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.
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These 11 universities are ranked in the top 30 of the three major world university rankings (QS2020, THE2020, ARWU2019), except for one in the ARWU2019 ranking and two in the QS2020 ranking; they all ranked in the top 100 in these three major world university rankings.
John McCarthy: Turing Award laureate in 1971 for considerable contributions to the foundation of artificial intelligence.
John Backus: Turing Award laureate in 1977 for contributions to the design of high-level programming systems, notably on FORTRAN.
John Cocke: Turing Award laureate in 1987 for contributions in the design and theory of compilers.
Ten laureates with the least number of publications: Edwin E. Catmull, Fernando J Corbato, Frances E Allen, Kenneth L Thompson, Charles P. Thacker, John Backus, Charles W Bachman, John Cocke, William M Kahan, Alan Kay.
Ten laureates with the highest number of publications: Yoshua Bengio, Michael Stonebreaker, Donald E Knuth, Judea Pearl, Geoffery E Hinton, Amir Pnueli, Ronald, Robert Tarjan, David Patterson, Edmund M Clarke.
References
Acuna, D. E., Allesina, S., & Kording, K. P. (2012). Predicting scientific success. Nature, 489(7415), 201–202.
Ahuja, M. K. (2002). Women in the information technology profession: A literature review, synthesis and research agenda. European Journal of Information Systems, 11(1), 20–34.
Borjas, G. J., & Doran, K. B. (2015). Prizes and productivity how winning the fields medal affects scientific output. Journal of Human Resources, 50(3), 728–758.
Boudreau, K. J., Guinan, E. C., Lakhani, K. R., & Riedl, C. (2016). Looking across and looking beyond the knowledge frontier: Intellectual distance, novelty, and resource allocation in science. Management Science, 62(10), 2765–2783.
Bromham, L., Dinnage, R., & Hua, X. (2016). Interdisciplinary research has consistently lower funding success. Nature, 534(7609), 684–687.
Camp, T. (2002). The incredible shrinking pipeline. ACM SIGCSE Bulletin, 34(2), 129–134.
Fortunato, S., Bergstrom, C. T., Börner, K., Evans, J. A., Helbing, D., Milojević, S., et al. (2018). Science of science. Science, 359(6379), eaao0185.
Foster, J. G., Rzhetsky, A., & Evans, J. A. (2015). Tradition and innovation in scientists’ research strategies. American Sociological Review, 80(5), 875–908.
Gros, C., (2018). An empirical study of the per capita yield of science Nobel prizes: Is the US era coming to an end? Royal Society Open Science, 5(5), 180167. https://doi.org/10.1098/rsos.180167.
Hillebrand, C. D. (2002). Nobel century: A biographical analysis of physics laureates. Interdisciplinary Science Reviews, 27(2), 87–93.
Je, H. (2005). An index to quantify an individual’s scientific research output. Proceedings of the National Academy of Sciences of the United States of America, 102(46), 16569–16572.
Jones, B. F. (2009). The burden of knowledge and the “death of the renaissance man” : Is innovation getting harder? The Review of Economic Studies, 76(1), 283–317.
Jones, B. F. (2010). Age and great invention. The Review of Economics and Statistics, 92(1), 1–14.
Jones, B. F., & Weinberg, B. A. (2011). Age dynamics in scientific creativity. Proceedings of the National Academy of Sciences, 108(47), 18910–18914.
Kim, D., Cerigo, D. B., Jeong, H., & Youn, H. (2016). Technological novelty profile and invention’s future impact. EPJ Data Science, 5(1), 1–15.
Larivière, V., Gingras, Y., Sugimoto, C. R., & Tsou, A. (2015). Team size matters: Collaboration and scientific impact since 1900. Journal of the Association for Information Science and Technology, 66(7), 1323–1332.
Larivière, V., Haustein, S., & Börner, K. (2015). Long-distance interdisciplinarity leads to higher scientific impact. PLOS ONE, 10(3), e0122565.
Li, J., Yin, Y., Fortunato, S., & Wang, D. (2020). Scientific elite revisited: Patterns of productivity, collaboration, authorship and impact. Journal of the Royal Society Interface, 17(165), 20200135.
Ma, Y., & Uzzi, B. (2018). Scientific prize network predicts who pushes the boundaries of science. Proceedings of the National Academy of Sciences, 115(50), 12608–12615.
Mazloumian, A., Eom, Y. H., Helbing, D., Lozano, S., & Fortunato, S. (2011). How citation boosts promote scientific paradigm shifts and nobel prizes. PLOS ONE, 6(5), e18975.
Petersen, A. M., Riccaboni, M., Stanley, H. E., & Pammolli, F. (2012). Persistence and uncertainty in the academic career. Proceedings of the National Academy of Sciences, 109(14), 5213–5218.
