首页|The Evolution of Scientific Visualisations: A Case Study Approach to Big Data for Varied Audiences
The Evolution of Scientific Visualisations: A Case Study Approach to Big Data for Varied Audiences
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NSTL
Visual representations of complex data are a cornerstone of how scientific information is shared. By taking large quantities of data and creating accessible visualisations that show relationships, patterns, outliers, and conclusions, important research can be communicated effectively to any audience. The nature of animal cognition is heavily debated with no consensus on what constitutes animal intelligence. Over the last half-century, the methods used to define intelligence have evolved to incorporate larger datasets and more complex theories—moving from relatively simple comparisons of brain mass and body mass to explorations of brain composition and how neuron count changes between specific groups of animals. The primary aim of this chapter is therefore to explore how visualisation choice influences the accessibility of complex scientific information, using animal cognition as a case study. As the datasets concerned with animal intelligence have increased in both size and complexity, have the visualisations that accompany them evolved as well? We first investigate how the basic presentation of visualisations (figure legends, inclusion of statistics, use of colour, etc.) has changed, before discussing alternative approaches that might improve communication with both scientific and general audiences. By building upon the types of visualisation techniques that everyone is taught at school (bar charts, XY scatter plots, pie charts, etc.), we show how small changes can improve our communication with both scientific and general audiences. We suggest that there is no single right way to visualise data, but careful consideration of the audience and the specific message can help, even where communications are constrained by time, technology, or medium.
Animal CognitionStatistical and Graphical VisualisationsScience CommunicationAccessible ScienceLarge Complex DatasetsVisualisation Alternatives
Andrew J. Lunn、Vivien Shaw、Isabelle C. Winder
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MZool Graduate from School of Natural Sciences, Bangor University, Bangor, UK
NSTL
