100+ datasets found
  1. C

    Data from: Sound and music recommendation with knowledge graphs [dataset]

    • dataverse.csuc.cat
    txt, zip
    Updated Oct 9, 2023
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    Sergio Oramas; Sergio Oramas; Vito Claudio Ostuni; Gabriel Vigliensoni; Gabriel Vigliensoni; Vito Claudio Ostuni (2023). Sound and music recommendation with knowledge graphs [dataset] [Dataset]. http://doi.org/10.34810/data444
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    txt(3751), zip(56553416)Available download formats
    Dataset updated
    Oct 9, 2023
    Dataset provided by
    CORA.Repositori de Dades de Recerca
    Authors
    Sergio Oramas; Sergio Oramas; Vito Claudio Ostuni; Gabriel Vigliensoni; Gabriel Vigliensoni; Vito Claudio Ostuni
    License

    https://dataverse.csuc.cat/api/datasets/:persistentId/versions/1.0/customlicense?persistentId=doi:10.34810/data444https://dataverse.csuc.cat/api/datasets/:persistentId/versions/1.0/customlicense?persistentId=doi:10.34810/data444

    Description

    Music Recommendation Dataset (KGRec-music). Number of items: 8,640. Number of users: 5,199. Number of items-users interactions: 751,531. All the data comes from songfacts.com and last.fm websites. Items are songs, which are described in terms of textual description extracted from songfacts.com, and tags from last.fm. Files and folders in the dataset: /descriptions: In this folder there is one file per item with the textual description of the item. The name of the file is the id of the item plus the ".txt" extension. /tags: In this folder there is one file per item with the tags of the item separated by spaces. Multiword tags are separated by -. The name of the file is the id of the item plus the ".txt" extension. Not all items have tags, there are 401 items without tags. implicit_lf_dataset.txt: This file contains the interactions between users and items. There is one line per interaction (a user that downloaded a sound in this case) with the following format, fields in one line are separated by tabs: user_id /t sound_id /t 1 /n. Sound Recommendation Dataset (KGRec-sound). Number of items: 21,552. Number of users: 20,000. Number of items-users interactions: 2,117,698. All the data comes from Freesound.org. Items are sounds, which are described in terms of textual description and tags created by the sound creator at uploading time. Files and folders in the dataset: /descriptions: In this folder there is one file per item with the textual description of the item. The name of the file is the id of the item plus the ".txt" extension. /tags: In this folder there is one file per item with the tags of the item separated by spaces. The name of the file is the id of the item plus the ".txt" extension. downloads_fs_dataset.txt: This file contains the interactions between users and items. There is one line per interaction (a user that downloaded a sound in this case) with the following format, fields in one line are separated by tabs: /nuser_id /t sound_id /t 1 /n. Two different datasets with users, items, implicit feedback interactions between users and items, item tags, and item text descriptions are provided, one for Music Recommendation (KGRec-music), and other for Sound Recommendation (KGRec-sound).

  2. MGD: Music Genre Dataset

    • zenodo.org
    • data.niaid.nih.gov
    zip
    Updated May 28, 2021
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    Gabriel P. Oliveira; Gabriel P. Oliveira; Mariana O. Silva; Mariana O. Silva; Danilo B. Seufitelli; Danilo B. Seufitelli; Anisio Lacerda; Mirella M. Moro; Mirella M. Moro; Anisio Lacerda (2021). MGD: Music Genre Dataset [Dataset]. http://doi.org/10.5281/zenodo.4778563
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    zipAvailable download formats
    Dataset updated
    May 28, 2021
    Dataset provided by
    Zenodohttp://zenodo.org/
    Authors
    Gabriel P. Oliveira; Gabriel P. Oliveira; Mariana O. Silva; Mariana O. Silva; Danilo B. Seufitelli; Danilo B. Seufitelli; Anisio Lacerda; Mirella M. Moro; Mirella M. Moro; Anisio Lacerda
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    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}
    }

  3. Data from: MusicOSet: An Enhanced Open Dataset for Music Data Mining

    • zenodo.org
    • data.niaid.nih.gov
    bin, zip
    Updated Jun 7, 2021
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    Mariana O. Silva; Mariana O. Silva; Laís Mota; Mirella M. Moro; Mirella M. Moro; Laís Mota (2021). MusicOSet: An Enhanced Open Dataset for Music Data Mining [Dataset]. http://doi.org/10.5281/zenodo.4904639
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    zip, binAvailable download formats
    Dataset updated
    Jun 7, 2021
    Dataset provided by
    Zenodohttp://zenodo.org/
    Authors
    Mariana O. Silva; Mariana O. Silva; Laís Mota; Mirella M. Moro; Mirella M. Moro; Laís Mota
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    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   |
  4. Live music revenue in MENA 2015, by country

