Be Prospective, Not Retrospective: A Philosophy for Advancing Reproducibility in Modern Biological Research

TL;DR

This paper advocates for a proactive approach to reproducibility in biological research, emphasizing early documentation and organization of data and code using modern tools like GitHub from the start of projects.

Contribution

It introduces a philosophy and a language-agnostic template architecture for integrating reproducibility practices at the beginning of research projects.

Findings

Early documentation improves reproducibility and collaboration.

A flexible template can be adapted for various research workflows.

Proactive practices enhance data and code validation.

Abstract

The ubiquity of computation in modern scientific research inflicts new challenges for reproducibility. While most journals now require code and data be made available, the standards for organization, annotation, and validation remain lax, making the data and code often difficult to decipher or practically use. I believe that this is due to the documentation, collation, and validation of code and data only being done in retrospect. In this essay, I reflect on my experience contending with these challenges and present a philosophy for prioritizing reproducibility in modern biological research where balancing computational analysis and wet-lab experiments is commonplace. Modern tools used in scientific workflows (such as GitHub repositories) lend themselves well to this philosophy where reproducibility begins at project inception, not completion. To that end, I present and provide a programming-language agnostic template architecture that can be immediately copied and made bespoke to your next paper, whether your lab work is wet, dry, or somewhere in between.