Skip to main content
Log in

Using content analysis to investigate the research paths chosen by scientists over time

  • Published:
Scientometrics Aims and scope Submit manuscript

Abstract

We present an application of a clustering technique to a large original dataset of SCI publications which is capable at disentangling the different research lines followed by a scientist, their duration over time and the intensity of effort devoted to each of them. Information is obtained by means of software-assisted content analysis, based on the co-occurrence of words in the full abstract and title of a set of SCI publications authored by 650 American star-physicists across 17 years. We estimated that scientists in our dataset over the time span contributed on average to 16 different research lines lasting on average 3.5 years and published nearly 5 publications in each single line of research. The technique is potentially useful for scholars studying science and the research community, as well as for research agencies, to evaluate if the scientist is new to the topic and for librarians, to collect timely biographic information.

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

Access this article

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

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

Similar content being viewed by others

References

  • Agrawal, A., & Henderson, R. (2002). Putting patents in context: Exploring knowledge transfer from MIT. Management Science , 48(1), 44–60.

    Article  Google Scholar 

  • Allison, P. D., & Stewart, J. A. (1974). Productivity differences among scientists: Evidence for accumulative advantage. American Sociological Review, 39(4), 596–606.

    Article  Google Scholar 

  • Cole, S., & Cole, J. R. (1967). Scientific output and recognition: A study in the operation of the reward system in science. American Sociological Review, 32(3), 377–390.

    Article  Google Scholar 

  • Courtial, J. P., Sigogneau, A., & Callon, M. (1997). Identifying strategic sciences and technologies through scientometrics. In W. B. Ahston & R. A. Klavans (Eds.), Keeping abreast of science and technology, technical intelligence for business. Columbus, OH: Battelle Press.

    Google Scholar 

  • Crane, D. (1972). Invisible colleges. Diffusion of knowledge in scientific communities. Chicago & London: The University of Chicago Press.

    Google Scholar 

  • Fox, M. F. (1983). Publication productivity among scientists: A critical review. Social Studies of Science, 13(2), 285–305.

    Article  Google Scholar 

  • Garfield, E. (1979). Citation indexing: Its theory and application in science, technology and humanities. New York: Wiley.

    Google Scholar 

  • Garner, C. A. (1979). Academic publication, market signaling and scientific research decisions. Economic Inquiry, 17(4), 575–584.

    Article  Google Scholar 

  • Gibbons, M., Limoges, C., Nowotny, H., Schwartzman, S., Scott, P., & Trow, M. (1994). The new production of knowledge. The dynamics of science and research in contemporary society. NY: Sage Publications.

    Google Scholar 

  • Godin, B. (2003). The emergence of S&T indicators: Why did governments supplement statistics with indicators? Research Policy, 32, 679–691.

    Article  Google Scholar 

  • Hackett, E. J. (2005). Essential tensions: Identity, control, and risk in research. Social Studies of Science, 35(5), 787–826.

    Article  Google Scholar 

  • Hackett, E. J., Conz, D., Parker, J., Bashford, J., & DeLay, S. (2004). Tokamaks and turbulence: Research ensembles, policy and technoscientific work. Research Policy, 33(5), 747–767.

    Article  Google Scholar 

  • Hagstrom, W. O. (1965). The scientific community. New York, London: Basic Books Inc.

    Google Scholar 

  • Hicks, D., & Hamilton, K. (1999). Does University-Industry collaboration adversely affect University research? Issues in Science and Technology Online, Summer 1999, 74-75, http://www.nap.edu/issues/15.4/images/realnum_big.jpg.

  • Klavans, R., & Boyack, K. W. (2005). Generation of large-scale maps of science and associated indicators. SANDIA Report SAND2005-7538, 01 Dec 2005.

  • Levi Montalcini, R. (1988). In praise of imperfection: My life and work. Alfred P. Sloan Foundation Series, New York: Basic Books.

  • Leydesdorff, L. (2002). Indicators of structural change in the dynamics of science: Entropy statistics of the SCI Journal Citation Reports. Scientometrics, 53(1), 131–159.

    Article  Google Scholar 

  • Manning, C., & Schütze, H. (1999). Foundations of statistical natural language processing. Cambridge: MIT Press.

    MATH  Google Scholar 

  • Merton, R. K. (1968). The Matthew effect in science. Science, New Series, 159(3810), 56–63.

    Google Scholar 

  • Moed, H. F., Burger, W. J. M., Frankfort, J. G., & Van Raan, A. F. J. (1985). The use of bibliometric data for the measurement of university research performance. Research Policy, 14, 131–149.

    Article  Google Scholar 

  • Murray, F., & Stern, S. (2007). Do formal intellectual property rights hinder the free flow of scientific knowledge? An empirical test of the anti-commons hypothesis. Journal of Economic Behavior & Organization, 63, 648–687.

    Article  Google Scholar 

  • Narin, F., & Hamilton, K. S. (1996). Bibliometric performance measures. Scientometrics, 36(3), 293–310.

    Article  Google Scholar 

  • Narin, F., Pinski, G., & Gee, H. H. (1976). Structure of the biomedical literature. Journal of the American Society for Information Science, 25, 45.

    Google Scholar 

  • Peritz, B. C. (1992). On the objectives of citation analysis: Problems of theory and method. Journal of the American Society for information Science, 43(6), 448–451.

    Article  Google Scholar 

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

    Google Scholar 

  • Rasmussen, E. (1992). Clustering Algorithms. In N. Frakes & Baeza-Yates (Eds.), Information retrieval: Data structures & algorithms. New Jersey: Prentice Hall.

    Google Scholar 

  • Salton, G. (1989). Automatic text processing: The transformation, analysis, and retrieval of information by computer. Reading, MA: Addison Wesley.

    Google Scholar 

  • Stephan, P. E., & Levin, S. G. (1992). Striking the Mother Lode in science: The importance of age, place, and time.  : Oxford University Press.

    Google Scholar 

  • Ziman, J. M. (1968). Public knowledge: An essay concerning the social dimension of science. London, Cambridge: University Press.

Download references

Acknowledgements

This work was supported by the CERIS, National Research Council of Italy and was done while one of the authors (Chiara Franzoni) was kindly gusted as visiting scholar at the Andrew Young School of Policy Studies (Georgia State University, Atlanta, GA). The authors wish to thank Paula Stephan and Francesco Lissoni for comments and suggestions. All usual disclaims apply.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Chiara Franzoni.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Franzoni, C., Simpkins, C.L., Li, B. et al. Using content analysis to investigate the research paths chosen by scientists over time. Scientometrics 83, 321–335 (2010). https://doi.org/10.1007/s11192-009-0061-7

Download citation

  • Received:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11192-009-0061-7

Keywords

Navigation