A September 2023 survey on exercise habits in the United States revealed that around 65 percent of male respondents took part in strength training. Meanwhile, just under one quarter of female respondents participated in yoga.

In 2023, it was found that 22.4 percent of men in the United States participated in sports, exercise, and recreational activities daily, compared to only 19.9 percent of women. These statistics highlight a notable difference in the daily engagement of different genders in sporting activities. Other factors influencing this participation include socioeconomic status, age, disability, ethnicity, geography, personal interests, and societal expectations. These barriers can prevent individuals from having equal access to, and opportunities for, sport participation. What role does gender play in sports participation? Historically, many sports have been segregated by gender, with men and women participating in separate leagues and competitions. This segregation has led to a lack of opportunities for women and girls to participate in sports at the same level as men and boys. Additionally, societal attitudes and stereotypes about gender can discourage women and girls from participating in sports or limit their access to resources and support for their athletic pursuits. This often results in fewer women and girls participating in sports and a lack of representation of women and girls in leadership roles within the sports industry. However, in recent years, there has been an increased focus on promoting gender equality in sports and providing equal opportunities for men and women to participate in sports. This includes initiatives to increase funding and support for women's sports, as well as efforts to challenge gender stereotypes and discrimination in the athletic world. Impact of the COVID-19 pandemic on sports participation The COVID-19 pandemic led to many people spending more time at home due to lockdowns, remote work, and school closures. This resulted in many people having more time to engage in sports and other physical activities, as seen in the share of the U.S. population engaged in sports and exercise peaking in 2020. With gyms and sports facilities closed or with limited access, many people turned to home-based workouts and other activities. This included activities such as running, cycling, and strength training that could all be done at home with minimal equipment. Online classes and streaming services also saw an increase in usage during the pandemic, providing people with access to a wide range of workout options and fitness programs.

This dataset is a comprehensive list of gym exercises that can be used to improve your fitness. It includes exercises for all levels of fitness, from beginners to advanced. The dataset also includes information on the muscles worked by each exercise, the equipment needed, and how to do the exercise safely.

This dataset can be used to create a personalized workout routine that meets your individual fitness goals. You can use the information in the dataset to choose exercises that target the muscles you want to strengthen or tone. You can also use the information to find exercises that are safe for your fitness level.

The dataset is a valuable resource for anyone who wants to improve their fitness. It can be used by beginners to learn the basics of gym exercises, by intermediate exercisers to find new and challenging exercises, and by advanced exercisers to fine-tune their workouts.

Here are some additional tips for using the dataset:

Start with a few exercises and gradually add more as you get stronger. Listen to your body and don't push yourself too hard. Warm up before you start your workout and cool down afterwards. Stay hydrated by drinking plenty of water. Eat a healthy diet to support your fitness goals.

A meticulously compiled dataset providing deep insights into the global fitness industry in 2025. This dataset covers high-demand topics such as the exponential growth of fitness clubs, emerging trends in boutique fitness studios, skyrocketing online fitness training statistics, the flourishing fitness equipment market, and changing consumer behavior and expenditure patterns in the fitness sector.

This dataset is about books. It has 89 rows and is filtered where the book subjects is Statistics-Problems, exercises, etc. It features 9 columns including author, publication date, language, and book publisher.

This data is prepared for CS334 Machine Learning course final project from Emory University. It can also be used and we are very welocome for doing any kind of data analysis and machine learning research by anyone. Data is scraped from vlr.gg: one of the three major websites (vlr.gg, rib.gg, thespike.gg) that give out past Valorant pro matches data. For actual in-game data you can check out rib.gg which may have advanced data for selling. Columns are described in abbreviation and here are the explanation:

name: str
team: str # might be different for the same team
agent: str
rating: float
acs: int # average combat score
k: int # kills
d: int # deaths
a: int # assists
tkmd: int # total kills minus deaths
kast: float # kill, assist, survive, trade %
adr: int # average damage per round
hs: float # headshot %
fk: int # first kills
fd: int # first deaths
fkmd: int # first kills minus first deaths
t # attack side
ct # defend side

Data may have missing values either because they were missing in the original source or the gameplay is so biased (score like 13:0) that one of the team or some of the players did not get to do anything.

