Every article here ships the material needed to re-run it. "Data available on request" is exactly the promise this journal exists to replace — where data cannot be public, we publish a verified, privacy-preserving surrogate instead of an IOU.
The reproducibility package
Every submission carries:
- an executable source or code — with the one entry-point command that regenerates the results;
- the data — or a privacy-preserving verifiable surrogate where the real data cannot be released;
- a pinned environment — lockfile, requirements or container, confirmed before acceptance;
- determinism information — seeds and any known sources of run-to-run variation.
We assess reproducibility by re-execution: where the article type carries runnable artifacts, the analysis is re-run from the package before publication, and the published article states the outcome as its badge. Article types without runnable artifacts (for example pure theory) document what exists instead — the requirement adapts to the paper, never silently disappears.
An availability statement on every article
Each published article carries a data-and-code availability statement — composed at submission from what the authors actually provide, so the statement and the artifacts cannot drift apart.
When the data cannot be public
Confidential microdata, clinical records, register data — a real and legitimate constraint, and not an exemption from verification. The route:
- The restriction is documented at submission: what the data are, why they cannot be released, and under what regime they were held.
- Where feasible, the authors release a safe companion dataset — either carefully anonymised real records or a synthetic surrogate, chosen by the data's actual disclosure-risk structure, not by fashion. The journal offers this as an opt-in, separately priced service run by the founding editors, whose research field is statistical disclosure control.
- The published result is then re-executed on the safe dataset, so the verification claim rests on an artifact readers can actually download.
- Raw sensitive data are handled under a data processing agreement, are never published, and are never processed by any cloud AI model — they stay in a controlled environment.
Honest limits
We say plainly what a safe dataset can and cannot carry: anonymisation is risk-based, not zero-risk; synthetic data are not automatically private (they face the same disclosure-risk audit); and some analyses — rare cells, extreme quantiles, fine geography — may not be faithfully reproducible on any safe dataset. When that happens, the article says so, rather than shipping a misleadingly "reproducible" file. Overclaiming privacy is itself a harm.
Licences and preservation
Code and data publish under open licences by default (see open access & licensing), and the package will be preserved alongside the article (see archiving & preservation).