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2,481 results for “gaming”
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>
Iterated Prisoner's Dilemma Best response data for games of length 200
<p>Iterated Prisoner's Dilemma Best response data for games of length 200, generated by a genetic algorthm. The opponents listed are the ones found in the Axelrod Pyhton library. This data was generated as part of a final year BSc project which can be found here: https://github.com/GitToby/FinalYearProject/</p> <p>The report can be found here: https://github.com/GitToby/FinalYearProject/blob/master/LaTeX/tex/main.pdf</p>
eConfidence School of Empathy game metrics
<p>Database includes metrics regarding in-game behaviour in eConfidence <em>School of Empathy </em>game. In supplementary file variable labels and descriptions are provided.</p>
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>
Video Game Platform Mapping
<p>The diggr platform mapping provides a mapping, reference and grouping for platform strings of various video game databases. It is meant to ease mapping and matching of releases, local releases or games in general which are to be found in these databases.</p> <p>This is meant as a first step towards unification of platform identification.</p> <p> </p>
GAED: Game Acceptance Evaluation Dataset
<p>The Game Acceptance Evaluation Dataset (GAED) contains statistical data aswell training and validation sets used in our experiments on <em>Neural Networks to evaluate Video Games Acceptance</em>.</p> <p>Please consider citing the following references if you found this dataset useful:</p> <p>[1] Augusto de Castro Vieira, Wladmir Cardoso Brandão. Evaluating Acceptance of Video Games using Convolutional Neural Networks for Sentiment Analysis of User Reviews. In Proceedings of the 30th ACM Conference on Hypertext and Social Media. 2019.</p> <p>[2] Augusto de Castro Vieira, Wladmir Cardoso Brandão. GA-Eval: A Neural Network based approach to evaluate Video Games Acceptance. In Proceedings of the 18th Brazilian Symposium on Computer Games and Digital Entertainment. 2019.</p> <p>[3] Augusto de Castro Vieira, Wladmir Cardoso Brandão. (2019). GAED: The Game Acceptance Evaluation Dataset (Version 1.0) [Data set]. Zenodo. </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>
CrowdWater game training study
<p>This data is connected to the CrowdWater project (www.crowdwater.ch), initiated through the Hydrology and Climate group at the University of Zurich. The content of these files is based on the CrowdWater game training study, which was conducted by the authors. A publication with the results of this study will be submitted within the next few days. The questions of the study are shown in the pdf file and the answers are shown in the csv file.</p> <p>Column name explanations:</p> <p>- "SG1" / "SG2" stands for staff gauge placement one / two (before and after the training respectively)</p> <p>- "ref_x" stands for the number x of the 60 reference pictures, that were assessed by the participants for the rating score</p> <p>- "first_refs" / "second_refs" stands for the rating of reference pictures before and after the training respectively</p> <p>- "SG1_ref_pic_id" / "SG2_ref_pic_id" - refers to the stream pictures (shown in the supplementary material of the soon to be submitted paper)</p> <p>- "SG1_suit_pic" / "SG1_suit_sg" / "SG1_size_cat" / "SG1_size" / "SG1_location" / "SG1_angle" / "SG1_line0" / "SG1_2sgs" refers to the individual assessment criteria for the placement score. Further explanation can be found in the methods section of the paper. The same applies for "SG2_..." and "app_..."</p> <p>- "SG1_sum" / "SG2_sum" refers to the summed up placement score, that consists of the individual assessment criteria described above.</p> <p>- "game_score" refers to the training score</p> <p>- "SG1_confidence_nr" / "SG1_confidence_simplified" refers to the self-assessed confidence of the user, once in numeric terms and once in words. The same applies for "SG2_confidence_...", "first_refs_confidence_..." and "second_refs_confidence_..."</p> <p> </p>
Programing language & Games
<p>With the development of science and technology, there are more and more electronic games on the market. The types of electronic games have also become more diversified. At present, there are many programming languages on the market that can be used to develop games. As a beginner of game development, it is difficult for us to choose an appropriate programming language to develop specific types of games. So we investigate some famous game and the programing languages they use.</p>
Figure 2 in Exploring Energy Management on GPUs in Game Architectures
Figure 2. - Photographic evidence from New Caledonia. (A) Inter-specific depredation of a ~1.2 m TL grey reef shark C. amblyrhynchos (GRS) that was depredat- ed by a ~2.6 m TL bullshark C. leucas (BS), which was itself almost entirely consumed probably by a larger conspecific after (B) consuming the grey reef shark (Photos courtesy of B. Claude).
