Skip to main content
SpringerLink
Log in

The evolution of scientific disciplines in applied sciences: dynamics and empirical properties of experimental physics

  • Published:
Scientometrics Aims and scope Submit manuscript

Abstract

How do scientific disciplines evolve? This is one of the fundamental problems of the dynamics of science. This study confronts this problem here by investigating the evolution of experimental physics, which plays a vital role for the progress of science in society. In particular, the main aim of this article is to analyze the structure and endogenous processes of experimental physics to explain and generalize, whenever possible, the properties of the evolution of applied sciences in the phase of continuous expansion of the universe of science. Empirical analysis here suggests the following properties of the dynamics of science: (a) scientific fission, the evolution of scientific disciplines generates a process of division into two or more research fields that evolve as autonomous entities, creating new disciplines of scientific specialization; (b) ambidextrous drivers of science, the evolution of scientific disciplines by scientific fission is due to scientific discoveries or new technologies; (c) higher growth rates of the scientific production are in new research fields of a scientific discipline rather than old ones; (d) average duration of the growth phase of scientific production in research fields is about 80 years, almost the period of one generation of scholars. Overall, then, this study explains, whenever possible, the relationships that support scientific change of disciplines to develop comprehensive properties of the evolution of science directed to economic, technological and social progress.

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

Access this article

Log in via an institution

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

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10

Similar content being viewed by others

Notes

  1. Many social studies of science investigate these topics with different perspectives, such as Adams (2012), Ávila-Robinson et al. (2019), Coccia and Bozeman (2016), Freedman (1960), Kuhn (1962), Lakatos (1968, 1978), Lee and Bozeman (2005), Merton (1957, 1968), Souzanchi Kashani and Roshani (2019), Stephan (1996), Zhou et al. (2019).

  2. See also Bernal (1939), Bush (1945), Callon (1994), Etzkowitz and Leydesdorff (1998), Johnson (1972), Nelson (1962), Nelson and Romer (1996), Nordhaus (1969), Rosenberg (1974).

  3. Lievrouw (1988, p.7ff) argues that researches are organized into four distinct "programs" in scientific communication: 1). Artifact studies: scientific information as an objective commodity, whose value is independent of its use; 2). User studies: scientific information as a commodity whose value depends on the practical needs of the user; 3). Network studies: scientific information as a social link, whose value is determined by its utility in the coherence of social networks; 4). Lab studies: scientific information as a social construction of scientists, with its value completely dependent on the changing perceptions of those individual scientists (so called because their authors typically employ participant observation or other ethnographic techniques to gather data in the scientists' workplace).

  4. Hypergraphs are mathematically equivalent to bipartite graphs in which articles (hyperedges) are represented as a distinct type of node that connects other things together. Latour points out that the old word “Thing” originally designated a type of archaic assembly, as the Icelandic Althing: “Thus, long before designating an object thrown out of the political sphere and standing there objectively and independently, the Ding or Thing has for many centuries meant the issue that brings people together because it divides them” (as quoted by Shi et al. 2015, p. 73).

  5. cf., Boyack (2004), Boyack et al. (2005), Fanelli and Glänzel (2013), Simonton (2002), Small (1999a, b), Smith et al. (2000), Sun et al. (2013).

  6. “ ‘normal science’ means research firmly based upon one or more past scientific achievements that some particular scientific community acknowledges for a time as supplying the foundation for its further practice’’ (Kuhn 1962, p. 10, original emphasis).

  7. Transmission Electron Microscopy (TEM) is a radical innovation that operates with electrons that are accelerated to a velocity approaching the speed of light; the associated wavelength is five orders of magnitude smaller than light wavelength and the resolution of the material imaging and structure determination is at atomic level (Hawkes 2007; Fultz and Howe 2007; Reimer and Kohl 2008). In short, TEM is a microscopy that can provide information of the surface feature, shape and structure of matter  and is an appropriate instrument to support scientific advances in cancer research, materials science, semiconductor research, metallurgy, and so on.

  8. High-energy electron diffraction is a technique to analyze and characterize the surface of crystalline materials.

  9. cf., Beaver and Rosen (1978), Coccia and Bozeman (2016), Coccia and Wang (2016), De Solla Price (1986), Frame and Carpenter (1979), Latour (1987a, b), Latour and Woolgar (1979), Mulkay (1975), Newman (2001), Sun et al. (2013), Storer (1970).

  10. cf., Fanelli and Glänzel (2013), Gibbons et al. (1994), Guimera et al. (2005), Kitcher (2001), Klein (1996), Sun et al. (2013), Wagner (2008).

  11. This study uses the terms of research field, research topic or keyword like interchangeable concepts because these concepts are rather similar in outputs of citation database under study here.

