This is an example of a public dataset on the AST Data Repository

This document provides guidance to State agencies on evaluating datasets with PII, PHI, or other forms of private or confidential data. This guidance includes a sample risk benefit analysis form and process to enable agencies to evaluate datasets for publication and help select appropriate privacy protections for open datasets.

This dataset includes public datasets for the use of workshop examples.

The Public Use Microdata Samples (PUMS) are computer-accessible files containing records for a sample of housing Units, with information on the characteristics of each housing Unit and the people in it for 1940-1990. Within the limits of sample size and geographical detail, these files allow users to prepare virtually any tabulations they require. Each datafile is documented in a codebook containing a data dictionary and supporting appendix information. Electronic versions for the codebooks are only available for the 1980 and 1990 datafiles. Identifying information has been removed to protect the confidentiality of the respondents. PUMS is produced by the United States Census Bureau (USCB) and is distributed by USCB, Inter-university Consortium for Political and Social Research (ICPSR), and Columbia University Center for International Earth Science Information Network (CIESIN).

Context

NYC Open Data is an opportunity to engage New Yorkers in the information that is produced and used by City government. We believe that every New Yorker can benefit from Open Data, and Open Data can benefit from every New Yorker. Source: https://opendata.cityofnewyork.us/overview/

Content

Thanks to NYC Open Data, which makes public data generated by city agencies available for public use, and Citi Bike, we've incorporated over 150 GB of data in 5 open datasets into Google BigQuery Public Datasets, including:

• Over 8 million 311 service requests from 2012-2016

• More than 1 million motor vehicle collisions 2012-present

• Citi Bike stations and 30 million Citi Bike trips 2013-present

• Over 1 billion Yellow and Green Taxi rides from 2009-present

• Over 500,000 sidewalk trees surveyed decennially in 1995, 2005, and 2015

This dataset is deprecated and not being updated.

Fork this kernel to get started with this dataset.

Acknowledgements

https://opendata.cityofnewyork.us/

https://cloud.google.com/blog/big-data/2017/01/new-york-city-public-datasets-now-available-on-google-bigquery

This dataset is publicly available for anyone to use under the following terms provided by the Dataset Source - https://data.cityofnewyork.us/ - and is provided "AS IS" without any warranty, express or implied, from Google. Google disclaims all liability for any damages, direct or indirect, resulting from the use of the dataset.

By accessing datasets and feeds available through NYC Open Data, the user agrees to all of the Terms of Use of NYC.gov as well as the Privacy Policy for NYC.gov. The user also agrees to any additional terms of use defined by the agencies, bureaus, and offices providing data. Public data sets made available on NYC Open Data are provided for informational purposes. The City does not warranty the completeness, accuracy, content, or fitness for any particular purpose or use of any public data set made available on NYC Open Data, nor are any such warranties to be implied or inferred with respect to the public data sets furnished therein.

The City is not liable for any deficiencies in the completeness, accuracy, content, or fitness for any particular purpose or use of any public data set, or application utilizing such data set, provided by any third party.

Banner Photo by @bicadmedia from Unplash.

Inspiration

On which New York City streets are you most likely to find a loud party?

Can you find the Virginia Pines in New York City?

Where was the only collision caused by an animal that injured a cyclist?

What’s the Citi Bike record for the Longest Distance in the Shortest Time (on a route with at least 100 rides)?

https://cloud.google.com/blog/big-data/2017/01/images/148467900588042/nyc-dataset-6.png" alt="enter image description here"> https://cloud.google.com/blog/big-data/2017/01/images/148467900588042/nyc-dataset-6.png

About

From website:

Public Data Sets on AWS provides a centralized repository of public data sets that can be seamlessly integrated into AWS cloud-based applications. AWS is hosting the public data sets at no charge for the community, and like all AWS services, users pay only for the compute and storage they use for their own applications. An initial list of data sets is already available, and more will be added soon.

Previously, large data sets such as the mapping of the Human Genome and the US Census data required hours or days to locate, download, customize, and analyze. Now, anyone can access these data sets from their Amazon Elastic Compute Cloud (Amazon EC2) instances and start computing on the data within minutes. Users can also leverage the entire AWS ecosystem and easily collaborate with other AWS users. For example, users can produce or use prebuilt server images with tools and applications to analyze the data sets. By hosting this important and useful data with cost-efficient services such as Amazon EC2, AWS hopes to provide researchers across a variety of disciplines and industries with tools to enable more innovation, more quickly.

