Please find below some useful resources shared by the speakers:
Slides
“Creating clear & informative images for scientific publications” – Tracey Weissgerber
“Graphical representations and statistical inferences” – Guillaume Rousselet
“Bioimages: common problems and pitfalls in publications” – Alberto Antonietti
“10 Solutions and tips for bioimage figures” – Helena Jambor
Some tips
“Processing images for papers & posters”
“Publishing images for papers & posters”
Colour maps
http://www.cookbook-r.com/
https://cran.r-project.org/
EJN editorial about graphical representations
https://onlinelibrary.wiley.
Beyond differences in means: robust graphical methods to compare two groups in neuroscience
https://onlinelibrary.wiley.
Removable interactions
Wagenmakers et al. Mem Cogn 40, 145–160 (2012). https://doi.org/10.
Blog post: https://janhove.github.
Erroneous analyses of interactions in neuroscience: a problem of significance
Nieuwenhuis et al. 2011
https://www.nature.com/
Scientists Rise up against Statistical Significance
Amrhein, Greenland, and McShane (2019)
https://www.nature.com/
Why you shouldn’t use a bar graph to show continuous data, and what to do instead
1. Everything you need to know in a visual Q&A – Twitter thread
2. Flipbook that provides line by line R code to make the graphs shown in the Q&A
3. Papers
a. 2015: http://journals.plos.org/plosbiology/article?id=10.1371/journal.pbio.1002128
b. 2019 follow-up (see Table 3 for a list of visualization tools; paper also contains information on other common visualization problems): https://www.ahajournals.org/doi/10.1161/CIRCULATIONAHA.118.037777
5. Free interactive dotplot tool: http://statistika.mfub.bg.ac.rs/interactive-dotplot/ (Paper: http://www.jbc.org/content/292/50/20592.full.pdf)
6. Free interactive line graph tool: http://statistika.mfub.bg.ac.rs/interactive-linegraph/ (Paper with supplement describing other strategies for showing individual-level data on a line graph: http://journals.plos.org/plosbiology/article?id=10.1371/journal.pbio.1002484)
Colorblindness simulator (Color Oracle): https://colororacle.org
Imaging & image-based figures:
1. Creating clear and informative image-based figures for scientific publications: “Creating Clear and Informative Image-based Figures for Scientific Publications”
2. Creating image-based figures for publications (a Fiji workflow): https://f1000research.com/articles/9-1373
3. How to report imaging methods: https://elifesciences.org/articles/55133
Designing better figures
1. Better figures for the life sciences: https://ecrlife420999811.wordpress.com/2018/08/29/better-figures-for-life-sciences/
2. A brief guide to designing effective figures for the scientific paper: https://pubmed.ncbi.nlm.nih.gov/21960472/
Resources for Creating Other Types of Graphs
1. Static Graphs Comparing Effect Sizes: The estimation stats website allows users to make graphs that examine the size of the difference between groups. This approach offers an alternative to statistical tests that focus on whether groups are significantly different and helps readers to assess the potential importance of an effect.
2. Comparing Differences in Variability: Most statistical tests compare differences in means or medians, however sometimes scientists want to compare differences in variability (i.e. Is the range of values observed larger in males than in females?). A recent paper shows how investigators with larger sample sizes can use shift functions for these comparisons and provides R code for making these graphs.
3. Creating Customized SHINY Apps: Investigators who are comfortable with R can use SHINY to create customized interactive graphics, as described in this paper
Links shared in the chat during the webinar:
Examples of useful graph representations for bayesian (Laplacian really) results:
https://cran.r-project.org/web/packages/bayesplot/vignettes/graphical-ppcs.html
http://mjskay.github.io/tidybayes/
About ordinal data and modelling:
https://psyarxiv.com/3vgwk/
Torrin M.Liddell & John K.Kruschke (2018) Analyzing ordinal data with metric models: What could possibly go wrong? Journal of Experimental Social Psychology, 79, 328-348
https://www.sciencedirect.com/science/article/abs/pii/S0022103117307746
Bürkner, Paul – Christian, and Matti Vuorre. 2019. “Ordinal Regression Models in Psychology: A Tutorial.”
https://journals.sagepub.com/doi/full/10.1177/2515245918823199
https://apps.automeris.io/wpd/
https://journals.plos.org/plosbiology/article?id=10.1371/journal.pbio.2005282
Some useful links shared during the webinar
Examples of useful graph representations for bayesian (Laplacian really) results:
Graphical posterior predictive checks using the bayesplot package
tidybayes: Bayesian analysis + tidy data + geoms
About ordinal data and modelling: