# Multi-Source Domain Adaptation for Bioimaging Data (MSCDA-BioIm) **MSCDA-BioIm** is a biomedical microscopy benchmark for evaluating test-time and in-context domain adaptation under realistic batch effects. Built from the large-scale JUMP-CP dataset, it targets mechanism-of-action (MoA) classification using five-channel images of compounds associated with eight well-defined MoA classes. The dataset is organized by experimental batches and imaging sources, enabling controlled evaluation of generalization to unseen batches, cross-source transfer, small target context sizes, and label-shifted target batches. A key feature of **MSCDA-BioIm** is the inclusion of negative control samples in every experimental batch. These unperturbed samples provide a stable reference for estimating batch-specific technical variation, making the dataset especially suitable for studying control-aware adaptation methods such as CS-ARM-BN. For more information, please see our [paper](https://arxiv.org/abs/2604.20824) and [code](https://github.com/ml-jku/cs-arm-bn)