After reviewing the structure of the dataset, here are the identified issues:

1. **Incorrect Column Descriptions**: 
    - **Issue**: The descriptions in the column names are misleading or incorrectly associated. 
    - **Evidence**: The dataset columns have descriptions in parentheses that do not correctly describe the data they hold. For example, `vote_count(Date of publish)` suggests that `vote_count` is the date of publication, which is incorrect. Similarly, `vote_average(popularity)` suggests that `vote_average` represents popularity, which is a misrepresentation as it likely represents average votes/ratings.
    - **Description**: The column names contain additional descriptions that mislead the reader regarding the content of each column. This can cause confusion when attempting to understand the dataset's structure and the type of data each column holds. Correct descriptions should directly reflect the data type and content of each column, such as simply "vote_count" for the number of votes and "vote_average" for the average rating.

2. **Misleading Column Name for Release Date Information**: 
    - **Issue**: The column that is expected to contain the release date information is named inaccurately.
    - **Evidence**: There is no column explicitly named for release dates, yet `vote_count(Date of publish)` seems to incorrectly suggest that vote counts are related to publication dates.
    - **Description**: Users looking for the release date or publication date of the movies would be misled by the current naming convention. There's a misunderstanding created by the parenthetical descriptions, suggesting a different meaning than the actual data presents. This issue highlights the necessity of clear and accurate column naming to ensure data is understandable and accurately interpreted.