Figure 1 - Photographic evidence from Seychelles. A in Exploring Energy Management on GPUs in Game Architectures
Figure 1 - Photographic evidence from Seychelles. A: Intra-specific depredation of a ~2 m TL tiger shark Galeocerdo cuvier (TS1) by a 4 m TL conspecific (TS2); B: Inter-specific depredation based on the catch of a ~1≈m TL grey reef shark Carcharhinus amblyrhynchos (GRS), that was almost entirely consumed by C: ~2 m TL bullshark C. leucas (BS), which was (D) itself depredated, probably by a large tiger shark (Photo courtesy of JBG).
Figure 1. – A in First steps towards Live-action Virtual Reality Games
Figure 1. – A: The Clipperton-La Passion Island (France) lies in the NorthEastern Tropical Ocean at a minimum distance of 945 km from the first emerged ground constituted by Socorro Island in the Mexican Exclusive Economic Zone. The closest potential sources for the colonization are Marquesas (French Polynesia) on the Eastern side and Galapagos (Ecuador) on the Western side, respectively (map modified from "Carte de l'île de la Passion", 2004, by C. Jost); B: Approximative location of the adult Caranx ignobilis identified on the 4 February 2016 (+ represent the location of the six dives).
Figure 2 in First steps towards Live-action Virtual Reality Games
Figure 2. – Photography of an adult individual of Caranx ignobilis on the eastern side reef slope of the Clipperton atoll on the 4 February 2016. The dorsal profile of steep and nearly straight snout, plus the silvery gray dorsal colour, shading to silvery white ventrally, with numerous scattered, very small black spots, are quite characteristic of this species of jack (Photo E. Clua).
FIGURE 2 in Molecular cytogenetics insights in two pelagic big-game fishes in the Atlantic, the tarpon, Megalops atlanticus (Elopiformes: Megalopidae), and the sailfish, Istiophorus platypterus (Istiophoriformes: Istiophoridae)
FIGURE 2 | Karyotypes of Megalops atlanticus (Megalopidae) and Istiophorus platypterus (Istiophoridae) after Giemsa staining, C-banding and FISH procedures. The small left boxes highlight the Ag-NORs and MM+/DAPI- sites, and the right ones the 18S (red) and 5S (green) rDNA sites. Scale bar = 5 µm.
FIGURE 1 in Molecular cytogenetics insights in two pelagic big-game fishes in the Atlantic, the tarpon, Megalops atlanticus (Elopiformes: Megalopidae), and the sailfish, Istiophorus platypterus (Istiophoriformes: Istiophoridae)
FIGURE 1 | Geographic distribution map of Megalops atlanticus (Megalopidae) and Istiophorus platypterus (Istiophoridae) across the Atlantic ocean. The shaded areas represents the occurrence and the yellow stars represent the collection sites of the species.
