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The latent structure of global scientific development


Science is essential to innovation and economic prosperity. Although studies have shown that national scientific development is affected by geographic, historic and economic factors, it remains unclear whether there are universal structures and trajectories of national scientific development that can inform forecasting and policy-making. Here, by examining the scientific ‘exports’—publications that are indexed in international databases—of countries, we reveal a three-cluster structure in the relatedness network of disciplines that underpin national scientific development and the organization of global science. Tracing the evolution of national research portfolios reveals that while nations are proceeding to more diverse research profiles individually, scientific production is increasingly specialized in global science over the past decades. By uncovering the underlying structure of scientific development and connecting it with economic development, our results may offer a new perspective on the evolution of global science.

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Fig. 1: Disciplinary specialization reflects geographical, historical and economic factors.
Fig. 2: The structure of the disciplinary proximity network and national development.
Fig. 3: The principle of relatedness dictates the development and loss of competencies.
Fig. 4: Nestedness and modularity of global science.
Fig. 5: Scientific production is correlated with national development indicators.

Data availability

Data used in this study are available at

Code availability

The code used for data processing and analysis is available at


  1. Publications Output: U.S. Trends and International Comparisons (National Science Board, 2019).

  2. Tollefson, J. China declared world’s largest producer of scientific articles. Nature 553, 390–390 (2018).

    CAS  PubMed  Article  Google Scholar 

  3. Zhou, P. & Leydesdorff, L. The emergence of China as a leading nation in science. Res. Policy 35, 83–104 (2006).

    Article  Google Scholar 

  4. Noorden, R. V. Science in East Asia—by the numbers. Nature 558, 500–501 (2018).

    PubMed  Article  CAS  Google Scholar 

  5. Li, K.-W. Capitalist Development and Economism in East Asia: The Rise of Hong Kong, Singapore, Taiwan and South Korea (Routledge, 2002).

  6. Livingstone, D. N. Putting Science in its Place: Geographies of Scientific Knowledge (Univ. Chicago Press, 2010).

  7. Seth, S. Putting knowledge in its place: science, colonialism, and the postcolonial. Postcolonial Stud. 12, 373–388 (2009).

    Article  Google Scholar 

  8. Kozlowski, J., Radosevic, S. & Ircha, D. History matters: the inherited disciplinary structure of the post-communist science in countries of central and eastern Europe and its restructuring. Scientometrics 45, 137–166 (1999).

    Article  Google Scholar 

  9. Hidalgo, C. A. et al. in Unifying Themes in Complex Systems IX Proc. Ninth International Conference on Complex Systems (eds Morales, A. J. et al.) 451–457 (Springer International, 2018).

  10. Chinazzi, M., Gonçalves, B., Zhang, Q. & Vespignani, A. Mapping the physics research space: a machine learning approach. EPJ Data Sci. 8, 33 (2019).

    Article  Google Scholar 

  11. Guevara, M. R., Hartmann, D., Aristarán, M., Mendoza, M. & Hidalgo, C. A. The research space: using career paths to predict the evolution of the research output of individuals, institutions, and nations. Scientometrics 109, 1695–1709 (2016).

    Article  Google Scholar 

  12. Boschma, R., Heimeriks, G. & Balland, P.-A. Scientific knowledge dynamics and relatedness in biotech cities. Res. Policy 43, 107–114 (2014).

    Article  Google Scholar 

  13. Stephan, P. How Economics Shapes Science (Harvard Univ. Press, 2012).

  14. Lee, L.-C., Lin, P.-H., Chuang, Y.-W. & Lee, Y.-Y. Research output and economic productivity: a Granger causality test. Scientometrics 89, 465–478 (2011).

    Article  Google Scholar 

  15. Kumar, R. R., Stauvermann, P. J. & Patel, A. Exploring the link between research and economic growth: an empirical study of China and USA. Qual. Quant. 50, 1073–1091 (2016).

