Lionbridge AI has assembled a wealth of resources for machine learning and natural language processing activities. In our previous articles, we explained why datasets are such an integral part of machine learning and natural language processing. Without training datasets, machine-learning algorithms would have no way of learning how to do text mining, text classification, or categorize products.
This article is the ultimate list of open datasets for machine learning. They range from the vast (looking at you, Kaggle) to the highly specific, such as financial news or Amazon product datasets.
First, some quick pointers to keep in mind when searching for datasets:
- Look for clean datasets because you don’t want to waste time cleaning the data yourself.
- Look for datasets without too many rows and columns, because those are easier to work with.
- There should be an interesting question that can be answered with the dataset.
Open Dataset Finders
Where can I download free, open datasets for machine learning?
The best way to learn machine learning is to practice with different projects. You can search and download free datasets online using these major dataset finders.
Kaggle: A data science site that contains a variety of externally-contributed interesting datasets. You can find all kinds of niche datasets in its master list, from ramen ratings to basketball data to and even Seattle pet licenses.
UCI Machine Learning Repository: One of the oldest sources of datasets on the web, and a great first stop when looking for interesting datasets. Although the data sets are user-contributed, and thus have varying levels of cleanliness, the vast majority are clean. You can download data directly from the UCI Machine Learning repository, without registration.
Public Government Datasets for Machine Learning
Where can I download public government datasets for machine learning?
Demographic data is a powerful tool for improving government and society, by serving as the basis for major economic decisions. Machine learning models that were trained using public government data can help policymakers to identify trends and prepare for issues related to population decline or growth, aging, and migration.
Data.gov: This site makes it possible to download data from multiple US government agencies. Data can range from government budgets to school performance scores. Be warned though: much of the data requires additional research.
EU Open Data Portal: The EU Open Data Portal provides access to open data published by EU institutions in fields as diverse as economics, employment, science, the environment, and education.
School System Finances: This dataset was developed through a survey of the finances of school systems in the US.
US Healthcare Data: Data about population health, diseases, drugs, and health plans have been collected from the FDA drug database and USDA Food composition database in this dataset.
The US National Center for Education Statistics: This site hosts data on educational institutions and education demographics from the US and around the world.
The UK Data Service: The UK’s largest collection of social, economic and population data can be found here.
Data USA: This site has a comprehensive visualization of US public data.
Finance & Economics Datasets for Machine Learning
Where can I download finance and economics datasets for machine learning?
Machine learning is proving to be a golden opportunity for the financial sector. Financial quantitative records are kept for decades, so the industry is perfectly suited for machine learning. In fact, machine learning is already transforming finance and investment banking for algorithmic trading, stock market predictions, and fraud detection. In economics, machine learning can be used to test economic models and predict citizen behavior.
Quandl: A good source for economic and financial data – useful for building models to predict economic indicators or stock prices.
World Bank Open Data: Datasets covering population demographics and a huge number of economic and development indicators from across the world.
IMF Data: The International Monetary Fund publishes data on international finances, debt rates, foreign exchange reserves, commodity prices and investments.
Financial Times Market Data: Up to date information on financial markets from around the world, including stock price indexes, commodities and foreign exchange.
Google Trends: Examine and analyze data on internet search activity and trending news stories around the world.
American Economic Association (AEA): A good source to find US macroeconomic data.
Image Datasets for Computer Vision
Where can I download image datasets for computer vision?
Image datasets are useful for training a wide range of computer vision applications, such as medical imaging technology, autonomous vehicles, and face recognition.
Labelme: A large dataset of annotated images.
ImageNet: The de-facto image dataset for new algorithms. Is organized according to the WordNet hierarchy, in which each node of the hierarchy is depicted by hundreds and thousands of images.
LSUN: Scene understanding with many ancillary tasks (room layout estimation, saliency prediction, etc.)
MS COCO: Generic image understanding and captioning.
COIL100 : 100 different objects imaged at every angle in a 360 rotation.
Visual Genome: Very detailed visual knowledge base with captioning of ~100K images.
Google’s Open Images: A collection of 9 million URLs to images “that have been annotated with labels spanning over 6,000 categories” under Creative Commons.