This report provides common criteria to help identify high value datasets and provide examples of common types of high value datasets. It was based on jurisdictional scans of high value dataset criteria, recent surveys, and international standards

This data package contains claims-based data about beneficiaries of Medicare program services including Inpatient, Outpatient, related to Chronic Conditions, Skilled Nursing Facility, Home Health Agency, Hospice, Carrier, Durable Medical Equipment (DME) and data related to Prescription Drug Events. It is necessary to mention that the values are estimated and counted, by using a random sample of fee-for-service Medicare claims.

[Sample Dataset] April 2024 Public Data File from Crossref. This dataset includes 100 random JSON records from the Crossref metadata corpus.

The data were collected during the user-centered analysis of usability of 41 open government data portals including EU27, applying a common methodology to them, considering aspects such as specification of open data set, feedback and requests, further broken down into 14 sub-criteria. Each aspect was assessed using a three-level Likert scale (fulfilled - 3, partially fulfilled - 2, and unfulfilled – 1), that belongs to the acceptability tasks. This dataset summarises a total of 1640 protocols obtained during the analysis of the selected portals carried out by 40 participants, who were selected on a voluntary basis. This is complemented with 4 summaries of these protocols, which include calculated average scores by category, aspect and country. These data allow comparative analysis of the national open data portals, help to find the key challenges that can negatively impact users’ experience, and identifies portals that can be considered as an example for the less successful open data portals.

This is a study to assess the application of process mining techniques on data from the Brazilian public services, made available on open data portals, aiming to identify bottlenecks and improvement opportunities in government processes. The datasets were obtained from the Brazilian Federal Government's Open Data Portal: dados.govCategorization:(1) event log(2) there is a complete date(3) list of data or information table(4) documents(5) no file founded(6) link to another portalLink of brasilian portal: https://dados.gov.br/homeList of content made available:open-data-sample.zip: all the files obtained from the representative sample of the studyopen-data-sample.xls: table categorizing the datasets obtained and classifying them as relevant for testing in the process mining toolsdataset137.csv: dataset with undergraduate degree records tested in the Disco, Celonis and ProM toolsdataset258.csv: dataset with software registration requests tested in the Disco, Celonis and ProM toolsdataset356.csv: dataset with public tender inspector registrations tested in the Disco, Celonis and ProM tools

This Public Health Portfolio (Directly Funded Research - Programme and Training Awards) dataset contains NIHR directly funded research awards where the funding is allocated to an award holder or host organisation to carry out a specific piece of research or complete a training award. The NIHR also invests significantly in centres of excellence, collaborations, services and facilities to support research in England. Collectively these form NIHR infrastructure support. NIHR infrastructure supported projects are available in the Public Health Portfolio (Infrastructure Support) dataset which you can find here.NIHR directly funded research awards (Programmes and Training Awards) that were funded between January 2006 and the present extraction date are eligible for inclusion in this dataset. An agreed inclusion/exclusion criteria is used to categorise awards as public health awards (see below). Following inclusion in the dataset, public health awards are second level coded to one of the four Public Health Outcomes Framework domains. These domains are: (1) wider determinants (2) health improvement (3) health protection (4) healthcare and premature mortality.More information on the Public Health Outcomes Framework domains can be found here.This dataset is updated quarterly to include new NIHR awards categorised as public health awards. Please note that for those Public Health Research Programme projects showing an Award Budget of £0.00, the project is undertaken by an on-call team for example, PHIRST, Public Health Review Team, or Knowledge Mobilisation Team, as part of an ongoing programme of work.Inclusion CriteriaThe NIHR Public Health Overview project team worked with colleagues across NIHR public health research to define the inclusion criteria for NIHR public health research. NIHR directly funded research awards are categorised as public health if they are determined to be ‘investigations of interventions in, or studies of, populations that are anticipated to have an effect on health or on health inequity at a population level.’ This definition of public health is intentionally broad to capture the wide range of NIHR public health research across prevention, health improvement, health protection, and healthcare services (both within and outside of NHS settings). This dataset does not reflect the NIHR’s total investment in public health research. The intention is to showcase a subset of the wider NIHR public health portfolio. This dataset includes NIHR directly funded research awards categorised as public health awards. This dataset does not include public health awards or projects funded by any of the three NIHR Research Schools or NIHR Health Protection Research Units.DisclaimersUsers of this dataset should acknowledge the broad definition of public health that has been used to develop the inclusion criteria for this dataset. Please note that this dataset is currently subject to a limited data quality review. We are working to improve our data collection methodologies. Please also note that some awards may also appear in other NIHR curated datasets. Further InformationFurther information on the individual awards shown in the dataset can be found on the NIHR’s Funding & Awards website here. Further information on individual NIHR Research Programme’s decision making processes for funding health and social care research can be found here.Further information on NIHR’s investment in public health research can be found as follows:The NIHR is one of the main funders of public health research in the UK. Public health research falls within the remit of a range of NIHR Directly Funded Research (Programmes and Training Awards), and NIHR Infrastructure Support. NIHR School for Public Health here.NIHR Public Health Policy Research Unit here. NIHR Health Protection Research Units here.NIHR Public Health Research Programme Health Determinants Research Collaborations (HDRC) here.NIHR Public Health Research Programme Public Health Intervention Responsive Studies Teams (PHIRST) here.

