[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"similar-leomaurodesenv--game-datasets":3,"tool-leomaurodesenv--game-datasets":65},[4,18,32,41,49,57],{"id":5,"name":6,"github_repo":7,"description_zh":8,"stars":9,"difficulty_score":10,"last_commit_at":11,"category_tags":12,"status":17},4358,"openclaw","openclaw\u002Fopenclaw","OpenClaw 是一款专为个人打造的本地化 AI 助手，旨在让你在自己的设备上拥有完全可控的智能伙伴。它打破了传统 AI 助手局限于特定网页或应用的束缚，能够直接接入你日常使用的各类通讯渠道，包括微信、WhatsApp、Telegram、Discord、iMessage 等数十种平台。无论你在哪个聊天软件中发送消息，OpenClaw 都能即时响应，甚至支持在 macOS、iOS 和 Android 设备上进行语音交互，并提供实时的画布渲染功能供你操控。\n\n这款工具主要解决了用户对数据隐私、响应速度以及“始终在线”体验的需求。通过将 AI 部署在本地，用户无需依赖云端服务即可享受快速、私密的智能辅助，真正实现了“你的数据，你做主”。其独特的技术亮点在于强大的网关架构，将控制平面与核心助手分离，确保跨平台通信的流畅性与扩展性。\n\nOpenClaw 非常适合希望构建个性化工作流的技术爱好者、开发者，以及注重隐私保护且不愿被单一生态绑定的普通用户。只要具备基础的终端操作能力（支持 macOS、Linux 及 Windows WSL2），即可通过简单的命令行引导完成部署。如果你渴望拥有一个懂你",349277,3,"2026-04-06T06:32:30",[13,14,15,16],"Agent","开发框架","图像","数据工具","ready",{"id":19,"name":20,"github_repo":21,"description_zh":22,"stars":23,"difficulty_score":24,"last_commit_at":25,"category_tags":26,"status":17},2268,"ML-For-Beginners","microsoft\u002FML-For-Beginners","ML-For-Beginners 是由微软推出的一套系统化机器学习入门课程，旨在帮助零基础用户轻松掌握经典机器学习知识。这套课程将学习路径规划为 12 周，包含 26 节精炼课程和 52 道配套测验，内容涵盖从基础概念到实际应用的完整流程，有效解决了初学者面对庞大知识体系时无从下手、缺乏结构化指导的痛点。\n\n无论是希望转型的开发者、需要补充算法背景的研究人员，还是对人工智能充满好奇的普通爱好者，都能从中受益。课程不仅提供了清晰的理论讲解，还强调动手实践，让用户在循序渐进中建立扎实的技能基础。其独特的亮点在于强大的多语言支持，通过自动化机制提供了包括简体中文在内的 50 多种语言版本，极大地降低了全球不同背景用户的学习门槛。此外，项目采用开源协作模式，社区活跃且内容持续更新，确保学习者能获取前沿且准确的技术资讯。如果你正寻找一条清晰、友好且专业的机器学习入门之路，ML-For-Beginners 将是理想的起点。",85267,2,"2026-04-18T11:00:28",[15,16,27,28,13,29,30,14,31],"视频","插件","其他","语言模型","音频",{"id":33,"name":34,"github_repo":35,"description_zh":36,"stars":37,"difficulty_score":38,"last_commit_at":39,"category_tags":40,"status":17},5784,"funNLP","fighting41love\u002FfunNLP","funNLP 是一个专为中文自然语言处理（NLP）打造的超级资源库，被誉为\"NLP 民工的乐园”。它并非单一的软件工具，而是一个汇集了海量开源项目、数据集、预训练模型和实用代码的综合性平台。\n\n面对中文 NLP 领域资源分散、入门门槛高以及特定场景数据匮乏的痛点，funNLP 提供了“一站式”解决方案。这里不仅涵盖了分词、命名实体识别、情感分析、文本摘要等基础任务的标准工具，还独特地收录了丰富的垂直领域资源，如法律、医疗、金融行业的专用词库与数据集，甚至包含古诗词生成、歌词创作等趣味应用。其核心亮点在于极高的全面性与实用性，从基础的字典词典到前沿的 BERT、GPT-2 模型代码，再到高质量的标注数据和竞赛方案，应有尽有。\n\n无论是刚刚踏入 NLP 领域的学生、需要快速验证想法的算法工程师，还是从事人工智能研究的学者，都能在这里找到急需的“武器弹药”。对于开发者而言，它能大幅减少寻找数据和复现模型的时间；对于研究者，它提供了丰富的基准测试资源和前沿技术参考。funNLP 以开放共享的精神，极大地降低了中文自然语言处理的开发与研究成本，是中文 AI 社区不可或缺的宝藏仓库。",79857,1,"2026-04-08T20:11:31",[30,16,29],{"id":42,"name":43,"github_repo":44,"description_zh":45,"stars":46,"difficulty_score":38,"last_commit_at":47,"category_tags":48,"status":17},5773,"cs-video-courses","Developer-Y\u002Fcs-video-courses","cs-video-courses 是一个精心整理的计算机科学视频课程清单，旨在为自学者提供系统化的学习路径。它汇集了全球知名高校（如加州大学伯克利分校、新南威尔士大学等）的完整课程录像，涵盖从编程基础、数据结构与算法，到操作系统、分布式系统、数据库等核心领域，并深入延伸至人工智能、机器学习、量子计算及区块链等前沿方向。\n\n面对网络上零散且质量参差不齐的教学资源，cs-video-courses 解决了学习者难以找到成体系、高难度大学级别课程的痛点。该项目严格筛选内容，仅收录真正的大学层级课程，排除了碎片化的简短教程或商业广告，确保用户能接触到严谨的学术内容。\n\n这份清单特别适合希望夯实计算机基础的开发者、需要补充特定领域知识的研究人员，以及渴望像在校生一样系统学习计算机科学的自学者。其独特的技术亮点在于分类极其详尽，不仅包含传统的软件工程与网络安全，还细分了生成式 AI、大语言模型、计算生物学等新兴学科，并直接链接至官方视频播放列表，让用户能一站式获取高质量的教育资源，免费享受世界顶尖大学的课堂体验。",79792,"2026-04-08T22:03:59",[29,15,16,14],{"id":50,"name":51,"github_repo":52,"description_zh":53,"stars":54,"difficulty_score":24,"last_commit_at":55,"category_tags":56,"status":17},7347,"lobehub","lobehub\u002Flobehub","LobeHub 是一个致力于工作与生活的智能体协作平台，旨在帮助用户发现、构建并与不断成长的 AI 智能体队友协同工作。它解决了当前 AI 应用中单点交互效率低、难以形成规模化协作网络的问题，将“智能体”确立为工作的基本单元，让人类与 AI 能够共同进化。\n\n无论是开发者、研究人员还是普通用户，都能通过 LobeHub 轻松设计多智能体协作流程。平台支持一键安装 MCP 插件、访问丰富的智能体市场，并提供本地与云端数据库管理、多用户协作等高级功能。其独特的技术亮点包括对多种大模型服务商的兼容、本地大模型部署支持、视觉识别、语音对话（TTS\u002FSTT）、文生图以及思维链（Chain of Thought）等能力。此外，LobeHub 还具备分支对话、工件生成、文件上传与知识库集成等实用特性，并适配桌面端、移动端及 PWA 场景，支持自定义主题。\n\n通过开源与自托管选项，LobeHub 为构建人机共演的未来协作网络提供了灵活、可扩展的基础设施。",75141,"2026-04-13T22:06:32",[30,16,13,14,15],{"id":58,"name":59,"github_repo":60,"description_zh":61,"stars":62,"difficulty_score":38,"last_commit_at":63,"category_tags":64,"status":17},2234,"scikit-learn","scikit-learn\u002Fscikit-learn","scikit-learn 是一个基于 Python 构建的开源机器学习库，依托于 SciPy、NumPy 等科学计算生态，旨在让机器学习变得简单高效。它提供了一套统一且简洁的接口，涵盖了从数据预处理、特征工程到模型训练、评估及选择的全流程工具，内置了包括线性回归、支持向量机、随机森林、聚类等在内的丰富经典算法。\n\n对于希望快速验证想法或构建原型的数据科学家、研究人员以及 Python 开发者而言，scikit-learn 是不可或缺的基础设施。它有效解决了机器学习入门门槛高、算法实现复杂以及不同模型间调用方式不统一的痛点，让用户无需重复造轮子，只需几行代码即可调用成熟的算法解决分类、回归、聚类等实际问题。\n\n其核心技术亮点在于高度一致的 API 设计风格，所有估算器（Estimator）均遵循相同的调用逻辑，极大地降低了学习成本并提升了代码的可读性与可维护性。此外，它还提供了强大的模型选择与评估工具，如交叉验证和网格搜索，帮助用户系统地优化模型性能。作为一个由全球志愿者共同维护的成熟项目，scikit-learn 以其稳定性、详尽的文档和活跃的社区支持，成为连接理论学习与工业级应用的最",65861,"2026-04-18T10:37:59",[14,29,16],{"id":66,"github_repo":67,"name":68,"description_en":69,"description_zh":70,"ai_summary_zh":70,"readme_en":71,"readme_zh":72,"quickstart_zh":73,"use_case_zh":74,"hero_image_url":75,"owner_login":76,"owner_name":77,"owner_avatar_url":78,"owner_bio":79,"owner_company":80,"owner_location":81,"owner_email":82,"owner_twitter":80,"owner_website":83,"owner_url":84,"languages":80,"stars":85,"forks":86,"last_commit_at":87,"license":88,"difficulty_score":38,"env_os":89,"env_gpu":90,"env_ram":90,"env_deps":91,"category_tags":94,"github_topics":95,"view_count":24,"oss_zip_url":80,"oss_zip_packed_at":80,"status":17,"created_at":105,"updated_at":106,"faqs":107,"releases":133},9325,"leomaurodesenv\u002Fgame-datasets","game-datasets",":video_game: A curated list of awesome game datasets, and tools to artificial intelligence in games","game-datasets 是一个专为游戏人工智能与数据挖掘领域打造的精选资源库。它系统性地整理了大量高质量的游戏数据集、实用开发工具以及相关学术资料，旨在解决研究人员和开发者在构建智能游戏应用时面临的“数据难找、工具分散”的痛点。\n\n无论是需要训练强化学习模型的研究员，还是希望分析玩家行为的数据科学家，亦或是想要获取实时电竞数据的游戏开发者，都能在这里找到所需的支持。资源库涵盖了从经典主机游戏到移动端、Web 端的多平台数据接口（API），包括暴雪战网、Steam 数据库、Dota 2 开放数据、宝可梦全世代数据等知名来源，同时也收录了关于超级马里奥制造器等特定游戏的解析工具。\n\n除了原始数据，game-datasets 还提供了市场研究资料、专业书籍推荐及各类辅助工具，帮助用户更高效地完成从数据获取到模型验证的全流程工作。项目采用开放的社区协作模式，持续更新维护，确保资源的时效性与实用性。对于希望深入探索游戏智能技术、开展数据驱动型游戏设计的专业人士而言，这是一个不可多得的入门指南与实战宝库。","# :video_game: Awesome Game Datasets [![Awesome](https:\u002F\u002Fawesome.re\u002Fbadge.svg)](https:\u002F\u002Fawesome.re)\n\n[![GitHub](https:\u002F\u002Fimg.shields.io\u002Fstatic\u002Fv1?label=Code&message=GitHub&color=blue&style=flat-square)](https:\u002F\u002Fgithub.com\u002Fleomaurodesenv\u002Fgame-datasets)\n[![CC-BY-4.0 license](https:\u002F\u002Fimg.shields.io\u002Fstatic\u002Fv1?label=License&message=CC-BY-4.0&color=blue&style=flat-square)](LICENSE)\n[![GitHub Workflow Status](https:\u002F\u002Fimg.shields.io\u002Fgithub\u002Factions\u002Fworkflow\u002Fstatus\u002Fleomaurodesenv\u002Fgame-datasets\u002Fcontinuous-integration.yml?label=Build&style=flat-square)](https:\u002F\u002Fgithub.com\u002Fleomaurodesenv\u002Fgame-datasets\u002Factions\u002Fworkflows\u002Fcontinuous-integration.yml)\n\nIn computer science, Artificial Intelligence (AI) is intelligence demonstrated by machines. Its definition, AI research as the study of \"intelligent agents\": any device that perceives its environment and takes actions that achieving its goals _Russell et. al (2016)_.\n\nWithal, Data Mining (DM) is the process of discovering patterns in data sets (or datasets) involving methods of machine learning, statistics, and database systems; DM focus on extract the information of datasets _Han (2011)_.\n\nThis repository guides work with **Artificial Intelligence** or **Data Mining** in digital games. Find datasets, tools, and materials to build your _application_ or _dataset_.\n\n**Contributing**\n\nFor suggestions or questions, open an issue. To contribute, read [this](CONTRIBUTING.md) and submit a pull request.\n\n______________________________________________________________________\n\n**Contents**\n\n- [:video_game: Awesome Game Datasets ](#video_game-awesome-game-datasets-)\n  - [API](#api)\n  - [Artificial Intelligence](#artificial-intelligence)\n    - [Mobile](#mobile)\n    - [Web](#web)\n  - [Books](#books)\n  - [Dataset](#dataset)\n    - [Related](#related)\n  - [Market Research](#market-research)\n  - [Miscellaneous](#miscellaneous)\n  - [License](#license)\n\n______________________________________________________________________\n\n## API\n\nAPI is _\"a set of functions and procedures allowing the creation of applications that access the features or data of an operating system, application, or other service\"_ (Google).\n\n- [Battle.net](https:\u002F\u002Fdevelop.battle.net\u002F) - Collection of games developed by [Blizzard](https:\u002F\u002Fwww.blizzard.com).\n- [Battlefield 4 Stats](https:\u002F\u002Fbf4db.com\u002Fstats) - Stats, rankings, and progression of a player.