  Computational materials science produces large quantities of data, both in
terms of high-throughput calculations and individual studies. Extracting
knowledge from this large and heterogeneous pool of data is challenging due to
the wide variety of computational methods and approximations, resulting in
significant veracity in the sheer amount of available data. Here, we present
MADAS, a Python framework for computing similarity relations between material
properties. It can be used to automate the download of data from various
sources, compute descriptors and similarities between materials, analyze the
relationship between materials through their properties, and can incorporate a
variety of existing machine learning methods. We explain the design of the
package and demonstrate its power with representative examples.
