UBelong Longitudinal Data, 2023-2025

The U-Belong dataset comprises longitudinal survey data collected as part of the UK MRC–funded U-Belong project, which investigates students’ social belonging, social connection, and mental health in UK higher education. The dataset includes repeated observations from undergraduate and postgraduate students recruited from a large UK university across two academic years (2023–2024 and 2024–2025), with baseline and follow-up survey waves enabling within-person longitudinal analyses.

The survey captures a rich set of constructs spanning social, psychological, and contextual domains. Core measures include students’ expectations and experiences of social connection, perceived mismatches between expectations and experiences, sense of belonging, social connectedness, engagement with university life, community connection, comfort with solitude, and experiences of everyday discrimination. Loneliness is assessed using multiple approaches, including the De Jong Gierveld Loneliness Scale, the UCLA Loneliness Scale, and a direct loneliness item. Mental health and wellbeing measures include validated assessments of anxiety, depression, mental wellbeing, and general wellbeing, alongside social anxiety, social avoidance, alcohol consumption, and peer pressure.

The dataset also includes detailed social network data collected using both aggregate network indicators and a brief egocentric “mini-network” module, as well as measures of social support, social media use and experiences, and academic and financial pressures. Extensive demographic, educational, and identity-related variables are provided, including age, sex, gender identity, sexual and romantic orientation, ethnicity, religion, disability status, migration status, caring responsibilities, first-generation student status, international student status, commuter status, care experience, and socioeconomic background. Information on course of study, prior educational experiences, and living arrangements is also included.

Participants are linked across waves using anonymised participant identifiers. The data are fully anonymised and accompanied by detailed documentation, including a comprehensive codebook describing variable construction, scales used, and wave availability. The dataset is suitable for secondary analyses examining social belonging, loneliness, mental health, inequalities, and student experiences in higher education, as well as for methodological work on longitudinal survey design and social network measurement.