    • statista.com
    Updated Apr 15, 2015
    + more versions
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    Statista (2015). Live music revenue in MENA 2015, by country [Dataset]. https://www.statista.com/statistics/666310/live-music-sales-mena-country/
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    Dataset updated
    Apr 15, 2015
    Dataset authored and provided by
    Statistahttp://statista.com/
    Time period covered
    2015
    Area covered
    MENA, Africa
    Description

    The graph presents live music revenue in the Middle East and North Africa in 2014, broken down by country. According to the data, live music revenue in the United Arab Emirates amounted to ** million U.S. dollars in 2015.

  5. Z

    Data from: MGD+: An Enhanced Music Genre Dataset with Success-based Networks...

    • data.niaid.nih.gov
    Updated Jun 28, 2023
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    Mirella M. Moro (2023). MGD+: An Enhanced Music Genre Dataset with Success-based Networks [Dataset]. https://data.niaid.nih.gov/resources?id=zenodo_8086642
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    Dataset updated
    Jun 28, 2023
    Dataset provided by
    Mariana O. Silva
    Danilo B. Seufitelli
    Mirella M. Moro
    Gabriel P. Oliveira
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    This dataset is built by using data from Spotify. It provides a daily 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 March 2022 (downloaded from CSV files), considering 68 distinct markets.

    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

    Artist Networks: Success-based artist collaboration networks

    Artists: Some artist data

    Hit Songs: Hit Song data and features

    Charts: Enhanced data from Spotify Daily Top 200 Charts

  6. G

    Sound recording and music publishing, summary statistics

    • open.canada.ca
    • www150.statcan.gc.ca
    • +2more
    csv, html, xml
    Updated Jan 24, 2025
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    Statistics Canada (2025). Sound recording and music publishing, summary statistics [Dataset]. https://open.canada.ca/data/en/dataset/59a2eebc-1d92-43c4-9212-944045dc25b6
    Explore at:
    csv, html, xmlAvailable download formats
    Dataset updated
    Jan 24, 2025
    Dataset provided by
    Statistics Canada
    License

    Open Government Licence - Canada 2.0https://open.canada.ca/en/open-government-licence-canada
    License information was derived automatically

    Description

    The summary statistics by North American Industry Classification System (NAICS) which include: operating revenue (dollars x 1,000,000), operating expenses (dollars x 1,000,000), salaries wages and benefits (dollars x 1,000,000), and operating profit margin (by percent), of record production and integrated record production/distribution (NAICS 512210 & 512220), music publishers (NAICS 512230), sound recording studios (NAICS 512240), and other sound recording industries (NAICS 512290), annual, for five years of data.

  7. o

    US Chart Hits 1950-2019 with Lyrics

    • opendatabay.com
    .undefined
    Updated Jul 7, 2025
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    Datasimple (2025). US Chart Hits 1950-2019 with Lyrics [Dataset]. https://www.opendatabay.com/data/ai-ml/5c92da56-152e-41aa-9630-0cb38c45f08a
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    .undefinedAvailable download formats
    Dataset updated
    Jul 7, 2025
    Dataset authored and provided by
    Datasimple
    License

    CC0 1.0 Universal Public Domain Dedicationhttps://creativecommons.org/publicdomain/zero/1.0/
    License information was derived automatically

    Area covered
    Entertainment & Media Consumption, United States
    Description

    This dataset provides a curated collection of the top 10 popular songs from the United States for each year, spanning from 1950 to 2019. It aims to offer insights into social patterns and cultural shifts over these decades within the US, and by extension, globally. Each entry includes the song's chart year, its rank, the performing artist(s), the song title, and the full lyrics. While most lyrics are in English, a few are in other languages, and some entries do not include lyrics.

    Columns

    • year: The specific year the song entered or was featured in the chart.
    • rank: The song's position in the annual chart, without normalisation.
    • artist: The name(s) of the artist(s) who performed the song, without normalisation.
    • song: The title or name of the song.
    • lyrics: The full lyrics of the song, with a pipe character ( | ) indicating a new line.

    Distribution

    The dataset is provided in CSV format. It contains 5 columns and 700 rows, representing 10 top songs for each of the 70 years covered.

