How many paid subscribers does Spotify have? As of the first quarter of 2025, Spotify had 268 million premium subscribers worldwide, up from 239 million in the corresponding quarter of 2024. Spotify’s subscriber base has increased dramatically in the last few years and has more than doubled since early 2019. Spotify and competitors Spotify is a music streaming service originally founded in 2006 in Sweden. The platform can be used from various devices and allows users to browse through a catalogue of music licensed through multiple record labels, as well as creating and sharing playlists with other users. Additionally, listeners are able to enjoy music for free with advertisements or are also given the option to purchase a subscription to allow for unlimited ad-free music streaming. Spotify’s largest competitors are Pandora, a company that offers a similar service and remains popular in the United States, and Apple Music, which was launched in 2015. While Pandora was once among the highest-grossing music apps in the Apple App Store, recent rankings show that global services like QQ Music, NetEase Cloud Music, and YouTube Music now generate higher monthly revenues.Users are also able to register Spotify accounts using Facebook directly through the website using an app. This enables them to connect with other Facebook friends and explore their music tastes and playlists. Spotify is a popular source for keeping up-to-date with music, and the ability to enjoy Spotify anywhere at any time allows consumers to shape their music consumption around their lifestyles and preferences.

Content

This is a dataset of Spotify tracks over a range of 125 different genres. Each track has some audio features associated with it. The data is in CSV format which is tabular and can be loaded quickly.

  Usage

The dataset can be used for:

Building a Recommendation System based on some user input or preference Classification purposes based on audio features and available genres Any other application that you can think of. Feel free to discuss!

  Column… See the full description on the dataset page: https://huggingface.co/datasets/maharshipandya/spotify-tracks-dataset.

Gain valuable insights into music trends, artist popularity, and streaming analytics with our comprehensive Spotify Dataset. Designed for music analysts, marketers, and businesses, this dataset provides structured and reliable data from Spotify to enhance market research, content strategy, and audience engagement.

Dataset Features

Track Information: Access detailed data on songs, including track name, artist, album, genre, and release date. Streaming Popularity: Extract track popularity scores, listener engagement metrics, and ranking trends. Artist & Album Insights: Analyze artist performance, album releases, and genre trends over time. Related Searches & Recommendations: Track related search terms and suggested content for deeper audience insights. Historical & Real-Time Data: Retrieve historical streaming data or access continuously updated records for real-time trend analysis.

Customizable Subsets for Specific Needs Our Spotify Dataset is fully customizable, allowing you to filter data based on track popularity, artist, genre, release date, or listener engagement. Whether you need broad coverage for industry analysis or focused data for content optimization, we tailor the dataset to your needs.

Popular Use Cases

Market Analysis & Trend Forecasting: Identify emerging music trends, genre popularity, and listener preferences. Artist & Label Performance Tracking: Monitor artist rankings, album success, and audience engagement. Competitive Intelligence: Analyze competitor music strategies, playlist placements, and streaming performance. AI & Machine Learning Applications: Use structured music data to train AI models for recommendation engines, playlist curation, and predictive analytics. Advertising & Sponsorship Insights: Identify high-performing tracks and artists for targeted advertising and sponsorship opportunities.

Whether you're optimizing music marketing, analyzing streaming trends, or enhancing content strategies, our Spotify Dataset provides the structured data you need. Get started today and customize your dataset to fit your business objectives.

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.

The csv-file holding the dataset contains the following columns: "user_id", "artistname", "trackname", "playlistname", where

user_id is a hash of the user's Spotify user name

artistname is the name of the artist

trackname is the title of the track and

playlistname is the name of the playlist that contains this track.

The separator used is , each entry is enclosed by double quotes and the escape character used is .

A description of the generation of the dataset and the dataset itself can be found in the following paper:

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.

💁‍♀️Please take a moment to carefully read through this description and metadata to better understand the dataset and its nuances before proceeding to the Suggestions and Discussions section.

Dataset Description:

This dataset compiles the tracks from Spotify's official "Top Tracks of 2023" playlist, showcasing the most popular and influential music of the year according to Spotify's streaming data. It represents a wide range array of genres, artists, and musical styles that have defined the musical landscapes of 2023. Each track in the dataset is detailed with a variety of features, popularity, and metadata. This dataset serves as an excellent resource for music enthusiasts, data analysts, and researchers aiming to explore music trends or develop music recommendation systems based on empirical data.