Labelled Faces in the Wild: 13,000 labeled images of human faces, for use in developing applications that involve facial recognition.
Stanford Dogs Dataset: Contains 20,580 images and 120 different dog breed categories.
Indoor Scene Recognition: A very specific dataset, useful as most scene recognition models are better ‘outside’. Contains 67 Indoor categories, and a total of 15620 images.
VisualQA: This dataset contains open-ended questions related to 265,016 images. The questions asked require an understanding of vision and language to answer.
Sentiment Analysis Datasets for Machine Learning
Where can I download sentiment analysis datasets for machine learning?
Sentiment analysis models require large, specialized datasets to learn effectively. The following list should hint at some of the endless ways that you can improve your sentiment analysis algorithm.
Multidomain Sentiment Analysis Dataset: A slightly older dataset that features product reviews from Amazon.
IMDB Reviews: An older, relatively small dataset for binary sentiment classification, features 25,000 movie reviews.
Stanford Sentiment Treebank: Standard sentiment dataset with sentiment annotations.
Sentiment140: A popular dataset, which uses 160,000 tweets with emoticons pre-removed.
Twitter US Airline Sentiment: Twitter data on US airlines from February 2015, classified as positive, negative, and neutral tweets.
Natural Language Processing Datasets
Where can I download open datasets for natural language processing?
Natural language processing is a massive field of research, but the following list includes a broad range of datasets for different natural language processing tasks, such as voice recognition and chatbots.
Enron Dataset: Email data from the senior management of Enron, organized into folders.
Amazon Reviews: Contains around 35 million reviews from Amazon spanning 18 years. Data include product and user information, ratings, and the plaintext review.
Google Books Ngrams: A collection of words from Google books.
Blogger Corpus: A collection 681,288 blog posts gathered from blogger.com. Each blog contains a minimum of 200 occurrences of commonly used English words.
Wikipedia Links Data: The full text of Wikipedia. The dataset contains almost 1.9 billion words from more than 4 million articles. You can search by word, phrase or part of a paragraph itself.
Gutenberg eBooks List: Annotated list of ebooks from Project Gutenberg.
Hansards Text Chunks from the Canadian Parliament: 1.3 million pairs of texts from the records of the 36th Canadian Parliament.
Jeopardy: Archive of more than 200,000 questions from the quiz show Jeopardy.
SMS Spam Collection in English: A dataset that consists of 5,574 English SMS spam messages.
Yelp Reviews: An open dataset released by Yelp, contains more than 5 million reviews.
UCI’s Spambase: A large spam email dataset, useful for spam filtering.
Datasets for Autonomous Vehicles
Where can I download open datasets for training autonomous vehicles?
Autonomous vehicles need to be trained with large amounts of high-quality datasets so that they can accurately perceive their environment and surrounding objects.
Berkeley DeepDrive BDD100k: Currently the largest dataset for self-driving AI. Contains over 100,000 videos of over 1,100-hour driving experiences across different times of the day and weather conditions. The annotated images come from New York and San Francisco areas.
Baidu Apolloscapes: Large image dataset that defines 26 different semantic items such as cars, bicycles, pedestrians, buildings, street lights, etc.
Comma.ai: More than 7 hours of highway driving. Details include car’s speed, acceleration, steering angle, and GPS coordinates.
Oxford’s Robotic Car: Over 100 repetitions of the same route through Oxford, UK, captured over a period of a year. The dataset captures different combinations of weather, traffic and pedestrians, along with long-term changes such as construction and roadworks.
Cityscape Dataset: A large dataset that records urban street scenes in 50 different cities.
KUL Belgium Traffic Sign Dataset: More than 10000+ traffic sign annotations from thousands of physically distinct traffic signs in the Flanders region in Belgium.
MIT AGE Lab: A sample of the 1,000+ hours of multi-sensor driving datasets collected at AgeLab.
LISA: Laboratory for Intelligent & Safe Automobiles, UC San Diego Datasets: This dataset includes traffic signs, vehicles detection, traffic lights, and trajectory patterns.
Still can’t find what you need? Lionbridge AI has over two decades years of expertise in building extensive, accurate datasets for machine learning projects. With 500,000 qualified linguists working across 300+ languages, we’re well positioned to build the custom dataset you’ve been searching for.