Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
570
datasets available to search
ShareScore release 0.7.1
Dataset results
570 results for “Sport”
RAGE pilot data from 2nd evaluation of the Sports Team Manager game on soft skills for employability
<p><strong>General description: </strong>The dataset includes data from the second evaluation pilot which tested the Sports team manager game developed by PlayGen for the Okkam use case.</p> <p><strong>Topic</strong><br> ACM CSS 2012: Human Computer Interaction (HCI) design and evaluation methods<br> PsycINFO Classification: 3620 Personnel Management & Selection & Training; 2228 Occupational & Employment Testing</p> <p><strong>Name entitites</strong><br> Organizational information: OKKAM, in collaboration with University of Trento<br> Geographical information: Italy<br> Time information: December 2017- November 2018</p> <p><strong>Types</strong>: Excel</p> <p><strong>RAGCS target group:</strong> end users: other user groups</p> <p><strong>Evaluation dimensions</strong><br> Evaluation object: Sports Team Manager game<br> Methodology/design: within subjects design for learning<br> Evaluation variables: usability, user experience, learning</p> <p><strong>Instruments:</strong> 1. Questionnaire on Usability Game User Experience Satisfaction Scale (GUESS; Phan, Keebler, & Chaparro, 2016) – Usability subscale; 2. questionnaire on User Experience including 3 subscales: Enjoyment (GUESS -Enjoyment subscale); Usefulness (Intrinsic Motivation Questionnaire, IMI; Ryan, 1982) - Subscale Value/Usefulness; Flow (Flow Short Scale, FSS, Rheinberg et al., 2003; Vollmeyer & Rheinberg, 2006); 3. Pre-post questionnaire on learning; 4. Focus interview</p> <p><strong>Knowledge/skill elements</strong><br> RAGCS skills: cognitive skills: evaluating, analysing; affective skills: interpersonal skills<br> ESCO skills: social interaction: <a href="http://data.europa.eu/esco/skill/8f18f987-33e2-4228-9efb-65de25d03330">http://data.europa.eu/esco/skill/8f18f987-33e2-4228-9efb-65de25d03330</a>; accept constructive criticism: <a href="http://data.europa.eu/esco/skill/a311ab20-75df-4aff-8016-3142c5659d30">http://data.europa.eu/esco/skill/a311ab20-75df-4aff-8016-3142c5659d30</a>; work in teams: <a href="http://data.europa.eu/esco/skill/60c78287-22eb-4103-9c8c-28deaa460da0">http://data.europa.eu/esco/skill/60c78287-22eb-4103-9c8c-28deaa460da0</a>; negotiate compromise: <a href="http://data.europa.eu/esco/skill/7954861c-86d4-4529-afbb-2c23dab9ac74">http://data.europa.eu/esco/skill/7954861c-86d4-4529-afbb-2c23dab9ac74</a>; lead others: <a href="http://data.europa.eu/esco/skill/75d8e5d9-bef3-418b-9011-01bff9f27207">http://data.europa.eu/esco/skill/75d8e5d9-bef3-418b-9011-01bff9f27207</a>; motivate others: <a href="http://data.europa.eu/esco/skill/e2d44a9b-f28c-489e-9861-b654b5ded507">http://data.europa.eu/esco/skill/e2d44a9b-f28c-489e-9861-b654b5ded507</a>; support colleagues: <a href="http://data.europa.eu/esco/skill/95a41cf5-4037-4c96-91a8-c34b41637224">http://data.europa.eu/esco/skill/95a41cf5-4037-4c96-91a8-c34b41637224</a>; manage time: <a href="http://data.europa.eu/esco/skill/d9013e0e-e937-43d5-ab71-0e917ee882b8">http://data.europa.eu/esco/skill/d9013e0e-e937-43d5-ab71-0e917ee882b8</a>; make decisions: <a href="http://data.europa.eu/esco/skill/d62d2b4c-a6f8-439e-8a1b-4f29ab5f2c47">http://data.europa.eu/esco/skill/d62d2b4c-a6f8-439e-8a1b-4f29ab5f2c47</a>; develop strategies to solve problems: <a href="http://data.europa.eu/esco/skill/7a8fb784-67fa-41e9-a75c-6b491d91f800">http://data.europa.eu/esco/skill/7a8fb784-67fa-41e9-a75c-6b491d91f800</a>; evaluate information: <a href="http://data.europa.eu/esco/skill/7dd94ad3-13d6-43fe-8b94-51fcbf67ced9">http://data.europa.eu/esco/skill/7dd94ad3-13d6-43fe-8b94-51fcbf67ced9</a><br> <br> <strong>Relationships</strong>: D8.4 Second RAGE Evaluation Report<br> Related dataset: <a href="https://doi.org/10.5281/zenodo.1209206">10.5281/zenodo.1209206</a></p>
