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This release contains information on the precinct-level returns for state elections in each of the fifty states of the United States for the year of 2016. Precinct-level elections are the smallest component in which electoral districts are divided into.
Overview
The source code outlining how this product gathers, transforms, revises and publishes its datasets is available at https://github.com/rearc-data/mit-state-level-returns-2016 .
Main Overview
This release contains information on the precinct-level returns for state elections in each of the fifty states of the United States for the year of 2016. Precinct-level elections are the smallest component in which electoral districts are divided into.
The included data is provided by Harvard Dataverse, which contains almost 100,000 datasets on various subjects. This specific dataset is maintained by the MIT Election Data and Science Lab (MEDSL). If you want to learn more or explore other datasets, visit the Harvard Dataverse homepage and the MEDSL homepage .
Data Source
This resource is presented in the CSV format. The dataset includes a codebook in which there is information on the columns and sources for the data. Additionally, there are also release and coverage notes provided.
More Information
- Source: MIT Election Data and Science Lab
- MIT Election Lab | Homepage
- Terms of Use
- Format: CSV
Contact Details
- If you find any issues with or have enhancement ideas for this product, open up a GitHub issue and we will gladly take a look at it. Better yet, submit a pull request. Any contributions you make are greatly appreciated ❤️.
- If you are looking for specific open datasets currently not available on ADX, please submit a request on our project board here .
- If you have questions about the source data, please contact MIT @ mitelectionlab@mit.edu .
- If you have any other questions or feedback, send us an email at data@rearc.io .
About Rearc
Rearc is a cloud, software and services company. We believe that empowering engineers drives innovation. Cloud-native architectures, modern software and data practices, and the ability to safely experiment can enable engineers to realize their full potential. We have partnered with several enterprises and startups to help them achieve agility. Our approach is simple — empower engineers with the best tools possible to make an impact within their industry.
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This listing is free and uses a single pricing dimension called Product Access (Units). That one dimension grants you access to the dataset as a subscriber. There are no tiers, usage add-ons, or instance sizes to choose between. You subscribe once and gain access to the data product. The vendor publishes free public datasets on AWS Data Exchange so subscribers can consume them in a consistent way.
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One unit grants you subscriber access to this single dataset. It is a logical access grant, not a compute resource or user seat. You subscribe once and can consume the data. There are no separate counts for users, hosts, or instances tied to this unit.
No. This listing is free and uses one access dimension with no usage metering. Your cost does not change based on how often you query or how much data you pull. There are no tiers or overage points that could trigger extra charges.
The vendor publishes free public datasets on AWS Data Exchange. As a subscriber, you consume the data in a consistent, cloud-native way through that service. This removes the work of sourcing and transforming the data yourself.
www.rearc.io
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Delivery details
AWS Data Exchange (ADX)
AWS Data Exchange is a service that helps AWS easily share and manage data entitlements from other organizations at scale.
Additional details
You will receive access to the following data sets.
Data set name | Type | Historical revisions | Future revisions | Sensitive information | Data dictionaries | Data samples |
|---|---|---|---|---|---|---|
State Precinct-Level Returns 2016 | Harvard Dataverse/MEDSL | All historical revisions | All future revisions | Not included | Not included |
Resources
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