Machine learning algorithms offer a promising approach for vibration-based Structural Health Monitoring (SHM) to assess damage in real time. However, the scarcity of labelled health-state data, especially considering various environmental conditions and damage cases, remains a significant challenge. Population-based Structural Health Monitoring (PBSHM) addresses this issue by enriching the available data via knowledge transfer across a population of similar structures. This approach is particularly powerful in bridge networks where structures can be classified into a few typologies. Scaling SHM from single assets to the entire network is crucial for modern risk assessment in transportation networks. However, PBSHM faces the challenge of obtaining and validating relevant technologies using datasets from multiple similar structures representing various health states. This chapter presents an experimental dataset from a model bridge, where the positions of supports were varied to represent different structures. The dataset includes a wide range of temperatures, including freezing effects, simulated using an environmental chamber. Multiple damage scenarios are also introduced to enable the investigation of damage detection and classification methods for both conventional SHM and PBSHM. This chapter provides an analysis of the dataset and demonstrates the assessment of damage under changing environmental conditions. The whole dataset contributes to advancing the field of PBSHM by providing valuable insights into the limitations of existing SHM methods towards damage assessment in diverse environmental conditions.