Given the plethora of ML potentials and their growing importance in computational chemistry, this review aims to provide an overview of the current landscape of molecular quantum chemical data sets and databases. We explore the key characteristics and functionalities of prominent resources, examining the type of information they store (e.g., molecular structures, various properties), the level of electronic structure theory employed (e.g., density functional theory (DFT), second-order Møller-Plesset perturbation theory (MP2)), the size and diversity of the chemical space they cover, and the methodologies used for data creation. While the possible selection of the data sets an databases is very broad, we mostly focus on the data collections which were specifically designed with the goal of developing and evaluating ML potentials of molecules or have a potential to be used for such applications. Data sets for both ground and excited state potentials are included and many data sets also contain properties that go beyond energies and forces. We also included data collections that provide the sets of 3D nuclear geometries and energies which are frequently used in the context of structure-property relations such as QM9 data set but in principle can be used to train ML potentials and, indeed, QM9 is sometimes used to benchmark ML potentials . Surely, there are more data sets generated in the plenitude of studies involving MLPs, which either served to highlight the particular strengths and shortcomings of a new methodology such as a new active learning protocol or were a "by-product" of investigating an interesting chemical phenomenon. Although these data collections are undoubtedly very useful, we have to exclude them to limit the scope of our current overview. Here were we aim to focus on the data sets generated wit as little as possible involvement of ML potentials to have a collection less biased to a particular ML potential. This is admittedly hard to avoid as, e.g., ANI-1x data set was generated using techniques such as normal mode sampling but the final composition was obtained via pool-based active learning with the ANI-type universal potential.