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

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

Context:

The EA FC 24 dataset marks a significant milestone by featuring women players for the first time ever in the FIFA series, specifically focusing on the ultimate team mode in FIFA 23.

Columns Description:

Here is a description of some columns in the EA FC 24 dataset:

Ratings: Player Rating Position: Player Position Version: Card Version PS: Price on Playstation, if 0 then it is not available in the market SKI: Skills rating of the player (ranging from 0 to 5) WF: Weak Foot Skills (ranging from 0 to 5) WR: Work rate of the player on the field, given in the formula (Attack Work rate / Defence Work rate), with each value being low, medium, or high PAC: Player Pace (Speed) SHO: Player Shooting power PAS: Player Pass DRI: Player Dribble DEF: Player Defence PHY: Player Physicality Body: Player height given in centimeters and feet, followed by the type of body of the player (for some players, the game may have custom bodies for them) Popularity: Popularity of using the player BS: Base stats IGS: In-game stats

The inclusion of women players in this dataset reflects the commitment of EA Sports to providing a more inclusive and diverse gaming experience in the FIFA series.

Video Games Sales Dataset

About Dataset

This Dataset provides up-to-date information on the sales performance and popularity of various video games worldwide. The data includes the name, platform, year of release, genre, publisher, and sales in North America, Europe, Japan, and other regions. It also features scores and ratings from both critics and users, including average critic score, number of critics reviewed, average user score, number of users reviewed, developer, and rating. This comprehensive and essential dataset offers valuable insights into the global video game market and is a must-have tool for gamers, industry professionals, and market researchers. by source

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Columns

Research Ideas / Data Use

• Market Analysis: The video game sales data can be used to analyze market trends and identify popular genres, platforms, and publishers. This can be useful for industry professionals to make informed decisions about game development and marketing strategies.
• Sales Forecasting: The sales data can be used to forecast future trends and predict the success of upcoming games.
• Consumer Insights: The data can be analyzed to gain insights into consumer preferences and buying habits, which can be used to tailor marketing strategies and improve customer satisfaction.
• Comparison of Competitors: The data can be used to compare the sales performance of competing video games and identify market leaders.
• Gaming Industry Performance: The data can be used to evaluate the overall performance of the gaming industry and track its growth over time.
• Gaming Popularity by Region: The data can be analyzed to determine which regions are the largest markets for video games and which genres are most popular in each region.
• Impact of Reviews: The data can be used to study the impact of critic and user reviews on sales and the relationship between scores and sales performance.
• Gaming Trends over Time: The data can be used to identify trends in the gaming industry over time and to track the evolution of the market.
• Gaming Demographics: The data can be used to analyze the demographic makeup of the gaming audience, including age, gender, and income.
• Impact of Gaming Industry on the Economy: The data can be used to evaluate the impact of the gaming industry on the economy and to assess its contribution to job creation and economic growth.

Acknowledgements

if this dataset was used in your work or studies, please credit the original source Please Credit ↑ ⠀⠀⠀

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

Context

The dataset provided include players data for the ultimate team mode in FIFA 23 .

Columns Description

here is a description of some columns in the dataset :

Ratings : Player Rating Position : Player Position Version : Card Version PS : Price on Playstation , if 0 then it is not available in market SKI : Skills rating of player ( from 0 to 5 ) WF : Weak Foot Skills ( from 0 to 5 ) WR : Work rate of player on the field , and given in the formula ( Attack Work rate / Defence Work rate ) , each value can be ( low , medium , high ) PAC : Player Pace (Speed) SHO : Player Shooting power PAS : Player Pass DRI : Player Dribble DEF : Player Defence PHY : Player Physicality Body : player height given in cm and feet followed by type of body of player ( for some players the game have custom body for them ) Popularity : popularity of using the player BS : Base stats IGS : In game stats

Discovering Hidden Trends in Global Video Games Sales

Platforms, Genres, and Profitable Regions

By Andy Bramwell [source]

About this dataset

This dataset contains sales data for video games from all around the world, across different platforms, genres and regions. From the thought-provoking latest release of RPGs to the thrilling adventures of racing games, this database provides an insight into what constitutes as a hit game in today’s gaming industry. Armed with this data and analysis, future developers can better understand what types of gameplay and mechanics resonate more with players to create a new gaming experience. Through its comprehensive analysis on various game titles, genres and platforms this dataset displays detailed insights into how video games can achieve global success as well as providing a wonderful window into the ever-changing trends of gaming culture

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• 🚨 Your notebook can be here! 🚨!

How to use the dataset

This dataset can be used to uncover hidden trends in Global Video Games Sales. To make the most of this data, it is important to understand the different columns and their respective values.