    Usage

    This dataset is ideal for: * Analysing social and cultural trends: Explore how popular music reflects societal changes over seven decades. * Musicology and historical research: Study the evolution of music styles, popular artists, and lyrical themes from 1950 to 2019. * Natural Language Processing (NLP): Utilise the lyrics for text analysis, sentiment analysis, topic modelling, or training language models. * Data visualisation: Create visualisations to illustrate music popularity trends, artist dominance, and lyrical content changes over time.

    Coverage

    • Geographic Scope: United States.
    • Time Range: From 1950 to 2019.
    • Data Availability Notes: The dataset includes 10 top songs for each year within this 70-year period. While most lyrics are in English, there is at least one non-English entry (e.g., Korean), and a small number of entries do not contain lyrics.

    License

    CC0

    Who Can Use It

    This dataset is suitable for: * Researchers and Academics: Those studying sociology, cultural history, linguistics, or music theory looking for a structured dataset of popular culture. * Data Scientists and Analysts: Individuals interested in applying NLP techniques to music lyrics or performing time-series analysis on music trends. * Developers: Those building applications related to music discovery, historical archives, or lyrical analysis. * Music Enthusiasts: Anyone with an interest in the history of popular music in the US.

    Dataset Name Suggestions

    • US Chart Hits 1950-2019 with Lyrics
    • American Pop Music Archive (1950-2019)
    • Historical US Song Chart Data
    • Decades of US Top Songs & Lyrics
    • US Music Chart Evolution (1950-2019)

    Attributes

    Original Data Source: Top US Songs from 1950 to 2019, w. lyrics

  8. E

    Amazon Music Statistics By Revenue, Users And Downloads (2025)

    • electroiq.com
    Updated Jul 2, 2025
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    Electro IQ (2025). Amazon Music Statistics By Revenue, Users And Downloads (2025) [Dataset]. https://electroiq.com/stats/amazon-music-statistics/
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    Dataset updated
    Jul 2, 2025
    Dataset authored and provided by
    Electro IQ
    License

    https://electroiq.com/privacy-policyhttps://electroiq.com/privacy-policy

    Time period covered
    2022 - 2032
    Area covered
    Global
    Description

    Introduction

    Amazon Music Statistics: Amazon Music is a popular music streaming platform, offering a variety of services, including Amazon Music Unlimited, Amazon Prime Music, and Amazon Music HD. As of 2023, Amazon Music boasts over 80 million songs in its catalog, providing a wide range of music options across genres. The service is available in more than 50 countries and is integrated with Amazon's smart devices, like Echo and Fire TV. Amazon Music Unlimited, the premium version of the service, offers access to an even larger selection of over 90 million songs. The platform also supports high-definition audio for subscribers of Amazon Music HD, with tracks available in lossless, CD-quality audio.

    Amazon Music has seen steady growth, with recent reports suggesting that it has gained a significant share of the global streaming market, though it still trails behind competitors like Spotify and Apple Music. Additionally, Amazon Music offers personalized playlists and radio stations, enhancing the user experience through tailored recommendations. This article will discuss the important Amazon Music statistics and key trends.

  9. The WASABI Dataset and RDF Knowledge Graph

    • zenodo.org
    • data.niaid.nih.gov
    tar
    Updated Feb 28, 2022
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    Michel Buffa; Elena Cabrio; Michael Fell; Fabien Gandon; Fabien Gandon; Alain Giboin; Alain Giboin; Romain Hennequin; Romain Hennequin; Fabrice Jauvat; Elmahdi Korfed; Franck Michel; Franck Michel; Johan Pauwels; Johan Pauwels; Guillaume Pellerin; Maroua Tikat; Marco Winckler; Marco Winckler; Michel Buffa; Elena Cabrio; Michael Fell; Fabrice Jauvat; Elmahdi Korfed; Guillaume Pellerin; Maroua Tikat (2022). The WASABI Dataset and RDF Knowledge Graph [Dataset]. http://doi.org/10.5281/zenodo.5603369
    Explore at:
    tarAvailable download formats
    Dataset updated
    Feb 28, 2022
    Dataset provided by
    Zenodohttp://zenodo.org/
    Authors
    Michel Buffa; Elena Cabrio; Michael Fell; Fabien Gandon; Fabien Gandon; Alain Giboin; Alain Giboin; Romain Hennequin; Romain Hennequin; Fabrice Jauvat; Elmahdi Korfed; Franck Michel; Franck Michel; Johan Pauwels; Johan Pauwels; Guillaume Pellerin; Maroua Tikat; Marco Winckler; Marco Winckler; Michel Buffa; Elena Cabrio; Michael Fell; Fabrice Jauvat; Elmahdi Korfed; Guillaume Pellerin; Maroua Tikat
    License

    Attribution-NonCommercial 4.0 (CC BY-NC 4.0)https://creativecommons.org/licenses/by-nc/4.0/
    License information was derived automatically

    Description

    The WASABI Dataset and RDF Knowledge Graph is rich dataset describing more than 2 millions commercial songs, 200K albums and 77K artists (mainly from pop/rock culture). It comprises data extracted from music databases on the Web, and resulting from the processing of song lyrics and from audio analysis.

