Further Reading: Statistical and Scientific Visualization

Tier 1: Essential Reading

Nature Figure Guidelines. nature.com/documents/nature-final-artwork.pdf The authoritative source for Nature's figure requirements. Download, read, and keep as a reference when preparing submissions.

Ho, Joses, Tayfun Tumkaya, Sameer Aryal, Hyungwon Choi, and Adam Claridge-Chang. "Moving beyond P values: data analysis with estimation graphics." Nature Methods 16, no. 7 (2019): 565-566. The paper introducing DABEST and estimation plots. Freely available at nature.com. Read alongside Case Study 2.

Wong, Bang. "Points of view: Color blindness." Nature Methods 8, no. 6 (2011): 441. The short paper introducing the Wong colorblind-safe palette. Widely cited and still relevant. Freely available.

Wilke, Claus O. Fundamentals of Data Visualization. O'Reilly Media, 2019. Wilke's textbook covers scientific visualization with many examples of publication-quality figures. Chapter on color covers colorblind safety extensively. Freely available at clauswilke.com/dataviz.

Amrhein, Valentin, Sander Greenland, and Blake McShane. "Scientists rise up against statistical significance." Nature 567 (2019): 305-307. The widely-cited editorial calling for the retirement of "statistical significance" as a binary concept. Relevant to the effect-size focus of Section 27.23 and Case Study 2.

Wasserstein, Ronald L., and Nicole A. Lazar. "The ASA's statement on p-values: context, process, and purpose." The American Statistician 70, no. 2 (2016): 129-133. The American Statistical Association's formal statement on p-value misuse. Freely available and foundational for modern statistical practice.

Cleveland, William S. The Elements of Graphing Data. Hobart Press, 1994. Cleveland's classic book on chart design, including many scientific examples. The chapter on QQ plots and diagnostics remains the best introduction to those tools.

Gelman, Andrew, and Jennifer Hill. Data Analysis Using Regression and Multilevel Models. Cambridge University Press, 2006. Chapters on visualizing regression results, diagnostics, and multilevel model output. Essential for anyone producing statistical figures that go beyond simple group comparisons.

Hollands, J. G., and Ian Spence. "Judging proportion with graphs: The summation model." Applied Cognitive Psychology 12, no. 2 (1998): 173-190. Classic research on perception of proportions in charts. Relevant to decisions about bar charts vs. pie charts vs. stacked areas in scientific contexts.

Tier 3: Tools and Online Resources

Resource URL / Source Description
DABEST (Python) github.com/ACCLAB/DABEST-python Estimation plots for biological statistics. See Case Study 2.
estimationstats.com estimationstats.com Live web tool for producing estimation plots from data. Requires no installation.
statannotations github.com/trevismd/statannotations Significance bracket annotations for seaborn plots.
matplotlib style gallery matplotlib.org/stable/gallery/style_sheets/style_sheets_reference.html Built-in style sheets including "seaborn-paper" that are close to publication-ready.
PLOS ONE figure guidelines journals.plos.org/plosone/s/figures Example guidelines from a major open-access journal.
IEEE Transactions figure guidelines ieee.org/publications/rights/IEEE-Guide-Figures-CE-Authors.pdf Engineering journal guidelines.
ColorBrewer colorbrewer2.org Color palette selection tool. Colorblind-safe options clearly marked.
matplotlib-scalebar github.com/ppinard/matplotlib-scalebar Scale bar for microscopy and similar images.
scipy.stats.probplot docs.scipy.org/doc/scipy/reference/generated/scipy.stats.probplot.html QQ plot function.
statsmodels regression diagnostics statsmodels.org/stable/diagnostic.html Diagnostic plots for regression models.
seaborn publishing theme seaborn.pydata.org/generated/seaborn.set_context.html sns.set_context("paper") adjusts sizes for publication.

A note on reading order: If you want one additional source, read the Ho et al. DABEST paper — it is short, influential, and paired with a working tool. For a broader perspective on scientific figure design, Wilke's Fundamentals of Data Visualization is free, up-to-date, and full of examples. For the p-value debate that underlies modern reporting practices, Wasserstein & Lazar's ASA statement is the authoritative reference.