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The ubiquity of computation in modern scientific research inflicts newchallenges for reproducibility. While most journals now require code and databe made available, the standards for organization, annotation, and validationremain lax, making the data and code often difficult to decipher or practicallyuse. I believe that this is due to the documentation, collation, and validationof code and data only being done in retrospect. In this essay, I reflect on myexperience contending with these challenges and present a philosophy forprioritizing reproducibility in modern biological research where balancingcomputational analysis and wet-lab experiments is commonplace. Modern toolsused in scientific workflows (such as GitHub repositories) lend themselves wellto this philosophy where reproducibility begins at project inception, notcompletion. To that end, I present and provide a programming-language agnostictemplate architecture that can be immediately copied and made bespoke to yournext paper, whether your lab work is wet, dry, or somewhere in between.

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