| --- |
| license: mit |
| language: |
| - en |
| tags: |
| - chemistry |
| - physics |
| - math |
| - biology |
| - code |
| pretty_name: sci-or |
| size_categories: |
| - n<1K |
| task_categories: |
| - question-answering |
| --- |
| |
| # Computational STEM QA Dataset |
|
|
| [](https://opensource.org/licenses/MIT) |
| [](https://turing.com) |
|
|
|
|
| ## 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 |
| | Field | Type | Description | |
| |-------|------|-------------| |
| | `conversation_id` | string | Unique identifier for each QA pair. | |
| | `domain` | string | Physics, Math, Chemistry, Biology. | |
| | `sub-domain` | string | Specific discipline. | |
| | `question` | string | STEM problem statement. | |
| | `answer` | string | Correct solution. | |
| | `code` | string | Full Python code. | |
|
|
| ## 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` `pandas`libraries |
| - 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/} |
| } |
| ``` |
