Baler -- Machine Learning Based Compression of Scientific Data

Understand

Storing and sharing increasingly large datasets is a challenge across scientific research and industry.

• In this paper, we document the development and applications of Baler - a Machine Learning based data compression tool for use across scientific disciplines and industry.
• Here, we present Baler's performance for the compression of High Energy Physics (HEP) data, as well as its application to Computational Fluid Dynamics (CFD) toy data as a proof-of-principle.
• We also present suggestions for cross-disciplinary guidelines to enable feasibility studies for machine learning based compression for scientific data.