Deep learning techniques have made an increasing impact on the field of remote sensing. However, deep neural networks based fusion of multimodal data from different remote sensors with heterogenous characteristics has not been fully explored, due to the lack of availability of big amounts of perfectly aligned multi-sensor image data with diverse scenes of high resolution, especially for synthetic aperture radar (SAR) data and optical imagery. In this paper, we publish the QXS-SAROPT dataset to foster deep learning research in SAR-optical data fusion. QXS-SAROPT comprises 20,000 pairs of corresponding image patches, collected from three port cities: San Diego, Shanghai and Qingdao acquired by the SAR satellite GaoFen-3 and optical satellites of Google Earth. Besides a detailed description of the dataset, we show exemplary results for two representative applications, namely SAR-optical image matching and SAR ship detection boosted by cross-modal information from optical images. Since QXS-SAROPT is a large open dataset with multiple scenes of the highest resolution of this kind, we believe it will support further developments in the field of deep learning based SAR-optical data fusion for remote sensing.