Let us start by investigating the number of duplicates in commonly used databases of experimental and hypothetical MOF structures. In the case of the experimental databases, it is important to realize that if the same structure has been refined multiple times or measured under different temperatures or with different unbound solvents, it will appear under multiple CSD reference codes. From a machine learning point of view, however, these materials are too similar to appear in both train and test sets (as for many applications such as high-pressure gas storage, the model could then make a very good prediction by just remembering the appropriate training data). We show the number of duplicates as inverse cumulative histograms, i.e. the bar heights indicate how often we find n or more times a duplicated graph type. In the columns, we show increasingly general (i.e. more structures are considered as duplicates) definitions of duplicates. The rows show the counts for different databases: The all-solvent-removed (ASR-CSD) subset of the 2019 MOF subset of the CSD, the CoRE database, the QMOF database, and the Boyd-Woo database (BW) of hypothetical MOFs.