Datasets:

AstroMLab
/

USAAAO_QA

language:
  - en
pretty_name: >-
  USA Astronomy and Astrophysics Organization (USAAAO) Open-ended Questionaire
  Dataset
tags:
  - astronomy
  - astrophysics
  - olympiad
  - physics
  - science
  - stem
  - multimodal
license: other
task_categories:
  - question-answering
  - visual-question-answering

USA Astronomy and Astrophysics Organization (USAAAO) Open-ended Questionaire

This dataset contains structured question–answer pairs from the USAAAO (2017-2019), converted from pdf to LaTeX sources, into a machine-readable JSONL format.

The dataset includes:

Short, medium, and long-form astrophysics problems
Multi-part questions (grouped via parent_id)
Full worked solutions
Associated figures (JPEG images) where applicable

The goal of this dataset is to support evaluation and benchmarking of large language models (LLMs) on:

Advanced scientific reasoning
Mathematical derivation
Multi-step physics problem solving
Multimodal reasoning (text + figures)

Dataset Structure

Each record contains:

id – unique question or sub-question identifier
parent_id – shared ID for multi-part questions (null for single-part)
year – exam year
part – sub-part label (e.g., "a", "b")
part_order – ordering index for sub-parts
question_type – typically "open-ended"
question_length – "short", "medium", or "long"
question – problem statement
answer – full solution text
image – relative path to image (if applicable)
caption – figure caption (if applicable)

Directory Layout

your-dataset-repo/ ├── README.md ├── data/ │ ├── 2017.jsonl │ ├── 2018.jsonl │ └── 2019.jsonl └── images/ ├── 2017/ ├── 2018/ └── 2019/

Loading the Dataset

If using the Hugging Face `datasets` library:

from datasets import load_dataset

dataset = load_dataset("your-org/your-dataset-name")

If loading locally from JSONL:


from datasets import load_dataset

dataset = load_dataset("json", data_files="data/2019.jsonl")

Citation

If you use this dataset, please cite:

@misc{usaaao_dataset_2025, author = {AstroMLab}, title = {USAAAO QA dataset}, year = {2025}, }