An evidence-first approach to data licensing
Useful licensing guidance starts with a specific question, a traceable source and a clear line between fact and interpretation.
How a guide is built
- A real question What an owner or buyer needs to decide.
- Primary sources Statutes, guidance, opinions, research.
- Practical guidance Checks and documents a reader can use.
Define the decision
Start from the question a data owner or buyer actually has to answer.
Go to primary sources
Statutes, agency guidance, court opinions and peer-reviewed or preprint research.
Write it fresh
Each guide is written for this publication, not adapted from another.
Separate the questions
Technical suitability, ownership, contract scope and privacy are treated apart.
Make it usable
Turn the analysis into checks and documents a reader can act on.
Limits of a general guide
Licensing decisions depend on the collection history, the proposed AI use and the actual agreement. A general guide cannot determine whether a particular dataset is lawful to license. Bring the underlying records and contract to qualified counsel.
What we refuse to publish
- Rankings or “best” lists without a verifiable method.
- Valuations of individual datasets, or price figures we cannot attribute to a named publisher with context.
- Names of buyers or partners we have no documented relationship with.
Research records are maintained separately from the public guides. We do not sell promotional placements or present companies as partners without a documented relationship.