Quantifying patterns of research-interest evolution

Abstract

To understand quantitatively how scientists choose and shift their research focus over time is of high importance, because it affects the ways in which scientists are trained, science is funded, knowledge is organized and discovered, and excellence is recognized and rewarded19. Despite extensive investigation into various factors that influence a scientist’s choice of research topics821, quantitative assessments of mechanisms that give rise to macroscopic patterns characterizing research-interest evolution of individual scientists remain limited. Here we perform a large-scale analysis of publication records, and we show that changes in research interests follow a reproducible pattern characterized by an exponential distribution. We identify three fundamental features responsible for the observed exponential distribution, which arise from a subtle interplay between exploitation and exploration in research-interest evolution5,22. We developed a random-walk-based model, allowing us to accurately reproduce the empirical observations. This work uncovers and quantitatively analyses macroscopic patterns that govern changes in research interests, thereby showing that there is a high degree of regularity underlying scientific research and individual careers.

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Figure 1: An example demonstrating the procedure to compose a topic tuple and topic vector.
Figure 2: Patterns in the research-interest evolution.
Figure 3: An illustration of the ‘seashore walk’.
Figure 4: Results of the ’seashore walk’.

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Acknowledgements

We thank A.-L. Barabasi for providing the initial dataset, A.-L. Barabási and G. Korniss for discussions. This work was supported by the Army Research Laboratory under Cooperative Agreement Number W911NF-09-2-0053. T.J. is supported by the Natural Science Foundation of China (61603309) and CCF-Tencent RAGR (20160107). D.W. is supported by the Air Force Office of Scientific Research under award number FA9550-15-1-0162 and FA9550-17-1-0089. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

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T.J., D.W. and B.K.S. designed the research. T.J. performed numerical simulations and analysed the empirical data. T.J., D.W. and B.K.S. prepared the paper.

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Correspondence to Tao Jia or Dashun Wang or Boleslaw K. Szymanski.

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Jia, T., Wang, D. & Szymanski, B. Quantifying patterns of research-interest evolution. Nat Hum Behav 1, 0078 (2017). https://doi.org/10.1038/s41562-017-0078

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