OpenBookQA has missing and inconsistent field names
I agree with @mariosasko . The transition to the original format could be done in one PR for the next minor release, clearly documenting all dataset changes just as @albertvillanova outlined them above and perhaps even providing a per dataset util method to convert the new valid format to the old for backward compatibility. Users who relied on the old format will update their code with either the util method for a quick fix or slightly more elaborate for the new. 
## 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 
 I agree with @mariosasko . The transition to the original format could be done in one PR for the next minor release, clearly documenting all dataset changes just as @albertvillanova outlined them above and perhaps even providing a per dataset util method to convert the new valid format to the old for backward compatibility. Users who relied on the old format will update their code with either the util method for a quick fix or slightly more elaborate for the new. 