The dataset provides valuable insights into the demographics and gaming habits of students, capturing various attributes that could be analyzed to uncover meaningful correlations. Each entry in the dataset includes the student's gender, identified as either "Female" or "Male," along with a unique school code that serves as an identifier for each institution. This allows for the categorization and grouping of students based on their respective schools, enabling comparative analyses across different educational institutions.

One of the key aspects covered in the dataset is the student's gaming experience, which includes the number of years they have been playing games. This attribute can indicate whether gaming is a long-term habit or a relatively new activity for the student. Additionally, the dataset records how frequently students engage in gaming, likely measured on a scale from 1 to 5, providing a quantitative representation of their gaming intensity. To further elaborate on gaming engagement, the dataset also tracks the average number of hours a student spends playing games daily. This metric can be crucial in understanding whether extended gaming sessions have an impact on academic performance. Moreover, the dataset distinguishes whether a student actively plays games or not, which can be particularly useful in comparative studies assessing the behaviors of gaming versus non-gaming students.

Beyond gaming habits, the dataset delves into socioeconomic factors by including the annual income of the student's family. This "Parent Revenue" variable can help researchers examine the potential influence of economic background on a student's gaming behavior and academic performance. Additionally, the education levels of both the student's father and mother are recorded, offering insights into whether parental education has any correlation with the student's gaming frequency, academic performance, or gaming choices.

Academic performance is another critical component of this dataset, represented by the "Grade" variable, which provides a measure of the student's academic standing. This information can be instrumental in investigating how gaming habits, parental background, and socioeconomic status contribute to or hinder academic success.

This dataset presents an excellent opportunity for analysis on platforms like Kaggle. Potential research directions could include exploring the relationship between gaming frequency and academic performance, investigating whether students from higher-income families spend more or fewer hours gaming, or analyzing if parental education has any impact on the types of games students play or their duration of play. By leveraging this dataset, researchers can identify trends and generate insights that may inform policies on gaming habits, parental involvement, and educational strategies.

Context

League of Legends is a popular global online game played by millions of players monthly. In the past few years, the League of Legends e-sports industry has shown phenomenal growth. Just recently in 2020, the World Championship finals drew 3.8 million peak viewers! While the e-sports industry still lags behind traditional sports in terms of popularity and viewership, it has shown exponential growth in certain regions with fast-growing economy, such as Vietnam and China, making it a prime target for sponsorship for foreign companies looking to spread brand awareness in these regions.

While the e-sports data industry is also showing gradual growth, there is not much available publicly in terms of published analysis of individual games. This may be due to the fact that the games are fast-changing compared to traditional sports--rules and game stats are frequently and arbitrarily changed by the developers. Nevertheless it is an interesting field for fun researches: hence the reason for many pet projects and graduate-level papers dedicated to this field.

All existing League of Legends games (minus custom games, including ones from competitions) are made available by Riot's API. However, having to request and parse the data for every single relevant game is quite annoying; this dataset intends to save that work for you. To make things (hopefully) easier, I parsed all JSON files returned by Riot API into CSV files, with each row corresponding to one game.

Components

This dataset consists of three parts: root games, root2tail, and tail games.

I found that quite often when trying to predict the outcome of a match prior to its play, the historical matches of a player prior to that game count as an important factor (Hall, 2017). For such purpose, root games contains 1087 games from which tail games branches out.

Tail games contains historical matches of each player for every game in root games. Root2tail maps root games's each player's account ID and that player's controlled champion ID to a list of matches that can be found in tail games.

To simplify the explanation, if you want to access historical matches of a player in root games file, 1. Get player's account ID and the game ID. 2. Load root2tail file. 3. Queue for matching row on account ID and game ID. 4. The corresponding row contains a list of game IDs that can be queued on tail_games files.

Note that root2tail documents most recent 5 matches, or a list of matches played within the past 5 weeks, prior to the game creation date of the corresponding "root game". It also only documents the most recent games by the player played with the same champion he/she played in the "root game". In cases where there is an empty list, it means the player has not played a single match with the same champion within the past 5 weeks.

Content

How was this data collected?

On 2020, December 5th, I fetched the list of current players in Challenger tier, then recursively gathered historical matches of those players to consist root games, so this is the data collection date.

What do the rows and columns of the csv data represent?