\n- [BoardGameGeek](https:\u002F\u002Fboardgamegeek.com\u002Fwiki\u002Fpage\u002FBGG_XML_API2) - Board games data.\n- [Counter-Strike](https:\u002F\u002Fgithub.com\u002Fpnxenopoulos\u002Fcsgo) - data parsing for Counter-Strike: Global Offensive (CSGO).\n- [Giant Bomb](https:\u002F\u002Fwww.giantbomb.com\u002Fapi\u002F) - Game data and players review.\n- [IGDB](https:\u002F\u002Fwww.igdb.com\u002Fapi) - General information of games from any platform.\n- [Marvel Developer](https:\u002F\u002Fdeveloper.marvel.com\u002F) - Information about Marvel's vast library of comics.\n- [metacritc](https:\u002F\u002Fwww.metacritic.com\u002Fgame) - Game reviews and evaluation.\n- [NEXARDA](https:\u002F\u002Fwww.nexarda.com\u002Fapi) - Games and price data.\n- [OpenCritic](https:\u002F\u002Fwww.opencritic.com) - Game reviews aggregator.\n- [OpenDota](https:\u002F\u002Fwww.opendota.com\u002F) - Platform providing Dota 2 data.\n- [PandaScore](https:\u002F\u002Fpandascore.co\u002F) - Realtime eSports data.\n- [PokéAPI](https:\u002F\u002Fpokeapi.co\u002F) - Pokémon data of all generations.\n- [Riot Games](https:\u002F\u002Fdeveloper.riotgames.com\u002F) - Active games, match history, and ranked statistics.\n- [smm-course-search](https:\u002F\u002Fgithub.com\u002Fleomaurodesenv\u002Fsmm-course-search) - Search courses from Super Mario Maker game.\n- [smm-course-viewer](https:\u002F\u002Fgithub.com\u002Fleomaurodesenv\u002Fsmm-course-viewer) - Read courses from Super Mario Maker saves.\n- [smm-maker-profile](https:\u002F\u002Fgithub.com\u002Fleomaurodesenv\u002Fsmm-maker-profile) - Fetch the user profile from Super Mario Maker game.\n- [Steam Database](https:\u002F\u002Fgithub.com\u002FSteamDatabase) - Series of tools to Steam data. [Website](https:\u002F\u002Fsteamdb.info\u002F).\n- [Steambase](https:\u002F\u002Fgithub.com\u002FSteambase) - Steam data tools and insights. [Website](https:\u002F\u002Fsteambase.io\u002F).\n- [Steam Spy](https:\u002F\u002Fgithub.com\u002Ftopics\u002Fsteamspy) - Game statistical from Steam users profiles.\n- [Steam Web API](https:\u002F\u002Fdeveloper.valvesoftware.com\u002Fwiki\u002FSteam_Web_API) - Query tool from Steam.\n- [TGDB](https:\u002F\u002Fgithub.com\u002FTheGamesDB\u002FTheGamesDB\u002F) - General information of games from any platform. [Website](https:\u002F\u002Fthegamesdb.net\u002F).\n- [TrendingNow.games](https:\u002F\u002Ftrendingnow.games\u002Fdata-feeds) - Real-time trending Steam game data with free CSV, RSS, and JSON feeds updated hourly.\n- [vgchartzScrape](https:\u002F\u002Fgithub.com\u002FGregorUT\u002FvgchartzScrape) - Crawler from [VGChartz](http:\u002F\u002Fwww.vgchartz.com).\n- [WhatoPlay](https:\u002F\u002Fwhatoplay.com) - Game reviews and ratings aggregator, and a recommender for discovering games.\n- [Xbox LIVE API](https:\u002F\u002Fgithub.com\u002Fxboxapi) - Games, apps, users stats and messages. [paper](https:\u002F\u002Fdoi.org\u002F10.1145\u002F1943552.1943569).\n\n______________________________________________________________________\n\n## Artificial Intelligence\n\n- [CyberBattleSim](https:\u002F\u002Fgithub.com\u002Fmicrosoft\u002FCyberBattleSim) - Experimentation platform to investigate automated agents. [Website](https:\u002F\u002Fwww.microsoft.com\u002Fen-us\u002Fresearch\u002Fproject\u002Fcyberbattlesim\u002F).\n- [Daimonin](https:\u002F\u002Fwww.daimonin.org\u002F) - Isometric MMORPG.\n- [Deliantra](http:\u002F\u002Fwww.deliantra.net\u002F) - Adventure game in a medieval environment.\n- [Dungeon and Cave Generation](https:\u002F\u002Fgithub.com\u002Fsentientdesigns\u002Fconstructive) - Constructive generation methods for dungeons and levels.\n- [Dungeon Crawl: Stone Soup](https:\u002F\u002Fgithub.com\u002Fcrawl\u002Fcrawl) - Roguelike adventure.\n- [Fighting Game AI Competition](http:\u002F\u002Fwww.ice.ci.ritsumei.ac.jp\u002F~ftgaic\u002F) - Controller for a fighting game.\n- [FlightGear Flight Simulator](https:\u002F\u002Fwww.flightgear.org\u002F) - Flight simulator.\n- [General Video Game AI](http:\u002F\u002Fwww.gvgai.net\u002F) - Controller for general video game playing. [Paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F1802.10363).\n- [Halite by Two Sigma](https:\u002F\u002Fwww.kaggle.com\u002Fc\u002Fhalite\u002F) - Collect the halite during a match in space.\n- [Hanabi Competition](http:\u002F\u002Fhanabi.aiclash.com\u002F) - Board game competition.\n- [Infinite Mario Bros](http:\u002F\u002Fwww.marioai.org\u002F) - Super Mario competition. Platformer AI antecedent. [Website](http:\u002F\u002Fjulian.togelius.com\u002Fmariocompetition2009\u002F).\n- [Malmo](https:\u002F\u002Fgithub.com\u002FMicrosoft\u002Fmalmo) - Platform built on top of [Minecraft](https:\u002F\u002Fwww.minecraft.net\u002Fen-us\u002F) game.\n- [microRTS](https:\u002F\u002Fgithub.com\u002Fsantiontanon\u002Fmicrorts) - RTS game competition. [PT-BR](https:\u002F\u002Fgithub.com\u002Frubensolv\u002FMicroRTS).\n- [MiniDungeons](https:\u002F\u002Fgithub.com\u002Fsentientdesigns\u002Fminidungeons) - Procedural dungeon-like game.\n- [Morai-Maker-Engine](https:\u002F\u002Fgithub.com\u002Fmguzdial3\u002FMorai-Maker-Engine) - Cooperative game level editor. [Paper](http:\u002F\u002Fdx.doi.org\u002F10.1145\u002F3290605.3300854).\n- [Ms. Pac-Man](http:\u002F\u002Fgameaibook.org\u002Fwp-content\u002Fuploads\u002F2016\u002F10\u002Fmspacman-master.zip) - Pac-Man game competition.\n- [OpenLieroX](http:\u002F\u002Fwww.openlierox.net\u002F) - Liero (similar to Worms) game.\n- [openmw](https:\u002F\u002Fgitlab.com\u002FOpenMW\u002Fopenmw) - Open-world RPG game. [Website](https:\u002F\u002Fopenmw.org\u002Fen\u002F).\n- [Platformer AI](https:\u002F\u002Fsites.google.com\u002Fsite\u002Fplatformersai\u002F) - Super Mario competition.\n- [polyworld](https:\u002F\u002Fgithub.com\u002Fpolyworld\u002Fpolyworld) - Artificial life system.\n- [qengine](https:\u002F\u002Fgithub.com\u002Fklaussilveira\u002Fqengine) - Retro game engine.\n- [Retro Contest](https:\u002F\u002Fgithub.com\u002Fopenai\u002Fretro) - Competition on SEGA Genesis games. [Website](https:\u002F\u002Fopenai.com\u002Fblog\u002Fretro-contest\u002F).\n- [Robocode](https:\u002F\u002Frobocode.sourceforge.io\u002F) - Robot battle tank competition.\n- [Showdown AI Competition](https:\u002F\u002Fgithub.com\u002Fscotchkorean27\u002Fshowdownaiclient) - Competition of Pokemon battle.\n- [StarCraft AI Competition](https:\u002F\u002Fsites.google.com\u002Fsite\u002Fstarcraftaic\u002F) - StarCraft game competition.\n- [Text-Based Adventure AI Competition](https:\u002F\u002Fgithub.com\u002FAtkrye\u002FIEEE-CIG-Text-Adventurer-Competition) - Text-adventure game competition. [Website](http:\u002F\u002Fatkrye.github.io\u002FIEEE-CIG-Text-Adventurer-Competition\u002F).\n- [The Genius](http:\u002F\u002Fthegenius.sourceforge.net\u002F) - Chess engine.\n- [The Open Racing Car Simulator](http:\u002F\u002Ftorcs.sourceforge.net\u002F) - Car racing simulator.\n- [Vegan on a Desert Island](https:\u002F\u002Fgitlab.com\u002Fvoadi\u002Fvoadi) - Adventure game on island survival.\n- [veloren](https:\u002F\u002Fgitlab.com\u002Fveloren\u002Fveloren) - Sandbox game. [Website](https:\u002F\u002Fveloren.net\u002F).\n- [Vindinium](https:\u002F\u002Fgithub.com\u002Fleomaurodesenv\u002Fvindinium) - Multi-player turn based on roguelike competition. [Documentation](https:\u002F\u002Fpythonhosted.org\u002Fvindinium\u002F).\n- [Visual Doom AI Competition](https:\u002F\u002Fgithub.com\u002Fmwydmuch\u002FViZDoom) - Doom game competition.\n- [Wargus](https:\u002F\u002Fgithub.com\u002FWargus\u002Fwargus) - Real-time strategy game. [Website](https:\u002F\u002Fwargus.github.io\u002F).\n\n### Mobile\n\n- [Habitica](https:\u002F\u002Fgithub.com\u002FHabitRPG\u002Fhabitica-android) - Gamify your life.\n- [Pixel Dungeon](https:\u002F\u002Fgithub.com\u002Fwatabou\u002Fpixel-dungeon) - Roguelike game.\n- [Shattered Pixel Dungeon](https:\u002F\u002Fgithub.com\u002F00-Evan\u002Fshattered-pixel-dungeon) - Improved Pixel Dungeon.\n\n### Web\n\n- [Digger](https:\u002F\u002Fgithub.com\u002Flutzroeder\u002Fdigger) - Boulderdash game.\n- [Duck Hunt](https:\u002F\u002Fgithub.com\u002FMattSurabian\u002FDuckHunt-JS) - Shooting game in ducks. [Play](http:\u002F\u002Fduckhuntjs.com\u002F).\n- [Infinite Mario Bros](https:\u002F\u002Fgithub.com\u002Frobertkleffner\u002Fmariohtml5) - Super Mario Bros. [Play](https:\u002F\u002Fopenhtml5games.github.io\u002Fgames-mirror\u002Fdist\u002Fmariohtml5\u002Fmain.html).