Data cleaning and enrichment through data integration: networking the Italian academia.

Area of Science:

• Bibliometrics
• Social Network Analysis
• Scientometrics

Background:

• Understanding academic collaborations is crucial for evaluating research impact and productivity.
• Existing bibliometric datasets often lack comprehensive semantic information or broad coverage.

Purpose of the Study:

• To construct and validate a comprehensive bibliometric network of co-authorship collaborations within the Italian academic community.
• To enrich the network with diverse semantic data for advanced analytical applications.

Main Methods:

• Integration of faculty data from the Italian Ministry of University and Research with publication data from Semantic Scholar.
• Development of a bibliometric network with 38,220 nodes (researchers) and 507,050 edges (collaborations).
• Validation of the network's reliability and analysis of its graph-theoretic properties.

Main Results:

• Creation of a large-scale, semantically rich bibliometric network.
• The network incorporates data on gender, bibliometric indexes, research fields, and temporal information.
• Successful validation addresses challenges in data integration, ensuring network reliability.

Conclusions:

• The developed Italian academic co-authorship network is a robust dataset for bibliometric and social network analysis.
• The network's rich semantic features enable in-depth experimental studies.
• This resource facilitates research in academic collaboration dynamics and scientific productivity.