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2,481 results for “gaming”
Costless renewable energy distribution model based on cooperative game theory for energy communities considering its members' active contributions
<p>This dataset was used in the case study of the following publication:</p> <p> - Luis Gomes, Zita Vale, "Costless renewable energy distribution model based on cooperative game theory for energy communities considering its members’ active contributions," Sustainable Cities and Society, Volume 101, 2024, 105060, ISSN 2210-6707, <a href="https://doi.org/10.1016/j.scs.2023.105060">https://doi.org/10.1016/j.scs.2023.105060</a> </p> <p><em>(if you used this dataset in your publications, please send us your information so we can add your publication to the list above)</em></p> <p> </p> <p>The dataset is composed by energy generation, consumption, and forecast (for generation, and for consumption) expressed in Wh. The data considers an energy community of 10 prosumers in 30 days.</p> <p>The dataset also has energy prices that have been collected from MIBEL (Iberian Electricity Market).</p> <p> </p> <p>We would be grateful if you could acknowledge the use of this dataset in your publications. Please use the Zenodo publication to cite this work.</p>
Software and data underlying the article 'A serious game approach for lake modeling and management: the EscapeBLOOM'
<p>Here we share the player version of the EscapeBLOOM, a dummy version showcasing the techniques to create a similar digital escape room, and the anonymized data of the quantitative survey as presented in the publication 'A serious game approach for lake modeling and management: the EscapeBLOOM'.</p> <p>Anyone is free to play or adjust the game for their own educational purposes. The dummy and supplementary material of the publication 'A serious game approach for lake modeling and management: The EscapeBLOOM' <a title="Persistent link using digital object identifier" href="https://doi.org/10.1016/j.envsoft.2024.105941" target="_blank" rel="noreferrer noopener">https://doi.org/10.1016/j.envsoft.2024.105941</a> together provide guides on how to create a new game from the start and may help to adjust the existing game.</p> <p>The data of the survey was used for the analysis of perceived learning in the publication 'A serious game approach for lake modeling and management: the EscapeBLOOM'.</p>
Figs 2, 3 in Seasonal variations in ixodid tick populations on a commercial game farm in the Limpopo Province, South Africa
Figs 2, 3. Numbers of Rhipicephalus (Boophilus) decoloratus collected in wetter and drier months (2), and in warmer and cooler months (3).
RAYUELA - Open Data (small) Preliminary Pilots - Data collected through a serious game created to identify patterns and profiles of young potential victims/perpetrators of cybercrimes.
<p>The data of this dataset have been collected in the pilots carried out by the RAYUELA project in different countries of the European Union. The participants are minors and the game sessions have been carried out in schools and summer camps in a supervised way.</p> <p>This is the first version of a larger dataset: https://doi.org/10.5281/zenodo.10604760</p>
Pokemon: combination of in-game stats and pokedex descriptors
<p>Recopilation of all 1025 pokemon along with their in-game stats and a sort description from their latest appearance in the pokedex</p>
VR Gaming Dataset - 2025
<p>This dataset includes ALVR statistics (.json) and Wireshark packet traces (.csv) for a wide range of single-user and multi-user PCVR configurations. Our configurations are based on ALVR and SteamVR. For each user, we stream a video game from a wired server (desktop or laptop) to a wireless head-mounted display (Meta Quest 2 or 3) over Wi-Fi. In each case, the data was captured on the server.</p>
SC2EGSet: StarCraft II Esport Game State Dataset