\n- [Onslaught! Arena](https:\u002F\u002Fgithub.com\u002Flostdecade\u002Fonslaught_arena) - Fight off hordes of medieval monsters. [Play](http:\u002F\u002Farcade.lostdecadegames.com\u002Fonslaught-arena\u002F).\n- [Starship](http:\u002F\u002Fmaettig.com\u002Fcode\u002Fcanvas\u002Fstarship-sorades-13k.zip) - Traditional starship game.\n- [TapAI](https:\u002F\u002Fgithub.com\u002Fleomaurodesenv\u002FTapAI) - User interactions with a tap on screen game.\n- [WebNES](https:\u002F\u002Fgithub.com\u002Fpubby) - Play rooms of NES in web browser. [Play](http:\u002F\u002Fpubby.github.io\u002Fwebnes\u002Findex_app.html).\n\n______________________________________________________________________\n\n## Books\n\n- Drachen, A. Mirza-Babaei, P. Nacke, L. (2018). _Games user research_. Oxford.\n- El-Nasr, S. Drachen, A. Canossa, A. (2013). _Game analytics: maximizing the value of player data_. Sprigner.\n- Han, J., Pei, J., Kamber, M. (2011). _Data mining: concepts and techniques_. Elsevier.\n- Hennig-Thurau, T. Houston, M. (2018). _Entertainment science: data analytics and practical theory for movies, games, music and books_. Springer.\n- Loh, A. Sheng, Y. Ifenthaler, D. (2015). _Serious games analytics: methodologies for performance measurement, assessment, and improvement_. Springer.\n- Millington, Ian, Funge, John (2020). _AI for Games, Third Edition_. CRC Press.\n- Russell, S. J., Norvig, P. (2016). _Artificial intelligence: a modern approach_. Malaysia; Pearson Education Limited.\n- Yannakakis, G. N., Togelius, J. (2018). _Artificial intelligence and games_. Springer.\n\n______________________________________________________________________\n\n## Dataset\n\n- [(LoL) League of Legends Ranked Games](https:\u002F\u002Fwww.kaggle.com\u002Fdatasnaek\u002Fleague-of-legends) - Matches details from ranked games.\n- [17K Mobile Strategy Games](https:\u002F\u002Fwww.kaggle.com\u002Ftristan581\u002F17k-apple-app-store-strategy-games) - Strategy games from the Apple App Store.\n- [2018 FIFA World Cup Squads](https:\u002F\u002Fwww.kaggle.com\u002Fcclayford\u002F2018-fifa-world-cup-squads) - Squad details for teams participating in the World Cup.\n- [320K Roblox App Google Store Reviews](https:\u002F\u002Fwww.kaggle.com\u002Fdatasets\u002Fbwandowando\u002F322k-roblox-app-google-store-reviews) - Google Store reviews of Roblox.\n- [380,000 Guesses Dataset - Higher or Lower?](https:\u002F\u002Fwww.kaggle.com\u002Fsdobson46\u002Fhigher-or-lower-game) - Real-world game data of guessing a number.\n- [Age of Empires 2: Definitive Edition 225.000 Games](https:\u002F\u002Fwww.kaggle.com\u002Fdatasets\u002Fnicoelbert\u002Faoe-matchups) - Data about match ups, outcomes and game states over the time in 225.000 AoE2 matches.\n- [Animal Crossing New Horizons Catalog](https:\u002F\u002Fwww.kaggle.com\u002Fdatasets\u002Fjessicali9530\u002Fanimal-crossing-new-horizons-nookplaza-dataset) - Comprehensive inventory of items, villagers, clothing, fish\u002Fbugs etc.\n- [Board Game Data](https:\u002F\u002Fwww.kaggle.com\u002Fmrpantherson\u002Fboard-game-data) - Data from board games.\n- [Board Games Dataset](https:\u002F\u002Fwww.kaggle.com\u002Fdatasets\u002Fsujaykapadnis\u002Fboard-games) - Board Games Dataset from [BoardGameGeek](https:\u002F\u002Fwww.boardgamegeek.com\u002F).\n- [Board Games Dataset](https:\u002F\u002Fwww.kaggle.com\u002Fdatasets\u002Fgabrio\u002Fboard-games-dataset) - Attributes and the ratings from board games in [BoardGameGeek](https:\u002F\u002Fwww.boardgamegeek.com\u002F).\n- [Boardgaming Online Game Records](https:\u002F\u002Fwww.kaggle.com\u002Fjingking\u002Fboardgaming-online-processed-game-records) - Playthroughs of board games.\n- [bravefrontier_data](https:\u002F\u002Fgithub.com\u002Fcheahjs\u002Fbravefrontier_data) - Mobile game data, items and missions information.\n- [CartolaFC](https:\u002F\u002Fwww.kaggle.com\u002Fschiller\u002Fcartolafc) - Popular brazilian fantasy football (from 2014 to 2017).\n- [Chess Game Dataset (Lichess)](https:\u002F\u002Fwww.kaggle.com\u002Fdatasets\u002Fdatasnaek\u002Fchess) - Chess games, including moves, victor, rating, opening details and more.\n- [Clash royale Dataset](https:\u002F\u002Fwww.kaggle.com\u002Fswappyk\u002Fclash-royale-dataset) - Cards data.\n- [Complete FIFA 2017 Player dataset (Global)](https:\u002F\u002Fwww.kaggle.com\u002Fartimous\u002Fcomplete-fifa-2017-player-dataset-global) - Players data.\n- [Condensing Steam: Distilling the Diversity of Gamer Behavior](http:\u002F\u002Facademictorrents.com\u002Fdetails\u002Feba3b48fcdaa9e69a927051f1678251a86a546f3) - Temporal games data.\n- [Connect-4 Data Set](https:\u002F\u002Farchive.ics.uci.edu\u002Fml\u002Fdatasets\u002FConnect-4) - Connect-4 game matches.\n- [CS:GO Competitive Matchmaking Data](https:\u002F\u002Fwww.kaggle.com\u002Fskihikingkevin\u002Fcsgo-matchmaking-damage) - Damage entries on rounds played.\n- [CS:GO Steam Reviews](https:\u002F\u002Fwww.kaggle.com\u002Fdatasets\u002Fnoahx1\u002Fcsgo-steam-reviews) - Steam Reviews.\n- [Data Game ClashRoyale](https:\u002F\u002Fwww.kaggle.com\u002Flucianomartins\u002Fdata-game-clashroyale) - Player data of Clash Royale game.\n- [Defense of the Ancients](https:\u002F\u002Fwww.kaggle.com\u002Fraxnamosa\u002Fdefense-of-the-ancients) - Monsters data from Warcraft III game.\n- [dnddata](https:\u002F\u002Fgithub.com\u002Foganm\u002Fdnddata) - Dataset of Dungeons and Dragons characters.\n- [Dota 2 Games (UCI)](https:\u002F\u002Farchive.ics.uci.edu\u002Fml\u002Fdatasets\u002FDota2+Games+Results) - Matches results.\n- [Elden Ring Steam Reviews](https:\u002F\u002Fwww.kaggle.com\u002Fdatasets\u002Fnoahx1\u002Felden-ring-steam-reviews) - Steam Reviews.\n- [FIFA 18 Complete Player Dataset](https:\u002F\u002Fwww.kaggle.com\u002Fthec03u5\u002Ffifa-18-demo-player-dataset) - Players and their attributes.\n- [Fortnite Statistics_80 Games](https:\u002F\u002Fdata.world\u002Fkreynol3\u002Ffortnite-statistics80-games) - End games statistics.\n- [Fortnite: Battle Royale - Weapon Attributes](https:\u002F\u002Fwww.kaggle.com\u002Fjruots\u002Ffortnite-battle-royale-weapon-attributes) - Stats of the weapons.\n- [GamingVideoSET](https:\u002F\u002Fgithub.com\u002FNabajeetBarman\u002FGamingHDRVideoSET) - A Dataset for Gaming Video Streaming Applications. [Paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F8463362)\n- [GOSU.AI Dota 2 Game Chats](https:\u002F\u002Fwww.kaggle.com\u002Fromovpa\u002Fgosuai-dota-2-game-chats) - Chats from matches replays.\n- [GTA-3D Dataset](https:\u002F\u002Fgithub.com\u002Foscarmcnulty\u002Fgta-3d-dataset) - 2D and 3D images from Grand Theft Auto 5 game.\n- [Hearthstone Cards](https:\u002F\u002Fwww.kaggle.com\u002Fjeradrose\u002Fhearthstone-cards) - Collection of cards.\n- [Heroes of Might and Magic 3 Units](https:\u002F\u002Fwww.kaggle.com\u002Fdaynearthur\u002Fheroes-of-might-and-magic-3-units) - Units of a game.\n- [History of Hearthstone](https:\u002F\u002Fwww.kaggle.com\u002Fromainvincent\u002Fhistory-of-hearthstone) - Collection of decks.\n- [Hogwarts Legacy Reviews](https:\u002F\u002Fwww.kaggle.com\u002Fdatasets\u002Fgeorgescutelnicu\u002Fhogwarts-legacy-reviews) - A list of Hogwarts Legacy reviews.\n- [LCS 2017 Summer Split Fantasy Player & Team Stats](https:\u002F\u002Fwww.kaggle.com\u002Fdanielwatabe\u002Flcs-2017-summer-split-fantasy-player-team-stats) - Player and team data.\n- [League of Legends Diamond Ranked Games (10 min)](https:\u002F\u002Fwww.kaggle.com\u002Fbobbyscience\u002Fleague-of-legends-diamond-ranked-games-10-min) - Classify ranked games.\n- [League of Legends Summoner Ids and Data - 2016](https:\u002F\u002Fwww.kaggle.com\u002Fxenogearcap\u002Fleague2016) - Game data.\n- [Magic The Gathering Cards](https:\u002F\u002Fwww.kaggle.com\u002Fmylesoneill\u002Fmagic-the-gathering-cards) - Cards data.\n- [Most Played Games of All Time](https:\u002F\u002Fwww.kaggle.com\u002Fdatasets\u002Ffaisaljanjua0555\u002Fmost-played-games-of-all-time) - Steam game stats.\n- [NBA Players](https:\u002F\u002Fwww.kaggle.com\u002Fdatasets\u002Fjustinas\u002Fnba-players-data) - Biometric, biographic and basic box score features from 1996 to 2019 season.\n- [Oldschool Runescape Polling Data](https:\u002F\u002Fwww.kaggle.com\u002Fnikkynak\u002Foldschool-runescape-polling-data) - Historical polling data.