OpenBookQA has missing and inconsistent field names
@albertvillanova, thanks for the detailed answer and the new perspectives. I understand the friction for the best design approach much better now.  Ultimately, it is essential to include all the missing fields and the correct data first. Whatever approach is determined to be optimal is important but not as crucial once all the data is there, and users can create lambda functions to create whatever structure serves them best. 
## Describe the bug
OpenBookQA implementation is inconsistent with the original dataset.

We need to:

1. The dataset field [question][stem] is flattened into question_stem. Unflatten it to match the original format.
2. Add missing additional fields:
    - 'fact1': row['fact1'],
    - 'humanScore': row['humanScore'],
    - 'clarity': row['clarity'],
    - 'turkIdAnonymized': row['turkIdAnonymized']
3. Ensure the structure and every data item in the original OpenBookQA matches our OpenBookQA version.

## Expected results
The structure and every data item in the original OpenBookQA matches our OpenBookQA version.

## Actual results
TBD

## Environment info
- `datasets` version: 2.1.0
- Platform: macOS-10.15.7-x86_64-i386-64bit
- Python version: 3.8.13
- PyArrow version: 7.0.0
- Pandas version: 1.4.2
OpenBookQA has missing and inconsistent field names 
 ## Describe the bug
OpenBookQA implementation is inconsistent with the original dataset.

We need to:

1. The dataset field [question][stem] is flattened into question_stem. Unflatten it to match the original format.
2. Add missing additional fields:
    - 'fact1': row['fact1'],
    - 'humanScore': row['humanScore'],
    - 'clarity': row['clarity'],
    - 'turkIdAnonymized': row['turkIdAnonymized']
3. Ensure the structure and every data item in the original OpenBookQA matches our OpenBookQA version.

## Expected results
The structure and every data item in the original OpenBookQA matches our OpenBookQA version.

## Actual results
TBD

## Environment info
- `datasets` version: 2.1.0
- Platform: macOS-10.15.7-x86_64-i386-64bit
- Python version: 3.8.13
- PyArrow version: 7.0.0
- Pandas version: 1.4.2 
 @albertvillanova, thanks for the detailed answer and the new perspectives. I understand the friction for the best design approach much better now.  Ultimately, it is essential to include all the missing fields and the correct data first. Whatever approach is determined to be optimal is important but not as crucial once all the data is there, and users can create lambda functions to create whatever structure serves them best. 