During a 2017 survey, over ** percent percent of respondents from Argentina stated that they listened to Argentine rock either regularly or from time to time. Second most popular genre was cumbia. Argentine music market is expected to generate *** million U.S. dollars in revenue in 2021.

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} }

This statistic shows a ranking of the most popular music styles and genres for music CDs purchased within the past six months in Germany from 2017 to 2020. In 2020, **** percent of Germans aged 14 years and older had bought a rock music CD for themselves within the last half year.

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

The statistic displays the results of a survey conducted by Cint on the distribution of music genres listened to in Belgium in 2017 and 2018. In 2018, **** percent of respondents stated that they like to listen to pop music.

Skateboard Music Genre Distribution

Genre-Usage by 32 Skateboard Companies (2017)

By data.world's Admin [source]

About this dataset

This folder contains datasets from The Pudding essay The Good, the Rad, and the Gnarly published in June 2018 which provides an in-depth examination of skateboard music genre usage across multiple companies. Not only does this dataset provide insight into trends and patterns in term of genre usage over time, but it also allows users to explore down to the artist level.

The folder contains two files: time_series.tsv and waffle.csv. The former contains data on ingredient lists from 211 chocolate chip cookie recipes alongside their scaled yield, while the latter consists of skateboard company genre usages percents multiplied by 1000 along with associated genres or fake genres used for testing purposes. Both datasets can be used to gain greater understanding into the inner workings of skateboard music taste and trends while still being able to examine particular artists' usage across time and companies if desired

Detailed below are column descriptions for both files:

• time_series.tsv: This file is made up of a number of columns that include 'genre', 'time', 'percentage used (% p)', 'maximum percentage across all genres (% maxp)', 'a peak (p_peak)', and finally a moving average percentage use (p_smooth). Each column is valuable when engaging with this dataset's layerd approach to exploring skateboard music trends over time alongside individual artists growing popularity compared with others in similar styles or even more broad categories such as Hip Hop, Electronic Music etc..

• waffle.csv: This file consists four columns - 'source','value','company','fake genre' - each helping paint a picture about how specific companies utilize various aspects within broader genres like Classic Rock, Indie/Alternative Music etc.. allowing viewers to delve right on down into specifics like exact artist or 80's metallic band etc.. Utilizing this dataset demands attention so as not mixup what particular genre using what company contributing which portion value wise relative overall favorite amongst boardsports enthusiast globally!

  Both these datasets are characterized by their temporal applicability that scale concerts pre-December 2017; hence allowing viewers engage framework bar none! All data available under the MIT License[link]!

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How to use the dataset

In this guide, we will take a look at how you can use this data set to better understand music in the skateboarding community.

Step 1: Exploring the Data Set

The first step is to get familiar with all of the columns contained in this dataset. The following table provides an overview of what is included:

| Header | Description | Data Type | |-------------|------------------------------------------------------------|-----------| | source | Genre of music from broad genre bins | text |
| value | Percentage of associated genres used for corresponding company, multiplied by 1000| number
| company | Skateboard company | text

Using these headers, you can examine which genres are most popular amongst different companies, allowing skaters to draw comparisons between them. This will help skaters form an understanding as to why some companies might enjoy certain music more than others. Additionally, you can track certain trends over time using this dataset - allowing insights into which genres may be becoming more or less popular on each touring team or within each brands video output over time. Finally, if it becomes necessary due to licensing issues or other restrictions one brand places upon its releases or press materials you may ...

In 2018, hip-hop and rap music accounted for 21.7 percent of total music consumption in the United States, more than double the percentage of R&B music sales. Other highly popular genres included pop and rock music, whereas just 1.1 percent of all music sold in the U.S. in 2018 was jazz.

Why are some genres more popular than others? 

Whilst music is a highly subjective medium in terms of the listener’s taste and preferences, the top genres in terms of consumption tend not to fluctuate heavily. The catchiness and familiarity of pop music is appealing to a wide range of music fans. Pop songs tend to be easy to listen to and remember, usually feature simple, snappy lyrics to avoid polarizing listeners, making pop overall less divisive than other genres because it is designed to generate mass appeal.

Conversely, religious music by its very nature is a niche genre in that it encompasses, describes or advocates certain beliefs, giving it the equal ability to alienate some listeners while appealing enormously to others, depending on their religious stance.

The hit genre of 2018 was hip-hop and rap, a music style notorious for its tendency to divide listeners. Singer Drake arguably influenced sales within the genre that year, with ‘Scorpion’ topping the list of best-selling albums in the United States based on total streams and ‘Scary Hours’ also making the top ten. Drake came tenth in the list of most successful music tours in North America, with revenue from his live concerts amounting to 79 million U.S. dollars, and second in the ranking was Jay-Z and Beyoncé with 166.4 million dollars in revenue, artists whose music is also strongly aligned with the rap and hip-hop genre.

Other artists in the genre who achieved significant influence in 2018 include Kendrick Lamar, Childish Gambino, Cardi B, Travis Scott and Post Malone, many of whom released songs that year which garnered hundreds of millions of audio streams. The sheer amount of hip-hop and rap music flooding the music industry has had a profound effect on the genre’s popularity, and musicians in the category tend to be prolific songwriters and active social media users. Equally, artists in the genre are arguably passionate about creating music which challenges social norms in a way that rock music has always been famous for.

