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JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics
Published on: October 19, 2021
New perspectives on analysing data from biological collections based on social network analytics
Pedro C de Siracusa1, Luiz M R Gadelha2, Artur Ziviani3
1National Laboratory for Scientific Computing (LNCC), Petrópolis, RJ, 25651-075, Brazil. pedrosiracusa@gmail.com.
Scientific Reports
|February 27, 2020
Summary
This study introduces network models to analyze biases in biological collections, revealing collector preferences and taxonomic composition. These models offer a new framework for understanding collection formation and dynamics.
Area of Science:
- Biodiversity science
- Network science
- Museum studies
Background:
- Biological collections are vital biodiversity data sources but contain biases.
- Taxonomic and collector biases influence collection composition.
- Understanding these biases is crucial for data interpretation.
Purpose of the Study:
- To propose network models for analyzing biological collection formation.
- To investigate collector and taxonomic biases within collections.
- To provide a framework for understanding collector activities and interests.
Main Methods:
- Developed two network models to represent collector-herbarium interactions.
- Applied models to a case study of the University of Brasília herbarium.
- Analyzed network topology, collector relevance, and collaborative behavior.
Main Results:
- Identified key collectors and their preferred taxonomic groups.
- Characterized the network structure and collaborative patterns of collectors.
- Demonstrated the utility of network analysis for understanding collection biases.
Conclusions:
- Network models offer a novel approach to studying biological collections.
- The framework can reveal underlying patterns in collection development.
- Future work can incorporate temporal and geographical data for richer insights.
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