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The Evolution of Scientific Visualisations: A Case Study Approach to Big Data for Varied Audiences

Lunn, Andrew J.; Shaw, Vivien; Winder, Isabelle C.

Authors

Andrew J. Lunn

Isabelle C. Winder



Contributors

Leonard Shapiro
Editor

Paul M. Rea
Editor

Abstract

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.

Citation

Lunn, A. J., Shaw, V., & Winder, I. C. (2022). The Evolution of Scientific Visualisations: A Case Study Approach to Big Data for Varied Audiences. In L. Shapiro, & P. M. Rea (Eds.), Biomedical Visualisation (51-84). Cham: Springer. https://doi.org/10.1007/978-3-031-10889-1_3

Online Publication Date Sep 15, 2022
Publication Date Sep 15, 2022
Deposit Date Nov 21, 2023
Publicly Available Date Sep 16, 2024
Publisher Springer
Pages 51-84
Series Title Advances in Experimental Medicine and Biology
Series Number 1120
Series ISSN 2214-8019
Book Title Biomedical Visualisation
Chapter Number 3
ISBN 9783031108884
DOI https://doi.org/10.1007/978-3-031-10889-1_3
Keywords Animal Cognition, Statistical and Graphical Visualisations, Science Communication, Accessible Science, Large Complex Datasets, Visualisation Alternatives
Public URL https://hull-repository.worktribe.com/output/4448733