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.

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

Dataset contains a comprehensive list of the most famous songs and most streamed songs as listed on Spotify.

It provides insights into each song's

• Attributes
• Popularity
• Presence on various music platforms

The dataset includes information such as track name

• Artist's name
• Release date
• Spotify playlists and charts
• Streaming statistics
• Apple Music presence
• Deezer presence
• Shazam charts
• Various audio features

Unlock powerful insights with our custom music datasets, offering access to millions of records from popular music platforms like Spotify, SoundCloud, Amazon Music, YouTube Music, and more. These datasets provide comprehensive data points such as track titles, artists, albums, genres, release dates, play counts, playlist details, popularity scores, user-generated tags, and much more, allowing you to analyze music trends, listener behavior, and industry patterns with precision. Use these datasets to optimize your music strategies by identifying trending tracks, analyzing artist performance, understanding playlist dynamics, and tracking audience preferences across platforms. Gain valuable insights into streaming habits, regional popularity, and emerging genres to make data-driven decisions that enhance your marketing campaigns, content creation, and audience engagement. Whether you’re a music producer, marketer, data analyst, or researcher, our music datasets empower you with the data needed to stay ahead in the ever-evolving music industry. Available in various formats such as JSON, CSV, and Parquet, and delivered via flexible options like API, S3, or email, these datasets ensure seamless integration into your workflows.

According to data gathered in the United States in April 2024, Generation Z is much more open to AI being used in the production of music. Nearly **** of the Gen Z respondents stated that they would be interested in listening to music that has been produced with the help of AI. Older generations seem more hesitant towards this topic, as only ** percent stated the same.

Introduction

Music Streaming Statistics: The music streaming sector has significantly transformed how people discover and enjoy music, providing seamless, on-demand access across various digital devices. As the industry moves away from physical formats and downloads toward cloud-based services, streaming has emerged as the primary method of music consumption worldwide.

Its continued expansion is fueled by mobile convenience, curated content, and intelligent recommendation algorithms that boost user interaction. Streaming platforms are also introducing features such as social sharing, live audio, and tailored user experiences to strengthen engagement and loyalty.

Moreover, increasing smartphone adoption and improved internet infrastructure in developing regions are driving broader access to these services. Monitoring shifts in user behavior, platform usage, and demographic engagement is essential to understanding the evolving landscape of the music streaming industry.

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}
}

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.

This dataset provides a list of lyrics from 1950 to 2019 describing music metadata as sadness, danceability, loudness, acousticness, etc. We also provide some informations as lyrics which can be used to natural language processing.

The audio data was scraped using Echo Nest® API integrated engine with spotipy Python’s package. The spotipy API permits the user to search for specific genres, artists,songs, release date, etc. To obtain the lyrics we used the Lyrics Genius® API as baseURL for requesting data based on the song title and artist name.

This dataset was designed to evaluate the effectiveness of music education by collecting data on student performance, physiological information, engagement, and other metrics in a classroom setting, enhanced by Internet of Things (IoT) devices and Artificial Intelligence (AI) algorithms. The data simulates a learning environment where IoT devices track various music performance metrics, physiological responses, and behavioral patterns of students during music lessons. The dataset is structured to enable evaluation of the impact of music education, focusing on skill development, engagement, and performance outcomes.

Features: Student Information:

Student_ID: A unique identifier for each student (e.g., "S001", "S002"). Age: The age of the student (integer). Gender: The gender of the student, either "Male" or "Female". Class_Level: The level of music education (e.g., "Beginner", "Intermediate", "Advanced"). Music Performance Metrics:

Accuracy: A score representing the student's accuracy in reproducing the music correctly (percentage). Rhythm: A metric representing the student's ability to keep rhythm consistently (percentage). Tempo: The tempo of the student's performance, measured in beats per minute (BPM). Pitch_Accuracy: A score representing the accuracy of the pitch in the student's performance (percentage). Duration: The total time (in seconds) that the student spent playing during the lesson. Volume: The loudness level of the student's performance, measured in decibels (dB). Physiological Data:

Heart_Rate: The heart rate of the student during the lesson, measured in beats per minute (BPM). Blood_Pressure: The systolic blood pressure of the student, measured in mmHg. Stress_Level: A subjective stress level rating from 1 to 10, indicating how stressed the student felt during the lesson. Engagement and Behavioral Data:

Engagement_Level: A rating (1-10) representing the student's engagement level during the lesson. Focus_Time: The total time (in seconds) the student was focused on the task. Behavioral_Patterns: A categorical variable indicating the level of distraction during the lesson: 0: No distractions 1: Mild distractions 2: Heavy distractions Learning Outcomes (Target Variables):

Performance_Score: The overall performance score for the student, ranging from 60 to 100 (percentage). Skill_Development: A rating (1-5) representing the student’s development in musical skills, where: 1: Poor 2: Fair 3: Good 4: Very good 5: Excellent Engagement_Score: A numeric score representing the level of engagement in the lesson, ranging from 1 to 10. Lesson Information:

Lesson_Type: The type of lesson the student is participating in, either "Theory" or "Practical". Instrument_Type: The type of musical instrument used in the lesson (e.g., "Piano", "Guitar", "Violin"). Timestamps:

Timestamp: The timestamp of the lesson session, recorded to track when each data point was collected.