SLD: Sports Leagues Dataset
<p>The Sports Leagues Dataset (SLD) contains statistical data of the major professional sports leagues in the United States: NFL (National Football League), NBA (National Basketball Association), NHL (National Hockey League) and MLB (Major League Baseball). One collect five topics (Player Expenses, Player Salaries, Players Performance, Team Salaries, Team Valuation) of two dimensions (Finance and Performance) in different seasons (2000-2007) from three data sources (Forbes, Spotrac and Sports Reference).</p> <p><br> Please consider citing <a href="https://doi.org/10.5281/zenodo.3256432">https://doi.org/10.5281/zenodo.3256432</a> if you found this dataset useful:</p> <p>[1] André Albino Bastos, Matheus de Oliveira Salim, Wladmir Cardoso Brandão. (2019). SLD: The Sports Leagues Dataset (Version 1.0) [Data set]. Zenodo.</p>
Exploring multi-camera views from user-generated sports videos
<div> <p><strong>The proliferation of mobile devices</strong> with video recording capabilities has revolutionized the creation, sharing, and consumption of audiovisual content, turning user-generated video (UGV) platforms into major data sources. </p> <div> <div> <p><strong>Despite this growth</strong>, there is a notable gap in the availability of public datasets featuring multi-angle recordings of sports events captured by various mobile cameras. This led to the creation of the <strong>MUVY Dataset</strong>, with the name stemming from <strong>Multiview User-generated Videos from YouTube.</strong></p> <div> <div> <p>The dataset offers a diverse collection of sports videos from multiple perspectives, without restrictions on video size. In its first version, it covers sports like, <strong>American football, artistic gymnastics, athletics, basketball, tennis, and cricket.</strong></p> <div> <div> <div> <p>The dataset addresses common challenges in user-generated videos, such as shaking, occlusions, blurring, and abrupt movements. Each video is accompanied by metadata including camera identification, YouTube URLs, extracted frames, and object annotations.</p> </div> </div> </div> </div> </div> </div> </div> </div>
Positive mental health among sports coaches: A six-month longitudinal study
<p><span>Positive mental health is important for sports coaches to keep functioning well over time, but little is known about its longitudinal stability</span><span>. </span><span>Consequently, this study examined </span><span>stability and change of emotional, psychological, and social well-being in a sample of 422 sports coaches </span><span>(<em>M<sub>age</sub></em> = 44.48, <em>SD</em> = 11.03) in terms of measurement invariance, rank-order stability, mean-level change, stability of interindividual differences, and structural stability. Participants completed measures of emotional, psychological, and social well-being at baseline and again at three and six months. Multiple perspectives of stability and change were examined using structural equation modeling. The results confirm that the measure of positive mental health functions equivalently over time. Stability of well-being remained high, with rank-order stability coefficients ranging from .67 to .79 at three and six months, and no mean-level change in well-being was observed. The results also indicate stable interindividual differences and structural stability of positive mental health over time. These results confirm the importance of addressing any symptoms of diminished positive mental health in the coaching population.</span></p>
Phenotypic data from: from buds to shoots: insights into grapevine development from the Witch’s Broom bud sport
Open the record for dataset details and reuse information.
Positive mental health among sports coaches: A six-month longitudinal study
Open the record for dataset details and reuse information.
