Keeping track of scripts used to generate figures is difficult. Before realising that Jupyter Notebooks could solve most of my problems, I would have directories with dozens of scripts with filenames of varying levels of ambiguity. Names that probably meant something to me at the time, but are hardly descriptive months or years later. Names like ISW_plume_plots.m, new_ISW_model_plots.m, and plot_model_behaviour.m. A certain PhD comic springs to mind.
Regardless of whether its Python, R, Julia, Matlab, or pretty much any other type of code, Jupyter Notebooks solve the problem. For example, I use a single notebook to archive the code for all figures in a paper and, more importantly, I can associate each set of code with the figure it generates. Rather than trying to remember what file I want, I need only remember which figure I want. (I say archive because I much prefer to do the bulk of my exploratory analysis in an editor. Alternatively, JupyterLab may work better for you.)
Scientific papers are built by taking existing ideas, applying them in new ways, adapting them to fit new situations, and improving them over time. Yet when it comes to drawing a schematic, many people start from scratch or never even start. Instead, start with an image search, let Inkscape do the hard work, and refine the best parts of other schematics.
Scientific figures are usually messy enough, there’s no need to aggravate the problem by including redundant labels. As with figure captions, problems arise with multi-panel plots. If the panels share axes, there’s no need to label each one.
The little things matter; for example, a typo. In theory, a typo is a minor mistake that makes no difference to the meaning of the writing. In practice, if you’re like me, your opinion of the quality of the rest of the work decreases. Moreover, you may inadvertently seek out further faults.
The same can be said for figures: poor attention to detail will spoil an otherwise perfectly good plot. For this reason, here’s a short list of easily adjustable details that will improve your figures.
When designing any figure with colour, consider the Hue-Saturation-Lightness (HSL) colour space. It is the most intuitive and simplest colour space to work with. For examples of why it is well suited to scientific figures, skip to the bottom. To learn the details, read on.
Images come in a variety of file types: jpg, png, pdf, eps, svg, tif, bmp, and countless other lesser-known ones. Each have their pros and cons, but they can be divided into two types: vector and raster. In science, we generally want vector images, unless we are dealing with photos.
Before leaving high school, every scientist should have learned all the things a graph should contain: a descriptive title, labels for every axis, appropriately spaced tick marks, and a legend if necessary. All pretty straightforward, so you would think any figure published in a scientific journal would adhere to this as a minimum. But I’ve come across far too many figures breaking one or more of these rules. The problem is not that people are excluding the information, rather they are putting everything in the figure caption. Consequently, the figure caption ends up being long-winded, procedural, and not at all interesting. Fortunately, it is easy to make the caption succinct and descriptive with a few quick adjustments to the figure.