{
    "identifier": "ark:\/88434\/mds2-3838",
    "accessLevel": "public",
    "contactPoint": {
        "hasEmail": "mailto:[email protected]",
        "fn": "Peter Bajcsy"
    },
    "programCode": [
        "006:045"
    ],
    "landingPage": "https:\/\/data.nist.gov\/od\/id\/mds2-3838",
    "title": "Detection Limits for SEM Image Segmentation",
    "description": "The dataset consists of six collections of SEM images, three trained U-net AI models, and CSV files with image quality metrics and trained AI model accuracy metrics. Each SEM image collection contains images augmented with Poisson noise and contrast.This work was performed with funding from the CHIPS Metrology Program, part of CHIPS for America, National Institute of Standards and Technology, U.S. Department of Commerce.",
    "language": [
        "en"
    ],
    "distribution": [
        {
            "downloadURL": "https:\/\/data.nist.gov\/od\/ds\/mds2-3838\/intensity_sets.zip",
            "format": "zip file with six subfolders containing SEM image collections",
            "description": "SEM images with varying noise and contrast values",
            "mediaType": "application\/zip",
            "title": "Six sets of simulated SEM images"
        },
        {
            "downloadURL": "https:\/\/data.nist.gov\/od\/ds\/mds2-3838\/mask_sets.zip",
            "format": "zip file contains mask images and the initial intensity image with max contrast and min noise",
            "description": "image masks defining foreground and background labels",
            "mediaType": "application\/zip",
            "title": "Six masks for the six simulated SEM image collections"
        },
        {
            "downloadURL": "https:\/\/data.nist.gov\/od\/ds\/mds2-3838\/AImodel_sets.zip",
            "format": "zip contains three folders with TensorFlow AI models",
            "description": "Three trained AI models on the first five of six simulated SEM image collections",
            "mediaType": "application\/zip",
            "title": "Three trained AI models (U-net architecture)"
        },
        {
            "downloadURL": "https:\/\/data.nist.gov\/od\/ds\/mds2-3838\/metrics_sets.zip",
            "format": "zip file contains a folder with image quality metrics and a folder with AI model accuracy",
            "description": "Image quality metrics extracted from six simulated SEM images, as well as three AI model accuracy metrics obtained by evaluating the trained AI models on the sixth of the simulated SEM image collections.",
            "mediaType": "application\/zip",
            "title": "Tabular files with image and AI model metrics"
        },
        {
            "accessURL": "https:\/\/pages.nist.gov\/detection_limits\/web\/index.html",
            "format": "URL",
            "description": "graphs",
            "title": "relationships between AI model accuracy and image quality metrics"
        },
        {
            "accessURL": "https:\/\/github.com\/usnistgov\/detection_limits",
            "description": "Python code",
            "title": "source code for detection limits"
        },
        {
            "downloadURL": "https:\/\/data.nist.gov\/od\/ds\/mds2-3838\/README.txt",
            "mediaType": "text\/plain",
            "title": "README"
        }
    ],
    "bureauCode": [
        "006:55"
    ],
    "modified": "2025-05-12 00:00:00",
    "publisher": {
        "@type": "org:Organization",
        "name": "National Institute of Standards and Technology"
    },
    "theme": [
        "Information Technology:Computational science",
        "Metrology:Dimensional metrology",
        "Nanotechnology:Nanoelectronics",
        "Mathematics and Statistics:Image and signal processing",
        "Mathematics and Statistics:Uncertainty quantification"
    ],
    "keyword": [
        "scanning electron microscopy",
        "SEM",
        "dimensional metrology",
        "detection limits",
        "AI model"
    ]
}