Comfort and wearability of orthodontic mouthguards during contact sports in adolescent patients undergoing fixed appliance orthodontic treatment: a randomised clincal trial
<p>Dataset for all analyses in the paper</p>
Sport Camera
ID no.: MHF 1893/I Museum of Photography in Kraków https://muzea.malopolska.pl/en/objects-list/950 Digitalisation: RDW MIC, Virtual Małopolska project Source: Objaverse 1.0 / Sketchfab
Part of the Sports Complex Izmaylovo
Photogrammetry of the Sports Complex Immaylovo part. Russia, Moscow Source: Objaverse 1.0 / Sketchfab
Sporting Institute Statistics Experiment
<p>Created for Exercise 3 in Digital Preservation Exercises (Course at Vienna University of Technology).</p> <p>Experiment that generates statistics from open data on sporting institutes in Vienna. The script is written in Python, the data comes as plain text as well as graphs in .eps format. See README.md for details.</p> <p>Results of the experiment are written in German.</p>
Record of the temporal evolution of the price of Decathlon sports footwear by gender and brand
<p>This dataset includes simulated data for a potential project of analysis of data for prices of sports shoes classified by brand and gender collected from a website at different dates.</p>
The Sports Etiquette
<div>Sports etiquette plays an important role in creating a positive and comfortable environment for everyone, so it is essential to adhere to the key rules of etiquette.</div>
BIBLIOMETRIC DATA COUNTRY BRAND AND SPORTS
<p>135 artículos que hablan sobre marca país y deporte</p>
Six-month stability of individual differences in sports coaches' burnout, self-compassion and social support
<p>Using a three-wave prospective cross-lagged panel design, the study examined the six-month stability of burnout, self-compassion, and social support among sports coaches in terms of measurement invariance, mean-level change, rank-order stability, and structural stability. The participating coaches (<em>N</em> = 422; <em>M<sub>age</sub></em> = 44.48, <em>SD</em> = 11.03) completed an online questionnaire measuring self-compassion, social support, coach burnout, and demographics at baseline and two follow-ups at three months and six months. The various forms of stability were assessed using structural equation modeling. There was no significant mean-level change in burnout, self-compassion, or social support, and all three constructs exhibited measurement invariance. Rank-order stability remained relatively high, ranging from 0.78 to 0.94 across the three time points. For all three constructs, covariances between latent factors were invariant over time, indicating high structural stability. While self-compassion and social support were positively related, both were negatively related to coach burnout. These results confirm the importance of preventing and addressing symptoms of burnout, low self-compassion, and poor social support in sports settings.</p>
NASA SPoRT Basin Average Training Data
<p>The included data files contain basin average SPoRT-LIS relative soil moisture [Total column (0-2 m depth) and four model layers (0-10, 10-40, 40-100, and 100-200 cm depth)] and MRMS QPE for each river basin. These files were used to train and tune the developed basin specific LSTM models. The number in the file name indicates the corresponding USGS site number. </p>
Samarali tikish uchun sport voqealarini qanday tahlil qilish kerak?
<p>Tikish dunyosi ko'plab sport muxlislari uchun qiziqarli va daromadli hobbiga aylangan. Ammo, muvaffaqiyatli tikish uchun sport voqealarini chuqur tahlil qilish talab etiladi. Ushbu maqolada sizga samarali tahlil qilish usullarini ko'rsatamiz va <a href="https://1wins-uz.com/">https://1wins-uz.com/</a> kabi platformalarda qanday qilib muvaffaqiyatli tikish qilish haqida batafsil ma'lumot beramiz.</p> <p><br>Sport tahlili nima va nima uchun muhim?<br>Sport tahlili - bu sport voqealarini, jamoa va o'yinchilarni o'rganish va ularning oldingi natijalarini tahlil qilish jarayonidir. Tikish paytida tahlil qilish sizga o'yin natijalarini oldindan bilishga yordam beradi, bu esa sizning muvaffaqiyatli garov qo'yish ehtimolingizni oshiradi. Quyida tahlil qilish uchun ba'zi muhim omillar keltirilgan:</p> <p>Statistika: O'yinchi va jamoalarning o'tmishdagi natijalari, gol urish darajasi, jarohatlar va boshqa statistika ko'rsatkichlari.