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Repository Approaches to Improving the Quality of Shared Data and Code

Author

Listed:
  • Ana Trisovic

    (Institute for Quantitative Social Science, Harvard University, 1737 Cambridge St, Cambridge, MA 02138, USA)

  • Katherine Mika

    (Institute for Quantitative Social Science, Harvard University, 1737 Cambridge St, Cambridge, MA 02138, USA)

  • Ceilyn Boyd

    (Institute for Quantitative Social Science, Harvard University, 1737 Cambridge St, Cambridge, MA 02138, USA)

  • Sebastian Feger

    (European Organization for Nuclear Research (CERN), 1, Esplanade des Particules, CH-1217 Meyrin, Switzerland
    LMU Munich, 1, Geschwister-Scholl-Platz, 80539 Munich, Germany)

  • Mercè Crosas

    (Institute for Quantitative Social Science, Harvard University, 1737 Cambridge St, Cambridge, MA 02138, USA)

Abstract

Sharing data and code for reuse has become increasingly important in scientific work over the past decade. However, in practice, shared data and code may be unusable, or published results obtained from them may be irreproducible. Data repository features and services contribute significantly to the quality, longevity, and reusability of datasets. This paper presents a combination of original and secondary data analysis studies focusing on computational reproducibility, data curation, and gamified design elements that can be employed to indicate and improve the quality of shared data and code. The findings of these studies are sorted into three approaches that can be valuable to data repositories, archives, and other research dissemination platforms.

Suggested Citation

  • Ana Trisovic & Katherine Mika & Ceilyn Boyd & Sebastian Feger & Mercè Crosas, 2021. "Repository Approaches to Improving the Quality of Shared Data and Code," Data, MDPI, vol. 6(2), pages 1-12, February.
    • Handle: RePEc:gam:jdataj:v:6:y:2021:i:2:p:15-:d:492455
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    References listed on IDEAS

    as
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    2. Christine L. Borgman, 2012. "The conundrum of sharing research data," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 63(6), pages 1059-1078, June.
    3. 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.
    4. Gary King, 2007. "An Introduction to the Dataverse Network as an Infrastructure for Data Sharing," Sociological Methods & Research, , vol. 36(2), pages 173-199, November.
    5. Monya Baker, 2016. "1,500 scientists lift the lid on reproducibility," Nature, Nature, vol. 533(7604), pages 452-454, May.
    Full references (including those not matched with items on IDEAS)

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