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Scientometrics

, Volume 100, Issue 3, pp 741–754 | Cite as

A topic model approach to measuring interdisciplinarity at the National Science Foundation

  • Leah G. Nichols
Article

Abstract

As the National Science Foundation (NSF) implements new cross-cutting initiatives and programs, interest in assessing the success of these experiments in fostering interdisciplinarity grows. A primary challenge in measuring interdisciplinarity is identifying and bounding the discrete disciplines that comprise interdisciplinary work. Using statistical text-mining techniques to extract topic bins, the NSF recently developed a topic map of all of their awards issued between 2000 and 2011. These new data provide a novel means for measuring interdisciplinarity by assessing the language or content of award proposals. Using the Directorate for Social, Behavioral, and Economic Sciences as a case study and drawing on the new topic model of the NSF’s awards, this paper explores new methods for quantifying interdisciplinarity in the NSF portfolio.

Keywords

Interdisciplinarity Topic model Network analysis 

Notes

Acknowledgments

The author would like to thank Dave Newman from the University of California, Irvine for his assistance with the topic model data; Myron Gutmann, Amber Story, and many others at the NSF for their perspectives and advice on this paper; the Sci2 Team for the use of their Science of Science Tool; Julia Lane who was until recently the Program Director for the Science of Science Policy Program for her vision and work in developing the NSF Portfolio Explorer; and the anonymous reviewers of this paper for their constructive comments and helpful suggestions.

Disclaimer

This work was completed as part of a AAAS Science and Technology Policy Fellowship and supported by the National Science Foundation (Award 1035631). Any opinions, findings, conclusions, or recommendations expressed in this material are those of the author and do not necessarily reflect the views of the National Science Foundation.

References

  1. AAAS Intersociety Working Group. (2011). AAAS Report XXXVI: Research and Development FY2012. Washington DC: American Association for the Advancement of Science.Google Scholar
  2. Blei, D. M., Ng, A., & Jordan, M. I. (2003). Latent dirichlet allocation. Journal of Machine Learning Research., 3, 993–1022.zbMATHGoogle Scholar
  3. Gerrish, S. M. & Blei, D. M. (2010). A language-based approach to measuring scholarly impact. Princeton University. Retrieved on April 26, 2013 from http://www.cs.princeton.edu/~blei/papers/GerrishBlei2010.pdf.
  4. Hu, D. (2009). Latent dirichlet allocation for text, images, and music. University of California, San Diego. Retrieved April 26, 2013 from http://www.cseweb.ucsd.edu/~dhu/docs/research_exam09.pdf.
  5. Leydesdorff, L. (2007). Betweenness centrality as an indicator of the interdisciplinarity of scientific journals. Journal for the American Society for Information Science and Technology., 58(9), 1303–1319.CrossRefGoogle Scholar
  6. Lu, K., & Wolfram, D. (2012). Measuring author research relatedness: A comparison of word-based, topic-based, and author cocitation approaches. Journal of the American Society for Information Science and Technology, 63(10), 1973–1986.CrossRefGoogle Scholar
  7. Masse, L. C., Moser, R. P., Stokols, D., Taylor, B. K., Marcus, S. E., Morgan, G. D., et al. (2008). Measuring collaboration and transdisciplinary integration in team science. American Journal of Preventive Medicine, 35, S151–S160.CrossRefGoogle Scholar
  8. Newman, D., Bonilla, E., & Buntine, W. (2011). Improving topic coherence with regularized topic models. Advances in Neural Information Processing Systems., 24, 496–504.Google Scholar
  9. Porter, A., & Rafols, I. (2009). Is science becoming more interdisciplinary? Measuring and mapping six research fields over time. Scientometrics., 81, 719–745.CrossRefGoogle Scholar
  10. Rafols, I., & Meyer, M. (2008). Diversity and network centralities as indicators of interdisciplinarity: Case studies in bionanoscience. Scientometrics., 82, 263–287.CrossRefGoogle Scholar
  11. Rosen-Zvi, M., Griffiths, T., Steyvers, M., & Smith, P. (2004). The Author-topic model for authors and documents (pp. 487–494). Banff: AUAI Press.Google Scholar
  12. Sci2 Team. (2009). Science of science (Sci2) tool. Indiana University and SciTech Strategies. http://sci2.cns.iu.edu. Accessed 14 Oct 2011.
  13. Sterling, A. (2007). A general framework for analysing diversity in science, technology, and society. Journal of the Royal Society Interface, 4, 707–719.CrossRefGoogle Scholar
  14. Stokols, D., Fuqua, J., Gress, J., Harvey, R., Phillips, K., Baezconde-Garbanati, L., et al. (2003). Evaluating transdisciplinary science. Nicotine & Tobacco Research, 5, S21–S39.CrossRefGoogle Scholar
  15. Wagner, C., Roessner, J., Bobb, K., Klein, J., Boyack, K., Keyton, J., et al. (2011). Approaches to understanding and measuring interdisciplinary scientific research (IDR): A review of the literature. Journal of Informetrics., 165, 14–26.CrossRefGoogle Scholar
  16. Wang, H., Ding, Y., Tang, J., Dong, X., He, B., Qiu, J., et al. (2011). Finding complex biological relationships in recent PubMed articles using Bio-LDA. PLoS One, 6(3), 1–14.Google Scholar

Copyright information

© Akadémiai Kiadó, Budapest, Hungary 2014

Authors and Affiliations

  1. 1.National Science FoundationArlingtonUSA

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