# HighDataCircles

> The official companion publication to r/HighDataCircles. Practical reference material about selling, buying, brokering, and licensing data.

The HTML pages and machine-readable exports share the same editorial sources. Guides contain direct answers, examples, limitations, and source notes. Examples and calculator defaults are hypothetical. Companies appear only as sourced examples within guides; the publication has no standalone company profiles.

## Guides

- [How to sell data: from a raw dataset to your first deal](https://highdatacircles.com/guides/how-to-sell-data/index.md): A practical guide to selling a dataset: establish your rights, choose a buyer use case, prepare a sample, price the license, and run a paid pilot.
- [How to start a data brokerage business](https://highdatacircles.com/guides/start-a-data-brokerage/index.md): Choose a brokerage model, validate a niche, negotiate supplier permissions, qualify buyers, and build a repeatable data brokerage operation.
- [How to find buyers for your dataset](https://highdatacircles.com/guides/find-data-buyers/index.md): Map your data to buyer teams, distinguish marketplaces from buyers, qualify demand, and write an evidence-led data sales pitch.
- [How to sell data to AI companies](https://highdatacircles.com/guides/sell-data-to-ai-companies/index.md): Understand AI data buying: training, evaluation, licensed content, enterprise workflows, and the evidence an AI data partner needs.
- [How to price a dataset and structure a data deal](https://highdatacircles.com/guides/data-pricing/index.md): Compare dataset pricing models, calculate contribution, define license scope, and use a paid pilot to test willingness to pay.
- [How to package a dataset buyers can evaluate](https://highdatacircles.com/guides/package-a-dataset/index.md): Prepare a dataset card, field dictionary, representative sample, quality report, and versioned delivery manifest for a commercial data product.
- [Data licensing: the terms to settle before a deal](https://highdatacircles.com/guides/data-licensing/index.md): A practical issue checklist for data licenses: permitted uses, training rights, redistribution, exclusivity, updates, deletion, warranties, and payment.
- [A buyer’s checklist for data due diligence](https://highdatacircles.com/guides/data-due-diligence/index.md): Evaluate a data vendor’s provenance, rights, coverage, quality, delivery, privacy controls, and commercial fit before buying a dataset.
- [Anonymizing data before a commercial release](https://highdatacircles.com/guides/anonymize-data-for-sale/index.md): Understand anonymisation, pseudonymisation, contextual identifiers, release review, and why removing names does not establish that a dataset is safe to sell.
- [Data broker laws: scope the rules before you sell](https://highdatacircles.com/guides/data-broker-laws/index.md): A starting checklist for data brokerage compliance, including jurisdiction, personal information, California DROP, EU privacy duties, and contractual rights.
- [How to run a paid data pilot that leads to a decision](https://highdatacircles.com/guides/paid-data-pilot/index.md): Design a data evaluation with a clear use case, sample, acceptance criteria, license scope, timeline, payment, and go/no-go decision.
- [Selling alternative data: what research buyers need](https://highdatacircles.com/guides/alternative-data/index.md): Prepare alternative data for research buyers with point-in-time history, source methodology, coverage, revision records, and a testable evaluation.
- [Direct sales, licensing partners, or data marketplaces?](https://highdatacircles.com/guides/choose-data-sales-channel/index.md): Choose a data sales channel by comparing customer ownership, exclusivity, channel fees, delivery work, and the evidence of real demand.
- [How to compare data vendors and dataset offers](https://highdatacircles.com/guides/compare-data-vendors/index.md): Compare datasets using task fit, coverage, timestamps, rights, quality, delivery, and total cost. Build a useful shortlist without relying on brand rankings.

## Structured reference

- [Content catalog](https://highdatacircles.com/api/catalog.json): Titles, answers, canonical URLs, dates, and source links.
- [Glossary](https://highdatacircles.com/api/glossary.json): Definitions with relevant guide links.
- [Complete reference](https://highdatacircles.com/llms-full.txt): Combined guide and glossary export.

## Tools and context

- [Free toolkit](https://highdatacircles.com/tools/): Seller readiness, a dataset card builder, deal economics, and editable templates.
- [Editorial policy](https://highdatacircles.com/editorial-policy/): Source selection, AI assistance, limitations, and corrections.
- [Research access](https://highdatacircles.com/for-agents/): Export formats and scope.
- [Publication feed](https://highdatacircles.com/rss.xml): Guides and market briefs.