Datasets:

cmudrc
/

OpenSeeSimE-Structural-Small

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: 10343
configs:
  - config_name: default
    data_files:
      - split: test
        path: data/test-*
license: mit
task_categories:
  - visual-question-answering
language:
  - en
size_categories:
  - 10K<n<100K
tags:
  - engineering
  - simulation
  - stratified-subset

OpenSeeSimE-Structural-Small

A stratified 10% subset of 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 (102,678 rows total)
Rows in this subset: 10,343 (10.07% of parent)
Source classes: Beams, Dog Bone, Hip Implant, Pressure Vessel, Wall Bracket
Parquet shards: 4 | Storage: ~15.60 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	2088	20.19
Pressure Vessel	2074	20.05
Dog Bone	2061	19.93
Hip Implant	2060	19.92
Wall Bracket	2060	19.92

By `media_type`

media_type	rows
image	5192
video	5151

By `(source_file, question_type)`

source_file	Binary	Multiple Choice	Spatial	Total
Beams	627	1045	416	2088
Dog Bone	619	1030	412	2061
Hip Implant	618	1030	412	2060
Pressure Vessel	622	1040	412	2074
Wall Bracket	618	1030	412	2060

Feature Schema

Identical to the parent dataset. See cmudrc/OpenSeeSimE-Structural for full documentation of simulation generation, ground-truth extraction, preprocessing, limitations, and intended use.

{
    '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

@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