Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
2,481
datasets available to search
ShareScore release 0.7.1
Dataset results
2,481 results for “games”
Top selling games in Steam platform
<p>The dataset is extracted from the list of best-selling games available in Spanish on the Steam platform. It contains information related to the content of the game, to its development and marketing (price and discounts), as well as different metrics derived from user opinions.</p>
Chess Game Dataset
<p>This is the dataset for all of the chess enthusiasts and chess.com members. It has been created via the chess.com API.</p><p>Sourced from: <a href="https://www.kaggle.com/datasets/adityajha1504/chesscom-user-games-60000-games">60,000+ Chess Game Dataset</a></p>
RAYUELA - Open Data - 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>
WKU experimental epidemic game using research version of Operation Outbreak app
<p>This dataset contains the full list of participants and events in the experimental epidemic game at Wenzhou-Kean University (WKU) in China, run between November 20 and December 4 of 2023 using a customized version of the Operation Outbreak mobile app and cloud backend for research uses. The following blog post provides some more information about this simulation:</p> <p>https://colabobio.medium.com/667295c43907</p> <p> </p>
SyDRA: An Approach to Understand Game Engine Architecture
<p>Game engines are tools to facilitate video game development. They provide graphics, sound, and physics simulation features, which would have to be otherwise implemented by developers. Even though essential for modern commercial video game development, game engines are complex and developers often struggle to understand their architecture, leading to maintainability and evolution issues that negatively affect video game productions. In this paper, we present the Subsystem-Dependency Recovery Approach (SyDRA), which helps game engine developers understand game engine architecture and therefore make informed game engine development choices. By applying this approach to 10 open-source game engines, we obtain architectural models that can be used to compare game engine architectures and identify and solve issues of excessive coupling and folder nesting. Through a controlled experiment, we show that the inspection of the architectural models derived from SyDRA enables developers to complete tasks related to architectural understanding and impact analysis in less time and with higher correctness than without these models.</p>
Raw data for the article "Games on Climate Change: Identifying Development Potentials through Advanced Classification and Game Characteristics Mapping"
<p>Raw data used for the article "Gerber, Andreas, Markus Ulrich, Flurin X. Wäger, Marta Roca-Puigròs, João S.V. Gonçalves, and Patrick Wäger. 2021. "Games on Climate Change: Identifying Development Potentials through Advanced Classification and Game Characteristics Mapping" <em>Sustainability</em> 13, no. 4: 1997. <a href="https://doi.org/10.3390/su13041997">https://doi.org/10.3390/su13041997</a>"</p> <p>The documents include the raw data (both as .csv and .xlsx files with the same content), as well as the publication (.pdf file). The data collection process and the data itself are described in the publication. The data is published as "supplementary material" on the publisher's homepage.</p>
Raw data for the book chapter "Review of Haptic and Computerized (Simulation) Games on Climate Change"
<p>Raw data used for the book chapter "Gerber, A., Ulrich, M., Wäger, P. (2021). Review of Haptic and Computerized (Simulation) Games on Climate Change. In: Wardaszko, M., Meijer, S., Lukosch, H., Kanegae, H., Kriz, W.C., Grzybowska-Brzezińska, M. (eds) Simulation Gaming Through Times and Disciplines. ISAGA 2019. Lecture Notes in Computer Science(), vol 11988. Springer, Cham. <a href="https://doi.org/10.1007/978-3-030-72132-9_24">https://doi.org/10.1007/978-3-030-72132-9_24</a>"</p> <p>The documents include the raw data (both as .csv and .xlsx files with the same content), as well as the publication (.pdf file). The data collection process and the data itself are described in the publication. The data is published as "supplementary material" on the publisher's homepage.</p>
Players' Profiles and Satisfaction for Game Elements across Levels: Dataset
<p>Dataset produced in a study to measure the impact of levels' generation and adaptation to the players' preferences.</p> <p>The study was conducted in the context of a Master's thesis on Game Adaptivity.</p>
Data-Driven Study of Long-Term Gaming Experience (Tribalwars)
<p>This is the accompanying dataset for our Paper "Data-Driven Study of Long-Term Gaming Experience" at the 14th International Conference on Quality of Multimedia Experience (QoMEX) - Please city this work if you use this dataset.</p> <p>This dataset contains hourly crawled player metadata of the Tribalwars casual game game round "dep13".</p> <p>With this dataset and a set of possible tooling, we hope to provide a foundation into further data-driven QoE studies.</p> <p>You will find further information about prerequisites and the dataset within the <strong>eval.ipynb</strong>.</p>