Root2tail is self-explanatory. As for the other files, each row represents a single game. The columns are quite confusing, however, as it is a flattened version of a JSON file with nested lists of dictionaries.

I tried to think of the simplest way to make the columns comprehensible, but looking at the original JSON file is most likely the simplest way to understand the structure. Use tools like https://jsonformatter.curiousconcept.com/ to inspect the dummy_league_match.json file.

A very simple explanation: participant.stats._ and participant.timeline._ contains pretty much all match-related statistics of a player during the game.

Also, note that the "accountId" fields use encrypted account IDs which are specific to my API key. If you want to do additional research using player account IDs, you should fetch the match file first and get your own list of player account IDs.

Acknowledgements

The following are great resources I got a lot of help from: 1. https://riot-watcher.readthedocs.io/en/latest/ 2. https://riot-api-libraries.readthedocs.io/en/latest/

These two actually explain everything you need to get started on your own project with Riot API.

The following are links to related projects that could maybe help you get ideas!

1. Kim, Seouk Jun, https://towardsdatascience.com/discussing-the-champion-specific-player-win-rate-factor-in-league-of-legends-match-prediction-3d83d7e50a94 (2020)
2. Huang, Thomas, Kim, David, and Leung, Gregory, https://thomasythuang.github.io/League-Predictor/ (2015)
3. Jiang, Jinhang, https://towardsdatascience.com/lol-match-prediction-using-early-laning-phase-data-machine-learning-4...

Mortal Online player activity dataset from MMO Populations, combining monthly enhanced players and 30-day daily estimates generated from public signals.

Fortnite Player Performance

Understanding Player Performance with Granular Data

By Kristian Reynolds [source]

About this dataset

This dataset contains 88 end-game Fortnite statistics, giving a comprehensive look at player performance over the course of 80 games. Discover the time of day, date, mental state and more that contribute to winning strategies! Measure success across eliminations, assists, revives, accuracy percentage, hits scored and head shots landed. Explore distance traveled and materials gathered or used to gauge efficiency while playing. Examine damage taken versus damage dealt to other players and structures alike. Use this data to reveal peak performance trends in Fortnite gameplay

More Datasets

For more datasets, click here.

Featured Notebooks

• 🚨 Your notebook can be here! 🚨!

How to use the dataset

This dataset is a great resource for analyzing and tracking the performance of Fortnite players. It contains 88 end game stats that provide insights into player performance, such as eliminations, assists and revives. This dataset can help you gain a better understanding of your own performance or another player’s overall effectiveness in the game.

• Analyzing Performance: This dataset can be used to analyze your own or other players’ overall performance in Fortnite across multiple games by looking at statistics like eliminations, assists, revives and head shots (by looking at comparisons between different games).
• Tracking Performance: The dataset also has valuable data that enables you to track any changes in performance over time since it includes data on when the games were played (Date) as well as when they ended (Time of Day). This can be used to measure progress or stagnation in your play over time by comparing different stats like accuracy and distance traveled per game.
• Improving Performance: By combining this data with other information about gear and character builds, one can use this information to look for patterns between successful playstyles across multiple matches or build an optimal loadout for their particular playstyle preferences or intentions see what works best their intended approach

Research Ideas

• Using this dataset to develop player performance indicators that can be used to compare players across games. The indicators can measure each player's ability in terms of eliminations, assists, headshots accuracy and other data points.
• Establishing correlations between the mental state and performance level of a player by analyzing how their stats vary before and after playing under different mental states.
• Analyzing the relationship between overall game performance (such as placement) and specific statistics (such as materials gathered or damage taken). This could provide useful insights into what aspects of gameplay are more important for high-level play in Fortnite

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

License: Dataset copyright by authors - You are free to: - Share - copy and redistribute the material in any medium or format for any purpose, even commercially. - Adapt - remix, transform, and build upon the material for any purpose, even commercially. - You must: - Give appropriate credit - Provide a link to the license, and indicate if changes were made. - ShareAlike - You must distribute your contributions under the same license as the original. - Keep intact - all notices that refer to this license, including copyright notices.