Data Collection and Processing:

Obtaining the Data:

The data was obtained directly from the Spotify Web API, specifically from the "Top Tracks of 2023" official playlist curated by Spotify. The Spotify API provides detailed information about tracks, artists, and albums through various endpoints.

Data Processing:

To process and structure the data, I developed Python scripts using data science libraries such as pandas for data manipulation and spotipy for API interactions specifically for Spotify data retrieval.

Workflow:

1. Authentification
2. API Requests
3. Data Cleaning and Transformation
4. Saving the Data

Attribute Descriptions:

• artist_name: the artist name
• track_name: the title of the track
• is_explicit: Indicates whether the track contains explicit content
• album_release_date: The date when the track was released
• genres: A list of genres associated with the track's artist(s)
• danceability: A measure from 0.0 to 1.0 indicating how suitable a track is for dancing based on a combination of musical elements
• valence: A measure from 0.0 to 1.0 indicating the musical positiveness conveyed by a track
• energy: A measure from 0.0 to 1.0 representing a perceptual measure of intensity and activity
• loudness: The overall loudness of a track in decibels (dB)
• acousticness: A measure from 0.0 to 1.0 whether the track is acoustic.
• instrumentalness: Predicts whether a track contains no vocals
• liveness: Detects the presence of an audience in the recordings
• speechiness: Detects the presence of spoken words in a track
• key: The key the track is in. Integers map to pitches using standard Pitch Class notation.
• tempo: The overall estimated tempo of a track in beats per minute (BPM)
• mode: Modality of the track
• duration_ms: The length of the track in milliseconds
• time_signature: An estimated overall time signature of a track
• popularity: A score between 0 and 100, with 100 being the most popular

Possible Data Projects

• Trends Analysis
• Genre Popularity
• Mood and Music
• Comparison with other tracks

Disclaimer and Responsible Use:

• This dataset, derived from Spotify's "Top Tracks of 2023" playlist, is intended for educational, research, and analysis purposes only. Users are urged to use this data responsibly and ethically.
• Users should comply with Spotify's Terms of Service and Developer Policies when using this dataset.
• The dataset includes music track information such as names and artist details, which are subject to copyright. While the dataset presents this information for analytical purposes, it does not convey any rights to the music itself.
• Users of the dataset must ensure that their use does not infringe on the rights of copyright holders. Any analysis, distribution, or derivative work should respect the intellectual property rights of all parties and comply with applicable laws.
• The dataset is provided "as is," without warranty, and the creator disclaims any legal liability for the use of the dataset by others. Users are responsible for ensuring their use of the dataset is legal and ethical.
• For the most accurate and up-to-date information regarding Spotify's music, playlists, and policies, users are encouraged to refer directly to Spotify's official website. This ensures that users have access to the latest details directly from the source.
• The creator/maintainer of this dataset is not affiliated with Spotify, any third-party entities, or artists mentioned within the dataset. This project is independent and has not been authorized, sponsored, or otherwise approved by Spotify or any other mentioned entities.

Contribution

I encourage users who discover new insights, propose dataset enhancements, or craft analytics that illuminate aspects of the dataset's focus to share their findings with the community. - Kaggle Notebooks: To facilitate sharing and collaboration, users are encouraged to create and share their analyses through Kaggle notebooks. For ease of use, start your notebook by clicking "New Notebook" atop this dataset’s page on K...

About Dataset

Overview:

This dataset provides detailed metadata and audio analysis for a wide collection of Spotify music tracks across various genres. It includes track-level information such as popularity, tempo, energy, danceability, and other musical features that can be used for music recommendation systems, genre classification, or trend analysis. The dataset is a rich source for exploring music consumption patterns and user preferences based on song characteristics.

Dataset Details:

This dataset contains rows of individual music tracks, each described by both metadata (such as track name, artist, album, and genre) and quantitative audio features. These features reflect different musical attributes such as energy, acousticness, instrumentalness, valence, and more, making it ideal for audio machine learning projects and exploratory data analysis.

Schema and Column Descriptions:

Key Features:

• Comprehensive Track Data: Metadata combined with detailed audio analysis.
• Genre Diversity: Includes tracks from various music genres.
• Audio Feature Rich: Suitable for audio classification, recommendation engines, or clustering.
• Machine Learning Friendly: Clean and numerical format ideal for ML models.

