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A quantitative measure to compare the disciplinary profiles of research systems and their evolution over time

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  • Bongioanni, Irene
  • Daraio, Cinzia
  • Ruocco, Giancarlo

Abstract

By modeling research systems as complex systems we generalize similarity measures used in the literature during the last two decades. We propose to use the mathematical tools developed within the spin-glasses literature to evaluate similarity within systems and between systems in a unified manner. Our measure is based on the ‘overlap’ of disciplinary profiles of a set of research systems and can readily be integrated in the framework of traditional bibliometric profile analysis. The investigation of the distribution of the overlaps provides useful insights on the dynamics of the general system, that is whether it converges toward a unique disciplinary structure or to a differentiated pattern.

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  • Bongioanni, Irene & Daraio, Cinzia & Ruocco, Giancarlo, 2014. "A quantitative measure to compare the disciplinary profiles of research systems and their evolution over time," Journal of Informetrics, Elsevier, vol. 8(3), pages 710-727.
    • Handle: RePEc:eee:infome:v:8:y:2014:i:3:p:710-727
    DOI: 10.1016/j.joi.2014.06.006
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    References listed on IDEAS

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    1. Almeida, J.A.S. & Pais, A.A.C.C. & Formosinho, S.J., 2009. "Science indicators and science patterns in Europe," Journal of Informetrics, Elsevier, vol. 3(2), pages 134-142.
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    4. Qiuju Zhou & Ronald Rousseau & Liying Yang & Ting Yue & Guoliang Yang, 2012. "A general framework for describing diversity within systems and similarity between systems with applications in informetrics," Scientometrics, Springer;Akadémiai Kiadó, vol. 93(3), pages 787-812, December.
    5. Yuxian Liu & Ronald Rousseau, 2014. "Citation analysis and the development of science: A case study using articles by some Nobel prize winners," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 65(2), pages 281-289, February.
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    Cited by:

    1. Cinzia Daraio & Francesco Fabbri & Giulia Gavazzi & Maria Grazia Izzo & Luca Leuzzi & Giammarco Quaglia & Giancarlo Ruocco, 2018. "Assessing the interdependencies between scientific disciplinary profiles," Scientometrics, Springer;Akadémiai Kiadó, vol. 116(3), pages 1785-1803, September.
    2. Ning Li, 2017. "Evolutionary patterns of national disciplinary profiles in research: 1996–2015," Scientometrics, Springer;Akadémiai Kiadó, vol. 111(1), pages 493-520, April.
    3. Sasaki, Hajime & Sakata, Ichiro, 2021. "Identifying potential technological spin-offs using hierarchical information in international patent classification," Technovation, Elsevier, vol. 100(C).
    4. Hyun-Lim Yang & Tai-Woo Chang & Yerim Choi, 2018. "Exploring the Research Trend of Smart Factory with Topic Modeling," Sustainability, MDPI, vol. 10(8), pages 1-15, August.
    5. Park, Inchae & Yoon, Byungun, 2018. "Technological opportunity discovery for technological convergence based on the prediction of technology knowledge flow in a citation network," Journal of Informetrics, Elsevier, vol. 12(4), pages 1199-1222.
    6. Abramo, Giovanni & D'Angelo, Ciriaco Andrea & Di Costa, Flavia, 2022. "Revealing the scientific comparative advantage of nations: Common and distinctive features," Journal of Informetrics, Elsevier, vol. 16(1).
    7. Jielan Ding & Per Ahlgren & Liying Yang & Ting Yue, 2018. "Disciplinary structures in Nature, Science and PNAS: journal and country levels," Scientometrics, Springer;Akadémiai Kiadó, vol. 116(3), pages 1817-1852, September.

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