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Researchers concentrate their efforts to understand the different female relations with science, using approaches that review their scientific and technological participation, as well as, seeking to understand their academic trajectory and performance. In this context, this study aimed to analyze the participation of women using as database the set of PhD graduates who have their curricula entered in the Lattes Platform. The data were collected and selected obtaining a set of 125,515 curricula of women who had completed their PhD. The PhD data were grouped according to the large areas of expertise (fields of science) and academic training, in which it was possible to analyze the academic evolution and the scientific and technological production of the group in a temporal manner. The different types of studies that help to understand the general aspect of women active mainly in science, besides being relevant, exhibit the characteristics of their research. This may be useful for the generation of national scientific indicators, for the management of information in the scientific area and for technological development. It is also useful to encourage and valuate participation of women in science.

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When publishing an article with other authors, initial links must be formed by a collaboration between authors, a scientific collaboration network. In this context, the papers are represented by the edges, and the authors are represented the nodes, forming a network. At this moment, the following question arises: How does the evolution of the network occur over time? Understanding what factors are essential for creating a new connection to answer this question is necessary. Therefore, the purpose of this article is to foresee connections in co-authorship networks formed by PhDs with curricula registered in Lattes Platform in the areas of Information Sciences and Biology. The following steps are performed: initially the data is extracted and organized. This step is essential for the continuity of the process. Then, co-authorship networks are generated based on articles published together. Subsequently, the attributes to be used are defined and some metrics are calculated. Finally, machine learning algorithms estimate future scientific collaborations in the selected areas. The Lattes Platform has 6.6 million resumes for researchers and represents one of the most relevant and recognized scientific repositories worldwide. As a result, random forest and logistic regression algorithms showed the highest hit rates, and preferential attachment attribute was identified as the most influential in the emergence of new scientific collaborations. Through the results, it is possible to establish the evolution of the network of scientific associations of researchers at a national level, assisting development agencies in selecting of future outstanding researchers.

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Predicting the stock market is a widely studied field, either due to the curiosity in finding an explanation for the behavior of financial assets or for financial purposes. Among these studies the best techniques use neural networks as a prediction tool. More specifically, the best networks for this purpose are called recurrent neural networks (RNN), and provide an extra option when dealing with a sequence of values. However, a great part of the studies is intended to predict the result of few stocks, therefore, this work aims to predict the behavior of a large number of stocks. For this, similar stocks were grouped based on their correlation and later the algorithm K-means was applied so that similar groups were clustered. After this process, the Long Short-Term Memory (LSTM) - a type of RNN - was used in order to predict the price of a certain group of assets. Results showed that clustering stocks did not influence the effectiveness of the network and that investors and portfolio managers can use it to simply their daily tasks.