Uncorrelated Corrupted Dataset is an evaluation set that consists of realistic visible-infrared (V-I) corruptions allowing for models' corruption robustness evaluation. Initially proposed for multimodal person re-identification, our dataset can also be used for the evaluation of V-I cross-modal approaches. Corruptions of the visible modality are the twenty corruptions proposed by Chen & al. in the "Benchmarks for Corruption Invariant Person Re-identification" paper. Corruptions of the infrared modalities have been proposed in our paper, introducing 19 corruptions that respect the infrared modality encoding. In practice, the corruptions are applied randomly and independently to the visible and the infrared cameras, making it more suited to a not co-located camera setting.