Using Jupyter for Reproducible Scientific Workflows

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 magnetism another mathematics-where domain-specific software was exposed to the Jupyter environment. This enables high level control simulations computation, interactive exploration results, batch processing on HPC resources, reproducible workflow documentation notebooks. first study, Ubermag drives existing micromagnetics through a language embedded Python. second dedicated kernel interfaces GAP system discrete algebra its programming language. light these studies, discuss benefits approach, including progress toward more reusable research results outputs, notably use infrastructure such JupyterHub Binder.