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README.md

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+[![arXiv](https://img.shields.io/badge/arXiv-2604.20824-b31b1b.svg)](https://arxiv.org/abs/2604.20824)
+[![Github](https://img.shields.io/badge/Github-181717?logo=github&logoColor=white)](https://github.com/ml-jku/cs-arm-bn)
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 <p align="center">
   <img src="figures/data_overview.png" alt="Dataset overview" width="750"/>
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-  <em>Overview of the benchmark setting: experimental batches contain perturbed samples and negative controls, enabling control-stabilized adaptation under batch effects and label shift.</em>
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-[![arXiv](https://img.shields.io/badge/arXiv-2604.20824-b31b1b.svg)](https://arxiv.org/abs/2604.20824)
-[![Github](https://img.shields.io/badge/Github-181717?logo=github&logoColor=white)](https://github.com/ml-jku/cs-arm-bn)
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 # 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.