Reproducibility is a publication requirement. Every article ships the code and data needed to rerun it. Where the data cannot be public, a verified, privacy-preserving surrogate is provided in their place. "Available on request" is not sufficient.
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 surrogate where the real data cannot be
released
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A verified synthetic twin of the restricted data, built in a secure enclave: artificial records that reproduce the paper's results without exposing any real one. "Verified" means checked on two sides. It must reproduce the analysis, which is the utility test, and it must pass a disclosure-risk audit, which is the privacy test. Clearing both is what lets a reader rerun the surrogate and trust what comes out. The founders of FAIR Press Journals work in statistical disclosure control. Raw data never leave the enclave and never touch a cloud AI model.
- a pinned environment, meaning a lockfile, requirements file or container, confirmed before acceptance
- determinism information, meaning 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 rerun from the package before publication, and the published article states the outcome as its badge. Papers with no runnable artifacts (pure theory, for example) instead state that no code or data apply, and list any machine-checkable proof or symbolic-computation files where those exist.
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 and register data are a real and legitimate constraint, and they are not an exemption from verification. Here is the route.
- The restriction is documented at submission, covering 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 to fit the data's actual
disclosure-risk structure. The journal offers this as an opt-in,
separately priced service run by the founders of FAIR Press Journals, whose research field is
statistical disclosure control.
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Submitting never requires it, and the article charge never includes it. The founders of FAIR Press Journals build the safe companion dataset inside a secure enclave and quote it per dataset. The raw sensitive data never leave that environment and never touch a cloud AI model, and choosing the service has no bearing on the accept or reject decision.
- 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.
Limits
Anonymisation lowers disclosure risk without removing it. Synthetic data are not private by construction, and they face the same disclosure-risk audit as anonymised records. And some analyses, such as rare cells, extreme quantiles or fine geography, may fail to reproduce faithfully on any safe dataset. When that happens the article says so. 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).