Columns

File: Fortnite Statistics.csv | Column name | Description | |:-------------------------|:--------------------------------------------------------------| | Date | Date of the game. (Date) | | Time of Day | Time of day the game was played. (Time) | | Placed | Player's placement in the game. (Integer) | | Mental State | Player's mental state during the game. (String) | | Eliminations | Number of eliminations the player achieved. (Integer) | | Assists | Number of assists the player achieved. (Integer) | | Revives | Number of revives the player achieved. (Integer) | | Accuracy | Player's accuracy in the game. (Float) | | Hits ...

General video gaming use among the U.S. population increased significantly during the COVID-19 pandemic. Between May and December 2020, U.S. teens aged 15 to 19 years spent an average of 112.8 daily minutes on playing games and using computers for leisure, up from 73.8 minutes per day in the corresponding period of 2019. In 2024, the daily time spent on such activities among this age group decreased to 78.6 minutes per day.

Destiny 2 player activity dataset from MMO Populations, combining monthly enhanced players and 30-day daily estimates generated from public signals.

This dataset was inspired by another Kaggle dataset: Popular Video Games 1980 - 2023 🎮, acting as a comprehensive collection of information about some of the most popular video games released. It serves as a valuable resource for researchers, gamers, and enthusiasts interested in exploring the evolution of the gaming industry over the past decades.

This dataset provides a wealth of information about each game, including its title, release date, genre, platform(s), summary, among other data. With this dataset, users can analyze trends, identify patterns, and gain insights into the popularity and commercial success of video games across different platforms and genres.

The difference between this dataset and its inspiration is that the amount of data is greater (60k rows), in addition to having the new column "Platforms", indicating which platforms the game was released on. Also, the "Reviews" column (with the texts of user reviews) has been removed, keeping only the column with the total number of reviews that the game has.

📅 - The data was collected in mid-June 2023, so some values may be slightly different today.

Cross-sectional survey of individuals aged 16–25 residing in Spain (n = 1,000), fielded in June 2025. The dataset captures demographics, gaming practices, exposure to gamblified mechanics (e.g., loot boxes, randomized rewards, time-limited offers), spending bands, emotional responses, and perceptions among non-players.The survey questionnaire (available as related material) consists of three sections: 1) sociodemographic data; 2) gamers (n=927); and 3) non-gamers (n=73). To distinguish between gamers and non-gamers, a filter question was placed at the end of section 1 (“Do you regularly play video games, mobile games, or digital games?”).The first section collects information on: sex/gender, municipality of residence, province/community of residence according to Nielsen area, size of municipality of residence, level of education completed, and current employment status.Block two collects information on estimated weekly hours of play, the device usually used, estimated monthly money spent on video games, and the two most played games. It also includes five-point Likert scale questions (disagree/agree) on issues related to random mechanisms, payments, or gaming habits.The third section repeats the same pattern of five-point Likert scale questions (disagree/agree) but from the perspective of non-video game players.

A survey conducted in the second quarter of 2025 found that around 91.5 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 82 percent of internet users were gamers.

Here are a few use cases for this project:

1. Sports Analytics: Use the "Basketball Players" model to automatically track players' movements, ball possession, and referee decisions during live games or post-game analysis. This data can be used by coaches, analysts, and teams to inform and improve strategies, tactics, and player performance.

2. Real-time Game Commentary: Integrate the model into sports broadcasting platforms, providing real-time updates and statistics to commentators, allowing them to focus on in-depth analysis and storytelling while the model handles identification and stat-tracking.

3. Automated Sports Highlights: Utilize the model to automatically create highlights from basketball games by identifying key moments, such as successful shots, blocks, and referee decisions. This can streamline post-production process for sports media outlets and social media channels.

4. Training and Skill Development: Leverage the "Basketball Players" model to create feedback tools for players, identifying areas of improvement in team dynamics and individual technique during practice sessions or games.

5. Fan Experience: Employ the model in smartphone apps or AR devices, providing fans with real-time information on their favorite teams and players during live games, enhancing their overall experience and engagement.

This data set has 2 other notebooks, One for collecting data: https://www.kaggle.com/code/makspl/collecting-data-script Another for analysing and modelling: https://www.kaggle.com/code/makspl/eda-modelling?scriptVersionId=161968252

Data collected used Riot Games public APi, I created a function which made API calls and formated player statistics into a dictionary which became a row in a csv file.
Around 2200 unique players, with around 300 players from each rank.

