We have the opportunity to write a headline review for Journal of the Royal Society Interface on a topic overlapping the computer and life sciences in the area of systems pharmacology. We are hitting the home stretch towards getting this paper submitted. Consequently, we have established the following guidelines:
- April 24: Any empty section, even if in the outline, gets dropped due to lack of interest.
- May 1: Any section not merged & ready for revisions gets dropped due to lack of completion. Invitations go out to contributing authors to approve the manuscript.
- May 1 - 14: Revisions, flow, and clarity.
- Final edits and author approval. Authors must approve by Noon Eastern
Time on May 17 to be included as co-authors. File a pull
request to add the appropriate line to
author_contact_approval.md
At this stage it is ideal if each pull request touches no more than one subsection as this minimizes the number of people who will need to sign off on changes.
The most current version of the master branch is built by continuous integration
and available via gh-pages. To see
what's incoming, check the
open pull requests.
If you want to claim a section (time is short - see timeline above!) issue number
188 is the place to do it.
A Headline Review is one in a short, targeted series of high-level reviews within a particular topic of a burgeoning research area. We encourage authors to write in a style that opens the door to a broad range of readers working at the physical sciences - life sciences interface. We intend the reviews to address critical developments in an area of cross-disciplinary research and, when possible, to place such research in a broader context. This is not a place for comprehensive literature surveys.
We do encourage you to speculate in an informed way, and to be topical and provocative about the subject without worrying unduly about space, (the provisional target length is 8 -12,000 words). Please think of this as an article which will be a landmark in your area, and will come to be considered as a classic paper of the literature.
I was recently inspired by Harold Pimentel's crowd-sourced collection of deep learning papers. Instead of having one individual write this, I thought that this invitation provided a wonderful opportunity to take advantage of the wisdom of crowds to bring a team together around this topic.
This repository provides a home for the paper. We'll operate on a pull request model. Anyone whose contributions meet the ICJME standards of authorship will be included as an author on the manuscript. I can't guarantee that it will be accepted, but I look forward to trying this approach out.
We are now actively writing the review. Markdown files can be found in the
sections/ folder. Please claim a section, create a fork, and contribute that
section back via a pull request. To see what a pull request into the paper
entails, check out PR #147
from @evancofer.
We are now actively outlining the review sections and will begin writing soon. The goal is to have a complete draft in about a month. The action items from the 8/25 status report below are still applicable. In addition, you can:
- Sign up to write in #116 and share which sections you are most interested in. We are in need of experts in biomedical imaging applications in particular.
- Review the stubs in the
sectionssubdirectory and respond to the prompts with a pull request. - Outline sections that do not have stubs with a pull request and discuss them with other co-authors in the pull request comments.
Over the first three weeks of this project, we've developed an initial guiding question; collaboratively read, summarized, and discussed existing literature through github issues; and we're now refining our guiding question. If you want to begin to contribute to this review now, there are a few steps that you may want to take to get up to speed quickly.
- Read through issue #2. This will give an idea of what our perspective was as we were starting out and planning to read papers.
- Peruse some of the papers that the group has already reviewed, and take a look at the review. Fill in gaps that you see in the short summary/discussion of the paper.
- Choose some papers in an area that you care about, review them, and summarize them.
- Dive into #88 and help us to further refine the specific question that we're going to deal with in this review.
In about a week, we plan to move into the phase where we start to vigorously argue about the answer to the question that we coalesce on with #88 for each area that the review will cover.
When you make a pull request, Travis CI will test whether your changes break the build process to generate the formatted manuscript. The build process aims to detect common errors, such as invalid references. If your build fails, see the Travis CI logs for the cause of failure and revise your pull request accordingly.
When a pull request is merged, Travis CI performs the build and writes the results to the gh-pages and references branches.
The gh-pages branch hosts the following URLs:
- HTML manuscript at https://greenelab.github.io/deep-review/
short URL: https://git.io/vytJN - PDF manuscript at https://greenelab.github.io/deep-review/deep-review.pdf
short URL: https://git.io/vytJ5
For continuous integration configuration details, see .travis.yml.
This 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/greenelab/deep-review.
Since CC BY is not ideal for code and data, certain repository components are also released under the CC0 1.0 public domain dedication (LICENSE-CC0.md).
All files matched by the following blog patterns are dual licensed under CC BY 4.0 and CC0 1.0:
*.sh*.py*.yml*.json*.bib*.tsv.gitignore
All other files are only available under CC BY 4.0, including:
*.md*.html*.pdf
Please open an issue for any question related to licensing.