Comprehensive dataset of 22 Running stores in Turkey as of August, 2025. Includes verified contact information (email, phone), geocoded addresses, customer ratings, reviews, business categories, and operational details. Perfect for market research, lead generation, competitive analysis, and business intelligence. Download a complimentary sample to evaluate data quality and completeness.

🏃 View data in the dashboard

Trail running races are much like regular road running races except that they are off-road by definition, and generally longer than your typical road race. The premier global trail running race is the Ultra-Trail du Mont-Blanc, held in Chamonix-Mont-Blanc, France. It is a 174 km (108 mi) race with 10,000 m (33,000 ft) of elevation gain. This race is organized by the UTMB Group. In addition to the UTMB, they organize 40+ prominent races worldwide called the “UTMB World Series”. Their website, UTMB.world, contains the results of these events. But, to my surprise, it also contains the results of tens of thousands of other trail running races.

Data was scraped from UTMB.world in two steps: First, I had to find all unique race identifiers (race UIDs) since they are erratic, and then I had to scrape each year held. In total, I found 15,679 race UIDs ranging from 4 to 45,846. The total number of races held was 38,460. See the "Scripts used in data collection" notebook for all methods. Second I scraped the content of these all unique race UIDs.

This data collection contains raw scraped data and an easy-to-read CSV file. Please look at the raw file's documentation for more information. The CSV file is much smaller and easy to interpret because: categorical fields were summarized to integers and the array of result times was aggregated to First, Last and Mean finish times as well as the number of participants that did not finish (N DNF).

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

Centre of mass acceleration data was collected while runners ran a long distance overground run. This was for a PhD project investigating running bomechanics related to running performance and runnng injuries.

Centre of mass acceleration data was captured from a tri-axial accelerometer attached to the lower back of runners while they ran for 8 km around an outdoor athletics track.

Comprehensive dataset of 3,288 Running stores in United States as of June, 2025. Includes verified contact information (email, phone), geocoded addresses, customer ratings, reviews, business categories, and operational details. Perfect for market research, lead generation, competitive analysis, and business intelligence. Download a complimentary sample to evaluate data quality and completeness.

Data accompanying manuscript Data of 7 runners during a Marathon is provided to accompany the manuscript “Quantifying and correcting for speed and stride frequency effects on running mechanics in fatiguing outdoor running”. For each runner the following files are provided: - Global Navigation Satellite System (GNSS) running speed - Inertial Measurement unit (IMU) running speed - Acceleration of foot sensor - Acceleration of lower leg/tibia sensor - Knee angles GNSS running speed GNSS running speed during the full marathon was based on different sports watches. Sampling frequencies between sports watches differed but was on average 0.7 (0.4) Hz. In line with the manuscript, missing latitude-longitude data was linearly interpolated before speed was computed as the distance between two latitude-longitude coordinates based on the Haversine formula. GNSS speeds above 20 km/h were deemed extremely unlikely and replaced with spline interpolation. GNSS speed was then resampled to 240 Hz to match the sampling frequency of the IMUs. Note that GNSS and IMU data are not yet time synchronized! - Filename: SubXX_gnss_speed.csv - Size of matrix: [1xN] IMU running speed IMU running speed was solely used for time synchronization of the GNSS running speed with IMU data. The scaled biomechanical model (as described in the manuscript) provided the velocity of the pelvis segment at 240 Hz. Pelvis IMU speed was then computed as the resultant pelvis IMU velocity. - Filename: SubXX_imu_speed.csv - Size of matrix: [Nx1] Acceleration of foot sensor Accelerations of the right foot were used for initial contact detection in the manuscript. 3D accelerations of a sensor on the right foot are provided in a sensor-fixed coordinate system. The sensor was placed on the midfoot within the shoes, the sensor was aligned with the long axis of the foot. The positive axis of the first dimension points towards the center of the ankle joint. The positive axis of the second dimension points to the right. The positive axis of the third dimensions is directed approximately upwards. - Filename: SubXX_rfoot_acc.csv - Size of matrix: [Nx3] Acceleration of lower leg/tibia sensor Accelerations of the lower leg were one of the quantities of interest in the manuscript. 1D acceleration of a sensor on the right lower leg at 240 Hz is provided in a sensor-fixed coordinate system. The sensor was aligned with the axial direction of the tibia. - Filename: SubXX_rtibia_acc.csv - Size of matrix: [Nx1] Knee angles Knee flexion/extension angles were one of the quantities of interest in the manuscript. Knee flexion/extension angles of the right lower leg at 240 Hz are provided. Knee flexion angles were defined 0° when the leg was fully extended during neutral standing. Flexion resulted in positive knee flexion angles. - Filename: SubXX_rknee_angle.csv - Size of matrix: [Nx1]

