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13 results for “spotify”
P4KxSpotify: A Dataset of Pitchfork Music Reviews and Spotify Musical Features
<p>18,403 music reviews scraped from Pitchfork, including relevant metadata such as author, review date, record release year, score, and genre, along with those album's audio features pulled from Spotify's API.</p>
Playlist2vec: Spotify Million Playlist Dataset
<p>This dataset was created using Spotify developer API. It consists of user-created as well as Spotify-curated playlists.<br> The dataset consists of 1 million playlists, 3 million unique tracks, 3 million unique albums, and 1.3 million artists.<br> The data is stored in a SQL database, with the primary entities being songs, albums, artists, and playlists.<br> Each of the aforementioned entities are represented by unique IDs (Spotify URI).<br> Data is stored into following tables:</p> <ul> <li> album</li> <li> artist</li> <li> track</li> <li> playlist</li> <li> track_artist1</li> <li> track_playlist1</li> </ul> <p><strong>album</strong></p> <p><em>| id | name | uri |</em></p> <p><strong>id</strong>: Album ID as provided by Spotify<br> <strong>name</strong>: Album Name as provided by Spotify<br> <strong>uri</strong>: Album URI as provided by Spotify</p> <p><br> <strong>artist</strong></p> <p><em>| id | name | uri |</em></p> <p><strong>id</strong>: Artist ID as provided by Spotify<br> <strong>name</strong>: Artist Name as provided by Spotify<br> <strong>uri</strong>: Artist URI as provided by Spotify</p> <p><br> <strong>track</strong></p> <p><em>| id | name | duration | popularity | explicit | preview_url | uri | album_id |</em></p> <p><strong>id</strong>: Track ID as provided by Spotify<br> <strong>name</strong>: Track Name as provided by Spotify<br> <strong>duration</strong>: Track Duration (in milliseconds) as provided by Spotify<br> <strong>popularity</strong>: Track Popularity as provided by Spotify<br> <strong>explicit</strong>: Whether the track has explicit lyrics or not. (true or false)<br> <strong>preview_url</strong>: A link to a 30 second preview (MP3 format) of the track. Can be null<br> <strong>uri</strong>: Track Uri as provided by Spotify<br> <strong>album_id</strong>: Album Id to which the track belongs</p> <p><br> <strong>playlist</strong></p> <p><em>| id | name | followers | uri | total_tracks |</em></p> <p><strong>id</strong>: Playlist ID as provided by Spotify<br> <strong>name</strong>: Playlist Name as provided by Spotify<br> <strong>followers</strong>: Playlist Followers as provided by Spotify<br> <strong>uri</strong>: Playlist Uri as provided by Spotify<br> <strong>total_tracks</strong>: Total number of tracks in the playlist.</p> <p> </p> <p><strong>track_artist1</strong></p> <p><em>| track_id | artist_id |</em><br> <br> Track-Artist association table</p> <p> </p> <p><strong>track_playlist1</strong></p> <p><em>| track_id | playlist_id |</em></p> <p>Track-Playlist association table</p> <p> </p> <p> - - - - - <em>SETUP</em> - - - - - </p> <p><br> The data is in the form of a SQL dump. The download size is about 10 GB, and the database populated from it comes out to about 35GB.</p> <p><strong>spotifydbdumpschemashare.sql</strong> contains the schema for the database (for reference):<br> <strong>spotifydbdumpshare.sql</strong> is the actual data dump.</p> <p><br> <strong>Setup steps:</strong><br> 1. Create database <dbname><br> 2. mysql -u <username> -p <dbname> < spotifydbdumpshare.sql</p> <p> </p> <p>- - - - - <em>PAPER</em> - - - - -</p> <p><br> The description of this dataset can be found in the following paper:</p> <p>Papreja P., Venkateswara H., Panchanathan S. (2020) Representation, Exploration and Recommendation of Playlists. In: Cellier P., Driessens K. (eds) Machine Learning and Knowledge Discovery in Databases. ECML PKDD 2019. Communications in Computer and Information Science, vol 1168. Springer, Cham</p>
Spotify and Youtube
<p>This is the statistics for the Top 10 songs of various spotify artists and their YouTube videos. The Creators above generated the data and uploaded it to Kaggle on February 6-7 2023. The license to use this data is "CC0: Public Domain", allowing the data to be copied, modified, distributed, and worked on without having to ask permission. The data is in numerical and textual CSV format as attached.</p><p>This dataset contains the statistics and attributes of the top 10 songs of various artists in the world. As described by the creators above, it includes 26 variables for each of the songs collected from spotify. These variables are briefly described next:</p><ul><li><strong>Track</strong>: name of the song, as visible on the Spotify platform.