Usage:

This dataset is valuable for:

• 🎵 Music Recommendation Systems: Building collaborative or content-based recommenders.
• 📊 Data Visualization & Dashboards: Analyzing genre or mood trends over time.
• 🤖 Machine Learning Projects: Predicting song popularity or clustering similar tracks.
• 🧠 Music Psychology & Behavioral Studies: Exploring how music features relate to emotions or behavior.

Data Maintenance:

• Source: https://huggingface.co/datasets/ConquestAce/spotify-songs
• Last Updated: 2025/04/26
• License: Check original source for usage terms.

Additional Notes:

• This dataset can be enhanced by merging it with user listening behavior data, lyrics datasets, or chart positions for more advanced analysis.
• Some columns like key, mode, and explicit may need to be mapped for better readability in visualization.

Dataset Card for Spotify Million Song Dataset

  Dataset Summary

This is Spotify Million Song Dataset. This dataset contains song names, artists names, link to the song and lyrics. This dataset can be used for recommending songs, classifying or clustering songs.

  Supported Tasks and Leaderboards

[More Information Needed]

  Languages

[More Information Needed]

  Dataset Structure





  Data Instances

[More Information Needed]

  Data… See the full description on the dataset page: https://huggingface.co/datasets/vishnupriyavr/spotify-million-song-dataset.

💁‍♀️Please take a moment to carefully read through this description and metadata to better understand the dataset and its nuances before proceeding to the Suggestions and Discussions section.

Dataset Description:

This dataset provides a comprehensive collection of setlists from Taylor Swift’s official era tours, curated expertly by Spotify. The playlist, available on Spotify under the title "Taylor Swift The Eras Tour Official Setlist," encompasses a diverse range of songs that have been performed live during the tour events of this global artist. Each dataset entry corresponds to a song featured in the playlist.

Taylor Swift, a pivotal figure in both country and pop music scenes, has had a transformative impact on the music industry. Her tours are celebrated not just for their musical variety but also for their theatrical elements, narrative style, and the deep emotional connection they foster with fans worldwide. This dataset aims to provide fans and researchers an insight into the evolution of Swift's musical and performance style through her tours, capturing the essence of what makes her tour unique.

Data Collection and Processing:

Obtaining the Data: The data was obtained directly from the Spotify Web API, specifically focusing on the setlist tracks by the artist. The Spotify API provides detailed information about tracks, artists, and albums through various endpoints.

Data Processing: To process and structure the data, Python scripts were developed using data science libraries such as pandas for data manipulation and spotipy for API interactions, specifically for Spotify data retrieval.

Workflow:

Authentication API Requests Data Cleaning and Transformation Saving the Data

Attribute Descriptions:

• artist_name: the name of the artist (Taylor Swift)
• track_name: the title of the track
• is_explicit: Indicates whether the track contains explicit content
• album_release_date: The date when the track was released
• genres: A list of genres associated with Beyoncé
• danceability: A measure from 0.0 to 1.0 indicating how suitable a track is for - dancing based on a combination of musical elements
• valence: A measure from 0.0 to 1.0 indicating the musical positiveness conveyed by a track
• energy: A measure from 0.0 to 1.0 representing a perceptual measure of intensity and activity
• loudness: The overall loudness of a track in decibels (dB)
• acousticness: A measure from 0.0 to 1.0 whether the track is acoustic
• instrumentalness: Predicts whether a track contains no vocals
• liveness: Detects the presence of an audience in the recordings speechiness: Detects the presence of spoken words in a track
• key: The key the track is in. Integers map to pitches using standard Pitch Class notation
• tempo: The overall estimated tempo of a track in beats per minute (BPM)
• mode: Modality of the track
• duration_ms: The length of the track in milliseconds
• time_signature: An estimated overall time signature of a track
• popularity: A score between 0 and 100, with 100 being the most popular

Note: Popularity score reflects the score recorded on the day that retrieves this dataset. The popularity score could fluctuate daily.

Potential Applications:

• Predictive Analytics: Researchers might use this dataset to predict future setlist choices for tours based on album success, song popularity, and fan feedback.

Disclaimer and Responsible Use:

This dataset, derived from Spotify focusing on Taylor Swift's The Eras Tour setlist data, is intended for educational, research, and analysis purposes only. Users are urged to use this data responsibly, ethically, and within the bounds of legal stipulations.

