The GiantSteps+ EDM Key Dataset includes 600 two-minute sound excerpts from various EDM subgenres, annotated with single-key labels. This dataset focus in problematic Beatport excerpts, so it is biased, but it is interesting to test the robustness of key recognition systems. These 600 tracks have been analysed by Daniel G. Camhi and Ángel Faraldo, providing pitch-class set descriptions, key and modal changes, comments and confidence levels for the individual tracks. This dataset is a revision of the original GiantSteps Key Dataset, available in Github (<https://github.com/GiantSteps/giantsteps-key-dataset>) and initially described in: Knees, P., Faraldo, Á., Herrera, P., Vogl, R., Böck, S., Hörschläger, F., Le Goff, M. (2015). Two Datasets for Tempo Estimation and Key Detection in Electronic Dance Music Annotated from User Corrections. In Proceedings of the 16th International Society for Music Information Retrieval Conference, 364–370. Málaga, Spain. The original audio samples belong to online audio snippets from Beatport, an online music store for DJ's and Electronic Dance Music Producers (<http:\\www.beatport.com>). If this dataset were used in further research, we would appreciate the citation of the current DOI (10.5281/zenodo.1101082) and the following doctoral dissertation, where a detailed description of the properties of this dataset can be found: Ángel Faraldo (2017). Tonality Estimation in Electronic Dance Music: A Computational and Musically Informed Examination. PhD Thesis. Universitat Pompeu Fabra, Barcelona.