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Tracing the knowledge-building dynamics in new stem cell technologies through techno-scientific networks

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Abstract

This study assesses the knowledge-building dynamics of emerging technologies, their participating country-level actors, and their interrelations. We examine research on induced pluripotent stem (iPS) cells, a recently discovered stem cell species. Compared to other studies, our approach conflates the totality of publications and patents of a field, and their references, into single “techno-scientific networks” across intellectual bases (IB) and research fronts (RF). Diverse mapping approaches—co-citation, direct citation, and bibliographic coupling networks—are used, driven by the problems tackled by iPS cell researchers. Besides the study of the field of iPS cells as a whole, we assessed the roles of relevant countries in terms of “knowledge exploration,” “knowledge nurturing,” “knowledge exploitation,” and cognitive content. The results show that a fifth of nodes in IB and edges in RF interconnect science (S) and technology (T). S and T domains tell different, yet complementing stories: S overstresses upstream activities, and T captures the increasing influential role of application domains and general technologies. Both S and T reflect the path-dependent nature of iPS cells in embryonic stem cell technologies. Building on the feedback between IB and RF, we examine the dominating role of the United States. Japan, the pioneer, falls behind in quantity, yet its global influence remains intact. New entrants, such as China, are advancing rapidly, yet, cognitively, the bulk of efforts are still upstream. Our study demonstrates the need for bibliometric assessment studies to account for S&T co-evolution. The multiple data source-based, integrated bibliometric approaches of this study are initial efforts toward this direction.

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References

  • Alkemade, F., & Suurs, R. A. (2012). Patterns of expectations for emerging sustainable technologies. Technological Forecasting and Social Change, 79(3), 448–456.

    Article  Google Scholar 

  • Anaya-Ruiz, M., & Perez-Santos, M. (2015). Innovation status of gene therapy for breast cancer. Asian Pacific Journal of Cancer Prevention, 16(9), 4133–4136.

    Article  Google Scholar 

  • Arthur, W. B. (2009). The nature of technology: What it is and how it evolves. New York: Simon and Schuster.

    Google Scholar 

  • Ávila-Robinson, A. (2013). Understanding the dynamics of emerging technologies through knowledge structures: The case of micro/nanotechnologies. Tokyo Institute of Technology (unpublished dissertation).

  • Ávila-Robinson, A., & Miyazaki, K. (2013a). Evolutionary paths of change of emerging nanotechnological innovation systems—The case of ZnO nanostructures. Scientometrics, 95(3), 829–849.

    Article  Google Scholar 

  • Ávila-Robinson, A., & Miyazaki, K. (2013b). Dynamics of scientific knowledge bases as proxies for discerning technological emergence—The case of MEMS/NEMS technologies. Technological Forecasting and Social Change, 80(6), 1071–1084.

    Article  Google Scholar 

  • Ávila-Robinson, A., & Miyazaki, K. (2014). Assessing nanotechnology potentials: interplay between the paths of knowledge evolution and the patterns of competence building. International Journal of Technology Intelligence and Planning, 10(1), 1–28.

    Article  Google Scholar 

  • Ávila-Robinson, A., & Sengoku, S. (2017). Multilevel exploration of the realities of interdisciplinary research centers for the management of knowledge integration. Technovation. doi:10.1016/j.technovation.2017.01.003.

  • Barfoot, J., Kemp, E., Doherty, K., Blackburn, C., Sengoku, S., van Servellen, A., et al. (2013). Stem cell research: Trends and perspectives on the evolving international landscape. Amsterdam: Elsevier BV.

    Google Scholar 

  • Bengisu, M., & Nekhili, R. (2006). Forecasting emerging technologies with the aid of science and technology databases. Technological Forecasting and Social Change, 73(7), 835–844.

    Article  Google Scholar 

  • Bergek, A., Hekkert, M., Jacobsson, S., Markard, J., Sandén, B., & Truffer, B. (2015). Technological innovation systems in contexts: Conceptualizing contextual structures and interaction dynamics. Environmental Innovation and Societal Transitions, 16, 51–64.

    Article  Google Scholar 

  • Birkinshaw, J., Bessant, J., & Delbridge, R. (2007). Finding, forming, and performing: Creating networks for discontinuous innovation. California Management Review, 49(3), 67–84.