Overview

The Associated Press is sharing data from the COVID Impact Survey, which provides statistics about physical health, mental health, economic security and social dynamics related to the coronavirus pandemic in the United States.

Conducted by NORC at the University of Chicago for the Data Foundation, the probability-based survey provides estimates for the United States as a whole, as well as in 10 states (California, Colorado, Florida, Louisiana, Minnesota, Missouri, Montana, New York, Oregon and Texas) and eight metropolitan areas (Atlanta, Baltimore, Birmingham, Chicago, Cleveland, Columbus, Phoenix and Pittsburgh).

The survey is designed to allow for an ongoing gauge of public perception, health and economic status to see what is shifting during the pandemic. When multiple sets of data are available, it will allow for the tracking of how issues ranging from COVID-19 symptoms to economic status change over time.

The survey is focused on three core areas of research:

• Physical Health: Symptoms related to COVID-19, relevant existing conditions and health insurance coverage.
• Economic and Financial Health: Employment, food security, and government cash assistance.
• Social and Mental Health: Communication with friends and family, anxiety and volunteerism. (Questions based on those used on the U.S. Census Bureau’s Current Population Survey.) ## Using this Data - IMPORTANT This is survey data and must be properly weighted during analysis: DO NOT REPORT THIS DATA AS RAW OR AGGREGATE NUMBERS!!

Instead, use our queries linked below or statistical software such as R or SPSS to weight the data.

Queries

If you'd like to create a table to see how people nationally or in your state or city feel about a topic in the survey, use the survey questionnaire and codebook to match a question (the variable label) to a variable name. For instance, "How often have you felt lonely in the past 7 days?" is variable "soc5c".

Nationally: Go to this query and enter soc5c as the variable. Hit the blue Run Query button in the upper right hand corner.

Local or State: To find figures for that response in a specific state, go to this query and type in a state name and soc5c as the variable, and then hit the blue Run Query button in the upper right hand corner.

The resulting sentence you could write out of these queries is: "People in some states are less likely to report loneliness than others. For example, 66% of Louisianans report feeling lonely on none of the last seven days, compared with 52% of Californians. Nationally, 60% of people said they hadn't felt lonely."

Margin of Error

The margin of error for the national and regional surveys is found in the attached methods statement. You will need the margin of error to determine if the comparisons are statistically significant. If the difference is:

• At least twice the margin of error, you can report there is a clear difference.
• At least as large as the margin of error, you can report there is a slight or apparent difference.
• Less than or equal to the margin of error, you can report that the respondents are divided or there is no difference. ## A Note on Timing Survey results will generally be posted under embargo on Tuesday evenings. The data is available for release at 1 p.m. ET Thursdays.

About the Data

The survey data will be provided under embargo in both comma-delimited and statistical formats.

Each set of survey data will be numbered and have the date the embargo lifts in front of it in the format of: 01_April_30_covid_impact_survey. The survey has been organized by the Data Foundation, a non-profit non-partisan think tank, and is sponsored by the Federal Reserve Bank of Minneapolis and the Packard Foundation. It is conducted by NORC at the University of Chicago, a non-partisan research organization. (NORC is not an abbreviation, it part of the organization's formal name.)

