For a qualitative and quantitative assessment of context prediction and recognition methods, real-world data sets are inevitable. By collecting sensor data on a single notebook over a period of a few months we got a rather large log file of homogeneous and heterogeneous features reflecting the users activities during this time frame. In this paper we present which devices were exploited as sensors, which information was logged and how this information was stored for further processing by classification algorithms.