  12. Main research topics in experimental physics are described by: Barger and Olsson (1973), Bleaney and Bleaney (1965), Cheng (2010), Halliday et al. (2014), Heyde (1994), Jackson (1999), Kleppner and Kolenkow (2014), Lilley (2001), Martin (2006), Martin and Shaw (2008); Perkins (2000), Phillips (1994), Squires (2001), Taylor (1997), Young and Freedman (2012).

References

  • Adams, J. (2012). The rise of research networks. Nature,490(7420), 335–356.

    Google Scholar 

  • Adams, J. (2013). The fourth age of research. Nature,497(7451), 557–560.

    Article  Google Scholar 

  • Alexander, J. (1979). Paradigm Revision and Parsonianism. Canadian Journal of Sociology,4, 343–358.

    Google Scholar 

  • Alexander, J. (1983). Theoretical logic in sociology (Vol. 2). Berkeley: University of California Press.

    Google Scholar 

  • Andersen, H. (1998). Characteristics of scientific revolutions. Endeavour,22(1), 3–6.

    Google Scholar 

  • Ávila-Robinson, A., Islam, N., & Sengoku, S. (2019). Co-evolutionary and systemic study on the evolution of emerging stem cell-based therapies. Technological Forecasting and Social Change,138, 324–339. https://doi.org/10.1016/j.techfore.2018.10.012.

    Article  Google Scholar 

  • Barger, V. D., & Olsson, M. G. (1973). Classical mechanics: A modern perspective. New York: McGraw-Hill.

    MATH  Google Scholar 

  • Ben-David, J., & Collins, R. (1966). Social factors in the origins of new science: The case of psychology. American Sociological Review,4, 451–465.

    Google Scholar 

  • Bernal, J. D. (1939). The social function of science. Cambridge: MIT Press.

    Google Scholar 

  • Bettencourt, L. M., Kaiser, D. I., & Kaur, J. (2009). Scientific discovery and topological transitions in collaboration networks. Journal of Informetrics,3, 210–221.

    Google Scholar 

  • Bleaney, B. I., & Bleaney, B. (1965). Electricity & magnetism. Oxford Uni: Press.

    MATH  Google Scholar 

  • Bol, T., de Vaan, M., & van de Rijt, A. (2018). The Matthew effect in science funding. PNAS,115(19), 4887–4890. https://doi.org/10.1073/pnas.1719557115.

    Article  Google Scholar 

  • Boring, E. G. (1927). The problem of originality in science. The American Journal of Psychology,39, 70–90.

    Google Scholar 

  • Börner, K., Boyack K.W., Milojević S., & Morris S. (2012). An introduction to modeling science: basic model types, key definitions, and a general framework for the comparison of process models. In Scharnhorst et al. Models of science dynamics (pp. 3–22). New York: Springer.

  • Börner, K., Glänzel, W., Scharnhorst, A., & van den Besselaar, P. V. (2011). Modeling science: Studying the structure and dynamics of science. Scientometrics,89, 347–348.

    Google Scholar 

  • Börner, K., & Scharnhorst, A. (2009). Visual conceptualizations and models of science. Journal of Informetrics,3, 161–172.

    Google Scholar 

  • Boyack, K. W. (2004). Mapping knowledge domains: Characterizing PNAS. Proceedings of The National Academy of Sciences of the United States of America (PNAS),101(suppl. 1), 5192–5199.

    Google Scholar 

  • Boyack, K. W., Börner, K., & Klavans, R. (2009). Mapping the structure and evolution of chemistry research. Scientometrics,79, 45–60. https://doi.org/10.1007/s11192-009-0403-5.

    Article  Google Scholar 

  • Boyack, K. W., Klavans, R., & Börner, K. (2005). Mapping the backbone of science. Scientometrics,64(3), 351–374.

    Google Scholar 

  • Bush, V. (1945). Science: The endless frontier. North Stratford: Ayer Co.

    Google Scholar 

  • Büttner, J., Renn, J., & Schemmel, M. (2003). Exploring the limits of classical physics: Planck, Einstein, and the structure of a scientific revolution. Studies in History and Philosophy of Modern Physics,34(1), 37–59.

    MathSciNet  MATH  Google Scholar 

  • Cahlík, T., & Jiřina, M. (2006). Law of cumulative advantages in the evolution of scientific fields. Scientometrics,66(3), 441–449. https://doi.org/10.1007/s11192-006-0032-1.

    Article  Google Scholar 

  • Callon, M. (1994). Is science a public good? Fifth Mullins lecture. Science, Technology, and Human Values,19(4), 395–424.

    Google Scholar 

  • Callon, M. (1986). Some elements of a sociology of translation: domestication of the scallops and the fishermen of St Brieuc Bay. In J. Law (Ed.), Power, action, and belief: A new sociology of knowledge? (pp. 196–229). London: Routledge & Kegan Paul.

    Google Scholar 

  • Cheng, T. P. (2010). Relativity, gravitation and cosmology: A basic introduction. Cambridge: Cambridge University Press.

    MATH  Google Scholar 

  • Chubin, D. E. (1976). The conceptualization of scientific specialties. The Sociological Quarterly,17(4), 448–476.

