I will examine the datasets to identify any data misalignment issues. Data misalignment issues in CSV files can include misaligned columns, missing headers, and irregular row lengths. Here are the issues I found:

### Issue 1

```json
{
    "issue": "Data misalignment in `recent-grads.csv`",
    "evidence": "Row 3: '0 50 660' instead of '0.050661', '65 000.0' instead of '65000.0'",
    "description": "In the `recent-grads.csv` file, the values in row 3 are misaligned. Instead of '0.050661', it is displayed as '0 50 660', and '65000.0' is displayed as '65 000.0'. This misalignment indicates that the data is not correctly parsed, likely due to incorrect delimiters or spaces in the original data."
}
```

### Issue 2

```json
{
    "issue": "Missing header in `all-ages.csv`",
    "evidence": "Row 0: 'All ages Bachelor's degree 448564 45199 ...'",
    "description": "The first row in the `all-ages.csv` file appears to contain actual data instead of column headers. This suggests that the header row is missing, causing potential misalignment when interpreting the dataset."
}
```

### Issue 3

```json
{
    "issue": "Inconsistent row lengths in `grad-students.csv`",
    "evidence": "Row 5: '15,21 45211 ...' (20 columns), Row 10: '14,20 42013 ...' (22 columns)",
    "description": "The `grad-students.csv` file has inconsistent row lengths. Row 5 has 20 columns, while row 10 has 22 columns. This inconsistency can lead to difficulties in data analysis and processing, as it indicates irregularities in data entry or formatting."
}
```

These issues highlight potential problems in the dataset files that need to be addressed to ensure data integrity and proper analysis.