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134 results for “game data”
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>
CrowdWater game data
<p>This data is connected to the CrowdWater project (www.crowdwater.ch), initiated through the Hydrology and Climate group at the University of Zurich. This is the data from the CrowdWater game (made and maintained by SPOTTERON), collected between May 2018 and February 2019. All user ids have been modified, so as to keep all participants anonymous. There are three files:</p> <p>- cwgame_spot_export.csv (one spot per row)</p> <p>- cwgame_vote_export_anonym.csv (one vote per row)</p> <p>- cwgame_report_export_anonym.csv (one report per row)</p>
Data supporting the "Health Libraries Sharing Game"
<p>This dataset provides the necessary files to reproduce the "Health libraries sharing game". This game was created for the workshop "Health libraries: sharing through gaming" held in Basel on Wednesday the 19th of June 2019, as part of the EAHIL conference.</p> <p>Inspired by the game "Bucket of Doom", this game aims to help health librarians address challenging professional situations (based on real situations experienced by the authors). The players will have to be creative to overcome each challenge. </p> <p>The game is composed of cards that include possible situations to resolve, some tools, as well as resources available to solve those questions. The cards are accompanied by a "How to play" file explaining the rules of the game and a "Name sheet" file with the combination of funny names that participants can choose at the beginning of the game. The README.txt file describes the files and the content of the different folders of the dataset.</p> <p>Please consult the following article for further information about the creation of the game:</p> <p>Gómez-Sánchez, A., Kerdelhue, G., Isabel-Gómez, R., González-Cantalejo, M., Iriarte, P., & Muller, F. (2019). Health libraries: sharing through gaming. Journal of EAHIL, 15(3), 8-11. https://doi.org/10.32384/jeahil15329 </p>
Extended data integration of motivational aspects in gamification and game-based learning educational designs
<p>This is the extended data for a systematic review article about the integration of motivational aspects in gamification and game-based learning educational designs related to teacher´s training and teacher´s professional development.</p>
Data underlying the research paper "Articulating Social Issues with Open Data: Exploring a Game Jam Approach"
<p>Contains research data underlying the following research paper:</p> <blockquote> <p>Davide Di Staso, Lærke Christiansen, Fernando Kleiman, and Marijn Janssen. 2024. Articulating Social Issues with Open Data: Exploring a Game Jam Approach. In Proceedings of the 8th International Conference on Game Jams, Hackathons and Game Creation Events (ICGJ ’24), October 11, 2024, Copenhagen, Denmark. ACM, New York, NY, USA, 7 pages. https://doi.org/10.1145/3697789.3697798</p> </blockquote> <p>The authors acknowledge the financial support from the European Union’s Horizon 2020 research and innovation program under the Marie Skłodowska-Curie grant agreement No. 955569, "Towards a sustainable Open Data ECOsystem" (ODECO).</p>
Data of 14 participants in the RCT titled Acute effects of virtual reality exercise bike games on psychophysiological outcomes in college North-African adolescents with cerebral palsy: A randomized clinical trial
<p>It is an Excel file including the numerical data of the 14 participants included in the study titled: <a name="_Hlk151119211"></a><span>Acute effects of virtual reality exercise bike games on psychophysiological outcomes in college North-African adolescents with cerebral palsy: A randomized clinical trial</span></p>
Data for Manuscript: Instrumental Validity of the Motion Detection Accuracy of a Smartphone Based Training Game
<p><strong>Background: </strong>In the project TRIMOTEP we developed a low-cost augmented reality training game. Aim of the training game ist to support patients after total hip replacement in their rehabilitation. The project was funded by the Austrian Research Promotion Agency (FFG, grant number 862050). As hardware the training game uses a headset, an android smartphone and a step board. The goal of the training game is to dodge animals and objects while performing exercises. A current version of the training game can be downloaded here: https://trimotep.fh-joanneum.at/exer-game-ar_walker/ . The training game is based on Google ARCore and uses a movement detection approach to recognise different exercises. To detect movements ARCore uses the smartphone inbuilt inertial measurement unit and the front camera (https://developers.google.com/ar/discover). In order to investigate the possibilities of the training game, it is necessary to examine the accuracy of movement detection in more detail.</p> <p><strong>Data: </strong>To investigate the accuracy, comparative measurements were carried out with 30 healthy subjects. During the measurements, the subjects motion was recorded simultaneously with the training game and an optoelectronic motion capture system (Vicon). Two trials were recorded with each subject.</p> <p>First Trial: subjects followed a protocol</p> <p>Second Trial: subjects played the training game for one minute</p> <p>The training game measures the movement of the smartphone (and therefore of the headset and the head). The optoelectronic motion capture system uses a marker set consisting of four markers. Those markers are labeled HMD_F, HMD_B, HMD_R, HMD_L. Markers HMD_R and HMD_L as well as HMD_B and HMD_F form an axis in a karthesian coordinate system. This coordinate system is rotated by 8 degrees compared to the training game along the transversal axis.</p> <p><strong>Structure of the Data Set:</strong> The data set includes an excel sheet with general data of the subjects and a figure showing the tilt between the two coordinate systems. Further one folder contains the measurement data of the training game as json files. Another folder contains the measurement data of the optoelectronic motion capturing system as csv files.</p> <p> </p> <p>For further information or help to process the data please contact:</p> <p>Bernhard Guggenberger, bernhard.guggenberger2@fh-joanneum.at</p>
German Gaming Magazines Sales Data 1980-2000
