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Global neuroscience and mental health research: a bibliometrics case study

Author

Listed:
  • Alison M. J. Buchan

    (University of Toronto
    University of Toronto)

  • Eva Jurczyk

    (University of Toronto)

  • Ruth Isserlin

    (University of Toronto)

  • Gary D. Bader

    (University of Toronto)

Abstract

This case study of the impact of publications in the area of Neurosciences and Mental Health was completed as part of an institutional analysis of health research activity at the University of Toronto. Our data show that selecting top researchers by total publication output favoured clinical research over all other research disciplines active in the subjects. The use of citation rate based measures broadened the research disciplines in the top group, to include researchers in Public Health (highest impact in the analysis), Commerce and Basic Sciences. In addition, focusing on impact rather than output increased the participation of women in the top group. The number of female scientists increased from 20 to 31 % in the University of Toronto cohort when citations to publications were compared. Social network analysis showed that the top 100 researchers in both cohorts were highly collaborative, with several researchers forming bridges between individual clusters. There were two areas of research, neurodegeneration/movement disorders and cerebrovascular disease, represented by strong clusters in each analysis. The University of Toronto analysis identified two areas neuro-oncology/neuro-development and mental health/schizophrenia that were not represented in the global researcher networks. Information about the areas and relative strength of researcher collaborative networks will inform future strategic planning.

Suggested Citation

  • Alison M. J. Buchan & Eva Jurczyk & Ruth Isserlin & Gary D. Bader, 2016. "Global neuroscience and mental health research: a bibliometrics case study," Scientometrics, Springer;Akadémiai Kiadó, vol. 109(1), pages 515-531, October.
    • Handle: RePEc:spr:scient:v:109:y:2016:i:1:d:10.1007_s11192-016-2094-z
    DOI: 10.1007/s11192-016-2094-z
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    References listed on IDEAS

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    1. Ciriaco Andrea D'Angelo & Cristiano Giuffrida & Giovanni Abramo, 2011. "A heuristic approach to author name disambiguation in bibliometrics databases for large-scale research assessments," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 62(2), pages 257-269, February.
    2. Li Tang & John P. Walsh, 2010. "Bibliometric fingerprints: name disambiguation based on approximate structure equivalence of cognitive maps," Scientometrics, Springer;Akadémiai Kiadó, vol. 84(3), pages 763-784, September.
    3. James D. O’Leary & Mark W. Crawford & Eva Jurczyk & Alison Buchan, 2015. "Benchmarking bibliometrics in biomedical research: research performance of the University of Toronto’s Faculty of Medicine, 2008–2012," Scientometrics, Springer;Akadémiai Kiadó, vol. 105(1), pages 311-321, October.
    4. Ciriaco Andrea D'Angelo & Cristiano Giuffrida & Giovanni Abramo, 2011. "A heuristic approach to author name disambiguation in bibliometrics databases for large‐scale research assessments," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 62(2), pages 257-269, February.
    5. Erjia Yan & Ying Ding, 2009. "Applying centrality measures to impact analysis: A coauthorship network analysis," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 60(10), pages 2107-2118, October.
    6. Isidro F. Aguillo, 2012. "Is Google Scholar useful for bibliometrics? A webometric analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 91(2), pages 343-351, May.
    7. Éric Archambault & David Campbell & Yves Gingras & Vincent Larivière, 2009. "Comparing bibliometric statistics obtained from the Web of Science and Scopus," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 60(7), pages 1320-1326, July.
    8. van Rijnsoever, Frank J. & Hessels, Laurens K., 2011. "Factors associated with disciplinary and interdisciplinary research collaboration," Research Policy, Elsevier, vol. 40(3), pages 463-472, April.
    9. He, Zi-Lin & Geng, Xue-Song & Campbell-Hunt, Colin, 2009. "Research collaboration and research output: A longitudinal study of 65 biomedical scientists in a New Zealand university," Research Policy, Elsevier, vol. 38(2), pages 306-317, March.
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    Cited by:

    1. Murat Kocak & Carlos García-Zorita & Sergio Marugán-Lázaro & Murat Perit Çakır & Elías Sanz-Casado, 2019. "Mapping and clustering analysis on neuroscience literature in Turkey: a bibliometric analysis from 2000 to 2017," Scientometrics, Springer;Akadémiai Kiadó, vol. 121(3), pages 1339-1366, December.

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