The 'Rank' column identifies each game's ranking according to its global sales (highest to lowest). This can help you identify which games are most popular globally. The 'Game Title' column contains the name of each video game, which allows you to easily discern one entry from another. The 'Platform' column lists the type of platform on which each game was released, e.g., PlayStation 4 or Xbox One, so that you can make comparisons between platforms as well as specific games for each platform. The 'Year' column provides an additional way of making year-on-year comparisons and tracking changes over time in global video game sales.
In addition, this dataset also contains metadata such as genre ('Genre'), publisher ('Publisher'), and review score ('Review') that add context when considering a particular title's performance in terms of global sales rankings. For example, it might be more compelling to compare two similar genres than two disparate ones when analyzing how successful a select set of titles have been at generating revenue in comparison with others released globally within that timeline. Lastly but no less important are the three variables dedicated exclusively for geographic breakdowns: North America ('North America'), Europe (Europe), Japan (Japan), Rest of World (Rest of World), and Global (Global). This allows us to see how certain regions contribute individually or collectively towards a given title's overall sales figures; by comparing these metrics regionally or collectively an interesting picture arises -- from which inferences about consumer preferences and supplier priorities emerge!

Overall this powerful dataset allows researchers and marketers alike a deep dive into market performance for those persistent questions about demand patterns across demographics around the world!

Research Ideas

• Analyzing the effects of genre and platform on a game's success - By comparing different genres and platforms, one can get a better understanding of what type of games have the highest sales in different regions across the globe. This could help developers decide which type of gaming content to create in order to maximize their profits.
• Tracking changes in global video games trends over time - This dataset could be used to analyze how various elements such as genre or platform affect success over various years, allowing developers an inside look into what kind of videos are being favored at any given moment across the world.
• Identifying highly successful games and their key elements- Developers could look at this data to find any common factors such as publisher or platform shared by successful titles to uncover characteristics that lead to a high rate-of-return when creating video games or other forms media entertainment

Acknowledgements

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

License

See the dataset description for more information.

Columns

File: Video Games Sales.csv | Column name | Description | |:------------------|:------------------------------------------------------------| | Rank | The ranking of the game in terms of global sales. (Integer) | | Game Title | The title of the game. (String) | | Platform | The platform the game was released on. (String) ...

Wizard101 player activity dataset from MMO Populations, combining monthly enhanced players and 30-day daily estimates generated from public signals.

This dataset contains a list of video games with sales greater than 100,000 copies. It was generated by a scrape of vgchartz.com.

Fields include

• Rank - Ranking of overall sales

• Name - The games name

• Platform - Platform of the games release (i.e. PC,PS4, etc.)

• Year - Year of the game's release

• Genre - Genre of the game

• Publisher - Publisher of the game

• NA_Sales - Sales in North America (in millions)

• EU_Sales - Sales in Europe (in millions)

• JP_Sales - Sales in Japan (in millions)

• Other_Sales - Sales in the rest of the world (in millions)

• Global_Sales - Total worldwide sales.

The script to scrape the data is available at https://github.com/GregorUT/vgchartzScrape. It is based on BeautifulSoup using Python. There are 16,598 records. 2 records were dropped due to incomplete information.

Context

The dataset provided include players data for the ultimate team mode in fifa 21 .

Columns Description

here is a description of some columns in the dataset : - Ratings : Player Rating - Position : Player Position - Version : Card Version - PS : Price on Playstation , if 0 then it is not available in market - SKI : Skills rating of player ( from 0 to 5 ) - WF : Weak Foot Skills ( from 0 to 5 ) - WR : Work rate of player on the field , and given in the formula ( Attack Work rate / Defence Work rate ) , each value can be ( low , medium , high ) - PAC : Player Pace (Speed) - SHO : Player Shooting power
- PAS : Player Pass - DRI : Player Dribble - DEF : Player Defence - PHY : Player Physicality - Body : player height given in cm and feet followed by type of body of player ( for some players the game have custom body for them ) - Popularity : popularity of using the player - BS : Base stats - IGS : In game stats

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

Find Ps

## Overview

Find Ps is a dataset for object detection tasks - it contains People annotations for 305 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

Context

The dataset provided include players data for the ultimate team mode in FIFA 23 .

Columns Description

here is a description of some columns in the dataset :

Ratings : Player Rating Position : Player Position Version : Card Version PS : Price on Playstation , if 0 then it is not available in market SKI : Skills rating of player ( from 0 to 5 ) WF : Weak Foot Skills ( from 0 to 5 ) WR : Work rate of player on the field , and given in the formula ( Attack Work rate / Defence Work rate ) , each value can be ( low , medium , high ) PAC : Player Pace (Speed) SHO : Player Shooting power PAS : Player Pass DRI : Player Dribble DEF : Player Defence PHY : Player Physicality Body : player height given in cm and feet followed by type of body of player ( for some players the game have custom body for them ) Popularity : popularity of using the player BS : Base stats IGS : In game stats

All GTA 5 Cheats for PS4, PS5 and PS3 cheat codes dataset covering input sequences and effects.

