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    Automated quality evaluation for a more effective data peer review


    Düsterhus, André and Hense, Andreas (2014) Automated quality evaluation for a more effective data peer review. Data Science Journal, 13. pp. 67-78. ISSN 1683-1470

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    Abstract

    A peer review scheme comparable to that used in traditional scientific journals is a major element missing in bringing publications of raw data up to standards equivalent to those of traditional publications. This paper introduces a quality evaluation process designed to analyse the technical quality as well as the content of a dataset. This process is based on quality tests, the results of which are evaluated with the help of the knowledge of an expert. As a result, the quality is estimated by a single value only. Further, the paper includes an application and a critical discussion on the potential for success, the possible introduction of the process into data centres, and practical implications of the scheme.

    Item Type: Article
    Keywords: Data peer review; Data publication; Quality evaluation; Statistical quality assurance; Meteorological data;
    Academic Unit: Faculty of Social Sciences > Geography
    Item ID: 12288
    Identification Number: https://doi.org/10.2481/dsj.14-009
    Depositing User: André Düsterhus
    Date Deposited: 30 Jan 2020 12:32
    Journal or Publication Title: Data Science Journal
    Publisher: Ubiquity Press
    Refereed: Yes
    URI:

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