Reproducibility

Reproducibility is the ability to obtain consistent results when a study is reanalyzed using the same data, methods, and code, and the analysis is rerun under the same conditions. In practice, the term is used with varying meanings across fields. A common distinction is that reproducibility refers to re-running the original analysis with the same data and code, while replicability refers to obtaining similar results with new data or independent experiments. Terminology is not uniform, and different disciplines may use related terms differently.

Key elements that influence reproducibility include access to data and code, complete and precise methods, and the availability of the computational environment used for analysis. Practices such as preregistration, thorough documentation, version control, and the use of open-source software support reproducibility. Sharing data and code, along with clear licensing and metadata, helps others verify findings and reuse components for further work. Containerization and workflow tools can reduce discrepancies caused by software versions or operating systems.

Challenges to reproducibility include selective reporting, p-hacking, insufficient or opaque methods, and incomplete data or code sharing. Privacy concerns, data ownership, and proprietary software or data can also hinder reproducibility. Structural incentives in research funding and publishing sometimes emphasize novel results over verification, contributing to a reproducibility crisis in some fields.

Efforts to improve reproducibility encompass journal and funder policies, community standards, and educational initiatives. When effectively implemented, reproducibility strengthens trust, enables verification, and accelerates cumulative scientific progress by making analyses and data more transparent and reusable.