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Super Mario Maker Temporal Dataset
Sports & Recreation
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Free
About
This dataset, known as SMMnet - Super Mario Maker, offers a rich collection of temporal data derived from the online game Super Mario Maker. It provides a unique opportunity to study dynamic game environments and player interactions, serving as a promising foundation for applying Artificial Intelligence and Data Mining techniques. The dataset captures over 115 thousand game maps, features interactions from more than 880 thousand players, and records over 7 million player activities. These interactions include creating maps, playing others' maps, clearing maps, achieving first clears, beating time records, and liking maps. The data notably presents temporal changes for each game map over a three-month period.
Columns
The dataset is structured across seven distinct files, each representing a different aspect of the game data. While a full schema describes how these tables are linked via primary and foreign keys, the
clears.csv file, for instance, includes the following columns:- catch: Further details about this column are not available in the provided information.
- timestamp: Indicates the time of an event.
- id: Refers to the game map identifier.
- player: Refers to the player identifier. The schema is designed with non-normalized tables suitable for a Relational Database Management System (RDBMS).
Distribution
The SMMnet dataset is delivered as a collection of seven separate CSV files:
courses.csv, course-meta.csv, players.csv, plays.csv, clears.csv, likes.csv, and records.csv. For example, the clears.csv file has a size of 112.83 MB. While precise row counts for each file are not explicitly stated, the dataset encompasses millions of player interactions and covers a substantial number of unique game maps and players, indicating its significant volume.Usage
This dataset is an ideal resource for developing and testing learning models across various domains. Its applications include, but are not limited to:
- Player Modelling: Analysing player experience, activity, and behaviour patterns.
- Data Mining: Performing predictions, discovering hidden patterns, and uncovering insights within gaming data.
- Social Network Analysis: Identifying communities, predicting links between players, and ranking player influence. More specific use cases involve detecting game influencers (e.g., Twitch and YouTube users), predicting the popularity of a game over time, and identifying the characteristics of popular games.
Coverage
The dataset's scope is global, reflecting interactions from millions of players across the world. It provides temporal insights, tracking changes in game maps and player activities over a period of three months. This temporal aspect allows for analysis of how game maps and player engagement evolve over time.
License
Attribution 4.0 International (CC BY 4.0)
Who Can Use It
This dataset is primarily intended for researchers and data scientists. It is particularly valuable for those working in the fields of Artificial Intelligence, Data Mining, and Social Network Analysis. Examples of ideal users include academics studying game design and player psychology, data analysts seeking to understand online community dynamics, and developers interested in predictive modelling for game engagement or popularity.
Dataset Name Suggestions
- SMMnet - Super Mario Maker
- Super Mario Maker Temporal Dataset
- Super Mario Maker Game Interaction Data
Attributes
Original Data Source: Super Mario Maker Temporal Dataset
QUALITY