Web3Survivor/Onlineresearch

Computational STEM QA Dataset

 

Dataset Summary

This dataset contains computationally intensive, self-contained, and unambiguous STEM reasoning problems across Physics, Mathematics, Biology, and Chemistry.

Problems require multi-step reasoning, symbolic manipulation, numerical accuracy, or simulation-based verification. These tasks expose failure modes in state-of-the-art LLMs, making this dataset a strong benchmark for evaluating deep reasoning.

Each example includes:

• —conversation_id
• —domain and sub-domain
• —A rigorous question with LaTeX
• —A deterministic **answer`
• —Optional Python code for simulation or verification

Dataset Structure

Example

{
  "conversation_id": "201186",
  "domain": "Physics",
  "sub-domain": "Classical Mechanics",
  "question": "A block of mass m slides down a frictionless incline... Compute the acceleration using $a = g \sin(\theta)$.",
  "answer": "Using Newton's laws, the acceleration is $a = g \sin(\theta)$.",
  "code": "import math\ng = 9.81\ntheta = math.radians(30)\na = g * math.sin(theta)\nprint(a)"
}

Dataset Characteristics

• —Self-contained and unambiguous
• —Heavy use of LaTeX in STEM reasoning
• —All examples require precise computation and can not be solved analytically
• —Designed to stress-test LLM reasoning
• —Full python code to solve the problem

Dataset Format

This dataset is provided in standard JSON format as a top-level array containing all problem records.

Example:

[
  {"conversation_id": "1", "domain": "Physics", "sub-domain": "Mechanics", "question": "...", "answer": "...", "code": "..."},
  {"conversation_id": "2", "domain": "Math", "sub-domain": "Algebra", "question": "...", "answer": "...", "code": "..."}
]

Intended Uses

• —Fine-tuning STEM reasoning models
• —Evaluating LLM computation accuracy
• —Benchmarking symbolic + numeric reasoning
• —Developing STEM tutoring agents
• —Creating reward models requiring strict correctness

Limitations

• —Numeric results may vary slightly due to floating point behavior
• —Python code assumes availability of only numpy,scipy pandaslibraries
• —Some models may require preprocessing of LaTeX

Citation

@dataset{saurabh_2025_stemqa,
  title        = {Computational STEM QA Dataset},
  author       = {Saurabh Patil,Anshuman Lall,Marko Pavlovic,Tejas Ukarde,Chinmayee Shukla,Mahesh Joshi,Kihwan Han},
  year         = {2025},
  url          = {https://huggingface.co/datasets/TuringEnterprises/Turing-Open-Reasoning/}
}