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Increasing trend of scientists to switch between topics

Author

Listed:
  • An Zeng

    (Beijing Normal University)

  • Zhesi Shen

    (Chinese Academy of Sciences)

  • Jianlin Zhou

    (Beijing Normal University)

  • Ying Fan

    (Beijing Normal University)

  • Zengru Di

    (Beijing Normal University)

  • Yougui Wang

    (Beijing Normal University)

  • H. Eugene Stanley

    (Boston University)

  • Shlomo Havlin

    (Bar-Ilan University)

Abstract

Despite persistent efforts in understanding the creativity of scientists over different career stages, little is known about the underlying dynamics of research topic switching that drives innovation. Here, we analyze the publication records of individual scientists, aiming to quantify their topic switching dynamics and its influence. We find that the co-citing network of papers of a scientist exhibits a clear community structure where each major community represents a research topic. Our analysis suggests that scientists have a narrow distribution of number of topics. However, researchers nowadays switch more frequently between topics than those in the early days. We also find that high switching probability in early career is associated with low overall productivity, yet with high overall productivity in latter career. Interestingly, the average citation per paper, however, is in all career stages negatively correlated with the switching probability. We propose a model that can explain the main observed features.

Suggested Citation

  • An Zeng & Zhesi Shen & Jianlin Zhou & Ying Fan & Zengru Di & Yougui Wang & H. Eugene Stanley & Shlomo Havlin, 2019. "Increasing trend of scientists to switch between topics," Nature Communications, Nature, vol. 10(1), pages 1-11, December.
    • Handle: RePEc:nat:natcom:v:10:y:2019:i:1:d:10.1038_s41467-019-11401-8
    DOI: 10.1038/s41467-019-11401-8
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    Cited by:

    1. Yi Zhang & Mengjia Wu & Guangquan Zhang & Jie Lu, 2023. "Stepping beyond your comfort zone: Diffusion‐based network analytics for knowledge trajectory recommendation," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 74(7), pages 775-790, July.
    2. Huang, Shengzhi & Huang, Yong & Bu, Yi & Luo, Zhuoran & Lu, Wei, 2023. "Disclosing the interactive mechanism behind scientists’ topic selection behavior from the perspective of the productivity and the impact," Journal of Informetrics, Elsevier, vol. 17(2).
    3. Feng Shi & James Evans, 2023. "Surprising combinations of research contents and contexts are related to impact and emerge with scientific outsiders from distant disciplines," Nature Communications, Nature, vol. 14(1), pages 1-13, December.
    4. Clémentine Cottineau, 2022. "What do analyses of city size distributions have in common?," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(3), pages 1439-1463, March.
    5. Seokbeom Kwon & Kazuyuki Motohashi & Kenta Ikeuchi, 2022. "Chasing two hares at once? Effect of joint institutional change for promoting commercial use of university knowledge and scientific research," The Journal of Technology Transfer, Springer, vol. 47(4), pages 1242-1272, August.
    6. Lubna Zafar & Nayyer Masood & Samreen Ayaz, 2023. "Impact of field of study (FoS) on authors’ citation trend," Scientometrics, Springer;Akadémiai Kiadó, vol. 128(4), pages 2557-2576, April.
    7. Confraria, Hugo & Ciarli, Tommaso & Noyons, Ed, 2024. "Countries' research priorities in relation to the Sustainable Development Goals," Research Policy, Elsevier, vol. 53(3).
    8. Ma, Yinghong & Song, Le & Ji, Zhaoxun & Wang, Qian & Yu, Qinglin, 2020. "Scholar’s career switch adhesive with research topics: An evidence from China," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 557(C).
    9. Zhao, Yi & Liu, Lifan & Zhang, Chengzhi, 2022. "Is coronavirus-related research becoming more interdisciplinary? A perspective of co-occurrence analysis and diversity measure of scientific articles," Technological Forecasting and Social Change, Elsevier, vol. 175(C).
    10. Zhao, Zhi-Dan & Chen, Jiahao & Lu, Yichuan & Zhao, Na & Jiang, Dazhi & Wang, Bing-Hong, 2021. "Dynamic patterns of open review process," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 582(C).
    11. Li, Heyang & Wu, Meijun & Wang, Yougui & Zeng, An, 2022. "Bibliographic coupling networks reveal the advantage of diversification in scientific projects," Journal of Informetrics, Elsevier, vol. 16(3).
    12. Cui, Haochuan & Zeng, An & Fan, Ying & Di, Zengru, 2021. "Quantifying the impact of a teamwork publication," Journal of Informetrics, Elsevier, vol. 15(4).
    13. Liang, Zhentao & Ba, Zhichao & Mao, Jin & Li, Gang, 2023. "Research complexity increases with scientists’ academic age: Evidence from library and information science," Journal of Informetrics, Elsevier, vol. 17(1).
    14. Zhang, Lin & Qi, Fan & Sivertsen, Gunnar & Liang, Liming & Campbell, David, 2023. "Gender differences in the patterns and consequences of changing specialization in scientific careers," SocArXiv ep5bx, Center for Open Science.
    15. Ebadi, Ashkan & Tremblay, Stéphane & Goutte, Cyril & Schiffauerova, Andrea, 2020. "Application of machine learning techniques to assess the trends and alignment of the funded research output," Journal of Informetrics, Elsevier, vol. 14(2).
    16. Ning-Ning Wang & Zhen Jin & Xiao-Long Peng, 2019. "Community Detection with Self-Adapting Switching Based on Affinity," Complexity, Hindawi, vol. 2019, pages 1-16, November.
    17. Yu, Xiaoyao & Szymanski, Boleslaw K. & Jia, Tao, 2021. "Become a better you: Correlation between the change of research direction and the change of scientific performance," Journal of Informetrics, Elsevier, vol. 15(3).
    18. Katchanov, Yurij L. & Markova, Yulia V., 2022. "Dynamics of senses of new physics discourse: Co-keywords analysis," Journal of Informetrics, Elsevier, vol. 16(1).
    19. Ma, Guoshuai & Yuhua, Qian & Zhang, Yayu & Yan, Hongren & Cheng, Honghong & Hu, Zhiguo, 2022. "The recognition of kernel research team," Journal of Informetrics, Elsevier, vol. 16(4).
    20. Lu Liu & Benjamin F. Jones & Brian Uzzi & Dashun Wang, 2023. "Data, measurement and empirical methods in the science of science," Nature Human Behaviour, Nature, vol. 7(7), pages 1046-1058, July.
    21. Xing, Yanmeng & Wang, Fenghua & Zeng, An & Ying, Fan, 2021. "Solving the cold-start problem in scientific credit allocation," Journal of Informetrics, Elsevier, vol. 15(3).

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