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Reproducible Research Practices and Transparency across the Biomedical Literature

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  • Shareen A Iqbal
  • Joshua D Wallach
  • Muin J Khoury
  • Sheri D Schully
  • John P A Ioannidis

Abstract

There is a growing movement to encourage reproducibility and transparency practices in the scientific community, including public access to raw data and protocols, the conduct of replication studies, systematic integration of evidence in systematic reviews, and the documentation of funding and potential conflicts of interest. In this survey, we assessed the current status of reproducibility and transparency addressing these indicators in a random sample of 441 biomedical journal articles published in 2000–2014. Only one study provided a full protocol and none made all raw data directly available. Replication studies were rare (n = 4), and only 16 studies had their data included in a subsequent systematic review or meta-analysis. The majority of studies did not mention anything about funding or conflicts of interest. The percentage of articles with no statement of conflict decreased substantially between 2000 and 2014 (94.4% in 2000 to 34.6% in 2014); the percentage of articles reporting statements of conflicts (0% in 2000, 15.4% in 2014) or no conflicts (5.6% in 2000, 50.0% in 2014) increased. Articles published in journals in the clinical medicine category versus other fields were almost twice as likely to not include any information on funding and to have private funding. This study provides baseline data to compare future progress in improving these indicators in the scientific literature.Examination of recent trends in reproducibility and transparency practices in biomedical research reveals an ongoing lack of access to full datasets and detailed protocols for both clinical and non-clinical studies.Author Summary: There is increasing interest in the scientific community about whether published research is transparent and reproducible. Lack of replication and non-transparency decreases the value of research. Several biomedical journals have started to encourage or require authors to submit detailed protocols, full datasets, and disclose information on funding and potential conflicts of interest. In this study, we investigate the reproducibility and transparency practices across the full spectrum of published biomedical literature from 2000–2014. We identify an ongoing lack of access to full datasets and detailed protocols for both clinical and non-clinical biomedical investigation. We also map the availability of information on funding and conflicts of interest in this literature. The results from this study provide baseline data to compare future progress in improving these indicators in the scientific literature. We believe that this information may be essential to sensitize stakeholders in science about the need for improving reproducibility and transparency practices.

Suggested Citation

  • Shareen A Iqbal & Joshua D Wallach & Muin J Khoury & Sheri D Schully & John P A Ioannidis, 2016. "Reproducible Research Practices and Transparency across the Biomedical Literature," PLOS Biology, Public Library of Science, vol. 14(1), pages 1-13, January.
    • Handle: RePEc:plo:pbio00:1002333
    DOI: 10.1371/journal.pbio.1002333
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    References listed on IDEAS

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    1. John P A Ioannidis, 2005. "Why Most Published Research Findings Are False," PLOS Medicine, Public Library of Science, vol. 2(8), pages 1-1, August.
    2. John P A Ioannidis, 2014. "How to Make More Published Research True," PLOS Medicine, Public Library of Science, vol. 11(10), pages 1-6, October.
    3. Keith Baggerly, 2010. "Disclose all data in publications," Nature, Nature, vol. 467(7314), pages 401-401, September.
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    2. Estelle Dumas-Mallet & Katherine Button & Thomas Boraud & Marcus Munafo & François Gonon, 2016. "Replication Validity of Initial Association Studies: A Comparison between Psychiatry, Neurology and Four Somatic Diseases," PLOS ONE, Public Library of Science, vol. 11(6), pages 1-20, June.
    3. Jacques Muthusi & Samuel Mwalili & Peter Young, 2019. "%svy_logistic_regression: A generic SAS macro for simple and multiple logistic regression and creating quality publication-ready tables using survey or non-survey data," PLOS ONE, Public Library of Science, vol. 14(9), pages 1-14, September.
    4. Christopher Allen & David M A Mehler, 2019. "Open science challenges, benefits and tips in early career and beyond," PLOS Biology, Public Library of Science, vol. 17(5), pages 1-14, May.
    5. Sheyu Li & Valentyn Litvin & Charles F. Manski, 2022. "Partial Identification of Personalized Treatment Response with Trial-reported Analyses of Binary Subgroups," NBER Working Papers 30461, National Bureau of Economic Research, Inc.
    6. John P A Ioannidis, 2018. "Meta-research: Why research on research matters," PLOS Biology, Public Library of Science, vol. 16(3), pages 1-6, March.
    7. Masselus, Lise & Petrik, Christina & Ankel-Peters, Jörg, 2024. "Lost in the Design Space? Construct Validity in the Microfinance Literature," OSF Preprints nwp8k, Center for Open Science.
    8. Stavroula Kousta & Christine Ferguson & Emma Ganley, 2016. "Meta-Research: Broadening the Scope of PLOS Biology," PLOS Biology, Public Library of Science, vol. 14(1), pages 1-2, January.
    9. Sadri, Arash, 2022. "The Ultimate Cause of the “Reproducibility Crisis”: Reductionist Statistics," MetaArXiv yxba5, Center for Open Science.

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