This dataset includes a list of the highest-grossing mobile games of all time that have grossed at least $1 billion in revenue. The dataset covers all mobile platforms and includes information such as the game's rank, name, publisher, platform, year of release, and global sales.

Database of Characters in Mobile Games

All the character in the following games are supported:

Arknights (crawled from https://prts.wiki) Fate/Grand Order (crawled from https://fgo.wiki) Azur Lane (crawled from https://wiki.biligame.com/blhx) Girls' Front-Line (crawled from https://iopwiki.com/) Genshin Impact (crawled from https://genshin-impact.fandom.com/ja/wiki/%E5%8E%9F%E7%A5%9E_Wiki)

The source code and python library is hosted on narugo1992/gchar, and the scheduled job is… See the full description on the dataset page: https://huggingface.co/datasets/deepghs/game_characters.

Complete dataset used in the research study on Exploring the Relationship Between Mobile Games and the Development of Spatial Skills by Dr. Martha Perry

Complete dataset used in the research study on Privacy Concerns in Mobile Games: The Rise of Data Collection Practices by Dr. Sandra Scott

Complete dataset used in the research study on Mobile Games and the Gamification of Healthcare by Dr. Jeffrey Reed

In February 2025, Block Blast! was the most-downloaded gaming app in the Google Play Store worldwide. The casual game generated more than 19.6 million downloads from Android users. Roblox was the second-most popular gaming app title with approximately 12.5 million downloads from global users.

Complete dataset used in the research study on How Mobile Games Engage Players in Ethical Decision-Making by Dr. Kenneth Nelson

In March 2025, total video games sales in the United States amounted to 4.69 billion U.S. dollars, representing a six percent year-over-year decrease. Generally speaking, the video game industry has its most important months in November and December, as video game software and hardware make very popular Christmas gifts. In December 2024, total U.S. video game sales surpassed 7.54 billion U.S. dollars. Birth of the video game industry Although the largest regional market in terms of sales, as well as number of gamers, is Asia Pacific, the United States is also an important player within the global video games industry. In fact, many consider the United States as the birthplace of gaming as we know it today, fueled by the arcade game fever in the ’60s and the introduction of the first personal computers and home gaming consoles in the ‘70s. Furthermore, the children of those eras are the game developers and game players of today, the ones who have driven the movement for better software solutions, better graphics, better sound and more advanced interaction not only for video games, but also for computers and communication technologies of today. An ever-changing market However, the video game industry in the United States is not only growing, it is also changing in many ways. Due to increased internet accessibility and development of technologies, more and more players are switching from single-player console or PC video games towards multiplayer games, as well as social networking games and last, but not least, mobile games, which are gaining tremendous popularity around the world. This can be evidenced in the fact that mobile games accounted for 51 percent of the revenue of the games market worldwide, ahead of both console games and downloaded or boxed PC games.

Complete dataset used in the research study on Mobile Games and Their Role in Raising Awareness About Social Issues by Dr. Kevin Stewart

The Evolution of a Global Phenomenon

The gaming industry is more than just a form of entertainment—it’s a cultural juggernaut that reflects the pulse of innovation, creativity, and community in the digital era. Gaming trends encapsulate the evolution of platforms, genres, player behavior, and industry economics, offering a window into how the industry adapts and thrives in a constantly changing landscape.

Key Trends Shaping the Gaming World

Platform Ecosystems and the Battle for Dominance: The gaming ecosystem is a three-way battlefield between consoles, PCs, and mobile devices, each with its own loyal fanbase. While traditional consoles like PlayStation and Xbox thrive on exclusivity, PC gaming dominates the competitive and modding scenes. Meanwhile, mobile gaming’s meteoric rise is redefining accessibility, offering high-quality gaming experiences on the go. Cloud gaming platforms like Stadia and GeForce Now are further blurring these lines, creating a unified yet competitive space.

