Reproduceability

Reproduceability, often spelled reproducibility, is the capacity to obtain the same results by re-running the original analysis on the same data and code. It relies on well-documented methods and a preserved computational environment, so other researchers can verify reported findings by recomputing figures, statistics, and models.

Distinction between terms: In many domains, reproducibility refers to recomputation with the same dataset and code, while replicability denotes obtaining consistent results with new data or using different methods. However, usage varies, and some communities treat the terms as synonyms. Clear definitions are essential in reporting.

Importance: Reproduceability supports verification, transparency, and trust in science and engineering. It enables error detection, builds on prior work, and strengthens the foundations of knowledge by allowing others to build upon the same results.

Requirements and practices: Sharing data and code, providing detailed methods, and capturing the computational environment are central. Version control, open licenses, and documentation help. Techniques include containerization, such as Docker or Singularity, environment manifests (Conda or Pip), and literate programming with notebooks and provenance records. Reproducible workflows should specify random seeds and seed management.

Challenges: Data privacy, proprietary restrictions, and large or opaque datasets hinder sharing. Non-deterministic algorithms, hardware differences, and evolving software can complicate recomputation. Long-term accessibility of software and data formats is also a concern.

Improving reproducibility involves planning from project inception, preregistration of analyses where appropriate, and adopting standards for metadata, data curation, and reporting. Funders and journals increasingly require data and code availability to enhance reproducibility.