Quantifying the Research Diversification of Physicists

Abstract

Scientists may shift research interests and span multiple research areas in their careers, reflecting the research diversification of scientists. Quantifying the scientists’ research diversity can help to understand the research patterns of scientists. In this paper, we study the research diversification of scientists in Physics based on the Physics and Astronomy Classification Scheme (PACS) which can well reflect the research topics of physics papers. For each scientist, we first build a PACS codes co-occurrence network and reveal the research diversity by analyzing the connectivity and community structure of this network. Then we use diversity indicators to measure the research diversification of scientists and analyze the distribution of each indicator. Finally, we investigate the relationship between scientists’ diversity indicators and their scientific impact using multiple regression analysis. The results show that the numbers of connected components of most PACS codes co-occurrence networks are less than 5, and some networks have significant community structures. The diversity indicators show the heterogeneity of the research diversity of physicists. We also find that some diversity indicators are weakly correlated with scientific impact indicators. Based on our findings, we suggest that physicists should focus on their main research fields and span multiple research fields over their entire careers which could promote their scientific impact.

Keywords

Research diversification / co-occurrence network / community structure / scientific impact

References

[1] Abramo G, D’Angelo C A, Di Costa F. Specialization versus diversification in research activities: The extent, intensity and relatedness of field diversification by individual scientists. Scientometrics, 2017, 112(3): 1403-1418.

[2] Abramo G, D’Angelo C A, Di Costa F. The effects of gender, age and academic rank on research diversification. Scientometrics, 2018, 114(2): 373-387.

[3] Abramo G, D’Angelo C A, Di Costa F. The effect of multidisciplinary collaborations on research diversification. Scientometrics, 2018, 116(1): 423-433.

[4] Calcagno V, Demoinet E, Gollner K, Guidi L, Ruths D, de Mazancourt C. Flows of research manuscripts among scientific journals reveal hidden submission patterns. Science, 2012, 338(6110): 1065-1069.

[5] Chakraborty T, Tammana V, Ganguly N, Mukherjee A. Understanding and modeling diverse scientific careers of researchers. Journal of Informetrics, 2015, 9(1): 69-78.

[6] Chen P, Redner S. Community structure of the physical review citation network. Journal of Informetrics, 2010, 4(3): 278-290.

[7] Deng S, Xia S. Mapping the interdisciplinarity in information behavior research: a quantitative study using diversity measure and co-occurrence analysis. Scientometrics, 2020, 124(1): 489-513.

[8] Deville P, Wang D, Sinatra R, Song C, Blondel V D, Barabási A L. Career on the move: Geography, stratification, and scientific impact. Scientific Reports, 2014, 4: 4770.

[9] Fortunato S, Hric D. Community detection in networks: A user guide. Physics Reports, 2016, 659: 1-44.

[10] Foster J G, Rzhetsky A, Evans J A. Tradition and innovation in scientists’ research strategies. American Sociological Review, 2015, 80(5): 875-908.

[11] Gomez C J, Herman A C, Parigi P. Moving more, but closer: Mapping the growing regionalization of global scientific mobility using ORCID. Journal of Informetrics, 2020, 14(3): 101044.

[12] Herron P, Mehta A, Cao C, Lenoir T. Research diversification and impact: The case of national nanoscience development. Scientometrics, 2016, 109(2): 629-659.

[13] Jamali H R, Abbasi A, Bornmann L. Research diversification and its relationship with publication counts and impact: A case study based on Australian professors. Journal of Information Science, 2020, 46(1): 131-144.

[14] Jia T, Wang D, Szymanski B K. Quantifying patterns of research-interest evolution. Nature Human Behaviour, 2017, 1(4): 0078.

[15] Li J, Yin Y, Fortunato S, Wang D. Scientific elite revisited: Patterns of productivity, collaboration, authorship and impact. Journal of the Royal Society Interface, 2020, 17(165): 20200135.

[16] Milojević S. Principles of scientific research team formation and evolution. Proceedings of the National Academy of Sciences of the United States of America, 2014, 111(11): 3984-3989.

[17] Moschini U, Fenialdi E, Daraio C, Ruocco G, Molinari E. A comparison of three multidisciplinarity indices based on the diversity of Scopus subject areas of authors’ documents, their bibliography and their citing papers. Scientometrics, 2020, 125(2): 1145-1158.

[18] Newman M E. Fast algorithm for detecting community structure in networks. Physical Review E, 2004, 69(6): 066133.

[19] Pan R K, Sinha S, Kaski K, Saramäki J. The evolution of interdisciplinarity in physics research. Scientific Reports, 2012, 2: 551.

[20] Radicchi F, Castellano C. Rescaling citations of publications in physics. Physical Review E, 2011, 83(4): 046116.

[21] Rubinov M, Sporns O. Weight-conserving characterization of complex functional brain networks. NeuroImage, 2011, 56(4): 2068-2079.

[22] Shannon C E. A mathematical theory of communication. Bell System Technical Journal, 1948, 27(3): 379-423.

[23] Simpson E H. Measurement of diversity. Nature, 1949, 163(4148): 688-688.

[24] Sinatra R, Wang D, Deville P, Song C, Barabási A L. Quantifying the evolution of individual scientific impact. Science, 2016, 354(6312): aaf5239.

[25] Smith A. From PACS to PhySH. Nature Reviews Physics, 2019, 1(1): 8-11.

[26] Uzzi B, Mukherjee S, Stringer M, Jones B. Atypical combinations and scientific impact. Science, 2013, 342(6157): 468-472.

[27] Waltman L, Van Eck N J. A new methodology for constructing a publication-level classification system of science. Journal of the American Society for Information Science and Technology, 2012, 63(12): 2378-2392.

[28] Zhang L, Sun B, Jiang L, Huang Y (2021). On the relationship between interdisciplinarity and impact: Distinct effects on academic and broader impact. Research Evaluation rvab007.

[29] Zeng A, Shen Z, Zhou J, Fan Y, Di Z, Wang Y, Stanley H E, Havlin S. Increasing trend of scientists to switch between topics. Nature Communications, 2019, 10(1): 3439.

[30] Zeng A, Shen Z, Zhou J, Wu J, Fan Y, Wang Y, Stanley H E. The science of science: From the perspective of complex systems. Physics Reports, 2017, 714: 1-73.