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In the world of artificial intelligence, nothing is more important than datasets. They are the raw material that feeds every machine learning model. Without carefully collected and labeled training data, even the most advanced algorithms struggle to perform.
But what does a dataset actually look like? Some are as simple as spreadsheets, while others are billions of text documents or hours of labeled video. Let’s explore the main types of datasets — tabular, text, image, audio, and video — to understand how they look, what they’re used for, and how much they typically cost.
When people think of data, they often imagine a table — and that’s exactly what a tabular dataset is. Picture a spreadsheet in Excel or Google Sheets, with rows representing individual records and columns representing features. Each row could be a customer, a transaction, or a sensor reading. Each column could be age, income, purchase amount, or timestamp.
For example, the Iris dataset, one of the most famous in machine learning, has rows describing flowers and columns showing petal length, sepal width, and species. The Titanic dataset has rows of passengers with columns like age, class, and whether they survived. And large-scale open datasets like the NYC Taxi dataset contain billions of rows detailing taxi rides, including fares, tips, and routes.
These datasets are used everywhere: in finance to predict credit risk, in retail to forecast sales, in healthcare to predict outcomes from patient records, and in fraud detection to catch anomalies. In short, tabular datasets are the bread and butter of classical machine learning.
The good news is that many tabular datasets are available for free from Kaggle, UCI, or government open-data portals. But high-value commercial datasets, such as marketing databases or financial feeds, can cost anywhere from $0.20 per record to thousands of dollars per year in subscription fees.
If you’ve used ChatGPT or any other large language model (LLM), you’ve already seen the power of text datasets. These are collections of human language — sentences, paragraphs, or documents — often paired with labels or metadata.
Some text datasets look like question–answer pairs, perfect for training chatbots. Others are just huge collections of plain text, scraped from books, news, or websites. For example, Wikipedia is one of the most widely used open datasets in AI research, while Common Crawl offers petabytes of text from across the web.
Smaller, more focused datasets exist too: the IMDB reviews dataset is great for training sentiment analysis models, while the 20 Newsgroups dataset contains thousands of forum posts sorted by topic. For training massive LLMs, researchers often use The Pile, a curated 825GB dataset that combines sources from literature to scientific papers.
Text datasets are used to train AI in chatbots, search engines, machine translation, spam detection, summarization, and sentiment analysis. In fact, almost every modern AI assistant relies on them.
Many free text datasets are available, but high-quality, specialized corpora — such as legal documents, medical text, or proprietary news archives — can be expensive. For example, the Linguistic Data Consortium (LDC) offers large curated text datasets, with prices ranging from hundreds of dollars per corpus to $27,500 per year for full institutional memberships.
The world of computer vision runs on image datasets. These look like folders full of pictures (JPEGs or PNGs), often paired with labels. A simple dataset might just have “cat” and “dog” images in separate folders. More advanced datasets include annotations like bounding boxes (drawn rectangles around objects) or segmentation masks (pixel-by-pixel outlines).
One of the most famous image datasets is ImageNet, which contains over 14 million labeled images across 20,000 categories. This dataset is what sparked the deep learning revolution in computer vision. Another is COCO (Common Objects in Context), which includes 330,000 images labeled for 80 object categories — but also adds details about object positions and interactions. Smaller benchmark datasets like CIFAR-10 (tiny images of 10 classes) or MNIST (handwritten digits) are often used for beginners.
Image datasets are the foundation for object detection, facial recognition, medical imaging AI, autonomous vehicles, and OCR (optical character recognition). If you’ve ever seen an AI detect cats in a photo, it was trained on an image dataset.
Like other data types, many image datasets are free to use in research. But commercial image datasets, especially specialized ones like medical scans or synthetic faces, can be costly. Prices range from a few cents per image at bulk scale to $20,000 or more for curated, domain-specific datasets.
Audio datasets are collections of sound recordings, usually in WAV or MP3 format, often with transcripts. Each file might be a spoken sentence, an environmental sound, or a music clip.
For speech recognition, the LibriSpeech dataset is a go-to resource — about 1,000 hours of audiobook recordings with transcripts. Mozilla’s Common Voice is another open dataset, with over 33,000 hours of crowd-contributed voice data across more than 130 languages. These are invaluable for training automatic speech recognition (ASR) systems.
Other datasets focus on different audio tasks: UrbanSound8K contains city noises like sirens, horns, and dogs barking, used for sound event detection. For speaker identification, datasets like VoxCeleb provide clips of thousands of individuals.
Audio datasets are used in voice assistants, transcription apps, speaker verification, emotion detection, and text-to-speech. In other words, any time an AI listens and responds, an audio dataset was behind it.
The pricing varies: many are free and open, but high-quality commercial speech corpora can cost thousands to tens of thousands of dollars, often priced per hour of audio. For example, some licensed broadcast news datasets cost over $14,000 for non-members.
Finally, there are video datasets, which add a new dimension: time. A video dataset isn’t just a single image — it’s a sequence of frames, sometimes with labels for actions, objects, or events.
A simple video dataset might label clips as “playing guitar” or “riding a bike.” Larger ones, like Kinetics, include hundreds of thousands of short YouTube clips labeled with human actions. The UCF101 dataset contains 13,000 sports and activity clips, while autonomous driving datasets like nuScenes provide multi-camera driving footage with annotations of cars, pedestrians, and traffic lights.
Video datasets are crucial for action recognition, self-driving cars, surveillance AI, gesture recognition, and multimodal AI systems. They’re how AI learns to not only see, but also understand events as they unfold over time.
Free academic datasets exist, but commercial video data is expensive. Stock video clips can cost $50–$200 each, while custom-collected datasets for AI (e.g., thousands of hours of labeled driving video) can cost hundreds of thousands of dollars.
Datasets come in many forms: spreadsheets, text files, photo libraries, sound clips, or video archives. But they all serve the same purpose: to provide training data that allows AI systems to learn patterns and make predictions.
👉 If you’re experimenting with free open datasets or investing in commercial datasets for production, remember this golden rule: your AI is only as good as the training data it learns from.
Looking for high-quality, real-world datasets to power your projects? Contact us and explore how OORT DataHub can provide the datasets you need to train reliable, scalable AI systems.