We start by noting that there exist a large number of available databases of framework materials -that is, for crystalline frameworks, more precisely. Among databases of experimental crystalline structures, we can cite the open-access Crystallography Open Database, or the commercial Inorganic Crystal Structure Database (ICSD) and Cambridge Structural Database (CSD, for organic and metalorganic structures). From these general databases with structures deposited by researchers upon characterization and publication, researchers have created and published more focused databases, limited to specific families of materials, and often curated for the purpose of high-throughput exploration. Among these, we can list the CSD MOF subset, the CoRE MOF database, the CURATED COF database, etc. These experimental databases are complemented by databases of hypothetical materials, which have been proposed for zeolites, MOFs, COFs , and others. They include materials whose structures are predicted by different computational algorithms, typically by a systematic combination of a list of topologies and building blocks, or by generative algorithms -although it is still difficult to predict to date whether a given hypothetical structure is experimentally "feasible" to synthesize. In any case, it is clear that while databases of crystalline structures abound, the same is not true of experimentallymeasured physical or chemical properties. Those are scarce, often unstructured and in highly heterogeneous formats, and locked in the existing publications -requiring the use of textand data-mining approaches to be recovered. It is therefore natural to complement the purely structural databases with computational data, generated by quantum chemistry calculations or molecular simulations, in studies that are generally referred to as high-throughput computational screening.