ABOUT DATA SET - summonerName - player username - summonerLevel - Experience accumilated across multiple lol games (different to in game champion level(think of this as total time spent playing games)) - rank - Leader board system which seperates players into different brackets, indicator of skill - wins - games won out of 25 recent - losses - games lost out of 25 recent games - winRate - number of wins divided by total games played (25) - kills - average kills aquired over past 25 games - deaths - avg deaths aquired over past 25 games - assists - avg number of people this player helped to kill - prefLane - most played lane out of the 25 (ADC and SUPPORT play together in the bottom lane) - campsKilled - jungle minions killed - minionsKilled - lane minions killed - goldEarned - avg of gold accumilated in each game - turretTakedowns - avg number of towers the player has destroyed (not total towers destroyed in a game) - visionScore - avg point system revolving around revealing hidden areas of map and destroying enemy vision wards - dragonKills - avg number of dragons the player has killed (killing dragons lends the team extra buffs such as more damage or health) - longestTimeSpentLiving - time in seconds - totalDamageDealt - avg of total damage dealt to enemy players - totalDamageTaken - avg of total damage taken from enemy players - gameDuration - avg time spent playing a single match in minutes - gameStart - avg time in hours where the player will start playing games, (eg 15.06 == 15:04)

People play Internet games for a variety of reasons but the motivations and outcomes of this gameplay are not always 'healthy'. Utilising a mixed methods approach, this research will help to better understand the pathways to problematic and non-problematic gameplay. With the aim of developing a conceptualisation of 'healthy gaming', this research will help to prevent negative outcomes and promote adaptive engagement with Internet games and help to refine public health agendas and, in turn, this will help to avoid the stigmatisation of non-problematic Internet gameplay.

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.

In July 2025, total video games sales in the United States amounted to **** billion U.S. dollars, representing a five percent year-over-year increase. 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 **** 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 ** percent of the revenue of the games market worldwide, ahead of both console games and downloaded or boxed PC games.

This dataset provides granular records of how players unlock achievements across various games, capturing player progression, session context, and monetization events. It is designed to help game designers analyze engagement patterns, optimize achievement systems, and correlate player actions with monetization opportunities for improved game design and revenue strategies.

OAN helps you reach gamers across the world. Our gaming audience data offers categorized audience segments into gamer behavior and gaming trends. This powerful dataset provides a deep understanding of the gaming industry by delivering unique categories such as: demography, interest, hardware, spenders, genres and titles, e-sports fans and players.

By understanding this data, businesses can make data-driven decisions to optimize their marketing strategies, game development, and monetization efforts.

The Gaming Taxonomy contains a broad scope of Gaming related topics, based on the user's browser and mobile app activity through the last 30 days. There are also gamer audiences categorized by specific Hardware Products and Brands, based on the Intent of these devices' purchase. Furthermore, we offer segments for: - Virtual Reality - Interest in Gaming Subscriptions - Payments - Micropayments - Devices and Platforms.

We also cover the area of E-sports Enthusiasts and Fandoms Members. In spirit of looking beyond simple game genres, we categorize Games according to their Themes (e.g. Historical), which are definitely important aspects of user experience and purchase decisions. Since Mobile Gaming is a very important part of the Gaming Industry, we distinct special Mobile Gaming segments, which are analogous to the ordinary Gaming segments, with additional categorizations of the Telecommunication Network Providers.

Gaming audience data is just a part of all audience data we provide. We deliver millions of users’ profiles gathered globally and grouped into IAB-compliant segments. You can choose which target groups you want to reach. Contact us to check all the possibilities: team@oan.pl

How you can use our data?

There are two main areas where you can use our data: - Marketers - targeting online campaigns With our high-quality audience data, you can easily reach specific audiences across the world in programmatic campaigns. Show them personalized ads adjusted to their specific profiles. - Ad tech companies Enriching 1st party data or using our raw data by your own data science team.

The EVIDENT serious game explores consumer behaviour in response to a malfunctioning home appliance. Specifically, it examines how consumers approach decisions to repair or replace a broken home appliance and the impact of behavioural biases on these decisions. There are two key aims addressed within the EVIDENT serious game. 1) Determine the impact of socio-demographic factors, environmental literacy, and financial literacy on consumer willingness to pay for the repair of home appliances. 2) Determine the impact of information and education mediated through a serious game on consumer in-game and real-world repair/replace decision-making.

