# IF.TTT — Sciences
IF.TTT for science and research: provenance and reproducibility for reports, analyses, and model outputs.

Research reality

# Reproducibility is governance in a lab coat.

Scientific workflows already understand receipts: methods, datasets, and citations. IF.TTT is the same idea applied to AI outputs and operational decisions: publish what can be verified, and mark what can’t.

### Third‑party pressure

- Peer reviewers need the ability to reproduce claims.
- Funding/compliance bodies need provenance for decisions and outputs.
- Collaborators need proof that the artifact they received is the artifact you published.

### What IF.TTT provides

Integrity
Hash receipts: same bytes, same hash, independent verification.
Clarity
Explicitly separates evidence (verified) from interpretation (not verified).
Portability
Offline bundles for constrained or future review environments.
PROCESS
PROVENANCE

1. 1
   Hash the source
   Dataset / paper / prompt pack → `source_sha256`.
2. 2
   Hash the output
   The output you publish → `output_sha256`.
3. 3
   Publish the trace
   Receipt binds source ↔ output with trace id and versions.

## Portable verification artifacts

So reviewers can validate without trusting your infrastructure.

    Trace receipt: https://infrafabric.io/static/trace/<shareId>
    Pack (raw): https://infrafabric.io/static/pack/<shareId>.md
    Pack (HTML): https://infrafabric.io/static/pack/<shareId>
    Offline bundles: https://infrafabric.io/static/hosted/review/trace-bundles/<id>/index.html

## Live example

Example trace receipt https://infrafabric.io/static/trace/6qRgcR01kw_qNo63Dbs_ob9n
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