Active Places NI records key contact, location and facility specification information on sports facilities throughout Northern Ireland.

Track the status of more than $200 billion in sports rights deals across eight professional and college sports and 10 platforms. The Information's Sports Rights Database shows who controls the most important streaming deals and when they are up for renegotiation.

See our stories NBA Wants More in Sports Deals: Media and Tech Firms Are Resistant and Amazon in Talks With Disney About ESPN Streaming Partnership

This dataset corresponds to actual data from the functioning of a sports facility and refers to all new users who signed up between June 1st 2014 and October 31st 2019. Demographic and service level agreement (SLA) data is collected by operators in the process of enrolling users in the activities they intend to practice. The data regarding the frequency of the sports facility and classes were obtained by the access control system where each user identifies himself with an RFID card to access the facilities on the days and times agreed in his SLA.

Current ownership information for professional sports teams including primary and minority owners

Comprehensive dataset of 3 Sports in Kentucky, 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.

Historical data of sports franchise transactions including sales, ownership changes, relocations, and expansions

Dataset

This dataset was created by Dongrui Shen

Contents

It contains the following files:

This dictionary was produced within the French national project EuRADic (European and Arabic Dictionaries and Corpora), as part of the Technolangue programme funded by the French Ministry of Industry. A needs study in the field of sport terminology, which covered an overall category of users, led to a partial restructuring of an already existing database (English-French) and a re-evaluation of options. All choices were based on the results of this needs study, which clearly highlighted the impact of open corpora (Internet) and exploration tools for the production of dictionaries as well as for the carrying out of terminography activity. The existing database (English-French) was subject to a systematic revision and updating with the following purposes:•Spell checking•Term production verificationThe principles of terminology unit production were modified according to current technical progress. All nomenclature entries were subject to a systematic revision.•Equivalence compliance verificationEquivalences were systematically checked and corrected (7,044 corrections or adjustments).•Source verification For reasons of scientific rigour (and right protection), sources and references that were exploited for each dictionary entry were systematically checked (40,000 verifications for a total of 11,256 modifications).The results are presented in the form of MS ACCESS databases.The current set consists of an English-French bilingual database: The English-French bilingual database consists of 63,161 entries with, for each language •Mandatory information: term, reference/source, grammar •Mandatory and common information: field (sport), domain, additional circumscription •Optional information: definition and source OR linguistic and source reference, combinatorics, other form, synonym, variantThe query enables to search for:•French term •English term •sport (field, domain and special features ++)The EuRADic sport dictionary is provided under the following different subsets:ELRA-T0372-01 Multilingual Dictionary of Sports – English-French-Greek-Arabic-German-Spanish-Portuguese multilingual databaseELRA-T0372-02 Multilingual Dictionary of Sports - English-French bilingual databaseELRA-T0372-03 Multilingual Dictionary of Sports - English-French-Greek trilingual databaseELRA-T0372-04 Multilingual Dictionary of Sports - English-French-Arabic trilingual databaseELRA-T0372-05 Multilingual Dictionary of Sports - English-French-German trilingual databaseELRA-T0372-06 Multilingual Dictionary of Sports - English-French-Spanish trilingual databaseELRA-T0372-07 Multilingual Dictionary of Sports - English-French-Portuguese trilingual database

Active Places NI records key contact, location and facility specification information on sports facilities throughout Northern Ireland.

Details of Veterans Services Local Staff Management.

Comprehensive dataset of 3 Sports in Iowa, 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.

soccer database with tons of football players and their ovr feel free to add an recommendation to add to the databata I will add the icon soon

Abstract The Norwegian Endurance Athlete ECG Database contains 12-lead ECG recordings from 28 elite athletes from various sports in Norway. All recordings are 10 seconds resting ECGs recorded with a General Electric (GE) MAC VUE 360 electrocardiograph. All ECGs are interpreted with both the GE Marquette SL12 algorithm (version 23 (v243)) and one cardiologist with training in interpretation of athlete's ECG. The data was collected at the University of Oslo in February and March 2020.