Context

The dataset provided include players data for the ultimate team mode in FIFA 22 .

Columns Description

here is a description of some columns in the dataset : - Ratings : Player Rating - Position : Player Position - Version : Card Version - PS : Price on Playstation , if 0 then it is not available in market - SKI : Skills rating of player ( from 0 to 5 ) - WF : Weak Foot Skills ( from 0 to 5 ) - WR : Work rate of player on the field , and given in the formula ( Attack Work rate / Defence Work rate ) , each value can be ( low , medium , high ) - PAC : Player Pace (Speed) - SHO : Player Shooting power
- PAS : Player Pass - DRI : Player Dribble - DEF : Player Defence - PHY : Player Physicality - Body : player height given in cm and feet followed by type of body of player ( for some players the game have custom body for them ) - Popularity : popularity of using the player - BS : Base stats - IGS : In game stats

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

FIFA 23 is a football simulation video game published by Electronic Arts. It is the 30th and final installment in the FIFA series that is developed by EA Sports, and released worldwide on 30 September 2022 for PC, Nintendo Switch, PlayStation 4, PlayStation 5, Xbox One, Xbox Series X/S and Google Stadia.

The role of performance analysis within football is more important than ever. Whether it’s the opposition, potential transfer targets or last weekend’s fixture, analysing performances and data can be the difference between success and failure.

This is data from a scoping review that was conducted. The methods for this review are summarised here:

The aim of this study is to synthesise current evidence related to use of psycho-social groups as part of community-based mental health interventions in South Asia using a realist lens with specific research questions as follows: (i) What types of psycho-social, group directed mental health interventions are being delivered by community mental health workers in South Asia? (ii) What outcomes do they deliver and how are they measured? (iii) What are possible mechanisms that trigger positive outcomes? What constrains positive outcomes?

Framing the scoping review Our focus was group interventions that are targeted primarily at adults from different family groups with a component designed to accomplish at least one of the following: - prevent or treat mental health problem/s; - support people who live with mental health problems and their carers; - improve resilience in the face of mental health problems. We proposed that interventions should have a clear psychosocial component. While interventions could be short, and engage with existing groups, they should involve multiple sessions. Group interventions could also be part of larger interventions with individual, family, or screening components. The detailed exclusion criteria are provided in the supplementary material.
Sample: Adults living in the community and affected by mental health problems. We included both studies of people with mental health problems and of those who care for them. Interventions should be carried out in, and benefit citizens of, the SAARC. Interventions who targeted both adults and young people (aged 14 and above) were also included. Phenomenon of Interest: Psychosocial group interventions with a stated intention to support mental health in SAARC countries that are delivered by community workers or primary care health workers. Those workers should have no tertiary level training in medicine, social work, psychology, or one of the allied health professions, and they should not be training in a tertiary setting. However, these workers may be regarded as experts by their community and may have undergone rigorous apprenticeships in traditional forms of health/ medicine and physical, mental, and spiritual care provision. A minimal description of the intervention should be available, covering who delivered it, what the content of the intervention was, and at whom the intervention was aimed. Design: Study protocols, implementation studies, qualitative studies, experience reports, evaluations, case studies, randomised controlled trials Evaluation: Studies should report, or, in the case of study protocols, specify quantitative or qualitative outcomes of the intervention. Reports of implemented interventions should also mention barriers to and facilitators of success. Research type: mixed methods, quantitative research, qualitative research, study protocol, experience report Ethics was not required as the data presented in this data set was all secondary date.

Overwatch is a team-based multiplayer first-person shooter video game developed and published by Blizzard Entertainment, which released on May 24, 2016 for PlayStation 4, Xbox One, and Windows. Described as a "hero shooter", Overwatch assigns players into two teams of six, with each player selecting from a roster of nearly 30 characters, known as "heroes", each with a unique style of play whose roles are divided into three general categories that fit their role: Offense, Defense, Tank, and Support. Players on a team work together to secure and defend control points on a map or escort a payload across the map in a limited amount of time.

I discovered this dataset on the Overwatch Subreddit here: https://www.reddit.com/r/Overwatch/comments/7o8hmg/my_friend_has_recorded_every_game_hes_played/

and there is sort of same dataset here: https://www.kaggle.com/mylesoneill/overwatch-game-records/home

Data was messy, so I try to clean it and make better and easier for visualising and analysing.

Columns as: time, result, map, team_role, character_1, character_2, character_3 and psychological_condition were transformed (clustered) into easy to use format.

Thanks to JustWingIt for collecting this amazing data!

And Myles O'Neill for bringing this data to Kaggle!

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