Visible, Trackable, Forkable: Opening the Process of Science

TL;DR

This paper advocates for opening the entire scientific process—making it visible, trackable, and forkable—to accelerate progress, improve trustworthiness, and enable scalable quality assurance in science.

Contribution

It introduces a framework for open scientific workflows inspired by software development practices, emphasizing transparency, traceability, and collaborative branching.

Findings

Open science reforms have focused on outputs, not the process.

A visible, trackable, forkable workflow enhances verifiability and collaboration.

Software development demonstrates benefits of such workflows for scientific progress.

Abstract

The way science is currently practiced shows conclusions but hides how they were reached. Researchers work privately, polish their results, publish a finished paper, and defend it. Errors are punished by retraction rather than corrected by amendment. Alternative directions are pursued through competing papers with no shared history. The reasoning, the dead ends, the trade-offs, the corrections: everything that would let others understand how a conclusion was reached is invisible. Two decades of open science reform have addressed this by opening specific artifacts: papers, data, code, notebooks, protocols. Each is valuable, but the unit remains a finished product. None opens the thinking process itself: the evolving sequence of questions, interpretations, dead ends, and direction changes that constitutes the actual scientific contribution. This paper argues that opening the process of science (not just its outputs) would produce a step change in the speed of scientific progress, the accessibility of scientific reasoning, the trustworthiness of scientific claims, and the scalability of scientific quality assurance. We identify three properties the workflow needs: visible (the process is open, not just the product), trackable (every change is recorded and attributable), and forkable (anyone can branch from any point with shared history preserved). A visible, trackable flow is inherently verifiable: by humans, by automated tools, by AI agents. Software development adopted this flow decades ago, and the results (faster correction, broader contribution, maintained quality at scale) demonstrate the opportunity for science.