\n- [OpenDota](https:\u002F\u002Fblog.opendota.com\u002F2017\u002F03\u002F24\u002Fdatadump2\u002F) - Continuous database of Dota 2 matches.\n- [Overwatch Game Records](https:\u002F\u002Fwww.kaggle.com\u002Fmylesoneill\u002Foverwatch-game-records) - Stats of one player from thousands of matches.\n- [Overwatch Ranked Data](https:\u002F\u002Fwww.kaggle.com\u002Fsimonho87\u002Foverwatch-ranked-data) - Player and match data.\n- [Overwatch](https:\u002F\u002Fwww.kaggle.com\u002Fedopic\u002Foverwatch) - Heros characteristics.\n- [Path of exile game statistic](https:\u002F\u002Fwww.kaggle.com\u002Fgagazet\u002Fpath-of-exile-league-statistic) - Players data.\n- [Platform Experience Dataset](https:\u002F\u002Fdrive.google.com\u002Fdrive\u002Ffolders\u002F0B93_a48_LnJ0VEc3NklYbWpVZXM) - Super Mario Bros matches. [Paper](https:\u002F\u002Fdoi.org\u002F10.1109\u002FACII.2015.7344647).\n- [Pokémon for Data Mining and Machine Learning](https:\u002F\u002Fwww.kaggle.com\u002Fdatasets\u002Falopez247\u002Fpokemon) - Stats of 721 Pokémon of the first six generations.\n- [Pokémon GO Pokédex](https:\u002F\u002Fgithub.com\u002FBiuni\u002FPokemonGO-Pokedex) - Encyclopedia Pokemon.\n- [Pokemon with stats](https:\u002F\u002Fwww.kaggle.com\u002Fabcsds\u002Fpokemon) - Pokemon data with stats and types.\n- [Pokemon Wonder Trade Results](https:\u002F\u002Fdata.world\u002Fnotgibs\u002Fpokemon-wonder-trade-results) - Results of wonder trades in Pokemon Moon version.\n- [Pokemon- Weedle's Cave](https:\u002F\u002Fwww.kaggle.com\u002Fterminus7\u002Fpokemon-challenge) - Battle data of Pokemon.\n- [PokemonGO](https:\u002F\u002Fwww.kaggle.com\u002Fabcsds\u002Fpokemongo) - Pokemon and battle stats.\n- [Predict'em All](https:\u002F\u002Fwww.kaggle.com\u002Fsemioniy\u002Fpredictemall) - Pokemon appear in PokemonGo over time.\n- [PUBG Match Deaths and Statistics](https:\u002F\u002Fwww.kaggle.com\u002Fskihikingkevin\u002Fpubg-match-deaths) - Matches data.\n- [Scrabble](https:\u002F\u002Fgithub.com\u002Fonzie9\u002FQuackle_Self_Play) - Data Quackle game matches.\n- [Self Driving Car](https:\u002F\u002Fwww.kaggle.com\u002Fdatasets\u002Faslanahmedov\u002Fself-driving-carbehavioural-cloning) - Behavioural Cloning Complete Guide.\n- [SkillCraft-StarCraft](https:\u002F\u002Fwww.kaggle.com\u002Fdanofer\u002Fskillcraft) - StarCraft 2 league-level performance.\n- [SMMnet](https:\u002F\u002Fwww.kaggle.com\u002Fleomauro\u002Fsmmnet) - Network data from Super Mario Maker.\n- [StarCraft 2 (UCI)](https:\u002F\u002Farchive.ics.uci.edu\u002Fml\u002Fdatasets\u002FSkillCraft1+Master+Table+Dataset) - Data stream of matches. [Paper](https:\u002F\u002Fdoi.org\u002F10.1371\u002Fjournal.pone.0075129).\n- [StarCraft II matches history](https:\u002F\u002Fwww.kaggle.com\u002Falimbekovkz\u002Fstarcraft-ii-matches-history) - Results of matches.\n- [StarCraft II Replay Analysis](https:\u002F\u002Fwww.kaggle.com\u002Fsfu-summit\u002Fstarcraft-ii-replay-analysis) - Aggregation of the replays.\n- [Starcraft: Scouting The Enemy](https:\u002F\u002Fwww.kaggle.com\u002Fkinguistics\u002Fstarcraft-scouting-the-enemy) - Player reconnaissance in professional-level.\n- [StarData](https:\u002F\u002Fgithub.com\u002FTorchCraft\u002FStarData) - Matches, videos, etc. [Website](http:\u002F\u002Fnova.wolfwork.com\u002FdataMining.html), [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F1708.02139).\n- [Super Trunfo - Dinossaurs 2](https:\u002F\u002Fwww.kaggle.com\u002Fkandebonfim\u002Fsuper-trunfo-dinossaurs-2) - Cards of this game.\n- [Terra Mystica Snellman Statistics](https:\u002F\u002Fwww.kaggle.com\u002Flemonkoala\u002Fterra-mystica) - Game logs and statistics.\n- [The Complete Pokemon Dataset](https:\u002F\u002Fwww.kaggle.com\u002Fdatasets\u002Frounakbanik\u002Fpokemon) - Pokemon data from all generations.\n- [The Quick, Draw! Dataset](https:\u002F\u002Fgithub.com\u002Fgooglecreativelab\u002Fquickdraw-dataset) - Collection of 50 million drawings across 345 categories.\n- [Travian buildings](https:\u002F\u002Fwww.kaggle.com\u002Fcblesa\u002Ftravian-buildings) - Time, cost and bonus of buildings.\n- [World of Warcraft Avatar History](https:\u002F\u002Fwww.kaggle.com\u002Fmylesoneill\u002Fwarcraft-avatar-history) - Collection of records.\n- [World of Warcraft Battlegrounds](https:\u002F\u002Fwww.kaggle.com\u002Fcblesa\u002Fworld-of-warcraft-battlegrounds) - Details of battlegrounds.\n\n### Related\n\n- [Computer Games Dataset](https:\u002F\u002Fwww.kaggle.com\u002Fdatasets\u002Fiamsouravbanerjee\u002Fcomputer-games-dataset) - Gaming World: A Comprehensive Computer Games Dataset.\n- [Google Play Store Apps](https:\u002F\u002Fwww.kaggle.com\u002Flava18\u002Fgoogle-play-store-apps) - Data from Play Store apps.\n- [JVC Game Reviews](https:\u002F\u002Fwww.kaggle.com\u002Ffloval\u002Fjvc-game-reviews) - Video game data from [JeuxVideo.com](http:\u002F\u002Fwww.jeuxvideo.com\u002F).\n- [Kickstarter Datasets](https:\u002F\u002Fwebrobots.io\u002Fkickstarter-datasets\u002F) - Projects details.\n- [Metacritic games](https:\u002F\u002Fwww.kaggle.com\u002Fdestring\u002Fmetacritic-reviewed-games-since-2000) - Games data from [metacritc](https:\u002F\u002Fwww.metacritic.com).\n- [NEXARDA Franchises](https:\u002F\u002Fwww.nexarda.com\u002Fpages\u002Fcomplete-list-of-video-game-franchises) - Franchises data from [nexarda.com](https:\u002F\u002Fwww.nexarda.com).\n- [NEXARDA Games](https:\u002F\u002Fwww.nexarda.com\u002Fpages\u002Fcomplete-list-of-video-games) - Games data from [nexarda.com](https:\u002F\u002Fwww.nexarda.com).\n- [NEXARDA Studios](https:\u002F\u002Fwww.nexarda.com\u002Fpages\u002Fcomplete-list-of-video-game-studios) - Developers and publishers data from [nexarda.com](https:\u002F\u002Fwww.nexarda.com).\n- [Nintendo Games](https:\u002F\u002Fwww.kaggle.com\u002Fdatasets\u002Fjoebeachcapital\u002Fnintendo-games) - Nintendo games for all platforms scraped from [metacritc](https:\u002F\u002Fwww.metacritic.com).\n- [Over 13,000 Steam Games](https:\u002F\u002Fwww.kaggle.com\u002Fkingburrito666\u002Fover-13000-steam-games) - Prices of video games from Steam.\n- [PC Games Sales](https:\u002F\u002Fwww.kaggle.com\u002Fdatasets\u002Fkhaiid\u002Fmost-selling-pc-games) - Dataset of the best selling PC games.\n- [PEW-Gaming-Broadband](https:\u002F\u002Fdata.world\u002Fjshep512\u002Fpew-gaming-broadband) - Questions about video games.\n- [Steam Game Data](https:\u002F\u002Fgithub.com\u002FCraigKelly\u002Fsteam-data) - Combination of Steam API and Steam Spy.\n- [Steam Games Dataset](https:\u002F\u002Fwww.kaggle.com\u002Fdatasets\u002Fnikatomashvili\u002Fsteam-games-dataset) - Dataset scraped from Steam search system.\n- [Steam Review Datasets](https:\u002F\u002Fgithub.com\u002Fmulhod\u002Fsteam_reviews) - Steam user reviews.\n- [Steam Store Games](https:\u002F\u002Fwww.kaggle.com\u002Fdatasets\u002Fnikdavis\u002Fsteam-store-games) - Information about 27,000 games scraped from Steam and SteamSpy APIs.\n- [Steam Video Games](https:\u002F\u002Fwww.kaggle.com\u002Fdatasets\u002Ftamber\u002Fsteam-video-games) - Steam user interactions.\n- [Vandal Game Reviews](https:\u002F\u002Fwww.kaggle.com\u002Ffloval\u002F12-000-video-game-reviews-from-vandal) - Game data from [Vandal.com](https:\u002F\u002Fvandal.elespanol.com\u002F).\n- [Video Game DATA](https:\u002F\u002Fwww.kaggle.com\u002Fjuttugarakesh\u002Fvideo-game-data) - Video games released.\n- [Video Game Sales with Ratings](https:\u002F\u002Fwww.kaggle.com\u002Frush4ratio\u002Fvideo-game-sales-with-ratings) - Video game sales and rating from [metacritc](https:\u002F\u002Fwww.metacritic.com).\n- [Video Game Sales](https:\u002F\u002Fwww.kaggle.com\u002Fdatasets\u002Fgregorut\u002Fvideogamesales) - Sales data from games.\n- [Video Games Data](https:\u002F\u002Fwww.kaggle.com\u002Fdatasets\u002Fmaso0dahmed\u002Fvideo-games-data) - Video games synopse.\n- [Video Games Review](https:\u002F\u002Fwww.kaggle.com\u002Flaunay10christian\u002Fvideo-games-review) - Reviews on [JeuxVideo.com](http:\u002F\u002Fwww.jeuxvideo.com\u002F).\n- [Video Games Sales 2019](https:\u002F\u002Fwww.kaggle.com\u002Fashaheedq\u002Fvideo-games-sales-2019) - Sales and scores for games.\n\n______________________________________________________________________\n\n## Market Research\n\n- [Euro-Monitor, Video Games](https:\u002F\u002Fwww.euromonitor.com\u002F) - Strategic Market Researcher.