Spaceborne synthetic aperture radar (SAR) has been applied to detect oil slicks. Previous studies applied machine learning and deep learning techniques to automate oil slick detection. However, a large amount of data is generally required to train such a model. It highlights the importance and necessity of a publicly available oil slick dataset. This dataset provides annotations of oil slicks located in longitude from 30°E to 36°E and in latitude from 31°N to 34.7°N in the Eastern Mediterranean Sea, observed from Sentinel-1 SAR in 2019. The annotation and inspection of the oil slicks were initially done in the framework of a previous study by the authors (Yang, Y.-J. et al., 2024). On top of that, images with oceanic and other phenomena, which can also manifest similar SAR signatures and are considered look-alikes, are also included in the dataset as a no-oil set. A well-developed oil spill detection system should be able to not only detect oil spills but also avoid the detection of these look-alikes. These look-alikes were collected on a larger extent, ranging between 27.1212703°E and 36.0881997°E in longitudes and 29.2991798°N and 36.3715771°N in latitudes. There are, in total, 1365 image patches with 3225 oil objects in the oil set and 2290 image patches in the no-oil set.