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2,481 results for “gaming”
Evaluation of restoration on post–mining areas using a game theory - data
<p>Supplementary data to the article - numerical values from graphs (Fig 1-2, 5-12) + input data for calculations. The resulting NE probability values can be verified at equsis.com (outside of Fig 8, the limit is 32 cases).</p>
Perceived image quality of real time ray tracing in video games survey image archive
<p>These screenshots were used in my Bachelor’s Thesis Perceived image quality of real time ray tracing in video games survey. Images have been labeled with RT ON or RT OFF depending on whether the screenshot contains ray traced graphics or not. Original survey did not have these labels.<br> <br> Images 1-5 are from Control, Images 6-10 are from Shadow of the Tomb Raider, Images 11-15 are from Cyberpunk 2077 and Images 16-20 are from Metro Exodus and Metro Exodus Enhanced Edition.</p>
Meta Analysis of Conservation Games Data
<p>This is the first release of Meta Analysis of Conservation Games Dataset. This was created for the analysis in the paper "Payments don't reconcile agriculture and conservation" by Andrew Reid Bell, O. Sarobidy Rakotonarivo, Apurva Bhargava, A. Bradley Duthie, Wei Zhang, Becca Sargent, Spike Lewis, and Adams Kipchumba.</p>
Artefact for Kaki: Concurrent Update Synthesis for Regular Policies via Petri Games
<p>This is the artefact for the paper "Kaki: Concurrent Update Synthesis for Regular Policies via Petri Games".</p> <p>The files in this package aim to assist you in running Kaki and generating the results presented in the paper. The details on running Kaki, generating the problems and reproducing the results can be read in the README.md file.</p>
Compare Analysis with a High-quality Genome of Weedy Rice Reveals the Evolutionary Game of De-domestication
<p>A02.SV.vcf is the SV information between the A02 genome and Nipponbare genome.</p> <p>All.population.SV.vcf is the data of the SV genotyping information based on the super pan-genome.</p>
DrawMon: A Distributed System for Detection of Atypical Sketch Content in Concurrent Pictionary Games
<p>Dataset for training CanvasNet, a model for detecting atypical sketch instances. CanvasNet processes the rendered image as input and outputs a list of atypical activities (if any) along with associated meta-information (atypical content category, 2-D spatial location).</p> <p>For more details, visit our page https://drawm0n.github.io/.</p>
12 - The Game of Science
<p>Session #12 of the REINFORCE Critical & Scientific Thinking course, entitled, 'The Game of Science', is given by Stefano Gattei, Professor of Science in Society, at the University of Trento, Italy.</p> <p>During the session, Prof. Gattei covers the Game of Science, as described by Popper, with a study of the Rules and the Supreme Rule, which governs them all.</p>
Dogs can find the payoff-dominant outcome in the Assurance game
<p>Studies on coordination often present animals with the choice of either cooperating or remaining inactive; however, in nature, animals may also choose to act alone. These situations can be modeled with the Assurance game, an economic game that has recently been used to explore social decision-making in primates. Here, we investigated the ability of dyads of pet dogs to coordinate in the Assurance game. Pairs were presented with two alternatives: they could individually solve an apparatus baited with a low-value reward (Hare) or they could work together to solve a cooperative apparatus baited with one high-value reward for each dog (Stag). All individuals matched their partner's choices, but once we controlled for side-bias, only four dyads consistently coordinated on the payoff-dominant strategy (Stag-Stag). Thus, some dogs are capable of finding coordinated outcomes, as do primates, at least in a context in which their partner's actions are visible and coordination always results in the biggest payoff for both individuals.</p>
complete table of data for article: Changes in technical/physical performance of young soccer players throughout soccer game simulation.
