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Generating process of emerging topics in the life sciences

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  • Ryosuke L. Ohniwa

    (University of Tsukuba
    National Taiwan University)

  • Aiko Hibino

    (Hirosaki University)

Abstract

Clarifying the mechanism of how emerging topics in science and technology research fields are generated is useful for both researchers and agencies to identify potential emerging topics of the future. In the present study, we use bibliometric analyses targeting data of about 30 million published articles from 1970 to 2017 on PubMed, the largest article database in the life science field, to test our hypothesis that existing emerging topics contribute to the generation of new emerging topics in that field. We first collected emerging keywords from medical subject headings attached to each article using our previously reported methodology (Ohniwa et al. in Scientometrics 85(1):111–127, 2010, https://doi.org/10.1007/s11192-010-0252-2), and performed co-word analyses of each emerging keyword 1-year prior to it becoming an emerging keyword. About 75% of total emerging keywords, at 1-year prior to becoming identified as emerging, co-appeared with other emerging keywords in the same article. Furthermore, most of the keywords co-appeared again at the point when the target keyword was identified as emerging, which is consistent with our hypothesis regarding the mechanism that emerging topics generate emerging topics.

Suggested Citation

  • 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.
    • Handle: RePEc:spr:scient:v:121:y:2019:i:3:d:10.1007_s11192-019-03248-z
    DOI: 10.1007/s11192-019-03248-z
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    References listed on IDEAS

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    1. Lee, Changyong & Kwon, Ohjin & Kim, Myeongjung & Kwon, Daeil, 2018. "Early identification of emerging technologies: A machine learning approach using multiple patent indicators," Technological Forecasting and Social Change, Elsevier, vol. 127(C), pages 291-303.
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    6. 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.
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    Cited by:

    1. 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).
    2. 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.

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