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.
This is a preview of subscription content, log in to check access.
Buy single article
Instant access to the full article PDF.
Price includes VAT for USA
Abbasi, A., Wigand, R. T., & Hossain, L. (2014). Measuring social capital through network analysis and its influence on individual performance. Library & Information Science Research, 36, 66–73.
Aguillo, I. F. (2012). Is Google Scholar useful for bibliometrics? A webometric analysis. Scientometrics, 91, 343–351.
Altbach, P.G. (2010) The state of the Rankings. Inside Higher Ed. https://www.insidehighered.com/views/2010/11/11/state-rankings. Accessed 15 Dec 2015.
Anderson, M. S., & Steneck, N. H. (2011). Realizing gains and staying out of trouble. In M. S. Anderson & N. H. Steneck (Eds.), International research collaborations (pp. 243–247). NewYork: Routledge.
Archambault, E., Campbell, D., Gingras, Y., & Lariviere, V. (2009). Comparing bibliometric statistics obtained from the Web of Science and Scopus. Journal of the American Society for Information Science and Technology, 60, 1320–1326.
Archambault, E., & Lariviere, V. (2010). The limits of Bibliometrics for the analysis of the Social Sciences and Humanities Literature. World Social Science Report 2010.
Bornmann, L., & Leydesdorff, L. (2014). Scientometrics in a changing research landscape Bibliometrics has become an integral part of research quality evaluation and has been changing the practice of research. EMBO Reports, 15, 1228–1232.
Bornmann, L., & Mutz, R. (2011). Further steps towards an ideal method of measuring citation performance: The avoidance of citation (ratio) averages in field-normalization. Journal of Informetrics, 5(1), 228–230.
D’Angelo, C. A., Giuffrida, C., & Abramo, G. (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, 62, 257–269.
Frank, C., & Nason, E. (2009). Health research: measuring the social, health and economic benefits. CMAJ: Canadian Medical Association Journal, 180(5), 528–534. doi:10.1503/cmaj.090016.
Garfield. E., (1977). The mystery of the transposed journal lists—wherein Bradford’s law of scattering is generalized according to Garfield’s law of concentration, Current Content No. 7 5(August 4 I971) Reprinted in Essays of an Information Scientist, Volume 1 Philadelphia: ISI Press, pp. 222–223.
He, H.-L., Geng, X.-S., & Cambell-Hunt, C. (2009). Research collaboration and research output: A longitudinal study of 65 biomedical scientists in a New Zealand university. Research Policy, 38, 306–317.
Hicks, D., Wouters, P., Waiman, L., de Rijcke, S., & Rafols, I. (2015). Bibliometrics: The Leiden Manifesto for research metrics. Nature, 520, 429–431.
Kofia, V., Isserlin, R., Buchan, A.M.J., & Bader, G.D. (2015). Social Network: a Cytoscape app for visualizing co-authorship networks. F1000Research, 4:481. doi:10.12688/f1000research.6804.3.
Lee, S., & Bozman, B. (2005). The Impact of Research collaboration on Scientific Productivity. Social Studies of Science, 35, 673–702.
Leydesdorff, L., de Moya-Anegón, F., & de Nooy, W. (2015). Aggregated journal–journal citation relations in scopus and web of science matched and compared in terms of networks, maps, and interactive overlays. Journal of the American Society for Information Science and Technology,. doi:10.1002/asi.23372.
Meneghini, R., & Packer, A. L. (2007). Is there science beyond English? Initiatives to increase the quality and visibility of non-English publications might help to break down language barriers in scientific communication. EMBO Reports, 8, 112–116.
Mongeon, P, & Paul-Hus, A. (2014). The journal coverage of bibliometric databases: A comparison of Scopus and Web of Science. Metrics Seattle. Full-text doi:10.13140/2.1.4759.7762. Available from: Adèle Paul-Hus, Nov 14, 2014.
Nepusz, T., Yu, H., & Paccanaro, A. (2012). Detecting overlapping protein complexes in protein-protein interaction networks. Nature Methods, 9, 471–472.
Newman, M. E. J. (2001). The structure of scientific collaboration networks. Proceedings of the National Academy of Science of the United States of America, 98(2), 404–409.
Newman, M. E. (2004). Coauthorship networks and patterns of scientific collaboration. Proceedings of the National Academy of Sciences of the United States of America, 101(Suppl 1), 5200–5205.
O’Leary, J. D., Crawford, M. W., Jurczyk, E., & Buchan, A. M. J. (2015). Benchmarking bibliometrics in biomedical research: research performance of the University of Toronto’s Faculty of Medicine, 2008–2012. Scientometrics, 105(1), 311–321.
Opthof, T. (2011). Differences in citation frequence of clinical and basic science papers in cardiovascular research. Medical & Biological Engineering & Computing, 49, 613–621.
Shahabuddin, S. M. (2013). Mapping neuroscience research in India—a bibliometric approach. Current Science, 104, 1619–1626.
Tang, L., & Walsh, J. P. (2010). Bibliometric fingerprints: name disambiguation based on approximate structure equivalence of cognitive maps. Scientometrics,. doi:10.1007/s11192-010-0196-6.
van Rijnsoever, F. J., & Hessels, L. K. (2011). Factors associated with disciplinary and interdisciplinary research collaboration. Research Policy, 40, 463–472.
Yan, E., & Ding, Y. (2009). Applying centrality measures to impact analysis: A coauthorship network analysis. Journal of the American Society for Information Science and Technology, 60(10), 2107–2118.
The authors would like to acknowledge the assistance of Lilia Smale and Aurora Mendelsohn in the Evaluation Group, Faculty of Medicine, University of Toronto, for their invaluable assistance with the SNA visualizations. This study was funded by the Faculty of Medicine at the University of Toronto.
About this article
Cite this article
Buchan, A.M.J., Jurczyk, E., Isserlin, R. et al. Global neuroscience and mental health research: a bibliometrics case study. Scientometrics 109, 515–531 (2016). https://doi.org/10.1007/s11192-016-2094-z
- Mental health
- Social network analysis
- Research evaluation
- Case study