The most common music listening location among Gen Z and Millennial adults in the United States was revealed to be the home, though ** percent of Gen Z respondents and ** percent of Millennials also reported listening to music in a car. The home and the car were also the most popular listening locations among adults aged 35 or above, though listening to music on public transport, at the gym, or at music venues was less popular among older adults.  

How is music consumption affected by the generation of the listener? 

Gen Z and Millennial consumers tend to be more digitally focused than their older counterparts. Whilst Millennials are generally acknowledged as being digital natives, Gen Z is more focused on mobile devices, but in both cases it is clear that the ability to consume media on the go is important. Equally, genre preferences are affected by age, and not necessarily in ways one might expect. 

For example, a survey held in July 2018 revealed that older generations are generally much more likely than younger listeners to enjoy classic rock - not surprising, given that the genre is aligned with music produced between the ***** and *****. However, the data also showed that respondents aged 16 to 19 years old and adults aged 65 or above had something unexpected in common. Listeners in these age categories were similarly likely to name show music and musicals as their favorites, whereas adults aged 25 to 64 years old had little interest in such music. 

When it comes to how Americans listen to music, regardless of genre, habits and preferences also differ with age. The majority of younger music fans’ listening time is dedicated to Spotify, whereas older adults prefer to stream their music via Amazon Prime Music and YouTube.

Context

Top 100 songs of each year on Spotify from 2010 to 2019.

Spotify API documentation: https://developer.spotify.com/documentation/web-api/reference/#/operations/get-several-audio-features

Content

Data extracted from: - Spotify Top Hits of 2010: https://open.spotify.com/playlist/37i9dQZF1DXc6IFF23C9jj?si=ccd192867a1a4c40 - Spotify Top Hits of 2011: https://open.spotify.com/playlist/37i9dQZF1DXcagnSNtrGuJ?si=c60cb6e3988e4989 - Spotify Top Hits of 2012: https://open.spotify.com/playlist/37i9dQZF1DX0yEZaMOXna3?si=36ac956e89074f55 - Spotify Top Hits of 2013: https://open.spotify.com/playlist/37i9dQZF1DX3Sp0P28SIer?si=96dfc6cefd19411d - Spotify Top Hits of 2014: https://open.spotify.com/playlist/37i9dQZF1DX0h0QnLkMBl4?si=d1f91043b80f4cc1 - Spotify Top Hits of 2015: https://open.spotify.com/playlist/37i9dQZF1DX9ukdrXQLJGZ?si=777880d80a5646e9 - Spotify Top Hits of 2016: https://open.spotify.com/playlist/37i9dQZF1DX8XZ6AUo9R4R?si=512201d3bafb4952 - Spotify Top Hits of 2017: https://open.spotify.com/playlist/37i9dQZF1DWTE7dVUebpUW?si=aeff30eb6a9141b8 - Spotify Top Hits of 2018: https://open.spotify.com/playlist/37i9dQZF1DXe2bobNYDtW8?si=61a2b8573cb14306 - Spotify Top Hits of 2019: https://open.spotify.com/playlist/37i9dQZF1DWVRSukIED0e9?si=d6b665ed0c394160

With the use of: http://organizeyourmusic.playlistmachinery.com/ Data was collected on March 20, 2022

This statistic presents the share of the top 30 streams on Spotify worldwide as of November 2017, by genre. According to the source, ** percent of the top-streamed songs worldwide in this period were pop songs.

Hello fellow coders,

This huge dataset contains all the songs in Spotify's Daily Top 200 charts in 35+1 (global) countries around the world for a period of over 3 years (2017-2020).

We are 6 university students who used this database for our Big Data class, hence we already did most (if not all) of the necessary data cleaning. Our research question was to understand the impact of many variables on a songs' popularity and see if there was any significant national difference.

You can find 2 files attached:

• "Database to Calculate Popularity" includes all the daily entries (8mln+) for the songs which made it to the top 200 . Among these data, quite intuitively, you will find the same song being in the charts for more than one day. We then created a popularity score, unique for a given song in a given country, which took into account the position in the charts and the days it stayed there.

• "Final Database" includes many data for each song. It aggregates the populairty for songs into a single score for each. For each song several variables were retrieved by using Spotify's API (such as artist, country, genre, ...)

The following notes will clarify doubts you may have on data, if you still have some feel free to drop a question in the discussion section!

NB you can see that the 8+mln songs of the first database are reduced to "only" tens of thousands in the other. Why? This is because the POPULARITY score was created by US and aggregated into a single score the whole period a same song stayed in the charts of the same country. The popularity given by Spotify takes into account the time at which data are seen, hence a song which dominated the charts a few years back now scores very low in this parameter. This is why we created our new score which includes the number of days a song stayed in the charts and at which position, adjusted with a modificator to give more weight to top positions.

