Reproducible

Reproducible is an adjective meaning capable of being reproduced or duplicated. In science and technology, it is most often used to describe results or analyses that can be re-run and yield the same outcomes.

In scientific research, reproducibility generally means that another researcher can obtain the same results using the original data and analysis workflow. Many fields also distinguish replicability, which refers to obtaining consistent results when the study is repeated with new data or independent methods. Because terminology varies by discipline, it is important to consider field-specific definitions when discussing reproducibility and replication.

In computational and data-intensive research, reproducibility requires access to data, code, and the computational environment used to produce results. Reproducible work typically includes clear documentation, version-controlled code, and a record of software dependencies and hardware settings so others can re-create analyses and figures.

Practices that support reproducibility include:

- Sharing data and code under clear licenses.

- Using version control and publishing it with the project.

- Providing comprehensive methods descriptions and computational notebooks or scripts.

- Capturing the computational environment (containers like Docker, or environment files such as conda).

- Employing automated workflows and well-documented pipelines.

- Setting fixed random seeds where appropriate to reduce nondeterminism.

Limitations and challenges include privacy concerns, proprietary or large datasets, non-deterministic computations, and differences in hardware or software environments. Some studies involve ethical or legal restrictions on data sharing, which can hinder reproducibility.

The aim of reproducibility is to increase transparency, enable validation, and promote reuse of data, methods, and findings, thereby strengthening trust in scientific and technical results.