</li><li><strong>Artist</strong>: name of the artist.</li><li><strong>Url_spotify</strong>: the Url of the artist.</li><li><strong>Album</strong>: the album in wich the song is contained on Spotify.</li><li><strong>Album_type</strong>: indicates if the song is relesead on Spotify as a single or contained in an album.</li><li><strong>Uri</strong>: a spotify link used to find the song through the API.</li><li><strong>Danceability</strong>: describes how suitable a track is for dancing based on a combination of musical elements including tempo, rhythm stability, beat strength, and overall regularity. A value of 0.0 is least danceable and 1.0 is most danceable.</li><li><strong>Energy</strong>: is a measure from 0.0 to 1.0 and represents a perceptual measure of intensity and activity. Typically, energetic tracks feel fast, loud, and noisy. For example, death metal has high energy, while a Bach prelude scores low on the scale. Perceptual features contributing to this attribute include dynamic range, perceived loudness, timbre, onset rate, and general entropy.</li><li><strong>Key</strong>: the key the track is in. Integers map to pitches using standard Pitch Class notation. E.g. 0 = C, 1 = C♯/D♭, 2 = D, and so on. If no key was detected, the value is -1.</li><li><strong>Loudness</strong>: the overall loudness of a track in decibels (dB). Loudness values are averaged across the entire track and are useful for comparing relative loudness of tracks. Loudness is the quality of a sound that is the primary psychological correlate of physical strength (amplitude). Values typically range between -60 and 0 db.</li><li><strong>Speechiness</strong>: detects the presence of spoken words in a track. The more exclusively speech-like the recording (e.g. talk show, audio book, poetry), the closer to 1.0 the attribute value. Values above 0.66 describe tracks that are probably made entirely of spoken words. Values between 0.33 and 0.66 describe tracks that may contain both music and speech, either in sections or layered, including such cases as rap music. Values below 0.33 most likely represent music and other non-speech-like tracks.</li><li><strong>Acousticness</strong>: a confidence measure from 0.0 to 1.0 of whether the track is acoustic. 1.0 represents high confidence the track is acoustic.</li><li><strong>Instrumentalness</strong>: predicts whether a track contains no vocals. "Ooh" and "aah" sounds are treated as instrumental in this context. Rap or spoken word tracks are clearly "vocal". The closer the instrumentalness value is to 1.0, the greater likelihood the track contains no vocal content. Values above 0.5 are intended to represent instrumental tracks, but confidence is higher as the value approaches 1.0.</li><li><strong>Liveness</strong>: detects the presence of an audience in the recording. Higher liveness values represent an increased probability that the track was performed live. A value above 0.8 provides strong likelihood that the track is live.</li><li><strong>Valence</strong>: a measure from 0.0 to 1.0 describing the musical positiveness conveyed by a track. Tracks with high valence sound more positive (e.g. happy, cheerful, euphoric), while tracks with low valence sound more negative (e.g. sad, depressed, angry).</li><li><strong>Tempo</strong>: the overall estimated tempo of a track in beats per minute (BPM). In musical terminology, tempo is the speed or pace of a given piece and derives directly from the average beat duration.</li><li><strong>Duration_ms</strong>: the duration of the track in milliseconds.</li><li><strong>Stream</strong>: number of streams of the song on Spotify.</li><li><strong>Url_youtube</strong>: url of the video linked to the song on Youtube, if it have any.</li><li><strong>Title</strong>: title of the videoclip on youtube.</li><li><strong>Channel</strong>: name of the channel that have published the video.</li><li><strong>Views</strong>: number of views.</li><li><strong>Likes</strong>: number of likes.</li><li><strong>Comments</strong>: number of comments.</li><li><strong>Description</strong>: description of the video on Youtube.</li><li><strong>Licensed</strong>: Indicates whether the video represents licensed content, which means that the content was uploaded to a channel linked to a YouTube content partner and then claimed by that partner.</li><li><strong>official_video</strong>: boolean value that indicates if the video found is the official video of the song.</li></ul><p>The data was last updated on February 7, 2023.</p>
Top 10 most-played albums on Spotify for various European countries
