Test dataset (cropped from dataset 1) for AIVE benchmarking. Voxel values are inverted from original BSE signal. Spatial units are in nm. 10nm per slice.
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All raw membrane predictions and AIVE processed data from Figures 1 & 2 of manuscript, showing direct comparison of results generated via different models. The trained models are RF (Random Forest), J48 (the java compatible extension of Ross Quinlan’s C4.5 classifier), MLP (Multi-Layer Perceptron), DT (Decision Table), JRip (java compatible propositional rule-based RIPPER), and PART (Projective Adaptive Resonance Theory neural network). Slice thickness is 10nm.
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Also, this feature is unstable and may not work properly, and checksums of downloaded files are not verified.
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Raw annotations and AIVE processed data for mitochondria from Figure 3, using classifications generated by one human on consecutive days, or two U-Nets with 3D anisotropic architecture (different random seeds). Membrane predictions by a random forest model, which were used to conduct various forms of AIVE, are also provided. Slice thickness is 10nm.
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Tiff stack for half-scale overview FIB-SEM dataset 1. Slice thickness is 10nm.
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Tiff stack for half-scale overview FIB-SEM dataset 2. Slice thickness is 10nm.
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Also, this feature is unstable and may not work properly, and checksums of downloaded files are not verified.
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It is possible to download files by specifying the file list with rsync command, etc.
Tiff stack for half-scale overview FIB-SEM dataset 3. Slice thickness is 10nm.
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Archives and downloads the selected files into an uncompressed zip file.
Depending on the number and size of files to be downloaded, this can take an enormous amount of time.
Also, this feature is unstable and may not work properly, and checksums of downloaded files are not verified.
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Tiff stack for muscle tissue. Slice thickness is 20nm.
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Archives and downloads the selected files into an uncompressed zip file.
Depending on the number and size of files to be downloaded, this can take an enormous amount of time.
Also, this feature is unstable and may not work properly, and checksums of downloaded files are not verified.
We recommend using rsync, aspera, globus, etc.
Download a list of selected files.
It is possible to download files by specifying the file list with rsync command, etc.