    Google Scholar 

  • Coccia, M. (2005a). A taxonomy of public research bodies: A systemic approach. Prometheus, 23(1), 63–82.

    Google Scholar 

  • Coccia, M. (2005b). Metrics to measure the technology transfer absorption: Analysis of the relationship between institutes and adopters in northern Italy. International Journal of Technology Transfer and Commercialization,4(4), 462–486. https://doi.org/10.1504/IJTTC.2005.006699.

    Article  Google Scholar 

  • Coccia, M. (2006). Analysis and classification of public research institutes. World Review of Science, Technology and Sustainable Development,3(1), 1–16. https://doi.org/10.1504/WRSTSD.2006.008759.

    Article  Google Scholar 

  • Coccia, M. (2010). Democratization is the driving force for technological and economic change. Technological Forecasting & Social Change,77(2), 248–264. https://doi.org/10.1016/j.techfore.2009.06.007.

    Article  Google Scholar 

  • Coccia, M. (2014). Path-breaking target therapies for lung cancer and a far-sighted health policy to support clinical and cost effectiveness. Health Policy and Technology,1(3), 74–82. https://doi.org/10.1016/j.hlpt.2013.09.007.

    Article  Google Scholar 

  • Coccia, M. (2015a). Technological paradigms and trajectories as determinants of the R&D corporate change in drug discovery industry. International Journal of Knowledge and Learning, 10(1), 29.

    Google Scholar 

  • Coccia, M. (2015b). General sources of general purpose technologies in complex societies: Theory of global leadership-driven innovation, warfare and human development. Technology in Society,42, 199–226. https://doi.org/10.1016/j.techsoc.2015.05.008.

    Article  Google Scholar 

  • Coccia, M. (2016). Radical innovations as drivers of breakthroughs: Characteristics and properties of the management of technology leading to superior organizational performance in the discovery process of R&D labs. Technology Analysis & Strategic Management,28(4), 381–395. https://doi.org/10.1080/09537325.2015.1095287.

    Article  Google Scholar 

  • Coccia, M. (2018a). General properties of the evolution of research fields: A scientometric study of human microbiome, evolutionary robotics and astrobiology. Scientometrics,117(2), 1265–1283. https://doi.org/10.1007/s11192-018-2902-8.

    Article  Google Scholar 

  • Coccia, M. (2018b). Evolution of the economics of science in the Twenty Century. Journal of Economics Library,5(1), 65–84. https://doi.org/10.1453/jel.v5i1.1577.

    Article  Google Scholar 

  • Coccia, M. (2018c). Theorem of not independence of any technological innovation. Journal of Economics Bibliography,5(1), 29–35. https://doi.org/10.1453/jeb.v5i1.1578.

    Article  Google Scholar 

  • Coccia, M. (2019a). The role of superpowers in conflict development and resolutions. In A. Farazmand (Ed.), Global encyclopedia of public administration, public policy, and governance. Switzerland: Springer. https://doi.org/10.1007/978-3-319-31816-5_3709-1.

    Chapter  Google Scholar 

  • Coccia, M. (2019b). Revolutions and evolutions. In A. Farazmand (Ed.), Global encyclopedia of public administration, public policy, and governance. Switzerland: Springer. https://doi.org/10.1007/978-3-319-31816-5_3708-1.

    Chapter  Google Scholar 

  • Coccia, M. (2020). Deep learning technology for improving cancer care in society: New directions in cancer imaging driven by artificial intelligence. Technology in Society,60, 1–11.

    Google Scholar 

  • Coccia, M., & Bozeman, B. (2016). Allometric models to measure and analyze the evolution of international research collaboration. Scientometrics,108(3), 1065–1084.

    Google Scholar 

  • Coccia, M., & Wang, L. (2015). Path-breaking directions of nanotechnology-based chemotherapy and molecular cancer therapy. Technological Forecasting & Social Change,94(May), 155–169. https://doi.org/10.1016/j.techfore.2014.09.007.

    Article  Google Scholar 

  • Coccia, M., & Wang, L. (2016). Evolution and convergence of the patterns of international scientific collaboration. Proceedings of the National Academy of Sciences of the United States of America,113(8), 2057–2061. https://doi.org/10.1073/pnas.1510820113.

    Article  Google Scholar 

  • Coccia, M., & Watts, J. (2020). A theory of the evolution of technology: Technological parasitism and the implications for innovation management. Journal of Engineering and Technology Management,55, 101552. https://doi.org/10.1016/j.jengtecman.2019.11.003.

    Article  Google Scholar 

  • Cohen, I. B. (1952). Orthodoxy and Scientific Progress. Proceedings of the American Philosophical Society,96, 505–512.

    Google Scholar 

  • Cohen, M. D., March, J. G., & Olsen, J. P. (1972). A garbage can model of organizational choice. Administrative Science Quarterly,17(1), 1–25.

