Context

We were building a set of waifu-centered projects (the main one being WaifuChain) and needed a dataset of waifus upon which to build several tools, like our waifu-based AI, so we scrapped MyWaifuList's database and published it here. See the github repo of this dataset for more information regarding the project and the tools we used to obtain this dataset, along with instructions on how to use them.

Content

All data has been extracted from MyWaifuList, a website where users can upload waifus and vote on whether a waifu is good or "trash".

There's the following information on each waifu:

"age": null, 
"alternative_name": "\u7d50\u57ce \u660e\u65e5\u5948", 
"birthday_day": 30, 
"birthday_month": "September", 
"birthday_year": "", 
"blood_type": "", 
"bust": "82.00", 
"creator": {
  "id": 42, 
  "name": "Railtracks", 
  "rolename": ""
}, 
"creator_id": 42, 
"description": "Asuna is a friend of Kirito and is a sub-leader of the guild Knights of the Blood (KoB), a medium-sized guild of about thirty players, also called the strongest guild in Aincrad. Being one of the few girls that are in SAO, and even more so that she's extremely pretty, she receives many invitations and proposals. She is a skilled player earning the title \"Lightning Flash\" for her extraordinary skill with the sword. Her game alias is the same as her real world name.\r

\r FR: Asuna, de son vrai de son vrai nom Asuna Y\u00fbki, est une joueuse de 17 ans tr\u00e8s peu exp\u00e9riment\u00e9e en mati\u00e8re de jeu vid\u00e9o. Et pour dire, Sword art Online est son premier jeu vid\u00e9o car \u00e0 la base elle trouve que ceux-ci ne sont qu'une perte de temps.", "display_picture": "images/58.jpeg", "height": "168.00", "hip": "83.00", "id": 58, "likes": 1442, "name": "Yuuki Asuna", "origin": "Japan", "series": { "description": "In 2022, a virtual reality massively multiplayer online role-playing game (VRMMORPG) called Sword Art Online (SAO) is released. With the NerveGear, a helmet that stimulates the user's five senses via their brain, players can experience and control their in-game characters with their minds. Both the game and the NerveGear was created by Akihiko Kayaba.\r \r On November 6, 10,000 players log into the SAO's mainframe cyberspace for the first time, only to discover that they are unable to log out. Kayaba appears and tells the players that they must beat all 100 floors of Aincrad, a steel castle which is the setting of SAO, if they wish to be free. Those who suffer in-game deaths or forcibly remove the NerveGear out-of-game will suffer real-life deaths.\r \r The main character, Kirigaya "Kirito" Kazuto, was also one of 1,000 testers in the game's previous closed beta. With the advantage of previous VR gaming experience and a drive to protect other beta testers from discrimination, he isolates himself from the greater group and plays the game alone, bearing the mantle of "beater", a "beta tester" and "cheater". As the players progress through the game Kirito eventually befriends a young girl named Asuna Yuuki, who form a relationship that later turns into in-game marriage. After the duo discover that Akihiko Kayaba was playing the game as the leader of the guild Asuna joined, they confront and destroy him, freeing themselves and the other players from the game.", "id": 49, "name": "Sword Art Online", "slug": "sword-art-online" }, "slug": "yuuki-asuna", "tags": [ { "id": 1869, "name": "worst anime" }, { "id": 1870, "name": "trash" }, { "id": 1927, "name": "idiot" } ], "trash": 2093, "waist": "60.00", "weight": "55.00"

Acknowledgements

This project wouldn't be possible without MyWaifuList and it's community.

Inspiration

Who is best girl?

Here are a few use cases for this project:

1. Sports Media Analysis: This model can be used by sports media outlets for a deeper, real-time analysis of football matches. This includes player count, referee's involvement, tracking the ball, and movement of goalkeepers. The information can be used in commentaries, reviews, and post-match analysis.

2. Gaming Industry: The "20-3-23 2-1" model can be utilized in the game development industry to improve the realism of football games. It can help generate real-time data about player behaviors and actions which can be used for creating advanced gameplay algorithms.