Data for the national estimates are collected using the AmeriSpeak Panel, NORC’s probability-based panel designed to be representative of the U.S. household population. Interviews are conducted with adults age 18 and over representing the 50 states and the District of Columbia. Panel members are randomly drawn from AmeriSpeak with a target of achieving 2,000 interviews in each survey. Invited panel members may complete the survey online or by telephone with an NORC telephone interviewer.

Once all the study data have been made final, an iterative raking process is used to adjust for any survey nonresponse as well as any noncoverage or under and oversampling resulting from the study specific sample design. Raking variables include age, gender, census division, race/ethnicity, education, and county groupings based on county level counts of the number of COVID-19 deaths. Demographic weighting variables were obtained from the 2020 Current Population Survey. The count of COVID-19 deaths by county was obtained from USA Facts. The weighted data reflect the U.S. population of adults age 18 and over.

Data for the regional estimates are collected using a multi-mode address-based (ABS) approach that allows residents of each area to complete the interview via web or with an NORC telephone interviewer. All sampled households are mailed a postcard inviting them to complete the survey either online using a unique PIN or via telephone by calling a toll-free number. Interviews are conducted with adults age 18 and over with a target of achieving 400 interviews in each region in each survey.Additional details on the survey methodology and the survey questionnaire are attached below or can be found at https://www.covid-impact.org.

Attribution

Results should be credited to the COVID Impact Survey, conducted by NORC at the University of Chicago for the Data Foundation.

AP Data Distributions

​To learn more about AP's data journalism capabilities for publishers, corporations and financial institutions, go here or email kromano@ap.org.

Success.ai offers a cutting-edge solution for businesses and organizations seeking Company Financial Data on private and public companies. Our comprehensive database is meticulously crafted to provide verified profiles, including contact details for financial decision-makers such as CFOs, financial analysts, corporate treasurers, and other key stakeholders. This robust dataset is continuously updated and validated using AI technology to ensure accuracy and relevance, empowering businesses to make informed decisions and optimize their financial strategies.

Key Features of Success.ai's Company Financial Data:

Global Coverage: Access data from over 70 million businesses worldwide, including public and private companies across all major industries and regions. Our datasets span 250+ countries, offering extensive reach for your financial analysis and market research.

Detailed Financial Profiles: Gain insights into company financials, including revenue, profit margins, funding rounds, and operational costs. Profiles are enriched with key contact details, including work emails, phone numbers, and physical addresses, ensuring direct access to decision-makers.

Industry-Specific Data: Tailored datasets for sectors such as financial services, manufacturing, technology, healthcare, and energy, among others. Each dataset is customized to meet the unique needs of industry professionals and analysts.

Real-Time Accuracy: With continuous updates powered by AI-driven validation, our financial data maintains a 99% accuracy rate, ensuring you have access to the most reliable and up-to-date information available.

Compliance and Security: All data is collected and processed in strict adherence to global compliance standards, including GDPR, ensuring ethical and lawful usage.

Why Choose Success.ai for Company Financial Data?

Best Price Guarantee: We pride ourselves on offering the most competitive pricing in the industry, ensuring you receive unparalleled value for comprehensive financial data.

AI-Validated Accuracy: Our advanced AI algorithms meticulously verify every data point to ensure precision and reliability, helping you avoid costly errors in your financial decision-making.

Customized Data Solutions: Whether you need data for a specific region, industry, or type of business, we tailor our datasets to align perfectly with your requirements.

Scalable Data Access: From small startups to global enterprises, our platform caters to businesses of all sizes, delivering scalable solutions to suit your operational needs.

Comprehensive Use Cases for Financial Data:

1. Strategic Financial Planning:

Leverage our detailed financial profiles to create accurate budgets, forecasts, and strategic plans. Gain insights into competitors’ financial health and market positions to make data-driven decisions.

1. Mergers and Acquisitions (M&A):

Access key financial details and contact information to streamline your M&A processes. Identify potential acquisition targets or partners with verified profiles and financial data.

1. Investment Analysis:

Evaluate the financial performance of public and private companies for informed investment decisions. Use our data to identify growth opportunities and assess risk factors.

1. Lead Generation and Sales:

Enhance your sales outreach by targeting CFOs, financial analysts, and other decision-makers with verified contact details. Utilize accurate email and phone data to increase conversion rates.

1. Market Research:

Understand market trends and financial benchmarks with our industry-specific datasets. Use the data for competitive analysis, benchmarking, and identifying market gaps.