Release dates for role playing computer games (RPGs)
<p>Release date information from Steam using web data scraping through Python coding. <br>Code can be located on GitHub: <a href="https://github.com/kyoraven/genderGames">kyoraven/genderGames (github.com)</a></p>
Gender in role playing computer games (RPGs)
<p>Modified data from:</p> <p>Rennick, S., Clinton, M., Ioannidou, E., Oh, L., Clooney, C., T, E., Healy, E., & Roberts, S. G. (2023). <em>The Video Game Dialogue Corpus</em> [Data set]. GitHub, Incorporated. <a href="https://github.com/seannyD/VideoGameDialogueCorpusPublic">https://github.com/seannyD/VideoGameDialogueCorpusPublic</a></p> <p>Stephanie Rennick, Melanie Clinton, Elena Ioannidou, Liana Oh, Charlotte Clooney, E. T., Edward Healy, Seán G. Roberts (2023) Gender bias in video game dialogue. <em>Royal Society Open Science</em> 10(5). <a href="https://royalsocietypublishing.org/doi/10.1098/rsos.221095" rel="nofollow">https://royalsocietypublishing.org/doi/10.1098/rsos.221095</a></p> <p>Modified data was filtered and analysed using Excel and Python. Python coding can be found: <a href="https://github.com/kyoraven/genderGames/tree/code">kyoraven/genderGames at code (github.com)</a> </p>
Indie role playing computer games (RPGs)
<p>Indie role playing computer games (RPGs) from Steam, with 'choices matter' tag from publisher Choice of Games. Name, publishing date, price, and description fields only. 116 records. Please note that Choice of Games has a scripting language that enables authors/developers to use, publish and sell their work. An analysis of those works may be conducted at a later date.</p> <p>v0.1.1 now includes gender options, orientation options</p>
Survey with game development companies on personal data protection
<p><strong>Dataset linked to the article: </strong>Investigating the Implementation of Data Protection Laws in Brazilian Game Companies: An Initial Study</p>
Gaming Horizons corpus dataset
<p>This is the corpus dataset which was used to analyse the official H2020 discourse in the ICT area, in order to identify priorities, implicit biases and unexplored assumptions, in general and in relation to the gaming and gamification subdomains. The analysis is based in a social science and humanities approach and resulted in a research deliverable which can be downloaded from https://www.gaminghorizons.eu/deliverables/ </p> <p> </p>
Group size effects and critical mass in public goods games
<pre>This dataset accompanies the paper "Group size effects and critical mass in public goods games", https://doi.org/10.1038/s41598-019-41988-3 It records participant decisions in a set of binary one-shot Public Goods Games with curvilinear payoff function (see details in the paper). The explanation of the data fields is present also in the metadata of the file: # Cooperation: 0=defection, 1=cooperation, 99=the participant did not make the decision # Group: interacting group size (N) # Treatment: Critical Mass Nc # dropout: 1=the participant did not make the decision, 0=the participant made the decision # incompleted: 1=participant did not make all the decisions of the experiment; 0=participant did made all the decisions of the experiment # female: 1=female, 0=male </pre> <p> </p>
Large scale and information effects on cooperation in public good games
<pre>This dataset accompanies the paper "Large scale and information effects on cooperation in public good games", https://doi.org/10.1038/s41598-019-50964-w It records participant decisions in a set of Public Goods Games (see details in the paper). Explanation of the data fields: #participant_id: identification number for each participant in each treatment #player_alive: (for each round) 1=participant is still playing; 0=participant has been banned for forgetting three decisions or did not show up the first day. #group_avg_contribution: Average contribution #player_contribution: participant individual contribution to the PGG #round_number: round number #gender #age #treatment: see the publication for details regarding the different treatments # IBSEN metadata # 2017A2ECOCOOPANSA01ONL0000ESPMAD </pre>
Dataset and models of TMLR 2024 Paper "Identifying and Clustering Counter Relationships of Team Compositions in PvP Games for Efficient Balance Analysis"
<p>This is a part of dataset and models of the paper published in TMLR 2024 (Transactions on Machine Learning Research, <a href="https://jmlr.org/tmlr/" target="_blank" rel="noopener">https://jmlr.org/tmlr/</a>).</p> <p>Including training datasets, testing datasets, and models.</p> <p>The example program for using this file will be put on the author's github repo branch: <a href="https://github.com/DSobscure/cgi_drl_platform/tree/game_balance_measures_tmlr" target="_blank" rel="noopener">https://github.com/DSobscure/cgi_drl_platform/tree/game_balance_measures_tmlr</a></p> <p> </p>
