Literate computing has emerged as an important tool for computational studies and open science, with growing folklore of best practices. In this work, we report two case studies - one in computational magnetism and another in computational mathematics - where domain-specific software was exposed to the Jupyter environment. This enables high-level control of simulations and computation, interactive exploration of computational results, batch processing on HPC resources, and reproducible workflow documentation in Jupyter notebooks. In the first study, Ubermag drives existing computational micromagnetics software through a domain-specific language embedded in Python. In the second study, a dedicated Jupyter kernel interfaces with the GAP system for computational discrete algebra and its dedicated programming language. In light of these case studies, we discuss the benefits of this approach, including progress toward more reproducible and reusable research results and outputs, notably through the use of infrastructure such as JupyterHub and Binder.

中文翻译：

---

使用Jupyter进行可重复的科学工作流程

随着最佳实践的不断发展，精确计算已成为计算研究和开放科学的重要工具。在这项工作中，我们报告了两个案例研究-一个是计算磁性的，另一个是计算数学的-特定领域的软件暴露于Jupyter环境。这样就可以对模拟和计算进行高级控制，对计算结果进行交互式浏览，对HPC资源进行批处理，并在Jupyter笔记本中提供可复制的工作流程文档。在第一个研究中，Ubermag通过嵌入在Python中的特定领域语言来驱动现有的计算微磁软件。在第二项研究中，专用的Jupyter内核与GAP系统接口，用于计算离散代数及其专用编程语言。根据这些案例研究，