reproducibilityreproducing

Reproducibility and reproducing, sometimes discussed together as reproducibility-reproducing, describe how scientific results can be confirmed by others. Reproducibility generally means that, given the same data and analysis methods, others can obtain the same results when following the published workflow under the same conditions. Reproducing refers to performing the work anew—often with different data or a different implementation—to determine whether the same conclusions hold when the protocol is followed. In computational disciplines, reproducibility highlights the sharing of data, code, and the computational environment so analyses can be rerun to verify results.

Field-specific usage varies: some communities reserve replication for independent data collection and experiments, while others use reproducibility as the umbrella term for both recomputation and independent confirmation. The term "reproducibility crisis" has been used to describe widespread difficulties in achieving reproducible results due to incomplete documentation, selective reporting, small samples, or incompatible software and data formats.

Improving reproducibility-reproducing involves practices such as preregistration, open data and open code, clear methodological descriptions, and the use of version control, literate programming, and containerized environments to stabilize software dependencies. Many journals and funders require data and code availability and the sharing of analysis workflows (for example, through notebooks or workflow descriptions).

Limitations include privacy and data-use restrictions, licensing constraints, proprietary software, and the reality that some results depend on stochastic processes or unavailable external factors. The goal of reproducibility-reproducing is to enhance transparency and trust while balancing ethical, legal, and practical considerations.