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“Brazilian style science”—an analysis of the difference between Brazilian and international Computer Science departments and graduate programs using social networks analysis and bibliometrics

  • Ricardo LindenEmail author
  • Lênin Ferreira Barbosa
  • Luciano Antonio Digiampietri
Original Article

Abstract

In this paper, we compare the Brazilian Computer Science Graduate Programs of levels 6 and 7 (which CAPES assume to be equivalent to good international ones) and those departments best ranked in three different international rankings. We used both bibliometric and social networks analysis metrics, and we could see that there is a great difference between the results achieved by the national and international programs. The results from a principal component analysis show that the Brazilian programs are a class of their own and do not share the characteristics of the best international departments. We also analyzed the CAPES grade system and show that it is consistent, even if based on different metrics than those used internationally. It is safe to conclude that there is a “Brazilian Science” that is different from the worldwide accepted one and that derives, in a certain way, from an observer effect. We discuss the observer effect in Science and how scientific output could actually be measured.

References

  1. Alves A, Yanasse H, Soma N (2011a) Sucupira: a system for information extraction of the Lattes platform to identify academic social networks. In: 2011 6th Iberian Conference on Information systems and technologies (CISTI), pp 1–6Google Scholar
  2. Alves AD, Yanasse HH, Soma NY (2011b). Lattesminer: a multilingual dsl for information extraction from lattes platform. In: Proceedings of the compilation of the co-located workshops on DSM’11, TMC’11, AGERE! 2011, AOOPES’11, NEAT’11, & VMIL’11, SPLASH’11 workshops, ACM, pp 85–92Google Scholar
  3. Arruda D, Bezerra F, Neris V, Rocha De Toro P, Wainera J (2009) Brazilian computer science research: gender and regional distributions. Scientometrics 79(3):651–665CrossRefGoogle Scholar
  4. Bergstrom CT, West JD, Wiseman MA (2008) The eigenfactor metrics. J Neurosci 28:11433–11434CrossRefGoogle Scholar
  5. Berkowitz SD (1982) An introduction to structural analysis: the network approach to social research. Butterworths, LondonGoogle Scholar
  6. Bollen J, Rodriquez MA, Van de Sompel H (2006) Journal status. Scientometrics 69:669–687CrossRefGoogle Scholar
  7. Breiger R (2004) The analysis of social networks. In: Hardy M, Bryman A (eds) Handbook of data analysis. Sage Publications, London, pp 505–526Google Scholar
  8. Cole JR, Zuckerman H (1984) The productivity puzzle: persistence and change in patterns of publication of men and women scientists. Adv Motiv Achiev 2(2):1Google Scholar
  9. Costa B, da Silva Pedro E, de Macedo G (2013) Scientific collaboration in biotechnology: the case of the northeast region in brazil. Scientometrics 95(2):571–592CrossRefGoogle Scholar
  10. da Cunha e Melo PL (2011) Produtividade, internacionalização e visibilidade da comunidade científica Brasileira na virada do milênio. PhD thesis, Universidade Federal do Rio de JaneiroGoogle Scholar
  11. Digiampietri LA, Mena-Chalco JP, Vaz de Melo POS, Malheiro APR, Meira DNO, Franco LF, Oliveira LB (2014) BraX-ray: an x-ray of the Brazilian computer science graduate programs. PLoS ONE 9(4):e94541CrossRefGoogle Scholar
  12. Duffy RD, Jadidian A, Webster GD, Sandell KJ (2011) The research productivity of academic psychologists: assessment, trends, and best practice recommendations. Scientometrics 89(1):207–227CrossRefGoogle Scholar
  13. Franceschet M (2010) A comparison of bibliometric indicators for computer science scholars and journals on web of science and google scholar. Scientometrics 83(1):243–258CrossRefGoogle Scholar
  14. Franceschet M (2011) Collaboration in computer science: a network science approach. J Am Soc Inf Sci Technol 62(10):1992–2012CrossRefGoogle Scholar
  15. Freire V, Figueiredo D (2011) Ranking in collaboration networks using a group based metric. J Braz Comput Soc 17:1–12MathSciNetCrossRefGoogle Scholar
  16. Gallivan MJ, Benbunan-Fich R (2006) Examining the relationship between gender and the research productivity of IS faculty. In: Proceedings of the 2006 ACM SIGMIS CPR conference on computer personnel research: forty four years of computer personnel research: achievements, challenges and the future, ACM, pp 103–113Google Scholar
