audit-reproducibility

To verify manuscript numbers against analysis outputs, you run an audit that extracts numeric claims like point estimates and p-values from the text, compares them against pipeline results, and generates a structured PASS/FAIL report highlighting any mismatches for review.

Yes, you can audit reproducibility across R, Stata, and Python pipelines. The process reads outputs from common formats generated by these environments, parses numerical claims from the manuscript, and checks consistency across all specified analysis results.

Replication protocols enforce tolerance thresholds by applying predefined limits defined in a protocol file to the audit process. The system compares extracted manuscript claims against analysis outputs and flags any numerical drift exceeding these strict tolerance boundaries as a failed check.

Numeric claims extracted from a manuscript for a reproducibility audit include point estimates, standard errors, p-values, and sample sizes. The process parses these specific figures from the text and cross-references them with the corresponding analysis pipeline outputs.

You need to run a reproducibility audit on your research manuscript immediately before submission and before releasing replication packages. This pre-submission check ensures consistency between reported results and computations, preventing drift between manuscript text and analysis outputs.

A structured reproducibility audit report includes a detailed PASS or FAIL status for each extracted numeric claim measured against predefined tolerances. It also highlights specific mismatches between manuscript text and analysis outputs, directing your attention to inconsistencies for manual review.