IDEAS home Printed from https://ideas.repec.org/a/spr/scient/v107y2016i1d10.1007_s11192-016-1872-y.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11192-016-1872-y
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.

    File URL: https://libkey.io/10.1007/s11192-016-1872-y?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    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. 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.
    3. 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.
    4. 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.
    5. 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.
    6. 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.
    7. 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.
    8. 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.
    9. 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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Hao Wu & Bo Li & Yijian Pei & Jun He, 2014. "Unsupervised author disambiguation using Dempster–Shafer theory," Scientometrics, Springer;Akadémiai Kiadó, vol. 101(3), pages 1955-1972, December.
    2. Deyun Yin & Kazuyuki Motohashi & Jianwei Dang, 2020. "Large-scale name disambiguation of Chinese patent inventors (1985–2016)," Scientometrics, Springer;Akadémiai Kiadó, vol. 122(2), pages 765-790, February.
    3. Jan Schulz, 2016. "Using Monte Carlo simulations to assess the impact of author name disambiguation quality on different bibliometric analyses," Scientometrics, Springer;Akadémiai Kiadó, vol. 107(3), pages 1283-1298, June.
    4. Jinseok Kim & Jason Owen-Smith, 2021. "ORCID-linked labeled data for evaluating author name disambiguation at scale," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(3), pages 2057-2083, March.
    5. Jinseok Kim & Jenna Kim, 2020. "Effect of forename string on author name disambiguation," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 71(7), pages 839-855, July.
    6. Kim, Jinseok & Diesner, Jana, 2015. "The effect of data pre-processing on understanding the evolution of collaboration networks," Journal of Informetrics, Elsevier, vol. 9(1), pages 226-236.
    7. Jinseok Kim & Jinmo Kim & Jason Owen-Smith, 2019. "Generating automatically labeled data for author name disambiguation: an iterative clustering method," Scientometrics, Springer;Akadémiai Kiadó, vol. 118(1), pages 253-280, January.
    8. Andrea Ancona & Roy Cerqueti & Gianluca Vagnani, 2023. "A novel methodology to disambiguate organization names: an application to EU Framework Programmes data," Scientometrics, Springer;Akadémiai Kiadó, vol. 128(8), pages 4447-4474, August.
    9. Mark-Christoph Müller & Florian Reitz & Nicolas Roy, 2017. "Data sets for author name disambiguation: an empirical analysis and a new resource," Scientometrics, Springer;Akadémiai Kiadó, vol. 111(3), pages 1467-1500, June.
    10. Lee Branstetter & Neil Gandal & Nadav Kuniesky, 2017. "Network-Mediated Knowledge Spillovers: A Cross-Country Comparative Analysis of Information Security Innovations," NBER Working Papers 23808, National Bureau of Economic Research, Inc.
    11. Jinseok Kim & Jenna Kim & Jason Owen‐Smith, 2021. "Ethnicity‐based name partitioning for author name disambiguation using supervised machine learning," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 72(8), pages 979-994, August.
    12. Chin-Chang Tsai & Elizabeth A. Corley & Barry Bozeman, 2016. "Collaboration experiences across scientific disciplines and cohorts," Scientometrics, Springer;Akadémiai Kiadó, vol. 108(2), pages 505-529, August.
    13. Jinseok Kim, 2018. "Evaluating author name disambiguation for digital libraries: a case of DBLP," Scientometrics, Springer;Akadémiai Kiadó, vol. 116(3), pages 1867-1886, September.
    14. Song, Min & Kim, Erin Hea-Jin & Kim, Ha Jin, 2015. "Exploring author name disambiguation on PubMed-scale," Journal of Informetrics, Elsevier, vol. 9(4), pages 924-941.
    15. Shuiqing Huang & Bo Yang & Sulan Yan & Ronald Rousseau, 2014. "Institution name disambiguation for research assessment," Scientometrics, Springer;Akadémiai Kiadó, vol. 99(3), pages 823-838, June.
    16. Gandal Neil & Kunievsky Nadav & Branstetter Lee, 2021. "Network-Mediated Knowledge Spillovers in ICT/Information Security," Review of Network Economics, De Gruyter, vol. 19(2), pages 85-114, January.
    17. Li Zhang & Wei Lu & Jinqing Yang, 2023. "LAGOS‐AND: A large gold standard dataset for scholarly author name disambiguation," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 74(2), pages 168-185, February.
    18. Pellegrino, Gabriele & Penner, Orion & Piguet, Etienne & de Rassenfosse, Gaétan, 2023. "Productivity gains from migration: Evidence from inventors," Research Policy, Elsevier, vol. 52(1).
    19. Dongwook Shin & Taehwan Kim & Joongmin Choi & Jungsun Kim, 2014. "Author name disambiguation using a graph model with node splitting and merging based on bibliographic information," Scientometrics, Springer;Akadémiai Kiadó, vol. 100(1), pages 15-50, July.
    20. Jian Wang & Kaspars Berzins & Diana Hicks & Julia Melkers & Fang Xiao & Diogo Pinheiro, 2012. "A boosted-trees method for name disambiguation," Scientometrics, Springer;Akadémiai Kiadó, vol. 93(2), pages 391-411, November.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:scient:v:107:y:2016:i:1:d:10.1007_s11192-016-1872-y. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.