3. Sports Betting Companies: These companies can use this model to gather detailed statistics about players' performance, referees' activity, and goalkeepers' defense strategies. This elaborate information could offer more accurate and diversified bet options.

4. Player Performance Tracking: Football clubs can use this model for tracking the performance and growth of their players during matches. It provides useful data regarding a player's movement on the field, their interaction with the ball, up against different opposition, etc.

5. Virtual Reality Training: Training centers or clubs can use this model to create VR based training modules. Identifying specific players, referees, the ball, and goalkeepers can help design simulations that reflect real game situations, helping players to enhance their tactics and skills.

The purpose of this study was to determine the relative intensity of physical activity from playing the virtual reality game Beat Saber®. Male (n=6) and female (n=4) participants (ages 18-27 years) completed two laboratory visits: 1) In the first visit, participants completed a graded exercise test to measure their V̇O2max and HRmax, gas exchange threshold (GET) and reparatory compensation point (RCP); 2) In the second visit, participants played Beat Saber® while ventilatory parameters were measured. Relative intensity for each participant achieved during gameplay was reported as V̇O2 expressed as a percentage of GET, RCP, and V̇O2max, metabolic equivalents (METs), rating of perceived exertion (RPE), and percent heart rate max (%HRmax)). Gameplay intensity quantified using GET and RCP values, indicated that playing Beat Saber® elicits moderate intensity physical activity. In conclusion, Beat Saber® can be used to meet physical activities guidelines as defined by CSEP, however, in novice players, Beat Saber® may not be an appropriate mode of physical activity to meet intensity recommendations as defined by CSEP.

Here are a few use cases for this project:

1. Sports Analytics: The model can be used to analyze game strategies by providing insights about player movements, ball possession, and team formations during a soccer match. Search Keywords: Sports Analytics, Game Analysis, Player Tracking.

2. Broadcasting Enhancements: The model can enrich live broadcasts and replays by automatically recognizing and tagging players and their actions, aiding commentators and enhancing viewer experiences. Search Keywords: Broadcasting, Sports Media, Player Identification.

3. Virtual Reality Gaming: It can be incorporated into VR/AR gaming to create realistic player movements by mapping real-life player actions onto game characters. Search Keywords: Virtual Reality, Gaming, Player Simulation.

4. Training and Coaching: The model can assist coaches in assessing player performance, analyzing team strategies, and providing personalized training based on player's in-game actions. Search Keywords: Training, Coaching, Player Performance.

5. Security and Crowd Management: Inside stadiums, the model can recognize unauthorized access to the field by distinguishing between players, referees, and other individuals. Search Keywords: Stadium Security, Crowd Management, Unauthorized Access.

This dataset was collected from participants playing a first-person VR horror survival game set on a deserted space station. The game was designed to induce stress through environmental challenges, resource scarcity, and threatening enemies.

Data includes physiological, behavioral, and self-reported stress measurements. Physiological signals were recorded using the Empatica E4 wristband, capturing Electrodermal Activity (EDA) at 4 Hz, a physiological indicator of arousal. Behavioral motion data were gathered from the Oculus Quest 2 headset and controllers, recording position, rotation, velocity, and acceleration at 64 Hz. In-game actions, such as button presses and pressure intensity, were also logged.

Before the gaming session, participants completed two questionnaires:

• Perceived Stress Scale (PSS) questionnaire: A standard phsycological 10-item questionnaire rated on a 5-point Likert scale (0-4) to assess perceived stress over the past month.

• Demographic questionnaire (accessible at https://forms.gle/i4syZb2CpXd91r9M8): Collected unchanging personal data, including age, gender, gaming habits, and VR experience.

After gameplay, participants reviewed their recordings and annotated perceived stress levels using DANTE (Dimensional ANnotation Tool for Emotions), a web-based tool that enables continuous stress assessment on a scale from -1 to 1 (step of 0.001). A color-coded bar indicated low, medium, and high stress levels.

The game’s structured design ensured consistent exposure to stress-inducing stimuli across all participants, enabling reliable analysis of physiological and behavioral responses in an immersive VR environment.

