  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.
