Open Cross-Stitch Thread Conversion Dataset
A free, open dataset mapping every 552 DMC floss colors to their nearest equivalents across 8 brands — Anchor, Madeira, Cosmo, Sullivans, J&P Coats, Dimensions, Bucilla, and Candamar — each tagged with a match quality (exact, close, or approximate). Built for stitchers, designers, and developers who want the raw data, not just a lookup box.
What's in it
- 552 DMC colors with name + hex value
- Equivalents for 8 brands, each with a match-quality rating
- Two formats: flat CSV (spreadsheet-ready) and structured JSON (API-ready)
- Regenerated on every site update, so it never drifts out of date
By the numbers
How well each brand actually covers the DMC range — a question the per-color match data lets us answer directly:
| Brand | Colors covered | Exact | Close | Approximate | No equivalent |
|---|---|---|---|---|---|
| Anchor | 552 / 552 | 312 | 240 | 0 | 0 |
| Madeira | 549 / 552 | 26 | 523 | 0 | 3 |
| Cosmo | 547 / 552 | 0 | 546 | 1 | 5 |
| Sullivans | 540 / 552 | 1 | 538 | 1 | 12 |
| J&P Coats | 444 / 552 | 0 | 444 | 0 | 108 |
| Dimensions | 314 / 552 | 0 | 314 | 0 | 238 |
| Bucilla | 300 / 552 | 0 | 300 | 0 | 252 |
| Candamar | 223 / 552 | 0 | 223 | 0 | 329 |
License & attribution
Released under CC BY 4.0 — free to use, share, and adapt (including commercially), as long as you credit the source. Please attribute as:
Thread-conversion data by Stitchies (getstitchies.com), CC BY 4.0
How to cite in a project
- Blog / chart: link to
https://getstitchies.com/datawith the credit line above. - Code: fetch
https://getstitchies.com/data/thread-conversions.json(CORS-enabled, cached 24h).
Accuracy & methodology
Thread conversions are inherently approximate — brands mix dye differently and screen color depends on your display, so always check a physical thread before buying. Match quality reflects how close the nearest equivalent is. Anchor and Sullivans data is verified against established references; Madeira is partially verified and Cosmo is being actively scrubbed for accuracy. Spotted an error? Let us know — corrections make the dataset better for everyone.