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.
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Interface = ‘a point where two systems, subjects, organizations, etc. meet and interact’ (Oxford English Dictionary).
Graphene is a material that in its purest form is a one-atom thick layer of carbon foil. In graphene, carbon atoms are densely packed in a regular (hexagonal) pattern. These hexagonal patterns are the basic structural element of related materials, including graphite, charcoal, carbon nanotubes and fullerenes. Graphene can also be thought of being an indefinitely large aromatic molecule. The hexagonal arrangement of carbon atoms in a single layer results in quantum mechanical effects that manifest at macro scale as extraordinary properties. Its interactions with other materials and with light in combination with its inherently two-dimensional nature produce unique properties, such as the bipolar transistor effect, ballistic transport of charges and large quantum oscillations. High-quality graphene is strong, light, almost transparent and an excellent conductor of heat and electricity.
‘Free form’ is to be understood as a one-atom thick layer of graphene placed on a substrate. The only function of the substrate is to support and fix the graphene layer. The graphene in the layer behaves as if it were freestanding.
See the short description of Koshland’s Cha–Cha–Cha theory in footnote 6 on p 4.
The three different types of scientific discovery according to Koshland (2007) are:
Charge discoveries, not necessarily simple, solve scientific problems that are quite obvious and fit within the at that moment existing theoretical framework.
Challenge discoveries are a response to an accumulation of facts or concepts that are unexplained by or incongruous with scientific theories of the time, ‘paradigm shifts’ (Kuhn 1962).
Chance discoveries are those that are often called serendipitous. Van Andel (1994) denotes discoveries of this type ‘unsought findings’.
A more accurate approximation for the date of a discovery would be the date a manuscript was submitted for the first time to a publisher. This submission date is not available in the databases and could therefore not be used. The publication itself is not necessarily the first attempt to publish the discovery resulting in an even more imprecise time stamp for the discovery.
According to Thomson-Reuters information terms for a topic are extracted from the title, the abstract, the author keywords of a publication, and from KeyWords Plus®. ‘KeyWords Plus are words and phrases harvested from the titles of the cited articles.’
In this study we focus on publications containing original scientific work, and therefore only publications of the WoS types ‘article’ and ‘letter’ are used in the analysis.
EPO Worldwide Patent Statistical Database (PATSTAT).
The Cooperative Patent Classification (CPC), which is the result of cooperation between the EPO and US Patent and Trademark Office (USPTO) replaced in 2013 the ECLA, ICO, and the classification of the USPTO (US Patent Classification).
We used ECLA and ICO codes C01B31/04H, H01F41/42, H01F42/44, H01F10/00C, H01L29/16G, M01B204/00, M01B204/02, M01B204/06, M01B204/20, T01F10/00C, T01L29/16G.
Patent rights are nowadays national rights granted by a sovereign state.
In the US the list of citations appear on the front-page of the patent publication.
The volume in which the ‘Novoselov paper’ was published.
Inspection of the patent file for the patent application from 2004 citing the ‘Novoselov paper’ leads to the conclusion that at a later stage in the patenting process this citation was added; most probably in 2006.
We included only publications with document types research articles and letter, from 2004 to 2005.
In this paper the classification of a publication is the classification as it appears in the Web of Science database.
The science levels are assigned to journals on the basis of the addresses of the authors of the publications in a journal. Based on the addresses the affiliation of an author is classified as university, hospital, or company. The distribution of the three classes determines the science level assigned to a journal.
These pictures were presented before at the STI 2012 conference (Winnink 2012).
First author = the author first mentioned on a publication, first organisation = the affiliation of the first author of a publication. Ambiguity in names and the effects for the analysis are discussed in section “Influence of ambiguity of author and organisation names”.
Only publications covered in TR/CWTS WoS of type article and letter are taken into account to focus on publications containing original research.
Adams, J. (2005). Early citation counts correlate with accumulated impact. Scientometrics, 63, 567–581. doi:10.1007/s11192-005-0228-9.
Andersen, P. D., & Borup, M. (2009). Foresight and strategy in national research councils and research programmes. Technology Analysis & Strategic Management, 21(8), 917–932.
Bañuls, V. A., & Salmeron, J. L. (2008). Foresighting key areas in the information technology industry. Technovation, 28(3), 103–111.
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.
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.
Breiner, S., Cuhls, K., & Grupp, H. (1994). Technology foresight using a Delphi approach—A Japanese–German cooperation. R&D Management, 24(2), 141–153.
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.
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.
Coates, J. F., Mahaffie, J. B., & Hines, A. (1994). Technological forecasting: 1970–1993. Technological Forecasting and Social Change, 47(1), 23–33.
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.
Frenken, J. W. M. (2013). Personal communication (unpublished).
Geim, A. K., & Novoselov, K. S. (2007). The rise of graphene. Nature Materials, 6(3), 183–191.
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.
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.
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.
Isenson, R. S. (Ed.). (1969). Project hindsight (final report). Technical report, US Dept. of Defense.
Jewkes, J., Sawers, D., & Stillerman, R. (1958, 1969). The sources of invention. London: MacMillan.
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.
Koshland, D. E. (2007). The Cha-Cha-Cha theory of scientific discovery. Science, 317(5839), 761–762.
Kuhn, T. S. (1962). The structure of scientific revolutions. Chicago: The University of Chicago Press.
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.
Martin, B. R. (1995). Foresight in science and technology. Technology Analysis and Strategic Management, 7(2), 139–168.
Meade, N., & Islam, T. (1998). Technological forecasting—Model selection, model stability, and combining models. Management Science, 44(8), 1115–1130.
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.
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. http://www.nobelprize.org/nobel_prizes/physics/laureates/2010/advanced-physicsprize2010.pdf.
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.
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.
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).
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.
Small, H. (1977). Co-citation model of a scientific specialty—Longitudinal-study of collagen research. Social Studies of Science, 7(2), 139–166.
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.
Tu, Y.-N., & Seng, J.-L. (2012). Indices of novelty for emerging topic detection. Information Processing and Management, 48(2), 303–325.
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.
Van Raan, A. F. (2004). Sleeping beauties in science. Scientometrics, 59(3), 467–472.
Wallace, P. R. (1947). 1946 Annual meeting at New York, January 30, 31 and February 1, 1947. Physical Review, 71, 460–489.
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).
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.
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.
Young, P. (1993). Technological growth curves: A competition of forecasting models. Technological Forecasting and Social Change, 44(4), 375–389.
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.
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Winnink, J.J., Tijssen, R.J.W. Early stage identification of breakthroughs at the interface of science and technology: lessons drawn from a landmark publication. Scientometrics 102, 113–134 (2015). https://doi.org/10.1007/s11192-014-1451-z
- Early stage
- Science–technology interface
- Weak signals