<p><strong>SC2EGSet: StarCraft II Esport Game State Dataset</strong></p> <p>This repository contains pre-processed data that was generated from the <a href="https://doi.org/10.5281/zenodo.5575796">SC2ReSet: StarCraft II Esports Replaypack Set</a></p> <p><strong>Data Modeling</strong></p> <p>Our official aplication programing interface (API) implementation supports downloading, unpacking, loading and data access features. Please refer to: <a href="https://github.com/Kaszanas/SC2_Datasets">GitHub SC2_Datasets</a></p> <p><strong>License Information</strong></p> <p>This dataset is licensed under the following license: Creative Commons Attribution 4.0 International (CC BY 4.0 International)</p> <p><strong>Changelog</strong></p> <p>Please refer to the CHANGELOG.md file within this repository for full changelog.</p> <p><strong>Citation</strong></p> <p>If you use this dataset you can chose to cite the projects and/or articles presented below.</p> <p><strong><a href="https://www.researchgate.net/publication/373767449_SC2EGSet_StarCraft_II_Esport_Replay_and_Game-state_Dataset">Dataset Description Pre-print</a></strong></p> <p>To cite the article that introduces <a href="https://doi.org/10.5281/zenodo.5575796">SC2ReSet</a> and <a href="https://doi.org/10.5281/zenodo.5503997">SC2EGSet</a> use this:</p> <div> <div>```<br>@article{Bialecki2023,</div> <div> author = {Bia{\l}ecki, Andrzej</div> <div> and Jakubowska, Natalia</div> <div> and Dobrowolski, Pawe{\l}</div> <div> and Bia{\l}ecki, Piotr</div> <div> and Krupi{\'{n}}ski, Leszek</div> <div> and Szczap, Andrzej</div> <div> and Bia{\l}ecki, Robert</div> <div> and Gajewski, Jan},</div> <div> title = {SC2EGSet: StarCraft II Esport Replay and Game-state Dataset},</div> <div> journal = {Scientific Data},</div> <div> year = {2023},</div> <div> month = {Sep},</div> <div> day = {08},</div> <div> volume = {10},</div> <div> number = {1},</div> <div> pages = {600},</div> <div> issn = {2052-4463},</div> <div> doi = {10.1038/s41597-023-02510-7},</div> <div> url = {https://doi.org/10.1038/s41597-023-02510-7}</div> <div>}<br>```</div> <div> </div> <div><strong><a href="https://doi.org/10.5281/zenodo.5575796">SC2ReSet: StarCraft II Esport Replaypack Set</a></strong></div> </div> <p>To cite the replay collection that was used to generate the dataset use this:</p> <pre><code>@dataset{bialecki_andrzej_2022_5575797, author = {Białecki, Andrzej}, title = {SC2ReSet: StarCraft II Esport Replaypack Set}, month = mar, year = 2025, publisher = {Zenodo}, version = {2.0.0}, doi = {10.5281/zenodo.5575797}, url = {https://doi.org/10.5281/zenodo.5575797} } </code></pre>
Data Set Analisis Perilaku dan Interaksi pada YouTuber Gaming berdasarkan Persebaran Gender
<p>Data Set Analisis Perilaku dan Interaksi pada YouTuber Gaming berdasarkan Persebaran Gender</p>
RePAST Quiz-game-tour (usage data)
<p>RePAST Quiz-game-tour (usage data)</p>
Video Games Sales
<p>This dataset contains a list of video games with sales greater than 100,000 copies. It was generated by a scrape of <a href="http://www.vgchartz.com/">vgchartz.com</a>.</p> <p>Fields include</p> <ul> <li> <p>Rank - Ranking of overall sales</p> </li> <li> <p>Name - The games name</p> </li> <li> <p>Platform - Platform of the games release (i.e. PC,PS4, etc.)</p> </li> <li> <p>Year - Year of the game's release</p> </li> <li> <p>Genre - Genre of the game</p> </li> <li> <p>Publisher - Publisher of the game</p> </li> <li> <p>NA_Sales - Sales in North America (in millions)</p> </li> <li> <p>EU_Sales - Sales in Europe (in millions)</p> </li> <li> <p>JP_Sales - Sales in Japan (in millions)</p> </li> <li> <p>Other_Sales - Sales in the rest of the world (in millions)</p> </li> <li> <p>Global_Sales - Total worldwide sales.</p> </li> </ul> <p>The script to scrape the data is available at <a href="https://github.com/GregorUT/vgchartzScrape">https://github.com/GregorUT/vgchartzScrape</a>.<br> It is based on BeautifulSoup using Python.<br> There are 16,598 records. 2 records were dropped due to incomplete information.</p>
Volunteer classifications of images from the Cropland Capture game
<p>Each entry represents a single classification of a single image by a volunteer rater.</p> <p>The dataset contains six columns:</p> <p>imgid: The unique identifier for each image used in the Cropland Capture campaign<br> userid: The unique identifier for each volunteer in the Cropland Capture campaign<br> rating: The answer provided; can be only one of the following:<br> 1: yes cropland<br> 2: no cropland<br> 0: maybe <br> date: Timestamp of the rating<br> ratingid: The unique identifier of the rating (this is different for each data row)<br> platform: What interface did the volunteer use to provide this rating?<br> 1: iPhone5<br> 2: iPhone, other models<br> 3: iPad<br> 4: Browser<br> >100: Android; different numbers indicate the screen size in pixels </p> <p>For more information, please see the following publications:</p> <p>Salk, CF, T Sturn, L See, S Fritz (2017). Limitations of majority agreement in crowdsourced image interpretation. <em>Transactions in GIS</em>, 21: 207–223.</p> <p>Salk, CF, T Sturn, L See, S Fritz (2016). Local knowledge and professional background have a minimal impact on volunteer citizen science performance in a land-cover classification task. <em>Remote Sensing</em>, 8: 744.</p> <p>Salk, CF, T Sturn, L See, S Fritz and C Perger (2016). Assessing quality of volunteer crowdsourcing contributions: Lessons from the Cropland Capture game. <em>International Journal of Digital Earth</em>, 9(4): 410-426.</p>