Genre Renaissance and Niche Revolution: Genres like Action, Adventure, and RPGs remain staples of the industry, but niche categories like Simulation, Survival, and Indie Platformers are thriving, often backed by passionate communities. The rise of sub-genres, such as Souls-like or Roguelikes, highlights players' demand for unique and challenging experiences. Battle Royale, an overnight sensation, continues to evolve, proving how a single genre can dominate global gaming culture.

User Feedback as the Voice of Gaming Culture: In a world where Metacritic scores, Steam reviews, and community forums hold immense power, user ratings are no longer just numbers—they’re the currency of credibility. Developers are increasingly engaging with their player base through iterative updates, live-service models, and fan-driven content, shaping games into living, breathing experiences.

Geographical Influences on Gaming Styles: Gaming culture varies significantly across regions, with Asia pioneering mobile gaming and competitive esports scenes, North America leading AAA production, and Europe fostering indie innovation. Cultural influences can be seen in game design—Japan’s intricate storytelling (e.g., JRPGs), the West’s open-world epics, and China’s mobile-first dominance are reshaping global trends.

The Economics of Creativity: The contrast between indie darlings and blockbuster AAA games tells a fascinating story of budgets and creativity. While high-budget games like Elden Ring or Cyberpunk 2077 push graphical and narrative boundaries, indie hits like Hollow Knight or Among Us show that clever gameplay and community engagement often outshine expensive production values. Microtransactions, season passes, and in-game economies continue to drive revenue models in this live-service era.

Immersive Technology and the Future of Gaming: Cutting-edge technologies like VR, AR, and haptics are redefining immersion, making players feel like they’re truly in the game world. With the promise of metaverse gaming, persistent virtual worlds with real economies and player-driven stories are just on the horizon.

Esports, Streaming, and the Social Layer: Gaming has transcended solo entertainment to become a massive spectator sport, with platforms like Twitch and YouTube Gaming hosting millions of viewers daily. Esports tournaments for titles like League of Legends, Valorant, and Dota 2 bring gaming communities together, while individual streamers influence trends, game launches, and even patch updates.

Why Gaming Trends Matter in the Culture of Play Shaping Player Communities: Gaming is no longer a solitary activity; it’s a shared culture. Understanding trends helps developers and publishers connect with their audience on a deeper level. Driving Industry Innovation: Trends guide how the industry reinvents itself, with players demanding fresh experiences, cross-platform connectivity, and more social interaction. Defining the Future of Digital Entertainment: Gaming’s blend of technology, storytelling, and community has placed it at the forefront of digital culture, making it an industry to watch for groundbreaking innovation. Gaming trends are more than market insights—they’re a reflection of how people play, compete, and connect in the modern world. The industry's evolution is a testament to its ability to adapt to shifting player expectations while remaining a cornerstone of global culture.

Explanation of Dataset Variables

1. Date: Represents the date of the game's release.
2. Platform: Indicates the gaming platform, such as PC, Console, Mobile, or VR.
3. Daily Active Users (DAU): The number of users actively playing the game daily.
4. New Registrations: The number of new users who registered to play the game.
5. Session Duration (minutes): The average time (in minutes) that players spend in a single gaming session.
6. In-game Pu...

Complete dataset used in the research study on How Hyper-Casual Mobile Games Dominate the App Store: A Market Analysis by Dr. Ruth Wood

Welcome to the ultimate Android vs iOS battle with this Smartphone SoC dataset!

Includes three .csv files. Any analysis is appreciated, even if it is a short one 😎

Context

Benchmarks allow for easy comparison between multiple devices by scoring their performance on a standardized series of tests, and they are useful in many instances: When buying a new phone or tablet

Content

smartphone cpu_stats.csv is the main data. Updated performance rating of smartphone SoCs as of 2022. Includes summary of Geekbench 5 and AnTuTu v9 scores. Includes CPU specs such as clock speed, core count, core config, and GPU.

