| --- |
| license: mit |
| language: |
| - en |
| configs: |
| - config_name: default |
| data_files: |
| - split: test |
| path: "data_filtered.jsonl" |
| task_categories: |
| - question-answering |
| - text-generation |
| tags: |
| - scientific-reasoning |
| - video-understanding |
| - physics |
| - chemistry |
| - benchmark |
| --- |
| |
| # VideoScienceBench |
|
|
| A benchmark for evaluating **video understanding** and **scientific reasoning** in vision-language models. Each example pairs a textual description of an experiment (what is shown) with the correct scientific explanation (expected phenomenon). |
|
|
| ## Dataset Summary |
|
|
| | Attribute | Value | |
| |-----------|-------| |
| | **Examples** | 160 | |
| | **Domains** | Physics, Chemistry | |
| | **Format** | JSONL (prompt + expected phenomenon + vid) | |
|
|
| ## Data Creation Pipeline |
|
|
| Each researcher selects two or more scientific concepts and references relevant educational materials or videos to design a prompt. Prompts undergo peer and model review, followed by model-based quality checking, before being finalized for dataset inclusion. |
|
|
| ## Dataset Structure |
|
|
| Each line is a JSON object with: |
|
|
| | Field | Type | Description | |
| |-------|------|-------------| |
| | keywords | list[str] | Relevant scientific concepts | |
| | field | str | Scientific discipline (e.g., Physics) | |
| | prompt | str | Textual description of what is shown in the video/experiment | |
| | expected phenomenon | str | The correct scientific explanation | |
| | vid | str | Video identifier | |
|
|
| ## Example |
|
|
| ```json |
| { |
| "keywords": ["Buoyancy", "Gas Laws", "Pressure"], |
| "field": "Physics", |
| "prompt": "A sealed plastic bottle is filled with water containing a floating eyedropper with an air bubble inside. A person squeezes the sides of the bottle.", |
| "expected phenomenon": "The eyedropper immediately sinks when the bottle is squeezed, then rises again when released, as increased pressure compresses the air bubble, reducing buoyancy.", |
| "vid": "98" |
| } |
| ``` |
|
|
| ## Usage |
|
|
| ```python |
| from datasets import load_dataset |
| |
| dataset = load_dataset("lmgame/VideoScienceBench") |
| # Access the test split (configured in the dataset card) |
| data = dataset["test"] |
| ``` |
|
|
| ## License |
|
|
| MIT |
|
|
