Reproducibility

Reproducibility is the ability of an independent researcher to obtain the same results as a study by using the original data, code and computational methods. It is distinct from replicability, obtaining consistent findings by running a new, independent study to test the same question; a result can be reproducible, the numbers check out, without being replicable, the effect does not hold up in a fresh sample.

In computational and data-driven research, reproducibility depends on the analysis pipeline being fully specified and shareable, versioned code, a pinned software environment, a documented scientific workflow, and access to the underlying data managed under sound research data management practice. Many fields have documented a "reproducibility crisis," where a meaningful share of published results could not be reproduced or replicated when independently checked.

Reproducibility matters for scientific credibility, for regulators and peer reviewers who need to verify claims, and for organizations reusing internal analysis, an unreproducible dashboard or model is a liability the moment its author leaves. It is closely tied to the FAIR data principles and to open data practice, since data or code that cannot be accessed cannot be reproduced. Common pitfalls include undocumented manual steps in a pipeline, unpinned software versions that silently change behavior, and random seeds left unset.