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Improving co-authorship network structures by combining multiple data sources: evidence from Italian academic statisticians

Author

Listed:
  • Vittorio Fuccella

    (University of Salerno)

  • Domenico De Stefano

    (University of Trieste)

  • Maria Prosperina Vitale

    (University of Salerno)

  • Susanna Zaccarin

    (University of Trieste)

Abstract

The aim of the present contribution is to merge bibliographic data for members of a bounded scientific community in order to derive a complete unified archive, with top-international and nationally oriented production, as a new basis to carry out network analysis on a unified co-authorship network. A two-step procedure is used to deal with the identification of duplicate records and the author name disambiguation. Specifically, for the second step we strongly drew inspiration from a well-established unsupervised disambiguation method proposed in the literature following a network-based approach and requiring a restricted set of record attributes. Evidences from Italian academic statisticians were provided by merging data from three bibliographic archives. Non-negligible differences were observed in network results in the comparison of disambiguated and not disambiguated data sets, especially in network measures at individual level.

Suggested Citation

  • Vittorio Fuccella & Domenico De Stefano & Maria Prosperina Vitale & Susanna Zaccarin, 2016. "Improving co-authorship network structures by combining multiple data sources: evidence from Italian academic statisticians," Scientometrics, Springer;Akadémiai Kiadó, vol. 107(1), pages 167-184, April.
    • Handle: RePEc:spr:scient:v:107:y:2016:i:1:d:10.1007_s11192-016-1872-y
    DOI: 10.1007/s11192-016-1872-y
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    References listed on IDEAS

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    Cited by:

    1. Silvia Bacci & Bruno Bertaccini & Alessandra Petrucci, 2023. "Insights from the co-authorship network of the Italian academic statisticians," Scientometrics, Springer;Akadémiai Kiadó, vol. 128(8), pages 4269-4303, August.
    2. Beatriz Barros & Ana Fernández-Zubieta & Raul Fidalgo-Merino & Francisco Triguero, 2018. "Scientific knowledge percolation process and social impact: A case study on the biotechnology and microbiology perceptions on Twitter," Science and Public Policy, Oxford University Press, vol. 45(6), pages 804-814.

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