ML ALL_benchmarks.csv is the Geekbench ML Benchmark data. This tells you how well each smartphone device performs when performing Machine Learning tasks. The data is gathered from user-submitted Geekbench ML results from the Geekbench Browser. To make sure the results accurately reflect the average performance of each device, the dataset only includes devices with at least five unique results in the Geekbench Browser.

antutu android vs ios_v4.csv is the AnTuTu benchmarks data. It includes information about CPU, GPU, MEM, UX and Total score.

Antutu Benchmarks

1. Total Score

Benchmark apps gives your device an overall numerical score as well as individual scores for each test it performs. The overall score is created by adding the results of those individual scores. These score numbers don't mean much on their own, they're just helpful for comparing different devices. For example, if your device's score is 300000, a device with a score of 600000 is about twice as fast. You can use individual test scores to compare the relative performance of specific parts of different devices. For example, you could compare how fast your phone's storage performs compared to another phone's storage.

2. CPU Score

The first part of the overall score is your CPU score. The CPU score in turn includes the output of CPU Mathematical Operations, CPU Common Algorithms, and CPU Multi-Core. In simpler words, the CPU score means how fast your phone processes commands. Your device's central processing unit (CPU) does most of the number-crunching. A faster CPU can run apps faster, so everything on your device will seem faster. Of course, once you get to a certain point, CPU speed won't affect performance much. However, a faster CPU may still help when running more demanding applications, such as high-end games.

3. GPU Score

The second part of the overall score is your GPU score. This score is comprised of the output of graphical components like Metal, OpenGL or Vulkan, depending on your device. The GPU score means how well your phone displays 2D and 3D graphics. Your device's graphics processing unit (GPU) handles accelerated graphics. When you play a game, your GPU kicks into gear and renders the 3D graphics or accelerates the shiny 2D graphics. Many interface animations and other transitions also use the GPU. The GPU is optimized for these sorts of graphics operations. The CPU could perform them, but it's more general-purpose and would take more time and battery power. You can say that your GPU does the graphics number-crunching, so a higher score here is better.

4. MEM score

The third part of the overall score is your MEM score. The MEM score includes the results of the output of RAM Access, ROM APP IO, ROM Sequential Read and Write, and ROM Random Access. In simpler words, the MEM score means how fast and how much memory your phone possesses. RAM stands for random-access memory; while ROM stands for read-only memory. Your device uses RAM as working memory, while flash storage or an internal SD card is used for long-term storage. The faster it can write to and read data from its RAM, the faster your device will perform. Your RAM is constantly being used on your device, whatever you're doing. While RAM is volatile in nature, ROM is its opposite. RAM mostly stores temporary data, while ROM is used to store permanent data like the firmware of your phone. Both the RAM and ROM make up the memory of your phone, helping it to perform tasks efficiently.

5. UX Score

The fourth and final part of the overall score is your UX score. The UX score is made up of the results of the output of the Data Security, Data Processing, Image Processing, User Experience, and Video CTS and Decode tests. The UX score means an overall score that represents how the device's "user experience" will be in the real world. It's a number you can look at to get a feel for a device's overall performance without digging into the above benchmarks or relying too much on the overall score.

Acknowledgements

Sourced from Geekbench and AnTuTu.

If you enjoyed this dataset, here's some similar datasets you may like 😎

Content

Scraped from 'https://pokemondb.net/pokedex/national', this dataset includes a CSV file containing some features of all 893 pokemons up to generation 8, including the pokemon's national number, name, type(s), type defense effectiveness multiplier, and an image folder.

Inspiration

The CSV file as well as the picture folder will come up in a later project and serve as a database for a mobile/web application designed to provide new pokemon players with type advantages during pokemon battles using intuitive UI.

Future developments

Pokemon type prediction through image processing (predicting the type(s) of a pokemon through its picture by color groups, wings/no wings, etc). An unbiased tier list based on type advantages between all pokemons (for example: Bulbasaur is a grass-type pokemon, Squirtle is a water-type pokemon. Since grass is strong against water, Bulbasaur's TypeAdvantageScore is increased by 1, while Squirtle's TypeDisadvantageScore is increased by 1).