The serious game itself is a life-simulation game in which users are tasked with maintaining their virtual home while ensuring their avatar remains comfortable (i.e. basic needs such as hunger, warmth and hygiene are met) while monitoring their financial and energy consumption. Within this game, users learn that an appliance has malfunctioned, and a repairperson is called. Users must then determine how best to proceed by entering a negotiation with the repairperson.

The experiment consists of the following sections: 1) demographic information; 2) financial literacy; 3) environmental literacy; 4) serious game. The game receives as input the replies of the participant on the demographics information section to provide a personalized gameplay experience. Replies regarding participant's age ("What is your age?"), role ("Which of the following apply to you?"), income ("What is your household's annual income?"), gender ("Which character would you like to play with?") and family status ("How many people live in your home (including you) - Children") will be used to adjust players' avatar, starting amount of money, size of the house, age of the player and the negotiation process with the repair person.

The negotiation process differs based on the participants' role ("Which of the following apply to you?"). In this question, the participant can choose one of the following replies: 1) I am a homeowner, 2) I am a tenant (i.e. I pay someone to rent my accommodation), 3) I am a landlord (i.e. I receive payment for accommodation from someone else). Participants who rent (2) or are landlords (3) will be assigned to an additional in-game scenario to explore the unique context in which their energy decisions are made. Random allocation to a role will be applied for participants who select multiple options (i.e., homeowners who are also landlords).

More information on the EVIDENT Serious Game Experiment can be found on the public deliverables of the EVIDENT project https://evident-h2020.eu/deliverables/. More specifically, the serious game implementation design is described in deliverable D2.3 Serious game implementation design, the design of the experiment is reported in D2.2 Optimised Protocols Design, and the experiment preparatory actions are described in D3.1 Specifications of preparatory actions for RCT, surveys and serious game and D3.2 Implementation of preparatory actions for RCT, surveys and serious game.

Finally, the EVIDENT serious game can be found in the following locations:

EVIDENT Website: https://evident-h2020.eu/seriousgame

Google Play: https://play.google.com/store/apps/details?id=com.CERTH.EvidentSeriousGame

App Store: https://apps.apple.com/gr/app/evident-serious-game/id6447255106

EVIDENT Platform (participation in the experiment): https://platform.evident-h2020.eu/sessions/participate_session/1560d6e6-732a-470c-807a-c70472d51c53

LLMafia - Asynchronous LLM Agent

Our Mafia game dataset of an Asynchronous LLM Agent playing games of Mafia with multiple human players.

🌐 Project | 📃 Paper | 💻 Code

A virtual game of Mafia, played by human players and an LLM agent player. The agent integrates in the asynchronous group conversation by constantly simulating the decision to send a message.

Time to Talk: 🕵️‍♂️ LLM Agents for Asynchronous Group Communication in Mafia Games Niv Eckhaus, Uri Berger, Gabriel… See the full description on the dataset page: https://huggingface.co/datasets/niveck/LLMafia.

What does this contain?

This is a dataset contains whatever information that was scrapable, regarding some 80000 games from the Steam official website. Most of the columns contains valuable information that could give a better insight of the game, but cleaning them is required for most of them. make sure to refer the column descriptors for more information. I have not included all reviews and reviews related time series data for the games in this dataset because, it constantly keeps changing and it can be easily fetched using the steam api.

Checkout this Dashboard Webapp made by Raphael Guyot using this dataset

Review related time series can be fetched from this Steam API Link: https://store.steampowered.com/appreviewhistogram/945360 User reviews data can be fetched from this Steam API Link: https://store.steampowered.com/appreviews/945360?json=1

Here, replace 945360 with the Game ID (available inside the url of the game) of the game for which you are going to fetch the data.

