Central to the development of robust ML potentials is the availability of high-quality training data. Quantum chemical data sets and databases serve as the foundation for these models, providing essential information on molecular structures, energies, forces, and other properties. The breadth and depth of these data sets and databases directly influence the ability of ML models to learn complex chemical phenomena and generalize to new systems. Furthermore, accessible and well-curated data sets and databases are essential for reproducibility and benchmarking in the field.