sacchi Arthur Smith Mike Taylor Tim Clark Achieving human and machine accessibility of cited data in scholarly publications PeerJ Computer Science Data citation Machine accessibility Data archiving Data accessibility |
| title | Achieving human and machine accessibility of cited data in scholarly publications |
| title_full | Achieving human and machine accessibility of cited data in scholarly publications |
| title_fullStr | Achieving human and machine accessibility of cited data in scholarly publications |
| title_full_unstemmed | Achieving human and machine accessibility of cited data in scholarly publications |
| title_short | Achieving human and machine accessibility of cited data in scholarly publications |
| title_sort | achieving human and machine accessibility of cited data in scholarly publications |
| topic | Data citation Machine accessibility Data archiving Data accessibility |
| url | https://peerj.com/articles/cs-1.pdf |
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