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Theory‐changing breakthroughs in science: The impact of research teamwork on scientific discoveries

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  • Jos J. Winnink
  • Robert J. W. Tijssen
  • Anthony F. J. van Raan

Abstract

We have developed and tested an evidence‐based method for early‐stage identification of scientific discoveries. Scholarly publications are analyzed to track and trace breakthrough processes as well as their impact on world science. The focus in this study is on the incremental discovery of the ubiquitin‐mediated proteolytic system in the late 1970s by a small international team of collaborating researchers. Analysis of their groundbreaking research articles, all produced within a relatively short period of time, and the network of citing articles shows the cumulative effects of the intense collaboration within a small group of researchers working on the same subject. Using bibliographic data from the Web of Science database and the PATSTAT patents database in combination with expert opinions shows that these discoveries accumulated into a new technology. These first findings suggest that potential breakthrough discoveries can be identified at a relatively early stage by careful analysis of publication and citation patterns.

Suggested Citation

  • Jos J. Winnink & Robert J. W. Tijssen & Anthony F. J. van Raan, 2016. "Theory‐changing breakthroughs in science: The impact of research teamwork on scientific discoveries," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 67(5), pages 1210-1223, May.
    • Handle: RePEc:bla:jinfst:v:67:y:2016:i:5:p:1210-1223
    DOI: 10.1002/asi.23505
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    References listed on IDEAS

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    1. Deepa Nath & Sadaf Shadan, 2009. "The ubiquitin system," Nature, Nature, vol. 458(7237), pages 421-421, March.
    2. Ponomarev, Ilya V. & Williams, Duane E. & Hackett, Charles J. & Schnell, Joshua D. & Haak, Laurel L., 2014. "Predicting highly cited papers: A Method for Early Detection of Candidate Breakthroughs," Technological Forecasting and Social Change, Elsevier, vol. 81(C), pages 49-55.
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    6. Ilya V. Ponomarev & Brian K. Lawton & Duane E. Williams & Joshua D. Schnell, 2014. "Breakthrough paper indicator 2.0: can geographical diversity and interdisciplinarity improve the accuracy of outstanding papers prediction?," Scientometrics, Springer;Akadémiai Kiadó, vol. 100(3), pages 755-765, September.
    7. S. Phineas Upham & Lori Rosenkopf & Lyle H. Ungar, 2010. "Positioning knowledge: schools of thought and new knowledge creation," Scientometrics, Springer;Akadémiai Kiadó, vol. 83(2), pages 555-581, May.
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    Cited by:

    1. Xue Wang & Xuemei Yang & Jian Du & Xuwen Wang & Jiao Li & Xiaoli Tang, 2021. "A deep learning approach for identifying biomedical breakthrough discoveries using context analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(7), pages 5531-5549, July.
    2. Winnink, J.J. & Tijssen, Robert J.W. & van Raan, A.F.J., 2019. "Searching for new breakthroughs in science: How effective are computerised detection algorithms?," Technological Forecasting and Social Change, Elsevier, vol. 146(C), pages 673-686.
    3. Dag W. Aksnes & Liv Langfeldt & Paul Wouters, 2019. "Citations, Citation Indicators, and Research Quality: An Overview of Basic Concepts and Theories," SAGE Open, , vol. 9(1), pages 21582440198, February.
    4. Li, Xin & Ma, Xiaodi & Feng, Ye, 2024. "Early identification of breakthrough research from sleeping beauties using machine learning," Journal of Informetrics, Elsevier, vol. 18(2).
    5. Xian Li & Ronald Rousseau & Liming Liang & Fangjie Xi & Yushuang Lü & Yifan Yuan & Xiaojun Hu, 2022. "Is low interdisciplinarity of references an unexpected characteristic of Nobel Prize winning research?," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(4), pages 2105-2122, April.
    6. Ryosuke L. Ohniwa & Kunio Takeyasu & Aiko Hibino, 2022. "Researcher dynamics in the generation of emerging topics in life sciences and medicine," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(2), pages 871-884, February.
    7. Fenghua Wang & Ying Fan & An Zeng & Zengru Di, 2019. "Can we predict ESI highly cited publications?," Scientometrics, Springer;Akadémiai Kiadó, vol. 118(1), pages 109-125, January.

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