  We introduce a Python package that provides simply and unified access to a
collection of datasets from fundamental physics research - including particle
physics, astroparticle physics, and hadron- and nuclear physics - for
supervised machine learning studies. The datasets contain hadronic top quarks,
cosmic-ray induced air showers, phase transitions in hadronic matter, and
generator-level histories. While public datasets from multiple fundamental
physics disciplines already exist, the common interface and provided reference
models simplify future work on cross-disciplinary machine learning and transfer
learning in fundamental physics. We discuss the design and structure and line
out how additional datasets can be submitted for inclusion.
  As showcase application, we present a simple yet flexible graph-based neural
network architecture that can easily be applied to a wide range of supervised
learning tasks. We show that our approach reaches performance close to
dedicated methods on all datasets. To simplify adaptation for various problems,
we provide easy-to-follow instructions on how graph-based representations of
data structures, relevant for fundamental physics, can be constructed and
provide code implementations for several of them. Implementations are also
provided for our proposed method and all reference algorithms.
