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.

Around 48 million people in the United States went running or jogging at least once in 2023. Although this figure was higher than what was reported in the previous year, the number of individuals who were running or jogging in the United States was still marginally lower in 2023 than in 2019 and 2020.

The aim of the analysis was to evaluate how single or combinations of weather parameters (temperature, humidity, wind speed, solar load) affect peak performance during endurance running events and identify which events are most vulnerable to varying weather conditions.

Results for the marathon, 50 km race-walk, 20 km race-walk, 10,000 m, 5,000 m and 3,000 m-steeplechase were obtained from the official websites of the largest competitions in the world. Finish times for all races were collected from the first year of each competition for which data were available online until the end of 2019. The collection of these data was completed between February 2016 and September 2020

We obtained the date, time, and location for each race from its official website while the relevant longitude and latitude were obtained from www.locationiq.com. Weather data (air temperature, dew point, wind speed, and cloud coverage) corresponding to the time at half-way in each race were obtained from the closest meteorological station using the official dataset of the National Oceanic and Atmospheric Administration (www.ncei.noaa.gov/data/global-hourly). In cases where these data were not available, we retrieved the information from widely-used meteorology websites (www.wunderground.com and www.weatherspark.com). Wind speed was adjusted for height above the ground and air friction coefficient (i.e., large city with tall buildings). Dew point data were converted to relative humidity. For cases where cloud coverage was not available in the National Oceanic and Atmospheric Administration datasets, the cloud coverage (in okta) was computed using relative humidity data based on previous methodology and applying coefficients of 0.25 for low and high as well as 0.5 for middle clouds, as previously suggested. Solar radiation was calculated using the date, time, and coordinates of each race, while accounting for cloud coverage. Thereafter, the Heat Index, Simplified WBGT and WBGT, were calculated using previous methodology.

Includes the raw IMU data for 20 participants performing seven different running exercises each.

20 participants: 16 m, 4 f, 16 to 31 yo, healthy, do sports regularly Seven exercises: Carioca left, carioca right, heel-to-butt, high-knee running, sideskips left, sideskips right, regular running Four IMUs: accelerometer + gyroscope each, two at wrists, two at ankles Ten seconds per recording under supervision One .json file per recording with sensor values and timestamps

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.

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).

The statistic shows the preferred race distance, according to a survey carried out in 2017. 43 percent of the survey respondents said that they preferred to run half - marathons.

A survey among runners in the United States in February 2022 revealed physical health and fitness as the main reason to go jogging. Meanwhile, 60 percent of runners said they did the activity for the benefit of their mental health.

About the Dataset This dataset was created as part of a study aimed at predicting the calories burned by individuals during running. The dataset includes demographic information such as gender, age, height, and weight, as well as exercise parameters like running time, running speed, distance, and average heart rate. These data can be used to estimate how many calories a person burns while running.

Columns Gender: The gender of the runner (Male/Female). Age: The age of the runner (between 18-65). Height(cm): The height of the runner (between 150-200 cm). Weight(kg): The weight of the runner (between 50-100 kg). BMI: The Body Mass Index of the runner (between 18.5-30). Running Time(min): The duration of the run (between 30-120 minutes). Running Speed(km/h): The average speed during the run (between 7-15 km/h). Distance(km): The distance covered during the run (between 3-15 km). Average Heart Rate: The average heart rate during the run (between 120-180 bpm). Calories Burned: The amount of calories burned during the run (between 200-1000 calories). Use Cases This dataset can be used in various machine learning projects, such as:

Calorie Burn Prediction: Using machine learning algorithms to predict the amount of calories burned during a run. Health and Fitness: Developing health and fitness applications by analyzing user data to provide insights. Data Analysis: Exploring the relationships between demographic characteristics and exercise parameters through data analysis and visualization techniques.

License This dataset is shared under the Creative Commons Attribution 4.0 International (CC BY 4.0) license. This means you are free to share and adapt the data, but you must provide appropriate credit to the original source.

Creator This dataset was created as part of a research project aimed at predicting calorie burn during running.

The statistic shows the factors which impact the participation in running events, according to a survey carried out in 2017. 61 percent of the survey respondents said that a scenic course would influence them to participate in a running event.

According to a survey conducted in October 2021 in Japan, the share of jogging participants amounted to 11.1 percent. This included people who ran marathon distances or trained for marathons. Participation rates remained unfazed by the advent of the COVID-19 pandemic.

The statistic shows the running related activities for which phone or app on phone are commonly used in the United States in 2017. According to the survey, 45 percent of respondents used their phones to track mileage.

