Fig 1.
Tuberculosis research publications (2005–2014).
A) Top ten countries with highest number of articles published and relative contributions (%). White bars indicate high TB burden countries. B) Number of papers published by Brazilian authors by year. C) Top ten Brazilian institutions with highest number of articles. Fiocruz: Oswaldo Cruz Foundation; USP: University of São Paulo; UFRJ: Federal University of Rio de Janeiro; UNESP: São Paulo State University; UFMG: Federal University of Minas Gerais; UFRGS: Federal University of Rio Grande do Sul; PUC-RS: Pontifical Catholic University of Rio Grande do Sul; UNICAMP: State University of Campinas; UFBA: Federal University of Bahia; UFES: Federal University of Espírito Santo.
Fig 2.
Term map of Brazilian TB research.
The map shows 1,651 terms extracted from titles and abstracts of TB publications involving Brazilian institutions. The closer two terms are located to each other, the stronger their relation. Each term is represented by a circle and its diameter and label size indicate the number of publications that have the corresponding term in their title or abstract. Each term occurs in at least five publications. A) Colors indicate clusters of terms that have co-occurred more frequently in the dataset. B) Colors indicate the occurrence of a term in publications involving Fiocruz relative to Brazil’s average, where blue represents a low occurrence, green average, and red a high occurrence.
Fig 3.
Evolution of TB research networks involving Brazilian institutions (2005–2009 and 2010–2014).
Each node represents one institution and two institutions were considered connected if their members shared the authorship of a paper. Nodes are color coded—red for Brazil and blue for foreign organizations. The size of the nodes is proportionate to their degree centrality. For visualization purposes only the giant component is shown. The top three Brazilian organizations with highest degree centrality are labeled.
Table 1.
Connectivity indicators of the TB research network involving Brazilian institutions (2005–2014).
Table 2.
Top three most influential Brazilian institutions in the national TB research network according to centrality measurements.*
Table 3.
Top ten most influential scientists in the national TB research network according to the degree and betweeness centrality indexes.*
Fig 4.
Intrainstitutional TB research network in Fiocruz (2005–2014).
Each node is a researcher and two researchers were considered connected if they shared the authorship of an article. Researchers were color coded according to the technical/scientific or administrative unit to which they belong in Fiocruz. The thickness of the lines represents frequency of collaborations between the researchers. IOC: Oswaldo Cruz Institute; INI: National Institute of Infectology Evandro Chagas; ENSP: National School of Public Health Sérgio Arouca; CPqAM: Aggeu Magalhães Research Center (Fiocruz Pernambuco); CPqRR: René Rachou Research Center (Fiocruz Minas Gerais); CPqGM: Gonçalo Muniz Research Center (Fiocruz Bahia); Farmanguinhos: Institute of Drugs Technology; ILMD: Leônidas and Maria Deane Institute (Fiocruz Amazônia); INCQS: National Institute for Quality Control in Health; CDTS: Center for Technological Development in Health; PROCC: Scientific Computing Program; ICICT: Institute of Communication and Scientific Information and Technology in Health; ICC: Carlos Chagas Institute (Fiocruz Paraná); IFF: Fernandes Figueira Institute; Fiocruz MS: Fiocruz Mato Grosso do Sul; Biomanguinhos: Immunobiological Technology Institute; Cecal: Laboratory Animal Breeding Center; DIREH: Directorate of Human Resources.
Fig 5.
TB network and research communities within Fiocruz (2005–2014).
Each node is a community formed by researchers affiliated with Fiocruz. Two communities were considered connected if their members shared the authorship of an article. The size of the nodes is proportional to the number of members of the community. The thickness of the lines indicates the frequency of collaboration. For visualization purposes only the giant component is shown. The communities were numbered from 1 to 9.