Speaker
Description
Scientific collaboration is an essential driver of research progress and innovation in Science.
As evidence of this, collaboration is increasing in all disciplines, and government policies in international exchange programs aim to promote collaboration among researchers.
Collaborations develop through informal mechanisms (e.g., advice, face-to-face contacts, and exchange of personal knowledge) and formal activities (e.g., writing papers and participating in research projects). Scientific collaborations have several advantages concerning productivity but can also have drawbacks. Even with the comprehensive discussion on co-authorship analysis and productivity, the academic literature still needs to improve the analysis of the pros and cons of co-authorship on scientific productivity, especially comparing different research fields and research communities.
In this contribution, we propose a comparison of the co-authorship networks between two different fields of study for Italian scholars, the statistics community (SECS-S (Statistics, composed of 5 distinct subfields) and management community (SECS-P/08 Management), both belonging to the macro-group named “Area 13” (Economics and statistics).
Data about scholars’ scientific production have been retrieved from the Scopus platform in order to build different longitudinal co-authorship networks at subfield level.
We will compare the different co-authorship networks by considering their topology and authors’ position. We will also analyze productivity and co-authorship structures over time in order to identify the differences in scientific collaboration patterns emerging in the fields under analysis. Proper graph measures will be selected to assess the role of the collaboration configurations in the scholars’ productivity frameworks, with the main aim of comparing the two macro-area in that regard.
Furthermore, we build his/her own ego-network for each scholar to analyze the co-authorship dynamics, underlining new publishing behaviors concerning different individual characteristics (e.g., academic position). This analysis is crucial to understand how the authors are related and how the collaboration patterns change across time and between disciplines.
| Topics | • Collaboration and scientific networks |
|---|---|
| Keywords | collaboration network, Bibliometric databases, Co-authorship data, Network topology, Scientific performance |
