Data source
Data from Open Beauty Facts, delivered clean.
4 datasets pulled from Open Beauty Facts's releases, checked field by field and shipped the way you want them — daily, weekly, or hourly, your call.
- 4 datasets
- 1 industry
- Real rows on request
What Datadory delivers from Open Beauty Facts
4Open Beauty Facts — Global Cosmetics Product Database
INCIDecoder / INKEEDecoder — Ingredient Encyclopedia and Product INCI Database
COSDNA — Cosmetics Ingredient Analysis Database
Sephora — Structured Product Catalog
Pick a catch, see the rows.
Name any Open Beauty Facts dataset and we send real rows from it — not a screenshot of rows. 1,744 datasets. Pick your catch.
Get a sampleAPI, files, or your warehouse. Daily, weekly, or hourly.
Straight answers about Open Beauty Facts data
How many products are in the Open Beauty Facts database?
The cumulative snapshot holds 73,522 cosmetics and personal-care products spread across 1,471 browse pages, one row per product barcode at SKU level. Counts move as volunteers contribute, so Datadory re-states the exact figure with each delivery rather than letting a stale number drift into your models.
What fields does an Open Beauty Facts record include?
Thirty documented fields per product: barcode and naming, the printed INCI ingredient list, allergen and additive layers, a hierarchical category tree, certification labels, packaging, stores, countries sold, manufacturing places, creator and edit timestamps, completion-state tags, quality-error flags, a 0-1 completeness score and an app-scan popularity count.
Does every Open Beauty Facts record have a full ingredient list?
No - completion varies, because records are contributed by volunteers photographing labels. The schema exposes a completeness score, state tags and quality-error flags precisely so incomplete records can be filtered. Datadory ships those quality columns alongside every row so you can set your own threshold instead of inheriting ours.
How far back does Open Beauty Facts data go?
Each record carries its creation timestamp and last-modified timestamp back to the start of the project, so edit history survives every pull. Trailing change files capture additions and modifications over the previous two weeks, which makes incremental pipelines straightforward between fuller refreshes.