Dataset cards
The dataset card
Selecting any result opens its dataset card in the right-hand panel. This is the full profile of a dataset, organized into tabs so you can scan exactly what you need.
At the top you'll find the dataset title, its relevance score, a Save button to add it to My Datasets in your workspace, and Open Dataset Source to view or download it from its original location.
The tabs
Description & metadata
The Description and Metadata tabs cover the essentials of what a dataset is and where it comes from.
| Field | What it tells you |
|---|---|
| Provider | The organization or author who built the dataset |
| License | License type, such as CC BY, MIT, and so on |
| Data type | Image, Video, Text, Audio, or Multimodal |
| Category | Usage type — Computer Vision, LLM, Image Classification, and more |
| Main language | Primary language of the data (en, fr, es, …) |
| Domain | Field such as medical, legal, finance, geospatial, retail |
| File format | JSONL, CSV, Parquet, PNG, and others — multiple are possible |
| Data size | Number of rows, files, or samples (e.g. 35,000 images) |
| Storage size | Approximate file size in MB |
You may also see up to three thumbnail images illustrating the dataset, plus the source URL used by Open Dataset Source.
Analysis & scores
The Analysis tab is where Dataset Finder goes beyond a basic listing. Each dataset is rated on quality dimensions with a star rating out of 5, each with an explanatory comment, so you can judge usability before you commit.
Ease of use
How hard the dataset is to work with, from 1 star (hard to work with) to 5 stars (very easy).
Cleaning needed
How much data cleaning is required, from 1 star (not clean at all) to 5 stars (super clean, no cleaning needed).
Label richness
The quality and depth of labels, from 1 star (no labels) to 5 stars (very precise and detailed labels).
Other analysis fields
- List of labels — the labels available, with counts and descriptions
- Class distribution — the share of each class, when available (e.g. 90% cats, 10% dogs)
- Beginner friendly — whether it suits less experienced users
- Source reliability — verified, user-uploaded, academic, government, or private
- PII content — whether personal data is present, confirmed or potential
- Dimensionality — the shape of the data (e.g. 50×50 images, paragraph-level text)
- Fine-tuning ready — guidance on which training it suits
Business applications
The Business Applications tab helps you see how a dataset translates into real AI work.
- Primary use cases — the AI use cases where this dataset is useful
- Useful for models — existing models you can train or fine-tune with it (for example YOLO, or fine-tuning Mistral)
- Known limitations — constraints to keep in mind when applying it
How to enrich
A separate How to enrich tab gives recommendations for improving the dataset: cleanup, balancing, adding labels, or complementary datasets to combine it with. If a dataset is close but not quite enough on its own, this is where you'll find ideas for closing the gap, including when a custom dataset could fill what's missing.
Q&A
The Q&A tab answers common questions about a dataset in a quick, scannable format: pairs of questions and answers covering the practical things people usually want to know before using it.
It's the fastest way to get specific answers without reading every other tab. Typical questions cover things like what the dataset contains, how it's licensed, whether it needs cleaning, what it's best suited for, and any catches to be aware of.
When you save a dataset to My Datasets, you can also add your own Q&A entries to it, each a question and its answer, to capture notes for yourself or your team. Use ＋ Add Q&A on the dataset to add an entry.