    Article  Google Scholar 

  16. Hornyak, T. Chilean research grows despite poor investment. Nat. Index (2016).

  17. Bronfman, L. A panorama of Chilean astronomy. Messenger 107, 14–18 (2002).

    Google Scholar 

  18. Bajak, A. Chile’s chance to embrace science for the twenty-first century. Nature 552, S53–S55 (2017).

    CAS  PubMed  Article  Google Scholar 

  19. Yeom, H. W. South Korean science needs restructuring. Nature 558, 511–513 (2018).

    CAS  PubMed  Article  Google Scholar 

  20. Science and Engineering Indicators 2018 (National Science Board, 2018).

  21. May, R. M. The Scientific Wealth of Nations. Science 275, 793–796 (1997).

    CAS  Article  Google Scholar 

  22. Comte, A. The Positive Philosophy of Auguste Comte (C. Blanchard, 1855).

  23. Basalla, G. The spread of western science. Science 156, 611–622 (1967).

    CAS  PubMed  Article  Google Scholar 

  24. Anderson, W. Remembering the spread of western science. Hist. Rec. Aust. Sci. 29, 73 (2018).

    Article  Google Scholar 

  25. Raina, D. From west to non‐west? Basalla’s three‐stage model revisited. Sci. Cult. 8, 497–516 (1999).

    Article  Google Scholar 

  26. Moya-Anegón, F. & Herrero-Solana, V. Worldwide topology of the scientific subject profile: a macro approach in the country level. PLoS ONE 8, e83222 (2013).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  27. Cimini, G., Gabrielli, A. & Labini, F. S. The scientific competitiveness of nations. PLoS ONE 9, e113470 (2014).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  28. Hidalgo, C. A. Economic complexity theory and applications. Nat. Rev. Phys. 3, 92–113 (2021).

    Article  Google Scholar 

  29. Hong, I., Frank, M. R., Rahwan, I., Jung, W.-S. & Youn, H. The universal pathway to innovative urban economies. Sci. Adv. 6, eaba4934 (2020).

    PubMed  PubMed Central  Article  Google Scholar 

  30. Alabdulkareem, A. et al. Unpacking the polarization of workplace skills. Sci. Adv. 4, eaao6030 (2018).

    PubMed  PubMed Central  Article  Google Scholar 

  31. Hidalgo, C. A., Klinger, B., Barabasi, A.-L. & Hausmann, R. The product space conditions the development of nations. Science 317, 482–487 (2007).

    CAS  PubMed  Article  Google Scholar 

  32. Boschma, R. A. & Koen, F. Evolutionary economics and industry location. Rev. Reg. Res. 23, 183–200 (2003).

    Google Scholar 

  33. Boschma, R. A. & Frenken, K. Why is economic geography not an evolutionary science? Towards an evolutionary economic geography. J. Econ. Geogr. 6, 273–302 (2006).

    Article  Google Scholar 

  34. Boschma, R. & Koen, F. in The New Oxford Handbook of Economic Geography (eds Clark, G. L. et al.) 213–229 (Oxford Univ. Press, 2018).

  35. Hidalgo, C. A. & Hausmann, R. The building blocks of economic complexity. Proc. Natl Acad. Sci. USA 106, 10570–10575 (2009).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  36. Boyack, K. W. & Klavans, R. Creation of a highly detailed, dynamic, global model and map of science. J. Assoc. Inf. Sci. Technol. 65, 670–685 (2014).

    Article  Google Scholar 

  37. Boyack, K. W. & Klavans, R. Co-citation analysis, bibliographic coupling, and direct citation: which citation approach represents the research front most accurately? J. Am. Soc. Inf. Sci. Technol. 61, 2389–2404 (2010).

    Article  Google Scholar 

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

    CAS  Article  Google Scholar 

  39. Boyack, K. W., Börner, K. & Klavans, R. Mapping the structure and evolution of chemistry research. Scientometrics 79, 45–60 (2009).

  40. King, D. A. The scientific impact of nations. Nature 430, 311–316 (2004).

    CAS  PubMed  Article  Google Scholar 

  41. Bray, F. et al. Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J. Clin. 68, 394–424 (2018).

    PubMed  Article  Google Scholar 

  42. Evans, J. A., Shim, J.-M. & Ioannidis, J. P. A. Attention to local health burden and the global disparity of health research. PLoS ONE 9, e90147 (2014).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  43. World Health Statistics 2018: Monitoring Health for the SDGs (World Health Organization, 2018).

