IDEAS home Printed from https://ideas.repec.org/a/spr/scient/v126y2021i3d10.1007_s11192-020-03860-4.html
   My bibliography  Save this article

Turing Award elites revisited: patterns of productivity, collaboration, authorship and impact

Author

Listed:
  • Yinyu Jin

    (Beijing Academy of Artificial Intelligence)

  • Sha Yuan

    (Beijing Academy of Artificial Intelligence)

  • Zhou Shao

    (Nanjing University of Science and Technology
    Tsinghua University)

  • Wendy Hall

    (University of Southampton)

  • Jie Tang

    (Tsinghua University)

Abstract

The Turing Award is recognized as the most influential and prestigious award in the field of computer science (CS). With the rise of the science of science, a large amount of bibliographic data has been analyzed in an attempt to understand the hidden mechanism of scientific evolution. These include the analysis of the Nobel Prize, including physics, chemistry, medicine, etc. In this article, we extract and analyze the data of 72 Turing Award laureates from the complete bibliographic data, fill the gap in the lack of Turing Award analysis, and discover the development characteristics of CS as an independent discipline. First, we show most Turing Award laureates have long-term and high-quality educational backgrounds, and more than 61% of them have a degree in mathematics, which indicates that mathematics has played a significant role in the development of CS. Secondly, the data shows that not all scholars have high productivity and high h-index; that is, the number of publications and h-index is not the leading indicator for evaluating the Turing Award. Third, the average age of awardees has increased from 40 to around 70 in recent years. This may be because new breakthroughs take longer, and some new technologies need time to prove their influence. Besides, we have also found that in the past 10 years, international collaboration has experienced explosive growth, showing a new paradigm in the form of collaboration. It is also worth noting that in recent years, the emergence of female winners has also been eye-catching. Finally, by analyzing the personal publication records, we find that many people are more likely to publish high-impact articles during their high-yield periods.

Suggested Citation

  • Yinyu Jin & Sha Yuan & Zhou Shao & Wendy Hall & Jie Tang, 2021. "Turing Award elites revisited: patterns of productivity, collaboration, authorship and impact," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(3), pages 2329-2348, March.
    • Handle: RePEc:spr:scient:v:126:y:2021:i:3:d:10.1007_s11192-020-03860-4
    DOI: 10.1007/s11192-020-03860-4
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11192-020-03860-4
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s11192-020-03860-4?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 F. Jones, 2009. "The Burden of Knowledge and the "Death of the Renaissance Man": Is Innovation Getting Harder?," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 76(1), pages 283-317.
    2. George J. Borjas & Kirk B. Doran, 2015. "Prizes and Productivity: How Winning the Fields Medal Affects Scientific Output," Journal of Human Resources, University of Wisconsin Press, vol. 50(3), pages 728-758.
    3. repec:nas:journl:v:115:y:2018:p:12608-12615 is not listed on IDEAS
    4. Daniel E. Acuna & Stefano Allesina & Konrad P. Kording, 2012. "Predicting scientific success," Nature, Nature, vol. 489(7415), pages 201-202, September.
    5. Lindell Bromham & Russell Dinnage & Xia Hua, 2016. "Interdisciplinary research has consistently lower funding success," Nature, Nature, vol. 534(7609), pages 684-687, June.
    6. Caroline S Wagner & Edwin Horlings & Travis A Whetsell & Pauline Mattsson & Katarina Nordqvist, 2015. "Do Nobel Laureates Create Prize-Winning Networks? An Analysis of Collaborative Research in Physiology or Medicine," PLOS ONE, Public Library of Science, vol. 10(7), pages 1-13, July.
    7. Benjamin F. Jones, 2010. "Age and Great Invention," The Review of Economics and Statistics, MIT Press, vol. 92(1), pages 1-14, February.
    8. Amin Mazloumian & Young-Ho Eom & Dirk Helbing & Sergi Lozano & Santo Fortunato, 2011. "How Citation Boosts Promote Scientific Paradigm Shifts and Nobel Prizes," PLOS ONE, Public Library of Science, vol. 6(5), pages 1-6, May.
    9. Vincent Larivière & Yves Gingras & Cassidy R. Sugimoto & Andrew Tsou, 2015. "Team size matters: Collaboration and scientific impact since 1900," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 66(7), pages 1323-1332, July.
    10. Kevin J. Boudreau & Eva C. Guinan & Karim R. Lakhani & Christoph Riedl, 2016. "Looking Across and Looking Beyond the Knowledge Frontier: Intellectual Distance, Novelty, and Resource Allocation in Science," Management Science, INFORMS, vol. 62(10), pages 2765-2783, October.
    11. Alfredo Yegros-Yegros & Ismael Rafols & Pablo D’Este, 2015. "Does Interdisciplinary Research Lead to Higher Citation Impact? The Different Effect of Proximal and Distal Interdisciplinarity," PLOS ONE, Public Library of Science, vol. 10(8), pages 1-21, August.
    12. Sha Yuan & Zhou Shao & Xingxing Wei & Jie Tang & Wendy Hall & Yongli Wang & Ying Wang & Ye Wang, 2020. "Science behind AI: the evolution of trend, mobility, and collaboration," Scientometrics, Springer;Akadémiai Kiadó, vol. 124(2), pages 993-1013, August.
    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. Ao, Weiyi & Lyu, Dongqing & Ruan, Xuanmin & Li, Jiang & Cheng, Ying, 2023. "Scientific creativity patterns in scholars’ academic careers: Evidence from PubMed," Journal of Informetrics, Elsevier, vol. 17(4).
    2. Fabio Zagonari & Paolo Foschi, 2024. "Coping with the Inequity and Inefficiency of the H-Index: A Cross-Disciplinary Empirical Analysis," Publications, MDPI, vol. 12(2), pages 1-30, April.
    3. Wu, Jiang & Ou, Guiyan & Liu, Xiaohui & Dong, Ke, 2022. "How does academic education background affect top researchers’ performance? Evidence from the field of artificial intelligence," Journal of Informetrics, Elsevier, vol. 16(2).
    4. Peng, Xianzhe & Xu, Huixin & Shi, Jin, 2024. "Are the bibliometric growth patterns of excellent scholars similar? From the analysis of ACM Fellows," Journal of Informetrics, Elsevier, vol. 18(3).
    5. 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.
    6. Yu, Shuo & Alqahtani, Fayez & Tolba, Amr & Lee, Ivan & Jia, Tao & Xia, Feng, 2022. "Collaborative Team Recognition: A Core Plus Extension Structure," Journal of Informetrics, Elsevier, vol. 16(4).
    7. Chih-Hsing Liu & Jun-You Lin, 2024. "Collaboration-based scientific productivity: evidence from Nobel laureates," Scientometrics, Springer;Akadémiai Kiadó, vol. 129(7), pages 3735-3768, July.