• Compliance with Terms of Service: Users should adhere to Spotify's Terms of Service and Developer Policies when utilizing this dataset.
• Copyright Notice: The dataset presents music track information including names and artist details for analytical purposes and does not convey any rights to the music itself. Users must ensure that their use does not infringe on the copyright holders' rights. Any analysis, distribution, or derivative work should respect the intellectual property rights of all involved parties and comply with applicable laws.
• No Warranty Disclaimer: The dataset is provided "as is," without warranty, and the creator disclaims any legal liability for its use by others.
• Ethical Use: Users are encouraged to consider the ethical implications of their analyses and the potential impact on artists and the broader community.
• Data Accuracy and Timeliness: The dataset reflects a snapshot in time and may not represent the most current information available. Users are encouraged to verify the data's accuracy and timeliness.
• Source Verification: For the most accurate and up-to-date information, users are encouraged to refer directly to Spotify's official website.
• Independence Declaration: ...

This dataset was created using Spotify developer API. It consists of user-created as well as Spotify-curated playlists.
The dataset consists of 1 million playlists, 3 million unique tracks, 3 million unique albums, and 1.3 million artists.
The data is stored in a SQL database, with the primary entities being songs, albums, artists, and playlists.
Each of the aforementioned entities are represented by unique IDs (Spotify URI).
Data is stored into following tables:

• album
• artist
• track
• playlist
• track_artist1
• track_playlist1

album

| id | name | uri |

id: Album ID as provided by Spotify
name: Album Name as provided by Spotify
uri: Album URI as provided by Spotify

artist

| id | name | uri |

id: Artist ID as provided by Spotify
name: Artist Name as provided by Spotify
uri: Artist URI as provided by Spotify

track

| id | name | duration | popularity | explicit | preview_url | uri | album_id |

id: Track ID as provided by Spotify
name: Track Name as provided by Spotify
duration: Track Duration (in milliseconds) as provided by Spotify
popularity: Track Popularity as provided by Spotify
explicit: Whether the track has explicit lyrics or not. (true or false)
preview_url: A link to a 30 second preview (MP3 format) of the track. Can be null
uri: Track Uri as provided by Spotify
album_id: Album Id to which the track belongs

playlist

| id | name | followers | uri | total_tracks |

id: Playlist ID as provided by Spotify
name: Playlist Name as provided by Spotify
followers: Playlist Followers as provided by Spotify
uri: Playlist Uri as provided by Spotify
total_tracks: Total number of tracks in the playlist.

track_artist1

| track_id | artist_id |

Track-Artist association table

track_playlist1

| track_id | playlist_id |

Track-Playlist association table

- - - - - SETUP - - - - -

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.

spotifydbdumpschemashare.sql contains the schema for the database (for reference):
spotifydbdumpshare.sql is the actual data dump.

Setup steps:
1. Create database

- - - - - PAPER - - - - -

The description of this dataset can be found in the following paper:

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

On October 22, 2024, 'APT.' by ROSÉ and Bruno Mars was the most-streamed track on Spotify with 14.6 million streams worldwide, followed by 'Die With A Smile" by Lady Gaga and Bruno Mars, reaching over 11 million Spotify streams on Spotify that day. Billie Eilish's 'BIRDS OF A FEATHER' came third with just over 7.6 million streams. How do music artists get so many streams on Spotify? Firstly, Spotify is one of the most successful and popular music streaming services in the United States, and as of the first half of 2018 had the biggest share of music streaming subscribers in the world. With Spotify’s vast audience, featuring on the platform is a good start for emerging and popular artists hoping to make an impact. Secondly, there is no exact science to ‘going viral’. From the famous egg photo on Instagram posted in early 2019 to wildly successful music video ‘Gangnam Style’ released back in 2012, viral content comes in all shapes and sizes. Purposeful viral marketing is one way in which something could go viral, and is one of the reasons why some songs have so many streams in a short space of time. This type of marketing involves a tactical approach and pre-planning in an attempt to push the content into the public eye and encourage it to spread as quickly as possible. However, many artists who go viral do not expect to. Accessible, catchy content created by an already popular artist is already poised to do well, i.e. the latest song or album from U.S. singer Drake. This is an example of incidental viral marketing, when content spreads by itself partially as a result of an established and engaged audience. Indeed, Spotify’s most-streamed tracks generally originate from a well-known figure with a large following. But for smaller or entirely unknown content creators, going viral or experiencing their 15 minutes of fame can simply be a case of posting the right thing at the right time.