The dataset contains information extracted from the website “Codewars” about different programming exercises created with the intention of fostering or developing abilities and competences. Said exercises are called “katas”. Our dataset collects information from recently solved katas and their statistics, applying the "Approved" filter. The total number of katas in that category amounts to 6.932 (18th March, 2021). Information about the date of execution is not available beyond the previously mentioned filter, but the date of publication of the kata is known. Taking this factor into account, the dataset contains katas published between March 2013 and March 2021.

This table contains 1260 series, with data for years 1990 - 1998 (not all combinations necessarily have data for all years), and was last released on 2007-01-29. This table contains data described by the following dimensions (Not all combinations are available): Geography (30 items: Austria; Belgium (Flemish speaking); Belgium; Belgium (French speaking) ...), Sex (2 items: Males; Females ...), Age group (3 items: 11 years;13 years;15 years ...), Frequency of exercise (7 items: Everyday; Once a week;2 to 3 times a week;4 to 6 times a week ...).

Created for Exercise 3 in Digital Preservation Exercises (Course at Vienna University of Technology).

Experiment that generates statistics from open data on sporting institutes in Vienna. The script is written in Python, the data comes as plain text as well as graphs in .eps format. See README.md for details.

Results of the experiment are written in German.

Google data search exercises can be used to practice finding data or statistics on a topic of interest, including using Google's own internal tools and by using advanced operators.

The COVID-19 pandemic that spread across the world at the beginning of 2020 was not only a big threat to public health, but also to the entire sports industry. Many professional and amateur leagues and events were canceled and the public was advised to not spend time in large groups or in public areas. During an April 2020 survey in the United States, 27 percent of respondents stated that they had been exercising less often than usual as a result of the crisis.

Statistics illustrates consumption, production, prices, and trade of Exercise Books in Estonia from 2007 to 2024.

This research examines the relationship between physical exercise and subjective well-being via the mediation of body image and self-esteem, thereby providing some suggestions on the improvement of subjective well-being in college students. A total of 671 college students from three universities of science and engineering in Sichuan, China voluntarily participated in the survey. Descriptive statistics, Pearson’s product-moment correlation, and mediation model analysis were conducted using the SPSS statistics 19.0. The results showed that (1) the physical exercise level was positively and significantly correlated with the subjective well-being level in each dimension (R = 0.12–0.64, p < 0.01) (2) college students with the medium and high level of exercise have higher subjective well-being than those with the low level of exercise, and (3) body image and self-esteem played a complete mediation role between physical exercise and subjective well-being. The mediation analysis revealed two paths: first, the single mediating path via self-esteem [indirect effect = 0.087, 95% CI: (0.037, 0.141)] and second, the serial mediating path via body image and self-esteem [indirect effect = 0.038, 95% CI: (0.021, 0.158)]. Some practical implications have been discussed on the physical exercise intervention for promoting the subjective well-being level in college students.

These files contain the GWAS summary statistics as described in van der Zee, M.D., et al. (2021, manuscript submitted). If summary statistics are used, please make sure to include a reference to the original manuscript.

Statistics illustrates consumption, production, prices, and trade of Exercise Books in Denmark from 2007 to 2024.

