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Exploring temporal relationships between scientific and technical fronts: a case of biotechnology field

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Listed:
  • Mu-Hsuan Huang

    (National Taiwan University)

  • Ssu-Han Chen

    (National Taiwan University)

  • Chia-Ying Lin

    (National Taiwan University)

  • Dar-Zen Chen

    (National Taiwan University)

Abstract

Biotechnology is an expanding interdisciplinary field in which the interactions of science and technology (S&T) are more and more intensified. Question raised regarding the dynamic interactions between S&T encourages us to propose a series of methodologies for examination. Using high-impact publications and patents as the proxy measures, two document sets are transformed into the scientific and technical front trajectories respectively, and then each subject is categorized into either basic science, or applied technology, or co-existence. The results show that, in the biotechnology field, subjects of embryonic or mesenchymal stem cells, RNA interference, microRNA, and microbial fuel cell are in the basic science phase; those of plant breeding, seed diversity, and taste receptors have been applied to practice. There also exists interactions between S&T in the subjects of disease treatment and gene analysis platform, in which the behavior of technology precedes science, science precedes technology, or synchronous development can be observed.

Suggested Citation

  • Mu-Hsuan Huang & Ssu-Han Chen & Chia-Ying Lin & Dar-Zen Chen, 2014. "Exploring temporal relationships between scientific and technical fronts: a case of biotechnology field," Scientometrics, Springer;Akadémiai Kiadó, vol. 98(2), pages 1085-1100, February.
    • Handle: RePEc:spr:scient:v:98:y:2014:i:2:d:10.1007_s11192-013-1054-0
    DOI: 10.1007/s11192-013-1054-0
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    References listed on IDEAS

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    Cited by:

    1. Junmo Kim & Juneseuk Shin, 2018. "Mapping extended technological trajectories: integration of main path, derivative paths, and technology junctures," Scientometrics, Springer;Akadémiai Kiadó, vol. 116(3), pages 1439-1459, September.
    2. Guijie Zhang & Luning Liu & Fangfang Wei, 2019. "Key nodes mining in the inventor–author knowledge diffusion network," Scientometrics, Springer;Akadémiai Kiadó, vol. 118(3), pages 721-735, March.
    3. Hui-Yun Sung & Hsi-Yin Yeh & Jin-Kwan Lin & Ssu-Han Chen, 2017. "A visualization tool of patent topic evolution using a growing cell structure neural network," Scientometrics, Springer;Akadémiai Kiadó, vol. 111(3), pages 1267-1285, June.

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