<p>The table contains the data of 21 subjects</p> <p>The letters in the third column indicate playing position Midfielders (M), attackers (A) and defenders (D).</p> <p>The abbreviations that appear in the columns are:</p> <p>-Goal shooting accuracy from 20 m (GSA).</p> <p>-Dribbling (DRIB).</p> <p>-Long pass accuracy from 30 m (LPA).</p> <p>-Speed without ball in 20 x 20 m (S).</p> <p>-Perception of fatigue (PF).</p> <p>After each abbreviation, the period in which it was measured according to phase criteria (phase 1, phase 2, phase 3) or intensities, low (L), medium (M) or high (H) is indicated.</p> <p>The CMJ i and CMJ f columns show the countermovement jump height values at the beginning and end of the study, respectively.</p>
Effects of human and non-human predation risk on antipredator movement behaviors of an upland game bird
<p>Predators can elicit antipredator behaviors in prey such as proactive and reactive movements, but both are rarely investigated simultaneously. Impacts of human predation risk on antipredator behaviors can potentially be greater than non-human predators, resulting in increased effects on populations and community structure. Therefore, we compared the influence of human and non-human predation risk on proactive and reactive antipredator movement behaviors of a commonly harvested game bird, male Eastern wild turkeys (<em>Meleagris</em> <em>gallopavo</em>, hereafter turkey). We used simultaneously collected GPS locations from 31 turkeys and 36 coyotes (<em>Canis</em> <em>latrans</em>) to investigate antipredator behavior of turkeys to coyotes. To assess antipredator behaviors by turkeys to hunters, we used 1,661 hunting tracks collected while monitoring 109 turkeys. Specifically, for proactive movements, we quantified how predation risk influenced resource selection. To investigate reactive movements, we quantified changes in movement behavior of turkeys after encountering hunters and coyotes. Coyotes and turkeys were sympatric on the landscape as home range overlap was high, but lack of core area overlap, encounters, and similar resource selection suggested use of different areas on the landscape. Turkeys selected areas associated with decreased coyote risk and closer to hardwoods. Coyotes preferred shrubs and open areas, suggesting turkeys avoided coyote risk and the habitats coyotes preferred. We detected 17 coyote and turkey contacts, and probability of a contact decreased by 16.6% for every 100m farther from a forest edge. Turkeys did not display reactive movement behaviors after a direct encounter with coyotes, as step lengths were similar prior and after encounters, which did not differ from random step lengths. After the onset of hunting, turkeys selected to be farther away from public access points and closer to private property, suggesting proactive avoidance of areas associated with increased hunter predation risk. We detected 31 hunter and turkey contacts, step lengths after hunter contacts were approximately double compared to random step lengths. The probability of a hunter-turkey contact decreased by 5.5% for every 100m farther from a secondary road. Collectively, antipredator movement behaviors by turkeys suggest coyote risk to be low over a broad temporal window as we only documented proactive movement behaviors. Conversely, hunter risk is high for a short temporal window while hunting is occurring, as we documented both proactive and reactive movement behavior responses. Overall, we provide insight into how human-induced fear can cause antipredator behavioral responses greater than non-human fear, potentially causing changes in species distribution and community structure.</p>
Dataset to Study Flexibility Service Providers' Gaming Potential and its Impact on TSO-DSO Coordinated Markets
<p>We publish the dataset and nomenclature table used to study the Flexibility Service Providers’ Gaming Potential and its Impact on TSO-DSO Coordinated Markets (paper published in the 2023 SEST conference).</p> <p>The dataset is composed by an interconnected system consisting of the IEEE 14-bus (TN) transmission network connected to two distribution networks: the Matpower systems 69-bus (DN_69), and 141-bus (DN_141). All systems topology and some parameters are based on the corresponding cases in Matpower [1]. Injections and loads of the nodes are adapted to create an anticipated negative imbalance (total load surpassing total generation) in the interconnected system, resolved by upward flexibility. In addition, the lines’ upper limits are adjusted to create anticipated congestion in the networks. The interconnected system is fully represented in "Network.xlsx".<br>Upward and downward flexibility bids are randomly generated and allocated to the nodes. Their quantities are aligned with the nodes’ base injection/load and their marginal values are in the range [10, 26] euro/MW for downward, and marginal costs in the range [30, 73] euro/MW for upward, totaling 536 bids. </p> <p>4 bids lists are available in this dataset, to allow the strategic behavior analysis with different levels of liquidity:</p> <p>- In the list of bids "case 0", all 536 bids are present.</p> <p>- In the list of bids "case 1", 40% of bids in the transmission list are deleted (distribution bids are the same as in case 0).</p> <p>- In the list of bids "case 2", 98% of the bids in the distribution bids are deleted (transmission bids are the same as in case 0).</p> <p>- In the list of bids "case 3", only bids needed to solve distribution systems congestions are kept (all the others are deleted) (transmission bids are the same as in case 0).</p> <p>Source of the systems' topology:</p> <p>[1] R. D. Zimmerman, C. E. Murillo-Sanchez, and R. J. Thomas, “Mat-power: Steady-state operations, planning, and analysis tools for power systems research and education,” IEEE Transactions on power systems, vol. 26, no. 1, pp. 12–19, 2010.</p> <p>Please notice that this dataset does not replace the information provided by Matpower related to the aforementioned systems. It rather uses those systems topology and some of their parameters to build a case study to investigate TSO-DSO coordination market models for the procurement of flexibility. For the full description of these systems, please visit: <a href="https://matpower.org/">MATPOWER – Free, open-source tools for electric power system simulation and optimization</a>.</p>
