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Identification and characterisation of technological topics in the field of Molecular Biology

Author

Listed:
  • Ivana Roche

    (INIST-CNRS)

  • Dominique Besagni

    (INIST-CNRS)

  • Claire François

    (INIST-CNRS)

  • Marianne Hörlesberger

    (AIT Austrian Institute of Technology GmbH, Tech Gate Vienna)

  • Edgar Schiebel

    (AIT Austrian Institute of Technology GmbH, Tech Gate Vienna)

Abstract

Following up the European project PromTech the aim of which was to detect emerging technologies by studying the scientific literature, we chose one field, Molecular Biology, to identify and characterize emerging topics within that domain. We combined two analytical approaches: the first one introduces a model of the terminological evolution of the field based on bibliometric indicators and the second one operates a diachronic clustering analysis. Our objective is to bring answers to questions such as: Which technological aspects can be detected? Which of them are already established and which of them are new? How are the topics linked to each other?

Suggested Citation

  • Ivana Roche & Dominique Besagni & Claire François & Marianne Hörlesberger & Edgar Schiebel, 2010. "Identification and characterisation of technological topics in the field of Molecular Biology," Scientometrics, Springer;Akadémiai Kiadó, vol. 82(3), pages 663-676, March.
    • Handle: RePEc:spr:scient:v:82:y:2010:i:3:d:10.1007_s11192-010-0178-8
    DOI: 10.1007/s11192-010-0178-8
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    Citations

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    Cited by:

    1. Yuan Zhou & Heng Lin & Yufei Liu & Wei Ding, 2019. "A novel method to identify emerging technologies using a semi-supervised topic clustering model: a case of 3D printing industry," Scientometrics, Springer;Akadémiai Kiadó, vol. 120(1), pages 167-185, July.
    2. 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).
    3. 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).
    4. Rotolo, Daniele & Hicks, Diana & Martin, Ben R., 2015. "What is an emerging technology?," Research Policy, Elsevier, vol. 44(10), pages 1827-1843.
    5. Woondong Yeo & Seonho Kim & Byoung-Youl Coh & Jaewoo Kang, 2013. "A quantitative approach to recommend promising technologies for SME innovation: a case study on knowledge arbitrage from LCD to solar cell," Scientometrics, Springer;Akadémiai Kiadó, vol. 96(2), pages 589-604, August.
    6. Small, Henry & Boyack, Kevin W. & Klavans, Richard, 2014. "Identifying emerging topics in science and technology," Research Policy, Elsevier, vol. 43(8), pages 1450-1467.
    7. Shuo Xu & Liyuan Hao & Xin An & Hongshen Pang & Ting Li, 2020. "Review on emerging research topics with key-route main path analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 122(1), pages 607-624, January.
    8. Chen, Guo & Xiao, Lu, 2016. "Selecting publication keywords for domain analysis in bibliometrics: A comparison of three methods," Journal of Informetrics, Elsevier, vol. 10(1), pages 212-223.
    9. Samira Ranaei & Arho Suominen & Alan Porter & Stephen Carley, 2020. "Evaluating technological emergence using text analytics: two case technologies and three approaches," Scientometrics, Springer;Akadémiai Kiadó, vol. 122(1), pages 215-247, January.
    10. Breitzman, Anthony & Thomas, Patrick, 2015. "The Emerging Clusters Model: A tool for identifying emerging technologies across multiple patent systems," Research Policy, Elsevier, vol. 44(1), pages 195-205.

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