<p>This dataset presents the Top 10 most listened-to albums on Spotify for five European countries: Spain, France, Italy, the United Kingdom, and Germany. The information has been compiled from data available on the website <a href="https://chartmasters.org/">https://chartmasters.org/</a>. The dataset, in CSV format, includes essential details such as the ranking position, album title, artist, and the total number of plays. Organized to provide an overview, the dataset offers insights into current musical preferences in various European regions, showcasing the most popular albums on the Spotify platform.</p>
Almost a million Spotify tracks
<p>Dataset contains information about ~0.9 million Spotify tracks.<br>Data sampled using Spotify API</p> <p>Each object may be uniquely identified by track_id</p> <p><br>Description of the data:</p> <p><strong>track_id:</strong> id of the track<br><strong>streams:</strong> number of times the track has been listened to<br><strong>artist_followers:</strong> number of the followers of the track's author<br><strong>genres:</strong> track genres<br><strong>album_total_tracks:</strong> number of tracks in the album the track is a part of<br><strong>track_artists:</strong> name of the track's author<br><strong>artist_popularity:</strong> popularity of the track's author estimated by Spotify<br><strong>explicit:</strong> whether the lyrics contain obscene words<br><strong>tempo:</strong> track's tempo estimated by Spotify<br><strong>chart:</strong> chart the track is in (if any)<br><strong>album_release_date:</strong> the date on which the album the track is a part of was released<br><strong>energy:</strong> track's energy estimated by Spotify<br><strong>key:</strong> track's tonality estimated by Spotify<br><strong>added_at:</strong> moment in time when the track was uploaded<br><strong>popularity: </strong>track's popularity<br><strong>track_album_album:</strong> type of the album the track is a part of<br><strong>duration_ms:</strong> length of the track in milliseconds<br><strong>available_markets:</strong> in what countries the track is available<br><strong>track_track_number:</strong> track's disc number according to Spotify (the number of the track in the album it belongs to)<br><strong>rank:</strong> position in the chart (if the track is a part of a chart)<br><strong>mode:</strong> modality of the track<br><strong>time_signature:</strong> time signature of the track<br><strong>album_name:</strong> name of the album the track is a part of<br><strong>speechiness:</strong> speechiness of the track estimated by Spotify<br><strong>region:</strong> region of the chart (if track is a part of any chart)<br><strong>danceability:</strong> danceability of the track estimated by Spotify<br><strong>valence:</strong> valence of the track estimated by Spotify<br><strong>acousticness:</strong> acousticness of the track estimated by Spotify<br><strong>liveness:</strong> liveness of the track estimated by Spotify<br><strong>trend:</strong> change in track's position within the chart (if track is a part of a chart)<br><strong>instrumentalness:</strong> instrumentalness of the track estimated by Spotify<br><strong>loudness:</strong> loudness of the track estimated by Spotify<br><strong>name:</strong> track title as displayed at Spotify</p>
Spotify Playlists Dataset
<p><br> This dataset is based on the subset of users in the #nowplaying dataset who publish their #nowplaying tweets via Spotify. In principle, the dataset holds users, their playlists and the tracks contained in these playlists.</p> <p>The csv-file holding the dataset contains the following columns: "user_id", "artistname", "trackname", "playlistname", where</p> <ul> <li>user_id is a hash of the user's Spotify user name</li> <li>artistname is the name of the artist</li> <li>trackname is the title of the track and</li> <li>playlistname is the name of the playlist that contains this track.</li> </ul> <p>The separator used is , each entry is enclosed by double quotes and the escape character used is \.</p> <p>A description of the generation of the dataset and the dataset itself can be found in the following paper:</p> <p>Pichl, Martin; Zangerle, Eva; Specht, Günther: "Towards a Context-Aware Music Recommendation Approach: What is Hidden in the Playlist Name?" in 15th IEEE International Conference on Data Mining Workshops (ICDM 2015), pp. 1360-1365, IEEE, Atlantic City, 2015.<br> </p>
Spotify Top 50 European Countries
Top 50 tracks data from european countries
Spotify Top 50 European Countries
Top 50 tracks data from european countries
