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570 results for “Sport”
Behavioral, physiological, and neural signatures of surprise during naturalistic sports viewing
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Data from: Carbon accumulation of cool season sports turfgrass species in distinctive soil layers
<p>Carbon sequestered by turfgrasses may contribute to reducing atmospheric CO<sub>2 </sub>levels, to improved soil health and to increased turfgrass quality. Therfore in a field study conducted in the Netherlands, the amount of soil C accumulated by nine cool season turfgrass monocultures and 12 mixtures of turfgrass species during the first three years of establishment was analysed and compared. Thatch, mat and other soil layers and the layers were sampled and thickness of these layers was quantified. From these samples, dry matter, C and N concentrations, and CN ratio were measured.</p> <p>The study was conducted on a 3 years old turfgrass field of the turfgrass seed company DLF. The site was located in the Netherlands (51°32´N, 4°20´E), on a sandy soil (Hortic Anthrasol as described in the FAO/Unesco soil map of the world (2006)). The monocultures consisted of different varieties of the (sub)species <em>Lolium perenne (lp), Poa pratensis (Pp), Festuca arundinacea (Fa), Festuca rubra commutata (Frc), Festuca rubr trichophylla (Frt), Festuca rubra rubra (Frr), Festuca ovina duriuscala (Fod), Festuca ovina vulgaris (Fov), Agrostis stolonifera (As). </em>Varieties were treated as replicates per (sub)species, which resulted in some variation in the number of replicates, as not all species were available in the same number of varieties.<em> </em>Varieties of the<em> (s</em>ub)species and mixtures were on the market as commercial turfgrass seeds. </p> <p>In 2016 a soil profile sampler with a depth of 20 cm, a horizontal length of 10 cm and a width of 2 cm was used to take an undisturbed soil profile in each plot and the thickness of each layer, thatch, matt and remainder soil, was measured using the protocol as described in Evers et al. (2024). Plant biomass in the plots was quantified by taking cores of the top 20 cm of the soil with a core sampler (diameter 28 mm). Cores were divided into thatch, mat, the remainder soil till 10 cm depth, and 10-20 cm depth, respectively, based on the earlier measurement of layer thicknesses in the field. Sediment of each section was then carefully washed out with tap water, after which the remaining below-ground (dead and living) plant biomass was dried at 65°C until stable weight and weighed. Total C and N analyses were carried out at the General Instrumentation Department of Radboud University with a Vario Micro Cube Element Analyzer (Elementar, Langenselbold, Germany), from which C and N concentrations (in % of dry matter or in mg cm<sup>-3</sup> C from total plant biomass in a layer) and CN ratios were calculated.</p> <p>Statistical analyses were carried out using the open source program R version 3.5.2 (2018-12-20). Differences in thickness of thatch and mat as well as differences in the C accumulation and C- and N concentration in thatch, mat and soil layers between (sub)species of turfgrasses in were based on the calculated means per species. Normality of residuals and the equality of variances was checked with diagnostic plots and Levene’s test, respectively. Non-normal and heteroscedastic data were either log transformed in linear models from the car package, or general least square (gls) models using varIdent from the nlme package were used. All data were further analyzed with ANOVA-type3 from the car package, followed by the Tukey post hoc test of the emeans package. Correlations between thatch and mat thickness were analyzed with linear regression models in R of the ggplot package. Similar procedures were performed for correlation between thatch, mat or soil thickness and C accumulation as well as for the correlation between C concentration and N concentration on C accumulation in a particular layer.</p>
SOCIAL MEDIA DATA 3 SPORTING EVENTS
<p>Comments on social networks (Facebook, Instagram, Twitter and YouTube) about the brand Spain linked to three chosen sporting events (mega, medium and<br>local): a football mega event (Qatar Football World Cup), a semi-massive tennis event (Davis Cup 2022) and a local marathon event (XLI<br>Marathon Valencia Trinidad Alfonso 2022</p>
Data of Female members of National Federations Sport Governing Boards. Database GESPORT Project.
