In 2019, most dance events in Italy took place in December. As of this month, the number of performances peaked at roughly 30 thousand. On the other hand, September recorded the lowest figure, namely 18.7 thousand.

In 2019, dance events in Italy recorded the highest revenue in December. As of this month, the revenue peaked at roughly 99 million euros. August registered the second-highest figure, namely nearly 93 million euros.

In 2019, dance events in Italy recorded the highest expenditure at the box office in December. As of this month, the spending peaked at roughly 35.2 million euros. Overall, the audience expenditure, which also considers other expenses apart from the ticket costs, reached 98.2 million euros in December 2019.

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.

We learn high fidelity human depths by leveraging a collection of social media dance videos scraped from the TikTok mobile social networking application. It is by far one of the most popular video sharing applications across generations, which include short videos (10-15 seconds) of diverse dance challenges as shown above. We manually find more than 300 dance videos that capture a single person performing dance moves from TikTok dance challenge compilations for each month, variety, type of dances, which are moderate movements that do not generate excessive motion blur. For each video, we extract RGB images at 30 frame per second, resulting in more than 100K images. We segmented these images using Removebg application, and computed the UV coordinates from DensePose.

Download TikTok Dataset:

Please use the dataset only for the research purpose.

The dataset can be viewed and downloaded from the Kaggle page. (you need to make an account in Kaggle to be able to download the data. It is free!)

The dataset can also be downloaded from here (42 GB). The dataset resolution is: (1080 x 604)

The original YouTube videos corresponding to each sequence and the dance name can be downloaded from here (2.6 GB).

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

In 2019, approximately 3 percent of the respondents in a nationally representative survey in Wales advised that they danced in the last four weeks.

More information about sport in Wales can be found in the Dossier: Sport in Wales.

Japan Retail Price: Tokyo: CA: Lesson fees: Dance data was reported at 2,322.000 JPY in Oct 2018. This records an increase from the previous number of 2,251.000 JPY for Sep 2018. Japan Retail Price: Tokyo: CA: Lesson fees: Dance data is updated monthly, averaging 2,242.000 JPY from Jan 2005 (Median) to Oct 2018, with 166 observations. The data reached an all-time high of 2,374.000 JPY in Feb 2010 and a record low of 2,179.000 JPY in May 2006. Japan Retail Price: Tokyo: CA: Lesson fees: Dance data remains active status in CEIC and is reported by Statistical Bureau. The data is categorized under Global Database’s Japan – Table JP.P002: Retail Price: Tokyo.

This pilot study attempted to identify a relationship between dance and mirror neuron activity in people with Parkinson’s disease by investigating Mu rhythm desynchronization in electroencephalographic (EEG) data before and after regular participation in a program of dance classes. During the EEG recordings, the participants observed a sequence of videos showing either choreographic (complex) or daily (simple) movements, each preceded by a baseline image (dark screen) and a control video (moving blocks). The results showed a statistically significant increase in Mu rhythm desynchronization in the alpha 1 band at the central channels after 6 months of dance classes. Control comparisons with occipital channels showed no such increase. Mu rhythm suppression has been demonstrated to reflect the activity of the human mirror neuron system, respond to variations in motor expertise, and seem to be impaired in Parkinson’s disease. The Mu wave desynchronization increase shown here, after 6 months of dance classes, is an objective measurement of the benefits of such practice for people with Parkinson’s disease (PD).

This statistic depicts the result of a survey on the share of people who attended dance classes during the previous month in England in 2016, by age. In 2016, it was found that 29.5 percent of respondents in the age group of 22 to 34 stated that they attended dance classes during the previous month.