APIs to Power Your Financial Strategies:

Enrichment API: Integrate real-time updates into your systems with our Enrichment API. Keep your financial data accurate and current to drive dynamic decision-making and maintain a competitive edge.

Lead Generation API: Supercharge your lead generation efforts with access to verified contact details for key financial decision-makers. Perfect for personalized outreach and targeted campaigns.

Tailored Solutions for Industry Professionals:

Financial Services Firms: Gain detailed insights into revenue streams, funding rounds, and operational costs for competitor analysis and client acquisition.

Corporate Finance Teams: Enhance decision-making with precise data on industry trends and benchmarks.

Consulting Firms: Deliver informed recommendations to clients with access to detailed financial datasets and key stakeholder profiles.

Investment Firms: Identify potential investment opportunities with verified data on financial performance and market positioning.

What Sets Success.ai Apart?

Extensive Database: Access detailed financial data for 70M+ companies worldwide, including small businesses, startups, and large corporations.

Ethical Practices: Our data collection and processing methods are fully comp...

This dataset supports the SWAMP Data Dashboard, a public-facing tool developed by the Surface Water Ambient Monitoring Program (SWAMP) to provide accessible, user-friendly access to water quality monitoring data across California. The dashboard and its associated datasets are designed to help the public, researchers, and decision-makers explore and download monitoring data collected from California’s surface waters.

This dataset includes five distinct resources:

• SWAMP Stations – Geospatial and descriptive information about SWAMP monitoring sites.
• Water Quality Results – Field and lab analysis results for chemical and physical parameters measured in water samples.
• Toxicity Summary Results – Summarized results from aquatic toxicity tests. Summary records are entries in the database that summarize the results from multiple replicate toxicity tests of the same sample water.
• Habitat Results – Data on physical habitat conditions typically collected alongside biological monitoring to provide context for interpreting water quality conditions. Includes scores for the California Stream Condition Index (CSCI) and Algal Stream Condition Index (ASCI).
• Tissue Summary Results – Annual summary statistics of contaminant concentrations in aquatic organism tissue samples. The data are derived from raw individual and composite tissue sample results.

These data are collected by SWAMP and its partners to support water quality assessments, identify trends, and inform water resource management. The SWAMP Data Dashboard provides interactive visualizations and filtering tools to explore this data by region, parameter, and more.

The SWAMP dataset is sourced from the California Environmental Data Exchange Network (CEDEN), which serves as the central repository for water quality data collected by various monitoring programs throughout the state. As such, there is some overlap between this dataset and the broader CEDEN datasets also published on the California Open Data Portal (see Related Resources). This SWAMP dataset represents a curated subset of CEDEN data, specifically tailored for use in the SWAMP Data Dashboard.

Access the SWAMP Data Dashboard: https://gispublic.waterboards.ca.gov/swamp-data/

*This dataset is provisional and subject to revision. It should not be used for regulatory purposes.

Data innovations happen daily: the semantic web, the cloud, visualization, mapping, sensors, spatial data infrastructures, etc. This portion of the Training Day will focus on recent access to public data initiatives in Canada with an emphasis on open government and open data. In this session participants will be introduced to data and participatory democracy, open data definitions and examples of good government policy. In addition, we will look at what some community groups are doing, the leadership in Canada’s big cities and the Province of BC by administrations and citizens. This will include licenses, open data initiatives, hackfests, hackathons, applications, challenges and opportunities. It is hoped that this overview will provide participants with insight about what is new in the Canadian access to public data world.

Unlock the power of ready-to-use data sourced from developer communities and repositories with Developer Community and Code Datasets.

Data Sources:

1. GitHub: Access comprehensive data about GitHub repositories, developer profiles, contributions, issues, social interactions, and more.

2. StackShare: Receive information about companies, their technology stacks, reviews, tools, services, trends, and more.

3. DockerHub: Dive into data from container images, repositories, developer profiles, contributions, usage statistics, and more.

Developer Community and Code Datasets are a treasure trove of public data points gathered from tech communities and code repositories across the web.

With our datasets, you'll receive:

• Usernames;
• Companies;
• Locations;
• Job Titles;
• Follower Counts;
• Contact Details;
• Employability Statuses;
• And More.

Choose from various output formats, storage options, and delivery frequencies:

• Get datasets in CSV, JSON, or other preferred formats.
• Opt for data delivery via SFTP or directly to your cloud storage, such as AWS S3.
• Receive datasets either once or as per your agreed-upon schedule.

