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Collaboration Networks from a Large CV Database: Dynamics, Topology and Bonus Impact

Overview of attention for article published in PLOS ONE, March 2014
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Title
Collaboration Networks from a Large CV Database: Dynamics, Topology and Bonus Impact
Published in
PLOS ONE, March 2014
DOI 10.1371/journal.pone.0090537
Pubmed ID
Authors

Eduardo B. Araújo, André A. Moreira, Vasco Furtado, Tarcisio H. C. Pequeno, José S. Andrade

Abstract

Understanding the dynamics of research production and collaboration may reveal better strategies for scientific careers, academic institutions, and funding agencies. Here we propose the use of a large and multidisciplinary database of scientific curricula in Brazil, namely, the Lattes Platform, to study patterns of scientific production and collaboration. Detailed information about publications and researchers is available in this database. Individual curricula are submitted by the researchers themselves so that coauthorship is unambiguous. Researchers can be evaluated by scientific productivity, geographical location and field of expertise. Our results show that the collaboration network is growing exponentially for the last three decades, with a distribution of number of collaborators per researcher that approaches a power-law as the network gets older. Moreover, both the distributions of number of collaborators and production per researcher obey power-law behaviors, regardless of the geographical location or field, suggesting that the same universal mechanism might be responsible for network growth and productivity. We also show that the collaboration network under investigation displays a typical assortative mixing behavior, where teeming researchers (i.e., with high degree) tend to collaborate with others alike.

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Mendeley readers

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Geographical breakdown

Country Count As %
Japan 1 2%
United States 1 2%
Poland 1 2%
Germany 1 2%
Unknown 47 92%

Demographic breakdown

Readers by professional status Count As %
Researcher 8 16%
Student > Ph. D. Student 6 12%
Student > Doctoral Student 5 10%
Professor 4 8%
Other 3 6%
Other 12 24%
Unknown 13 25%
Readers by discipline Count As %
Social Sciences 7 14%
Computer Science 6 12%
Business, Management and Accounting 4 8%
Economics, Econometrics and Finance 4 8%
Environmental Science 3 6%
Other 10 20%
Unknown 17 33%