Research integrity audit

Screen a manuscript's evidence at the smallest meaningful unit: one experimental image panel, or one independently measured data series. Hashes, feature matches, and statistical tests find candidates; they do not establish misconduct, or even duplication, on their own.

Inputs and workspace

Accept a PDF, a directory of manuscript images, and/or source data (CSV, TSV, Excel, or a directory of them). Resolve tagged or attached paths before running anything. Choose tracks from the input and the request:

• Figure track (scripts/audit_figures.py): PDFs and image directories.
• Data track (scripts/audit_data.py): Source Data files, supplementary tables, and numeric tables transcribed from the PDF.

A PDF usually warrants both unless the user limits scope. Ask for a page range only when the user did not specify one and scanning the whole PDF would materially change scope.

Create a new analysis directory such as analysis/integrity-audit-YYYYMMDD-HHMM/ with figures/ and data/ as the two script workspaces. Never modify source files, overwrite a prior audit, or silently omit an unreadable file.

Locate both scripts from the resource paths returned by use_skill. If imports fail, load local-env-setup, create a project-local environment, and install the packages named in compatibility. Do not continue with the hash-only figure fallback when the user requested a strict or exhaustive review.

Figure track

F1. Prepare sources

For a PDF:

python audit_figures.py prepare --input PAPER.pdf --output AUDIT_DIR/figures --pages "1-40,49-54"

The script extracts qualifying embedded raster images first. It renders a page only when no large embedded image is available and the page looks like a figure page, or when --render-fallback all is explicitly used. Review sources.json, skipped.json, and sources-contact-sheet.png; confirm that every requested figure is represented. A page render still contains captions and page furniture, so crop the figure before panel splitting.

For a directory:

python audit_figures.py prepare --input FIGURE_DIR --output AUDIT_DIR/figures

The script recursively inventories supported images, normalizes EXIF orientation into audit copies, and records hashes and original paths. It does not alter the directory.

F2. Verify panel boundaries

prepare writes conservative panel proposals to panels.json. They are only proposals. View every source at full resolution and edit the manifest until:

• every data-bearing photograph, microscopy field, histology tile, plate, wound, gel/blot region, or other experimental image has its own box;
• repeated grids are split into individual experimental units, with stable labels such as Fig2-D-r1-c2 rather than anonymous indices;
• labels, legends, scale bars, and axes are not mistaken for independent data panels;
• adjacent boxes do not overlap accidentally;
• expected derivatives share a derivation_group (for example raw channels and merge, overview and inset, or known longitudinal views);
• kind records the modality when known (microscopy, histology, western-blot, gel, plate, wound, ivis, chart, or schematic).

Run:

python audit_figures.py materialize --workspace AUDIT_DIR/figures

Inspect panels-contact-sheet.png immediately. Fix bad crops and rerun. Do not scan until manifest-warnings.json has no unexplained out-of-bounds, duplicate-ID, or overlapping-box warning. Preserve parent/context crops when a tighter data-only crop is needed for matching.

F3. Run all-pairs screening

python audit_figures.py scan --workspace AUDIT_DIR/figures --features required

The scan combines exact pixel hashes, perceptual hashes, normalized correlation, and SIFT + RANSAC geometry. It writes candidates.csv, candidates.json, quality-flags.csv, and scan-summary.json. Review every candidate, not only the first page of the table. Re-scan after any crop change.

Automatic scores are triage signals. Repeated labels, axes, membrane grids, plate rims, scale bars, and regular tissue texture often produce false matches. Conversely, different crops, contrast changes, rotation, mirroring, or recompression can hide a duplicate from hashes and global correlation.

F4. Confirm or exclude candidates

Generate evidence for selected pairs or the highest-ranked unresolved pairs:

python audit_figures.py evidence --workspace AUDIT_DIR/figures --pair PANEL_A,PANEL_B
python audit_figures.py evidence --workspace AUDIT_DIR/figures --top 20

Inspect the full panels, data-only crops, match-line view, registered red/green overlay, and metrics together. For circular plates or other strong borders, repeat with a tighter interior crop. For blots, compare both whole blot context and protein-by-lane crops. For microscopy, distinguish same-field channel derivation from cross-condition reuse. Consult references/review-protocol.md for modality-specific checks and verdicts.

