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235 results for “soccer”
Head-to-Ball Impacts in Collegiate Soccer Players
Open the record for dataset details and reuse information.
Replication files for "Building social cohesion between Christians and Muslims through soccer in post-ISIS Iraq"
<p>Replication files for main analyses, and supplementary analyses (comparison group, Muslim player attitudes, fan attitudes, match-level data) for:</p> <p><strong>Mousa, Salma. </strong>"<a href="https://science.sciencemag.org/content/369/6505/866">Building social cohesion between Christians and Muslims through soccer in Post-ISIS Iraq</a>.<strong>" <em>Science</em>. </strong>Vol. 369, Issue 6505, pp. 866-870. DOI: 10.1126/science.abb3153</p> <p>Each R file describes the needed datasets at the top of the script. </p>
Figure 4. (a)Our Humanoid soccer robot, (b) Overview of the Control System-Design and Implementation of an Autonomous Humanoid Robot Based on Fuzzy Rule-Based Motion Controller
<p>Figure 4 shows the block diagram of the software which runs in the robot’s main processor.<br> The program consists of 4 main blocks:<br> • Hardware Interface: Contains all low level routines to access hardware of the robot including<br> sensors and actuators.<br> • Vision: Contains image processing algorithms such as recognition of landmarks and other<br> object. Self localization is done using particle filtering. Particles are scored by comparing a<br> simulated image from each particle with the current frame captured by camera. Using<br> “Sampling-Importance Resampling” method, a new distribution of the particles is created after<br> each step.<br> Particles are also updated using a motion model. Final distribution of the particles converges to<br> the real pose of the robot.<br> • Planning: Planning system of the robot is based on a multi layer, and multi thread structure.<br> The layers are named Strategy, Role, Behavior and Motion. Each layer contains a Scenario<br> which runs in parallel with the scenarios in the other layers. A scenario in a higher level can<br> terminate and change the scenario running in the lower level; however it is usually done in<br> synchronization with the lower level scenario to avoid conflicts and instabilities. (Such as<br> stopping the walking motion while one of the feet is still in the air).<br> • Network: Mainly responsible for the wireless communication of the robot with the other robots<br> or the referee box. This is done via WLAN.<br> • Motion Control: manages all the actuators of the robot, and controls locomotion or any other<br> action of the robot according to the requests from Cognition.<br> • Sensor Control: manages other sensors, and interacts with the Sub-Controller.</p>
Figure 3. (a) Our Humanoid soccer robot, (b) Overview of the Control System-Design and Implementation of an Autonomous Humanoid Robot Based on Fuzzy Rule-Based Motion Controller
<p>The PERSIA Humanoid robot designed for has multipurpose capability. This robot<br> equipped with main board for motion control, vision sensor, other balancing sensors, servo motors<br> and etc. Figure 3 shows picture of the robot and overview of the Persia humanoid robot control<br> system.</p>
Architecture, morphology and strength of the quadriceps muscle in male and female soccer players at the national level: a cross-sectional study
<p>This is the dataset for the corresponding publication. The dataset includes the "raw" data as well as the analysis script used for the calculation of the group differences and correlations.</p>
GR-SCBET-Y03Y07 Soccer bets and teams DB from coupons, Greece, Aug.2003 - Mar.2007
<p>========================================================</p> <p> Dataset: GR-SCBET-Y03Y07</p> <p> Soccer bets and teams DB from coupons<br /> Greece, Aug.2003 - Mar.2007<br /> <br /> Release Notes</p> <p> Copyright (c) 2016 by Harris V. Georgiou</p> <p>========================================================<br /> Release: Aug 3, 2016</p> <p> - Version: 1.1a<br /> - Format: .xls<br /> ========================================================</p> <p><br /> This file contains important information about the current version of the dataset package. Downloading and using this material hints that you accept the EULA/Terms-of-Use (please read carefully).</p> <p>We welcome your comments and suggestions.</p> <p>_______________________________________________<br /> WHAT'S IN THIS PACKAGE?