Making an executable paper with the Python in Heliophysics Community to foster open science and improve reproducibility

FRONTIERS IN ASTRONOMY AND SPACE SCIENCES(2023)

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摘要
We share the story of how we made this paper, the first executable paper in Heliophysics, through cross-disciplinary collaboration to highlight the benefits of our process. Executable papers are interactive documents that put a publication's text inline with the code used in the research in a containerized environment with the data and dependencies needed to run the code. This approach enables readers to reproduce every step taken to arrive at the publication's conclusions and to easily build upon and extend the work-all important components of open science. Open science is, broadly speaking, transparent and accessible knowledge that is shared and developed through collaborative networks. In this work, we present an adaptable workflow to compare magnetosphere models to spacecraft observations. It is one example of many other workflows that can be developed through collaborations between software developers and scientists in a move towards open science. Most of the authors are members of the Python in Heliophysics Community (PyHC), an international, multi-organizational community that serves as a knowledge base for performing Heliophysics research in the Python programming language. PyHC promotes the executable paper format as a supplemental tool to improve the reproducibility of publications and support open science. A key takeaway is that our collaboration made such a complex task an easy feat in the end. Additionally, the executable version of our paper makes it trivial for others to reproduce our work, and it gives them a better launching point to extend it. These facts underscore the success of our approach. In highlighting this new open science approach, we hope to be an example to our field and encourage this way of doing science.
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关键词
Python in Heliophysics Community, PyHC, executable paper, open science, improving reproducibility, magnetosphere models, cross-disciplinary collaboration, deepnote
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