\n- [Grand View Research, Digital Media](https:\u002F\u002Fwww.grandviewresearch.com\u002Findustry\u002Fdigital-media) - Syndicated market research studies.\n- [Newzoo](https:\u002F\u002Fnewzoo.com\u002F) - View on the games market. Unparalleled insights and value.\n- [Statista, Video Games](https:\u002F\u002Fwww.statista.com\u002Ftopics\u002F868\u002Fvideo-games\u002F) - Market and opinion research institutes and data derived from the economic sector.\n\n______________________________________________________________________\n\n## Miscellaneous\n\n- [Academic Torrents](http:\u002F\u002Facademictorrents.com\u002F) - Sharing enormous datasets.\n- [Awesome ACG](https:\u002F\u002Fgithub.com\u002Fsoruly\u002Fawesome-acg) - Technologies related to anime, comic and games.\n- [Awesome Esports](https:\u002F\u002Fgithub.com\u002Fstrift\u002Fawesome-esports) - Competitiosn using video games.\n- [Awesome Gamedev](https:\u002F\u002Fgithub.com\u002FCalinou\u002Fawesome-gamedev) - Collection of open-source games.\n- [AWS Datasets](https:\u002F\u002Faws.amazon.com\u002Fdatasets\u002F) - Amazon public datasets.\n- [data.world](https:\u002F\u002Fdata.world) - Datasets.\n- [datasets-games](https:\u002F\u002Fgithub.com\u002Fcncplyr\u002Fdatasets-games) - Datasets from a variety of games.\n- [Games of Coding](https:\u002F\u002Fgithub.com\u002Fmichelpereira\u002Fawesome-gamesofcoding) - Games to teach programming language.\n- [Games on GitHub](https:\u002F\u002Fgithub.com\u002Fleereilly\u002Fgames) - Popular videos games hosted in GitHub.\n- [GitHub Activity Data](https:\u002F\u002Fconsole.cloud.google.com\u002Fmarketplace\u002Fdetails\u002Fgithub\u002Fgithub-repos?filter=solution-type:dataset&id=46ee22ab-2ca4-4750-81a7-3ee0f0150dcb) - Activity from open source GitHub repositories.\n- [Gym OpenAI](https:\u002F\u002Fgithub.com\u002Fopenai\u002Fgym) - Game toolkit for reinforcement learning algorithms.\n- [Kaggle](http:\u002F\u002Fkaggle.com\u002F) - Data Science competitions, datasets and projects.\n- [Libre Game Wiki](https:\u002F\u002Flibregamewiki.org\u002FMain_Page) - Free gaming encyclopedia.\n- [Open HTML5 Games](https:\u002F\u002Fgithub.com\u002FOpenHTML5Games) - JavaScript and HTML5 games.\n- [Open-source games](https:\u002F\u002Fpt.wikipedia.org\u002Fwiki\u002FLista_de_jogos_de_c%C3%B3digo_aberto) - Open-source games (PT-BR).\n- [Reddit - Datasets](https:\u002F\u002Fwww.reddit.com\u002Fr\u002Fdatasets) - Forum of datasets.\n- [UCI](https:\u002F\u002Farchive.ics.uci.edu\u002F) - Datasets.\n\n______________________________________________________________________\n\n## License\n\n\u003Ca rel=\"license\" href=\"LICENSE\">\u003Cimg alt=\"Creative Commons License\" style=\"border-width:0\" src=\"https:\u002F\u002Fmirrors.creativecommons.org\u002Fpresskit\u002Fbuttons\u002F88x31\u002Fsvg\u002Fby-sa.svg\" \u002F>\u003C\u002Fa>\n\n- License: [Creative Commons Attribution-ShareAlike 4.0 International License](LICENSE)\n","# :video_game: 优秀的游戏数据集 [![Awesome](https:\u002F\u002Fawesome.re\u002Fbadge.svg)](https:\u002F\u002Fawesome.re)\n\n[![GitHub](https:\u002F\u002Fimg.shields.io\u002Fstatic\u002Fv1?label=代码&message=GitHub&color=blue&style=flat-square)](https:\u002F\u002Fgithub.com\u002Fleomaurodesenv\u002Fgame-datasets)\n[![CC-BY-4.0 许可证](https:\u002F\u002Fimg.shields.io\u002Fstatic\u002Fv1?label=许可证&message=CC-BY-4.0&color=blue&style=flat-square)](LICENSE)\n[![GitHub 工作流状态](https:\u002F\u002Fimg.shields.io\u002Fgithub\u002Factions\u002Fworkflow\u002Fstatus\u002Fleomaurodesenv\u002Fgame-datasets\u002Fcontinuous-integration.yml?label=构建&style=flat-square)](https:\u002F\u002Fgithub.com\u002Fleomaurodesenv\u002Fgame-datasets\u002Factions\u002Fworkflows\u002Fcontinuous-integration.yml)\n\n在计算机科学中，人工智能（AI）是指机器所表现出的智能。其定义为：人工智能研究是关于“智能代理”的研究——即任何能够感知其环境并采取行动以实现其目标的设备 _Russell 等人 (2016)_。\n\n与此同时，数据挖掘（DM）是从数据集中发现模式的过程，涉及机器学习、统计学和数据库系统的方法；数据挖掘的重点在于从数据集中提取信息 _Han (2011)_。\n\n本仓库旨在指导如何在数字游戏中使用**人工智能**或**数据挖掘**。您可以在这里找到数据集、工具和资料，用于构建您的_应用程序_或_数据集_。\n\n**贡献**\n\n如您有任何建议或问题，请提交一个议题。若想参与贡献，请阅读[此处](CONTRIBUTING.md)，然后提交一个拉取请求。\n\n______________________________________________________________________\n\n**目录**\n\n- [:video_game: 优秀的游戏数据集 ](#video_game-awesome-game-datasets-)\n  - [API](#api)\n  - [人工智能](#artificial-intelligence)\n    - [移动端](#mobile)\n    - [Web](#web)\n  - [书籍](#books)\n  - [数据集](#dataset)\n    - [相关](#related)\n  - [市场研究](#market-research)\n  - [杂项](#miscellaneous)\n  - [许可证](#license)\n\n______________________________________________________________________\n\n## API\n\nAPI 是指“一组函数和过程，允许创建访问操作系统、应用程序或其他服务的功能或数据的应用程序”（Google）。\n\n- [Battle.net](https:\u002F\u002Fdevelop.battle.net\u002F) - 由 [Blizzard](https:\u002F\u002Fwww.blizzard.com) 开发的游戏集合。\n- [Battlefield 4 统计](https:\u002F\u002Fbf4db.com\u002Fstats) - 玩家的统计数据、排名和进度。\n- [BoardGameGeek](https:\u002F\u002Fboardgamegeek.com\u002Fwiki\u002Fpage\u002FBGG_XML_API2) - 棋盘游戏数据。\n- [反恐精英](https:\u002F\u002Fgithub.com\u002Fpnxenopoulos\u002Fcsgo) - 反恐精英：全球攻势（CSGO）的数据解析。\n- [Giant Bomb](https:\u002F\u002Fwww.giantbomb.com\u002Fapi\u002F) - 游戏数据及玩家评论。\n- [IGDB](https:\u002F\u002Fwww.igdb.com\u002Fapi) - 来自任何平台的游戏通用信息。\n- [漫威开发者](https:\u002F\u002Fdeveloper.marvel.com\u002F) - 关于漫威庞大漫画库的信息。\n- [metacritc](https:\u002F\u002Fwww.metacritic.com\u002Fgame) - 游戏评论与评分。\n- [NEXARDA](https:\u002F\u002Fwww.nexarda.com\u002Fapi) - 游戏及价格数据。\n- [OpenCritic](https:\u002F\u002Fwww.opencritic.com) - 游戏评论聚合平台。\n- [OpenDota](https:\u002F\u002Fwww.opendota.com\u002F) - 提供 Dota 2 数据的平台。\n- [PandaScore](https:\u002F\u002Fpandascore.co\u002F) - 实时电子竞技数据。\n- [PokéAPI](https:\u002F\u002Fpokeapi.co\u002F) - 全世代宝可梦数据。\n- [Riot Games](https:\u002F\u002Fdeveloper.riotgames.com\u002F) - 在线游戏、对战历史及排位统计。\n- [smm-course-search](https:\u002F\u002Fgithub.com\u002Fleomaurodesenv\u002Fsmm-course-search) - 搜索《超级马里奥制造》游戏中的关卡。\n- [smm-course-viewer](https:\u002F\u002Fgithub.com\u002Fleomaurodesenv\u002Fsmm-course-viewer) - 读取《超级马里奥制造》存档中的关卡。\n- [smm-maker-profile](https:\u002F\u002Fgithub.com\u002Fleomaurodesenv\u002Fsmm-maker-profile) - 获取《超级马里奥制造》游戏中的用户资料。\n- [Steam 数据库](https:\u002F\u002Fgithub.com\u002FSteamDatabase) - 一系列用于处理 Steam 数据的工具。[官网](https:\u002F\u002Fsteamdb.info\u002F)。\n- [Steambase](https:\u002F\u002Fgithub.com\u002FSteambase) - Steam 数据工具与洞察。[官网](https:\u002F\u002Fsteambase.io\u002F)。\n- [Steam Spy](https:\u002F\u002Fgithub.com\u002Ftopics\u002Fsteamspy) - 基于 Steam 用户资料的游戏统计数据。\n- [Steam Web API](https:\u002F\u002Fdeveloper.valvesoftware.com\u002Fwiki\u002FSteam_Web_API) - Steam 查询工具。\n- [TGDB](https:\u002F\u002Fgithub.com\u002FTheGamesDB\u002FTheGamesDB\u002F) - 来自任何平台的游戏通用信息。[官网](https:\u002F\u002Fthegamesdb.net\u002F)。\n- [TrendingNow.games](https:\u002F\u002Ftrendingnow.games\u002Fdata-feeds) - 实时热门 Steam 游戏数据，提供免费的 CSV、RSS 和 JSON 格式数据源，每小时更新。\n- [vgchartzScrape](https:\u002F\u002Fgithub.com\u002FGregorUT\u002FvgchartzScrape) - 来自 [VGChartz](http:\u002F\u002Fwww.vgchartz.com) 的爬虫。\n- [WhatoPlay](https:\u002F\u002Fwhatoplay.com) - 游戏评论与评分聚合平台，同时也是帮助发现新游戏的推荐引擎。\n- [Xbox LIVE API](https:\u002F\u002Fgithub.com\u002Fxboxapi) - 游戏、应用、用户统计及消息。[论文](https:\u002F\u002Fdoi.org\u002F10.1145\u002F1943552.1943569)。\n\n______________________________________________________________________\n\n## 人工智能\n\n- [CyberBattleSim](https:\u002F\u002Fgithub.com\u002Fmicrosoft\u002FCyberBattleSim) - 用于研究自动化智能体的实验平台。[官网](https:\u002F\u002Fwww.microsoft.com\u002Fen-us\u002Fresearch\u002Fproject\u002Fcyberbattlesim\u002F)。\n- [Daimonin](https:\u002F\u002Fwww.daimonin.org\u002F) - 等距视角大型多人在线角色扮演游戏。\n- [Deliantra](http:\u002F\u002Fwww.deliantra.net\u002F) - 中世纪背景下的冒险游戏。\n- [地牢与洞穴生成](https:\u002F\u002Fgithub.com\u002Fsentientdesigns\u002Fconstructive) - 用于地牢和关卡的构造式生成方法。\n- [地牢爬行：石汤](https:\u002F\u002Fgithub.com\u002Fcrawl\u002Fcrawl) - 类Roguelike冒险游戏。\n- [格斗游戏AI竞赛](http:\u002F\u002Fwww.ice.ci.ritsumei.ac.jp\u002F~ftgaic\u002F) - 格斗游戏控制器。\n- [FlightGear飞行模拟器](https:\u002F\u002Fwww.flightgear.org\u002F) - 飞行模拟器。\n- [通用视频游戏AI](http:\u002F\u002Fwww.gvgai.net\u002F) - 用于通用视频游戏的控制器。[论文](https:\u002F\u002Farxiv.org\u002Fabs\u002F1802.10363)。\n- [Two Sigma的Halite](https:\u002F\u002Fwww.kaggle.com\u002Fc\u002Fhalite\u002F) - 在太空对战中收集卤石。\n- [花火牌比赛](http:\u002F\u002Fhanabi.aiclash.com\u002F) - 桌游比赛。\n- [无限超级马里奥兄弟](http:\u002F\u002Fwww.marioai.org\u002F) - 超级马里奥比赛。平台跳跃类AI的前身。[官网](http:\u002F\u002Fjulian.togelius.com\u002Fmariocompetition2009\u002F)。