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In recent years, research on the detection and mitigation of non-line-of-sight (NLOS) conditions in the context of ultra-wideband ranging has received increasing attention. As a result, numerous statistical and machine learning methods have been proposed, and a selection of datasets has been made available to the community. In an attempt to benchmark the performance of state-of-the-art NLOS classification and error correction techniques on a newly-built ultra-wideband testbed at our premises, we have observed how reusing publicly-available datasets and applying existing solutions is a complex and error-prone task. Indeed, a multitude of minor details in the selection, pre-processing, collection, labeling, and blending of datasets can have a profound impact on the correctness of the employed methods and on the achieved performance. In this paper, we summarize the lessons we have learned, pointing out …