{"url":"/dataset/dataset-for-zaugnet","name":"Dataset for ZAugNet","full_name":"Self-Supervised Z-Slice Augmentation for 3D Bio-Imaging via Knowledge Distillation","description_markdown":"Dataset used to train ZAugNet, a neural network for Z-slice augmentation, that encompasses a variety of shapes, textures, and microscopy techniques, as described below:\r\nAscidian Embryos: This dataset consists of 3D confocal images of P. mammillata embryos, captured using fluorescence microscopy. The plasma membrane was imaged using a PH::Tomato construct, and images were taken at 20°C with a Leica TCS SP8 inverted microscope, resulting in cubic voxel datasets. Credits: Rémi Dumollard, Alex McDougall.\r\nCell Nuclei: This dataset includes 3D confocal images of colorectal cancer organoids, stained with DAPI. The images were captured using a Nikon Spatial Array Confocal (NSPARC) detector with 40x objective, providing high-resolution data on organoid structures. Credits: Yekaterina A. Miroshnikova.\r\nFilaments of Microtubules: This dataset features 3D images of microtubules in Mouse Embryonic Fibroblasts, captured using a Zeiss LSM 900 Airyscan2 with a high-resolution 63x oil objective. The images focus on the microtubule network and were post-processed using Airyscan technology. Credits: Benoit Vianay, Alexandra Colin.\r\nHuman Embryos: This dataset contains a time-lapse image of a human embryo obtained as part of in vitro fertilization procedure, captured using an EmbryoScope Plus incubator. The images were acquired across 11 focal planes at 15-minute intervals, providing detailed temporal data of embryo development. Credits: Elsa Labrune.\r\nThese datasets offer diverse microscopy techniques and biological subjects, supporting a variety of training scenarios for neural networks. Additional information about these datasets can be found in the Methods section of the associated paper.","description_withheld":null,"homepage":"https://doi.org/10.5281/zenodo.14961732","introduced_date":"2025-03-05","introduced_date_note":null,"introduced_by":{"paper":"/paper/zaugnet-for-z-slice-augmentation-in-bio","title":"Self-Supervised Z-Slice Augmentation for 3D Bio-Imaging via Knowledge Distillation","first_author":"Alessandro Pasqui","url":null},"license":{"name":"Creative Commons Attribution 4.0 International","url":null},"modalities":[],"tasks":[],"languages":[{"name":"English","url":"/datasets/language/english"}],"variants":["Dataset for ZAugNet"],"data_loaders":[],"num_papers_in_archive":1,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[],"papers_with_a_benchmark_row":[],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":0,"samples_harvested":0,"samples_ran":0,"samples_unverified":0,"pointer_only_for_licence":0,"papers_with_no_sample_that_ran":0,"note":"the per-paper counts above, summed; not a rate"},"papers_note":"The archive never published its papers-using-dataset list; these are papers with a leaderboard row on this dataset's benchmarks."}