IDEAS home Printed from https://ideas.repec.org/a/pal/palcom/v11y2024i1d10.1057_s41599-024-02781-4.html
   My bibliography  Save this article

Science’s greatest discoverers: a shift towards greater interdisciplinarity, top universities and older age

Author

Listed:
  • Alexander Krauss

    (London School of Economics
    Spanish National Research Council)

Abstract

What are the unique features and characteristics of the scientists who have made the greatest discoveries in science? To address this question, we assess all major scientific discoverers, defined as all nobel-prize and major non-nobel-prize discoverers, and their demographic, institutional and economic traits. What emerges is a general profile of the scientists who have driven over 750 of science’s greatest advances. We find that interdisciplinary scientists who completed two or more degrees in different academic fields by the time of discovery made about half—54%—of all nobel-prize discoveries and 42% of major non-nobel-prize discoveries over the same period; this enables greater interdisciplinary methodological training for making new scientific achievements. Science is also becoming increasingly elitist, with scientists at the top 25 ranked universities accounting for 30% of both all nobel-prize and non-nobel-prize discoveries. Scientists over the age of 50 made only 7% of all nobel-prize discoveries and 15% of non-nobel-prize discoveries and those over the age of 60 made only 1% and 3%, respectively. The gap in years between making nobel-prize discoveries and receiving the award is also increasing over time across scientific fields—illustrating that it is taking longer to recognise and select major breakthroughs. Overall, we find that those who make major discoveries are increasingly interdisciplinary, older and at top universities. We also assess here the role and distribution of factors like geographic location, gender, religious affiliation and country conditions of these leading scientists, and how these factors vary across time and scientific fields. The findings suggest that more discoveries could be made if science agencies and research institutions provide greater incentives for researchers to work against the common trend of narrow specialisation and instead foster interdisciplinary research that combines novel methods across fields.

Suggested Citation

  • Alexander Krauss, 2024. "Science’s greatest discoverers: a shift towards greater interdisciplinarity, top universities and older age," Palgrave Communications, Palgrave Macmillan, vol. 11(1), pages 1-11, December.
  • Handle: RePEc:pal:palcom:v:11:y:2024:i:1:d:10.1057_s41599-024-02781-4
    DOI: 10.1057/s41599-024-02781-4
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1057/s41599-024-02781-4
    File Function: Abstract
    Download Restriction: Access to full text is restricted to subscribers.

