Machine learning based on data extracted from experimental crystallographic databases [such as the Cambridge Structural Database (CSD) or the Open Crystallographic Database (COD) ] is particularly prone to this kind of data leakage as one structure can appear multiple times under different identifiers in the database. This can, for instance, be the case because there are different refinements for the same structure or because the measurement was performed at different temperatures. The presence of duplicates in MOF databases has been reported before but is seldom taken into account in machine learning studies. In Case Study 1, we illustrate that for MOFs data leakage is indeed a severe and perhaps underestimated problem.