\n- [Malmo](https:\u002F\u002Fgithub.com\u002FMicrosoft\u002Fmalmo) - 基于[Minecraft](https:\u002F\u002Fwww.minecraft.net\u002Fen-us\u002F)游戏构建的平台。\n- [microRTS](https:\u002F\u002Fgithub.com\u002Fsantiontanon\u002Fmicrorts) - RTS游戏比赛。[PT-BR](https:\u002F\u002Fgithub.com\u002Frubensolv\u002FMicroRTS)。\n- [MiniDungeons](https:\u002F\u002Fgithub.com\u002Fsentientdesigns\u002Fminidungeons) - 程序化地牢风格游戏。\n- [Morai-Maker-Engine](https:\u002F\u002Fgithub.com\u002Fmguzdial3\u002FMorai-Maker-Engine) - 合作游戏关卡编辑器。[论文](http:\u002F\u002Fdx.doi.org\u002F10.1145\u002F3290605.3300854)。\n- [吃豆人小姐](http:\u002F\u002Fgameaibook.org\u002Fwp-content\u002Fuploads\u002F2016\u002F10\u002Fmspacman-master.zip) - 吃豆人游戏比赛。\n- [OpenLieroX](http:\u002F\u002Fwww.openlierox.net\u002F) - Liero（类似Worms）游戏。\n- [openmw](https:\u002F\u002Fgitlab.com\u002FOpenMW\u002Fopenmw) - 开放世界角色扮演游戏。[官网](https:\u002F\u002Fopenmw.org\u002Fen\u002F)。\n- [平台跳跃AI](https:\u002F\u002Fsites.google.com\u002Fsite\u002Fplatformersai\u002F) - 超级马里奥比赛。\n- [polyworld](https:\u002F\u002Fgithub.com\u002Fpolyworld\u002Fpolyworld) - 人工生命系统。\n- [qengine](https:\u002F\u002Fgithub.com\u002Fklaussilveira\u002Fqengine) - 复古游戏引擎。\n- [复古大赛](https:\u002F\u002Fgithub.com\u002Fopenai\u002Fretro) - SEGA Genesis游戏比赛。[官网](https:\u002F\u002Fopenai.com\u002Fblog\u002Fretro-contest\u002F)。\n- [Robocode](https:\u002F\u002Frobocode.sourceforge.io\u002F) - 机器人坦克对战比赛。\n- [Showdown AI竞赛](https:\u002F\u002Fgithub.com\u002Fscotchkorean27\u002Fshowdownaiclient) - 宝可梦对战比赛。\n- [星际争霸AI竞赛](https:\u002F\u002Fsites.google.com\u002Fsite\u002Fstarcraftaic\u002F) - 星际争霸游戏比赛。\n- [基于文本的冒险AI竞赛](https:\u002F\u002Fgithub.com\u002FAtkrye\u002FIEEE-CIG-Text-Adventurer-Competition) - 文本冒险游戏比赛。[官网](http:\u002F\u002Fatkrye.github.io\u002FIEEE-CIG-Text-Adventurer-Competition\u002F)。\n- [The Genius](http:\u002F\u002Fthegenius.sourceforge.net\u002F) - 国际象棋引擎。\n- [开放赛车模拟器](http:\u002F\u002Ftorcs.sourceforge.net\u002F) - 赛车模拟器。\n- [荒岛素食者](https:\u002F\u002Fgitlab.com\u002Fvoadi\u002Fvoadi) - 岛上生存冒险游戏。\n- [veloren](https:\u002F\u002Fgitlab.com\u002Fveloren\u002Fveloren) - 沙盒游戏。[官网](https:\u002F\u002Fveloren.net\u002F)。\n- [Vindinium](https:\u002F\u002Fgithub.com\u002Fleomaurodesenv\u002Fvindinium) - 多人回合制Roguelike比赛。[文档](https:\u002F\u002Fpythonhosted.org\u002Fvindinium\u002F)。\n- [视觉毁灭战士AI竞赛](https:\u002F\u002Fgithub.com\u002Fmwydmuch\u002FViZDoom) - 毁灭战士游戏比赛。\n- [Wargus](https:\u002F\u002Fgithub.com\u002FWargus\u002Fwargus) - 实时战略游戏。[官网](https:\u002F\u002Fwargus.github.io\u002F)。\n\n### 移动端\n\n- [Habitica](https:\u002F\u002Fgithub.com\u002FHabitRPG\u002Fhabitica-android) - 将生活游戏化。\n- [像素地牢](https:\u002F\u002Fgithub.com\u002Fwatabou\u002Fpixel-dungeon) - Roguelike游戏。\n- [破碎像素地牢](https:\u002F\u002Fgithub.com\u002F00-Evan\u002Fshattered-pixel-dungeon) - 改进版的像素地牢。\n\n### 网页端\n\n- [Digger](https:\u002F\u002Fgithub.com\u002Flutzroeder\u002Fdigger) - Boulderdash游戏。\n- [打鸭子](https:\u002F\u002Fgithub.com\u002FMattSurabian\u002FDuckHunt-JS) - 打鸭子射击游戏。[游玩](http:\u002F\u002Fduckhuntjs.com\u002F)。\n- [无限超级马里奥兄弟](https:\u002F\u002Fgithub.com\u002Frobertkleffner\u002Fmariohtml5) - 超级马里奥兄弟。[游玩](https:\u002F\u002Fopenhtml5games.github.io\u002Fgames-mirror\u002Fdist\u002Fmariohtml5\u002Fmain.html)。\n- [突袭！竞技场](https:\u002F\u002Fgithub.com\u002Flostdecade\u002Fonslaught_arena) - 击退中世纪怪物大军。[游玩](http:\u002F\u002Farcade.lostdecadegames.com\u002Fonslaught-arena\u002F)。\n- [星际飞船](http:\u002F\u002Fmaettig.com\u002Fcode\u002Fcanvas\u002Fstarship-sorades-13k.zip) - 经典星际飞船游戏。\n- [TapAI](https:\u002F\u002Fgithub.com\u002Fleomaurodesenv\u002FTapAI) - 用户通过点击屏幕进行互动的游戏。\n- [WebNES](https:\u002F\u002Fgithub.com\u002Fpubby) - 在网页浏览器中玩NES游戏。[游玩](http:\u002F\u002Fpubby.github.io\u002Fwebnes\u002Findex_app.html)。\n\n______________________________________________________________________\n\n## 图书\n\n- Drachen, A. Mirza-Babaei, P. Nacke, L. (2018). _游戏用户研究_. 牛津。\n- El-Nasr, S. Drachen, A. Canossa, A. (2013). _游戏分析：最大化玩家数据的价值_. Sprigner。\n- Han, J., Pei, J., Kamber, M. (2011). _数据挖掘：概念与技术_. Elsevier。\n- Hennig-Thurau, T. Houston, M. (2018). _娱乐科学：电影、游戏、音乐和书籍的数据分析与实践理论_. Springer。\n- Loh, A. Sheng, Y. Ifenthaler, D. (2015). _严肃游戏分析：绩效测量、评估和改进的方法论_. Springer。\n- Millington, Ian, Funge, John (2020). _游戏中的人工智能，第三版_. CRC Press。\n- Russell, S. J., Norvig, P. (2016). _人工智能：现代方法_. 马来西亚；培生教育有限公司。\n- Yannakakis, G. N., Togelius, J. (2018). _人工智能与游戏_. Springer。\n\n______________________________________________________________________\n\n## 数据集\n\n- [(LoL) 英雄联盟排位赛数据](https:\u002F\u002Fwww.kaggle.com\u002Fdatasnaek\u002Fleague-of-legends) - 排位赛对局详情。\n- [1.7万款苹果应用商店策略游戏](https:\u002F\u002Fwww.kaggle.com\u002Ftristan581\u002F17k-apple-app-store-strategy-games) - 苹果应用商店中的策略游戏。\n- [2018年国际足联世界杯参赛球队名单](https:\u002F\u002Fwww.kaggle.com\u002Fcclayford\u002F2018-fifa-world-cup-squads) - 参加世界杯各队的球员名单。\n- [32万条Roblox应用谷歌商店评论](https:\u002F\u002Fwww.kaggle.com\u002Fdatasets\u002Fbwandowando\u002F322k-roblox-app-google-store-reviews) - Roblox应用在谷歌商店的用户评论。\n- [38万次“猜大还是小”游戏数据集](https:\u002F\u002Fwww.kaggle.com\u002Fsdobson46\u002Fhigher-or-lower-game) - 猜数字游戏的真实数据。\n- [帝国时代2：决定版22.5万场游戏数据](https:\u002F\u002Fwww.kaggle.com\u002Fdatasets\u002Fnicoelbert\u002Faoe-matchups) - 22.5万场《帝国时代2》比赛中的对战、结果及游戏状态数据。\n- [动物森友会新视野目录](https:\u002F\u002Fwww.kaggle.com\u002Fdatasets\u002Fjessicali9530\u002Fanimal-crossing-new-horizons-nookplaza-dataset) - 包含物品、村民、服装、鱼类\u002F昆虫等的全面清单。\n- [桌游数据](https:\u002F\u002Fwww.kaggle.com\u002Fmrpantherson\u002Fboard-game-data) - 来自各类桌游的数据。\n- [桌游数据集](https:\u002F\u002Fwww.kaggle.com\u002Fdatasets\u002Fsujaykapadnis\u002Fboard-games) - 来自[BoardGameGeek](https:\u002F\u002Fwww.boardgamegeek.com\u002F)的桌游数据集。\n- [桌游数据集](https:\u002F\u002Fwww.kaggle.com\u002Fdatasets\u002Fgabrio\u002Fboard-games-dataset) - 来自[BoardGameGeek](https:\u002F\u002Fwww.boardgamegeek.com\u002F)的桌游属性及评分数据。\n- [在线桌游对局记录](https:\u002F\u002Fwww.kaggle.com\u002Fjingking\u002Fboardgaming-online-processed-game-records) - 桌游对局的完整记录。\n- [bravefrontier_data](https:\u002F\u002Fgithub.com\u002Fcheahjs\u002Fbravefrontier_data) - 手机游戏数据，包括道具和任务信息。\n- [CartolaFC](https:\u002F\u002Fwww.kaggle.com\u002Fschiller\u002Fcartolafc) - 流行的巴西虚拟足球游戏（2014年至2017年）。\n- [国际象棋游戏数据集（Lichess）](https:\u002F\u002Fwww.kaggle.com\u002Fdatasets\u002Fdatasnaek\u002Fchess) - 国际象棋对局数据，包含每步走法、胜负、等级分、开局细节等。\n- [皇室战争数据集](https:\u002F\u002Fwww.kaggle.com\u002Fswappyk\u002Fclash-royale-dataset) - 卡牌数据。\n- [完整的FIFA 2017全球球员数据集](https:\u002F\u002Fwww.kaggle.com\u002Fartimous\u002Fcomplete-fifa-2017-player-dataset-global) - 球员数据。\n- [Steam游戏行为多样性研究](http:\u002F\u002Facademictorrents.com\u002Fdetails\u002Feba3b48fcdaa9e69a927051f1678251a86a546f3) - 历史性游戏数据。\n- [四子连珠数据集](https:\u002F\u002Farchive.ics.uci.edu\u002Fml\u002Fdatasets\u002FConnect-4) - 四子连珠游戏对局。\n- [CS:GO竞技匹配数据](https:\u002F\u002Fwww.kaggle.com\u002Fskihikingkevin\u002Fcsgo-matchmaking-damage) - 各回合伤害记录。\n- [CS:GO Steam评论](https:\u002F\u002Fwww.kaggle.com\u002Fdatasets\u002Fnoahx1\u002Fcsgo-steam-reviews) - Steam平台上的用户评论。\n- [Clash Royale游戏玩家数据](https:\u002F\u002Fwww.kaggle.com\u002Flucianomartins\u002Fdata-game-clashroyale) - Clash Royale游戏中的玩家数据。\n- [魔兽争霸III中的怪物数据](https:\u002F\u002Fwww.kaggle.com\u002Fraxnamosa\u002Fdefense-of-the-ancients) - 魔兽争霸III游戏中怪物的相关数据。\n- [dnddata](https:\u002F\u002Fgithub.com\u002Foganm\u002Fdnddata) - 龙与地下城角色数据集。\n- [Dota 2比赛结果（UCI）](https:\u002F\u002Farchive.ics.uci.edu\u002Fml\u002Fdatasets\u002FDota2+Games+Results) - Dota 2比赛的结果数据。\n- [艾尔登法环Steam评论](https:\u002F\u002Fwww.kaggle.com\u002Fdatasets\u002Fnoahx1\u002Felden-ring-steam-reviews) - Steam平台上的用户评论。\n- [FIFA 18完整球员数据集](https:\u002F\u002Fwww.kaggle.com\u002Fthec03u5\u002Ffifa-18-demo-player-dataset) - 球员及其各项属性数据。\n- [堡垒之夜统计_80场比赛](https:\u002F\u002Fdata.world\u002Fkreynol3\u002Ffortnite-statistics80-games) - 结束时的游戏统计数据。\n- [堡垒之夜：大逃杀武器属性](https:\u002F\u002Fwww.kaggle.com\u002Fjruots\u002Ffortnite-battle-royale-weapon-attributes) - 武器的各项统计数据。\n- [GamingVideoSET](https:\u002F\u002Fgithub.com\u002FNabajeetBarman\u002FGamingHDRVideoSET) - 用于游戏视频流应用的数据集。[论文](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F8463362)\n- [GOSU.AI Dota 2游戏聊天记录](https:\u002F\u002Fwww.kaggle.com\u002Fromovpa\u002Fgosuai-dota-2-game-chats) - 来自比赛回放的聊天记录。\n- [GTA-3D数据集](https:\u002F\u002Fgithub.com\u002Foscarmcnulty\u002Fgta-3d-dataset) - 侠盗猎车手5中的2D和3D图像。\n- [炉石传说卡牌](https:\u002F\u002Fwww.kaggle.com\u002Fjeradrose\u002Fhearthstone-cards) - 卡牌收藏。