@article{16083,
  keywords = {Analytical models, python, Computational modeling, Documentation, Tools, Libraries, Kernel},
  author = {Marijan Beg and Juliette Belin and Thomas Kluyver and Alexander Konovalov and Benjamin Ragan-Kelley and Nicolas Thiery and Hans Fangohr},
  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.},
  year = {2021},
  journal = {Computing in Science \& Engineering},
  volume = {23},
  pages = {36-46},
  month = {Jan-01-2021},
  publisher = {IEEE},
  issn = {1521-9615},
  url = {https://ieeexplore.ieee.org/document/9325550},
  doi = {10.1109/MCSE.2021.3052101},
}