A curated collection of datasets, APIs, and tools for applying artificial intelligence and data mining to video games.
Awesome Game Datasets is a curated repository listing datasets, APIs, tools, and academic resources specifically for artificial intelligence and data mining applications in video games. It solves the problem of fragmented resources by providing a centralized, community-maintained index to accelerate research and development in game AI, analytics, and procedural content generation.
AI researchers, data scientists, and game developers focused on building intelligent game agents, analyzing player behavior, or generating game content algorithmically. It is also valuable for academics and students studying game AI or data mining.
Developers choose this project because it aggregates hard-to-find, game-specific data and tools in one place, saving significant time in resource discovery. Its community-driven curation ensures the listed resources are relevant and actively used in the field.
:video_game: A curated list of awesome game datasets, and tools to artificial intelligence in games
Open-Awesome is built by the community, for the community. Submit a project, suggest an awesome list, or help improve the catalog on GitHub.
The README lists datasets from diverse popular games such as Dota 2, StarCraft, and Pokémon, covering match results, player stats, and assets, providing a broad starting point for research.
It aggregates public APIs from major platforms like Steam, Riot Games, and IGDB, enabling access to live and historical game data for integration into applications.
Includes links to environments like OpenAI Retro and ViZDoom, which are standard for developing and benchmarking game-playing AI agents, as noted in the AI competition sections.
References key books, research papers, and market analysis tools like Newzoo, grounding practical resources in established theory and industry insights.
The repository only provides links to external datasets; users must independently handle data downloading, cleaning, and validation, which can be time-consuming.
As a community-maintained list, external resources may become outdated or inaccessible without active maintenance, risking broken links for users.
Datasets and APIs are sourced from various providers like Kaggle and UCI, with no assessment of data quality, documentation completeness, or consistency.