Here is the description of the files, with each row corresponding to a participant:

• stress.csv contains the following columns:

  • TimeStamp: A time counter starting from 0 for each session.
  • UserId: A unique identifier for the participant.
  • NameVideo: The name of the video associated with the recorded data.
  • AnnoType: The type of annotation (e.g., stress, valence, arousal).
  • Value: The recorded stress level at the corresponding moment.

• EDA.csv includes:

  • First row: An initial timestamp in numeric format, representing Unix epoch time.
  • Second row: The sampling rate value (in Hz).
  • Remaining rows: EDA signal values recorded over time..

• S#.csv (files containing motion behavioral data collected from Oculus) include:

  • Movement data for the head and hands: velocity, angular velocity, acceleration, and angular acceleration.
  • Buttons data: Grip pressure and trigger pressure for both hands.
  • A column TimeStamp in YYYY-MM-DD HH:MM:SS format.

• PerceivedStressScale.csv: Contains responses to the PSS questionnaire.

• EarlyQuestionnaire.csv: Includes demographic data.

If you use this dataset, please cite the following paper: Stress Assessment in Virtual Reality Horror Games Using Players' Behavioural and Physiological Data, 2025 (Forthcoming).

DOI: 10.1109/TG.2025.3563258

Here are a few use cases for this project:

1. Sports Analysis: The "Analytical FC" model could be used for in-depth match analysis. It could provide data on player performance, including time on the pitch, distance run, or even identifying specific interactions with the ball. Furthermore, tracking referees could ensure fair play by monitoring their position in relation to the play.

2. Game Highlights Creation: By identifying the key classes- player, referee, ball, and goalkeeper- this model can aid in automated generation of game highlights. The key moments such as goals, fouls, or saves could be pinpointed based on the simultaneous movements of the ball, players, and the referee.

3. Player Training and Development: Coaches could use this model to review and analyze players' performances during training or matches. It could provide insights like on-field positioning, reaction times and player-ball engagement.

4. Virtual Reality Gaming: In virtual reality sports games, "Analytical FC" could be used to create more lifelike simulations by modeling the actions of virtual players, referees, and the ball based on the real-world dataset.

5. Injury Prevention and Rehabilitation: By analyzing the movement and actions of players, the model could help in identifying unnatural movements or situations that typically lead to injuries. This could help in formulating injury prevention strategies and in guiding injured athletes' rehabilitation process.

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Context

• The metaverse, a living and breathing space that blends physical and digital, is quickly evolving from a science fiction dream into a reality with endless possibilities. A world where people can interact virtually, create and exchange digital assets for real-world value, own digital land, engage with digitized real-world products and services, and much more.

• Major tech giants are beginning to recognize the viability and potential of metaverses, following Facebook’s groundbreaking Meta rebrand announcement. In addition to tech companies, entertainment brands like Disney have also announced plans to take the leap into virtual reality.

• While the media hype is deafening, your average netizen isn’t fully aware of what a metaverse is, how it operates and, most importantly—what benefits and opportunities it can offer them as a user.

https://cdn.images.express.co.uk/img/dynamic/22/590x/Metaverse-tokens-cryptocurrency-explained-ethereum-killers-new-coins-digital-currency-meta-news-1518777.jpg?r=1638256864800" alt="">

What Is The Metaverse?

• In its digital iteration, a metaverse is a virtual world based on blockchain technology. This all-encompassing space allows users to work and play in a virtual reflection of real-life and fantasy scenarios, an online reality, ranging from sci-fi and dragons to more practical and familiar settings like shopping centers, offices, and even homes.

• Users can access metaverses via computer, handheld device, or complete immersion with a VR headset. Those entering the metaverse get to experience living in a digital realm, where they will be able to work, play, shop, exercise, and socialize. Users will be able to create their own avatars based on face recognition, set up their own businesses of any kind, buy real estate, create in-world content and asset,s and attend concerts from real-world superstars—all in one virtual environment,

• With that said, a metaverse is a virtual world with a virtual economy. In most cases, it is an online reality powered by decentralized finance (DeFi), where users exchange value and assets via cryptocurrencies and Non-Fungible Tokens.

