A data set (or dataset) is a collection of data. Most commonly a data set corresponds to the contents of a single database table, or a single statistical data matrix, where every column of the table represents a particular variable, and each row corresponds to a given member of the data set in question. The data set lists values for each of the variables, such as height and weight of an object, for each member of the data set. Each value is known as a datum. The data set may comprise data for one or more members, corresponding to the number of rows.
The term data set may also be used more loosely, to refer to the data in a collection of closely related tables, corresponding to a particular experiment or event. Less used names for this kind of data sets are data corpus and data stock. An example of this type is the data sets collected by space agencies performing experiments with instruments aboard space probes. Data sets that are so large that traditional data processing applications are inadequate to deal with them are known as big data.
In the open data discipline, data set is the unit to measure the information released in a public open data repository. The European Open Data portal aggregates more than half a million data sets. In this field other definitions have been proposed but currently there is not an official one. Some other issues (real-time data sources, non-relational data sets, etc.) increases the difficulty to reach a consensus about it.
Several characteristics define a data set’s structure and properties. These include the number and types of the attributes or variables, and various statistical measures applicable to them, such as standard deviation and kurtosis.
The values may be numbers, such as real numbers or integers, for example representing a person’s height in centimeters, but may also be nominal data, for example representing a person’s ethnicity. More generally, values may be of any of the kinds described as a level of measurement. For each variable, the values are normally all of the same kind. However, there may also be missing values, which must be indicated in some way.
In statistics, data sets usually come from actual observations obtained by sampling a statistical population, and each row corresponds to the observations on one element of that population. Data sets may further be generated by algorithms for the purpose of testing certain kinds of software. Some modern statistical analysis software such as SPSS still present their data in the classical data set fashion. If data is missing or suspicious an imputation method may be used to complete a data set.
Classic data sets
Several classic data sets have been used extensively in the statistical literature:
- Iris flower data set – Multivariate data set introduced by Ronald Fisher (1936).
- MNIST database – Images of handwritten digits commonly used to test classification, clustering, and image processing algorithms
- Categorical data analysis – Data sets used in the book, An Introduction to Categorical Data Analysis.
- Robust statistics – Data sets used in Robust Regression and Outlier Detection Provided on-line at the University of Cologne.
- Time series – Data used in Chatfield’s book, The Analysis of Time Series, are provided on-line by StatLib.
- Extreme values – Data used in the book, An Introduction to the Statistical Modeling of Extreme Values are a snapshot of the data as it was provided on-line by Stuart Coles, the book’s author.
- Bayesian Data Analysis – Data used in the book are provided on-line by Andrew Gelman, one of the book’s authors.