Recent years have seen the development and widespread use of an important number of databases of materials, whether experimental or hypothetical. Data-based approaches to the discovery of novel materials -or the identification of known materials with optimal per-formance for a specific application -are becoming a common research theme. 9,10 However, despite the many databases of crystalline or framework materials available, there has been very little systematic effort to date to analyze their topology. We think that this blind spot in previous research efforts is due to the combination of two factors. First, the determination of a material's topology requires the knowledge of chemical bonds in the system: while many heuristics exist for specific subclasses of systems, the determination of bonds is not a trivial problem, especially at large scale. Secondly, applying topology determination algorithms to a large number of structures (tens of thousands or more) requires the use of very fast, optimized software, which was not readily available until recently. In this article, we detail our methodological work to determine the topology of materials from databases at a large scale, and analyze the resulting data. We demonstrate the usefulness of this approach by applying to more than 170,000 inorganic crystalline structures from the Materials Project database, and publishing this new data set of chemical environment and topology information. This is the first systematic, high-throughput determination of material topology in a data base of this size. We highlight how this can be statistically analyzed, and propose two concrete examples of how topological considerations can be used in high-throughput computational screening for the identification of materials with desirable properties.