<p>A data set that collects sales and distribution data of seventeen German gaming magazines 1980-2000, based on the print run lists ("Auflagelisten") published by the <i>Informationsgemeinschaft zur Feststellung der Verbreitung von Werbeträgern</i> (<a href="https://en.wikipedia.org/wiki/Informationsgemeinschaft_zur_Feststellung_der_Verbreitung_von_Werbetr%C3%A4gern"><i>Information Community for the Assessment of the Circulation of Media</i></a> – IVW). More information on the dataset and its structure can be found here: <a href="https://chludens.hypotheses.org/1228">https://chludens.hypotheses.org/1228</a></p>
Data from: A high-performance brain-computer interface for finger decoding and quadcopter game control in an individual with paralysis
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Data from: Winter game crop plots for gamebirds retain hedgerow breeding songbirds in an improved grassland landscape
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Data from: Luck, skill, and depth of competition in games and social hierarchies
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The color communication game: how categorical understanding of colors can be shown without considering color naming data
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Supplementary data on learning about German farmers' willingness to cooperate from public goods games and expert predictions
<p>Supplemental files on learning about German farmers' willingness to cooperate from public goods games and expert predictions. </p>
Head movement and heartbeat data while playing VR games
<p>This dataset was collected while 30 different participants played five different VR games (Aircar, Beat Saber, Moss, Arizona Sunshine, and SUPERHOT). It includes the head movement data, heartbeat data, and SSQ data.</p>
Toll Dilemma Game Data: Measuring the Cooperation of the People with the Government in Iran in the Form of Seven designed Scenarios
<p>The toll dilemma game was designed in the form of a robot and distributed in the Telegram messenger. More than 1,200 people participated in this game, who acted as testee in the game. Each actor entered one of seven pre-designed scenarios, and how he or she worked with the government was evaluated in a simulated environment. The current data are entered in SPSS software. </p>
Applying an Inconsistency Repair Mechanism for clone-and-own Code Smell Analysis: the Apo-games Case Study (Evaluation Data)
<p>This is a repository containing the artifacts and the results of the evaluation of the solution paper "Applying an Inconsistency Repair Mechanism for <em>clone-and-own</em> Code Smell Analysis: the Apo-games Case Study"</p>
Banding and recovery data on several game bird species to study hunting selectivity
<p><span>Selective hunting has various impacts that need to be considered for the conservation and management of harvested populations. The consequences of selective harvest have mostly been studied in trophy hunting and fishing, where selection of specific phenotypes is intentional. Recent studies however show that selection can also occur unintentionally.</span> <span>With at least 52 million birds harvested each year in Europe, it is particularly relevant to evaluate the selectivity of hunting on this taxon. Here we considered </span><span>211 806</span><span> individuals belonging to 7 hunted bird species to study unintentional selectivity in harvest. Using linear mixed models, we compared morphological traits (mass, wing and tarsus size) and body condition at the time of banding between birds that were subsequently recovered from hunting during the same year as their banding, and birds that were not recovered. We did not find any patterns showing systematic differences between recovery categories, among our model species, for the traits we studied. Moreover, when a difference existed between recovery categories, it was so small that its biological relevance can be challenged. Hunting of birds in Europe therefore does not show any form of strong selectivity on the morphological and physiological traits that we studied, and should hence not lead to any change of these traits either by plastic or evolutionary response. </span></p>
Mielke & Carvalho 2022 Chimpanzee play sequences are structured hierarchically as games - Data
<p>Data and scripts for the 2022 manuscript 'Chimpanzee play sequences are structured hierarchically as games' - preprint here: </p> <p>https://doi.org/10.1101/2022.06.14.496075</p> <p>Dataset and scripts generated on 20/09/2022. For potential changes and all information see:</p> <p>https://github.com/AlexMielke1988/Mielke-Carvalho_Chimpanzee-Play</p>
Federated Learning Games for Reconfigurable Intelligent Surfaces via Causal Representations: SW and Data
<p>This upload contains the main simulation code and related datasets used in the conference paper entitled <a href="https://ieeexplore.ieee.org/document/10437657" target="_blank" rel="nofollow noreferrer noopener">Federated Learning Games for Reconfigurable Intelligent Surfaces via Causal Representations</a>, which was presented at <a href="https://globecom2023.ieee-globecom.org/" target="_blank" rel="nofollow noreferrer noopener">IEEE GLOBECOM 2023</a>.</p>
Replication data for Evaluation of movements with greater trunk acceleration and their properties during volleyball games
<p>The present study aimed to elucidate the movements that require greater trunk acceleration and their frequency during volleyball matches and compare their acceleration components. A triaxial accelerometer was used to measure trunk acceleration during volleyball matches. The moments that generated resultant accelerations >4G, >5G and >6G were extracted, and movements that coincided with the extracted moments were identified. The ratios and frequencies of the extracted movements were calculated, and the resultant, mediolateral, vertical and anteroposterior accelerations among the top seven volleyball-specific extracted movements were compared. For attackers, 361, 185 and 97 movements were extracted with acceleration thresholds of 4G, 5G and 6G, respectively. At 6G, landing (77%) was the most frequently observed movement. For receivers, 297, 115 and 38 movements were extracted with acceleration thresholds of 4G, 5G and 6G, respectively. At 4G, running (49%) and steps (31%) were the most frequently observed movements. Resultant and vertical accelerations of spike landing were significantly greater than those of the other six movements (p < 0.001, respectively). Stationary or directional steps observed in receivers showed greater anteroposterior acceleration. Frequency or magnitude of trunk acceleration during these movements might determine the risk of injury during volleyball.</p>
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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.