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Public Availability of Published Research Data in High-Impact Journals

Author

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  • Alawi A Alsheikh-Ali
  • Waqas Qureshi
  • Mouaz H Al-Mallah
  • John P A Ioannidis

Abstract

Background: There is increasing interest to make primary data from published research publicly available. We aimed to assess the current status of making research data available in highly-cited journals across the scientific literature. Methods and Results: We reviewed the first 10 original research papers of 2009 published in the 50 original research journals with the highest impact factor. For each journal we documented the policies related to public availability and sharing of data. Of the 50 journals, 44 (88%) had a statement in their instructions to authors related to public availability and sharing of data. However, there was wide variation in journal requirements, ranging from requiring the sharing of all primary data related to the research to just including a statement in the published manuscript that data can be available on request. Of the 500 assessed papers, 149 (30%) were not subject to any data availability policy. Of the remaining 351 papers that were covered by some data availability policy, 208 papers (59%) did not fully adhere to the data availability instructions of the journals they were published in, most commonly (73%) by not publicly depositing microarray data. The other 143 papers that adhered to the data availability instructions did so by publicly depositing only the specific data type as required, making a statement of willingness to share, or actually sharing all the primary data. Overall, only 47 papers (9%) deposited full primary raw data online. None of the 149 papers not subject to data availability policies made their full primary data publicly available. Conclusion: A substantial proportion of original research papers published in high-impact journals are either not subject to any data availability policies, or do not adhere to the data availability instructions in their respective journals. This empiric evaluation highlights opportunities for improvement.

Suggested Citation

  • Alawi A Alsheikh-Ali & Waqas Qureshi & Mouaz H Al-Mallah & John P A Ioannidis, 2011. "Public Availability of Published Research Data in High-Impact Journals," PLOS ONE, Public Library of Science, vol. 6(9), pages 1-4, September.
    • Handle: RePEc:plo:pone00:0024357
    DOI: 10.1371/journal.pone.0024357
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    References listed on IDEAS

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    1. Andreas Lundh & Marija Barbateskovic & Asbjørn Hróbjartsson & Peter C Gøtzsche, 2010. "Conflicts of Interest at Medical Journals: The Influence of Industry-Supported Randomised Trials on Journal Impact Factors and Revenue – Cohort Study," PLOS Medicine, Public Library of Science, vol. 7(10), pages 1-7, October.
    2. Neal S Young, 2008. "Why Current Publication May Distort Science," Working Papers id:1757, eSocialSciences.
    3. Neal S Young & John P A Ioannidis & Omar Al-Ubaydli, 2008. "Why Current Publication Practices May Distort Science," PLOS Medicine, Public Library of Science, vol. 5(10), pages 1-5, October.
    4. Keith Baggerly, 2010. "Disclose all data in publications," Nature, Nature, vol. 467(7314), pages 401-401, September.
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    1. Rut Lucas-Dominguez & Adolfo Alonso-Arroyo & Antonio Vidal-Infer & Rafael Aleixandre-Benavent, 2021. "The sharing of research data facing the COVID-19 pandemic," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(6), pages 4975-4990, June.
    2. Stephanie B Linek & Benedikt Fecher & Sascha Friesike & Marcel Hebing, 2017. "Data sharing as social dilemma: Influence of the researcher’s personality," PLOS ONE, Public Library of Science, vol. 12(8), pages 1-24, August.
    3. Zeng, Tong & Wu, Longfeng & Bratt, Sarah & Acuna, Daniel E., 2020. "Assigning credit to scientific datasets using article citation networks," Journal of Informetrics, Elsevier, vol. 14(2).
    4. Sixto-Costoya Andrea & Robinson-Garcia Nicolas & Leeuwen Thed & Costas Rodrigo, 2021. "Exploring the relevance of ORCID as a source of study of data sharing activities at the individual-level: a methodological discussion," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(8), pages 7149-7165, August.
    5. Schweinsberg, Martin & Feldman, Michael & Staub, Nicola & van den Akker, Olmo R. & van Aert, Robbie C.M. & van Assen, Marcel A.L.M. & Liu, Yang & Althoff, Tim & Heer, Jeffrey & Kale, Alex & Mohamed, Z, 2021. "Same data, different conclusions: Radical dispersion in empirical results when independent analysts operationalize and test the same hypothesis," Organizational Behavior and Human Decision Processes, Elsevier, vol. 165(C), pages 228-249.

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