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Excellence networks in science: A Web-based application based on Bayesian multilevel logistic regression (BMLR) for the identification of institutions collaborating successfully

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  • Bornmann, Lutz
  • Stefaner, Moritz
  • de Moya Anegón, Felix
  • Mutz, Rüdiger

Abstract

In this study we present an application which can be accessed via www.excellence-networks.net and which represents networks of scientific institutions worldwide. The application is based on papers (articles, reviews and conference papers) published between 2007 and 2011. It uses (network) data, on which the SCImago Institutions Ranking is based (Scopus data from Elsevier). Using this data, institutional networks have been estimated with statistical models (Bayesian multilevel logistic regression, BMLR) for a number of Scopus subject areas. Within single subject areas, we have investigated and visualized how successfully overall an institution (reference institution) has collaborated (compared to all the other institutions in a subject area), and with which other institutions (network institutions) a reference institution has collaborated particularly successfully. The “best paper rate” (statistically estimated) was used as an indicator for evaluating the collaboration success of an institution. This gives the proportion of highly cited papers from an institution, and is considered generally as an indicator for measuring impact in bibliometrics.

Suggested Citation

  • Bornmann, Lutz & Stefaner, Moritz & de Moya Anegón, Felix & Mutz, Rüdiger, 2016. "Excellence networks in science: A Web-based application based on Bayesian multilevel logistic regression (BMLR) for the identification of institutions collaborating successfully," Journal of Informetrics, Elsevier, vol. 10(1), pages 312-327.
    • Handle: RePEc:eee:infome:v:10:y:2016:i:1:p:312-327
    DOI: 10.1016/j.joi.2016.01.005
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    References listed on IDEAS

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    2. Bornmann, Lutz & Leydesdorff, Loet & Wang, Jian, 2013. "Which percentile-based approach should be preferred for calculating normalized citation impact values? An empirical comparison of five approaches including a newly developed citation-rank approach (P1," Journal of Informetrics, Elsevier, vol. 7(4), pages 933-944.
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    Cited by:

    1. José Alberto Molina & Alfredo Ferrer & David Iñiguez & Alejandro Rivero & Gonzalo Ruiz & Alfonso Tarancón, 2020. "Network analysis to measure academic performance in economics," Empirical Economics, Springer, vol. 58(3), pages 995-1018, March.
    2. Gauffriau, Marianne, 2017. "A categorization of arguments for counting methods for publication and citation indicators," Journal of Informetrics, Elsevier, vol. 11(3), pages 672-684.
    3. Mutz, Rüdiger & Bornmann, Lutz & Daniel, Hans-Dieter, 2017. "Are there any frontiers of research performance? Efficiency measurement of funded research projects with the Bayesian stochastic frontier analysis for count data," Journal of Informetrics, Elsevier, vol. 11(3), pages 613-628.
    4. Lutz Bornmann & Rüdiger Mutz & Robin Haunschild & Felix Moya-Anegon & Mirko Almeida Madeira Clemente & Moritz Stefaner, 2021. "Mapping the impact of papers on various status groups in excellencemapping.net: a new release of the excellence mapping tool based on citation and reader scores," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(11), pages 9305-9331, November.
    5. José Alberto Molina & David Iñiguez & Gonzalo Ruiz & Alfonso Tarancón, 2018. "The Nobel Prize in Economics: individual or collective merits?," Boston College Working Papers in Economics 966, Boston College Department of Economics.
    6. Malesios, C., 2016. "Measuring the robustness of the journal h-index with respect to publication and citation values: A Bayesian sensitivity analysis," Journal of Informetrics, Elsevier, vol. 10(3), pages 719-731.
    7. Chowdhury, K.P., 2023. "Nonparametric functional analysis under joint estimation with applications to identifying highly cited papers," Journal of Informetrics, Elsevier, vol. 17(4).
    8. Tahamtan, Iman & Bornmann, Lutz, 2018. "Core elements in the process of citing publications: Conceptual overview of the literature," Journal of Informetrics, Elsevier, vol. 12(1), pages 203-216.

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