Wikidata Dump spotify-musicbrainz-tidal-mashup
<p>RDF dump of wikidata produced with <a href="//wdumps.toolforge.org/">wdumper</a>.</p><p>alex's test proof of concept for marc he stayed up late doing<br><a href="//wdumps.toolforge.org/dump/2837">View on wdumper</a></p><p><b>entity count<b>: 0, <b>statement count</b>: 0, <b>triple count</b>: 0</b></b></p>
spotify data
<p>from kaggle</p>
Menghubungkan Data Musik dari Spotify dan YouTube dengan Menggunakan Vocabularies Extended dan Linked Data
<p><strong>Menghubungkan Data Musik dari Spotify dan YouTube dengan Menggunakan Vocabularies dan Linked Data. Dataset diambil dari link </strong><a href="https://www.kaggle.com/datasets/salvatorerastelli/spotify-and-youtube">Spotify and Youtube (kaggle.com)</a></p>
Spotify Million Playlist: Recsys Challenge 2018 Dataset
<p>Spotify Million Playlist Dataset Challenge</p> <p>Summary</p> <p>The Spotify Million Playlist Dataset Challenge consists of a dataset and evaluation to enable research in music recommendations. It is a continuation of the RecSys Challenge 2018, which ran from January to July 2018. The dataset contains 1,000,000 playlists, including playlist titles and track titles, created by users on the Spotify platform between January 2010 and October 2017. The evaluation task is automatic playlist continuation: given a seed playlist title and/or initial set of tracks in a playlist, to predict the subsequent tracks in that playlist. This is an open-ended challenge intended to encourage research in music recommendations, and no prizes will be awarded (other than bragging rights).</p> <p>Background</p> <p>Playlists like Today’s Top Hits and RapCaviar have millions of loyal followers, while Discover Weekly and Daily Mix are just a couple of our personalized playlists made especially to match your unique musical tastes.</p> <p>Our users love playlists too. In fact, the Digital Music Alliance, in their 2018 Annual Music Report, state that 54% of consumers say that playlists are replacing albums in their listening habits.</p> <p>But our users don’t love just listening to playlists, they also love creating them. To date, over 4 billion playlists have been created and shared by Spotify users. People create playlists for all sorts of reasons: some playlists group together music categorically (e.g., by genre, artist, year, or city), by mood, theme, or occasion (e.g., romantic, sad, holiday), or for a particular purpose (e.g., focus, workout). Some playlists are even made to land a dream job, or to send a message to someone special.</p> <p>The other thing we love here at Spotify is playlist research. By learning from the playlists that people create, we can learn all sorts of things about the deep relationship between people and music. Why do certain songs go together? What is the difference between “Beach Vibes” and “Forest Vibes”? And what words do people use to describe which playlists?</p> <p>By learning more about nature of playlists, we may also be able to suggest other tracks that a listener would enjoy in the context of a given playlist. This can make playlist creation easier, and ultimately help people find more of the music they love.</p> <p>Dataset</p> <p>To enable this type of research at scale, in 2018 we sponsored the RecSys Challenge 2018, which introduced the Million Playlist Dataset (MPD) to the research community. Sampled from the over 4 billion public playlists on Spotify, this dataset of 1 million playlists consist of over 2 million unique tracks by nearly 300,000 artists, and represents the largest public dataset of music playlists in the world. The dataset includes public playlists created by US Spotify users between January 2010 and November 2017. The challenge ran from January to July 2018, and received 1,467 submissions from 410 teams. A summary of the challenge and the top scoring submissions was published in the ACM Transactions on Intelligent Systems and Technology.</p> <p>In September 2020, we re-released the dataset as an open-ended challenge on AIcrowd.com. The dataset can now be downloaded by registered participants from the Resources page.</p> <p>Each playlist in the MPD contains a playlist title, the track list (including track IDs and metadata), and other metadata fields (last edit time, number of playlist edits, and more). All data is anonymized to protect user privacy. Playlists are sampled with some randomization, are manually filtered for playlist quality and to remove offensive content, and have some dithering and fictitious tracks added to them. As such, the dataset is not representative of the true distribution of playlists on the Spotify platform, and must not be interpreted as such in any research or analysis performed on the dataset.