\n- [魔法门之英雄无敌3单位](https:\u002F\u002Fwww.kaggle.com\u002Fdaynearthur\u002Fheroes-of-might-and-magic-3-units) - 游戏中的单位数据。\n- [炉石传说历史](https:\u002F\u002Fwww.kaggle.com\u002Fromainvincent\u002Fhistory-of-hearthstone) - 牌组集合。\n- [霍格沃茨之遗评论](https:\u002F\u002Fwww.kaggle.com\u002Fdatasets\u002Fgeorgescutelnicu\u002Fhogwarts-legacy-reviews) - 《霍格沃茨之遗》的评论列表。\n- [LCS 2017夏季赛梦幻联赛选手与队伍数据](https:\u002F\u002Fwww.kaggle.com\u002Fdanielwatabe\u002Flcs-2017-summer-split-fantasy-player-team-stats) - 选手和队伍的相关数据。\n- [英雄联盟钻石段位10分钟排位赛](https:\u002F\u002Fwww.kaggle.com\u002Fbobbyscience\u002Fleague-of-legends-diamond-ranked-games-10-min) - 排位赛分类数据。\n- [2016年英雄联盟召唤师ID及数据](https:\u002F\u002Fwww.kaggle.com\u002Fxenogearcap\u002Fleague2016) - 游戏相关数据。\n- [万智牌卡牌](https:\u002F\u002Fwww.kaggle.com\u002Fmylesoneill\u002Fmagic-the-gathering-cards) - 卡牌数据。\n- [史上最多游玩游戏](https:\u002F\u002Fwww.kaggle.com\u002Fdatasets\u002Ffaisaljanjua0555\u002Fmost-played-games-of-all-time) - Steam平台上的游戏统计数据。\n- [NBA球员](https:\u002F\u002Fwww.kaggle.com\u002Fdatasets\u002Fjustinas\u002Fnba-players-data) - 1996年至2019赛季的生物特征、个人资料及基础数据统计。\n- [老派Runescape投票数据](https:\u002F\u002Fwww.kaggle.com\u002Fnikkynak\u002Foldschool-runescape-polling-data) - 历史投票数据。\n- [OpenDota](https:\u002F\u002Fblog.opendota.com\u002F2017\u002F03\u002F24\u002Fdatadump2\u002F) - Dota 2比赛的持续数据库。\n- [守望先锋游戏记录](https:\u002F\u002Fwww.kaggle.com\u002Fmylesoneill\u002Foverwatch-game-records) - 单个玩家在数千场对局中的统计数据。\n- [守望先锋排位赛数据](https:\u002F\u002Fwww.kaggle.com\u002Fsimonho87\u002Foverwatch-ranked-data) - 玩家及对局数据。\n- [守望先锋](https:\u002F\u002Fwww.kaggle.com\u002Fedopic\u002Foverwatch) - 英雄特性。\n- [流亡者之路游戏统计](https:\u002F\u002Fwww.kaggle.com\u002Fgagazet\u002Fpath-of-exile-league-statistic) - 玩家数据。\n- [平台体验数据集](https:\u002F\u002Fdrive.google.com\u002Fdrive\u002Ffolders\u002F0B93_a48_LnJ0VEc3NklYbWpVZXM) - 超级马里奥兄弟对局数据。[论文](https:\u002F\u002Fdoi.org\u002F10.1109\u002FACII.2015.7344647)。\n- [用于数据挖掘和机器学习的宝可梦](https:\u002F\u002Fwww.kaggle.com\u002Fdatasets\u002Falopez247\u002Fpokemon) - 前六代共721只宝可梦的统计数据。\n- [Pokémon GO图鉴](https:\u002F\u002Fgithub.com\u002FBiuni\u002FPokemonGO-Pokedex) - 宝可梦百科全书。\n- [带统计数据的宝可梦](https:\u002F\u002Fwww.kaggle.com\u002Fabcsds\u002Fpokemon) - 包含统计数据和属性的宝可梦数据。\n- [宝可梦奇妙交换结果](https:\u002F\u002Fdata.world\u002Fnotgibs\u002Fpokemon-wonder-trade-results) - 宝可梦月球版中奇妙交换的结果。\n- [宝可梦挑战：Weedle的洞穴](https:\u002F\u002Fwww.kaggle.com\u002Fterminus7\u002Fpokemon-challenge) - 宝可梦对战数据。\n- [PokémonGO](https:\u002F\u002Fwww.kaggle.com\u002Fabcsds\u002Fpokemongo) - 宝可梦及对战统计数据。\n- [Predict'em All](https:\u002F\u002Fwww.kaggle.com\u002Fsemioniy\u002Fpredictemall) - 宝可梦在PokémonGo中随时间出现的情况。\n- [绝地求生比赛死亡与统计](https:\u002F\u002Fwww.kaggle.com\u002Fskihikingkevin\u002Fpubg-match-deaths) - 比赛数据。\n- [拼字游戏](https:\u002F\u002Fgithub.com\u002Fonzie9\u002FQuackle_Self_Play) - Quackle游戏对局数据。\n- [自动驾驶汽车](https:\u002F\u002Fwww.kaggle.com\u002Fdatasets\u002Faslanahmedov\u002Fself-driving-carbehavioural-cloning) - 行为克隆完整指南。\n- [SkillCraft-StarCraft](https:\u002F\u002Fwww.kaggle.com\u002Fdanofer\u002Fskillcraft) - StarCraft 2职业联赛水平的表现。\n- [SMMnet](https:\u002F\u002Fwww.kaggle.com\u002Fleomauro\u002Fsmmnet) - 来自超级马里奥制造者的网络数据。\n- [StarCraft 2（UCI）](https:\u002F\u002Farchive.ics.uci.edu\u002Fml\u002Fdatasets\u002FSkillCraft1+Master+Table+Dataset) - 比赛数据流。[论文](https:\u002F\u002Fdoi.org\u002F10.1371\u002Fjournal.pone.0075129)。\n- [StarCraft II比赛历史](https:\u002F\u002Fwww.kaggle.com\u002Falimbekovkz\u002Fstarcraft-ii-matches-history) - 比赛结果。\n- [StarCraft II回放分析](https:\u002F\u002Fwww.kaggle.com\u002Fsfu-summit\u002Fstarcraft-ii-replay-analysis) - 回放数据的汇总。\n- [星际争霸：侦察敌军](https:\u002F\u002Fwww.kaggle.com\u002Fkinguistics\u002Fstarcraft-scouting-the-enemy) - 职业级玩家的侦察行动。\n- [StarData](https:\u002F\u002Fgithub.com\u002FTorchCraft\u002FStarData) - 比赛、视频等。[官网](http:\u002F\u002Fnova.wolfwork.com\u002FdataMining.html)，[论文](https:\u002F\u002Farxiv.org\u002Fabs\u002F1708.02139)。\n- [超级王牌 - 恐龙2](https:\u002F\u002Fwww.kaggle.com\u002Fkandebonfim\u002Fsuper-trunfo-dinossaurs-2) - 该游戏的卡片。\n- [Terra Mystica Snellman统计](https:\u002F\u002Fwww.kaggle.com\u002Flemonkoala\u002Fterra-mystica) - 游戏日志和统计数据。\n- [完整的宝可梦数据集](https:\u002F\u002Fwww.kaggle.com\u002Fdatasets\u002Frounakbanik\u002Fpokemon) - 来自所有世代的宝可梦数据。\n- [Quick, Draw! 数据集](https:\u002F\u002Fgithub.com\u002Fgooglecreativelab\u002Fquickdraw-dataset) - 涵盖345个类别的5000万幅绘画作品。\n- [Travian建筑](https:\u002F\u002Fwww.kaggle.com\u002Fcblesa\u002Ftravian-buildings) - 建筑的时间、成本及奖励。\n- [魔兽世界角色历史](https:\u002F\u002Fwww.kaggle.com\u002Fmylesoneill\u002Fwarcraft-avatar-history) - 记录集合。\n- [魔兽世界战场](https:\u002F\u002Fwww.kaggle.com\u002Fcblesa\u002Fworld-of-warcraft-battlegrounds) - 战场详细信息。\n\n### 相关资源\n\n- [电脑游戏数据集](https:\u002F\u002Fwww.kaggle.com\u002Fdatasets\u002Fiamsouravbanerjee\u002Fcomputer-games-dataset) - 游戏世界：全面的电脑游戏数据集。\n- [Google Play商店应用](https:\u002F\u002Fwww.kaggle.com\u002Flava18\u002Fgoogle-play-store-apps) - 来自Play商店的应用数据。\n- [JVC游戏评论](https:\u002F\u002Fwww.kaggle.com\u002Ffloval\u002Fjvc-game-reviews) - 来自[JeuxVideo.com](http:\u002F\u002Fwww.jeuxvideo.com\u002F)的视频游戏数据。\n- [Kickstarter数据集](https:\u002F\u002Fwebrobots.io\u002Fkickstarter-datasets\u002F) - 项目详情。\n- [Metacritic游戏](https:\u002F\u002Fwww.kaggle.com\u002Fdestring\u002Fmetacritic-reviewed-games-since-2000) - 来自[metacritc](https:\u002F\u002Fwww.metacritic.com)的游戏数据。\n- [NEXARDA特许经营品牌](https:\u002F\u002Fwww.nexarda.com\u002Fpages\u002Fcomplete-list-of-video-game-franchises) - 来自[nexarda.com](https:\u002F\u002Fwww.nexarda.com)的特许经营品牌数据。\n- [NEXARDA游戏](https:\u002F\u002Fwww.nexarda.com\u002Fpages\u002Fcomplete-list-of-video-games) - 来自[nexarda.com](https:\u002F\u002Fwww.nexarda.com)的游戏数据。\n- [NEXARDA工作室](https:\u002F\u002Fwww.nexarda.com\u002Fpages\u002Fcomplete-list-of-video-game-studios) - 来自[nexarda.com](https:\u002F\u002Fwww.nexarda.com)的开发者和发行商数据。\n- [任天堂游戏](https:\u002F\u002Fwww.kaggle.com\u002Fdatasets\u002Fjoebeachcapital\u002Fnintendo-games) - 从[metacritc](https:\u002F\u002Fwww.metacritic.com)抓取的跨平台任天堂游戏。\n- [超过13,000款Steam游戏](https:\u002F\u002Fwww.kaggle.com\u002Fkingburrito666\u002Fover-13000-steam-games) - Steam平台上视频游戏的价格信息。\n- [PC游戏销量](https:\u002F\u002Fwww.kaggle.com\u002Fdatasets\u002Fkhaiid\u002Fmost-selling-pc-games) - 最畅销PC游戏的数据集。\n- [PEW-游戏-宽带](https:\u002F\u002Fdata.world\u002Fjshep512\u002Fpew-gaming-broadband) - 关于视频游戏的问题。\n- [Steam游戏数据](https:\u002F\u002Fgithub.com\u002FCraigKelly\u002Fsteam-data) - 结合了Steam API和Steam Spy的数据。\n- [Steam游戏数据集](https:\u002F\u002Fwww.kaggle.com\u002Fdatasets\u002Fnikatomashvili\u002Fsteam-games-dataset) - 从Steam搜索系统抓取的数据集。\n- [Steam评论数据集](https:\u002F\u002Fgithub.com\u002Fmulhod\u002Fsteam_reviews) - Steam用户评论。\n- [Steam商店游戏](https:\u002F\u002Fwww.kaggle.com\u002Fdatasets\u002Fnikdavis\u002Fsteam-store-games) - 从Steam和SteamSpy API抓取的27,000款游戏信息。\n- [Steam视频游戏](https:\u002F\u002Fwww.kaggle.com\u002Fdatasets\u002Ftamber\u002Fsteam-video-games) - Steam用户的互动数据。\n- [Vandal游戏评论](https:\u002F\u002Fwww.kaggle.com\u002Ffloval\u002F12-000-video-game-reviews-from-vandal) - 来自[Vandal.com](https:\u002F\u002Fvandal.elespanol.com\u002F)的游戏数据。\n- [视频游戏数据](https:\u002F\u002Fwww.kaggle.com\u002Fjuttugarakesh\u002Fvideo-game-data) - 已发布的视频游戏。\n- [带评分的视频游戏销量](https:\u002F\u002Fwww.kaggle.com\u002Frush4ratio\u002Fvideo-game-sales-with-ratings) - 来自[metacritc](https:\u002F\u002Fwww.metacritic.com)的视频游戏销量和评分。\n- [视频游戏销量](https:\u002F\u002Fwww.kaggle.com\u002Fdatasets\u002Fgregorut\u002Fvideogamesales) - 游戏的销售数据。\n- [视频游戏数据](https:\u002F\u002Fwww.kaggle.com\u002Fdatasets\u002Fmaso0dahmed\u002Fvideo-games-data) - 视频游戏简介。\n- [视频游戏评论](https:\u002F\u002Fwww.kaggle.com\u002Flaunay10christian\u002Fvideo-games-review) - 在[JeuxVideo.com](http:\u002F\u002Fwww.jeuxvideo.com\u002F)上的评论。\n- [2019年视频游戏销量](https:\u002F\u002Fwww.kaggle.com\u002Fashaheedq\u002Fvideo-games-sales-2019) - 游戏的销量和评分。\n\n______________________________________________________________________\n\n## 市场研究\n\n- [欧睿国际，视频游戏](https:\u002F\u002Fwww.euromonitor.com\u002F) - 战略市场研究员。\n- [Grand View Research，数字媒体](https:\u002F\u002Fwww.grandviewresearch.com\u002Findustry\u002Fdigital-media) - 联合市场研究报告。\n- [Newzoo](https:\u002F\u002Fnewzoo.com\u002F) - 对游戏市场的见解。无与伦比的洞察力和价值。\n- [Statista，视频游戏](https:\u002F\u002Fwww.statista.com\u002Ftopics\u002F868\u002Fvideo-games\u002F) - 市场和观点研究机构以及来自经济部门的数据。\n\n______________________________________________________________________\n\n## 其他\n\n- [Academic Torrents](http:\u002F\u002Facademictorrents.com\u002F) - 分享海量数据集。