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The field of research on activity recognition is relatively young compared to others, like computer vision. In more mature fields, algorithms are usually tested on standardized, reference datasets. This way, algorithms coming from different groups can be tested in a fair manner, which accelerates the process of developing new knowledge. Collecting a reference dataset under realistic settings for activity recognition poses many challenges due to the large amount of sensors and sensor modalities which are needed to provide a sufficiently complete playground. We here report on some lessons learned while collecting such a reference dataset with a heterogeneous setup. We argue for the importance of a few principles to obtain a clean dataset, starting from the sampling and acquisition, down to the synchronization and labeling of the data.