English
 
Help Privacy Policy Disclaimer
  Advanced SearchBrowse

Item

ITEM ACTIONSEXPORT

Released

Journal Article

Using Jupyter for Reproducible Scientific Workflows

MPS-Authors
/persons/resource/persons255347

Fangohr,  H.
Computational Science, Scientific Service Units, Max Planck Institute for the Structure and Dynamics of Matter, Max Planck Society;

External Resource
Fulltext (public)

2102.09562.pdf
(Preprint), 4MB

Supplementary Material (public)
There is no public supplementary material available
Citation

Beg, M., Taka, J., Kluyver, T., Konovalov, A., Ragan-Kelley, M., Thiery, N., et al. (2021). Using Jupyter for Reproducible Scientific Workflows. Computing in Science & Engineering, 23(2), 36-46. doi:10.1109/MCSE.2021.3052101.


Cite as: http://hdl.handle.net/21.11116/0000-0008-1E6C-F
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 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.