    Article  Google Scholar 

  • Björk, B.-C., & Solomon, D. (2013). The publishing delay in scholarly peer-reviewed journals. Journal of Informetrics, 7(4), 914–923.

    Article  Google Scholar 

  • Borgatti, S. P., Everett, M. G., & Freeman, L. C. (2002). UCINET for windows: Software for social network analysis. Harvard, MA: Analytic Technologies.

  • Borgatti, S. P., Everett, M. G., & Johnson, J. C. (2013). Analyzing social networks. Thousand Oaks, CA: SAGE Publications Limited.

    Google Scholar 

  • Börner, K., Chen, C., & Boyack, K. W. (2003). Visualizing knowledge domains. Annual Review of Information Science and Technology, 37(1), 179–255.

    Article  Google Scholar 

  • Bousfield, D., McEntyre, J., Velankar, S., Papadatos, G., Bateman, A., & Cochrane, G., et al. (2016). Patterns of database citation in articles and patents indicate long-term scientific and industry value of biological data resources. F1000Research. doi:10.12688/f1000research.7911.1.

  • Boyack, K. W., & Klavans, R. (2010). Co-citation analysis, bibliographic coupling, and direct citation: Which citation approach represents the research front most accurately? Journal of the American Society for Information Science and Technology, 61(12), 2389–2404.

    Article  Google Scholar 

  • Breschi, S., & Catalini, C. (2010). Tracing the links between science and technology: An exploratory analysis of scientists’ and inventors’ networks. Research Policy, 39(1), 14–26.

    Article  Google Scholar 

  • Breschi, S., Malerba, F., & Orsenigo, L. (2000). Technological regimes and schumpeterian patterns of innovation. The Economic Journal, 110(463), 388–410.

    Article  Google Scholar 

  • Callaert, J., Grouwels, J., & Van Looy, B. (2012). Delineating the scientific footprint in technology: Identifying scientific publications within non-patent references. Scientometrics, 91(2), 383–398.

    Article  Google Scholar 

  • Chen, C. (2006). CiteSpace II: Detecting and visualizing emerging trends and transient patterns in scientific literature. Journal of the American Society for Information Science and Technology, 57(3), 359–377.

    Article  Google Scholar 

  • Chen, S.-H., Huang, M.-H., & Chen, D.-Z. (2012). Identifying and visualizing technology evolution: A case study of smart grid technology. Technological Forecasting and Social Change, 79(6), 1099–1110.

    Article  Google Scholar 

  • Chen, C., & Leydesdorff, L. (2014). Patterns of connections and movements in dual-map overlays: A new method of publication portfolio analysis. Journal of the Association for Information Science and Technology, 65(2), 334–351.

    Article  Google Scholar 

  • Chiang, S.-Y. (2012). An application of Lotka–Volterra model to Taiwan’s transition from 200 mm to 300 mm silicon wafers. Technological Forecasting and Social Change, 79(2), 383–392.

    Article  Google Scholar 

  • Cobo, M., López-Herrera, A., Herrera-Viedma, E., & Herrera, F. (2011). Science mapping software tools: Review, analysis, and cooperative study among tools. Journal of the American Society for Information Science and Technology, 62, 1382–1402.

    Article  MATH  Google Scholar 

  • Consoli, D., & Ramlogan, R. (2011). Patterns of organization in the development of medical know-how: The case of glaucoma research. Industrial and Corporate Change, 21(2), 315–343.

    Article  Google Scholar 

  • Cozzens, S., Gatchair, S., Kang, J., Kim, K.-S., Lee, H. J., Ordóñez, G., et al. (2010). Emerging technologies: quantitative identification and measurement. Technology Analysis and Strategic Management, 22(3), 361–376.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • David, P. A. (1994). Why are institutions the ‘carriers of history’?: Path dependence and the evolution of conventions, organizations and institutions. Structural Change and Economic Dynamics, 5(2), 205–220.

    Article  Google Scholar 

  • David, P. A., & Foray, D. (1995). Accessing and expanding the science and technology knowledge base. STI Review, No. 16. Paris: OECD.

  • Day, G. S., Schoemaker, P. J., & Gunther, R. E. (2004). Wharton on managing emerging technologies. Hoboken, NJ: Wiley.

    Google Scholar 

  • De Nooy, W., Mrvar, A., & Batagelj, V. (2011). Exploratory social network analysis with Pajek. New York, NY: Cambridge University Press.

