The paper is excellent and provides a much-needed source of verification information for SAR detection of oil spills

in particular it is excellent that it covers and provides information on commonly-encountered interferences - including  internal waves, winds

(low and high), areas of mixing and vertical advection in the ocean,  meso- and sub-mesoscale eddies, biogenic surface films,  rain cells,  radio frquency interference, etc.  

This is the first and important data set to be used for automatic algorithm evaluation

no errors were found

Comments on “Dataset of oil slicks look-alikes and remarkable SAR …” by Yang et al.,

The detection of oil slicks in the real ocean is crucial for effective natural disaster management using satellite technology, such as Synthetic Aperture Radar (SAR). However, the complexity of oceanic phenomena makes real-world oil slick identification challenging. This difficulty is partly due to the lack of a robust benchmark dataset suitable for training effective detection models. The manuscript by Yang et al. attempts to address this critical need by providing a categorized dataset. I recommend the publication of this manuscript following the incorporation of the suggestions below.

1 Data Hosting and Model Interoperability: Is it possible to establish a website to host not only the data but also the model codes, similar to initiatives like the PIV challenge (see https://www.pivchallenge.org/)? This would facilitate fair comparison between different models and ensure ease of use for the wider research community.

2 Terminology (Line 47): Please replace "vertical advection in the ocean" with the more precise oceanographic term: "upwelling in the ocean."  

3 Equation (1): Please provide a brief justification for choosing the parameter threshold as three times the standard deviation.

4 Annotation Criteria (Lines 111-112): Regarding the data labeled "jointly by two human interpreters," please comment on the specific criteria used for achieving consensus between the interpreters.

5 Figure 3 Caption: The caption should be more informative. For example, the meaning of the vertical line must be explicitly mentioned, even though it is referenced in the main text.

6 Clustering Justification (Lines 189-190): Regarding the "K-means clustering methods with 12 and 5 classes for nw and nc subsets," please provide further detail or comment on the rationale for selecting 12 and 5 classes, respectively.

7 Data Pre-processing (Line 325): When geolocation information is available, the removal of land and islands could be easily and efficiently handled using the global-land-mask Python package (https://github.com/toddkarin/global-land-mask). This should be considered for data cleaning.

8 Figure 13: It would significantly improve clarity to visually indicate the upwelling area in all sub-figures.

9 Typographical Error (Line 33): Please remove the redundant "in" in the sentence.

10  Notation Consistency (Equation (3)): Ensure the notation used for the IoU (Intersection over Union) in Equation (3) is consistent with its use elsewhere in the main text.

11 Model Identification (Line 559): Please explicitly provide the names of the "two models" being referenced.