The Ultimate Guide to Machine Learning & AI Datasets for 2025

Back when I first wrote this post in 2022, the AI landscape was a different place. Finding a good dataset felt like a treasure hunt, piecing together links from university pages, old GitHub repos, and forum posts. I started this list to keep track of the gems I stumbled upon during literature reviews or through the grapevine on Twitter.

Fast forward to today, and the game has changed entirely. While the treasure hunt is still real, the map is vast and overflowing. The rise of Large Language Models (LLMs), multimodal AI, and open-source collaboration has led to an explosion of high-quality, large-scale datasets. It i’s no longer just about finding a dataset; it’s about finding the right one for your project in a sea of incredible options.

This is the 2025 edition of that original list. It’s been completely revamped to guide you —through the most relevant and powerful datasets, codebases, and platforms available today.

Related Posts

For those just getting started, you might also want to check out my other post on [Data Challenge Competitions], where datasets come neatly packaged with a problem statement, making it easy to dive in.

Top 10 Conference Shared Tasks/ Data competitions

Also Part-2 of this blog is also worth a read.

AI and ML Datasets to start with – Part 2

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List of Machine Learning and Artificial Intelligence datasets that was published in 2022

1. ICLR OpenReview 2019 web pages [Link1]
2. FIRE -Forum of Information Retrieval India involving multi-lingual languages [Link]
3. UCI Machine Learning Repository
4. https://archive.ics.uci.edu/ml/datasets.html
5. Carnegie Mellon University – Machine Learning Course Projects, Spring 2015 http://www.cs.cmu.edu/~ninamf/courses/601sp15/projects.html, Fall 2010, http://www.cs.cmu.edu/afs/cs/academic/class/10601-f10/projects.html
6. Awesome-public-datasets and Awesome-machine-learning in Github
7. ACL Knowledge datasets and collections
8. Linguistic Data Consortium Catalog
9. The Dryad Digital Data Repository
10. Kaggle
11. Google: Machine Learning Student projects based on Natural Language Processing.
12. Avocado Research Email Collection and Enron Email Dataset
13.  FiveThirtyEight Data
14. This blog post gives links to many available datasets.

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2025 Updated List: The Modern Hubs & Platforms (Your First Stop)

These aren’t just lists of datasets; they are living ecosystems. For any new project in 2025, you should start your search here.

1. Hugging Face Hub

• Link: https://huggingface.co/datasets
• Why it’s essential for 2025: If you’re doing anything with Transformers, LLMs, or diffusion models, this is your home. The Hugging Face Hub is the de-facto standard for the modern AI community. It hosts over 50,000 datasets, all easily accessible through their datasets library. More importantly, it directly integrates with thousands of pre-trained models and codebases, creating a seamless workflow from data to model deployment.
• What you’ll find: Everything from instruction-tuning datasets for LLMs (like Open-Orca) to massive multimodal collections and niche scientific data.

2. Papers with Code

• Link: https://paperswithcode.com/datasets
• Why it’s essential for 2025: This is the bridge between cutting-edge research and practical application. Papers with Code links academic papers to the code and datasets used to produce their results. It’s the best way to find the exact data used to set a new state-of-the-art (SOTA) benchmark on a specific task.
• What you’ll find: The latest and greatest datasets from conferences like NeurIPS, ICML, and CVPR, complete with leaderboards showing which models perform best.

3. Kaggle

• Link: https://www.kaggle.com/datasets
• Why it’s still a top contender: Kaggle has evolved from just a competition platform into a massive community with a vast repository of user-contributed datasets. It’s an excellent place to find clean, interesting, and often business-oriented datasets that are perfect for portfolio projects. The integrated notebooks make it incredibly easy to start exploring data immediately.
• What you’ll find: Tabular data for finance and marketing, unique image collections, and text data for sentiment analysis.

4. Google Dataset Search

• Link: https://datasetsearch.research.google.com/
• Why it’s essential for 2025: Think of it as Google, but specifically for data. It indexes datasets from thousands of repositories across the web, including academic institutions, government portals, and public domains. It’s a powerful search tool when you have a very specific domain in mind.

