MAGs generated by MetaCC binning from the human gut short-read, the wastewater (WW) short-read, the cow rumen long-read, and the sheep gut long-read metaHi-C datasets

*Also find Metacritic Movies and Metacritic TV Shows datasets.*

Metacritic Games and Reviews Dataset

This dataset contains a collection of video games and their corresponding reviews from Metacritic, a popular aggregate review site. The data provides insights into various video games across different platforms, including PC, PlayStation, Xbox, and others. Each game entry includes critical reviews, user reviews, ratings, and other relevant information that can be used for analysis, natural language processing, machine learning, and predictive modeling.

Important Note: *The games in this collection are selected from Metacritic's Best Games of All Time list, which only includes titles that have received at least 7 reviews, ensuring a minimum level of critical and user input.*

Up-to-dateness: *This dataset is accurate as of March 14, 2025, and includes the most current rankings and game details available at that time.*

Content

The dataset contains general information and scores of 13K+ games and their corresponding 1.6M+ user/critic reviews collected by sending automated requests to Metacritic's public backend API using Python's requests and pandas libraries.

Potential Uses

• Sentiment analysis and natural language processing on reviews.
• Predictive modeling for predicting user ratings based on features like genre, publisher, and developer.
• Data analysis on trends in game quality, genres, or platform performance.
• Comparing critical reviews and user reviews to understand the divergence in ratings.

This dataset is perfect for researchers, game enthusiasts, and data scientists who are interested in exploring the gaming industry through data analysis.

Acknowledgements

• The datasets are scraped from metacritic.com
  
• The code for scraping the dataset can be found on my GitHub repository

This curated dataset contains detailed music reviews scraped from Metacritic, covering hundreds of albums across genres. Delivered in CSV format, it includes critic scores, review excerpts, album names, artists, release years, and publication sources. Ideal for machine learning, sentiment analysis, data journalism, and research in music trends or media bias.

Whether you're building a recommender system, training a model on music-related sentiment, or visualizing the evolution of critical reception in music — this dataset gives you a powerful head start.

titles - game names platform - game platform, games can have an implementation on several platforms metascore - the rating put down metacritic.com userscore - user rating, may not be available for new games genre - date - date of reliz

The dataset contains 6 columns and 199 rows of pc games with the highest metascore available on metacritic. The title and publisher column contains the title of the game and the name of the company that published the game. The rating column contains the rating of the game with "-" which means there is no rating. The user_score column contains the average rating given by the user to the game. The metascore column contains the results of the assessment conducted by the metacritics of the game.

This dataset provides details and scores for Metacritic's 3,200 best television shows of all time. It compiles information and reviews from Metacritic, a prominent review aggregation website. Each entry offers insights into various TV programmes, including critical and user reviews, ratings, and other relevant information suitable for analysis, natural language processing, machine learning, and predictive modelling. The shows included are from Metacritic's 'Best TV Shows of All Time' list, ensuring each title has received a minimum of seven reviews. This dataset is up-to-date as of 14th March 2025, reflecting the most current rankings and show details available at that time.

Columns

• id: A unique identifier for each show, useful for merging with related review data.
• title: The name of the television show.
• releaseDate: The initial release date of the show.
• rating: The ESRB (Entertainment Software Rating Board) rating for the show, such as TV-MA, TV-14.
• genres: The genre or multiple genres associated with the show (e.g., Comedy, Drama).
• description: A summary of the television show.
• duration: The average duration of an episode in minutes.
• tagline: A brief, memorable phrase associated with the show.
• num_seasons: The total number of seasons released for the show.
• metascore: The aggregated score based on professional critic reviews.

Distribution

The dataset is typically provided in a tabular format, such as CSV. It contains general information and scores for over 3,000 television shows, along with more than 96,000 user and critic reviews. While specific row counts for individual files are not specified, there are 3,200 unique TV show entries.

Usage

This dataset is ideal for: * Conducting sentiment analysis and natural language processing on reviews. * Developing predictive models to forecast user ratings based on features like genre, number of seasons, or cast performance. * Performing data analysis on trends in TV show quality, popular genres, or cast performance over time. * Comparing critical reviews and user reviews to understand any divergence in ratings and perceptions.

Coverage

The dataset covers Metacritic's best television shows of all time, selected from a global scope. It is accurate as of 14th March 2025. The collection focuses on titles that have accumulated at least seven reviews.

License

CC0

Who Can Use It

This dataset is well-suited for researchers, TV enthusiasts, and data scientists eager to explore the television industry through data analysis. It supports a variety of projects from academic studies to personal explorations of TV content.

Dataset Name Suggestions

• Metacritic's Best TV Shows Data
• Top Rated TV Shows by Metascore
• Metacritic TV Series Dataset
• Metacritic TV Show Rankings
• Global TV Show Ratings Dataset

Attributes

Original Data Source: Metacritics Best TV Shows and Reviews - 2025

I wanted to scrape reviews of all the movies on metacritic.com for practicing web scraping. The resulting file has 8 columns, which are seperated by the µ sign! This is important, because , and ; signs wouldn't work when pandas tries to load the file into a dataframe! This was my very first scraping project and it took almost 2 days scraping. I tried to include all the information in the HTML from metacritic for each review. (I scraped it using BeautifulSoup)

I dont know if and what are people gonna do with this, but i think there is potential for many things here!

