In 2023, the number of participants in dance, step, and other choreographed exercise to music in the United States amounted to approximately 26.24 million. This showed growth of over four percent over the previous years' figure of 25.16 million.

There were estimated to be approximately 9,100 dancers and choreographers working in the United Kingdom as of the third quarter of 2024, compared with 8,100 in the previous quarter.

The market size of the dance studio industry in the United States amounted to roughly 4.3 billion U.S. dollars in 2023. This figure was expected to increase by 2.6 percent in 2024, reaching an estimated 4.4 billion U.S. dollars.

An explanation of the parameters is given in the section 2.2. Units and annotations are shown in column 2. Means, standard deviation and coefficient of variation are given, if available.

The Check-In Dataset is the second public-use dataset in the Dunham's Data series, a unique data collection created by Kate Elswit (Royal Central School of Speech and Drama, University of London) and Harmony Bench (The Ohio State University) to explore questions and problems that make the analysis and visualization of data meaningful for dance history through the case study of choreographer Katherine Dunham. The Check-In Dataset accounts for the comings and goings of Dunham's nearly 200 dancers, drummers, and singers and discerns who among them were working in the studio and theatre together over the fourteen years from 1947 to 1960. As with the Everyday Itinerary Dataset, the first public-use dataset from Dunham's Data, data on check-ins come from scattered sources. Due to information available, it has a greater level of ambiguity as many dates are approximated in order to achieve accurate chronological sequence. By showing who shared time and space together, the Check-In Dataset can be used to trace potential lines of transmission of embodied knowledge within and beyond the Dunham Company. Dunham's Data: Digital Methods for Dance Historical Inquiry is funded by the United Kingdom Arts and Humanities Research Council (AHRC AH/R012989/1, 2018-2022) and is part of a larger suite of ongoing digital collaborations by Bench and Elswit, Movement on the Move. The Dunham's Data team also includes digital humanities postdoctoral research assistant Antonio Jiménez-Mavillard and dance history postdoctoral research assistants Takiyah Nur Amin and Tia-Monique Uzor. For more information about Dunham's Data, please see the Dunham's Data website. Also, visit the Dunham's Data research blog to view the interactive visualizations based on the Dunham's Data.

The statistic displays the number of people who participated in creative or artistic dance for more than 150 minutes a week in England from 2017 to 2018, by gender. As of May 2018, approximately 121 thousand male respondents and 422 thousand female respondents in England participated in creative or artistic dance with at least moderate intensity for more than 150 minutes a week.

Financial overview and grant giving statistics of Dance to Be Free

Financial overview and grant giving statistics of Dance Time Inc.

Financial overview and grant giving statistics of Dance From the Heart

Everybody Dance Now is a dataset of videos that can be used for training and motion transfer. It contains long single-dancer videos that can be used to train and evaluate the model. All subjects have consented to allowing the data to be used for research purposes.

When using this resource, please cite Wallace, B., Nymoen, K., Martin, C.P & Tøressen, J. DeepDance: Motion capture data of improvised dance (2019) (version 2.0). Zenodo 10.5281/zenodo.7948501

Abstract

This dataset comprises full-body motion capture of improvised dance as well as corresponding audio files. 30 dancers were recorded individually, improvising to six different audio files. The motion was captured in units of mm at 240Hz using a Qualisys infra-red optical system. The experiment was carried out at the University of Oslo in October 2019. For each dancer, 3 performances are recorded for each musical piece, resulting in 540 1-minute motion capture files. The dataset was collected for use as training data in deep learning for motion generation. This dataset also includes MATLAB code to visualize the motion capture files.

Music

• Skarphedinsson, M. Wallace, B. (2019). “Song a”
• Skarphedinsson, M. Wallace, B. (2019). “Song b”
• Skarphedinsson, M. Wallace, B. (2019). “Song c”
• Skarphedinsson, M. Wallace, B. (2019). “Song d”
• Skarphedinsson, M. Wallace, B. (2019). “Song f”
• LaClair, J. Bounce. Jesse LaClair, (2018) Referenced here as “Song e”

Data Description

The following data types are provided:

• Motion (marker position): Recorded with Qualisys Track Manager and saved as tab-separated .tsv files.
• Stimuli: audio .wav files containing 1 minute of the tracks described above.
• MATLAB script for animating the tsv files. (requires the MoCap Toolbox)

Note: Recordings which contained errors such as missing markers have been replaced by subject 001.

Acknowledgements

This work was partially supported by the Research Council of Norway through its Centres of Excellence scheme, project number 262762.

Conflicts of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Financial overview and grant giving statistics of Dance for Dreams

The statistic illustrates the results of a survey on dancing participation in the last four weeks of adults from 2007 to 2020 in Scotland. In 2020, it was found that six percent of respondents stated that they participated in dancing in the last four weeks.

