/deprecated-ag1000g-phase3-data-paper

Repository for building a manuscript describing the Ag1000G phase 3 data resource.

Primary LanguageJupyter NotebookMIT LicenseMIT

Ag1000G phase 3 data resource paper

This repository is for building a manuscript describing the Ag1000G phase 3 data resource.

This is a work in progress. Any data made available via this repository are subject to the Ag1000G terms of use.

Contributor setup

Fork this repository to your own github user account, then clone locally, e.g.:

git clone --recursive git@github.com:{myusername}/ag1000g-phase3-data-paper.git

Run the conda environment installation script:

cd /path/to/local/clone/of/ag1000g-phase3-data-paper
./binder/install-conda.sh

Once conda is installed, activate the conda environment:

source binder/env.sh

Run a jupyter notebook server, e.g.:

jupyter notebook

...or:

jupyter lab

Approach

  • This is a public repo. Meaning no personal information, e.g., no email addresses, no reviewer comments or comments from consortium
  • This repo uses CI (continuous integration) to build the paper, the build must pass before PR can be merged, ensure no-one breaks the paper

Structure of repo

  • notebooks contains Jupyter notebooks, perhaps organised in subdirectories if analysis encompasses several steps.
  • artwork contains image files (PNGs) etc included
  • Files named descriptively not by likely figure position.

Style

Images

  • Prefer PNG or PDF (vector).
  • Preferred format may depend on whether using latex or pandoc (manubot) - PDF figures are good with latex but maybe not with pandoc
  • Prefer 120-300 DPI
  • Style rules
    • Max 8 inches wide
    • Min 6 pt font size
    • Max 10 pt font size

Code

  • All code should be reproducible by all contributors on DataLab i.e. read data directly from GCS
  • Python module or setup notebook to hold common code and variables (avoid copying boilerplate) TBA
  • Avoid too much indirection - max one level (import Python module or %run setup notebook)

Writing code and review process

  1. Work in your own fork preferred (but not essential).
  • if branch is in main repo, prefix with your username
  • branches should include the number of the quire issue they are addressing
  • branch title marked as WIP
  1. Submit PRs.
  • Check CI passes
  • remove WIP label
  • link to PR from relevant quire issue(s)
  • request review
  • No further pushes to branch (to avoid conflicts)
  • upon merge, quire issue can be closed.
  1. Review.
  • Reviews should check notebooks by rerunning on datalab
  • Minor changes can be requested using "request changes"
  • More substantive changes can be made by making a PR to the branch in question. Avoid pushing directly to avoid conflicts.