Video gaming has been rising rapidly to become one of the primary entertainment media, especially during the COVID-19 pandemic. Playing video games has been reported to associate with many psychological and behavioral traits. However, little is known about the connections between game players' behaviors in the virtual environment and environmental perceptions. Thus, the current data set offers valuable resources regarding environmental worldviews and behaviors in the virtual world of 640 Animal Crossing: New Horizons (ACNH) game players from 29 countries around the globe. The data set consists of six major categories: 1) socio-demographic profile, 2) COVID-19 concern, 3) environmental perception, 4) game-playing habit, 5) in-game behavior, and 6) game-playing feeling. By making this data set open, we aim to provide policymakers, game producers, and researchers with valuable resources for understanding the interactions between behaviors in the virtual world and environmental perceptions, which could help produce video games in compliance with the United Nations (UN) Sustainable Development Goals.

See more: https://doi.org/10.1162/dint_a_00111

Other repository: Quan-Hoang Vuong; Manh-Toan Ho; Viet-Phuong La; Tam-Tri Le; Thanh Huyen T. Nguyen; Minh-Hoang Nguyen. A multinational dataset of game players’ behaviors in a virtual world and environmental perceptions(V1). 2021. Science Data Bank. 2021-10-09. cstr:31253.11.sciencedb.j00104.00098; https://datapid.cn/31253.11.sciencedb.j00104.00098

In January 2025, total video games sales in the United States amounted to 4.5billion U.S. dollars, representing a 15 percent year-over-year decrease. Generally speaking, the video game industry has its most important months in November and December, as video game software and hardware make very popular Christmas gifts. In December 2024, total U.S. video game sales surpassed 7.54 billion U.S. dollars. Birth of the video game industry Although the largest regional market in terms of sales, as well as number of gamers, is Asia Pacific, the United States is also an important player within the global video games industry. In fact, many consider the United States as the birthplace of gaming as we know it today, fueled by the arcade game fever in the ’60s and the introduction of the first personal computers and home gaming consoles in the ‘70s. Furthermore, the children of those eras are the game developers and game players of today, the ones who have driven the movement for better software solutions, better graphics, better sound and more advanced interaction not only for video games, but also for computers and communication technologies of today. An ever-changing market However, the video game industry in the United States is not only growing, it is also changing in many ways. Due to increased internet accessibility and development of technologies, more and more players are switching from single-player console or PC video games towards multiplayer games, as well as social networking games and last, but not least, mobile games, which are gaining tremendous popularity around the world. This can be evidenced in the fact that mobile games accounted for 51 percent of the revenue of the games market worldwide, ahead of both console games and downloaded or boxed PC games.

A survey conducted in the third quarter of 2024 found that over 92 percent of female internet users aged 16 to 24 years worldwide played video games on any kind of device. During the survey period, 93 percent of male respondents in the same age group stated that they played video games. Worldwide, over 83 percent of internet users were gamers.

In this study, we investigated the extent to which adolescents who spend time playing violent video games exhibit higher levels of aggressive behaviour when compared with those who do not. A large sample of British adolescent participants (n = 1004) aged 14 and 15 years and an equal number of their carers were interviewed. Young people provided reports of their recent gaming experiences. Further, the violent contents of these games were coded using official E.U. and US ratings, and carers provided evaluations of their adolescents' aggressive behaviours in the past month. Following a preregistered analysis plan, multiple regression analyses tested the hypothesis that recent violent game play is linearly and positively related to carer assessments of aggressive behaviour. Results did not support this prediction, nor did they support the idea that the relationship between these factors follows a nonlinear parabolic function. There was no evidence for a critical tipping point relating violent game engagement to aggressive behaviour. Sensitivity and exploratory analyses indicated these null effects extended across multiple operationalizations of violent game engagement and when the focus was on another behavioural outcome, namely, prosocial behaviour. The discussion presents an interpretation of this pattern of effects in terms of both the ongoing scientific and policy debates around violent video games, and emerging standards for robust evidence-based policy concerning young people's technology use.

These datasets contain reviews from the Steam video game platform, and information about which games were bundled together.

Metadata includes

• reviews

• purchases, plays, recommends (likes)

• product bundles

• pricing information

Basic Statistics:

• Reviews: 7,793,069

• Users: 2,567,538

• Items: 15,474

• Bundles: 615

Context

Video games have greatly contributed, and continue to contribute to the expansion of the entertainment industry. When the first video game, Pong, was launched in an arcade machine in 1972, it ignited a video game craze that quickly swept over the youth. With this, businesses such as Atari Games and Nintendo saw the golden opportunity of investing in a developing entertainment sector and began churning out gaming software and hardware. This caused the rise of the video game industry, which has generated over $109 billion in revenue and 2.2 billion gamers since its conception 50 years ago.

