Issue |
J. Space Weather Space Clim.
Volume 10, 2020
Topical Issue - Space Weather research in the Digital Age and across the full data lifecycle
|
|
---|---|---|
Article Number | 12 | |
Number of page(s) | 8 | |
DOI | https://doi.org/10.1051/swsc/2020011 | |
Published online | 06 April 2020 |
Technical Article
Reproducible Software Environment: a tool enabling computational reproducibility in geospace sciences and facilitating collaboration
Center for Geospace Studies, SRI International, Menlo Park, 94025 CA, USA
* Corresponding author: asti.bhatt@sri.com
Received:
8
December
2019
Accepted:
4
March
2020
The Reproducible Software Environment (Resen) is an open-source software tool enabling computationally reproducible scientific results in the geospace science community. Resen was developed as part of a larger project called the Integrated Geoscience Observatory (InGeO), which aims to help geospace researchers bring together diverse datasets from disparate instruments and data repositories, with software tools contributed by instrument providers and community members. The main goals of InGeO are to remove barriers in accessing, processing, and visualizing geospatially resolved data from multiple sources using methodologies and tools that are reproducible. The architecture of Resen combines two mainstream open source software tools, Docker and JupyterHub, to produce a software environment that not only facilitates computationally reproducible research results, but also facilitates effective collaboration among researchers. In this technical paper, we discuss some challenges for performing reproducible science and a potential solution via Resen, which is demonstrated using a case study of a geospace event. Finally we discuss how the usage of mainstream, open-source technologies seems to provide a sustainable path towards enabling reproducible science compared to proprietary and closed-source software.
Key words: Geospace science / open source software / data collaboration / computational reproducibility
© A. Bhatt et al., Published by EDP Sciences 2020
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.