Number and percentage of individuals who participate regularly in sport activities by sex, age group and other demographic characteristics, Canada, Geographical region of Canada, province or territory.

Women's Soccer Participation in High Schools

Women's Soccer Participation in High Schools

By Eva Murray [source]

About this dataset

This dataset contains information on participation in high school soccer in the United States from 2006 to 2014. It includes data on the number of schools participating, the number of students participating, and the gender split of participants. This dataset can be used to understand the popularity of soccer among high school students and compare participation rates between boys and girls

How to use the dataset

Research Ideas

• Analyzing the correlation between boys and girls soccer participation in high school and the level of success of each gender's national soccer team.
• Determining which states have the largest disparities between boys and girls soccer participation rates.
• Analyzing how participation rates have changed over time, both nationally and by state

Acknowledgements

Data Source

License

License: Dataset copyright by authors - You are free to: - Share - copy and redistribute the material in any medium or format for any purpose, even commercially. - Adapt - remix, transform, and build upon the material for any purpose, even commercially. - You must: - Give appropriate credit - Provide a link to the license, and indicate if changes were made. - ShareAlike - You must distribute your contributions under the same license as the original. - Keep intact - all notices that refer to this license, including copyright notices.

Columns

File: partcipation_statistics_06_14_2020 10_22.csv | Column name | Description | |:------------------------|:---------------------------------------------------------------------| | Year | The year the data was collected. (Integer) | | State | The state the data was collected from. (String) | | Sport | The sport the data is for. (String) | | Boys School | The number of schools that offered a boys soccer program. (Integer) | | Girls School | The number of schools that offered a girls soccer program. (Integer) | | Boys Participation | The number of boys who participated in soccer. (Integer) | | Girls Participation | The number of girls who participated in soccer. (Integer) |

Acknowledgements

If you use this dataset in your research, please credit Eva Murray.

This dataset contains the number of working and non-working members in youth sport institutions in the State of Qatar, categorized by gender (male/female). The data reflects the engagement of youth members in operational and non-operational roles within sports institutions.The dataset is valuable for analyzing gender participation trends, labor force involvement in the youth sports sector, and planning future programs aimed at youth empowerment and inclusive sports development. It aligns with national youth and sports policy objectives under the Ministry of Sports and Youth.

This dataset provides detailed statistics on individuals who participated in activities organized by youth and sports institutions in the State of Qatar. Data is disaggregated by type of activity, age group, nationality (Qatari, non-Qatari), and gender (male, female). It enables analysis of participation trends in various recreational, cultural, scientific, and physical activities among different population segments.

This dataset, titled "Global College Statistics Dataset", is a synthetic dataset designed to provide a comprehensive overview of academic and demographic data across 50,000 records. It includes key features such as college IDs (e.g., "College 1" to "College 100"), countries, total students, male and female student counts, CGPA, annual family income, academic branches, sports participation, research papers published, placement rates, and faculty counts. The dataset reflects simulated correlations, such as higher family income influencing CGPA and research output impacting placement rates. Created for analytical purposes, this synthetic dataset offers valuable insights into global education trends, student demographics, and institutional performance in a controlled and reproducible environment.

this graph was created in Loocker studiomPowerBi and Tableau:

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F16731800%2F288fa8de92c3630f212ad5ea0d7d864a%2Fgraph1.jpg?generation=1721501353427055&alt=media" alt=""> https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F16731800%2Fba947629d2b148eeb1f80e18bd946493%2Fgraph2.jpg?generation=1721501359166368&alt=media" alt=""> https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F16731800%2F3255b68dea89e749f2b3d01e038475b4%2Fgraph3.png?generation=1721501364380517&alt=media" alt="">

Overview This file contains detailed information on collegiate sports programs across various institutions in the United States. It encompasses data on student enrollment, sports participation, revenue, and expenditures, all categorized by gender and sport. This dataset serves as a valuable resource for analyzing trends, financial aspects, and gender disparities in collegiate sports.

Key Insights Enrollment Data The dataset includes the total number of male and female students enrolled in each institution, providing insights into the gender distribution of the student body. This data is crucial for understanding the demographics of students participating in collegiate sports and can highlight trends in gender representation over time.

Sports Participation Participation data is broken down by gender and sport, allowing for a detailed analysis of gender representation in different sports. This helps in identifying which sports have higher or lower participation rates among men and women, providing a basis for addressing any disparities.

