A quality and popularity based ranking method of research datasets

KOYA, Kushwanth and CHOWDHURY, Gobinda (2022). A quality and popularity based ranking method of research datasets. In: APIT 2022: 2022 4th Asia Pacific Information Technology Conference. New York, Association for Computing Machinery, 103-110.

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Link to published version:: https://doi.org/10.1145/3512353.3512368
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    Abstract

    Research outputs are the final products in the scientific research process and their quality is progressively being evaluated by various methods such as altmetrics, bibliometrics, impact factors and citation count etc. However, a significant component of scientific research involves creating/collecting/curating research datasets and globally, funding agencies and governments are mandating an open access policy on research datasets. Though repositories exist to store the datasets, there is no metricised guidance, indicating the quality of datasets for researchers wishing to reuse. We propose a novel method for ranking and visualising research datasets based on their quality and popularity, constructed through a normalised citation count since the year of origin, total cites and the impact factor of the journals which publish the articles citing the dataset. Additionally, we present the process flow for a proposed digital information system for the access of datasets according to their discipline and rank based on the variables. The proposed method is expected to assist researchers, globally, to choose the right datasets for their research, encourage researchers to share their datasets and promote interdisciplinary research.

    Item Type: Book Section
    Additional Information: "The accepted and registered papers can be published in the ACM International Conference Proceedings (ISBN: 978-1-4503-9557-1), which will be indexed by Ei Compendex and Scopus, and submitted to be reviewed by Thomson Reuters Conference Proceedings Citation Index (ISI Web of Science)."
    Identification Number: https://doi.org/10.1145/3512353.3512368
    Page Range: 103-110
    SWORD Depositor: Symplectic Elements
    Depositing User: Symplectic Elements
    Date Deposited: 28 Oct 2021 14:08
    Last Modified: 22 Mar 2022 10:30
    URI: http://shura.shu.ac.uk/id/eprint/29219

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