What Are Metaverse Tokens?

• Metaverse tokens are a unit of virtual currency used to make digital transactions within the metaverse. Since metaverses are built on the blockchain, transactions on underlying networks are near-instant. Blockchains are designed to ensure trust and security, making the metaverse the perfect environment for an economy free of corruption and financial fraud.

• Holders of metaverse tokens can access multiple services and applications inside the virtual space. Some tokens give special in-game abilities. Other tokens represent unique items, like clothing for virtual avatars or membership for a community. If you’ve played MMO games like World of Warcraft, the concept of in-game items and currencies are very familiar. However, unlike your traditional virtual world games, metaverse tokens have value inside and outside the virtual worlds. Metaverse tokens in the form of cryptocurrency can be exchanged for fiat currencies. Or if they’re an NFT, they can be used to authenticate ownership to tethered real-world assets like collectibles, works or art, or even cups of coffee.

• Some examples of metaverse tokens include SAND of the immensely popular Sandbox metaverse. In The Sandbox, users can create a virtual world driven by NFTs. Another token is MANA of the Decentraland project, where users can use MANA to purchase plots of digital real estate called “LAND”. It is even possible to monetize the plots of LAND purchased by renting them to other users for fixed fees. The ENJ token of the Enjin metaverse is the native asset of an ecosystem with the world’s largest game/app NFT networks.

Dataset Information

• The dataset brings 198 metaverse cryptos. Pls refer to the file Metaverse coins.csv to find the list of metaverse crypto coins.

• The dataset will be updated on a weekly basis with more and more additional metaverse tokens, Stay tuned ⏳

Here are a few use cases for this project:

1. Board Game Assistants: This Dice model can be used in a digital assistant for board games. It would help users track dice results automatically, thereby enhancing the experience for games involving dice such as Monopoly, Yahtzee, or Dungeons and Dragons.

2. Educational Games Development: Educational organizations and ed-tech companies can use this model to develop interactive learning games or applications that teach probability, math or statistics through a dice game.

3. Gambling Supervision: Casinos or online gambling platforms can apply the model to monitor dice games and ensure fair play, automatically and meticulously track game statistics, and verify or dispute any contentious throws.

4. Virtual Reality Gaming: The Dice model can be integrated into VR gaming systems to interact with physical dice. For instance, in a VR board game setup, the model can compute the numbers rolled on the dice and translate that into the virtual game.

5. Assistive Technology for Visually-Impaired: Application for visually impaired people, where the app can detect the number rolled on a dice and communicate it via audio, enabling visually impaired people to participate in dice-based games.

Here are a few use cases for this project:

1. Sports Analytics and Performance Tracking: Use Vision stat v2.1 to analyze player performances, movements, and interactions in real-time or in post-game analysis, providing valuable insights for coaches to improve team strategies and individual player development.

2. Automated Game Highlights and Summaries: Vision stat v2.1 can quickly identify key moments in a game (goals, corners, saves, referee decisions) to automatically create game highlights or summaries, saving time for sports media and content creators.

3. Virtual and Augmented Reality Applications: Incorporate Vision stat v2.1 into VR and AR experiences to overlay real-time information about players, team positions, and game events onto live or recorded footage, enhancing the viewing experience for fans.

4. Smart Stadium Solutions: Integrate Vision stat v2.1 into the security and monitoring systems of sports venues to improve crowd management, detect unauthorized individuals on the field, and ensure a safe and enjoyable experience for attendees.

5. Betting and Fantasy Sports: Use the advanced statistics and live game data generated by Vision stat v2.1 to enhance betting platforms and fantasy sports apps, providing users a more comprehensive understanding for making informed decisions.

