  In the era of data-driven science, conducting computational experiments that
involve analysing large datasets using heterogeneous computational clusters, is
part of the everyday routine for many scientists. Moreover, to ensure the
credibility of their results, it is very important for these analyses to be
easily reproducible by other researchers. Although various technologies, that
could facilitate the work of scientists in this direction, have been introduced
in the recent years, there is still a lack of open source platforms that
combine them to this end. In this work, we describe and demonstrate SCHeMa, an
open-source platform that facilitates the execution and reproducibility of
computational analysis on heterogeneous clusters, leveraging containerization,
experiment packaging, workflow management, and machine learning technologies.