In this industry with over 47 million daily active users, Steam has been operating for almost 16 years. Its constant improvement to better accommodate users has made its development notable in the video game industry.

Steam is a digital distribution platform tailored to gamers and game developers. While it initially catered to PC games, the platform soon expanded its availability to home video game consoles such as the Xbox and Sony PlayStation. In Steam, gamers can log in to the website to conveniently purchase and play games online, a better alternative to buying physical copies of the games and manually downloading it on the computer.

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Content

A lot of gamers write reviews at the game page and have an option of choosing whether they would recommend this game to others or not. However, determining this sentiment automatically from text can help Steam to automatically tag such reviews extracted from other forums across the internet and can help them better judge the popularity of games.

Game overview information for both train and test are available in single file game_overview.csv inside train.zip

Acknowledgements

Steam digital distribution.

Inspiration

• Predict whether the reviewer recommended the game titles available in the test set on the basis of review text and other information.

Developmental psychology_raw data and code also see https://osf.io/2h6bu/

Data collected from accessibility evaluation with WCAG 2.1 in serious games. Applies a combinedmanual method including educational interactive simulations.Applies a combined manual method including educational interactive simulations.The data were recorded in a spreadsheet by applying: 1) Automatic tools to check color contrast andbrightness that can cause alterations to people with epilepsy. 2)A manual method was then appliedwith the WCAG 2.1.

Description:

👉 Download the dataset here

Ticket to Ride Games is a popular strategic board game where players compete to connect various cities on a map by placing their train pieces along specific routes. The objective is to complete the longest and most valuable routes while blocking opponents' paths. The game involves strategic planning, as each player's moves impact the overall board configuration and the availability of routes. This dataset is designed for enthusiasts, researchers, and Al developers interested in analyzing board game strategies, computer vision tasks, and data-driven game mechanics. It provides a comprehensive look at how different players approach the game, offering insights into decision-making processes, route optimization, and game dynamics.

Download Dataset

Content:

The dataset contains high-resolution images capturing various board configurations during the game, showcasing the players' city connections. The images are taken from four different angles to provide a complete view of the board's layout. Additionally, the dataset includes a well-structure CSV file that labels each player's city connections, detailing which cities have been successfully link by train routes. This labeling allows for in-depth analysis and pattern recognition, making it an ideal resource for those interest in game theory, Al training, or visual recognition models.

Key Features:

Images: Over [insert number] high-quality images of board configurations during gameplay, capturing different stages and strategies employe by players.

Angles: Each configuration is photograph from four distinct angles, ensuring comprehensive visual data for analysis.

CSV Labeling: The accompanying CSV file provides detail labeling of players' city connections, specifying which routes have been claim by each player. This structured data enables various analytical approaches, including statistical analysis, machine learning, and Al model training.

Versatile Applications: The dataset can be use for computer vision tasks, such as object detection and image segmentation, as well as for developing Al models to simulate or predict player strategies in board games.

Research Potential: Ideal for academic research, game development, and Al training, this dataset offers a rich source of data for exploring the complexities of board game strategies and player behaviors.

This dataset is sourced from Kaggle.

The dataset consists of interview transcripts with people who spend a lot of time playing video games. The interviewees include people who play video games competitively for at least 30 hours a week and people who have sought help for compulsive gaming. The interviews are follow-up interviews, and the same individuals were interviewed for the first time a year earlier. For the dataset containing the first round of interviews, see dataset FSD3678 archived at FSD. In the first part of the follow-up interviews, the interviewees were asked whether there had been any changes in their digital gaming habits compared to a year ago. The interviewees were also asked about any changes in their career, family and friends. Next, they were asked to give a day-by-day description of what a normal week of digital gaming was like for them and to describe in as much detail as possible one digital gaming experience from the previous month. Additionally, the interviews included questions about the interviewees' other hobbies and their satisfaction with their current job. In relation to gaming, the interviewees were asked whether they felt that they spent too much time playing digital games. Background information included, among others, the interviewee's gender, information on which interviewee group the interviewee was part of, and the date of the interview. The interview identifier makes it possible to compare data between each interviewee's first interview and follow-up interview. The data were organised into an easy to use HTML version at FSD.