  44. Serrano, M. A., Boguna, M. & Vespignani, A. Extracting the multiscale backbone of complex weighted networks. Proc. Natl Acad. Sci. USA 106, 6483–6488 (2009).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  45. Traag, V. A., Waltman, L. & van Eck, N. J. From Louvain to Leiden: guaranteeing well-connected communities. Sci. Rep. 9, 5233 (2019).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  46. Klavans, R. & Boyack, K. W. The research focus of nations: economic vs. altruistic motivations. PLoS ONE 12, e0169383 (2017).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  47. Fantom, N. & Serajuddin, U. The World Bank’s Classification of Countries by Income (The World Bank, 2016).

  48. Pinheiro, F. L., Hartmann, D., Boschma, R. & Hidalgo, C. A. The time and frequency of unrelated diversification. Res. Policy (2021).

  49. Bustos, S., Gomez, C., Hausmann, R. & Hidalgo, C. A. The dynamics of nestedness predicts the evolution of industrial ecosystems. PLoS ONE 7, e49393 (2012).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  50. Gao, J., Zhang, Y.-C. & Zhou, T. Computational socioeconomics. Phys. Rep. 817, 1–104 (2019).

    Article  Google Scholar 

  51. Morrison, G. et al. On economic complexity and the fitness of nations. Sci. Rep. 7, 15332 (2017).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  52. Inglesi-Lotz, R., Balcilar, M. & Gupta, R. Time-varying causality between research output and economic growth in US. Scientometrics 100, 203–216 (2014).

    Article  Google Scholar 

  53. Vinkler, P. Correlation between the structure of scientific research, scientometric indicators and GDP in EU and non-EU countries. Scientometrics 74, 237–254 (2008).

    CAS  Article  Google Scholar 

  54. Gelman, A. & Stern, H. The difference between “significant” and “not significant” is not itself statistically significant. Am. Stat. 60, 328–331 (2006).

    Article  Google Scholar 

  55. Krammer, S. M. S. Science, technology, and innovation for economic competitiveness: the role of smart specialization in less-developed countries. Technol. Forecast. Soc. Change 123, 95–107 (2017).

    Article  Google Scholar 

  56. Powell, W. W. & Snellman, K. The knowledge economy. Annu. Rev. Sociol. 30, 199–220 (2004).

    Article  Google Scholar 

  57. Glänzel, W. National characteristics in international scientific co-authorship relations. Scientometrics 51, 69–115 (2001).

  58. Gordin, M. D. Scientific Babel: How Science was Done Before and After Global English (Univ. Chicago Press, 2015).

  59. Éric, A., Étienne, V.-G., Grégoire, C., Vincent, L. & Gingrasb, Y. Benchmarking scientific output in the social sciences and humanities: the limits of existing databases. Scientometrics 68, 329–342 (2006).

    Article  Google Scholar 

  60. Sugimoto, C. R. & Vincent, L. Measuring Research: What Everyone Needs to Know (Oxford Univ. Press, 2018).

  61. GDP (current US$) World Development Indicators (World Bank, 2019);

  62. Barber, M. J. Modularity and community detection in bipartite networks. Phys. Rev. E 76, 066102 (2007).

    Article  CAS  Google Scholar 

  63. Almeida‐Neto, M., Guimarães, P., Guimarães, P. R., Loyola, R. D. & Ulrich, W. A consistent metric for nestedness analysis in ecological systems: reconciling concept and measurement. Oikos 117, 1227–1239 (2008).

    Article  Google Scholar 

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L.M., D.M. and Y.Y.A. acknowledges funding support from the Air Force Office of Scientific Research under award no. FA9550-19-1-0391. The funder had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript. We thank S. Milojević, J. Yoon, S. Kojaku, B.-K. Lee, B. Weinberg, P. Stephan, S. Schnell, T. Zhou, J. Huang, J. Gao, Y. Bu, Q. Zhang, B. Uzzi, H. Youn and N. Dehmamy for helpful discussion and comments.

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Authors and Affiliations



L.M. and D.M. conceived the study. All authors contributed to the design of the study. V.L. prepared the primary datasets. L.M., D.M., V.L. and Y.Y.A. performed analysis. All authors contributed to the interpretation of the results and writing of the manuscript.

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Correspondence to Yong-Yeol Ahn.

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Miao, L., Murray, D., Jung, WS. et al. The latent structure of global scientific development. Nat Hum Behav (2022).

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