Here are some potential issues related to inconsistency in dataset scoring, identified from the uploaded documents:

1. **Issue**: Inconsistent Weights in Quality Dimensions
   - **Evidence**: “We further analyze that the inconsistence between the model accuracy ranking and dataset quality ranking is caused by equal weights wi of all quality dimensions in Eq. (6).” 
   - **Description**: The inconsistency between model accuracy ranking and dataset quality ranking is attributed to the equal weighting of all quality dimensions. Adjusting the weights to reflect the importance of specific dimensions for particular tasks, such as increasing the weight of correctness for text sentiment classification, can resolve this inconsistency【17:1†source】.

2. **Issue**: Varied Evaluation Metrics Across Dataset Lifecycle Stages
   - **Evidence**: “While current research on dataset quality has achieved significant results, particularly in the dimension of dataset quality, there are still some limitations that need to be addressed. These include: Lack of uniformity in the evaluation metrics used during the construction of datasets.”
   - **Description**: There is a lack of uniformity in the evaluation metrics used during the different stages of dataset lifecycle, leading to inconsistencies in quality assessment. Standardizing the evaluation metrics across all stages could improve consistency and reliability in dataset scoring【17:10†source】.

3. **Issue**: Inconsistent Data Quality Assessment for Unstructured Data
   - **Evidence**: “Few works consider the general quality measurements for unstructured data. Other works however mainly measure quality of an individual data piece [5] other than the quality of a collection of data pieces.”
   - **Description**: The assessment methods for unstructured data are inconsistent, often focusing on individual data pieces rather than the dataset as a whole. This leads to a misalignment in quality scoring when applied to large, diverse datasets【17:1†source】.

4. **Issue**: Inconsistent Data Quality Metrics for Different Data Types
   - **Evidence**: “Data quality assessment metrics at each stage of the dataset’s lifecycle are summarized in the Table 2. Metrics include data imbalance and data distribution consistency.”
   - **Description**: Different data types (image, text, voice) are evaluated using varied metrics, leading to inconsistencies in scoring across datasets. Standardizing metrics that are applicable to all data types could ensure more consistent and comparable quality assessments【17:7†source】.

These issues highlight the need for a more standardized and task-specific approach to dataset quality assessment to ensure consistent and reliable scoring across different datasets and applications.