    Google Scholar 

  • Constant, E. W. (2000). The evolution of war and technology. In J. Zirman (Ed.), Technological knowledge as an evolutionary process (pp. 281–298). Cambridge: Cambridge University Press.

    Google Scholar 

  • Crane, D. (1972). Invisible colleges: Diffusion of knowledge in scientific communities. Chicago: University of Chicago Press.

    Google Scholar 

  • Dampier, W. C. (1953). Shorter history of science. New York: Macmillan Company Armed.

    Google Scholar 

  • de Beaver, B. D., & Rosen, R. (1978). Studies in scientific collaboration. Part 1 The professional origins of scientific co-authorship. Scientometrics,1, 65–84.

    Google Scholar 

  • De Solla Price, D. J. (1986). Little science, big science and beyond, Ch. 3. New York: Columbia University Press.

    Google Scholar 

  • De Solla Price, D., & Beaver, D. (1966). Collaboration in an Invisible College. American Psychologist, XXI,6, 1011–1018.

    Google Scholar 

  • Dogan, M., & Pahre, R. (1990). Creative marginality. Innovation at the intersections of social sciences. Boulder: Westview Press.

    Google Scholar 

  • Edge D.O. & Mulkay M.J. (1974). Case studies of scientific specialties. Working paper: University of Edinburgh, Science Studies Unit. German translation published in Kölner Zeitschrift für Soziologie und Sozialpsychologie.

  • Etzkowitz, H., & Leydesdorff, L. (1998). The endless transition: a Triple Helix of university-industry-government relations. Minerva,36(3), 203–208.

    Google Scholar 

  • Evans, J. A., & Foster, J. G. (2011). Metaknowledge. Science,331(6018), 721–725.

    MathSciNet  MATH  Google Scholar 

  • Fanelli, D., & Glänzel, W. (2013). Bibliometric evidence for a hierarchy of the sciences. PLoS ONE,8(6), e66938. https://doi.org/10.1371/journal.pone.0066938.

    Article  Google Scholar 

  • Foote, R. (2007). Mathematics and complex systems. Science,318(5849), 410–412.

    MathSciNet  MATH  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. https://doi.org/10.1126/science.aao0185.

    Article  Google Scholar 

  • Frame, J. D., & Carpenter, M. P. (1979). International research collaboration. Social Studies of Science,9(4), 481–497.

    Google Scholar 

  • Freedman, P. (1960). The principles of scientific research (First edition 1949). London: Pergamon Press.

    Google Scholar 

  • Fultz, B., & Howe, J. (2007). Transmission electron microscopy and diffractometry of materials. New York: Springer.

    Google Scholar 

  • Garg, K. C., Sharma, P., & Sharma, L. (1993). Bradford's law in relation to the evolution of a field. A case study of solar power research. Scientometrics,27(3), 669–685.

    Google Scholar 

  • Genovesi, A. (1786). Elementi di Fisica Sperimentale, Publisher presso Giuseppe di Bisogno, Napoli (Italy)

  • Gibbons, M., Limoges, C., Nowotny, H., Schwatzman, S., Scott, P., & Trow, M. (1994). The new production of knowledge: The dynamics of science and research in contemporary society. London: Sage Publications.

    Google Scholar 

  • Godin, B. (2001). Defining research: is research always systematic? Project on the history and sociology of S&T statistics, No.5, OST: Montreal.

  • Golinski, J. (1998). Making natural knowledge: constructivism and the history of science, chapter 2 (pp. 47–78). Cambridge: Cambridge University Press.

    Google Scholar 

  • Good, G. A. (2000). The assembly of geophysics: Scientific disciplines as frameworks of consensus. Studies in History and Philosophy of Science Part B: Studies in History and Philosophy of Modern Physics.,31(3), 259–292.

    MathSciNet  Google Scholar 

  • Guimera, R., Uzzi, B., Spiro, J., & Amaral, L. (2005). Team assembly mechanisms determine collaboration network structure and team performance. Science,308, 697–702.

    Google Scholar 

  • Guntau, M., & Laitko, H. (1988). On the origin and nature of scientific disciplines. In W. R. Woodward & R. S. Cohen (Eds.), World views and scientific discipline formation, chapter 1 (pp. 17–89). Berlin: Akademie Verlag.

    Google Scholar 

  • Hagstrom, W. O. (1970). Factors related to the use of different modes of publishing research in four scientific fields. In C. E. Nelson & D. K. Pollock (Eds.), Communication among scientists and engineers (pp. 85–124). Lexington: Mass. Lexington Books.

    Google Scholar 

  • Halliday, D., Resnick, R., & Walker, J. (2014). Fundamental of Physics (Vol. 10). New York: Wiley.

    MATH  Google Scholar 

  • Haskins C.P. (1965). Report of the President by Carnegie Institution of Washington Yearbook 63, 1963–1964, Washington, D.C.