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Foundational Datasets for LLMs & Natural Language Processing

The world of NLP has been remade by LLMs. Here are the datasets that power them and allow you to fine-tune them.

• The Pile™: Link – A massive 825 GiB English text corpus created by EleutherAI, it was one of the foundational open-source datasets for training large language models. Still a crucial reference.
• RedPajama-Data: Link – An open-source project to replicate the LLaMA training dataset. It contains over 30 trillion tokens and is vital for anyone wanting to pre-train or deeply understand LLMs.
• Instruction-Tuning Datasets (Alpaca, Dolly, Open-Orca): These datasets aren’t for pre-training, but for teaching a base LLM how to follow instructions and chat. You can find them all on the Hugging Face Hub. They are essential for creating your own custom, helpful AI assistants.
• Anthropic HH-RLHF: Link – A key dataset for understanding and implementing Reinforcement Learning from Human Feedback (RLHF), the technique used to make models like ChatGPT safer and more aligned with human preferences.
• ACL Anthology: Link – Still a fantastic resource for finding datasets associated with papers from top NLP conferences.

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Cutting-Edge Datasets for Computer Vision & Multimodality

Vision is no longer just about classification. It’s about generation, understanding, and connecting with language.

• LAION-5B: Link – The 5.85 billion image-text pair dataset that powered the Stable Diffusion revolution. It is the go-to resource for training large-scale text-to-image models from scratch.
• COCO (Common Objects in Context): Link – A classic that remains the gold standard for object detection, segmentation, and captioning. If you’re testing a fundamental vision model, you’re probably using COCO.
• ImageNet: Link – The dataset that kickstarted the deep learning revolution. While its original classification task is largely solved, its massive scale makes it invaluable for pre-training vision models.
• Visual Genome: Link – An incredibly detailed dataset connecting objects, attributes, and relationships within images. Perfect for deep scene understanding and visual question answering (VQA).

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Specialized Datasets: Science, Code, and More

AI is now a tool for scientific discovery and specialized applications.

• AlphaFold Protein Structure Database: Link – A revolutionary database from DeepMind and EMBL-EBI containing predictions for the 3D structures of millions of proteins. A cornerstone of AI for biology.
• The Stack: Link – A massive 6.4 TB dataset of permissively licensed source code in over 300 programming languages. It’s the engine behind powerful code generation models like StarCoder.
• HumanEval: Link – The standard benchmark for evaluating the functional correctness of code generated by AI models.

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Classic & Foundational Archives

These are the libraries our current giants were built upon. They are still excellent for fundamental learning and specific use cases.

• UCI Machine Learning Repository: Link – The grandfather of all dataset repositories. For decades, this has been the place to go for small, clean, and classic datasets for learning and benchmarking traditional ML algorithms (like SVMs, decision trees, etc.).
• Linguistic Data Consortium (LDC) Catalog: Link – A source for high-quality (but often commercial/pay-walled) language data for government, industry, and academic research.
• Awesome Public Datasets on GitHub: Link – A vast, community-curated list covering nearly every domain imaginable. A bit of a firehose, but a fantastic resource for browsing when you’re looking for inspiration.

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How to Choose the Right Dataset in 2025

With so many options, how do you pick one?

1. Define Your Goal First: Are you pre-training a model from scratch, fine-tuning an existing one, or just performing analysis? Your goal dictates the scale and type of data you need.
2. Check the License: This is more important than ever. Can you use the data for commercial purposes? Does it have a research-only license? Always check before you build.
3. Assess Quality and Bias: Large datasets scraped from the web (like LAION or The Pile) are powerful but can contain significant biases, noise, and unsafe content. Be aware of the limitations and plan for data cleaning and model alignment.
4. Consider Your Compute: Training on RedPajama is a task for a major organization. Fine-tuning with an instruction dataset can be done on a single consumer GPU. Match the dataset to your available resources.

The world of data is moving faster than ever. I hope this updated guide helps you navigate it and build something amazing.

What are your go-to datasets for 2025? Did I miss any of your favorites? Share them in the comments below!