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. 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, dataset 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 datasets. 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 datasets, 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 (i.e., not consisting of numerical values), 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 (Rousseeuw and Leroy, 1986). 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.
- The Bupa liver data – Used in several papers in the machine learning (data mining) literature.
- Anscombe's quartet – Small data set illustrating the importance of graphing the data to avoid statistical fallacies
- Snijders, C.; Matzat, U.; Reips, U.-D. (2012). "'Big Data': Big gaps of knowledge in the field of Internet". International Journal of Internet Science. 7: 1–5.
- "European open data portal". European open data portal. European Commission. Retrieved 2016-09-23.
- "Dataset definition – MELODA". www.meloda.org. Retrieved 2016-08-17.
- Atz, U (2014). "The tau of data: A new metric to assess the timeliness of data in catalogues" (PDF). CEDEM 2014 Proceedings. Retrieved 2016-08-01.
- Jan M. Żytkow, Jan Rauch (1999). Principles of data mining and knowledge discovery. ISBN 978-3-540-66490-1.
- United Nations Statistical Commission; United Nations Economic Commission for Europe (2007). Statistical Data Editing: Impact on Data Quality: Volume 3 of Statistical Data Editing, Conference of European Statisticians Statistical standards and studies. United Nations Publications. p. 20. ISBN 9211169526. Retrieved 19 July 2015.
- Fisher, R.A. (1936). "The Use of Multiple Measurements in Taxonomic Problems" (PDF). Annals of Eugenics. 7: 179–188. doi:10.1111/j.1469-1809.1936.tb02137.x.
- Cogence – a curated collection of open government and other data sets
- Datahub – a community-managed home for open data sets
- GCMD – the Global Change Master Directory containing over 20,000 descriptions of Earth science and environmental science data sets and services
- Relational data set repository
- Research Pipeline – a wiki/website with links to data sets on many different topics
- StatLib–JASA Data Archive
- UCI – a machine learning repository
- UK Government Public Data