    This is version 2 of the dataset. It consists of two representation formats:

    • The JSON format provides all data extracted from the MongoDB database that backs up the web application
    • The RDF Knowledge Graph that represents the same data following the WASABI ontology.

    WASABI project homepage: http://wasabihome.i3s.unice.fr/

    Github: https://github.com/micbuffa/WasabiDataset

  10. Weekly time spent with music 2015-2019

    • statista.com
    Updated May 29, 2024
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    Statista (2024). Weekly time spent with music 2015-2019 [Dataset]. https://www.statista.com/statistics/828195/time-spent-music/
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    Dataset updated
    May 29, 2024
    Dataset authored and provided by
    Statistahttp://statista.com/
    Area covered
    United States
    Description

    Data on the amount of time spent listening to music in the United States in 2019 revealed that consumers spent an average of 26.9 hours per week enjoying their favorite tunes, down from 28.3 hours per week in 2018. Weekly consumption in 2017 was even higher at 32.1 hours.

  11. ChoCo: the Chord Corpus

    • zenodo.org
    • data.niaid.nih.gov
    zip
    Updated Mar 8, 2023
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    Jacopo de Berardinis; Andrea Poltronieri; Albert Meroño-Peñuela; Valentina Presutti; Jacopo de Berardinis; Andrea Poltronieri; Albert Meroño-Peñuela; Valentina Presutti (2023). ChoCo: the Chord Corpus [Dataset]. http://doi.org/10.5281/zenodo.7706751
    Explore at:
    zipAvailable download formats
    Dataset updated
    Mar 8, 2023
    Dataset provided by
    Zenodohttp://zenodo.org/
    Authors
    Jacopo de Berardinis; Andrea Poltronieri; Albert Meroño-Peñuela; Valentina Presutti; Jacopo de Berardinis; Andrea Poltronieri; Albert Meroño-Peñuela; Valentina Presutti
    Description

    Overview

    We are happy to announce the new release of ChoCo, which has now reached v1.0.0. This ChoCo release includes updates and improvements to ChoCo's licensing and symbolic collections. We have adopted a dual licensing scheme for ChoCo's data, and regenerated all symbolic collections following improvements to the JAMifier, providing a more meaningful and complete version of JAMS files.

    📄 Licensing Updates

    We have adopted a dual licensing scheme for ChoCo's data. Only three subsets are released using the CC-BY-NC-SA 4.0 license, while all the others follow the CC-BY 4.0 license. We include the LICENSE.md file in the release which contains all the details.

    🎼 Symbolic Collection Updates

    All symbolic collections have been regenerated following improvements to the JAMifier. Measures and beats now always start from 1, and time signatures are included in the JAMS files. Temporal information such as onset and duration is now expressed in measures and beats, making our JAMS files and KG more meaningful.

    ❇️ Knowledge Graph Updates

    The Knowledge Graph was regenerated to reflect the changes made to the JAMifier and the JAMS files produced.

  12. f

    Main Dataset for "Evolution of Popular Music: USA 1960–2010"

    • figshare.com
    txt
    Updated Jan 19, 2016
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    Matthias Mauch (2016). Main Dataset for "Evolution of Popular Music: USA 1960–2010" [Dataset]. http://doi.org/10.6084/m9.figshare.1309953.v1
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    txtAvailable download formats
    Dataset updated
    Jan 19, 2016
    Dataset provided by
    figshare
    Authors
    Matthias Mauch
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    This is a large file (~20MB) called EvolutionPopUSA_MainData.csv, in comma-separated data format with column headers. Each row corresponds to a recording. The file is viewable in any text editor, and can also be opened in Excel or imported to other data processing programs. Below is a list of the column headers, with annotations. public_idunique ID of the recording artist_namename of the recording artist artist_name_cleanartist name all upper case, no spaces, with secondary artists ("featuring") removed. track_namename of the track, i.e. usually name of the song first_entrydate of the first entry into the Billboard Hot 100 quarter, year, fiveyear, decadetransformations of first_entry to coarser time periods eraera the track belongs to (1,...,4), as determined by Foote segmentation on the PC data (see below) clustercluster membership of the track, as derived by k-means clustering on the PC data (see below) hTopic_01, ... , hTopic_08harmonic Topic weights, see description in the paper tTopic_01, ... , tTopic_08timbral Topic weights, see description in the paper PC1, ... , PC14principal components of the harmonic and timbral Topics harm_…193 columns of chord change counts; the chord change is indicated in the column label (e.g. harm_M.2.M means major chord followed by another major chord 2 semitones up). timb_01, ... , timb_3535 columns of timbre class counts (see description in supplementary information)