</p> <p>- Overview<br /> - Available file formats<br /> - Files and Datasets<br /> - License Agreement</p> <p>_______________________________________________<br /> OVERVIEW</p> <p>Athletic betting and successful risk management is one of the most challenging tasks in Machine Learning and A.I. in general. With only limited information and very sparsely distributed data, an algorithm has to model the teams' competency and predict outcomes of games, even when two teams face each other for the first time.</p> <p>This package contains two main datasets, generated from soccer betting coupons in Greece throughout a 3.5 year period and including every game that was included for any championship and any team. The first dataset corresponds to 43.758 matches with complete odds, final result and goals. The second is team-based statistics from these matches for a total of 1.126 teams from various championships (European and other).</p> <p>These datasets can be used as benchmark training sets for odds-oriented or team-oriented predictive models and algorithms, relevant not only to athletic betting but also other contexts, lile in in financial risk management. In the case of team-oriented modeling, special ELO-like ranking algorithms can be developed and tested against the official team/country rankings, as in UEFA and FIFA, but tailored to specific teams or championships, in order to evaluate and fine-tune the models.</p> <p>Note: Team names are in Greek, but they can be used as-is for proper referencing to unique IDs, i.e., practically they can be ignored.</p> <p>_______________________________________________<br /> AVAILABLE FILE FORMATS</p> <p>The datasets are available in the following formats (included):</p> <p>*.xls : MS-Excel/LibreOffice worksheet data file (exportable)</p> <p> </p>
Awareness of fifth metatarsal stress fractures among soccer coaches in Japan: A cross-sectional study
<p>Although a fifth metatarsal stress fracture is the most frequent stress fracture in soccer players, awareness of fifth metatarsal stress fractures among soccer coaches is unclear. Therefore, we performed an online survey of soccer coaches affiliated with the Japan Football Association to assess their awareness of fifth metatarsal stress fractures. A total of 150 soccer coaches were invited for an original online survey. Data on participants' age, sex, types of coaching licence, coaching category, types of training surface, awareness of fifth metatarsal stress fractures, and measures employed to prevent fifth metatarsal stress fractures were collected using the survey. Data from 117 coaches were analysed. Eighty-seven of the 117 coaches were aware of fifth metatarsal stress fractures; however, only 30% reported awareness of preventive and treatment measures for fifth metatarsal stress fractures. Licensed coaches (i.e., licensed higher than level C) were also more likely to be aware of fifth metatarsal stress fractures than unlicensed coaches were. Furthermore, although playing on artificial turf is an established risk factor for numerous sports injuries, soccer coaches who usually trained on artificial turf were more likely to be unaware of the risks associated with fifth metatarsal stress fractures than coaches who trained on other surfaces were (e.g., clay fields).<strong> </strong>Soccer coaches in the study population were generally aware of fifth metatarsal stress fractures; however, most were unaware of specific treatment or preventive training strategies for fifth metatarsal stress fractures. Additionally, coaches who practised on artificial turf were not well educated on fifth metatarsal stress fractures. Our findings suggest the need for increased awareness of fifth metatarsal stress fractures and improved education of soccer coaches regarding injury prevention strategies. </p>
Ancient Greek Soccer Players
This is a scan of a sculpture from Friederichienabend Museum in Vienna, Austria. Dated 2nd century A.D., marble. A group of young men are presumably representing early stages of football game. Made with Momento Beta. Unfortunately I was unable to preserve textures. Source: Objaverse 1.0 / Sketchfab
Supplementary materials for "Forecasting Soccer Matches With Betting Odds: A Tale of Two Markets"
<p>Replication package for International Journal of Forecasting article "Forecasting Soccer Matches With Betting Odds: A Tale of Two Markets".</p>
Bibliometric data for Neuropsychophysiological Aspects of Soccer Performance
Open the record for dataset details and reuse information.