\n- [Awesome ACG](https:\u002F\u002Fgithub.com\u002Fsoruly\u002Fawesome-acg) - 与动漫、漫画和游戏相关的技术。\n- [Awesome Esports](https:\u002F\u002Fgithub.com\u002Fstrift\u002Fawesome-esports) - 使用视频游戏进行的竞技比赛。\n- [Awesome Gamedev](https:\u002F\u002Fgithub.com\u002FCalinou\u002Fawesome-gamedev) - 开源游戏集合。\n- [AWS数据集](https:\u002F\u002Faws.amazon.com\u002Fdatasets\u002F) - 亚马逊公共数据集。\n- [data.world](https:\u002F\u002Fdata.world) - 数据集。\n- [datasets-games](https:\u002F\u002Fgithub.com\u002Fcncplyr\u002Fdatasets-games) - 各种游戏的数据集。\n- [Coding游戏](https:\u002F\u002Fgithub.com\u002Fmichelpereira\u002Fawesome-gamesofcoding) - 用于教授编程语言的游戏。\n- [GitHub上的游戏](https:\u002F\u002Fgithub.com\u002Fleereilly\u002Fgames) - 在GitHub上托管的热门视频游戏。\n- [GitHub活动数据](https:\u002F\u002Fconsole.cloud.google.com\u002Fmarketplace\u002Fdetails\u002Fgithub\u002Fgithub-repos?filter=solution-type:dataset&id=46ee22ab-2ca4-4750-81a7-3ee0f0150dcb) - 来自开源GitHub仓库的活动数据。\n- [Gym OpenAI](https:\u002F\u002Fgithub.com\u002Fopenai\u002Fgym) - 用于强化学习算法的游戏工具包。\n- [Kaggle](http:\u002F\u002Fkaggle.com\u002F) - 数据科学竞赛、数据集和项目。\n- [Libre Game Wiki](https:\u002F\u002Flibregamewiki.org\u002FMain_Page) - 自由游戏百科全书。\n- [Open HTML5 Games](https:\u002F\u002Fgithub.com\u002FOpenHTML5Games) - JavaScript和HTML5游戏。\n- [开源游戏](https:\u002F\u002Fpt.wikipedia.org\u002Fwiki\u002FLista_de_jogos_de_c%C3%B3digo_aberto) - 开源游戏（PT-BR）。\n- [Reddit - 数据集](https:\u002F\u002Fwww.reddit.com\u002Fr\u002Fdatasets) - 数据集论坛。\n- [UCI](https:\u002F\u002Farchive.ics.uci.edu\u002F) - 数据集。\n\n______________________________________________________________________\n\n## 许可证\n\n\u003Ca rel=\"license\" href=\"LICENSE\">\u003Cimg alt=\"知识共享许可\" style=\"border-width:0\" src=\"https:\u002F\u002Fmirrors.creativecommons.org\u002Fpresskit\u002Fbuttons\u002F88x31\u002Fsvg\u002Fby-sa.svg\" \u002F>\u003C\u002Fa>\n\n- 许可证：[知识共享 署名-相同方式共享 4.0 国际许可协议](LICENSE)","# game-datasets 快速上手指南\n\n`game-datasets` 并非一个需要安装的可执行软件或代码库，而是一个**精选的游戏人工智能（AI）与数据挖掘（DM）资源清单**。它汇集了用于研究、开发和分析的数字游戏数据集、API 接口、开源游戏引擎及相关学术书籍。\n\n本指南将指导开发者如何高效利用该仓库中的资源来构建自己的 AI 应用或数据集。\n\n## 环境准备\n\n由于本项目是资源索引，无需特定的系统环境，但使用其中的具体资源通常需要以下基础开发环境：\n\n*   **操作系统**：Windows, macOS 或 Linux 均可。\n*   **版本控制**：已安装 [Git](https:\u002F\u002Fgit-scm.com\u002F)，用于克隆相关子项目。\n*   **编程语言环境**（根据目标资源选择）：\n    *   **Python** (推荐): 大多数 AI\u002FDM 工具和数据处理脚本基于 Python (需安装 `pip`, `virtualenv`)。\n    *   **Node.js**: 部分 Web 游戏演示或 API 工具可能需要。\n    *   **C++ \u002F Java**: 部分传统游戏引擎或竞赛平台（如 StarCraft AI, Robocode）可能需要。\n*   **数据科学库** (建议预装)：\n    ```bash\n    pip install pandas numpy scikit-learn matplotlib\n    ```\n*   **网络环境**：访问 GitHub、Kaggle 及各游戏厂商开发者官网（如 Blizzard, Riot Games, Steam）。\n    *   *提示*：部分国外 API 或数据集下载可能较慢，建议配置合适的网络代理或使用国内镜像源（如清华源、阿里源）安装 Python 依赖。\n\n## 获取资源\n\n你不需要“安装”此列表，而是需要根据需求**克隆仓库**或**访问链接**获取具体数据。\n\n### 1. 克隆资源索引仓库\n方便在本地浏览所有链接和文档：\n```bash\ngit clone https:\u002F\u002Fgithub.com\u002Fleomaurodesenv\u002Fgame-datasets.git\ncd game-datasets\n```\n\n### 2. 获取具体数据集或工具\n根据 `README` 中的分类（如 **Dataset**, **API**, **Artificial Intelligence**），找到你感兴趣的项目链接。\n\n**示例：获取《英雄联盟》排名赛数据集**\n该项目托管在 Kaggle 上，需前往对应页面下载，或使用 Kaggle CLI（如果已配置）：\n```bash\n# 假设已安装 kaggle-cli 并配置了 token\nkaggle datasets download -d datasnaek\u002Fleague-of-legends\nunzip league-of-legends.zip\n```\n\n**示例：克隆一个 AI 竞赛平台 (如 Malmo\u002FMinecraft)**\n```bash\ngit clone https:\u002F\u002Fgithub.com\u002FMicrosoft\u002Fmalmo.git\ncd malmo\n# 后续需参考该项目自身的 INSTALL.md 进行环境配置\n```\n\n## 基本使用\n\n使用流程通常为：**选择场景 -> 获取数据\u002F引擎 -> 编写分析或训练代码**。\n\n### 场景一：使用现有数据集进行数据分析\n以分析 **Steam 游戏数据** 为例：\n\n1.  **查找资源**：在列表中定位到 `Steam Database` 或 `Steam Spy` 相关条目。\n2.  **获取数据**：访问提供的 GitHub 工具或网站下载 CSV\u002FJSON 数据。\n3.  **编写代码**：使用 Python 进行简单分析。\n\n```python\nimport pandas as pd\n\n# 假设已下载名为 steam_games.csv 的数据集\ndf = pd.read_csv('steam_games.csv')\n\n# 简单示例：查看评分最高的前 5 款游戏\ntop_rated = df.sort_values(by='average_rating', ascending=False).head(5)\nprint(top_rated[['name', 'average_rating']])\n```\n\n### 场景二：搭建 AI 训练环境\n以 **General Video Game AI (GVGAI)** 为例，用于训练通用游戏智能体：\n\n1.  **访问项目**：点击列表中的 [General Video Game AI](http:\u002F\u002Fwww.gvgai.net\u002F) 链接。\n2.  **下载框架**：按照其官方指引下载 Java 框架或 Python 包装器。\n3.  **运行基准测试**：\n    ```bash\n    # 伪代码示例，具体命令需参考 GVGAI 官方文档\n    java -jar gvgai.jar -g zelda -l 0 -p SimpleRandom\n    ```\n\n### 场景三：调用游戏 API 获取实时数据\n以 **PokéAPI** (宝可梦数据) 为例：\n\n1.  **直接调用**：无需安装 SDK，直接使用 HTTP 请求。\n2.  **代码示例**：\n    ```python\n    import requests\n\n    url = \"https:\u002F\u002Fpokeapi.co\u002Fapi\u002Fv2\u002Fpokemon\u002Fditto\"\n    response = requests.get(url)\n\n    if response.status_code == 200:\n        data = response.json()\n        print(f\"Name: {data['name']}, Height: {data['height']}\")\n    ```\n\n## 核心资源分类速查\n\n*   **API 接口**：适合需要实时数据的应用（如战绩查询、游戏推荐系统）。推荐关注 *Riot Games*, *Steam Web API*, *IGDB*。\n*   **Dataset (数据集)**：适合离线训练模型或进行统计分析。推荐关注 *Kaggle* 托管的 *League of Legends*, *Age of Empires 2* 等大规模对局数据。\n*   **Artificial Intelligence (AI 平台)**：适合强化学习研究和算法竞赛。推荐 *Malmo (Minecraft)*, *StarCraft AI*, *ViZDoom*。\n*   **Books (书籍)**：列表底部提供了经典的 AI 与游戏分析教材，建议查阅 *Artificial intelligence: a modern approach* 和 *Game analytics*。\n\n> **注意**：本仓库遵循 **CC-BY-4.0** 协议。在使用具体数据集或工具时，请务必点击对应链接，仔细阅读该项目单独的许可证和使用条款。","一家独立游戏工作室的数据分析师正试图构建一个预测模型，以评估新游戏在 Steam 平台的潜在销量与用户评价趋势。\n\n### 没有 game-datasets 时\n- **数据源分散且难寻**：团队需要在 GitHub、Kaggle 及各个游戏论坛中盲目搜索，花费数周时间才凑齐零散的 CS:GO 或 Dota 2 比赛记录。\n- **接口文档缺失**：即使找到了如 Riot Games 或 Battle.net 的原始 API，也缺乏统一的调用指南和示例代码，导致开发环境配置反复报错。\n- **数据格式不统一**：收集到的数据集结构各异，有的缺少关键字段，有的包含大量噪声，清洗和标准化工作占据了 80% 的项目时间。\n- **合规风险不明**：难以确认某些爬取数据的使用许可，存在侵犯版权或违反服务条款的法律隐患。\n\n### 使用 game-datasets 后\n- **资源一站式获取**：直接通过 game-datasets 索引到经过筛选的高质量数据集，如 OpenDota 的实时比赛数据和 Steam Spy 的销售估算，半天内即可完成数据储备。\n- **开发效率倍增**：利用列表中整理的 API 文档和工具链接（如 IGDB 和 Giant Bomb），快速打通数据管道，无需重复造轮子。\n- **数据质量可控**：采纳库中推荐的成熟数据集，字段定义清晰且经过社区验证，大幅减少了数据预处理的工作量。\n- **授权清晰安心**：每个条目均标注了明确的许可证信息（如 CC-BY-4.0），确保商业分析项目的合法合规性。\n\ngame-datasets 将原本耗时数月的数据筹备期压缩至几天，让团队能专注于核心算法优化而非繁琐的数据搜集。","https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Fleomaurodesenv_game-datasets_435d275f.png","leomaurodesenv","Leonardo Moraes","https:\u002F\u002Foss.gittoolsai.com\u002Favatars\u002Fleomaurodesenv_950ca763.jpg","Principal Machine Learning Engineer | Product Manager | Tutor",null,"Sao Carlos, SP - Brazil","leo.mauro.desenv@gmail.com","https:\u002F\u002Fleomaurodesenv.github.io\u002F","https:\u002F\u002Fgithub.com\u002Fleomaurodesenv",1037,76,"2026-04-17T03:50:35","CC-BY-4.0","","未说明",{"notes":92,"python":90,"dependencies":93},"该项目是一个游戏数据集、工具和资源的精选列表（Awesome List），并非一个需要安装和运行的单一软件工具。它主要包含指向外部 API、开源游戏项目、竞赛平台、书籍和数据集下载链接的引用。因此，没有统一的操作系统、GPU、内存或 Python 版本要求。具体的环境需求取决于用户选择使用列表中哪个特定的子项目或数据集。",[],[16],[96,97,98,99,100,101,102,103,104],"game","dataset","database","games","data-mining","artificial-intelligence","awesome-list","awesome","awesome-game","2026-03-27T02:49:30.150509","2026-04-19T06:02:10.945182",[108,113,118,123,128],{"id":109,"question_zh":110,"answer_zh":111,"source_url":112},41849,"这个仓库接受游戏代码实现或教程类的提交吗？","不接受。该仓库的定位是游戏数据集和相关资源列表，具体的游戏代码实现（如 FNAF 的 Python 逻辑或 Flask 网站搭建）不属于贡献范围，此类 Issue 会被直接关闭。","https:\u002F\u002Fgithub.com\u002Fleomaurodesenv\u002Fgame-datasets\u002Fissues\u002F26",{"id":114,"question_zh":115,"answer_zh":116,"source_url":117},41850,"发现仓库中的外部链接失效或数据集过时该怎么办？","社区成员可以通过开启一个新的 Issue 来报告发现的失效链接或过时数据。维护者鼓励这种做法以保持仓库资源的最新性和有效性，未来也可能引入自动化工作流来定期检查链接状态。","https:\u002F\u002Fgithub.com\u002Fleomaurodesenv\u002Fgame-datasets\u002Fissues\u002F18",{"id":119,"question_zh":120,"answer_zh":121,"source_url":122},41846,"我可以添加付费的市场研究报告到这个仓库吗？","该仓库主要专注于免费使用或开源的项目。虽然付费报告（如 Statista, Newzoo 等）很有价值，但建议将其作为新主题（例如“行业洞察”）单独添加，而不是混入现有的数据集列表中。维护者已提议修改 CONTRIBUTING 文件以容纳此类内容。","https:\u002F\u002Fgithub.com\u002Fleomaurodesenv\u002Fgame-datasets\u002Fissues\u002F7",{"id":124,"question_zh":125,"answer_zh":126,"source_url":127},41847,"如何向仓库贡献新的数据集或 API 资源？","请先阅读仓库根目录下的 `CONTRIBUTING.md` 文件了解贡献指南。确认资源符合要求后，请直接发起一个 Pull Request (PR)，并将新资源添加到 README 中对应的章节（例如 `#related` 部分）。","https:\u002F\u002Fgithub.com\u002Fleomaurodesenv\u002Fgame-datasets\u002Fissues\u002F27",{"id":129,"question_zh":130,"answer_zh":131,"source_url":132},41848,"为什么仓库不重命名为更贴切的名称（如 game-analytics-ai）？","维护者同意仓库内容已超出单纯的数据集范畴，涵盖分析与 AI。但由于该仓库链接已在多处分享和引用，更改名称会导致原有链接失效，因此暂时保留原名。","https:\u002F\u002Fgithub.com\u002Fleomaurodesenv\u002Fgame-datasets\u002Fissues\u002F4",[]]