MGD: Music Genre Dataset

Over recent years, the world has seen a dramatic change in the way people consume music, moving from physical records to streaming services. Since 2017, such services have become the main source of revenue within the global recorded music market. Therefore, this dataset is built by using data from Spotify. It provides a weekly chart of the 200 most streamed songs for each country and territory it is present, as well as an aggregated global chart.

Considering that countries behave differently when it comes to musical tastes, we use chart data from global and regional markets from January 2017 to December 2019, considering eight of the top 10 music markets according to IFPI: United States (1st), Japan (2nd), United Kingdom (3rd), Germany (4th), France (5th), Canada (8th), Australia (9th), and Brazil (10th).

We also provide information about the hit songs and artists present in the charts, such as all collaborating artists within a song (since the charts only provide the main ones) and their respective genres, which is the core of this work. MGD also provides data about musical collaboration, as we build collaboration networks based on artist partnerships in hit songs. Therefore, this dataset contains:

Genre Networks: Success-based genre collaboration networks

Genre Mapping: Genre mapping from Spotify genres to super-genres

Artist Networks: Success-based artist collaboration networks

Artists: Some artist data

Hit Songs: Hit Song data and features

Charts: Enhanced data from Spotify Weekly Top 200 Charts

This dataset was originally built for a conference paper at ISMIR 2020. If you make use of the dataset, please also cite the following paper:

Gabriel P. Oliveira, Mariana O. Silva, Danilo B. Seufitelli, Anisio Lacerda, and Mirella M. Moro. Detecting Collaboration Profiles in Success-based Music Genre Networks. In Proceedings of the 21st International Society for Music Information Retrieval Conference (ISMIR 2020), 2020.

@inproceedings{ismir/OliveiraSSLM20, title = {Detecting Collaboration Profiles in Success-based Music Genre Networks}, author = {Gabriel P. Oliveira and Mariana O. Silva and Danilo B. Seufitelli and Anisio Lacerda and Mirella M. Moro}, booktitle = {21st International Society for Music Information Retrieval Conference} pages = {726--732}, year = {2020} }

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   |

Context

This dataset consists of 24000 tracks from 30 genres, and is a shrunk version of maharshipandya/spotify-tracks-dataset dataset. All non-heuristic data is cut and cleaned for better usability and performance. All data taken from Spotify API and is open source. This dataset can be used to train prediction models based on user preferences, or categorise tracks by corresponding heuristic.

  Column Description

danceability: Danceability describes how suitable a track is… See the full description on the dataset page: https://huggingface.co/datasets/engels/spotify-tracks-lite.

Analysis of ‘K-Pop Hits Through The Years’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/sberj127/kpop-hits-through-the-years on 12 November 2021.

--- Dataset description provided by original source is as follows ---

What is the data?

The datasets contain the top songs from the said era or year accordingly (as presented in the name of each dataset). Note that only the KPopHits90s dataset represents an era (1989-2001). Although there is a lack of easily available and reliable sources to show the actual K-Pop hits per year during the 90s, this era was still included as this time period was when the first generation of K-Pop stars appeared. Each of the other datasets represent a specific year after the 90s.

How was it obtained?

A song is considered to be a K-Pop hit during that era or year if it is included in the annual series of K-Pop Hits playlists, which is created officially by Apple Music. Note that for the dataset that represents the 90s, the playlist 90s K-Pop Essentials was used as the reference.

1. These playlists were transferred into Spotify through the Tune My Music site. After transferring, the site also presented all the missing songs from each Spotify playlist when compared to the original Apple Music playlists.
   • Any data besides the names and artists of the hit songs were not directly obtained from Apple Music since these other details of songs in this music service are only available for those enrolled as members of the Apple Developer Program.
2. The presented missing songs from each playlist was manually searched and, if found, added to the respective Spotify playlist.
   • For the songs that were found, there are three types: (1) the song by the original artist, (2) the instrumental of the original song and (3) a cover of the song. When the first type is not found, the two other types are searched and are compared to each other. The one that sounded the most like the original song (from the Apple Music playlist) is chosen as the substitute in the Spotify playlist.
   • Presented is a link containing all the missing data per playlist (when the initial Spotify playlists were compared to the original Apple Music playlists) and the action done to each one.
3. The necessary identification details and specific audio features of each track were obtained through the use of the Spotipy library and Spotify Web API documentation.

Why did you make this?