Background Athletes often have increased thickness in the left ventricular wall and extended chambers in both the left and right ventricle compared to untrained people at the same age [1]. These changes occur as a result of the heart adapting to large amounts of exercise. These changes can be seen on an echocardiogram, but the changes also give electrical manifestations that can be observed on an electrocardiogram (ECG). Even if these changes are considered healthy, they can be confused with pathological changes that are related to sudden cardiac death (SCD) [2]. In addition, studies show that the incidence of SCD is higher in athletes than in non-athletes of the same age [3,4]. Current measures and procedures for detecting athletes with an increased risk of SCD are characterized by low accuracy and low precision. This emphasizes that ECG interpretation of athletes is an area that requires increased focus.

Methods Twenty-eight healthy athletes were recruited for this study. 19 (68%) of the participants were men and 9 (32%) were women. Participant's ages ranged from 20 to 43 years (Mean = 25 years, standard deviation = 4.7 years). The distribution among sports was 24 rowers (86%), 2 kayakers (7%) and 2 cyclists (7%). The average amount of training hours for 2017 was 822 hours with a standard deviation of 117 hours, in 2018 the average amount of training was 820 hours with a standard deviation of 113 hours and in 2019 the average amount of training was 798 hours with a standard deviation of 171 hours.

The study protocol and consent form were approved by the Norwegian Centre for Research Data (application ID: 389013) and the University of Oslo, and the ethical considerations were approved by the Regional Committees for Medical and Health Research Ethics (application ID: 51205). All participants were informed and gave written consent before the test was initiated, they also agreed to have their ECG shared in an open database after the project was finished. The test subjects were lying horizontally on a bed, relaxing, while electrodes were attached to perform a 12-lead ECG recording. The recordings were performed as a standard 10 seconds resting ECG. The device used was a GE MAC VUE 360. The device's built-in interpretation algorithm, Marquette 12SL (version 23 (v243)), performed automatic interpretation of all ECGs.

All ECG recordings were examined by a cardiologist, with specialization in athletes' hearts, after the recordings were completed. The cardiologist interpreted the ECGs according to the international criteria for ECG interpretation of athletes.

Data Description Each of the 28 waveform files consists of 12 arrays, representing the twelve leads. The ECGs were obtained using a General Electric (GE) MAC VUE 360 electrocardiograph and interpreted using the built-in ECGs are GE Marquette SL12 algorithm (version 23 (v243)) and a cardiologist with training in interpretation of athlete's ECG.

The waveform files are stored in .dat -files with a corresponding .hea file containing all the metadata. This file formats are compatible with the Python WaveForm DataBase (WFDB) package and this makes it easy to import the data.

All ECG waveforms are sampled and stored with a sampling frequency of 500Hz and a length of 5000 samples (10 seconds). The header file contains information about the total amount of leads, samples per lead and additional information about each lead. The last two lines in the header file contains the diagnose given by the Marquette SL12 (SL12) algorithm and the cardiologist (C). ``` ath_001 12 500 5000 ath_001.dat 16 50000/mV 16 0 10251 49595 0 I ath_001.dat 16 50000/mV 16 0 -1096 35223 0 II ath_001.dat 16 50000/mV 16 0 -10267 60826 0 III ath_001.dat 16 50000/mV 16 0 -3724 3505 0 AVR ath_001.dat 16 50000/mV 16 0 9391 26379 0 AVL ath_001.dat 16 50000/mV 16 0 -5395 57481 0 AVF ath_001.dat 16 50000/mV 16 0 13580 61759 0 V1 ath_001.dat 16 50000/mV 16 0 11410 33501 0 V2 ath_001.dat 16 50000/mV 16 0 14721 52508 0 V3 ath_001.dat 16 50000/mV 16 0 16103 51083 0 V4 ath_001.dat 16 50000/mV 16 0 6662 44197 0 V5 ath_001.dat 16 50000/mV 16 0 -3806 11333 0 V6

SL12: sinus bradycardia with marked sinus arrhythmia, Right Axis Deviation, Borderline ECG C: Sinus arrhythmia, Normal ECG ```

Usage Notes The intended use of this database is for the development of better algorithms designed to make better diagnostics for athletes based on ECG. One of the unique features of this database is that the ECGs are annotated by both a trained cardiologist and by a state-of-the-art ECG software (GE Marquette SL12).

