Here are some potential issues identified in the dataset files based on the content analysis of `task.json` and `README.md`:

1. **Issue: Discrepancy in Task Descriptions**
   - **Evidence:** In `README.md`, the task is described as "The purpose of this task is to measure the ability of models to understand the semantics of a natural language utterance and map into a precise SQL query..." However, in `task.json`, the description is "Map a natural-language utterance into an SQL query that can be executed on databases".
   - **Description:** The task descriptions in `README.md` and `task.json` should be consistent. The `README.md` provides a more detailed purpose, whereas `task.json` gives a straightforward task directive. Aligning these descriptions would help avoid confusion and ensure clarity in documentation.

2. **Issue: Inconsistent Metric Specification**
   - **Evidence:** In `task.json`, the metrics listed are `["exact_str_match", "bleu", "rouge"]`, with a preferred score of "bleu". However, in `README.md`, no explicit mention of the metric preference is stated under the performance metrics section, nor is there a mention of the "rouge" metric.
   - **Description:** The metrics and preferred scoring methods need to be consistently documented across all files to ensure evaluators use the correct methods when assessing model performance on this task. The discrepancy could lead to inconsistent evaluation outcomes.

3. **Issue: Missing Link to Detailed Results**
   - **Evidence:** In `README.md`, it mentions "These plots are auto-generated from the score files in the [results](results/) subdirectory." However, the provided `results.zip` file is inaccessible within this environment, and no direct links or methods to access these detailed results are provided in the documents.
   - **Description:** The documents refer to detailed results and performance plots which are crucial for understanding model performance, but no accessible method or link is provided to access these results directly. This could hinder the complete evaluation of the task performance and model capabilities.

These issues could potentially impact the usability and integrity of the dataset, and addressing them could improve clarity and consistency across documentation and dataset usage.