  Generating context specific data quality deficits is necessary to
experimentally assess data quality of data-driven (artificial intelligence (AI)
or machine learning (ML)) applications. In this paper we present badgers, an
extensible open-source Python library to generate data quality deficits
(outliers, imbalanced data, drift, etc.) for different modalities (tabular
data, time-series, text, etc.). The documentation is accessible at
https://fraunhofer-iese.github.io/badgers/ and the source code at
https://github.com/Fraunhofer-IESE/badgers