  • Hawkes, P. (2007). The beginnings of electron microscopy transmission electron microscopy and diffractometry of materials. New York: Springer.

    Google Scholar 

  • Heyde, K. (1994). Basic ideas and concepts in nuclear physics. Boca Raton: CRC Press.

    MATH  Google Scholar 

  • Hughes, S. S. (1977). The virus: A history of the concept. New York: Science History Publications.

    Google Scholar 

  • International Union of Crystallography. (1992). Report of the Executive Committee for 1991. Acta Crystallographica,A48, 922–946.

    Google Scholar 

  • Jackson, J. D. (1999). Classical electrodynamics. New York: Wiley.

    MATH  Google Scholar 

  • Jamali, H. R., & Nicholas, D. (2010). Interdisciplinarity and the information-seeking behavior of scientists. Information Processing and Management,46, 233–243.

    Google Scholar 

  • Jeffrey, P. (2003). Smoothing the Waters: Observations on the process of cross-disciplinary research collaboration. Social Studies of Science,33(4), 539–562.

    Google Scholar 

  • Johnson, H. G. (1972). Some economic aspects of science. Minerva,10(1), 10–18.

    MathSciNet  Google Scholar 

  • Kitcher, P. (2001). Science, Truth, and democracy, chaps. 5–6. New York: Oxford University Press.

    Google Scholar 

  • Klavans, R., & Boyack, K. W. (2009). Toward a consensus map of science. Journal of American Society for Information Science and Technology,60, 455–476.

    Google Scholar 

  • Klein, J. T. (1996). Crossing boundaries. Knowledge, disciplinarities and interdisciplinarities. Charlottesville, VA: University Press of Virginia.

    Google Scholar 

  • Kleppner, D., & Kolenkow, R. (2014). An introduction to mechanics. New York: McGraw-Hill.

    MATH  Google Scholar 

  • Knorr, K. D., Strasser, H., & Zilian, H. G. (1975). Determinants and controls of scientific development. Netherlands: Springer.

    Google Scholar 

  • Kot, S. M. (1987). The stochastic model of evolution of scientific disciplines. Scientometrics,12, 197–205. https://doi.org/10.1007/BF02016292.

    Article  Google Scholar 

  • Kuhn, T. S. (1962). The structure of scientific revolutions (2nd ed.). Chicago: The University of Chicago Press.

    Google Scholar 

  • Lakatos, I. (1968). Criticism and the methodology of scientific research programmes. Proceedings of the Aristotelian Society, New Series,69, 149–186.

    Google Scholar 

  • Lakatos, I. (1978). The Methodology of scientific research programmes: Philosophical papers (Vol. 1). Cambridge, MA: Cambridge University Press.

    MATH  Google Scholar 

  • Latour, B. (1987a). Science in action. Cambridge, MA: Harvard University Press.

    Google Scholar 

  • Latour, B. (1987b). Science in action: How to follow scientists and engineers through society. Cambridge: Harvard University Press.

    Google Scholar 

  • Latour, B. (1999). Pandora’s hope: Essays on the reality of science studies. Cambridge: Harvard University Press.

    Google Scholar 

  • Latour, B., & Woolgar, S. (1979). Laboratory life: The social construction of scientific facts. London and Beverly Hills: Sage.

    Google Scholar 

  • Latour, B., & Woolgar, S. (1986). Laboratory life: The construction of scientific facts. Princeton: Princeton University Press.

    Google Scholar 

  • Law, J. (1976). The development of specialties in science: The case of X-ray protein crystallography. In L. Gerard, M. Roy, M. Michael, & W. Peter (Eds.), Perspectives on the emergence of scientific discipline (pp. 123–152). Chicago, IL: Aldine Publishing Company.

    Google Scholar 

  • Lee, S., & Bozeman, B. (2005). The impact of research collaboration on scientific productivity. Social Studies of Science,35(5), 673–702.

    Google Scholar 

  • Lemaine, G., MacLeod, R., Mulkay, M., & Weingart, P. (Eds.). (1976). Perspectives on the emergence of scientific disciplines, the hague and Paris: Mouton (pp. 1–23). Chicago: Aldine.

    Google Scholar 

  • Levine, D., & Steinhardt, R. (1984). Quasicrystals: A New class of ordered structures. Physical Review Letters,53(26), 2477–2480.

    Google Scholar 

  • Levi-Strauss, C. (1966). The savage mind. Chicago: Chicago University Press.

    Google Scholar 

  • Leydesdorff, L., & Cozzens, S. E. (1993). The delineation of specialties in terms of journals using the dynamic journal set of the Science Citation Index. Scientometrics,26(133), 154.

    Google Scholar 

  • Lievrouw, L. A. (1988). Four programs of research in scientific communication. Knowledge in Society,1(2), 6–22. https://doi.org/10.1007/BF02687210.

    Article  Google Scholar 

  • Lilley, J. (2001). Nuclear physics principles and applications. New York: Wiley.