Overview

The World of Warcraft Avatar History Dataset is a collection of records that detail information about player characters in the game over time. It includes information about their character level, race, class, location, and social guild. The Kaggle version of this dataset includes only the information from 2008 (and the dataset in general only includes information from the 'Horde' faction of players in the game from a single game server).

• Full Dataset Source and Information: http://mmnet.iis.sinica.edu.tw/dl/wowah/
• Code used to clean the data: https://github.com/myles-oneill/WoWAH-parser

Ideas for Using the Dataset

From the perspective of game system designers, players' behavior is one of the most important factors they must consider when designing game systems. To gain a fundamental understanding of the game play behavior of online gamers, exploring users' game play time provides a good starting point. This is because the concept of game play time is applicable to all genres of games and it enables us to model the system workload as well as the impact of system and network QoS on users' behavior. It can even help us predict players' loyalty to specific games.

Open Questions

• Understand user gameplay behavior (game sessions, movement, leveling)
• Understand user interactions (guilds)
• Predict players unsubscribing from the game based on activity
• What are the most popular zones in WoW, what level players tend to inhabit each?

Wrath of the Lich King

An expansion to World of Warcraft, "Wrath of the Lich King" (Wotlk) was released on November 13, 2008. It introduced new zones for players to go to, a new character class (the death knight), and a new level cap of 80 (up from 70 previously). This event intersects nicely with the dataset and is probably interesting to investigate.

Map

This dataset doesn't include a shapefile (if you know of one that exists, let me know!) to show where the zones the dataset talks about are. Here is a list of zones an information from this version of the game, including their recommended levels: http://wowwiki.wikia.com/wiki/Zones_by_level_(original) .

Update (Version 3): dmi3kno has generously put together some supplementary zone information files which have now been included in this dataset. Some notes about the files:

Note that some zone names contain Chinese characters. Unicode names are preserved as a key to the original dataset. What this addition will allow is to understand properties of the zones a bit better - their relative location to each other, competititive properties, type of gameplay and, hopefully, their contribution to character leveling. Location coordinates contain some redundant (and possibly duplicate) records as they are collected from different sources. Working with uncleaned location coordinate data will allow users to demonstrate their data wrangling skills (both working with strings and spatial data).

Overview

This dataset is part of the study titled "Player Experience in Video Game Character Analysis: A Study of Female Characters", conducted at Mapúa University. The research aims to integrate player experience into an existing framework for video game character analysis.

Content

The dataset includes:

A partial transcript of 5 semi-structured interviews with the key informants. Originally, 8 interviews were conducted, but the audio/video recordings for 3 interviews were lost and thus their transcripts are not available.

Significant codes presented in tabulated form.

Data Collection Method

Data were collected through in-depth interviews conducted via Facebook Messenger and Discord from March to April 2024. Participants were various video game players from different backgrounds and age groups, ranging from 20 to 40 years old. Due to technical issues, the recordings of 3 interviews were lost, resulting in only 5 available transcripts.

Data Processing and Analysis

The 5 available interviews were transcribed verbatim. Data were analyzed using thematic analysis, involving initial coding, theme development, and refinement.

Usage data

The dataset is organized into several sections within a single Word document (.docx). This word document has headings for navigation and a definition of terms.

Limitations

The dataset only includes 5 out of 8 due to technical difficulties encountered after the recording of the interview. This may impact the comprehensiveness of the findings.

Contextual Reference

The manuscript associated with this dataset heavily references the works "A Structural Model for Player-Characters as Semiotic Constructs." (DOI: https://doi.org/10.26503/TODIGRA.V2I2.37) and "Object, me, symbiote, other: A social typology of player-avatar relationships." (DOI:https://doi.org/10.5210/FM.V20I2.5433) which explore the foundational frameworks on video game character analysis.

For any further information or clarifications, please contact wbdg2000@gmail.com

https://i.imgur.com/PLS0HB3.gif" alt="Example Video from Deploy Tab">

Here are a few use cases for this project:

1. Sports Analytics: The Soccer Players computer vision model can be used to analyze player performance during games by tracking player and ball positions, individual player actions, and goal-scoring events, allowing coaches and trainers to make data-driven decisions for improving performance and strategies.

2. Automated Highlight Reels: The model can be used to automatically curate soccer match highlights by identifying crucial moments such as goals, outstanding player performances, and referee decisions. This can streamline the video editing process for broadcasting and streaming companies.

