, Volume 94, Issue 1, pp 133–161 | Cite as

Assessing researcher interdisciplinarity: a case study of the University of Hawaii NASA Astrobiology Institute

  • Michael GowanlockEmail author
  • Rich Gazan


In this study, we combine bibliometric techniques with a machine learning algorithm, the sequential information bottleneck, to assess the interdisciplinarity of research produced by the University of Hawaii NASA Astrobiology Institute (UHNAI). In particular, we cluster abstract data to evaluate Thomson Reuters Web of Knowledge subject categories as descriptive labels for astrobiology documents, assess individual researcher interdisciplinarity, and determine where collaboration opportunities might occur. We find that the majority of the UHNAI team is engaged in interdisciplinary research, and suggest that our method could be applied to additional NASA Astrobiology Institute teams in particular, or other interdisciplinary research teams more broadly, to identify and facilitate collaboration opportunities.


Astrobiology Bibliometrics Information bottleneck method Interdisciplinary science Machine learning Text mining 



We thank David Schanzenbach for devising scripts, and Mahdi Belcaid and the anonymous reviewers for insightful comments. This material is based upon work supported by the National Aeronautics and Space Administration through the NASA Astrobiology Institute under Cooperative Agreement No. NNA08DA77A issued through the Office of Space Science.

Supplementary material

11192_2012_765_MOESM1_ESM.pdf (29 kb)
PDF (28 KB)


