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Reproducible experiment design

Build a complete, bounded non-clinical run matrix from maintained factors and levels, with seeded ordering and source-bound AI factor review.

Input

Factors and purpose: id,name,purpose F1,Print speed,Compare print speed settings for visible edge quality. F2,Cooling setting,Compare controlled cooling settings while reviewing print quality. Maintained factor levels: id,factor,value L1…

Result

Two print-speed levels and two cooling levels produce 4 combinations. With 2 replicates, the complete matrix contains 8 runs in 2 blocks of 4. Seed 42 reproducibly orders runs within each block. Each row retains its combination, replicat…

A reproducible experiment matrix starts with explicit factors, maintained levels and a bounded run count. The tool enumerates a full factorial design or exactly the allowed combinations supplied in a CSV. Replicates, block size and ordering options determine the complete run list. The AI reviews the maintained factor purposes and cites original evidence; it never invents levels or changes the matrix.

Your texts, pages and images are processed on our own servers in the EU. Trust Center

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Who it is for

Suitable for non-clinical manufacturing trials, material comparisons, software configuration experiments and educational demonstrations. It helps technical teams document a transparent starting plan. Domain expertise, safe operating conditions, measurement design and any required approval remain separate responsibilities.

Typical uses

  • Provide factor purposes and factor-specific level IDs.
  • Choose full factorial or an explicit list of admissible combinations, then set replicates and a complete-run limit.
  • Preserve source order or sort IDs before creating blocks.
  • Randomize within existing blocks or globally before assigning blocks, using a fixed seed.
  • Each output row shows a stable run ID, execution sequence, combination ID, replicate, block and exact level settings.
  • Separate AI review rows distinguish measurement, control, possible confounding and unclear purpose.
  • The plan does not claim that scheduled blocks eliminate confounding.

Example

Input

Factors and purpose: id,name,purpose F1,Print speed,Compare print speed settings for visible edge quality. F2,Cooling setting,Compare controlled cooling settings while reviewing print quality. Maintained factor levels: id,factor,value L1,F1,Slow L2,F1,Fast L3,F2,Low L4,F2,High Combination mode: Full factorial Replicates per combination: 2 Runs per block: 4 Run ordering: Randomize within blocks Reproducibility seed: 42 Maximum complete run count: 180 Factor order: Source order Level order: Source order Replication order: Interleave replicates Non-clinical work area: Manufacturing Maintained objective: Compare maintained printer settings on non-clinical sample parts. Design reference: DESIGN1 AI evidence length: Brief evidence

Result

Two print-speed levels and two cooling levels produce 4 combinations. With 2 replicates, the complete matrix contains 8 runs in 2 blocks of 4. Seed 42 reproducibly orders runs within each block. Each row retains its combination, replicate and level IDs. AI factor-purpose excerpts appear separately and do not modify the deterministic run count or settings.

A fixed example, shortened. Real results are AI-generated: please check them.

Price and limits

Price
usually 1–3 credits
Available with
Free, With package
Input limit
One page with up to 90,000 characters of HTML

Privacy

Processed on our own servers in the EU (Poland). Inputs and results are deleted after 7 days (Free) or 30 days (packages), or right after you see the result if you choose not to store them. Images are deleted right after processing.

In the API

Tool name: experiment_design. Create a job with POST /api/v1/jobs and fetch the result with GET /api/v1/jobs/{id}. You find your API key in your account.

curl -X POST https://do4u.eu/api/v1/jobs \
  -H "Authorization: Bearer d4u_…" -H "Content-Type: application/json" \
  -d '{"tool": "experiment_design", "url": "https://example.com/"}'

Full API documentation →

Questions and answers

Is this an optimal experimental design?

No. The tool enumerates supplied combinations and orders the resulting runs. It does not calculate statistical power, fit a response model, optimize a design or establish causal conclusions.

Can unsafe or forbidden combinations be excluded?

Explicit mode includes only the maintained combinations listed in the input. The tool validates references, not physical safety. Full-factorial mode includes every supplied level combination and must only be used where that complete set is appropriate.

Can this produce clinical or human-treatment plans?

No. The supported scope is non-clinical. The output is a planning draft, not an approval, treatment recommendation, execution instruction or safety guarantee.

Is my text stored?

Only as long as needed: 7 days on Free, 30 days with a package – or not at all if you choose not to store it. Processing happens on our own servers in the EU (Poland).

Can I use the result right away?

Results are AI-generated. Please read them before you publish or send them.