data set

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.{{Cite book |url=https://books.google.com/books?id=uTzeRZFmaBgC&pg=PA100 |title=Principles of data mining and knowledge discovery |author=Jan M. Żytkow, Jan Rauch |isbn=978-3-540-66490-1 |year=2000|publisher=Springer }}

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. Missing values may exist, which must be indicated somehow.

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.{{cite book |title=Statistical Data Editing: Impact on Data Quality: Volume 3 of Statistical Data Editing, Conference of European Statisticians Statistical standards and studies |author=United Nations Statistical Commission |author2=United Nations Economic Commission for Europe |year=2007 |publisher=United Nations Publications |isbn=978-9211169522 |page=20 |url=https://unece.org/DAM/stats/publications/editing/SDE3.pdf }}

Several classic data sets have been used extensively in the statistical literature:

• Iris flower data set – Multivariate data set introduced by Ronald Fisher (1936).{{cite journal|author=Fisher, R.A.|title=The Use of Multiple Measurements in Taxonomic Problems|journal=Annals of Eugenics|volume=7|pages=179–188|year=1963|issue=2|url=http://digital.library.adelaide.edu.au/coll/special//fisher/138.pdf|doi=10.1111/j.1469-1809.1936.tb02137.x|hdl=2440/15227|hdl-access=free|access-date=2007-05-22|archive-date=2011-09-28|archive-url=https://web.archive.org/web/20110928044802/http://digital.library.adelaide.edu.au/coll/special//fisher/138.pdf|url-status=dead}} [https://archive.ics.uci.edu/ml/datasets/Iris Provided online by University of California-Irvine Machine Learning Repository].{{cite web |url=https://archive.ics.uci.edu/ml/datasets/Iris |title=UCI Machine Learning Repository: Iris Data Set |access-date=2023-05-02 |url-status=live |archive-url=https://web.archive.org/web/20230426065109/https://archive.ics.uci.edu/ml/datasets/Iris |archive-date=2023-04-26}}
• 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, [https://stats.oarc.ucla.edu/other/examples/icda/ provided online] by UCLA Advanced Research Computing.{{cite web |url=https://stats.oarc.ucla.edu/other/examples/icda/ |title=Textbook Examples An Introduction to Categorical Data Analysis by Alan Agresti |access-date=2023-05-02 |url-status=live |archive-url=https://web.archive.org/web/20230131013107/https://stats.oarc.ucla.edu/other/examples/icda/ |archive-date=2023-01-31}}
• Robust statistics – Data sets used in Robust Regression and Outlier Detection (Rousseeuw and Leroy, 1968). [https://web.archive.org/web/20050207032959/http://www.uni-koeln.de/themen/statistik/data/rousseeuw/ Provided online] at the University of Cologne.{{cite web |url=http://www.uni-koeln.de/themen/statistik/data/rousseeuw/ |title=The ROUSSEEUW datasets |url-status=dead |archive-url=https://web.archive.org/web/20050207032959/http://www.uni-koeln.de/themen/statistik/data/rousseeuw/ |archive-date=2005-02-07}}
• Time series – Data used in Chatfield's book, The Analysis of Time Series, are [https://web.archive.org/web/20110102201323/http://lib.stat.cmu.edu/modules.php?op=modload&name=PostWrap&file=index&page=datasets/ provided on-line] by StatLib.{{cite web |url=http://lib.stat.cmu.edu/modules.php?op=modload&name=PostWrap&file=index&page=datasets/ |title=StatLib :: Data, Software and News from the Statistics Community |url-status=dead |archive-url=https://web.archive.org/web/20110102201323/http://lib.stat.cmu.edu/modules.php?op=modload&name=PostWrap&file=index&page=datasets/ |archive-date=2011-01-02}}
• Extreme values – Data used in the book, An Introduction to the Statistical Modeling of Extreme Values are [https://web.archive.org/web/20060910161517/http://homes.stat.unipd.it/coles/public_html/ismev/ismev.dat 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 [http://www.stat.columbia.edu/~gelman/book/data/ provided on-line] ([https://web.archive.org/web/20230122121643/http://www.stat.columbia.edu/~gelman/book/data/ archive link]) by Andrew Gelman, one of the book's authors.
• The [https://web.archive.org/web/20171023174701/http://ftp.ics.uci.edu:80/pub/machine-learning-databases/liver-disorders/ 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.

Loading datasets using Python:

$ pip install datasets

from datasets import load_dataset

dataset = load_dataset(NAME OF DATASET)

• List of datasets for machine-learning research
• List of datasets in computer vision and image processing
• Data blending
• Data (computer science)
• Sampling
• Data store
• Interoperability
• Data collection system

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{{Wiktionary}}

• [https://www.data.gov/ Data.gov] – the U.S. Government's open data
• [https://earthdata.nasa.gov/gcmd GCMD] – the Global Change Master Directory containing over 34,000 descriptions of Earth science and environmental science data sets and services
• [https://data.humdata.org/ Humanitarian Data Exchange(HDX)] – The Humanitarian Data Exchange (HDX) is an open humanitarian data sharing platform managed by the United Nations Office for the Coordination of Humanitarian Affairs.
• [https://opendata.cityofnewyork.us/ NYC Open Data] – free public data published by New York City agencies and other partners.
• [https://relational.fel.cvut.cz/ Relational data set repository] {{Webarchive|url=https://web.archive.org/web/20180307150058/https://relational.fit.cvut.cz/ |date=2018-03-07 }}
• [https://web.archive.org/web/20190214051201/http://www.researchpipeline.com/mediawiki/index.php?title=Main_Page Research Pipeline] – a wiki/website with links to data sets on many different topics
• [http://lib.stat.cmu.edu/jasadata/ StatLib–JASA Data Archive]
• [https://archive.ics.uci.edu/ UCI] – a machine learning repository
• [https://data.gov.uk/ UK Government Public Data]
• [https://data.worldbank.org/ World Bank Open Data] – Free and open access to global development data by World Bank

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Category:Computer data

Category:Statistical data sets