  1. Bjurström, A., & Polk, M. (2011). Climate change and interdisciplinarity: A co-citation analysis of IPCC Third Assessment Report. Scientometrics, 87, 525–550.CrossRefGoogle Scholar
  2. Bornmann, L., & Waltman, L. (2011). The detection of “hot regions” in the geography of science—A visualization approach by using density maps. Journal of Informetrics, 5(4), 547–553.CrossRefGoogle Scholar
  3. Boyack, K. W., Klavans, R., & Börner, K. (2005). Mapping the backbone of science. Scientometrics, 64, 351–374.CrossRefGoogle Scholar
  4. Brewer, G. D. (1999). The challenges of interdisciplinarity. Policy Sciences, 32, 327–337.CrossRefGoogle Scholar
  5. Chawla, N. V., Bowyer, K. W., Hall, L. O., & Kegelmeyer, W. P. (2002). SMOTE: Synthetic Minority Over-sampling Technique. Journal of Artificial Intelligence Research, 16, 321–357.zbMATHGoogle Scholar
  6. Cockell, C. (2002). Astrobiology—A new opportunity for interdisciplinary thinking. Space Policy, 18(4), 263–266.CrossRefGoogle Scholar
  7. Derrick, G., Sturk, H., Haynes, A., Chapman, S., & Hall, W. (2010). A cautionary bibliometric tale of two cities. Scientometrics, 84, 317–320.CrossRefGoogle Scholar
  8. Gargaud, M., & Tirard, S. (2011). Exobiology: An example of interdisciplinarity at work. In J. -Pierre Lasota (Ed.), Astronomy at the Frontiers of Science, Vol. 1 of Integrated Science & Technology Program (pp. 337–350). Dordrecht: Springer.Google Scholar
  9. Jacsó, P. (2005). As we may search: Comparison of major features of the Web of Science, Scopus, and Google Scholar citation-based and citation-enhanced databases. Current Science, 89, 1537–1547.Google Scholar
  10. Kostoff, R. (1998). The use and misuse of citation analysis in research evaluation. Scientometrics, 43, 27–43.CrossRefGoogle Scholar
  11. Kostoff, R., del Río, J. A., Humenik, J. A., García, E. O., & Ramírez, A. M. (2001). Citation mining: Integrating text mining and bibliometrics for research user profiling. Journal of the American Society for Information Science and Technology, 52(13), 1148–1156.CrossRefGoogle Scholar
  12. Kousha, K., & Thelwall, M. (2008). Sources of Google Scholar citations outside the Science Citation Index: A comparison between four science disciplines. Scientometrics, 74, 273–294.CrossRefGoogle Scholar
  13. Morillo, F., Bordons, M., & Gómez, I. (2001). An approach to interdisciplinarity through bibliometric indicators. Scientometrics, 51, 203–222.CrossRefGoogle Scholar
  14. National Academies. Committee on Facilitating Interdisciplinary Research, of the Committee on Science, Engineering, and Public Policy. (2005). Facilitating Interdisciplinary Research. Washington, DC: National Academies Press.Google Scholar
  15. National Science Foundation. (2011). Introduction to interdisciplinary research. Accessed November 21, 2011, from
  16. Oliver, C. A., & Fergusson, J. (2007). Astrobiology: A pathway to adult science literacy? Acta Astronautica, 61(78), 716–723.Google Scholar
  17. Porter, A., Cohen, A., Roessner, J. D., & Perreault, M. (2007). Measuring researcher interdisciplinarity. Scientometrics, 72, 117–147.CrossRefGoogle Scholar
  18. Porter, A., & Rafols, I. (2009). Is science becoming more interdisciplinary? Measuring and mapping six research fields over time. Scientometrics, 81, 719–745.CrossRefGoogle Scholar
  19. Porter, M. F. (1980). An algorithm for suffix stripping. Program, 14(3), 130–137.CrossRefGoogle Scholar
  20. Rafols, I., & Meyer, M. (2010). Diversity and network coherence as indicators of interdisciplinarity: Case studies in bionanoscience. Scientometrics, 82, 263–287.CrossRefGoogle Scholar
  21. Slonim, N., Friedman, N., & Tishby, N. (2002). Unsupervised document classification using sequential information maximization. In Proceedings of the 25th annual international ACM SIGIR conference on research and development in information retrieval (pp. 129–136). New York, USA.Google Scholar
  22. Small, H. (2010). Maps of science as interdisciplinary discourse: Co-citation contexts and the role of analogy. Scientometrics, 83, 835–849.CrossRefGoogle Scholar
  23. Staley, J. (2003). Astrobiology, The transcendent science: the promise of astrobiology as an integrative approach for science and engineering education and research. Current Opinion in Biotechnology, 14(3), 347–354.MathSciNetCrossRefGoogle Scholar
  24. Sugimoto, C. (2011). Looking across communicative genres: A call for inclusive indicators of interdisciplinarity. Scientometrics, 86, 449–461.CrossRefGoogle Scholar
  25. Upham, S., & Small, H. (2010). Emerging research fronts in science and technology: Patterns of new knowledge development. Scientometrics, 83, 15–38.CrossRefGoogle Scholar
  26. van Leeuwen, T. N. (2007). Modelling of bibliometric approaches and importance of output verification in research performance assessment. Research Evaluation, 16(2), 93–105.CrossRefGoogle Scholar
  27. van Raan, A. F. J., & van Leeuwen, T. N. (2002). Assessment of the scientific basis of interdisciplinary, applied research: Application of bibliometric methods in Nutrition and Food Research. Research Policy, 31(4), 611–632.CrossRefGoogle Scholar
  28. Wagner, C. S., Roessner, J. D., Bobb, K., Klein, J. T., Boyack, K. W., Keyton, J., Rafols, I., & Börner, K. (2011). Approaches to understanding and measuring interdisciplinary scientific research (IDR): A review of the literature. Journal of Informetrics, 5(1), 14–26.CrossRefGoogle Scholar
  29. Witten, I. H., & Frank, E. (2005). Data mining: Practical machine learning tools and techniques, 2nd edn. San Francisco: Morgan Kaufmann.zbMATHGoogle Scholar
  30. Zhang, J., Vogeley, M. S., & Chen, C. (2011). Scientometrics of big science: A case study of research in the Sloan Digital Sky Survey. Scientometrics, 86, 1–14.CrossRefGoogle Scholar
  31. Zhang, L., Liu, X., Janssens, F., Liang, L., & Glänzel, W. (2010). Subject clustering analysis based on ISI category classification. Journal of Informetrics, 4(2), 185–193.CrossRefGoogle Scholar

Copyright information

© Akadémiai Kiadó, Budapest, Hungary 2012

Authors and Affiliations

  1. 1.Department of Information & Computer Sciences, University of Hawaii NASA Astrobiology InstituteUniversity of HawaiiHonoluluUSA
  2. 2.Department of Information & Computer Sciences, Library & Information Science Program, University of Hawaii NASA Astrobiology InstituteUniversity of HawaiiHonoluluUSA

Personalised recommendations