Project title: Data-Centric Bridge Monitoring and Assessment Project Description: Statistical analysis and modeling of monitoring data captured from a self-sensing railway bridge built as part of the Stafford Area Improvements Programme Publication title: Real-time statistical modelling of data generated from self-sensing bridges Authors: F. Din-Houn Lau(1,2), Liam J Butler(2,3), Niall M. Adams(1,5), Mohammed ZEB Elshafie(3,4), Mark A. Girolami(1,2) Author affiliations: 1. Department of Mathematics, Imperial College London 2. The Lloyd's Register Foundation Programme on Data-Centric Engineering, The Alan Turing Institute 3. Cambridge Centre for Smart Infrastructure and Construction, Department of Engineering, University of Cambridge 4. Department of Civil and Architectural Engineering, Qatar University 5. Data Science Institute, Imperial College London General description of data: The following data sets are those recorded during field monitoring of trains passing over the Staffordshire Self-Sensing Railway Bridges. The remaining figures (Figs. 7, 8, 9, 10, and 11) were generated using the raw data and the statistical models described within the paper. Figure 4. Fibre optic sensor raw data from a single sensor (units: wavelength, seconds) and train event. Figure 5. Detrended sensor data for multiple train passage events (units: microstrain, seconds) Figure 6. Sensor data for multiple train passage events (units: microstrain, time)