Title: Shifts 2.0: Extending The Dataset of Real Distributional Shifts. Abstract: Distributional shift, or the mismatch between training and deployment data, is a significant obstacle to the usage of machine learning in high-stakes industrial applications, such as autonomous driving and medicine. This creates a need to be able to assess how robustly ML models generalize as well as the quality of their uncertainty estimates. Standard ML datasets do not allow these properties to be assessed, as the training, validation and test data are often identically distributed. Recently, a range of dedicated benchmarks have appeared, featuring both distributionally matched and shifted data. The Shifts dataset stands out in terms of the diversity of tasks and data modalities it features. Unlike most benchmarks, which are dominated by 2D image data, Shifts contains tabular weather forecasting, machine translation, and vehicle motion prediction tasks. This enables models to be assessed on a diverse set of industrial-scale tasks and either universal or directly applicable task-specific conclusions to be reached. In this paper, we extend the Shifts Dataset with two datasets sourced from industrial, high-risk applications of high societal importance. Specifically, we consider the tasks of segmentation of white matter Multiple Sclerosis lesions in 3D magnetic resonance brain images and the estimation of power consumption in marine cargo vessels. Both tasks feature ubiquitous distributional shifts and strict safety requirements due to the high cost of errors. These new datasets will allow researchers to explore robust generalization and uncertainty estimation in new situations. This work provides a description of the dataset and baseline results for both tasks.