Acknowledgements

Data were retrieved from 'https://pokemondb.net/pokedex/national' using Python's BeautifulSoup package.

Here are a few use cases for this project:

1. Sports Analytics: The model could be used to analyze team strategies and players' performance in basketball games, by identifying times when various players possess the basketball.

2. Activity Tracking: Players, coaches, and fitness professionals could use this model to track training sessions, observing how often individuals possess the ball, trajectory of the ball, and interactions between players.

3. Video Production: Sports broadcasters could use the model during live coverage or replay analysis. It can automatically highlight the person with the ball, making it easier for audience to follow the game.

4. Surveillance and Security: The model can monitor public basketball courts to ensure users' safety or to track usage statistics for park and recreation planning.

5. Interactive Games and Apps: The model could be used to design mobile apps or video games that allow users to virtually 'play' basketball, identifying when the user or another player has the 'ball'.

The dataset was carefully structured to encompass a broad range of demographic variables, such as age, gender, and occupation, which are critical for examining variations in responses to astroturfing among different social media user groups. Participants shared detailed insights into their mobile operating systems, usage habits, and satisfaction levels, offering valuable data for evaluating their digital engagement and technological proficiency—key factors influencing susceptibility to astroturfing. User preferences for various mobile operating system features were systematically measured using a 5-point Likert scale, ranging from ‘Very Poor’ (1) to ‘Excellent’ (5), assessing aspects such as graphical user interfaces and brand trust. This comprehensive dataset facilitated advanced modeling to detect patterns and shifts in public opinion resulting from astroturfing exposure, shedding light on its impact on user perception and behavior. Table 1 presents the demographic distribution and additional dataset details. Feature Description Unique Values Start Time of Form Fill Records the exact date and time a user begins filling out the survey. [Various timestamps] Timestamp of Form Submission Records the exact date and time each participant completed the survey, enabling analysis of responses over time. [Various timestamps] Email Address Used to uniquely identify respondents, ensuring that each participant could only submit the survey once. [Unique per respondent] Age Categorised into groups such as 18-24, 25-34, 35-44, and 45-54 years old, capturing demographic variations in mobile operating system preferences. ['18-24', '25-34', '35-44', '45-54'] Gender It includes diverse gender identities and shows preferences or tendencies associated with different genders. ['Male', 'Female'] Occupation Identifies the respondent's job role, providing insights into how professional life influences mobile operating system choices. ['Self-employed,' 'Student,' 'Professional,' 'Other'] Which mobile operating system do you currently use? Determines the current mobile OS of respondents, segmenting data for OS-specific analysis. ['Arnoid OS,' 'Orange,' 'Other'] How long have you been using your current mobile operating system? Measures the duration of usage to correlate with loyalty and user satisfaction. ['Less than 1 year', '1-2 years', '3-5 years', 'More than 5 years'] How often do you upgrade your mobile device? Indicates the frequency of hardware updates, which can impact software preferences and user experiences. ['Every year, 'Every 2 years', 'Less frequently'] Rate your overall satisfaction with your current mobile operating system Quantifies user contentment, providing a direct measure of OS success among users. Ratings from 1 to 5 What do you like most about your current mobile operating system? Collects qualitative data on user preferences, highlighting strengths of different OSes. [Various text responses] What do you dislike most about your current mobile operating system? Gathers user criticisms, which can guide potential areas for OS improvements. [Various text responses] Graphical User Interface Evaluates user ratings on the visual design and intuitiveness of their OS's interface. Ratings from 1 to 5 Ease of Use and Accessibility Features Assesses how user-friendly and accessible the OS is, which can be critical for adoption rates. Ratings from 1 to 5 Performance & Speed It rates the operating system's efficiency and responsiveness, which are important factors for user satisfaction. Ratings from 1 to 5 Audio Quality Surveys user satisfaction with the sound quality provided by their device's operating system. Ratings from 1 to 5 Camera Quality Assesses satisfaction with the camera performance, a significant feature for many users. Ratings from 1 to 5 Battery Life and Charging Speed Evaluates user satisfaction with their device's battery endurance and recharge rate. Ratings from 1 to 5 Best Value for Money Determines user perception of their OS's cost-effectiveness, which can influence purchasing decisions. Ratings from 1 to 5 Customer Support and Services Rates the quality and responsiveness of customer service provided by the OS brand. Ratings from 1 to 5 Screen Resolution and Quality Gauges user satisfaction with their device's display clarity and quality. Ratings from 1 to 5 Gaming Performance Assesses how well the operating system handles games, an important aspect for many users. Ratings from 1 to 5 Privacy & Security Evaluates user ratings on their OS's ability to protect personal information and prevent security breaches. Ratings from 1 to 5 Storage Options and Expandability Rates how well the OS manages storage and the ease of expanding it. Ratings from 1 to 5 Durability and Resistance to Damage (e.g., Water Resistance, Drop Protection) Surveys how users perceive their device's robustness, including resistance to environmental factors like water and drops. Ratings from 1 to 5 Offers the Best Integration...

