Skip to content

Research at St Andrews

Using Jupyter for reproducible scientific workflows

Research output: Contribution to journalArticlepeer-review

Standard

Using Jupyter for reproducible scientific workflows. / Beg, Marijan; Belin, Juliette; Kluyver, Thomas; Konovalov, Alexander; Ragan-Kelley, Min; Thiery, Nicolas; Fangohr, Hans.

In: Computing in Science and Engineering, Vol. Early Access, 15.01.2021.

Research output: Contribution to journalArticlepeer-review

Harvard

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

APA

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, Early Access. https://doi.org/10.1109/MCSE.2021.3052101

Vancouver

Beg M, Belin J, Kluyver T, Konovalov A, Ragan-Kelley M, Thiery N et al. Using Jupyter for reproducible scientific workflows. Computing in Science and Engineering. 2021 Jan 15;Early Access. https://doi.org/10.1109/MCSE.2021.3052101

Author

Beg, Marijan ; Belin, Juliette ; Kluyver, Thomas ; Konovalov, Alexander ; Ragan-Kelley, Min ; Thiery, Nicolas ; Fangohr, Hans. / Using Jupyter for reproducible scientific workflows. In: Computing in Science and Engineering. 2021 ; Vol. Early Access.

Bibtex - Download

@article{68fa4ba8e35c4f62a1fa8db302f3661b,
title = "Using Jupyter for reproducible scientific workflows",
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.",
keywords = "Jupyter",
author = "Marijan Beg and Juliette Belin and Thomas Kluyver and Alexander Konovalov and Min Ragan-Kelley and Nicolas Thiery and Hans Fangohr",
note = "Funding: 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).",
year = "2021",
month = jan,
day = "15",
doi = "10.1109/MCSE.2021.3052101",
language = "English",
volume = "Early Access",
journal = "Computing in Science and Engineering",
issn = "1521-9615",
publisher = "IEEE Computer Society",

}

RIS (suitable for import to EndNote) - Download

TY - JOUR

T1 - Using Jupyter for reproducible scientific workflows

AU - Beg, Marijan

AU - Belin, Juliette

AU - Kluyver, Thomas

AU - Konovalov, Alexander

AU - Ragan-Kelley, Min

AU - Thiery, Nicolas

AU - Fangohr, Hans

N1 - Funding: 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).

PY - 2021/1/15

Y1 - 2021/1/15

N2 - 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.

AB - 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.

KW - Jupyter

U2 - 10.1109/MCSE.2021.3052101

DO - 10.1109/MCSE.2021.3052101

M3 - Article

AN - SCOPUS:85099730291

VL - Early Access

JO - Computing in Science and Engineering

JF - Computing in Science and Engineering

SN - 1521-9615

ER -

Related by author

  1. GAP – Groups, Algorithms, and Programming, Version 4.11.1

    The GAP Group, Behrends, R., Breuer, T., Horn, M., Hulpke, A., Jefferson, C. A., Konovalov, A., Linton, S. A., Lübeck, F., Mitchell, J. D., Pfeiffer, M. J., Siccha, S. & Torpey, M. C., 2 Mar 2021

    Research output: Non-textual formSoftware

  2. GAP – Groups, Algorithms, and Programming, Version 4.11.0

    The GAP Group, Behrends, R., Breuer, T., Horn, M., Hulpke, A., Jefferson, C. A., Konovalov, A., Linton, S. A., Lübeck, F., Mitchell, J. D., Pfeiffer, M. J., Siccha, S. & Torpey, M. C., 29 Feb 2020

    Research output: Non-textual formSoftware

  3. Software Carpentry: Programming with GAP: Version 3.0

    Konovalov, A., Torpey, M., Jefferson, C. A. & Software Carpentry team, 13 Aug 2019, Zenodo.

    Research output: Other contribution

ID: 272713944

Top