Machine learning (ML) models are increasingly becoming an essential item of the standard computational and theoretical chemistry toolkit. This growth stems from the ability to efficiently develop highly accurate trained models. In the chemistry field, these ML models are applied across various areas, including studying reactive processes, sampling equilibrium states, creating precise force fields, and generating as well as exploring chemical space. Today, a wide variety of complex, robust models are available, though their quality is largely constrained by the data used for training. Traditionally, efforts have focused on gathering large datasets, following trends from computer science with the assumption that more data could enhance model performance. However, it has become clear that poor data quality can undermine even the best models. Increasingly, the ML field emphasizes the balance between data quality and quantity . This shift has led to a smart data approach prioritizing curated, reliable datasets, which is particularly important in chemical applications where selecting the right data type for specific target properties is crucial. .