    File URL: https://libkey.io/10.1057/s41599-024-02781-4?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Benjamin Jones & E.J. Reedy & Bruce A. Weinberg, 2014. "Age and Scientific Genius," NBER Working Papers 19866, National Bureau of Economic Research, Inc.
    2. Elisabeth Maria Schlagberger & Lutz Bornmann & Johann Bauer, 2016. "At what institutions did Nobel laureates do their prize-winning work? An analysis of biographical information on Nobel laureates from 1994 to 2014," Scientometrics, Springer;Akadémiai Kiadó, vol. 109(2), pages 723-767, November.
    3. Ho Fai Chan & Benno Torgler, 2015. "The implications of educational and methodological background for the career success of Nobel laureates: an investigation of major awards," Scientometrics, Springer;Akadémiai Kiadó, vol. 102(1), pages 847-863, January.
    4. Xiao Han T Zeng & Jordi Duch & Marta Sales-Pardo & João A G Moreira & Filippo Radicchi & Haroldo V Ribeiro & Teresa K Woodruff & Luís A Nunes Amaral, 2016. "Differences in Collaboration Patterns across Discipline, Career Stage, and Gender," PLOS Biology, Public Library of Science, vol. 14(11), pages 1-19, November.
    5. Fengli Xu & Lingfei Wu & James Evans, 2022. "Flat teams drive scientific innovation," Proceedings of the National Academy of Sciences, Proceedings of the National Academy of Sciences, vol. 119(23), pages 2200927119-, June.
    6. Lingfei Wu & Dashun Wang & James A. Evans, 2019. "Large teams develop and small teams disrupt science and technology," Nature, Nature, vol. 566(7744), pages 378-382, February.
    7. Per Lunnemann & Mogens H. Jensen & Liselotte Jauffred, 2019. "Gender bias in Nobel prizes," Palgrave Communications, Palgrave Macmillan, vol. 5(1), pages 1-4, December.
    8. David A. King, 2004. "The scientific impact of nations," Nature, Nature, vol. 430(6997), pages 311-316, July.
    9. Samuel Bjork & Avner Offer & Gabriel Söderberg, 2014. "Time series citation data: the Nobel Prize in economics," Scientometrics, Springer;Akadémiai Kiadó, vol. 98(1), pages 185-196, January.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Krauss, Alexander, 2024. "How nobel-prize breakthroughs in economics emerge and the field's influential empirical methods," Journal of Economic Behavior & Organization, Elsevier, vol. 221(C), pages 657-674.
    2. Krauss, Alexander, 2024. "How nobel-prize breakthroughs in economics emerge and the field's influential empirical methods," LSE Research Online Documents on Economics 123039, London School of Economics and Political Science, LSE Library.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Bilal Barış Alkan & Leyla Karakuş & Bekir Direkci, 2023. "Knowledge discovery from the texts of Nobel Prize winners in literature: sentiment analysis and Latent Dirichlet Allocation," Scientometrics, Springer;Akadémiai Kiadó, vol. 128(9), pages 5311-5334, September.
    2. Antonio De Nicola & Gregorio D’Agostino, 2021. "Assessment of gender divide in scientific communities," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(5), pages 3807-3840, May.
    3. Thomas Heinze & Arlette Jappe & David Pithan, 2019. "From North American hegemony to global competition for scientific leadership? Insights from the Nobel population," PLOS ONE, Public Library of Science, vol. 14(4), pages 1-14, April.
    4. Ho Fai Chan & Benno Torgler, 2020. "Gender differences in performance of top cited scientists by field and country," Scientometrics, Springer;Akadémiai Kiadó, vol. 125(3), pages 2421-2447, December.
    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. Zhang, Ming-Ze & Wang, Tang-Rong & Lyu, Peng-Hui & Chen, Qi-Mei & Li, Ze-Xia & Ngai, Eric W.T., 2024. "Impact of gender composition of academic teams on disruptive output," Journal of Informetrics, Elsevier, vol. 18(2).
    7. Jelnov, Pavel & Weiss, Yoram, 2022. "Influence in economics and aging," Labour Economics, Elsevier, vol. 77(C).
    8. R. Bjørk, 2020. "The journals in physics that publish Nobel Prize research," Scientometrics, Springer;Akadémiai Kiadó, vol. 122(2), pages 817-823, February.
    9. Hayat D. Bedru & Chen Zhang & Feng Xie & Shuo Yu & Iftikhar Hussain, 2023. "CLARA: citation and similarity-based author ranking," Scientometrics, Springer;Akadémiai Kiadó, vol. 128(2), pages 1091-1117, February.
    10. Victoria Anauati & Sebastian Galiani & Ramiro H. Gálvez, 2016. "Quantifying The Life Cycle Of Scholarly Articles Across Fields Of Economic Research," Economic Inquiry, Western Economic Association International, vol. 54(2), pages 1339-1355, April.
    11. Ho F. Chan & Franklin G. Mixon & Benno Torgler, 2018. "Relation of early career performance and recognition to the probability of winning the Nobel Prize in economics," Scientometrics, Springer;Akadémiai Kiadó, vol. 114(3), pages 1069-1086, March.
    12. Yang, Alex J., 2024. "Unveiling the impact and dual innovation of funded research," Journal of Informetrics, Elsevier, vol. 18(1).
    13. Lu Liu & Benjamin F. Jones & Brian Uzzi & Dashun Wang, 2023. "Data, measurement and empirical methods in the science of science," Nature Human Behaviour, Nature, vol. 7(7), pages 1046-1058, July.
    14. Julián D. Cortés & Daniel A. Andrade, 2022. "Winners and runners-up alike?—a comparison between awardees and special mention recipients of the most reputable science award in Colombia via a composite citation indicator," Palgrave Communications, Palgrave Macmillan, vol. 9(1), pages 1-14, December.
    15. John P A Ioannidis & Ioana-Alina Cristea & Kevin W Boyack, 2020. "Work honored by Nobel prizes clusters heavily in a few scientific fields," PLOS ONE, Public Library of Science, vol. 15(7), pages 1-11, July.
    16. Hamdi A. Al-Jamimi & Galal M. BinMakhashen & Lutz Bornmann, 2022. "Use of bibliometrics for research evaluation in emerging markets economies: a review and discussion of bibliometric indicators," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(10), pages 5879-5930, October.
    17. Pandelis Mitsis, 2022. "The Nobel Prize time gap," Palgrave Communications, Palgrave Macmillan, vol. 9(1), pages 1-11, December.
    18. Wang, Yukai & Yang, Zhongkai & Liu, Lanjian & Wang, Xianwen, 2020. "Gender bias in patenting process," Journal of Informetrics, Elsevier, vol. 14(3).
    19. Yue Wang & Ning Li & Bin Zhang & Qian Huang & Jian Wu & Yang Wang, 2023. "The effect of structural holes on producing novel and disruptive research in physics," Scientometrics, Springer;Akadémiai Kiadó, vol. 128(3), pages 1801-1823, March.
    20. Jingda Ding & Yifan Chen & Chao Liu, 2023. "Exploring the research features of Nobel laureates in Physics based on the semantic similarity measurement," Scientometrics, Springer;Akadémiai Kiadó, vol. 128(9), pages 5247-5275, September.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:pal:palcom:v:11:y:2024:i:1:d:10.1057_s41599-024-02781-4. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: https://www.nature.com/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.