3. Virtual Assistant for Soccer Enthusiasts: The Soccer Players model can be integrated into a mobile application, allowing users to take pictures or upload images from soccer matches and receive instant information about the teams (USA, NED), player roles (goalie, outfield player, referee), and other relevant classes such as ball and goal locations, enhancing their understanding and engagement with the sport.

4. Real-Time Augmented Reality (AR) Applications: The model can be used to create AR experiences for soccer fans attending live matches, providing pop-up information about players (such as player stats, team affiliations, etc.) and game events (goals, referee decisions) when viewing the live match through an AR device or smartphone.

5. Training and Scouting Tools: Soccer scouts and trainers can use the Soccer Players model to evaluate potential recruits or assess the performance of their own players during practice sessions. By rapidly identifying key actions (goals, saves, tackles) and providing context for each play, the model can help scouts and trainers make informed decisions faster.

Here are a few use cases for this project:

1. Gaming Assistance: "dogedash" can be integrated into video games, particularly platformer or action games, to provide real-time object identification and assistance to players. It can help users understand game elements, identify threats or opportunities, and suggest strategies or actions tailored to the current in-game situation.

2. Automated Game Testing: Developers can use "dogedash" to automate the testing of object interactions, functionality, and performance within their games. By identifying and tracking object classes like coin, enemy, and missile, the model can help detect any anomalies or bugs and ensure that the game is running correctly.

3. Streamer and Content Creator Tool: "dogedash" can be incorporated into software used by game streamers and content creators. By providing real-time object identification, it can create overlays, visual cues, or informative pop-ups for viewers, enhancing their experience and understanding of the game being played.

4. Accessible Gaming: "dogedash" can be harnessed to create adaptive controller interfaces for gamers with physical or cognitive impairments. By identifying and tracking key in-game objects like doge, spiky_enemy, and fire, the model can help map alternative control schemes, user-specific timings, and other adjustments tailored to the gamer's individual needs.

5. Game Design Analysis: "dogedash" can be employed as a research tool for game designers and academics studying game mechanics, level design, and player interaction. By analyzing a range of games and identifying object classes, the model can provide insights into how these elements impact player engagement, difficulty, and overall experience.

This repository includes the data of a Randomized Controlled Trial (RCT) that was reported on in the article titled "A Randomized Controlled Trial Assessing the Efficacy of a Virtual Reality Biofeedback Video Game: Anxiety Outcomes and Appraisal Processes" by Weerdmeester and colleagues (2020). The study assessed the efficacy of a virtual reality biofeedback video game (DEEP) in reducing anxiety symptoms. In addition, engagement and cognitive appraisals including self-efficacy, locus of control, and threat-challenge were explored as potential mechanisms of change. Undergraduates with elevated anxiety symptoms (N = 112) were randomly assigned to four training sessions in the lab with either DEEP, a biofeedback video game, or a smartphone-guided breathing application. Trait anxiety was measured at screening (two weeks prior to the training), pre-test, post-test, and three months later. State anxiety was assessed before and after each individual training session. Engagement as well as all cognitive appraisals were assessed after each training session.This repository includes the following files:- A word file which describes the complete method and procedure of the corresponding Randomized Controlled Trial study, including a description of how the data was analysed.- Two data-sets(one in .sav and one in .CSV format) with all the data that was reported on in the corresponding article, includingraw data of participant characteristics and single-item questions as well as the sum-scores, ratio-scores or difference scores of the main outcomes.- A data codebook (in .xlsx format) including a description of of the variables in the data-set including the labels, values and the scales of the corresponding questionnaire items.Information regarding missing values:Missing values on the variables 'gamehrswk' and 'gamehrswknd' are the result of these questions being skipped because participants answered 'No' on a previous question asking whether or not they play video games (variable 'doyougame').Missingness on other values was due to participants dropping out of the training or not filling out the online follow-up questionnaire. Information about the extent of and reasons for attrition are included in Figure 1, which is included in DEEP_RCT_Data_Method_Procedure.docx. The method section in this document also describes how missingness was dealt with in the analyses.

Gaming has increasingly become a part of life in Africa. Currently, no data on gaming disorders or their association with mental disorders exist for African countries. This exploratory study investigates (1) the prevalence of insomnia, excessive daytime sleepiness, anxiety and depression among African gamers based in Gabon and Tunisia and (2) the association between these conditions and gamer types (i.e., non-problematic, engaged, problematic and addicted). The questionnaire could only be completed once by participants with the same email address, and duplicates and incomplete forms were discarded. Responses were collected in multiple sites based in nine African countries between November 2015 and June 2017 (Rwanda, Gabon, Cameroon, Nigeria, Morocco, Tunisia, Senegal, Ivory Coast and South Africa). Because of local restrictions related to the expiration of some ethical certificates, this dataset currently provides aggregate data from Gabon and Tunisia.

