, Volume 109, Issue 3, pp 1695–1709 | Cite as

The research space: using career paths to predict the evolution of the research output of individuals, institutions, and nations

  • Miguel R. GuevaraEmail author
  • Dominik Hartmann
  • Manuel Aristarán
  • Marcelo Mendoza
  • César A. Hidalgo


In recent years scholars have built maps of science by connecting the academic fields that cite each other, are cited together, or that cite a similar literature. But since scholars cannot always publish in the fields they cite, or that cite them, these science maps are only rough proxies for the potential of a scholar, organization, or country, to enter a new academic field. Here we use a large dataset of scholarly publications disambiguated at the individual level to create a map of science—or research space—where links connect pairs of fields based on the probability that an individual has published in both of them. We find that the research space is a significantly more accurate predictor of the fields that individuals and organizations will enter in the future than citation based science maps. At the country level, however, the research space and citations based science maps are equally accurate. These findings show that data on career trajectories—the set of fields that individuals have previously published in—provide more accurate predictors of future research output for more focalized units—such as individuals or organizations—than citation based science maps.


Maps of science Research policy Innovation policy Career paths Scientograms RCA 

Mathematics Subject Classification

68U35 94A17 05C90 91D30 68R10 

JEL Classification

I230 I280 94A17 



M.G and C.H were supported by the Massachusetts Institute of Technology MIT Media Lab Consortia and MIT Chile Seed Fund. M.G was supported by the Universidad de Playa Ancha, Chile (ING01-1516) and the Universidad Técnica Federico Santa María, Chile (PIIC). D.H was supported by the Marie Curie International Outgoing Fellowship within the EU 7th Framework Programme for Research and Technical Development: Connecting_EU!—PIOF-GA-2012-328828. M.M was supported by Basal Project FB-0821. C.H was supported by the Metaknowledge Network at the University of Chicago.

Supplementary material

11192_2016_2125_MOESM1_ESM.pdf (3.6 mb)
Supplementary material 1 (PDF 3696 kb)