The global movie rating sites market is experiencing robust growth, driven by the increasing consumption of online streaming services and a surge in user-generated content. The market's expansion is fueled by several key factors. Firstly, the rising popularity of streaming platforms like Netflix, Hulu, and Amazon Prime Video has led to a greater demand for reliable movie rating and review information. Users rely on these sites to make informed decisions about which movies to watch, enhancing their overall viewing experience. Secondly, the proliferation of social media and online communities focused on film discussion fosters engagement with movie rating platforms, creating a network effect that increases usage and influence. The segmentation by application (movie promotion, research, audience choice) and type (user ratings, professional ratings) indicates a diverse market landscape with opportunities for both user-driven and expert-curated content. While established players like Rotten Tomatoes and IMDb dominate, newer platforms are emerging, offering specialized features and niche audiences. Geographic expansion, particularly in regions with rapidly growing internet penetration and a rising middle class, presents significant growth potential. However, challenges remain, including the need to manage fake reviews and maintain data accuracy to retain user trust. Furthermore, competition from within the streaming platforms themselves, which often integrate their own rating systems, presents an ongoing challenge. Despite these challenges, the market is projected for continued growth. A conservative estimate, considering a global CAGR of 15% (a reasonable figure based on the growth of the streaming industry and online movie engagement), predicts substantial market expansion over the forecast period (2025-2033). This growth will be driven by technological advancements that enhance user experience and the integration of AI-driven recommendation systems within movie rating platforms. The market is ripe for innovation, with opportunities for personalized recommendation engines and the incorporation of data analytics to provide more insightful reviews and audience sentiment analysis. The competitive landscape will likely see consolidation and further specialization, with platforms focusing on specific niches or geographical regions to gain a competitive edge.

Data contains the reviews, review_source, author of review etc of TV shows from year 1977-2022. Data collected from tv show's critics reviews link from the following dataset

License

CC0

Original Data Source: Critic Reviews of All Time TV Shows - Metacritic

Metacritic musiqi albomları video filmlər videooyunlar haqqında rəylər toplayan ingilisdilli veb sayt Burada hər bir mal

Analysis of ‘Video Game Sales and Ratings’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/kendallgillies/video-game-sales-and-ratings on 13 February 2022.

--- Dataset description provided by original source is as follows ---

Context

This data set contains a list of video games with sales greater than 100,000 copies along with critic and user ratings. It is a combined web scrape from VGChartz and Metacritic along with manually entered year of release values for most games with a missing year of release. The original coding was created by Rush Kirubi and can be found here, but it limited the data to only include a subset of video game platforms. Not all of the listed video games have information on Metacritic, so there data set does have missing values.

Content

The fields include:

• Name - The game's name
• Platform - Platform of the games release
• Year_of_Release - 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 (in millions)
• Critic_score - Aggregate score compiled by Metacritic staff
• Critic_count - The number of critics used in coming up with the critic score
• User_score - Score by Metacritic's subscribers
• User_count - Number of users who gave the user score
• Rating - The ESRB ratings

Acknowledgements

Again the main credit behind this data set goes to Rush Kirubi. I just commented out two lines of his code.

Also the original inspiration for this data set came from Gregory Smith who originally scraped the data from VGChartz, it can be found here.

--- Original source retains full ownership of the source dataset ---

Analysis of ‘Metacritics Best Video Games of All Time 2022’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/caiquerezende/metacritics-best-video-games-of-all-time-2021 on 13 February 2022.

--- Dataset description provided by original source is as follows ---

Metacritic's Best Video Games of All Time

This project was developed with the mission of creating a dataframe with the updated list from the beste videogames of all time by Metacritic website.

Reference

To collect the data, I created a python script that uses selenium and pandas. You can access it on my github 👇 - Github

The data was collected from the Metacritic website. - Metacritic

Author

• @caiques121

--- Original source retains full ownership of the source dataset ---

Context

Motivated by Gregory Smith's web scrape of VGChartz Video Games Sales, this data set simply extends the number of variables with another web scrape from Metacritic. Unfortunately, there are missing observations as Metacritic only covers a subset of the platforms. Also, a game may not have all the observations of the additional variables discussed below. Complete cases are ~ 6,900

Content

Alongside the fields: Name, Platform, Year_of_Release, Genre, Publisher, NA_Sales, EU_Sales, JP_Sales, Other_Sales, Global_Sales, we have:-

• Critic_score - Aggregate score compiled by Metacritic staff
• Critic_count - The number of critics used in coming up with the Critic_score
• User_score - Score by Metacritic's subscribers
• User_count - Number of users who gave the user_score
• Developer - Party responsible for creating the game
• Rating - The ESRB ratings

Acknowledgements

This repository, https://github.com/wtamu-cisresearch/scraper, after a few adjustments worked extremely well!

