  Screwdriving is one of the most popular industrial processes. As such, it is
increasingly common to automate that procedure by using various robots. Even
though the automation increases the efficiency of the screwdriving process, if
the process is not monitored correctly, faults may occur during operation,
which can impact the effectiveness and quality of assembly. Machine Learning
(ML) has the potential to detect those undesirable events and limit their
impact. In order to do so, first a dataset that fully describes the operation
of an industrial robot performing automated screwdriving must be available.
  This report describes a dataset created using a UR3e series robot and OnRobot
Screwdriver. We create different scenarios and introduce 4 types of anomalies
to the process while all available robot and screwdriver sensors are
continuously recorded. The resulting data contains 2042 samples of normal and
anomalous robot operation. Brief ML benchmarks using this data are also
provided, showcasing the data's suitability and potential for further analysis
and experimentation.
