repo
The Hugging Face git repository.
git+https
application/x-parquet
default/*/*.parquet
default_splits
Splits for the default config.
train
LabShield/review - 'default' subset
fullpath
default/(?:partial-)?(train)/.+parquet$
sample_id
scenario
operation_level
safety_level
instruction
observation
left_wrist_rgbd
path
right_wrist_rgbd
head_rgbd
torso_rgbd
instruction_abstract
open_prp
perception
unsafe_factors
reasoning
hazard_patterns
analysis
planning
decision
plan_steps
close_vqa
qa_id
type
question
choices
gold_choice
subtype
reasoning_tasks
choice_questions
review
🛡️ LabShield: A Multimodal Benchmark for Laboratory Safety Official dataset for the paper: "LabShield: A Multimodal Benchmark for Safety-Critical Reasoning and Planning in Scientific Laboratories". [Project Website (Coming Soon)] | [Paper (NeurIPS 2026 Submission)] | [Code (Coming Soon)] 📌 Introduction LabShield is a rigorous, multi-view benchmark designed to assess the safety awareness and decision-making reliability of Multimodal Large Language Models (MLLMs) in… See the full description on the dataset page: https://huggingface.co/datasets/LabShield/review.
LabShield/review
LabShield Review
LabShield-Review
visual-question-answering
image-to-text
English
apache-2.0
< 1K
json
Image
Tabular
Text
Datasets
pandas
Polars
Croissant
🇺🇸 Region: US
safety
benchmark
embodied-ai
robotics
laboratory-automation
The dataset is currently focused on laboratory environments. The safety evaluations are based on static VQA proxies and do not yet account for real-time dynamic robot control feedback loop.
Visual data is captured from a single robotic platform (Astribot), which may introduce perspective and hardware-specific biases. Safety logic is aligned with GHS and OSHA standards.
None. The dataset contains no human faces, identifiers, or sensitive personal data.
Intended for evaluating the safety reasoning and planning capabilities of MLLMs in laboratory settings. Not for training autonomous systems without further safety validation.
Provides a foundation for safer AI-driven laboratory automation and contributes to the reduction of physical risks in scientific research.
Tasks synthesized by LLMs followed by rigorous multi-stage manual filtering and expert annotation by domain-specific PhD researchers. Inter-annotator agreement (Fleiss' Kappa) is 0.82.