Visualizing natural history collection data provides insight into collection development and bias

Area of Science:

• Biodiversity informatics
• Museum studies
• Data science

Background:

• Natural history collections house billions of records on global biodiversity.
• Existing bias assessments aggregate data, potentially obscuring collection-specific issues.
• Understanding bias sources is crucial for accurate biodiversity data interpretation.

Purpose of the Study:

• To advocate for collection-level bias assessment.
• To demonstrate interactive visualization for analyzing collection development and bias.
• To provide a case study for applying these methods to individual natural history collections.

Main Methods:

• Utilized Sankey diagrams for visualizing the development of an entomology type collection.
• Employed interactive visualization to explore historical data and identify bias origins.
• Focused on the Academy of Natural Sciences of Drexel University's entomology collection.

Main Results:

• Sankey diagrams effectively illustrate the historical trajectory and growth patterns of the collection.
• Visualization revealed insights into the accumulation of biases within the collection over time.
• The case study provides a model for understanding collection-specific biases.

Conclusions:

• Collection-level bias assessment is essential for improving biodiversity data quality.
• Interactive visualization, specifically Sankey diagrams, is a valuable tool for this assessment.
• This approach can enhance our understanding of biodiversity knowledge and its limitations.