    Book  Google Scholar 

  • Ebert, A. D., Yu, J., Rose, F. F., Mattis, V. B., Lorson, C. L., Thomson, J. A., et al. (2009). Induced pluripotent stem cells from a spinal muscular atrophy patient. Nature, 457(7227), 277–280.

    Article  Google Scholar 

  • Fenn, J., & Raskino, M. (2008). Mastering the hype cycle: how to choose the right innovation at the right time. Boston: Harvard Business Press.

    Google Scholar 

  • Franco, L. A., Meadows, M., & Armstrong, S. J. (2013). Exploring individual differences in scenario planning workshops: A cognitive style framework. Technological Forecasting and Social Change, 80(4), 723–734.

    Article  Google Scholar 

  • Galibert, O., Rosset, S., Tannier, X., & Grandry, F., (2010). Hybrid citation extraction from patents. In N. Calzolari, K. Choukri, B. Maegaard, J. Mariani, S. Piperidis, M. Rosner, D. Tapias (Eds.), LREC 2010, seventh international conference on language resources and evaluation, Valleta, Malta.

  • Garber, K. (2015). RIKEN suspends first clinical trial involving induced pluripotent stem cells. Nature Biotechnology, 33(9), 890–891.

    Article  Google Scholar 

  • Hekkert, M. P., & Negro, S. O. (2009). Functions of innovation systems as a framework to understand sustainable technological change: Empirical evidence for earlier claims. Technological Forecasting and Social Change, 76(4), 584–594.

    Article  Google Scholar 

  • Hilgartner, S., & Lewenstein, B. (2004). The speculative world of emerging technologies (unpublished work).

  • Ho, J.-Y., & O’Sullivan, E. (2017). Strategic standardisation of smart systems: A roadmapping process in support of innovation. Technological Forecasting and Social Change, 115, 301–312.

    Article  Google Scholar 

  • Hung, S.-C., & Chu, Y.-Y. (2006). Stimulating new industries from emerging technologies: Challenges for the public sector. Technovation, 26(1), 104–110.

    Article  Google Scholar 

  • Inoue, H., Nagata, N., Kurokawa, H., & Yamanaka, S. (2014). iPS cells: A game changer for future medicine. The EMBO Journal, 33(5), 409–417.

    Article  Google Scholar 

  • Jacobsson, S. (2008). The emergence and troubled growth of a ‘biopower’innovation system in Sweden. Energy Policy, 36(4), 1491–1508.

    Article  Google Scholar 

  • Jansen, D., von Görtz, R., & Heidler, R. (2010). Knowledge production and the structure of collaboration networks in two scientific fields. Scientometrics, 83(1), 219–241.

    Article  Google Scholar 

  • Jarneving, B. (2007). Bibliographic coupling and its application to research-front and other core documents. Journal of Informetrics, 1(4), 287–307.

    Article  Google Scholar 

  • Kauffman, S., & Macready, W. (1995). Technological evolution and adaptive organizations: Ideas from biology may find applications in economics. Complexity, 1(2), 26–43.

    Article  MathSciNet  Google Scholar 

  • Keller, J., & Heiko, A. (2014). The influence of information and communication technology (ICT) on future foresight processes—Results from a Delphi survey. Technological Forecasting and Social Change, 85, 81–92.

    Article  Google Scholar 

  • Kissin, I. (2015). Scientometrics of drug discovery efforts: Pain-related molecular targets. Drug Design, Development and Therapy, 9(1), 3393–3404.

    Article  Google Scholar 

  • Krafft, J., Quatraro, F., & Saviotti, P. P. (2011). The knowledge-base evolution in biotechnology: A social network analysis. Economics of Innovation and New Technology, 20(5), 445–475.

    Article  Google Scholar 

  • Kukk, P., Moors, E., & Hekkert, M. (2015). The complexities in system building strategies—the case of personalized cancer medicines in England. Technological Forecasting and Social Change, 98, 47–59.

    Article  Google Scholar 

  • Kuusi, O., & Meyer, M. (2007). Anticipating technological breakthroughs: Using bibliographic coupling to explore the nanotubes paradigm. Scientometrics, 70(3), 759–777.

    Article  Google Scholar 

  • Larédo, P., Robinson, D. K., Delemarle, A., Lagnau, A., Revollo, M., & Villard, L. (2015). Mapping and characterising the dynamics of emerging technologies to inform policy. Final Report IFRIS Institut Francilien Recherche Innovation Société, Project No. ANR-10-ORA-007.