A Reputation Game Simulation: Emergent Social Phenomena from Information Theory
<p>Here, the data underlying the article "A Reputation Game Simulation: Emergent Social Phenomena from Information Theory" (<a href="https://doi.org/10.1002/andp.202100277">https://doi.org/10.1002/andp.202100277</a>) is provided.<br> <br> The data is structured according to the figures it has been used for. There are</p> <ul> <li>example simulations with basic communication strategies in the folder "single_simulations_3_agents" (Figures 4,5,8,D1)</li> <li>statistical simulations with 3 agents and special communication strategies in the folder "statistical_simulations_3_agents" (Figures 9-13, the upper panel of figure 15, figures 16-18, D2 and the left panels of figure D3)</li> <li>statistical simulations with 4 agents and special communication strategies in the folder "statistical_simulations_4_agents" (Figure 14, the middle panel of figure 15, the middle panels of figure D3 and the upper panels of figures D4, D5)</li> <li>statistical simulations with 5 agents and special communication strategies in the folder "statistical_simulations_5_agents" (The lower panel of figure 15, the right panels of figure D3 and the lower panels of figures D4,D5)</li> <li>propaganda simulations in the folder "propaganda_simulations" (Figure 7)</li> </ul> <p><br> Each simulation is represented by a .json file in which all events that happened during the simulation are collected. Generally, there are three types of events: communications, self-updates (information that the speaker gained about itself is processed) and updates (information that the receiver gained about the speaker and the topic is processed). Additionally, the first line specifies the parameters of each simulation, and the last few lines summarize the final status of the simulation. In the following all important abbreviations are explained:</p> <ul> <li>parameters <ul> <li>decpeting: whether or not agents in generally make dishonest statements</li> <li>listening: whether or not agents in listen to their communication partners</li> <li>disturbing: whether or not agents are particularly risk-taking when making dishonest statements</li> <li>x_est: intrinsic honesties of the agents</li> <li>RSeed: the used random seed</li> <li>NA: number of agents</li> <li>NR: number of rounds</li> </ul> </li> <li>communication <ul> <li>a: speaker</li> <li>b: receiver</li> <li>c: topic</li> <li>J: transmitted message in the form of</li> </ul> </li> <li>self_update <ul> <li>id: number of agent who is updating knowledge about itself</li> <li>Nl, Nt: number of dishonest/honest statements the agent has observed from itself so far</li> <li>I_<id>: knowledge that the agents has about itself after the update in the form of</li> </ul> </li> <li>update <ul> <li>id: number of agent who is updating its knowledge</li> <li>I_<id1>: knowledge that the updating agent has about agent <id1> in the form of</li> <li>Jothers_<id1>_<id2>: last statement that the updating agent heared agent <id1> make about agent <id2></li> <li>Iothers_<id1>_<id2>: what the updating agent believes that agent <id1> thinks about agent <id2> after the update</li> <li>Cothers_<id1>_<id2>: what the updating agent believes after the update that agent <id1> wants it to think about agent <id2></li> <li>new_friends/enemies: id of the agent, the updating agent after the update considers a friend/enemy</li> <li>new_K: normalized surprise the updating agent experienced in the last communication (used to calculate kappa)</li> <li>kappa: median of the last ten normalized surprises the updating agent experienced</li> </ul> </li> <li>final_status <ul> <li>id/name: number if the described agent</li> <li>x: the agent's honesty</li> <li>I: the agent's knowledge about all others</li> <li>Nc/Nt/Nl: total number of conversations/honest statements/dishonest statements the agent has made</li> <li>K: the last 10 normalized surprises the agent experienced</li> <li>kappa: the median of K</li> <li>friends/enemies: list of the agent's friends/enemies</li> <li>Jothers/Iothers/Cothers: same as above, now as full array, i.e. the combined information about all others</li> <li>openess/mind/decepting/strategic/egocentric/deceptive/flattering/aggressive/shameless/disturbing: the agent's character traits</li> </ul> </li> </ul>
Waterbirds counts in the Drugeon bassin, France (game reserves and hunting areas) in 2020
