LabGEOPI Drives Innovation Policy Analysis with Big Data and Artificial Intelligence
Campinas, SP The digital age has transformed the way public policies are formulated and evaluated. The Laboratory for Studies on the Organization of Research and Innovation (LabGEOPI) at Unicamp is at the forefront of this transformation, using Big Data and Artificial Intelligence (AI) to improve the analysis of innovation policies and provide more accurate input for decision-making.
LabGEOPI employs advanced techniques for collecting, processing, and analyzing large volumes of data (Big Data), combined with Artificial Intelligence algorithms, to identify patterns, trends, and correlations in Science, Technology, and Innovation (ST&I) policies. This allows for a deeper understanding of the dynamics of innovation and its impacts. Public managers, policymakers, researchers, and companies can benefit from the Big Data and AI-based analyses performed by LabGEOPI. The goal is to offer tools and insights that enable the design of more effective policies adapted to contemporary challenges.
The laboratory develops and applies analytical models that use machine learning, natural language processing (NLP), and complex network analysis to extract knowledge from diverse data sources, such as scientific publication databases, patents, research funding, news, and social media. The research conducted by LabGEOPI, located at the Institute of Geosciences of Unicamp in Campinas (SP), has a national and international impact, contributing to the advancement of knowledge on how new digital technologies can be applied in the management and evaluation of innovation policies.
LabGEOPI is continuously exploring new frontiers in the use of Big Data and AI, developing research projects and training professionals to handle these technologies. The application of these tools is growing and fundamental to keeping pace with the speed of transformations in the innovation ecosystem. The use of Big Data and Artificial Intelligence in the analysis of innovation policies allows us to overcome the limitations of traditional methods, offering a more granular, dynamic, and predictive view. This results in better-informed, more efficient policies capable of driving scientific, technological, and economic development more effectively.
