Finding Data Sources
- Leigh Phan
Recommended Data Collections
Whether you are looking for research data, or looking to practice your data science skills, below are some data collections we recommend.
Source | Description | Access and Notes |
---|---|---|
UC Irvine Machine Learning Repository | The UCI Machine Learning Repository is a collection of databases, domain theories, and data generators that are used by the machine learning community for the empirical analysis of machine learning algorithms. | Managed and hosted by UC Irvine. Free and open access. |
Kaggle.com | Kaggle is an online community that hosts competitions for machine learning. Kaggle also holds a collection of datasets, open for public use, although it requires users to create an account. Kaggle was acquired by Google in 2017. | Requires user email address to create an account. |
Tidy Tuesday | A weekly data project aimed at the R programming language ecosystem. As this project was borne out of the Datasets are posted each week and available on the R for Data Science GitHub. | Free and open access. UCLA Library Data Science Center periodically hosts Tidy Tuesday welcoming all to practice working with data together. |
UCLA Dataverse | Collection of research data and findings from the UCLA research community. | Free and open access. |
GitHub Awesome Public Datasets | Curated lists of public datasets on GitHub. | Free and open access. |
The General Index | The General Index consists of 3 tables derived from 107,233,728 journal articles. | Free and open access. |
Additional Information
Submit a Request
This service is available to
All | |
---|---|
Library Staff | * |
UCLA | * |
Campus-Affiliated | |
Other |
If this service is not available to you, and you have questions, please send an email to techhelp@library.ucla.edu for more information.
UCLA Library Service Catalog Home | UCLA Library Catalog | UCLA Library Website | Library News & Events | About UCLA IT Service Catalog | Feedback
A-Z Index
-
Page:
-
Page:
-
Page:
-
Page:
-
Page:
-
Page:
-
Page:
-
Page:
-
Page:
-
Page:
-
Page:
-
Page:
-
Page:
-
Page:
-
Page:
-
Page:
-
Page:
-
Page:
-
Page:
-
Page:
-
Page:
-
Page:
-
Page:
-
Page:
-
Page:
-
Page:
-
Page:
-
Page:
-
Page:
-
Page:
-
Page:
-
Page:
-
Page:
-
Page:
-
Page:
-
Page:
-
Page:
-
Page:
-
Page:
-
Page:
-
Page:
-
Page:
-
Page:
-
Page:
-
Page:
-
Page:
-
Page:
-
Page:
-
Page:
-
Page:
-
Page:
-
Page:
-
Page:
-
Page:
-
Page:
-
Page:
-
Page:
-
Page:
-
Page:
-
Page:
-
Page:
-
Page:
-
Page:
-
Page:
-
Page:
-
Page:
-
Page:
-
Page:
-
Page:
-
Page:
-
Page:
-
Page:
-
Page:
-
Page:
-
Page:
-
Page:
-
Page:
-
Page:
-
Page:
-
Page:
-
Page:
-
Page:
-
Page:
-
Page:
-
Page:
-
Page:
-
Page:
-
Page:
-
Page:
-
Page:
-
Page:
-
Page:
-
Page:
-
Page:
-
Page:
-
Page:
-
Page:
-
Page:
-
Page:
-
Page:
-
Page:
-
Page:
-
Page:
-
Page:
-
Page:
-
Page:
-
Page:
-
Page:
-
Page:
-
Page:
-
Page:
-
Page:
-
Page:
-
Page:
-
Page:
-
Page:
-
Page:
-
Page:
-
Page:
-
Page:
-
Page:
-
Page:
-
Page:
-
Page:
-
Page:
-
Page:
-
Page:
-
Page:
-
Page:
-
Page:
-
Page:
-
Page:
-
Page:
-
Page:
-
Page:
-
Page:
-
Page:
-
Page:
-
Page:
-
Page:
-
Page:
-
Page:
-
Page:
-
Page:
-
Page:
-
Page:
-
Page:
-
Page:
-
Page:
-
Page:
-
Page:
-
Page:
-
Page:
-
Page:
-
Page:
-
Page:
-
Page:
-
Page:
-
Page:
-
Page:
-
Page:
-
Page:
-
Page:
-
Page:
-
Page:
-
Page:
-
Page:
-
Page:
-
Page:
-
Page:
-
Page:
-
Page:
-
Page:
-
Page:
-
Page:
-
Page: