Skip to main content
Log in

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

  • Published:
Scientometrics Aims and scope Submit manuscript

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

Notes

  1. https://amturing.acm.org/.

  2. http://open.baai.ac.cn/data-set-detail/MTI2NDk=/NjY=/true.

  3. https://www.acm.org/.

  4. 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.

  5. John McCarthy: Turing Award laureate in 1971 for considerable contributions to the foundation of artificial intelligence.

  6. John Backus: Turing Award laureate in 1977 for contributions to the design of high-level programming systems, notably on FORTRAN.

  7. John Cocke: Turing Award laureate in 1987 for contributions in the design and theory of compilers.

  8. 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.

  9. 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.

  10. https://public.tableau.com/profile/megan.jin#!/vizhome/researchsubject/Dashboard.

References

  • Acuna, D. E., Allesina, S., & Kording, K. P. (2012). Predicting scientific success. Nature, 489(7415), 201–202.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Bromham, L., Dinnage, R., & Hua, X. (2016). Interdisciplinary research has consistently lower funding success. Nature, 534(7609), 684–687.

    Article  Google Scholar 

  • Camp, T. (2002). The incredible shrinking pipeline. ACM SIGCSE Bulletin, 34(2), 129–134.

    Article  Google Scholar 

  • Fortunato, S., Bergstrom, C. T., Börner, K., Evans, J. A., Helbing, D., Milojević, S., et al. (2018). Science of science. Science, 359(6379), eaao0185.

    Article  Google Scholar 

  • Foster, J. G., Rzhetsky, A., & Evans, J. A. (2015). Tradition and innovation in scientists’ research strategies. American Sociological Review, 80(5), 875–908.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Jones, B. F. (2010). Age and great invention. The Review of Economics and Statistics, 92(1), 1–14.

    Article  Google Scholar 

  • Jones, B. F., & Weinberg, B. A. (2011). Age dynamics in scientific creativity. Proceedings of the National Academy of Sciences, 108(47), 18910–18914.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Larivière, V., Haustein, S., & Börner, K. (2015). Long-distance interdisciplinarity leads to higher scientific impact. PLOS ONE, 10(3), e0122565.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Roberts, E. S., Kassianidou, M., & Irani, L. (2002). Encouraging women in computer science. ACM SIGCSE Bulletin, 34(2), 84–88.

    Article  Google Scholar 

  • Sinatra, R., Wang, D., Deville, P., Song, C., & Barabási, A. L. (2016). Quantifying the evolution of individual scientific impact. Science, 354(6312), aaf5239.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

Download references

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.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Sha Yuan.

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

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

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

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11192-020-03860-4

Keywords

Navigation