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Outline the envisioned workflow for a scientist. With this we can build a better idea of what needs teaching, blue-printing, etc
First suggestion for a workflow:
- start new data analysis by creating a empty directory
- type
openscience init
to create a skeleton- runs
git init
, creates a "sensible"Dockerfile
- setups up aliases for running things in the docker container?
- runs
- create code, run it with
openscience run <cmd>
which executes it inside the docker container - create a notebook or
.md
with code blocks that has narrative mixed with steps for reproducing parts of the analysis git commit
all along- push repo to git hub at some point(?)
- as analysis comes to an end create a new ipynb/md that is the paper, preview it with
openscience paper
(?)
(I will edit this entry as we iterate)
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