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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.",
    "alternateName": [
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        "LabShield Review"
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        "name": "LabShield-Review",
        "url": "https://huggingface.co/LabShield"
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        "image-to-text",
        "English",
        "apache-2.0",
        "< 1K",
        "json",
        "Image",
        "Tabular",
        "Text",
        "Datasets",
        "pandas",
        "Polars",
        "Croissant",
        "🇺🇸 Region: US",
        "safety",
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        "embodied-ai",
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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."
}