Sophisticated soccer players can skillfully manipulate a ball with their feet depending on the external environment. This ability of goal-directed control in the lower limbs has not been fully elucidated, although upper limb movements have been studied extensively using motor adaptation tasks. The purpose of this study was to clarify how the goal-directed movements of the lower limbs is acquired by conducting an experiment of visuomotor adaptation in ball-kicking movements. In this study, healthy young participants with and without experience playing soccer or futsal performed ball-kicking movements. They were instructed to move a cursor representing the right foot position and shoot a virtual ball to a target on a display in front of them. During the learning trials, the trajectories of the virtual ball were rotated by 15° either clockwise or counterclockwise relative to the actual ball direction. As a result, participants adapted their lower limb movements to novel visuomotor perturbation regardless of the soccer playing experience, and changed their whole trajectories not just the kicking position during adaptation. These results indicate that the goal-directed lower limb movements can be adapted to the novel environment. Moreover, it was suggested that fundamental structure of visuomotor adaptation is common between goal-directed movements in the upper and lower limbs.

Here are a few use cases for this project:

1. Sports Analytics: Analysts can use the "Team_Maximes" model to collect real-time data during basketball games. This could involve tracking player movement, identifying possession changes, checking violation of some game rules, and making statistics on the success rate of teams from different shooting zones.

2. Media Broadcasting: TV broadcasters and Sports networks could use this model to enhance viewers' experience with real-time graphics, game statistics, player tracking, and to predict next moves. Additionally, it can be used in automatic gathering of game highlights.

3. Sports Betting Platforms: The firms can use the model as a tool to provide live data inputs that are critical to making betting decisions such as current scores, player statistics, and timing left.

4. Virtual Reality Training: Software developers could use this model to provide real-world, statistical data-driven scenarios for VR training programs for basketball players. This would allow players to practice against different simulated match scenarios aided by real-time data.

5. Crowd Management: Given the visual perspective, the model can help in strategic crowd management in live games, optimizing security by providing potential insights on crowd distribution and movement patterns.

Here are a few use cases for this project:

1. Sports Equipment Inventory Assurance: In sports retail, where pickleball equipment is sold, the model can be extensively used to count and manage inventory effectively. This computer vision model can automate the process of identifying pickleball courts and balls which can lead to time efficiency.

2. Sports Analytics and Broadcasting Technology: The "Pickleball Vision" model can be utilized for real-time analysis in tournaments. The model can accurately track ball movements, allowing analysts and commentators to provide in-depth game insights for viewers, providing high-resolution replays and near-instant analysis of gameplay.

3. Automated Refereeing: In professional or competitive pickleball matches, the model could be applied for automated decision-making assistance such as in/out calls, helping minimize human error in the officiating process.

4. Player Training and Performance Improvement: Players and coaches can use the tool to analyze game strategies, ball trajectories, and court positions. This can help them make adjustments and improve player skill sets.

5. Designing Virtual Reality/Augmented Reality Games: Independent game developers or gaming companies can use this model to create more realistic pickleball AR/VR games. The model can be used to enhance the gameplay experience by identifying real-life ball and court elements for the game.

The intention-behavior gap is a common phenomenon where people fail to follow through on their intentions to change their behavior and pursue their future goals. Previous research has shown that people are more likely to act in favor of their future selves when they feel similar/connected to their future self and can vividly describe them. This study compared an imagination exercise with an integrated imagination and exposure exercise using virtual reality (VR) to embody age-morphed future selves to an imagination only exercise. We expected that strengthening the similarity/connectedness and the vividness of the future self would reduce the intention-behavior gap, and exposure to the future self would have the greatest effect. Surprisingly, the results showed that strengthening connectedness reduced the intention-behavior gap, but strengthening similarity increased the gap. Additionally, the exercises were equally effective in reducing the intention-behavior gap. These findings suggest that both feeling connected to and recognizing dissimilarity to one’s future self play different roles in future-oriented behavior change.

Here are a few use cases for this project:

1. Sports Broadcasting and Analysis: The model can be used to automate player tracking, ball movements, identifying referee decisions, and analysing on-field tactics during live or recorded soccer matches, providing in-depth game insights and enhancing viewing experience.

2. Game Play Simulation: This model can be used to analyze the gameplay in real-life soccer matches. This gameplay data can be used in developing more realistic soccer video games or for AI training to mimic human strategies.

3. Player Performance Evaluation: Sports coaching teams could use the model to observe and analyze the performance of individual players and the team more broadly, by tracking player movements, ball possession, and goalkeeper performance.

