Where Reproducibility Policy Actually Lives: A Stratified Audit of 150 Clinical Journals — Data and Code

Open data and code for a cross-sectional audit of the documented editorial infrastructure to evaluate computational research across a field-normalised (Scopus SNIP) stratified random sample of 150 MEDLINE-indexed clinical journals in six specialties (general internal medicine, cardiology, infectious diseases, oncology, neurology, pediatrics). Five indicators — statistics/methods editor, AI/machine-learning editor, data-availability statement, code deposit, and executable artifact — were hand-coded from public editorial-board and author-instruction pages, separating disclosure (a statement that data exist), deposit (a requirement to share), and verification (a check that a computation runs). Codes were verified against a timestamped source-page archive; the data-statement and board codes were cross-checked by blind automated agents; and a boundary-enriched 50-cell subset was re-coded by an independent second coder (Cohen's κ=0.69, three of five indicators). Contents: Frozen sampling frame and draw order (600 drawn, 150 retained) Per-journal coded indicators with source URLs Publisher-family policy units (149 readable author-policy records → 50 analytic units within the 150-journal sample) Three-profile HTTP reachability probe results and the automated-comparator (machine-vs-human) codes Codebook, coding rules, and analysis.py, which regenerates the reported prevalences, intervals, statistics, Tables 1-4 and Table S1 (make_figures.py regenerates Figs 1-3; Figs 2-3 are data-computed and Fig 1 is a schematic; the PubMed trend is not regenerated) Journal-level policy is reported openly as a matter of public record; editorial roles are coded for presence only and no individual editor is named.