Reproducibility
The ability to obtain the same results by re-running an analysis with the same data and methods.
Definition
Reproducibility is the property that an analysis or computation, given the same inputs and procedure, yields the same results when independently re-run. It is a cornerstone of credible science and reliable engineering.
Containerization and workflow tools help by capturing the exact software environment, but true reproducibility also demands recording data provenance and the intent behind each analysis choice. It is as much a discipline of documentation as a technical feature.
Reproducibility is a discipline as much as a technical feature: it requires versioned code and data, recorded environments and dependencies, fixed random seeds, and documentation of the intent behind each choice. Containerization captures the software environment, but true reproducibility also demands data provenance and clear methods. Results that cannot be independently reproduced cannot be trusted or built upon, which is why reproducibility is increasingly required for credible scientific work.
What it requires
- Versioned code and data with recorded provenance.
- Documented environment and dependencies.
- Fixed random seeds where randomness is used.
- Clear records of parameters and numerical settings.
Why it matters
Results that cannot be reproduced cannot be trusted or built upon. Reproducibility guards against errors, enables independent checking, and is increasingly required for published scientific work. In computation it also depends on controlling sources of nondeterminism.
Fusion connection
Kronos records the code, parameters, and environment for its simulations so that Hyperion results can be reproduced and independently verified, reflecting the program's commitment to auditable, credible methods.