4. Virtual Reality Applications: VR applications aiming to put users into the game could use this model to accurately identify and replicate realistic soccer game elements.

5. Security and Surveillance at Football Events: The model could help identify the relative positions of players, referees and security personnel during crowded football matches in real time, thus helping ensure smooth crowd management and immediate responses to security incidents.

Here are a few use cases for this project:

1. Sports Analytics: YoloV5 can be used to automate analytics in sports games, particularly in games like badminton. By identifying individual players and objects like the net and shuttlecock, the model could track player movements, interactions with the shuttlecock, and count the number of times the shuttlecock hits the net.

2. Training and Coaching: The model can assist coaches in understanding their players' performance better by monitoring their footwork, strategy implementation, speed, and other performance metrics during practice sessions or matches.

3. Gaming & Virtual Reality: The model could be applied in the development of interactive sports video games or VR simulations, where real-world actions of players are captured and transformed into in-game movements.

4. Sports Equipment Testing: Companies could use the model during the quality testing phase of sports equipment—like rackets and shuttlecocks—by tracking the movement and response of the equipment under various conditions.

5. Sports Broadcasting and Journalism: This model could be used to aid sports journalists and broadcasters by automatically generating statistics and key highlights of the game (e.g., number of net hits, shuttlecock speed and trajectory, player positioning) in real-time, making covering, analyzing, and summarizing games more efficient.

Here are a few use cases for this project:

1. Sports Broadcasting Enhancement: The "Football Object Detection" model can be used in sports broadcasting to enhance the viewing experience by automatically identifying and highlighting key elements of the game such as players, referee, ball, etc. It could create real-time annotations and infographics to improve viewer comprehension.

2. Sports Analytics: In football analysis, the model can aid in player performance evaluation by tracking specific players, the ball, or the goalkeeper during games. The generated data can be used to assess strategies, possession, player movements, and scoring opportunities.

3. Coaching Aid: Football coaches could utilize this model to analyze and strategize gameplay. It could help in scrutinizing the performance of individual players and the team as a whole during practices and match reviews, potentially aiding in improved decision-making.

4. Interactive Gaming and Virtual Reality: The model could find application in the development of football-themed video games and VR experiences. It can help in achieving realistic movements of virtual players, goalkeepers, referees, and ball dynamics.

5. Security and Crowd Management: For crowded football events, this model could be used to monitor crowd behavior, track any prohibited items, or assess emergency situations, contributing to improved stadium security and safety.

This is an educational game that is based in the human body. It is going to have different parts in the series telling different stories for each part of the human body and is going to be realistic as though the player is live in the body operating or flying some sort of fighting ship. The players role is to play the part of for example, a white blood cell fighting viruses, The player will learn while having fun. The game will be authenticated I am going by doctors as consultants to endorse the game features.. There is no other game on the market with all this incorporated into it so will be the first of its kind so is marked as the intellectual property of Naipthan Phillips. Anyone who copies this idea will have to compensate Naipthan Phillips for his idea. All others that contribute to the game will be credited for their input if selected.[NP: The TP IPR games development dataset 2 (health and education)]

Here are a few use cases for this project:

1. Sports Analytics: This model could be used to track the performance and movement patterns of individual players, goalkeepers, and the ball during a live game or post-match analysis. Useful metrics could include ball possession time, player positions, strategies etc.

2. Interactive Sports Games Creation: The model could provide critical inputs for developing realistic sports video games or AR/VR experiences by taking real-world player data and integrating it into the gaming environment.

3. Training and Coaching: Coaches could utilize this model to analyze team strategies and training sessions, tracking players' movements and their interaction with the ball. Visualization of these dynamics could inform and improve training techniques and game plans.

4. Sports Governance: The model could contribute in real-time decision making, foul detection and rule violation identification in football games, aiding referees and sports governing bodies.

5. Media and Spectator Experience: Media companies could leverage this model to enhance the viewing experiences of spectators, by providing real-time, detailed analysis of the game progression including player movements and ball handling.