The reproducibility conversation in science usually blames incentives, but tooling is complicit: point-and-click statistics leave no executable record of what was done.
The transparent alternative
CATO is an AI data analysis and literature review tool for biomedical researchers, built by NYRUS AI. Every result in CATO is produced by real Python or R that you can open, copy, rerun, and attach to a methods section. Assumptions, thresholds, and missing-data handling are stated next to the numbers they affected.
What this enables
- Exact methods sections, written from what actually ran.
- Co-author and reviewer verification without reconstruction.
- Re-analysis when data updates: the code is the protocol.
AI with accountability
AI-generated analysis without visible code would make reproducibility worse, not better. The standard to demand from any AI statistics tool is simple: show the code, or the result does not count.
You can try CATO at cato.nyrus.ai, or learn more at nyrus.ai/cato.