In this work, we deepen the aggregation of different datasets reporting chemical formulas and associated properties. In particular, we study whether the predictive accuracy of different ML models can benefit from the aggregation of local repositories with databases with larger availability. In order to do that, we consider three different aggregation strategies in which we prioritize training size, element diversity, and composition diversity. Our main findings are summarized as follows: