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Unrestricted Versus Regulated Open Data Governance: A Bibliometric Comparison of SARS-CoV-2 Nucleotide Sequence Databases

Nathanael Sheehan,Federico Botta,Sabina Leonelli

Published 2024 in Data Science Journal

ABSTRACT

Two distinct modes of data governance have emerged in accessing and reusing viral data pertaining to COVID-19: an unrestricted model, espoused by data repositories part of the International Nucleotide Sequence Database Collaboration and a regulated model promoted by the Global Initiative on Sharing All Influenza data. In this paper, we focus on publications mentioning either infrastructure in the period between January 2020 and January 2023, thus capturing a period of acute response to the COVID-19 pandemic. Through a variety of bibliometric and network science methods, we compare the extent to which either data infrastructure facilitated collaboration from different countries around the globe to understand how data reuse can enhance forms of diversity between institutions, countries, and funding groups. Our findings reveal disparities in representation and usage between the two data infrastructures. We conclude that both approaches offer useful lessons, with the unrestricted model providing insights into complex data linkage and the regulated model demonstrating the importance of global representation.

PUBLICATION RECORD

  • Publication year

    2024

  • Venue

    Data Science Journal

  • Publication date

    Unknown publication date

  • Fields of study

    Political Science, Medicine, Computer Science, Environmental Science

  • Identifiers
    DOI 10.5334/dsj-2024-029
  • External record

    Open on Semantic Scholar

  • Source metadata

    Semantic Scholar

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