  13. o

    Comusic: Good things come to those who collaborate

    • explore.openaire.eu
    • data.niaid.nih.gov
    Updated Apr 8, 2019
    + more versions
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    Mariana O. Silva; Laís Mota; Mirella M. Moro (2019). Comusic: Good things come to those who collaborate [Dataset]. http://doi.org/10.5281/zenodo.4904676
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    Dataset updated
    Apr 8, 2019
    Authors
    Mariana O. Silva; Laís Mota; Mirella M. Moro
    Description

    Comusic is an ongoing project that seeks to study the impact of collaboration networks' topological features on musical success. To that end, we analyze and identify such characterizations in a musical success-based network; that is, a network composed only of successful artists. Our findings offer a new perspective on success in the music industry, unraveling how collaboration profiles can contribute to an artist's popularity. Our Methodology Initially, using data from Billboard and the Spotify platform, we model a "successful" collaborative network and apply tools of network science to study its structure. By means of topological metrics, we defined four categories of collaboration profiles and, applying a clustering algorithm, we identified three communities with different collaboration patterns and notable discrepancies in musical success levels. Then, we conduct a statistical correlation analysis to evaluate the correlation between collaboration profiles and the artist's success. Our Findings By detecting cluster and their respective patterns of network collaboration, we focus on analyzing the impact of these profiles on successful musical artists. Considering topological metrics, we define four main categories of collaboration profiles: Interaction, Distance, Influence and Similarity. Among them, we find that the first three affect musical success more intensely than Similarity. Our Contributions Our findings provide evidence that: there are indeed distinct success factors for music collaboration profiles that are socially measurable, and there exist common factors to successful collaboration in the music market. Furthermore, our exploratory approach based on collaborative networks can easily be extended to other areas of knowledge (e.g., arts and science). Files Successfull Network: The successful musical collaboration network. (8,88 MB) Billboard Charts: Some Billboard Charts data. (3,04 MB) Ego Networks: All the 30 ego networks. (38 KB) Time Series: All the time series. (807 KB) {"references": ["Silva, M. O., Rocha, L. M., & Moro, M. M. (2019, April). Collaboration profiles and their impact on musical success. In Proceedings of the 34th ACM/SIGAPP Symposium on Applied Computing (pp. 2070-2077)."]}

  14. T

    United States - Sources of Revenue: Print Music for Music Publishers, All...

    • tradingeconomics.com
    csv, excel, json, xml
    Updated Dec 3, 2020
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    TRADING ECONOMICS (2020). United States - Sources of Revenue: Print Music for Music Publishers, All Establishments, Employer Firms [Dataset]. https://tradingeconomics.com/united-states/sources-of-revenue-print-music-for-music-publishers-all-establishments-employer-firms-fed-data.html
    Explore at:
    xml, json, csv, excelAvailable download formats
    Dataset updated
    Dec 3, 2020
    Dataset authored and provided by
    TRADING ECONOMICS
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Time period covered
    Jan 1, 1976 - Dec 31, 2025
    Area covered
    United States
    Description

    United States - Sources of Revenue: Print Music for Music Publishers, All Establishments, Employer Firms was 194.00000 Mil. of $ in January of 2022, according to the United States Federal Reserve. Historically, United States - Sources of Revenue: Print Music for Music Publishers, All Establishments, Employer Firms reached a record high of 321.00000 in January of 2011 and a record low of 157.00000 in January of 2020. Trading Economics provides the current actual value, an historical data chart and related indicators for United States - Sources of Revenue: Print Music for Music Publishers, All Establishments, Employer Firms - last updated from the United States Federal Reserve on June of 2025.