Clean Data in JSON Format has been uploaded :smile: Both JSON and CSV files contains same data, but in JSON one it has been cleaned, so while downloading, choose any one of the filetype Refer Metadata for scripts used for Scraping

JSON Sample data

{'img_url': 'https://steamcdn-a.akamaihd.net/steam/apps/730/header.jpg?t=1592263625',
 'date': 'Aug 21, 2012',
 'developer': 'Valve, Hidden Path Entertainment',
 'publisher': 'Valve',
 'full_desc': {'sort': 'game',
 'desc': 'About This Game Counter-Strike: Global Offensive (CS: GO) expands upon the team-based action gameplay that it pioneered when it was launched 19 years ago.CS: GO features new maps, characters, weapons, and game modes, and delivers updated versions of the classic CS content (de_dust2, etc.)."Counter-Strike took the gaming industry by surprise when the unlikely MOD became the most played online PC action game in the world almost immediately after its release in August 1999," said Doug Lombardi at Valve. "For the past 12 years, it has continued to be one of the most-played games in the world, headline competitive gaming tournaments and selling over 25 million units worldwide across the franchise. CS: GO promises to expand on CS\' award-winning gameplay and deliver it to gamers on the PC as well as the next gen consoles and the Mac."'},
 'requirements': {'minimum': {'windows': {'processor': ' Intel® Core™ 2 Duo E6600 or AMD Phenom™ X3 8750 processor or better',
  'memory': ' 2 GB RAM',
  'graphics': ' Video card must be 256 MB or more and should be a ',
  'os': ' Windows® 7/Vista/XP'},
  'linux': {'processor': ' 64-bit Dual core from Intel or AMD at 2.8 GHz',
  'memory': ' 4 GB RAM',
  'graphics': ' nVidia GeForce 8600/9600GT, ATI/AMD Radeon HD2600/3600 (Graphic Drivers: nVidia 310, AMD 12.11), OpenGL 2.1',
  'os': ' Ubuntu 12.04'}},
 'recommended': {}},
 'popu_tags': ['Shooter',
 'Multiplayer',
 'Competitive',
 'Action',
 'Team-',
 'Basede',
 'Sports',
 'Tactical',
 'First-',
 'Person',
 'Online',
 'Strategy',
 'Military',
 'Difficult',
 'Trading',
 'Realistic',
 'Fast-',
 'Paced',
 'Moddable+'],
 'price': 'free',
 'url_info': {'url': 'https://store.steampowered.com/app/730/CounterStrike_Global_Offensive/?snr=1_7_7_230_150_1',
 'id': '730',
 'type': 'app',
 'url_name': 'CounterStrike Global Offensive'},
 'name': 'Counter-Strike: Global Offensive',
 'categories': ['Steam Achievements Full',
 'controller supportSteam',
 'Trading Cards Steam',
 'Workshop In-App Purchases Valve',
 'Anti-Cheat enabledStats Remote',
 'Play on',
 'Phone Remote Play',
 'on Tablet Remote',
 'Play on']}

What can be done with this?

I have divided the dataset into 2 parts, one with information based on numbers and other based on text. One can use the number based dataset to practice data cleaning procedures and regrexes, after cleaning, procedures like EDA, feature selection, clustering and many more things can be done. Text based dataset can be used in NLP projects.

Acknowledgements

I would like to thank Hariharan.S.V and Shankar Narayanan for helping me with the data scraping and data cleaning.

This dataset consists of games with purposes other than just entertainment (serious games, role-play games, social simulations, computer simulations, browser games, board games, etc.) incorporating concepts of urban planning and Ecosystem Services (ES)/Nature-based Solutions (NbS). The games, termed ‘Urban Nature Games’, were developed by universities, researchers, NGOs, or other credible organizations, and were available in English. They were compiled from multiple sources (the last additions were made in May 2023). The full database comprises 69 games (sheet “All 69 Games described” in the .xls file), that were rated of high to low relevance to urban planning and ES/NbS, according to defined criteria. More extensive game assessments were conducted for Urban Nature Games of medium to the highest relevance (sheet “Ratings 37 High + Medium Games” in the .xls file), according to defined dimensions of assessment. High-relevance games were also assessed in terms of the urban planning processes they relate to and the Nature-based Solutions they exemplify (sheet “Categorized 22 High-relev Games” in the .xls file). The database can be consulted for game selection under different circumstances and needs of educators, practitioners, and researchers interested in incorporating Nature-based Solutions in urban planning. Practical considerations such as distribution, the number of players, costs, or duration of gameplay (available in the .xls sheet “All 69 Games described”), often represent constraints that pragmatically help select suitable games. The database can also be further developed with games that may be released in the future or are available in languages other than English.

Specific abbreviations used in the dataset: ES = ecosystem services; NbS = nature-based solutions; UP = urban planning; NN = Nature for Nature; NS = Nature for Society; NC = Nature as Culture;