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.

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

• 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, to ensure no-one breaks the paper

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

• Prefer SVG
• Prefer 120-300 DPI
• Style rules
  • Max 8 inches wide
  • Min 6 pt font size
  • Max 10 pt font size

• 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)

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

2. 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.

3. 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.

Manubot is a system for writing scholarly manuscripts via GitHub. Manubot automates citations and references, versions manuscripts using git, and enables collaborative writing via GitHub. An overview manuscript presents the benefits of collaborative writing with Manubot and its unique features. The rootstock repository is a general purpose template for creating new Manubot instances, as detailed in SETUP.md. See USAGE.md for documentation how to write a manuscript.

Please open an issue for questions related to Manubot usage, bug reports, or general inquiries.

The directories are as follows:

• content contains the manuscript source, which includes markdown files as well as inputs for citations and references. See USAGE.md for more information.
• output contains the outputs (generated files) from Manubot including the resulting manuscripts. You should not edit these files manually, because they will get overwritten.
• webpage is a directory meant to be rendered as a static webpage for viewing the HTML manuscript.
• build contains commands and tools for building the manuscript.
• ci contains files necessary for deployment via continuous integration.

The easiest way to run Manubot is to use continuous integration to rebuild the manuscript when the content changes. If you want to build a Manubot manuscript locally, install the conda environment as described in build. Then, you can build the manuscript on POSIX systems by running the following commands from this root directory.

# Activate the manubot conda environment (assumes conda version >= 4.4)
conda activate manubot

# Build the manuscript, saving outputs to the output directory
bash build/build.sh

# At this point, the HTML & PDF outputs will have been created. The remaining
# commands are for serving the webpage to view the HTML manuscript locally.
# This is required to view local images in the HTML output.

# Configure the webpage directory
manubot webpage

# You can now open the manuscript webpage/index.html in a web browser.
# Alternatively, open a local webserver at http://localhost:8000/ with the
# following commands.
cd webpage
python -m http.server

Sometimes it's helpful to monitor the content directory and automatically rebuild the manuscript when a change is detected. The following command, while running, will trigger both the build.sh script and manubot webpage command upon content changes:

bash build/autobuild.sh

Whenever a pull request is opened, CI (continuous integration) will test whether the changes break the build process to generate a formatted manuscript. The build process aims to detect common errors, such as invalid citations. If your pull request build fails, see the CI logs for the cause of failure and revise your pull request accordingly.

When a commit to the master branch occurs (for example, when a pull request is merged), CI builds the manuscript and writes the results to the gh-pages and output branches. The gh-pages branch uses GitHub Pages to host the following URLs:

• HTML manuscript at https://malariagen.github.io/ag1000g-phase3-data-paper/
• PDF manuscript at https://malariagen.github.io/ag1000g-phase3-data-paper/manuscript.pdf

For continuous integration configuration details, see .github/workflows/manubot.yaml if using GitHub Actions or .travis.yml if using Travis CI.

Except when noted otherwise, the entirety of this repository is licensed under a CC BY 4.0 License (LICENSE.md), which allows reuse with attribution. Please attribute by linking to https://github.com/malariagen/ag1000g-phase3-data-paper.

All files are licensed under CC BY 4.0.

Please open an issue for any question related to licensing.