    Google Scholar 

  • Martin, B., & Shaw, G. (2008). Particle physics. New York: Wiley.

    MATH  Google Scholar 

  • Martin, B. R. (2006). Nuclear and particle physics: An introduction. New York: Wiley.

    Google Scholar 

  • Merton, R. K. (1957). Priorities in scientific discovery: A chapter in the sociology of science. American Sociological Review,22(6), 635–659. https://doi.org/10.2307/2089193.

    Article  Google Scholar 

  • Merton, R. K. (1968). The matthew effect in science. Science,159(3810), 56–63. https://doi.org/10.1126/science.159.3810.56.

    Article  Google Scholar 

  • Monge G., Cassini J.-D., Bertholon P., Hassenfratz J.H., & Panckoucke C.J. (1793–1822). Dictionnaire de physique. Paris: Hotel de Thou

  • Moran, J. (2010). Interdisciplinarity. New York: Routledge.

    Google Scholar 

  • Morillo, F., Bordons, M., & Gómez, I. (2003). Interdisciplinarity in science: A tentative typology of disciplines and research areas. Journal of the American society for information science and technology,54(13), 1237–1249.

    Google Scholar 

  • Mulkay, M. J. (1969). Some aspects of cultural growth in the natural sciences. Social Research,36, 22–52.

    Google Scholar 

  • Mulkay, M. J. (1974). Conceptual displacement and migration in science: a prefatory paper. Science Studies,4, 205–234.

    Google Scholar 

  • Mulkay, M. J. (1975). Three models of scientific development. The Sociological Review,23, 509–526.

    Google Scholar 

  • Mullins, N. C. (1972). The development of a scientific specialty: The phage group and the origins of molecular biology. Minerva,10, 51–82.

    Google Scholar 

  • Mullins, N. C. (1973). The development of specialties in social sciences: The case of ethnomethodology. Science Studies,3, 245–273.

    Google Scholar 

  • Mullins, N. C. (1974). A sociological theory of normal and revolutionary science. In K. D. Knorr, H. Strasser, & H. G. Zilian (Eds.), Determinants and controls of scientific development. Boston: Reidel.

    Google Scholar 

  • Nelson, R. R. (1962). The link between science and invention: The case of the transistor, in the rate and direction of inventive activity: Economic and social factors (pp. 549–583). Princeton University Press: Princeton.

    Google Scholar 

  • Nelson, R. R., & Romer, P. M. (1996). Science, economic growth, and public policy. Challenge,39(1), 9–21. https://doi.org/10.1080/05775132.1996.11471873.

    Article  Google Scholar 

  • Newell, A., & Simon, H. A. (1972). Human problem solving. New York: Prentice-Hall.

    Google Scholar 

  • Newman, M. E. J. (2001). The Structure of scientific collaboration networks. Proceedings of The National Academy of Sciences of the United States of America (PNAS),98(2), 404–409.

    MathSciNet  MATH  Google Scholar 

  • Newman, M. E. J. (2004). Coauthorship Networks and Patterns of Scientific Collaboration. Proceedings of The National Academy of Sciences of the United States of America (PNAS),101(suppl. 1), 5200–5205.

    Google Scholar 

  • NHGRI. (2020). National Human Genome Research Institute, National Institutes of Health, Retrieved March 2020, from https://www.genome.gov/about-nhgri/Organization

  • Nordhaus, W. (1969). Invention. Growth and Welfare: MIT Press, Massachusetts Cambridge.

    Google Scholar 

  • Noyons, E. C. M., & van Raan, A. F. J. (1998). Monitoring scientific developments from a dynamic perspective: Self-organized structuring to map neural network research. Journal of the American Society for Information Science,49, 68–81.

    Google Scholar 

  • NYU Department of Physics. (2019). Syllabus for Advanced Experimental Physics, Retrieved November, 2019, from https://physics.nyu.edu/undergraduate/SyllabusforAdvancedExperimentalPhysics.pdf

  • Pan, R. K., Kaski, K., & Fortunato, S. (2012). World citation and collaboration networks: Uncovering the Role of geography in science. Scientific Reports,2(902), 1–7.

    Google Scholar 

  • Pauling, L. (1987). So-called icosahedral and decagonal quasicrystals are twins of an 820-atom cubic crystal. Physical Review Letters,58(4), 365. https://doi.org/10.1103/PhysRevLett.58.365.

    Article  Google Scholar 

  • Payette, N. (2012). Agent-based models of science. In Scharnhorst et al. Models of science dynamics (pp. 127–157) Springer

  • Perc, M. (2014). The Matthew effect in empirical data. Journal of the Royal Society, Interface,11, 20140378. https://doi.org/10.1098/rsif.2014.0378.

    Article  Google Scholar 

  • Perkins, D. H. (2000). Introduction to high energy physics (4th ed.). Cambridge: Cambridge University Press.

    Google Scholar 

  • Phillips, A. C. (1994). The physics of stars. New York: Wiley.

