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

We describe a bibliometric network characterizing co-authorship collaborations in the entire Italian academic community. The network, consisting of 38,220 nodes and 507,050 edges, is built upon two distinct data sources: faculty information provided by the Italian Ministry of University and Research and publications available in Semantic Scholar. Both nodes and edges are associated with a large variety of semantic data, including gender, bibliometric indexes, authors' and publications' research fields, and temporal information. While linking data between the two original sources posed many challenges, the network has been carefully validated to assess its reliability and to understand its graph-theoretic characteristics. By resembling several features of social networks, our dataset can be profitably leveraged in experimental studies in the wide social network analytics domain as well as in more specific bibliometric contexts.

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Publication Details

Journal
Scientific Data
Published
2025-02-21
DOI
https://doi.org/10.1038/s41597-025-04608-6
Primary Topic
Research Data Management Practices
Type
article
Field-Weighted Citation Impact
0.00

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article

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

Blerina Sinaimeri, Irene Finocchi, Alessio Martino, Fariba Ranjbar
Scientific Data
Research Data Management Practices
article

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

Blerina Sinaimeri, Irene Finocchi, Alessio Martino, Fariba Ranjbar
article en

Abstract

We describe a bibliometric network characterizing co-authorship collaborations in the entire Italian academic community. The network, consisting of 38,220 nodes and 507,050 edges, is built upon two distinct data sources: faculty information provided by the Italian Ministry of University and Research and publications available in Semantic Scholar. Both nodes and edges are associated with a large variety of semantic data, including gender, bibliometric indexes, authors' and publications' research fields, and temporal information. While linking data between the two original sources posed many challenges, the network has been carefully validated to assess its reliability and to understand its graph-theoretic characteristics. By resembling several features of social networks, our dataset can be profitably leveraged in experimental studies in the wide social network analytics domain as well as in more specific bibliometric contexts.

Scientific DataVol. 12(1)
Libera Università Internazionale degli Studi Sociali Guido Carli (IT)
Ministero dell’Istruzione, dell’Università e della Ricerca
Industry, innovation and infrastructure
Openalex Percentile: Top 96%
Research Data Management Practices
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