<p>This dataset describes waterbird observations performed in game reserves and in hunting areas of the Drugeon river basin, France, in 2020.</p> <p>For each game reserve, a similar hunting area was selected in the same or an adjacent commune. If the hunting reserve was a stretch of river, as in Bonnevaux or Houtaud, another stretch of river was surveyed for comparison. In Frasne, the hunting reserve covers the Lothaud pond. Therefore, the adjacent Lucien pond was surveyed as a huntable area. The other nine wetland reserves are located in marshes and therefore nine other marshes were surveyed. Once this work was done, 10 other wetlands were also surveyed to complete the study. In total, 12 hunting reserves and 22 huntable areas, all in wetlands, were surveyed twice during the autumn of 2020. One of the 13 reserves was not surveyed (just forgotten in the sampling plan). The first survey was carried out between 24 August and 5 September, i.e. just before the opening of the waterfowl hunting season on 6 September. The second pass was carried out between 19 and 29 October, i.e. 44 to 54 days after the opening of the hunting season.</p> <p>Each sector was surveyed on foot, with round trips spaced at approximately 20 m intervals. The use of the Caynax application on Android allowed to control the location of the route, the regular spacing of the passages and the length of the route taken on each wetland. For both ponds, the water bodies were circled and birds were counted along the banks and on the water table. All characteristic wetland birds were noted, specifying whether they had been seen landing or in transit flight. Only birds landing are taken into account in this study. The observations were then recorded in an Excel table with the name of the area, its hunting status, the surface area, the number of kilometres covered during the survey and the date of the survey (see table <a href="https://zenodo.org/api/files/1546581e-0a8a-4966-9250-cc37eeafb414/db0.txt">db0.txt</a> and article cited below for more details).</p> <p><strong>FILE DESCRIPTION:</strong></p> <p><a href="https://zenodo.org/record/7539822/files/CartePAIGN_BB.jpg?download=1">CartePAIGN_BB.jpg </a>map of the areas studied. Green, game reserves sampled; red , hunting areas sampled: blue, one game reserve not sampled.</p> <p><a href="https://zenodo.org/record/7539822/files/db0.txt?download=1">db0.txt</a> counts for each area sampled</p> <ul> <li>com, commune</li> <li>lieu, place name</li> <li>pair, pair ID</li> <li>res, game reserve (OUI = yes, NON = no)</li> <li>ha, area sampled</li> <li>km, number of kilometers walked</li> <li>mois, month</li> <li>The next 28 colums are species ID, for full names see table <a href="https://zenodo.org/record/7539822/files/Statuts.txt?download=1">Statuts.txt</a>.</li> </ul> <p><a href="https://zenodo.org/record/7539822/files/Game reserves and hunting areas sampled.kml?download=1">Game reserves and hunting areas sampled.kml </a> kml file of the areas sampled (game reserves in green, hunting areas sampled in red; in blue, one reserve not sampled)</p> <p><a href="https://zenodo.org/record/7539822/files/Statuts.txt?download=1">Statuts.txt</a> list of species recorded</p> <ul> <li>espece, specie name (in French)</li> <li>nom latin, latin name</li> <li>nom anglais, species name (in English)</li> <li>sp, species ID</li> <li>protection, protection status: Protégée, protected; Chassable, huntable</li> <li>migrateur, migratory status: Migrateur tardif, late migratory; Migrateur précoce, early migratory</li> </ul> <p><strong>SUPPLEMENTARY FILES</strong></p> <p><a href="http://zenodo.org/record/7539822/files/R_code_for_Analysis.zip?download=1">R_code_for_analysis.zip</a> Workflow (in French) and R code for the analyses carried out in the article Michelat & Giraudoux (2023).</p> <p> </p>
Steam Games
<p>Videogames data scrapped from steam. Contains:</p> <p><strong>'is_dlc' → If it is a DLC of another game</strong></p> <p><strong>'img_src' → Header image src</strong></p> <p><strong>'short_description' → Short game description (in spanish)</strong></p> <p><strong>'recent_reviews' → Valoration in recent reviews</strong></p> <p><strong>'recent_reviews_count' -> Number of recent reviews</strong></p> <p><strong>'all_reviews' → Valoration in all times reviews</strong></p> <p><strong>'all_reviews_count' → Total number of reviews</strong></p> <p><strong>'reviews_anomally' → Whether is ther an anomally in reviews</strong></p> <p><strong>'release_date' → Release date</strong></p> <p><strong>'developer' → Developer</strong></p> <p><strong>'developer_url' → URL to Developer web page</strong></p> <p><strong>'publisher' → Publisher</strong></p> <p><strong>'publisher_url' → URL to publisher web page</strong></p> <p><strong>'tags' → Categories of this videogame</strong></p> <p><strong>'discount_original_price' → Videogame original price (in case there is a discount)</strong></p> <p><strong>'discount_final_price' → Videogame discounted price</strong></p> <p><strong>'discount' → % of discount</strong></p> <p><strong>'price' → Videogame original price (in case there aren't any discount)</strong></p> <p><strong>'game_content' → More content linked to this videogame</strong></p> <p><strong>'name' → Name</strong></p> <p><strong>'genre' → Genre</strong></p> <p><strong>'website' → Videogame's original website</strong></p> <p><strong>'metacritic_score' → Metacritic score</strong></p> <p><strong>'metacritic_url' → URL to metacritic game's page.</strong></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.