Using Jupyter for reproducible scientific workflows

Marijan Beg¹, Juliette Taka², Thomas Kluyver³, Alexander Konovalov⁴, Min Ragan-Kelley⁵, Nicolas Thiéry⁶, and Hans Fangohr⁷

¹ Faculty of Engineering and Physical Sciences, University of Southampton, Southampton SO17 1BJ, United Kingdom
² Logilab, 75013 Paris, France
³ European XFEL GmbH, 22869 Schenefeld, Germany
⁴ School of Computer Science, University of St Andrews, St Andrews KY16 9AJ, United Kingdom
⁵ Simula Research Laboratory, 1364 Fornebu, Norway
⁶ Laboratoire de Recherche en Informatique, Université Paris-Saclay, CNRS, 91405 Orsay, France
⁷ Max Planck Institute for the Structure and Dynamics of Matter, 22761 Hamburg, Germany

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About

This repository contains all the necessary data and information required to reproduce Figure 3 in:

M. Beg, J. Taka, T. Kluyver, A. Konovalov, M. Ragan-Kelley, N. Thiéry, and H. Fangohr. Using Jupyter for reproducible scientific workflows. Computing in Science & Engineering 23, 36 (2021).

Binder

You can run the Jupyter notebook in the cloud via Binder. To access Binder, use the badge in the table above.

License

Licensed under the BSD 3-Clause "New" or "Revised" License. For details, please refer to the LICENSE file.

How to cite

M. Beg, J. Taka, T. Kluyver, A. Konovalov, M. Ragan-Kelley, N. Thiéry, and H. Fangohr. Using Jupyter for reproducible scientific workflows. Computing in Science & Engineering 23, 36 (2021).
M. Beg, J. Taka, T. Kluyver, A. Konovalov, M. Ragan-Kelley, N. Thiéry, and H. Fangohr. Accompanying repository for "Using Jupyter for reproducible scientific workflows". GitHub: https://github.com/marijanbeg/2021-paper-jupyter-reproducible-workflows, DOI: 10.5281/zenodo.4382224 (2021).

Acknowledgments