A dataset containing diverse billboard charts from their creations to today (updated each week)

Current charts files

• billboard200.csv for the Billboard 200 chart
• hot100.csv for the Hot 100 chart
• radio.csv for the Radio chart
• streaming_songs.csv for the Streaming songs chart
• digital_songs.csv for the Digital song sales chart

Datasets updated every Wednesday at 2 a.m.

Contains details about :

• Date
• Song's title
• Artist's name
• Rank
• Last week rank
• Peak position
• Weeks in charts
• Image Url

Notes :

• Some songs or artists lists contains , by default, it was changed to ; for songs and | for artist to avoid any problem with the , used as a separator
• Some songs don't have an image url and it was replaced by # instead of None

The Billboard Hot 100 is a chart that ranks the best-performing singles of the United States. Its data, published by Billboard magazine and compiled by Nielsen Sound Scan, is based collectively on each single's weekly physical and digital sales, as well as airplay and streaming. At the end of a year, Billboard will publish an annual list of the 100 most successful songs throughout that year on the Hot 100 chart based on the information.

Billboard year end chart works These charts are a cumulative measure of a single or album's performance in the United States, based upon the Billboard magazine charts during any given chart year. Other factors including the total weeks a song spent on the chart and at its peak position were calculated into its year-end total.

Billboard Hot is determined The Hot 100 is ranked by radio airplay audience impressions as measured by Nielsen BDS, sales data compiled by Nielsen Sound scan (both at retail and digitally) and streaming activity provided by online music sources. There are several component charts that contribute to the overall calculation of the Hot 100.

The Billboard Global 200 is a weekly record chart published by Billboard magazine. The chart ranks the top songs globally and is based on digital sales and online streaming from over 200 territories worldwide.

Stories about the Billboard 200 albums chart generally post on Sunday afternoons, while stories about the Billboard Hot 100 generally post each Monday afternoon. Other stories, podcasts, videos and more covering our full menu of charts post throughout the week.

In the US, Billboard represents the cream of all the objective data. And their efforts to collect all the data from all these various sources to create an objective, final tally of each artist's popularity in a given week, still has merit. ... This is why the billboard chart is important.

So here we had collected list of Billboard Hot 100 singles from the year 1992 to 2014.

This dataset is designed for music analytics, recommendation systems, and cultural studies [1]. It brings together information from Billboard Hot-100 charts, Genius for song lyrics, and Spotify for audio features [1]. The dataset provides detailed information for popular songs spanning the years 2000 to 2023 [1].

Columns

The main CSV file contains the following columns [2]: * ranking: The rank of the song for a given year. * song: The title of the song. * band_singer: The name of the singer or band performing the song. * songurl: A URL specific to the song. * titletext: Additional title text. * url: A general URL. * year: The year of the chart. * lyrics: The lyrics of the song. * uri: A Uniform Resource Identifier. * danceability: A Spotify-derived feature indicating how suitable a track is for dancing, based on musical elements like tempo, rhythm stability, beat strength, and overall regularity [2, 3].

Distribution

The dataset is provided as a main CSV file [2]. While specific total row counts are not available, the data covers various distributions. For example, song rankings range from 1 to 100 [2], and years covered are from 2000 to 2023 [4]. Danceability scores range from approximately 0.19 to 0.96 [3]. Unique values are present for song titles, artists, and URLs, with a notable portion of artists falling into an 'Other' category beyond top acts like Drake and Rihanna [4].

Usage

This dataset is ideal for various applications and use cases [5]: * Music analytics: For understanding trends in popular music [1]. * Recommendation systems: To build models for suggesting songs [1]. * Cultural studies: For research into music and its societal impact [1]. * Data visualisation: To create visual insights from music data [6]. * Exploratory data analysis: For discovering patterns and insights [6]. * Natural Language Processing (NLP): For analysing lyrical content [6].

Coverage

The dataset covers songs from the Billboard Hot-100 charts [1] and has a global region coverage [7]. The time range for the data is from 2000 to 2023 [1, 4].

License

CCO

Who Can Use It

This dataset is suitable for a variety of users [5]: * Data scientists: For machine learning projects such as building recommender systems or predictive models. * Data analysts: To perform detailed analysis of music industry trends and song characteristics. * Researchers: For academic studies in fields like musicology, digital humanities, or social sciences. * Developers: For creating applications that utilise rich music metadata.

Dataset Name Suggestions

• Billboard Top Hits 2000-2023
• Hot-100 Song Lyrics and Audio Features
• Music Chart Data with Spotify Integration
• Popular Songs 2000-2023 Analysis Dataset

Attributes

Original Data Source: Original Data Source:

Complete Billboard Hot 100 chart data since 2000

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   |

The high quality singers have to be identified based on the rankings given by Billboard from 1992 to 2014.