Financial Data The dataset provides detailed information on the revenue and expenditures for men's and women's sports programs. This financial data enables a comprehensive analysis of the economic aspects of collegiate sports, including how funds are allocated and spent, as well as the financial sustainability of different sports programs.

Institutional Classification Institutions are classified by type and sector, such as NCAA Division I, II, and III. This classification helps in comparing different categories of schools, facilitating a deeper understanding of how institutional type and sector influence sports programs and their outcomes.

Context Geography: United States of America Time Period: 2015 - 2019 Unit of Analysis: US Collegiate Sports Dataset This dataset provides a robust foundation for researchers, policymakers, and educational institutions to analyze various aspects of collegiate sports programs, including gender equity, financial health, and participation trends.

📊 Dataset Description

This dataset contains comprehensive leaderboard data from the wondr ITB Ultra Endurance Challenge 2025, one of Indonesia's most prestigious endurance running events. The challenge runs from August 17 to September 13, 2025, bringing together athletes from the Institut Teknologi Bandung (ITB) community and general public participants.

🎯 Overview

The Beyond Run platform hosts virtual and physical running challenges that connect athletes across Indonesia. This particular dataset captures real-time performance metrics from two distinct categories of participants:

• Keluarga Besar ITB (ITB Alumni & Community) - 462+ participants
• UMUM (General Public) - 147+ participants

📁 Dataset Structure

The dataset is provided in multiple formats for maximum compatibility:

Files Included:

├── beyondrun_leaderboard.csv     # Main dataset (UTF-8 encoded)
├── beyondrun_leaderboard.json     # Structured JSON with metadata
├── beyondrun_analysis.csv       # Pre-computed statistics
├── gender_distribution.csv      # Gender analysis by category
├── top_performers_by_category.csv   # Ranked top 20 per category
└── data_dictionary.txt        # Column descriptions

📋 Features (Columns)

🔍 Key Statistics

Total Participants: 609+
Date Range: August 17, 2025 - September 13, 2025
Categories: 2 (ITB Alumni, General Public)
Gender Distribution: ~68% Male, ~32% Female
Average Distance: 125.3 km
Maximum Distance: 282.79 km
Data Points: 4,872+ (including filtered views)

💡 Potential Use Cases

Sports Analytics

• Performance prediction modeling
• Endurance pattern analysis
• Gender-based performance studies
• Community vs public athlete comparison

Machine Learning Applications

• Regression: Distance prediction based on demographics
• Classification: Category/gender prediction
• Clustering: Athlete segmentation
• Time Series: Performance progression analysis
• Anomaly Detection: Identifying exceptional performers

Data Visualization Projects

• Interactive leaderboard dashboards
• Geographic distribution mapping
• Performance distribution analysis
• Gender equity in sports visualization

Research Applications

• Sports science studies
• Community engagement analysis
• Event participation patterns
• Indonesian athletic performance benchmarking

🛠️ Technical Information

Data Collection Method

- Primary: Selenium WebDriver with Chrome (headless mode)
- Fallback: BeautifulSoup HTML parsing
- API: RESTful endpoint discovery attempted
- Rate Limiting: Respectful 2-second delays
- Error Handling: Comprehensive retry logic

Data Quality

• Completeness: 99.8% (minimal missing values)
• Accuracy: Verified against live leaderboard
• Consistency: Standardized formats across all fields
• Timeliness: Real-time data as of collection date
• Deduplication: Verified unique participants

📈 Update Frequency

This dataset is updated: - During Event: Daily automated updates - Post-Event: Weekly for final standings - Historical: Maintained for year-over-year analysis

🔧 Code Examples

Quick Start - Load Data

import pandas as pd
import json

# Load CSV
df = pd.read_csv('beyondrun_leaderboard.csv')
print(f"Total participants: {len(df)}")
print(df.groupby('category')['distance_km'].describe())

# Load JSON for structured access
with open('beyondrun_leaderboard.json', 'r', encoding='utf-8') as f:
  data = json.load(f)
  print(f"Event: {data['event']}")
  print(f"Period: {data['period']}")

Analysis Example

# Top performers by gender
top_by_gender = df.groupby(['category', 'gender']).apply(
  lambda x: x.nlargest(3, 'distance_km')[['name', 'distance_km']]
)

# Performance distribution
import matplotlib.pyplot as plt
df.boxplot(column='distance_km', by='category', figsize=(10, 6))
plt.title('Distance Distribution by Category')
plt.show()

🏷️ Tags

sports running endurance athletics indonesia fitness `leade...

