Obtaining robust "dates-as-data" inference from the calibration of multiple radiocarbon determinations

The recent explosion in the availability of dates within archaeological science has ushered in a data science revolution. Computational analyses of large, collated sets of dates have the potential to provide unprecedented inference on our past, on rates of change, and on population dynamics. It is essential however that the methods underpinning these “big-data” analyses are rigorous and robust. This concern is particularly relevant for radiocarbon dating, the archaeological method of choice for dating the last 55,000 years. Recent advances in measurement techniques have increased the number of potentially dateable radiocarbon samples on any archaeological site into the thousands. However, their need for calibration introduces considerable, and complex, uncertainties in our dates that must be incorporated into any inference. This project will provide novel, statistically rigorous yet easily accessible, tools for the archaeological community that will allow better, and more robust, inference on large datasets of radiocarbon determinations.