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Researcher dynamics in the generation of emerging topics in life sciences and medicine

Author

Listed:
  • Ryosuke L. Ohniwa

    (University of Tsukuba
    National Taiwan University)

  • Kunio Takeyasu

    (National Taiwan University
    Kyoto University)

  • Aiko Hibino

    (Hirosaki University)

Abstract

Understanding the driving forces that generate emerging topics (ETs or Emerging Research Topics) will assist in the sound development of science and technologies. In the present study, we aim to clarify the researcher dynamics of generating and developing ETs in life sciences and medicine over the past half-century by analyzing the pre-, contemporary-, and post-participation of researchers publishing articles containing the emerging keywords that are elements of ETs. Our results suggest that, while manpower needs for publication have increased, less manpower is actually required to generate ETs these days and that pre-participation in certain research topics has become important to generate regular ETs but not Nobel Prize-class ones. Finally, we discovered that, in this post-genomic era, those researchers who generate ETs also continue to focus on those fields. These trends illustrate a mode shift in the scientific practice of researchers that have generated and developed ETs over the last 50 years as well as highlight the significance of funding projects with high probabilities of generating high-impact ETs.

Suggested Citation

  • Ryosuke L. Ohniwa & Kunio Takeyasu & Aiko Hibino, 2022. "Researcher dynamics in the generation of emerging topics in life sciences and medicine," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(2), pages 871-884, February.
    • Handle: RePEc:spr:scient:v:127:y:2022:i:2:d:10.1007_s11192-021-04233-1
    DOI: 10.1007/s11192-021-04233-1
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    References listed on IDEAS

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    1. Xu, Shuo & Hao, Liyuan & Yang, Guancan & Lu, Kun & An, Xin, 2021. "A topic models based framework for detecting and forecasting emerging technologies," Technological Forecasting and Social Change, Elsevier, vol. 162(C).
    2. Huang, Lu & Chen, Xiang & Ni, Xingxing & Liu, Jiarun & Cao, Xiaoli & Wang, Changtian, 2021. "Tracking the dynamics of co-word networks for emerging topic identification," Technological Forecasting and Social Change, Elsevier, vol. 170(C).
    3. Hanning Guo & Scott Weingart & Katy Börner, 2011. "Mixed-indicators model for identifying emerging research areas," Scientometrics, Springer;Akadémiai Kiadó, vol. 89(1), pages 421-435, October.
    4. Xu, Haiyun & Winnink, Jos & Yue, Zenghui & Zhang, Huiling & Pang, Hongshen, 2021. "Multidimensional Scientometric indicators for the detection of emerging research topics," Technological Forecasting and Social Change, Elsevier, vol. 163(C).
    5. Small, Henry & Boyack, Kevin W. & Klavans, Richard, 2014. "Identifying emerging topics in science and technology," Research Policy, Elsevier, vol. 43(8), pages 1450-1467.
    6. Rotolo, Daniele & Hicks, Diana & Martin, Ben R., 2015. "What is an emerging technology?," Research Policy, Elsevier, vol. 44(10), pages 1827-1843.
    7. Yan, Erjia, 2014. "Research dynamics: Measuring the continuity and popularity of research topics," Journal of Informetrics, Elsevier, vol. 8(1), pages 98-110.
    8. Ryosuke L. Ohniwa & Aiko Hibino, 2019. "Generating process of emerging topics in the life sciences," Scientometrics, Springer;Akadémiai Kiadó, vol. 121(3), pages 1549-1561, December.
    9. Yuan Zhou & Fang Dong & Yufei Liu & Zhaofu Li & JunFei Du & Li Zhang, 2020. "Forecasting emerging technologies using data augmentation and deep learning," Scientometrics, Springer;Akadémiai Kiadó, vol. 123(1), pages 1-29, April.
    10. Robert R. Braam & Henk F. Moed & Anthony F. J. van Raan, 1991. "Mapping of science by combined co‐citation and word analysis. I. Structural aspects," Journal of the American Society for Information Science, Association for Information Science & Technology, vol. 42(4), pages 233-251, May.
    11. Ulrich Schmoch & Bernd Beckert & Petra Schaper-Rinkel, 2019. "Impact assessment of a support programme of science-based emerging technologies," Scientometrics, Springer;Akadémiai Kiadó, vol. 118(3), pages 1141-1161, March.
    12. Petersen, Alexander M. & Rotolo, Daniele & Leydesdorff, Loet, 2016. "A triple helix model of medical innovation: Supply, demand, and technological capabilities in terms of Medical Subject Headings," Research Policy, Elsevier, vol. 45(3), pages 666-681.
    13. Jos J. Winnink & Robert J. W. Tijssen & Anthony F. J. van Raan, 2016. "Theory‐changing breakthroughs in science: The impact of research teamwork on scientific discoveries," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 67(5), pages 1210-1223, May.
    14. María José Martín-Sempere & Jesús Rey-Rocha & Belén Garzón-García, 2002. "The effect of team consolidation on research collaboration and performance of scientists. Case study of Spanish university researchers in Geology," Scientometrics, Springer;Akadémiai Kiadó, vol. 55(3), pages 377-394, November.
    15. Xu, Shuo & Hao, Liyuan & An, Xin & Yang, Guancan & Wang, Feifei, 2019. "Emerging research topics detection with multiple machine learning models," Journal of Informetrics, Elsevier, vol. 13(4).
    16. Yuan Zhou & Fang Dong & Yufei Liu & Liang Ran, 2021. "A deep learning framework to early identify emerging technologies in large-scale outlier patents: an empirical study of CNC machine tool," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(2), pages 969-994, February.
    17. Blaise Cronin, 2001. "Hyperauthorship: A postmodern perversion or evidence of a structural shift in scholarly communication practices?," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 52(7), pages 558-569.
    18. Loet Leydesdorff & Daniele Rotolo & Ismael Rafols, 2012. "Bibliometric perspectives on medical innovation using the medical subject Headings of PubMed," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 63(11), pages 2239-2253, November.
    19. Robert R. Braam & Henk F. Moed & Anthony F. J. van Raan, 1991. "Mapping of science by combined co‐citation and word analysis. II: Dynamical aspects," Journal of the American Society for Information Science, Association for Information Science & Technology, vol. 42(4), pages 252-266, May.
    20. Yutao Sun & Cong Cao, 2020. "The dynamics of the studies of China’s science, technology and innovation (STI): a bibliometric analysis of an emerging field," Scientometrics, Springer;Akadémiai Kiadó, vol. 124(2), pages 1335-1365, August.
    21. Qi Wang, 2018. "A bibliometric model for identifying emerging research topics," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 69(2), pages 290-304, February.
    22. Wolfgang Glänzel & Bart Thijs, 2012. "Using ‘core documents’ for detecting and labelling new emerging topics," Scientometrics, Springer;Akadémiai Kiadó, vol. 91(2), pages 399-416, May.
    23. Ryosuke L. Ohniwa & Aiko Hibino & Kunio Takeyasu, 2010. "Trends in research foci in life science fields over the last 30 years monitored by emerging topics," Scientometrics, Springer;Akadémiai Kiadó, vol. 85(1), pages 111-127, October.
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