Desarrollo de un sistema de toma de decisiones para apoyo a los investigadores ecuatorianos basado en motores de búsqueda y de recomendación : automatización del proceso de extracción, depuración, y almacenamiento de datos de Scopus mediante la API de Elsevier.

Abstract

This paper addresses the growing need for collaboration and efficiency in scientific research, reflected in the increase in documents and the complexity of interactions between experts. To address these challenges, two complementary applications were developed: ResNet, a search engine that visualizes co-authorship networks between researchers, and ResearchDecide, a recommendation system for the formation of research teams. The integration of these tools gave rise to Centinela, a comprehensive solution designed to optimize scientific collaboration. Centinela combines the capabilities of ResNet and ResearchDecide, offering a platform that facilitates both the visualization of research networks and the management of scientific teams. For the frontend, Angular was used, providing a dynamic and responsive interface. The backend was built with Django Rest Framework, ensuring a robust and scalable API, and a hexagonal architecture was implemented to facilitate scalability and system maintenance. The project focuses on automating data extraction, cleaning, and storage, improving the integrity and consistency of information, reducing human errors, and increasing efficiency. Sentinel represents a significant advance in the integration of research tools, effectively addressing the problems of collaboration and data management in the scientific field.