  17. Garfield E (1955) Citation indexes for science. a new dimension in documentation through association of ideas. Science 122:1123–1127CrossRefGoogle Scholar
  18. Glanzel W, Schubert A (2004) Analysing scientific networks through coauthorship. In: Handbook of quantitative science and technology research, Kluwer Academic Publishers, Dordrecht, pp 257–276Google Scholar
  19. Hirsch JE (2005) An index to quantify an individual’s scientific research output. Proc Natl Acad Sci USA 102(46):16569–16572CrossRefzbMATHGoogle Scholar
  20. KCUE (Korean Council for Higher Education) Key programs and resources, Internet Site available at. http://english.kcue.or.kr/resources/resources_01_01.php. Accessed Apr 2017
  21. Laender A, de Lucena C, Maldonado J, de Souza e Silva E, Ziviani N (2008) Assessing the research and education quality of the top Brazilian computer science graduate programs. ACM Spec Interest Group Comput Sci Educ 40(2):135–145Google Scholar
  22. Leite P, Mugnaini R, Leta J (2011) A new indicator for international visibility: exploring Brazilian scientific community. Scientometrics 88:311–319CrossRefGoogle Scholar
  23. Lemieux V, Ouimet M (2008) Análise Estrutural das Redes Sociais. Instituto Piaget, LisboaGoogle Scholar
  24. Ley M (2002). The DBLP computer science bibliography: evolution, research issues, perspectives. In: Proceedings of the 9th international symposium on string processing and information retrieval, SPIRE 2002, Springer, London, UK, pp 1–10Google Scholar
  25. Lindsey D (1989) Using citation counts as a measure of quality in science: measuring what’s measurable rather than what’s valid. Scientometrics 15:189–203CrossRefGoogle Scholar
  26. Martins WS, Gonçalves MA, Laender AHF, Ziviani N (2010) Assessing the quality of scientific conferences based on bibliographic citations. Scientometrics 83(1):133–155CrossRefGoogle Scholar
  27. Mena-Chalco JP, Cesar RM Jr (2009) ScriptLattes: an open-source knowledge extraction system from the Lattes platform. J Braz Comput Soc 15:31–39CrossRefGoogle Scholar
  28. Menezes GV, Ziviani N, Laender AHF, Almeida V (2009) A geographical analysis of knowledge production in computer science. In: Proceedings of the 18th international conference on World Wide Web, pp 1041–1050Google Scholar
  29. Mugnaini R, Digiampietri LA, de Oliveira LC, Ferreira SMSP (2012) Normalização de nomes de autores em fontes de informação institucionais: proposta de um método automático de verificação de erros. Em Questão 18(3):263–279Google Scholar
  30. Newman MEJ (2004) Coauthorship networks and patterns of scientific collaboration. Natl Acad Sci 101:5200–5205CrossRefGoogle Scholar
  31. Orduna-Malea E, Ayllón JM, Martín-Martín A, López-Cózar ED (2015) Methods for estimating the size of Google Scholar. Scientometrics 104(3):931–949CrossRefGoogle Scholar
  32. Ortega JL, Aguillo IF (2014) Microsoft academic search and google scholar citations: comparative analysis of author profiles. J Assoc Inf Sci Technol 65:1149–1156CrossRefGoogle Scholar
  33. Reddit (2014) Top 40 countries by the number of scientific papers published. https://www.reddit.com/r/dataisbeautiful/comments/20k5dk/top_40_countries_by_the_number_of_scientific/. Last Accessed 19 Apr 2017
  34. Scimago (2017) Scimago journal and country rank. http://www.scimagojr.com/countryrank.php. Last Accessed 19 Apr 2017
  35. Scott J (2009) Social network analysis: a handbook, 2nd edn. SAGE, Thousand OaksGoogle Scholar
  36. Ulrik B, Erlebach T (2005) Network analysis: methodological foundations. Springer, BerlinzbMATHGoogle Scholar
  37. Vardi MY (2009) Conferences vs. journals in computing research. Commun ACM 52(5):5CrossRefGoogle Scholar
  38. Wainer J, Vieira P (2013) Correlations between bibliometrics and peer evaluation for all disciplines: the evaluation of Brazilian scientists. Scientometrics 96(2):395–410CrossRefGoogle Scholar
  39. Wasserman S, Faust K (2009) Social network analysis: methods and applications, 19th edn. Cambridge University Press, CambridgezbMATHGoogle Scholar
  40. Wasserman S, Galaskiewicz J (1994) Advances in social network analysis research in the social and behavioral sciences. SAGE, Thousand OaksCrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Austria 2017

Authors and Affiliations

  1. 1.Computer Engineering SchoolFaculdade Salesiana Maria AuxiliadoraMacaéBrazil
  2. 2.School of Arts, Sciences and HumanitiesUniversity of São PauloSão PauloBrazil

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