Data visualization is a complex topic in the experimental sciences. While there are many ways to display data, many researchers choose to use bar plots. Generally, these plots only depict a group mean and standard error (or deviation). Unfortunately, most data are not as clean as bar plots make them seem, and since bar plots reveal very little about the distribution of the data, this kind of visualization can be misleading [1,2,3]. A further issue is that of the bar itself, which implies that the base of the y-axis is meaningful, which is not necessarily the case. The bar can then mislead readers .
We are a group of young scientists who recently started an initiative called “#barbarplots” aimed at raising awareness and improving scientific communication. As part of this campaign, 169 individuals pledged over 3,400€ in a Kickstarter project to send this package to editors of top journals. Journal editors like you have the power to greatly influence trends in the field. We would like to suggest that you begin a conversation with your editorial board about your journal’s stance on data visualization, and whether you wish to encourage authors to use more informative techniques to plot distributions of data. Many high impact journals, such as Nature Neuroscience, the Journal of Neuroscience, and PLoS Biology, have already taken the step to #barbarplots in an effort to make articles more transparent.
To better spread the word, we wish to encourage your participation in our social media campaign. A t-shirt is enclosed in this package: put it on, take a selfie, and share it on Twitter or Facebook with the hashtag #barbarplots.
We hope that this initiative will encourage discussions between colleagues on the merits of various data visualization techniques and on transparency in science in general.