Section 1: Introduction

Brief overview of dataset contents:

Current database contains anonymised data collected during exercise testing services performed on male and female participants (cycling, rowing, kayaking and running) provided by the Human Performance Laboratory, School of Medicine, Trinity College Dublin, Dublin 2, Ireland.

835 graded incremental exercise test files (285 cycling, 266 rowing / kayaking, 284 running)

Description file with each row representing a test file - COLUMNS: file name (AXXX), sport (cycling, running, rowing or kayaking)

Anthropometric data of participants by sport (age, gender, height, body mass, BMI, skinfold thickness,% body fat, lean body mass and haematological data; namely, haemoglobin concentration (Hb), haematocrit (Hct), red blood cell (RBC) count and white blood cell (WBC) count )

Test data (HR, VO2 and lactate data) at rest and across a range of exercise intensities

Derived physiological indices quantifying each individual’s endurance profile

Following a request from athletes seeking assessment by phone or e-mail the test protocol, risks, benefits and test and medical requirements, were explained verbally or by return e-mail. Subsequently, an appointment for an exercise assessment was arranged following the regulatory reflection period (7 days). Following this regulatory period each participant’s verbal consent was obtained pre-test, for participants under 18 years of age parent / guardian consent was obtained in writing. Ethics approval was obtained from the Faculty of Health Sciences ethics committee and all testing procedures were performed in compliance with Declaration of Helsinki guidelines.

All consenting participants were required to attend the laboratory on one occasion in a rested, carbohydrate loaded and well-hydrated state, and for male participants’ clean shaven in the facial region. All participants underwent a pre-test medical examination, including assessment of resting blood pressure, pulmonary function testing and haematological (Coulter Counter Act Diff, Beckmann Coulter, CA,US) review performed by a qualified medical doctor prior to exercise testing. Any person presenting with any cardiac abnormalities, respiratory difficulties, symptoms of cold or influenza, musculoskeletal injury that could impair performance, diabetes, hypertension, metabolic disorders, or any other contra-indicatory symptoms were excluded. In addition, participants completed a medical questionnaire detailing training history, previous personal and family health abnormalities, recent illness or injury, menstrual status for female participants, as well as details of recent travel and current vaccination status, and current medications, supplements and allergies. Barefoot height in metre (Holtain, Crymych, UK), body mass (counter balanced scales) in kilogram (Seca, Hamburg, Germany) and skinfold thickness in millimetre using a Harpenden skinfold caliper (Bath International, West Sussex, UK) were recorded pre-exercise.

Section 2: Testing protocols

2.1: Cycling

A continuous graded incremental exercise test (GxT) to volitional exhaustion was performed on an electromagnetically braked cycle ergometer (Lode Excalibur Sport, Groningen, The Netherlands). Participants initially identified a cycling position in which they were most comfortable by adjusting saddle height, saddle fore-aft position relative to the crank axis, saddle to handlebar distance and handlebar height. Participant’s feet were secured to the ergometer using their own cycling shoes with cleats and accompanying pedals. The protocol commenced with a 15-min warm-up at a workload of 120 Watt (W), followed by a 10-min rest. The GxT began with a 3-min stationary phase for resting data collection, followed by an active phase commencing at a workload of 100 or 120 W for female and male participants, respectively, and subsequently increasing by a 20, 30 or 40 W incremental increase every 3-min depending on gender and current competition category. During assessment participants maintained a constant self-selected cadence chosen during their warm-up (permitted window was 5 rev.min−1 within a permitted absolute range of 75 to 95 rev.min−1) and the test was terminated when a participant was no longer able to maintain a constant cadence.

