  In general, large datasets enable deep learning models to perform with good
accuracy and generalizability. However, massive high-fidelity simulation
datasets (from molecular chemistry, astrophysics, computational fluid dynamics
(CFD), etc. can be challenging to curate due to dimensionality and storage
constraints. Lossy compression algorithms can help mitigate limitations from
storage, as long as the overall data fidelity is preserved. To illustrate this
point, we demonstrate that deep learning models, trained and tested on data
from a petascale CFD simulation, are robust to errors introduced during lossy
compression in a semantic segmentation problem. Our results demonstrate that
lossy compression algorithms offer a realistic pathway for exposing
high-fidelity scientific data to open-source data repositories for building
community datasets. In this paper, we outline, construct, and evaluate the
requirements for establishing a big data framework, demonstrated at
https://blastnet.github.io/, for scientific machine learning.
