Commit f9280135 authored by Luke Johnston's avatar Luke Johnston

Add revisions based on MR comments

parent 4818e315
# Specific recommendations {#recommendations}
```{r, child="preamble-note.md"}
......@@ -32,13 +31,23 @@ explanations and comparisons between tools, services, and workflows.
- **Writing platform**: [RStudio]
- **Dissemination** for getting a DOI and for discoverability:
- **Code and other project files**: [Zenodo]
- **Preprint manuscripts**: [bioRxiv] or [PeerJ Preprints] or [OSF Preprints]
- **Preprint manuscripts**: [bioRxiv], [medRxiv], or [OSF Preprints]
- **Posters**: [figshare] or [PeerJ Preprints]
- **Slides**: ??? [figshare]?
- **All activities**: For R projects, preferably everything is done in [RStudio].
- **All activities**: For R projects, preferably everything is done in
[RStudio]. See the [workflow section](#workflow) below for more detail. For
Python projects the environment is a bit more complicated and we are still
thinking through how it would look.
<!--
For Python projects, most work can be done in [JupyterLab], however other tools
will also need to be used. See the [workflow section](#workflow) below for more
detail.
will also need to be used.
For Jupyter Notebook, it might make sense to always have Rmd as the backend and
then use RStudio as a Markdown and Git GUI. There is no other platform with as
much support for different publishing option through a GUI, so I think it will
be used for writing. For git, there is git kraken and nbdime and git jupyterlab
exteisnion as an alternative.
-->
[Git]: https://git-scm.com/
[GitHub]: https://github.com/
......@@ -51,7 +60,7 @@ detail.
[JupyterLab]: https://jupyterlab.readthedocs.io/en/stable/
[Zenodo]: https://zenodo.org/
[bioRxiv]: https://www.biorxiv.org/
[PeerJ Preprints]: https://peerj.com/preprints/
[medRxiv]: https://www.medrxiv.org/
[OSF Preprints]: https://osf.io/preprints/
[figshare]: https://figshare.com/
......@@ -152,8 +161,9 @@ then excludes it from being part of a ROS workflow.
There are many programming and statistical computing languages available,
both open source and proprietary. However, of them all we recommend using [R]
and [Python]. Both languages are open source, have active and (mostly) welcoming
communities, have very well developed packages and extensions for all types of
and [Python]. Both languages are open source, have active communities, are
working at being more welcoming and inclusive,
have very well developed packages and extensions for all types of
analyses projects, are well maintained and documented, are (mostly) readable,
are widely used in the scientific community, and are the two most widely used
languages in the world for data science. The R community in particular is very
......
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