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Research at St Andrews

Using Jupyter for reproducible scientific workflows

Research output: Contribution to journalArticlepeer-review

Author(s)

Marijan Beg, Juliette Belin, Thomas Kluyver, Alexander Konovalov, Min Ragan-Kelley, Nicolas Thiery, Hans Fangohr

School/Research organisations

Abstract

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 a dedicated software was exposed into the Jupyter environment. This enabled interactive and batch computational exploration of data, simulations, data analysis, and workflow documentation and outcome 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 towards more reproducible and re-usable research results and outputs, notably through the use of infrastructure such as JupyterHub and Binder.

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Details

Original languageEnglish
Number of pages11
JournalComputing in Science and Engineering
VolumeEarly Access
DOIs
Publication statusPublished - 15 Jan 2021

    Research areas

  • Jupyter

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