This dataset contains 10,703,690 records of running training during 2019 and 2020, from 36,412 athletes from around the world. The records were obtained through web scraping of a large social network for athletes on the internet.The data with the athletes' activities are contained in dataframe objects (tabular data) and saved in the Parquet file format using the Pandas library, part of the Python ecosystem for data science. Each Pandas dataframe contains the following data (as different columns) for each athlete (as different rows), the first word identifies the name of the column in the dataframe:- datetime: date of the running activity;- athlete: a computer-generated ID for the athlete (integer);- distance: distance of running (floating-point number, in kilometers);- duration: duration of running (floating-point number, in minutes);- gender: gender (string 'M' of 'F');- age_group: age interval (one of the strings '18 - 34', '35 - 54', or '55 +');- country: country of origin of the athlete (string);- major: marathon(s) and year(s) the athlete ran (comma-separated list of strings).For convenience, we created files with the athletes' activities data sampled at different frequencies: day 'd', week 'w', month 'm', and quarter 'q' (i.e., there are files with the distance and duration of running accumulated at each day, week, month, and quarter) for each year, 2019 and 2020. Accordingly, the files are named 'run_ww_yyyy_f.parquet', where 'yyyy' is '2019' or '2020' and 'f' is 'd', 'w', 'm' or 'q' (without quotes). The dataset also contains data with different government’s stringency indexes for the COVID-19 pandemic. These data are saved as text files and were obtained from https://ourworldindata.org/covid-stringency-index. The Jupyter notebooks that we created and made available in the https://github.com/BMClab/covid19 repository exemplify the use of the data.

Human Running

## Overview

Human Running is a dataset for object detection tasks - it contains Running annotations for 2,118 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

This dataset has been shared by Nike, encompassing a comprehensive record of all my running activities logged through the Nike Run Club App over three years. This rich dataset includes detailed information on each run, capturing metrics such as distance, pace, and time among others. It reflects my journey of commitment and endurance, punctuated by rigorous training sessions, recovery runs, and personal milestones. A significant highlight of this dataset is its documentation of my dedicated preparation for a major athletic goal—the Paris Marathon 2023. Through this dataset, one can trace my progress, challenges overcome, and the evolution of my running performance over time, offering valuable insights into the discipline and resilience required to train for such a prestigious long-distance running event.

This statistic shows the number of participants in trail running in the United States from 2006 to 2017. In 2017, there were approximately **** million participants in trail running in the U.S., up from **** million the previous year.

The data set comprises raw and processed lower extremity gait kinematics and kinetics signals of 39 subjects in different file formats (c3d and txt). A file of metadata (in txt and xls formats), including demographics, running characteristics, foot-strike patterns, and muscle strength and flexibility measurements is provided. In addition, a model file (mdh) and a pipeline file (v3s) for the Visual 3D software program are also provided. The data were collected using a three-dimensional (3D) motion-capture system and an instrumented treadmill while the subjects ran at 2.5 m/s, 3.5 m/s, and 4.5 m/s wearing standard neutral shoes.

This statistic shows the number of youth participants in running in the United States from 2006 to 2020. According to the source, the number of youth participants (aged between six and 17 years) in running amounted to approximately 10.9 million in 2020.

Project aims to investigate congestion within running events. On this record available and open are; race director surveys, runner surveys, post running event surveys, excel document of flow rate, density and raw timing data from running events.

Data collected and not publicly available includes focus group meeting audio recordings and transcriptions (3 meetings 1hr in duration each) and video footage of running event start lines (approximately 2 hrs of video footage).

This dataset is about books. It has 2 rows and is filtered where the book is Up and running : your 8-week plan to go from 0-5k and beyond and disover the life-changing power of running!. It features 7 columns including author, publication date, language, and book publisher.