  15. Sound recording and music publishing, summary statistics, by North American...

    • open.canada.ca
    • www150.statcan.gc.ca
    • +3more
    csv, html, xml
    Updated Jan 17, 2023
    + more versions
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    Statistics Canada (2023). Sound recording and music publishing, summary statistics, by North American Industry Classification System (NAICS), inactive [Dataset]. https://open.canada.ca/data/en/dataset/87571393-4199-492a-85ae-071a128c86fb
    Explore at:
    html, csv, xmlAvailable download formats
    Dataset updated
    Jan 17, 2023
    Dataset provided by
    Statistics Canadahttps://statcan.gc.ca/en
    License

    Open Government Licence - Canada 2.0https://open.canada.ca/en/open-government-licence-canada
    License information was derived automatically

    Description

    This table contains 76 series, with data for years 2005 - 2011 (not all combinations necessarily have data for all years), and was last released on 2015-08-12. This table contains data described by the following dimensions (Not all combinations are available): Geography (6 items: Canada; Ontario; Quebec; Atlantic provinces ...), North American Industry Classification System (NAICS) (4 items: Record production and integrated record production/distribution; Sound recording studios; Other sound recording industries; Music publishers ...), Summary statistics (4 items: Operating revenue; Operating expenses; Operating profit margin; Salaries; wages and benefits ...).

  16. P

    Amazon Digital Music Dataset

    • paperswithcode.com
    Updated Sep 26, 2024
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    Yupeng Hou; Jiacheng Li; Zhankui He; An Yan; Xiusi Chen; Julian McAuley (2024). Amazon Digital Music Dataset [Dataset]. https://paperswithcode.com/dataset/amazon-digital-music
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    Dataset updated
    Sep 26, 2024
    Authors
    Yupeng Hou; Jiacheng Li; Zhankui He; An Yan; Xiusi Chen; Julian McAuley
    Description

    This dataset includes reviews (ratings, text, helpfulness votes), product metadata (descriptions, category information, price, brand, and image features), and links (also viewed/also bought graphs).

  17. f

    Final BB Year-end charts '13-'22.xlsx

    • figshare.com
    xlsx
    Updated Jul 23, 2024
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    Thu Tran (2024). Final BB Year-end charts '13-'22.xlsx [Dataset]. http://doi.org/10.6084/m9.figshare.26357506.v1
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    xlsxAvailable download formats
    Dataset updated
    Jul 23, 2024
    Dataset provided by
    figshare
    Authors
    Thu Tran
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    The American political landscape favors autonomy and independence while dismissing vulnerability and dependency. This article examines the lyrical expressions of autonomy and vulnerability through gender, race, and genre on the Billboard Year-end charts from 2013 to 2022. In this study, autonomy and vulnerability are categorized into four categories: socioeconomics, sex, violence, and emotion. Overall, men perform socioeconomic autonomy more than women, but among men, white men are more likely to express emotional vulnerability than men of color. Conversely, women mention emotional autonomy and sexual vulnerability the most in their songs. This research hopes to show the multifaceted meaning of autonomy and vulnerability and change the sentiment toward these words by providing a deeper context behind the most well-received songs in America to see what prompts artists to be autonomous/vulnerable.

  18. Music On Demand Market Analysis, Size, and Forecast 2025-2029: North America...

    • technavio.com
    Updated Apr 25, 2025
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    Technavio (2025). Music On Demand Market Analysis, Size, and Forecast 2025-2029: North America (US and Canada), Europe (France, Germany, Italy, and UK), APAC (China, India, Japan, and South Korea), and Rest of World (ROW) [Dataset]. https://www.technavio.com/report/music-on-demand-market-industry-analysis
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    Dataset updated
    Apr 25, 2025
    Dataset provided by
    TechNavio
    Authors
    Technavio
    Time period covered
    2021 - 2025
    Area covered
    Canada, United States, Global
    Description

    Snapshot img

    Music On Demand Market Size 2025-2029

    The music on demand market size is forecast to increase by USD 72.58 billion, at a CAGR of 17.9% between 2024 and 2029.

    The market is experiencing significant growth, driven by the surge in mobile advertisement spending and the increasing adoption of cloud services. These trends are transforming the way consumers access and listen to music, with the proliferation of modified apps offering free music access. The market's dynamic landscape presents both opportunities and challenges for players. On the one hand, the rise in mobile advertisement spending opens up new avenues for monetization through targeted advertising, while the adoption of cloud services enables seamless music streaming and storage solutions. These trends are expected to attract a larger user base, particularly among the younger demographic.
    However, the market is not without its challenges. The increasing competition from free music streaming services and piracy concerns pose significant threats to market growth. Additionally, the need to secure licensing agreements and comply with copyright laws adds complexity to the business model. Companies seeking to capitalize on market opportunities must navigate these challenges effectively, focusing on innovation, differentiation, and strategic partnerships.
    

    What will be the Size of the Music On Demand Market during the forecast period?