1. Statistical scope and object: According to the "Taichung Performing Arts Group Guidance and Management Autonomy Ordinance," non-profit performing arts groups engaged in music, drama, dance, traditional opera, modern drama, and folk arts, which apply for registration with the City Cultural Affairs Bureau, are the statistical objects.2. Definition of statistical items:a. Music: Vocal performance (solo, choir, opera, musicals, pop music, etc.), traditional Chinese music performance (wind instruments, string instruments, plucked instruments, Chinese orchestra, northern and southern music, etc.), Western music performance (wind instruments, string instruments, keyboard instruments, chamber music, symphony orchestra, etc.), percussion, jazz music, religious music, others, and comprehensive.b. Dance: Modern dance, ballet, folk dance, tap dance, jazz dance, others, and comprehensive.c. Traditional opera: Traditional opera (gezaixi, Hakka opera, northern opera, southern opera, other Taiwanese theater genres, Pingju, various Chinese provincial theater genres, etc.), puppet theater (glove puppetry, puppetry, shadow play, etc.).d. Modern drama: Including spoken drama, mime, musicals, puppetry, children's drama, etc.e. Folk arts: Festivals (national holidays, temple fairs, Hakka festivals, Taiwanese indigenous festivals, etc.), ceremonies (Taiwanese indigenous, others), arts and crafts (parade arts, folk sports, acrobatics, traditional children's games, chess/bridge arts, etc.), cultural relics (common folk cultural relics, religious cultural relics, local documents, etc.).

FIPE: Consumer Price Index (CPI): MoM: 2nd Week: Personal Expenses: Recreation and Culture: Recreation: Dance House data was reported at 1.573 % in Apr 2025. This records an increase from the previous number of 0.000 % for Mar 2025. FIPE: Consumer Price Index (CPI): MoM: 2nd Week: Personal Expenses: Recreation and Culture: Recreation: Dance House data is updated monthly, averaging 0.214 % from Feb 2000 (Median) to Apr 2025, with 303 observations. The data reached an all-time high of 4.157 % in Jul 2022 and a record low of -3.882 % in Aug 2002. FIPE: Consumer Price Index (CPI): MoM: 2nd Week: Personal Expenses: Recreation and Culture: Recreation: Dance House data remains active status in CEIC and is reported by Institute of Economic Research Foundation. The data is categorized under Brazil Premium Database’s Inflation – Table BR.IB008: Consumer Price Index: June1994=100: São Paulo: São Paulo: Month-on-Month: Second Week: FIPE.

FIPE: Consumer Price Index (CPI): MoM: 3rd Week: Personal Expenses: Recreation and Culture: Recreation: Dance House data was reported at 0.699 % in Apr 2025. This records a decrease from the previous number of 0.869 % for Mar 2025. FIPE: Consumer Price Index (CPI): MoM: 3rd Week: Personal Expenses: Recreation and Culture: Recreation: Dance House data is updated monthly, averaging 0.208 % from Feb 2000 (Median) to Apr 2025, with 303 observations. The data reached an all-time high of 3.834 % in Mar 2013 and a record low of -5.569 % in Aug 2002. FIPE: Consumer Price Index (CPI): MoM: 3rd Week: Personal Expenses: Recreation and Culture: Recreation: Dance House data remains active status in CEIC and is reported by Institute of Economic Research Foundation. The data is categorized under Brazil Premium Database’s Inflation – Table BR.IB009: Consumer Price Index: June1994=100: São Paulo: São Paulo: Month-on-Month: Third Week: FIPE.

Twenty three retirement villages offering independent living accommodations were participated in the DAnCE study. Data were collected at the village site in a staggered manner starting on September 2012 and the follow-up data ended on August 2014. Data was entered using Microsoft Access Software. Participants were assessed for their cognitive status in face to face interview, then undertook physical measurements to determine their falls risk, and completed a self-report questionnaires including basic socio-demographic, question about history of falls, quality of life, depression, past 3-month physical activity, whether they had been ever diagnosed with 13 listed chronic conditions and what medications they take. During 12 months they had to post data on their falls on a monthly basis. After 12 months they repeated all the baseline measurements, except the cognitive status.

Honeybees are known for their ability to communicate about resources in their environment.
They inform the other foragers by performing specific dance sequences according to the spatial
characteristics of the resource. The purpose of our study is to provide a new tool for honeybees
dances recording, usable in the field, in a practical and fully automated way, without condemning
the harvest of honey. We designed and equipped an outdoor prototype of a production hive, later
called “GeoDanceHive”, allowing the continuous recording of honeybees’ behavior such as dances
and their analysis. The GeoDanceHive is divided into two sections, one for the colony and the other
serving as a recording studio. The time record of dances can be set up from minutes to several months.
To validate the encoding and sampling quality, we used an artificial feeder and visual decoding
to generate maps with the vector endpoints deduced from the dance information. The use of the
GeoDanceHive is designed for a wide range of users, who can meet different objectives, such as
researchers or professional beekeepers. Thus, our hive is a powerful tool for honeybees studies in the
field and could highly contribute to facilitating new research approaches and a better understanding
landscape ecology of key pollinators.

