Many machine learning practitioners recognize benchmark sets as drivers of progress. For instance, researchers in image classification can easily compare the performance of competing approaches, as they can compare model performance on the same tasks on the same dataset (e.g., ImageNet ). Over the last years, similar benchmark datasets have been reported for generative models for molecules or quantum machine learning. However, there is currently no widely used reference set for machine learning on metal-organic frameworks (even though the QMOF dataset makes important steps towards this goal). As a first step towards more comparable machine learning for MOFs, our package implements a consistent interface for collections of structures (StructureDatasets), along with some corresponding properties (e.g., gas adsorption or electronic properties) with which all datasets can be used via the same interface. Our package implements reference datasets based on the QMOF database, the ARC database, the BW database, the ARABG database, as well as on a subset of the CoRE-MOF database. A challenge with the currently existing datasets is that different properties are computed for different structures. However, for many learning applications, it can be useful to have multiple properties for the same structure. To address this, we used reproducible computational workflows to compute diverse gas separation properties (CO 2 , CH 4 , H 2 , N 2 , O 2 isotherms; H 2 S, H 2 O, Kr, Xe Henry coefficient as well as parasitic energy for carbon capture from natural gas and a coalfired power plant ) for nearly seven thousand materials from the QMOF database (which contains many nonporous materials) and make them accessible via our mofdscribe package. This makes it, to the best of our knowledge, the first database of some gas adsorption properties that are collected alongside many other properties (e.g., bandgap computed with different functionals) of the same structure. We intend to update the database in parallel with the QMOF database.