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

Area of Science:

• Scientific communication
• Data visualization
• Animal cognition

Background:

• Visual representations are crucial for sharing complex scientific data effectively.
• The study of animal intelligence has evolved with larger, more complex datasets and theories.
• Current understanding of animal intelligence lacks consensus.

Purpose of the Study:

• To explore how visualization choices impact the accessibility of complex scientific information.
• To examine the evolution of data visualization in animal cognition research.
• To identify methods for improving scientific communication for diverse audiences.

Main Methods:

• Investigating changes in basic visualization presentation (e.g., figure legends, statistics, color).
• Discussing alternative visualization approaches for broader communication.
• Analyzing the use of fundamental visualization techniques (e.g., bar charts, scatter plots).

Main Results:

• Data visualization methods in animal cognition have evolved alongside dataset complexity.
• Basic presentation elements significantly influence information accessibility.
• Simple modifications to standard charts can enhance communication.

Conclusions:

• There is no single optimal method for data visualization.
• Audience and message specificity are critical for effective visualization.
• Careful consideration of visualization choices improves scientific communication across audiences.