As someone who has a particular curiosity to the field of data science and a genuine love for the musicality in the K-Pop scene, this data set was created to make something out of the strong interest I have for these separate subjects.

Acknowledgements

I would like to express my sincere gratitude to Apple Music for creating the annual K-Pop playlists, Spotify for making their API very accessible, Spotipy for making it easier to get the desired data from the Spotify Web API, Tune My Music for automating the process of transferring one's library into another service's library and, of course, all those involved in the making of these songs and artists included in these datasets for creating such high quality music and concepts digestible even for the general public.

--- Original source retains full ownership of the source dataset ---

Content

This is a dataset of Spotify tracks over a range of 125 different genres. Each track has some audio features associated with it. The data is in CSV format which is tabular and can be loaded quickly.

Usage

The dataset can be used for:

• Building a Recommendation System based on some user input or preference
• Classification purposes based on audio features and available genres
• Any other application that you can think of. Feel free to discuss!

Column Description

• track_id: The Spotify ID for the track
• artists: The artists' names who performed the track. If there is more than one artist, they are separated by a ;
• album_name: The album name in which the track appears
• track_name: Name of the track
• popularity: The popularity of a track is a value between 0 and 100, with 100 being the most popular. The popularity is calculated by algorithm and is based, in the most part, on the total number of plays the track has had and how recent those plays are. Generally speaking, songs that are being played a lot now will have a higher popularity than songs that were played a lot in the past. Duplicate tracks (e.g. the same track from a single and an album) are rated independently. Artist and album popularity is derived mathematically from track popularity.
• duration_ms: The track length in milliseconds
• explicit: Whether or not the track has explicit lyrics (true = yes it does; false = no it does not OR unknown)
• danceability: Danceability 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
• energy: Energy 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
• key: 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
• loudness: The overall loudness of a track in decibels (dB)
• mode: Mode indicates the modality (major or minor) of a track, the type of scale from which its melodic content is derived. Major is represented by 1 and minor is 0
• speechiness: Speechiness 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
• acousticness: A confidence measure from 0.0 to 1.0 of whether the track is acoustic. 1.0 represents high confidence the track is acoustic
• instrumentalness: 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
• liveness: 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
• valence: 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)
• tempo: 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
• time_signature: An estimated time signature. The time signature (meter) is a notational convention to specify how many beats are in each bar (or measure). The time signature ranges from 3 to 7 indicating time signatures of 3/4, to 7/4.
• track_genre: The genre in which the track belongs

Acknowledgement

Image credits: BPR world

Dataset contains information about ~0.9 million Spotify tracks.
Data sampled using Spotify API

Each object may be uniquely identified by track_id

Description of the data:

track_id: id of the track
streams: number of times the track has been listened to
artist_followers: number of the followers of the track's author
genres: track genres
album_total_tracks: number of tracks in the album the track is a part of
track_artists: name of the track's author
artist_popularity: popularity of the track's author estimated by Spotify
explicit: whether the lyrics contain obscene words
tempo: track's tempo estimated by Spotify
chart: chart the track is in (if any)
album_release_date: the date on which the album the track is a part of was released
energy: track's energy estimated by Spotify
key: track's tonality estimated by Spotify
added_at: moment in time when the track was uploaded
popularity: track's popularity
track_album_album: type of the album the track is a part of
duration_ms: length of the track in milliseconds
available_markets: in what countries the track is available
track_track_number: track's disc number according to Spotify (the number of the track in the album it belongs to)
rank: position in the chart (if the track is a part of a chart)
mode: modality of the track
time_signature: time signature of the track
album_name: name of the album the track is a part of
speechiness: speechiness of the track estimated by Spotify
region: region of the chart (if track is a part of any chart)
danceability: danceability of the track estimated by Spotify
valence: valence of the track estimated by Spotify
acousticness: acousticness of the track estimated by Spotify
liveness: liveness of the track estimated by Spotify
trend: change in track's position within the chart (if track is a part of a chart)
instrumentalness: instrumentalness of the track estimated by Spotify
loudness: loudness of the track estimated by Spotify
name: track title as displayed at Spotify

LFM-1b dataset extended by acoustic track features and cultural cues describing users

This dataset is based on the LFM-1b dataset (cf. http://www.cp.jku.at/datasets/LFM-1b/), however, adds acoustic features describing the tracks to the original dataset as well as cultural aspects describing users (taken from Hofstede's six dimension model and the World Happiness Report) on the country-level.

