Based on the initial review of the uploaded dataset script `coil100.py`, I identified a couple of potential issues according to the usual standards expected for dataset documentation and code quality:

1. **Obscure Dataset URL Issue**:
    - **Evidence**: The dataset URL mentioned in the script is `"http://www.cs.columbia.edu/CAVE/databases/SLAM_coil-20_coil-100/coil-100/coil-100.zip"`. This URL is not using a secure HTTP connection (HTTPS), which can be a potential security risk for users trying to download the dataset.
    - **Description**: The script contains a dataset URL that is not using HTTPS, which could pose a security risk to users and also lead to potential man-in-the-middle attacks during dataset download. It is common best practice to use HTTPS to ensure the integrity and security of exchanged data.

2. **Incorrect Citation Issue**:
    - **Evidence**: The citation provided in the script is for a different dataset, `"Columbia object image library (coil-20)"`, but the script is for the COIL-100 dataset.
    - **Description**: The citation included in the script seems to be incorrect or at least misleading. The citation mentions the COIL-20 dataset (`title={Columbia object image library (coil-20)}`) instead of the COIL-100 dataset, which the script is meant to handle. This misalignment may confuse users of this dataset and mislead academic referencing.

Please address these issues to ensure that the dataset adheres to the standard protocols of dataset documentation and coding practices.