On the open-source landscape of PLOS Computational Biology
Recently, following an editorial by N.S. on reproducibility and the future of MRI research [2], we wrote a blog post presenting an analysis of the open-source landscape for the journal Magnetic Resonance in Medicine (MRM), which broadly focuses on MRI research for medical applications. Using open-so...
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| Veröffentlicht in: | PLoS computational biology 2021-02, Vol.17 (2), p.e1008725-e1008725 |
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| creator | Boudreau, Mathieu Poline, Jean-Baptiste Bellec, Pierre Stikov, Nikola |
| description | Recently, following an editorial by N.S. on reproducibility and the future of MRI research [2], we wrote a blog post presenting an analysis of the open-source landscape for the journal Magnetic Resonance in Medicine (MRM), which broadly focuses on MRI research for medical applications. Using open-source languages like Python opens the gateway to a wide selection of other open-source tools (e.g., continuous integration, Jupyter Notebook, Binder, etc.) which are mostly incompatible with licensed software like MATLAB. Reproducibility tools used by percentage. https://doi.org/10.1371/journal.pcbi.1008725.t002 In addition to sharing code, an emerging trend in the open science community is to provide an easily reproducible coding environment that requires only a web browser to run demos or reproduce figures. |
| doi_str_mv | 10.1371/journal.pcbi.1008725 |
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| subjects | Biology Biology and Life Sciences Computer and Information Sciences Computer applications Engineering and Technology Keywords Magnetic resonance imaging Medical research Medicine and Health Sciences Open source software Programming languages Reproducibility Research and Analysis Methods Scholarly publishing Science Policy Social Sciences Software Source code |
| title | On the open-source landscape of PLOS Computational Biology |
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