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Gsm_hdata: a bibliometric tool to analyze data from google scholar metrics

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Abstract

Google Scholar Metrics has been considered an alternative source for analyzing the impact of academic publications. Its free access, user-friendly interface, and wide coverage have contributed to its growing popularity. However, it presents several challenges, such as the absence of advanced search engines and data extraction tools, the lack of bibliometric standardization, and the non-identification of self-citations, making it difficult to use in large-scale research. To address these challenges, Gsm_hdata was developed as a bibliometric tool for research, extraction, and analysis of citation data from the h5-index of Google Scholar Metrics. The tool consists of four resources that perform automatic searches and data extraction of indexed publications, identify and calculate journal self-citation indicators, and allow for yearly data updates. Initial tests have shown that the tool is efficient and accurate. However, some manual cleaning and verification steps are still necessary. There is room for improvement in the tool, including the implementation of a certification system for the publication search engine to avoid errors with homonymous or similarly titled publications. With technical improvements and more robust tests, Gsm_hdata has the potential to become a relevant tool for bibliometric studies. As well as being used in empirical research, this innovative tool can be used by funding agencies that choose the h5-index as a journal impact indicator.

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Acknowledgements

Adilson Luiz Pinto would like to thank the Conselho Nacional de Desenvolvimento Científico e Tecnológico - CNPq. Process 402042/2022-0 and 200937/2022-7. Post-Doctorate Abroad. Protocol 7763808095369450.

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Authors and Affiliations

  1. Federal University of Santa Catarina, Florianópolis, Brazil

    Fábio Lorensi do Canto, Adilson Luiz Pinto & Edson Mário Gavron

  2. Centro Federal de Educação Tecnológica de Minas Gerais, Belo Horizonte, Brazil

    Thiago Magela Rodrigues Dias

  3. Federal Technological University of Paraná, Curitiba, Brazil

    Marcos Talau

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  1. Fábio Lorensi do Canto
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  2. Adilson Luiz Pinto
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Correspondence to Fábio Lorensi do Canto.

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do Canto, F.L., Pinto, A.L., Dias, T.M.R. et al. Gsm_hdata: a bibliometric tool to analyze data from google scholar metrics. Mobile Netw Appl (2023). https://doi.org/10.1007/s11036-023-02258-9

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