We present the MTG-Jamendo Dataset, a new open dataset for music auto-tagging. It is built using music available at Jamendo under Creative Commons licenses and tags provided by content uploaders. The dataset contains over 55,000 full audio tracks with 195 tags from genre, instrument, and mood/theme categories. We provide elaborated data splits for researchers and report the performance of a simple baseline approach on five different sets of tags: genre, instrument, mood/theme, top-50, and overall. This repository contains metadata. For scripts and instructions on how to download and use the dataset please see the related GitHub repository. Citation If you use the MTG-Jamendo Dataset or part of it, please cite our ICML2019 ML4MD paper: Bogdanov, D., Won M., Tovstogan P., Porter A., & Serra X. (2019). The MTG-Jamendo Dataset for Automatic Music Tagging. Machine Learning for Music Discovery Workshop, International Conference on Machine Learning (ICML 2019). BibTeX version: @conference {bogdanov2019mtg, author = "Bogdanov, Dmitry and Won, Minz and Tovstogan, Philip and Porter, Alastair and Serra, Xavier", title = "The MTG-Jamendo Dataset for Automatic Music Tagging", booktitle = "Machine Learning for Music Discovery Workshop, International Conference on Machine Learning (ICML 2019)", year = "2019", address = "Long Beach, CA, United States", url = "http://hdl.handle.net/10230/42015" } Acknowledgments This work was funded by the predoctoral grant MDM-2015-0502-17-2 from the Spanish Ministry of Economy and Competitiveness linked to the Maria de Maeztu Units of Excellence Programme (MDM-2015-0502). This work has received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No. 765068 "MIP-Frontiers". This work has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 688382 "AudioCommons".