Dataset Description: The AI-Enhanced English and Chinese Language Learning Dataset is a comprehensive collection of data aimed at advancing language education through the use of artificial intelligence. This dataset includes detailed records from various language learning platforms, combining both traditional classroom activities and AI-driven learning applications. The dataset is suitable for exploring different AI techniques to improve English and Chinese language acquisition, focusing on adaptive learning, feedback analysis, and language practice. Data spans from February 2019 to August 2024, covering diverse language learning scenarios across multiple institutions, including digital language labs, mobile apps, and AI-powered tutoring systems.

The dataset includes hourly data collected from language learners engaging in various activities such as grammar exercises, conversational practice, writing assessments, and interactive quizzes. The data is sourced from multiple regions, including English-speaking and Mandarin-speaking communities, making it ideal for comparative studies on AI-driven learning outcomes. The records encompass a variety of linguistic features and learning metrics, offering valuable insights into student engagement, progress, and performance across different learning contexts.

Features: Timestamp: Hourly timestamp indicating the time of each learning session. Learner ID: A unique identifier for each learner. Age: The age of the learner. Gender: Gender of the learner (Male, Female, Other). Native Language: The primary language spoken by the learner. Country of Residence: The country where the learner is based. Language Proficiency Level (Initial): The learner's initial language proficiency in English or Chinese (Beginner, Intermediate, Advanced). Type of Activity: Type of learning activity (Listening, Speaking, Reading, Writing). Lesson Content Type: The specific focus of the lesson (Grammar, Vocabulary, Pronunciation, etc.). Number of Lessons Completed: Cumulative count of lessons completed by the learner. Time Spent on Learning: Total time spent on language learning (in minutes). Learning Platform or Tool Used: Platform or tool used for learning (App, Website, Classroom Software). Homework Completion Rate: Percentage of homework assignments completed. Participation in Interactive Exercises: Frequency of participation in interactive exercises like quizzes and games. Frequency of Practice Sessions: Number of practice sessions per week. Test Scores: Scores from language proficiency tests, covering various areas such as grammar, listening, and vocabulary. Speaking Fluency Scores: Scores evaluating pronunciation accuracy and speech rate. Reading Comprehension Scores: Assessment scores for reading comprehension tasks. Writing Quality: Evaluation of writing quality based on grammatical accuracy and vocabulary use. Change in Proficiency Level: Measured change in language proficiency over time. Assignment Grades: Grades received on language assignments. Error Correction Rate: The rate at which learners correct their mistakes. Feedback from Instructors/Tutors: Qualitative feedback provided by instructors or AI tutors. Study Session Duration: Average duration of study sessions. Learning Consistency: Number of days per week studied. User Activity Type: Type of user activity (Active or Passive Participation). Engagement with Additional Learning Materials: Frequency of accessing extra learning resources (e.g., videos, articles). Peer Interaction Score: Score representing participation in study groups or discussion forums. Motivation Level: Self-reported level of motivation. Learning Environment: Type of learning environment (Home, School, Language Center). Learning Mode: Mode of learning (Self-Paced or Instructor-Led). Accessibility of Learning Resources: Availability of learning materials to the learner. Use of AI Tools: Whether AI tools like chatbots or speech recognition software were used. Language Learning Goals: Purpose of language learning (Academic, Professional, Personal). This dataset offers rich data for researchers and educators to analyze the impact of AI on language learning outcomes, make cross-linguistic comparisons, and develop personalized AI-driven language education models.

