Reproducibility and Open Data
A result no one else can reproduce is a claim, not a finding; open data and code turn claims into verifiable science.
The Reproducibility Standard
A computational result is reproducible when an independent party, given the code, inputs, and environment, obtains the same result. This is a stricter standard than replication, which allows an independent reimplementation. Reproducibility is the minimum for computational science to be checkable, because a result that cannot be regenerated cannot be verified, debugged, or extended.
What Must Be Shared
- The source code at the exact version used, under version control with a citable identifier.
- The input files and parameters, complete enough to run without guesswork.
- The computational environment: compiler, library versions, and platform, ideally captured in a container.
- The output data and the scripts that turn output into figures and tables.
Open Data
Sharing data openly, under clear licenses and with persistent identifiers such as DOIs, lets others check and build on results. Repositories that mint DOIs give data and code a permanent citable location. Metadata matters as much as the files: a dataset without documentation of how it was produced is hard to trust or reuse.
Barriers and Limits
Not everything can be open. Some inputs are proprietary or export-controlled, and some results carry commercial or security sensitivity. The honest response is to be explicit about what is shared and what is withheld, and to share as much of the reproducibility chain as possible so that the open portion is genuinely checkable.
Kronos maintains a published reproducibility record: physics deposits with persistent DOIs, cold-reproduction runbooks that regenerate key results on a clean machine, and a two-tier repro standard distinguishing byte-for-byte reproduction from reproduction within numerical tolerance. That discipline is what lets an outside reader confirm a physics number rather than take it on faith.