COVID-2019 has been recognized as a global threat, and several studies are being conducted in order to contribute to the fight and prevention of this pandemic. This work presents a scholarly production dataset focused on COVID-19, providing an overview of scientific research activities, making it possible to identify countries, scientists and research groups most active in this task force to combat the coronavirus disease. The dataset is composed of 40,212 records of articles' metadata collected from Scopus, PubMed, arXiv and bioRxiv databases from January 2019 to July 2020. Those data were extracted by using the techniques of Python Web Scraping and preprocessed with Pandas Data Wrangling. In addition, the pipeline to preprocess and generate the dataset are versioned with the Data Version Control tool (DVC) and are thus easily reproducible and auditable.

This article proposes a novel approach that leverages graph theory, machine learning, and graph embedding to evaluate research groups comprehensively. Assessing the performance and impact of research groups is crucial for funding agencies and research institutions, but many traditional methods often fail to capture the complex relationships between the evaluated elements. In this sense, our methodology transforms publication data into graph structures, allowing the visualization and quantification of relationships between researchers, publications, and institutions. By incorporating symmetry properties, we offer a more in-depth evaluation of research groups cohesiveness and structure over time. This temporal evaluation methodology bridges the gap between unstructured scientometrics networks and the evaluation process, making it a valuable tool for decision-making procedures. A case study is defined to demonstrate the potential to provide valuable insights into the dynamics and limitations of research groups, which ultimately reinforces the feasibility of the proposed approach when supporting decision making for funding agencies and research institutions.

Legislative documents are crucial to democratic societies, defining the legal framework for social life. In Brazil, legislative texts are particularly complex due to extensive technical jargon, intricate sentence structures, and frequent references to prior legislation. The country’s civil law tradition and multicultural context introduce further interpretative and linguistic challenges. Moreover, the study of Brazilian Portuguese legislative texts remains underexplored, lacking legal-specific models and datasets. To address these gaps, this work proposes a data-driven approach utilizing large language models (LLMs) to analyze these documents and extract knowledge graphs (KGs). A case study was conducted using 1869proposals from the Legislative Assembly of Rio Grande do Norte (ALRN), spanning January 2019 to April 2024. The Llama 3.2 3B Instruct model was employed to extract KGs representing entities and their relationships. The findings support the method’s effectiveness in producing coherent graphs faithful to the original content. Nevertheless, challenges remain in resolving entity ambiguity and achieving full relationship coverage. Additionally, readability analyses using metrics for Brazilian Portuguese revealed that ALRN proposals require superior reading skills due to their technical style. Ultimately, this study advances legal artificial intelligence by providing insights into Brazilian legislative texts and promoting transparency and accessibility through natural language processing techniques.

Departamentos acadêmicos lidam com demandas complexas — a exemplo de dúvidas sobre matrículas e disciplinas —, mas chatbots tradicionais, baseados em regras, falham ao oferecer respostas contextualizadas, gerando frustração e custos operacionais elevados. Desse modo, este estudo propõe o Lusi 2.0, um chatbot baseado em Large Language Models (LLMs) e Retrieval-Augmented Generation (RAG), o qual recupera informações semânticas de Projetos Pedagógicos de Cursos (PPCs) e gera respostas precisas e contextualizadas, reduzindo a necessidade de intervenção manual. Adicionalmente, um experimento controlado comparará sua eficácia com um chatbot baseado em regras, envolvendo, pelo menos, 30 alunos de um departamento universitário. Logo, os resultados permitirão validar sua capacidade de humanizar serviços públicos, substituindo burocracia por diálogos empáticos, além de contribuir para aplicações práticas de LLMs em contextos educacionais.