Wikipedia video games similarity dataset with expert annotations
<p>A video games NLP dataset extracted from Wikipedia.</p> <p>For all articles, the figures and tables have been filtered out, as well as the categories and "see also" sections.</p> <p>The article structure, and particularly the sub-titles and paragraphs are kept in these picese.</p> <p>Provided as well are 90 seeds with recommended articles, annotated by human experts.</p>
Performance results of different scheduling algorithms used in the simulation of a modern game engine
<p><strong>Performance results of different scheduling algorithms used in the simulation of a modern game engine</strong></p> <p>These results are a companion to the paper entitled "<em>Exploring scheduling algorithms for parallel task graphs: a modern game engine case study</em>" by M. Regragui et al.</p> <p><strong>General information</strong></p> <p>This dataset contains raw outputs and scripts to visualize and analyze the scheduling results from our game engine simulator.<br> The result analysis can be directly reproduced using the script run_analysis.sh. A series of Jupyter Notebook files are also available to help visualize the results.</p> <p><strong>File information</strong></p> <p>- All Scenario*.ipynb files contain python scripts to visualize and analyze the simulation results.<br> - The Scenario*.py files contain python scripts that can be run directly with Jupyter Notebook.<br> - The requirements.txt file contains the names and versions of python packages necessary to reproduce the analysis.<br> - The run_analysis.sh file contains a bash script to install the required python packages and run the Scenario*.py scripts.</p> <p>The results are organized in five folders:</p> <p>1. Result_1 contains the results for Scenario 1 generated using file input_scenario_1.txt.<br> 2. Result_2 contains the results for Scenario 2 generated using file input_scenario_2.txt.<br> 3. Result_3 contains the results for Scenario 3 generated using file input_scenario_3.txt.<br> 4. Result_CP_1 contains the results for the critical path of Scenarios 1 and 2 generated using file input_CP_scenario_1.txt.<br> 5. Result_CP_3 contains the results for the critical path of Scenario 3 generated using file input_CP_scenario_3.txt.</p> <p>Each result file (e.g., HLF_NonSorted_Random_1_200_10.txt) contains 200 lines representing information of the 200 frames that were simulated. Each line contains four values: the frame number, the duration of the frame (in microseconds), a critical path estimation for the previous frame (in microseconds), and the load parameter (value between 0 and 1).</p> <p>The outputs of this analysis include some PDF files representing the figures in the paper (in order) and some CSV files representing the values shown in tables. The standard output shows the p-values computed in parts of the statistical analysis.</p> <p><strong>Software and hardware information</strong></p> <p>The simulation results were generated on an Intel Core i7-1185G7 processor, with 32 GB of LPDDR4 RAM (3200 MHz). The machine ran on Ubuntu 20.04.3 LTS (5.14.0-1034-oem), and g++ 9.4.0 was used for the simulator's compilation (-O3 flag).</p> <p>The results were analyzed using Python 3.8.10, pip 20.0.2 and jupyter-notebook 6.0.3. The following packages and their respective versions were used:</p> <p>- pandas 1.3.2<br> - numpy 1.21.2<br> - matplotlib 3.4.3<br> - seaborn 0.11.2<br> - scipy 1.7.1<br> - pytz 2019.3<br> - python-dateutil 2.7.3<br> - kiwisolver 1.3.2 <br> - pyparsing 2.4.7 <br> - cycler 0.10.0 <br> - Pillow 7.0.0<br> - six 1.14.0 </p> <p><strong>Simulation information</strong></p> <p>Simulation results were generated from 4 to 20 resources. Each configuration was run with 50 different RNG seeds (1 up to 50).</p> <p>Each simulation is composed of 200 frames. The load parameter (lag) starts at zero and increases by 0.01 with each frame up to a value equal to 100% in frame 101. After that, the load parameter starts to decrease in the same rhythm down to 0.01 in frame 200.</p> <p><strong>Algorithms abbreviation in presentation order</strong></p> <p>FIFO serves as the baseline for comparisons.</p> <p>1. FIFO: First In First Out.<br> 2. LPT: Longest Processing Time First.<br> 3. SPT: Shortest Processing Time First.<br> 4. SLPT: LPT at a subtask level.<br> 5. SSPT: SPT at a subtask level.<br> 6. HRRN: Highest Response Ratio Next. <br> 7. WT: Longest Waiting Time First.<br> 8. HLF: Hu's Level First with unitary processing time of each task.<br> 9. HLFET: HLF with estimated times.<br> 10. CG: Coffman-Graham's Algorithm.<br> 11. DCP: Dynamic Critical Path Priority.</p> <p><strong>Metrics</strong></p> <p>* SF: slowest frame (maximum frame execution time)<br> * DF: number of delayed frames (with 16.667 ms as the due date)<br> * CS: cumulative slowdown (with 16.667 ms as the due date)<br> </p>
