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| "conformsTo": "http://mlcommons.org/croissant/1.1", |
| "name": "review", |
| "description": "\n\t\n\t\t\n\t\t🛡️ LabShield: A Multimodal Benchmark for Laboratory Safety\n\t\n\nOfficial dataset for the paper: \"LabShield: A Multimodal Benchmark for Safety-Critical Reasoning and Planning in Scientific Laboratories\".\n[Project Website (Coming Soon)] | [Paper (NeurIPS 2026 Submission)] | [Code (Coming Soon)]\n\n\n\t\n\t\t\n\t\t📌 Introduction\n\t\n\nLabShield 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.", |
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| "LabShield Review" |
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| "@type": "Organization", |
| "name": "LabShield-Review", |
| "url": "https://huggingface.co/LabShield" |
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| "keywords": [ |
| "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" |
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| "license": "https://choosealicense.com/licenses/apache-2.0/", |
| "url": "https://huggingface.co/datasets/LabShield/review", |
| "rai:dataLimitations": "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.", |
| "rai:dataBiases": "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.", |
| "rai:personalSensitiveInformation": "None. The dataset contains no human faces, identifiers, or sensitive personal data.", |
| "rai:dataUseCases": "Intended for evaluating the safety reasoning and planning capabilities of MLLMs in laboratory settings. Not for training autonomous systems without further safety validation.", |
| "rai:hasSyntheticData": true, |
| "rai:dataSocialImpact": "Provides a foundation for safer AI-driven laboratory automation and contributes to the reduction of physical risks in scientific research.", |
| "prov:wasGeneratedBy": "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." |
| } |
