MusicOSet is an open and enhanced dataset of musical elements (artists, songs and albums) based on musical popularity classification. Provides a directly accessible collection of data suitable for numerous tasks in music data mining (e.g., data visualization, classification, clustering, similarity search, MIR, HSS and so forth). To create MusicOSet, the potential information sources were divided into three main categories: music popularity sources, metadata sources, and acoustic and lyrical features sources. Data from all three categories were initially collected between January and May 2019. Nevertheless, the update and enhancement of the data happened in June 2019. The attractive features of MusicOSet include: Integration and centralization of different musical data sources Calculation of popularity scores and classification of hits and non-hits musical elements, varying from 1962 to 2018 Enriched metadata for music, artists, and albums from the US popular music industry Availability of acoustic and lyrical resources Unrestricted access in two formats: SQL database and compressed .csv files | Data | # Records | |:-----------------:|:---------:| | Songs | 20,405 | | Artists | 11,518 | | Albums | 26,522 | | Lyrics | 19,664 | | Acoustic Features | 20,405 | | Genres | 1,561 |

{"references": ["Silva, M. O., Rocha, L. M., and Moro, M. M. (2019). MusicOSet: An Enhanced Open Dataset for Music Data Mining. In XXXIV Simp\u00f3sio Brasileiro de Banco de Dados: Dataset Showcase Workshop, SBBD 2019 Companion, Fortaleza, CE, Brazil."]}