</p> <p>Dataset Contains</p> <p>1000 examples of each scenario:</p> <p>Title only (no tracks) Title and first track Title and first 5 tracks First 5 tracks only Title and first 10 tracks First 10 tracks only Title and first 25 tracks Title and 25 random tracks Title and first 100 tracks Title and 100 random tracks</p> <p>Download Link</p> <p>Full Details: <a href="https://www.aicrowd.com/challenges/spotify-million-playlist-dataset-challenge">https://www.aicrowd.com/challenges/spotify-million-playlist-dataset-challenge</a><br> Download Link: <a href="https://www.aicrowd.com/challenges/spotify-million-playlist-dataset-challenge/dataset_files">https://www.aicrowd.com/challenges/spotify-million-playlist-dataset-challenge/dataset_files</a></p>
Most Streamed Spotify Songs 2024
<h1>Description:</h1> <p>This dataset presents a comprehensive compilation of the most streamed songs on Spotify in 2024. It provides extensive insights into each track's attributes, popularity, and presence on various music platforms, offering a valuable resource for music analysts, enthusiasts, and industry professionals. The dataset includes information such as track name, artist, release date, ISRC, streaming statistics, and presence on platforms like YouTube, TikTok, and more.</p> <h1>Key Features:</h1> <ul> <li><strong>Track Name</strong>: Name of the song.</li> <li><strong>Album Name</strong>: Name of the album the song belongs to.</li> <li><strong>Artist</strong>: Name of the artist(s) of the song.</li> <li><strong>Release Date</strong>: Date when the song was released.</li> <li><strong>ISRC</strong>: International Standard Recording Code for the song.</li> <li><strong>All Time Rank</strong>: Ranking of the song based on its all-time popularity.</li> <li><strong>Track Score</strong>: Score assigned to the track based on various factors.</li> <li><strong>Spotify Streams</strong>: Total number of streams on Spotify.</li> <li><strong>Spotify Playlist Count</strong>: Number of Spotify playlists the song is included in.</li> <li><strong>Spotify Playlist Reach</strong>: Reach of the song across Spotify playlists.</li> <li><strong>Spotify Popularity</strong>: Popularity score of the song on Spotify.</li> <li><strong>YouTube Views</strong>: Total views of the song's official video on YouTube.</li> <li><strong>YouTube Likes</strong>: Total likes on the song's official video on YouTube.</li> <li><strong>TikTok Posts</strong>: Number of TikTok posts featuring the song.</li> <li><strong>TikTok Likes</strong>: Total likes on TikTok posts featuring the song.</li> <li><strong>TikTok Views</strong>: Total views on TikTok posts featuring the song.</li> <li><strong>YouTube Playlist Reach</strong>: Reach of the song across YouTube playlists.</li> <li><strong>Apple Music Playlist Count</strong>: Number of Apple Music playlists the song is included in.</li> <li><strong>AirPlay Spins</strong>: Number of times the song has been played on radio stations.</li> <li><strong>SiriusXM Spins</strong>: Number of times the song has been played on SiriusXM.</li> <li><strong>Deezer Playlist Count</strong>: Number of Deezer playlists the song is included in.</li> <li><strong>Deezer Playlist Reach</strong>: Reach of the song across Deezer playlists.</li> <li><strong>Amazon Playlist Count</strong>: Number of Amazon Music playlists the song is included in.</li> <li><strong>Pandora Streams</strong>: Total number of streams on Pandora.</li> <li><strong>Pandora Track Stations</strong>: Number of Pandora stations featuring the song.</li> <li><strong>Soundcloud Streams</strong>: Total number of streams on Soundcloud.</li> <li><strong>Shazam Counts</strong>: Total number of times the song has been Shazamed.</li> <li><strong>TIDAL Popularity</strong>: Popularity score of the song on TIDAL.</li> <li><strong>Explicit Track</strong>: Indicates whether the song contains explicit content.</li> </ul> <h1>Potential Use Cases:</h1> <ul> <li><strong>Music Analysis</strong>: Analyze trends in audio features to understand popular song characteristics.</li> <li><strong>Platform Comparison</strong>: Compare song popularity across different music platforms.</li> <li><strong>Artist Impact</strong>: Study the relationship between artist attributes and song success.</li> <li><strong>Temporal Trends</strong>: Identify changes in music attributes and preferences over time.</li> <li><strong>Cross-Platform Presence</strong>: Investigate song performance across various streaming services.</li> </ul>
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