A multilayer and spatial description of the Erasmus mobility network.

Area of Science:

• Higher Education Studies
• Network Science
• Data Science

Background:

• The Erasmus Programme facilitates European Higher Education Institution (HEI) collaboration.
• Mobility flows of students, teachers, and staff create complex networks connecting HEIs, regions, and countries.
• Existing data lacks comprehensive integration for network analysis.

Purpose of the Study:

• To present a linked and verified dataset of the Erasmus Programme's spatial network.
• To enrich this network with socio-economic and geographical data for deeper analysis.
• To provide a resource for understanding institutional attractiveness and mobility patterns.

Main Methods:

• Constructed a multiplex, multipartite, multi-labelled, spatial network of the Erasmus Programme.
• Integrated institutional socio-economic data from the European Tertiary Education Register (ETER) and Global Research Identifier Database (GRID).
• Geocoded HEI headquarters and characterized surrounding environments using Points of Interest (POI) data.

Main Results:

• A comprehensive, linked dataset of the Erasmus Programme network was created.
• The dataset incorporates socio-economic and geographical attributes of participating HEIs.
• This enriched data enables detailed analysis of mobility dynamics and institutional attractiveness.

Conclusions:

• The linked dataset offers a novel resource for studying European higher education mobility.
• It facilitates a more nuanced understanding of factors influencing institutional attractiveness.
• This work supports research on collaboration networks and regional development in higher education.