Reproducible Software Environment: a tool enabling computational reproducibility in geospace sciences and facilitating collaboration | Journal of Space Weather and Space Climate

Topical Issue - Space Weather research in the Digital Age and across the full data lifecycle

Open Access

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

Baker M. 2016. Is there a reproducibility crisis? Nature 533: 452–454. https://doi.org/10.1038/533452. [Google Scholar]
Bhandari Neupane J, Neupane RP, Luo Y, Yoshida WY, Sun R, Williams PG. 2019. Characterization of Leptazolines A–D, polar oxazolines from the Cyanobacterium Leptolyngbya sp., reveals a glitch with the “Willoughby-Hoye” scripts for calculating NMR chemical shifts. Org Lett 21(20): 8449–8453. PMID: 31591889. https://doi.org/10.1021/acs.orglett.9b03216. [CrossRef] [Google Scholar]
Docker Inc. 2019. Docker. Available from https://docs.docker.com/install/ (Last accessed 6 December 2019). [Google Scholar]
EOSDIS N. 2018. NASA Earth Science Data Systems Program Highlights 2018. https://cdn.earthdata.nasa.gov/conduit/upload/11232/ESDISHighlights.pdf. [Google Scholar]
Integrated Geoscience Observatory Team. 2019a. Resen. Version 2019.1.0rc2. Available from https://github.com/EarthCubeInGeo/resen (Last accessed 6 December 2019). [Google Scholar]
Integrated Geoscience Observatory Team. 2019b. Resen-core. Version 2019.1.0rc2. Available from https://github.com/EarthCubeInGeo/resen-core (Last accessed 6 December 2019) [Google Scholar]
Lyons LR, Nishimura Y, Zhang SR, Coster AJ, Bhatt A, Kendall E, Deng Y. 2019. Identification of auroral zone activity driving large? Scale traveling ionospheric disturbances. J Geophys Res: Space Phys 124: 700–714. https://doi.org/10.1029/2018JA025980. [CrossRef] [Google Scholar]
Makela J. 2015a. Data from the CEDAR Madrigal database, CEDAR Madrigal Database. Available from https://w3id.org/cedar?experiment_list=experiments/2015/uao/06nov15&file_list=minime05_uao_20151106.cedar.002.hdf5 (Last accessed 25 November 2019). [Google Scholar]
Makela J. 2015b. Data from the CEDAR Madrigal database, CEDAR Madrigal Database. Available from https://w3id.org/cedar?experiment_list=experiments/2015/ann/06nov15&file_list=minime08_ann_20151106.cedar.003.hdf5 (Last accessed 25 November 2019). [Google Scholar]
Matsuo T. 2020. Recent progress on inverse and data assimilation procedure for high-latitude ionospheric electrodynamics, Springer International Publishing, Cham, pp. 219–232. https://doi.org/10.1007/978-3-030-26732-2. [Google Scholar]
McGranaghan RM, Bhatt A, Matsuo T, Mannucci AJ, Semeter JL, Datta-Barua S. 2017. Ushering in a new frontier in geospace through data science. J Geophys Res: Space Phys 122(12): 12586–12590. https://doi.org/10.1002/2017JA024835. [CrossRef] [Google Scholar]
Met Office. 2010–2015. Cartopy: A cartographic python library with a Matplotlib interface, Exeter, Devon, UK. http://scitools.org.uk/cartopy. [Google Scholar]
Morley SK, Koller J, Welling DT, Larsen BA, Henderson MG, Niehof JT. 2011. Spacepy – A Python-based library of tools for the space sciences. In Proceedings of the 9th Python in Science Conference (SciPy 2010), Austin, TX. [Google Scholar]
National Academies of Sciences, Engineering, and Medicine. 2018. Open Source Software Policy Options for NASA Earth and Space Sciences, The National Academies Press, Washington, DC. https://doi.org/10.17226/25217. [Google Scholar]
Project Jupyter. 2019. Jupyter Lab. Version 0.35.5. Available from https://jupyter.org/install (Last accessed 6 December 2019). [Google Scholar]
Rideout W. 2017. MadrigalWeb. Version 3.1.11. Available from https://pypi.org/project/madrigalWeb/ (Last accessed 6 December 2019). [Google Scholar]
Shiokawa K, Otsuka Y, Ogawa T, Balan N, Igarashi K, Ridley AJ, Knipp DJ, Saito A, Yumoto K. 2002. A large-scale traveling ionospheric disturbance during the magnetic storm of 15 September 1999. J Geophys Res: Space Phys 107(A6): 1088. https://doi.org/10.1029/2001JA000245. [NASA ADS] [CrossRef] [Google Scholar]
Song Q, Ding F, Wan W, Ning B, Liu L. 2012. Global propagation features of large-scale traveling ionospheric disturbances during the magnetic storm of 7–10 November 2004. Ann Geophys 30: 683–694. https://doi.org/10.5194/angeo-30-683-2012. [CrossRef] [Google Scholar]
Stall S, Yarmey LR, Boehm R, Cousijn H, Cruse P, et al. 2018. Advancing FAIR data in Earth, space, and environmental science. Eos 99. https://doi.org/10.1029/2018EO109301. Published on 05 November 2018. [CrossRef] [Google Scholar]
Stoneback R, Klenzing J, Burrell A, Spence C, Depew M, et al. 2019. pysat/pysat v2.1 (Version v2.1). Zenodo. https://doi.org/10.5281/zenodo.3546270. [Google Scholar]
Stoneback RA, Burrell AG, Klenzing J, Depew MD. 2018. PYSAT: Python Satellite Data Analysis Toolkit. J Geophys Res: Space Phys 123(6): 5271–5283. https://doi.org/10.1029/2018JA025297. [CrossRef] [Google Scholar]
Super Dual Auroral Radar Network Software Team. 2018. Data and Visualization Toolkit-Python. Version 0.8. Available from https://github.com/vtsuperdarn/davitpy (Last accessed 6 December 2019). [Google Scholar]
Teytelman L. 2018. No more excuses for non-reproducible methods. Nature 560: 411. https://doi.org/10.1038/d41586-018-06008-w. [CrossRef] [Google Scholar]

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.