It's getting to be CogSci submission time, and this year I am thinking more about trying to set uniform standards for submission. Following my previous post on onboarding, here's a pre-submission checklist that I'm encouraging folks in my lab to follow. Note that, as described in that post, all our papers are written in RStudio using R Markdown, so the paper should be a single document that compiles all analyses and figures into a single PDF. This process helps deal with much of the error-checking of results that used to be the bulk of my presubmission checking.
- Is the first paragraph engaging and clear to an outsider who doesn't know this subfield?
- Are multiple alternative hypotheses stated clearly in the introduction and linked to supporting prior literature?
- Does the paragraph before the first model/experiment clearly lay out the plan of the paper?
- Does the abstract describe the main contribution of the paper in terms that are accessible to a broad audience?
- Does the first paragraph of the general discussion clearly describe the contributions of the paper to someone who hasn't read the results in detail?
- Is there a statement of limitations on the work (even a few lines) in the general discussion?
- Are libraries sourced and constants defined at the top of the doc
- Are code blocks short and legible?
- If there is complicated code, have you walked a collaborator or friend through it to catch obvious weirdness?
- If there is extensive data preprocessing, have you considered factoring to a script where you can check outputs and store an intermediate tidy data frame?
- For long/complex analyses, have you considered factoring helper functions to a separate script?
- Are all fonts > 9 pts (as an absolute minimum) when rendered into the document?
- Is your aspect ratio for panels a bit wider than it is high? (maybe around the golden ratio, ~1.6:1 or 3:2).
- Are your axes titled appropriately?
- Is your caption clear and informative? Does it describe all aspects of the plot, including error bars?
- Is the legend in a useful place? Have you considered direct labeling of the plots using directlabels::geom_dl() or putting the legend below the plot to save space?
- Have you used a good color palette? consider langcog::scale_colour_solarized()
- Are you using an unobtrusive theme like theme_bw()? have you considered using something minimalist like ggthemes::theme_few()?
- Is there a github repository?
- Does it have all the data and code, including writeup?
- Is the repo organized clearly, with "raw_data," "analysis," and "writeup" directories? (plus "helper" etc. as needed).
- Is there an R project, and is it packratted for package versioning?
- Can someone else re-render your paper from scratch (get a buddy to try this)? if that takes a while, can you cache intermediate data?
- Is there a README.md that briefly describes what the project is and how the repo is organized?
Authorship, formatting, and miscellaneous
- Have all authors ok'd submission?
- Have you checked on grant acknowledgements?
- Are all section headings maximally informative?
- Are results and methods described in the past tense?
- Are there links to your preregistration, experiment code, and data/analysis repository?
- Did you spell check, since R Studio doesn't have inline spellchecking?
- Did you check for referent-less "this" (my pet prescriptive peeve, as in "This shows that ...")?
Good luck on your submissions!
*Note: These may seem a bit formulaic, but A) CogSci papers are very short (6 pages), and B) often a writing formula is a good starting point for creative departures.