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    Evaluating institutional open access performance: Sensitivity analysis

    Access Status
    Fulltext not available
    Authors
    Huang, Karl
    Neylon, Cameron
    Hosking, Richard
    Montgomery, Lucy
    Wilson, Katie
    Ozaygen, Alkim
    Brookes-Kenworthy, Chloe
    Date
    2020
    Type
    Journal Article
    
    Metadata
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    Citation
    Huang, C.-K. and Neylon, C. and Hosking, R. and Montgomery, L. and Wilson, K. and Ozaygen, A. and Brookes-Kenworthy, C. 2020. Evaluating institutional open access performance: Sensitivity analysis.
    DOI
    10.1101/2020.03.19.998542
    Faculty
    Faculty of Humanities
    School
    School of Media, Creative Arts and Social Inquiry
    URI
    http://hdl.handle.net/20.500.11937/85129
    Collection
    • Curtin Research Publications
    Abstract

    In the article “Evaluating institutional open access performance: Methodology, challenges and assessment” we develop the first comprehensive and reproducible workflow that integrates multiple bibliographic data sources for evaluating institutional open access (OA) performance. The major data sources include Web of Science, Scopus, Microsoft Academic, and Unpaywall. However, each of these databases continues to update, both actively and retrospectively. This implies the results produced by the proposed process are potentially sensitive to both the choice of data source and the versions of them used. In addition, there remain the issue relating to selection bias in sample size and margin of error. The current work shows that the levels of sensitivity relating to the above issues can be significant at the institutional level. Hence, the transparency and clear documentation of the choices made on data sources (and their versions) and cut-off boundaries are vital for reproducibility and verifiability.

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