For the creation of the dataset, we extract all users for which the original dataset contains country information for. We extract the listening events of these users and match the tracks against the Spotify API to subsequently retrieve the acoustic features of these tracks (cf. [Spotify Audio Feature Description](https://developer.spotify.com/documentation/web-api/reference/object-model/#audio-features-object)). The final dataset contains only events of users with country information and tracks with acoustic features, which can be matched with the country-level data of the World Happiness Report and Hofstede's cultural dimensions to add cultural and socio-economic aspects for users.

This new dataset contains

• 55,190 users
• 3,471,884 tracks including acoustic features
• 351,469,333 listening events of those users for tracks we have obtained acoustic features for
• Hofstede's cultural dimensions for 47 countries
• World Happiness Report (WHR) data for 164 countries

Files
All files are tab-separated, with no quoting of strings. The dataset contains the following files, whose content we describe in more detail in the following parts.

* acoustic_features_lfm_id.tsv: acoustic features for all tracks in the dataset, identified by their LFM track identifier
* events.tsv: listening events for all users
* hofstede.tsv: Hofstede's cultural dimensions
* users.tsv: user metadata
* world_happiness_report_2018.tsv: World Happiness Report data

For further information on the contents of these files, please cf. the Readme file.

Please cite the following paper when using the dataset:
Zangerle, E., Pichl, M. and Schedl, M., 2020. User Models for Culture-Aware Music Recommendation: Fusing Acoustic and Cultural Cues. Transactions of the International Society for Music Information Retrieval, 3(1), pp.1–16. DOI: http://doi.org/10.5334/tismir.37

Overview

Welcome to the Music Informatics for Radio Across the GlobE (MIRAGE) MetaCorpus. The current (v0.2) development release consists of metadata (e.g., artist name, track title) and musicological features (e.g., instrument list, voice type, tempo) for 1 million events streaming on 10,000 internet radio stations across the globe, with 100 events from each station.

Users who wish to access, interact with, and/or export metadata from the MIRAGE-MetaCorpus may also visit the MIRAGE online dashboard at the following url:

• https://pearl-laboratory.github.io/mirage-mc/

Attribution

The current MIRAGE-MetaCorpus is available under a CC4 license. Users may cite the dataset here:

Sears, David R.W. “Music Informatics for Radio Across the Globe (MIRAGE) Metacorpus -- 2024”. Zenodo, July 19, 2024. https://doi.org/10.5281/zenodo.12786202.

Users accessing the MIRAGE-MetaCorpus using the online dashboard should also cite the following ISMIR paper:

Ngan V.T. Nguyen, Elizabeth A.M. Acosta, Tommy Dang, and David R.W. Sears. "Exploring Internet Radio Across the Globe with the MIRAGE Online Dashboard," in Proceedings of the 25th International Society for Music Information Retrieval Conference (San Francisco, CA, 2024).

Data Sources

This repository of the MIRAGE-MetaCorpus contains 81 metadata variables from the following open-access sources:

• Radio Garden (RG) -- https://radio.garden
• Natural Earth map data set (NE) -- https://www.naturalearthdata.com/
• Internet Radio Station Stream Encoder (SE)
• Annotator Review (AR)
• Monitoring/Matching Algorithm (MA)
• WikiData (WD) -- https://www.wikidata.org
• MusicBrainz (MB) -- https://musicbrainz.org/

Each event also includes attribution metadata from the following commercial sources:

• Spotify (SP) -- https://open.spotify.com/
  • Note that users may examine an additional 19 metadata variables on the MIRAGE online dashboard that were obtained from the Spotify API.
• Musixmatch (MX) -- https://www.musixmatch.com/
• YouTube (YT) -- https://www.youtube.com/
• Genius (GE) -- https://genius.com/
• AZlyrics (AZ) -- https://www.azlyrics.com/

Data Sets

The metadata reflect information about each event's location (e.g., city, country), station (name, format, url), event (id, local time at station, etc.), artist (name, voice type, etc.), and track (e.g., title, year of release, etc.). For that reason, the MIRAGE-MetaCorpus includes the following datasets:

• MIRAGE.csv -- the complete metacorpus (1 million)
• events.csv -- all event-level metadata (1 million)
• tracks.csv -- all track-level metadata (414,886)
• artists.csv -- all artist-level metadata (259,783)
• stations.csv -- all station-level metadata (10,000)
• locations.csv -- all location-level metadata (4,324)