  • Lee, P.-C., & Su, H.-N. (2011). Quantitative mapping of scientific research—the case of electrical conducting polymer nanocomposite. Technological Forecasting and Social Change, 78(1), 132–151.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Lopez, P. (2009). GROBID: Combining automatic bibliographic data recognition and term extraction for scholarship publications. In Proceedings of the 13th European conference on digital library (ECDL), Corfu, Greece.

  • Lopez, P. (2010). Automatic extraction and resolution of bibliographical references in patent documents. In H. Cunningham, A. Hanbury, & S. Rüger (Eds.), Advances in multidisciplinary retrieval (pp. 120–135). Berlin: Springer.

    Chapter  Google Scholar 

  • Malerba, F. (2005). Sectoral systems: How and why innovation differs across sectors. In J. Fagerberg, D. C. Mowery, & R. R. Nelson (Eds.), The Oxford handbook of innovation. New York: Oxford University Press.

    Google Scholar 

  • March, J. G. (1991). Exploration and exploitation in organizational learning. Organization Science, 2(1), 71–87.

    Article  Google Scholar 

  • Markard, J., & Truffer, B. (2008). Technological innovation systems and the multi-level perspective: Towards an integrated framework. Research Policy, 37(4), 596–615.

    Article  Google Scholar 

  • Martínez, C. (2011). Patent families: When do different definitions really matter? Scientometrics, 86(1), 39–63.

    Article  MathSciNet  Google Scholar 

  • McCallum, A. K. (2002). MALLET: A machine learning for language toolkit. http://mallet.cs.umass.edu.

  • Medcof, J. W. (2010). Exploration, exploitation and technology management. International Journal of Technology Intelligence and Planning, 6(4), 301–316.

    Article  Google Scholar 

  • Metcalfe, J. S. (2002). Knowledge of growth and the growth of knowledge. Journal of Evolutionary Economics, 12(1–2), 3–15.

    Article  Google Scholar 

  • Metcalfe, J. S., James, A., & Mina, A. (2005). Emergent innovation systems and the delivery of clinical services: The case of intra-ocular lenses. Research Policy, 34(9), 1283–1304.

    Article  Google Scholar 

  • Meyer, M. (2000). What is special about patent citations? Differences between scientific and patent citations. Scientometrics, 49(1), 93–123.

    Article  Google Scholar 

  • Michel, J., & Bettels, B. (2001). Patent citation analysis. A closer look at the basic input data from patent search reports. Scientometrics, 51(1), 185–201.

    Article  Google Scholar 

  • Mina, A., Ramlogan, R., Tampubolon, G., & Metcalfe, J. S. (2007). Mapping evolutionary trajectories: Applications to the growth and transformation of medical knowledge. Research Policy, 36(5), 789–806.

    Article  Google Scholar 

  • Miyazaki, K. (1995). Building competences in the firm: Lessons from Japanese and European Optoelectronics. New York: St. Martin’s Press.

    Book  Google Scholar 

  • Momeni, A., & Rost, K. (2016). Identification and monitoring of possible disruptive technologies by patent-development paths and topic modeling. Technological Forecasting and Social Change, 104, 16–29.

    Article  Google Scholar 

  • Morlacchi, P., & Nelson, R. R. (2011). How medical practice evolves: Learning to treat failing hearts with an implantable device. Research Policy, 40(4), 511–525.

    Article  Google Scholar 

  • Murray, F. (2002). Innovation as co-evolution of scientific and technological networks: Exploring tissue engineering. Research Policy, 31(8–9), 1389–1403.

    Article  Google Scholar 

  • Nanba, H., Anzen, N., & Okumura, M. (2008). Automatic extraction of citation information in Japanese patent applications. International Journal on Digital Libraries, 9(2), 151–161.

    Article  Google Scholar 

  • Neal, H. A., Smith, T. L., & McCormick, J. B. (2008). Beyond Sputnik: US Science policy in the 21st century. Ann Arbor, MI: The University of Michigan Press.

    Book  Google Scholar 

  • Nelson, R. R. (2004). The market economy, and the scientific commons. Research Policy, 33(3), 455–471.

    Article  Google Scholar 

  • Nelson, R. R., Buterbaugh, K., Perl, M., & Gelijns, A. (2011). How medical know-how progresses. Research Policy, 40(10), 1339–1344.

