Advertisement

Scientometrics

, Volume 79, Issue 2, pp 421–434 | Cite as

Mapping institutions and their weak ties in a specialty: A case study of cystic fibrosis body composition research

  • Liying YangEmail author
  • Steven A. Morris
  • Elizabeth M. Barden
Article

Abstract

The paper demonstrates visualization technique that show the collaboration structure of institutions in the specialty and the researchers that function as weak ties among them. Institution names were extracted from the collection of papers and disambiguated using the Derwent Analytics (v1.2) software product. Institutions were clustered into collaboration groups based on their co-occurrence in papers. A crossmap of clustered institutions against research fronts, which were derived using bibliographic coupling analysis, shows the research fronts that specific institutions participate in, their collaborator institutions and the research fronts in which those collaborations occurred. A crossmap of institutions to author teams, derived from co-authorship analysis, reveals research teams in the specialty and their general institutional affiliation, and further identifies the researchers that function as weak ties and the institutions that they link. The case study reveals that the techniques introduced in this paper can be used to extract a large amount of useful information about institutions participating in a research specialty.

Keywords

Cystic Fibrosis Social Network Analysis Subject Matter Expert Research Front Paper Author 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Björneborn, L., Ingwersen, P. (2001), Perspectives of webometrics. Scientometrics, 50(1): 65–82.CrossRefGoogle Scholar
  2. Börner, K., Penumarthy, S., Meiss, M., Ke, W. M. (2006), Mapping the diffusion of scholarly knowledge among major us research institutions. Scientometrics, 68(3): 415–426.CrossRefGoogle Scholar
  3. Chubin, D. E. (1976), Conceptualization of scientific specialties. Sociological Quarterly, 17(4): 448–476.CrossRefGoogle Scholar
  4. Debackere, K., Clarysse, B. (1998). Advanced bibliometric methods to model the relationship between entry behavior and networking in emerging technological communities. Journal of the American Society for Information Science, 49(1): 49.CrossRefGoogle Scholar
  5. De Nooy, W., Mrvar, A., Batagelj, V. (2005), Exploratory Social Network Analysis with Pajek. New York, Cambridge University Press.Google Scholar
  6. Otte, E., Rousseau, R. (2002), Social network analysis: a powerful strategy, also for the information sciences. Journal of Information Science, 28(6): 441–453.CrossRefGoogle Scholar
  7. Granovetter, M. S. (1973), The strength of weak ties. American Journal of Sociology, 778(6): 1360–1380.CrossRefGoogle Scholar
  8. Havemann, F., Heinz, M., Kretschmer, H. (2006), Collaboration and distances between German immunological institutes. Journal of Biomedical Discovery and Collaboration, 1(1): 6–6.CrossRefGoogle Scholar
  9. Katz, J. S., Martin, B. R. (1997), What is research collaboration? Research Policy, 26: 1–18.CrossRefGoogle Scholar
  10. Kretschmer, H., Aguillo, I. F. (2004), Visibility of collaboration on the web. Scientometrics, 61(3): 405–426.CrossRefGoogle Scholar
  11. Kretschmer, H., Kretschmer, U., Kretschmer, T. (2005), Visibility of collaboration between immunology institutions on the web including aspects of gender studies. Paper presented at the 10th International Conference of the International Society for Scientometrics and Informetrics, (pp.750–760). Stockholm, Sweden: Karolinska University Press.Google Scholar
  12. Leydesdorff, L. (1998), Theories of citation? Scientometrics, 43(1): 5–25.CrossRefGoogle Scholar
  13. Melin, G., Persson, O. (1996), Studying research collaboration using co-authorships. Scientometrics, 36(3): 363–377.CrossRefGoogle Scholar
  14. Morris, S. A., Boyack, K. W. (2005), Visualizing 60 years of anthrax research, 10th International Conference of the International Society for Scientometrics and Informetrics, (pp. 45–55). Stockholm, Sweden: Karolinska University Press.Google Scholar
  15. Morris, S. A., Yen, G. (2004), Crossmaps: Visualization of overlapping relationships in collections of journal papers. Proceedings of the National Academy of Sciences of the United States, 101(suppl. 1): 5291–5296.CrossRefGoogle Scholar
  16. Morris, S. A., Yen, G., Wu, Z., Asnake, B. (2003), Time line visualization of research fronts. Journal of the American Society for Information Science and Technology, 54(5): 413–422.CrossRefGoogle Scholar
  17. Nagpaul, P. S. (2002), Visualizing cooperation networks of elite institutions in India. Scientometrics, 54(2): 213–228.CrossRefGoogle Scholar
  18. Pencharz, PB, Durie, PR (2000), Pathogenesis of malnutrition in cystic fibrosis, and its treatment. Clinical Nutrition, 19(6): 387–394.CrossRefGoogle Scholar
  19. Salton, G. (1989), Automatic Text Processing: the Transformation, Analysis and Retrieval of Information by Compute. Reading, MA: Addison-Wesley, 1989.Google Scholar
  20. Wagner, C. S. (2005), Six case studies of international collaboration in science. Scientometrics, 62(1): 3–26.CrossRefGoogle Scholar
  21. Wasserman, S., Faust, K., Iacobucci, D. (1994), Social Network Analysis: Methods and Applications. Cambridge. Cambridge University Press.Google Scholar

Copyright information

© Springer Science+Business Media B.V. 2008

Authors and Affiliations

  • Liying Yang
    • 1
    Email author
  • Steven A. Morris
    • 2
  • Elizabeth M. Barden
    • 3
  1. 1.National Science Library of Chinese Academy of ScienceBeijingP. R. China
  2. 2.Baker-Hughes Inc.HoustonUSA
  3. 3.Barden ConsultingBedfordUSA

Personalised recommendations