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Storing and sharing increasingly large datasets is a challenge acrossscientific research and industry. In this paper, we document the developmentand applications of Baler - a Machine Learning based data compression tool foruse across scientific disciplines and industry. Here, we present Baler'sperformance for the compression of High Energy Physics (HEP) data, as well asits application to Computational Fluid Dynamics (CFD) toy data as aproof-of-principle. We also present suggestions for cross-disciplinaryguidelines to enable feasibility studies for machine learning based compressionfor scientific data.

Baler -- Machine Learning Based Compression of Scientific Data