The Music Atlas is based entirely off of Spotify data
"License to Developer. Subject to and conditional upon your compliance at all times with these Developer Terms, particularly the limitations in Section IV below, Spotify grants to you a limited, non-exclusive, non-transferable, non-sublicensable, revocable right during the Term (as defined in Section IX.10)...". For non-streaming SDAs (e.g., Music Atlas) Spotify Grants permission to develop and distribute Non-Streaming SDAs that comply with the Branding Guidelines for use with the Spotify Service.
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Overview of the data:
# | Dataset | Description | size (# rows) | Relevant Columns | Relevant Visualization(s) |
---|---|---|---|---|---|
1 | Genre Dataset | Track-level metadata from popular Spotify Curated Genre Playlists | ~822k | track_id, album_id, artist_id, track_audio_features, genre | Genre Map Playlist Relationship Map |
2 | Playlist Dataset | Track-level metadata from popular playlists on Spotify. Note, this is not the same as the Genre Dataset because we aren't requiring the playlist to be a Spotify Curated Playlist. | ~1.67mil | playlist_id, track_id, artist_id, track_audio_features | Genre Map Playlist Relationship Map |
3 | Artist Dataset | Track and Album-level metadata for a small set of hand picked artists. | ~1k | artist_id, track_id, album_id, track_audio_features, release_date | Artist Sound Progression Map |
I am grateful to Chris Olah and the rest of the Distill Pub team for there insightful blog posts. Also, a big thank you to Sophie Engle for formally introducing me to data visualization.
Work was complete by Kai unless noted otherwise.
Concepts & Writting: Kai introduced and wrote the central concept and structure of this article.
Figures & Code: Some figures were drafted in RawGraphs. For all figures, the final version was implemented in D3. All code was written by Kai.
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