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datasets available to search
ShareScore release 0.9.0
Dataset results
35 results for “High-throughput imaging”
Cosegmentation for Plant Phenotyping+ (CosegPP+) Data Repository Collected Via a High-Throughput Imaging System
<p>CosegPP+ is an extension of CosegPP (https://doi.org/10.5281/zenodo.5117176) with binary masks for a collection of cosegmentation and segmentation algorithms. </p> <p>We thank Vincent Stoeger for acquiring the dataset using LemnaTec at the University of Nebraska-Lincoln.</p> <p>If you use this dataset, please cite this paper:</p> <table> <tbody> <tr></tr> <tr> <td> <div>Quiñones, R., Samal, A., Das Choudhury, S., & Muñoz-Arriola, F. (2023). OSC-CO2: coattention and cosegmentation framework for plant state change with multiple features. <em>Frontiers in Plant Science</em>, <em>14</em>, 1211409.</div> </td> </tr> <tr> <td> </td> </tr> </tbody> </table> <p> </p>
Source Data for "Imaging biological tissue with high-throughput single-pixel compressive holography"
<p>This file contains five subfolders, which are archived with relevant data that are necessary for reconstructing the holographic images of biological samples and resolution targets, respectively. <br> Here we introduce in order:<br> 1. 'dataset 1' is prepared for holographic reconstruction of stained tissue from mouse tails;<br> 2. 'dataset 2' is provided for holographic reconstruction of 80-um unstained tissue from mouse brains.<br> 3. 'dataset 3' is provided for verification of amplitude resolution in large-FOV mode;<br> 4. 'dataset 4' is provided for verification of amplitude resolution in high-resolution mode;<br> 5. 'dataset 5' is provided for verification of phase resolution in high-resolution mode;<br> 6. 'additional dataset 1' is prepared for additional holographic reconstruction of another stained tissue from mouse tails;<br> 7. 'additional dataset 2' is prepared for additional holographic reconstruction of 100-um unstained tissue from mouse brains;<br> 8. 'additional dataset 3' is prepared for additional holographic reconstruction of 120-um unstained tissue from mouse brains;<br> 9. 'additional dataset 4' is prepared for additional holographic reconstruction of 10-um unstained tissue from mouse brains;</p> <p>Both subfolders have the same structures, including the MATLAB data and raw data collected from the data acquisition card, which are necessary for holographic imaging reconstruction.<br> Here we introduce in order:<br> *) biological_sample.mat: The raw data of imaging biological sample. The format of the data has been converted from .tdms to .mat file.</p> <p>*) target_sample.mat: The raw data of imaging resolution target. The format of the data has been converted from .tdms to .mat file.</p> <p>*) background_curvature.mat: The raw data used to correct for phase contaminations from system aberrations. The format of the data has been converted from .tdms to .mat file.</p> <p>*) biological_sample_rawdata.tdms: The raw data of imaging biological sample. The data was collected through DAC and was in the format of TDMS.</p> <p>*) target_sample_rawdata.tdms: The raw data of imaging resolution target. The data was collected through DAC and was in the format of TDMS.</p> <p>*) background_curvature_rawdata.tdms: The raw data used to correct for phase contaminations from system aberrations. The data was collected through DAC and was in the format of TDMS.<br> </p>
Cosegmentation for Plant Phenotyping (CosegPP) Data Repository Collected Via a High-Throughput Imaging System
<p>CosegPP is a data repository that contains four datasets for plant phenotyping. Each dataset contains: </p> <ol> <li>two species physically different for challenging segmentation. Buckwheat is a thin plant with a variety sizes of leaves and Sunflower is a bushy plant that contains flowering;</li> <li>the most commonly used induced environments in plant phenotyping such as a control and drought-induced; </li> <li>a temporal resolution that begins with the plants vegetative stage and ends with the plant fully matured;</li> <li>modalities (infrared, visible, near infrared) that are commonly used in plant phenotyping analysis; and </li> <li>multiple perspectives that are becoming widely acquired in plant phenotyping analysis due to its potential for three dimensional analysis.</li> </ol> <p>We thank Vincent Stoeger for acquiring the dataset using LemnaTec at the University of Nebraska-Lincoln.</p> <p>If you use this dataset, please cite this paper:</p> <p>Quiñones R, Munoz-Arriola F, Choudhury SD, Samal A (2021) Multi-feature data repository development and analytics for image cosegmentation in high-throughput plant phenotyping. PLoS ONE 16(9): e0257001. <a href="https://doi.org/10.1371/journal.pone.0257001">https://doi.org/10.1371/journal.pone.0257001</a></p>
Data from: Flow imaging microscopy as a novel tool for high-throughput evaluation of elastin-like polymer coacervates
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Sashimi: A toolkit for facilitating high-throughput organismal image segmentation using deep learning