Never call a pair confirmed from an inlier count or NCC alone. Confirmation requires geometrically consistent correspondence across independent random details in the data region, a plausible transform, visual agreement after registration, and review of the experimental relationship. Record strong negative controls from visually similar nonmatching panels when possible.

F5. Review uninformative images

Treat automated quality flags as prompts. Mark a panel uninformative only for a specific reason such as blank/placeholder content, corruption, unreadably low resolution, a caption mismatch, or unrelated residual artwork. A negative result, schematic, control, or visually sparse field is not "useless" merely because it contains little signal.

Data track

Read references/data-protocol.md before reviewing data findings.

D1. Collect the numbers

Prefer Source Data and supplementary files over values read from plots. For tables that exist only in the PDF, transcribe them into a CSV exactly as printed: keep trailing zeros and signs, one column per group, and verify the transcription against the rendered page. Do not read values off charts unless the user asks; if you do, say so and skip digit-level checks for those values. Also collect every reported mean with its SD and n, and every test reported with statistic, degrees of freedom, and p (t, F, χ², r, z).

python audit_data.py prepare --input SOURCE_DATA_DIR --output AUDIT_DIR/data

--input may be repeated and accepts files or directories. prepare dumps every sheet to tables/ with the precision the authors displayed, and writes series.json with one proposed series per vertical block of numeric cells. Obvious index columns are proposed with "include": false. When the paper has no tables of raw values, skip prepare: create AUDIT_DIR/data/ and write series.json with only means and tests.

D2. Verify the series manifest

Proposals are only a starting point. Edit series.json until every included series is one independently measured variable, design and summary columns are excluded, expected derivations share a derivation_group, and each label names figure, panel, group, and variable. Add reported means and percentages with their SD and n to means (GRIM, GRIMMER), and reported test results to tests in APA form (t(18) = 2.31, p = .032) for p-value recomputation. The manifest rules are in references/data-protocol.md.

D3. Run all-pairs screening

python audit_data.py scan --workspace AUDIT_DIR/data

The scan runs repeated-run detection and fixed-relation checks across all series pairs, decimal and terminal-digit tests per series and pooled per source, Benford where applicable, GRIM and GRIMMER on means, and p-value recomputation on tests. It writes findings.csv, findings.json, and scan-summary.json. The distributional tests share one Benjamini-Hochberg family. Rescan after any manifest change.

D4. Confirm or exclude findings

Review every flagged row, not only the first. For each, confirm the cells in tables/, locate the series in the paper, and look for a declared shared control, normalization, formula, or unit conversion. Weigh shared runs and exact fixed relations far above distributional anomalies; a single digit or Benford deviation is not a concern on its own. Record negative controls.

Report

The final report must include:

1. exact inputs, page scope, figure/source and table counts, and unreadable or skipped files;
2. number of reviewed panels and series, all-pairs comparisons, and means, SDs, and tests checked or untestable;
3. methods, whether the SIFT pass actually ran, and which data checks were applicable;
4. one verdict table covering both tracks: confirmed duplicate, high-confidence concern, needs raw data, expected derivative/longitudinal view, and excluded false positive;
5. for figures: panel IDs, source/page, bounding boxes, metrics, and evidence paths; for data: series IDs and labels, cell ranges, the relation or statistic, p and q with the family size, and the paper location;
6. separately listed quality/uninformative image findings;
7. limitations, especially uncertain panel boundaries, unsplit lanes, transcribed rather than source values, and inapplicable tests.

Use neutral language: the audit identifies reuse, similarity, and numerical inconsistency, not intent. Never compute or report a composite fraud or risk score. Do not claim the review is exhaustive unless coverage accounting shows that every in-scope source, experimental-image unit, and data series was inspected.