Why choose our Datasets?

1. Fresh and accurate data: Access complete, clean, and structured data from scraping professionals, ensuring the highest quality.

2. Time and resource savings: Let us handle data extraction and processing cost-effectively, freeing your resources for strategic tasks.

3. Customized solutions: Share your unique data needs, and we'll tailor our data harvesting approach to fit your requirements perfectly.

4. Legal compliance: Partner with a trusted leader in ethical data collection. Oxylabs is trusted by Fortune 500 companies and adheres to GDPR and CCPA standards.

Pricing Options:

Standard Datasets: choose from various ready-to-use datasets with standardized data schemas, priced from $1,000/month.

Custom Datasets: Tailor datasets from any public web domain to your unique business needs. Contact our sales team for custom pricing.

Experience a seamless journey with Oxylabs:

• Understanding your data needs: We work closely to understand your business nature and daily operations, defining your unique data requirements.
• Developing a customized solution: Our experts create a custom framework to extract public data using our in-house web scraping infrastructure.
• Delivering data sample: We provide a sample for your feedback on data quality and the entire delivery process.
• Continuous data delivery: We continuously collect public data and deliver custom datasets per the agreed frequency.

Empower your data-driven decisions with Oxylabs Developer Community and Code Datasets!

Building a comprehensive data inventory as required by section 6.3 of the Directive on Open Government: “Establishing and maintaining comprehensive inventories of data and information resources of business value held by the department to determine their eligibility and priority, and to plan for their effective release.” Creating a data inventory is among the first steps in identifying federal data that is eligible for release. Departmental data inventories has been published on the Open Government portal, Open.Canada.ca, so that Canadians can see what federal data is collected and have the opportunity to indicate what data is of most interest to them, helping departments to prioritize data releases based on both external demand and internal capacity. The objective of the inventory is to provide a landscape of all federal data. While it is recognized that not all data is eligible for release due to the nature of the content, departments are responsible for identifying and including all datasets of business values as part of the inventory exercise with the exception of datasets whose title contains information that should not be released to be released to the public due to security or privacy concerns. These titles have been excluded from the inventory. Departments were provided with an open data inventory template with standardized elements to populate, and upload in the metadata catalogue, the Open Government Registry. These elements are described in the data dictionary file. Departments are responsible for maintaining up-to-date data inventories that reflect significant additions to their data holdings. For purposes of this open data inventory exercise, a dataset is defined as: “An organized collection of data used to carry out the business of a department or agency, that can be understood alone or in conjunction with other datasets”. Please note that the Open Data Inventory is no longer being maintained by Government of Canada organizations and is therefore not being updated. However, we will continue to provide access to the dataset for review and analysis.

The American Community Survey (ACS) Public Use Microdata Sample (PUMS) contains a sample of responses to the ACS. The ACS PUMS dataset includes variables for nearly every question on the survey, as well as many new variables that were derived after the fact from multiple survey responses (such as poverty status). Each record in the file represents a single person, or, in the household-level dataset, a single housing unit. In the person-level file, individuals are organized into households, making possible the study of people within the contexts of their families and other household members. Individuals living in Group Quarters, such as nursing facilities or college facilities, are also included on the person file. ACS PUMS data are available at the nation, state, and Public Use Microdata Area (PUMA) levels. PUMAs are special non-overlapping areas that partition each state into contiguous geographic units containing roughly 100,000 people each. ACS PUMS files for an individual year, such as 2020, contain data on approximately one percent of the United States population.

A supervised learning task involves constructing a mapping from an input data space (normally described by several features) to an output space. A set of training examples---examples with known output values---is used by a learning algorithm to generate a model. This model is intended to approximate the mapping between the inputs and outputs. This model can be used to generate predicted outputs for inputs that have not been seen before. Within supervised learning, one type of task is a classification learning task, in which each output consists of one or more classes to which the corresponding input belongs. For example, we may have data consisting of observations of sunspots. In a classification learning task, our goal may be to learn to classify sunspots into one of several types. Each example may correspond to one candidate sunspot with various measurements or just an image. A learning algorithm would use the supplied examples to generate a model that approximates the mapping between each supplied set of measurements and the type of sunspot. This model can then be used to classify previously unseen sunspots based on the candidate's measurements. In this chapter, we explain several basic classification algorithms.