    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. Fontana, Magda & Iori, Martina & Leone Sciabolazza, Valerio & Souza, Daniel, 2022. "The interdisciplinarity dilemma: Public versus private interests," Research Policy, Elsevier, vol. 51(7).
    2. Jingjing Ren & Fang Wang & Minglu Li, 2023. "Dynamics and characteristics of interdisciplinary research in scientific breakthroughs: case studies of Nobel-winning research in the past 120 years," Scientometrics, Springer;Akadémiai Kiadó, vol. 128(8), pages 4383-4419, August.
    3. 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.
    4. Lina Xu & Steven Dellaportas & Zhiqiang Yang & Jin Wang, 2023. "More on the relationship between interdisciplinary accounting research and citation impact," Accounting and Finance, Accounting and Finance Association of Australia and New Zealand, vol. 63(4), pages 4779-4803, December.
    5. Banal-Estañol, Albert & Macho-Stadler, Inés & Pérez-Castrillo, David, 2019. "Evaluation in research funding agencies: Are structurally diverse teams biased against?," Research Policy, Elsevier, vol. 48(7), pages 1823-1840.
    6. Xin Liu & Yi Bu & Ming Li & Jiang Li, 2024. "Monodisciplinary collaboration disrupts science more than multidisciplinary collaboration," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 75(1), pages 59-78, January.
    7. Feng Shi & James Evans, 2023. "Surprising combinations of research contents and contexts are related to impact and emerge with scientific outsiders from distant disciplines," Nature Communications, Nature, vol. 14(1), pages 1-13, December.
    8. Whalen, Ryan, 2018. "Boundary spanning innovation and the patent system: Interdisciplinary challenges for a specialized examination system," Research Policy, Elsevier, vol. 47(7), pages 1334-1343.
    9. Yin, Yian & Wang, Dashun, 2017. "The time dimension of science: Connecting the past to the future," Journal of Informetrics, Elsevier, vol. 11(2), pages 608-621.
    10. Seolmin Yang & So Young Kim, 2023. "Knowledge-integrated research is more disruptive when supported by homogeneous funding sources: a case of US federally funded research in biomedical and life sciences," Scientometrics, Springer;Akadémiai Kiadó, vol. 128(6), pages 3257-3282, June.
    11. Kwon, Seokbeom, 2022. "Interdisciplinary knowledge integration as a unique knowledge source for technology development and the role of funding allocation," Technological Forecasting and Social Change, Elsevier, vol. 181(C).
    12. Fontana, Magda & Iori, Martina & Montobbio, Fabio & Sinatra, Roberta, 2020. "New and atypical combinations: An assessment of novelty and interdisciplinarity," Research Policy, Elsevier, vol. 49(7).
    13. Sha Yuan & Zhou Shao & Xingxing Wei & Jie Tang & Wendy Hall & Yongli Wang & Ying Wang & Ye Wang, 2020. "Science behind AI: the evolution of trend, mobility, and collaboration," Scientometrics, Springer;Akadémiai Kiadó, vol. 124(2), pages 993-1013, August.
    14. Ufuk Akcigit & Murat Celik & Daron Acemoglu, 2014. "Young, Restless and Creative: Openness to Disruption and Creative Innovations," 2014 Meeting Papers 377, Society for Economic Dynamics.
    15. Jürgen Janger & Nicole Schmidt-Padickakudy & Anna Strauss-Kollin, 2019. "International Differences in Basic Research Grant Funding. A Systematic Comparison," WIFO Studies, WIFO, number 61664, January.
    16. Jeffrey L. Furman & Florenta Teodoridis, 2020. "Automation, Research Technology, and Researchers’ Trajectories: Evidence from Computer Science and Electrical Engineering," Organization Science, INFORMS, vol. 31(2), pages 330-354, March.
    17. Matthias Krapf, 2015. "Age and complementarity in scientific collaboration," Empirical Economics, Springer, vol. 49(2), pages 751-781, September.
    18. Chung-Souk Han, 2011. "On the demographical changes of U.S. research doctorate awardees and corresponding trends in research fields," Scientometrics, Springer;Akadémiai Kiadó, vol. 89(3), pages 845-865, December.
    19. Zhentao Liang & Jin Mao & Gang Li, 2023. "Bias against scientific novelty: A prepublication perspective," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 74(1), pages 99-114, January.
    20. Jung, Taehyun & Ejermo, Olof, 2014. "Demographic patterns and trends in patenting: Gender, age, and education of inventors," Technological Forecasting and Social Change, Elsevier, vol. 86(C), pages 110-124.

    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:spr:scient:v:126:y:2021:i:3:d:10.1007_s11192-020-03860-4. 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: http://www.springer.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.