FIG. 3 in Gaming and divination pieces, markers of ownership, or all three? Zooarchaeology and the interpretation of knuckle bones found in tombs of the Iberian necropolis of El Poblado (Coimbra del Barranco Ancho, Murcia, Spain)
FIG. 3. — Minimum number of individuals (MNI) calculated from the astragali assemblage of Sheep (Ovis Aries Linnaeus, 1758), Goat (Capra hircus Linnaeus, 1758), Roe deer (Capreolus capreolus Linnaeus, 1758), Red deer (Cervus ela- phus Linnaeus, 1758) and Pig (Sus scrofa Linnaeus, 1758).
FIG. 6 in Gaming and divination pieces, markers of ownership, or all three? Zooarchaeology and the interpretation of knuckle bones found in tombs of the Iberian necropolis of El Poblado (Coimbra del Barranco Ancho, Murcia, Spain)
FIG. 6. — Some examples of modified bone or ivory objects from Tomb 43. A, B, two imitation astragali in "plantar" and "lateral" view; C, different faces of the ivory dice recovered (according to Blasco Martín 2016: 249). Scale bar: 1 cm. Photo credit: J. Gómez Carrasco.
FIG. 4. — Tomb 116 in Gaming and divination pieces, markers of ownership, or all three? Zooarchaeology and the interpretation of knuckle bones found in tombs of the Iberian necropolis of El Poblado (Coimbra del Barranco Ancho, Murcia, Spain)
FIG. 4. — Tomb 116, "Triangular pyramid" shaped knuckle bones (caprines). Scale bar: 1 cm. Photo credit: J. Gómez Carrasco.
Game of Thrones characters interactions
<p>Game of Thrones characters interactions</p>
Serious game_dataset
<p>The datasets contain the input and the output of The Serious Game regarding the scenarios of Peschiera Borromeo WWTP case study:</p> <p>- Input dataset used to develop the Serious Game and perform the simulations on Peschiera Borromeo WWTP case study.</p> <p>- Ouptut dataset obtained from the Serious Game simulation scenarios that could be played selecting Peschiera Borromeo WWTP case study.</p>
Consensus of Game Engine Architectures: an Overview of Coupling and Subsystems in Game Engines
<p>The video game industry is one of the most innovative, competitive, and rapidly growing industries. The industry's successes along with the increasing gamers’ expectations result in always larger and more complex games. These games thus must be developed with game engines, which have become correspondingly more sophisticated. Today, game engine developers find game engine development challenging. To support the process of creating and maintaining game engines, we propose applying an approach based on a consensus algorithm to a set of game engine architectures. Our approach generates a model that suggests the most commonly used subsystems in game engine architectures, ranked by their degree of coupling. The model can be used by developers as a starting point when deciding what subsystems to include when building a game engine and points out the most coupled subsystems, which can play an important role towards higher subsystems maintainability and reusability. We evaluate our approach by comparing the results of our approach against a predefined ground truth data. The result of our approach matches the subsystems defined in the ground truth data and it shows that the most coupled subsystems are core, low-level rendering, third-party SDKs and world editor. Additionally, when comparing game engine architectures, we observe that most architectures are composed of nearly the same set of subsystems. </p>
A Dive into the State of the Practice of Brazilian Game Software Ecosystems - Codes and Relationships Dictionary (PT-BR)
<p>This spreadsheet is related to Grounded Theory open and axial coding processes regarding the answers captured by the survey about the identification of positives, negatives, and opportunities in Brazilian Game Software Ecosystems (https://zenodo.org/record/6632485#.YqV2XajMKW). It is essential to note that the responses are in the native language of the target audience (Brazilian Portuguese).</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.