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.

翻译：当前科学实践的方式呈现结论，却隐藏了获得结论的过程。研究者私下开展工作，打磨结果，发表最终论文，并为之辩护。错误通过撤稿受到惩罚，而非通过修正得到纠正。替代性研究方向通过相互竞争且无共享历史的论文来推进。推理过程、死胡同、权衡取舍、修正——所有能让他人理解结论如何得出的要素都是不可见的。过去二十年的开放科学改革通过开放特定成果（论文、数据、代码、笔记、实验方案）来应对这一问题。每项成果都有价值，但单元仍然是最终产品。没有任何一项开放了思维过程本身：即构成实际科学贡献的不断演进的问题、解释、死胡同和方向转变。本文主张，开放科学过程（而不仅仅是其产出）将在科学进步速度、科学推理的可及性、科学主张的可信度以及科学质量保障的可扩展性方面产生质的飞跃。我们确定了工作流程所需的三个属性：可见（过程透明，而不仅仅是产品）、可追溯（每项变更均有记录且可归因）、可派生（任何人可在保持共享历史的前提下从任意节点分支）。一个可见、可追溯的流程本质上具有可验证性：可由人类、自动化工具、AI代理进行验证。软件开发早在数十年前就已采用这一流程，其成果（更快的纠错、更广泛的贡献、维持大规模质量）展示了科学领域的机遇。