We hope these recommendations, compiled from a number of excellent resources on data visualization (Lane and Sándor, 2009, Tufte, 2001 and Wainer, 1996), may be used by both internal and external reviewers to help evaluate figures for clarity and completeness. We sampled 288 articles published in 2010 from six neuroscience journals (Frontiers in Systems Neuroscience, Human Brain Mapping, Journal of Neuroscience, Nature

Neuroscience, Baf-A1 molecular weight NeuroImage, and Neuron) and examined the 1,451 figures therein. We surveyed four basic features that were applicable to nearly all graphs and addressed Wainer’s points above. The survey asked the following questions: (1) Is the dependent variable or quantity of interest labeled? (2) Is the scale of the dependent variable indicated? (3) Where applicable, is a measure of uncertainty displayed? (4) Is the

type of uncertainty (e.g., standard error bars or confidence intervals) defined in the figure or accompanying legend? Examples of these graphical features are shown in Figure 1A for two-dimensional (2D) and 3D data sets. Survey results, shown in Figure 1B, overwhelmingly suggest that graphical displays become less informative as the dimensions and complexity of data sets increase. Compared to graphs of 2D data, 3D displays provide poorer descriptions of the outcome of interest and rarely provide an indication of uncertainty. Only 43% of CAL-101 solubility dmso 3D graphics label the dependent variable (meaning that if you were asked, “What is being plotted here?” you would be able to answer less than half of the time) and only 20% portray the uncertainty of reported effects. Even for 2D data, the proportion of graphs displaying uncertainty

is lower when explanatory variables are continuous (and typically take on many values) than when they are categorical (and typically represent a few conditions; Figure 1C). Of 2D figures that do indicate uncertainty, nearly 30% fail to define the type of uncertainty or variability being portrayed. Mephenoxalone Given the plurality of interpretations connoted by an error bar (e.g., a standard deviation [SD] of the sample, a standard error of the mean [SEM], a range, a parametric confidence interval [CI] of the mean, a bootstrap CI, a Bayesian probability interval, a prediction interval, etc.), it is unclear how including it without a proper label would offer readers any further understanding of the data; in contrast, the poor labeling or omission of error bars has been shown to encourage misinterpretation (Cumming and Finch, 2005, Vaux, 2004 and Wainer, 1996). A breakdown of results by journal (see supplementary analysis at http://mialab.mrn.