    Article  Google Scholar 

  • NIH. (2017). NIH stem cell information home page. In stem cell information [World Wide Web site]. Bethesda, MD: National Institutes of Health, U.S. Department of Health and Human Services, 2016 [cited January 19, 2017]. http://stemcells.nih.gov/info/basics/1.htm

  • Perez-Santos, M., Anaya-Ruiz, M., & Bandala, C. (2017). Contribution of Latin American countries to cancer research and patent generation: Recent patents. Recent Patents on Anti-Cancer Drug Discovery, 12(1), 81–93.

    Article  Google Scholar 

  • Persson, O. (1994). The intellectual base and research fronts of JASIS 1986–1990. Journal of the American Society for Information Science, 45(1), 31–38.

    Article  Google Scholar 

  • Porter, A. L., & Cunningham, S. W. (2004). Tech mining: Exploiting new technologies for competitive advantage. Hoboken, NJ: Wiley.

    Book  Google Scholar 

  • Ramlogan, R., & Consoli, D. (2008). Knowledge, understanding and the dynamics of medical innovation. Munich Personal RePEc Archive MPRA Paper No. 9112.

  • Robinson, D. K., Huang, L., Guo, Y., & Porter, A. L. (2013). Forecasting innovation pathways (FIP) for new and emerging science and technologies. Technological Forecasting and Social Change, 80(2), 267–285.

    Article  Google Scholar 

  • Rosenkopf, L. (2000). Managing dynamic knowledge networks. In G. S. Day, P. J. Schoemaker, & R. E. Gunther (Eds.), Wharton on managing emerging technologies (pp. 337–357). New York: Wiley.

    Google Scholar 

  • Rotolo, D., Hicks, D., & Martin, B. R. (2015). What is an emerging technology? Research Policy, 44(10), 1827–1843.

    Article  Google Scholar 

  • Saviotti, P. P. (2007). On the dynamics of generation and utilisation of knowledge: The local character of knowledge. Structural Change and Economic Dynamics, 18(4), 387–408.

    Article  Google Scholar 

  • Schiebel, E. (2012). Visualization of research fronts and knowledge bases by three-dimensional areal densities of bibliographically coupled publications and co-citations. Scientometrics, 91(2), 557–566.

    Article  Google Scholar 

  • Schmoch, U. (2007). Double-boom cycles and the comeback of science-push and market-pull. Research Policy, 36(7), 1000–1015.

    Article  Google Scholar 

  • Scott, C. T., McCormick, J. B., DeRouen, M. C., & Owen-Smith, J. (2011). Democracy derived? New trajectories in pluripotent stem cell research. Cell, 145(6), 820–826.

    Article  Google Scholar 

  • Sengoku, S. (2015). Innovation and commercialisation of induced pluripotent stem cells. In A. A. Vertès, N. Qureshi, A. I. Caplan, & E. B. Lee (Eds.), Stem cells in regenerative medicine: Science, regulation and business strategies (pp. 423–446). West Sussex, UK: Wiley.

    Google Scholar 

  • Shibata, N., Kajikawa, Y., Takeda, Y., & Matsushima, K. (2009). Comparative study on methods of detecting research fronts using different types of citation. Journal of the American Society for Information Science and Technology, 60(3), 571–580.

    Article  Google Scholar 

  • Shibata, N., Kajikawa, Y., Takeda, Y., Sakata, I., & Matsushima, K. (2011). Detecting emerging research fronts in regenerative medicine by the citation network analysis of scientific publications. Technological Forecasting and Social Change, 78(2), 274–282.

    Article  Google Scholar 

  • Sternitzke, C. (2009). Patents and publications as sources of novel and inventive knowledge. Scientometrics, 79(3), 551–561.

    Article  Google Scholar 

  • Suzuki, J., Gemba, K., Tamada, S., Yasaki, Y., & Goto, A. (2006). Analysis of propensity to patent and science-dependence of large Japanese manufacturers of electrical machinery. Scientometrics, 68(2), 265–288.

    Article  Google Scholar 

  • Takahashi, K., & Yamanaka, S. (2006). Induction of pluripotent stem cells from mouse embryonic and adult fibroblast cultures by defined factors. Cell, 126(4), 663–676.

