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Badges for sharing data and code at Biostatistics: an observational study

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      Abstract

      Background:  Reproducible research includes sharing data and code.  The reproducibility policy at the journal Biostatistics rewards articles with badges for data and code sharing.  This study investigates the effect of badges at increasing reproducible research, specifically, data and code sharing, at Biostatistics. Methods:  The setting of this observational study is the Biostatistics and Statistics in Medicine (control journal) online research archives.  The data consisted of 240 randomly sampled articles from 2006 to 2013 (30 articles per year) per journal, a total sample of 480 articles.  Data analyses included: plotting probability of data and code sharing by article submission date, and Bayesian logistic regression modelling to test for a difference in the probability of making data and code available after the introduction of badges at Biostatistics.  Results:  The probability of data sharing was higher at Biostatistics than the control journal but the probability of code sharing was comparable for both journals.  The probability of data sharing increased by 3.5 times (95% credible interval: 1.4 to 7.4 times, p-value probability that sharing increased: 0.996) after badges were introduced at Biostatistics.  On an absolute scale, however, this difference was only a 7.3% increase in data sharing (95% CI: 2 to 14%, p-value: 0.996).  Badges did not have an impact on code sharing at the journal (mean increase: 1.1 times, 95% credible interval: 0.45 to 2.14 times, p-value probability that sharing increased: 0.549).  Conclusions:  The effect of badges at Biostatistics was a 7.3% increase in the data sharing rate, 5 times less than the effect of badges on data sharing at Psychological Science (37.9% badge effect).  Though the effect of badges at Biostatistics did not impact code sharing, and was associated with only a moderate effect on data sharing, badges are an interesting step that journals are taking to incentivise and promote reproducible research.

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      Reproducible research in computational science.

       Roger Peng (2011)
      Computational science has led to exciting new developments, but the nature of the work has exposed limitations in our ability to evaluate published findings. Reproducibility has the potential to serve as a minimum standard for judging scientific claims when full independent replication of a study is not possible.
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        What does research reproducibility mean?

        The language and conceptual framework of "research reproducibility" are nonstandard and unsettled across the sciences. In this Perspective, we review an array of explicit and implicit definitions of reproducibility and related terminology, and discuss how to avoid potential misunderstandings when these terms are used as a surrogate for "truth."
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          The availability of research data declines rapidly with article age

          Policies ensuring that research data are available on public archives are increasingly being implemented at the government [1], funding agency [2-4], and journal [5,6] level. These policies are predicated on the idea that authors are poor stewards of their data, particularly over the long term [7], and indeed many studies have found that authors are often unable or unwilling to share their data [8-11]. However, there are no systematic estimates of how the availability of research data changes with time since publication. We therefore requested datasets from a relatively homogenous set of 516 articles published between 2 and 22 years ago, and found that availability of the data was strongly affected by article age. For papers where the authors gave the status of their data, the odds of a dataset being extant fell by 17% per year. In addition, the odds that we could find a working email address for the first, last or corresponding author fell by 7% per year. Our results reinforce the notion that, in the long term, research data cannot be reliably preserved by individual researchers, and further demonstrate the urgent need for policies mandating data sharing via public archives.
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            Author and article information

            Affiliations
            [1 ]Institute of Health and Biomedical Innovation, Queensland University of Technology, Brisbane, Queensland, 4001, Australia
            [1 ]Division of Environmental Genomics and Systems Biology, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
            [2 ]Open Humans (openhumans.org), Frankfurt am Main, Germany
            Queensland University of Technology, Australia
            [1 ]Stress Research Institute, Stockholm University, Stockholm, Sweden
            [2 ]Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden
            Queensland University of Technology, Australia
            Author notes

            No competing interests were disclosed.

            Contributors
            Role: Conceptualization, Role: Investigation, Role: Methodology, Role: Writing – Original Draft Preparation, ORCID: https://orcid.org/0000-0003-3637-2423
            Role: Conceptualization, Role: Data Curation, Role: Formal Analysis, Role: Methodology, Role: Software, Role: Supervision, Role: Validation, Role: Visualization, Role: Writing – Review & Editing, ORCID: https://orcid.org/0000-0001-6339-0374
            Journal
            F1000Res
            F1000Res
            F1000Research
            F1000Research
            F1000 Research Limited (London, UK )
            2046-1402
            19 January 2018
            2018
            : 7
            5843843
            10.12688/f1000research.13477.1
            Copyright: © 2018 Rowhani-Farid A and Barnett AG

            This is an open access article distributed under the terms of the Creative Commons Attribution Licence, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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            Funding
            Funded by: Queensland University of Technology
            This study was supported in kind by the Institute of Health and Biomedical Innovation at the Queensland University of Technology.
            The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
            Categories
            Research Article
            Articles

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