# datacriterion.com

> Official-source monthly data packs, methodology, samples and trust routes without placeholder navigation.


## Pages

- [Terms of Service](https://datacriterion.com/faq/terms-of-service/): Datacriterion sells standalone, downloadable data packages.
- [Refund Policy](https://datacriterion.com/faq/refund-policy/): We provide clear documentation, sample data, and preview images prior to purchase to ensure you know exactly what you are receiving.
- [EU Macro Core Sample](https://datacriterion.com/samples/eu-macro-core/): Request the EU macro sample route to inspect schema, naming, horizon logic and fit before moving to the full EU Macro Core pack.
- [US Housing Activity Sample](https://datacriterion.com/samples/us-housing-activity/): Request the housing sample route to inspect the monthly housing-cycle schema, documentation style and fit before moving to the full housing pack.
- [US Retail Sector Sample](https://datacriterion.com/samples/us-retail-sector/): Request the retail sample route to inspect structure, naming, coverage logic and fit before moving to the full US Retail Sector data pack.
- [EU Macro Core](https://datacriterion.com/flagships/eu-macro-core/): Explore the EU Macro Core Monthly Data Pack - Ten Year 2016-2025, a normalized EU27 macro dataset covering inflation, unemployment, industry and retail volume.
- [US Housing Activity](https://datacriterion.com/flagships/us-housing-activity/): Explore the US Housing Activity Monthly Data Pack - Five Year 2021-2025, a structured housing dataset covering permits, starts, completions, sales and price context.
- [US Retail Sector](https://datacriterion.com/flagships/us-retail-sector/): Explore the US Retail Sector Monthly Data Pack - Annual 2025, a cleaned Census retail sales dataset with visible sample, methodology and license routes.
- [Contact](https://datacriterion.com/contact/): Use the correct Datacriterion inbox for sample, fit, bundle and license questions when the standard evaluation path does not fully resolve the case.
- [Commercial License](https://datacriterion.com/licenses/commercial/): Read the full Datacriterion Commercial license terms for client-facing derived outputs and broader commercial team use before purchase.
- [Pro License](https://datacriterion.com/licenses/pro/): Read the full Datacriterion Pro license terms for internal commercial team use before purchase.
- [Basic License](https://datacriterion.com/licenses/basic/): Read the full Datacriterion Basic license terms for solo use, evaluation, editorial and independent research before purchase.
- [Licenses](https://datacriterion.com/licenses/): Check solo, internal team and commercial-use boundaries so reuse rights are clear before purchase.
- [FAQ](https://datacriterion.com/faq/): Read the questions buyers ask most about samples, documentation, licensing, custom scope and checkout before contacting Datacriterion.
- [Methodology](https://datacriterion.com/methodology/): Review the source, normalization, validation and delivery logic behind Datacriterion data packs before you buy or request a sample.
- [Evaluation Guide](https://datacriterion.com/samples/evaluation-guide/): Use this guide to evaluate scope, horizon, validation and license fit before requesting a sample or moving toward a full data pack.
- [Samples](https://datacriterion.com/samples/): Use the open evaluation guide or request a flagship sample to inspect structure, scope and licensing fit before buying a full Datacriterion pack.
- [Flagships](https://datacriterion.com/flagships/): Compare the three flagship data packs that anchor the current Datacriterion MVP by horizon, scope, pricing and next-step evaluation path.
- [Catalog](https://datacriterion.com/catalog/): Browse the full Datacriterion catalog across five data families, fifteen SKUs, current launch priorities, price tiers and evaluation routes.
- [Home](https://datacriterion.com/): Datacriterion packages official-source monthly data into analysis-ready files with visible sample, methodology, FAQ and license paths for analysts, researchers and small teams.
- [Privacy Policy](https://datacriterion.com/faq/privacy-policy/): At Datacriterion, your privacy is important to us.

## Optional

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