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Where is your field going? A machine learning approach to study the relative motion of the domains of physics

Author

Listed:
  • Andrea Palmucci
  • Hao Liao
  • Andrea Napoletano
  • Andrea Zaccaria

Abstract

We propose an original approach to describe the scientific progress in a quantitative way. Using innovative Machine Learning techniques we create a vector representation for the PACS codes and we use them to represent the relative movements of the various domains of Physics in a multi-dimensional space. This methodology unveils about 25 years of scientific trends, enables us to predict innovative couplings of fields, and illustrates how Nobel Prize papers and APS milestones drive the future convergence of previously unrelated fields.

Suggested Citation

  • Andrea Palmucci & Hao Liao & Andrea Napoletano & Andrea Zaccaria, 2020. "Where is your field going? A machine learning approach to study the relative motion of the domains of physics," PLOS ONE, Public Library of Science, vol. 15(6), pages 1-16, June.
    • Handle: RePEc:plo:pone00:0233997
    DOI: 10.1371/journal.pone.0233997
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    References listed on IDEAS

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    1. David Chavalarias & Jean-Philippe Cointet, 2013. "Phylomemetic Patterns in Science Evolution—The Rise and Fall of Scientific Fields," PLOS ONE, Public Library of Science, vol. 8(2), pages 1-11, February.
    2. Sun, Xiaoling & Ding, Kun & Lin, Yuan, 2016. "Mapping the evolution of scientific fields based on cross-field authors," Journal of Informetrics, Elsevier, vol. 10(3), pages 750-761.
    3. Cimini, Giulio & Zaccaria, Andrea & Gabrielli, Andrea, 2016. "Investigating the interplay between fundamentals of national research systems: Performance, investments and international collaborations," Journal of Informetrics, Elsevier, vol. 10(1), pages 200-211.
    4. Mark Herrera & David C Roberts & Natali Gulbahce, 2010. "Mapping the Evolution of Scientific Fields," PLOS ONE, Public Library of Science, vol. 5(5), pages 1-6, May.
    5. Emanuele Pugliese & Giulio Cimini & Aurelio Patelli & Andrea Zaccaria & Luciano Pietronero & Andrea Gabrielli, 2017. "Unfolding the innovation system for the development of countries: co-evolution of Science, Technology and Production," Papers 1707.05146, arXiv.org, revised Dec 2017.
    6. repec:nas:journl:v:115:y:2018:p:12590-12594 is not listed on IDEAS
    7. repec:nas:journl:v:115:y:2018:p:12608-12615 is not listed on IDEAS
    8. repec:nas:journl:v:115:y:2018:p:12624-12629 is not listed on IDEAS
    9. Tao Jia & Dashun Wang & Boleslaw K. Szymanski, 2017. "Quantifying patterns of research-interest evolution," Nature Human Behaviour, Nature, vol. 1(4), pages 1-7, April.
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    Cited by:

    1. Tacchella, Andrea & Zaccaria, Andrea & Miccheli, Marco & Pietronero, Luciano, 2023. "Relatedness in the era of machine learning," Chaos, Solitons & Fractals, Elsevier, vol. 176(C).
    2. Andrea Tacchella & Andrea Zaccaria & Marco Miccheli & Luciano Pietronero, 2021. "Relatedness in the Era of Machine Learning," Papers 2103.06017, arXiv.org.
    3. Sabrina Aufiero & Giordano De Marzo & Angelica Sbardella & Andrea Zaccaria, 2023. "Mapping job complexity and skills into wages," Papers 2304.05251, arXiv.org.
    4. Francisco Galuppo Azevedo & Fabricio Murai, 2021. "Evaluating the state-of-the-art in mapping research spaces: A Brazilian case study," PLOS ONE, Public Library of Science, vol. 16(3), pages 1-27, March.
    5. Yu, Xiaoyao & Szymanski, Boleslaw K. & Jia, Tao, 2021. "Become a better you: Correlation between the change of research direction and the change of scientific performance," Journal of Informetrics, Elsevier, vol. 15(3).
    6. Katchanov, Yurij L. & Markova, Yulia V., 2022. "Dynamics of senses of new physics discourse: Co-keywords analysis," Journal of Informetrics, Elsevier, vol. 16(1).

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