In April 2017, the Intelligence Advanced Research Projects Activity (IARPA) held a dry run for the data collection portion of its Nail to Nail (N2N) Fingerprint Challenge. This data collection event was designed to ensure that the real data collection event held in September 2017 would be successful. To this end, biometric data from unhabituated individuals needed to be collected. That data is now released by NIST as Special Database 301.In total, 14 fingerprint sensors were deployed during the data collection, amassing a series of rolled and plain images. The devices include rolled fingerprints captured by skilled experts from the Federal Bureau of Investigation (FBI) Biometric Training Team. Captures of slaps, palms, and other plain impression fingerprint impressions were additionally recorded. NIST also partnered with the FBI and Schwarz Forensic Enterprises to design activity scenarios in which subjects would likely leave fingerprints on different objects. The activities and associated objects were chosen in order to use a number of latent print development techniques and simulate the types of objects often found in real law enforcement case work. NIST also collected some mugshot-style face and iris images of the subjects who participated in the dry run. These data are also available for download.

Comprehensive dataset of 219 Running stores in Texas, United States as of July, 2025. Includes verified contact information (email, phone), geocoded addresses, customer ratings, reviews, business categories, and operational details. Perfect for market research, lead generation, competitive analysis, and business intelligence. Download a complimentary sample to evaluate data quality and completeness.

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.

as well as reproducing the paper

Germany Electricity Generation: Running Water data was reported at 1,305,168.000 MWh in Jan 2025. This records a decrease from the previous number of 1,323,512.000 MWh for Dec 2024. Germany Electricity Generation: Running Water data is updated monthly, averaging 1,286,396.000 MWh from Jan 2002 (Median) to Jan 2025, with 277 observations. The data reached an all-time high of 1,747,816.000 MWh in May 2005 and a record low of 646,486.000 MWh in Nov 2018. Germany Electricity Generation: Running Water data remains active status in CEIC and is reported by Statistisches Bundesamt. The data is categorized under Global Database’s Germany – Table DE.RB006: Electricity Generation. [COVID-19-IMPACT]

Ghana E-Commerce Transactions: AOV: Sports: Running data was reported at 115.436 USD in 22 Nov 2024. This records an increase from the previous number of 110.607 USD for 21 Nov 2024. Ghana E-Commerce Transactions: AOV: Sports: Running data is updated daily, averaging 135.933 USD from Mar 2019 (Median) to 22 Nov 2024, with 129 observations. The data reached an all-time high of 4,057.932 USD in 29 Aug 2023 and a record low of 37.457 USD in 02 Dec 2023. Ghana E-Commerce Transactions: AOV: Sports: Running data remains active status in CEIC and is reported by Grips Intelligence Inc.. The data is categorized under Global Database’s Ghana – Table GH.GI.EC: E-Commerce Transactions: by Category.

Jordan E-Commerce Transactions: Volume: Sports: Running data was reported at 1.000 Unit in 17 Oct 2024. This stayed constant from the previous number of 1.000 Unit for 16 Oct 2024. Jordan E-Commerce Transactions: Volume: Sports: Running data is updated daily, averaging 3.000 Unit from Feb 2019 (Median) to 17 Oct 2024, with 167 observations. The data reached an all-time high of 22.000 Unit in 07 Oct 2023 and a record low of 1.000 Unit in 17 Oct 2024. Jordan E-Commerce Transactions: Volume: Sports: Running data remains active status in CEIC and is reported by Grips Intelligence Inc.. The data is categorized under Global Database’s Jordan – Table JO.GI.EC: E-Commerce Transactions: by Category.

Files contain all data and code necessary to reproduce findings in the paper.

