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The structural shift and collaboration capacity in GenBank Networks: A longitudinal study
Jian Qin1, Jeff Hemsley1, Sarah E Bratt1
1School of Information Studies, Syracuse University, Syracuse, NY.
Quantitative Science Studies
|April 18, 2022
Summary
Analyzing GenBank metadata reveals evolving scientific collaboration networks. This study identifies a trend towards flatter network structures and optimal conditions for enhancing research collaboration capacity.
Area of Science:
- Bibliometrics and Scientometrics
- Network Science
- Bioinformatics and Data Science
Background:
- Scientific collaboration networks are crucial for research advancement.
- Publication-based analysis has limitations in capturing full collaboration dynamics.
- Metadata in scientific data repositories offers a complementary data source.
Purpose of the Study:
- To analyze the structures and dynamics of GenBank collaboration networks (1992-2018).
- To identify trends in collaboration network evolution.
- To develop new metrics for assessing collaboration enablers.
Main Methods:
- Utilized network science methodologies on GenBank metadata.
- Conducted longitudinal analysis of large-scale data from 1992 to 2018.
- Incorporated metadata from both data production and publication stages.
Main Results:
- Uncovered the historical evolution of GenBank collaboration networks.
- Identified a trend of flattening network structures over time.
- Determined the optimal assortative mixing range for enhanced collaboration capacity.
Conclusions:
- GenBank metadata provides novel insights into scientific collaboration beyond publication data.
- Collaboration networks exhibit evolving structures and require adaptive strategies.
- New metrics can effectively assess the impact of human capital, cyberinfrastructure, and science policy on collaboration.
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