Netherlands Consumer Price Index (CPI): RC: Services: RS: Music, Dance and Sports Lessons data was reported at 134.900 2000=100 in Dec 2006. This stayed constant from the previous number of 134.900 2000=100 for Nov 2006. Netherlands Consumer Price Index (CPI): RC: Services: RS: Music, Dance and Sports Lessons data is updated monthly, averaging 114.500 2000=100 from Jan 2000 (Median) to Dec 2006, with 84 observations. The data reached an all-time high of 134.900 2000=100 in Dec 2006 and a record low of 99.000 2000=100 in Mar 2000. Netherlands Consumer Price Index (CPI): RC: Services: RS: Music, Dance and Sports Lessons data remains active status in CEIC and is reported by Statistics Netherlands. The data is categorized under Global Database’s Netherlands – Table NL.I007: Consumer Price Index: 2000=100.

In 2024, the number of participants in dance, step, and other choreographed exercise to music in the United States amounted to approximately **** million. This showed growth over the previous year's figure of **** million.

ABSTRACT Introduction The quality of life of middle-aged and elderly women is affected by the physiological effects of aging on the locomotor system; moderate aerobic exercise is one of the practices that can delay these deleterious effects. Square dancing has functional characteristics of aerobic exercise, but there are still no studies on its effects on motor function in middle-aged and elderly women who practice it regularly. Objective Explore the long-duration square dance exercises’ effect on motor function in middle-aged and elderly women. Methods 45 middle-aged and elderly women, divided into experimental and control groups, participated. The experimental group (n=25) performed square dancing exercises of 90 minutes four times a week for six months. Indicators of physical function, vital capacities, and motor function indices were collected. Results After exercise, improved grip strength of the middle-aged women in the square dance group and the 1-minute sessions was observed; in particular, the mean value of the selection response reduced from 516.20±83.87 before exercise to 440.28±58.07, a very significant difference. Conclusion Long-term square dance exercise has a particular effect on improving the cardiopulmonary function of middle-aged and elderly women and significantly improved motor function. Evidence Level II; Therapeutic Studies - Investigating the result.

The purpose is to strengthen the life education of contemporary college students and give better play to the vital role of life education in preventing college students’ mental diseases. Specifically, it discusses the role of dance therapy (DT) in College Students’ Life Education (CSLE). Firstly, based on educational psychology (EP), this manuscript analyzes the relevant theoretical concepts of EP and life education and discusses the importance of life education to contemporary college students. Secondly, following a Questionnaire Survey (QS) and using deep learning (DL) Convolutional Neural Network (CNN) and Facial Emotion Recognition (FER), this manuscript reviews and examines the CSLE’s current situation and the DT effect. Research findings are summarized combined with the QS results and scores of 20 subjects before and after five activities in 3 months. (I) After DT intervention, the positive dimensions of college students’ life values have improved, especially self-development and dedication, and their quality of life is refined. Thus, DT group counseling proves the positive role of DT in CSLE. (II) After DT intervention, 96.5% of the members think DT is effective. Therefore, EP-based DT is more effective and scientific in CSLE. The research findings provide a DT-based teaching concept for CSLE, explore the feasibility and effectiveness of life education, and enrich the DT scheme of CSLE. The research provides a practical reference for further applying DT in college students’ psychological education.

Chile Consumer Price Index (CPI): Education: Others: Class: Dance & Music data was reported at 28,842.680 1998=100 in Dec 2008. This stayed constant from the previous number of 28,842.680 1998=100 for Nov 2008. Chile Consumer Price Index (CPI): Education: Others: Class: Dance & Music data is updated monthly, averaging 26,769.320 1998=100 from Dec 1998 (Median) to Dec 2008, with 121 observations. The data reached an all-time high of 29,821.780 1998=100 in Jul 2008 and a record low of 21,373.820 1998=100 in Jan 1999. Chile Consumer Price Index (CPI): Education: Others: Class: Dance & Music data remains active status in CEIC and is reported by National Institute of Statistics. The data is categorized under Global Database’s Chile – Table CL.I014: Consumer Price Index: Greater Santiago: Dec1998=100.