Behaviormetrika

, Volume 44, Issue 2, pp 385–403 | Cite as

An International Research Comparative Study of the Degree of Cooperation between disciplines within mathematics and mathematical sciences: proposal and application of new indices for identifying the specialized field of researchers

  • Yuji Mizukami
  • Yosuke Mizutani
  • Keisuke Honda
  • Shigenori Suzuki
  • Junji Nakano
Original Paper

Abstract

Based on 10 years of data on academic papers from 2005 to 2014 available on the “Web of Science”, we conducted an in-depth review and analysis of collaborative academic research activities and relationships in mathematics/mathematical science and 21 other fields. We focused on the “academic papers co-authored with researchers in other fields” and derived new methods for measuring the degree of research cooperation among disciplines. Our new methods consist of two steps. The first step involves measuring and deriving new indices for identifying the specialized field of each researcher and the degree of concentration of each researcher’s work in his or her field of specialization. The second step, based on the indices, utilizes the Lorenz curve and Gini coefficient to identify the characteristics of each field. We analyzed the temporal trends in the data for 15 major countries from 2007 to 2014, to determine if the degree of cooperation between disciplines is changing. We also analyzed the characteristics of different regions: the European Union, North America, Asia without Japan, and Japan. We confirmed the effectiveness of our new methods and obtained some interesting findings that offer a new perspective on academic research activities.

Keywords

Interdisciplinary field Cooperative degree Mathematics Lorenz curve Gini coefficient 

Notes

Acknowledgements

This study was funded by the Institute of Statistical Mathematics joint research program (28-Kyoken-4406). The data were provided by Thomson Reuters. We would like to thank the two anonymous referees for their invaluable suggestions.

References

  1. Barney G (1965) Organizational scientists: their professional careers. Bobbs-Merrill, New YorkGoogle Scholar
  2. Cho M, Fujigaki Y, Hirakawa H, Tomizawa H, Hayashi T, Makino J (2004) “Scientometrics introduction for research evaluation and science studies”, Maruzen. ISBN-10:4621073990 (in Japanese) Google Scholar
  3. Sen A, Foster EJ (1997) On economic inequality. Clarendon PressGoogle Scholar
  4. Gini C (1936) On the measure of concentration with special reference to income and statistics. Colorado College Publication, General Series No. 208, pp 73–79Google Scholar
  5. Lorenz MO (1905) Methods of measuring the concentration of wealth. Publ Am Stat Assoc 9(70):209–219Google Scholar
  6. Negishi M, Yamazaki S, Sun Y, Nishizawa M (2001) “Research evaluation”, Maruzen. ISBN-10:4621048902 (in Japanese) Google Scholar
  7. Porter AL, Rafols I (2009) Is science becoming more interdisciplinary? Measuring and mapping six research fields over time. Scientometrics 81:719–745CrossRefGoogle Scholar
  8. Rafols I, Meyer M (2010) Diversity measures and network centralities as indicators of interdisciplinarity: case studies in bionanoscience. Scientometrics 82:263–287CrossRefGoogle Scholar
  9. Rogers M (1983) Diffusion of innovations. Free Press, New YorkGoogle Scholar
  10. Stirling A (2007) A general framework for analyzing diversity in science, technology and society. J R Soc 4:707–719CrossRefGoogle Scholar
  11. Thomson Reuters (2012a) Web of science subject core collection areas. http://ipscience-help.thomsonreuters.com/inCites2Live/filterValuesGroup/researchAreaSchema/wosDetail/wosCategories.html. Accessed 11 May 2017
  12. Thomson Reuters Community (2012b) Connection rules between essential science indicators subject areas and web of science subject core collection areas. http://community.thomsonreuters.com/t5/InCites-Customer-Forum/Subect-Schemas-in-InCites/td-p/32069. Accessed 11 May 2017
  13. Thomson Reuters (2016a) Essential science indicators subject areas. http://ipscience-help.thomsonreuters.com/inCites2Live/filterValuesGroup/researchAreaSchema/esiDetail/esiCategories.html. Accessed 11 May 2017
  14. Thomson Reuters (2016b) SCIENCEWATCH, journal list. http://sciencewatch.com/info/journal-list. Accessed 11 May 2017
  15. Vergidis P, Karavasiou A, Paraschakis K, Bliziotis I, Falagas M (2005) Bibliometric analysis of global trends for research productivity in microbiology. Eur J Clin Microbiol Infect Dis 24(5):342–346CrossRefGoogle Scholar
  16. Wagner CS, Roessner JD, Bobb K, Klein JT, Boyack KW, Keyton J, Börner K (2011) Approaches to understanding and measuring interdisciplinary scientific research (IDR): a review of the literature. J Informetr 5(1):14–26CrossRefGoogle Scholar

Copyright information

© The Behaviormetric Society 2017

Authors and Affiliations

  • Yuji Mizukami
    • 1
  • Yosuke Mizutani
    • 2
  • Keisuke Honda
    • 3
  • Shigenori Suzuki
    • 4
  • Junji Nakano
    • 3
  1. 1.College of Industrial TechnologyNihon UniversityChibaJapan
  2. 2.Former Org.: Demand Side Science, Inc.TokyoJapan
  3. 3.The Institute of Statistical MathematicsTokyoJapan
  4. 4.Research Institute of Info-Communication MedicineTokyoJapan

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