<p>This database has been built by the authors. The data has been collected from the websites of the national federations of Italy, Portugal, Turkey, Spain and the United Kingdom in 2018.</p> <p>With the support of the European Commission. Erasmus+ Project. "Corporate governance in sport organizations: a gendered approach". Project Reference -EPP-1-2017-1-ES-SPO-SCP</p>
Research data management in the German-speaking Sports Sciences - Survey on the Status Quo
<p>The data set contains survey data on the status quo of research data management within the German-speaking sports science community. The survey was conducted as an online survey in the period from August 16<sup>th</sup> to September 30<sup>th</sup>, 2023.</p>
ERC Locus Ludi. Play and Games in Antiquity. Play, Dance, Sport, War
<p>Mark Golden, Winnipeg, a widely known specialist of ancient childhood and sport. His talk: Play, Dance, Sport, War: Ancient Greek Bodies in Motion, was given at the International Conference, Play and Games in Antiquity. Definition, Transmission, Reception, September 17-19, 2018, Swiss Museum of Games.</p> <p>Movie/Music: <a href="https://www.edwanmusic.com/">https://www.edwanmusic.com/</a></p> <p>More about: www.locusludi.ch</p>
Availability and trends in sports foods available for sale at New Zealand supermarketsy of sports foods globally and in New Zealand supermarkets
<p>Sports foods are specially formulated to help people achieve specific nutritional or sporting performance goals. Anecdotal evidence suggests increasing availability and marketing of such products to consumers, however, very few studies have looked at in-store product availability. Data for 2013 to 2018 were collected from the Nutritrack database, an online searchable database of all unique packaged foods and beverages sold at four main supermarket chains in New Zealand. Availability of sports foods and on-pack marketing techniques were assessed in 2018 using descriptive analysis, and changes in proportions over time were assessed using Chi-Square analyses. In 2018, the proportion of packaged foods available in major New Zealand supermarkets which were classified as sports foods was 2.1% (n=325), which had increased from 1.8% (n=247) in 2013. Sports foods also appeared in more food groups and subcategories in 2018 compared with 2013 (11 vs. 6 food groups, and 25 vs. 19 subcategories, respectively). The use of on-pack marketing techniques also increased over time, with Nutrient Claims present on 87% of sports foods in 2013 and 98% in 2018. The implications of the increase in product availability and on-pack marketing of sports foods in New Zealand supermarkets warrants consideration from public health, sporting, and consumer sectors.</p> <p>Sports foods are specially formulated to help people achieve specific nutritional or sporting performance goals. Anecdotal evidence suggests increasing availability and marketing of such products to consumers, however, very few studies have looked at in-store product availability. Data for 2013 to 2018 were collected from the Nutritrack database, an online searchable database of all unique packaged foods and beverages sold at four main supermarket chains in New Zealand. Availability of sports foods and on-pack marketing techniques were assessed in 2018 using descriptive analysis, and changes in proportions over time were assessed using Chi-Square analyses. In 2018, the proportion of packaged foods available in major New Zealand supermarkets which were classified as sports foods was 2.1% (n=325), which had increased from 1.8% (n=247) in 2013. Sports foods also appeared in more food groups and subcategories in 2018 compared with 2013 (11 vs. 6 food groups, and 25 vs. 19 subcategories, respectively). Use of on-pack marketing techniques also increased over time, with Nutrient Claims present on 87% of sports foods in 2013 and 98% in 2018. The implications of the increase in product availability and on-pack marketing of sports foods in New Zealand supermarkets warrants consideration from public health, sporting, and consumer sectors.</p> <p> </p>
Women on sports boards. GESPORT. Database.
<p>This database has been built by the authors. The data has been collected from the websites of the national federations of Italy, Portugal, Turkey, Spain and the United Kingdom in 2018. This complements the data base 10.5281/zenodo.6598291.</p>
Gender policies in sports organizations
<p> </p> <p> The data has been collected from the national sports federations of Italy, Portugal, Turkey, Spain, and the United Kingdom in 2021 and 2022.</p> <p>With the support of the European Commission. Erasmus+ Project. "Corporate governance in sport organizations: a gendered approach". Project Reference -EPP-1-2017-1-ES-SPO-SCP</p>
Gesport_Women on sports boards in NSFs of five countries: Italy, Portugal, Spain, Turkey and the United Kingdom
<p>This database has been built by the authors with the support of the Erasmus+ programme of the European Union under Grant number 590521-EPP-1-2017-1-ES-SPO-SC.</p> <p>The data has been collected from the websites of the national federations of Italy, Portugal, Turkey, Spain, and the United Kingdom in 2018 and in 2022.</p>
A Large-Scale Empirical Study of Android Sports Apps in the Google Play Store
<p>This repository contains the dataset for our study "A Large-Scale Empirical Study of Android Sports Apps in the Google Play Store" and this will help to replicate our study, also the <a href="https://github.com/mooselab/Sports-Apps-Analysis">replication package</a> to direct you to help replicate it for your dataset too. </p> <p>Note: The dataset given are protected with password, and the password is available in our published paper</p>
Phenotypic data from: from buds to shoots: insights into grapevine development from the Witch's Broom bud sport
<p><strong>Background </strong></p> <p>Bud sports occur spontaneously in plants when new growth exhibits a distinct phenotype from the rest of the parent plant. The Witch's Broom bud sport occurs occasionally in various grapevine (<em>Vitis vinifera</em>) varieties and displays a suite of developmental defects, including dwarf features and reduced fertility. While it is highly detrimental for grapevine growers, it also serves as a useful tool for studying grapevine development. We used the Witch's Broom bud sport in grapevine to understand the developmental trajectories of the bud sports, as well as the potential genetic basis. We analyzed the phenotypes of two independent cases of the Witch's Broom bud sport, in the Dakapo and Merlot varieties of grapevine, alongside wild type counterparts. To do so, we quantified various shoot traits, performed 3D X-ray Computed Tomography on dormant buds, and landmarked leaves from the samples. We also performed Illumina and Oxford Nanopore sequencing on the samples and called genetic variants using these sequencing datasets.</p> <p><strong>Results</strong></p> <p>The Dakapo and Merlot cases of Witch's Broom displayed severe developmental defects, with no fruit/clusters formed and dwarf vegetative features. However, the Dakapo and Merlot cases of Witch's Broom studied were also phenotypically different from one another, with distinct differences in bud and leaf development. We identified 968–974 unique genetic mutations in our two Witch's Broom cases that are potential causal variants of the bud sports. Examining gene function and validating these genetic candidates through PCR and Sanger-sequencing revealed one strong candidate mutation in Merlot Witch's Broom impacting the gene GSVIVG01008260001.</p> <p><strong>Conclusions</strong></p> <p>The Witch's Broom bud sports in both varieties studied had dwarf phenotypes, but the two instances studied were also vastly different from one another and likely have distinct genetic bases. Future work on Witch's Broom bud sports in grapevine could provide more insight into development and the genetic pathways involved in grapevine.</p>
Evaluating the Prevalence and Impact of Chronic Ankle Insta-bility in Collegiate Athletes across Various Sports: A Cross-Sectional Study.
