As our first and main experiment, we consider the aggregation of each dataset A with data points collected from the respective dataset B. To assess the benefits of the aggregation, we first evaluate the performance of ML estimators before integrating any new data points; this will be indicated as baseline setting. This is done, as usual, by training on a subset (80%) of dataset A and computing prediction errors on the corresponding test set (20%). For DL models, 10% of the training size is reserved for a validation set. In the aggregation process, data points collected from B only increase the size of the original training set (and validation, for DL) of A. For consistency, performance is always assessed on the original test set of A. We consider three different aggregation strategies: