Contains linguistic annotated data from the Online-Forum PC Games (https://forum.pcgames.de). The forum is concerned about gaming. All posts (approx. 2.4 mio) where scraped in April 2019 (details see Kissling 2019), resulting in 120 mio tokens of almost 70'000 authors. The data is saved in a SQL-database and can be accessed using eg. pg_restore. The database itself and the tables of the database contain detailed self-descriptions. In this database you find tokenized, part-of-speech-tagged and party lemmatized information of every token in the forum and its metadata (usernames and their location in the forum structure, e.g. which post(s), thread, subforum it belongs to). The order of the words in a post cannot be reconstructed with this corpus. Usernames were replaced with author_ids to protect the personal rights of the post authors. Additional information: As this corpus was analyzed in terms of productivity and language contact of German and English (Kissling 2020), there is additional information about German base forms found in present day English, mainly focussing on the formula "German_verb_stem + -en = English verb infinitive". Therefore the API of the Oxford Dictionary of English was used. You will find the results of the API request done with Oxford Dictionary of English in the table infinitives. The corpus can be used without using this information, too. Calculations were performed at sciCORE (http://scicore.unibas.ch/) scientific computing core facility at University of Basel on 2019-09-10. This database contains all of the primary corpus of Kissling (2020). Sources: Kissling, J. (2019). Computerunterstütztes Verfahren zur Erhebung eigener Textkorpus-Daten. Methodenentwicklung und Anwendung auf 2.4 Mio. Posts des Forums PC Games.de [certification thesis]. Universität Basel. Kissling, J. (2020). Produktivität englischer Verben im Deutschen [master thesis]. Universität Basel.

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.

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.

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.

Context

Wondering if a model can detect visual differences through gaming graphics? If it is possible, does the model have real world applications?

Check out these images from Virtual Battle Station 2 and see if you can build the model and identify any further trends in this visual dataset.

Content

The dataset consists of before and after scenes with one major difference (new building, road, bush or car) and lots of smaller minor differences (lighting, weather, ...). The goal is to automatically detect the major differences among the noisy background of other smaller differences.

Acknowledgements

The original dataset page is down but the full dataset can still be downloaded here: https://computervisiononline.com/dataset/1105138664 Credit: Nicolas Bourdis, Denis Marraud, Hichem Sahbi

Inspiration

How well do simulated video game images apply to real world problems?

Dataset Overview

This dataset contains 25,600 self-play chess games generated using Leela Chess Zero (Lc0) with specific configurations to explore unique gameplay dynamics. The games were generated using:

• Policy Temperature: 2.25
• Backend: cuda-fp16
• Time Control: 1.0

The games are stored in the PGN (Portable Game Notation) format, which is widely used for recording chess games. This dataset can serve as a resource for:

• Training and evaluating chess engines
• Analyzing chess strategies and opening theory
• Conducting AI and machine learning experiments in chess

Files

• games-1.0s.pgn: The main dataset file containing all 25,600 games in PGN format.

Dataset Features

1. Game Format: Each game is recorded in PGN format, including headers for metadata such as player names, result, and opening.
2. Generated by AI: All games were played by the AI model Leela Chess Zero using the following configuration:
   • Policy Temperature: 2.25
   • Backend: cuda-fp16
   • Time Control: 1.0
3. Diverse Playstyles: The higher policy temperature introduces greater diversity in the decision-making process, providing a wide range of strategies and outcomes.
4. High-Quality Self-Play: Games generated by Lc0 are renowned for their depth of calculation and understanding of positional play.

Usage

Chess Engine Training and Evaluation

This dataset can be used to fine-tune or evaluate chess engines by: - Extracting positions for supervised learning. - Analyzing endgames or middlegame strategies.

AI Research

• Investigate the effects of a high policy temperature on gameplay.
• Explore the diversity and quality of play in self-play scenarios.

Chess Strategy Analysis

• Study opening trends and preferences.
• Analyze game outcomes under the given configurations.

License

This dataset is shared under the Creative Commons Attribution 4.0 International License (CC BY 4.0). You are free to use, share, and adapt the data, provided you give appropriate credit to the creators.

Acknowledgments

• Leela Chess Zero: A groundbreaking open-source chess engine that leverages neural networks for unparalleled chess understanding.
• CUDA: The GPU acceleration technology that enabled efficient game generation.

Feedback

If you use this dataset or have suggestions for improvements, please leave feedback or share your project in the Kaggle discussion forums.

This dataset contains a list of video games with sales greater than 100,000 copies. It was generated by a scrape of vgchartz.com.

Fields include

• Rank - Ranking of overall sales

• Name - The games name

• Platform - Platform of the games release (i.e. PC,PS4, etc.)

• Year - Year of the game's release

• Genre - Genre of the game

• Publisher - Publisher of the game

• NA_Sales - Sales in North America (in millions)

• EU_Sales - Sales in Europe (in millions)

• JP_Sales - Sales in Japan (in millions)

• Other_Sales - Sales in the rest of the world (in millions)

• Global_Sales - Total worldwide sales.

The script to scrape the data is available at https://github.com/GregorUT/vgchartzScrape. It is based on BeautifulSoup using Python. There are 16,598 records. 2 records were dropped due to incomplete information.

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.

YM2413-MDB is an 80s FM video game music dataset with multi-label emotion annotations. It includes 669 audio and MIDI files of music from Sega and MSX PC games in the 80s using YM2413, a programmable sound generator based on FM. The collected game music is arranged with a subset of 15 monophonic instruments and one drum instrument. They were converted from binary commands of the YM2413 sound chip. Each song was labeled with 19 emotion tags by two annotators and validated by three verifiers to obtain refined tags

For more detailed information about the dataset, please refer to our paper: YM2413-MDB: A Multi-Instrumental FM Video Game Music Dataset with Emotion Annotations.

File Description

1) Pure data

- original_vgms: crawled vgm files from SMS POWER and VGMRIPs

- wav: rendered vgm files using VGMPlay

2) MIDI data

- midi/vgmplay_log_to_midi: converted midi files

- midi/adjust_tempo: add postprocessing(metrically aligned using wav_downbeat files) after midi conversion

- midi/adjust_tempo_remove_delayed_inst: add postprocessing(metrically aligned using wav_downbeat files, remove delayed instrument) after midi conversion

3) Metadata

- emotion_annotation/verified_annotation.csv: contains emotion annotation for each songs

- tags_kor_eng.txt: Korean <-> English tag dictionary

4) Useful middle-time step data

- wav_downbeat: extracted downbeat values using TCNBeatTracker of madmom

- vgm_txts: disassembled vgm files as txt using vgm2txt

- ydr: YM2413 Disassembly Raw(YDR). command list of vgm files. generated by reading vgm_txts

Update Log

- version 1.0.1: Fix ticks per beat value adjust to tempo where tempo values are not 150. Also, madmom downbeat files are updated from DBNBeatTracker(ISMIR, 2015) to TCNBeatTracker(Newer one EUSIPCO, 2019).

This dataset includes screenshots, test subejcts' actions and payoffs, and also their answers to the questionnaires of the experiments done for our reseach project on emotions towards computer players. The corresponding manuscript is "Existence and suppression of emotions towards algorithmic computer players in games".

This is the dataset and model used for Tiny Towns Scorer, a computer vision project completed as part of CS 4664: Data-Centric Computing Capstone at Virginia Tech. The goal of the project was to calculate player scores in the board game Tiny Towns.

The dataset consists of 226 images and associated annotations, intended for object detection. The images are photographs of players' game boards over the course of a game of Tiny Towns, as well as photos of individual game pieces taken after the game. Photos were taken using hand-held smartphones. Images are in JPG and PNG formats. The annotations are provided in TFRecord 1.0 and CVAT for Images 1.1 formats.

The weights for the trained RetinaNet-portion of the model are also provided.

The Region encourages creative diversity in the field of video games and strengthens its professional ecosystem, through selective and reimbursable production aid.

Any work that meets the criteria is eligible for this system. following:

• The project must be a video game, online or offline, on console, mobile phone, PC, social networks and on any distribution medium excluding “Pay” games to win",
• The project must have an overall development cost greater than or equal to €50,000,
• The project must have a minimum of 50% of the production expenses made in Île-de-France. -France,
• The project must not include sequences that could be subject to a PEGI 18 (Pan-European Game Information) classification.

The dataset presents the list of production companies and games benefiting from the scheme:

• Year the aid was awarded
< li>Title of the game (this may or may have changed at the time of its release)
• Commentary (1st game developed, or specificity)
• Website of the production company
• Game website
• Amount of regional aid

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).

This dataset is connected to an experiment, where each participant played three versions of a tablet game. They played each version twice. The first version was a training version and contained "blank" minions, white capsule shapes with no animations. The other two versions contained either small, green minions with no weapons and minimal armour or large, red minions with weapons and armour. The participants played the training version first and then the other two versions in a randomised order.

Subject characteristics in T3, n = 158.

In the first half of 2024, video ads accounted for 77.8 percent of all mobile game advertising creatives on digital platforms worldwide. Images made 18.8 percent of all creatives.

Between January 2023 and June 2024, casual mobile game advertisers accounted for 28.59 percent of all mobile game advertisers on digital platforms worldwide. Puzzles ranked second, with 13.87 percent.