  Difficulties in replication and reproducibility of empirical evidences in
machine learning research have become a prominent topic in recent years.
Ensuring that machine learning research results are sound and reliable requires
reproducibility, which verifies the reliability of research findings using the
same code and data. This promotes open and accessible research, robust
experimental workflows, and the rapid integration of new findings. Evaluating
the degree to which research publications support these different aspects of
reproducibility is one goal of the present work. For this we introduce an
ontology of reproducibility in machine learning and apply it to methods for
graph neural networks. Building on these efforts we turn towards another
critical challenge in machine learning, namely the curse of dimensionality,
which poses challenges in data collection, representation, and analysis, making
it harder to find representative data and impeding the training and inference
processes. Using the closely linked concept of geometric intrinsic dimension we
investigate to which extend the used machine learning models are influenced by
the intrinsic dimension of the data sets they are trained on.