Heart rate (HR) data were recorded continuously by radio-telemetry using a Cosmed HR monitor (Cosmed, Rome, Italy). During the test, blood samples were collected from the middle finger of the right hand at the end of the second minute of each 3-min interval. The fingertip was cleaned to remove any sweat or blood and lanced using a long point sterile lancet (Braun, Melsungen, Germany). The blood sample was collected into a heparinised capillary tube (Brand, Wertheim, Germany) by holding the tube horizontal to the droplet and allowing transfer by capillary action. Subsequently, a 25μL aliquot of whole blood was drawn from the capillary tube using a YSI syringepet (YSI, OH, USA) and added into the chamber of a YSI 1500 Sport lactate analyser (YSI, OH, USA) for determination of non-lysed [Lac] in mmol.L−1. The lactate analyser was calibrated to the manufacturer’s requirements (± 0.05 mmol.L−1) before each test using a standard solution (YSI, OH, USA) of known concentration (5 mmol.L−1) and analyser linearity was confirmed using either a 15 or 30 mmol.L-1 standard solution (YSI, OH, USA).

Gas exchange variables including respiration rate (Rf in breaths.min-1), minute ventilation (VE in L.min-1), oxygen consumption (VO2 in L.min-1 and in mL.kg-1.min-1) and carbon dioxide production (VCO2 in L.min-1), were measured on a breath-by-breath basis throughout the test, using a cardiopulmonary exercise testing unit (CPET) and an associated software package (Cosmed, Rome, Italy). Participants wore a face mask (Hans Rudolf, KA, USA) which was connected to the CPET unit. The metabolic unit was calibrated prior to each test using ambient air and an alpha certified gas mixture containing 16% O2, 5% CO2 and 79% N2 (Cosmed, Rome, Italy). Volume calibration was performed using a 3L gas calibration syringe (Cosmed, Rome, Italy). Barometric pressure recorded by the CPET was confirmed by recording barometric pressure using a laboratory grade barometer.

Following testing mean HR and mean VO2 data at rest and during each exercise increment were computed and tabulated over the final minute of each 3-min interval. A graphical plot of [Lac], mean VO2 and mean HR versus cycling workload was constructed and analysed to quantify physiological endurance indices, see Data Analysis section. Data for VO2 peak in L.min-1 (absolute) and in mL.kg-1.min-1 (relative) and VE peak in L.min-1 were reported as the peak data recorded over any 10 consecutive breaths recorded during the last minute of the final exercise increment.

2.2: Running protocol

A continuous graded incremental exercise test (GxT) to volitional exhaustion was performed on a motorised treadmill (Powerjog, Birmingham, UK). The running protocol, performed at a gradient of 0%, commenced with a 15-min warm-up at a velocity (km.h-1) which was lower than the participant’s reported typical weekly long run (>60 min) on-road training velocity. Subsequently, the warm-up was followed by a 10 minute rest / dynamic stretching phase. From a safety perspective during all running GxT participants wore a suspended lightweight safety harness to minimise any potential falls risk. The GxT began with a 3-min stationary phase for resting data collection, followed by an active phase commencing at a sub-maximal running velocity which was lower than the participant’s reported typical weekly long run (>60 min) on-road training velocity, and subsequently increased by ≥ 1 km.h-1 every 3-min depending on gender and current competition category. The test was terminated when a participant was no longer able to maintain the imposed treadmill.

Measurement variables, equipment and pre-test calibration procedures, timing and procedure for measurement of selected variables and subsequent data analysis were as outlined in Section 2.1.

2.3: Rowing / kayaking protocol

A discontinuous graded incremental exercise test (GxT) to volitional exhaustion was performed on a Concept 2C rowing ergometer (Concept, VA, US) in rowers or a Dansprint kayak ergometer (Dansprint, Hvidovre, Denmark) in flat-water kayakers. The protocol commenced with a 15-min low-intensity warm-up at a workload (W) dependent on gender, sport and competition category, followed by a 10-min rest. For rowing the flywheel damping (120, 125 or 130W) was set dependent on gender and competition category. For kayaking the bungee cord tension was adjusted by individual participants to suit their requirements. A discontinuous protocol of 3-min exercise at a targeted load followed by a 1-min rest phase to facilitate stationary earlobe capillary blood sample collection and resetting of ergometer display (Dansprint ergometer) was used. The GxT began with a 3-min stationary phase for resting data collection, followed by an active phase commencing at a sub-maximal load 80 to 120 W for rowing, 50 to 90 W for kayaking and subsequently increased by 20,30 or 40 W every 3-min depending on gender, sport and current competition category. The test was terminated when a participant was no longer able to maintain the targeted workload.

Measurement variables, equipment and pre-test calibration procedures, timing and procedure for measurement of selected variables and subsequent data analysis were as outlined in Section 2.1.

