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Need Help Working with Data?

Data & Visualization Services Department

This department provides training and computing resources related to data analysis, data management, and data visualization. The department provides free consultive and instructional assistance to UM-affiliated patrons. 

About this Guide

This guide can be used to help you locate data, data repositories, and statistics to use in your research.

Finding Data and Statistics

The Finding Data and Statistics section of the guide links out to the Data & Visualization Services departmental page where you can learn more about the services that the department provides around finding and working with data.

Data at UM

The Data at UM section of the guide highlights the data sources that the University of Miami Libraries has purchased or subscribed to for the University community. 

Open (Free) Data

The Open (Free) Data section of the guide shares selected open data sources where you can access data for free. 

Data Purchase Program

The Data Purchase Program section of the guide provides you with an opportunity to request data for the University of Miami Libraries to purchase. 

 

 

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Accessing Data through the University of Miami Libraries

The University of Miami Libraries supports access to data and statistics via subscriptions to data access databases and the purchasing of licensed datasets. Find a list of these data resources below. For additional data access resources, refer to the Databases A-Z list or see the lists at the very bottom of the page.

Data Sources at UM Use Key

- Data source accessible via subscription paid for by the University of Miami Libraries (UM authentication required)

 - Free (Open) data source

 - Must be accessed on campus

 

Access University of Miami Libraries Licensed Datasets

The following is a list of licensed datasets that have been purchased by the University of Miami Libraries.

  • Power outages in Florida by city and utility. 
    • Dataset of electrical power outages in Florida at the city level, organized by utility provider and recorded at hourly intervals from 2018 through 2025. The data were derived from PowerOutage.us, an online platform that aggregates and tracks power outages across the United States in near real time. The dataset supports analysis of outage patterns, infrastructure reliability, and the impact of extreme weather events on electrical service.
  • Property Appraiser of Miami-Dade County data. 
    • Collection of datasets containing property assessment, ownership, tax roll, sales, land, building, and tangible personal property information maintained by the Property Appraiser of Miami-Dade County. Includes current assessment data, historical tax roll event snapshots, sales records, and property characteristics for real estate parcels and tangible personal property accounts. Data covers the timeframe from 2009 to May 4th, 2026. 
  • US Consumer Historical Dataset. (Data Axle)  
    • Consists of annual snapshots of historical address level data from Data Axle (previously known as InfoGroup) US Consumers database. Includes annual datasets with household, marketing and geographic information. The data can be represented as a time series of consumer records from 2006 to 2025.

Need Access to Data that We Don't Have?

If you are looking for a data source or dataset that we do not yet provide access to, please consider submitting a data purchase request for the data. See the Data Purchase Program section of this research guide for more information on how to apply. 

Additional Data Sources

In addition to the data sources above, the following sections will highlight selected free data sources.

For information on biomedical & health science data sources, please view the following research guides:

For further information on biomedical and health sciences data, visit the Louis Calder Memorial Library website.

For information on marine and atmospheric sciences data sources, please view the following research guides:

For further information on marine and atmospheric sciences data, visit the Rosenstiel School of Marine, Atmospheric, and Earth Science Library website.

For information on spatial/GIS data sources, please view the following pages of the GIS Resources at the University Libraries research guide:

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University of Miami Libraries’ Dataset Purchase Program

Program Overview

The University of Miami Libraries’ Dataset Purchase Program is designed to support the University of Miami research community by providing the opportunity to request specific datasets to be purchased by the Libraries. 

Who Can Request a Dataset Purchase

University of Miami affiliated faculty, staff, and students are eligible to submit a request for the purchase of a dataset. 

Datasets that are Eligible for Purchase

The Dataset Purchase Program can only consider purchasing datasets that: 

  • Can be purchased one-time. Datasets available through a subscription are not eligible for the Dataset Purchase Program. 

  • Can be made available to all University of Miami faculty, staff, and students. 

  • Do not have restrictions on access.  

  • Do not require proprietary software for use. 

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Why Cite Data?

It is important to properly cite data just like you would cite other scholarly publications such as articles and books. Citing data ensures that the author or creator of the data receives appropriate credit. Additionally, citing data can improve the credibility of your research and allow anyone who is reviewing your work to have a better understanding of the sources used and where to locate the data themselves. 

Elements of a Dataset Citation

While the citation standards for datasets are not uniformly agreeded upon, most citations will include the following elements:

  • Author/Creator
  • Title
  • Publication Year
  • Publisher
  • Version (if relevant)
  • Access Information (such as a DOI)

Data Citation Examples

O’Donohue, W. (2017). Content analysis of undergraduate psychology textbooks (ICPSR 36966; Version V1) [Data set]. ICPSR. https://doi.org/10.3886/ICPSR36966.v1. APA's resource on Data set references
  • Parenthetical citation: (O’Donohue, 2017)
  • Narrative citation: O’Donohue (2017)

MIT Election Data and Science Lab. “U.S. Senate Precinct-Level Returns 2020.” Version 1.1. Harvard Dataverse, March 17, 2022. https://doi.org/10.7910/DVN/ER9XTV.

Chicago Manual of Style's resource on Citing Databases and Datasets.

The MLA Handbook does not specifically reference Datasets. Use this format: Author. Title of dataset. Publisher, Publication Date, Location. Publisher name, Date of publication (format DD Month YYYY), location. DOI/URL of data

Bureau of Transportation Statistics. 2020 National Census of Ferry Operators. United States Department of Transportation, 01 March, 2022, Washington DC. www.bts.gov/NCFO

Some repositories or journals provide an automatic citation for the data published in their repository. In the case, use the listed citation for the data.