In 2024, revenue from digital album downloads amounted to 166.7 million U.S. dollars, less than half the figure recorded in 2018. Meanwhile, subscription and streaming revenues have been increasing annually and reached 14.88 billion that same year, making up the vast majority of revenues for the entire music industry. Digital music – additional informationThe increase in digital music revenue, more specifically subscription and streaming services, may be down to the accessibility and availability of digital music. For legal downloads, consumers can pick between services such as Spotify, Apple Music, and Pandora. Of course, there are countless numbers of illegal sites which distribute digital music also. In 2019, approximately 34 percent of global internet users aged 16 to 24 admitted to accessing music through music ripping, the most popular method being copyright infringement.Digital music sales have made a huge impact on the listings of best-selling lists with digital sales either being combined with physical sales or set apart. The list of the top-selling digital songs in the United States in 2020 features artists such as The Weeknd, Tones and I, and Megan Thee Stallion. The title for the top selling digital song goes to ‘Blinding Lights’ by The Weeknd. The song sold over 372 thousand units in the United States from January to July 2020. Nevertheless, the share of the digital music market does not always directly correspond to the value of the digital market. The value of digital music singles downloads in 2024 amounted to 162.4 million U.S. dollars, which marked a drop in value from 678.5 million in 2017. The value of digital album sales also saw a decrease from 668.5 million in 2017 to 166.7 million in 2024.

Music streaming revenue has increased astronomically in the last ten years alone – growing from 1.9 billion U.S. dollars in 2014 to 14.9 billion in 2024. Streaming has become a popular pastime for U.S. music fans and a major source of revenue for the industry, though many traditional consumers lament the resulting decline of physical music formats. Physical CD shipments have dwindled, whilst digital music platforms are flourishing.  The world of digital music   Platforms like Spotify have millions of users worldwide, and millions of tracks are streamed via the service each week. In the U.S., more than 25 percent of adults under the age of 35 are Spotify users, as well as almost 20 percent of adults aged 55 or above. Unsurprisingly, the vast majority of digital music revenue in the United States is derived from subscriptions and streaming, and successful musicians like Drake, Eminem, and Ariana Grande  amass billions of streams each year.  Whilst many artists in the music industry generate most of their income from touring, streaming is also incredibly lucrative, generating millions of dollars in earnings.  

Music is a volatile industry, where its dynamic nature can directly influence artist career behavior. That is, musical careers can suffer ups and downs depending on the current market moment. This dataset provides data about hot streak periods in musical careers, which are defined by high-impact bursts occurring in sequence.

Success in the music industry has a temporal structure, as the audience tastes change over time. Here, we use the Billboard Hot 100 charts with Spotify data to represent success over time. For musical careers, we build their time series from the debut date (i.e., date of the first release obtained from Spotify) to the last chart collected. Thus, each point in the time series represents the success of such an artist in a given week, according to the Hot 100 chart.

Therefore, we present MUHSIC (Music-oriented Hot Streak Information Collection), which contains:

Charts: enhanced data on all weekly Hot 100 Charts

Artists: artist success time series with hot streak information

Genres: genre success time series with hot streak information (the genre is the aggregated of all its artists)

Hot Streaks: summarized hot streak information

Dataset

This dataset was created by Pavan Sanagapati

Contents

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

This dataset was studied on Temporal Analysis and Visualisation of Music paper, in the following link:

       https://sol.sbc.org.br/index.php/eniac/article/view/12155

This dataset provides a list of lyrics from 1950 to 2019 describing music metadata as sadness, danceability, loudness, acousticness, etc. We also provide some informations as lyrics which can be used to natural language processing.

The audio data was scraped using Echo Nest® API integrated engine with spotipy Python’s package. The spotipy API permits the user to search for specific genres, artists,songs, release date, etc. To obtain the lyrics we used the Lyrics Genius® API as baseURL for requesting data based on the song title and artist name.

Introduction

Apple Music Statistics: Since its 2015 release, Apple Music has become a major force in the worldwide music streaming market. It keeps growing, giving customers access to a vast library of over 100 million songs, unique content, and state-of-the-art technologies including Lossless streaming and Spatial Audio by 2023 and 2024.

Apple Music is a notable platform because of its unique technology, easy integration inside the Apple ecosystem, and dedication to sustainability. It has over 110 million users and generates considerable income. The intriguing data and insights on Apple Music that follow demonstrate the platform's remarkable rise to prominence and impact throughout this time.

The sales based on format of musical recordings for the record production and integrated record production and distribution industries, sound recording and music publishing (NAICS 512210 and 512220), for two years of data.

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 ...).

hiratehseen/music-data dataset hosted on Hugging Face and contributed by the HF Datasets community