Scientific Data

Scientific data management and analysis are undergoing a transformation driven by the exponential growth of data generated by scientific simulations and experiments. Current research focuses on improving data compression techniques (e.g., using neural networks and error-controlled methods), enhancing data quality through AI-assisted annotation and metadata enrichment (leveraging large language models), and developing novel methods for uncertainty quantification and efficient data exploration. These advancements are crucial for enabling reproducible research, facilitating scientific discovery, and supporting the development of more robust and trustworthy AI models in various scientific domains.