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Author Spotlight: Emerging Technologies and Advanced Tools for Decoding Metabolomics Data Analysis

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Collaboration Networks and Career Trajectories: What Do Metadata from Data Repositories Tell Us?

Hemsley Jeff¹, Qin Jian¹, Bratt Sarah¹

¹Syracuse University, USA.

Proceedings of the Association for Information Science and Technology. Association for Information Science and Technology

|September 11, 2023

Summary

Scientists

Keywords:

Assessment metrics Collaboration capacity Collaboration networks Metadata analytics Research performance assessment

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Area of Science:

Science of Science
Bibliometrics
Network Science

Background:

Scientific collaborations are crucial for pooling expertise and advancing research.
Understanding factors influencing a scientist's ability to form new collaborations is essential for career development and research productivity.

Purpose of the Study:

To identify key factors contributing to a scientist's collaboration capacity.
To introduce and empirically validate a new framework for measuring collaboration capacity.

Main Methods:

Utilized longitudinal network data from GenBank, a cyberinfrastructure-enabled data repository.
Constructed yearly scientific collaboration maps to analyze network dynamics.
Tested hypotheses related to early-career network position and productivity trends.

Main Results:

A scientist's early network position significantly predicts their future collaboration capacity.
Increasing research productivity is positively associated with higher collaboration capacity.
The study provides initial empirical support for the proposed collaboration capacity framework.

Conclusions:

Collaboration capacity is influenced by early career network integration and sustained productivity.
The developed framework offers a novel approach to understanding and quantifying scientist collaboration potential.
Findings contribute to the field of Science of Science by providing measurable insights into research collaboration dynamics.