Object-Centric Event Log for Age of Empires Game Interactions
<p>The dataset contains object-centric event logs in the OCEL 2.0 ( <a href="https://www.ocel-standard.org/" target="_blank" rel="noopener">https://www.ocel-standard.org/ </a>) format.</p> <p>The event logs originate from 100,000 Age of Empires 2 matches. In this real-time strategy game, players control units (like villagers or archers) and build structures (like houses or lumber camps) to create an efficient economy and win against the other players. Players can control multiple units at once, and they can utilize game mechanics to automate parts of the process for them, so they must not trigger every event themselves. The beginning of the game focuses on building economic structures that are as efficient as possible. Normative process descriptions, so-called build orders, describe battle-tested interaction patterns for the beginning of the game, similar to chess openings.</p> <p>The large object-centric event log contains 1000 matches and has the following properties:</p> <table> <tbody> <tr> <td><strong>Property</strong></td> <td><strong>Value</strong></td> </tr> <tr> <td>Objects</td> <td>361,935</td> </tr> <tr> <td>Object Types</td> <td>30</td> </tr> <tr> <td>Events</td> <td>2,372,505</td> </tr> <tr> <td>Event Types</td> <td>829</td> </tr> </tbody> </table> <p> </p> <p>The following table describes the most important object types:<br><br></p> <table> <tbody> <tr> <td><strong>Object Type or (Group of Object Types)</strong></td> <td><strong><span>Explanation</span> </strong></td> </tr> <tr> <td>Match</td> <td>The match represents the competition of two players.</td> </tr> <tr> <td>Player</td> <td>There is one object per player. They are connected to all events that involve player input.</td> </tr> <tr> <td>Session</td> <td>There is one session per player in a match. The session is connected to all events happening on the machine of a player. The events can involve the player directly or they can also be game logic-based events triggered by the game engine.</td> </tr> <tr> <td>Villager</td> <td>Worker units to gather resources and build infrastructure.</td> </tr> <tr> <td>Town Center</td> <td>Central buildings for villager production and resource drop-off. Capable of setting automated gather points to assign tasks for newly created villagers.</td> </tr> <tr> <td>(Resource Drop-Off Group)</td> <td>Includes Lumber Camps, Mining Camps, and Mills. These facilities not only serve as drop-off points but can automatically command workers to gather the corresponding resources upon build completion.</td> </tr> <tr> <td>Farms</td> <td>Agricultural units for a continuous food supply. Farms can sometimes be replenished automatically, depending on game settings or upgrades.</td> </tr> <tr> <td>(Military Buildings)</td> <td>Structures for training military units and producing siege weaponry. Capable of setting gather points to automate unit deployment.</td> </tr> <tr> <td>(Research Buildings)</td> <td> <p>Facilities dedicated to technological advancements and upgrades.</p> </td> </tr> <tr> <td>(Military Units)</td> <td> <p>Units used for combat operations.</p> </td> </tr> </tbody> </table> <p> </p> <p>The following table describes the most important activities:</p> <p> </p> <table> <tbody> <tr> <td><strong>Event Type</strong></td> <td><strong><span>Explanation</span></strong></td> </tr> <tr> <td>Command Build [Structure]</td> <td>Issued by players to direct units to construct buildings.</td> </tr> <tr> <td>Start Build [Structure]</td> <td>Marks the beginning of the construction of a building by a designated group of villagers.</td> </tr> <tr> <td>Complete Build [Structure]</td> <td>Signals the completion of a building's construction, making the building operational and freeing up capacity of the constructing units.</td> </tr> <tr> <td>Gather [Resource]</td> <td>Represents the command to a unit to collect resources such as wood, stone, food, or gold.</td> </tr> <tr> <td>Command Research [Technology]</td> <td>Issued by players to initiate a research task in a research building.</td> </tr> <tr> <td>Start Research [Technology]</td> <td>Marks the beginning of the research process once resources arrived.</td> </tr> <tr> <td>Complete Research [Technology]</td> <td>Denotes the completion of a research task, unlocking new technologies or enhancements, and freeing up production capacity.</td> </tr> <tr> <td>Command Queue [Unit]</td> <td>Issued by players to add units to the production queue of a building.</td> </tr> <tr> <td>Start Production [Unit]</td> <td>Marks the beginning of unit production within a facility, as soon as there is capacity.</td> </tr> <tr> <td>Complete Queue [Unit]</td> <td>Signals the end of unit production, resulting in the deployment of a new unit and freeing up production capacity.</td> </tr> </tbody> </table> <p> </p> <p>The zip file contains filtered object-centric event logs that only contain 10 matches to explore the data set with faster loading time.</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>
MAPA MENTAL DAS REGRAS & FORMULÁRIO DE DECISÃO – BestPrice Game
<p>Diagrama na forma de mapa mental das regras e formulário de decisão do jogo BestPrice</p> <p> </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.