3.1: Data analysis

Constructed graphical plots (HR, VO2 and [Lac] versus load / velocity) were analysed to quantify the following; load / velocity at TLac, HR at TLac, [Lac] at TLac, % of VO2 peak at TLac, % of HRmax at TLac, load / velocity and HR at a nominal [Lac] of 2 mmol.L-1, load / velocity, VO2 and [Lac} at a nominal HR of

Participants, A-Es, and injury count in snowboarding male and female athletes involved in the TT program.

OVERVIEW

Title of Dataset: Urinary incontinence in female weightlifters

Reference: doi: 10.1371/journal.pone.0278376. PMID: 36449558; PMCID: PMC9710785.

Author Information

Name: Marianne Huebner
Institution: Michigan State University
Address: East Lansing, MI 48824

Period of data collection: 27 April – 20 May 2022

Geographic region of data collection: Online survey in USA with participants from 29 countries in IWF regions Africa, Asia, Europe, Oceania, PanAmerican

LIST OF FILES
Dataset: wlisi_zenodo.xlsx
Data dictionary: wlisi_meta.xlsx

METHODOLOGICAL INFORMATION

Description of methods used for collection/generation of data: The survey was distributed by the Master Committee of the International Weightlifting Federation (IWF) to the National Master Chairs. They then used email or social media to communicate the study to the women weightlifters. The Survey was available in four languages (English, German, French, Spanish), translated and tested by native speakers. In addition, the survey was advertised in weightlifting interest groups via Facebook and Instagram. The survey was administered online via Qualtrics (Provo, UT, USA).

Methods for processing the data: Data were downloaded from Qualtrics (Provo, UT, USA) to Excel and then pre-processed in the statistical software R v. 4.0.3. (https://www.r-project.org)

Variable formats (numeric, character) were checked and transformed, as appropriate.

Data quality checks: Exclusion criteria were younger than 30 years (n=1), missing age (n=1), currently pregnant (n=3). To account for the possibility of male participants missing responses to age of menstruation or prior pregnancies (n=15), were also excluded. Since the focus was on competitive weightlifters, missing response to age of first competition (n=34) or no snatch or clean and jerk in the last 6 months (n=14) were also exclusion criteria. This resulted in an analysis data set of 824 women. Univariate distributions were evaluated numerically and graphically.

DATA-SPECIFIC INFORMATION

1. Number of variables: 27
2. Number of cases/rows: 824
3. Variable List: wlisi_meta.xlsx
4. Missing data codes: NA

ObjectiveThis study aimed to examine the relationships among psychological resilience, exercise motivation, and sports participation in adolescents, with a focus on demographic differences, the mediating role of exercise motivation, and structural invariance across gender.MethodsA total of 2,588 adolescents from grades 7 to 12 were recruited using stratified random sampling, ensuring representation across school levels and rural–urban residence. Demographic differences were analyzed using independent sample t-tests and one-way ANOVA. Structural equation modeling (SEM) was employed to conduct mediation analysis and multi-group invariance testing.ResultsSignificant demographic differences were observed. Males reported higher levels of psychological resilience, exercise motivation, and sports participation compared to females (p 

ObjectivesHip and groin pain is common in Gaelic games players, but data are limited to elite males with poor representation of females. The aim of this study was to examine the prevalence, severity and factors associated with hip and groin pain and Femoroacetabular Impingement Syndrome (FAIS) in male and female Gaelic games players.MethodsA representative national sample of Gaelic games players completed a survey providing demographic information and details related to self-reported episodes of hip and groin pain and FAIS diagnosis within the last year. Players from multiple age grades, codes (Football/Hurling/Camogie) and levels of Gaelic games were included.ResultsA total of 775 players responded to the survey. The annual prevalence of hip and groin pain was 54.8%. Almost half of players (48.8%) continued to participate in sport, while 18.7% ceased participation and 32.5% reported reduced participation. Although 40% of episodes lasted no longer than 3 weeks, there was a high recurrence rate (33.5%). FAIS was reported by eight players, representing 1.9% of hip and groin complaints. Logistic regression models indicate male sex, playing both codes of Gaelic games and participating in additional sport were significant factors in predicting hip and groin pain.ConclusionHip and groin pain is prevalent in Gaelic Games with FAIS accounting for a small proportion of cases. However, consideration of indicators of severity (participation impact/symptom duration/medical attention) is essential in understanding the context and magnitude of these hip and groin issues. Male players and players engaging in multiple sports are more likely to experience hip and groin pain.

