Research Trend
Research is increasingly leveraging computational methods, particularly machine learning and large language models (LLMs), to analyze and understand the vast and rapidly evolving landscape of scientific publications. Current efforts focus on identifying research trends, predicting future research directions, and automating tasks like topic labeling and research proposal generation, often employing techniques like topic modeling, graph convolutional networks, and various deep learning architectures for time series analysis. This automated analysis facilitates more efficient knowledge discovery, aids in resource allocation, and enables researchers to better understand the interconnectedness and evolution of scientific fields, ultimately improving research productivity and impact.
Papers
Mapping Climate Change Research via Open Repositories & AI: advantages and limitations for an evidence-based R&D policy-making
Nicandro Bovenzi, Nicolau Duran-Silva, Francesco Alessandro Massucci, Francesco Multari, César Parra-Rojas, Josep Pujol-Llatse
Mapping STI ecosystems via Open Data: overcoming the limitations of conflicting taxonomies. A case study for Climate Change Research in Denmark
Nicandro Bovenzi, Nicolau Duran-Silva, Francesco Alessandro Massucci, Francesco Multari, Cèsar Parra-Rojas, Josep Pujol-Llatse