KnitR/Jupyter

[bemert]

Knitr and IPython notebook allow scientists to compose documents with inline code and figures.

https://rpubs.com/marschmi/105639 https://www.r-bloggers.com/reproducible-research-training-wheels-and-knitr/ http://jupyter-notebook-beginner-guide.readthedocs.io/en/latest/what_is_jupyter.html

These tools can help computational scientists write more readable and reproducible code as well as more transparent figures/results. By no means are these tools sufficient for reproducible computational biology research.

[oryoruk]

I find notebooks beneficial in transparency and reproducibility also.

9 times out of 10 I preprocess the data I get, before inputting it to an analysis pipeline. I try to use notebooks in documenting these preprocessing steps of publicly available datasets.

[gwaybio]

I have found Jupyter notebooks to be extremely helpful in transparent, easy to follow, reproducible research. I don't think its necessary though. I have not tried knitr before

[apexamodi]

Jupyter seems like a great way to document code and I like that you can run the code within the notebook, which would be useful for collaborators or PIs when looking at your notebook/work. However, in terms of code sharing I would likely just use Github. Also R and python seem to be coming up with easy ways to create a notebook/documentation already built in.

[linzho]

Jupyter is fun to code in, and definitely for showing results via inline figures. Also super useful like Onur said for documenting steps. However, from my own experience, for reproducibility reasons I think straight up python scripts are easier to implement and share with other lab members.