Rewrite like a human tech writer for Venture Beat without plagiarism and without reducing the word count of the original article, "Kaggle 
The organization is devoted to data science, learning data science and the data itself. Their portal offers easy access to notebooks filled with Python and R code, as well as some lessons for learning how to use them and even some competitions. One corner is a big collection of datasets that range from essential to bizarre. From omicron daily cases, tabulated by country, to the winning numbers of the South Korean lottery. 

Data.gov 
Governments run on data and the US government sometimes shares it. Data.gov is a central clearing house listing many data sources like the Integrated Postsecondary Education Data System, filled with data about college, or the US Geological Survey’s collection of topographic data about every square mile of the country. And in an extra bit of meta surprise, they also offer a listing of data hubs in the individual agencies, bureaus and departments for further digging. 
PLOS 
The Public Library of Science was founded in 2001 to be an alternative to the for-profit scientific journals that dominate the world of research. Along the way, it also created PLOS Open Data, a collection of open datasets that are usually connected to the research in the journal. If you have a question about the analysis or you just want to rerun the numbers differently, there’s a good chance the data will be available. This has been a crucial opportunity for scientists creating meta analysis by combining the research from multiple studies to search for larger patterns and issues. 

Open Science 
The Open Science Data Cloud is another mechanism where scientists from many different disciplines can share their lab data with each other. Some of the biggest projects include Harvard’s Cultural Observatory’s Bookworm, a collection of books and other textual materials, and Bionimbus, a collection of biological and biomedical data for studying cells. 
Amazon 
AWS offers a wide collection of datasets and also preloads them into some of their best services like EMR, often to use as an example. Many of these include some of the biggest government datasets like the NEXRAD weather radar system or the Landsat images. The company has been pushing environmental awareness in this area so many of the collections focus on natural data as part of the Amazon Sustainability Data Initiative and Earth on AWS. In January, they updated bioacoustic recordings of Orca sounds with streaming audio from around Puget Sound. 
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7) Azure Open Datasets
The Azure Open Datasets are curated and preprocessed to make them easier to use with Azure’s instances and AI routines. Many of the big government sets like the weather data are routinely polled and updated so the freshest information is available in the same location. Economists can track inflation with details from the Producer Price Index compiled by the US Department of Commerce. Urban planners, for instance, might be interested in New York City’s yellow taxi cab records that contain pick up and drop off times but no personal information. 
8)  Google Dataset Search
Google Dataset Search is one of the top open-source datasets to train machine learning models with AI algorithms. It is home to around 25 million datasets for the use of data to train ML models efficiently and effectively. Programmers or developers can search with a simple keyword to seek open-source datasets in thousands of repositories in the Internet world. It helps to foster a data-sharing ecosystem for data to train ML models with AI algorithms and machine learning. It has promised ample growth in the variety and coverage of datasets for machine learning models.  

9) FiveThirtyEight 
The popular data journalism site FiveThirtyEight often includes the data that constitutes the foundation for their analysis and writing. The NHL predictions, for instance, are based on thousands of simulations that are updated after each game. Political polling on questions like whether voters prefer a Republican or Democrat generic ballot are ready for your own statistical investigations. And if you’re curious which polls are more trustworthy, FiveThirtyEight distributes their meta analysis on pollster ratings too. 
10) Yelp 
As of this writing, Yelp distributes a subset of their vast collection of opinions about the restaurants, shops and other establishments. The current batch contains almost 7 million reviews of more than 150,000 businesses from eleven major cities. Yelp expects the text and photos will make great opportunities to train natural language processing algorithms and other AI applications, but maybe you’ll come up with a different idea. 
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11) DBpedia 
Many datasets are fairly raw and unstructured. DBpedia is an effort to create an open knowledge graph full of ontological information that can be queried with SPARQL. The structure makes it possible to create queries that include strong inference and don’t just rely upon raw keywords to find the answer. Most of the information comes from the various Wikipedias. 
12) Facebook 
Many of the bits of cultural flotsam are found in Facebook’s social network and one way to search them is through Meta’s Graph API. We’re all just nodes in this huge data structure and your code can poke around it through the API seeing, more or less, the same things that you might see if you logged in. 
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13) GitHub
While many think of repositories like GitHub as places for code, many also store data inside, sometimes alongside some code but also just as a standalone source. The approach brings all the built-in features to track the evolution of the files over time, something that’s often missing from many databases. Some quick searches often reveal several repositories that might do what you need. MIT’s course on Deep Learning, for instance, stores sample material for class assignments like training autonomous cars. If you’re studying NFT’s, some Python analytics may do what you need. Thousands of repositories are squirreled away. 
14) Appen Datasets Resource Center
Appen Datasets Resource Center provides high-quality licensable datasets that are sufficient data to train ML models. There is an extensive catalog of ‘Off-the-Shelf’ open-source datasets that consist of more than 11,000 hours of audio as well as over 25,000 images with 8.7 million words in 80 languages. This open-source dataset offers to enhance accuracy in machine learning models with high performance in AI algorithms. It is focused on meeting the needs of the global customer base who wants data to train ML models."