Procedural Knowledge Libraries (PKLs) are frameworks for capturing the full arc of scientific inquiry, not just its outcomes. Whereas traditional libraries store static end products, PKLs preserve the process that leads to those results, including hypotheses, failures, decisions, and iterations. By addressing the loss of tacit knowledge -- typically buried in notebooks, emails, or memory -- PKLs lay a foundation for reproducible, collaborative, and adaptive research. PKLs provide executable, version-controlled records that contextualize each step of a research process. For example, a researcher using Jupyter notebooks could share not just final outputs, but also the reasoning, discarded approaches, and intermediate analyses that informed them. This work proposes a framework for implementing PKLs within the Jupyter ecosystem, supported by a lens-based transformation model and procedural storage schema.

翻译：过程知识库（PKLs）是一种旨在捕捉完整科学探究过程而不仅仅是其成果的框架。传统图书馆存储的是静态的最终产物，而PKLs则保存了导致这些结果的过程，包括假设、失败、决策和迭代。通过解决隐性知识（通常埋藏在笔记本、电子邮件或记忆中）的流失问题，PKLs为可重复、可协作和自适应研究奠定了基础。PKLs提供可执行的、版本控制的记录，为研究过程的每一步提供背景信息。例如，使用Jupyter笔记本的研究人员不仅可以共享最终输出，还可以共享指导这些输出的推理过程、被舍弃的方法以及中间分析。本研究提出了一个在Jupyter生态系统中实现PKLs的框架，该框架由一个基于透镜的转换模型和过程存储模式提供支持。