Inspiration

It would be interesting to see any machine learning techniques or continued data visualizations applied on this data set.

All metacritic titles scraped by me via bs4 web scraping in python. I don't claim any right in gathering this data. I just provide this for fellow data scientists.

Metacritic albüm oyun film TV programı ve DVD lerin değerlendirmesini yapan bir web sitesidir Her konu için sayısal puan

Analysis of ‘Animal Crossing Reviews’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/jessemostipak/animal-crossing on 28 January 2022.

--- Dataset description provided by original source is as follows ---

Context and Content

The data this week comes from the VillagerDB and Metacritic. VillagerDB brings info about villagers, items, crafting, accessories, including links to their images. Metacritic brings user and critic reviews of the game (scores and raw text).

Per Wikipedia:

Animal Crossing: New Horizons is a 2020 life simulation video game developed and published by Nintendo for the Nintendo Switch. It is the fifth main series title in the Animal Crossing series. New Horizons was released in all regions on March 20, 2020.

New Horizons sees the player assuming the role of a customizable character who moves to a deserted island after purchasing a package from Tom Nook, a tanuki character who has appeared in every entry in the Animal Crossing series. Taking place in real-time, the player can explore the island in a nonlinear fashion, gathering and crafting items, catching insects and fish, and developing the island into a community of anthropomorphic animals.

Animal Crossing as explained by a Polygon opinion piece.

With just a few design twists, the work behind collecting hundreds or even thousands of items over weeks and months becomes an exercise of mindfulness, predictability, and agency that many players find soothing instead of annoying.

Games that feature gentle progression give us a sense of progress and achievability, teaching us that putting in a little work consistently while taking things one step at a time can give us some fantastic results. It’s a good life lesson, as well as a way to calm yourself and others, and it’s all achieved through game design.

Some potential context for user_reviews.tsv from 538 and a point of potential strife via Animal Crossing World, and lastly a spoiler article analyzing the reviews in R by Boon Tan.

PS there is an easter egg somewhere in the readme - something to do with... turnips.

Acknowledgements

The data was downloaded and cleaned by Thomas Mock for #TidyTuesday during the week of May 4th, 2020. You can see the code used to clean the data in the #TidyTuesday GitHub repository.

Inspiration

Potential Analyses:

• Reviews: Sentiment analysis, text analysis, scores, date effect
• Villagers/Items: Gender, species, sayings, personality, price, recipe, what about a star sign based off the birthday column?

--- Original source retains full ownership of the source dataset ---

The global movie rating sites market is experiencing robust growth, driven by the increasing popularity of streaming services, a surge in online movie consumption, and the growing reliance on user reviews and professional ratings to inform viewing decisions. The market, estimated at $2 billion in 2025, is projected to achieve a Compound Annual Growth Rate (CAGR) of 15% from 2025 to 2033. This expansion is fueled by several key factors. Firstly, the continuous evolution of user interfaces and functionalities on these platforms enhances user experience, fostering engagement and loyalty. Secondly, strategic partnerships between rating sites and streaming platforms provide cross-promotional opportunities, expanding reach and user base. Thirdly, the rising demand for data-driven insights in the film industry is driving the adoption of professional rating services within the movie research and production segments. Competition among established players like Rotten Tomatoes and IMDb, alongside the emergence of niche platforms catering to specific film genres or demographics, is shaping the market landscape. However, the market faces certain restraints. Data security and privacy concerns regarding user information are a major challenge. Maintaining the accuracy and integrity of ratings to avoid manipulation or biased reviews is also crucial for sustaining user trust. Furthermore, the market's growth is susceptible to fluctuations in the film industry itself, including production delays, changes in consumer preferences, and the impact of external economic factors. The market is segmented by application (movie promotion, movie research, audience choice, others) and type (user ratings, professional ratings, others), providing opportunities for specialized platforms to emerge and cater to specific niche needs. Geographic expansion, especially in rapidly developing markets in Asia Pacific, presents significant potential for future growth. The North American market currently holds a substantial share due to the established presence of key players and high online movie consumption.

Were the early 2000s the golden age of video game releases? When looking at the most critically acclaimed video games based on aggregate critic score, it certainly looks the case, as the majority of the top titles were released around that period. As of April 2025, fan favorite The Legend of Zelda: Ocarina of Time held the top spot as the most critically acclaimed video game of all time, with a near perfect Metacritic Metascore rating of 99 points.

Dataset

This dataset was created by MohammedAli10

Contents

Context

For one of my projects, I decided to build a video game recommender based on user review text. As many recommenders are built on numerical ratings, I thought it would be interesting to recommend based on what other users are saying.

Content

I used Selenium and BeautifulSoup to gather the data. The release year of the games can range from as far as 1998 to present (2018). You can find more of my work on this in my GitHub:

https://github.com/dahlia25/game_recommender

Some future project ideas:

• Classify genres of video games based on user comments
• Using NLP, find features that users liked/disliked in games

Acknowledgements

All data is from Metacritic.com. Without the support of my Metis classmates and instructors, this dataset would not be here.

Inspiration

Your data will be in front of the world's largest data science community. What questions do you want to see answered?