Skip to main content
SpringerLink
Log in

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

  • Published:
Scientometrics Aims and scope Submit manuscript

Abstract

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12

Similar content being viewed by others

Notes

  1. Keywords are not always a free-text field.

  2. The classification of documents is a requirement for an astrobiology publication information retrieval system. Our research group is inclined to create such a system. See http://airframe.ics.hawaii.edu/ for more information.

  3. This is also the year that the journal Astrobiology began publication. While astrobiology research was, and continues to be published in other journals, this indicates that astrobiology research may not have coalesced as a field prior to 2001.

  4. The scripts can be found here: http://vo.ads.harvard.edu/adswww-lib/.

  5. For example, a cluster with SCs constituting 30, 18, 16 and 12 % of the abstracts would have three dominant SCs.

References

  • Bjurström, A., & Polk, M. (2011). Climate change and interdisciplinarity: A co-citation analysis of IPCC Third Assessment Report. Scientometrics, 87, 525–550.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Boyack, K. W., Klavans, R., & Börner, K. (2005). Mapping the backbone of science. Scientometrics, 64, 351–374.

    Article  Google Scholar 

  • Brewer, G. D. (1999). The challenges of interdisciplinarity. Policy Sciences, 32, 327–337.

    Article  Google Scholar 

  • 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.

    MATH  Google Scholar 

  • Cockell, C. (2002). Astrobiology—A new opportunity for interdisciplinary thinking. Space Policy, 18(4), 263–266.

    Article  Google Scholar 

  • Derrick, G., Sturk, H., Haynes, A., Chapman, S., & Hall, W. (2010). A cautionary bibliometric tale of two cities. Scientometrics, 84, 317–320.

    Article  Google Scholar 

  • 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 

  • 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 

  • Kostoff, R. (1998). The use and misuse of citation analysis in research evaluation. Scientometrics, 43, 27–43.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Morillo, F., Bordons, M., & Gómez, I. (2001). An approach to interdisciplinarity through bibliometric indicators. Scientometrics, 51, 203–222.

    Article  Google Scholar 

  • 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.

  • National Science Foundation. (2011). Introduction to interdisciplinary research. Accessed November 21, 2011, from http://www.nsf.gov/od/oia/additional_resources/interdisciplinary_research/.

  • Oliver, C. A., & Fergusson, J. (2007). Astrobiology: A pathway to adult science literacy? Acta Astronautica, 61(78), 716–723.

    Google Scholar 

  • Porter, A., Cohen, A., Roessner, J. D., & Perreault, M. (2007). Measuring researcher interdisciplinarity. Scientometrics, 72, 117–147.

    Article  Google Scholar 

  • Porter, A., & Rafols, I. (2009). Is science becoming more interdisciplinary? Measuring and mapping six research fields over time. Scientometrics, 81, 719–745.

    Article  Google Scholar 

  • Porter, M. F. (1980). An algorithm for suffix stripping. Program, 14(3), 130–137.

    Article  Google Scholar 

  • Rafols, I., & Meyer, M. (2010). Diversity and network coherence as indicators of interdisciplinarity: Case studies in bionanoscience. Scientometrics, 82, 263–287.

    Article  Google Scholar 

  • 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.

  • Small, H. (2010). Maps of science as interdisciplinary discourse: Co-citation contexts and the role of analogy. Scientometrics, 83, 835–849.

    Article  Google Scholar 

  • 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.

    Article  MathSciNet  Google Scholar 

  • Sugimoto, C. (2011). Looking across communicative genres: A call for inclusive indicators of interdisciplinarity. Scientometrics, 86, 449–461.

    Article  Google Scholar 

  • Upham, S., & Small, H. (2010). Emerging research fronts in science and technology: Patterns of new knowledge development. Scientometrics, 83, 15–38.

    Article  Google Scholar 

  • van Leeuwen, T. N. (2007). Modelling of bibliometric approaches and importance of output verification in research performance assessment. Research Evaluation, 16(2), 93–105.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Witten, I. H., & Frank, E. (2005). Data mining: Practical machine learning tools and techniques, 2nd edn. San Francisco: Morgan Kaufmann.

    MATH  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

Download references

Acknowledgments

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.

Author information

Authors and Affiliations

  1. Department of Information & Computer Sciences, University of Hawaii NASA Astrobiology Institute, University of Hawaii, POST 310, 1680 East-West Road, Honolulu, HI, 96822, USA

    Michael Gowanlock

  2. Department of Information & Computer Sciences, Library & Information Science Program, University of Hawaii NASA Astrobiology Institute, University of Hawaii, Hamilton Library 2H, 2550 McCarthy Mall, Honolulu, HI, 96822, USA

    Rich Gazan

Authors
  1. Michael Gowanlock
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Rich Gazan
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Michael Gowanlock.

Electronic supplementary material

Below is the link to the electronic supplementary material.

PDF (28 KB)

Rights and permissions

Reprints and permissions

About this article

Cite this article

Gowanlock, M., Gazan, R. Assessing researcher interdisciplinarity: a case study of the University of Hawaii NASA Astrobiology Institute. Scientometrics 94, 133–161 (2013). https://doi.org/10.1007/s11192-012-0765-y

Download citation

  • Received:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11192-012-0765-y

Keywords

Search

Navigation