<br>Yaqin kelajakdagi o'yinlar: Jamoalarning yaqin kelajakdagi o'yin jadvali va undagi muhim voqealar.<br>Jamoa holati: Jamoalarning hozirgi holati, jarohatlar va diskvalifikatsiyalar.</p> <p>Taktikalar: Murabbiylarning taktikalari va jamoa uslubi.<br>O'zbekistonda 1Win<br>1Win O'zbekistondagi eng mashhur bukmekerlardan biri bo'lib, o'yinchilarga keng imkoniyatlar taqdim etadi. Ushbu platformada foydalanuvchilar uchun qulay interfeys va mobil ilova mavjud bo'lib, har qanday qurilmada samarali ishlaydi.</p> <p>Bukmekerlik va kazino xizmatlari bilan bir qatorda, 1Win platformasi turli sport tadbirlariga tikish imkoniyatini beradi. Tikish tarixi va batafsil statistikaga kirish imkoniyati foydalanuvchilarga muvaffaqiyatli garov qo'yishga yordam beradi. Bu platformada siz turli xil to'lov usullaridan foydalanib, depozitlarni to'ldirishingiz va yutuqlarni yechib olishingiz mumkin.</p> <p>1Winning afzalliklari<br>1Win platformasi bir qator afzalliklarga ega. Quyida ularning ba'zilarini ko'rib chiqamiz:</p> <p>Qulay interfeys: Platforma oson va tez tushuniladigan dizaynga ega, bu esa yangi foydalanuvchilar uchun juda qulay.<br>Mobil ilova: 1Winning rasmiy mobil ilovasi mavjud bo'lib, u orqali siz istalgan vaqtda va joyda pul tikishingiz mumkin.<br>To'lov usullari: Ko'p turdagi to'lov usullari orqali o'yinlar uchun depozit qilish va yutuqlarni yechib olish imkoniyati.<br>Jonli efirlar: Sport tadbirlarini jonli tomosha qilish imkoniyati.<br>Texnik yordam: Kechayu kunduz ishlaydigan texnik yordam xizmati.<br>Tikish uchun sport voqealarini tahlil qilish bo'yicha maslahatlar<br>Muvaffaqiyatli tikish qilish uchun quyidagi qadamlarni ko'rib chiqing:</p> <p>Statistikani tahlil qiling: Jamoa va o'yinchilarning o'tmishdagi natijalarini chuqur o'rganing. Statistika sizga o'yin natijalarini oldindan bilishda yordam beradi.<br>Jamoa holatini tekshiring: Jarohatlar, diskvalifikatsiyalar va boshqa muhim ma'lumotlarni bilib oling.<br>O'yin sharoitlarini e'tiborga oling: O'yin qaerda va qaysi sharoitda o'tkazilishini biling. Uy o'yinlari odatda jamoalar uchun ko'proq ustunlik beradi.<br>Ekspert fikrlarini o'rganing: Sport mutaxassislari va analitiklarning fikrlarini o'rganing.<br>O'z tahlilingizni qiling: Har bir o'yin uchun o'z tahlilingizni tayyorlang va boshqa manbalardan olingan ma'lumotlarni tasdiqlang.</p> <p>Tikish - bu nafaqat omadga bog'liq, balki chuqur tahlil va rejalashtirishni talab qiladigan jarayon. Oʻzbekistonda 1Win kabi platformalarda muvaffaqiyatli tikish qilish uchun statistik ma'lumotlar, jamoa holati va ekspert fikrlarini hisobga olishingiz kerak. 1Win O'zbekistondagi foydalanuvchilar uchun juda ko'p imkoniyatlar taqdim etadi va bu platforma orqali siz o'z tikish tajribangizni oshirishingiz mumkin. Samimiy va puxta tahlil qilingan yondashuv orqali muvaffaqiyatga erishishingiz mumkin.</p>
How does the proximity to restaurants and sports facilities influence the Body Mass Index in Geneva?
<p>Sport facilities and restaurants of the canton of Geneva, located by means of adresses, are published. They were obtained from the Geographic Information System of Geneva (Système d'Information du Territoire à Genève, SITG). The BMI dataset is strictly confidential and could not be published.</p>
Ground reaction force metrics are not strongly correlated with tibial bone load when running across speeds and slopes: implications for science, sport and wearable tech
<p>An interactive user interface and the raw data from the manuscript titled: "Ground reaction force metrics are not strongly correlated with tibial bone load when running across speeds and slopes: implications for science, sport and wearable tech". </p>
Bibliometric data for sports performance profiling
Open the record for dataset details and reuse information.
SPoD-Ex Videoabstract für Patient*innen - Demenzprävention durch Bewegung und Sport
<p>Eine unabhängige Patienteninformation zur Demenzprävention durch Bewegung. Ergebnisse des Forschungsprojekts SPoD-Ex.</p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
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.