Lesson 358 of 2116
Dataset Discovery: Finding Data You Didn't Know Existed
For any research question, the bottleneck is often data. AI can map the dataset landscape in ways Google never could.
Lesson map
What this lesson covers
Learning path
The main moves in order
- 1The landscape is messier than it should be
- 2dataset discovery
- 3open data
- 4data repositories
Concept cluster
Terms to connect while reading
Section 1
The landscape is messier than it should be
Research datasets live in a dozen different places: Zenodo, Figshare, OSF, ICPSR, domain-specific repositories, government data portals, supplementary materials of old papers. Google Dataset Search helps, but LLMs can triangulate across these in ways keyword search can't.
The dataset-discovery prompt
- Always verify that the dataset actually exists — LLMs hallucinate datasets too
- Check the license — 'open' is not one thing; CC0, CC-BY, and research-only have different rules
- Read the codebook or data dictionary BEFORE you commit to the dataset
- Ask: who curates this? When was it last updated?
FAIR in practice
The FAIR principles (Findable, Accessible, Interoperable, Reusable) are increasingly required by funders. When you publish your own data, check each axis: DOI assigned (F), public repository (A), standard format (I), clear license and metadata (R).
Key terms in this lesson
The big idea: LLMs can map the dataset ecosystem faster than search engines, but every discovery needs verification. Hallucinated datasets are real.
End-of-lesson quiz
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