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Journal data policies: Exploring how the understanding of editors and authors corresponds to the policies themselves

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  • Thu-Mai Christian
  • Amanda Gooch
  • Todd Vision
  • Elizabeth Hull

Abstract

Despite the increase in the number of journals issuing data policies requiring authors to make data underlying reporting findings publicly available, authors do not always do so, and when they do, the data do not always meet standards of quality that allow others to verify or extend published results. This phenomenon suggests the need to consider the effectiveness of journal data policies to present and articulate transparency requirements, and how well they facilitate (or hinder) authors’ ability to produce and provide access to data, code, and associated materials that meet quality standards for computational reproducibility. This article describes the results of a research study that examined the ability of journal-based data policies to: 1) effectively communicate transparency requirements to authors, and 2) enable authors to successfully meet policy requirements. To do this, we conducted a mixed-methods study that examined individual data policies alongside editors’ and authors’ interpretation of policy requirements to answer the following research questions. Survey responses from authors and editors along with results from a content analysis of data policies found discrepancies among editors’ assertion of data policy requirements, authors’ understanding of policy requirements, and the requirements stated in the policy language as written. We offer explanations for these discrepancies and offer recommendations for improving authors’ understanding of policies and increasing the likelihood of policy compliance.

Suggested Citation

  • Thu-Mai Christian & Amanda Gooch & Todd Vision & Elizabeth Hull, 2020. "Journal data policies: Exploring how the understanding of editors and authors corresponds to the policies themselves," PLOS ONE, Public Library of Science, vol. 15(3), pages 1-15, March.
  • Handle: RePEc:plo:pone00:0230281
    DOI: 10.1371/journal.pone.0230281
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    References listed on IDEAS

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    1. Benedikt Fecher & Sascha Friesike & Marcel Hebing, 2015. "What Drives Academic Data Sharing?," PLOS ONE, Public Library of Science, vol. 10(2), pages 1-25, February.
    2. Vlaeminck, Sven & Herrmann, Lisa-Kristin, 2015. "Data Policies and Data Archives: A New Paradigm for Academic Publishing in Economic Sciences?," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, pages 145-155.
    3. Paul Sturges & Marianne Bamkin & Jane H.S. Anders & Bill Hubbard & Azhar Hussain & Melanie Heeley, 2015. "Research data sharing: Developing a stakeholder-driven model for journal policies," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 66(12), pages 2445-2455, December.
    4. Victoria Stodden & Jennifer Seiler & Zhaokun Ma, 2018. "An empirical analysis of journal policy effectiveness for computational reproducibility," Proceedings of the National Academy of Sciences, Proceedings of the National Academy of Sciences, vol. 115(11), pages 2584-2589, March.
    5. Carol Tenopir & Elizabeth D Dalton & Suzie Allard & Mike Frame & Ivanka Pjesivac & Ben Birch & Danielle Pollock & Kristina Dorsett, 2015. "Changes in Data Sharing and Data Reuse Practices and Perceptions among Scientists Worldwide," PLOS ONE, Public Library of Science, vol. 10(8), pages 1-24, August.
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    Cited by:

    1. Kraft-Todd, Gordon T. & Rand, David G., 2021. "Practice what you preach: Credibility-enhancing displays and the growth of open science," Organizational Behavior and Human Decision Processes, Elsevier, vol. 164(C), pages 1-10.

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