Financial overview and grant giving statistics of Dance Project Inc

Dance archives play a crucial role in preserving the historical record of dance, housing materials used to reflect, revive, study, and analyse this art form. However, with declining physical visitor numbers, it is essential to explore how users access and utilise this knowledge digitally. Therefore, the doctoral research project investigated the future development of online dance archive resources by examining the information-seeking behaviour's of dance researchers and the working practices of dance archivists. This dataset was compiled between 2018-2020 as survey the landscape of dance archives and to contextualise one of the study's participant sample: dance archivists. As a result a comprehensive UK dance archive collection database was compiled using publicly available online sources. The UK Theatre Collections Database (Association of Performing Arts Collections, 2009) served as the initial foundation in compiling this dataset. The dataset compiled details of archives across the UK which hold dance, or dance-related, collections, material or items. It provides details inclusive of the archive name and geographical location. Additionally, it surveys dance archives' catalogue, detailing whether catalogues can be found online or offline and which software the dance archive has used to produce the catalogue. This information was gathered both from publicly information and further investigated through correspondences with the dance archives via email. Moreover, the dataset provides a URL link to the search tool provided by the dance archive online which users can use to search the archive for information, it also lists metadata and interface affordances and outlines particular collections, material or items related to dance which can be found using the search tool[1]. The subsequent development and extension of this list revealed that over sixty archives within the UK and Northern Ireland contain dance or dance-related collections or materials. This newly compiled database provided a statistical understanding of dance archives that had previously been unavailable. Furthermore, it facilitated an informed decision regarding the recruitment pool of dance archivists for the study. This dataset supports future research by providing a statistical understanding of dance archives within the UK and NI.[1] A basic search for words such as ‘dance’ or ‘performance’ were used to locate material relating to dance within the archive searching facilities. This forms the listed dance, or dance-related collections value within the dataset.

Dunham's Data is a three year project (2018-2021) funded by the United Kingdom Arts and Humanities Research Council, under the direction of Kate Elswit (Principle Investigator (PI), University of London, Royal Central School of Speech and Drama) and Harmony Bench (Clinical Investigator (CI), Ohio State University). The project explores the kinds of questions and problems that make the analysis and visualization of data meaningful for dance history. It does so through the case study of choreographer Katherine Dunham, cataloging a daily itinerary of Dunham's touring and travel (including country, city, hotel, and venue, whenever possible) from the 1930s-60s, the dancers, drummers, and singers in her employ during that time, and the repertory they performed. The datasets included with this collection represent the years 1950-1953.

Data and metadata used in "Machine learning reveals the waggle drift’s role in the honey bee dance communication system"

All timestamps are given in ISO 8601 format.

The following files are included:

Berlin2019_waggle_phases.csv, Berlin2021_waggle_phases.csv

Automatic individual detections of waggle phases during our recording periods in 2019 and 2021.

• timestamp: Date and time of the detection.

• cam_id: Camera ID (0: left side of the hive, 1: right side of the hive).

• x_median, y_median: Median position of the bee during the waggle phase (for 2019 given in millimeters after applying a homography, for 2021 in the original image coordinates).

• waggle_angle: Body orientation of the bee during the waggle phase in radians (0: oriented to the right, PI / 4: oriented upwards).

Berlin2019_dances.csv

Automatic detections of dance behavior during our recording period in 2019.

• dancer_id: Unique ID of the individual bee.

• dance_id: Unique ID of the dance.

• ts_from, ts_to: Date and time of the beginning and end of the dance.

• cam_id: Camera ID (0: left side of the hive, 1: right side of the hive).

• median_x, median_y: Median position of the individual during the dance.

• feeder_cam_id: ID of the feeder that the bee was detected at prior to the dance.

Berlin2019_followers.csv

Automatic detections of attendance and following behavior, corresponding to the dances in Berlin2019_dances.csv.

• dance_id: Unique ID of the dance being attended or followed.

• follower_id: Unique ID of the individual attending or following the dance.

• ts_from, ts_to: Date and time of the beginning and end of the interaction.

• label: “attendance” or “follower”

• cam_id: Camera ID (0: left side of the hive, 1: right side of the hive).

Berlin2019_dances_with_manually_verified_times.csv

A sample of dances from Berlin2019_dances.csv where the exact timestamps have been manually verified to correspond to the beginning of the first and last waggle phase down to a precision of ca. 166 ms (video material was recorded at 6 FPS).

• dance_id: Unique ID of the dance.

• dancer_id: Unique ID of the dancing individual.

• cam_id: Camera ID (0: left side of the hive, 1: right side of the hive).

• feeder_cam_id: ID of the feeder that the bee was detected at prior to the dance.

• dance_start, dance_end: Manually verified date and times of the beginning and end of the dance.

Berlin2019_dance_classifier_labels.csv

Manually annotated waggle phases or following behavior for our recording season in 2019 that was used to train the dancing and following classifier. Can be merged with the supplied individual detections.

• timestamp: Timestamp of the individual frame the behavior was observed in.

• frame_id: Unique ID of the video frame the behavior was observed in.

• bee_id: Unique ID of the individual bee.

• label: One of “nothing”, “waggle”, “follower”

Berlin2019_dance_classifier_unlabeled.csv

Additional unlabeled samples of timestamp and individual ID with the same format as Berlin2019_dance_classifier_labels.csv, but without a label. The data points have been sampled close to detections of our waggle phase classifier, so behaviors related to the waggle dance are likely overrepresented in that sample.

