Data set

For IBM mainframe term for a file, see Data set (IBM mainframe).

A data set (or dataset) is a collection of data, usually presented in tabular form. Each column represents a particular variable. Each row corresponds to a given member of the data set in question. Its values for each of the variables, such as height and weight of an object or values of random numbers. Each value is known as a datum. The data set may comprise data for one or more members, corresponding to the number of rows.

Nontabular data sets can take the form of marked up strings of characters, such as an XML file.

Almost all data sets, although they may often be written using high-level languages and base-ten numbers, end up transcoded into machine language for processing by the computers involved. Thus, for all their semantic diversity and tabular or nontabular forms, most datasets can be expressed in binary code as a long string of zeros and ones.

History

Historically, the term originated in the mainframe field, where it had a well-defined meaning, very close to contemporary computer file^{[citation needed]}. This topic is not covered here.

Properties

A data set has several characteristics which define its structure and properties. These include the number and types of the attributes or variables and the various statistical measures which may be applied to them such as standard deviation and kurtosis.^[1]

In the simplest case, there is only one variable, and then the data set consists of a single column of values, often represented as a list. In spite of the name, such a univariate data set is not a set in the usual mathematical sense, since a given value may occur multiple times. Normally the order does not matter, and then the collection of values may be considered to be a multiset rather than an (ordered) list^{[original research?]}.

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 will normally all be of the same kind. However, there may also be "missing values", which need to 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 PSPP still present their data in the classical data set fashion.

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).^[2]
• Categorical data analysis - Data sets used in the book, An Introduction to Categorical Data Analysis, by Agresti are provided on-line by StatLib.
• 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 provided on-line by Stuart Coles, the book's author. [Dead link]

• 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.

See also

• Interoperability

Notes

1. ^ Jan M. Żytkow, Jan Rauch (1999). Principles of data mining and knowledge discovery. ISBN 9783540664901. http://books.google.co.uk/books?id=uTzeRZFmaBgC&pg=PA100 
2. ^ Fisher, R.A. (1936). "The Use of Multiple Measurements in Taxonomic Problems". Annals of Eugenics 7: 179–188. doi:10.1111/j.1469-1809.1936.tb02137.x. http://digital.library.adelaide.edu.au/coll/special//fisher/138.pdf. 

External links

• Research Pipeline - A wiki/website with links to datasets on many different topics.
• StatLib--Datasets Archive
• StatLib--JASA Data Archive
• Data.gov
• UK Government Public Data
• GCMD - The Global Change Master Directory contains more than 20,000 descriptions of Earth science data sets and services covering all aspects of Earth and environmental sciences.