<p>In the cross-sectional study "Evaluating the Prevalence and Impact of Chronic Ankle Instability in Collegiate Athletes across Various Sports," researchers sought to quantify how widespread chronic ankle instability (CAI) is among collegiate athletes and to determine its effects on athletic performance and training. The study involved 385 athletes from diverse sports, including basketball, soccer, track and field, swimming, and other sports, and revealed that CAI was present in nearly half of the participants. The investigation highlighted significant disparities in prevalence rates among different sports, with basketball and soccer athletes exhibiting higher rates of CAI. Furthermore, the study assessed the impact of CAI on several performance metrics such as training intensity, performance scores, and recovery times, finding that athletes with CAI generally showed lower performance levels and longer recovery times. These results emphasize the need for targeted preventive and rehabilitative strategies to mitigate the effects of CAI and support athlete health and performance.</p>
Cloud to Thing Continuum based Sports Monitoring System using Machine Learning and Deep Learning Model
<p><span>Sports monitoring and analysis have seen significant advancements with the integration of cloud computing and continuum paradigms, facilitated by machine learning and deep learning techniques. In this study, we present a novel approach for sports monitoring that seamlessly transitions from traditional cloud-based architectures to a continuum paradigm, enabling real-time analysis and insights into player performance and team dynamics. Leveraging machine learning and deep learning algorithms, our framework offers enhanced capabilities for player tracking, action recognition, and performance evaluation in various sports scenarios. This research proposes a Cloud-to-Thing Continuum based Sports Monitoring System utilizing Machine Learning (ML) and Deep Learning (DL) models. The system integrates data acquisition, preprocessing, feature extraction, cloud-based processing, continuum paradigm integration, and decision-making stages. It leverages innovative techniques such as Improved Mask R-CNN for pose estimation, hybrid metaheuristic algorithms with Generative Adversarial Network (GAN) for classification, and fuzzy decision-making Based on the integrated analysis, decisions are made regarding player performance, team strategies, and tactical adjustments. The continuum approach ensures a balance between centralized cloud processing and distributed edge processing, optimizing resource utilization and reducing latency. Through this system, real-time analysis of sports events is achieved, enabling immediate feedback for time-sensitive applications.</span></p>
DEVELOPMENT OF HIGH PERFORMANCE SPORTS IN RUSSIA AT THE PRESENT STAGE
<p><span>This article examines the implementation of a common strategy and standards in the field of sport development, where fair and equal competition between athletes is necessary, which is important for preserving reputation and fairness in the sport environment, as well as protecting the health of athletes. Increasing transparency and ethics in sport contributes to its development and attractiveness for both participants and spectators. The formation of the sports industry through integration and joint sports events plays a key role not only in changing the economic situation in Russia, but also in improving relations between countries and popularizing sports as a means of maintaining health and friendship.</span></p>
Dataset: Big 5 Sporting Goods Corporation (BGFV) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Dataset: Academy Sports and Outdoors, Inc. (ASO) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Dataset: Academy Sports and Outdoors, Inc. (ASO) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Dataset: Connexa Sports Technologies Inc. (YYAI) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
RAGE pilot data from 1st evaluation of the Sports Team Manager game on soft skills for employability
<p><strong>General description: </strong>The dataset includes data from the first 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: May-June 2017</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<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>: D8.3 First RAGE Evaluation Report<br> Related dataset: <a href="https://doi.org/10.5281/zenodo.2564742">10.5281/zenodo.2564742</a></p>
ScienceDex guides
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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.