Data for "Statistical Adjustment for Tactical Choices When Evaluating Team's Offensive Output Across Five Major European Club Soccer Leagues"
<p>Data files for a paper titled "Statistical Adjustment for Tactical Choices When Evaluating Team's Offensive Output Across Five Major European Club Soccer Leagues"</p>
Satellite Imagery Dataset - Runway and Soccer Field
<p>Satellite Imagery (Google) Dataset of Runways and Soccer Fields</p> <p> </p> <p>Papers which used the dataset</p> <ul> <li><strong>A comparison of Haar-like, LBP and HOG approaches to concrete and asphalt runway detection in high resolution imagery</strong> (<a href="https://jkreuz.github.io/publications/papers/JCruz2015_JCIS.pdf">pdf</a>)<br>JEC Cruz, EH Shiguemori, LNF Guimarães<br>Journal of Computational Interdisciplinary Sciences, 2015</li> </ul> <ul> <li><strong>Concrete and asphalt runway detection in high resolution images using LBP cascade classifier</strong> (<a href="http://plutao.sid.inpe.br/col/sid.inpe.br/plutao/2013/12.12.17.25.22/doc/Cruz_concrete.pdf">pdf</a>)(<a href="https://ieeexplore.ieee.org/abstract/document/6855892">pdf</a>)<br>JEC Cruz, EH Shiguemori, LNF Guimarães<br>BRICS Congress on Computational Intelligence, 2013</li> </ul> <p> </p> <p>more information can be found in <a href="https://jkreuz.github.io/publications/">https://jkreuz.github.io/publications/</a></p>
complete table of data for article: Changes in technical/physical performance of young soccer players throughout soccer game simulation.
<p>The table contains the data of 21 subjects</p> <p>The letters in the third column indicate playing position Midfielders (M), attackers (A) and defenders (D).</p> <p>The abbreviations that appear in the columns are:</p> <p>-Goal shooting accuracy from 20 m (GSA).</p> <p>-Dribbling (DRIB).</p> <p>-Long pass accuracy from 30 m (LPA).</p> <p>-Speed without ball in 20 x 20 m (S).</p> <p>-Perception of fatigue (PF).</p> <p>After each abbreviation, the period in which it was measured according to phase criteria (phase 1, phase 2, phase 3) or intensities, low (L), medium (M) or high (H) is indicated.</p> <p>The CMJ i and CMJ f columns show the countermovement jump height values at the beginning and end of the study, respectively.</p>
EFFECT OF VISUAL STIMULI ON THE JUMPING ABILITY OF AMATEUR SOCCER PLAYERS DATA
<p>Data from the study "EFFECT OF VISUAL STIMULI ON THE JUMPING ABILITY OF AMATEUR SOCCER PLAYERS"</p>
Soccer-based Adaptation of the Diabetes Prevention Program
ClinicalTrials.gov study NCT03595384. IPD Sharing: YES. Countries: 1. Publications: 2.
A Replicate Crossover Trial on Nutritional Supplementation in Association Football (Soccer)
ClinicalTrials.gov study NCT07190989. IPD Sharing: NO. Countries: 1. Publications: 55.
Whole-Body Photobiomodulation Use in Professional Soccer Players During a State Championship
ClinicalTrials.gov study NCT07224646. IPD Sharing: NO. Countries: 1. Publications: 25.
Fitness Tests Conducted at Intervals of 30-15 Minutes Provide Superior Sport-specific Accuracy Compared to Treadmill Tests in Elite Female Soccer Players.
ClinicalTrials.gov study NCT07390149. IPD Sharing: NO. Countries: 1. Publications: 0.
Strategic Daytime Napping Enhances Agility and Lowers Perceived Exertion But Does Not Improve Fatigue Resistance in Adolescent Soccer Players
ClinicalTrials.gov study NCT07314645. IPD Sharing: NO. Countries: 1. Publications: 1.
Effects of the FIFA11+ Warm-up Program on Speed, Agility, and Vertical Jump Performance in Adult Female Amateur Soccer Players
ClinicalTrials.gov study NCT03683758. IPD Sharing: NO. Countries: 1. Publications: 3.
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.