Complete dataset used in the research study on The Role of Mobile Games in Shaping Virtual Friendships by Dr. Katherine Foster

Three hybrid vehicles equipped with AMMOS units were deployed during the 2015 Pan Am and Parapan Am Games as part of the high-resolution atmospheric monitoring network, the Mesonet, built by Environment and Climate Change Canada (ECCC) in support of the Games. AMMOS vehicles travelled prescribed routes (often simultaneously) between the Lake Ontario shore in Toronto and suburban/rural areas to the north and west. These three mobile stations collected data in locations where fixed stations cannot, such as along roadways surrounded by large buildings in downtown Toronto known as “urban canyons.” The AMMOS units collected temperature and humidity (aspirated), pressure, wind speed and direction, GPS location and vehicle speed, insolation, and black globe temperature at one-second intervals. The AMMOS vehicles also carried fine particulate air quality sensors, and one AMMOS vehicle carried a prototype AirSENCE air quality sampling system. Note that the Legacy Archive dataset only provides quality-controlled meteorological data averaged at 1-minute intervals. Other data may be obtained by contacting the lead scientist. The AMMOS mobile observations complemented those from the Mesonet, helped monitor weather and air quality conditions during the Games, and thoroughly sampled lake-breeze fronts for study post-Games. Three summer students and 6 ECCC scientists operated the 3 vehicles, mostly in pairs (1 student with 1 scientist). Nearly 10 000 km were travelled over 22 intensive observation days.

New Zealand NZ: Internet Users: Individuals: % of Population data was reported at 88.470 % in 2016. This records an increase from the previous number of 88.223 % for 2015. New Zealand NZ: Internet Users: Individuals: % of Population data is updated yearly, averaging 61.405 % from Dec 1990 (Median) to 2016, with 26 observations. The data reached an all-time high of 88.470 % in 2016 and a record low of 0.000 % in 1990. New Zealand NZ: Internet Users: Individuals: % of Population data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s New Zealand – Table NZ.World Bank: Telecommunication. Internet users are individuals who have used the Internet (from any location) in the last 3 months. The Internet can be used via a computer, mobile phone, personal digital assistant, games machine, digital TV etc.; ; International Telecommunication Union, World Telecommunication/ICT Development Report and database.; Weighted average; Please cite the International Telecommunication Union for third-party use of these data.

Pakistan PK: Internet Users: Individuals: % of Population data was reported at 15.515 % in 2016. This records an increase from the previous number of 14.000 % for 2015. Pakistan PK: Internet Users: Individuals: % of Population data is updated yearly, averaging 6.416 % from Dec 1990 (Median) to 2016, with 22 observations. The data reached an all-time high of 15.515 % in 2016 and a record low of 0.000 % in 1990. Pakistan PK: Internet Users: Individuals: % of Population data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Pakistan – Table PK.World Bank: Telecommunication. Internet users are individuals who have used the Internet (from any location) in the last 3 months. The Internet can be used via a computer, mobile phone, personal digital assistant, games machine, digital TV etc.; ; International Telecommunication Union, World Telecommunication/ICT Development Report and database.; Weighted average; Please cite the International Telecommunication Union for third-party use of these data.