With the aim to investigate psychological resilience factors and vulnerabilities of a dual career construction in late adolescence and how they impact the life course, we have collected longitudinal mixed methods data from 391/453 student-athletes, aged 16/17 at the baseline (T1/T2) and enrolled in 6/7 elite sport upper secondary schools across Finland, their parents/guardians, and coaches. The current dataset consists of: (1) self-report questionnaires of student-athletes gathered 6 times (a) at the beginning of upper secondary school, (b) at the end of the first year, (c) at the end of the second school year, (d) at the beginning of the third school year, (e) at the end of the third school year, and (f) at the beginning of the fourth year; (2) life story interviews, including art-based, visual storytelling, with 18 talented and elite student-athletes at the matching time-points during the upper secondary; (3) self-report questionnaires of the participants’ parents/legal guardians at the beginning and the end of the upper secondary; (4) one-time, semi-structured interviews with 10 male, youth ice-hockey coaches from a club in Finland; (5) one-time, semi-structured interviews with 10 female and male cross-country ski coaches in Finland; and (6) one-time, semi-structured interviews with 15 female and male athletics (track-and-field) coaches from a club in Finland. Student-athlete questionnaires were constructed to examine development of the participants’ motivation, identity, psychological well-being, future orientation and career adaptability resources in and across sport and school contexts. Parental questionnaires were constructed to examine, for example, the role of parenting styles, expectations, own education, athletic background and well-being in the outcome measures of adolescent participants. Life story interviews were designed to obtain a deeper understanding of how young people make sense of their life trajectories in particular socio-cultural contexts, marked by concrete events, relationships, and transitions. Coaches were interviewed to explore the discourses that underpin their coaching philosophy and views on holistic development (e.g., dual career, lifelong participation, life-skills) of gendered athletes and how the derived meanings shape their coaching practice. Data were collected in 2015-2018. Follow-up data on dual career status, athletic and academic achievements, employment, economic situation, dimensions of emerging adulthood (IDEA), mood states, motivation, identity, psychological well-being, career adaptability resources, and impact of COVID-19 on young people’s life course were collected in Autumn 2021, when they transitioned to early adulthood (n=238).

Data on total minutes of exercise, club membership, sports event attendance and opportunity to be physically active to Local Authority District level. Figures also provided for male and female.

The General Directorate of Sport of the City of Madrid together with the municipal districts offers each sports season a wide range of sports activities of a closed nature both in sports modalities, as well as in number of places and schedules. In this data set, you can obtain monthly for each district and sports facility information on the offer for each of the groups, the number of occupied places broken down by men and women participants, the number of free places, the sports modality. It is understood that a group has a closed place when the participants are assigned a fixed place. You can also get monthly information for each district and sports facility on the number of sessions, offer and occupancy in the sessions of open and free-to-use directed sports activities with prior reservation. The information published corresponds to the current sports season and is updated on a monthly basis in the first days of each month. The data correspond to the sports activities that take place in the municipal sports centers that are managed and the provision of the service is charged directly by the Madrid City Council. It does not include those sports facilities of municipal ownership whose management is carried out by a third party.

This dataset contains comprehensive information on 2,392 high school students, detailing their demographics, study habits, parental involvement, extracurricular activities, and academic performance. The target variable, GradeClass, classifies students' grades into distinct categories, providing a robust dataset for educational research, predictive modeling, and statistical analysis.

Table of Contents

1. Student Information
   • Student ID
   • Demographic Details
   • Study Habits
2. Parental Involvement
3. Extracurricular Activities
4. Academic Performance
5. Target Variable: Grade Class

Student Information

Student ID

• StudentID: A unique identifier assigned to each student (1001 to 3392).

Demographic Details

• Age: The age of the students ranges from 15 to 18 years.
• Gender: Gender of the students, where 0 represents Male and 1 represents Female.
• Ethnicity: The ethnicity of the students, coded as follows:
  • 0: Caucasian
  • 1: African American
  • 2: Asian
  • 3: Other
• ParentalEducation: The education level of the parents, coded as follows:
  • 0: None
  • 1: High School
  • 2: Some College
  • 3: Bachelor's
  • 4: Higher

Study Habits

• StudyTimeWeekly: Weekly study time in hours, ranging from 0 to 20.
• Absences: Number of absences during the school year, ranging from 0 to 30.
• Tutoring: Tutoring status, where 0 indicates No and 1 indicates Yes.