  1. Abramo, G., & D’Angelo, C. A. (2014). How do you define and measure research productivity? Scientometrics. doi: 10.1007/s11192-014-1269-8.Google Scholar
  2. Balassa, B. (1965). Trade liberalisation and “revealed” comparative advantage1. The Manchester School, 33(2), 99–123. doi: 10.1111/j.1467-9957.1965.tb00050.x.CrossRefGoogle Scholar
  3. Bollen, J., Van de Sompel, H., Hagberg, A., Bettencourt, L., Chute, R., Rodriguez, M. A., et al. (2009). Clickstream data yields high-resolution maps of science. PLoS ONE, 4(3), e4803. doi: 10.1371/journal.pone.0004803.CrossRefGoogle Scholar
  4. Börner, K., Klavans, R., Patek, M., Zoss, A. M., Biberstine, J. R., Light, R. P., et al. (2012). Design and update of a classification system: The UCSD map of science. PLoS ONE, 7(7), e39464. doi: 10.1371/journal.pone.0039464.CrossRefGoogle Scholar
  5. Boyack, K. W., Klavans, R., & Börner, K. (2005). Mapping the backbone of science. Scientometrics, 64(3), 351–374. doi: 10.1007/s11192-005-0255-6.CrossRefGoogle Scholar
  6. Cimini, G., Gabrielli, A., & Sylos Labini, F. (2014). The scientific competitiveness of nations. PLoS ONE, 9(12), e113470. doi: 10.1371/journal.pone.0113470.CrossRefGoogle Scholar
  7. Collins, H. (2010). Tacit and explicit knowledge. Chicago: University of Chicago Press.CrossRefGoogle Scholar
  8. Cybermetrics Lab. (2015). About Us | Ranking Web of Universities. Retrieved February 25, 2016, from
  9. DataViva. (2016). Retrieved February 3, 2016, from
  10. Elhorst, J. P., & Zigova, K. (2014). Competition in research activity among economic departments: Evidence by negative spatial autocorrelation. Geographical Analysis, 46(2), 104–125. doi: 10.1111/gean.12031.CrossRefGoogle Scholar
  11. Fox, M. F. (2005). Gender, family characteristics, and publication productivity among scientists. Social Studies of Science, 35(1), 131–150.MathSciNetCrossRefGoogle Scholar
  12. Granovetter, M. (1985). Economic action and social structure: The problem of embeddedness. American Journal of Sociology, 91(3), 481–510.CrossRefGoogle Scholar
  13. Guevara, M., & Mendoza, M. (2013). Revealing comparative advantages in the backbone of science. In Proceedings of the 2013 workshop on computational scientometrics: Theory and applications (pp. 31–36). New York, NY: ACM. doi: 10.1145/2508497.2508503.
  14. Harzing, A.-W., & Giroud, A. (2014). The competitive advantage of nations: An application to academia. Journal of Informetrics, 8(1), 29–42. doi: 10.1016/j.joi.2013.10.007.CrossRefGoogle Scholar
  15. Klavans, R., & Boyack, K. W. (2009). Toward a consensus map of science. Journal of the American Society for Information Science and Technology, 60(3), 455–476. doi: 10.1002/asi.20991.CrossRefGoogle Scholar
  16. Leydesdorff, L., & Rafols, I. (2009). A global map of science based on the ISI subject categories. Journal of the American Society for Information Science and Technology, 60(2), 348–362. doi: 10.1002/asi.20967.CrossRefGoogle Scholar
  17. Moya-Anegón, F., Vargas-Quesada, B., Herrero-Solana, V., Chinchilla-Rodríguez, Z., Corera-Álvarez, E., & Munoz-Fernández, F. J. (2004). A new technique for building maps of large scientific domains based on the cocitation of classes and categories. Scientometrics, 61(1), 129–145. doi: 10.1023/B:SCIE.0000037368.31217.34.CrossRefGoogle Scholar
  18. Neffke, F., & Henning, M. (2013). Skill relatedness and firm diversification. Strategic Management Journal, 34(3), 297–316.CrossRefGoogle Scholar
  19. Neffke, F., Otto, A., & Hidalgo, C. A. (2016). The mobility of displaced workers: How the local industry mix affects job search strategies. Retrieved from
  20. Rafols, I., Porter, A. L., & Leydesdorff, L. (2010). Science overlay maps: A new tool for research policy and library management. Journal of the American Society for Information Science and Technology, 61(9), 1871–1887. doi: 10.1002/asi.21368.CrossRefGoogle Scholar
  21. Rosvall, M., & Bergstrom, C. T. (2008). Maps of random walks on complex networks reveal community structure. Proceedings of the National Academy of Sciences, 105(4), 1118–1123. doi: 10.1073/pnas.0706851105.CrossRefGoogle Scholar
  22. Small, H. (1973). Co-citation in the scientific literature: A new measure of the relationship between two documents. Journal of the American Society for Information Science, 24(4), 265–269.CrossRefGoogle Scholar
  23. Small, H. (1999). Visualizing science by citation mapping. Journal of the American Society for Information Science, 50(9), 799–813. doi: 10.1002/(SICI)1097-4571(1999)50:9<799:AID-ASI9>3.0.CO;2-G.CrossRefGoogle Scholar
  24. Waltman, L., van Eck, N. J., & Noyons, E. C. M. (2010). A unified approach to mapping and clustering of bibliometric networks. Journal of Informetrics, 4(4), 629–635. doi: 10.1016/j.joi.2010.07.002.CrossRefGoogle Scholar
  25. Zhuge, H. (2006). Discovery of knowledge flow in science. Communications of the ACM, 49(5), 101–107. doi: 10.1145/1125944.1125948.CrossRefGoogle Scholar

Copyright information

© Akadémiai Kiadó, Budapest, Hungary 2016

Authors and Affiliations

  1. 1.Macro Connections, The MIT Media LabMassachusetts Institute of TechnologyCambridgeUSA
  2. 2.Department of Computer ScienceUniversidad de Playa AnchaValparaisoChile
  3. 3.Chair for Economics of InnovationUniversity of HohenheimStuttgartGermany
  4. 4.Department of InformaticsUniversidad Técnica Federico Santa MaríaSantiagoChile

Personalised recommendations