, Volume 102, Issue 1, pp 113–134 | Cite as

Early stage identification of breakthroughs at the interface of science and technology: lessons drawn from a landmark publication

  • J. J. WinninkEmail author
  • Robert J. W. Tijssen


Certain scholarly publications or patent publications may signal breakthroughs in basic scientific research or radical new technological developments. Are there bibliographical indicators that enable an analysis of R&D dynamics to help identify these ‘local revolutions’ in science and technology? The focus of this paper is on early stage identification of potential breakthroughs in science that may evolve into new technology. We analyse bibliographic information for a typical example of such a breakthrough to pinpoint information that has the potential to be used as bibliographic indicator. The typical example used is the landmark research paper by Novoselov et al. (Science 306(5696): 666–669, 2004) concerning graphene. After an initial accumulation of theoretical knowledge about graphene over a period of 50 years this publication of the discovery of a method to produce graphene had an immediate and significant impact on the R&D community; it provides a link between theory, experimental verification, and new technological applications. The publication of this landmark discovery marks a sharp rise in the number of scholarly publications, and not much later an increase in the number of filings for related patent applications. Noticeable within 2 years after publication is an above average influx of researchers and of organisations. Changes in the structure of co-citation term maps point to renewed interest from theoretical physicists. The analysis uncovered criteria that can help in identifying at early stage potential breakthroughs that link science and technology.


Breakthrough Early stage Graphene Science–technology interface Weak signals 



The authors kindly thank Prof. J.W.M. Frenken, Leiden Institute of Physics—Leiden University, and Prof. A.F.J. van Raan, Centre for Science and Technology Studies—Leiden University, for their useful comments on an earlier version of this manuscript. We also like to thank the anonymous reviewers for their valuable comments during peer review of this manuscript.

Supplementary material

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Supplementary material 1 (PDF 9469 kb)
11192_2014_1451_MOESM2_ESM.pdf (69 kb)
Supplementary material 2 (PDF 69 kb)


