  There has been an increasing number of applications of machine learning to
the field of Computer Algebra in recent years, including to the prominent
sub-field of Symbolic Integration. However, machine learning models require an
abundance of data for them to be successful and there exist few benchmarks on
the scale required. While methods to generate new data already exist, they are
flawed in several ways which may lead to bias in machine learning models
trained upon them. In this paper, we describe how to use the Risch Algorithm
for symbolic integration to create a dataset of elementary integrable
expressions. Further, we show that data generated this way alleviates some of
the flaws found in earlier methods.