OverviewBiomechanics dataset of human subjects (N=1798) walking and or running on a treadmill. Data include 3D marker positions over trials ranging from 25-60 seconds. Also included are demographic information and calculated variables of interest (step with, stride rate, peak knee flexion angle, etc...), sample processing code, and data analysis tutorials.This dataset accompanies an article with the following citation:Ferber R, Brett A, Fukuchi RK, Hettinga B, Osis ST. (2024). A Biomechanical Dataset of 1,798 Healthy and Injured Subjects During Treadmill Walking and Running. Scientific Data - Nature. 11:1232 | https://doi.org/10.1038/s41597-024-04011-7Data DescriptionContained within this dataset are 4 categories of files. They consist of datafiles (.JSON format ->2506 files), metadata (.CSV format ->2 files), Matlab processing code (.M, .MAT format -> 8 files) and Matlab tutorial files (.M, .MLX, .MAT format -> 8 files). All code which accompanies this dataset (processing and tutorials) can be found in the "supplementary_materials.zip" file.Data files are contained within the zipped folder "ric_data" which itself a contains series of folders with names representing the subject ID. Each subject ID folder contains timestamped datafile(s) in ".json" format with each containing walking and/or running data from a single collection session.MethodsThree-dimensional (3D) marker trajectory data were captured using either a 3-camera or an 8-camera VICON motion capture system (Bonita or MX3+, Vicon Motion Systems Oxford, UK) while participants walked or ran on a treadmill. Spherical retro-reflective markers were placed on anatomical landmarks and rigid plates with clusters of 3-4 markers were placed on each of seven lower body segments as per Pohl et al. (Gait Posture. 2010;32(4):559-563.). The marker-set consisted of seven rigid segments and followed International Society of Biomechanics standards. To allow for unobstructed movement during running, anatomical markers were removed following a one second static trial where subjects stood upon a template with their feet positioned straight ahead and 0.3m apart with arms crossed over their chest. Following a warmup period of 2-5mins, kinematic data were collected for approximately 60 seconds while participants walked and then ran at a self-selected speed.Data were collected at the University of Calgary Running Injury Clinic as part of research studies or as part of clinical practice between 2009 and 2017. All subjects provided informed consent and all data were collected under approval from the University of Calgary's Conjoint Health Research Ethics Board (CHREB) (Ethics IDs: E–21705, E–22194, E–24339). In total, n=1197 (67%) can be considered unique datasets and have not been published in previous scientific manuscripts. However, 33% of the dataset (n=601) were recruited for specific research studies and as such, have been used in previously published works including comparisons between recreational and competitive runners, healthy and knee osteoarthritis patients , developing novel methods for MoCap marker placement, and determination of subgroups in healthy and injured runners. Please see accompanying paper for references to these studies.More detailsMore details regarding this dataset can be found in the README file. This file contains more detailed descriptions of the contents of the datafiles, processing code, and tutorial code.LicensingThe data is protected under a CC BY 4.0 license. All scripts and functions are protected under a permissive MIT license which can be found in the file LICENSE.txt.

The statistic shows the running related websites most often visited by runners in the United States in 2016. According to the survey, ** percent of respondents regularly visited local club websites.

The Running Apparel Report is Segmented by Product Types (Tops, Bottoms, Outerwear, Socks and Accessories), Fabric and Material (Synthetic, and More), End User (Men, Women, Kids), Distribution Channels (Offline Stores, Online Stores), and Geography (North America, South America, Europe, Asia-Pacific, Middle East and Africa). The Market Forecasts are Provided in Terms of Value (USD) and Volume (Units).

The statistic shows runners' favorite running race distance according to a survey carried out in late 2017. Twenty-two percent of the survey respondents said that their favorite race distance was ten kilometers.

This statistic shows the number of young adult participants in running in the United States from 2006 to 2017. In 2017, there were approximately *** million young adult participants (aged between 18 and 24 years) in running in the U.S.

Urban Running Club Market Outlook

According to our latest research, the global Urban Running Club market size stood at USD 2.7 billion in 2024, reflecting the growing enthusiasm for fitness and community-based wellness activities in urban environments. The market is projected to expand at a robust CAGR of 6.2% over the forecast period, reaching a value of USD 4.6 billion by 2033. This growth trajectory is primarily driven by increasing health awareness, urbanization, and the proliferation of social and competitive running platforms that cater to diverse demographics. As per our analysis, the market’s upward momentum is further supported by the integration of digital engagement tools and wellness initiatives, which are transforming the urban running experience globally.

A key growth factor for the Urban Running Club market is the rising prioritization of health and wellness among urban populations. As sedentary lifestyles and work-related stress become more prevalent in cities, individuals are increasingly seeking accessible and engaging ways to stay active. Urban running clubs offer an appealing solution by providing structured group activities, professional coaching, and a sense of accountability, which collectively foster regular participation. Moreover, the growing body of research linking physical activity to improved mental health is motivating urban dwellers to join running clubs not just for fitness, but also for stress relief and social connection. This holistic approach to well-being is driving both membership numbers and retention rates, contributing to steady market expansion.

