Computer Science Research Resources

Explore key concepts and essential resources for computer science (CS) research. UH Libraries provide access to articles, books, research data, and more to support your work.

Introduction to Machine Learning & AI Datasets

Machine learning and AI research are driven by the availability of diverse datasets that support model training and algorithm evaluation. This page offers curated links to key repositories, categorized by domain, to help you find the right datasets for your projects. Whether you're working in natural language processing, computer vision, or general machine learning, these resources provide valuable data for researchers, data scientists, and students alike. Explore the collections below to discover datasets that will enhance and accelerate your AI and machine learning endeavors.

General Machine Learning Datasets

This section highlights various sources of datasets that are well-suited for machine learning tasks such as classification, regression, and predictive modeling. These platforms provide access to diverse collections of data, supporting a wide range of machine learning techniques for both academic and practical applications.

UCI Machine Learning Repository
A long-standing and trusted resource, the UCI Machine Learning Repository offers datasets for a variety of tasks including classification, clustering, and regression. It serves as a go-to repository for benchmarking algorithms and evaluating model performance in machine learning.
Kaggle Datasets
Kaggle offers a vast collection of datasets across multiple domains, many of which come from competitions and community contributions. It's an excellent platform for experimenting with machine learning techniques and tackling real-world problems.
OpenML
OpenML is a collaborative platform where datasets are shared and explored within the machine learning community. It allows users to experiment with datasets, run machine learning experiments, and compare models, creating an interactive and dynamic space for research.
Data.gov
Data.gov provides access to a variety of datasets from U.S. government agencies, including data on health, agriculture, and transportation. These datasets are valuable for developing machine learning models that address societal and policy-related challenges.

Natural Language Processing (NLP) Datasets

This section highlights key sources of datasets for natural language processing tasks. Whether you're working on text analysis, language modeling, or other NLP applications, these resources offer high-quality data to support your research and development.

Hugging Face (NLP)
A popular platform that provides access to a large collection of preprocessed datasets for a wide range of NLP tasks, such as text classification, translation, summarization, and more. Hugging Face makes it easy to load and integrate datasets directly into your NLP pipeline.
Common Crawl
Common Crawl is a non-profit organization that crawls the web and freely provides a massive dataset of web pages. This dataset is invaluable for training large-scale NLP models, especially in tasks like language modeling, information retrieval, and document classification.
OpenSLR
OpenSLR is a repository of speech and language resources that contains a collection of speech and NLP datasets for tasks like speech recognition and language modeling. It's widely used in both academic and industry research.
LexisNexis
LexisNexis offers a range of high-quality paid NLP datasets across domains such as legal, business, and news media. These datasets are ideal for training models related to information retrieval, legal document classification, and entity extraction.
Note: Access to datasets on LexisNexis requires a paid subscription.

Computer Vision Datasets

This section highlights trusted repositories where you can find high-quality datasets for various computer vision tasks. These platforms provide datasets for training and evaluating models for image recognition, object detection, segmentation, and more.

Roboflow
Roboflow offers a collection of datasets for tasks like object detection, image classification, and segmentation, with tools for data augmentation and annotation.
COVE
COVE is an online repository for computer vision datasets sponsored by the Computer Vision Foundation. It serves as a centralized reference for datasets in the field and aims to support the computer vision research community.
VisualData
VisualData is a growing repository focused on computer vision datasets. It offers a curated collection of datasets for image classification, semantic segmentation, object detection, and other vision tasks. The platform is designed for both research and industrial use, with high-quality datasets.
Hugging Face (Computer Vision)
Hugging Face provides a rich collection of datasets for computer vision tasks, including image classification, object detection, and segmentation. It offers tools for easy dataset integration and experimentation within the Hugging Face ecosystem

Time Series Datasets

These repositories provide access to datasets specifically tailored for time series analysis, including tasks such as forecasting, anomaly detection, and trend analysis.

Time Series Data Library (TSDL)
The Time Series Data Library is an open-source resource offering a collection of time series datasets from diverse domains like finance, healthcare, and economics. It’s specifically designed for those working on time series forecasting and analysis.
FRED (Federal Reserve Economic Data)
FRED offers a comprehensive collection of economic time series data. It’s a valuable resource for economic forecasting, with datasets covering various financial, economic, and monetary indicators.
Yahoo Finance
Yahoo Finance offers financial time series data, including historical stock prices, exchange rates, and economic indicators. Note: Access to datasets requires a Gold Subscription purchase.
Quandl
Quandl provides access to a variety of financial, economic, and alternative datasets, including time series data for forecasting and analysis. Many of the high-quality datasets are available via paid subscription.

Tips for Using Datasets

Read the Documentation: Understand data structure and preprocessing needs.
Check Licensing: Ensure proper usage rights, especially for paid datasets.
Prepare for Preprocessing: Clean or format data as required.
Watch for Bias: Be mindful of potential data biases.
Use Augmentation: Enhance smaller datasets with augmentation techniques.

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