Adoption of the lattes platform as the data source for the characterization of scientific networks
DOI:
https://doi.org/10.5007/1518-2924.2016v21n47p16Keywords:
Lattes Platform, Data Extract, Information retrieval, Science collaborationAbstract
Studies scientific productions data has received attention from researchers in several areas, which aim to acquire knowledge about the evolution of research general them. Such studies allow for a review of scientific production paragraph several purposes and hum of the challenges this type of analysis is in repositories diversity containing data in different formats and structures. The resumes the Lattes Platform is currently characterized how important tool for what researchers, academics and students, data your register, being widely used, characterizing how largest data repositories about scientific production, technical, artistic and vocational, containing millions of researchers registered. In this work is proposal a platform for extraction entire data set of Curriculum Lattes composing great hum scientific data repository, in addition, are implemented technical for bibliometric analysis of data and identification of scientific collaboration networks. Results are presented as studies aimed that get an overview of the Curriculum Lattes and repository as the content of these resumes may be used one paragraph characterization of scientific collaboration networks. Done is the resumes Lattes that are an extremely rich source of scientific data and your adoption for bibliometric and based on scientific network analysis studies may provide results important for how understanding the Brazilian science has been held. The difficulty when analyzing whole Lattes Platform data repository is related large data repository and still due when using techniques such as collaborations identification shortly efficient and computationally complex. Therefore, this work presents the full potential of the Lattes Platform for bibliometric analyzes of researchers, for this proposal a platform to collect and analyze the entire data set with low computational cost and with satisfactory accuracy.
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