Dataset SLR Game Software Engineering 2009-21
<p>This dataset records the review process and results of the study "The Consolidation of Game Software Engineering: A Systematic Literature Review of Software Engineering for Industry-scale Computer Games."</p>
Game reviews of VR HMDs
<p>These games and reviews are collected from VR games on three digital distribution platforms (Steam, Viveport, and Oculus).</p> <p>Viveport_games_message.csv, Oculus_games_message.csv, and Steam_games_message.csv are the game information on the three platforms for RQ1</p> <p>.VR HMDs_game_reviews.csv is users' reviews about HMDs for RQ2.</p> <p><br>If you use this data, please cite our article.<br>Yijun Lu, Kaoru Ota, and Mianxiong Dong. 2024. An Empirical Study of VR Head-Mounted Displays Based on VR Games Reviews. https://doi.org/10.1145/3665988</p> <p> </p> <p> </p> <p> </p>
Data from questionnaire from village adjacent Ugalla game reserve
<p>Data were collected using a semi-structured interview questionnaire, which involved 141 respondents’ members of four trade unions, namely SIFICO, TWIKILAGI, TABORA FISH and TABORA BEEKEEPERS, who were contacted and interviewed. With the application of the Yamen formula, we efficiently identified 141 individuals who were considered appropriate and representative of the population sample size to participate in the interviews.To ensure the questionnaires are trustworthy, we conducted a reliability analysis test. Additionally, we checked the data validity using a Pearson correlation coefficient.</p>
bachtiarrizkyal/Etika-TI: Dataset Analisis Dampak Bermain Game Mobile Legends
<p>Dataset berisi data hasil respon survey terkait "Analisis Dampak Bermain Game Mobile Legends Terhadap Perilaku Toxic Anak-Anak Hingga Dewasa"</p>
DockGame: Cooperative Games for Multimeric Rigid Protein Docking
<p>Raw datasets used for DB5.5 dataset and DIPS dataset used in DockGame, with associated code at https://github.com/vsomnath/dockgame </p> <p>These datasets can also be downloaded from https://zlab.umassmed.edu/benchmark (DB5.5) and https://www.dropbox.com/s/sqknqofy58nlosh/DIPS.zip?dl=0 (DIPS). Note that our organization would be slightly different from these downloaded versions.</p> <p>We follow EquiDock (https://github.com/octavian-ganea/equidock_public) for the DB5 dataset splits.</p> <p><strong>Paper Abstract</strong></p> <p>Protein interactions and assembly formation are fundamental to most biological processes. Predicting the assembly structure from constituent proteins -- referred to as the protein docking task -- is thus a crucial step in protein design applications. Most traditional and deep learning methods for docking have focused mainly on binary docking, following either a search-based, regression-based, or generative modeling paradigm. In this paper, we focus on the less-studied multimeric (i.e., two or more proteins) docking problem. We introduce DockGame, a novel game-theoretic framework for docking -- we view protein docking as a cooperative game between proteins, where the final assembly structure(s) constitute stable equilibria w.r.t. the underlying game potential. Since we do not have access to the true potential, we consider two approaches - i) learning a surrogate game potential guided by physics-based energy functions and computing equilibria by simultaneous gradient updates, and ii) sampling from the Gibbs distribution of the true potential by learning a diffusion generative model over the action spaces (rotations and translations) of all proteins. Empirically, on the Docking Benchmark 5.5 (DB5.5) dataset, DockGame has much faster runtimes than traditional docking methods, can generate multiple plausible assembly structures, and achieves comparable performance to existing binary docking baselines, despite solving the harder task of coordinating multiple protein chains.</p>
Venus of Milo game asset
This is a Game ready asset based on the Venus de Milo. I made a retopo over a high poly 3d scanned model using Instant Meshes. The scanned model was made using Meshroom to create a photogrammetry model based on a little statue I have in my house . Then I fixed the topology using Blender 2.9 and optimized the model using modificators. I exported the low poly version to Substance Painter and bake the PBR maps. Finally I created the textures from scratch. Source: Objaverse 1.0 / Sketchfab
Sickle Game Ready Prop Realistic
modeled by virtualmonkey, textured by me blender+substance painter 4096x PBR Sickle game asset Source: Objaverse 1.0 / Sketchfab
Kashan Vase (Game Ready / 2K PBR)
Support the Free 3D Model Library for only $1 per month: https://www.patreon.com/meerdigital Check out more cool 3D art from Meerschaum Digital: https://linktr.ee/meerdigital Enhance your projects with this premium model of an ancient vase. This model has a very lean polygon count at 1,876, making it perfect for real-time applications or quick render times in ray tracing engines. The geometry comprising this model has been whittled down from a photoscanned model to the absolute bare minimum possible while still retaining most of its visual quality. Features: * 1,876 tris, 938 vertices * 2048px by 2048px textures for PBR workflows (Albedo/Color, Normal, Roughness & Ambient Occlusion) * Non-overlapping UV Map * Real world scale Kashan ware ewer, 12th C CE by MInneapolis Institute of Art is dedicated to the public domain, original model here: https://sketchfab.com/3d-models/kashan-ware-ewer-12th-c-ce-e0e8594096f34d329906de73682e9b74 Source: Objaverse 1.0 / Sketchfab
Native American Game Stone
Native American stone used for games. 105 images, Canon EOS 80D, 35mm, F/16, ISO 100, Agisoft Metashape, Windows 10. Source: Objaverse 1.0 / Sketchfab
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.