Concatenation: a simple concatenation of all points from the train set of A with the whole dataset B. Duplicated instances are removed by taking the median across reported target properties. The primary advantage of this strategy is that the size of the resulting dataset is maximized. This is generally believed to strengthen the robustness of the estimators and potentially discover new patterns. However, a possible drawback is a saturation effect which arises from the compounded presence of redundant data points, hindering model learning and generalization. In particular, different associated values and experimental conditions may have the overall effect of increasing the degree of noise in the dataset.