lmgame/VideoScienceBench
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… See the full description on the dataset page: https://huggingface.co/datasets/lmgame/VideoScienceBench.
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
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:
Example
{
"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
from datasets import load_dataset
dataset = load_dataset("lmgame/VideoScienceBench")
# Access the test split (configured in the dataset card)
data = dataset["test"]License
MIT