  The game Starcraft is one of the most interesting arenas to test new machine
learning and computational intelligence techniques; however, StarCraft matches
take a long time and creating a good dataset for training can be hard. Besides,
analyzing match logs to extract the main characteristics can also be done in
many different ways to the point that extracting and processing data itself can
take an inordinate amount of time and of course, depending on what you choose,
can bias learning algorithms. In this paper we present a simplified dataset
extracted from the set of matches published by Robinson and Watson, which we
have called RedDwarfData, containing several thousand matches processed to
frames, so that temporal studies can also be undertaken. This dataset is
available from GitHub under a free license. An initial analysis and appraisal
of these matches is also made.