Berlin2021_waggle_phase_classifier_labels.csv

Manually annotated detections of our waggle phase detector (bb_wdd2) that were used to train the neural network filter (bb_wdd_filter) for the 2021 data.

• detection_id: Unique ID of the waggle phase.

• label: One of “waggle”, “activating”, “ventilating”, “trembling”, “other”. Where “waggle” denoted a waggle phase, “activating” is the shaking signal, “ventilating” is a bee fanning her wings. “trembling” denotes a tremble dance, but the distinction from the “other” class was often not clear, so “trembling” was merged into “other” for training.

• orientation: The body orientation of the bee that triggered the detection in radians (0: facing to the right, PI /4: facing up).

• metadata_path: Path to the individual detection in the same directory structure as created by the waggle dance detector.

Berlin2021_waggle_phase_classifier_ground_truth.zip

The output of the waggle dance detector (bb_wdd2) that corresponds to Berlin2021_waggle_phase_classifier_labels.csv and is used for training. The archive includes a directory structure as output by the bb_wdd2 and each directory includes the original image sequence that triggered the detection in an archive and the corresponding metadata. The training code supplied in bb_wdd_filter directly works with this directory structure.

Berlin2019_tracks.zip

Detections and tracks from the recording season in 2019 as produced by our tracking system. As the full data is several terabytes in size, we include the subset of our data here that is relevant for our publication which comprises over 46 million detections. We included tracks for all detected behaviors (dancing, following, attending) including one minute before and after the behavior. We also included all tracks that correspond to the labeled and unlabeled data that was used to train the dance classifier including 30 seconds before and after the data used for training.
We grouped the exported data by date to make the handling easier, but to efficiently work with the data, we recommend importing it into an indexable database.

The individual files contain the following columns:

• cam_id: Camera ID (0: left side of the hive, 1: right side of the hive).

• timestamp: Date and time of the detection.

• frame_id: Unique ID of the video frame of the recording from which the detection was extracted.

• track_id: Unique ID of an individual track (short motion path from one individual). For longer tracks, the detections can be linked based on the bee_id.

• bee_id: Unique ID of the individual bee.

• bee_id_confidence: Confidence between 0 and 1 that the bee_id is correct as output by our tracking system.

• x_pos_hive, y_pos_hive: Spatial position of the bee in the hive on the side indicated by cam_id. Given in millimeters after applying a homography on the video material.

• orientation_hive: Orientation of the bees’ thorax in the hive in radians (0: oriented to the right, PI / 4: oriented upwards).

Berlin2019_feeder_experiment_log.csv

Experiment log for our feeder experiments in 2019.

• date: Date given in the format year-month-day.

• feeder_cam_id: Numeric ID of the feeder.

• coordinates: Longitude and latitude of the feeder. For feeders 1 and 2 this is only given once and held constant. Feeder 3 had varying locations.

• time_opened, time_closed: Date and time when the feeder was set up or closed again.
  sucrose_solution: Concentration of the sucrose solution given as sugar:water (in terms of weight). On days where feeder 3 was open, the other two feeders offered water without sugar.

Software used to acquire and analyze the data:

• bb_pipeline: Tag localization and decoding pipeline

• bb_pipeline_models: Pretrained localizer and decoder models for bb_pipeline

• bb_binary: Raw detection data storage format

• bb_irflash: IR flash system schematics and arduino code

• bb_imgacquisition: Recording and network storage

• bb_behavior: Database interaction and data (pre)processing, feature extraction

• bb_tracking: Tracking of bee detections over time

• bb_wdd2: Automatic detection and decoding of honey bee waggle dances

• bb_wdd_filter: Machine learning model to improve the accuracy of the waggle dance detector

• bb_dance_networks: Detection of dancing and following behavior from trajectories

The Student Enrolment Series of the Statistics of Non-University Teachings aims to show the evolution of its basic variables and statistical indicators. The data offered may imply slight differences for some variables with respect to the data that appear in the Detailed Results of the corresponding course, in case they respond to subsequent revisions to improve the temporal comparability of the information.

United States Employment: NF: LH: Theater, Dance & Other Performing Co data was reported at 102.500 Person th in May 2018. This records an increase from the previous number of 100.000 Person th for Apr 2018. United States Employment: NF: LH: Theater, Dance & Other Performing Co data is updated monthly, averaging 76.300 Person th from Jan 1990 (Median) to May 2018, with 341 observations. The data reached an all-time high of 102.500 Person th in May 2018 and a record low of 60.200 Person th in Jan 1990. United States Employment: NF: LH: Theater, Dance & Other Performing Co data remains active status in CEIC and is reported by Bureau of Labor Statistics. The data is categorized under Global Database’s USA – Table US.G024: Current Employment Statistics Survey: Employment: Non Farm.

As of February 2021, almost 30 percent of public square dancers in China aged over 46 years. In comparison, young people below 18 years old amounted to about 13 percent. Public square dancing is a popular exercise routine performed to music, usually in a large group, in squares, plazas or parks.