To get started in Python you can use this code to import the ECG-signals and metadata ``` import wfdb import numpy as np import os

directory = "./your/directory/" ECGs = [] for ecgfilename in sorted(os.listdir(directory )): if ecgfilename.endswith(".dat"): ecg = wfdb.rdsamp(directory + ecgfilename.split(".")[0]) ECGs.append(ecg) ECGs = np.asarray(ECGs) ``` The numpy array (ECGs) now contains all ECG signals and metadata.

Despite the fact that the measurements were taken from top-trained athletes it is not confirmed whether they had athletic remodeling of the heart or not. No echocardiographic or other examinations were performed to investigate the structure of the heart.

Release Notes 1.0.0 Initial release of the dataset.

Ethics The authors declare no ethics concerns.

Acknowledgements I will thank Professor Emeritus Knut Gjessdal for providing his medical expertise and interpreting all of the ECGs. This work was done at the University of Oslo and I will thank Professor Ørjan Grrøttem Martinsen for providing appropriate facilities for ECG measurements.

Details of Veterans Services Local Staff Management.

The database contains the physical characteristics and athletic performance of middle school students between the ages of 11 and 19. The data includes age, sex, weight, height, BMI, sprint and long jump performance. This database can be used for a number of research purposes: analysis of student performance, physical characteristics of adolescents, application of artificial intelligence in sport, etc.

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Databases of highly networked individuals have been indispensable in studying narratives and influence on social media. To support studies on Twitter in India, we present a systematically categorized database of accounts of influence on Twitter in India, identified and annotated through an iterative process of friends, networks, and self-described profile information, verified manually. We built an initial set of accounts based on the friend network of a seed set of accounts based on real-world renown in various fields, and then snowballed friends of friends" multiple times, and rank ordered individuals based on the number of in-group connections, and overall followers. We then manually classified identified accounts under the categories of entertainment, sports, business, government, institutions, journalism, civil society accounts that have independent standing outside of social media, as well as a category ofdigital first" referring to accounts that derive their primary influence from online activity. Overall, we annotated 11580 unique accounts across all categories. The database is useful studying various questions related to the role of influencers in polarisation, misinformation, extreme speech, political discourse etc.

Comprehensive dataset of 5 Sports in Missouri, 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.

Information layer of the Topographic Database of the Province of Lodi. A020204 - Sports equipment. This class includes bleachers in sports facilities.

Historical data on the modern Olympics from Athens 1896 to Rio 2016. The data was scraped from www.sports-reference.com in May 2018. The file athlete_events.csv contains 271116 rows and 15 columns. Each row corresponds to an individual athlete competing in an individual Olympic event (athlete-events). The columns are:1. ID - Row numbers2. Name - Athlete's name3. Sex - M or F4. Age - Integer5. Height - In centimeters6. Weight - In kilograms7. Team - Team name8. NOC - National Olympic Committee 3-letter code9. Games - Year and season10. Year - Integer11. Season - Summer or Winter12. City - Host city13. Sport - Sport14. Event - Event15. Medal - Gold, Silver, Bronze, or NAFor more information:About the sports-reference Olympic database: http://olympstats.com/2016/08/21/the-olymadmen-and-olympstats-and-sports-reference/About how I scraped the data: https://rgriff23.github.io/2018/05/27/olympic-history-1-web-scraping.htmlAbout how I wrangled the data: https://rgriff23.github.io/2018/05/28/olympic-history-2-data-wrangling-1.htmlhttps://rgriff23.github.io/2018/05/28/olympic-history-3-data-wrangling-2.htmlMy GitHub repo for this project, including analyses using this data:https://github.com/rgriff23/Olympic_history