After examining the initial parts of both files, here are the identified issues according to the hint provided (`improper data formatting`):

### Issue 1
- **issue**: `Inconsistent Data Format`
- **evidence**: `{
  "data_set_description": {
    "format": "Sparse_ARFF",
    "upload_date": "2020-06-26T16:32:43",
    ...
  }
}`
- **description**: The metadata in `description.json` indicates that the dataset's intended format is `Sparse_ARFF`, which implies a specific structure common in machine learning datasets. However, the uploaded dataset `dataset_rb_leipzig.csv` is in CSV format. This inconsistency suggests an improper data formatting issue as the dataset does not match its described format.

### Issue 2
- **issue**: `Missing Data Represented Inconsistently in CSV`
- **evidence**: `
9,VfB Stuttgart;Home;;;;;Win
...
11,Hannover 96;Home;;;;;Win
`
- **description**: In `dataset_rb_leipzig.csv`, there are rows where missing data seems to be indicated by consecutive semicolons `;;;;`. This representation of missing data is inconsistent and can lead to errors during data parsing or analysis. It lacks a standardized missing data notation (such as `NA`), which makes it harder to properly handle missing values in data processing tasks.

These issues highlight the importance of proper data documentation and the need for consistent data format and representation to facilitate data usability and reliability.