Moreover, data format standardization is another area requiring attention. The FAIR (Findable, Accessible, Interoperable, Reusable) principles provide a guiding framework, but the diversity of formats in use today makes unification challenging. Efforts to promote standard formats, such as ioChem-BD and Quantum Chemistry Schema, could help streamline data exchange and enhance interoperability across different platforms and research groups. Attempts to provide standard formats interoperable between software are naturally undertaken in the big software ecosystems gluing together different quantum chemical and/or ML codes like is done in MLatom and ASE. Another big issue with the growing number of data sets and databases is that they are often generated at different levels severely hampering their use in ML potentials as, e.g., training a ML potential on a merged data set would be very problematic if possible at all to achieve any reasonable result. One of the solutions is recalculating properties at the same level for the merged data sets but it is very computationally costly undertaking. Recently, some of us suggested a potentially simple solution to this problem by creating allin-one machine learning potentials which take in input the level of theory as an additional feature: we showed that this approach allows to train a single, accurate ML model on a heterogeneous data containing data at different levels of theory. This seems to be one of the most promising direction for the future explorations which we also pursue in our labs. Furthermore, maintaining updatable data sets and databases, particularly for "living" (i.e., updatable) methods like UAIQM, is crucial for the continued evolution of ML potentials. Ensuring these data sets and databases are regularly updated and accessible will require coordinated efforts within the research community, alongside the development of tools and standards that facilitate easy data curation and integration.