Based on the review of the provided files, here are some potential issues identified:

### Issue 1
**Issue:** Inconsistent Licensing Information  
**Evidence:** 
```
'license': ''
```
in `dataset_infos.json`.

**Description:** The `dataset_infos.json` file lacks licensing information for the `bc2gm_corpus` dataset. It is crucial to specify the license under which the dataset is distributed to ensure users are aware of the terms and conditions for using the dataset.

### Issue 2
**Issue:** Missing Metadata in `README.md`  
**Evidence:** The file is not directly readable, but if the README lacks comprehensive metadata such as author information, contact details, or dataset usage instructions, it can be problematic.

**Description:** The README file is supposed to contain essential metadata about the dataset, including author information, contact details, and instructions for usage. Missing this information can make it difficult for users to properly attribute the dataset or understand how to use it.

### Issue 3
**Issue:** Potential Data Redundancy or Insufficient Data Validation  
**Evidence:** From the description:
```
"The results varied with a highest achieved F1 score of 0.8721...by combining the results from all submissions, an F score of 0.9066 is feasible."
```

**Description:** The dataset description in `dataset_infos.json` mentions combining results to achieve a higher F1 score. It is important to ensure that the combined dataset does not introduce redundancy or overfitting issues. Proper validation steps should be described to assure data integrity and quality.

### Issue 4
**Issue:** Lack of Detail on Data Preprocessing Steps  
**Evidence:** From the description:
```
"This dataset has been converted to CoNLL format for NER using the following tool: https://github.com/spyysalo/standoff2conll"
```

**Description:** While the dataset description mentions the conversion tool used, it lacks detailed steps on how the conversion was performed. Detailed preprocessing steps are essential for reproducibility and understanding the data transformation pipeline.

### Issue 5
**Issue:** Inadequate Description of Dataset Splits  
**Evidence:** 
```
'splits': {'train': {'name': 'train', 'num_examples': 12501}, 'validation': {'name': 'validation', 'num_examples': 2501}, 'test': {'name': 'test', 'num_examples': 5001}}
```

**Description:** The dataset split information in `dataset_infos.json` provides the number of examples but does not describe the criteria used for splitting the data into train, validation, and test sets. This information is important to ensure the splits are representative and unbiased.

### Issue 6
**Issue:** Missing Feature Descriptions  
**Evidence:** 
```
'features': {'id': {'dtype': 'string'}, 'tokens': {'feature': {'dtype': 'string'}, 'ner_tags': {'feature': {'num_classes': 3, 'names': ['O', 'B-GENE', 'I-GENE']}}
```

**Description:** While the feature types and tag names are provided, a detailed description of each feature and its significance is missing. Comprehensive feature descriptions are crucial for users to understand the structure and content of the dataset.

These issues highlight areas where the dataset documentation and structure could be improved to enhance usability, transparency, and quality assurance.