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)

We also provide this additional file with genre tags for every song, which we used for validation. Here, too, the rows correspond to recordings. The data sets can be joined via the recording_id field.

The Billboard Hot 100 is a chart that ranks the best-performing singles of the United States. Its data, published by Billboard magazine and compiled by Nielsen Sound Scan, is based collectively on each single's weekly physical and digital sales, as well as airplay and streaming. At the end of a year, Billboard will publish an annual list of the 100 most successful songs throughout that year on the Hot 100 chart based on the information.

So here we had collected list of Billboard Hot 100 singles from the year 1992 to 2014.

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

Billboard 200 Amerika Birleşik Devletleri nde yayınlanan Billboard adlı müzik dergisinin her hafta çıkardığı müzik albüm

Dataset

This dataset was created by GUGGILAM DHARMA TEJA

Contents

It contains the following files:

MUHSIC: Music-oriented Hot Streak Information Collection

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, this dataset 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

I wanted to create a data of billboard top 100 artists of various years in a single sheet.

I merged the data of billboard top 100 artists for different years from 1992 to 2014 by gathering from the billboard website...

UK Singles Chart Böyük Britaniyanın rəsmi hit paradı Hit parad nüfuzuna görə yalnız ABş ın Billboard Hot 100 hitparadı i

The dataset is first introduced in the following paper: Siqi Wu, Marian-Andrei Rizoiu, and Lexing Xie. Beyond Views: Measuring and Predicting Engagement in Online Videos. In AAAI International Conference on Weblogs and Social Media (ICWSM), 2018. Tweeted videos dataset This dataset contains YouTube videos published between July 1st and August 31st, 2016. To be collected, the video needs (a) be mentioned on Twitter during aforementioned collection period; (b) have insight statistics available; (c) have at least 100 views within the first 30 days after upload. Quality videos datasets These datasets contain videos deemed of high quality by domain experts. Vevo videos: Videos of verified Vevo artists, as of August 31st, 2016. Billboard16 videos: Videos of 2016 Billboard Hot 100 chart. Top news videos: Videos of top 100 most viewed News channels. freebase_mid_type_name.csv It maps a freebase mid to a real-world entity. See more details in this data description.

This dataset contains Shazam query timings ('offsets') and query dates corresponding to 20 hit songs from the Billboard Year End Hot 100 2015 chart. Queries were aggregated from 1 January 2014 to 31 May 2016, inclusive. Number of queries per song range from 3,020,785 to 19,974,795, with a total of 188,271,243 queries across the 20 songs. Data are stored in .csv files (one file per song) ranging in size from 62.9MB to 416.1MB. The total size of the dataset is around 4GB.

Context

This dataset is made to find the best quality singer in between the year 1992 to 2014. Every year there are different singers who are at top 100. In order to find best one this analysis is done.

Content

The dataset consists of top 100 singers in order from the year 1992 to 2014 in a tabular column consists of Rank, Title, Artist, Year and Score.

Acknowledgements

The data for doing this analysis is taken from the Billboard Magazine (Top 100 Hot Singles).

Inspiration

I would like to see answered for all related questions regarding this data set.

According to a study on representation and equality in the music industry, only *** percent of producers were female while approximately **** percent were male. The share of female music producers has been increasing since 2017, despite the setback in 2020 and still leaving a significant gap in terms of proportionate representation. Gender inequality in the music industry Even though music audiences are as diverse as ever, and recent data has also indicated that male and female listeners account for similar shares of digital music users in the United States, there are still significant gaps when it comes to the representation of different groups. The share of female songwriters across the top 100 songs in 2020 stood at below ** percent - a figure that has pretty much remained unchanged in the past decade. But this disparity not only unfolds behind the scenes: In 2020, just over ** percent of artists on Billboard’s top 100 charts were female, and in genres like hip-hop or alternative, this share was even lower. Grammy Awards The fact that the music industry remains a male-dominated landscape is also reflected in the Grammy Awards. While the show made headlines by merging male and female categories back in 2012, the imbalances have remained. Data on the gender distribution of Grammy nominees collected between 2013 and 2021 shows that less than ** percent of nominees for awards like Record of the Year, Album of the Year, and Producer of the Year were female. And even though the playing field was much more balanced in the Best New Artist category, many artists still fail to get the spotlight they deserve.

Hot 100 Airplay Amerika Birleşik Devletleri nde Billboard dergisi tarafından açıklanan müzik listesidir RekorlarEn yükse