Brazilian e-Science Workshop

Over the past decades, science has undergone a major transformation driven by advances in computational methods, which have become established as the third pillar of research, alongside theory and experimentation. This paradigm, known as e-Science, is grounded in intensive computational support for scientific investigation.

In this context, researchers explore vast data collections and large-scale computational resources, often geographically distributed, requiring advanced techniques for data distribution and parallel processing. These characteristics pose significant challenges related to data modeling and management, the development of robust scientific software, and the interpretation of results. Moreover, the inherently multidisciplinary nature of e-Science demands close collaboration among experts from diverse domains.

Recent advances in Computer Science offer promising pathways to address these challenges. Technologies and research areas such as workflow modeling, ontologies, Web services, high-performance computing (HPC), cloud computing, distributed databases, data provenance, Data-Centric AI, and digital curation play a fundamental role in tackling major scientific challenges. At the same time, the pervasiveness of large volumes of data (Big Data) across all areas of knowledge reinforces the need for effective methods to transform raw data into meaningful discoveries.

The advent of Big Data has consolidated the so-called fourth paradigm — data-intensive science— motivating biologists, astronomers, engineers, and mathematicians to confront complex computational problems. Data Science, by integrating methods, processes, and algorithms from fields such as Mathematics, Statistics, and Computer Science, has emerged as an essential and complementary discipline to e-Science, focused on large-scale knowledge extraction.