Parental Involvement

• ParentalSupport: The level of parental support, coded as follows:
  • 0: None
  • 1: Low
  • 2: Moderate
  • 3: High
  • 4: Very High

Extracurricular Activities

• Extracurricular: Participation in extracurricular activities, where 0 indicates No and 1 indicates Yes.
• Sports: Participation in sports, where 0 indicates No and 1 indicates Yes.
• Music: Participation in music activities, where 0 indicates No and 1 indicates Yes.
• Volunteering: Participation in volunteering, where 0 indicates No and 1 indicates Yes.

Academic Performance

• GPA: Grade Point Average on a scale from 2.0 to 4.0, influenced by study habits, parental involvement, and extracurricular activities.

Target Variable: Grade Class

• GradeClass: Classification of students' grades based on GPA:
  • 0: 'A' (GPA >= 3.5)
  • 1: 'B' (3.0 <= GPA < 3.5)
  • 2: 'C' (2.5 <= GPA < 3.0)
  • 3: 'D' (2.0 <= GPA < 2.5)
  • 4: 'F' (GPA < 2.0)

Conclusion

This dataset offers a comprehensive view of the factors influencing students' academic performance, making it ideal for educational research, development of predictive models, and statistical analysis.

Dataset Usage and Attribution Notice

This dataset, shared by Rabie El Kharoua, is original and has never been shared before. It is made available under the CC BY 4.0 license, allowing anyone to use the dataset in any form as long as proper citation is given to the author. A DOI is provided for proper referencing. Please note that duplication of this work within Kaggle is not permitted.

Exclusive Synthetic Dataset

This dataset is synthetic and was generated for educational purposes, making it ideal for data science and machine learning projects. It is an original dataset, owned by Mr. Rabie El Kharoua, and has not been previously shared. You are free to use it under the license outlined on the data card. The dataset is offered without any guarantees. Details about the data provider will be shared soon.

Concussion BASICS Supplementary TablesSupplementary tables corresponding to the Concussion BASICS project. Tables A and B are relevant to Concussion BASICS: Part 1 - The Normative Study. Table C is relevant to Concussion BASICS: Part 3 - Diagnostic Accuracy Following Sport-Related COncussionDryad Tables.docx

Description of the Study Design The running intervention of this study was organized into seven units of about 50-60 minutes each, conducted over a time period of two weeks. The standardized running route lead through a mostly forest area at a local recreation area and was about five kilometers long. The design of this study included two groups of participants who were tested at three time points of assessment. A sample of 68 participants was recruited for this study. Out of this sample, 48 participants completed all required MRI scans and psychometric assessments and participated in the running intervention. We primarily recruited rather unathletic people showing no or only low regular engagement in sports activities. Participants indicated to exercise about half an hour per week (M = 0.53; SD = 1.2). Participants were randomly assigned to two intervention groups, which received the running intervention time-delayed. The first group ("intervention group") performed the running intervention between the first (t1) and the second test session (t2), while the second group ("wait group") received the training between t2 and the third test session (t3).At each time point of assessment (t1, t2, and t3), the German version of the Center for Epidemiological Studies Depression Scale (CES-D; Hautzinger et al., 2012) was administrated to test intervention related changes in depressive symptoms. Available Data / Folder Structure / Data Dictionary The dataset include the MRI data of n=48 participants who completed all three MRI scans (at t1, t2, t3). Specifically, for each single participant (e.g., “sub-season101”) 3 subfolders of MRI data (“ses-1”, “ses-2”, and “ses-3”, for each time point of assessment) are available. The CES-D scores for each participant and time point of assessment (CES-D_1, CES-D_2, CES-D_3) can be found in the “phenotype” folder (“CES-D.tsv”) In addition, the file “participants.tsv” contains the grouping variable (either “1” for training group 1 or “2” for training group 2), along with age (in years), sex (“F” female, “M” male), size (in meter) and weight (in kg) of the participants. For quality assurance we performed the mriqc-pipline (https://mriqc.readthedocs.io/en/latest/) for all subjects, which can be found under der folder “derivatives”/subfolder “mriqc”. The raw BIDS data was created using BIDScoin 3.0.8 All provenance information and settings can be found in ./code/bidscoin For more information see: https://github.com/Donders-Institute/bidscoin

