reproducibiliteitsverplichtingen

Reproducibility in science is the degree to which the results of a study can be duplicated when the analysis is conducted again using the same data and methods. In practice, achieving reproducibility often requires access to the original data, the code or pipelines, and a clear description of the computational environment.

Terminology is not always consistent across fields. Repeatability refers to obtaining the same results under identical conditions and with the same investigators. Reproducibility (sometimes termed computational reproducibility) means that independent researchers can recompute results using the same data and analysis. Replicability generally refers to obtaining consistent results across studies that test the same hypothesis but use independent data and possibly different methods.

Why it matters: It supports verification, builds trust, enables meta-analyses, and reduces the risk of erroneous conclusions.

Barriers include data privacy, access restrictions, incomplete documentation, software dependencies, and computationally intensive analyses.

Strategies to improve reproducibility include preregistration, open data and software, version control, literate programming, containerization, standardized workflows, and detailed methods.