Roberts, E. S., Kassianidou, M., & Irani, L. (2002). Encouraging women in computer science. ACM SIGCSE Bulletin, 34(2), 84–88.
Sinatra, R., Wang, D., Deville, P., Song, C., & Barabási, A. L. (2016). Quantifying the evolution of individual scientific impact. Science, 354(6312), aaf5239.
Sinha, A., Shen, Z., Song, Y., Ma, H., Eide, D., Hsu, B.J., & Wang, K. (2015). An overview of microsoft academic service (mas) and applications. In: Proceedings of the 24th international conference on world wide web (pp. 243–246).
Spertus, E. (1991). Why are there so few female computer scientists? MIT Artificial Intelligence Laboratory Technical Report 1315.
Stephan, P., & Levin, S. (1993). Age and the nobel prize revisited. Scientometrics, 28(3), 387–399.
Tang, J., Zhang, J., Yao, L., Li, J., Zhang, L., & Su, Z. (2008). Arnetminer: Extraction and mining of academic social networks. In: Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining (pp. 990–998).
Tyutyunnik, A., & Tyutyunnik, V. (2013). Scientometric analysis of nobel laureates by country and age. Scientific Prospects 92
Uzzi, B., Mukherjee, S., Stringer, M., & Jones, B. (2013). Atypical combinations and scientific impact. Science, 342(6157), 468–472.
Van Noorden, R. (2014). Google scholar pioneer on search engine’s future. Nature News. https://doi.org/10.1038/nature.2014.16269.
Wagner, C. S., Horlings, E., Whetsell, T. A., Mattsson, P., & Nordqvist, K. (2015). Do nobel laureates create prize-winning networks? An analysis of collaborative research in physiology or medicine. PLOS ONE, 10(7), e0134164.
Wu, L., Wang, D., & Evans, J. A. (2017). Large teams have developed science and technology; small teams have disrupted it. Small Teams Have Disrupted It (September 8, 2017)
Wuchty, S., Jones, B. F., & Uzzi, B. (2007). The increasing dominance of teams in production of knowledge. Science, 316(5827), 1036–1039.
Yang, K., & Meho, L. I. (2006). Citation analysis: A comparison of google scholar, scopus, and web of science. Proceedings of the American Society for information science and technology, 43(1), 1–15.
Yegros-Yegros, A., Rafols, I., & D’Este, P. (2015). Does interdisciplinary research lead to higher citation impact? The different effect of proximal and distal interdisciplinarity. PLOS ONE, 10(8), e0135095.
Yuan, S., Shao, Z., Wei, X., Tang, J., Hall, W., Wang, Y., & Wang, Y., Wang, Y. (2020). Science behind AI: The evolution of trend, mobility, and collaboration. Scientometrics, 124, 993–1013. https://doi.org/10.1007/s11192-020-03423-7.
Acknowledgements
The work is supported by the National Natural Science Foundation of China (NSFC) under Grant No. 61806111, NSFC for Distinguished Young Scholar under Grant No. 61825602 and National Key R&D Program of China under Grant No. 2020AAA010520002.
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Appendix
Appendix
Name | Year | Subfield | Major subfield |
---|---|---|---|
Edwin E. Catmull | 2019 | Animation | Computer graphics |
Patrick M. Hanrahan | 2019 | Animation | Computer graphics |
Yoshua Bengio | 2018 | Deep learning | Artificial intelligence |
Geoffrey E. Hinton | 2018 | Deep learning | Artificial intelligence |
Yann Lecun | 2018 | Deep learning | Artificial intelligence |
John L. Hennessy | 2017 | Microprocessor | Computer architecture |
David Patterson | 2017 | Microprocessor | Computer architecture |
Sir Tim Berners-Lee | 2016 | World Wide Web | Computer architecture |
Whitfield Diffie | 2015 | Public-key cryptography | Cryptography |
Martin Hellman | 2015 | Public-key cryptography | Cryptography |
Michael Stonebraker | 2014 | INGRES | Database |
Leslie Lamport | 2013 | Distributed and concurrent system | Computer architecture |
Shafi Goldwasser | 2012 | Complexity-theoretic foundation | Cryptography |
Silvio Micali | 2012 | Complexity-theoretic foundation | Cryptography |
Judea Pearl | 2011 | Causal reasoning | Artificial intelligence |
Leslie Gabriel Valiant | 2010 | PAC | Theoretical CS |
Charles P. (Chuck) Thacker | 2009 | Modern PC | Computer architecture |
Barbara Liskov | 2008 | Data abstraction, fault tolerance | Programming technology |
Edmund Melson Clarke | 2007 | Model checking | Theoretical CS |