NB We calculated popularity as follows: we assigned a score from 1 to 200 to each song. #1 Ranked gets 200, #2 ranked gets 199, … , #200 ranked gets 1. we multiplied by a modificator which is 3 for #1 2.2 for the #2 1.7 for the #3 1.3 for #4-10 1 for # 11-50 0.85 for #51-100 0.8 for #101-200 we did it for every day we summed up the daily score for a SAME song in a SAME country (NB same song in different country has different popularity)

NB in the final database the title of a same song is repeated more than once. Why? Because we recorded the songs WITHIN each of the 35+1 countries, hence if a same song became popular in more than one county (which is the case for most songs) it will figure in the charts of both countries. Nonethless, the popularity score of the SAME song could be DIFFERENT in two different countries, as each of countries has its own tastes in music!

NB we used NLP and LDA techniques to assign a Tone, Emotion and Topic to the songs in ENGLISH SPEAKING COUNTRIES only. Most of them were correctly recorded but in some cases the lyrics could not be retrieved hence the data are missing.

Below you can find a description of every variable: -Title: Name of a song -URI: Unique identifier of a song created by Spotify -Country Global and 34 countries where Spotify operates, namely Argentina, Australia, Austria, Belgium, Brazil, Canada, Chile, Colombia, Costa Rica, Denmark, Ecuador, Finland, France, Germany, Great Britain, Indonesia, Ireland, Italy, Mexico, Malaysia, Netherlands, New Zealand, Norway, Peru, Philippines, Poland, Portugal, Singapore, Spain, Sweden, Switzerland, Taiwan, Turkey, USA -Popularity_ The popularity score calculated taking into account both the number of days a song stayed in the Top200 and the position it stayed in every day, weighting more the top positions -Artist: Name of the songs' artist -Album/Single: Whether the song was published as a single or as part of an album or compilation -Genre: The predominant genre of an artist according to Spotify’s classification -Artist_followers: The number of followers the artist has on Spotify on the 5th of November 2020 -Explicit: Whether the song is rated as ‘Parental Advisory Explicit Content’ or not -Album: Name of the album the song belongs to -Release_date: Date on which the song was published -Track_number: The position of the song on its respective album -Track _album: Total songs present in the album -Danceability: 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: It 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. Perceptual features contributing to this attribute include dynamic range, perceived loudness, timbre, onset rate, and general entropy -Key: The estim...

This statistic displays the results of a survey about the most popular genres of South Korean pop music (K-pop) in France in 2017. In 2017, about **** percent of respondents in France reported that rap or hip-hop was their favorite K-pop genre.

This statistic displays the results of a survey about the most popular genres of South Korean pop music (K-pop) in Turkey in 2017. In 2017, about **** percent of respondents in Turkey reported that rap or hip-hop was their favorite K-pop genre.

Context

During some studies developed using Million Song Dataset (https://labrosa.ee.columbia.edu/millionsong/), we needed to clean the subset up to get data in a convinient way to our interests.

Content

This data consists of the results of cleaning up the MSD subset, available in https://labrosa.ee.columbia.edu/millionsong/pages/getting-dataset#subset.

Acknowledgements

This is a work developed by Marcos Pedro Ferreira Leal (mleal@ime.usp.br), Shayenne da Luz Moura (shayenne@ime.usp.br) e Thais Rodrigues Neubauer (thais.neubauer@usp.br).

Inspiration

Some of the questions we want to answer (http://www.cs.colostate.edu/~cs555/CS555-Fall2017-HW3.pdf): Q1. For each artist, what is the most commonly tagged genre? Q2. What is the average tempo across all the songs in the dataset? Q3. What is the median danceability score across all the songs in the dataset? Q4. Who are the top ten artists for fast songs (based on their tempo)? Q5. What are top ten songs based on their hotness in each genre? Please also provide the artist name and title for these songs. Q6. On a per-year basis, what is the mean variance of loudness across the songs within the dataset? Q7. How many songs does each artist have in this dataset?
Q8. What are the top ten most popular terms (genres) that songs in the dataset have been tagged with?

This statistic displays the results of a survey about the most popular genres of South Korean pop music (K-pop) in Australia in 2017. In 2017, about **** percent of respondents in Australia reported that dance was their favorite K-pop genre.

This statistic displays the results of a survey about the most popular genres of South Korean pop music (K-pop) in Malaysia in 2017. In 2017, about **** percent of respondents in Malaysia reported that Rap or hip-hop was their favorite K-pop genre.

This statistic displays the results of a survey about the most popular genres of South Korean pop music (K-pop) in Indonesia in 2017. In 2017, about **** percent of respondents in Indonesia reported that dance was their favorite K-pop genre.

This statistic displays the results of a survey about the most popular genres of South Korean pop music (K-pop) in the United Kingdom in 2017. In 2017, about **** percent of respondents in the UK reported that dance was their favorite K-pop genre.

This statistic displays the results of a survey about the most popular genres of South Korean pop music (K-pop) in the United States in 2017. In 2017, about **** percent of respondents in the U.S. reported that electronic music was their favorite K-pop genre.

This statistic displays the results of a survey about the most popular genres of South Korean pop music (K-pop) in Thailand in 2017. In 2017, about **** percent of respondents in Taiwan reported that R&B was their favorite K-pop genre.