Double space: make it easy on the reader (and editor) by double spacing the entire text, including references and figure legends.

Use big fonts: again, make the paper easy to read. Tracking 30 words across one line in a tiny font is hard, especially if you are reading for hours at a time. Instead, use a font that provides about 12-15 words per line of text.

Use continuous line numbers: reviewers like to refer to specific line numbers and frequently comment on their absence.

Avoid subjective wording: reviewers will often object to words/phrases like “unprecedented”, “paradigm shift”, “amazing”, “dramatic”, and “remarkable”. Best to present your results, and let the readers make up their minds about the magnitude of the advance.

Avoid acronyms: a rule of thumb might be to use an acronym if the term is used at least five times. Do use common acronyms, like SST, CDW, NPP, AMOC. Avoid, if at all possible, inventing acronyms that are unique to your paper.

Avoid words like “influence”: instead, state the direction of the effect you’re describing. So, instead of hypothetically writing “Precipitation influences net primary production” write “Precipitation increases net primary production”. Better yet, use numbers.

Avoid using “significant” to mean “big” or “major”: too easily confused with the results of a statistical text. Even if you are reporting the results of a statistical test, it’s better to report the numerical results instead. In fact, best to avoid "significantly" entirely.

Define uncertainties: probably half of initial submissions do not fully define the meaning of error bars and/or uncertainties. 95% confidence intervals, ranges, 2 sigma? Is the box plot showing the interquartile range, or something else? Tell us, in the figure legend.

Consider accessibility: try to use colors in a way that won’t create problems for the many readers with some form of color blindness (for a good discussion and design suggestions see somersault1824.com/tips-for-…).

Avoid rainbow color schemes: see #endrainbow for discussion, much of it from @ed_hawkins. For more, and palettes: matplotlib.org/cmocean/ giss.nasa.gov/tools/panoply/… colorbrewer2.org/#type=seque… cran.r-project.org/web/packa…

Use divergent colors when appropriate: in an anomaly graph, it’s often helpful to set zero as white and then ramp up to two colors. For example, panel f in Extended Data figure 6 in Cody Routson’s recent @nature paper nature.com/articles/s41586-0…. nature.com/articles/s41586-0…

Simplify figures: we often see maps with colors and contours used to display the same data. This can be confusing, as the reader may think that different datasets are being displayed on the same map. Normally, one or the other is sufficient.

Use a declarative title: reviewers (and editors) will often recommend a declarative title. Instead of a hypothetical title like “Trends in groundwater storage” try “A doubling of groundwater loss since 2010”.

Follow a template for your abstract: The abstract/first paragraph of @nature papers is fairly standardized, and it can be helpful to follow our template, even for non-@nature papers. nature.com/documents/nature-…

Provide short titles for figure legends: sometimes this can be descriptive, like “Study area”. But if possible, make the title declarative, like “Habitat fragmentation increases cloud condensation nuclei” (hypothetical!).

Provide inline figures and legends: at least for the purposes of review at @nature, it’s fine to provide figures and legends as part of a single PDF, with the figures placed at an appropriate location in the text.