  1. Adams, J. (2005). Early citation counts correlate with accumulated impact. Scientometrics, 63, 567–581. doi: 10.1007/s11192-005-0228-9.CrossRefGoogle Scholar
  2. Andersen, P. D., & Borup, M. (2009). Foresight and strategy in national research councils and research programmes. Technology Analysis & Strategic Management, 21(8), 917–932.CrossRefGoogle Scholar
  3. Bañuls, V. A., & Salmeron, J. L. (2008). Foresighting key areas in the information technology industry. Technovation, 28(3), 103–111.CrossRefGoogle Scholar
  4. Bettencourt, L. M., Kaiser, D., Kaur, J., Castillo-Chávez, C., & Wojick, D. (2008). Population modeling of the emergence and development of scientific fields. Scientometrics, 75(3), 495–518.CrossRefGoogle Scholar
  5. Boehm, H., Clauss, A., Hofmann, U., & Fischer, G. (1962). Dunnste kohlenstofffolien. Zeitschrift fur Naturforschung Part B-Chemie Biochemie Biophysik Biologie und Verwandten Gebiete, B, 17(3), 150–153.Google Scholar
  6. Breiner, S., Cuhls, K., & Grupp, H. (1994). Technology foresight using a Delphi approach—A Japanese–German cooperation. R&D Management, 24(2), 141–153.CrossRefGoogle Scholar
  7. Chen, C., Chen, Y., Horowitz, M., Hou, H., Liu, Z., & Pellegrino, D. (2009). Towards an explanatory and computational theory of scientific discovery. Journal of Informetrics, 3(3), 191–209.CrossRefGoogle Scholar
  8. Coates, V., Farooque, M., Klavans, R., Lapid, K., Linstone, H. A., Pistorius, C., et al. (2001). On the future of technological forecasting. Technological Forecasting and Social Change, 67(1), 1–17.CrossRefGoogle Scholar
  9. Coates, J. F., Mahaffie, J. B., & Hines, A. (1994). Technological forecasting: 1970–1993. Technological Forecasting and Social Change, 47(1), 23–33.CrossRefGoogle Scholar
  10. Daim, T. U., Rueda, G., Martin, H., & Gerdsri, P. (2006). Forecasting emerging technologies: Use of bibliometrics and patent analysis. Technological Forecasting and Social Change, 73(8):981–1012. Tech Mining: Exploiting Science and Technology Information Resources.Google Scholar
  11. Frenken, J. W. M. (2013). Personal communication (unpublished).Google Scholar
  12. Geim, A. K., & Novoselov, K. S. (2007). The rise of graphene. Nature Materials, 6(3), 183–191.CrossRefGoogle Scholar
  13. Grupp, H., & Schmoch, U. (1992). Dynamics of science-based innovation, chapter 9—At the crossroads in laser medicine and polyimide chemistry: Patent assessment of the expansion of knowledge, (pp. 269–301). Berlin: Springer.Google Scholar
  14. Hand, D. J. (2009). Mining the past to determine the future: Problems and possibilities. International Journal of Forecasting, 25(3):441–451. Special Section: Time Series Monitoring.Google Scholar
  15. Hollingsworth, J. R. (2008). Scientific discoveries: An institutionalist and path dependent perspective. In Hannaway, C. (Ed.), Biomedicine in the twentieth century: Practices, policies, and politics (pp. 317–353). Amsterdam: IOS Press.Google Scholar
  16. Isenson, R. S. (Ed.). (1969). Project hindsight (final report). Technical report, US Dept. of Defense.Google Scholar
  17. Jewkes, J., Sawers, D., & Stillerman, R. (1958, 1969). The sources of invention. London: MacMillan.Google Scholar
  18. Julius, M., Berkoff, C. E., Strack, A. E., Krasovec, F., & Bender, A. D. (1977). A very early warning system for the rapid identification and transfer of new technology. Journal of the American Society for Information Science, 28(3), 170–174.CrossRefGoogle Scholar
  19. Koshland, D. E. (2007). The Cha-Cha-Cha theory of scientific discovery. Science, 317(5839), 761–762.Google Scholar
  20. Kuhn, T. S. (1962). The structure of scientific revolutions. Chicago: The University of Chicago Press.Google Scholar
  21. Leydesdorff, L., & Rafols, I. (2011). Local emergence and global diffusion of research technologies: An exploration of patterns of network formation. Journal of the American Society for Information Science and Technology, 62(5), 846–860.CrossRefGoogle Scholar
  22. Martin, B. R. (1995). Foresight in science and technology. Technology Analysis and Strategic Management, 7(2), 139–168.CrossRefGoogle Scholar
  23. Meade, N., & Islam, T. (1998). Technological forecasting—Model selection, model stability, and combining models. Management Science, 44(8), 1115–1130.CrossRefzbMATHGoogle Scholar
  24. Mishra, S., Deshmukh, S. G., & Vrat, P. (2002). Matching of technological forecasting technique to a technology. Technological Forecasting and Social Change, 69(1), 1–27.CrossRefGoogle Scholar
  25. Nobel Prize Physics. (2010). Scientific background on the Nobel Prize in physics 2010—GRAPHENE, compiled by the class for physics of the Royal Swedish Academy of Sciences.
  26. Novoselov, K., Geim, A., Morozov, S., Jiang, D., Zhang, Y., Dubonos, S., et al. (2004). Electric field effect in atomically thin carbon films. Science, 306(5696), 666–669.CrossRefGoogle Scholar
  27. Ponomarev, I. V., Williams, D. E., Hackett, C. J., Schnell, J. D., & Haak, L. L. (2014). Predicting highly cited papers: A method for early detection of candidate breakthroughs. Technological Forecasting and Social Change, 81, 49–55.CrossRefGoogle Scholar
  28. Ponomarev, I., Williams, D., Lawton, B., Cross, D. H., Seger, Y., Schnell, J., et al. (2012). Breakthrough paper indicator: Early detection and measurement of ground-breaking research. In Jeffery, K. G., & Dvořák, J. (Eds.), E-Infrastructures for research and innovation: Linking information systems to improve scientific knowledge production: Proceedings of the 11th international conference on current research information systems (pp. 295–304).Google Scholar
  29. Shen, Y. C., Chang, S. H., Lin, G. T., Yu, H. C., et al. (2010). A hybridselection model for emerging technology. Technological Forecasting and Social Change, 77(1), 151–166.CrossRefGoogle Scholar
  30. Small, H. (1977). Co-citation model of a scientific specialty—Longitudinal-study of collagen research. Social Studies of Science, 7(2), 139–166.CrossRefGoogle Scholar
  31. Tijssen, R. J. W. (2010). Discarding the ‘basic science/applied science’ dichotomy: A knowledge utilization triangle classification system of research journals. Journal of the American Society for Information Science and Technology, 61(9), 1842–1852.CrossRefGoogle Scholar
  32. Tu, Y.-N., & Seng, J.-L. (2012). Indices of novelty for emerging topic detection. Information Processing and Management, 48(2), 303–325.CrossRefGoogle Scholar
  33. Van Andel, P. (1994). Anatomy of the unsought finding. Serendipity: Origin, history, domains, traditions, appearances, patterns and programmability. The British Journal for the Philosophy of Science, 45(2), 631–648.CrossRefGoogle Scholar
  34. Van Raan, A. F. (2004). Sleeping beauties in science. Scientometrics, 59(3), 467–472.CrossRefGoogle Scholar
  35. Wallace, P. R. (1947). 1946 Annual meeting at New York, January 30, 31 and February 1, 1947. Physical Review, 71, 460–489.CrossRefGoogle Scholar
  36. Winnink, J. J. (2012). Searching for structural shifts in science: Graphene R&D before and after Novoselov et al. (2004). In Archambault, E., Gingras, Y., & Larivière, V., (Eds.), Proceedings of the 17th international conference on science and technology indicators (STI 2012) (Vol. 2, pp. 837–846).Google Scholar
  37. Winnink, J., & Tijssen, R. (2014). Early stage identification of ‘charge’ breakthroughs at the interface of science and technology: Lessons drawn from a landmark publication. Supplementary material. Google Scholar
  38. Yoon, B., & Park, Y. (2007). Development of new technology forecasting algorithm: Hybrid approach for morphology analysis and conjoint analysis of patent information. IEEE Transactions on Engineering Management, 54(3), 588–599.CrossRefGoogle Scholar
  39. Young, P. (1993). Technological growth curves: A competition of forecasting models. Technological Forecasting and Social Change, 44(4), 375–389.CrossRefGoogle Scholar

Copyright information

© Akadémiai Kiadó, Budapest, Hungary 2014

Authors and Affiliations

  1. 1.Centre for Science and Technology Studies (CWTS)Leiden UniversityLeidenThe Netherlands
  2. 2.Leiden University Dual PhD Centre The HagueThe HagueThe Netherlands
  3. 3.Netherlands Patent OfficeThe HagueThe Netherlands
  4. 4.DST-NRF Centre of Excellence in Scientometrics and STI PolicyStellenbosch UniversityStellenboschSouth Africa

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