2 datasets found
  1. Z

    MGD: Music Genre Dataset

    • data.niaid.nih.gov
    • zenodo.org
    Updated May 28, 2021
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    Danilo B. Seufitelli (2021). MGD: Music Genre Dataset [Dataset]. https://data.niaid.nih.gov/resources?id=zenodo_4778562
    Explore at:
    Dataset updated
    May 28, 2021
    Dataset provided by
    Anisio Lacerda
    Danilo B. Seufitelli
    Gabriel P. Oliveira
    Mariana O. Silva
    Mirella M. Moro
    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} }

  2. Number of internet users in China 2005-2024

    • statista.com
    Updated Mar 18, 2025
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    Statista (2025). Number of internet users in China 2005-2024 [Dataset]. https://www.statista.com/statistics/265140/number-of-internet-users-in-china/
    Explore at:
    Dataset updated
    Mar 18, 2025
    Dataset authored and provided by
    Statistahttp://statista.com/
    Area covered
    China
    Description

    In 2024, China reported adding 16 million new users to its massive 1.1 billion internet population. The first half-year data in 2024 revealed that nearly half of the new internet users were between 10 and 18 years old, while a third were older adults aged above 50 years. The largest online community In 2023, China accounted for about one-fifth of the 5.5 billion internet users worldwide. However, compared to its total population, China’s internet penetration rate is lower than in other Asian countries. Penetration rates in both South Korea and Japan were extensively higher. The market potentials Internet usage in China is further characterized by a large regional discrepancy. In rural regions, the internet access rate is much lower than the national level. On the other side, the Chinese market became a mobile-first nation. Since 2014, more Chinese people have accessed the internet via mobile device than computers. The number of mobile internet users in China increased steadily over the previous decade.

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Share
FacebookFacebook
TwitterTwitter
Email
Click to copy link
Link copied
Close
Cite
Danilo B. Seufitelli (2021). MGD: Music Genre Dataset [Dataset]. https://data.niaid.nih.gov/resources?id=zenodo_4778562

MGD: Music Genre Dataset

Explore at:
Dataset updated
May 28, 2021
Dataset provided by
Anisio Lacerda
Danilo B. Seufitelli
Gabriel P. Oliveira
Mariana O. Silva
Mirella M. Moro
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} }

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