A subset of the MIRAGE-MetaCorpus is also available for events with metadata from online music libraries that reliably matched the event's description in the radio station's stream encoder:

• MIRAGE_reliable.csv (473,850)
• events_reliable.csv (473,850)
• tracks_reliable.csv (204,969)
• artists_reliable.csv (80,005)
• stations_reliable.csv (9,284)
• locations_reliable.csv (4,142)

Contact

If you are a copyright owner for any of the metadata that appears in the MIRAGE-MetaCorpus and would like us to remove your metadata, please contact the developer team at the following email address: miragedashboard@gmail.com

The dataset comprises developer test results of Maven projects with flaky tests across a range of consecutive commits from the projects' git commit histories. The Maven projects are a subset of those investigated in an OOPSLA 2020 paper. The commit range for this dataset has been chosen as the flakiness-introducing commit (FIC) and iDFlakies-commit (see the OOPSLA paper for details). The commit hashes have been obtained from the IDoFT dataset.

The dataset will be presented at the 1st International Flaky Tests Workshop 2024 (FTW 2024). Please refer to our extended abstract for more details about the motivation for and context of this dataset.

The following table provides a summary of the data.

Slug (Module) FIC Hash Tests Commits Av. Commits/Test Flaky Tests Tests w/ Consistent Failures Total Distinct Histories

TooTallNate/Java-WebSocket 822d40 146 75 75 24 1 2.6x10^9

apereo/java-cas-client (cas-client-core) 5e3655 157 65 61.7 3 2 1.0x10^7

eclipse-ee4j/tyrus (tests/e2e/standard-config) ce3b8c 185 16 16 12 0 261

feroult/yawp (yawp-testing/yawp-testing-appengine) abae17 1 191 191 1 1 8

fluent/fluent-logger-java 5fd463 19 131 105.6 11 2 8.0x10^32

fluent/fluent-logger-java 87e957 19 160 122.4 11 3 2.1x10^31

javadelight/delight-nashorn-sandbox d0d651 81 113 100.6 2 5 4.2x10^10

javadelight/delight-nashorn-sandbox d19eee 81 93 83.5 1 5 2.6x10^9

sonatype-nexus-community/nexus-repository-helm 5517c8 18 32 32 0 0 18

spotify/helios (helios-services) 23260 190 448 448 0 37 190

spotify/helios (helios-testing) 78a864 43 474 474 0 7 43

The columns are composed of the following variables:

Slug (Module): The project's GitHub slug (i.e., the project's URL is https://github.com/{Slug}) and, if specified, the module for which tests have been executed.

FIC Hash: The flakiness-introducing commit hash for a known flaky test as described in this OOPSLA 2020 paper. As different flaky tests have different FIC hashes, there may be multiple rows for the same slug/module with different FIC hashes.

Tests: The number of distinct test class and method combinations over the entire considered commit range.

Commits: The number of commits in the considered commit range

Av. Commits/Test: The average number of commits per test class and method combination in the considered commit range. The number of commits may vary for each test class, as some tests may be added or removed within the considered commit range.

Flaky Tests: The number of distinct test class and method combinations that have more than one test result (passed/skipped/error/failure + exception type, if any + assertion message, if any) across 30 repeated test suite executions on at least one commit in the considered commit range.

Tests w/ Consistent Failures: The number of distinct test class and method combinations that have the same error or failure result (error/failure + exception type, if any + assertion message, if any) across all 30 repeated test suite executions on at least one commit in the considered commit range.

Total Distinct Histories: The number of distinct test results (passed/skipped/error/failure + exception type, if any + assertion message, if any) for all test class and method combinations along all commits for that test in the considered commit range.

👍 If this dataset was useful to you, leave your vote at the top of the page 👍

The dataset provides information on the daily top 200 tracks listened to by users of the Spotify digital platform around the world.

I put together this dataset because I really love music (I listen to it for several hours a day) and have not found a similar dataset with track genres on kaggle.

The dataset can be useful for beginners in the field of working with data. It contains missing values, arrays in columns, and so on, which can be great practice when conducting an EDA phase.

Soon, my example will appear here as possible, based on the specified dataset, go on a musical journey around the world and understand how the musical tastes of humanity have changed around the world)))

In addition, I will be very happy to see the work of the community on this dataset.

Also, in case of interest in data by country, I am ready to place it upon request.

You can contact me through: telegram @natarov_ivan