![]() ![]() The chart also has poor contrast that will cause issues for printing and photocopying. The one of the left is inaccessible for persons with common red/green protanopia. This figure shows two versions of a pie chart. Another tip: instead of having a separate legend, label data series directly on the graph. Use different shapes for markers, line patterns, and fill textures (Figures 14.2, 14.3). In your data visualizations, don’t use colour alone to convey an idea. The image below, from a Nature Methods article called “Points of view: Color blindness” by Bang Wong, shows how common colour schemes in scientific articles can be easily fixed to make them accessible to all readers. See this blog post by physicist Paul Tol to learn more about colour schemes for accessible design in scientific communication. Use tools like Color Oracle or the Coblis - Color Blindness Simulator to check your data visualizations. Contrast is important for distinguishing colours on the screen, but also for printing in black and white or photocopying. ![]() ![]() Use resources like ColorBrewer 2.0 and Chroma.js to find or create colour-blind friendly palettes with suitable contrast. All readers will benefit if the colours in your data visualization are easy to distinguish in tone, shade, and contrast. The most common form of colour-blindness makes it difficult to distinguish between some shades of red and green. Not everyone has the same sensitivity to colour – around 5% of the population experiences some form of colour-blindness. This philosophy for design ensures equality and inclusion for all of human diversity. When creating your tables and figures, keep in mind the principle of Universal Design: design that makes the product accessible to all people regardless of age, disability, and other possible barriers. The other side to presenting your data is to make it accessible to all readers. This eventually led to corrections in retractions of dozens of papers from the Fukuyama lab, and many other labs as well. This caught several papers from a single research group where spectra had been manipulation to remove impurities. Journals like Organic Letters have hired data analysts to examine spectra and other data submitted to the journal. unethical practices since articles that have manipulation of NMR spectra will be retracted and the authors placed on a watch list.ĭon’t be fooled by how easy it is to delete peaks! Be cautious and make sure your peers and coauthors are aware of ethical vs. Some students are not aware that using the “solvent suppression” feature in iNMR constitutes manipulation of data. NMR spectra are one of the most common places to find data manipulation in chemistry. Which do you think most accurately and honestly portrays the data? ![]() These examples show how the choice of Y-axis scale can make it look like there is a significant trend in the data (Left) or like there is no trend at all (Right). Design choices can, either purposefully or inadvertently, introduce bias in favour of the author’s interpretation of the data ( See the example below). For example, by adjusting the Y-axis scale (Figure 14.1), trends in data can be either exaggerated or made to seem insignificant. Scientists should be aware of the many ways that data can be misrepresented. Examples of misleading data visualizationĮthical design requires that graphics be honest and accurate.This chapter explains the principles of accessible and ethical graphic design, and includes several examples of what not to do! Sections in this chapter Presenting your data so that it is accessible.Communicating your data honestly and ethically.This chapter will cover two key aspects of communicating data using graphics: ![]()
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