    Article  Google Scholar 

  • Takeda, Y., & Kajikawa, Y. (2009). Optics: A bibliometric approach to detect emerging research domains and intellectual bases. Scientometrics, 78(3), 543–558.

    Article  Google Scholar 

  • Tamada, S., Naito, Y., Kodama, F., Gemba, K., & Suzuki, J. (2006). Significant difference of dependence upon scientific knowledge among different technologies. Scientometrics, 68(2), 289–302.

    Article  Google Scholar 

  • Tushman, M. L., & O’Reilly, C. A. (1996). The ambidextrous organizations: Managing evolutionary and revolutionary change. California Management Review, 38(4), 8–30.

    Article  Google Scholar 

  • Upham, S. P., & Small, H. (2010). Emerging research fronts in science and technology: Patterns of new knowledge development. Scientometrics, 83(1), 15–38.

    Article  Google Scholar 

  • Van Den Besselaar, P., & Heimeriks, G. (2006). Mapping research topics using word-reference co-occurrences: A method and an exploratory case study. Scientometrics, 68(3), 377–393.

    Article  Google Scholar 

  • Van Eck, N. J., & Waltman, L. (2010). Software survey: VOSviewer, a computer program for bibliometric mapping. Scientometrics, 84(2), 523–538.

    Article  Google Scholar 

  • Van Merkerk, R. O., & Robinson, D. K. (2006). Characterizing the emergence of a technological field: Expectations, agendas and networks in Lab-on-a-chip technologies. Technology Analysis and Strategic Management, 18(3–4), 411–428.

    Article  Google Scholar 

  • Van Merkerk, R. O., & Smits, R. E. (2008). Tailoring CTA for emerging technologies. Technological Forecasting and Social Change, 75(3), 312–333.

    Article  Google Scholar 

  • Verbeek, A., Debackere, K., Luwel, M., Andries, P., Zimmermann, E., & Deleus, F. (2002). Linking science to technology: Using bibliographic references in patents to build linkage schemes. Scientometrics, 54(3), 399–420.

    Article  Google Scholar 

  • Walsh, S. T. (2004). Roadmapping a disruptive technology: A case study: The emerging microsystems and top-down nanosystems industry. Technological Forecasting and Social Change, 71(1), 161–185.

    Article  Google Scholar 

  • Watatani, K., Xie, Z., Nakatsuji, N., & Sengoku, S. (2013). Global competencies of regional stem cell research: Bibliometrics for investigating and forecasting research trends. Regenerative Medicine, 8(5), 659–668.

    Article  Google Scholar 

  • Whitesides, G. (2010). Solving problems. Lab on a Chip, 10(18), 2317–2318.

    Article  Google Scholar 

  • Wirth, S., & Markard, J. (2011). Context matters: How existing sectors and competing technologies affect the prospects of the Swiss Bio-SNG innovation system. Technological Forecasting and Social Change, 78(4), 635–649.

    Article  Google Scholar 

  • Yan, E. (2014). Research dynamics: Measuring the continuity and popularity of research topics. Journal of Informetrics, 8(1), 98–110.

    Article  Google Scholar 

  • Ziman, J. (2003). Technological innovation as an evolutionary process. Cambridge: Cambridge University Press.

    Google Scholar 

  • Zitt, M., Lelu, A., & Bassecoulard, E. (2011). Hybrid citation-word representations in science mapping: Portolan charts of research fields? Journal of the American Society for Information Science and Technology, 62(1), 19–39.

    Article  Google Scholar 

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Acknowledgements

We thank the Editor and anonymous reviewers for their helpful comments. This work was financially supported by MEXT/JSPS World Premier International Research Center (WPI) Initiative [AAR] and by MEXT/JSPS Kakenhi Grant No. 26301022 [AAR, SS] (Project leader Prof. Shintaro Sengoku). Initial stages of this study were supported by Cabinet Office of Japan/JSPS Funding Program for World-Leading Next-Generation Innovative R&D on Science and Technology (NEXT Program, Grant Number LZ009) [AAR, SS]. An earlier version of this manuscript was presented at the Portland International Center for Management of Engineering and Technology (PICMET) 2014 conference (Portland, US). All remaining errors are our own.

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Ávila-Robinson, A., Sengoku, S. Tracing the knowledge-building dynamics in new stem cell technologies through techno-scientific networks. Scientometrics 112, 1691–1720 (2017). https://doi.org/10.1007/s11192-017-2436-5

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