OpenBookQA has missing and inconsistent field names
I don't have a strong opinion on this, besides the fact that whatever decision we agree on, should be applied to all datasets.

There is always the tension between:
- preserving each dataset original structure (which has the advantage of not forcing users to learn other structure for the same dataset),
-  and on the other hand performing some king of standardization/harmonization depending on the task (this has the advantage that once learnt, the same structure applies to all datasets; this has been done for e.g.  POS tagging: all datasets have been adapted to a certain "standard" structure).
   - Another advantage: datasets can easily be interchanged (or joined) to be used by the same model

Recently, in the BigScience BioMedical hackathon, they adopted a different approach:
- they implement a "source" config, respecting the original structure as much as possible
- they implement additional config for each task, with a "standard" nested structure per task, which is most useful for users.
## 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 don't have a strong opinion on this, besides the fact that whatever decision we agree on, should be applied to all datasets.

There is always the tension between:
- preserving each dataset original structure (which has the advantage of not forcing users to learn other structure for the same dataset),
-  and on the other hand performing some king of standardization/harmonization depending on the task (this has the advantage that once learnt, the same structure applies to all datasets; this has been done for e.g.  POS tagging: all datasets have been adapted to a certain "standard" structure).
   - Another advantage: datasets can easily be interchanged (or joined) to be used by the same model

Recently, in the BigScience BioMedical hackathon, they adopted a different approach:
- they implement a "source" config, respecting the original structure as much as possible
- they implement additional config for each task, with a "standard" nested structure per task, which is most useful for users.