Technological advancements are also playing a pivotal role in the evolution of the Urban Running Club market. The widespread adoption of fitness tracking apps, GPS-enabled wearables, and social media platforms has significantly enhanced the running club experience. Clubs are leveraging these technologies to organize virtual events, track member progress, and facilitate real-time communication, making participation more interactive and personalized. Additionally, partnerships with fitness brands and the integration of wellness programs—such as nutrition workshops and mindfulness sessions—have added value to club memberships. This tech-enabled transformation is not only attracting younger, tech-savvy members but also enabling clubs to scale their operations and reach a wider audience, further fueling market growth.

Another significant driver is the increasing emphasis on community engagement and corporate wellness initiatives. Many organizations are recognizing the benefits of supporting employee health through running clubs, leading to the rise of corporate and community-based clubs. These clubs often collaborate with local governments, non-profits, and brands to organize public races, charity events, and wellness challenges, thereby strengthening their presence and impact in urban areas. Such initiatives foster a sense of belonging and purpose among members, which is especially important in densely populated cities where individuals may feel isolated. This community-centric approach is not only expanding the market’s reach but also enhancing the social value of urban running clubs.

Regionally, North America and Europe continue to dominate the Urban Running Club market, accounting for a significant share of global revenues. However, Asia Pacific is emerging as a high-growth region, driven by rapid urbanization, increasing disposable incomes, and a burgeoning middle class that values fitness and social activities. Latin America and the Middle East & Africa are also witnessing rising interest, albeit from a smaller base, as urban infrastructure improves and health awareness campaigns gain traction. The regional landscape is characterized by diverse club models and varying levels of digital adoption, reflecting the unique cultural and socioeconomic dynamics of each market.

Club Type Analysis

The Urban Running Club market is segmented by club type into Social Running Clubs, Competitive Runn

The statistic shows the preferred race distance, according to a survey carried out in 2017. ** percent of the survey respondents said that they preferred to run half - marathons.

Subjects`anthropometric data, training history and weekly running mileage.

The Trail Running Shoes Market Report is Segmented by Product Type (Light Trail Shoes, Rugged/Technical Trail Shoes), End User (Men, Women, Kids), Category (Mass, Premium), Distribution Channel (Online Retail Stores, Specialty Stores, Supermarkets/Hypermarkets, Others), and Geography (North America, Europe, Asia-Pacific, South America, Middle East and Africa). The Market Forecasts are Provided in Terms of Value (USD).

This dataset is about books. It has 4 rows and is filtered where the book subjects is Marathon running-Psychological aspects. It features 9 columns including author, publication date, language, and book publisher.

This is a MD iMAP hosted service layer. Find more information at http://imap.maryland.gov. Maryland Sports (http://www.marylandsports.us/) has identified sport venues located within the State of Maryland. These venues offer opportunities to participate in free and fee-based - organized and pick-up - indoor and outdoor sports and physical fitness related activities in the area of Running Sports. Last Updated: 08/2014 Feature Service Layer Link: https://mdgeodata.md.gov/imap/rest/services/Society/MD_SportVenues/FeatureServer ADDITIONAL LICENSE TERMS: The Spatial Data and the information therein (collectively "the Data") is provided "as is" without warranty of any kind either expressed implied or statutory. The user assumes the entire risk as to quality and performance of the Data. No guarantee of accuracy is granted nor is any responsibility for reliance thereon assumed. In no event shall the State of Maryland be liable for direct indirect incidental consequential or special damages of any kind. The State of Maryland does not accept liability for any damages or misrepresentation caused by inaccuracies in the Data or as a result to changes to the Data nor is there responsibility assumed to maintain the Data in any manner or form. The Data can be freely distributed as long as the metadata entry is not modified or deleted. Any data derived from the Data must acknowledge the State of Maryland in the metadata.

Discover Market Research Intellect's Running Clothes Market Report, worth USD 45 billion in 2024 and projected to hit USD 65 billion by 2033, registering a CAGR of 5.5% between 2026 and 2033.Gain in-depth knowledge of emerging trends, growth drivers, and leading companies.