    Google Scholar 

  • Piaget, J. (1972). The epistemology of interdisciplinary relationships. Paris: Organization for Economic Cooperation and Development.

    Google Scholar 

  • Planck, M. (1950). Scientific autobiography (pp. 33–34). London: Williams and Norgate.

    Google Scholar 

  • Polanyi, M. (1958). Personal knowledge. London: Routledge and Kegan Paul.

    Google Scholar 

  • Polanyi, M. (1963). The potential theory of absorption (p. 94). London: In Knowing and Being, Routledge.

    Google Scholar 

  • Politecnico di Milano. (2019). Fondamenti di Fisica Sperimentale, 2019–2020. Retrieved November, 2019, from https://www11.ceda.polimi.it/schedaincarico/schedaincarico/controller/scheda_pubblica/SchedaPublic.do?&evn_default=evento&c_classe=712874&polij_device_category=DESKTOP&__pj0=0&__pj1=a794e2436c25eddb8fb6e908fe63792e.

  • Popper, K. (1959). The logic of scientific discovery. London: Hutchinson.

    MATH  Google Scholar 

  • Pratt, J. B. (1907). Truth and its verification. The Journal of Philosophy, Psychology and Scientific Methods,4(12), 320–324.

    Google Scholar 

  • Reimer, L., & Kohl, H. (2008). Transmission electron microscopy: Physics of image formation. New York: Springer.

    Google Scholar 

  • Relman, D. A. (2002). New technologies, human-microbe interactions, and the search for previously unrecognized pathogens. Journal of Infectious Diseases,186(Suppl. 2), S254–S258.

    Google Scholar 

  • Richards, R. J. (1992). The meaning of evolution: The Morphological construction and ideological reconstruction of darwin’s theory. Chicago: University of Chicago Press.

    Google Scholar 

  • Riesch, H. (2014). Philosophy, history and sociology of science; Interdisciplinary and complex social identities. Studies in History and Philosophy of Science,48, 30–37.

    Google Scholar 

  • Rosenberg, N. (1974). Science, invention and economic growth. Economic Journal,84(333), 90–108. https://doi.org/10.2307/2230485.

    Article  Google Scholar 

  • Rousmaniere, F. H. (1909). The bases for generalization in scientific methods. The Journal of Philosophy, Psychology and Scientific Methods,6(8), 202–205.

    Google Scholar 

  • Ruttan, V. W. (2006). Is War necessary for economic growth?. Military Procurement and Technology Development: Oxford University Press, New York.

    Google Scholar 

  • Scharnhorst, A., Börner, K., & Besselaar, P. (2012). Models of science dynamics: Encounters between complexity theory and information sciences. New York: Springer.

    Google Scholar 

  • Schröder, W. (2010). History of geophysics. Acta Geodaetica et Geophysica Hungarica.,45(2), 253–261. https://doi.org/10.1556/AGeod.45.2010.2.9.

    Article  Google Scholar 

  • Science. (1965). The evolution of science. Science-New Series,148(3671), 737.

    Google Scholar 

  • ScienceDirect. (2019). Advanced Research. Retrieved October, 2019, from https://www2.scopus.com/search/form.uri?display=basic.

  • Seidman, S. S. (1987). Models of scientific development in sociology. Humboldt Journal of Social Relations,15(1), 119–139.

    MathSciNet  Google Scholar 

  • Shechtman, D., Blech, I., Gratias, D., & Cahn, J. W. (1984). Metallic phase with long range orientational order and no translation symmetry. Physical Review Letters,53(20), 1951–1953.

    Google Scholar 

  • Shi, F., Foster, J. G., & Evans, J. A. (2015). Weaving the fabric of science: Dynamic network models of science's unfolding structure. Social Networks,43, 73–85. https://doi.org/10.1016/j.socnet.2015.02.006.

    Article  Google Scholar 

  • Simonton, D. K. (2002). Great psychologists and their times: Scientific insights into psychology’s history. Washington, DC: APA Books.

    Google Scholar 

  • Simonton, D. K. (2004). Psychology’s status as a scientific discipline: Its empirical placement within an implicit hierarchy of the sciences. Review of General Psychology,8(1), 59–67.

    Google Scholar 

  • Sintonen, M. (1990). Basic and applied sciences—can the distinction (still) be drawn? Science & technology Studies,3(2), 23–31.

    Google Scholar 

  • Small, H. (1999a). Visualizing science by citation mapping. Journal of the American Society for Information Science and Technology,50(3), 799–813.

    Google Scholar 

  • Small, M. L. (1999b). Departmental conditions and the emergence of new disciplines: two cases in the legitimation of African-American studies. Theory and Society,28(5), 659–707.

    Google Scholar 

  • Smith, L. D., Best, L. A., Stubbs, D. A., Johnston, J., & Bastiani, A. A. (2000). Scientific graphs and the hierarchy of the sciences: A latourian survey of inscription practices. Social Studies of Science,30(1), 73–94.