Physical fitness is a key element of a healthy life, and being overweight or lacking physical exercise will lead to health problems. Therefore, assessing an individual’s physical health status from a non-medical, cost-effective perspective is essential. This paper aimed to evaluate the national physical health status through national physical examination data, selecting 12 indicators to divide the physical health status into four levels: excellent, good, pass, and fail. The existing challenge lies in the fact that most literature on physical fitness assessment mainly focuses on the two major groups of sports athletes and school students. Unfortunately, there is no reasonable index system has been constructed. The evaluation method has limitations and cannot be applied to other groups. This paper builds a reasonable health indicator system based on national physical examination data, breaks group restrictions, studies national groups, and hopes to use machine learning models to provide helpful health suggestions for citizens to measure their physical status. We analyzed the significance of the selected indicators through nonparametric tests and exploratory statistical analysis. We used seven machine learning models to obtain the best multi-classification model for the physical fitness test level. Comprehensive research showed that MLP has the best classification effect, with macro-precision reaching 74.4% and micro-precision reaching 72.8%. Furthermore, the recall rates are also above 70%, and the Hamming loss is the smallest, i.e., 0.272. The practical implications of these findings are significant. Individuals can use the classification model to understand their physical fitness level and status, exercise appropriately according to the measurement indicators, and adjust their lifestyle, which is an important aspect of health management.

Statistics illustrates consumption, production, prices, and trade of Exercise Books in Benelux from 2007 to 2024.

**Description of the three datasets **

1) PremierLeaguePlayersDataset: This dataset includes statistics ranging from general information such as the goals and assists in a season, to more precise statistics like key passes and dribble attempts. It also includes the player of the year for a given season. Interesting predictive analysis could be done with this attribute. This dataset ranges from the 02/03 season, to the 20/21 season.

2) League Standings: This dataset includes the final standings of a given season. The data ranges from the 10/11 season, to the 20/21. The attributes are the same you may find on the official Premier League site or Sky Sports site (where the data actually comes from)

3) Full Dataset: This dataset merges the two datasets described above. For a given player and season, you have the final ranking of his team. An interesting analysis would be to see the players involvement in the teams goals.

This Synthetic Gym Members Exercise Dataset is created for educational and research purposes in fitness, public health, and data science. It provides detailed demographic, physiological, and workout-related information about gym members, enabling analysis of exercise patterns, health metrics, and fitness progress. The dataset can be utilized for building predictive models and exploring personalized workout and fitness management strategies.

Dataset Features

• Age: Age of the gym member in years.
• Gender: Biological sex of the gym member (Male/Female).
• Weight (kg): Weight of the individual in kilograms.
• Height (m): Height of the individual in meters.
• Max_BPM: Maximum heartbeats per minute during exercise.
• Avg_BPM: Average heartbeats per minute during exercise.
• Resting_BPM: Resting heartbeats per minute.
• Session_Duration (hours): Duration of the exercise session in hours.
• Calories_Burned: Total calories burned during the workout session.
• Workout_Type: Type of workout performed (e.g., HIIT, Yoga, Cardio).
• Fat_Percentage: Body fat percentage of the individual.
• Water_Intake (liters): Water intake during the workout session in liters.
• Workout_Frequency (days/week): Number of workout days per week.
• Experience_Level: Experience level of the gym member (1 = Beginner, 2 = Intermediate, 3 = Advanced).
• BMI: Body Mass Index, calculated as weight (kg) / (height (m))².

Distribution

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Usage

This dataset is suited for the following applications:

• Health and Fitness Insights: Analyze relationships between BMI, workout types, and health metrics like fat percentage or heart rate.
• Personalized Exercise Plans: Develop algorithms to recommend tailored workout routines based on individual fitness levels and goals.
• Calorie Burn Prediction: Build predictive models to estimate calories burned during workout sessions based on key features.
• Public Health Research: Study exercise trends and their impact on health outcomes.
• Fitness Tracking: Use data to monitor individual or group fitness progress over time. ### Coverage This synthetic dataset is anonymized and adheres to data privacy standards. It is designed for research and learning purposes, with diverse cases representing various fitness levels, workout types, and health metrics.

License

CC0 (Public Domain)

Who Can Use It

• Data Science Practitioners: For practicing data preprocessing, regression, and classification tasks related to fitness and health.
• Fitness Professionals and Researchers: To explore trends and patterns in gym members' workout habits and health outcomes.
• Public Health Analysts: To design effective strategies promoting physical activity and healthy lifestyles.
• Policy Makers and Regulators: For data-driven decision-making to promote fitness and public health initiatives.