    Explore in-depth regional segment analysis with market size data - historical 2019-2023 and forecasts 2025-2029 - in the full report.
    Request Free Sample

    In the ever-evolving music industry, the on-demand market continues to shape the landscape with its dynamic nature. This sector encompasses various applications, including streaming services, web applications, and mobile applications, all offering users uninterrupted access to a vast library of music. Data security is paramount in this industry, with user accounts and privacy policies ensuring the protection of personal information. Music charts, a reflection of user preferences, are updated in real-time, showcasing the evolving trends. Content moderation and copyright infringement are ongoing concerns, with metadata tagging and lossy audio enabling efficient content delivery while maintaining legal compliance. Subscription revenue models and payment gateways facilitate seamless transactions, while royalty payments ensure fair compensation for artists.

    User experience (UX) is a critical differentiator, with recommendation algorithms, playlist creation, and on-demand playback enhancing the listening experience. High-resolution audio and podcast integration cater to diverse user preferences, while server infrastructure and audio decoding ensure optimal performance. Advertising revenue and customer support provide additional revenue streams, with social features and API integrations fostering user engagement. The integration of smart speakers and audio streaming protocols further expands the market's reach. In this continuously unfolding market, data analytics and user segmentation enable targeted marketing efforts, while A/B testing optimizes user experience. The incorporation of cloud computing, lossless audio, and user interface (UI) design enhances the overall functionality and accessibility of on-demand music services.

    How is this Music On Demand Industry segmented?

    The music on demand industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments.

    Type
    
      Music streaming
      Radio on demand
    
    
    End-user
    
      Individual users
      Commercial users
    
    
    Method
    
      Premium subscription
      Ad-supported free model
    
    
    Content Type
    
      Audio
      Video
    
    
    Platform
    
      Smartphones
      PCs
      Tablets
      Smart TVs
      Others
    
    
    Geography
    
      North America
    
        US
        Canada
    
    
      Europe
    
        France
        Germany
        Italy
        UK
    
    
      APAC
    
        China
        India
        Japan
        South Korea
    
    
      Rest of World (ROW)
    

    By Type Insights

    The music streaming segment is estimated to witness significant growth during the forecast period.

    In the dynamic music industry, two primary types of on-demand streaming services have emerged: paid and free. The paid music streaming segment is projected to expand significantly during the forecast period, driven by the growing preference for an uninterrupted listening experience and access to advanced features. These premium services offer benefits such as no advertisements, personalized song recommendations, a vast music catalog, playlist customization, and third-party integrations, like Amazon Alexa. As a result, an increasing number of consumers are transitioning from free subscriptions to paid ones, which are typically offered on a subscription basis. Ensuring data security and user experience (UX) are top priorities f

  19. Brazil regional spotify charts

    • kaggle.com
    zip
    Updated Apr 14, 2024
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    Filipe Moura (2024). Brazil regional spotify charts [Dataset]. https://www.kaggle.com/datasets/filipeasm/brazil-regional-spotify-charts
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    zip(10117250 bytes)Available download formats
    Dataset updated
    Apr 14, 2024
    Authors
    Filipe Moura
    License

    MIT Licensehttps://opensource.org/licenses/MIT
    License information was derived automatically

    Area covered
    Brazil
    Description

    This dataset provides a regional detailed overview of the Brazil digital music consumption in Spotify between 2021-2023. It includes acoustic features and all genres/artists that are listened at least one time in those years. The data is provided by the Spotify API for Developers and the SpotifyCharts wich are used to collect the acoustic features and the summarized most listened songs in city, respectively.

    Data description

    It contemplates 17 cities of 16 different states in Brazil that achieved 5190 unique tracks, 487 different genres and 2056 artists. The covered cities are: Belém, Belo Horizonte, Brasília, Campinas, Campo Grande, Cuiabá, Curitiba, Florianópolis, Fortaleza, Goiânia, Manaus, Porto Alegre, Recife, Rio de Janeiro, Salvador, São Paulo and Uberlândia. Each city has 119 different weekly's charts wich the week period is described by the file name.

    Acoustic features

    The covered acoustic features are provided by Spotify and are described as: - Acousticness: Measures from 0.0 to 1.0 of wheter the track is acoustic; 1.0 indicates a totally acoustic song and 0.0 means a song without any acoustic element - 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: 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. - 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. Values above 0.5 are intended to represent instrumental tracks, but confidence is higher as the value approaches 1.0. - 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. - 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. - Loudness: 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. - 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: 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. - 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". - 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).

