Using Jupyter for reproducible scientific workflows
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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.
Beg , M , Belin , J , Kluyver , T , Konovalov , A , Ragan-Kelley , M , Thiery , N & Fangohr , H 2021 , ' Using Jupyter for reproducible scientific workflows ' , Computing in Science and Engineering , vol. Early Access . https://doi.org/10.1109/MCSE.2021.3052101
Computing in Science and Engineering
Copyright © IEEE 2020. This work has been made available online in accordance with publisher policies or with permission. Permission for further reuse of this content should be sought from the publisher or the rights holder. This is the author created accepted manuscript following peer review and may differ slightly from the final published version. The final published version of this work is available at https://doi.org/10.1109/MCSE.2021.3052101.
DescriptionFunding: This work was financially supported by the OpenDreamKit Horizon 2020 European Research Infrastructure project (676541) and the EPSRC Programme grant on Skyrmionics (EP/N032128/1).
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