Data contained aggregate information describing epidemiology of self-reported measures of insomnia (with the Insomnia Severity Index), excessive daytime sleepiness (with Epworth Sleepiness Scale), anxiety (with Hospital Anxiety and Depression Scale-A), depression (Hospital and Anxiety Depression Scale-D) and gaming disorder (with game addiction scale short form) between gamers in Tunisia and Gabon. The participants who formed this convenience sample were contacted by email. The online questionnaire included a consent form on the second page, following a description of the study in French and English. Consent was required to participate in this project. The average time to answer all questions was 20 minutes. Data available are as follow: mean hours of gaming per week, period from when the participant considered him or herself a gamer, type of device used for gaming purposes, age, sex, and category of gamers.

The present research is a pilot investigation which documents sleep disorders, anxiety and depression among an African sample with a focus on gamers. It should be replicated with the general population with a longitudinal cohort study to understand the global picture of gaming disorder. Similarly, more attention should be brought to the sleep health of African populations. More research on gaming addiction needs to be performed in low- and middle-income countries where little is known about internet gaming disorder.

Percentage of Canadians' time spent online and using video streaming services and video gaming services, in a typical week.

BackgroundThe realm of virtual games, video games, and e-sports has witnessed remarkable and substantial growth, captivating a diverse and global audience. However, some studies indicate that this surge is often linked to a desire to escape from real life, a phenomenon known as escapism. Much like substance abuse, escapism has been identified as a significant motivator, leading to adverse outcomes, including addiction. Therefore, it is crucial to comprehend the existing research on the connection between escapism and engagement in virtual gaming. This understanding can shed light on the reasons behind such practices and their potential impact on mental and public health.PurposeThe objective of this systematic review is investigate the findings pertaining to association between escapism and the practice of virtual games, such as video-games and e-sport.MethodsPUBMED and SCOPUS database were systematically searched. Six independent researchers screened articles for relevance. We extracted data regarding escapism-related measures, emotional/mental health-related measures and demographic information relevant to the review purpose.ResultsThe search yielded 357 articles, 36 were included. Results showed that: (i) Escapist motivation (EM) is one of the main motives for playing virtual games; (ii) EM is related to negative clinical traits; (iii) EM predicts negative psychological/emotional/mental health outcomes; (iv) EM is associated with impaired/negative perception of the real-world life; (v) EM predicts non-adaptive real social life; and (vi) EM is associated with dysfunctional gaming practices in some cases. However, EM can have beneficial effects, fostering confidence, determination, a sense of belonging in virtual communities, and representation through avatars. Furthermore, the reviewed findings suggest that EM was positively linked to mitigating loneliness in anxious individuals and promoting social activities that preserved mental health among typical individuals during the pandemic.ConclusionOur review reinforces the evidence linking EM in the context of virtual games to poor mental health and non-adaptive social behavior. The ensuing discussion explores the intricate connection between escapism and mental health, alongside examining the broad implications of virtual gaming practices on underlying motivations for escapism in the realms of social cognition, health promotion, and public health.

OSAI introduces OpenTTGames - an open dataset aimed at evaluation of different computer vision tasks in Table Tennis: ball detection, semantic segmentation of humans, table and scoreboard and fast in-game events spotting.

It includes full-HD videos of table tennis games recorded at 120 fps with an industrial camera. Every video is equipped with an annotation containing the frame numbers and corresponding targets for this particular frame: manually labeled in-game events (ball bounces, net hits, or empty event targets) and/or ball coordinates and segmentation masks, which were labeled with deep learning-aided annotation models.

League of Legends (LOL) is the most popular game on PC, drawing 8 million concurrent players. A common activity of gamers, besides playing games, is to watch other players presenting tips and tricks. Streaming platforms allow some players to show gameplays and live games. Twitch.tv is the world´s leading live streaming platform.

Considering that hate speech is a ubiquitous problem in online gaming, we collected 985,766 comments from five videos of the top 10 LOL streamers in Twitch.tv platform.

The dataset is freely available in a single file, ensembling all videos/players; and divided by players as well.

These comments are a rich data source for opinion mining, sentiment analysis, topic modeling, and hate speech detection (including sexism and racism).