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Data from: High-throughput adaptive sampling for whole-slide histopathology image analysis (HASHI) via convolutional neural networks: application to invasive breast cancer detection
Precise detection of invasive cancer on whole-slide images (WSI) is a critical first step in digital pathology tasks of diagnosis and grading. Convolutional neural network (CNN) is the most popular representation learning method for computer vision tasks, which have been successfully applied in digital pathology, including tumor and mitosis detection. However, CNNs are typically only tenable with relatively small image sizes (200x200 pixels). Only recently, Fully convolutional networks (FCN) are able to deal with larger image sizes (500x500 pixels) for semantic segmentation. Hence, the direct application of CNNs to WSI is not computationally feasible because for a WSI, a CNN would require billions or trillions of parameters. To alleviate this issue, this paper presents a novel method, High-throughput Adaptive Sampling for whole-slide Histopathology Image analysis (HASHI), which involves: i) a new efficient adaptive sampling method based on probability gradient and quasi-Monte Carlo sampling, and, ii) a powerful representation learning classifier based on CNNs. We applied HASHI to automated detection of invasive breast cancer on WSI. HASHI was trained and validated using three different data cohorts involving near 500 cases and then independently tested on 195 studies from The Cancer Genome Atlas. The results show that (1) the adaptive sampling method is an effective strategy to deal with WSI without compromising prediction accuracy by obtaining comparative results of a dense sampling (~6 million of samples in 24 hours) with far fewer samples (~2,000 samples in 1 minute), and (2) on an independent test dataset, HASHI is effective and robust to data from multiple sites, scanners, and platforms, achieving an average Dice coefficient of 76%.
Data from: Quantitative detection of rare interphase chromosome breaks and translocations by high-throughput imaging
We report a method for the sensitive detection of rare chromosome breaks and translocations in interphase cells. HiBA-FISH (High-throughput break-apart FISH) combines high-throughput imaging with the measurement of the spatial separation of FISH probes flanking target genome regions of interest. As proof-of-principle, we apply hiBA-FISH to detect with high sensitivity and specificity rare chromosome breaks and translocations in the anaplastic large cell lymphoma breakpoint regions of NPM1 and ALK. This method complements existing approaches to detect translocations by overcoming the need for precise knowledge of translocation breakpoints and it extends traditional FISH by its quantitative nature.
Data from: High-throughput adaptive sampling for whole-slide histopathology image analysis (HASHI) via convolutional neural networks: application to invasive breast cancer detection
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Data from: Quantitative detection of rare interphase chromosome breaks and translocations by high-throughput imaging
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Data from: Dissecting the phenotypic components of crop plant growth and drought responses based on high-throughput image analysis
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Data from: High-throughput synapse-resolving two-photon fluorescence microendoscopy for deep-brain volumetric imaging in vivo
Optical imaging has become a powerful tool for studying brains in vivo. The opacity of adult brains makes microendoscopy, with an optical probe such as a gradient index (GRIN) lens embedded into brain tissue to provide optical relay, the method of choice for imaging neurons and neural activity in deeply buried brain structures. Incorporating a Bessel focus scanning module into two-photon fluorescence microendoscopy, we extended the excitation focus axially and improved its lateral resolution. Scanning the Bessel focus in 2D, we imaged volumes of neurons at high-throughput while resolving fine structures such as synaptic terminals. We applied this approach to the volumetric anatomical imaging of dendritic spines and axonal boutons in the mouse hippocampus, and functional imaging of GABAergic neurons in the mouse lateral hypothalamus in vivo.
Bioactivity screening of environmental chemicals using imaging-based high-throughput phenotypic profiling
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Optimization of Human Neural Progenitor Cells for an Imaging-Based High-Throughput Phenotypic Profiling Assay for Developmental Neurotoxicity Screening
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Data from: High-throughput synapse-resolving two-photon fluorescence microendoscopy for deep-brain volumetric imaging in vivo
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Field dependent deep learning enables high-throughput whole-cell 3D super-resolution imaging
<p><a href="https://zenodo.org/api/files/edf611c5-3daa-479a-8470-3406cf4df3b5/example_data.zip?versionId=43aab935-acd9-4b81-9a0d-298eaae73fd1">example data</a></p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.