References and further readings

This page houses a list of links you might find useful in your graph design journey. It doesn't even come close to being a full "bibliography" of this graph design guide, but it does reflect a curated list of sources I found helpful or stimulating. I'll steadily add more as I come across them; I only want to add resources here that are approachable and value-added, so you can rest assured that only the most "sharable" resources I've found are listed here.

• Franceroni et al. 2021 -- A comprehensive review of the data visualization literature, organized in a "what works and what doesn't" structure. The single most comprehensive and accessible resource for someone who wants to dive straight into this field's literature. 

• The Springer guide to crafting Figures and Tables for a scientific journal article -- Makes the case for why crafting graphs may be the single most important use of your time, if you want your research to have a real impact. Provides some practical tips along the way but also demonstrates how far we need to go before journals demand really high standards for our graphs. 

• Muth 2023 -- A nice blog post detailing how (and why) to use color hue, saturation, and luminance to communicate relative importance of elements in a graph. I also like the blunt style and vibrant visual examples used. 

• Boers 2018 -- A recent attempt to catalogue a list of prescriptions for researchers to use for designing their figures for their manuscripts. Not comprehensive but contains many good ideas and examples. 

• Lovei 2021 -- I haven't read this book chapter on constructing figures for scientific manuscripts "cover to cover" at time of writing, but you might find it an interesting presentation and parallel perspective to mine. 

• Andrey Churkin's 2024 Youtube video entitled Principles of Beautiful Figures for Research Papers -- A really nice distillation of a lot of graph design principles and best practices in a highly visual format. 

• Mason 2019 -- A popular science article making the case that scientists have a lot of room to grow when it comes to our visualizations. I found it to be a compelling read.

• Stephenie Evergreen's Data Visualization blog -- A trusted colleague recently sent me a link to this resource. I've only skimmed it so far, but I can already say it covers a lot of relevant topics, such as how to make your graphs accessible and why you should be cautious when using color as a visual channel. 

• Designing science graphs for data ananysis and presentation -- A trusted colleague also pointed me toward this resource. I've only looked closely at the first few pages, but already, I can tell this guide and mine are in alignment about all the major elements.