The RUN dataset is based on OpenStreetMap (OSM). The map contains rich layers and an abundance of entities of different types. Each entity is complex and can contain (at least) four labels: name, type, is building=y/n, and house number. An entity can spread over several tiles. As the maps do not overlap, only very few entities are shared among them. The RUN dataset aligns NL navigation instructions to coordinates of their corresponding route on the OSM map.

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.

This dataset contains data collected during fatigue detection experiments in running using IMUs.Subjects underwent a fatiguing protocol consisting of three distinct consecutive runs on an athletic track:1. The first run consisted of a 4000 m run (10 laps) at a constant speed, determined as 100% of the average speed of the subject during the best performance in the previous year on a 5 to 10 km race;2. The second run was performed according to a fatiguing protocol. The speed in this fatiguing protocol started at the same level of the first run and increased progres-sively of by 0.2km/h every 100 m. Perceived fatigue was assessed by means of a Borg Rating of Perceived Extertion (RPE) Scale (min-max score 6-20) [20], asked to the runner every 100 m. The fatiguing protocol was terminated once the RPE was higher than 16 (RPE between hard and very hard) , or, if such requirement was not met, after 1200m;3. The third run consisted of a 1200m run (3 laps), in which speed was kept constant and equal to the first 4000 m run. pXXX_XXX_0-2K: contains the Segment and Joint data exported from MVN for the first half of the first runpXXX_XXX_2-4K: contains the Segment and Joint data exported from MVN for the second half of the first runpXXX_XXX_postfatigue1200m: : contains the Segment and Joint data exported from MVN for the third run pXXX_strides: contains the segmented strides from each subject TableFeats: contains values used for the machine learning pipeline, after normalization over each single subject

This dataset provides information about the number of properties, residents, and average property values for Running Fox Way cross streets in Parker, CO.

This dataset contains fully labeled C3D files, OpenSim files and further processed biomechanical data

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: http://geodata.md.gov/imap/rest/services/Society/MD_SportVenues/FeatureServer/55 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.

Humans can run without falling down, usually despite uneven terrain or occasional pushes. Even without such external perturbations, intrinsic sources like sensorimotor noise perturb the running motion incessantly, making each step variable. Here, using simple and generalizable models, we show that even such small step-to-step variability contains considerable information about strategies used to run stably. Deviations in the center of mass motion predict the corrective strategies during the next stance, well in advance of foot touchdown. Horizontal motion is stabilized by total leg impulse modulations, whereas the vertical motion is stabilized by differentially modulating the impulse within stance. We implement these human-derived control strategies on a simple computational biped, showing that it runs stably for hundreds of steps despite incessant noise-like perturbations or larger discrete perturbations. This running controller derived from natural variability echoes behaviors observed in previous animal and robot studies.

1. Do prey run as fast as they can to avoid capture? This is a common assumption in studies of animal performance, yet a recent mathematical model (Wheatley et al. 2015) of escape behaviour predicts that animals should instead use speeds below their maximum capabilities even when running from predators. Fast speeds may compromise motor control and accuracy of limb placement, particularly as the animal runs along narrow structures like beams or branches. Mistakes decrease speed and increase the probability of capture. 2. We tested several key assumptions and predictions of Wheatley et al.'s (2015) model using wild-caught northern quolls (Dasyurus hallucatus), a squirrel-sized marsupial carnivore. We quantified the speeds of quolls as they traversed beams of differing width and expected animals should balance the benefits of higher speeds against the increased probability of mistakes when selecting speeds. 3. We first explored whether the probability of mistakes when running along a beam increased at faster running speeds (speed-accuracy trade-off) and when the difficulty of a task was greater (narrower beam). In addition, we quantified the costs of locomotor mistakes to test the assumption that mistakes decreased overall running speed. Finally, we tested whether individual northern quolls modulated their running speeds when moving on narrow beams, which would decrease the probability of making catastrophic mistakes. 4. We found quolls were more likely to make mistakes when running faster and on the narrower beam. Locomotor mistakes increased the total time needed to traverse the entire beam, and each mistake decreased average escape speed by around 50%, representing a substantial cost for slips or trips. To circumvent the costs of these mistakes, quolls voluntarily reduced speeds in situations when they are more likely to make a mistake (i.e. narrower beams), thereby allowing them to decrease the total time it took to traverse the beam. 5. Our data provide support for the assumptions and predictions of Wheatley et al.'s (2015) model of optimal escape speeds, and suggest that animals optimise rather than simply maximise speeds when running along challenging substrates. Our work provides a foundation for understanding the movement behaviour of animals when their objective is to escape predatory attacks, and demonstrates that animals should select their escape strategy based on how both the speed of movement and motor control affect task success.