E. Allen Emerson | 2007 | Model checking | Theoretical CS |
Joseph Sifakis | 2007 | Model checking | Theoretical CS |
Frances (“Fran”) Elizabeth Allen | 2006 | Compilers | Programming technology |
Peter Naur | 2005 | ALGOL 60 | Programming technology |
Vinton (“Vint”) Gray Cerf | 2004 | TCP/IP | Computer architecture |
Robert (“Bob”) Elliot Kahn | 2004 | TCP/IP | Computer architecture |
Alan Kay | 2003 | Object oriented programming | Programming technology |
Leonard (Len) Max Adleman | 2002 | RSA | Cryptography |
Ronald (Ron) Linn Rivest | 2002 | RSA | Cryptography |
Adi Shamir | 2002 | RSA | Cryptography |
Ole-Johan Dahl | 2001 | Object oriented programming | Programming technology |
Kristen Nygaard | 2001 | Object oriented programming | Programming technology |
Andrew Chi-Chih Yao | 2000 | Complexity | Theoretical CS |
Frederick (“Fred”) Brooks | 1999 | System/360 | Computer architecture |
Frederick (“Fred”) Brooks | 1999 | System/360 | Operating systems |
James (“Jim”) Nicholas Gray | 1998 | Transaction processing | Database |
Douglas Engelbart | 1997 | Interactive computing | Computer architecture |
Amir Pnueli | 1996 | Temporal logic | Theoretical CS |
Manuel Blum | 1995 | Public key encryption | Cryptography |
Manuel Blum | 1995 | Computational complexity | Theoretical CS |
Edward A. (“ED”) Feigenbaum | 1994 | Large-scale AI system | Artificial intelligence |
Dabbala Rajagopal (“Raj”) Reddy | 1994 | Large-scale AI system | Artificial intelligence |
Juris Hartmanis | 1993 | Computational complexity | Theoretical CS |
Richard (“Dick”) Edwin Stearns | 1993 | Computational complexity | Theoretical CS |
Butler W. Lampson | 1992 | Distributed system | Computer architecture |
Arthur John Robin Gorell Milner | 1991 | LCF, ML | Theoretical CS |
Fernando J. (“Corby”) Corbato | 1990 | CTSS | Operating systems |
William (“Velvel”) Morton Kahan | 1989 | Floating-point computation | Numerical methods |
Ivan Sutherland | 1988 | Sketchpad | Computer graphics |
John Cocke | 1987 | RISC | Computer architecture |
John E. Hopcroft | 1986 | Analysis of algorithms | Theoretical CS |
Robert (Bob) Endre Tarjan | 1986 | Analysis of algorithms | Theoretical CS |
Richard (“Dick”) Manning Karp | 1985 | Combinatorial algorithms | Theoretical CS |
Niklaus E. Wirth | 1984 | PASCAL | Programming technology |
Dennis M. Ritchie | 1983 | UNIX | Operating systems |
Kenneth Lane Thompson | 1983 | UNIX | Operating systems |
Stephen Arthur Cook | 1982 | Computational complexity | Theoretical CS |
Edgar F. (“Ted”) Codd | 1981 | Relational model | Database |
C. Antony R. Hoare | 1980 | Programming language definition and design | Programming technology |
Kenneth E. (“Ken”) Iverson | 1979 | APL | Programming technology |
Robert (Bob) W. Floyd | 1978 | Software engineering | Theoretical CS |
John Backus | 1977 | High level programing system | Programming technology |
Michael O. Rabin | 1976 | Automata | Theoretical CS |
Dana Stewart Scott | 1976 | Automata | Theoretical CS |
Allen Newell | 1975 | List processing | Artificial intelligence |
Herbert Alexander Simon | 1975 | List processing | Artificial intelligence |
Donald (“Don”) Ervin Knuth | 1974 | Programming language design | Programming technology |
Charles William Bachman | 1973 | IDS | Database |
Edsger Wybe Dijkstra | 1972 | High level programing language | Programming technology |
John Mccarthy | 1971 | LISP | Artificial intelligence |
James Hardy (“Jim”) Wilkinson | 1970 | Linear algebra | Numerical Methods |
Marvin Minsky | 1969 | Learning | Artificial intelligence |
Richard W. Hamming | 1968 | Automatic Coding System | Numerical methods |
Maurice V. Wilkes | 1967 | EDSAC | Computer architecture |
Alan J. Perlis | 1966 | Advanced Programming | Programming technology |
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Jin, Y., Yuan, S., Shao, Z. et al. Turing Award elites revisited: patterns of productivity, collaboration, authorship and impact. Scientometrics 126, 2329–2348 (2021). https://doi.org/10.1007/s11192-020-03860-4
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DOI: https://doi.org/10.1007/s11192-020-03860-4