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  Using Jupyter for Reproducible Scientific Workflows

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.

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https://dx.doi.org/10.1109/MCSE.2021.3052101 (Publisher version)
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https://arxiv.org/abs/2102.09562 (Preprint)
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 Creators:
Beg, M.1, Author
Taka, J.2, Author
Kluyver, T.3, Author
Konovalov, A.4, Author
Ragan-Kelley, M.5, Author
Thiery, N.6, Author
Fangohr, H.7, Author              
Affiliations:
1Faculty of Engineering and Physical Sciences, University of Southampton, ou_persistent22              
2Logilab, ou_persistent22              
3European XFEL GmbH, ou_persistent22              
4School of Computer Science, University of St Andrews, ou_persistent22              
5Simula Research Laboratory AS, ou_persistent22              
6Laboratoire de Recherche en Informatique, Universite Paris-Saclay, ou_persistent22              
7Computational Science, Scientific Service Units, Max Planck Institute for the Structure and Dynamics of Matter, Max Planck Society, ou_3267028              

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 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.

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Language(s): eng - English
 Dates: 2021-01-152021-03
 Publication Status: Published in print
 Pages: 11
 Publishing info: -
 Table of Contents: -
 Rev. Type: Peer
 Identifiers: DOI: 10.1109/MCSE.2021.3052101
arXiv: 2102.09562
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Grant ID : 676541
Funding program : Horizon 2020 (H2020)
Funding organization : European Commission (EC)

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Title: Computing in Science & Engineering
  Other : Comput. Sci. Eng.
Source Genre: Journal
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Publ. Info: College Park, Md. : AIP
Pages: - Volume / Issue: 23 (2) Sequence Number: - Start / End Page: 36 - 46 Identifier: ISSN: 1521-9615
CoNE: https://pure.mpg.de/cone/journals/resource/954928593018