Beach Volleyball

The data this week comes from Adam Vagnar who also blogged about this dataset. There's a LOT of data here - match-level results, player details, and match-level statistics for some matches. For all this dataset all the matches are played 2 vs 2, so there are columns for 2 winners (1 team) and 2 losers (1 team). The data is relatively ready for analysis and clean, although there are some duplicated columns and the data is wide due to the 2-players per team.

Check out the data dictionary, or Wikipedia for some longer-form details around what the various match statistics mean.

Most of the data is from the international FIVB tournaments but about 1/3 is from the US-centric AVP.

The FIVB Beach Volleyball World Tour (known between 2003 and 2012 as the FIVB Beach Volleyball Swatch World Tour for sponsorship reasons) is the worldwide professional beach volleyball tour for both men and women organized by the Fédération Internationale de Volleyball (FIVB). The World Tour was introduced for men in 1989 while the women first competed in 1992.

Winning the World Tour is considered to be one of the highest honours in international beach volleyball, being surpassed only by the World Championships, and the Beach Volleyball tournament at the Summer Olympic Games.

FiveThirtyEight examined the disadvantage of serving in beach volleyball, although they used Olympic-level data. Again, Adam Vagnar also covered this data on his blog.

What is Tidy Tuesday?

TidyTuesday A weekly data project aimed at the R ecosystem. As this project was borne out of the R4DS Online Learning Community and the R for Data Science textbook, an emphasis was placed on understanding how to summarize and arrange data to make meaningful charts with ggplot2, tidyr, dplyr, and other tools in the tidyverse ecosystem. However, any code-based methodology is welcome - just please remember to share the code used to generate the results.

Join the R4DS Online Learning Community in the weekly #TidyTuesday event! Every week we post a raw dataset, a chart or article related to that dataset, and ask you to explore the data. While the dataset will be “tamed”, it will not always be tidy!

We will have many sources of data and want to emphasize that no causation is implied. There are various moderating variables that affect all data, many of which might not have been captured in these datasets. As such, our guidelines are to use the data provided to practice your data tidying and plotting techniques. Participants are invited to consider for themselves what nuancing factors might underlie these relationships.

The intent of Tidy Tuesday is to provide a safe and supportive forum for individuals to practice their wrangling and data visualization skills independent of drawing conclusions. While we understand that the two are related, the focus of this practice is purely on building skills with real-world data.

This dataset contains information on long-distance running events held in various places, including event dates, names, participant counts, athlete performance times, age categories, speeds, and birth years. It provides insights into the demographic and performance characteristics of urban marathons over two centuries.

Exploring Trends and Patterns in Marathon Data

column explanation

Year of Event: The year when the marathon event took place.

Event Dates: The exact date of the event in day, month, and year format (e.g., 06.01.2018 represents January 6, 2018).

Event Name: The name of the marathon or running event, often including the location and country abbreviation (e.g., "Selva Costera (CHI)" indicates it took place in Chile).

Event Distance/Length: The length of the race course, usually measured in kilometers (e.g., 50 km).

Event Number of Finishers: The total number of participants who successfully finished the event.

Athlete Performance: The recorded completion time of each athlete, showing hours, minutes, and seconds (e.g., "4:51:39 h" means 4 hours, 51 minutes, and 39 seconds).

Athlete Club: The sports club or team with which the athlete is affiliated. This may be blank if the athlete is unaffiliated.

Athlete Country: The country code representing the athlete's nationality (e.g., CHI for Chile, ARG for Argentina).

Athlete Year of Birth: The athlete's birth year, which helps to estimate the athlete's age during the event.

Athlete Gender: The athlete's gender, represented by "M" for male and "F" for female.

Athlete Age Category: The age category of the athlete, typically indicated by a letter representing gender (M or F) and a number representing age range (e.g., M35 refers to a male in the 35-year-old age category).

Athlete Average Speed: The average speed of the athlete during the event, calculated based on distance and performance time, measured in kilometers per hour (km/h).

Athlete ID: A unique identifier assigned to each athlete in the dataset, useful for distinguishing individual records.