During the 21st century, sea-level rise will have a wide range of effects on coastal environments, human development and infrastructure in coastal areas. Consequently there is a need to develop modeling or other analytical approaches that can be used to evaluate potential impacts to inform coastal management. This shapefile provides the data that were used to develop and evaluate the performance of a Bayesian network (BN) that was developed to predict long-term shoreline change associated with sea-level rise. The data consist of information compiled as part of the U.S. Geological Survey Coastal Vulnerability Index for the U.S. Atlantic coast. In this work, the Bayesian network is used to define relationships between driving forces, geologic constraints, and coastal response which are represented by observations of local rates of relative sea-level rise, wave height, tide range, geomorphic classification, coastal slope, and shoreline change rate. Using this information, the BN is used to make probabilistic predictions of shoreline retreat in response to different future sea-level rise rates.