README.md

---
dataset_info:
  features:
  - name: file_name
    dtype: string
  - name: source_file
    dtype: string
  - name: question
    dtype: string
  - name: question_type
    dtype: string
  - name: question_id
    dtype: int32
  - name: answer
    dtype: string
  - name: answer_choices
    list: string
  - name: correct_choice_idx
    dtype: int32
  - name: image
    dtype: image
  - name: video
    dtype: video
  - name: media_type
    dtype: string
  splits:
  - name: test
    num_examples: 1120
configs:
- config_name: default
  data_files:
  - split: test
    path: data/test-*
license: mit
task_categories:
- visual-question-answering
language:
- en
size_categories:
- 1K<n<10K
tags:
- engineering
- simulation
- stratified-subset
---

# OpenSeeSimE-Structural-Mini

A **stratified 1% subset** of [`cmudrc/OpenSeeSimE-Structural`](https://huggingface.co/datasets/cmudrc/OpenSeeSimE-Structural) for evaluating vision-language models at a reduced compute footprint while preserving the joint distribution of simulation type, question type, media type, and question id.

## Subset Provenance

- **Parent dataset**: [`cmudrc/OpenSeeSimE-Structural`](https://huggingface.co/datasets/cmudrc/OpenSeeSimE-Structural) (102,678 rows total)
- **Rows in this subset**: **1,120** (1.09% of parent)
- **Source classes**: `Beams`, `Dog Bone`, `Hip Implant`, `Pressure Vessel`, `Wall Bracket`
- **Parquet shards**: 1   |   **Storage**: ~1.73 GB
- **Sampling**: per-stratum shuffle with `numpy.random.default_rng(42)`, then take `ceil(n * fraction)` from each stratum. Any non-empty stratum contributes at least 1 row.
- **Strata**: `(source_file, question_type, media_type, question_id)` — all four jointly.
- **Nesting**: the 1% subset is a literal subset of the 10% subset (same shuffled prefix is taken for every fraction).

## Composition

### By `source_file`

| source_file     |   rows |   pct |
|:----------------|-------:|------:|
| Beams           |    240 | 21.43 |
| Dog Bone        |    220 | 19.64 |
| Hip Implant     |    220 | 19.64 |
| Pressure Vessel |    220 | 19.64 |
| Wall Bracket    |    220 | 19.64 |

### By `media_type`

| media_type   |   rows |
|:-------------|-------:|
| image        |    560 |
| video        |    560 |

### By `(source_file, question_type)`

| source_file     |   Binary |   Multiple Choice |   Spatial |   Total |
|:----------------|---------:|------------------:|----------:|--------:|
| Beams           |       72 |               120 |        48 |     240 |
| Dog Bone        |       66 |               110 |        44 |     220 |
| Hip Implant     |       66 |               110 |        44 |     220 |
| Pressure Vessel |       66 |               110 |        44 |     220 |
| Wall Bracket    |       66 |               110 |        44 |     220 |

## Feature Schema

Identical to the parent dataset. See [`cmudrc/OpenSeeSimE-Structural`](https://huggingface.co/datasets/cmudrc/OpenSeeSimE-Structural) for full documentation of simulation generation, ground-truth extraction, preprocessing, limitations, and intended use.

```python
{
    'file_name':          str,        # Unique identifier
    'source_file':        str,        # Base simulation model
    'question':           str,        # Question text
    'question_type':      str,        # 'Binary', 'Multiple Choice', 'Spatial'
    'question_id':        int,        # Question identifier (1-20)
    'answer':             str,        # Ground truth answer
    'answer_choices':     list[str],  # Options
    'correct_choice_idx': int,        # Index of correct answer
    'image':              Image,      # PIL Image (1920x1440) or null for video rows
    'video':              Video,      # Video bytes or null for image rows
    'media_type':         str,        # 'image' or 'video'
}
```

## Intended Use

- Benchmark evaluation of vision-language models on engineering simulation question answering at reduced compute cost
- Smoke-testing of evaluation pipelines before running the full benchmark
- Comparative studies where storage or bandwidth constraints matter

## License

MIT — same as parent. Free for academic and commercial use with attribution.

## Citation

```bibtex
@article{ezemba2024opensesime,
  title={OpenSeeSimE: A Large-Scale Benchmark to Assess Vision-Language Model Question Answering Capabilities in Engineering Simulations},
  author={Ezemba, Jessica and Pohl, Jason and Tucker, Conrad and McComb, Christopher},
  year={2025}
}
```

## Contact

**Jessica Ezemba** — jezemba@andrew.cmu.edu  
Department of Mechanical Engineering, Carnegie Mellon University