    Data Science Applications:

    • Time Series Analysis: Identify seasonal behaviors and the deviation of each city during those 2 years
    • Trend Analysis: Identify patterns and trends in digital music consumption based in genres and/or acoustic features in each city to understand seasonal changes
    • Clustering Tasks: Group cities based on genre and/or acoustic features to identify different regional patterns between Brazil's regions and describe the difference between each group
  20. T

    United States - Sources of Revenue: Sale of Recordings for Music Publishers,...

    • tradingeconomics.com
    csv, excel, json, xml
    Updated Jan 2, 2021
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    TRADING ECONOMICS (2021). United States - Sources of Revenue: Sale of Recordings for Music Publishers, All Establishments, Employer Firms [Dataset]. https://tradingeconomics.com/united-states/sources-of-revenue-sale-of-recordings-for-music-publishers-all-establishments-employer-firms-fed-data.html
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    xml, excel, json, csvAvailable download formats
    Dataset updated
    Jan 2, 2021
    Dataset authored and provided by
    TRADING ECONOMICS
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Time period covered
    Jan 1, 1976 - Dec 31, 2025
    Area covered
    United States
    Description

    United States - Sources of Revenue: Sale of Recordings for Music Publishers, All Establishments, Employer Firms was 293.00000 Mil. of $ in January of 2022, according to the United States Federal Reserve. Historically, United States - Sources of Revenue: Sale of Recordings for Music Publishers, All Establishments, Employer Firms reached a record high of 293.00000 in January of 2022 and a record low of 38.00000 in January of 2015. Trading Economics provides the current actual value, an historical data chart and related indicators for United States - Sources of Revenue: Sale of Recordings for Music Publishers, All Establishments, Employer Firms - last updated from the United States Federal Reserve on June of 2025.

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Sergio Oramas; Sergio Oramas; Vito Claudio Ostuni; Gabriel Vigliensoni; Gabriel Vigliensoni; Vito Claudio Ostuni (2023). Sound and music recommendation with knowledge graphs [dataset] [Dataset]. http://doi.org/10.34810/data444

Data from: Sound and music recommendation with knowledge graphs [dataset]

Related Article
Explore at:
txt(3751), zip(56553416)Available download formats
Dataset updated
Oct 9, 2023
Dataset provided by
CORA.Repositori de Dades de Recerca
Authors
Sergio Oramas; Sergio Oramas; Vito Claudio Ostuni; Gabriel Vigliensoni; Gabriel Vigliensoni; Vito Claudio Ostuni
License

https://dataverse.csuc.cat/api/datasets/:persistentId/versions/1.0/customlicense?persistentId=doi:10.34810/data444https://dataverse.csuc.cat/api/datasets/:persistentId/versions/1.0/customlicense?persistentId=doi:10.34810/data444

Description

Music Recommendation Dataset (KGRec-music). Number of items: 8,640. Number of users: 5,199. Number of items-users interactions: 751,531. All the data comes from songfacts.com and last.fm websites. Items are songs, which are described in terms of textual description extracted from songfacts.com, and tags from last.fm. Files and folders in the dataset: /descriptions: In this folder there is one file per item with the textual description of the item. The name of the file is the id of the item plus the ".txt" extension. /tags: In this folder there is one file per item with the tags of the item separated by spaces. Multiword tags are separated by -. The name of the file is the id of the item plus the ".txt" extension. Not all items have tags, there are 401 items without tags. implicit_lf_dataset.txt: This file contains the interactions between users and items. There is one line per interaction (a user that downloaded a sound in this case) with the following format, fields in one line are separated by tabs: user_id /t sound_id /t 1 /n. Sound Recommendation Dataset (KGRec-sound). Number of items: 21,552. Number of users: 20,000. Number of items-users interactions: 2,117,698. All the data comes from Freesound.org. Items are sounds, which are described in terms of textual description and tags created by the sound creator at uploading time. Files and folders in the dataset: /descriptions: In this folder there is one file per item with the textual description of the item. The name of the file is the id of the item plus the ".txt" extension. /tags: In this folder there is one file per item with the tags of the item separated by spaces. The name of the file is the id of the item plus the ".txt" extension. downloads_fs_dataset.txt: This file contains the interactions between users and items. There is one line per interaction (a user that downloaded a sound in this case) with the following format, fields in one line are separated by tabs: /nuser_id /t sound_id /t 1 /n. Two different datasets with users, items, implicit feedback interactions between users and items, item tags, and item text descriptions are provided, one for Music Recommendation (KGRec-music), and other for Sound Recommendation (KGRec-sound).

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