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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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    1. Milojević, Staša, 2013. "Accuracy of simple, initials-based methods for author name disambiguation," Journal of Informetrics, Elsevier, vol. 7(4), pages 767-773.
    2. Diana Hicks, 1999. "The difficulty of achieving full coverage of international social science literature and the bibliometric consequences," Scientometrics, Springer;Akadémiai Kiadó, vol. 44(2), pages 193-215, February.
    3. Jiang Wu & Xiu-Hao Ding, 2013. "Author name disambiguation in scientific collaboration and mobility cases," Scientometrics, Springer;Akadémiai Kiadó, vol. 96(3), pages 683-697, September.
    4. Sanjeev Goyal & Marco J. van der Leij & José Luis Moraga-Gonzalez, 2006. "Economics: An Emerging Small World," Journal of Political Economy, University of Chicago Press, vol. 114(2), pages 403-432, April.
    5. Ricardo G. Cota & Anderson A. Ferreira & Cristiano Nascimento & Marcos André Gonçalves & Alberto H. F. Laender, 2010. "An unsupervised heuristic-based hierarchical method for name disambiguation in bibliographic citations," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 61(9), pages 1853-1870, September.
    6. Pascal Cuxac & Jean-Charles Lamirel & Valerie Bonvallot, 2013. "Efficient supervised and semi-supervised approaches for affiliations disambiguation," Scientometrics, Springer;Akadémiai Kiadó, vol. 97(1), pages 47-58, October.
    7. Vetle I. Torvik & Marc Weeber & Don R. Swanson & Neil R. Smalheiser, 2005. "A probabilistic similarity metric for Medline records: A model for author name disambiguation," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 56(2), pages 140-158, January.
    8. Ventura, Samuel L. & Nugent, Rebecca & Fuchs, Erica R.H., 2015. "Seeing the non-stars: (Some) sources of bias in past disambiguation approaches and a new public tool leveraging labeled records," Research Policy, Elsevier, vol. 44(9), pages 1672-1701.
    9. Domenico De Stefano & Susanna Zaccarin, 2016. "Co-authorship networks and scientific performance: an empirical analysis using the generalized extreme value distribution," Journal of Applied Statistics, Taylor & Francis Journals, vol. 43(1), pages 262-279, January.
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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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