A systematic metadata harvesting workflow for analysing scientific networks

Area of Science:

• Science of Science
• Bibliometrics
• Social Network Analysis

Background:

• Scientific networks are crucial for understanding research landscapes.
• Limited access to comprehensive datasets hinders network analysis.
• Publicly available citation metadata offers a solution.

Purpose of the Study:

• To develop a replicable workflow for constructing scientific networks.
• To leverage open citation data from Crossref and OpenCitatons.
• To enable multi-dimensional analysis of academic research.

Main Methods:

• Utilized citation metadata from Crossref and OpenCitatons.
• Developed a Python-based workflow for scientific network creation.
• Applied social network analysis techniques including centrality analysis.

Main Results:

• Successfully created scientific networks using open data.
• Demonstrated the workflow with a case study on Scientometrics journal metadata.
• Identified prominent authors through centrality analysis.

Conclusions:

• The proposed workflow facilitates the creation of scientific networks from open metadata.
• This approach enhances the understanding of academic scholarship.
• The methodology is adaptable for various scientific fields and research questions.