Chemical reactions are naturally subjected to a wide range of conditions. Therefore, data covering these conditions is fundamental to the design and validation of accurate ML reactive models. ML-based approaches require large amounts of data for training and perform poorly when used "out of distribution" scenarios, i.e. conditions significantly different from those present in the training data. For this reason, both general-purpose and reaction-specific datasets were produced.