Visualizando a literatura científica sobre COVID-19

Abstract

Due to the global pandemic of COVID-19, thousands of scientific articles about the disease were and continue to be published every day. However, this incredible rate of scientific productivity leads to information overload, which makes it more difficult for researchers, physicians and other healthcare professionals to keep up with the latest scientific discoveries and developments in this area of research. Our ability to generate and store data increases at a faster rate than our ability to analyze it, and if no information is extracted, the storage will have been useless. In view of the above, information visualization has undoubtedly been widely used in the coverage of the coronavirus pandemic, as it expands the cognitive capacity of the human being in data exploration processes, favoring access to information. When coupled with analytical strategies derived from data mining, visualization has its potential maximized, as it allows associating computational capacity with knowledge and the human ability to perceive patterns. Therefore, this study aims, through information visualization and data mining techniques, to analyze in an exploratory way the scientific literature on COVID-19 during the global pandemic of the disease. Thus, different types of analyzes can be performed to achieve this objective, including co-authorship analysis, in order to show the hidden relationships of research collaboration; geospatial analysis, to represent the geographic data related to the articles. However, it is necessary to carry out some activities that precede and accompany this analysis process, such as obtaining scientific articles to be used as an object of study, preparing the data collected, and choosing the visualization techniques to be applied to the data.