This project contains the database used to re-parametrize the ReaxFF force field for LiF, an inorganic compound. The purpose of the database is to improve the accuracy and reliability of ReaxFF calculations for LiF. The results and method used were published in the article Enhancing ReaxFF for Lithium-ion battery simulations: An interactive reparameterization protocol. This database was made using the simulation obtained using the protocol published in Enhancing ReaxFF repository. Installation To use the database and interact with it, ensure that you have the following Python requirements installed: Minimum Requirements: Python 3.9 or above Atomic Simulation Environment (ASE) library Jupyter Lab Requirements for Re-running or Performing New Simulations: SCM (Software for Chemistry & Materials) Amsterdam Modeling Suite PLAMS (Python Library for Automating Molecular Simulation) library You can install the required Python packages using pip: pip install -r requirements.txt Warning: Make sure to have the appropriate licenses and installations of SCM Amsterdam Modeling Suite and any other necessary software for running simulations. Folder Structure The project has the following folder structure: . ├── CONTRIBUTING.md ├── CREDITS.md ├── LICENSE ├── README.md ├── requirements.txt ├── assets ├── data │ ├── LiF.db │ ├── LiF.json │ └── LiF.yaml ├── notebooks │ ├── browsing_db.ipynb │ └── running_simulation.ipynb └── tools ├── db ├── plams_experimental └── scripts CONTRIBUTING.md: This file provides guidelines and instructions for contributing to the repository. It outlines the contribution process, coding conventions, and other relevant information for potential contributors. CREDITS.md: This file acknowledges and credits the individuals or organizations that have contributed to the repository. LICENSE: This file contains the license information for the repository (CC BY 4.0). It specifies the terms and conditions under which the repository's contents are distributed and used. README.md: This file. requirements.txt: This file lists the required Python packages and their versions. (see installation section) assets: This folder contains any additional assets, such as images or documentation, related to the repository. data: This folder contains the data files used in the repository. LiF.db: This file is the SQLite database file that includes the DFT data used for the ReaxFF force field. Specifically, it contains data related to the inorganic compound LiF. LiF.json: This file provides the database metadata in a human-readable format using JSON. LiF.yaml: This file also contains the database metadata in a more human-readable format, still using YAML. notebooks: This folder contains Jupyter notebooks that provide demonstrations and examples of how to use and analyze the database. browsing_db.ipynb: This notebook demonstrates how to handle, select, read, and understand the data points in the LiF.db database using the ASE database Python interface. It serves as a guide for exploring and navigating the database effectively. running_simulation.ipynb: In this notebook, you will find an example of how to get a data point from the LiF.db database and use it to perform a new simulation. The notebook showcases how to utilize either the PLAMS library or the AMSCalculator and ASE Python library to conduct simulations based on the retrieved data and then store it as a new data point in the LiF.db database. It provides step-by-step instructions and code snippets for a seamless simulation workflow. tools: This directory contains a collection of Python modules and scripts that are useful for reading, analyzing, and re-running simulations stored in the database. These tools are indispensable for ensuring that this repository adheres to the principles of Interoperability and Reusability, as outlined by the FAIR principles. db: This Python module provides functionalities for handling, reading, and storing data into the database. plasm_experimental: This Python module includes the necessary components for using the AMSCalculator with PLASM and the SCM software package, utilizing the ASE API. It facilitates running simulations, performing calculations. scripts: This directory contains additional scripts for dvanced usage scenarios of this repository. Interacting with the Database There are three ways to interact with the database: using the ASE db command line, the web interface, and the ASE Python interface. ASE db Command-line To interact with the database using the ASE db terminal command, follow these steps: Open a terminal and navigate to the directory containing the LiF.db file. Run the following command to start the ASE db terminal: ase db LiF.db You can now use the available commands in the terminal to query and manipulate the database. More information can be found in the ASE database documentation. Web Interface To interact with the database using the web interface, follow these steps: Open a terminal and navigate to the directory

This .zip contains the raw accelerometry data used to develop SEAR (speed estimation algorithm for running), the results of which are described in the following paper:

Davis, J., Oeding, B., and Gruber, A., 2022. Estimating Running Speed From Wrist- or Waist-Worn Wearable Accelerometer Data: A Machine Learning Approach. Journal for the Measurement of Physical Behaviour. DOI: 10.1123/jmpb.2022-0011

For a detailed description of the data, see the Data README.md file.

This archived Paleoclimatology Study is available from the NOAA National Centers for Environmental Information (NCEI), under the World Data Service (WDS) for Paleoclimatology. The associated NCEI study type is Plant Macrofossil. The data include parameters of plant macrofossil (population abundance) with a geographic location of West Virginia, United States Of America. The time period coverage is from 19893 to 12672 in calendar years before present (BP). See metadata information for parameter and study location details. Please cite this study when using the data.

2055 Global export shipment records of Running Board with prices, volume & current Buyer's suppliers relationships based on actual Global export trade database.

87469 Global import shipment records of Shoes running with prices, volume & current Buyer’s suppliers relationships based on actual Global import trade database.

The data are input data files to run the forest simulation model Landis-II for Isle Royale National Park. Files include: a) Initial_Comm, which includes the location of each mapcode, b) Cohort_ages, which includes the ages for each tree species-cohort within each mapcode, c) Ecoregions, which consist of different regions of soils and climate, d) Ecoregion_codes, which define the ecoregions, and e) Species_Params, which link the potential establishment and growth rates for each species with each ecoregion.