    Google Scholar 

  • Souzanchi Kashani, E., & Roshani, S. (2019). Evolution of innovation system literature: Intellectual bases and emerging trends. Technological Forecasting and Social Change,146, 68–80.

    Google Scholar 

  • Spencer, H. (1857). Progress: It’s law and cause. Westminster Review,67(April), 445–465.

    Google Scholar 

  • Squires, G. L. (2001). Practical physics (4th ed.). Cambridge: Cambridge University PRee.

    Google Scholar 

  • Stephan, P. E. (1996). The economics of science. Journal of Economic Literature,34(3), 1199–1235.

    Google Scholar 

  • Stephan, P. E., & Levin, S. G. (1992). How science is done; Why science is done. Striking the mother lode in science: The importance of age, place and time, chapter 2 (pp. 11–24). New York: Oxford University Press.

    Google Scholar 

  • Storer, N. W. (1967). The hard sciences and the soft: Some sociological observations. Bulletin of the Medical Library Association,55(1), 75–84.

    Google Scholar 

  • Storer, N. W. (1970). The internationality of science and the nationality of scientists. Int Soc Sci J,22(1), 80–93.

    Google Scholar 

  • Strevens, M. (2006). The role of the Matthew effect in science. Studies in History and Philosophy of Science Part A,37(2), 159–170. https://doi.org/10.1016/j.shpsa.2005.07.009.

    Article  Google Scholar 

  • Sun, X., Kaur, J., Milojevic, S., Flammini, A., & Menczer, F. (2013). Social dynamics of science. Scientific Reports,3(1069), 1–6. https://doi.org/10.1038/srep01069.

    Article  Google Scholar 

  • Taylor J.R. (1997). An introduction to error analysis. University Science Books.

  • Thiel, P. A. (2004). An introduction to the surface science of quasicrystals. Progress in Surface Science,75(3–8), 69–86.

    Google Scholar 

  • 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.

    Google Scholar 

  • Tiryakian, E. (1979). The significance of schools in the development of sociology. In W. Snizek, et al. (Eds.), Contemporary issues in theory and research. Westport, CN: Greenwood Press.

    Google Scholar 

  • van Raan, A. F. J. (2000). On growth, ageing, and fractal differentiation of science. Scientometrics,47, 347–362.

    Google Scholar 

  • Van Raan, A. F. J., & Peters, H. P. F. (1989). Dynamics of a scientific field analysed by co-subfield structures. Scientometrics,15(5–6), 607–620. https://doi.org/10.1007/BF02017073.

    Article  Google Scholar 

  • Wagner, C. (2008). The new invisible college: Science for development. Washington, DC: Brookings Institution Press.

    Google Scholar 

  • Wagner, C. S., & Leydesdorff, L. (2003). Seismology as a dynamic, distributed area of scientific research. Scientometrics,58(1), 91–114.

    Google Scholar 

  • Wassermann, G. D. (1989). Theories, Systemic Models (SYMOs), laws and facts in the sciences. Synthese,79(3), 89–514.

    Google Scholar 

  • Waterson, A. P., & Wilkinson, L. (1978). An introduction to the history of virology. Cambridge: Cambridge University Press.

    Google Scholar 

  • Wray, K. B. (2005). Rethinking scientific specialization. Social Studies of Science,35(1), 151–164.

    Google Scholar 

  • Wuchty, S., Jones, B. F., & Uzzi, B. (2007). The increasing dominance of teams in production of knowledge. Science,316(1036), 1036–1039.

    Google Scholar 

  • Young, H. D., & Freedman, R. A. (2012). University physics. Boston: Addison-Wesley.

    Google Scholar 

  • Zhou, Y., Dong, F., Kong, D., & Liu, Y. (2019). Unfolding the convergence process of scientific knowledge for the early identification of emerging technologies. Technological Forecasting and Social Change,144, 205–220.

    Google Scholar 

Download references

Acknowledgements

I gratefully acknowledge financial support from National Research Council of Italy–Direzione Generale Relazioni Internazionali for funding this research project developed at Yale University in 2019 (grant-CNR n. 62489–2018). The author declares that he has no relevant or material financial interests that relate to the research discussed in this paper.

Author information

Authors and Affiliations

  1. CNR, National Research Council of Italy, Collegio Carlo Alberto, Via Real Collegio, Moncalieri, 30-10024, Turin, Italy

    Mario Coccia

  2. Yale University, 310 Cedar Street, Lauder Hall, New Haven, CT, 06510, USA

    Mario Coccia

Authors
  1. Mario Coccia
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Mario Coccia.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Coccia, M. The evolution of scientific disciplines in applied sciences: dynamics and empirical properties of experimental physics. Scientometrics 124, 451–487 (2020). https://doi.org/10.1007/s11192-020-03464-y

Download citation

  • Received:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11192-020-03464-y

Keywords

JEL codes

Search

Navigation